diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS
index 036e4a25526ac0..352049ffbd9211 100644
--- a/.github/CODEOWNERS
+++ b/.github/CODEOWNERS
@@ -88,6 +88,7 @@
 /src/frontends/tensorflow_common/  @openvinotoolkit/openvino-tf-frontend-maintainers
 /src/frontends/tensorflow_lite/  @openvinotoolkit/openvino-tf-frontend-maintainers
 /src/frontends/pytorch/ @openvinotoolkit/openvino-pytorch-frontend-maintainers
+/src/frontends/jax/ @openvinotoolkit/openvino-jax-frontend-maintainers
 
 # OpenVINO ONNX Frontend:
 /src/frontends/onnx/ @openvinotoolkit/openvino-onnx-frontend-maintainers
@@ -99,7 +100,7 @@
 /tests/layer_tests/ @openvinotoolkit/openvino-tests-maintainers @openvinotoolkit/openvino-mo-maintainers
 /tests/layer_tests/pytorch_tests/ @openvinotoolkit/openvino-pytorch-frontend-maintainers
 /tests/layer_tests/tensorflow_tests @openvinotoolkit/openvino-tf-frontend-maintainers
-/tests/layer_tests/jax_tests @openvinotoolkit/openvino-tf-frontend-maintainers
+/tests/layer_tests/jax_tests @openvinotoolkit/openvino-tf-frontend-maintainers @openvinotoolkit/openvino-jax-frontend-maintainers
 /tests/model_hub_tests @openvinotoolkit/openvino-tf-frontend-maintainers
 /tests/model_hub_tests/pytorch @openvinotoolkit/openvino-pytorch-frontend-maintainers
 
diff --git a/.github/actions/smart-ci/smart_ci.py b/.github/actions/smart-ci/smart_ci.py
index 80d285da0e8d0e..1c8558f4779108 100644
--- a/.github/actions/smart-ci/smart_ci.py
+++ b/.github/actions/smart-ci/smart_ci.py
@@ -225,7 +225,7 @@ def main():
 
     skip_workflow = False
     if is_merge_queue or (args.pr and not run_full_scope):
-        if args.skip_when_only_listed_labels_set:
+        if args.skip_when_only_listed_labels_set and changed_component_names:
             excepted_labels = set(args.skip_when_only_listed_labels_set.split(','))
             excepted_labels_only = changed_component_names - excepted_labels == set()
             skip_workflow = excepted_labels_only
diff --git a/.github/components.yml b/.github/components.yml
index 8ec4b759db39e6..9c5d7c0089c9ca 100644
--- a/.github/components.yml
+++ b/.github/components.yml
@@ -13,6 +13,9 @@ LP_transformations:
     - TFL_FE
     - ONNX_FE
     - PDPD_FE
+    - JAX_FE
+  build:
+    - TOKENIZERS # TF_FE tests depends on tokenizers build
 
 preprocessing:
   revalidate:
@@ -32,12 +35,14 @@ CPU:
     - PyTorch_FE
     - TF_FE
     - ONNX_FE
+    - JAX_FE
   build:
     - AUTO
     - HETERO
     - AUTO_BATCH
     - TEMPLATE
     - IR_FE
+    - TOKENIZERS # TF_FE tests depends on tokenizers build
 
 GPU:
   build:
@@ -124,7 +129,7 @@ TF_FE:
   build:
     - CPU
     - Python_API
-    - TOKENIZERS
+    - TOKENIZERS # TF_FE tests depends on tokenizers build
 
 TFL_FE:
   revalidate:
@@ -139,7 +144,13 @@ PyTorch_FE:
   build:
     - CPU
     - Python_API
-    - TOKENIZERS
+
+JAX_FE:
+  revalidate:
+    - MO
+  build:
+    - CPU
+    - Python_API
 
 C_API:
   build:
@@ -155,6 +166,7 @@ Python_API:
     - samples
     - MO
     - tools
+    - TF_FE
   build:
     - CPU
     - HETERO
@@ -164,9 +176,10 @@ Python_API:
     - IR_FE
     - ONNX_FE
     - PDPD_FE
-    - TF_FE
     - TFL_FE
     - PyTorch_FE
+    - JAX_FE
+    - TOKENIZERS # TF_FE tests depends on tokenizers build
 
 JS_API:
   build:
@@ -200,6 +213,7 @@ MO:
     - TF_FE
   build:
     - Python_API
+    - TOKENIZERS # TF_FE tests depends on tokenizers build
 
 tools:
   build:
@@ -223,5 +237,6 @@ ONNX_RT:
   build: []
 
 TOKENIZERS:
-  revalidate: []
+  revalidate:
+    - TF_FE
   build: []
diff --git a/.github/dependency_review.yml b/.github/dependency_review.yml
index 7a1b3a4386a9da..11639f4d2d4b22 100644
--- a/.github/dependency_review.yml
+++ b/.github/dependency_review.yml
@@ -3,8 +3,10 @@ allow-licenses:
   - 'BSD-2-Clause'
   - 'BSD-3-Clause'
   - 'BSD-2-Clause AND BSD-3-Clause'
+  - 'BSD-3-Clause AND BSD-3-Clause-Clear'
   - 'MIT'
   - 'Apache-2.0'
+  - 'Apache-2.0 AND BSD-3-Clause'
   - 'ISC'
   - 'Apache-2.0 AND MIT'
   - 'BlueOak-1.0.0'
diff --git a/.github/dockerfiles/docker_tag b/.github/dockerfiles/docker_tag
index 67a7eec64a342f..ae48310adafe6f 100644
--- a/.github/dockerfiles/docker_tag
+++ b/.github/dockerfiles/docker_tag
@@ -1 +1 @@
-pr-24878
+pr-25303
diff --git a/.github/dockerfiles/ov_build/fedora_33/Dockerfile b/.github/dockerfiles/ov_build/fedora_33/Dockerfile
new file mode 100644
index 00000000000000..fc94c37d67a321
--- /dev/null
+++ b/.github/dockerfiles/ov_build/fedora_33/Dockerfile
@@ -0,0 +1,23 @@
+FROM openvinogithubactions.azurecr.io/dockerio/library/fedora:33
+
+USER root
+
+RUN yum update -y && yum install -y git
+
+# Install build dependencies
+ADD install_build_dependencies.sh /install_build_dependencies.sh
+RUN chmod +x /install_build_dependencies.sh && \
+    /install_build_dependencies.sh && \
+    rm -rf /var/lib/apt/lists/*
+
+# Install sscache
+ARG SCCACHE_VERSION="v0.7.5"
+ENV SCCACHE_HOME="/opt/sccache" \
+    SCCACHE_PATH="/opt/sccache/sccache"
+
+RUN mkdir ${SCCACHE_HOME} && cd ${SCCACHE_HOME} && \
+    SCCACHE_ARCHIVE="sccache-${SCCACHE_VERSION}-x86_64-unknown-linux-musl.tar.gz" && \
+    curl -SLO https://github.com/mozilla/sccache/releases/download/${SCCACHE_VERSION}/${SCCACHE_ARCHIVE} && \
+    tar -xzf ${SCCACHE_ARCHIVE} --strip-components=1 && rm ${SCCACHE_ARCHIVE}
+
+ENV PATH="$SCCACHE_HOME:$PATH"
diff --git a/.github/dockerfiles/ov_build/ubuntu_22_04_android_arm64/Dockerfile b/.github/dockerfiles/ov_build/ubuntu_22_04_android_arm64/Dockerfile
new file mode 100644
index 00000000000000..f3d8caa5220f28
--- /dev/null
+++ b/.github/dockerfiles/ov_build/ubuntu_22_04_android_arm64/Dockerfile
@@ -0,0 +1,55 @@
+FROM openvinogithubactions.azurecr.io/dockerhub/ubuntu:22.04
+
+USER root
+
+# APT configuration
+RUN echo 'Acquire::Retries "10";' > /etc/apt/apt.conf && \
+    echo 'APT::Get::Assume-Yes "true";' >> /etc/apt/apt.conf && \
+    echo 'APT::Get::Fix-Broken "true";' >> /etc/apt/apt.conf && \
+    echo 'APT::Get::no-install-recommends "true";' >> /etc/apt/apt.conf
+
+ENV DEBIAN_FRONTEND="noninteractive" \
+    TZ="Europe/London"
+
+RUN apt update && \
+    apt install software-properties-common git ca-certificates && \
+    add-apt-repository --yes --no-update ppa:git-core/ppa && \
+    apt update && \
+    apt install \
+        scons \
+        wget \
+        ninja-build \
+        build-essential \
+        python3-pip && \
+    # vcpkg requires cmake 3.19 or later
+    python3 -m pip install -U pip cmake~=3.28.0 && \
+    # vcpkg's tool dependencies
+    apt install curl zip unzip tar && \
+    # vcpkg 'python3' port dependencies
+    apt install autoconf libtool autoconf-archive && \
+    # vcpkg tree of dependencies require extra packages
+    apt install pkgconf linux-libc-dev && \
+    apt --no-install-recommends install default-jdk && \
+    rm -rf /var/lib/apt/lists/*
+
+# Install sscache
+ARG SCCACHE_VERSION="v0.7.5"
+ENV SCCACHE_HOME="/opt/sccache" \
+    SCCACHE_PATH="/opt/sccache/sccache"
+
+RUN mkdir ${SCCACHE_HOME} && cd ${SCCACHE_HOME} && \
+    SCCACHE_ARCHIVE="sccache-${SCCACHE_VERSION}-x86_64-unknown-linux-musl.tar.gz" && \
+    curl -SLO https://github.com/mozilla/sccache/releases/download/${SCCACHE_VERSION}/${SCCACHE_ARCHIVE} && \
+    tar -xzf ${SCCACHE_ARCHIVE} --strip-components=1 && rm ${SCCACHE_ARCHIVE}
+
+ENV PATH="$SCCACHE_HOME:$PATH"
+
+# Install Android SDK, NDK and Tools
+ENV ANDROID_TOOLS /deps/android_tools
+ENV ANDROID_NDK_HOME /deps/android_tools/ndk-bundle
+RUN mkdir -p ${ANDROID_NDK_HOME}
+ENV ANDROID_SDK_VERSION 29
+
+RUN wget https://dl.google.com/android/repository/commandlinetools-linux-7583922_latest.zip && \
+    unzip commandlinetools-linux-7583922_latest.zip
+RUN echo "yes" | ./cmdline-tools/bin/sdkmanager --sdk_root=${ANDROID_TOOLS} --install "ndk-bundle" "platform-tools" "platforms;android-${ANDROID_SDK_VERSION}"
diff --git a/.github/dockerfiles/ov_build/ubuntu_22_04_riscv/Dockerfile b/.github/dockerfiles/ov_build/ubuntu_22_04_riscv/Dockerfile
new file mode 100644
index 00000000000000..5eda853da56545
--- /dev/null
+++ b/.github/dockerfiles/ov_build/ubuntu_22_04_riscv/Dockerfile
@@ -0,0 +1,68 @@
+FROM openvinogithubactions.azurecr.io/dockerhub/ubuntu:22.04
+
+USER root
+
+# APT configuration
+RUN echo 'Acquire::Retries "10";' > /etc/apt/apt.conf && \
+    echo 'APT::Get::Assume-Yes "true";' >> /etc/apt/apt.conf && \
+    echo 'APT::Get::Fix-Broken "true";' >> /etc/apt/apt.conf && \
+    echo 'APT::Get::no-install-recommends "true";' >> /etc/apt/apt.conf
+
+ENV DEBIAN_FRONTEND="noninteractive" \
+    TZ="Europe/London"
+
+RUN apt-get update && \
+    apt-get install software-properties-common && \
+    add-apt-repository --yes --no-update ppa:git-core/ppa && \
+    add-apt-repository --yes --no-update ppa:deadsnakes/ppa && \
+    apt-get update && \
+    # install compilers to build OpenVINO for RISC-V 64
+    apt-get install gcc-riscv64-linux-gnu g++-riscv64-linux-gnu && \
+    apt-get install \
+        curl \
+        git \
+        cmake \
+        ccache \
+        ninja-build \
+        fdupes \
+        patchelf \
+        ca-certificates \
+        gpg-agent \
+        tzdata \
+        # Python \
+        python3-dev \
+        python3-pip \
+        python3-venv \
+        python3-distutils \
+        # Compilers
+        gcc \
+        g++ \
+        && \
+    rm -rf /var/lib/apt/lists/*
+
+# Install RISC-V native debian packages
+RUN echo deb [arch=amd64] http://archive.ubuntu.com/ubuntu/ jammy main restricted > riscv64-sources.list && \
+    echo deb [arch=amd64] http://archive.ubuntu.com/ubuntu/ jammy-updates main restricted >> riscv64-sources.list && \
+    echo deb [arch=amd64] http://archive.ubuntu.com/ubuntu/ jammy universe >> riscv64-sources.list && \
+    echo deb [arch=amd64] http://archive.ubuntu.com/ubuntu/ jammy-updates universe >> riscv64-sources.list && \
+    echo deb [arch=amd64] http://archive.ubuntu.com/ubuntu/ jammy multiverse >> riscv64-sources.list && \
+    echo deb [arch=amd64] http://archive.ubuntu.com/ubuntu/ jammy-updates multiverse >> riscv64-sources.list && \
+    echo deb [arch=amd64] http://archive.ubuntu.com/ubuntu/ jammy-backports main restricted universe multiverse >> riscv64-sources.list && \
+    echo deb [arch=amd64] http://security.ubuntu.com/ubuntu/ jammy-security main restricted >> riscv64-sources.list && \
+    echo deb [arch=amd64] http://security.ubuntu.com/ubuntu/ jammy-security universe >> riscv64-sources.list && \
+    echo deb [arch=amd64] http://security.ubuntu.com/ubuntu/ jammy-security multiverse >> riscv64-sources.list && \
+    echo deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ jammy main >> riscv64-sources.list && \
+    echo deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ jammy universe >> riscv64-sources.list && \
+    echo deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ jammy-updates main >> riscv64-sources.list && \
+    echo deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ jammy-security main >> riscv64-sources.list && \
+    mv riscv64-sources.list /etc/apt/sources.list.d/
+
+RUN dpkg --add-architecture riscv64 && \
+    apt-get update -o Dir::Etc::sourcelist=/etc/apt/sources.list.d/riscv64-sources.list && \
+    apt-get install -y --no-install-recommends libpython3-dev:riscv64
+
+# Setup pip
+ENV PIP_VERSION="24.0"
+RUN curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py && \
+    python3 get-pip.py --no-cache-dir pip==${PIP_VERSION} && \
+    rm -f get-pip.py
diff --git a/.github/dockerfiles/ov_build/webassembly/Dockerfile b/.github/dockerfiles/ov_build/webassembly/Dockerfile
new file mode 100644
index 00000000000000..66765ed9341efe
--- /dev/null
+++ b/.github/dockerfiles/ov_build/webassembly/Dockerfile
@@ -0,0 +1,33 @@
+FROM openvinogithubactions.azurecr.io/dockerio/emscripten/emsdk:3.1.61
+
+USER root
+
+# APT configuration
+RUN echo 'Acquire::Retries "10";' > /etc/apt/apt.conf && \
+    echo 'APT::Get::Assume-Yes "true";' >> /etc/apt/apt.conf && \
+    echo 'APT::Get::Fix-Broken "true";' >> /etc/apt/apt.conf && \
+    echo 'APT::Get::no-install-recommends "true";' >> /etc/apt/apt.conf
+
+ENV DEBIAN_FRONTEND="noninteractive" \
+    TZ="Europe/London"
+
+RUN apt-get update && \
+    apt-get install software-properties-common && \
+    add-apt-repository --yes --no-update ppa:git-core/ppa && \
+    apt-get update && \
+    apt-get install \
+        git \
+        ca-certificates && \
+    rm -rf /var/lib/apt/lists/*
+
+# Install sscache
+ARG SCCACHE_VERSION="v0.7.5"
+ENV SCCACHE_HOME="/opt/sccache" \
+    SCCACHE_PATH="/opt/sccache/sccache"
+
+RUN mkdir ${SCCACHE_HOME} && cd ${SCCACHE_HOME} && \
+    SCCACHE_ARCHIVE="sccache-${SCCACHE_VERSION}-x86_64-unknown-linux-musl.tar.gz" && \
+    curl -SLO https://github.com/mozilla/sccache/releases/download/${SCCACHE_VERSION}/${SCCACHE_ARCHIVE} && \
+    tar -xzf ${SCCACHE_ARCHIVE} --strip-components=1 && rm ${SCCACHE_ARCHIVE}
+
+ENV PATH="$SCCACHE_HOME:$PATH"
diff --git a/.github/labeler.yml b/.github/labeler.yml
index aeb8e8f2655819..64a8661cf1e2e8 100644
--- a/.github/labeler.yml
+++ b/.github/labeler.yml
@@ -141,6 +141,7 @@
 'category: TF FE':
 - 'src/frontends/tensorflow/**/*'
 - 'src/frontends/tensorflow_common/**/*'
+- 'src/bindings/python/src/openvino/frontend/tensorflow/**/*'
 - 'tests/layer_tests/tensorflow_tests/**/*'
 - 'tests/layer_tests/tensorflow2_keras_tests/**/*'
 - 'tests/layer_tests/jax_tests/**/*'
@@ -161,6 +162,11 @@
 - any: ['tests/model_hub_tests/**',
         '!tests/model_hub_tests/tensorflow/**/*']
 
+'category: JAX FE':
+- 'src/frontends/jax/**/*'
+- 'src/bindings/python/src/openvino/frontend/jax/**/*'
+- 'tests/layer_tests/jax_tests/**/*'
+
 'category: tools':
 - any: ['tools/**',
         '!tools/mo/**/*',
diff --git a/.github/scripts/workflow_rerun/rerunner.py b/.github/scripts/workflow_rerun/rerunner.py
index f92130734c3fe6..b37f51e282a1cd 100644
--- a/.github/scripts/workflow_rerun/rerunner.py
+++ b/.github/scripts/workflow_rerun/rerunner.py
@@ -6,12 +6,14 @@
 import tempfile
 from pathlib import Path
 
+import requests
 from github import Github, Auth
 from workflow_rerun.argument_parser import get_arguments
 from workflow_rerun.constants import GITHUB_TOKEN, LOGGER
 from workflow_rerun.log_analyzer import LogAnalyzer
 from workflow_rerun.log_collector import collect_logs_for_run
 
+
 if __name__ == '__main__':
 
     args = get_arguments()
@@ -53,7 +55,12 @@
             LOGGER.info(f'RUNNING IN DRY RUN MODE, NOT RETRIGGERING, EXITING')
             sys.exit(0)
 
-        status = run.rerun()
+        # PyGitHub does not expose the "/repos/{owner}/{repo}/actions/runs/RUN_ID/rerun-failed-jobs" endpoint
+        # so we have to use requests
+        response = requests.post(url=f'https://api.github.com/repos/{repository_name}/actions/runs/{run_id}/rerun-failed-jobs',
+                                 headers={'Authorization': f'Bearer {GITHUB_TOKEN}'})
+        status = response.status_code == 201
+        
         if status:
             LOGGER.info(f'RUN RETRIGGERED SUCCESSFULLY: {run.html_url}')
         else:
diff --git a/.github/workflows/android_arm64.yml b/.github/workflows/android_arm64.yml
index ad42737805b584..de5b6c0011e34d 100644
--- a/.github/workflows/android_arm64.yml
+++ b/.github/workflows/android_arm64.yml
@@ -20,6 +20,7 @@ jobs:
     runs-on: ubuntu-latest
     outputs:
       affected_components: "${{ steps.smart_ci.outputs.affected_components }}"
+      changed_components: "${{ steps.smart_ci.outputs.changed_components }}"
       skip_workflow: "${{ steps.smart_ci.outputs.skip_workflow }}"
     steps:
       - name: checkout action
@@ -40,15 +41,37 @@ jobs:
           skip_when_only_listed_labels_set: 'docs'
           skip_when_only_listed_files_changed: '*.md,*.rst,*.png,*.jpg,*.svg,*/layer_tests_summary/*,*/conformance/*'
 
-  Build:
+  Docker:
     needs: Smart_CI
+    runs-on: aks-linux-4-cores-16gb-docker-build
+    container:
+      image: openvinogithubactions.azurecr.io/docker_build:0.2
+      volumes:
+        - /mount:/mount
+    outputs:
+      images: "${{ steps.handle_docker.outputs.images }}"
+    steps:
+      - name: Checkout
+        uses: actions/checkout@692973e3d937129bcbf40652eb9f2f61becf3332 # v4.1.7
+
+      - uses: ./.github/actions/handle_docker
+        id: handle_docker
+        with:
+          images: |
+            ov_build/ubuntu_22_04_android_arm64
+          registry: 'openvinogithubactions.azurecr.io'
+          dockerfiles_root_dir: '.github/dockerfiles'
+          changed_components: ${{ needs.smart_ci.outputs.changed_components }}
+
+  Build:
+    needs: [Smart_CI, Docker]
     timeout-minutes: 150
     defaults:
       run:
         shell: bash
     runs-on: aks-linux-16-cores-32gb
     container:
-      image: openvinogithubactions.azurecr.io/dockerhub/ubuntu:20.04
+      image: ${{ fromJSON(needs.docker.outputs.images).ov_build.ubuntu_22_04_android_arm64 }}
       volumes:
         - /mount:/mount
       options: -e SCCACHE_AZURE_BLOB_CONTAINER -e SCCACHE_AZURE_CONNECTION_STRING
@@ -64,8 +87,8 @@ jobs:
       OPENVINO_REPO: '/__w/openvino/openvino/openvino'
       VCPKG_ROOT: '/__w/openvino/openvino/vcpkg'
       BUILD_DIR: '/__w/openvino/openvino/build'
-      ANDROID_TOOLS: '/__w/openvino/openvino/android_tools'
-      ANDROID_NDK_HOME: '/__w/openvino/openvino/android_tools/ndk-bundle'
+      ANDROID_TOOLS: '/deps/android_tools'
+      ANDROID_NDK_HOME: '/deps/android_tools/ndk-bundle'
       ANDROID_SDK_VERSION: 29
       ANDROID_ABI_CONFIG: arm64-v8a
       VCPKG_DEFAULT_BINARY_CACHE: '/mount/caches/ccache/android_arm64/vcpkg_cache'
@@ -73,9 +96,6 @@ jobs:
       SCCACHE_AZURE_KEY_PREFIX: android_arm64
     if: "!needs.smart_ci.outputs.skip_workflow"
     steps:
-      - name: Install git
-        run: apt-get update && apt-get install --assume-yes --no-install-recommends git ca-certificates
-
       - name: Clone OpenVINO
         uses: actions/checkout@692973e3d937129bcbf40652eb9f2f61becf3332 # v4.1.7
         with:
@@ -107,35 +127,6 @@ jobs:
       - name: System info
         uses: ./openvino/.github/actions/system_info
 
-      #
-      # Dependencies
-      #
-
-      - name: Install dependencies
-        run: |
-          # generic dependencies
-          apt --assume-yes install ccache scons ninja-build build-essential python3-pip
-
-          # vcpkg requires cmake 3.19 or later
-          python3 -m pip install -U pip cmake~=3.28.0
-          # vcpkg's tool dependencies
-          apt --assume-yes install curl zip unzip tar
-          # vcpkg 'python3' port dependencies
-          apt --assume-yes install autoconf libtool autoconf-archive
-          # vcpkg tree of dependencies require extra packages
-          apt --assume-yes install pkgconf linux-libc-dev
-
-          # Install Android SDK, NDK and Tools
-          apt -y --no-install-recommends install unzip wget default-jdk
-          wget https://dl.google.com/android/repository/commandlinetools-linux-7583922_latest.zip
-          unzip commandlinetools-linux-7583922_latest.zip
-          echo "yes" | ./cmdline-tools/bin/sdkmanager --sdk_root=${ANDROID_TOOLS} --install "ndk-bundle" "platform-tools" "platforms;android-${{ env.ANDROID_SDK_VERSION }}"
-
-      - name: Install sccache
-        uses: mozilla-actions/sccache-action@2e7f9ec7921547d4b46598398ca573513895d0bd # v0.0.4
-        with:
-          version: "v0.7.5"
-
       #
       # Build
       #
diff --git a/.github/workflows/code_style.yml b/.github/workflows/code_style.yml
index ceff14e1e09774..223a7418156e43 100644
--- a/.github/workflows/code_style.yml
+++ b/.github/workflows/code_style.yml
@@ -31,7 +31,7 @@ jobs:
 
       - name: suggester / clang-format
         if: startsWith(github.event_name, 'pull_request')
-        uses: reviewdog/action-suggester@9e1cd88b79ba3c0023c94e44accd72344f032093 # v1.13.0
+        uses: reviewdog/action-suggester@a1d57ff096639094e0ba35ef3039e79316364796 # v1.15.0
         with:
           github_token: ${{ secrets.GITHUB_TOKEN }}
           level: warning
@@ -60,7 +60,7 @@ jobs:
       # always provide suggestions even for skipped scripts in ov_shellcheck tagret
       - name: ShellCheck action
         if: always()
-        uses: reviewdog/action-shellcheck@6e3a862f231c6895fbd335b70adef8f9243d5762 # v1.21.0
+        uses: reviewdog/action-shellcheck@52f34f737a16c65b8caa8c51ae1b23036afe5685 # v1.23.0
         with:
           level: style
           reporter: github-pr-review
diff --git a/.github/workflows/coverage.yml b/.github/workflows/coverage.yml
index a145276590d3e6..436f335276d169 100644
--- a/.github/workflows/coverage.yml
+++ b/.github/workflows/coverage.yml
@@ -137,6 +137,6 @@ jobs:
           lcov --capture --directory ${{ github.workspace }}/. --output-file coverage.info
           genhtml coverage.info --output-directory coverage-report
       - name: Collect coverage
-        uses: codecov/codecov-action@125fc84a9a348dbcf27191600683ec096ec9021c # v4.4.1
+        uses: codecov/codecov-action@e28ff129e5465c2c0dcc6f003fc735cb6ae0c673 # v4.5.0
         with:
           verbose: true
diff --git a/.github/workflows/fedora.yml b/.github/workflows/fedora.yml
index 66dd87a4466ca6..02cd0abf018319 100644
--- a/.github/workflows/fedora.yml
+++ b/.github/workflows/fedora.yml
@@ -20,6 +20,7 @@ jobs:
     runs-on: ubuntu-latest
     outputs:
       affected_components: "${{ steps.smart_ci.outputs.affected_components }}"
+      changed_components: "${{ steps.smart_ci.outputs.changed_components }}"
       skip_workflow: "${{ steps.smart_ci.outputs.skip_workflow }}"
     steps:
       - name: checkout action
@@ -40,15 +41,42 @@ jobs:
           skip_when_only_listed_labels_set: 'docs'
           skip_when_only_listed_files_changed: '*.md,*.rst,*.png,*.jpg,*.svg,*/layer_tests_summary/*,*/conformance/*'
 
-  Build:
+      - name: Show affected components
+        run: |
+          echo "${{ toJSON(steps.smart_ci.outputs.affected_components) }}"
+        shell: bash
+
+  Docker:
     needs: Smart_CI
+    runs-on: aks-linux-4-cores-16gb-docker-build
+    container:
+      image: openvinogithubactions.azurecr.io/docker_build:0.2
+      volumes:
+        - /mount:/mount
+    outputs:
+      images: "${{ steps.handle_docker.outputs.images }}"
+    steps:
+      - name: Checkout
+        uses: actions/checkout@692973e3d937129bcbf40652eb9f2f61becf3332 # v4.1.7
+
+      - uses: ./.github/actions/handle_docker
+        id: handle_docker
+        with:
+          images: |
+            ov_build/fedora_33
+          registry: 'openvinogithubactions.azurecr.io'
+          dockerfiles_root_dir: '.github/dockerfiles'
+          changed_components: ${{ needs.smart_ci.outputs.changed_components }}
+
+  Build:
+    needs: [Docker, Smart_CI]
     timeout-minutes: 150
     defaults:
       run:
         shell: bash
     runs-on: aks-linux-16-cores-32gb
     container:
-      image: fedora:33
+      image: ${{ fromJSON(needs.docker.outputs.images).ov_build.fedora_33 }}
       volumes:
         - /mount:/mount
       options: -e SCCACHE_AZURE_BLOB_CONTAINER -e SCCACHE_AZURE_CONNECTION_STRING
@@ -69,9 +97,6 @@ jobs:
       SCCACHE_AZURE_KEY_PREFIX: fedora33_x86_64_Release
     if: "!needs.smart_ci.outputs.skip_workflow"
     steps:
-      - name: Install git
-        run: yum update -y && yum install -y git
-
       - name: Clone OpenVINO
         uses: actions/checkout@692973e3d937129bcbf40652eb9f2f61becf3332 # v4.1.7
         with:
@@ -89,14 +114,6 @@ jobs:
       # Dependencies
       #
 
-      - name: Install build dependencies
-        run: bash ${OPENVINO_REPO}/install_build_dependencies.sh
-
-      - name: Install sccache
-        uses: mozilla-actions/sccache-action@2e7f9ec7921547d4b46598398ca573513895d0bd # v0.0.4
-        with:
-          version: "v0.7.5"
-
       - name: Install python dependencies
         run: |
           python3 -m pip install -U pip
@@ -204,14 +221,14 @@ jobs:
           if-no-files-found: 'error'
 
   RPM_Packages:
-    needs: Build
+    needs: [Docker, Build]
     timeout-minutes: 10
     defaults:
       run:
         shell: bash
-    runs-on: ubuntu-20.04
+    runs-on: aks-linux-4-cores-16gb
     container:
-      image: fedora:33
+      image: ${{ fromJSON(needs.docker.outputs.images).ov_build.fedora_33 }}
     env:
       RPM_PACKAGES_DIR: /__w/openvino/packages/
 
@@ -254,7 +271,7 @@ jobs:
           python3 -c 'from openvino import Core; Core().get_property("MULTI", "SUPPORTED_PROPERTIES")'
           python3 -c 'from openvino import Core; Core().get_property("HETERO", "SUPPORTED_PROPERTIES")'
           python3 -c 'from openvino import Core; Core().get_property("BATCH", "SUPPORTED_PROPERTIES")'
-          python3 -c 'from openvino.frontend import FrontEndManager; assert len(FrontEndManager().get_available_front_ends()) == 6'
+          python3 -c 'from openvino.frontend import FrontEndManager; assert len(FrontEndManager().get_available_front_ends()) == 7'
           benchmark_app --help
           ovc --help
 
diff --git a/.github/workflows/job_debian_packages.yml b/.github/workflows/job_debian_packages.yml
index d4734805a2cb4d..a7547a2483dd16 100644
--- a/.github/workflows/job_debian_packages.yml
+++ b/.github/workflows/job_debian_packages.yml
@@ -87,7 +87,7 @@ jobs:
           python3 -c 'from openvino import Core; Core().get_property("MULTI", "SUPPORTED_PROPERTIES")'
           python3 -c 'from openvino import Core; Core().get_property("HETERO", "SUPPORTED_PROPERTIES")'
           python3 -c 'from openvino import Core; Core().get_property("BATCH", "SUPPORTED_PROPERTIES")'
-          python3 -c 'from openvino.frontend import FrontEndManager; assert len(FrontEndManager().get_available_front_ends()) == 6'
+          python3 -c 'from openvino.frontend import FrontEndManager; assert len(FrontEndManager().get_available_front_ends()) == 7'
           benchmark_app --help
           opt_in_out --help
           ovc --help
diff --git a/.github/workflows/job_gpu_tests.yml b/.github/workflows/job_gpu_tests.yml
index 7ba71afec09748..7a5af97cdcde49 100644
--- a/.github/workflows/job_gpu_tests.yml
+++ b/.github/workflows/job_gpu_tests.yml
@@ -21,6 +21,8 @@ on:
         required: false
         default: '{"image": null}'
 
+permissions: read-all
+
 jobs:
   GPU:
     timeout-minutes: 80
diff --git a/.github/workflows/job_python_unit_tests.yml b/.github/workflows/job_python_unit_tests.yml
index c6b185ec4c6a4a..bab441e3e27453 100644
--- a/.github/workflows/job_python_unit_tests.yml
+++ b/.github/workflows/job_python_unit_tests.yml
@@ -121,7 +121,7 @@ jobs:
       #
 
       - name: Python API Tests
-        # if: ${{ fromJSON(inputs.affected-components).Python_API.test && runner.arch != 'ARM64' }} # Ticket: 126380, 127101
+        if: ${{ fromJSON(inputs.affected-components).Python_API.test }}
         run: |
           # for 'template' extension
           export LD_LIBRARY_PATH=${INSTALL_TEST_DIR}:$LD_LIBRARY_PATH
@@ -246,6 +246,14 @@ jobs:
         env:
           TEST_DEVICE: CPU
           TEST_PRECISION: FP16
+      
+      - name: JAX Layer Tests - JAX FE
+        if: ${{ fromJSON(inputs.affected-components).JAX_FE.test && runner.arch != 'ARM64' && runner.os != 'macOS' }}
+        run: python3 -m pytest ${LAYER_TESTS_INSTALL_DIR}/jax_tests/ -m precommit_jax_fe --junitxml=${INSTALL_TEST_DIR}/TEST-jax_fe.xml
+        env:
+          TEST_DEVICE: CPU
+          TEST_PRECISION: FP16
+          JAX_TRACE_MODE: JAXPR
 
       - name: TensorFlow Lite Layer Tests - TFL FE
         if: fromJSON(inputs.affected-components).TFL_FE.test
diff --git a/.github/workflows/job_samples_tests.yml b/.github/workflows/job_samples_tests.yml
index c9c13569342784..e453210d58b13b 100644
--- a/.github/workflows/job_samples_tests.yml
+++ b/.github/workflows/job_samples_tests.yml
@@ -7,11 +7,11 @@ on:
         description: 'Machine on which the tests would run'
         type: string
         required: true
-      image:
-        description: 'Docker image in which the tests would run'
+      container:
+        description: 'JSON to be converted to the value of the "container" configuration for the job'
         type: string
         required: false
-        default: null
+        default: '{"image": null}'
       affected-components:
         description: 'Components that are affected by changes in the commit defined by the Smart CI Action'
         type: string
@@ -21,9 +21,9 @@ permissions: read-all
 
 jobs:
   Samples:
+    timeout-minutes: 30
     runs-on: ${{ inputs.runner }}
-    container:
-      image: ${{ inputs.image }}
+    container: ${{ fromJSON(inputs.container) }}
     defaults:
       run:
         shell: bash
@@ -66,6 +66,8 @@ jobs:
         run: brew install coreutils
 
       - name: Fetch setup_python action
+        # Python is already installed on Ubuntu within Dockerfile
+        if: runner.os != 'Linux'
         uses: actions/checkout@692973e3d937129bcbf40652eb9f2f61becf3332 # v4.1.7
         with:
           sparse-checkout: |
@@ -74,6 +76,8 @@ jobs:
           path: 'openvino'
 
       - name: Setup Python 3.11
+        # Python is already installed on Ubuntu within Dockerfile
+        if: runner.os != 'Linux'
         uses: ./openvino/.github/actions/setup_python
         with:
           version: '3.11'
diff --git a/.github/workflows/job_tokenizers.yml b/.github/workflows/job_tokenizers.yml
index 684abf8e026a45..da9e096e7abd8b 100644
--- a/.github/workflows/job_tokenizers.yml
+++ b/.github/workflows/job_tokenizers.yml
@@ -96,10 +96,21 @@ jobs:
       # Dependencies
       #
 
-      - name: Install python dependencies
+      - name: Install OpenVINO Python wheel (Linux and macOS)
+        if: runner.os != 'Windows'
+        run: |
+          # Find and install wheel
+          pushd ${INSTALL_DIR}/tools
+            wheel_name=$(find . -name 'openvino-*.whl')
+            python3 -m pip install $wheel_name
+          popd
+
+      - name: Install OpenVINO Python wheel (Windows)
+        if: runner.os == 'Windows'
         run: |
-          # wheel packaging
-          python3 -m pip install -r ${{ env.OPENVINO_TOKENIZERS_REPO }}/requirements-build.txt
+          # Find and install wheel
+          $ovCoreWheelPath=Get-ChildItem -Path "${{ env.INSTALL_DIR }}\\tools" -Filter openvino-*.whl | % { $_.FullName }
+          python3 -m pip install "$ovCoreWheelPath"
 
       #
       # Build
@@ -108,20 +119,18 @@ jobs:
       - name: Build tokenizers wheel (Linux and macOS)
         if: runner.os != 'Windows'
         run: |
-          source ${INSTALL_DIR}/setupvars.sh
-          python -m build --wheel --outdir ${EXTENSION_BUILD_DIR} ${OPENVINO_TOKENIZERS_REPO}
+          # use OpenVINO wheel package only to build the extension
+          export OpenVINO_DIR=$(python3 -c "from openvino.utils import get_cmake_path; print(get_cmake_path(), end='')")
+          python -m pip wheel -v --no-deps --wheel-dir ${EXTENSION_BUILD_DIR} ${OPENVINO_TOKENIZERS_REPO}
         env:
-          CMAKE_ARGS: '-DBUILD_FAST_TOKENIZERS=OFF'
           CMAKE_BUILD_PARALLEL_LEVEL: '4'
-          CMAKE_GENERATOR: 'Unix Makefiles'
 
       - name: Build tokenizers wheel (Windows)
         if: runner.os == 'Windows'
         run: |
-          . "${{ env.INSTALL_DIR }}/setupvars.ps1"
-          python3 -m build --wheel --outdir ${env:EXTENSION_BUILD_DIR} ${env:OPENVINO_TOKENIZERS_REPO}
+          $env:OpenVINO_DIR=$(python3 -c "from openvino.utils import get_cmake_path; print(get_cmake_path(), end='')")
+          python3 -m pip wheel -v --no-deps --wheel-dir ${env:EXTENSION_BUILD_DIR} ${env:OPENVINO_TOKENIZERS_REPO}
         env:
-          CMAKE_ARGS: '-DBUILD_FAST_TOKENIZERS=OFF'
           CMAKE_BUILD_PARALLEL_LEVEL: '4'
 
       #
diff --git a/.github/workflows/linux.yml b/.github/workflows/linux.yml
index 633a414d713ad7..4358af5707b077 100644
--- a/.github/workflows/linux.yml
+++ b/.github/workflows/linux.yml
@@ -224,7 +224,7 @@ jobs:
         run: |
           /usr/bin/python3.8 -m pip install -U pip
           /usr/bin/python3.8 -m pip install -r ${OPENVINO_REPO}/src/bindings/python/wheel/requirements-dev.txt
-          cmake -UPYTHON* \
+          cmake -UPython* -U_Python* -UPYTHON* \
                 -UTBB* \
                 -DENABLE_SYSTEM_TBB=ON \
                 -DENABLE_PYTHON_PACKAGING=ON \
@@ -245,11 +245,9 @@ jobs:
 
       - name: CMake configure, build and install - OpenVINO JS API
         if: fromJSON(needs.smart_ci.outputs.affected_components).JS_API
-        run:
-          cmake -DCPACK_GENERATOR=NPM -DENABLE_SYSTEM_TBB=OFF -UTBB* -S ${OPENVINO_REPO} -B ${BUILD_DIR}
-
+        run: |
+          cmake -UTBB* -DCPACK_GENERATOR=NPM -DENABLE_SYSTEM_TBB=OFF -S ${OPENVINO_REPO} -B ${BUILD_DIR}
           cmake --build ${BUILD_DIR} --parallel
-
           cmake -DCMAKE_INSTALL_PREFIX=${INSTALL_DIR_JS} -P ${BUILD_DIR}/cmake_install.cmake
 
       #
@@ -317,7 +315,7 @@ jobs:
     uses: ./.github/workflows/job_samples_tests.yml
     with:
       runner: 'aks-linux-4-cores-16gb'
-      image: ${{ fromJSON(needs.docker.outputs.images).ov_test.ubuntu_20_04_x64 }}
+      container: '{"image": "${{ fromJSON(needs.docker.outputs.images).ov_test.ubuntu_20_04_x64 }}", "volumes": ["/mount:/mount"]}'
       affected-components: ${{ needs.smart_ci.outputs.affected_components }}
 
   JS_API:
diff --git a/.github/workflows/linux_arm64.yml b/.github/workflows/linux_arm64.yml
index d429398c05c810..9156fa711e752a 100644
--- a/.github/workflows/linux_arm64.yml
+++ b/.github/workflows/linux_arm64.yml
@@ -309,7 +309,7 @@ jobs:
     uses: ./.github/workflows/job_samples_tests.yml
     with:
       runner: 'aks-linux-16-cores-arm'
-      image: ${{ fromJSON(needs.docker.outputs.images).ov_test.ubuntu_20_04_arm64 }}
+      container: '{"image": "${{ fromJSON(needs.docker.outputs.images).ov_test.ubuntu_20_04_arm64 }}", "volumes": ["/mount:/mount"]}'
       affected-components: ${{ needs.smart_ci.outputs.affected_components }}
 
   JS_API:
diff --git a/.github/workflows/linux_conditional_compilation.yml b/.github/workflows/linux_conditional_compilation.yml
index 48756022f6244b..cdae9c77af9b40 100644
--- a/.github/workflows/linux_conditional_compilation.yml
+++ b/.github/workflows/linux_conditional_compilation.yml
@@ -317,6 +317,7 @@ jobs:
             -DENABLE_OV_TF_LITE_FRONTEND=OFF \
             -DENABLE_OV_PADDLE_FRONTEND=OFF \
             -DENABLE_OV_PYTORCH_FRONTEND=OFF \
+            -DENABLE_OV_JAX_FRONTEND=OFF \
             -DENABLE_OV_ONNX_FRONTEND=OFF \
             -DSELECTIVE_BUILD_STAT=${SELECTIVE_BUILD_STAT_DIR}/*.csv \
             -DCMAKE_C_COMPILER_LAUNCHER=${{ env.CMAKE_C_COMPILER_LAUNCHER }} \
diff --git a/.github/workflows/linux_riscv.yml b/.github/workflows/linux_riscv.yml
index c9629e1cf22477..6c26bb8b3859f0 100644
--- a/.github/workflows/linux_riscv.yml
+++ b/.github/workflows/linux_riscv.yml
@@ -1,8 +1,5 @@
 name: Linux RISC-V with Conan (Ubuntu 22.04, Python 3.10)
 on:
-  schedule:
-    # at 00:00 on Wednesday and Saturday
-    - cron: '0 0 * * 3,6'
   workflow_dispatch:
   pull_request:
   merge_group:
@@ -23,6 +20,7 @@ jobs:
     runs-on: ubuntu-latest
     outputs:
       affected_components: "${{ steps.smart_ci.outputs.affected_components }}"
+      changed_components: "${{ steps.smart_ci.outputs.changed_components }}"
       skip_workflow: "${{ steps.smart_ci.outputs.skip_workflow }}"
     steps:
       - name: checkout action
@@ -43,15 +41,37 @@ jobs:
           skip_when_only_listed_labels_set: 'docs'
           skip_when_only_listed_files_changed: '*.md,*.rst,*.png,*.jpg,*.svg,*/layer_tests_summary/*,*/conformance/*'
 
-  Build:
+  Docker:
     needs: Smart_CI
+    runs-on: aks-linux-4-cores-16gb-docker-build
+    container:
+      image: openvinogithubactions.azurecr.io/docker_build:0.2
+      volumes:
+        - /mount:/mount
+    outputs:
+      images: "${{ steps.handle_docker.outputs.images }}"
+    steps:
+      - name: Checkout
+        uses: actions/checkout@692973e3d937129bcbf40652eb9f2f61becf3332 # v4.1.7
+
+      - uses: ./.github/actions/handle_docker
+        id: handle_docker
+        with:
+          images: |
+            ov_build/ubuntu_22_04_riscv
+          registry: 'openvinogithubactions.azurecr.io'
+          dockerfiles_root_dir: '.github/dockerfiles'
+          changed_components: ${{ needs.smart_ci.outputs.changed_components }}
+
+  Build:
+    needs: [Smart_CI, Docker]
     timeout-minutes: 150
     defaults:
       run:
         shell: bash
     runs-on: aks-linux-16-cores-32gb
     container:
-      image: openvinogithubactions.azurecr.io/dockerhub/ubuntu:22.04
+      image: ${{ fromJSON(needs.docker.outputs.images).ov_build.ubuntu_22_04_riscv }}
       volumes:
         - /mount:/mount
     env:
@@ -70,12 +90,6 @@ jobs:
 
     if: ${{ !needs.smart_ci.outputs.skip_workflow && github.event_name != 'merge_group' }}
     steps:
-      - name: Set apt retries
-        run: echo 'Acquire::Retries "10";' > /etc/apt/apt.conf.d/80-retries
-
-      - name: Install git
-        run: apt-get update && apt-get install --assume-yes --no-install-recommends git ca-certificates
-
       - name: Clone OpenVINO
         uses: actions/checkout@692973e3d937129bcbf40652eb9f2f61becf3332 # v4.1.7
         with:
@@ -119,43 +133,16 @@ jobs:
       # Dependencies
       #
 
-      - name: Install build dependencies
+      - name: Install Python dependencies
         run: |
           # create build directory
           mkdir -p ${OPENVINO_BUILD_DIR}
 
-          # install compilers to build OpenVINO for RISC-V 64
-          apt --assume-yes install gcc-riscv64-linux-gnu g++-riscv64-linux-gnu
-          apt --assume-yes install -yf gcc g++ python3-pip python3-venv python3-dev
-          # generic dependencies
-          apt --assume-yes install cmake ccache ninja-build fdupes patchelf
-
           python3 -m venv ${OPENVINO_BUILD_DIR}/env
           source ${OPENVINO_BUILD_DIR}/env/bin/activate
           python3 -m pip install -r ${OPENVINO_REPO}/src/bindings/python/wheel/requirements-dev.txt
           python3 -m pip install conan
 
-      - name: Install RISC-V native debian packages
-        run: |
-          echo deb [arch=amd64] http://archive.ubuntu.com/ubuntu/ jammy main restricted > riscv64-sources.list
-          echo deb [arch=amd64] http://archive.ubuntu.com/ubuntu/ jammy-updates main restricted >> riscv64-sources.list
-          echo deb [arch=amd64] http://archive.ubuntu.com/ubuntu/ jammy universe >> riscv64-sources.list
-          echo deb [arch=amd64] http://archive.ubuntu.com/ubuntu/ jammy-updates universe >> riscv64-sources.list
-          echo deb [arch=amd64] http://archive.ubuntu.com/ubuntu/ jammy multiverse >> riscv64-sources.list
-          echo deb [arch=amd64] http://archive.ubuntu.com/ubuntu/ jammy-updates multiverse >> riscv64-sources.list
-          echo deb [arch=amd64] http://archive.ubuntu.com/ubuntu/ jammy-backports main restricted universe multiverse >> riscv64-sources.list
-          echo deb [arch=amd64] http://security.ubuntu.com/ubuntu/ jammy-security main restricted >> riscv64-sources.list
-          echo deb [arch=amd64] http://security.ubuntu.com/ubuntu/ jammy-security universe >> riscv64-sources.list
-          echo deb [arch=amd64] http://security.ubuntu.com/ubuntu/ jammy-security multiverse >> riscv64-sources.list
-          echo deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ jammy main >> riscv64-sources.list
-          echo deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ jammy universe >> riscv64-sources.list
-          echo deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ jammy-updates main >> riscv64-sources.list
-          echo deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ jammy-security main >> riscv64-sources.list
-          mv riscv64-sources.list /etc/apt/sources.list.d/
-          dpkg --add-architecture riscv64
-          apt-get update -o Dir::Etc::sourcelist=/etc/apt/sources.list.d/riscv64-sources.list
-          apt-get install -y --no-install-recommends libpython3-dev:riscv64
-
       - name: Create conan_toolchain.cmake file
         run: |
           source ${OPENVINO_BUILD_DIR}/env/bin/activate
diff --git a/.github/workflows/linux_sanitizers.yml b/.github/workflows/linux_sanitizers.yml
index fb298026f4b473..d52378bab8c95b 100644
--- a/.github/workflows/linux_sanitizers.yml
+++ b/.github/workflows/linux_sanitizers.yml
@@ -35,14 +35,14 @@ jobs:
         include:
           - SANITIZER: 'AddressAndLeak'
             SANITIZER_CMAKE_OPTION: '-DENABLE_SANITIZER=ON'
-          - SANITIZER: 'UndefinedBehavior'
-            SANITIZER_CMAKE_OPTION: '-DENABLE_UB_SANITIZER=ON'
+#          - SANITIZER: 'UndefinedBehavior'  # Ticket: 144163
+#            SANITIZER_CMAKE_OPTION: '-DENABLE_UB_SANITIZER=ON'
 #          - SANITIZER: 'Thread'  # Problems with protobuf
 #            SANITIZER_CMAKE_OPTION: '-DENABLE_THREAD_SANITIZER=ON'
     env:
       DEBIAN_FRONTEND: noninteractive # to prevent apt-get from waiting user input
-      CMAKE_BUILD_TYPE: 'Release'
-      CMAKE_GENERATOR: 'Ninja Multi-Config'
+      CMAKE_BUILD_TYPE: 'RelWithDebInfo'
+      CMAKE_GENERATOR: 'Ninja'
       GITHUB_WORKSPACE: '/__w/openvino/openvino'
       OPENVINO_REPO: /__w/openvino/openvino/openvino
       OPENVINO_CONTRIB_REPO: /__w/openvino/openvino/openvino_contrib
@@ -135,7 +135,6 @@ jobs:
             -DENABLE_NCC_STYLE=OFF \
             -DENABLE_TESTS=ON \
             -DENABLE_STRICT_DEPENDENCIES=OFF \
-            -DENABLE_SYSTEM_TBB=ON \
             -DENABLE_SYSTEM_OPENCL=ON \
             -DCMAKE_VERBOSE_MAKEFILE=ON \
             -DCPACK_GENERATOR=TGZ \
@@ -144,6 +143,7 @@ jobs:
             -DENABLE_OV_TF_LITE_FRONTEND=ON \
             -DENABLE_OV_PADDLE_FRONTEND=ON \
             -DENABLE_OV_PYTORCH_FRONTEND=ON \
+            -DENABLE_OV_JAX_FRONTEND=ON \
             -DENABLE_OV_ONNX_FRONTEND=ON \
             -DENABLE_ONEDNN_FOR_GPU=OFF \
             -DCMAKE_COMPILE_WARNING_AS_ERROR=OFF \
@@ -159,9 +159,9 @@ jobs:
 
       - name: Cmake install - OpenVINO
         run: |
-          cmake -DCMAKE_INSTALL_PREFIX=${INSTALL_DIR} -P ${BUILD_DIR}/cmake_install.cmake
-          cmake -DCMAKE_INSTALL_PREFIX=${INSTALL_TEST_DIR} -DCOMPONENT=tests -P ${BUILD_DIR}/cmake_install.cmake
-          cmake -DCMAKE_INSTALL_PREFIX=${INSTALL_DIR} -DCOMPONENT=python_wheels -P ${BUILD_DIR}/cmake_install.cmake
+          cmake -DCMAKE_INSTALL_PREFIX=${INSTALL_DIR} -P ${BUILD_DIR}/cmake_install.cmake --config ${{ env.CMAKE_BUILD_TYPE }}
+          cmake -DCMAKE_INSTALL_PREFIX=${INSTALL_TEST_DIR} -DCOMPONENT=tests -P ${BUILD_DIR}/cmake_install.cmake --config ${{ env.CMAKE_BUILD_TYPE }}
+          cmake -DCMAKE_INSTALL_PREFIX=${INSTALL_DIR} -DCOMPONENT=python_wheels -P ${BUILD_DIR}/cmake_install.cmake --config ${{ env.CMAKE_BUILD_TYPE }}
 
       - name: Remove unused files to free space
         run: rm -rf ${BUILD_DIR}/*
@@ -360,7 +360,7 @@ jobs:
                 --gtest_output=xml:${INSTALL_TEST_DIR}/TEST-TensorFlowLiteFrontend.xml
 
       - name: Transformations func tests
-        if: always()
+        if: ${{ 'false' }} # Ticket: 143900
         run: |
           source ${INSTALL_DIR}/setupvars.sh
 
@@ -389,7 +389,7 @@ jobs:
                 --gtest_output=xml:${INSTALL_TEST_DIR}/TEST-CPUUnitTests.xml
 
       - name: ov_subgraphs_dumper_tests tests
-        if: ${{ 'false' }} # Ticket: 134419
+        if: always()
         run: |
           source ${INSTALL_DIR}/setupvars.sh
           ${INSTALL_TEST_DIR}/ov_subgraphs_dumper_tests --gtest_print_time=1 \
diff --git a/.github/workflows/webassembly.yml b/.github/workflows/webassembly.yml
index 9ce66a9fb02b14..902fb0dfcb00f0 100644
--- a/.github/workflows/webassembly.yml
+++ b/.github/workflows/webassembly.yml
@@ -20,6 +20,7 @@ jobs:
     runs-on: ubuntu-latest
     outputs:
       affected_components: "${{ steps.smart_ci.outputs.affected_components }}"
+      changed_components: "${{ steps.smart_ci.outputs.changed_components }}"
       skip_workflow: "${{ steps.smart_ci.outputs.skip_workflow }}"
     steps:
       - name: checkout action
@@ -40,14 +41,41 @@ jobs:
           skip_when_only_listed_labels_set: 'docs'
           skip_when_only_listed_files_changed: '*.md,*.rst,*.png,*.jpg,*.svg,*/layer_tests_summary/*,*/conformance/*'
 
-  Build:
+      - name: Show affected components
+        run: |
+          echo "${{ toJSON(steps.smart_ci.outputs.affected_components) }}"
+        shell: bash
+
+  Docker:
     needs: Smart_CI
+    runs-on: aks-linux-4-cores-16gb-docker-build
+    container:
+      image: openvinogithubactions.azurecr.io/docker_build:0.2
+      volumes:
+        - /mount:/mount
+    outputs:
+      images: "${{ steps.handle_docker.outputs.images }}"
+    steps:
+      - name: Checkout
+        uses: actions/checkout@692973e3d937129bcbf40652eb9f2f61becf3332 # v4.1.7
+
+      - uses: ./.github/actions/handle_docker
+        id: handle_docker
+        with:
+          images: |
+            ov_build/webassembly
+          registry: 'openvinogithubactions.azurecr.io'
+          dockerfiles_root_dir: '.github/dockerfiles'
+          changed_components: ${{ needs.smart_ci.outputs.changed_components }}
+
+  Build:
+    needs: [Docker, Smart_CI]
     defaults:
       run:
         shell: bash
     runs-on: aks-linux-16-cores-32gb
     container:
-      image: emscripten/emsdk
+      image: ${{ fromJSON(needs.docker.outputs.images).ov_build.webassembly }}
       volumes:
         - /mount:/mount
       options: -e SCCACHE_AZURE_BLOB_CONTAINER -e SCCACHE_AZURE_CONNECTION_STRING
@@ -62,20 +90,12 @@ jobs:
       SCCACHE_AZURE_KEY_PREFIX: webassembly_Release
     if: "!needs.smart_ci.outputs.skip_workflow"
     steps:
-      - name: Install git
-        run: apt-get update && apt-get install --assume-yes --no-install-recommends git ca-certificates
-
       - name: Clone OpenVINO
         uses: actions/checkout@692973e3d937129bcbf40652eb9f2f61becf3332 # v4.1.7
         with:
           path: 'openvino'
           submodules: 'true'
 
-      - name: Install sccache
-        uses: mozilla-actions/sccache-action@2e7f9ec7921547d4b46598398ca573513895d0bd # v0.0.4
-        with:
-          version: "v0.7.5"
-
       - name: emcmake cmake - configure
         run: |
           emcmake cmake \
diff --git a/.github/workflows/windows_conditional_compilation.yml b/.github/workflows/windows_conditional_compilation.yml
index 96e6c44145ddd3..977f9aee91bcce 100644
--- a/.github/workflows/windows_conditional_compilation.yml
+++ b/.github/workflows/windows_conditional_compilation.yml
@@ -319,6 +319,7 @@ jobs:
             -DENABLE_OV_TF_LITE_FRONTEND=OFF `
             -DENABLE_OV_PADDLE_FRONTEND=OFF `
             -DENABLE_OV_PYTORCH_FRONTEND=OFF `
+            -DENABLE_OV_JAX_FRONTEND=OFF `
             -DENABLE_OV_ONNX_FRONTEND=OFF `
             -DSELECTIVE_BUILD_STAT=${{ env.SELECTIVE_BUILD_STAT_DIR }}\*.csv `
             -S ${{ env.OPENVINO_REPO }} `
diff --git a/CMakeLists.txt b/CMakeLists.txt
index adfdc58a8de05b..494a3e4ce21d61 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -41,7 +41,10 @@ if(POLICY CMP0149)
     cmake_policy(SET CMP0149 NEW)
 endif()
 
-project(OpenVINO DESCRIPTION "OpenVINO toolkit")
+project(OpenVINO
+        DESCRIPTION "OpenVINO toolkit"
+        HOMEPAGE_URL "https://docs.openvino.ai/2024/home.html"
+        LANGUAGES C CXX)
 
 find_package(OpenVINODeveloperScripts REQUIRED
              PATHS "${OpenVINO_SOURCE_DIR}/cmake/developer_package"
diff --git a/cmake/coverage.cmake b/cmake/coverage.cmake
index d58200400c904f..061d6c493cdf41 100644
--- a/cmake/coverage.cmake
+++ b/cmake/coverage.cmake
@@ -112,6 +112,13 @@ if(ENABLE_OV_IR_FRONTEND)
         PREFIX "${OV_COVERAGE_BASE_DIRECTORY}")
 endif()
 
+if(ENABLE_OV_JAX_FRONTEND)
+    ov_coverage_extract(INPUT "openvino" OUTPUT "jax_frontend"
+        PATTERNS "${OV_COVERAGE_BASE_DIRECTORY}/src/frontends/jax/*")
+    ov_coverage_genhtml(INFO_FILE "jax_frontend"
+        PREFIX "${OV_COVERAGE_BASE_DIRECTORY}")
+endif()
+
 if(ENABLE_OV_ONNX_FRONTEND)
     ov_coverage_extract(INPUT "openvino" OUTPUT "onnx_frontend"
         PATTERNS "${OV_COVERAGE_BASE_DIRECTORY}/src/frontends/onnx/*")
diff --git a/cmake/dependencies.cmake b/cmake/dependencies.cmake
index 69dedb3e3bd527..219d464682b016 100644
--- a/cmake/dependencies.cmake
+++ b/cmake/dependencies.cmake
@@ -89,10 +89,10 @@ function(ov_download_tbb)
         # TODO: add target_path to be platform specific as well, to avoid following if
         # build oneTBB 2021.2.1 with Visual Studio 2019 (MSVC 14.21)
         RESOLVE_DEPENDENCY(TBB
-                ARCHIVE_WIN "oneapi-tbb-2021.2.2-win.zip"
+                ARCHIVE_WIN "oneapi-tbb-2021.2.5-win-trim.zip"
                 TARGET_PATH "${TEMP}/tbb"
                 ENVIRONMENT "TBBROOT"
-                SHA256 "103b19a8af288c6a7d83ed3f0d2239c4afd0dd189fc12aad1d34b3c9e78df94b"
+                SHA256 "a9384a25861946648db58f7f294c4aa2b1be7ae52748024fef3e13ca2762a1ba"
                 USE_NEW_LOCATION TRUE)
     elseif(ANDROID AND X86_64)
         RESOLVE_DEPENDENCY(TBB
@@ -104,10 +104,10 @@ function(ov_download_tbb)
     elseif(LINUX AND X86_64 AND OPENVINO_GNU_LIBC AND OV_LIBC_VERSION VERSION_GREATER_EQUAL 2.17)
         # build oneTBB 2021.2.1 with gcc 4.8 (glibc 2.17)
         RESOLVE_DEPENDENCY(TBB
-                ARCHIVE_LIN "oneapi-tbb-2021.2.4-lin.tgz"
+                ARCHIVE_LIN "oneapi-tbb-2021.2.5-lin-trim.tgz"
                 TARGET_PATH "${TEMP}/tbb"
                 ENVIRONMENT "TBBROOT"
-                SHA256 "6523661559a340e88131472ea9a595582c306af083e55293b7357d11b8015546"
+                SHA256 "9bea2c838df3085d292989d643523dc1cedce9b46d5a03eec90104151b49a180"
                 USE_NEW_LOCATION TRUE)
     elseif(YOCTO_AARCH64)
         RESOLVE_DEPENDENCY(TBB
@@ -119,34 +119,34 @@ function(ov_download_tbb)
     elseif(APPLE AND X86_64)
         # build oneTBB 2021.2.1 with OS version 11.4
         RESOLVE_DEPENDENCY(TBB
-                ARCHIVE_MAC "oneapi-tbb-2021.2.1-mac.tgz"
+                ARCHIVE_MAC "oneapi-tbb-2021.2.5-mac-trim.tgz"
                 TARGET_PATH "${TEMP}/tbb"
                 ENVIRONMENT "TBBROOT"
-                SHA256 "c57ce4b97116cd3093c33e6dcc147fb1bbb9678d0ee6c61a506b2bfe773232cb"
+                SHA256 "5a6035fcf7b9d3bd8183ecc31b3e2b2026a749152b5b879f8e4d147e09479efc"
                 USE_NEW_LOCATION TRUE)
     elseif(WIN32 AND AARCH64)
         # build oneTBB 2021.2.1 with Visual Studio 2022 (MSVC 14.35)
         RESOLVE_DEPENDENCY(TBB
-                ARCHIVE_WIN "oneapi-tbb-2021.2.1-win-arm64.zip"
+                ARCHIVE_WIN "oneapi-tbb-2021.2.5-win-arm64-trim.zip"
                 TARGET_PATH "${TEMP}/tbb"
                 ENVIRONMENT "TBBROOT"
-                SHA256 "09fe7f5e7be589aa34ccd20fdfd7cad9e0afa89d1e74ecdb008a75d0af71d6e1"
+                SHA256 "c26b7593e1808c2dd15a768cd4ea1ee14aa0aa2dacb210b86e326ab7960d2473"
                 USE_NEW_LOCATION TRUE)
     elseif(LINUX AND AARCH64 AND OPENVINO_GNU_LIBC AND OV_LIBC_VERSION VERSION_GREATER_EQUAL 2.17)
-        # build oneTBB 2021.2.1 with gcc 4.8 (glibc 2.17)
+        # build oneTBB with glibc 2.17
         RESOLVE_DEPENDENCY(TBB
-                ARCHIVE_LIN "oneapi-tbb-2021.2.5-lin-arm64.tgz"
+                ARCHIVE_LIN "oneapi-tbb-2021.13.0-rc1-lin-arm64-trim.tgz"
                 TARGET_PATH "${TEMP}/tbb"
                 ENVIRONMENT "TBBROOT"
-                SHA256 "86b24e4db254136bf88f0a8195ae478494e34db109e08569b1c059b174494865"
+                SHA256 "7fe49525217de9536980a820d90645784216ad4a61e11799b8c95129dcdeeecf"
                 USE_NEW_LOCATION TRUE)
     elseif(APPLE AND AARCH64)
-        # build oneTBB 2021.2.1 with export MACOSX_DEPLOYMENT_TARGET=11.0
+        # build oneTBB with export MACOSX_DEPLOYMENT_TARGET=11.0
         RESOLVE_DEPENDENCY(TBB
-                ARCHIVE_MAC "oneapi-tbb-2021.2.5-mac-arm64.tgz"
+                ARCHIVE_MAC "oneapi-tbb-2021.13.0-rc1-mac-arm64-trim.tgz"
                 TARGET_PATH "${TEMP}/tbb"
                 ENVIRONMENT "TBBROOT"
-                SHA256 "bb1f84b1dcc50787f35c99b4b6ecea733d3068ca3467b5ebd2e369c4f7eccb53"
+                SHA256 "d3ce1c00e46a187baee459458e8d13d3421dc7242bff0c977b95d8d66d74441a"
                 USE_NEW_LOCATION TRUE)
     else()
         message(WARNING "Prebuilt TBB is not available on current platform")
diff --git a/cmake/developer_package/OpenVINODeveloperScriptsConfig.cmake b/cmake/developer_package/OpenVINODeveloperScriptsConfig.cmake
index 904ad86e31d8f0..ee8111bd185d85 100644
--- a/cmake/developer_package/OpenVINODeveloperScriptsConfig.cmake
+++ b/cmake/developer_package/OpenVINODeveloperScriptsConfig.cmake
@@ -211,6 +211,8 @@ set(CMAKE_POLICY_DEFAULT_CMP0068 NEW)
 set(CMAKE_POLICY_DEFAULT_CMP0074 NEW)
 # CMake 3.13+: option() honors normal variables.
 set(CMAKE_POLICY_DEFAULT_CMP0077 NEW)
+# CMake 3.15+: export(PACKAGE) does not populate package registry by default.
+set(CMAKE_POLICY_DEFAULT_CMP0090 NEW)
 # CMake 3.15: Modules FindPython3, FindPython2 and FindPython use LOCATION for lookup strategy
 set(CMAKE_POLICY_DEFAULT_CMP0094 NEW)
 # CMake 3.19+: An imported target missing its location property fails during generation.
@@ -222,6 +224,11 @@ set(CMAKE_POLICY_DEFAULT_CMP0135 NEW)
 # CMake 3.27+ :Visual Studio Generators select latest Windows SDK by default.
 set(CMAKE_POLICY_DEFAULT_CMP0149 NEW)
 
+set(CMAKE_FIND_USE_PACKAGE_REGISTRY OFF CACHE BOOL "Disables search in user / system package registries")
+set(CMAKE_FIND_PACKAGE_NO_PACKAGE_REGISTRY ON CACHE BOOL "Disables search in user package registries")
+set(CMAKE_FIND_PACKAGE_NO_SYSTEM_PACKAGE_REGISTRY ON CACHE BOOL "Disables search in system package registries")
+set(CMAKE_EXPORT_PACKAGE_REGISTRY OFF CACHE BOOL "Disables package registry. Required for 3rd party projects like rapidjson, gflags")
+set(CMAKE_EXPORT_NO_PACKAGE_REGISTRY ON CACHE BOOL "Disables package registry. Required for 3rd party projects like rapidjson, gflags")
 set(CMAKE_WARN_DEPRECATED OFF CACHE BOOL "Don't warn about obsolete cmake versions in 3rdparty")
 set(CMAKE_WARN_ON_ABSOLUTE_INSTALL_DESTINATION ON CACHE BOOL "Warn about absolute paths in destination")
 
diff --git a/cmake/developer_package/api_validator/api_validator.cmake b/cmake/developer_package/api_validator/api_validator.cmake
index 0fd1f2c03a3343..ff6b0fbaa061e9 100644
--- a/cmake/developer_package/api_validator/api_validator.cmake
+++ b/cmake/developer_package/api_validator/api_validator.cmake
@@ -2,7 +2,11 @@
 # SPDX-License-Identifier: Apache-2.0
 #
 
-if(WIN32)
+macro(ov_search_api_validator)
+    if(NOT ENABLE_API_VALIDATOR)
+        return()
+    endif()
+
     # CMAKE_VS_WINDOWS_TARGET_PLATFORM_VERSION is only set when
     # Visual Studio generators are used, but we need it
     # when we use Ninja as well
@@ -12,7 +16,7 @@ if(WIN32)
             set(CMAKE_VS_WINDOWS_TARGET_PLATFORM_VERSION ${WINDOWS_SDK_VERSION})
             message(STATUS "Use ${CMAKE_VS_WINDOWS_TARGET_PLATFORM_VERSION} Windows SDK version")
         else()
-          message(FATAL_ERROR "WindowsSDKVersion environment variable is not set,\
+            message(FATAL_ERROR "WindowsSDKVersion environment variable is not set,\
 can't find Windows SDK version. Try to use vcvarsall.bat script")
         endif()
     endif()
@@ -41,6 +45,11 @@ can't find Windows SDK version. Try to use vcvarsall.bat script")
             message(STATUS "Found apivalidator: ${ONECORE_API_VALIDATOR}")
         endif()
     endif()
+endmacro()
+
+
+if(ENABLE_API_VALIDATOR)
+    ov_search_api_validator()
 endif()
 
 function(_ov_add_api_validator_post_build_step_recursive)
diff --git a/cmake/developer_package/compile_flags/os_flags.cmake b/cmake/developer_package/compile_flags/os_flags.cmake
index f846d8a0050afc..a49dce9bce7b50 100644
--- a/cmake/developer_package/compile_flags/os_flags.cmake
+++ b/cmake/developer_package/compile_flags/os_flags.cmake
@@ -75,11 +75,10 @@ macro(ov_dev_package_no_errors)
         endif()
     endif()
 
-    if (CMAKE_COMPILE_WARNING_AS_ERROR AND WIN32)
+    if(CMAKE_COMPILE_WARNING_AS_ERROR AND WIN32)
         if(CMAKE_CXX_COMPILER_ID STREQUAL "MSVC")
             if(CMAKE_VERSION VERSION_LESS 3.24)
-                string(REPLACE "/WX" "" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
-                string(REPLACE "/WX" "" CMAKE_C_FLAGS "${CMAKE_C_FLAGS}")
+                ov_add_compiler_flags(/WX-)
             endif()
             string(REPLACE "/WX" "" CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS}")
         endif()
diff --git a/cmake/developer_package/compile_flags/sdl.cmake b/cmake/developer_package/compile_flags/sdl.cmake
index ce47c9cd552f93..d399ec4f62fe0d 100644
--- a/cmake/developer_package/compile_flags/sdl.cmake
+++ b/cmake/developer_package/compile_flags/sdl.cmake
@@ -48,6 +48,7 @@ if(CMAKE_COMPILER_IS_GNUCXX OR OV_COMPILER_IS_CLANG OR OV_COMPILER_IS_INTEL_LLVM
     endif()
 elseif(CMAKE_CXX_COMPILER_ID STREQUAL "MSVC")
     set(OV_C_CXX_FLAGS "${OV_C_CXX_FLAGS} /sdl /guard:cf")
+    set(OV_LINKER_FLAGS "${OV_LINKER_FLAGS} /guard:cf")
 endif()
 
 if(ENABLE_QSPECTRE)
diff --git a/cmake/developer_package/cross_compile/python_helpers.cmake b/cmake/developer_package/cross_compile/python_helpers.cmake
index a6c7de739375f1..050c99db9a852f 100644
--- a/cmake/developer_package/cross_compile/python_helpers.cmake
+++ b/cmake/developer_package/cross_compile/python_helpers.cmake
@@ -49,6 +49,19 @@ endfunction()
 
 # Wrapper for find_package(Python3) to allow cross-compilation
 macro(ov_find_python3 find_package_mode)
+    # Settings for FindPython3.cmake
+    if(NOT DEFINED Python3_USE_STATIC_LIBS)
+        set(Python3_USE_STATIC_LIBS OFF)
+    endif()
+
+    if(NOT DEFINED Python3_FIND_VIRTUALENV)
+        set(Python3_FIND_VIRTUALENV FIRST)
+    endif()
+
+    if(NOT DEFINED Python3_FIND_IMPLEMENTATIONS)
+        set(Python3_FIND_IMPLEMENTATIONS CPython PyPy)
+    endif()
+
     if(CMAKE_VERSION VERSION_GREATER_EQUAL 3.18)
         set(python3_development_component Development.Module)
     else()
diff --git a/cmake/developer_package/features.cmake b/cmake/developer_package/features.cmake
index da2fbf6af4f5fe..d2fdcd4cf12049 100644
--- a/cmake/developer_package/features.cmake
+++ b/cmake/developer_package/features.cmake
@@ -5,8 +5,11 @@
 include(options)
 include(target_flags)
 
-set (CPACK_GENERATOR "TGZ" CACHE STRING "Cpack generator for OpenVINO")
-list (APPEND OV_OPTIONS CPACK_GENERATOR)
+if(WIN32)
+    set (CPACK_GENERATOR "ZIP" CACHE STRING "Cpack generator for OpenVINO")
+else()
+    set (CPACK_GENERATOR "TGZ" CACHE STRING "Cpack generator for OpenVINO")
+endif()
 
 ov_dependent_option (ENABLE_LTO "Enable Link Time Optimization" OFF "LINUX;NOT ARM;CMAKE_CXX_COMPILER_VERSION VERSION_GREATER 4.9" OFF)
 
diff --git a/cmake/developer_package/frontends/frontends.cmake b/cmake/developer_package/frontends/frontends.cmake
index 0b959322950106..87efaea0ceb387 100644
--- a/cmake/developer_package/frontends/frontends.cmake
+++ b/cmake/developer_package/frontends/frontends.cmake
@@ -319,7 +319,7 @@ macro(ov_add_frontend)
             if(OV_FRONTEND_LINKABLE_FRONTEND)
                 set(export_set EXPORT OpenVINOTargets)
                 set(archive_dest ARCHIVE DESTINATION ${OV_CPACK_ARCHIVEDIR} COMPONENT ${dev_component} ${OV_CPACK_COMP_CORE_DEV_EXCLUDE_ALL})
-                set(namelink NAMELINK_COMPONENT ${dev_component})
+                set(namelink NAMELINK_COMPONENT ${OV_CPACK_COMP_LINKS} ${OV_CPACK_COMP_LINKS_EXCLUDE_ALL})
             else()
                 set(namelink NAMELINK_SKIP)
             endif()
diff --git a/cmake/developer_package/ncc_naming_style/openvino.style b/cmake/developer_package/ncc_naming_style/openvino.style
index 8bfa4164490482..6c82ccb9b8c55a 100644
--- a/cmake/developer_package/ncc_naming_style/openvino.style
+++ b/cmake/developer_package/ncc_naming_style/openvino.style
@@ -1,6 +1,6 @@
 # custom OpenVINO values
 CppMethod: '^(operator\W+|[a-z_\d]+|signaling_NaN|quiet_NaN|OPENVINO_OP)$'
-ClassName: '^([A-Z][\w]+|b?float16|float8_e4m3|float8_e5m2|float4_e2m1|numeric_limits|ngraph_error|stopwatch|unsupported_op)$'
+ClassName: '^([A-Z][\w]+|b?float16|float8_e4m3|float8_e5m2|float4_e2m1|float8_e8m0|numeric_limits|ngraph_error|stopwatch|unsupported_op)$'
 StructName: '^([A-Z][\w]+|element_type_traits|hash|oi_pair|stat)$'
 FunctionName: '^(operator\W+|[a-z_\d]+)|PrintTo$'
 Namespace: '^([a-z\d_]*|InferenceEngine)$'
@@ -18,7 +18,7 @@ VariableReference: '^\w+$'
 
 EnumName: '^[A-Z][\w]+$'
 # excepts element_type
-EnumConstantName: '^([A-Z\d_]+|undefined|dynamic|boolean|bf16|f16|f32|f64|i4|i8|i16|i32|i64|u1|u2|u3|u4|u6|u8|u16|u32|u64|nf4|f8e4m3|f8e5m2|f4e2m1|string|asymmetric|align_corners|round_prefer_floor|round_prefer_ceil|floor|ceil|simple|nearest|linear|linear_onnx|cubic|area|scales|sizes|half_pixel|tf_half_pixel_for_nn|pytorch_half_pixel|asymetric)$'
+EnumConstantName: '^([A-Z\d_]+|undefined|dynamic|boolean|bf16|f16|f32|f64|i4|i8|i16|i32|i64|u1|u2|u3|u4|u6|u8|u16|u32|u64|nf4|f8e4m3|f8e5m2|f4e2m1|f8e8m0|string|asymmetric|align_corners|round_prefer_floor|round_prefer_ceil|floor|ceil|simple|nearest|linear|linear_onnx|cubic|area|scales|sizes|half_pixel|tf_half_pixel_for_nn|pytorch_half_pixel|asymetric)$'
 # TODO: align
 UsingDeclaration: '^.*$'
 TypedefName: '^.*$'
diff --git a/cmake/developer_package/options.cmake b/cmake/developer_package/options.cmake
index a54a8bed1d4e08..3ad42f156c706c 100644
--- a/cmake/developer_package/options.cmake
+++ b/cmake/developer_package/options.cmake
@@ -11,11 +11,13 @@ endif()
 macro(ov_option variable description value)
     option(${variable} "${description}" ${value})
     list(APPEND OV_OPTIONS ${variable})
+    set(OV_OPTIONS "${OV_OPTIONS}" CACHE INTERNAL "A list of OpenVINO cmake options")
 endmacro()
 
 macro(ov_dependent_option variable description def_value condition fallback_value)
     cmake_dependent_option(${variable} "${description}" ${def_value} "${condition}" ${fallback_value})
     list(APPEND OV_OPTIONS ${variable})
+    set(OV_OPTIONS "${OV_OPTIONS}" CACHE INTERNAL "A list of OpenVINO cmake options")
 endmacro()
 
 macro(ov_option_enum variable description value)
@@ -29,6 +31,7 @@ macro(ov_option_enum variable description value)
     endif()
 
     list(APPEND OV_OPTIONS ${variable})
+    set(OV_OPTIONS "${OV_OPTIONS}" CACHE INTERNAL "A list of OpenVINO cmake options")
 
     set(${variable} ${value} CACHE STRING "${description}")
     set_property(CACHE ${variable} PROPERTY STRINGS ${OPTION_ENUM_ALLOWED_VALUES})
diff --git a/cmake/developer_package/packaging/archive.cmake b/cmake/developer_package/packaging/archive.cmake
index 19c6ac2ae5fc11..853b7649e88ddf 100644
--- a/cmake/developer_package/packaging/archive.cmake
+++ b/cmake/developer_package/packaging/archive.cmake
@@ -90,6 +90,12 @@ macro(ov_define_component_include_rules)
     # scripts
     unset(OV_CPACK_COMP_INSTALL_DEPENDENCIES_EXCLUDE_ALL)
     unset(OV_CPACK_COMP_SETUPVARS_EXCLUDE_ALL)
+    # pkgconfig
+    set(OV_CPACK_COMP_PKG_CONFIG_EXCLUDE_ALL ${OV_CPACK_COMP_CORE_DEV_EXCLUDE_ALL})
+    # symbolic links
+    set(OV_CPACK_COMP_LINKS_EXCLUDE_ALL ${OV_CPACK_COMP_CORE_DEV_EXCLUDE_ALL})
+    # npu internal tools
+    unset(OV_CPACK_COMP_NPU_INTERNAL_EXCLUDE_ALL)
 endmacro()
 
 ov_define_component_include_rules()
diff --git a/cmake/developer_package/packaging/common-libraries.cmake b/cmake/developer_package/packaging/common-libraries.cmake
index c47c123ab4b31d..4ec96dc28b53e8 100644
--- a/cmake/developer_package/packaging/common-libraries.cmake
+++ b/cmake/developer_package/packaging/common-libraries.cmake
@@ -57,6 +57,9 @@ macro(ov_override_component_names)
     # merge C++ and C runtimes
     set(OV_CPACK_COMP_CORE_C "${OV_CPACK_COMP_CORE}")
     set(OV_CPACK_COMP_CORE_C_DEV "${OV_CPACK_COMP_CORE_DEV}")
+    # merge links and pkgconfig with dev component
+    set(OV_CPACK_COMP_LINKS "${OV_CPACK_COMP_CORE_DEV}")
+    set(OV_CPACK_COMP_PKG_CONFIG "${OV_CPACK_COMP_CORE_DEV}")
 endmacro()
 
 ov_override_component_names()
@@ -104,6 +107,12 @@ macro(ov_define_component_include_rules)
     # scripts
     set(OV_CPACK_COMP_INSTALL_DEPENDENCIES_EXCLUDE_ALL EXCLUDE_FROM_ALL)
     set(OV_CPACK_COMP_SETUPVARS_EXCLUDE_ALL EXCLUDE_FROM_ALL)
+    # pkgconfig
+    set(OV_CPACK_COMP_PKG_CONFIG_EXCLUDE_ALL ${OV_CPACK_COMP_CORE_DEV_EXCLUDE_ALL})
+    # symbolic links
+    set(OV_CPACK_COMP_LINKS_EXCLUDE_ALL ${OV_CPACK_COMP_CORE_DEV_EXCLUDE_ALL})
+    # npu internal tools
+    set(OV_CPACK_COMP_NPU_INTERNAL_EXCLUDE_ALL EXCLUDE_FROM_ALL)
 endmacro()
 
 ov_define_component_include_rules()
diff --git a/cmake/developer_package/packaging/debian/debian.cmake b/cmake/developer_package/packaging/debian/debian.cmake
index bdb005ab6c3706..c7f49419111cea 100644
--- a/cmake/developer_package/packaging/debian/debian.cmake
+++ b/cmake/developer_package/packaging/debian/debian.cmake
@@ -60,6 +60,9 @@ macro(ov_override_component_names)
     # merge all C / C++ samples as a single samples component
     set(OV_CPACK_COMP_CPP_SAMPLES "samples")
     set(OV_CPACK_COMP_C_SAMPLES "${OV_CPACK_COMP_CPP_SAMPLES}")
+    # merge links and pkgconfig with dev component
+    set(OV_CPACK_COMP_LINKS "${OV_CPACK_COMP_CORE_DEV}")
+    set(OV_CPACK_COMP_PKG_CONFIG "${OV_CPACK_COMP_CORE_DEV}")
 endmacro()
 
 ov_override_component_names()
@@ -111,6 +114,12 @@ macro(ov_define_component_include_rules)
     # scripts
     set(OV_CPACK_COMP_INSTALL_DEPENDENCIES_EXCLUDE_ALL EXCLUDE_FROM_ALL)
     set(OV_CPACK_COMP_SETUPVARS_EXCLUDE_ALL EXCLUDE_FROM_ALL)
+    # pkgconfig
+    set(OV_CPACK_COMP_PKG_CONFIG_EXCLUDE_ALL ${OV_CPACK_COMP_CORE_DEV_EXCLUDE_ALL})
+    # symbolic links
+    set(OV_CPACK_COMP_LINKS_EXCLUDE_ALL ${OV_CPACK_COMP_CORE_DEV_EXCLUDE_ALL})
+    # npu internal tools
+    set(OV_CPACK_COMP_NPU_INTERNAL_EXCLUDE_ALL EXCLUDE_FROM_ALL)
 endmacro()
 
 ov_define_component_include_rules()
diff --git a/cmake/developer_package/packaging/npm.cmake b/cmake/developer_package/packaging/npm.cmake
index 6d7ca6d04e680a..24453965125348 100644
--- a/cmake/developer_package/packaging/npm.cmake
+++ b/cmake/developer_package/packaging/npm.cmake
@@ -4,7 +4,7 @@
 
 include(GNUInstallDirs)
 
-# We have to specify RPATH, all runtime libs are in one dir 
+# We have to specify RPATH, all runtime libs are in one dir
 set(CMAKE_SKIP_INSTALL_RPATH OFF)
 
 #
@@ -32,6 +32,20 @@ endmacro()
 
 ov_npm_cpack_set_dirs()
 
+#
+# Override CPack components name for NPM generator
+# This is needed to change the granularity, i.e. merge several components
+# into a single one
+#
+
+macro(ov_override_component_names)
+    # merge links and pkgconfig with dev component
+    set(OV_CPACK_COMP_LINKS "${OV_CPACK_COMP_CORE_DEV}")
+    set(OV_CPACK_COMP_PKG_CONFIG "${OV_CPACK_COMP_CORE_DEV}")
+endmacro()
+
+ov_override_component_names()
+
 #
 # Override include / exclude rules for components
 # This is required to exclude some files from installation
@@ -67,6 +81,12 @@ macro(ov_define_component_include_rules)
     # scripts
     set(OV_CPACK_COMP_INSTALL_DEPENDENCIES_EXCLUDE_ALL EXCLUDE_FROM_ALL)
     set(OV_CPACK_COMP_SETUPVARS_EXCLUDE_ALL EXCLUDE_FROM_ALL)
+    # pkgconfig
+    unset(OV_CPACK_COMP_PKG_CONFIG_EXCLUDE_ALL)
+    # symbolic links
+    unset(OV_CPACK_COMP_LINKS_EXCLUDE_ALL)
+    # npu internal tools
+    set(OV_CPACK_COMP_NPU_INTERNAL_EXCLUDE_ALL EXCLUDE_FROM_ALL)
 endmacro()
 
 ov_define_component_include_rules()
diff --git a/cmake/developer_package/packaging/nsis.cmake b/cmake/developer_package/packaging/nsis.cmake
index 9dcda7315dab61..f5f9a233e8b87f 100644
--- a/cmake/developer_package/packaging/nsis.cmake
+++ b/cmake/developer_package/packaging/nsis.cmake
@@ -87,6 +87,20 @@ endmacro()
 
 ov_nsis_cpack_set_dirs()
 
+#
+# Override CPack components name for NSIS generator
+# This is needed to change the granularity, i.e. merge several components
+# into a single one
+#
+
+macro(ov_override_component_names)
+    # merge links and pkgconfig with dev component
+    set(OV_CPACK_COMP_LINKS "${OV_CPACK_COMP_CORE_DEV}")
+    set(OV_CPACK_COMP_PKG_CONFIG "${OV_CPACK_COMP_CORE_DEV}")
+endmacro()
+
+ov_override_component_names()
+
 #
 # Override include / exclude rules for components
 # This is required to exclude some files from installation
@@ -122,6 +136,12 @@ macro(ov_define_component_include_rules)
     # scripts
     unset(OV_CPACK_COMP_INSTALL_DEPENDENCIES_EXCLUDE_ALL)
     unset(OV_CPACK_COMP_SETUPVARS_EXCLUDE_ALL)
+    # pkgconfig
+    set(OV_CPACK_COMP_PKG_CONFIG_EXCLUDE_ALL ${OV_CPACK_COMP_CORE_DEV_EXCLUDE_ALL})
+    # symbolic links
+    set(OV_CPACK_COMP_LINKS_EXCLUDE_ALL ${OV_CPACK_COMP_CORE_DEV_EXCLUDE_ALL})
+    # npu internal tools
+    set(OV_CPACK_COMP_NPU_INTERNAL_EXCLUDE_ALL EXCLUDE_FROM_ALL)
 endmacro()
 
 ov_define_component_include_rules()
diff --git a/cmake/developer_package/packaging/packaging.cmake b/cmake/developer_package/packaging/packaging.cmake
index 41c0308acafba6..d62a8ca7fe2084 100644
--- a/cmake/developer_package/packaging/packaging.cmake
+++ b/cmake/developer_package/packaging/packaging.cmake
@@ -178,6 +178,10 @@ macro(ov_define_component_names)
     # scripts
     set(OV_CPACK_COMP_INSTALL_DEPENDENCIES "install_dependencies")
     set(OV_CPACK_COMP_SETUPVARS "setupvars")
+    # symbolic_links
+    set(OV_CPACK_COMP_LINKS "core_dev_links")
+    # pkgconfig
+    set(OV_CPACK_COMP_PKG_CONFIG "core_dev_pkgconfig")
 endmacro()
 
 ov_define_component_names()
diff --git a/cmake/developer_package/packaging/rpm/rpm.cmake b/cmake/developer_package/packaging/rpm/rpm.cmake
index 99f11730983f04..7c9fb4f22a372d 100644
--- a/cmake/developer_package/packaging/rpm/rpm.cmake
+++ b/cmake/developer_package/packaging/rpm/rpm.cmake
@@ -51,6 +51,9 @@ macro(ov_override_component_names)
     # merge all C / C++ samples as a single samples component
     set(OV_CPACK_COMP_CPP_SAMPLES "samples")
     set(OV_CPACK_COMP_C_SAMPLES "${OV_CPACK_COMP_CPP_SAMPLES}")
+    # merge links and pkgconfig with dev component
+    set(OV_CPACK_COMP_LINKS "${OV_CPACK_COMP_CORE_DEV}")
+    set(OV_CPACK_COMP_PKG_CONFIG "${OV_CPACK_COMP_CORE_DEV}")
 endmacro()
 
 ov_override_component_names()
@@ -102,6 +105,12 @@ macro(ov_define_component_include_rules)
     # scripts
     set(OV_CPACK_COMP_INSTALL_DEPENDENCIES_EXCLUDE_ALL EXCLUDE_FROM_ALL)
     set(OV_CPACK_COMP_SETUPVARS_EXCLUDE_ALL EXCLUDE_FROM_ALL)
+    # pkgconfig
+    set(OV_CPACK_COMP_PKG_CONFIG_EXCLUDE_ALL ${OV_CPACK_COMP_CORE_DEV_EXCLUDE_ALL})
+    # symbolic links
+    set(OV_CPACK_COMP_LINKS_EXCLUDE_ALL ${OV_CPACK_COMP_CORE_DEV_EXCLUDE_ALL})
+    # npu internal tools
+    set(OV_CPACK_COMP_NPU_INTERNAL_EXCLUDE_ALL EXCLUDE_FROM_ALL)
 endmacro()
 
 ov_define_component_include_rules()
diff --git a/cmake/features.cmake b/cmake/features.cmake
index bfa3a05481d77f..4063e2f8545ced 100644
--- a/cmake/features.cmake
+++ b/cmake/features.cmake
@@ -137,6 +137,7 @@ endif()
 ov_option(ENABLE_OV_PADDLE_FRONTEND "Enable PaddlePaddle FrontEnd" ON)
 ov_option(ENABLE_OV_IR_FRONTEND "Enable IR FrontEnd" ON)
 ov_option(ENABLE_OV_PYTORCH_FRONTEND "Enable PyTorch FrontEnd" ON)
+ov_option(ENABLE_OV_JAX_FRONTEND "Enable JAX FrontEnd" ON)
 ov_option(ENABLE_OV_IR_FRONTEND "Enable IR FrontEnd" ON)
 ov_option(ENABLE_OV_TF_FRONTEND "Enable TensorFlow FrontEnd" ON)
 ov_option(ENABLE_OV_TF_LITE_FRONTEND "Enable TensorFlow Lite FrontEnd" ON)
@@ -192,6 +193,8 @@ ov_dependent_option(ENABLE_JS "Enables JS API building" ON "NOT ANDROID;NOT EMSC
 
 ov_option(ENABLE_OPENVINO_DEBUG "Enable output for OPENVINO_DEBUG statements" OFF)
 
+ov_dependent_option (ENABLE_API_VALIDATOR "Enables API Validator usage" ON "WIN32" OFF)
+
 if(NOT BUILD_SHARED_LIBS AND ENABLE_OV_TF_FRONTEND)
     set(FORCE_FRONTENDS_USE_PROTOBUF ON)
 else()
diff --git a/cmake/packaging/debian.cmake b/cmake/packaging/debian.cmake
index 90b01a196cec84..b97f8eea67c8a6 100644
--- a/cmake/packaging/debian.cmake
+++ b/cmake/packaging/debian.cmake
@@ -234,6 +234,19 @@ macro(ov_cpack_settings)
         set(ir_copyright "generic")
     endif()
 
+    if(ENABLE_OV_JAX_FRONTEND)
+        set(CPACK_COMPONENT_JAX_DESCRIPTION "OpenVINO JAX Frontend")
+        set(CPACK_COMPONENT_JAX_DEPENDS "${OV_CPACK_COMP_CORE}")
+        set(CPACK_DEBIAN_JAX_PACKAGE_NAME "libopenvino-jax-frontend-${cpack_name_ver}")
+        # since we JAX FE is linkable target, we need to call ldconfig (i.e. `def_triggers`)
+        set(CPACK_DEBIAN_JAX_PACKAGE_CONTROL_EXTRA "${def_postinst};${def_postrm};${def_triggers}")
+        ov_debian_add_lintian_suppression(jax
+            # we have different package name strategy; it suggests libopenvino-jax-frontend202230
+            "package-name-doesnt-match-sonames")
+        list(APPEND frontends jax)
+        set(jax_copyright "generic")
+    endif()
+
     if(ENABLE_OV_ONNX_FRONTEND)
         set(CPACK_COMPONENT_ONNX_DESCRIPTION "OpenVINO ONNX Frontend")
         set(CPACK_COMPONENT_ONNX_DEPENDS "${OV_CPACK_COMP_CORE}")
diff --git a/cmake/packaging/rpm.cmake b/cmake/packaging/rpm.cmake
index 6bbeba02bdc11f..4d06ebff496421 100644
--- a/cmake/packaging/rpm.cmake
+++ b/cmake/packaging/rpm.cmake
@@ -208,6 +208,15 @@ macro(ov_cpack_settings)
         set(ir_copyright "generic")
     endif()
 
+    if(ENABLE_OV_JAX_FRONTEND)
+        set(CPACK_COMPONENT_JAX_DESCRIPTION "OpenVINO JAX Frontend")
+        set(CPACK_RPM_JAX_PACKAGE_NAME "libopenvino-jax-frontend-${cpack_name_ver}")
+        set(CPACK_RPM_JAX_POST_INSTALL_SCRIPT_FILE "${def_triggers}")
+        set(CPACK_RPM_JAX_POST_UNINSTALL_SCRIPT_FILE "${def_triggers}")
+        _ov_add_package(frontend_packages jax)
+        set(jax_copyright "generic")
+    endif()
+
     if(ENABLE_OV_ONNX_FRONTEND)
         set(CPACK_COMPONENT_ONNX_DESCRIPTION "OpenVINO ONNX Frontend")
         set(CPACK_RPM_ONNX_PACKAGE_NAME "libopenvino-onnx-frontend-${cpack_name_ver}")
diff --git a/cmake/templates/OpenVINOConfig.cmake.in b/cmake/templates/OpenVINOConfig.cmake.in
index ac527e969068ef..af8dd1596c4a39 100644
--- a/cmake/templates/OpenVINOConfig.cmake.in
+++ b/cmake/templates/OpenVINOConfig.cmake.in
@@ -10,6 +10,7 @@
 # The following components are supported:
 #
 #  * `Runtime`: OpenVINO C++ and C Core & Inference Runtime, frontend common
+#  * `JAX`: OpenVINO JAX frontend
 #  * `ONNX`: OpenVINO ONNX frontend
 #  * `Paddle`: OpenVINO Paddle frontend
 #  * `PyTorch`: OpenVINO PyTorch frontend
@@ -37,6 +38,9 @@
 #
 #  Frontend specific targets:
 #
+#   `openvino::frontend::jax`
+#   JAX FrontEnd target (optional)
+#
 #   `openvino::frontend::onnx`
 #   ONNX FrontEnd target (optional)
 #
@@ -63,6 +67,9 @@
 #   `OpenVINO_Runtime_FOUND`
 #   OpenVINO C++ Core & Inference Runtime is available
 #
+#   `OpenVINO_Frontend_JAX_FOUND`
+#   OpenVINO JAX frontend is available
+#
 #   `OpenVINO_Frontend_ONNX_FOUND`
 #   OpenVINO ONNX frontend is available
 #
@@ -510,7 +517,7 @@ unset(_OV_ENABLE_OPENVINO_BUILD_SHARED)
 
 set(_ov_imported_libs openvino::runtime openvino::runtime::c
    openvino::frontend::onnx openvino::frontend::paddle openvino::frontend::tensorflow
-   openvino::frontend::pytorch openvino::frontend::tensorflow_lite)
+   openvino::frontend::pytorch openvino::frontend::tensorflow_lite openvino::frontend::jax)
 
 if(_ov_as_external_package)
     foreach(target IN LISTS _ov_imported_libs)
@@ -549,6 +556,7 @@ set(${CMAKE_FIND_PACKAGE_NAME}_TensorFlow_FOUND @ENABLE_OV_TF_FRONTEND@)
 set(${CMAKE_FIND_PACKAGE_NAME}_TensorFlowLite_FOUND @ENABLE_OV_TF_LITE_FRONTEND@)
 set(${CMAKE_FIND_PACKAGE_NAME}_IR_FOUND @ENABLE_OV_IR_FRONTEND@)
 set(${CMAKE_FIND_PACKAGE_NAME}_PyTorch_FOUND @ENABLE_OV_PYTORCH_FRONTEND@)
+set(${CMAKE_FIND_PACKAGE_NAME}_JAX_FOUND @ENABLE_OV_JAX_FRONTEND@)
 
 set(${CMAKE_FIND_PACKAGE_NAME}_Frontend_ONNX_FOUND ${${CMAKE_FIND_PACKAGE_NAME}_ONNX_FOUND})
 set(${CMAKE_FIND_PACKAGE_NAME}_Frontend_Paddle_FOUND ${${CMAKE_FIND_PACKAGE_NAME}_Paddle_FOUND})
@@ -556,6 +564,7 @@ set(${CMAKE_FIND_PACKAGE_NAME}_Frontend_TensorFlow_FOUND ${${CMAKE_FIND_PACKAGE_
 set(${CMAKE_FIND_PACKAGE_NAME}_Frontend_TensorFlowLite_FOUND ${${CMAKE_FIND_PACKAGE_NAME}_TensorFlowLite_FOUND})
 set(${CMAKE_FIND_PACKAGE_NAME}_Frontend_IR_FOUND ${${CMAKE_FIND_PACKAGE_NAME}_IR_FOUND})
 set(${CMAKE_FIND_PACKAGE_NAME}_Frontend_PyTorch_FOUND ${${CMAKE_FIND_PACKAGE_NAME}_PyTorch_FOUND})
+set(${CMAKE_FIND_PACKAGE_NAME}_Frontend_JAX_FOUND ${${CMAKE_FIND_PACKAGE_NAME}_JAX_FOUND})
 
 # if no components specified, only Runtime is provided
 if(NOT ${CMAKE_FIND_PACKAGE_NAME}_FIND_COMPONENTS)
@@ -591,5 +600,6 @@ unset(${CMAKE_FIND_PACKAGE_NAME}_ONNX_FOUND)
 unset(${CMAKE_FIND_PACKAGE_NAME}_TensorFlow_FOUND)
 unset(${CMAKE_FIND_PACKAGE_NAME}_TensorFlowLite_FOUND)
 unset(${CMAKE_FIND_PACKAGE_NAME}_PyTorch_FOUND)
+unset(${CMAKE_FIND_PACKAGE_NAME}_JAX_FOUND)
 
 cmake_policy(POP)
diff --git a/docs/articles_en/about-openvino/additional-resources.rst b/docs/articles_en/about-openvino/additional-resources.rst
deleted file mode 100644
index cb8d0fc62f244a..00000000000000
--- a/docs/articles_en/about-openvino/additional-resources.rst
+++ /dev/null
@@ -1,34 +0,0 @@
-.. {#resources}
-
-Additional Resources
-====================
-
-
-
-.. meta::
-   :description: Learn more about OpenVINO from benchmark results, case studies
-                 and lists of supported models, operations and devices.
-
-.. toctree::
-   :maxdepth: 1
-   :hidden:
-
-   additional-resources/glossary
-   Legal and Responsible AI Information <./additional-resources/legal-information>
-   additional-resources/telemetry
-   Case Studies <https://www.intel.com/openvino-success-stories>
-
-
-:doc:`Performance Benchmarks <performance-benchmarks>` contain results from benchmarking models with OpenVINO on Intel hardware.
-
-:doc:`Glossary <additional-resources/glossary>` contains terms used in OpenVINO.
-
-:doc:`Legal and Responsible AI Information <additional-resources/legal-information>` provides trademark information and other legal statements.
-
-:doc:`OpenVINO™ Telemetry <additional-resources/telemetry>` has detailed information on the telemetry data collection.
-
-`Case Studies <https://www.intel.com/openvino-success-stories>`__ are articles about real-world examples of OpenVINO™ usage.
-
-
-
-
diff --git a/docs/articles_en/about-openvino/compatibility-and-support.rst b/docs/articles_en/about-openvino/compatibility-and-support.rst
index f2c467be34676e..f9ea2b2b34ba90 100644
--- a/docs/articles_en/about-openvino/compatibility-and-support.rst
+++ b/docs/articles_en/about-openvino/compatibility-and-support.rst
@@ -1,5 +1,3 @@
-.. {#compatibility_and_support}
-
 Compatibility and Support
 =========================
 
@@ -9,12 +7,15 @@ Compatibility and Support
    :hidden:
 
    compatibility-and-support/supported-devices
+   compatibility-and-support/supported-models
    compatibility-and-support/supported-operations-inference-devices
    compatibility-and-support/supported-operations-framework-frontend
 
 
 :doc:`Supported Devices <compatibility-and-support/supported-devices>` - compatibility information for supported hardware accelerators.
 
+:doc:`Supported Models <compatibility-and-support/supported-models>` - a list of selected models confirmed to work with given hardware.
+
 :doc:`Supported Operations <compatibility-and-support/supported-operations-inference-devices>` - a listing of framework layers supported by OpenVINO.
 
 :doc:`Supported Operations <compatibility-and-support/supported-operations-framework-frontend>` - a listing of layers supported by OpenVINO inference devices.
diff --git a/docs/articles_en/about-openvino/compatibility-and-support/supported-models.rst b/docs/articles_en/about-openvino/compatibility-and-support/supported-models.rst
index 0c1195063196e9..968c5b6571bca9 100644
--- a/docs/articles_en/about-openvino/compatibility-and-support/supported-models.rst
+++ b/docs/articles_en/about-openvino/compatibility-and-support/supported-models.rst
@@ -1,44 +1,45 @@
-Supported Models
-========================
+AI Models that run with OpenVINO
+=====================================
 
-.. toctree::
-   :maxdepth: 1
-   :hidden:
+This list of 500+ computer vision, LLM, and GenAI models includes pre-optimized OpenVINO
+models/notebooks plus models from public model zoos (ONNX Model Zoo, Pytorch Model Zoo, and
+HuggingFace). This list is not comprehensive and only includes models tested by Intel.
 
-   about-openvino/performance-benchmarks
-   about-openvino/compatibility-and-support
-   Release Notes <about-openvino/release-notes-openvino>
-   Additional Resources <about-openvino/additional-resources>
+**AI Models that run on Intel® Core Ultra ™ Processors with OpenVINO™ toolkit:**
 
 .. raw:: html
-      
+
    <link rel="stylesheet" type="text/css" href="../../_static/css/openVinoDataTables.css">
    <label>hide/reveal additional columns:</label><br/>
    <label class="column-container">
-       Topology
-      <input type="checkbox" id="topology" name="topology" value="topology" data-column="0" class="toggle-vis"/>
-      <label for="topology" class="checkmark"></label>
-   </label>
-   <label class="column-container">
-      Source framework
-      <input type="checkbox" checked id="source" name="source" value="source" data-column="1" class="toggle-vis"/>
-      <label for="source" class="checkmark"></label>
+      CPU
+      <input type="checkbox" id="AI PC CPU" name="AI PC CPU" value="AI PC CPU" data-column="3" class="toggle-vis"/>
+      <label for="AI PC CPU" class="checkmark"></label>
    </label>
    <label class="column-container">
-      Precision
-      <input type="checkbox" checked id="precision" name="precision" value="precision" data-column="2" class="toggle-vis"/>
-      <label for="precision" class="checkmark"></label>
+      GPU
+      <input type="checkbox" id="AI PC GPU" name="AI PC GPU" value="AI PC GPU" data-column="4" class="toggle-vis"/>
+      <label for="AI PC GPU" class="checkmark"></label>
    </label>
    <label class="column-container">
-      Arc
-      <input type="checkbox" checked id="arc" name="arc" value="arc" data-column="6" class="toggle-vis"/>
-      <label for="arc" class="checkmark"></label>
+      NPU
+      <input type="checkbox" id="AI PC NPU" name="AI PC NPU" value="AI PC NPU" data-column="5" class="toggle-vis"/>
+      <label for="AI PC NPU" class="checkmark"></label>
    </label>
 
+
 .. csv-table::
    :class: modeldata stripe
    :name: supportedModelsTable
    :header-rows: 1
-   :file:  ../../_static/models.csv
+   :file:  ../../_static/download/supported_models.csv
+
 
+| Note:
+| The results as of June 17 2024, for OpenVINO version 2024.2.
 
+| The validation process involved using the OpenVINO toolkit (natively or as a backend) to load
+  each model onto the designated hardware and successfully execute inference without encountering
+  any errors or failures. These successfully run models are indicated with a check mark below and
+  blanks are not tested. In the precision column where it is listed as optimum-intel default,
+  that corresponds to FP32 for small models and INT8 for models greater than 1B parameters.
diff --git a/docs/articles_en/about-openvino/performance-benchmarks.rst b/docs/articles_en/about-openvino/performance-benchmarks.rst
index ced358db18c044..cc0377f64857e5 100644
--- a/docs/articles_en/about-openvino/performance-benchmarks.rst
+++ b/docs/articles_en/about-openvino/performance-benchmarks.rst
@@ -205,10 +205,10 @@ You can also test performance for your system yourself, following the guide on
 
 
 * Intel® Distribution of OpenVINO™ toolkit performance results are based on release
-  2024.2, as of June 17, 2024.
+  2024.2, as of June 28, 2024.
 
 * OpenVINO Model Server performance results are based on release
-  2024.1, as of April 26, 2024.
+  2024.2, as of June 28, 2024.
 
 The results may not reflect all publicly available updates. Intel technologies' features and
 benefits depend on system configuration and may require enabled hardware, software, or service
diff --git a/docs/articles_en/about-openvino/performance-benchmarks/generative-ai-performance.rst b/docs/articles_en/about-openvino/performance-benchmarks/generative-ai-performance.rst
index a56b623c574a34..73c9f255ff3dbb 100644
--- a/docs/articles_en/about-openvino/performance-benchmarks/generative-ai-performance.rst
+++ b/docs/articles_en/about-openvino/performance-benchmarks/generative-ai-performance.rst
@@ -1,13 +1,15 @@
 Most Efficient Large Language Models for AI PC
 ==============================================
 
-This page is regularly updated to help you identify the best-performing LLMs on the Intel® Core™ Ultra processor family and AI PCs.
+This page is regularly updated to help you identify the best-performing LLMs on the
+Intel® Core™ Ultra processor family and AI PCs.
 
-The table below lists the key performance indicators for a selection of Large Language Models running on an Intel® Core™ Ultra 7-165H based system.
+The table below lists the key performance indicators for a selection of Large Language Models,
+running on an Intel® Core™ Ultra 7-165H based system, on built-in GPUs.
 
-.. raw:: html
+For complete information on the system config, see:
+`Hardware Platforms [PDF] <https://docs.openvino.ai/2024/_static/benchmarks_files/OV-2024.2-platform_list.pdf>`__
 
-   For complete information on the system config, see: <a href="https://docs.openvino.ai/2024/_static/benchmarks_files/OV-2024.2-platform_list.pdf">Hardware Platforms [PDF]</a>
 
 .. raw:: html
 
@@ -59,7 +61,11 @@ The table below lists the key performance indicators for a selection of Large La
    :class: modeldata stripe
    :name: supportedModelsTable
    :header-rows: 1
-   :file:  ../../_static/llm_models.csv
+   :file:  ../../_static/download/llm_models.csv
 
 
+This page is regularly updated to help you identify the best-performing LLMs on the
+Intel® Core™ Ultra processor family and AI PCs.
 
+For complete information on the system config, see:
+`Hardware Platforms [PDF] <https://docs.openvino.ai/2024/_static/benchmarks_files/OV-2024.2-platform_list.pdf>`__
\ No newline at end of file
diff --git a/docs/articles_en/about-openvino/performance-benchmarks/performance-benchmarks-faq.rst b/docs/articles_en/about-openvino/performance-benchmarks/performance-benchmarks-faq.rst
index 868c828845a092..6c731dc374010b 100644
--- a/docs/articles_en/about-openvino/performance-benchmarks/performance-benchmarks-faq.rst
+++ b/docs/articles_en/about-openvino/performance-benchmarks/performance-benchmarks-faq.rst
@@ -17,7 +17,13 @@ Performance Information F.A.Q.
 
 .. dropdown:: Where can I find the models used in the performance benchmarks?
 
-   All models used are included in the GitHub repository of :doc:`Open Model Zoo <../../documentation/legacy-features/model-zoo>`.
+   All models used are included in the GitHub repository of
+   :doc:`Open Model Zoo <../../documentation/legacy-features/model-zoo>`.
+
+   .. important::
+
+      Due to the deprecation of Open Model Zoo, models in the OpenVINO IR format are now
+      published on `Hugging Face <https://huggingface.co/OpenVINO>`__.
 
 .. dropdown:: Will there be any new models added to the list used for benchmarking?
 
diff --git a/docs/articles_en/about-openvino/release-notes-openvino.rst b/docs/articles_en/about-openvino/release-notes-openvino.rst
index 68c64e83a61aa0..c8c32126e3e10c 100644
--- a/docs/articles_en/about-openvino/release-notes-openvino.rst
+++ b/docs/articles_en/about-openvino/release-notes-openvino.rst
@@ -24,7 +24,8 @@ OpenVINO Release Notes
 What's new
 +++++++++++++++++++++++++++++
 
-* More Gen AI coverage and framework integrations to minimize code changes.
+* More :doc:`Gen AI <../learn-openvino/llm_inference_guide/genai-guide>` coverage and framework
+  integrations to minimize code changes.
 
   * Llama 3 optimizations for CPUs, built-in GPUs, and discrete GPUs for improved performance
     and efficient memory usage.
@@ -35,7 +36,7 @@ What's new
     Python Custom Operation empowers users to implement their own specialized operations into
     any model.
   * Notebooks expansion to ensure better coverage for new models. Noteworthy notebooks added:
-    DynamiCrafter, YOLOv10, and Chatbot notebook with Phi-3.
+    DynamiCrafter, YOLOv10, Chatbot notebook with Phi-3, and QWEN2.
 
 
 * Broader Large Language Model (LLM) support and more model compression techniques.
@@ -52,21 +53,22 @@ What's new
 
   * Model Serving Enhancements:
 
-    * OpenVINO Model Server (OVMS) now supports OpenAI-compatible API along with Continuous
+    * Preview: OpenVINO Model Server (OVMS) now supports OpenAI-compatible API along with Continuous
       Batching and PagedAttention, enabling significantly higher throughput for parallel
       inferencing, especially on Intel® Xeon® processors, when serving LLMs to many concurrent
       users.
-    * OpenVINO backend for Triton Server now supports built-in GPUs and discrete GPUs, in
-      addition to dynamic shapes support.
+    * OpenVINO backend for Triton Server now supports dynamic input shapes.
     * Integration of TorchServe through torch.compile OpenVINO backend for easy model deployment,
       provisioning to multiple instances, model versioning, and maintenance.
 
-  * Addition of the Generate API, a simplified API for text generation using large language
-    models with only a few lines of code. The API is available through the newly launched
-    OpenVINO GenAI package.
+  * Preview: addition of the :doc:`Generate API <../learn-openvino/llm_inference_guide/genai-guide>`,
+    a simplified API for text generation using large language models with only a few lines of
+    code. The API is available through the newly launched OpenVINO GenAI package.
+  * Support for Intel Atom® Processor X Series. For more details, see :doc:`System Requirements <./release-notes-openvino/system-requirements>`.
   * Preview: Support for Intel® Xeon® 6 processor.
 
 
+
 OpenVINO™ Runtime
 +++++++++++++++++++++++++++++
 
@@ -77,8 +79,6 @@ Common
   efficient LLM weight compression.
 * Common OV headers have been optimized, improving binary compilation time and reducing binary
   size.
-* Support for Intel Atom® Processor X Series has been added. For more details, see
-  :doc:`System Requirements <./release-notes-openvino/system-requirements>`.
 
 
 AUTO Inference Mode
@@ -115,13 +115,14 @@ GPU Device Plugin
 * Stable Diffusion FP16 performance improved on Core Ultra platforms, with significant pipeline
   improvement for models with dynamic-shaped input. Memory usage of the pipeline has been reduced,
   as well.
-* Improved performance of optimized permute_f_y kernel.
+* Optimized permute_f_y kernel performance has been improved.
 
 
 NPU Device Plugin
 -----------------------------
 
-* TBD
+* A new set of configuration options is now available.
+* Performance increase has been unlocked, with the new `2408 NPU driver <https://www.intel.com/content/www/us/en/download/794734/intel-npu-driver-windows.html>`__.
 
 
 OpenVINO Python API
@@ -185,8 +186,8 @@ OpenVINO Model Server
 
 * OpenVINO Model server can be now used for text generation use cases using OpenAI compatible API.
 * Added support for continuous batching and PagedAttention algorithms for text generation with
-  fast and efficient in high concurrency load especially on Intel Xeon processors. Learn more
-  about it.
+  fast and efficient in high concurrency load especially on Intel Xeon processors.
+  `Learn more about it <https://github.com/openvinotoolkit/model_server/tree/releases/2024/2/demos/continuous_batching>`__.
 
 
 Neural Network Compression Framework
@@ -233,10 +234,10 @@ Known Issues
 | **Component: TBB**
 | ID: TBB-1400/ TBB-1401
 | Description:
-|   In 2024.2, oneTBB 2021.2.x is used for Intel Distribution OpenVINO Ubuntu and Red Hat
-    archives, instead of system TBB/oneTBB, which improved the performance on some platforms
-    (EMR for example), but the latency of some models drop on SPR, user can build OpenVINO with
-    **-DSYSTEM_TBB=ON** to get better latency performance for these models.
+|   In 2024.2, oneTBB 2021.2.x is used for Intel Distribution of OpenVINO Ubuntu and Red Hat
+    archives, instead of system TBB/oneTBB. This improves performance on the new generation of
+    Xeon platforms but may increase latency of some models on the previous generation. You can
+    build OpenVINO with **-DSYSTEM_TBB=ON** to get better latency performance for these models.
 
 | **Component: python API**
 | ID: CVS-141744
diff --git a/docs/articles_en/about-openvino/release-notes-openvino/system-requirements.rst b/docs/articles_en/about-openvino/release-notes-openvino/system-requirements.rst
index 4d8381ec09bba3..a02d133453c8b9 100644
--- a/docs/articles_en/about-openvino/release-notes-openvino/system-requirements.rst
+++ b/docs/articles_en/about-openvino/release-notes-openvino/system-requirements.rst
@@ -19,6 +19,7 @@ CPU
 
    .. tab-item:: Supported Hardware
 
+      * Intel® Xeon® 6 processor (preview)
       * Intel Atom® Processor X Series
       * Intel Atom® processor with Intel® SSE4.2 support
       * Intel® Pentium® processor N4200/5, N3350/5, N3450/5 with Intel® HD Graphics
diff --git a/docs/articles_en/documentation/legacy-features.rst b/docs/articles_en/documentation/legacy-features.rst
index a470f9bcd9cc4b..489e6e77fe4191 100644
--- a/docs/articles_en/documentation/legacy-features.rst
+++ b/docs/articles_en/documentation/legacy-features.rst
@@ -59,6 +59,9 @@ offering.
     until they are fully removed.
 |   :doc:`See the Open Model ZOO documentation <legacy-features/model-zoo>`
 |   `Check the OMZ GitHub project <https://github.com/openvinotoolkit/open_model_zoo>`__
+|   As for public model databases, `Hugging Face <https://huggingface.co/OpenVINO>`__ has
+    become the recommended model source for OpenVINO.
+
 
 
 
diff --git a/docs/articles_en/documentation/legacy-features/install-dev-tools.rst b/docs/articles_en/documentation/legacy-features/install-dev-tools.rst
index 6466eb8711a381..de2b35529aeafc 100644
--- a/docs/articles_en/documentation/legacy-features/install-dev-tools.rst
+++ b/docs/articles_en/documentation/legacy-features/install-dev-tools.rst
@@ -8,14 +8,18 @@ Install OpenVINO™ Development Tools
    :description: Learn how to install OpenVINO™ Development Tools on Windows,
                  Linux, and macOS operating systems, using a PyPi package.
 
-OpenVINO Development Tools is a set of utilities that make it easy to develop and optimize models and applications for OpenVINO. It provides the following tools:
+OpenVINO Development Tools is a set of utilities that make it easy to develop and
+optimize models and applications for OpenVINO. It provides the following tools:
 
 * Model conversion API
 * Benchmark Tool
 * Accuracy Checker and Annotation Converter
 * Model Downloader and other Open Model Zoo tools
 
-The instructions on this page show how to install OpenVINO Development Tools. If you are a Python developer, it only takes a few simple steps to install the tools with PyPI. If you are developing in C/C++, OpenVINO Runtime must be installed separately before installing OpenVINO Development Tools.
+The instructions on this page show how to install OpenVINO Development Tools. If you are a
+Python developer, it only takes a few simple steps to install the tools with PyPI. If you
+are developing in C/C++, OpenVINO Runtime must be installed separately before installing
+OpenVINO Development Tools.
 
 In both cases, Python 3.8 - 3.11 needs to be installed on your machine before starting.
 
@@ -28,16 +32,28 @@ In both cases, Python 3.8 - 3.11 needs to be installed on your machine before st
 For Python Developers
 #####################
 
-If you are a Python developer, follow the steps in the :ref:`Installing OpenVINO Development Tools <install_dev_tools>` section on this page to install it. Installing OpenVINO Development Tools will also install OpenVINO Runtime as a dependency, so you don’t need to install OpenVINO Runtime separately. This option is recommended for new users.
+If you are a Python developer, follow the steps in the
+:ref:`Installing OpenVINO Development Tools <install_dev_tools>` section on this page to
+install it. Installing OpenVINO Development Tools will also install OpenVINO Runtime as
+a dependency, so you don’t need to install OpenVINO Runtime separately. This option is
+recommended for new users.
 
 .. _cpp_developers:
 
 For C/C++ Developers
 #######################
 
-If you are a C/C++ developer, you must first install OpenVINO Runtime separately to set up the C/C++ libraries, sample code, and dependencies for building applications with OpenVINO. These files are not included with the PyPI distribution. See the :doc:`Selector Tool <../../get-started/install-openvino>` page to install OpenVINO Runtime from an archive file for your operating system.
+If you are a C/C++ developer, you must first install OpenVINO Runtime separately to set
+up the C/C++ libraries, sample code, and dependencies for building applications with
+OpenVINO. These files are not included with the PyPI distribution. See the
+:doc:`Selector Tool <../../get-started/install-openvino>` page to install OpenVINO Runtime
+from an archive file for your operating system.
 
-Once OpenVINO Runtime is installed, you may install OpenVINO Development Tools for access to tools like ``mo``, Model Downloader, Benchmark Tool, and other utilities that will help you optimize your model and develop your application. Follow the steps in the :ref:`Installing OpenVINO Development Tools <install_dev_tools>` section on this page to install it.
+Once OpenVINO Runtime is installed, you may install OpenVINO Development Tools for access
+to tools like ``mo``, Model Downloader, Benchmark Tool, and other utilities that will help
+you optimize your model and develop your application. Follow the steps in the
+:ref:`Installing OpenVINO Development Tools <install_dev_tools>` section on this page
+to install it.
 
 .. _install_dev_tools:
 
@@ -45,14 +61,16 @@ Installing OpenVINO™ Development Tools
 ######################################
 
 Follow these step-by-step instructions to install OpenVINO Development Tools on your computer.
-There are two options to install OpenVINO Development Tools: installation into an existing environment with a deep learning framework that was used
-for model training or creation; or installation into a new environment.
+There are two options to install OpenVINO Development Tools: installation into an existing
+environment with a deep learning framework that was used for model training or creation;
+or installation into a new environment.
 
 Installation into an Existing Environment with the Source Deep Learning Framework
 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
-To install OpenVINO Development Tools (see the :ref:`Install the Package <install_the_package>` section of this article) into an existing environment
-with the deep learning framework used for the model training or creation, run the following command:
+To install OpenVINO Development Tools (see the :ref:`Install the Package <install_the_package>`
+section of this article) into an existing environment with the deep learning framework used
+for the model training or creation, run the following command:
 
 .. code-block:: sh
 
@@ -62,13 +80,16 @@ with the deep learning framework used for the model training or creation, run th
 Installation in a New Environment
 +++++++++++++++++++++++++++++++++
 
-If you do not have an environment with a deep learning framework for the input model or you encounter any compatibility issues between OpenVINO
-and your version of deep learning framework, you may install OpenVINO Development Tools with validated versions of frameworks into a new environment.
+If you do not have an environment with a deep learning framework for the input model or you
+encounter any compatibility issues between OpenVINO and your version of deep learning
+framework, you may install OpenVINO Development Tools with validated versions of
+frameworks into a new environment.
 
 Step 1. Set Up Python Virtual Environment
 -----------------------------------------
 
-Create a virtual Python environment to avoid dependency conflicts. To create a virtual environment, use the following command:
+Create a virtual Python environment to avoid dependency conflicts. To create a virtual
+environment, use the following command:
 
 .. tab-set::
 
@@ -117,7 +138,8 @@ Activate the newly created Python virtual environment by issuing this command:
 Step 3. Set Up and Update PIP to the Highest Version
 ----------------------------------------------------
 
-Make sure `pip` is installed in your environment and upgrade it to the latest version by issuing the following command:
+Make sure `pip` is installed in your environment and upgrade it to the latest version by
+issuing the following command:
 
 .. code-block:: sh
 
@@ -129,7 +151,8 @@ Make sure `pip` is installed in your environment and upgrade it to the latest ve
 Step 4. Install the Package
 ---------------------------
 
-To install and configure the components of the development package together with validated versions of specific frameworks, use the commands below.
+To install and configure the components of the development package together with validated
+versions of specific frameworks, use the commands below.
 
 .. code-block:: sh
 
@@ -137,9 +160,11 @@ To install and configure the components of the development package together with
 
 
 where the ``extras`` parameter specifies the source deep learning framework for the input model
-and is one or more of the following values separated with "," :  ``onnx``, ``pytorch``, ``tensorflow``, ``tensorflow2``.
+and is one or more of the following values separated with "," :  ``onnx``, ``pytorch``,
+``tensorflow``, ``tensorflow2``.
 
-For example, to install and configure dependencies required for working with TensorFlow 2.x and ONNX models, use the following command:
+For example, to install and configure dependencies required for working with TensorFlow 2.x
+and ONNX models, use the following command:
 
 .. code-block:: sh
 
@@ -148,9 +173,14 @@ For example, to install and configure dependencies required for working with Ten
 
 .. note::
 
-   Model conversion API support for TensorFlow 1.x environment has been deprecated. Use the ``tensorflow2`` parameter to install a TensorFlow 2.x environment that can convert both TensorFlow 1.x and 2.x models. If your model isn't compatible with the TensorFlow 2.x environment, use the `tensorflow` parameter to install the TensorFlow 1.x environment. The TF 1.x environment is provided only for legacy compatibility reasons.
+   Model conversion API support for TensorFlow 1.x environment has been deprecated. Use the
+   ``tensorflow2`` parameter to install a TensorFlow 2.x environment that can convert both
+   TensorFlow 1.x and 2.x models. If your model isn't compatible with the TensorFlow 2.x
+   environment, use the `tensorflow` parameter to install the TensorFlow 1.x environment.
+   The TF 1.x environment is provided only for legacy compatibility reasons.
 
-For more details on the openvino-dev PyPI package, see `pypi.org <https://pypi.org/project/openvino-dev/2023.2.0>`__ .
+For more details on the openvino-dev PyPI package, see
+`pypi.org <https://pypi.org/project/openvino-dev/2023.2.0>`__ .
 
 Step 5. Test the Installation
 ------------------------------
@@ -161,9 +191,13 @@ To verify the package is properly installed, run the command below (this may tak
 
    mo -h
 
-You will see the help message for ``mo`` if installation finished successfully. If you get an error, refer to the :doc:`Troubleshooting Guide <../../get-started/troubleshooting-install-config>` for possible solutions.
+You will see the help message for ``mo`` if installation finished successfully. If you get an
+error, refer to the :doc:`Troubleshooting Guide <../../get-started/troubleshooting-install-config>`
+for possible solutions.
 
-Congratulations! You finished installing OpenVINO Development Tools with C/C++ capability. Now you can start exploring OpenVINO's functionality through example C/C++ applications. See the "What's Next?" section to learn more!
+Congratulations! You finished installing OpenVINO Development Tools with C/C++ capability.
+Now you can start exploring OpenVINO's functionality through example C/C++ applications.
+See the "What's Next?" section to learn more!
 
 What's Next?
 ############
@@ -176,9 +210,12 @@ Get started with Python
 .. image:: ../../assets/images/get_started_with_python.gif
   :width: 400
 
-Try the `Python Quick Start Example <../../notebooks/vision-monodepth-with-output.html>`__ to estimate depth in a scene using an OpenVINO monodepth model in a Jupyter Notebook inside your web browser.
+Try the `Python Quick Start Example <../../notebooks/vision-monodepth-with-output.html>`__
+to estimate depth in a scene using an OpenVINO monodepth model in a Jupyter Notebook
+inside your web browser.
 
-Visit the :doc:`Tutorials <../../learn-openvino/interactive-tutorials-python>` page for more Jupyter Notebooks to get you started with OpenVINO, such as:
+Visit the :doc:`Tutorials <../../learn-openvino/interactive-tutorials-python>` page for more
+Jupyter Notebooks to get you started with OpenVINO, such as:
 
 * `OpenVINO Python API Tutorial <../../notebooks/openvino-api-with-output.html>`__
 * `Basic image classification program with Hello Image Classification <../../notebooks/hello-world-with-output.html>`__
@@ -191,9 +228,11 @@ Get started with C++
   :width: 400
 
 
-Try the :doc:`C++ Quick Start Example <../../learn-openvino/openvino-samples/get-started-demos>` for step-by-step instructions on building and running a basic image classification C++ application.
+Try the :doc:`C++ Quick Start Example <../../learn-openvino/openvino-samples/get-started-demos>`
+for step-by-step instructions on building and running a basic image classification C++ application.
 
-Visit the :doc:`Samples <../../learn-openvino/openvino-samples>` page for other C++ example applications to get you started with OpenVINO, such as:
+Visit the :doc:`Samples <../../learn-openvino/openvino-samples>` page for other C++
+example applications to get you started with OpenVINO, such as:
 
 * :doc:`Basic object detection with the Hello Reshape SSD C++ sample <../../learn-openvino/openvino-samples/hello-reshape-ssd>`
 * :doc:`Object classification sample <../../learn-openvino/openvino-samples/hello-classification>`
@@ -201,10 +240,20 @@ Visit the :doc:`Samples <../../learn-openvino/openvino-samples>` page for other
 Learn OpenVINO Development Tools
 ++++++++++++++++++++++++++++++++
 
-* Explore a variety of pre-trained deep learning models in the :doc:`Open Model Zoo <model-zoo>` and deploy them in demo applications to see how they work.
-* Want to import a model from another framework and optimize its performance with OpenVINO? Visit the :doc:`Convert a Model <transition-legacy-conversion-api/legacy-conversion-api>` page.
-* Accelerate your model's speed even further with quantization and other compression techniques using :doc:`Neural Network Compression Framework (NNCF) <../../openvino-workflow/model-optimization-guide/quantizing-models-post-training>`.
-* Benchmark your model's inference speed with one simple command using the :doc:`Benchmark Tool <../../learn-openvino/openvino-samples/benchmark-tool>`.
+* Explore a variety of pre-trained deep learning models in the
+  :doc:`Open Model Zoo <model-zoo>` and deploy them in demo applications to see how they work.
+
+  .. important::
+
+     Due to the deprecation of Open Model Zoo, models in the OpenVINO IR format are now
+     published on `Hugging Face <https://huggingface.co/OpenVINO>`__.
+
+* Want to import a model from another framework and optimize its performance with OpenVINO?
+  Visit the :doc:`Convert a Model <transition-legacy-conversion-api/legacy-conversion-api>` page.
+* Accelerate your model's speed even further with quantization and other compression techniques
+  using :doc:`Neural Network Compression Framework (NNCF) <../../openvino-workflow/model-optimization-guide/quantizing-models-post-training>`.
+* Benchmark your model's inference speed with one simple command using the
+  :doc:`Benchmark Tool <../../learn-openvino/openvino-samples/benchmark-tool>`.
 
 Additional Resources
 ####################
diff --git a/docs/articles_en/documentation/legacy-features/model-zoo.rst b/docs/articles_en/documentation/legacy-features/model-zoo.rst
index 281e217f75c9c4..f2b26f2a983efc 100644
--- a/docs/articles_en/documentation/legacy-features/model-zoo.rst
+++ b/docs/articles_en/documentation/legacy-features/model-zoo.rst
@@ -29,9 +29,29 @@ Model Zoo
    ../../omz_model_api_ovms_adapter
 
 
-Open Model Zoo for OpenVINO™ toolkit delivers a wide variety of free, pre-trained deep learning models and demo applications that provide full application templates to help you implement deep learning in Python, C++, or OpenCV Graph API (G-API). Models and demos are available in the `Open Model Zoo GitHub repo <https://github.com/openvinotoolkit/open_model_zoo>`__ and licensed under Apache License Version 2.0.
-
-Browse through over 200 neural network models, both :doc:`public <../../omz_models_group_public>` and from :doc:`Intel <../../omz_models_group_intel>`, and pick the right one for your solution. Types include object detection, classification, image segmentation, handwriting recognition, text to speech, pose estimation, and others. The Intel models have already been converted to work with OpenVINO™ toolkit, while public models can easily be converted using the :doc:`OpenVINO Model Conversion API <../../openvino-workflow/model-preparation>` utility.
-
-Get started with simple :doc:`step-by-step procedures <../../learn-openvino/openvino-samples/get-started-demos>` to learn how to build and run demo applications or discover the :doc:`full set of demos <../../omz_demos>` and adapt them for implementing specific deep learning scenarios in your applications.
+.. note::
+
+   Since the deprecation of Open Model Zoo, OpenVINO has significantly extended its presence on the
+   `Hugging Face <https://huggingface.co/OpenVINO>`__ model repository. It is currently
+   the recommended source of optimized OpenVINO IR models.
+
+Open Model Zoo for OpenVINO™ toolkit delivers a wide variety of free, pre-trained deep learning
+models and demo applications that provide full application templates to help you implement deep
+learning in Python, C++, or OpenCV Graph API (G-API). Models and demos are available in the
+`Open Model Zoo GitHub repo <https://github.com/openvinotoolkit/open_model_zoo>`__
+and licensed under Apache License Version 2.0.
+
+Browse through over 200 neural network models, both
+:doc:`public <../../omz_models_group_public>` and from
+:doc:`Intel <../../omz_models_group_intel>`, and pick the right one for your solution.
+Types include object detection, classification, image segmentation, handwriting recognition,
+text to speech, pose estimation, and others. The Intel models have already been converted
+to work with OpenVINO™ toolkit, while public models can easily be converted using the
+:doc:`OpenVINO Model Conversion API <../../openvino-workflow/model-preparation>` utility.
+
+Get started with simple
+:doc:`step-by-step procedures <../../learn-openvino/openvino-samples/get-started-demos>`
+to learn how to build and run demo applications or discover the
+:doc:`full set of demos <../../omz_demos>` and adapt them for implementing specific deep
+learning scenarios in your applications.
 
diff --git a/docs/articles_en/documentation/legacy-features/transition-legacy-conversion-api/legacy-conversion-api/[legacy]-model-optimizer-faq.rst b/docs/articles_en/documentation/legacy-features/transition-legacy-conversion-api/legacy-conversion-api/[legacy]-model-optimizer-faq.rst
index 6cc2de65a8a9a0..fb7e3004258253 100644
--- a/docs/articles_en/documentation/legacy-features/transition-legacy-conversion-api/legacy-conversion-api/[legacy]-model-optimizer-faq.rst
+++ b/docs/articles_en/documentation/legacy-features/transition-legacy-conversion-api/legacy-conversion-api/[legacy]-model-optimizer-faq.rst
@@ -53,7 +53,7 @@ For example, to add the description of the ``CustomReshape`` layer, which is an
       python3 generate_caffe_pb2.py --input_proto <PATH_TO_CUSTOM_CAFFE>/src/caffe/proto/caffe.proto
 
 
-   where ``PATH_TO_CUSTOM_CAFFE` is the path to the root directory of custom Caffe.
+   where ``PATH_TO_CUSTOM_CAFFE`` is the path to the root directory of custom Caffe.
 
 3. Now, Model Optimizer is able to load the model into memory and start working with your extensions if there are any.
 
@@ -927,7 +927,7 @@ Q102. What does the message "Operation _contrib_box_nms is not supported ..." me
 
 .. _question-103:
 
-Q103. What does the message "ModelOptimizer is not able to parse *.caffemodel" mean?
+Q103. What does the message "ModelOptimizer is not able to parse "\*.caffemodel" mean?
 #####################################################################################################################################################
 
 **A:** If a ``*.caffemodel`` file exists and is correct, the error occurred possibly because of the use of Python protobuf implementation. In some cases, error messages may appear during model parsing, for example: "``utf-8`` codec can't decode byte 0xe0 in position 4: invalid continuation byte in field: mo_caffe.SpatialTransformerParameter.transform_type". You can either use a newer Python version (3.8 - 3.11) or build the ``cpp`` implementation of ``protobuf`` yourself for your version of Python. For the complete instructions about building ``protobuf`` from sources, see the appropriate section in the :doc:`Converting Models with Model Optimizer <../legacy-conversion-api>` guide.
diff --git a/docs/articles_en/documentation/legacy-features/transition-legacy-conversion-api/legacy-conversion-api/[legacy]-supported-model-formats/[legacy]-conversion-tutorials/convert-tensorflow-deep-speech.rst b/docs/articles_en/documentation/legacy-features/transition-legacy-conversion-api/legacy-conversion-api/[legacy]-supported-model-formats/[legacy]-conversion-tutorials/convert-tensorflow-deep-speech.rst
index 71c28a5db9205d..45157106165732 100644
--- a/docs/articles_en/documentation/legacy-features/transition-legacy-conversion-api/legacy-conversion-api/[legacy]-supported-model-formats/[legacy]-conversion-tutorials/convert-tensorflow-deep-speech.rst
+++ b/docs/articles_en/documentation/legacy-features/transition-legacy-conversion-api/legacy-conversion-api/[legacy]-supported-model-formats/[legacy]-conversion-tutorials/convert-tensorflow-deep-speech.rst
@@ -42,8 +42,8 @@ To download the model, follow the instruction below:
   2. Download the `TensorFlow MetaGraph with pre-trained weights <https://github.com/mozilla/DeepSpeech/releases/download/v0.8.2/deepspeech-0.8.2-checkpoint.tar.gz>`__.
   3. Unpack it with a file archiver application.
 
-Freezing the Model into a *.pb File
-###################################
+Freezing the Model into a "\*.pb File"
+######################################
 
 After unpacking the archives above, you have to freeze the model. This requires
 TensorFlow version 1, which is not available under Python 3.8, so you need Python 3.7 or lower.
@@ -75,7 +75,7 @@ a fixed input length shape, thus the model is not reshapable.
 Refer to the :doc:`Using Shape Inference <../../../../../../openvino-workflow/running-inference/changing-input-shape>` guide.
 
 The second is that the frozen model still has two variables: ``previous_state_c`` and ``previous_state_h``, figure
-with the frozen *.pb model is below. It means that the model keeps training these variables at each inference.
+with the frozen \*.pb model is below. It means that the model keeps training these variables at each inference.
 
 .. image:: ../../../../../../assets/images/DeepSpeech-0.8.2.png
 
diff --git a/docs/articles_en/documentation/legacy-features/transition-legacy-conversion-api/legacy-conversion-api/[legacy]-supported-model-formats/[legacy]-conversion-tutorials/convert-tensorflow-efficient-det.rst b/docs/articles_en/documentation/legacy-features/transition-legacy-conversion-api/legacy-conversion-api/[legacy]-supported-model-formats/[legacy]-conversion-tutorials/convert-tensorflow-efficient-det.rst
index 47a6342ff7c989..daebe992d8441c 100644
--- a/docs/articles_en/documentation/legacy-features/transition-legacy-conversion-api/legacy-conversion-api/[legacy]-supported-model-formats/[legacy]-conversion-tutorials/convert-tensorflow-efficient-det.rst
+++ b/docs/articles_en/documentation/legacy-features/transition-legacy-conversion-api/legacy-conversion-api/[legacy]-supported-model-formats/[legacy]-conversion-tutorials/convert-tensorflow-efficient-det.rst
@@ -5,7 +5,7 @@ Converting TensorFlow EfficientDet Models
 
 
 .. meta::
-   :description: Learn how to convert an EfficientDet model 
+   :description: Learn how to convert an EfficientDet model
                  from TensorFlow to the OpenVINO Intermediate Representation.
 
 
@@ -14,7 +14,7 @@ Converting TensorFlow EfficientDet Models
    The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
 
    This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <../../../../../../learn-openvino/interactive-tutorials-python>`.
-   
+
 This tutorial explains how to convert EfficientDet public object detection models to the Intermediate Representation (IR).
 
 .. _efficientdet-to-ir:
@@ -55,9 +55,15 @@ The attribute ``image_size`` specifies the shape to be defined for the model con
 
     The color channel order (RGB or BGR) of an input data should match the channel order of the model training dataset. If they are different, perform the ``RGB<->BGR`` conversion specifying the command-line parameter: ``--reverse_input_channels``. Otherwise, inference results may be incorrect. For more information about the parameter, refer to the **When to Reverse Input Channels** section of the :doc:`Converting a Model to Intermediate Representation (IR) <../../[legacy]-setting-input-shapes>` guide.
 
-OpenVINO toolkit provides samples that can be used to infer EfficientDet model. 
+OpenVINO toolkit provides samples that can be used to infer EfficientDet model.
 For more information, refer to the :doc:`Open Model Zoo Demos <../../../../../../omz_demos>`.
 
+.. important::
+
+   Due to the deprecation of Open Model Zoo, models in the OpenVINO IR format have are now
+   published on `Hugging Face <https://huggingface.co/OpenVINO>`__.
+
+
 Interpreting Results of the TensorFlow Model and the IR
 #######################################################
 
diff --git a/docs/articles_en/documentation/legacy-features/transition-legacy-conversion-api/legacy-conversion-api/[legacy]-supported-model-formats/[legacy]-conversion-tutorials/convert-tensorflow-object-detection.rst b/docs/articles_en/documentation/legacy-features/transition-legacy-conversion-api/legacy-conversion-api/[legacy]-supported-model-formats/[legacy]-conversion-tutorials/convert-tensorflow-object-detection.rst
index 3eb5a93e6e0feb..8b1a04029962b1 100644
--- a/docs/articles_en/documentation/legacy-features/transition-legacy-conversion-api/legacy-conversion-api/[legacy]-supported-model-formats/[legacy]-conversion-tutorials/convert-tensorflow-object-detection.rst
+++ b/docs/articles_en/documentation/legacy-features/transition-legacy-conversion-api/legacy-conversion-api/[legacy]-supported-model-formats/[legacy]-conversion-tutorials/convert-tensorflow-object-detection.rst
@@ -89,6 +89,11 @@ Speech Recognition, Natural Language Processing and others. Refer to the links b
 * :doc:`OpenVINO Samples <../../../../../../learn-openvino/openvino-samples>`
 * :doc:`Open Model Zoo Demos <../../../../model-zoo>`
 
+.. important::
+
+   Due to the deprecation of Open Model Zoo, models in the OpenVINO IR format are now
+   published on `Hugging Face <https://huggingface.co/OpenVINO>`__.
+
 Feeding Input Images to the Samples
 ###################################
 
diff --git a/docs/articles_en/documentation/legacy-features/transition-legacy-conversion-api/legacy-conversion-api/[legacy]-supported-model-formats/[legacy]-conversion-tutorials/convert-tensorflow-yolo.rst b/docs/articles_en/documentation/legacy-features/transition-legacy-conversion-api/legacy-conversion-api/[legacy]-supported-model-formats/[legacy]-conversion-tutorials/convert-tensorflow-yolo.rst
index 10946ce84100da..bf1668a89007c9 100644
--- a/docs/articles_en/documentation/legacy-features/transition-legacy-conversion-api/legacy-conversion-api/[legacy]-supported-model-formats/[legacy]-conversion-tutorials/convert-tensorflow-yolo.rst
+++ b/docs/articles_en/documentation/legacy-features/transition-legacy-conversion-api/legacy-conversion-api/[legacy]-supported-model-formats/[legacy]-conversion-tutorials/convert-tensorflow-yolo.rst
@@ -236,7 +236,7 @@ To convert DarkNet YOLOv1 and YOLOv2 models to the OpenVINO format, follow these
 
 
 Installing DarkFlow
---------------------------------------------------------------
++++++++++++++++++++++
 
 You need DarkFlow to convert YOLOv1 and YOLOv2 models to TensorFlow. To install DarkFlow:
 
@@ -259,7 +259,7 @@ You need DarkFlow to convert YOLOv1 and YOLOv2 models to TensorFlow. To install
 
 
 Converting a DarkNet YOLOv1 or YOLOv2 Model to TensorFlow
---------------------------------------------------------------
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
 To convert YOLOv1 or YOLOv2 model to TensorFlow, go to the root directory of the cloned DarkFlow repository, place the previously downloaded \*.cfg and \*.weights files in the current directory and run the following command:
 
@@ -292,7 +292,7 @@ in ``built_graph``  subdirectory of the cloned DarkFlow repository.
 File ``<model_name>.pb`` is a TensorFlow representation of the YOLO model.
 
 Converting a TensorFlow YOLOv1 or YOLOv2 Model to the IR
----------------------------------------------------------
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
 Converted TensorFlow YOLO model is missing ``Region`` layer and its parameters. Original YOLO ``Region`` layer parameters are stored in the configuration ``<path_to_model>/<model_name>.cfg`` file under the ``[region]`` title.
 
diff --git a/docs/articles_en/documentation/legacy-features/transition-legacy-conversion-api/legacy-model-optimizer-extensibility/[legacy]-graph-traversal-and-modification.rst b/docs/articles_en/documentation/legacy-features/transition-legacy-conversion-api/legacy-model-optimizer-extensibility/[legacy]-graph-traversal-and-modification.rst
index b2be35f4452832..f742b4531f93d0 100644
--- a/docs/articles_en/documentation/legacy-features/transition-legacy-conversion-api/legacy-model-optimizer-extensibility/[legacy]-graph-traversal-and-modification.rst
+++ b/docs/articles_en/documentation/legacy-features/transition-legacy-conversion-api/legacy-model-optimizer-extensibility/[legacy]-graph-traversal-and-modification.rst
@@ -71,7 +71,7 @@ the ``mo.graph.port.Port`` class). The ``Port`` object has several attributes:
 
 * ``node`` - the instance of the ``Node`` object the port belongs to.
 * ``idx`` - the port number. Input and output ports are numbered independently, starting from ``0``. Thus,
-:doc:`ReLU <../../../openvino-ir-format/operation-sets/operation-specs/activation/relu-1>` operation has one input port (with index ``0``) and one output port (with index ``0``).
+  :doc:`ReLU <../../../openvino-ir-format/operation-sets/operation-specs/activation/relu-1>` operation has one input port (with index ``0``) and one output port (with index ``0``).
 * ``type`` - the type of the port. Could be equal to either ``"in"`` or ``"out"``.
 * ``data`` - the object that should be used to get attributes of the corresponding data node. This object has methods ``get_shape()`` / ``set_shape()`` and ``get_value()`` / ``set_value()`` to get/set shape/value of the corresponding data node. For example, ``in_port.data.get_shape()`` returns an input shape of a tensor connected to input port ``in_port`` (``in_port.type == 'in'``), ``out_port.data.get_value()`` returns a value of a tensor produced from output port ``out_port`` (``out_port.type == 'out'``).
 
diff --git a/docs/articles_en/documentation/openvino-ecosystem/datumaro.rst b/docs/articles_en/documentation/openvino-ecosystem/datumaro.rst
index c6b257ae3f17ca..e582b9359d227c 100644
--- a/docs/articles_en/documentation/openvino-ecosystem/datumaro.rst
+++ b/docs/articles_en/documentation/openvino-ecosystem/datumaro.rst
@@ -21,8 +21,6 @@ Plus, enjoy `Jupyter notebooks <https://github.com/openvinotoolkit/datumaro/tree
 Detailed Workflow
 #################
 
-``
-
 .. image:: ../../assets/images/datumaro.png
 
 1. To start working with Datumaro, download public datasets or prepare your own annotated dataset.
diff --git a/docs/articles_en/documentation/openvino-ecosystem/openvino-security-add-on.rst b/docs/articles_en/documentation/openvino-ecosystem/openvino-security-add-on.rst
index a261849c11315c..65c13ed68ce9b2 100644
--- a/docs/articles_en/documentation/openvino-ecosystem/openvino-security-add-on.rst
+++ b/docs/articles_en/documentation/openvino-ecosystem/openvino-security-add-on.rst
@@ -226,15 +226,16 @@ This example in this step uses the following names. Your configuration might use
 4. Save and close the network configuration file.
 5. Run two commands to activate the updated network configuration file. If you use ssh, you might lose network connectivity when issuing these commands. If so, reconnect to the network.
 
-.. code-block:: sh
+   .. code-block:: sh
 
-   sudo netplan generate
+      sudo netplan generate
 
-.. code-block:: sh
+   .. code-block:: sh
+
+      sudo netplan apply
 
-   sudo netplan apply
+      A bridge is created and an IP address is assigned to the new bridge.
 
-   A bridge is created and an IP address is assigned to the new bridge.
 6. Verify the new bridge:
 
    .. code-block:: sh
diff --git a/docs/articles_en/documentation/openvino-extensibility/custom-gpu-operations.rst b/docs/articles_en/documentation/openvino-extensibility/custom-gpu-operations.rst
index 9c544d4dbb2b66..015c416ac5c258 100644
--- a/docs/articles_en/documentation/openvino-extensibility/custom-gpu-operations.rst
+++ b/docs/articles_en/documentation/openvino-extensibility/custom-gpu-operations.rst
@@ -9,14 +9,21 @@ How to Implement Custom GPU Operations
                  enable operations not supported by OpenVINO.
 
 
-To enable operations not supported by OpenVINO™ out of the box, you may need an extension for OpenVINO operation set, and a custom kernel for the device you will target. This article describes custom kernel support for the GPU device.
+To enable operations not supported by OpenVINO™ out of the box, you may need an extension
+for OpenVINO operation set, and a custom kernel for the device you will target. This
+article describes custom kernel support for the GPU device.
 
-The GPU codepath abstracts many details about OpenCL. You need to provide the kernel code in OpenCL C and an XML configuration file that connects the kernel and its parameters to the parameters of the operation.
+The GPU codepath abstracts many details about OpenCL. You need to provide the kernel
+code in OpenCL C and an XML configuration file that connects the kernel and its
+parameters to the parameters of the operation.
 
 There are two options for using the custom operation configuration file:
 
-* Include a section with your kernels into the automatically-loaded ``<lib_path>/cldnn_global_custom_kernels/cldnn_global_custom_kernels.xml`` file.
-* Call the ``ov::Core::set_property()`` method from your application with the ``"CONFIG_FILE"`` key and the configuration file name as a value before loading the network that uses custom operations to the plugin:
+* Include a section with your kernels into the automatically-loaded
+  ``<lib_path>/cldnn_global_custom_kernels/cldnn_global_custom_kernels.xml`` file.
+* Call the ``ov::Core::set_property()`` method from your application with the
+  ``"CONFIG_FILE"`` key and the configuration file name as a value before loading
+  the network that uses custom operations to the plugin:
 
 .. tab-set::
 
@@ -35,14 +42,21 @@ There are two options for using the custom operation configuration file:
         :fragment: [part0]
 
 
-All OpenVINO samples, except the trivial ``hello_classification``, and most Open Model Zoo demos
-feature a dedicated command-line option ``-c`` to load custom kernels. For example, to load custom operations for the classification sample, run the command below:
+All OpenVINO samples, except the trivial ``hello_classification``, and most Open
+Model Zoo demos feature a dedicated command-line option ``-c`` to load custom kernels.
+For example, to load custom operations for the classification sample, run the command below:
 
 .. code-block:: cpp
 
    $ ./classification_sample -m <path_to_model>/bvlc_alexnet_fp16.xml -i ./validation_set/daily/227x227/apron.bmp -d GPU
    -c <absolute_path_to_config>/custom_layer_example.xml
 
+.. important::
+
+   Due to the deprecation of Open Model Zoo, models in the OpenVINO IR format are now
+   published on `Hugging Face <https://huggingface.co/OpenVINO>`__.
+
+
 .. _config-file-format:
 
 Configuration File Format
@@ -80,7 +94,8 @@ The ``CustomLayer`` node contains the entire configuration for a single custom o
       - Description
     * - ``name``
       - (1)
-      - The name of the operation type to be used. This name should be identical to the type used in the IR.
+      - The name of the operation type to be used. This name should be identical to
+        the type used in the IR.
     * - ``type``
       - (1)
       - Must be ``SimpleGPU`` .
@@ -111,7 +126,8 @@ The ``Source`` node points to a single OpenCL source file.
       - Description
     * - ``filename``
       - (1)
-      - Name of the file containing OpenCL source code. The path is relative to your executable. Multiple source nodes will have their sources concatenated in order.
+      - Name of the file containing OpenCL source code. The path is relative to your
+        executable. Multiple source nodes will have their sources concatenated in order.
 
 **Sub-nodes**: None
 
@@ -129,16 +145,19 @@ the sources during compilation (JIT).
       - Description
     * - ``name``
       - (1)
-      - The name of the defined JIT. For static constants, this can include the value as well, which is taken as a string.
+      - The name of the defined JIT. For static constants, this can include the value
+        as well, which is taken as a string.
     * - ``param``
       - (0/1)
       - This parameter value is used as the value of this JIT definition.
     * - ``type``
       - (0/1)
-      - The parameter type. Accepted values: ``int`` , ``float`` , and ``int[]`` , ``float[]`` for arrays.
+      - The parameter type. Accepted values: ``int`` , ``float`` , and ``int[]`` ,
+        ``float[]`` for arrays.
     * - ``default``
       - (0/1)
-      - The default value to be used if the specified parameters are missing from the operation in the OpenVINO IR.
+      - The default value to be used if the specified parameters are missing from the
+        operation in the OpenVINO IR.
 
 **Sub-nodes:** None
 
@@ -197,7 +216,8 @@ The ``Tensor`` node configures a single input or output tensor.
       - 0-based index in the operation input/output ports in the OpenVINO IR
     * - ``format``
       - (0/1)
-      - Data layout declaration for the tensor. Accepted values: ``BFYX`` , ``BYXF`` , ``YXFB`` , ``FYXB`` , and same values in all lowercase. Default value: ``BFYX``.
+      - Data layout declaration for the tensor. Accepted values: ``BFYX`` , ``BYXF`` ,
+        ``YXFB`` , ``FYXB`` , and same values in all lowercase. Default value: ``BFYX``.
 
 CompilerOptions Node and Sub-Node Structure
 +++++++++++++++++++++++++++++++++++++++++++
@@ -231,10 +251,14 @@ queuing an OpenCL program for execution.
       - Description
     * - ``global`` ``local``
       - (0/1) (0/1)
-      - An array of up to three integers or formulas for defining OpenCL work-sizes to be used during execution. The formulas can use the values of the B,F,Y,X dimensions and contain the operators: +,-,/,\*,%. All operators are evaluated in integer arithmetic. Default value: ``global=”B\*F\*Y\*X” local=””``
+      - An array of up to three integers or formulas for defining OpenCL work-sizes to
+        be used during execution. The formulas can use the values of the B,F,Y,X
+        dimensions and contain the operators: +,-,/,\*,%. All operators are evaluated
+        in integer arithmetic. Default value: ``global=”B\*F\*Y\*X” local=””``
     * - ``dim``
       - (0/1)
-      - A tensor to take the work-size from. Accepted values: ``input N`` , ``output`` , where ``N`` is an index of input tensor starting with 0. Default value: ``output``
+      - A tensor to take the work-size from. Accepted values: ``input N`` , ``output`` ,
+        where ``N`` is an index of input tensor starting with 0. Default value: ``output``
 
 **Sub-nodes**: None
 
@@ -242,7 +266,8 @@ Example Configuration File
 ##########################
 
 The following code sample provides an example configuration file in XML
-format. For information on the configuration file structure, see the `Configuration File Format <#config-file-format>`__.
+format. For information on the configuration file structure, see the
+`Configuration File Format <#config-file-format>`__.
 
 .. code-block:: xml
    :force:
@@ -291,21 +316,27 @@ For an example, see `Example Kernel <#example-kernel>`__.
     * - ``<TENSOR>_TYPE``
       - The datatype of the tensor: ``float`` , ``half`` , or ``char``
     * - ``<TENSOR>_FORMAT_<TENSOR_FORMAT>``
-      - The format of the tensor, BFYX, BYXF, YXFB , FYXB, or ANY. The format is concatenated to the defined name. You can use the tensor format to define codepaths in your code with ``#ifdef/#endif`` .
+      - The format of the tensor, BFYX, BYXF, YXFB , FYXB, or ANY. The format is
+        concatenated to the defined name. You can use the tensor format to define
+        codepaths in your code with ``#ifdef/#endif`` .
     * - ``<TENSOR>_LOWER_PADDING``
-      - An array of padding elements used for the tensor dimensions before they start. Always ordered as BFYX.
+      - An array of padding elements used for the tensor dimensions before they start.
+        Always ordered as BFYX.
     * - ``<TENSOR>_LOWER_PADDING_SIZE``
       - The size of the ``<TENSOR>_LOWER_PADDING`` array
     * - ``<TENSOR>_UPPER_PADDING``
-      - An array of padding elements used for the tensor dimensions after they end. Always ordered as BFYX.
+      - An array of padding elements used for the tensor dimensions after they end.
+        Always ordered as BFYX.
     * - ``<TENSOR>_UPPER_PADDING_SIZE``
       - The size of the ``<TENSOR>_UPPER_PADDING`` array
     * - ``<TENSOR>_PITCHES``
-      - The offset (in elements) between adjacent elements in each dimension. Always ordered as BFYX.
+      - The offset (in elements) between adjacent elements in each dimension.
+        Always ordered as BFYX.
     * - ``<TENSOR>_PITCHES_SIZE``
       - The size of the ``<TENSOR>_PITCHES`` array
     * - ``<TENSOR>_OFFSET``
-      - The number of elements from the start of the tensor to the first valid element, bypassing the lower padding.
+      - The number of elements from the start of the tensor to the first valid element,
+        bypassing the lower padding.
 
 All ``<TENSOR>`` values are automatically defined for every tensor
 bound to this operation, such as ``INPUT0``, ``INPUT1``, and ``OUTPUT0``, as shown
@@ -345,7 +376,9 @@ Example Kernel
 .. _debugging-tips:
 
 .. note::
-   As described in the previous section, all items such as the ``INPUT0_TYPE`` are actually defined as OpenCL (pre-)compiler inputs by OpenVINO for efficiency reasons. See the `Debugging Tips <#debugging-tips>`__ below for information on debugging the results.
+   As described in the previous section, all items such as the ``INPUT0_TYPE`` are
+   actually defined as OpenCL (pre-)compiler inputs by OpenVINO for efficiency reasons.
+   See the `Debugging Tips <#debugging-tips>`__ below for information on debugging the results.
 
 Debugging Tips
 ##############
@@ -359,5 +392,6 @@ your output can be truncated to fit the buffer. Also, because of
 buffering, you actually get an entire buffer of output when the
 execution ends.
 
-For more information, refer to the `printf Function <https://www.khronos.org/registry/OpenCL/sdk/1.2/docs/man/xhtml/printfFunction.html>`__.
+For more information, refer to the
+`printf Function <https://www.khronos.org/registry/OpenCL/sdk/1.2/docs/man/xhtml/printfFunction.html>`__.
 
diff --git a/docs/articles_en/documentation/openvino-extensibility/openvino-plugin-library/advanced-guides/low-precision-transformations/step1-prerequisites.rst b/docs/articles_en/documentation/openvino-extensibility/openvino-plugin-library/advanced-guides/low-precision-transformations/step1-prerequisites.rst
index 135dc2516c0276..5c36e8b509ebd6 100644
--- a/docs/articles_en/documentation/openvino-extensibility/openvino-plugin-library/advanced-guides/low-precision-transformations/step1-prerequisites.rst
+++ b/docs/articles_en/documentation/openvino-extensibility/openvino-plugin-library/advanced-guides/low-precision-transformations/step1-prerequisites.rst
@@ -15,10 +15,12 @@ Step 1. Prerequisites Transformations
    PullReshapeThroughDequantization <step1-prerequisites/pull-reshape-through-dequantization>
    PullTransposeThroughDequantization <step1-prerequisites/pull-transpose-through-dequantization>
    LinOpSequenceFusion <step1-prerequisites/lin-op-sequence-fusion>
+   ConvertSubtractConstant <step1-prerequisites/convert-subtract-constant>
 
 Prerequisites transformations are optional. The transformations prepare a model before running other low precision transformations. The transformations do not operate with dequantization operations or update precisions. Prerequisites transformations include:
 
 * :doc:`PullReshapeThroughDequantization <step1-prerequisites/lin-op-sequence-fusion>`
 * :doc:`PullTransposeThroughDequantization <step1-prerequisites/pull-transpose-through-dequantization>`
 * :doc:`LinOpSequenceFusion <step1-prerequisites/lin-op-sequence-fusion>`
+* :doc:`ConvertSubtractConstant <step1-prerequisites/convert-subtract-constant>`
 
diff --git a/docs/articles_en/documentation/openvino-extensibility/openvino-plugin-library/advanced-guides/low-precision-transformations/step2-markup.rst b/docs/articles_en/documentation/openvino-extensibility/openvino-plugin-library/advanced-guides/low-precision-transformations/step2-markup.rst
index 073ccc1c3d0da4..cfdd53d348a259 100644
--- a/docs/articles_en/documentation/openvino-extensibility/openvino-plugin-library/advanced-guides/low-precision-transformations/step2-markup.rst
+++ b/docs/articles_en/documentation/openvino-extensibility/openvino-plugin-library/advanced-guides/low-precision-transformations/step2-markup.rst
@@ -26,6 +26,7 @@ Step 2. Markup Transformations
    CreatePrecisionsDependentAttribute <step2-markup/create-precisions-dependent-attribute>
    PropagateThroughPrecisionPreserved <step2-markup/propagate-through-precision-preserved>
    PropagateToInput <step2-markup/propagate-to-input>
+   PropagateSharedValue <step2-markup/propagate-shared-value>
    UpdateSharedPrecisionPreserved <step2-markup/update-shared-precision-preserved>
 
 This step defines the optimal ``FakeQuantize`` decomposition precisions for the best inference performance via operations markup with runtime attribute instances. Attributes are created for input and output ports and operations. Transformations do not change the operation output port precisions. A model markup low precision logic is decomposed and implemented into the following common markup transformations. The order of transformations is important:
@@ -36,8 +37,9 @@ This step defines the optimal ``FakeQuantize`` decomposition precisions for the
 4. :doc:`MarkupPerTensorQuantization <step2-markup/markup-per-tensor-quantization>`
 5. :doc:`MarkupAvgPoolPrecisionPreserved <step2-markup/markup-avg-pool-precision-preserved>`
 6. :doc:`PropagatePrecisions <step2-markup/propagate-precisions>`
-7. :doc:`AlignQuantizationIntervals <step2-markup/align-quantization-intervals>`
-8. :doc:`AlignQuantizationParameters <step2-markup/align-quantization-parameters>`
+7. :doc:`PropagateSharedValue <step2-markup/propagate-shared-value>`
+8. :doc:`AlignQuantizationIntervals <step2-markup/align-quantization-intervals>`
+9. :doc:`AlignQuantizationParameters <step2-markup/align-quantization-parameters>`
 
 .. list-table::
     :header-rows: 1
diff --git a/docs/articles_en/documentation/openvino-extensibility/openvino-plugin-library/advanced-guides/low-precision-transformations/step3-main.rst b/docs/articles_en/documentation/openvino-extensibility/openvino-plugin-library/advanced-guides/low-precision-transformations/step3-main.rst
index bd5c449877ad87..121d19001f2a51 100644
--- a/docs/articles_en/documentation/openvino-extensibility/openvino-plugin-library/advanced-guides/low-precision-transformations/step3-main.rst
+++ b/docs/articles_en/documentation/openvino-extensibility/openvino-plugin-library/advanced-guides/low-precision-transformations/step3-main.rst
@@ -20,7 +20,7 @@ Step 3. Main Transformations
    ConcatTransformation <step3-main/movement/concat>
    ConvolutionTransformation <step3-main/convolution/convolution>
    ConvolutionBackpropDataTransformation <step3-main/convolution/convolution-backprop-data>
-   DepthToSpaceTransformation <step3-main/movement-depth-to-space>
+   DepthToSpaceTransformation <step3-main/movement/depth-to-space>
    FakeQuantizeDecompositionTransformation <step4-cleanup/fake-quantize-decomposition>
    FakeQuantizeTransformation <step3-main/quantization/fake-quantize>
    InterpolateTransformation <step3-main/image/interpolate>
@@ -29,6 +29,7 @@ Step 3. Main Transformations
    MatMulTransformation <step3-main/matrix/mat-mul>
    MaxPoolTransformation <step3-main/pooling/max-pool>
    MultiplyPartialTransformation <step3-main/arithmetic/multiply-partial>
+   MultiplyTransformation <step3-main/arithmetic/multiply>
    MVNTransformation <step3-main/normalization/mvn>
    NormalizeL2Transformation <step3-main/normalization/normalize-l2>
    PadTransformation<step3-main/movement/pad>
@@ -44,6 +45,7 @@ Step 3. Main Transformations
    ShuffleChannelsTransformation <step3-main/movement/shuffle-channels>
    SplitTransformation <step3-main/movement/split>
    StridedSliceTransformation <step3-main/movement/strided-slice>
+   SubtractTransformation <step3-main/arithmetic/subtract>
    TransposeTransformation <step3-main/movement/transpose>
    UnsqueezeTransformation <step3-main/shape/unsqueeze>
    VariadicSplitTransformation <step3-main/movement/variadic-split>
@@ -67,6 +69,7 @@ Main transformations are the majority of low precision transformations. Transfor
 * :doc:`MatMulTransformation <step3-main/matrix/mat-mul>`
 * :doc:`MaxPoolTransformation <step3-main/pooling/max-pool>`
 * :doc:`MultiplyPartialTransformation <step3-main/arithmetic/multiply-partial>`
+* :doc:`MultiplyTransformation <step3-main/arithmetic/multiply>`
 * :doc:`MVNTransformation <step3-main/normalization/mvn>`
 * :doc:`NormalizeL2Transformation <step3-main/normalization/normalize-l2>`
 * :doc:`PadTransformation <step3-main/movement/pad>`
@@ -82,6 +85,7 @@ Main transformations are the majority of low precision transformations. Transfor
 * :doc:`ShuffleChannelsTransformation <step3-main/movement/shuffle-channels>`
 * :doc:`SplitTransformation <step3-main/movement/split>`
 * :doc:`StridedSliceTransformation <step3-main/movement/strided-slice>`
+* :doc:`SubtractTransformation <step3-main/arithmetic/subtract>`
 * :doc:`TransposeTransformation <step3-main/movement/transpose>`
 * :doc:`UnsqueezeTransformation <step3-main/shape/unsqueeze>`
 * :doc:`VariadicSplitTransformation <step3-main/movement/variadic-split>`
diff --git a/docs/articles_en/documentation/openvino-extensibility/openvino-plugin-library/compiled-model.rst b/docs/articles_en/documentation/openvino-extensibility/openvino-plugin-library/compiled-model.rst
index d3c7d5d9c3e5a7..4bdbfa0ab4a189 100644
--- a/docs/articles_en/documentation/openvino-extensibility/openvino-plugin-library/compiled-model.rst
+++ b/docs/articles_en/documentation/openvino-extensibility/openvino-plugin-library/compiled-model.rst
@@ -8,19 +8,20 @@ Compiled Model
    :description: Use the ov::CompiledModel class as the base class for a compiled
                  model and to create an arbitrary number of ov::InferRequest objects.
 
-ov::CompiledModel class functionality:
+``ov::CompiledModel`` class functionality:
 
-* Compile an ov::Model instance to a backend specific graph representation
-* Create an arbitrary number of ov::InferRequest objects
-* Hold some common resources shared between different instances of ov::InferRequest. For example:
+* Compile an ``ov::Model`` instance to a backend specific graph representation
+* Create an arbitrary number of ``ov::InferRequest`` objects
+* Hold some common resources shared between different instances of ``ov::InferRequest``. For example:
 
-  * ov::ICompiledModel::m_task_executor task executor to implement asynchronous execution
-  * ov::ICompiledModel::m_callback_executor task executor to run an asynchronous inference request callback in a separate thread
+  * ``ov::ICompiledModel::m_task_executor`` task executor to implement asynchronous execution
+  * ``ov::ICompiledModel::m_callback_executor`` task executor to run an asynchronous inference request callback in a separate thread
 
 CompiledModel Class
 ###################
 
-OpenVINO Plugin API provides the interface ov::ICompiledModel which should be used as a base class for a compiled model. Based on that, a declaration of an compiled model class can look as follows:
+OpenVINO Plugin API provides the interface ``ov::ICompiledModel`` which should be used as a base class
+for a compiled model. Based on that, a declaration of an compiled model class can look as follows:
 
 .. doxygensnippet:: src/plugins/template/src/compiled_model.hpp
    :language: cpp
@@ -32,9 +33,12 @@ Class Fields
 
 The example class has several fields:
 
-* ``m_request_id`` - Tracks a number of created inference requests, which is used to distinguish different inference requests during profiling via the Intel® Instrumentation and Tracing Technology (ITT) library.
+* ``m_request_id`` - Tracks a number of created inference requests, which is used to distinguish
+  different inference requests during profiling via the Intel® Instrumentation and Tracing Technology (ITT) library.
 * ``m_cfg`` - Defines a configuration a compiled model was compiled with.
-* ``m_model`` - Keeps a reference to transformed ``ov::Model`` which is used in OpenVINO reference backend computations. Note, in case of other backends with backend specific graph representation ``m_model`` has different type and represents backend specific graph or just a set of computational kernels to perform an inference.
+* ``m_model`` - Keeps a reference to transformed ``ov::Model`` which is used in OpenVINO reference
+  backend computations. Note, in case of other backends with backend specific graph representation
+  ``m_model`` has different type and represents backend specific graph or just a set of computational kernels to perform an inference.
 * ``m_loaded_from_cache`` - Allows to understand that model was loaded from cache.
 
 CompiledModel Constructor
@@ -51,7 +55,11 @@ The implementation ``compile_model()`` is fully device-specific.
 compile_model()
 +++++++++++++++
 
-The function accepts a const shared pointer to ``ov::Model`` object and applies OpenVINO passes using ``transform_model()`` function, which defines plugin-specific conversion pipeline. To support low precision inference, the pipeline can include Low Precision Transformations. These transformations are usually hardware specific. You can find how to use and configure Low Precisions Transformations in :doc:`Low Precision Transformations <advanced-guides/low-precision-transformations>` guide.
+The function accepts a const shared pointer to ``ov::Model`` object and applies OpenVINO passes
+using ``transform_model()`` function, which defines plugin-specific conversion pipeline. To support
+low precision inference, the pipeline can include Low Precision Transformations. These
+transformations are usually hardware specific. You can find how to use and configure Low Precisions
+Transformations in :doc:`Low Precision Transformations <advanced-guides/low-precision-transformations>` guide.
 
 .. doxygensnippet:: src/plugins/template/src/compiled_model.cpp
    :language: cpp
@@ -65,7 +73,8 @@ The function accepts a const shared pointer to ``ov::Model`` object and applies
 export_model()
 ++++++++++++++
 
-The implementation of the method should write all data to the ``model_stream``, which is required to import a backend specific graph later in the ``Plugin::import_model`` method:
+The implementation of the method should write all data to the ``model_stream``, which is required
+to import a backend specific graph later in the ``Plugin::import_model`` method:
 
 .. doxygensnippet:: src/plugins/template/src/compiled_model.cpp
    :language: cpp
@@ -80,14 +89,25 @@ The method creates an synchronous inference request and returns it.
    :language: cpp
    :fragment: [compiled_model:create_sync_infer_request]
 
-While the public OpenVINO API has a single interface for inference request, which can be executed in synchronous and asynchronous modes, a plugin library implementation has two separate classes:
+While the public OpenVINO API has a single interface for inference request, which can be executed
+in synchronous and asynchronous modes, a plugin library implementation has two separate classes:
 
 * :doc:`Synchronous inference request <synch-inference-request>`, which defines pipeline stages and runs them synchronously in the ``infer`` method.
 
-* :doc:`Asynchronous inference request <asynch-inference-request>`, which is a wrapper for a synchronous inference request and can run a pipeline asynchronously. Depending on a device pipeline structure, it can have one or several stages:
-
-  * For single-stage pipelines, there is no need to define this method and create a class derived from ov::IAsyncInferRequest. For single stage pipelines, a default implementation of this method creates ov::IAsyncInferRequest wrapping a synchronous inference request and runs it asynchronously in the ``m_request_executor`` executor.
-  * For pipelines with multiple stages, such as performing some preprocessing on host, uploading input data to a device, running inference on a device, or downloading and postprocessing output data, schedule stages on several task executors to achieve better device use and performance. You can do it by creating a sufficient number of inference requests running in parallel. In this case, device stages of different inference requests are overlapped with preprocessing and postprocessing stage giving better performance.
+* :doc:`Asynchronous inference request <asynch-inference-request>`, which is a wrapper for a synchronous
+  inference request and can run a pipeline asynchronously. Depending on a device pipeline structure,
+  it can have one or several stages:
+
+  * For single-stage pipelines, there is no need to define this method and create a class derived
+    from ``ov::IAsyncInferRequest``. For single stage pipelines, a default implementation of this
+    method creates ``ov::IAsyncInferRequest`` wrapping a synchronous inference request and runs
+    it asynchronously in the ``m_request_executor`` executor.
+  * For pipelines with multiple stages, such as performing some preprocessing on host, uploading
+    input data to a device, running inference on a device, or downloading and postprocessing output
+    data, schedule stages on several task executors to achieve better device use and performance.
+    You can do it by creating a sufficient number of inference requests running in parallel.
+    In this case, device stages of different inference requests are overlapped with preprocessing
+    and postprocessing stage giving better performance.
 
 .. important::
 
diff --git a/docs/articles_en/documentation/openvino-extensibility/openvino-plugin-library/plugin-testing.rst b/docs/articles_en/documentation/openvino-extensibility/openvino-plugin-library/plugin-testing.rst
index 3705f0e4a285d1..ff3bdf878d1b13 100644
--- a/docs/articles_en/documentation/openvino-extensibility/openvino-plugin-library/plugin-testing.rst
+++ b/docs/articles_en/documentation/openvino-extensibility/openvino-plugin-library/plugin-testing.rst
@@ -5,7 +5,7 @@ Plugin Testing
 
 
 .. meta::
-   :description: Use the openvino::funcSharedTests library, which includes 
+   :description: Use the openvino::funcSharedTests library, which includes
                  a predefined set of functional tests and utilities to verify a plugin.
 
 
@@ -22,13 +22,13 @@ Test definitions are split into tests class declaration (see ``src/tests/functio
 2. **Single layer tests** (``single_layer_tests`` sub-folder). This groups of tests checks that a particular single layer can be inferenced on a device. An example of test instantiation based on test definition from ``openvino::funcSharedTests`` library:
 
 * From the declaration of convolution test class we can see that it's a parametrized GoogleTest based class with the ``convLayerTestParamsSet`` tuple of parameters:
-    
-.. doxygensnippet:: src/tests/functional/shared_test_classes/include/shared_test_classes/single_layer/convolution.hpp
+
+.. doxygensnippet:: src/tests/functional/shared_test_classes/include/shared_test_classes/single_op/convolution.hpp
    :language: cpp
    :fragment: [test_convolution:definition]
 
 * Based on that, define a set of parameters for ``Template`` plugin functional test instantiation:
-   
+
 .. doxygensnippet:: src/plugins/template/tests/functional/shared_tests_instances/single_layer_tests/convolution.cpp
    :language: cpp
    :fragment: [test_convolution:declare_parameters]
@@ -41,7 +41,7 @@ Test definitions are split into tests class declaration (see ``src/tests/functio
 
 3. **Sub-graph tests** (``subgraph_tests`` sub-folder). This group of tests is designed to tests small patterns or combination of layers. E.g. when a particular topology is being enabled in a plugin e.g. TF ResNet-50, there is no need to add the whole topology to test tests. In opposite way, a particular repetitive subgraph or pattern can be extracted from ``ResNet-50`` and added to the tests. The instantiation of the sub-graph tests is done in the same way as for single layer tests.
 
-.. note:: 
+.. note::
 
    Such sub-graphs or patterns for sub-graph tests should be added to ``openvino::ov_models`` library first (this library is a pre-defined set of small ``ov::Model``) and re-used in sub-graph tests after.
 
@@ -55,7 +55,7 @@ Test definitions are split into tests class declaration (see ``src/tests/functio
 To use these tests for your own plugin development, link the ``openvino::funcSharedTests`` library to your test binary and instantiate required test cases with desired parameters values.
 
 .. note::
-   
+
    A plugin may contain its own tests for use cases that are specific to hardware or need to be extensively tested.
 
 To build test binaries together with other build artifacts, use the ``make all`` command. For details, see :doc:`Build Plugin Using CMake <build-plugin-using-cmake>`.
@@ -67,7 +67,7 @@ OpenVINO Plugin tests are open for contribution.
 Add common test case definitions applicable for all plugins to the ``openvino::funcSharedTests`` target within the OpenVINO repository. Then, any other plugin supporting corresponding functionality can instantiate the new test.
 
 .. note::
-  
+
    When implementing a new subgraph test, add new single-layer tests for each operation of the subgraph if such test does not exist.
 
 
diff --git a/docs/articles_en/documentation/openvino-extensibility/transformation-api.rst b/docs/articles_en/documentation/openvino-extensibility/transformation-api.rst
index c179e628f6d0db..840b93adddc75f 100644
--- a/docs/articles_en/documentation/openvino-extensibility/transformation-api.rst
+++ b/docs/articles_en/documentation/openvino-extensibility/transformation-api.rst
@@ -18,14 +18,17 @@ Overview of Transformations API
    transformation-api/graph-rewrite-pass
    transformation-api/patterns-python-api
 
-OpenVINO Transformation mechanism allows to develop transformation passes to modify ``ov::Model``. You can use this mechanism to apply additional optimizations to the original Model or transform unsupported subgraphs and operations to new operations supported by the plugin.
+OpenVINO Transformation mechanism allows to develop transformation passes to modify ``ov::Model``.
+You can use this mechanism to apply additional optimizations to the original Model or transform
+unsupported subgraphs and operations to new operations supported by the plugin.
 This guide contains all the necessary information to start implementing OpenVINO™ transformations.
 
 Working with Model
 ##################
 
 Before moving to the transformation part, it is important to say a few words about the functions which allow modifying ``ov::Model``.
-This section extends the :doc:`model representation guide <../../openvino-workflow/running-inference/integrate-openvino-with-your-application/model-representation>` and introduces an API for ``ov::Model`` manipulation.
+This section extends the :doc:`model representation guide <../../openvino-workflow/running-inference/integrate-openvino-with-your-application/model-representation>`
+and introduces an API for ``ov::Model`` manipulation.
 
 Working with node input and output ports
 ++++++++++++++++++++++++++++++++++++++++
@@ -33,7 +36,8 @@ Working with node input and output ports
 Each OpenVINO operation has ``ov::Node`` input and output ports, except for ``Parameter`` and ``Constant`` types.
 The terms ``node`` and ``operation`` are used interchangeably in OpenVINO, but this article will maintain consistency in their use.
 
-Every port is associated with a node, allowing access to the node it belongs to, including its shape, type, all consumers for output ports and the producer node for input ports.
+Every port is associated with a node, allowing access to the node it belongs to, including
+its shape, type, all consumers for output ports and the producer node for input ports.
 
 Take a look at the code example:
 
@@ -44,7 +48,8 @@ Take a look at the code example:
 Node replacement
 ++++++++++++++++
 
-OpenVINO™ provides two ways for node replacement: via OpenVINO™ helper function and directly via port methods. We are going to review both of them.
+OpenVINO™ provides two ways for node replacement: via OpenVINO™ helper function and directly
+via port methods. We are going to review both of them.
 
 Let's start with OpenVINO™ helper functions. The most popular function is ``ov::replace_node(old_node, new_node)``.
 
@@ -56,7 +61,8 @@ Let's review a replacement case where a Negative operation is replaced with Mult
    :language: cpp
    :fragment: [ov:replace_node]
 
-``ov::replace_node`` has a constraint that number of output ports for both nodes must be the same. Otherwise, the attempt to replace the nodes will result in an exception.
+``ov::replace_node`` has a constraint that number of output ports for both nodes must be the same.
+Otherwise, the attempt to replace the nodes will result in an exception.
 
 The alternative way to do the same replacement is the following:
 
@@ -109,7 +115,8 @@ Transformation conditional compilation
 
 Transformation library has two internal macros to support conditional compilation feature.
 
-* ``MATCHER_SCOPE(region)`` - allows to disable the MatcherPass if matcher isn't used. The region name should be unique. This macro creates a local variable ``matcher_name`` which you should use as a matcher name.
+* ``MATCHER_SCOPE(region)`` - allows to disable the MatcherPass if matcher isn't used. The region
+  name should be unique. This macro creates a local variable ``matcher_name`` which you should use as a matcher name.
 * ``RUN_ON_MODEL_SCOPE(region)`` - allows to disable run_on_model pass if it isn't used. The region name should be unique.
 
 .. _transformation_writing_essentials:
@@ -122,22 +129,29 @@ To develop a transformation, follow these transformation rules:
 1. Friendly Names
 +++++++++++++++++
 
-Each ``ov::Node`` has a unique name and a friendly name. In transformations, only the friendly name matters because it represents the name from the model's perspective.
-To prevent losing the friendly name when replacing a node with another node or a subgraph, the original friendly name is set to the last node in the replacing subgraph. See the example below.
+Each ``ov::Node`` has a unique name and a friendly name. In transformations, only the friendly
+name matters because it represents the name from the model's perspective.
+To prevent losing the friendly name when replacing a node with another node or a subgraph,
+the original friendly name is set to the last node in the replacing subgraph. See the example below.
 
 .. doxygensnippet:: docs/articles_en/assets/snippets/ov_model_snippets.cpp
    :language: cpp
    :fragment: [ov:replace_friendly_name]
 
-In more complicated cases, when a replaced operation has several outputs and additional consumers are added to its outputs, the decision on how to set the friendly name is determined by an agreement.
+In more complicated cases, when a replaced operation has several outputs and additional
+consumers are added to its outputs, the decision on how to set the friendly name is determined by an agreement.
 
 2. Runtime Info
 +++++++++++++++
 
-Runtime info is a map ``std::map<std::string, ov::Any>`` located inside the ``ov::Node`` class. It represents additional attributes of the ``ov::Node``.
-These attributes, which can be set by users or plugins, need to be preserved when executing a transformation that changes ``ov::Model``, as they are not automatically propagated.
-In most cases, transformations have the following types: 1:1 (replace node with another node), 1:N (replace node with a sub-graph), N:1 (fuse sub-graph into a single node), N:M (any other transformation).
-Currently, there is no mechanism that automatically detects transformation types, so this runtime information needs to be propagated manually. See the example below:
+Runtime info is a map ``std::map<std::string, ov::Any>`` located inside the ``ov::Node`` class.
+It represents additional attributes of the ``ov::Node``.
+These attributes, which can be set by users or plugins, need to be preserved when executing
+a transformation that changes ``ov::Model``, as they are not automatically propagated.
+In most cases, transformations have the following types: 1:1 (replace node with another node),
+1:N (replace node with a sub-graph), N:1 (fuse sub-graph into a single node), N:M (any other transformation).
+Currently, there is no mechanism that automatically detects transformation types, so this
+runtime information needs to be propagated manually. See the example below:
 
 
 .. doxygensnippet:: docs/articles_en/assets/snippets/ov_model_snippets.cpp
@@ -146,12 +160,16 @@ Currently, there is no mechanism that automatically detects transformation types
 
 When a transformation has multiple fusions or decompositions, ``ov::copy_runtime_info`` must be called multiple times for each case.
 
-.. note:: ``copy_runtime_info`` removes ``rt_info`` from destination nodes. If you want to keep it, specify them in source nodes as following: ``copy_runtime_info({a, b, c}, {a, b})``
+.. note::
+
+   ``copy_runtime_info`` removes ``rt_info`` from destination nodes. If you want to keep it,
+   specify them in source nodes as following: ``copy_runtime_info({a, b, c}, {a, b})``
 
 3. Constant Folding
 +++++++++++++++++++
 
-If your transformation inserts constant sub-graphs that need to be folded, do not forget to use ``ov::pass::ConstantFolding()`` after your transformation or call constant folding directly for operation.
+If your transformation inserts constant sub-graphs that need to be folded, do not forget
+to use ``ov::pass::ConstantFolding()`` after your transformation or call constant folding directly for operation.
 The example below shows how constant subgraph can be constructed.
 
 .. doxygensnippet:: docs/articles_en/assets/snippets/ov_model_snippets.cpp
@@ -174,8 +192,12 @@ Common mistakes in transformations
 In transformation development process:
 
 * Do not use deprecated OpenVINO™ API. Deprecated methods are marked with ``OPENVINO_DEPRECATED`` macro in their definition.
-* Do not pass ``shared_ptr<Node>`` as input for another node if the type of the node is unknown or if it has multiple outputs. Instead, use explicit output ports.
-* If you replace a node with another node that produces different shape, note that the new shape will not be propagated until the first ``validate_nodes_and_infer_types`` call for ``ov::Model``. If you are using ``ov::pass::Manager``, it will automatically call this method after each transformation execution.
+* Do not pass ``shared_ptr<Node>`` as input for another node if the type of the node is unknown
+  or if it has multiple outputs. Instead, use explicit output ports.
+* If you replace a node with another node that produces different shape, note that
+  the new shape will not be propagated until the first ``validate_nodes_and_infer_types``
+  call for ``ov::Model``. If you are using ``ov::pass::Manager``, it will automatically call
+  this method after each transformation execution.
 * Do not forget to call the ``ov::pass::ConstantFolding`` pass if your transformation creates constant subgraphs.
 * Use latest OpSet if you are not developing downgrade transformation pass.
 * When developing a callback for ``ov::pass::MatcherPass``, do not change nodes that come after the root node in the topological order.
@@ -185,9 +207,11 @@ In transformation development process:
 Using pass manager
 ##################
 
-``ov::pass::Manager`` is a container class that can store a list of transformations and execute them. The main idea of this class is to have a high-level representation for grouped list of transformations.
-It can register and apply any `transformation pass <#transformations_types>`__ on a model.
-In addition, ``ov::pass::Manager`` has extended debug capabilities (find more information in the `how to debug transformations <#how-to-debug-transformations>`__ section).
+``ov::pass::Manager`` is a container class that can store a list of transformations and execute them.
+The main idea of this class is to have a high-level representation for grouped list of transformations.
+It can register and apply any `transformation pass <#transformations-types>`__ on a model.
+In addition, ``ov::pass::Manager`` has extended debug capabilities (find more information
+in the `how to debug transformations <#how-to-debug-transformations>`__ section).
 
 The example below shows basic usage of ``ov::pass::Manager``
 
@@ -206,7 +230,8 @@ Another example shows how multiple matcher passes can be united into single Grap
 How to debug transformations
 ############################
 
-If you are using ``ov::pass::Manager`` to run sequence of transformations, you can get additional debug capabilities by using the following environment variables:
+If you are using ``ov::pass::Manager`` to run sequence of transformations, you can get
+additional debug capabilities by using the following environment variables:
 
 .. code-block:: cpp
 
@@ -214,7 +239,9 @@ If you are using ``ov::pass::Manager`` to run sequence of transformations, you c
    OV_ENABLE_VISUALIZE_TRACING=1 -  enables visualization after each transformation. By default, it saves dot and svg files.
 
 
-.. note:: Make sure that you have dot installed on your machine; otherwise, it will silently save only dot file without svg file.
+.. note::
+
+   Make sure that you have dot installed on your machine; otherwise, it will silently save only dot file without svg file.
 
 See Also
 ########
diff --git a/docs/articles_en/documentation/openvino-ir-format/intermediate-representation-int8-inference.rst b/docs/articles_en/documentation/openvino-ir-format/intermediate-representation-int8-inference.rst
index a0cc4488ef15cc..4c9c3ab6f05c76 100644
--- a/docs/articles_en/documentation/openvino-ir-format/intermediate-representation-int8-inference.rst
+++ b/docs/articles_en/documentation/openvino-ir-format/intermediate-representation-int8-inference.rst
@@ -21,7 +21,7 @@ Such a model is called a Low Precision IR and can be generated in two ways:
 
 * By :doc:`quantizing regular IR with the Neural Network Compression Framework (NNCF) <../../openvino-workflow/model-optimization>`
 * Using model conversion of a model pre-trained for Low Precision inference: TensorFlow models (``.pb`` model file with ``FakeQuantize`` operations), quantized TensorFlow Lite models and ONNX quantized models.
-TensorFlow and ONNX quantized models can be prepared by `Neural Network Compression Framework <https://github.com/openvinotoolkit/nncf/blob/develop/README.md>`__.
+  TensorFlow and ONNX quantized models can be prepared by `Neural Network Compression Framework <https://github.com/openvinotoolkit/nncf/blob/develop/README.md>`__.
 
 For an operation to be executed in INT8, it must have `FakeQuantize` operations as inputs.
 For more details, see the :doc:`specification of FakeQuantize operation <operation-sets/operation-specs/quantization/fake-quantize-1>`.
diff --git a/docs/articles_en/documentation/openvino-ir-format/operation-sets/broadcast-rules.rst b/docs/articles_en/documentation/openvino-ir-format/operation-sets/broadcast-rules.rst
index bfa55a5a798ce6..c15b31b9051e20 100644
--- a/docs/articles_en/documentation/openvino-ir-format/operation-sets/broadcast-rules.rst
+++ b/docs/articles_en/documentation/openvino-ir-format/operation-sets/broadcast-rules.rst
@@ -5,8 +5,8 @@ Broadcast Rules For Elementwise Operations
 
 
 .. meta::
-  :description: Learn about Numpy and PDPD broadcast types in OpenVINO, that 
-                enable performing element-wise operation for inputs of arbitrary 
+  :description: Learn about Numpy and PDPD broadcast types in OpenVINO, that
+                enable performing element-wise operation for inputs of arbitrary
                 number of dimensions.
 
 The purpose of this document is to provide a set of common rules which are applicable for ops using broadcasting.
@@ -40,81 +40,82 @@ Rules
 Numpy examples
 ##############
 
-* ``A: Shape(,) -> scalar`` 
-  ``B: Shape(,) -> scalar`` 
+* ``A: Shape(,) -> scalar``
+  ``B: Shape(,) -> scalar``
   ``Result: Shape(,) -> scalar``
 
-* ``A: Shape(2, 3)`` 
-  ``B: Shape(   1)`` 
+* ``A: Shape(2, 3)``
+  ``B: Shape(   1)``
   ``Result: Shape(2, 3)``
 
-* ``A: Shape(   3)`` 
-  ``B: Shape(2, 3)`` 
+* ``A: Shape(   3)``
+  ``B: Shape(2, 3)``
   ``Result: Shape(2, 3)``
 
-* ``A: Shape(2, 3, 5)`` 
-  ``B: Shape(,) -> scalar`` 
+* ``A: Shape(2, 3, 5)``
+  ``B: Shape(,) -> scalar``
   ``Result: Shape(2, 3, 5)``
 
-* ``A: Shape(2, 1, 5)`` 
+* ``A: Shape(2, 1, 5)``
   ``B: Shape(1, 4, 5)``
   ``Result: Shape(2, 4, 5)``
 
-* ``A: Shape(   6, 5)`` 
-  ``B: Shape(2, 1, 5)`` 
+* ``A: Shape(   6, 5)``
+  ``B: Shape(2, 1, 5)``
   ``Result: Shape(2, 6, 5)``
 
-* ``A: Shape(2, 1, 5)`` 
-  ``B: Shape(   4, 1)`` 
-  ``Result: Shape(2, 4, 5)`` 
+* ``A: Shape(2, 1, 5)``
+  ``B: Shape(   4, 1)``
+  ``Result: Shape(2, 4, 5)``
 
-* ``A: Shape(3, 2, 1, 4)`` 
-  ``B: Shape(      5, 4)`` 
+* ``A: Shape(3, 2, 1, 4)``
+  ``B: Shape(      5, 4)``
   ``Result: Shape(3, 2, 5, 4)``
 
-* ``A: Shape(   1, 5, 3)`` 
-  ``B: Shape(5, 2, 1, 3)`` 
+* ``A: Shape(   1, 5, 3)``
+  ``B: Shape(5, 2, 1, 3)``
   ``Result: Shape(5, 2, 5, 3)``
 
-* ``A: Shape(3)`` 
-  ``B: Shape(2)`` 
+* ``A: Shape(3)``
+  ``B: Shape(2)``
   ``Result: broadcast won't happen due to dimensions mismatch``
 
-* ``A: Shape(3, 1, 5)`` 
-  ``B: Shape(4, 4, 5)`` 
+* ``A: Shape(3, 1, 5)``
+  ``B: Shape(4, 4, 5)``
   ``Result: broadcast won't happen due to dimensions mismatch on the leftmost axis``
 
 PDPD examples
 #############
 
-* ``A: Shape(2, 3, 4, 5)`` 
-  ``B: Shape(   3, 4   ) with axis = 1`` 
+* ``A: Shape(2, 3, 4, 5)``
+  ``B: Shape(   3, 4   ) with axis = 1``
   ``Result: Shape(2, 3, 4, 5)``
 
-* ``A: Shape(2, 3, 4, 5)`` 
-  ``B: Shape(   3, 1   ) with axis = 1`` 
+* ``A: Shape(2, 3, 4, 5)``
+  ``B: Shape(   3, 1   ) with axis = 1``
   ``Result: Shape(2, 3, 4, 5)``
 
-* ``A: Shape(2, 3, 4, 5)`` 
-  ``B: Shape(      4, 5) with axis=-1(default) or axis=2`` 
+* ``A: Shape(2, 3, 4, 5)``
+  ``B: Shape(      4, 5) with axis=-1(default) or axis=2``
   ``Result: Shape(2, 3, 4, 5)``
 
-* ``A: Shape(2, 3, 4, 5)`` 
-  ``B: Shape(1, 3      ) with axis = 0`` 
+* ``A: Shape(2, 3, 4, 5)``
+  ``B: Shape(1, 3      ) with axis = 0``
   ``Result: Shape(2, 3, 4, 5)``
 
-* ``A: Shape(2, 3, 4, 5)`` 
-  ``B: Shape(,)`` 
-  ``Result: Shape(2, 3, 4, 5)`` 
+* ``A: Shape(2, 3, 4, 5)``
+  ``B: Shape(,)``
+  ``Result: Shape(2, 3, 4, 5)``
 
-* ``A: Shape(2, 3, 4, 5)`` 
+* ``A: Shape(2, 3, 4, 5)``
   ``B: Shape(         5) with axis=-1(default) or axis = 3``
   ``Result: Shape(2, 3, 4, 5)``
 
-* ``A: Shape(8, 1, 6, 1)`` 
-  ``B: Shape(   7, 1, 5) with axis = 1`` 
+* ``A: Shape(8, 1, 6, 1)``
+  ``B: Shape(   7, 1, 5) with axis = 1``
   ``Result: broadcast won't happen due to dimensions mismatch, only B to A broadcast is supported for PDPD``
-Bidirectional Broadcast Rules 
+
+Bidirectional Broadcast Rules
 #############################
 
 Description
@@ -138,23 +139,23 @@ Rules
 Bidirectional examples
 ++++++++++++++++++++++
 
-* ``A: Shape(5)`` 
-  ``B: Shape(1)`` 
+* ``A: Shape(5)``
+  ``B: Shape(1)``
   ``Result: Shape(5)``
 
-* ``A: Shape(2, 3)`` 
-  ``B: Shape(   3)`` 
+* ``A: Shape(2, 3)``
+  ``B: Shape(   3)``
   ``Result: Shape(2, 3)``
 
-* ``A: Shape(3, 1)`` 
-  ``B: Shape(3, 4)`` 
+* ``A: Shape(3, 1)``
+  ``B: Shape(3, 4)``
   ``Result: Shape(3, 4)``
 
-* ``A: Shape(3, 4)`` 
-  ``B: Shape(,) -> scalar`` 
+* ``A: Shape(3, 4)``
+  ``B: Shape(,) -> scalar``
   ``Result: Shape(3, 4)``
 
-* ``A: Shape(   3, 1)`` 
-  ``B: Shape(2, 1, 6)`` 
+* ``A: Shape(   3, 1)``
+  ``B: Shape(2, 1, 6)``
   ``Result: Shape(2, 3, 6)``
 
diff --git a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs.rst b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs.rst
index 250ef955bb41a8..8f37b9dbdf8e9f 100644
--- a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs.rst
+++ b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs.rst
@@ -22,6 +22,8 @@ Operation Specifications
    Assign-6 <operation-specs/infrastructure/assign-6>
    Atan-1 <operation-specs/arithmetic/atan-1>
    Atanh-3 <operation-specs/arithmetic/atanh-3>
+   AUGRUCell <operation-specs/internal/augru-cell>
+   AUGRUSequence <operation-specs/internal/augru-sequence>
    AvgPool-1 <operation-specs/pooling/avg-pool-1>
    AvgPool-14 <operation-specs/pooling/avg-pool-14>
    BatchNormInference-1 <operation-specs/normalization/batch-norm-inference-1>
@@ -107,7 +109,7 @@ Operation Specifications
    Interpolate-1 <operation-specs/image/interpolate-1>
    Interpolate-4 <operation-specs/image/interpolate-4>
    Interpolate-11 <operation-specs/image/interpolate-11>
-   Inverse-14 <operation-specs/matrix/Inverse_14>
+   Inverse-14 <operation-specs/matrix/inverse-14>
    IsFinite-10 <operation-specs/comparison/isfinite-10>
    IsInf-10 <operation-specs/comparison/isinf-10>
    IsNaN-10 <operation-specs/comparison/isnan-10>
@@ -130,6 +132,7 @@ Operation Specifications
    MaxPool-1 <operation-specs/pooling/max-pool-1>
    MaxPool-8 <operation-specs/pooling/max-pool-8>
    MaxPool-14 <operation-specs/pooling/max-pool-14>
+   Shape Calculation Rules for Pooling Operators <operation-specs/pooling/pooling_shape_rules>
    Maximum-1 <operation-specs/arithmetic/maximum-1>
    Minimum-1 <operation-specs/arithmetic/minimum-1>
    Mish-4 <operation-specs/activation/mish-4>
diff --git a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/arithmetic/tanh-1.rst b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/arithmetic/tanh-1.rst
index 57a4e6f28091e8..bd2184251d0a26 100644
--- a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/arithmetic/tanh-1.rst
+++ b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/arithmetic/tanh-1.rst
@@ -5,7 +5,7 @@ Tanh
 
 
 .. meta::
-  :description: Learn about Tanh-1 - an element-wise, arithmetic operation, which 
+  :description: Learn about Tanh-1 - an element-wise, arithmetic operation, which
                 can be performed on a single tensor in OpenVINO.
 
 **Versioned name**: *Tanh-1*
@@ -34,7 +34,7 @@ For each element from the input tensor calculates corresponding element in the o
 **Outputs**:
 
 * **1**: The result of element-wise *Tanh* operation. A tensor of type *T* and the same shape as input tensor.
-**Types**
+  **Types**
 
 * *T*: any numeric type.
 
@@ -44,7 +44,7 @@ For each element from the input tensor calculates corresponding element in the o
 *Example 1*
 
 .. code-block:: xml
-   :force: 
+   :force:
 
     <layer ... type="Tanh">
         <input>
diff --git a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/condition/if-8.rst b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/condition/if-8.rst
index 7dd1a2d1659a0a..f53693792133f8 100644
--- a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/condition/if-8.rst
+++ b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/condition/if-8.rst
@@ -5,46 +5,46 @@ If
 
 
 .. meta::
-  :description: Learn about If-8 - an element-wise, condition operation, which 
+  :description: Learn about If-8 - an element-wise, condition operation, which
                 can be performed on multiple tensors in OpenVINO.
 
 **Versioned name**: *If-8*
 
 **Category**: *Condition*
 
-**Short description**: *If* operation contains two internal networks(subgraphs) such as ``then_body`` and ``else_body``, 
-and performs one of them depending on ``cond`` value. If ``cond`` is  ``True``, ``then_body`` is executed. If ``cond`` is  ``False``, 
-the operation executes the ``else_body`` subgraph. 
+**Short description**: *If* operation contains two internal networks(subgraphs) such as ``then_body`` and ``else_body``,
+and performs one of them depending on ``cond`` value. If ``cond`` is  ``True``, ``then_body`` is executed. If ``cond`` is  ``False``,
+the operation executes the ``else_body`` subgraph.
 
 **Detailed description**
 
-*If* must not contain empty subgraphs. Each of them must have at least one operation ``Result``. 
+*If* must not contain empty subgraphs. Each of them must have at least one operation ``Result``.
 Also the number of outputs from *If* always must be greater than zero and equal to the number of outputs from each subgraph.
 
 **If attributes**:
 
 * **Subgraphs**:
 
-  ``then_body``/``else_body`` are subgraphs that are executed depending on the ``cond`` value. 
-  The subgraph is described operation by operation as a typical IR network. 
+  ``then_body``/``else_body`` are subgraphs that are executed depending on the ``cond`` value.
+  The subgraph is described operation by operation as a typical IR network.
   The subgraph has inputs (``Parameter`` operations) and outputs (``Result`` operations).
 
-  * **Subgraph's inputs** - inputs to the subgraph which associated with *If* inputs via *port_map*. 
+  * **Subgraph's inputs** - inputs to the subgraph which associated with *If* inputs via *port_map*.
 
     The subgraph can have any number of inputs (even zero).
 
   * **Subgraph's outputs** - outputs from the subgraph which associated with *If* outputs via *port_map*.
 
-    The subgraph must contain at least one output. Each *If* output is associated with one output from the subgraph. 
-    Therefore the number of ``then_body`` outputs is equal to the number of outputs from *If* and 
+    The subgraph must contain at least one output. Each *If* output is associated with one output from the subgraph.
+    Therefore the number of ``then_body`` outputs is equal to the number of outputs from *If* and
     the number of ``else_body`` outputs.
     The type of the subgraph output and the type of the associated output from *If* must be equal.
 
 
 * **Port maps**:
 
-  *port_map* is a set of rules to map input or output data tensors of *If* operation onto the subgraph data tensors. 
-  The ``port_map`` entries can be ``input`` and ``output``. Each entry describes a corresponding mapping rule. 
+  *port_map* is a set of rules to map input or output data tensors of *If* operation onto the subgraph data tensors.
+  The ``port_map`` entries can be ``input`` and ``output``. Each entry describes a corresponding mapping rule.
   *If* has two *port_maps*: ``then_port_map`` for ``then_body`` and ``else_port_map`` for ``else_body``.
 
   * **Port map attributes**:
@@ -59,18 +59,18 @@ Also the number of outputs from *If* always must be greater than zero and equal
 
     * *internal_layer_id*
 
-      * **Description**: *internal_layer_id* is a ``Parameter`` or ``Result`` operation ID inside 
-      the subgraph to map to.
+      * **Description**: *internal_layer_id* is a ``Parameter`` or ``Result`` operation ID inside
+        the subgraph to map to.
       * **Range of values**: IDs of the ``Parameter`` or ``Result`` operations in the subgraph
       * **Type**: ``unsigned int``
       * **Default value**: None
-      * **Required**: *yes* 
+      * **Required**: *yes*
 
 **If Inputs**
 
 
-* **cond**: A scalar or 1D tensor with 1 element of ``boolean`` type specifying which subgraph to execute. 
-``True`` value means to execute the ``then_body``, ``False`` - ``else_body``. *Required*.
+* **cond**: A scalar or 1D tensor with 1 element of ``boolean`` type specifying which subgraph to execute.
+  ``True`` value means to execute the ``then_body``, ``False`` - ``else_body``. *Required*.
 
 * **Multiple other inputs**: Tensors of different types and shapes. *Optional*.
 
diff --git a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/detection/experimental-detectron-detection-output-6.rst b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/detection/experimental-detectron-detection-output-6.rst
index 4f494d910fa014..3e9ba293596d2a 100644
--- a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/detection/experimental-detectron-detection-output-6.rst
+++ b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/detection/experimental-detectron-detection-output-6.rst
@@ -5,8 +5,8 @@ ExperimentalDetectronDetectionOutput
 
 
 .. meta::
-  :description: Learn about ExperimentalDetectronDetectionOutput-6 - an object 
-                detection operation, which can be performed on four required 
+  :description: Learn about ExperimentalDetectronDetectionOutput-6 - an object
+                detection operation, which can be performed on four required
                 input tensors in OpenVINO.
 
 **Versioned name**: *ExperimentalDetectronDetectionOutput-6*
@@ -42,7 +42,7 @@ refined boxes according to the formulas:
 ``ctr_y = y0 + 0.5f * box_h``
 
 * ``dx``, ``dy``, ``d_log_w`` and ``d_log_h`` are deltas calculated according to the formulas below, and ``deltas_tensor`` is a
-second input:
+  second input:
 
 ``dx = deltas_tensor[roi_idx, 4 * class_idx + 0] / deltas_weights[0]``
 
@@ -104,7 +104,7 @@ second input:
 
   * **Description**: *class_agnostic_box_regression* attribute is a flag that specifies whether to delete background classes or not.
   * **Range of values**:
-  
+
     * ``true`` means background classes should be deleted
     * ``false`` means background classes should not be deleted
   * **Type**: ``boolean``
@@ -149,7 +149,7 @@ second input:
 
 .. code-block:: xml
    :force:
-   
+
    <layer ... type="ExperimentalDetectronDetectionOutput" version="opset6">
        <data class_agnostic_box_regression="false" deltas_weights="10.0,10.0,5.0,5.0" max_delta_log_wh="4.135166645050049" max_detections_per_image="100" nms_threshold="0.5" num_classes="81" post_nms_count="2000" score_threshold="0.05000000074505806"/>
        <input>
diff --git a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/detection/experimental-detectron-generate-proposals-single-image-6.rst b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/detection/experimental-detectron-generate-proposals-single-image-6.rst
index d3850dbb4296ec..4a3b947603c15c 100644
--- a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/detection/experimental-detectron-generate-proposals-single-image-6.rst
+++ b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/detection/experimental-detectron-generate-proposals-single-image-6.rst
@@ -5,8 +5,8 @@ ExperimentalDetectronGenerateProposalsSingleImage
 
 
 .. meta::
-  :description: Learn about ExperimentalDetectronGenerateProposalsSingleImage-6 - 
-                an object detection operation, which can be performed on four 
+  :description: Learn about ExperimentalDetectronGenerateProposalsSingleImage-6 -
+                an object detection operation, which can be performed on four
                 required input tensors.
 
 **Versioned name**: *ExperimentalDetectronGenerateProposalsSingleImage-6*
@@ -64,7 +64,7 @@ ExperimentalDetectronGenerateProposalsSingleImage
 * **1**: A 1D tensor of type *T* with 3 elements ``[image_height, image_width, scale_height_and_width]`` providing input image size info. **Required.**
 * **2**: A 2D tensor of type *T* with shape ``[height * width * number_of_channels, 4]`` providing anchors. **Required.**
 * **3**: A 3D tensor of type *T* with shape ``[number_of_channels * 4, height, width]`` providing deltas for anchors.
-Height and width for third and fourth inputs should be equal. **Required.**
+  Height and width for third and fourth inputs should be equal. **Required.**
 * **4**: A 3D tensor of type *T* with shape ``[number_of_channels, height, width]`` providing proposals scores. **Required.**
 
 **Outputs**
@@ -80,7 +80,7 @@ Height and width for third and fourth inputs should be equal. **Required.**
 
 .. code-block:: xml
    :force:
-   
+
    <layer ... type="ExperimentalDetectronGenerateProposalsSingleImage" version="opset6">
        <data min_size="0.0" nms_threshold="0.699999988079071" post_nms_count="1000" pre_nms_count="1000"/>
        <input>
diff --git a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/detection/psroi-pooling-1.rst b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/detection/psroi-pooling-1.rst
index d3ac83442e3c2d..c5de7a037bbbe9 100644
--- a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/detection/psroi-pooling-1.rst
+++ b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/detection/psroi-pooling-1.rst
@@ -5,7 +5,7 @@ PSROIPooling
 
 
 .. meta::
-  :description: Learn about PSROIPooling-1 - an object detection operation, 
+  :description: Learn about PSROIPooling-1 - an object detection operation,
                 which can be performed on two required input tensors.
 
 **Versioned name**: *PSROIPooling-1*
@@ -73,10 +73,10 @@ ROIs coordinates are specified in absolute values for the average mode and in no
 
 **Inputs**:
 
-*   **1**: 4D input tensor with shape ``[N, C, H, W]`` and type *T*  with feature maps. **Required.**
+* **1**: 4D input tensor with shape ``[N, C, H, W]`` and type *T*  with feature maps. **Required.**
 
-*   **2**: 2D input tensor with shape ``[num_boxes, 5]``. It contains a list of five element tuples that describe a region of interest: ``[batch_id, x_1, y_1, x_2, y_2]``. **Required.**
-Batch indices must be in the range of ``[0, N-1]``.
+* **2**: 2D input tensor with shape ``[num_boxes, 5]``. It contains a list of five element tuples that describe a region of interest: ``[batch_id, x_1, y_1, x_2, y_2]``. **Required.**
+  Batch indices must be in the range of ``[0, N-1]``.
 
 **Outputs**:
 
diff --git a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/detection/roi-align-rotated-15.rst b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/detection/roi-align-rotated-15.rst
index 1da1e33079c106..6663b048fdc6de 100644
--- a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/detection/roi-align-rotated-15.rst
+++ b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/detection/roi-align-rotated-15.rst
@@ -1,4 +1,5 @@
 .. {#openvino_docs_ops_detection_ROIAlignRotated_15}
+
 ROIAlignRotated
 ===============
 
@@ -56,9 +57,9 @@ Each ROI box's center is shifted by [-0.5, -0.5] before pooling to achive better
 * *spatial_scale*
 
   * **Description**: *spatial_scale* is a multiplicative spatial scale factor to that is applied to the ROI box(height, weight and center vector) before pooling.
-   WARNING!
-   Spatial scale is also applied to the center point of the ROI box. It means that scaling does not only change the size of the ROI box, but also its position.
-   For example, if the spatial scale is 2.0, ROI box center is [0.5, 0.5], box width is 1.0 and box height is 1.0, then after scaling the ROI box center will be [1.0, 1.0], box width will be 2.0 and box height will be 2.0.
+    WARNING!
+    Spatial scale is also applied to the center point of the ROI box. It means that scaling does not only change the size of the ROI box, but also its position.
+    For example, if the spatial scale is 2.0, ROI box center is [0.5, 0.5], box width is 1.0 and box height is 1.0, then after scaling the ROI box center will be [1.0, 1.0], box width will be 2.0 and box height will be 2.0.
   * **Range of values**: a positive floating-point number
   * **Type**: ``float``
   * **Required**: *yes*
diff --git a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/detection/roi-pooling-1.rst b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/detection/roi-pooling-1.rst
index 26377ca0aebfcc..7deb444d388ac8 100644
--- a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/detection/roi-pooling-1.rst
+++ b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/detection/roi-pooling-1.rst
@@ -5,7 +5,7 @@ ROIPooling
 
 
 .. meta::
-  :description: Learn about ROIPooling-1 - an object detection operation, 
+  :description: Learn about ROIPooling-1 - an object detection operation,
                 which can be performed on two required input tensors.
 
 **Versioned name**: *ROIPooling-1*
@@ -61,10 +61,10 @@ The box height and width have different representation based on **method** attri
 
 **Inputs**:
 
-*   **1**: 4D input tensor of shape ``[N, C, H, W]`` with feature maps of type *T*. **Required.**
+* **1**: 4D input tensor of shape ``[N, C, H, W]`` with feature maps of type *T*. **Required.**
 
-*   **2**: 2D input tensor of shape ``[NUM_ROIS, 5]`` describing region of interest box consisting of 5 element tuples of type *T*: ``[batch_id, x_1, y_1, x_2, y_2]``. **Required.**
-Batch indices must be in the range of ``[0, N-1]``.
+* **2**: 2D input tensor of shape ``[NUM_ROIS, 5]`` describing region of interest box consisting of 5 element tuples of type *T*: ``[batch_id, x_1, y_1, x_2, y_2]``. **Required.**
+  Batch indices must be in the range of ``[0, N-1]``.
 
 
 **Outputs**:
diff --git a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/image/interpolate-1.rst b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/image/interpolate-1.rst
index 470fa5b7099006..6968d556734d20 100644
--- a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/image/interpolate-1.rst
+++ b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/image/interpolate-1.rst
@@ -51,7 +51,7 @@ Interpolate
 * *pads_begin*
 
   * **Description**: *pads_beg* specify the number of pixels to add to the beginning of the image being interpolated.
-This is a scalar that specifies padding for each spatial dimension.
+    This is a scalar that specifies padding for each spatial dimension.
   * **Range of values**: list of non-negative integer numbers
   * **Type**: ``int``
   * **Default value**: 0
@@ -60,7 +60,7 @@ This is a scalar that specifies padding for each spatial dimension.
 * *pads_end*
 
   * **Description**: *pads_end* specify the number of pixels to add to the beginning of the image being interpolated.
-This is a scalar that specifies padding for each spatial dimension.
+    This is a scalar that specifies padding for each spatial dimension.
   * **Range of values**: list of non-negative integer numbers
   * **Type**: ``int``
   * **Default value**: 0
diff --git a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/image/interpolate-4.rst b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/image/interpolate-4.rst
index 7572f7c1bc97ac..202bcde7c0c60c 100644
--- a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/image/interpolate-4.rst
+++ b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/image/interpolate-4.rst
@@ -22,7 +22,12 @@ Interpolate
   * **Range of values**: one of ``nearest``, ``linear``, ``linear_onnx``, ``cubic``
   * **Type**: string
   * **Required**: *yes*
-  **Note**: Only 2D, 3D, 4D, 5D tensors with ``axes = {0, 1}``, ``axes = {0, 1, 2}``, ``axes = {2, 3}``,  ``axes = {2, 3, 4}`` respectively are supported for ``"mode" == "linear_onnx"``.
+
+    .. note::
+
+       Only 2D, 3D, 4D, 5D tensors with ``axes = {0, 1}``, ``axes = {0, 1, 2}``,
+       ``axes = {2, 3}``,  ``axes = {2, 3, 4}`` respectively are supported for
+       ``"mode" == "linear_onnx"``.
 
 * *shape_calculation_mode*
 
diff --git a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/infrastructure/loop-5.rst b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/infrastructure/loop-5.rst
index 375833ec7ee18e..0e1e206d821ad0 100644
--- a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/infrastructure/loop-5.rst
+++ b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/infrastructure/loop-5.rst
@@ -72,7 +72,7 @@ There are several combinations of these two inputs ``(trip_count, execution cond
 Loop operation description in the IR has regular sections: ``input`` and ``output``. They connect Loop body to the outer graph and specify condition(s).
 Loop operation description in the IR also has several special sections: ``body``, ``port_map`` and ``back_edges`` similar to the ones from the TensorIterator operation but having some important features described below.
 
-1. The body operation getting an input from the main graph should have an entry in the ``port_map`` section of the Loop operation. These edges connect input ports of the Loop with the body ``Parameter``s.
+1. The body operation getting an input from the main graph should have an entry in the ``port_map`` section of the Loop operation. These edges connect input ports of the Loop with the body ``Parameter``\ s.
 2. Input tensors to the Loop can be sliced along a specified axis, the Loop can iterates over all sliced parts. The corresponding ``input`` entry in the ``port_map`` should have ``axis`` attribute specifying the axis to slice. Therefore, inputs to the Loop operation corresponding to ``input`` entries in the ``port_map`` without ``axis`` attribute are used "as is" (without slicing).
 3. The body operation producing tensor to be used in the subsequent iterations (like in RNN models) should have a back edge described in the ``back_edges`` section of the operation. The back edge connects the respective body ``Parameter`` and ``Result`` operations. For such a case the Loop operation node provides input for the first iteration, while corresponding Loop operation output produces the tensor computed during the last iteration.
 4. Output tensors produced by a particular body operation across all iterations can be concatenated and returned as a Loop operation output (this is a "scan output" according to the ONNX* Loop operation `specification <https://github.com/onnx/onnx/blob/master/docs/Changelog.md#Loop-13>`__ ). The corresponding ``output`` entry in the ``port_map`` should have ``axis`` attribute specifying the axis to concatenate. Therefore, outputs from operations corresponding to ``output`` entries in the ``port_map`` without ``axis`` attribute are returned "as is" (without concatenation).
@@ -114,7 +114,7 @@ Loop operation description in the IR also has several special sections: ``body``
     * *axis*
 
       * **Description**: if *axis* is specified for ``output`` entry, then it is an axis to concatenate the body ``Result`` output across all iterations.
-      If *axis* is specified for ``input`` entry, then it is an axis to iterate through, it triggers the slicing of the input tensor.
+        If *axis* is specified for ``input`` entry, then it is an axis to iterate through, it triggers the slicing of the input tensor.
 
       * **Range of values**: an integer. Negative value means counting dimension from the end.
       * **Type**: ``int``
diff --git a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/movement/gather-7.rst b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/movement/gather-7.rst
index ebe248309a122d..cd1026cb052b18 100644
--- a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/movement/gather-7.rst
+++ b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/movement/gather-7.rst
@@ -13,7 +13,7 @@ Gather
 **Category**: *Data movement*
 
 **Short description**: *Gather* operation takes slices of data of the first input tensor according to the indices
- specified with the second input tensor and axis from the third input. Semantics of this operation is identical to
+specified with the second input tensor and axis from the third input. Semantics of this operation is identical to
 TensorFlow `Gather <https://www.tensorflow.org/api_docs/python/tf/gather>`__ operation.
 
 **Detailed description**
diff --git a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/movement/gather-8.rst b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/movement/gather-8.rst
index b2bb5bf0235c60..9bc5dc50a51fb6 100644
--- a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/movement/gather-8.rst
+++ b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/movement/gather-8.rst
@@ -195,7 +195,7 @@ Example 7 with indices out of the range:
 **Outputs**
 
 * **1**: The resulting tensor of type *T* that consists of elements from ``data`` tensor gathered by ``indices``. The shape
-of the output tensor is ``data.shape[:axis] + indices.shape[batch_dims:] + data.shape[axis + 1:]``
+  of the output tensor is ``data.shape[:axis] + indices.shape[batch_dims:] + data.shape[axis + 1:]``
 
 **Types**
 
diff --git a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/movement/scatter-elements-update-3.rst b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/movement/scatter-elements-update-3.rst
index 69eafbb10f7c26..175e838ab0b576 100644
--- a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/movement/scatter-elements-update-3.rst
+++ b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/movement/scatter-elements-update-3.rst
@@ -33,20 +33,20 @@ For instance, in a 3D tensor case, the update corresponding to the ``[i][j][k]``
 
 **Inputs**:
 
-*   **1**: ``data`` tensor of arbitrary rank ``r`` and of type *T*. **Required.**
+* **1**: ``data`` tensor of arbitrary rank ``r`` and of type *T*. **Required.**
 
-*   **2**: ``indices`` tensor with indices of type *T_IND*. The rank of the tensor is equal to the rank of ``data`` tensor. All index values are expected to be within bounds ``[0, s - 1]`` along axis of size ``s``. If multiple indices point to the
-same output location then the order of updating the values is undefined. If an index points to non-existing output
-tensor element or is negative then exception is raised. **Required.**
+* **2**: ``indices`` tensor with indices of type *T_IND*. The rank of the tensor is equal to the rank of ``data`` tensor. All index values are expected to be within bounds ``[0, s - 1]`` along axis of size ``s``. If multiple indices point to the
+  same output location then the order of updating the values is undefined. If an index points to non-existing output
+  tensor element or is negative then exception is raised. **Required.**
 
-*   **3**: ``updates`` tensor of shape equal to the shape of ``indices`` tensor and of type *T*. **Required.**
+* **3**: ``updates`` tensor of shape equal to the shape of ``indices`` tensor and of type *T*. **Required.**
 
-*   **4**: ``axis`` tensor with scalar or 1D tensor with one element of type *T_AXIS* specifying axis for scatter.
-The value can be in range ``[-r, r - 1]`` where ``r`` is the rank of ``data``. **Required.**
+* **4**: ``axis`` tensor with scalar or 1D tensor with one element of type *T_AXIS* specifying axis for scatter.
+  The value can be in range ``[-r, r - 1]`` where ``r`` is the rank of ``data``. **Required.**
 
 **Outputs**:
 
-*   **1**: tensor with shape equal to ``data`` tensor of the type *T*.
+* **1**: tensor with shape equal to ``data`` tensor of the type *T*.
 
 **Types**
 
diff --git a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/pooling/pooling_shape_rules.rst b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/pooling/pooling_shape_rules.rst
index cc5105a4f11697..df46e4afb30c01 100644
--- a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/pooling/pooling_shape_rules.rst
+++ b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/pooling/pooling_shape_rules.rst
@@ -1,6 +1,6 @@
 .. {#openvino_docs_pooling_shape_rules}
 
-Shape calculation rules for Pooling Operators
+Shape Calculation Rules for Pooling Operators
 =============================================
 
 .. meta::
diff --git a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/reduction/reduce-mean-1.rst b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/reduction/reduce-mean-1.rst
index 9aef4e981cf46f..768b440f626ea0 100644
--- a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/reduction/reduce-mean-1.rst
+++ b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/reduction/reduce-mean-1.rst
@@ -107,7 +107,7 @@ Particular cases:
             </port>
         </output>
     </layer>
-=
+
 
 .. code-block:: xml
    :force:
diff --git a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/sequence/scaled-dot-product-attention.rst b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/sequence/scaled-dot-product-attention.rst
index 922048a4f18754..2d18b9d3a6488c 100644
--- a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/sequence/scaled-dot-product-attention.rst
+++ b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/sequence/scaled-dot-product-attention.rst
@@ -60,11 +60,11 @@ omitting training-related parameter.
 
 * **4**: ``attention_mask`` - two options available. ``attention_mask`` is ignored if ``causal`` is set to ``True``. **Optional.**
 
-	* at least 3 dimensional tensor of type *T* or ``boolean`` and shape ``[N, ..., L, S]``.
+	* at least 2 dimensional tensor of type *T* or ``boolean`` and shape numpy-broadcastable to ``[N, ..., L, S]``. See :doc:`Numpy Broadcast Rules <../../broadcast-rules>` for broadcast details.
 
 	* a scalar of type *T* with value ``0``. Scalar zero value signals that applying an attention mask is not necessary (similar to specifying attention_mask=None in the provided pseudo-code).
 
-* **5**: ``scale`` a scalar tensor of type *T*, an alternative scale factor instead of 1/sqrt(query.shape[-1]) used by default in the pseudo-code above. **Optional.**
+* **5**: ``scale`` a scalar or single element 1D tensor of type *T*, an alternative scale factor instead of 1/sqrt(query.shape[-1]) used by default in the pseudo-code above. **Optional.**
 
 
 **Outputs**
@@ -78,7 +78,7 @@ omitting training-related parameter.
 
 **Dimensions**
 
-* ``N, ...`` - one or more batch dimensions. Each batch dimension should be either constant across the input tensors (query, key, and value), indicating that they have the same batch size, or they should be broadcastable to the same value.
+* ``N, ...`` - one or more batch dimensions. Each batch dimension should be either constant across the input tensors (query, key, and value), indicating that they have the same batch size, or they should be numpy-broadcastable to the same value. See :doc:`Numpy Broadcast Rules <../../broadcast-rules>` for broadcast details.
 
 * ``S`` - source sequence length
 
@@ -192,29 +192,29 @@ Other batch dimensions ``...`` are optional.
 			<input>
 				<!-- Multiple batch dimensions, broadcastable to the following values: N1 = 4, N2 = 6, N3 = 10-->
 				<port id="0" precision="FP32"> <!-- query -->
-					<dim>1</dim> <!-- N1 (repeat 4 times) -->
+					<dim>4</dim> <!-- N1 (repeat 1 time) -->
 					<dim>6</dim> <!-- N2 (repeat 1 time)-->
-					<dim>5</dim> <!-- N3 (repeat 2 times)-->
+					<dim>10</dim> <!-- N3 (repeat 1 time)-->
 					<dim>-1</dim> <!-- L -->
 					<dim>80</dim> <!-- E -->
 				</port>
 				<port id="1" precision="FP32"> <!-- key -->
-					<dim>2</dim> (repeat 2 times)<!-- N1 -->
-					<dim>2</dim> (repeat 3 times)<!-- N2 -->
-					<dim>2</dim> (repeat 5 times)<!-- N3 -->
+					<dim>1</dim> <!-- N1 (repeat 4 times) -->
+					<dim>6</dim> <!-- N2 (repeat 1 time) -->
+					<dim>10</dim> <!-- N3 (repeat 1 time) -->
 					<dim>-1</dim> <!-- S -->
 					<dim>80</dim> <!-- E -->
 				</port>
 				<port id="2" precision="FP32"> <!-- value -->
-					<dim>4</dim> <!-- N1 (repeat 1 time)-->
-					<dim>3</dim> <!-- N2 (repeat 2 times)-->
-					<dim>10</dim> <!-- N3 (repeat 1 time)-->
+					<dim>1</dim> <!-- N1 (repeat 4 times)-->
+					<dim>1</dim> <!-- N2 (repeat 6 times)-->
+					<dim>1</dim> <!-- N3 (repeat 10 times)-->
 					<dim>-1</dim> <!-- S -->
 					<dim>80</dim> <!-- Ev -->
 				</port>
 				<port id="3" precision="FP32"> <!-- attention_mask -->
 					<dim>1</dim> <!-- N1 (repeat 4 times)-->
-					<dim>2</dim> <!-- N2 (repeat 3 times)-->
+					<dim>1</dim> <!-- N2 (repeat 6 times)-->
 					<dim>1</dim> <!-- N3 (repeat 10 times)-->
 					<dim>-1</dim> <!-- L -->
 					<dim>-1</dim> <!-- S -->
@@ -231,3 +231,42 @@ Other batch dimensions ``...`` are optional.
 				</port>
 			</output>
 		</layer>
+
+*Example 5: With attention mask broadcasting*
+
+.. code-block:: xml
+   :force:
+
+    <layer id="285" name="aten::scaled_dot_product_attention_0" type="ScaledDotProductAttention" version="opset13">
+			<data causal="false" />
+			<input>
+				<!-- Example with simple dimensions, with N = 2, L = 16, S = 32, E = 80, Ev = 80-->
+				<port id="0" precision="FP32"> <!-- query -->
+					<dim>2</dim>  <!-- N -->
+					<dim>16</dim> <!-- L -->
+					<dim>80</dim> <!-- E -->
+				</port>
+				<port id="1" precision="FP32"> <!-- key -->
+					<dim>2</dim>  <!-- N -->
+					<dim>32</dim> <!-- S -->
+					<dim>80</dim> <!-- E -->
+				</port>
+				<port id="2" precision="FP32"> <!-- value -->
+					<dim>2</dim>  <!-- N -->
+					<dim>32</dim> <!-- S -->
+					<dim>80</dim> <!-- Ev -->
+				</port>
+				<port id="3" precision="FP32"> <!-- attention_mask -->
+					<dim>2</dim>  <!-- N -->
+					<dim>1</dim>  <!-- to be broadcasted to L -->
+					<dim>1</dim> <!-- to be broadcasted to S -->
+				</port>
+			</input>
+			<output>
+				<port id="4" precision="FP32">
+					<dim>2</dim>  <!-- N -->
+					<dim>16</dim> <!-- L -->
+					<dim>80</dim> <!-- Ev -->
+				</port>
+			</output>
+		</layer>
diff --git a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/shape/squeeze-1.rst b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/shape/squeeze-1.rst
index 1ccce710ee0f41..ae6db8fb5353a4 100644
--- a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/shape/squeeze-1.rst
+++ b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/shape/squeeze-1.rst
@@ -18,7 +18,7 @@ Squeeze
 
 * If only the first input is provided, every dimension that is equal to 1 will be removed from it.
 * With the second input provided, each value is an index of a dimension from the first tensor that is to be removed. Specified dimension should be equal to 1, otherwise it will be ignored and copied as is.
-Dimension indices can be specified directly, or by negative indices (counting dimensions from the end).
+  Dimension indices can be specified directly, or by negative indices (counting dimensions from the end).
 
 Note: Updated behavior since 2024.3, request of squeezing dimension not equal to 1 is expected to be ignored instead of causing an error.
 
diff --git a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/sort/non-max-suppression-1.rst b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/sort/non-max-suppression-1.rst
index 328dd37d0f2c44..662dbd5d282e83 100644
--- a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/sort/non-max-suppression-1.rst
+++ b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/sort/non-max-suppression-1.rst
@@ -5,8 +5,8 @@ NonMaxSuppression
 
 
 .. meta::
-  :description: Learn about NonMaxSuppression-1 - a sorting and maximization 
-                operation, which can be performed on two required and three 
+  :description: Learn about NonMaxSuppression-1 - a sorting and maximization
+                operation, which can be performed on two required and three
                 optional input tensors.
 
 **Versioned name**: *NonMaxSuppression-1*
@@ -52,24 +52,24 @@ class must not exceed ``max_output_boxes_per_class``.
 
 **Inputs**:
 
-*   **1**: ``boxes`` - floating-point tensor of shape ``[num_batches, num_boxes, 4]`` with box coordinates. **Required.**
+* **1**: ``boxes`` - floating-point tensor of shape ``[num_batches, num_boxes, 4]`` with box coordinates. **Required.**
 
-*   **2**: ``scores`` - floating-point tensor of shape ``[num_batches, num_classes, num_boxes]`` with box scores. **Required.**
+* **2**: ``scores`` - floating-point tensor of shape ``[num_batches, num_classes, num_boxes]`` with box scores. **Required.**
 
-*   **3**: ``max_output_boxes_per_class`` - integer scalar tensor specifying maximum number of boxes to be selected per class. Optional with default value 0 meaning select no boxes.
+* **3**: ``max_output_boxes_per_class`` - integer scalar tensor specifying maximum number of boxes to be selected per class. Optional with default value 0 meaning select no boxes.
 
-*   **4**: ``iou_threshold`` - floating-point scalar tensor specifying intersection over union threshold. Optional with default value 0 meaning keep all boxes.
+* **4**: ``iou_threshold`` - floating-point scalar tensor specifying intersection over union threshold. Optional with default value 0 meaning keep all boxes.
 
-*   **5**: ``score_threshold`` - floating-point scalar tensor specifying minimum score to consider box for the processing. Optional with default value 0.
+* **5**: ``score_threshold`` - floating-point scalar tensor specifying minimum score to consider box for the processing. Optional with default value 0.
 
 **Outputs**:
 
-*   **1**: ``selected_indices`` - integer tensor of shape ``[min(num_boxes, max_output_boxes_per_class * num_classes), 3]`` containing information about selected boxes as triplets ``[batch_index, class_index, box_index]``.
-The output tensor is filled with -1s for output tensor elements if the total number of selected boxes is less than the output tensor size.
+* **1**: ``selected_indices`` - integer tensor of shape ``[min(num_boxes, max_output_boxes_per_class * num_classes), 3]`` containing information about selected boxes as triplets ``[batch_index, class_index, box_index]``.
+  The output tensor is filled with -1s for output tensor elements if the total number of selected boxes is less than the output tensor size.
 
 **Example**
 
-.. code-block::  cpp 
+.. code-block::  cpp
 
   <layer ... type="NonMaxSuppression" ... >
       <data box_encoding="corner" sort_result_descending="1"/>
diff --git a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/sort/non-max-suppression-4.rst b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/sort/non-max-suppression-4.rst
index 161a68b255b1c9..72a5631194f2cf 100644
--- a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/sort/non-max-suppression-4.rst
+++ b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/sort/non-max-suppression-4.rst
@@ -60,20 +60,20 @@ class must not exceed ``max_output_boxes_per_class``.
 
 **Inputs**:
 
-*   **1**: ``boxes`` - tensor of type *T* and shape ``[num_batches, num_boxes, 4]`` with box coordinates. **Required.**
+* **1**: ``boxes`` - tensor of type *T* and shape ``[num_batches, num_boxes, 4]`` with box coordinates. **Required.**
 
-*   **2**: ``scores`` - tensor of type *T* and shape ``[num_batches, num_classes, num_boxes]`` with box scores. **Required.**
+* **2**: ``scores`` - tensor of type *T* and shape ``[num_batches, num_classes, num_boxes]`` with box scores. **Required.**
 
-*   **3**: ``max_output_boxes_per_class`` - scalar tensor of type *T_MAX_BOXES* specifying maximum number of boxes to be selected per class. Optional with default value 0 meaning select no boxes.
+* **3**: ``max_output_boxes_per_class`` - scalar tensor of type *T_MAX_BOXES* specifying maximum number of boxes to be selected per class. Optional with default value 0 meaning select no boxes.
 
-*   **4**: ``iou_threshold`` - scalar tensor of type *T_THRESHOLDS* specifying intersection over union threshold. Optional with default value 0 meaning keep all boxes.
+* **4**: ``iou_threshold`` - scalar tensor of type *T_THRESHOLDS* specifying intersection over union threshold. Optional with default value 0 meaning keep all boxes.
 
-*   **5**: ``score_threshold`` - scalar tensor of type *T_THRESHOLDS* specifying minimum score to consider box for the processing. Optional with default value 0.
+* **5**: ``score_threshold`` - scalar tensor of type *T_THRESHOLDS* specifying minimum score to consider box for the processing. Optional with default value 0.
 
 **Outputs**:
 
-*   **1**: ``selected_indices`` - tensor of type *T_IND* and shape ``[min(num_boxes, max_output_boxes_per_class) * num_batches * num_classes, 3]`` containing information about selected boxes as triplets ``[batch_index, class_index, box_index]``.
-The output tensor is filled with -1s for output tensor elements if the total number of selected boxes is less than the output tensor size.
+* **1**: ``selected_indices`` - tensor of type *T_IND* and shape ``[min(num_boxes, max_output_boxes_per_class) * num_batches * num_classes, 3]`` containing information about selected boxes as triplets ``[batch_index, class_index, box_index]``.
+  The output tensor is filled with -1s for output tensor elements if the total number of selected boxes is less than the output tensor size.
 
 **Types**
 
diff --git a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/sparse/embedding-bag-offsets-15.rst b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/sparse/embedding-bag-offsets-15.rst
new file mode 100644
index 00000000000000..9f0392c8e2d038
--- /dev/null
+++ b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/sparse/embedding-bag-offsets-15.rst
@@ -0,0 +1,184 @@
+.. {#openvino_docs_ops_sparse_EmbeddingBagOffsets_15}
+
+EmbeddingBagOffsets
+======================
+
+
+.. meta::
+  :description: Learn about EmbeddingBagOffsets-15 - a sparse operation, which
+                can be performed on three required and two optional input tensors.
+
+**Versioned name**: *EmbeddingBagOffsets-15*
+
+**Category**: *Sparse*
+
+**Short description**: Computes sums or means of "bags" of embeddings, without instantiating the intermediate embeddings.
+
+**Detailed description**:
+
+Operation EmbeddingBagOffsets is an implementation of ``torch.nn.EmbeddingBag`` with indices and offsets inputs being 1D tensors.
+
+For each index in ``indices`` this operator gathers values from ``emb_table`` embedding table. Then values at indices in the range of the same bag (based on ``offset`` input) are reduced according to ``reduction`` attribute.
+
+Values in ``offsets`` define starting index in ``indices`` tensor of each "bag",
+e.g. ``offsets`` with value ``[0, 3, 4, 4, 6]`` define 5 "bags" containing ``[3, 1, 0, 2, num_indices-6]`` elements corresponding to ``[indices[0:3], indices[3:4], empty_bag, indices[4:6], indices[6:]]`` slices of indices per bag.
+
+EmbeddingBagOffsets is an equivalent to following NumPy snippet:
+
+.. code-block:: py
+
+    def embedding_bag_offsets(
+        emb_table: np.ndarray,
+        indices: np.ndarray,
+        offsets: np.ndarray,
+        default_index: Optional[int] = None,
+        per_sample_weights: Optional[np.ndarray] = None,
+        reduction: Literal["sum", "mean"] = "sum",
+    ):
+        assert (
+            reduction == "sum" or per_sample_weights is None
+        ), "Attribute per_sample_weights is only supported in sum reduction."
+        if per_sample_weights is None:
+            per_sample_weights = np.ones_like(indices)
+        embeddings = []
+        for emb_idx, emb_weight in zip(indices, per_sample_weights):
+            embeddings.append(emb_table[emb_idx] * emb_weight)
+        previous_offset = offsets[0]
+        bags = []
+        offsets = np.append(offsets, len(indices))
+        for bag_offset in offsets[1:]:
+            bag_size = bag_offset - previous_offset
+            if bag_size != 0:
+                embedding_bag = embeddings[previous_offset:bag_offset]
+                reduced_bag = np.add.reduce(embedding_bag)
+                if reduction == "mean":
+                    reduced_bag = reduced_bag / bag_size
+                bags.append(reduced_bag)
+            else:
+                # Empty bag case
+                if default_index is not None and default_index != -1:
+                    bags.append(emb_table[default_index])
+                else:
+                    bags.append(np.zeros(emb_table.shape[1:]))
+            previous_offset = bag_offset
+        return np.stack(bags, axis=0)
+
+
+**Attributes**:
+
+* *reduction*
+
+  * **Description**: reduction mode. 
+  * **Range of values**:
+
+    * sum - compute weighted sum, using corresponding values of ``per_sample_weights`` as weights if provided.
+    * mean - compute average of values in bag. Input ``per_sample_weights`` is not supported and will raise exception.
+
+  * **Type**: ``string``
+  * **Default value**: sum
+  * **Required**: *no*
+
+**Inputs**:
+
+* **1**: ``emb_table`` tensor containing the embedding lookup table of the module of shape ``[num_emb, emb_dim1, emb_dim2, ...]`` and  of type *T*. **Required.**
+* **2**: ``indices`` tensor of shape ``[num_indices]`` and of type *T_IND*. **Required.**
+* **3**: ``offsets`` tensor of shape ``[batch]`` and of type *T_IND* containing the starting index positions of each "bag" in ``indices``. Maximum value of offsets cannot be greater than length of ``indices``. **Required.**
+* **4**: ``default_index`` scalar of type *T_IND* containing default index in embedding table to fill empty "bags". If set to ``-1`` or not provided, empty "bags" are filled with zeros. Reverse indexing using negative values is not supported. **Optional.**
+* **5**: ``per_sample_weights`` tensor of the same shape as ``indices`` and of type *T*. Supported only when *reduction* attribute is set to ``"sum"``. Each value in this tensor are multiplied with each value pooled from embedding table for each index. Optional, default is tensor of ones. **Optional.**
+
+**Outputs**:
+
+* **1**: tensor of shape ``[batch, emb_dim1, emb_dim2, ...]`` and of type *T* containing embeddings for each bag.
+
+**Types**
+
+* *T*: any numeric type.
+* *T_IND*: ``int32`` or ``int64``.
+
+**Example**
+
+*Example 1: per_sample_weights are provided, default_index is set to 0 to fill empty bag with values gathered form emb_table on given index.*
+
+.. code-block:: xml
+
+   <layer ... type="EmbeddingBagOffsets" ... >
+       <data reduction="sum"/>
+       <input>
+           <port id="0">     <!-- emb_table value is: [[-0.2, -0.6], [-0.1, -0.4], [-1.9, -1.8], [-1.,  1.5], [ 0.8, -0.7]] -->
+               <dim>5</dim>
+               <dim>2</dim>
+           </port>
+           <port id="1">     <!-- indices value is: [0, 2, 3, 4] -->
+               <dim>4</dim>
+           </port>
+           <port id="2">     <!-- offsets value is: [0, 2, 2] - 3 "bags" containing [2,0,4-2] elements, second "bag" is empty -->
+               <dim>3</dim>
+           </port>
+           <port id="3"/>    <!-- default_index value is: 0 -->
+           <port id="4"/>    <!-- per_sample_weights value is: [0.5, 0.5, 0.5, 0.5] -->
+               <dim>4</dim>
+           </port>
+       </input>
+       <output>
+           <port id="5">     <!-- output value is: [[-1.05, -1.2], [-0.2, -0.6], [-0.1, 0.4]] -->
+               <dim>3</dim>
+               <dim>2</dim>
+           </port>
+       </output>
+   </layer>
+
+*Example 2: per_sample_weights are provided, default_index is set to -1 to fill empty bag with 0.*
+
+.. code-block:: xml
+
+   <layer ... type="EmbeddingBagOffsets" ... >
+       <data reduction="sum"/>
+       <input>
+           <port id="0">     <!-- emb_table value is: [[-0.2, -0.6], [-0.1, -0.4], [-1.9, -1.8], [-1.,  1.5], [ 0.8, -0.7]] -->
+               <dim>5</dim>
+               <dim>2</dim>
+           </port>
+           <port id="1">     <!-- indices value is: [0, 2, 3, 4] -->
+               <dim>4</dim>
+           </port>
+           <port id="2">     <!-- offsets value is: [0, 2, 2] - 3 "bags" containing [2,0,4-2] elements, second "bag" is empty -->
+               <dim>3</dim>
+           </port>
+           <port id="3"/>    <!-- default_index value is: -1 - fill empty bag with 0-->
+           <port id="4"/>    <!-- per_sample_weights value is: [0.5, 0.2, -2, 1] -->
+               <dim>4</dim>
+           </port>
+       </input>
+       <output>
+           <port id="5">     <!-- output value is: [[-0.48, -0.66], [0., 0.], [2.8, -3.7]] -->
+               <dim>3</dim>
+               <dim>2</dim>
+           </port>
+       </output>
+   </layer>
+
+*Example 3: Example of reduction set to mean.*
+
+.. code-block:: xml
+
+   <layer ... type="EmbeddingBagOffsets" ... >
+       <data reduction="mean"/>
+       <input>
+           <port id="0">     <!-- emb_table value is: [[-0.2, -0.6], [-0.1, -0.4], [-1.9, -1.8], [-1.,  1.5], [ 0.8, -0.7]] -->
+               <dim>5</dim>
+               <dim>2</dim>
+           </port>
+           <port id="1">     <!-- indices value is: [0, 2, 3, 4] -->
+               <dim>4</dim>
+           </port>
+           <port id="2">     <!-- offsets value is: [0, 2, 2] - 3 "bags" containing [2,0,4-2] elements, second "bag" is empty -->
+               <dim>3</dim>
+           </port>
+       </input>
+       <output>
+           <port id="3">     <!-- output value is: [[-1.05, -1.2], [0., 0.], [-0.1, 0.4]] -->
+               <dim>3</dim>
+               <dim>2</dim>
+           </port>
+       </output>
+   </layer>
diff --git a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/sparse/embedding-bag-offsets-sum-3.rst b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/sparse/embedding-bag-offsets-sum-3.rst
index 0a0cb67afb0f06..c3eb163b16d98f 100644
--- a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/sparse/embedding-bag-offsets-sum-3.rst
+++ b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/sparse/embedding-bag-offsets-sum-3.rst
@@ -14,7 +14,48 @@ EmbeddingBagOffsetsSum
 
 **Short description**: Computes sums of "bags" of embeddings, without instantiating the intermediate embeddings.
 
-**Detailed description**: This is the second case of the PyTorch `EmbeddingBag <https://pytorch.org/docs/stable/nn.html#embeddingbag>`__ , it has indices in two 1D tensors provided as 2nd and 3rd inputs. For each index in ``indices`` this operator gets values from ``data`` embedding table and sums all values belonging to each bag. Values in ``offsets`` define starting index in ``indices`` tensor of each "bag", e.g. ``offsets`` with value ``[0,3,4,4,6]`` define 5 "bags" containing ``[3,1,0,2,n-6]`` elements.
+**Detailed description**:
+
+Operation EmbeddingBagOffsets is an implementation of ``torch.nn.EmbeddingBag`` with indices and offsets inputs being 1D tensors.
+
+For each index in ``indices`` this operator gathers values from ``emb_table`` embedding table. Then values at indices in the range of the same bag (based on ``offset`` input) are reduced according to ``reduction`` attribute.
+
+Values in ``offsets`` define starting index in ``indices`` tensor of each "bag",
+e.g. ``offsets`` with value ``[0, 3, 4, 4, 6]`` define 5 "bags" containing ``[3, 1, 0, 2, num_indices-6]`` elements corresponding to ``[indices[0:3], indices[3:4], empty_bag, indices[4:6], indices[6:]]`` slices of indices per bag.
+
+EmbeddingBagOffsetsSum is an equivalent to following NumPy snippet:
+
+.. code-block:: py
+
+    def embedding_bag_offsets(
+        emb_table: np.ndarray,
+        indices: np.ndarray,
+        offsets: np.ndarray,
+        default_index: Optional[int] = None,
+        per_sample_weights: Optional[np.ndarray] = None,
+    ):
+        if per_sample_weights is None:
+            per_sample_weights = np.ones_like(indices)
+        embeddings = []
+        for emb_idx, emb_weight in zip(indices, per_sample_weights):
+            embeddings.append(emb_table[emb_idx] * emb_weight)
+        previous_offset = offsets[0]
+        bags = []
+        offsets = np.append(offsets, len(indices))
+        for bag_offset in offsets[1:]:
+            bag_size = bag_offset - previous_offset
+            if bag_size != 0:
+                embedding_bag = embeddings[previous_offset:bag_offset]
+                reduced_bag = np.add.reduce(embedding_bag)
+                bags.append(reduced_bag)
+            else:
+                # Empty bag case
+                if default_index is not None and default_index != -1:
+                    bags.append(emb_table[default_index])
+                else:
+                    bags.append(np.zeros(emb_table.shape[1:]))
+            previous_offset = bag_offset
+        return np.stack(bags, axis=0)
 
 **Attributes**: EmbeddingBagOffsetsSum operation has no attributes.
 
@@ -22,9 +63,9 @@ EmbeddingBagOffsetsSum
 
 * **1**: ``emb_table`` tensor containing the embedding lookup table of the module of shape ``[num_emb, emb_dim1, emb_dim2, ...]`` and  of type *T*. **Required.**
 * **2**: ``indices`` tensor of shape ``[num_indices]`` and of type *T_IND*. **Required.**
-* **3**: ``offsets`` tensor of shape ``[batch]`` and of type *T_IND* containing the starting index positions of each "bag" in ``indices``. **Required.**
-* **4**: ``default_index`` scalar of type *T_IND* containing default index in embedding table to fill empty "bags". If not provided empty "bags" are filled with zeros. **Optional.**
-* **5**: ``per_sample_weights`` tensor of the same shape as ``indices`` and of type *T*. Each value in this tensor are multiplied with each value pooled from embedding table for each index. Optional, default is tensor of ones.
+* **3**: ``offsets`` tensor of shape ``[batch]`` and of type *T_IND* containing the starting index positions of each "bag" in ``indices``. Maximum value of offsets cannot be greater than length of ``indices``. **Required.**
+* **4**: ``default_index`` scalar of type *T_IND* containing default index in embedding table to fill empty "bags". If set to ``-1`` or not provided, empty "bags" are filled with zeros. Reverse indexing using negative values is not supported. **Optional.**
+* **5**: ``per_sample_weights`` tensor of the same shape as ``indices`` and of type *T*. Each value in this tensor are multiplied with each value pooled from embedding table for each index. Optional, default is tensor of ones. **Optional.**
 
 **Outputs**:
 
@@ -37,7 +78,9 @@ EmbeddingBagOffsetsSum
 
 **Example**
 
-.. code-block:: cpp
+*Example 1: per_sample_weights are provided, default_index is set to 0 to fill empty bag with values gathered form emb_table on given index.*
+
+.. code-block:: xml
 
    <layer ... type="EmbeddingBagOffsetsSum" ... >
        <input>
@@ -52,7 +95,7 @@ EmbeddingBagOffsetsSum
                <dim>3</dim>
            </port>
            <port id="3"/>    <!-- default_index value is: 0 -->
-           <port id="4"/>    <!-- per_sample_weigths value is: [0.5, 0.5, 0.5, 0.5] -->
+           <port id="4"/>    <!-- per_sample_weights value is: [0.5, 0.5, 0.5, 0.5] -->
                <dim>4</dim>
            </port>
        </input>
@@ -64,4 +107,31 @@ EmbeddingBagOffsetsSum
        </output>
    </layer>
 
+*Example 2: per_sample_weights are provided, default_index is set to -1 to fill empty bag with 0.*
+
+.. code-block:: xml
 
+   <layer ... type="EmbeddingBagOffsets" ... >
+       <input>
+           <port id="0">     <!-- emb_table value is: [[-0.2, -0.6], [-0.1, -0.4], [-1.9, -1.8], [-1.,  1.5], [ 0.8, -0.7]] -->
+               <dim>5</dim>
+               <dim>2</dim>
+           </port>
+           <port id="1">     <!-- indices value is: [0, 2, 3, 4] -->
+               <dim>4</dim>
+           </port>
+           <port id="2">     <!-- offsets value is: [0, 2, 2] - 3 "bags" containing [2,0,4-2] elements, second "bag" is empty -->
+               <dim>3</dim>
+           </port>
+           <port id="3"/>    <!-- default_index value is: -1 - fill empty bag with 0-->
+           <port id="4"/>    <!-- per_sample_weights value is: [0.5, 0.5, 0.5, 0.5] -->
+               <dim>4</dim>
+           </port>
+       </input>
+       <output>
+           <port id="5">     <!-- output value is: [[-1.05, -1.2], [0., 0.], [-0.1, 0.4]] -->
+               <dim>3</dim>
+               <dim>2</dim>
+           </port>
+       </output>
+   </layer>
diff --git a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/sparse/embedding-bag-packed-15.rst b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/sparse/embedding-bag-packed-15.rst
new file mode 100644
index 00000000000000..2892d49759f667
--- /dev/null
+++ b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/sparse/embedding-bag-packed-15.rst
@@ -0,0 +1,131 @@
+.. {#openvino_docs_ops_sparse_EmbeddingBagPacked_15}
+
+EmbeddingBagPacked
+=====================
+
+
+.. meta::
+  :description: Learn about EmbeddingBagPacked-15 - a sparse operation, which
+                can be performed on two required and one optional input tensor.
+
+**Versioned name**: *EmbeddingBagPacked-15*
+
+**Category**: *Sparse*
+
+**Short description**: Computes sums or means of "bags" of embeddings, without instantiating the intermediate embeddings.
+
+**Detailed description**:
+
+Operation EmbeddingBagPacked is an implementation of ``torch.nn.EmbeddingBag`` with indices input being 2D tensor of shape ``[batch, indices_per_bag]``.
+Operation is equivalent to *gather_op = Gather(emb_table, indices, axis=0)* followed by reduction:
+
+  * *sum* - *ReduceSum(Multiply(gather_op, Unsqueeze(per_sample_weights, -1)), axis=1)*,
+  * *mean* - *ReduceMean(gather_op, axis=1)*.
+
+**Attributes**:
+
+* *reduction*
+
+  * **Description**: reduction mode. 
+  * **Range of values**:
+
+    * sum - compute weighted sum, using corresponding values of ``per_sample_weights`` as weights if provided.
+    * mean - compute average of values in bag. Input ``per_sample_weights`` is not supported and will raise exception.
+
+  * **Type**: ``string``
+  * **Default value**: sum
+  * **Required**: *no*
+
+**Inputs**:
+
+* **1**: ``emb_table`` tensor containing the embedding lookup table of the module of shape ``[num_emb, emb_dim1, emb_dim2, ...]`` and of type *T*. **Required.**
+* **2**: ``indices`` tensor of shape ``[batch, indices_per_bag]`` and of type *T_IND*. **Required.**
+* **3**: ``per_sample_weights`` tensor of the same shape as ``indices`` and of type *T* supported only in ``sum`` mode. Each value in this tensor are multiplied with each value pooled from embedding table for each index. Optional, default is tensor of ones. **Optional.**
+
+**Outputs**:
+
+* **1**: tensor of shape ``[batch, emb_dim1, emb_dim2, ...]`` and of type *T* containing embeddings for each bag.
+
+**Types**
+
+* *T*: any numeric type.
+* *T_IND*: ``int32`` or ``int64``.
+
+**Example**
+
+*Example 1: reduction set to sum, per_sample_weights are not provided.*
+
+.. code-block:: xml
+
+   <layer ... type="EmbeddingBagPacked" ... >
+       <data reduction="sum"/>
+       <input>
+           <port id="0">     <!-- emb_table value is: [[-0.2, -0.6], [-0.1, -0.4], [-1.9, -1.8], [-1.,  1.5], [ 0.8, -0.7]] -->
+               <dim>5</dim>
+               <dim>2</dim>
+           </port>
+           <port id="1">     <!-- indices value is: [[0, 2], [1, 2], [3, 4]] -->
+               <dim>3</dim>
+               <dim>2</dim>
+           </port>
+       </input>
+       <output>
+           <port id="2">     <!-- output value is: [[-2.1, -2.4], [-2., -2.2], [-0.2, 0.8]] -->
+               <dim>3</dim>
+               <dim>2</dim>
+           </port>
+       </output>
+   </layer>
+
+*Example 2: reduction set to sum and per_sample_weights are provided.*
+
+.. code-block:: xml
+
+   <layer ... type="EmbeddingBagPacked" ... >
+       <data reduction="sum"/>
+       <input>
+           <port id="0">     <!-- emb_table value is: [[-0.2, -0.6], [-0.1, -0.4], [-1.9, -1.8], [-1.,  1.5], [ 0.8, -0.7]] -->
+               <dim>5</dim>
+               <dim>2</dim>
+           </port>
+           <port id="1">     <!-- indices value is: [[0, 2], [1, 2], [3, 4]] -->
+               <dim>3</dim>
+               <dim>2</dim>
+           </port>
+           <port id="2"/>    <!-- per_sample_weights value is: [[0.5, 0.5], [0.3, 0.7], [2., -1.]] -->
+               <dim>3</dim>
+               <dim>2</dim>
+           </port>
+       </input>
+       <output>
+           <port id="3">     <!-- output value is: [[-1.05, -1.2], [-1.36, -1.38], [-2.8, 3.7]] -->
+               <dim>3</dim>
+               <dim>2</dim>
+           </port>
+       </output>
+   </layer>
+
+*Example 3: reduction set to mean, per_sample_weights are not provided.*
+
+.. code-block:: xml
+
+   <layer ... type="EmbeddingBagPacked" ... >
+       <data reduction="mean"/>
+       <input>
+           <port id="0">     <!-- emb_table value is: [[-0.2, -0.6], [-0.1, -0.4], [-1.9, -1.8], [-1.,  1.5], [ 0.8, -0.7]] -->
+               <dim>5</dim>
+               <dim>2</dim>
+           </port>
+           <port id="1">     <!-- indices value is: [[0, 2], [1, 2], [3, 4]] -->
+               <dim>3</dim>
+               <dim>2</dim>
+           </port>
+       </input>
+       <output>
+           <port id="2">     <!-- output value is: [[-1.05, -1.2], [-1., -1.1], [-0.1, 0.4]] -->
+               <dim>3</dim>
+               <dim>2</dim>
+           </port>
+       </output>
+   </layer>
+
diff --git a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/sparse/embedding-bag-packed-sum-3.rst b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/sparse/embedding-bag-packed-sum-3.rst
index 9ef623ca7755eb..b6cad12be869ac 100644
--- a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/sparse/embedding-bag-packed-sum-3.rst
+++ b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/sparse/embedding-bag-packed-sum-3.rst
@@ -14,7 +14,10 @@ EmbeddingBagPackedSum
 
 **Short description**: Computes sums of "bags" of embeddings, without instantiating the intermediate embeddings.
 
-**Detailed description**: This is the first case of the PyTorch `EmbeddingBag <https://pytorch.org/docs/stable/nn.html#embeddingbag>`__ , it has indices in the tensor of format ``[batch, indices_per_bag]``. If 3rd input is not provided, this operation is equivalent to *Gather* followed by *ReduceSum(axis=0)*. However, *EmbeddingBagPackedSum* is much more time and memory efficient than using a chain of these operations.
+**Detailed description**:
+
+Operation EmbeddingBagPackedSum is an implementation of ``torch.nn.EmbeddingBag`` in ``sum`` mode, which indices input being 2D tensor of shape ``[batch, indices_per_bag]``.
+Operation is equivalent to *ReduceSum(Multiply(Gather(emb_table, indices, axis=0), Unsqueeze(per_sample_weights, -1)), axis=1)*.
 
 **Attributes**: EmbeddingBagPackedSum operation has no attributes.
 
@@ -35,7 +38,7 @@ EmbeddingBagPackedSum
 
 **Example**
 
-.. code-block:: cpp
+.. code-block:: xml
 
    <layer ... type="EmbeddingBagPackedSum" ... >
        <input>
@@ -47,13 +50,13 @@ EmbeddingBagPackedSum
                <dim>3</dim>
                <dim>2</dim>
            </port>
-           <port id="2"/>    <!-- per_sample_weigths value is: [[0.5, 0.5], [0.5, 0.5], [0.5, 0.5]] -->
+           <port id="2"/>    <!-- per_sample_weights value is: [[0.5, 0.5], [0.5, 0.5], [0.5, 0.5]] -->
                <dim>3</dim>
                <dim>2</dim>
            </port>
        </input>
        <output>
-           <port id="4">     <!-- output value is: [[-1.05, -1.2], [-1., -1.1], [-0.1, 0.4]] -->
+           <port id="3">     <!-- output value is: [[-1.05, -1.2], [-1., -1.1], [-0.1, 0.4]] -->
                <dim>3</dim>
                <dim>2</dim>
            </port>
diff --git a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/type/string-tensor-pack-15.rst b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/type/string-tensor-pack-15.rst
new file mode 100644
index 00000000000000..77f44f8d687741
--- /dev/null
+++ b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/type/string-tensor-pack-15.rst
@@ -0,0 +1,147 @@
+.. {#openvino_docs_ops_type_StringTensorPack_15}
+
+StringTensorPack
+===================
+
+
+.. meta::
+  :description: Learn about StringTensorPack-15 - operation which packs a concatenated batch of strings into a batched string tensor.
+
+**Versioned name**: *StringTensorPack-15*
+
+**Category**: *Type*
+
+**Short description**: *StringTensorPack* transforms a concatenated strings data (encoded as 1D tensor of u8 element type) into
+a string tensor using *begins* and *ends* indices.
+
+**Detailed description**
+
+Consider inputs:
+
+* *begins* = [0, 5]
+* *ends* = [5, 13]
+* *symbols* = "IntelOpenVINO"
+
+*StringTensorPack* uses indices from ``begins`` and ``ends`` to transform concatenated string ``symbols`` into ``output``, 
+a string tensor. The ``output.shape`` is equal to ``begins.shape`` and ``ends.shape``, 
+and in this case ``output`` holds values ``["Intel", "OpenVINO"]``.
+
+When defining *begins* and *ends*, the notation ``[a, b)`` is used. This means that the range starts with ``a`` and includes all values up to, 
+but not including, ``b``. That is why in the example given the length of "IntelOpenVINO" is 12, but *ends* vector contains 13. The shapes of ``begins`` and ``ends`` are required to be equal.
+
+**Inputs**
+
+* **1**: ``begins`` - ND tensor of non-negative integer numbers of type *T_IDX*, containing indices of each string's beginnings. **Required.** 
+
+* **2**: ``ends`` - ND tensor of non-negative integer numbers of type *T_IDX*, containing indices of each string's endings. **Required.** 
+
+* **3**: ``symbols`` - 1D tensor of concatenated strings data encoded in utf-8 bytes, of type *u8*. **Required.** 
+
+**Outputs**
+
+* **1**: ``output`` - ND string tensor of the same shape as *begins* and *ends*.
+
+**Types**
+
+* *T_IDX*: ``int32`` or ``int64``.
+
+**Examples**
+
+*Example 1: 1D begins and ends*
+
+.. code-block:: xml
+   :force:
+
+    <layer ... type="StringTensorPack" ... >
+        <input>
+            <port id="0" precision="I64">
+                <dim>2</dim>     <!-- begins = [0, 5] -->
+            </port>
+            <port id="1" precision="I64">
+                <dim>2</dim>     <!-- ends = [5, 13] -->
+            </port>
+            <port id="2" precision="U8">
+                <dim>13</dim>    <!-- symbols = "IntelOpenVINO" encoded in an utf-8 array -->
+            </port>
+        </input>
+        <output>
+            <port id="0" precision="STRING">
+                <dim>2</dim>     <!-- output = ["Intel", "OpenVINO"] -->
+            </port>
+        </output>
+    </layer>
+
+*Example 2: input with an empty string*
+
+.. code-block:: xml
+   :force:
+
+    <layer ... type="StringTensorPack" ... >
+        <input>
+            <port id="0" precision="I64">
+                <dim>2</dim>     <!-- begins = [0, 3, 3, 8, 9] -->
+            </port>
+            <port id="1" precision="I64">
+                <dim>2</dim>     <!-- ends = [3, 3, 8, 9, 13] -->
+            </port>
+            <port id="2" precision="U8">
+                <dim>13</dim>    <!-- symbols = "OMZGenAI 2024" encoded in an utf-8 array -->
+            </port>
+        </input>
+        <output>
+            <port id="0" precision="STRING">
+                <dim>5</dim>     <!-- output = ["OMZ", "", "GenAI", " ", "2024"] -->
+            </port>
+        </output>
+    </layer>
+
+*Example 3: skipped symbols*
+
+.. code-block:: xml
+   :force:
+
+    <layer ... type="StringTensorPack" ... >
+        <input>
+            <port id="0" precision="I64">
+                <dim>2</dim>     <!-- begins = [0, 8] -->
+            </port>
+            <port id="1" precision="I64">
+                <dim>2</dim>     <!-- ends = [1, 9] -->
+            </port>
+            <port id="2" precision="U8">
+                <dim>13</dim>    <!-- symbols = "123456789" encoded in an utf-8 array -->
+            </port>
+        </input>
+        <output>
+            <port id="0" precision="STRING">
+                <dim>5</dim>     <!-- output = ["1", "9"] -->
+            </port>
+        </output>
+    </layer>
+
+*Example 4: 2D begins and ends*
+
+.. code-block:: xml
+   :force:
+
+    <layer ... type="StringTensorPack" ... >
+        <input>
+            <port id="0" precision="I64">
+                <dim>2</dim>     <!-- begins = [[0, 5], [13, 16]] -->
+                <dim>2</dim>
+            </port>
+            <port id="1" precision="I64">
+                <dim>2</dim>     <!-- ends = [[5, 13], [16, 21]] -->
+                <dim>2</dim>
+            </port>
+            <port id="2" precision="U8">
+                <dim>21</dim>    <!-- symbols = "IntelOpenVINOOMZGenAI" -->
+            </port>
+        </input>
+        <output>
+            <port id="0" precision="STRING">
+                <dim>2</dim>     <!-- output = [["Intel", "OpenVINO"], ["OMZ", "GenAI"]] -->
+                <dim>2</dim>
+            </port>
+        </output>
+    </layer>
diff --git a/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/type/string-tensor-unpack-15.rst b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/type/string-tensor-unpack-15.rst
new file mode 100644
index 00000000000000..8ae8a8f8234b21
--- /dev/null
+++ b/docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/type/string-tensor-unpack-15.rst
@@ -0,0 +1,133 @@
+.. {#openvino_docs_ops_type_StringTensorUnpack_15}
+
+StringTensorUnpack
+===================
+
+
+.. meta::
+  :description: Learn about StringTensorUnpack-15 - operation which unpacks a batch of strings into three tensors.
+
+**Versioned name**: *StringTensorUnpack-15*
+
+**Category**: *Type*
+
+**Short description**: *StringTensorUnpack* operation transforms a given batch of strings into three tensors - two storing begin 
+and end indices of the strings and another containing the concatenated string data, respectively.
+
+**Detailed description**
+
+Consider an ``input`` string tensor containing values ``["Intel", "OpenVINO"]``.
+
+The operator will transform the tensor into three outputs:
+
+* *begins* = [0, 5]
+    * ``begins[0]`` is equal to 0, because the first string starts at the beginning index.
+    * ``begins[1]`` is equal to 5, because length of the string "Intel" is equal to 5.
+    * ``begins.shape`` is equal to [2], because the ``input`` is a batch of 2 strings.
+
+* *ends* = [5, 13]
+    * ``ends[0]`` is equal to 5, because length of the string "Intel" is equal to 5.
+    * ``ends[1]`` is equal to 13, because length of the string "OpenVINO" is 8, and it needs to be summed up with length of the string "Intel".
+    * ``ends.shape`` is equal to ``[2]``, because the ``input`` is a batch of 2 strings.
+
+* *symbols* = "IntelOpenVINO"
+    * ``symbols`` contains concatenated string data encoded in utf-8 bytes, interpretable using ``begins`` and ``ends``.
+    * ``symbols.shape`` is equal to ``[13]``, because it's the length of concatenated ``input`` strings.
+
+When defining *begins* and *ends*, the notation ``[a, b)`` is used. This means that the range starts with ``a`` and includes all values up to, 
+but not including, ``b``. That is why in the example given the length of "IntelOpenVINO" is 12, but *ends* vector contains 13.
+
+**Inputs**
+  
+* **1**: ``data`` - ND tensor of type *string*. **Required.** 
+
+**Outputs**
+
+* **1**: ``begins`` - ND tensor of non-negative integer numbers of type *int32* and of the same shape as ``data`` input.
+
+* **2**: ``ends`` - ND tensor of non-negative integer numbers of type *int32* and of the same shape as ``data`` input.
+
+* **3**: ``symbols`` - 1D tensor of concatenated strings data encoded in utf-8 bytes, of type *u8* and size equal to the sum of the lengths of each string from the ``data`` input.
+
+**Examples**
+
+*Example 1: 1D input*
+
+For ``input = ["Intel", "OpenVINO"]``
+
+.. code-block:: xml
+   :force:
+
+    <layer ... type="StringTensorUnpack" ... >
+        <input>
+            <port id="0" precision="STRING">
+                <dim>2</dim>     <!-- batch of strings -->
+            </port>
+        </input>
+        <output>
+            <port id="0" precision="I32">
+                <dim>2</dim>     <!-- begins = [0, 5] -->
+            </port>
+            <port id="1" precision="I32">
+                <dim>2</dim>     <!-- ends = [5, 13] -->
+            </port>
+            <port id="2" precision="U8">
+                <dim>13</dim>     <!-- symbols = "IntelOpenVINO" encoded in an utf-8 array -->
+            </port>
+        </output>
+    </layer>
+
+*Example 2: input with an empty string*
+
+For ``input = ["OMZ", "", "GenAI", " ", "2024"]``
+
+.. code-block:: xml
+   :force:
+
+    <layer ... type="StringTensorUnpack" ... >
+        <input>
+            <port id="0" precision="STRING">
+                <dim>5</dim>     <!-- batch of strings -->
+            </port>
+        </input>
+        <output>
+            <port id="0" precision="I32">
+                <dim>2</dim>     <!-- begins = [0, 3, 3, 8, 9] -->
+            </port>
+            <port id="1" precision="I32">
+                <dim>2</dim>     <!-- ends = [3, 3, 8, 9, 13] -->
+            </port>
+            <port id="2" precision="U8">
+                <dim>13</dim>    <!-- symbols = "OMZGenAI 2024" encoded in an utf-8 array -->
+            </port>
+        </output>
+    </layer>
+
+*Example 3: 2D input*
+
+For ``input = [["Intel", "OpenVINO"], ["OMZ", "GenAI"]]``
+
+.. code-block:: xml
+   :force:
+
+    <layer ... type="StringTensorUnpack" ... >
+        <input>
+            <port id="0" precision="STRING">
+                <dim>2</dim>
+                <dim>2</dim>
+            </port>
+        </input>
+        <output>
+            <port id="0" precision="I32">
+                <dim>2</dim>     <!-- begins = [[0, 5], [13, 16]] -->
+                <dim>2</dim>
+            </port>
+            <port id="1" precision="I32">
+                <dim>2</dim>     <!-- ends = [[5, 13], [16, 21]] -->
+                <dim>2</dim>
+            </port>
+            <port id="2" precision="U8">
+                <dim>21</dim>    <!-- symbols = "IntelOpenVINOOMZGenAI" encoded in an utf-8 array -->
+            </port>
+        </output>
+    </layer>
diff --git a/docs/articles_en/get-started/install-openvino.rst b/docs/articles_en/get-started/install-openvino.rst
index 166df8eb104cd7..f8fefc4abacece 100644
--- a/docs/articles_en/get-started/install-openvino.rst
+++ b/docs/articles_en/get-started/install-openvino.rst
@@ -21,7 +21,7 @@ Install OpenVINO™ 2024.2
 
    <script type="module" crossorigin src="../_static/selector-tool/assets/index-f34d1fad.js"></script>
    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-   <iframe id="selector" src="../_static/selector-tool/selector-1c16038.html" style="width: 100%; border: none" title="Download Intel® Distribution of OpenVINO™ Toolkit"></iframe>
+   <iframe id="selector" src="../_static/selector-tool/selector-0fa99c3.html" style="width: 100%; border: none" title="Download Intel® Distribution of OpenVINO™ Toolkit"></iframe>
 
 OpenVINO 2024.2, described here, is not a Long-Term-Support version!
 All currently supported versions are:
@@ -50,7 +50,7 @@ All currently supported versions are:
       | \* **Of the Linux systems, version 22.04 includes drivers for NPU.**
       |  **For Windows, CPU inference on ARM64 is not supported.**
 
-.. dropdown:: Effortless GenAI integration with OpenVINO GenAI Flavor**
+.. dropdown:: Effortless GenAI integration with OpenVINO GenAI Flavor
 
    A new OpenVINO GenAI Flavor streamlines application development by providing
    LLM-specific interfaces for easy integration of language models, handling tokenization and
diff --git a/docs/articles_en/get-started/install-openvino/install-openvino-apt.rst b/docs/articles_en/get-started/install-openvino/install-openvino-apt.rst
index 058b93f3b9dd75..004f74f74043fd 100644
--- a/docs/articles_en/get-started/install-openvino/install-openvino-apt.rst
+++ b/docs/articles_en/get-started/install-openvino/install-openvino-apt.rst
@@ -252,13 +252,8 @@ You can also try the following:
 
 * Learn more about :doc:`OpenVINO Workflow <../../../openvino-workflow>`.
 * To prepare your models for working with OpenVINO, see :doc:`Model Preparation <../../../openvino-workflow/model-preparation>`.
-* See pre-trained deep learning models in our :doc:`Open Model Zoo <../../../documentation/legacy-features/model-zoo>`.
+* See pre-trained deep learning models on `Hugging Face <https://huggingface.co/OpenVINO>`__
 * Learn more about :doc:`Inference with OpenVINO Runtime <../../../openvino-workflow/running-inference>`.
 * See sample applications in :doc:`OpenVINO toolkit Samples Overview <../../../learn-openvino/openvino-samples>`.
 * Take a glance at the OpenVINO `product home page <https://software.intel.com/en-us/openvino-toolkit>`__ .
 
-
-
-
-
-
diff --git a/docs/articles_en/get-started/install-openvino/install-openvino-archive-linux.rst b/docs/articles_en/get-started/install-openvino/install-openvino-archive-linux.rst
index 294d966df9b37c..c64c27678b4fa4 100644
--- a/docs/articles_en/get-started/install-openvino/install-openvino-archive-linux.rst
+++ b/docs/articles_en/get-started/install-openvino/install-openvino-archive-linux.rst
@@ -25,10 +25,8 @@ Install OpenVINO™ Runtime on Linux from an Archive File
        Debian9 armhf         V     n/a    n/a
        Ubuntu20 arm64        V     n/a    n/a
        CentOS7 x86_64        V      V     n/a
-       Ubuntu18 x86_64       V      V     n/a
-       Ubuntu20 x86_64       V      V     n/a
+       Ubuntu20 x86_64       V      V      V
        Ubuntu22 x86_64       V      V      V
-       Ubuntu24 x86_64       V      V      V
        RHEL8 x86_64          V      V     n/a
       ===================  =====  =====  =====
 
@@ -60,11 +58,6 @@ Install OpenVINO™ Runtime on Linux from an Archive File
 
             * GCC 9.3.0
 
-         .. tab-item:: Ubuntu 18.04
-            :sync: ubuntu-18
-
-            * GCC 7.5.0
-
          .. tab-item:: RHEL 8
             :sync: rhel-8
 
@@ -74,7 +67,7 @@ Install OpenVINO™ Runtime on Linux from an Archive File
             :sync: centos-7
 
             * GCC 8.3.1
-            Use the following instructions to install it:
+              Use the following instructions to install it:
 
             Install GCC 8.3.1 via devtoolset-8
 
@@ -91,8 +84,6 @@ Install OpenVINO™ Runtime on Linux from an Archive File
                gcc -v
 
 
-
-
 Installing OpenVINO Runtime
 ############################################################
 
@@ -117,7 +108,7 @@ Step 1: Download and Install the OpenVINO Core Components
 
       cd <user_home>/Downloads
 
-4. Download the `OpenVINO Runtime archive file for your system <https://storage.openvinotoolkit.org/repositories/openvino/packages/2024.1/linux/>`_, extract the files, rename the extracted folder and move it to the desired path:
+4. Download the `OpenVINO Runtime archive file for your system <https://storage.openvinotoolkit.org/repositories/openvino/packages/2024.2/linux/>`_, extract the files, rename the extracted folder and move it to the desired path:
 
    .. tab-set::
 
@@ -132,9 +123,9 @@ Step 1: Download and Install the OpenVINO Core Components
                .. code-block:: sh
 
 
-                  curl -L https://storage.openvinotoolkit.org/repositories/openvino/packages/2024.1/linux/l_openvino_toolkit_ubuntu22_2024.1.0.15008.f4afc983258_x86_64.tgz --output openvino_2024.1.0.tgz
-                  tar -xf openvino_2024.1.0.tgz
-                  sudo mv l_openvino_toolkit_ubuntu24_2024.1.0.15008.f4afc983258_x86_64 /opt/intel/openvino_2024.1.0
+                  curl -L https://storage.openvinotoolkit.org/repositories/openvino/packages/2024.2/linux/l_openvino_toolkit_ubuntu24_2024.2.0.15519.5c0f38f83f6_x86_64.tgz --output openvino_2024.2.0.tgz
+                  tar -xf openvino_2024.2.0.tgz
+                  sudo mv l_openvino_toolkit_ubuntu24_2024.2.0.15519.5c0f38f83f6_x86_64 /opt/intel/openvino_2024.2.0
 
             .. tab-item:: Ubuntu 22.04
                :sync: ubuntu-22
@@ -142,9 +133,9 @@ Step 1: Download and Install the OpenVINO Core Components
                .. code-block:: sh
 
 
-                  curl -L https://storage.openvinotoolkit.org/repositories/openvino/packages/2024.1/linux/l_openvino_toolkit_ubuntu22_2024.1.0.15008.f4afc983258_x86_64.tgz --output openvino_2024.1.0.tgz
-                  tar -xf openvino_2024.1.0.tgz
-                  sudo mv l_openvino_toolkit_ubuntu22_2024.1.0.15008.f4afc983258_x86_64 /opt/intel/openvino_2024.1.0
+                  curl -L https://storage.openvinotoolkit.org/repositories/openvino/packages/2024.2/linux/l_openvino_toolkit_ubuntu22_2024.2.0.15519.5c0f38f83f6_x86_64.tgz --output openvino_2024.2.0.tgz
+                  tar -xf openvino_2024.2.0.tgz
+                  sudo mv l_openvino_toolkit_ubuntu22_2024.2.0.15519.5c0f38f83f6_x86_64 /opt/intel/openvino_2024.2.0
 
             .. tab-item:: Ubuntu 20.04
                :sync: ubuntu-20
@@ -152,20 +143,9 @@ Step 1: Download and Install the OpenVINO Core Components
                .. code-block:: sh
 
 
-                  curl -L https://storage.openvinotoolkit.org/repositories/openvino/packages/2024.1/linux/l_openvino_toolkit_ubuntu20_2024.1.0.15008.f4afc983258_x86_64.tgz --output openvino_2024.1.0.tgz
-                  tar -xf openvino_2024.1.0.tgz
-                  sudo mv l_openvino_toolkit_ubuntu20_2024.1.0.15008.f4afc983258_x86_64 /opt/intel/openvino_2024.1.0
-
-
-            .. tab-item:: Ubuntu 18.04
-               :sync: ubuntu-18
-
-               .. code-block:: sh
-
-
-                  curl -L https://storage.openvinotoolkit.org/repositories/openvino/packages/2024.1/linux/l_openvino_toolkit_ubuntu18_2024.1.0.15008.f4afc983258_x86_64.tgz --output openvino_2024.1.0.tgz
-                  tar -xf openvino_2024.1.0.tgz
-                  sudo mv l_openvino_toolkit_ubuntu18_2024.1.0.15008.f4afc983258_x86_64 /opt/intel/openvino_2024.1.0
+                  curl -L https://storage.openvinotoolkit.org/repositories/openvino/packages/2024.2/linux/l_openvino_toolkit_ubuntu20_2024.2.0.15519.5c0f38f83f6_x86_64.tgz --output openvino_2024.2.0.tgz
+                  tar -xf openvino_2024.2.0.tgz
+                  sudo mv l_openvino_toolkit_ubuntu20_2024.2.0.15519.5c0f38f83f6_x86_64 /opt/intel/openvino_2024.2.0
 
             .. tab-item:: RHEL 8
                :sync: rhel-8
@@ -173,18 +153,18 @@ Step 1: Download and Install the OpenVINO Core Components
                .. code-block:: sh
 
 
-                  curl -L https://storage.openvinotoolkit.org/repositories/openvino/packages/2024.1/linux/l_openvino_toolkit_rhel8_2024.1.0.15008.f4afc983258_x86_64.tgz --output openvino_2024.1.0.tgz
-                  tar -xf openvino_2024.1.0.tgz
-                  sudo mv l_openvino_toolkit_rhel8_2024.1.0.15008.f4afc983258_x86_64 /opt/intel/openvino_2024.1.0
+                  curl -L https://storage.openvinotoolkit.org/repositories/openvino/packages/2024.2/linux/l_openvino_toolkit_rhel8_2024.2.0.15519.5c0f38f83f6_x86_64.tgz --output openvino_2024.2.0.tgz
+                  tar -xf openvino_2024.2.0.tgz
+                  sudo mv l_openvino_toolkit_rhel8_2024.2.0.15519.5c0f38f83f6_x86_64 /opt/intel/openvino_2024.2.0
 
             .. tab-item:: CentOS 7
                :sync: centos-7
 
                .. code-block:: sh
 
-                  curl -L https://storage.openvinotoolkit.org/repositories/openvino/packages/2024.1/linux/l_openvino_toolkit_centos7_2024.1.0.15008.f4afc983258_x86_64.tgz --output openvino_2024.1.0.tgz
-                  tar -xf openvino_2024.1.0.tgz
-                  sudo mv l_openvino_toolkit_centos7_2024.1.0.15008.f4afc983258_x86_64 /opt/intel/openvino_2024.1.0
+                  curl -L https://storage.openvinotoolkit.org/repositories/openvino/packages/2024.2/linux/l_openvino_toolkit_centos7_2024.2.0.15519.5c0f38f83f6_x86_64.tgz --output openvino_2024.2.0.tgz
+                  tar -xf openvino_2024.2.0.tgz
+                  sudo mv l_openvino_toolkit_centos7_2024.2.0.15519.5c0f38f83f6_x86_64 /opt/intel/openvino_2024.2.0
 
 
       .. tab-item:: ARM 64-bit
@@ -192,26 +172,25 @@ Step 1: Download and Install the OpenVINO Core Components
 
          .. code-block:: sh
 
-
-            curl -L https://storage.openvinotoolkit.org/repositories/openvino/packages/2024.1/linux/l_openvino_toolkit_ubuntu20_2024.1.0.15008.f4afc983258_arm64.tgz -O openvino_2024.1.0.tgz
-            tar -xf openvino_2024.1.0.tgz
-            sudo mv l_openvino_toolkit_ubuntu20_2024.1.0.15008.f4afc983258_arm64 /opt/intel/openvino_2024.1.0
+            curl -L https://storage.openvinotoolkit.org/repositories/openvino/packages/2024.2/linux/l_openvino_toolkit_ubuntu20_2024.2.0.15519.5c0f38f83f6_arm64.tgz -O openvino_2024.2.0.tgz
+            tar -xf openvino_2024.2.0.tgz
+            sudo mv l_openvino_toolkit_ubuntu20_2024.2.0.15519.5c0f38f83f6_arm64 /opt/intel/openvino_2024.2.0
 
       .. tab-item:: ARM 32-bit
          :sync: arm-32
 
          .. code-block:: sh
 
-            curl -L https://storage.openvinotoolkit.org/repositories/openvino/packages/2024.1/linux/l_openvino_toolkit_debian9_2024.1.0.15008.f4afc983258_armhf.tgz -O openvino_2024.1.0.tgz
-            tar -xf openvino_2024.1.0.tgz
-            sudo mv l_openvino_toolkit_debian9_2024.1.0.15008.f4afc983258_armhf /opt/intel/openvino_2024.1.0
+            curl -L https://storage.openvinotoolkit.org/repositories/openvino/packages/2024.2/linux/l_openvino_toolkit_debian9_2024.2.0.15519.5c0f38f83f6_armhf.tgz -O openvino_2024.2.0.tgz
+            tar -xf openvino_2024.2.0.tgz
+            sudo mv l_openvino_toolkit_debian9_2024.2.0.15519.5c0f38f83f6_armhf /opt/intel/openvino_2024.2.0
 
 
 5. Install required system dependencies on Linux. To do this, OpenVINO provides a script in the extracted installation directory. Run the following command:
 
    .. code-block:: sh
 
-      cd /opt/intel/openvino_2024.1.0
+      cd /opt/intel/openvino_2024.2.0
       sudo -E ./install_dependencies/install_openvino_dependencies.sh
 
 6. (Optional) Install *numpy* Python Library:
@@ -220,11 +199,11 @@ Step 1: Download and Install the OpenVINO Core Components
 
       This step is required only when you decide to use Python API.
 
-   You can use the ``requirements.txt`` file from the ``/opt/intel/openvino_2024.1.0/python`` folder:
+   You can use the ``requirements.txt`` file from the ``/opt/intel/openvino_2024.2.0/python`` folder:
 
    .. code-block:: sh
 
-      cd /opt/intel/openvino_2024.1.0
+      cd /opt/intel/openvino_2024.2.0
       python3 -m pip install -r ./python/requirements.txt
 
 7. For simplicity, it is useful to create a symbolic link as below:
@@ -233,7 +212,7 @@ Step 1: Download and Install the OpenVINO Core Components
 
       cd /opt/intel
 
-      sudo ln -s openvino_2024.1.0 openvino_2024
+      sudo ln -s openvino_2024.2.0 openvino_2024
 
    .. note::
       If you have already installed a previous release of OpenVINO 2024, a symbolic link to the ``openvino_2024`` folder may already exist.
diff --git a/docs/articles_en/get-started/install-openvino/install-openvino-archive-macos.rst b/docs/articles_en/get-started/install-openvino/install-openvino-archive-macos.rst
index 2421ec54c32354..03902a8edb7dc6 100644
--- a/docs/articles_en/get-started/install-openvino/install-openvino-archive-macos.rst
+++ b/docs/articles_en/get-started/install-openvino/install-openvino-archive-macos.rst
@@ -63,7 +63,7 @@ Step 1: Install OpenVINO Core Components
       cd <user_home>/Downloads
 
 
-4. Download the `OpenVINO Runtime archive file for macOS <https://storage.openvinotoolkit.org/repositories/openvino/packages/2024.1/macos/>`__, extract the files, rename the extracted folder and move it to the desired path:
+4. Download the `OpenVINO Runtime archive file for macOS <https://storage.openvinotoolkit.org/repositories/openvino/packages/2024.2/macos/>`__, extract the files, rename the extracted folder and move it to the desired path:
 
    .. tab-set::
 
@@ -73,9 +73,9 @@ Step 1: Install OpenVINO Core Components
          .. code-block:: sh
 
 
-            curl -L https://storage.openvinotoolkit.org/repositories/openvino/packages/2024.1/macos/m_openvino_toolkit_macos_12_6_2024.1.0.15008.f4afc983258_x86_64.tgz --output openvino_2024.1.0.tgz
-            tar -xf openvino_2024.1.0.tgz
-            sudo mv m_openvino_toolkit_macos_12_6_2024.1.0.15008.f4afc983258_x86_64 /opt/intel/openvino_2024.1.0
+            curl -L https://storage.openvinotoolkit.org/repositories/openvino/packages/2024.2/macos/m_openvino_toolkit_macos_12_6_2024.2.0.15519.5c0f38f83f6_x86_64.tgz --output openvino_2024.2.0.tgz
+            tar -xf openvino_2024.2.0.tgz
+            sudo mv m_openvino_toolkit_macos_12_6_2024.2.0.15519.5c0f38f83f6_x86_64 /opt/intel/openvino_2024.2.0
 
       .. tab-item:: ARM, 64-bit
          :sync: arm-64
@@ -83,9 +83,9 @@ Step 1: Install OpenVINO Core Components
          .. code-block:: sh
 
 
-            curl -L https://storage.openvinotoolkit.org/repositories/openvino/packages/2024.1/macos/m_openvino_toolkit_macos_12_6_2024.1.0.15008.f4afc983258_arm64.tgz --output openvino_2024.1.0.tgz
-            tar -xf openvino_2024.1.0.tgz
-            sudo mv m_openvino_toolkit_macos_12_6_2024.1.0.15008.f4afc983258_arm64 /opt/intel/openvino_2024.1.0
+            curl -L https://storage.openvinotoolkit.org/repositories/openvino/packages/2024.2/macos/m_openvino_toolkit_macos_12_6_2024.2.0.15519.5c0f38f83f6_arm64.tgz --output openvino_2024.2.0.tgz
+            tar -xf openvino_2024.2.0.tgz
+            sudo mv m_openvino_toolkit_macos_12_6_2024.2.0.15519.5c0f38f83f6_arm64 /opt/intel/openvino_2024.2.0
 
 
 5. (Optional) Install *numpy* Python Library:
@@ -94,11 +94,11 @@ Step 1: Install OpenVINO Core Components
 
       This step is required only when you decide to use Python API.
 
-   You can use the ``requirements.txt`` file from the ``/opt/intel/openvino_2024.1.0/python`` folder:
+   You can use the ``requirements.txt`` file from the ``/opt/intel/openvino_2024.2.0/python`` folder:
 
    .. code-block:: sh
 
-      cd /opt/intel/openvino_2024.1.0
+      cd /opt/intel/openvino_2024.2.0
       python3 -m pip install -r ./python/requirements.txt
 
 6. For simplicity, it is useful to create a symbolic link as below:
@@ -106,7 +106,7 @@ Step 1: Install OpenVINO Core Components
    .. code-block:: sh
 
 
-      sudo ln -s /opt/intel/openvino_2024.1.0 /opt/intel/openvino_2024
+      sudo ln -s /opt/intel/openvino_2024.2.0 /opt/intel/openvino_2024
 
 
    .. note::
diff --git a/docs/articles_en/get-started/install-openvino/install-openvino-archive-windows.rst b/docs/articles_en/get-started/install-openvino/install-openvino-archive-windows.rst
index ffa5e612ab1571..c9e32e907837b0 100644
--- a/docs/articles_en/get-started/install-openvino/install-openvino-archive-windows.rst
+++ b/docs/articles_en/get-started/install-openvino/install-openvino-archive-windows.rst
@@ -78,19 +78,19 @@ Step 1: Download and Install OpenVINO Core Components
       ``C:\Program Files (x86)\Intel`` is the recommended folder. You may also use a different path if desired or if you don't have administrator privileges on your computer.
 
 
-2. Download the `OpenVINO Runtime archive file for Windows <https://storage.openvinotoolkit.org/repositories/openvino/packages/2024.1/windows/>`__ to your local ``Downloads`` folder.
+2. Download the `OpenVINO Runtime archive file for Windows <https://storage.openvinotoolkit.org/repositories/openvino/packages/2024.2/windows/>`__ to your local ``Downloads`` folder.
 
    If you prefer using command-lines, run the following commands in the command prompt window you opened:
 
    .. code-block:: sh
 
       cd <user_home>/Downloads
-      curl -L https://storage.openvinotoolkit.org/repositories/openvino/packages/2024.0/windows/w_openvino_toolkit_windows_2024.1.0.15008.f4afc983258_x86_64.zip --output openvino_2024.1.0.zip --output openvino_2024.0.0.zip
+      curl -L https://storage.openvinotoolkit.org/repositories/openvino/packages/2024.2/windows/w_openvino_toolkit_windows_2024.2.0.15519.5c0f38f83f6_x86_64.zip --output openvino_2024.2.0.zip --output openvino_2024.2.0.zip
 
 
    .. note::
 
-      A ``.sha256`` file is provided together with the archive file to validate your download process. To do that, download the ``.sha256`` file from the same repository and run ``CertUtil -hashfile openvino_2024.1.0.zip SHA256``. Compare the returned value in the output with what's in the ``.sha256`` file: if the values are the same, you have downloaded the correct file successfully; if not, create a Support ticket `here <https://www.intel.com/content/www/us/en/support/contact-intel.html>`__.
+      A ``.sha256`` file is provided together with the archive file to validate your download process. To do that, download the ``.sha256`` file from the same repository and run ``CertUtil -hashfile openvino_2024.2.0.zip SHA256``. Compare the returned value in the output with what's in the ``.sha256`` file: if the values are the same, you have downloaded the correct file successfully; if not, create a Support ticket `here <https://www.intel.com/content/www/us/en/support/contact-intel.html>`__.
 
 
 3. Use your favorite tool to extract the archive file, rename the extracted folder, and move it to the ``C:\Program Files (x86)\Intel`` directory.
@@ -99,9 +99,9 @@ Step 1: Download and Install OpenVINO Core Components
 
    .. code-block:: sh
 
-      tar -xf openvino_2024.1.0.zip
-      ren w_openvino_toolkit_windows_2024.1.0.15008.f4afc983258_x86_64 openvino_2024.1.0
-      move openvino_2024.1.0 "C:\Program Files (x86)\Intel"
+      tar -xf openvino_2024.2.0.zip
+      ren w_openvino_toolkit_windows_2024.2.0.15519.5c0f38f83f6_x86_64 openvino_2024.2.0
+      move openvino_2024.2.0 "C:\Program Files (x86)\Intel"
 
 
 4. (Optional) Install *numpy* Python Library:
@@ -110,11 +110,11 @@ Step 1: Download and Install OpenVINO Core Components
 
       This step is required only when you decide to use Python API.
 
-   You can use the ``requirements.txt`` file from the ``C:\Program Files (x86)\Intel\openvino_2024.1.0\python`` folder:
+   You can use the ``requirements.txt`` file from the ``C:\Program Files (x86)\Intel\openvino_2024.2.0\python`` folder:
 
    .. code-block:: sh
 
-      cd "C:\Program Files (x86)\Intel\openvino_2024.1.0"
+      cd "C:\Program Files (x86)\Intel\openvino_2024.2.0"
       python -m pip install -r .\python\requirements.txt
 
 
@@ -123,7 +123,7 @@ Step 1: Download and Install OpenVINO Core Components
    .. code-block:: sh
 
       cd C:\Program Files (x86)\Intel
-      mklink /D openvino_2024 openvino_2024.1.0
+      mklink /D openvino_2024 openvino_2024.2.0
 
 
    .. note::
diff --git a/docs/articles_en/get-started/install-openvino/install-openvino-brew.rst b/docs/articles_en/get-started/install-openvino/install-openvino-brew.rst
index a5f5abeaf34db9..11bd8d90b6b641 100644
--- a/docs/articles_en/get-started/install-openvino/install-openvino-brew.rst
+++ b/docs/articles_en/get-started/install-openvino/install-openvino-brew.rst
@@ -96,7 +96,14 @@ Now that you've installed OpenVINO Runtime, you can try the following things:
 
 * Learn more about :doc:`OpenVINO Workflow <../../../openvino-workflow>`.
 * To prepare your models for working with OpenVINO, see :doc:`Model Preparation <../../../openvino-workflow/model-preparation>`.
-* See pre-trained deep learning models in our :doc:`Open Model Zoo <../../../documentation/legacy-features/model-zoo>`.
+* See pre-trained deep learning models in our
+  :doc:`Open Model Zoo <../../../documentation/legacy-features/model-zoo>`.
+
+  .. important::
+
+     Due to the deprecation of Open Model Zoo, models in the OpenVINO IR format are now
+     published on `Hugging Face <https://huggingface.co/OpenVINO>`__.
+
 * Learn more about :doc:`Inference with OpenVINO Runtime <../../../openvino-workflow/running-inference>`.
 * See sample applications in :doc:`OpenVINO toolkit Samples Overview <../../../learn-openvino/openvino-samples>`.
 * Check out the OpenVINO `product home page <https://software.intel.com/en-us/openvino-toolkit>`__.
diff --git a/docs/articles_en/get-started/install-openvino/install-openvino-docker-linux.rst b/docs/articles_en/get-started/install-openvino/install-openvino-docker-linux.rst
index 6aa5240098da10..11c2425d231dd3 100644
--- a/docs/articles_en/get-started/install-openvino/install-openvino-docker-linux.rst
+++ b/docs/articles_en/get-started/install-openvino/install-openvino-docker-linux.rst
@@ -1,9 +1,6 @@
-.. {#openvino_docs_install_guides_installing_openvino_docker}
-
 Install Intel® Distribution of OpenVINO™ toolkit from a Docker Image
 =======================================================================
 
-
 .. meta::
    :description: Learn how to use a prebuilt Docker image or create an image
                  manually to install OpenVINO™ Runtime on Linux and Windows operating systems.
@@ -33,7 +30,7 @@ The Docker CI repository includes guides on how to
 
 To start using Dockerfiles the following conditions must be met:
 
-- Linux OS or Windows (under :ref:`Windows Subsystem for Linux (WSL2) <wsl-install>`)
+- Linux OS or Windows (under :ref:`Windows Subsystem for Linux (WSL2) <wsl_install>`)
 - Installed docker engine or compatible container engine
 - Permissions to run containers (sudo or docker group membership)
 
@@ -42,10 +39,14 @@ To start using Dockerfiles the following conditions must be met:
    OpenVINO's `Docker <https://docs.docker.com/>`__ and :doc:`Bare Metal <../install-openvino>`
    distributions are identical, so the documentation applies to both.
 
+   Note that starting with OpenVINO 2024.4, Ubuntu docker images will no longer be provided
+   and will be replaced by Debian-based ones.
+
 .. note::
 
    OpenVINO development environment in a docker container is also available in the
-   `notebook repository <https://github.com/openvinotoolkit/openvino_notebooks>`__.    It can be implemented in
+   `notebook repository <https://github.com/openvinotoolkit/openvino_notebooks>`__.
+   It can be implemented in
    `OpenShift RedHat OpenData Science (RHODS) <https://github.com/openvinotoolkit/operator/blob/main/docs/notebook_in_rhods.md>`__.
 
 More information about Docker CI for Intel® Distribution of OpenVINO™ toolset can be found
diff --git a/docs/articles_en/get-started/install-openvino/install-openvino-genai.rst b/docs/articles_en/get-started/install-openvino/install-openvino-genai.rst
index 5927d11b18e8c8..cd6714149e7211 100644
--- a/docs/articles_en/get-started/install-openvino/install-openvino-genai.rst
+++ b/docs/articles_en/get-started/install-openvino/install-openvino-genai.rst
@@ -9,16 +9,30 @@ For a quickstart guide, refer to the :doc:`GenAI API Guide <../../learn-openvino
 
 To see GenAI in action, check the Jupyter notebooks:
 `LLM-powered Chatbot <https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/llm-chatbot/README.md>`__ and
-`LLM Instruction-following pipeline <https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/llm-question-answering/README.md>`__
+`LLM Instruction-following pipeline <https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/llm-question-answering/README.md>`__.
 
-The OpenVINO GenAI flavor is available for installation via Archive and PyPI distributions:
+The OpenVINO GenAI flavor is available for installation via PyPI and Archive distributions:
+
+PyPI Installation
+###############################
+
+To install the GenAI flavor of OpenVINO via PyPI, follow the standard :doc:`installation steps <install-openvino-pip>`,
+but use the *openvino-genai* package instead of *openvino*:
+
+.. code-block:: python
+
+   python -m pip install openvino-genai
 
 Archive Installation
 ###############################
 
+The OpenVINO GenAI archive package includes the OpenVINO™ Runtime and :doc:`Tokenizers <../../learn-openvino/llm_inference_guide/ov-tokenizers>`.
 To install the GenAI flavor of OpenVINO from an archive file, follow the standard installation steps for your system
 but instead of using the vanilla package file, download the one with OpenVINO GenAI:
 
+Linux
+++++++++++++++++++++++++++
+
 .. tab-set::
 
    .. tab-item:: x86_64
@@ -31,32 +45,69 @@ but instead of using the vanilla package file, download the one with OpenVINO Ge
 
             .. code-block:: sh
 
-
-               curl -L https://storage.openvinotoolkit.org/repositories/openvino/packages/2024.1/linux/l_openvino_toolkit_ubuntu22_2024.1.0.15008.f4afc983258_x86_64.tgz --output openvino_2024.1.0.tgz
-               tar -xf openvino_2024.1.0.tgz
-               sudo mv l_openvino_toolkit_ubuntu24_2024.1.0.15008.f4afc983258_x86_64 /opt/intel/openvino_2024.1.0
+               curl -L https://storage.openvinotoolkit.org/repositories/openvino_genai/packages/2024.2/linux/openvino_genai_ubuntu24_2024.2.0.0_x86_64.tar.gz --output openvino_genai_2024.2.0.0.tgz
+               tar -xf openvino_genai_2024.2.0.0.tgz
 
          .. tab-item:: Ubuntu 22.04
             :sync: ubuntu-22
 
             .. code-block:: sh
 
-               curl -L https://storage.openvinotoolkit.org/repositories/openvino/packages/2024.1/linux/l_openvino_toolkit_ubuntu22_2024.1.0.15008.f4afc983258_x86_64.tgz --output openvino_2024.1.0.tgz
-               tar -xf openvino_2024.1.0.tgz
-               sudo mv l_openvino_toolkit_ubuntu22_2024.1.0.15008.f4afc983258_x86_64 /opt/intel/openvino_2024.1.0
+               curl -L https://storage.openvinotoolkit.org/repositories/openvino_genai/packages/2024.2/linux/openvino_genai_ubuntu22_2024.2.0.0_x86_64.tar.gz --output openvino_genai_2024.2.0.0.tgz
+               tar -xf openvino_genai_2024.2.0.0.tgz
+
+         .. tab-item:: Ubuntu 20.04
+            :sync: ubuntu-20
+
+            .. code-block:: sh
+
+               curl -L https://storage.openvinotoolkit.org/repositories/openvino_genai/packages/2024.2/linux/openvino_genai_ubuntu20_2024.2.0.0_x86_64.tar.gz  --output openvino_genai_2024.2.0.0.tgz
+               tar -xf openvino_genai_2024.2.0.0.tgz
+
+
+   .. tab-item:: ARM 64-bit
+      :sync: arm-64
+
+      .. code-block:: sh
+
+         curl -L https://storage.openvinotoolkit.org/repositories/openvino_genai/packages/2024.2/linux/openvino_genai_ubuntu20_2024.2.0.0_arm64.tar.gz -O openvino_genai_2024.2.0.0.tgz
+         tar -xf openvino_genai_2024.2.0.0.tgz
+
+
+Windows
+++++++++++++++++++++++++++
+
+.. code-block:: sh
+
+   cd <user_home>/Downloads
+   curl -L https://storage.openvinotoolkit.org/repositories/openvino_genai/packages/2024.2/windows/openvino_genai_windows_2024.2.0.0_x86_64.zip --output openvino_genai_2024.2.0.0.zip
+
+macOS
+++++++++++++++++++++++++++
+
+.. tab-set::
+
+   .. tab-item:: x86, 64-bit
+      :sync: x86-64
+
+      .. code-block:: sh
+
+         curl -L https://storage.openvinotoolkit.org/repositories/openvino_genai/packages/2024.2/macos/openvino_genai_macos_12_6_2024.2.0.0_x86_64.tar.gz --output openvino_genai_2024.2.0.0.tgz
+         tar -xf openvino_genai_2024.2.0.0.tgz
+
+   .. tab-item:: ARM, 64-bit
+      :sync: arm-64
+
+      .. code-block:: sh
+
+         curl -L https://storage.openvinotoolkit.org/repositories/openvino_genai/packages/2024.2/macos/openvino_genai_macos_11_0_2024.2.0.0_arm64.tar.gz --output openvino_genai_2024.2.0.0.tgz
+         tar -xf openvino_genai_2024.2.0.0.tgz
+
 
 Here are the full guides:
 :doc:`Linux <install-openvino-archive-linux>`,
 :doc:`Windows <install-openvino-archive-windows>`, and
 :doc:`macOS <install-openvino-archive-macos>`.
 
-PyPI Installation
-###############################
-
-To install the GenAI flavor of OpenVINO via PyPI, follow the standard :doc:`installation steps <install-openvino-pip>`,
-but use the *openvino-genai* package instead of *openvino*:
 
-.. code-block:: python
-
-   python -m pip install openvino-genai
 
diff --git a/docs/articles_en/get-started/install-openvino/install-openvino-vcpkg.rst b/docs/articles_en/get-started/install-openvino/install-openvino-vcpkg.rst
index 3074bd8135e4e7..828b5205169fea 100644
--- a/docs/articles_en/get-started/install-openvino/install-openvino-vcpkg.rst
+++ b/docs/articles_en/get-started/install-openvino/install-openvino-vcpkg.rst
@@ -102,6 +102,12 @@ Now that you've installed OpenVINO Runtime, you can try the following things:
 * Learn more about :doc:`OpenVINO Workflow <../../../openvino-workflow>`.
 * To prepare your models for working with OpenVINO, see :doc:`Model Preparation <../../../openvino-workflow/model-preparation>`.
 * See pre-trained deep learning models in our :doc:`Open Model Zoo <../../../documentation/legacy-features/model-zoo>`.
+
+  .. important::
+
+     Due to the deprecation of Open Model Zoo, models in the OpenVINO IR format are now
+     published on `Hugging Face <https://huggingface.co/OpenVINO>`__.
+
 * Learn more about :doc:`Inference with OpenVINO Runtime <../../../openvino-workflow/running-inference>`.
 * See sample applications in :doc:`OpenVINO toolkit Samples Overview <../../../learn-openvino/openvino-samples>`.
 * Check out the OpenVINO `product home page <https://software.intel.com/en-us/openvino-toolkit>`__ .
diff --git a/docs/articles_en/get-started/install-openvino/install-openvino-yocto.rst b/docs/articles_en/get-started/install-openvino/install-openvino-yocto.rst
index afa00cd6551e85..0ff1b95c8eb212 100644
--- a/docs/articles_en/get-started/install-openvino/install-openvino-yocto.rst
+++ b/docs/articles_en/get-started/install-openvino/install-openvino-yocto.rst
@@ -66,8 +66,6 @@ Step 1: Set up the environment
       CORE_IMAGE_EXTRA_INSTALL:append = " openvino-inference-engine-samples"
       # Include OpenVINO Python API package in the target image.
       CORE_IMAGE_EXTRA_INSTALL:append = " openvino-inference-engine-python3"
-      # Include model conversion API in the target image.
-      CORE_IMAGE_EXTRA_INSTALL:append = " openvino-model-optimizer"
 
 Step 2: Build a Yocto Image with OpenVINO Packages
 ##################################################
@@ -102,9 +100,6 @@ If the image build is successful, it will return the list of packages as below:
    openvino-inference-engine-python3
    openvino-inference-engine-samples
    openvino-inference-engine-src
-   openvino-model-optimizer
-   openvino-model-optimizer-dbg
-   openvino-model-optimizer-dev
 
 Additional Resources
 ####################
diff --git a/docs/articles_en/get-started/install-openvino/install-openvino-yum.rst b/docs/articles_en/get-started/install-openvino/install-openvino-yum.rst
index a5559e937e50d6..cbb25eac817f97 100644
--- a/docs/articles_en/get-started/install-openvino/install-openvino-yum.rst
+++ b/docs/articles_en/get-started/install-openvino/install-openvino-yum.rst
@@ -226,6 +226,12 @@ You can also try the following things:
 * Learn more about :doc:`OpenVINO Workflow <../../../openvino-workflow>`.
 * To prepare your models for working with OpenVINO, see :doc:`Model Preparation <../../../openvino-workflow/model-preparation>`.
 * See pre-trained deep learning models in our :doc:`Open Model Zoo <../../../documentation/legacy-features/model-zoo>`.
+
+  .. important::
+
+     Due to the deprecation of Open Model Zoo, models in the OpenVINO IR format are now
+     published on `Hugging Face <https://huggingface.co/OpenVINO>`__.
+
 * Learn more about :doc:`Inference with OpenVINO Runtime <../../../openvino-workflow/running-inference>`.
 * See sample applications in :doc:`OpenVINO toolkit Samples Overview <../../../learn-openvino/openvino-samples>`.
 * Take a glance at the OpenVINO `product home page <https://software.intel.com/en-us/openvino-toolkit>`__ .
diff --git a/docs/articles_en/get-started/install-openvino/install-openvino-zypper.rst b/docs/articles_en/get-started/install-openvino/install-openvino-zypper.rst
index 20166da049fee7..ee0b2633299c66 100644
--- a/docs/articles_en/get-started/install-openvino/install-openvino-zypper.rst
+++ b/docs/articles_en/get-started/install-openvino/install-openvino-zypper.rst
@@ -170,6 +170,12 @@ You can also try the following things:
 * Learn more about :doc:`OpenVINO Workflow <../../../openvino-workflow>`.
 * To prepare your models for working with OpenVINO, see :doc:`Model Preparation <../../../openvino-workflow/model-preparation>`.
 * See pre-trained deep learning models in our :doc:`Open Model Zoo <../../../documentation/legacy-features/model-zoo>`.
+
+  .. important::
+
+     Due to the deprecation of Open Model Zoo, models in the OpenVINO IR format are now
+     published on `Hugging Face <https://huggingface.co/OpenVINO>`__.
+
 * Learn more about :doc:`Inference with OpenVINO Runtime <../../../openvino-workflow/running-inference>`.
 * See sample applications in :doc:`OpenVINO toolkit Samples Overview <../../../learn-openvino/openvino-samples>`.
 * Take a glance at the OpenVINO `product home page <https://software.intel.com/en-us/openvino-toolkit>`__ .
diff --git a/docs/articles_en/learn-openvino.rst b/docs/articles_en/learn-openvino.rst
index e955c1e6f8abbf..19e96db09bd543 100644
--- a/docs/articles_en/learn-openvino.rst
+++ b/docs/articles_en/learn-openvino.rst
@@ -6,7 +6,7 @@ Learn OpenVINO
 
 .. meta::
    :description: Explore learning materials, including interactive
-                 Python learn-openvino/interactive-tutorials-python and sample console applications that explain
+                 Python tutorials and sample console applications that explain
                  how to use OpenVINO features.
 
 
@@ -26,7 +26,7 @@ accommodating different learning needs, which means you should find it useful if
 as well as an experienced user.
 
 | :doc:`Tutorials <learn-openvino/interactive-tutorials-python>`
-| A collection of interactive Python learn-openvino/interactive-tutorials-python. It introduces you to the OpenVINO™ toolkit explaining how to use the Python API and tools for optimized deep learning inference. The learn-openvino/interactive-tutorials-python are available in Jupyter notebooks and can be run in your browser. No installation required.
+| A collection of interactive Python tutorials. It introduces you to the OpenVINO™ toolkit explaining how to use the Python API and tools for optimized deep learning inference. The tutorials are available in Jupyter notebooks and can be run in your browser. No installation required.
 
 | :doc:`OpenVINO Samples <learn-openvino/openvino-samples>`
 | The OpenVINO samples (Python and C++) are simple console applications that show how to use specific OpenVINO API features. They can assist you in executing tasks such as loading a model, running inference, querying particular device capabilities, etc.
diff --git a/docs/articles_en/learn-openvino/llm_inference_guide.rst b/docs/articles_en/learn-openvino/llm_inference_guide.rst
index 874e53899e1cfd..6db776a3c1f5fb 100644
--- a/docs/articles_en/learn-openvino/llm_inference_guide.rst
+++ b/docs/articles_en/learn-openvino/llm_inference_guide.rst
@@ -26,19 +26,36 @@ conversion to advanced use cases.
 
 The advantages of using OpenVINO for LLM deployment:
 
-*	**OpenVINO offers optimized LLM inference**; provides a full C/C++ API, leading to faster operation than Python-based runtimes; includes a Python API for rapid development, with the option for further optimization in C++.
-
-*	**Compatible with diverse hardware**, supports CPUs, GPUs, and neural accelerators across ARM and x86/x64 architectures, integrated Intel® Processor Graphics, discrete Intel® Arc™ A-Series Graphics, and discrete Intel® Data Center GPU Flex Series; features automated optimization to maximize performance on target hardware.
-
-*	**Requires fewer dependencies** than frameworks like Hugging Face and PyTorch, resulting in a smaller binary size and reduced memory footprint, making deployments easier and updates more manageable.
-
-*	**Provides compression and precision management techniques** such as 8-bit and 4-bit weight compression, including embedding layers, and storage format reduction. This includes fp16 precision for non-compressed models and int8/int4 for compressed models, like GPTQ models from Hugging Face.
-
-*	**Supports a wide range of deep learning models and architectures** including text, image, and audio generative models like Llama 2, MPT, OPT, Stable Diffusion, Stable Diffusion XL. This enables the development of multimodal applications, allowing for write-once, deploy-anywhere capabilities.
-
-*	**Enhances inference capabilities**: fused inference primitives such as Scaled Dot Product Attention, Rotary Positional Embedding, Group Query Attention, and Mixture of Experts. It also offers advanced features like in-place KV-cache, dynamic quantization, KV-cache quantization and encapsulation, dynamic beam size configuration, and speculative sampling.
-
-*	**Provides stateful model optimization**: models from the Hugging Face Transformers are converted into a stateful form, optimizing inference performance and memory usage in long-running text generation tasks by managing past KV-cache tensors more efficiently internally. This feature is automatically activated for many supported models, while unsupported ones remain stateless. Learn more about the :doc:`Stateful models and State API <../openvino-workflow/running-inference/stateful-models>`.
+* **OpenVINO offers optimized LLM inference**:
+  provides a full C/C++ API, leading to faster operation than Python-based runtimes; includes a
+  Python API for rapid development, with the option for further optimization in C++.
+* **Compatible with diverse hardware**:
+  supports CPUs, GPUs, and neural accelerators across ARM and x86/x64 architectures, integrated
+  Intel® Processor Graphics, discrete Intel® Arc™ A-Series Graphics, and discrete Intel® Data
+  Center GPU Flex Series; features automated optimization to maximize performance on target
+  hardware.
+* **Requires fewer dependencies**:
+  than frameworks like Hugging Face and PyTorch, resulting in a smaller binary size and reduced
+  memory footprint, making deployments easier and updates more manageable.
+* **Provides compression and precision management techniques**:
+  such as 8-bit and 4-bit weight compression, including embedding layers, and storage format
+  reduction. This includes fp16 precision for non-compressed models and int8/int4 for compressed
+  models, like GPTQ models from `Hugging Face <https://huggingface.co/models>`__.
+* **Supports a wide range of deep learning models and architectures**:
+  including text, image, and audio generative models like Llama 2, MPT, OPT, Stable Diffusion,
+  Stable Diffusion XL. This enables the development of multimodal applications, allowing for
+  write-once, deploy-anywhere capabilities.
+* **Enhances inference capabilities**:
+  fused inference primitives such as Scaled Dot Product Attention, Rotary Positional Embedding,
+  Group Query Attention, and Mixture of Experts. It also offers advanced features like in-place
+  KV-cache, dynamic quantization, KV-cache quantization and encapsulation, dynamic beam size
+  configuration, and speculative sampling.
+* **Provides stateful model optimization**:
+  models from the Hugging Face Transformers are converted into a stateful form, optimizing
+  inference performance and memory usage in long-running text generation tasks by managing past
+  KV-cache tensors more efficiently internally. This feature is automatically activated for many
+  supported models, while unsupported ones remain stateless. Learn more about the
+  :doc:`Stateful models and State API <../openvino-workflow/running-inference/stateful-models>`.
 
 OpenVINO offers three main paths for Generative AI use cases:
 
@@ -121,4 +138,5 @@ approaches.
         - Best suited for high-performance, resource-optimized production environments
       * - Model Serving
         - Paid service, based on CPU/GPU usage with Hugging Face
-        - Free code solution, run script for own server; costs may incur for cloud services like AWS but generally cheaper than Hugging Face rates
+        - Free code solution, run script for own server; costs may incur for cloud services
+          like AWS but generally cheaper than Hugging Face rates
diff --git a/docs/articles_en/learn-openvino/llm_inference_guide/genai-guide.rst b/docs/articles_en/learn-openvino/llm_inference_guide/genai-guide.rst
index 821f16062c271b..e1b0a170848ec3 100644
--- a/docs/articles_en/learn-openvino/llm_inference_guide/genai-guide.rst
+++ b/docs/articles_en/learn-openvino/llm_inference_guide/genai-guide.rst
@@ -54,9 +54,10 @@ will not work with these instructions, make sure to
             #include <iostream>
 
             int main(int argc, char* argv[]) {
-            std::string model_path = argv[1];
-            ov::genai::LLMPipeline pipe(model_path, "CPU");//target device is CPU
-            std::cout << pipe.generate("The Sun is yellow because"); //input context
+               std::string model_path = argv[1];
+               ov::genai::LLMPipeline pipe(model_path, "CPU");
+               std::cout << pipe.generate("The Sun is yellow because");
+            }
 
 The `LLMPipeline` is the main object used for decoding. You can construct it directly from the
 folder with the converted model. It will automatically load the main model, tokenizer, detokenizer,
@@ -74,6 +75,17 @@ below, where a lambda function outputs words to the console immediately upon gen
 
 .. tab-set::
 
+   .. tab-item:: Python
+      :sync: py
+
+      .. code-block:: python
+
+         import openvino_genai as ov_genai
+         pipe = ov_genai.LLMPipeline(model_path, "CPU")
+         
+         streamer = lambda x: print(x, end='', flush=True)
+         pipe.generate("The Sun is yellow because", streamer=streamer)
+
    .. tab-item:: C++
 
       .. code-block:: cpp
@@ -85,14 +97,43 @@ below, where a lambda function outputs words to the console immediately upon gen
             std::string model_path = argv[1];
             ov::genai::LLMPipeline pipe(model_path, "CPU");
 
-            auto streamer = [](std::string word) { std::cout << word << std::flush; };
-            std::cout << pipe.generate("The Sun is yellow because", streamer);
+            auto streamer = [](std::string word) { 
+               std::cout << word << std::flush; 
+               // Return flag indicating whether generation should be stopped.
+               // false means continue generation.
+               return false;
+            };
+            pipe.generate("The Sun is yellow because", ov::genai::streamer(streamer));
          }
 
 You can also create your custom streamer for more sophisticated processing:
 
 .. tab-set::
 
+   .. tab-item:: Python
+      :sync: py
+
+      .. code-block:: python
+
+         import openvino_genai as ov_genai
+
+         class CustomStreamer(ov_genai.StreamerBase):
+            def __init__(self, tokenizer):
+               ov_genai.StreamerBase.__init__(self)
+               self.tokenizer = tokenizer
+            def put(self, token_id) -> bool:
+               # Decode tokens and process them.
+               # Streamer returns a flag indicating whether generation should be stopped.
+               # In Python, `return` can be omitted. In that case, the function will return None
+               # which will be converted to False, meaning that generation should continue.
+               # return stop_flag
+            def end(self):
+               # Decode tokens and process them.
+
+         pipe = ov_genai.LLMPipeline(model_path, "CPU")
+         pipe.generate("The Sun is yellow because", streamer=CustomStreamer())
+
+
    .. tab-item:: C++
 
       .. code-block:: cpp
@@ -101,20 +142,31 @@ You can also create your custom streamer for more sophisticated processing:
 
          class CustomStreamer: publict StreamerBase {
          public:
-            void put(int64_t token) {/* decode tokens and do process them*/};
-
-            void end() {/* decode tokens and do process them*/};
+            bool put(int64_t token) {
+               bool stop_flag = false; 
+               /* 
+               custom decoding/tokens processing code
+               tokens_cache.push_back(token);
+               std::string text = m_tokenizer.decode(tokens_cache);
+               ...
+               */
+               return stop_flag;  // Flag indicating whether generation should be stopped. If True, generation stops.
+            };
+
+            void end() {
+               /* custom finalization */
+            };
          };
 
          int main(int argc, char* argv[]) {
-            CustomStreamer custom_streamer;
+            auto custom_streamer = std::make_shared<CustomStreamer>();
 
             std::string model_path = argv[1];
-            ov::LLMPipeline pipe(model_path, "CPU");
-            cout << pipe.generate("The Sun is yellow because", custom_streamer);
+            ov::genai::LLMPipeline pipe(model_path, "CPU");
+            pipe.generate("The Sun is yellow because", ov::genai::streamer(custom_streamer));
          }
 
-Optimizing the Chat Scenario
+Using GenAI in Chat Scenario
 ################################
 
 For chat scenarios where inputs and outputs represent a conversation, maintaining KVCache across inputs
@@ -131,16 +183,15 @@ mark a conversation session, as you can see in these simple examples:
          import openvino_genai as ov_genai
          pipe = ov_genai.LLMPipeline(model_path)
 
-         config = {'num_groups': 3, 'group_size': 5, 'diversity_penalty': 1.1}
-         pipe.set_generation_cofnig(config)
+         pipe.set_generation_cofnig({'max_new_tokens': 100)
 
          pipe.start_chat()
          while True:
-             print('question:')
-             prompt = input()
+            print('question:')
+            prompt = input()
             if prompt == 'Stop!':
-                 break
-             print(pipe(prompt))
+               break
+            print(pipe.generate(prompt))
          pipe.finish_chat()
 
 
@@ -153,14 +204,18 @@ mark a conversation session, as you can see in these simple examples:
             std::string prompt;
 
             std::string model_path = argv[1];
-            ov::LLMPipeline pipe(model_path, "CPU");
+            ov::genai::LLMPipeline pipe(model_path, "CPU");
+            
+            ov::genai::GenerationConfig config = pipe.get_generation_config();
+            config.max_new_tokens = 100;
+            pipe.set_generation_cofnig(config)
 
             pipe.start_chat();
             for (size_t i = 0; i < questions.size(); i++) {
                std::cout << "question:\n";
                std::getline(std::cin, prompt);
 
-               std::cout << pipe(prompt) << std::endl>>;
+               std::cout << pipe.generate(prompt) << std::endl;
             }
             pipe.finish_chat();
          }
@@ -171,60 +226,43 @@ Optimizing Generation with Grouped Beam Search
 Leverage grouped beam search decoding and configure generation_config for better text generation
 quality and efficient batch processing in GenAI applications.
 
-Use grouped beam search decoding:
+Specify generation_config to use grouped beam search:
 
 .. tab-set::
 
-   .. tab-item:: C++
-
-      .. code-block:: cpp
-
-         int main(int argc, char* argv[]) {
-            std::string model_path = argv[1];
-            ov::LLMPipeline pipe(model_path, "CPU");
-            ov::GenerationConfig config = pipe.get_generation_config();
-            config.max_new_tokens = 256;
-            config.num_groups = 3;
-            config.group_size = 5;
-            config.diversity_penalty = 1.0f;
+   .. tab-item:: Python
+      :sync: py
 
-            cout << pipe.generate("The Sun is yellow because", config);
-         }
+      .. code-block:: python
 
-Specify generation_config to use grouped beam search:
+         import openvino_genai as ov_genai
+         pipe = ov_genai.LLMPipeline(model_path, "CPU")
+         config = pipe.get_generation_config()
+         config.max_new_tokens = 256
+         config.num_beam_groups = 3
+         config.num_beams = 15
+         config.diversity_penalty = 1.0
+         pipe.generate("The Sun is yellow because", config)
 
-.. tab-set::
 
    .. tab-item:: C++
+      :sync: cpp
 
       .. code-block:: cpp
 
          int main(int argc, char* argv[]) {
-            std::string prompt;
-
             std::string model_path = argv[1];
-            ov::LLMPipeline pipe(model_path, "CPU");
-
-            ov::GenerationConfig config = pipe.get_generation_config();
+            ov::genai::LLMPipeline pipe(model_path, "CPU");
+            ov::genai::GenerationConfig config = pipe.get_generation_config();
             config.max_new_tokens = 256;
-            config.num_groups = 3;
-            config.group_size = 5;
+            config.num_beam_groups = 3;
+            config.num_beams = 15;
             config.diversity_penalty = 1.0f;
 
-            auto streamer = [](std::string word) { std::cout << word << std::flush; };
-
-            pipe.start_chat();
-            for (size_t i = 0; i < questions.size(); i++) {
-
-               std::cout << "question:\n";
-               cout << prompt << endl;
-
-               auto answer = pipe(prompt, config, streamer);
-               // no need to print answer, streamer will do that
-            }
-            pipe.finish_chat();
+            cout << pipe.generate("The Sun is yellow because", config);
          }
 
+
 Comparing with Hugging Face Results
 #######################################
 
@@ -237,6 +275,7 @@ Compare and analyze results with those generated by Hugging Face models.
       .. code-block:: python
 
          from transformers import AutoTokenizer, AutoModelForCausalLM
+         import openvino_genai as ov_genai
 
          tokenizer = AutoTokenizer.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
          model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
@@ -249,12 +288,8 @@ Compare and analyze results with those generated by Hugging Face models.
          hf_output = tokenizer.decode(hf_encoded_output[0, encoded_prompt.shape[1]:])
          print(f'hf_output: {hf_output}')
 
-         import sys
-         sys.path.append('build-Debug/')
-         import py_generate_pipeline as genai # set more friendly module name
-
-         pipe = genai.LLMPipeline('text_generation/causal_lm/TinyLlama-1.1B-Chat-v1.0/pytorch/dldt/FP16/')
-         ov_output = pipe(prompt, max_new_tokens=max_new_tokens)
+         pipe = ov_genai.LLMPipeline('TinyLlama-1.1B-Chat-v1.0')
+         ov_output = pipe.generate(prompt, max_new_tokens=max_new_tokens)
          print(f'ov_output: {ov_output}')
 
          assert hf_output == ov_output
diff --git a/docs/articles_en/learn-openvino/llm_inference_guide/llm-inference-hf.rst b/docs/articles_en/learn-openvino/llm_inference_guide/llm-inference-hf.rst
index a0edebf83d1749..4ee10dd9b10637 100644
--- a/docs/articles_en/learn-openvino/llm_inference_guide/llm-inference-hf.rst
+++ b/docs/articles_en/learn-openvino/llm_inference_guide/llm-inference-hf.rst
@@ -3,14 +3,16 @@
 Run LLMs with Hugging Face and Optimum Intel
 =====================================================
 
-The steps below show how to load and infer LLMs from Hugging Face using Optimum Intel.
-They also show how to convert models into OpenVINO IR format so they can be optimized
-by NNCF and used with other OpenVINO tools.
+The steps below show how to load and infer LLMs from
+`Hugging Face <https://huggingface.co/models>`__ using Optimum Intel. They also show how to
+convert models into OpenVINO IR format so they can be optimized by NNCF and used with
+other OpenVINO tools.
 
 Prerequisites
 ############################################################
 
-* Create a Python environment by following the instructions on the :doc:`Install OpenVINO PIP <../../get-started/install-openvino>` page.
+* Create a Python environment by following the instructions on the
+  :doc:`Install OpenVINO PIP <../../get-started/install-openvino>` page.
 * Install the necessary dependencies for Optimum Intel:
 
 .. code-block:: console
@@ -20,7 +22,8 @@ Prerequisites
 Loading a Hugging Face Model to Optimum Intel
 ############################################################
 
-To start using OpenVINO as a backend for Hugging Face, change the original Hugging Face code in two places:
+To start using OpenVINO as a backend for Hugging Face, change the original Hugging Face
+code in two places:
 
 .. code-block:: diff
 
@@ -225,10 +228,15 @@ OpenVINO runtime provides a set of optimizations for more efficient LLM inferenc
 includes **Dynamic quantization** of activations of 4/8-bit quantized MatMuls and
 **KV-cache quantization**.
 
-* **Dynamic quantization** enables quantization of activations of MatMul operations that have 4 or 8-bit quantized weights (see :doc:`LLM Weight Compression <../../openvino-workflow/model-optimization-guide/weight-compression>`).
-  It improves inference latency and throughput of LLMs, though it may cause insignificant deviation in generation accuracy.  Quantization is performed in a
-  group-wise manner, with configurable group size. It means that values in a group share quantization parameters. Larger group sizes lead to faster inference but lower accuracy. Recommended group size values are ``32``, ``64``, or ``128``. To enable Dynamic quantization, use the corresponding
-  inference property as follows:
+* **Dynamic quantization** enables quantization of activations of MatMul operations
+  that have 4 or 8-bit quantized weights (see
+  :doc:`LLM Weight Compression <../../openvino-workflow/model-optimization-guide/weight-compression>`).
+  It improves inference latency and throughput of LLMs, though it may cause
+  insignificant deviation in generation accuracy.  Quantization is performed in a group-wise
+  manner, with configurable group size. It means that values in a group share quantization
+  parameters. Larger group sizes lead to faster inference but lower accuracy. Recommended
+  group size values are ``32``, ``64``, or ``128``. To enable Dynamic quantization, use
+  the corresponding inference property as follows:
 
 
   .. code-block:: python
@@ -238,9 +246,12 @@ includes **Dynamic quantization** of activations of 4/8-bit quantized MatMuls an
          ov_config={"DYNAMIC_QUANTIZATION_GROUP_SIZE": "32", "PERFORMANCE_HINT": "LATENCY"}
      )
 
-* **KV-cache quantization** allows lowering the precision of Key and Value cache in LLMs. This helps reduce memory consumption during inference, improving latency and throughput. KV-cache can be quantized into the following precisions:
-  ``u8``, ``bf16``, ``f16``.  If ``u8`` is used, KV-cache quantization is also applied in a group-wise manner. Thus, it can use ``DYNAMIC_QUANTIZATION_GROUP_SIZE`` value if defined.
-  Otherwise, the group size ``32`` is used by default. KV-cache quantization can be enabled as follows:
+* **KV-cache quantization** allows lowering the precision of Key and Value cache in LLMs.
+  This helps reduce memory consumption during inference, improving latency and throughput.
+  KV-cache can be quantized into the following precisions: ``u8``, ``bf16``, ``f16``.
+  If ``u8`` is used, KV-cache quantization is also applied in a group-wise manner. Thus,
+  it can use ``DYNAMIC_QUANTIZATION_GROUP_SIZE`` value if defined. Otherwise, the group
+  size ``32`` is used by default. KV-cache quantization can be enabled as follows:
 
 
   .. code-block:: python
diff --git a/docs/articles_en/learn-openvino/llm_inference_guide/llm-inference-native-ov.rst b/docs/articles_en/learn-openvino/llm_inference_guide/llm-inference-native-ov.rst
index 12850aa5ab31f7..c1923e543e3b64 100644
--- a/docs/articles_en/learn-openvino/llm_inference_guide/llm-inference-native-ov.rst
+++ b/docs/articles_en/learn-openvino/llm_inference_guide/llm-inference-native-ov.rst
@@ -3,28 +3,41 @@
 Run LLMs with Base OpenVINO
 ===============================
 
-To run Generative AI models using native OpenVINO APIs you need to follow regular **Convert -> Optimize -> Deploy** path with a few simplifications.
+To run Generative AI models using native OpenVINO APIs you need to follow regular
+**Convert -> Optimize -> Deploy** path with a few simplifications.
 
-To convert a model from Hugging Face, you can use Optimum-Intel export feature that allows you to export model in the OpenVINO format without invoking conversion API and tools directly.
-In this case, the conversion process is a bit more simplified. You can still use a regular conversion path if the model comes from outside of Hugging Face ecosystem, i.e., in source framework format (PyTorch, etc.)
+To convert a model from `Hugging Face <https://huggingface.co/models>`__, you can use
+Optimum-Intel export feature that allows you to export model in the OpenVINO format without
+invoking conversion API and tools directly. In this case, the conversion process is a bit
+more simplified. You can still use a regular conversion path if the model comes from
+outside of Hugging Face ecosystem, i.e., in source framework format (PyTorch, etc.)
 
-Model optimization can be performed within Hugging Face or directly using NNCF as described in :doc:`Weight Compression <../../openvino-workflow/model-optimization-guide/weight-compression>`.
+Model optimization can be performed within Hugging Face or directly using NNCF as described in
+:doc:`Weight Compression <../../openvino-workflow/model-optimization-guide/weight-compression>`.
 
 .. note::
 
-   It is recommended to use models in 4-bit precision, as maintaining the model in its original precision may result in significantly decreased performance.
+   It is recommended to use models in 4-bit precision, as maintaining the model in its
+   original precision may result in significantly decreased performance.
 
-Inference code that uses native API cannot benefit from Hugging Face pipelines. You need to write your custom code or take it from the available examples. Below are some examples of popular Generative AI scenarios:
+Inference code that uses native API cannot benefit from Hugging Face pipelines.
+You need to write your custom code or take it from the available examples. Below are
+some examples of popular Generative AI scenarios:
 
-* In case of LLMs for text generation, you need to handle tokenization, inference and token selection loop, and de-tokenization. If token selection involves beam search, it also needs to be written.
-* For image generation models, you need to make a pipeline that includes several model inferences: inference for source (e.g., text) encoder models, inference loop for diffusion process and inference for the decoding part. Scheduler code is also required.
+* In case of LLMs for text generation, you need to handle tokenization, inference and
+  token selection loop, and de-tokenization. If token selection involves beam search,
+  it also needs to be written.
+* For image generation models, you need to make a pipeline that includes several model
+  inferences: inference for source (e.g., text) encoder models, inference loop for
+  diffusion process and inference for the decoding part. Scheduler code is also required.
 
 To write such pipelines, you can follow the examples provided as part of OpenVINO:
 
 * `OpenVINO Latent Consistency Model C++ image generation pipeline <https://github.com/openvinotoolkit/openvino.genai/tree/master/image_generation/lcm_dreamshaper_v7/cpp>`__
 * `OpenVINO Stable Diffusion (with LoRA) C++ image generation pipeline <https://github.com/openvinotoolkit/openvino.genai/tree/master/image_generation/stable_diffusion_1_5/cpp>`__
 
-To perform inference, models must be first converted to OpenVINO IR format using Hugging Face Optimum-Intel API.
+To perform inference, models must be first converted to OpenVINO IR format using
+Hugging Face Optimum-Intel API.
 
 An inference pipeline for a text generation LLM is set up in the following stages:
 
@@ -179,4 +192,3 @@ Additional Resources
 * :doc:`Stateful Models Low-Level Details <../../openvino-workflow/running-inference/stateful-models>`
 * :doc:`Working with Textual Data <../../openvino-workflow/running-inference/string-tensors>`
 
-
diff --git a/docs/articles_en/learn-openvino/llm_inference_guide/ov-tokenizers.rst b/docs/articles_en/learn-openvino/llm_inference_guide/ov-tokenizers.rst
index 571743701ce01a..d670c9940b707c 100644
--- a/docs/articles_en/learn-openvino/llm_inference_guide/ov-tokenizers.rst
+++ b/docs/articles_en/learn-openvino/llm_inference_guide/ov-tokenizers.rst
@@ -338,7 +338,7 @@ Additional Resources
 
 * `OpenVINO Tokenizers repo <https://github.com/openvinotoolkit/openvino_tokenizers>`__
 * `OpenVINO Tokenizers Notebook <https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/openvino-tokenizers>`__
-* `Text generation C++ samples that support most popular models like LLaMA 2 <https://github.com/openvinotoolkit/openvino.genai/tree/master/text_generation/causal_lm/cpp>`__
+* `Text generation C++ samples that support most popular models like LLaMA 2 <https://github.com/openvinotoolkit/openvino.genai/tree/master/samples/cpp/greedy_causal_lm>`__
 * `OpenVINO GenAI Repo <https://github.com/openvinotoolkit/openvino.genai>`__
 
 
diff --git a/docs/articles_en/openvino-workflow/deployment-locally/local-distribution-libraries.rst b/docs/articles_en/openvino-workflow/deployment-locally/local-distribution-libraries.rst
index 9889f15c0ecbd9..90796e37e9abf3 100644
--- a/docs/articles_en/openvino-workflow/deployment-locally/local-distribution-libraries.rst
+++ b/docs/articles_en/openvino-workflow/deployment-locally/local-distribution-libraries.rst
@@ -10,20 +10,34 @@ Libraries for Local Distribution
                  needed to deploy the application.
 
 
-With local distribution, each C or C++ application/installer has its own copies of OpenVINO Runtime binaries. However, OpenVINO has a scalable plugin-based architecture, which means that some components can be loaded in runtime only when they are really needed. This guide helps you understand what minimal set of libraries is required to deploy the application.
-
-Local distribution is also suitable for OpenVINO binaries built from source using `Build instructions <https://github.com/openvinotoolkit/openvino/wiki#how-to-build>`__,
-but this guide assumes that OpenVINO Runtime is built dynamically. For `Static OpenVINO Runtime <https://github.com/openvinotoolkit/openvino/blob/master/docs/dev/static_libaries.md>`__, select the required OpenVINO capabilities at the CMake configuration stage using `CMake Options for Custom Compilation <https://github.com/openvinotoolkit/openvino/blob/master/docs/dev/cmake_options_for_custom_compilation.md>`__, then build and link the OpenVINO components to the final application.
+With local distribution, each C or C++ application/installer has its own copies of OpenVINO Runtime binaries.
+However, OpenVINO has a scalable plugin-based architecture, which means that some components
+can be loaded in runtime only when they are really needed. This guide helps you understand
+what minimal set of libraries is required to deploy the application.
+
+Local distribution is also suitable for OpenVINO binaries built from source using
+`Build instructions <https://github.com/openvinotoolkit/openvino/wiki#how-to-build>`__,
+but this guide assumes that OpenVINO Runtime is built dynamically.
+For `Static OpenVINO Runtime <https://github.com/openvinotoolkit/openvino/blob/master/docs/dev/static_libaries.md>`__,
+select the required OpenVINO capabilities at the CMake configuration stage using
+`CMake Options for Custom Compilation <https://github.com/openvinotoolkit/openvino/blob/master/docs/dev/cmake_options_for_custom_compilation.md>`__,
+then build and link the OpenVINO components to the final application.
 
 .. note::
 
-   The steps below are independent of the operating system and refer to the library file name without any prefixes (like ``lib`` on Unix systems) or suffixes (like ``.dll`` on Windows OS). Do not put ``.lib`` files on Windows OS to the distribution because such files are needed only at a linker stage.
+   The steps below are independent of the operating system and refer to the library file name
+   without any prefixes (like ``lib`` on Unix systems) or suffixes (like ``.dll`` on Windows OS).
+   Do not put ``.lib`` files on Windows OS to the distribution because such files are needed
+   only at a linker stage.
 
 
 Library Requirements for C++ and C Languages
 ############################################
 
-Regardless of the programming language of an application, the ``openvino`` library must always be included in its final distribution. This core library manages all inference and frontend plugins. The ``openvino`` library depends on the TBB libraries which are used by OpenVINO Runtime to optimally saturate devices with computations.
+Regardless of the programming language of an application, the ``openvino`` library must always
+be included in its final distribution. This core library manages all inference and frontend plugins.
+The ``openvino`` library depends on the TBB libraries which are used by OpenVINO Runtime
+to optimally saturate devices with computations.
 
 If your application is in C language, you need to additionally include the ``openvino_c`` library.
 
@@ -48,7 +62,9 @@ For each inference device, OpenVINO Runtime has its own plugin library:
 
 Depending on which devices are used in the app, the corresponding libraries should be included in the distribution package.
 
-As shown in the picture above, some plugin libraries may have OS-specific dependencies which are either backend libraries or additional supports files with firmware, etc. Refer to the table below for details:
+As shown in the picture above, some plugin libraries may have OS-specific dependencies
+which are either backend libraries or additional supports files with firmware, etc.
+Refer to the table below for details:
 
 .. tab-set::
 
@@ -114,7 +130,8 @@ Libraries for Execution Modes
 The ``HETERO``, ``MULTI``, ``BATCH``, and ``AUTO`` execution modes can also be used by the application explicitly or implicitly.
 Use the following recommendation scheme to decide whether to add the appropriate libraries to the distribution package:
 
-- If :doc:`AUTO <../running-inference/inference-devices-and-modes/auto-device-selection>` is used explicitly in the application or ov::Core::compile_model is used without specifying a device, put ``openvino_auto_plugin`` to the distribution.
+- If :doc:`AUTO <../running-inference/inference-devices-and-modes/auto-device-selection>` is used
+  explicitly in the application or ``ov::Core::compile_model`` is used without specifying a device, put ``openvino_auto_plugin`` to the distribution.
 
   .. note::
 
@@ -122,9 +139,12 @@ Use the following recommendation scheme to decide whether to add the appropriate
      If you are not sure which inference devices are available on the target system, put all inference plugin libraries in the distribution.
      If ov::device::priorities is used for `AUTO` to specify a limited device list, grab the corresponding device plugins only.
 
-- If :doc:`MULTI <../running-inference/inference-devices-and-modes/multi-device>` is used explicitly, put ``openvino_auto_plugin`` in the distribution.
-- If :doc:`HETERO <../running-inference/inference-devices-and-modes/hetero-execution>` is either used explicitly or ov::hint::performance_mode is used with GPU, put ``openvino_hetero_plugin`` in the distribution.
-- If :doc:`BATCH <../running-inference/inference-devices-and-modes/automatic-batching>` is either used explicitly or ``ov::hint::performance_mode`` is used with GPU, put ``openvino_batch_plugin`` in the distribution.
+- If :doc:`MULTI <../running-inference/inference-devices-and-modes/multi-device>` is used explicitly,
+  put ``openvino_auto_plugin`` in the distribution.
+- If :doc:`HETERO <../running-inference/inference-devices-and-modes/hetero-execution>` is either
+  used explicitly or ``ov::hint::performance_mode`` is used with GPU, put ``openvino_hetero_plugin`` in the distribution.
+- If :doc:`BATCH <../running-inference/inference-devices-and-modes/automatic-batching>` is either
+  used explicitly or ``ov::hint::performance_mode`` is used with GPU, put ``openvino_batch_plugin`` in the distribution.
 
 Frontend Libraries for Reading Models
 +++++++++++++++++++++++++++++++++++++
@@ -138,46 +158,58 @@ OpenVINO Runtime uses frontend libraries dynamically to read models in different
 - ``openvino_paddle_frontend`` is used to read the Paddle file format.
 - ``openvino_pytorch_frontend`` is used to convert PyTorch model via ``openvino.convert_model`` API.
 
-Depending on the model format types that are used in the application in ov::Core::read_model, select the appropriate libraries.
+Depending on the model format types that are used in the application in ``ov::Core::read_model``, select the appropriate libraries.
 
 .. note::
 
-   To optimize the size of the final distribution package, it is recommended to convert models to OpenVINO IR by using :doc:`model conversion API <../model-preparation>`. This way you do not have to keep TensorFlow, TensorFlow Lite, ONNX, PaddlePaddle, and other frontend libraries in the distribution package.
+   To optimize the size of the final distribution package, it is recommended to convert models
+   to OpenVINO IR by using :doc:`model conversion API <../model-preparation>`. This way you
+   do not have to keep TensorFlow, TensorFlow Lite, ONNX, PaddlePaddle, and other frontend
+   libraries in the distribution package.
 
 Examples
 ####################
 
-**CPU + OpenVINO IR in C application**
+.. dropdown:: CPU + OpenVINO IR in C application
 
-In this example, the application is written in C, performs inference on CPU, and reads models stored in the OpenVINO IR format.
+   In this example, the application is written in C, performs inference on CPU, and reads models stored in the OpenVINO IR format.
 
-The following libraries are used: ``openvino_c``, ``openvino``, ``openvino_intel_cpu_plugin``, and ``openvino_ir_frontend``.
+   The following libraries are used: ``openvino_c``, ``openvino``, ``openvino_intel_cpu_plugin``, and ``openvino_ir_frontend``.
 
-- The ``openvino_c`` library is a main dependency of the application. The app links against this library.
-- The ``openvino`` library is used as a private dependency for ``openvino_c`` and is also used in the deployment.
-- ``openvino_intel_cpu_plugin`` is used for inference.
-- ``openvino_ir_frontend`` is used to read source models.
+   - The ``openvino_c`` library is a main dependency of the application. The app links against this library.
+   - The ``openvino`` library is used as a private dependency for ``openvino_c`` and is also used in the deployment.
+   - ``openvino_intel_cpu_plugin`` is used for inference.
+   - ``openvino_ir_frontend`` is used to read source models.
 
-**MULTI execution on GPU and CPU in `tput` mode**
+.. dropdown:: MULTI execution on GPU and CPU in tput mode
 
-In this example, the application is written in C++, performs inference :doc:`simultaneously on GPU and CPU devices <../running-inference/inference-devices-and-modes/multi-device>` with the ov::hint::PerformanceMode::THROUGHPUT property set, and reads models stored in the ONNX format.
+   In this example, the application is written in C++, performs inference
+   :doc:`simultaneously on GPU and CPU devices <../running-inference/inference-devices-and-modes/multi-device>` with
+   the ``ov::hint::PerformanceMode::THROUGHPUT`` property set, and reads models stored in the ONNX format.
 
-The following libraries are used: ``openvino``, ``openvino_intel_gpu_plugin``, ``openvino_intel_cpu_plugin``, ``openvino_auto_plugin``, ``openvino_auto_batch_plugin``, and ``openvino_onnx_frontend``.
+   The following libraries are used: ``openvino``, ``openvino_intel_gpu_plugin``, ``openvino_intel_cpu_plugin``,
+   ``openvino_auto_plugin``, ``openvino_auto_batch_plugin``, and ``openvino_onnx_frontend``.
 
-- The ``openvino`` library is a main dependency of the application. The app links against this library.
-- ``openvino_intel_gpu_plugin`` and ``openvino_intel_cpu_plugin`` are used for inference.
-- ``openvino_auto_plugin`` is used for Multi-Device Execution.
-- ``openvino_auto_batch_plugin`` can be also put in the distribution to improve the saturation of :doc:`Intel® GPU <../running-inference/inference-devices-and-modes/gpu-device>` device. If there is no such plugin, :doc:`Automatic Batching <../running-inference/inference-devices-and-modes/automatic-batching>` is turned off.
-- ``openvino_onnx_frontend`` is used to read source models.
+   - The ``openvino`` library is a main dependency of the application. The app links against this library.
+   - ``openvino_intel_gpu_plugin`` and ``openvino_intel_cpu_plugin`` are used for inference.
+   - ``openvino_auto_plugin`` is used for Multi-Device Execution.
+   - ``openvino_auto_batch_plugin`` can be also put in the distribution to improve the saturation
+     of :doc:`Intel® GPU <../running-inference/inference-devices-and-modes/gpu-device>` device.
+     If there is no such plugin, :doc:`Automatic Batching <../running-inference/inference-devices-and-modes/automatic-batching>` is turned off.
+   - ``openvino_onnx_frontend`` is used to read source models.
 
-**Auto-Device Selection between GPU and CPU**
+.. dropdown:: Auto-Device Selection between GPU and CPU
 
-In this example, the application is written in C++, performs inference with the :doc:`Automatic Device Selection <../running-inference/inference-devices-and-modes/auto-device-selection>` mode, limiting device list to GPU and CPU, and reads models :doc:`created using C++ code <../running-inference/integrate-openvino-with-your-application/model-representation>`.
+   In this example, the application is written in C++, performs inference
+   with the :doc:`Automatic Device Selection <../running-inference/inference-devices-and-modes/auto-device-selection>`
+   mode, limiting device list to GPU and CPU, and reads models
+   :doc:`created using C++ code <../running-inference/integrate-openvino-with-your-application/model-representation>`.
 
-The following libraries are used: ``openvino``, ``openvino_auto_plugin``, ``openvino_intel_gpu_plugin``, and ``openvino_intel_cpu_plugin``.
+   The following libraries are used: ``openvino``, ``openvino_auto_plugin``, ``openvino_intel_gpu_plugin``, and ``openvino_intel_cpu_plugin``.
 
-- The ``openvino`` library is a main dependency of the application. The app links against this library.
-- ``openvino_auto_plugin`` is used to enable Automatic Device Selection.
-- ``openvino_intel_gpu_plugin`` and ``openvino_intel_cpu_plugin`` are used for inference. AUTO selects between CPU and GPU devices according to their physical existence on the deployed machine.
-- No frontend library is needed because ``ov::Model`` is created in code.
+   - The ``openvino`` library is a main dependency of the application. The app links against this library.
+   - ``openvino_auto_plugin`` is used to enable Automatic Device Selection.
+   - ``openvino_intel_gpu_plugin`` and ``openvino_intel_cpu_plugin`` are used for inference. AUTO
+     selects between CPU and GPU devices according to their physical existence on the deployed machine.
+   - No frontend library is needed because ``ov::Model`` is created in code.
 
diff --git a/docs/articles_en/openvino-workflow/deployment-locally/optimial-binary-size-conditional-compilation.rst b/docs/articles_en/openvino-workflow/deployment-locally/optimial-binary-size-conditional-compilation.rst
index ed7604117a1a14..93adbf17b044f7 100644
--- a/docs/articles_en/openvino-workflow/deployment-locally/optimial-binary-size-conditional-compilation.rst
+++ b/docs/articles_en/openvino-workflow/deployment-locally/optimial-binary-size-conditional-compilation.rst
@@ -241,7 +241,7 @@ Build OpenVINO with conditional compilation enabled:
     cd %OPENVINO_HOME%
     md build_cc
     cd build_cc
-    cmake -G Ninja -Wno-dev -DCMAKE_BUILD_TYPE=Debug -DENABLE_CPPLINT=OFF -DCMAKE_VERBOSE_MAKEFILE=ON -DCMAKE_COMPILE_WARNING_AS_ERROR=OFF -DENABLE_FASTER_BUILD=ON -DENABLE_SANITIZER=OFF -DTHREADING=TBB -DBUILD_SHARED_LIBS=OFF -DENABLE_PROFILING_ITT=ON -DSELECTIVE_BUILD=COLLECT -DENABLE_INTEL_GPU=OFF -DENABLE_MULTI=OFF -DENABLE_AUTO=OFF -DENABLE_AUTO_BATCH=OFF -DENABLE_HETERO=OFF -DENABLE_TEMPLATE=OFF -DENABLE_OV_ONNX_FRONTEND=OFF -DENABLE_OV_PADDLE_FRONTEND=OFF -DENABLE_OV_PYTORCH_FRONTEND=OFF -DENABLE_OV_TF_FRONTEND=OFF -DCMAKE_INSTALL_PREFIX=install ..
+    cmake -G Ninja -Wno-dev -DCMAKE_BUILD_TYPE=Debug -DENABLE_CPPLINT=OFF -DCMAKE_VERBOSE_MAKEFILE=ON -DCMAKE_COMPILE_WARNING_AS_ERROR=OFF -DENABLE_FASTER_BUILD=ON -DENABLE_SANITIZER=OFF -DTHREADING=TBB -DBUILD_SHARED_LIBS=OFF -DENABLE_PROFILING_ITT=ON -DSELECTIVE_BUILD=COLLECT -DENABLE_INTEL_GPU=OFF -DENABLE_MULTI=OFF -DENABLE_AUTO=OFF -DENABLE_AUTO_BATCH=OFF -DENABLE_HETERO=OFF -DENABLE_TEMPLATE=OFF -DENABLE_OV_ONNX_FRONTEND=OFF -DENABLE_OV_PADDLE_FRONTEND=OFF -DENABLE_OV_PYTORCH_FRONTEND=OFF -DENABLE_OV_JAX_FRONTEND=OFF -DENABLE_OV_TF_FRONTEND=OFF -DCMAKE_INSTALL_PREFIX=install ..
 
     cmake --build . --config Debug
 
@@ -274,7 +274,7 @@ Generate final optimal binaries size of OpenVINO package
     md build
     cd build
 
-    cmake -G "Visual Studio 16 2019" -A x64 -DENABLE_CPPLINT=OFF -DCMAKE_VERBOSE_MAKEFILE=ON -DCMAKE_COMPILE_WARNING_AS_ERROR=OFF -DCMAKE_BUILD_TYPE=Release -DENABLE_FASTER_BUILD=ON -DENABLE_PROFILING_ITT=OFF -DSELECTIVE_BUILD=ON -DENABLE_INTEL_GPU=OFF -DENABLE_MULTI=OFF -DENABLE_AUTO=OFF -DENABLE_AUTO_BATCH=OFF -DENABLE_HETERO=OFF -DENABLE_TEMPLATE=OFF -DENABLE_OV_ONNX_FRONTEND=OFF -DENABLE_OV_PADDLE_FRONTEND=OFF -DENABLE_OV_PYTORCH_FRONTEND=OFF -DENABLE_OV_TF_FRONTEND=OFF -DSELECTIVE_BUILD_STAT=%OPENVINO_HOME%\cc_data\*.csv -DBUILD_SHARED_LIBS=OFF -DENABLE_LTO=ON -DENABLE_ONEDNN_FOR_GPU=OFF -DENABLE_OV_TF_LITE_FRONTEND=OFF -DENABLE_PROFILING_FIRST_INFERENCE=OFF ..
+    cmake -G "Visual Studio 16 2019" -A x64 -DENABLE_CPPLINT=OFF -DCMAKE_VERBOSE_MAKEFILE=ON -DCMAKE_COMPILE_WARNING_AS_ERROR=OFF -DCMAKE_BUILD_TYPE=Release -DENABLE_FASTER_BUILD=ON -DENABLE_PROFILING_ITT=OFF -DSELECTIVE_BUILD=ON -DENABLE_INTEL_GPU=OFF -DENABLE_MULTI=OFF -DENABLE_AUTO=OFF -DENABLE_AUTO_BATCH=OFF -DENABLE_HETERO=OFF -DENABLE_TEMPLATE=OFF -DENABLE_OV_ONNX_FRONTEND=OFF -DENABLE_OV_PADDLE_FRONTEND=OFF -DENABLE_OV_PYTORCH_FRONTEND=OFF -DENABLE_OV_JAX_FRONTEND=OFF -DENABLE_OV_TF_FRONTEND=OFF -DSELECTIVE_BUILD_STAT=%OPENVINO_HOME%\cc_data\*.csv -DBUILD_SHARED_LIBS=OFF -DENABLE_LTO=ON -DENABLE_ONEDNN_FOR_GPU=OFF -DENABLE_OV_TF_LITE_FRONTEND=OFF -DENABLE_PROFILING_FIRST_INFERENCE=OFF ..
 
     cmake --build . --config Release
 
diff --git a/docs/articles_en/openvino-workflow/model-optimization-guide/compressing-models-during-training/filter-pruning.rst b/docs/articles_en/openvino-workflow/model-optimization-guide/compressing-models-during-training/filter-pruning.rst
index ae4d03877f18ac..7a8a11a24bf56a 100644
--- a/docs/articles_en/openvino-workflow/model-optimization-guide/compressing-models-during-training/filter-pruning.rst
+++ b/docs/articles_en/openvino-workflow/model-optimization-guide/compressing-models-during-training/filter-pruning.rst
@@ -228,8 +228,7 @@ To restore the model from checkpoint you should use the following API:
          :language: python
          :fragment: [load_checkpoint]
 
-For more details on saving/loading checkpoints in the NNCF, see the following
-`documentation <https://github.com/openvinotoolkit/nncf/blob/develop/docs/Usage.md#saving-and-loading-compressed-models>`__.
+For more details, see the following `documentation <https://github.com/openvinotoolkit/nncf/blob/develop/docs/usage/training_time_compression/other_algorithms/Pruning.md>`__.
 
 Deploying pruned model
 ######################
diff --git a/docs/articles_en/openvino-workflow/model-optimization-guide/compressing-models-during-training/quantization-aware-training-tensorflow.rst b/docs/articles_en/openvino-workflow/model-optimization-guide/compressing-models-during-training/quantization-aware-training-tensorflow.rst
index 41a2ea615214a8..57abad31a42354 100644
--- a/docs/articles_en/openvino-workflow/model-optimization-guide/compressing-models-during-training/quantization-aware-training-tensorflow.rst
+++ b/docs/articles_en/openvino-workflow/model-optimization-guide/compressing-models-during-training/quantization-aware-training-tensorflow.rst
@@ -91,7 +91,8 @@ To restore the model from checkpoint, use the following API:
    :fragment: [load_checkpoint]
 
 
-For more details on saving/loading checkpoints in the NNCF, see the corresponding `NNCF documentation <https://github.com/openvinotoolkit/nncf/blob/develop/docs/Usage.md#saving-and-loading-compressed-models>`__.
+For more details on saving/loading checkpoints in the NNCF, see the corresponding
+`NNCF documentation <https://github.com/openvinotoolkit/nncf/blob/develop/docs/usage/training_time_compression/other_algorithms/Usage.md#saving-and-loading-compressed-models>`__.
 
 Deploying quantized model
 #########################
diff --git a/docs/articles_en/openvino-workflow/model-optimization-guide/weight-compression.rst b/docs/articles_en/openvino-workflow/model-optimization-guide/weight-compression.rst
index 7ce2893107e626..a393a0925cba3c 100644
--- a/docs/articles_en/openvino-workflow/model-optimization-guide/weight-compression.rst
+++ b/docs/articles_en/openvino-workflow/model-optimization-guide/weight-compression.rst
@@ -13,27 +13,32 @@ memory footprint, a crucial factor for Large Language Models (LLMs).
 
 Unlike full model quantization, where weights and activations are quantized,
 weight compression in `Neural Network Compression Framework (NNCF) <https://github.com/openvinotoolkit/nncf>`__
-only targets the model's weights. This approach
-allows the activations to remain as floating-point numbers, preserving most
-of the model's accuracy while improving its speed and reducing
-its size.
+only targets the model's weights. This approach allows the activations to remain as
+floating-point numbers, preserving most of the model's accuracy while improving its
+speed and reducing its size.
 
 The reduction in size is especially noticeable with larger models,
 for instance the 7 billion parameter Llama 2 model can be reduced
-from about 25GB to 4GB using 4-bit weight compression. With smaller models (i.e. less than 1B parameters),
-weight compression may result in more accuracy reduction than with larger models.
+from about 25GB to 4GB using 4-bit weight compression. With smaller models (i.e. less
+than 1B parameters), weight compression may result in more accuracy reduction than
+with larger models.
 
 LLMs and other models that require
 extensive memory to store the weights during inference can benefit
 from weight compression as it:
 
-* enables inference of exceptionally large models that cannot be accommodated in the device memory;
+* enables inference of exceptionally large models that cannot be accommodated in the
+  device memory;
 
-* reduces storage and memory overhead, making models more lightweight and less resource intensive for deployment;
+* reduces storage and memory overhead, making models more lightweight and less resource
+  intensive for deployment;
 
-* improves inference speed by reducing the latency of memory access when computing the operations with weights, for example, Linear layers. The weights are smaller and thus faster to load from memory;
+* improves inference speed by reducing the latency of memory access when computing the
+  operations with weights, for example, Linear layers. The weights are smaller and thus
+  faster to load from memory;
 
-* unlike quantization, does not require sample data to calibrate the range of activation values.
+* unlike quantization, does not require sample data to calibrate the range of
+  activation values.
 
 Currently, `NNCF <https://github.com/openvinotoolkit/nncf>`__
 provides weight quantization to 8 and 4-bit integer data types as a compression
@@ -50,7 +55,8 @@ Transformer-based models. Models with 8-bit compressed weights are performant on
 vast majority of supported CPU and GPU platforms.
 
 
-The code snippet below shows how to do 8-bit quantization of the model weights represented in OpenVINO IR using NNCF:
+The code snippet below shows how to do 8-bit quantization of the model weights represented
+in OpenVINO IR using NNCF:
 
 .. tab-set::
 
@@ -70,7 +76,8 @@ where INT4 is considered as the primary precision and INT8 is the backup one.
 It usually results in a smaller model size and lower inference latency, although the accuracy
 degradation could be higher, depending on the model.
 
-The code snippet below shows how to do 4-bit quantization of the model weights represented in OpenVINO IR using NNCF:
+The code snippet below shows how to do 4-bit quantization of the model weights represented
+in OpenVINO IR using NNCF:
 
 .. tab-set::
 
@@ -108,13 +115,16 @@ memory reduction, speed gain, and accuracy loss.
 
 
 
-The INT4 method has several parameters that can provide different performance-accuracy trade-offs after optimization:
+The INT4 method has several parameters that can provide different performance-accuracy
+trade-offs after optimization:
 
 * ``mode`` - there are two optimization modes: symmetric and asymmetric.
 
   **Symmetric Compression** - ``INT4_SYM``
 
-  INT4 Symmetric mode involves quantizing weights to an unsigned 4-bit integer symmetrically with a fixed zero point of 8. This mode is faster than the INT8, making it ideal for situations where **speed and size reduction are prioritized over accuracy**.
+  INT4 Symmetric mode involves quantizing weights to an unsigned 4-bit integer
+  symmetrically with a fixed zero point of 8. This mode is faster than the INT8, making
+  it ideal for situations where **speed and size reduction are prioritized over accuracy**.
 
   .. code-block:: python
 
@@ -125,7 +135,10 @@ The INT4 method has several parameters that can provide different performance-ac
 
   **Asymmetric Compression** - ``INT4_ASYM``
 
-  INT4 Asymmetric mode also uses an unsigned 4-bit integer but quantizes weights asymmetrically with a non-fixed zero point. This mode slightly compromises speed in favor of better accuracy compared to the symmetric mode. This mode is useful when **minimal accuracy loss is crucial**, but a faster performance than INT8 is still desired.
+  INT4 Asymmetric mode also uses an unsigned 4-bit integer but quantizes weights
+  asymmetrically with a non-fixed zero point. This mode slightly compromises speed in
+  favor of better accuracy compared to the symmetric mode. This mode is useful when
+  **minimal accuracy loss is crucial**, but a faster performance than INT8 is still desired.
 
   .. code-block:: python
 
@@ -134,19 +147,31 @@ The INT4 method has several parameters that can provide different performance-ac
 
     compressed_model = compress_weights(model, mode=CompressWeightsMode.INT4_ASYM)
 
-* ``group_size`` controls the size of the group of weights that share the same quantization parameters. Shared quantization parameters help to speed up the calculation of activation values as they are dequantized and quantized between layers. However, they can reduce accuracy. The following group sizes are recommended: ``128``, ``64``, ``32`` (``128`` is default value).
+* ``group_size`` controls the size of the group of weights that share the same
+  quantization parameters. Shared quantization parameters help to speed up the
+  calculation of activation values as they are dequantized and quantized between
+  layers. However, they can reduce accuracy. The following group sizes are
+  recommended: ``128``, ``64``, ``32`` (``128`` is default value).
 
-  `Smaller Group Size`: Leads to a more accurate model but increases the model's footprint and reduces inference speed.
+  `Smaller Group Size`: Leads to a more accurate model but increases the model's
+  footprint and reduces inference speed.
 
-  `Larger Group Size`: Results in faster inference and a smaller model, but might compromise accuracy.
+  `Larger Group Size`: Results in faster inference and a smaller model, but might
+  compromise accuracy.
 
-* ``ratio`` controls the ratio between INT4 and INT8 compressed layers in the model. Ratio is a decimal between 0 and 1. For example, 0.8 means that 80% of layers will be compressed to INT4, while the rest will be compressed to INT8 precision. The default value for ratio is 1.
+* ``ratio`` controls the ratio between INT4 and INT8 compressed layers in the model.
+  Ratio is a decimal between 0 and 1. For example, 0.8 means that 80% of layers will be
+  compressed to INT4, while the rest will be compressed to INT8 precision. The default
+  value for ratio is 1.
 
-  `Higher Ratio (more INT4)`: Reduces the model size and increase inference speed but might lead to higher accuracy degradation.
+  `Higher Ratio (more INT4)`: Reduces the model size and increase inference speed but
+  might lead to higher accuracy degradation.
 
-  `Lower Ratio (more INT8)`: Maintains better accuracy but results in a larger model size and potentially slower inference.
+  `Lower Ratio (more INT8)`: Maintains better accuracy but results in a larger model size
+  and potentially slower inference.
 
-  In this example, 90% of the model's layers are quantized to INT4 asymmetrically with a group size of 64:
+  In this example, 90% of the model's layers are quantized to INT4 asymmetrically with
+  a group size of 64:
 
   .. code-block:: python
 
@@ -160,25 +185,43 @@ The INT4 method has several parameters that can provide different performance-ac
       ratio=0.9,
     )
 
-* ``dataset`` - calibration dataset for data-aware weight compression. It is required for some compression options, for example, some types ``sensitivity_metric`` can use data for precision selection.
+* ``dataset`` - calibration dataset for data-aware weight compression. It is required
+  for some compression options, for example, some types ``sensitivity_metric`` can use
+  data for precision selection.
 
-* ``sensitivity_metric`` - controls the metric to estimate the sensitivity of compressing layers in the bit-width selection algorithm. Some of the metrics require dataset to be provided. The following types are supported:
+* ``sensitivity_metric`` - controls the metric to estimate the sensitivity of compressing
+  layers in the bit-width selection algorithm. Some of the metrics require dataset to be
+  provided. The following types are supported:
 
-  * ``nncf.SensitivityMetric.WEIGHT_QUANTIZATION_ERROR`` - data-free metric computed as the inverted 8-bit quantization noise. Weights with highest value of this metric can be accurately quantized channel-wise to 8-bit. The idea is to leave these weights in 8 bit, and quantize the rest of layers to 4-bit group-wise. Since group-wise is more accurate than per-channel, accuracy should not degrade.
+  * ``nncf.SensitivityMetric.WEIGHT_QUANTIZATION_ERROR`` - data-free metric computed as
+    the inverted 8-bit quantization noise. Weights with highest value of this metric can
+    be accurately quantized channel-wise to 8-bit. The idea is to leave these weights in
+    8 bit, and quantize the rest of layers to 4-bit group-wise. Since group-wise is more
+    accurate than per-channel, accuracy should not degrade.
 
-  * ``nncf.SensitivityMetric.HESSIAN_INPUT_ACTIVATION`` - requires dataset. The average Hessian trace of weights with respect to the layer-wise quantization error multiplied by L2 norm of 8-bit quantization noise.
+  * ``nncf.SensitivityMetric.HESSIAN_INPUT_ACTIVATION`` - requires dataset. The average
+    Hessian trace of weights with respect to the layer-wise quantization error multiplied
+    by L2 norm of 8-bit quantization noise.
 
-  * ``nncf.SensitivityMetric.MEAN_ACTIVATION_VARIANCE`` - requires dataset. The mean variance of the layers' inputs multiplied by inverted 8-bit quantization noise.
+  * ``nncf.SensitivityMetric.MEAN_ACTIVATION_VARIANCE`` - requires dataset. The mean
+    variance of the layers' inputs multiplied by inverted 8-bit quantization noise.
 
-  * ``nncf.SensitivityMetric.MAX_ACTIVATION_VARIANCE`` - requires dataset. The maximum variance of the layers' inputs multiplied by inverted 8-bit quantization noise.
+  * ``nncf.SensitivityMetric.MAX_ACTIVATION_VARIANCE`` - requires dataset. The maximum
+    variance of the layers' inputs multiplied by inverted 8-bit quantization noise.
 
-  * ``nncf.SensitivityMetric.MEAN_ACTIVATION_MAGNITUDE`` - requires dataset. The mean magnitude of the layers' inputs multiplied by inverted 8-bit quantization noise.
+  * ``nncf.SensitivityMetric.MEAN_ACTIVATION_MAGNITUDE`` - requires dataset. The mean
+    magnitude of the layers' inputs multiplied by inverted 8-bit quantization noise.
 
-* ``all_layers`` - boolean parameter that enables INT4 weight quantization of all Fully-Connected and Embedding layers, including the first and last layers in the model.
+* ``all_layers`` - boolean parameter that enables INT4 weight quantization of all
+  Fully-Connected and Embedding layers, including the first and last layers in the model.
 
-* ``awq`` - boolean parameter that enables the AWQ method for more accurate INT4 weight quantization. Especially helpful when the weights of all the layers are quantized to 4 bits. The method can sometimes result in reduced accuracy when used with Dynamic Quantization of activations. Requires dataset.
+* ``awq`` - boolean parameter that enables the AWQ method for more accurate INT4 weight
+  quantization. Especially helpful when the weights of all the layers are quantized to
+  4 bits. The method can sometimes result in reduced accuracy when used with
+  Dynamic Quantization of activations. Requires dataset.
 
-For data-aware weight compression refer to the following `example <https://github.com/openvinotoolkit/nncf/tree/develop/examples/llm_compression/openvino/tiny_llama>`__.
+For data-aware weight compression refer to the following
+`example <https://github.com/openvinotoolkit/nncf/tree/develop/examples/llm_compression/openvino/tiny_llama>`__.
 
 The example below shows data-free 4-bit weight quantization
 applied on top of OpenVINO IR. Before trying the example, make sure Optimum Intel
@@ -191,11 +234,11 @@ is installed in your environment by running the following command:
 The first example loads a pre-trained Hugging Face model using the Optimum Intel API,
 compresses it to INT4 using NNCF, and then executes inference with a text phrase.
 
-If the model comes from Hugging Face and is supported by Optimum, it can
-be easier to use the Optimum Intel API to perform weight compression. The compression
-type is specified when the model is loaded using the ``load_in_8bit=True`` or ``load_in_4bit=True`` parameter.
-The second example uses the Weight Compression API from Optimum Intel instead of NNCF
-to compress the model to INT8.
+If the model comes from `Hugging Face <https://huggingface.co/models>`__ and is supported
+by Optimum, it may be easier to use the Optimum Intel API to perform weight compression.
+The compression type is specified when the model is loaded using the ``load_in_8bit=True``
+or ``load_in_4bit=True`` parameter. The second example uses the Weight Compression API
+from Optimum Intel instead of NNCF to compress the model to INT8.
 
 .. tab-set::
 
@@ -291,14 +334,17 @@ Make sure to install GPTQ dependencies by running the following command:
   results = pipe(phrase)
   print(results)
 
-An `example of a model <https://huggingface.co/TheBloke/Llama-2-7B-Chat-GPTQ>`__ that has been optimized using GPTQ.
+An `example of a model <https://huggingface.co/TheBloke/Llama-2-7B-Chat-GPTQ>`__
+that has been optimized using GPTQ.
 
 Compression Metrics Examples
 ########################################
 
-The table below shows examples of text-generation Language Models with different optimization settings in a data-free setup, where no dataset is used at the optimization step.
-The Perplexity metric is a measurement of response accuracy, where a higher complexity score indicates a lower accuracy.
-It is measured on the `Lambada OpenAI dataset <https://github.com/openai/gpt-2/issues/131#issuecomment-497136199>`__.
+The table below shows examples of text-generation Language Models with different
+optimization settings in a data-free setup, where no dataset is used at the optimization step.
+The Perplexity metric is a measurement of response accuracy, where a higher complexity
+score indicates a lower accuracy. It is measured on the
+`Lambada OpenAI dataset <https://github.com/openai/gpt-2/issues/131#issuecomment-497136199>`__.
 
 .. list-table::
    :widths: 40 55 25 25
@@ -370,7 +416,8 @@ It is measured on the `Lambada OpenAI dataset <https://github.com/openai/gpt-2/i
      - 8.0
 
 
-The following table shows accuracy metric in a data-aware 4-bit weight quantization setup measured on the `Wikitext dataset <https://arxiv.org/pdf/1609.07843.pdf>`__.
+The following table shows accuracy metric in a data-aware 4-bit weight quantization
+setup measured on the `Wikitext dataset <https://arxiv.org/pdf/1609.07843.pdf>`__.
 
 .. list-table::
    :widths: 40 55 25 25
@@ -406,21 +453,28 @@ The following table shows accuracy metric in a data-aware 4-bit weight quantizat
      - 2.6
 
 
-\*Perplexity metric in both tables was measured without the Dynamic Quantization feature enabled in the OpenVINO runtime.
+\*Perplexity metric in both tables was measured without the Dynamic Quantization feature
+enabled in the OpenVINO runtime.
 
 Auto-tuning of Weight Compression Parameters
 ############################################
 
-To find the optimal weight compression parameters for a particular model, refer to the `example <https://github.com/openvinotoolkit/nncf/tree/develop/examples/llm_compression/openvino/tiny_llama_find_hyperparams>`__ , where weight compression parameters are being searched from the subset of values. To speed up the search, a self-designed
-validation pipeline called `WhoWhatBench <https://github.com/openvinotoolkit/openvino.genai/tree/master/llm_bench/python/who_what_benchmark>`__ is used.
-The pipeline can quickly evaluate the changes in the accuracy of the optimized model compared to the baseline.
+To find the optimal weight compression parameters for a particular model, refer to the
+`example <https://github.com/openvinotoolkit/nncf/tree/develop/examples/llm_compression/openvino/tiny_llama_find_hyperparams>`__ ,
+where weight compression parameters are being searched from the subset of values.
+To speed up the search, a self-designed validation pipeline called
+`WhoWhatBench <https://github.com/openvinotoolkit/openvino.genai/tree/master/llm_bench/python/who_what_benchmark>`__
+is used. The pipeline can quickly evaluate the changes in the accuracy of the optimized
+model compared to the baseline.
 
 Additional Resources
 ####################
 
 - `Data-aware Weight Compression Example <https://github.com/openvinotoolkit/nncf/tree/develop/examples/llm_compression/openvino/tiny_llama>`__
 - `Tune Weight Compression Parameters Example <https://github.com/openvinotoolkit/nncf/tree/develop/examples/llm_compression/openvino/tiny_llama_find_hyperparams>`__
-- `OpenVINO GenAI Repo <https://github.com/openvinotoolkit/openvino.genai>`__: Repository containing example pipelines that implement image and text generation tasks. It also provides a tool to benchmark LLMs.
+- `OpenVINO GenAI Repo <https://github.com/openvinotoolkit/openvino.genai>`__
+  : Repository containing example pipelines that implement image and text generation
+  tasks. It also provides a tool to benchmark LLMs.
 - `WhoWhatBench <https://github.com/openvinotoolkit/openvino.genai/tree/master/llm_bench/python/who_what_benchmark>`__
 - `NNCF GitHub <https://github.com/openvinotoolkit/nncf>`__
 - :doc:`Post-training Quantization <quantizing-models-post-training>`
diff --git a/docs/articles_en/openvino-workflow/model-optimization.rst b/docs/articles_en/openvino-workflow/model-optimization.rst
index 2cf08990d6b1d6..17903c760e1c4c 100644
--- a/docs/articles_en/openvino-workflow/model-optimization.rst
+++ b/docs/articles_en/openvino-workflow/model-optimization.rst
@@ -13,23 +13,48 @@ Model Optimization Guide
    model-optimization-guide/weight-compression
 
 
-Model optimization is an optional offline step of improving the final model performance and reducing the model size by applying special optimization methods, such as 8-bit quantization, pruning, etc. OpenVINO offers three optimization paths implemented in `Neural Network Compression Framework (NNCF) <https://github.com/openvinotoolkit/nncf>`__:
+Model optimization is an optional offline step of improving the final model performance
+and reducing the model size by applying special optimization methods, such as 8-bit
+quantization, pruning, etc. OpenVINO offers three optimization paths implemented in
+`Neural Network Compression Framework (NNCF) <https://github.com/openvinotoolkit/nncf>`__:
 
-- :doc:`Post-training Quantization <model-optimization-guide/quantizing-models-post-training>` is designed to optimize the inference of deep learning models by applying the post-training 8-bit integer quantization that does not require model retraining or fine-tuning.
+- :doc:`Post-training Quantization <model-optimization-guide/quantizing-models-post-training>`
+  is designed to optimize the inference of deep learning models by applying the
+  post-training 8-bit integer quantization that does not require model retraining
+  or fine-tuning.
+
+- :doc:`Training-time Optimization <model-optimization-guide/compressing-models-during-training>`
+  , a suite of advanced methods for training-time model optimization within the DL
+  framework, such as PyTorch and TensorFlow 2.x. It supports methods like
+  Quantization-aware Training, Structured and Unstructured Pruning, etc.
 
-- :doc:`Training-time Optimization <model-optimization-guide/compressing-models-during-training>`, a suite of advanced methods for training-time model optimization within the DL framework, such as PyTorch and TensorFlow 2.x. It supports methods like Quantization-aware Training, Structured and Unstructured Pruning, etc.
+- :doc:`Weight Compression <model-optimization-guide/weight-compression>`,
+  an easy-to-use method for Large Language Models footprint reduction and inference acceleration.
 
-- :doc:`Weight Compression <model-optimization-guide/weight-compression>`, an easy-to-use method for Large Language Models footprint reduction and inference acceleration.
+.. note::
 
-.. note:: OpenVINO also supports optimized models (for example, quantized) from source frameworks such as PyTorch, TensorFlow, and ONNX (in Q/DQ; Quantize/DeQuantize format). No special steps are required in this case and optimized models can be converted to the OpenVINO Intermediate Representation format (IR) right away.
+   OpenVINO also supports optimized models (for example, quantized) from source
+   frameworks such as PyTorch, TensorFlow, and ONNX (in Q/DQ; Quantize/DeQuantize format).
+   No special steps are required in this case and optimized models can be converted to
+   the OpenVINO Intermediate Representation format (IR) right away.
 
-Post-training Quantization is the fastest way to optimize an arbitrary DL model and should be applied first, but it is limited in terms of achievable accuracy-performance trade-off. The recommended approach to obtain OpenVINO quantized model is to convert a model from original framework to ``ov.Model`` and ensure that the model works correctly in OpenVINO, for example, by calculating the model metrics. Then, ``ov.Model`` can be used as input for the ``nncf.quantize()`` method to get the quantized model or as input for the ``nncf.compress_weights()`` method to compress weights of Large Language Models (see the diagram below).
+Post-training Quantization is the fastest way to optimize an arbitrary DL model and should
+be applied first, but it is limited in terms of achievable accuracy-performance trade-off.
+The recommended approach to obtain OpenVINO quantized model is to convert
+`a model from original framework <https://huggingface.co/models>`__ to ``ov.Model``
+and ensure that the model works correctly in OpenVINO, for example, by calculating the
+model metrics. Then, ``ov.Model`` can be used as input for the ``nncf.quantize()``
+method to get the quantized model or as input for the ``nncf.compress_weights()``
+method to compress weights of Large Language Models (see the diagram below).
 
-In case of unsatisfactory accuracy or performance after Post-training Quantization, Training-time Optimization can be used as an option.
+In case of unsatisfactory accuracy or performance after Post-training Quantization,
+Training-time Optimization can be used as an option.
 
 .. image:: ../assets/images/DEVELOPMENT_FLOW_V3_crunch.svg
 
-Once the model is optimized using the aforementioned methods, it can be used for inference using the regular OpenVINO inference workflow. No changes to the inference code are required.
+Once the model is optimized using the aforementioned methods, it can be used for
+inference using the regular OpenVINO inference workflow. No changes to the inference
+code are required.
 
 .. image:: ../assets/images/WHAT_TO_USE.svg
 
@@ -40,5 +65,5 @@ Additional Resources
 - :doc:`Training-time Optimization <model-optimization-guide/compressing-models-during-training>`
 - :doc:`Weight Compression <model-optimization-guide/weight-compression>`
 - :doc:`Deployment optimization <running-inference/optimize-inference>`
-- `HuggingFace Optimum Intel <https://huggingface.co/docs/optimum/intel/optimization_ov>`__
+- `Hugging Face Optimum Intel <https://huggingface.co/docs/optimum/intel/optimization_ov>`__
 
diff --git a/docs/articles_en/openvino-workflow/model-preparation/conversion-parameters.rst b/docs/articles_en/openvino-workflow/model-preparation/conversion-parameters.rst
index bb1626559f6987..6a7023fc16afc3 100644
--- a/docs/articles_en/openvino-workflow/model-preparation/conversion-parameters.rst
+++ b/docs/articles_en/openvino-workflow/model-preparation/conversion-parameters.rst
@@ -7,9 +7,15 @@ Conversion Parameters
 .. meta::
    :description: Model Conversion API provides several parameters to adjust model conversion.
 
-This document describes all available parameters for ``openvino.convert_model``, ``ovc``, and ``openvino.save_model`` without focusing on a particular framework model format. Use this information for your reference as a common description of the conversion API capabilities in general. Part of the options can be not relevant to some specific frameworks. Use :doc:`Supported Model Formats <../model-preparation>` page for more dedicated framework-dependent tutorials.
+This document describes all available parameters for ``openvino.convert_model``, ``ovc``,
+and ``openvino.save_model`` without focusing on a particular framework model format.
+Use this information for your reference as a common description of the conversion API
+capabilities in general. Part of the options can be not relevant to some specific
+frameworks. Use :doc:`Supported Model Formats <../model-preparation>` page for more
+dedicated framework-dependent tutorials.
 
-In most cases when it is required to convert a model the following simple syntax can be used:
+You can obtain a model from `Hugging Face <https://huggingface.co/models>`__. When you
+need to convert it, in most cases, you can use the following simple syntax:
 
 .. tab-set::
 
@@ -36,15 +42,34 @@ In most cases when it is required to convert a model the following simple syntax
 
           ovc path_to_your_model
 
-Providing just a path to the model or model object as ``openvino.convert_model`` argument is frequently enough to make a successful conversion. However, depending on the model topology and original deep learning framework, additional parameters may be required, which are described below.
-
-- ``example_input`` parameter available in Python ``openvino.convert_model`` only is intended to trace the model to obtain its graph representation. This parameter is crucial for converting PyTorch models and may sometimes be required for TensorFlow models. For more details, refer to the :doc:`PyTorch Model Conversion <convert-model-pytorch>` or :doc:`TensorFlow Model Conversion <convert-model-tensorflow>`.
-
-- ``input`` parameter to set or override shapes for model inputs. It configures dynamic and static dimensions in model inputs depending on your inference requirements. For more information on this parameter, refer to the :doc:`Setting Input Shapes <setting-input-shapes>` guide.
-
-- ``output`` parameter to select one or multiple outputs from the original model. This is useful when the model has outputs that are not required for inference in a deployment scenario. By specifying only necessary outputs, you can create a more compact model that infers faster.
-
-- ``compress_to_fp16`` parameter that is provided by ``ovc`` CLI tool and ``openvino.save_model`` Python function, gives controls over the compression of model weights to FP16 format when saving OpenVINO model to IR. This option is enabled by default which means all produced IRs are saved using FP16 data type for weights which saves up to 2x storage space for the model file and in most cases doesn't sacrifice model accuracy. In case it does affect accuracy, the compression can be disabled by setting this flag to ``False``:
+Providing just a path to the model or model object as ``openvino.convert_model`` argument
+is frequently enough to make a successful conversion. However, depending on the model
+topology and original deep learning framework, additional parameters may be required,
+which are described below.
+
+- ``example_input`` parameter available in Python ``openvino.convert_model`` only is
+  intended to trace the model to obtain its graph representation. This parameter is crucial
+  for converting PyTorch models and may sometimes be required for TensorFlow models.
+  For more details, refer to the :doc:`PyTorch Model Conversion <convert-model-pytorch>`
+  or :doc:`TensorFlow Model Conversion <convert-model-tensorflow>`.
+
+- ``input`` parameter to set or override shapes for model inputs. It configures dynamic
+  and static dimensions in model inputs depending on your inference requirements. For more
+  information on this parameter, refer to the :doc:`Setting Input Shapes <setting-input-shapes>`
+  guide.
+
+- ``output`` parameter to select one or multiple outputs from the original model.
+  This is useful when the model has outputs that are not required for inference in a
+  deployment scenario. By specifying only necessary outputs, you can create a more
+  compact model that infers faster.
+
+- ``compress_to_fp16`` parameter that is provided by ``ovc`` CLI tool and
+  ``openvino.save_model`` Python function, gives controls over the compression of
+  model weights to FP16 format when saving OpenVINO model to IR. This option is enabled
+  by default which means all produced IRs are saved using FP16 data type for weights
+  which saves up to 2x storage space for the model file and in most cases doesn't
+  sacrifice model accuracy. In case it does affect accuracy, the compression can be
+  disabled by setting this flag to ``False``:
 
 .. tab-set::
 
@@ -75,23 +100,59 @@ For details on how plugins handle compressed ``FP16`` models, see
    Refer to the :doc:`Post-training optimization <../model-optimization-guide/quantizing-models-post-training>` guide for more
    information about that.
 
-- ``extension`` parameter which makes possible conversion of the models consisting of operations that are not supported by OpenVINO out-of-the-box. It requires implementing of an OpenVINO extension first, please refer to :doc:`Frontend Extensions <../../documentation/openvino-extensibility/frontend-extensions>` guide.
-
-- ``share_weigths`` parameter with default value ``True`` allows reusing memory with original weights. For models loaded in Python and then passed to ``openvino.convert_model``, that means that OpenVINO model will share the same areas in program memory where the original weights are located. For models loaded from files by ``openvino.convert_model``, file memory mapping is used to avoid extra memory allocation. When enabled, the original model cannot be modified (Python object cannot be deallocated and original model file cannot be deleted) for the whole lifetime of OpenVINO model. Even model inference by original framework can lead to model modification. If it is not desired, set ``share_weights=False`` when calling ``openvino.convert_model``.
-
-.. note:: ``ovc`` does not have ``share_weights`` option and always uses sharing to reduce conversion time and consume less amount of memory during the conversion.
+- ``extension`` parameter which makes possible conversion of the models consisting of
+  operations that are not supported by OpenVINO out-of-the-box. It requires implementing of
+  an OpenVINO extension first, please refer to
+  :doc:`Frontend Extensions <../../documentation/openvino-extensibility/frontend-extensions>`
+  guide.
+
+- ``share_weigths`` parameter with default value ``True`` allows reusing memory with
+  original weights. For models loaded in Python and then passed to ``openvino.convert_model``,
+  that means that OpenVINO model will share the same areas in program memory where the
+  original weights are located. For models loaded from files by ``openvino.convert_model``,
+  file memory mapping is used to avoid extra memory allocation. When enabled, the
+  original model cannot be modified (Python object cannot be deallocated and original
+  model file cannot be deleted) for the whole lifetime of OpenVINO model. Even model
+  inference by original framework can lead to model modification. If it is not desired,
+  set ``share_weights=False`` when calling ``openvino.convert_model``.
+
+  .. note::
+
+     ``ovc`` does not have ``share_weights`` option and always uses sharing to reduce
+     conversion time and consume less amount of memory during the conversion.
+
+- ``output_model`` parameter in ``ovc`` and ``openvino.save_model`` specifies name for
+  output ``.xml`` file with the resulting OpenVINO IR. The accompanying ``.bin`` file
+  name will be generated automatically by replacing ``.xml`` extension with ``.bin``
+  extension. The value of ``output_model`` must end with ``.xml`` extension. For ``ovc``
+  command line tool, ``output_model`` can also contain a name of a directory. In this case,
+  the resulting OpenVINO IR files will be put into that directory with a base name of
+  ``.xml`` and ``.bin`` files matching the original model base name passed to ``ovc`` as a
+  parameter. For example, when calling ``ovc your_model.onnx --output_model directory_name``,
+  files ``directory_name/your_model.xml`` and ``directory_name/your_model.bin`` will be
+  created. If ``output_model`` is not used, then the current directory is used as
+  a destination directory.
+
+  .. note::
+
+     ``openvino.save_model`` does not support a directory for ``output_model``
+     parameter value because ``openvino.save_model`` gets OpenVINO model object
+     represented in a memory and there is no original model file name available for
+     output file name generation. For the same reason, ``output_model`` is a mandatory
+     parameter for ``openvino.save_model``.
+
+- ``verbose`` parameter activates extra diagnostics printed to the standard output.
+  Use for debugging purposes in case there is an issue with the conversion and to collect
+  information for better bug reporting to OpenVINO team.
 
-- ``output_model`` parameter in ``ovc`` and ``openvino.save_model`` specifies name for output ``.xml`` file with the resulting OpenVINO IR. The accompanying ``.bin`` file name will be generated automatically by replacing ``.xml`` extension with ``.bin`` extension. The value of ``output_model`` must end with ``.xml`` extension. For ``ovc`` command line tool, ``output_model`` can also contain a name of a directory. In this case, the resulting OpenVINO IR files will be put into that directory with a base name of ``.xml`` and ``.bin`` files matching the original model base name passed to ``ovc`` as a parameter. For example, when calling ``ovc your_model.onnx --output_model directory_name``, files ``directory_name/your_model.xml`` and ``directory_name/your_model.bin`` will be created. If ``output_model`` is not used, then the current directory is used as a destination directory.
-
-.. note:: ``openvino.save_model`` does not support a directory for ``output_model`` parameter value because ``openvino.save_model`` gets OpenVINO model object represented in a memory and there is no original model file name available for output file name generation. For the same reason, ``output_model`` is a mandatory parameter for ``openvino.save_model``.
-
-- ``verbose`` parameter activates extra diagnostics printed to the standard output. Use for debugging purposes in case there is an issue with the conversion and to collect information for better bug reporting to OpenVINO team.
+.. note::
 
-.. note:: Weights sharing does not equally work for all the supported model formats. The value of this flag is considered as a hint for the conversion API, and actual sharing is used only if it is implemented and possible for a particular model representation.
+   Weights sharing does not equally work for all the supported model formats. The value
+   of this flag is considered as a hint for the conversion API, and actual sharing is
+   used only if it is implemented and possible for a particular model representation.
 
-You can always run ``ovc -h`` or ``ovc --help`` to recall all the supported parameters for ``ovc``.
+You can always run ``ovc -h`` or ``ovc --help`` to recall all the supported
+parameters for ``ovc``.
 
 Use ``ovc --version`` to check the version of OpenVINO package installed.
 
-
-
diff --git a/docs/articles_en/openvino-workflow/model-preparation/convert-model-onnx.rst b/docs/articles_en/openvino-workflow/model-preparation/convert-model-onnx.rst
index b52552ad55730e..9c903388a5c3bd 100644
--- a/docs/articles_en/openvino-workflow/model-preparation/convert-model-onnx.rst
+++ b/docs/articles_en/openvino-workflow/model-preparation/convert-model-onnx.rst
@@ -11,18 +11,30 @@ Converting an ONNX Model
 Introduction to ONNX
 ####################
 
-`ONNX <https://github.com/onnx/onnx>`__ is a representation format for deep learning models that enables AI developers to easily transfer models between different frameworks.
+`ONNX <https://github.com/onnx/onnx>`__ is a representation format for deep learning models
+that enables AI developers to easily transfer models between different frameworks.
 
-.. note:: An ONNX model file can be loaded by ``openvino.Core.read_model`` or ``openvino.Core.compile_model`` methods by OpenVINO runtime API without the need to prepare an OpenVINO IR first. Refer to the :doc:`inference example <../running-inference/integrate-openvino-with-your-application>` for more details. Using ``openvino.convert_model`` is still recommended if the model load latency is important for the inference application.
+.. note::
+
+   An ONNX model file can be loaded by ``openvino.Core.read_model`` or
+   ``openvino.Core.compile_model`` methods by OpenVINO runtime API without the need to
+   prepare an OpenVINO IR first. Refer to the
+   :doc:`inference example <../running-inference/integrate-openvino-with-your-application>`
+   for more details. Using ``openvino.convert_model`` is still recommended if the model
+   load latency is important for the inference application.
 
 Converting an ONNX Model
 ########################
 
-This page provides instructions on model conversion from the ONNX format to the OpenVINO IR format.
+This page provides instructions on model conversion from the ONNX format to the
+OpenVINO IR format.
 
-For model conversion, you need an ONNX model either directly downloaded from a public repository or converted from any framework that supports exporting to the ONNX format.
+For model conversion, you need an ONNX model either directly downloaded from
+an online database, for example `Hugging Face <https://huggingface.co/models>`__ , or
+converted from any framework that supports exporting to the ONNX format.
 
-To convert an ONNX model, run model conversion with the path to the input model ``.onnx`` file:
+To convert an ONNX model, run model conversion with the path to the input
+model ``.onnx`` file:
 
 .. tab-set::
 
diff --git a/docs/articles_en/openvino-workflow/model-preparation/convert-model-paddle.rst b/docs/articles_en/openvino-workflow/model-preparation/convert-model-paddle.rst
index c70724b4047f95..97c968bc4a7692 100644
--- a/docs/articles_en/openvino-workflow/model-preparation/convert-model-paddle.rst
+++ b/docs/articles_en/openvino-workflow/model-preparation/convert-model-paddle.rst
@@ -8,16 +8,31 @@ Converting a PaddlePaddle Model
    :description: Learn how to convert a model from the
                  PaddlePaddle format to the OpenVINO Model.
 
-This page provides general instructions on how to convert a model from the PaddlePaddle format to the OpenVINO IR format using OpenVINO model conversion API. The instructions are different depending on the PaddlePaddle model format.
+This page provides general instructions on how to convert a model from the PaddlePaddle
+format to the OpenVINO IR format using OpenVINO model conversion API. The instructions
+are different depending on the PaddlePaddle model format.
 
-.. note:: PaddlePaddle model serialized in a file can be loaded by ``openvino.Core.read_model`` or ``openvino.Core.compile_model`` methods by OpenVINO runtime API without preparing OpenVINO IR first. Refer to the :doc:`inference example <../running-inference/integrate-openvino-with-your-application>` for more details. Using ``openvino.convert_model`` is still recommended if model load latency matters for the inference application.
+.. note::
+
+   PaddlePaddle model serialized in a file can be loaded by ``openvino.Core.read_model``
+   or ``openvino.Core.compile_model`` methods by OpenVINO runtime API without preparing
+   OpenVINO IR first. Refer to the
+   :doc:`inference example <../running-inference/integrate-openvino-with-your-application>`
+   for more details. Using ``openvino.convert_model`` is still recommended if model load
+   latency matters for the inference application.
 
 Converting PaddlePaddle Model Files
 ###################################
 
-PaddlePaddle inference model includes ``.pdmodel`` (storing model structure) and ``.pdiparams`` (storing model weight). For details on how to export a PaddlePaddle inference model, refer to the `Exporting PaddlePaddle Inference Model <https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/beginner/model_save_load_cn.html>`__ Chinese guide.
+PaddlePaddle inference model includes ``.pdmodel`` (storing model structure) and
+``.pdiparams`` (storing model weight). For details on how to export a PaddlePaddle
+inference model, refer to the
+`Exporting PaddlePaddle Inference Model <https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/beginner/model_save_load_cn.html>`__
+Chinese guide.
 
-To convert a PaddlePaddle model, use the ``ovc`` or ``openvino.convert_model`` and specify the path to the input ``.pdmodel`` model file:
+You can download a PaddlePaddle model from `Hugging Face <https://huggingface.co/models>`__.
+To convert the model, use the ``ovc`` or ``openvino.convert_model``
+and specify the path to the input ``.pdmodel`` model file:
 
 
 .. tab-set::
diff --git a/docs/articles_en/openvino-workflow/model-preparation/convert-model-pytorch.rst b/docs/articles_en/openvino-workflow/model-preparation/convert-model-pytorch.rst
index 7289c6cbe020c0..6ac806daf0cda0 100644
--- a/docs/articles_en/openvino-workflow/model-preparation/convert-model-pytorch.rst
+++ b/docs/articles_en/openvino-workflow/model-preparation/convert-model-pytorch.rst
@@ -7,7 +7,9 @@ Converting a PyTorch Model
                  PyTorch format to the OpenVINO Model.
 
 
-To convert a PyTorch model, use the ``openvino.convert_model`` function.
+You can download a PyTorch model from `PyTorch Hub <https://pytorch.org/hub/>`__ or
+`Hugging Face <https://huggingface.co/models>`__.
+To convert the model, use the ``openvino.convert_model`` function.
 
 Here is the simplest example of PyTorch model conversion using a model from ``torchvision``:
 
diff --git a/docs/articles_en/openvino-workflow/model-preparation/convert-model-tensorflow-lite.rst b/docs/articles_en/openvino-workflow/model-preparation/convert-model-tensorflow-lite.rst
index ecf08f9ff55575..93738f48f1d39f 100644
--- a/docs/articles_en/openvino-workflow/model-preparation/convert-model-tensorflow-lite.rst
+++ b/docs/articles_en/openvino-workflow/model-preparation/convert-model-tensorflow-lite.rst
@@ -9,7 +9,10 @@ Converting a TensorFlow Lite Model
                  TensorFlow Lite format to the OpenVINO Model.
 
 
-To convert an TensorFlow Lite model, run model conversion with the path to the ``.tflite`` model file:
+You can download a TensorFlow Lite model from
+`Kaggle <https://www.kaggle.com/models?framework=tfLite&subtype=module,placeholder&tfhub-redirect=true>`__
+or `Hugging Face <https://huggingface.co/models>`__.
+To convert the model, run model conversion with the path to the ``.tflite`` model file:
 
 .. tab-set::
 
@@ -28,16 +31,27 @@ To convert an TensorFlow Lite model, run model conversion with the path to the `
 
          ovc your_model_file.tflite
 
-.. note:: TensorFlow Lite model file can be loaded by ``openvino.Core.read_model`` or ``openvino.Core.compile_model`` methods by OpenVINO runtime API without preparing OpenVINO IR first. Refer to the :doc:`inference example <../running-inference/integrate-openvino-with-your-application>` for more details. Using ``openvino.convert_model`` is still recommended if model load latency matters for the inference application.
+.. note::
+
+   TensorFlow Lite model file can be loaded by ``openvino.Core.read_model`` or
+   ``openvino.Core.compile_model`` methods by OpenVINO runtime API without preparing
+   OpenVINO IR first. Refer to the
+   :doc:`inference example <../running-inference/integrate-openvino-with-your-application>`
+   for more details. Using ``openvino.convert_model`` is still recommended if model
+   load latency matters for the inference application.
 
 Supported TensorFlow Lite Layers
 ###################################
 
-For the list of supported standard layers, refer to the :doc:`Supported Operations <../../about-openvino/compatibility-and-support/supported-operations-framework-frontend>` page.
+For the list of supported standard layers, refer to the
+:doc:`Supported Operations <../../about-openvino/compatibility-and-support/supported-operations-framework-frontend>`
+page.
 
 Supported TensorFlow Lite Models
 ###################################
 
-More than eighty percent of public TensorFlow Lite models are supported from open sources `TensorFlow Hub <https://tfhub.dev/s?deployment-format=lite&subtype=module,placeholder>`__ and `MediaPipe <https://developers.google.com/mediapipe>`__.
+More than eighty percent of public TensorFlow Lite models are supported from open
+sources `Kaggle <https://www.kaggle.com/models?framework=tfLite&subtype=module,placeholder&tfhub-redirect=true>`__
+and `MediaPipe <https://developers.google.com/mediapipe>`__.
 Unsupported models usually have custom TensorFlow Lite operations.
 
diff --git a/docs/articles_en/openvino-workflow/model-preparation/convert-model-tensorflow.rst b/docs/articles_en/openvino-workflow/model-preparation/convert-model-tensorflow.rst
index 84aa153cfb82be..e66a5a87c48cd3 100644
--- a/docs/articles_en/openvino-workflow/model-preparation/convert-model-tensorflow.rst
+++ b/docs/articles_en/openvino-workflow/model-preparation/convert-model-tensorflow.rst
@@ -8,13 +8,39 @@ Converting a TensorFlow Model
    :description: Learn how to convert a model from a TensorFlow format to the OpenVINO Model.
 
 
-This page provides general instructions on how to run model conversion from a TensorFlow format to the OpenVINO IR format. The instructions are different depending on whether your model was created with TensorFlow v1.X or TensorFlow v2.X.
+This page provides general instructions on how to run model conversion from a TensorFlow
+format to the OpenVINO IR format. The instructions are different depending on whether
+your model was created with TensorFlow v1.X or TensorFlow v2.X.
 
-.. note:: TensorFlow models can be loaded by ``openvino.Core.read_model`` or ``openvino.Core.compile_model`` methods by OpenVINO runtime API without preparing OpenVINO IR first. Refer to the :doc:`inference example <../running-inference/integrate-openvino-with-your-application>` for more details. Using ``openvino.convert_model`` is still recommended if model load latency matters for the inference application.
+TensorFlow models can be obtained from
+`Kaggle <https://www.kaggle.com/models?framework=tfLite&subtype=module,placeholder&tfhub-redirect=true>`__
+or `Hugging Face <https://huggingface.co/models>`__.
 
-.. note:: ``openvino.convert_model`` uses sharing of model weights by default. That means that OpenVINO model will share the same areas in program memory where the original weights are located, for this reason the original model cannot be modified (Python object cannot be deallocated and original model file cannot be deleted) for the whole lifetime of OpenVINO model. Model inference for TensorFlow models can lead to model modification, so original TF model should not be inferred during the lifetime of OpenVINO model. If it is not desired, set ``share_weights=False`` when calling ``openvino.convert_model``.
+.. note::
+
+   TensorFlow models can be loaded by ``openvino.Core.read_model`` or
+   ``openvino.Core.compile_model`` methods by OpenVINO runtime API without preparing
+   OpenVINO IR first. Refer to the
+   :doc:`inference example <../running-inference/integrate-openvino-with-your-application>`
+   for more details. Using ``openvino.convert_model`` is still recommended if model load
+   latency matters for the inference application.
+
+.. note::
+
+   ``openvino.convert_model`` uses sharing of model weights by default. That means that
+   OpenVINO model will share the same areas in program memory where the original weights
+   are located, for this reason the original model cannot be modified (Python object
+   cannot be deallocated and original model file cannot be deleted) for the whole
+   lifetime of OpenVINO model. Model inference for TensorFlow models can lead to model
+   modification, so original TF model should not be inferred during the lifetime of
+   OpenVINO model. If it is not desired, set ``share_weights=False`` when calling
+   ``openvino.convert_model``.
+
+.. note::
 
-.. note:: Examples below that convert TensorFlow models from a file, do not require any version of TensorFlow to be installed on the system, except in cases when the ``tensorflow`` module is imported explicitly.
+   Examples below that convert TensorFlow models from a file, do not require any version
+   of TensorFlow to be installed on the system, except in cases when the ``tensorflow``
+   module is imported explicitly.
 
 Converting TensorFlow 2 Models
 ##############################
@@ -25,7 +51,8 @@ Below are the instructions on how to convert each of them.
 SavedModel Format
 +++++++++++++++++
 
-A model in the SavedModel format consists of a directory with a ``saved_model.pb`` file and two subfolders: ``variables`` and ``assets`` inside.
+A model in the SavedModel format consists of a directory with a ``saved_model.pb``
+file and two subfolders: ``variables`` and ``assets`` inside.
 To convert a model, run conversion with the directory as the model argument:
 
 .. tab-set::
@@ -59,8 +86,9 @@ SavedModel format. Here is an example of how to do it:
    model = tf.keras.models.load_model('model.h5')
    tf.saved_model.save(model,'model')
 
-Converting a Keras H5 model with a custom layer to the SavedModel format requires special considerations.
-For example, the model with a custom layer ``CustomLayer`` from ``custom_layer.py`` is converted as follows:
+Converting a Keras H5 model with a custom layer to the SavedModel format requires special
+considerations. For example, the model with a custom layer ``CustomLayer`` from
+``custom_layer.py`` is converted as follows:
 
 .. code-block:: py
    :force:
@@ -82,7 +110,8 @@ Converting TensorFlow 1 Models
 Converting Frozen Model Format
 +++++++++++++++++++++++++++++++
 
-To convert a TensorFlow model, run model conversion with the path to the input model ``*.pb*`` file:
+To convert a TensorFlow model, run model conversion with the path to the input
+model ``*.pb*`` file:
 
 .. tab-set::
 
@@ -107,9 +136,12 @@ Converting Non-Frozen Model Formats
 
 There are three ways to store non-frozen TensorFlow models.
 
-1. **SavedModel format**. In this case, a model consists of a special directory with a ``.pb`` file
-and several subfolders: ``variables``, ``assets``, and ``assets.extra``. For more information about the SavedModel directory, refer to the `README <https://github.com/tensorflow/tensorflow/tree/master/tensorflow/python/saved_model#components>`__ file in the TensorFlow repository.
-To convert such TensorFlow model, run the conversion similarly to other model formats and pass a path to the directory as a model argument:
+1. **SavedModel format**. In this case, a model consists of a special directory with a
+   ``.pb`` file and several subfolders: ``variables``, ``assets``, and ``assets.extra``.
+   For more information about the SavedModel directory, refer to the
+   `README <https://github.com/tensorflow/tensorflow/tree/master/tensorflow/python/saved_model#components>`__
+   file in the TensorFlow repository. To convert such TensorFlow model, run the conversion
+   similarly to other model formats and pass a path to the directory as a model argument:
 
 .. tab-set::
 
@@ -128,9 +160,12 @@ To convert such TensorFlow model, run the conversion similarly to other model fo
 
          ovc path_to_saved_model_dir
 
-2. **Checkpoint**. In this case, a model consists of two files: ``inference_graph.pb`` (or ``inference_graph.pbtxt``) and ``checkpoint_file.ckpt``.
-If you do not have an inference graph file, refer to the `Freezing Custom Models in Python <#Freezing-Custom-Models-in-Python>`__  section.
-To convert the model with the inference graph in ``.pb`` format, provide paths to both files as an argument for ``ovc`` or ``openvino.convert_model``:
+2. **Checkpoint**. In this case, a model consists of two files: ``inference_graph.pb``
+   (or ``inference_graph.pbtxt``) and ``checkpoint_file.ckpt``.
+   If you do not have an inference graph file, refer to the
+   `Freezing Custom Models in Python <#freezing-custom-models-in-python>`__ section.
+   To convert the model with the inference graph in ``.pb`` format, provide paths to both
+   files as an argument for ``ovc`` or ``openvino.convert_model``:
 
 .. tab-set::
 
@@ -149,11 +184,16 @@ To convert the model with the inference graph in ``.pb`` format, provide paths t
 
          ovc path_to_inference_graph.pb path_to_checkpoint_file.ckpt
 
-To convert the model with the inference graph in the ``.pbtxt`` format, specify the path to ``.pbtxt`` file instead of the ``.pb`` file. The conversion API automatically detects the format of the provided file, there is no need to specify the model file format explicitly when calling ``ovc`` or ``openvino.convert_model`` in all examples in this document.
+To convert the model with the inference graph in the ``.pbtxt`` format, specify the path
+to ``.pbtxt`` file instead of the ``.pb`` file. The conversion API automatically detects
+the format of the provided file, there is no need to specify the model file format
+explicitly when calling ``ovc`` or ``openvino.convert_model`` in all examples in this document.
 
-3. **MetaGraph**. In this case, a model consists of three or four files stored in the same directory: ``model_name.meta``, ``model_name.index``,
-``model_name.data-00000-of-00001`` (the numbers may vary), and ``checkpoint`` (optional).
-To convert such a TensorFlow model, run the conversion providing a path to `.meta` file as an argument:
+3. **MetaGraph**. In this case, a model consists of three or four files stored in the same
+   directory: ``model_name.meta``, ``model_name.index``,
+   ``model_name.data-00000-of-00001`` (the numbers may vary), and ``checkpoint`` (optional).
+   To convert such a TensorFlow model, run the conversion providing a path to ``.meta``
+   file as an argument:
 
 .. tab-set::
 
@@ -176,9 +216,11 @@ To convert such a TensorFlow model, run the conversion providing a path to `.met
 Freezing Custom Models in Python
 ++++++++++++++++++++++++++++++++
 
-When a model is defined in Python code, you must create an inference graph file. Graphs are usually built in a form
-that allows model training. That means all trainable parameters are represented as variables in the graph.
-To be able to use such a graph with the model conversion API, it should be frozen first before passing to the ``openvino.convert_model`` function:
+When a model is defined in Python code, you must create an inference graph file. Graphs are
+usually built in a form that allows model training. That means all trainable parameters are
+represented as variables in the graph. To be able to use such a graph with the model
+conversion API, it should be frozen first before passing to the
+``openvino.convert_model`` function:
 
 .. code-block:: py
    :force:
@@ -192,22 +234,28 @@ To be able to use such a graph with the model conversion API, it should be froze
 
 Where:
 
-* ``sess`` is the instance of the TensorFlow Session object where the network topology is defined.
-* ``["name_of_the_output_node"]`` is the list of output node names in the graph; ``frozen`` graph will include only those nodes from the original ``sess.graph_def`` that are directly or indirectly used to compute given output nodes. The ``'name_of_the_output_node'`` is an example of a possible output node name. You should derive the names based on your own graph.
+* ``sess`` is the instance of the TensorFlow Session object where the network topology
+  is defined.
+* ``["name_of_the_output_node"]`` is the list of output node names in the graph;
+  ``frozen`` graph will include only those nodes from the original ``sess.graph_def``
+  that are directly or indirectly used to compute given output nodes. The
+  ``'name_of_the_output_node'`` is an example of a possible output node name.
+  You should derive the names based on your own graph.
 
 Converting TensorFlow Models from Memory Using Python API
 ############################################################
 
 Model conversion API supports passing TensorFlow/TensorFlow2 models directly from memory.
 
-* ``Trackable``. The object returned by ``hub.load()`` can be converted to ``ov.Model`` with ``convert_model()``.
+* ``Trackable``. The object returned by ``hub.load()`` can be converted to
+  ``ov.Model`` with ``convert_model()``.
 
   .. code-block:: py
      :force:
 
      import tensorflow_hub as hub
      import openvino as ov
-    
+
      model = hub.load("https://tfhub.dev/google/movenet/singlepose/lightning/4")
      ov_model = ov.convert_model(model)
 
@@ -234,7 +282,10 @@ Model conversion API supports passing TensorFlow/TensorFlow2 models directly fro
      model = tf.keras.applications.ResNet50(weights="imagenet")
      ov_model = ov.convert_model(model)
 
-* ``tf.keras.layers.Layer``. The ``ov.Model`` converted from ``tf.keras.layers.Layer`` does not contain original input and output names. So it is recommended to convert the model to ``tf.keras.Model`` before conversion or use ``hub.load()`` for TensorFlow Hub models.
+* ``tf.keras.layers.Layer``. The ``ov.Model`` converted from ``tf.keras.layers.Layer``
+  does not contain original input and output names. So it is recommended to convert the
+  model to ``tf.keras.Model`` before conversion or use ``hub.load()`` for
+  TensorFlow Hub models.
 
   .. code-block:: py
      :force:
@@ -265,7 +316,12 @@ Model conversion API supports passing TensorFlow/TensorFlow2 models directly fro
      model = MyModule(name="simple_module")
      ov_model = ov.convert_model(model, input=[-1])
 
-.. note:: There is a known bug in ``openvino.convert_model`` on using ``tf.Variable`` nodes in the model graph. The results of the conversion of such models are unpredictable. It is recommended to save a model with ``tf.Variable`` into TensorFlow Saved Model format and load it with ``openvino.convert_model``.
+.. note::
+
+   There is a known bug in ``openvino.convert_model`` on using ``tf.Variable`` nodes in
+   the model graph. The results of the conversion of such models are unpredictable. It
+   is recommended to save a model with ``tf.Variable`` into TensorFlow Saved Model format
+   and load it with ``openvino.convert_model``.
 
 * ``tf.compat.v1.Graph``
 
@@ -328,7 +384,9 @@ Model conversion API supports passing TensorFlow/TensorFlow2 models directly fro
 Supported TensorFlow and TensorFlow 2 Keras Layers
 ##################################################
 
-For the list of supported standard layers, refer to the :doc:`Supported Operations <../../about-openvino/compatibility-and-support/supported-operations-framework-frontend>` page.
+For the list of supported standard layers, refer to the
+:doc:`Supported Operations <../../about-openvino/compatibility-and-support/supported-operations-framework-frontend>`
+page.
 
 Summary
 #######
@@ -339,5 +397,3 @@ In this document, you learned:
 * Which TensorFlow models are supported.
 * How to freeze a TensorFlow model.
 
-
-
diff --git a/docs/articles_en/openvino-workflow/model-preparation/convert-model-to-ir.rst b/docs/articles_en/openvino-workflow/model-preparation/convert-model-to-ir.rst
index ac008631e49fff..be67f581173309 100644
--- a/docs/articles_en/openvino-workflow/model-preparation/convert-model-to-ir.rst
+++ b/docs/articles_en/openvino-workflow/model-preparation/convert-model-to-ir.rst
@@ -18,7 +18,8 @@ Convert to OpenVINO IR
    Convert from PaddlePaddle <convert-model-paddle>
 
 
-:doc:`IR (Intermediate Representation) <../../documentation/openvino-ir-format>` is OpenVINO own format consisting of  ``.xml`` and ``.bin`` files.
+:doc:`IR (Intermediate Representation) <../../documentation/openvino-ir-format>` is
+OpenVINO own format consisting of  ``.xml`` and ``.bin`` files.
 Convert the model into OpenVINO IR for `better performance <#ir-conversion-benefits>`__.
 
 Convert Models
@@ -616,4 +617,5 @@ Additional Resources
 ####################
 
 * :doc:`Transition guide from the legacy to new conversion API <../../documentation/legacy-features/transition-legacy-conversion-api>`
+* `Download models from Hugging Face <https://huggingface.co/models>`__.
 
diff --git a/docs/articles_en/openvino-workflow/model-preparation/setting-input-shapes.rst b/docs/articles_en/openvino-workflow/model-preparation/setting-input-shapes.rst
index 7ab05fed2ec56a..9e17fdaad9999c 100644
--- a/docs/articles_en/openvino-workflow/model-preparation/setting-input-shapes.rst
+++ b/docs/articles_en/openvino-workflow/model-preparation/setting-input-shapes.rst
@@ -20,7 +20,8 @@ performance and memory consumption.
 For more information on changing input shapes in runtime, refer to the
 :doc:`Changing input shapes <../running-inference/changing-input-shape>` guide.
 To learn more about dynamic shapes in runtime, refer to the
-:doc:`Dynamic Shapes <../running-inference/dynamic-shapes>` guide.
+:doc:`Dynamic Shapes <../running-inference/dynamic-shapes>` guide. To download models,
+you can visit `Hugging Face <https://huggingface.co/models>`__.
 
 The OpenVINO Runtime API may present certain limitations in inferring models with undefined
 dimensions on some hardware. See the :doc:`Feature support matrix <../../about-openvino/compatibility-and-support/supported-devices>`
@@ -49,9 +50,13 @@ and specify the input shape of ``[2,300,300,3]``:
 
          ovc MobileNet.pb --input [2,300,300,3]
 
-If a model has multiple inputs, the input shape should be specified in ``input`` parameter as a list. In ``ovc``, this is a command separate list, and in ``openvino.convert_model`` this is a Python list or tuple with number of elements matching the number of inputs in the model. Use input names from the original model to define the mapping between inputs and shapes specified.
-The following example demonstrates the conversion of the ONNX OCR model with a pair of inputs ``data`` and ``seq_len``
-and specifies shapes ``[3,150,200,1]`` and ``[3]`` for them respectively:
+If a model has multiple inputs, the input shape should be specified in ``input`` parameter
+as a list. In ``ovc``, this is a command separate list, and in ``openvino.convert_model``
+this is a Python list or tuple with number of elements matching the number of inputs in
+the model. Use input names from the original model to define the mapping between inputs
+and shapes specified. The following example demonstrates the conversion of the ONNX OCR
+model with a pair of inputs ``data`` and ``seq_len`` and specifies shapes ``[3,150,200,1]``
+and ``[3]`` for them respectively:
 
 .. tab-set::
 
@@ -71,7 +76,9 @@ and specifies shapes ``[3,150,200,1]`` and ``[3]`` for them respectively:
 
          ovc ocr.onnx --input data[3,150,200,1],seq_len[3]
 
-If the order of inputs is defined in the input model and the order is known for the user, names could be omitted. In this case, it is important to specify shapes in the same order of input model inputs:
+If the order of inputs is defined in the input model and the order is known for the user,
+names could be omitted. In this case, it is important to specify shapes in the
+same order of input model inputs:
 
 .. tab-set::
 
@@ -91,12 +98,19 @@ If the order of inputs is defined in the input model and the order is known for
 
          ovc ocr.onnx --input [3,150,200,1],[3]
 
-Whether the model has a specified order of inputs depends on the original framework. Usually, it is convenient to set shapes without specifying the names of the parameters in the case of PyTorch model conversion because a PyTorch model is considered as a callable that usually accepts positional parameters. On the other hand, names of inputs are convenient when converting models from model files, because naming of inputs is a good practice for many frameworks that serialize models to files.
+Whether the model has a specified order of inputs depends on the original framework.
+Usually, it is convenient to set shapes without specifying the names of the parameters
+in the case of PyTorch model conversion because a PyTorch model is considered as
+a callable that usually accepts positional parameters. On the other hand, names of inputs
+are convenient when converting models from model files, because naming of inputs is
+a good practice for many frameworks that serialize models to files.
 
-The ``input`` parameter allows overriding original input shapes if it is supported by the model topology.
-Shapes with dynamic dimensions in the original model can be replaced with static shapes for the converted model, and vice versa.
-The dynamic dimension can be marked in model conversion API parameter as ``-1`` or ``?`` when using ``ovc``.
-For example, launch model conversion for the ONNX OCR model and specify dynamic batch dimension for inputs:
+The ``input`` parameter allows overriding original input shapes if it is supported by
+the model topology. Shapes with dynamic dimensions in the original model can be replaced
+with static shapes for the converted model, and vice versa. The dynamic dimension can be
+marked in model conversion API parameter as ``-1`` or ``?`` when using ``ovc``.
+For example, launch model conversion for the ONNX OCR model and specify dynamic batch
+dimension for inputs:
 
 .. tab-set::
 
@@ -116,9 +130,13 @@ For example, launch model conversion for the ONNX OCR model and specify dynamic
 
          ovc ocr.onnx --input "data[?,150,200,1],seq_len[?]"
 
-To optimize memory consumption for models with undefined dimensions in run-time, model conversion API provides the capability to define boundaries of dimensions.
-The boundaries of undefined dimension can be specified with ellipsis in the command line or with ``openvino.Dimension`` class in Python.
-For example, launch model conversion for the ONNX OCR model and specify a boundary for the batch dimension 1..3, which means that the input tensor will have batch dimension minimum 1 and maximum 3 in inference:
+To optimize memory consumption for models with undefined dimensions in run-time,
+model conversion API provides the capability to define boundaries of dimensions.
+The boundaries of undefined dimension can be specified with ellipsis in the command
+line or with ``openvino.Dimension`` class in Python.
+For example, launch model conversion for the ONNX OCR model and specify a boundary for
+the batch dimension 1..3, which means that the input tensor will have batch dimension
+minimum 1 and maximum 3 in inference:
 
 .. tab-set::
 
@@ -139,5 +157,9 @@ For example, launch model conversion for the ONNX OCR model and specify a bounda
 
          ovc ocr.onnx --input data[1..3,150,200,1],seq_len[1..3]
 
-In practice, not every model is designed in a way that allows change of input shapes. An attempt to change the shape for such models may lead to an exception during model conversion, later in model inference, or even to wrong results of inference without explicit exception raised. A knowledge about model topology is required to set shapes appropriately.
+In practice, not every model is designed in a way that allows change of input shapes.
+An attempt to change the shape for such models may lead to an exception during model
+conversion, later in model inference, or even to wrong results of inference without
+explicit exception raised. A knowledge about model topology is required to set
+shapes appropriately.
 
diff --git a/docs/articles_en/openvino-workflow/running-inference.rst b/docs/articles_en/openvino-workflow/running-inference.rst
index 3ccd9f3ff7cc2e..95c6bc66e902b8 100644
--- a/docs/articles_en/openvino-workflow/running-inference.rst
+++ b/docs/articles_en/openvino-workflow/running-inference.rst
@@ -39,9 +39,9 @@ OpenVINO IR provides by far the best first-inference latency scores.
    For more detailed information on how to convert, read, and compile supported model formats
    see the :doc:`Model Preparation article <model-preparation>`.
 
-   Note that TensorFlow models can be run using the
+   Note that PyTorch models can be run using the
    :doc:`torch.compile feature <torch-compile>`, as well as the standard ways of
-   :doc:`converting TensorFlow <model-preparation/convert-model-pytorch>`
+   :doc:`converting Pytorch <model-preparation/convert-model-pytorch>`
    or running its inference.
 
 OpenVINO Runtime uses a plugin architecture. Its plugins are software components that contain complete implementation for inference on a particular Intel® hardware device: CPU, GPU, etc. Each plugin implements the unified API and provides additional hardware-specific APIs for configuring devices or API interoperability between OpenVINO Runtime and underlying plugin backend.
diff --git a/docs/articles_en/openvino-workflow/running-inference/inference-devices-and-modes.rst b/docs/articles_en/openvino-workflow/running-inference/inference-devices-and-modes.rst
index 19328ec7a9c1f9..c11696ce07bc18 100644
--- a/docs/articles_en/openvino-workflow/running-inference/inference-devices-and-modes.rst
+++ b/docs/articles_en/openvino-workflow/running-inference/inference-devices-and-modes.rst
@@ -83,4 +83,3 @@ Accordingly, the code that loops over all available devices of the "GPU" type on
          :language: cpp
          :fragment: [part3]
 
-
diff --git a/docs/articles_en/openvino-workflow/running-inference/inference-devices-and-modes/auto-device-selection.rst b/docs/articles_en/openvino-workflow/running-inference/inference-devices-and-modes/auto-device-selection.rst
index d087e369ff117d..69bdf26eb0cf79 100644
--- a/docs/articles_en/openvino-workflow/running-inference/inference-devices-and-modes/auto-device-selection.rst
+++ b/docs/articles_en/openvino-workflow/running-inference/inference-devices-and-modes/auto-device-selection.rst
@@ -61,15 +61,14 @@ How AUTO Works
 ##############
 
 To put it simply, when loading the model to the first device on the list fails, AUTO will try to load it to the next device in line, until one of them succeeds.
-What is important, **AUTO starts inference with the CPU of the system by default**, as it provides very low latency and can start inference with no additional delays.
+What is important, **AUTO starts inference with the CPU of the system by default unless there is model cached for the best suited device**, as it provides very low latency and can start inference with no additional delays.
 While the CPU is performing inference, AUTO continues to load the model to the device best suited for the purpose and transfers the task to it when ready.
 This way, the devices which are much slower in compiling models, GPU being the best example, do not impact inference at its initial stages.
 For example, if you use a CPU and a GPU, the first-inference latency of AUTO will be better than that of using GPU alone.
 
 Note that if you choose to exclude CPU from the priority list or disable the initial
 CPU acceleration feature via ``ov::intel_auto::enable_startup_fallback``, it will be
-unable to support the initial model compilation stage. The models with dynamic
-input/output or :doc:`stateful operations <../stateful-models>`
+unable to support the initial model compilation stage. The models with :doc:`stateful operations <../stateful-models>`
 will be loaded to the CPU if it is in the candidate list. Otherwise,
 these models will follow the normal flow and be loaded to the device based on priority.
 
@@ -510,8 +509,9 @@ For more information, refer to the :doc:`Benchmark Tool <../../../learn-openvino
 Additional Resources
 ####################
 
-- :doc:`Debugging AUTO <auto-device-selection/debugging-auto-device>`
-- :doc:`Running on Multiple Devices Simultaneously <multi-device>`
-- :doc:`Inference Devices and Modes <../inference-devices-and-modes>`
+* `Automatic Device Selection with OpenVINO™ Notebook <https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/auto-device/auto-device.ipynb>`__
+* :doc:`Debugging AUTO <auto-device-selection/debugging-auto-device>`
+* :doc:`Running on Multiple Devices Simultaneously <multi-device>`
+* :doc:`Inference Devices and Modes <../inference-devices-and-modes>`
 
 
diff --git a/docs/articles_en/openvino-workflow/running-inference/inference-devices-and-modes/cpu-device/performance-hint-and-thread-scheduling.rst b/docs/articles_en/openvino-workflow/running-inference/inference-devices-and-modes/cpu-device/performance-hint-and-thread-scheduling.rst
index 5abd4dbcec6742..dc158fe9352042 100644
--- a/docs/articles_en/openvino-workflow/running-inference/inference-devices-and-modes/cpu-device/performance-hint-and-thread-scheduling.rst
+++ b/docs/articles_en/openvino-workflow/running-inference/inference-devices-and-modes/cpu-device/performance-hint-and-thread-scheduling.rst
@@ -7,8 +7,8 @@ Performance Hints and Thread Scheduling
                  on performance hints automatically.
 
 To simplify the configuration of hardware devices, it is recommended to use the
-:doc:` ov::hint::PerformanceMode::LATENCY and ov::hint::PerformanceMode::THROUGHPUT <../../../../optimize-inference/high-level-performance-hints>`
-high-level performance hints. Both performance hints ensure optimal portability
+ov::hint::PerformanceMode::LATENCY and ov::hint::PerformanceMode::THROUGHPUT
+:doc:`high-level performance hints <../../optimize-inference/high-level-performance-hints>`. Both performance hints ensure optimal portability
 and scalability of applications across various platforms and models.
 
 - ``ov::inference_num_threads`` limits the number of logical processors used for CPU inference.
@@ -44,7 +44,8 @@ the model precision and the ratio of P-cores and E-cores.
     P-cores is short for Performance-cores and E-cores stands for Efficient-cores. These
     types of cores are available starting with the 12th Gen Intel® Core™ processors.
 
-.. _Core Type Table of Latency Hint:
+.. _core_type_latency:
+
 +----------------------------+---------------------+---------------------+
 |                            | INT8 Model          | FP32 Model          |
 +============================+=====================+=====================+
@@ -69,7 +70,7 @@ Then the default settings for low-level performance properties on Windows and Li
 +--------------------------------------+------------------------------------------------------------------------+--------------------------------------------------------------------+
 | ``ov::inference_num_threads``        | is equal to the number of P-cores or P-cores+E-cores on one socket     | is equal to the number of P-cores or P-cores+E-cores on one socket |
 +--------------------------------------+------------------------------------------------------------------------+--------------------------------------------------------------------+
-| ``ov::hint::scheduling_core_type``   | `Core Type Table of Latency Hint`_                                     | `Core Type Table of Latency Hint`_                                 |
+| ``ov::hint::scheduling_core_type``   | :ref:`Core Type Table of Latency Hint <core_type_latency>`             | :ref:`Core Type Table of Latency Hint <core_type_latency>`         |
 +--------------------------------------+------------------------------------------------------------------------+--------------------------------------------------------------------+
 | ``ov::hint::enable_hyper_threading`` | No                                                                     | No                                                                 |
 +--------------------------------------+------------------------------------------------------------------------+--------------------------------------------------------------------+
diff --git a/docs/articles_en/openvino-workflow/running-inference/inference-devices-and-modes/gpu-device.rst b/docs/articles_en/openvino-workflow/running-inference/inference-devices-and-modes/gpu-device.rst
index 0e3506484b42ae..adad5dcf1e594d 100644
--- a/docs/articles_en/openvino-workflow/running-inference/inference-devices-and-modes/gpu-device.rst
+++ b/docs/articles_en/openvino-workflow/running-inference/inference-devices-and-modes/gpu-device.rst
@@ -476,6 +476,7 @@ Since OpenVINO relies on the OpenCL kernels for the GPU implementation, many gen
 Additional Resources
 #######################################
 
+* `Working with GPUs in OpenVINO™ Notebook <https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/gpu-device/gpu-device.ipynb>`__
 * :doc:`Inference Devices and Modes <../inference-devices-and-modes>`.
 * :doc:`Optimization guide <../optimize-inference>`.
 * `GPU plugin developer documentation <https://github.com/openvinotoolkit/openvino/blob/master/src/plugins/intel_gpu/README.md>`__
diff --git a/docs/articles_en/openvino-workflow/running-inference/inference-devices-and-modes/hetero-execution.rst b/docs/articles_en/openvino-workflow/running-inference/inference-devices-and-modes/hetero-execution.rst
index 0ba7d26436ee9e..24f820755555b0 100644
--- a/docs/articles_en/openvino-workflow/running-inference/inference-devices-and-modes/hetero-execution.rst
+++ b/docs/articles_en/openvino-workflow/running-inference/inference-devices-and-modes/hetero-execution.rst
@@ -22,7 +22,7 @@ Execution via the heterogeneous mode can be divided into two independent steps:
 These two steps are not interconnected and affinities can be set in one of two ways, used separately or in combination (as described below): in the ``manual`` or the ``automatic`` mode.
 
 Defining and Configuring the Hetero Device
-++++++++++++++++++++++++++++++++++++++++++
+##########################################
 
 Following the OpenVINO™ naming convention, the Hetero execution plugin is assigned the label of ``"HETERO".`` It may be defined with no additional parameters, resulting in defaults being used, or configured further with the following setup options:
 
@@ -37,10 +37,10 @@ Following the OpenVINO™ naming convention, the Hetero execution plugin is assi
 
 
 Manual and Automatic modes for assigning affinities
-+++++++++++++++++++++++++++++++++++++++++++++++++++
+###################################################
 
 The Manual Mode
---------------------
++++++++++++++++++++++
 
 It assumes setting affinities explicitly for all operations in the model using `ov::Node::get_rt_info <https://docs.openvino.ai/2024/api/c_cpp_api/classov_1_1_node.html#doxid-classov-1-1-node-1a6941c753af92828d842297b74df1c45a>`__ with the ``"affinity"`` key.
 
@@ -66,7 +66,7 @@ Randomly selecting operations and setting affinities may lead to decrease in mod
 
 
 The Automatic Mode
---------------------
++++++++++++++++++++++
 
 It decides automatically which operation is assigned to which device according to the support from dedicated devices (``GPU``, ``CPU``, etc.) and query model step is called implicitly by Hetero device during model compilation.
 
@@ -92,7 +92,7 @@ It does not take into account device peculiarities such as the inability to infe
 
 
 Using Manual and Automatic Modes in Combination
------------------------------------------------
++++++++++++++++++++++++++++++++++++++++++++++++
 
 In some cases you may need to consider manually adjusting affinities which were set automatically. It usually serves minimizing the number of total subgraphs to optimize memory transfers. To do it, you need to "fix" the automatically assigned affinities like so:
 
@@ -121,7 +121,7 @@ Importantly, the automatic mode will not work if any operation in a model has it
    `ov::Core::query_model <https://docs.openvino.ai/2024/api/c_cpp_api/classov_1_1_core.html#doxid-classov-1-1-core-1acdf8e64824fe4cf147c3b52ab32c1aab>`__ does not depend on affinities set by a user. Instead, it queries for an operation support based on device capabilities.
 
 Configure fallback devices
-++++++++++++++++++++++++++
+##########################
 
 If you want different devices in Hetero execution to have different device-specific configuration options, you can use the special helper property `ov::device::properties <https://docs.openvino.ai/2024/api/c_cpp_api/structov_1_1device_1_1_properties.html#doxid-group-ov-runtime-cpp-prop-api-1ga794d09f2bd8aad506508b2c53ef6a6fc>`__:
 
@@ -146,7 +146,7 @@ If you want different devices in Hetero execution to have different device-speci
 In the example above, the ``GPU`` device is configured to enable profiling data and uses the default execution precision, while ``CPU`` has the configuration property to perform inference in ``fp32``.
 
 Handling of Difficult Topologies
-++++++++++++++++++++++++++++++++
+################################
 
 Some topologies are not friendly to heterogeneous execution on some devices, even to the point of being unable to execute.
 For example, models having activation operations that are not supported on the primary device are split by Hetero into multiple sets of subgraphs which leads to suboptimal execution.
@@ -154,7 +154,7 @@ If transmitting data from one subgraph to another part of the model in the heter
 In such cases, you can define the heaviest part manually and set the affinity to avoid sending data back and forth many times during one inference.
 
 Analyzing Performance of Heterogeneous Execution
-++++++++++++++++++++++++++++++++++++++++++++++++
+################################################
 
 After enabling the ``OPENVINO_HETERO_VISUALIZE`` environment variable, you can dump GraphViz ``.dot`` files with annotations of operations per devices.
 
@@ -186,7 +186,7 @@ Here is an example of the output for Googlenet v1 running on HDDL (device no lon
 
 
 Sample Usage
-++++++++++++++++++++
+#####################
 
 OpenVINO™ sample programs can use the Heterogeneous execution used with the ``-d`` option:
 
diff --git a/docs/articles_en/openvino-workflow/running-inference/inference-devices-and-modes/npu-device.rst b/docs/articles_en/openvino-workflow/running-inference/inference-devices-and-modes/npu-device.rst
index ea39001b4f3fd4..5a21f2f7ccc0f8 100644
--- a/docs/articles_en/openvino-workflow/running-inference/inference-devices-and-modes/npu-device.rst
+++ b/docs/articles_en/openvino-workflow/running-inference/inference-devices-and-modes/npu-device.rst
@@ -20,8 +20,9 @@ NPU Plugin is now available through all relevant OpenVINO distribution channels.
 
 NPU Plugin needs an NPU Driver to be installed on the system for both compiling and executing a model.
 Follow the instructions below to install the latest NPU drivers:
-* Windows driver: https://www.intel.com/content/www/us/en/download/794734/intel-npu-driver-windows.html
-* Linux driver: https://github.com/intel/linux-npu-driver/releases
+
+* `Windows driver <https://www.intel.com/content/www/us/en/download/794734/intel-npu-driver-windows.html>`__
+* `Linux driver <https://github.com/intel/linux-npu-driver/releases>`__
 
 
 The plugin uses the graph extension API exposed by the driver to convert the OpenVINO specific representation
@@ -151,7 +152,8 @@ guaranteed.
 Additional Resources
 #############################
 
-* `Vision colorization Notebook <notebooks/vision-image-colorization-with-output.html>`__
+* `Working with NPUs in OpenVINO™ Notebook <https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/hello-npu/hello-npu.ipynb>`__
+* `Vision colorization Notebook <./../../../notebooks/vision-image-colorization-with-output.html>`__
 * `Classification Benchmark C++ Demo <https://github.com/openvinotoolkit/open_model_zoo/tree/master/demos/classification_benchmark_demo/cpp>`__
 * `3D Human Pose Estimation Python Demo <https://github.com/openvinotoolkit/open_model_zoo/tree/master/demos/3d_segmentation_demo/python>`__
 * `Object Detection C++ Demo <https://github.com/openvinotoolkit/open_model_zoo/tree/master/demos/object_detection_demo/cpp>`__
diff --git a/docs/articles_en/openvino-workflow/running-inference/integrate-openvino-with-your-application.rst b/docs/articles_en/openvino-workflow/running-inference/integrate-openvino-with-your-application.rst
index ce5e6fd20722a1..222c8760d0a880 100644
--- a/docs/articles_en/openvino-workflow/running-inference/integrate-openvino-with-your-application.rst
+++ b/docs/articles_en/openvino-workflow/running-inference/integrate-openvino-with-your-application.rst
@@ -432,7 +432,8 @@ To build your project using CMake with the default build tools currently availab
 Additional Resources
 ####################
 
-* See the :doc:`OpenVINO Samples <../../learn-openvino/openvino-samples>` page or the `Open Model Zoo Demos <https://docs.openvino.ai/2024/omz_demos.html>`__ page for specific examples of how OpenVINO pipelines are implemented for applications like image classification, text prediction, and many others.
+* See the :doc:`OpenVINO Samples <../../learn-openvino/openvino-samples>` page for specific examples of how OpenVINO pipelines are implemented for applications like image classification, text prediction, and many others.
+* Models in the OpenVINO IR format on `Hugging Face <https://huggingface.co/models>`__.
 * :doc:`OpenVINO™ Runtime Preprocessing <optimize-inference/optimize-preprocessing>`
 * :doc:`String Tensors <string-tensors>`
 * :doc:`Using Encrypted Models with OpenVINO <../../documentation/openvino-security/openvino-encrypted-models>`
diff --git a/docs/articles_en/openvino-workflow/running-inference/string-tensors.rst b/docs/articles_en/openvino-workflow/running-inference/string-tensors.rst
index 438a2a5d9ae259..22223649144d9f 100644
--- a/docs/articles_en/openvino-workflow/running-inference/string-tensors.rst
+++ b/docs/articles_en/openvino-workflow/running-inference/string-tensors.rst
@@ -10,11 +10,11 @@ String Tensors
 OpenVINO tensors can hold not only numerical data, like floating-point or integer numbers,
 but also textual information, represented as one or multiple strings.
 Such a tensor is called a string tensor and can be passed as input or retrieved as output of a text-processing model, such as
-`tokenizers and detokenizers <https://github.com/openvinotoolkit/openvino_contrib/tree/master/modules/custom_operations/user_ie_extensions/tokenizer/python>`__.
+`tokenizers and detokenizers <https://github.com/openvinotoolkit/openvino_tokenizers/tree/master>`__.
 
 While this section describes basic API to handle string tensors, more practical examples that leverage both
 string tensors and OpenVINO tokenizer can be found in
-`GenAI Samples <https://github.com/openvinotoolkit/openvino.genai/tree/master/text_generation/causal_lm/cpp>`__.
+`GenAI Samples <https://github.com/openvinotoolkit/openvino.genai/tree/master/samples/cpp/greedy_causal_lm>`__.
 
 
 Representation
diff --git a/docs/articles_en/openvino-workflow/torch-compile.rst b/docs/articles_en/openvino-workflow/torch-compile.rst
index 280b7c01ca190e..759ca125d6dcd5 100644
--- a/docs/articles_en/openvino-workflow/torch-compile.rst
+++ b/docs/articles_en/openvino-workflow/torch-compile.rst
@@ -48,7 +48,7 @@ For OpenVINO installed via channels other than pip, such as conda, and versions
 
 
 .. image:: ../assets/images/torch_compile_backend_openvino.svg
-   :alt: torch.compile execution diagram 
+   :alt: torch.compile execution diagram
    :width: 992px
    :height: 720px
    :scale: 60%
@@ -196,7 +196,7 @@ The ``torch.compile`` feature is part of PyTorch 2.0, and is based on:
   (PEP 523) to dynamically modify Python bytecode right before it is executed (PyTorch operators
   that cannot be extracted to FX graph are executed in the native Python environment).
   It maintains the eager-mode capabilities using
-  `Guards <https://pytorch.org/docs/stable/dynamo/guards-overview.html>`__ to ensure the
+  `Guards <https://pytorch.org/docs/stable/torch.compiler_guards_overview.html>`__ to ensure the
   generated graphs are valid.
 
 * **AOTAutograd** - generates the backward graph corresponding to the forward graph captured by TorchDynamo.
diff --git a/docs/dev/build_linux.md b/docs/dev/build_linux.md
index 814deaa3deec3c..d3e1319f9d2c1a 100644
--- a/docs/dev/build_linux.md
+++ b/docs/dev/build_linux.md
@@ -12,13 +12,7 @@ The software was validated on:
 - [CMake](https://cmake.org/download/) 3.13 or higher
 - GCC 7.5 or higher to build OpenVINO Runtime
 - Python 3.8 - 3.11 for OpenVINO Runtime Python API
-- (Optional) Install Intel® Graphics Compute Runtime for OpenCL™ Driver package to enable inference on Intel integrated GPUs. Select a driver package from the table below depending on what version of Ubuntu you are installing on.
-
-  | Ubuntu | Driver package |
-  | --- | ----------- |
-  | 22.04 | [23.13.26032.30](https://github.com/intel/compute-runtime/releases/tag/23.13.26032.30) |
-  | 20.04 | [22.24.23453](https://github.com/intel/compute-runtime/releases/tag/22.24.23453) |
-  | 18.04 | [21.38.21026](https://github.com/intel/compute-runtime/releases/tag/21.38.21026) |
+- (Optional) Install Intel® Graphics Compute Runtime for OpenCL™ Driver package to enable inference on Intel integrated GPUs.
 
 ## How to build
 
@@ -45,6 +39,8 @@ The software was validated on:
      mkdir build && cd build
    ```
 
+> **NOTE**: It is recommended to disable the oneAPI environment before compiling OpenVINO from source on Linux, as it may cause build failures. 
+
 4. OpenVINO Runtime uses a CMake-based build system. In the created `build` directory, run `cmake` to fetch project dependencies and create Unix makefiles, then run `make` to build the project:
    ```sh
      cmake -DCMAKE_BUILD_TYPE=Release ..
diff --git a/docs/dev/build_windows.md b/docs/dev/build_windows.md
index eaf695f898ba18..2515000e5ec4f0 100644
--- a/docs/dev/build_windows.md
+++ b/docs/dev/build_windows.md
@@ -6,20 +6,20 @@ Supported configurations:
 - Windows 10 x86 64-bit or higher with Visual Studio 2019 or higher build for X64 architecture.
 - Windows on ARM (shortly WoA) to build for ARM64 architecture. OpenVINO was validated on [Windows DevKit 2023](https://developer.qualcomm.com/hardware/windows-on-snapdragon/windows-dev-kit-2023)
 
-## Software requirements 
+## Software requirements
 
 - [CMake](https://cmake.org/download/) 3.13 or higher
 - Microsoft Visual Studio 2019 or higher, version 16.3 or later
-  > **NOTE**: Native Microsoft Visual Studio for WoA is available since 2022. 
+  > **NOTE**: Native Microsoft Visual Studio for WoA has been available since version 3.11.
 - Python 3.8 - 3.11 for OpenVINO Runtime Python API
-  > **NOTE**: Python for ARM64 is available since [3.11](https://www.python.org/downloads/windows/) version. 
+  > **NOTE**: Python for ARM64 is available since [3.11](https://www.python.org/downloads/windows/) version.
 - [Git for Windows*]
 - (Windows on ARM only) [LLVM for Windows on ARM (WoA)](https://github.com/llvm/llvm-project/releases/download/llvmorg-15.0.6/LLVM-15.0.6-woa64.exe)
-  > **NOTE**: After installation, make sure `clang-cl` compiler is available from `PATH`. 
+  > **NOTE**: After installation, make sure `clang-cl` compiler is available from `PATH`.
 
 ## How to build
 
-> **NOTE**: By default, the build enables the OpenVINO Runtime GPU plugin to infer models on your Intel® Processor Graphics. This requires you to download and install the Intel® Graphics Driver for Windows (26.20) [driver package](https://www.intel.com/content/www/us/en/download/19344/intel-graphics-windows-dch-drivers.html) before running the build. If you don't want to use the GPU plugin, use the `-DENABLE_INTEL_GPU=OFF` CMake build option and skip the installation of the Intel® Graphics Driver.
+> **NOTE**: By default, the build enables the OpenVINO Runtime GPU plugin to infer models on your Intel® Processor Graphics. This requires you to download and install the [Intel® Graphics Driver for Windows](https://www.intel.com/content/www/us/en/download/19344/intel-graphics-windows-dch-drivers.html) before running the build. If you don't want to use the GPU plugin, use the `-DENABLE_INTEL_GPU=OFF` CMake build option and skip the installation of the Intel® Graphics Driver.
 
 1. Clone submodules:
     ```sh
@@ -37,12 +37,12 @@ Supported configurations:
     ```sh
     cmake -G "Visual Studio 17 2022" <path/to/openvino>
     ```
-    
+
    > **HINT**: **Generating PDB Files and Debugging Your Build** <br>
    > If you intend to generate PDB files and debug your build, it is essential to set the CMake build type appropriately.
    > You should utilize one of the following CMake build type options: <br>
    >* `-DCMAKE_BUILD_TYPE=RelWithDebInfo`: This option generates PDB files with release information, making it suitable for debugging optimized builds. <br>
-   >* `-DCMAKE_BUILD_TYPE=Debug`: This option generates PDB files optimized for debugging, providing comprehensive debugging information. 
+   >* `-DCMAKE_BUILD_TYPE=Debug`: This option generates PDB files optimized for debugging, providing comprehensive debugging information.
 
 4. Build generated solution in Visual Studio or run `cmake --build . --config Release --verbose -j<number_of_jobs>` to build from the command line. View the number of available processing units with `WMIC cpu get numberofLogicalProcessors`. Be aware that this process may take some time.
 
@@ -62,7 +62,7 @@ Supported configurations:
   3. After the build process finishes, export the newly built Python libraries to the user environment variables:
      ```
      set PYTHONPATH=<openvino_repo>/bin/<arch>/Release/python;%PYTHONPATH%
-     set OPENVINO_LIB_PATHS=<openvino_repo>/bin/<arch>/Release;%OPENVINO_LIB_PATH%
+     set OPENVINO_LIB_PATHS=<openvino_repo>/bin/<arch>/Release;<openvino_repo>/temp/tbb/bin
      ```
      or install the wheel with pip:
      ```
diff --git a/docs/dev/cmake_options_for_custom_compilation.md b/docs/dev/cmake_options_for_custom_compilation.md
index a5184dbc460979..d370285f99c8b4 100644
--- a/docs/dev/cmake_options_for_custom_compilation.md
+++ b/docs/dev/cmake_options_for_custom_compilation.md
@@ -42,6 +42,8 @@ This document provides description and default values for CMake options that can
         * `ON` is default.
     * `ENABLE_OV_PYTORCH_FRONTEND` enables [PyTorch] frontend plugin for OpenVINO Runtime:
         * `ON` is default.
+    * `ENABLE_OV_JAX_FRONTEND` enables [JAX] frontend plugin for OpenVINO Runtime:
+        * `ON` is default.
     * `ENABLE_OV_IR_FRONTEND` enables OpenVINO Intermediate Representation frontend plugin for OpenVINO Runtime:
         * `ON` is default.
 * `OPENVINO_EXTRA_MODULES` specifies path to add extra OpenVINO modules to the build.
diff --git a/docs/dev/ov_dependencies_24_2.txt b/docs/dev/ov_dependencies_24_2.txt
new file mode 100644
index 00000000000000..0f24303ec47d0c
--- /dev/null
+++ b/docs/dev/ov_dependencies_24_2.txt
@@ -0,0 +1,374 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+#This file provides a comprehensive list of all dependencies of OpenVINO 2024.2
+#The file is part of the automation pipeline for posting OpenVINO IR models on the HuggingFace Hub, including OneBOM dependency checks.
+
+
+absl-py
+accuracy-checker
+addict
+alabaster
+appnope
+argparse
+astor
+astpretty
+astroid
+astunparse
+atomicwrites
+attrs
+babel
+backcall
+backports-functools-lru-cache
+bandit
+beautifulsoup4
+black
+Boost
+Bootstrap
+BrainMaGe
+cached-property
+cachetools
+certifi
+chardet
+charset-normalizer
+chosen-js
+clang
+click
+CNPY
+colorama
+ComputeLibrary
+coverage
+cycler
+cython
+dataclasses
+decorator
+Deformable-ConvNets
+defusedxml
+Detectron2
+distlib
+distro
+docopt
+docutils
+Doxygen
+Vovkos doxyrest
+enum34
+eradicate
+execnet
+fastjsonschema
+Ffmpeg
+filelock
+flake8
+flake8_builtins
+flake8_coding
+flake8_commas
+flake8_pep3101
+flake8_quotes
+flake8-annotations-complexity
+flake8-broken-line
+flake8-bugbear
+flake8-class-attributes-order
+flake8-comprehensions
+flake8-debugger
+flake8-docstrings
+flake8-eradicate
+flake8-executable
+flake8-expression-complexity
+flake8-plugin-utils
+flake8-print
+flake8-pytest-style
+flake8-rst-docstrings
+flake8-string-format
+flake8-variables-names
+flatbuffers
+fonttools
+freetype
+funcsigs
+functools32
+future
+futures
+gast
+generator
+gflags
+gitdb
+gitpython
+google-auth
+google-auth-oauthlib
+google cpplint
+google fonts
+google-java-format
+google-pasta
+Google gtest-parallel
+googletest
+graphviz
+grpcio
+Gstreamer
+h5py
+hamcrest-core
+Hyperopt
+idna
+idna
+imagesize
+importlib-metadata
+importlib-resources
+import-order
+iniconfig
+INTEL/CAFFE
+Intel Integrated Performance Primitives - Intel IPP
+intel/IntelSEAPI 
+intel/libva
+ipython
+isort
+itt API
+jedi
+jinja2
+json
+json-c
+jsonschema
+pboettch json-schema-validator
+linjackson78 jstyleson
+John Hauser Berkeley SoftFloat
+jquery - jquery/jquery
+Junit
+keras-nightly
+keras-preprocessing
+kiwisolver
+lazy-object-proxy
+libjpeg-turbo
+Libpng
+Llohse libnpy
+llvm
+lxml
+mando
+facebookresearch/maskrcnn-benchmark
+markdown
+markupsafe
+mathjax mathjax
+matplotlib
+mccabe
+mistune
+mongo-python-driver
+more-itertools
+mxnet
+mypy
+mypy-extensions
+networkx
+nibabel
+Nithinn ncc
+numpy
+oauthlib
+OneDNN
+OneTBB
+onnx
+onnxruntime
+Open Model Zoo
+omz-tools
+OpenCL
+OpenCL headers
+OpenCL ICD Loader
+OpenCL-hpp headers
+OpenCV
+opencv-python
+OpenJDK
+OpenVINO OSS
+openvino-telemetry
+opt-einsum
+packaging
+paddlepaddle
+PapaParse
+pandas
+parso
+pathlib2
+pathspec
+pbr
+pep8-naming
+pexpect
+pickleshare
+pillow
+platformdirs
+pluggy
+pluggy
+progress
+prompt-toolkit
+protobuf
+ptyprocess
+pugixml
+py
+pyasn1
+pyasn1-modules
+pycodestyle
+py-cpuinfo
+py-leveldb
+Pybind11
+pydata-sphinx-theme
+pydocstyle
+pyenchant
+pyflakes
+pygments
+pylint
+pymongo
+pyparsing
+pyrsistent
+pytest
+pytest-dependency
+pytest-forked
+pytest-html
+pytest-metadata
+pytest-metadata
+pytest-timeout
+pytest-xdist
+Pytorch
+Python
+python-attrs
+python-dateutil
+python-fastjsonschema
+python pandas
+Python-pillow
+pytz
+pyyaml
+radon
+Rapidjson
+requests
+requests-oauthlib
+restructuredtext-lint
+retrying
+Roboto Fonts
+rsa
+ruGPT3Medium
+safestringlib
+scikit-build
+scipy
+setuptools
+six
+smmap
+snowballstemmer
+soupsieve
+sphinx
+sphinxcontrib-applehelp
+sphinxcontrib-devhelp
+sphinxcontrib-htmlhelp
+sphinxcontrib-jsmath
+sphinxcontrib-qthelp
+sphinxcontrib-serializinghtml
+sphinx-copybutton
+sphinx-inline-tabs
+sphinx-panels
+sphinx-sitemap
+stevedore
+tensorboard
+tensorboard-data-server
+tensorboard-plugin-wit
+tensorflow
+tensorflow
+tensorflow-estimator
+termcolor
+test-generator
+threading_building_blocks
+foutaise/texttable/
+toml
+tomli
+Torch7
+tox
+traitlets
+Transformers
+typed-ast
+types-pkg_resources
+typing
+typing-extensions
+urllib3
+Viewer
+virtualenv
+Torchvision
+wcwidth
+werkzeug
+wheel
+wrapt
+xbyak
+zipp
+Zlib
+backports
+contextlib2
+dill
+keras
+singledispatch
+Fluid
+libusb (for MYRIAD)
+libGNA (for GNA)
+Benchmark
+GitPython
+huggingface/transformers
+Hamcrest JavaHamcrest
+Openvinotoolkit model-server
+Openvinotoolkit nncf
+pytorch/vision
+Sphinx documentation builder
+FlatBuffers
+POCO C++ Libraries
+Google Mock
+Google Snappy
+Microsoft onnxruntime
+ML commons/inference
+take-action
+bitsandbytes
+diffusers
+einops
+optimum-intel
+tiktoken
+timm
+tokenizers
+torch
+optimum
+psutil
+vscode-extension-samples
+fast_tokenizer
+re2
+icu4c
+sentencepiece
+auto-gptq
+transformers-stream-generator
+eigen3
+gtk3-devel 
+gstreamer1-devel 
+gstreamer1-plugins-base-devel 
+ffmpeg-devel 
+libmfx-devel 
+cmake 
+git 
+python38-devel 
+python38-pip 
+gcc-c++  
+gcc 
+python3-dev 
+python3-pip 
+ninja-build 
+libgtk-3-dev 
+libpng-dev 
+libjpeg-dev 
+libwebp-dev 
+libtiff5-dev 
+libopenexr-dev 
+libopenblas-dev 
+libx11-dev 
+libavutil-dev 
+libavcodec-dev 
+libavformat-dev 
+libswscale-dev 
+libavresample-dev 
+libtbb2 
+libssl-dev 
+libva-dev 
+libmfx-dev 
+libgstreamer1.0-dev 
+libgstreamer-plugins-base1.0-dev
+epel-release-latest-8.noarch.rpm
+rpmfusion-free-release-8.noarch.rpm
+pytest-repeat 0.9.3
+pip
+make
+httpx
+ultralytics
+llama.cpp
+lixsmm
+
+
+
+
+
diff --git a/docs/dev/static_libaries.md b/docs/dev/static_libaries.md
index 3570b4532d9157..0f694cda73f07d 100644
--- a/docs/dev/static_libaries.md
+++ b/docs/dev/static_libaries.md
@@ -47,6 +47,7 @@ cmake -DENABLE_INTEL_GPU=OFF \
       -DENABLE_OV_TF_FRONTEND=OFF \
       -DENABLE_OV_TF_LITE_FRONTEND=OFF \
       -DENABLE_OV_PYTORCH_FRONTEND=OFF \
+      -DENABLE_OV_JAX_FRONTEND=OFF \
       -DENABLE_INTEL_CPU=ON \
       -DENABLE_OV_IR_FRONTEND=ON
 ```
diff --git a/docs/nbdoc/consts.py b/docs/nbdoc/consts.py
index ded7c9637e0999..83c035c7766160 100644
--- a/docs/nbdoc/consts.py
+++ b/docs/nbdoc/consts.py
@@ -6,7 +6,7 @@
 repo_owner = "openvinotoolkit"
 repo_name = "openvino_notebooks"
 repo_branch = "tree/main"
-artifacts_link = "http://repository.toolbox.iotg.sclab.intel.com/projects/ov-notebook/0.1.0-latest/20240605220807/dist/rst_files/"
+artifacts_link = "http://repository.toolbox.iotg.sclab.intel.com/projects/ov-notebook/0.1.0-latest/20240701220806/dist/rst_files/"
 blacklisted_extensions = ['.xml', '.bin']
 notebooks_repo = "https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/"
 notebooks_binder = "https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?filepath="
diff --git a/docs/notebooks/3D-pose-estimation-with-output.rst b/docs/notebooks/3D-pose-estimation-with-output.rst
index 8d52a5d3589f29..ba06f88f2bd864 100644
--- a/docs/notebooks/3D-pose-estimation-with-output.rst
+++ b/docs/notebooks/3D-pose-estimation-with-output.rst
@@ -12,13 +12,13 @@ out the algorithms. **Make sure you have properly installed
 the**\ `Jupyter
 extension <https://github.com/jupyter-widgets/pythreejs#jupyterlab>`__\ **and
 been using JupyterLab to run the demo as suggested in the
-``README.md``**
+"README.md"**
 
    **NOTE**: *To use a webcam, you must run this Jupyter notebook on a
    computer with a webcam. If you run on a remote server, the webcam
    will not work. However, you can still do inference on a video file in
    the final step. This demo utilizes the Python interface in
-   ``Three.js`` integrated with WebGL to process data from the model
+   "Three.js" integrated with WebGL to process data from the model
    inference. These results are processed and displayed in the
    notebook.*
 
@@ -26,8 +26,8 @@ been using JupyterLab to run the demo as suggested in the
 recommended browser on one of the following operating systems:* *Ubuntu,
 Windows: Chrome* *macOS: Safari*
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Imports <#imports>`__
@@ -52,7 +52,7 @@ Prerequisites
 
 
 
-**The ``pythreejs`` extension may not display properly when using a
+**The "pythreejs" extension may not display properly when using a
 Jupyter Notebook release. Therefore, it is recommended to use Jupyter
 Lab instead.**
 
@@ -67,84 +67,84 @@ Lab instead.**
     Collecting pythreejs
       Using cached pythreejs-2.4.2-py3-none-any.whl.metadata (5.4 kB)
     Collecting openvino-dev>=2024.0.0
-      Using cached openvino_dev-2024.1.0-15008-py3-none-any.whl.metadata (16 kB)
+      Using cached openvino_dev-2024.2.0-15519-py3-none-any.whl.metadata (16 kB)
     Collecting opencv-python
-      Using cached opencv_python-4.10.0.82-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (20 kB)
+      Using cached opencv_python-4.10.0.84-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (20 kB)
     Collecting torch
       Using cached https://download.pytorch.org/whl/cpu/torch-2.3.1%2Bcpu-cp38-cp38-linux_x86_64.whl (190.4 MB)
     Collecting onnx
       Using cached onnx-1.16.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (16 kB)
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     Collecting ipydatawidgets>=1.1.1 (from pythreejs)
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       Using cached networkx-3.1-py3-none-any.whl.metadata (5.3 kB)
     Collecting openvino-telemetry>=2023.2.1 (from openvino-dev>=2024.0.0)
       Using cached openvino_telemetry-2024.1.0-py3-none-any.whl.metadata (2.3 kB)
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-    Collecting openvino==2024.1.0 (from openvino-dev>=2024.0.0)
-      Using cached openvino-2024.1.0-15008-cp38-cp38-manylinux2014_x86_64.whl.metadata (8.8 kB)
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+    Collecting openvino==2024.2.0 (from openvino-dev>=2024.0.0)
+      Using cached openvino-2024.2.0-15519-cp38-cp38-manylinux2014_x86_64.whl.metadata (8.9 kB)
     Collecting filelock (from torch)
-      Using cached filelock-3.14.0-py3-none-any.whl.metadata (2.8 kB)
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+      Using cached filelock-3.15.4-py3-none-any.whl.metadata (2.9 kB)
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-      Using cached fsspec-2024.6.0-py3-none-any.whl.metadata (11 kB)
+      Using cached fsspec-2024.6.1-py3-none-any.whl.metadata (11 kB)
     Collecting protobuf>=3.20.2 (from onnx)
-      Using cached protobuf-5.27.0-cp38-abi3-manylinux2014_x86_64.whl.metadata (592 bytes)
+      Using cached protobuf-5.27.2-cp38-abi3-manylinux2014_x86_64.whl.metadata (592 bytes)
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+    Requirement already satisfied: stack-data in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from ipython>=6.1.0->ipywidgets>=7.2.1->pythreejs) (0.6.3)
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+    Requirement already satisfied: parso<0.9.0,>=0.8.3 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from jedi>=0.16->ipython>=6.1.0->ipywidgets>=7.2.1->pythreejs) (0.8.4)
+    Requirement already satisfied: ptyprocess>=0.5 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from pexpect>4.3->ipython>=6.1.0->ipywidgets>=7.2.1->pythreejs) (0.7.0)
+    Requirement already satisfied: wcwidth in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from prompt-toolkit!=3.0.37,<3.1.0,>=3.0.30->ipython>=6.1.0->ipywidgets>=7.2.1->pythreejs) (0.2.13)
+    Requirement already satisfied: executing>=1.2.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from stack-data->ipython>=6.1.0->ipywidgets>=7.2.1->pythreejs) (2.0.1)
+    Requirement already satisfied: asttokens>=2.1.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from stack-data->ipython>=6.1.0->ipywidgets>=7.2.1->pythreejs) (2.4.1)
+    Requirement already satisfied: pure-eval in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from stack-data->ipython>=6.1.0->ipywidgets>=7.2.1->pythreejs) (0.2.2)
+    Requirement already satisfied: six>=1.12.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from asttokens>=2.1.0->stack-data->ipython>=6.1.0->ipywidgets>=7.2.1->pythreejs) (1.16.0)
     Using cached pythreejs-2.4.2-py3-none-any.whl (3.4 MB)
-    Using cached openvino_dev-2024.1.0-15008-py3-none-any.whl (4.7 MB)
-    Using cached openvino-2024.1.0-15008-cp38-cp38-manylinux2014_x86_64.whl (38.7 MB)
-    Using cached opencv_python-4.10.0.82-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (62.5 MB)
+    Using cached openvino_dev-2024.2.0-15519-py3-none-any.whl (4.7 MB)
+    Using cached openvino-2024.2.0-15519-cp38-cp38-manylinux2014_x86_64.whl (38.7 MB)
+    Using cached opencv_python-4.10.0.84-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (62.5 MB)
     Using cached onnx-1.16.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (15.9 MB)
     Using cached ipydatawidgets-4.3.5-py2.py3-none-any.whl (271 kB)
     Using cached networkx-3.1-py3-none-any.whl (2.1 MB)
     Using cached numpy-1.24.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.3 MB)
     Using cached openvino_telemetry-2024.1.0-py3-none-any.whl (23 kB)
-    Using cached protobuf-5.27.0-cp38-abi3-manylinux2014_x86_64.whl (309 kB)
-    Using cached filelock-3.14.0-py3-none-any.whl (12 kB)
-    Using cached fsspec-2024.6.0-py3-none-any.whl (176 kB)
+    Using cached protobuf-5.27.2-cp38-abi3-manylinux2014_x86_64.whl (309 kB)
+    Using cached filelock-3.15.4-py3-none-any.whl (16 kB)
+    Using cached fsspec-2024.6.1-py3-none-any.whl (177 kB)
     Using cached sympy-1.12.1-py3-none-any.whl (5.7 MB)
     Using cached traittypes-0.2.1-py2.py3-none-any.whl (8.6 kB)
     Installing collected packages: openvino-telemetry, mpmath, traittypes, sympy, protobuf, numpy, networkx, fsspec, filelock, torch, openvino, opencv-python, onnx, openvino-dev, ipydatawidgets, pythreejs
-    Successfully installed filelock-3.14.0 fsspec-2024.6.0 ipydatawidgets-4.3.5 mpmath-1.3.0 networkx-3.1 numpy-1.24.4 onnx-1.16.1 opencv-python-4.10.0.82 openvino-2024.1.0 openvino-dev-2024.1.0 openvino-telemetry-2024.1.0 protobuf-5.27.0 pythreejs-2.4.2 sympy-1.12.1 torch-2.3.1+cpu traittypes-0.2.1
+    Successfully installed filelock-3.15.4 fsspec-2024.6.1 ipydatawidgets-4.3.5 mpmath-1.3.0 networkx-3.1 numpy-1.24.4 onnx-1.16.1 opencv-python-4.10.0.84 openvino-2024.2.0 openvino-dev-2024.2.0 openvino-telemetry-2024.1.0 protobuf-5.27.2 pythreejs-2.4.2 sympy-1.12.1 torch-2.3.1+cpu traittypes-0.2.1
     Note: you may need to restart the kernel to use updated packages.
 
 
@@ -158,28 +158,28 @@ Imports
     import collections
     import time
     from pathlib import Path
-    
+
     import cv2
     import ipywidgets as widgets
     import numpy as np
     from IPython.display import clear_output, display
     import openvino as ov
-    
+
     # Fetch `notebook_utils` module
     import requests
-    
+
     r = requests.get(
         url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
     with open("notebook_utils.py", "w") as f:
         f.write(r.text)
-    
+
     r = requests.get(
         url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/engine3js.py",
     )
     with open("engine3js.py", "w") as f:
         f.write(r.text)
-    
+
     import notebook_utils as utils
     import engine3js as engine
 
@@ -201,19 +201,19 @@ directory structure and downloads the selected model.
 
     # directory where model will be downloaded
     base_model_dir = "model"
-    
+
     # model name as named in Open Model Zoo
     model_name = "human-pose-estimation-3d-0001"
     # selected precision (FP32, FP16)
     precision = "FP32"
-    
+
     BASE_MODEL_NAME = f"{base_model_dir}/public/{model_name}/{model_name}"
     model_path = Path(BASE_MODEL_NAME).with_suffix(".pth")
     onnx_path = Path(BASE_MODEL_NAME).with_suffix(".onnx")
-    
+
     ir_model_path = f"model/public/{model_name}/{precision}/{model_name}.xml"
     model_weights_path = f"model/public/{model_name}/{precision}/{model_name}.bin"
-    
+
     if not model_path.exists():
         download_command = f"omz_downloader " f"--name {model_name} " f"--output_dir {base_model_dir}"
         ! $download_command
@@ -222,12 +222,12 @@ directory structure and downloads the selected model.
 .. parsed-literal::
 
     ################|| Downloading human-pose-estimation-3d-0001 ||################
-    
+
     ========== Downloading model/public/human-pose-estimation-3d-0001/human-pose-estimation-3d-0001.tar.gz
-    
-    
+
+
     ========== Unpacking model/public/human-pose-estimation-3d-0001/human-pose-estimation-3d-0001.tar.gz
-    
+
 
 
 Convert Model to OpenVINO IR format
@@ -252,19 +252,20 @@ IR format.
 .. parsed-literal::
 
     ========== Converting human-pose-estimation-3d-0001 to ONNX
-    Conversion to ONNX command: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/bin/python -- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/omz_tools/internal_scripts/pytorch_to_onnx.py --model-path=model/public/human-pose-estimation-3d-0001 --model-name=PoseEstimationWithMobileNet --model-param=is_convertible_by_mo=True --import-module=model --weights=model/public/human-pose-estimation-3d-0001/human-pose-estimation-3d-0001.pth --input-shape=1,3,256,448 --input-names=data --output-names=features,heatmaps,pafs --output-file=model/public/human-pose-estimation-3d-0001/human-pose-estimation-3d-0001.onnx
-    
+    Conversion to ONNX command: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/bin/python -- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/omz_tools/internal_scripts/pytorch_to_onnx.py --model-path=model/public/human-pose-estimation-3d-0001 --model-name=PoseEstimationWithMobileNet --model-param=is_convertible_by_mo=True --import-module=model --weights=model/public/human-pose-estimation-3d-0001/human-pose-estimation-3d-0001.pth --input-shape=1,3,256,448 --input-names=data --output-names=features,heatmaps,pafs --output-file=model/public/human-pose-estimation-3d-0001/human-pose-estimation-3d-0001.onnx
+
     ONNX check passed successfully.
-    
+
     ========== Converting human-pose-estimation-3d-0001 to IR (FP32)
-    Conversion command: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/bin/python -- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/bin/mo --framework=onnx --output_dir=model/public/human-pose-estimation-3d-0001/FP32 --model_name=human-pose-estimation-3d-0001 --input=data '--mean_values=data[128.0,128.0,128.0]' '--scale_values=data[255.0,255.0,255.0]' --output=features,heatmaps,pafs --input_model=model/public/human-pose-estimation-3d-0001/human-pose-estimation-3d-0001.onnx '--layout=data(NCHW)' '--input_shape=[1, 3, 256, 448]' --compress_to_fp16=False
-    
-    [ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release. Please use OpenVINO Model Converter (OVC). OVC represents a lightweight alternative of MO and provides simplified model conversion API. 
+    Conversion command: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/bin/python -- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/bin/mo --framework=onnx --output_dir=model/public/human-pose-estimation-3d-0001/FP32 --model_name=human-pose-estimation-3d-0001 --input=data '--mean_values=data[128.0,128.0,128.0]' '--scale_values=data[255.0,255.0,255.0]' --output=features,heatmaps,pafs --input_model=model/public/human-pose-estimation-3d-0001/human-pose-estimation-3d-0001.onnx '--layout=data(NCHW)' '--input_shape=[1, 3, 256, 448]' --compress_to_fp16=False
+
+    [ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release.
+    In 2025.0 MO command line tool and openvino.tools.mo.convert_model() will be removed. Please use OpenVINO Model Converter (OVC) or openvino.convert_model(). OVC represents a lightweight alternative of MO and provides simplified model conversion API.
     Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
     [ SUCCESS ] Generated IR version 11 model.
-    [ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/3D-pose-estimation-webcam/model/public/human-pose-estimation-3d-0001/FP32/human-pose-estimation-3d-0001.xml
-    [ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/3D-pose-estimation-webcam/model/public/human-pose-estimation-3d-0001/FP32/human-pose-estimation-3d-0001.bin
-    
+    [ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/3D-pose-estimation-webcam/model/public/human-pose-estimation-3d-0001/FP32/human-pose-estimation-3d-0001.xml
+    [ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/3D-pose-estimation-webcam/model/public/human-pose-estimation-3d-0001/FP32/human-pose-estimation-3d-0001.bin
+
 
 
 Select inference device
@@ -277,14 +278,14 @@ select device from dropdown list for running inference using OpenVINO
 .. code:: ipython3
 
     core = ov.Core()
-    
+
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
         value="AUTO",
         description="Device:",
         disabled=False,
     )
-    
+
     device
 
 
@@ -319,7 +320,7 @@ created to infer the compiled model.
     compiled_model = core.compile_model(model=model, device_name=device.value)
     infer_request = compiled_model.create_infer_request()
     input_tensor_name = model.inputs[0].get_any_name()
-    
+
     # get input and output names of nodes
     input_layer = compiled_model.input(0)
     output_layers = list(compiled_model.outputs)
@@ -359,25 +360,25 @@ input for the 3D model. This is how you obtain the output heat maps, PAF
     def model_infer(scaled_img, stride):
         """
         Run model inference on the input image
-    
+
         Parameters:
             scaled_img: resized image according to the input size of the model
             stride: int, the stride of the window
         """
-    
+
         # Remove excess space from the picture
         img = scaled_img[
             0 : scaled_img.shape[0] - (scaled_img.shape[0] % stride),
             0 : scaled_img.shape[1] - (scaled_img.shape[1] % stride),
         ]
-    
+
         img = np.transpose(img, (2, 0, 1))[None,]
         infer_request.infer({input_tensor_name: img})
         # A set of three inference results is obtained
         results = {name: infer_request.get_tensor(name).data[:] for name in {"features", "heatmaps", "pafs"}}
         # Get the results
         results = (results["features"][0], results["heatmaps"][0], results["pafs"][0])
-    
+
         return results
 
 Draw 2D Pose Overlays
@@ -417,8 +418,8 @@ from Open Model Zoo.
             [13, 14],  # neck - r_hip - r_knee - r_ankle
         ]
     )
-    
-    
+
+
     body_edges_2d = np.array(
         [
             [0, 1],  # neck - nose
@@ -440,25 +441,25 @@ from Open Model Zoo.
             [13, 14],  # neck - r_hip - r_knee - r_ankle
         ]
     )
-    
-    
+
+
     def draw_poses(frame, poses_2d, scaled_img, use_popup):
         """
         Draw 2D pose overlays on the image to visualize estimated poses.
         Joints are drawn as circles and limbs are drawn as lines.
-    
+
         :param frame: the input image
         :param poses_2d: array of human joint pairs
         """
         for pose in poses_2d:
             pose = np.array(pose[0:-1]).reshape((-1, 3)).transpose()
             was_found = pose[2] > 0
-    
+
             pose[0], pose[1] = (
                 pose[0] * frame.shape[1] / scaled_img.shape[1],
                 pose[1] * frame.shape[0] / scaled_img.shape[0],
             )
-    
+
             # Draw joints.
             for edge in body_edges_2d:
                 if was_found[edge[0]] and was_found[edge[1]]:
@@ -481,7 +482,7 @@ from Open Model Zoo.
                         -1,
                         cv2.LINE_AA,
                     )
-    
+
         return frame
 
 Main Processing Function
@@ -498,18 +499,18 @@ webcam feed or a video file.
         """
         2D image as input, using OpenVINO as inference backend,
         get joints 3D coordinates, and draw 3D human skeleton in the scene
-    
+
         :param source:      The webcam number to feed the video stream with primary webcam set to "0", or the video path.
         :param flip:        To be used by VideoPlayer function for flipping capture image.
         :param use_popup:   False for showing encoded frames over this notebook, True for creating a popup window.
         :param skip_frames: Number of frames to skip at the beginning of the video.
         """
-    
+
         focal_length = -1  # default
         stride = 8
         player = None
         skeleton_set = None
-    
+
         try:
             # create video player to play with target fps  video_path
             # get the frame from camera
@@ -517,16 +518,16 @@ webcam feed or a video file.
             player = utils.VideoPlayer(source, flip=flip, fps=30, skip_first_frames=skip_frames)
             # start capturing
             player.start()
-    
+
             input_image = player.next()
             # set the window size
             resize_scale = 450 / input_image.shape[1]
             windows_width = int(input_image.shape[1] * resize_scale)
             windows_height = int(input_image.shape[0] * resize_scale)
-    
+
             # use visualization library
             engine3D = engine.Engine3js(grid=True, axis=True, view_width=windows_width, view_height=windows_height)
-    
+
             if use_popup:
                 # display the 3D human pose in this notebook, and origin frame in popup window
                 display(engine3D.renderer)
@@ -536,43 +537,43 @@ webcam feed or a video file.
                 # set the 2D image box, show both human pose and image in the notebook
                 imgbox = widgets.Image(format="jpg", height=windows_height, width=windows_width)
                 display(widgets.HBox([engine3D.renderer, imgbox]))
-    
+
             skeleton = engine.Skeleton(body_edges=body_edges)
-    
+
             processing_times = collections.deque()
-    
+
             while True:
                 # grab the frame
                 frame = player.next()
                 if frame is None:
                     print("Source ended")
                     break
-    
+
                 # resize image and change dims to fit neural network input
                 # (see https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/human-pose-estimation-3d-0001)
                 scaled_img = cv2.resize(frame, dsize=(model.inputs[0].shape[3], model.inputs[0].shape[2]))
-    
+
                 if focal_length < 0:  # Focal length is unknown
                     focal_length = np.float32(0.8 * scaled_img.shape[1])
-    
+
                 # inference start
                 start_time = time.time()
                 # get results
                 inference_result = model_infer(scaled_img, stride)
-    
+
                 # inference stop
                 stop_time = time.time()
                 processing_times.append(stop_time - start_time)
                 # Process the point to point coordinates of the data
                 poses_3d, poses_2d = engine.parse_poses(inference_result, 1, stride, focal_length, True)
-    
+
                 # use processing times from last 200 frames
                 if len(processing_times) > 200:
                     processing_times.popleft()
-    
+
                 processing_time = np.mean(processing_times) * 1000
                 fps = 1000 / processing_time
-    
+
                 if len(poses_3d) > 0:
                     # From here, you can rotate the 3D point positions using the function "draw_poses",
                     # or you can directly make the correct mapping below to properly display the object image on the screen
@@ -585,28 +586,28 @@ webcam feed or a video file.
                         -y + np.ones(poses_3d[:, 2::4].shape) * 100,
                         -x,
                     )
-    
+
                     poses_3d = poses_3d.reshape(poses_3d.shape[0], 19, -1)[:, :, 0:3]
                     people = skeleton(poses_3d=poses_3d)
-    
+
                     try:
                         engine3D.scene_remove(skeleton_set)
                     except Exception:
                         pass
-    
+
                     engine3D.scene_add(people)
                     skeleton_set = people
-    
+
                     # draw 2D
                     frame = draw_poses(frame, poses_2d, scaled_img, use_popup)
-    
+
                 else:
                     try:
                         engine3D.scene_remove(skeleton_set)
                         skeleton_set = None
                     except Exception:
                         pass
-    
+
                 cv2.putText(
                     frame,
                     f"Inference time: {processing_time:.1f}ms ({fps:.1f} FPS)",
@@ -617,7 +618,7 @@ webcam feed or a video file.
                     1,
                     cv2.LINE_AA,
                 )
-    
+
                 if use_popup:
                     cv2.imshow(title, frame)
                     key = cv2.waitKey(1)
@@ -631,9 +632,9 @@ webcam feed or a video file.
                         frame,
                         params=[cv2.IMWRITE_JPEG_QUALITY, 90],
                     )[1].tobytes()
-    
+
                 engine3D.renderer.render(engine3D.scene, engine3D.cam)
-    
+
         except KeyboardInterrupt:
             print("Interrupted")
         except RuntimeError as e:
@@ -680,10 +681,10 @@ picture on the left to interact.
 .. code:: ipython3
 
     USE_WEBCAM = False
-    
+
     cam_id = 0
     video_path = "https://github.com/intel-iot-devkit/sample-videos/raw/master/face-demographics-walking.mp4"
-    
+
     source = cam_id if USE_WEBCAM else video_path
-    
+
     run_pose_estimation(source=source, flip=isinstance(source, int), use_popup=False)
diff --git a/docs/notebooks/3D-segmentation-point-clouds-with-output.rst b/docs/notebooks/3D-segmentation-point-clouds-with-output.rst
index 8e4954bc00a95f..c73718778a29a6 100644
--- a/docs/notebooks/3D-segmentation-point-clouds-with-output.rst
+++ b/docs/notebooks/3D-segmentation-point-clouds-with-output.rst
@@ -22,8 +22,8 @@ segmentation, to scene semantic parsing. It is highly efficient and
 effective, showing strong performance on par or even better than state
 of the art.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Imports <#imports>`__
 -  `Prepare the Model <#prepare-the-model>`__
@@ -216,7 +216,7 @@ chair for example.
 
 .. parsed-literal::
 
-    /tmp/ipykernel_3063563/2434168836.py:12: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
+    /tmp/ipykernel_2758943/2434168836.py:12: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
       ax.scatter3D(X, Y, Z, s=5, cmap="jet", marker="o", label="chair")
 
 
@@ -317,7 +317,7 @@ select device from dropdown list for running inference using OpenVINO
 
 .. parsed-literal::
 
-    /tmp/ipykernel_3063563/2804603389.py:23: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
+    /tmp/ipykernel_2758943/2804603389.py:23: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
       ax.scatter(XCur, YCur, ZCur, s=5, cmap="jet", marker="o", label=classes[i])
 
 
diff --git a/docs/notebooks/action-recognition-webcam-with-output.rst b/docs/notebooks/action-recognition-webcam-with-output.rst
index 3b3455ba9b487a..38cbcff848b171 100644
--- a/docs/notebooks/action-recognition-webcam-with-output.rst
+++ b/docs/notebooks/action-recognition-webcam-with-output.rst
@@ -35,8 +35,8 @@ Transformer <https://en.wikipedia.org/wiki/Transformer_(machine_learning_model)>
 and
 `ResNet34 <https://pytorch.org/vision/main/models/generated/torchvision.models.resnet34.html>`__.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Imports <#imports>`__
 -  `The models <#the-models>`__
diff --git a/docs/notebooks/action-recognition-webcam-with-output_files/action-recognition-webcam-with-output_22_0.png b/docs/notebooks/action-recognition-webcam-with-output_files/action-recognition-webcam-with-output_22_0.png
index acae41e0b1ada0..e398fc093af7c9 100644
--- a/docs/notebooks/action-recognition-webcam-with-output_files/action-recognition-webcam-with-output_22_0.png
+++ b/docs/notebooks/action-recognition-webcam-with-output_files/action-recognition-webcam-with-output_22_0.png
@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:f9519b2a12072147ebf54e1d7a840ccde81b965fa1844f42f79e66c6513d844a
-size 68147
+oid sha256:339e8a574ee9bb1160f222834ed747e724e89bf7148ebd01b15ab89c702cf16e
+size 68902
diff --git a/docs/notebooks/all_notebooks_paths.txt b/docs/notebooks/all_notebooks_paths.txt
index 481450364a1eca..636415ae99c7be 100644
--- a/docs/notebooks/all_notebooks_paths.txt
+++ b/docs/notebooks/all_notebooks_paths.txt
@@ -12,12 +12,12 @@ notebooks/clip-language-saliency-map/clip-language-saliency-map.ipynb
 notebooks/clip-zero-shot-image-classification/clip-zero-shot-classification.ipynb
 notebooks/controlnet-stable-diffusion/controlnet-stable-diffusion.ipynb
 notebooks/convert-to-openvino/convert-to-openvino.ipynb
-notebooks/convert-to-openvino/legacy-mo-convert-to-openvino.ipynb
 notebooks/cross-lingual-books-alignment/cross-lingual-books-alignment.ipynb
 notebooks/ct-segmentation-quantize/ct-segmentation-quantize-nncf.ipynb
 notebooks/ddcolor-image-colorization/ddcolor-image-colorization.ipynb
 notebooks/decidiffusion-image-generation/decidiffusion-image-generation.ipynb
 notebooks/depth-anything/depth-anything.ipynb
+notebooks/depth-anything/depth-anything-v2.ipynb
 notebooks/detectron2-to-openvino/detectron2-to-openvino.ipynb
 notebooks/distilbert-sequence-classification/distilbert-sequence-classification.ipynb
 notebooks/distil-whisper-asr/distil-whisper-asr.ipynb
@@ -37,9 +37,9 @@ notebooks/hello-npu/hello-npu.ipynb
 notebooks/hello-segmentation/hello-segmentation.ipynb
 notebooks/hello-world/hello-world.ipynb
 notebooks/hugging-face-hub/hugging-face-hub.ipynb
+notebooks/hunyuan-dit-image-generation/hunyuan-dit-image-generation.ipynb
 notebooks/image-bind/image-bind.ipynb
 notebooks/image-classification-quantization/image-classification-quantization.ipynb
-notebooks/image-inpainting/image-inpainting.ipynb
 notebooks/instant-id/instant-id.ipynb
 notebooks/instruct-pix2pix-image-editing/instruct-pix2pix-image-editing.ipynb
 notebooks/knowledge-graphs-conve/knowledge-graphs-conve.ipynb
@@ -51,17 +51,18 @@ notebooks/latent-consistency-models-image-generation/lcm-lora-controlnet.ipynb
 notebooks/llava-multimodal-chatbot/llava-multimodal-chatbot.ipynb
 notebooks/llava-multimodal-chatbot/videollava-multimodal-chatbot.ipynb
 notebooks/llava-next-multimodal-chatbot/llava-next-multimodal-chatbot.ipynb
-notebooks/llm-agent-langchain/llm-agent-langchain.ipynb
+notebooks/llm-agent-react/llm-agent-react-langchain.ipynb
+notebooks/llm-chatbot/llm-chatbot-generate-api.ipynb
 notebooks/llm-chatbot/llm-chatbot.ipynb
 notebooks/llm-question-answering/llm-question-answering.ipynb
 notebooks/llm-rag-langchain/llm-rag-langchain.ipynb
+notebooks/llm-rag-llamaindex/llm-rag-llamaindex.ipynb
 notebooks/magika-content-type-recognition/magika-content-type-recognition.ipynb
 notebooks/meter-reader/meter-reader.ipynb
 notebooks/mms-massively-multilingual-speech/mms-massively-multilingual-speech.ipynb
 notebooks/mobileclip-video-search/mobileclip-video-search.ipynb
 notebooks/mobilevlm-language-assistant/mobilevlm-language-assistant.ipynb
 notebooks/model-server/model-server.ipynb
-notebooks/model-tools/model-tools.ipynb
 notebooks/music-generation/music-generation.ipynb
 notebooks/named-entity-recognition/named-entity-recognition.ipynb
 notebooks/nano-llava-multimodal-chatbot/nano-llava-multimodal-chatbot.ipynb
@@ -75,12 +76,13 @@ notebooks/optimize-preprocessing/optimize-preprocessing.ipynb
 notebooks/paddle-ocr-webcam/paddle-ocr-webcam.ipynb
 notebooks/paddle-to-openvino/paddle-to-openvino-classification.ipynb
 notebooks/paint-by-example/paint-by-example.ipynb
+notebooks/parler-tts-text-to-speech/parler-tts-text-to-speech.ipynb
 notebooks/person-counting-webcam/person-counting.ipynb
 notebooks/person-tracking-webcam/person-tracking.ipynb
 notebooks/photo-maker/photo-maker.ipynb
 notebooks/pix2struct-docvqa/pix2struct-docvqa.ipynb
+notebooks/pixart/pixart.ipynb
 notebooks/pose-estimation-webcam/pose-estimation.ipynb
-notebooks/pyannote-speaker-diarization/pyannote-speaker-diarization.ipynb
 notebooks/pytorch-post-training-quantization-nncf/pytorch-post-training-quantization-nncf.ipynb
 notebooks/pytorch-quantization-aware-training/pytorch-quantization-aware-training.ipynb
 notebooks/pytorch-quantization-sparsity-aware-training/pytorch-quantization-sparsity-aware-training.ipynb
@@ -100,9 +102,8 @@ notebooks/softvc-voice-conversion/softvc-voice-conversion.ipynb
 notebooks/sound-generation-audioldm2/sound-generation-audioldm2.ipynb
 notebooks/sparsity-optimization/sparsity-optimization.ipynb
 notebooks/speculative-sampling/speculative-sampling.ipynb
-notebooks/speech-recognition-quantization/speech-recognition-quantization-data2vec.ipynb
+notebooks/speechbrain-emotion-recognition/speechbrain-emotion-recognition.ipynb
 notebooks/speech-recognition-quantization/speech-recognition-quantization-wav2vec2.ipynb
-notebooks/speech-to-text/speech-to-text.ipynb
 notebooks/stable-cascade-image-generation/stable-cascade-image-generation.ipynb
 notebooks/stable-diffusion-ip-adapter/stable-diffusion-ip-adapter.ipynb
 notebooks/stable-diffusion-keras-cv/stable-diffusion-keras-cv.ipynb
@@ -112,6 +113,7 @@ notebooks/stable-diffusion-v2/stable-diffusion-v2-infinite-zoom.ipynb
 notebooks/stable-diffusion-v2/stable-diffusion-v2-optimum-demo.ipynb
 notebooks/stable-diffusion-v2/stable-diffusion-v2-text-to-image-demo.ipynb
 notebooks/stable-diffusion-v2/stable-diffusion-v2-text-to-image.ipynb
+notebooks/stable-diffusion-v3/stable-diffusion-v3.ipynb
 notebooks/stable-diffusion-xl/segmind-vegart.ipynb
 notebooks/stable-diffusion-xl/stable-diffusion-xl.ipynb
 notebooks/stable-video-diffusion/stable-video-diffusion.ipynb
@@ -124,7 +126,6 @@ notebooks/tensorflow-hub/tensorflow-hub.ipynb
 notebooks/tensorflow-object-detection-to-openvino/tensorflow-instance-segmentation-to-openvino.ipynb
 notebooks/tensorflow-object-detection-to-openvino/tensorflow-object-detection-to-openvino.ipynb
 notebooks/tensorflow-quantization-aware-training/tensorflow-quantization-aware-training.ipynb
-notebooks/text-prediction/text-prediction.ipynb
 notebooks/tflite-selfie-segmentation/tflite-selfie-segmentation.ipynb
 notebooks/tflite-to-openvino/tflite-to-openvino.ipynb
 notebooks/tiny-sd-image-generation/tiny-sd-image-generation.ipynb
@@ -133,12 +134,10 @@ notebooks/triposr-3d-reconstruction/triposr-3d-reconstruction.ipynb
 notebooks/typo-detector/typo-detector.ipynb
 notebooks/vehicle-detection-and-recognition/vehicle-detection-and-recognition.ipynb
 notebooks/vision-background-removal/vision-background-removal.ipynb
-notebooks/vision-image-colorization/vision-image-colorization.ipynb
 notebooks/vision-monodepth/vision-monodepth.ipynb
 notebooks/vision-paddlegan-anime/vision-paddlegan-anime.ipynb
 notebooks/vision-paddlegan-superresolution/vision-paddlegan-superresolution.ipynb
-notebooks/whisper-subtitles-generation/whisper-convert.ipynb
-notebooks/whisper-subtitles-generation/whisper-nncf-quantize.ipynb
+notebooks/whisper-subtitles-generation/whisper-subtitles-generation.ipynb
 notebooks/wuerstchen-image-generation/wuerstchen-image-generation.ipynb
 notebooks/yolov10-optimization/yolov10-optimization.ipynb
 notebooks/yolov7-optimization/yolov7-optimization.ipynb
diff --git a/docs/notebooks/amused-lightweight-text-to-image-with-output.rst b/docs/notebooks/amused-lightweight-text-to-image-with-output.rst
index 4619212090a46f..50e3731ee0663b 100644
--- a/docs/notebooks/amused-lightweight-text-to-image-with-output.rst
+++ b/docs/notebooks/amused-lightweight-text-to-image-with-output.rst
@@ -14,8 +14,8 @@ small parameter count and few forward pass generation process, amused
 can generate many images quickly. This benefit is seen particularly at
 larger batch sizes.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Load and run the original
@@ -76,12 +76,6 @@ Load and run the original pipeline
     image.save("text2image_256.png")
 
 
-.. parsed-literal::
-
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/transformers/transformer_2d.py:34: FutureWarning: `Transformer2DModelOutput` is deprecated and will be removed in version 1.0.0. Importing `Transformer2DModelOutput` from `diffusers.models.transformer_2d` is deprecated and this will be removed in a future version. Please use `from diffusers.models.modeling_outputs import Transformer2DModelOutput`, instead.
-      deprecate("Transformer2DModelOutput", "1.0.0", deprecation_message)
-
-
 
 .. parsed-literal::
 
@@ -202,29 +196,29 @@ Convert the Text Encoder
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4481: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-716/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4565: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
       warnings.warn(
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_attn_mask_utils.py:86: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-716/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_attn_mask_utils.py:86: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if input_shape[-1] > 1 or self.sliding_window is not None:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_attn_mask_utils.py:162: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-716/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_attn_mask_utils.py:162: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if past_key_values_length > 0:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:622: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-716/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:621: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       encoder_states = () if output_hidden_states else None
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:627: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-716/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:626: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if output_hidden_states:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:276: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-716/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:275: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:284: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-716/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:283: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if causal_attention_mask.size() != (bsz, 1, tgt_len, src_len):
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:316: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-716/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:315: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:650: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-716/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:649: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if output_hidden_states:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:653: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-716/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:652: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if not return_dict:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:745: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-716/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:744: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if not return_dict:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:1230: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-716/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:1231: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if not return_dict:
 
 
@@ -275,7 +269,7 @@ Convert the U-ViT transformer
 Convert VQ-GAN decoder (VQVAE)
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
- Function ``get_latents`` is
+Function ``get_latents`` is
 needed to return real latents for the conversion. Due to the VQVAE
 implementation autogenerated tensor of the required shape is not
 suitable. This function repeats part of ``AmusedPipeline``.
@@ -333,13 +327,13 @@ suitable. This function repeats part of ``AmusedPipeline``.
 
 .. parsed-literal::
 
-    /tmp/ipykernel_3064357/3779428577.py:34: TracerWarning: Converting a tensor to a Python list might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /tmp/ipykernel_2636725/3779428577.py:34: TracerWarning: Converting a tensor to a Python list might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       shape=shape.tolist(),
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/vq_model.py:144: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-716/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/autoencoders/vq_model.py:144: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if not force_not_quantize:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/upsampling.py:146: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-716/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/upsampling.py:146: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       assert hidden_states.shape[1] == self.channels
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/upsampling.py:162: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-716/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/upsampling.py:162: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if hidden_states.shape[0] >= 64:
 
 
@@ -477,7 +471,7 @@ And insert wrappers instances in the pipeline:
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/configuration_utils.py:140: FutureWarning: Accessing config attribute `_execution_device` directly via 'AmusedPipeline' object attribute is deprecated. Please access '_execution_device' over 'AmusedPipeline's config object instead, e.g. 'scheduler.config._execution_device'.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-716/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/configuration_utils.py:140: FutureWarning: Accessing config attribute `_execution_device` directly via 'AmusedPipeline' object attribute is deprecated. Please access '_execution_device' over 'AmusedPipeline's config object instead, e.g. 'scheduler.config._execution_device'.
       deprecate("direct config name access", "1.0.0", deprecation_message, standard_warn=False)
 
 
@@ -571,7 +565,7 @@ Prepare calibration dataset
 
 
 We use a portion of
-`conceptual_captions <https://huggingface.co/datasets/conceptual_captions>`__
+`conceptual_captions <https://huggingface.co/datasets/google-research-datasets/conceptual_captions>`__
 dataset from Hugging Face as calibration data. To collect intermediate
 model inputs for calibration we customize ``CompiledModel``.
 
@@ -612,7 +606,7 @@ model inputs for calibration we customize ``CompiledModel``.
             pipe.transformer.transformer = CompiledModelDecorator(ov_transformer_model, calibration_data, keep_prob=1.0)
             disable_progress_bar(pipe)
 
-            dataset = datasets.load_dataset("conceptual_captions", split="train").shuffle(seed=42)
+            dataset = datasets.load_dataset("google-research-datasets/conceptual_captions", split="train", trust_remote_code=True).shuffle(seed=42)
 
             # Run inference for data collection
             pbar = tqdm(total=calibration_dataset_size)
@@ -686,14 +680,6 @@ model.
     INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, onnx, openvino
 
 
-.. parsed-literal::
-
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/datasets/load.py:1491: FutureWarning: The repository for conceptual_captions contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/conceptual_captions
-    You can avoid this message in future by passing the argument `trust_remote_code=True`.
-    Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.
-      warnings.warn(
-
-
 
 .. parsed-literal::
 
@@ -702,7 +688,7 @@ model.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/configuration_utils.py:140: FutureWarning: Accessing config attribute `_execution_device` directly via 'AmusedPipeline' object attribute is deprecated. Please access '_execution_device' over 'AmusedPipeline's config object instead, e.g. 'scheduler.config._execution_device'.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-716/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/configuration_utils.py:140: FutureWarning: Accessing config attribute `_execution_device` directly via 'AmusedPipeline' object attribute is deprecated. Please access '_execution_device' over 'AmusedPipeline's config object instead, e.g. 'scheduler.config._execution_device'.
       deprecate("direct config name access", "1.0.0", deprecation_message, standard_warn=False)
 
 
@@ -713,17 +699,17 @@ model.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -734,17 +720,17 @@ model.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -752,9 +738,9 @@ model.
 
     INFO:nncf:3 ignored nodes were found by types in the NNCFGraph
     INFO:nncf:182 ignored nodes were found by name in the NNCFGraph
-    INFO:nncf:Not adding activation input quantizer for operation: 37 __module.transformer.embed.conv/aten::_convolution/Convolution
-    INFO:nncf:Not adding activation input quantizer for operation: 2883 __module.transformer.mlm_layer.conv1/aten::_convolution/Convolution
-    INFO:nncf:Not adding activation input quantizer for operation: 3243 __module.transformer.mlm_layer.conv2/aten::_convolution/Convolution
+    INFO:nncf:Not adding activation input quantizer for operation: 120 __module.transformer.embed.conv/aten::_convolution/Convolution
+    INFO:nncf:Not adding activation input quantizer for operation: 2154 __module.transformer.mlm_layer.conv1/aten::_convolution/Convolution
+    INFO:nncf:Not adding activation input quantizer for operation: 2993 __module.transformer.mlm_layer.conv2/aten::_convolution/Convolution
 
 
 
@@ -764,41 +750,41 @@ model.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/experimental/tensor/tensor.py:84: RuntimeWarning: invalid value encountered in multiply
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-716/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/experimental/tensor/tensor.py:92: RuntimeWarning: invalid value encountered in multiply
       return Tensor(self.data * unwrap_tensor_data(other))
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/experimental/tensor/tensor.py:84: RuntimeWarning: invalid value encountered in multiply
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-716/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/experimental/tensor/tensor.py:92: RuntimeWarning: invalid value encountered in multiply
       return Tensor(self.data * unwrap_tensor_data(other))
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/experimental/tensor/tensor.py:84: RuntimeWarning: invalid value encountered in multiply
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-716/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/experimental/tensor/tensor.py:92: RuntimeWarning: invalid value encountered in multiply
       return Tensor(self.data * unwrap_tensor_data(other))
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/experimental/tensor/tensor.py:84: RuntimeWarning: invalid value encountered in multiply
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-716/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/experimental/tensor/tensor.py:92: RuntimeWarning: invalid value encountered in multiply
       return Tensor(self.data * unwrap_tensor_data(other))
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/experimental/tensor/tensor.py:84: RuntimeWarning: invalid value encountered in multiply
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-716/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/experimental/tensor/tensor.py:92: RuntimeWarning: invalid value encountered in multiply
       return Tensor(self.data * unwrap_tensor_data(other))
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/experimental/tensor/tensor.py:84: RuntimeWarning: invalid value encountered in multiply
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-716/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/experimental/tensor/tensor.py:92: RuntimeWarning: invalid value encountered in multiply
       return Tensor(self.data * unwrap_tensor_data(other))
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
 
 
 
@@ -822,7 +808,7 @@ Demo generation with quantized pipeline
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/configuration_utils.py:140: FutureWarning: Accessing config attribute `_execution_device` directly via 'AmusedPipeline' object attribute is deprecated. Please access '_execution_device' over 'AmusedPipeline's config object instead, e.g. 'scheduler.config._execution_device'.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-716/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/configuration_utils.py:140: FutureWarning: Accessing config attribute `_execution_device` directly via 'AmusedPipeline' object attribute is deprecated. Please access '_execution_device' over 'AmusedPipeline's config object instead, e.g. 'scheduler.config._execution_device'.
       deprecate("direct config name access", "1.0.0", deprecation_message, standard_warn=False)
 
 
@@ -866,7 +852,7 @@ a rough estimate of generation quality.
         pipe.transformer.transformer = core.compile_model(ov_transformer_model_path, device.value)
 
         disable_progress_bar(pipe)
-        dataset = datasets.load_dataset("conceptual_captions", "unlabeled", split="validation").shuffle(seed=42)
+        dataset = datasets.load_dataset("google-research-datasets/conceptual_captions", "unlabeled", split="validation", trust_remote_code=True).shuffle(seed=42)
         dataset = islice(dataset, validation_set_size)
 
         inception_score = InceptionScore(normalize=True, splits=1)
@@ -906,11 +892,7 @@ a rough estimate of generation quality.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/datasets/load.py:1491: FutureWarning: The repository for conceptual_captions contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/conceptual_captions
-    You can avoid this message in future by passing the argument `trust_remote_code=True`.
-    Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.
-      warnings.warn(
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torchmetrics/utilities/prints.py:43: UserWarning: Metric `InceptionScore` will save all extracted features in buffer. For large datasets this may lead to large memory footprint.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-716/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torchmetrics/utilities/prints.py:43: UserWarning: Metric `InceptionScore` will save all extracted features in buffer. For large datasets this may lead to large memory footprint.
       warnings.warn(\*args, \*\*kwargs)  # noqa: B028
 
 
@@ -922,9 +904,9 @@ a rough estimate of generation quality.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/configuration_utils.py:140: FutureWarning: Accessing config attribute `_execution_device` directly via 'AmusedPipeline' object attribute is deprecated. Please access '_execution_device' over 'AmusedPipeline's config object instead, e.g. 'scheduler.config._execution_device'.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-716/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/configuration_utils.py:140: FutureWarning: Accessing config attribute `_execution_device` directly via 'AmusedPipeline' object attribute is deprecated. Please access '_execution_device' over 'AmusedPipeline's config object instead, e.g. 'scheduler.config._execution_device'.
       deprecate("direct config name access", "1.0.0", deprecation_message, standard_warn=False)
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torchmetrics/image/inception.py:176: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1807.)
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-716/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torchmetrics/image/inception.py:176: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at ../aten/src/ATen/native/ReduceOps.cpp:1807.)
       return kl.mean(), kl.std()
 
 
@@ -941,8 +923,8 @@ a rough estimate of generation quality.
 
 .. parsed-literal::
 
-    Quantized pipeline Inception Score: 9.630992889404297
-    Quantization speed-up: 2.10x
+    Quantized pipeline Inception Score: 9.63099479675293
+    Quantization speed-up: 2.09x
 
 
 Interactive inference
diff --git a/docs/notebooks/animate-anyone-with-output.rst b/docs/notebooks/animate-anyone-with-output.rst
index 9312ff261b123c..00b78cb83331f9 100644
--- a/docs/notebooks/animate-anyone-with-output.rst
+++ b/docs/notebooks/animate-anyone-with-output.rst
@@ -34,17 +34,14 @@ Learn more in `GitHub
 repo <https://github.com/MooreThreads/Moore-AnimateAnyone>`__ and
 `paper <https://arxiv.org/pdf/2311.17117.pdf>`__.
 
-.. container:: alert alert-warning
 
-   ::
+.. warning::
 
-      <p style="font-size:1.25em"><b>! WARNING !</b></p>
-      <p>
-          This tutorial requires at least <b>96 GB</b> of RAM for model conversion and <b>40 GB</b> for inference. Changing the values of <code>HEIGHT</code>, <code>WIDTH</code> and <code>VIDEO_LENGTH</code> variables will change the memory consumption but will also affect accuracy.
-      </p>
+   This tutorial requires at least **96 GB** of RAM for model conversion and **40 GB** for inference. Changing the values of ``HEIGHT``, ``WIDTH`` and ``VIDEO_LENGTH`` variables will change the memory consumption but will also affect accuracy.
+
+
+**Table of contents:**
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
 
 -  `Prerequisites <#prerequisites>`__
 -  `Prepare base model <#prepare-base-model>`__
@@ -67,6 +64,11 @@ Table of contents:
 .. |image0| image:: https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/notebooks/animate-anyone/animate-anyone.gif
 
 
+
+
+
+
+
 Prerequisites
 -------------
 
@@ -154,11 +156,11 @@ Note that we clone a fork of original repo with tweaked forward methods.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
       torch.utils._pytree._register_pytree_node(
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
       torch.utils._pytree._register_pytree_node(
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
       torch.utils._pytree._register_pytree_node(
 
 
@@ -279,7 +281,7 @@ Download weights
 
 .. parsed-literal::
 
-    config.json:   0%|          | 0.00/547 [00:00<?, ?B/s]
+    README.md:   0%|          | 0.00/6.84k [00:00<?, ?B/s]
 
 
 
@@ -291,13 +293,13 @@ Download weights
 
 .. parsed-literal::
 
-    .gitattributes:   0%|          | 0.00/1.46k [00:00<?, ?B/s]
+    config.json:   0%|          | 0.00/547 [00:00<?, ?B/s]
 
 
 
 .. parsed-literal::
 
-    README.md:   0%|          | 0.00/6.84k [00:00<?, ?B/s]
+    .gitattributes:   0%|          | 0.00/1.46k [00:00<?, ?B/s]
 
 
 
@@ -309,37 +311,37 @@ Download weights
 
 .. parsed-literal::
 
-    .gitattributes:   0%|          | 0.00/1.52k [00:00<?, ?B/s]
+    README.md:   0%|          | 0.00/154 [00:00<?, ?B/s]
 
 
 
 .. parsed-literal::
 
-    README.md:   0%|          | 0.00/154 [00:00<?, ?B/s]
+    .gitattributes:   0%|          | 0.00/1.52k [00:00<?, ?B/s]
 
 
 
 .. parsed-literal::
 
-    reference_unet.pth:   0%|          | 0.00/3.44G [00:00<?, ?B/s]
+    denoising_unet.pth:   0%|          | 0.00/3.44G [00:00<?, ?B/s]
 
 
 
 .. parsed-literal::
 
-    motion_module.pth:   0%|          | 0.00/1.82G [00:00<?, ?B/s]
+    reference_unet.pth:   0%|          | 0.00/3.44G [00:00<?, ?B/s]
 
 
 
 .. parsed-literal::
 
-    denoising_unet.pth:   0%|          | 0.00/3.44G [00:00<?, ?B/s]
+    pose_guider.pth:   0%|          | 0.00/4.35M [00:00<?, ?B/s]
 
 
 
 .. parsed-literal::
 
-    pose_guider.pth:   0%|          | 0.00/4.35M [00:00<?, ?B/s]
+    motion_module.pth:   0%|          | 0.00/1.82G [00:00<?, ?B/s]
 
 
 .. code:: ipython3
@@ -398,7 +400,7 @@ Load pretrained weights
 Convert model to OpenVINO IR
 ----------------------------
 
- The pose sequence is initially
+The pose sequence is initially
 encoded using Pose Guider and fused with multi-frame noise, followed by
 the Denoising UNet conducting the denoising process for video
 generation. The computational block of the Denoising UNet consists of
@@ -412,8 +414,14 @@ Finally, the VAE decoder decodes the result into a video clip.
 
 |image01|
 
-The pipeline contains 6 PyTorch modules: - VAE encoder - VAE decoder -
-Image encoder - Reference UNet - Denoising UNet - Pose Guider
+The pipeline contains 6 PyTorch modules:
+
+- VAE encoder
+- VAE decoder
+- Image encoder
+- Reference UNet
+- Denoising UNet
+- Pose Guider
 
 For reducing memory consumption, weights compression optimization can be
 applied using `NNCF <https://github.com/openvinotoolkit/nncf>`__. Weight
@@ -503,7 +511,6 @@ of the pipeline, it will be better to convert them to separate models.
 
 .. parsed-literal::
 
-    WARNING:nncf:NNCF provides best results with torch==2.2.*, while current torch version is 2.3.1+cpu. If you encounter issues, consider switching to torch==2.2.*
     INFO:nncf:Statistics of the bitwidth distribution:
     ┍━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┑
     │   Num bits (N) │ % all parameters (layers)   │ % ratio-defining parameters (layers)   │
@@ -519,17 +526,17 @@ of the pipeline, it will be better to convert them to separate models.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -572,17 +579,17 @@ of the pipeline, it will be better to convert them to separate models.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -646,17 +653,17 @@ step.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -747,17 +754,17 @@ step.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -801,17 +808,17 @@ efficiently integrate pose control signals into the denoising process.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -840,7 +847,7 @@ required for both reference and denoising UNets.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4481: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4565: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
       warnings.warn(
 
 
@@ -861,17 +868,17 @@ required for both reference and denoising UNets.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -1211,7 +1218,7 @@ Video post-processing
 .. raw:: html
 
     <video controls  >
-     <source 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" 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diff --git a/docs/notebooks/async-api-with-output.rst b/docs/notebooks/async-api-with-output.rst
index 73435822bbac2e..a498c9b34a73ce 100644
--- a/docs/notebooks/async-api-with-output.rst
+++ b/docs/notebooks/async-api-with-output.rst
@@ -11,8 +11,8 @@ device is busy with inference, the application can perform other tasks
 in parallel (for example, populating inputs or scheduling other
 requests) rather than wait for the current inference to complete first.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Imports <#imports>`__
 -  `Prepare model and data
@@ -352,7 +352,7 @@ Test performance in Sync Mode
 .. parsed-literal::
 
     Source ended
-    average throuput in sync mode: 60.54 fps
+    average throuput in sync mode: 60.97 fps
 
 
 Async Mode
@@ -491,7 +491,7 @@ Test the performance in Async Mode
 .. parsed-literal::
 
     Source ended
-    average throuput in async mode: 103.70 fps
+    average throuput in async mode: 105.34 fps
 
 
 Compare the performance
@@ -634,5 +634,5 @@ Test the performance with ``AsyncInferQueue``
 
 .. parsed-literal::
 
-    average throughput in async mode with async infer queue: 148.11 fps
+    average throughput in async mode with async infer queue: 149.73 fps
 
diff --git a/docs/notebooks/async-api-with-output_files/async-api-with-output_23_0.png b/docs/notebooks/async-api-with-output_files/async-api-with-output_23_0.png
index 589ab94948728b..4c12110dc64fec 100644
--- a/docs/notebooks/async-api-with-output_files/async-api-with-output_23_0.png
+++ b/docs/notebooks/async-api-with-output_files/async-api-with-output_23_0.png
@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:0d82d618fb2b2ef25ecd8ad941de1d1173b3e21a3340314cc584dca9b32d6c55
-size 29416
+oid sha256:f244959cfb281521cd783e5942c77a18a614a8f6bfd4cb96b6f6d9c14a5cab17
+size 29396
diff --git a/docs/notebooks/auto-device-with-output.rst b/docs/notebooks/auto-device-with-output.rst
index d05af42d51629e..4abf3cc4f87076 100644
--- a/docs/notebooks/auto-device-with-output.rst
+++ b/docs/notebooks/auto-device-with-output.rst
@@ -30,13 +30,13 @@ first inference.
 
    auto
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Import modules and create Core <#import-modules-and-create-core>`__
 -  `Convert the model to OpenVINO IR
    format <#convert-the-model-to-openvino-ir-format>`__
--  `(1) Simplify selection logic <#1-simplify-selection-logic>`__
+-  `(1) Simplify selection logic <#simplify-selection-logic>`__
 
    -  `Default behavior of Core::compile_model API without
       device_name <#default-behavior-of-corecompile_model-api-without-device_name>`__
@@ -69,10 +69,10 @@ Import modules and create Core
 .. code:: ipython3
 
     import platform
-    
+
     # Install required packages
     %pip install -q "openvino>=2023.1.0" Pillow torch torchvision tqdm --extra-index-url https://download.pytorch.org/whl/cpu
-    
+
     if platform.system() != "Windows":
         %pip install -q "matplotlib>=3.4"
     else:
@@ -89,13 +89,13 @@ Import modules and create Core
 
     import time
     import sys
-    
+
     import openvino as ov
-    
+
     from IPython.display import Markdown, display
-    
+
     core = ov.Core()
-    
+
     if not any("GPU" in device for device in core.available_devices):
         display(
             Markdown(
@@ -139,11 +139,11 @@ For more information about model conversion API, see this
 
     import torchvision
     from pathlib import Path
-    
+
     base_model_dir = Path("./model")
     base_model_dir.mkdir(exist_ok=True)
     model_path = base_model_dir / "resnet50.xml"
-    
+
     if not model_path.exists():
         pt_model = torchvision.models.resnet50(weights="DEFAULT")
         ov_model = ov.convert_model(pt_model, input=[[1, 3, 224, 224]])
@@ -176,25 +176,26 @@ By default, ``compile_model`` API will select **AUTO** as
 
     # Set LOG_LEVEL to LOG_INFO.
     core.set_property("AUTO", {"LOG_LEVEL": "LOG_INFO"})
-    
+
     # Load the model onto the target device.
     compiled_model = core.compile_model(ov_model)
-    
+
     if isinstance(compiled_model, ov.CompiledModel):
         print("Successfully compiled model without a device_name.")
 
 
 .. parsed-literal::
 
-    [23:27:27.6972]I[plugin.cpp:418][AUTO] device:CPU, config:LOG_LEVEL=LOG_INFO
-    [23:27:27.6973]I[plugin.cpp:418][AUTO] device:CPU, config:PERFORMANCE_HINT=LATENCY
-    [23:27:27.6973]I[plugin.cpp:418][AUTO] device:CPU, config:PERFORMANCE_HINT_NUM_REQUESTS=0
-    [23:27:27.6973]I[plugin.cpp:418][AUTO] device:CPU, config:PERF_COUNT=NO
-    [23:27:27.6973]I[plugin.cpp:423][AUTO] device:CPU, priority:0
-    [23:27:27.6973]I[schedule.cpp:17][AUTO] scheduler starting
-    [23:27:27.6973]I[auto_schedule.cpp:131][AUTO] select device:CPU
-    [23:27:27.8462]I[auto_schedule.cpp:109][AUTO] device:CPU compiling model finished
-    [23:27:27.8464]I[plugin.cpp:451][AUTO] underlying hardware does not support hardware context
+    [23:25:26.9165]I[plugin.cpp:421][AUTO] device:CPU, config:LOG_LEVEL=LOG_INFO
+    [23:25:26.9166]I[plugin.cpp:421][AUTO] device:CPU, config:PERFORMANCE_HINT=LATENCY
+    [23:25:26.9166]I[plugin.cpp:421][AUTO] device:CPU, config:PERFORMANCE_HINT_NUM_REQUESTS=0
+    [23:25:26.9166]I[plugin.cpp:421][AUTO] device:CPU, config:PERF_COUNT=NO
+    [23:25:26.9166]I[plugin.cpp:426][AUTO] device:CPU, priority:0
+    [23:25:26.9166]I[schedule.cpp:17][AUTO] scheduler starting
+    [23:25:26.9166]I[auto_schedule.cpp:134][AUTO] select device:CPU
+    [23:25:27.0671]I[auto_schedule.cpp:336][AUTO] Device: [CPU]: Compile model took 150.493079 ms
+    [23:25:27.0674]I[auto_schedule.cpp:112][AUTO] device:CPU compiling model finished
+    [23:25:27.0675]I[plugin.cpp:454][AUTO] underlying hardware does not support hardware context
     Successfully compiled model without a device_name.
 
 
@@ -208,7 +209,7 @@ By default, ``compile_model`` API will select **AUTO** as
 .. parsed-literal::
 
     Deleted compiled_model
-    [23:27:27.8575]I[schedule.cpp:303][AUTO] scheduler ending
+    [23:25:27.0786]I[schedule.cpp:308][AUTO] scheduler ending
 
 
 Explicitly pass AUTO as device_name to Core::compile_model API
@@ -223,9 +224,9 @@ improve readability of your code.
 
     # Set LOG_LEVEL to LOG_NONE.
     core.set_property("AUTO", {"LOG_LEVEL": "LOG_NONE"})
-    
+
     compiled_model = core.compile_model(model=ov_model, device_name="AUTO")
-    
+
     if isinstance(compiled_model, ov.CompiledModel):
         print("Successfully compiled model using AUTO.")
 
@@ -275,25 +276,25 @@ function, we will reuse it for preparing input data.
 
     # Fetch `notebook_utils` module
     import requests
-    
+
     r = requests.get(url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py")
     open("notebook_utils.py", "w").write(r.text)
-    
+
     from notebook_utils import download_file
 
 .. code:: ipython3
 
     from PIL import Image
-    
+
     # Download the image from the openvino_notebooks storage
     image_filename = download_file(
         "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/coco.jpg",
         directory="data",
     )
-    
+
     image = Image.open(str(image_filename))
     input_transform = torchvision.models.ResNet50_Weights.DEFAULT.transforms()
-    
+
     input_tensor = input_transform(image)
     input_tensor = input_tensor.unsqueeze(0).numpy()
     image
@@ -324,10 +325,10 @@ Load the model to GPU device and perform inference
         # Start time.
         gpu_load_start_time = time.perf_counter()
         compiled_model = core.compile_model(model=ov_model, device_name="GPU")  # load to GPU
-    
+
         # Execute the first inference.
         results = compiled_model(input_tensor)[0]
-    
+
         # Measure time to the first inference.
         gpu_fil_end_time = time.perf_counter()
         gpu_fil_span = gpu_fil_end_time - gpu_load_start_time
@@ -353,11 +354,11 @@ executed on CPU until GPU is ready.
     # Start time.
     auto_load_start_time = time.perf_counter()
     compiled_model = core.compile_model(model=ov_model)  # The device_name is AUTO by default.
-    
+
     # Execute the first inference.
     results = compiled_model(input_tensor)[0]
-    
-    
+
+
     # Measure time to the first inference.
     auto_fil_end_time = time.perf_counter()
     auto_fil_span = auto_fil_end_time - auto_load_start_time
@@ -410,7 +411,7 @@ Class and callback definition
         :member: latency_list: Record the latency of each inference execution over @interval seconds duration.
         :member: interval: The metrics will be updated every @interval seconds
         """
-    
+
         def __init__(self, interval):
             """
             Create and initilize one instance of class PerformanceMetrics.
@@ -420,11 +421,11 @@ Class and callback definition
             """
             self.fps = 0
             self.latency = 0
-    
+
             self.start_time = time.perf_counter()
             self.latency_list = []
             self.interval = interval
-    
+
         def update(self, infer_request: ov.InferRequest) -> bool:
             """
             Update the metrics if current ongoing @interval seconds duration is expired. Record the latency only if it is not expired.
@@ -446,8 +447,8 @@ Class and callback definition
                 return True
             else:
                 return False
-    
-    
+
+
     class InferContext:
         """
         Inference context. Record and update peforamnce metrics via @metrics, set @feed_inference to False once @remaining_update_num <=0
@@ -455,7 +456,7 @@ Class and callback definition
         :member: remaining_update_num: the remaining times for peforamnce metrics updating.
         :member: feed_inference: if feed inference request is required or not.
         """
-    
+
         def __init__(self, update_interval, num):
             """
             Create and initilize one instance of class InferContext.
@@ -467,7 +468,7 @@ Class and callback definition
             self.metrics = PerformanceMetrics(update_interval)
             self.remaining_update_num = num
             self.feed_inference = True
-    
+
         def update(self, infer_request: ov.InferRequest):
             """
             Update the context. Set @feed_inference to False if the number of remaining performance metric updates (@remaining_update_num) reaches 0
@@ -476,13 +477,13 @@ Class and callback definition
             """
             if self.remaining_update_num <= 0:
                 self.feed_inference = False
-    
+
             if self.metrics.update(infer_request):
                 self.remaining_update_num = self.remaining_update_num - 1
                 if self.remaining_update_num <= 0:
                     self.feed_inference = False
-    
-    
+
+
     def completion_callback(infer_request: ov.InferRequest, context) -> None:
         """
         callback for the inference request, pass the @infer_request to @context for updating
@@ -491,8 +492,8 @@ Class and callback definition
         :returns: None
         """
         context.update(infer_request)
-    
-    
+
+
     # Performance metrics update interval (seconds) and number of times.
     metrics_update_interval = 10
     metrics_update_num = 6
@@ -508,29 +509,29 @@ Loop for inference and update the FPS/Latency every
 .. code:: ipython3
 
     THROUGHPUT_hint_context = InferContext(metrics_update_interval, metrics_update_num)
-    
+
     print("Compiling Model for AUTO device with THROUGHPUT hint")
     sys.stdout.flush()
-    
+
     compiled_model = core.compile_model(model=ov_model, config={"PERFORMANCE_HINT": "THROUGHPUT"})
-    
+
     infer_queue = ov.AsyncInferQueue(compiled_model, 0)  # Setting to 0 will query optimal number by default.
     infer_queue.set_callback(completion_callback)
-    
+
     print(f"Start inference, {metrics_update_num: .0f} groups of FPS/latency will be measured over {metrics_update_interval: .0f}s intervals")
     sys.stdout.flush()
-    
+
     while THROUGHPUT_hint_context.feed_inference:
         infer_queue.start_async(input_tensor, THROUGHPUT_hint_context)
-    
+
     infer_queue.wait_all()
-    
+
     # Take the FPS and latency of the latest period.
     THROUGHPUT_hint_fps = THROUGHPUT_hint_context.metrics.fps
     THROUGHPUT_hint_latency = THROUGHPUT_hint_context.metrics.latency
-    
+
     print("Done")
-    
+
     del compiled_model
 
 
@@ -538,12 +539,12 @@ Loop for inference and update the FPS/Latency every
 
     Compiling Model for AUTO device with THROUGHPUT hint
     Start inference,  6 groups of FPS/latency will be measured over  10s intervals
-    throughput:  177.73fps, latency:  32.02ms, time interval: 10.01s
-    throughput:  179.52fps, latency:  32.63ms, time interval: 10.00s
-    throughput:  178.56fps, latency:  32.79ms, time interval: 10.00s
-    throughput:  177.70fps, latency:  32.99ms, time interval: 10.01s
-    throughput:  178.80fps, latency:  32.69ms, time interval: 10.02s
-    throughput:  177.72fps, latency:  33.00ms, time interval: 10.01s
+    throughput:  178.13fps, latency:  31.99ms, time interval: 10.00s
+    throughput:  180.15fps, latency:  32.47ms, time interval: 10.00s
+    throughput:  179.79fps, latency:  32.55ms, time interval: 10.01s
+    throughput:  178.57fps, latency:  32.85ms, time interval: 10.01s
+    throughput:  179.62fps, latency:  32.61ms, time interval: 10.00s
+    throughput:  179.43fps, latency:  32.69ms, time interval: 10.00s
     Done
 
 
@@ -558,30 +559,30 @@ Loop for inference and update the FPS/Latency for each
 .. code:: ipython3
 
     LATENCY_hint_context = InferContext(metrics_update_interval, metrics_update_num)
-    
+
     print("Compiling Model for AUTO Device with LATENCY hint")
     sys.stdout.flush()
-    
+
     compiled_model = core.compile_model(model=ov_model, config={"PERFORMANCE_HINT": "LATENCY"})
-    
+
     # Setting to 0 will query optimal number by default.
     infer_queue = ov.AsyncInferQueue(compiled_model, 0)
     infer_queue.set_callback(completion_callback)
-    
+
     print(f"Start inference, {metrics_update_num: .0f} groups fps/latency will be out with {metrics_update_interval: .0f}s interval")
     sys.stdout.flush()
-    
+
     while LATENCY_hint_context.feed_inference:
         infer_queue.start_async(input_tensor, LATENCY_hint_context)
-    
+
     infer_queue.wait_all()
-    
+
     # Take the FPS and latency of the latest period.
     LATENCY_hint_fps = LATENCY_hint_context.metrics.fps
     LATENCY_hint_latency = LATENCY_hint_context.metrics.latency
-    
+
     print("Done")
-    
+
     del compiled_model
 
 
@@ -589,12 +590,12 @@ Loop for inference and update the FPS/Latency for each
 
     Compiling Model for AUTO Device with LATENCY hint
     Start inference,  6 groups fps/latency will be out with  10s interval
-    throughput:  135.52fps, latency:  6.87ms, time interval: 10.01s
-    throughput:  137.89fps, latency:  6.85ms, time interval: 10.00s
-    throughput:  137.71fps, latency:  6.82ms, time interval: 10.01s
-    throughput:  137.83fps, latency:  6.83ms, time interval: 10.01s
-    throughput:  137.80fps, latency:  6.83ms, time interval: 10.01s
-    throughput:  138.34fps, latency:  6.84ms, time interval: 10.00s
+    throughput:  138.97fps, latency:  6.66ms, time interval: 10.00s
+    throughput:  142.02fps, latency:  6.67ms, time interval: 10.01s
+    throughput:  141.78fps, latency:  6.62ms, time interval: 10.00s
+    throughput:  141.76fps, latency:  6.63ms, time interval: 10.00s
+    throughput:  141.93fps, latency:  6.66ms, time interval: 10.01s
+    throughput:  142.25fps, latency:  6.64ms, time interval: 10.00s
     Done
 
 
@@ -606,16 +607,16 @@ Difference in FPS and latency
 .. code:: ipython3
 
     import matplotlib.pyplot as plt
-    
+
     TPUT = 0
     LAT = 1
     labels = ["THROUGHPUT hint", "LATENCY hint"]
-    
+
     fig1, ax1 = plt.subplots(1, 1)
     fig1.patch.set_visible(False)
     ax1.axis("tight")
     ax1.axis("off")
-    
+
     cell_text = []
     cell_text.append(
         [
@@ -624,7 +625,7 @@ Difference in FPS and latency
         ]
     )
     cell_text.append(["%.2f%s" % (LATENCY_hint_fps, " FPS"), "%.2f%s" % (LATENCY_hint_latency, " ms")])
-    
+
     table = ax1.table(
         cellText=cell_text,
         colLabels=["FPS (Higher is better)", "Latency (Lower is better)"],
@@ -639,7 +640,7 @@ Difference in FPS and latency
     table.auto_set_column_width(0)
     table.auto_set_column_width(1)
     table.scale(1, 3)
-    
+
     fig1.tight_layout()
     plt.show()
 
@@ -653,28 +654,28 @@ Difference in FPS and latency
     # Output the difference.
     width = 0.4
     fontsize = 14
-    
+
     plt.rc("font", size=fontsize)
     fig, ax = plt.subplots(1, 2, figsize=(10, 8))
-    
+
     rects1 = ax[0].bar([0], THROUGHPUT_hint_fps, width, label=labels[TPUT], color="#557f2d")
     rects2 = ax[0].bar([width], LATENCY_hint_fps, width, label=labels[LAT])
     ax[0].set_ylabel("frames per second")
     ax[0].set_xticks([width / 2])
     ax[0].set_xticklabels(["FPS"])
     ax[0].set_xlabel("Higher is better")
-    
+
     rects1 = ax[1].bar([0], THROUGHPUT_hint_latency, width, label=labels[TPUT], color="#557f2d")
     rects2 = ax[1].bar([width], LATENCY_hint_latency, width, label=labels[LAT])
     ax[1].set_ylabel("milliseconds")
     ax[1].set_xticks([width / 2])
     ax[1].set_xticklabels(["Latency (ms)"])
     ax[1].set_xlabel("Lower is better")
-    
+
     fig.suptitle("Performance Hints")
     fig.legend(labels, fontsize=fontsize)
     fig.tight_layout()
-    
+
     plt.show()
 
 
diff --git a/docs/notebooks/auto-device-with-output_files/auto-device-with-output_27_0.png b/docs/notebooks/auto-device-with-output_files/auto-device-with-output_27_0.png
index 9e2d67fe539252..54de802ad396dc 100644
--- a/docs/notebooks/auto-device-with-output_files/auto-device-with-output_27_0.png
+++ b/docs/notebooks/auto-device-with-output_files/auto-device-with-output_27_0.png
@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:50f5fe192a152bfb8036a782ee93ab26543c9a30fc626e67aab5e5a7dba9e35a
-size 27240
+oid sha256:807232c3d860a113c5a6bb3549f746e61548c95977692ad10c94a6ce8f17d12c
+size 26625
diff --git a/docs/notebooks/auto-device-with-output_files/auto-device-with-output_28_0.png b/docs/notebooks/auto-device-with-output_files/auto-device-with-output_28_0.png
index 1f42981e0fb5b4..7447b7e75aaf75 100644
--- a/docs/notebooks/auto-device-with-output_files/auto-device-with-output_28_0.png
+++ b/docs/notebooks/auto-device-with-output_files/auto-device-with-output_28_0.png
@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:5a4bfe1e79236b08d4848e6ff59eccf1e2c459d74cb8940b54e8cad3ef01a948
-size 40033
+oid sha256:d0304a4d2a5cd64a60a96e78eea3ee2afa0f6c8115767ee620e6509d109d8269
+size 39949
diff --git a/docs/notebooks/bark-text-to-audio-with-output.rst b/docs/notebooks/bark-text-to-audio-with-output.rst
index 1c2c30cc4f5b80..c6d67c50fb30eb 100644
--- a/docs/notebooks/bark-text-to-audio-with-output.rst
+++ b/docs/notebooks/bark-text-to-audio-with-output.rst
@@ -54,8 +54,8 @@ tokens into audio codec tokens to generate the full waveform. To enable
 the community to use Bark via public code, EnCodec codec from Facebook
 is used to act as an audio representation.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Download and Convert models <#download-and-convert-models>`__
diff --git a/docs/notebooks/blip-visual-language-processing-with-output.rst b/docs/notebooks/blip-visual-language-processing-with-output.rst
index 8a00c7cdf6dc7e..88b1c999c03b89 100644
--- a/docs/notebooks/blip-visual-language-processing-with-output.rst
+++ b/docs/notebooks/blip-visual-language-processing-with-output.rst
@@ -30,8 +30,8 @@ The tutorial consists of the following parts:
 5. Compare original and optimized models
 6. Launch interactive demo
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Background <#background>`__
 
@@ -270,7 +270,7 @@ text and vision modalities and postprocessing of generation results.
 
     import platform
 
-    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu "torch>=2.1.0" torchvision "transformers>=4.26.0" "gradio>=4.19" "openvino>=2023.3.0" "datasets>==2.14.6" "nncf>=2.8.1" "tqdm"
+    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu "torch>=2.1.0" torchvision "transformers>=4.26.0" "gradio>=4.19" "openvino>=2023.3.0" "datasets>=2.14.6" "nncf>=2.8.1" "tqdm"
     if platform.system() != "Windows":
         %pip install -q "matplotlib>=3.4"
     else:
@@ -831,7 +831,7 @@ understanding of vision, language and commonsense knowledge to answer.
         Prepares a vision-text dataset for quantization.
         """
         split = f"train[:{opt_init_steps}]" if not streaming else "train"
-        dataset = load_dataset("HuggingFaceM4/VQAv2", split=split, streaming=streaming)
+        dataset = load_dataset("HuggingFaceM4/VQAv2", split=split, streaming=streaming, trust_remote_code=True)
         dataset = dataset.shuffle(seed=42)
         if streaming:
             dataset = dataset.take(opt_init_steps)
@@ -878,17 +878,17 @@ Quantize vision model
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -899,17 +899,17 @@ Quantize vision model
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -926,17 +926,17 @@ Quantize vision model
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -947,17 +947,17 @@ Quantize vision model
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -988,17 +988,17 @@ Quantize text encoder
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -1009,17 +1009,17 @@ Quantize text encoder
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -1036,17 +1036,17 @@ Quantize text encoder
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -1057,17 +1057,17 @@ Quantize text encoder
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -1113,17 +1113,17 @@ The optimization process contains the following steps:
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
diff --git a/docs/notebooks/clip-language-saliency-map-with-output.rst b/docs/notebooks/clip-language-saliency-map-with-output.rst
index 9ac43fe4b4266d..08248f3aa58168 100644
--- a/docs/notebooks/clip-language-saliency-map-with-output.rst
+++ b/docs/notebooks/clip-language-saliency-map-with-output.rst
@@ -79,8 +79,8 @@ used to build the saliency map. Here is how it can be done:
 5. Update the corresponding region on the ``saliency map``.
 6. Repeat steps 2-5 multiple times (``n_iters``).
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Initial Implementation with Transformers and
    Pytorch <#initial-implementation-with-transformers-and-pytorch>`__
diff --git a/docs/notebooks/clip-zero-shot-classification-with-output.rst b/docs/notebooks/clip-zero-shot-classification-with-output.rst
index 40cca0e2ec9f15..f8d06e6dd91422 100644
--- a/docs/notebooks/clip-zero-shot-classification-with-output.rst
+++ b/docs/notebooks/clip-zero-shot-classification-with-output.rst
@@ -36,8 +36,8 @@ The notebook contains the following steps:
 8. Compare performance of converted and quantized models.
 9. Launch interactive demo
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Instantiate model <#instantiate-model>`__
 -  `Run PyTorch model inference <#run-pytorch-model-inference>`__
@@ -318,6 +318,8 @@ Quantize model to INT8 using NNCF
 ---------------------------------
 
 
+
+
 The goal of this part of tutorial is to demonstrate how to speed up the
 model by applying 8-bit post-training quantization from
 `NNCF <https://github.com/openvinotoolkit/nncf/>`__ (Neural Network
@@ -474,7 +476,7 @@ model.
         """
         Prepares a vision-text dataset for quantization.
         """
-        dataset = load_dataset("conceptual_captions")
+        dataset = load_dataset("google-research-datasets/conceptual_captions", trust_remote_code=True)
         train_dataset = dataset["train"].shuffle(seed=42)
         dataloader = torch.utils.data.DataLoader(train_dataset, collate_fn=collate_fn, batch_size=1)
         calibration_data = prepare_calibration_data(dataloader, opt_init_steps)
@@ -563,17 +565,17 @@ Create a quantized model from the pre-trained ``FP16`` model.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -584,17 +586,17 @@ Create a quantized model from the pre-trained ``FP16`` model.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -605,17 +607,17 @@ Create a quantized model from the pre-trained ``FP16`` model.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -626,17 +628,17 @@ Create a quantized model from the pre-trained ``FP16`` model.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -697,7 +699,7 @@ Compare File Size
 Compare inference time of the FP16 IR and quantized models
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
- To measure the inference
+To measure the inference
 performance of the ``FP16`` and ``INT8`` models, we use median inference
 time on calibration dataset. So we can approximately estimate the speed
 up of the dynamic quantized models.
@@ -740,6 +742,7 @@ up of the dynamic quantized models.
 Interactive demo
 ----------------
 
+
 Now, it is your turn! You can provide your own image and comma-separated
 list of labels for zero-shot classification.
 
diff --git a/docs/notebooks/controlnet-stable-diffusion-with-output.rst b/docs/notebooks/controlnet-stable-diffusion-with-output.rst
index 1e9957a95b8646..fd4eb02ad019f6 100644
--- a/docs/notebooks/controlnet-stable-diffusion-with-output.rst
+++ b/docs/notebooks/controlnet-stable-diffusion-with-output.rst
@@ -141,8 +141,8 @@ of the target in the image:
 This tutorial focuses mainly on conditioning by pose. However, the
 discussed steps are also applicable to other annotation modes.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Instantiating Generation
diff --git a/docs/notebooks/convert-to-openvino-with-output.rst b/docs/notebooks/convert-to-openvino-with-output.rst
index c3a12ea70bd6cd..cd272f1436edd3 100644
--- a/docs/notebooks/convert-to-openvino-with-output.rst
+++ b/docs/notebooks/convert-to-openvino-with-output.rst
@@ -4,8 +4,8 @@ OpenVINO™ Model conversion
 This notebook shows how to convert a model from original framework
 format to OpenVINO Intermediate Representation (IR).
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `OpenVINO IR format <#openvino-ir-format>`__
 -  `Fetching example models <#fetching-example-models>`__
@@ -35,7 +35,7 @@ Table of contents:
 
 .. parsed-literal::
 
-    Requirement already satisfied: pip in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (24.0)
+    Requirement already satisfied: pip in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (24.1.1)
     Note: you may need to restart the kernel to use updated packages.
     Note: you may need to restart the kernel to use updated packages.
 
@@ -181,11 +181,11 @@ NLP model from Hugging Face and export it in ONNX format:
 
 .. parsed-literal::
 
-    2024-06-05 23:48:38.001731: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-06-05 23:48:38.036985: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-07-01 23:46:23.388228: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-07-01 23:46:23.422415: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2024-06-05 23:48:38.551703: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/distilbert/modeling_distilbert.py:231: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+    2024-07-01 23:46:24.062925: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/distilbert/modeling_distilbert.py:230: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
       mask, torch.tensor(torch.finfo(scores.dtype).min)
 
 
@@ -662,12 +662,12 @@ frameworks conversion guides.
 
 .. parsed-literal::
 
-    2024-06-05 23:48:58.596260: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:266] failed call to cuInit: CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE: forward compatibility was attempted on non supported HW
-    2024-06-05 23:48:58.596295: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:168] retrieving CUDA diagnostic information for host: iotg-dev-workstation-07
-    2024-06-05 23:48:58.596299: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:175] hostname: iotg-dev-workstation-07
-    2024-06-05 23:48:58.596508: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:199] libcuda reported version is: 470.223.2
-    2024-06-05 23:48:58.596524: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:203] kernel reported version is: 470.182.3
-    2024-06-05 23:48:58.596528: E tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:312] kernel version 470.182.3 does not match DSO version 470.223.2 -- cannot find working devices in this configuration
+    2024-07-01 23:46:43.443603: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:266] failed call to cuInit: CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE: forward compatibility was attempted on non supported HW
+    2024-07-01 23:46:43.443639: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:168] retrieving CUDA diagnostic information for host: iotg-dev-workstation-07
+    2024-07-01 23:46:43.443643: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:175] hostname: iotg-dev-workstation-07
+    2024-07-01 23:46:43.443849: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:199] libcuda reported version is: 470.223.2
+    2024-07-01 23:46:43.443867: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:203] kernel reported version is: 470.182.3
+    2024-07-01 23:46:43.443871: E tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:312] kernel version 470.182.3 does not match DSO version 470.223.2 -- cannot find working devices in this configuration
 
 
 Migration from Legacy conversion API
@@ -725,6 +725,13 @@ Resnet50 model that was exported to the ONNX format:
     ov_model = mo.convert_model(ONNX_CV_MODEL_PATH, layout="nchw")
 
 
+.. parsed-literal::
+
+    [ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release.
+    In 2025.0 MO command line tool and openvino.tools.mo.convert_model() will be removed. Please use OpenVINO Model Converter (OVC) or openvino.convert_model(). OVC represents a lightweight alternative of MO and provides simplified model conversion API. 
+    Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
+
+
 .. parsed-literal::
 
     huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
@@ -768,6 +775,17 @@ and the layout of an original model:
     # alternatively use source_layout and target_layout parameters
     ov_model = mo.convert_model(ONNX_CV_MODEL_PATH, source_layout="nchw", target_layout="nhwc")
 
+
+.. parsed-literal::
+
+    [ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release.
+    In 2025.0 MO command line tool and openvino.tools.mo.convert_model() will be removed. Please use OpenVINO Model Converter (OVC) or openvino.convert_model(). OVC represents a lightweight alternative of MO and provides simplified model conversion API. 
+    Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
+    [ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release.
+    In 2025.0 MO command line tool and openvino.tools.mo.convert_model() will be removed. Please use OpenVINO Model Converter (OVC) or openvino.convert_model(). OVC represents a lightweight alternative of MO and provides simplified model conversion API. 
+    Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
+
+
 Specifying Mean and Scale Values
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
@@ -806,6 +824,14 @@ more examples.
         scale_values=[255 * x for x in [0.229, 0.224, 0.225]],
     )
 
+
+.. parsed-literal::
+
+    [ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release.
+    In 2025.0 MO command line tool and openvino.tools.mo.convert_model() will be removed. Please use OpenVINO Model Converter (OVC) or openvino.convert_model(). OVC represents a lightweight alternative of MO and provides simplified model conversion API. 
+    Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
+
+
 Reversing Input Channels
 ^^^^^^^^^^^^^^^^^^^^^^^^
 
@@ -836,6 +862,14 @@ the color channels before inference.
     
     ov_model = mo.convert_model(ONNX_CV_MODEL_PATH, reverse_input_channels=True)
 
+
+.. parsed-literal::
+
+    [ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release.
+    In 2025.0 MO command line tool and openvino.tools.mo.convert_model() will be removed. Please use OpenVINO Model Converter (OVC) or openvino.convert_model(). OVC represents a lightweight alternative of MO and provides simplified model conversion API. 
+    Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
+
+
 Cutting Off Parts of a Model
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
diff --git a/docs/notebooks/convnext-classification-with-output.rst b/docs/notebooks/convnext-classification-with-output.rst
index 6e7c73162fa6c7..4e175694fc9ff0 100644
--- a/docs/notebooks/convnext-classification-with-output.rst
+++ b/docs/notebooks/convnext-classification-with-output.rst
@@ -22,8 +22,8 @@ maintaining the simplicity and efficiency of standard ConvNets. The
 several pretrained ConvNeXt model. In this tutorial we will use ConvNeXt
 Tiny model.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Get a test image <#get-a-test-image>`__
@@ -50,29 +50,27 @@ Prerequisites
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
 
 
 Get a test image
 ----------------
 
- First of all lets get a test
+First of all lets get a test
 image from an open dataset.
 
 .. code:: ipython3
 
     import requests
-    
+
     from torchvision.io import read_image
     import torchvision.transforms as transforms
-    
-    
+
+
     img_path = "cats_image.jpeg"
     r = requests.get("https://huggingface.co/datasets/huggingface/cats-image/resolve/main/cats_image.jpeg")
-    
+
     with open(img_path, "wb") as f:
         f.write(r.content)
     image = read_image(img_path)
@@ -86,19 +84,19 @@ image from an open dataset.
 Get a pretrained model
 ----------------------
 
- Torchvision provides a
+Torchvision provides a
 mechanism of `listing and retrieving available
 models <https://pytorch.org/vision/stable/models.html#listing-and-retrieving-available-models>`__.
 
 .. code:: ipython3
 
     import torchvision.models as models
-    
+
     # List available models
     all_models = models.list_models()
     # List of models by type. Classification models are in the parent module.
     classification_models = models.list_models(module=models)
-    
+
     print(classification_models)
 
 
@@ -126,7 +124,7 @@ initialize pre-trained models
 Define a preprocessing and prepare an input data
 ------------------------------------------------
 
- You can use
+You can use
 ``torchvision.transforms`` to make a preprocessing or
 use\ `preprocessing transforms from the model
 wight <https://pytorch.org/vision/stable/models.html#using-the-pre-trained-models>`__.
@@ -134,10 +132,10 @@ wight <https://pytorch.org/vision/stable/models.html#using-the-pre-trained-model
 .. code:: ipython3
 
     import torch
-    
-    
+
+
     preprocess = models.ConvNeXt_Tiny_Weights.DEFAULT.transforms()
-    
+
     input_data = preprocess(image)
     input_data = torch.stack([input_data], dim=0)
 
@@ -159,17 +157,17 @@ And print results
     r = requests.get(
         url="https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/datasets/imagenet/imagenet_2012.txt",
     )
-    
+
     with open(imagenet_classes_file_path, "w") as f:
         f.write(r.text)
-    
+
     imagenet_classes = open(imagenet_classes_file_path).read().splitlines()
-    
-    
+
+
     def print_results(outputs: torch.Tensor):
         _, predicted_class = outputs.max(1)
         predicted_probability = torch.softmax(outputs, dim=1)[0, predicted_class].item()
-    
+
         print(f"Predicted Class: {predicted_class.item()}")
         print(f"Predicted Label: {imagenet_classes[predicted_class.item()]}")
         print(f"Predicted Probability: {predicted_probability}")
@@ -183,7 +181,7 @@ And print results
 
     Predicted Class: 281
     Predicted Label: n02123045 tabby, tabby cat
-    Predicted Probability: 0.5808374285697937
+    Predicted Probability: 0.5351971983909607
 
 
 Convert the model to OpenVINO Intermediate representation format
@@ -203,12 +201,12 @@ interface. However, it can also be saved on disk using
 .. code:: ipython3
 
     from pathlib import Path
-    
+
     import openvino as ov
-    
-    
+
+
     ov_model_xml_path = Path("models/ov_convnext_model.xml")
-    
+
     if not ov_model_xml_path.exists():
         ov_model_xml_path.parent.mkdir(parents=True, exist_ok=True)
         converted_model = ov.convert_model(model, example_input=torch.randn(1, 3, 224, 224))
@@ -229,7 +227,7 @@ Select device from dropdown list for running inference using OpenVINO
 .. code:: ipython3
 
     import ipywidgets as widgets
-    
+
     core = ov.Core()
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
@@ -237,7 +235,7 @@ Select device from dropdown list for running inference using OpenVINO
         description="Device:",
         disabled=False,
     )
-    
+
     device
 
 
@@ -252,7 +250,7 @@ Select device from dropdown list for running inference using OpenVINO
 .. code:: ipython3
 
     core = ov.Core()
-    
+
     compiled_model = core.compile_model(ov_model_xml_path, device_name=device.value)
 
 Use the OpenVINO IR model to run an inference
diff --git a/docs/notebooks/cross-lingual-books-alignment-with-output.rst b/docs/notebooks/cross-lingual-books-alignment-with-output.rst
index f80b3eb809c33f..2ef4a8e021d735 100644
--- a/docs/notebooks/cross-lingual-books-alignment-with-output.rst
+++ b/docs/notebooks/cross-lingual-books-alignment-with-output.rst
@@ -37,8 +37,8 @@ Prerequisites
 -  ``seaborn`` - for alignment matrix visualization
 -  ``ipywidgets`` - for displaying HTML and JS output in the notebook
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Get Books <#get-books>`__
 -  `Clean Text <#clean-text>`__
diff --git a/docs/notebooks/ct-segmentation-quantize-nncf-with-output.rst b/docs/notebooks/ct-segmentation-quantize-nncf-with-output.rst
index 602a5753f5c417..59623292d4026a 100644
--- a/docs/notebooks/ct-segmentation-quantize-nncf-with-output.rst
+++ b/docs/notebooks/ct-segmentation-quantize-nncf-with-output.rst
@@ -53,8 +53,8 @@ demonstration purposes, this tutorial will download one converted CT
 scan and use that scan for quantization and inference. For production
 purposes, use a representative dataset for quantizing the model.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Imports <#imports>`__
 -  `Settings <#settings>`__
@@ -152,10 +152,10 @@ Imports
 
 .. parsed-literal::
 
-    2024-06-05 23:50:19.239580: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-06-05 23:50:19.274774: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-07-01 23:47:08.962451: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-07-01 23:47:08.997285: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2024-06-05 23:50:19.860831: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-07-01 23:47:09.575374: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 .. parsed-literal::
@@ -435,7 +435,7 @@ this notebook.
 .. parsed-literal::
 
     [ WARNING ]  Please fix your imports. Module %s has been moved to %s. The old module will be deleted in version %s.
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/monai/networks/nets/basic_unet.py:168: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/monai/networks/nets/basic_unet.py:168: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if x_e.shape[-i - 1] != x_0.shape[-i - 1]:
 
 
@@ -486,17 +486,17 @@ steps:
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -507,17 +507,17 @@ steps:
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -534,18 +534,18 @@ Convert quantized model to OpenVINO IR model and save it.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/torch/quantization/layers.py:337: TracerWarning: Converting a tensor to a Python number might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/torch/quantization/layers.py:339: TracerWarning: Converting a tensor to a Python number might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       return self._level_low.item()
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/torch/quantization/layers.py:345: TracerWarning: Converting a tensor to a Python number might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/torch/quantization/layers.py:347: TracerWarning: Converting a tensor to a Python number might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       return self._level_high.item()
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/monai/networks/nets/basic_unet.py:168: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/monai/networks/nets/basic_unet.py:168: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if x_e.shape[-i - 1] != x_0.shape[-i - 1]:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:1116: TracerWarning: Output nr 1. of the traced function does not match the corresponding output of the Python function. Detailed error:
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:1116: TracerWarning: Output nr 1. of the traced function does not match the corresponding output of the Python function. Detailed error:
     Tensor-likes are not close!
     
-    Mismatched elements: 249914 / 262144 (95.3%)
-    Greatest absolute difference: 4.319500803947449 at index (0, 0, 125, 295) (up to 1e-05 allowed)
-    Greatest relative difference: 10952.699411314505 at index (0, 0, 220, 387) (up to 1e-05 allowed)
+    Mismatched elements: 247635 / 262144 (94.5%)
+    Greatest absolute difference: 4.091559410095215 at index (0, 0, 441, 89) (up to 1e-05 allowed)
+    Greatest relative difference: 6430.725591012176 at index (0, 0, 48, 134) (up to 1e-05 allowed)
       _check_trace(
 
 
@@ -668,18 +668,18 @@ be run in the notebook with ``! benchmark_app`` or
     [ INFO ] Parsing input parameters
     [Step 2/11] Loading OpenVINO Runtime
     [ INFO ] OpenVINO:
-    [ INFO ] Build ................................. 2024.1.0-15008-f4afc983258-releases/2024/1
+    [ INFO ] Build ................................. 2024.2.0-15519-5c0f38f83f6-releases/2024/2
     [ INFO ] 
     [ INFO ] Device info:
     [ INFO ] AUTO
-    [ INFO ] Build ................................. 2024.1.0-15008-f4afc983258-releases/2024/1
+    [ INFO ] Build ................................. 2024.2.0-15519-5c0f38f83f6-releases/2024/2
     [ INFO ] 
     [ INFO ] 
     [Step 3/11] Setting device configuration
     [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.LATENCY.
     [Step 4/11] Reading model files
     [ INFO ] Loading model files
-    [ INFO ] Read model took 8.75 ms
+    [ INFO ] Read model took 8.87 ms
     [ INFO ] Original model I/O parameters:
     [ INFO ] Model inputs:
     [ INFO ]     x (node: x) : f32 / [...] / [?,?,?,?]
@@ -693,7 +693,7 @@ be run in the notebook with ``! benchmark_app`` or
     [ INFO ] Model outputs:
     [ INFO ]     ***NO_NAME*** (node: __module.final_conv/aten::_convolution/Add) : f32 / [...] / [?,1,16..,16..]
     [Step 7/11] Loading the model to the device
-    [ INFO ] Compile model took 150.71 ms
+    [ INFO ] Compile model took 192.84 ms
     [Step 8/11] Querying optimal runtime parameters
     [ INFO ] Model:
     [ INFO ]   NETWORK_NAME: Model0
@@ -728,9 +728,9 @@ be run in the notebook with ``! benchmark_app`` or
     [Step 9/11] Creating infer requests and preparing input tensors
     [ ERROR ] Input x is dynamic. Provide data shapes!
     Traceback (most recent call last):
-      File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/openvino/tools/benchmark/main.py", line 486, in main
+      File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/openvino/tools/benchmark/main.py", line 486, in main
         data_queue = get_input_data(paths_to_input, app_inputs_info)
-      File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/openvino/tools/benchmark/utils/inputs_filling.py", line 123, in get_input_data
+      File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/openvino/tools/benchmark/utils/inputs_filling.py", line 123, in get_input_data
         raise Exception(f"Input {info.name} is dynamic. Provide data shapes!")
     Exception: Input x is dynamic. Provide data shapes!
 
@@ -747,18 +747,18 @@ be run in the notebook with ``! benchmark_app`` or
     [ INFO ] Parsing input parameters
     [Step 2/11] Loading OpenVINO Runtime
     [ INFO ] OpenVINO:
-    [ INFO ] Build ................................. 2024.1.0-15008-f4afc983258-releases/2024/1
+    [ INFO ] Build ................................. 2024.2.0-15519-5c0f38f83f6-releases/2024/2
     [ INFO ] 
     [ INFO ] Device info:
     [ INFO ] AUTO
-    [ INFO ] Build ................................. 2024.1.0-15008-f4afc983258-releases/2024/1
+    [ INFO ] Build ................................. 2024.2.0-15519-5c0f38f83f6-releases/2024/2
     [ INFO ] 
     [ INFO ] 
     [Step 3/11] Setting device configuration
     [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.LATENCY.
     [Step 4/11] Reading model files
     [ INFO ] Loading model files
-    [ INFO ] Read model took 10.69 ms
+    [ INFO ] Read model took 10.93 ms
     [ INFO ] Original model I/O parameters:
     [ INFO ] Model inputs:
     [ INFO ]     x (node: x) : f32 / [...] / [1,1,512,512]
@@ -772,7 +772,7 @@ be run in the notebook with ``! benchmark_app`` or
     [ INFO ] Model outputs:
     [ INFO ]     ***NO_NAME*** (node: __module.final_conv/aten::_convolution/Add) : f32 / [...] / [1,1,512,512]
     [Step 7/11] Loading the model to the device
-    [ INFO ] Compile model took 275.30 ms
+    [ INFO ] Compile model took 227.54 ms
     [Step 8/11] Querying optimal runtime parameters
     [ INFO ] Model:
     [ INFO ]   NETWORK_NAME: Model49
@@ -809,17 +809,17 @@ be run in the notebook with ``! benchmark_app`` or
     [ INFO ] Fill input 'x' with random values 
     [Step 10/11] Measuring performance (Start inference synchronously, limits: 15000 ms duration)
     [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
-    [ INFO ] First inference took 29.48 ms
+    [ INFO ] First inference took 31.23 ms
     [Step 11/11] Dumping statistics report
     [ INFO ] Execution Devices:['CPU']
-    [ INFO ] Count:            969 iterations
-    [ INFO ] Duration:         15001.31 ms
+    [ INFO ] Count:            970 iterations
+    [ INFO ] Duration:         15001.74 ms
     [ INFO ] Latency:
-    [ INFO ]    Median:        15.23 ms
-    [ INFO ]    Average:       15.28 ms
-    [ INFO ]    Min:           14.97 ms
-    [ INFO ]    Max:           17.10 ms
-    [ INFO ] Throughput:   64.59 FPS
+    [ INFO ]    Median:        15.20 ms
+    [ INFO ]    Average:       15.26 ms
+    [ INFO ]    Min:           14.94 ms
+    [ INFO ]    Max:           19.19 ms
+    [ INFO ] Throughput:   64.66 FPS
 
 
 Visually Compare Inference Results
@@ -904,7 +904,7 @@ seed is displayed to enable reproducing specific runs of this cell.
 
 .. parsed-literal::
 
-    Visualizing results with seed 1717624288
+    Visualizing results with seed 1719870497
 
 
 
@@ -987,8 +987,8 @@ performs inference, and displays the results on the frames loaded in
 
 .. parsed-literal::
 
-    Loaded model to AUTO in 0.23 seconds.
-    Total time for 68 frames: 2.33 seconds, fps:29.56
+    Loaded model to AUTO in 0.19 seconds.
+    Total time for 68 frames: 2.41 seconds, fps:28.61
 
 
 References
diff --git a/docs/notebooks/ct-segmentation-quantize-nncf-with-output_files/ct-segmentation-quantize-nncf-with-output_37_1.png b/docs/notebooks/ct-segmentation-quantize-nncf-with-output_files/ct-segmentation-quantize-nncf-with-output_37_1.png
index 60f97677de531c..66c0fdf7363487 100644
--- a/docs/notebooks/ct-segmentation-quantize-nncf-with-output_files/ct-segmentation-quantize-nncf-with-output_37_1.png
+++ b/docs/notebooks/ct-segmentation-quantize-nncf-with-output_files/ct-segmentation-quantize-nncf-with-output_37_1.png
@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:1a4a6ad8cf666b4ce12cb07fa53b22a2ba0257697d926d9846ee4c18752fc553
-size 380300
+oid sha256:f98b4bde92e898a1005c5a07234464687d828ba03e25ac44551a12f75504c878
+size 379807
diff --git a/docs/notebooks/ddcolor-image-colorization-with-output.rst b/docs/notebooks/ddcolor-image-colorization-with-output.rst
index 24f1c30e773fa3..42e12cca2092f5 100644
--- a/docs/notebooks/ddcolor-image-colorization-with-output.rst
+++ b/docs/notebooks/ddcolor-image-colorization-with-output.rst
@@ -25,10 +25,8 @@ In this tutorial we consider how to convert and run DDColor using
 OpenVINO. Additionally, we will demonstrate how to optimize this model
 using `NNCF <https://github.com/openvinotoolkit/nncf/>`__.
 
-🪄 Let’s start to explore magic of image colorization!
-
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+🪄 Let’s start to explore magic of image colorization! #### Table of
+contents:
 
 -  `Prerequisites <#prerequisites>`__
 -  `Load PyTorch model <#load-pytorch-model>`__
@@ -59,14 +57,10 @@ Prerequisites
 .. code:: ipython3
 
     import platform
-    import os
-
-    os.environ["GIT_CLONE_PROTECTION_ACTIVE"] = "false"
-
-    %pip install -q timm "torch>=2.1" "torchvision" "opencv_python" "pillow" "PyYAML" "scipy" "scikit-image" "datasets" "gradio>=4.19"  --extra-index-url https://download.pytorch.org/whl/cpu
+    
+    %pip install -q "nncf>=2.11.0" "torch>=2.1" "torchvision" "timm" "opencv_python" "pillow" "PyYAML" "scipy" "scikit-image" "datasets" "gradio>=4.19"  --extra-index-url https://download.pytorch.org/whl/cpu
     %pip install -Uq --pre "openvino" --extra-index-url https://storage.openvinotoolkit.org/simple/wheels/nightly
-    %pip install -q "git+https://github.com/openvinotoolkit/nncf.git"
-
+    
     if platform.python_version_tuple()[1] in ["8", "9"]:
         %pip install -q "gradio-imageslider<=0.0.17" "typing-extensions>=4.9.0"
     else:
@@ -77,8 +71,7 @@ Prerequisites
 
     Note: you may need to restart the kernel to use updated packages.
     ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
-    openvino-dev 2024.1.0 requires openvino==2024.1.0, but you have openvino 2024.3.0.dev20240605 which is incompatible.
-    Note: you may need to restart the kernel to use updated packages.
+    openvino-dev 2024.2.0 requires openvino==2024.2.0, but you have openvino 2024.3.0.dev20240627 which is incompatible.
     Note: you may need to restart the kernel to use updated packages.
     Note: you may need to restart the kernel to use updated packages.
 
@@ -87,24 +80,24 @@ Prerequisites
 
     import sys
     from pathlib import Path
-
+    
     repo_dir = Path("DDColor")
-
+    
     if not repo_dir.exists():
         !git clone https://github.com/piddnad/DDColor.git
-
+    
     sys.path.append(str(repo_dir))
 
 
 .. parsed-literal::
 
     Cloning into 'DDColor'...
-    remote: Enumerating objects: 223, done.
-    remote: Counting objects: 100% (69/69), done.
-    remote: Compressing objects: 100% (35/35), done.
-    remote: Total 223 (delta 51), reused 36 (delta 33), pack-reused 154
-    Receiving objects: 100% (223/223), 13.34 MiB | 22.50 MiB/s, done.
-    Resolving deltas: 100% (72/72), done.
+    remote: Enumerating objects: 230, done.
+    remote: Counting objects: 100% (76/76), done.
+    remote: Compressing objects: 100% (39/39), done.
+    remote: Total 230 (delta 54), reused 40 (delta 36), pack-reused 154
+    Receiving objects: 100% (230/230), 13.34 MiB | 21.61 MiB/s, done.
+    Resolving deltas: 100% (75/75), done.
 
 
 .. code:: ipython3
@@ -128,30 +121,17 @@ models from DDColor family.
 .. code:: ipython3
 
     import torch
-
+    
     model_name = "ddcolor_paper_tiny"
-
+    
     ddcolor_model = DDColorHF.from_pretrained(f"piddnad/{model_name}")
-
-
+    
+    
     colorizer = ImageColorizationPipelineHF(model=ddcolor_model, input_size=512)
-
+    
     ddcolor_model.to("cpu")
     colorizer.device = torch.device("cpu")
 
-
-
-.. parsed-literal::
-
-    config.json:   0%|          | 0.00/258 [00:00<?, ?B/s]
-
-
-
-.. parsed-literal::
-
-    pytorch_model.bin:   0%|          | 0.00/220M [00:00<?, ?B/s]
-
-
 Run PyTorch model inference
 ---------------------------
 
@@ -161,12 +141,12 @@ Run PyTorch model inference
 
     import cv2
     import PIL
-
+    
     IMG_PATH = "DDColor/assets/test_images/Ansel Adams _ Moore Photography.jpeg"
-
-
+    
+    
     img = cv2.imread(IMG_PATH)
-
+    
     PIL.Image.fromarray(img[:, :, ::-1])
 
 
@@ -205,9 +185,9 @@ loading on device using ``core.complie_model``.
 
     import openvino as ov
     import torch
-
+    
     OV_COLORIZER_PATH = Path("ddcolor.xml")
-
+    
     if not OV_COLORIZER_PATH.exists():
         ov_model = ov.convert_model(ddcolor_model, example_input=torch.ones((1, 3, 512, 512)), input=[1, 3, 512, 512])
         ov.save_model(ov_model, OV_COLORIZER_PATH)
@@ -222,16 +202,16 @@ Select one of supported devices for inference using dropdown list.
 .. code:: ipython3
 
     import ipywidgets as widgets
-
+    
     core = ov.Core()
-
+    
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
         value="AUTO",
         description="Device:",
         disabled=False,
     )
-
+    
     device
 
 
@@ -253,36 +233,36 @@ Select one of supported devices for inference using dropdown list.
     import numpy as np
     import torch
     import torch.nn.functional as F
-
-
+    
+    
     def process(img, compiled_model):
         # Preprocess input image
         height, width = img.shape[:2]
-
+    
         # Normalize to [0, 1] range
         img = (img / 255.0).astype(np.float32)
         orig_l = cv2.cvtColor(img, cv2.COLOR_BGR2Lab)[:, :, :1]  # (h, w, 1)
-
+    
         # Resize rgb image -> lab -> get grey -> rgb
         img = cv2.resize(img, (512, 512))
         img_l = cv2.cvtColor(img, cv2.COLOR_BGR2Lab)[:, :, :1]
         img_gray_lab = np.concatenate((img_l, np.zeros_like(img_l), np.zeros_like(img_l)), axis=-1)
         img_gray_rgb = cv2.cvtColor(img_gray_lab, cv2.COLOR_LAB2RGB)
-
+    
         # Transpose HWC -> CHW and add batch dimension
         tensor_gray_rgb = torch.from_numpy(img_gray_rgb.transpose((2, 0, 1))).float().unsqueeze(0)
-
+    
         # Run model inference
         output_ab = compiled_model(tensor_gray_rgb)[0]
-
+    
         # Postprocess result
         # resize ab -> concat original l -> rgb
         output_ab_resize = F.interpolate(torch.from_numpy(output_ab), size=(height, width))[0].float().numpy().transpose(1, 2, 0)
         output_lab = np.concatenate((orig_l, output_ab_resize), axis=-1)
         output_bgr = cv2.cvtColor(output_lab, cv2.COLOR_LAB2BGR)
-
+    
         output_img = (output_bgr * 255.0).round().astype(np.uint8)
-
+    
         return output_img
 
 .. code:: ipython3
@@ -325,7 +305,7 @@ improve model inference speed.
         description="Quantization",
         disabled=False,
     )
-
+    
     to_quantize
 
 
@@ -340,15 +320,15 @@ improve model inference speed.
 .. code:: ipython3
 
     import requests
-
+    
     OV_INT8_COLORIZER_PATH = Path("ddcolor_int8.xml")
     compiled_int8_model = None
-
+    
     r = requests.get(
         url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/skip_kernel_extension.py",
     )
     open("skip_kernel_extension.py", "w").write(r.text)
-
+    
     %load_ext skip_kernel_extension
 
 Collect quantization dataset
@@ -363,14 +343,14 @@ dataset from Hugging Face as calibration data.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-
+    
     from datasets import load_dataset
-
+    
     subset_size = 300
     calibration_data = []
-
+    
     if not OV_INT8_COLORIZER_PATH.exists():
-        dataset = load_dataset("ummagumm-a/colorization_dataset", split="train").shuffle(seed=42)
+        dataset = load_dataset("ummagumm-a/colorization_dataset", split="train", streaming=True).shuffle(seed=42).take(subset_size)
         for idx, batch in enumerate(dataset):
             if idx >= subset_size:
                 break
@@ -380,29 +360,10 @@ dataset from Hugging Face as calibration data.
             img_l = cv2.cvtColor(np.stack([img, img, img], axis=2), cv2.COLOR_BGR2Lab)[:, :, :1]
             img_gray_lab = np.concatenate((img_l, np.zeros_like(img_l), np.zeros_like(img_l)), axis=-1)
             img_gray_rgb = cv2.cvtColor(img_gray_lab, cv2.COLOR_LAB2RGB)
-
+    
             image = np.expand_dims(img_gray_rgb.transpose((2, 0, 1)).astype(np.float32), axis=0)
             calibration_data.append(image)
 
-
-
-.. parsed-literal::
-
-    Downloading readme:   0%|          | 0.00/574 [00:00<?, ?B/s]
-
-
-
-.. parsed-literal::
-
-    Downloading data:   0%|          | 0.00/127M [00:00<?, ?B/s]
-
-
-
-.. parsed-literal::
-
-    Generating train split:   0%|          | 0/1000 [00:00<?, ? examples/s]
-
-
 Perform model quantization
 ~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -411,9 +372,9 @@ Perform model quantization
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-
+    
     import nncf
-
+    
     if not OV_INT8_COLORIZER_PATH.exists():
         ov_model = core.read_model(OV_COLORIZER_PATH)
         quantized_model = nncf.quantize(
@@ -431,10 +392,10 @@ Perform model quantization
 
 .. parsed-literal::
 
-    2024-06-05 23:53:12.619257: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-06-05 23:53:12.657603: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-07-01 23:50:30.822181: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-07-01 23:50:30.862201: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2024-06-05 23:53:13.253249: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-07-01 23:50:31.313473: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 
@@ -444,17 +405,17 @@ Perform model quantization
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -465,17 +426,17 @@ Perform model quantization
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -487,7 +448,7 @@ Run INT8 model inference
 .. code:: ipython3
 
     from IPython.display import display
-
+    
     if OV_INT8_COLORIZER_PATH.exists():
         compiled_int8_model = core.compile_model(OV_INT8_COLORIZER_PATH, device.value)
         img = cv2.imread("DDColor/assets/test_images/Ansel Adams _ Moore Photography.jpeg")
@@ -507,9 +468,9 @@ Compare FP16 and INT8 model size
 .. code:: ipython3
 
     fp16_ir_model_size = OV_COLORIZER_PATH.with_suffix(".bin").stat().st_size / 2**20
-
+    
     print(f"FP16 model size: {fp16_ir_model_size:.2f} MB")
-
+    
     if OV_INT8_COLORIZER_PATH.exists():
         quantized_model_size = OV_INT8_COLORIZER_PATH.with_suffix(".bin").stat().st_size / 2**20
         print(f"INT8 model size: {quantized_model_size:.2f} MB")
@@ -547,18 +508,18 @@ Tool <https://docs.openvino.ai/2024/learn-openvino/openvino-samples/benchmark-to
     [ INFO ] Parsing input parameters
     [Step 2/11] Loading OpenVINO Runtime
     [ INFO ] OpenVINO:
-    [ INFO ] Build ................................. 2024.3.0-15599-de4d00a5970
-    [ INFO ]
+    [ INFO ] Build ................................. 2024.3.0-15837-76a668b0662
+    [ INFO ] 
     [ INFO ] Device info:
     [ INFO ] AUTO
-    [ INFO ] Build ................................. 2024.3.0-15599-de4d00a5970
-    [ INFO ]
-    [ INFO ]
+    [ INFO ] Build ................................. 2024.3.0-15837-76a668b0662
+    [ INFO ] 
+    [ INFO ] 
     [Step 3/11] Setting device configuration
     [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.THROUGHPUT.
     [Step 4/11] Reading model files
     [ INFO ] Loading model files
-    [ INFO ] Read model took 42.73 ms
+    [ INFO ] Read model took 41.64 ms
     [ INFO ] Original model I/O parameters:
     [ INFO ] Model inputs:
     [ INFO ]     x (node: x) : f32 / [...] / [1,3,512,512]
@@ -567,14 +528,14 @@ Tool <https://docs.openvino.ai/2024/learn-openvino/openvino-samples/benchmark-to
     [Step 5/11] Resizing model to match image sizes and given batch
     [ INFO ] Model batch size: 1
     [ INFO ] Reshaping model: 'x': [1,3,512,512]
-    [ INFO ] Reshape model took 0.03 ms
+    [ INFO ] Reshape model took 0.04 ms
     [Step 6/11] Configuring input of the model
     [ INFO ] Model inputs:
     [ INFO ]     x (node: x) : u8 / [N,C,H,W] / [1,3,512,512]
     [ INFO ] Model outputs:
     [ INFO ]     ***NO_NAME*** (node: __module.refine_net.0.0/aten::_convolution/Add) : f32 / [...] / [1,2,512,512]
     [Step 7/11] Loading the model to the device
-    [ INFO ] Compile model took 1527.31 ms
+    [ INFO ] Compile model took 1431.82 ms
     [Step 8/11] Querying optimal runtime parameters
     [ INFO ] Model:
     [ INFO ]   NETWORK_NAME: Model0
@@ -586,7 +547,7 @@ Tool <https://docs.openvino.ai/2024/learn-openvino/openvino-samples/benchmark-to
     [ INFO ]     AFFINITY: Affinity.CORE
     [ INFO ]     CPU_DENORMALS_OPTIMIZATION: False
     [ INFO ]     CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE: 1.0
-    [ INFO ]     DYNAMIC_QUANTIZATION_GROUP_SIZE: 0
+    [ INFO ]     DYNAMIC_QUANTIZATION_GROUP_SIZE: 32
     [ INFO ]     ENABLE_CPU_PINNING: True
     [ INFO ]     ENABLE_HYPER_THREADING: True
     [ INFO ]     EXECUTION_DEVICES: ['CPU']
@@ -608,20 +569,20 @@ Tool <https://docs.openvino.ai/2024/learn-openvino/openvino-samples/benchmark-to
     [ INFO ]   PERF_COUNT: False
     [Step 9/11] Creating infer requests and preparing input tensors
     [ WARNING ] No input files were given for input 'x'!. This input will be filled with random values!
-    [ INFO ] Fill input 'x' with random values
+    [ INFO ] Fill input 'x' with random values 
     [Step 10/11] Measuring performance (Start inference asynchronously, 6 inference requests, limits: 15000 ms duration)
     [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
-    [ INFO ] First inference took 548.36 ms
+    [ INFO ] First inference took 540.84 ms
     [Step 11/11] Dumping statistics report
     [ INFO ] Execution Devices:['CPU']
     [ INFO ] Count:            72 iterations
-    [ INFO ] Duration:         16675.11 ms
+    [ INFO ] Duration:         16700.79 ms
     [ INFO ] Latency:
-    [ INFO ]    Median:        1385.45 ms
-    [ INFO ]    Average:       1386.39 ms
-    [ INFO ]    Min:           1326.48 ms
-    [ INFO ]    Max:           1447.89 ms
-    [ INFO ] Throughput:   4.32 FPS
+    [ INFO ]    Median:        1387.86 ms
+    [ INFO ]    Average:       1390.31 ms
+    [ INFO ]    Min:           1341.22 ms
+    [ INFO ]    Max:           1461.83 ms
+    [ INFO ] Throughput:   4.31 FPS
 
 
 .. code:: ipython3
@@ -636,18 +597,18 @@ Tool <https://docs.openvino.ai/2024/learn-openvino/openvino-samples/benchmark-to
     [ INFO ] Parsing input parameters
     [Step 2/11] Loading OpenVINO Runtime
     [ INFO ] OpenVINO:
-    [ INFO ] Build ................................. 2024.3.0-15599-de4d00a5970
-    [ INFO ]
+    [ INFO ] Build ................................. 2024.3.0-15837-76a668b0662
+    [ INFO ] 
     [ INFO ] Device info:
     [ INFO ] AUTO
-    [ INFO ] Build ................................. 2024.3.0-15599-de4d00a5970
-    [ INFO ]
-    [ INFO ]
+    [ INFO ] Build ................................. 2024.3.0-15837-76a668b0662
+    [ INFO ] 
+    [ INFO ] 
     [Step 3/11] Setting device configuration
     [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.THROUGHPUT.
     [Step 4/11] Reading model files
     [ INFO ] Loading model files
-    [ INFO ] Read model took 67.54 ms
+    [ INFO ] Read model took 66.82 ms
     [ INFO ] Original model I/O parameters:
     [ INFO ] Model inputs:
     [ INFO ]     x (node: x) : f32 / [...] / [1,3,512,512]
@@ -656,14 +617,14 @@ Tool <https://docs.openvino.ai/2024/learn-openvino/openvino-samples/benchmark-to
     [Step 5/11] Resizing model to match image sizes and given batch
     [ INFO ] Model batch size: 1
     [ INFO ] Reshaping model: 'x': [1,3,512,512]
-    [ INFO ] Reshape model took 0.03 ms
+    [ INFO ] Reshape model took 0.04 ms
     [Step 6/11] Configuring input of the model
     [ INFO ] Model inputs:
     [ INFO ]     x (node: x) : u8 / [N,C,H,W] / [1,3,512,512]
     [ INFO ] Model outputs:
     [ INFO ]     ***NO_NAME*** (node: __module.refine_net.0.0/aten::_convolution/Add) : f32 / [...] / [1,2,512,512]
     [Step 7/11] Loading the model to the device
-    [ INFO ] Compile model took 2704.43 ms
+    [ INFO ] Compile model took 2613.37 ms
     [Step 8/11] Querying optimal runtime parameters
     [ INFO ] Model:
     [ INFO ]   NETWORK_NAME: Model0
@@ -675,7 +636,7 @@ Tool <https://docs.openvino.ai/2024/learn-openvino/openvino-samples/benchmark-to
     [ INFO ]     AFFINITY: Affinity.CORE
     [ INFO ]     CPU_DENORMALS_OPTIMIZATION: False
     [ INFO ]     CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE: 1.0
-    [ INFO ]     DYNAMIC_QUANTIZATION_GROUP_SIZE: 0
+    [ INFO ]     DYNAMIC_QUANTIZATION_GROUP_SIZE: 32
     [ INFO ]     ENABLE_CPU_PINNING: True
     [ INFO ]     ENABLE_HYPER_THREADING: True
     [ INFO ]     EXECUTION_DEVICES: ['CPU']
@@ -697,20 +658,20 @@ Tool <https://docs.openvino.ai/2024/learn-openvino/openvino-samples/benchmark-to
     [ INFO ]   PERF_COUNT: False
     [Step 9/11] Creating infer requests and preparing input tensors
     [ WARNING ] No input files were given for input 'x'!. This input will be filled with random values!
-    [ INFO ] Fill input 'x' with random values
+    [ INFO ] Fill input 'x' with random values 
     [Step 10/11] Measuring performance (Start inference asynchronously, 6 inference requests, limits: 15000 ms duration)
     [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
-    [ INFO ] First inference took 290.81 ms
+    [ INFO ] First inference took 280.85 ms
     [Step 11/11] Dumping statistics report
     [ INFO ] Execution Devices:['CPU']
-    [ INFO ] Count:            150 iterations
-    [ INFO ] Duration:         15775.52 ms
+    [ INFO ] Count:            156 iterations
+    [ INFO ] Duration:         15707.80 ms
     [ INFO ] Latency:
-    [ INFO ]    Median:        627.63 ms
-    [ INFO ]    Average:       626.88 ms
-    [ INFO ]    Min:           535.42 ms
-    [ INFO ]    Max:           721.71 ms
-    [ INFO ] Throughput:   9.51 FPS
+    [ INFO ]    Median:        596.53 ms
+    [ INFO ]    Average:       597.25 ms
+    [ INFO ]    Min:           463.75 ms
+    [ INFO ]    Max:           675.97 ms
+    [ INFO ] Throughput:   9.93 FPS
 
 
 Interactive inference
@@ -723,16 +684,16 @@ Interactive inference
     import gradio as gr
     from gradio_imageslider import ImageSlider
     from functools import partial
-
-
+    
+    
     def generate(image, use_int8=True):
         image_in = cv2.imread(image)
         image_out = process(image_in, compiled_model if not use_int8 else compiled_int8_model)
         image_in_pil = PIL.Image.fromarray(cv2.cvtColor(image_in, cv2.COLOR_BGR2RGB))
         image_out_pil = PIL.Image.fromarray(cv2.cvtColor(image_out, cv2.COLOR_BGR2RGB))
         return (image_in_pil, image_out_pil)
-
-
+    
+    
     with gr.Blocks() as demo:
         with gr.Row(equal_height=False):
             image = gr.Image(type="filepath")
@@ -752,8 +713,8 @@ Interactive inference
             ],
             inputs=[image],
         )
-
-
+    
+    
     if __name__ == "__main__":
         try:
             demo.queue().launch(debug=False)
@@ -767,7 +728,7 @@ Interactive inference
 .. parsed-literal::
 
     Running on local URL:  http://127.0.0.1:7860
-
+    
     To create a public link, set `share=True` in `launch()`.
 
 
diff --git a/docs/notebooks/ddcolor-image-colorization-with-output_files/ddcolor-image-colorization-with-output_25_0.jpg b/docs/notebooks/ddcolor-image-colorization-with-output_files/ddcolor-image-colorization-with-output_25_0.jpg
index 96f01e15434e6e..6702ef9044f1aa 100644
--- a/docs/notebooks/ddcolor-image-colorization-with-output_files/ddcolor-image-colorization-with-output_25_0.jpg
+++ b/docs/notebooks/ddcolor-image-colorization-with-output_files/ddcolor-image-colorization-with-output_25_0.jpg
@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:c70e32cab8d811d577a589a2212a382d02aa7c51f0093e8537ee86828314c592
-size 167271
+oid sha256:61803cf538d3616aecebcb6b9cc1b585c9ef3c6b82a593fed64ad214aa14fe62
+size 167157
diff --git a/docs/notebooks/ddcolor-image-colorization-with-output_files/ddcolor-image-colorization-with-output_25_0.png b/docs/notebooks/ddcolor-image-colorization-with-output_files/ddcolor-image-colorization-with-output_25_0.png
index d4f05aeb95e1e6..c6df43ea31b9b6 100644
--- a/docs/notebooks/ddcolor-image-colorization-with-output_files/ddcolor-image-colorization-with-output_25_0.png
+++ b/docs/notebooks/ddcolor-image-colorization-with-output_files/ddcolor-image-colorization-with-output_25_0.png
@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:634bf7305f368aea78f429424e11e7f00fc341a485d74823434ac1d624a6515d
-size 1521926
+oid sha256:25650ff46f8d2997e1d30ef05bcea87f0e57ed197d110ee8ff85fc29ac1f5478
+size 1522105
diff --git a/docs/notebooks/decidiffusion-image-generation-with-output.rst b/docs/notebooks/decidiffusion-image-generation-with-output.rst
index e951bc6ad867e5..cbd3dba7a40667 100644
--- a/docs/notebooks/decidiffusion-image-generation-with-output.rst
+++ b/docs/notebooks/decidiffusion-image-generation-with-output.rst
@@ -39,8 +39,8 @@ The notebook contains the following steps:
 5. Compare results of original and optimized pipelines.
 6. Run Interactive demo for DeciDiffusion model.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Prepare DeciDiffusion models for OpenVINO format
@@ -89,7 +89,7 @@ install required packages
 
 .. code:: ipython3
 
-    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu  "diffusers" "transformers" "torch>=2.1" "pillow" "openvino>=2023.1.0" "gradio>=4.19" "datasets>=2.14.6" "huggingface-hub>=0.19.4" "nncf>=2.7.0" "peft==0.6.2" "opencv-python"
+    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu  "diffusers<0.29.0" "transformers" "torch>=2.1" "pillow" "openvino>=2023.1.0" "gradio>=4.19" "datasets>=2.14.6" "huggingface-hub>=0.19.4" "nncf>=2.7.0" "peft==0.6.2" "opencv-python"
 
 Prepare DeciDiffusion models for OpenVINO format conversion
 -----------------------------------------------------------
@@ -1210,7 +1210,7 @@ Prepare calibration dataset
 
 
 We use a portion of
-`conceptual_captions <https://huggingface.co/datasets/conceptual_captions>`__
+`conceptual_captions <https://huggingface.co/datasets/google-research-datasets/conceptual_captions>`__
 dataset from Hugging Face as calibration data. To collect intermediate
 model inputs for calibration we should customize ``CompiledModel``.
 
@@ -1245,7 +1245,7 @@ model inputs for calibration we should customize ``CompiledModel``.
         pipeline.unet = CompiledModelDecorator(original_unet, prob=0.3)
         pipeline.set_progress_bar_config(disable=True)
     
-        dataset = datasets.load_dataset("conceptual_captions", split="train", streaming=True).shuffle(seed=42)
+        dataset = datasets.load_dataset("google-research-datasets/conceptual_captions", split="train", streaming=True, trust_remote_code=True).shuffle(seed=42)
     
         pbar = tqdm(total=subset_size)
         for batch in dataset:
@@ -1440,7 +1440,7 @@ pipelines, we use median inference time on calibration subset.
     import time
     
     validation_size = 10
-    calibration_dataset = datasets.load_dataset("conceptual_captions", split="train", streaming=True)
+    calibration_dataset = datasets.load_dataset("conceptual_captions", split="train", streaming=True, trust_remote_code=True)
     validation_data = []
     for idx, batch in enumerate(calibration_dataset):
         if idx >= validation_size:
diff --git a/docs/notebooks/depth-anything-v2-with-output.rst b/docs/notebooks/depth-anything-v2-with-output.rst
new file mode 100644
index 00000000000000..731488f78c7007
--- /dev/null
+++ b/docs/notebooks/depth-anything-v2-with-output.rst
@@ -0,0 +1,1176 @@
+Depth estimation with DepthAnythingV2 and OpenVINO
+==================================================
+
+`Depth Anything V2 <https://depth-anything-v2.github.io/>`__ is a
+solution for robust relative depth estimation. Without pursuing fancy
+techniques, this project aim to reveal crucial findings to pave the way
+towards building a powerful monocular depth estimation model. This model
+is an improvement of the `Depth Anything
+V1 <https://depth-anything.github.io/>`__. Notably, compared with V1,
+this version produces much finer and more robust depth predictions
+through three key practices: replacing all labeled real images with
+synthetic images, scaling up the capacity of our teacher model and
+teaching student models via the bridge of large-scale pseudo-labeled
+real images.
+
+The pipeline of training of Depth Anything V2 is shown below. It
+consists of three steps: train a reliable teacher model purely on
+high-quality synthetic image; produce precise pseudo depth on
+large-scale unlabeled real images; train final student models on
+pseudo-labeled real images for robust generalization.
+
+.. figure:: https://depth-anything-v2.github.io/static/images/pipeline.png
+   :alt: image.png
+
+   image.png
+
+More details about model can be found in `project web
+page <https://depth-anything-v2.github.io/>`__,
+`paper <https://arxiv.org/abs/2406.09414>`__ and official
+`repository <https://github.com/DepthAnything/Depth-Anything-V2>`__
+
+In this tutorial we will explore how to convert and run DepthAnythingV2
+using OpenVINO. An additional part demonstrates how to run quantization
+with `NNCF <https://github.com/openvinotoolkit/nncf/>`__ to speed up the
+model.
+
+**Table of contents:**
+
+
+-  `Prerequisites <#prerequisites>`__
+-  `Load and run PyTorch model <#load-and-run-pytorch-model>`__
+
+   -  `Prepare input data <#prepare-input-data>`__
+   -  `Run model inference <#run-model-inference>`__
+
+-  `Convert Model to OpenVINO IR
+   format <#convert-model-to-openvino-ir-format>`__
+-  `Run OpenVINO model inference <#run-openvino-model-inference>`__
+
+   -  `Select inference device <#select-inference-device>`__
+   -  `Run inference on image <#run-inference-on-image>`__
+   -  `Run inference on video <#run-inference-on-video>`__
+
+-  `Quantization <#quantization>`__
+
+   -  `Prepare calibration dataset <#prepare-calibration-dataset>`__
+   -  `Run quantization <#run-quantization>`__
+
+      -  `Compare model file size <#compare-model-file-size>`__
+
+   -  `Compare inference time of the FP16 and INT8
+      models <#compare-inference-time-of-the-fp16-and-int8-models>`__
+
+-  `Interactive demo <#interactive-demo>`__
+
+Prerequisites
+-------------
+
+
+
+.. code:: ipython3
+
+    from pathlib import Path
+    
+    
+    repo_dir = Path("Depth-Anything-V2")
+    
+    if not repo_dir.exists():
+        !git clone https://huggingface.co/spaces/depth-anything/Depth-Anything-V2
+    %cd Depth-Anything-V2
+
+
+.. parsed-literal::
+
+    Cloning into 'Depth-Anything-V2'...
+    remote: Enumerating objects: 154, done.
+    remote: Counting objects: 100% (150/150), done.
+    remote: Compressing objects: 100% (147/147), done.
+    remote: Total 154 (delta 43), reused 0 (delta 0), pack-reused 4 (from 1)
+    Receiving objects: 100% (154/154), 7.76 MiB | 13.62 MiB/s, done.
+    Resolving deltas: 100% (43/43), done.
+    Filtering content: 100% (5/5), 28.66 MiB | 24.37 MiB/s, done.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything-V2
+
+
+.. code:: ipython3
+
+    import platform
+    
+    %pip install -q "openvino>=2024.2.0" "datasets>=2.14.6" "nncf>=2.11.0" "tqdm"
+    %pip install -q "typing-extensions>=4.9.0" eval-type-backport "gradio>=4.19"
+    %pip install -q -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cpu
+    
+    if platform.python_version_tuple()[1] in ["8", "9"]:
+        %pip install -q "gradio-imageslider<=0.0.17" "typing-extensions>=4.9.0"
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+    Note: you may need to restart the kernel to use updated packages.
+    Note: you may need to restart the kernel to use updated packages.
+    Note: you may need to restart the kernel to use updated packages.
+
+
+Load and run PyTorch model
+--------------------------
+
+
+
+To be able run PyTorch model on CPU, we should disable xformers
+attention optimizations first.
+
+.. code:: ipython3
+
+    attention_file_path = Path("./depth_anything_v2/dinov2_layers/attention.py")
+    orig_attention_path = attention_file_path.parent / ("orig_" + attention_file_path.name)
+    
+    if not orig_attention_path.exists():
+        attention_file_path.rename(orig_attention_path)
+    
+        with orig_attention_path.open("r") as f:
+            data = f.read()
+            data = data.replace("XFORMERS_AVAILABLE = True", "XFORMERS_AVAILABLE = False")
+            with attention_file_path.open("w") as out_f:
+                out_f.write(data)
+
+Prepare input data
+~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    from PIL import Image
+    
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
+    )
+    
+    open("notebook_utils.py", "w").write(r.text)
+    from notebook_utils import download_file
+    
+    download_file(
+        "https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/3f779fc1-c1b2-4dec-915a-64dae510a2bb",
+        "furseal.png",
+    )
+    
+    Image.open("furseal.png").resize((600, 400))
+
+
+
+.. parsed-literal::
+
+    furseal.png:   0%|          | 0.00/2.55M [00:00<?, ?B/s]
+
+
+
+
+.. image:: depth-anything-v2-with-output_files/depth-anything-v2-with-output_8_1.png
+
+
+
+Run model inference
+~~~~~~~~~~~~~~~~~~~
+
+
+
+``DepthAnythingV2.from_pretrained`` method creates PyTorch model class
+instance and load model weights. There are 3 available models in
+repository depends on VIT encoder size: \* Depth-Anything-V2-Small
+(24.8M) \* Depth-Anything-V2-Base (97.5M) \* Depth-Anything-V2-Large
+(335.3M)
+
+We will use ``Depth-Anything-V2-Small``, but the same steps for running
+model and converting to OpenVINO are applicable for other models from
+DepthAnythingV2 family.
+
+.. code:: ipython3
+
+    from huggingface_hub import hf_hub_download
+    
+    encoder = "vits"
+    model_type = "Small"
+    model_id = f"depth_anything_v2_{encoder}"
+    
+    model_path = hf_hub_download(repo_id=f"depth-anything/Depth-Anything-V2-{model_type}", filename=f"{model_id}.pth", repo_type="model")
+
+Preprocessed image passed to model forward and model returns depth map
+in format ``B`` x ``H`` x ``W``, where ``B`` is input batch size, ``H``
+is preprocessed image height, ``W`` is preprocessed image width.
+
+.. code:: ipython3
+
+    import cv2
+    import torch
+    import torch.nn.functional as F
+    
+    from depth_anything_v2.dpt import DepthAnythingV2
+    
+    model = DepthAnythingV2(encoder=encoder, features=64, out_channels=[48, 96, 192, 384])
+    model.load_state_dict(torch.load(model_path, map_location="cpu"))
+    model.eval()
+    
+    raw_img = cv2.imread("furseal.png")
+    image, (h, w) = model.image2tensor(raw_img)
+    image = image.to("cpu").to(torch.float32)
+    
+    with torch.no_grad():
+        depth = model.forward(image)
+    
+    depth = F.interpolate(depth[:, None], (h, w), mode="bilinear", align_corners=True)[0, 0]
+    
+    output = depth.cpu().numpy()
+
+
+.. parsed-literal::
+
+    xFormers not available
+    xFormers not available
+
+
+.. code:: ipython3
+
+    from matplotlib import pyplot as plt
+    import numpy as np
+    import cv2
+    
+    
+    def get_depth_map(output, w, h):
+        depth = cv2.resize(output, (w, h))
+    
+        depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
+        depth = depth.astype(np.uint8)
+    
+        depth = cv2.applyColorMap(depth, cv2.COLORMAP_INFERNO)
+    
+        return depth
+
+.. code:: ipython3
+
+    h, w = raw_img.shape[:-1]
+    res_depth = get_depth_map(output, w, h)
+    plt.imshow(res_depth[:, :, ::-1])
+
+
+
+
+.. parsed-literal::
+
+    <matplotlib.image.AxesImage at 0x7f1afc2af340>
+
+
+
+
+.. image:: depth-anything-v2-with-output_files/depth-anything-v2-with-output_14_1.png
+
+
+Convert Model to OpenVINO IR format
+-----------------------------------
+
+
+
+OpenVINO supports PyTorch models via conversion to OpenVINO Intermediate
+Representation (IR). OpenVINO model conversion API should be used for
+these purposes. ``ov.convert_model`` function accepts original PyTorch
+model instance and example input for tracing and returns ``ov.Model``
+representing this model in OpenVINO framework. Converted model can be
+used for saving on disk using ``ov.save_model`` function or directly
+loading on device using ``core.complie_model``.
+
+.. code:: ipython3
+
+    import openvino as ov
+    
+    OV_DEPTH_ANYTHING_PATH = Path(f"{model_id}.xml")
+    
+    if not OV_DEPTH_ANYTHING_PATH.exists():
+        ov_model = ov.convert_model(model, example_input=torch.rand(1, 3, 518, 518), input=[1, 3, 518, 518])
+        ov.save_model(ov_model, OV_DEPTH_ANYTHING_PATH)
+
+
+.. parsed-literal::
+
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything-V2/depth_anything_v2/dinov2_layers/patch_embed.py:73: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+      assert H % patch_H == 0, f"Input image height {H} is not a multiple of patch height {patch_H}"
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything-V2/depth_anything_v2/dinov2_layers/patch_embed.py:74: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+      assert W % patch_W == 0, f"Input image width {W} is not a multiple of patch width: {patch_W}"
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything-V2/depth_anything_v2/dinov2.py:183: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+      if npatch == N and w == h:
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything-V2/depth_anything_v2/dpt.py:147: TracerWarning: Converting a tensor to a Python integer might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+      out = F.interpolate(out, (int(patch_h * 14), int(patch_w * 14)), mode="bilinear", align_corners=True)
+
+
+Run OpenVINO model inference
+----------------------------
+
+
+
+Now, we are ready to run OpenVINO model
+
+Select inference device
+~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+For starting work, please select inference device from dropdown list.
+
+.. code:: ipython3
+
+    import ipywidgets as widgets
+    
+    core = ov.Core()
+    
+    device = widgets.Dropdown(
+        options=core.available_devices + ["AUTO"],
+        value="AUTO",
+        description="Device:",
+        disabled=False,
+    )
+    
+    device
+
+
+
+
+.. parsed-literal::
+
+    Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
+
+
+
+.. code:: ipython3
+
+    compiled_model = core.compile_model(OV_DEPTH_ANYTHING_PATH, device.value)
+
+Run inference on image
+~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+For simplicity of usage, model authors provide helper functions for
+preprocessing input image. The main conditions are that image size
+should be divisible on 14 (size of vit patch) and normalized in [0, 1]
+range.
+
+.. code:: ipython3
+
+    from depth_anything_v2.util.transform import Resize, NormalizeImage, PrepareForNet
+    from torchvision.transforms import Compose
+    
+    transform = Compose(
+        [
+            Resize(
+                width=518,
+                height=518,
+                resize_target=False,
+                ensure_multiple_of=14,
+                resize_method="lower_bound",
+                image_interpolation_method=cv2.INTER_CUBIC,
+            ),
+            NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+            PrepareForNet(),
+        ]
+    )
+
+.. code:: ipython3
+
+    h, w = raw_img.shape[:-1]
+    
+    image = cv2.cvtColor(raw_img, cv2.COLOR_BGR2RGB) / 255.0
+    image = transform({"image": image})["image"]
+    image = torch.from_numpy(image).unsqueeze(0)
+    
+    res = compiled_model(image)[0]
+
+.. code:: ipython3
+
+    depth_color = get_depth_map(res[0], w, h)
+
+.. code:: ipython3
+
+    plt.imshow(depth_color[:, :, ::-1])
+
+
+
+
+.. parsed-literal::
+
+    <matplotlib.image.AxesImage at 0x7f1ab59d2730>
+
+
+
+
+.. image:: depth-anything-v2-with-output_files/depth-anything-v2-with-output_24_1.png
+
+
+Run inference on video
+~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    download_file(
+        "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/video/Coco%20Walking%20in%20Berkeley.mp4",
+        "./Coco Walking in Berkeley.mp4",
+    )
+    
+    VIDEO_FILE = "./Coco Walking in Berkeley.mp4"
+    # Number of seconds of input video to process. Set `NUM_SECONDS` to 0 to process
+    # the full video.
+    NUM_SECONDS = 4
+    # Set `ADVANCE_FRAMES` to 1 to process every frame from the input video
+    # Set `ADVANCE_FRAMES` to 2 to process every second frame. This reduces
+    # the time it takes to process the video.
+    ADVANCE_FRAMES = 2
+    # Set `SCALE_OUTPUT` to reduce the size of the result video
+    # If `SCALE_OUTPUT` is 0.5, the width and height of the result video
+    # will be half the width and height of the input video.
+    SCALE_OUTPUT = 0.5
+    # The format to use for video encoding. The 'vp09` is slow,
+    # but it works on most systems.
+    # Try the `THEO` encoding if you have FFMPEG installed.
+    # FOURCC = cv2.VideoWriter_fourcc(*"THEO")
+    FOURCC = cv2.VideoWriter_fourcc(*"vp09")
+    
+    # Create Path objects for the input video and the result video.
+    output_directory = Path("output")
+    output_directory.mkdir(exist_ok=True)
+    result_video_path = output_directory / f"{Path(VIDEO_FILE).stem}_depth_anything.mp4"
+
+
+
+.. parsed-literal::
+
+    Coco Walking in Berkeley.mp4:   0%|          | 0.00/877k [00:00<?, ?B/s]
+
+
+.. code:: ipython3
+
+    cap = cv2.VideoCapture(str(VIDEO_FILE))
+    ret, image = cap.read()
+    if not ret:
+        raise ValueError(f"The video at {VIDEO_FILE} cannot be read.")
+    input_fps = cap.get(cv2.CAP_PROP_FPS)
+    input_video_frame_height, input_video_frame_width = image.shape[:2]
+    
+    target_fps = input_fps / ADVANCE_FRAMES
+    target_frame_height = int(input_video_frame_height * SCALE_OUTPUT)
+    target_frame_width = int(input_video_frame_width * SCALE_OUTPUT)
+    
+    cap.release()
+    print(f"The input video has a frame width of {input_video_frame_width}, " f"frame height of {input_video_frame_height} and runs at {input_fps:.2f} fps")
+    print(
+        "The output video will be scaled with a factor "
+        f"{SCALE_OUTPUT}, have width {target_frame_width}, "
+        f" height {target_frame_height}, and run at {target_fps:.2f} fps"
+    )
+
+
+.. parsed-literal::
+
+    The input video has a frame width of 640, frame height of 360 and runs at 30.00 fps
+    The output video will be scaled with a factor 0.5, have width 320,  height 180, and run at 15.00 fps
+
+
+.. code:: ipython3
+
+    def normalize_minmax(data):
+        """Normalizes the values in `data` between 0 and 1"""
+        return (data - data.min()) / (data.max() - data.min())
+    
+    
+    def convert_result_to_image(result, colormap="viridis"):
+        """
+        Convert network result of floating point numbers to an RGB image with
+        integer values from 0-255 by applying a colormap.
+    
+        `result` is expected to be a single network result in 1,H,W shape
+        `colormap` is a matplotlib colormap.
+        See https://matplotlib.org/stable/tutorials/colors/colormaps.html
+        """
+        result = result.squeeze(0)
+        result = normalize_minmax(result)
+        result = result * 255
+        result = result.astype(np.uint8)
+        result = cv2.applyColorMap(result, cv2.COLORMAP_INFERNO)[:, :, ::-1]
+        return result
+    
+    
+    def to_rgb(image_data) -> np.ndarray:
+        """
+        Convert image_data from BGR to RGB
+        """
+        return cv2.cvtColor(image_data, cv2.COLOR_BGR2RGB)
+
+.. code:: ipython3
+
+    import time
+    from IPython.display import (
+        HTML,
+        FileLink,
+        Pretty,
+        ProgressBar,
+        Video,
+        clear_output,
+        display,
+    )
+    
+    
+    def process_video(compiled_model, video_file, result_video_path):
+        # Initialize variables.
+        input_video_frame_nr = 0
+        start_time = time.perf_counter()
+        total_inference_duration = 0
+    
+        # Open the input video
+        cap = cv2.VideoCapture(str(video_file))
+    
+        # Create a result video.
+        out_video = cv2.VideoWriter(
+            str(result_video_path),
+            FOURCC,
+            target_fps,
+            (target_frame_width * 2, target_frame_height),
+        )
+    
+        num_frames = int(NUM_SECONDS * input_fps)
+        total_frames = cap.get(cv2.CAP_PROP_FRAME_COUNT) if num_frames == 0 else num_frames
+        progress_bar = ProgressBar(total=total_frames)
+        progress_bar.display()
+    
+        try:
+            while cap.isOpened():
+                ret, image = cap.read()
+                if not ret:
+                    cap.release()
+                    break
+    
+                if input_video_frame_nr >= total_frames:
+                    break
+    
+                h, w = image.shape[:-1]
+                input_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) / 255.0
+                input_image = transform({"image": input_image})["image"]
+                # Reshape the image to network input shape NCHW.
+                input_image = np.expand_dims(input_image, 0)
+    
+                # Do inference.
+                inference_start_time = time.perf_counter()
+                result = compiled_model(input_image)[0]
+                inference_stop_time = time.perf_counter()
+                inference_duration = inference_stop_time - inference_start_time
+                total_inference_duration += inference_duration
+    
+                if input_video_frame_nr % (10 * ADVANCE_FRAMES) == 0:
+                    clear_output(wait=True)
+                    progress_bar.display()
+                    # input_video_frame_nr // ADVANCE_FRAMES gives the number of
+                    # Frames that have been processed by the network.
+                    display(
+                        Pretty(
+                            f"Processed frame {input_video_frame_nr // ADVANCE_FRAMES}"
+                            f"/{total_frames // ADVANCE_FRAMES}. "
+                            f"Inference time per frame: {inference_duration:.2f} seconds "
+                            f"({1/inference_duration:.2f} FPS)"
+                        )
+                    )
+    
+                # Transform the network result to a RGB image.
+                result_frame = to_rgb(convert_result_to_image(result))
+                # Resize the image and the result to a target frame shape.
+                result_frame = cv2.resize(result_frame, (target_frame_width, target_frame_height))
+                image = cv2.resize(image, (target_frame_width, target_frame_height))
+                # Put the image and the result side by side.
+                stacked_frame = np.hstack((image, result_frame))
+                # Save a frame to the video.
+                out_video.write(stacked_frame)
+    
+                input_video_frame_nr = input_video_frame_nr + ADVANCE_FRAMES
+                cap.set(1, input_video_frame_nr)
+    
+                progress_bar.progress = input_video_frame_nr
+                progress_bar.update()
+    
+        except KeyboardInterrupt:
+            print("Processing interrupted.")
+        finally:
+            clear_output()
+            processed_frames = num_frames // ADVANCE_FRAMES
+            out_video.release()
+            cap.release()
+            end_time = time.perf_counter()
+            duration = end_time - start_time
+    
+            print(
+                f"Processed {processed_frames} frames in {duration:.2f} seconds. "
+                f"Total FPS (including video processing): {processed_frames/duration:.2f}."
+                f"Inference FPS: {processed_frames/total_inference_duration:.2f} "
+            )
+            print(f"Video saved to '{str(result_video_path)}'.")
+        return stacked_frame
+
+.. code:: ipython3
+
+    stacked_frame = process_video(compiled_model, VIDEO_FILE, result_video_path)
+
+
+.. parsed-literal::
+
+    Processed 60 frames in 13.35 seconds. Total FPS (including video processing): 4.49.Inference FPS: 10.45 
+    Video saved to 'output/Coco Walking in Berkeley_depth_anything.mp4'.
+
+
+.. code:: ipython3
+
+    def display_video(stacked_frame):
+        video = Video(result_video_path, width=800, embed=True)
+        if not result_video_path.exists():
+            plt.imshow(stacked_frame)
+            raise ValueError("OpenCV was unable to write the video file. Showing one video frame.")
+        else:
+            print(f"Showing video saved at\n{result_video_path.resolve()}")
+            print("If you cannot see the video in your browser, please click on the " "following link to download the video ")
+            video_link = FileLink(result_video_path)
+            video_link.html_link_str = "<a href='%s' download>%s</a>"
+            display(HTML(video_link._repr_html_()))
+            display(video)
+
+.. code:: ipython3
+
+    display_video(stacked_frame)
+
+
+.. parsed-literal::
+
+    Showing video saved at
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything-V2/output/Coco Walking in Berkeley_depth_anything.mp4
+    If you cannot see the video in your browser, please click on the following link to download the video 
+
+
+
+.. raw:: html
+
+    output/Coco Walking in Berkeley_depth_anything.mp4<br>
+
+
+
+.. raw:: html
+
+    <video controls  width="800" >
+     <source 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type="video/mp4">
+     Your browser does not support the video tag.
+     </video>
+
+
+Quantization
+------------
+
+
+
+`NNCF <https://github.com/openvinotoolkit/nncf/>`__ enables
+post-training quantization by adding quantization layers into model
+graph and then using a subset of the training dataset to initialize the
+parameters of these additional quantization layers. Quantized operations
+are executed in ``INT8`` instead of ``FP32``/``FP16`` making model
+inference faster.
+
+The optimization process contains the following steps:
+
+1. Create a calibration dataset for quantization.
+2. Run ``nncf.quantize()`` to obtain quantized model.
+3. Save the ``INT8`` model using ``openvino.save_model()`` function.
+
+Please select below whether you would like to run quantization to
+improve model inference speed.
+
+.. code:: ipython3
+
+    to_quantize = widgets.Checkbox(
+        value=True,
+        description="Quantization",
+        disabled=False,
+    )
+    
+    to_quantize
+
+
+
+
+.. parsed-literal::
+
+    Checkbox(value=True, description='Quantization')
+
+
+
+.. code:: ipython3
+
+    # Fetch `skip_kernel_extension` module
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/skip_kernel_extension.py",
+    )
+    open("skip_kernel_extension.py", "w").write(r.text)
+    
+    OV_DEPTH_ANYTHING_INT8_PATH = Path(f"{model_id}_int8.xml")
+    
+    %load_ext skip_kernel_extension
+
+Prepare calibration dataset
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+We use a portion of
+`Nahrawy/VIDIT-Depth-ControlNet <https://huggingface.co/datasets/Nahrawy/VIDIT-Depth-ControlNet>`__
+dataset from Hugging Face as calibration data.
+
+.. code:: ipython3
+
+    %%skip not $to_quantize.value
+    
+    import datasets
+    
+    if not OV_DEPTH_ANYTHING_INT8_PATH.exists():
+        subset_size = 300
+        calibration_data = []
+        dataset = datasets.load_dataset("Nahrawy/VIDIT-Depth-ControlNet", split="train", streaming=True).shuffle(seed=42).take(subset_size)
+        for batch in dataset:
+            image = np.array(batch["image"])[...,:3]
+            image = image / 255.0
+            image = transform({'image': image})['image']
+            image = np.expand_dims(image, 0)
+            calibration_data.append(image)
+
+
+
+.. parsed-literal::
+
+    Resolving data files:   0%|          | 0/42 [00:00<?, ?it/s]
+
+
+Run quantization
+~~~~~~~~~~~~~~~~
+
+
+
+Create a quantized model from the pre-trained converted OpenVINO model.
+> **NOTE**: Quantization is time and memory consuming operation. Running
+quantization code below may take some time.
+
+.. code:: ipython3
+
+    %%skip not $to_quantize.value
+    
+    import nncf
+    
+    if not OV_DEPTH_ANYTHING_INT8_PATH.exists():
+        model = core.read_model(OV_DEPTH_ANYTHING_PATH)
+        quantized_model = nncf.quantize(
+            model=model,
+            subset_size=subset_size,
+            model_type=nncf.ModelType.TRANSFORMER,
+            calibration_dataset=nncf.Dataset(calibration_data),
+        )
+        ov.save_model(quantized_model, OV_DEPTH_ANYTHING_INT8_PATH)
+
+
+.. parsed-literal::
+
+    INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino
+
+
+.. parsed-literal::
+
+    2024-07-01 23:58:14.192557: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-07-01 23:58:14.226081: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
+    2024-07-01 23:58:14.785892: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+.. parsed-literal::
+
+    INFO:nncf:36 ignored nodes were found by name in the NNCFGraph
+    INFO:nncf:48 ignored nodes were found by name in the NNCFGraph
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Let us check predictions with the quantized model using the same input
+data.
+
+.. code:: ipython3
+
+    %%skip not $to_quantize.value
+    
+    def visualize_results(orig_img:Image.Image, optimized_img:Image.Image):
+        """
+        Helper function for results visualization
+    
+        Parameters:
+           orig_img (Image.Image): generated image using FP16 model
+           optimized_img (Image.Image): generated image using quantized model
+        Returns:
+           fig (matplotlib.pyplot.Figure): matplotlib generated figure contains drawing result
+        """
+        orig_title = "FP16 model"
+        control_title = "INT8 model"
+        figsize = (20, 20)
+        fig, axs = plt.subplots(1, 2, figsize=figsize, sharex='all', sharey='all')
+        list_axes = list(axs.flat)
+        for a in list_axes:
+            a.set_xticklabels([])
+            a.set_yticklabels([])
+            a.get_xaxis().set_visible(False)
+            a.get_yaxis().set_visible(False)
+            a.grid(False)
+        list_axes[0].imshow(np.array(orig_img))
+        list_axes[1].imshow(np.array(optimized_img))
+        list_axes[0].set_title(orig_title, fontsize=15)
+        list_axes[1].set_title(control_title, fontsize=15)
+    
+        fig.subplots_adjust(wspace=0.01, hspace=0.01)
+        fig.tight_layout()
+        return fig
+
+.. code:: ipython3
+
+    %%skip not $to_quantize.value
+    
+    image = cv2.cvtColor(cv2.imread('furseal.png'), cv2.COLOR_BGR2RGB) / 255.0
+    image = transform({'image': image})['image']
+    image = torch.from_numpy(image).unsqueeze(0)
+    
+    int8_compiled_model = core.compile_model(OV_DEPTH_ANYTHING_INT8_PATH, device.value)
+    int8_res = int8_compiled_model(image)[0]
+    int8_depth_color = get_depth_map(int8_res[0], w, h)
+
+.. code:: ipython3
+
+    %%skip not $to_quantize.value
+    
+    visualize_results(depth_color[:, :, ::-1], int8_depth_color[:, :, ::-1])
+
+
+
+.. image:: depth-anything-v2-with-output_files/depth-anything-v2-with-output_43_0.png
+
+
+.. code:: ipython3
+
+    %%skip not $to_quantize.value
+    
+    int8_result_video_path = output_directory / f"{Path(VIDEO_FILE).stem}_depth_anything_int8.mp4"
+    stacked_frame = process_video(int8_compiled_model, VIDEO_FILE, int8_result_video_path)
+    display_video(stacked_frame)
+
+
+.. parsed-literal::
+
+    Processed 60 frames in 12.73 seconds. Total FPS (including video processing): 4.71.Inference FPS: 12.94 
+    Video saved to 'output/Coco Walking in Berkeley_depth_anything_int8.mp4'.
+    Showing video saved at
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything-V2/output/Coco Walking in Berkeley_depth_anything.mp4
+    If you cannot see the video in your browser, please click on the following link to download the video 
+
+
+
+.. raw:: html
+
+    output/Coco Walking in Berkeley_depth_anything.mp4<br>
+
+
+
+.. raw:: html
+
+    <video controls  width="800" >
+     <source 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type="video/mp4">
+     Your browser does not support the video tag.
+     </video>
+
+
+Compare model file size
+^^^^^^^^^^^^^^^^^^^^^^^
+
+
+
+.. code:: ipython3
+
+    %%skip not $to_quantize.value
+    
+    fp16_ir_model_size = OV_DEPTH_ANYTHING_PATH.with_suffix(".bin").stat().st_size / 2**20
+    quantized_model_size = OV_DEPTH_ANYTHING_INT8_PATH.with_suffix(".bin").stat().st_size / 2**20
+    
+    print(f"FP16 model size: {fp16_ir_model_size:.2f} MB")
+    print(f"INT8 model size: {quantized_model_size:.2f} MB")
+    print(f"Model compression rate: {fp16_ir_model_size / quantized_model_size:.3f}")
+
+
+.. parsed-literal::
+
+    FP16 model size: 47.11 MB
+    INT8 model size: 24.41 MB
+    Model compression rate: 1.930
+
+
+Compare inference time of the FP16 and INT8 models
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+To measure the inference performance of OpenVINO FP16 and INT8 models,
+use `Benchmark
+Tool <https://docs.openvino.ai/2024/learn-openvino/openvino-samples/benchmark-tool.html>`__.
+
+   **NOTE**: For the most accurate performance estimation, it is
+   recommended to run ``benchmark_app`` in a terminal/command prompt
+   after closing other applications.
+
+.. code:: ipython3
+
+    import re
+    
+    
+    def get_fps(benchmark_output: str):
+        parsed_output = [line for line in benchmark_output if "Throughput:" in line]
+        fps = re.findall(r"\d+\.\d+", parsed_output[0])[0]
+        return fps
+    
+    
+    if OV_DEPTH_ANYTHING_INT8_PATH.exists():
+        benchmark_output = !benchmark_app -m $OV_DEPTH_ANYTHING_PATH -d $device.value -api async
+        original_fps = get_fps(benchmark_output)
+        print(f"FP16 Throughput: {original_fps} FPS")
+    
+        benchmark_output = !benchmark_app -m $OV_DEPTH_ANYTHING_INT8_PATH -d $device.value -api async
+        optimized_fps = get_fps(benchmark_output)
+        print(f"INT8 Throughput: {optimized_fps} FPS")
+        print(f"Speed-up: {float(optimized_fps) / float(original_fps):.2f}")
+
+
+.. parsed-literal::
+
+    FP16 Throughput: 10.63 FPS
+    INT8 Throughput: 14.30 FPS
+    Speed-up: 1.35
+
+
+Interactive demo
+----------------
+
+
+
+You can apply model on own images. You can move the slider on the
+resulting image to switch between the original image and the depth map
+view.
+
+Please select below whether you would like to use the quantized model to
+launch the interactive demo.
+
+.. code:: ipython3
+
+    quantized_model_present = OV_DEPTH_ANYTHING_INT8_PATH.exists()
+    
+    use_quantized_model = widgets.Checkbox(
+        value=True if quantized_model_present else False,
+        description="Use quantized model",
+        disabled=False,
+    )
+    
+    use_quantized_model
+
+
+
+
+.. parsed-literal::
+
+    Checkbox(value=True, description='Use quantized model')
+
+
+
+.. code:: ipython3
+
+    import gradio as gr
+    import cv2
+    import numpy as np
+    import os
+    import tempfile
+    from gradio_imageslider import ImageSlider
+    
+    css = """
+    #img-display-container {
+        max-height: 100vh;
+        }
+    #img-display-input {
+        max-height: 80vh;
+        }
+    #img-display-output {
+        max-height: 80vh;
+        }
+    """
+    
+    
+    title = "# Depth Anything with OpenVINO"
+    
+    if use_quantized_model.value:
+        compiled_model = core.compile_model(OV_DEPTH_ANYTHING_INT8_PATH, device.value)
+    
+    
+    def predict_depth(model, image):
+        return model(image)[0]
+    
+    
+    with gr.Blocks(css=css) as demo:
+        gr.Markdown(title)
+        gr.Markdown("### Depth Prediction demo")
+        gr.Markdown("You can slide the output to compare the depth prediction with input image")
+    
+        with gr.Row():
+            input_image = gr.Image(label="Input Image", type="numpy", elem_id="img-display-input")
+            depth_image_slider = ImageSlider(label="Depth Map with Slider View", elem_id="img-display-output", position=0)
+        raw_file = gr.File(label="16-bit raw depth (can be considered as disparity)")
+        submit = gr.Button("Submit")
+    
+        def on_submit(image):
+            original_image = image.copy()
+    
+            h, w = image.shape[:2]
+    
+            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) / 255.0
+            image = transform({"image": image})["image"]
+            image = np.expand_dims(image, 0)
+    
+            depth = predict_depth(compiled_model, image)
+            depth = cv2.resize(depth[0], (w, h), interpolation=cv2.INTER_LINEAR)
+    
+            raw_depth = Image.fromarray(depth.astype("uint16"))
+            tmp = tempfile.NamedTemporaryFile(suffix=".png", delete=False)
+            raw_depth.save(tmp.name)
+    
+            depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
+            depth = depth.astype(np.uint8)
+            colored_depth = cv2.applyColorMap(depth, cv2.COLORMAP_INFERNO)[:, :, ::-1]
+    
+            return [(original_image, colored_depth), tmp.name]
+    
+        submit.click(on_submit, inputs=[input_image], outputs=[depth_image_slider, raw_file])
+    
+        example_files = os.listdir("assets/examples")
+        example_files.sort()
+        example_files = [os.path.join("assets/examples", filename) for filename in example_files]
+        examples = gr.Examples(
+            examples=example_files,
+            inputs=[input_image],
+            outputs=[depth_image_slider, raw_file],
+            fn=on_submit,
+            cache_examples=False,
+        )
+    
+    
+    if __name__ == "__main__":
+        try:
+            demo.queue().launch(debug=False)
+        except Exception:
+            demo.queue().launch(share=True, debug=False)
+    # if you are launching remotely, specify server_name and server_port
+    # demo.launch(server_name='your server name', server_port='server port in int')
+    # Read more in the docs: https://gradio.app/docs/
+
+
+.. parsed-literal::
+
+    Running on local URL:  http://127.0.0.1:7860
+    
+    To create a public link, set `share=True` in `launch()`.
+
+
+
+
+
+
+
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diff --git a/docs/notebooks/depth-anything-v2-with-output_files/depth-anything-v2-with-output_8_1.png b/docs/notebooks/depth-anything-v2-with-output_files/depth-anything-v2-with-output_8_1.png
new file mode 100644
index 00000000000000..096d76fe63cc45
--- /dev/null
+++ b/docs/notebooks/depth-anything-v2-with-output_files/depth-anything-v2-with-output_8_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3f279ac10beacc7f27d1af73f7917d060c4c33db80b09d78c85dc864bd4bb3a7
+size 424224
diff --git a/docs/notebooks/depth-anything-with-output.rst b/docs/notebooks/depth-anything-with-output.rst
index f9d03cb4fcdeaf..50d946f009543c 100644
--- a/docs/notebooks/depth-anything-with-output.rst
+++ b/docs/notebooks/depth-anything-with-output.rst
@@ -19,8 +19,8 @@ using OpenVINO. An additional part demonstrates how to run quantization
 with `NNCF <https://github.com/openvinotoolkit/nncf/>`__ to speed up the
 model.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Load and run PyTorch model <#load-and-run-pytorch-model>`__
@@ -75,13 +75,13 @@ Prerequisites
 .. parsed-literal::
 
     Cloning into 'Depth-Anything'...
-    remote: Enumerating objects: 421, done.
-    remote: Counting objects: 100% (144/144), done.
-    remote: Compressing objects: 100% (105/105), done.
-    remote: Total 421 (delta 101), reused 43 (delta 39), pack-reused 277
-    Receiving objects: 100% (421/421), 237.89 MiB | 26.31 MiB/s, done.
-    Resolving deltas: 100% (144/144), done.
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything
+    remote: Enumerating objects: 436, done.
+    remote: Counting objects: 100% (156/156), done.
+    remote: Compressing objects: 100% (115/115), done.
+    remote: Total 436 (delta 113), reused 41 (delta 41), pack-reused 280
+    Receiving objects: 100% (436/436), 237.89 MiB | 24.04 MiB/s, done.
+    Resolving deltas: 100% (156/156), done.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything
     Note: you may need to restart the kernel to use updated packages.
     Note: you may need to restart the kernel to use updated packages.
     WARNING: typer 0.12.3 does not provide the extra 'all'
@@ -273,13 +273,13 @@ loading on device using ``core.complie_model``.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything/torchhub/facebookresearch_dinov2_main/dinov2/layers/patch_embed.py:73: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything/torchhub/facebookresearch_dinov2_main/dinov2/layers/patch_embed.py:73: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       assert H % patch_H == 0, f"Input image height {H} is not a multiple of patch height {patch_H}"
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything/torchhub/facebookresearch_dinov2_main/dinov2/layers/patch_embed.py:74: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything/torchhub/facebookresearch_dinov2_main/dinov2/layers/patch_embed.py:74: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       assert W % patch_W == 0, f"Input image width {W} is not a multiple of patch width: {patch_W}"
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything/torchhub/facebookresearch_dinov2_main/vision_transformer.py:183: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything/torchhub/facebookresearch_dinov2_main/vision_transformer.py:183: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if npatch == N and w == h:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything/depth_anything/dpt.py:133: TracerWarning: Converting a tensor to a Python integer might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything/depth_anything/dpt.py:133: TracerWarning: Converting a tensor to a Python integer might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       out = F.interpolate(out, (int(patch_h * 14), int(patch_w * 14)), mode="bilinear", align_corners=True)
 
 
@@ -571,7 +571,7 @@ Run inference on video
 
 .. parsed-literal::
 
-    Processed 60 frames in 13.62 seconds. Total FPS (including video processing): 4.41.Inference FPS: 10.01 
+    Processed 60 frames in 13.25 seconds. Total FPS (including video processing): 4.53.Inference FPS: 10.44 
     Video saved to 'output/Coco Walking in Berkeley_depth_anything.mp4'.
 
 
@@ -598,7 +598,7 @@ Run inference on video
 .. parsed-literal::
 
     Showing video saved at
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything/output/Coco Walking in Berkeley_depth_anything.mp4
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything/output/Coco Walking in Berkeley_depth_anything.mp4
     If you cannot see the video in your browser, please click on the following link to download the video 
 
 
@@ -735,10 +735,10 @@ quantization code below may take some time.
 
 .. parsed-literal::
 
-    2024-06-06 00:01:26.132845: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-06-06 00:01:26.165931: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-07-02 00:07:04.850517: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-07-02 00:07:04.883202: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2024-06-06 00:01:26.726838: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-07-02 00:07:05.467923: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 
@@ -748,17 +748,17 @@ quantization code below may take some time.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -769,17 +769,17 @@ quantization code below may take some time.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -796,17 +796,17 @@ quantization code below may take some time.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -817,17 +817,17 @@ quantization code below may take some time.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -902,10 +902,10 @@ data.
 
 .. parsed-literal::
 
-    Processed 60 frames in 12.67 seconds. Total FPS (including video processing): 4.74.Inference FPS: 12.82 
+    Processed 60 frames in 13.18 seconds. Total FPS (including video processing): 4.55.Inference FPS: 11.58 
     Video saved to 'output/Coco Walking in Berkeley_depth_anything_int8.mp4'.
     Showing video saved at
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything/output/Coco Walking in Berkeley_depth_anything.mp4
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/depth-anything/Depth-Anything/output/Coco Walking in Berkeley_depth_anything.mp4
     If you cannot see the video in your browser, please click on the following link to download the video 
 
 
@@ -985,9 +985,9 @@ Tool <https://docs.openvino.ai/2024/learn-openvino/openvino-samples/benchmark-to
 
 .. parsed-literal::
 
-    FP16 Throughput: 10.66 FPS
-    INT8 Throughput: 14.02 FPS
-    Speed-up: 1.32
+    FP16 Throughput: 10.57 FPS
+    INT8 Throughput: 14.19 FPS
+    Speed-up: 1.34
 
 
 Interactive demo
diff --git a/docs/notebooks/detectron2-to-openvino-with-output.rst b/docs/notebooks/detectron2-to-openvino-with-output.rst
index 8f1e2eba67ce44..555d09c9b33801 100644
--- a/docs/notebooks/detectron2-to-openvino-with-output.rst
+++ b/docs/notebooks/detectron2-to-openvino-with-output.rst
@@ -15,8 +15,8 @@ using OpenVINO™. We will use ``Faster R-CNN FPN x1`` model and
 `COCO <https://cocodataset.org/#home>`__ dataset as examples for object
 detection and instance segmentation respectively.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 
diff --git a/docs/notebooks/detectron2-to-openvino-with-output_files/detectron2-to-openvino-with-output_22_0.jpg b/docs/notebooks/detectron2-to-openvino-with-output_files/detectron2-to-openvino-with-output_22_0.jpg
index 009298597f3ff1..ab0c1b78ebfca1 100644
--- a/docs/notebooks/detectron2-to-openvino-with-output_files/detectron2-to-openvino-with-output_22_0.jpg
+++ b/docs/notebooks/detectron2-to-openvino-with-output_files/detectron2-to-openvino-with-output_22_0.jpg
@@ -1,3 +1,3 @@
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-oid sha256:eb6a3c05535408d011ad8aa46258978d309afda0fe57ab8c97ea81078386469c
-size 58366
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+size 57923
diff --git a/docs/notebooks/detectron2-to-openvino-with-output_files/detectron2-to-openvino-with-output_22_0.png b/docs/notebooks/detectron2-to-openvino-with-output_files/detectron2-to-openvino-with-output_22_0.png
index 84292f2d1d35ad..84b2911e640610 100644
--- a/docs/notebooks/detectron2-to-openvino-with-output_files/detectron2-to-openvino-with-output_22_0.png
+++ b/docs/notebooks/detectron2-to-openvino-with-output_files/detectron2-to-openvino-with-output_22_0.png
@@ -1,3 +1,3 @@
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+size 509091
diff --git a/docs/notebooks/detectron2-to-openvino-with-output_files/detectron2-to-openvino-with-output_32_0.jpg b/docs/notebooks/detectron2-to-openvino-with-output_files/detectron2-to-openvino-with-output_32_0.jpg
index 42770b2466d915..7b80e48c89ae43 100644
--- a/docs/notebooks/detectron2-to-openvino-with-output_files/detectron2-to-openvino-with-output_32_0.jpg
+++ b/docs/notebooks/detectron2-to-openvino-with-output_files/detectron2-to-openvino-with-output_32_0.jpg
@@ -1,3 +1,3 @@
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-size 54727
+oid sha256:10b9ef56b21a181e5a72b2ad211355d22211d9e88458bef06738ab527d64f4c1
+size 56024
diff --git a/docs/notebooks/detectron2-to-openvino-with-output_files/detectron2-to-openvino-with-output_32_0.png b/docs/notebooks/detectron2-to-openvino-with-output_files/detectron2-to-openvino-with-output_32_0.png
index 946beed11b4517..f5d36c77d54d6e 100644
--- a/docs/notebooks/detectron2-to-openvino-with-output_files/detectron2-to-openvino-with-output_32_0.png
+++ b/docs/notebooks/detectron2-to-openvino-with-output_files/detectron2-to-openvino-with-output_32_0.png
@@ -1,3 +1,3 @@
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-size 457983
+oid sha256:2ba18e298e6381279761c526d16d39d57daaeb1804a72983b4c969c176cc0ed0
+size 457758
diff --git a/docs/notebooks/distil-whisper-asr-with-output.rst b/docs/notebooks/distil-whisper-asr-with-output.rst
index 1e27e7ebf6a893..72c69751103cda 100644
--- a/docs/notebooks/distil-whisper-asr-with-output.rst
+++ b/docs/notebooks/distil-whisper-asr-with-output.rst
@@ -36,8 +36,8 @@ convert the model to OpenVINO™ IR format. To further improve OpenVINO
 Distil-Whisper model performance ``INT8`` post-training quantization
 from `NNCF <https://github.com/openvinotoolkit/nncf/>`__ is applied.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Load PyTorch model <#load-pytorch-model>`__
@@ -186,7 +186,7 @@ by Hugging Face datasets implementation.
         return input_features
     
     
-    dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+    dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True)
     sample = dataset[0]
     input_features = extract_input_features(sample)
 
@@ -475,7 +475,7 @@ seconds is optimal. To activate batching, pass the argument batch_size.
 
 .. code:: ipython3
 
-    dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation")
+    dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation", trust_remote_code=True)
     sample_long = dataset[0]
     
     
@@ -687,7 +687,7 @@ improves quantization quality.
                                                                  apply_caching=True)
     
         try:
-            calibration_dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True)
+            calibration_dataset = load_dataset("openslr/librispeech_asr", "clean", split="validation", streaming=True, trust_remote_code=True)
             for sample in tqdm(islice(calibration_dataset, calibration_dataset_size), desc="Collecting calibration data",
                                total=calibration_dataset_size):
                 input_features = extract_input_features(sample)
@@ -833,7 +833,7 @@ models.
     %%skip not $to_quantize.value
     
     dataset = load_dataset(
-        "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation"
+        "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True
     )
     sample = dataset[0]
     input_features = extract_input_features(sample)
@@ -944,7 +944,7 @@ decoder-with-past model forwards, and for the whole model inference too.
         mean_decoder_with_time_infer_time = sum(decoder_with_past_infer_times)
         return word_accuracy, (mean_whole_infer_time, mean_encoder_infer_time, mean_decoder_with_time_infer_time)
     
-    test_dataset = load_dataset("librispeech_asr", "clean", split="test", streaming=True)
+    test_dataset = load_dataset("openslr/librispeech_asr", "clean", split="test", streaming=True, trust_remote_code=True)
     test_dataset = test_dataset.shuffle(seed=42).take(TEST_DATASET_SIZE)
     test_samples = [sample for sample in test_dataset]
     
diff --git a/docs/notebooks/distilbert-sequence-classification-with-output.rst b/docs/notebooks/distilbert-sequence-classification-with-output.rst
index 545a5b3429f262..a345deeabbe402 100644
--- a/docs/notebooks/distilbert-sequence-classification-with-output.rst
+++ b/docs/notebooks/distilbert-sequence-classification-with-output.rst
@@ -7,8 +7,8 @@ identify, extract, quantify, and study affective states and subjective
 information. This notebook demonstrates how to convert and run a
 sequence classification model using OpenVINO.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Imports <#imports>`__
 -  `Initializing the Model <#initializing-the-model>`__
@@ -36,31 +36,31 @@ Imports
 .. parsed-literal::
 
     Looking in indexes: https://pypi.org/simple, https://download.pytorch.org/whl/cpu
-    Requirement already satisfied: openvino>=2023.1.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (2024.3.0.dev20240605)
-    Requirement already satisfied: transformers in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (4.41.2)
-    Requirement already satisfied: torch>=2.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (2.3.1+cpu)
-    Requirement already satisfied: tqdm in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (4.66.4)
-    Requirement already satisfied: numpy<2.0.0,>=1.16.6 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino>=2023.1.0) (1.23.5)
-    Requirement already satisfied: openvino-telemetry>=2023.2.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino>=2023.1.0) (2024.1.0)
-    Requirement already satisfied: packaging in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino>=2023.1.0) (24.0)
-    Requirement already satisfied: filelock in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from transformers) (3.14.0)
-    Requirement already satisfied: huggingface-hub<1.0,>=0.23.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from transformers) (0.23.3)
-    Requirement already satisfied: pyyaml>=5.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from transformers) (6.0.1)
-    Requirement already satisfied: regex!=2019.12.17 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from transformers) (2024.5.15)
-    Requirement already satisfied: requests in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from transformers) (2.32.3)
-    Requirement already satisfied: tokenizers<0.20,>=0.19 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from transformers) (0.19.1)
-    Requirement already satisfied: safetensors>=0.4.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from transformers) (0.4.3)
-    Requirement already satisfied: typing-extensions>=4.8.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch>=2.1) (4.12.1)
-    Requirement already satisfied: sympy in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch>=2.1) (1.12.1)
-    Requirement already satisfied: networkx in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch>=2.1) (3.1)
-    Requirement already satisfied: jinja2 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch>=2.1) (3.1.4)
-    Requirement already satisfied: fsspec in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch>=2.1) (2024.3.1)
-    Requirement already satisfied: MarkupSafe>=2.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from jinja2->torch>=2.1) (2.1.5)
-    Requirement already satisfied: charset-normalizer<4,>=2 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from requests->transformers) (3.3.2)
-    Requirement already satisfied: idna<4,>=2.5 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from requests->transformers) (3.7)
-    Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from requests->transformers) (2.2.1)
-    Requirement already satisfied: certifi>=2017.4.17 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from requests->transformers) (2024.6.2)
-    Requirement already satisfied: mpmath<1.4.0,>=1.1.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from sympy->torch>=2.1) (1.3.0)
+    Requirement already satisfied: openvino>=2023.1.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (2024.3.0.dev20240627)
+    Requirement already satisfied: transformers in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (4.42.3)
+    Requirement already satisfied: torch>=2.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (2.3.1+cpu)
+    Requirement already satisfied: tqdm in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (4.66.4)
+    Requirement already satisfied: numpy<2.0.0,>=1.16.6 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino>=2023.1.0) (1.23.5)
+    Requirement already satisfied: openvino-telemetry>=2023.2.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino>=2023.1.0) (2024.1.0)
+    Requirement already satisfied: packaging in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino>=2023.1.0) (24.1)
+    Requirement already satisfied: filelock in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from transformers) (3.15.4)
+    Requirement already satisfied: huggingface-hub<1.0,>=0.23.2 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from transformers) (0.23.4)
+    Requirement already satisfied: pyyaml>=5.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from transformers) (6.0.1)
+    Requirement already satisfied: regex!=2019.12.17 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from transformers) (2024.5.15)
+    Requirement already satisfied: requests in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from transformers) (2.32.3)
+    Requirement already satisfied: safetensors>=0.4.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from transformers) (0.4.3)
+    Requirement already satisfied: tokenizers<0.20,>=0.19 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from transformers) (0.19.1)
+    Requirement already satisfied: typing-extensions>=4.8.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch>=2.1) (4.12.2)
+    Requirement already satisfied: sympy in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch>=2.1) (1.12.1)
+    Requirement already satisfied: networkx in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch>=2.1) (3.1)
+    Requirement already satisfied: jinja2 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch>=2.1) (3.1.4)
+    Requirement already satisfied: fsspec in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch>=2.1) (2024.5.0)
+    Requirement already satisfied: MarkupSafe>=2.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from jinja2->torch>=2.1) (2.1.5)
+    Requirement already satisfied: charset-normalizer<4,>=2 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from requests->transformers) (3.3.2)
+    Requirement already satisfied: idna<4,>=2.5 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from requests->transformers) (3.7)
+    Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from requests->transformers) (2.2.2)
+    Requirement already satisfied: certifi>=2017.4.17 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from requests->transformers) (2024.6.2)
+    Requirement already satisfied: mpmath<1.4.0,>=1.1.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from sympy->torch>=2.1) (1.3.0)
     Note: you may need to restart the kernel to use updated packages.
 
 
@@ -143,9 +143,9 @@ optimal execution on end-point target devices.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4481: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4565: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
       warnings.warn(
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/distilbert/modeling_distilbert.py:231: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/distilbert/modeling_distilbert.py:230: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
       mask, torch.tensor(torch.finfo(scores.dtype).min)
 
 
diff --git a/docs/notebooks/dolly-2-instruction-following-with-output.rst b/docs/notebooks/dolly-2-instruction-following-with-output.rst
index 4344393ba488c5..1863874479b765 100644
--- a/docs/notebooks/dolly-2-instruction-following-with-output.rst
+++ b/docs/notebooks/dolly-2-instruction-following-with-output.rst
@@ -81,8 +81,8 @@ dataset can be found in `Databricks blog
 post <https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm>`__
 and `repo <https://github.com/databrickslabs/dolly>`__
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Convert model using Optimum-CLI
@@ -124,7 +124,10 @@ documentation <https://huggingface.co/docs/optimum/intel/inference>`__.
 
 .. code:: ipython3
 
-    %pip install -Uq pip
+    import os
+
+    os.environ["GIT_CLONE_PROTECTION_ACTIVE"] = "false"
+
     %pip uninstall -q -y optimum optimum-intel
     %pip install --pre -Uq openvino openvino-tokenizers[transformers] --extra-index-url https://storage.openvinotoolkit.org/simple/wheels/nightly
     %pip install -q "diffusers>=0.16.1" "transformers>=4.33.0" "torch>=2.1" "nncf>=2.10.0" onnx "gradio>=4.19" --extra-index-url https://download.pytorch.org/whl/cpu
@@ -136,7 +139,7 @@ Convert model using Optimum-CLI tool
 
 
 `Optimum Intel <https://huggingface.co/docs/optimum/intel/index>`__ is
-the interface between the 
+the interface between the
 `Transformers <https://huggingface.co/docs/transformers/index>`__ and
 `Diffusers <https://huggingface.co/docs/diffusers/index>`__ libraries
 and OpenVINO to accelerate end-to-end pipelines on Intel architectures.
@@ -190,12 +193,13 @@ to make it
 `symmetric <https://github.com/openvinotoolkit/nncf/blob/develop/docs/compression_algorithms/Quantization.md#symmetric-quantization>`__
 you can add ``--sym``.
 
-For INT4 quantization you can also specify the following arguments : -
-The ``--group-size`` parameter will define the group size to use for
-quantization, -1 it will results in per-column quantization. - The
-``--ratio`` parameter controls the ratio between 4-bit and 8-bit
-quantization. If set to 0.9, it means that 90% of the layers will be
-quantized to int4 while 10% will be quantized to int8.
+For INT4 quantization you can also specify the following arguments :
+
+- The ``--group-size`` parameter will define the group size to use for
+  quantization, -1 it will results in per-column quantization.
+- The ``--ratio`` parameter controls the ratio between 4-bit and 8-bit
+  quantization. If set to 0.9, it means that 90% of the layers will be
+  quantized to int4 while 10% will be quantized to int8.
 
 Smaller group_size and ratio values usually improve accuracy at the
 sacrifice of the model size and inference latency.
@@ -207,7 +211,7 @@ sacrifice of the model size and inference latency.
 
     from IPython.display import Markdown, display
     import ipywidgets as widgets
-    
+
     prepare_int4_model = widgets.Checkbox(
         value=True,
         description="Prepare INT4 model",
@@ -223,7 +227,7 @@ sacrifice of the model size and inference latency.
         description="Prepare FP16 model",
         disabled=False,
     )
-    
+
     display(prepare_int4_model)
     display(prepare_int8_model)
     display(prepare_fp16_model)
@@ -250,15 +254,15 @@ sacrifice of the model size and inference latency.
 .. code:: ipython3
 
     from pathlib import Path
-    
+
     model_id = "databricks/dolly-v2-3b"
     model_path = Path("dolly-v2-3b")
-    
+
     fp16_model_dir = model_path / "FP16"
     int8_model_dir = model_path / "INT8_compressed_weights"
     int4_model_dir = model_path / "INT4_compressed_weights"
-    
-    
+
+
     def convert_to_fp16():
         if (fp16_model_dir / "openvino_model.xml").exists():
             return
@@ -268,8 +272,8 @@ sacrifice of the model size and inference latency.
         display(Markdown("**Export command:**"))
         display(Markdown(f"`{export_command}`"))
         ! $export_command
-    
-    
+
+
     def convert_to_int8():
         if (int8_model_dir / "openvino_model.xml").exists():
             return
@@ -279,8 +283,8 @@ sacrifice of the model size and inference latency.
         display(Markdown("**Export command:**"))
         display(Markdown(f"`{export_command}`"))
         ! $export_command
-    
-    
+
+
     def convert_to_int4():
         if (int4_model_dir / "openvino_model.xml").exists():
             return
@@ -290,8 +294,8 @@ sacrifice of the model size and inference latency.
         display(Markdown("**Export command:**"))
         display(Markdown(f"`{export_command}`"))
         ! $export_command
-    
-    
+
+
     if prepare_fp16_model.value:
         convert_to_fp16()
     if prepare_int8_model.value:
@@ -304,7 +308,7 @@ sacrifice of the model size and inference latency.
     fp16_weights = fp16_model_dir / "openvino_model.bin"
     int8_weights = int8_model_dir / "openvino_model.bin"
     int4_weights = int4_model_dir / "openvino_model.bin"
-    
+
     if fp16_weights.exists():
         print(f"Size of FP16 model is {fp16_weights.stat().st_size / 1024 / 1024:.2f} MB")
     for precision, compressed_weights in zip([8, 4], [int8_weights, int4_weights]):
@@ -335,14 +339,14 @@ select device from dropdown list for running inference using OpenVINO
         available_models.append("INT8")
     if fp16_model_dir.exists():
         available_models.append("FP16")
-    
+
     model_to_run = widgets.Dropdown(
         options=available_models,
         value=available_models[0],
         description="Model to run:",
         disabled=False,
     )
-    
+
     model_to_run
 
 
@@ -358,16 +362,16 @@ select device from dropdown list for running inference using OpenVINO
 
     import ipywidgets as widgets
     import openvino as ov
-    
+
     core = ov.Core()
-    
+
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
         value="CPU",
         description="Device:",
         disabled=False,
     )
-    
+
     device
 
 
@@ -420,7 +424,7 @@ guide <https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide.html>`__
     from pathlib import Path
     from transformers import AutoTokenizer
     from optimum.intel.openvino import OVModelForCausalLM
-    
+
     if model_to_run.value == "INT4":
         model_dir = int4_model_dir
     elif model_to_run.value == "INT8":
@@ -428,13 +432,13 @@ guide <https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide.html>`__
     else:
         model_dir = fp16_model_dir
     print(f"Loading model from {model_dir}")
-    
+
     tokenizer = AutoTokenizer.from_pretrained(model_dir)
-    
+
     current_device = device.value
-    
+
     ov_config = {"PERFORMANCE_HINT": "LATENCY", "NUM_STREAMS": "1", "CACHE_DIR": ""}
-    
+
     ov_model = OVModelForCausalLM.from_pretrained(model_dir, device=current_device, ov_config=ov_config)
 
 
@@ -617,14 +621,14 @@ into model with providing additional context.
     RESPONSE_KEY = "### Response:"
     END_KEY = "### End"
     INTRO_BLURB = "Below is an instruction that describes a task. Write a response that appropriately completes the request."
-    
+
     # This is the prompt that is used for generating responses using an already trained model.  It ends with the response
     # key, where the job of the model is to provide the completion that follows it (i.e. the response itself).
     PROMPT_FOR_GENERATION_FORMAT = """{intro}
-    
+
     {instruction_key}
     {instruction}
-    
+
     {response_key}
     """.format(
         intro=INTRO_BLURB,
@@ -646,17 +650,17 @@ the code below find its id for using it as generation stop-criteria.
     def get_special_token_id(tokenizer: AutoTokenizer, key: str) -> int:
         """
         Gets the token ID for a given string that has been added to the tokenizer as a special token.
-    
+
         When training, we configure the tokenizer so that the sequences like "### Instruction:" and "### End" are
         treated specially and converted to a single, new token.  This retrieves the token ID each of these keys map to.
-    
+
         Args:
             tokenizer (PreTrainedTokenizer): the tokenizer
             key (str): the key to convert to a single token
-    
+
         Raises:
             RuntimeError: if more than one ID was generated
-    
+
         Returns:
             int: the token ID for the given key
         """
@@ -664,13 +668,13 @@ the code below find its id for using it as generation stop-criteria.
         if len(token_ids) > 1:
             raise ValueError(f"Expected only a single token for '{key}' but found {token_ids}")
         return token_ids[0]
-    
-    
+
+
     tokenizer_response_key = next(
         (token for token in tokenizer.additional_special_tokens if token.startswith(RESPONSE_KEY)),
         None,
     )
-    
+
     end_key_token_id = None
     if tokenizer_response_key:
         try:
@@ -700,7 +704,7 @@ parameter and returns model response.
     ):
         """
         Text generation function
-    
+
         Parameters:
           user_text (str): User-provided instruction for a generation.
           top_p (float):  Nucleus sampling. If set to < 1, only the smallest set of most probable tokens with probabilities that add up to top_p or higher are kept for a generation.
@@ -712,13 +716,13 @@ parameter and returns model response.
           model_output (str) - model-generated text
           perf_text (str) - updated perf text filed content
         """
-    
+
         # Prepare input prompt according to model expected template
         prompt_text = PROMPT_FOR_GENERATION_FORMAT.format(instruction=user_text)
-    
+
         # Tokenize the user text.
         model_inputs = tokenizer(prompt_text, return_tensors="pt")
-    
+
         # Start generation on a separate thread, so that we don't block the UI. The text is pulled from the streamer
         # in the main thread. Adds timeout to the streamer to handle exceptions in the generation thread.
         streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
@@ -734,7 +738,7 @@ parameter and returns model response.
         )
         t = Thread(target=ov_model.generate, kwargs=generate_kwargs)
         t.start()
-    
+
         # Pull the generated text from the streamer, and update the model output.
         model_output = ""
         per_token_time = []
@@ -768,14 +772,14 @@ elements.
     ):
         """
         Helper function for performance estimation
-    
+
         Parameters:
           current_time (float): This step time in seconds.
           current_perf_text (str): Current content of performance UI field.
           new_gen_text (str): New generated text.
           per_token_time (List[float]): history of performance from previous steps.
           num_tokens (int): Total number of generated tokens.
-    
+
         Returns:
           update for performance text field
           update for a total number of tokens
@@ -790,27 +794,27 @@ elements.
                 num_tokens,
             )
         return current_perf_text, num_tokens
-    
-    
+
+
     def reset_textbox(instruction: str, response: str, perf: str):
         """
         Helper function for resetting content of all text fields
-    
+
         Parameters:
           instruction (str): Content of user instruction field.
           response (str): Content of model response field.
           perf (str): Content of performance info filed
-    
+
         Returns:
           empty string for each placeholder
         """
         return "", "", ""
-    
-    
+
+
     def select_device(device_str: str, current_text: str = "", progress: gr.Progress = gr.Progress()):
         """
         Helper function for uploading model on the device.
-    
+
         Parameters:
           device_str (str): Device name.
           current_text (str): Current content of user instruction field (used only for backup purposes, temporally replacing it on the progress bar during model loading).
@@ -821,7 +825,7 @@ elements.
         if device_str != ov_model._device:
             ov_model.request = None
             ov_model._device = device_str
-    
+
             for i in progress.tqdm(range(1), desc=f"Model loading on {device_str}"):
                 ov_model.compile()
         return current_text
@@ -852,7 +856,7 @@ generation parameters:
 .. code:: ipython3
 
     available_devices = ov.Core().available_devices + ["AUTO"]
-    
+
     examples = [
         "Give me recipe for pizza with pineapple",
         "Write me a tweet about new OpenVINO release",
@@ -865,13 +869,13 @@ generation parameters:
         "Write instructions on how to become a good AI engineer",
         "Write a love letter to my best friend",
     ]
-    
+
     with gr.Blocks() as demo:
         gr.Markdown(
             "# Instruction following using Databricks Dolly 2.0 and OpenVINO.\n"
             "Provide insturction which describes a task below or select among predefined examples and model writes response that performs requested task."
         )
-    
+
         with gr.Row():
             with gr.Column(scale=4):
                 user_text = gr.Textbox(
@@ -918,7 +922,7 @@ generation parameters:
                     interactive=True,
                     label="Temperature",
                 )
-    
+
         user_text.submit(
             run_generation,
             [user_text, top_p, temperature, top_k, max_new_tokens, performance],
@@ -936,13 +940,13 @@ generation parameters:
             [user_text, model_output, performance],
         )
         device.change(select_device, [device, user_text], [user_text])
-    
+
     if __name__ == "__main__":
         try:
             demo.queue().launch(debug=False, height=800)
         except Exception:
             demo.queue().launch(debug=False, share=True, height=800)
-    
+
     # If you are launching remotely, specify server_name and server_port
     # EXAMPLE: `demo.launch(server_name='your server name', server_port='server port in int')`
     # To learn more please refer to the Gradio docs: https://gradio.app/docs/
diff --git a/docs/notebooks/dynamicrafter-animating-images-with-output.rst b/docs/notebooks/dynamicrafter-animating-images-with-output.rst
index 5f431841999aa8..5504281d547091 100644
--- a/docs/notebooks/dynamicrafter-animating-images-with-output.rst
+++ b/docs/notebooks/dynamicrafter-animating-images-with-output.rst
@@ -98,8 +98,8 @@ as higher conformity to the input image.
 
    </table >
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Load the original model <#load-the-original-model>`__
@@ -123,14 +123,16 @@ Prerequisites
 
 .. code:: ipython3
 
-    %pip install -Uq pip
-    %pip install --pre -Uq openvino --extra-index-url https://storage.openvinotoolkit.org/simple/wheels/nightly
+    %pip install "openvino>=2024.2.0"
     %pip install -q "gradio>=4.19" omegaconf decord einops pytorch_lightning kornia open_clip_torch transformers av opencv-python torch --extra-index-url https://download.pytorch.org/whl/cpu
 
 
 .. parsed-literal::
 
-    Note: you may need to restart the kernel to use updated packages.
+    Requirement already satisfied: openvino>=2024.2.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (2024.3.0.dev20240627)
+    Requirement already satisfied: numpy<2.0.0,>=1.16.6 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino>=2024.2.0) (1.23.5)
+    Requirement already satisfied: openvino-telemetry>=2023.2.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino>=2024.2.0) (2024.1.0)
+    Requirement already satisfied: packaging in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino>=2024.2.0) (24.1)
     Note: you may need to restart the kernel to use updated packages.
     Note: you may need to restart the kernel to use updated packages.
 
@@ -139,26 +141,31 @@ Prerequisites
 
     import sys
     from pathlib import Path
-    
-    
+
+
     dynamicrafter_path = Path("dynamicrafter")
-    
+
     if not dynamicrafter_path.exists():
         dynamicrafter_path.mkdir(parents=True, exist_ok=True)
         !git clone https://github.com/Doubiiu/DynamiCrafter.git dynamicrafter
-    
+        %cd dynamicrafter
+        !git checkout 26e665cd6c174234238d2ded661e2e56f875d360 -q  # to avoid breaking changes
+        %cd ..
+
     sys.path.append(str(dynamicrafter_path))
 
 
 .. parsed-literal::
 
     Cloning into 'dynamicrafter'...
-    remote: Enumerating objects: 294, done.
-    remote: Counting objects: 100% (131/131), done.
-    remote: Compressing objects: 100% (75/75), done.
-    remote: Total 294 (delta 79), reused 76 (delta 56), pack-reused 163
-    Receiving objects: 100% (294/294), 72.40 MiB | 26.63 MiB/s, done.
-    Resolving deltas: 100% (94/94), done.
+    remote: Enumerating objects: 320, done.
+    remote: Counting objects: 100% (157/157), done.
+    remote: Compressing objects: 100% (93/93), done.
+    remote: Total 320 (delta 95), reused 88 (delta 64), pack-reused 163
+    Receiving objects: 100% (320/320), 72.40 MiB | 21.86 MiB/s, done.
+    Resolving deltas: 100% (110/110), done.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/dynamicrafter-animating-images/dynamicrafter
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/dynamicrafter-animating-images
 
 
 Load and run the original pipeline
@@ -173,14 +180,14 @@ We will use model for 256x256 resolution as example. Also, models for
 .. code:: ipython3
 
     import os
-    
+
     from huggingface_hub import hf_hub_download
     from omegaconf import OmegaConf
-    
+
     from dynamicrafter.scripts.evaluation.funcs import load_model_checkpoint
     from dynamicrafter.utils.utils import instantiate_from_config
-    
-    
+
+
     def download_model():
         REPO_ID = "Doubiiu/DynamiCrafter"
         if not os.path.exists("./checkpoints/dynamicrafter_256_v1/"):
@@ -188,7 +195,7 @@ We will use model for 256x256 resolution as example. Also, models for
         local_file = os.path.join("./checkpoints/dynamicrafter_256_v1/model.ckpt")
         if not os.path.exists(local_file):
             hf_hub_download(repo_id=REPO_ID, filename="model.ckpt", local_dir="./checkpoints/dynamicrafter_256_v1/", local_dir_use_symlinks=False)
-    
+
         ckpt_path = "checkpoints/dynamicrafter_256_v1/model.ckpt"
         config_file = "dynamicrafter/configs/inference_256_v1.0.yaml"
         config = OmegaConf.load(config_file)
@@ -197,16 +204,16 @@ We will use model for 256x256 resolution as example. Also, models for
         model = instantiate_from_config(model_config)
         model = load_model_checkpoint(model, ckpt_path)
         model.eval()
-    
+
         return model
-    
-    
+
+
     model = download_model()
 
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/huggingface_hub/file_download.py:1194: UserWarning: `local_dir_use_symlinks` parameter is deprecated and will be ignored. The process to download files to a local folder has been updated and do not rely on symlinks anymore. You only need to pass a destination folder as`local_dir`.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/huggingface_hub/file_download.py:1194: UserWarning: `local_dir_use_symlinks` parameter is deprecated and will be ignored. The process to download files to a local folder has been updated and do not rely on symlinks anymore. You only need to pass a destination folder as`local_dir`.
     For more details, check out https://huggingface.co/docs/huggingface_hub/main/en/guides/download#download-files-to-local-folder.
       warnings.warn(
 
@@ -236,10 +243,10 @@ file.
 .. code:: ipython3
 
     import torch
-    
+
     import openvino as ov
-    
-    
+
+
     def convert(model: torch.nn.Module, xml_path: str, example_input, input_shape=None):
         xml_path = Path(xml_path)
         if not xml_path.exists():
@@ -250,7 +257,7 @@ file.
                 else:
                     converted_model = ov.convert_model(model, example_input=example_input, input=input_shape)
             ov.save_model(converted_model, xml_path, compress_to_fp16=False)
-    
+
             # cleanup memory
             torch._C._jit_clear_class_registry()
             torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore()
@@ -274,21 +281,21 @@ Convert CLIP text encoder
 .. code:: ipython3
 
     from dynamicrafter.lvdm.modules.encoders.condition import FrozenOpenCLIPEmbedder
-    
-    
+
+
     COND_STAGE_MODEL_OV_PATH = Path("models/cond_stage_model.xml")
-    
-    
+
+
     class FrozenOpenCLIPEmbedderWrapper(FrozenOpenCLIPEmbedder):
-    
+
         def forward(self, tokens):
             z = self.encode_with_transformer(tokens.to(self.device))
             return z
-    
-    
+
+
     cond_stage_model = FrozenOpenCLIPEmbedderWrapper(device="cpu")
-    
-    
+
+
     convert(
         cond_stage_model,
         COND_STAGE_MODEL_OV_PATH,
@@ -305,53 +312,53 @@ resolutions.
 .. code:: ipython3
 
     EMBEDDER_OV_PATH = Path("models/embedder_ir.xml")
-    
-    
+
+
     dummy_input = torch.rand([1, 3, 767, 767], dtype=torch.float32)
-    
+
     model.embedder.model.visual.input_patchnorm = None  # fix error: visual model has not  attribute 'input_patchnorm'
     convert(model.embedder, EMBEDDER_OV_PATH, example_input=dummy_input, input_shape=[1, 3, -1, -1])
 
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/kornia/utils/image.py:226: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/kornia/utils/image.py:226: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if input.numel() == 0:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/kornia/geometry/transform/affwarp.py:573: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/kornia/geometry/transform/affwarp.py:573: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if size == input_size:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/kornia/geometry/transform/affwarp.py:579: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/kornia/geometry/transform/affwarp.py:579: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       antialias = antialias and (max(factors) > 1)
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/kornia/geometry/transform/affwarp.py:581: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/kornia/geometry/transform/affwarp.py:581: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if antialias:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/kornia/geometry/transform/affwarp.py:584: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/kornia/geometry/transform/affwarp.py:584: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       sigmas = (max((factors[0] - 1.0) / 2.0, 0.001), max((factors[1] - 1.0) / 2.0, 0.001))
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/kornia/geometry/transform/affwarp.py:589: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/kornia/geometry/transform/affwarp.py:589: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       ks = int(max(2.0 * 2 * sigmas[0], 3)), int(max(2.0 * 2 * sigmas[1], 3))
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/kornia/geometry/transform/affwarp.py:589: TracerWarning: Converting a tensor to a Python integer might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/kornia/geometry/transform/affwarp.py:589: TracerWarning: Converting a tensor to a Python integer might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       ks = int(max(2.0 * 2 * sigmas[0], 3)), int(max(2.0 * 2 * sigmas[1], 3))
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/kornia/filters/gaussian.py:55: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/kornia/filters/gaussian.py:55: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
       sigma = tensor([sigma], device=input.device, dtype=input.dtype)
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/kornia/filters/gaussian.py:55: TracerWarning: Converting a tensor to a Python float might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/kornia/filters/gaussian.py:55: TracerWarning: Converting a tensor to a Python float might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       sigma = tensor([sigma], device=input.device, dtype=input.dtype)
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/kornia/core/check.py:77: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/kornia/core/check.py:78: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if x_shape_to_check[i] != dim:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/kornia/filters/kernels.py:92: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/kornia/filters/kernels.py:92: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
       mean = tensor([[mean]], device=sigma.device, dtype=sigma.dtype)
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/kornia/enhance/normalize.py:101: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/kornia/enhance/normalize.py:101: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if len(mean.shape) == 0 or mean.shape[0] == 1:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/kornia/enhance/normalize.py:103: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/kornia/enhance/normalize.py:103: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if len(std.shape) == 0 or std.shape[0] == 1:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/kornia/enhance/normalize.py:107: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/kornia/enhance/normalize.py:107: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if mean.shape and mean.shape[0] != 1:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/kornia/enhance/normalize.py:108: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/kornia/enhance/normalize.py:108: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if mean.shape[0] != data.shape[1] and mean.shape[:2] != data.shape[:2]:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/kornia/enhance/normalize.py:112: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/kornia/enhance/normalize.py:112: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if std.shape and std.shape[0] != 1:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/kornia/enhance/normalize.py:113: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/kornia/enhance/normalize.py:113: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if std.shape[0] != data.shape[1] and std.shape[:2] != data.shape[:2]:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/kornia/enhance/normalize.py:116: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/kornia/enhance/normalize.py:116: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
       mean = torch.as_tensor(mean, device=data.device, dtype=data.dtype)
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/kornia/enhance/normalize.py:117: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/kornia/enhance/normalize.py:117: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
       std = torch.as_tensor(std, device=data.device, dtype=data.dtype)
 
 
@@ -363,10 +370,10 @@ Convert AE encoder
 .. code:: ipython3
 
     ENCODER_FIRST_STAGE_OV_PATH = Path("models/encode_first_stage_ir.xml")
-    
-    
+
+
     dummy_input = torch.rand([1, 3, 256, 256], dtype=torch.float32)
-    
+
     convert(
         model.first_stage_model.encoder,
         ENCODER_FIRST_STAGE_OV_PATH,
@@ -376,7 +383,7 @@ Convert AE encoder
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/dynamicrafter-animating-images/dynamicrafter/lvdm/modules/networks/ae_modules.py:67: TracerWarning: Converting a tensor to a Python integer might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/dynamicrafter-animating-images/dynamicrafter/lvdm/modules/networks/ae_modules.py:67: TracerWarning: Converting a tensor to a Python integer might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       w_ = w_ * (int(c)**(-0.5))
 
 
@@ -388,18 +395,18 @@ Convert Diffusion U-Net model
 .. code:: ipython3
 
     MODEL_OV_PATH = Path("models/model_ir.xml")
-    
-    
+
+
     class ModelWrapper(torch.nn.Module):
         def __init__(self, diffusion_model):
             super().__init__()
             self.diffusion_model = diffusion_model
-    
+
         def forward(self, xc, t, context=None, fs=None, temporal_length=None):
             outputs = self.diffusion_model(xc, t, context=context, fs=fs, temporal_length=temporal_length)
             return outputs
-    
-    
+
+
     convert(
         ModelWrapper(model.model.diffusion_model),
         MODEL_OV_PATH,
@@ -415,22 +422,22 @@ Convert Diffusion U-Net model
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/dynamicrafter-animating-images/dynamicrafter/lvdm/modules/networks/openaimodel3d.py:556: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/dynamicrafter-animating-images/dynamicrafter/lvdm/modules/networks/openaimodel3d.py:556: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if l_context == 77 + t*16: ## !!! HARD CODE here
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/dynamicrafter-animating-images/dynamicrafter/lvdm/modules/networks/openaimodel3d.py:205: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/dynamicrafter-animating-images/dynamicrafter/lvdm/modules/networks/openaimodel3d.py:205: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if batch_size:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/dynamicrafter-animating-images/dynamicrafter/lvdm/modules/networks/openaimodel3d.py:232: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/dynamicrafter-animating-images/dynamicrafter/lvdm/modules/networks/openaimodel3d.py:232: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if self.use_temporal_conv and batch_size:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/dynamicrafter-animating-images/dynamicrafter/lvdm/modules/networks/openaimodel3d.py:76: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/dynamicrafter-animating-images/dynamicrafter/lvdm/modules/networks/openaimodel3d.py:76: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       assert x.shape[1] == self.channels
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/dynamicrafter-animating-images/dynamicrafter/lvdm/modules/networks/openaimodel3d.py:99: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/dynamicrafter-animating-images/dynamicrafter/lvdm/modules/networks/openaimodel3d.py:99: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       assert x.shape[1] == self.channels
 
 
 Convert AE decoder
 ~~~~~~~~~~~~~~~~~~
 
- ``Decoder`` receives a
+``Decoder`` receives a
 ``bfloat16`` tensor. numpy doesn’t support this type. To avoid problems
 with the conversion lets replace ``decode`` method to convert bfloat16
 to float32.
@@ -438,24 +445,24 @@ to float32.
 .. code:: ipython3
 
     import types
-    
-    
+
+
     def decode(self, z, **kwargs):
         z = self.post_quant_conv(z)
         z = z.float()
         dec = self.decoder(z)
         return dec
-    
-    
+
+
     model.first_stage_model.decode = types.MethodType(decode, model.first_stage_model)
 
 .. code:: ipython3
 
     DECODER_FIRST_STAGE_OV_PATH = Path("models/decoder_first_stage_ir.xml")
-    
-    
+
+
     dummy_input = torch.rand([16, 4, 32, 32], dtype=torch.float32)
-    
+
     convert(
         model.first_stage_model.decoder,
         DECODER_FIRST_STAGE_OV_PATH,
@@ -472,7 +479,7 @@ Select device from dropdown list for running inference using OpenVINO.
 .. code:: ipython3
 
     import ipywidgets as widgets
-    
+
     core = ov.Core()
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
@@ -480,7 +487,7 @@ Select device from dropdown list for running inference using OpenVINO.
         description="Device:",
         disabled=False,
     )
-    
+
     device
 
 
@@ -512,49 +519,49 @@ return ``torch.Tensor``\ s instead of ``np.array``\ s.
 .. code:: ipython3
 
     import open_clip
-    
-    
+
+
     class CondStageModelWrapper(torch.nn.Module):
         def __init__(self, cond_stage_model):
             super().__init__()
             self.cond_stage_model = cond_stage_model
-    
+
         def encode(self, tokens):
             if isinstance(tokens, list):
                 tokens = open_clip.tokenize(tokens[0])
             outs = self.cond_stage_model(tokens)[0]
-    
+
             return torch.from_numpy(outs)
-    
-    
+
+
     class EncoderFirstStageModelWrapper(torch.nn.Module):
         def __init__(self, encode_first_stage):
             super().__init__()
             self.encode_first_stage = encode_first_stage
-    
+
         def forward(self, x):
             outs = self.encode_first_stage(x)[0]
-    
+
             return torch.from_numpy(outs)
-    
-    
+
+
     class EmbedderWrapper(torch.nn.Module):
         def __init__(self, embedder):
             super().__init__()
             self.embedder = embedder
-    
+
         def forward(self, x):
             outs = self.embedder(x)[0]
-    
+
             return torch.from_numpy(outs)
-    
-    
+
+
     class CModelWrapper(torch.nn.Module):
         def __init__(self, diffusion_model, out_channels):
             super().__init__()
             self.diffusion_model = diffusion_model
             self.out_channels = out_channels
-    
+
         def forward(self, xc, t, context, fs, temporal_length):
             inputs = {
                 "xc": xc,
@@ -563,19 +570,19 @@ return ``torch.Tensor``\ s instead of ``np.array``\ s.
                 "fs": fs,
             }
             outs = self.diffusion_model(inputs)[0]
-    
+
             return torch.from_numpy(outs)
-    
-    
+
+
     class DecoderFirstStageModelWrapper(torch.nn.Module):
         def __init__(self, decoder_first_stage):
             super().__init__()
             self.decoder_first_stage = decoder_first_stage
-    
+
         def forward(self, x):
             x.float()
             outs = self.decoder_first_stage(x)[0]
-    
+
             return torch.from_numpy(outs)
 
 And insert wrappers instances in the pipeline:
@@ -596,31 +603,31 @@ Interactive inference
 .. code:: ipython3
 
     import time
-    
+
     from einops import repeat
     from pytorch_lightning import seed_everything
     import torchvision.transforms as transforms
-    
+
     from dynamicrafter.scripts.evaluation.funcs import save_videos, batch_ddim_sampling, get_latent_z
-    from dynamicrafter.lvdm.models.samplers.ddim import DDIMSampler
-    
-    
+    from lvdm.models.samplers.ddim import DDIMSampler
+
+
     def register_buffer(self, name, attr):
         if isinstance(attr, torch.Tensor):
             if attr.device != torch.device("cpu"):
                 attr = attr.to(torch.device("cpu"))
         setattr(self, name, attr)
-    
-    
+
+
     # monkey patching to replace the original method 'register_buffer' that uses CUDA
     DDIMSampler.register_buffer = types.MethodType(register_buffer, DDIMSampler)
-    
-    
+
+
     def get_image(image, prompt, steps=5, cfg_scale=7.5, eta=1.0, fs=3, seed=123, model=model):
         result_dir = "results"
         if not os.path.exists(result_dir):
             os.mkdir(result_dir)
-    
+
         seed_everything(seed)
         transform = transforms.Compose(
             [
@@ -639,31 +646,31 @@ Interactive inference
         frames = model.temporal_length
         h, w = 256 // 8, 256 // 8
         noise_shape = [batch_size, channels, frames, h, w]
-    
+
         # text cond
         with torch.no_grad(), torch.cpu.amp.autocast():
             text_emb = model.get_learned_conditioning([prompt])
-    
+
             # img cond
             img_tensor = torch.from_numpy(image).permute(2, 0, 1).float().to(model.device)
             img_tensor = (img_tensor / 255.0 - 0.5) * 2
-    
+
             image_tensor_resized = transform(img_tensor)  # 3,h,w
             videos = image_tensor_resized.unsqueeze(0)  # bchw
-    
+
             z = get_latent_z(model, videos.unsqueeze(2))  # bc,1,hw
-    
+
             img_tensor_repeat = repeat(z, "b c t h w -> b c (repeat t) h w", repeat=frames)
-    
+
             cond_images = model.embedder(img_tensor.unsqueeze(0))  # blc
-    
+
             img_emb = model.image_proj_model(cond_images)
-    
+
             imtext_cond = torch.cat([text_emb, img_emb], dim=1)
-    
+
             fs = torch.tensor([fs], dtype=torch.long, device=model.device)
             cond = {"c_crossattn": [imtext_cond], "fs": fs, "c_concat": [img_tensor_repeat]}
-    
+
             ## inference
             batch_samples = batch_ddim_sampling(model, cond, noise_shape, n_samples=1, ddim_steps=steps, ddim_eta=eta, cfg_scale=cfg_scale)
             ## b,samples,c,t,h,w
@@ -672,17 +679,17 @@ Interactive inference
             prompt_str = prompt_str[:40]
             if len(prompt_str) == 0:
                 prompt_str = "empty_prompt"
-    
+
         save_videos(batch_samples, result_dir, filenames=[prompt_str], fps=8)
         print(f"Saved in {prompt_str}. Time used: {(time.time() - start):.2f} seconds")
-    
+
         return os.path.join(result_dir, f"{prompt_str}.mp4")
 
 .. code:: ipython3
 
     import gradio as gr
-    
-    
+
+
     i2v_examples_256 = [
         ["dynamicrafter/prompts/256/art.png", "man fishing in a boat at sunset", 50, 7.5, 1.0, 3, 234],
         ["dynamicrafter/prompts/256/boy.png", "boy walking on the street", 50, 7.5, 1.0, 3, 125],
@@ -690,11 +697,11 @@ Interactive inference
         ["dynamicrafter/prompts/256/fire_and_beach.jpg", "a campfire on the beach and the ocean waves in the background", 50, 7.5, 1.0, 3, 111],
         ["dynamicrafter/prompts/256/guitar0.jpeg", "bear playing guitar happily, snowing", 50, 7.5, 1.0, 3, 122],
     ]
-    
-    
+
+
     def dynamicrafter_demo():
         css = """#input_img {max-width: 256px !important} #output_vid {max-width: 256px; max-height: 256px}"""
-    
+
         with gr.Blocks(analytics_enabled=False, css=css) as dynamicrafter_iface:
             with gr.Tab(label="Image2Video_256x256"):
                 with gr.Column():
@@ -714,7 +721,7 @@ Interactive inference
                             i2v_end_btn = gr.Button("Generate")
                         with gr.Row():
                             i2v_output_video = gr.Video(label="Generated Video", elem_id="output_vid", autoplay=True, show_share_button=True)
-    
+
                     gr.Examples(
                         examples=i2v_examples_256,
                         inputs=[i2v_input_image, i2v_input_text, i2v_steps, i2v_cfg_scale, i2v_eta, i2v_motion, i2v_seed],
@@ -727,13 +734,13 @@ Interactive inference
                     outputs=[i2v_output_video],
                     fn=get_image,
                 )
-    
+
         return dynamicrafter_iface
-    
-    
+
+
     demo = dynamicrafter_demo()
-    
-    
+
+
     try:
         demo.queue().launch(debug=False)
     except Exception:
@@ -746,7 +753,7 @@ Interactive inference
 .. parsed-literal::
 
     Running on local URL:  http://127.0.0.1:7860
-    
+
     To create a public link, set `share=True` in `launch()`.
 
 
diff --git a/docs/notebooks/efficient-sam-with-output.rst b/docs/notebooks/efficient-sam-with-output.rst
index b78a5d8df01ef0..c85d1e371471fe 100644
--- a/docs/notebooks/efficient-sam-with-output.rst
+++ b/docs/notebooks/efficient-sam-with-output.rst
@@ -28,8 +28,8 @@ In this tutorial we consider how to convert and run EfficientSAM using
 OpenVINO. We also demonstrate how to quantize model using
 `NNCF <https://github.com/openvinotoolkit/nncf.git>`__
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Load PyTorch model <#load-pytorch-model>`__
@@ -72,12 +72,12 @@ Prerequisites
 .. code:: ipython3
 
     import platform
-    
+
     if platform.system() != "Windows":
         %pip install -q "matplotlib>=3.4"
     else:
         %pip install -q "matplotlib>=3.4,<3.7"
-    
+
     %pip install -q "openvino>=2023.3.0" "nncf>=2.7.0" opencv-python "gradio>=4.13" torch torchvision tqdm  --extra-index-url https://download.pytorch.org/whl/cpu
 
 
@@ -90,9 +90,9 @@ Prerequisites
 .. code:: ipython3
 
     from pathlib import Path
-    
+
     repo_dir = Path("EfficientSAM")
-    
+
     if not repo_dir.exists():
         !git clone https://github.com/yformer/EfficientSAM.git
     %cd $repo_dir
@@ -105,9 +105,9 @@ Prerequisites
     remote: Counting objects: 100% (85/85), done.
     remote: Compressing objects: 100% (33/33), done.
     remote: Total 424 (delta 76), reused 52 (delta 52), pack-reused 339
-    Receiving objects: 100% (424/424), 262.14 MiB | 26.65 MiB/s, done.
+    Receiving objects: 100% (424/424), 262.14 MiB | 26.74 MiB/s, done.
     Resolving deltas: 100% (246/246), done.
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/efficient-sam/EfficientSAM
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/efficient-sam/EfficientSAM
 
 
 Load PyTorch model
@@ -136,12 +136,12 @@ one of them as example.
         build_efficient_sam_vits,
     )
     import zipfile
-    
+
     MODELS_LIST = {
         "efficient-sam-vitt": build_efficient_sam_vitt,
         "efficient-sam-vits": build_efficient_sam_vits,
     }
-    
+
     # Since EfficientSAM-S checkpoint file is >100MB, we store the zip file.
     with zipfile.ZipFile("weights/efficient_sam_vits.pt.zip", "r") as zip_ref:
         zip_ref.extractall("weights")
@@ -151,16 +151,16 @@ Select one from supported models:
 .. code:: ipython3
 
     import ipywidgets as widgets
-    
+
     model_ids = list(MODELS_LIST)
-    
+
     model_id = widgets.Dropdown(
         options=model_ids,
         value=model_ids[0],
         description="Model:",
         disabled=False,
     )
-    
+
     model_id
 
 
@@ -177,13 +177,13 @@ build PyTorch model
 .. code:: ipython3
 
     pt_model = MODELS_LIST[model_id.value]()
-    
+
     pt_model.eval();
 
 Run PyTorch model inference
 ---------------------------
 
- Now, when we selected and
+Now, when we selected and
 loaded PyTorch model, we can check its result
 
 Prepare input data
@@ -203,9 +203,9 @@ bounding box, 3 - right-bottom point of bounding box.
 .. code:: ipython3
 
     from PIL import Image
-    
+
     image_path = "figs/examples/dogs.jpg"
-    
+
     image = Image.open(image_path)
     image
 
@@ -232,8 +232,8 @@ points. We also provided some helper function for results visualization.
     import torch
     import matplotlib.pyplot as plt
     import numpy as np
-    
-    
+
+
     def prepare_input(input_image, points, labels, torch_tensor=True):
         img_tensor = np.ascontiguousarray(input_image)[None, ...].astype(np.float32) / 255
         img_tensor = np.transpose(img_tensor, (0, 3, 1, 2))
@@ -244,16 +244,16 @@ points. We also provided some helper function for results visualization.
             pts_sampled = torch.from_numpy(pts_sampled)
             pts_labels = torch.from_numpy(pts_labels)
         return img_tensor, pts_sampled, pts_labels
-    
-    
+
+
     def postprocess_results(predicted_iou, predicted_logits):
         sorted_ids = np.argsort(-predicted_iou, axis=-1)
         predicted_iou = np.take_along_axis(predicted_iou, sorted_ids, axis=2)
         predicted_logits = np.take_along_axis(predicted_logits, sorted_ids[..., None, None], axis=2)
-    
+
         return predicted_logits[0, 0, 0, :, :] >= 0
-    
-    
+
+
     def show_points(coords, labels, ax, marker_size=375):
         pos_points = coords[labels == 1]
         neg_points = coords[labels == 0]
@@ -275,14 +275,14 @@ points. We also provided some helper function for results visualization.
             edgecolor="white",
             linewidth=1.25,
         )
-    
-    
+
+
     def show_box(box, ax):
         x0, y0 = box[0], box[1]
         w, h = box[2] - box[0], box[3] - box[1]
         ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor="yellow", facecolor=(0, 0, 0, 0), lw=5))
-    
-    
+
+
     def show_anns(mask, ax):
         ax.set_autoscale_on(False)
         img = np.ones((mask.shape[0], mask.shape[1], 4))
@@ -299,17 +299,17 @@ The complete model inference example demonstrated below
 
     input_points = [[580, 350], [650, 350]]
     input_labels = [1, 1]
-    
+
     example_input = prepare_input(image, input_points, input_labels)
-    
+
     predicted_logits, predicted_iou = pt_model(*example_input)
-    
+
     predicted_mask = postprocess_results(predicted_iou.detach().numpy(), predicted_logits.detach().numpy())
 
 .. code:: ipython3
 
     image = Image.open(image_path)
-    
+
     plt.figure(figsize=(20, 20))
     plt.axis("off")
     plt.imshow(image)
@@ -348,11 +348,11 @@ disk using ``openvino.save_model``.
 .. code:: ipython3
 
     import openvino as ov
-    
+
     core = ov.Core()
-    
+
     ov_model_path = Path(f"{model_id.value}.xml")
-    
+
     if not ov_model_path.exists():
         ov_model = ov.convert_model(pt_model, example_input=example_input)
         ov.save_model(ov_model, ov_model_path)
@@ -362,23 +362,23 @@ disk using ``openvino.save_model``.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/efficient-sam/EfficientSAM/efficient_sam/efficient_sam.py:220: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/efficient-sam/EfficientSAM/efficient_sam/efficient_sam.py:220: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if (
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/efficient-sam/EfficientSAM/efficient_sam/efficient_sam_encoder.py:241: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/efficient-sam/EfficientSAM/efficient_sam/efficient_sam_encoder.py:241: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       assert (
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/efficient-sam/EfficientSAM/efficient_sam/efficient_sam_encoder.py:163: TracerWarning: Converting a tensor to a Python float might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/efficient-sam/EfficientSAM/efficient_sam/efficient_sam_encoder.py:163: TracerWarning: Converting a tensor to a Python float might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       size = int(math.sqrt(xy_num))
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/efficient-sam/EfficientSAM/efficient_sam/efficient_sam_encoder.py:164: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/efficient-sam/EfficientSAM/efficient_sam/efficient_sam_encoder.py:164: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       assert size * size == xy_num
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/efficient-sam/EfficientSAM/efficient_sam/efficient_sam_encoder.py:166: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/efficient-sam/EfficientSAM/efficient_sam/efficient_sam_encoder.py:166: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if size != h or size != w:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/efficient-sam/EfficientSAM/efficient_sam/efficient_sam_encoder.py:251: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/efficient-sam/EfficientSAM/efficient_sam/efficient_sam_encoder.py:251: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       assert x.shape[2] == num_patches
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/efficient-sam/EfficientSAM/efficient_sam/efficient_sam.py:85: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/efficient-sam/EfficientSAM/efficient_sam/efficient_sam.py:85: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if num_pts > self.decoder_max_num_input_points:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/efficient-sam/EfficientSAM/efficient_sam/efficient_sam.py:92: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/efficient-sam/EfficientSAM/efficient_sam/efficient_sam.py:92: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       elif num_pts < self.decoder_max_num_input_points:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/efficient-sam/EfficientSAM/efficient_sam/efficient_sam.py:126: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/efficient-sam/EfficientSAM/efficient_sam/efficient_sam.py:126: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if output_w > 0 and output_h > 0:
 
 
@@ -400,7 +400,7 @@ Select inference device from dropdown list
         description="Device:",
         disabled=False,
     )
-    
+
     device
 
 
@@ -432,11 +432,11 @@ Now, we can take a look on OpenVINO model prediction
 
     example_input = prepare_input(image, input_points, input_labels, torch_tensor=False)
     result = compiled_model(example_input)
-    
+
     predicted_logits, predicted_iou = result[0], result[1]
-    
+
     predicted_mask = postprocess_results(predicted_iou, predicted_logits)
-    
+
     plt.figure(figsize=(20, 20))
     plt.axis("off")
     plt.imshow(image)
@@ -490,7 +490,7 @@ quantization.
         description="Quantization",
         disabled=False,
     )
-    
+
     to_quantize
 
 
@@ -506,12 +506,12 @@ quantization.
 
     # Fetch `skip_kernel_extension` module
     import requests
-    
+
     r = requests.get(
         url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/skip_kernel_extension.py",
     )
     open("skip_kernel_extension.py", "w").write(r.text)
-    
+
     %load_ext skip_kernel_extension
 
 Prepare calibration datasets
@@ -529,22 +529,22 @@ creates DataLoader for preparing inputs for EfficientSAM model.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     from zipfile import ZipFile
-    
+
     r = requests.get(
         url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py',
     )
-    
+
     open('notebook_utils.py', 'w').write(r.text)
-    
+
     from notebook_utils import download_file
-    
+
     DATA_URL = "https://ultralytics.com/assets/coco128.zip"
     OUT_DIR = Path('.')
-    
+
     download_file(DATA_URL, directory=OUT_DIR, show_progress=True)
-    
+
     if not (OUT_DIR / "coco128/images/train2017").exists():
         with ZipFile('coco128.zip' , "r") as zip_ref:
             zip_ref.extractall(OUT_DIR)
@@ -559,21 +559,21 @@ creates DataLoader for preparing inputs for EfficientSAM model.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     import torch.utils.data as data
-    
+
     class COCOLoader(data.Dataset):
         def __init__(self, images_path):
             self.images = list(Path(images_path).iterdir())
             self.labels_dir = images_path.parents[1] / 'labels' / images_path.name
-    
+
         def get_points(self, image_path, image_width, image_height):
             file_name = image_path.name.replace('.jpg', '.txt')
             label_file =  self.labels_dir / file_name
             if not label_file.exists():
                 x1, x2 = np.random.randint(low=0, high=image_width, size=(2, ))
                 y1, y2 = np.random.randint(low=0, high=image_height, size=(2, ))
-            else:    
+            else:
                 with label_file.open("r") as f:
                     box_line = f.readline()
                 _, x1, y1, x2, y2 = box_line.split()
@@ -582,7 +582,7 @@ creates DataLoader for preparing inputs for EfficientSAM model.
                 x2 = int(float(x2) * image_width)
                 y2 = int(float(y2) * image_height)
             return [[x1, y1], [x2, y2]]
-    
+
         def __getitem__(self, index):
             image_path = self.images[index]
             image = Image.open(image_path)
@@ -592,14 +592,14 @@ creates DataLoader for preparing inputs for EfficientSAM model.
             labels = [1, 1] if index % 2 == 0 else [2, 3]
             batched_images, batched_points, batched_point_labels = prepare_input(image, points, labels, torch_tensor=False)
             return {'batched_images': np.ascontiguousarray(batched_images)[0], 'batched_points': np.ascontiguousarray(batched_points)[0], 'batched_point_labels': np.ascontiguousarray(batched_point_labels)[0]}
-        
+
         def __len__(self):
             return len(self.images)
 
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     coco_dataset = COCOLoader(OUT_DIR / 'coco128/images/train2017')
     calibration_loader = torch.utils.data.DataLoader(coco_dataset)
 
@@ -622,11 +622,11 @@ architecture type, we should specify ``transformer`` in ``model_type``.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     import nncf
-    
+
     calibration_dataset = nncf.Dataset(calibration_loader)
-    
+
     model = core.read_model(ov_model_path)
     quantized_model = nncf.quantize(model,
                                     calibration_dataset,
@@ -642,10 +642,10 @@ architecture type, we should specify ``transformer`` in ``model_type``.
 
 .. parsed-literal::
 
-    2024-06-06 00:16:32.701538: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-06-06 00:16:32.734051: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-07-02 00:21:49.366604: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-07-02 00:21:49.398532: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2024-06-06 00:16:33.371409: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-07-02 00:21:50.014189: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 
@@ -655,17 +655,17 @@ architecture type, we should specify ``transformer`` in ``model_type``.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -676,17 +676,17 @@ architecture type, we should specify ``transformer`` in ``model_type``.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -703,17 +703,17 @@ architecture type, we should specify ``transformer`` in ``model_type``.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -724,17 +724,17 @@ architecture type, we should specify ``transformer`` in ``model_type``.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -751,15 +751,15 @@ Verify quantized model inference
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     compiled_model = core.compile_model(quantized_model, device.value)
-    
+
     result = compiled_model(example_input)
-    
+
     predicted_logits, predicted_iou = result[0], result[1]
-    
+
     predicted_mask = postprocess_results(predicted_iou, predicted_logits)
-    
+
     plt.figure(figsize=(20, 20))
     plt.axis("off")
     plt.imshow(image)
@@ -788,7 +788,7 @@ Save quantize model on disk
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     quantized_model_path = Path(f"{model_id.value}_int8.xml")
     ov.save_model(quantized_model, quantized_model_path)
 
@@ -800,10 +800,10 @@ Compare quantized model size
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     fp16_weights = ov_model_path.with_suffix('.bin')
     quantized_weights = quantized_model_path.with_suffix('.bin')
-    
+
     print(f"Size of FP16 model is {fp16_weights.stat().st_size / 1024 / 1024:.2f} MB")
     print(f"Size of INT8 quantized model is {quantized_weights.stat().st_size / 1024 / 1024:.2f} MB")
     print(f"Compression rate for INT8 model: {fp16_weights.stat().st_size / quantized_weights.stat().st_size:.3f}")
@@ -839,18 +839,18 @@ models, we use ``bencmark_app``.
     [ INFO ] Parsing input parameters
     [Step 2/11] Loading OpenVINO Runtime
     [ INFO ] OpenVINO:
-    [ INFO ] Build ................................. 2024.3.0-15599-de4d00a5970
-    [ INFO ] 
+    [ INFO ] Build ................................. 2024.3.0-15837-76a668b0662
+    [ INFO ]
     [ INFO ] Device info:
     [ INFO ] AUTO
-    [ INFO ] Build ................................. 2024.3.0-15599-de4d00a5970
-    [ INFO ] 
-    [ INFO ] 
+    [ INFO ] Build ................................. 2024.3.0-15837-76a668b0662
+    [ INFO ]
+    [ INFO ]
     [Step 3/11] Setting device configuration
     [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.THROUGHPUT.
     [Step 4/11] Reading model files
     [ INFO ] Loading model files
-    [ INFO ] Read model took 29.27 ms
+    [ INFO ] Read model took 49.00 ms
     [ INFO ] Original model I/O parameters:
     [ INFO ] Model inputs:
     [ INFO ]     batched_images (node: batched_images) : f32 / [...] / [?,?,?,?]
@@ -870,7 +870,7 @@ models, we use ``bencmark_app``.
     [ INFO ]     ***NO_NAME*** (node: aten::reshape/Reshape_3) : f32 / [...] / [?,?,3,?,?]
     [ INFO ]     ***NO_NAME*** (node: aten::reshape/Reshape_2) : f32 / [...] / [?,?,3]
     [Step 7/11] Loading the model to the device
-    [ INFO ] Compile model took 1448.36 ms
+    [ INFO ] Compile model took 1374.04 ms
     [Step 8/11] Querying optimal runtime parameters
     [ INFO ] Model:
     [ INFO ]   NETWORK_NAME: Model0
@@ -882,7 +882,7 @@ models, we use ``bencmark_app``.
     [ INFO ]     AFFINITY: Affinity.CORE
     [ INFO ]     CPU_DENORMALS_OPTIMIZATION: False
     [ INFO ]     CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE: 1.0
-    [ INFO ]     DYNAMIC_QUANTIZATION_GROUP_SIZE: 0
+    [ INFO ]     DYNAMIC_QUANTIZATION_GROUP_SIZE: 32
     [ INFO ]     ENABLE_CPU_PINNING: True
     [ INFO ]     ENABLE_HYPER_THREADING: True
     [ INFO ]     EXECUTION_DEVICES: ['CPU']
@@ -906,22 +906,22 @@ models, we use ``bencmark_app``.
     [ WARNING ] No input files were given for input 'batched_images'!. This input will be filled with random values!
     [ WARNING ] No input files were given for input 'batched_points'!. This input will be filled with random values!
     [ WARNING ] No input files were given for input 'batched_point_labels'!. This input will be filled with random values!
-    [ INFO ] Fill input 'batched_images' with random values 
-    [ INFO ] Fill input 'batched_points' with random values 
-    [ INFO ] Fill input 'batched_point_labels' with random values 
+    [ INFO ] Fill input 'batched_images' with random values
+    [ INFO ] Fill input 'batched_points' with random values
+    [ INFO ] Fill input 'batched_point_labels' with random values
     [Step 10/11] Measuring performance (Start inference asynchronously, 6 inference requests, limits: 15000 ms duration)
     [ INFO ] Benchmarking in full mode (inputs filling are included in measurement loop).
-    [ INFO ] First inference took 677.52 ms
+    [ INFO ] First inference took 672.94 ms
     [Step 11/11] Dumping statistics report
     [ INFO ] Execution Devices:['CPU']
     [ INFO ] Count:            49 iterations
-    [ INFO ] Duration:         15956.35 ms
+    [ INFO ] Duration:         15915.46 ms
     [ INFO ] Latency:
-    [ INFO ]    Median:        1926.16 ms
-    [ INFO ]    Average:       1900.48 ms
-    [ INFO ]    Min:           644.61 ms
-    [ INFO ]    Max:           1989.53 ms
-    [ INFO ] Throughput:   3.07 FPS
+    [ INFO ]    Median:        1916.49 ms
+    [ INFO ]    Average:       1893.66 ms
+    [ INFO ]    Min:           633.01 ms
+    [ INFO ]    Max:           1977.93 ms
+    [ INFO ] Throughput:   3.08 FPS
 
 
 .. code:: ipython3
@@ -936,18 +936,18 @@ models, we use ``bencmark_app``.
     [ INFO ] Parsing input parameters
     [Step 2/11] Loading OpenVINO Runtime
     [ INFO ] OpenVINO:
-    [ INFO ] Build ................................. 2024.3.0-15599-de4d00a5970
-    [ INFO ] 
+    [ INFO ] Build ................................. 2024.3.0-15837-76a668b0662
+    [ INFO ]
     [ INFO ] Device info:
     [ INFO ] AUTO
-    [ INFO ] Build ................................. 2024.3.0-15599-de4d00a5970
-    [ INFO ] 
-    [ INFO ] 
+    [ INFO ] Build ................................. 2024.3.0-15837-76a668b0662
+    [ INFO ]
+    [ INFO ]
     [Step 3/11] Setting device configuration
     [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.THROUGHPUT.
     [Step 4/11] Reading model files
     [ INFO ] Loading model files
-    [ INFO ] Read model took 42.94 ms
+    [ INFO ] Read model took 42.67 ms
     [ INFO ] Original model I/O parameters:
     [ INFO ] Model inputs:
     [ INFO ]     batched_images (node: batched_images) : f32 / [...] / [?,?,?,?]
@@ -967,7 +967,7 @@ models, we use ``bencmark_app``.
     [ INFO ]     ***NO_NAME*** (node: aten::reshape/Reshape_3) : f32 / [...] / [?,?,3,?,?]
     [ INFO ]     ***NO_NAME*** (node: aten::reshape/Reshape_2) : f32 / [...] / [?,?,3]
     [Step 7/11] Loading the model to the device
-    [ INFO ] Compile model took 1975.21 ms
+    [ INFO ] Compile model took 1885.12 ms
     [Step 8/11] Querying optimal runtime parameters
     [ INFO ] Model:
     [ INFO ]   NETWORK_NAME: Model0
@@ -979,7 +979,7 @@ models, we use ``bencmark_app``.
     [ INFO ]     AFFINITY: Affinity.CORE
     [ INFO ]     CPU_DENORMALS_OPTIMIZATION: False
     [ INFO ]     CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE: 1.0
-    [ INFO ]     DYNAMIC_QUANTIZATION_GROUP_SIZE: 0
+    [ INFO ]     DYNAMIC_QUANTIZATION_GROUP_SIZE: 32
     [ INFO ]     ENABLE_CPU_PINNING: True
     [ INFO ]     ENABLE_HYPER_THREADING: True
     [ INFO ]     EXECUTION_DEVICES: ['CPU']
@@ -1003,22 +1003,22 @@ models, we use ``bencmark_app``.
     [ WARNING ] No input files were given for input 'batched_images'!. This input will be filled with random values!
     [ WARNING ] No input files were given for input 'batched_points'!. This input will be filled with random values!
     [ WARNING ] No input files were given for input 'batched_point_labels'!. This input will be filled with random values!
-    [ INFO ] Fill input 'batched_images' with random values 
-    [ INFO ] Fill input 'batched_points' with random values 
-    [ INFO ] Fill input 'batched_point_labels' with random values 
+    [ INFO ] Fill input 'batched_images' with random values
+    [ INFO ] Fill input 'batched_points' with random values
+    [ INFO ] Fill input 'batched_point_labels' with random values
     [Step 10/11] Measuring performance (Start inference asynchronously, 6 inference requests, limits: 15000 ms duration)
     [ INFO ] Benchmarking in full mode (inputs filling are included in measurement loop).
-    [ INFO ] First inference took 609.28 ms
+    [ INFO ] First inference took 623.83 ms
     [Step 11/11] Dumping statistics report
     [ INFO ] Execution Devices:['CPU']
     [ INFO ] Count:            55 iterations
-    [ INFO ] Duration:         16666.59 ms
+    [ INFO ] Duration:         16362.06 ms
     [ INFO ] Latency:
-    [ INFO ]    Median:        1799.56 ms
-    [ INFO ]    Average:       1780.10 ms
-    [ INFO ]    Min:           641.70 ms
-    [ INFO ]    Max:           1867.14 ms
-    [ INFO ] Throughput:   3.30 FPS
+    [ INFO ]    Median:        1762.26 ms
+    [ INFO ]    Average:       1740.85 ms
+    [ INFO ]    Min:           488.03 ms
+    [ INFO ]    Max:           1807.11 ms
+    [ INFO ] Throughput:   3.36 FPS
 
 
 Interactive segmentation demo
@@ -1034,37 +1034,37 @@ Interactive segmentation demo
     from PIL import ImageDraw, Image
     import cv2
     import matplotlib.pyplot as plt
-    
+
     example_images = [
         "https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/b8083dd5-1ce7-43bf-8b09-a2ebc280c86e",
         "https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/9a90595d-70e7-469b-bdaf-469ef4f56fa2",
         "https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/b626c123-9fa2-4aa6-9929-30565991bf0c",
     ]
-    
+
     examples_dir = Path("examples")
     examples_dir.mkdir(exist_ok=True)
-    
+
     for img_id, image_url in enumerate(example_images):
         r = requests.get(image_url)
         img_path = examples_dir / f"example_{img_id}.jpg"
         with img_path.open("wb") as f:
             f.write(r.content)
-    
-    
+
+
     def sigmoid(x):
         return 1 / (1 + np.exp(-x))
-    
-    
+
+
     def clear():
         return None, None, [], []
-    
-    
+
+
     def format_results(masks, scores, logits, filter=0):
         annotations = []
         n = len(scores)
         for i in range(n):
             annotation = {}
-    
+
             mask = masks[i]
             tmp = np.where(mask != 0)
             if np.sum(mask) < filter:
@@ -1081,8 +1081,8 @@ Interactive segmentation demo
             annotation["area"] = annotation["segmentation"].sum()
             annotations.append(annotation)
         return annotations
-    
-    
+
+
     def point_prompt(masks, points, point_label, target_height, target_width):  # numpy
         h = masks[0]["segmentation"].shape[0]
         w = masks[0]["segmentation"].shape[1]
@@ -1102,8 +1102,8 @@ Interactive segmentation demo
                         onemask -= mask
         onemask = onemask >= 1
         return onemask, 0
-    
-    
+
+
     def show_mask(
         annotation,
         ax,
@@ -1120,7 +1120,7 @@ Interactive segmentation demo
         areas = np.sum(annotation, axis=(1, 2))
         sorted_indices = np.argsort(areas)[::1]
         annotation = annotation[sorted_indices]
-    
+
         index = (annotation != 0).argmax(axis=0)
         if random_color:
             color = np.random.random((mask_sum, 1, 1, 3))
@@ -1129,23 +1129,23 @@ Interactive segmentation demo
         transparency = np.ones((mask_sum, 1, 1, 1)) * 0.6
         visual = np.concatenate([color, transparency], axis=-1)
         mask_image = np.expand_dims(annotation, -1) * visual
-    
+
         mask = np.zeros((height, weight, 4))
-    
+
         h_indices, w_indices = np.meshgrid(np.arange(height), np.arange(weight), indexing="ij")
         indices = (index[h_indices, w_indices], h_indices, w_indices, slice(None))
-    
+
         mask[h_indices, w_indices, :] = mask_image[indices]
         if bbox is not None:
             x1, y1, x2, y2 = bbox
             ax.add_patch(plt.Rectangle((x1, y1), x2 - x1, y2 - y1, fill=False, edgecolor="b", linewidth=1))
-    
+
         if not retinamask:
             mask = cv2.resize(mask, (target_width, target_height), interpolation=cv2.INTER_NEAREST)
-    
+
         return mask
-    
-    
+
+
     def process(
         annotations,
         image,
@@ -1159,7 +1159,7 @@ Interactive segmentation demo
     ):
         if isinstance(annotations[0], dict):
             annotations = [annotation["segmentation"] for annotation in annotations]
-    
+
         original_h = image.height
         original_w = image.width
         if better_quality:
@@ -1178,10 +1178,10 @@ Interactive segmentation demo
             target_height=original_h,
             target_width=original_w,
         )
-    
+
         if isinstance(annotations, torch.Tensor):
             annotations = annotations.cpu().numpy()
-    
+
         if withContours:
             contour_all = []
             temp = np.zeros((original_h, original_w, 1))
@@ -1201,22 +1201,22 @@ Interactive segmentation demo
             cv2.drawContours(temp, contour_all, -1, (255, 255, 255), 2 // scale)
             color = np.array([0 / 255, 0 / 255, 255 / 255, 0.9])
             contour_mask = temp / 255 * color.reshape(1, 1, -1)
-    
+
         image = image.convert("RGBA")
         overlay_inner = Image.fromarray((inner_mask * 255).astype(np.uint8), "RGBA")
         image.paste(overlay_inner, (0, 0), overlay_inner)
-    
+
         if withContours:
             overlay_contour = Image.fromarray((contour_mask * 255).astype(np.uint8), "RGBA")
             image.paste(overlay_contour, (0, 0), overlay_contour)
-    
+
         return image
-    
-    
+
+
     # Description
     title = "<center><strong><font size='8'>Efficient Segment Anything with OpenVINO and EfficientSAM <font></strong></center>"
-    
-    
+
+
     description_p = """# Interactive Instance Segmentation
                     - Point-prompt instruction
                     <ol>
@@ -1230,15 +1230,15 @@ Interactive segmentation demo
                     <li> Click the button of Segment with Box Prompt </li>
                     </ol>
                   """
-    
+
     # examples
     examples = [[img] for img in examples_dir.glob("*.jpg")]
-    
+
     default_example = examples[0]
-    
+
     css = "h1 { text-align: center } .about { text-align: justify; padding-left: 10%; padding-right: 10%; }"
-    
-    
+
+
     def segment_with_boxs(
         image,
         seg_image,
@@ -1252,48 +1252,48 @@ Interactive segmentation demo
     ):
         if global_points is None or len(global_points) < 2 or global_points[0] is None:
             return image, global_points, global_point_label
-    
+
         input_size = int(input_size)
         w, h = image.size
         scale = input_size / max(w, h)
         new_w = int(w * scale)
         new_h = int(h * scale)
         image = image.resize((new_w, new_h))
-    
+
         scaled_points = np.array([[int(x * scale) for x in point] for point in global_points])
         scaled_points = scaled_points[:2]
         scaled_point_label = np.array(global_point_label)[:2]
-    
+
         if scaled_points.size == 0 and scaled_point_label.size == 0:
             return image, global_points, global_point_label
-    
+
         nd_image = np.array(image)
         img_tensor = nd_image.astype(np.float32) / 255
         img_tensor = np.transpose(img_tensor, (2, 0, 1))
-    
+
         pts_sampled = np.reshape(scaled_points, [1, 1, -1, 2])
         pts_sampled = pts_sampled[:, :, :2, :]
         pts_labels = np.reshape(np.array([2, 3]), [1, 1, 2])
-    
+
         results = compiled_model([img_tensor[None, ...], pts_sampled, pts_labels])
         predicted_logits = results[0]
         predicted_iou = results[1]
         all_masks = sigmoid(predicted_logits[0, 0, :, :, :]) >= 0.5
         predicted_iou = predicted_iou[0, 0, ...]
-    
+
         max_predicted_iou = -1
         selected_mask_using_predicted_iou = None
         selected_predicted_iou = None
-    
+
         for m in range(all_masks.shape[0]):
             curr_predicted_iou = predicted_iou[m]
             if curr_predicted_iou > max_predicted_iou or selected_mask_using_predicted_iou is None:
                 max_predicted_iou = curr_predicted_iou
                 selected_mask_using_predicted_iou = all_masks[m : m + 1]
                 selected_predicted_iou = predicted_iou[m : m + 1]
-    
+
         results = format_results(selected_mask_using_predicted_iou, selected_predicted_iou, predicted_logits, 0)
-    
+
         annotations = results[0]["segmentation"]
         annotations = np.array([annotations])
         fig = process(
@@ -1306,12 +1306,12 @@ Interactive segmentation demo
             bbox=scaled_points.reshape([4]),
             withContours=withContours,
         )
-    
+
         global_points = []
         global_point_label = []
         return fig, global_points, global_point_label
-    
-    
+
+
     def segment_with_points(
         image,
         global_points,
@@ -1328,32 +1328,32 @@ Interactive segmentation demo
         new_w = int(w * scale)
         new_h = int(h * scale)
         image = image.resize((new_w, new_h))
-    
+
         if global_points is None or len(global_points) < 1 or global_points[0] is None:
             return image, global_points, global_point_label
         scaled_points = np.array([[int(x * scale) for x in point] for point in global_points])
         scaled_point_label = np.array(global_point_label)
-    
+
         if scaled_points.size == 0 and scaled_point_label.size == 0:
             return image, global_points, global_point_label
-    
+
         nd_image = np.array(image)
         img_tensor = (nd_image).astype(np.float32) / 255
         img_tensor = np.transpose(img_tensor, (2, 0, 1))
-    
+
         pts_sampled = np.reshape(scaled_points, [1, 1, -1, 2])
         pts_labels = np.reshape(np.array(global_point_label), [1, 1, -1])
-    
+
         results = compiled_model([img_tensor[None, ...], pts_sampled, pts_labels])
         predicted_logits = results[0]
         predicted_iou = results[1]
         all_masks = sigmoid(predicted_logits[0, 0, :, :, :]) >= 0.5
         predicted_iou = predicted_iou[0, 0, ...]
-    
+
         results = format_results(all_masks, predicted_iou, predicted_logits, 0)
         annotations, _ = point_prompt(results, scaled_points, scaled_point_label, new_h, new_w)
         annotations = np.array([annotations])
-    
+
         fig = process(
             annotations=annotations,
             image=image,
@@ -1365,13 +1365,13 @@ Interactive segmentation demo
             use_retina=use_retina,
             withContours=withContours,
         )
-    
+
         global_points = []
         global_point_label = []
         # return fig, None
         return fig, global_points, global_point_label
-    
-    
+
+
     def get_points_with_draw(image, cond_image, global_points, global_point_label, evt: gr.SelectData):
         print(global_points)
         if len(global_points) == 0:
@@ -1389,10 +1389,10 @@ Interactive segmentation demo
         )
         global_points.append([x, y])
         global_point_label.append(1 if label == "Add Mask" else 0)
-    
+
         if image is not None:
             draw = ImageDraw.Draw(image)
-    
+
             draw.ellipse(
                 [
                     (x - point_radius, y - point_radius),
@@ -1400,10 +1400,10 @@ Interactive segmentation demo
                 ],
                 fill=point_color,
             )
-    
+
         return image, global_points, global_point_label
-    
-    
+
+
     def get_points_with_draw_(image, cond_image, global_points, global_point_label, evt: gr.SelectData):
         if len(global_points) == 0:
             image = copy.deepcopy(cond_image)
@@ -1422,7 +1422,7 @@ Interactive segmentation demo
         )
         global_points.append([x, y])
         global_point_label.append(1 if label == "Add Mask" else 0)
-    
+
         if image is not None:
             draw = ImageDraw.Draw(image)
             draw.ellipse(
@@ -1432,7 +1432,7 @@ Interactive segmentation demo
                 ],
                 fill=point_color,
             )
-    
+
         if len(global_points) == 2:
             x1, y1 = global_points[0]
             x2, y2 = global_points[1]
@@ -1456,17 +1456,17 @@ Interactive segmentation demo
                 global_points[0][1] = y2
                 global_points[1][0] = x1
                 global_points[1][1] = y1
-    
+
         return image, global_points, global_point_label
-    
-    
+
+
     cond_img_p = gr.Image(label="Input with Point", value=default_example[0], type="pil")
     cond_img_b = gr.Image(label="Input with Box", value=default_example[0], type="pil")
-    
+
     segm_img_p = gr.Image(label="Segmented Image with Point-Prompt", interactive=False, type="pil")
     segm_img_b = gr.Image(label="Segmented Image with Box-Prompt", interactive=False, type="pil")
-    
-    
+
+
     with gr.Blocks(css=css, title="Efficient SAM") as demo:
         global_points = gr.State([])
         global_point_label = gr.State([])
@@ -1474,16 +1474,16 @@ Interactive segmentation demo
             with gr.Column(scale=1):
                 # Title
                 gr.Markdown(title)
-    
+
         with gr.Tab("Point mode"):
             # Images
             with gr.Row(variant="panel"):
                 with gr.Column(scale=1):
                     cond_img_p.render()
-    
+
                 with gr.Column(scale=1):
                     segm_img_p.render()
-    
+
             # Submit & Clear
             # ###
             with gr.Row():
@@ -1491,72 +1491,72 @@ Interactive segmentation demo
                     with gr.Column():
                         segment_btn_p = gr.Button("Segment with Point Prompt", variant="primary")
                         clear_btn_p = gr.Button("Clear", variant="secondary")
-    
+
                     gr.Markdown("Try some of the examples below ⬇️")
                     gr.Examples(
                         examples=examples,
                         inputs=[cond_img_p],
                         examples_per_page=4,
                     )
-    
+
                 with gr.Column():
                     # Description
                     gr.Markdown(description_p)
-    
+
         with gr.Tab("Box mode"):
             # Images
             with gr.Row(variant="panel"):
                 with gr.Column(scale=1):
                     cond_img_b.render()
-    
+
                 with gr.Column(scale=1):
                     segm_img_b.render()
-    
+
             # Submit & Clear
             with gr.Row():
                 with gr.Column():
                     with gr.Column():
                         segment_btn_b = gr.Button("Segment with Box Prompt", variant="primary")
                         clear_btn_b = gr.Button("Clear", variant="secondary")
-    
+
                     gr.Markdown("Try some of the examples below ⬇️")
                     gr.Examples(
                         examples=examples,
                         inputs=[cond_img_b],
                         examples_per_page=4,
                     )
-    
+
                 with gr.Column():
                     # Description
                     gr.Markdown(description_p)
-    
+
         cond_img_p.select(
             get_points_with_draw,
             inputs=[segm_img_p, cond_img_p, global_points, global_point_label],
             outputs=[segm_img_p, global_points, global_point_label],
         )
-    
+
         cond_img_b.select(
             get_points_with_draw_,
             [segm_img_b, cond_img_b, global_points, global_point_label],
             [segm_img_b, global_points, global_point_label],
         )
-    
+
         segment_btn_p.click(
             segment_with_points,
             inputs=[cond_img_p, global_points, global_point_label],
             outputs=[segm_img_p, global_points, global_point_label],
         )
-    
+
         segment_btn_b.click(
             segment_with_boxs,
             inputs=[cond_img_b, segm_img_b, global_points, global_point_label],
             outputs=[segm_img_b, global_points, global_point_label],
         )
-    
+
         clear_btn_p.click(clear, outputs=[cond_img_p, segm_img_p, global_points, global_point_label])
         clear_btn_b.click(clear, outputs=[cond_img_b, segm_img_b, global_points, global_point_label])
-    
+
     demo.queue()
     try:
         demo.launch(debug=False)
@@ -1570,7 +1570,7 @@ Interactive segmentation demo
 .. parsed-literal::
 
     Running on local URL:  http://127.0.0.1:7860
-    
+
     To create a public link, set `share=True` in `launch()`.
 
 
diff --git a/docs/notebooks/efficient-sam-with-output_files/efficient-sam-with-output_16_1.png b/docs/notebooks/efficient-sam-with-output_files/efficient-sam-with-output_16_1.png
index ff05849fba88f0..a579f7cf8d9b79 100644
--- a/docs/notebooks/efficient-sam-with-output_files/efficient-sam-with-output_16_1.png
+++ b/docs/notebooks/efficient-sam-with-output_files/efficient-sam-with-output_16_1.png
@@ -1,3 +1,3 @@
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-oid sha256:45a2aec656ead3328284564339cc490e379c5ad16d1a1a4922f99eb84037b818
-size 1259656
+oid sha256:e23a04e4766a7681a0ccb191624e27243abc5b472a93f8c75913482b2e4677b7
+size 1260060
diff --git a/docs/notebooks/efficient-sam-with-output_files/efficient-sam-with-output_24_1.png b/docs/notebooks/efficient-sam-with-output_files/efficient-sam-with-output_24_1.png
index 59851a8fe68139..e9dde9eaa7df76 100644
--- a/docs/notebooks/efficient-sam-with-output_files/efficient-sam-with-output_24_1.png
+++ b/docs/notebooks/efficient-sam-with-output_files/efficient-sam-with-output_24_1.png
@@ -1,3 +1,3 @@
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-oid sha256:7dd1210236f949071574ecd9097962a3db3156b0c44810aa2f2b6d03576f6ca3
-size 1260203
+oid sha256:5bb4c9271a08dd69c873d9cc7f980750e19bf7c385193389edee6ecd43bc6c01
+size 1261465
diff --git a/docs/notebooks/efficient-sam-with-output_files/efficient-sam-with-output_35_1.png b/docs/notebooks/efficient-sam-with-output_files/efficient-sam-with-output_35_1.png
index 903ec2ee00b8b1..6a0e8658b23756 100644
--- a/docs/notebooks/efficient-sam-with-output_files/efficient-sam-with-output_35_1.png
+++ b/docs/notebooks/efficient-sam-with-output_files/efficient-sam-with-output_35_1.png
@@ -1,3 +1,3 @@
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-oid sha256:89fc9efa319522df2dd0638e28e020e8695a8c791db61da44f75703b1d493470
-size 1262684
+oid sha256:ab8dd706c2c1f138e44def1caddc86762a5c055d1c528bd8b4fd35934e307ef7
+size 1261457
diff --git a/docs/notebooks/encodec-audio-compression-with-output.rst b/docs/notebooks/encodec-audio-compression-with-output.rst
index 41f2b975c3bc98..62970a41a19a32 100644
--- a/docs/notebooks/encodec-audio-compression-with-output.rst
+++ b/docs/notebooks/encodec-audio-compression-with-output.rst
@@ -26,8 +26,8 @@ and original `repo <https://github.com/facebookresearch/encodec>`__.
 
    image.png
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Instantiate audio compression
@@ -129,7 +129,7 @@ bandwidth.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/utils/weight_norm.py:28: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/utils/weight_norm.py:28: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.
       warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.")
 
 
@@ -289,7 +289,7 @@ similar as possible to the original.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/utils/weight_norm.py:28: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/utils/weight_norm.py:28: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.
       warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.")
 
 
@@ -389,13 +389,13 @@ with ``ov.save_model``.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/encodec/modules/conv.py:60: TracerWarning: Converting a tensor to a Python float might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/encodec/modules/conv.py:60: TracerWarning: Converting a tensor to a Python float might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       ideal_length = (math.ceil(n_frames) - 1) * stride + (kernel_size - padding_total)
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/encodec/modules/conv.py:85: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/encodec/modules/conv.py:85: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       assert padding_left >= 0 and padding_right >= 0, (padding_left, padding_right)
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/encodec/modules/conv.py:87: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/encodec/modules/conv.py:87: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       max_pad = max(padding_left, padding_right)
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/encodec/modules/conv.py:89: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/encodec/modules/conv.py:89: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if length <= max_pad:
 
 
@@ -415,11 +415,11 @@ with ``ov.save_model``.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/encodec/quantization/core_vq.py:358: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/encodec/quantization/core_vq.py:358: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
       quantized_out = torch.tensor(0.0, device=q_indices.device)
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/encodec/quantization/core_vq.py:359: TracerWarning: Iterating over a tensor might cause the trace to be incorrect. Passing a tensor of different shape won't change the number of iterations executed (and might lead to errors or silently give incorrect results).
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/encodec/quantization/core_vq.py:359: TracerWarning: Iterating over a tensor might cause the trace to be incorrect. Passing a tensor of different shape won't change the number of iterations executed (and might lead to errors or silently give incorrect results).
       for i, indices in enumerate(q_indices):
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/encodec/modules/conv.py:103: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/encodec/modules/conv.py:103: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       assert (padding_left + padding_right) <= x.shape[-1]
 
 
diff --git a/docs/notebooks/fast-segment-anything-with-output.rst b/docs/notebooks/fast-segment-anything-with-output.rst
index cac2e1485c6fac..dec0dd864785db 100644
--- a/docs/notebooks/fast-segment-anything-with-output.rst
+++ b/docs/notebooks/fast-segment-anything-with-output.rst
@@ -28,8 +28,8 @@ the prompt.
 
    pipeline
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 
@@ -145,7 +145,7 @@ model and generate a segmentation map.
 
 .. parsed-literal::
 
-    100%|██████████| 138M/138M [00:01<00:00, 100MB/s]
+    100%|██████████| 138M/138M [00:01<00:00, 101MB/s]
 
 
 
@@ -157,8 +157,8 @@ model and generate a segmentation map.
 .. parsed-literal::
 
     
-    image 1/1 /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/fast-segment-anything/coco_bike.jpg: 768x1024 37 objects, 658.4ms
-    Speed: 4.0ms preprocess, 658.4ms inference, 586.5ms postprocess per image at shape (1, 3, 768, 1024)
+    image 1/1 /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/fast-segment-anything/coco_bike.jpg: 768x1024 37 objects, 645.4ms
+    Speed: 4.0ms preprocess, 645.4ms inference, 583.2ms postprocess per image at shape (1, 3, 768, 1024)
 
 
 The model returns segmentation maps for all the objects on the image.
@@ -198,13 +198,13 @@ tracing. The FastSAM model itself is based on YOLOv8 model.
 
     Ultralytics YOLOv8.2.24 🚀 Python-3.8.10 torch-2.3.1+cpu CPU (Intel Core(TM) i9-10920X 3.50GHz)
     
-    PyTorch: starting from 'FastSAM-x.pt' with input shape (1, 3, 1024, 1024) BCHW and output shape(s) ((1, 37, 21504), (1, 32, 256, 256)) (138.2 MB)
+    PyTorch: starting from 'FastSAM-x.pt' with input shape (1, 3, 1024, 1024) BCHW and output shape(s) ((1, 37, 21504), (1, 32, 256, 256)) (138.3 MB)
     
-    OpenVINO: starting export with openvino 2024.1.0-15008-f4afc983258-releases/2024/1...
+    OpenVINO: starting export with openvino 2024.2.0-15519-5c0f38f83f6-releases/2024/2...
     OpenVINO: export success ✅ 6.2s, saved as 'FastSAM-x_openvino_model/' (276.1 MB)
     
-    Export complete (9.2s)
-    Results saved to /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/fast-segment-anything
+    Export complete (9.1s)
+    Results saved to /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/fast-segment-anything
     Predict:         yolo predict task=segment model=FastSAM-x_openvino_model imgsz=1024  
     Validate:        yolo val task=segment model=FastSAM-x_openvino_model imgsz=1024 data=ultralytics/datasets/sa.yaml  
     Visualize:       https://netron.app
@@ -313,8 +313,8 @@ pipeline.
 .. parsed-literal::
 
     
-    image 1/1 /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/fast-segment-anything/coco_bike.jpg: 1024x1024 42 objects, 503.6ms
-    Speed: 7.6ms preprocess, 503.6ms inference, 38.0ms postprocess per image at shape (1, 3, 1024, 1024)
+    image 1/1 /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/fast-segment-anything/coco_bike.jpg: 1024x1024 42 objects, 502.2ms
+    Speed: 7.4ms preprocess, 502.2ms inference, 33.8ms postprocess per image at shape (1, 3, 1024, 1024)
 
 
 One can observe the converted model outputs in the next cell, they is
@@ -539,15 +539,15 @@ repo <../yolov8-optimization/>`__.
 
     INFO:nncf:3 ignored nodes were found by name in the NNCFGraph
     INFO:nncf:8 ignored nodes were found by types in the NNCFGraph
-    INFO:nncf:Not adding activation input quantizer for operation: 275 __module.model.22/aten::sigmoid/Sigmoid
-    INFO:nncf:Not adding activation input quantizer for operation: 325 __module.model.22.dfl.conv/aten::_convolution/Convolution
-    INFO:nncf:Not adding activation input quantizer for operation: 351 __module.model.22/aten::sub/Subtract
-    INFO:nncf:Not adding activation input quantizer for operation: 352 __module.model.22/aten::add/Add
-    INFO:nncf:Not adding activation input quantizer for operation: 365 __module.model.22/aten::add/Add_1
-    378 __module.model.22/aten::div/Divide
+    INFO:nncf:Not adding activation input quantizer for operation: 271 __module.model.22/aten::sigmoid/Sigmoid
+    INFO:nncf:Not adding activation input quantizer for operation: 312 __module.model.22.dfl.conv/aten::_convolution/Convolution
+    INFO:nncf:Not adding activation input quantizer for operation: 349 __module.model.22/aten::sub/Subtract
+    INFO:nncf:Not adding activation input quantizer for operation: 350 __module.model.22/aten::add/Add
+    INFO:nncf:Not adding activation input quantizer for operation: 362 __module.model.22/aten::add/Add_1
+    374 __module.model.22/aten::div/Divide
     
-    INFO:nncf:Not adding activation input quantizer for operation: 366 __module.model.22/aten::sub/Subtract_1
-    INFO:nncf:Not adding activation input quantizer for operation: 389 __module.model.22/aten::mul/Multiply
+    INFO:nncf:Not adding activation input quantizer for operation: 363 __module.model.22/aten::sub/Subtract_1
+    INFO:nncf:Not adding activation input quantizer for operation: 386 __module.model.22/aten::mul/Multiply
 
 
 
@@ -557,17 +557,17 @@ repo <../yolov8-optimization/>`__.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -578,17 +578,17 @@ repo <../yolov8-optimization/>`__.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -656,9 +656,9 @@ calibration dataset to measure the performance.
 
 .. parsed-literal::
 
-    Segmented in 23 seconds
-    Resulting in 5.57 fps
-    That is 2.96 times faster!
+    Segmented in 22 seconds
+    Resulting in 5.82 fps
+    That is 3.09 times faster!
 
 
 Try out the converted pipeline
diff --git a/docs/notebooks/film-slowmo-with-output.rst b/docs/notebooks/film-slowmo-with-output.rst
index 5a3c12de3717db..51342e3cc06830 100644
--- a/docs/notebooks/film-slowmo-with-output.rst
+++ b/docs/notebooks/film-slowmo-with-output.rst
@@ -38,8 +38,8 @@ a model source.
    video encoder. Ubuntu has it preinstalled, but for Windows, you
    should install it manually.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Prepare images <#prepare-images>`__
diff --git a/docs/notebooks/freevc-voice-conversion-with-output.rst b/docs/notebooks/freevc-voice-conversion-with-output.rst
index a70c8f37121789..8b50a7449daabc 100644
--- a/docs/notebooks/freevc-voice-conversion-with-output.rst
+++ b/docs/notebooks/freevc-voice-conversion-with-output.rst
@@ -28,8 +28,8 @@ devices. It consists of the following steps:
 -  Convert models to OpenVINO Intermediate Representation.
 -  Inference using only OpenVINO’s IR models.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Pre-requisites <#pre-requisites>`__
 -  `Imports and settings <#imports-and-settings>`__
@@ -77,12 +77,12 @@ Check if FreeVC is installed and append its path to ``sys.path``
 
     from pathlib import Path
     import sys
-
-
+    
+    
     free_vc_repo = "FreeVC"
     if not Path(free_vc_repo).exists():
         !git clone https://github.com/OlaWod/FreeVC.git
-
+    
     sys.path.append(free_vc_repo)
 
 
@@ -93,7 +93,7 @@ Check if FreeVC is installed and append its path to ``sys.path``
     remote: Counting objects: 100% (65/65), done.
     remote: Compressing objects: 100% (41/41), done.
     remote: Total 131 (delta 39), reused 24 (delta 24), pack-reused 66
-    Receiving objects: 100% (131/131), 15.28 MiB | 27.90 MiB/s, done.
+    Receiving objects: 100% (131/131), 15.28 MiB | 21.50 MiB/s, done.
     Resolving deltas: 100% (43/43), done.
 
 
@@ -102,19 +102,19 @@ Check if FreeVC is installed and append its path to ``sys.path``
     # Fetch `notebook_utils` module
     import requests
     import gdown
-
+    
     r = requests.get(
         url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
-
+    
     open("notebook_utils.py", "w").write(r.text)
     from notebook_utils import download_file
-
+    
     wavlm_large_dir_path = Path("FreeVC/wavlm")
     wavlm_large_path = wavlm_large_dir_path / "WavLM-Large.pt"
-
+    
     wavlm_url = "https://drive.google.com/uc?id=12-cB34qCTvByWT-QtOcZaqwwO21FLSqU&confirm=t&uuid=a703c43c-ccce-436c-8799-c11b88e9e7e4"
-
+    
     if not wavlm_large_path.exists():
         gdown.download(wavlm_url, str(wavlm_large_path))
 
@@ -123,8 +123,8 @@ Check if FreeVC is installed and append its path to ``sys.path``
 
     Downloading...
     From: https://drive.google.com/uc?id=12-cB34qCTvByWT-QtOcZaqwwO21FLSqU&confirm=t&uuid=a703c43c-ccce-436c-8799-c11b88e9e7e4
-    To: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/freevc-voice-conversion/FreeVC/wavlm/WavLM-Large.pt
-    100%|██████████| 1.26G/1.26G [00:34<00:00, 36.3MB/s]
+    To: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/freevc-voice-conversion/FreeVC/wavlm/WavLM-Large.pt
+    100%|██████████| 1.26G/1.26G [00:34<00:00, 36.7MB/s]
 
 
 .. code:: ipython3
@@ -132,7 +132,7 @@ Check if FreeVC is installed and append its path to ``sys.path``
     freevc_chpt_dir = Path("checkpoints")
     freevc_chpt_name = "freevc.pth"
     freevc_chpt_path = freevc_chpt_dir / freevc_chpt_name
-
+    
     if not freevc_chpt_path.exists():
         download_file(
             f"https://storage.openvinotoolkit.org/repositories/openvino_notebooks/models/freevc/{freevc_chpt_name}",
@@ -152,10 +152,10 @@ Check if FreeVC is installed and append its path to ``sys.path``
     audio1_url = f"https://huggingface.co/spaces/OlaWod/FreeVC/resolve/main/{audio1_name}"
     audio2_name = "p226_002.wav"
     audio2_url = f"https://huggingface.co/spaces/OlaWod/FreeVC/resolve/main/{audio2_name}"
-
+    
     if not Path(audio1_name).exists():
         download_file(audio1_url)
-
+    
     if not Path(audio2_name).exists():
         download_file(audio2_url)
 
@@ -182,20 +182,20 @@ Imports and settings
     import logging
     import os
     import time
-
+    
     import librosa
     import numpy as np
     import torch
     from scipy.io.wavfile import write
     from tqdm import tqdm
-
+    
     import openvino as ov
-
+    
     import utils
     from models import SynthesizerTrn
     from speaker_encoder.voice_encoder import SpeakerEncoder
     from wavlm import WavLM, WavLMConfig
-
+    
     logger = logging.getLogger()
     logger.setLevel(logging.CRITICAL)
 
@@ -209,7 +209,7 @@ Redefine function ``get_model`` from ``utils`` to exclude CUDA
         cmodel = WavLM(cfg)
         cmodel.load_state_dict(checkpoint["model"])
         cmodel.eval()
-
+    
         return cmodel
 
 Models initialization
@@ -218,9 +218,9 @@ Models initialization
 
     hps = utils.get_hparams_from_file("FreeVC/configs/freevc.json")
     os.makedirs("outputs/freevc", exist_ok=True)
-
+    
     net_g = SynthesizerTrn(hps.data.filter_length // 2 + 1, hps.train.segment_size // hps.data.hop_length, **hps.model)
-
+    
     utils.load_checkpoint(freevc_chpt_path, net_g, optimizer=None, strict=True)
     cmodel = get_cmodel()
     smodel = SpeakerEncoder("FreeVC/speaker_encoder/ckpt/pretrained_bak_5805000.pt", device="cpu")
@@ -228,7 +228,7 @@ Models initialization
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/utils/weight_norm.py:28: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/utils/weight_norm.py:28: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.
       warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.")
 
 
@@ -254,19 +254,19 @@ Inference
             # tgt
             wav_tgt, _ = librosa.load(tgt, sr=hps.data.sampling_rate)
             wav_tgt, _ = librosa.effects.trim(wav_tgt, top_db=20)
-
+    
             g_tgt = smodel.embed_utterance(wav_tgt)
             g_tgt = torch.from_numpy(g_tgt).unsqueeze(0)
-
+    
             # src
             wav_src, _ = librosa.load(src, sr=hps.data.sampling_rate)
             wav_src = torch.from_numpy(wav_src).unsqueeze(0)
-
+    
             c = utils.get_content(cmodel, wav_src)
-
+    
             tgt_audio = net_g.infer(c, g=g_tgt)
             tgt_audio = tgt_audio[0][0].data.cpu().float().numpy()
-
+    
             timestamp = time.strftime("%m-%d_%H-%M", time.localtime())
             write(
                 os.path.join("outputs/freevc", "{}.wav".format(timestamp)),
@@ -277,7 +277,7 @@ Inference
 
 .. parsed-literal::
 
-    2it [00:03,  1.55s/it]
+    2it [00:03,  1.61s/it]
 
 
 Result audio files should be available in ‘outputs/freevc’
@@ -315,13 +315,13 @@ OpenVINO’s IR format. We keep the original name of the model in code:
 
     OUTPUT_DIR = Path("output")
     BASE_MODEL_NAME = "cmodel"
-
+    
     OUTPUT_DIR.mkdir(exist_ok=True)
-
+    
     ir_cmodel_path = Path(OUTPUT_DIR / (BASE_MODEL_NAME + "_ir")).with_suffix(".xml")
-
+    
     length = 32000
-
+    
     dummy_input = torch.randn(1, length)
 
 Converting to OpenVINO’s IR format.
@@ -329,17 +329,17 @@ Converting to OpenVINO’s IR format.
 .. code:: ipython3
 
     core = ov.Core()
-
-
+    
+    
     class ModelWrapper(torch.nn.Module):
         def __init__(self, model):
             super().__init__()
             self.model = model
-
+    
         def forward(self, input):
             return self.model(input)[0]
-
-
+    
+    
     if not ir_cmodel_path.exists():
         ir_cmodel = ov.convert_model(ModelWrapper(cmodel), example_input=dummy_input)
         ov.save_model(ir_cmodel, ir_cmodel_path)
@@ -349,13 +349,13 @@ Converting to OpenVINO’s IR format.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/freevc-voice-conversion/FreeVC/wavlm/modules.py:495: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/freevc-voice-conversion/FreeVC/wavlm/modules.py:495: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       assert embed_dim == self.embed_dim
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/freevc-voice-conversion/FreeVC/wavlm/modules.py:496: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/freevc-voice-conversion/FreeVC/wavlm/modules.py:496: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       assert list(query.size()) == [tgt_len, bsz, embed_dim]
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/freevc-voice-conversion/FreeVC/wavlm/modules.py:500: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/freevc-voice-conversion/FreeVC/wavlm/modules.py:500: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       assert key_bsz == bsz
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/freevc-voice-conversion/FreeVC/wavlm/modules.py:502: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/freevc-voice-conversion/FreeVC/wavlm/modules.py:502: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       assert src_len, bsz == value.shape[:2]
 
 
@@ -364,14 +364,14 @@ Select device from dropdown list for running inference using OpenVINO
 .. code:: ipython3
 
     import ipywidgets as widgets
-
+    
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
         value="AUTO",
         description="Device:",
         disabled=False,
     )
-
+    
     device
 
 
@@ -396,16 +396,16 @@ Convert ``SpeakerEncoder``
 
     OUTPUT_DIR = Path("output")
     BASE_MODEL_NAME = "smodel"
-
+    
     OUTPUT_DIR.mkdir(exist_ok=True)
-
+    
     ir_smodel_path = Path(OUTPUT_DIR / (BASE_MODEL_NAME + "ir")).with_suffix(".xml")
-
-
+    
+    
     length = 32000
-
+    
     dummy_input = torch.randn(1, length, 40)
-
+    
     if not ir_smodel_path.exists():
         ir_smodel = ov.convert_model(smodel, example_input=dummy_input)
         ov.save_model(ir_smodel, ir_smodel_path)
@@ -419,18 +419,18 @@ based on ``speaker_encoder.voice_encoder.SpeakerEncoder`` class methods
 
     from speaker_encoder.hparams import sampling_rate, mel_window_step, partials_n_frames
     from speaker_encoder import audio
-
-
+    
+    
     def compute_partial_slices(n_samples: int, rate, min_coverage):
         """
         Computes where to split an utterance waveform and its corresponding mel spectrogram to
         obtain partial utterances of <partials_n_frames> each. Both the waveform and the
         mel spectrogram slices are returned, so as to make each partial utterance waveform
         correspond to its spectrogram.
-
+    
         The returned ranges may be indexing further than the length of the waveform. It is
         recommended that you pad the waveform with zeros up to wav_slices[-1].stop.
-
+    
         :param n_samples: the number of samples in the waveform
         :param rate: how many partial utterances should occur per second. Partial utterances must
         cover the span of the entire utterance, thus the rate should not be lower than the inverse
@@ -446,14 +446,14 @@ based on ``speaker_encoder.voice_encoder.SpeakerEncoder`` class methods
         utterances.
         """
         assert 0 < min_coverage <= 1
-
+    
         # Compute how many frames separate two partial utterances
         samples_per_frame = int((sampling_rate * mel_window_step / 1000))
         n_frames = int(np.ceil((n_samples + 1) / samples_per_frame))
         frame_step = int(np.round((sampling_rate / rate) / samples_per_frame))
         assert 0 < frame_step, "The rate is too high"
         assert frame_step <= partials_n_frames, "The rate is too low, it should be %f at least" % (sampling_rate / (samples_per_frame * partials_n_frames))
-
+    
         # Compute the slices
         wav_slices, mel_slices = [], []
         steps = max(1, n_frames - partials_n_frames + frame_step + 1)
@@ -462,17 +462,17 @@ based on ``speaker_encoder.voice_encoder.SpeakerEncoder`` class methods
             wav_range = mel_range * samples_per_frame
             mel_slices.append(slice(*mel_range))
             wav_slices.append(slice(*wav_range))
-
+    
         # Evaluate whether extra padding is warranted or not
         last_wav_range = wav_slices[-1]
         coverage = (n_samples - last_wav_range.start) / (last_wav_range.stop - last_wav_range.start)
         if coverage < min_coverage and len(mel_slices) > 1:
             mel_slices = mel_slices[:-1]
             wav_slices = wav_slices[:-1]
-
+    
         return wav_slices, mel_slices
-
-
+    
+    
     def embed_utterance(
         wav: np.ndarray,
         smodel: ov.CompiledModel,
@@ -484,7 +484,7 @@ based on ``speaker_encoder.voice_encoder.SpeakerEncoder`` class methods
         Computes an embedding for a single utterance. The utterance is divided in partial
         utterances and an embedding is computed for each. The complete utterance embedding is the
         L2-normed average embedding of the partial utterances.
-
+    
         :param wav: a preprocessed utterance waveform as a numpy array of float32
         :param smodel: compiled speaker encoder model.
         :param return_partials: if True, the partial embeddings will also be returned along with
@@ -509,7 +509,7 @@ based on ``speaker_encoder.voice_encoder.SpeakerEncoder`` class methods
         max_wave_length = wav_slices[-1].stop
         if max_wave_length >= len(wav):
             wav = np.pad(wav, (0, max_wave_length - len(wav)), "constant")
-
+    
         # Split the utterance into partials and forward them through the model
         mel = audio.wav_to_mel_spectrogram(wav)
         mels = np.array([mel[s] for s in mel_slices])
@@ -517,11 +517,11 @@ based on ``speaker_encoder.voice_encoder.SpeakerEncoder`` class methods
             mels = torch.from_numpy(mels).to(torch.device("cpu"))
             output_layer = smodel.output(0)
             partial_embeds = smodel(mels)[output_layer]
-
+    
         # Compute the utterance embedding from the partial embeddings
         raw_embed = np.mean(partial_embeds, axis=0)
         embed = raw_embed / np.linalg.norm(raw_embed, 2)
-
+    
         if return_partials:
             return embed, partial_embeds, wav_slices
         return embed
@@ -561,14 +561,14 @@ function to OpenVINO IR format.
     BASE_MODEL_NAME = "net_g"
     onnx_net_g_path = Path(OUTPUT_DIR / (BASE_MODEL_NAME + "_fp32")).with_suffix(".onnx")
     ir_net_g_path = Path(OUTPUT_DIR / (BASE_MODEL_NAME + "ir")).with_suffix(".xml")
-
+    
     dummy_input_1 = torch.randn(1, 1024, 81)
     dummy_input_2 = torch.randn(1, 256)
-
+    
     # define forward as infer
     net_g.forward = net_g.infer
-
-
+    
+    
     if not ir_net_g_path.exists():
         ir_net_g_model = ov.convert_model(net_g, example_input=(dummy_input_1, dummy_input_2))
         ov.save_model(ir_net_g_model, ir_net_g_path)
@@ -578,12 +578,12 @@ function to OpenVINO IR format.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:1116: TracerWarning: Output nr 1. of the traced function does not match the corresponding output of the Python function. Detailed error:
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:1116: TracerWarning: Output nr 1. of the traced function does not match the corresponding output of the Python function. Detailed error:
     Tensor-likes are not close!
-
-    Mismatched elements: 25632 / 25920 (98.9%)
-    Greatest absolute difference: 0.05565535183995962 at index (0, 0, 18928) (up to 1e-05 allowed)
-    Greatest relative difference: 506443.2686567164 at index (0, 0, 10753) (up to 1e-05 allowed)
+    
+    Mismatched elements: 25917 / 25920 (100.0%)
+    Greatest absolute difference: 0.6957888901233673 at index (0, 0, 6412) (up to 1e-05 allowed)
+    Greatest relative difference: 26109.2899320933 at index (0, 0, 23414) (up to 1e-05 allowed)
       _check_trace(
 
 
@@ -600,22 +600,22 @@ Define function for synthesizing.
     def synthesize_audio(src, tgt):
         wav_tgt, _ = librosa.load(tgt, sr=hps.data.sampling_rate)
         wav_tgt, _ = librosa.effects.trim(wav_tgt, top_db=20)
-
+    
         g_tgt = embed_utterance(wav_tgt, compiled_smodel)
         g_tgt = torch.from_numpy(g_tgt).unsqueeze(0)
-
+    
         # src
         wav_src, _ = librosa.load(src, sr=hps.data.sampling_rate)
         wav_src = np.expand_dims(wav_src, axis=0)
-
+    
         output_layer = compiled_cmodel.output(0)
         c = compiled_cmodel(wav_src)[output_layer]
         c = c.transpose((0, 2, 1))
-
+    
         output_layer = compiled_ir_net_g_model.output(0)
         tgt_audio = compiled_ir_net_g_model((c, g_tgt))[output_layer]
         tgt_audio = tgt_audio[0][0]
-
+    
         return tgt_audio
 
 And now we can check inference using only IR models.
@@ -623,13 +623,13 @@ And now we can check inference using only IR models.
 .. code:: ipython3
 
     result_wav_names = []
-
+    
     with torch.no_grad():
         for line in tqdm(zip(srcs, tgts)):
             src, tgt = line
-
+    
             output_audio = synthesize_audio(src, tgt)
-
+    
             timestamp = time.strftime("%m-%d_%H-%M", time.localtime())
             result_name = f"{timestamp}.wav"
             result_wav_names.append(result_name)
@@ -642,7 +642,7 @@ And now we can check inference using only IR models.
 
 .. parsed-literal::
 
-    2it [00:01,  1.31it/s]
+    2it [00:01,  1.21it/s]
 
 
 Result audio files should be available in ‘outputs/freevc’ and you can
@@ -654,7 +654,7 @@ Source audio (source of text):
 .. code:: ipython3
 
     import IPython.display as ipd
-
+    
     ipd.Audio(srcs[0])
 
 
@@ -662,7 +662,7 @@ Source audio (source of text):
 
 .. raw:: html
 
-
+    
     <audio  controls="controls" >
         <source 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" type="audio/x-wav" />
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@@ -682,7 +682,7 @@ Target audio (source of voice):
 
 .. raw:: html
 
-
+    
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" type="audio/x-wav" />
         Your browser does not support the audio element.
@@ -702,9 +702,9 @@ Result audio:
 
 .. raw:: html
 
-
+    
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" 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" type="audio/x-wav" />
         Your browser does not support the audio element.
     </audio>
 
@@ -718,27 +718,27 @@ inference. Use rate corresponding to the value of
 .. code:: ipython3
 
     import gradio as gr
-
-
+    
+    
     audio1 = gr.Audio(label="Source Audio", type="filepath")
     audio2 = gr.Audio(label="Reference Audio", type="filepath")
     outputs = gr.Audio(label="Output Audio", type="filepath")
     examples = [[audio1_name, audio2_name]]
-
+    
     title = "FreeVC with Gradio"
     description = 'Gradio Demo for FreeVC and OpenVINO™. Upload a source audio and a reference audio, then click the "Submit" button to inference.'
-
-
+    
+    
     def infer(src, tgt):
         output_audio = synthesize_audio(src, tgt)
-
+    
         timestamp = time.strftime("%m-%d_%H-%M", time.localtime())
         result_name = f"{timestamp}.wav"
         write(result_name, hps.data.sampling_rate, output_audio)
-
+    
         return result_name
-
-
+    
+    
     iface = gr.Interface(
         infer,
         [audio1, audio2],
@@ -758,7 +758,7 @@ inference. Use rate corresponding to the value of
 .. parsed-literal::
 
     Running on local URL:  http://127.0.0.1:7860
-
+    
     To create a public link, set `share=True` in `launch()`.
 
 
@@ -772,6 +772,8 @@ inference. Use rate corresponding to the value of
 
 
 
+    
+
 
 
 .. code:: ipython3
diff --git a/docs/notebooks/gpu-device-with-output.rst b/docs/notebooks/gpu-device-with-output.rst
index 7970745c386f72..fb8a12b0c09826 100644
--- a/docs/notebooks/gpu-device-with-output.rst
+++ b/docs/notebooks/gpu-device-with-output.rst
@@ -1,8 +1,8 @@
 Working with GPUs in OpenVINO™
 ==============================
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Introduction <#introduction>`__
 
diff --git a/docs/notebooks/grammar-correction-with-output.rst b/docs/notebooks/grammar-correction-with-output.rst
index b4b1a0c93d710b..a5dc3264449a00 100644
--- a/docs/notebooks/grammar-correction-with-output.rst
+++ b/docs/notebooks/grammar-correction-with-output.rst
@@ -45,8 +45,8 @@ It consists of the following steps:
 -  Compare original and optimized pipelines from performance and
    accuracy standpoints
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `How does it work? <#how-does-it-work>`__
 -  `Prerequisites <#prerequisites>`__
@@ -614,17 +614,17 @@ some time to complete.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -635,17 +635,17 @@ some time to complete.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -662,17 +662,17 @@ some time to complete.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
diff --git a/docs/notebooks/grounded-segment-anything-with-output.rst b/docs/notebooks/grounded-segment-anything-with-output.rst
index 87ed2c245bf494..a999264e866f73 100644
--- a/docs/notebooks/grounded-segment-anything-with-output.rst
+++ b/docs/notebooks/grounded-segment-anything-with-output.rst
@@ -24,8 +24,8 @@ More details about the model can be found in the
 
    image
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Clone repository and install
    requirements <#clone-repository-and-install-requirements>`__
@@ -111,17 +111,17 @@ segmentation you can select vanilla ``SAM``.
 
     Cloning into 'GroundingDINO'...
     remote: Enumerating objects: 379, done.
-    remote: Counting objects: 100% (177/177), done.
-    remote: Compressing objects: 100% (64/64), done.
-    remote: Total 379 (delta 137), reused 113 (delta 113), pack-reused 202
-    Receiving objects: 100% (379/379), 14.03 MiB | 23.40 MiB/s, done.
+    remote: Counting objects: 100% (176/176), done.
+    remote: Compressing objects: 100% (65/65), done.
+    remote: Total 379 (delta 136), reused 111 (delta 111), pack-reused 203
+    Receiving objects: 100% (379/379), 14.03 MiB | 21.77 MiB/s, done.
     Resolving deltas: 100% (195/195), done.
     Cloning into 'EfficientSAM'...
     remote: Enumerating objects: 424, done.
     remote: Counting objects: 100% (85/85), done.
     remote: Compressing objects: 100% (33/33), done.
     remote: Total 424 (delta 76), reused 52 (delta 52), pack-reused 339
-    Receiving objects: 100% (424/424), 262.14 MiB | 23.25 MiB/s, done.
+    Receiving objects: 100% (424/424), 262.14 MiB | 25.69 MiB/s, done.
     Resolving deltas: 100% (246/246), done.
 
 
@@ -498,10 +498,10 @@ class, but the inference will be done using OpenVINO optimized model.
 
 .. parsed-literal::
 
-    2024-06-06 00:30:58.009326: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-06-06 00:30:58.048500: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-07-02 00:36:14.468864: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-07-02 00:36:14.508743: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2024-06-06 00:30:58.610355: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-07-02 00:36:15.070486: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 Convert predicted boxes to supervision box detections format
diff --git a/docs/notebooks/handwritten-ocr-with-output.rst b/docs/notebooks/handwritten-ocr-with-output.rst
index 65328d4e592bfa..c331b975faab1b 100644
--- a/docs/notebooks/handwritten-ocr-with-output.rst
+++ b/docs/notebooks/handwritten-ocr-with-output.rst
@@ -18,8 +18,8 @@ and
 charlists are used. Both models are available on `Open Model
 Zoo <https://github.com/openvinotoolkit/open_model_zoo/>`__.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Imports <#imports>`__
 -  `Settings <#settings>`__
diff --git a/docs/notebooks/hello-detection-with-output.rst b/docs/notebooks/hello-detection-with-output.rst
index 9c6e2259a747db..fb364b60e0eb5e 100644
--- a/docs/notebooks/hello-detection-with-output.rst
+++ b/docs/notebooks/hello-detection-with-output.rst
@@ -16,8 +16,8 @@ corner, ``(x_max, y_max)`` are the coordinates of the bottom right
 bounding box corner and ``conf`` is the confidence for the predicted
 class.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Imports <#imports>`__
 -  `Download model weights <#download-model-weights>`__
diff --git a/docs/notebooks/hello-npu-with-output.rst b/docs/notebooks/hello-npu-with-output.rst
index 2cf3118d428e41..bd57df83f2f7ac 100644
--- a/docs/notebooks/hello-npu-with-output.rst
+++ b/docs/notebooks/hello-npu-with-output.rst
@@ -4,8 +4,8 @@ Hello NPU
 Working with NPU in OpenVINO™
 -----------------------------
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Introduction <#introduction>`__
 
@@ -910,9 +910,6 @@ different performance hints.
 Discover the power of Neural Processing Unit (NPU) with OpenVINO through
 these interactive Jupyter notebooks:
 
-Introduction
-''''''''''''
-
 - `hello-world <https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/hello-world>`__:
   Start your OpenVINO journey by performing inference on an OpenVINO IR
   model.
@@ -923,9 +920,6 @@ Introduction
 Model Optimization and Conversion
 '''''''''''''''''''''''''''''''''
 
--  `model-tools <https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/model-tools>`__:
-   Discover how to download, convert, and benchmark models from the Open
-   Model Zoo.
 -  `tflite-to-openvino <https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/tflite-to-openvino>`__:
    Learn the process of converting TensorFlow Lite models to OpenVINO IR
    format.
@@ -944,9 +938,6 @@ Advanced Computer Vision Techniques
 -  `handwritten-ocr <https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/handwritten-ocr>`__:
    Apply optical character recognition to handwritten Chinese and
    Japanese text.
--  `image-inpainting <https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/image-inpainting>`__:
-   Explore the art of image in-painting and restore images with missing
-   parts.
 -  `vehicle-detection-and-recognition <https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/vehicle-detection-and-recognition>`__:
    Use pre-trained models for vehicle detection and recognition in
    images.
diff --git a/docs/notebooks/hello-segmentation-with-output.rst b/docs/notebooks/hello-segmentation-with-output.rst
index 06274d745af433..69a591cee2f638 100644
--- a/docs/notebooks/hello-segmentation-with-output.rst
+++ b/docs/notebooks/hello-segmentation-with-output.rst
@@ -10,8 +10,8 @@ Zoo <https://github.com/openvinotoolkit/open_model_zoo/>`__ is used.
 ADAS stands for Advanced Driver Assistance Services. The model
 recognizes four classes: background, road, curb and mark.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Imports <#imports>`__
 -  `Download model weights <#download-model-weights>`__
@@ -25,10 +25,10 @@ Table of contents:
 .. code:: ipython3
 
     import platform
-    
+
     # Install required packages
     %pip install -q "openvino>=2023.1.0" opencv-python tqdm
-    
+
     if platform.system() != "Windows":
         %pip install -q "matplotlib>=3.4"
     else:
@@ -52,16 +52,16 @@ Imports
     import matplotlib.pyplot as plt
     import numpy as np
     import openvino as ov
-    
+
     # Fetch `notebook_utils` module
     import requests
-    
+
     r = requests.get(
         url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
-    
+
     open("notebook_utils.py", "w").write(r.text)
-    
+
     from notebook_utils import segmentation_map_to_image, download_file
 
 Download model weights
@@ -72,15 +72,15 @@ Download model weights
 .. code:: ipython3
 
     from pathlib import Path
-    
+
     base_model_dir = Path("./model").expanduser()
-    
+
     model_name = "road-segmentation-adas-0001"
     model_xml_name = f"{model_name}.xml"
     model_bin_name = f"{model_name}.bin"
-    
+
     model_xml_path = base_model_dir / model_xml_name
-    
+
     if not model_xml_path.exists():
         model_xml_url = (
             "https://storage.openvinotoolkit.org/repositories/open_model_zoo/2023.0/models_bin/1/road-segmentation-adas-0001/FP32/road-segmentation-adas-0001.xml"
@@ -88,7 +88,7 @@ Download model weights
         model_bin_url = (
             "https://storage.openvinotoolkit.org/repositories/open_model_zoo/2023.0/models_bin/1/road-segmentation-adas-0001/FP32/road-segmentation-adas-0001.bin"
         )
-    
+
         download_file(model_xml_url, model_xml_name, base_model_dir)
         download_file(model_bin_url, model_bin_name, base_model_dir)
     else:
@@ -117,7 +117,7 @@ select device from dropdown list for running inference using OpenVINO
 .. code:: ipython3
 
     import ipywidgets as widgets
-    
+
     core = ov.Core()
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
@@ -125,7 +125,7 @@ select device from dropdown list for running inference using OpenVINO
         description="Device:",
         disabled=False,
     )
-    
+
     device
 
 
@@ -145,17 +145,17 @@ Load the Model
 .. code:: ipython3
 
     core = ov.Core()
-    
+
     model = core.read_model(model=model_xml_path)
     compiled_model = core.compile_model(model=model, device_name=device.value)
-    
+
     input_layer_ir = compiled_model.input(0)
     output_layer_ir = compiled_model.output(0)
 
 Load an Image
 -------------
 
- A sample image from the
+A sample image from the
 `Mapillary Vistas <https://www.mapillary.com/dataset/vistas>`__ dataset
 is provided.
 
@@ -166,19 +166,19 @@ is provided.
         "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/empty_road_mapillary.jpg",
         directory="data",
     )
-    
+
     # The segmentation network expects images in BGR format.
     image = cv2.imread(str(image_filename))
-    
+
     rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
     image_h, image_w, _ = image.shape
-    
+
     # N,C,H,W = batch size, number of channels, height, width.
     N, C, H, W = input_layer_ir.shape
-    
+
     # OpenCV resize expects the destination size as (width, height).
     resized_image = cv2.resize(image, (W, H))
-    
+
     # Reshape to the network input shape.
     input_image = np.expand_dims(resized_image.transpose(2, 0, 1), 0)
     plt.imshow(rgb_image)
@@ -194,7 +194,7 @@ is provided.
 
 .. parsed-literal::
 
-    <matplotlib.image.AxesImage at 0x7f35bceac760>
+    <matplotlib.image.AxesImage at 0x7f086d40aac0>
 
 
 
@@ -211,7 +211,7 @@ Do Inference
 
     # Run the inference.
     result = compiled_model([input_image])[output_layer_ir]
-    
+
     # Prepare data for visualization.
     segmentation_mask = np.argmax(result, axis=1)
     plt.imshow(segmentation_mask.transpose(1, 2, 0))
@@ -221,7 +221,7 @@ Do Inference
 
 .. parsed-literal::
 
-    <matplotlib.image.AxesImage at 0x7f3580514910>
+    <matplotlib.image.AxesImage at 0x7f08241e53a0>
 
 
 
@@ -238,14 +238,14 @@ Prepare Data for Visualization
 
     # Define colormap, each color represents a class.
     colormap = np.array([[68, 1, 84], [48, 103, 141], [53, 183, 120], [199, 216, 52]])
-    
+
     # Define the transparency of the segmentation mask on the photo.
     alpha = 0.3
-    
+
     # Use function from notebook_utils.py to transform mask to an RGB image.
     mask = segmentation_map_to_image(segmentation_mask, colormap)
     resized_mask = cv2.resize(mask, (image_w, image_h))
-    
+
     # Create an image with mask.
     image_with_mask = cv2.addWeighted(resized_mask, alpha, rgb_image, 1 - alpha, 0)
 
@@ -258,16 +258,16 @@ Visualize data
 
     # Define titles with images.
     data = {"Base Photo": rgb_image, "Segmentation": mask, "Masked Photo": image_with_mask}
-    
+
     # Create a subplot to visualize images.
     fig, axs = plt.subplots(1, len(data.items()), figsize=(15, 10))
-    
+
     # Fill the subplot.
     for ax, (name, image) in zip(axs, data.items()):
         ax.axis("off")
         ax.set_title(name)
         ax.imshow(image)
-    
+
     # Display an image.
     plt.show(fig)
 
diff --git a/docs/notebooks/hello-world-with-output.rst b/docs/notebooks/hello-world-with-output.rst
index b2cc053a7bd24e..c4f8f3a1f0b7a4 100644
--- a/docs/notebooks/hello-world-with-output.rst
+++ b/docs/notebooks/hello-world-with-output.rst
@@ -13,8 +13,8 @@ created, refer to the `TensorFlow to
 OpenVINO <tensorflow-classification-to-openvino-with-output.html>`__
 tutorial.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Imports <#imports>`__
 -  `Download the Model and data
diff --git a/docs/notebooks/hugging-face-hub-with-output.rst b/docs/notebooks/hugging-face-hub-with-output.rst
index 9f0a8e2b2e128b..3128464f1c97f7 100644
--- a/docs/notebooks/hugging-face-hub-with-output.rst
+++ b/docs/notebooks/hugging-face-hub-with-output.rst
@@ -17,8 +17,8 @@ Throughout this notebook we will learn: 1. How to load a HF pipeline
 using the ``transformers`` package and then convert it to OpenVINO. 2.
 How to load the same pipeline using Optimum Intel package.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Converting a Model from the HF Transformers
    Package <#converting-a-model-from-the-hf-transformers-package>`__
@@ -85,10 +85,10 @@ Imports
 .. code:: ipython3
 
     from pathlib import Path
-    
+
     import numpy as np
     import torch
-    
+
     from transformers import AutoModelForSequenceClassification
     from transformers import AutoTokenizer
 
@@ -111,9 +111,9 @@ tutorials <https://huggingface.co/learn/nlp-course/chapter2/2?fw=pt#behind-the-p
 .. code:: ipython3
 
     MODEL = "cardiffnlp/twitter-roberta-base-sentiment-latest"
-    
+
     tokenizer = AutoTokenizer.from_pretrained(MODEL, return_dict=True)
-    
+
     # The torchscript=True flag is used to ensure the model outputs are tuples
     # instead of ModelOutput (which causes JIT errors).
     model = AutoModelForSequenceClassification.from_pretrained(MODEL, torchscript=True)
@@ -121,8 +121,6 @@ tutorials <https://huggingface.co/learn/nlp-course/chapter2/2?fw=pt#behind-the-p
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
-      warnings.warn(
     Some weights of the model checkpoint at cardiffnlp/twitter-roberta-base-sentiment-latest were not used when initializing RobertaForSequenceClassification: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']
     - This IS expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
     - This IS NOT expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
@@ -138,20 +136,20 @@ Let’s do a classification of a simple prompt below.
 .. code:: ipython3
 
     text = "HF models run perfectly with OpenVINO!"
-    
+
     encoded_input = tokenizer(text, return_tensors="pt")
     output = model(**encoded_input)
     scores = output[0][0]
     scores = torch.softmax(scores, dim=0).numpy(force=True)
-    
-    
+
+
     def print_prediction(scores):
         for i, descending_index in enumerate(scores.argsort()[::-1]):
             label = model.config.id2label[descending_index]
             score = np.round(float(scores[descending_index]), 4)
             print(f"{i+1}) {label} {score}")
-    
-    
+
+
     print_prediction(scores)
 
 
@@ -165,7 +163,7 @@ Let’s do a classification of a simple prompt below.
 Converting the Model to OpenVINO IR format
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
- We use the OpenVINO `Model
+We use the OpenVINO `Model
 conversion
 API <https://docs.openvino.ai/2024/openvino-workflow/model-preparation.html#convert-a-model-with-python-convert-model>`__
 to convert the model (this one is implemented in PyTorch) to OpenVINO
@@ -177,9 +175,9 @@ Note how we reuse our real ``encoded_input``, passing it to the
 .. code:: ipython3
 
     import openvino as ov
-    
+
     save_model_path = Path("./models/model.xml")
-    
+
     if not save_model_path.exists():
         ov_model = ov.convert_model(model, example_input=dict(encoded_input))
         ov.save_model(ov_model, save_model_path)
@@ -187,7 +185,7 @@ Note how we reuse our real ``encoded_input``, passing it to the
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4481: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4565: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
       warnings.warn(
 
 
@@ -201,16 +199,16 @@ First, we pick a device to do the model inference
 .. code:: ipython3
 
     import ipywidgets as widgets
-    
+
     core = ov.Core()
-    
+
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
         value="AUTO",
         description="Device:",
         disabled=False,
     )
-    
+
     device
 
 
@@ -228,12 +226,12 @@ model inference.
 .. code:: ipython3
 
     compiled_model = core.compile_model(save_model_path, device.value)
-    
+
     # Compiled model call is performed using the same parameters as for the original model
     scores_ov = compiled_model(encoded_input.data)[0]
-    
+
     scores_ov = torch.softmax(torch.tensor(scores_ov[0]), dim=0).detach().numpy()
-    
+
     print_prediction(scores_ov)
 
 
@@ -314,22 +312,17 @@ documentation <https://huggingface.co/docs/optimum/intel/inference>`__.
     from optimum.intel.openvino import OVModelForSequenceClassification
 
 
-.. parsed-literal::
-
-    INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino
-
-
 .. parsed-literal::
 
     huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
     To disable this warning, you can either:
     	- Avoid using `tokenizers` before the fork if possible
     	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
-    2024-06-06 00:32:38.966766: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-06-06 00:32:39.002435: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-07-02 00:37:41.589687: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-07-02 00:37:41.625311: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2024-06-06 00:32:39.621181: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
+    2024-07-02 00:37:42.027035: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
       torch.utils._pytree._register_pytree_node(
 
 
@@ -359,15 +352,13 @@ inference run.
 .. code:: ipython3
 
     model = OVModelForSequenceClassification.from_pretrained(MODEL, export=True, device=device.value)
-    
+
     # The save_pretrained() method saves the model weights to avoid conversion on the next load.
     model.save_pretrained("./models/optimum_model")
 
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
-      warnings.warn(
     Framework not specified. Using pt to export the model.
     Some weights of the model checkpoint at cardiffnlp/twitter-roberta-base-sentiment-latest were not used when initializing RobertaForSequenceClassification: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']
     - This IS expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
@@ -384,8 +375,6 @@ inference run.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4481: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
-      warnings.warn(
     Compiling the model to AUTO ...
 
 
@@ -440,53 +429,53 @@ Full list of supported arguments available via ``--help``
 
 .. parsed-literal::
 
-    2024-06-06 00:32:51.454579: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-07-02 00:37:54.170558: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
     usage: optimum-cli export openvino [-h] -m MODEL [--task TASK]
-                                       [--cache_dir CACHE_DIR]
                                        [--framework {pt,tf}] [--trust-remote-code]
-                                       [--pad-token-id PAD_TOKEN_ID] [--fp16]
-                                       [--int8]
                                        [--weight-format {fp32,fp16,int8,int4,int4_sym_g128,int4_asym_g128,int4_sym_g64,int4_asym_g64}]
+                                       [--library {transformers,diffusers,timm,sentence_transformers}]
+                                       [--cache_dir CACHE_DIR]
+                                       [--pad-token-id PAD_TOKEN_ID]
                                        [--ratio RATIO] [--sym]
                                        [--group-size GROUP_SIZE]
-                                       [--dataset DATASET] [--all-layers]
+                                       [--dataset DATASET] [--all-layers] [--awq]
+                                       [--scale-estimation]
+                                       [--sensitivity-metric SENSITIVITY_METRIC]
+                                       [--num-samples NUM_SAMPLES]
                                        [--disable-stateful]
-                                       [--disable-convert-tokenizer]
-                                       [--convert-tokenizer]
-                                       [--library {transformers,diffusers,timm,sentence_transformers}]
+                                       [--disable-convert-tokenizer] [--fp16]
+                                       [--int8] [--convert-tokenizer]
                                        output
-    
+
     optional arguments:
       -h, --help            show this help message and exit
-    
+
     Required arguments:
       -m MODEL, --model MODEL
                             Model ID on huggingface.co or path on disk to load
                             model from.
       output                Path indicating the directory where to store the
                             generated OV model.
-    
+
     Optional arguments:
       --task TASK           The task to export the model for. If not specified,
                             the task will be auto-inferred based on the model.
                             Available tasks depend on the model, but are among:
-                            ['zero-shot-object-detection', 'automatic-speech-
-                            recognition', 'semantic-segmentation', 'text2text-
-                            generation', 'stable-diffusion', 'audio-
-                            classification', 'mask-generation', 'image-to-text',
-                            'text-to-audio', 'image-to-image', 'zero-shot-image-
-                            classification', 'token-classification', 'feature-
-                            extraction', 'image-classification', 'image-
-                            segmentation', 'conversational', 'sentence-
-                            similarity', 'audio-frame-classification', 'text-
-                            generation', 'text-classification', 'fill-mask',
-                            'stable-diffusion-xl', 'audio-xvector', 'multiple-
-                            choice', 'object-detection', 'masked-im', 'question-
-                            answering', 'depth-estimation']. For decoder models,
-                            use `xxx-with-past` to export the model using past key
-                            values in the decoder.
-      --cache_dir CACHE_DIR
-                            Path indicating where to store cache.
+                            ['audio-frame-classification', 'multiple-choice',
+                            'mask-generation', 'conversational', 'image-
+                            segmentation', 'feature-extraction', 'audio-xvector',
+                            'token-classification', 'object-detection', 'sentence-
+                            similarity', 'audio-classification', 'image-
+                            classification', 'text-to-audio', 'text2text-
+                            generation', 'semantic-segmentation', 'question-
+                            answering', 'stable-diffusion', 'image-to-text',
+                            'depth-estimation', 'automatic-speech-recognition',
+                            'zero-shot-image-classification', 'image-to-image',
+                            'stable-diffusion-xl', 'zero-shot-object-detection',
+                            'text-classification', 'fill-mask', 'text-generation',
+                            'masked-im']. For decoder models, use `xxx-with-past`
+                            to export the model using past key values in the
+                            decoder.
       --framework {pt,tf}   The framework to use for the export. If not provided,
                             will attempt to use the local checkpoint's original
                             framework or what is available in the environment.
@@ -495,36 +484,63 @@ Full list of supported arguments available via ``--help``
                             for repositories you trust and in which you have read
                             the code, as it will execute on your local machine
                             arbitrary code present in the model repository.
+      --weight-format {fp32,fp16,int8,int4,int4_sym_g128,int4_asym_g128,int4_sym_g64,int4_asym_g64}
+                            he weight format of the exported model.
+      --library {transformers,diffusers,timm,sentence_transformers}
+                            The library used to load the model before export. If
+                            not provided, will attempt to infer the local
+                            checkpoint's library
+      --cache_dir CACHE_DIR
+                            The path to a directory in which the downloaded model
+                            should be cached if the standard cache should not be
+                            used.
       --pad-token-id PAD_TOKEN_ID
                             This is needed by some models, for some tasks. If not
                             provided, will attempt to use the tokenizer to guess
                             it.
-      --fp16                Compress weights to fp16
-      --int8                Compress weights to int8
-      --weight-format {fp32,fp16,int8,int4,int4_sym_g128,int4_asym_g128,int4_sym_g64,int4_asym_g64}
-                            The weight format of the exporting model, e.g. f32
-                            stands for float32 weights, f16 - for float16 weights,
-                            i8 - INT8 weights, int4_* - for INT4 compressed
-                            weights.
-      --ratio RATIO         Compression ratio between primary and backup
-                            precision. In the case of INT4, NNCF evaluates layer
-                            sensitivity and keeps the most impactful layers in
-                            INT8precision (by default 20% in INT8). This helps to
-                            achieve better accuracy after weight compression.
+      --ratio RATIO         A parameter used when applying 4-bit quantization to
+                            control the ratio between 4-bit and 8-bit
+                            quantization. If set to 0.8, 80% of the layers will be
+                            quantized to int4 while 20% will be quantized to int8.
+                            This helps to achieve better accuracy at the sacrifice
+                            of the model size and inference latency. Default value
+                            is 0.8.
       --sym                 Whether to apply symmetric quantization
       --group-size GROUP_SIZE
                             The group size to use for quantization. Recommended
                             value is 128 and -1 uses per-column quantization.
       --dataset DATASET     The dataset used for data-aware compression or
                             quantization with NNCF. You can use the one from the
-                            list ['wikitext2','c4','c4-new','ptb','ptb-new'] for
-                            LLLMs or
-                            ['conceptual_captions','laion/220k-GPT4Vision-
+                            list ['wikitext2','c4','c4-new'] for language models
+                            or ['conceptual_captions','laion/220k-GPT4Vision-
                             captions-from-LIVIS','laion/filtered-wit'] for
                             diffusion models.
       --all-layers          Whether embeddings and last MatMul layers should be
                             compressed to INT4. If not provided an weight
                             compression is applied, they are compressed to INT8.
+      --awq                 Whether to apply AWQ algorithm. AWQ improves
+                            generation quality of INT4-compressed LLMs, but
+                            requires additional time for tuning weights on a
+                            calibration dataset. To run AWQ, please also provide a
+                            dataset argument. Note: it's possible that there will
+                            be no matching patterns in the model to apply AWQ, in
+                            such case it will be skipped.
+      --scale-estimation    Indicates whether to apply a scale estimation
+                            algorithm that minimizes the L2 error between the
+                            original and compressed layers. Providing a dataset is
+                            required to run scale estimation. Please note, that
+                            applying scale estimation takes additional memory and
+                            time.
+      --sensitivity-metric SENSITIVITY_METRIC
+                            The sensitivity metric for assigning quantization
+                            precision to layers. Can be one of the following:
+                            ['weight_quantization_error',
+                            'hessian_input_activation',
+                            'mean_activation_variance', 'max_activation_variance',
+                            'mean_activation_magnitude'].
+      --num-samples NUM_SAMPLES
+                            The maximum number of samples to take from the dataset
+                            for quantization.
       --disable-stateful    Disable stateful converted models, stateless models
                             will be generated instead. Stateful models are
                             produced by default when this key is not used. In
@@ -539,11 +555,10 @@ Full list of supported arguments available via ``--help``
       --disable-convert-tokenizer
                             Do not add converted tokenizer and detokenizer
                             OpenVINO models.
+      --fp16                Compress weights to fp16
+      --int8                Compress weights to int8
       --convert-tokenizer   [Deprecated] Add converted tokenizer and detokenizer
                             with OpenVINO Tokenizers.
-      --library {transformers,diffusers,timm,sentence_transformers}
-                            The library on the model. If not provided, will
-                            attempt to infer the local checkpoint's library
 
 
 The command line export for model from example above with FP16 weights
@@ -564,13 +579,10 @@ compression:
 
 .. parsed-literal::
 
-    2024-06-06 00:32:56.261281: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
-    INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
+    2024-07-02 00:37:58.949844: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
       torch.utils._pytree._register_pytree_node(
     `--fp16` option is deprecated and will be removed in a future version. Use `--weight-format` instead.
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
-      warnings.warn(
     Framework not specified. Using pt to export the model.
     Some weights of the model checkpoint at cardiffnlp/twitter-roberta-base-sentiment-latest were not used when initializing RobertaForSequenceClassification: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']
     - This IS expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
@@ -578,10 +590,8 @@ compression:
     Using framework PyTorch: 2.3.1+cpu
     Overriding 1 configuration item(s)
     	- use_cache -> False
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4481: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
-      warnings.warn(
     OpenVINO Tokenizers is not available. To deploy models in production with C++ code, please follow installation instructions: https://github.com/openvinotoolkit/openvino_tokenizers?tab=readme-ov-file#installation
-    
+
     Tokenizer won't be converted.
 
 
@@ -615,7 +625,7 @@ Model inference is exactly the same as for the original model!
     output = model(**encoded_input)
     scores = output[0][0]
     scores = torch.softmax(scores, dim=0).numpy(force=True)
-    
+
     print_prediction(scores)
 
 
diff --git a/docs/notebooks/hunyuan-dit-image-generation-with-output.rst b/docs/notebooks/hunyuan-dit-image-generation-with-output.rst
new file mode 100644
index 00000000000000..03e9e8695e9f17
--- /dev/null
+++ b/docs/notebooks/hunyuan-dit-image-generation-with-output.rst
@@ -0,0 +1,1038 @@
+Image generation with HunyuanDIT and OpenVINO
+=============================================
+
+Hunyuan-DiT is a powerful text-to-image diffusion transformer with
+fine-grained understanding of both English and Chinese.
+
+|image0|
+
+The model architecture expertly blends diffusion models and transformer
+networks to unlock the potential of text-to-image generation. The
+diffusion transformer consists of encoder and decoder blocks that work
+together to translate the text prompt into a visual representation. Each
+block contains three key modules: self-attention, cross-attention, and a
+feed-forward network. Self-attention analyzes relationships within the
+image, while cross-attention fuses the text encoding from CLIP and T5,
+guiding the image generation process based on the user’s input. The
+Hunyuan-DiT block, specifically, consists of these encoder and decoder
+blocks. The encoder block processes the image patches, capturing
+patterns and dependencies, while the decoder reconstructs the image from
+the encoded information. The decoder also includes a skip module that
+directly connects to the encoder, facilitating information flow and
+enhancing detail reconstruction. Rotary Positional Embedding (RoPE)
+ensures that the model understands the spatial relationships between
+image patches, accurately reconstructing the visual composition.
+Additionally, Centralized Interpolative Positional Encoding enables
+multi-resolution training, allowing Hunyuan-DiT to handle various image
+sizes seamlessly.
+
+More details about model can be found in original
+`repository <https://github.com/Tencent/HunyuanDiT>`__, `project web
+page <https://dit.hunyuan.tencent.com/>`__ and
+`paper <https://arxiv.org/abs/2405.08748>`__.
+
+In this tutorial we consider how to convert and run Hunyuan-DIT model
+using OpenVINO. Additionally, we will use
+`NNCF <https://github.com/openvinotoolkit/nncf>`__ for optimizing model
+in low precision.
+
+
+**Table of contents:**
+
+
+-  `Prerequisites <#prerequisites>`__
+-  `Download PyTorch model <#download-pytorch-model>`__
+-  `Build PyTorch pipeline <#build-pytorch-pipeline>`__
+-  `Convert and Optimize models with OpenVINO and
+   NNCF <#convert-and-optimize-models-with-openvino-and-nncf>`__
+
+   -  `DiT <#dit>`__
+   -  `Text Encoder <#text-encoder>`__
+   -  `Text Embedder <#text-embedder>`__
+   -  `VAE Decoder <#vae-decoder>`__
+
+-  `Create Inference pipeline <#create-inference-pipeline>`__
+
+   -  `Run model <#run-model>`__
+
+-  `Interactive demo <#interactive-demo>`__
+
+.. |image0| image:: https://raw.githubusercontent.com/Tencent/HunyuanDiT/main/asset/framework.png
+
+Prerequisites
+-------------
+
+
+.. code:: ipython3
+
+    %pip install -q "torch>=2.1" torchvision einops timm peft accelerate transformers diffusers huggingface-hub tokenizers sentencepiece protobuf loguru --extra-index-url https://download.pytorch.org/whl/cpu
+    %pip install -q "nncf>=2.11" "gradio>=4.19" "pillow" "opencv-python"
+    %pip install -pre -Uq openvino --extra-index-url https://storage.openvinotoolkit.org/simple/wheels/nightly
+
+.. code:: ipython3
+
+    from pathlib import Path
+    import sys
+
+    repo_dir = Path("HunyuanDiT")
+
+    if not repo_dir.exists():
+        !git clone https://github.com/tencent/HunyuanDiT
+        %cd HunyuanDiT
+        !git checkout ebfb7936490287616c38519f87084a34a1d75362
+        %cd ..
+
+    sys.path.append(str(repo_dir))
+
+Download PyTorch model
+----------------------
+
+
+
+For starting work with model, we should download it from HuggingFace
+Hub. We will use
+`Distilled <https://huggingface.co/Tencent-Hunyuan/Distillation>`__
+version of
+`hunyuan-DIT <https://huggingface.co/Tencent-Hunyuan/HunyuanDiT>`__. In
+the first time, model downloading may take some time.
+
+.. code:: ipython3
+
+    import huggingface_hub as hf_hub
+
+    weights_dir = Path("ckpts")
+    weights_dir.mkdir(exist_ok=True)
+    models_dir = Path("models")
+    models_dir.mkdir(exist_ok=True)
+
+    OV_DIT_MODEL = models_dir / "dit.xml"
+    OV_TEXT_ENCODER = models_dir / "text_encoder.xml"
+    OV_TEXT_EMBEDDER = models_dir / "text_embedder.xml"
+    OV_VAE_DECODER = models_dir / "vae_decoder.xml"
+
+    model_conversion_required = not all([OV_DIT_MODEL.exists(), OV_TEXT_ENCODER.exists(), OV_TEXT_EMBEDDER.exists(), OV_VAE_DECODER.exists()])
+    distilled_repo_id = "Tencent-Hunyuan/Distillation"
+    orig_repo_id = "Tencent-Hunyuan/HunyuanDiT"
+
+    if model_conversion_required and not (weights_dir / "t2i").exists():
+        hf_hub.snapshot_download(repo_id=orig_repo_id, local_dir=weights_dir, allow_patterns=["t2i/*"], ignore_patterns=["t2i/model/*"])
+        hf_hub.hf_hub_download(repo_id=distilled_repo_id, filename="pytorch_model_distill.pt", local_dir=weights_dir / "t2i/model")
+
+Build PyTorch pipeline
+----------------------
+
+
+
+The code bellow, initialize PyTorch inference pipeline for hunyuan-DIT
+model.
+
+.. code:: ipython3
+
+    from hydit.inference import End2End
+    from hydit.config import get_args
+
+    gen = None
+
+    if model_conversion_required:
+        args = get_args({})
+        args.load_key = "distill"
+        args.model_root = weights_dir
+
+        # Load models
+        gen = End2End(args, weights_dir)
+
+
+.. parsed-literal::
+
+    /home/ea/work/notebooks_env/lib/python3.8/site-packages/diffusers/models/transformers/transformer_2d.py:34: FutureWarning: `Transformer2DModelOutput` is deprecated and will be removed in version 1.0.0. Importing `Transformer2DModelOutput` from `diffusers.models.transformer_2d` is deprecated and this will be removed in a future version. Please use `from diffusers.models.modeling_outputs import Transformer2DModelOutput`, instead.
+      deprecate("Transformer2DModelOutput", "1.0.0", deprecation_message)
+
+
+.. parsed-literal::
+
+    flash_attn import failed: No module named 'flash_attn'
+
+
+Convert and Optimize models with OpenVINO and NNCF
+--------------------------------------------------
+
+
+
+Starting from 2023.0 release, OpenVINO supports PyTorch models directly
+via Model Conversion API. ``ov.convert_model`` function accepts instance
+of PyTorch model and example inputs for tracing and returns object of
+``ov.Model`` class, ready to use or save on disk using ``ov.save_model``
+function.
+
+The pipeline consists of four important parts:
+
+-  Clip and T5 Text Encoder to create condition to generate an image
+   from a text prompt.
+-  DIT for step-by-step denoising latent image representation.
+-  Autoencoder (VAE) for decoding latent space to image.
+
+For reducing model memory consumption and improving performance we will
+use weights compression. The `Weights
+Compression <https://docs.openvino.ai/2024/openvino-workflow/model-optimization-guide/weight-compression.html>`__
+algorithm is aimed at compressing the weights of the models and can be
+used to optimize the model footprint and performance of large models
+where the size of weights is relatively larger than the size of
+activations, for example, Large Language Models (LLM). Compared to INT8
+compression, INT4 compression improves performance even more, but
+introduces a minor drop in prediction quality.
+
+Let us convert and optimize each part:
+
+DiT
+~~~
+
+
+
+.. code:: ipython3
+
+    import torch
+    import nncf
+    import gc
+    import openvino as ov
+
+
+    def cleanup_torchscript_cache():
+        """
+        Helper for removing cached model representation
+        """
+        torch._C._jit_clear_class_registry()
+        torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore()
+        torch.jit._state._clear_class_state()
+
+
+    if not OV_DIT_MODEL.exists():
+        latent_model_input = torch.randn(2, 4, 64, 64)
+        t_expand = torch.randint(0, 1000, [2])
+        prompt_embeds = torch.randn(2, 77, 1024)
+        attention_mask = torch.randint(0, 2, [2, 77])
+        prompt_embeds_t5 = torch.randn(2, 256, 2048)
+        attention_mask_t5 = torch.randint(0, 2, [2, 256])
+        ims = torch.tensor([[512, 512, 512, 512, 0, 0], [512, 512, 512, 512, 0, 0]])
+        style = torch.tensor([0, 0])
+        freqs_cis_img = (
+            torch.randn(1024, 88),
+            torch.randn(1024, 88),
+        )
+        model_args = (
+            latent_model_input,
+            t_expand,
+            prompt_embeds,
+            attention_mask,
+            prompt_embeds_t5,
+            attention_mask_t5,
+            ims,
+            style,
+            freqs_cis_img[0],
+            freqs_cis_img[1],
+        )
+
+        gen.model.to(torch.device("cpu"))
+        gen.model.to(torch.float32)
+        gen.model.args.use_fp16 = False
+        ov_model = ov.convert_model(gen.model, example_input=model_args)
+        ov_model = nncf.compress_weights(ov_model, mode=nncf.CompressWeightsMode.INT4_SYM, ratio=0.8, group_size=64)
+        ov.save_model(ov_model, OV_DIT_MODEL)
+        del ov_model
+        cleanup_torchscript_cache()
+        del gen.model
+        gc.collect()
+
+
+.. parsed-literal::
+
+    INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, onnx, openvino
+
+
+Text Encoder
+~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    if not OV_TEXT_ENCODER.exists():
+        gen.clip_text_encoder.to("cpu")
+        gen.clip_text_encoder.to(torch.float32)
+        ov_model = ov.convert_model(
+            gen.clip_text_encoder, example_input={"input_ids": torch.ones([1, 77], dtype=torch.int64), "attention_mask": torch.ones([1, 77], dtype=torch.int64)}
+        )
+        ov_model = nncf.compress_weights(ov_model, mode=nncf.CompressWeightsMode.INT4_SYM, ratio=0.8, group_size=64)
+        ov.save_model(ov_model, OV_TEXT_ENCODER)
+        del ov_model
+        cleanup_torchscript_cache()
+        del gen.clip_text_encoder
+        gc.collect()
+
+Text Embedder
+~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    if not OV_TEXT_EMBEDDER.exists():
+        gen.embedder_t5.model.to("cpu")
+        gen.embedder_t5.model.to(torch.float32)
+
+        ov_model = ov.convert_model(gen.embedder_t5, example_input=(torch.ones([1, 256], dtype=torch.int64), torch.ones([1, 256], dtype=torch.int64)))
+        ov_model = nncf.compress_weights(ov_model, mode=nncf.CompressWeightsMode.INT4_SYM, ratio=0.8, group_size=64)
+        ov.save_model(ov_model, OV_TEXT_EMBEDDER)
+        del ov_model
+        cleanup_torchscript_cache()
+        del gen.embedder_t5
+        gc.collect()
+
+VAE Decoder
+~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    if not OV_VAE_DECODER.exists():
+        vae_decoder = gen.vae
+        vae_decoder.to("cpu")
+        vae_decoder.to(torch.float32)
+
+        vae_decoder.forward = vae_decoder.decode
+
+        ov_model = ov.convert_model(vae_decoder, example_input=torch.zeros((1, 4, 128, 128)))
+        ov.save_model(ov_model, OV_VAE_DECODER)
+        del ov_model
+        cleanup_torchscript_cache()
+        del vae_decoder
+        del gen.vae
+        gc.collect()
+
+.. code:: ipython3
+
+    del gen
+    gc.collect();
+
+Create Inference pipeline
+-------------------------
+
+
+
+.. code:: ipython3
+
+    import inspect
+    from typing import Any, Callable, Dict, List, Optional, Union
+
+    import torch
+    from diffusers.configuration_utils import FrozenDict
+    from diffusers.image_processor import VaeImageProcessor
+    from diffusers.models import AutoencoderKL, UNet2DConditionModel
+    from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+    from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
+    from diffusers.schedulers import KarrasDiffusionSchedulers
+    from diffusers.utils.torch_utils import randn_tensor
+    from transformers import BertModel, BertTokenizer
+    from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
+
+
+    def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
+        """
+        Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
+        Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
+        """
+        std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
+        std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
+        # rescale the results from guidance (fixes overexposure)
+        noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
+        # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
+        noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
+        return noise_cfg
+
+
+    class OVHyDiTPipeline(DiffusionPipeline):
+        def __init__(
+            self,
+            vae: AutoencoderKL,
+            text_encoder: Union[BertModel, CLIPTextModel],
+            tokenizer: Union[BertTokenizer, CLIPTokenizer],
+            unet: UNet2DConditionModel,
+            scheduler: KarrasDiffusionSchedulers,
+            feature_extractor: CLIPImageProcessor,
+            progress_bar_config: Dict[str, Any] = None,
+            embedder_t5=None,
+            embedder_tokenizer=None,
+        ):
+            self.embedder_t5 = embedder_t5
+            self.embedder_tokenizer = embedder_tokenizer
+
+            if progress_bar_config is None:
+                progress_bar_config = {}
+            if not hasattr(self, "_progress_bar_config"):
+                self._progress_bar_config = {}
+            self._progress_bar_config.update(progress_bar_config)
+
+            if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
+                new_config = dict(scheduler.config)
+                new_config["steps_offset"] = 1
+                scheduler._internal_dict = FrozenDict(new_config)
+
+            if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
+                new_config = dict(scheduler.config)
+                new_config["clip_sample"] = False
+                scheduler._internal_dict = FrozenDict(new_config)
+
+            self.vae = vae
+            self.text_encoder = text_encoder
+            self.tokenizer = tokenizer
+            self.unet = unet
+            self.scheduler = scheduler
+            self.feature_extractor = feature_extractor
+            self.vae_scale_factor = 2**3
+            self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
+
+        def encode_prompt(
+            self,
+            prompt,
+            num_images_per_prompt,
+            do_classifier_free_guidance,
+            negative_prompt=None,
+            prompt_embeds: Optional[torch.FloatTensor] = None,
+            negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+            embedder=None,
+        ):
+            r"""
+            Encodes the prompt into text encoder hidden states.
+
+            Args:
+                prompt (`str` or `List[str]`, *optional*):
+                    prompt to be encoded
+                num_images_per_prompt (`int`):
+                    number of images that should be generated per prompt
+                do_classifier_free_guidance (`bool`):
+                    whether to use classifier free guidance or not
+                negative_prompt (`str` or `List[str]`, *optional*):
+                    The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                    `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+                    less than `1`).
+                prompt_embeds (`torch.FloatTensor`, *optional*):
+                    Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                    provided, text embeddings will be generated from `prompt` input argument.
+                negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                    Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                    weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                    argument.
+                embedder:
+                    T5 embedder
+            """
+            if embedder is None:
+                text_encoder = self.text_encoder
+                tokenizer = self.tokenizer
+                max_length = self.tokenizer.model_max_length
+            else:
+                text_encoder = embedder
+                tokenizer = self.embedder_tokenizer
+                max_length = 256
+
+            if prompt is not None and isinstance(prompt, str):
+                batch_size = 1
+            elif prompt is not None and isinstance(prompt, list):
+                batch_size = len(prompt)
+            else:
+                batch_size = prompt_embeds.shape[0]
+
+            if prompt_embeds is None:
+                text_inputs = tokenizer(
+                    prompt,
+                    padding="max_length",
+                    max_length=max_length,
+                    truncation=True,
+                    return_attention_mask=True,
+                    return_tensors="pt",
+                )
+                text_input_ids = text_inputs.input_ids
+                attention_mask = text_inputs.attention_mask
+
+                prompt_embeds = text_encoder([text_input_ids, attention_mask])
+                prompt_embeds = torch.from_numpy(prompt_embeds[0])
+                attention_mask = attention_mask.repeat(num_images_per_prompt, 1)
+            else:
+                attention_mask = None
+
+            bs_embed, seq_len, _ = prompt_embeds.shape
+            # duplicate text embeddings for each generation per prompt, using mps friendly method
+            prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+            prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+            # get unconditional embeddings for classifier free guidance
+            if do_classifier_free_guidance and negative_prompt_embeds is None:
+                uncond_tokens: List[str]
+                if negative_prompt is None:
+                    uncond_tokens = [""] * batch_size
+                elif prompt is not None and type(prompt) is not type(negative_prompt):
+                    raise TypeError(f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}.")
+                elif isinstance(negative_prompt, str):
+                    uncond_tokens = [negative_prompt]
+                elif batch_size != len(negative_prompt):
+                    raise ValueError(
+                        f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                        f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                        " the batch size of `prompt`."
+                    )
+                else:
+                    uncond_tokens = negative_prompt
+
+                max_length = prompt_embeds.shape[1]
+                uncond_input = tokenizer(
+                    uncond_tokens,
+                    padding="max_length",
+                    max_length=max_length,
+                    truncation=True,
+                    return_tensors="pt",
+                )
+                uncond_attention_mask = uncond_input.attention_mask
+                negative_prompt_embeds = text_encoder([uncond_input.input_ids, uncond_attention_mask])
+                negative_prompt_embeds = torch.from_numpy(negative_prompt_embeds[0])
+                uncond_attention_mask = uncond_attention_mask.repeat(num_images_per_prompt, 1)
+            else:
+                uncond_attention_mask = None
+
+            if do_classifier_free_guidance:
+                # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+                seq_len = negative_prompt_embeds.shape[1]
+
+                negative_prompt_embeds = negative_prompt_embeds
+
+                negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
+                negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+            return prompt_embeds, negative_prompt_embeds, attention_mask, uncond_attention_mask
+
+        def prepare_extra_step_kwargs(self, generator, eta):
+            # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+            # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+            # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+            # and should be between [0, 1]
+
+            accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+            extra_step_kwargs = {}
+            if accepts_eta:
+                extra_step_kwargs["eta"] = eta
+
+            # check if the scheduler accepts generator
+            accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
+            if accepts_generator:
+                extra_step_kwargs["generator"] = generator
+            return extra_step_kwargs
+
+        def check_inputs(
+            self,
+            prompt,
+            height,
+            width,
+            callback_steps,
+            negative_prompt=None,
+            prompt_embeds=None,
+            negative_prompt_embeds=None,
+        ):
+            if height % 8 != 0 or width % 8 != 0:
+                raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+            if (callback_steps is None) or (callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)):
+                raise ValueError(f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}.")
+            if prompt is not None and prompt_embeds is not None:
+                raise ValueError(
+                    f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two."
+                )
+            elif prompt is None and prompt_embeds is None:
+                raise ValueError("Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.")
+            elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+                raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+            if negative_prompt is not None and negative_prompt_embeds is not None:
+                raise ValueError(
+                    f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
+                    f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+                )
+
+            if prompt_embeds is not None and negative_prompt_embeds is not None:
+                if prompt_embeds.shape != negative_prompt_embeds.shape:
+                    raise ValueError(
+                        "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
+                        f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
+                        f" {negative_prompt_embeds.shape}."
+                    )
+
+        def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, generator, latents=None):
+            shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
+            if isinstance(generator, list) and len(generator) != batch_size:
+                raise ValueError(
+                    f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                    f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+                )
+
+            if latents is None:
+                latents = randn_tensor(shape, generator=generator, device=torch.device("cpu"), dtype=dtype)
+
+            # scale the initial noise by the standard deviation required by the scheduler
+            latents = latents * self.scheduler.init_noise_sigma
+            return latents
+
+        def __call__(
+            self,
+            height: int,
+            width: int,
+            prompt: Union[str, List[str]] = None,
+            num_inference_steps: Optional[int] = 50,
+            guidance_scale: Optional[float] = 7.5,
+            negative_prompt: Optional[Union[str, List[str]]] = None,
+            num_images_per_prompt: Optional[int] = 1,
+            eta: Optional[float] = 0.0,
+            generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+            latents: Optional[torch.FloatTensor] = None,
+            prompt_embeds: Optional[torch.FloatTensor] = None,
+            prompt_embeds_t5: Optional[torch.FloatTensor] = None,
+            negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+            negative_prompt_embeds_t5: Optional[torch.FloatTensor] = None,
+            output_type: Optional[str] = "pil",
+            return_dict: bool = True,
+            callback: Optional[Callable[[int, int, torch.FloatTensor, torch.FloatTensor], None]] = None,
+            callback_steps: int = 1,
+            guidance_rescale: float = 0.0,
+            image_meta_size: Optional[torch.LongTensor] = None,
+            style: Optional[torch.LongTensor] = None,
+            freqs_cis_img: Optional[tuple] = None,
+            learn_sigma: bool = True,
+        ):
+            # 1. Check inputs. Raise error if not correct
+            self.check_inputs(prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds)
+
+            # 2. Define call parameters
+            if prompt is not None and isinstance(prompt, str):
+                batch_size = 1
+            elif prompt is not None and isinstance(prompt, list):
+                batch_size = len(prompt)
+            else:
+                batch_size = prompt_embeds.shape[0]
+
+            # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+            # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+            # corresponds to doing no classifier free guidance.from
+            do_classifier_free_guidance = guidance_scale > 1.0
+
+            prompt_embeds, negative_prompt_embeds, attention_mask, uncond_attention_mask = self.encode_prompt(
+                prompt,
+                num_images_per_prompt,
+                do_classifier_free_guidance,
+                negative_prompt,
+                prompt_embeds=prompt_embeds,
+                negative_prompt_embeds=negative_prompt_embeds,
+            )
+            prompt_embeds_t5, negative_prompt_embeds_t5, attention_mask_t5, uncond_attention_mask_t5 = self.encode_prompt(
+                prompt,
+                num_images_per_prompt,
+                do_classifier_free_guidance,
+                negative_prompt,
+                prompt_embeds=prompt_embeds_t5,
+                negative_prompt_embeds=negative_prompt_embeds_t5,
+                embedder=self.embedder_t5,
+            )
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            if do_classifier_free_guidance:
+                prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
+                attention_mask = torch.cat([uncond_attention_mask, attention_mask])
+                prompt_embeds_t5 = torch.cat([negative_prompt_embeds_t5, prompt_embeds_t5])
+                attention_mask_t5 = torch.cat([uncond_attention_mask_t5, attention_mask_t5])
+
+            # 4. Prepare timesteps
+            self.scheduler.set_timesteps(num_inference_steps, device=torch.device("cpu"))
+            timesteps = self.scheduler.timesteps
+
+            # 5. Prepare latent variables
+            num_channels_latents = 4
+            latents = self.prepare_latents(
+                batch_size * num_images_per_prompt,
+                num_channels_latents,
+                height,
+                width,
+                prompt_embeds.dtype,
+                generator,
+                latents,
+            )
+
+            # 6. Prepare extra step kwargs.
+            extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+            # 7. Denoising loop
+            num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+            with self.progress_bar(total=num_inference_steps) as progress_bar:
+                for i, t in enumerate(timesteps):
+                    # expand the latents if we are doing classifier free guidance
+                    latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+                    latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+                    # expand scalar t to 1-D tensor to match the 1st dim of latent_model_input
+                    t_expand = torch.tensor([t] * latent_model_input.shape[0], device=latent_model_input.device)
+
+                    ims = image_meta_size if image_meta_size is not None else torch.tensor([[1024, 1024, 1024, 1024, 0, 0], [1024, 1024, 1024, 1024, 0, 0]])
+
+                    noise_pred = torch.from_numpy(
+                        self.unet(
+                            [
+                                latent_model_input,
+                                t_expand,
+                                prompt_embeds,
+                                attention_mask,
+                                prompt_embeds_t5,
+                                attention_mask_t5,
+                                ims,
+                                style,
+                                freqs_cis_img[0],
+                                freqs_cis_img[1],
+                            ]
+                        )[0]
+                    )
+                    if learn_sigma:
+                        noise_pred, _ = noise_pred.chunk(2, dim=1)
+
+                    # perform guidance
+                    if do_classifier_free_guidance:
+                        noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                        noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                    if do_classifier_free_guidance and guidance_rescale > 0.0:
+                        # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
+                        noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
+
+                    # compute the previous noisy sample x_t -> x_t-1
+                    results = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=True)
+                    latents = results.prev_sample
+                    pred_x0 = results.pred_original_sample if hasattr(results, "pred_original_sample") else None
+
+                    # call the callback, if provided
+                    if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                        progress_bar.update()
+                        if callback is not None and i % callback_steps == 0:
+                            callback(i, t, latents, pred_x0)
+
+            has_nsfw_concept = None
+            if not output_type == "latent":
+                image = torch.from_numpy(self.vae(latents / 0.13025)[0])
+            else:
+                image = latents
+
+            if has_nsfw_concept is None:
+                do_denormalize = [True] * image.shape[0]
+            else:
+                do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
+
+            image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
+
+            if not return_dict:
+                return (image, has_nsfw_concept)
+
+            return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
+
+Run model
+~~~~~~~~~
+
+
+
+Please select inference device using dropdown widget:
+
+.. code:: ipython3
+
+    import openvino as ov
+    import ipywidgets as widgets
+
+    core = ov.Core()
+
+    device = widgets.Dropdown(
+        options=core.available_devices + ["AUTO"],
+        value="AUTO",
+        description="Device:",
+        disabled=False,
+    )
+
+    device
+
+
+
+
+.. parsed-literal::
+
+    Dropdown(description='Device:', index=3, options=('CPU', 'GPU.0', 'GPU.1', 'AUTO'), value='AUTO')
+
+
+
+.. code:: ipython3
+
+    import gc
+
+    core = ov.Core()
+    ov_dit = core.read_model(OV_DIT_MODEL)
+    dit = core.compile_model(ov_dit, device.value)
+    ov_text_encoder = core.read_model(OV_TEXT_ENCODER)
+    text_encoder = core.compile_model(ov_text_encoder, device.value)
+    ov_text_embedder = core.read_model(OV_TEXT_EMBEDDER)
+
+    text_embedder = core.compile_model(ov_text_embedder, device.value)
+    vae_decoder = core.compile_model(OV_VAE_DECODER, device.value)
+
+    del ov_dit, ov_text_encoder, ov_text_embedder
+
+    gc.collect();
+
+.. code:: ipython3
+
+    from transformers import AutoTokenizer
+
+    tokenizer = AutoTokenizer.from_pretrained("./ckpts/t2i/tokenizer/")
+    embedder_tokenizer = AutoTokenizer.from_pretrained("./ckpts/t2i/mt5")
+
+
+.. parsed-literal::
+
+    You are using the default legacy behaviour of the <class 'transformers.models.t5.tokenization_t5.T5Tokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565
+    /home/ea/work/notebooks_env/lib/python3.8/site-packages/transformers/convert_slow_tokenizer.py:562: UserWarning: The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option which is not implemented in the fast tokenizers. In practice this means that the fast version of the tokenizer can produce unknown tokens whereas the sentencepiece version would have converted these unknown tokens into a sequence of byte tokens matching the original piece of text.
+      warnings.warn(
+
+
+.. code:: ipython3
+
+    from hydit.constants import SAMPLER_FACTORY, NEGATIVE_PROMPT
+
+.. code:: ipython3
+
+    sampler = "ddpm"
+    kwargs = SAMPLER_FACTORY[sampler]["kwargs"]
+    scheduler = SAMPLER_FACTORY[sampler]["scheduler"]
+
+.. code:: ipython3
+
+    from diffusers import schedulers
+
+    scheduler_class = getattr(schedulers, scheduler)
+    scheduler = scheduler_class(**kwargs)
+
+.. code:: ipython3
+
+    ov_pipe = OVHyDiTPipeline(vae_decoder, text_encoder, tokenizer, dit, scheduler, None, None, embedder_t5=text_embedder, embedder_tokenizer=embedder_tokenizer)
+
+.. code:: ipython3
+
+    from hydit.modules.posemb_layers import get_2d_rotary_pos_embed, get_fill_resize_and_crop
+
+
+    def calc_rope(height, width, patch_size=2, head_size=88):
+        th = height // 8 // patch_size
+        tw = width // 8 // patch_size
+        base_size = 512 // 8 // patch_size
+        start, stop = get_fill_resize_and_crop((th, tw), base_size)
+        sub_args = [start, stop, (th, tw)]
+        rope = get_2d_rotary_pos_embed(head_size, *sub_args)
+        return rope
+
+.. code:: ipython3
+
+    from hydit.utils.tools import set_seeds
+
+    height, width = 880, 880
+    style = torch.as_tensor([0, 0])
+    target_height = int((height // 16) * 16)
+    target_width = int((width // 16) * 16)
+
+    size_cond = [height, width, target_width, target_height, 0, 0]
+    image_meta_size = torch.as_tensor([size_cond] * 2)
+    freqs_cis_img_cache = {}
+
+    if (target_height, target_width) not in freqs_cis_img_cache:
+        freqs_cis_img_cache[(target_height, target_width)] = calc_rope(target_height, target_width)
+
+    freqs_cis_img = freqs_cis_img_cache[(target_height, target_width)]
+    images = ov_pipe(
+        prompt="cute cat",
+        negative_prompt=NEGATIVE_PROMPT,
+        height=target_height,
+        width=target_width,
+        num_inference_steps=10,
+        image_meta_size=image_meta_size,
+        style=style,
+        return_dict=False,
+        guidance_scale=7.5,
+        freqs_cis_img=freqs_cis_img,
+        generator=set_seeds(42),
+    )
+
+
+
+.. parsed-literal::
+
+      0%|          | 0/10 [00:00<?, ?it/s]
+
+
+.. code:: ipython3
+
+    images[0][0]
+
+
+
+
+.. image:: hunyuan-dit-image-generation-with-output_files/hunyuan-dit-image-generation-with-output_30_0.png
+
+
+
+Interactive demo
+----------------
+
+
+
+.. code:: ipython3
+
+    import gradio as gr
+
+
+    def inference(input_prompt, negative_prompt, seed, num_steps, height, width, progress=gr.Progress(track_tqdm=True)):
+        style = torch.as_tensor([0, 0])
+        target_height = int((height // 16) * 16)
+        target_width = int((width // 16) * 16)
+
+        size_cond = [height, width, target_width, target_height, 0, 0]
+        image_meta_size = torch.as_tensor([size_cond] * 2)
+        freqs_cis_img = calc_rope(target_height, target_width)
+        images = ov_pipe(
+            prompt=input_prompt,
+            negative_prompt=negative_prompt,
+            height=target_height,
+            width=target_width,
+            num_inference_steps=num_steps,
+            image_meta_size=image_meta_size,
+            style=style,
+            return_dict=False,
+            guidance_scale=7.5,
+            freqs_cis_img=freqs_cis_img,
+            generator=set_seeds(seed),
+        )
+        return images[0][0]
+
+
+    with gr.Blocks() as demo:
+        with gr.Row():
+            with gr.Column():
+                prompt = gr.Textbox(label="Input prompt", lines=3)
+                with gr.Row():
+                    infer_steps = gr.Slider(
+                        label="Number Inference steps",
+                        minimum=1,
+                        maximum=200,
+                        value=15,
+                        step=1,
+                    )
+                    seed = gr.Number(
+                        label="Seed",
+                        minimum=-1,
+                        maximum=1_000_000_000,
+                        value=42,
+                        step=1,
+                        precision=0,
+                    )
+                with gr.Accordion("Advanced settings", open=False):
+                    with gr.Row():
+                        negative_prompt = gr.Textbox(
+                            label="Negative prompt",
+                            value=NEGATIVE_PROMPT,
+                            lines=2,
+                        )
+                    with gr.Row():
+                        oriW = gr.Number(
+                            label="Width",
+                            minimum=768,
+                            maximum=1024,
+                            value=880,
+                            step=16,
+                            precision=0,
+                            min_width=80,
+                        )
+                        oriH = gr.Number(
+                            label="Height",
+                            minimum=768,
+                            maximum=1024,
+                            value=880,
+                            step=16,
+                            precision=0,
+                            min_width=80,
+                        )
+                        cfg_scale = gr.Slider(label="Guidance scale", minimum=1.0, maximum=16.0, value=7.5, step=0.5)
+                with gr.Row():
+                    advanced_button = gr.Button()
+            with gr.Column():
+                output_img = gr.Image(
+                    label="Generated image",
+                    interactive=False,
+                )
+            advanced_button.click(
+                fn=inference,
+                inputs=[
+                    prompt,
+                    negative_prompt,
+                    seed,
+                    infer_steps,
+                    oriH,
+                    oriW,
+                ],
+                outputs=output_img,
+            )
+
+        with gr.Row():
+            gr.Examples(
+                [
+                    ["一只小猫"],
+                    ["a kitten"],
+                    ["一只聪明的狐狸走在阔叶树林里, 旁边是一条小溪, 细节真实, 摄影"],
+                    ["A clever fox walks in a broadleaf forest next to a stream, realistic details, photography"],
+                    ["请将“杞人忧天”的样子画出来"],
+                    ['Please draw a picture of "unfounded worries"'],
+                    ["枯藤老树昏鸦，小桥流水人家"],
+                    ["Withered vines, old trees and dim crows, small bridges and flowing water, people's houses"],
+                    ["湖水清澈，天空湛蓝，阳光灿烂。一只优雅的白天鹅在湖边游泳。它周围有几只小鸭子，看起来非常可爱，整个画面给人一种宁静祥和的感觉。"],
+                    [
+                        "The lake is clear, the sky is blue, and the sun is bright. An elegant white swan swims by the lake. There are several little ducks around it, which look very cute, and the whole picture gives people a sense of peace and tranquility."
+                    ],
+                    ["一朵鲜艳的红色玫瑰花，花瓣撒有一些水珠，晶莹剔透，特写镜头"],
+                    ["A bright red rose flower with petals sprinkled with some water drops, crystal clear, close-up"],
+                    ["风格是写实，画面主要描述一个亚洲戏曲艺术家正在表演，她穿着华丽的戏服，脸上戴着精致的面具，身姿优雅，背景是古色古香的舞台，镜头是近景"],
+                    [
+                        "The style is realistic. The picture mainly depicts an Asian opera artist performing. She is wearing a gorgeous costume and a delicate mask on her face. Her posture is elegant. The background is an antique stage and the camera is a close-up."
+                    ],
+                ],
+                [prompt],
+            )
+
+    try:
+        demo.launch(debug=False)
+    except Exception:
+        demo.launch(share=True, debug=False)
+    # if you are launching remotely, specify server_name and server_port
+    # demo.launch(server_name='your server name', server_port='server port in int')
+    # Read more in the docs: https://gradio.app/docs/
+
+
+.. parsed-literal::
+
+    Running on local URL:  http://127.0.0.1:7860
+
+    To create a public link, set `share=True` in `launch()`.
+
+
+
+
+
+
+
+
+.. parsed-literal::
+
+    Keyboard interruption in main thread... closing server.
+
diff --git a/docs/notebooks/hunyuan-dit-image-generation-with-output_files/hunyuan-dit-image-generation-with-output_30_0.jpg b/docs/notebooks/hunyuan-dit-image-generation-with-output_files/hunyuan-dit-image-generation-with-output_30_0.jpg
new file mode 100644
index 00000000000000..30cd809fec4a42
--- /dev/null
+++ b/docs/notebooks/hunyuan-dit-image-generation-with-output_files/hunyuan-dit-image-generation-with-output_30_0.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2a20707bc7aa8dd65e0982c5c4faad7958400585b56ba8b5df528a3ec8c7b0f3
+size 54311
diff --git a/docs/notebooks/hunyuan-dit-image-generation-with-output_files/hunyuan-dit-image-generation-with-output_30_0.png b/docs/notebooks/hunyuan-dit-image-generation-with-output_files/hunyuan-dit-image-generation-with-output_30_0.png
new file mode 100644
index 00000000000000..7e0c0030728cd6
--- /dev/null
+++ b/docs/notebooks/hunyuan-dit-image-generation-with-output_files/hunyuan-dit-image-generation-with-output_30_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6989eab001fd21f892e97feb3fe7443815acc416d52d61cdd1c00053670c5c7c
+size 828989
diff --git a/docs/notebooks/image-bind-with-output.rst b/docs/notebooks/image-bind-with-output.rst
index 3b2ccf42de7bdf..75a0508e3a3c33 100644
--- a/docs/notebooks/image-bind-with-output.rst
+++ b/docs/notebooks/image-bind-with-output.rst
@@ -67,8 +67,8 @@ represented on the image below:
 In this tutorial, we consider how to use ImageBind for multimodal
 zero-shot classification.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Instantiate PyTorch model <#instantiate-pytorch-model>`__
@@ -641,7 +641,7 @@ quantize image and text models.
         """
         Prepares a vision-text dataset for quantization by collecting vision and text data.
         """
-        dataset = load_dataset("conceptual_captions", streaming=False)
+        dataset = load_dataset("google-research-datasets/conceptual_captions", streaming=False, trust_remote_code=True)
         train_dataset = dataset["train"].shuffle(seed=0)
         dataloader = torch.utils.data.DataLoader(train_dataset, collate_fn=collate_fn, batch_size=1)
         vision_data, text_data = collect_vision_text_data(dataloader, opt_init_steps)
@@ -677,7 +677,7 @@ consists of 5-second-long recordings organized into 50 semantic classes.
         Collects audio data from the dataloader by calling the `collect_audio_data` function.
         Returns a list containing the collected calibration audio data batches.
         """
-        audio_dataset = load_dataset("ashraq/esc50", streaming=True)
+        audio_dataset = load_dataset("ashraq/esc50", streaming=True, trust_remote_code=True)
         train_dataset = audio_dataset["train"].shuffle(seed=42, buffer_size=1000)
 
         def collate_fn(examples):
@@ -764,6 +764,8 @@ Quantize ImageBind model for text modality
 Quantize ImageBind model for audio modality
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
+
+
 .. code:: ipython3
 
     if not int8_model_paths[ModalityType.AUDIO].exists():
diff --git a/docs/notebooks/image-classification-quantization-with-output.rst b/docs/notebooks/image-classification-quantization-with-output.rst
index b6ce19111d1b31..cc959bc3cf9d9c 100644
--- a/docs/notebooks/image-classification-quantization-with-output.rst
+++ b/docs/notebooks/image-classification-quantization-with-output.rst
@@ -19,8 +19,8 @@ This tutorial consists of the following steps:
 -  Compare performance of the original and quantized models.
 -  Compare results on one picture.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prepare the Model <#prepare-the-model>`__
 -  `Prepare Dataset <#prepare-dataset>`__
@@ -101,7 +101,7 @@ Model preparation stage has the following steps:
     remote: Counting objects: 100% (281/281), done.
     remote: Compressing objects: 100% (96/96), done.
     remote: Total 282 (delta 135), reused 269 (delta 128), pack-reused 1
-    Receiving objects: 100% (282/282), 9.22 MiB | 23.02 MiB/s, done.
+    Receiving objects: 100% (282/282), 9.22 MiB | 18.15 MiB/s, done.
     Resolving deltas: 100% (135/135), done.
 
 
@@ -173,7 +173,7 @@ Preprocessing for model obtained from training
 
 .. parsed-literal::
 
-    100%|██████████| 170498071/170498071 [00:06<00:00, 24821501.62it/s]
+    100%|██████████| 170498071/170498071 [00:07<00:00, 22491087.99it/s]
 
 
 .. parsed-literal::
@@ -245,10 +245,10 @@ about supported parameters can be found on this
 
 .. parsed-literal::
 
-    2024-06-06 00:33:36.759161: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-06-06 00:33:36.792130: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-07-02 00:38:38.778759: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-07-02 00:38:38.811166: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2024-06-06 00:33:37.462996: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-07-02 00:38:39.448394: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 
@@ -258,17 +258,17 @@ about supported parameters can be found on this
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -279,17 +279,17 @@ about supported parameters can be found on this
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -420,18 +420,18 @@ Tool <https://docs.openvino.ai/2024/learn-openvino/openvino-samples/benchmark-to
     [ INFO ] Parsing input parameters
     [Step 2/11] Loading OpenVINO Runtime
     [ INFO ] OpenVINO:
-    [ INFO ] Build ................................. 2024.1.0-15008-f4afc983258-releases/2024/1
+    [ INFO ] Build ................................. 2024.2.0-15519-5c0f38f83f6-releases/2024/2
     [ INFO ] 
     [ INFO ] Device info:
     [ INFO ] AUTO
-    [ INFO ] Build ................................. 2024.1.0-15008-f4afc983258-releases/2024/1
+    [ INFO ] Build ................................. 2024.2.0-15519-5c0f38f83f6-releases/2024/2
     [ INFO ] 
     [ INFO ] 
     [Step 3/11] Setting device configuration
     [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.THROUGHPUT.
     [Step 4/11] Reading model files
     [ INFO ] Loading model files
-    [ INFO ] Read model took 9.77 ms
+    [ INFO ] Read model took 9.60 ms
     [ INFO ] Original model I/O parameters:
     [ INFO ] Model inputs:
     [ INFO ]     x (node: x) : f32 / [...] / [1,3,32,32]
@@ -445,7 +445,7 @@ Tool <https://docs.openvino.ai/2024/learn-openvino/openvino-samples/benchmark-to
     [ INFO ] Model outputs:
     [ INFO ]     x.17 (node: aten::linear/Add) : f32 / [...] / [1,10]
     [Step 7/11] Loading the model to the device
-    [ INFO ] Compile model took 181.72 ms
+    [ INFO ] Compile model took 221.82 ms
     [Step 8/11] Querying optimal runtime parameters
     [ INFO ] Model:
     [ INFO ]   NETWORK_NAME: Model2
@@ -482,17 +482,17 @@ Tool <https://docs.openvino.ai/2024/learn-openvino/openvino-samples/benchmark-to
     [ INFO ] Fill input 'x' with random values 
     [Step 10/11] Measuring performance (Start inference asynchronously, 12 inference requests, limits: 15000 ms duration)
     [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
-    [ INFO ] First inference took 2.97 ms
+    [ INFO ] First inference took 3.31 ms
     [Step 11/11] Dumping statistics report
     [ INFO ] Execution Devices:['CPU']
-    [ INFO ] Count:            88560 iterations
-    [ INFO ] Duration:         15003.70 ms
+    [ INFO ] Count:            88584 iterations
+    [ INFO ] Duration:         15002.96 ms
     [ INFO ] Latency:
     [ INFO ]    Median:        1.85 ms
     [ INFO ]    Average:       1.85 ms
-    [ INFO ]    Min:           1.57 ms
-    [ INFO ]    Max:           9.36 ms
-    [ INFO ] Throughput:   5902.54 FPS
+    [ INFO ]    Min:           1.18 ms
+    [ INFO ]    Max:           9.56 ms
+    [ INFO ] Throughput:   5904.44 FPS
 
 
 .. code:: ipython3
@@ -507,18 +507,18 @@ Tool <https://docs.openvino.ai/2024/learn-openvino/openvino-samples/benchmark-to
     [ INFO ] Parsing input parameters
     [Step 2/11] Loading OpenVINO Runtime
     [ INFO ] OpenVINO:
-    [ INFO ] Build ................................. 2024.1.0-15008-f4afc983258-releases/2024/1
+    [ INFO ] Build ................................. 2024.2.0-15519-5c0f38f83f6-releases/2024/2
     [ INFO ] 
     [ INFO ] Device info:
     [ INFO ] AUTO
-    [ INFO ] Build ................................. 2024.1.0-15008-f4afc983258-releases/2024/1
+    [ INFO ] Build ................................. 2024.2.0-15519-5c0f38f83f6-releases/2024/2
     [ INFO ] 
     [ INFO ] 
     [Step 3/11] Setting device configuration
     [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.THROUGHPUT.
     [Step 4/11] Reading model files
     [ INFO ] Loading model files
-    [ INFO ] Read model took 14.86 ms
+    [ INFO ] Read model took 14.69 ms
     [ INFO ] Original model I/O parameters:
     [ INFO ] Model inputs:
     [ INFO ]     x (node: x) : f32 / [...] / [1,3,32,32]
@@ -532,7 +532,7 @@ Tool <https://docs.openvino.ai/2024/learn-openvino/openvino-samples/benchmark-to
     [ INFO ] Model outputs:
     [ INFO ]     x.17 (node: aten::linear/Add) : f32 / [...] / [1,10]
     [Step 7/11] Loading the model to the device
-    [ INFO ] Compile model took 335.02 ms
+    [ INFO ] Compile model took 345.52 ms
     [Step 8/11] Querying optimal runtime parameters
     [ INFO ] Model:
     [ INFO ]   NETWORK_NAME: Model2
@@ -569,17 +569,17 @@ Tool <https://docs.openvino.ai/2024/learn-openvino/openvino-samples/benchmark-to
     [ INFO ] Fill input 'x' with random values 
     [Step 10/11] Measuring performance (Start inference asynchronously, 12 inference requests, limits: 15000 ms duration)
     [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
-    [ INFO ] First inference took 1.95 ms
+    [ INFO ] First inference took 2.08 ms
     [Step 11/11] Dumping statistics report
     [ INFO ] Execution Devices:['CPU']
-    [ INFO ] Count:            166536 iterations
-    [ INFO ] Duration:         15001.86 ms
+    [ INFO ] Count:            165636 iterations
+    [ INFO ] Duration:         15001.23 ms
     [ INFO ] Latency:
-    [ INFO ]    Median:        1.01 ms
-    [ INFO ]    Average:       1.04 ms
-    [ INFO ]    Min:           0.74 ms
-    [ INFO ]    Max:           7.29 ms
-    [ INFO ] Throughput:   11101.03 FPS
+    [ INFO ]    Median:        1.02 ms
+    [ INFO ]    Average:       1.05 ms
+    [ INFO ]    Min:           0.77 ms
+    [ INFO ]    Max:           8.23 ms
+    [ INFO ] Throughput:   11041.50 FPS
 
 
 Compare results on four pictures
diff --git a/docs/notebooks/image-inpainting-with-output.rst b/docs/notebooks/image-inpainting-with-output.rst
deleted file mode 100644
index 5777eb68aa510b..00000000000000
--- a/docs/notebooks/image-inpainting-with-output.rst
+++ /dev/null
@@ -1,350 +0,0 @@
-Image In-painting with OpenVINO™
---------------------------------
-
-This notebook demonstrates how to use an image in-painting model with
-OpenVINO, using `GMCNN
-model <https://github.com/shepnerd/inpainting_gmcnn>`__ from `Open Model
-Zoo <https://github.com/openvinotoolkit/open_model_zoo/>`__. This model,
-given a tampered image, is able to create something very similar to the
-original image. The Following pipeline will be used in this notebook.
-|pipeline|
-
-Table of contents:
-^^^^^^^^^^^^^^^^^^
-
--  `Download the Model <#download-the-model>`__
--  `Convert Tensorflow model to OpenVINO IR
-   format <#convert-tensorflow-model-to-openvino-ir-format>`__
--  `Load the model <#load-the-model>`__
--  `Determine the input shapes of the
-   model <#determine-the-input-shapes-of-the-model>`__
--  `Create a square mask <#create-a-square-mask>`__
--  `Load and Resize the Image <#load-and-resize-the-image>`__
--  `Generating the Masked Image <#generating-the-masked-image>`__
--  `Preprocessing <#preprocessing>`__
--  `Inference <#inference>`__
--  `Save the Restored Image <#save-the-restored-image>`__
-
-.. |pipeline| image:: https://user-images.githubusercontent.com/4547501/165792473-ba784c0d-0a37-409f-a5f6-bb1849c1d140.png
-
-.. code:: ipython3
-
-    import platform
-    
-    %pip install -q "openvino>=2023.1.0" "opencv-python" tqdm
-    
-    if platform.system() != "Windows":
-        %pip install -q "matplotlib>=3.4"
-    else:
-        %pip install -q "matplotlib>=3.4,<3.7"
-
-
-.. parsed-literal::
-
-    
-    [notice] A new release of pip is available: 23.2.1 -> 23.3.1
-    [notice] To update, run: pip install --upgrade pip
-    Note: you may need to restart the kernel to use updated packages.
-
-
-.. code:: ipython3
-
-    from pathlib import Path
-    
-    import cv2
-    import matplotlib.pyplot as plt
-    import numpy as np
-    from zipfile import ZipFile
-    import openvino as ov
-    
-    # Fetch `notebook_utils` module
-    import requests
-    
-    r = requests.get(
-        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
-    )
-    
-    open("notebook_utils.py", "w").write(r.text)
-    from notebook_utils import download_file
-
-Download the Model
-~~~~~~~~~~~~~~~~~~
-
-
-
-Download ``gmcnn-places2-tf``\ model (this step will be skipped if the
-model is already downloaded) and then unzip it. Downloaded model stored
-in TensorFlow frozen graph format. The steps how this frozen graph can
-be obtained from original model checkpoint can be found in this
-`instruction <https://docs.openvino.ai/2024/omz_models_model_gmcnn_places2_tf.html#steps-to-reproduce-conversion-to-frozen-graph>`__
-
-.. code:: ipython3
-
-    # A directory where the model will be downloaded.
-    base_model_dir = "model"
-    # The name of the model from Open Model Zoo.
-    model_name = "gmcnn-places2-tf"
-    
-    model_path = Path(f"{base_model_dir}/public/{model_name}/frozen_model.pb")
-    if not model_path.exists():
-        model_url = f"https://storage.openvinotoolkit.org/repositories/open_model_zoo/public/2022.1/gmcnn-places2-tf/{model_name}.zip"
-        download_file(model_url, model_name, base_model_dir)
-    else:
-        print("Already downloaded")
-    
-    with ZipFile(f"{base_model_dir}/{model_name}" + "", "r") as zip_ref:
-        zip_ref.extractall(
-            path=Path(
-                base_model_dir,
-                "public",
-            )
-        )
-
-
-.. parsed-literal::
-
-    Already downloaded
-
-
-Convert Tensorflow model to OpenVINO IR format
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-The pre-trained model is in TensorFlow format. To use it with OpenVINO,
-convert it to OpenVINO IR format with model conversion API. For more
-information about model conversion, see this
-`page <https://docs.openvino.ai/2024/openvino-workflow/model-preparation.html>`__.
-This step is also skipped if the model is already converted.
-
-.. code:: ipython3
-
-    model_dir = Path(base_model_dir, "public", "ir")
-    ir_path = Path(f"{model_dir}/frozen_model.xml")
-    
-    # Run model conversion API to convert model to OpenVINO IR FP32 format, if the IR file does not exist.
-    if not ir_path.exists():
-        ov_model = ov.convert_model(model_path, input=[[1, 512, 680, 3], [1, 512, 680, 1]])
-        ov.save_model(ov_model, str(ir_path))
-    else:
-        print(f"{ir_path} already exists.")
-
-
-.. parsed-literal::
-
-    model/public/ir/frozen_model.xml already exists.
-
-
-Load the model
-~~~~~~~~~~~~~~
-
-
-
-Now, load the OpenVINO IR model and perform as follows:
-
-1. Initialize OpenVINO Runtime (Core).
-2. Read the network from ``*.bin`` and ``*.xml`` files (weights and
-   architecture)
-3. Compile the model for the “CPU”.
-4. Get input and output nodes.
-
-Only a few lines of code are required to run the model:
-
-.. code:: ipython3
-
-    core = ov.Core()
-    
-    # Read the model.xml and weights file
-    model = core.read_model(model=ir_path)
-
-.. code:: ipython3
-
-    import ipywidgets as widgets
-    
-    device = widgets.Dropdown(
-        options=core.available_devices + ["AUTO"],
-        value="AUTO",
-        description="Device:",
-        disabled=False,
-    )
-    
-    device
-
-
-
-
-.. parsed-literal::
-
-    Dropdown(description='Device:', index=2, options=('CPU', 'GPU', 'AUTO'), value='AUTO')
-
-
-
-.. code:: ipython3
-
-    # Load the model on to the device
-    compiled_model = core.compile_model(model=model, device_name=device.value)
-    # Store the input and output nodes
-    input_layer = compiled_model.input(0)
-    output_layer = compiled_model.output(0)
-
-Determine the input shapes of the model
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-Note that both input shapes are the same. However, the second input has
-1 channel (monotone).
-
-.. code:: ipython3
-
-    N, H, W, C = input_layer.shape
-
-Create a square mask
-~~~~~~~~~~~~~~~~~~~~
-
-
-
-Next, create a single channeled mask that will be laid on top of the
-original image.
-
-.. code:: ipython3
-
-    def create_mask(image_width, image_height, size_x=30, size_y=30, number=1):
-        """
-        Create a square mask of defined size on a random location.
-    
-        :param: image_width: width of the image
-        :param: image_height: height of the image
-        :param: size: size in pixels of one side
-        :returns:
-                mask: grayscale float32 mask of size shaped [image_height, image_width, 1]
-        """
-    
-        mask = np.zeros((image_height, image_width, 1), dtype=np.float32)
-        for _ in range(number):
-            start_x = np.random.randint(image_width - size_x)
-            start_y = np.random.randint(image_height - size_y)
-            cv2.rectangle(
-                img=mask,
-                pt1=(start_x, start_y),
-                pt2=(start_x + size_x, start_y + size_y),
-                color=(1, 1, 1),
-                thickness=cv2.FILLED,
-            )
-        return mask
-
-.. code:: ipython3
-
-    # Generate a square mask of size WxH with number of "holes".
-    mask = create_mask(image_width=W, image_height=H, size_x=50, size_y=50, number=15)
-    # This mask will be laid over the input image as noise.
-    plt.figure(figsize=(16, 12))
-    plt.imshow(cv2.cvtColor(mask, cv2.COLOR_BGR2RGB));
-
-
-
-.. image:: image-inpainting-with-output_files/image-inpainting-with-output_15_0.png
-
-
-Load and Resize the Image
-~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-This image will be altered by using the mask. You can process any image
-you like. Just change the URL below.
-
-.. code:: ipython3
-
-    img_path = Path("data/laptop.png")
-    
-    if not img_path.exists():
-        # Download an image.
-        url = "https://user-images.githubusercontent.com/29454499/281372079-fa8d84c4-8bf9-4a82-a1b9-5a74ad42ce47.png"
-        image_file = download_file(
-            url,
-            filename="laptop.png",
-            directory="data",
-            show_progress=False,
-            silent=True,
-            timeout=30,
-        )
-    
-    # Read the image.
-    image = cv2.imread(str(img_path))
-    # Resize the image to meet network expected input sizes.
-    resized_image = cv2.resize(src=image, dsize=(W, H), interpolation=cv2.INTER_AREA)
-    plt.figure(figsize=(16, 12))
-    plt.imshow(cv2.cvtColor(resized_image, cv2.COLOR_BGR2RGB));
-
-
-
-.. image:: image-inpainting-with-output_files/image-inpainting-with-output_17_0.png
-
-
-Generating the Masked Image
-~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-This multiplication of the image and the mask gives the result of the
-masked image layered on top of the original image. The ``masked_image``
-will be the first input to the GMCNN model.
-
-.. code:: ipython3
-
-    # Generating a masked image.
-    masked_image = (resized_image * (1 - mask) + 255 * mask).astype(np.uint8)
-    plt.figure(figsize=(16, 12))
-    plt.imshow(cv2.cvtColor(masked_image, cv2.COLOR_BGR2RGB));
-
-
-
-.. image:: image-inpainting-with-output_files/image-inpainting-with-output_19_0.png
-
-
-Preprocessing
-~~~~~~~~~~~~~
-
-
-
-The model expects the input dimensions to be ``NHWC``.
-
--  masked_image.shape = (512,680,3) —–> model expects = (1,512,680,3)
--  resized_mask.shape = (512,680,1) —–> model expects = (1,512,680,1)
-
-.. code:: ipython3
-
-    masked_image = masked_image[None, ...]
-    mask = mask[None, ...]
-
-Inference
-~~~~~~~~~
-
-
-
-Do inference with the given masked image and the mask. Then, show the
-restored image.
-
-.. code:: ipython3
-
-    result = compiled_model([ov.Tensor(masked_image.astype(np.float32)), ov.Tensor(mask.astype(np.float32))])[output_layer]
-    result = result.squeeze().astype(np.uint8)
-    plt.figure(figsize=(16, 12))
-    plt.imshow(cv2.cvtColor(result, cv2.COLOR_BGR2RGB));
-
-
-
-.. image:: image-inpainting-with-output_files/image-inpainting-with-output_23_0.png
-
-
-Save the Restored Image
-~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-Save the restored image to the data directory to download it.
-
-.. code:: ipython3
-
-    cv2.imwrite("data/laptop_restored.png", result);
diff --git a/docs/notebooks/image-inpainting-with-output_files/image-inpainting-with-output_15_0.png b/docs/notebooks/image-inpainting-with-output_files/image-inpainting-with-output_15_0.png
deleted file mode 100644
index 9672f85850845f..00000000000000
--- a/docs/notebooks/image-inpainting-with-output_files/image-inpainting-with-output_15_0.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:bcd410b6efb941d2c605b9700489d965f6556a77bd2b1b1c8e81b6611ae60e92
-size 16186
diff --git a/docs/notebooks/image-inpainting-with-output_files/image-inpainting-with-output_17_0.png b/docs/notebooks/image-inpainting-with-output_files/image-inpainting-with-output_17_0.png
deleted file mode 100644
index 630c40142d5d25..00000000000000
--- a/docs/notebooks/image-inpainting-with-output_files/image-inpainting-with-output_17_0.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:1784e76f880bfc703c807391eb8d7960c99b2bd83230a53a09100ef0c587a263
-size 544222
diff --git a/docs/notebooks/image-inpainting-with-output_files/image-inpainting-with-output_19_0.png b/docs/notebooks/image-inpainting-with-output_files/image-inpainting-with-output_19_0.png
deleted file mode 100644
index 8762c383538b8d..00000000000000
--- a/docs/notebooks/image-inpainting-with-output_files/image-inpainting-with-output_19_0.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:47ecdc3f633406ae13fa0c2e006655c088c39fdeea5928d700362491533ce840
-size 492981
diff --git a/docs/notebooks/image-inpainting-with-output_files/image-inpainting-with-output_23_0.png b/docs/notebooks/image-inpainting-with-output_files/image-inpainting-with-output_23_0.png
deleted file mode 100644
index 886e64b2e43870..00000000000000
--- a/docs/notebooks/image-inpainting-with-output_files/image-inpainting-with-output_23_0.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:3d613aa493580e662df403d6269fc6ea72d87a05cd3b362db03c62f25fade2f5
-size 593391
diff --git a/docs/notebooks/instant-id-with-output.rst b/docs/notebooks/instant-id-with-output.rst
index a0fe572f7a17c2..6528cc371492bd 100644
--- a/docs/notebooks/instant-id-with-output.rst
+++ b/docs/notebooks/instant-id-with-output.rst
@@ -51,8 +51,7 @@ additional part demonstrates how to run optimization with
 pipeline.
 
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
 
 - `Prerequisites <#prerequisites>`__
 - `Convert and prepare Face IdentityNet <#convert-and-prepare-face-identitynet>`__
@@ -92,6 +91,7 @@ Table of contents:
 
 .. |applications.png| image:: https://github.com/InstantID/InstantID/blob/main/assets/applications.png?raw=true
 
+
 Prerequisites
 -------------
 
@@ -499,6 +499,8 @@ Now, let’s see models inference result
 Select Inference Device for Face Recognition
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
+
+
 .. code:: ipython3
 
     import openvino as ov
@@ -1690,6 +1692,7 @@ Create pipeline
 ~~~~~~~~~~~~~~~
 
 
+
 .. code:: ipython3
 
     ov_pipe = OVStableDiffusionXLInstantIDPipeline(
@@ -1708,6 +1711,7 @@ Run inference
 ~~~~~~~~~~~~~
 
 
+
 .. code:: ipython3
 
     prompt = "Anime girl"
@@ -1849,7 +1853,7 @@ image.
     ov_int8_controlnet_path = MODELS_DIR / 'controlnet_optimized.xml'
 
     num_samples = int(np.ceil(subset_size / num_inference_steps))
-    dataset = datasets.load_dataset("wider_face", split="train", streaming=True).shuffle(seed=42)
+    dataset = datasets.load_dataset("wider_face", split="train", streaming=True, trust_remote_code=True).shuffle(seed=42)
     face_info = []
     for batch in dataset:
         try:
diff --git a/docs/notebooks/instruct-pix2pix-image-editing-with-output.rst b/docs/notebooks/instruct-pix2pix-image-editing-with-output.rst
index c0c7787c271f2b..0a6c5b9eccb160 100644
--- a/docs/notebooks/instruct-pix2pix-image-editing-with-output.rst
+++ b/docs/notebooks/instruct-pix2pix-image-editing-with-output.rst
@@ -30,8 +30,8 @@ Notebook contains the following steps:
    `NNCF <https://github.com/openvinotoolkit/nncf/>`__ quantization.
 4. Compare results of original and optimized pipelines.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Create Pytorch Models pipeline <#create-pytorch-models-pipeline>`__
@@ -61,10 +61,10 @@ Install necessary packages
 .. code:: ipython3
 
     import platform
-    
+
     %pip install -q "transformers>=4.25.1" torch accelerate "gradio>4.19" "datasets>=2.14.6" diffusers pillow opencv-python --extra-index-url https://download.pytorch.org/whl/cpu
     %pip install -q "openvino>=2023.1.0"
-    
+
     if platform.system() != "Windows":
         %pip install -q "matplotlib>=3.4"
     else:
@@ -78,7 +78,7 @@ Create Pytorch Models pipeline
 ``StableDiffusionInstructPix2PixPipeline`` is an end-to-end inference
 pipeline that you can use to edit images from text instructions with
 just a few lines of code provided as part
- `diffusers <https://huggingface.co/docs/diffusers/index>`__ library.
+`diffusers <https://huggingface.co/docs/diffusers/index>`__ library.
 
 First, we load the pre-trained weights of all components of the model.
 
@@ -93,7 +93,7 @@ First, we load the pre-trained weights of all components of the model.
         StableDiffusionInstructPix2PixPipeline,
         EulerAncestralDiscreteScheduler,
     )
-    
+
     model_id = "timbrooks/instruct-pix2pix"
     pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float32, safety_checker=None)
     scheduler_config = pipe.scheduler.config
@@ -103,7 +103,7 @@ First, we load the pre-trained weights of all components of the model.
     unet.eval()
     vae = pipe.vae
     vae.eval()
-    
+
     del pipe
 
 Convert Models to OpenVINO IR
@@ -157,12 +157,12 @@ hidden states.
     from pathlib import Path
     import openvino as ov
     import gc
-    
+
     core = ov.Core()
-    
+
     TEXT_ENCODER_OV_PATH = Path("text_encoder.xml")
-    
-    
+
+
     def cleanup_torchscript_cache():
         """
         Helper for removing cached model representation
@@ -170,8 +170,8 @@ hidden states.
         torch._C._jit_clear_class_registry()
         torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore()
         torch.jit._state._clear_class_state()
-    
-    
+
+
     def convert_encoder(text_encoder: torch.nn.Module, ir_path: Path):
         """
         Convert Text Encoder mode.
@@ -185,7 +185,7 @@ hidden states.
         input_ids = torch.ones((1, 77), dtype=torch.long)
         # switch model to inference mode
         text_encoder.eval()
-    
+
         # disable gradients calculation for reducing memory consumption
         with torch.no_grad():
             # Export model to IR format
@@ -200,13 +200,13 @@ hidden states.
         del ov_model
         cleanup_torchscript_cache()
         print(f"Text Encoder successfully converted to IR and saved to {ir_path}")
-    
-    
+
+
     if not TEXT_ENCODER_OV_PATH.exists():
         convert_encoder(text_encoder, TEXT_ENCODER_OV_PATH)
     else:
         print(f"Text encoder will be loaded from {TEXT_ENCODER_OV_PATH}")
-    
+
     del text_encoder
     gc.collect()
 
@@ -246,8 +246,8 @@ into two independent models.
 .. code:: ipython3
 
     VAE_ENCODER_OV_PATH = Path("vae_encoder.xml")
-    
-    
+
+
     def convert_vae_encoder(vae: torch.nn.Module, ir_path: Path):
         """
         Convert VAE model for encoding to IR format.
@@ -259,15 +259,15 @@ into two independent models.
         Returns:
             None
         """
-    
+
         class VAEEncoderWrapper(torch.nn.Module):
             def __init__(self, vae):
                 super().__init__()
                 self.vae = vae
-    
+
             def forward(self, image):
                 return self.vae.encode(x=image)["latent_dist"].sample()
-    
+
         vae_encoder = VAEEncoderWrapper(vae)
         vae_encoder.eval()
         image = torch.zeros((1, 3, 512, 512))
@@ -277,16 +277,16 @@ into two independent models.
         del ov_model
         cleanup_torchscript_cache()
         print(f"VAE encoder successfully converted to IR and saved to {ir_path}")
-    
-    
+
+
     if not VAE_ENCODER_OV_PATH.exists():
         convert_vae_encoder(vae, VAE_ENCODER_OV_PATH)
     else:
         print(f"VAE encoder will be loaded from {VAE_ENCODER_OV_PATH}")
-    
+
     VAE_DECODER_OV_PATH = Path("vae_decoder.xml")
-    
-    
+
+
     def convert_vae_decoder(vae: torch.nn.Module, ir_path: Path):
         """
         Convert VAE model for decoding to IR format.
@@ -298,18 +298,18 @@ into two independent models.
         Returns:
             None
         """
-    
+
         class VAEDecoderWrapper(torch.nn.Module):
             def __init__(self, vae):
                 super().__init__()
                 self.vae = vae
-    
+
             def forward(self, latents):
                 return self.vae.decode(latents)
-    
+
         vae_decoder = VAEDecoderWrapper(vae)
         latents = torch.zeros((1, 4, 64, 64))
-    
+
         vae_decoder.eval()
         with torch.no_grad():
             ov_model = ov.convert_model(vae_decoder, example_input=latents, input=[((1, 4, 64, 64),)])
@@ -317,13 +317,13 @@ into two independent models.
         del ov_model
         cleanup_torchscript_cache()
         print(f"VAE decoder successfully converted to IR and saved to {ir_path}")
-    
-    
+
+
     if not VAE_DECODER_OV_PATH.exists():
         convert_vae_decoder(vae, VAE_DECODER_OV_PATH)
     else:
         print(f"VAE decoder will be loaded from {VAE_DECODER_OV_PATH}")
-    
+
     del vae
     gc.collect()
 
@@ -360,12 +360,12 @@ Model predicts the ``sample`` state for the next step.
 .. code:: ipython3
 
     import numpy as np
-    
+
     UNET_OV_PATH = Path("unet.xml")
-    
+
     dtype_mapping = {torch.float32: ov.Type.f32, torch.float64: ov.Type.f64}
-    
-    
+
+
     def convert_unet(unet: torch.nn.Module, ir_path: Path):
         """
         Convert U-net model to IR format.
@@ -387,7 +387,7 @@ Model predicts the ``sample`` state for the next step.
             shape = ov.PartialShape(tuple(input_tensor.shape))
             element_type = dtype_mapping[input_tensor.dtype]
             input_info.append((shape, element_type))
-    
+
         unet.eval()
         with torch.no_grad():
             ov_model = ov.convert_model(unet, example_input=dummy_inputs, input=input_info)
@@ -395,8 +395,8 @@ Model predicts the ``sample`` state for the next step.
         del ov_model
         cleanup_torchscript_cache()
         print(f"Unet successfully converted to IR and saved to {ir_path}")
-    
-    
+
+
     if not UNET_OV_PATH.exists():
         convert_unet(unet, UNET_OV_PATH)
         gc.collect()
@@ -455,13 +455,13 @@ decoder part of the variational auto encoder.
     from typing import Union, List, Optional, Tuple
     import PIL
     import cv2
-    
-    
+
+
     def scale_fit_to_window(dst_width: int, dst_height: int, image_width: int, image_height: int):
         """
         Preprocessing helper function for calculating image size for resize with peserving original aspect ratio
         and fitting image to specific window size
-    
+
         Parameters:
           dst_width (int): destination window width
           dst_height (int): destination window height
@@ -473,15 +473,15 @@ decoder part of the variational auto encoder.
         """
         im_scale = min(dst_height / image_height, dst_width / image_width)
         return int(im_scale * image_width), int(im_scale * image_height)
-    
-    
+
+
     def preprocess(image: PIL.Image.Image):
         """
         Image preprocessing function. Takes image in PIL.Image format, resizes it to keep aspect ration and fits to model input window 512x512,
         then converts it to np.ndarray and adds padding with zeros on right or bottom side of image (depends from aspect ratio), after that
         converts data to float32 data type and change range of values from [0, 255] to [-1, 1], finally, converts data layout from planar NHWC to NCHW.
         The function returns preprocessed input tensor and padding size, which can be used in postprocessing.
-    
+
         Parameters:
           image (PIL.Image.Image): input image
         Returns:
@@ -499,15 +499,15 @@ decoder part of the variational auto encoder.
         image = 2.0 * image - 1.0
         image = image.transpose(0, 3, 1, 2)
         return image, pad
-    
-    
+
+
     def randn_tensor(
         shape: Union[Tuple, List],
         dtype: Optional[np.dtype] = np.float32,
     ):
         """
         Helper function for generation random values tensor with given shape and data type
-    
+
         Parameters:
           shape (Union[Tuple, List]): shape for filling random values
           dtype (np.dtype, *optiona*, np.float32): data type for result
@@ -515,15 +515,15 @@ decoder part of the variational auto encoder.
           latents (np.ndarray): tensor with random values with given data type and shape (usually represents noise in latent space)
         """
         latents = np.random.randn(*shape).astype(dtype)
-    
+
         return latents
-    
-    
+
+
     class OVInstructPix2PixPipeline(DiffusionPipeline):
         """
         OpenVINO inference pipeline for InstructPix2Pix
         """
-    
+
         def __init__(
             self,
             tokenizer: CLIPTokenizer,
@@ -540,7 +540,7 @@ decoder part of the variational auto encoder.
             self.vae_scale_factor = 8
             self.scheduler = scheduler
             self.load_models(core, device, text_encoder, vae_encoder, unet, vae_decoder)
-    
+
         def load_models(
             self,
             core: ov.Core,
@@ -552,7 +552,7 @@ decoder part of the variational auto encoder.
         ):
             """
             Function for loading models on device using OpenVINO
-    
+
             Parameters:
               core (Core): OpenVINO runtime Core class instance
               device (str): inference device
@@ -573,7 +573,7 @@ decoder part of the variational auto encoder.
             self.unet_out = self.unet.output(0)
             self.vae_decoder = core.compile_model(vae_decoder, device, ov_config)
             self.vae_decoder_out = self.vae_decoder.output(0)
-    
+
         def __call__(
             self,
             prompt: Union[str, List[str]],
@@ -587,7 +587,7 @@ decoder part of the variational auto encoder.
         ):
             """
             Function invoked when calling the pipeline for generation.
-    
+
             Parameters:
                 prompt (`str` or `List[str]`):
                     The prompt or prompts to guide the image generation.
@@ -616,9 +616,9 @@ decoder part of the variational auto encoder.
                     [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
             Returns:
                 image ([List[Union[np.ndarray, PIL.Image.Image]]): generaited images
-    
+
             """
-    
+
             # 1. Define call parameters
             batch_size = 1 if isinstance(prompt, str) else len(prompt)
             # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
@@ -627,25 +627,25 @@ decoder part of the variational auto encoder.
             do_classifier_free_guidance = guidance_scale > 1.0 and image_guidance_scale >= 1.0
             # check if scheduler is in sigmas space
             scheduler_is_in_sigma_space = hasattr(self.scheduler, "sigmas")
-    
+
             # 2. Encode input prompt
             text_embeddings = self._encode_prompt(prompt)
-    
+
             # 3. Preprocess image
             orig_width, orig_height = image.size
             image, pad = preprocess(image)
             height, width = image.shape[-2:]
-    
+
             # 4. set timesteps
             self.scheduler.set_timesteps(num_inference_steps)
             timesteps = self.scheduler.timesteps
-    
+
             # 5. Prepare Image latents
             image_latents = self.prepare_image_latents(
                 image,
                 do_classifier_free_guidance=do_classifier_free_guidance,
             )
-    
+
             # 6. Prepare latent variables
             num_channels_latents = 4
             latents = self.prepare_latents(
@@ -656,7 +656,7 @@ decoder part of the variational auto encoder.
                 text_embeddings.dtype,
                 latents,
             )
-    
+
             # 7. Denoising loop
             num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
             with self.progress_bar(total=num_inference_steps) as progress_bar:
@@ -665,14 +665,14 @@ decoder part of the variational auto encoder.
                     # The latents are expanded 3 times because for pix2pix the guidance\
                     # is applied for both the text and the input image.
                     latent_model_input = np.concatenate([latents] * 3) if do_classifier_free_guidance else latents
-    
+
                     # concat latents, image_latents in the channel dimension
                     scaled_latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
                     scaled_latent_model_input = np.concatenate([scaled_latent_model_input, image_latents], axis=1)
-    
+
                     # predict the noise residual
                     noise_pred = self.unet([scaled_latent_model_input, t, text_embeddings])[self.unet_out]
-    
+
                     # Hack:
                     # For karras style schedulers the model does classifier free guidance using the
                     # predicted_original_sample instead of the noise_pred. So we need to compute the
@@ -681,7 +681,7 @@ decoder part of the variational auto encoder.
                         step_index = (self.scheduler.timesteps == t).nonzero().item()
                         sigma = self.scheduler.sigmas[step_index].numpy()
                         noise_pred = latent_model_input - sigma * noise_pred
-    
+
                     # perform guidance
                     if do_classifier_free_guidance:
                         noise_pred_text, noise_pred_image, noise_pred_uncond = (
@@ -694,7 +694,7 @@ decoder part of the variational auto encoder.
                             + guidance_scale * (noise_pred_text - noise_pred_image)
                             + image_guidance_scale * (noise_pred_image - noise_pred_uncond)
                         )
-    
+
                     # For karras style schedulers the model does classifier free guidance using the
                     # predicted_original_sample instead of the noise_pred. But the scheduler.step function
                     # expects the noise_pred and computes the predicted_original_sample internally. So we
@@ -702,26 +702,26 @@ decoder part of the variational auto encoder.
                     # predicted_original_sample is correct.
                     if scheduler_is_in_sigma_space:
                         noise_pred = (noise_pred - latents) / (-sigma)
-    
+
                     # compute the previous noisy sample x_t -> x_t-1
                     latents = self.scheduler.step(torch.from_numpy(noise_pred), t, torch.from_numpy(latents)).prev_sample.numpy()
-    
+
                     # call the callback, if provided
                     if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                         progress_bar.update()
-    
+
             # 8. Post-processing
             image = self.decode_latents(latents, pad)
-    
+
             # 9. Convert to PIL
             if output_type == "pil":
                 image = self.numpy_to_pil(image)
                 image = [img.resize((orig_width, orig_height), PIL.Image.Resampling.LANCZOS) for img in image]
             else:
                 image = [cv2.resize(img, (orig_width, orig_width)) for img in image]
-    
+
             return image
-    
+
         def _encode_prompt(
             self,
             prompt: Union[str, List[str]],
@@ -730,7 +730,7 @@ decoder part of the variational auto encoder.
         ):
             """
             Encodes the prompt into text encoder hidden states.
-    
+
             Parameters:
                 prompt (str or list(str)): prompt to be encoded
                 num_images_per_prompt (int): number of images that should be generated per prompt
@@ -739,7 +739,7 @@ decoder part of the variational auto encoder.
                 text_embeddings (np.ndarray): text encoder hidden states
             """
             batch_size = len(prompt) if isinstance(prompt, list) else 1
-    
+
             # tokenize input prompts
             text_inputs = self.tokenizer(
                 prompt,
@@ -749,15 +749,15 @@ decoder part of the variational auto encoder.
                 return_tensors="np",
             )
             text_input_ids = text_inputs.input_ids
-    
+
             text_embeddings = self.text_encoder(text_input_ids)[self.text_encoder_out]
-    
+
             # duplicate text embeddings for each generation per prompt, using mps friendly method
             if num_images_per_prompt != 1:
                 bs_embed, seq_len, _ = text_embeddings.shape
                 text_embeddings = np.tile(text_embeddings, (1, num_images_per_prompt, 1))
                 text_embeddings = np.reshape(text_embeddings, (bs_embed * num_images_per_prompt, seq_len, -1))
-    
+
             # get unconditional embeddings for classifier free guidance
             if do_classifier_free_guidance:
                 uncond_tokens: List[str]
@@ -770,21 +770,21 @@ decoder part of the variational auto encoder.
                     truncation=True,
                     return_tensors="np",
                 )
-    
+
                 uncond_embeddings = self.text_encoder(uncond_input.input_ids)[self.text_encoder_out]
-    
+
                 # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                 seq_len = uncond_embeddings.shape[1]
                 uncond_embeddings = np.tile(uncond_embeddings, (1, num_images_per_prompt, 1))
                 uncond_embeddings = np.reshape(uncond_embeddings, (batch_size * num_images_per_prompt, seq_len, -1))
-    
+
                 # For classifier free guidance, you need to do two forward passes.
                 # Here, you concatenate the unconditional and text embeddings into a single batch
                 # to avoid doing two forward passes
                 text_embeddings = np.concatenate([text_embeddings, uncond_embeddings, uncond_embeddings])
-    
+
             return text_embeddings
-    
+
         def prepare_image_latents(
             self,
             image,
@@ -794,7 +794,7 @@ decoder part of the variational auto encoder.
         ):
             """
             Encodes input image to latent space using VAE Encoder
-    
+
             Parameters:
                image (np.ndarray): input image tensor
                num_image_per_prompt (int, *optional*, 1): number of image generated for promt
@@ -802,12 +802,12 @@ decoder part of the variational auto encoder.
             Returns:
                image_latents: image encoded to latent space
             """
-    
+
             image = image.astype(np.float32)
-    
+
             batch_size = batch_size * num_images_per_prompt
             image_latents = self.vae_encoder(image)[self.vae_encoder_out]
-    
+
             if batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] == 0:
                 # expand image_latents for batch_size
                 additional_image_per_prompt = batch_size // image_latents.shape[0]
@@ -816,13 +816,13 @@ decoder part of the variational auto encoder.
                 raise ValueError(f"Cannot duplicate `image` of batch size {image_latents.shape[0]} to {batch_size} text prompts.")
             else:
                 image_latents = np.concatenate([image_latents], axis=0)
-    
+
             if do_classifier_free_guidance:
                 uncond_image_latents = np.zeros_like(image_latents)
                 image_latents = np.concatenate([image_latents, image_latents, uncond_image_latents], axis=0)
-    
+
             return image_latents
-    
+
         def prepare_latents(
             self,
             batch_size: int,
@@ -835,7 +835,7 @@ decoder part of the variational auto encoder.
             """
             Preparing noise to image generation. If initial latents are not provided, they will be generated randomly,
             then prepared latents scaled by the standard deviation required by the scheduler
-    
+
             Parameters:
                batch_size (int): input batch size
                num_channels_latents (int): number of channels for noise generation
@@ -856,15 +856,15 @@ decoder part of the variational auto encoder.
                 latents = randn_tensor(shape, dtype=dtype)
             else:
                 latents = latents
-    
+
             # scale the initial noise by the standard deviation required by the scheduler
             latents = latents * self.scheduler.init_noise_sigma.numpy()
             return latents
-    
+
         def decode_latents(self, latents: np.array, pad: Tuple[int]):
             """
             Decode predicted image from latent space using VAE Decoder and unpad image result
-    
+
             Parameters:
                latents (np.ndarray): image encoded in diffusion latent space
                pad (Tuple[int]): each side padding sizes obtained on preprocessing step
@@ -885,8 +885,8 @@ decoder part of the variational auto encoder.
 .. code:: ipython3
 
     import matplotlib.pyplot as plt
-    
-    
+
+
     def visualize_results(
         orig_img: PIL.Image.Image,
         processed_img: PIL.Image.Image,
@@ -895,7 +895,7 @@ decoder part of the variational auto encoder.
     ):
         """
         Helper function for results visualization
-    
+
         Parameters:
            orig_img (PIL.Image.Image): original image
            processed_img (PIL.Image.Image): processed image after editing
@@ -938,14 +938,14 @@ can provide device selecting one from available in dropdown list.
 .. code:: ipython3
 
     import ipywidgets as widgets
-    
+
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
         value="AUTO",
         description="Device:",
         disabled=False,
     )
-    
+
     device
 
 
@@ -960,10 +960,10 @@ can provide device selecting one from available in dropdown list.
 .. code:: ipython3
 
     from transformers import CLIPTokenizer
-    
+
     tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
     scheduler = EulerAncestralDiscreteScheduler.from_config(scheduler_config)
-    
+
     ov_pipe = OVInstructPix2PixPipeline(
         tokenizer,
         scheduler,
@@ -1017,7 +1017,7 @@ seed for latent state initialization and number of steps.
 
     import io
     import requests
-    
+
     default_url = "https://user-images.githubusercontent.com/29454499/223343459-4ac944f0-502e-4acf-9813-8e9f0abc8a16.jpg"
     # read uploaded image
     image = PIL.Image.open(io.BytesIO(image_widget.value[-1]["content"]) if image_widget.value else requests.get(default_url, stream=True).raw)
@@ -1099,7 +1099,7 @@ improve model inference speed.
         description="Quantization",
         disabled=False,
     )
-    
+
     to_quantize
 
 
@@ -1118,12 +1118,12 @@ Let’s load ``skip magic`` extension to skip quantization if
 
     # Fetch `skip_kernel_extension` module
     import requests
-    
+
     r = requests.get(
         url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/skip_kernel_extension.py",
     )
     open("skip_kernel_extension.py", "w").write(r.text)
-    
+
     %load_ext skip_kernel_extension
 
 Prepare calibration dataset
@@ -1139,29 +1139,29 @@ model inputs for calibration we should customize ``CompiledModel``.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     import datasets
     from tqdm.notebook import tqdm
     from transformers import Pipeline
     from typing import Any, Dict, List
-    
+
     class CompiledModelDecorator(ov.CompiledModel):
         def __init__(self, compiled_model, prob: float, data_cache: List[Any] = None):
             super().__init__(compiled_model)
             self.data_cache = data_cache if data_cache else []
             self.prob = np.clip(prob, 0, 1)
-    
+
         def __call__(self, *args, **kwargs):
             if np.random.rand() >= self.prob:
                 self.data_cache.append(*args)
             return super().__call__(*args, **kwargs)
-    
+
     def collect_calibration_data(pix2pix_pipeline: Pipeline, subset_size: int) -> List[Dict]:
         original_unet = pix2pix_pipeline.unet
         pix2pix_pipeline.unet = CompiledModelDecorator(original_unet, prob=0.3)
         dataset = datasets.load_dataset("fusing/instructpix2pix-1000-samples", split="train", streaming=True).shuffle(seed=42)
         pix2pix_pipeline.set_progress_bar_config(disable=True)
-    
+
         # Run inference for data collection
         pbar = tqdm(total=subset_size)
         diff = 0
@@ -1175,7 +1175,7 @@ model inputs for calibration we should customize ``CompiledModel``.
                 break
             pbar.update(collected_subset_size - diff)
             diff = collected_subset_size
-    
+
         calibration_dataset = pix2pix_pipeline.unet.data_cache
         pix2pix_pipeline.set_progress_bar_config(disable=False)
         pix2pix_pipeline.unet = original_unet
@@ -1184,7 +1184,7 @@ model inputs for calibration we should customize ``CompiledModel``.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     UNET_INT8_OV_PATH = Path("unet_int8.xml")
     if not UNET_INT8_OV_PATH.exists():
         subset_size = 300
@@ -1216,9 +1216,9 @@ Create a quantized model from the pre-trained converted OpenVINO model.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     import nncf
-    
+
     if UNET_INT8_OV_PATH.exists():
         print("Loading quantized model")
         quantized_unet = core.read_model(UNET_INT8_OV_PATH)
@@ -1261,16 +1261,16 @@ data.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     print('Pipeline settings')
     print(f'Input text: {text_prompt.value}')
     print(f'Seed: {seed.value}')
     print(f'Number of steps: {num_steps.value}')
     np.random.seed(seed.value)
-    
+
     int8_pipe = OVInstructPix2PixPipeline(tokenizer, scheduler, core, TEXT_ENCODER_OV_PATH, VAE_ENCODER_OV_PATH, UNET_INT8_OV_PATH, VAE_DECODER_OV_PATH, device=device.value)
     int8_processed_image = int8_pipe(text_prompt.value, image, num_steps.value)
-    
+
     fig = visualize_results(processed_image[0], int8_processed_image[0], img1_title="FP16 result", img2_title="INT8 result")
 
 
@@ -1307,9 +1307,9 @@ models, we use median inference time on calibration subset.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     import time
-    
+
     calibration_dataset = datasets.load_dataset("fusing/instructpix2pix-1000-samples", split="train", streaming=True)
     validation_data = []
     validation_size = 10
@@ -1318,7 +1318,7 @@ models, we use median inference time on calibration subset.
         prompt = batch["edit_prompt"]
         input_image = batch["input_image"].convert("RGB")
         validation_data.append((prompt, input_image))
-    
+
     def calculate_inference_time(pix2pix_pipeline, calibration_dataset, size=10):
         inference_time = []
         pix2pix_pipeline.set_progress_bar_config(disable=True)
@@ -1333,7 +1333,7 @@ models, we use median inference time on calibration subset.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     fp_latency = calculate_inference_time(ov_pipe, validation_data)
     int8_latency = calculate_inference_time(int8_pipe, validation_data)
     print(f"Performance speed up: {fp_latency / int8_latency:.3f}")
@@ -1360,7 +1360,7 @@ Interactive demo with Gradio
         description="Precision:",
         disabled=False,
     )
-    
+
     pipe_precision
 
 
@@ -1377,27 +1377,27 @@ Interactive demo with Gradio
     import gradio as gr
     from pathlib import Path
     import numpy as np
-    
+
     default_url = "https://user-images.githubusercontent.com/29454499/223343459-4ac944f0-502e-4acf-9813-8e9f0abc8a16.jpg"
     path = Path("data/example.jpg")
     path.parent.mkdir(parents=True, exist_ok=True)
-    
+
     r = requests.get(default_url)
-    
+
     with path.open("wb") as f:
         f.write(r.content)
-    
+
     pipeline = int8_pipe if pipe_precision.value == "INT8" else ov_pipe
-    
-    
+
+
     def generate(img, text, seed, num_steps, _=gr.Progress(track_tqdm=True)):
         if img is None:
             raise gr.Error("Please upload an image or choose one from the examples list")
         np.random.seed(seed)
         result = pipeline(text, img, num_steps)[0]
         return result
-    
-    
+
+
     demo = gr.Interface(
         generate,
         [
@@ -1415,7 +1415,7 @@ Interactive demo with Gradio
         gr.Image(label="Result"),
         examples=[[path, "Make it in galaxy"]],
     )
-    
+
     try:
         demo.queue().launch(debug=False)
     except Exception:
diff --git a/docs/notebooks/knowledge-graphs-conve-with-output.rst b/docs/notebooks/knowledge-graphs-conve-with-output.rst
index 498925ca6c0a27..dfccf692aa522e 100644
--- a/docs/notebooks/knowledge-graphs-conve-with-output.rst
+++ b/docs/notebooks/knowledge-graphs-conve-with-output.rst
@@ -16,8 +16,8 @@ Knowledge Graph Embeddings” (https://arxiv.org/abs/1707.01476). The
 sample dataset can be downloaded from:
 https://github.com/TimDettmers/ConvE/tree/master/countries/countries_S1
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Windows specific settings <#windows-specific-settings>`__
 -  `Import the packages needed for successful
@@ -222,7 +222,7 @@ Download Model Checkpoint
 
 .. parsed-literal::
 
-    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/knowledge-graphs-conve/models/conve.pt')
+    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/knowledge-graphs-conve/models/conve.pt')
 
 
 
@@ -384,7 +384,7 @@ typical to use metrics such as Mean Reciprocal Rank, Hits@10 etc.
 
 .. parsed-literal::
 
-    Average time taken for inference: 0.6391008694966634 ms
+    Average time taken for inference: 0.6698866685231527 ms
     Mean accuracy of the model on the test dataset: 0.875
 
 
@@ -531,7 +531,7 @@ select device from dropdown list for running inference using OpenVINO
 
 .. parsed-literal::
 
-    Average time taken for inference: 0.7032553354899088 ms
+    Average time taken for inference: 0.5994141101837158 ms
     Mean accuracy of the model on the test dataset: 0.10416666666666667
 
 
@@ -550,7 +550,7 @@ Determine the platform specific speedup obtained through OpenVINO graph optimiza
 
 .. parsed-literal::
 
-    Speedup with OpenVINO optimizations: 0.91 X
+    Speedup with OpenVINO optimizations: 1.12 X
 
 
 Benchmark the converted OpenVINO model using benchmark app
@@ -584,18 +584,18 @@ inference can also be obtained by looking at the benchmark app results.
     [ INFO ] Parsing input parameters
     [Step 2/11] Loading OpenVINO Runtime
     [ INFO ] OpenVINO:
-    [ INFO ] Build ................................. 2024.1.0-15008-f4afc983258-releases/2024/1
+    [ INFO ] Build ................................. 2024.2.0-15519-5c0f38f83f6-releases/2024/2
     [ INFO ] 
     [ INFO ] Device info:
     [ INFO ] CPU
-    [ INFO ] Build ................................. 2024.1.0-15008-f4afc983258-releases/2024/1
+    [ INFO ] Build ................................. 2024.2.0-15519-5c0f38f83f6-releases/2024/2
     [ INFO ] 
     [ INFO ] 
     [Step 3/11] Setting device configuration
     [ WARNING ] Performance hint was not explicitly specified in command line. Device(CPU) performance hint will be set to PerformanceMode.THROUGHPUT.
     [Step 4/11] Reading model files
     [ INFO ] Loading model files
-    [ INFO ] Read model took 5.04 ms
+    [ INFO ] Read model took 4.55 ms
     [ INFO ] Original model I/O parameters:
     [ INFO ] Model inputs:
     [ INFO ]     e1 (node: e1) : i64 / [...] / []
@@ -611,13 +611,12 @@ inference can also be obtained by looking at the benchmark app results.
     [ INFO ] Model outputs:
     [ INFO ]     ***NO_NAME*** (node: aten::softmax/Softmax) : f32 / [...] / [1,271]
     [Step 7/11] Loading the model to the device
-    [ INFO ] Compile model took 64.51 ms
+    [ INFO ] Compile model took 71.77 ms
     [Step 8/11] Querying optimal runtime parameters
     [ INFO ] Model:
     [ INFO ]   NETWORK_NAME: Model0
     [ INFO ]   OPTIMAL_NUMBER_OF_INFER_REQUESTS: 12
     [ INFO ]   NUM_STREAMS: 12
-    [ INFO ]   AFFINITY: Affinity.CORE
     [ INFO ]   INFERENCE_NUM_THREADS: 24
     [ INFO ]   PERF_COUNT: NO
     [ INFO ]   INFERENCE_PRECISION_HINT: <Type: 'float32'>
@@ -634,6 +633,7 @@ inference can also be obtained by looking at the benchmark app results.
     [ INFO ]   CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE: 1.0
     [ INFO ]   DYNAMIC_QUANTIZATION_GROUP_SIZE: 0
     [ INFO ]   KV_CACHE_PRECISION: <Type: 'float16'>
+    [ INFO ]   AFFINITY: Affinity.CORE
     [Step 9/11] Creating infer requests and preparing input tensors
     [ WARNING ] No input files were given for input 'e1'!. This input will be filled with random values!
     [ WARNING ] No input files were given for input 'rel'!. This input will be filled with random values!
@@ -641,17 +641,17 @@ inference can also be obtained by looking at the benchmark app results.
     [ INFO ] Fill input 'rel' with random values 
     [Step 10/11] Measuring performance (Start inference asynchronously, 12 inference requests, limits: 10000 ms duration)
     [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
-    [ INFO ] First inference took 1.24 ms
+    [ INFO ] First inference took 1.40 ms
     [Step 11/11] Dumping statistics report
     [ INFO ] Execution Devices:['CPU']
-    [ INFO ] Count:            100548 iterations
-    [ INFO ] Duration:         10001.41 ms
+    [ INFO ] Count:            101232 iterations
+    [ INFO ] Duration:         10000.87 ms
     [ INFO ] Latency:
-    [ INFO ]    Median:        1.02 ms
-    [ INFO ]    Average:       1.03 ms
+    [ INFO ]    Median:        1.00 ms
+    [ INFO ]    Average:       1.02 ms
     [ INFO ]    Min:           0.65 ms
-    [ INFO ]    Max:           8.40 ms
-    [ INFO ] Throughput:   10053.38 FPS
+    [ INFO ]    Max:           12.95 ms
+    [ INFO ] Throughput:   10122.32 FPS
 
 
 Conclusions
diff --git a/docs/notebooks/kosmos2-multimodal-large-language-model-with-output.rst b/docs/notebooks/kosmos2-multimodal-large-language-model-with-output.rst
index 4fd6dcd0a39190..654298e2e39166 100644
--- a/docs/notebooks/kosmos2-multimodal-large-language-model-with-output.rst
+++ b/docs/notebooks/kosmos2-multimodal-large-language-model-with-output.rst
@@ -33,8 +33,8 @@ more accurate, informational, and comprehensive answers.
 
    image
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Install requirements <#install-requirements>`__
 -  `Original model inference <#original-model-inference>`__
@@ -66,7 +66,7 @@ Install requirements
 
 .. parsed-literal::
 
-    Requirement already satisfied: pip in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (24.0)
+    Requirement already satisfied: pip in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (24.1.1)
     Note: you may need to restart the kernel to use updated packages.
     Note: you may need to restart the kernel to use updated packages.
     Note: you may need to restart the kernel to use updated packages.
@@ -133,10 +133,10 @@ example <https://huggingface.co/microsoft/kosmos-2-patch14-224>`__
 
 .. parsed-literal::
 
-    2024-06-06 00:35:34.439294: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-06-06 00:35:34.473205: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-07-02 00:40:27.165807: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-07-02 00:40:27.199544: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2024-06-06 00:35:34.967434: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-07-02 00:40:27.792699: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
     Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
 
 
@@ -356,11 +356,11 @@ Vision model accept ``pixel_values`` and returns ``image_embeds``.
 .. parsed-literal::
 
     [ WARNING ]  Please fix your imports. Module %s has been moved to %s. The old module will be deleted in version %s.
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4481: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4481: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
       warnings.warn(
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/kosmos2/modeling_kosmos2.py:466: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/kosmos2/modeling_kosmos2.py:466: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/kosmos2/modeling_kosmos2.py:506: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/kosmos2/modeling_kosmos2.py:506: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
 
 
@@ -388,7 +388,7 @@ Convert Image To Text Projection model
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:165: UserWarning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the .grad field to be populated for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations. (Triggered internally at aten/src/ATen/core/TensorBody.h:489.)
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:165: UserWarning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the .grad field to be populated for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations. (Triggered internally at aten/src/ATen/core/TensorBody.h:489.)
       if a.grad is not None:
 
 
@@ -523,13 +523,13 @@ generated text by ``AutoProcessor``.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/kosmos2/modeling_kosmos2.py:805: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/kosmos2/modeling_kosmos2.py:805: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if max_pos > self.weights.size(0):
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/kosmos2/modeling_kosmos2.py:1114: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/kosmos2/modeling_kosmos2.py:1114: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if input_shape[-1] > 1:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/kosmos2/modeling_kosmos2.py:921: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/kosmos2/modeling_kosmos2.py:921: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if attention_mask.size() != (batch_size, 1, seq_length, src_len):
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/kosmos2/modeling_kosmos2.py:1207: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/kosmos2/modeling_kosmos2.py:1207: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if past_key_values_length > 0:
 
 
diff --git a/docs/notebooks/kosmos2-multimodal-large-language-model-with-output_files/kosmos2-multimodal-large-language-model-with-output_29_0.jpg b/docs/notebooks/kosmos2-multimodal-large-language-model-with-output_files/kosmos2-multimodal-large-language-model-with-output_29_0.jpg
index f8c2edd227a7c7..1b2d8a23f49737 100644
--- a/docs/notebooks/kosmos2-multimodal-large-language-model-with-output_files/kosmos2-multimodal-large-language-model-with-output_29_0.jpg
+++ b/docs/notebooks/kosmos2-multimodal-large-language-model-with-output_files/kosmos2-multimodal-large-language-model-with-output_29_0.jpg
@@ -1,3 +1,3 @@
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-size 121469
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+size 119263
diff --git a/docs/notebooks/kosmos2-multimodal-large-language-model-with-output_files/kosmos2-multimodal-large-language-model-with-output_29_0.png b/docs/notebooks/kosmos2-multimodal-large-language-model-with-output_files/kosmos2-multimodal-large-language-model-with-output_29_0.png
index 6dfdae1f02dd71..3cce0093693746 100644
--- a/docs/notebooks/kosmos2-multimodal-large-language-model-with-output_files/kosmos2-multimodal-large-language-model-with-output_29_0.png
+++ b/docs/notebooks/kosmos2-multimodal-large-language-model-with-output_files/kosmos2-multimodal-large-language-model-with-output_29_0.png
@@ -1,3 +1,3 @@
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-oid sha256:4d7ff778d8a6131d02d03afc15c4ff72695e117e44e9e41d0cd1ade176d07190
-size 1150902
+oid sha256:2abbef76cb1c0a3076d56bde584f79860cc52f45c248a103811e598b47d46f4a
+size 1151191
diff --git a/docs/notebooks/kosmos2-multimodal-large-language-model-with-output_files/kosmos2-multimodal-large-language-model-with-output_8_0.jpg b/docs/notebooks/kosmos2-multimodal-large-language-model-with-output_files/kosmos2-multimodal-large-language-model-with-output_8_0.jpg
index 36b6b0fea92eb5..02265db70f48b6 100644
--- a/docs/notebooks/kosmos2-multimodal-large-language-model-with-output_files/kosmos2-multimodal-large-language-model-with-output_8_0.jpg
+++ b/docs/notebooks/kosmos2-multimodal-large-language-model-with-output_files/kosmos2-multimodal-large-language-model-with-output_8_0.jpg
@@ -1,3 +1,3 @@
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-size 123612
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+size 122206
diff --git a/docs/notebooks/kosmos2-multimodal-large-language-model-with-output_files/kosmos2-multimodal-large-language-model-with-output_8_0.png b/docs/notebooks/kosmos2-multimodal-large-language-model-with-output_files/kosmos2-multimodal-large-language-model-with-output_8_0.png
index 00b3c37439a39e..b99e539adc149e 100644
--- a/docs/notebooks/kosmos2-multimodal-large-language-model-with-output_files/kosmos2-multimodal-large-language-model-with-output_8_0.png
+++ b/docs/notebooks/kosmos2-multimodal-large-language-model-with-output_files/kosmos2-multimodal-large-language-model-with-output_8_0.png
@@ -1,3 +1,3 @@
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-oid sha256:a6f20b1ac6e439ce83cdf50c7a44d13853072fb966d5ca52e1a83279be35b44a
-size 1150696
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+size 1151028
diff --git a/docs/notebooks/language-quantize-bert-with-output.rst b/docs/notebooks/language-quantize-bert-with-output.rst
index d260745104512a..d818bae6088b75 100644
--- a/docs/notebooks/language-quantize-bert-with-output.rst
+++ b/docs/notebooks/language-quantize-bert-with-output.rst
@@ -22,8 +22,8 @@ and datasets. It consists of the following steps:
 -  Compare the performance of the original, converted and quantized
    models.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Imports <#imports>`__
 -  `Settings <#settings>`__
@@ -90,10 +90,10 @@ Imports
 
 .. parsed-literal::
 
-    2024-06-06 00:36:52.607166: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-06-06 00:36:52.641403: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-07-02 00:41:45.725166: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-07-02 00:41:45.787216: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2024-06-06 00:36:53.239122: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-07-02 00:41:46.298436: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 .. parsed-literal::
@@ -191,7 +191,7 @@ PyTorch model formats are supported:
 .. parsed-literal::
 
     [ WARNING ]  Please fix your imports. Module %s has been moved to %s. The old module will be deleted in version %s.
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4481: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4481: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
       warnings.warn(
 
 
@@ -267,17 +267,17 @@ The optimization process contains the following steps:
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -288,17 +288,17 @@ The optimization process contains the following steps:
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -314,17 +314,17 @@ The optimization process contains the following steps:
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -335,17 +335,17 @@ The optimization process contains the following steps:
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -522,9 +522,9 @@ Frames Per Second (FPS) for images.
 
 .. parsed-literal::
 
-    PyTorch model on CPU: 0.068 seconds per sentence, SPS: 14.61
-    IR FP32 model in OpenVINO Runtime/AUTO: 0.021 seconds per sentence, SPS: 47.63
-    OpenVINO IR INT8 model in OpenVINO Runtime/AUTO: 0.010 seconds per sentence, SPS: 103.25
+    PyTorch model on CPU: 0.067 seconds per sentence, SPS: 14.88
+    IR FP32 model in OpenVINO Runtime/AUTO: 0.021 seconds per sentence, SPS: 47.87
+    OpenVINO IR INT8 model in OpenVINO Runtime/AUTO: 0.010 seconds per sentence, SPS: 105.04
 
 
 Finally, measure the inference performance of OpenVINO ``FP32`` and
@@ -554,18 +554,18 @@ in OpenVINO.
     [Step 2/11] Loading OpenVINO Runtime
     [ WARNING ] Default duration 120 seconds is used for unknown device AUTO
     [ INFO ] OpenVINO:
-    [ INFO ] Build ................................. 2024.1.0-15008-f4afc983258-releases/2024/1
+    [ INFO ] Build ................................. 2024.2.0-15519-5c0f38f83f6-releases/2024/2
     [ INFO ] 
     [ INFO ] Device info:
     [ INFO ] AUTO
-    [ INFO ] Build ................................. 2024.1.0-15008-f4afc983258-releases/2024/1
+    [ INFO ] Build ................................. 2024.2.0-15519-5c0f38f83f6-releases/2024/2
     [ INFO ] 
     [ INFO ] 
     [Step 3/11] Setting device configuration
     [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.LATENCY.
     [Step 4/11] Reading model files
     [ INFO ] Loading model files
-    [ INFO ] Read model took 19.23 ms
+    [ INFO ] Read model took 17.78 ms
     [ INFO ] Original model I/O parameters:
     [ INFO ] Model inputs:
     [ INFO ]     input_ids (node: input_ids) : i64 / [...] / [1,?]
@@ -576,7 +576,7 @@ in OpenVINO.
     [Step 5/11] Resizing model to match image sizes and given batch
     [ INFO ] Model batch size: 1
     [ INFO ] Reshaping model: 'input_ids': [1,128], '63': [1,128], 'token_type_ids': [1,128]
-    [ INFO ] Reshape model took 10.36 ms
+    [ INFO ] Reshape model took 5.07 ms
     [Step 6/11] Configuring input of the model
     [ INFO ] Model inputs:
     [ INFO ]     input_ids (node: input_ids) : i64 / [...] / [1,128]
@@ -585,7 +585,7 @@ in OpenVINO.
     [ INFO ] Model outputs:
     [ INFO ]     logits (node: __module.classifier/aten::linear/Add) : f32 / [...] / [1,2]
     [Step 7/11] Loading the model to the device
-    [ INFO ] Compile model took 362.31 ms
+    [ INFO ] Compile model took 420.24 ms
     [Step 8/11] Querying optimal runtime parameters
     [ INFO ] Model:
     [ INFO ]   NETWORK_NAME: Model0
@@ -626,17 +626,17 @@ in OpenVINO.
     [ INFO ] Fill input 'token_type_ids' with random values 
     [Step 10/11] Measuring performance (Start inference synchronously, limits: 120000 ms duration)
     [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
-    [ INFO ] First inference took 21.08 ms
+    [ INFO ] First inference took 31.50 ms
     [Step 11/11] Dumping statistics report
     [ INFO ] Execution Devices:['CPU']
-    [ INFO ] Count:            6222 iterations
-    [ INFO ] Duration:         120015.61 ms
+    [ INFO ] Count:            5611 iterations
+    [ INFO ] Duration:         120019.71 ms
     [ INFO ] Latency:
-    [ INFO ]    Median:        19.16 ms
-    [ INFO ]    Average:       19.20 ms
-    [ INFO ]    Min:           18.52 ms
-    [ INFO ]    Max:           23.65 ms
-    [ INFO ] Throughput:   51.84 FPS
+    [ INFO ]    Median:        21.20 ms
+    [ INFO ]    Average:       21.30 ms
+    [ INFO ]    Min:           20.42 ms
+    [ INFO ]    Max:           32.46 ms
+    [ INFO ] Throughput:   46.75 FPS
 
 
 .. code:: ipython3
@@ -652,18 +652,18 @@ in OpenVINO.
     [Step 2/11] Loading OpenVINO Runtime
     [ WARNING ] Default duration 120 seconds is used for unknown device AUTO
     [ INFO ] OpenVINO:
-    [ INFO ] Build ................................. 2024.1.0-15008-f4afc983258-releases/2024/1
+    [ INFO ] Build ................................. 2024.2.0-15519-5c0f38f83f6-releases/2024/2
     [ INFO ] 
     [ INFO ] Device info:
     [ INFO ] AUTO
-    [ INFO ] Build ................................. 2024.1.0-15008-f4afc983258-releases/2024/1
+    [ INFO ] Build ................................. 2024.2.0-15519-5c0f38f83f6-releases/2024/2
     [ INFO ] 
     [ INFO ] 
     [Step 3/11] Setting device configuration
     [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.LATENCY.
     [Step 4/11] Reading model files
     [ INFO ] Loading model files
-    [ INFO ] Read model took 25.23 ms
+    [ INFO ] Read model took 24.23 ms
     [ INFO ] Original model I/O parameters:
     [ INFO ] Model inputs:
     [ INFO ]     input_ids (node: input_ids) : i64 / [...] / [1,?]
@@ -674,7 +674,7 @@ in OpenVINO.
     [Step 5/11] Resizing model to match image sizes and given batch
     [ INFO ] Model batch size: 1
     [ INFO ] Reshaping model: 'input_ids': [1,128], '63': [1,128], 'token_type_ids': [1,128]
-    [ INFO ] Reshape model took 12.77 ms
+    [ INFO ] Reshape model took 7.03 ms
     [Step 6/11] Configuring input of the model
     [ INFO ] Model inputs:
     [ INFO ]     input_ids (node: input_ids) : i64 / [...] / [1,128]
@@ -683,7 +683,7 @@ in OpenVINO.
     [ INFO ] Model outputs:
     [ INFO ]     logits (node: __module.classifier/aten::linear/Add) : f32 / [...] / [1,2]
     [Step 7/11] Loading the model to the device
-    [ INFO ] Compile model took 1122.18 ms
+    [ INFO ] Compile model took 1191.60 ms
     [Step 8/11] Querying optimal runtime parameters
     [ INFO ] Model:
     [ INFO ]   NETWORK_NAME: Model0
@@ -724,15 +724,15 @@ in OpenVINO.
     [ INFO ] Fill input 'token_type_ids' with random values 
     [Step 10/11] Measuring performance (Start inference synchronously, limits: 120000 ms duration)
     [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
-    [ INFO ] First inference took 18.38 ms
+    [ INFO ] First inference took 16.72 ms
     [Step 11/11] Dumping statistics report
     [ INFO ] Execution Devices:['CPU']
-    [ INFO ] Count:            11981 iterations
-    [ INFO ] Duration:         120000.10 ms
+    [ INFO ] Count:            12042 iterations
+    [ INFO ] Duration:         120008.71 ms
     [ INFO ] Latency:
-    [ INFO ]    Median:        10.28 ms
-    [ INFO ]    Average:       9.93 ms
-    [ INFO ]    Min:           8.10 ms
-    [ INFO ]    Max:           11.42 ms
-    [ INFO ] Throughput:   99.84 FPS
+    [ INFO ]    Median:        9.93 ms
+    [ INFO ]    Average:       9.88 ms
+    [ INFO ]    Min:           8.53 ms
+    [ INFO ]    Max:           11.09 ms
+    [ INFO ] Throughput:   100.34 FPS
 
diff --git a/docs/notebooks/latent-consistency-models-image-generation-with-output.rst b/docs/notebooks/latent-consistency-models-image-generation-with-output.rst
index 99d123c3e01bf3..aa38db2fa18149 100644
--- a/docs/notebooks/latent-consistency-models-image-generation-with-output.rst
+++ b/docs/notebooks/latent-consistency-models-image-generation-with-output.rst
@@ -44,8 +44,8 @@ An additional part demonstrates how to run quantization with
 `NNCF <https://github.com/openvinotoolkit/nncf/>`__ to speed up
 pipeline.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Prepare models for OpenVINO format
@@ -874,7 +874,7 @@ Prepare calibration dataset
 
 
 We use a portion of
-`conceptual_captions <https://huggingface.co/datasets/conceptual_captions>`__
+`conceptual_captions <https://huggingface.co/datasets/google-research-datasets/conceptual_captions>`__
 dataset from Hugging Face as calibration data. To collect intermediate
 model inputs for calibration we should customize ``CompiledModel``.
 
@@ -904,7 +904,7 @@ model inputs for calibration we should customize ``CompiledModel``.
         original_unet = lcm_pipeline.unet
         lcm_pipeline.unet = CompiledModelDecorator(original_unet, prob=0.3)
     
-        dataset = datasets.load_dataset("conceptual_captions", split="train").shuffle(seed=42)
+        dataset = datasets.load_dataset("google-research-datasets/conceptual_captions", split="train", trust_remote_code=True).shuffle(seed=42)
         lcm_pipeline.set_progress_bar_config(disable=True)
         safety_checker = lcm_pipeline.safety_checker
         lcm_pipeline.safety_checker = None
@@ -1004,17 +1004,17 @@ Create a quantized model from the pre-trained converted OpenVINO model.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -1025,17 +1025,17 @@ Create a quantized model from the pre-trained converted OpenVINO model.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -1051,17 +1051,17 @@ Create a quantized model from the pre-trained converted OpenVINO model.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -1136,7 +1136,7 @@ pipelines, we use median inference time on calibration subset.
     import time
     
     validation_size = 10
-    calibration_dataset = datasets.load_dataset("conceptual_captions", split="train")
+    calibration_dataset = datasets.load_dataset("google-research-datasets/conceptual_captions", split="train", trust_remote_code=True)
     validation_data = []
     for idx, batch in enumerate(calibration_dataset):
         if idx >= validation_size:
diff --git a/docs/notebooks/latent-consistency-models-optimum-demo-with-output.rst b/docs/notebooks/latent-consistency-models-optimum-demo-with-output.rst
index bf0a94ba4bfa21..7712734fdc058e 100644
--- a/docs/notebooks/latent-consistency-models-optimum-demo-with-output.rst
+++ b/docs/notebooks/latent-consistency-models-optimum-demo-with-output.rst
@@ -28,8 +28,8 @@ minimal steps (from 2 to 4) on any pre-trained LDMs (e.g. Stable
 Diffusion). To read more about LCM please refer to
 https://latent-consistency-models.github.io/
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Full precision model on the
@@ -121,10 +121,10 @@ https://huggingface.co/docs/diffusers/en/api/pipelines/latent_consistency_models
 
 .. parsed-literal::
 
-    2024-06-06 00:42:53.373765: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-06-06 00:42:53.408053: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-07-02 00:47:34.768202: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-07-02 00:47:34.804068: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2024-06-06 00:42:53.902704: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-07-02 00:47:35.404078: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 
@@ -216,11 +216,6 @@ and there is no need to do it manually
     ov_pipeline.reshape(batch_size=1, height=512, width=512, num_images_per_prompt=1)
 
 
-.. parsed-literal::
-
-    INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino
-
-
 .. parsed-literal::
 
     Framework not specified. Using pt to export the model.
@@ -309,8 +304,8 @@ and there is no need to do it manually
 
     Compiling the vae_decoder to CPU ...
     Compiling the unet to CPU ...
-    Compiling the vae_encoder to CPU ...
     Compiling the text_encoder to CPU ...
+    Compiling the vae_encoder to CPU ...
 
 
 .. code:: ipython3
diff --git a/docs/notebooks/latent-consistency-models-optimum-demo-with-output_files/latent-consistency-models-optimum-demo-with-output_15_1.jpg b/docs/notebooks/latent-consistency-models-optimum-demo-with-output_files/latent-consistency-models-optimum-demo-with-output_15_1.jpg
index 1b79b454ff2a0f..3f40c377022d15 100644
--- a/docs/notebooks/latent-consistency-models-optimum-demo-with-output_files/latent-consistency-models-optimum-demo-with-output_15_1.jpg
+++ b/docs/notebooks/latent-consistency-models-optimum-demo-with-output_files/latent-consistency-models-optimum-demo-with-output_15_1.jpg
@@ -1,3 +1,3 @@
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-oid sha256:ede98f62d1d27162d7d604073e9f8de2fb5d951d493981d40fed6cde05723048
-size 33866
+oid sha256:3369ec4b31d4fda14d817b071b1e61e05730c10fed5b7a75ce154a15c057b776
+size 35079
diff --git a/docs/notebooks/latent-consistency-models-optimum-demo-with-output_files/latent-consistency-models-optimum-demo-with-output_15_1.png b/docs/notebooks/latent-consistency-models-optimum-demo-with-output_files/latent-consistency-models-optimum-demo-with-output_15_1.png
index 897d59a667a53b..1f29b65823ea83 100644
--- a/docs/notebooks/latent-consistency-models-optimum-demo-with-output_files/latent-consistency-models-optimum-demo-with-output_15_1.png
+++ b/docs/notebooks/latent-consistency-models-optimum-demo-with-output_files/latent-consistency-models-optimum-demo-with-output_15_1.png
@@ -1,3 +1,3 @@
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-oid sha256:9010559959f775533c438237a49d197fa05cab0e22c7ed546dba031a90a5b4eb
-size 441523
+oid sha256:01503cea842ebbba11297166f8d6fdffe41afc2f232e1fa7109915157215f9ed
+size 458933
diff --git a/docs/notebooks/latent-consistency-models-optimum-demo-with-output_files/latent-consistency-models-optimum-demo-with-output_8_1.jpg b/docs/notebooks/latent-consistency-models-optimum-demo-with-output_files/latent-consistency-models-optimum-demo-with-output_8_1.jpg
index 69ccf67710d43e..cf7a9c7c2ef4d8 100644
--- a/docs/notebooks/latent-consistency-models-optimum-demo-with-output_files/latent-consistency-models-optimum-demo-with-output_8_1.jpg
+++ b/docs/notebooks/latent-consistency-models-optimum-demo-with-output_files/latent-consistency-models-optimum-demo-with-output_8_1.jpg
@@ -1,3 +1,3 @@
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-oid sha256:9246fabbebb05dc5ecc65e4f4e61c860fe49e4b175a3efdcbd4351689720b594
-size 38529
+oid sha256:b79b4f5940c84683ecdcc5afe8d8ccce5737b6e595a0b06d4cd5032d9fb62fba
+size 40257
diff --git a/docs/notebooks/latent-consistency-models-optimum-demo-with-output_files/latent-consistency-models-optimum-demo-with-output_8_1.png b/docs/notebooks/latent-consistency-models-optimum-demo-with-output_files/latent-consistency-models-optimum-demo-with-output_8_1.png
index 41dd987612842b..f360b6c13be651 100644
--- a/docs/notebooks/latent-consistency-models-optimum-demo-with-output_files/latent-consistency-models-optimum-demo-with-output_8_1.png
+++ b/docs/notebooks/latent-consistency-models-optimum-demo-with-output_files/latent-consistency-models-optimum-demo-with-output_8_1.png
@@ -1,3 +1,3 @@
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-oid sha256:c5809256a03aff9396dc484356572bdc0a532c31a43a3a25ee663ca04899068b
-size 463403
+oid sha256:2fefd49f701e3370c2ea136deb2a48e8d8babfff31e524e93099e26f5064f0bb
+size 470455
diff --git a/docs/notebooks/lcm-lora-controlnet-with-output.rst b/docs/notebooks/lcm-lora-controlnet-with-output.rst
index c93e5b02dee8aa..e631b83a1756ac 100644
--- a/docs/notebooks/lcm-lora-controlnet-with-output.rst
+++ b/docs/notebooks/lcm-lora-controlnet-with-output.rst
@@ -47,8 +47,8 @@ LoRA, OpenVINO and quantization with
 `NNCF <https://github.com/openvinotoolkit/nncf/>`__. Let us get
 “controlling”!
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Background <#background>`__
 
@@ -138,7 +138,7 @@ OpenVINO, see the following
 ControlNet
 ~~~~~~~~~~
 
- ControlNet is a neural network
+ControlNet is a neural network
 structure to control diffusion models by adding extra conditions. Using
 this new framework, we can capture a scene, structure, object, or
 subject pose from an inputted image, and then transfer that quality to
@@ -232,18 +232,18 @@ Prepare PyTorch models
 .. code:: ipython3
 
     from pathlib import Path
-    
+
     controlnet_id = "lllyasviel/control_v11p_sd15_normalbae"
     adapter_id = "latent-consistency/lcm-lora-sdv1-5"
     stable_diffusion_id = "runwayml/stable-diffusion-v1-5"
-    
+
     TEXT_ENCODER_OV_PATH = Path("model/text_encoder.xml")
     UNET_OV_PATH = Path("model/unet_controlnet.xml")
     CONTROLNET_OV_PATH = Path("model/controlnet-normalbae.xml")
     VAE_DECODER_OV_PATH = Path("model/vae_decoder.xml")
     TOKENIZER_PATH = Path("model/tokenizer")
     SCHEDULER_PATH = Path("model/scheduler")
-    
+
     skip_models = TEXT_ENCODER_OV_PATH.exists() and UNET_OV_PATH.exists() and CONTROLNET_OV_PATH.exists() and VAE_DECODER_OV_PATH.exists()
 
 Load Original Diffusers pipeline and prepare models for conversion
@@ -274,12 +274,12 @@ ControlNet model 3. Load LoRA weights to the pipeline using
 
     from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
     import gc
-    
-    
+
+
     def load_original_pytorch_pipeline_components(controlnet_id: str, stable_diffusion_id: str, adapter_id: str):
         """
         Helper function for loading Stable Diffusion ControlNet pipeline and applying LCM LoRA
-    
+
         Parameters:
           controlnet_id: model id from HuggingFace hub or local path for loading ControlNet model
           stable_diffusion_id: model id from HuggingFace hub or local path for loading Stable Diffusion model
@@ -290,7 +290,7 @@ ControlNet model 3. Load LoRA weights to the pipeline using
           unet: Stable Diffusion U-Net
           vae: Stable Diffusion Variational Autoencoder (VAE)
         """
-    
+
         # load controlnet model
         controlnet = ControlNetModel.from_pretrained(controlnet_id)
         # load stable diffusion pipeline
@@ -348,18 +348,18 @@ color-coded image.
     import matplotlib.pyplot as plt
     from PIL import Image
     import numpy as np
-    
+
     example_image_url = "https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae/resolve/main/images/input.png"
     r = requests.get(example_image_url)
     with open("example.png", "wb") as f:
         f.write(r.content)
-    
+
     processor = NormalBaeDetector.from_pretrained("lllyasviel/Annotators")
-    
+
     image = load_image("example.png")
     control_image = processor(image)
-    
-    
+
+
     def visualize_results(
         orig_img: Image.Image,
         normal_img: Image.Image,
@@ -368,7 +368,7 @@ color-coded image.
     ):
         """
         Helper function for results visualization
-    
+
         Parameters:
            orig_img (Image.Image): original image
            normal_img (Image.Image): image with bwith surface normal information
@@ -406,14 +406,14 @@ color-coded image.
         if result_img is not None:
             list_axes[2].imshow(np.array(result_img))
             list_axes[2].set_title("Result", fontsize=15)
-    
+
         fig.subplots_adjust(wspace=0.01 if is_horizontal else 0.00, hspace=0.01 if is_horizontal else 0.1)
         fig.tight_layout()
         if save_fig:
             fig.savefig("result.png", bbox_inches="tight")
         return fig
-    
-    
+
+
     fig = visualize_results(image, control_image)
 
 
@@ -471,8 +471,8 @@ blocks, which serves additional context for the UNet model.
     import torch
     import openvino as ov
     from functools import partial
-    
-    
+
+
     def cleanup_torchscript_cache():
         """
         Helper for removing cached model representation
@@ -480,8 +480,8 @@ blocks, which serves additional context for the UNet model.
         torch._C._jit_clear_class_registry()
         torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore()
         torch.jit._state._clear_class_state()
-    
-    
+
+
     def flattenize_inputs(inputs):
         """
         Helper function for resolve nested input structure (e.g. lists or tuples of tensors)
@@ -495,16 +495,16 @@ blocks, which serves additional context for the UNet model.
             else:
                 flatten_inputs.append(input_data)
         return flatten_inputs
-    
-    
+
+
     dtype_mapping = {
         torch.float32: ov.Type.f32,
         torch.float64: ov.Type.f64,
         torch.int32: ov.Type.i32,
         torch.int64: ov.Type.i64,
     }
-    
-    
+
+
     def prepare_input_info(input_dict):
         """
         Helper function for preparing input info (shapes and data types) for conversion based on example inputs
@@ -518,25 +518,25 @@ blocks, which serves additional context for the UNet model.
             if input_data.ndim == 4:
                 updated_shape[2] = -1
                 updated_shape[3] = -1
-    
+
             input_info.append((dtype_mapping[input_data.dtype], updated_shape))
         return input_info
-    
-    
+
+
     inputs = {
         "sample": torch.randn((1, 4, 64, 64)),
         "timestep": torch.tensor(1, dtype=torch.float32),
         "encoder_hidden_states": torch.randn((1, 77, 768)),
         "controlnet_cond": torch.randn((1, 3, 512, 512)),
     }
-    
-    
+
+
     # Prepare conditional inputs for U-Net
     if not UNET_OV_PATH.exists():
         controlnet.eval()
         with torch.no_grad():
             down_block_res_samples, mid_block_res_sample = controlnet(**inputs, return_dict=False)
-    
+
     if not CONTROLNET_OV_PATH.exists():
         input_info = prepare_input_info(inputs)
         with torch.no_grad():
@@ -548,7 +548,7 @@ blocks, which serves additional context for the UNet model.
         print("ControlNet successfully converted to IR")
     else:
         print(f"ControlNet will be loaded from {CONTROLNET_OV_PATH}")
-    
+
     del controlnet
     gc.collect()
 
@@ -578,8 +578,8 @@ generated by ControlNet.
 .. code:: ipython3
 
     from typing import Tuple
-    
-    
+
+
     class UnetWrapper(torch.nn.Module):
         def __init__(
             self,
@@ -597,7 +597,7 @@ generated by ControlNet.
             self.encoder_hidden_states_dtype = encoder_hidden_states
             self.down_block_additional_residuals_dtype = down_block_additional_residuals
             self.mid_block_additional_residual_dtype = mid_block_additional_residual
-    
+
         def forward(
             self,
             sample: torch.Tensor,
@@ -618,20 +618,20 @@ generated by ControlNet.
                 down_block_additional_residuals=down_block_additional_residuals,
                 mid_block_additional_residual=mid_block_additional_residual,
             )
-    
-    
+
+
     if not UNET_OV_PATH.exists():
         inputs.pop("controlnet_cond", None)
         inputs["down_block_additional_residuals"] = down_block_res_samples
         inputs["mid_block_additional_residual"] = mid_block_res_sample
         input_info = prepare_input_info(inputs)
-    
+
         wrapped_unet = UnetWrapper(unet)
         wrapped_unet.eval()
-    
+
         with torch.no_grad():
             ov_model = ov.convert_model(wrapped_unet, example_input=inputs)
-    
+
         for (input_dtype, input_shape), input_tensor in zip(input_info, ov_model.inputs):
             input_tensor.get_node().set_partial_shape(ov.PartialShape(input_shape))
             input_tensor.get_node().set_element_type(input_dtype)
@@ -696,7 +696,7 @@ hidden states.
             input_ids = torch.ones((1, 77), dtype=torch.long)
             # switch model to inference mode
             text_encoder.eval()
-    
+
             # disable gradients calculation for reducing memory consumption
             with torch.no_grad():
                 ov_model = ov.convert_model(
@@ -708,8 +708,8 @@ hidden states.
                 del ov_model
             cleanup_torchscript_cache()
             print("Text Encoder successfully converted to IR")
-    
-    
+
+
     if not TEXT_ENCODER_OV_PATH.exists():
         convert_encoder(text_encoder, TEXT_ENCODER_OV_PATH)
     else:
@@ -764,19 +764,19 @@ diffusion
         Returns:
             None
         """
-    
+
         class VAEDecoderWrapper(torch.nn.Module):
             def __init__(self, vae):
                 super().__init__()
                 self.vae = vae
-    
+
             def forward(self, latents):
                 return self.vae.decode(latents)
-    
+
         if not ir_path.exists():
             vae_decoder = VAEDecoderWrapper(vae)
             latents = torch.zeros((1, 4, 64, 64))
-    
+
             vae_decoder.eval()
             with torch.no_grad():
                 ov_model = ov.convert_model(vae_decoder, example_input=latents, input=[-1, 4, -1, -1])
@@ -784,13 +784,13 @@ diffusion
             del ov_model
             cleanup_torchscript_cache()
             print("VAE decoder successfully converted to IR")
-    
-    
+
+
     if not VAE_DECODER_OV_PATH.exists():
         convert_vae_decoder(vae, VAE_DECODER_OV_PATH)
     else:
         print(f"VAE decoder will be loaded from {VAE_DECODER_OV_PATH}")
-    
+
     del vae
 
 
@@ -818,13 +818,13 @@ OpenVINO.
     from transformers import CLIPTokenizer
     from typing import Union, List, Optional, Tuple
     import cv2
-    
-    
+
+
     def scale_fit_to_window(dst_width: int, dst_height: int, image_width: int, image_height: int):
         """
         Preprocessing helper function for calculating image size for resize with peserving original aspect ratio
         and fitting image to specific window size
-    
+
         Parameters:
           dst_width (int): destination window width
           dst_height (int): destination window height
@@ -836,15 +836,15 @@ OpenVINO.
         """
         im_scale = min(dst_height / image_height, dst_width / image_width)
         return int(im_scale * image_width), int(im_scale * image_height)
-    
-    
+
+
     def preprocess(image: Image.Image, dst_height: int = 512, dst_width: int = 512):
         """
         Image preprocessing function. Takes image in PIL.Image format, resizes it to keep aspect ration and fits to model input window 512x512,
         then converts it to np.ndarray and adds padding with zeros on right or bottom side of image (depends from aspect ratio), after that
         converts data to float32 data type and change range of values from [0, 255] to [-1, 1], finally, converts data layout from planar NHWC to NCHW.
         The function returns preprocessed input tensor and padding size, which can be used in postprocessing.
-    
+
         Parameters:
           image (Image.Image): input image
           dst_width: destination image width
@@ -863,15 +863,15 @@ OpenVINO.
         image = image.astype(np.float32) / 255.0
         image = image.transpose(0, 3, 1, 2)
         return image, pad
-    
-    
+
+
     def randn_tensor(
         shape: Union[Tuple, List],
         dtype: Optional[torch.dtype] = torch.float32,
     ):
         """
         Helper function for generation random values tensor with given shape and data type
-    
+
         Parameters:
           shape (Union[Tuple, List]): shape for filling random values
           dtype (torch.dtype, *optiona*, torch.float32): data type for result
@@ -880,13 +880,13 @@ OpenVINO.
         """
         latents = torch.randn(shape, dtype=dtype)
         return latents.numpy()
-    
-    
+
+
     class OVControlNetStableDiffusionPipeline(DiffusionPipeline):
         """
         OpenVINO inference pipeline for Stable Diffusion with ControlNet guidence
         """
-    
+
         def __init__(
             self,
             tokenizer: CLIPTokenizer,
@@ -903,7 +903,7 @@ OpenVINO.
             self.vae_scale_factor = 8
             self.scheduler = scheduler
             self.load_models(core, device, controlnet, text_encoder, unet, vae_decoder)
-    
+
         def load_models(
             self,
             core: ov.Core,
@@ -915,7 +915,7 @@ OpenVINO.
         ):
             """
             Function for loading models on device using OpenVINO
-    
+
             Parameters:
               core (Core): OpenVINO runtime Core class instance
               device (str): inference device
@@ -931,7 +931,7 @@ OpenVINO.
             self.register_to_config(unet=core.compile_model(unet, device))
             ov_config = {"INFERENCE_PRECISION_HINT": "f32"} if device != "CPU" else {}
             self.vae_decoder = core.compile_model(vae_decoder, device, ov_config)
-    
+
         def __call__(
             self,
             prompt: Union[str, List[str]],
@@ -947,7 +947,7 @@ OpenVINO.
         ):
             """
             Function invoked when calling the pipeline for generation.
-    
+
             Parameters:
                 prompt (`str` or `List[str]`):
                     The prompt or prompts to guide the image generation.
@@ -975,9 +975,9 @@ OpenVINO.
                     [PIL](https://pillow.readthedocs.io/en/stable/): `Image.Image` or `np.array`.
             Returns:
                 image ([List[Union[np.ndarray, Image.Image]]): generaited images
-    
+
             """
-    
+
             # 1. Define call parameters
             batch_size = 1 if isinstance(prompt, str) else len(prompt)
             if guidance_scale < 1 and negative_prompt:
@@ -992,17 +992,17 @@ OpenVINO.
                 do_classifier_free_guidance=do_classifier_free_guidance,
                 negative_prompt=negative_prompt,
             )
-    
+
             # 3. Preprocess image
             orig_width, orig_height = image.size
             image, pad = preprocess(image, height, width)
             if do_classifier_free_guidance:
                 image = np.concatenate(([image] * 2))
-    
+
             # 4. set timesteps
             self.scheduler.set_timesteps(num_inference_steps)
             timesteps = self.scheduler.timesteps
-    
+
             # 5. Prepare latent variables
             num_channels_latents = 4
             latents = self.prepare_latents(
@@ -1012,7 +1012,7 @@ OpenVINO.
                 width,
                 latents=latents,
             )
-    
+
             # 6. Denoising loop
             with self.progress_bar(total=num_inference_steps) as progress_bar:
                 for i, t in enumerate(timesteps):
@@ -1021,14 +1021,14 @@ OpenVINO.
                     # is applied for both the text and the input image.
                     latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
                     latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-    
+
                     result = self.controlnet(
                         [latent_model_input, t, text_embeddings, image],
                         share_inputs=True,
                         share_outputs=True,
                     )
                     down_and_mid_blok_samples = [sample * controlnet_conditioning_scale for _, sample in result.items()]
-    
+
                     # predict the noise residual
                     noise_pred = self.unet(
                         [
@@ -1040,28 +1040,28 @@ OpenVINO.
                         share_inputs=True,
                         share_outputs=True,
                     )[0]
-    
+
                     # perform guidance
                     if do_classifier_free_guidance:
                         noise_pred_uncond, noise_pred_text = noise_pred[0], noise_pred[1]
                         noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-    
+
                     # compute the previous noisy sample x_t -> x_t-1
                     latents = self.scheduler.step(torch.from_numpy(noise_pred), t, torch.from_numpy(latents)).prev_sample.numpy()
                     progress_bar.update()
-    
+
             # 7. Post-processing
             image = self.decode_latents(latents, pad)
-    
+
             # 8. Convert to PIL
             if output_type == "pil":
                 image = self.numpy_to_pil(image)
                 image = [img.resize((orig_width, orig_height), Image.Resampling.LANCZOS) for img in image]
             else:
                 image = [cv2.resize(img, (orig_width, orig_width)) for img in image]
-    
+
             return image
-    
+
         def _encode_prompt(
             self,
             prompt: Union[str, List[str]],
@@ -1071,7 +1071,7 @@ OpenVINO.
         ):
             """
             Encodes the prompt into text encoder hidden states.
-    
+
             Parameters:
                 prompt (str or list(str)): prompt to be encoded
                 num_images_per_prompt (int): number of images that should be generated per prompt
@@ -1081,7 +1081,7 @@ OpenVINO.
                 text_embeddings (np.ndarray): text encoder hidden states
             """
             batch_size = len(prompt) if isinstance(prompt, list) else 1
-    
+
             # tokenize input prompts
             text_inputs = self.tokenizer(
                 prompt,
@@ -1091,15 +1091,15 @@ OpenVINO.
                 return_tensors="np",
             )
             text_input_ids = text_inputs.input_ids
-    
+
             text_embeddings = self.text_encoder(text_input_ids, share_inputs=True, share_outputs=True)[0]
-    
+
             # duplicate text embeddings for each generation per prompt
             if num_images_per_prompt != 1:
                 bs_embed, seq_len, _ = text_embeddings.shape
                 text_embeddings = np.tile(text_embeddings, (1, num_images_per_prompt, 1))
                 text_embeddings = np.reshape(text_embeddings, (bs_embed * num_images_per_prompt, seq_len, -1))
-    
+
             # get unconditional embeddings for classifier free guidance
             if do_classifier_free_guidance:
                 uncond_tokens: List[str]
@@ -1117,21 +1117,21 @@ OpenVINO.
                     truncation=True,
                     return_tensors="np",
                 )
-    
+
                 uncond_embeddings = self.text_encoder(uncond_input.input_ids, share_inputs=True, share_outputs=True)[0]
-    
+
                 # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                 seq_len = uncond_embeddings.shape[1]
                 uncond_embeddings = np.tile(uncond_embeddings, (1, num_images_per_prompt, 1))
                 uncond_embeddings = np.reshape(uncond_embeddings, (batch_size * num_images_per_prompt, seq_len, -1))
-    
+
                 # For classifier free guidance, we need to do two forward passes.
                 # Here we concatenate the unconditional and text embeddings into a single batch
                 # to avoid doing two forward passes
                 text_embeddings = np.concatenate([uncond_embeddings, text_embeddings])
-    
+
             return text_embeddings
-    
+
         def prepare_latents(
             self,
             batch_size: int,
@@ -1144,7 +1144,7 @@ OpenVINO.
             """
             Preparing noise to image generation. If initial latents are not provided, they will be generated randomly,
             then prepared latents scaled by the standard deviation required by the scheduler
-    
+
             Parameters:
                batch_size (int): input batch size
                num_channels_latents (int): number of channels for noise generation
@@ -1165,15 +1165,15 @@ OpenVINO.
                 latents = randn_tensor(shape, dtype=dtype)
             else:
                 latents = latents
-    
+
             # scale the initial noise by the standard deviation required by the scheduler
             latents = latents * self.scheduler.init_noise_sigma
             return latents
-    
+
         def decode_latents(self, latents: np.array, pad: Tuple[int]):
             """
             Decode predicted image from latent space using VAE Decoder and unpad image result
-    
+
             Parameters:
                latents (np.ndarray): image encoded in diffusion latent space
                pad (Tuple[int]): each side padding sizes obtained on preprocessing step
@@ -1215,7 +1215,7 @@ the original pipeline scheduler with
 
     from diffusers import LCMScheduler
     from transformers import AutoTokenizer
-    
+
     if not TOKENIZER_PATH.exists():
         tokenizer = AutoTokenizer.from_pretrained(stable_diffusion_id, subfolder="tokenizer")
         tokenizer.save_pretrained(TOKENIZER_PATH)
@@ -1237,16 +1237,16 @@ select device from dropdown list for running inference using OpenVINO
 .. code:: ipython3
 
     import ipywidgets as widgets
-    
+
     core = ov.Core()
-    
+
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
         value="CPU",
         description="Device:",
         disabled=False,
     )
-    
+
     device
 
 
@@ -1301,7 +1301,7 @@ Let’s see model in action
 
     prompt = "A head full of roses"
     torch.manual_seed(4257)
-    
+
     result = ov_pipe(prompt, control_image, 4)
     result[0]
 
@@ -1368,7 +1368,7 @@ improve model inference speed.
 
     skip_for_device = "GPU" in device.value
     to_quantize = widgets.Checkbox(value=not skip_for_device, description="Quantization", disabled=skip_for_device)
-    
+
     to_quantize
 
 Let’s load ``skip magic`` extension to skip quantization if
@@ -1381,9 +1381,9 @@ Let’s load ``skip magic`` extension to skip quantization if
         url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/skip_kernel_extension.py",
     )
     open("skip_kernel_extension.py", "w").write(r.text)
-    
+
     int8_pipe = None
-    
+
     %load_ext skip_kernel_extension
 
 Prepare calibration datasets
@@ -1401,32 +1401,32 @@ To collect intermediate model inputs for calibration we should customize
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     import datasets
     from tqdm.notebook import tqdm
     from transformers import set_seed
     from typing import Any, Dict, List
-    
+
     set_seed(1)
-    
+
     class CompiledModelDecorator(ov.CompiledModel):
         def __init__(self, compiled_model, prob: float):
             super().__init__(compiled_model)
             self.data_cache = []
             self.prob = np.clip(prob, 0, 1)
-    
+
         def __call__(self, *args, **kwargs):
             if np.random.rand() >= self.prob:
                 self.data_cache.append(*args)
             return super().__call__(*args, **kwargs)
-    
+
     def collect_calibration_data(pipeline: OVControlNetStableDiffusionPipeline, subset_size: int) -> List[Dict]:
         original_unet = pipeline.unet
         pipeline.unet = CompiledModelDecorator(original_unet, prob=0.3)
-    
+
         dataset = datasets.load_dataset("fusing/instructpix2pix-1000-samples", split="train", streaming=True).shuffle(seed=42)
         pipeline.set_progress_bar_config(disable=True)
-    
+
         # Run inference for data collection
         pbar = tqdm(total=subset_size)
         diff = 0
@@ -1437,7 +1437,7 @@ To collect intermediate model inputs for calibration we should customize
                 continue
             image = batch["input_image"]
             control_image = processor(image)
-    
+
             _ = pipeline(prompt, image=control_image, num_inference_steps=4)
             collected_subset_size = len(pipeline.unet.data_cache)
             control_images.append((min(collected_subset_size, subset_size), control_image))
@@ -1446,7 +1446,7 @@ To collect intermediate model inputs for calibration we should customize
                 break
             pbar.update(collected_subset_size - diff)
             diff = collected_subset_size
-    
+
         control_calibration_dataset = pipeline.unet.data_cache
         pipeline.set_progress_bar_config(disable=False)
         pipeline.unet = original_unet
@@ -1455,7 +1455,7 @@ To collect intermediate model inputs for calibration we should customize
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     CONTROLNET_INT8_OV_PATH = Path("model/controlnet-normalbae_int8.xml")
     UNET_INT8_OV_PATH = Path("model/unet_controlnet_int8.xml")
     if not (CONTROLNET_INT8_OV_PATH.exists() and UNET_INT8_OV_PATH.exists()):
@@ -1475,7 +1475,7 @@ the last ControlNet input is a preprocessed ``control_image``.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     if not CONTROLNET_INT8_OV_PATH.exists():
         control_calibration_data = []
         prev_idx = 0
@@ -1500,9 +1500,9 @@ improvement in SD models and increased quantization time.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     import nncf
-    
+
     if not UNET_INT8_OV_PATH.exists():
         unet = core.read_model(UNET_OV_PATH)
         quantized_unet = nncf.quantize(
@@ -1518,7 +1518,7 @@ improvement in SD models and increased quantization time.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     if not CONTROLNET_INT8_OV_PATH.exists():
         controlnet = core.read_model(CONTROLNET_OV_PATH)
         quantized_controlnet = nncf.quantize(
@@ -1537,9 +1537,9 @@ the same input data.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     from IPython.display import display
-    
+
     int8_pipe = OVControlNetStableDiffusionPipeline(
         tokenizer,
         scheduler,
@@ -1550,12 +1550,12 @@ the same input data.
         VAE_DECODER_OV_PATH,
         device=device.value
     )
-    
+
     prompt = "A head full of roses"
     torch.manual_seed(4257)
-    
+
     int8_result = int8_pipe(prompt, control_image, 4)
-    
+
     fig = visualize_results(result[0], int8_result[0])
     fig.axes[0].set_title('FP16 result', fontsize=15)
     fig.axes[1].set_title('INT8 result', fontsize=15)
@@ -1587,9 +1587,9 @@ pipelines, we use median inference time on calibration subset.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     import time
-    
+
     validation_size = 10
     calibration_dataset = datasets.load_dataset("fusing/instructpix2pix-1000-samples", split="train", streaming=True).take(validation_size)
     validation_data = []
@@ -1598,7 +1598,7 @@ pipelines, we use median inference time on calibration subset.
         image = batch["input_image"]
         control_image = processor(image)
         validation_data.append((prompt, control_image))
-    
+
     def calculate_inference_time(pipeline, calibration_dataset):
         inference_time = []
         pipeline.set_progress_bar_config(disable=True)
@@ -1613,7 +1613,7 @@ pipelines, we use median inference time on calibration subset.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     fp_latency = calculate_inference_time(ov_pipe, validation_data)
     int8_latency = calculate_inference_time(int8_pipe, validation_data)
     print(f"Performance speed up: {fp_latency / int8_latency:.3f}")
@@ -1632,10 +1632,10 @@ Compare model file sizes
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     fp16_ir_model_size = UNET_OV_PATH.with_suffix(".bin").stat().st_size / 2**20
     quantized_model_size = UNET_INT8_OV_PATH.with_suffix(".bin").stat().st_size / 2**20
-    
+
     print(f"FP16 UNet size: {fp16_ir_model_size:.2f} MB")
     print(f"INT8 UNet size: {quantized_model_size:.2f} MB")
     print(f"UNet compression rate: {fp16_ir_model_size / quantized_model_size:.3f}")
@@ -1651,10 +1651,10 @@ Compare model file sizes
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     fp16_ir_model_size = CONTROLNET_OV_PATH.with_suffix(".bin").stat().st_size / 2**20
     quantized_model_size = CONTROLNET_INT8_OV_PATH.with_suffix(".bin").stat().st_size / 2**20
-    
+
     print(f"FP16 ControlNet size: {fp16_ir_model_size:.2f} MB")
     print(f"INT8 ControlNet size: {quantized_model_size:.2f} MB")
     print(f"ControlNet compression rate: {fp16_ir_model_size / quantized_model_size:.3f}")
@@ -1680,11 +1680,11 @@ options for generation: ``Guidance scale``, ``Seed`` and ``Steps``.
 .. code:: ipython3
 
     import gradio as gr
-    
+
     MAX_SEED = np.iinfo(np.int32).max
-    
+
     quantized_model_present = int8_pipe is not None
-    
+
     gr.close_all()
     with gr.Blocks() as demo:
         with gr.Row():
@@ -1713,16 +1713,16 @@ options for generation: ``Guidance scale``, ``Seed`` and ``Steps``.
             with gr.Column(visible=quantized_model_present) as quantization_step:
                 int_result = gr.Image(label="Result (Quantized)")
         examples = gr.Examples([["example.png", "a head full of roses"]], [inp_img, inp_prompt])
-    
+
         def extract_normal_map(img):
             if img is None:
                 raise gr.Error("Please upload the image or use one from the examples list")
             return processor(img)
-    
+
         def generate(img, prompt, negative_prompt, seed, num_steps, guidance_scale):
             torch.manual_seed(seed)
             control_img = extract_normal_map(img)
-    
+
             result = ov_pipe(
                 prompt,
                 control_img,
@@ -1741,7 +1741,7 @@ options for generation: ``Guidance scale``, ``Seed`` and ``Steps``.
                 )[0]
                 return control_img, result, int8_result
             return control_img, result
-    
+
         output_images = [out_normal, out_result]
         if quantized_model_present:
             output_images.append(int_result)
@@ -1750,8 +1750,8 @@ options for generation: ``Guidance scale``, ``Seed`` and ``Steps``.
             [inp_img, inp_prompt, inp_neg_prompt, inp_seed, inp_steps, guidance_scale],
             output_images,
         )
-    
-    
+
+
     try:
         demo.queue().launch(debug=False)
     except Exception:
diff --git a/docs/notebooks/legacy-mo-convert-to-openvino-with-output.rst b/docs/notebooks/legacy-mo-convert-to-openvino-with-output.rst
deleted file mode 100644
index dff89f9832ad8e..00000000000000
--- a/docs/notebooks/legacy-mo-convert-to-openvino-with-output.rst
+++ /dev/null
@@ -1,1658 +0,0 @@
-OpenVINO™ model conversion API
-==============================
-
-This notebook shows how to convert a model from original framework
-format to OpenVINO Intermediate Representation (IR).
-
-Table of contents:
-^^^^^^^^^^^^^^^^^^
-
--  `OpenVINO IR format <#openvino-ir-format>`__
--  `IR preparation with Python conversion API and Model Optimizer
-   command-line
-   tool <#ir-preparation-with-python-conversion-api-and-model-optimizer-command-line-tool>`__
--  `Fetching example models <#fetching-example-models>`__
--  `Basic conversion <#basic-conversion>`__
--  `Model conversion parameters <#model-conversion-parameters>`__
-
-   -  `Setting Input Shapes <#setting-input-shapes>`__
-   -  `Cutting Off Parts of a Model <#cutting-off-parts-of-a-model>`__
-   -  `Embedding Preprocessing
-      Computation <#embedding-preprocessing-computation>`__
-
-      -  `Specifying Layout <#specifying-layout>`__
-      -  `Changing Model Layout <#changing-model-layout>`__
-      -  `Specifying Mean and Scale
-         Values <#specifying-mean-and-scale-values>`__
-      -  `Reversing Input Channels <#reversing-input-channels>`__
-
-   -  `Compressing a Model to FP16 <#compressing-a-model-to-fp16>`__
-
--  `Convert Models Represented as Python
-   Objects <#convert-models-represented-as-python-objects>`__
-
-.. code:: ipython3
-
-    # Required imports. Please execute this cell first.
-    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu \
-    "openvino-dev>=2024.0.0" "requests" "tqdm" "transformers[onnx]>=4.21.1" "torch>=2.1" "torchvision"
-
-
-.. parsed-literal::
-
-    Note: you may need to restart the kernel to use updated packages.
-
-
-OpenVINO IR format
-------------------
-
-
-
-OpenVINO `Intermediate Representation
-(IR) <https://docs.openvino.ai/2024/documentation/openvino-ir-format.html>`__
-is the proprietary model format of OpenVINO. It is produced after
-converting a model with model conversion API. Model conversion API
-translates the frequently used deep learning operations to their
-respective similar representation in OpenVINO and tunes them with the
-associated weights and biases from the trained model. The resulting IR
-contains two files: an ``.xml`` file, containing information about
-network topology, and a ``.bin`` file, containing the weights and biases
-binary data.
-
-IR preparation with Python conversion API and Model Optimizer command-line tool
--------------------------------------------------------------------------------
-
-
-
-There are two ways to convert a model from the original framework format
-to OpenVINO IR: Python conversion API and Model Optimizer command-line
-tool. You can choose one of them based on whichever is most convenient
-for you. There should not be any differences in the results of model
-conversion if the same set of parameters is used. For more details,
-refer to `Model
-Preparation <https://docs.openvino.ai/2024/openvino-workflow/model-preparation.html>`__
-documentation.
-
-.. code:: ipython3
-
-    # Model Optimizer CLI tool parameters description
-    
-    ! mo --help
-
-
-.. parsed-literal::
-
-    usage: main.py [options]
-    
-    optional arguments:
-      -h, --help            show this help message and exit
-      --framework FRAMEWORK
-                            Name of the framework used to train the input model.
-    
-    Framework-agnostic parameters:
-      --model_name MODEL_NAME, -n MODEL_NAME
-                            Model_name parameter passed to the final create_ir
-                            transform. This parameter is used to name a network in
-                            a generated IR and output .xml/.bin files.
-      --output_dir OUTPUT_DIR, -o OUTPUT_DIR
-                            Directory that stores the generated IR. By default, it
-                            is the directory from where the Model Conversion is
-                            launched.
-      --freeze_placeholder_with_value FREEZE_PLACEHOLDER_WITH_VALUE
-                            Replaces input layer with constant node with provided
-                            value, for example: "node_name->True". It will be
-                            DEPRECATED in future releases. Use "input" option to
-                            specify a value for freezing.
-      --static_shape        Enables IR generation for fixed input shape (folding
-                            `ShapeOf` operations and shape-calculating sub-graphs
-                            to `Constant`). Changing model input shape using the
-                            OpenVINO Runtime API in runtime may fail for such an
-                            IR.
-      --use_new_frontend    Force the usage of new Frontend for model conversion
-                            into IR. The new Frontend is C++ based and is
-                            available for ONNX* and PaddlePaddle* models. Model
-                            Conversion API uses new Frontend for ONNX* and
-                            PaddlePaddle* by default that means `use_new_frontend`
-                            and `use_legacy_frontend` options are not specified.
-      --use_legacy_frontend
-                            Force the usage of legacy Frontend for model
-                            conversion into IR. The legacy Frontend is Python
-                            based and is available for TensorFlow*, ONNX*, MXNet*,
-                            Caffe*, and Kaldi* models.
-      --input_model INPUT_MODEL, -m INPUT_MODEL, -w INPUT_MODEL
-                            Tensorflow*: a file with a pre-trained model (binary
-                            or text .pb file after freezing). Caffe*: a model
-                            proto file with model weights.
-      --input INPUT         Quoted list of comma-separated input nodes names with
-                            shapes, data types, and values for freezing. The order
-                            of inputs in converted model is the same as order of
-                            specified operation names. The shape and value are
-                            specified as comma-separated lists. The data type of
-                            input node is specified in braces and can have one of
-                            the values: f64 (float64), f32 (float32), f16
-                            (float16), i64 (int64), i32 (int32), u8 (uint8),
-                            boolean (bool). Data type is optional. If it's not
-                            specified explicitly then there are two options: if
-                            input node is a parameter, data type is taken from the
-                            original node dtype, if input node is not a parameter,
-                            data type is set to f32. Example, to set `input_1`
-                            with shape [1,100], and Parameter node `sequence_len`
-                            with scalar input with value `150`, and boolean input
-                            `is_training` with `False` value use the following
-                            format:
-                            "input_1[1,100],sequence_len->150,is_training->False".
-                            Another example, use the following format to set input
-                            port 0 of the node `node_name1` with the shape [3,4]
-                            as an input node and freeze output port 1 of the node
-                            "node_name2" with the value [20,15] of the int32 type
-                            and shape [2]:
-                            "0:node_name1[3,4],node_name2:1[2]{i32}->[20,15]".
-      --output OUTPUT       The name of the output operation of the model or list
-                            of names. For TensorFlow*, do not add :0 to this
-                            name.The order of outputs in converted model is the
-                            same as order of specified operation names.
-      --input_shape INPUT_SHAPE
-                            Input shape(s) that should be fed to an input node(s)
-                            of the model. Shape is defined as a comma-separated
-                            list of integer numbers enclosed in parentheses or
-                            square brackets, for example [1,3,227,227] or
-                            (1,227,227,3), where the order of dimensions depends
-                            on the framework input layout of the model. For
-                            example, [N,C,H,W] is used for ONNX* models and
-                            [N,H,W,C] for TensorFlow* models. The shape can
-                            contain undefined dimensions (? or -1) and should fit
-                            the dimensions defined in the input operation of the
-                            graph. Boundaries of undefined dimension can be
-                            specified with ellipsis, for example
-                            [1,1..10,128,128]. One boundary can be undefined, for
-                            example [1,..100] or [1,3,1..,1..]. If there are
-                            multiple inputs in the model, --input_shape should
-                            contain definition of shape for each input separated
-                            by a comma, for example: [1,3,227,227],[2,4] for a
-                            model with two inputs with 4D and 2D shapes.
-                            Alternatively, specify shapes with the --input option.
-      --example_input EXAMPLE_INPUT
-                            Sample of model input in original framework. For
-                            PyTorch it can be torch.Tensor. For Tensorflow it can
-                            be tf.Tensor or numpy.ndarray. For PaddlePaddle it can
-                            be Paddle Variable.
-      --batch BATCH, -b BATCH
-                            Set batch size. It applies to 1D or higher dimension
-                            inputs. The default dimension index for the batch is
-                            zero. Use a label 'n' in --layout or --source_layout
-                            option to set the batch dimension. For example,
-                            "x(hwnc)" defines the third dimension to be the batch.
-      --mean_values MEAN_VALUES
-                            Mean values to be used for the input image per
-                            channel. Values to be provided in the (R,G,B) or
-                            [R,G,B] format. Can be defined for desired input of
-                            the model, for example: "--mean_values
-                            data[255,255,255],info[255,255,255]". The exact
-                            meaning and order of channels depend on how the
-                            original model was trained.
-      --scale_values SCALE_VALUES
-                            Scale values to be used for the input image per
-                            channel. Values are provided in the (R,G,B) or [R,G,B]
-                            format. Can be defined for desired input of the model,
-                            for example: "--scale_values
-                            data[255,255,255],info[255,255,255]". The exact
-                            meaning and order of channels depend on how the
-                            original model was trained. If both --mean_values and
-                            --scale_values are specified, the mean is subtracted
-                            first and then scale is applied regardless of the
-                            order of options in command line.
-      --scale SCALE, -s SCALE
-                            All input values coming from original network inputs
-                            will be divided by this value. When a list of inputs
-                            is overridden by the --input parameter, this scale is
-                            not applied for any input that does not match with the
-                            original input of the model. If both --mean_values and
-                            --scale are specified, the mean is subtracted first
-                            and then scale is applied regardless of the order of
-                            options in command line.
-      --reverse_input_channels [REVERSE_INPUT_CHANNELS]
-                            Switch the input channels order from RGB to BGR (or
-                            vice versa). Applied to original inputs of the model
-                            if and only if a number of channels equals 3. When
-                            --mean_values/--scale_values are also specified,
-                            reversing of channels will be applied to user's input
-                            data first, so that numbers in --mean_values and
-                            --scale_values go in the order of channels used in the
-                            original model. In other words, if both options are
-                            specified, then the data flow in the model looks as
-                            following: Parameter -> ReverseInputChannels -> Mean
-                            apply-> Scale apply -> the original body of the model.
-      --source_layout SOURCE_LAYOUT
-                            Layout of the input or output of the model in the
-                            framework. Layout can be specified in the short form,
-                            e.g. nhwc, or in complex form, e.g. "[n,h,w,c]".
-                            Example for many names: "in_name1([n,h,w,c]),in_name2(
-                            nc),out_name1(n),out_name2(nc)". Layout can be
-                            partially defined, "?" can be used to specify
-                            undefined layout for one dimension, "..." can be used
-                            to specify undefined layout for multiple dimensions,
-                            for example "?c??", "nc...", "n...c", etc.
-      --target_layout TARGET_LAYOUT
-                            Same as --source_layout, but specifies target layout
-                            that will be in the model after processing by
-                            ModelOptimizer.
-      --layout LAYOUT       Combination of --source_layout and --target_layout.
-                            Can't be used with either of them. If model has one
-                            input it is sufficient to specify layout of this
-                            input, for example --layout nhwc. To specify layouts
-                            of many tensors, names must be provided, for example:
-                            --layout "name1(nchw),name2(nc)". It is possible to
-                            instruct ModelOptimizer to change layout, for example:
-                            --layout "name1(nhwc->nchw),name2(cn->nc)". Also "*"
-                            in long layout form can be used to fuse dimensions,
-                            for example "[n,c,...]->[n*c,...]".
-      --compress_to_fp16 [COMPRESS_TO_FP16]
-                            If the original model has FP32 weights or biases, they
-                            are compressed to FP16. All intermediate data is kept
-                            in original precision. Option can be specified alone
-                            as "--compress_to_fp16", or explicit True/False values
-                            can be set, for example: "--compress_to_fp16=False",
-                            or "--compress_to_fp16=True"
-      --extensions EXTENSIONS
-                            Paths or a comma-separated list of paths to libraries
-                            (.so or .dll) with extensions. For the legacy MO path
-                            (if `--use_legacy_frontend` is used), a directory or a
-                            comma-separated list of directories with extensions
-                            are supported. To disable all extensions including
-                            those that are placed at the default location, pass an
-                            empty string.
-      --transform TRANSFORM
-                            Apply additional transformations. Usage: "--transform
-                            transformation_name1[args],transformation_name2..."
-                            where [args] is key=value pairs separated by
-                            semicolon. Examples: "--transform LowLatency2" or "--
-                            transform Pruning" or "--transform
-                            LowLatency2[use_const_initializer=False]" or "--
-                            transform "MakeStateful[param_res_names= {'input_name_
-                            1':'output_name_1','input_name_2':'output_name_2'}]"
-                            Available transformations: "LowLatency2",
-                            "MakeStateful", "Pruning"
-      --transformations_config TRANSFORMATIONS_CONFIG
-                            Use the configuration file with transformations
-                            description. Transformations file can be specified as
-                            relative path from the current directory, as absolute
-                            path or as arelative path from the mo root directory.
-      --silent [SILENT]     Prevent any output messages except those that
-                            correspond to log level equals ERROR, that can be set
-                            with the following option: --log_level. By default,
-                            log level is already ERROR.
-      --log_level {CRITICAL,ERROR,WARN,WARNING,INFO,DEBUG,NOTSET}
-                            Logger level of logging massages from MO. Expected one
-                            of ['CRITICAL', 'ERROR', 'WARN', 'WARNING', 'INFO',
-                            'DEBUG', 'NOTSET'].
-      --version             Version of Model Optimizer
-      --progress [PROGRESS]
-                            Enable model conversion progress display.
-      --stream_output [STREAM_OUTPUT]
-                            Switch model conversion progress display to a
-                            multiline mode.
-      --share_weights [SHARE_WEIGHTS]
-                            Map memory of weights instead reading files or share
-                            memory from input model. Currently, mapping feature is
-                            provided only for ONNX models that do not require
-                            fallback to the legacy ONNX frontend for the
-                            conversion.
-    
-    TensorFlow*-specific parameters:
-      --input_model_is_text [INPUT_MODEL_IS_TEXT]
-                            TensorFlow*: treat the input model file as a text
-                            protobuf format. If not specified, the Model Optimizer
-                            treats it as a binary file by default.
-      --input_checkpoint INPUT_CHECKPOINT
-                            TensorFlow*: variables file to load.
-      --input_meta_graph INPUT_META_GRAPH
-                            Tensorflow*: a file with a meta-graph of the model
-                            before freezing
-      --saved_model_dir SAVED_MODEL_DIR
-                            TensorFlow*: directory with a model in SavedModel
-                            format of TensorFlow 1.x or 2.x version.
-      --saved_model_tags SAVED_MODEL_TAGS
-                            Group of tag(s) of the MetaGraphDef to load, in string
-                            format, separated by ','. For tag-set contains
-                            multiple tags, all tags must be passed in.
-      --tensorflow_custom_operations_config_update TENSORFLOW_CUSTOM_OPERATIONS_CONFIG_UPDATE
-                            TensorFlow*: update the configuration file with node
-                            name patterns with input/output nodes information.
-      --tensorflow_object_detection_api_pipeline_config TENSORFLOW_OBJECT_DETECTION_API_PIPELINE_CONFIG
-                            TensorFlow*: path to the pipeline configuration file
-                            used to generate model created with help of Object
-                            Detection API.
-      --tensorboard_logdir TENSORBOARD_LOGDIR
-                            TensorFlow*: dump the input graph to a given directory
-                            that should be used with TensorBoard.
-      --tensorflow_custom_layer_libraries TENSORFLOW_CUSTOM_LAYER_LIBRARIES
-                            TensorFlow*: comma separated list of shared libraries
-                            with TensorFlow* custom operations implementation.
-    
-    Caffe*-specific parameters:
-      --input_proto INPUT_PROTO, -d INPUT_PROTO
-                            Deploy-ready prototxt file that contains a topology
-                            structure and layer attributes
-      --caffe_parser_path CAFFE_PARSER_PATH
-                            Path to Python Caffe* parser generated from
-                            caffe.proto
-      --k K                 Path to CustomLayersMapping.xml to register custom
-                            layers
-      --disable_omitting_optional [DISABLE_OMITTING_OPTIONAL]
-                            Disable omitting optional attributes to be used for
-                            custom layers. Use this option if you want to transfer
-                            all attributes of a custom layer to IR. Default
-                            behavior is to transfer the attributes with default
-                            values and the attributes defined by the user to IR.
-      --enable_flattening_nested_params [ENABLE_FLATTENING_NESTED_PARAMS]
-                            Enable flattening optional params to be used for
-                            custom layers. Use this option if you want to transfer
-                            attributes of a custom layer to IR with flattened
-                            nested parameters. Default behavior is to transfer the
-                            attributes without flattening nested parameters.
-    
-    MXNet-specific parameters:
-      --input_symbol INPUT_SYMBOL
-                            Symbol file (for example, model-symbol.json) that
-                            contains a topology structure and layer attributes
-      --nd_prefix_name ND_PREFIX_NAME
-                            Prefix name for args.nd and argx.nd files.
-      --pretrained_model_name PRETRAINED_MODEL_NAME
-                            Name of a pretrained MXNet model without extension and
-                            epoch number. This model will be merged with args.nd
-                            and argx.nd files
-      --save_params_from_nd [SAVE_PARAMS_FROM_ND]
-                            Enable saving built parameters file from .nd files
-      --legacy_mxnet_model [LEGACY_MXNET_MODEL]
-                            Enable MXNet loader to make a model compatible with
-                            the latest MXNet version. Use only if your model was
-                            trained with MXNet version lower than 1.0.0
-      --enable_ssd_gluoncv [ENABLE_SSD_GLUONCV]
-                            Enable pattern matchers replacers for converting
-                            gluoncv ssd topologies.
-    
-    Kaldi-specific parameters:
-      --counts COUNTS       Path to the counts file
-      --remove_output_softmax [REMOVE_OUTPUT_SOFTMAX]
-                            Removes the SoftMax layer that is the output layer
-      --remove_memory [REMOVE_MEMORY]
-                            Removes the Memory layer and use additional inputs
-                            outputs instead
-
-
-.. code:: ipython3
-
-    # Python conversion API parameters description
-    from openvino.tools import mo
-    
-    
-    mo.convert_model(help=True)
-
-
-.. parsed-literal::
-
-    Optional parameters:
-      --help 
-    			Print available parameters.
-      --framework 
-    			Name of the framework used to train the input model.
-    
-    Framework-agnostic parameters:
-      --input_model 
-    			Model object in original framework (PyTorch, Tensorflow) or path to
-    			model file.
-    			Tensorflow*: a file with a pre-trained model (binary or text .pb file
-    			after freezing).
-    			Caffe*: a model proto file with model weights
-    			
-    			Supported formats of input model:
-    			
-    			PaddlePaddle
-    			paddle.hapi.model.Model
-    			paddle.fluid.dygraph.layers.Layer
-    			paddle.fluid.executor.Executor
-    			
-    			PyTorch
-    			torch.nn.Module
-    			torch.jit.ScriptModule
-    			torch.jit.ScriptFunction
-    			
-    			TF
-    			tf.compat.v1.Graph
-    			tf.compat.v1.GraphDef
-    			tf.compat.v1.wrap_function
-    			tf.compat.v1.session
-    			
-    			TF2 / Keras
-    			tf.keras.Model
-    			tf.keras.layers.Layer
-    			tf.function
-    			tf.Module
-    			tf.train.checkpoint
-      --input 
-    			Input can be set by passing a list of InputCutInfo objects or by a list
-    			of tuples. Each tuple can contain optionally input name, input
-    			type or input shape. Example: input=("op_name", PartialShape([-1,
-    			3, 100, 100]), Type(np.float32)). Alternatively input can be set by
-    			a string or list of strings of the following format. Quoted list of comma-separated
-    			input nodes names with shapes, data types, and values for freezing.
-    			If operation names are specified, the order of inputs in converted
-    			model will be the same as order of specified operation names (applicable
-    			for TF2, ONNX, MxNet).
-    			The shape and value are specified as comma-separated lists. The data
-    			type of input node is specified
-    			in braces and can have one of the values: f64 (float64), f32 (float32),
-    			f16 (float16), i64
-    			(int64), i32 (int32), u8 (uint8), boolean (bool). Data type is optional.
-    			If it's not specified explicitly then there are two options: if input
-    			node is a parameter, data type is taken from the original node dtype,
-    			if input node is not a parameter, data type is set to f32. Example, to set
-    			`input_1` with shape [1,100], and Parameter node `sequence_len` with
-    			scalar input with value `150`, and boolean input `is_training` with
-    			`False` value use the following format: "input_1[1,100],sequence_len->150,is_training->False".
-    			Another example, use the following format to set input port 0 of the node
-    			`node_name1` with the shape [3,4] as an input node and freeze output
-    			port 1 of the node `node_name2` with the value [20,15] of the int32 type
-    			and shape [2]: "0:node_name1[3,4],node_name2:1[2]{i32}->[20,15]".
-    			
-      --output 
-    			The name of the output operation of the model or list of names. For TensorFlow*,
-    			do not add :0 to this name.The order of outputs in converted model is the
-    			same as order of specified operation names.
-      --input_shape 
-    			Input shape(s) that should be fed to an input node(s) of the model. Input
-    			shapes can be defined by passing a list of objects of type PartialShape,
-    			Shape, [Dimension, ...] or [int, ...] or by a string of the following
-    			format. Shape is defined as a comma-separated list of integer numbers
-    			enclosed in parentheses or square brackets, for example [1,3,227,227]
-    			or (1,227,227,3), where the order of dimensions depends on the framework
-    			input layout of the model. For example, [N,C,H,W] is used for ONNX* models
-    			and [N,H,W,C] for TensorFlow* models. The shape can contain undefined
-    			dimensions (? or -1) and should fit the dimensions defined in the input
-    			operation of the graph. Boundaries of undefined dimension can be specified
-    			with ellipsis, for example [1,1..10,128,128]. One boundary can be
-    			undefined, for example [1,..100] or [1,3,1..,1..]. If there are multiple
-    			inputs in the model, --input_shape should contain definition of shape
-    			for each input separated by a comma, for example: [1,3,227,227],[2,4]
-    			for a model with two inputs with 4D and 2D shapes. Alternatively, specify
-    			shapes with the --input option.
-      --example_input 
-    			Sample of model input in original framework.
-    			For PyTorch it can be torch.Tensor.
-    			For Tensorflow it can be tf.Tensor or numpy.ndarray.
-    			For PaddlePaddle it can be Paddle Variable.
-      --batch 
-    			Set batch size. It applies to 1D or higher dimension inputs.
-    			The default dimension index for the batch is zero.
-    			Use a label 'n' in --layout or --source_layout option to set the batch
-    			dimension.
-    			For example, "x(hwnc)" defines the third dimension to be the batch.
-    			
-      --mean_values 
-    			Mean values to be used for the input image per channel. Mean values can
-    			be set by passing a dictionary, where key is input name and value is mean
-    			value. For example mean_values={'data':[255,255,255],'info':[255,255,255]}.
-    			Or mean values can be set by a string of the following format. Values to
-    			be provided in the (R,G,B) or [R,G,B] format. Can be defined for desired
-    			input of the model, for example: "--mean_values data[255,255,255],info[255,255,255]".
-    			The exact meaning and order of channels depend on how the original model
-    			was trained.
-      --scale_values 
-    			Scale values to be used for the input image per channel. Scale values
-    			can be set by passing a dictionary, where key is input name and value is
-    			scale value. For example scale_values={'data':[255,255,255],'info':[255,255,255]}.
-    			Or scale values can be set by a string of the following format. Values
-    			are provided in the (R,G,B) or [R,G,B] format. Can be defined for desired
-    			input of the model, for example: "--scale_values data[255,255,255],info[255,255,255]".
-    			The exact meaning and order of channels depend on how the original model
-    			was trained. If both --mean_values and --scale_values are specified,
-    			the mean is subtracted first and then scale is applied regardless of
-    			the order of options in command line.
-      --scale 
-    			All input values coming from original network inputs will be divided
-    			by this value. When a list of inputs is overridden by the --input parameter,
-    			this scale is not applied for any input that does not match with the original
-    			input of the model. If both --mean_values and --scale  are specified,
-    			the mean is subtracted first and then scale is applied regardless of
-    			the order of options in command line.
-      --reverse_input_channels 
-    			Switch the input channels order from RGB to BGR (or vice versa). Applied
-    			to original inputs of the model if and only if a number of channels equals
-    			3. When --mean_values/--scale_values are also specified, reversing
-    			of channels will be applied to user's input data first, so that numbers
-    			in --mean_values and --scale_values go in the order of channels used
-    			in the original model. In other words, if both options are specified,
-    			then the data flow in the model looks as following: Parameter -> ReverseInputChannels
-    			-> Mean apply-> Scale apply -> the original body of the model.
-      --source_layout 
-    			Layout of the input or output of the model in the framework. Layout can
-    			be set by passing a dictionary, where key is input name and value is LayoutMap
-    			object. Or layout can be set by string of the following format. Layout
-    			can be specified in the short form, e.g. nhwc, or in complex form, e.g.
-    			"[n,h,w,c]". Example for many names: "in_name1([n,h,w,c]),in_name2(nc),out_name1(n),out_name2(nc)".
-    			Layout can be partially defined, "?" can be used to specify undefined
-    			layout for one dimension, "..." can be used to specify undefined layout
-    			for multiple dimensions, for example "?c??", "nc...", "n...c", etc.
-    			
-      --target_layout 
-    			Same as --source_layout, but specifies target layout that will be in
-    			the model after processing by ModelOptimizer.
-      --layout 
-    			Combination of --source_layout and --target_layout. Can't be used
-    			with either of them. If model has one input it is sufficient to specify
-    			layout of this input, for example --layout nhwc. To specify layouts
-    			of many tensors, names must be provided, for example: --layout "name1(nchw),name2(nc)".
-    			It is possible to instruct ModelOptimizer to change layout, for example:
-    			--layout "name1(nhwc->nchw),name2(cn->nc)".
-    			Also "*" in long layout form can be used to fuse dimensions, for example
-    			"[n,c,...]->[n*c,...]".
-      --compress_to_fp16 
-    			If the original model has FP32 weights or biases, they are compressed
-    			to FP16. All intermediate data is kept in original precision. Option
-    			can be specified alone as "--compress_to_fp16", or explicit True/False
-    			values can be set, for example: "--compress_to_fp16=False", or "--compress_to_fp16=True"
-    			
-      --extensions 
-    			Paths to libraries (.so or .dll) with extensions, comma-separated
-    			list of paths, objects derived from BaseExtension class or lists of
-    			objects. For the legacy MO path (if `--use_legacy_frontend` is used),
-    			a directory or a comma-separated list of directories with extensions
-    			are supported. To disable all extensions including those that are placed
-    			at the default location, pass an empty string.
-      --transform 
-    			Apply additional transformations. 'transform' can be set by a list
-    			of tuples, where the first element is transform name and the second element
-    			is transform parameters. For example: [('LowLatency2', {{'use_const_initializer':
-    			False}}), ...]"--transform transformation_name1[args],transformation_name2..."
-    			where [args] is key=value pairs separated by semicolon. Examples:
-    			 "--transform LowLatency2" or
-    			 "--transform Pruning" or
-    			 "--transform LowLatency2[use_const_initializer=False]" or
-    			 "--transform "MakeStateful[param_res_names=
-    			{'input_name_1':'output_name_1','input_name_2':'output_name_2'}]""
-    			Available transformations: "LowLatency2", "MakeStateful", "Pruning"
-    			
-      --transformations_config 
-    			Use the configuration file with transformations description or pass
-    			object derived from BaseExtension class. Transformations file can
-    			be specified as relative path from the current directory, as absolute
-    			path or as relative path from the mo root directory.
-      --silent 
-    			Prevent any output messages except those that correspond to log level
-    			equals ERROR, that can be set with the following option: --log_level.
-    			By default, log level is already ERROR.
-      --log_level 
-    			Logger level of logging massages from MO.
-    			Expected one of ['CRITICAL', 'ERROR', 'WARN', 'WARNING', 'INFO',
-    			'DEBUG', 'NOTSET'].
-      --version 
-    			Version of Model Optimizer
-      --progress 
-    			Enable model conversion progress display.
-      --stream_output 
-    			Switch model conversion progress display to a multiline mode.
-      --share_weights 
-    			Map memory of weights instead reading files or share memory from input
-    			model.
-    			Currently, mapping feature is provided only for ONNX models
-    			that do not require fallback to the legacy ONNX frontend for the conversion.
-    			
-    
-    PaddlePaddle-specific parameters:
-      --example_output 
-    			Sample of model output in original framework. For PaddlePaddle it can
-    			be Paddle Variable.
-    
-    TensorFlow*-specific parameters:
-      --input_model_is_text 
-    			TensorFlow*: treat the input model file as a text protobuf format. If
-    			not specified, the Model Optimizer treats it as a binary file by default.
-    			
-      --input_checkpoint 
-    			TensorFlow*: variables file to load.
-      --input_meta_graph 
-    			Tensorflow*: a file with a meta-graph of the model before freezing
-      --saved_model_dir 
-    			TensorFlow*: directory with a model in SavedModel format of TensorFlow
-    			1.x or 2.x version.
-      --saved_model_tags 
-    			Group of tag(s) of the MetaGraphDef to load, in string format, separated
-    			by ','. For tag-set contains multiple tags, all tags must be passed in.
-    			
-      --tensorflow_custom_operations_config_update 
-    			TensorFlow*: update the configuration file with node name patterns
-    			with input/output nodes information.
-      --tensorflow_object_detection_api_pipeline_config 
-    			TensorFlow*: path to the pipeline configuration file used to generate
-    			model created with help of Object Detection API.
-      --tensorboard_logdir 
-    			TensorFlow*: dump the input graph to a given directory that should be
-    			used with TensorBoard.
-      --tensorflow_custom_layer_libraries 
-    			TensorFlow*: comma separated list of shared libraries with TensorFlow*
-    			custom operations implementation.
-    
-    MXNet-specific parameters:
-      --input_symbol 
-    			Symbol file (for example, model-symbol.json) that contains a topology
-    			structure and layer attributes
-      --nd_prefix_name 
-    			Prefix name for args.nd and argx.nd files.
-      --pretrained_model_name 
-    			Name of a pretrained MXNet model without extension and epoch number.
-    			This model will be merged with args.nd and argx.nd files
-      --save_params_from_nd 
-    			Enable saving built parameters file from .nd files
-      --legacy_mxnet_model 
-    			Enable MXNet loader to make a model compatible with the latest MXNet
-    			version. Use only if your model was trained with MXNet version lower
-    			than 1.0.0
-      --enable_ssd_gluoncv 
-    			Enable pattern matchers replacers for converting gluoncv ssd topologies.
-    			
-    
-    Caffe*-specific parameters:
-      --input_proto 
-    			Deploy-ready prototxt file that contains a topology structure and
-    			layer attributes
-      --caffe_parser_path 
-    			Path to Python Caffe* parser generated from caffe.proto
-      --k 
-    			Path to CustomLayersMapping.xml to register custom layers
-      --disable_omitting_optional 
-    			Disable omitting optional attributes to be used for custom layers.
-    			Use this option if you want to transfer all attributes of a custom layer
-    			to IR. Default behavior is to transfer the attributes with default values
-    			and the attributes defined by the user to IR.
-      --enable_flattening_nested_params 
-    			Enable flattening optional params to be used for custom layers. Use
-    			this option if you want to transfer attributes of a custom layer to IR
-    			with flattened nested parameters. Default behavior is to transfer
-    			the attributes without flattening nested parameters.
-    
-    Kaldi-specific parameters:
-      --counts 
-    			Path to the counts file
-      --remove_output_softmax 
-    			Removes the SoftMax layer that is the output layer
-      --remove_memory 
-    			Removes the Memory layer and use additional inputs outputs instead
-    			
-    
-
-
-Fetching example models
------------------------
-
-
-
-This notebook uses two models for conversion examples:
-
--  `Distilbert <https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english>`__
-   NLP model from Hugging Face
--  `Resnet50 <https://pytorch.org/vision/stable/models/generated/torchvision.models.resnet50.html#torchvision.models.ResNet50_Weights>`__
-   CV classification model from torchvision
-
-.. code:: ipython3
-
-    from pathlib import Path
-    
-    # create a directory for models files
-    MODEL_DIRECTORY_PATH = Path("model")
-    MODEL_DIRECTORY_PATH.mkdir(exist_ok=True)
-
-Fetch
-`distilbert <https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english>`__
-NLP model from Hugging Face and export it in ONNX format:
-
-.. code:: ipython3
-
-    from transformers import AutoModelForSequenceClassification, AutoTokenizer
-    from transformers.onnx import export, FeaturesManager
-    
-    
-    ONNX_NLP_MODEL_PATH = MODEL_DIRECTORY_PATH / "distilbert.onnx"
-    
-    # download model
-    hf_model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
-    # initialize tokenizer
-    tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
-    
-    # get model onnx config function for output feature format sequence-classification
-    model_kind, model_onnx_config = FeaturesManager.check_supported_model_or_raise(hf_model, feature="sequence-classification")
-    # fill onnx config based on pytorch model config
-    onnx_config = model_onnx_config(hf_model.config)
-    
-    # export to onnx format
-    export(
-        preprocessor=tokenizer,
-        model=hf_model,
-        config=onnx_config,
-        opset=onnx_config.default_onnx_opset,
-        output=ONNX_NLP_MODEL_PATH,
-    )
-
-
-.. parsed-literal::
-
-    2024-06-05 23:49:21.145736: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-06-05 23:49:21.179983: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
-    To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2024-06-05 23:49:21.824078: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/distilbert/modeling_distilbert.py:231: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
-      mask, torch.tensor(torch.finfo(scores.dtype).min)
-
-
-
-
-.. parsed-literal::
-
-    (['input_ids', 'attention_mask'], ['logits'])
-
-
-
-Fetch
-`Resnet50 <https://pytorch.org/vision/stable/models/generated/torchvision.models.resnet50.html#torchvision.models.ResNet50_Weights>`__
-CV classification model from torchvision:
-
-.. code:: ipython3
-
-    from torchvision.models import resnet50, ResNet50_Weights
-    
-    
-    # create model object
-    pytorch_model = resnet50(weights=ResNet50_Weights.DEFAULT)
-    # switch model from training to inference mode
-    pytorch_model.eval()
-
-
-
-
-.. parsed-literal::
-
-    ResNet(
-      (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
-      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-      (relu): ReLU(inplace=True)
-      (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
-      (layer1): Sequential(
-        (0): Bottleneck(
-          (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
-          (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
-          (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
-          (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (relu): ReLU(inplace=True)
-          (downsample): Sequential(
-            (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
-            (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          )
-        )
-        (1): Bottleneck(
-          (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
-          (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
-          (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
-          (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (relu): ReLU(inplace=True)
-        )
-        (2): Bottleneck(
-          (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
-          (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
-          (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
-          (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (relu): ReLU(inplace=True)
-        )
-      )
-      (layer2): Sequential(
-        (0): Bottleneck(
-          (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
-          (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
-          (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
-          (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (relu): ReLU(inplace=True)
-          (downsample): Sequential(
-            (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
-            (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          )
-        )
-        (1): Bottleneck(
-          (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
-          (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
-          (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
-          (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (relu): ReLU(inplace=True)
-        )
-        (2): Bottleneck(
-          (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
-          (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
-          (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
-          (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (relu): ReLU(inplace=True)
-        )
-        (3): Bottleneck(
-          (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
-          (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
-          (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
-          (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (relu): ReLU(inplace=True)
-        )
-      )
-      (layer3): Sequential(
-        (0): Bottleneck(
-          (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
-          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
-          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
-          (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (relu): ReLU(inplace=True)
-          (downsample): Sequential(
-            (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
-            (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          )
-        )
-        (1): Bottleneck(
-          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
-          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
-          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
-          (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (relu): ReLU(inplace=True)
-        )
-        (2): Bottleneck(
-          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
-          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
-          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
-          (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (relu): ReLU(inplace=True)
-        )
-        (3): Bottleneck(
-          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
-          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
-          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
-          (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (relu): ReLU(inplace=True)
-        )
-        (4): Bottleneck(
-          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
-          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
-          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
-          (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (relu): ReLU(inplace=True)
-        )
-        (5): Bottleneck(
-          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
-          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
-          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
-          (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (relu): ReLU(inplace=True)
-        )
-      )
-      (layer4): Sequential(
-        (0): Bottleneck(
-          (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
-          (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
-          (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
-          (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (relu): ReLU(inplace=True)
-          (downsample): Sequential(
-            (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
-            (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          )
-        )
-        (1): Bottleneck(
-          (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
-          (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
-          (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
-          (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (relu): ReLU(inplace=True)
-        )
-        (2): Bottleneck(
-          (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
-          (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
-          (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
-          (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (relu): ReLU(inplace=True)
-        )
-      )
-      (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
-      (fc): Linear(in_features=2048, out_features=1000, bias=True)
-    )
-
-
-
-Convert PyTorch model to ONNX format:
-
-.. code:: ipython3
-
-    import torch
-    import warnings
-    
-    
-    ONNX_CV_MODEL_PATH = MODEL_DIRECTORY_PATH / "resnet.onnx"
-    
-    if ONNX_CV_MODEL_PATH.exists():
-        print(f"ONNX model {ONNX_CV_MODEL_PATH} already exists.")
-    else:
-        with warnings.catch_warnings():
-            warnings.filterwarnings("ignore")
-            torch.onnx.export(model=pytorch_model, args=torch.randn(1, 3, 780, 520), f=ONNX_CV_MODEL_PATH)
-        print(f"ONNX model exported to {ONNX_CV_MODEL_PATH}")
-
-
-.. parsed-literal::
-
-    ONNX model model/resnet.onnx already exists.
-
-
-Basic conversion
-----------------
-
-
-
-To convert a model to OpenVINO IR, use the following command:
-
-.. code:: ipython3
-
-    # Model Optimizer CLI
-    
-    ! mo --input_model model/distilbert.onnx --output_dir model
-
-
-.. parsed-literal::
-
-    huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
-    To disable this warning, you can either:
-    	- Avoid using `tokenizers` before the fork if possible
-    	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
-
-
-.. parsed-literal::
-
-    [ INFO ] Generated IR will be compressed to FP16. If you get lower accuracy, please consider disabling compression explicitly by adding argument --compress_to_fp16=False.
-    Find more information about compression to FP16 at https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_FP16_Compression.html
-    [ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release. Please use OpenVINO Model Converter (OVC). OVC represents a lightweight alternative of MO and provides simplified model conversion API. 
-    Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
-    [ SUCCESS ] Generated IR version 11 model.
-    [ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/convert-to-openvino/model/distilbert.xml
-    [ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/convert-to-openvino/model/distilbert.bin
-
-
-.. code:: ipython3
-
-    # Python conversion API
-    from openvino.tools import mo
-    
-    # mo.convert_model returns an openvino.runtime.Model object
-    ov_model = mo.convert_model(ONNX_NLP_MODEL_PATH)
-    
-    # then model can be serialized to *.xml & *.bin files
-    from openvino.runtime import serialize
-    
-    serialize(ov_model, xml_path=MODEL_DIRECTORY_PATH / "distilbert.xml")
-
-
-.. parsed-literal::
-
-    huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
-    To disable this warning, you can either:
-    	- Avoid using `tokenizers` before the fork if possible
-    	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
-
-
-Model conversion parameters
----------------------------
-
-
-
-Both Python conversion API and Model Optimizer command-line tool provide
-the following capabilities: \* overriding original input shapes for
-model conversion with ``input`` and ``input_shape`` parameters. `Setting
-Input Shapes
-guide <https://docs.openvino.ai/2024/openvino-workflow/model-preparation/setting-input-shapes.html>`__.
-\* cutting off unwanted parts of a model (such as unsupported operations
-and training sub-graphs) using the ``input`` and ``output`` parameters
-to define new inputs and outputs of the converted model. `Cutting Off
-Parts of a Model
-guide <https://docs.openvino.ai/2023.3/openvino_docs_MO_DG_prepare_model_convert_model_Cutting_Model.html>`__.
-\* inserting additional input pre-processing sub-graphs into the
-converted model by using the ``mean_values``, ``scales_values``,
-``layout``, and other parameters. `Embedding Preprocessing Computation
-article <https://docs.openvino.ai/2023.3/openvino_docs_MO_DG_Additional_Optimization_Use_Cases.html>`__.
-\* compressing the model weights (for example, weights for convolutions
-and matrix multiplications) to FP16 data type using ``compress_to_fp16``
-compression parameter. `Compression of a Model to FP16
-guide <https://docs.openvino.ai/2024/openvino-workflow/model-preparation/conversion-parameters.html>`__.
-
-If the out-of-the-box conversion (only the ``input_model`` parameter is
-specified) is not successful, it may be required to use the parameters
-mentioned above to override input shapes and cut the model.
-
-Setting Input Shapes
-~~~~~~~~~~~~~~~~~~~~
-
-
-
-Model conversion is supported for models with dynamic input shapes that
-contain undefined dimensions. However, if the shape of data is not going
-to change from one inference request to another, it is recommended to
-set up static shapes (when all dimensions are fully defined) for the
-inputs. Doing it at this stage, instead of during inference in runtime,
-can be beneficial in terms of performance and memory consumption. To set
-up static shapes, model conversion API provides the ``input`` and
-``input_shape`` parameters.
-
-For more information refer to `Setting Input Shapes
-guide <https://docs.openvino.ai/2024/openvino-workflow/model-preparation/setting-input-shapes.html>`__.
-
-.. code:: ipython3
-
-    # Model Optimizer CLI
-    
-    ! mo --input_model model/distilbert.onnx --input input_ids,attention_mask --input_shape [1,128],[1,128] --output_dir model
-    
-    # alternatively
-    ! mo --input_model model/distilbert.onnx --input input_ids[1,128],attention_mask[1,128] --output_dir model
-
-
-.. parsed-literal::
-
-    huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
-    To disable this warning, you can either:
-    	- Avoid using `tokenizers` before the fork if possible
-    	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
-
-
-.. parsed-literal::
-
-    [ INFO ] Generated IR will be compressed to FP16. If you get lower accuracy, please consider disabling compression explicitly by adding argument --compress_to_fp16=False.
-    Find more information about compression to FP16 at https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_FP16_Compression.html
-    [ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release. Please use OpenVINO Model Converter (OVC). OVC represents a lightweight alternative of MO and provides simplified model conversion API. 
-    Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
-    [ SUCCESS ] Generated IR version 11 model.
-    [ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/convert-to-openvino/model/distilbert.xml
-    [ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/convert-to-openvino/model/distilbert.bin
-
-
-.. parsed-literal::
-
-    huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
-    To disable this warning, you can either:
-    	- Avoid using `tokenizers` before the fork if possible
-    	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
-
-
-.. parsed-literal::
-
-    [ INFO ] Generated IR will be compressed to FP16. If you get lower accuracy, please consider disabling compression explicitly by adding argument --compress_to_fp16=False.
-    Find more information about compression to FP16 at https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_FP16_Compression.html
-    [ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release. Please use OpenVINO Model Converter (OVC). OVC represents a lightweight alternative of MO and provides simplified model conversion API. 
-    Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
-    [ SUCCESS ] Generated IR version 11 model.
-    [ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/convert-to-openvino/model/distilbert.xml
-    [ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/convert-to-openvino/model/distilbert.bin
-
-
-.. code:: ipython3
-
-    # Python conversion API
-    from openvino.tools import mo
-    
-    
-    ov_model = mo.convert_model(
-        ONNX_NLP_MODEL_PATH,
-        input=["input_ids", "attention_mask"],
-        input_shape=[[1, 128], [1, 128]],
-    )
-    
-    # alternatively specify input shapes, using the input parameter
-    ov_model = mo.convert_model(ONNX_NLP_MODEL_PATH, input=[("input_ids", [1, 128]), ("attention_mask", [1, 128])])
-
-The input_shape parameter allows overriding original input shapes to
-ones compatible with a given model. Dynamic shapes, i.e. with dynamic
-dimensions, can be replaced in the original model with static shapes for
-the converted model, and vice versa. The dynamic dimension can be marked
-in the model conversion API parameter as ``-1`` or ``?``. For example,
-launch model conversion for the ONNX Bert model and specify a dynamic
-sequence length dimension for inputs:
-
-.. code:: ipython3
-
-    # Model Optimizer CLI
-    
-    ! mo --input_model model/distilbert.onnx --input input_ids,attention_mask --input_shape [1,-1],[1,-1] --output_dir model
-
-
-.. parsed-literal::
-
-    huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
-    To disable this warning, you can either:
-    	- Avoid using `tokenizers` before the fork if possible
-    	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
-
-
-.. parsed-literal::
-
-    [ INFO ] Generated IR will be compressed to FP16. If you get lower accuracy, please consider disabling compression explicitly by adding argument --compress_to_fp16=False.
-    Find more information about compression to FP16 at https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_FP16_Compression.html
-    [ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release. Please use OpenVINO Model Converter (OVC). OVC represents a lightweight alternative of MO and provides simplified model conversion API. 
-    Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
-    [ SUCCESS ] Generated IR version 11 model.
-    [ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/convert-to-openvino/model/distilbert.xml
-    [ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/convert-to-openvino/model/distilbert.bin
-
-
-.. code:: ipython3
-
-    # Python conversion API
-    from openvino.tools import mo
-    
-    
-    ov_model = mo.convert_model(
-        ONNX_NLP_MODEL_PATH,
-        input=["input_ids", "attention_mask"],
-        input_shape=[[1, -1], [1, -1]],
-    )
-
-To optimize memory consumption for models with undefined dimensions in
-runtime, model conversion API provides the capability to define
-boundaries of dimensions. The boundaries of undefined dimensions can be
-specified with ellipsis. For example, launch model conversion for the
-ONNX Bert model and specify a boundary for the sequence length
-dimension:
-
-.. code:: ipython3
-
-    # Model Optimizer CLI
-    
-    ! mo --input_model model/distilbert.onnx --input input_ids,attention_mask --input_shape [1,10..128],[1,10..128] --output_dir model
-
-
-.. parsed-literal::
-
-    huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
-    To disable this warning, you can either:
-    	- Avoid using `tokenizers` before the fork if possible
-    	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
-
-
-.. parsed-literal::
-
-    [ INFO ] Generated IR will be compressed to FP16. If you get lower accuracy, please consider disabling compression explicitly by adding argument --compress_to_fp16=False.
-    Find more information about compression to FP16 at https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_FP16_Compression.html
-    [ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release. Please use OpenVINO Model Converter (OVC). OVC represents a lightweight alternative of MO and provides simplified model conversion API. 
-    Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
-    [ SUCCESS ] Generated IR version 11 model.
-    [ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/convert-to-openvino/model/distilbert.xml
-    [ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/convert-to-openvino/model/distilbert.bin
-
-
-.. code:: ipython3
-
-    # Python conversion API
-    from openvino.tools import mo
-    
-    
-    ov_model = mo.convert_model(
-        ONNX_NLP_MODEL_PATH,
-        input=["input_ids", "attention_mask"],
-        input_shape=[[1, "10..128"], [1, "10..128"]],
-    )
-
-Cutting Off Parts of a Model
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-The following examples show when model cutting is useful or even
-required:
-
--  A model has pre- or post-processing parts that cannot be translated
-   to existing OpenVINO operations.
--  A model has a training part that is convenient to be kept in the
-   model but not used during inference.
--  A model is too complex to be converted at once because it contains
-   many unsupported operations that cannot be easily implemented as
-   custom layers.
--  A problem occurs with model conversion or inference in OpenVINO
-   Runtime. To identify the issue, limit the conversion scope by an
-   iterative search for problematic areas in the model.
--  A single custom layer or a combination of custom layers is isolated
-   for debugging purposes.
-
-For a more detailed description, refer to the `Cutting Off Parts of a
-Model
-guide <https://docs.openvino.ai/2023.3/openvino_docs_MO_DG_prepare_model_convert_model_Cutting_Model.html>`__.
-
-.. code:: ipython3
-
-    # Model Optimizer CLI
-    
-    # cut at the end
-    ! mo --input_model model/distilbert.onnx --output /classifier/Gemm --output_dir model
-    
-    
-    # cut from the beginning
-    ! mo --input_model model/distilbert.onnx --input /distilbert/embeddings/LayerNorm/Add_1,attention_mask --output_dir model
-
-
-.. parsed-literal::
-
-    huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
-    To disable this warning, you can either:
-    	- Avoid using `tokenizers` before the fork if possible
-    	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
-
-
-.. parsed-literal::
-
-    [ INFO ] Generated IR will be compressed to FP16. If you get lower accuracy, please consider disabling compression explicitly by adding argument --compress_to_fp16=False.
-    Find more information about compression to FP16 at https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_FP16_Compression.html
-    [ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release. Please use OpenVINO Model Converter (OVC). OVC represents a lightweight alternative of MO and provides simplified model conversion API. 
-    Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
-    [ SUCCESS ] Generated IR version 11 model.
-    [ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/convert-to-openvino/model/distilbert.xml
-    [ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/convert-to-openvino/model/distilbert.bin
-
-
-.. parsed-literal::
-
-    huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
-    To disable this warning, you can either:
-    	- Avoid using `tokenizers` before the fork if possible
-    	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
-
-
-.. parsed-literal::
-
-    [ INFO ] Generated IR will be compressed to FP16. If you get lower accuracy, please consider disabling compression explicitly by adding argument --compress_to_fp16=False.
-    Find more information about compression to FP16 at https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_FP16_Compression.html
-    [ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release. Please use OpenVINO Model Converter (OVC). OVC represents a lightweight alternative of MO and provides simplified model conversion API. 
-    Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
-    [ SUCCESS ] Generated IR version 11 model.
-    [ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/convert-to-openvino/model/distilbert.xml
-    [ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/convert-to-openvino/model/distilbert.bin
-
-
-.. code:: ipython3
-
-    # Python conversion API
-    from openvino.tools import mo
-    
-    
-    # cut at the end
-    ov_model = mo.convert_model(ONNX_NLP_MODEL_PATH, output="/classifier/Gemm")
-    
-    # cut from the beginning
-    ov_model = mo.convert_model(
-        ONNX_NLP_MODEL_PATH,
-        input=["/distilbert/embeddings/LayerNorm/Add_1", "attention_mask"],
-    )
-
-Embedding Preprocessing Computation
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-Input data for inference can be different from the training dataset and
-requires additional preprocessing before inference. To accelerate the
-whole pipeline, including preprocessing and inference, model conversion
-API provides special parameters such as ``mean_values``,
-``scale_values``, ``reverse_input_channels``, and ``layout``. Based on
-these parameters, model conversion API generates OpenVINO IR with
-additionally inserted sub-graphs to perform the defined preprocessing.
-This preprocessing block can perform mean-scale normalization of input
-data, reverting data along channel dimension, and changing the data
-layout. For more information on preprocessing, refer to the `Embedding
-Preprocessing Computation
-article <https://docs.openvino.ai/2023.3/openvino_docs_MO_DG_Additional_Optimization_Use_Cases.html>`__.
-
-Specifying Layout
-^^^^^^^^^^^^^^^^^
-
-
-
-Layout defines the meaning of dimensions in a shape and can be specified
-for both inputs and outputs. Some preprocessing requires to set input
-layouts, for example, setting a batch, applying mean or scales, and
-reversing input channels (BGR<->RGB). For the layout syntax, check the
-`Layout API
-overview <https://docs.openvino.ai/2024/openvino-workflow/running-inference/optimize-inference/optimize-preprocessing/layout-api-overview.html>`__.
-To specify the layout, you can use the layout option followed by the
-layout value.
-
-The following command specifies the ``NCHW`` layout for a Pytorch
-Resnet50 model that was exported to the ONNX format:
-
-.. code:: ipython3
-
-    # Model Optimizer CLI
-    
-    ! mo --input_model model/resnet.onnx --layout nchw --output_dir model
-
-
-.. parsed-literal::
-
-    huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
-    To disable this warning, you can either:
-    	- Avoid using `tokenizers` before the fork if possible
-    	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
-
-
-.. parsed-literal::
-
-    [ INFO ] Generated IR will be compressed to FP16. If you get lower accuracy, please consider disabling compression explicitly by adding argument --compress_to_fp16=False.
-    Find more information about compression to FP16 at https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_FP16_Compression.html
-    [ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release. Please use OpenVINO Model Converter (OVC). OVC represents a lightweight alternative of MO and provides simplified model conversion API. 
-    Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
-    [ SUCCESS ] Generated IR version 11 model.
-    [ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/convert-to-openvino/model/resnet.xml
-    [ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/convert-to-openvino/model/resnet.bin
-
-
-.. code:: ipython3
-
-    # Python conversion API
-    from openvino.tools import mo
-    
-    
-    ov_model = mo.convert_model(ONNX_CV_MODEL_PATH, layout="nchw")
-
-Changing Model Layout
-^^^^^^^^^^^^^^^^^^^^^
-
-
-
-Changing the model layout may be necessary if it differs from the one
-presented by input data. Use either ``layout`` or ``source_layout`` with
-``target_layout`` to change the layout.
-
-.. code:: ipython3
-
-    # Model Optimizer CLI
-    
-    ! mo --input_model model/resnet.onnx --layout "nchw->nhwc" --output_dir model
-    
-    # alternatively use source_layout and target_layout parameters
-    ! mo --input_model model/resnet.onnx --source_layout nchw --target_layout nhwc --output_dir model
-
-
-.. parsed-literal::
-
-    huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
-    To disable this warning, you can either:
-    	- Avoid using `tokenizers` before the fork if possible
-    	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
-
-
-.. parsed-literal::
-
-    [ INFO ] Generated IR will be compressed to FP16. If you get lower accuracy, please consider disabling compression explicitly by adding argument --compress_to_fp16=False.
-    Find more information about compression to FP16 at https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_FP16_Compression.html
-    [ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release. Please use OpenVINO Model Converter (OVC). OVC represents a lightweight alternative of MO and provides simplified model conversion API. 
-    Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
-    [ SUCCESS ] Generated IR version 11 model.
-    [ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/convert-to-openvino/model/resnet.xml
-    [ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/convert-to-openvino/model/resnet.bin
-
-
-.. parsed-literal::
-
-    huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
-    To disable this warning, you can either:
-    	- Avoid using `tokenizers` before the fork if possible
-    	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
-
-
-.. parsed-literal::
-
-    [ INFO ] Generated IR will be compressed to FP16. If you get lower accuracy, please consider disabling compression explicitly by adding argument --compress_to_fp16=False.
-    Find more information about compression to FP16 at https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_FP16_Compression.html
-    [ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release. Please use OpenVINO Model Converter (OVC). OVC represents a lightweight alternative of MO and provides simplified model conversion API. 
-    Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
-    [ SUCCESS ] Generated IR version 11 model.
-    [ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/convert-to-openvino/model/resnet.xml
-    [ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/convert-to-openvino/model/resnet.bin
-
-
-.. code:: ipython3
-
-    # Python conversion API
-    from openvino.tools import mo
-    
-    
-    ov_model = mo.convert_model(ONNX_CV_MODEL_PATH, layout="nchw->nhwc")
-    
-    # alternatively use source_layout and target_layout parameters
-    ov_model = mo.convert_model(ONNX_CV_MODEL_PATH, source_layout="nchw", target_layout="nhwc")
-
-Specifying Mean and Scale Values
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-
-
-Model conversion API has the following parameters to specify the values:
-``mean_values``, ``scale_values``, ``scale``. Using these parameters,
-model conversion API embeds the corresponding preprocessing block for
-mean-value normalization of the input data and optimizes this block so
-that the preprocessing takes negligible time for inference.
-
-.. code:: ipython3
-
-    # Model Optimizer CLI
-    
-    ! mo --input_model model/resnet.onnx --mean_values [123,117,104] --scale 255 --output_dir model
-    
-    ! mo --input_model model/resnet.onnx --mean_values [123,117,104] --scale_values [255,255,255] --output_dir model
-
-
-.. parsed-literal::
-
-    huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
-    To disable this warning, you can either:
-    	- Avoid using `tokenizers` before the fork if possible
-    	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
-
-
-.. parsed-literal::
-
-    [ INFO ] Generated IR will be compressed to FP16. If you get lower accuracy, please consider disabling compression explicitly by adding argument --compress_to_fp16=False.
-    Find more information about compression to FP16 at https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_FP16_Compression.html
-    [ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release. Please use OpenVINO Model Converter (OVC). OVC represents a lightweight alternative of MO and provides simplified model conversion API. 
-    Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
-    [ SUCCESS ] Generated IR version 11 model.
-    [ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/convert-to-openvino/model/resnet.xml
-    [ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/convert-to-openvino/model/resnet.bin
-
-
-.. parsed-literal::
-
-    huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
-    To disable this warning, you can either:
-    	- Avoid using `tokenizers` before the fork if possible
-    	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
-
-
-.. parsed-literal::
-
-    [ INFO ] Generated IR will be compressed to FP16. If you get lower accuracy, please consider disabling compression explicitly by adding argument --compress_to_fp16=False.
-    Find more information about compression to FP16 at https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_FP16_Compression.html
-    [ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release. Please use OpenVINO Model Converter (OVC). OVC represents a lightweight alternative of MO and provides simplified model conversion API. 
-    Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
-    [ SUCCESS ] Generated IR version 11 model.
-    [ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/convert-to-openvino/model/resnet.xml
-    [ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/convert-to-openvino/model/resnet.bin
-
-
-.. code:: ipython3
-
-    # Python conversion API
-    from openvino.tools import mo
-    
-    
-    ov_model = mo.convert_model(ONNX_CV_MODEL_PATH, mean_values=[123, 117, 104], scale=255)
-    
-    ov_model = mo.convert_model(ONNX_CV_MODEL_PATH, mean_values=[123, 117, 104], scale_values=[255, 255, 255])
-
-Reversing Input Channels
-^^^^^^^^^^^^^^^^^^^^^^^^
-
-
-
-Sometimes, input images for your application can be of the ``RGB`` (or
-``BGR``) format, and the model is trained on images of the ``BGR`` (or
-``RGB``) format, which is in the opposite order of color channels. In
-this case, it is important to preprocess the input images by reverting
-the color channels before inference.
-
-.. code:: ipython3
-
-    # Model Optimizer CLI
-    
-    ! mo --input_model model/resnet.onnx --reverse_input_channels --output_dir model
-
-
-.. parsed-literal::
-
-    huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
-    To disable this warning, you can either:
-    	- Avoid using `tokenizers` before the fork if possible
-    	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
-
-
-.. parsed-literal::
-
-    [ INFO ] Generated IR will be compressed to FP16. If you get lower accuracy, please consider disabling compression explicitly by adding argument --compress_to_fp16=False.
-    Find more information about compression to FP16 at https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_FP16_Compression.html
-    [ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release. Please use OpenVINO Model Converter (OVC). OVC represents a lightweight alternative of MO and provides simplified model conversion API. 
-    Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
-    [ SUCCESS ] Generated IR version 11 model.
-    [ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/convert-to-openvino/model/resnet.xml
-    [ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/convert-to-openvino/model/resnet.bin
-
-
-.. code:: ipython3
-
-    # Python conversion API
-    from openvino.tools import mo
-    
-    
-    ov_model = mo.convert_model(ONNX_CV_MODEL_PATH, reverse_input_channels=True)
-
-Compressing a Model to FP16
-~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-Optionally all relevant floating-point weights can be compressed to FP16
-data type during the model conversion, creating a compressed FP16 model.
-This smaller model occupies about half of the original space in the file
-system. While the compression may introduce a drop in accuracy, for most
-models, this decrease is negligible.
-
-.. code:: ipython3
-
-    # Model Optimizer CLI
-    
-    ! mo --input_model model/resnet.onnx --compress_to_fp16=True --output_dir model
-
-
-.. parsed-literal::
-
-    huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
-    To disable this warning, you can either:
-    	- Avoid using `tokenizers` before the fork if possible
-    	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
-
-
-.. parsed-literal::
-
-    [ INFO ] Generated IR will be compressed to FP16. If you get lower accuracy, please consider disabling compression explicitly by adding argument --compress_to_fp16=False.
-    Find more information about compression to FP16 at https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_FP16_Compression.html
-    [ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release. Please use OpenVINO Model Converter (OVC). OVC represents a lightweight alternative of MO and provides simplified model conversion API. 
-    Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
-    [ SUCCESS ] Generated IR version 11 model.
-    [ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/convert-to-openvino/model/resnet.xml
-    [ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/convert-to-openvino/model/resnet.bin
-
-
-.. code:: ipython3
-
-    # Python conversion API
-    from openvino.tools import mo
-    
-    
-    ov_model = mo.convert_model(ONNX_CV_MODEL_PATH, compress_to_fp16=True)
-
-Convert Models Represented as Python Objects
---------------------------------------------
-
-
-
-Python conversion API can pass Python model objects, such as a Pytorch
-model or TensorFlow Keras model directly, without saving them into files
-and without leaving the training environment (Jupyter Notebook or
-training scripts).
-
-.. code:: ipython3
-
-    # Python conversion API
-    from openvino.tools import mo
-    
-    
-    ov_model = mo.convert_model(pytorch_model)
-
-
-.. parsed-literal::
-
-    WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.base has been moved to tensorflow.python.trackable.base. The old module will be deleted in version 2.11.
-
-
-``convert_model()`` accepts all parameters available in the MO
-command-line tool. Parameters can be specified by Python classes or
-string analogs, similar to the command-line tool.
-
-.. code:: ipython3
-
-    # Python conversion API
-    from openvino.tools import mo
-    
-    
-    ov_model = mo.convert_model(
-        pytorch_model,
-        input_shape=[1, 3, 100, 100],
-        mean_values=[127, 127, 127],
-        layout="nchw",
-    )
-    
-    ov_model = mo.convert_model(pytorch_model, source_layout="nchw", target_layout="nhwc")
-    
-    ov_model = mo.convert_model(pytorch_model, compress_to_fp16=True, reverse_input_channels=True)
diff --git a/docs/notebooks/llava-multimodal-chatbot-with-output.rst b/docs/notebooks/llava-multimodal-chatbot-with-output.rst
index 4af29384c0b588..437312b7afeee2 100644
--- a/docs/notebooks/llava-multimodal-chatbot-with-output.rst
+++ b/docs/notebooks/llava-multimodal-chatbot-with-output.rst
@@ -41,8 +41,8 @@ The tutorial consists from following steps:
 -  Prepare OpenVINO-based inference pipeline
 -  Run OpenVINO model
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `About model <#about-model>`__
 -  `Prerequisites <#prerequisites>`__
@@ -56,11 +56,11 @@ Table of contents:
    -  `Convert and Optimize Model <#convert-and-optimize-model>`__
 
       -  `Instantiate PyTorch model
-          <#instantiate-pytorch-model-uparrow#table-of-content>`__
+          <#instantiate-pytorch-model>`__
       -  `Compress Model weights to 4 and 8 bits using NNCF
-          <#compress-model-weights-to-4-and-8-bits-using-nncf-uparrow#table-of-content>`__
+          <#compress-model-weights-to-4-and-8-bits-using-nncf>`__
       -  `Convert model to OpenVINO IR format
-          <#convert-model-to-openvino-ir-format-uparrow#table-of-content>`__
+          <#convert-model-to-openvino-ir-format>`__
 
 -  `Prepare OpenVINO based inference
    pipeline <#prepare-openvino-based-inference-pipeline>`__
@@ -115,7 +115,7 @@ Install required dependencies
 .. code:: ipython3
 
     import sys
-    
+
     %pip install -q "torch>=2.1.0" "torchvision" "torchaudio" --index-url https://download.pytorch.org/whl/cpu
     %pip install -q "openvino>=2023.2.0" "nncf>=2.7.0"  "sentencepiece" "tokenizers>=0.12.1" "transformers>=4.37.2" "gradio>=4.19" "einops"
 
@@ -123,7 +123,7 @@ Install required dependencies
 .. parsed-literal::
 
     Note: you may need to restart the kernel to use updated packages.
-    
+
     [notice] A new release of pip is available: 23.3.2 -> 24.0
     [notice] To update, run: pip install --upgrade pip
     Note: you may need to restart the kernel to use updated packages.
@@ -132,12 +132,12 @@ Install required dependencies
 .. code:: ipython3
 
     from pathlib import Path
-    
+
     repo_dir = Path("LLaVA")
-    
+
     if not repo_dir.exists():
         !git clone https://github.com/haotian-liu/LLaVA.git
-    
+
     sys.path.insert(0, str(repo_dir.resolve()))
 
 Build model tokenizer and image processor
@@ -160,9 +160,9 @@ instruction.
 
     from transformers import AutoTokenizer, AutoConfig, CLIPImageProcessor
     from llava.model.language_model.llava_mpt import LlavaMptForCausalLM
-    
+
     model_id = "liuhaotian/LLaVA-Lightning-MPT-7B-preview"
-    
+
     config = AutoConfig.from_pretrained(model_id)
     tokenizer = AutoTokenizer.from_pretrained(model_id)
     image_processor = CLIPImageProcessor.from_pretrained(config.mm_vision_tower)
@@ -181,14 +181,14 @@ instruction.
         DEFAULT_IM_END_TOKEN,
         DEFAULT_IMAGE_TOKEN,
     )
-    
+
     mm_use_im_start_end = getattr(config, "mm_use_im_start_end", False)
     mm_use_im_patch_token = getattr(config, "mm_use_im_patch_token", True)
     if mm_use_im_patch_token:
         tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
     if mm_use_im_start_end:
         tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)
-    
+
     if hasattr(config, "max_sequence_length"):
         context_len = config.max_sequence_length
     else:
@@ -261,20 +261,20 @@ on disk using ``ov.save_model``.
     import nncf
     from typing import Optional, Tuple, List
     import torch.nn.functional as F
-    
+
     warnings.filterwarnings("ignore")
-    
-    
+
+
     class ModelWrapper(torch.nn.Module):
         """
         Model wrapper class for export for spliting original forward logic on preparing multimodal data and inference using it.
         That allows us to sperate image encoder and token embeddings model from general flow.
         """
-    
+
         def __init__(self, model):
             super().__init__()
             self.model = model
-    
+
         def forward(
             self,
             input_ids: torch.LongTensor = None,
@@ -296,19 +296,19 @@ on disk using ``ov.save_model``.
                 outputs.last_hidden_state.to(self.model.transformer.wte.weight.device),
                 self.model.transformer.wte.weight.to(outputs.last_hidden_state.dtype),
             )
-    
+
             return (logits, tuple(outputs.past_key_values))
-    
-    
+
+
     def patch_model_forward(model):
         """
         Helper function for patching model forward for model with past.
         It makes model more convinient for export to TorchScript format avoiding limitation
         that list of tensors can not be correctly traced as model input
         """
-    
+
         orig_forward = model.forward
-    
+
         @wraps(orig_forward)
         def ts_patched_forward(
             input_ids: torch.Tensor,
@@ -322,11 +322,11 @@ on disk using ``ov.save_model``.
                 attention_mask=attention_mask,
             )
             return outs
-    
+
         model.forward = ts_patched_forward
         return model
-    
-    
+
+
     def flattenize_inputs(inputs):
         """
         Helper function for making nested inputs flattens
@@ -340,8 +340,8 @@ on disk using ``ov.save_model``.
             else:
                 flatten_inputs.append(input_data)
         return flatten_inputs
-    
-    
+
+
     def cleanup_torchscript_cache():
         """
         Helper for removing cached model representation
@@ -349,8 +349,8 @@ on disk using ``ov.save_model``.
         torch._C._jit_clear_class_registry()
         torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore()
         torch.jit._state._clear_class_state()
-    
-    
+
+
     def postprocess_converted_model(
         ov_model,
         example_input=None,
@@ -363,7 +363,7 @@ on disk using ``ov.save_model``.
         acording to requested specification
         """
         flatten_example_inputs = flattenize_inputs(example_input) if example_input else []
-    
+
         if input_names:
             for inp_name, m_input, input_data in zip(input_names, ov_model.inputs, flatten_example_inputs):
                 input_node = m_input.get_node()
@@ -375,14 +375,14 @@ on disk using ``ov.save_model``.
                         shape[k] = -1
                 input_node.set_partial_shape(ov.PartialShape(shape))
                 m_input.get_tensor().set_names({inp_name})
-    
+
         if output_names:
             for out, out_name in zip(ov_model.outputs, output_names):
                 out.get_tensor().set_names({out_name})
         ov_model.validate_nodes_and_infer_types()
         return ov_model
-    
-    
+
+
     def convert_llava_mpt(
         pt_model: torch.nn.Module,
         model_path: Path,
@@ -391,7 +391,7 @@ on disk using ``ov.save_model``.
     ):
         """
         LLaVA MPT model conversion function
-    
+
         Params:
           pt_model: PyTorch model
           model_path: path for saving model
@@ -421,7 +421,7 @@ on disk using ``ov.save_model``.
             del ov_model
             gc.collect()
             print("Image Encoder model successfully converted")
-    
+
         if not token_embedding_model_path.exists():
             model.forward = model.get_model().embed_tokens
             ov_model = ov.convert_model(model, example_input=torch.ones((1, 10), dtype=torch.long))
@@ -430,7 +430,7 @@ on disk using ``ov.save_model``.
             del ov_model
             gc.collect()
             print("Token Embedding model successfully converted")
-    
+
         if first_stage_model_path.exists() and second_stage_model_path.exists():
             print("LLaVA model successfully converted")
             del pt_model
@@ -449,7 +449,7 @@ on disk using ``ov.save_model``.
             dynamic_shapes[inputs[-1]] = {2: "past_sequence + sequence"}
             dynamic_shapes[inputs[-2]] = {2: "past_sequence + sequence"}
             outputs.extend([f"present.{idx}.key", f"present.{idx}.value"])
-    
+
         inputs.extend(["attention_mask"])
         if not first_stage_model_path.exists():
             ov_model = ov.convert_model(model_wrap, example_input=example_input_first_stage)
@@ -461,7 +461,7 @@ on disk using ``ov.save_model``.
             cleanup_torchscript_cache()
             del ov_model
             gc.collect()
-    
+
         if not second_stage_model_path.exists():
             model_wrap = patch_model_forward(model_wrap)
             example_input_second_stage = {
@@ -505,7 +505,7 @@ Convert model to OpenVINO format and save it on disk.
 
 Let’s consider each step more deeply.
 
-Instantiate PyTorch model `:math:`\Uparrow` <#table-of-content>`__
+Instantiate PyTorch model
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 
@@ -516,7 +516,7 @@ from `HuggingFace hub <https://huggingface.co/models>`__ during first
 run. It may takes some time and requires at least 13 Gb free space on
 disk.
 
-Compress Model weights to 4 and 8 bits using NNCF `:math:`\Uparrow` <#table-of-content>`__
+Compress Model weights to 4 and 8 bits using NNCF
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 
@@ -559,7 +559,7 @@ documentation <https://docs.openvino.ai/2024/openvino-workflow/model-optimizatio
 
    **Note**: There is no speedup for INT4 compressed models on dGPU.
 
-Convert model to OpenVINO IR format `:math:`\Uparrow` <#table-of-content>`__
+Convert model to OpenVINO IR format
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 
@@ -573,14 +573,14 @@ compression instead of INT8 weight compression.
 .. code:: ipython3
 
     import ipywidgets as widgets
-    
+
     compression_mode = widgets.Dropdown(
         options=["INT4", "INT8"],
         value="INT4",
         description="Compression mode:",
         disabled=False,
     )
-    
+
     compression_mode
 
 
@@ -600,7 +600,7 @@ compression instead of INT8 weight compression.
     else:
         compressed_model_dir = Path("llava-mpt/INT8_compressed_weights")
         llava_wc_parameters = dict(mode=nncf.CompressWeightsMode.INT8)
-    
+
     if not compressed_model_dir.exists():
         compressed_model_dir.mkdir(exist_ok=True, parents=True)
         config.save_pretrained(compressed_model_dir)
@@ -608,10 +608,10 @@ compression instead of INT8 weight compression.
         vision_tower = model.get_vision_tower()
         if not vision_tower.is_loaded:
             vision_tower.load_model()
-    
+
         if mm_use_im_start_end:
             model.resize_token_embeddings(len(tokenizer))
-    
+
         model.eval()
         with torch.no_grad():
             convert_llava_mpt(
@@ -649,17 +649,17 @@ compression instead of INT8 weight compression.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -677,17 +677,17 @@ compression instead of INT8 weight compression.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -711,17 +711,17 @@ compression instead of INT8 weight compression.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -737,17 +737,17 @@ compression instead of INT8 weight compression.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -771,17 +771,17 @@ compression instead of INT8 weight compression.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -810,8 +810,8 @@ documentation <https://huggingface.co/docs/transformers/main_classes/text_genera
     from transformers import AutoConfig
     import numpy as np
     import torch
-    
-    
+
+
     class OVLlavaMPTForCausalLM(GenerationMixin):
         def __init__(self, core, model_dir, device):
             self.image_encoder = core.compile_model(model_dir / "image_encoder.xml", device)
@@ -829,11 +829,11 @@ documentation <https://huggingface.co/docs/transformers/main_classes/text_genera
             self.main_input_name = "input_ids"
             self.device = torch.device("cpu")
             self.num_pkv = 2
-    
+
         def can_generate(self):
             """Returns True to validate the check that the model using `GenerationMixin.generate()` can indeed generate."""
             return True
-    
+
         def __call__(
             self,
             input_ids: torch.LongTensor,
@@ -844,7 +844,7 @@ documentation <https://huggingface.co/docs/transformers/main_classes/text_genera
             **kwargs,
         ) -> CausalLMOutputWithPast:
             return self.forward(input_ids, images, attention_mask, prefix_mask, past_key_values)
-    
+
         def forward(
             self,
             input_ids: torch.LongTensor,
@@ -865,27 +865,27 @@ documentation <https://huggingface.co/docs/transformers/main_classes/text_genera
                 past_key_values = tuple(past_key_value for pkv_per_layer in past_key_values for past_key_value in pkv_per_layer)
                 # Add the past_key_values to the decoder inputs
                 inputs = dict(zip(self.key_value_input_names, past_key_values))
-    
+
             else:
                 return self.forward_with_image(input_ids, images, attention_mask)
             inputs["input_ids"] = np.array(input_ids)
-    
+
             if "attention_mask" in self.input_names:
                 inputs["attention_mask"] = np.array(attention_mask)
-    
+
             # Run inference
             self.request.start_async(inputs, share_inputs=True)
             self.request.wait()
-    
+
             logits = torch.from_numpy(self.request.get_tensor("logits").data)
-    
+
             # Tuple of length equal to : number of layer * number of past_key_value per decoder layer (2 corresponds to the self-attention layer)
             past_key_values = tuple(self.request.get_tensor(key).data for key in self.key_value_output_names)
             # Tuple of tuple of length `n_layers`, with each tuple of length equal to 2 (k/v of self-attention)
-    
+
             past_key_values = tuple(past_key_values[i : i + self.num_pkv] for i in range(0, len(past_key_values), self.num_pkv))
             return CausalLMOutputWithPast(logits=logits, past_key_values=past_key_values)
-    
+
         def forward_with_image(self, input_ids, images, attention_mask):
             """First step inference method, that resolves multimodal data"""
             input_embed, attention_mask = self.prepare_multimodal_input(input_ids, images, attention_mask)
@@ -894,13 +894,13 @@ documentation <https://huggingface.co/docs/transformers/main_classes/text_genera
             pkv = list(outs.values())[1:]
             pkv = tuple(pkv[i : i + self.num_pkv] for i in range(0, len(pkv), self.num_pkv))
             return CausalLMOutputWithPast(logits=torch.from_numpy(logits), past_key_values=pkv)
-    
+
         def prepare_multimodal_input(self, input_ids, images, attention_mask):
             """Preprocessing function for embedding multimodal data"""
             image_features = []
             if images is not None:
                 image_features = self.image_encoder(images)[0]
-    
+
             new_input_embeds = []
             cur_image_idx = 0
             for batch_idx, cur_input_ids in enumerate(input_ids):
@@ -940,10 +940,10 @@ documentation <https://huggingface.co/docs/transformers/main_classes/text_genera
                 cur_new_input_embeds = [torch.from_numpy(x) for x in cur_new_input_embeds]
                 cur_new_input_embeds = torch.cat(cur_new_input_embeds, dim=0)
                 new_input_embeds.append(cur_new_input_embeds)
-    
+
             if any(x.shape != new_input_embeds[0].shape for x in new_input_embeds):
                 max_len = max(x.shape[0] for x in new_input_embeds)
-    
+
                 new_input_embeds_align = []
                 for cur_new_embed in new_input_embeds:
                     cur_new_embed = torch.cat(
@@ -958,7 +958,7 @@ documentation <https://huggingface.co/docs/transformers/main_classes/text_genera
                     )
                     new_input_embeds_align.append(cur_new_embed)
                 new_input_embeds = torch.stack(new_input_embeds_align, dim=0)
-    
+
                 if attention_mask is not None:
                     new_attention_mask = []
                     for cur_attention_mask, cur_new_labels, cur_new_labels_align in zip(attention_mask, _new_labels, new_labels):
@@ -985,7 +985,7 @@ documentation <https://huggingface.co/docs/transformers/main_classes/text_genera
                     assert attention_mask.shape == new_labels.shape
             else:
                 new_input_embeds = torch.stack(new_input_embeds, dim=0)
-    
+
                 if attention_mask is not None:
                     new_attn_mask_pad_left = torch.full(
                         (
@@ -997,9 +997,9 @@ documentation <https://huggingface.co/docs/transformers/main_classes/text_genera
                     )
                     attention_mask = torch.cat((new_attn_mask_pad_left, attention_mask), dim=1)
                     assert attention_mask.shape == new_input_embeds.shape[:2]
-    
+
             return new_input_embeds, attention_mask
-    
+
         def prepare_inputs_for_generation(self, input_ids, past_key_values=None, **kwargs):
             """
             This function is used during running GenerationMixin.generate for preparing model specific inputs for
@@ -1024,14 +1024,14 @@ documentation <https://huggingface.co/docs/transformers/main_classes/text_genera
                 "past_key_values": past_key_values,
                 "images": kwargs.get("images", None),
             }
-    
+
         def _reorder_cache(self, past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor) -> Tuple[Tuple[torch.Tensor]]:
             """
             This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
             [`~PreTrainedModel.beam_sample`] is called.
             This is required to match `past_key_values` with the correct beam_idx at every generation step.
             """
-    
+
             # from transformers.models.gpt2.modeling_gpt2.GPT2LMHeadModel._reorder_cache
             return tuple(tuple(np.take(past_state, beam_idx, 0) for past_state in layer_past) for layer_past in past_key_values)
 
@@ -1055,20 +1055,20 @@ Select device from dropdown list for running inference using OpenVINO.
 .. code:: ipython3
 
     import ipywidgets as widgets
-    
+
     core = ov.Core()
-    
+
     support_devices = core.available_devices
     if "NPU" in support_devices:
         support_devices.remove("NPU")
-    
+
     device = widgets.Dropdown(
         options=support_devices + ["AUTO"],
         value="AUTO",
         description="Device:",
         disabled=False,
     )
-    
+
     device
 
 
@@ -1103,8 +1103,8 @@ PyTorch implementation we will use PyTorch tensors as input.
     import requests
     from PIL import Image
     from io import BytesIO
-    
-    
+
+
     def load_image(image_file):
         if image_file.startswith("http") or image_file.startswith("https"):
             response = requests.get(image_file)
@@ -1112,13 +1112,13 @@ PyTorch implementation we will use PyTorch tensors as input.
         else:
             image = Image.open(image_file).convert("RGB")
         return image
-    
-    
+
+
     image_file = "https://llava-vl.github.io/static/images/view.jpg"
-    
+
     image = load_image(image_file)
     image_tensor = image_processor.preprocess(image, return_tensors="pt")["pixel_values"]
-    
+
     text_message = "What are the things I should be cautious about when I visit here?"
     print(f"Question: {text_message}")
     image
@@ -1161,21 +1161,21 @@ accumulating history of provided messages and images.
     from llava.constants import IMAGE_TOKEN_INDEX
     from transformers import TextStreamer
     from llava.conversation import conv_templates, SeparatorStyle
-    
+
     # Prepare
     streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
     conv_mode = "mpt"
-    
+
     conv = conv_templates[conv_mode].copy()
     roles = ("user", "assistant")
-    
+
     if mm_use_im_start_end:
         inp = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + "\n" + text_message
     else:
         inp = DEFAULT_IMAGE_TOKEN + "\n" + text_message
     conv.append_message(conv.roles[0], inp)
     conv.append_message(conv.roles[1], None)
-    
+
     prompt = conv.get_prompt()
     input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0)
     stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
@@ -1183,7 +1183,7 @@ accumulating history of provided messages and images.
     stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
     streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
     print("Answer:")
-    
+
     output_ids = ov_model.generate(
         input_ids,
         images=image_tensor,
@@ -1212,25 +1212,25 @@ Interactive demo
     import gradio as gr
     from threading import Event, Thread
     from transformers import TextIteratorStreamer
-    
+
     title_markdown = """
     # 🌋 LLaVA: Large Language and Vision Assistant
     """
-    
+
     tos_markdown = """
     ### Terms of use
     By using this service, users are required to agree to the following terms:
     The service is a research preview intended for non-commercial use only. It only provides limited safety measures and may generate offensive content. It must not be used for any illegal, harmful, violent, racist, or sexual purposes. The service may collect user dialogue data for future research.
     """
-    
+
     conv = conv_templates[conv_mode].copy()
     conv.messages = []
-    
-    
+
+
     def clear_history(textbox, imagebox, chatbot):
         """
         callback function for clearing chat windows in interface on clear button click
-    
+
         Params:
           textbox: current textbox for user messages state
           imagebox: current imagebox state
@@ -1239,14 +1239,14 @@ Interactive demo
           empty textbox, imagebox and chatbot states
         """
         conv.messages = []
-    
+
         return None, None, None
-    
-    
+
+
     def user(message, history):
         """
         callback function for updating user messages in interface on submit button click
-    
+
         Params:
           message: current message
           history: conversation history
@@ -1255,20 +1255,20 @@ Interactive demo
         """
         # Append the user's message to the conversation history
         return "", history + [[message, ""]]
-    
-    
+
+
     def bot(image, history, temperature=0.2, top_p=0.7, max_new_tokens=1024):
         """
         callback function for running chatbot on submit button click
-    
+
         Params:
           history: conversation history
           temperature:  parameter for control the level of creativity in AI-generated text.
                         By adjusting the `temperature`, you can influence the AI model's probability distribution, making the text more focused or diverse.
           top_p: parameter for control the range of tokens considered by the AI model based on their cumulative probability.
-    
+
         """
-    
+
         text = history[-1][0]
         if len(text) <= 0 and image is None:
             conv.skip_next = True
@@ -1282,7 +1282,7 @@ Interactive demo
         conv.append_message(conv.roles[0], text)
         conv.append_message(conv.roles[1], None)
         conv.skip_next = False
-    
+
         # Construct the input message string for the model by concatenating the current system message and conversation history
         prompt = conv.get_prompt()
         image = conv.get_images(return_pil=True)
@@ -1307,19 +1307,19 @@ Interactive demo
             use_cache=True,
             stopping_criteria=[stopping_criteria],
         )
-    
+
         stream_complete = Event()
-    
+
         def generate_and_signal_complete():
             """
             genration function for single thread
             """
             ov_model.generate(**generate_kwargs)
             stream_complete.set()
-    
+
         t1 = Thread(target=generate_and_signal_complete)
         t1.start()
-    
+
         # Initialize an empty string to store the generated text
         partial_text = ""
         for new_text in streamer:
@@ -1329,11 +1329,11 @@ Interactive demo
             conv.messages[-1][-1] = partial_text
             history[-1][1] = partial_text
             yield history
-    
-    
+
+
     with gr.Blocks(title="LLaVA") as demo:
         gr.Markdown(title_markdown)
-    
+
         with gr.Row():
             with gr.Column():
                 imagebox = gr.Image(type="pil")
@@ -1362,7 +1362,7 @@ Interactive demo
                         interactive=True,
                         label="Max output tokens",
                     )
-    
+
             with gr.Column(scale=3):
                 with gr.Column(scale=6):
                     chatbot = gr.Chatbot(height=400)
@@ -1378,9 +1378,9 @@ Interactive demo
                             submit_btn = gr.Button(value="Submit", visible=True)
                     with gr.Row(visible=True) as button_row:
                         clear_btn = gr.Button(value="🗑️  Clear history", interactive=True)
-    
+
         gr.Markdown(tos_markdown)
-    
+
         submit_event = textbox.submit(
             fn=user,
             inputs=[textbox, chatbot],
@@ -1405,7 +1405,7 @@ Interactive demo
             chatbot,
             queue=True,
         )
-    
+
     # if you are launching remotely, specify server_name and server_port
     # demo.launch(server_name='your server name', server_port='server port in int')
     # Read more in the docs: https://gradio.app/docs/
diff --git a/docs/notebooks/llava-next-multimodal-chatbot-with-output.rst b/docs/notebooks/llava-next-multimodal-chatbot-with-output.rst
index b9264591b925e4..772d4935100644 100644
--- a/docs/notebooks/llava-next-multimodal-chatbot-with-output.rst
+++ b/docs/notebooks/llava-next-multimodal-chatbot-with-output.rst
@@ -23,8 +23,8 @@ stateful transformation on LLM part and model optimization techniques
 like weights compression and quantization using
 `NNCF <https://github.com/openvinotoolkit/nncf>`__
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Download PyTorch model <#download-pytorch-model>`__
@@ -69,12 +69,12 @@ Prerequisites
 .. code:: ipython3
 
     from pathlib import Path
-
+    
     MODEL_DIR = Path("model")
     IMAGE_ENCODER_PATH = MODEL_DIR / "image_encoder.xml"
     INPUT_EMBEDDING_PATH = MODEL_DIR / "input_embeddings.xml"
     LANGUAGE_MODEL_PATH = MODEL_DIR / "language_model.xml"
-
+    
     requires_pt_model_loading = not all([p.exists() for p in [IMAGE_ENCODER_PATH, INPUT_EMBEDDING_PATH, LANGUAGE_MODEL_PATH]])
 
 Download PyTorch model
@@ -87,18 +87,18 @@ Download PyTorch model
     from transformers import LlavaNextProcessor, LlavaNextForConditionalGeneration
     import torch
     import gc
-
+    
     processor = LlavaNextProcessor.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf")
     image_encoder_model, input_embedding_model, language_model = None, None, None
-
-
+    
+    
     class ImageEncoder(torch.nn.Module):
         def __init__(self, config, vision_tower, multi_modal_projector):
             super().__init__()
             self.config = config
             self.vision_tower = vision_tower
             self.multi_modal_projector = multi_modal_projector
-
+    
         def forward(self, pixel_values):
             batch_size, num_patches, num_channels, height, width = pixel_values.shape
             reshaped_pixel_values = pixel_values.view(batch_size * num_patches, num_channels, height, width)
@@ -110,8 +110,8 @@ Download PyTorch model
                 selected_image_feature = selected_image_feature
             image_features = self.multi_modal_projector(selected_image_feature)
             return image_features
-
-
+    
+    
     if requires_pt_model_loading:
         model = LlavaNextForConditionalGeneration.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf", low_cpu_mem_usage=True)
         model.config.save_pretrained(MODEL_DIR)
@@ -136,8 +136,8 @@ Download PyTorch model
     Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
 
 
-Convert model to OpenVINO Intermediate Representation
------------------------------------------------------
+OpenVINO## Convert model to OpenVINO Intermediate Representation `back
+to top ⬆️ <#Table-of-contents:>`__
 
 OpenVINO supports PyTorch models via conversion to OpenVINO Intermediate
 Representation (IR). `OpenVINO model conversion
@@ -208,8 +208,8 @@ Image Encoder is represented in LLaVA by pretrained CLIP model.
     import torch
     import openvino as ov
     import gc
-
-
+    
+    
     def cleanup_torchscript_cache():
         """
         Helper for removing cached model representation
@@ -217,14 +217,14 @@ Image Encoder is represented in LLaVA by pretrained CLIP model.
         torch._C._jit_clear_class_registry()
         torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore()
         torch.jit._state._clear_class_state()
-
-
+    
+    
     if not IMAGE_ENCODER_PATH.exists():
         ov_image_encoder = ov.convert_model(image_encoder_model, example_input=torch.zeros((1, 5, 3, 336, 336)))
         ov.save_model(ov_image_encoder, IMAGE_ENCODER_PATH)
         del ov_image_encoder
         cleanup_torchscript_cache()
-
+    
     del image_encoder_model
     gc.collect();
 
@@ -242,16 +242,16 @@ use it separately.
 .. code:: ipython3
 
     llm_input = None
-
+    
     if not LANGUAGE_MODEL_PATH.exists():
         llm_input = input_embedding_model(torch.ones((2, 2), dtype=torch.int64))
-
+    
     if not INPUT_EMBEDDING_PATH.exists():
         ov_input_embeddings_model = ov.convert_model(input_embedding_model, example_input=torch.ones((2, 2), dtype=torch.int64))
         ov.save_model(ov_input_embeddings_model, INPUT_EMBEDDING_PATH)
         del ov_input_embeddings_model
         cleanup_torchscript_cache()
-
+    
     del input_embedding_model
     gc.collect();
 
@@ -302,28 +302,28 @@ documentation <https://docs.openvino.ai/2024/openvino-workflow/running-inference
     from typing import Optional, Tuple, List
     from openvino.runtime import opset13
     import numpy as np
-
-
+    
+    
     def model_has_state(ov_model: ov.Model):
         # TODO: Provide a better way based on the variables availability, but OV Python API doesn't expose required methods
         return len(ov_model.get_sinks()) > 0
-
-
+    
+    
     def model_has_input_output_name(ov_model: ov.Model, name: str):
         """
         Helper function for checking that model has specified input or output name
-
+    
         Parameters:
           ov_model (ov.Model):   # TODO: Can we derive the dimensions from the model topology?
           name (str):
               name of input or output
-
+    
         Returns:
           True if input or output with requested name exists else False
         """
         return name in sum([list(t.get_names()) for t in ov_model.inputs + ov_model.outputs], [])
-
-
+    
+    
     def fuse_cache_reorder(
         ov_model: ov.Model,
         not_kv_inputs: List[str],
@@ -332,14 +332,14 @@ documentation <https://docs.openvino.ai/2024/openvino-workflow/running-inference
     ):
         """
         Fuses reored_cache during generate cycle into ov.Model. Used with stateful models, because we can not modify model state directly.
-
+    
         Adds a new beam_idx parameter and Gather op per each kv-cache input in a given model.
         Should be run before make_stateful. Implements optimumum's _reorder_cache
         inside the model in the beginning of each iteration.
         Gather works along given gather_dim dimension that may vary from model to model.
         KV-cache inputs are identified based on names in key_value_input_names.
         Append the new beam_idx parameter to not_kv_inputs.
-
+    
         Parameters:
           ov_model (`ov.Model`):
               openvino model for processing
@@ -350,7 +350,7 @@ documentation <https://docs.openvino.ai/2024/openvino-workflow/running-inference
           gather_dim (int):
               dimension for gathering cache during reorder pass
         """
-
+    
         if model_has_input_output_name(ov_model, "beam_idx"):
             raise ValueError("Model already has fused cache")
         input_batch = ov_model.input("inputs_embeds").get_partial_shape()[0]
@@ -366,12 +366,12 @@ documentation <https://docs.openvino.ai/2024/openvino-workflow/running-inference
             for consumer in consumers:
                 consumer.replace_source_output(gather.output(0))
         ov_model.validate_nodes_and_infer_types()
-
-
+    
+    
     def build_state_initializer(ov_model: ov.Model, batch_dim: int):
         """
         Build initialization ShapeOf Expression for all ReadValue ops
-
+    
         Parameters:
           ov_model (ov.Model):
               openvino model
@@ -393,8 +393,8 @@ documentation <https://docs.openvino.ai/2024/openvino-workflow/running-inference
                 broadcast = opset13.broadcast(opset13.constant(0.0, dtype=op.get_output_element_type(0)), shape)
                 op.set_arguments([broadcast])
         ov_model.validate_nodes_and_infer_types()
-
-
+    
+    
     def make_stateful(
         ov_model: ov.Model,
         not_kv_inputs: List[str],
@@ -406,7 +406,7 @@ documentation <https://docs.openvino.ai/2024/openvino-workflow/running-inference
     ):
         """
         Hides kv-cache inputs and outputs inside the model as variables.
-
+    
         Parameters:
             ov_model (ov.Model):
                 openvino model
@@ -424,9 +424,9 @@ documentation <https://docs.openvino.ai/2024/openvino-workflow/running-inference
                 precalculated number of beams and batch for shapes initialization
         """
         from openvino._offline_transformations import apply_make_stateful_transformation
-
+    
         input_output_map = {}
-
+    
         if num_beams_and_batch is not None:
             # Set batch size for input_ids and attention mask to avoid dynamic dimension got propagated from the end of the model back to ReadValue
             for input in not_kv_inputs:
@@ -441,16 +441,16 @@ documentation <https://docs.openvino.ai/2024/openvino-workflow/running-inference
                 shape = input.get_partial_shape()
                 shape[batch_dim] = num_beams_and_batch * num_attention_heads
                 input.get_node().set_partial_shape(shape)
-
+    
         if num_beams_and_batch is not None:
             # Re-validation model if shapes are altered above
             ov_model.validate_nodes_and_infer_types()
-
+    
         apply_make_stateful_transformation(ov_model, input_output_map)
         if num_beams_and_batch is None:
             build_state_initializer(ov_model, batch_dim)
-
-
+    
+    
     def patch_stateful(ov_model):
         key_value_input_names = [key.get_any_name() for key in ov_model.inputs[2:-1]]
         key_value_output_names = [key.get_any_name() for key in ov_model.outputs[1:]]
@@ -459,7 +459,7 @@ documentation <https://docs.openvino.ai/2024/openvino-workflow/running-inference
             return
         batch_dim = 0
         num_attention_heads = 1
-
+    
         fuse_cache_reorder(ov_model, not_kv_inputs, key_value_input_names, batch_dim)
         make_stateful(
             ov_model,
@@ -475,7 +475,7 @@ documentation <https://docs.openvino.ai/2024/openvino-workflow/running-inference
 
     make_stateful_model = True
     core = ov.Core()
-
+    
     if not LANGUAGE_MODEL_PATH.exists():
         pkv = language_model(inputs_embeds=llm_input, attention_mask=torch.ones((2, 2), dtype=torch.int64))[1]
         model_inputs = ["attention_mask", "position_ids"]
@@ -495,10 +495,10 @@ documentation <https://docs.openvino.ai/2024/openvino-workflow/running-inference
                 "position_ids": position_ids,
             },
         )
-
+    
         for input, input_name in zip(ov_model.inputs, model_inputs):
             input.get_tensor().set_names({input_name})
-
+    
         for output, output_name in zip(ov_model.outputs, model_outputs):
             output.get_tensor().set_names({output_name})
         if make_stateful_model:
@@ -556,13 +556,13 @@ documentation <https://docs.openvino.ai/2024/openvino-workflow/model-optimizatio
 .. code:: ipython3
 
     import ipywidgets as widgets
-
+    
     to_compress_weights = widgets.Checkbox(
         value=True,
         description="Weights Compression",
         disabled=False,
     )
-
+    
     to_compress_weights
 
 
@@ -577,13 +577,13 @@ documentation <https://docs.openvino.ai/2024/openvino-workflow/model-optimizatio
 .. code:: ipython3
 
     import nncf
-
+    
     compression_configuration = {
         "mode": nncf.CompressWeightsMode.INT4_SYM,
         "group_size": 64,
         "ratio": 0.6,
     }
-
+    
     LANGUAGE_MODEL_PATH_INT4 = LANGUAGE_MODEL_PATH.parent / LANGUAGE_MODEL_PATH.name.replace(".xml", "-int4.xml")
     if to_compress_weights.value and not LANGUAGE_MODEL_PATH_INT4.exists():
         ov_model = core.read_model(LANGUAGE_MODEL_PATH)
@@ -631,7 +631,7 @@ inference faster. The optimization process contains the following steps:
         description="Quantization",
         disabled=False,
     )
-
+    
     to_quantize
 
 
@@ -646,15 +646,15 @@ inference faster. The optimization process contains the following steps:
 .. code:: ipython3
 
     IMAGE_ENCODER_PATH_INT8 = IMAGE_ENCODER_PATH.parent / IMAGE_ENCODER_PATH.name.replace(".xml", "-int8.xml")
-
-
+    
+    
     import requests
-
+    
     r = requests.get(
         url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/skip_kernel_extension.py",
     )
     open("skip_kernel_extension.py", "w").write(r.text)
-
+    
     %load_ext skip_kernel_extension
 
 Prepare datasets
@@ -670,15 +670,15 @@ model.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-
+    
     import requests
     from io import BytesIO
     import numpy as np
     from PIL import Image
     from requests.packages.urllib3.exceptions import InsecureRequestWarning
     requests.packages.urllib3.disable_warnings(InsecureRequestWarning)
-
-
+    
+    
     def get_pil_from_url(url):
         """
         Downloads and converts an image from a URL to a PIL Image object.
@@ -686,7 +686,7 @@ model.
         response = requests.get(url, verify=False, timeout=20)
         image = Image.open(BytesIO(response.content))
         return image.convert("RGB")
-
+    
     def collate_fn(example, image_column="image_url"):
         """
         Preprocesses an example by loading and transforming image and text data.
@@ -705,18 +705,18 @@ model.
                 return None
         except Exception:
             return None
-
+    
         inputs = processor.image_processor(images=[image], return_tensors="pt")
         return inputs
 
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-
+    
     import torch
     from datasets import load_dataset
     from tqdm.notebook import tqdm
-
+    
     def prepare_calibration_data(dataloader, init_steps):
         """
         This function prepares calibration data from a dataloader for a specified number of initialization steps.
@@ -737,13 +737,13 @@ model.
                             }
                         )
         return data
-
-
+    
+    
     def prepare_dataset(opt_init_steps=50, max_train_samples=1000):
         """
         Prepares a vision-text dataset for quantization.
         """
-        dataset = load_dataset("conceptual_captions")
+        dataset = load_dataset("google-research-datasets/conceptual_captions", trust_remote_code=True)
         train_dataset = dataset["train"].shuffle(seed=42)
         dataloader = torch.utils.data.DataLoader(train_dataset, collate_fn=collate_fn, batch_size=1)
         calibration_data = prepare_calibration_data(dataloader, opt_init_steps)
@@ -752,7 +752,7 @@ model.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-
+    
     vcalibration_data = []
     if not IMAGE_ENCODER_PATH_INT8.exists():
         calibration_data = prepare_dataset()
@@ -770,14 +770,14 @@ Create a quantized model from the pre-trained model.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-
-
+    
+    
     if not IMAGE_ENCODER_PATH_INT8.exists():
         if len(calibration_data) == 0:
             raise RuntimeError(
                 'Calibration dataset is empty. Please check internet connection and try to download images manually.'
             )
-
+    
         ov_model = core.read_model(IMAGE_ENCODER_PATH)
         calibration_dataset = nncf.Dataset(calibration_data)
         quantized_model = nncf.quantize(
@@ -821,8 +821,8 @@ documentation <https://huggingface.co/docs/transformers/main_classes/text_genera
         unpad_image,
     )
     import openvino as ov
-
-
+    
+    
     class OVLlavaForCausalLM(GenerationMixin):
         def __init__(
             self,
@@ -851,11 +851,11 @@ documentation <https://huggingface.co/docs/transformers/main_classes/text_genera
             self.image_newline = torch.zeros(self.config.text_config.hidden_size, dtype=torch.float32)
             self.pad_token_id = self.config.pad_token_id if self.config.pad_token_id is not None else -1
             self.past_len = 0
-
+    
         def can_generate(self):
             """Returns True to validate the check that the model using `GenerationMixin.generate()` can indeed generate."""
             return True
-
+    
         def __call__(
             self,
             input_ids: torch.LongTensor,
@@ -875,7 +875,7 @@ documentation <https://huggingface.co/docs/transformers/main_classes/text_genera
                 image_sizes,
                 **kwargs,
             )
-
+    
         def forward(
             self,
             input_ids: torch.LongTensor,
@@ -900,49 +900,49 @@ documentation <https://huggingface.co/docs/transformers/main_classes/text_genera
                 # inputs["attention_mask"] = attention_mask
                 if "beam_idx" in self.input_names:
                     inputs["beam_idx"] = self.next_beam_idx if self.next_beam_idx is not None else np.arange(batch_size, dtype=int)
-
+    
                 if not self.stateful:
                     first_layer_past_key_value = torch.from_numpy(past_key_values[0][0][:, :, :, 0])
                 else:
                     first_layer_past_key_value = torch.from_numpy(self.request.query_state()[0].state.data[:, :, :, 0])
-
+    
                 # Sum all dimensions of head_dim (-2) to avoid random errors such as: https://github.com/huggingface/transformers/pull/28032#issuecomment-1863691941
                 batch_index, non_attended_tokens = torch.where(first_layer_past_key_value.float().sum(-2) == 0)
-
+    
                 # Get the target length
                 target_length = input_ids.shape[1]
                 past_length = first_layer_past_key_value.shape[-1]
-
+    
                 extended_attention_mask = torch.ones(
                     (attention_mask.shape[0], past_length),
                     dtype=attention_mask.dtype,
                     device=attention_mask.device,
                 )
-
+    
                 # Filter out only the tokens that can be un-attended, this can happen
                 # if one uses Llava + Fused modules where the cache on the
                 # first iteration is already big enough, or if one passes custom cache
                 valid_indices = non_attended_tokens < extended_attention_mask.size(-1)
                 new_batch_index = batch_index[valid_indices]
                 new_non_attended_tokens = non_attended_tokens[valid_indices]
-
+    
                 # Zero-out the places where we don't need to attend
                 extended_attention_mask[new_batch_index, new_non_attended_tokens] = 0
-
+    
                 attention_mask = torch.cat((extended_attention_mask, attention_mask[:, -target_length:]), dim=1)
                 position_ids = torch.sum(attention_mask, dim=1).unsqueeze(-1) - 1
                 inputs["attention_mask"] = attention_mask
                 inputs["position_ids"] = position_ids
-
+    
             else:
                 inputs = self.prepare_multimodal_input(input_ids, pixel_values, attention_mask, position_ids, image_sizes)
-
+    
             # Run inference
             self.request.start_async(inputs, share_inputs=True)
             self.request.wait()
-
+    
             logits = torch.from_numpy(self.request.get_tensor(self.output_names[0]).data)
-
+    
             if not self.stateful:
                 # Tuple of length equal to : number of layer * number of past_key_value per decoder layer (2 corresponds to the self-attention layer)
                 past_key_values = tuple(self.request.get_tensor(key).data for key in self.key_value_output_names)
@@ -952,7 +952,7 @@ documentation <https://huggingface.co/docs/transformers/main_classes/text_genera
                 past_key_values = ((),)
             self.past_len += inputs["inputs_embeds"].shape[1]
             return CausalLMOutputWithPast(logits=logits, past_key_values=past_key_values)
-
+    
         def prepare_multimodal_input(self, input_ids, pixel_values, attention_mask, position_ids, image_sizes=None):
             """Preprocessing function for embedding multimodal data"""
             inputs = {}
@@ -974,7 +974,7 @@ documentation <https://huggingface.co/docs/transformers/main_classes/text_genera
                 # Set initial value for the next beam_idx input that will be used at the current iteration
                 # and will be optionally updated by _reorder_cache at the next iterations if beam_search is used
                 self.next_beam_idx = np.arange(batch_size, dtype=int)
-
+    
             if "beam_idx" in self.input_names:
                 inputs["beam_idx"] = self.next_beam_idx if self.next_beam_idx is not None else np.arange(batch_size, dtype=int)
             if pixel_values is None:
@@ -988,16 +988,16 @@ documentation <https://huggingface.co/docs/transformers/main_classes/text_genera
             image_features = torch.from_numpy(res[0])
             split_sizes = [image.shape[0] for image in pixel_values]
             image_features = torch.split(image_features, split_sizes, dim=0)
-
+    
             # NOTE we only support multimodal_patch_merge_type == "spatial_unpad"
             height = width = self.config.vision_config.image_size // self.config.vision_config.patch_size
-
+    
             new_image_features = []
             for image_idx, image_feature in enumerate(image_features):
                 if image_feature.shape[0] > 1:
                     base_image_feature = image_feature[0]
                     image_feature = image_feature[1:]
-
+    
                     if height * width != base_image_feature.shape[0]:
                         raise ValueError("The number of patches is not consistent with the image size.")
                     num_patch_height, num_patch_width = get_anyres_image_grid_shape(
@@ -1023,7 +1023,7 @@ documentation <https://huggingface.co/docs/transformers/main_classes/text_genera
                     image_feature = torch.cat((image_feature, self.image_newline[None]), dim=0)
                 new_image_features.append(image_feature)
             image_features = torch.stack(new_image_features, dim=0)
-
+    
             (
                 inputs_embeds,
                 attention_mask,
@@ -1032,9 +1032,9 @@ documentation <https://huggingface.co/docs/transformers/main_classes/text_genera
             inputs["inputs_embeds"] = inputs_embeds
             inputs["attention_mask"] = attention_mask
             inputs["position_ids"] = position_ids
-
+    
             return inputs
-
+    
         def _merge_input_ids_with_image_features(self, image_features, inputs_embeds, input_ids, attention_mask, labels):
             num_images, num_image_patches, embed_dim = image_features.shape
             batch_size, sequence_length = input_ids.shape
@@ -1045,7 +1045,7 @@ documentation <https://huggingface.co/docs/transformers/main_classes/text_genera
             # Compute the maximum embed dimension
             max_embed_dim = (num_special_image_tokens.max() * (num_image_patches - 1)) + sequence_length
             batch_indices, non_image_indices = torch.where(input_ids != self.config.image_token_index)
-
+    
             # 2. Compute the positions where text should be written
             # Calculate new positions for text tokens in merged image-text sequence.
             # `special_image_token_mask` identifies image tokens. Each image token will be replaced by `nb_text_tokens_per_images - 1` text tokens.
@@ -1056,7 +1056,7 @@ documentation <https://huggingface.co/docs/transformers/main_classes/text_genera
             if left_padding:
                 new_token_positions += nb_image_pad[:, None]  # offset for left padding
             text_to_overwrite = new_token_positions[batch_indices, non_image_indices]
-
+    
             # 3. Create the full embedding, already padded to the maximum position
             final_embedding = torch.zeros(
                 batch_size,
@@ -1080,14 +1080,14 @@ documentation <https://huggingface.co/docs/transformers/main_classes/text_genera
                 text_to_overwrite.to(target_device),
             )
             attention_mask = attention_mask.to(target_device)
-
+    
             # 4. Fill the embeddings based on the mask. If we have ["hey" "<image>", "how", "are"]
             # we need to index copy on [0, 577, 578, 579] for the text and [1:576] for the image features
             final_embedding[batch_indices, text_to_overwrite] = inputs_embeds[batch_indices, non_image_indices]
             final_attention_mask[batch_indices, text_to_overwrite] = attention_mask[batch_indices, non_image_indices]
             if labels is not None:
                 final_labels[batch_indices, text_to_overwrite] = labels[batch_indices, non_image_indices]
-
+    
             # 5. Fill the embeddings corresponding to the images. Anything that is still zeros needs filling
             image_to_overwrite = torch.all(final_embedding == 0, dim=-1)
             image_to_overwrite &= image_to_overwrite.cumsum(-1) - 1 >= nb_image_pad[:, None].to(target_device)
@@ -1096,19 +1096,19 @@ documentation <https://huggingface.co/docs/transformers/main_classes/text_genera
                     f"The input provided to the model are wrong. The number of image tokens is {torch.sum(special_image_token_mask)} while"
                     f" the number of image given to the model is {num_images}. This prevents correct indexing and breaks batch generation."
                 )
-
+    
             final_embedding[image_to_overwrite] = image_features.contiguous().reshape(-1, embed_dim).to(target_device)
             final_attention_mask |= image_to_overwrite
             position_ids = (final_attention_mask.cumsum(-1) - 1).masked_fill_((final_attention_mask == 0), 1)
-
+    
             # 6. Mask out the embedding at padding positions, as we later use the past_key_value value to determine the non-attended tokens.
             batch_indices, pad_indices = torch.where(input_ids == self.pad_token_id)
             indices_to_mask = new_token_positions[batch_indices, pad_indices]
-
+    
             final_embedding[batch_indices, indices_to_mask] = 0
-
+    
             return final_embedding, final_attention_mask, position_ids
-
+    
         def prepare_inputs_for_generation(
             self,
             input_ids,
@@ -1124,7 +1124,7 @@ documentation <https://huggingface.co/docs/transformers/main_classes/text_genera
                     cache_length = past_length = past_key_values[0][0].shape[2]
                 else:
                     cache_length = past_length = self.past_len
-
+    
                 # Keep only the unprocessed tokens:
                 # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
                 # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
@@ -1142,7 +1142,7 @@ documentation <https://huggingface.co/docs/transformers/main_classes/text_genera
                 # older attention values, as their corresponding values are not part of the input.
                 if cache_length < past_length and attention_mask is not None:
                     attention_mask = attention_mask[:, -(cache_length + input_ids.shape[1]) :]
-
+    
             position_ids = kwargs.get("position_ids", None)
             if attention_mask is not None and position_ids is None:
                 # create position_ids on the fly for batch gllavaenerationsubset_siz
@@ -1150,13 +1150,13 @@ documentation <https://huggingface.co/docs/transformers/main_classes/text_genera
                 position_ids.masked_fill_(attention_mask == 0, 1)
                 if past_key_values:
                     position_ids = position_ids[:, -input_ids.shape[1] :]
-
+    
             # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
             if inputs_embeds is not None and past_key_values is None:
                 model_inputs = {"inputs_embeds": inputs_embeds}
             else:
                 model_inputs = {"input_ids": input_ids}
-
+    
             model_inputs.update(
                 {
                     "position_ids": position_ids,
@@ -1182,18 +1182,18 @@ Select device
 .. code:: ipython3
 
     core = ov.Core()
-
+    
     support_devices = core.available_devices
     if "NPU" in support_devices:
         support_devices.remove("NPU")
-
+    
     device = widgets.Dropdown(
         options=support_devices + ["AUTO"],
         value="CPU",
         description="Device:",
         disabled=False,
     )
-
+    
     device
 
 
@@ -1212,7 +1212,7 @@ Select device
         description="INT4 language model",
         disabled=not LANGUAGE_MODEL_PATH_INT4.exists(),
     )
-
+    
     use_int4_lang_model
 
 
@@ -1231,7 +1231,7 @@ Select device
         description="INT8 image encoder",
         disabled=not IMAGE_ENCODER_PATH_INT8.exists(),
     )
-
+    
     use_int8_image_encoder
 
 
@@ -1247,23 +1247,23 @@ Select device
 
     lang_model_path = LANGUAGE_MODEL_PATH_INT4 if use_int4_lang_model.value else LANGUAGE_MODEL_PATH
     image_encoder_path = IMAGE_ENCODER_PATH_INT8 if use_int8_image_encoder.value else IMAGE_ENCODER_PATH
-
+    
     ov_llava_model = OVLlavaForCausalLM(core, image_encoder_path, INPUT_EMBEDDING_PATH, lang_model_path, device.value)
 
 .. code:: ipython3
 
     from PIL import Image
     import requests
-
-
+    
+    
     from transformers import TextStreamer
-
+    
     url = "https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/d5fbbd1a-d484-415c-88cb-9986625b7b11"
     image = Image.open(requests.get(url, stream=True).raw)
     question = "What is unusual on this image?"
     prompt = f"[INST] <image>\n{question}[/INST]"
     streamer = TextStreamer(processor, skip_special_tokens=True, skip_prompt=True)
-
+    
     inputs = processor(prompt, image, return_tensors="pt")
     print(f"Question:\n{question}")
     image
@@ -1311,7 +1311,7 @@ Interactive demo
     from threading import Thread
     from PIL import Image
     import torch
-
+    
     example_image_urls = [
         (
             "https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/1d6a0188-5613-418d-a1fd-4560aae1d907",
@@ -1324,40 +1324,40 @@ Interactive demo
     ]
     for url, file_name in example_image_urls:
         Image.open(requests.get(url, stream=True).raw).save(file_name)
-
-
+    
+    
     def bot_streaming(message, history):
         print(message)
         if message["files"]:
-            image = message["files"][-1]["path"]
+            image = message["files"][-1]["path"] if isinstance(message["files"][-1], dict) else message["files"][-1]
         else:
             # if there's no image uploaded for this turn, look for images in the past turns
             # kept inside tuples, take the last one
             for hist in history:
                 if isinstance(hist[0], tuple):
                     image = hist[0][0]
-
+    
         if image is None:
             gr.Error("You need to upload an image for LLaVA to work.")
         prompt = f"[INST] <image>\n{message['text']} [/INST]"
         image = Image.open(image).convert("RGB")
         inputs = processor(prompt, image, return_tensors="pt")
-
+    
         streamer = TextIteratorStreamer(processor, **{"skip_special_tokens": True})
         generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=100)
-
+    
         thread = Thread(target=ov_llava_model.generate, kwargs=generation_kwargs)
         thread.start()
-
+    
         text_prompt = f"[INST]  \n{message['text']} [/INST]"
-
+    
         buffer = ""
         for new_text in streamer:
             buffer += new_text
             generated_text_without_prompt = buffer[len(text_prompt) :]
             yield generated_text_without_prompt
-
-
+    
+    
     demo = gr.ChatInterface(
         fn=bot_streaming,
         title="LLaVA NeXT",
@@ -1369,7 +1369,7 @@ Interactive demo
         stop_btn="Stop Generation",
         multimodal=True,
     )
-
+    
     try:
         demo.launch(debug=False)
     except Exception:
diff --git a/docs/notebooks/llm-agent-langchain-with-output.rst b/docs/notebooks/llm-agent-langchain-with-output.rst
deleted file mode 100644
index 78df48deac6e5c..00000000000000
--- a/docs/notebooks/llm-agent-langchain-with-output.rst
+++ /dev/null
@@ -1,678 +0,0 @@
-Create LLM Agent using OpenVINO
-===============================
-
-LLM are limited to the knowledge on which they have been trained and the
-additional knowledge provided as context, as a result, if a useful piece
-of information is missing the provided knowledge, the model cannot “go
-around” and try to find it in other sources. This is the reason why we
-need to introduce the concept of Agents.
-
-The core idea of agents is to use a language model to choose a sequence
-of actions to take. In agents, a language model is used as a reasoning
-engine to determine which actions to take and in which order. Agents can
-be seen as applications powered by LLMs and integrated with a set of
-tools like search engines, databases, websites, and so on. Within an
-agent, the LLM is the reasoning engine that, based on the user input, is
-able to plan and execute a set of actions that are needed to fulfill the
-request.
-
-.. figure:: https://github.com/openvinotoolkit/openvino_notebooks/assets/91237924/22fa5396-8381-400f-a78f-97e25d57d807
-   :alt: agent
-
-   agent
-
-`LangChain <https://python.langchain.com/docs/get_started/introduction>`__
-is a framework for developing applications powered by language models.
-LangChain comes with a number of built-in agents that are optimized for
-different use cases.
-
-This notebook explores how to create an AI Agent step by step using
-OpenVINO and LangChain.
-
-Table of contents:
-^^^^^^^^^^^^^^^^^^
-
--  `Prerequisites <#prerequisites>`__
--  `Create tools <#create-tools>`__
--  `Create prompt template <#create-prompt-template>`__
--  `Create LLM <#create-llm>`__
-
-   -  `Download model <#select-model>`__
-   -  `Select inference device for
-      LLM <#select-inference-device-for-llm>`__
-
--  `Create agent <#create-agent>`__
--  `Run the agent <#run-agent>`__
--  `Interactive Demo <#interactive-demo>`__
-
-   -  `Use built-in tool <#use-built-in-tool>`__
-
-Prerequisites
--------------
-
-
-
-.. code:: ipython3
-
-    import os
-    
-    os.environ["GIT_CLONE_PROTECTION_ACTIVE"] = "false"
-    
-    %pip install -Uq pip
-    %pip uninstall -q -y optimum optimum-intel
-    %pip install --pre -Uq openvino openvino-tokenizers[transformers] --extra-index-url https://storage.openvinotoolkit.org/simple/wheels/nightly
-    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu\
-    "git+https://github.com/huggingface/optimum-intel.git"\
-    "git+https://github.com/openvinotoolkit/nncf.git"\
-    "torch>=2.1"\
-    "datasets"\
-    "accelerate"\
-    "gradio"\
-    "transformers>=4.38.1" "langchain>=0.2.0" "langchain-community>=0.2.0" "wikipedia"
-
-Create a tools
---------------
-
-
-
-First, we need to create some tools to call. In this example, we will
-create 3 custom functions to do basic calculation. For `more
-information <https://python.langchain.com/docs/modules/tools/>`__ on
-creating custom tools.
-
-.. code:: ipython3
-
-    from langchain_core.tools import tool
-    
-    
-    @tool
-    def multiply(first_int: int, second_int: int) -> int:
-        """Multiply two integers together."""
-        return first_int * second_int
-    
-    
-    @tool
-    def add(first_int: int, second_int: int) -> int:
-        "Add two integers."
-        return first_int + second_int
-    
-    
-    @tool
-    def exponentiate(base: int, exponent: int) -> int:
-        "Exponentiate the base to the exponent power."
-        return base**exponent
-
-Tools are interfaces that an agent, chain, or LLM can use to interact
-with the world. They combine a few things:
-
-1. The name of the tool
-2. A description of what the tool is
-3. JSON schema of what the inputs to the tool are
-4. The function to call
-5. Whether the result of a tool should be returned directly to the user
-
-.. code:: ipython3
-
-    print(f"name of `multiply` tool: {multiply.name}")
-    print(f"description of `multiply` tool: {multiply.description}")
-
-
-.. parsed-literal::
-
-    name of `multiply` tool: multiply
-    description of `multiply` tool: multiply(first_int: int, second_int: int) -> int - Multiply two integers together.
-
-
-Now that we have created all of them, and we can create a list of tools
-that we will use downstream.
-
-.. code:: ipython3
-
-    tools = [multiply, add, exponentiate]
-
-Create prompt template
-----------------------
-
-
-
-A prompt for a language model is a set of instructions or input provided
-by a user to guide the model’s response, helping it understand the
-context and generate relevant and coherent language-based output, such
-as answering questions, completing sentences, or engaging in a
-conversation.
-
-Different agents have different prompting styles for reasoning. In this
-example, we will use `ReAct agent <https://react-lm.github.io/>`__ with
-its typical prompt template. For a full list of built-in agents see
-`agent
-types <https://python.langchain.com/docs/modules/agents/agent_types/>`__.
-
-.. figure:: https://github.com/openvinotoolkit/openvino_notebooks/assets/91237924/a83bdf7f-bb9d-4b1f-9a0a-3fe4a76ba1ae
-   :alt: react
-
-   react
-
-A ReAct prompt consists of few-shot task-solving trajectories, with
-human-written text reasoning traces and actions, as well as environment
-observations in response to actions. ReAct prompting is intuitive and
-flexible to design, and achieves state-of-the-art few-shot performances
-across a variety of tasks, from question answering to online shopping!
-
-In an prompt template for agent, ``agent_scratchpad`` should be a
-sequence of messages that contains the previous agent tool invocations
-and the corresponding tool outputs.
-
-.. code:: ipython3
-
-    from langchain.prompts import PromptTemplate
-    
-    prompt = PromptTemplate.from_template(
-        """Answer the following questions as best you can. You have access to the following tools:
-    
-        {tools}
-    
-        Use the following format:
-    
-        Question: the input question you must answer
-        Thought: you should always think about what to do
-        Action: the action to take, should be one of [{tool_names}]
-        Action Input: the input to the action\nObservation: the result of the action
-        ... (this Thought/Action/Action Input/Observation can repeat N times)
-        Thought: I now know the final answer
-        Final Answer: the final answer to the original input question
-    
-        Begin!
-    
-        Question: {input}
-        Thought:{agent_scratchpad}"""
-    )
-
-Create LLM
-----------
-
-
-
-Large Language Models (LLMs) are a core component of LangChain.
-LangChain does not serve its own LLMs, but rather provides a standard
-interface for interacting with many different LLMs. In this example, we
-select ``neural-chat-7b-v3-1`` as LLM in agent pipeline.
-
-**neural-chat-7b-v3-1** - Mistral-7b model fine-tuned using Intel Gaudi.
-The model fine-tuned on the open source dataset
-`Open-Orca/SlimOrca <https://huggingface.co/datasets/Open-Orca/SlimOrca>`__
-and aligned with `Direct Preference Optimization (DPO)
-algorithm <https://arxiv.org/abs/2305.18290>`__. More details can be
-found in `model
-card <https://huggingface.co/Intel/neural-chat-7b-v3-1>`__ and `blog
-post <https://medium.com/@NeuralCompressor/the-practice-of-supervised-finetuning-and-direct-preference-optimization-on-habana-gaudi2-a1197d8a3cd3>`__.
-
-Download model
-~~~~~~~~~~~~~~
-
-
-
-To run LLM locally, we have to download the model in the first step. It
-is possible to `export your
-model <https://github.com/huggingface/optimum-intel?tab=readme-ov-file#export>`__
-to the OpenVINO IR format with the CLI, and load the model from local
-folder.
-
-.. code:: ipython3
-
-    from pathlib import Path
-    
-    model_id = "Intel/neural-chat-7b-v3-1"
-    model_path = "neural-chat-7b-v3-1-ov-int4"
-    
-    if not Path(model_path).exists():
-        !optimum-cli export openvino --model {model_id} --weight-format int4 {model_path}
-
-Select inference device for LLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-.. code:: ipython3
-
-    import openvino as ov
-    import ipywidgets as widgets
-    
-    core = ov.Core()
-    
-    support_devices = core.available_devices
-    if "NPU" in support_devices:
-        support_devices.remove("NPU")
-    
-    device = widgets.Dropdown(
-        options=support_devices + ["AUTO"],
-        value="CPU",
-        description="Device:",
-        disabled=False,
-    )
-    
-    device
-
-
-
-
-.. parsed-literal::
-
-    Dropdown(description='Device:', options=('CPU', 'GPU.0', 'GPU.1', 'AUTO'), value='CPU')
-
-
-
-OpenVINO models can be run locally through the ``HuggingFacePipeline``
-class in LangChain. To deploy a model with OpenVINO, you can specify the
-``backend="openvino"`` parameter to trigger OpenVINO as backend
-inference framework. For `more
-information <https://python.langchain.com/docs/integrations/llms/openvino/>`__.
-
-.. code:: ipython3
-
-    from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline
-    
-    ov_config = {"PERFORMANCE_HINT": "LATENCY", "NUM_STREAMS": "1", "CACHE_DIR": ""}
-    
-    ov_llm = HuggingFacePipeline.from_model_id(
-        model_id=model_path,
-        task="text-generation",
-        backend="openvino",
-        model_kwargs={"device": device.value, "ov_config": ov_config},
-        pipeline_kwargs={"max_new_tokens": 1024},
-    )
-
-
-.. parsed-literal::
-
-    2024-05-01 12:57:42.013703: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-05-01 12:57:42.015389: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
-    2024-05-01 12:57:42.049792: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
-    2024-05-01 12:57:42.050591: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
-    To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2024-05-01 12:57:42.819557: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
-    /home/ea/work/my_optimum_intel/optimum_env/lib/python3.8/site-packages/bitsandbytes/cextension.py:34: UserWarning: The installed version of bitsandbytes was compiled without GPU support. 8-bit optimizers, 8-bit multiplication, and GPU quantization are unavailable.
-      warn("The installed version of bitsandbytes was compiled without GPU support. "
-
-
-.. parsed-literal::
-
-    /home/ea/work/my_optimum_intel/optimum_env/lib/python3.8/site-packages/bitsandbytes/libbitsandbytes_cpu.so: undefined symbol: cadam32bit_grad_fp32
-    INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino
-
-
-.. parsed-literal::
-
-    No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
-    WARNING[XFORMERS]: xFormers can't load C++/CUDA extensions. xFormers was built for:
-        PyTorch 2.0.1+cu118 with CUDA 1108 (you have 2.1.2+cpu)
-        Python  3.8.18 (you have 3.8.10)
-      Please reinstall xformers (see https://github.com/facebookresearch/xformers#installing-xformers)
-      Memory-efficient attention, SwiGLU, sparse and more won't be available.
-      Set XFORMERS_MORE_DETAILS=1 for more details
-    Compiling the model to CPU ...
-
-
-You can get additional inference speed improvement with [Dynamic
-Quantization of activations and KV-cache quantization] on
-CPU(https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide/llm-inference-hf.html#enabling-openvino-runtime-optimizations).
-These options can be enabled with ``ov_config`` as follows:
-
-.. code:: ipython3
-
-    ov_config = {
-        "KV_CACHE_PRECISION": "u8",
-        "DYNAMIC_QUANTIZATION_GROUP_SIZE": "32",
-        "PERFORMANCE_HINT": "LATENCY",
-        "NUM_STREAMS": "1",
-        "CACHE_DIR": "",
-    }
-
-Create agent
-------------
-
-
-
-Now that we have defined the tools, prompt template and LLM, we can
-create the agent_executor.
-
-The agent executor is the runtime for an agent. This is what actually
-calls the agent, executes the actions it chooses, passes the action
-outputs back to the agent, and repeats.
-
-.. code:: ipython3
-
-    from custom_output_parser import ReActSingleInputOutputParser
-    from langchain.agents import AgentExecutor, create_react_agent
-    
-    output_parser = ReActSingleInputOutputParser()
-    
-    agent = create_react_agent(ov_llm, tools, prompt, output_parser=output_parser)
-    agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
-
-Run the agent
--------------
-
-
-
-We can now run the agent with a math query. Before getting the final
-answer, a agent executor will also produce intermediate steps of
-reasoning and actions. The format of these messages will follow your
-prompt template.
-
-.. code:: ipython3
-
-    agent_executor.invoke({"input": "Take 3 to the fifth power and multiply that by the sum of twelve and three"})
-
-
-.. parsed-literal::
-
-    
-    
-    > Entering new AgentExecutor chain...
-    Answer the following questions as best you can. You have access to the following tools:
-    
-        multiply: multiply(first_int: int, second_int: int) -> int - Multiply two integers together.
-    add: add(first_int: int, second_int: int) -> int - Add two integers.
-    exponentiate: exponentiate(base: int, exponent: int) -> int - Exponentiate the base to the exponent power.
-    
-        Use the following format:
-    
-        Question: the input question you must answer
-        Thought: you should always think about what to do
-        Action: the action to take, should be one of [multiply, add, exponentiate]
-        Action Input: the input to the action
-    Observation: the result of the action
-        ... (this Thought/Action/Action Input/Observation can repeat N times)
-        Thought: I now know the final answer
-        Final Answer: the final answer to the original input question
-    
-        Begin!
-    
-        Question: Take 3 to the fifth power and multiply that by the sum of twelve and three
-        Thought: We need to exponentiate 3 to the power of 5, then multiply the result by the sum of 12 and 3
-        Action: exponentiate
-        Action Input: base: 3, exponent: 5
-        Observation: 243
-        Action: add
-        Action Input: first_int: 12, second_int: 3
-        Observation: 15
-        Action: multiply
-        Action Input: first_int: 243, second_int: 15
-        Observation: 3645
-        Thought: I now know the final answer
-        Final Answer: 3645
-    
-    > Finished chain.
-
-
-
-
-.. parsed-literal::
-
-    {'input': 'Take 3 to the fifth power and multiply that by the sum of twelve and three',
-     'output': '3645'}
-
-
-
-Interactive Demo
-----------------
-
-
-
-Let’s create a interactive agent using
-`Gradio <https://www.gradio.app/>`__.
-
-Use built-in tool
-~~~~~~~~~~~~~~~~~
-
-
-
-LangChain has provided a list of all `built-in
-tools <https://python.langchain.com/docs/integrations/tools/>`__. In
-this example, we will use ``Wikipedia`` python package to query key
-words generated by agent.
-
-.. code:: ipython3
-
-    from langchain.tools import WikipediaQueryRun
-    from langchain_community.utilities import WikipediaAPIWrapper
-    
-    
-    wikipedia = WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper())
-    print(f"description of `wikipedia` tool: {wikipedia.description}")
-    
-    tools = [wikipedia]
-    
-    agent = create_react_agent(ov_llm, tools, prompt, output_parser=output_parser)
-    agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
-
-
-.. parsed-literal::
-
-    description of `wikipedia` tool: A wrapper around Wikipedia. Useful for when you need to answer general questions about people, places, companies, facts, historical events, or other subjects. Input should be a search query.
-
-
-.. code:: ipython3
-
-    from threading import Thread
-    import gradio as gr
-    from transformers import TextIteratorStreamer
-    
-    examples = [
-        ["What is OpenVINO ?"],
-        ["Who is 44th presedent of USA ?"],
-        ["what is Obama's first name and who is him ?"],
-        ["How many people live in Canada ?"],
-        ["How tall is the Eiffel Tower ?"],
-    ]
-    
-    
-    def partial_text_processor(partial_text, new_text):
-        """
-        helper for updating partially generated answer, used by default
-    
-        Params:
-          partial_text: text buffer for storing previosly generated text
-          new_text: text update for the current step
-        Returns:
-          updated text string
-    
-        """
-        new_text = new_text.replace("[INST]", "").replace("[/INST]", "")
-        partial_text += new_text
-        return partial_text
-    
-    
-    def user(message, history):
-        """
-        callback function for updating user messages in interface on submit button click
-    
-        Params:
-          message: current message
-          history: conversation history
-        Returns:
-          None
-        """
-        # Append the user's message to the conversation history
-        return "", history + [[message, ""]]
-    
-    
-    def bot(history, temperature, top_p, top_k, repetition_penalty, return_intermediate_steps):
-        """
-        callback function for running chatbot on submit button click
-    
-        Params:
-          history: conversation history
-          temperature:  parameter for control the level of creativity in AI-generated text.
-                        By adjusting the `temperature`, you can influence the AI model's probability distribution, making the text more focused or diverse.
-          top_p: parameter for control the range of tokens considered by the AI model based on their cumulative probability.
-          top_k: parameter for control the range of tokens considered by the AI model based on their cumulative probability, selecting number of tokens with highest probability.
-          repetition_penalty: parameter for penalizing tokens based on how frequently they occur in the text.
-          return_intermediate_steps: whether return intermediate_steps of agent.
-    
-        """
-        streamer = TextIteratorStreamer(
-            ov_llm.pipeline.tokenizer,
-            timeout=60.0,
-            skip_prompt=True,
-            skip_special_tokens=True,
-        )
-    
-        ov_llm.pipeline._forward_params = dict(
-            max_new_tokens=512,
-            temperature=temperature,
-            do_sample=temperature > 0.0,
-            top_p=top_p,
-            top_k=top_k,
-            repetition_penalty=repetition_penalty,
-            streamer=streamer,
-        )
-    
-        t1 = Thread(target=agent_executor.invoke, args=({"input": history[-1][0]},))
-        t1.start()
-    
-        # Initialize an empty string to store the generated text
-        partial_text = ""
-        final_answer = False
-    
-        for new_text in streamer:
-            if "Answer" in new_text:
-                final_answer = True
-            if final_answer or return_intermediate_steps:
-                partial_text = partial_text_processor(partial_text, new_text)
-                history[-1][1] = partial_text
-                yield history
-    
-    
-    def request_cancel():
-        ov_llm.pipeline.model.request.cancel()
-    
-    
-    with gr.Blocks(
-        theme=gr.themes.Soft(),
-        css=".disclaimer {font-variant-caps: all-small-caps;}",
-    ) as demo:
-        gr.Markdown(f"""<h1><center>OpenVINO Agent for {wikipedia.name}</center></h1>""")
-        chatbot = gr.Chatbot(height=500)
-        with gr.Row():
-            with gr.Column():
-                msg = gr.Textbox(
-                    label="Chat Message Box",
-                    placeholder="Chat Message Box",
-                    show_label=False,
-                    container=False,
-                )
-            with gr.Column():
-                with gr.Row():
-                    return_cot = gr.Checkbox(value=True, label="Return intermediate steps")
-                    submit = gr.Button("Submit")
-                    stop = gr.Button("Stop")
-                    clear = gr.Button("Clear")
-        with gr.Row():
-            with gr.Accordion("Advanced Options:", open=False):
-                with gr.Row():
-                    with gr.Column():
-                        with gr.Row():
-                            temperature = gr.Slider(
-                                label="Temperature",
-                                value=0.1,
-                                minimum=0.0,
-                                maximum=1.0,
-                                step=0.1,
-                                interactive=True,
-                                info="Higher values produce more diverse outputs",
-                            )
-                    with gr.Column():
-                        with gr.Row():
-                            top_p = gr.Slider(
-                                label="Top-p (nucleus sampling)",
-                                value=1.0,
-                                minimum=0.0,
-                                maximum=1,
-                                step=0.01,
-                                interactive=True,
-                                info=(
-                                    "Sample from the smallest possible set of tokens whose cumulative probability "
-                                    "exceeds top_p. Set to 1 to disable and sample from all tokens."
-                                ),
-                            )
-                    with gr.Column():
-                        with gr.Row():
-                            top_k = gr.Slider(
-                                label="Top-k",
-                                value=50,
-                                minimum=0.0,
-                                maximum=200,
-                                step=1,
-                                interactive=True,
-                                info="Sample from a shortlist of top-k tokens — 0 to disable and sample from all tokens.",
-                            )
-                    with gr.Column():
-                        with gr.Row():
-                            repetition_penalty = gr.Slider(
-                                label="Repetition Penalty",
-                                value=1.1,
-                                minimum=1.0,
-                                maximum=2.0,
-                                step=0.1,
-                                interactive=True,
-                                info="Penalize repetition — 1.0 to disable.",
-                            )
-        gr.Examples(examples, inputs=msg, label="Click on any example and press the 'Submit' button")
-    
-        submit_event = msg.submit(
-            fn=user,
-            inputs=[msg, chatbot],
-            outputs=[msg, chatbot],
-            queue=False,
-        ).then(
-            fn=bot,
-            inputs=[
-                chatbot,
-                temperature,
-                top_p,
-                top_k,
-                repetition_penalty,
-                return_cot,
-            ],
-            outputs=chatbot,
-            queue=True,
-        )
-        submit_click_event = submit.click(
-            fn=user,
-            inputs=[msg, chatbot],
-            outputs=[msg, chatbot],
-            queue=False,
-        ).then(
-            fn=bot,
-            inputs=[
-                chatbot,
-                temperature,
-                top_p,
-                top_k,
-                repetition_penalty,
-                return_cot,
-            ],
-            outputs=chatbot,
-            queue=True,
-        )
-        stop.click(
-            fn=request_cancel,
-            inputs=None,
-            outputs=None,
-            cancels=[submit_event, submit_click_event],
-            queue=False,
-        )
-        clear.click(lambda: None, None, chatbot, queue=False)
-    
-    # if you are launching remotely, specify server_name and server_port
-    #  demo.launch(server_name='your server name', server_port='server port in int')
-    # if you have any issue to launch on your platform, you can pass share=True to launch method:
-    # demo.launch(share=True)
-    # it creates a publicly shareable link for the interface. Read more in the docs: https://gradio.app/docs/
-    demo.launch()
-
-.. code:: ipython3
-
-    # please run this cell for stopping gradio interface
-    demo.close()
diff --git a/docs/notebooks/llm-agent-react-langchain-with-output.rst b/docs/notebooks/llm-agent-react-langchain-with-output.rst
new file mode 100644
index 00000000000000..42f5664d5b177a
--- /dev/null
+++ b/docs/notebooks/llm-agent-react-langchain-with-output.rst
@@ -0,0 +1,836 @@
+Create ReAct Agent using OpenVINO and LangChain
+===============================================
+
+LLM are limited to the knowledge on which they have been trained and the
+additional knowledge provided as context, as a result, if a useful piece
+of information is missing the provided knowledge, the model cannot “go
+around” and try to find it in other sources. This is the reason why we
+need to introduce the concept of Agents.
+
+The core idea of agents is to use a language model to choose a sequence
+of actions to take. In agents, a language model is used as a reasoning
+engine to determine which actions to take and in which order. Agents can
+be seen as applications powered by LLMs and integrated with a set of
+tools like search engines, databases, websites, and so on. Within an
+agent, the LLM is the reasoning engine that, based on the user input, is
+able to plan and execute a set of actions that are needed to fulfill the
+request.
+
+.. figure:: https://github.com/openvinotoolkit/openvino_notebooks/assets/91237924/22fa5396-8381-400f-a78f-97e25d57d807
+   :alt: agent
+
+   agent
+
+`LangChain <https://python.langchain.com/docs/get_started/introduction>`__
+is a framework for developing applications powered by language models.
+LangChain comes with a number of built-in agents that are optimized for
+different use cases.
+
+This notebook explores how to create an AI Agent step by step using
+OpenVINO and LangChain.
+
+**Table of contents:**
+
+
+-  `Prerequisites <#prerequisites>`__
+-  `Create tools <#create-tools>`__
+-  `Create prompt template <#create-prompt-template>`__
+-  `Create LLM <#create-llm>`__
+
+   -  `Download model <#select-model>`__
+   -  `Select inference device for
+      LLM <#select-inference-device-for-llm>`__
+
+-  `Create agent <#create-agent>`__
+-  `Run the agent <#run-agent>`__
+-  `Interactive Demo <#interactive-demo>`__
+
+   -  `Use built-in tool <#use-built-in-tool>`__
+   -  `Create customized tools <#create-customized-tools>`__
+   -  `Create AI agent demo with Gradio
+      UI <#create-ai-agent-demo-with-gradio-ui>`__
+
+Prerequisites
+-------------
+
+
+
+.. code:: ipython3
+
+    import os
+    
+    os.environ["GIT_CLONE_PROTECTION_ACTIVE"] = "false"
+    
+    %pip install -Uq pip
+    %pip uninstall -q -y optimum optimum-intel
+    %pip install --pre -Uq openvino openvino-tokenizers[transformers] --extra-index-url https://storage.openvinotoolkit.org/simple/wheels/nightly
+    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu\
+    "git+https://github.com/huggingface/optimum-intel.git"\
+    "git+https://github.com/openvinotoolkit/nncf.git"\
+    "torch>=2.1"\
+    "datasets"\
+    "accelerate"\
+    "gradio>=4.19"\
+    "transformers>=4.38.1" "langchain>=0.2.3" "langchain-community>=0.2.4" "wikipedia"
+
+Create a tools
+--------------
+
+
+
+First, we need to create some tools to call. In this example, we will
+create 3 custom functions to do basic calculation. For `more
+information <https://python.langchain.com/docs/modules/tools/>`__ on
+creating custom tools.
+
+.. code:: ipython3
+
+    from langchain_core.tools import tool
+    
+    
+    @tool
+    def multiply(first_int: int, second_int: int) -> int:
+        """Multiply two integers together."""
+        return first_int * second_int
+    
+    
+    @tool
+    def add(first_int: int, second_int: int) -> int:
+        "Add two integers."
+        return first_int + second_int
+    
+    
+    @tool
+    def exponentiate(base: int, exponent: int) -> int:
+        "Exponentiate the base to the exponent power."
+        return base**exponent
+
+.. code:: ipython3
+
+    print(f"name of `multiply` tool: {multiply.name}")
+    print(f"description of `multiply` tool: {multiply.description}")
+
+
+.. parsed-literal::
+
+    name of `multiply` tool: multiply
+    description of `multiply` tool: Multiply two integers together.
+
+
+Tools are interfaces that an agent, chain, or LLM can use to interact
+with the world. They combine a few things:
+
+1. The name of the tool
+2. A description of what the tool is
+3. JSON schema of what the inputs to the tool are
+4. The function to call
+5. Whether the result of a tool should be returned directly to the user
+
+Now that we have created all of them, and we can create a list of tools
+that we will use downstream.
+
+.. code:: ipython3
+
+    tools = [multiply, add, exponentiate]
+
+Create prompt template
+----------------------
+
+
+
+A prompt for a language model is a set of instructions or input provided
+by a user to guide the model’s response, helping it understand the
+context and generate relevant and coherent language-based output, such
+as answering questions, completing sentences, or engaging in a
+conversation.
+
+Different agents have different prompting styles for reasoning. In this
+example, we will use `ReAct agent <https://react-lm.github.io/>`__ with
+its typical prompt template. For a full list of built-in agents see
+`agent
+types <https://python.langchain.com/docs/modules/agents/agent_types/>`__.
+
+.. figure:: https://github.com/openvinotoolkit/openvino_notebooks/assets/91237924/a83bdf7f-bb9d-4b1f-9a0a-3fe4a76ba1ae
+   :alt: react
+
+   react
+
+A ReAct prompt consists of few-shot task-solving trajectories, with
+human-written text reasoning traces and actions, as well as environment
+observations in response to actions. ReAct prompting is intuitive and
+flexible to design, and achieves state-of-the-art few-shot performances
+across a variety of tasks, from question answering to online shopping!
+
+In an prompt template for agent, ``input`` is user’s query and
+``agent_scratchpad`` should be a sequence of messages that contains the
+previous agent tool invocations and the corresponding tool outputs.
+
+.. code:: ipython3
+
+    PREFIX = """[INST]Respond to the human as helpfully and accurately as possible. You have access to the following tools:"""
+    
+    FORMAT_INSTRUCTIONS = """Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).
+    
+    Valid "action" values: "Final Answer" or {tool_names}
+    
+    Provide only ONE action per $JSON_BLOB, as shown:
+    
+    ```
+    {{{{
+      "action": $TOOL_NAME,
+      "action_input": $INPUT
+    }}}}
+    ```
+    
+    Follow this format:
+    
+    Question: input question to answer
+    Thought: consider previous and subsequent steps
+    Action:
+    ```
+    $JSON_BLOB
+    ```
+    Observation: action result
+    ... (repeat Thought/Action/Observation N times)
+    Thought: I know what to respond
+    Action:
+    ```
+    {{{{
+      "action": "Final Answer",
+      "action_input": "Final response to human"
+    }}}}
+    ```[/INST]"""
+    
+    SUFFIX = """Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation:.
+    Thought:[INST]"""
+    
+    HUMAN_MESSAGE_TEMPLATE = "{input}\n\n{agent_scratchpad}"
+
+Create LLM
+----------
+
+
+
+Large Language Models (LLMs) are a core component of LangChain.
+LangChain does not serve its own LLMs, but rather provides a standard
+interface for interacting with many different LLMs. In this example, we
+select ``Mistral-7B-Instruct-v0.3`` as LLM in agent pipeline.
+
+-  **Mistral-7B-Instruct-v0.3** - The Mistral-7B-Instruct-v0.3 Large
+   Language Model (LLM) is an instruct fine-tuned version of the
+   Mistral-7B-v0.3. You can find more details about model in the `model
+   card <https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3>`__,
+   `paper <https://arxiv.org/abs/2310.06825>`__ and `release blog
+   post <https://mistral.ai/news/announcing-mistral-7b/>`__.
+   >\ **Note**: run model with demo, you will need to accept license
+   agreement. >You must be a registered user in Hugging Face Hub.
+   Please visit `HuggingFace model
+   card <https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3>`__,
+   carefully read terms of usage and click accept button. You will need
+   to use an access token for the code below to run. For more
+   information on access tokens, refer to `this section of the
+   documentation <https://huggingface.co/docs/hub/security-tokens>`__.
+   >You can login on Hugging Face Hub in notebook environment, using
+   following code:
+
+.. code:: python
+
+       ## login to huggingfacehub to get access to pretrained model 
+
+       from huggingface_hub import notebook_login, whoami
+
+       try:
+           whoami()
+           print('Authorization token already provided')
+       except OSError:
+           notebook_login()
+
+Download model
+~~~~~~~~~~~~~~
+
+
+
+To run LLM locally, we have to download the model in the first step. It
+is possible to `export your
+model <https://github.com/huggingface/optimum-intel?tab=readme-ov-file#export>`__
+to the OpenVINO IR format with the CLI, and load the model from local
+folder.
+
+.. code:: ipython3
+
+    from pathlib import Path
+    
+    model_id = "mistralai/Mistral-7B-Instruct-v0.3"
+    model_path = "Mistral-7B-Instruct-v0.3-ov-int4"
+    
+    if not Path(model_path).exists():
+        !optimum-cli export openvino --model {model_id} --task text-generation-with-past --trust-remote-code --weight-format int4 {model_path}
+
+Select inference device for LLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    import openvino as ov
+    import ipywidgets as widgets
+    
+    core = ov.Core()
+    
+    support_devices = core.available_devices
+    if "NPU" in support_devices:
+        support_devices.remove("NPU")
+    
+    device = widgets.Dropdown(
+        options=support_devices + ["AUTO"],
+        value="CPU",
+        description="Device:",
+        disabled=False,
+    )
+    
+    device
+
+
+
+
+.. parsed-literal::
+
+    Dropdown(description='Device:', options=('CPU', 'GPU', 'AUTO'), value='CPU')
+
+
+
+OpenVINO models can be run locally through the ``HuggingFacePipeline``
+class in LangChain. To deploy a model with OpenVINO, you can specify the
+``backend="openvino"`` parameter to trigger OpenVINO as backend
+inference framework. For `more
+information <https://python.langchain.com/docs/integrations/llms/openvino/>`__.
+
+.. code:: ipython3
+
+    from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline
+    from transformers.generation.stopping_criteria import StoppingCriteriaList, StoppingCriteria
+    
+    
+    class StopSequenceCriteria(StoppingCriteria):
+        """
+        This class can be used to stop generation whenever a sequence of tokens is encountered.
+    
+        Args:
+            stop_sequences (`str` or `List[str]`):
+                The sequence (or list of sequences) on which to stop execution.
+            tokenizer:
+                The tokenizer used to decode the model outputs.
+        """
+    
+        def __init__(self, stop_sequences, tokenizer):
+            if isinstance(stop_sequences, str):
+                stop_sequences = [stop_sequences]
+            self.stop_sequences = stop_sequences
+            self.tokenizer = tokenizer
+    
+        def __call__(self, input_ids, scores, **kwargs) -> bool:
+            decoded_output = self.tokenizer.decode(input_ids.tolist()[0])
+            return any(decoded_output.endswith(stop_sequence) for stop_sequence in self.stop_sequences)
+    
+    
+    ov_config = {"PERFORMANCE_HINT": "LATENCY", "NUM_STREAMS": "1", "CACHE_DIR": ""}
+    stop_tokens = ["Observation:"]
+    
+    ov_llm = HuggingFacePipeline.from_model_id(
+        model_id=model_path,
+        task="text-generation",
+        backend="openvino",
+        model_kwargs={
+            "device": device.value,
+            "ov_config": ov_config,
+            "trust_remote_code": True,
+        },
+        pipeline_kwargs={"max_new_tokens": 2048},
+    )
+    ov_llm = ov_llm.bind(skip_prompt=True, stop=["Observation:"])
+    
+    tokenizer = ov_llm.pipeline.tokenizer
+    ov_llm.pipeline._forward_params["stopping_criteria"] = StoppingCriteriaList([StopSequenceCriteria(stop_tokens, tokenizer)])
+
+
+.. parsed-literal::
+
+    2024-06-07 23:17:16.804739: I tensorflow/core/util/port.cc:111] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-06-07 23:17:16.807973: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
+    2024-06-07 23:17:16.850235: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
+    2024-06-07 23:17:16.850258: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
+    2024-06-07 23:17:16.850290: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+    2024-06-07 23:17:16.859334: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
+    2024-06-07 23:17:17.692415: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    You set `add_prefix_space`. The tokenizer needs to be converted from the slow tokenizers
+    The argument `trust_remote_code` is to be used along with export=True. It will be ignored.
+    Compiling the model to GPU ...
+
+
+You can get additional inference speed improvement with [Dynamic
+Quantization of activations and KV-cache quantization] on
+CPU(https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide/llm-inference-hf.html#enabling-openvino-runtime-optimizations).
+These options can be enabled with ``ov_config`` as follows:
+
+.. code:: ipython3
+
+    ov_config = {
+        "KV_CACHE_PRECISION": "u8",
+        "DYNAMIC_QUANTIZATION_GROUP_SIZE": "32",
+        "PERFORMANCE_HINT": "LATENCY",
+        "NUM_STREAMS": "1",
+        "CACHE_DIR": "",
+    }
+
+Create agent
+------------
+
+
+
+Now that we have defined the tools, prompt template and LLM, we can
+create the agent_executor.
+
+The agent executor is the runtime for an agent. This is what actually
+calls the agent, executes the actions it chooses, passes the action
+outputs back to the agent, and repeats.
+
+.. code:: ipython3
+
+    from langchain.agents import AgentExecutor, StructuredChatAgent
+    
+    agent = StructuredChatAgent.from_llm_and_tools(
+        ov_llm,
+        tools,
+        prefix=PREFIX,
+        suffix=SUFFIX,
+        human_message_template=HUMAN_MESSAGE_TEMPLATE,
+        format_instructions=FORMAT_INSTRUCTIONS,
+    )
+    agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
+
+Run the agent
+-------------
+
+
+
+We can now run the agent with a math query. Before getting the final
+answer, a agent executor will also produce intermediate steps of
+reasoning and actions. The format of these messages will follow your
+prompt template.
+
+.. code:: ipython3
+
+    agent_executor.invoke({"input": "Take 3 to the fifth power and multiply that by the sum of twelve and three, then square the whole result"})
+
+
+.. parsed-literal::
+
+    
+    
+    > Entering new AgentExecutor chain...
+    Thought: I can use the exponentiate and add tools to solve the first part, and then use the multiply tool for the second part, and finally the exponentiate tool again to square the result.
+    
+    Action:
+    ```
+    {
+      "action": "exponentiate",
+      "action_input": {"base": 3, "exponent": 5}
+    }
+    ```
+    Observation:
+    Observation: 243
+    Thought: Now I need to add twelve and three
+    
+    Action:
+    ```
+    {
+      "action": "add",
+      "action_input": {"first_int": 12, "second_int": 3}
+    }
+    ```
+    Observation:
+    Observation: 15
+    Thought: Now I need to multiply the result by 243
+    
+    Action:
+    ```
+    {
+      "action": "multiply",
+      "action_input": {"first_int": 243, "second_int": 15}
+    }
+    ```
+    Observation:
+    Observation: 3645
+    Thought: Finally, I need to square the result
+    
+    Action:
+    ```
+    {
+      "action": "exponentiate",
+      "action_input": {"base": 3645, "exponent": 2}
+    }
+    ```
+    Observation:
+    Observation: 13286025
+    Thought: I know what to respond
+    
+    Action:
+    ```
+    {
+      "action": "Final Answer",
+      "action_input": "The final answer is 13286025"
+    }
+    ```
+    
+    > Finished chain.
+
+
+
+
+.. parsed-literal::
+
+    {'input': 'Take 3 to the fifth power and multiply that by the sum of twelve and three, then square the whole result',
+     'output': 'The final answer is 13286025'}
+
+
+
+Interactive Demo
+----------------
+
+
+
+Let’s create a interactive agent using
+`Gradio <https://www.gradio.app/>`__.
+
+Use built-in tools
+~~~~~~~~~~~~~~~~~~
+
+
+
+LangChain has provided a list of all `built-in
+tools <https://python.langchain.com/docs/integrations/tools/>`__. In
+this example, we will use ``Wikipedia`` python package to query key
+words generated by agent.
+
+.. code:: ipython3
+
+    from langchain_community.tools import WikipediaQueryRun
+    from langchain_community.utilities import WikipediaAPIWrapper
+    from langchain_core.pydantic_v1 import BaseModel, Field
+    from langchain_core.callbacks import CallbackManagerForToolRun
+    from typing import Optional
+    
+    
+    class WikipediaQueryRunWrapper(WikipediaQueryRun):
+        def _run(
+            self,
+            text: str,
+            run_manager: Optional[CallbackManagerForToolRun] = None,
+        ) -> str:
+            """Use the Wikipedia tool."""
+            return self.api_wrapper.run(text)
+    
+    
+    api_wrapper = WikipediaAPIWrapper(top_k_results=2, doc_content_chars_max=1000)
+    
+    
+    class WikiInputs(BaseModel):
+        """inputs to the wikipedia tool."""
+    
+        text: str = Field(description="query to look up on wikipedia.")
+    
+    
+    wikipedia = WikipediaQueryRunWrapper(
+        description="A wrapper around Wikipedia. Useful for when you need to answer general questions about people, places, companies, facts, historical events, or other subjects. Input should be a search query.",
+        args_schema=WikiInputs,
+        api_wrapper=api_wrapper,
+    )
+
+.. code:: ipython3
+
+    wikipedia.invoke({"text": "OpenVINO"})
+
+
+
+
+.. parsed-literal::
+
+    'Page: OpenVINO\nSummary: OpenVINO is an open-source software toolkit for optimizing and deploying deep learning models. It enables programmers to develop scalable and efficient AI solutions with relatively few lines of code. It supports several popular model formats and categories, such as large language models, computer vision, and generative AI.\nActively developed by Intel, it prioritizes high-performance inference on Intel hardware but also supports ARM/ARM64 processors and encourages contributors to add new devices to the portfolio.\nBased in C++, it offers the following APIs: C/C++, Python, and Node.js (an early preview).\nOpenVINO is cross-platform and free for use under Apache License 2.0.\n\nPage: Stable Diffusion\nSummary: Stable Diffusion is a deep learning, text-to-image model released in 2022 based on diffusion techniques. It is considered to be a part of the ongoing artificial intelligence boom.\nIt is primarily used to generate detailed images conditioned on text descriptions, t'
+
+
+
+Create customized tools
+~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+In this examples, we will create 2 customized tools for
+``image generation`` and ``weather qurey``.
+
+.. code:: ipython3
+
+    import urllib.parse
+    import json5
+    
+    
+    @tool
+    def painting(prompt: str) -> str:
+        """
+        AI painting (image generation) service, input text description, and return the image URL drawn based on text information.
+        """
+        prompt = urllib.parse.quote(prompt)
+        return json5.dumps({"image_url": f"https://image.pollinations.ai/prompt/{prompt}"}, ensure_ascii=False)
+    
+    
+    painting.invoke({"prompt": "a cat"})
+
+
+
+
+.. parsed-literal::
+
+    '{image_url: "https://image.pollinations.ai/prompt/a%20cat"}'
+
+
+
+.. code:: ipython3
+
+    @tool
+    def weather(
+        city_name: str,
+    ) -> str:
+        """
+        Get the current weather for `city_name`
+        """
+    
+        if not isinstance(city_name, str):
+            raise TypeError("City name must be a string")
+    
+        key_selection = {
+            "current_condition": [
+                "temp_C",
+                "FeelsLikeC",
+                "humidity",
+                "weatherDesc",
+                "observation_time",
+            ],
+        }
+        import requests
+    
+        resp = requests.get(f"https://wttr.in/{city_name}?format=j1")
+        resp.raise_for_status()
+        resp = resp.json()
+        ret = {k: {_v: resp[k][0][_v] for _v in v} for k, v in key_selection.items()}
+    
+        return str(ret)
+    
+    
+    weather.invoke({"city_name": "London"})
+
+
+
+
+.. parsed-literal::
+
+    "{'current_condition': {'temp_C': '9', 'FeelsLikeC': '8', 'humidity': '93', 'weatherDesc': [{'value': 'Sunny'}], 'observation_time': '04:39 AM'}}"
+
+
+
+Create AI agent demo with Gradio UI
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    tools = [wikipedia, painting, weather]
+    
+    agent = StructuredChatAgent.from_llm_and_tools(
+        ov_llm,
+        tools,
+        prefix=PREFIX,
+        suffix=SUFFIX,
+        human_message_template=HUMAN_MESSAGE_TEMPLATE,
+        format_instructions=FORMAT_INSTRUCTIONS,
+    )
+    agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
+
+.. code:: ipython3
+
+    import gradio as gr
+    
+    examples = [
+        ["Based on current weather in London, show me a picture of Big Ben through its URL"],
+        ["What is OpenVINO ?"],
+        ["Create an image of pink cat and return its URL"],
+        ["How many people live in Canada ?"],
+        ["What is the weather like in New York now ?"],
+    ]
+    
+    
+    def partial_text_processor(partial_text, new_text):
+        """
+        helper for updating partially generated answer, used by default
+    
+        Params:
+          partial_text: text buffer for storing previosly generated text
+          new_text: text update for the current step
+        Returns:
+          updated text string
+    
+        """
+        partial_text += new_text
+        return partial_text
+    
+    
+    def user(message, history):
+        """
+        callback function for updating user messages in interface on submit button click
+    
+        Params:
+          message: current message
+          history: conversation history
+        Returns:
+          None
+        """
+        # Append the user's message to the conversation history
+        return "", history + [[message, ""]]
+    
+    
+    def bot(history):
+        """
+        callback function for running chatbot on submit button click
+    
+        Params:
+          history: conversation history
+    
+        """
+        partial_text = ""
+    
+        for new_text in agent_executor.stream(
+            {"input": history[-1][0]},
+        ):
+            if "output" in new_text.keys():
+                partial_text = partial_text_processor(partial_text, new_text["output"])
+                history[-1][1] = partial_text
+                yield history
+    
+    
+    def request_cancel():
+        ov_llm.pipeline.model.request.cancel()
+    
+    
+    with gr.Blocks(
+        theme=gr.themes.Soft(),
+        css=".disclaimer {font-variant-caps: all-small-caps;}",
+    ) as demo:
+        names = [tool.name for tool in tools]
+        gr.Markdown(f"""<h1><center>OpenVINO Agent for {str(names)}</center></h1>""")
+        chatbot = gr.Chatbot(height=500)
+        with gr.Row():
+            with gr.Column():
+                msg = gr.Textbox(
+                    label="Chat Message Box",
+                    placeholder="Chat Message Box",
+                    show_label=False,
+                    container=False,
+                )
+            with gr.Column():
+                with gr.Row():
+                    submit = gr.Button("Submit")
+                    stop = gr.Button("Stop")
+                    clear = gr.Button("Clear")
+        gr.Examples(examples, inputs=msg, label="Click on any example and press the 'Submit' button")
+    
+        submit_event = msg.submit(
+            fn=user,
+            inputs=[msg, chatbot],
+            outputs=[msg, chatbot],
+            queue=False,
+        ).then(
+            fn=bot,
+            inputs=[
+                chatbot,
+            ],
+            outputs=chatbot,
+            queue=True,
+        )
+        submit_click_event = submit.click(
+            fn=user,
+            inputs=[msg, chatbot],
+            outputs=[msg, chatbot],
+            queue=False,
+        ).then(
+            fn=bot,
+            inputs=[
+                chatbot,
+            ],
+            outputs=chatbot,
+            queue=True,
+        )
+        stop.click(
+            fn=request_cancel,
+            inputs=None,
+            outputs=None,
+            cancels=[submit_event, submit_click_event],
+            queue=False,
+        )
+        clear.click(lambda: None, None, chatbot, queue=False)
+    
+    # if you are launching remotely, specify server_name and server_port
+    #  demo.launch(server_name='your server name', server_port='server port in int')
+    # if you have any issue to launch on your platform, you can pass share=True to launch method:
+    # demo.launch(share=True)
+    # it creates a publicly shareable link for the interface. Read more in the docs: https://gradio.app/docs/
+    demo.launch()
+
+
+.. parsed-literal::
+
+    
+    
+    > Entering new AgentExecutor chain...
+    Thought: I need to use the weather tool to get the current weather in London, then use the painting tool to generate a picture of Big Ben based on the weather information.
+    
+    Action:
+    ```
+    {
+      "action": "weather",
+      "action_input": "London"
+    }
+    ```
+    
+    Observation:
+    Observation: {'current_condition': {'temp_C': '9', 'FeelsLikeC': '8', 'humidity': '93', 'weatherDesc': [{'value': 'Sunny'}], 'observation_time': '04:39 AM'}}
+    Thought: I have the current weather in London. Now I can use the painting tool to generate a picture of Big Ben based on the weather information.
+    
+    Action:
+    ```
+    {
+      "action": "painting",
+      "action_input": "Big Ben, sunny day"
+    }
+    ```
+    
+    Observation:
+    Observation: {image_url: "https://image.pollinations.ai/prompt/Big%20Ben%2C%20sunny%20day"}
+    Thought: I have the image URL of Big Ben on a sunny day. Now I can respond to the human with the image URL.
+    
+    Action:
+    ```
+    {
+      "action": "Final Answer",
+      "action_input": "Here is the image of Big Ben on a sunny day: https://image.pollinations.ai/prompt/Big%20Ben%2C%20sunny%20day"
+    }
+    ```
+    Observation:
+    
+    > Finished chain.
+
+
+.. code:: ipython3
+
+    # please run this cell for stopping gradio interface
+    demo.close()
diff --git a/docs/notebooks/llm-chatbot-generate-api-with-output.rst b/docs/notebooks/llm-chatbot-generate-api-with-output.rst
new file mode 100644
index 00000000000000..84a22d9f1dd28d
--- /dev/null
+++ b/docs/notebooks/llm-chatbot-generate-api-with-output.rst
@@ -0,0 +1,1404 @@
+Create an LLM-powered Chatbot using OpenVINO Generate API
+=========================================================
+
+In the rapidly evolving world of artificial intelligence (AI), chatbots
+have emerged as powerful tools for businesses to enhance customer
+interactions and streamline operations. Large Language Models (LLMs) are
+artificial intelligence systems that can understand and generate human
+language. They use deep learning algorithms and massive amounts of data
+to learn the nuances of language and produce coherent and relevant
+responses. While a decent intent-based chatbot can answer basic,
+one-touch inquiries like order management, FAQs, and policy questions,
+LLM chatbots can tackle more complex, multi-touch questions. LLM enables
+chatbots to provide support in a conversational manner, similar to how
+humans do, through contextual memory. Leveraging the capabilities of
+Language Models, chatbots are becoming increasingly intelligent, capable
+of understanding and responding to human language with remarkable
+accuracy.
+
+Previously, we already discussed how to build an instruction-following
+pipeline using OpenVINO and Optimum Intel, please check out `Dolly
+example <../dolly-2-instruction-following>`__ for reference. In this
+tutorial, we consider how to use the power of OpenVINO for running Large
+Language Models for chat. We will use a pre-trained model from the
+`Hugging Face
+Transformers <https://huggingface.co/docs/transformers/index>`__
+library. The `Hugging Face Optimum
+Intel <https://huggingface.co/docs/optimum/intel/index>`__ library
+converts the models to OpenVINO™ IR format. To simplify the user
+experience, we will use `OpenVINO Generate
+API <https://github.com/openvinotoolkit/openvino.genai>`__ for
+generation of instruction-following inference pipeline.
+
+The tutorial consists of the following steps:
+
+-  Install prerequisites
+-  Download and convert the model from a public source using the
+   `OpenVINO integration with Hugging Face
+   Optimum <https://huggingface.co/blog/openvino>`__.
+-  Compress model weights to 4-bit or 8-bit data types using
+   `NNCF <https://github.com/openvinotoolkit/nncf>`__
+-  Create a chat inference pipeline with `OpenVINO Generate
+   API <https://github.com/openvinotoolkit/openvino.genai/blob/master/src/README.md>`__.
+-  Run chat pipeline
+
+**Table of contents:**
+
+
+-  `Prerequisites <#prerequisites>`__
+-  `Select model for inference <#select-model-for-inference>`__
+-  `Convert model using Optimum-CLI
+   tool <#convert-model-using-optimum-cli-tool>`__
+-  `Compress model weights <#compress-model-weights>`__
+
+   -  `Weights Compression using
+      Optimum-CLI <#weights-compression-using-optimum-cli>`__
+   -  `Weight compression with AWQ <#weight-compression-with-awq>`__
+
+      -  `Select device for inference and model
+         variant <#select-device-for-inference-and-model-variant>`__
+
+-  `Instantiate pipeline with OpenVINO Generate
+   API <#instantiate-pipeline-with-openvino-generate-api>`__
+-  `Run Chatbot <#run-chatbot>`__
+
+   -  `Prepare text streamer to get results
+      runtime <#prepare-text-streamer-to-get-results-runtime>`__
+   -  `Setup of the chatbot life process
+      function <#setup-of-the-chatbot-life-process-function>`__
+   -  `Next Step <#next-step>`__
+
+Prerequisites
+-------------
+
+
+
+Install required dependencies
+
+.. code:: ipython3
+
+    import os
+
+    os.environ["GIT_CLONE_PROTECTION_ACTIVE"] = "false"
+
+    %pip install -Uq pip
+    %pip uninstall -q -y optimum optimum-intel
+    %pip install -q "openvino-genai>=2024.2"
+    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu\
+    "git+https://github.com/huggingface/optimum-intel.git"\
+    "git+https://github.com/openvinotoolkit/nncf.git"\
+    "torch>=2.1"\
+    "datasets" \
+    "accelerate"\
+    "gradio>=4.19"\
+    "onnx" "einops" "transformers_stream_generator" "tiktoken" "transformers>=4.40" "bitsandbytes"
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+    Note: you may need to restart the kernel to use updated packages.
+    Note: you may need to restart the kernel to use updated packages.
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. code:: ipython3
+
+    import os
+    from pathlib import Path
+    import requests
+    import shutil
+
+    # fetch model configuration
+
+    config_shared_path = Path("../../utils/llm_config.py")
+    config_dst_path = Path("llm_config.py")
+
+    if not config_dst_path.exists():
+        if config_shared_path.exists():
+            try:
+                os.symlink(config_shared_path, config_dst_path)
+            except Exception:
+                shutil.copy(config_shared_path, config_dst_path)
+        else:
+            r = requests.get(url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/llm_config.py")
+            with open("llm_config.py", "w", encoding="utf-8") as f:
+                f.write(r.text)
+    elif not os.path.islink(config_dst_path):
+        print("LLM config will be updated")
+        if config_shared_path.exists():
+            shutil.copy(config_shared_path, config_dst_path)
+        else:
+            r = requests.get(url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/llm_config.py")
+            with open("llm_config.py", "w", encoding="utf-8") as f:
+                f.write(r.text)
+
+Select model for inference
+--------------------------
+
+
+
+The tutorial supports different models, you can select one from the
+provided options to compare the quality of open source LLM solutions.
+>\ **Note**: conversion of some models can require additional actions
+from user side and at least 64GB RAM for conversion.
+
+The available options are:
+
+-  **tiny-llama-1b-chat** - This is the chat model finetuned on top of
+   `TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T <https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T>`__.
+   The TinyLlama project aims to pretrain a 1.1B Llama model on 3
+   trillion tokens with the adoption of the same architecture and
+   tokenizer as Llama 2. This means TinyLlama can be plugged and played
+   in many open-source projects built upon Llama. Besides, TinyLlama is
+   compact with only 1.1B parameters. This compactness allows it to
+   cater to a multitude of applications demanding a restricted
+   computation and memory footprint. More details about model can be
+   found in `model
+   card <https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0>`__
+-  **mini-cpm-2b-dpo** - MiniCPM is an End-Size LLM developed by
+   ModelBest Inc. and TsinghuaNLP, with only 2.4B parameters excluding
+   embeddings. After Direct Preference Optimization (DPO) fine-tuning,
+   MiniCPM outperforms many popular 7b, 13b and 70b models. More details
+   can be found in
+   `model_card <https://huggingface.co/openbmb/MiniCPM-2B-dpo-fp16>`__.
+-  **gemma-2b-it** - Gemma is a family of lightweight, state-of-the-art
+   open models from Google, built from the same research and technology
+   used to create the Gemini models. They are text-to-text, decoder-only
+   large language models, available in English, with open weights,
+   pre-trained variants, and instruction-tuned variants. Gemma models
+   are well-suited for a variety of text generation tasks, including
+   question answering, summarization, and reasoning. This model is
+   instruction-tuned version of 2B parameters model. More details about
+   model can be found in `model
+   card <https://huggingface.co/google/gemma-2b-it>`__. >\ **Note**: run
+   model with demo, you will need to accept license agreement. >You must
+   be a registered user in Hugging Face Hub. Please visit `HuggingFace
+   model card <https://huggingface.co/google/gemma-2b-it>`__, carefully
+   read terms of usage and click accept button. You will need to use an
+   access token for the code below to run. For more information on
+   access tokens, refer to `this section of the
+   documentation <https://huggingface.co/docs/hub/security-tokens>`__.
+   >You can login on Hugging Face Hub in notebook environment, using
+   following code:
+
+.. code:: python
+
+       ## login to huggingfacehub to get access to pretrained model
+   [back to top ⬆️](#Table-of-contents:)
+
+
+
+       from huggingface_hub import notebook_login, whoami
+
+       try:
+           whoami()
+           print('Authorization token already provided')
+       except OSError:
+           notebook_login()
+
+-  **phi3-mini-instruct** - The Phi-3-Mini is a 3.8B parameters,
+   lightweight, state-of-the-art open model trained with the Phi-3
+   datasets that includes both synthetic data and the filtered publicly
+   available websites data with a focus on high-quality and reasoning
+   dense properties. More details about model can be found in `model
+   card <https://huggingface.co/microsoft/Phi-3-mini-4k-instruct>`__,
+   `Microsoft blog <https://aka.ms/phi3blog-april>`__ and `technical
+   report <https://aka.ms/phi3-tech-report>`__.
+-  **red-pajama-3b-chat** - A 2.8B parameter pre-trained language model
+   based on GPT-NEOX architecture. It was developed by Together Computer
+   and leaders from the open-source AI community. The model is
+   fine-tuned on OASST1 and Dolly2 datasets to enhance chatting ability.
+   More details about model can be found in `HuggingFace model
+   card <https://huggingface.co/togethercomputer/RedPajama-INCITE-Chat-3B-v1>`__.
+-  **gemma-7b-it** - Gemma is a family of lightweight, state-of-the-art
+   open models from Google, built from the same research and technology
+   used to create the Gemini models. They are text-to-text, decoder-only
+   large language models, available in English, with open weights,
+   pre-trained variants, and instruction-tuned variants. Gemma models
+   are well-suited for a variety of text generation tasks, including
+   question answering, summarization, and reasoning. This model is
+   instruction-tuned version of 7B parameters model. More details about
+   model can be found in `model
+   card <https://huggingface.co/google/gemma-7b-it>`__. >\ **Note**: run
+   model with demo, you will need to accept license agreement. >You must
+   be a registered user in Hugging Face Hub. Please visit `HuggingFace
+   model card <https://huggingface.co/google/gemma-7b-it>`__, carefully
+   read terms of usage and click accept button. You will need to use an
+   access token for the code below to run. For more information on
+   access tokens, refer to `this section of the
+   documentation <https://huggingface.co/docs/hub/security-tokens>`__.
+   >You can login on Hugging Face Hub in notebook environment, using
+   following code:
+
+.. code:: python
+
+       ## login to huggingfacehub to get access to pretrained model
+
+       from huggingface_hub import notebook_login, whoami
+
+       try:
+           whoami()
+           print('Authorization token already provided')
+       except OSError:
+           notebook_login()
+
+-  **llama-2-7b-chat** - LLama 2 is the second generation of LLama
+   models developed by Meta. Llama 2 is a collection of pre-trained and
+   fine-tuned generative text models ranging in scale from 7 billion to
+   70 billion parameters. llama-2-7b-chat is 7 billions parameters
+   version of LLama 2 finetuned and optimized for dialogue use case.
+   More details about model can be found in the
+   `paper <https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/>`__,
+   `repository <https://github.com/facebookresearch/llama>`__ and
+   `HuggingFace model
+   card <https://huggingface.co/meta-llama/Llama-2-7b-chat-hf>`__.
+   >\ **Note**: run model with demo, you will need to accept license
+   agreement. >You must be a registered user in Hugging Face Hub.
+   Please visit `HuggingFace model
+   card <https://huggingface.co/meta-llama/Llama-2-7b-chat-hf>`__,
+   carefully read terms of usage and click accept button. You will need
+   to use an access token for the code below to run. For more
+   information on access tokens, refer to `this section of the
+   documentation <https://huggingface.co/docs/hub/security-tokens>`__.
+   >You can login on Hugging Face Hub in notebook environment, using
+   following code:
+
+.. code:: python
+
+       ## login to huggingfacehub to get access to pretrained model
+
+       from huggingface_hub import notebook_login, whoami
+
+       try:
+           whoami()
+           print('Authorization token already provided')
+       except OSError:
+           notebook_login()
+
+-  **llama-3-8b-instruct** - Llama 3 is an auto-regressive language
+   model that uses an optimized transformer architecture. The tuned
+   versions use supervised fine-tuning (SFT) and reinforcement learning
+   with human feedback (RLHF) to align with human preferences for
+   helpfulness and safety. The Llama 3 instruction tuned models are
+   optimized for dialogue use cases and outperform many of the available
+   open source chat models on common industry benchmarks. More details
+   about model can be found in `Meta blog
+   post <https://ai.meta.com/blog/meta-llama-3/>`__, `model
+   website <https://llama.meta.com/llama3>`__ and `model
+   card <https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct>`__.
+   >\ **Note**: run model with demo, you will need to accept license
+   agreement. >You must be a registered user in Hugging Face Hub.
+   Please visit `HuggingFace model
+   card <https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct>`__,
+   carefully read terms of usage and click accept button. You will need
+   to use an access token for the code below to run. For more
+   information on access tokens, refer to `this section of the
+   documentation <https://huggingface.co/docs/hub/security-tokens>`__.
+   >You can login on Hugging Face Hub in notebook environment, using
+   following code:
+
+.. code:: python
+
+       ## login to huggingfacehub to get access to pretrained model
+
+       from huggingface_hub import notebook_login, whoami
+
+       try:
+           whoami()
+           print('Authorization token already provided')
+       except OSError:
+           notebook_login()
+
+-  **qwen2-1.5b-instruct/qwen2-7b-instruct** - Qwen2 is the new series
+   of Qwen large language models.Compared with the state-of-the-art open
+   source language models, including the previous released Qwen1.5,
+   Qwen2 has generally surpassed most open source models and
+   demonstrated competitiveness against proprietary models across a
+   series of benchmarks targeting for language understanding, language
+   generation, multilingual capability, coding, mathematics, reasoning,
+   etc. For more details, please refer to
+   `model_card <https://huggingface.co/Qwen/Qwen2-7B-Instruct>`__,
+   `blog <https://qwenlm.github.io/blog/qwen2/>`__,
+   `GitHub <https://github.com/QwenLM/Qwen2>`__, and
+   `Documentation <https://qwen.readthedocs.io/en/latest/>`__.
+-  **qwen1.5-0.5b-chat/qwen1.5-1.8b-chat/qwen1.5-7b-chat** - Qwen1.5 is
+   the beta version of Qwen2, a transformer-based decoder-only language
+   model pretrained on a large amount of data. Qwen1.5 is a language
+   model series including decoder language models of different model
+   sizes. It is based on the Transformer architecture with SwiGLU
+   activation, attention QKV bias, group query attention, mixture of
+   sliding window attention and full attention. You can find more
+   details about model in the `model
+   repository <https://huggingface.co/Qwen>`__.
+-  **qwen-7b-chat** - Qwen-7B is the 7B-parameter version of the large
+   language model series, Qwen (abbr. Tongyi Qianwen), proposed by
+   Alibaba Cloud. Qwen-7B is a Transformer-based large language model,
+   which is pretrained on a large volume of data, including web texts,
+   books, codes, etc. For more details about Qwen, please refer to the
+   `GitHub <https://github.com/QwenLM/Qwen>`__ code repository.
+-  **chatglm3-6b** - ChatGLM3-6B is the latest open-source model in the
+   ChatGLM series. While retaining many excellent features such as
+   smooth dialogue and low deployment threshold from the previous two
+   generations, ChatGLM3-6B employs a more diverse training dataset,
+   more sufficient training steps, and a more reasonable training
+   strategy. ChatGLM3-6B adopts a newly designed `Prompt
+   format <https://github.com/THUDM/ChatGLM3/blob/main/PROMPT_en.md>`__,
+   in addition to the normal multi-turn dialogue. You can find more
+   details about model in the `model
+   card <https://huggingface.co/THUDM/chatglm3-6b>`__
+-  **mistral-7b** - The Mistral-7B-v0.1 Large Language Model (LLM) is a
+   pretrained generative text model with 7 billion parameters. You can
+   find more details about model in the `model
+   card <https://huggingface.co/mistralai/Mistral-7B-v0.1>`__,
+   `paper <https://arxiv.org/abs/2310.06825>`__ and `release blog
+   post <https://mistral.ai/news/announcing-mistral-7b/>`__.
+-  **zephyr-7b-beta** - Zephyr is a series of language models that are
+   trained to act as helpful assistants. Zephyr-7B-beta is the second
+   model in the series, and is a fine-tuned version of
+   `mistralai/Mistral-7B-v0.1 <https://huggingface.co/mistralai/Mistral-7B-v0.1>`__
+   that was trained on on a mix of publicly available, synthetic
+   datasets using `Direct Preference Optimization
+   (DPO) <https://arxiv.org/abs/2305.18290>`__. You can find more
+   details about model in `technical
+   report <https://arxiv.org/abs/2310.16944>`__ and `HuggingFace model
+   card <https://huggingface.co/HuggingFaceH4/zephyr-7b-beta>`__.
+-  **neural-chat-7b-v3-1** - Mistral-7b model fine-tuned using Intel
+   Gaudi. The model fine-tuned on the open source dataset
+   `Open-Orca/SlimOrca <https://huggingface.co/datasets/Open-Orca/SlimOrca>`__
+   and aligned with `Direct Preference Optimization (DPO)
+   algorithm <https://arxiv.org/abs/2305.18290>`__. More details can be
+   found in `model
+   card <https://huggingface.co/Intel/neural-chat-7b-v3-1>`__ and `blog
+   post <https://medium.com/@NeuralCompressor/the-practice-of-supervised-finetuning-and-direct-preference-optimization-on-habana-gaudi2-a1197d8a3cd3>`__.
+-  **notus-7b-v1** - Notus is a collection of fine-tuned models using
+   `Direct Preference Optimization
+   (DPO) <https://arxiv.org/abs/2305.18290>`__. and related
+   `RLHF <https://huggingface.co/blog/rlhf>`__ techniques. This model is
+   the first version, fine-tuned with DPO over zephyr-7b-sft. Following
+   a data-first approach, the only difference between Notus-7B-v1 and
+   Zephyr-7B-beta is the preference dataset used for dDPO. Proposed
+   approach for dataset creation helps to effectively fine-tune Notus-7b
+   that surpasses Zephyr-7B-beta and Claude 2 on
+   `AlpacaEval <https://tatsu-lab.github.io/alpaca_eval/>`__. More
+   details about model can be found in `model
+   card <https://huggingface.co/argilla/notus-7b-v1>`__.
+-  **youri-7b-chat** - Youri-7b-chat is a Llama2 based model. `Rinna
+   Co., Ltd. <https://rinna.co.jp/>`__ conducted further pre-training
+   for the Llama2 model with a mixture of English and Japanese datasets
+   to improve Japanese task capability. The model is publicly released
+   on Hugging Face hub. You can find detailed information at the
+   `rinna/youri-7b-chat project
+   page <https://huggingface.co/rinna/youri-7b>`__.
+-  **baichuan2-7b-chat** - Baichuan 2 is the new generation of
+   large-scale open-source language models launched by `Baichuan
+   Intelligence inc <https://www.baichuan-ai.com/home>`__. It is trained
+   on a high-quality corpus with 2.6 trillion tokens and has achieved
+   the best performance in authoritative Chinese and English benchmarks
+   of the same size.
+-  **internlm2-chat-1.8b** - InternLM2 is the second generation InternLM
+   series. Compared to the previous generation model, it shows
+   significant improvements in various capabilities, including
+   reasoning, mathematics, and coding. More details about model can be
+   found in `model repository <https://huggingface.co/internlm>`__.
+
+.. code:: ipython3
+
+    from llm_config import SUPPORTED_LLM_MODELS
+    import ipywidgets as widgets
+
+.. code:: ipython3
+
+    model_languages = list(SUPPORTED_LLM_MODELS)
+
+    model_language = widgets.Dropdown(
+        options=model_languages,
+        value=model_languages[0],
+        description="Model Language:",
+        disabled=False,
+    )
+
+    model_language
+
+
+
+
+.. parsed-literal::
+
+    Dropdown(description='Model Language:', options=('English', 'Chinese', 'Japanese'), value='English')
+
+
+
+.. code:: ipython3
+
+    model_ids = list(SUPPORTED_LLM_MODELS[model_language.value])
+
+    model_id = widgets.Dropdown(
+        options=model_ids,
+        value=model_ids[0],
+        description="Model:",
+        disabled=False,
+    )
+
+    model_id
+
+
+
+
+.. parsed-literal::
+
+    Dropdown(description='Model:', options=('qwen2-0.5b-instruct', 'tiny-llama-1b-chat', 'qwen2-1.5b-instruct', 'g…
+
+
+
+.. code:: ipython3
+
+    model_configuration = SUPPORTED_LLM_MODELS[model_language.value][model_id.value]
+    print(f"Selected model {model_id.value}")
+
+
+.. parsed-literal::
+
+    Selected model qwen2-0.5b-instruct
+
+
+Convert model using Optimum-CLI tool
+------------------------------------
+
+
+
+`Optimum Intel <https://huggingface.co/docs/optimum/intel/index>`__ is
+the interface between the
+`Transformers <https://huggingface.co/docs/transformers/index>`__ and
+`Diffusers <https://huggingface.co/docs/diffusers/index>`__ libraries
+and OpenVINO to accelerate end-to-end pipelines on Intel architectures.
+It provides ease-to-use cli interface for exporting models to `OpenVINO
+Intermediate Representation
+(IR) <https://docs.openvino.ai/2024/documentation/openvino-ir-format.html>`__
+format.
+
+The command bellow demonstrates basic command for model export with
+``optimum-cli``
+
+::
+
+   optimum-cli export openvino --model <model_id_or_path> --task <task> <out_dir>
+
+where ``--model`` argument is model id from HuggingFace Hub or local
+directory with model (saved using ``.save_pretrained`` method),
+``--task`` is one of `supported
+task <https://huggingface.co/docs/optimum/exporters/task_manager>`__
+that exported model should solve. For LLMs it will be
+``text-generation-with-past``. If model initialization requires to use
+remote code, ``--trust-remote-code`` flag additionally should be passed.
+
+Compress model weights
+----------------------
+
+
+
+The `Weights
+Compression <https://docs.openvino.ai/2024/openvino-workflow/model-optimization-guide/weight-compression.html>`__
+algorithm is aimed at compressing the weights of the models and can be
+used to optimize the model footprint and performance of large models
+where the size of weights is relatively larger than the size of
+activations, for example, Large Language Models (LLM). Compared to INT8
+compression, INT4 compression improves performance even more, but
+introduces a minor drop in prediction quality.
+
+Weights Compression using Optimum-CLI
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+You can also apply fp16, 8-bit or 4-bit weight compression on the
+Linear, Convolutional and Embedding layers when exporting your model
+with the CLI by setting ``--weight-format`` to respectively fp16, int8
+or int4. This type of optimization allows to reduce the memory footprint
+and inference latency. By default the quantization scheme for int8/int4
+will be
+`asymmetric <https://github.com/openvinotoolkit/nncf/blob/develop/docs/compression_algorithms/Quantization.md#asymmetric-quantization>`__,
+to make it
+`symmetric <https://github.com/openvinotoolkit/nncf/blob/develop/docs/compression_algorithms/Quantization.md#symmetric-quantization>`__
+you can add ``--sym``.
+
+For INT4 quantization you can also specify the following arguments:
+
+- The ``--group-size`` parameter will define the group size to use for
+  quantization, -1 it will results in per-column quantization.
+- The ``--ratio`` parameter controls the ratio between 4-bit and 8-bit
+  quantization. If set to 0.9, it means that 90% of the layers will be
+  quantized to int4 while 10% will be quantized to int8.
+
+Smaller group_size and ratio values usually improve accuracy at the
+sacrifice of the model size and inference latency.
+
+   **Note**: There may be no speedup for INT4/INT8 compressed models on
+   dGPU.
+
+.. code:: ipython3
+
+    from IPython.display import Markdown, display
+
+    prepare_int4_model = widgets.Checkbox(
+        value=True,
+        description="Prepare INT4 model",
+        disabled=False,
+    )
+    prepare_int8_model = widgets.Checkbox(
+        value=False,
+        description="Prepare INT8 model",
+        disabled=False,
+    )
+    prepare_fp16_model = widgets.Checkbox(
+        value=False,
+        description="Prepare FP16 model",
+        disabled=False,
+    )
+
+    display(prepare_int4_model)
+    display(prepare_int8_model)
+    display(prepare_fp16_model)
+
+
+
+.. parsed-literal::
+
+    Checkbox(value=True, description='Prepare INT4 model')
+
+
+
+.. parsed-literal::
+
+    Checkbox(value=False, description='Prepare INT8 model')
+
+
+
+.. parsed-literal::
+
+    Checkbox(value=False, description='Prepare FP16 model')
+
+
+Weight compression with AWQ
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+`Activation-aware Weight
+Quantization <https://arxiv.org/abs/2306.00978>`__ (AWQ) is an algorithm
+that tunes model weights for more accurate INT4 compression. It slightly
+improves generation quality of compressed LLMs, but requires significant
+additional time for tuning weights on a calibration dataset. We use
+``wikitext-2-raw-v1/train`` subset of the
+`Wikitext <https://huggingface.co/datasets/Salesforce/wikitext>`__
+dataset for calibration.
+
+Below you can enable AWQ to be additionally applied during model export
+with INT4 precision.
+
+   **Note**: Applying AWQ requires significant memory and time.
+
+..
+
+   **Note**: It is possible that there will be no matching patterns in
+   the model to apply AWQ, in such case it will be skipped.
+
+.. code:: ipython3
+
+    enable_awq = widgets.Checkbox(
+        value=False,
+        description="Enable AWQ",
+        disabled=not prepare_int4_model.value,
+    )
+    display(enable_awq)
+
+
+
+.. parsed-literal::
+
+    Checkbox(value=False, description='Enable AWQ')
+
+
+We can now save floating point and compressed model variants
+
+.. code:: ipython3
+
+    from pathlib import Path
+
+    pt_model_id = model_configuration["model_id"]
+    pt_model_name = model_id.value.split("-")[0]
+    fp16_model_dir = Path(model_id.value) / "FP16"
+    int8_model_dir = Path(model_id.value) / "INT8_compressed_weights"
+    int4_model_dir = Path(model_id.value) / "INT4_compressed_weights"
+
+
+    def convert_to_fp16():
+        if (fp16_model_dir / "openvino_model.xml").exists():
+            return
+        remote_code = model_configuration.get("remote_code", False)
+        export_command_base = "optimum-cli export openvino --model {} --task text-generation-with-past --weight-format fp16".format(pt_model_id)
+        if remote_code:
+            export_command_base += " --trust-remote-code"
+        export_command = export_command_base + " " + str(fp16_model_dir)
+        display(Markdown("**Export command:**"))
+        display(Markdown(f"`{export_command}`"))
+        ! $export_command
+
+
+    def convert_to_int8():
+        if (int8_model_dir / "openvino_model.xml").exists():
+            return
+        int8_model_dir.mkdir(parents=True, exist_ok=True)
+        remote_code = model_configuration.get("remote_code", False)
+        export_command_base = "optimum-cli export openvino --model {} --task text-generation-with-past --weight-format int8".format(pt_model_id)
+        if remote_code:
+            export_command_base += " --trust-remote-code"
+        export_command = export_command_base + " " + str(int8_model_dir)
+        display(Markdown("**Export command:**"))
+        display(Markdown(f"`{export_command}`"))
+        ! $export_command
+
+
+    def convert_to_int4():
+        compression_configs = {
+            "zephyr-7b-beta": {
+                "sym": True,
+                "group_size": 64,
+                "ratio": 0.6,
+            },
+            "mistral-7b": {
+                "sym": True,
+                "group_size": 64,
+                "ratio": 0.6,
+            },
+            "minicpm-2b-dpo": {
+                "sym": True,
+                "group_size": 64,
+                "ratio": 0.6,
+            },
+            "gemma-2b-it": {
+                "sym": True,
+                "group_size": 64,
+                "ratio": 0.6,
+            },
+            "notus-7b-v1": {
+                "sym": True,
+                "group_size": 64,
+                "ratio": 0.6,
+            },
+            "neural-chat-7b-v3-1": {
+                "sym": True,
+                "group_size": 64,
+                "ratio": 0.6,
+            },
+            "llama-2-chat-7b": {
+                "sym": True,
+                "group_size": 128,
+                "ratio": 0.8,
+            },
+            "llama-3-8b-instruct": {
+                "sym": True,
+                "group_size": 128,
+                "ratio": 0.8,
+            },
+            "gemma-7b-it": {
+                "sym": True,
+                "group_size": 128,
+                "ratio": 0.8,
+            },
+            "chatglm2-6b": {
+                "sym": True,
+                "group_size": 128,
+                "ratio": 0.72,
+            },
+            "qwen-7b-chat": {"sym": True, "group_size": 128, "ratio": 0.6},
+            "red-pajama-3b-chat": {
+                "sym": False,
+                "group_size": 128,
+                "ratio": 0.5,
+            },
+            "default": {
+                "sym": False,
+                "group_size": 128,
+                "ratio": 0.8,
+            },
+        }
+
+        model_compression_params = compression_configs.get(model_id.value, compression_configs["default"])
+        if (int4_model_dir / "openvino_model.xml").exists():
+            return
+        remote_code = model_configuration.get("remote_code", False)
+        export_command_base = "optimum-cli export openvino --model {} --task text-generation-with-past --weight-format int4".format(pt_model_id)
+        int4_compression_args = " --group-size {} --ratio {}".format(model_compression_params["group_size"], model_compression_params["ratio"])
+        if model_compression_params["sym"]:
+            int4_compression_args += " --sym"
+        if enable_awq.value:
+            int4_compression_args += " --awq --dataset wikitext2 --num-samples 128"
+        export_command_base += int4_compression_args
+        if remote_code:
+            export_command_base += " --trust-remote-code"
+        export_command = export_command_base + " " + str(int4_model_dir)
+        display(Markdown("**Export command:**"))
+        display(Markdown(f"`{export_command}`"))
+        ! $export_command
+
+
+    if prepare_fp16_model.value:
+        convert_to_fp16()
+    if prepare_int8_model.value:
+        convert_to_int8()
+    if prepare_int4_model.value:
+        convert_to_int4()
+
+
+
+**Export command:**
+
+
+
+``optimum-cli export openvino --model Qwen/Qwen2-0.5B-Instruct --task text-generation-with-past --weight-format int4 --group-size 128 --ratio 0.8 qwen2-0.5b-instruct/INT4_compressed_weights``
+
+
+.. parsed-literal::
+
+    2024-07-02 00:50:04.949521: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-07-02 00:50:04.983760: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
+    2024-07-02 00:50:05.502308: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
+      torch.utils._pytree._register_pytree_node(
+    Framework not specified. Using pt to export the model.
+    Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
+    Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
+    Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
+    Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
+    Using framework PyTorch: 2.3.1+cpu
+    Overriding 1 configuration item(s)
+    	- use_cache -> True
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_attn_mask_utils.py:114: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+      if (input_shape[-1] > 1 or self.sliding_window is not None) and self.is_causal:
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/optimum/exporters/onnx/model_patcher.py:300: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+      if past_key_values_length > 0:
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/qwen2/modeling_qwen2.py:120: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+      if seq_len > self.max_seq_len_cached:
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/qwen2/modeling_qwen2.py:667: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+      if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+    Mixed-Precision assignment ━━━━━━━━━━━━━━━━━━━━ 100% 168/168 • 0:00:02 • 0:00:00
+    INFO:nncf:Statistics of the bitwidth distribution:
+    ┍━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┑
+    │   Num bits (N) │ % all parameters (layers)   │ % ratio-defining parameters (layers)   │
+    ┝━━━━━━━━━━━━━━━━┿━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┿━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┥
+    │              8 │ 43% (81 / 169)              │ 21% (80 / 168)                         │
+    ├────────────────┼─────────────────────────────┼────────────────────────────────────────┤
+    │              4 │ 57% (88 / 169)              │ 79% (88 / 168)                         │
+    ┕━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┙
+    Applying Weight Compression ━━━━━━━━━━━━━━━━━━━ 100% 169/169 • 0:00:09 • 0:00:00
+    Replacing `(?!\S)` pattern to `(?:$|[^\S])` in RegexSplit operation
+
+
+Let’s compare model size for different compression types
+
+.. code:: ipython3
+
+    fp16_weights = fp16_model_dir / "openvino_model.bin"
+    int8_weights = int8_model_dir / "openvino_model.bin"
+    int4_weights = int4_model_dir / "openvino_model.bin"
+
+    if fp16_weights.exists():
+        print(f"Size of FP16 model is {fp16_weights.stat().st_size / 1024 / 1024:.2f} MB")
+    for precision, compressed_weights in zip([8, 4], [int8_weights, int4_weights]):
+        if compressed_weights.exists():
+            print(f"Size of model with INT{precision} compressed weights is {compressed_weights.stat().st_size / 1024 / 1024:.2f} MB")
+        if compressed_weights.exists() and fp16_weights.exists():
+            print(f"Compression rate for INT{precision} model: {fp16_weights.stat().st_size / compressed_weights.stat().st_size:.3f}")
+
+
+.. parsed-literal::
+
+    Size of model with INT4 compressed weights is 358.86 MB
+
+
+Select device for inference and model variant
+'''''''''''''''''''''''''''''''''''''''''''''
+
+
+
+   **Note**: There may be no speedup for INT4/INT8 compressed models on
+   dGPU.
+
+.. code:: ipython3
+
+    import openvino as ov
+
+    core = ov.Core()
+
+    support_devices = core.available_devices
+    if "NPU" in support_devices:
+        support_devices.remove("NPU")
+
+    device = widgets.Dropdown(
+        options=support_devices + ["AUTO"],
+        value="CPU",
+        description="Device:",
+        disabled=False,
+    )
+
+    device
+
+
+
+
+.. parsed-literal::
+
+    Dropdown(description='Device:', options=('CPU', 'AUTO'), value='CPU')
+
+
+
+The cell below demonstrates how to instantiate model based on selected
+variant of model weights and inference device
+
+.. code:: ipython3
+
+    available_models = []
+    if int4_model_dir.exists():
+        available_models.append("INT4")
+    if int8_model_dir.exists():
+        available_models.append("INT8")
+    if fp16_model_dir.exists():
+        available_models.append("FP16")
+
+    model_to_run = widgets.Dropdown(
+        options=available_models,
+        value=available_models[0],
+        description="Model to run:",
+        disabled=False,
+    )
+
+    model_to_run
+
+
+
+
+.. parsed-literal::
+
+    Dropdown(description='Model to run:', options=('INT4',), value='INT4')
+
+
+
+Instantiate pipeline with OpenVINO Generate API
+-----------------------------------------------
+
+
+
+`OpenVINO Generate
+API <https://github.com/openvinotoolkit/openvino.genai/blob/master/src/README.md>`__
+can be used to create pipelines to run an inference with OpenVINO
+Runtime.
+
+Firstly we need to create pipeline with ``LLMPipeline``. ``LLMPipeline``
+is the main object used for decoding. You can construct it straight away
+from the folder with the converted model. It will automatically load the
+``main model``, ``tokenizer``, ``detokenizer`` and default
+``generation configuration``. We will provide directory with model and
+device for ``LLMPipeline``. After that we will configure parameters for
+decoding. We can get default config with ``get_generation_config()``,
+setup parameters and apply the updated version with
+``set_generation_config(config)`` or put config directly to
+``generate()``. It’s also possible to specify the needed options just as
+inputs in the ``generate()`` method, as shown below. Then we just run
+``generate`` method and get the output in text format. We do not need to
+encode input prompt according to model expected template or write
+post-processing code for logits decoder, it will be done easily with
+LLMPipeline.
+
+.. code:: ipython3
+
+    from transformers import AutoTokenizer
+    from openvino_tokenizers import convert_tokenizer
+    from openvino_genai import LLMPipeline
+
+    if model_to_run.value == "INT4":
+        model_dir = int4_model_dir
+    elif model_to_run.value == "INT8":
+        model_dir = int8_model_dir
+    else:
+        model_dir = fp16_model_dir
+    print(f"Loading model from {model_dir}\n")
+
+    # optionally convert tokenizer if used cached model without it
+    if not (model_dir / "openvino_tokenizer.xml").exists() or not (model_dir / "openvino_detokenizer.xml").exists():
+        hf_tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
+        ov_tokenizer, ov_detokenizer = convert_tokenizer(hf_tokenizer, with_detokenizer=True)
+        ov.save_model(ov_tokenizer, model_dir / "openvino_tokenizer.xml")
+        ov.save_model(ov_tokenizer, model_dir / "openvino_detokenizer.xml")
+
+
+    pipe = LLMPipeline(str(model_dir), device.value)
+    print(pipe.generate("The Sun is yellow bacause", temperature=1.2, top_k=4, do_sample=True, max_new_tokens=50))
+
+
+.. parsed-literal::
+
+    Loading model from qwen2-0.5b-instruct/INT4_compressed_weights
+
+     the light of the sun travels faster through the air than the light of a lamp. 120% of the sunlight is absorbed by plants in an area. 10% of the sunlight passes through the sky and is lost. If
+
+
+Run Chatbot
+-----------
+
+
+
+Now, when model created, we can setup Chatbot interface using
+`Gradio <https://www.gradio.app/>`__. The diagram below illustrates how
+the chatbot pipeline works
+
+.. figure:: https://user-images.githubusercontent.com/29454499/255523209-d9336491-c7ba-4dc1-98f0-07f23743ce89.png
+   :alt: generation pipeline
+
+   generation pipeline
+
+As can be seen, the pipeline very similar to instruction-following with
+only changes that previous conversation history additionally passed as
+input with next user question for getting wider input context. On the
+first iteration, it is provided instructions joined to conversation
+history (if exists) converted to token ids using a tokenizer, then
+prepared input provided to the model. The model generates probabilities
+for all tokens in logits format. The way the next token will be selected
+over predicted probabilities is driven by the selected decoding
+methodology. You can find more information about the most popular
+decoding methods in this
+`blog <https://huggingface.co/blog/how-to-generate>`__. The result
+generation updates conversation history for next conversation step. It
+makes stronger connection of next question with previously provided and
+allows user to make clarifications regarding previously provided
+answers. `More about that, please, see
+here. <https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide.html>`__
+
+To make experience easier, we will use `OpenVINO Generate
+API <https://github.com/openvinotoolkit/openvino.genai/blob/master/src/README>`__.
+Firstly we will create pipeline with ``LLMPipeline``. ``LLMPipeline`` is
+the main object used for decoding. You can construct it straight away
+from the folder with the converted model. It will automatically load the
+main model, tokenizer, detokenizer and default generation configuration.
+After that we will configure parameters for decoding. We can get default
+config with ``get_generation_config()``, setup parameters and apply the
+updated version with ``set_generation_config(config)`` or put config
+directly to ``generate()``. It’s also possible to specify the needed
+options just as inputs in the ``generate()`` method, as shown below.
+Then we just run ``generate`` method and get the output in text format.
+We do not need to encode input prompt according to model expected
+template or write post-processing code for logits decoder, it will be
+done easily with ``LLMPipeline``.
+
+| There are several parameters that can control text generation quality:
+  \* ``Temperature`` is a parameter used to control the level of
+  creativity in AI-generated text. By adjusting the ``temperature``, you
+  can influence the AI model’s probability distribution, making the text
+  more focused or diverse.
+| Consider the following example: The AI model has to complete the
+  sentence “The cat is \____.” with the following token probabilities:
+
+::
+
+   playing: 0.5
+   sleeping: 0.25
+   eating: 0.15
+   driving: 0.05
+   flying: 0.05
+
+   - **Low temperature** (e.g., 0.2): The AI model becomes more focused and deterministic, choosing tokens with the highest probability, such as "playing."
+   - **Medium temperature** (e.g., 1.0): The AI model maintains a balance between creativity and focus, selecting tokens based on their probabilities without significant bias, such as "playing," "sleeping," or "eating."
+   - **High temperature** (e.g., 2.0): The AI model becomes more adventurous, increasing the chances of selecting less likely tokens, such as "driving" and "flying."
+
+-  ``Top-p``, also known as nucleus sampling, is a parameter used to
+   control the range of tokens considered by the AI model based on their
+   cumulative probability. By adjusting the ``top-p`` value, you can
+   influence the AI model’s token selection, making it more focused or
+   diverse. Using the same example with the cat, consider the following
+   top_p settings:
+
+   -  **Low top_p** (e.g., 0.5): The AI model considers only tokens with
+      the highest cumulative probability, such as “playing.”
+   -  **Medium top_p** (e.g., 0.8): The AI model considers tokens with a
+      higher cumulative probability, such as “playing,” “sleeping,” and
+      “eating.”
+   -  **High top_p** (e.g., 1.0): The AI model considers all tokens,
+      including those with lower probabilities, such as “driving” and
+      “flying.”
+
+-  ``Top-k`` is an another popular sampling strategy. In comparison with
+   Top-P, which chooses from the smallest possible set of words whose
+   cumulative probability exceeds the probability P, in Top-K sampling K
+   most likely next words are filtered and the probability mass is
+   redistributed among only those K next words. In our example with cat,
+   if k=3, then only “playing”, “sleeping” and “eating” will be taken
+   into account as possible next word.
+-  ``Repetition Penalty`` This parameter can help penalize tokens based
+   on how frequently they occur in the text, including the input prompt.
+   A token that has already appeared five times is penalized more
+   heavily than a token that has appeared only one time. A value of 1
+   means that there is no penalty and values larger than 1 discourage
+   repeated
+   tokens.https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide.html
+
+Prepare text streamer to get results runtime
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+Load the ``detokenizer``, use it to convert token_id to string output
+format. We will collect print-ready text in a queue and give the text
+when it is needed. It will help estimate performance.
+
+.. code:: ipython3
+
+    import re
+    from queue import Queue
+    from openvino_genai import StreamerBase
+
+    core = ov.Core()
+
+    detokinizer_path = Path(model_dir, "openvino_detokenizer.xml")
+
+
+    class TextStreamerIterator(StreamerBase):
+        def __init__(self, tokenizer):
+            super().__init__()
+            self.tokenizer = tokenizer
+            self.compiled_detokenizer = core.compile_model(detokinizer_path.as_posix())
+            self.text_queue = Queue()
+            self.stop_signal = None
+
+        def __iter__(self):
+            return self
+
+        def __next__(self):
+            value = self.text_queue.get()
+            if value == self.stop_signal:
+                raise StopIteration()
+            else:
+                return value
+
+        def put(self, token_id):
+            openvino_output = self.compiled_detokenizer([[0, token_id]])
+            text = str(openvino_output["string_output"][0])
+            # remove labels/special symbols
+            text = re.sub("<.*>", "", text)
+            self.text_queue.put(text)
+
+        def end(self):
+            self.text_queue.put(self.stop_signal)
+
+Setup of the chatbot life process function
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+``bot`` function is the entry point for starting chat. We setup config
+here, collect history to string and put it to ``generate()`` method.
+After that it’s generate new chatbot message and we add it to history.
+
+.. code:: ipython3
+
+    from uuid import uuid4
+    from threading import Event, Thread
+
+    pipe = LLMPipeline(str(model_dir), device.value)
+
+    max_new_tokens = 80
+
+    start_message = model_configuration["start_message"]
+    history_template = model_configuration.get("history_template")
+    current_message_template = model_configuration.get("current_message_template")
+
+
+    def convert_history_to_input(history):
+        """
+        function for conversion history stored as list pairs of user and assistant messages to tokens according to model expected conversation template
+        Params:
+          history: dialogue history
+        Returns:
+          history in token format
+        """
+        new_prompt = f"{start_message}"
+        if history_template is None:
+            for user_msg, model_msg in history:
+                new_prompt += user_msg + "\n" + model_msg + "\n"
+            return new_prompt
+        else:
+            new_prompt = "".join(["".join([history_template.format(num=round, user=item[0], assistant=item[1])]) for round, item in enumerate(history[:-1])])
+            new_prompt += "".join(
+                [
+                    "".join(
+                        [
+                            current_message_template.format(
+                                num=len(history) + 1,
+                                user=history[-1][0],
+                                assistant=history[-1][1],
+                            )
+                        ]
+                    )
+                ]
+            )
+
+        return new_prompt
+
+
+    def default_partial_text_processor(partial_text: str, new_text: str):
+        """
+        helper for updating partially generated answer, used by default
+
+        Params:
+          partial_text: text buffer for storing previosly generated text
+          new_text: text update for the current step
+        Returns:
+          updated text string
+
+        """
+        partial_text += new_text
+        return partial_text
+
+
+    text_processor = model_configuration.get("partial_text_processor", default_partial_text_processor)
+
+
+    def bot(message, history, temperature, top_p, top_k, repetition_penalty):
+        """
+        callback function for running chatbot on submit button click
+
+        Params:
+          message: new message from user
+          history: conversation history
+          temperature:  parameter for control the level of creativity in AI-generated text.
+                        By adjusting the `temperature`, you can influence the AI model's probability distribution, making the text more focused or diverse.
+          top_p: parameter for control the range of tokens considered by the AI model based on their cumulative probability.
+          top_k: parameter for control the range of tokens considered by the AI model based on their cumulative probability, selecting number of tokens with highest probability.
+          repetition_penalty: parameter for penalizing tokens based on how frequently they occur in the text.
+          active_chat: chat state, if true then chat is running, if false then we should start it here.
+        Returns:
+          message: reset message and make it ""
+          history: updated history with message and answer from chatbot
+          active_chat: if we are here, the chat is running or will be started, so return True
+        """
+        streamer = TextStreamerIterator(pipe.get_tokenizer())
+
+        config = pipe.get_generation_config()
+        config.temperature = temperature
+        config.top_p = top_p
+        config.top_k = top_k
+        config.do_sample = temperature > 0.0
+        config.max_new_tokens = max_new_tokens
+        config.repetition_penalty = repetition_penalty
+
+        # history = [['message', 'chatbot answer'], ...]
+        history.append([message, ""])
+        new_prompt = convert_history_to_input(history)
+
+        stream_complete = Event()
+
+        def generate_and_signal_complete():
+            """
+            genration function for single thread
+            """
+            global start_time
+            pipe.generate(new_prompt, config, streamer)
+            stream_complete.set()
+
+        t1 = Thread(target=generate_and_signal_complete)
+        t1.start()
+
+        partial_text = ""
+        for new_text in streamer:
+            partial_text = text_processor(partial_text, new_text)
+            history[-1][1] = partial_text
+            yield "", history, streamer
+
+
+    def stop_chat(streamer):
+        if streamer is not None:
+            streamer.end()
+        return None
+
+
+    def stop_chat_and_clear_history(streamer):
+        if streamer is not None:
+            streamer.end()
+        return None, None
+
+
+    def get_uuid():
+        """
+        universal unique identifier for thread
+        """
+        return str(uuid4())
+
+.. code:: ipython3
+
+    import gradio as gr
+
+    chinese_examples = [
+        ["你好!"],
+        ["你是谁?"],
+        ["请介绍一下上海"],
+        ["请介绍一下英特尔公司"],
+        ["晚上睡不着怎么办？"],
+        ["给我讲一个年轻人奋斗创业最终取得成功的故事。"],
+        ["给这个故事起一个标题。"],
+    ]
+
+    english_examples = [
+        ["Hello there! How are you doing?"],
+        ["What is OpenVINO?"],
+        ["Who are you?"],
+        ["Can you explain to me briefly what is Python programming language?"],
+        ["Explain the plot of Cinderella in a sentence."],
+        ["What are some common mistakes to avoid when writing code?"],
+        ["Write a 100-word blog post on “Benefits of Artificial Intelligence and OpenVINO“"],
+    ]
+
+    japanese_examples = [
+        ["こんにちは！調子はどうですか?"],
+        ["OpenVINOとは何ですか?"],
+        ["あなたは誰ですか?"],
+        ["Pythonプログラミング言語とは何か簡単に説明してもらえますか?"],
+        ["シンデレラのあらすじを一文で説明してください。"],
+        ["コードを書くときに避けるべきよくある間違いは何ですか?"],
+        ["人工知能と「OpenVINOの利点」について100語程度のブログ記事を書いてください。"],
+    ]
+
+    examples = chinese_examples if (model_language.value == "Chinese") else japanese_examples if (model_language.value == "Japanese") else english_examples
+
+
+    with gr.Blocks(
+        theme=gr.themes.Soft(),
+        css=".disclaimer {font-variant-caps: all-small-caps;}",
+    ) as demo:
+        streamer = gr.State(None)
+        conversation_id = gr.State(get_uuid)
+        gr.Markdown(f"""<h1><center>OpenVINO {model_id.value} Chatbot</center></h1>""")
+        chatbot = gr.Chatbot(height=500)
+        with gr.Row():
+            with gr.Column():
+                msg = gr.Textbox(
+                    label="Chat Message Box",
+                    placeholder="Chat Message Box",
+                    show_label=False,
+                    container=False,
+                )
+            with gr.Column():
+                with gr.Row():
+                    submit = gr.Button("Submit")
+                    stop = gr.Button("Stop")
+                    clear = gr.Button("Clear")
+        with gr.Row():
+            with gr.Accordion("Advanced Options:", open=False):
+                with gr.Row():
+                    with gr.Column():
+                        with gr.Row():
+                            temperature = gr.Slider(
+                                label="Temperature",
+                                value=0.1,
+                                minimum=0.0,
+                                maximum=1.0,
+                                step=0.1,
+                                interactive=True,
+                                info="Higher values produce more diverse outputs",
+                            )
+                    with gr.Column():
+                        with gr.Row():
+                            top_p = gr.Slider(
+                                label="Top-p (nucleus sampling)",
+                                value=1.0,
+                                minimum=0.0,
+                                maximum=1,
+                                step=0.01,
+                                interactive=True,
+                                info=(
+                                    "Sample from the smallest possible set of tokens whose cumulative probability "
+                                    "exceeds top_p. Set to 1 to disable and sample from all tokens."
+                                ),
+                            )
+                    with gr.Column():
+                        with gr.Row():
+                            top_k = gr.Slider(
+                                label="Top-k",
+                                value=50,
+                                minimum=0.0,
+                                maximum=200,
+                                step=1,
+                                interactive=True,
+                                info="Sample from a shortlist of top-k tokens — 0 to disable and sample from all tokens.",
+                            )
+                    with gr.Column():
+                        with gr.Row():
+                            repetition_penalty = gr.Slider(
+                                label="Repetition Penalty",
+                                value=1.1,
+                                minimum=1.0,
+                                maximum=2.0,
+                                step=0.1,
+                                interactive=True,
+                                info="Penalize repetition — 1.0 to disable.",
+                            )
+        gr.Examples(examples, inputs=msg, label="Click on any example and press the 'Submit' button")
+
+        submit_event = msg.submit(
+            fn=bot,
+            inputs=[msg, chatbot, temperature, top_p, top_k, repetition_penalty],
+            outputs=[msg, chatbot, streamer],
+            queue=True,
+        )
+        submit_click_event = submit.click(
+            fn=bot,
+            inputs=[msg, chatbot, temperature, top_p, top_k, repetition_penalty],
+            outputs=[msg, chatbot, streamer],
+            queue=True,
+        )
+        stop.click(fn=stop_chat, inputs=streamer, outputs=[streamer], queue=False)
+        clear.click(fn=stop_chat_and_clear_history, inputs=streamer, outputs=[chatbot, streamer], queue=False)
+
+    # if you are launching remotely, specify server_name and server_port
+    #  demo.launch(server_name='your server name', server_port='server port in int')
+    # if you have any issue to launch on your platform, you can pass share=True to launch method:
+    # demo.launch(share=True)
+    # it creates a publicly shareable link for the interface. Read more in the docs: https://gradio.app/docs/
+    demo.launch()
+
+
+.. parsed-literal::
+
+    Running on local URL:  http://127.0.0.1:7860
+
+    To create a public link, set `share=True` in `launch()`.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+.. code:: ipython3
+
+    # please uncomment and run this cell for stopping gradio interface
+    # demo.close()
+
+Next Step
+~~~~~~~~~
+
+
+
+Besides chatbot, we can use LangChain to augmenting LLM knowledge with
+additional data, which allow you to build AI applications that can
+reason about private data or data introduced after a model’s cutoff
+date. You can find this solution in `Retrieval-augmented generation
+(RAG) example <../llm-rag-langchain/>`__.
diff --git a/docs/notebooks/llm-chatbot-with-output.rst b/docs/notebooks/llm-chatbot-with-output.rst
index 9cf4739790dcac..db00f0a5ca1945 100644
--- a/docs/notebooks/llm-chatbot-with-output.rst
+++ b/docs/notebooks/llm-chatbot-with-output.rst
@@ -25,7 +25,12 @@ Language Models for chat. We will use a pre-trained model from the
 Transformers <https://huggingface.co/docs/transformers/index>`__
 library. To simplify the user experience, the `Hugging Face Optimum
 Intel <https://huggingface.co/docs/optimum/intel/index>`__ library is
-used to convert the models to OpenVINO™ IR format.
+used to convert the models to OpenVINO™ IR format and to create
+inference pipeline. The inference pipeline can also be created using
+`OpenVINO Generate
+API <https://github.com/openvinotoolkit/openvino.genai/tree/master/src>`__,
+the example of that, please, see in the notebook `LLM chatbot with
+OpenVINO Generate API <./llm-chatbot-generate-api.ipynb>`__
 
 The tutorial consists of the following steps:
 
@@ -38,8 +43,8 @@ The tutorial consists of the following steps:
 -  Create a chat inference pipeline
 -  Run chat pipeline
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Select model for inference <#select-model-for-inference>`__
@@ -49,6 +54,7 @@ Table of contents:
 
    -  `Weights Compression using
       Optimum-CLI <#weights-compression-using-optimum-cli>`__
+   -  `Weight compression with AWQ <#weight-compression-with-awq>`__
 
 -  `Select device for inference and model
    variant <#select-device-for-inference-and-model-variant>`__
@@ -79,7 +85,16 @@ Install required dependencies
     "datasets" \
     "accelerate"\
     "gradio>=4.19"\
-    "onnx" "einops" "transformers_stream_generator" "tiktoken" "transformers>=4.38.1" "bitsandbytes"
+    "onnx" "einops" "transformers_stream_generator" "tiktoken" "transformers>=4.40" "bitsandbytes"
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+    Note: you may need to restart the kernel to use updated packages.
+    Note: you may need to restart the kernel to use updated packages.
+    Note: you may need to restart the kernel to use updated packages.
+
 
 .. code:: ipython3
 
@@ -101,7 +116,7 @@ Install required dependencies
                 shutil.copy(config_shared_path, config_dst_path)
         else:
             r = requests.get(url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/llm_config.py")
-            with open("llm_config.py", "w") as f:
+            with open("llm_config.py", "w", encoding="utf-8") as f:
                 f.write(r.text)
     elif not os.path.islink(config_dst_path):
         print("LLM config will be updated")
@@ -109,7 +124,7 @@ Install required dependencies
             shutil.copy(config_shared_path, config_dst_path)
         else:
             r = requests.get(url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/llm_config.py")
-            with open("llm_config.py", "w") as f:
+            with open("llm_config.py", "w", encoding="utf-8") as f:
                 f.write(r.text)
 
 Select model for inference
@@ -173,7 +188,7 @@ The available options are:
        except OSError:
            notebook_login()
 
--  **phi3-mini-instruct<|end|>** - The Phi-3-Mini is a 3.8B parameters,
+-  **phi3-mini-instruct** - The Phi-3-Mini is a 3.8B parameters,
    lightweight, state-of-the-art open model trained with the Phi-3
    datasets that includes both synthetic data and the filtered publicly
    available websites data with a focus on high-quality and reasoning
@@ -286,6 +301,18 @@ The available options are:
        except OSError:
            notebook_login()
 
+-  **qwen2-1.5b-instruct/qwen2-7b-instruct** - Qwen2 is the new series
+   of Qwen large language models.Compared with the state-of-the-art open
+   source language models, including the previous released Qwen1.5,
+   Qwen2 has generally surpassed most open source models and
+   demonstrated competitiveness against proprietary models across a
+   series of benchmarks targeting for language understanding, language
+   generation, multilingual capability, coding, mathematics, reasoning,
+   etc. For more details, please refer to
+   `model_card <https://huggingface.co/Qwen/Qwen2-7B-Instruct>`__,
+   `blog <https://qwenlm.github.io/blog/qwen2/>`__,
+   `GitHub <https://github.com/QwenLM/Qwen2>`__, and
+   `Documentation <https://qwen.readthedocs.io/en/latest/>`__.
 -  **qwen1.5-0.5b-chat/qwen1.5-1.8b-chat/qwen1.5-7b-chat** - Qwen1.5 is
    the beta version of Qwen2, a transformer-based decoder-only language
    model pretrained on a large amount of data. Qwen1.5 is a language
@@ -420,7 +447,7 @@ The available options are:
 
     model_id = widgets.Dropdown(
         options=model_ids,
-        value=model_ids[2],
+        value=model_ids[0],
         description="Model:",
         disabled=False,
     )
@@ -444,7 +471,7 @@ The available options are:
 
 .. parsed-literal::
 
-    Selected model phi-3-mini-instruct
+    Selected model qwen2-7b-instruct
 
 
 Convert model using Optimum-CLI tool
@@ -453,7 +480,7 @@ Convert model using Optimum-CLI tool
 
 
 `Optimum Intel <https://huggingface.co/docs/optimum/intel/index>`__ is
-the interface between the 
+the interface between the
 `Transformers <https://huggingface.co/docs/transformers/index>`__ and
 `Diffusers <https://huggingface.co/docs/diffusers/index>`__ libraries
 and OpenVINO to accelerate end-to-end pipelines on Intel architectures.
@@ -477,7 +504,8 @@ that exported model should solve. For LLMs it will be
 ``text-generation-with-past``. If model initialization requires to use
 remote code, ``--trust-remote-code`` flag additionally should be passed.
 
-<|end|>## Compress model weights
+Compress model weights
+----------------------
 
 The `Weights
 Compression <https://docs.openvino.ai/2024/openvino-workflow/model-optimization-guide/weight-compression.html>`__
@@ -504,12 +532,13 @@ to make it
 `symmetric <https://github.com/openvinotoolkit/nncf/blob/develop/docs/compression_algorithms/Quantization.md#symmetric-quantization>`__
 you can add ``--sym``.
 
-For INT4 quantization you can also specify the following arguments : -
-The ``--group-size`` parameter will define the group size to use for
-quantization, -1 it will results in per-column quantization. - The
-``--ratio`` parameter controls the ratio between 4-bit and 8-bit
-quantization. If set to 0.9, it means that 90% of the layers will be
-quantized to int4 while 10% will be quantized to int8.
+For INT4 quantization you can also specify the following arguments:
+
+- The ``--group-size`` parameter will define the group size to use for
+  quantization, -1 it will results in per-column quantization.
+- The ``--ratio`` parameter controls the ratio between 4-bit and 8-bit
+  quantization. If set to 0.9, it means that 90% of the layers will be
+  quantized to int4 while 10% will be quantized to int8.
 
 Smaller group_size and ratio values usually improve accuracy at the
 sacrifice of the model size and inference latency.
@@ -560,6 +589,39 @@ sacrifice of the model size and inference latency.
     Checkbox(value=False, description='Prepare FP16 model')
 
 
+Weight compression with AWQ
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+`Activation-aware Weight
+Quantization <https://arxiv.org/abs/2306.00978>`__ (AWQ) is an algorithm
+that tunes model weights for more accurate INT4 compression. It slightly
+improves generation quality of compressed LLMs, but requires significant
+additional time for tuning weights on a calibration dataset. We use
+``wikitext-2-raw-v1/train`` subset of the
+`Wikitext <https://huggingface.co/datasets/Salesforce/wikitext>`__
+dataset for calibration.
+
+Below you can enable AWQ to be additionally applied during model export
+with INT4 precision.
+
+   **Note**: Applying AWQ requires significant memory and time.
+
+..
+
+   **Note**: It is possible that there will be no matching patterns in
+   the model to apply AWQ, in such case it will be skipped.
+
+.. code:: ipython3
+
+    enable_awq = widgets.Checkbox(
+        value=False,
+        description="Enable AWQ",
+        disabled=not prepare_int4_model.value,
+    )
+    display(enable_awq)
+
 We can now save floating point and compressed model variants
 
 .. code:: ipython3
@@ -673,6 +735,8 @@ We can now save floating point and compressed model variants
         int4_compression_args = " --group-size {} --ratio {}".format(model_compression_params["group_size"], model_compression_params["ratio"])
         if model_compression_params["sym"]:
             int4_compression_args += " --sym"
+        if enable_awq.value:
+            int4_compression_args += " --awq --dataset wikitext2 --num-samples 128"
         export_command_base += int4_compression_args
         if remote_code:
             export_command_base += " --trust-remote-code"
@@ -689,7 +753,19 @@ We can now save floating point and compressed model variants
     if prepare_int4_model.value:
         convert_to_int4()
 
-Let’s compare model size for different compression types
+
+
+**Export command:**
+
+
+
+``optimum-cli export openvino --model Qwen/Qwen2-7B-Instruct --task text-generation-with-past --weight-format int4 --group-size 128 --ratio 0.8 qwen2-7b-instruct/INT4_compressed_weights``
+
+
+.. parsed-literal::
+
+   Let’s compare model size for different compression types
+
 
 .. code:: ipython3
 
@@ -708,7 +784,7 @@ Let’s compare model size for different compression types
 
 .. parsed-literal::
 
-    Size of model with INT4 compressed weights is 2339.74 MB
+    Size of model with INT4 compressed weights is 4929.13 MB
 
 
 Select device for inference and model variant
@@ -743,7 +819,7 @@ Select device for inference and model variant
 
 .. parsed-literal::
 
-    Dropdown(description='Device:', options=('CPU', 'GPU', 'AUTO'), value='CPU')
+    Dropdown(description='Device:', options=('CPU', 'GPU.0', 'GPU.1', 'AUTO'), value='CPU')
 
 
 
@@ -829,6 +905,9 @@ guide <https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide.html>`__
 
     ov_config = {"PERFORMANCE_HINT": "LATENCY", "NUM_STREAMS": "1", "CACHE_DIR": ""}
 
+    if "GPU" in device.value and "qwen2-7b-instruct" in model_id.value:
+        ov_config["GPU_ENABLE_SDPA_OPTIMIZATION"] = "NO"
+
     # On a GPU device a model is executed in FP16 precision. For red-pajama-3b-chat model there known accuracy
     # issues caused by this, which we avoid by setting precision hint to "f32".
     if model_id.value == "red-pajama-3b-chat" and "GPU" in core.available_devices and device.value in ["GPU", "AUTO"]:
@@ -848,45 +927,12 @@ guide <https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide.html>`__
 
 .. parsed-literal::
 
-    INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino
+    Loading model from qwen2-7b-instruct/INT4_compressed_weights
 
 
 .. parsed-literal::
 
-    No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
-    2024-04-23 22:13:04.208987: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-04-23 22:13:04.210866: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
-    2024-04-23 22:13:04.245998: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
-    2024-04-23 22:13:04.246894: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
-    To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2024-04-23 22:13:04.941663: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
-    /home/ea/work/my_optimum_intel/optimum_env/lib/python3.8/site-packages/bitsandbytes/cextension.py:34: UserWarning: The installed version of bitsandbytes was compiled without GPU support. 8-bit optimizers, 8-bit multiplication, and GPU quantization are unavailable.
-      warn("The installed version of bitsandbytes was compiled without GPU support. "
-
-
-.. parsed-literal::
-
-    /home/ea/work/my_optimum_intel/optimum_env/lib/python3.8/site-packages/bitsandbytes/libbitsandbytes_cpu.so: undefined symbol: cadam32bit_grad_fp32
-
-
-.. parsed-literal::
-
-    WARNING[XFORMERS]: xFormers can't load C++/CUDA extensions. xFormers was built for:
-        PyTorch 2.0.1+cu118 with CUDA 1108 (you have 2.1.2+cpu)
-        Python  3.8.18 (you have 3.8.10)
-      Please reinstall xformers (see https://github.com/facebookresearch/xformers#installing-xformers)
-      Memory-efficient attention, SwiGLU, sparse and more won't be available.
-      Set XFORMERS_MORE_DETAILS=1 for more details
     Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
-
-
-.. parsed-literal::
-
-    Loading model from phi-3-mini-instruct/INT4_compressed_weights
-
-
-.. parsed-literal::
-
     The argument `trust_remote_code` is to be used along with export=True. It will be ignored.
     Compiling the model to CPU ...
 
@@ -1340,26 +1386,6 @@ answers.https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide.html
     # it creates a publicly shareable link for the interface. Read more in the docs: https://gradio.app/docs/
     demo.launch()
 
-
-.. parsed-literal::
-
-    Running on local URL:  http://127.0.0.1:7860
-
-    To create a public link, set `share=True` in `launch()`.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
 .. code:: ipython3
 
     # please uncomment and run this cell for stopping gradio interface
diff --git a/docs/notebooks/llm-question-answering-with-output.rst b/docs/notebooks/llm-question-answering-with-output.rst
index aa65cf61238f8f..f893abbca5ff0b 100644
--- a/docs/notebooks/llm-question-answering-with-output.rst
+++ b/docs/notebooks/llm-question-answering-with-output.rst
@@ -16,9 +16,12 @@ In this tutorial, we consider how to run an instruction-following text
 generation pipeline using popular LLMs and OpenVINO. We will use
 pre-trained models from the `Hugging Face
 Transformers <https://huggingface.co/docs/transformers/index>`__
-library. To simplify the user experience, the `Hugging Face Optimum
+library. The `Hugging Face Optimum
 Intel <https://huggingface.co/docs/optimum/intel/index>`__ library
-converts the models to OpenVINO™ IR format.
+converts the models to OpenVINO™ IR format. To simplify the user
+experience, we will use `OpenVINO Generate
+API <https://github.com/openvinotoolkit/openvino.genai>`__ for
+generation of instruction-following inference pipeline.
 
 The tutorial consists of the following steps:
 
@@ -28,20 +31,21 @@ The tutorial consists of the following steps:
    Optimum <https://huggingface.co/blog/openvino>`__.
 -  Compress model weights to INT8 and INT4 with `OpenVINO
    NNCF <https://github.com/openvinotoolkit/nncf>`__
--  Create an instruction-following inference pipeline
+-  Create an instruction-following inference pipeline with `Generate
+   API <https://github.com/openvinotoolkit/openvino.genai>`__
 -  Run instruction-following pipeline
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Select model for inference <#select-model-for-inference>`__
--  `Instantiate Model using Optimum
-   Intel <#instantiate-model-using-optimum-intel>`__
+-  `Download and convert model to OpenVINO IR via Optimum Intel
+   CLI <#download-and-convert-model-to-openvino-ir-via-optimum-intel-cli>`__
 -  `Compress model weights <#compress-model-weights>`__
 
-   -  `Weights Compression using Optimum
-      Intel <#weights-compression-using-optimum-intel>`__
+   -  `Weights Compression using Optimum Intel
+      CLI <#weights-compression-using-optimum-intel-cli>`__
    -  `Weights Compression using
       NNCF <#weights-compression-using-nncf>`__
 
@@ -51,8 +55,8 @@ Table of contents:
    pipeline <#create-an-instruction-following-inference-pipeline>`__
 
    -  `Setup imports <#setup-imports>`__
-   -  `Prepare template for user
-      prompt <#prepare-template-for-user-prompt>`__
+   -  `Prepare text streamer to get results
+      runtime <#prepare-text-streamer-to-get-results-runtime>`__
    -  `Main generation function <#main-generation-function>`__
    -  `Helpers for application <#helpers-for-application>`__
 
@@ -66,10 +70,19 @@ Prerequisites
 
 .. code:: ipython3
 
-    %pip install -Uq pip
     %pip uninstall -q -y optimum optimum-intel
-    %pip install --pre -Uq openvino openvino-tokenizers[transformers] --extra-index-url https://storage.openvinotoolkit.org/simple/wheels/nightly
-    %pip install -q "torch>=2.1" "nncf>=2.7" "transformers>=4.36.0" onnx "optimum>=1.16.1" "accelerate" "datasets>=2.14.6" "gradio>=4.19" "git+https://github.com/huggingface/optimum-intel.git" --extra-index-url https://download.pytorch.org/whl/cpu
+    %pip install -q "openvino-genai>=2024.2"
+    %pip install -q "torch>=2.1" "nncf>=2.7" "transformers>=4.40.0" onnx "optimum>=1.16.1" "accelerate" "datasets>=2.14.6" "gradio>=4.19" "git+https://github.com/huggingface/optimum-intel.git" --extra-index-url https://download.pytorch.org/whl/cpu
+
+
+.. parsed-literal::
+
+    WARNING: Skipping optimum as it is not installed.
+    WARNING: Skipping optimum-intel as it is not installed.
+    Note: you may need to restart the kernel to use updated packages.
+    Note: you may need to restart the kernel to use updated packages.
+    Note: you may need to restart the kernel to use updated packages.
+
 
 Select model for inference
 --------------------------
@@ -211,81 +224,60 @@ The available options are:
 
 .. parsed-literal::
 
-    Selected model llama-3-8b-instruct
+    Selected model dolly-v2-3b
 
 
-Instantiate Model using Optimum Intel
--------------------------------------
+Download and convert model to OpenVINO IR via Optimum Intel CLI
+---------------------------------------------------------------
 
 
 
-Optimum Intel can be used to load optimized models from the `Hugging
-Face Hub <https://huggingface.co/docs/optimum/intel/hf.co/models>`__ and
-create pipelines to run an inference with OpenVINO Runtime using Hugging
-Face APIs. The Optimum Inference models are API compatible with Hugging
-Face Transformers models. This means we just need to replace
-``AutoModelForXxx`` class with the corresponding ``OVModelForXxx``
-class.
+Listed model are available for downloading via the `HuggingFace
+hub <https://huggingface.co/models>`__. We will use optimum-cli
+interface for exporting it into OpenVINO Intermediate Representation
+(IR) format.
 
-Below is an example of the RedPajama model
+Optimum CLI interface for converting models supports export to OpenVINO
+(supported starting optimum-intel 1.12 version). General command format:
 
-.. code:: diff
+.. code:: bash
 
-   -from transformers import AutoModelForCausalLM
-   +from optimum.intel.openvino import OVModelForCausalLM
-   from transformers import AutoTokenizer, pipeline
+   optimum-cli export openvino --model <model_id_or_path> --task <task> <output_dir>
 
-   model_id = "togethercomputer/RedPajama-INCITE-Chat-3B-v1"
-   -model = AutoModelForCausalLM.from_pretrained(model_id)
-   +model = OVModelForCausalLM.from_pretrained(model_id, export=True)
-
-Model class initialization starts with calling ``from_pretrained``
-method. When downloading and converting the Transformers model, the
-parameter ``export=True`` should be added. We can save the converted
-model for the next usage with the ``save_pretrained`` method. Tokenizer
-class and pipelines API are compatible with Optimum models.
-
-To optimize the generation process and use memory more efficiently, the
-``use_cache=True`` option is enabled. Since the output side is
-auto-regressive, an output token hidden state remains the same once
-computed for every further generation step. Therefore, recomputing it
-every time you want to generate a new token seems wasteful. With the
-cache, the model saves the hidden state once it has been computed. The
-model only computes the one for the most recently generated output token
-at each time step, re-using the saved ones for hidden tokens. This
-reduces the generation complexity from :math:`O(n^3)` to :math:`O(n^2)`
-for a transformer model. More details about how it works can be found in
-this
-`article <https://scale.com/blog/pytorch-improvements#Text%20Translation>`__.
-With this option, the model gets the previous step’s hidden states
-(cached attention keys and values) as input and additionally provides
-hidden states for the current step as output. It means for all next
-iterations, it is enough to provide only a new token obtained from the
-previous step and cached key values to get the next token prediction.
+where ``--model`` argument is model id from HuggingFace Hub or local
+directory with model (saved using ``.save_pretrained`` method),
+``--task`` is one of `supported
+task <https://huggingface.co/docs/optimum/exporters/task_manager>`__
+that exported model should solve. For LLMs it will be
+``text-generation-with-past``. If model initialization requires to use
+remote code, ``--trust-remote-code`` flag additionally should be passed.
+Full list of supported arguments available via ``--help`` For more
+details and examples of usage, please check `optimum
+documentation <https://huggingface.co/docs/optimum/intel/inference#export>`__.
 
 Compress model weights
 ----------------------
 
- The Weights Compression
-algorithm is aimed at compressing the weights of the models and can be
-used to optimize the model footprint and performance of large models
-where the size of weights is relatively larger than the size of
-activations, for example, Large Language Models (LLM). Compared to INT8
-compression, INT4 compression improves performance even more but
-introduces a minor drop in prediction quality.
 
-Weights Compression using Optimum Intel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The Weights Compression algorithm is aimed at compressing the weights of
+the models and can be used to optimize the model footprint and
+performance of large models where the size of weights is relatively
+larger than the size of activations, for example, Large Language Models
+(LLM). Compared to INT8 compression, INT4 compression improves
+performance even more but introduces a minor drop in prediction quality.
+
+Weights Compression using Optimum Intel CLI
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 
 
 Optimum Intel supports weight compression via NNCF out of the box. For
-8-bit compression we pass ``load_in_8bit=True`` to ``from_pretrained()``
-method of ``OVModelForCausalLM``. For 4 bit compression we provide
-``quantization_config=OVWeightQuantizationConfig(bits=4, ...)`` argument
-containing number of bits and other compression parameters. An example
-of this approach usage you can find in `llm-chatbot
-notebook <../llm-chatbot>`__
+8-bit compression we pass ``--weight-format int8`` to ``optimum-cli``
+command line. For 4 bit compression we provide ``--weight-format int4``
+and some other options containing number of bits and other compression
+parameters. An example of this approach usage you can find in
+`llm-chatbot notebook <../llm-chatbot>`__
 
 Weights Compression using NNCF
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -306,7 +298,7 @@ compression.
 
 .. code:: ipython3
 
-    from IPython.display import display
+    from IPython.display import display, Markdown
     
     prepare_int4_model = widgets.Checkbox(
         value=True,
@@ -353,9 +345,6 @@ compression.
     import logging
     import openvino as ov
     import nncf
-    from optimum.intel.openvino import OVModelForCausalLM, OVWeightQuantizationConfig
-    import gc
-    
     
     nncf.set_log_level(logging.ERROR)
     
@@ -370,20 +359,22 @@ compression.
     def convert_to_fp16():
         if (fp16_model_dir / "openvino_model.xml").exists():
             return
-        ov_model = OVModelForCausalLM.from_pretrained(pt_model_id, export=True, compile=False, load_in_8bit=False)
-        ov_model.half()
-        ov_model.save_pretrained(fp16_model_dir)
-        del ov_model
-        gc.collect()
+        export_command_base = "optimum-cli export openvino --model {} --task text-generation-with-past --weight-format fp16".format(pt_model_id)
+        export_command = export_command_base + " " + str(fp16_model_dir)
+        display(Markdown("**Export command:**"))
+        display(Markdown(f"`{export_command}`"))
+        ! $export_command
     
     
     def convert_to_int8():
         if (int8_model_dir / "openvino_model.xml").exists():
             return
-        ov_model = OVModelForCausalLM.from_pretrained(pt_model_id, export=True, compile=False, load_in_8bit=True)
-        ov_model.save_pretrained(int8_model_dir)
-        del ov_model
-        gc.collect()
+        int8_model_dir.mkdir(parents=True, exist_ok=True)
+        export_command_base = "optimum-cli export openvino --model {} --task text-generation-with-past --weight-format int8".format(pt_model_id)
+        export_command = export_command_base + " " + str(int8_model_dir)
+        display(Markdown("**Export command:**"))
+        display(Markdown(f"`{export_command}`"))
+        ! $export_command
     
     
     def convert_to_int4():
@@ -410,15 +401,15 @@ compression.
         model_compression_params = compression_configs.get(model_id.value, compression_configs["default"])
         if (int4_model_dir / "openvino_model.xml").exists():
             return
-        ov_model = OVModelForCausalLM.from_pretrained(
-            pt_model_id,
-            export=True,
-            compile=False,
-            quantization_config=OVWeightQuantizationConfig(bits=4, **model_compression_params),
-        )
-        ov_model.save_pretrained(int4_model_dir)
-        del ov_model
-        gc.collect()
+        export_command_base = "optimum-cli export openvino --model {} --task text-generation-with-past --weight-format int4".format(pt_model_id)
+        int4_compression_args = " --group-size {} --ratio {}".format(model_compression_params["group_size"], model_compression_params["ratio"])
+        if model_compression_params["sym"]:
+            int4_compression_args += " --sym"
+        export_command_base += int4_compression_args
+        export_command = export_command_base + " " + str(int4_model_dir)
+        display(Markdown("**Export command:**"))
+        display(Markdown(f"`{export_command}`"))
+        ! $export_command
     
     
     if prepare_fp16_model.value:
@@ -431,71 +422,7 @@ compression.
 
 .. parsed-literal::
 
-    INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino
-
-
-.. parsed-literal::
-
-    2024-04-19 10:35:50.012050: I tensorflow/core/util/port.cc:111] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-04-19 10:35:50.025002: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
-    2024-04-19 10:35:50.060073: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
-    2024-04-19 10:35:50.060108: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
-    2024-04-19 10:35:50.060134: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
-    2024-04-19 10:35:50.068691: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
-    2024-04-19 10:35:50.069448: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
-    To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2024-04-19 10:35:51.045741: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
-    The installed version of bitsandbytes was compiled without GPU support. 8-bit optimizers, 8-bit multiplication, and GPU quantization are unavailable.
-    Framework not specified. Using pt to export the model.
-
-
-
-.. parsed-literal::
-
-    Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]
-
-
-.. parsed-literal::
-
-    Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
-    Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
-    Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
-    Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
-    Using framework PyTorch: 2.2.2+cpu
-    Overriding 1 configuration item(s)
-    	- use_cache -> True
-    /home/ea/miniconda3/lib/python3.11/site-packages/transformers/modeling_utils.py:4225: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
-      warnings.warn(
-    The cos_cached attribute will be removed in 4.39. Bear in mind that its contents changed in v4.38. Use the forward method of RoPE from now on instead. It is not used in the `LlamaAttention` class
-    The sin_cached attribute will be removed in 4.39. Bear in mind that its contents changed in v4.38. Use the forward method of RoPE from now on instead. It is not used in the `LlamaAttention` class
-    /home/ea/miniconda3/lib/python3.11/site-packages/optimum/exporters/openvino/model_patcher.py:311: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      if sequence_length != 1:
-
-
-
-.. parsed-literal::
-
-    Output()
-
-
-
-.. raw:: html
-
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
-
-
-
-
-.. raw:: html
-
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
-
-
-
-.. parsed-literal::
-
-    Configuration saved in llama-3-8b-instruct/INT4_compressed_weights/openvino_config.json
+    INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, onnx, openvino
 
 
 Let’s compare model size for different compression types
@@ -517,7 +444,11 @@ Let’s compare model size for different compression types
 
 .. parsed-literal::
 
-    Size of model with INT4 compressed weights is 4435.75 MB
+    Size of FP16 model is 5297.21 MB
+    Size of model with INT8 compressed weights is 2656.29 MB
+    Compression rate for INT8 model: 1.994
+    Size of model with INT4 compressed weights is 2154.54 MB
+    Compression rate for INT4 model: 2.459
 
 
 Select device for inference and model variant
@@ -550,7 +481,7 @@ Select device for inference and model variant
 
 .. parsed-literal::
 
-    Dropdown(description='Device:', options=('CPU', 'GPU.0', 'GPU.1', 'AUTO'), value='CPU')
+    Dropdown(description='Device:', options=('CPU', 'AUTO'), value='CPU')
 
 
 
@@ -578,13 +509,14 @@ Select device for inference and model variant
 
 .. parsed-literal::
 
-    Dropdown(description='Model to run:', options=('INT4',), value='INT4')
+    Dropdown(description='Model to run:', options=('INT4', 'INT8', 'FP16'), value='INT4')
 
 
 
 .. code:: ipython3
 
     from transformers import AutoTokenizer
+    from openvino_tokenizers import convert_tokenizer
     
     if model_to_run.value == "INT4":
         model_dir = int4_model_dir
@@ -594,27 +526,17 @@ Select device for inference and model variant
         model_dir = fp16_model_dir
     print(f"Loading model from {model_dir}")
     
-    model_name = model_configuration["model_id"]
-    ov_config = {"PERFORMANCE_HINT": "LATENCY", "NUM_STREAMS": "1", "CACHE_DIR": ""}
-    
-    tok = AutoTokenizer.from_pretrained(model_name)
-    
-    ov_model = OVModelForCausalLM.from_pretrained(
-        model_dir,
-        device=device.value,
-        ov_config=ov_config,
-    )
-
-
-.. parsed-literal::
-
-    Loading model from llama-3-8b-instruct/INT4_compressed_weights
+    # optionally convert tokenizer if used cached model without it
+    if not (model_dir / "openvino_tokenizer.xml").exists() or not (model_dir / "openvino_detokenizer.xml").exists():
+        hf_tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
+        ov_tokenizer, ov_detokenizer = convert_tokenizer(hf_tokenizer, with_detokenizer=True)
+        ov.save_model(ov_tokenizer, model_dir / "openvino_tokenizer.xml")
+        ov.save_model(ov_tokenizer, model_dir / "openvino_detokenizer.xml")
 
 
 .. parsed-literal::
 
-    Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
-    Compiling the model to CPU ...
+    Loading model from dolly-v2-3b/INT8_compressed_weights
 
 
 Create an instruction-following inference pipeline
@@ -626,11 +548,7 @@ The ``run_generation`` function accepts user-provided text input,
 tokenizes it, and runs the generation process. Text generation is an
 iterative process, where each next token depends on previously generated
 until a maximum number of tokens or stop generation condition is not
-reached. To obtain intermediate generation results without waiting until
-when generation is finished, we will use
-`TextIteratorStreamer <https://huggingface.co/docs/transformers/main/en/internal/generation_utils#transformers.TextIteratorStreamer>`__,
-provided as part of HuggingFace `Streaming
-API <https://huggingface.co/docs/transformers/main/en/generation_strategies#streaming>`__.
+reached.
 
 The diagram below illustrates how the instruction-following pipeline
 works
@@ -640,13 +558,61 @@ works
 
    generation pipeline)
 
-As can be seen, on the first iteration, the user provided instructions
-converted to token ids using a tokenizer, then prepared input provided
-to the model. The model generates probabilities for all tokens in logits
-format The way the next token will be selected over predicted
-probabilities is driven by the selected decoding methodology. You can
-find more information about the most popular decoding methods in this
-`blog <https://huggingface.co/blog/how-to-generate>`__.
+As can be seen, on the first iteration, the user provided instructions.
+Instructions is converted to token ids using a tokenizer, then prepared
+input provided to the model. The model generates probabilities for all
+tokens in logits format. The way the next token will be selected over
+predicted probabilities is driven by the selected decoding methodology.
+You can find more information about the most popular decoding methods in
+this `blog <https://huggingface.co/blog/how-to-generate>`__.
+
+To simplify user experience we will use `OpenVINO Generate
+API <https://github.com/openvinotoolkit/openvino.genai/blob/master/src/README.md>`__.
+Firstly we will create pipeline with ``LLMPipeline``. ``LLMPipeline`` is
+the main object used for decoding. You can construct it straight away
+from the folder with the converted model. It will automatically load the
+``main model``, ``tokenizer``, ``detokenizer`` and default
+``generation configuration``. After that we will configure parameters
+for decoding. We can get default config with
+``get_generation_config()``, setup parameters and apply the updated
+version with ``set_generation_config(config)`` or put config directly to
+``generate()``. It’s also possible to specify the needed options just as
+inputs in the ``generate()`` method, as shown below. Then we just run
+``generate`` method and get the output in text format. We do not need to
+encode input prompt according to model expected template or write
+post-processing code for logits decoder, it will be done easily with
+LLMPipeline.
+
+To obtain intermediate generation results without waiting until when
+generation is finished, we will write class-iterator based on
+``StreamerBase`` class of ``openvino_genai``.
+
+.. code:: ipython3
+
+    from openvino_genai import LLMPipeline
+    
+    pipe = LLMPipeline(model_dir.as_posix(), device.value)
+    print(pipe.generate("The Sun is yellow bacause", temperature=1.2, top_k=4, do_sample=True, max_new_tokens=150))
+
+
+.. parsed-literal::
+
+     of the presence of chlorophyll
+    in its leaves. Chlorophyll absorbs all
+    visible sunlight and this causes it to
+    turn from a green to yellow colour.
+    The Sun is yellow bacause of the presence of chlorophyll in its leaves. Chlorophyll absorbs all
+    visible sunlight and this causes it to
+    turn from a green to yellow colour.
+    The yellow colour of the Sun is the
+    colour we perceive as the colour of the
+    sun. It also causes us to perceive the
+    sun as yellow. This property is called
+    the yellow colouration of the Sun and it
+    is caused by the presence of chlorophyll
+    in the leaves of plants.
+    Chlorophyll is also responsible for the green colour of plants
+
 
 There are several parameters that can control text generation quality:
 
@@ -699,23 +665,6 @@ There are several parameters that can control text generation quality:
    if k=3, then only “playing”, “sleeping” and “eating” will be taken
    into account as possible next word.
 
-To optimize the generation process and use memory more efficiently, the
-``use_cache=True`` option is enabled. Since the output side is
-auto-regressive, an output token hidden state remains the same once
-computed for every further generation step. Therefore, recomputing it
-every time you want to generate a new token seems wasteful. With the
-cache, the model saves the hidden state once it has been computed. The
-model only computes the one for the most recently generated output token
-at each time step, re-using the saved ones for hidden tokens. This
-reduces the generation complexity from O(n^3) to O(n^2) for a
-transformer model. More details about how it works can be found in this
-`article <https://scale.com/blog/pytorch-improvements#Text%20Translation>`__.
-With this option, the model gets the previous step’s hidden states
-(cached attention keys and values) as input and additionally provides
-hidden states for the current step as output. It means for all next
-iterations, it is enough to provide only a new token obtained from the
-previous step and cached key values to get the next token prediction.
-
 The generation cycle repeats until the end of the sequence token is
 reached or it also can be interrupted when maximum tokens will be
 generated. As already mentioned before, we can enable printing current
@@ -734,72 +683,55 @@ Setup imports
     from time import perf_counter
     from typing import List
     import gradio as gr
-    from transformers import AutoTokenizer, TextIteratorStreamer
     import numpy as np
+    from openvino_genai import StreamerBase
+    from queue import Queue
+    import re
 
-Prepare template for user prompt
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Prepare text streamer to get results runtime
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 
 
-For effective generation, model expects to have input in specific
-format. The code below prepare template for passing user instruction
-into model with providing additional context.
+Load the ``detokenizer``, use it to convert token_id to string output
+format. We will collect print-ready text in a queue and give the text
+when it is needed. It will help estimate performance.
 
 .. code:: ipython3
 
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    tokenizer_kwargs = model_configuration.get("toeknizer_kwargs", {})
-    
-    
-    def get_special_token_id(tokenizer: AutoTokenizer, key: str) -> int:
-        """
-        Gets the token ID for a given string that has been added to the tokenizer as a special token.
+    core = ov.Core()
     
-        Args:
-            tokenizer (PreTrainedTokenizer): the tokenizer
-            key (str): the key to convert to a single token
+    detokinizer_dir = Path(model_dir, "openvino_detokenizer.xml")
     
-        Raises:
-            RuntimeError: if more than one ID was generated
     
-        Returns:
-            int: the token ID for the given key
-        """
-        token_ids = tokenizer.encode(key)
-        if len(token_ids) > 1:
-            raise ValueError(f"Expected only a single token for '{key}' but found {token_ids}")
-        return token_ids[0]
+    class TextIteratorStreamer(StreamerBase):
+        def __init__(self, tokenizer):
+            super().__init__()
+            self.tokenizer = tokenizer
+            self.compiled_detokenizer = core.compile_model(detokinizer_dir.as_posix())
+            self.text_queue = Queue()
+            self.stop_signal = None
     
+        def __iter__(self):
+            return self
     
-    response_key = model_configuration.get("response_key")
-    tokenizer_response_key = None
-    
-    if response_key is not None:
-        tokenizer_response_key = next(
-            (token for token in tokenizer.additional_special_tokens if token.startswith(response_key)),
-            None,
-        )
+        def __next__(self):
+            value = self.text_queue.get()
+            if value == self.stop_signal:
+                raise StopIteration()
+            else:
+                return value
     
-    end_key_token_id = None
-    if tokenizer_response_key:
-        try:
-            end_key = model_configuration.get("end_key")
-            if end_key:
-                end_key_token_id = get_special_token_id(tokenizer, end_key)
-            # Ensure generation stops once it generates "### End"
-        except ValueError:
-            pass
+        def put(self, token_id):
+            openvino_output = self.compiled_detokenizer([[0, token_id]])
+            text = str(openvino_output["string_output"][0])
+            # remove labels/special symbols
+            text = text.lstrip("!")
+            text = re.sub("<.*>", "", text)
+            self.text_queue.put(text)
     
-    prompt_template = model_configuration.get("prompt_template", "{instruction}")
-    end_key_token_id = end_key_token_id or tokenizer.eos_token_id
-    pad_token_id = end_key_token_id or tokenizer.pad_token_id
-
-
-.. parsed-literal::
-
-    Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
-
+        def end(self):
+            self.text_queue.put(self.stop_signal)
 
 Main generation function
 ~~~~~~~~~~~~~~~~~~~~~~~~
@@ -835,30 +767,21 @@ parameter and returns model response.
           perf_text (str) - updated perf text filed content
         """
     
-        # Prepare input prompt according to model expected template
-        prompt_text = prompt_template.format(instruction=user_text)
-    
-        # Tokenize the user text.
-        model_inputs = tokenizer(prompt_text, return_tensors="pt", **tokenizer_kwargs)
+        # setup config for decoding stage
+        config = pipe.get_generation_config()
+        config.temperature = temperature
+        if top_k > 0:
+            config.top_k = top_k
+        config.top_p = top_p
+        config.do_sample = True
+        config.max_new_tokens = max_new_tokens
     
         # Start generation on a separate thread, so that we don't block the UI. The text is pulled from the streamer
-        # in the main thread. Adds timeout to the streamer to handle exceptions in the generation thread.
-        streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
-        generate_kwargs = dict(
-            model_inputs,
-            streamer=streamer,
-            max_new_tokens=max_new_tokens,
-            do_sample=True,
-            top_p=top_p,
-            temperature=float(temperature),
-            top_k=top_k,
-            eos_token_id=end_key_token_id,
-            pad_token_id=pad_token_id,
-        )
-        t = Thread(target=ov_model.generate, kwargs=generate_kwargs)
+        # in the main thread.
+        streamer = TextIteratorStreamer(pipe.get_tokenizer())
+        t = Thread(target=pipe.generate, args=(user_text, config, streamer))
         t.start()
     
-        # Pull the generated text from the streamer, and update the model output.
         model_output = ""
         per_token_time = []
         num_tokens = 0
@@ -866,7 +789,7 @@ parameter and returns model response.
         for new_text in streamer:
             current_time = perf_counter() - start
             model_output += new_text
-            perf_text, num_tokens = estimate_latency(current_time, perf_text, new_text, per_token_time, num_tokens)
+            perf_text, num_tokens = estimate_latency(current_time, perf_text, per_token_time, num_tokens)
             yield model_output, perf_text
             start = perf_counter()
         return model_output, perf_text
@@ -885,7 +808,6 @@ elements.
     def estimate_latency(
         current_time: float,
         current_perf_text: str,
-        new_gen_text: str,
         per_token_time: List[float],
         num_tokens: int,
     ):
@@ -895,7 +817,6 @@ elements.
         Parameters:
           current_time (float): This step time in seconds.
           current_perf_text (str): Current content of performance UI field.
-          new_gen_text (str): New generated text.
           per_token_time (List[float]): history of performance from previous steps.
           num_tokens (int): Total number of generated tokens.
     
@@ -903,9 +824,8 @@ elements.
           update for performance text field
           update for a total number of tokens
         """
-        num_current_toks = len(tokenizer.encode(new_gen_text))
-        num_tokens += num_current_toks
-        per_token_time.append(num_current_toks / current_time)
+        num_tokens += 1
+        per_token_time.append(1 / current_time)
         if len(per_token_time) > 10 and len(per_token_time) % 4 == 0:
             current_bucket = per_token_time[:-10]
             return (
@@ -927,6 +847,7 @@ elements.
         Returns:
           empty string for each placeholder
         """
+    
         return "", "", ""
 
 Run instruction-following pipeline
@@ -1019,7 +940,6 @@ generation parameters:
                     interactive=True,
                     label="Temperature",
                 )
-    
         user_text.submit(
             run_generation,
             [user_text, top_p, temperature, top_k, max_new_tokens, performance],
@@ -1035,7 +955,6 @@ generation parameters:
             [user_text, model_output, performance],
             [user_text, model_output, performance],
         )
-    
     if __name__ == "__main__":
         demo.queue()
         try:
@@ -1046,17 +965,3 @@ generation parameters:
     # If you are launching remotely, specify server_name and server_port
     # EXAMPLE: `demo.launch(server_name='your server name', server_port='server port in int')`
     # To learn more please refer to the Gradio docs: https://gradio.app/docs/
-
-
-.. parsed-literal::
-
-    Running on local URL:  http://127.0.0.1:7860
-    
-    To create a public link, set `share=True` in `launch()`.
-
-
-
-
-
-
-
diff --git a/docs/notebooks/llm-rag-langchain-with-output.rst b/docs/notebooks/llm-rag-langchain-with-output.rst
index afbac3035baf28..16eea95595a3b5 100644
--- a/docs/notebooks/llm-rag-langchain-with-output.rst
+++ b/docs/notebooks/llm-rag-langchain-with-output.rst
@@ -37,8 +37,8 @@ with OpenVINO to optimize their inference performance.
 
    RAG
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Select model for inference <#select-model-for-inference>`__
@@ -49,6 +49,9 @@ Table of contents:
 
    -  `LLM conversion and Weights Compression using
       Optimum-CLI <#llm-conversion-and-weights-compression-using-optimum-cli>`__
+
+      -  `Weight compression with AWQ <#weight-compression-with-awq>`__
+
    -  `Convert embedding model using
       Optimum-CLI <#convert-embedding-model-using-optimum-cli>`__
    -  `Convert rerank model using
@@ -94,7 +97,7 @@ Install required dependencies
     "datasets"\
     "accelerate"\
     "gradio"\
-    "onnx" "einops" "transformers_stream_generator" "tiktoken" "transformers>=4.38.1" "bitsandbytes" "faiss-cpu" "sentence_transformers" "langchain>=0.2.0" "langchain-community>=0.2.0" "langchainhub" "unstructured" "scikit-learn" "python-docx" "pypdf" 
+    "onnx" "einops" "transformers_stream_generator" "tiktoken" "transformers>=4.40" "bitsandbytes" "faiss-cpu" "sentence_transformers" "langchain>=0.2.0" "langchain-community>=0.2.0" "langchainhub" "unstructured" "scikit-learn" "python-docx" "pypdf" 
 
 
 .. parsed-literal::
@@ -129,7 +132,7 @@ Install required dependencies
                 shutil.copy(config_shared_path, config_dst_path)
         else:
             r = requests.get(url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/llm_config.py")
-            with open("llm_config.py", "w") as f:
+            with open("llm_config.py", "w", encoding="utf-8") as f:
                 f.write(r.text)
     elif not os.path.islink(config_dst_path):
         print("LLM config will be updated")
@@ -137,7 +140,7 @@ Install required dependencies
             shutil.copy(config_shared_path, config_dst_path)
         else:
             r = requests.get(url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/llm_config.py")
-            with open("llm_config.py", "w") as f:
+            with open("llm_config.py", "w", encoding="utf-8") as f:
                 f.write(r.text)
     
     
@@ -153,6 +156,12 @@ Install required dependencies
         with open("text_example_cn.pdf", "wb") as f:
             f.write(content.read())
 
+
+.. parsed-literal::
+
+    LLM config will be updated
+
+
 Select model for inference
 --------------------------
 
@@ -259,7 +268,7 @@ quality.
 
 .. parsed-literal::
 
-    Dropdown(description='Model:', index=9, options=('tiny-llama-1b-chat', 'gemma-2b-it', 'red-pajama-3b-chat', 'g…
+    Dropdown(description='Model:', index=4, options=('qwen1.5-7b-chat', 'qwen-7b-chat', 'chatglm3-6b', 'baichuan2-…
 
 
 
@@ -271,7 +280,7 @@ quality.
 
 .. parsed-literal::
 
-    Selected LLM model neural-chat-7b-v3-1
+    Selected LLM model qwen1.5-1.8b-chat
 
 
 `Optimum Intel <https://huggingface.co/docs/optimum/intel/index>`__ is
@@ -372,6 +381,39 @@ sacrifice of the model size and inference latency.
     Checkbox(value=False, description='Prepare FP16 model')
 
 
+Weight compression with AWQ
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+
+
+`Activation-aware Weight
+Quantization <https://arxiv.org/abs/2306.00978>`__ (AWQ) is an algorithm
+that tunes model weights for more accurate INT4 compression. It slightly
+improves generation quality of compressed LLMs, but requires significant
+additional time for tuning weights on a calibration dataset. We use
+``wikitext-2-raw-v1/train`` subset of the
+`Wikitext <https://huggingface.co/datasets/Salesforce/wikitext>`__
+dataset for calibration.
+
+Below you can enable AWQ to be additionally applied during model export
+with INT4 precision.
+
+   **Note**: Applying AWQ requires significant memory and time.
+
+..
+
+   **Note**: It is possible that there will be no matching patterns in
+   the model to apply AWQ, in such case it will be skipped.
+
+.. code:: ipython3
+
+    enable_awq = widgets.Checkbox(
+        value=False,
+        description="Enable AWQ",
+        disabled=not prepare_int4_model.value,
+    )
+    display(enable_awq)
+
 .. code:: ipython3
 
     pt_model_id = llm_model_configuration["model_id"]
@@ -481,6 +523,8 @@ sacrifice of the model size and inference latency.
         int4_compression_args = " --group-size {} --ratio {}".format(model_compression_params["group_size"], model_compression_params["ratio"])
         if model_compression_params["sym"]:
             int4_compression_args += " --sym"
+        if enable_awq.value:
+            int4_compression_args += " --awq --dataset wikitext2 --num-samples 128"
         export_command_base += int4_compression_args
         if remote_code:
             export_command_base += " --trust-remote-code"
@@ -516,7 +560,7 @@ Let’s compare model size for different compression types
 
 .. parsed-literal::
 
-    Size of model with INT4 compressed weights is 5069.90 MB
+    Size of model with INT4 compressed weights is 1343.75 MB
 
 
 Convert embedding model using Optimum-CLI
@@ -545,7 +589,7 @@ filter them out according the LLM you selected.
 
 .. parsed-literal::
 
-    Dropdown(description='Embedding Model:', options=('bge-small-en-v1.5', 'bge-large-en-v1.5'), value='bge-small-…
+    Dropdown(description='Embedding Model:', options=('bge-small-zh-v1.5', 'bge-large-zh-v1.5'), value='bge-small-…
 
 
 
@@ -557,7 +601,7 @@ filter them out according the LLM you selected.
 
 .. parsed-literal::
 
-    Selected bge-small-en-v1.5 model
+    Selected bge-small-zh-v1.5 model
 
 
 OpenVINO embedding model and tokenizer can be exported by
@@ -639,8 +683,6 @@ Select device for embedding model inference
     core = ov.Core()
     
     support_devices = core.available_devices
-    if "NPU" in support_devices:
-        support_devices.remove("NPU")
     
     embedding_device = widgets.Dropdown(
         options=support_devices + ["AUTO"],
@@ -670,6 +712,26 @@ Select device for embedding model inference
     Embedding model will be loaded to CPU device for text embedding
 
 
+Optimize the BGE embedding model’s parameter precision when loading
+model to NPU device.
+
+.. code:: ipython3
+
+    USING_NPU = embedding_device.value == "NPU"
+    
+    npu_embedding_dir = embedding_model_id.value + "-npu"
+    npu_embedding_path = Path(npu_embedding_dir) / "openvino_model.xml"
+    if USING_NPU and not Path(npu_embedding_dir).exists():
+        r = requests.get(
+            url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
+        )
+        with open("notebook_utils.py", "w") as f:
+            f.write(r.text)
+        import notebook_utils as utils
+    
+        shutil.copytree(embedding_model_id.value, npu_embedding_dir)
+        utils.optimize_bge_embedding(Path(embedding_model_id.value) / "openvino_model.xml", npu_embedding_path)
+
 Select device for rerank model inference
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -760,11 +822,13 @@ of LangChain.
 
     from langchain_community.embeddings import OpenVINOBgeEmbeddings
     
-    embedding_model_name = embedding_model_id.value
-    embedding_model_kwargs = {"device": embedding_device.value}
+    embedding_model_name = npu_embedding_dir if USING_NPU else embedding_model_id.value
+    batch_size = 1 if USING_NPU else 4
+    embedding_model_kwargs = {"device": embedding_device.value, "compile": False}
     encode_kwargs = {
         "mean_pooling": embedding_model_configuration["mean_pooling"],
         "normalize_embeddings": embedding_model_configuration["normalize_embeddings"],
+        "batch_size": batch_size,
     }
     
     embedding = OpenVINOBgeEmbeddings(
@@ -772,6 +836,9 @@ of LangChain.
         model_kwargs=embedding_model_kwargs,
         encode_kwargs=encode_kwargs,
     )
+    if USING_NPU:
+        embedding.ov_model.reshape(1, 512)
+    embedding.ov_model.compile()
     
     text = "This is a test document."
     embedding_result = embedding.embed_query(text)
@@ -894,6 +961,9 @@ inference framework.
     
     ov_config = {"PERFORMANCE_HINT": "LATENCY", "NUM_STREAMS": "1", "CACHE_DIR": ""}
     
+    if "GPU" in llm_device.value and "qwen2-7b-instruct" in llm_model_id.value:
+        ov_config["GPU_ENABLE_SDPA_OPTIMIZATION"] = "NO"
+    
     # On a GPU device a model is executed in FP16 precision. For red-pajama-3b-chat model there known accuracy
     # issues caused by this, which we avoid by setting precision hint to "f32".
     if llm_model_id.value == "red-pajama-3b-chat" and "GPU" in core.available_devices and llm_device.value in ["GPU", "AUTO"]:
diff --git a/docs/notebooks/llm-rag-llamaindex-with-output.rst b/docs/notebooks/llm-rag-llamaindex-with-output.rst
new file mode 100644
index 00000000000000..0d08dca77b0448
--- /dev/null
+++ b/docs/notebooks/llm-rag-llamaindex-with-output.rst
@@ -0,0 +1,1052 @@
+Create a RAG system using OpenVINO and LlamaIndex
+=================================================
+
+**Retrieval-augmented generation (RAG)** is a technique for augmenting
+LLM knowledge with additional, often private or real-time, data. LLMs
+can reason about wide-ranging topics, but their knowledge is limited to
+the public data up to a specific point in time that they were trained
+on. If you want to build AI applications that can reason about private
+data or data introduced after a model’s cutoff date, you need to augment
+the knowledge of the model with the specific information it needs. The
+process of bringing the appropriate information and inserting it into
+the model prompt is known as Retrieval Augmented Generation (RAG).
+
+`LlamaIndex <https://docs.llamaindex.ai/en/stable/>`__ is a framework
+for building context-augmented generative AI applications with
+LLMs.LlamaIndex imposes no restriction on how you use LLMs. You can use
+LLMs as auto-complete, chatbots, semi-autonomous agents, and more. It
+just makes using them easier.
+
+The tutorial consists of the following steps:
+
+-  Install prerequisites
+-  Download and convert the model from a public source using the
+   `OpenVINO integration with Hugging Face
+   Optimum <https://huggingface.co/blog/openvino>`__.
+-  Compress model weights to 4-bit or 8-bit data types using
+   `NNCF <https://github.com/openvinotoolkit/nncf>`__
+-  Create a RAG chain pipeline
+-  Run Q&A pipeline
+
+In this example, the customized RAG pipeline consists of following
+components in order, where embedding, rerank and LLM will be deployed
+with OpenVINO to optimize their inference performance.
+
+.. figure:: https://github.com/openvinotoolkit/openvino_notebooks/assets/91237924/0076f6c7-75e4-4c2e-9015-87b355e5ca28
+   :alt: RAG
+
+   RAG
+
+**Table of contents:**
+
+
+-  `Prerequisites <#prerequisites>`__
+-  `Select model for inference <#select-model-for-inference>`__
+-  `login to huggingfacehub to get access to pretrained
+   model <#login-to-huggingfacehub-to-get-access-to-pretrained-model>`__
+-  `Convert model and compress model
+   weights <#convert-model-and-compress-model-weights>`__
+
+   -  `LLM conversion and Weights Compression using
+      Optimum-CLI <#llm-conversion-and-weights-compression-using-optimum-cli>`__
+
+      -  `Weight compression with AWQ <#weight-compression-with-awq>`__
+
+   -  `Convert embedding model using
+      Optimum-CLI <#convert-embedding-model-using-optimum-cli>`__
+   -  `Convert rerank model using
+      Optimum-CLI <#convert-rerank-model-using-optimum-cli>`__
+
+-  `Select device for inference and model
+   variant <#select-device-for-inference-and-model-variant>`__
+
+   -  `Select device for embedding model
+      inference <#select-device-for-embedding-model-inference>`__
+   -  `Select device for rerank model
+      inference <#select-device-for-rerank-model-inference>`__
+   -  `Select device for LLM model
+      inference <#select-device-for-llm-model-inference>`__
+
+-  `Load model <#load-model>`__
+
+   -  `Load embedding model <#load-embedding-model>`__
+   -  `Load rerank model <#load-rerank-model>`__
+   -  `Load LLM model <#load-llm-model>`__
+
+-  `Run QA over Document <#run-qa-over-document>`__
+
+Prerequisites
+-------------
+
+
+
+Install required dependencies
+
+.. code:: ipython3
+
+    import os
+    
+    os.environ["GIT_CLONE_PROTECTION_ACTIVE"] = "false"
+    
+    %pip install -Uq pip
+    %pip uninstall -q -y optimum optimum-intel
+    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu\
+    "llama-index" "pymupdf" "llama-index-readers-file" "llama-index-vector-stores-faiss" "llama-index-llms-openvino" "llama-index-embeddings-openvino" "llama-index-postprocessor-openvino-rerank" "transformers>=4.40"\
+    "git+https://github.com/huggingface/optimum-intel.git"\
+    "git+https://github.com/openvinotoolkit/nncf.git"\
+    "datasets"\
+    "accelerate"\
+    "gradio"
+    %pip install --pre -Uq openvino openvino-tokenizers[transformers] --extra-index-url https://storage.openvinotoolkit.org/simple/wheels/nightly
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+    WARNING: Skipping optimum as it is not installed.
+    WARNING: Skipping optimum-intel as it is not installed.
+    Note: you may need to restart the kernel to use updated packages.
+    ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
+    langchain-huggingface 0.0.1 requires huggingface-hub>=0.23.0, but you have huggingface-hub 0.20.3 which is incompatible.
+    Note: you may need to restart the kernel to use updated packages.
+    Note: you may need to restart the kernel to use updated packages.
+
+
+.. code:: ipython3
+
+    import os
+    from pathlib import Path
+    import requests
+    import shutil
+    import io
+    
+    # fetch model configuration
+    
+    config_shared_path = Path("../../utils/llm_config.py")
+    config_dst_path = Path("llm_config.py")
+    text_example_en_path = Path("text_example_en.pdf")
+    text_example_cn_path = Path("text_example_cn.pdf")
+    text_example_en = "https://github.com/openvinotoolkit/openvino_notebooks/files/15039728/Platform.Brief_Intel.vPro.with.Intel.Core.Ultra_Final.pdf"
+    text_example_cn = "https://github.com/openvinotoolkit/openvino_notebooks/files/15039713/Platform.Brief_Intel.vPro.with.Intel.Core.Ultra_Final_CH.pdf"
+    
+    if not config_dst_path.exists():
+        if config_shared_path.exists():
+            try:
+                os.symlink(config_shared_path, config_dst_path)
+            except Exception:
+                shutil.copy(config_shared_path, config_dst_path)
+        else:
+            r = requests.get(url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/llm_config.py")
+            with open("llm_config.py", "w", encoding="utf-8") as f:
+                f.write(r.text)
+    elif not os.path.islink(config_dst_path):
+        print("LLM config will be updated")
+        if config_shared_path.exists():
+            shutil.copy(config_shared_path, config_dst_path)
+        else:
+            r = requests.get(url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/llm_config.py")
+            with open("llm_config.py", "w", encoding="utf-8") as f:
+                f.write(r.text)
+    
+    
+    if not text_example_en_path.exists():
+        r = requests.get(url=text_example_en)
+        content = io.BytesIO(r.content)
+        with open("text_example_en.pdf", "wb") as f:
+            f.write(content.read())
+    
+    if not text_example_cn_path.exists():
+        r = requests.get(url=text_example_cn)
+        content = io.BytesIO(r.content)
+        with open("text_example_cn.pdf", "wb") as f:
+            f.write(content.read())
+
+
+.. parsed-literal::
+
+    LLM config will be updated
+
+
+Select model for inference
+--------------------------
+
+
+
+The tutorial supports different models, you can select one from the
+provided options to compare the quality of open source LLM solutions.
+
+   **Note**: conversion of some models can require additional actions
+   from user side and at least 64GB RAM for conversion.
+
+The available embedding model options are:
+
+-  `bge-small-en-v1.5 <https://huggingface.co/BAAI/bge-small-en-v1.5>`__
+-  `bge-small-zh-v1.5 <https://huggingface.co/BAAI/bge-small-zh-v1.5>`__
+-  `bge-large-en-v1.5 <https://huggingface.co/BAAI/bge-large-en-v1.5>`__
+-  `bge-large-zh-v1.5 <https://huggingface.co/BAAI/bge-large-zh-v1.5>`__
+
+BGE embedding is a general Embedding Model. The model is pre-trained
+using RetroMAE and trained on large-scale pair data using contrastive
+learning.
+
+The available rerank model options are:
+
+-  `bge-reranker-large <https://huggingface.co/BAAI/bge-reranker-large>`__
+-  `bge-reranker-base <https://huggingface.co/BAAI/bge-reranker-base>`__
+
+Reranker model with cross-encoder will perform full-attention over the
+input pair, which is more accurate than embedding model (i.e.,
+bi-encoder) but more time-consuming than embedding model. Therefore, it
+can be used to re-rank the top-k documents returned by embedding model.
+
+You can also find available LLM model options in
+`llm-chatbot <../llm-chatbot/README.md>`__ notebook.
+
+.. code:: ipython3
+
+    from pathlib import Path
+    import openvino as ov
+    import ipywidgets as widgets
+
+Convert model and compress model weights
+----------------------------------------
+
+
+
+The Weights Compression algorithm is aimed at compressing the weights of
+the models and can be used to optimize the model footprint and
+performance of large models where the size of weights is relatively
+larger than the size of activations, for example, Large Language Models
+(LLM). Compared to INT8 compression, INT4 compression improves
+performance even more, but introduces a minor drop in prediction
+quality.
+
+.. code:: ipython3
+
+    from llm_config import (
+        SUPPORTED_EMBEDDING_MODELS,
+        SUPPORTED_RERANK_MODELS,
+        SUPPORTED_LLM_MODELS,
+    )
+    
+    model_languages = list(SUPPORTED_LLM_MODELS)
+    
+    model_language = widgets.Dropdown(
+        options=model_languages,
+        value=model_languages[0],
+        description="Model Language:",
+        disabled=False,
+    )
+    
+    model_language
+
+
+
+
+.. parsed-literal::
+
+    Dropdown(description='Model Language:', options=('English', 'Chinese', 'Japanese'), value='English')
+
+
+
+.. code:: ipython3
+
+    llm_model_ids = [model_id for model_id, model_config in SUPPORTED_LLM_MODELS[model_language.value].items() if model_config.get("rag_prompt_template")]
+    
+    llm_model_id = widgets.Dropdown(
+        options=llm_model_ids,
+        value=llm_model_ids[-1],
+        description="Model:",
+        disabled=False,
+    )
+    
+    llm_model_id
+
+
+
+
+.. parsed-literal::
+
+    Dropdown(description='Model:', index=12, options=('tiny-llama-1b-chat', 'gemma-2b-it', 'red-pajama-3b-chat', '…
+
+
+
+.. code:: ipython3
+
+    llm_model_configuration = SUPPORTED_LLM_MODELS[model_language.value][llm_model_id.value]
+    print(f"Selected LLM model {llm_model_id.value}")
+
+
+.. parsed-literal::
+
+    Selected LLM model neural-chat-7b-v3-1
+
+
+`Optimum Intel <https://huggingface.co/docs/optimum/intel/index>`__ is
+the interface between the 
+`Transformers <https://huggingface.co/docs/transformers/index>`__ and
+`Diffusers <https://huggingface.co/docs/diffusers/index>`__ libraries
+and OpenVINO to accelerate end-to-end pipelines on Intel architectures.
+It provides ease-to-use cli interface for exporting models to `OpenVINO
+Intermediate Representation
+(IR) <https://docs.openvino.ai/2024/documentation/openvino-ir-format.html>`__
+format.
+
+The command bellow demonstrates basic command for model export with
+``optimum-cli``
+
+::
+
+   optimum-cli export openvino --model <model_id_or_path> --task <task> <out_dir>
+
+where ``--model`` argument is model id from HuggingFace Hub or local
+directory with model (saved using ``.save_pretrained`` method),
+``--task`` is one of `supported
+task <https://huggingface.co/docs/optimum/exporters/task_manager>`__
+that exported model should solve. For LLMs it will be
+``text-generation-with-past``. If model initialization requires to use
+remote code, ``--trust-remote-code`` flag additionally should be passed.
+
+LLM conversion and Weights Compression using Optimum-CLI
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+You can also apply fp16, 8-bit or 4-bit weight compression on the
+Linear, Convolutional and Embedding layers when exporting your model
+with the CLI by setting ``--weight-format`` to respectively fp16, int8
+or int4. This type of optimization allows to reduce the memory footprint
+and inference latency. By default the quantization scheme for int8/int4
+will be
+`asymmetric <https://github.com/openvinotoolkit/nncf/blob/develop/docs/compression_algorithms/Quantization.md#asymmetric-quantization>`__,
+to make it
+`symmetric <https://github.com/openvinotoolkit/nncf/blob/develop/docs/compression_algorithms/Quantization.md#symmetric-quantization>`__
+you can add ``--sym``.
+
+For INT4 quantization you can also specify the following arguments :
+
+-  The ``--group-size`` parameter will define the group size to use for
+   quantization, -1 it will results in per-column quantization.
+-  The ``--ratio`` parameter controls the ratio between 4-bit and 8-bit
+   quantization. If set to 0.9, it means that 90% of the layers will be
+   quantized to int4 while 10% will be quantized to int8.
+
+Smaller group_size and ratio values usually improve accuracy at the
+sacrifice of the model size and inference latency.
+
+   **Note**: There may be no speedup for INT4/INT8 compressed models on
+   dGPU.
+
+.. code:: ipython3
+
+    from IPython.display import Markdown, display
+    
+    prepare_int4_model = widgets.Checkbox(
+        value=True,
+        description="Prepare INT4 model",
+        disabled=False,
+    )
+    prepare_int8_model = widgets.Checkbox(
+        value=False,
+        description="Prepare INT8 model",
+        disabled=False,
+    )
+    prepare_fp16_model = widgets.Checkbox(
+        value=False,
+        description="Prepare FP16 model",
+        disabled=False,
+    )
+    
+    display(prepare_int4_model)
+    display(prepare_int8_model)
+    display(prepare_fp16_model)
+
+
+
+.. parsed-literal::
+
+    Checkbox(value=True, description='Prepare INT4 model')
+
+
+
+.. parsed-literal::
+
+    Checkbox(value=False, description='Prepare INT8 model')
+
+
+
+.. parsed-literal::
+
+    Checkbox(value=False, description='Prepare FP16 model')
+
+
+Weight compression with AWQ
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+
+
+`Activation-aware Weight
+Quantization <https://arxiv.org/abs/2306.00978>`__ (AWQ) is an algorithm
+that tunes model weights for more accurate INT4 compression. It slightly
+improves generation quality of compressed LLMs, but requires significant
+additional time for tuning weights on a calibration dataset. We use
+``wikitext-2-raw-v1/train`` subset of the
+`Wikitext <https://huggingface.co/datasets/Salesforce/wikitext>`__
+dataset for calibration.
+
+Below you can enable AWQ to be additionally applied during model export
+with INT4 precision.
+
+   **Note**: Applying AWQ requires significant memory and time.
+
+..
+
+   **Note**: It is possible that there will be no matching patterns in
+   the model to apply AWQ, in such case it will be skipped.
+
+.. code:: ipython3
+
+    enable_awq = widgets.Checkbox(
+        value=False,
+        description="Enable AWQ",
+        disabled=not prepare_int4_model.value,
+    )
+    display(enable_awq)
+
+
+
+.. parsed-literal::
+
+    Checkbox(value=False, description='Enable AWQ')
+
+
+.. code:: ipython3
+
+    pt_model_id = llm_model_configuration["model_id"]
+    pt_model_name = llm_model_id.value.split("-")[0]
+    fp16_model_dir = Path(llm_model_id.value) / "FP16"
+    int8_model_dir = Path(llm_model_id.value) / "INT8_compressed_weights"
+    int4_model_dir = Path(llm_model_id.value) / "INT4_compressed_weights"
+    
+    
+    def convert_to_fp16():
+        if (fp16_model_dir / "openvino_model.xml").exists():
+            return
+        remote_code = llm_model_configuration.get("remote_code", False)
+        export_command_base = "optimum-cli export openvino --model {} --task text-generation-with-past --weight-format fp16".format(pt_model_id)
+        if remote_code:
+            export_command_base += " --trust-remote-code"
+        export_command = export_command_base + " " + str(fp16_model_dir)
+        display(Markdown("**Export command:**"))
+        display(Markdown(f"`{export_command}`"))
+        ! $export_command
+    
+    
+    def convert_to_int8():
+        if (int8_model_dir / "openvino_model.xml").exists():
+            return
+        int8_model_dir.mkdir(parents=True, exist_ok=True)
+        remote_code = llm_model_configuration.get("remote_code", False)
+        export_command_base = "optimum-cli export openvino --model {} --task text-generation-with-past --weight-format int8".format(pt_model_id)
+        if remote_code:
+            export_command_base += " --trust-remote-code"
+        export_command = export_command_base + " " + str(int8_model_dir)
+        display(Markdown("**Export command:**"))
+        display(Markdown(f"`{export_command}`"))
+        ! $export_command
+    
+    
+    def convert_to_int4():
+        compression_configs = {
+            "zephyr-7b-beta": {
+                "sym": True,
+                "group_size": 64,
+                "ratio": 0.6,
+            },
+            "mistral-7b": {
+                "sym": True,
+                "group_size": 64,
+                "ratio": 0.6,
+            },
+            "minicpm-2b-dpo": {
+                "sym": True,
+                "group_size": 64,
+                "ratio": 0.6,
+            },
+            "gemma-2b-it": {
+                "sym": True,
+                "group_size": 64,
+                "ratio": 0.6,
+            },
+            "notus-7b-v1": {
+                "sym": True,
+                "group_size": 64,
+                "ratio": 0.6,
+            },
+            "neural-chat-7b-v3-1": {
+                "sym": True,
+                "group_size": 64,
+                "ratio": 0.6,
+            },
+            "llama-2-chat-7b": {
+                "sym": True,
+                "group_size": 128,
+                "ratio": 0.8,
+            },
+            "llama-3-8b-instruct": {
+                "sym": True,
+                "group_size": 128,
+                "ratio": 0.8,
+            },
+            "gemma-7b-it": {
+                "sym": True,
+                "group_size": 128,
+                "ratio": 0.8,
+            },
+            "chatglm2-6b": {
+                "sym": True,
+                "group_size": 128,
+                "ratio": 0.72,
+            },
+            "qwen-7b-chat": {"sym": True, "group_size": 128, "ratio": 0.6},
+            "red-pajama-3b-chat": {
+                "sym": False,
+                "group_size": 128,
+                "ratio": 0.5,
+            },
+            "default": {
+                "sym": False,
+                "group_size": 128,
+                "ratio": 0.8,
+            },
+        }
+    
+        model_compression_params = compression_configs.get(llm_model_id.value, compression_configs["default"])
+        if (int4_model_dir / "openvino_model.xml").exists():
+            return
+        remote_code = llm_model_configuration.get("remote_code", False)
+        export_command_base = "optimum-cli export openvino --model {} --task text-generation-with-past --weight-format int4".format(pt_model_id)
+        int4_compression_args = " --group-size {} --ratio {}".format(model_compression_params["group_size"], model_compression_params["ratio"])
+        if model_compression_params["sym"]:
+            int4_compression_args += " --sym"
+        if enable_awq.value:
+            int4_compression_args += " --awq --dataset wikitext2 --num-samples 128"
+        export_command_base += int4_compression_args
+        if remote_code:
+            export_command_base += " --trust-remote-code"
+        export_command = export_command_base + " " + str(int4_model_dir)
+        display(Markdown("**Export command:**"))
+        display(Markdown(f"`{export_command}`"))
+        ! $export_command
+    
+    
+    if prepare_fp16_model.value:
+        convert_to_fp16()
+    if prepare_int8_model.value:
+        convert_to_int8()
+    if prepare_int4_model.value:
+        convert_to_int4()
+
+Let’s compare model size for different compression types
+
+.. code:: ipython3
+
+    fp16_weights = fp16_model_dir / "openvino_model.bin"
+    int8_weights = int8_model_dir / "openvino_model.bin"
+    int4_weights = int4_model_dir / "openvino_model.bin"
+    
+    if fp16_weights.exists():
+        print(f"Size of FP16 model is {fp16_weights.stat().st_size / 1024 / 1024:.2f} MB")
+    for precision, compressed_weights in zip([8, 4], [int8_weights, int4_weights]):
+        if compressed_weights.exists():
+            print(f"Size of model with INT{precision} compressed weights is {compressed_weights.stat().st_size / 1024 / 1024:.2f} MB")
+        if compressed_weights.exists() and fp16_weights.exists():
+            print(f"Compression rate for INT{precision} model: {fp16_weights.stat().st_size / compressed_weights.stat().st_size:.3f}")
+
+
+.. parsed-literal::
+
+    Size of model with INT4 compressed weights is 5069.90 MB
+
+
+Convert embedding model using Optimum-CLI
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+Since some embedding models can only support limited languages, we can
+filter them out according the LLM you selected.
+
+.. code:: ipython3
+
+    embedding_model_id = list(SUPPORTED_EMBEDDING_MODELS[model_language.value])
+    
+    embedding_model_id = widgets.Dropdown(
+        options=embedding_model_id,
+        value=embedding_model_id[0],
+        description="Embedding Model:",
+        disabled=False,
+    )
+    
+    embedding_model_id
+
+
+
+
+.. parsed-literal::
+
+    Dropdown(description='Embedding Model:', options=('bge-small-en-v1.5', 'bge-large-en-v1.5'), value='bge-small-…
+
+
+
+.. code:: ipython3
+
+    embedding_model_configuration = SUPPORTED_EMBEDDING_MODELS[model_language.value][embedding_model_id.value]
+    print(f"Selected {embedding_model_id.value} model")
+
+
+.. parsed-literal::
+
+    Selected bge-small-en-v1.5 model
+
+
+OpenVINO embedding model and tokenizer can be exported by
+``feature-extraction`` task with ``optimum-cli``.
+
+.. code:: ipython3
+
+    export_command_base = "optimum-cli export openvino --model {} --task feature-extraction".format(embedding_model_configuration["model_id"])
+    export_command = export_command_base + " " + str(embedding_model_id.value)
+    
+    if not Path(embedding_model_id.value).exists():
+        ! $export_command
+
+Convert rerank model using Optimum-CLI
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    rerank_model_id = list(SUPPORTED_RERANK_MODELS)
+    
+    rerank_model_id = widgets.Dropdown(
+        options=rerank_model_id,
+        value=rerank_model_id[0],
+        description="Rerank Model:",
+        disabled=False,
+    )
+    
+    rerank_model_id
+
+
+
+
+.. parsed-literal::
+
+    Dropdown(description='Rerank Model:', options=('bge-reranker-large', 'bge-reranker-base'), value='bge-reranker…
+
+
+
+.. code:: ipython3
+
+    rerank_model_configuration = SUPPORTED_RERANK_MODELS[rerank_model_id.value]
+    print(f"Selected {rerank_model_id.value} model")
+
+
+.. parsed-literal::
+
+    Selected bge-reranker-large model
+
+
+Since ``rerank`` model is sort of sentence classification task, its
+OpenVINO IR and tokenizer can be exported by ``text-classification``
+task with ``optimum-cli``.
+
+.. code:: ipython3
+
+    export_command_base = "optimum-cli export openvino --model {} --task text-classification".format(rerank_model_configuration["model_id"])
+    export_command = export_command_base + " " + str(rerank_model_id.value)
+    
+    if not Path(rerank_model_id.value).exists():
+        ! $export_command
+
+Select device for inference and model variant
+---------------------------------------------
+
+
+
+   **Note**: There may be no speedup for INT4/INT8 compressed models on
+   dGPU.
+
+Select device for embedding model inference
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    core = ov.Core()
+    
+    support_devices = core.available_devices
+    
+    embedding_device = widgets.Dropdown(
+        options=support_devices + ["AUTO"],
+        value="CPU",
+        description="Device:",
+        disabled=False,
+    )
+    
+    embedding_device
+
+
+
+
+.. parsed-literal::
+
+    Dropdown(description='Device:', options=('CPU', 'GPU', 'AUTO'), value='CPU')
+
+
+
+.. code:: ipython3
+
+    print(f"Embedding model will be loaded to {embedding_device.value} device for text embedding")
+
+
+.. parsed-literal::
+
+    Embedding model will be loaded to CPU device for text embedding
+
+
+Select device for rerank model inference
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    rerank_device = widgets.Dropdown(
+        options=support_devices + ["AUTO"],
+        value="CPU",
+        description="Device:",
+        disabled=False,
+    )
+    
+    rerank_device
+
+
+
+
+.. parsed-literal::
+
+    Dropdown(description='Device:', options=('CPU', 'GPU', 'AUTO'), value='CPU')
+
+
+
+.. code:: ipython3
+
+    print(f"Rerenk model will be loaded to {rerank_device.value} device for text reranking")
+
+
+.. parsed-literal::
+
+    Rerenk model will be loaded to CPU device for text reranking
+
+
+Select device for LLM model inference
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    llm_device = widgets.Dropdown(
+        options=support_devices + ["AUTO"],
+        value="CPU",
+        description="Device:",
+        disabled=False,
+    )
+    
+    llm_device
+
+
+
+
+.. parsed-literal::
+
+    Dropdown(description='Device:', options=('CPU', 'GPU', 'AUTO'), value='CPU')
+
+
+
+.. code:: ipython3
+
+    print(f"LLM model will be loaded to {llm_device.value} device for response generation")
+
+
+.. parsed-literal::
+
+    LLM model will be loaded to CPU device for response generation
+
+
+Load models
+-----------
+
+
+
+Load embedding model
+~~~~~~~~~~~~~~~~~~~~
+
+
+
+Now a Hugging Face embedding model can be supported by OpenVINO through
+`OpenVINOEmbeddings <https://docs.llamaindex.ai/en/stable/examples/embeddings/openvino/>`__
+class of LlamaIndex.
+
+.. code:: ipython3
+
+    from llama_index.embeddings.huggingface_openvino import OpenVINOEmbedding
+    
+    
+    embedding = OpenVINOEmbedding(folder_name=embedding_model_id.value, device=embedding_device.value)
+    
+    embeddings = embedding.get_text_embedding("Hello World!")
+    print(len(embeddings))
+    print(embeddings[:5])
+
+
+.. parsed-literal::
+
+    Compiling the model to CPU ...
+
+
+.. parsed-literal::
+
+    384
+    [-0.003275666618719697, -0.01169075071811676, 0.04155930131673813, -0.03814813867211342, 0.02418304793536663]
+
+
+Load rerank model
+~~~~~~~~~~~~~~~~~
+
+
+
+Now a Hugging Face embedding model can be supported by OpenVINO through
+`OpenVINORerank <https://docs.llamaindex.ai/en/stable/examples/node_postprocessor/openvino_rerank/>`__
+class of LlamaIndex.
+
+   **Note**: Rerank can be skipped in RAG.
+
+.. code:: ipython3
+
+    from llama_index.postprocessor.openvino_rerank import OpenVINORerank
+    
+    reranker = OpenVINORerank(model=rerank_model_id.value, device=rerank_device.value, top_n=2)
+
+
+.. parsed-literal::
+
+    Compiling the model to CPU ...
+
+
+Load LLM model
+~~~~~~~~~~~~~~
+
+
+
+OpenVINO models can be run locally through the ``HuggingFacePipeline``
+class. To deploy a model with OpenVINO, you can specify the
+``backend="openvino"`` parameter to trigger OpenVINO as backend
+inference framework.
+
+.. code:: ipython3
+
+    available_models = []
+    if int4_model_dir.exists():
+        available_models.append("INT4")
+    if int8_model_dir.exists():
+        available_models.append("INT8")
+    if fp16_model_dir.exists():
+        available_models.append("FP16")
+    
+    model_to_run = widgets.Dropdown(
+        options=available_models,
+        value=available_models[0],
+        description="Model to run:",
+        disabled=False,
+    )
+    
+    model_to_run
+
+
+
+
+.. parsed-literal::
+
+    Dropdown(description='Model to run:', options=('INT4',), value='INT4')
+
+
+
+OpenVINO models can be run locally through the ``OpenVINOLLM`` class in
+`LlamaIndex <https://docs.llamaindex.ai/en/stable/examples/llm/openvino/>`__.
+If you have an Intel GPU, you can specify ``device_map="gpu"`` to run
+inference on it.
+
+.. code:: ipython3
+
+    from llama_index.llms.openvino import OpenVINOLLM
+    
+    if model_to_run.value == "INT4":
+        model_dir = int4_model_dir
+    elif model_to_run.value == "INT8":
+        model_dir = int8_model_dir
+    else:
+        model_dir = fp16_model_dir
+    print(f"Loading model from {model_dir}")
+    
+    ov_config = {"PERFORMANCE_HINT": "LATENCY", "NUM_STREAMS": "1", "CACHE_DIR": ""}
+    
+    if "GPU" in llm_device.value and "qwen2-7b-instruct" in llm_model_id.value:
+        ov_config["GPU_ENABLE_SDPA_OPTIMIZATION"] = "NO"
+    
+    # On a GPU device a model is executed in FP16 precision. For red-pajama-3b-chat model there known accuracy
+    # issues caused by this, which we avoid by setting precision hint to "f32".
+    if llm_model_id.value == "red-pajama-3b-chat" and "GPU" in core.available_devices and llm_device.value in ["GPU", "AUTO"]:
+        ov_config["INFERENCE_PRECISION_HINT"] = "f32"
+    
+    llm = OpenVINOLLM(
+        model_name=str(model_dir),
+        tokenizer_name=str(model_dir),
+        context_window=3900,
+        max_new_tokens=2,
+        model_kwargs={"ov_config": ov_config, "trust_remote_code": True},
+        generate_kwargs={"temperature": 0.7, "top_k": 50, "top_p": 0.95},
+        device_map=llm_device.value,
+    )
+    
+    response = llm.complete("2 + 2 =")
+    print(str(response))
+
+
+.. parsed-literal::
+
+    The argument `trust_remote_code` is to be used along with export=True. It will be ignored.
+
+
+.. parsed-literal::
+
+    Loading model from neural-chat-7b-v3-1/INT4_compressed_weights
+
+
+.. parsed-literal::
+
+    Compiling the model to CPU ...
+    /home/ethan/intel/openvino_notebooks/openvino_env/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:492: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.7` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.
+      warnings.warn(
+    /home/ethan/intel/openvino_notebooks/openvino_env/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:497: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.
+      warnings.warn(
+    Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.
+
+
+.. parsed-literal::
+
+    4
+
+
+Run QA over Document
+--------------------
+
+
+
+A typical RAG application has two main components:
+
+-  **Indexing**: a pipeline for ingesting data from a source and
+   indexing it. This usually happen offline.
+
+-  **Retrieval and generation**: the actual RAG chain, which takes the
+   user query at run time and retrieves the relevant data from the
+   index, then passes that to the model.
+
+The most common full sequence from raw data to answer looks like:
+
+**Indexing**
+
+1. ``Load``: First we need to load our data. We’ll use DocumentLoaders
+   for this.
+2. ``Split``: Text splitters break large Documents into smaller chunks.
+   This is useful both for indexing data and for passing it in to a
+   model, since large chunks are harder to search over and won’t in a
+   model’s finite context window.
+3. ``Store``: We need somewhere to store and index our splits, so that
+   they can later be searched over. This is often done using a
+   VectorStore and Embeddings model.
+
+.. figure:: https://github.com/openvinotoolkit/openvino_notebooks/assets/91237924/dfed2ba3-0c3a-4e0e-a2a7-01638730486a
+   :alt: Indexing pipeline
+
+   Indexing pipeline
+
+.. code:: ipython3
+
+    from llama_index.core import VectorStoreIndex, StorageContext
+    from llama_index.core.node_parser import SentenceSplitter
+    from llama_index.core import Settings
+    from llama_index.readers.file import PyMuPDFReader
+    from llama_index.vector_stores.faiss import FaissVectorStore
+    import faiss
+    
+    if model_language.value == "English":
+        text_example_path = "text_example_en.pdf"
+    else:
+        text_example_path = "text_example_cn.pdf"
+    
+    loader = PyMuPDFReader()
+    documents = loader.load(file_path=text_example_path)
+    
+    # dimensions of embedding model
+    d = embedding._model.request.outputs[0].get_partial_shape()[2].get_length()
+    faiss_index = faiss.IndexFlatL2(d)
+    Settings.embed_model = embedding
+    llm.max_new_tokens = 2048
+    Settings.llm = llm
+    
+    
+    vector_store = FaissVectorStore(faiss_index=faiss_index)
+
+**Retrieval and generation**
+
+1. ``Retrieve``: Given a user input, relevant splits are retrieved from
+   storage using a Retriever.
+2. ``Generate``: A LLM produces an answer using a prompt that includes
+   the question and the retrieved data.
+
+.. figure:: https://github.com/openvinotoolkit/openvino_notebooks/assets/91237924/f0545ddc-c0cd-4569-8c86-9879fdab105a
+   :alt: Retrieval and generation pipeline
+
+   Retrieval and generation pipeline
+
+.. code:: ipython3
+
+    storage_context = StorageContext.from_defaults(vector_store=vector_store)
+    index = VectorStoreIndex.from_documents(
+        documents,
+        storage_context=storage_context,
+        transformations=[SentenceSplitter(chunk_size=500, chunk_overlap=50)],
+    )
+    query_engine = index.as_query_engine(streaming=True, similarity_top_k=10, node_postprocessors=[reranker])
+    if model_language.value == "English":
+        query = "What can Intel vPro® Enterprise systems offer?"
+    else:
+        query = "英特尔博锐® Enterprise系统提供哪些功能？"
+    
+    streaming_response = query_engine.query(query)
+    streaming_response.print_response_stream()
+
+
+.. parsed-literal::
+
+    Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.
+
+
+.. parsed-literal::
+
+    Intel vPro® Enterprise systems offer a range of features and capabilities, including dynamic root of trust, system management mode (SMM) protections, memory encryption with multi-key support, OS kernel protection, out-of-band management with remote KVM control, unique device identifier, device history, and in-band manageability plug-ins. These features are part of the strong portfolio of security and manageability technologies that form the foundation of the Intel vPro platform, which delivers differentiated capabilities to organizations of all sizes.
diff --git a/docs/notebooks/magika-content-type-recognition-with-output.rst b/docs/notebooks/magika-content-type-recognition-with-output.rst
index 6a10f6651118fd..4e55ba6b147eaa 100644
--- a/docs/notebooks/magika-content-type-recognition-with-output.rst
+++ b/docs/notebooks/magika-content-type-recognition-with-output.rst
@@ -41,8 +41,7 @@ post <https://opensource.googleblog.com/2024/02/magika-ai-powered-fast-and-effic
 
 In this tutorial we consider how to bring OpenVINO power into Magika.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
 
 -  `Prerequisites <#prerequisites>`__
 -  `Define model loading class <#define-model-loading-class>`__
@@ -58,6 +57,7 @@ Table of contents:
 Prerequisites
 -------------
 
+
 .. code:: ipython3
 
     %pip install -q magika "openvino>=2024.1.0" "gradio>=4.19"
diff --git a/docs/notebooks/meter-reader-with-output.rst b/docs/notebooks/meter-reader-with-output.rst
index 9b9e5948dada2a..aaf967c84cbeea 100644
--- a/docs/notebooks/meter-reader-with-output.rst
+++ b/docs/notebooks/meter-reader-with-output.rst
@@ -19,8 +19,8 @@ to build up a multiple inference task pipeline:
 
    workflow
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Import <#import>`__
 -  `Prepare the Model and Test
@@ -44,10 +44,10 @@ Table of contents:
 .. code:: ipython3
 
     import platform
-    
+
     # Install openvino package
     %pip install -q "openvino>=2023.1.0" opencv-python tqdm
-    
+
     if platform.system() != "Windows":
         %pip install -q "matplotlib>=3.4"
     else:
@@ -75,21 +75,21 @@ Import
     import tarfile
     import matplotlib.pyplot as plt
     import openvino as ov
-    
+
     # Fetch `notebook_utils` module
     import requests
-    
+
     r = requests.get(
         url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
-    
+
     open("notebook_utils.py", "w").write(r.text)
     from notebook_utils import download_file, segmentation_map_to_image
 
 Prepare the Model and Test Image
 --------------------------------
 
- Download PPYOLOv2 and
+Download PPYOLOv2 and
 DeepLabV3P pre-trained models from PaddlePaddle community.
 
 .. code:: ipython3
@@ -103,9 +103,9 @@ DeepLabV3P pre-trained models from PaddlePaddle community.
     IMG_LINK = "https://user-images.githubusercontent.com/91237924/170696219-f68699c6-1e82-46bf-aaed-8e2fc3fa5f7b.jpg"
     IMG_FILE_NAME = IMG_LINK.split("/")[-1]
     IMG_PATH = Path(f"{DATA_DIR}/{IMG_FILE_NAME}")
-    
+
     os.makedirs(MODEL_DIR, exist_ok=True)
-    
+
     download_file(DET_MODEL_LINK, directory=MODEL_DIR, show_progress=True)
     file = tarfile.open(f"model/{DET_FILE_NAME}")
     res = file.extractall("model")
@@ -113,7 +113,7 @@ DeepLabV3P pre-trained models from PaddlePaddle community.
         print(f'Detection Model Extracted to "./{MODEL_DIR}".')
     else:
         print("Error Extracting the Detection model. Please check the network.")
-    
+
     download_file(SEG_MODEL_LINK, directory=MODEL_DIR, show_progress=True)
     file = tarfile.open(f"model/{SEG_FILE_NAME}")
     res = file.extractall("model")
@@ -121,7 +121,7 @@ DeepLabV3P pre-trained models from PaddlePaddle community.
         print(f'Segmentation Model Extracted to "./{MODEL_DIR}".')
     else:
         print("Error Extracting the Segmentation model. Please check the network.")
-    
+
     download_file(IMG_LINK, directory=DATA_DIR, show_progress=True)
     if IMG_PATH.is_file():
         print(f'Test Image Saved to "./{DATA_DIR}".')
@@ -165,7 +165,7 @@ DeepLabV3P pre-trained models from PaddlePaddle community.
 Configuration
 -------------
 
- Add parameter configuration for
+Add parameter configuration for
 reading calculation.
 
 .. code:: ipython3
@@ -178,54 +178,54 @@ reading calculation.
     RECTANGLE_WIDTH = 1570
     TYPE_THRESHOLD = 40
     COLORMAP = np.array([[28, 28, 28], [238, 44, 44], [250, 250, 250]])
-    
+
     # There are 2 types of meters in test image datasets
     METER_CONFIG = [
         {"scale_interval_value": 25.0 / 50.0, "range": 25.0, "unit": "(MPa)"},
         {"scale_interval_value": 1.6 / 32.0, "range": 1.6, "unit": "(MPa)"},
     ]
-    
+
     SEG_LABEL = {"background": 0, "pointer": 1, "scale": 2}
 
 Load the Models
 ---------------
 
- Define a common class for model
+Define a common class for model
 loading and inference
 
 .. code:: ipython3
 
     # Initialize OpenVINO Runtime
     core = ov.Core()
-    
-    
+
+
     class Model:
         """
         This class represents a OpenVINO model object.
-    
+
         """
-    
+
         def __init__(self, model_path, new_shape, device="CPU"):
             """
             Initialize the model object
-    
+
             Param:
                 model_path (string): path of inference model
                 new_shape (dict): new shape of model input
-    
+
             """
             self.model = core.read_model(model=model_path)
             self.model.reshape(new_shape)
             self.compiled_model = core.compile_model(model=self.model, device_name=device)
             self.output_layer = self.compiled_model.output(0)
-    
+
         def predict(self, input_image):
             """
             Run inference
-    
+
             Param:
                 input_image (np.array): input data
-    
+
             Retuns:
                 result (np.array)): model output data
             """
@@ -235,7 +235,7 @@ loading and inference
 Data Process
 ------------
 
- Including the preprocessing and
+Including the preprocessing and
 postprocessing tasks of each model.
 
 .. code:: ipython3
@@ -243,13 +243,13 @@ postprocessing tasks of each model.
     def det_preprocess(input_image, target_size):
         """
         Preprocessing the input data for detection task
-    
+
         Param:
             input_image (np.array): input data
             size (int): the image size required by model input layer
         Retuns:
             img.astype (np.array): preprocessed image
-    
+
         """
         img = cv2.resize(input_image, (target_size, target_size))
         img = np.transpose(img, [2, 0, 1]) / 255
@@ -259,41 +259,41 @@ postprocessing tasks of each model.
         img -= img_mean
         img /= img_std
         return img.astype(np.float32)
-    
-    
+
+
     def filter_bboxes(det_results, score_threshold):
         """
         Filter out the detection results with low confidence
-    
+
         Param：
             det_results (list[dict]): detection results
             score_threshold (float)： confidence threshold
-    
+
         Retuns：
             filtered_results (list[dict]): filter detection results
-    
+
         """
         filtered_results = []
         for i in range(len(det_results)):
             if det_results[i, 1] > score_threshold:
                 filtered_results.append(det_results[i])
         return filtered_results
-    
-    
+
+
     def roi_crop(image, results, scale_x, scale_y):
         """
         Crop the area of detected meter of original image
-    
+
         Param：
             img (np.array)：original image。
             det_results (list[dict]): detection results
             scale_x (float): the scale value in x axis
             scale_y (float): the scale value in y axis
-    
+
         Retuns：
             roi_imgs (list[np.array]): the list of meter images
             loc (list[int]): the list of meter locations
-    
+
         """
         roi_imgs = []
         loc = []
@@ -309,22 +309,22 @@ postprocessing tasks of each model.
             roi_imgs.append(sub_img)
             loc.append([xmin, ymin, xmax, ymax])
         return roi_imgs, loc
-    
-    
+
+
     def roi_process(input_images, target_size, interp=cv2.INTER_LINEAR):
         """
         Prepare the roi image of detection results data
         Preprocessing the input data for segmentation task
-    
+
         Param：
             input_images (list[np.array])：the list of meter images
             target_size (list|tuple)： height and width of resized image， e.g [heigh,width]
             interp (int)：the interp method for image reszing
-    
+
         Retuns：
             img_list (list[np.array])：the list of processed images
             resize_img (list[np.array]): for visualization
-    
+
         """
         img_list = list()
         resize_list = list()
@@ -341,48 +341,48 @@ postprocessing tasks of each model.
             resize_img /= img_std
             img_list.append(resize_img)
         return img_list, resize_list
-    
-    
+
+
     def erode(seg_results, erode_kernel):
         """
         Erode the segmentation result to get the more clear instance of pointer and scale
-    
+
         Param：
             seg_results (list[dict])：segmentation results
             erode_kernel (int): size of erode_kernel
-    
+
         Return：
             eroded_results (list[dict])： the lab map of eroded_results
-    
+
         """
         kernel = np.ones((erode_kernel, erode_kernel), np.uint8)
         eroded_results = seg_results
         for i in range(len(seg_results)):
             eroded_results[i] = cv2.erode(seg_results[i].astype(np.uint8), kernel)
         return eroded_results
-    
-    
+
+
     def circle_to_rectangle(seg_results):
         """
         Switch the shape of label_map from circle to rectangle
-    
+
         Param：
             seg_results (list[dict])：segmentation results
-    
+
         Return：
             rectangle_meters (list[np.array])：the rectangle of label map
-    
+
         """
         rectangle_meters = list()
         for i, seg_result in enumerate(seg_results):
             label_map = seg_result
-    
+
             # The size of rectangle_meter is determined by RECTANGLE_HEIGHT and RECTANGLE_WIDTH
             rectangle_meter = np.zeros((RECTANGLE_HEIGHT, RECTANGLE_WIDTH), dtype=np.uint8)
             for row in range(RECTANGLE_HEIGHT):
                 for col in range(RECTANGLE_WIDTH):
                     theta = PI * 2 * (col + 1) / RECTANGLE_WIDTH
-    
+
                     # The radius of meter circle will be mapped to the height of rectangle image
                     rho = CIRCLE_RADIUS - row - 1
                     y = int(CIRCLE_CENTER[0] + rho * math.cos(theta) + 0.5)
@@ -390,19 +390,19 @@ postprocessing tasks of each model.
                     rectangle_meter[row, col] = label_map[y, x]
             rectangle_meters.append(rectangle_meter)
         return rectangle_meters
-    
-    
+
+
     def rectangle_to_line(rectangle_meters):
         """
         Switch the dimension of rectangle label map from 2D to 1D
-    
+
         Param：
             rectangle_meters (list[np.array])：2D rectangle OF label_map。
-    
+
         Return：
             line_scales (list[np.array])： the list of scales value
             line_pointers (list[np.array])：the list of pointers value
-    
+
         """
         line_scales = list()
         line_pointers = list()
@@ -419,18 +419,18 @@ postprocessing tasks of each model.
             line_scales.append(line_scale)
             line_pointers.append(line_pointer)
         return line_scales, line_pointers
-    
-    
+
+
     def mean_binarization(data_list):
         """
         Binarize the data
-    
+
         Param：
             data_list (list[np.array])：input data
-    
+
         Return：
             binaried_data_list (list[np.array])：output data。
-    
+
         """
         batch_size = len(data_list)
         binaried_data_list = data_list
@@ -443,18 +443,18 @@ postprocessing tasks of each model.
                 else:
                     binaried_data_list[i][col] = 1
         return binaried_data_list
-    
-    
+
+
     def locate_scale(line_scales):
         """
         Find location of center of each scale
-    
+
         Param：
             line_scales (list[np.array])：the list of binaried scales value
-    
+
         Return：
             scale_locations (list[list])：location of each scale
-    
+
         """
         batch_size = len(line_scales)
         scale_locations = list()
@@ -480,18 +480,18 @@ postprocessing tasks of each model.
                         find_start = False
             scale_locations.append(locations)
         return scale_locations
-    
-    
+
+
     def locate_pointer(line_pointers):
         """
         Find location of center of pointer
-    
+
         Param：
             line_scales (list[np.array])：the list of binaried pointer value
-    
+
         Return：
             scale_locations (list[list])：location of pointer
-    
+
         """
         batch_size = len(line_pointers)
         pointer_locations = list()
@@ -515,21 +515,21 @@ postprocessing tasks of each model.
                         break
             pointer_locations.append(location)
         return pointer_locations
-    
-    
+
+
     def get_relative_location(scale_locations, pointer_locations):
         """
         Match location of pointer and scales
-    
+
         Param：
             scale_locations (list[list])：location of each scale
             pointer_locations (list[list])：location of pointer
-    
+
         Return：
             pointed_scales (list[dict])： a list of dict with:
                                          'num_scales': total number of scales
                                          'pointed_scale': predicted number of scales
-    
+
         """
         pointed_scales = list()
         for scale_location, pointer_location in zip(scale_locations, pointer_locations):
@@ -542,18 +542,18 @@ postprocessing tasks of each model.
             result = {"num_scales": num_scales, "pointed_scale": pointed_scale}
             pointed_scales.append(result)
         return pointed_scales
-    
-    
+
+
     def calculate_reading(pointed_scales):
         """
         Calculate the value of meter according to the type of meter
-    
+
         Param：
             pointed_scales (list[list])：predicted number of scales
-    
+
         Return：
             readings (list[float])： the list of values read from meter
-    
+
         """
         readings = list()
         batch_size = len(pointed_scales)
@@ -582,14 +582,14 @@ select device from dropdown list for running inference using OpenVINO
 .. code:: ipython3
 
     import ipywidgets as widgets
-    
+
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
         value="AUTO",
         description="Device:",
         disabled=False,
     )
-    
+
     device
 
 
@@ -621,16 +621,16 @@ bounds of input batch size.
     }
     seg_model_path = f"{MODEL_DIR}/meter_seg_model/model.pdmodel"
     seg_model_shape = {"image": [ov.Dimension(1, 2), 3, 512, 512]}
-    
+
     erode_kernel = 4
     score_threshold = 0.5
     seg_batch_size = 2
     input_shape = 608
-    
+
     # Intialize the model objects
     detector = Model(det_model_path, det_model_shape, device.value)
     segmenter = Model(seg_model_path, seg_model_shape, device.value)
-    
+
     # Visulize a original input photo
     image = cv2.imread(img_file)
     rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
@@ -641,7 +641,7 @@ bounds of input batch size.
 
 .. parsed-literal::
 
-    <matplotlib.image.AxesImage at 0x7f9eff572a90>
+    <matplotlib.image.AxesImage at 0x7feb57002760>
 
 
 
@@ -652,7 +652,7 @@ bounds of input batch size.
 Run meter detection model
 ~~~~~~~~~~~~~~~~~~~~~~~~~
 
- Detect the location of the
+Detect the location of the
 meter and prepare the ROI images for segmentation.
 
 .. code:: ipython3
@@ -662,24 +662,24 @@ meter and prepare the ROI images for segmentation.
     scale_factor = np.array([[1, 2]]).astype("float32")
     input_image = det_preprocess(image, input_shape)
     inputs_dict = {"image": input_image, "im_shape": im_shape, "scale_factor": scale_factor}
-    
+
     # Run meter detection model
     det_results = detector.predict(inputs_dict)
-    
+
     # Filter out the bounding box with low confidence
     filtered_results = filter_bboxes(det_results, score_threshold)
-    
+
     # Prepare the input data for meter segmentation model
     scale_x = image.shape[1] / input_shape * 2
     scale_y = image.shape[0] / input_shape
-    
+
     # Create the individual picture for each detected meter
     roi_imgs, loc = roi_crop(image, filtered_results, scale_x, scale_y)
     roi_imgs, resize_imgs = roi_process(roi_imgs, METER_SHAPE)
-    
+
     # Create the pictures of detection results
     roi_stack = np.hstack(resize_imgs)
-    
+
     if cv2.imwrite(f"{DATA_DIR}/detection_results.jpg", roi_stack):
         print('The detection result image has been saved as "detection_results.jpg" in data')
         plt.imshow(cv2.cvtColor(roi_stack, cv2.COLOR_BGR2RGB))
@@ -697,7 +697,7 @@ meter and prepare the ROI images for segmentation.
 Run meter segmentation model
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
- Get the results of segmentation
+Get the results of segmentation
 task on detected ROI.
 
 .. code:: ipython3
@@ -705,7 +705,7 @@ task on detected ROI.
     seg_results = list()
     mask_list = list()
     num_imgs = len(roi_imgs)
-    
+
     # Run meter segmentation model on all detected meters
     for i in range(0, num_imgs, seg_batch_size):
         batch = roi_imgs[i : min(num_imgs, i + seg_batch_size)]
@@ -715,12 +715,12 @@ task on detected ROI.
     for i in range(len(seg_results)):
         results.append(np.argmax(seg_results[i], axis=0))
     seg_results = erode(results, erode_kernel)
-    
+
     # Create the pictures of segmentation results
     for i in range(len(seg_results)):
         mask_list.append(segmentation_map_to_image(seg_results[i], COLORMAP))
     mask_stack = np.hstack(mask_list)
-    
+
     if cv2.imwrite(f"{DATA_DIR}/segmentation_results.jpg", cv2.cvtColor(mask_stack, cv2.COLOR_RGB2BGR)):
         print('The segmentation result image has been saved as "segmentation_results.jpg" in data')
         plt.imshow(mask_stack)
@@ -738,7 +738,7 @@ task on detected ROI.
 Postprocess the models result and calculate the final readings
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
- Use OpenCV function to find the
+Use OpenCV function to find the
 location of the pointer in a scale map.
 
 .. code:: ipython3
@@ -752,13 +752,13 @@ location of the pointer in a scale map.
     pointer_locations = locate_pointer(binaried_pointers)
     pointed_scales = get_relative_location(scale_locations, pointer_locations)
     meter_readings = calculate_reading(pointed_scales)
-    
+
     rectangle_list = list()
     # Plot the rectangle meters
     for i in range(len(rectangle_meters)):
         rectangle_list.append(segmentation_map_to_image(rectangle_meters[i], COLORMAP))
     rectangle_meters_stack = np.hstack(rectangle_list)
-    
+
     if cv2.imwrite(
         f"{DATA_DIR}/rectangle_meters.jpg",
         cv2.cvtColor(rectangle_meters_stack, cv2.COLOR_RGB2BGR),
@@ -786,7 +786,7 @@ Get the reading result on the meter picture
     # Create a final result photo with reading
     for i in range(len(meter_readings)):
         print("Meter {}: {:.3f}".format(i + 1, meter_readings[i]))
-    
+
     result_image = image.copy()
     for i in range(len(loc)):
         cv2.rectangle(result_image, (loc[i][0], loc[i][1]), (loc[i][2], loc[i][3]), (0, 150, 0), 3)
diff --git a/docs/notebooks/mms-massively-multilingual-speech-with-output.rst b/docs/notebooks/mms-massively-multilingual-speech-with-output.rst
index 46f9e8619d8ca9..e6d7483c3ce159 100644
--- a/docs/notebooks/mms-massively-multilingual-speech-with-output.rst
+++ b/docs/notebooks/mms-massively-multilingual-speech-with-output.rst
@@ -29,8 +29,8 @@ it. Additional models quantization step is employed to improve models
 inference speed. In the end of the notebook there’s a Gradio-based
 interactive demo.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Prepare an example audio <#prepare-an-example-audio>`__
@@ -127,7 +127,7 @@ Specify ``streaming=True`` to not download the entire dataset.
     from datasets import load_dataset
     
     
-    mls_dataset = load_dataset("facebook/multilingual_librispeech", SAMPLE_LANG.value, split="test", streaming=True)
+    mls_dataset = load_dataset("facebook/multilingual_librispeech", SAMPLE_LANG.value, split="test", streaming=True, trust_remote_code=True)
     mls_dataset = iter(mls_dataset)  # make it iterable
     
     example = next(mls_dataset)  # get one example
@@ -330,7 +330,7 @@ Let’s check another language.
 
 .. code:: ipython3
 
-    mls_dataset = load_dataset("facebook/multilingual_librispeech", SAMPLE_LANG.value, split="test", streaming=True)
+    mls_dataset = load_dataset("facebook/multilingual_librispeech", SAMPLE_LANG.value, split="test", streaming=True, trust_remote_code=True)
     mls_dataset = iter(mls_dataset)
     
     example = next(mls_dataset)
@@ -606,7 +606,7 @@ dataset for the selected language.
 
     %%skip not $to_quantize.value
     
-    mls_dataset = iter(load_dataset("facebook/multilingual_librispeech", SAMPLE_LANG.value, split="validation", streaming=True))
+    mls_dataset = iter(load_dataset("facebook/multilingual_librispeech", SAMPLE_LANG.value, split="validation", streaming=True, trust_remote_code=True))
     example = next(mls_dataset)
 
 Create calibration dataset for quantization.
@@ -815,7 +815,7 @@ speech recognition models.
     from jiwer import wer
     
     TEST_DATASET_SIZE = 20
-    test_dataset = load_dataset("facebook/multilingual_librispeech", SAMPLE_LANG.value, split="test", streaming=True)
+    test_dataset = load_dataset("facebook/multilingual_librispeech", SAMPLE_LANG.value, split="test", streaming=True, trust_remote_code=True)
     test_dataset = test_dataset.take(TEST_DATASET_SIZE)
     
     def calculate_transcription_time_and_accuracy(lid_model, asr_model):
diff --git a/docs/notebooks/mobileclip-video-search-with-output.rst b/docs/notebooks/mobileclip-video-search-with-output.rst
index 0cd108262a0269..50c724abf040ed 100644
--- a/docs/notebooks/mobileclip-video-search-with-output.rst
+++ b/docs/notebooks/mobileclip-video-search-with-output.rst
@@ -61,11 +61,11 @@ Prerequisites
 .. parsed-literal::
 
     Cloning into 'ml-mobileclip'...
-    remote: Enumerating objects: 45, done.
-    remote: Counting objects: 100% (45/45), done.
-    remote: Compressing objects: 100% (36/36), done.
-    remote: Total 45 (delta 9), reused 44 (delta 8), pack-reused 0
-    Unpacking objects: 100% (45/45), 428.50 KiB | 3.17 MiB/s, done.
+    remote: Enumerating objects: 68, done.
+    remote: Counting objects: 100% (68/68), done.
+    remote: Compressing objects: 100% (51/51), done.
+    remote: Total 68 (delta 19), reused 65 (delta 16), pack-reused 0
+    Unpacking objects: 100% (68/68), 447.59 KiB | 3.52 MiB/s, done.
 
 
 .. code:: ipython3
@@ -437,8 +437,8 @@ Perform search
 
 .. parsed-literal::
 
-    Image encoding took 0.1 ms
-    Text encoding took 0.0107 ms
+    Image encoding took 0.115 ms
+    Text encoding took 0.011 ms
 
 
 
@@ -516,7 +516,7 @@ be used separately. Let’s convert each part to OpenVINO.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/mobileclip/modules/common/transformer.py:125: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/mobileclip/modules/common/transformer.py:125: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if seq_len != self.num_embeddings:
 
 
@@ -608,8 +608,8 @@ Perform search
 
 .. parsed-literal::
 
-    Image encoding took 0.0309 ms
-    Text encoding took 0.00588 ms
+    Image encoding took 0.0303 ms
+    Text encoding took 0.00599 ms
 
 
 
diff --git a/docs/notebooks/mobilevlm-language-assistant-with-output.rst b/docs/notebooks/mobilevlm-language-assistant-with-output.rst
index 0b27f54d43f808..54fc451b8764cf 100644
--- a/docs/notebooks/mobilevlm-language-assistant-with-output.rst
+++ b/docs/notebooks/mobilevlm-language-assistant-with-output.rst
@@ -22,8 +22,8 @@ See more information on official
 `GitHub <https://github.com/Meituan-AutoML/MobileVLM>`__ project page
 and `paper <https://arxiv.org/abs/2312.16886>`__.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Install requirements <#install-requirements>`__
 -  `Clone MobileVLM repository <#clone-mobilevlm-repository>`__
@@ -58,7 +58,7 @@ Install requirements
     ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
     mobileclip 0.1.0 requires torch==1.13.1, but you have torch 2.3.1+cpu which is incompatible.
     mobileclip 0.1.0 requires torchvision==0.14.1, but you have torchvision 0.18.1+cpu which is incompatible.
-    optimum-intel 1.18.0.dev0+2c79d98 requires transformers<4.42.0,>=4.36.0, but you have transformers 4.33.3 which is incompatible.
+    optimum-intel 1.19.0.dev0+384dda5 requires transformers<4.42.0,>=4.36.0, but you have transformers 4.33.3 which is incompatible.
     Note: you may need to restart the kernel to use updated packages.
 
 
@@ -110,13 +110,13 @@ Import required packages
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/utils/generic.py:311: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/utils/generic.py:311: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
       torch.utils._pytree._register_pytree_node(
-    2024-06-06 00:46:44.654609: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-06-06 00:46:44.688475: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-07-02 00:52:36.568927: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-07-02 00:52:36.602827: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2024-06-06 00:46:45.198736: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/utils/generic.py:311: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
+    2024-07-02 00:52:37.111524: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/utils/generic.py:311: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
       torch.utils._pytree._register_pytree_node(
 
 
@@ -324,15 +324,15 @@ compression instead of INT8 weight compression.
 .. parsed-literal::
 
     [ WARNING ]  Please fix your imports. Module %s has been moved to %s. The old module will be deleted in version %s.
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/llama/modeling_llama.py:595: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/llama/modeling_llama.py:595: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if input_shape[-1] > 1:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/llama/modeling_llama.py:119: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/llama/modeling_llama.py:119: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if seq_len > self.max_seq_len_cached:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/llama/modeling_llama.py:348: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/llama/modeling_llama.py:348: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/llama/modeling_llama.py:355: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/llama/modeling_llama.py:355: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/llama/modeling_llama.py:365: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/llama/modeling_llama.py:365: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
 
 
@@ -343,17 +343,17 @@ compression instead of INT8 weight compression.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -376,17 +376,17 @@ compression instead of INT8 weight compression.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -414,7 +414,7 @@ compression instead of INT8 weight compression.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:165: UserWarning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the .grad field to be populated for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations. (Triggered internally at aten/src/ATen/core/TensorBody.h:489.)
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:165: UserWarning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the .grad field to be populated for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations. (Triggered internally at aten/src/ATen/core/TensorBody.h:489.)
       if a.grad is not None:
 
 
@@ -425,17 +425,17 @@ compression instead of INT8 weight compression.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -458,17 +458,17 @@ compression instead of INT8 weight compression.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
diff --git a/docs/notebooks/model-server-with-output.rst b/docs/notebooks/model-server-with-output.rst
index 1fc1a4fe4c5d14..0853b0682d3723 100644
--- a/docs/notebooks/model-server-with-output.rst
+++ b/docs/notebooks/model-server-with-output.rst
@@ -34,8 +34,8 @@ deployment:
 
    ovms_diagram
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Serving with OpenVINO Model
    Server <#serving-with-openvino-model-server>`__
@@ -61,7 +61,7 @@ Table of contents:
 Serving with OpenVINO Model Server
 ----------------------------------
 
- OpenVINO Model Server (OVMS) is
+OpenVINO Model Server (OVMS) is
 a high-performance system for serving models. Implemented in C++ for
 scalability and optimized for deployment on Intel architectures, the
 model server uses the same architecture and API as TensorFlow Serving
@@ -79,7 +79,7 @@ To quickly start using OpenVINO™ Model Server, follow these steps:
 Step 1: Prepare Docker
 ----------------------
 
- Install `Docker
+Install `Docker
 Engine <https://docs.docker.com/engine/install/>`__, including its
 `post-installation <https://docs.docker.com/engine/install/linux-postinstall/>`__
 steps, on your development system. To verify installation, test it,
@@ -93,10 +93,10 @@ image and a message.
 
 .. parsed-literal::
 
-    
+
     Hello from Docker!
     This message shows that your installation appears to be working correctly.
-    
+
     To generate this message, Docker took the following steps:
      1. The Docker client contacted the Docker daemon.
      2. The Docker daemon pulled the "hello-world" image from the Docker Hub.
@@ -105,22 +105,22 @@ image and a message.
         executable that produces the output you are currently reading.
      4. The Docker daemon streamed that output to the Docker client, which sent it
         to your terminal.
-    
+
     To try something more ambitious, you can run an Ubuntu container with:
      $ docker run -it ubuntu bash
-    
+
     Share images, automate workflows, and more with a free Docker ID:
      https://hub.docker.com/
-    
+
     For more examples and ideas, visit:
      https://docs.docker.com/get-started/
-    
+
 
 
 Step 2: Preparing a Model Repository
 ------------------------------------
 
- The models need to be placed
+The models need to be placed
 and mounted in a particular directory structure and according to the
 following rules:
 
@@ -171,9 +171,9 @@ following rules:
 .. code:: ipython3
 
     import platform
-    
+
     %pip install -q "openvino>=2023.1.0" opencv-python tqdm
-    
+
     if platform.system() != "Windows":
         %pip install -q "matplotlib>=3.4"
     else:
@@ -182,21 +182,21 @@ following rules:
 .. code:: ipython3
 
     import os
-    
+
     # Fetch `notebook_utils` module
     import requests
-    
+
     r = requests.get(
         url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
-    
+
     open("notebook_utils.py", "w").write(r.text)
     from notebook_utils import download_file
-    
+
     dedicated_dir = "models"
     model_name = "detection"
     model_version = "1"
-    
+
     MODEL_DIR = f"{dedicated_dir}/{model_name}/{model_version}"
     XML_PATH = "horizontal-text-detection-0001.xml"
     BIN_PATH = "horizontal-text-detection-0001.bin"
@@ -207,7 +207,7 @@ following rules:
     model_bin_url = (
         "https://storage.openvinotoolkit.org/repositories/open_model_zoo/2022.3/models_bin/1/horizontal-text-detection-0001/FP32/horizontal-text-detection-0001.bin"
     )
-    
+
     download_file(model_xml_url, XML_PATH, MODEL_DIR)
     download_file(model_bin_url, BIN_PATH, MODEL_DIR)
 
@@ -235,21 +235,21 @@ following rules:
 Step 3: Start the Model Server Container
 ----------------------------------------
 
- Pull and start the container:
+Pull and start the container:
 
 Searching for an available serving port in local.
 
 .. code:: ipython3
 
     import socket
-    
+
     sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
     sock.bind(("localhost", 0))
     sock.listen(1)
     port = sock.getsockname()[1]
     sock.close()
     print(f"Port {port} is available")
-    
+
     os.environ["port"] = str(port)
 
 
@@ -705,7 +705,7 @@ available port on your system. For example:\ ``-p 9020:9000``
 Step 4: Prepare the Example Client Components
 ---------------------------------------------
 
- OpenVINO Model Server exposes
+OpenVINO Model Server exposes
 two sets of APIs: one compatible with ``TensorFlow Serving`` and another
 one, with ``KServe API``, for inference. Both APIs work on ``gRPC`` and
 ``REST``\ interfaces. Supporting two sets of APIs makes OpenVINO Model
@@ -750,7 +750,7 @@ Request Model Status
 .. code:: ipython3
 
     address = "localhost:" + str(port)
-    
+
     # Bind the grpc address to the client object
     client = make_grpc_client(address)
     model_status = client.get_model_status(model_name=model_name)
@@ -790,16 +790,16 @@ Load input image
         "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/intel_rnb.jpg",
         directory="data",
     )
-    
+
     # Text detection models expect an image in BGR format.
     image = cv2.imread(str(image_filename))
     fp_image = image.astype("float32")
-    
+
     # Resize the image to meet network expected input sizes.
     input_shape = model_metadata["inputs"]["image"]["shape"]
     height, width = input_shape[2], input_shape[3]
     resized_image = cv2.resize(fp_image, (height, width))
-    
+
     # Reshape to the network input shape.
     input_image = np.expand_dims(resized_image.transpose(2, 0, 1), 0)
     plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
@@ -831,10 +831,10 @@ Request Prediction on a Numpy Array
 .. code:: ipython3
 
     inputs = {"image": input_image}
-    
+
     # Run inference on model server and receive the result data
     boxes = client.predict(inputs=inputs, model_name=model_name)["boxes"]
-    
+
     # Remove zero only boxes.
     boxes = boxes[~np.all(boxes == 0, axis=1)]
     print(boxes)
@@ -862,17 +862,17 @@ Visualization
     def convert_result_to_image(bgr_image, resized_image, boxes, threshold=0.3, conf_labels=True):
         # Define colors for boxes and descriptions.
         colors = {"red": (255, 0, 0), "green": (0, 255, 0)}
-    
+
         # Fetch the image shapes to calculate a ratio.
         (real_y, real_x), (resized_y, resized_x) = (
             bgr_image.shape[:2],
             resized_image.shape[:2],
         )
         ratio_x, ratio_y = real_x / resized_x, real_y / resized_y
-    
+
         # Convert the base image from BGR to RGB format.
         rgb_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2RGB)
-    
+
         # Iterate through non-zero boxes.
         for box in boxes:
             # Pick a confidence factor from the last place in an array.
@@ -884,10 +884,10 @@ Visualization
                 (x_min, y_min, x_max, y_max) = [
                     (int(max(corner_position * ratio_y, 10)) if idx % 2 else int(corner_position * ratio_x)) for idx, corner_position in enumerate(box[:-1])
                 ]
-    
+
                 # Draw a box based on the position, parameters in rectangle function are: image, start_point, end_point, color, thickness.
                 rgb_image = cv2.rectangle(rgb_image, (x_min, y_min), (x_max, y_max), colors["green"], 3)
-    
+
                 # Add text to the image based on position and confidence.
                 # Parameters in text function are: image, text, bottom-left_corner_textfield, font, font_scale, color, thickness, line_type.
                 if conf_labels:
@@ -901,7 +901,7 @@ Visualization
                         1,
                         cv2.LINE_AA,
                     )
-    
+
         return rgb_image
 
 .. code:: ipython3
diff --git a/docs/notebooks/model-tools-with-output.rst b/docs/notebooks/model-tools-with-output.rst
deleted file mode 100644
index 1afe0b0a33c43d..00000000000000
--- a/docs/notebooks/model-tools-with-output.rst
+++ /dev/null
@@ -1,537 +0,0 @@
-Working with Open Model Zoo Models
-==================================
-
-This tutorial shows how to download a model from `Open Model
-Zoo <https://github.com/openvinotoolkit/open_model_zoo>`__, convert it
-to OpenVINO™ IR format, show information about the model, and benchmark
-the model.
-
-Table of contents:
-^^^^^^^^^^^^^^^^^^
-
--  `OpenVINO and Open Model Zoo
-   Tools <#openvino-and-open-model-zoo-tools>`__
--  `Preparation <#preparation>`__
-
-   -  `Model Name <#model-name>`__
-   -  `Imports <#imports>`__
-   -  `Settings and Configuration <#settings-and-configuration>`__
-
--  `Download a Model from Open Model
-   Zoo <#download-a-model-from-open-model-zoo>`__
--  `Convert a Model to OpenVINO IR
-   format <#convert-a-model-to-openvino-ir-format>`__
--  `Get Model Information <#get-model-information>`__
--  `Run Benchmark Tool <#run-benchmark-tool>`__
-
-   -  `Benchmark with Different
-      Settings <#benchmark-with-different-settings>`__
-
-OpenVINO and Open Model Zoo Tools
----------------------------------
-
-
-
-OpenVINO and Open Model Zoo tools are listed in the table below.
-
-+------------+--------------+-----------------------------------------+
-| Tool       | Command      | Description                             |
-+============+==============+=========================================+
-| Model      | ``omz_downlo | Download models from Open Model Zoo.    |
-| Downloader | ader``       |                                         |
-+------------+--------------+-----------------------------------------+
-| Model      | ``omz_conver | Convert Open Model Zoo models to        |
-| Converter  | ter``        | OpenVINO’s IR format.                   |
-+------------+--------------+-----------------------------------------+
-| Info       | ``omz_info_d | Print information about Open Model Zoo  |
-| Dumper     | umper``      | models.                                 |
-+------------+--------------+-----------------------------------------+
-| Benchmark  | ``benchmark_ | Benchmark model performance by          |
-| Tool       | app``        | computing inference time.               |
-+------------+--------------+-----------------------------------------+
-
-.. code:: ipython3
-
-    # Install openvino package
-    %pip install -q "openvino-dev>=2024.0.0" torch torchvision --extra-index-url https://download.pytorch.org/whl/cpu
-
-
-.. parsed-literal::
-
-    Note: you may need to restart the kernel to use updated packages.
-
-
-Preparation
------------
-
-
-
-Model Name
-~~~~~~~~~~
-
-
-
-Set ``model_name`` to the name of the Open Model Zoo model to use in
-this notebook. Refer to the list of
-`public <https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/public/index.md>`__
-and
-`Intel <https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/intel/index.md>`__
-pre-trained models for a full list of models that can be used. Set
-``model_name`` to the model you want to use.
-
-.. code:: ipython3
-
-    # model_name = "resnet-50-pytorch"
-    model_name = "mobilenet-v2-pytorch"
-
-Imports
-~~~~~~~
-
-
-
-.. code:: ipython3
-
-    import json
-    from pathlib import Path
-    
-    import openvino as ov
-    from IPython.display import Markdown, display
-    
-    # Fetch `notebook_utils` module
-    import requests
-    
-    r = requests.get(
-        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
-    )
-    
-    open("notebook_utils.py", "w").write(r.text)
-    from notebook_utils import DeviceNotFoundAlert, NotebookAlert
-
-Settings and Configuration
-~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-Set the file and directory paths. By default, this notebook downloads
-models from Open Model Zoo to the ``open_model_zoo_models`` directory in
-your ``$HOME`` directory. On Windows, the $HOME directory is usually
-``c:\users\username``, on Linux ``/home/username``. To change the
-folder, change ``base_model_dir`` in the cell below.
-
-The following settings can be changed:
-
--  ``base_model_dir``: Models will be downloaded into the ``intel`` and
-   ``public`` folders in this directory.
--  ``omz_cache_dir``: Cache folder for Open Model Zoo. Specifying a
-   cache directory is not required for Model Downloader and Model
-   Converter, but it speeds up subsequent downloads.
--  ``precision``: If specified, only models with this precision will be
-   downloaded and converted.
-
-.. code:: ipython3
-
-    base_model_dir = Path("model")
-    omz_cache_dir = Path("cache")
-    precision = "FP16"
-    
-    # Check if an GPU is available on this system to use with Benchmark App.
-    core = ov.Core()
-    gpu_available = "GPU" in core.available_devices
-    
-    print(f"base_model_dir: {base_model_dir}, omz_cache_dir: {omz_cache_dir}, gpu_availble: {gpu_available}")
-
-
-.. parsed-literal::
-
-    base_model_dir: model, omz_cache_dir: cache, gpu_availble: False
-
-
-Download a Model from Open Model Zoo
-------------------------------------
-
-
-
-Specify, display and run the Model Downloader command to download the
-model.
-
-.. code:: ipython3
-
-    ## Uncomment the next line to show help in omz_downloader which explains the command-line options.
-    
-    # !omz_downloader --help
-
-.. code:: ipython3
-
-    download_command = f"omz_downloader --name {model_name} --output_dir {base_model_dir} --cache_dir {omz_cache_dir}"
-    display(Markdown(f"Download command: `{download_command}`"))
-    display(Markdown(f"Downloading {model_name}..."))
-    ! $download_command
-
-
-
-Download command:
-``omz_downloader --name mobilenet-v2-pytorch --output_dir model --cache_dir cache``
-
-
-
-Downloading mobilenet-v2-pytorch…
-
-
-.. parsed-literal::
-
-    ################|| Downloading mobilenet-v2-pytorch ||################
-    
-    ========== Downloading model/public/mobilenet-v2-pytorch/mobilenet_v2-b0353104.pth
-    
-    
-
-
-Convert a Model to OpenVINO IR format
--------------------------------------
-
-
-
-Specify, display and run the Model Converter command to convert the
-model to OpenVINO IR format. Model conversion may take a while. The
-output of the Model Converter command will be displayed. When the
-conversion is successful, the last lines of the output will include:
-``[ SUCCESS ] Generated IR version 11 model.`` For downloaded models
-that are already in OpenVINO IR format, conversion will be skipped.
-
-.. code:: ipython3
-
-    ## Uncomment the next line to show Help in omz_converter which explains the command-line options.
-    
-    # !omz_converter --help
-
-.. code:: ipython3
-
-    convert_command = f"omz_converter --name {model_name} --precisions {precision} --download_dir {base_model_dir} --output_dir {base_model_dir}"
-    display(Markdown(f"Convert command: `{convert_command}`"))
-    display(Markdown(f"Converting {model_name}..."))
-    
-    ! $convert_command
-
-
-
-Convert command:
-``omz_converter --name mobilenet-v2-pytorch --precisions FP16 --download_dir model --output_dir model``
-
-
-
-Converting mobilenet-v2-pytorch…
-
-
-.. parsed-literal::
-
-    ========== Converting mobilenet-v2-pytorch to ONNX
-    Conversion to ONNX command: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/bin/python -- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/omz_tools/internal_scripts/pytorch_to_onnx.py --model-name=mobilenet_v2 --weights=model/public/mobilenet-v2-pytorch/mobilenet_v2-b0353104.pth --import-module=torchvision.models --input-shape=1,3,224,224 --output-file=model/public/mobilenet-v2-pytorch/mobilenet-v2.onnx --input-names=data --output-names=prob
-    
-    ONNX check passed successfully.
-    
-    ========== Converting mobilenet-v2-pytorch to IR (FP16)
-    Conversion command: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/bin/python -- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/bin/mo --framework=onnx --output_dir=model/public/mobilenet-v2-pytorch/FP16 --model_name=mobilenet-v2-pytorch --input=data '--mean_values=data[123.675,116.28,103.53]' '--scale_values=data[58.624,57.12,57.375]' --reverse_input_channels --output=prob --input_model=model/public/mobilenet-v2-pytorch/mobilenet-v2.onnx '--layout=data(NCHW)' '--input_shape=[1, 3, 224, 224]' --compress_to_fp16=True
-    
-    [ INFO ] Generated IR will be compressed to FP16. If you get lower accuracy, please consider disabling compression explicitly by adding argument --compress_to_fp16=False.
-    Find more information about compression to FP16 at https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_FP16_Compression.html
-    [ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release. Please use OpenVINO Model Converter (OVC). OVC represents a lightweight alternative of MO and provides simplified model conversion API. 
-    Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
-    [ SUCCESS ] Generated IR version 11 model.
-    [ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/model-tools/model/public/mobilenet-v2-pytorch/FP16/mobilenet-v2-pytorch.xml
-    [ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/model-tools/model/public/mobilenet-v2-pytorch/FP16/mobilenet-v2-pytorch.bin
-    
-
-
-Get Model Information
----------------------
-
-
-
-The Info Dumper prints the following information for Open Model Zoo
-models:
-
--  Model name
--  Description
--  Framework that was used to train the model
--  License URL
--  Precisions supported by the model
--  Subdirectory: the location of the downloaded model
--  Task type
-
-This information can be shown by running
-``omz_info_dumper --name model_name`` in a terminal. The information can
-also be parsed and used in scripts.
-
-In the next cell, run Info Dumper and use ``json`` to load the
-information in a dictionary.
-
-.. code:: ipython3
-
-    model_info_output = %sx omz_info_dumper --name $model_name
-    model_info = json.loads(model_info_output.get_nlstr())
-    
-    if len(model_info) > 1:
-        NotebookAlert(
-            f"There are multiple IR files for the {model_name} model. The first model in the "
-            "omz_info_dumper output will be used for benchmarking. Change "
-            "`selected_model_info` in the cell below to select a different model from the list.",
-            "warning",
-        )
-    
-    model_info
-
-
-
-
-.. parsed-literal::
-
-    [{'name': 'mobilenet-v2-pytorch',
-      'composite_model_name': None,
-      'description': 'MobileNet V2 is image classification model pre-trained on ImageNet dataset. This is a PyTorch* implementation of MobileNetV2 architecture as described in the paper "Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation" <https://arxiv.org/abs/1801.04381>.\nThe model input is a blob that consists of a single image of "1, 3, 224, 224" in "RGB" order.\nThe model output is typical object classifier for the 1000 different classifications matching with those in the ImageNet database.',
-      'framework': 'pytorch',
-      'license_url': 'https://raw.githubusercontent.com/pytorch/vision/master/LICENSE',
-      'accuracy_config': '/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/omz_tools/models/public/mobilenet-v2-pytorch/accuracy-check.yml',
-      'model_config': '/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/omz_tools/models/public/mobilenet-v2-pytorch/model.yml',
-      'precisions': ['FP16', 'FP32'],
-      'subdirectory': 'public/mobilenet-v2-pytorch',
-      'task_type': 'classification',
-      'input_info': [{'name': 'data',
-        'shape': [1, 3, 224, 224],
-        'layout': 'NCHW'}],
-      'model_stages': []}]
-
-
-
-Having information of the model in a JSON file enables extraction of the
-path to the model directory, and building the path to the OpenVINO IR
-file.
-
-.. code:: ipython3
-
-    selected_model_info = model_info[0]
-    model_path = base_model_dir / Path(selected_model_info["subdirectory"]) / Path(f"{precision}/{selected_model_info['name']}.xml")
-    print(model_path, "exists:", model_path.exists())
-
-
-.. parsed-literal::
-
-    model/public/mobilenet-v2-pytorch/FP16/mobilenet-v2-pytorch.xml exists: True
-
-
-Run Benchmark Tool
-------------------
-
-
-
-By default, Benchmark Tool runs inference for 60 seconds in asynchronous
-mode on CPU. It returns inference speed as latency (milliseconds per
-image) and throughput values (frames per second).
-
-.. code:: ipython3
-
-    ## Uncomment the next line to show Help in benchmark_app which explains the command-line options.
-    # !benchmark_app --help
-
-.. code:: ipython3
-
-    benchmark_command = f"benchmark_app -m {model_path} -t 15"
-    display(Markdown(f"Benchmark command: `{benchmark_command}`"))
-    display(Markdown(f"Benchmarking {model_name} on CPU with async inference for 15 seconds..."))
-    
-    ! $benchmark_command
-
-
-
-Benchmark command:
-``benchmark_app -m model/public/mobilenet-v2-pytorch/FP16/mobilenet-v2-pytorch.xml -t 15``
-
-
-
-Benchmarking mobilenet-v2-pytorch on CPU with async inference for 15
-seconds…
-
-
-.. parsed-literal::
-
-    [Step 1/11] Parsing and validating input arguments
-    [ INFO ] Parsing input parameters
-    [Step 2/11] Loading OpenVINO Runtime
-    [ INFO ] OpenVINO:
-    [ INFO ] Build ................................. 2024.1.0-15008-f4afc983258-releases/2024/1
-    [ INFO ] 
-    [ INFO ] Device info:
-    [ INFO ] CPU
-    [ INFO ] Build ................................. 2024.1.0-15008-f4afc983258-releases/2024/1
-    [ INFO ] 
-    [ INFO ] 
-    [Step 3/11] Setting device configuration
-    [ WARNING ] Performance hint was not explicitly specified in command line. Device(CPU) performance hint will be set to PerformanceMode.THROUGHPUT.
-    [Step 4/11] Reading model files
-    [ INFO ] Loading model files
-    [ INFO ] Read model took 27.44 ms
-    [ INFO ] Original model I/O parameters:
-    [ INFO ] Model inputs:
-    [ INFO ]     data (node: data) : f32 / [N,C,H,W] / [1,3,224,224]
-    [ INFO ] Model outputs:
-    [ INFO ]     prob (node: prob) : f32 / [...] / [1,1000]
-    [Step 5/11] Resizing model to match image sizes and given batch
-    [ INFO ] Model batch size: 1
-    [Step 6/11] Configuring input of the model
-    [ INFO ] Model inputs:
-    [ INFO ]     data (node: data) : u8 / [N,C,H,W] / [1,3,224,224]
-    [ INFO ] Model outputs:
-    [ INFO ]     prob (node: prob) : f32 / [...] / [1,1000]
-    [Step 7/11] Loading the model to the device
-    [ INFO ] Compile model took 162.51 ms
-    [Step 8/11] Querying optimal runtime parameters
-    [ INFO ] Model:
-    [ INFO ]   NETWORK_NAME: main_graph
-    [ INFO ]   OPTIMAL_NUMBER_OF_INFER_REQUESTS: 6
-    [ INFO ]   NUM_STREAMS: 6
-    [ INFO ]   AFFINITY: Affinity.CORE
-    [ INFO ]   INFERENCE_NUM_THREADS: 24
-    [ INFO ]   PERF_COUNT: NO
-    [ INFO ]   INFERENCE_PRECISION_HINT: <Type: 'float32'>
-    [ INFO ]   PERFORMANCE_HINT: THROUGHPUT
-    [ INFO ]   EXECUTION_MODE_HINT: ExecutionMode.PERFORMANCE
-    [ INFO ]   PERFORMANCE_HINT_NUM_REQUESTS: 0
-    [ INFO ]   ENABLE_CPU_PINNING: True
-    [ INFO ]   SCHEDULING_CORE_TYPE: SchedulingCoreType.ANY_CORE
-    [ INFO ]   MODEL_DISTRIBUTION_POLICY: set()
-    [ INFO ]   ENABLE_HYPER_THREADING: True
-    [ INFO ]   EXECUTION_DEVICES: ['CPU']
-    [ INFO ]   CPU_DENORMALS_OPTIMIZATION: False
-    [ INFO ]   LOG_LEVEL: Level.NO
-    [ INFO ]   CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE: 1.0
-    [ INFO ]   DYNAMIC_QUANTIZATION_GROUP_SIZE: 0
-    [ INFO ]   KV_CACHE_PRECISION: <Type: 'float16'>
-    [Step 9/11] Creating infer requests and preparing input tensors
-    [ WARNING ] No input files were given for input 'data'!. This input will be filled with random values!
-    [ INFO ] Fill input 'data' with random values 
-    [Step 10/11] Measuring performance (Start inference asynchronously, 6 inference requests, limits: 15000 ms duration)
-    [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
-    [ INFO ] First inference took 5.52 ms
-    [Step 11/11] Dumping statistics report
-    [ INFO ] Execution Devices:['CPU']
-    [ INFO ] Count:            20316 iterations
-    [ INFO ] Duration:         15008.24 ms
-    [ INFO ] Latency:
-    [ INFO ]    Median:        4.30 ms
-    [ INFO ]    Average:       4.30 ms
-    [ INFO ]    Min:           2.64 ms
-    [ INFO ]    Max:           12.94 ms
-    [ INFO ] Throughput:   1353.66 FPS
-
-
-Benchmark with Different Settings
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-The ``benchmark_app`` tool displays logging information that is not
-always necessary. A more compact result is achieved when the output is
-parsed with ``json``.
-
-The following cells show some examples of ``benchmark_app`` with
-different parameters. Below are some useful parameters:
-
--  ``-d`` A device to use for inference. For example: CPU, GPU, MULTI.
-   Default: CPU.
--  ``-t`` Time expressed in number of seconds to run inference. Default:
-   60.
--  ``-api`` Use asynchronous (async) or synchronous (sync) inference.
-   Default: async.
--  ``-b`` Batch size. Default: 1.
-
-Run ``! benchmark_app --help`` to get an overview of all possible
-command-line parameters.
-
-In the next cell, define the ``benchmark_model()`` function that calls
-``benchmark_app``. This makes it easy to try different combinations. In
-the cell below that, you display available devices on the system.
-
-   **Note**: In this notebook, ``benchmark_app`` runs for 15 seconds to
-   give a quick indication of performance. For more accurate
-   performance, it is recommended to run inference for at least one
-   minute by setting the ``t`` parameter to 60 or higher, and run
-   ``benchmark_app`` in a terminal/command prompt after closing other
-   applications. Copy the **benchmark command** and paste it in a
-   command prompt where you have activated the ``openvino_env``
-   environment.
-
-.. code:: ipython3
-
-    def benchmark_model(model_xml, device="CPU", seconds=60, api="async", batch=1):
-        core = ov.Core()
-        model_path = Path(model_xml)
-        if ("GPU" in device) and ("GPU" not in core.available_devices):
-            DeviceNotFoundAlert("GPU")
-        else:
-            benchmark_command = f"benchmark_app -m {model_path} -d {device} -t {seconds} -api {api} -b {batch}"
-            display(Markdown(f"**Benchmark {model_path.name} with {device} for {seconds} seconds with {api} inference**"))
-            display(Markdown(f"Benchmark command: `{benchmark_command}`"))
-    
-            benchmark_output = %sx $benchmark_command
-            print("command ended")
-            benchmark_result = [line for line in benchmark_output if not (line.startswith(r"[") or line.startswith("      ") or line == "")]
-            print("\n".join(benchmark_result))
-
-.. code:: ipython3
-
-    core = ov.Core()
-    
-    # Show devices available for OpenVINO Runtime
-    for device in core.available_devices:
-        device_name = core.get_property(device, "FULL_DEVICE_NAME")
-        print(f"{device}: {device_name}")
-
-
-.. parsed-literal::
-
-    CPU: Intel(R) Core(TM) i9-10920X CPU @ 3.50GHz
-
-
-You can select inference device using device widget
-
-.. code:: ipython3
-
-    import ipywidgets as widgets
-    
-    device = widgets.Dropdown(
-        options=core.available_devices + ["AUTO"],
-        value="CPU",
-        description="Device:",
-        disabled=False,
-    )
-    
-    device
-
-
-
-
-.. parsed-literal::
-
-    Dropdown(description='Device:', options=('CPU', 'AUTO'), value='CPU')
-
-
-
-.. code:: ipython3
-
-    benchmark_model(model_path, device=device.value, seconds=15, api="async")
-
-
-
-**Benchmark mobilenet-v2-pytorch.xml with CPU for 15 seconds with async
-inference**
-
-
-
-Benchmark command:
-``benchmark_app -m model/public/mobilenet-v2-pytorch/FP16/mobilenet-v2-pytorch.xml -d CPU -t 15 -api async -b 1``
-
-
-.. parsed-literal::
-
-    command ended
-    
-
diff --git a/docs/notebooks/music-generation-with-output.rst b/docs/notebooks/music-generation-with-output.rst
index 809627d0132720..11742a0fb72ab7 100644
--- a/docs/notebooks/music-generation-with-output.rst
+++ b/docs/notebooks/music-generation-with-output.rst
@@ -30,8 +30,8 @@ We will use a model implementation from the `Hugging Face
 Transformers <https://huggingface.co/docs/transformers/index>`__
 library.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 
@@ -45,11 +45,11 @@ Table of contents:
 -  `Convert models to OpenVINO Intermediate representation (IR)
    format <#convert-models-to-openvino-intermediate-representation-ir-format>`__
 
-   -  `0. Set Up Variables <#0--set-up-variables>`__
-   -  `1. Convert Text Encoder <#1--convert-text-encoder>`__
+   -  `0. Set Up Variables <#set-up-variables>`__
+   -  `1. Convert Text Encoder <#convert-text-encoder>`__
    -  `2. Convert MusicGen Language
       Model <#2--convert-musicgen-language-model>`__
-   -  `3. Convert Audio Decoder <#3--convert-audio-decoder>`__
+   -  `3. Convert Audio Decoder <#convert-audio-decoder>`__
 
 -  `Embedding the converted models into the original
    pipeline <#embedding-the-converted-models-into-the-original-pipeline>`__
@@ -95,7 +95,7 @@ Imports
     from pathlib import Path
     from typing import Optional, Tuple
     import warnings
-    
+
     from IPython.display import Audio
     import openvino as ov
     import numpy as np
@@ -106,20 +106,20 @@ Imports
         BaseModelOutputWithPastAndCrossAttentions,
         CausalLMOutputWithCrossAttentions,
     )
-    
+
     # Ignore tracing warnings
     warnings.filterwarnings("ignore", category=TracerWarning)
 
 
 .. parsed-literal::
 
-    2024-06-06 00:49:52.057444: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-06-06 00:49:52.090852: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-07-02 00:55:03.002895: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-07-02 00:55:03.036941: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2024-06-06 00:49:52.582434: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/utils/generic.py:311: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
+    2024-07-02 00:55:03.639649: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/utils/generic.py:311: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
       torch.utils._pytree._register_pytree_node(
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/utils/generic.py:311: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/utils/generic.py:311: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
       torch.utils._pytree._register_pytree_node(
 
 
@@ -141,29 +141,29 @@ generate a text-conditioned music sample.
 
     import sys
     from packaging.version import parse
-    
-    
+
+
     if sys.version_info < (3, 8):
         import importlib_metadata
     else:
         import importlib.metadata as importlib_metadata
     loading_kwargs = {}
-    
+
     if parse(importlib_metadata.version("transformers")) >= parse("4.40.0"):
         loading_kwargs["attn_implementation"] = "eager"
-    
-    
+
+
     # Load the pipeline
     model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-small", torchscript=True, return_dict=False, **loading_kwargs)
 
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
       warnings.warn(
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/utils/generic.py:311: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/utils/generic.py:311: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
       torch.utils._pytree._register_pytree_node(
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/utils/weight_norm.py:28: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/utils/weight_norm.py:28: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.
       warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.")
 
 
@@ -173,11 +173,11 @@ length.
 .. code:: ipython3
 
     sample_length = 8  # seconds
-    
+
     n_tokens = sample_length * model.config.audio_encoder.frame_rate + 3
     sampling_rate = model.config.audio_encoder.sampling_rate
     print("Sampling rate is", sampling_rate, "Hz")
-    
+
     model.to("cpu")
     model.eval();
 
@@ -201,14 +201,14 @@ vocabulary. It helps the model understand the context of a sentence.
 .. code:: ipython3
 
     processor = AutoProcessor.from_pretrained("facebook/musicgen-small")
-    
+
     inputs = processor(
         text=["80s pop track with bassy drums and synth"],
         return_tensors="pt",
     )
-    
+
     audio_values = model.generate(**inputs, do_sample=True, guidance_scale=3, max_new_tokens=n_tokens)
-    
+
     Audio(audio_values[0].cpu().numpy(), rate=sampling_rate)
 
 
@@ -216,9 +216,9 @@ vocabulary. It helps the model understand the context of a sentence.
 
 .. raw:: html
 
-    
+
     <audio  controls="controls" >
-        <source 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" 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" type="audio/wav" />
         Your browser does not support the audio element.
     </audio>
 
@@ -289,7 +289,7 @@ runtime <https://docs.openvino.ai/2024/openvino-workflow/running-inference.html>
 
     if not t5_ir_path.exists():
         t5_ov = ov.convert_model(model.text_encoder, example_input={"input_ids": inputs["input_ids"]})
-    
+
         ov.save_model(t5_ov, t5_ir_path)
         del t5_ov
         gc.collect()
@@ -325,7 +325,7 @@ the indices of audio codes, ``encoder_hidden_states`` and
 
     # Set model config `torchscript` to True, so the model returns a tuple as output
     model.decoder.config.torchscript = True
-    
+
     if not musicgen_0_ir_path.exists():
         decoder_input = {
             "input_ids": torch.ones(8, 1, dtype=torch.int64),
@@ -333,7 +333,7 @@ the indices of audio codes, ``encoder_hidden_states`` and
             "encoder_attention_mask": torch.ones(2, 12, dtype=torch.int64),
         }
         mg_ov_0_step = ov.convert_model(model.decoder, example_input=decoder_input)
-    
+
         ov.save_model(mg_ov_0_step, musicgen_0_ir_path)
         del mg_ov_0_step
         gc.collect()
@@ -362,14 +362,14 @@ second time, with an increased number of inputs.
             ]
             * 24
         )
-    
+
         mg_ov = ov.convert_model(model.decoder, example_input=decoder_input)
         for input in mg_ov.inputs[3:]:
             input.get_node().set_partial_shape(ov.PartialShape([-1, 16, -1, 64]))
             input.get_node().set_element_type(ov.Type.f32)
-    
+
         mg_ov.validate_nodes_and_infer_types()
-    
+
         ov.save_model(mg_ov, musicgen_ir_path)
         del mg_ov
         gc.collect()
@@ -387,17 +387,17 @@ corresponding `OpenVINO example <../encodec-audio-compression>`__.
 .. code:: ipython3
 
     if not audio_decoder_ir_path.exists():
-    
+
         class AudioDecoder(torch.nn.Module):
             def __init__(self, model):
                 super().__init__()
                 self.model = model
-    
+
             def forward(self, output_ids):
                 return self.model.decode(output_ids, [None])
-    
+
         audio_decoder_input = {"output_ids": torch.ones((1, 1, 4, n_tokens - 3), dtype=torch.int64)}
-    
+
         with torch.no_grad():
             audio_decoder_ov = ov.convert_model(AudioDecoder(model.audio_encoder), example_input=audio_decoder_input)
         ov.save_model(audio_decoder_ov, audio_decoder_ir_path)
@@ -431,14 +431,14 @@ from the dropdown list:
 .. code:: ipython3
 
     import ipywidgets as widgets
-    
+
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
         value="AUTO",
         description="Device:",
         disabled=False,
     )
-    
+
     device
 
 
@@ -477,13 +477,13 @@ inference into the ``decode`` method.
             super().__init__()
             self.encoder = core.compile_model(encoder_ir, device.value)
             self.config = config
-    
+
         def forward(self, input_ids, **kwargs):
             last_hidden_state = self.encoder(input_ids)[self.encoder.outputs[0]]
             last_hidden_state = torch.tensor(last_hidden_state)
             return BaseModelOutputWithPastAndCrossAttentions(last_hidden_state=last_hidden_state)
-    
-    
+
+
     class MusicGenWrapper(torch.nn.Module):
         def __init__(
             self,
@@ -501,7 +501,7 @@ inference into the ``decode`` method.
             self.num_codebooks = num_codebooks
             self.build_delay_pattern_mask = build_delay_pattern_mask
             self.apply_delay_pattern_mask = apply_delay_pattern_mask
-    
+
         def forward(
             self,
             input_ids: torch.LongTensor = None,
@@ -521,21 +521,21 @@ inference into the ``decode`` method.
                     encoder_attention_mask,
                     *past_key_values,
                 )
-    
+
             output = model(arguments)
             return CausalLMOutputWithCrossAttentions(
                 logits=torch.tensor(output[model.outputs[0]]),
                 past_key_values=tuple([output[model.outputs[i]] for i in range(1, 97)]),
             )
-    
-    
+
+
     class AudioDecoderWrapper(torch.nn.Module):
         def __init__(self, decoder_ir, config):
             super().__init__()
             self.decoder = core.compile_model(decoder_ir, device.value)
             self.config = config
             self.output_type = namedtuple("AudioDecoderOutput", ["audio_values"])
-    
+
         def decode(self, output_ids, audio_scales):
             output = self.decoder(output_ids)[self.decoder.outputs[0]]
             return self.output_type(audio_values=torch.tensor(output))
@@ -554,17 +554,17 @@ Now we initialize the wrapper objects and load them to the HF pipeline
         model.decoder.apply_delay_pattern_mask,
     )
     audio_encoder_ov = AudioDecoderWrapper(audio_decoder_ir_path, model.audio_encoder.config)
-    
+
     del model.text_encoder
     del model.decoder
     del model.audio_encoder
     gc.collect()
-    
+
     model.text_encoder = text_encode_ov
     model.decoder = musicgen_decoder_ov
     model.audio_encoder = audio_encoder_ov
-    
-    
+
+
     def prepare_inputs_for_generation(
         self,
         decoder_input_ids,
@@ -589,21 +589,21 @@ Now we initialize the wrapper objects and load them to the HF pipeline
                 self.generation_config.pad_token_id,
                 max_length=self.generation_config.max_length,
             )
-    
+
         # apply the delay pattern mask
         decoder_input_ids = self.decoder.apply_delay_pattern_mask(decoder_input_ids, decoder_delay_pattern_mask)
-    
+
         if guidance_scale is not None and guidance_scale > 1:
             # for classifier free guidance we need to replicate the decoder args across the batch dim (we'll split these
             # before sampling)
             decoder_input_ids = decoder_input_ids.repeat((2, 1))
             if decoder_attention_mask is not None:
                 decoder_attention_mask = decoder_attention_mask.repeat((2, 1))
-    
+
         if past_key_values is not None:
             # cut decoder_input_ids if past is used
             decoder_input_ids = decoder_input_ids[:, -1:]
-    
+
         return {
             "input_ids": None,  # encoder_outputs is defined. input_ids not needed
             "encoder_outputs": encoder_outputs,
@@ -616,8 +616,8 @@ Now we initialize the wrapper objects and load them to the HF pipeline
             "cross_attn_head_mask": cross_attn_head_mask,
             "use_cache": use_cache,
         }
-    
-    
+
+
     model.prepare_inputs_for_generation = partial(prepare_inputs_for_generation, model)
 
 We can now infer the pipeline backed by OpenVINO models.
@@ -625,14 +625,14 @@ We can now infer the pipeline backed by OpenVINO models.
 .. code:: ipython3
 
     processor = AutoProcessor.from_pretrained("facebook/musicgen-small")
-    
+
     inputs = processor(
         text=["80s pop track with bassy drums and synth"],
         return_tensors="pt",
     )
-    
+
     audio_values = model.generate(**inputs, do_sample=True, guidance_scale=3, max_new_tokens=n_tokens)
-    
+
     Audio(audio_values[0].cpu().numpy(), rate=sampling_rate)
 
 
@@ -640,9 +640,9 @@ We can now infer the pipeline backed by OpenVINO models.
 
 .. raw:: html
 
-    
+
     <audio  controls="controls" >
-        <source src="data:audio/wav;base64,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" type="audio/wav" />
         Your browser does not support the audio element.
     </audio>
 
@@ -674,7 +674,7 @@ package <https://www.gradio.app/docs/interface>`__
 .. code:: ipython3
 
     import gradio as gr
-    
+
     demo = gr.Interface(
         _generate,
         inputs=[
@@ -693,7 +693,7 @@ package <https://www.gradio.app/docs/interface>`__
         demo.launch(debug=False)
     except Exception:
         demo.launch(share=True, debug=False)
-    
+
     # If you are launching remotely, specify server_name and server_port
     # EXAMPLE: `demo.launch(server_name='your server name', server_port='server port in int')`
     # To learn more please refer to the Gradio docs: https://gradio.app/docs/
@@ -702,7 +702,7 @@ package <https://www.gradio.app/docs/interface>`__
 .. parsed-literal::
 
     Running on local URL:  http://127.0.0.1:7860
-    
+
     To create a public link, set `share=True` in `launch()`.
 
 
diff --git a/docs/notebooks/named-entity-recognition-with-output.rst b/docs/notebooks/named-entity-recognition-with-output.rst
index 8572c7a096829c..8d4dd5e7f438ee 100644
--- a/docs/notebooks/named-entity-recognition-with-output.rst
+++ b/docs/notebooks/named-entity-recognition-with-output.rst
@@ -25,8 +25,8 @@ To simplify the user experience, the `Hugging Face
 Optimum <https://huggingface.co/docs/optimum>`__ library is used to
 convert the model to OpenVINO™ IR format and quantize it.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Download the NER model <#download-the-ner-model>`__
@@ -175,6 +175,7 @@ corresponding ``OVModelForXxx`` class. So we use
         num_samples=100,
         dataset_split="train",
         preprocess_batch=True,
+        trust_remote_code=True,
     )
     
     # The directory where the quantized model will be saved
@@ -203,17 +204,17 @@ corresponding ``OVModelForXxx`` class. So we use
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -224,17 +225,17 @@ corresponding ``OVModelForXxx`` class. So we use
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -251,17 +252,17 @@ corresponding ``OVModelForXxx`` class. So we use
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -272,17 +273,17 @@ corresponding ``OVModelForXxx`` class. So we use
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
diff --git a/docs/notebooks/nano-llava-multimodal-chatbot-with-output.rst b/docs/notebooks/nano-llava-multimodal-chatbot-with-output.rst
index 3ca5b3d8870040..873f65c38134ce 100644
--- a/docs/notebooks/nano-llava-multimodal-chatbot-with-output.rst
+++ b/docs/notebooks/nano-llava-multimodal-chatbot-with-output.rst
@@ -10,8 +10,8 @@ this tutorial, we consider how to convert and run nanoLLaVA model using
 OpenVINO. Additionally, we will optimize model using
 `NNCF <https://github.com/openvinotoolkit/nncf>`__
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Load PyTorch model <#load-pytorch-model>`__
@@ -93,49 +93,49 @@ Prerequisites
 
 .. parsed-literal::
 
-    example_1.png:   0%|          | 0.00/200k [00:00<?, ?B/s]
+    README.md:   0%|          | 0.00/3.47k [00:00<?, ?B/s]
 
 
 
 .. parsed-literal::
 
-    .gitattributes:   0%|          | 0.00/1.52k [00:00<?, ?B/s]
+    merges.txt:   0%|          | 0.00/1.67M [00:00<?, ?B/s]
 
 
 
 .. parsed-literal::
 
-    merges.txt:   0%|          | 0.00/1.67M [00:00<?, ?B/s]
+    config.json:   0%|          | 0.00/1.28k [00:00<?, ?B/s]
 
 
 
 .. parsed-literal::
 
-    README.md:   0%|          | 0.00/3.47k [00:00<?, ?B/s]
+    example_1.png:   0%|          | 0.00/200k [00:00<?, ?B/s]
 
 
 
 .. parsed-literal::
 
-    added_tokens.json:   0%|          | 0.00/80.0 [00:00<?, ?B/s]
+    configuration_llava_qwen2.py:   0%|          | 0.00/8.87k [00:00<?, ?B/s]
 
 
 
 .. parsed-literal::
 
-    config.json:   0%|          | 0.00/1.28k [00:00<?, ?B/s]
+    .gitattributes:   0%|          | 0.00/1.52k [00:00<?, ?B/s]
 
 
 
 .. parsed-literal::
 
-    configuration_llava_qwen2.py:   0%|          | 0.00/8.87k [00:00<?, ?B/s]
+    generation_config.json:   0%|          | 0.00/172 [00:00<?, ?B/s]
 
 
 
 .. parsed-literal::
 
-    generation_config.json:   0%|          | 0.00/172 [00:00<?, ?B/s]
+    added_tokens.json:   0%|          | 0.00/80.0 [00:00<?, ?B/s]
 
 
 
@@ -147,19 +147,19 @@ Prerequisites
 
 .. parsed-literal::
 
-    special_tokens_map.json:   0%|          | 0.00/510 [00:00<?, ?B/s]
+    tokenizer_config.json:   0%|          | 0.00/1.32k [00:00<?, ?B/s]
 
 
 
 .. parsed-literal::
 
-    vocab.json:   0%|          | 0.00/2.78M [00:00<?, ?B/s]
+    special_tokens_map.json:   0%|          | 0.00/510 [00:00<?, ?B/s]
 
 
 
 .. parsed-literal::
 
-    tokenizer_config.json:   0%|          | 0.00/1.32k [00:00<?, ?B/s]
+    vocab.json:   0%|          | 0.00/2.78M [00:00<?, ?B/s]
 
 
 
@@ -200,10 +200,10 @@ previous step.
 
 .. parsed-literal::
 
-    2024-06-06 00:55:00.994609: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-06-06 00:55:01.029081: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-06-27 01:02:36.883651: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-06-27 01:02:36.917268: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2024-06-06 00:55:01.640957: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-06-27 01:02:37.523024: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 Run PyTorch Model Inference
@@ -252,8 +252,8 @@ Run PyTorch Model Inference
 
 .. parsed-literal::
 
-    The image showcases a charming, white lamb, with its body painted in shades of pink and yellow. The lamb's fur appears fluffy and fluffy, with its ears and legs slightly darker than the rest of its body. The lamb's face is particularly cute, with its black nose and small black eyes adding a touch of contrast to its white fur. The lamb's face is lit up with a radiant smile, making it seem even more endearing. The lamb's eyes are a captivating shade of brown, adding a touch of warmth to its overall appearance.
-    The lamb is positioned in the middle of the image, and its body fills a large part
+    The image features a white lama, with its fluffy woolly body covering most of the image, including the upper part of the image. The lama's face is predominantly white, and it has a black nose and two small black eyes. It also has a small white mouth and two small black ears. The lama's ears are quite cute, and it has its tongue out, which is pink and seems to be sticking out. The lama has a small black nose and a small black mouth, and it has a small black ear.
+    The lama's face is adorned with pink eyes and two black noses. The eyes are
 
 
 Convert and Optimize model
@@ -512,17 +512,17 @@ space.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -641,17 +641,17 @@ token prediction.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -674,17 +674,17 @@ token prediction.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
diff --git a/docs/notebooks/notebooks_tags.json b/docs/notebooks/notebooks_tags.json
deleted file mode 100644
index fc712c32db8886..00000000000000
--- a/docs/notebooks/notebooks_tags.json
+++ /dev/null
@@ -1,11 +0,0 @@
-{
-    "3D-pose-estimation": [
-        "Convert Model",
-        "Download Model",
-        "ONNX"
-    ],
-    "3D-segmentation-point-clouds": [
-        "Dynamic Shape",
-        "ONNX"
-    ]
-}
diff --git a/docs/notebooks/notebooks_with_binder_buttons.txt b/docs/notebooks/notebooks_with_binder_buttons.txt
index e036efc5e790d3..ce9cb50da47907 100644
--- a/docs/notebooks/notebooks_with_binder_buttons.txt
+++ b/docs/notebooks/notebooks_with_binder_buttons.txt
@@ -15,11 +15,9 @@ hello-segmentation
 hello-world
 hugging-face-hub
 image-classification-quantization
-image-inpainting
 knowledge-graphs-conve
 magika-content-type-recognition
 meter-reader
-model-tools
 music-generation
 object-detection
 openvino-api
@@ -31,7 +29,6 @@ person-tracking
 pose-estimation
 pytorch-onnx-to-openvino
 291-rmbg-background-removal
-speech-to-text
 style-transfer
 tensorflow-classification-to-openvino
 tensorflow-hub
@@ -40,5 +37,4 @@ tensorflow-object-detection-to-openvino
 tflite-selfie-segmentation
 vehicle-detection-and-recognition
 vision-background-removal
-vision-image-colorization
 vision-monodepth
diff --git a/docs/notebooks/notebooks_with_colab_buttons.txt b/docs/notebooks/notebooks_with_colab_buttons.txt
index ff711842296f06..3af767082904c6 100644
--- a/docs/notebooks/notebooks_with_colab_buttons.txt
+++ b/docs/notebooks/notebooks_with_colab_buttons.txt
@@ -6,6 +6,7 @@ clip-language-saliency-map
 convert-to-openvino
 cross-lingual-books-alignment
 depth-anything
+depth-anything-v2
 detectron2-to-openvino
 distilbert-sequence-classification
 fast-segment-anything
@@ -20,7 +21,6 @@ knowledge-graphs-conve
 language-quantize-bert
 magika-content-type-recognition
 mobileclip-video-search
-model-tools
 music-generation
 named-entity-recognition
 nano-llava-multimodal-chatbot
@@ -42,7 +42,6 @@ softvc-voice-conversion
 sparsity-optimization
 speculative-sampling
 speech-recognition-quantization-wav2vec2
-speech-to-text
 style-transfer
 surya-line-level-text-detection
 table-question-answering
@@ -51,7 +50,6 @@ tensorflow-hub
 tensorflow-instance-segmentation-to-openvino
 tensorflow-object-detection-to-openvino
 tensorflow-quantization-aware-training
-text-prediction
 tflite-selfie-segmentation
 tflite-to-openvino
 tiny-sd-image-generation
@@ -59,7 +57,7 @@ vision-background-removal
 vision-monodepth
 vision-paddlegan-anime
 vision-paddlegan-superresolution
-whisper-convert
+whisper-subtitles-generation
 yolov8-instance-segmentation
 yolov8-keypoint-detection
 yolov8-obb
diff --git a/docs/notebooks/object-detection-with-output.rst b/docs/notebooks/object-detection-with-output.rst
index 20a786ede3fc09..fedeadc07c06b9 100644
--- a/docs/notebooks/object-detection-with-output.rst
+++ b/docs/notebooks/object-detection-with-output.rst
@@ -14,8 +14,8 @@ Additionally, you can also upload a video file.
    server, the webcam will not work. However, you can still do inference
    on a video.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Preparation <#preparation>`__
 
@@ -72,6 +72,8 @@ Install requirements
     magika 0.5.1 requires numpy<2.0,>=1.24; python_version >= "3.8" and python_version < "3.9", but you have numpy 1.23.5 which is incompatible.
     mobileclip 0.1.0 requires torch==1.13.1, but you have torch 2.3.1+cpu which is incompatible.
     mobileclip 0.1.0 requires torchvision==0.14.1, but you have torchvision 0.18.1+cpu which is incompatible.
+    optimum 1.20.0 requires transformers[sentencepiece]<4.42.0,>=4.26.0, but you have transformers 4.42.3 which is incompatible.
+    optimum-intel 1.19.0.dev0+384dda5 requires transformers<4.42.0,>=4.36.0, but you have transformers 4.42.3 which is incompatible.
     Note: you may need to restart the kernel to use updated packages.
     Note: you may need to restart the kernel to use updated packages.
 
@@ -80,7 +82,7 @@ Install requirements
 
 .. parsed-literal::
 
-    21503
+    23215
 
 
 
@@ -186,6 +188,13 @@ converted, this step is skipped.
         del ov_model
 
 
+.. parsed-literal::
+
+    [ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release.
+    In 2025.0 MO command line tool and openvino.tools.mo.convert_model() will be removed. Please use OpenVINO Model Converter (OVC) or openvino.convert_model(). OVC represents a lightweight alternative of MO and provides simplified model conversion API. 
+    Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
+
+
 .. parsed-literal::
 
     [ WARNING ]  The Preprocessor block has been removed. Only nodes performing mean value subtraction and scaling (if applicable) are kept.
diff --git a/docs/notebooks/object-detection-with-output_files/object-detection-with-output_19_0.png b/docs/notebooks/object-detection-with-output_files/object-detection-with-output_19_0.png
index afba0a309b559a..605d12bf897503 100644
--- a/docs/notebooks/object-detection-with-output_files/object-detection-with-output_19_0.png
+++ b/docs/notebooks/object-detection-with-output_files/object-detection-with-output_19_0.png
@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:0ed603a97f1687a8b9ce577079909662f0ef54ba3324885625da2057b1255286
-size 175062
+oid sha256:668d7a5846170133eed4910d82a65636a9b54aa440af7ad75a14aa67e010b3f9
+size 175042
diff --git a/docs/notebooks/oneformer-segmentation-with-output.rst b/docs/notebooks/oneformer-segmentation-with-output.rst
index 0a356d389e9839..15eda6237aed8f 100644
--- a/docs/notebooks/oneformer-segmentation-with-output.rst
+++ b/docs/notebooks/oneformer-segmentation-with-output.rst
@@ -24,8 +24,8 @@ of increased latency, however.
 
 .. |image0| image:: https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/oneformer_architecture.png
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Install required libraries <#install-required-libraries>`__
 -  `Prepare the environment <#prepare-the-environment>`__
diff --git a/docs/notebooks/openvino-api-with-output.rst b/docs/notebooks/openvino-api-with-output.rst
deleted file mode 100644
index 401a865d0addf1..00000000000000
--- a/docs/notebooks/openvino-api-with-output.rst
+++ /dev/null
@@ -1,1083 +0,0 @@
-OpenVINO™ Runtime API Tutorial
-==============================
-
-This notebook explains the basics of the OpenVINO Runtime API.
-
-The notebook is divided into sections with headers. The next cell
-contains global requirements for installation and imports. Each section
-is standalone and does not depend on any previous sections. All models
-used in this tutorial are provided as examples. These model files can be
-replaced with your own models. The exact outputs will be different, but
-the process is the same.
-
-Table of contents:
-^^^^^^^^^^^^^^^^^^
-
--  `Loading OpenVINO Runtime and Showing
-   Info <#loading-openvino-runtime-and-showing-info>`__
--  `Loading a Model <#loading-a-model>`__
-
-   -  `OpenVINO IR Model <#openvino-ir-model>`__
-   -  `ONNX Model <#onnx-model>`__
-   -  `PaddlePaddle Model <#paddlepaddle-model>`__
-   -  `TensorFlow Model <#tensorflow-model>`__
-   -  `TensorFlow Lite Model <#tensorflow-lite-model>`__
-   -  `PyTorch Model <#pytorch-model>`__
-
--  `Getting Information about a
-   Model <#getting-information-about-a-model>`__
-
-   -  `Model Inputs <#model-inputs>`__
-   -  `Model Outputs <#model-outputs>`__
-
--  `Doing Inference on a Model <#doing-inference-on-a-model>`__
--  `Reshaping and Resizing <#reshaping-and-resizing>`__
-
-   -  `Change Image Size <#change-image-size>`__
-   -  `Change Batch Size <#change-batch-size>`__
-
--  `Caching a Model <#caching-a-model>`__
-
-.. code:: ipython3
-
-    # Required imports. Please execute this cell first.
-    %pip install -q "openvino>=2023.1.0"
-    %pip install -q requests tqdm ipywidgets
-    
-    # Fetch `notebook_utils` module
-    import requests
-    
-    r = requests.get(
-        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
-    )
-    
-    open("notebook_utils.py", "w").write(r.text)
-    
-    from notebook_utils import download_file
-
-
-.. parsed-literal::
-
-    Note: you may need to restart the kernel to use updated packages.
-    Note: you may need to restart the kernel to use updated packages.
-
-
-Loading OpenVINO Runtime and Showing Info
------------------------------------------
-
-
-
-Initialize OpenVINO Runtime with ``ov.Core()``
-
-.. code:: ipython3
-
-    import openvino as ov
-    
-    core = ov.Core()
-
-OpenVINO Runtime can load a network on a device. A device in this
-context means a CPU, an Intel GPU, a Neural Compute Stick 2, etc. The
-``available_devices`` property shows the available devices in your
-system. The “FULL_DEVICE_NAME” option to ``core.get_property()`` shows
-the name of the device.
-
-.. code:: ipython3
-
-    devices = core.available_devices
-    
-    for device in devices:
-        device_name = core.get_property(device, "FULL_DEVICE_NAME")
-        print(f"{device}: {device_name}")
-
-
-.. parsed-literal::
-
-    CPU: Intel(R) Core(TM) i9-10920X CPU @ 3.50GHz
-
-
-Select device for inference
-~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-You can specify which device from available devices will be used for
-inference using this widget
-
-.. code:: ipython3
-
-    import ipywidgets as widgets
-    
-    device = widgets.Dropdown(
-        options=core.available_devices,
-        value=core.available_devices[0],
-        description="Device:",
-        disabled=False,
-    )
-    
-    device
-
-
-
-
-.. parsed-literal::
-
-    Dropdown(description='Device:', options=('CPU',), value='CPU')
-
-
-
-Loading a Model
----------------
-
-
-
-After initializing OpenVINO Runtime, first read the model file with
-``read_model()``, then compile it to the specified device with the
-``compile_model()`` method.
-
-`OpenVINO™ supports several model
-formats <https://docs.openvino.ai/2024/openvino-workflow/model-preparation/convert-model-to-ir.html>`__
-and enables developers to convert them to its own OpenVINO IR format
-using a tool dedicated to this task.
-
-OpenVINO IR Model
-~~~~~~~~~~~~~~~~~
-
-
-
-An OpenVINO IR (Intermediate Representation) model consists of an
-``.xml`` file, containing information about network topology, and a
-``.bin`` file, containing the weights and biases binary data. Models in
-OpenVINO IR format are obtained by using model conversion API. The
-``read_model()`` function expects the ``.bin`` weights file to have the
-same filename and be located in the same directory as the ``.xml`` file:
-``model_weights_file == Path(model_xml).with_suffix(".bin")``. If this
-is the case, specifying the weights file is optional. If the weights
-file has a different filename, it can be specified using the ``weights``
-parameter in ``read_model()``.
-
-The OpenVINO `Model Conversion
-API <https://docs.openvino.ai/2024/openvino-workflow/model-preparation.html>`__
-tool is used to convert models to OpenVINO IR format. Model conversion
-API reads the original model and creates an OpenVINO IR model (``.xml``
-and ``.bin`` files) so inference can be performed without delays due to
-format conversion. Optionally, model conversion API can adjust the model
-to be more suitable for inference, for example, by alternating input
-shapes, embedding preprocessing and cutting training parts off. For
-information on how to convert your existing TensorFlow, PyTorch or ONNX
-model to OpenVINO IR format with model conversion API, refer to the
-`tensorflow-to-openvino <tensorflow-classification-to-openvino-with-output.html>`__
-and
-`pytorch-onnx-to-openvino <pytorch-to-openvino-with-output.html>`__
-notebooks.
-
-.. code:: ipython3
-
-    ir_model_url = "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/models/002-example-models/"
-    ir_model_name_xml = "classification.xml"
-    ir_model_name_bin = "classification.bin"
-    
-    download_file(ir_model_url + ir_model_name_xml, filename=ir_model_name_xml, directory="model")
-    download_file(ir_model_url + ir_model_name_bin, filename=ir_model_name_bin, directory="model")
-
-
-
-.. parsed-literal::
-
-    model/classification.xml:   0%|          | 0.00/179k [00:00<?, ?B/s]
-
-
-
-.. parsed-literal::
-
-    model/classification.bin:   0%|          | 0.00/4.84M [00:00<?, ?B/s]
-
-
-
-
-.. parsed-literal::
-
-    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/openvino-api/model/classification.bin')
-
-
-
-.. code:: ipython3
-
-    import openvino as ov
-    
-    core = ov.Core()
-    classification_model_xml = "model/classification.xml"
-    
-    model = core.read_model(model=classification_model_xml)
-    compiled_model = core.compile_model(model=model, device_name=device.value)
-
-ONNX Model
-~~~~~~~~~~
-
-
-
-`ONNX <https://onnx.ai/>`__ is an open format built to represent machine
-learning models. ONNX defines a common set of operators - the building
-blocks of machine learning and deep learning models - and a common file
-format to enable AI developers to use models with a variety of
-frameworks, tools, runtimes, and compilers. OpenVINO supports reading
-models in ONNX format directly,that means they can be used with OpenVINO
-Runtime without any prior conversion.
-
-Reading and loading an ONNX model, which is a single ``.onnx`` file,
-works the same way as with an OpenVINO IR model. The ``model`` argument
-points to the filename of an ONNX model.
-
-.. code:: ipython3
-
-    onnx_model_url = "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/models/002-example-models/segmentation.onnx"
-    onnx_model_name = "segmentation.onnx"
-    
-    download_file(onnx_model_url, filename=onnx_model_name, directory="model")
-
-
-
-.. parsed-literal::
-
-    model/segmentation.onnx:   0%|          | 0.00/4.41M [00:00<?, ?B/s]
-
-
-
-
-.. parsed-literal::
-
-    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/openvino-api/model/segmentation.onnx')
-
-
-
-.. code:: ipython3
-
-    import openvino as ov
-    
-    core = ov.Core()
-    onnx_model_path = "model/segmentation.onnx"
-    
-    model_onnx = core.read_model(model=onnx_model_path)
-    compiled_model_onnx = core.compile_model(model=model_onnx, device_name=device.value)
-
-The ONNX model can be exported to OpenVINO IR with ``save_model()``:
-
-.. code:: ipython3
-
-    ov.save_model(model_onnx, output_model="model/exported_onnx_model.xml")
-
-PaddlePaddle Model
-~~~~~~~~~~~~~~~~~~
-
-
-
-`PaddlePaddle <https://www.paddlepaddle.org.cn/documentation/docs/en/guides/index_en.html>`__
-models saved for inference can also be passed to OpenVINO Runtime
-without any conversion step. Pass the filename with extension to
-``read_model`` and exported an OpenVINO IR with ``save_model``
-
-.. code:: ipython3
-
-    paddle_model_url = "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/models/002-example-models/"
-    paddle_model_name = "inference.pdmodel"
-    paddle_params_name = "inference.pdiparams"
-    
-    download_file(paddle_model_url + paddle_model_name, filename=paddle_model_name, directory="model")
-    download_file(
-        paddle_model_url + paddle_params_name,
-        filename=paddle_params_name,
-        directory="model",
-    )
-
-
-
-.. parsed-literal::
-
-    model/inference.pdmodel:   0%|          | 0.00/1.03M [00:00<?, ?B/s]
-
-
-
-.. parsed-literal::
-
-    model/inference.pdiparams:   0%|          | 0.00/21.0M [00:00<?, ?B/s]
-
-
-
-
-.. parsed-literal::
-
-    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/openvino-api/model/inference.pdiparams')
-
-
-
-.. code:: ipython3
-
-    import openvino as ov
-    
-    core = ov.Core()
-    paddle_model_path = "model/inference.pdmodel"
-    
-    model_paddle = core.read_model(model=paddle_model_path)
-    compiled_model_paddle = core.compile_model(model=model_paddle, device_name=device.value)
-
-.. code:: ipython3
-
-    ov.save_model(model_paddle, output_model="model/exported_paddle_model.xml")
-
-TensorFlow Model
-~~~~~~~~~~~~~~~~
-
-
-
-TensorFlow models saved in frozen graph format can also be passed to
-``read_model``.
-
-.. code:: ipython3
-
-    pb_model_url = "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/models/002-example-models/classification.pb"
-    pb_model_name = "classification.pb"
-    
-    download_file(pb_model_url, filename=pb_model_name, directory="model")
-
-
-
-.. parsed-literal::
-
-    model/classification.pb:   0%|          | 0.00/9.88M [00:00<?, ?B/s]
-
-
-
-
-.. parsed-literal::
-
-    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/openvino-api/model/classification.pb')
-
-
-
-.. code:: ipython3
-
-    import openvino as ov
-    
-    core = ov.Core()
-    tf_model_path = "model/classification.pb"
-    
-    model_tf = core.read_model(model=tf_model_path)
-    compiled_model_tf = core.compile_model(model=model_tf, device_name=device.value)
-
-.. code:: ipython3
-
-    ov.save_model(model_tf, output_model="model/exported_tf_model.xml")
-
-TensorFlow Lite Model
-~~~~~~~~~~~~~~~~~~~~~
-
-
-
-`TFLite <https://www.tensorflow.org/lite>`__ models saved for inference
-can also be passed to OpenVINO Runtime. Pass the filename with extension
-``.tflite`` to ``read_model`` and exported an OpenVINO IR with
-``save_model``.
-
-This tutorial uses the image classification model
-`inception_v4_quant <https://tfhub.dev/tensorflow/lite-model/inception_v4_quant/1/default/1>`__.
-It is pre-trained model optimized to work with TensorFlow Lite.
-
-.. code:: ipython3
-
-    from pathlib import Path
-    
-    tflite_model_url = "https://www.kaggle.com/models/tensorflow/inception/frameworks/tfLite/variations/v4-quant/versions/1?lite-format=tflite"
-    tflite_model_path = Path("model/classification.tflite")
-    
-    download_file(
-        tflite_model_url,
-        filename=tflite_model_path.name,
-        directory=tflite_model_path.parent,
-    )
-
-
-
-.. parsed-literal::
-
-    model/classification.tflite:   0%|          | 0.00/40.9M [00:00<?, ?B/s]
-
-
-
-
-.. parsed-literal::
-
-    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/openvino-api/model/classification.tflite')
-
-
-
-.. code:: ipython3
-
-    import openvino as ov
-    
-    core = ov.Core()
-    
-    model_tflite = core.read_model(tflite_model_path)
-    compiled_model_tflite = core.compile_model(model=model_tflite, device_name=device.value)
-
-.. code:: ipython3
-
-    ov.save_model(model_tflite, output_model="model/exported_tflite_model.xml")
-
-PyTorch Model
-~~~~~~~~~~~~~
-
-
-
-`PyTorch <https://pytorch.org/>`__ models can not be directly passed to
-``core.read_model``. ``ov.Model`` for model objects from this framework
-can be obtained using ``ov.convert_model`` API. You can find more
-details in `pytorch-to-openvino <../pytorch-to-openvino>`__ notebook. In
-this tutorial we will use
-`resnet18 <https://pytorch.org/vision/main/models/generated/torchvision.models.resnet18.html>`__
-model form torchvision library. After conversion model using
-``ov.convert_model``, it can be compiled on device using
-``core.compile_model`` or saved on disk for the next usage using
-``ov.save_model``
-
-.. code:: ipython3
-
-    import openvino as ov
-    import torch
-    from torchvision.models import resnet18, ResNet18_Weights
-    
-    core = ov.Core()
-    
-    pt_model = resnet18(weights=ResNet18_Weights.IMAGENET1K_V1)
-    example_input = torch.zeros((1, 3, 224, 224))
-    ov_model_pytorch = ov.convert_model(pt_model, example_input=example_input)
-    
-    compiled_model_pytorch = core.compile_model(ov_model_pytorch, device_name=device.value)
-    
-    ov.save_model(ov_model_pytorch, "model/exported_pytorch_model.xml")
-
-Getting Information about a Model
----------------------------------
-
-
-
-The OpenVINO Model instance stores information about the model.
-Information about the inputs and outputs of the model are in
-``model.inputs`` and ``model.outputs``. These are also properties of the
-``CompiledModel`` instance. While using ``model.inputs`` and
-``model.outputs`` in the cells below, you can also use
-``compiled_model.inputs`` and ``compiled_model.outputs``.
-
-.. code:: ipython3
-
-    ir_model_url = "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/models/002-example-models/"
-    ir_model_name_xml = "classification.xml"
-    ir_model_name_bin = "classification.bin"
-    
-    download_file(ir_model_url + ir_model_name_xml, filename=ir_model_name_xml, directory="model")
-    download_file(ir_model_url + ir_model_name_bin, filename=ir_model_name_bin, directory="model")
-
-
-.. parsed-literal::
-
-    'model/classification.xml' already exists.
-    'model/classification.bin' already exists.
-
-
-
-
-.. parsed-literal::
-
-    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/openvino-api/model/classification.bin')
-
-
-
-Model Inputs
-~~~~~~~~~~~~
-
-
-
-Information about all input layers is stored in the ``inputs``
-dictionary.
-
-.. code:: ipython3
-
-    import openvino as ov
-    
-    core = ov.Core()
-    classification_model_xml = "model/classification.xml"
-    model = core.read_model(model=classification_model_xml)
-    model.inputs
-
-
-
-
-.. parsed-literal::
-
-    [<Output: names[input, input:0] shape[1,3,224,224] type: f32>]
-
-
-
-The cell above shows that the loaded model expects one input with the
-name *input*. If you loaded a different model, you may see a different
-input layer name, and you may see more inputs. You may also obtain info
-about each input layer using ``model.input(index)``, where index is a
-numeric index of the input layers in the model. If a model has only one
-input, index can be omitted.
-
-.. code:: ipython3
-
-    input_layer = model.input(0)
-
-It is often useful to have a reference to the name of the first input
-layer. For a model with one input, ``model.input(0).any_name`` gets this
-name.
-
-.. code:: ipython3
-
-    input_layer.any_name
-
-
-
-
-.. parsed-literal::
-
-    'input'
-
-
-
-The next cell prints the input layout, precision and shape.
-
-.. code:: ipython3
-
-    print(f"input precision: {input_layer.element_type}")
-    print(f"input shape: {input_layer.shape}")
-
-
-.. parsed-literal::
-
-    input precision: <Type: 'float32'>
-    input shape: [1,3,224,224]
-
-
-This cell shows that the model expects inputs with a shape of
-[1,3,224,224], and that this is in the ``NCHW`` layout. This means that
-the model expects input data with the batch size of 1 (``N``), 3
-channels (``C``) , and images with a height (``H``) and width (``W``)
-equal to 224. The input data is expected to be of ``FP32`` (floating
-point) precision.
-
-Model Outputs
-~~~~~~~~~~~~~
-
-
-
-.. code:: ipython3
-
-    import openvino as ov
-    
-    core = ov.Core()
-    classification_model_xml = "model/classification.xml"
-    model = core.read_model(model=classification_model_xml)
-    model.outputs
-
-
-
-
-.. parsed-literal::
-
-    [<Output: names[MobilenetV3/Predictions/Softmax] shape[1,1001] type: f32>]
-
-
-
-Model output info is stored in ``model.outputs``. The cell above shows
-that the model returns one output, with the
-``MobilenetV3/Predictions/Softmax`` name. Loading a different model will
-result in different output layer name, and more outputs might be
-returned. Similar to input, you may also obtain information about each
-output separately using ``model.output(index)``
-
-Since this model has one output, follow the same method as for the input
-layer to get its name.
-
-.. code:: ipython3
-
-    output_layer = model.output(0)
-    output_layer.any_name
-
-
-
-
-.. parsed-literal::
-
-    'MobilenetV3/Predictions/Softmax'
-
-
-
-Getting the output precision and shape is similar to getting the input
-precision and shape.
-
-.. code:: ipython3
-
-    print(f"output precision: {output_layer.element_type}")
-    print(f"output shape: {output_layer.shape}")
-
-
-.. parsed-literal::
-
-    output precision: <Type: 'float32'>
-    output shape: [1,1001]
-
-
-This cell shows that the model returns outputs with a shape of [1,
-1001], where 1 is the batch size (``N``) and 1001 is the number of
-classes (``C``). The output is returned as 32-bit floating point.
-
-Doing Inference on a Model
---------------------------
-
-
-
-   **NOTE** this notebook demonstrates only the basic synchronous
-   inference API. For an async inference example, please refer to `Async
-   API notebook <async-api-with-output.html>`__
-
-The diagram below shows a typical inference pipeline with OpenVINO
-
-.. figure:: https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/a91bc582-165b-41a2-ab08-12c812059936
-   :alt: image.png
-
-   image.png
-
-Creating OpenVINO Core and model compilation is covered in the previous
-steps. The next step is preparing inputs. You can provide inputs in one
-of the supported format: dictionary with name of inputs as keys and
-``np.arrays`` that represent input tensors as values, list or tuple of
-``np.arrays`` represented input tensors (their order should match with
-model inputs order). If a model has a single input, wrapping to a
-dictionary or list can be omitted. To do inference on a model, pass
-prepared inputs into compiled model object obtained using
-``core.compile_model``. The inference result represented as dictionary,
-where keys are model outputs and ``np.arrays`` represented their
-produced data as values.
-
-.. code:: ipython3
-
-    # Install opencv package for image handling
-    %pip install -q opencv-python
-
-
-.. parsed-literal::
-
-    Note: you may need to restart the kernel to use updated packages.
-
-
-**Load the network**
-
-.. code:: ipython3
-
-    ir_model_url = "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/models/002-example-models/"
-    ir_model_name_xml = "classification.xml"
-    ir_model_name_bin = "classification.bin"
-    
-    download_file(ir_model_url + ir_model_name_xml, filename=ir_model_name_xml, directory="model")
-    download_file(ir_model_url + ir_model_name_bin, filename=ir_model_name_bin, directory="model")
-
-
-.. parsed-literal::
-
-    'model/classification.xml' already exists.
-    'model/classification.bin' already exists.
-
-
-
-
-.. parsed-literal::
-
-    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/openvino-api/model/classification.bin')
-
-
-
-.. code:: ipython3
-
-    import openvino as ov
-    
-    core = ov.Core()
-    classification_model_xml = "model/classification.xml"
-    model = core.read_model(model=classification_model_xml)
-    compiled_model = core.compile_model(model=model, device_name=device.value)
-    input_layer = compiled_model.input(0)
-    output_layer = compiled_model.output(0)
-
-**Load an image and convert to the input shape**
-
-To propagate an image through the network, it needs to be loaded into an
-array, resized to the shape that the network expects, and converted to
-the input layout of the network.
-
-.. code:: ipython3
-
-    import cv2
-    
-    image_filename = download_file(
-        "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/coco_hollywood.jpg",
-        directory="data",
-    )
-    image = cv2.imread(str(image_filename))
-    image.shape
-
-
-
-.. parsed-literal::
-
-    data/coco_hollywood.jpg:   0%|          | 0.00/485k [00:00<?, ?B/s]
-
-
-
-
-.. parsed-literal::
-
-    (663, 994, 3)
-
-
-
-The image has a shape of (663,994,3). It is 663 pixels in height, 994
-pixels in width, and has 3 color channels. A reference to the height and
-width expected by the network is obtained and the image is resized to
-these dimensions.
-
-.. code:: ipython3
-
-    # N,C,H,W = batch size, number of channels, height, width.
-    N, C, H, W = input_layer.shape
-    # OpenCV resize expects the destination size as (width, height).
-    resized_image = cv2.resize(src=image, dsize=(W, H))
-    resized_image.shape
-
-
-
-
-.. parsed-literal::
-
-    (224, 224, 3)
-
-
-
-Now, the image has the width and height that the network expects. This
-is still in ``HWC`` format and must be changed to ``NCHW`` format.
-First, call the ``np.transpose()`` method to change to ``CHW`` and then
-add the ``N`` dimension (where ``N``\ = 1) by calling the
-``np.expand_dims()`` method. Next, convert the data to ``FP32`` with
-``np.astype()`` method.
-
-.. code:: ipython3
-
-    import numpy as np
-    
-    input_data = np.expand_dims(np.transpose(resized_image, (2, 0, 1)), 0).astype(np.float32)
-    input_data.shape
-
-
-
-
-.. parsed-literal::
-
-    (1, 3, 224, 224)
-
-
-
-**Do inference**
-
-Now that the input data is in the right shape, run inference. The
-``CompiledModel`` inference result is a dictionary where keys are the
-Output class instances (the same keys in ``compiled_model.outputs`` that
-can also be obtained with ``compiled_model.output(index)``) and values -
-predicted result in ``np.array`` format.
-
-.. code:: ipython3
-
-    # for single input models only
-    result = compiled_model(input_data)[output_layer]
-    
-    # for multiple inputs in a list
-    result = compiled_model([input_data])[output_layer]
-    
-    # or using a dictionary, where the key is input tensor name or index
-    result = compiled_model({input_layer.any_name: input_data})[output_layer]
-
-You can also create ``InferRequest`` and run ``infer`` method on
-request.
-
-.. code:: ipython3
-
-    request = compiled_model.create_infer_request()
-    request.infer(inputs={input_layer.any_name: input_data})
-    result = request.get_output_tensor(output_layer.index).data
-
-The ``.infer()`` function sets output tensor, that can be reached, using
-``get_output_tensor()``. Since this network returns one output, and the
-reference to the output layer is in the ``output_layer.index``
-parameter, you can get the data with
-``request.get_output_tensor(output_layer.index)``. To get a numpy array
-from the output, use the ``.data`` parameter.
-
-.. code:: ipython3
-
-    result.shape
-
-
-
-
-.. parsed-literal::
-
-    (1, 1001)
-
-
-
-The output shape is (1,1001), which is the expected output shape. This
-shape indicates that the network returns probabilities for 1001 classes.
-To learn more about this notion, refer to the `hello world
-notebook <hello-world-with-output.html>`__.
-
-Reshaping and Resizing
-----------------------
-
-
-
-Change Image Size
-~~~~~~~~~~~~~~~~~
-
-
-
-Instead of reshaping the image to fit the model, it is also possible to
-reshape the model to fit the image. Be aware that not all models support
-reshaping, and models that do, may not support all input shapes. The
-model accuracy may also suffer if you reshape the model input shape.
-
-First check the input shape of the model, then reshape it to the new
-input shape.
-
-.. code:: ipython3
-
-    ir_model_url = "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/models/002-example-models/"
-    ir_model_name_xml = "segmentation.xml"
-    ir_model_name_bin = "segmentation.bin"
-    
-    download_file(ir_model_url + ir_model_name_xml, filename=ir_model_name_xml, directory="model")
-    download_file(ir_model_url + ir_model_name_bin, filename=ir_model_name_bin, directory="model")
-
-
-
-.. parsed-literal::
-
-    model/segmentation.xml:   0%|          | 0.00/1.38M [00:00<?, ?B/s]
-
-
-
-.. parsed-literal::
-
-    model/segmentation.bin:   0%|          | 0.00/1.09M [00:00<?, ?B/s]
-
-
-
-
-.. parsed-literal::
-
-    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/openvino-api/model/segmentation.bin')
-
-
-
-.. code:: ipython3
-
-    import openvino as ov
-    
-    core = ov.Core()
-    segmentation_model_xml = "model/segmentation.xml"
-    segmentation_model = core.read_model(model=segmentation_model_xml)
-    segmentation_input_layer = segmentation_model.input(0)
-    segmentation_output_layer = segmentation_model.output(0)
-    
-    print("~~~~ ORIGINAL MODEL ~~~~")
-    print(f"input shape: {segmentation_input_layer.shape}")
-    print(f"output shape: {segmentation_output_layer.shape}")
-    
-    new_shape = ov.PartialShape([1, 3, 544, 544])
-    segmentation_model.reshape({segmentation_input_layer.any_name: new_shape})
-    segmentation_compiled_model = core.compile_model(model=segmentation_model, device_name=device.value)
-    # help(segmentation_compiled_model)
-    print("~~~~ RESHAPED MODEL ~~~~")
-    print(f"model input shape: {segmentation_input_layer.shape}")
-    print(f"compiled_model input shape: " f"{segmentation_compiled_model.input(index=0).shape}")
-    print(f"compiled_model output shape: {segmentation_output_layer.shape}")
-
-
-.. parsed-literal::
-
-    ~~~~ ORIGINAL MODEL ~~~~
-    input shape: [1,3,512,512]
-    output shape: [1,1,512,512]
-    ~~~~ RESHAPED MODEL ~~~~
-    model input shape: [1,3,544,544]
-    compiled_model input shape: [1,3,544,544]
-    compiled_model output shape: [1,1,544,544]
-
-
-The input shape for the segmentation network is [1,3,512,512], with the
-``NCHW`` layout: the network expects 3-channel images with a width and
-height of 512 and a batch size of 1. Reshape the network with the
-``.reshape()`` method of ``IENetwork`` to make it accept input images
-with a width and height of 544. This segmentation network always returns
-arrays with the input width and height of equal value. Therefore,
-setting the input dimensions to 544x544 also modifies the output
-dimensions. After reshaping, compile the network once again.
-
-Change Batch Size
-~~~~~~~~~~~~~~~~~
-
-
-
-Use the ``.reshape()`` method to set the batch size, by increasing the
-first element of ``new_shape``. For example, to set a batch size of two,
-set ``new_shape = (2,3,544,544)`` in the cell above.
-
-.. code:: ipython3
-
-    import openvino as ov
-    
-    segmentation_model_xml = "model/segmentation.xml"
-    segmentation_model = core.read_model(model=segmentation_model_xml)
-    segmentation_input_layer = segmentation_model.input(0)
-    segmentation_output_layer = segmentation_model.output(0)
-    new_shape = ov.PartialShape([2, 3, 544, 544])
-    segmentation_model.reshape({segmentation_input_layer.any_name: new_shape})
-    segmentation_compiled_model = core.compile_model(model=segmentation_model, device_name=device.value)
-    
-    print(f"input shape: {segmentation_input_layer.shape}")
-    print(f"output shape: {segmentation_output_layer.shape}")
-
-
-.. parsed-literal::
-
-    input shape: [2,3,544,544]
-    output shape: [2,1,544,544]
-
-
-The output shows that by setting the batch size to 2, the first element
-(``N``) of the input and output shape has a value of 2. Propagate the
-input image through the network to see the result:
-
-.. code:: ipython3
-
-    import numpy as np
-    import openvino as ov
-    
-    core = ov.Core()
-    segmentation_model_xml = "model/segmentation.xml"
-    segmentation_model = core.read_model(model=segmentation_model_xml)
-    segmentation_input_layer = segmentation_model.input(0)
-    segmentation_output_layer = segmentation_model.output(0)
-    new_shape = ov.PartialShape([2, 3, 544, 544])
-    segmentation_model.reshape({segmentation_input_layer.any_name: new_shape})
-    segmentation_compiled_model = core.compile_model(model=segmentation_model, device_name=device.value)
-    input_data = np.random.rand(2, 3, 544, 544)
-    
-    output = segmentation_compiled_model([input_data])
-    
-    print(f"input data shape: {input_data.shape}")
-    print(f"result data data shape: {segmentation_output_layer.shape}")
-
-
-.. parsed-literal::
-
-    input data shape: (2, 3, 544, 544)
-    result data data shape: [2,1,544,544]
-
-
-Caching a Model
----------------
-
-
-
-For some devices, like GPU, loading a model can take some time. Model
-Caching solves this issue by caching the model in a cache directory. If
-``core.compile_model(model=net, device_name=device_name, config=config_dict)``
-is set, caching will be used. This option checks if a model exists in
-the cache. If so, it loads it from the cache. If not, it loads the model
-regularly, and stores it in the cache, so that the next time the model
-is loaded when this option is set, the model will be loaded from the
-cache.
-
-In the cell below, we create a *model_cache* directory as a subdirectory
-of *model*, where the model will be cached for the specified device. The
-model will be loaded to the GPU. After running this cell once, the model
-will be cached, so subsequent runs of this cell will load the model from
-the cache.
-
-*Note: Model Caching is also available on CPU devices*
-
-.. code:: ipython3
-
-    ir_model_url = "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/models/002-example-models/"
-    ir_model_name_xml = "classification.xml"
-    ir_model_name_bin = "classification.bin"
-    
-    download_file(ir_model_url + ir_model_name_xml, filename=ir_model_name_xml, directory="model")
-    download_file(ir_model_url + ir_model_name_bin, filename=ir_model_name_bin, directory="model")
-
-
-.. parsed-literal::
-
-    'model/classification.xml' already exists.
-    'model/classification.bin' already exists.
-
-
-
-
-.. parsed-literal::
-
-    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/openvino-api/model/classification.bin')
-
-
-
-.. code:: ipython3
-
-    import time
-    from pathlib import Path
-    
-    import openvino as ov
-    
-    core = ov.Core()
-    
-    cache_path = Path("model/model_cache")
-    cache_path.mkdir(exist_ok=True)
-    # Enable caching for OpenVINO Runtime. To disable caching set enable_caching = False
-    enable_caching = True
-    config_dict = {"CACHE_DIR": str(cache_path)} if enable_caching else {}
-    
-    classification_model_xml = "model/classification.xml"
-    model = core.read_model(model=classification_model_xml)
-    
-    start_time = time.perf_counter()
-    compiled_model = core.compile_model(model=model, device_name=device.value, config=config_dict)
-    end_time = time.perf_counter()
-    print(f"Loading the network to the {device.value} device took {end_time-start_time:.2f} seconds.")
-
-
-.. parsed-literal::
-
-    Loading the network to the CPU device took 0.15 seconds.
-
-
-After running the previous cell, we know the model exists in the cache
-directory. Then, we delete the compiled model and load it again. Now, we
-measure the time it takes now.
-
-.. code:: ipython3
-
-    del compiled_model
-    start_time = time.perf_counter()
-    compiled_model = core.compile_model(model=model, device_name=device.value, config=config_dict)
-    end_time = time.perf_counter()
-    print(f"Loading the network to the {device.value} device took {end_time-start_time:.2f} seconds.")
-
-
-.. parsed-literal::
-
-    Loading the network to the CPU device took 0.07 seconds.
-
diff --git a/docs/notebooks/openvino-tokenizers-with-output.rst b/docs/notebooks/openvino-tokenizers-with-output.rst
index 8f978b6fabc75b..eb7c067c1a7047 100644
--- a/docs/notebooks/openvino-tokenizers-with-output.rst
+++ b/docs/notebooks/openvino-tokenizers-with-output.rst
@@ -14,8 +14,8 @@ designed to streamline tokenizer conversion for seamless integration
 into your projects. It supports Python and C++ environments and is
 compatible with all major platforms: Linux, Windows, and MacOS.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Tokenization Basics <#tokenization-basics>`__
 -  `Acquiring OpenVINO Tokenizers <#acquiring-openvino-tokenizers>`__
@@ -103,18 +103,19 @@ use ``pip install openvino-tokenizers[transformers]``.
 
     Note: you may need to restart the kernel to use updated packages.
     ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
-    openvino-dev 2024.1.0 requires openvino==2024.1.0, but you have openvino 2024.3.0.dev20240605 which is incompatible.
+    openvino-dev 2024.2.0 requires openvino==2024.2.0, but you have openvino 2024.3.0.dev20240627 which is incompatible.
+    openvino-genai 2024.2.0.0 requires openvino_tokenizers~=2024.2.0.0, but you have openvino-tokenizers 2024.3.0.0.dev20240627 which is incompatible.
     Note: you may need to restart the kernel to use updated packages.
     Looking in indexes: https://pypi.org/simple, https://download.pytorch.org/whl/cpu
-    Requirement already satisfied: torch>=2.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (2.3.1+cpu)
-    Requirement already satisfied: filelock in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch>=2.1) (3.14.0)
-    Requirement already satisfied: typing-extensions>=4.8.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch>=2.1) (4.12.1)
-    Requirement already satisfied: sympy in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch>=2.1) (1.12.1)
-    Requirement already satisfied: networkx in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch>=2.1) (3.1)
-    Requirement already satisfied: jinja2 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch>=2.1) (3.1.4)
-    Requirement already satisfied: fsspec in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch>=2.1) (2024.3.1)
-    Requirement already satisfied: MarkupSafe>=2.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from jinja2->torch>=2.1) (2.1.5)
-    Requirement already satisfied: mpmath<1.4.0,>=1.1.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from sympy->torch>=2.1) (1.3.0)
+    Requirement already satisfied: torch>=2.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (2.3.1+cpu)
+    Requirement already satisfied: filelock in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch>=2.1) (3.15.4)
+    Requirement already satisfied: typing-extensions>=4.8.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch>=2.1) (4.12.2)
+    Requirement already satisfied: sympy in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch>=2.1) (1.12.1)
+    Requirement already satisfied: networkx in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch>=2.1) (3.1)
+    Requirement already satisfied: jinja2 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch>=2.1) (3.1.4)
+    Requirement already satisfied: fsspec in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch>=2.1) (2024.5.0)
+    Requirement already satisfied: MarkupSafe>=2.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from jinja2->torch>=2.1) (2.1.5)
+    Requirement already satisfied: mpmath<1.4.0,>=1.1.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from sympy->torch>=2.1) (1.3.0)
     Note: you may need to restart the kernel to use updated packages.
 
 
@@ -193,7 +194,7 @@ The other method is to pass HuggingFace ``hf_tokenizer`` object to
      ]>,
      <Model: 'detokenizer'
      inputs[
-     <ConstOutput: names[Parameter_21] shape[?,?] type: i64>
+     <ConstOutput: names[Parameter_22] shape[?,?] type: i64>
      ]
      outputs[
      <ConstOutput: names[string_output] shape[?] type: string>
@@ -264,10 +265,10 @@ one:
 
 .. parsed-literal::
 
-    2024-06-06 00:58:07.002270: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-06-06 00:58:07.036768: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-07-02 01:03:15.895945: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-07-02 01:03:15.931306: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2024-06-06 00:58:07.614844: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-07-02 01:03:16.500703: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 .. parsed-literal::
@@ -466,6 +467,9 @@ models and tokenizers simplifies memory management.
 
 .. parsed-literal::
 
+    ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
+    mobileclip 0.1.0 requires torch==1.13.1, but you have torch 2.3.1+cpu which is incompatible.
+    mobileclip 0.1.0 requires torchvision==0.14.1, but you have torchvision 0.18.1+cpu which is incompatible.
     Note: you may need to restart the kernel to use updated packages.
 
 
@@ -479,18 +483,13 @@ models and tokenizers simplifies memory management.
 
 .. parsed-literal::
 
-    2024-06-06 00:58:44.506889: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
-    INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
+    2024-07-02 01:03:42.804844: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
       torch.utils._pytree._register_pytree_node(
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
-      warnings.warn(
     Framework not specified. Using pt to export the model.
     Using framework PyTorch: 2.3.1+cpu
     Overriding 1 configuration item(s)
     	- use_cache -> False
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4481: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
-      warnings.warn(
     Detokenizer is not supported, convert tokenizer only.
 
 
@@ -557,6 +556,6 @@ Links
    Types <https://github.com/openvinotoolkit/openvino_tokenizers?tab=readme-ov-file#supported-tokenizer-types>`__
 -  `OpenVINO.GenAI repository with the C++ example of OpenVINO
    Tokenizers
-   usage <https://github.com/openvinotoolkit/openvino.genai/tree/master/text_generation/causal_lm/cpp>`__
+   usage <https://github.com/openvinotoolkit/openvino.genai/tree/master/samples/cpp/greedy_causal_lm>`__
 -  `HuggingFace Tokenizers Comparison
    Table <https://github.com/openvinotoolkit/openvino_tokenizers?tab=readme-ov-file#output-match-by-model>`__
diff --git a/docs/notebooks/openvoice-with-output.rst b/docs/notebooks/openvoice-with-output.rst
index 64ad7e16c036b2..69d22802476f65 100644
--- a/docs/notebooks/openvoice-with-output.rst
+++ b/docs/notebooks/openvoice-with-output.rst
@@ -26,8 +26,8 @@ model <https://github.com/myshell-ai/OpenVoice>`__ to OpenVINO IR. In
 this tutorial we will explore how to convert and run OpenVoice using
 OpenVINO.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Clone repository and install
    requirements <#clone-repository-and-install-requirements>`__
@@ -79,10 +79,10 @@ Clone repository and install requirements
 
     Cloning into 'OpenVoice'...
     remote: Enumerating objects: 431, done.
-    remote: Counting objects: 100% (223/223), done.
-    remote: Compressing objects: 100% (105/105), done.
-    remote: Total 431 (delta 167), reused 118 (delta 118), pack-reused 208
-    Receiving objects: 100% (431/431), 3.84 MiB | 21.04 MiB/s, done.
+    remote: Counting objects: 100% (231/231), done.
+    remote: Compressing objects: 100% (106/106), done.
+    remote: Total 431 (delta 174), reused 125 (delta 125), pack-reused 200
+    Receiving objects: 100% (431/431), 3.82 MiB | 20.49 MiB/s, done.
     Resolving deltas: 100% (217/217), done.
     Note: you may need to restart the kernel to use updated packages.
 
@@ -208,7 +208,7 @@ True
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/utils/weight_norm.py:28: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/utils/weight_norm.py:28: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.
       warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.")
 
 
@@ -332,46 +332,46 @@ If IRs already exist skip conversion and read them directly
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/openvoice/OpenVoice/openvoice/attentions.py:283: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/openvoice/OpenVoice/openvoice/attentions.py:283: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       assert (
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/openvoice/OpenVoice/openvoice/attentions.py:346: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/openvoice/OpenVoice/openvoice/attentions.py:346: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       pad_length = max(length - (self.window_size + 1), 0)
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/openvoice/OpenVoice/openvoice/attentions.py:347: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/openvoice/OpenVoice/openvoice/attentions.py:347: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       slice_start_position = max((self.window_size + 1) - length, 0)
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/openvoice/OpenVoice/openvoice/attentions.py:349: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/openvoice/OpenVoice/openvoice/attentions.py:349: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if pad_length > 0:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/openvoice/OpenVoice/openvoice/transforms.py:114: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/openvoice/OpenVoice/openvoice/transforms.py:114: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if torch.min(inputs) < left or torch.max(inputs) > right:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/openvoice/OpenVoice/openvoice/transforms.py:119: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/openvoice/OpenVoice/openvoice/transforms.py:119: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if min_bin_width * num_bins > 1.0:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/openvoice/OpenVoice/openvoice/transforms.py:121: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/openvoice/OpenVoice/openvoice/transforms.py:121: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if min_bin_height * num_bins > 1.0:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/openvoice/OpenVoice/openvoice/transforms.py:171: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/openvoice/OpenVoice/openvoice/transforms.py:171: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       assert (discriminant >= 0).all()
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:1116: TracerWarning: Trace had nondeterministic nodes. Did you forget call .eval() on your model? Nodes:
-    	%3293 : Float(1, 2, 43, strides=[86, 43, 1], requires_grad=0, device=cpu) = aten::randn(%3288, %3289, %3290, %3291, %3292) # /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/openvoice/OpenVoice/openvoice/models.py:175:0
-    	%5559 : Float(1, 192, 156, strides=[29952, 1, 192], requires_grad=0, device=cpu) = aten::randn_like(%m_p, %5554, %5555, %5556, %5557, %5558) # /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/openvoice/OpenVoice/openvoice/models.py:487:0
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:1116: TracerWarning: Trace had nondeterministic nodes. Did you forget call .eval() on your model? Nodes:
+    	%3293 : Float(1, 2, 43, strides=[86, 43, 1], requires_grad=0, device=cpu) = aten::randn(%3288, %3289, %3290, %3291, %3292) # /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/openvoice/OpenVoice/openvoice/models.py:175:0
+    	%5559 : Float(1, 192, 153, strides=[29376, 1, 192], requires_grad=0, device=cpu) = aten::randn_like(%m_p, %5554, %5555, %5556, %5557, %5558) # /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/openvoice/OpenVoice/openvoice/models.py:487:0
     This may cause errors in trace checking. To disable trace checking, pass check_trace=False to torch.jit.trace()
       _check_trace(
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:1116: TracerWarning: Output nr 1. of the traced function does not match the corresponding output of the Python function. Detailed error:
-    The values for attribute 'shape' do not match: torch.Size([1, 1, 38912]) != torch.Size([1, 1, 39168]).
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:1116: TracerWarning: Output nr 1. of the traced function does not match the corresponding output of the Python function. Detailed error:
+    The values for attribute 'shape' do not match: torch.Size([1, 1, 39168]) != torch.Size([1, 1, 39424]).
       _check_trace(
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:1116: TracerWarning: Output nr 2. of the traced function does not match the corresponding output of the Python function. Detailed error:
-    The values for attribute 'shape' do not match: torch.Size([1, 1, 152, 43]) != torch.Size([1, 1, 153, 43]).
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:1116: TracerWarning: Output nr 2. of the traced function does not match the corresponding output of the Python function. Detailed error:
+    The values for attribute 'shape' do not match: torch.Size([1, 1, 153, 43]) != torch.Size([1, 1, 154, 43]).
       _check_trace(
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:1116: TracerWarning: Output nr 3. of the traced function does not match the corresponding output of the Python function. Detailed error:
-    The values for attribute 'shape' do not match: torch.Size([1, 1, 152]) != torch.Size([1, 1, 153]).
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:1116: TracerWarning: Output nr 3. of the traced function does not match the corresponding output of the Python function. Detailed error:
+    The values for attribute 'shape' do not match: torch.Size([1, 1, 153]) != torch.Size([1, 1, 154]).
       _check_trace(
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:1116: TracerWarning: Trace had nondeterministic nodes. Did you forget call .eval() on your model? Nodes:
-    	%1596 : Float(1, 192, 238, strides=[91392, 238, 1], requires_grad=0, device=cpu) = aten::randn_like(%m, %1591, %1592, %1593, %1594, %1595) # /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/openvoice/OpenVoice/openvoice/models.py:220:0
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:1116: TracerWarning: Trace had nondeterministic nodes. Did you forget call .eval() on your model? Nodes:
+    	%1596 : Float(1, 192, 238, strides=[91392, 238, 1], requires_grad=0, device=cpu) = aten::randn_like(%m, %1591, %1592, %1593, %1594, %1595) # /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/openvoice/OpenVoice/openvoice/models.py:220:0
     This may cause errors in trace checking. To disable trace checking, pass check_trace=False to torch.jit.trace()
       _check_trace(
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:1116: TracerWarning: Output nr 1. of the traced function does not match the corresponding output of the Python function. Detailed error:
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:1116: TracerWarning: Output nr 1. of the traced function does not match the corresponding output of the Python function. Detailed error:
     Tensor-likes are not close!
     
-    Mismatched elements: 33222 / 60928 (54.5%)
-    Greatest absolute difference: 0.021928173955529928 at index (0, 0, 263) (up to 1e-05 allowed)
-    Greatest relative difference: 61761.78723404255 at index (0, 0, 59794) (up to 1e-05 allowed)
+    Mismatched elements: 40608 / 60928 (66.6%)
+    Greatest absolute difference: 0.016898729372769594 at index (0, 0, 4830) (up to 1e-05 allowed)
+    Greatest relative difference: 27144.637546468402 at index (0, 0, 57834) (up to 1e-05 allowed)
       _check_trace(
 
 
@@ -521,7 +521,7 @@ Load speaker embeddings
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/functional.py:665: UserWarning: stft with return_complex=False is deprecated. In a future pytorch release, stft will return complex tensors for all inputs, and return_complex=False will raise an error.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/functional.py:665: UserWarning: stft with return_complex=False is deprecated. In a future pytorch release, stft will return complex tensors for all inputs, and return_complex=False will raise an error.
     Note: you can still call torch.view_as_real on the complex output to recover the old return format. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:873.)
       return _VF.stft(input, n_fft, hop_length, win_length, window,  # type: ignore[attr-defined]
 
@@ -676,7 +676,7 @@ And finally, run voice tone conversion with OpenVINO optimized model
 
     
     <audio  controls="controls" >
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     </audio>
 
@@ -694,7 +694,7 @@ And finally, run voice tone conversion with OpenVINO optimized model
 
     
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HAAcACQAGAAcACAAFAAUABgAFAAYABgAFAAYABQAHAAYABgAHAAYABQADAAYABwAGAAYACQAFAAQAAwACAAMAAwAFAAMAAQACAAQAAQADAAMAAgACAAQAAgAAAAUAAgACAAIAAQAEAAEAAQAEAAQAAgABAAEA//8DAAIAAQD+/wAAAQD+/wEA/////wEAAAADAAEAAgAAAAEABAD+/wAAAQACAAIAAQACAAIAAgAAAAEAAgD//wEA//8AAAEAAAACAAEAAAAAAAEAAgACAAEAAgADAAIAAQACAAIAAQACAAEAAQAFAAUAAwACAAMAAQADAAMAAwACAAIAAQABAAQABQAGAAYABAAGAAcABQAEAAYABQAFAAQABgAHAAUABgAHAAkACAAHAAgABwAGAAgABwAIAAgACQAIAAkACgAKAAgACQAHAAkACAAHAAkACAAIAAkACQAKAAsACgAJAAsACAAHAAgACQAJAAoACQAHAAgACgAIAAoACwAIAAsACgALAAsACgAMAAsACAAJAAcACgAJAAUACAAJAAoABwAJAAkACQAKAAoACgAIAAYABwAGAAYABQAHAAcACAAHAAgABgAFAAYABQAGAAYABgAFAAQABAAEAAQAAwABAAMAAwAAAP/////+//////////z//v/+//3//f/7//3/+//9//7/+/////z//f/8//z//P/9//3//v8AAP3//f8AAP7//v/9//3//f/9//3////+//7//v/9//z//P/9//3//f/7//v//P/8//3//P/7//v/+v/6//v/+//6//v/+v/6//r/+v/6//n/+P/5//r/+//6//v/+//6//r/+v/7//r/+v/4//r/+f/6//r/+v/6//r/+f/6//n/+f/6//n/+f/4//b/9f/3//f/+v/5//n/+v/4//n/+f/5//r//P/7//v/+v/5//r/+f/6//n/+f/4//n/+v/6//r/+//7//n//P/8//z//v/7//v//f/+//3//P////3//f////z//P/7//z//f/+//3//P/+//3//f/+//7//v/9//v/AAD///z//f/6//r/+//+//3//P/9//v/+v/7//v/+//9//r/+v/5//r/+//5//r/+//+//v/+f/7//v/+v/6//r/+v/8//z/+//5//v/+//+//r/+v/7//j//P/7//r//P/8//3/+//+//v//P/8//z/+v/7//z//P/+//3//P/8//3//P/9////AAAAAAEA/////////////wIAAQAAAAMAAQACAAEAAwABAAMAAQAAAP//AQABAP7//v8AAAAA/f/+//7//v/+/wAA///+////AAD9//v//////wAA///+//7/+//7//z//P/9//v//f/7//3//v/7//3/+//5//j/+P/4//j/9//3//n/+//3//j/9//1//b/+f/3//f/9v/3//b/9f/3//j/9v/1//j/9v/1//T/9v/2//X/8//1//P/9f/1//L/8//z//T/8//z//P/8//x/+//7//x//D/8P/y//L/8P/x//D/7//0//P/9f/0//X/9f/1//b/8//0//P/9P/2//X/9v/4//n/+P/6//r/+f/4//f/+P/3//j/+f/5//f/9//3//j/+P/2//j/+P/2//T/9P/1//L/9P/0//T/9P/z//L/8v/0//T/8//y//L/8f/x//D/8f/z//H/8f/z//T/9f/1//b/9P/1//T/8//0//X/9v/2//b/9//2//j/9f/2//b/9v/3//j/+P/2//b/9v/1//b/9f/3//f/9v/2//f/+P/3//j/+P/4//j/+v/4//n/+v/7//v/+//8//3/+//6//z//f/7//v//P/7//r/+v/7//v/+v/6//r/+//6//r/+f/6//v/+f/8//r/+v/8//3//f/9//n/+v/6//j/9//8//r/+f/5//r/+f/7//r/+P/4//r//P/6//v/+v/8//3/+//8//3//P/8//v/+//7//r//f/+/////v/9//7/+//+//7/AAD///////8AAAIAAQACAAMAAQAAAP////////3//v/+//7/AAABAAAAAQD///3//v/9//z//v/9//v/+f/4//n/+v/6//v/+//4//j/+P/4//r/+//7//r//f/5//r/+v/4//v/+f/5//n/9//5//j/9//3//b/9v/2//P/+P/5//X/8//y//P/8f/0//X/9v/4//f/+P/2//f/9f/3//n/9P/2//b/9v/4//v/9P/1//j/9v/3//b/9//4//f/9v/2//b/9//3//f/9v/1//b/8//1//P/8//y//P/8v/y//P/8//z//P/9P/0//P/8//z//L/9f/2//T/9v/2//b/9//4//n/+P/6//n/+v/6//j/+f/4//j/+v/6//r/+f/5//z/+//7//3//f/6//r/+//7//3//f/9//z//P/6//n/+P/7//v/+P/4//b/9v/3//f/9//3//f/+P/2//X/9f/0//b/9P/1//P/8//1//P/8v/y//H/8v/y//H/8v/y//P/8v/x//H/8v/w/+//9P/z//P/8v/z//H/8f/z//T/9f/1//T/9//2//T/9f/0//f/9f/1//b/9//4//f/9v/1//T/9v/6//j/9//1//b/9//2//f/9//0//f/9//3//T/9P/2//X/9P/1//b/9//2//f/+P/3//b/9f/1//f/9//3//f/9v/3//b/9v/4//f/9//2//b/9f/2//b/9v/1//X/9v/3//b/9v/2//X/9f/0//T/9P/0//T/9P/z//P/8v/0//P/9P/y//L/8//z//P/9P/0//T/8//z//L/8v/x//P/9f/y//P/9P/1//T/8v/z//P/9v/1//T/9v/1//X/9f/1//X/9P/0//X/9v/0//T/9P/1//b/9f/2//X/9P/2//f/9//2//b/9//5//n/+//6//v/+v/7//z//P/7//r/+f/6//v//P/9//3//P/8//3//v////////////7/AgADAP//AQACAAAAAAACAAEAAgADAAIA" 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type="audio/x-wav" />
         Your browser does not support the audio element.
     </audio>
 
@@ -1015,7 +1015,7 @@ voice tone conversion online.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/gradio/components/dropdown.py:94: UserWarning: The `max_choices` parameter is ignored when `multiselect` is False.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/gradio/components/dropdown.py:94: UserWarning: The `max_choices` parameter is ignored when `multiselect` is False.
       warnings.warn(
 
 
diff --git a/docs/notebooks/optical-character-recognition-with-output.rst b/docs/notebooks/optical-character-recognition-with-output.rst
index a97f8e9ef522c1..c6c72952fc6855 100644
--- a/docs/notebooks/optical-character-recognition-with-output.rst
+++ b/docs/notebooks/optical-character-recognition-with-output.rst
@@ -19,8 +19,8 @@ Zoo <https://github.com/openvinotoolkit/open_model_zoo>`__. For more
 information, refer to the
 `model-tools <model-tools-with-output.html>`__ tutorial.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Imports <#imports>`__
 -  `Settings <#settings>`__
@@ -64,7 +64,7 @@ Table of contents:
 .. parsed-literal::
 
     ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
-    openvino-tokenizers 2024.3.0.0.dev20240605 requires openvino~=2024.3.0.0.dev, but you have openvino 2024.1.0 which is incompatible.
+    openvino-tokenizers 2024.3.0.0.dev20240627 requires openvino~=2024.3.0.0.dev, but you have openvino 2024.2.0 which is incompatible.
     Note: you may need to restart the kernel to use updated packages.
     Note: you may need to restart the kernel to use updated packages.
 
@@ -368,20 +368,21 @@ Converting text-recognition-resnet-fc…
 .. parsed-literal::
 
     ========== Converting text-recognition-resnet-fc to ONNX
-    Conversion to ONNX command: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/bin/python -- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/omz_tools/internal_scripts/pytorch_to_onnx.py --model-path=/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/omz_tools/models/public/text-recognition-resnet-fc --model-path=model/public/text-recognition-resnet-fc --model-name=get_model --import-module=model '--model-param=file_config=r"model/public/text-recognition-resnet-fc/vedastr/configs/resnet_fc.py"' '--model-param=weights=r"model/public/text-recognition-resnet-fc/vedastr/ckpt/resnet_fc.pth"' --input-shape=1,1,32,100 --input-names=input --output-names=output --output-file=model/public/text-recognition-resnet-fc/resnet_fc.onnx
+    Conversion to ONNX command: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/bin/python -- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/omz_tools/internal_scripts/pytorch_to_onnx.py --model-path=/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/omz_tools/models/public/text-recognition-resnet-fc --model-path=model/public/text-recognition-resnet-fc --model-name=get_model --import-module=model '--model-param=file_config=r"model/public/text-recognition-resnet-fc/vedastr/configs/resnet_fc.py"' '--model-param=weights=r"model/public/text-recognition-resnet-fc/vedastr/ckpt/resnet_fc.pth"' --input-shape=1,1,32,100 --input-names=input --output-names=output --output-file=model/public/text-recognition-resnet-fc/resnet_fc.onnx
     
     ONNX check passed successfully.
     
     ========== Converting text-recognition-resnet-fc to IR (FP16)
-    Conversion command: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/bin/python -- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/bin/mo --framework=onnx --output_dir=model/public/text-recognition-resnet-fc/FP16 --model_name=text-recognition-resnet-fc --input=input '--mean_values=input[127.5]' '--scale_values=input[127.5]' --output=output --input_model=model/public/text-recognition-resnet-fc/resnet_fc.onnx '--layout=input(NCHW)' '--input_shape=[1, 1, 32, 100]' --compress_to_fp16=True
+    Conversion command: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/bin/python -- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/bin/mo --framework=onnx --output_dir=model/public/text-recognition-resnet-fc/FP16 --model_name=text-recognition-resnet-fc --input=input '--mean_values=input[127.5]' '--scale_values=input[127.5]' --output=output --input_model=model/public/text-recognition-resnet-fc/resnet_fc.onnx '--layout=input(NCHW)' '--input_shape=[1, 1, 32, 100]' --compress_to_fp16=True
     
+    [ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release.
+    In 2025.0 MO command line tool and openvino.tools.mo.convert_model() will be removed. Please use OpenVINO Model Converter (OVC) or openvino.convert_model(). OVC represents a lightweight alternative of MO and provides simplified model conversion API. 
+    Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
     [ INFO ] Generated IR will be compressed to FP16. If you get lower accuracy, please consider disabling compression explicitly by adding argument --compress_to_fp16=False.
     Find more information about compression to FP16 at https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_FP16_Compression.html
-    [ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release. Please use OpenVINO Model Converter (OVC). OVC represents a lightweight alternative of MO and provides simplified model conversion API. 
-    Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
     [ SUCCESS ] Generated IR version 11 model.
-    [ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/optical-character-recognition/model/public/text-recognition-resnet-fc/FP16/text-recognition-resnet-fc.xml
-    [ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/optical-character-recognition/model/public/text-recognition-resnet-fc/FP16/text-recognition-resnet-fc.bin
+    [ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/optical-character-recognition/model/public/text-recognition-resnet-fc/FP16/text-recognition-resnet-fc.xml
+    [ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/optical-character-recognition/model/public/text-recognition-resnet-fc/FP16/text-recognition-resnet-fc.bin
     
 
 
diff --git a/docs/notebooks/optimize-preprocessing-with-output.rst b/docs/notebooks/optimize-preprocessing-with-output.rst
index b30e169423531f..6163118c9718a3 100644
--- a/docs/notebooks/optimize-preprocessing-with-output.rst
+++ b/docs/notebooks/optimize-preprocessing-with-output.rst
@@ -23,8 +23,8 @@ This tutorial include following steps:
 -  Comparing results on one picture.
 -  Comparing performance.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Settings <#settings>`__
 -  `Imports <#imports>`__
@@ -207,8 +207,8 @@ and save it to the disk.
 
 .. parsed-literal::
 
-    2024-06-06 01:01:17.864372: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:266] failed call to cuInit: CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE: forward compatibility was attempted on non supported HW
-    2024-06-06 01:01:17.864557: E tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:312] kernel version 470.182.3 does not match DSO version 470.223.2 -- cannot find working devices in this configuration
+    2024-07-02 01:06:11.398399: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:266] failed call to cuInit: CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE: forward compatibility was attempted on non supported HW
+    2024-07-02 01:06:11.398596: E tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:312] kernel version 470.182.3 does not match DSO version 470.223.2 -- cannot find working devices in this configuration
 
 
 .. parsed-literal::
@@ -366,7 +366,7 @@ for mean/scale normalization.
 
 .. parsed-literal::
 
-    <openvino._pyopenvino.preprocess.InputTensorInfo at 0x7f4ec064a1f0>
+    <openvino._pyopenvino.preprocess.InputTensorInfo at 0x7f0c1168c3b0>
 
 
 
@@ -397,7 +397,7 @@ may be specified is input data
 
 .. parsed-literal::
 
-    <openvino._pyopenvino.preprocess.InputModelInfo at 0x7f4ec0650b70>
+    <openvino._pyopenvino.preprocess.InputModelInfo at 0x7f0c1168af30>
 
 
 
@@ -435,7 +435,7 @@ then such conversion will be added explicitly.
 
 .. parsed-literal::
 
-    <openvino._pyopenvino.preprocess.PreProcessSteps at 0x7f4ec0650330>
+    <openvino._pyopenvino.preprocess.PreProcessSteps at 0x7f0c1168a730>
 
 
 
@@ -649,6 +649,6 @@ Compare performance
 
 .. parsed-literal::
 
-    IR model in OpenVINO Runtime/CPU with manual image preprocessing: 0.0153 seconds per image, FPS: 65.57
-    IR model in OpenVINO Runtime/CPU with preprocessing API: 0.0140 seconds per image, FPS: 71.22
+    IR model in OpenVINO Runtime/CPU with manual image preprocessing: 0.0148 seconds per image, FPS: 67.40
+    IR model in OpenVINO Runtime/CPU with preprocessing API: 0.0139 seconds per image, FPS: 72.12
 
diff --git a/docs/notebooks/paddle-ocr-webcam-with-output.rst b/docs/notebooks/paddle-ocr-webcam-with-output.rst
index c2a092ee2fdca0..c923222ac61c19 100644
--- a/docs/notebooks/paddle-ocr-webcam-with-output.rst
+++ b/docs/notebooks/paddle-ocr-webcam-with-output.rst
@@ -22,8 +22,8 @@ the PaddleOCR is as follows:
    server, the webcam will not work. You can still do inference on a
    video file.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Imports <#imports>`__
 
@@ -200,7 +200,7 @@ Download the Model for Text **Detection**
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/paddle-o…
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/paddle-o…
 
 
 .. parsed-literal::
@@ -246,7 +246,7 @@ Download the Model for Text **Recognition**
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/paddle-o…
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/paddle-o…
 
 
 .. parsed-literal::
diff --git a/docs/notebooks/paddle-ocr-webcam-with-output_files/paddle-ocr-webcam-with-output_30_0.png b/docs/notebooks/paddle-ocr-webcam-with-output_files/paddle-ocr-webcam-with-output_30_0.png
index 9f00e3e04e20de..51e1515cc9e4fc 100644
--- a/docs/notebooks/paddle-ocr-webcam-with-output_files/paddle-ocr-webcam-with-output_30_0.png
+++ b/docs/notebooks/paddle-ocr-webcam-with-output_files/paddle-ocr-webcam-with-output_30_0.png
@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:7769f0a5ccc478c3f33005eb556365ab725f495ef55a296d7576f716c2cdb99a
-size 591026
+oid sha256:609413bf30940a0e5bf2c8654347afcfc119b69016cb6391cf44306deda683f2
+size 593130
diff --git a/docs/notebooks/paddle-to-openvino-classification-with-output.rst b/docs/notebooks/paddle-to-openvino-classification-with-output.rst
index 0e161d1ac207b6..59aad9de840cbc 100644
--- a/docs/notebooks/paddle-to-openvino-classification-with-output.rst
+++ b/docs/notebooks/paddle-to-openvino-classification-with-output.rst
@@ -14,8 +14,8 @@ IR model.
 Source of the
 `model <https://www.paddlepaddle.org.cn/hubdetail?name=mobilenet_v3_large_imagenet_ssld&en_category=ImageClassification>`__.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Preparation <#preparation>`__
 
@@ -64,7 +64,7 @@ Imports
     ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
     paddleclas 2.5.2 requires easydict, which is not installed.
     paddleclas 2.5.2 requires gast==0.3.3, but you have gast 0.4.0 which is incompatible.
-    paddleclas 2.5.2 requires opencv-python==4.6.0.66, but you have opencv-python 4.10.0.82 which is incompatible.
+    paddleclas 2.5.2 requires opencv-python==4.6.0.66, but you have opencv-python 4.10.0.84 which is incompatible.
     Note: you may need to restart the kernel to use updated packages.
     Note: you may need to restart the kernel to use updated packages.
 
@@ -78,11 +78,11 @@ Imports
 
 .. parsed-literal::
 
-    --2024-06-06 01:02:58--  http://nz2.archive.ubuntu.com/ubuntu/pool/main/o/openssl/libssl1.1_1.1.1f-1ubuntu2.19_amd64.deb
+    --2024-07-02 01:07:43--  http://nz2.archive.ubuntu.com/ubuntu/pool/main/o/openssl/libssl1.1_1.1.1f-1ubuntu2.19_amd64.deb
     Resolving proxy-dmz.intel.com (proxy-dmz.intel.com)... 10.241.208.166
     Connecting to proxy-dmz.intel.com (proxy-dmz.intel.com)|10.241.208.166|:911... connected.
     Proxy request sent, awaiting response... 404 Not Found
-    2024-06-06 01:02:58 ERROR 404: Not Found.
+    2024-07-02 01:07:43 ERROR 404: Not Found.
     
     dpkg: error: cannot access archive 'libssl1.1_1.1.1f-1ubuntu2.19_amd64.deb': No such file or directory
 
@@ -113,8 +113,8 @@ Imports
 
 .. parsed-literal::
 
-    2024-06-06 01:03:00 INFO: Loading faiss with AVX512 support.
-    2024-06-06 01:03:00 INFO: Successfully loaded faiss with AVX512 support.
+    2024-07-02 01:07:45 INFO: Loading faiss with AVX512 support.
+    2024-07-02 01:07:45 INFO: Successfully loaded faiss with AVX512 support.
 
 
 Settings
@@ -198,7 +198,7 @@ inference on that image, and then show the top three prediction results.
 
 .. parsed-literal::
 
-    [2024/06/06 01:03:28] ppcls WARNING: The current running environment does not support the use of GPU. CPU has been used instead.
+    [2024/07/02 01:08:12] ppcls WARNING: The current running environment does not support the use of GPU. CPU has been used instead.
     Labrador retriever, 0.75138
     German short-haired pointer, 0.02373
     Great Dane, 0.01848
@@ -264,7 +264,7 @@ clipping values.
 
 .. parsed-literal::
 
-    2024-06-06 01:03:29 WARNING: Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+    2024-07-02 01:08:13 WARNING: Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
 
 
 .. parsed-literal::
@@ -276,7 +276,7 @@ clipping values.
 
 .. parsed-literal::
 
-    <matplotlib.image.AxesImage at 0x7fd0b0255130>
+    <matplotlib.image.AxesImage at 0x7f33f85b7610>
 
 
 
@@ -455,7 +455,7 @@ Note that many optimizations are possible to improve the performance.
 
 .. parsed-literal::
 
-    PaddlePaddle model on CPU: 0.0075 seconds per image, FPS: 133.88
+    PaddlePaddle model on CPU: 0.0072 seconds per image, FPS: 139.22
     
     PaddlePaddle result:
     Labrador retriever, 0.75138
@@ -516,7 +516,7 @@ select device from dropdown list for running inference using OpenVINO
 
 .. parsed-literal::
 
-    OpenVINO IR model in OpenVINO Runtime (AUTO): 0.0029 seconds per image, FPS: 340.10
+    OpenVINO IR model in OpenVINO Runtime (AUTO): 0.0028 seconds per image, FPS: 361.41
     
     OpenVINO result:
     Labrador retriever, 0.74909
diff --git a/docs/notebooks/paint-by-example-with-output.rst b/docs/notebooks/paint-by-example-with-output.rst
index c3e240f6b63799..aabc72e0321a6f 100644
--- a/docs/notebooks/paint-by-example-with-output.rst
+++ b/docs/notebooks/paint-by-example-with-output.rst
@@ -1,8 +1,8 @@
 Paint By Example: Exemplar-based Image Editing with Diffusion Models
 ====================================================================
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Stable Diffusion in Diffusers
    library <#stable-diffusion-in-diffusers-library>`__
@@ -25,7 +25,7 @@ Table of contents:
 Stable Diffusion in Diffusers library
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
- To work with Stable Diffusion,
+To work with Stable Diffusion,
 we will use the Hugging Face
 `Diffusers <https://github.com/huggingface/diffusers>`__ library. To
 experiment with in-painting we can use Diffusers which exposes the
@@ -64,16 +64,16 @@ This might take several minutes because it is over 5GB
 
     from diffusers import DiffusionPipeline
     from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
-    
-    
+
+
     pipeline = DiffusionPipeline.from_pretrained("Fantasy-Studio/Paint-By-Example")
-    
+
     scheduler_inpaint = DDIMScheduler.from_config(pipeline.scheduler.config)
 
 .. code:: ipython3
 
     import gc
-    
+
     extractor = pipeline.feature_extractor
     image_encoder = pipeline.image_encoder
     image_encoder.eval()
@@ -81,7 +81,7 @@ This might take several minutes because it is over 5GB
     unet_inpaint.eval()
     vae_inpaint = pipeline.vae
     vae_inpaint.eval()
-    
+
     del pipeline
     gc.collect();
 
@@ -96,45 +96,45 @@ Download default images.
 
     # Fetch `notebook_utils` module
     import requests
-    
+
     r = requests.get(
         url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
-    
+
     open("notebook_utils.py", "w").write(r.text)
-    
+
     from notebook_utils import download_file
-    
+
     download_file(
         "https://github-production-user-asset-6210df.s3.amazonaws.com/103226580/286377210-edc98e97-0e43-4796-b771-dacd074c39ea.png",
         "0.png",
         "data/image",
     )
-    
+
     download_file(
         "https://github-production-user-asset-6210df.s3.amazonaws.com/103226580/286377233-b2c2d902-d379-415a-8183-5bdd37c52429.png",
         "1.png",
         "data/image",
     )
-    
+
     download_file(
         "https://github-production-user-asset-6210df.s3.amazonaws.com/103226580/286377248-da1db61e-3521-4cdb-85c8-1386d360ce22.png",
         "2.png",
         "data/image",
     )
-    
+
     download_file(
         "https://github-production-user-asset-6210df.s3.amazonaws.com/103226580/286377279-fa496f17-e850-4351-87c5-2552dfbc4633.jpg",
         "bird.jpg",
         "data/reference",
     )
-    
+
     download_file(
         "https://github-production-user-asset-6210df.s3.amazonaws.com/103226580/286377298-06a25ff2-84d8-4d46-95cd-8c25efa690d8.jpg",
         "car.jpg",
         "data/reference",
     )
-    
+
     download_file(
         "https://github-production-user-asset-6210df.s3.amazonaws.com/103226580/286377318-8841a801-1933-4523-a433-7d2fb64c47e6.jpg",
         "dog.jpg",
@@ -155,7 +155,7 @@ notebook <stable-diffusion-v2-with-output.html>`__
     import torch
     import numpy as np
     import openvino as ov
-    
+
     model_dir = Path("model")
     model_dir.mkdir(exist_ok=True)
     sd2_inpainting_model_dir = Path("model/paint_by_example")
@@ -172,8 +172,8 @@ Functions to convert to OpenVINO IR format
         torch._C._jit_clear_class_registry()
         torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore()
         torch.jit._state._clear_class_state()
-    
-    
+
+
     def convert_image_encoder(image_encoder: torch.nn.Module, ir_path: Path):
         """
         Convert Image Encoder model to IR.
@@ -184,22 +184,22 @@ Functions to convert to OpenVINO IR format
         Returns:
             None
         """
-    
+
         class ImageEncoderWrapper(torch.nn.Module):
             def __init__(self, image_encoder):
                 super().__init__()
                 self.image_encoder = image_encoder
-    
+
             def forward(self, image):
                 image_embeddings, negative_prompt_embeds = self.image_encoder(image, return_uncond_vector=True)
                 return image_embeddings, negative_prompt_embeds
-    
+
         if not ir_path.exists():
             image_encoder = ImageEncoderWrapper(image_encoder)
             image_encoder.eval()
             input_ids = torch.randn((1, 3, 224, 224))
             # switch model to inference mode
-    
+
             # disable gradients calculation for reducing memory consumption
             with torch.no_grad():
                 ov_model = ov.convert_model(image_encoder, example_input=input_ids, input=([1, 3, 224, 224],))
@@ -207,8 +207,8 @@ Functions to convert to OpenVINO IR format
                 del ov_model
                 cleanup_torchscript_cache()
             print("Image Encoder successfully converted to IR")
-    
-    
+
+
     def convert_unet(
         unet: torch.nn.Module,
         ir_path: Path,
@@ -242,15 +242,15 @@ Functions to convert to OpenVINO IR format
                 shape = ov.PartialShape(tuple(input_tensor.shape))
                 element_type = dtype_mapping[input_tensor.dtype]
                 input_info.append((shape, element_type))
-    
+
             with torch.no_grad():
                 ov_model = ov.convert_model(unet, example_input=dummy_inputs, input=input_info)
                 ov.save_model(ov_model, ir_path)
                 del ov_model
                 cleanup_torchscript_cache()
             print("U-Net successfully converted to IR")
-    
-    
+
+
     def convert_vae_encoder(vae: torch.nn.Module, ir_path: Path, width: int = 512, height: int = 512):
         """
         Convert VAE model to IR format.
@@ -264,16 +264,16 @@ Functions to convert to OpenVINO IR format
         Returns:
             None
         """
-    
+
         class VAEEncoderWrapper(torch.nn.Module):
             def __init__(self, vae):
                 super().__init__()
                 self.vae = vae
-    
+
             def forward(self, image):
                 latents = self.vae.encode(image).latent_dist.sample()
                 return latents
-    
+
         if not ir_path.exists():
             vae_encoder = VAEEncoderWrapper(vae)
             vae_encoder.eval()
@@ -284,8 +284,8 @@ Functions to convert to OpenVINO IR format
             del ov_model
             cleanup_torchscript_cache()
             print("VAE encoder successfully converted to IR")
-    
-    
+
+
     def convert_vae_decoder(vae: torch.nn.Module, ir_path: Path, width: int = 64, height: int = 64):
         """
         Convert VAE decoder model to IR format.
@@ -299,20 +299,20 @@ Functions to convert to OpenVINO IR format
         Returns:
             None
         """
-    
+
         class VAEDecoderWrapper(torch.nn.Module):
             def __init__(self, vae):
                 super().__init__()
                 self.vae = vae
-    
+
             def forward(self, latents):
                 latents = 1 / 0.18215 * latents
                 return self.vae.decode(latents)
-    
+
         if not ir_path.exists():
             vae_decoder = VAEDecoderWrapper(vae)
             latents = torch.zeros((1, 4, width, height))
-    
+
             vae_decoder.eval()
             with torch.no_grad():
                 ov_model = ov.convert_model(vae_decoder, example_input=latents, input=([1, 4, width, height],))
@@ -326,12 +326,12 @@ Do the conversion of the in-painting model:
 .. code:: ipython3
 
     IMAGE_ENCODER_OV_PATH_INPAINT = sd2_inpainting_model_dir / "image_encoder.xml"
-    
+
     if not IMAGE_ENCODER_OV_PATH_INPAINT.exists():
         convert_image_encoder(image_encoder, IMAGE_ENCODER_OV_PATH_INPAINT)
     else:
         print(f"Image encoder will be loaded from {IMAGE_ENCODER_OV_PATH_INPAINT}")
-    
+
     del image_encoder
     gc.collect();
 
@@ -354,18 +354,18 @@ Do the conversion of the VAE Encoder model
 .. code:: ipython3
 
     VAE_ENCODER_OV_PATH_INPAINT = sd2_inpainting_model_dir / "vae_encoder.xml"
-    
+
     if not VAE_ENCODER_OV_PATH_INPAINT.exists():
         convert_vae_encoder(vae_inpaint, VAE_ENCODER_OV_PATH_INPAINT, 512, 512)
     else:
         print(f"VAE encoder will be loaded from {VAE_ENCODER_OV_PATH_INPAINT}")
-    
+
     VAE_DECODER_OV_PATH_INPAINT = sd2_inpainting_model_dir / "vae_decoder.xml"
     if not VAE_DECODER_OV_PATH_INPAINT.exists():
         convert_vae_decoder(vae_inpaint, VAE_DECODER_OV_PATH_INPAINT, 64, 64)
     else:
         print(f"VAE decoder will be loaded from {VAE_DECODER_OV_PATH_INPAINT}")
-    
+
     del vae_inpaint
     gc.collect();
 
@@ -393,63 +393,63 @@ This is the detailed flowchart for the pipeline:
 
     import inspect
     from typing import Optional, Union, Dict
-    
+
     import PIL
     import cv2
-    
+
     from transformers import CLIPImageProcessor
     from diffusers.pipelines.pipeline_utils import DiffusionPipeline
     from openvino.runtime import Model
-    
-    
+
+
     def prepare_mask_and_masked_image(image: PIL.Image.Image, mask: PIL.Image.Image):
         """
         Prepares a pair (image, mask) to be consumed by the Stable Diffusion pipeline. This means that those inputs will be
         converted to ``np.array`` with shapes ``batch x channels x height x width`` where ``channels`` is ``3`` for the
         ``image`` and ``1`` for the ``mask``.
-    
+
         The ``image`` will be converted to ``np.float32`` and normalized to be in ``[-1, 1]``. The ``mask`` will be
         binarized (``mask > 0.5``) and cast to ``np.float32`` too.
-    
+
         Args:
             image (Union[np.array, PIL.Image]): The image to inpaint.
                 It can be a ``PIL.Image``, or a ``height x width x 3`` ``np.array``
             mask (_type_): The mask to apply to the image, i.e. regions to inpaint.
                 It can be a ``PIL.Image``, or a ``height x width`` ``np.array``.
-    
+
         Returns:
             tuple[np.array]: The pair (mask, masked_image) as ``torch.Tensor`` with 4
                 dimensions: ``batch x channels x height x width``.
         """
         if isinstance(image, (PIL.Image.Image, np.ndarray)):
             image = [image]
-    
+
         if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):
             image = [np.array(i.convert("RGB"))[None, :] for i in image]
             image = np.concatenate(image, axis=0)
         elif isinstance(image, list) and isinstance(image[0], np.ndarray):
             image = np.concatenate([i[None, :] for i in image], axis=0)
-    
+
         image = image.transpose(0, 3, 1, 2)
         image = image.astype(np.float32) / 127.5 - 1.0
-    
+
         # preprocess mask
         if isinstance(mask, (PIL.Image.Image, np.ndarray)):
             mask = [mask]
-    
+
         if isinstance(mask, list) and isinstance(mask[0], PIL.Image.Image):
             mask = np.concatenate([np.array(m.convert("L"))[None, None, :] for m in mask], axis=0)
             mask = mask.astype(np.float32) / 255.0
         elif isinstance(mask, list) and isinstance(mask[0], np.ndarray):
             mask = np.concatenate([m[None, None, :] for m in mask], axis=0)
-    
+
         mask = 1 - mask
-    
+
         mask[mask < 0.5] = 0
         mask[mask >= 0.5] = 1
-    
+
         masked_image = image * mask
-    
+
         return mask, masked_image
 
 Class for the pipeline which will connect all the models together: VAE
@@ -496,7 +496,7 @@ decode –> image encode –> tokenizer –> Unet –> VAE model –> scheduler
             self.height = self.unet.input(0).shape[2] * 8
             self.width = self.unet.input(0).shape[3] * 8
             self.image_processor = image_processor
-    
+
         def prepare_mask_latents(
             self,
             mask,
@@ -507,7 +507,7 @@ decode –> image encode –> tokenizer –> Unet –> VAE model –> scheduler
         ):
             """
             Prepare mask as Unet nput and encode input masked image to latent space using vae encoder
-    
+
             Parameters:
               mask (np.array): input mask array
               masked_image (np.array): masked input image tensor
@@ -520,15 +520,15 @@ decode –> image encode –> tokenizer –> Unet –> VAE model –> scheduler
             """
             mask = torch.nn.functional.interpolate(torch.from_numpy(mask), size=(height // 8, width // 8))
             mask = mask.numpy()
-    
+
             # encode the mask image into latents space so we can concatenate it to the latents
             masked_image_latents = self.vae_encoder(masked_image)[self._vae_e_output]
             masked_image_latents = 0.18215 * masked_image_latents
-    
+
             mask = np.concatenate([mask] * 2) if do_classifier_free_guidance else mask
             masked_image_latents = np.concatenate([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents
             return mask, masked_image_latents
-    
+
         def __call__(
             self,
             image: PIL.Image.Image,
@@ -575,10 +575,10 @@ decode –> image encode –> tokenizer –> Unet –> VAE model –> scheduler
             # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
             # corresponds to doing no classifier free guidance.
             do_classifier_free_guidance = guidance_scale > 1.0
-    
+
             # get reference image embeddings
             image_embeddings = self._encode_image(reference_image, do_classifier_free_guidance=do_classifier_free_guidance)
-    
+
             # prepare mask
             mask, masked_image = prepare_mask_and_masked_image(image, mask_image)
             # set timesteps
@@ -586,11 +586,11 @@ decode –> image encode –> tokenizer –> Unet –> VAE model –> scheduler
             extra_set_kwargs = {}
             if accepts_offset:
                 extra_set_kwargs["offset"] = 1
-    
+
             self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
             timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, 1)
             latent_timestep = timesteps[:1]
-    
+
             # get the initial random noise unless the user supplied it
             latents, meta = self.prepare_latents(latent_timestep)
             mask, masked_image_latents = self.prepare_mask_latents(
@@ -598,7 +598,7 @@ decode –> image encode –> tokenizer –> Unet –> VAE model –> scheduler
                 masked_image,
                 do_classifier_free_guidance=do_classifier_free_guidance,
             )
-    
+
             # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
             # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
             # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
@@ -607,7 +607,7 @@ decode –> image encode –> tokenizer –> Unet –> VAE model –> scheduler
             extra_step_kwargs = {}
             if accepts_eta:
                 extra_step_kwargs["eta"] = eta
-    
+
             for t in self.progress_bar(timesteps):
                 # expand the latents if we are doing classifier free guidance
                 latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
@@ -619,7 +619,7 @@ decode –> image encode –> tokenizer –> Unet –> VAE model –> scheduler
                 if do_classifier_free_guidance:
                     noise_pred_uncond, noise_pred_text = noise_pred[0], noise_pred[1]
                     noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-    
+
                 # compute the previous noisy sample x_t -> x_t-1
                 latents = self.scheduler.step(
                     torch.from_numpy(noise_pred),
@@ -629,14 +629,14 @@ decode –> image encode –> tokenizer –> Unet –> VAE model –> scheduler
                 )["prev_sample"].numpy()
             # scale and decode the image latents with vae
             image = self.vae_decoder(latents)[self._vae_d_output]
-    
+
             image = self.postprocess_image(image, meta, output_type)
             return {"sample": image}
-    
+
         def _encode_image(self, image: PIL.Image.Image, do_classifier_free_guidance: bool = True):
             """
             Encodes the image into image encoder hidden states.
-    
+
             Parameters:
                 image (PIL.Image.Image): base image to encode
                 do_classifier_free_guidance (bool): whether to use classifier free guidance or not
@@ -646,19 +646,19 @@ decode –> image encode –> tokenizer –> Unet –> VAE model –> scheduler
             processed_image = self.image_processor(image)
             processed_image = processed_image["pixel_values"][0]
             processed_image = np.expand_dims(processed_image, axis=0)
-    
+
             output = self.image_encoder(processed_image)
             image_embeddings = output[self.image_encoder.output(0)]
             negative_embeddings = output[self.image_encoder.output(1)]
-    
+
             image_embeddings = np.concatenate([negative_embeddings, image_embeddings])
-    
+
             return image_embeddings
-    
+
         def prepare_latents(self, latent_timestep: torch.Tensor = None):
             """
             Function for getting initial latents for starting generation
-    
+
             Parameters:
                 latent_timestep (torch.Tensor, *optional*, None):
                     Predicted by scheduler initial step for image generation, required for latent image mixing with nosie
@@ -672,12 +672,12 @@ decode –> image encode –> tokenizer –> Unet –> VAE model –> scheduler
             if isinstance(self.scheduler, LMSDiscreteScheduler):
                 noise = noise * self.scheduler.sigmas[0].numpy()
             return noise, {}
-    
+
         def postprocess_image(self, image: np.ndarray, meta: Dict, output_type: str = "pil"):
             """
             Postprocessing for decoded image. Takes generated image decoded by VAE decoder, unpad it to initila image size (if required),
             normalize and convert to [0, 255] pixels range. Optionally, convertes it from np.ndarray to PIL.Image format
-    
+
             Parameters:
                 image (np.ndarray):
                     Generated image
@@ -709,12 +709,12 @@ decode –> image encode –> tokenizer –> Unet –> VAE model –> scheduler
                     orig_height, orig_width = meta["src_height"], meta["src_width"]
                     image = [cv2.resize(img, (orig_width, orig_width)) for img in image]
             return image
-    
+
         def get_timesteps(self, num_inference_steps: int, strength: float):
             """
             Helper function for getting scheduler timesteps for generation
             In case of image-to-image generation, it updates number of steps according to strength
-    
+
             Parameters:
                num_inference_steps (int):
                   number of inference steps for generation
@@ -724,10 +724,10 @@ decode –> image encode –> tokenizer –> Unet –> VAE model –> scheduler
             """
             # get the original timestep using init_timestep
             init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
-    
+
             t_start = max(num_inference_steps - init_timestep, 0)
             timesteps = self.scheduler.timesteps[t_start:]
-    
+
             return timesteps, num_inference_steps - t_start
 
 Select inference device
@@ -741,16 +741,16 @@ select device from dropdown list for running inference using OpenVINO
 
     from openvino import Core
     import ipywidgets as widgets
-    
+
     core = Core()
-    
+
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
         value="AUTO",
         description="Device:",
         disabled=False,
     )
-    
+
     device
 
 
@@ -776,14 +776,14 @@ This can take a while to run.
 .. code:: ipython3
 
     ov_config = {"INFERENCE_PRECISION_HINT": "f32"} if device.value != "CPU" else {}
-    
-    
+
+
     def get_ov_pipeline():
         image_encoder_inpaint = core.compile_model(IMAGE_ENCODER_OV_PATH_INPAINT, device.value)
         unet_model_inpaint = core.compile_model(UNET_OV_PATH_INPAINT, device.value)
         vae_decoder_inpaint = core.compile_model(VAE_DECODER_OV_PATH_INPAINT, device.value, ov_config)
         vae_encoder_inpaint = core.compile_model(VAE_ENCODER_OV_PATH_INPAINT, device.value, ov_config)
-    
+
         ov_pipe_inpaint = OVStableDiffusionInpaintingPipeline(
             image_processor=extractor,
             image_encoder=image_encoder_inpaint,
@@ -792,10 +792,10 @@ This can take a while to run.
             vae_decoder=vae_decoder_inpaint,
             scheduler=scheduler_inpaint,
         )
-    
+
         return ov_pipe_inpaint
-    
-    
+
+
     ov_pipe_inpaint = get_ov_pipeline()
 
 Quantization
@@ -830,17 +830,17 @@ improve model inference speed.
 .. code:: ipython3
 
     import ipywidgets as widgets
-    
+
     UNET_INT8_OV_PATH = Path("model/unet_int8.xml")
     int8_ov_pipe_inpaint = None
-    
-    
+
+
     to_quantize = widgets.Checkbox(
         value=True,
         description="Quantization",
         disabled=False,
     )
-    
+
     to_quantize
 
 
@@ -862,10 +862,10 @@ Let’s load ``skip magic`` extension to skip quantization if
         url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/skip_kernel_extension.py",
     )
     open("skip_kernel_extension.py", "w").write(r.text)
-    
+
     if to_quantize.value and "GPU" in device.value:
         to_quantize.value = False
-    
+
     %load_ext skip_kernel_extension
 
 Prepare calibration dataset
@@ -882,13 +882,13 @@ to create a calibration dataset.
     import PIL
     import requests
     from io import BytesIO
-    
-    
+
+
     def download_image(url):
         response = requests.get(url)
         return PIL.Image.open(BytesIO(response.content)).convert("RGB")
-    
-    
+
+
     example1 = [
         "https://github.com/Fantasy-Studio/Paint-by-Example/blob/main/examples/image/example_1.png?raw=true",
         "https://github.com/Fantasy-Studio/Paint-by-Example/blob/main/examples/mask/example_1.png?raw=true",
@@ -905,8 +905,8 @@ to create a calibration dataset.
         "https://github.com/Fantasy-Studio/Paint-by-Example/blob/main/examples/reference/example_3.jpg?raw=true",
     ]
     examples = [example1, example2, example3]
-    
-    
+
+
     img_examples = []
     for init_image_url, mask_image_url, example_image_url in examples:
         init_image = download_image(init_image_url).resize((512, 512))
@@ -920,53 +920,53 @@ To collect intermediate model inputs for calibration we should customize
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     from tqdm.notebook import tqdm
     from transformers import set_seed
     from typing import Any, Dict, List
-    
-    
+
+
     class CompiledModelDecorator(ov.CompiledModel):
         def __init__(self, compiled_model, data_cache: List[Any] = None):
             super().__init__(compiled_model)
             self.data_cache = data_cache if data_cache else []
-    
+
         def __call__(self, *args, **kwargs):
             self.data_cache.append(*args)
             return super().__call__(*args, **kwargs)
-    
-    
+
+
     def collect_calibration_data(pipeline) -> List[Dict]:
         original_unet = pipeline.unet
         pipeline.unet = CompiledModelDecorator(original_unet)
         pipeline.set_progress_bar_config(disable=True)
         prev_example_image = None
         for init_image, mask_image, example_image in img_examples:
-    
+
             _ = pipeline(
-                image=init_image, 
-                mask_image=mask_image, 
+                image=init_image,
+                mask_image=mask_image,
                 reference_image=example_image,
             )
             if prev_example_image:
                 _ = pipeline(
-                    image=init_image, 
-                    mask_image=mask_image, 
+                    image=init_image,
+                    mask_image=mask_image,
                     reference_image=prev_example_image,
                 )
             prev_example_image = example_image
-    
-    
+
+
         calibration_dataset = pipeline.unet.data_cache
         pipeline.set_progress_bar_config(disable=False)
         pipeline.unet = original_unet
-        
+
         return calibration_dataset
 
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     UNET_INT8_OV_PATH = Path("model/unet_int8.xml")
     if not UNET_INT8_OV_PATH.exists():
         unet_calibration_data = collect_calibration_data(ov_pipe_inpaint)
@@ -984,10 +984,10 @@ Create a quantized model from the pre-trained converted OpenVINO model.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     import nncf
-    
-    
+
+
     def get_quantized_pipeline():
         if UNET_INT8_OV_PATH.exists():
             print("Loading quantized model")
@@ -1001,13 +1001,13 @@ Create a quantized model from the pre-trained converted OpenVINO model.
                 model_type=nncf.ModelType.TRANSFORMER,
             )
             ov.save_model(quantized_unet, UNET_INT8_OV_PATH)
-    
+
         unet_optimized = core.compile_model(UNET_INT8_OV_PATH, device.value)
-    
+
         image_encoder_inpaint = core.compile_model(IMAGE_ENCODER_OV_PATH_INPAINT, device.value)
         vae_decoder_inpaint = core.compile_model(VAE_DECODER_OV_PATH_INPAINT, device.value, ov_config)
         vae_encoder_inpaint = core.compile_model(VAE_ENCODER_OV_PATH_INPAINT, device.value, ov_config)
-    
+
         int8_ov_pipe_inpaint = OVStableDiffusionInpaintingPipeline(
             image_processor=extractor,
             image_encoder=image_encoder_inpaint,
@@ -1016,10 +1016,10 @@ Create a quantized model from the pre-trained converted OpenVINO model.
             vae_decoder=vae_decoder_inpaint,
             scheduler=scheduler_inpaint,
         )
-    
+
         return int8_ov_pipe_inpaint
-    
-    
+
+
     int8_ov_pipe_inpaint = get_quantized_pipeline()
 
 
@@ -1035,17 +1035,17 @@ Create a quantized model from the pre-trained converted OpenVINO model.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -1056,17 +1056,17 @@ Create a quantized model from the pre-trained converted OpenVINO model.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -1082,17 +1082,17 @@ Create a quantized model from the pre-trained converted OpenVINO model.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -1103,17 +1103,17 @@ Create a quantized model from the pre-trained converted OpenVINO model.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -1127,8 +1127,8 @@ OV pipeline:
 .. code:: ipython3
 
     init_image, mask_image, example_image = img_examples[1]
-    
-    
+
+
     ov_image = ov_pipe_inpaint(image=init_image, mask_image=mask_image, reference_image=example_image, seed=2)
 
 Quantized pipeline:
@@ -1136,20 +1136,20 @@ Quantized pipeline:
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     int8_image = int8_ov_pipe_inpaint(image=init_image, mask_image=mask_image, reference_image=example_image, seed=2)
 
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     import matplotlib.pyplot as plt
     from PIL import Image
-    
+
     def visualize_results(orig_img:Image.Image, optimized_img:Image.Image):
         """
         Helper function for results visualization
-    
+
         Parameters:
            orig_img (Image.Image): generated image using FP16 models
            optimized_img (Image.Image): generated image using quantized models
@@ -1171,12 +1171,12 @@ Quantized pipeline:
         list_axes[1].imshow(np.array(optimized_img))
         list_axes[0].set_title(orig_title, fontsize=15)
         list_axes[1].set_title(control_title, fontsize=15)
-    
+
         fig.subplots_adjust(wspace=0.01, hspace=0.01)
         fig.tight_layout()
         return fig
-    
-    
+
+
     visualize_results(ov_image["sample"][0], int8_image["sample"][0])
 
 
@@ -1187,7 +1187,7 @@ Quantized pipeline:
 .. code:: ipython3
 
     %%skip $to_quantize.value
-    
+
     display(ov_image["sample"][0])
 
 Compare UNet file size
@@ -1198,10 +1198,10 @@ Compare UNet file size
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     fp16_ir_model_size = UNET_OV_PATH_INPAINT.with_suffix(".bin").stat().st_size / 1024
     quantized_model_size = UNET_INT8_OV_PATH.with_suffix(".bin").stat().st_size / 1024
-    
+
     print(f"FP16 model size: {fp16_ir_model_size:.2f} KB")
     print(f"INT8 model size: {quantized_model_size:.2f} KB")
     print(f"Model compression rate: {fp16_ir_model_size / quantized_model_size:.3f}")
@@ -1225,17 +1225,17 @@ can choose both, FP16 and INT8.
 .. code:: ipython3
 
     available_models = ["FP16"]
-    
+
     if UNET_INT8_OV_PATH.exists():
         available_models.append("INT8")
-    
+
     model_to_use = widgets.Select(
         options=available_models,
         value="FP16",
         description="Select model:",
         disabled=False,
     )
-    
+
     model_to_use
 
 
@@ -1255,8 +1255,8 @@ can choose both, FP16 and INT8.
     else:
         chosen_pipeline = ov_pipe_inpaint or get_ov_pipeline()
         int8_ov_pipe_inpaint = None
-    
-    
+
+
     gc.collect();
 
 Choose a source image and a reference image, draw a mask in source image
@@ -1265,16 +1265,16 @@ and push “Paint!”
 .. code:: ipython3
 
     # Code adapated from https://huggingface.co/spaces/Fantasy-Studio/Paint-by-Example/blob/main/app.py
-    
+
     import os
     import gradio as gr
-    
-    
+
+
     def predict(input_dict, reference, seed, steps):
         """
         This function runs when the 'paint' button is pressed. It takes 3 input images. Takes generated image decoded by VAE decoder, unpad it to initila image size (if required),
         normalize and convert to [0, 255] pixels range. Optionally, convertes it from np.ndarray to PIL.Image format
-    
+
         Parameters:
             input_dict (Dict):
                 Contains two images in a dictionary
@@ -1293,7 +1293,7 @@ and push “Paint!”
                 Postprocessed images
         """
         width, height = input_dict["image"].size
-    
+
         # If the image is not 512x512 then resize
         if width < height:
             factor = width / 512.0
@@ -1303,10 +1303,10 @@ and push “Paint!”
             factor = height / 512.0
             height = 512
             width = int((width / factor) / 8.0) * 8
-    
+
         init_image = input_dict["image"].convert("RGB").resize((width, height))
         mask = input_dict["mask"].convert("RGB").resize((width, height))
-    
+
         # If the image is not a 512x512 square then crop
         if width > height:
             buffer = (width - height) / 2
@@ -1318,15 +1318,15 @@ and push “Paint!”
             mask = mask.crop((0, buffer, 512, height - buffer))
         else:
             input_image = init_image
-    
+
         if not os.path.exists("output"):
             os.mkdir("output")
         input_image.save("output/init.png")
         mask.save("output/mask.png")
         reference.save("output/ref.png")
-    
+
         mask = [mask]
-    
+
         result = chosen_pipeline(
             image=input_image,
             mask_image=mask,
@@ -1336,14 +1336,14 @@ and push “Paint!”
         )[
             "sample"
         ][0]
-    
+
         out_dir = Path("output")
         out_dir.mkdir(exist_ok=True)
         result.save("output/result.png")
-    
+
         return result
-    
-    
+
+
     example = {}
     title = f"# {model_to_use.value} pipeline"
     ref_dir = "data/reference"
@@ -1352,8 +1352,8 @@ and push “Paint!”
     ref_list.sort()
     image_list = [os.path.join(image_dir, file) for file in os.listdir(image_dir) if file.endswith(".png")]
     image_list.sort()
-    
-    
+
+
     image_blocks = gr.Blocks()
     with image_blocks as demo:
         gr.Markdown(title)
@@ -1373,7 +1373,7 @@ and push “Paint!”
                         type="pil",
                         label="Reference Image",
                     )
-    
+
                 with gr.Column():
                     image_out = gr.Image(label="Output", elem_id="output-img")
                     steps = gr.Slider(
@@ -1385,10 +1385,10 @@ and push “Paint!”
                         interactive=True,
                     )
                     seed = gr.Slider(0, 10000, label="Seed (0 = random)", value=0, step=1)
-    
+
                     with gr.Row(elem_id="prompt-container"):
                         btn = gr.Button("Paint!")
-    
+
             with gr.Row():
                 with gr.Column():
                     gr.Examples(
@@ -1404,13 +1404,13 @@ and push “Paint!”
                         label="Examples - Reference Image",
                         examples_per_page=12,
                     )
-    
+
             btn.click(
                 fn=predict,
                 inputs=[image, reference, seed, steps],
                 outputs=[image_out],
             )
-    
+
     # Launching the Gradio app
     try:
         image_blocks.launch(debug=False, height=680)
diff --git a/docs/notebooks/parler-tts-text-to-speech-with-output.rst b/docs/notebooks/parler-tts-text-to-speech-with-output.rst
new file mode 100644
index 00000000000000..428e353fea2310
--- /dev/null
+++ b/docs/notebooks/parler-tts-text-to-speech-with-output.rst
@@ -0,0 +1,511 @@
+Text-to-speech (TTS) with Parler-TTS and OpenVINO
+=================================================
+
+Parler-TTS is a lightweight text-to-speech (TTS) model that can generate
+high-quality, natural sounding speech in the style of a given speaker
+(gender, pitch, speaking style, etc). It is a reproduction of work from
+the paper `Natural language guidance of high-fidelity text-to-speech
+with synthetic
+annotations <https://www.text-description-to-speech.com/>`__ by Dan Lyth
+and Simon King, from Stability AI and Edinburgh University respectively.
+
+|image0|
+
+Text-to-speech models trained on large-scale datasets have demonstrated
+impressive in-context learning capabilities and naturalness. However,
+control of speaker identity and style in these models typically requires
+conditioning on reference speech recordings, limiting creative
+applications. Alternatively, natural language prompting of speaker
+identity and style has demonstrated promising results and provides an
+intuitive method of control. However, reliance on human-labeled
+descriptions prevents scaling to large datasets.
+
+This work bridges the gap between these two approaches. The authors
+propose a scalable method for labeling various aspects of speaker
+identity, style, and recording conditions. This method then is applied
+to a 45k hour dataset, which is used to train a speech language model.
+Furthermore, the authors propose simple methods for increasing audio
+fidelity, significantly outperforming recent work despite relying
+entirely on found data.
+
+`GitHub repository <https://github.com/huggingface/parler-tts>`__
+
+`HuggingFace page <https://huggingface.co/parler-tts>`__
+
+**Table of contents:**
+
+
+-  `Prerequisites <#prerequisites>`__
+-  `Load the original model and
+   inference <#load-the-original-model-and-inference>`__
+-  `Convert the model to OpenVINO
+   IR <#convert-the-model-to-openvino-ir>`__
+-  `Compiling models and inference <#compiling-models-and-inference>`__
+-  `Interactive inference <#interactive-inference>`__
+
+.. |image0| image:: https://images.squarespace-cdn.com/content/v1/657816dfbefe0533e8a69d9a/30c96e25-acc5-4019-acdd-648da6142c4c/architecture_v3.png?format=2500w
+
+Prerequisites
+-------------
+
+
+
+.. code:: ipython3
+
+    import os
+    
+    os.environ["GIT_CLONE_PROTECTION_ACTIVE"] = "false"
+    
+    %pip install -q "openvino>=2024.2.0"
+    %pip install -q git+https://github.com/huggingface/parler-tts.git "gradio>=4.19" transformers "torch>=2.2" --extra-index-url https://download.pytorch.org/whl/cpu
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+    ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
+    googleapis-common-protos 1.63.2 requires protobuf!=3.20.0,!=3.20.1,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0.dev0,>=3.20.2, but you have protobuf 3.19.6 which is incompatible.
+    mobileclip 0.1.0 requires torch==1.13.1, but you have torch 2.3.1+cpu which is incompatible.
+    mobileclip 0.1.0 requires torchvision==0.14.1, but you have torchvision 0.18.1+cpu which is incompatible.
+    onnx 1.16.1 requires protobuf>=3.20.2, but you have protobuf 3.19.6 which is incompatible.
+    paddlepaddle 2.6.1 requires protobuf>=3.20.2; platform_system != "Windows", but you have protobuf 3.19.6 which is incompatible.
+    tensorflow 2.12.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 3.19.6 which is incompatible.
+    tensorflow-datasets 4.9.2 requires protobuf>=3.20, but you have protobuf 3.19.6 which is incompatible.
+    tensorflow-metadata 1.14.0 requires protobuf<4.21,>=3.20.3, but you have protobuf 3.19.6 which is incompatible.
+    tf2onnx 1.16.1 requires protobuf~=3.20, but you have protobuf 3.19.6 which is incompatible.
+    visualdl 2.5.3 requires protobuf>=3.20.0, but you have protobuf 3.19.6 which is incompatible.
+    Note: you may need to restart the kernel to use updated packages.
+
+
+Load the original model and inference
+-------------------------------------
+
+
+
+.. code:: ipython3
+
+    import torch
+    from parler_tts import ParlerTTSForConditionalGeneration
+    from transformers import AutoTokenizer
+    import soundfile as sf
+    
+    device = "cpu"
+    
+    repo_id = "parler-tts/parler_tts_mini_v0.1"
+    model = ParlerTTSForConditionalGeneration.from_pretrained(repo_id).to(device)
+    tokenizer = AutoTokenizer.from_pretrained(repo_id)
+    
+    prompt = "Hey, how are you doing today?"
+    description = "A female speaker with a slightly low-pitched voice delivers her words quite expressively, in a very confined sounding environment with clear audio quality. She speaks very fast."
+    
+    input_ids = tokenizer(description, return_tensors="pt").input_ids.to(device)
+    prompt_input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
+    
+    generation = model.generate(input_ids=input_ids, prompt_input_ids=prompt_input_ids)
+    audio_arr = generation.cpu().numpy().squeeze()
+    sf.write("parler_tts_out.wav", audio_arr, model.config.sampling_rate)
+
+
+.. parsed-literal::
+
+    2024-07-02 01:08:42.916890: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-07-02 01:08:42.950912: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+      warnings.warn(
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/utils/weight_norm.py:28: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.
+      warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.")
+    You set `add_prefix_space`. The tokenizer needs to be converted from the slow tokenizers
+    Using the model-agnostic default `max_length` (=2580) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.
+
+
+.. code:: ipython3
+
+    import IPython.display as ipd
+    
+    ipd.Audio("parler_tts_out.wav")
+
+
+
+
+.. raw:: html
+
+    
+    <audio  controls="controls" >
+        <source 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type="audio/x-wav" />
+        Your browser does not support the audio element.
+    </audio>
+
+
+
+
+Convert the model to OpenVINO IR
+--------------------------------
+
+
+
+Let’s define the conversion function for PyTorch modules. We use
+``ov.convert_model`` function to obtain OpenVINO Intermediate
+Representation object and ``ov.save_model`` function to save it as XML
+file.
+
+.. code:: ipython3
+
+    import openvino as ov
+    
+    
+    def convert(model: torch.nn.Module, xml_path: str, example_input):
+        xml_path = Path(xml_path)
+        if not xml_path.exists():
+            xml_path.parent.mkdir(parents=True, exist_ok=True)
+            with torch.no_grad():
+                converted_model = ov.convert_model(model, example_input=example_input)
+    
+            ov.save_model(converted_model, xml_path)
+    
+            # cleanup memory
+            torch._C._jit_clear_class_registry()
+            torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore()
+            torch.jit._state._clear_class_state()
+
+In the pipeline two models are used: Text Encoder (``T5EncoderModel``)
+and Decoder (``ParlerTTSDecoder``). Lets convert them one by one.
+
+.. code:: ipython3
+
+    from pathlib import Path
+    
+    
+    TEXT_ENCODER_OV_PATH = Path("models/text_encoder_ir.xml")
+    
+    
+    example_input = {
+        "input_ids": torch.ones((1, 39), dtype=torch.int64),
+    }
+    
+    text_encoder_ov_model = convert(model.text_encoder, TEXT_ENCODER_OV_PATH, example_input)
+
+
+.. parsed-literal::
+
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4371: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
+      warnings.warn(
+
+
+The Decoder Model performs in generation pipeline and we can separate it
+into two stage. In the first stage the model generates
+``past_key_values`` into output for the second stage. In the second
+stage the model produces tokens during several runs.
+
+.. code:: ipython3
+
+    DECODER_STAGE_1_OV_PATH = Path("models/decoder_stage_1_ir.xml")
+    
+    
+    class DecoderStage1Wrapper(torch.nn.Module):
+        def __init__(self, decoder):
+            super().__init__()
+            self.decoder = decoder
+    
+        def forward(self, input_ids=None, encoder_hidden_states=None, encoder_attention_mask=None, prompt_hidden_states=None):
+            return self.decoder(
+                input_ids=input_ids,
+                return_dict=False,
+                encoder_hidden_states=encoder_hidden_states,
+                encoder_attention_mask=encoder_attention_mask,
+                prompt_hidden_states=prompt_hidden_states,
+            )
+    
+    
+    example_input = {
+        "input_ids": torch.ones((9, 1), dtype=torch.int64),
+        "encoder_hidden_states": torch.ones((1, 39, 1024), dtype=torch.float32),
+        "encoder_attention_mask": torch.ones((1, 39), dtype=torch.int64),
+        "prompt_hidden_states": torch.ones((1, 9, 1024), dtype=torch.float32),
+    }
+    
+    decoder_1_ov_model = convert(DecoderStage1Wrapper(model.decoder.model.decoder), DECODER_STAGE_1_OV_PATH, example_input)
+
+
+.. parsed-literal::
+
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_attn_mask_utils.py:86: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+      if input_shape[-1] > 1 or self.sliding_window is not None:
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_attn_mask_utils.py:162: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+      if past_key_values_length > 0:
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/parler_tts/modeling_parler_tts.py:221: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+      if seq_len > self.weights.size(0):
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/parler_tts/modeling_parler_tts.py:328: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+      if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/parler_tts/modeling_parler_tts.py:335: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+      if attention_mask.size() != (bsz, 1, tgt_len, src_len):
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/parler_tts/modeling_parler_tts.py:367: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+      if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
+
+
+.. code:: ipython3
+
+    DECODER_STAGE_2_OV_PATH = Path("models/decoder_stage_2_ir.xml")
+    
+    
+    class DecoderStage2Wrapper(torch.nn.Module):
+        def __init__(self, decoder):
+            super().__init__()
+            self.decoder = decoder
+    
+        def forward(self, input_ids=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_values=None):
+            past_key_values = tuple(tuple(past_key_values[i : i + 4]) for i in range(0, len(past_key_values), 4))
+            return self.decoder(
+                input_ids=input_ids,
+                return_dict=False,
+                encoder_hidden_states=encoder_hidden_states,
+                encoder_attention_mask=encoder_attention_mask,
+                past_key_values=past_key_values,
+            )
+    
+    
+    example_input = {
+        "input_ids": torch.ones((9, 1), dtype=torch.int64),
+        "encoder_hidden_states": torch.ones((1, 39, 1024), dtype=torch.float32),
+        "encoder_attention_mask": torch.ones((1, 39), dtype=torch.int64),
+        "past_key_values": (
+            (
+                torch.ones(1, 16, 10, 64, dtype=torch.float32),
+                torch.ones(1, 16, 10, 64, dtype=torch.float32),
+                torch.ones(1, 16, 39, 64, dtype=torch.float32),
+                torch.ones(1, 16, 39, 64, dtype=torch.float32),
+            )
+            * 24
+        ),
+    }
+    
+    decoder_2_ov_model = convert(DecoderStage2Wrapper(model.decoder.model.decoder), DECODER_STAGE_2_OV_PATH, example_input)
+
+
+.. parsed-literal::
+
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/parler_tts/modeling_parler_tts.py:287: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+      if (
+
+
+Compiling models and inference
+------------------------------
+
+
+
+Select device from dropdown list for running inference using OpenVINO.
+
+.. code:: ipython3
+
+    import ipywidgets as widgets
+    
+    core = ov.Core()
+    device = widgets.Dropdown(
+        options=core.available_devices + ["AUTO"],
+        value="AUTO",
+        description="Device:",
+        disabled=False,
+    )
+    
+    device
+
+
+
+
+.. parsed-literal::
+
+    Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
+
+
+
+Let’s create callable wrapper classes for compiled models to allow
+interaction with original pipeline. Note that all of wrapper classes
+return ``torch.Tensor``\ s instead of ``np.array``\ s. In the
+``DecoderWrapper`` we separates the pipeline into two stages.
+
+.. code:: ipython3
+
+    from collections import namedtuple
+    
+    EncoderOutput = namedtuple("EncoderOutput", "last_hidden_state")
+    DecoderOutput = namedtuple("DecoderOutput", ("last_hidden_state", "past_key_values", "hidden_states", "attentions", "cross_attentions"))
+    
+    
+    class TextEncoderModelWrapper(torch.nn.Module):
+        def __init__(self, encoder_ir_path, config):
+            self.encoder = core.compile_model(encoder_ir_path, device.value)
+            self.config = config
+            self.dtype = self.config.torch_dtype
+    
+        def __call__(self, input_ids, **_):
+            last_hidden_state = self.encoder(input_ids)[0]
+            return EncoderOutput(torch.from_numpy(last_hidden_state))
+    
+    
+    class DecoderWrapper(torch.nn.Module):
+        def __init__(self, decoder_stage_1_ir_path, decoder_stage_2_ir_path):
+            super().__init__()
+            self.decoder_stage_1 = core.compile_model(decoder_stage_1_ir_path, device.value)
+            self.decoder_stage_2 = core.compile_model(decoder_stage_2_ir_path, device.value)
+    
+        def __call__(self, input_ids=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_values=None, prompt_hidden_states=None, **kwargs):
+            inputs = {}
+            if input_ids is not None:
+                inputs["input_ids"] = input_ids
+            if encoder_hidden_states is not None:
+                inputs["encoder_hidden_states"] = encoder_hidden_states
+            if encoder_attention_mask is not None:
+                inputs["encoder_attention_mask"] = encoder_attention_mask
+            if prompt_hidden_states is not None:
+                inputs["prompt_hidden_states"] = prompt_hidden_states
+            if past_key_values is not None:
+                past_key_values = tuple(past_key_value for pkv_per_layer in past_key_values for past_key_value in pkv_per_layer)
+                inputs["past_key_values"] = past_key_values
+                arguments = (
+                    input_ids,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                    *past_key_values,
+                )
+                outs = self.decoder_stage_2(arguments)
+            else:
+                outs = self.decoder_stage_1(inputs)
+    
+            outs = [torch.from_numpy(out) for out in outs.values()]
+            past_key_values = list(list(outs[i : i + 4]) for i in range(1, len(outs), 4))
+    
+            return DecoderOutput(outs[0], past_key_values, None, None, None)
+
+Now we can replace the original models by our wrapped OpenVINO models
+and run inference.
+
+.. code:: ipython3
+
+    model.text_encoder = TextEncoderModelWrapper(TEXT_ENCODER_OV_PATH, model.text_encoder.config)
+    model.decoder.model.decoder = DecoderWrapper(DECODER_STAGE_1_OV_PATH, DECODER_STAGE_2_OV_PATH)
+
+.. code:: ipython3
+
+    generation = model.generate(input_ids=input_ids, prompt_input_ids=prompt_input_ids)
+    audio_arr = generation.cpu().numpy().squeeze()
+    sf.write("parler_tts_out.wav", audio_arr, model.config.sampling_rate)
+
+
+.. parsed-literal::
+
+    Using the model-agnostic default `max_length` (=2580) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.
+
+
+.. code:: ipython3
+
+    import IPython.display as ipd
+    
+    ipd.Audio("parler_tts_out.wav")
+
+
+
+
+.. raw:: html
+
+    
+    <audio  controls="controls" >
+        <source 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c/l7+ZP5m/mH+WP5U/lT+VP5Q/k7+UP5T/lL+Tv5N/k/+UP5R/lT+Wf5b/ln+Vf5V/lj+W/5a/ln+WP5W/lX+VP5V/lb+Wf5c/lz+W/5a/lr+Wv5V/lD+Uv5Y/l3+Xv5e/l/+Wf5P/kn+Tv5Y/lz+WP5T/lT+Vf5T/lH+U/5X/lb+UP5N/lD+Vf5W/lL+TP5J/kz+UP5V/ln+W/5a/lj+V/5Y/lj+Vf5S/lL+VP5V/lT+UP5N/kn+Rv5F/kb+S/5U/l7+X/5Z/lX+Wf5d/l3+Wv5c/l3+Wf5R/lD+VP5S/kv+Sf5O/lD+Sv5E/kb+SP5F/kH+Rf5L/kz+Sv5M/lD+Uv5V/l3+ZP5l/mL+Y/5m/mT+Xv5d/mD+Xf5V/lL+Wf5h/mT+Y/5i/mH+Xv5c/l3+Xv5d/mL+a/5w/mn+X/5c/mD+ZP5o/mv+bf5s/mn+Z/5n/mf+aP5n/mb+Zv5q/nH+df50/nH+bf5r/mz+dP59/n7+d/5z/nj+ff56/nP+cv51/nX+cv5x/nP+dv55/n/+hP6E/oD+f/6C/oX+h/6K/oz+if6F/oT+iP6J/oX+gv6F/on+i/6N/pD+kf6N/or+jf6U/pb+kf6M/oz+jv6M/oj+h/6I/of+hf6H/o/+lv6W/pP+kf6R/o7+i/6M/pD+lP6X/pj+lf6Q/o7+kv6U/o/+if6J/oz+if6F/on+kv6R/on+iP6R/pT+jP6F/oz+lP6Q/ob+iP6U/pv+mv6c/qb+r/6u/qn+qP6q/qr+p/6k/qT+pP6j/qH+of6g/p/+nv6f/p/+nv6f/qP+qf6r/qf+ov6k/qv+tf67/r7+vf67/rz+wP6//rb+rf6u/rT+sP6k/p/+p/6t/qX+nP6k/rb+vv65/r3+0/7l/tj+tf6d/p7+sf7P/vH+AP/o/sL+yP79/hj/2P5g/hT+J/5x/rX+2/7k/tP+tP6a/pT+nv63/t/+A/8H/+j+yf7F/sn+uf6g/qP+y/7t/uH+tv6i/rj+1/7X/sD+uv7T/uz+6P7X/tb+6/4D/wn/8/7M/q/+uP7c/vP+7f7p/vf+/P7k/sr+0/7t/u/+3P7Z/uf+6P7b/tj+3/7V/sD+xP7k/vX+5P7W/ub++P7v/t/+5P7z/vT+8P72/vz+8f7j/uz+AP8A/+z+3/7o/vX+/P7//gD/+/72/vX+8/7o/uL+7/4B/wD/6/7j/vL+Av/6/ub+4P7r/vb++P77/gX/D/8R/wr/Af/6/vj+AP8N/xb/Ef8H/wT/Cf8L/wP//f4C/wz/Df8E//v+/v4H/w3/Cv8A//v+A/8W/yT/IP8S/wb///77/vr+Av8R/x3/Hv8X/wr///7+/gj/GP8g/yD/HP8Y/xH/B/8A/wL/Df8a/x//GP8N/w3/Gf8h/x3/FP8U/xn/Gf8V/xX/F/8S/wn/Bv8L/w3/Cv8J/w//EP8I/wH/Bv8Q/xX/E/8S/xT/Gf8b/xf/Dv8J/w3/GP8d/xn/E/8Q/w7/Cf8D/wX/EP8a/xv/F/8W/xr/HP8Z/xb/Fv8W/xX/Fv8d/yT/I/8b/xf/G/8f/yD/If8l/yT/Gv8Q/w3/D/8T/xr/Iv8i/xb/Df8T/x3/Gv8S/xX/Jf8u/yf/G/8W/xj/Gf8a/xv/G/8c/yH/KP8n/x7/Gv8g/yr/LP8r/y3/MP8u/yr/K/8x/zL/K/8n/y7/Of89/zX/LP8p/yr/Kf8m/yf/Lf8x/y7/K/8w/zv/Qf8//zf/Mf8u/zH/N/88/zv/Nf8y/zT/Nv80/zH/Nf88/z//Pf86/zr/Of8z/y3/L/81/zv/Pf89/zv/Of87/0H/Rf9B/zz/Pv9I/07/S/9K/1D/Vf9R/0r/Tf9X/1b/TP9H/0v/T/9N/0z/Uf9V/1P/Vf9f/2b/Wv9A/y3/L/89/0b/R/9H/0v/UP9Q/0v/R/9K/1H/V/9e/2j/cv9x/2f/Xv9b/1z/Xv9k/2v/bv9r/2j/af9n/17/Vf9T/1f/Xf9j/2j/aP9h/1v/Xv9k/2X/Y/9k/2j/af9m/2T/Zf9m/2b/aP9s/2z/af9n/2f/aP9o/2r/b/90/3f/dv9y/2z/av9u/3f/fv+B/4H/ff92/3L/dP94/3r/ef93/3b/dv93/3j/dP9u/23/dP9//4L/fv97/3z/ff97/3r/e/97/3v/fP+A/4H/f/9+/3//fv96/3j/fP+A/4H/f/9//4H/gf+A/4D/gv+F/4X/hP+B/4L/hP+G/4X/hv+K/43/i/+G/4T/hv+J/4r/iv+M/47/jP+I/4b/hf+G/4b/h/+H/4j/if+L/47/kP+R/5H/kv+T/5b/mv+d/57/nf+d/5z/nP+f/6P/pf+k/6H/n/+f/6D/oP+g/6D/o/+o/6r/qP+k/6P/pf+l/6P/ov+l/6j/qf+q/6z/rf+s/6r/q/+u/7D/sP+u/67/sf+0/7T/s/+x/7H/s/+2/7j/vP+//8D/vv+7/7r/vf+//8D/v/+9/7v/u/+7/73/wP/D/8L/v/+9/77/wf/B/8D/w//I/8r/yf/G/8b/xv/F/8b/y//P/9D/zP/M/8//0f/P/83/zv/R/9L/0P/R/9X/2f/Z/9f/2P/b/97/4P/h/+L/4f/f/97/4P/h/9//3f/d/97/4P/j/+X/5f/h/97/3f/h/+X/5//o/+f/6P/q/+z/6//n/+L/4f/l/+n/6//q/+r/7f/w//L/8v/x//L/9f/4//r//P/+/wAAAgAEAAQAAgADAAYACQAHAAQABAAJAAwACgAIAAkADAANAAsACAAGAAYACQAMAAwACQAIAAoADQAQABEAEgAVABYAFwAYABgAGQAaABsAHAAeACAAIwAlACQAIgAjACUAJwApACsALQAsACsALAAuAC4ALgAuADAAMQAxAC8ALgAuAC0AKwApACgAKQAqACsALAAuADAAMgA1ADcAOAA5ADsAPwBDAEIAPQA5ADgAOwA/AD8APgA+AD8AQQBAAD0AOwA9AEIARQBEAEIAQQBBAEEAQAA/AEEARQBHAEQAPwA+AEIARgBJAEsATABKAEYARABGAEoASwBKAEgASQBOAFMAVgBWAFMATwBOAE8AUQBSAFEATgBLAEkASQBKAEsATABPAFEAUgBQAFEAUwBTAFMAVABXAFkAWQBXAFYAUwBRAFEAUwBVAFUAVQBYAFsAXQBbAFgAVwBYAFsAXQBgAGIAZABjAF8AWABUAFUAWABbAF0AXgBfAF4AXABZAFgAWABXAFUAUwBVAFsAYABfAF0AXwBiAGQAZABlAGYAZwBpAG0AcgB0AHMAdQB5AHsAeAB0AHUAdgB1AHEAbABnAGUAZgBrAG8AbgBpAGcAaQBsAG4AcAByAHIAbwBtAG8AdAB4AHsAfQB/AIEAggCDAIYAigCNAI4AkACTAJUAlACTAJQAlgCUAI8AiwCLAIwAjACKAIYAgQB9AHwAfQB+AHsAdwB3AHoAfgCCAIUAiACKAIwAjACMAIsAjACOAJAAkACQAJIAkwCVAJgAmwCdAJwAmQCXAJYAlACRAJAAjwCOAI4AkgCXAJsAmwCZAJkAnACgAKMAowCiAKMAowCgAJgAkQCNAI4AjgCMAIgAgQB6AHgAfgCHAIwAiwCIAIoAjQCNAI0AjwCVAJwAnwCfAJwAnACfAKQApwCoAKsArgCuAKgAowChAKAAnACWAJMAkQCQAI4AjwCPAIwAiQCLAI8AkgCSAJIAlACVAJQAlgCfAKoAswC1ALEAqgClAKMAowClAKkAsgC7AMIAxQDJAMsAygDEAMEAxgDNAM0AxAC5ALQAtACwAKYAnACbAKQArgC0ALkAwwDKAMgAvQC4ALwAwQDAALwAtwCzAKwApwCoAK4AtgC8AL4AvgDBAM0A3ADkAOUA7QD9AAkBBwEDAQYBDAELAQgBDwEaARsBEwEOAQ4BCQH+APUA8gDrAOEA4ADwAAIBCAEGAQUBBwEEAf4A+ADwAOQA2QDWANgA2QDZAN8A6wD1APwACAEcASsBLQElASMBKwEzATIBLAEsATABLgEhARABBQEAAf4ABAEQARYBDwEAAfoA/wAKARQBHgEpATEBOQFFAU0BSQFBAUABQgE6AScBGwEfASQBGwEUASMBQwFcAWQBaAFuAWsBVwE8ASwBLAEtASQBFwEMAQMB+wD5AAIBEAEUAQsBAQEGARcBJwEzATwBQQFBAT0BNQErASUBMQFMAWEBZAFkAXUBjgGXAYwBgAF/AYABfQGAAYwBkwGOAYcBhwGEAXgBawFtAXsBhAGEAYIBhwGMAYYBbwFKASsBJAE0AUkBVQFeAW0BfQGHAYgBiAGGAX8BbwFgAV4BbwGOAbYB4gEHAhcCDgL0AdgBuAGQAWkBVAFTAVABQQE1AT8BUgFWAVMBagGiAd4B+QHuAdABuwG6AcEBswGIAWQBawGUAa8BpQGPAYkBkgGaAZ4BmwGPAYoBrwH8AUECVgJPAlQCZQJpAlYCNQIBArwBkwG4ARICTQJAAhUC8wG8AUkBvQBwAIgA1QAiAW0BzgE0AmsCSQLTATkBuAB4AHkAoQDmAFUB6AFqAqkCrQKrArICmwJbAi8CQwJlAkUC+QHgAQQCBAKpAUoBTgGeAdQB0AHIAdcB5gHtAQQCJAIsAh8CGwINArQBGgGiAIkAngClAL0AGAGbAQICQQJzAogCZAIlAggCFQIlAjMCVQJzAl8CLgI2AoICrgJlAs8BSgH0ALMAjwDHAHIBTgLwAi0DIwP3ApcC6AEOAWsAOABVAJoADwG1AUQCbwJNAiwCJQIVAgoCNAJyAl8C7wGbAbAB0AF/AeYAoQDpAFcBgQGCAbIBJAKOAqoCcAIGApsBWwFpAb0BFgItAv4BwQGdAXcBKgHJAJQAuQA5AeEBZwKUAnMCNALVAS0BZgARAHwAQAHHARwCpwJXA6ADSAOqAhgCfQHIADwAAwDd/5v/l/8+AGYBbwL0AhYDCwPEAg0C9QD4/7//jwD2AR8DigNiAwsDpgIfAoUBDAHLAKEAdwB8APIAwgFaAjoCowFpAfMBowKiAvcBXgE4ASoB5QCyAPMAhwEIAlQCewJdArsBngBz/73+yv6K/6MArgF2Au4CAgOiAgYClAFpAUYB+wDAAOgAdAEVAoQCyAIbA5oD8AOHAz8CyQD2/9H/1v/o/30AtgHcAiADdgJqAW8Aqf8l//j+OP/4/xgBFAJYAuMBSAH8APAA+AAeAWkBoQGjAZcBowG4AdYBLQKvAu8CogL+AWEB5QCXAKoANAH6Ab4CaAOlA+0CSwGu//7+Jv9x/4v/rP8QAL0AlQExAv8BCwFMAJkAiwEPAtwBqAHsATgCEQLBAecBhALsAqIC7gGCAZ4ByAGPAVkB6QEhA9UDRgMoApkBlQEnAfT/zf6i/n3/qwB7Ab8BugG6AZUB3QCz//7+Wv9CANIADQGOAVICrgJ8AlsCkwKTAusBBgGCAGkAdgChAPEAQgGYATkCCgNGA2MC8AACAPH/NABjALwAnQHrAhgEjAQLBOkC0gEkAacAGADJ/0UAVwEAAqYB3ACaAP8AVQEwAeoA5wDwALQAiQAJARwC+wJOA5MDJQRkBH0DsgE0AM7/MACfANAA8wA0AYEBnwFvASsBPAGyAQ8C5AFvAS4BFgG2ACEA+v9+ABsBSQE6AU8BVAHkADUA//+vAP8BWQMzBEgEyANBAw8D7QJkAokB5wDGANEAlgD9/0r/1/7r/qH/yQAIAgsDkwNxA7sC7QGAAXEBYwFBAU8BogHpAeYBugGGAS8BvQCiACkB4wEuAjECoQKTAzoEDQSGAzkDswItARj/C/7L/igAbABu/5v+I//DAGQCPwNAA74CQgIuAlMCGAJKAaAA/gBTApcD8gOWA0YDVgNbA8MCfwEaADL/6v73/jH/5/9WAfkC0AOCA8sCewJPAlsBd/+q/Rv9Cf7l/+cBUQOkAx4DqwLbAiMDswLFAVUBngG4ARABZQC+ANYBYQKwAXYA0v8NAIIAjABKAHIAdgHwAg4EbQRCBM4DEQMbAjkBlgAeAO//YABNAeEBhwGzADEAGgAPABUAdgAOAWMBZAFzAcIBSwIxA3UEeAWFBdUEKQSJA0YCUQCG/nz9Dv0+/WD+AADOAFYA0P9TAEUBiwFWAYwBCwLrATIB2QAyAYkBgAGtAX8CdAPpA+cDtgNBA2gCkwFkAe8BlgKeAtwB0gAjANL/U/9q/pr9Z/2e/eX9hv7i/3cBWwKXAhgDPgRIBYMFHQWABKYDhQKGAfsAogAjALn/0P97AH4BnAJ3A6EDMgPbAvYC9AI7Ai0BrADqAFUBiAGnAcYBsAFhATQBUgFcAfEAQwDY//P/aQD4AHABsQG0AbQB6wEaAoMBzf+//av8If2C/vv/WwG+AvEDpATjBNcEZwSVA+cCyQLkAq0CXAKHAvMCvAK+Ac4AZgD//yz/Xf4W/iH+Mv6J/l7/NgB+AGYAiQAZAfIBEANcBF0FrAV8BSEFdwRLA/oBCgFwAMv/KP/W/r3+kP54/tz+kf8MAFEA+wAsAikDUgMGAx4D0wNwBAsEcQJrABv///63/60ApQGGAhUDOgMuAwgDdgJfAVsACQAzACQAnv/v/mX+S/4x/1sBBQTSBRYGKgWzA0ECYgFVAZMBawETAV8BZAJAA1ED2wJDAogBwgBIAPz/T/9Q/uz9l/6d/08ABAEhAgID3QIsAggCYgIpAg0BEwAqACIBPwIWA2QD6QLuAWoB+QH9AkUDZgIGAfb/c/8+/xT/5/7r/oP/6gDSAlwEqgSMA58B4f8T/1T/JQDeAE4B1AHAAqsD3wNcA+oC5wKWAj0Bhf/O/kr/2//h/xgAEQHtAZEBbwDQ/9T/XP8C/sr8ivzW/CH94v21//kBcAPeAwUEOQT6AxQDFQJyARYBGwETAuYDMwXDBBYDqgHxABUAs/6e/Yb98/0q/mr+Wv+3AI8BtAEIAhwDMAQMBGoCMgCZ/kz+OP/NAHECyAOTBGYE6AKIAG7+XP0B/dz8Zv19/60CDQVDBe4DggJxAUMAAv9p/qH+8f7p/gH/h//Y/17/vP4M/18A1QHgApED2AOHAx8DiwO9BIQFGgUSBEQDogKwAXQAKv+u/R78d/uf/Av/PgGPAm4DAgSeA/kB8P+L/tD9Tv04/TH+IwAKAhIDZQOiA9wDjQNmAtcAgv+B/n/9kPxw/LH9+v9KAtEDQwSlA1gCJQG+APsABAFrAM3/IQCBAfkCgwMfA7wCBwOAAxADawFc/7D9iPz++5f8X/4zAOkA3QA2AegBrgE3AOz+Af/b/0AANACbAIcBDQKsAQ0BIAEkAoQDTQTnA6AChAExAT8BEQHoAHABiAJNAzkDhAJgAb7//f37/Cn9DP7r/m//ZP+p/qn9PP2K/bX9I/2J/PL8UP7H//4AMgIxA1oDwAJbAsgCigPyAyUEtgSqBWsGhQb0BegEfAPBAd7/I/7d/On7pPq1+O32s/ZS+Gv6eft8+5X7h/w2/m0AMAMvBrAIRAoiC2oLlQokCJIELQEG/0j+hf4w/9X/JQAIAIf/nP49/bX7pvqA+gP7ivvO+yX89vwq/nf/6wDhAkcFSwfqB8MGUwSGARz/Zv2H/MX8af4lAQYEKAZNB1oH1AWTArv+EPwf+/z6+fqc+2z9tv9nAUACeALXAWAATP/5/xYC7QN+BCsEYgPnAbv/lf3x+5P6avkL+cP5EPuY/Lz+hQHIA1sEsAM+A5cDBQToA54D4QPGBLQFEQbGBQQFsANMAd/9pPoh+X75fPos+837pPwB/Uf8IPvJ+o/73PxY/hgA6wFnA44EogVbBiEGBwXnA0wD3AILAuUAFgBKAIgB8gJtA9kCQQJrAqcCtwGL/0/9x/uQ+ir57vev98b45fpv/cv/iAGAAt0C/wJJA8EDygO+AvEAn/+b/3UAQwGnAZgB7gDc/z7/hP/M/wj/wv2c/R3//gCrAbEAvf7//Kj8G/5vAFcCawMdBJoEcQRQA3ABYP/h/af9e/4S/3/+WP19/Iv7zPk3+Kv4RvsN/nL/vP/J/+//WgAfAacBZQFFAc8CngV7B2YHrAZWBpsF2AMrAqIBcAGEAH//av+w/x7/+f0+/bH8cPvt+YL5Wfo7+2T7F/ue+g766Pmk+qX75Puj+1f8qP6HAXID7wPJA1AEKwZoCDkJOgg2B7oHzghqCH4GyAThA2gCUv+C+3H4jPaO9WL12PVC9ir2//WD9r339PiY+fv5H/uv/SEBKAQLBjkHOAjMCIoIsAfaBkkG7QWFBUcEWwFR/SX65/hz+If3+PZP+Df7AP4cADACCQSABGoD8AHRAMv/yP4a/pf93Px3/Eb9q/4D/wP++Px9/Mv7ivqY+YD50PmE+mb8XP/cAdIC0QLGAu8CgAO6BNwFnAVkBDoEqwWWBhsFGgKU/yL+Rv25/BL8cPrp9w32v/Xd9Vv1N/W99jT5MPsA/eL/ggM2BjAH5wboBcEEYwTtBLYEQwKn/jT8W/uY+ij54/eY91P4NPpM/YwAdAL/AmED6gO6A5oCcAGHAA7/2PwL+4v64fpQ+8/7Yvyp/Jf8ufwo/T/90Pyd/CL96f2Q/mL/XQCfAMn/DP+2/0wBGAJMAa7/Tf51/f/87fxT/Rj+K//aAGsDSQb1B0sH2ARTAq4AZv/b/X387vu1++b6nfnJ+KH4UPhI9wb2c/UW9vj3rfpt/ZT/NgHRApoEYgbQB0sIUQeDBaQEhwWHBkcFpgG0/RP7tPnz+GH4Y/eu9XL0d/WG+DX79fvz+//8+/6hAMUBUQN4BTkHuwf5BjkFogKY/7L8FPpc95n07PK68yL3pvuA/xACtwOzBMgEAgTvAtgBjgBg/1X/wwB7AhQDdwJUAez/R/7E/GP7Vfl89nX0rvQr9vz2Svfp+Dz8t/9ZAm8EtgUjBREDSQEiAPj9mfre+O/65P44AVEBfAEYA+8EOAWjAyIB3/58/bT8svtO+oT5OPr9+4z9+f0G/Rf7Svnc+Kz5Jvpe+Yb4jPnV/CEB4ATcBo8G5gTyA5UE+QS9Aj3+aPpG+dT5nvmO9+P03POU9ev4nvu7/JD9zf8yA9gFdAabBdcEHAUOBmcGEAVSAqz/Vf4v/mL+V/7P/bX8i/tb+2P8J/3V+4D4QvUc9Br1jfbQ9vv1C/bt+B7+zgLfBC4FSQZCCY8M1w1SDHgJuwdLCLYJDwnpBMj+eflX9rD0Q/PC8fnwx/HX82T11fSm8gbxj/Ev9Gb4Nf4sBc0LuhCIE9IT1RANC+IETADX/Fv5ifbP9aP2IPcZ9z74+/pR/Zn91fwc/en+2wCTAQIBUAC9AC8CzwLpADv9bfoG+un6RfvM+kL64/ly+Vb5W/po/Hv+NABxAtcFcgmIC4ULMwoqCBsFGwF//Wn7C/qv91L04/Gi8YPy3PKc8hHzJvW3+BL9bAEuBWEIdAsuDmYPPQ4uC38HSARLAsQBrQElALj8cfl6+CX5n/iK9cDxR/Ar8rP1SPgZ+df5efx/AF0D4AO3A+YE8wbmBxsHmQUtBM8CdQE6AL3+nfxd+t/4IfiW90r3r/eq+Oj5uPtf/g0BygLuAzsFDgYuBfcC1QDj/j/8ufnB+fH81AD9AosDhAOkAjQAu/xi+bT2BvX59GT24/fE+GX6Gf6SAgkFoQQoA6kClwMwBWcGZQY/BTYEVAS7BHYDEQBM/Pz5N/kJ+dz4Y/j39mP0GfJc8sr1X/qf/Vz/nQHZBQ4L4A73D8YObQyyCeUG/QOYAHD8K/hE9df0r/Z7+b37k/w3/Mn7I/y5/Dv8YPqN+Hb4rvqv/moDZwdaCW8JaQleCtEKNwhTAuL7sfct9iH2xvb19xv5YPkP+aX54fts/ln/ov5D/qz/AAJFA7ECFAFm//r9AP3a/GD9iv2B/J/6CvmL+C/5ofpu/Ef+bwCRA4gHxQqnCzsK4QeeBaMDOQK1AVoBtP+4/Df6WPm9+H/2H/MO8aTx5vNE9j/4O/rD/D8AigSWCC0LJAxFDDQMNQybDEQN7wx9Cs0G8QNIAgcA2ftx9g7xVuw/6Sjpw+uz7rDwYfN8+Lj+pQMXB3sKyQ2zDxwQ3Q9iDi0KxAOv/Vr5FfZ481TywfJ189nzv/R29jD4Gfqu/fkCrQdiCn4MNw/cEE8PeAuuBwQE4f6W+GrzO/C47Wrr+ep47X/xPfXV+Cz9lgGGBBYGKgjoC14QChRqFjQXNhV3Dx0Hwf6y91jxkuv458jn9em17N7vGPSf+OP7EP6+ADgECgd1CEgJ5gmlCcsI5ggUCvsJ/gajAiT/N/xJ+Mfz7/DX8Iby0fSa9zz79f/BBdgLmRD2Ep0TxxN1E+QR+w4IC68Fxf7h9zvz3PCd7kzrZOjG5xXpdeoy64jsu+959IT5lf6YBMULQxLNFWgWIBbkFTEUuA/dCSUFDAIZ/4r7S/gt9rH0BfMo8cfvje/G8BLzcfU398D4uvoV/WP/rwH1Az8FvgSDA2gDKARqA1EAI/1I/Cv9p/1o/QH+PgBIA2AGngmhDCAOew2UC1EJkAZXA9IA4f90/8z9y/qp99D0nPEZ7l7rGOo46ivsqvAC9zb9fQLaB5oNXBKdFeMYvhzGHuYc5xjAFcoS6wzhAw77zfQt8NzrZ+ho5hnlDORi5NTmPuqG7YTxrff6/w8JWhEvFzkZ7BdzFTQTkhDWDOII5gWqA1IBrv62+/33r/Ma8BruI+3Y7CjugfFT9e33vPkl/B3/mQFkA9IEdgX6BHYE7QTuBPwBOPyb9nPzYPIy8vPySfVA+W7+VAQHCkIOYhDZEGcQfQ+HDuUNVA34C1EJqQVpAYv8/fYJ8S3rFubd4qbibeWh6ZLtOvEe9jb9ZgWMDNIRWhZVGzUgVCPaIzQiBB+GGuIUOQ59Bgj+LvYz8M3r9ucV5eHk2udG7DbwavO69rn6b/84BLEHFwncCSQMOQ+eD58LzgWfAX3/5/2W/GT8SP2i/qcAnQNYBmcHEAeKBtkFWQSSAnsBYQCs/WT5FfVh8Y/tZ+l45c/hdd4s3WHg8ufU8KD4yP/KB8AQShmfH1AifiFqH28eER6jGxgWUg9KCcoDy/1Q99zwruqg5T/j1uOA5VHmf+ZU50TpOuy38DL38P6WBmcN/xKzFjoYXhgEGD8XUBa/Fu0Z6R4bI/QkmyQaIoMcgROmCDb+IvUC7Zvlo9/T2/PZSNmL2evaS91Z4Ijk4+pW8+v7ugIUCKYNyBP2GPcbUx1oHtEfECEbIScfYxviFkASywzMBRf+P/fE8UTt/elM6CfnBeVN4sjgheCP31Pd/tuc3f3hQOgh8Iv4bf+nBNMKFhNIGgEdnxxQHT4griJeIsofQBxjGNEU8xGxDmEJVQKn+zn2x/Bp6jHkv9/Q3Z/eJOJL51/s1PCT9QT7EwCrA/EFpQeLCZwMWxGCFrAZGRo3GWAYLxcLFZkSixBfDq4LnAljCRkKdQlhBpIBBPwv9qLw/usG6N3jrN9J3ZjeeuOw6bru1PE/9LD3g/yCAW4FfAgEDOIQaBYXGwceNB9/HjobdxU5D+4Kdgj3BIT+vfat8Pnsm+ni5Effqdot2XbcK+RB7Wb0X/ls/j4ErQgJCq8JFwrhCycOfhAUE5QVLBclF8cUvQ98CQEF5AN4BC8E2AIXAnkCxgKwAaH+afnu8s/thuyj7lbx0fKo89j0MvY+9z/4kPkh+0P9DQEoB98OtRZXHcghoSOJI3siQCCtG/YUYQ6jCYcFg/8b913uLedo4eXbXNb60X3Q39J12GXfWeaY7fv1Ef8nBzMN5hFgFpcaoB0rH9cfFSBXH7scGRg8EigMgQZ6AfH8kvgt9PTvSOxL6fHmheWh5WfnMupd7ffwQfXJ+ZT9JgD3AeQDhwbuCasNNRF5FM8XOBvnHfoeRh4mHA8ZxRUhE6MQQwwYBXv9kPjy9RPyU+tV5Gvgwt+U4DDi1eQC6Ezr8+9h93EATAjKDR8SQRahGZMbLhyUG/oZYhh1F54VjRDuCC4CtP3q+Ifxdul+5KnjEeXe5lLo8+ju6L7pYeyG70vxN/Ja9O73V/tG/jkCfwdADFEPuBGAFA0X3xjbGjMdzh3fGuIV8BGzD+4MoQcWAPf37fDm62PojuRi37Xaztnf3RXl9+xv9G37NgJmCV4RCBkWHj0fdB3HGqQYYhdVFvYTTw/LCXsGgwZTB5cFPAFU/WD8Fv53AM8BXQE//1D8aflu9s/y/e5M7NLqtOjf5G/hneF25Y/phOs+7ZbyxfxZCdIU9xwbIdYh3yDoHxIfCR3RGOYSyAwdCOcFCgXUARD5iOx04vrerN9I37bc0tvP34XnnO8M9pL6kf2//9IBuAMFBYYGpAmqDaUPSA43DMgMtw/tEZwR2Q/qDmkQSRTnFw0XGRCrBvb/ovy8+Nzxl+rR5uvmUugy6a7pKOpl6obqwOuM70D2ff4hBkMMHxJBGZYgjyT7IsId9xivFq0VmRN/D0kKsQWjAgAAOfv18ibpqeG83nPfiuHO447mfequ7zn1jvmX++H7HPxO/RX/+AB8Aw4HdQrBC+IKIApoC/gNWw9aDi4M6QpKCzUMrQs3CPMB6PoH9gz1+/bU+EL4z/Ve9BP2oPkh+yH4nPJP7wjxQvaP+zr/1gElBPIF5QYQB0cG+wNzAGz9yvzY/kkChgWtB74IcQmeCkMMgA3eDfkNPA6+DYMLfgiYBqAF3AKj/B71J/Dw7hbvse1U6u/mveWJ50nrGe8S8lf16/pCA14MdRMtFxQYfRdOFqkUOhL2DosLFAkbCPsHYQdLBZUB3/ww+Iv0avKI8U/xivFt8tvz3fRe9GryZfC976zwf/LE9Jz39vom/oMADAI8A4wEhAa2CS0O8BK0FvwYNBqXGnwZ8RX3D/kIBgNW/1f9SPsA+B30LfHD76nuMuxR6CjlWOWY6Qfw//WE+pz+ZQNzCEAMyg1ZDQMM1gpEChgK7AmsCVkJbAg5BikDuQCg/6/+ZPwr+Qv3Offr+I36X/vL+878MP+zAu8FjAeWBwYHPgbpBFYDtAJmAyYErQOSAncCsQOeBHADAADQ+7P4g/el98X3U/cy95r4Ovsx/QH9CvvI+Cv3PvYD9tP26vgq/FwABgUPCUsLpwsNCwIKFQg/Bc4CIwL0Aq8DRQPVAef/yv2D+9D4ifWe8v3xgfRZ+Mz6Y/sO/Bz+cwA9AU8ALv91/6gBBQUeCPgJsgrWCjwKTgg2BeUBAv+X/MT66PkI+sr68Ps9/Sz+oP5g/+oARgJJApQB3gGdA+UFJghECk4LWwqMCM0HYgdgBFP+xfjK9k33VvcZ9vz0F/WD9tz4Dfux+/36KPus/V0BZQS6BgUJsQrtCpEK6wpeCx8K9waYA4UB5wAZAf0AOf+I+5f3S/Vo9PLyu++Z60/oG+f857fpxerr6mHrLe0W8MrzwPgL/1YFQwr8DWYRiRTdFkMYqBiiF6MVgRQ8Ff8VBxQRD2UJ2QRIAcz9/vnw9RTyWO9n7tnuzu8u8WbzQfYX+fb7m/8rBKgI7Au+DfwOyhBfE5wVTRbzFUAWlxcfGPUVTRG9C2wGugG9/Sz6n/au883ySPRa9h73bfYL9VvzEfKk8mT1SPiB+V76lv0/A2MIeAqdCZwHLwYxBg4H5wZ/BLkAO/3s+VH1Gu/H6KbjnN8z3K/Zvti22Tjc/d5w4Gng8eA+5CTqofCN9pT8nwOGC2AT8hnzHQQfeh4jHkke4h2PHDAbaxrcGfEYbhfcFKUQCAsPBWf/GPp59T3ySfDC7rXtcu598Vb1Sfiq+hr+FgN0CCkNYxGrFdoZgh2+IN0jfia7J04n4yXeI3UgsBr7Eu4KmQO7/NL1K++q6fzlLuR2423iUOAZ3oTdzt524JHhG+NT5uzqt+8R9NP3tfrh/DP/1wGVA54DKQOsA1EEEQPQ/4P8Qfob+CD1rPFJ7snqPOdr5Jri6+Dg3mrdrN2i39ficOeR7XD0LvsMArcJuhHLGHIegCPiKMQuqTSZOVQ8TjyAOjA47zTkLn4luRryEHMIJADZ96TwGeuP5mPi7t7t3KbcCd4F4VLleept8JH30f83CN0P1BaLHc8j/Si3LNIu0C4ZLLgmXx/pFjUO5QWs/Xf0IOpm4BLZg9OQzcfGK8FhvtW9eL7SwPXFPc261IDbQuLO6fnxF/qNAQIIzQ0GFCgbmSHWJHQkpyIpIc8ecxltEZ8JSwQJAfb9xfmb9NzvP+0i7bPtD+1I7KHuJ/Xq/MECOgfdDOMT8RmkHQEgYSLXJNkmuydbJiEijxwFGJEUBhCvCbcD/P+w/WH7afmT+Cr4PveU9nP3qvl4/A4ATwRbB7YH2AYrB0MIsQcaBacCdgFhAKf+Jf0k/EP6pfZV8rLu6uu96QXo6uVX4gfeS9vT2nzaUNge1ejSYtJu02XWOdt/4DzluOrB8oz8WgX/C/4R8hhYIJgmDSs1LmwwYjGrMEIuTCq3JIMdUhUsDZsFe/7A9+HxMe2G6ezm/+Xd5pXod+ol7T/x6/Xv+Zf94QFxBh8KFw12EPoTLhbvFsIXThmXGiwbBhygHRIf3x/EIOUhCiKTIGweHRzBGPwT/g5OCpQEGf2Q9Y7v6umz4ljaxdKVzDTH3sJRwBu/Yb6svizBncW3ykbQ9tZ83vXlye0a90YBIArtEFMXpR74Jb4rwi+ZMkc0aDTtMsUvYiqGIhsZkQ99Boz9mfQb7Hjkht0Y167RMM4QzRHO4dCX1VHczeSA7sP4pQJGC7US3RkAIQYnISsoLhMx4TLPMT0ucypyJ+Ujpx6XGB4Tfw51CgAHwwPr/7r75fh3+IT50fp2/N7+aAEuAzkE2gTRBP8D6QKcAV//c/yt+vj6g/vz+Xb2EvPN8O3uYeyI6E/juN2M2UfXTtVu0g7Qc9DQ07zYr94X5pvuI/c4/+0G3A3DE4UZ4B/QJfcp+yxcMFUzNDPPLp4nMx+eFc8Kpf/o9L/queH02pDWNdPjzyLN3csgzKPNqdCh1WXcXOT37PP1eP/0CR4VdB+oJ1QuFTX8OyZBP0MlQ5NCA0KPQFY9eTjNMtksFyZxHa0S1QbX+q7ugOKl11rPZck6xbbD/8U3yy7RM9dX3gnnOvAk+RkCMQuaE74atSAvJTAnQSbcInEdQBYpDkYGkf489oLtuuV+30TaDdbL09zTq9VG2Y3fOehj8cL5wAHDCecQZxbHGqAefiHVIsciRiHCHYoY8BI0DegFnfyT8yvt5OiW5JzfIdsW2HnWINbU1vrXb9lN3LThJukq8Qb50wBvCIoPXhYwHXEjcCiWLNwwWzU6Ock7tDydO4k4ZzQNMP8qFSQ2G4YRCgjt/gj2ae1N5THezdib1YLUHtX51lTZd9uH3XDgoOSR6cHuTvRn+ugAxQfaDiIVWhm4G7EdlR/vH+UdsRrCF+YUdhHxDToLRAmwB6kGGwYLBfUCnQB+/of77/YX8vPuZu3s6yjqKumE6bbqK+x17fLtme2u7TzvRPHu8UnxNvGb8kX03/Se9ID0DvU79r33IPkd+g77Z/zv/Rr/GAC8AXEEBQhODEIRfRZzG/kfBSQrJzgp1CqQLLgtMy0oK3ko3yQXHwIXHQ6IBfP8+fNO6+rjAN5Q2a7Vu9L4z7fNNs01zxvTD9g+3nHmt/Ac/G0H6xGNG7okgS0JNSg6vjzSPR4+CT2TOb0zhCyyJFgcXRMSCu4AGfiD70zn49+R2UHU5M/EzD3LLcsWzMnNe9At1GXYsNwD4Xrl++k87uDxn/Sn9sr4qvvX/noB0AMbB8MLlxCEFAAY9BsSIF0jrSWQJwApZinCKK4nSCYZJAohZx34GBgT9AucBJr9avak7uLmAeBC2rPVotIj0bXQ/9Bj0lvVqNm73pHkfutY83/70QP5DHEXbiJNLCc0jzqIQPRFnEm7Ss5Js0dvRFw/HTgGL54kLhnEDHv/3PH75NzZlNBjyNzAjLpHtja0+rNvtcG4Ab7uxCfNSdbR3xTpdvG0+AX/zQQVClwOQhEWE2cUQRUyFRYUSRI+EE4O/gzjDAwO4A+9EXkTKxXSFkwYShk8GbwXPhXYEv4Q8g6+C0oHEwJb/Ff2tvAN7PjnreNe3yrcs9qZ2mbbNd0h4Prjz+gg7yX3OgBkCf4R6hleIasoyS8sNkQ7Cz/FQYFDQEQWRJ9Csj6SN0YuuiSAG1MRJQX79w/sdOIl2pfRx8hgwdC8yLr6uei5ObtovuvC9MduzdbTZdur48rrAvMj+Yf+cQN5B/YJ7woyC5sLiwzvDVQPDRDyDwAQjxG3FE8YxxsWIPklLiypMBIzfjTmNJ8y3CwWJRIduhQRCxkAj/Sf6DPcCNAYxXe7/7K+rE2q2qsQsA62Tb5gyeXWIOa59l4IZRorLEo9QE1AW6ZmJ29adLd1XnNDbjJn6F22UbpCEDLYIFkPKP0J6rvWAsVxtkKrnaIQnD2Yr5etmd+c2qBWpgSur7eMwvPNttkJ5vzysv9zChISQRfiGxIhJibQKakrYyysLEMsaCoCJ/IiJB+yG1IYShVKE3ASABImEaEPxg0+DG0LsAq8CEIFmAHg/lT8bPgT84PtW+hU43He/NmW1dPQrszbyorLdM1H0GXVhd1j567x8vxHChMZhie8NPpAN0zcVb1dF2RnaIJpIGdfYihc3VMvSMY4nyZFEwQAme0u3MPLnLwMrwejlJhnkHWLy4mKimCNG5NynOmoIrfFxfTTluE57zP9rQo8FoUfgSfvLks1qTnqO4o8sTsVObU0US/2KQ4lESCAGgAV6hCwDk8Nngt7CXgH7wWxBCwDsAAd/Un5GvZZ8zLwsOyh6SLnY+QH4aPdnNq51xvVadO+0rjS/9Mg2CzfA+c77jH2pACbDKoXZCFZKwI2AEB6SPdPlVZZW8NdW14eXRNZKVK3SU1AnjS7JdcU1ANL8wDjPtOExNC2NKo7nyKWg46OiJaFeoaTii+Rp5omp3C1A8Rr0rPgie4A/B4KThlJKN01mUIcT8pZCGBjYZNfult7VQ1NP0SiPO81Bi+LJ7AfLhd3DbICwPeL7bPkld0Y2KfT6s8/zQrM4cv9yyfMqsyUzbnOS9DV0ojWENsr4BLmR+3v9VL/QQg9EPMXMyDPKCgxKjn4QPRHZk2hUU9V5FdHWLNWVFQxURRMnESDO6sw2SI0EgQBMvF64hjUmcbmus6wn6c/n9GXUZFrjLeKQY2Jk6+ccqhEtoPEv9EW3ojqYvc9BBERUB4gLCY6n0cUU2haT1ywWeNUak8hSb9BLjrCM5oubim+IsAZmQ5tAun2GO3a5PndHNnJ1uvVgtTU0djOaswtyrTH8MWpxsXKidE/2WPga+bV64zxF/hU/xUHug/IGeMkrS/jOF1AhEZRS3BOC1DRUBVRjVDNTnNL/UVEPhQ1iCuqIagWiAqs/hz0POr532fVecuiwou687INrB2mZ6F7nvad75/mo1ypNrBWuCDBwMkH0qTaOuST7jj5eQQFEXQeECtjNU09PUMrR9pIrEifR6BGDUagRX9EmUFgPCk1pCwBI+UXhwuT/xj2VO9M6QbijNku0W7Jp8GKuQqyzqwoq2ittrKpuUzBiMlg0mTbZuRQ7pj61QkbG7wsbT2OTKdZAWQGaw5vRHFlcglyWG8Xam1iJljvSiQ7vimyFwIG/fV16LncVdHwxYS7pLKGqlGitJp7lc6TcJVsmRCfPabmrlW4KMFgyIXOQdWN3bbmoO+Y+OQCtQ5sGlMkIyxzMoc3azt0PupAnUKGQz9ED0X/RMxCbj75OCwztSzeJJsb2hHfCAkBeflU8eHox+Ch2JfPDsarvZu3ErQ8sy213rirvDzAv8TjygPSBtqX5Dvz7ATRFuIm5zQtQXFLU1MLWYddyWEjZsVpM2tAaY5jM1poTdI9wiyQGxoL//vG7knjZtjgzFnAFbNrpTWYPY1ohmuEq4Y1jPeThpyWpIerUrFYtsS7esOyzrjc2esg+xMKVxcjIU8n0ivGMNU2vz1HRRdNQVRhWS1b/1hFU5FLzEMbPYY3aDLlLDImmh10ElkE0/PE4nTTCsd1vb22RbOQssayQLITsXGwSLExtNW5hsLUzUzb7OoI/HAMHRpeJeYvJzoxQ/tK3lJBW4Bi32YzaM9mOGL8WQBP5kKxNsYqhh/tFDIKnf5O8pvlSdg0ylC8K7Cnpr2fJJuqmAaY4JjEmvac4J4GodWkLqvOs1q+Ccup2c7oz/YLA64N7hY2H3YnfTBJOm1ElE4IWD1fi2JpYa9c6VWPTnVHnECaOUYyoSoNIj4XYgkw+Zbok9lYzTDE0r3XuQG40bcxuAu4abectwW6Lr8Tx7nR596f7UT8TAnTEwocLSOTKnwyTzoGQk5K21KUWS5cM1rTVGhN90SrPJI1uS8oKpwjCxvJD+QBX/KV4p3TdMYnvCC1erCgrIqoD6RLn22aAZYHk66S9JU3nbynCbTvwNrNKdoI5Wzulff6ARYOlBsRKu04y0YfUiVa7F64YAZgBF4yXBlbwVmrVgBRwkgdPgIxiSGVEMP/vvBr5KLamdKFy/jE374xudmzEq/jq+irIbANuEDCps3I2f3lKPGm+hMDqQs2Fccf1CpqNa0+Z0azTPhQE1L1T1BM8Ej7RXNC4T2hONUy/iuOIzwZGg3T/6PyfeZh2wHRpMe+v/a4Y7KdqwOlIJ9nmm6XwpaLmLucYKNLrLa2tcHhzE/YG+Ra8EX94wrMGIEmmzNyPx1JN1BDVcdYkVpyWidZt1coVmpThU5RRxY+EDN5JtMYHAuz/qD0tuzN5QPffdiL0szM5MaLwQy++7wfvkvBecY4zeXUa93k5qfwmPl4AQ4J4RCvGD4gzieELxo3Oz55RO9ItUr8ScpHl0QcQLU6uTXFMZUtUidvHpATRgfN+avr6N2I0SHHsL6ZtwuxvKoApSmgOZxpmXyYMJqonqGlyq6ruYHFq9H43VzqpPa3AuwOuBsLKTk2O0ISTCBTcleWWSBaelkyWOtWnlVFU7BOgkchPu8yKyaJGFkLyf819jvuNeeg4CrantPhzCvGR8BfvEq7I72IwfTHwM8e2FDg9ecf7z32/v3rBvMQbxudJfIu+zY5PVVBQUM3Q+JBS0A6P3M++DwIOos1eS93J28dFBJ2Bkz70fAT5x/e59U8zszGNL9stxawG6rnpVSjdaLfo+an960RtZa8eMTWzM7Vm9+A6o72nwNTEeceVyvdNVk+HEVfSj1O51CqUrtTElRrU11Rj038R/JApTgIL0QkDxlcDqkE1/uf8+LrlOSZ3d3WgdD0ytrGwcTbxPXGrsqQzxHVu9pv4EXmWezm8nz6jQPIDS8YDSIxKz8zXTnwPF0+oT5rPug9DT2gOzc5jDWcMEsqIiLcFxQM6/8q9A/pxt6k1c/N+8aZwD+64LPsrS6pXqbapcynS6wSszy7mMNvy9XSZ9rQ4nXsWfdFA/gPFB3NKe40pj0URNNILEwcTv1Okk80UFBQ8E6CSwBGmT6MNUwrlyBDFvIMwwRC/cv1Ae7j5aXdnNVbzq/INsUYxBPFr8dvy+/P6dQd2lDff+QK6oLwPfgdAZ4KFhQJHUAljCx7Mo825jhWOoE7NTzjO2M64DcsNMAubyeYHrwUQwqz/5f1E+zm4vrZiNGbyfPBiLrbs46uA6uIqXSqua2tspa4FL/9xR/Ni9TN3HTmevFa/Y8JrRU1IYwrKjTLOqA/aEPqRiZKZkxKTSxNP0zJSeVEsz0hNdIr7iHnF4sORQbL/qv3qfB/6djh29lM0gHMbcfbxJ/EwcbAyuHPi9U624rgiuXO6t3wy/d+/wEIORF6GtQisSn0Lqcy2TTmNWY2wDblNmw24DTgMSAtfSYjHo0UWgoiAFD2J+275NncFdUdzQDFHr3ntduvn6vCqVyqG62XsWK3z702xIzKdtGO2eDiTO3l+KoF9RKhH7IquDO8OhpAV0TcR9NKO03+TsdPA08qTAxHyT/GNsEskyK/GFMPUwby/R/2WO5E5jbeydY20IHKD8aIwybDqMS8xxfMLtFm1q7bgOEl6GvvKPeL/6oIFxIZGxojvSnZLqEyhDW9NzA5uDlmOSs4pDWGMRUsyyXJHtsWCw7IBIH7UfIq6RbgSNcBz3THr8Cpumy1Q7Gerr2tk67lsHm0MLn4vrvFYs3h1UDfhOmL9BIAygtUFzgiEizINHM8BkM/SAdMrE6EUHNR+1DETt1KeEWQPhM2Syz+IfMXcA5GBUr8h/MI66niXdp70pHLEcZJwoLA0cDTwubFucljzuLT7tlv4LvnL/Cn+aUDxg3AFxYhNSnRL/s03zigO209cz6NPls9oDpzNgMxUyphImwZ6A8uBmX8k/Kz6MXe7NR6y9HCILtrtMiufqrUp9emZqdmqcSsX7EUt9e9q8V/zj3Y5+J17ov6iQYREiUdzSeyMWk680GaSHJOMVN7ViZYKliDVjhTYU4YSI5AITg2L/glZhyLEpwIwv4P9anr1eLT2sfT2M06yQLGG8RwwwXEzsWvyJrMktF71yDefuXN7QL3lgDtCcwSKhvIIkEphy7dMlQ2szjQOcE5mDgiNiIyriwMJnIeABbtDHcDufmy74LlfNvq0ffIyMCQuYWzwK5DqxipQairqDuq8Kz9sKe2Dr4BxxjR49sf56HyKP5oCToUrh7vKAIzrzyiRX5N8FO/WMNb41wTXIFZklWtUPhKZ0T+PNk0Bix1IiEYOg0eAkz3P+045DLcFtX9zhPKRsZRwx3B9L8rwNTB2cQ3yebOq9Up3SHlbO3V9SL+TQZ4DpYWRR4UJdIqgC8QM001BTZCNTczCjC1KyEmYR+1F2QPlQZf/dXzD+o44JjWj81pxVW+drjts7mwqq6WrYytx656sc+12bt2w1HMCNZZ4BzrI/Y9AUwMOxfuIUssMzZtP6dHoU5PVLJYslsuXRxdgFtdWMFT2k3mRh0/sDbFLV4kahoJEIgFI/vm8APn9N0e1oLPAsqzxanCrMB/v12/tMB5wz/H6Mu80cPYceBF6D/wf/i+AI4I0Q+aFsocICKWJj8q6yxFLjIu0yw2KkcmIyEoG5cUdw3fBRX+RPZV7jHmC94/1gfPj8gaw+6+ILyeumW6cLt9vS/AcsObxwHNsdOP24rkb+65+NECagyBFRQeGCatLQ41OTzZQoxIC00bUIhRVlG/T+1M50jbQyE+6DcWMZgpjiEQGfcPQwZt/AfzTupQ4lTbv9WF0S/OYMsbyYnH3MZhx2DJ0Mxx0SLX1N0Z5T3s9fKX+XUAYQcODoAUvhpSIJ4kiCd3KaMq0CrHKbQn2iQyIY8c/xbIECkKPgMj/Pb0t+1e5hbfQNg30hDNxshmxf3CfcHVwBHBTMKWxBDI7cwT09zZnOBT53LuFfbc/aYFzw13Fv4enCYYLZkyGzeEOhQ9Mz/rQOBB1EG8QIU+CDtgNu8wECvvJKAeKxiNEdYKOwTa/Zr3e/Hq61/n1eP34L7eh92G3YbePOCH4lXljugx7ELwovQu+QP+TAPECLUNoRGQFLwWOhgTGXQZgBklGTQYeBaqE50PgQrPBPH+I/mX837uwOkG5R3gG9sr1mrRCc1XyZrG68RLxKTE0sXFx4nKBc7w0SjW6dpr4I7mH+089Pn7/APICzETJBpGIGQl8ymVLjwzbTcEOxk+U0AQQUBAYT7CO3c48DS0MYoupCrWJbcggxvQFbAPDgqABYgBkf3d+df2PfTI8envLu9c7+jv1vBK8tDz6PQA9uf3hPoX/Wf/xAEHBJAFWgbtBlYHFQc5BnoF5wSEA7YAL/3c+bT2NvN87/LrcOhh5LHf4dpI1uvRAs4GyxfJy8fGxirGLMarxpHHUsmCzADRAdYG21TgT+bh7M7zPft4A1YMPxW6HY8llCy9MlY4yj0nQyFIdUzyTztSBVORUnRRzU8uTYRJcEVcQdg8XjdFMUUrYCUDHw0Y+BAQCkgD4/yD917z+e/a7OXp7uai4yPgMN1V23XaXdou29bcnt7a35XgQeEJ4uriEOSo5Wvn5ejz6anq3epX6kvpMugj58Tl7eP+4Vzg8d5m3bPbLtog2YjYQthI2NHYONqx3A/g4+Ph5x3s0vAI9pj7dwHLB88OjhazHoombC0+M284Uz3YQd9FmUk5TYNQ+FJRVJJUvFPSURJP30ttSK1EhEDiO5k2gjDBKccizhueFAENRwUL/n/3U/FJ657lmOAT3MfXuNMZ0P7Md8rTyEzIk8gbybrJpMr2y53Nls/n0WjUA9f12UTdQuA+4pvjc+UK6ITqS+zf7c/vrfHB8jPztvN19BL1fPUG9r32Y/cW+EP56fqV/Dr+UwD3ApMF3QdnCswN0BHWFcAZux2iISklayi5Kw0vNDJANUw47DqCPBs9WD2DPTw9MzyvOgY5FzePNHQx+S0XKrUlFyGeHB4YCBNZDcEHqQKg/S74rfK77UHpxuRg4JPcXdlG1lDT+9BJz6zND8wny1rLNMxFzcbO3NAE09DUkdav2PbaKt213wHjmObN6bTswO+v8ub0mfaS+On67fxh/uL/2wHmA4kF7wZ2CCEKyAtkDeMONBCLEUQTPBXxFlQY+BkiHEYezR/iIBUijSMEJUQmTScnKOQojykGKgAqbCl7KFcn+SVfJJYijSAjHn4b6xhwFsQTyhCkDU8KmgaPAnH+VPoV9tzxIO4Q61DofOWT4tDfYd1g277ZP9jQ1tLVqdUO1mfWqNZQ15PYHNq926zd7t804nXkB+fh6X/suO778HbzsfVr9yD5Ovtp/UH/+ADfArUEGQY7B4QI7wk3C3MM0A0cDxgQ/xAiEk4TIxTJFLgV7BbwF5UYFRmdGSsa0BqRGyAcKBzUG5sbhRsmG1kadxnKGB8YGhehFdMT2RHfDwwOYgy3CuII1AaGBO0BFf83/I35IvfV9JjylfDt7mHtiut56cnnxub65ejk3uNy44vjluOG49TjkuRW5RnmTOfj6DPqGOtH7B/u8+8d8fzxPvPV9FP2w/di+Qn7gfz9/ab/FAHqAZQCrAMNBS8GHQdGCJsJtQqVC4UMYA3HDfENaw4qD6MP0A9LED0RBxIwEgwSIBJqEpoSihJLEv4RzBHMEb8RRRFeEG8PsQ7qDcwMVAu4CTAI3gasBUcEeQKDAND+Wf2i+3f5O/dk9ebzafLc8Jrv5+6K7gXuN+2H7EnsN+zY60/rMOul6y7sdOy77E3t8e1s7vLuuu+N8DPx5vHk8u7zsPRR9ST2DPfJ93/4bPlg+h775fsN/VP+Of/R/5YAlgFyAhYDwwOBBC0F5gXfBtQHXQimCDYJEArBCjkL3Au4DGsN0w0rDmkORg7uDecNLg4wDsYNdA1lDQ4NIQwZC2EKpQmHCFQHfwbWBdsEjANHAisBBgC+/lv94vt2+ln5nPju9wj3B/Yw9YT0zfPo8tvxyfDw73vvQ+/77p3ube6G7rvu6+4c7znvKO8o753vaPD68DDxhvFC8gjzhPPw85n0Z/U49jL3VPg/+cz5ZPpQ+0H87fym/cT+/v/mALIBzAL9A8YEPQXpBeMGxQdlCPoImAkWCncK5wpRC3wLfwumC+cL7QuvC24LOAvWCkYKtgkWCSUI+wb9BTQFRAQcAx4CewHbAOz/x/6o/Y78c/t0+o75jfh395f2//VZ9W/0ivP58pLyDvKB8SXx6vCX8DfwCfAX8DHwQfBk8K7wFPGE8e7xTvK28kPz7/OX9DP16/XL9qH3Sfjk+J/5b/o7+w/8/vz0/dv+wf+zAJcBWQIQA9EDhgQkBdAFmgZJB7IHBgiDCAAJLQkiCTsJiQnDCckJwAm8CZ4JXwkbCc4ITAiUB+UGWAbDBQ8FXgS3A+0C/QEsAZAA1v/C/qX97vx+/NT7yPqy+dr4IfhS93321PVf9QL1rvRl9Bv0r/Mf85zyYPJv8onyevJf8oLy9/J+89/zKfSR9CD1vvVf9gT3ovcp+Lb4cPlF+vD6Vfu0+1X8Kf3q/X7+EP/C/3wAFQGPAQwCkgIKA24D2QNZBNMELwV/Bd4FOgZuBoQGqgbqBhYHFAcDBwMH8AaYBhYGuAWYBXgFIwW1BGQEHwSlA+ACBgJOAbgAIQB+/9z+SP7B/T/9tPwT/GL7ufod+nr5xvgT+Hb36PZg9u31k/U89eL0tPTP9AP1A/Xc9OH0K/V89aT1wPUC9mn24vZs9/73ePje+GL5HfrY+mL73PuK/Gv9OP7F/jD/q/8+AMIAGgFZAbcBSALXAisDXgOyAx0EUQRBBDcEWARnBDgEAwQCBBQEDAQDBBEEBgS2A1cDKAMKA8QCdQJYAkcC/AGMATkB5wBaAMH/d/9e/wP/cP4d/hf+6P1n/fj80fya/Bn8k/tA++r6aPr9+dT5qflM+QX5BvkC+bz4g/iv+PP46Pi7+NP4Bfnq+LH44/h5+e/5MPqt+oH7MPx2/Kb8Dv18/br98P1E/or+r/79/p//QgCNALQAGAGnAQgCMgJsAsUCBAMSAxYDIAMPA9oCrwK3AuEC/QLnAqsCcwJpAnUCUQLoAYgBawFeAREBogBrAGgAPQDa/5z/r/+//4H/KP8A//L+t/5S/gX+5/3W/bP9fv1A/Qr99/z6/Oj8uvyj/Lj8v/yU/Gz8b/xl/Bv80vvW++37rvtS+3b7Ivym/J38bfyP/Nj82vya/HD8gfyw/O78Rv2g/c/9y/2//c79+P0f/in+FP79/Q3+Qv5t/mX+QP48/nj+yv7k/rT+hv6m/vD+BP/Y/sL+5v4D//r+Bf9D/2z/Xv9q/77//P/i/9H/KwCjANkAEQGiARkC+AG7ASECxQK4AjwCcQJjA+YDkQNkA/UDeQRVBCsEjwTiBIUEEAQ5BH0EFwRsA1IDiAM+A5QCRAI6AsEB6gB4AHgAJgBN/5n+ZP4n/nj9rPwk/Lv7M/um+jb6xPlF+eX4tviN+Ej46fdw9+/2qfa29r/2d/Yz9nj2G/d/94j3qPcN+Gv4pfj8+Hb5xfnY+f75X/rG+h37j/sj/K/8Of3t/bD+Qv/E/5EAhwEwApICJgP1A4UE1QR5BZsGogdNCCMJZQpoC7cL8gvJDL0N6Q17DWMNvQ2tDeIMJwwsDJEMkgwYDJYLMAuUCosJRwgZBxkGHgX2A58CUAFEAHD/jv6B/Xv8ofu1+mD50feo9hH2a/Ur9L7y5/GA8bHwdO/N7hvvV++s7ubtHO7F7pnu6O1N7vzvOfEM8bPwgvG68ufyUPJt8pbz0/S19dX2cfjk+dj65Pt8/Rr/9/8KAPL/WACYAYADQgU3BtwGUwjTCj8NpQ5rD34Q/hFkE2QUBxU5FQAV3hRZFTkWzBbQFqkWsBbbFvUWwxYCFrEURRMvEjARsw+5DesLnwpsCd0HFAZjBLkC5gAQ/3n98fsA+qD3WfWO8wHyO/A77n3sVOuE6pvpfuh558DmLuaV5Q/lw+SB5ALka+M043Ljq+OP44LjGOQ55VLmNedO6OLpmusM7V3u+u/p8cbza/Um90H5oPv6/T4AhwLxBI4HZgpCDcIPyBG1E+cVKhgAGmIbvhxLHsAf4yDjIe8i0SNIJH8kyCT+JK8kyCOqIpYhWyC6HskcvBqZGFYWCxTUEZsPOA2yCjAIvwVMA8oAOv6Q+8H46/VE89rwju5J7B7qK+h45vnkoeNb4hXh3N/Y3hLeXN2B3Jbb5NqU2ozapNrg2lvbGNz93PndFt9a4LzhMOPB5H7mX+hO6k7shu4W8ebzwfal+cb8OQC3A/IG9QkODU0QaxM6FuQYmhtGHrogBSNLJWonHClmKpErrCxiLX0tLi2+LBosDSuoKSQohCaZJGUiICDTHT8bTBg9FU8SXw8rDNIIpwW1ArL/gfxk+Zb2+/Nh8dbuiux16m7oeubG5E/j4+Fy4C/fP9563aXcz9sv29vavNq42s/a+9oy23zb79uM3Dvd8d3I3tvfKOGW4hDkmeVF5yvpTuud7Q3wsvKj9dn4Mvyf/yoD1gaHCiwO1RGaFWoZHR2eIPsjQydmKj8tui/iMcUzWTV8NiI3WzcvN4U2VjXRMyQyKDCSLW4qHCfAIwMgnBvQFiISlw3OCK8Dm/7f+VP1x/Bu7IzoA+WI4Tbeatsy2TLXRNW/0/HSqNJ/0nDSvNJp00DUPdWb1mHYQdr/28bd0d/74e7jnuVP5yzpEevL7Fnu1e9C8Zjy2PMV9Vj2offl+CL6ZfvG/Ez+1v9QAecC3QQvB5EJ3AtIDhARDhTuFqgZgRygH9Ui7CXpKN4rsi44MWEzPDXBNtE3XziKOHE4AjgGN2E1KzOMMH8t1SmAJakgfRv1FfsPrwlMA938Svas71HpUuN03ZbXCdIuzfbIBsVZwUe+BbxnulC58bhuuYy6CbwRvvDAhcRQyCTMVdAg1UDaSt855EjpfO6a84D4P/3TAQQGrgnzDAMQzRInFQ0XpxgNGikb3RseHP8bpRs+G98aexr9GXUZChnPGK8YoBi4GBcZwBmnGr8b/xxQHqYfFSGvImAk7yVCJ28oiymBKhkrPisBK3AqbynXJ6Al3CKLH54bHRcqEtMM9waAAKT5tPLD66rkat1Y1r7Pj8mdw/q97riQtMywpK1Oq+apUal6qYSqiKxjr+eyG7cRvKnBrccqzlLVCN3h5JTsP/QF/LMD+wrPEVEYfh4mJDQpsC2AMXU0jTYJOA05cTkHOfI3ejazNIcy9y8wLUkqPycoJDohjx4FHIoZVRekFWsUYRNqErgRgRHDEVgSJhMkFEYVdhafF6kYbxnVGeMZrRkuGT8YsxZ3FJIREA7/CWYFRACW+m/0/e1m553gi9lP0kXLrMR0vnW40rLkrdCpdaa+o+ehK6F0oZiis6QIqIGsr7Fit+a9fMXXzW/WLt9i6Bfy3ftUBY4OrxeAIJ4o+S/GNv48UkKVRvhJp0x/TklPEU8KTj9MmkknRiJCsz3hOMszry6vKbUktR/gGncWhRLsDrILAgn4BoMFggTnA64D0gNVBD0FeAbXBzQJjwrlCxoN/Q1xDm4O4g20DNkKWwg+BXQBA/0I+J3yuuxV5ojfkdib0bDK18M7vQi3UrEarH2ntKPfoPueEp5InrGfJqJ9pdKpV68CtoO9tsW+zp/Y9+Jm7fz3/AI7DiQZYCMfLYs2Uj/9RolNO1MRWLRbAF43X41f117mXOpZM1bMUZVMpEZHQKo5zDLGK+AkRh7pF8gRJQw6B/wCSv8//A/6rPjP9133ifdy+On5rfu2/REAmgIHBSsHAQl2CmMLrgtoC5YKHQnbBtUDJQDT+9v2SPE167Tkzt2h1mLPNMglwUm617MCrtCoNqRVoHKdr5vwmiObfpw5nzmjNKgkrky1ub0Kx+fQZtu55pvycv78CXUV+iAgLHE29T/fSABR21dOXalhEWU9Z+xnUGezZQ5jMl9DWqBUb06TRxhAVTiLMLoo5yBfGW4SEgwtBtgATPyP+HH15/In8VvwYfD68Aryj/N+9bP3FfqW/CD/lAHMA6YFDQf6B2sIRghcB6AFRANzABP94fjb81Tukeh44tfbytSzzdjGN8DTueSzqa4iqjWm+qLDoLyfw5+7oM2iN6bpqpqwKLfAvnXHCtE52wHmcPFG/QwJnhQrIK4rrDaiQIpJoFHeWPpeymNVZ5ppf2oXaqJoHmZKYixdTVcsUapKZkN9O4QzzSszJJwcURWeDn0I1ALV/bX5TPZX8/Dwd+/z7vzuRO/471zxVfOE9az3xvnU+8b9g//qANEBLAIZAqsBvQAX/8H85vmT9q3yRe6c6dfkyt9G2mvUi87DyPjCKb2et6+ye67qqv2n3KWypIWkTKUbpxSqPq5+s8e5J8GfyQXTKd3552XzKv/xCpQWGyJrLTI4O0KVS0NUAVx2YpVnhWtCbqFvnm9fbt9r+WfNYtlcalZPT0dHoz4NNs8triV9HYgVRw7nB0gCRv3L+Nr0l/E8783t+eyA7IrsUu2k7gDwPvG08pP0gfYb+H/5+vpq/Ez9bv0f/Z78t/sb+t33T/WO8nHv0+u850Tjed5r2SrUzM5lyR3EHr97uj22erJYr+usMqtBqlCqgqu4rb2wl7SNuca/Ccf7zpDXD+GT6772CQJTDdQYiyT6L5o6XUSDTRhW0V1iZLtp423JcEVyLnJqcBFte2j7YphcO1UZTblEXzzRM+cqEiLlGWESLQtmBKX+HPpH9sryDfCL7untZe377GXt4e6t8AvyMvPC9Kv2NPgA+Wn5zvkF+rT52fis9zL2P/TF8fPu7uu96FblweET3mra1tZC03fPbMtmx8HDiMB7vXu6xbeytVm0grP4ssuySLO/tDu3dboZvi/CHsc6zWfUN9x85HLtUPfgAaMMVxceIhAt4TctQtlL+1RjXZpkdGo8bw5zaHWudQp0M3FqbS5oOGEaWaFQ7kedPrY03yqXIccYQxBNCF4Bn/ve9ujys+9S7d7rW+uP6xPsvOzR7Y7vofFj85v0oPW79rL3Hvjr90b3Q/be9B/zGPG27ujr0ui75cDi09/x3CraedfS1E/SE9DszXbLuMhRxrTEi8Mtwo3AP7+cvle+Dr7bvRa+374uwDLCIcXKyMXMJtGb1pndtuUu7uX2ewBQC+YWXyJqLVE4Q0PuTdNXr2BuaN9u4nOLd9d5YXqzePR02W/DaVliSFkJT4FEFTqNL9YkaBq5ENIHr/+Y+ODyde4N65zoTecZ57fn5+iJ6mnsUu5D8FTyR/SW9RH2Evbt9XP1RvRb8vbvPu1G6kDnWuR14WDeXtsF2X3XStb61MHTDdPb0sPSgtIi0qnRE9F80A/QsM8IzwDO5MzwywbL8snFyMDHD8fUxlrH1cgQy7nNCdHE1VXcQ+TJ7NP18/9yC74X/yP3L+w76UdsU9td+2a9bt90JXmve758M3yNeZ908G0cZitd0VIxRwc7ES+KI14Yog2aA5v6+fL07HroO+UK4w7iYuK746fl/ufG6sDtd/DA8s70qfbl9yT4jveT9lX1mPMp8Rnun+oF557jiuCN3WzaYtcJ1Z7Tt9LQ0e3Qb9CC0O/QX9GW0YfRWdFX0ZjRvNFO0VjQXM+nzvrNAs3ay+TKUMofyoLKwsvjzanQKtT22H3fcecp8G/5qQMjD4MbDihaNGlAPUyNV+Zh5Wo6cqt3N3sQfTx9Zns+d/1wP2lVYBlWe0oFPoAxSyVoGQQOlQNf+mDywuvm5uPjReKe4Qriz+Ov5vvpQ+2K8Nfz6vaA+XX7n/zL/An8tPrx+HP2AvP87vXqCOfi4oPedtoy15nUVtJy0E3PCs9Yz9PPZ9BY0dXSn9Qj1g7XsNeU2K3ZONqx2Y7Ytdc711HWctQ/0r3QGtC8z2jPvM9Z0SbUvNc+3EHi6em08jf8lgYRElYetyrfNuVCqk6oWV5jpmthcip3uXlWemB5nHZpccVpjGB/VoFLOz8YMiUl/hiKDbwCEvkB8X3qXuXh4VvgkODe4e7j2ean6v3uZvOI9xT73f35/5UBcQLxAe3/B/3v+ZP2cvJ67T3oQeOk3mjarNaA08jQlM44zd3MMM28zXTOns9O0UnTPNXm1hbY1Nh12Tja1dqx2q/Zadhw16PWh9UV1NHSMdJO0ifTytQv10TaP96f453qv/Jg+34EnA7UGXwl0jC/O4RG7FBOWkRi7mhObt1xI3N1cndwI23AZwdgxFYFTQFDQTi5LCMhUhaZDPADUfy/9U7wQ+z06Uvps+nC6qXsl+878932PPp2/VEAPQIhA30DcQNEAmf/gPuv9xL0u+8+6nbkfd9g23zXp9Nt0DzO8sxIzD7M4Mz8zVLP6NDe0hHVDtd/2HTZPNrx2lfbH9tS2lLZaNhw1yTWqtSH0w/TH9Oc097URNeb2nLe7OK96CDwXfi5AH0JZhNIHvEonzLfO3tFAE8ZVzJd/2EuZkJpO2oDaW5m/GJJXuBXClB+R5Y+UTXYK40itBllEc8JLAN5/Zj4uvQ88vrwYPA98Azx+/JE9Q73jvh/+rX8Ff4h/o/9Cf0T/PD58vb78xXxfu0U6cvkT+EV3mLapNb305jSntFX0C/P886oz57QYdEj0irTUdQ91eDVc9b51iHXwdYv1ujV3NVx1WTUYNNS01LUntXB1k/YIts739vjo+gN7qD0MPwxBIEMUhWDHo4nKjCXOPlA3EiaTwxVh1k3XeNfRGFHYehfNF2HWVdVi1B2St5CuzpJM38sLCXYHLMUHA7WCJUDCf5Q+Tr2MfRO8tHwgvA78fnxavJZ8zv1OfdE+Hb4tPg8+Vv5f/jO9qr0JfI17/zrjei65H7gXNzo2CfWodMY0d7Occ34zDbNws1SzujOws8N0ZHS1dOL1NLUBdVg1c3V8dV61Y7U3tMc1DjVetaK1wfZ2NsH4MLkf+mj7tv0LPwSBDYMjxT7HC4lIi0nNSo9aURESgxPg1OvV75aLFxpXApc91rMWJ1VwVEKTQ5HLUCAOWQz3iwXJdgcsBXrD1MKEQTX/dH4N/Vw8hnwOe7O7OTr4uv+7ITuZO+i71Xw//Gv8z30xvMt86vyqvHT72vtnepC54jjG+BC3XzaVNc01N/RftCjzwbPsM6szgPP9s+k0YXTz9R91VzW7dee2XnaTdrJ2anZDNqN2r7aotrY2h/ckN554UfkY+e264fxO/hJ/9UGFg+/F2EgEikTMu06mkLUSIdOdVTWWRhd6l28Xc5dfl04W65WQlEbTNVGekAkOc0x7yogJCMdeRaWECwLtwVUAKD7+fcy9eHyofBc7o/s7etc7JzstOtZ6inqZett7OHrX+p86aDpv+nt6E3ng+Xv47TiyOHP4Fvfmt1L3M/bo9sh21Tattlr2T/ZJtkz2SXZmtiz1xXXB9cJ13zWb9WA1CHUStSu1AzVbtU91grY8tpt3vLhreVX6kjwIvdi/uQFsw2+FfgdhSZUL8E3ID+WReFLGlJDV25a+Vv4XKZdL10OW9NXOFQOUNxK/kRRP9I5pjOqLAQmiiB4G5UVHQ9YCaUEFwAT+y328PH67frpkeZI5G7iGuDn3TTd9t2m3pTe5N6V4NriZuRS5YnmBOjp6BnpPOlZ6ZnoyubW5E3jf+HB3rvbgtkP2JHW6dTM04fTotPZ04zU7NV317vYCNra2+jdbt8s4J3gMeHO4SziQuJH4n3iPePy5Mrnhuvi7xv1yPsdBIQNIBe0ILIqaTVSQGlKC1NIWlpgI2VHaIVpy2gnZtFhN1y+VXpOREY0PdkzAys1I1AcxxVUD4QJQgWoAqwAVv7w+2/6z/n2+Dn3JPU/8xDxEe7k6nDoTuZM44bfkdw421/a1dgh15LWR9di2J/ZdNvC3c3fl+Ht48vm5eh16XfpH+oF69bqXumd5xnmTOTV4S7f19yW2hjYpNW501jSMdE+0L3Py89o0LHRvNNa1lnZ5dxg4d3mHO3w81n7QQOACyMUSR21JsAvCjjOP0pHDk5PU89WEVmGWu1a0llkV1BU0lB4TBZHd0GCPAc4GDOmLdIoZSWqInofzRt4GLEVzBJYD4ELQwcxAmf8wvaR8fvrbOXd3rrZAdax0q/PBM4jzkrP/NDI0/vXqtzu4DXlP+pw72HzyfV395/4fPjL9lT0ePGH7TvoveI53jjaqtXt0IXNBcyNy2nLAMzOzYTQutN917Pbm9+94pflteig64ftf+5I7yfw7vDi8YDzgfVN94z52/17BJYLzxFxGIUhfCx5NgA+q0RcTEJU6VmKXKpdhl5kXv1bdlf7UQdMKkVePYA1NC4jJ98f3BjcEtUNKwm/BOsAo/1p+jv3qfSt8l/wSO1C6kPo3+YD5bHiAuF64IXgu+CG4STj9eSB5mHoP+tq7nnwK/Gh8aDycvPg8qbwlu2R6srnxuTz4G7cQNh11fDTutJ/0R3RRNJl1KzWT9ns3PTgG+RJ5rrow+vJ7Xnt3uv+6vvqPOo46GvmKubw5tTnduk57RXztvm3AE8JQhQ0IAErQzRxPVxHoVAZV0lal1s9XNFbD1mPUzZMPURGPEM08ytQI+canRP6DbsJZAbrA3QCwAGEAQoCjQMUBSMF1APpAhoDoQKo/xH7RPfY9Efywe5h6ybpl+c65trlEufM6J/pAOqX66bugPGt8nHy/PHx8fjxGfF27k/qPOay4zPi/9+h3LbZr9j52EPZiNma2ljc090U3yTh6uOF5bPk3uJn4l7jX+Pt4JjdI9w/3SrfYuCr4fTk7+p28j76TQJcC00VHx9OKGExgTpFQvlG+EhiSiJMTEyjSAhC2DvAN84zdi1tJe0ekhuoGQ8XTRR2E9wUiRY3FwYYOhqLHHMc0RlGF0EWjBRRD2sHkQBq/Kf4vPKz67vmCeXo5KzkzuSp5lbqt+4K84z3gfw3AXEE0wVaBtsGWwbuAoX8sPWM8PnraeXx3JXVQ9GozujLnMl+yYLLQ86c0YfWktyi4evkGOiC7LLwMfLb8LzuGu046wrotuMl3yLbaNiK10XYudnn2zbgtedw8Yv7jQVqEGUcJyhlMk07YkPUSUVNzk36THhLEEiZQQw5pzBfKVMitRo1EyYNKQktBwYHUwhZCtEMahCkFVwbqR8FIpcjBSVeJa4jYyA1HMMWsA9PCFkCUf0e95Hvcukg55Dn4Ocl5zXnA+pb7y71afmO+xf93v+pA9gFZAR+ANn88/nH9UDv6udw4VbbntROzjLKvsd9xPa/Cb01vu7BZsSTxIjFPcqE0XTXUdpc3H3gZOZB66ftLu/r8az1zfgB+7/9yAGoBckHHwlBDMER9RZpGYcaAB6gJMMq+iyyLC4ujzJuNuI2iDXINc83xDhsN9I1sjWcNUYzOC/1KxAqWic7IgUcABdwE8kPAguTBY0AxPyu+vj5dPlu+AX4+fn+/aUBhQP+BOoHsQsrDpgODQ43DVgLxAf7Arz9+/di8S/qFuO93ITXRdNVz3fLtMhCyIvJlMrRygDMbs/C08rWXNjj2czbFd1S3UXdVd3g3LHbx9rl2lfbEtuF2knbMt554uHmNOtM8Of2k/4FBnoMdxLQGFsfFyWDKRctUDD+Mro0fjV7Nco0hjPdMekvyy3nK5MqhSlKKDUnCieKJ4EnrCZAJsgm2SYWJWgidyDCHlUbIBZREfQNhwrPBRUBDP4r/AX6Cvi99+L4yfk2+q37gf7TAHkBrwGbAgEDRQEo/mL7ePiN8/XsNOcm48Demdh10p3Ok8xhyuzHtsYKx6/HUcgAysnMAM/lzyfRXdRQ2JLaB9vz24/ec+Ht4nTjzuSL55Tq/ew970Dy9PVt+V38w/+nBH0Kiw8mE7wW7xs0Il8nfiopLUsxdDZROpc7iDsVPD09PT0yO2g4bDa9NLsxei0AKk0ovSaQI7YfYB2QHBYb7BeQFH4SNBGMD4gNkgtHCWYG6wOxAsQBvv8o/b77nPsc+375z/fI9rz1S/RA877yV/Fb7n3rYOrO6XLnkeOu4KHfdN6W2yXY6dWk1NXSLtC3zfHLL8r3x/rFLcWBxQnGXcZKx97J9M1E0hzWZdpT4HbnBO4X85f3kfyaAY0FSgi3CjMNDQ+4D9sPoxBcEj8UvBWIF+AazB/FJIcowisvMP01TzuQPjpAm0GsQldCVUB0PSg6IzaBMRYtECmHJCQfHhqYFvET9xD8DRoM6ArtCF0GPgUSBnkGowRDAvsBFQN2AmD/aPxM+5b6ffi09ZHzZ/EM7pTq8+ia6CTnN+QY4gLiUeJO4Zrfh97R3Z3cO9s42r3YtdX+0X3PZs4JzWrKeseQxRzFHcaDyMnLS8+B05nZPuEs6Njsb/D+9Ir6Vv9/AsQEugbeB9oHjgf0B84IVwneCb4Lnw+OFCQZOh3lIeonxi5TNeM6Xj/EQjtFIUdjSP9HEEVPQIE7JTf9MVMrfCQcHxAbSBfFEzMRNQ/eDKIK2QlaCnMKqwlhCQcK4wnkB5MFWAS8Atz+K/qx9xP31PTY70bro+kK6YLmyuLr4HfhN+Ii4pfiWuT75UHmDeaD5urm7uXX49fh3d/W3LPYkNTb0OfMochIxc7Dp8MVxJzFOMm5zvHULNuC4fDnDO6j89f4hv1TAU0E2gboCOgJwwkiCZMIPAiRCFQKfw0YEcMUdhm2HyAmHytZL6s07zq+P8hBmkLvQ9tEPUMkP9A6Yjd9M9wtpCfmIqofOhwkGBUVHRS5E/gRfg9sDsEOJg6rC1kJ2Ah8CNkFsQGt/vn8Hfpv9YjxLPB67xXt+emm6OjoTOhL5h/lGOaj5wHo6Ofr6KrqZ+uk6pzpKOmU6MTmq+MW4J3cHtkp1b/Qnsydyb3HOcbwxA7Fncfuy3XQKNV224bjCesN8K3zRvij/SsB/QF4ApQExAZfBu8DqAL+AxgGLgdZCMkLjBGtF+wc4yGVJ/YtFzQOOYk80T5oQERBm0ABPlo6ATfpM6sv4ilbJOIgsB6YG44XFRV0FWwWSxXgEvgRuxJYEp0P1Ay6C3UKwwYNAjD/tP2p+s31K/Ik8Vvw5+1n6+fqTOt76gbp5ejK6b3pxuju6KLqzus760TqRupk6izp5Oax5JXird/H24XXRdMQzy/LC8i0xUvEbcRnxmrJlsyI0H3WBN745E7qYu+F9XX7+P5IAMgBWQQPBnEFCwQpBF4FmwX/BCYGVAqlDwsUKhjCHaskJCtnMAc1OzlqPGk+nD/AP/89qDpIN5I0ZTHYLPEnOCRkIQ8eThrYF1YXQRcrFusUExURFqgVMBOWEG0PnQ5ADKQImAVuA6sAuvzf+PL1FfOp79DsnOsN66fp5+dY5+XnEOh/51rnGejg6EHp4una6ifrbOqd6Rvps+eK5KDgId2H2enUDtB3zALKeccBxSXEX8WJxxfKI8571AvcR+Pz6XHwMvZI+vT8YP/NAV8DxQP1A8EElAV5BcIE0wR5Bk8JzQwfEYkWlhyVInIoYS7mMxg41zrnPHk+oD7NPAU6ljdBNewxuC0BKhgnuyNkH6UbEBrbGSoZ3RdpFxAYMRi5FpQU7hJzEVQP4gzOCqwIjwXCAWL+j/tt+O70GvJJ8I7ubey/6hHqgelQ6Grn5+f46P7oIOjr55nov+jW5wjn2ebv5Uzj8d/t3FHZ+9MaztTJF8cvxCjBBcBcwWXDNMV6yIPOo9Wc2//gtucr7670bPd4+V788/6h/wn/6/6a/xsANgAIAYkDdwfrC3UQVRXsGgsh5ybiK0sw3zR2OdQ8CT6CPUI8hzrdN2E0FjG2LsksVipEJ4YkwiJwIcwfSR4QHgYfeh9WHoccORvQGQ0XhhPqEBYPIQywB8UDagHY/pv6ePa/9Er0I/JZ7vrrE+wT7C/qY+j/6KDqUeo/6Czn3eci6MLmcuW45SnmpOR94W/eldus19nSxc7nywHJqsVZw/rCb8PBwy3FQMlKz3fVe9sm4vroX+4J8iT1Afid+dP5Evop+yH8NvxK/Gn9Qv9EAQcEXgjiDbETyRl4IC0nBS3zMUI2fzkEOy874DoPOhs4MTVjMkwwki6zLKEqpigjJ1kmCSalJSwlICVeJdkkGiPcIHUeFhucFqwSWBAgDkoK9AVFA6MBvf7Q+nj4Jfg996T0qfK18nvy3+/V7DvsCe0R7EjpjOeu51fnVeWI43DjnOM14ujfTd763F3ai9Yp09fQjs6Vy57IgcZBxcvEq8VByAXMRdAS1a3acuBM5QfpYey/76nyufRB9oL3J/gK+Pb35/jz+pz91wD+BP0JWw/lFJgaBiCpJNYoQC2JMVY0MTU3NVA12zQNM6owGy9TLhstTitFKpwqAys2Kvoo4yivKXMpbicMJYojBiInH4cbwhitFpYTVA/UC9YJbQdLA2z/K/46/nv8DfkB9wn3IPav8nzvMu/u72/ue+tl6kXrvuqc58vkm+RX5W7kW+Ie4ZbgA98m3GjZO9ee1EzRZ86MzOjK6sh1x5HHGMluy57OztJo183bReAx5dfpLu1w76vx6vMz9Wb1xPUS95n4x/mW+y3//gOBCHoMGRH4Fh4dYyLKJucqry6NMTcz1jOUM48yMTEBMAsv7i2dLKMrWStEK9oqdyrGKmwrSitDKn0pSykfKMAkeiBuHVAb9BcGE9YOsAzUCoAH8wM+AqQBzf/U/Br7DPst+i33M/Rq84rzBfIU7wntXOwV6z/oeeVL5PDj4uJQ4XPgSOB732fd19qs2NXW39S70oXQRc5KzCPLw8p9ykrKVctbzmPSK9YG2t/eEuQQ6OPq7+0d8aLyM/IM8qnzrPWS9m73+Pmm/eMAAgSFCEsOyRPKGG8elCRqKRksvi0yL7gvwy5wLfMswyynK+wpGymlKTQqqynWKEQp6ioMLJ0rpCptKlkqxSi0JcEiWiAPHVoYCBR7EUoPqAueB3UF2ARkA7oA9f7X/m3+dfxU+kv5Hvhy9WvyufCi70nt3ukw56vlHuQp4r7gLeCc36/e/t2R3YLcnNro2OvXrNZT1JPRes/FzbvLE8plyr/Ma8+Q0ZTUuNmZ3+rj1OZ96knv7/IC9BP04fSX9dP0vvO+9Ob3OvsQ/scBDge8DNgR4RZcHKohFSa9KaksGy6vLVYsMytEKvgolCfrJhMnTSdXJ9QnMSnbKgws3izWLacuMS7wK8Eo0iUnI+cf5RsMGAIVRBIiD/wLqQkZCJEGEwVgBGIE2AMJArj/wf3H+xf5EvaL82vxB+9x7EvqieiD5j7kmOL74cjhUuGf4Obf+96k3QvcYdpk2NnVOtND0dXPFc78y/TKEMx2zqHQs9L01Tva5t2k4DDkLemU7a7vs/Bs8vPzV/Nf8ezwAvMJ9gD53/xAAtoHWww5EJ4UmxlQHisiPiWtJ14pAipcKcMnSSbqJZEmZCf0J6co0CkQK+QraiwbLectJi5xLR8spCrKKAkmmiKCH1MdVRuCGAcVCBLuD/ENnQvMCToJ9giSB1MFkAMsApv/u/uM+Cv3AfZZ8w3w3u187FHqdOdw5ZbknOMD4tjgleAY4IHeptys2yjb5dnZ1wnWrdTj0nbQvc67zoDPq8/iz+rRotX52DjbGt644pXnCeul7VHwAPJ88Qnw5e8X8S7yTPP79VX6xP6LAoQGOgskEPIU7hnJHm8iZiQuJSolXyRbIyAj3iPAJFIlHyZ2J5so6igOKSIqByyFLeotly3eLE8rqCi5JYAjzyGkH7IcyBl2FxoV5BFnDjMMwgu1C6IK7QjCB+QG9QTsAWL/9P0k/OL4cPU480/xGO5I6gfolucE50rlc+Nr4onhB+Bp3o/dT93e3Ovbr9pC2WfX+dRY0lvQu89s0NDRftN81cDXENqF3Hrf3uIy5lLpgux+71TxjfG98MvvV+/w7/vxMfX8+E79TwJmB68LUw8zEzkXYhqBHGceISCzIAsgoh9pIJchXCJPIx0lECcoKNYoLioDLCYtMS3YLHEsVSv1KNslLyOaIbsgrB/9HQYcGhrWF+oUIBKFEK8PUQ56DFgLkAotCPkDfQAj//D96/qB9+H1EPWf8vTujuzR67/qk+ii5oLlC+Sl4UXfrd2V3MLbXtsC2+vZQ9jT1rTVgdR40xfTHNMr08LTL9V+1hzXcdj126vgmOS95znrcO7E75Tvve+B8K7wnPAl8oD1/fgg/DoAiQWKCrcOLxPtFzEbjBypHScfmx/QHu4e1yB8IrMihCNOJtYoGSkSKVArPy7ALm4tHy2WLR4swiiIJmQm8yXWI7kh5CDXH+kc/xgRFnEUBhM1EWYPDw7rDEELwwgdBioEqgKZAM/9OfsR+UL2UvKS7lLsD+uJ6ajnG+bg5GPjluHc31jeBt0R3GLbYNrB2PfWUtV6033RXdCq0G/RndHS0WbTONbu2DDbCN7d4YTl6udu6czquOuz62zrFOy27XvvT/EI9OX3K/yBAHkFLAtzEDgUxBbRGCUaFBobGdQYHRooHN8ddx/WIcEk6ib4JzcpgCulLTou3i0BLhcuQSyeKKUluiR0JD8j1CGeIQsiMSGSHqQbkRnBFywVPBI5EDEPmg2QCjsHDAWHAzUBJf7D+1b6gPiR9ZfykvAB7wbt0eoF6YPnpOUx45bgcd4d3WXcitsZ2pLYp9cR1/LVUdRF02nT/dMQ1LjTrdMp1NvU1dXZ12rb4d/g47nm8OgO65Psy+xQ7K/sbu6Q8Hby/fTi+D797wCWBJQJZQ/pE10WHxgUGkQbAhuiGpwbcR0UHwIh/SP4Jn0oTSkpK6UtxC5dLiIuSy4RLQcqRydDJtYlsSSmI/EjyCRHJAwiTB/ZHFQaWxd0FFMSuRCyDvYLTwlUB2cF1QJMAPP+VP6x/Ib5KPaS8yTxN+5d60TphOd45UbjRuE+3yXdr9s92wvbS9pA2VTY8tal1HnS2dFi0m/Ss9GV0bXS3NNB1DLVOdjC3Bbhy+SF6MHr/+wu7Dfrl+vM7OHtMe+v8UH1Afmm/NgADwbJCxQRZxW5GOYaixvNGscZoBmDGgIcIB4EIQwkZiZZKLcqNy3RLpMvZzDuMKAvYywRKe8mZiXyI1sjAySnJBAknSINIRQfQBxKGSwXsBXzE7kRWg/dDBoKZwdLBa4DAwINAOj9kfvZ+Nb14/I28Nzt9et76vjo4eZb5BPiUOC13hHdpdt72iTZZ9eL1brT7dGd0IbQc9FX0tHSb9Mr1FPUYtQV1sHZh90z4OLiZeYo6YjpqejF6AbqMOtS7JfuAvJz9Zv4UPwJAT8GMAteD3wSlxQDFtIW7BbiFsAX0hloHAcfzCGRJNYmvSjtKkYt1i5ELwEvFC77KxcpvyaKJdQkTiSeJNMljCatJb8juiF2H38cfxl1F/UVxhPjEHQOvQy0Cv4HuwWjBKoDiwGa/t77Sfkp9sfyK/B+7uDs2urh6D3nVOWk4q7fdN063Enb8dlS2NzWgdX4057SLdKB0qrSkNI208/U9NXD1YDVytZV2fjbxt5c4gfme+jc6VTrBO0j7sXu3u+28dXzTPam+bP9yAHlBWgK1Q4mEjYUuxX8FrIXLhheGY8bBx4eIAgiMCRsJkAopykWK7Qs4S3dLbgsKCuFKZYnkSV9JO8kAyZ1Jjcm8yVSJSUjZx+4GzYZHxdwFMEROBBYD6oNOAuPCQoJ/gdaBUoCDgC0/bv5yPTl8J/uuuyL6rjoludh5oTkbeKj4A7fed0L3MbaMdnX1snTndAmzhfNjs0NzxbRddPQ1YLXPtiN2EDZqdqx3DjfBeKO5EPm8ObP5oLm9+bo6EXsbPDd9G/54/3WATEFNQj3Cj4NCw+8EGgSnBMiFMwUvhYKGrEdFCGhJJYoByzsLZwuEy8sL+oteitHKd4nSCY7JDgjdSTgJqAohylsKt4qUimiJZUhfB6sGyUYgxQUErYQAQ+UDMMKeQqgCqoJxQcmBo8EWwEw/Pj2XfPD8NvtAut56eboi+fb5Dziv+CY36zdaNvR2azY6NZ21FjS89Cyz4XOY87Oz+LRidPA1A3WjNcf2cjaU9xU3fjdF9/+4AfjzeTK5kHpmuuM7cXvt/LD9YD4s/sFAKIENwitCroMbg5aD+IPPhEHFJUXBxtBHpQhyCQWJzEo3Sj2KR8rQStcKqopmSn9KD0n+CXfJvMo6imWKbopliopKnonMSTNIWMfvBvgF38VFxQJErQPBQ8aELIQcw+IDRsMKAoqBqYAg/un94H0gPHM7qXs9+p/6fvnQuZ95Anj4uGe4AHfG9232nbXu9Oy0P/OBc4ZzaTMdM15z9PR/dNM1iDZD9wg3t/e3d7U3pXep9283GvdKeCq4+bmaerT7lvzE/ec+gn/vgMrBxIJgwrHCxYMhAuXC2oNhxAYFCgY6xytIWIlBChjKsMsVy5bLjIt1yuAKoso+yU0JHckMibYJw0ppCp3LO8sXytDKfcneiYsI70eOxvKGKYVjhGgDhUORA5bDRAM4QsBDEkKdQZaAgb/oftc9+jyU++f7CPqoOds5fDjMeOe4nHhft9x3cfbAtp+16bUadLh0GPP2c34zBDNq82Qzl3QnNO612Pbrd2R3ove+90J3QXcr9vM3GPfu+JO5i7qU+4p8mT1ofiO/L4AJwSOBnUI7wmZCtMK3wtaDpER1hSKGAgdcSGlJNsmGilZK3wsLyxjK64qcCliJ9ElEyaMJ5Yo/Cj8Kc4r4yw4LNYq+SkIKdAmvCMYIbkedhuoFx0VNhRIE3MRERAfECkQcA6NCxwJ8QatA0r/C/ta96/zNfCx7ePrzumO5yHmW+X649fhGeDq3gndONr81w/X/tWL08HQOs+xzgrOqs37zhzSwdUX2UncDt9G4Hvfut1m3ObbDtwT3Vffi+LI5aDooeuK70P09fgX/dgAUwTfBsgHgwdlBy4IqgnAC+kOIxNsFwIbXR4WIo4lvifhKAAqDivlKjYpPycsJtclsiXuJegmLSj+KGspBCqqKpYqfin7J4AmniTKIWgeXhvkGIIWIRRdEm4RkhAlD5QNgAx3C3cJggaCA7cAZf1B+RP1i/F17nzr3ujT5hflnuPT4qfiGuJu4Bze3NuH2cTWINRp0nDRcNBqzxTPpM+p0AzSP9RA10PalNwl3unegd7z3DrbnNqk2wjeR+EN5RrpK+0K8aH0+/ci+/79YABNAgQEoAXuBgcIoQlTDL4PEhM8FsYZph0hIeAjPiY4KBIplSivJzQn1iYbJqglbCYPKFkp9CmbKnorrivEKpwpBClSKHYmxyOnIW8gDB/hHMgafhlBGAoWVRNoEU0Q9A4WDXQLHgoOCNMERwEK/qT6w/Yh82rwPu4a7CHqR+jo5RnjLeG14ErglN5h3B3bPNob2NDUC9KE0H7P1M6Ez8nRidT81l7ZZtv02/zaKNp42t3aXtoj2tHb/t7+4XLkUOff6nHuyfE89YD40Ppc/DH+kACfAv4DfwXtB/0K5A1kEPYSLBb4GXQdsB/dIPkhRiMMJEckISXxJkkoOCj0J60oQSlQKDInMSiZKoMrTipLKYEp6CgRJsYiLSGCIMgeIhxBGkwZ3ReaFcgTABMvEmIQFg4MDPcJKwfZA70AAf48+zz4PfV08uPvhe1N6x3p8+YU5arjTuJf4NPdadu/2YvYHNeD1XXU+9MZ02nRMdDS0MDSa9Sl1X7Xydnr2nXa6tkv2lraA9q82qDdMuGR4zvlxucb6wbul/Da88L3Ovvc/fr/agHvAW4CGwTABkUJrAvyDhQTzhZ0Gb0bUh66IC4iyiJIIwAklCSfJHUk9SRqJuwndyhtKAUpUCoAK5MqOyrCKs4qxyh8JQsjpiGhH7AcvBq8GvYashnRF+8WfhbSFAwSpg+nDbgK3wbIA/IB3/+4/Ln5APis9mj0YfGD7uLrKOmr5uPkaON+4S3f8dzW2szYWdf51h/XvdbK1SzVJtXS1KrTh9KM0rLTPdX91gTZsNpN227bR9zR3QLf8d/u4Qnlj+fC6D3qXe0X8fXzi/YD+pb9o/+LABsCiwRhBlUHFwmbDIAQKhP+FGMXbxr5HJoeRSCfIsMkdSX/JOIkzCXPJhInOydSKPAptypmKiMqhiqiKqkpTiiEJ7ImmCSWIXEftx75HTwcjhoVGuwZeBjuFbUT5BFJD+ELOQnqB40G+APtAJf+m/zY+T/2wPLo737tR+tC6V/nduWJ447hTt/n3A3bMNqX2RDYltVt01zSvNHJ0A3QetDL0QvTQtQg1izYFtnp2ArZvNm52eDYDNkq27ndid/N4bXlA+oc7aHvvPKa9QD38fcH+mT8Pv2K/er/bARuCNoKgA2KEXIVqBcGGSQbwR15HxYgpiDEIfsi6SMNJREnoSmFKyksdix/LY4u5C2eKx4qtCp6Ky4quSecJtcmIybtIyQisSEFIRQfHh0OHI4aiBdjFMoS0xGgD3kMCQpvCE0GLgP9/1n9Efv3+OT2XPRM8X3uOex66ZTlwuGH33jeLd1t2/XZ1NiT11TWUNX60wjShdBx0BjRLdHi0FjRpNLi08LUptWP1irXmtc02OXYv9mB247eEuIA5WPn0OlR7MXubfE39GT22/eq+Tv8XP5i/+wAoQRnCf4MfA+4EsgWwBnzGtobXB2cHi4fLyA/IkwkpyV1J6kqCC6vL40vEC8JLxIvky5yLQ4s/ip3Ku4p7igPKBsonShnKGEnKSZnJEQhdx2yGvkY0xY7FNQSthK8Ef4OTQzzCn0JWQZnAi3/Xfz9+GD1R/Jw74HsJurA6ETnz+QZ4hDgV95h3K3akdko2MvVTdN50bTPlc1gzDTNEc9h0FXR3tJt1MDUH9Ta0zHUg9QL1YTWfNjf2eza6dz83xrj6uUc6b/s2e/z8aLzXPUE97T41PpQ/cn/iwIjBi0KxQ3yEFsUvhcjGoYbzhwtHh0fBiAEIu4kVCfbKMMqfy2rL0owTDDDMAsxHDCQLs8tBy44LtktSi3dLFMsaCsvKuwouid0Jtck4CLJIJgeAxw3GQwX4BXPFNwSVxAJDq4LkQgaBU4CDQBX/Rv6J/dp9ALxLe1D6pzoHucw5Wvj5uGr34vcu9nX1wLWyNMa0m/RutBYz2DOqc40zwTP1M5oz9PPUs8sz5rQX9LP0t/Sd9Tt1jzY0tjd2jjef+BV4SHj4OaH6qrsz+5p8ir2Qvhn+X37m/5cAWADwAU/CU0N4BCkEx8W3BiOG1wdTh6WH9MhECSFJSonAyrpLA0u9i2NLssv8i/tLrUu5C+rMPkvGC8LL78uNy15K64qLSr3KIonnSZ8JU0jwSDQHtsc4xmYFlwUCxN0EWYPkQ26C/gIZgUBAvL+nPsx+KX15fPP8RXvsewH6xrpQ+Zj43zhFeAX3nvbINlG10nV6tLy0A7Qq8+wzjXNi8w2zQDO1c2IzWfO389P0ODPUdD40T3TltOo1GbXU9oU3J7dIODJ4orkcebi6eztq/BK8kb0qfZp+O35uvzJAKAE2QdUC/IOeRH8EiQVfRiWG4IdPx+YIaQjliSLJe4nLSujLfEu/y8CMS8xXDCoLwIw9jBrMRcxmzBeMO0vyC5RLW8sOSy1Kyoq+Se3JS8jGSAzHWAbHBosGJ8VtROlEi4RqA78C84JOAdbA//+dPvL+Fn2IvSD8hLxDu9w7LLp3ubY4x3hMN+s3eDb49ki2FDW6dN60QjQZc9uzvDM+csGzEbMJMxLzD3NM85mzpvODdBe0hfU/NRA1j3YvNkp2ujaPN044GLiZeT357PsTPAa8gT0OfdM+tv7IP33/9MD0AbvCOsLHxDXEysWYxiyGygfOCEnIn0jqSWfJ9soVSrkLMEvfjH9MWEyJTOBM+8yRDJ3MgMzsTJ+MY0wSDDUL5cuKy0uLDEreSkdJ5sk9SEKH0wcMhpbGCMWzBMfEiER+Q8hDsQL4QgSBW8A1vsE+O/0WPJk8C7vPe7e7M7qTuin5fPiPuCs3WPbZ9lo1/nUI9KVz/zNRs3czHbMOcwdzMHL+so5yhPKiMofy57La8wAziDQEtKr037Vy9fr2WXb99x831fiVuTc5Zboyeyj8NjyW/SY9mL51Psr/kIB8wRoCI4L5Q5DEgwVgheIGhUeByHOIigk5CXKJ08ppCpuLLIuuzD3MZgyLDPcM1c0cDRpNHw0VTSLM2EygDH1MDIwDC/4LQwtnSteKQgnKSUrI2kggB1nG8QZfBeYFC8SfRCCDsYLDwnABuwD2v9x+wf4j/Uh86vw4u647RjsWukY5jPjzOCa3rXcXdtJ2rXYN9Yl0x7Qhc2Ly1zK/ckRyhPK3smnyZ/JwMn1yUDKscp7y+bM6c4D0dfSo9Sw1p/YDdp927ndfODa4ufkrudQ65nu6/Av8zX2avkc/Mn+EQJdBd0HEgr3DEwQFxNzFW8YKhyKH/AhDySbJjMpGCtQLHAttS7ML3Iw4TCWMa8yzDOeNGM1XDYIN542TTUONCEztzGDL3wtTiwxK14peCdOJh4luiKoH1odrBsuGcgV/RJHEUsPSQxXCUEH9gRwAZb9uvpr+GL13PEr73btkevo6CTmzuOo4ZHf5N2x3HTbtdli13HU+tCrzVvLFcpMyQDJpMnsyrzLtMuzy0XMtMxyzFXMbc1Uz+jQPtJd1EPXt9lG2wHdpd9V4hzkteWH6HLs9O9J8i70dfYB+Yn7Yv7gAZkF6gjTC64ObBHEE+oViBjXG1UfaCL9JFMncSkhK1AsQS0/LkUvHDC9MG8xWTJXM000VTVWNsk2VTZbNVI09jLRMGYu0ywyLGUrzikQKKYm4SQlIi0f7hwDG20YVxWwEmMQhw03CqkHFQYSBLAA3fzk+Y/34/Tk8WPvhe1767zojuVu4qbfed0x3KjbRttx2trYbdZQ0/XP4sxjyqHIxse+xwPIKMh7yIXJAssQzH3MJ825zqvQHNIu06XUl9ZR2JrZIttX3brf4OFl5APoOOzG71XypfQi92D5Pft6/Z0ABgTpBn8JaAx1DxAShRTCF80bnR+QIiIlvSfjKRIr1SsFLYIueS/FLzAwOTFyMlczNzSeNTo3CTifN6k2yzWjNJYyEjA3LjAt/CsaKmAodieEJmgkjSFBH4MdEhuWF0IU3hGlD9cMDgr9B+0FvwLV/oj7IPmq9sXzLPFG71Xtm+py55/kQ+Id4FfeUN3Y3Cbcl9oV2OzUftEezhLLpcgox5/GpsbgxoDH7cjIyhLMbsymzG3NXM7dznjPLtHb00vW9teM2YnbY9233mLgc+Oq59HrN+8B8kj08fVd91j5H/wv/zICZwXjCC0M/Q7NESgV5xiMHOUf5SI8Jb0m4ydNKecqMiwpLUIulS+uMGAxLTKiM581cjeFOLk4QzhmNy82lzTFMg8xlC8nLsAspSvLKpgpsyejJewjCiImH4sbRhiJFYwSLg9dDGYKLQjgBFUBwf7g/J769ffA9fnzffHD7afpL+Zv4xfhWN+X3qjeyd5C3q/c/9lm1kjSE84yyg7H1cR1w/LCm8N6xbjHVslhyrHLbc22ziDPm88Y0SfTqtR91WLWm9e82NbZytsb30vjt+c67IDwkvPy9KL1K/fT+aL8F//SAVkFEgkMDFcO4BBFFEwYahxDIJwjRCZTKCsq/yuCLVQuny75LqEvPzCVMDEx3zKONS842TmUOrc6EzpZOOI1azMdMbUufSwZK0Uq0yhsJkckUiOgIsMg8B16G44ZBRdvE8IPoAxkCYYFyQEE/8X8M/qW98L1dvSC8nfvHOwf6VjmnuNx4W7ggeAJ4Vnh2OD/3qjbINff0WnMb8e7w9PB4cHcw1bHLssTzpHPHtAT0FHPIc6rzdrOUtEb1M7Wa9l621vcdNwb3TffouLn5rDrM/Ar8yb0QvQH9e32mfkE/WsBVQaDCisNrw4CENMRfxQlGG4chiDBIy0mNSgDKm0rdSxcLScuaC6yLV0sjytdLN8uNDJnNe43Uzn+ONw24jNKMWov0y1gLEArAiq3J2YkeCH/H14fZB70HIUbsBmRFnASng6WC3wI1QSFAVf/uP3K++X52/gx+GD2xPJJ7gTqKea54lzg1d/q4HvihOOh46/iceDQ3CbY/9KczffHf8KHvou94b9jxKDJ1s6I06rWLNdz1WXTotJC04zUMNYO2GfZYtlj2ADYeNnU3JrhcOeH7U7ywvSj9aD2e/jL+lj9lgCFBBAIJAr2CqsLHw2WDx8TmBdlHMkglCQOKDgrmS3wLocvoC/jLtks7SmOJzsncimWLZYyejdNOws9Kzw7OZE1UjL7L5ou6C0ELdsqYyflI5QhSiBLH40eWR4OHngccxkGFs4SVA9WC4UHYwRmASX+a/sP+lL5m/eH9Avxt+0m6nDm5eOq42Llteeg6ZzqCupQ53fiHNzb1CrNrcVuv5m7I7tYvl3EV8tT0QjV2dWu02XPDMvZyL/JPc1B0qDX4duV3XXcyNlj14rW1tdh23bgluWi6c3sC/DA86H3k/vW/ygEhwciCSoJeAjhBx4I1QkpDZMRkhZCHN4iyinEL+szGzYyNrEzli5OKFMjsCECJK0pZTFKOSw/hUFuQDo9MTkcNfMx2DCyMYoyVTEuLuIqcyhOJgQkVCKeIbIgQx7GGowXoxQWEQsN6AkYCFAGswNHASEANf9y/IT3FvJy7WDprOVu49/jheZ86UDreese6tHmbOGk2oXTaswYxQC+w7gWtzO55L3jw4TKvtCO1FDUj9C3y+zHAcZxxu/JIdCr1sHabtuR2SXW8dG+ziPPEdSw24bjyOq88cX3qvuh/XP/NwIJBW4GGQbWBJADOAP6BJoJpRDWGC8hNClDMGs1JzjMOPU3rDWGMbQrxCUkIr0i9ye9MPw6CURgSdRJFkbdP+U4BDMuMAUxmTPXNJYzKDHFLvsrPiiDJP0hUiB1HnYcBRukGQ0XLRMUD/oK/wVbAA78dfph+o75IvfF8wDw2eu357LkuePd5EbngOkG6gToneNd3dPVy81hxmPAE7zjubq6zL6RxMrJJ80zzkjM7MZ1vxG5jrbJuEe/NMnj1EjfRuWm5WHhUtpt0vHLJckaywPRHtnx4ZLqTvK++Ar+rALZBlYKrgxPDccLkAiEBfEE4QfDDacVDh8cKdQxPzcoOfg42zfiNRAzTDCkLksuNy8wMhQ47z/ORipKzEm/RmBB4jlWMlMugi/HM8c3SDrGO7I7XDjBMUoq8SNiHv4Y5xRbEzETGBLRD/0N6QzPCuIG4QJ3AJ/+Lvv09Z7wJ+wI6AHkH+Fy4KzhcuOd5InkruKZ3nzYfdEQy9XFUMEUvfO5Vrlhu4K+N8Fiwx/FSsVxwie9JrgEtti3Dr7JyG3W1uKL6c3o7OFc17bLSMK2vtzCac0O25LosfMk+wf/+wDjAl8F5gfsCRMLpgo0CB8FbARLCGQQ8xqMJvUxLTsTQD9APz3sOE40ITBpLecskC4oMtc3fj+IRzFNdk6GSw1GZz9COOcxxC5oMFo1Hzp/PMQ8kDs4ODYy5CpGJNYeFBpkFpIUyRMQEgMPIgwNCmUHVANZ/wr9jfvi+Mr0rPA/7Qnq/+b+5F/kROSg4xHihN+225LWldCoysrFpcIWwVDA/L/JwDTDM8bgx07HvsRowFO6rrP7rmquw7IFvK7Jjdlf5/Luse7D53vcqM/UxJS/zMHKyvnXAubE8XH5Yv19/4YBOwTSBz4MhxDNEgIScw/hDTYPrhPVGiokYy4YNw08vTxKOls2jzJ1MBkxQDSoOGI9f0IJSL5Mfk46TARH1kCwOpg0Yi9RLfAv6DUKPDRAC0IzQaI8ezQIK6MivxsaFpoS+RF7EjERmw3kCV0H3ARgAR7+avwg+wv4APPe7e7p4uZL5H3ipuEP4bnfLN132eXU08+WyoXFQ8GQvoe9hr07viTALcOHxffEG8FguzK1hq//q7usYbKKu4fGj9JD3obmYOi34wjb39DrxkW/4ry2wfrMxNvH6j73Tf/AAlMDZwM7BK8FawdnCUkLZAz+DAcPnBTkHdEo4jJ/Os0+LD+5OwQ2vzBNLpUv7TPZOeA/NUXTSetN7lBeURhO0ke9QHg6IDXoMJov6DKOOcs//EJHQ1BBQDx8M+sokB+xGPcTihHTEWYTeRPyEDANVQnJBDX/D/q89n30s/FP7o3r/+ks6ZPo0+cf5qvigt1T17zQbMpIxZLBkb7zu7q6wbs9vgfBksSNyQHOA866xxW9VrHxpoCgN6F1qtW5Ictk25fos+/U7bXjN9aYyifD/L9uwTzIv9Mu4Sjts/Xy+m3+ugF9BVoJpQzmDtwPjg+4DgAPPhJYGaUjJi8rOUY/X0AtPZI3tjGuLVwtpDFyORRC70gVTSRP7k9YT35MKEfmQB48ujmEOGQ3RjeDOQI94D7cPVc7gjiWNNouiCgII9sdKhg4EwAR/xCdEJ8O/wtBCT0FSv+9+Dnz8e6D613pNunp6oTtsO+b75frj+MZ2eTNM8P0uoK3G7n3vBTA1sHRwrvCcsGpwA7CNMSKw3a+mbZPrjanrKP3pjqyi8Ji08nh4Ovt7p/oldrGylG/q7oSvGLCJM0u25rpAfW5+7D+MADxATIESQavB4oIbgkLCw8OIROiGiwkZC5mN4w9DkA5Pxs8+zcUNLExFzLgNVA8oEMmSh1PZ1LVU9JSrE59Rwg/SjhbNdk1AjgWO0U/UUM1RElAaDmTMqssBycZIi0f7R1rHNIZFBeOFAwR/Au3BogC5/7Y+rv2YPNQ8IbsTeg65ZXkeebh6bzsfOx655rdkc/dvv2u9KSuo6OpN7Mzvk3JutFV1CjRJMvgw0+6hK4epFKfHKH5p6iy8L/kzPTVGNok2+DaY9nC1nLUrdNN0/HQbsxFyR3M5NZW5+z4zAfUEdUVLhMcC7EBM/z1/doGZhR5Ixkx6TrrP65Aaz5pOkk28DNBNHQ2cjliPcVCkEiNTORNBE6XTo5Pok/hTSNK6URhPwU7njgGOC85MTzeP3FB/j5xORgzBS0oJxEiwx7nHAEblhh8FrEUihHOC1kE4Pwp9lnwDOz36dHpcerL6ojq8el66dfohObj4PLX78yRwE+zqKf6oYik5qzCtvG/CshgzWXN/scHwOi3xa+8p/ihDqGApQOuQ7nixYnRmdny3A/ct9e10AfJ88MDxCTJFNF+2XzhUOkf8Sv4T/1IAD0CiwQkB7sIrwhlCEEKrQ94GK4jPTCUPFtGL0vPSd5C2zheL8gpFCp7MKE7wUgnVKVaMlsjV+ZQxUpkRlNE4kMORLdEEkYJR7NF4EGaPTI6iTaPMeYs5CrgKuwpyibcIiIf6RoaFmASpBAhD28LTAUm/vT2/e/46fDlJOTd41zkVOXQ5sroW+pG6U3jStgPymO62ar1nrKb3aJDsHG9IMcNzVzOz8k1wT25wbSXsrSwKq/rruKvDLLStje/l8np0pTZa9233ZfZddI4zB/KU8wW0UTX9N4u6Azy+/p+AfwEIAZeBtQG1QeiCRoNKBOOG7Ek0yxYM7E4Rj2oQPRB+ECZPgE8yzlhOOM4oTxRQ5RK0E8WUvZROVDFTd5LFktUSjFINUUVQ8FBPD8WO8E3/jbLNgg09i6IKuInTSXCIcgepB3fHD4avhXbEMgLPQUI/Tv1PvBW7q/tiOy+6v7oGuc/5OTgEt+g3yzgU9071iLMZ78TsJWhiJqfnrCqjbi2xPvN6NHIzRLDardar4OrRKvyruG1y7y1wDrCBMT7xvXJeMxmz7zSx9ST1KXTXNRL10DbZd9B5Izqq/Hc98v71v2h/3UCMAYYCkMOrRO7GmkiLylPLgkyBjUIOKM7fT8xQpRC/0DQPhQ9cTzxPXVCG0lNT+ZSlFMEUvlOsEvNSb5JHEp9SfVH1UUuQlg8CTbZMQEwfS5mLPAqoyq6KYcm1iF7HbMZghXiEN8MqAnvBaQAW/qX9Cvwu+yp6fjm5uTa4rHfjdtz2PHXpNiL11vTv8ygwx+3Iak8n76dY6PPq6m0lL2HxDHGc8JUvTm6qrjxtnS1c7VItnO2lbYEuY++fcXyy3/R7tUw2KvXtNWo1MfVpNiE3JLha+jG8Pn4Df9uAlAEUQb/CA0Mlw9hFJgaHSF4JkQqgy3EMS04qECGSRpQPVJJT+1HED4jNWExIzX2PgZLDVYTXkVhXl6lVrtNckYrQb89Ej0wP3ZB8EDVPZA63TdENHsvnisEKuooySWWIAYb1hW5EGIMfAoVC6QLawkeBF39LPYX7gjl4dwR2MXWy9ai1kXX1tmd3Efci9d7z47EULbxpjqckpqqoAaqJ7R8vqHGtsghxAS9qLfAtEGzSLMutXy3a7ipuJO6Bb+8xKLKrtAs1jfZ7tju1pnV1dVh15fahOBC6TfzJfyIAvAFwQYWBnkFPAYQCTsO3xWEH2Ip/zBNNes3ijvcQNpFZ0iZSJBHOUXjQPo7Djo9PRNEz0sxU59ZIV3GW6JW+VAyTBJHeEEtPiw/L0JbQwNCNUBZPmM6tzP0LIsosyVYInoekxuTGc4W0xJbD54NQQz4CHwDiP0e+DDypuq24srcY9mj1ozT8NG401zXFNkD1/vRRsrDvnSwP6S+ntKfZKT2qomz+Ls2wBK/17vaueC4XrfqtQq2Bbe9tli1jLX5uFa+w8MOyYTOD9Mx1U3Va9XQ1gjZj9s73zvlNe1O9fL76AB+BJMGKQdoBy4Jeg3VE1YbXyMkK3Yx9TXiObw+MURXSNBJv0ixRfdAvzuwOCc6J0CwSJpRMFmoXaldoFmFU/pMi0YAQf49RD47QHlBM0EtQJ4+pjv1NrQxES3rKIYk9h/hG10Y/BTuERoQow8iD9sMiAgsA1j9ZPbf7Qfl4t0T2bDVZtNm01XWXNqA3DzbfNbCza7AprHOpdegNKI2p1CujLZovfu/Xb62u3O6P7oMuvu5JbpJuYa2irNLs/W2Ur3KxFLMhtKk1WXVudP90hLUwtYb21Phseiu7071vvlj/RUAngGgAlsErAeYDN0SYRq+IrYq4TAHNVQ47zuzP6JCHUT/QxJCbz6kOmY5YDzbQtlKqVLEWDxbEFm9UyNOpEm2RVBC8EAsQuRDhUM3QQI/Zz2/Oi02CDG5LM4oYSQiIE4dchvgGDkV6RHLD68NEgpNBd4A8/z29+zwKunk4obesNrF1hTU0dPn1BbVhNOq0A7MOMSOuXWvOKkSp4inOqpXrxC1Q7gguAG3/LaVt9a3lbi7uqu8wLsluCu1fbXGuHy9RcNHyk3RSdae2LXZEtvR3IHequCQ5IDqR/GX90v9sQI7B98J3QoeDGoP7BTpG/cjkCwTNLM4gTqTO9M9W0EfRRhIdUmdSAVGbUOAQqRDjUY8S2tRaVf0Wjhb/Fj1VDpPr0h7QxJBqUCPQDpA9T8uP508HjgdM9wuSSvtJ+YkQyIvH9QarxUfEakNewrRBiIDUAAQ/tH6avVr7lzn9uDH2q7U0c95zYPNnM6iz8nPsc3fx7u+4rTIrAmnoaN7oymn46yqsWS0N7bGtyK4/rYDtom2WLdnthC0zrILtPO2ebrtvr3EEMuQ0OjUftg625nc59x83ZXfgOMK6RXwP/hIAJMGXAozDD0NZw50EFwUxRoaI6Yr8TK1OFM96EBiQ+5EqkVHRblDJ0IhQt1DRkbnSIlMTlGHVYhXwleZV9dWF1R/TylLSkjDRXtCdT8mPtM9hzz6OZ030zUXM4YubilMJZMh2RxuFxgTbRACDpkK1QbkA10BqP01+Dfy1Oyv5+zhB9x215XULNJtzy7NJcz4yqvHgMKeva655rRgrneoJaZmpzKqHK5HtLy7scCxwHC9A7r4tkuz7q+6r72zwbkTv0LDLMfpygXO+9Dp1MbZCN5r4FrhUuJi5Mnnnuwe8xD7OQPxCU4OoBDCEWESRxPFFfwapCIAK2MyWDgnPb1AmEKLQi1Bij+rPg0/ZkAPQvBDoUZpSpJOF1KEVI9VcVTGULRLIEehQ2VAPj1OOxo7QjtEOns4CjedNb0yHi78KA8kxR71GAIUMhHbDzgOoAvJCBMGsALA/Zr3dvEe7FznvuJ93jLb0diK1uPTaNGUz5TNLcqSxR7BLr2luDGzoa7YrI2tZK9Xshm3eryKv/e+ebw0uji437UztDe1OLltvi/DTcfyyrjNe88w0f7T5Nfl21jfXuJN5TroSesD7wP0UfonAVQH4QuKDssPnxBCEtMVwxtuI2YrVTKZNxg75TxAPa483DtrO/I70j3WQDZESkf/SVpM501HTvRNvk2ITWFM/Ek/R/FEwUJBQBU+Hz39PEE8FjrCNroyOy7hKYkmLySwIT8eYxrlFnYTMQ8xCqQFRQJy//v7b/dd8oztS+l75RniYN8z3craYtci06rO/cm2xH2/FLxGu6q7VLvvuS649rW7smCvCK6Yr9OyX7Ywuiu+u8BIwIS947oKut6637wpwJrEDslOzG3Or9Ao1N/YIN5R4zbojOzP77zx8fKr9MH3KfyOAcoHaQ5XFMkYQRzkH+MjYScNKu0sEDEMNms6Mz2gPos/n0ASQrBDMEWTRg1IeklOSnJKukrtS6dNzU7fTi5O10xxSvFGFkOFP0A8VjlsN842izZkNUkz/DBiLl0qmySGHsQZaBZXE/UPlgyOCZwGawPm//P7VPcb8tvsFeil4y7f79qo16DVDdTh0ffOA8xYyUfGFsIwvbK4AbXHsU2v2678sEK0sLb7tzu5rrocu8S5tLd3tlq2zLbHt+25Zb14wV7F3MgOzCXPU9KU1YTY1trp3IDf4+LK5h7rOPA79oP8CAITBnQIggkkCpULug6jE7cZJCA7Jq4rgjCPNDI3+jeoN883GjmoOng78jtTPQpAW0NwRgxJMkvRTMJNtU0zTFZJQkYiRNZCbUH1P48/XUCTQJ8+MDv7NxE1DzG6K+EmFySxItsg6x26GhsY1xUzE74PdAtuBuIATPtr9t/yofDx7gLtpOoS6B7lIOH2253WS9Irz37MzMlWx1vFlcO9wQ7A374bvoW9Vb3vve++Br9BvTW6nbfCtqO3s7nWvA/Bb8U5yJ/I4ccXyCHKRs2s0DjU1tfj2uzcjd7i4E7kZejB7DnxZvWo+PT6G/3//84DKwirDA8RQxVuGZkdPSFsI9kjWiM8I1AkxSZ1KuouZDM+Nx06njt0Ozw6mTmlOqw8Hj5uPi0+nj2BPD879zreO6g8NzzmOnU5mDeVNMUwXi0XK9UpWylMKcAo4Sb3I+YgrB1vGSsURA/mC8oJBghKBpYEcwJm/8T7U/g+9QLyVu586sPmJ+OY3zzcgtkD2BDYCdlm2fLX29Qv0ZnNM8pVx6jFRMV0xazFG8bfxmbHNcfXxjTHL8jQyMrIDMlrym7MAs78zjHQONLT1JzXndrK3Xfg+eG44u/jS+Zd6aLsdPBY9an60v4DARUCZQNbBYYHyQmhDDEQyxPaFroZGB29ILEjmyU/J0EpCSuMK/wq3ipWLNouDDFsMp0zFDVINns23TVANeI0QTRDM7wyOzPxM5wzOzLfMMgv3C1fKkwmJSMsIacfSR5QHYkcRRtzGbAXHxYPFCMR+g1AC7YIygWTAqD/Iv3Y+oz4Pvbs84fx/u4x7Czpa+Z25BDjbeF23xTet91L3YXbhtht1dHSsNBuz5LPmtBY0YXR5tGf0qbScNEA0IfP98+q0J/REdOL1HPVItZZ1+LY4Nlr2qPb891L4KbhgOIn5BXnxerB7v3yJ/dK+pf7W/vZ+jv70/xb/6sC0QaGC+4PMxNQFe4WfhjPGaUaWhubHKseNyHYI3kmDSkrK1EshixxLJEspSwnLDUriCqUKiYrHyzWLTkwCDK3MQ4vQCtqJ8cjgiBfHu8drB50H6EfJR/yHQccyhmRF+4UShExDSMKzghCCGgHYQbTBWUF8QMJAXX9K/pk9830H/Jt70nteewg7SbuKe7Y7NHqWOga5Wvhlt5v3Wjdt91v3qPfS+Br37Ldp9xz3OLbj9qL2YPZwdm52TjaFdye3n/gbuHl4cThkeC83nDdK92b3efew+EM5kzqP+337hTwtfDP8N7wgfHL8qT0UPfn+pT+LAF/AmYDowROBjkIRQozDMMNMA8FEUMTQxWuFgsY8RkSHJIdFB4EHuYd/B16HpUfLiHWIl0kCiblJyop5SgkJwUlhyOuIhAitCHiIVkiWSJ0IegfIh5THK4ajBnyGGgYoRfXFiYWBhX/EpEQnw4lDV8LMAltB3YGeQWjA1UBef9D/kz9e/z4+2j7EPrG9x31ovKJ8BDvfe6U7qLuMe4z7YfrB+k95kfkquPi43zkvOWi5wPppejD5qzkEuO04Xng0t/d3xHgFOAn4H3g3+BU4XLiZeQ95uPmVeZ15fDk9+TI5ZznFupq7DPuou/E8EXxGvH/8Nzx9fPi9g766vz2/gwAiQD1AIwBUAJiA/UE4gapCPoJEQtkDC8OZBDKEv8UZRaLFqwVnxQkFG4UbBUnF4cZ6ht7Hf0d6h2iHf8c7Rv9GtkaZBveG+AbnRssG0Ia3BicFwwX3hZYFkEV8hOrEnARaBDmD9oPvA8fDxMOvQz8CtIIxgZ0BdMEQwRFA+wBiQA//wX+2fzP+//6b/oD+pP5FPma+BP4Kfet9fbzk/Kp8fXwaPBU8MnwN/EO8WXwvu9C77/uMe7O7Z7tZ+0j7Svtt+2R7l/v8O8o8OLvOu+Z7kvuMu4r7mfuG+8f8CfxQfKq80H1ifY+9333ZPfx9mH2R/YT96X4kfp8/Bb+C/9F/x3/CP8l/1T/k/8YAAQBRQKxAyEFYwZFB8IHBAg9CH8IwQj8CCgJSgl/CfAJswq4C9cM7w3iDoIPkA/vDt4N6AyBDK4MKA2wDSwOcQ45DncNdwyRC9EKIwqmCZcJ5wk0CjQK+AmsCVEJzggvCJMH8QYlBjEFWgTeA7sDvwPNA+0DFgQVBMMDNQOfAhMCfwHmAG4ALwAQAO//zv+4/4v/FP9h/rv9Uf0C/Z/8OfwG/BL8L/w2/Cj8D/zf+4X7DvuT+hj6mPkv+Qr5M/l6+af5rvmh+YH5R/kD+dH4qvhw+Cj4//cI+Bb4//fj9/X3Kfg9+CL4C/gg+EH4SvhU+In42PgK+RP5JPla+ZP5ofmQ+ZX5zPkl+oP65fpX+9z7YvzM/BP9TP2U/e/9R/6Q/tf+Jf90/73/CwBqAM0AIAFuAc8BQQKjAuECEQNNA48DxAP0AzIEgATGBPUEEgUhBR4FFQUhBU0FjAXHBf4FOAZ0BqYGyAbdBucG6wbsBu0G6AbbBswGwAbABtAG7wYMBw8H9AbUBsIGtAaSBlkGIQb6Bd8FyAWzBZYFZgUmBe8EzgSwBIEEQgQFBMsDiAM/AwYD6gLWArECdQInAscBVAHdAHsAOQAIANj/qP98/07/E//L/oP+Qv7+/a39Uv35/K38bPw3/BH8+fvj+8H7kftg+zn7E/vi+qr6gPpt+mT6WPpJ+j76N/os+hv6DPr/+fL56/n6+Rr6Nfo++j/6Tfpq+or6qvrV+gj7Lvs6+zr7SPtp+5L7vfvz+zb8efyp/Mj85fwL/TP9U/1w/Zf9xf3x/Rf+P/50/q7+4v4O/zv/cv+t/97/AgAcADQATgBuAJoA2AAiAWkBoAHHAeQBAwIpAlUCiALAAv0COgNsA44DoQOzA9ID/gMqBFEEdQSdBMcE7AQNBTAFVwV9BZoFqwW0BbkFuwW5Ba8FoQWVBZAFlQWhBa0FswWrBZMFcgVPBSoFBAXhBMEEnARpBCsE8AO+A48DWwMhA+ICoQJeAiAC7AG7AYcBTQEOAcoAfwAxAOn/pf9f/xP/y/6O/lr+Jf7q/ar9bv06/Qj91Pyk/H78Xvw4/Af81fuw+5T7e/tg+0b7KfsC+9j6tvqi+pP6gPpv+mj6Z/pg+lD6Qvo/+kf6TvpP+kv6SPpI+kn6SPpK+lP6Zfp6+o36oPq2+s765fr8+hb7Nfta+4H7pfvG++b7Cvww/FT8d/yd/M/8Cv1L/Yj9v/3y/ST+Wv6U/tD+C/9I/4//3v8sAHEAsgD6AE0BogHvATUCeQK9AgADQgOFA8oDEARXBJ8E5gQpBWYFnQXNBfMFEgY0BlsGgwahBrkG0gbwBhEHKwc3BzkHNActByQHFAf7Bt4GxAasBpIGcwZPBikG/wXMBZMFWwUlBe0ErARlBCAE3gOZA00DAAO5AnECHwLFAXABIwHUAH8AMADz/7z/ef8n/9T+h/46/un9nP1Y/Rz94Pyl/Gz8N/wG/Nf7p/t1+0T7GPvv+sj6ofp++l/6QPol+hP6Avrk+bv5m/mQ+Yv5gfl5+X/5i/mO+Yj5jfmh+bT5vfnH+dv58/kI+h36Pvpp+pj6yvoA+zj7b/ul+9z7EvxH/ID8xfwT/WT9tP0E/lX+pP71/kr/ov/3/0cAmQDvAEgBnAHtATwCigLWAh8DagO4AwQESwSOBNQEHAVeBZYFyAX9BTMGYAaABpcGrQbCBtMG5Qb7BhAHGgcWBwwHAgf1BuEGxwauBpQGeAZYBjUGDgbgBa4FfQVNBRsF4gSmBGcEKQTuA7kDhwNPAw4DyAKDAkAC/QG6AXUBKgHbAI8ASwAMAMv/i/9O/xX/2/6g/mX+K/7v/bL9eP1A/Qr91vyq/IH8VPwj/PX7z/um+3j7Uvs7+yv7E/v1+t/60vrE+rH6ovqc+pn6k/qQ+pf6o/qs+rX6xvrg+vz6Fvsu+0r7afuL+6/71fv/+y78YfyT/ML89Pws/Wj9ov3c/Rr+Xv6i/uP+Iv9k/6b/5/8nAGcAqgDuADMBdQGzAewBIwJbApMCzQIGAzoDZwOTA78D6wMSBDUEWgSBBKIEugTNBOAE8QT8BAMFCQUNBQ8FEQURBQsF/gTuBOIE1gTGBLAElwR8BF4EPgQgBAME4gO9A5YDbgNFAxkD7QLBApECXAIpAvsBzQGcAWgBNQECAdEAoQByAEMAEwDj/7T/g/9P/xz/7v7D/pX+Zf44/g/+6f3F/aP9g/1h/Tz9Gv3+/Ob8zvy2/KL8kPx+/Gz8XPxR/Ez8S/xL/En8RvxD/EH8QvxI/Fb8aPx6/Ij8l/yp/Lz8zvzi/Pv8F/0x/Uz9b/2W/bn91v33/R/+Rv5p/ov+tP7f/gP/JP9J/3T/nP/B/+j/EwA8AF4AfQCfAMQA5wAIASgBSAFlAYIBngG6AdUB7wEHAhoCKwI+AlQCZwJ1AoECjQKXAp4CowKqArECswKxAq8CrAKoAqUCpAKhApUCgwJyAmUCVwJIAjgCKAISAvgB4AHKAbIBlAF3AVwBQgElAQYB6gDNAK0AjgB0AFwAPQAaAPn/2v+7/5z/gf9p/0//MP8S//n+4f7H/q3+lv6C/m7+Wf5H/jf+J/4W/gX+9P3m/dv90f3H/b39t/22/bT9rv2p/aj9q/2t/bL9vf3J/c/90f3X/eX99P0B/g3+Hf4u/j/+Uf5m/nr+i/6c/rP+y/7h/vb+C/8i/zj/Tf9m/4H/mf+s/8H/2P/u/wIAFgAtAEUAWwBwAIUAlwCnALYAyADYAOQA7gD6AAkBFgEjAS4BOAE/AUUBTAFTAVkBWQFZAVkBXAFdAV4BXQFbAVcBUgFNAUoBRQE9ATQBJwEaAQ4BAwH3AOkA3ADQAMUAuQCpAJgAhwB2AGgAXABOAD0AKAAXAAkA/P/u/97/z//A/7D/oP+S/4P/dP9n/1v/T/9C/zT/Kv8g/xb/DP8D//z+9P7r/uT+3/7Z/tP+zP7H/sX+xf7F/sL+vf65/rv+v/7C/sX+yv7Q/tH+0f7T/tn+3v7h/uj+8/79/gL/Bv8O/xr/Jf8v/zr/RP9M/1H/W/9p/3b/gP+I/5H/nP+l/6//uv/H/9P/3f/m/+//+P8DAA4AGAAhACoAMwA6AD8ARgBQAFgAXwBkAGoAbQBtAGwAbwB2AHwAfgB9AH4AfwCBAIEAgAB+AHsAeAB1AHIAcQBuAGgAYABZAFYAUwBOAEgAQgA6ADEAJwAgABkAEQAGAP3/9//w/+j/4f/c/9X/y/+//7b/sP+s/6X/nv+W/47/hv+B/3z/df9u/2j/Zf9g/1f/T/9K/0n/Sf9I/0T/QP87/zn/Of84/zT/L/8t/y//MP8w/zD/Mv81/zX/NP81/zn/PP89/z3/Pf8//0P/SP9M/1D/U/9X/1v/Xv9h/2f/b/91/3v/gf+K/5L/l/+b/5//pf+t/7X/vP/C/8f/zf/U/9n/3v/j/+j/7P/x//j/AAAGAAgACgAQABcAHAAeACEAJQAnACYAJgAnACsALgAvAC4ALAAsAC0ALQAtAC4AMAAuACcAIgAiACMAHgAWABIAEgAPAAcAAAD8//n/9f/y//D/6v/h/9n/1f/S/8v/xf/C/8D/uv+y/6r/o/+b/5X/k/+S/4//iP+C/3z/d/9y/2//bP9o/2P/X/9c/1X/T/9N/07/Tf9I/0X/Rv9G/0P/Pv88/zv/Ov84/zj/Of85/zj/Nv82/zr/QP9F/0T/P/89/0D/Rf9G/0f/S/9P/1P/Vf9Y/1v/Xv9i/2j/b/91/3f/e/+C/4j/if+L/5H/mf+f/6L/pf+r/7D/s/+3/7v/vf+//8T/zP/T/9j/2//d/93/3v/i/+j/7f/t/+v/7P/u/+//8P/w//H/8v/y//P/8v/u/+r/6v/u/+7/6P/j/+H/4f/g/93/2//X/9H/zv/N/8v/x//C/7//u/+0/67/q/+p/6X/oP+d/5v/lf+M/4X/gv9//3r/dv90/3H/a/9m/2P/Yv9g/1z/V/9S/0//Tv9N/0z/Sf9G/0T/QP89/zv/Ov83/zP/MP8y/zT/NP8y/zH/Mf8y/zT/Nf8z/y//L/81/zz/Pv88/zv/Pv9C/0T/Rv9K/03/T/9R/1X/WP9Z/1r/X/9n/2//dP93/3j/eP94/3z/g/+J/43/j/+S/5X/l/+b/5//o/+o/6z/r/+w/7L/tf+7/73/vv+//8H/w//C/8H/w//F/8b/xv/I/8j/xf/D/8X/xv/E/8D/v//B/8D/vv+8/73/vP+3/7H/r/+w/6//rP+q/6f/of+c/5r/mP+T/4v/h/+I/4r/iP+C/3r/df9z/3P/cf9r/2f/Z/9n/2L/Wf9T/1T/Vv9U/1D/Tf9N/0z/Sf9F/0T/RP9D/0L/Qf9B/0D/Pf86/zr/PP89/zz/Of84/zr/PP88/zr/Of85/zr/PP9C/0j/Sv9J/0r/Tv9Q/1D/Uv9Y/1//Yf9i/2b/af9p/2j/av9v/3T/d/95/3r/ev97/4H/iP+N/43/jf+P/5L/lP+W/5f/l/+W/5j/nP+h/6P/pP+m/6j/qP+n/6b/pv+m/6f/qf+q/6j/pf+n/6n/qP+m/6X/qP+q/6r/qP+k/6H/nf+b/5v/mv+X/5X/kv+O/4n/hv+G/4X/gP96/3f/eP94/3T/cP9t/2z/af9k/1//Xf9e/1//Xv9c/1f/Uv9N/0v/TP9O/0//TP9I/0X/Q/9B/z//P/8//zz/Nv8z/zb/Of84/zL/Lv8v/zL/NP82/zn/Ov85/zf/Nf80/zP/Mv81/zj/O/86/zf/N/87/0D/Qv9A/z7/QP9F/0j/R/9G/0n/Tv9Q/0//UP9T/1X/U/9T/1X/WP9Y/1f/WP9a/1v/Xf9h/2P/Yf9e/17/Yv9o/2n/af9o/2n/bf9x/3P/cf9u/2//dP96/3z/fP99/33/ev91/3X/eP98/3z/eP91/3T/df91/3X/cv9w/3L/df93/3T/cv9y/3X/d/93/3b/dP9z/3L/cv9y/3L/cf9y/3H/b/9s/2r/av9s/27/b/9v/2z/Zv9e/1z/Yf9o/2r/ZP9e/13/Yf9k/2H/Xf9c/1z/W/9a/1z/Xv9e/1r/Wv9d/1//Xv9h/2n/bv9q/1//Wf9a/1//Yf9h/2H/YP9d/1n/WP9V/1H/Tf9M/0//Uv9U/1T/VP9R/03/S/9M/07/Tv9M/0z/Tv9Q/0//Sf9D/0D/Q/9I/0v/Sv9I/0f/Rv9C/0D/Qv9G/0f/Qv8+/z//RP9H/0b/RP9E/0T/Qv9A/z3/PP87/zz/Pv8//zz/Of86/z7/P/87/zb/Nf85/z7/QP8+/zv/O/8//0b/Sv9I/0b/Rf9J/0z/Tf9L/0n/R/9J/0v/Tf9N/0z/Sv9I/0f/Sf9M/07/TP9J/0r/Tf9P/0//T/9Q/1P/VP9V/1T/VP9V/1f/Wv9c/17/Xv9e/1z/XP9d/1//X/9f/2H/Y/9j/2H/Yf9j/2T/Yf9e/1z/Xf9c/1v/Wv9Z/1j/Wf9c/1//YP9c/1r/W/9e/2D/Xv9d/1z/W/9Z/1f/V/9Z/1v/Wv9X/1b/Vv9W/1P/Tv9L/03/Uf9T/1D/Sv9F/0T/RP9E/0T/Q/9B/z//Pv9B/0X/Rf9C/z//Qf9E/0X/RP9C/0D/QP9D/0X/Rv9E/0P/Q/9D/0P/RP9H/0r/Sv9H/0T/Qv9B/0P/Rv9I/0j/RP9B/z//QP9B/0P/Rf9G/0X/RP9D/0L/QP8//0H/R/9L/0z/Sf9I/0n/Sv9K/0n/Sf9L/07/Uf9U/1X/U/9P/07/Tv9Q/1H/Uf9P/03/Tv9R/1P/T/9K/0n/TP9P/1D/U/9X/1j/VP9R/1H/U/9W/1j/WP9W/1P/Uf9T/1b/WP9a/1v/V/9S/1D/VP9X/1X/U/9U/1b/VP9Q/03/Tf9M/0r/Tf9Q/0//Sv9I/0n/Sf9G/0P/Q/9E/0L/QP9B/0X/SP9I/0f/Rf9E/0X/SP9J/0f/RP9C/0H/P/8+/z7/Pv8+/z//Qf9C/0L/Q/9F/0X/QP88/zz/P/9B/0L/Rf9I/0b/Qv8//z7/PP89/0L/SP9I/0X/Rv9M/0//Tf9M/1L/Wv9d/1r/Wv9c/13/Xf9f/1//Xf9b/1//Zf9k/1r/Uv9U/1r/XP9a/1n/Wf9W/1H/UP9S/1X/V/9Y/1r/Wv9a/13/Yv9m/2T/Yv9i/2T/Zv9n/2f/Z/9m/2b/aP9p/2n/av9s/2v/Zv9h/1//Yf9k/2X/Z/9o/2f/ZP9h/1//Xf9d/2D/ZP9j/1z/U/9N/03/T/9R/1H/Uv9T/1b/V/9U/1H/Uf9W/1n/WP9X/1n/XP9c/1j/Vf9X/1z/X/9f/1v/WP9Y/1r/XP9f/2P/Z/9m/2P/Xv9b/1r/W/9f/2P/Yv9e/1v/X/9k/2X/Y/9l/2z/cv9z/3T/d/96/3v/e/99/4L/hv+I/4f/h/+J/4v/jf+L/4n/if+L/4v/if+K/4//k/+S/5H/kv+V/5f/mf+e/6H/of+f/5z/mv+X/5n/oP+m/6T/n/+a/5X/j/+L/4//mf+g/6L/pP+l/6P/m/+W/5n/of+m/6b/pP+i/6D/n/+e/53/nP+d/5//ov+k/6X/pv+l/6X/ov+f/5z/nf+d/5r/lf+V/5r/nv+f/6L/pf+h/5f/kv+Z/6P/pP+h/6P/p/+k/53/nf+i/6L/mv+W/5v/nf+Y/5T/k/+Q/4n/hf+J/4z/if+F/4j/jv+O/4b/fP91/3T/d/9+/4T/iP+J/4b/gf9//4X/kP+Y/5z/oP+j/6H/m/+c/6T/rf+y/7f/vP+5/7P/sP+y/7L/s/+5/8D/u/+s/6f/sP+z/6f/nv+l/6//q/+i/6b/r/+p/5r/mv+n/6z/of+W/5X/k/+P/5H/nv+r/6//sf+2/7v/vP+8/73/uv+1/7r/xv/N/8j/x//P/9b/0f/K/8z/zv/I/8L/xf/O/9D/zf/N/8r/xP/A/8X/yv/I/8L/wf/A/73/xP/Y/+f/5P/a/9j/1f/I/77/x//X/9n/0P/L/8r/xP/D/8//1P/D/7P/wv/c/9r/wP+3/8T/yv/F/87/4P/Z/7v/sf/G/9H/v/+2/8z/3//S/8H/yf/b/9n/zP/N/9j/4f/q//H/6v/Z/9r/7v/z/9z/x//R/+X/5v/c/9j/1v/S/9j/5//t/+X/4P/g/9f/yv/O/93/3f/T/9n/5f/i/+L/BQAjAP7/u/+8//3/GADu/9f///8cAAMA8v8OABQA6f/c/xUAMgDv/6v/0/8sADgABgD6/xwAIgD9/+T/7P/9/xIAKwAzACEAEgAWABAA//8KACgAGQDn/+v/LQBBAAMA5P8mAGYAUAAsAEAASAAJANb/AQBHAFAAPQA+ACgA9f8CAFYAZgAIAOf/WwCuAEYAvv/e/1EATwAEAB0AgwCWAFMAQABwAIAAXABCADwAOgBZAJIAnAB6AJMAzwCXAPz/5P9zAKUAJgAMAMgAEwEuAIX/UQBKAcQAr/8AAEoBhgGZAEIA6AAXAUkAsv/7/1UAUwChAGUBtAE+AdEAvABNAHD/O/8wAD8BWwHiALEAuQBbAKD/Qv+a/xQABgDW/5QACAIuAgkA6f2d/ggBoQE/AE4AUQIuAmv+Kfx8/zUEAAR3ADIAlAM8BAUADP1aAJoGXQkkB/oCqf96/tD/TAKeA7sDrQQyBmkFLQIwAKYAFgCG/fn93gOPCEkFdP6e/bkCpwQ0AOH8NwAeBcsETAHLAN4C5wFK/Vv7PQCfB6AJuQNS+034Lf04BIMFkwDF/YYDjgx2DecDm/qk+5ADXgcxBMkBEAVKCCIF9P9WAeoH3QlHBIj/4QGmBnEH+wVABhYGvgLQAC8FngrjCAID/QIZCYYL4wbXAk4EGAdiCIsLNhAxDyoHawFzA70GBwY1B3oOxhJnC1YAOf+YBv8JegaQBNEHJAr8BzkFXwQXAxUBmwGKBKUFvQPBAZkA7v5X/jUBbwQeA4v/5v8ZA2ACq/0z/K//UAHB/Uv67fpe/Jr7Afvv+xf7Y/d69S747PtV/db9//15+oXznPCO9hL/pAAz+/z26vhu/WL+V/q29RD2BfwWAukCsf+Y/Qz+8P0f/Mn7Cf8TA6YDIAD1+0D7mv7fAfYAI/5y/4oEgAU8/9P5a/wLAnsCrP9QAM8CJAGU/Qb/ZgORAnf9Bf0uAk0EeABh/SX+RP4R/dn/ZgX8BD3+QPz8AmcHywHc+sf83AI9A0z/pv4QATEBwf/2APQC8wDN/P/7Av5K/kT9z/68AU0B5f1j/L79lv5//ngA+wNkBDoAXvsI+c/4ffpt/ysGQAlnBUj+fPpY/JoANwNsA/sC3gI+Aq4Ahf/a/48ASwD4//EABgJhAT8AHgHvAlICY//X/Rr/uQA3Ac8BpQLPAXT/Pf5z/mn9LPt7+xT/UwFI/1P8b/yC/oH/DP+8/Rj7jPjZ+cX+/wC7/Pz2WfbD+fv7QPz//Pr8Kfn481Lzm/ct+xf7Jfpj+sP5E/fT9MT0MPUM9Wf23PmU+1v40PL97/7wZfPY9Yv4Pvq3+IL0C/GC8ODxifN69cf3BPnn94r1jfQI9kz4zfjq9tv0q/XI+cL9cf2h+Kjz1fJa9ir7X/6g/sv7pPch9h76LwElBfICGf4n/G/+AgJFBEEF5gVLBhcGNwXTA1MCqQHzAmoGywqpDcYMYghGBNkEEgrSDnQOpQmCBMYB2gGkBOoI+gq5B3IBP/7dANMFewjpB6IFlAKT/4L+OQCdAgwDpQFBAGL/Wv50/Zf9Jv6X/RL8TPvU+1X8vPtc+sT4K/cp9lP23PZD9q/0wvO/8/7y+/C176DwJPL38Yjw8O+n8EjxGvGt8FXwxe8R73TuaO3C61XrHO6X8n/0EfJ27pXtce9J8bbxhPFr8SfxyfAt8cLy5vSR9hX3cfaX9ez1r/dm+bT5OvmR+f/6gPyN/WX+5P6v/n/+gP9MAT4CHwK3AvgESwfLB80GEgaFBvgHEwolDNkMlgumCdYIYQlyChAM2Q71ETMTzxGiD9wOiA8uECQQIhDaEN4RHRIPEVEPLA5lDnMPIBD5D6sPwQ+lD2gOPQyFCmAKjwvWDCINPgzMCpIJkAjfBuADhQCx/hf/TACRAO//qP/8/57/t/07+435rfik9z/2NfWH9ArzFfD17NTrQu1m78rvse2W6nvo/Oca6KTndeY95ZnkqeQ55cflfuXp473hhuAo4fvifOTD5AzkIuOL4k/iXeIM48jkKOeg6NTnVOV64zfkRufR6nntQO+e8ILxmvFc8Q7ybvSX97/5LPra+ST6S/vS/LP+RwE1BGoGgAdKCKsJiQtSDe4OihD6EfoSyRPiFD8WZxcxGNQYVRlwGSwZEhmsGQAbnhz5HbAexR6OHmEeQx4aHuEdbh1UHH8awRgeGIwY8BiEGKAX9BZ8FqcVNRR/EggR+g/+DnsNJAt0CFAGFwU3BOsC8ABy/q/7Cfn69oP17/Or8STvMu3d613qXehu5gDll+OA4dfectwE28Pakdv+3D7epN493ojdt9yN2wfastge2EfYrtjc2KPYGtil19TX89it2jzcEN1E3XDdMt7N3w7idOR35tfn1+gs6nbsi+9e8uzzWPTb9H/2NPlA/DT/CgKcBJ0GKgjXCQwMlQ4IEUATMBWYFlgX5RcJGSIb1x2RIOYipSS/JUcmZiY5Jv0lNiZcJygpkiq7KuApDCn3KG0pwymlKUIp1Cg1KA0nVyWkI7IivSIuIxAj0yGoHzgdEBtJGagX+hU7FGUSShC+DfIKWQgvBkQEUAI+APn9P/vz93L0QfGA7uzrbek551LlSuO+4Mjd0tpB2GvWmNWz1SLWWtZx1tLWXddc11LWiNSR0r7QL88nztfN9s0MzhPOf86Tz+3Q7NFm0r7SfdPl1NPW2tiK2uTbd93a39/ikeU650vo6+mr7AjwNPP69Yf40frT/Dj/9wIECPAMVxBHEr4TMBVSFkQX9Rj7G6UfwCIDJRInWSmAKxYtPS5SL1kwDzFiMYExiTF4MYQxGTJKM3806DRVNGYzyDKNMkQyoDGqMIEvJC6fLCYr0ClnKLEm0SQZI4MhpR8oHTAaMxeXFIMS4RBnD7INegu2CJQFXwJb/5L8tvlr9rry/+5k67jn9uOw4H/eNt0R3Ibai9gM1sDS0M4wy/fImcjoyWbMIs++0GTQ0M68zQjO4c7ozrnN/8t+yprJlMmXym3MnM7v0IDTH9YW2NjYvti82IrZVtsT3pjhU+V46NLqH+0t8MDz0fb2+Pb6rv3dAKED5wWnCHoMthBhFIAXvhoUHo0goSEDIrQi8CNyJWAnJCp4LWgwZTLMMxU1CDYpNpQ12jRENKUz+TKkMu4yqjN+NEI11TXaNfw0XjOLMeYvXS7CLCErtimTKIgnPSZyJCIikB8YHeUayhh7FtMT3xDDDcoKdAgcB2QGUgUvAyMAtvzt+Gr0WO+M6nLmZuKq3dLYhNXK1PHVS9eL11nW89PK0GDNHMpCxzPFg8SbxTfIdctQzgLQNNBAzzrOHc6+ztrOdM37ytvIN8hSyezLnM/D063XCdvo3Rzg/OBN4C3fft8t4nXm9eoi7yPzxfZ0+UT7N/0BABYDpAX0B/gKmg62EfsTzBZmG+0gWCX9J+ApkSsYLL8q3yjKKDsr0y7/MW40fTYdOOU40ThMOLM3HTeANsA1sTRcMzgyzjErMusywzOaNAk1PjTLMVwuSCtWKTAoCCd7Ja4jziHFH3Id5RpcGBMWHBRXEnoQVA4DDL8JhgcbBY0CXwDh/m/94vq49mDxZuvo5FzeONn41mHXhtil2JnXINY/1CzRq8zdx47EwsMFxcrGnsdCx9fGwcdOyonNLNBs0evQnM4fy9fHDMb1xfjGuchhy+LOhdKL1efXDNo93G3egOA+4jvjdeMR5LXmoOsG8fX0jveF+n/+KQJABIkFowewCoMNBBDEE3cZOR+UIpQjeCR/Jp4osClsKiUsjy4nMJIwOjFMMw020zcMOKs30DeNOP44XjjcNnY1+zQnNQA1EjT4MnQyWzL/MU4x6DD3MJ4w+i46LFcp2iZwJKshqx7oG6MZxRcWFmcUoBLaEEoPyQ2wC5MIDgVJAmkAHP4++oP1rPEL7+jrtOYg4EDaQ9bc04/SRNJl0qvRjM8dzZ/Ll8phyJLE0cAovxTAl8KDxTfIjcq1zAXPZNHt0nTSlc8vy97GFsSDw+bEjscYy6DP/9Qv2vDdGOC44ajjd+VE5j/mmOb25+XpJuyv73X1f/xvAicG4QjyC4MO9A73DY4OcRLpF04cZh/MItgmDCqqKwctUC+FMeExbjDYLjEuSi4pL4QxUTXqOI86JzrqOLM3bzblNDsztzGEMNsv3C8eMN0v5S4ELiYuFS9lL7Qt8ilgJWAhbB4dHCca1RhsGFUYbBc+FYgSORA/Ds8Lvwj6BU8ERAOOAZz+Hvvb95b0n/A47InoDOad47bfVtor1eXR09D70ALR6c+PzdPKtcgQx6TE7sCevVu9LMGYxzzOdtNt1o/WBtQ30PXM8MphyVfHFMUJxLDFSMpw0BrWO9qS3T7hxuRB5tfkYeKe4TrjnOXA5+nqmfDa9+r9lwFiBNkHKguODEMMXQzgDdUPuxEXFTcbjCIOKNkqkCxALtUucC2EKxgr9itULNMrGixZLqMxcTRxNg84FDnFOAs3gzSjMZ8uFiwAK5wrAy0mLr4uFi86L8oujy3JK8opXyfjIykfURo0F6YWchemF6YWbBWeFE8TQBDJC6MH4ATrAssAiv7C/ED7+Phq9WLx7e0I6+bnO+SH4AHd/dgS1EjPa8wczBnNks2BzLnJnMVOwY6+Wb7vvwvC6cT+yUzRAtid2ifY19LMzbDKbMk9yZjJT8pRy5LMX86M0d3Wvt0B5JTniui86DPp5ujT5rLkB+Yh7DH0VPrM/eUADwXYCJgKZAuTDQ8RQBP+EoYSeBSEGLgc1yAcJv0rqi+oL9Qtoiz8K4MqzCgFKZ4rbS6pL/IvqDC/MVkyizL4MkAzPjLBL/AsxyotKeEnaSdeKEsq0ivoK5Yqmih8JhkkGiHFHf4aLxmeF4EVahOlEiQTIhNgEcUO6Qy3C6wJNwaNAvf/Uf6w/LP6kPhN9rbz1vDg7anq1+a44k/fFd0Y2+rXhtOYz4rNscwKy7bH9MNxwYvA2MDDwivHWs2c0sXUf9Qp1KbUNNTF0OnK18WNxC3HNcs6zjvQFdPY1zbd+eCa4oPjxOSf5QrlAuTs5JfoHe2V8NLz/fjZ/10FmAdSCFMKbg03DysPog8jEjsVVhfAGYsewSQzKdcqrSsILYstFixTKoQqRCyfLWguDjCBMr8z1jI9MWYw1y/PLiAu0S7uL44viC2hKwAr5io7KiYpoigFKXUplCi9JeshGx86HgkezBytGjAZehjUFlYThg8tDdIL2Qk2B20F8wRWBEQCMf9j/Ez6cvhc9tjz0vBw7SLqEeev43zfJdst2CzXndYt1FfPIcrOxhzFysITv5O8ob5OxRnNrdK21ZjXpNje1x/VG9KV0C/Qzs5/y3XIX8lbzy7XatxA3u7fBOSX6IjpCOYF4rLhKOVr6dvsuvBB9g783v/zAc4EZwllDUIO1gwTDPwMLQ5OD2cSjhhqHxIk3ibuKdYs2SybKY0mfSY8KGkpXCq9LMsvFDGBMKwwmTLfMyMyYS4UK+IoCyf1JfkmyCkTLEUsMitxKgkqsSjMJWUiLiCgHzwfDR0tGRkWwxX/FhMXbxUIFMETmxKlDisJLAWRA7kCcAFNAPL/pf91/oD8Vfr791X10PK38F3u4OqB5mziJ98w3EvZVdf01rXWm9NPzHLD0b3lvbbBrMW4yJjMMtJs13LZGdjA1UXUZNMn0rzQD9Aj0MnPaM6fzVPQi9ds4D7m7eby5B3kJ+Wg5TTkTeNO5hTt6/MK+Jv6WP5yA4kHcAnhCncNwQ9HD7QMkAvNDdERtBUiGhsgyyVeKCMo/SfhKOgoeScnJ68ptCwoLQ4szSzIL/cx0DEfMS4xKzBGLCIn5SNSI/YjLSWdJ+cqCC2lLHkqBij3JRkkHiL/Hxoe5RzoG5gZbhWqEUAR4xP5FfwUFxJqD6MMQAjgAv/+xP3+/U/+ff5Z/iH9wvpE+C32g/OK71vriui05vjjtd+E2/PYdNdC1arRZM3UyFXDG73ZuBm6XMGsyhbRStMU1FLWjNnl2sfYudSv0S3RNtLH0hbSSNEK0sbUr9gr3UfiVOck6gbpbOVJ4xrlaukw7QDwWPSW+0cDywfGCCAJswoVDKMLzgoqDEEPCRGyED4RLhXcGiwf8iEIJSooDimgJ+QmhShXKicqlCmLK48vWzKHMowxWDDsLRkq2iZ0JXMkPyIfIIsgfiOMJiEotSjxKGconCbsIwYhTR7sG84ZnBeLFagUORVbFQwTVA9QDb8NyA0sC3IHMwUkBD4Cjv88/gL/GQDo/8L+Xv13+9n4J/ab8zrwiesP58fkfeT/46bhuN022WPU386vyMXCmb49vc2+ucJ1yFzPyNVh2TnZVNfo1vLYNtva2nnXQdOm0HLQutFE08nUFNfH2iPfaeKr48DjCeTP5Lzlluf76/Hy5/lf/vEALQTBCHkMfg3cDJsMxQwyDGULXAxzD7sSCxWuF68bcR/8IFEhCiM6JnIo7ijtKeosATDpMJQwgTGjM3M0uTK2L4IsnSjoIwAgah62HrAfPSHGI58mZiiUKKYnJiZ4JBEj0yG2H0UcxhitFn8V1xMDEoYR9BHIECINeQkZCNAHEQb0As8AewBvAIj/d/71/Vn94Pv2+Sz4Cfbh8hfvbOuz53fjkN+y3fjd1t1e2krTI8t+xMO/CryIuQW6jr6oxZvMC9JL1mzZjtqh2XzYCdnQ2l3bCtnD1BjRHNAN0nPVdNh32nfcX99x4gTk5OMH5H/mS+vn8HX2X/zPAoUIBAxxDVIOcg/zD/AOGg3PC1wLZQucDEUQxhV9GgsdDR8pIg8loCXwJDcm4inSLF0t+y1IMbE1wjcUNwc2YzVGM5IurihHI44e2xrDGQccvh9HIkAjICSgJeYm7CZ9JQ0jdyB5HrActhlOFZARfxAkEeIQIg9/DUkMlQmjBOf/7v0E/tn9LP2D/fL+rP/O/oj91vzz++T5Jfep9DHy3O7M6hPnOOTm4enfbt7n3H/ZqtJJyZvAp7vLuie8Wr7swaLHVM660yDX2Nm63LfeyN7x3dLd8N0z3CHYCtR20prT79U72BXaStvr28PcrN6K4dXk9Ojx7pX26v1aA2cHWQv1DuIQAxHNEOsQGhB6DaYK5AkOC08MsQ1lEcQXbR1YHwgfTCDLI64mqicfKRgt7DHLNMA1zDYuODE4JzYnM8YvVivrJQohlR3VGo4YQxj1GvoeryF+IsQiVCOqIy4j5yERIAseeBxNGz0ZdBVsEV8PBg/MDXwKuQYYBOQB9v4g/PT6RvvD+y78Kf0//hv+sfxm+7H6Sflg9h7z1PDn7srrYOcW47/foNy42PnTxs7qyAvCKbvPthO3WbvHwDfFO8mZzkLV+toM3infD+Am4XXhaOCm3gjdj9u52YzXBNZg1rzYadtC3Drb89rk3Wzj6OiP7YvzePxMBnANJxHbE/kWvhhQF0UU2BI3E0wSuw6YC0QMfA9kEZcRvxNpGeAebSALIHIi5SceLEIttS5kMwE5qTuGO3E7iTsZOYQzlC0wKQElpx+bGvUXSxf1FvUWcBgDG8sc5Rw6HNob6hsjHCscehsHGuUY3xjEGJYWWRIJDrsKeAdvA2j/Lvw0+SD2KfRX9Ir19fXB9XL2IfhF+SP5mfg3+E/3jPXv8ybzI/KD75ProefC45jebtd+z5zILsMavuS4XbVhtoC8bcQQyhPN8dDU1ybfz+LT4ifj8OWW6L7nLeR24eDgG+Aj3ULZKdd2123YeNju14fYwdus4fDoIvBN93L/RwiPD54TkBV/F1UZTxmHF3IWFBcGFycUZhBuD2MR4BI9EvURnhT9GDQcHh4FIbYlhSpdLkky7DafOpc7fzonOZo3azSPL74q7yZgI5AfaByEGu0Y1hZDFYEV2xZ/FxEXzhZYFxYYphh3GaUamBv7G9wbwxr7FwQUShAbDWsJDwVoAQD/V/xj+Jj0r/L48dTwxO9/8JTytfNS82Dz2PTp9dr00fL28QbyjfAi7OjlyN9G2mrUWs21xTu/IrsSuUS4JLm6vILCQ8i7zBrR49Zg3X3ilOXn533q2ewQ7g/uRu2+6yLpi+Xc4TvfG96y3cPcWNtW263e+uS/60zxqfbv/QwHOQ8UFGMW4xiaHLsflCASIA4gMyChHnAbHBnHGGgYJhafE48TrxWLF78YaBs9IDglAyn/LDEyrTZAOOc3FDiPOPY24TKULkArbCcPItIcfxkfF7QToQ8pDfEMKQ1hDHMLAAxJDjcR2hMLFgwY+hlnG5EbMRrnF1EVFhKWDVAIZgPT/pf54fN/707t2OvA6SHo1+hS6z/t+e0c73/xqfMd9HfzGvP58t/xKe8l6z3mreC22l7UdM1YxkvAVrxbuvi5grsrv9HDJciYzLTSbNqX4abmcep87nvy0vT+9Cr0YPMw8rDvIOzd6L3mDuWP4lTfUN133o/iWuc/6y/vSvXY/YgGFA3aEfsWMx3KIgsmdieHKDwpYCj9Ja8jHiL8H10ckhhFFiQVChR+E8gUThdQGTUbQB9vJZIq2CyTLkYyPDYFN9009zIJMkYveSl6I9wf7RyfF40QNwsKCe4H6QXMA4cDaQUzCOwKpw3BEBIU5RatGJkZLRobGkMYahQeEKsM+AgdA8/78fVs8s/ubOmB5CbjouSO5d/k9+Rq53/qGOzQ7HDuxPDp8frwqO5s68/muuAt2hbUT85hyMPCub4KvSm9vb02vrG/6cP2yrzSHtlV3inkF+tu8ZT1C/ht+vL8Dv6A/Az5nfUY83fwW+wh5/Lif+Ez4injyeOn5ZLqSvLE+loCIgkZEHsXgR6EJKQpFy5kMfQyBDMuMiswQywUJ3Yi/x4wG1MWihLXEXkSNRHeDvEP1xUjHL0esB+KIxEqtC6BL2IvzjAZMr4wXy0DKosmfCEaGzMVURB2C3kGlgJnABj/H/5T/okANQQNCGQLWQ4+ESgUpBbHFysXxRXFFJgTYBC6CpQEfP+K+lP0rO246NLlZ+OZ4D3eAd133Jzcat794Zvl5Oex6TTsS+6+7aXqZucj5Sbix9xJ1k7Rac69y+jH6MPLwUjCV8S5xm7JYs3q0ifZLt895fzr1/Iu+Hj7Bf7aAO0CpAJeANP9W/uk92fyle3K6ijpK+ef5czmFOtJ8Lv0PvlT/80Ghw5PFnMeFibDK6svazMwN/g4iTdwNJwxdi5bKegiUh3nGOcTMQ5TCswJvgoWC9QL8Q6yE9UXWhsJIJklfCn6Kl8s7y6EMOsuWSs+KG4lBiEFG2MV2xAvDNYGRgLh/zH/9f7P/jD/fQDWAvYFBglbC38NXBBNE2cUGhP5ECYPjAzsB1YC0P1e+kr27vBk64PmEOJB3iHcv9uT27zahNpH3OzeJ+C1333fZ+DV4A3f3NuJ2bvYv9f81C7Res6/zcDNB830yzTMjM4f0v3VZNrM34flcuqc7vryf/ff+qz8Of5sANUBgAAH/Q36lPi89v7yEu/z7dLv7/GG8gzz1vXz+roAdgbeDBkUDRsHIYAmnitwL4wxKzMXNcM1YzMjL8QrRinQJJ4dBxfnE4YSkg9KC9AIVgkjCwcN0w/nEx0Y4hsFIHQkeieCKFkpIysoLJQqiScOJXYiqR0NF1QRkA03Ci8GXQKI/xj90frD+Xf64/tR/bD/yQNmCMULxA1PD3EQfRBzD+QN3gsJCWEF3gAF+wP0eO226Lfkld+p2UvVVtP10cLPG87nzmvRKNNN04XTMdVk12bYNthr2NnZdNvC29Pa6tmV2RXZAtiI1wrZBdyK3uvfmOHO5JToMuv07M3vMvT19x35wviJ+cb7/fyu+8L54PmI+/T7bvpx+eT6iP1+/44BsQV0C2YQ5xPlF18doSImJv8omSzSL4swEC+TLbIs4ipuJ8cjGSF/Hs8ahBa6Ep0PKQ1QDKgN8Q+pEXMT2xY7Gzcedh9PIeAk+CdyKHUnNCcDJ34ksh9EG2MYXRXOEMoLtAcYBC4As/zE+hD61vmF+sH8uf85ApoE4geHCw0OZw+8EOYRVxGdDvwKJgdlAqT8PvfY8ifuGOh94WDbV9Ubz0XKbsimyM/IzMgqygvNuc920Y/T6dZq2tXcvd5T4RrkU+Vg5K3iAeKI4uXiBOJx4JPfsd+m3+Xe2t4Q4avkQudQ6Pnpw+068hn1r/ZB+X39aAEyA5gDWgR+BasF9QSBBYsIRQwaDjkOEQ+NEQ0UnhXXF/EbLiBHIg4j3SRIJ4snRCWJI1MkqyXzJH4i/R97HUAaLRe7FZMVYRU/FSAWehcCGEoY+RmQHLUdLR2UHQ4gDSIvIdMetx3EHc4cPxpeF4AUqxALDEYI/gUDBLQBIwAOAGoAJwDj/5oA5AEPA84EDAjHC+UN7g0mDRMM4Al6BlUDLQGp/lb6xvRc7/7pmONi3AHWkNGqznjMmMr5yOvHPchPyhHNGs++0KPTBtj92zfe59+64hvmIuh26JXoWunF6abobOat5HTkYOUg5q3lSORA44bj0ORw5oronutC73nyK/Uo+H37Bf5O/8oA/wNICKQLeQ0SDzoR5hJGE9gTuRYrG9UdKh3CG9Mcqh/KIBwfZB0CHqgfsh/zHTUcGRvAGQMY9xYeF6sXwRdBF20W6RXWFoAZPxxOHXod2h4ZIaQh+R/EHoYfEiBpHkMc0xtLG00XqhC8CyQKAgkUBgMD+QH/Af0ARv/F/uD/dwEZA0UF4gckCsoLAQ1oDZgMdQtMC6sLkwoBBwsCDv0L+HTyeuy65kjh3tuK1pLREs1WyRfHucaax8jIM8puzGXPK9JE1GbWnNkD3sTi7Ob16b/reuyS7F3s6es765TqH+qF6Szo/eWl4//hfeEf4pPjVuUP5+7oYOs77s3wD/Md9r365P/AAycGtghADIIPTBHDEtwVGRqwHFccBxsYGygcWRyUG3wbehz1HN0bKhoBGfcXYBYBFQMVDhbbFgMX6xaPFu4VGxYUGOYazRzsHcofKiL8IushRiE1Ir0iRiGLH4kfpx8GHUsYtxQcEygRuA2DCrwIJwfFBMYCWAKtAtICgQNtBWwHRgjvCM8KHQ0VDv8Now4REEwQOg7jCnAHrAM+/6/6bPbP8R/sp+Uq3+zYFdNWzlvL2cnvyEjIWch2yTLL38x9ztzQt9S42ZTeReLk5DTngOln65HsJu1p7T/tVOyN6kDo7uXz41ziEOER4HvfW9+z35XgB+Ln40fmq+lD7g/ztfZm+dP8vwHFBnkKmA2bEfEVkhglGXcZkxpgGwcbuhqsG7Uc9RvnGZoYYRiIF5IVYhRKFckWwxaCFdgUOxWhFcUVnxaVGOEa8RzrHpQgQyFiIU8iLyRuJU8lGSWOJR0leiJCH6wd5xyHGrIWvxPvEfMOPgp7BmcFPgUXBCYDRARYBggHjgYJB+sIxgopDAEOKRAmEZIQpw/dDiwNLgoWB5MEdgGY/Kj22vD86kLkLt1J1ynT2c9SzNXIfcbxxaPGfcdWyDTKyc0d0pnVKdgs20TfcOO/5qTpzOyc79XwMfCY7vjso+uI6lzpt+d35QLjzeDu3m3duNxS3UbfG+I85UPoLOta7jDymPZP+2cA9wVVC54P7xIgFikZFxvxGxkd+B7XH2seLhxXG30bcRrrF+MVSxXFFFAT+hHKEeoRaBExEYMSpxQUFggX8hjBG+wdOB8sIU8k8iauJ2knnCcAKGkn4yVmJA0jNSHpHrEcKBpwFgQSfQ5SDGQKIQiIBkkGbAb6BaUFgAYYCEkJLgqpC4INqw4iD+EP4BDREDkPOA2eC4wJ+AVvARz94/i685Dthec14tbcpdZt0O7L4ck1yXLId8dNx3XIN8r3y1fOMdLs1hPbY94A4mDmOOo97PDsyO0f7wjwvO9b7lDsxunk5gzko+HW35Lel92w3BfcWdy43enflOLG5aHpEO778lD4n/1NAo4GXQvmENUVChkmG0wdIh97H5Aeyh2LHcMc9hr2GCgX1RTpEbgP+g6JDnQN1gz/DfMPvBCQEHkRIxQyF9gZ7BzbIHUkviZPKN4p4yrXKoMqnypVKpUo/SXGI30hzB0QGSoVvRJrEE8NTgpcCPsGgQVyBIUETAUDBuYGiQiMCh4MSw2UDr8PLxArEHMQsxCfD+IMnwmxBnQDCP/r+VD1V/HO7OPmT+By2rDVTtHszJDJa8j5yFfJx8jUyDDLL8/B0l7ViNgs3RzireXm5/zpdey97knwOfG98YHxBvBX7TLqcudN5Unj+eCu3gndL9zQ2+3b8twM3/zhqeUg6vTuYPNc97X7ugC2BTQKxg69E/4XYxqDG+QcuR7EH2EfUR5NHRscZxpsGFgW9RN0EagP7g6yDpAOBA9UELoRpRLuE6MWPRpSHbYfaiKfJUQo3SksK5gsPi18LD0rhyqxKV4noiPWH5scaRn/FeESOBCIDd8KMgnoCBUJ1wiXCBcJCQqvCjMLUAzlDQoPiQ8UEOUQLhE9EF8OLQyeCVwGgwJt/gL6x/Sx7nfo6+Iw3qvZ5NQ/0JfMJMpSyOfGsMZSyA7LoM0Z0JTTHNg73A3fheHO5HfoOOvU7ALu9+4k71Hu7exL60/p+OZ65MXhqd6M20HZGNiW11rXxtd72YPcR+AZ5NbnBuw88S330vywAXQGrwuiED0U1RanGdYcNB8hIDog5R+EHsobohj0FYYT/BDuDvINZQ1VDBQLsApjC60Mmg6mEYMVNBlsHKgf/yLrJX4odCvTLosx5DJOMzQzGjKcL2UsTCkoJmgiLh4CGtYVbRFQDVgKYAiDBr0EGQT/BE0G2wYVB0YIqQobDaoOpQ/UEDsSFhPgEucRxhCaD94N9wq+BpUBB/xO9jfwsOlf40bej9r/1jvSm8wKyOPFksW7xSfGy8cZyyvP0dLb1erYZdwW4KHj5+bo6ZHspe7J773vsu5d7VTsVuuD6WjmneIm34bckNr02LfXI9eS1y3Zqdtz3mXhF+UW6vHvg/VO+t3+vgOdCMYMKBBnEw8X6hoGHlwfzh5/HZoc0BvfGbgW/RPDEgESUxAvDgsN/gz5DPUMGA6kEHsTBBbcGDccKB9VIcsjHycvKu8rKS0IL+owSTECMCYuDSw1KbklRCK7HoQaHxbQEqcQZw6xC7YJLQkTCVsIrgdXCEsKUgymDYEOZg96EHoR1xE0EQ4Qbw+EDxMP0wzfCEAEbv8I+trzhu3053bjgN9G26XWYNI1z+HMkcpJyBjHxcfwycrM688c0x/WH9mt3Kng+uMY5gLovuqK7cnuTO5p7djs3eu+6ffmc+Ru4o/ght5D3PTZHdhI12TX+9ca2WfbLd/e48zoxu3J8rb3n/y+AdAGLgvrDgwT6hc6HKAehB86INEgDSCLHY0aOBhIFgcUlhF1D6YNDQwTCw8LnQtkDPgN7hB8FEoXbxlGHDMg/CPNJnIp1SxgMOsyXjQ8NVM1BTSHMZsuQysMJ1UiIx6XGtMWmRLfDlMMVQoUCNkFewQzBKQEmQUGB7cIdAo5DNsN3Q4XDy8P4Q//EJ0RChE2D0UMLAjlArr8aPa68ODrQudU4lPd1djI1KfQlsxjyWfHR8b7xQHHL8mAy6rNudBA1QvawN3Q4GLkJujO6kzsy+2W747wCPCl7gjt+Opd6N3l3+PZ4VDfuty72jXZwteZ1mXWntdv2sLe9eMi6QXuFvNu+Gf9xwF3BiMM/BG+FmgazR3dII8igiKJIW0gHB9RHTMb2hgLFuYSIxA/Dv4MMAw0DD4Nug4uEBgSIxXdGEMcTh/eIhQn5iq3LTMwAzOPNeA2BzehNnI1szKELvMplSUZIXQcaBhNFVcSwA4ECzEIeAZNBZAEzAQtBhEIwwkYCy0MDw3UDcIOAhBYEUgSXxJFEdAOHQuQBpABQPyf9tfwS+s05mHhk9wE2DnUH9H0zX3Kssd/xlLGCsYaxj7Iwsyh0TfVUthc3KfgfeMN5T/nmepf7TLu1+2A7dPs1erZ5zPlc+MB4jbgFN7v2//ZTNjB1oPVYtVe12bbNODH5GXpoO4f9DT5Bf4gA1MI/gwyEWIVKxmjG9wc3h3wHhsfyh29G8sZtxcGFS4SChC7Du0Njw2YDaANqw22DoERORWCGHUbMR+oI2InvynlK9gu8jEzNMw1Nje5N0s2UjMUMLQsVSg2IxYfxBzQGogXWRMSEFMOAw1CC8UJzwlrC0kNOA5ODo4OjQ/DEGARahGaERIS0xH0D+UM3wlBBxwEkv8F+oH0YO8z6vDke+CV3aPbU9lG1knTxNABzqPKCsjxxxbKoszKznjRCNUE2EXZ/9ku3LffveJl5ITln+bu5uHlSeQm423ivuFT4VHh9eB932bdy9vd2gjaYdno2SXcZN+b4pbl8ug/7U3yMvcT+x/+agGBBZQJqQwrD0cSARYBGWoayhraGm8aRxn3FygXqxYAFkAVnxTAE4IS3xHrEjYVchdhGcobpR7VIAkiZSO/JXQo3SqBLcMwazMINAcz9jEWMUovXCyyKTYo7yaGJEEhjB73HK4byRl6F7sVERXbFP8TRRK3EEIQYxDMD04ODQ2tDEcM1wrfCJ0HIgcpBskDbQDr/Gf5qPX88RzvMe2B61PpvuYq5DzhEN3L1w3TItBLzgPMfcl1yG3JrMrcyhbLH82W0I7TWdUb15PZC9zS3WXfYeFq49/k3eW85jvn2+bO5arkjeMa4i/gJt583JDbkNtP3HXdKd8K4hPmEur97GjvxvJq9zf8XwBmBP8I0Q3sEfEUNxcqGfUajBySHY8djxw0G+AZSRhSFsgUhhRFFRMWmRYsF9AXQxjqGJ0aQx3PH+oheSTcJ90qaixbLS0vmDHoMocynTEFMQ0w0S29KvsnCyaNJAgjQSE0HwUd1xqIGOsVURNTEQAQ0w6ZDaoMDgwpC8IJiggVCOAHIwcFBhgFJwRkAsT/NP1P+575UfdM9CfxW+6u64ro1+Q44SjeEtsE1y7S7M0QyxrJp8d4xy7J58s5zv/P8tEF1JHV6tZA2bjcCeBI4ubjeuWo5vLmouZB5svl+uTI4zLiDeCg3b/b19ps2i3a1Nom3Zng2+N05iTptuwH8Vj1HfmA/DgAvwSGCW4NLBC1EssVtBgmGgYaZxnzGFEYIBfHFeoUvRQXFcAVVRZ1Fl8W7RaPGJwaIRw1HdkeiSGIJN0mmiiwKpEtfjBYMsgyazL4MYExlTDYLoUsTCqwKG4nwSVTI8Eg2h6THQIcgBl1Ft0TPhIzEfgPXA4IDbEMDQ30DLoLBwreCC8I/QbbBIoC3wCs/xX+n/uV+IP1t/If8HntnOqW54fkYeH43SvaCtYE0vzOqc2nzcfNp81ozt3Q0NNH1WHVRtYI2SDcu91F3oTf7uE45Cvl3+Qn5I3j/OIC4mbgqt6p3XjdHd3y277az9pq3MXeTeEn5IznXOtW7x3zOfav+F/7Hf92AyQHuwkODMQOThGtErQSJRLYEQ4SWxIbEkcRwRBNEWsS8RLOEl4TjRV8GK8a+xttHZQf5SHHI28laSfMKT4sYy7+L+owNTEXMakwsC/tLZsrUSl2J/AlbSTbIoEhlCDCH1weFByIGb0XAReNFpQVUxSXE2oT1xI2ESQPpg3RDM8LIwonCEoGVQTUAcP+kPuZ+P31xvPv8TXwKO6e69voGuYS417fY9tU2NHW9NV41JnS29G+0tfT2dOQ07XUU9e72dvaS9v129fcnN1U3g/fit+53/jfNeC/33beWN0Z3fbc6tt92jPahtt83UPfJuG64+bmGurs7EbvXfGl8232d/k4/Hz+fwByAkYE6QVSB2wIVAl3CusL+gwRDesM1A3FD0kRsxE7EjgUKBd9Gc4aIBwjHk0g1yHSItojQiXzJr8obyrQK+gs5C2pLrQutC0JLHAqTyl3KIsncSZwJd8koyQOJIYiaSDUHkMe3B2XHMQaiRkXGUgYWxYfFMcSNhJNEZMPmA3ZCw8K4Ad1BQ8DpABC/lr8D/uh+Tv3L/Si8dLvjO3k6crlCOOu4c3fK9wz2CrWE9bR1VfUFNOx07TVhden2LfZ1NqV2yfcD93a3aPd39wR3Vbe8t723c3c/ty23RrdKtu02d7ZKtvA3GneLeAl4rbkC+hH60HtNO6p737yxPUP+Cv5DPqU+6r9av8ZAD0ARwG6AyQG+wbvBgkImwrJDIENGQ4TENgSDRXFFucYQhv6HFAeOyCCIuQjPyT0JLcmcygMKfooViknKokqCCoSKUoo8ifSJ2cncSZxJSclciVgJZIkyCOPI0UjCCIiIIAeSR3mGz4a4BjqF7YW3RS6EqcQYA6fC+EI6AazBX4E0QIKAaX/Sf4X/Aj5Qvak9H/zhPGw7insIOpU5zrjS9/p3Dnbp9i+1VjUY9TW0znS0NET1BHXK9jk18LYRttC3T/dW9xK3A/dm9113encQtyk2zLb1tpV2uTZKdox2xrcYtzg3LreyeH05MHnveo87ojxvvPt9PL1d/dd+SP7l/wF/pf/xgDtAHgA2ADfArIF+AeVCWMLdA3jDn8PdRDAEuEV2hiVG3EeDiGrIm0jMCQ6JQcmeCYfJzQoFilHKR4pGyk6KVMpfSmnKWIpoSgNKAUo5ickJ2AmgSYdJ+kmnCUsJCgjBSJUIJweZh1XHNYa9Bj3Fq8U8BFFD1UN0wvwCa0H0gWOBPwCeQC0/dD70/qz+dL3uvX88xXyK++J63LoYOZp5NfhU9+D3XvbCdgc1PvRFdJv0vXRRdLv1IbYbNp22rTa/tsI3fLcrdwx3ejd390w3WDccNtv2gHaYdq22mbaQ9pU2y3ds97/3yvidOXi6M7rgO4k8ULzpPTf9XT3DfkQ+nL6r/o7+zP8Yv2G/uT/MAJwBV8I3gmuCoAMXg+TEYES0RP1Ft8anh0fH7ogwSIoJHskeyTIJDQlmSU9JgsnkCfmJ5koayldKVMofSePJ8gnYifnJhgnfScnJzAmVSWZJIMjSCJyIbQgSB9WHbwbbBppGJEVHRPEEaEQpg49DGQKAAktB9sE5QKXATgAT/6K/Kf7GfuW+QD3sfRw8wjy4u6l6qvnkOYO5Uvh1txn2rHZ1dcN1CDRZtGm03DVxdYz2XfchN6U3hTeQd6l3nzeGd4d3lveP96N3Uvck9r12ETYmNg92f/Zltsz3qDg3uHP4hjluugT7DjuEPCx8pn1S/dA94b2g/Z+95z4Kfmd+QP7d/3q/50BNwO3BdkIZgv7DHMOehC3EscUCBfVGakczh5dIMkh6CJHIysjYSMiJPckryWTJpUnMShJKGUoyigeKUIpuCmpKlArACseKl0pmChVJ+Ml+ySLJNwjvSKGIRsg/h11G2YZ6RcnFvITQxKBEYUQTQ6jC+oJ5gg2B6YEkAK7ASsBrf+D/dD7zfqM+WD3vPRg8jPwhu1K6knn6ORi4vPeO9t62JbWYtT80WjRytNm17XZqdof3JPeQODA3wLe6NwV3cDd7d0p3bfbXtqb2dDYKtdh1WXVwteI2tXbX9wh3kPh/uNn5bLmQemt7JDvU/Fx8k/zwfOs84Pz3vPM9N/1+vau+HD7q/40AdwCtARpBxgKmwtzDDgOaBHbFIYXrRn3G1YeVSDOIcIiLSNrIz4kvyX7JjsnIye7J+UotSn3KWUqYCtuLActFS2dLKsruCpMKhoqTin4Jxkn8SZbJoMkQyK3IGwfOh1pGlQYKBeaFS8TPRHNEMEQYw/rDAILIQrfCDcGJwNIAZAAkv+j/Y37/flC+G31BfJl767tkeuH6MjlDuS+4QzdMteM00PTA9TJ0+LTjdYL2zPegN6B3Srdf91/3eXcI9yJ2wHbN9qs2FDWJdSC01bUN9WR1YfW+NjN25bd0d4F4RXkTuYq5zDoceq77GvtG+3i7U3wdPKI8lPxWvHD8x33hvlD+y3+qgKYBvAHeQesB4oJ7gvfDeQPmBJvFb4XzxkEHNMd0R7qHzIi9iRmJiom2iWqJt4nOSjiJwcoMym1KnsrMiuVKrsq4yv6LOAs5Cs5KwUrIyoqKFgmqSVEJd8jziEoIJoe5htYGNgVJBW6FE0TwxFtEX8RAxDIDLIJBQjmBhEF0wJLAW0A8v47/Dn5A/dO9Srzy/Bo7xfvAO6I6oLl/eBw3aHZhdU/02PUkdcy2q/bQN0Z37bfXt6e3GLce90J3uDcpNqm2E7X5tXa0/TR7dFZ1HnXIdmO2fTaBt6R4LjgGODI4c3l0ui76HrnIOiX6hzsaOt16qLrlO4X8Vvy6fM293/78v4oAUkD2AXjB74ITAmRCggMxAyHDesPpBN6FosXuBjGG5AfsSEHIqcimCR0Jr0mBybqJdkm6CdfKJcoUCmuKgssqiyDLC4sByytK8Iquik1Kc8onifZJb0keiSPIxgheB5ZHQMdZBtdGBYWthWtFS0U0RFyEEAQoQ+nDSELDAk+By8FGAOCATYAjP6K/Nz6jfmz99P08fFN8FzvLe0A6VjksOBG3aPYzNMJ0unU/9mY3bTeB9/L3yTgut7H2//Y4NdG2HPYrtY601DQgM/Jz3XPPM+f0czWYNuE3Ljbb9wE34rglt+K3vPfn+Jp4+vhDuHG4m/lk+bA5q7oAe0r8TPzsvRg+Mz9ywEEA6IDmgWJBzAHgwW7BcIILAwmDvUPXBOQF5YacRzRHiUi7CQrJr8moCdBKJ0nGSZGJQImtSdRKZYqDywDLs4vfDASMKcv3y/fL4UuRCytKiMqYimkJ94lFyWuJIIj2CGvIMsfEx6pG/EZQxlJGDYWLBRSE+ESWBHZDscMbwuvCfoGYgTtAgYCeABR/qn8q/sA+sf2RfN88RjxNO/g6ePitt082z/ZUtZl1DjWYdtl4GTifOGR3+TdMtyS2fLVzdLZ0Z3SYNKGzyTMlMv8zWHQN9Fq0r/VjtkK20Xantnw2erZIdk92TDbRN2I3e3cJd624RrlQeaT5gfp5u0t8q7zNvQS9z38OgDsAFcAUwGAA4oERwQABdsHkQsGD9kSVBf6GrAcyx0WIN8iCSRzI0sjyiSEJrEmryVbJZUmhSgCKgkrjSwIL5IxuTItMgIxCTC+LpAsZCqHKZcpDinHJygndSfoJnok3yEUIUwhRSDrHVMcHBxXG60YjRXfExgTZhHIDv8MhQyvC0QJaQbSBCoEugIoAO392vyv+xf52fWd8wby7u676ZbkO+Fr3lfagNZ11rrabd8Y4ZHgeuC74PXepdpB1u7TJNM90r/QK8/RzdHMksxCzYrOTtDu0ibWcdiu2L7XU9eo16HX89bO1v7XvtkH2xnc7d2k4HPj4eVM6BHr0u0C8NzxP/Rz94b6b/x7/df+tgD9ATYC2gKMBcgJrw27EBEUExhxGzMdQx7UH4AhRyKCIlMjnSQjJaUkciSEJU0n5yh2KqEsPi9CMdgxGjHTL7QupS0ILNIpDSiXJ7onDyejJfokpiVOJtclFyUkJR8lYiNDIKgdBxzZGYUWxhMRExsTuxEzD7cN4g25DcMLTAlQCEwI6gZ6A+v/wP3T+3X4k/Sk8qbygPFC7fDnbeRX4k3fkNs92pPcv9+84B3gAOAF4Pzdxtnf1erT9NKf0RfQ/M4SzjrNW80Vz2LR1tKQ05/U6tUj1tDUE9Mn0hzSc9Ii02zUSNaF2DPbX96a4VHkgeai6MvqWuzb7P/sL+7z8FH0Hvd6+U/8p/93AkEE6wVkCF4LJg7qEB0UGhe9GD4ZKRofHAkeCx8QIFgiUiUzJ3gniye7KI4qpiuxK80rCy0CLzIwsi88LjotAC2VLForHCrpKXcqnSoRKpIpWymqKDwnCCbAJX8lHiQhIuQgKyA9HrUaoBfEFgwXIxbbEzsSOhI6EosQ5Q0TDB4LYQlMBksDbgHA/wf9//kx+CT3o/Qg8NLrXeki5+biDt513OXee+HK4FLend2o3h3erNrs1jTVTNTw0ZDOXczby63LXssRzDrOl9D90b7SX9NU0wLSHtDuzq/OpM59zsXOENBw0pbVAtkk3NbemuHF5Jzn+eju6ADpaeq07KzuQ/Cq8nf2f/os/Yf+LQA3AwIHVgoPDeIP+BKwFaYXFBknGswabhvzHHwf5SE0I+IjCyXQJmcobClPKnoryyznLY0uiS7zLU4t5yxELOEqQilzKIcoiSgMKL0nESh1KGgoWCirKLAonCfwJb8k7SNPIpwf9xxJGwkaVxh0FmcVeRXRFY4VrBSwE7USOhHIDoIL6wdeBCMBuf5d/Tb89fmh9tfzefIU8eHtzOmA55DnhueI5ffiAuIp4hLhG9782gXZpdcA1jPUgtKz0BbPys4E0CnR7dBL0AHRo9L90j7RBM/uzaTNE81fzI3M780F0I7Sn9XU2GTbO90c31jhH+Ov467jgeSC5qjoJuql61TuQvJG9nH58vts/hEBsAMwBpIIvQq1DMgOJBFeE/QUFhaAF4QZwRvGHYofKiGkIgIkUyVoJgIncSdZKK8pjyp+KiAqPSqbKooqFyrRKbQpWSn1KCgpvCmwKdMoOyiRKPEoPijaJvYlrCX3JGojwyGnILwfcR7rHKUbqxrCGQAZrRiNGOkXSxYGFM0R1g+XDXsK2gbfAzQCFwE//4T88/k3+Kn2YvSO8QrvKO2L69zpAujd5YPjnOGf4NTfBt5F2/7Y+Ncq11LVz9II0YbQl9CO0GHQCtBbz4rOHs4JzozNbMx/y5TLXcwJzZLNpc6A0KDSldRz1k/Y59kV2zHckd0J32Pg/OFZ5DznwOmV63rtOPCE83728fhr+z7+DwGPA/UFewj2CkoNxQ+QEiwVGRerGIoaoBxCHl8frSCHImgkySXOJsgnrih6KWoqfCsiLCEsFiyLLActvSzoK3Mriit8KxEr5ypNK6ArWivYKqEqeirmKQ0pXiiwJ4Um/yS4I8UimiEUINceTR7rHe0ccRs1GlcZ+RdeFQoSSQ+kDSIMiAkIBiEDpwGXAH3+cPvR+DH3nPUx82zwJ+457PDpZOdG5ZPjlOE13zjd2Ns52sbXStXg01XTi9If0cbPGc/MzkrObM1ZzEDLcso5yl7KO8qwyX7JS8q6ywHNEM6Bz3bRT9OY1JfVt9b21zrZytre3CXfMeE147blk+gO6/PsDu8S8o/1kfjg+h39rf9OAsEEQAf6CbEMJg+HEQcUXBY5GOEZzRvgHZsf/yCOIl0k1iWsJlknXih6KS4qkioSK6wr/ysDLBssfCwBLYIt4i3jLXUtHy1hLcEtOC3HK8QqAyuKK+4qYClIKCoo9ye+JgIl2CNqI+wixiEkIIQeLh0XHO8aUxkhF7AUgRKzEPcO/QzPCrkI5QYiBfkCIgDq/PX5gfcs9ZnyG/A57sLs7Oph6LTlhuO34cHfjN1t25HZydfy1SbUjdI50TLQaM+izrnN4cxNzLfLqspYyY7Iq8gQyRHJ7MhbyXDKkct2zITN9M5T0EPRJdKV027VDNdf2BvaqtyM3wriIORU5tjoXuun7d/vS/L69Mz3nPpA/ar/HwL8BCsIFQtYDUUPXRGlE8UVoBdqGUMbIx0fHz4hICNbJCwlPyaoJ7UoHymaKcQqEiyfLJUs5izBLV8uVi4zLmouki41Lrgtsy3rLa0tAi2nLOQsEi2QLI8rryoYKmUpPyjVJqkl9iRdJDEjRyE7H7sdmhwFG6MYFhYlFMASKxH3Dm4MEAr0B8gFPwNpAJr9C/ue+BT2d/MX8RrvMu396oroMOYQ5PHhrd9o3ULbF9nA1nPUkdIo0e/PwM7TzVXNCM2AzKXLuMrjyRjJWsjgx87H/sdLyNTIvcnayt3Lz8z/zYPPHdGi0inUydVq1/zYm9pc3DXeOeCa4kfl1OcS6mPsJu8b8sz0V/cy+k39FQBeAqIEGwdVCQELnQzKDlYRnROFFYQXvhnZG6gdbB87IcUi7SMKJU4mZScPKLco1SkcK+MrLyy6LNYt7y5tL4Avvy9QMMkwzTBjMNEvUi//LsQueC4dLuMt0C2QLc4spiuGKq8pAyk+KConriXsIzkisCDlHm8cqRlwFwEWnxSdEkkQVw6xDJYKvAe5BCQCy/8g/Qj68PY99Pvx8e/Y7Xrr6uiC5oHko+Je4I7dstpZ2GvWVdTY0XTP2M0LzWPMWMsiymHJR8lRyefIBcgmx7zGw8bnxuzG8cZTx0fIockFy0vMpc1bz27Rf9Mh1U7WgddB2YPbut2T33bhBOQw50bq1+w07+Lx3/S/9z/6d/yd/t8ASAOpBbYHfAl+Cx0O9xBVExwV2xbxGBob7hxzHuYfXiHYIlAknyWEJhwn9idZKb4qgivQK2AsWi0fLk0uRi6SLh4vgy+nL68vmi9aLyQvMy9dLzkvxS5kLjgu2i35LOgrNCviKmYqVSnZJ2gmOCUNJIUihyBpHpAc/BpCGSQX5xTvEiQRBQ9dDIQJ5QZvBMcBzP6w+7T4+PWO82bxP+/a7FHq8ee85VbjheCS3eragNjs1RvTjNC2znTNOczTyprJ5ciDyOnH48bVxTfFA8XMxGTEJcR+xFXFKsbAxnTHvMiRynrMGM5yz8jQRNLz087Vt9eS2YHbzN2C4Ebjv+UP6J/qg+1l8BDztvWF+FX76v1KAJ0C2ATnBvMIPQugDbUPZREUExQVPRc+GQkbvxxsHgcggyHKIrgjbSRPJYsmyienKEwpNSpnK14syiwBLXEtFy6kLvIuDy8EL9cuoC52Lk4uJS4XLjEuMy7ZLT0tsCxJLNcrQiuzKjoqoCmpKFkn0SUhJFsioSABH14dnxvgGTUYaxZDFN8Rpw+2Da8LNAlUBlYDXABg/XD6qvcQ9Y7yKvDw7bTrKOlL5n3jA+Gd3uTb6tgr1uvT4NGvz2/NfssLyu/I8cf2xvjF/MQexIHDJsPswsvC6cJgwwrEo8QZxazFscY8yATKosv/zHfOcNDE0uTUj9Ys2Ena5NyE3+PhL+Sn5lHpH+wN7//xyPRx9zn6Jv3T//ABzAP8BZUIHQs2DQkP8RACEw8V9BayGGAaHRzvHacfCSEXIhQjKCQ4JS4mKydCKEUpCiq8KogrOSyELKAsCy3MLVAuRS4RLjQujy6sLnkuSi49LhouyS14LUIt+yySLD0sHizvK1krdCqmKQIpIyi3JvYkYSMpIvMgSB8fHe0aHxmgF/YV3ROPEV8PNg28CuoHIQWjAkAArf3n+hr4RvVl8qPvM+3h6kLoUeWB4gzgld232q7XD9X30v7Q5c7izDjLy8lgyADH0sXcxBTEjcNXw0DDCsPTwvrCncNyxC7F4sXNxgDIX8nWym3MMs4s0D/SNdTh1WrXN9l+2//dZ+DM4oblneix63nuC/Gc8zT2xfhZ+/P9dQDGAgIFSQeFCZILjQ2uD+sR/RPIFXYXHhmhGu8bLx18HrcfyiDdIRIjOiQZJckljSZnJx4oryhYKSAquCrzKgYrOCuAK68rxSvbK+AruyuIK3wrlyukK6ArtSvfK8crNytsKtMpgCkoKYgotif9Jngm7CX1JHIjriEaIM0eeR3UG/kZIxhNFjgUzxFLD98MkQpTCCUG7QN2Abb+9vtv+e72GvQR8VDuB+zO6S7nNORF4YDet9vm2FDWHtQi0ivQVM7MzHrLFsqZyEXHQMZdxWTEZsOxwnnCq8INw3nD+MOtxK3F68ZIyK/JFctzzNTNVM/50J/SMdTy1UPYFdvd3UTgmOJT5WfoUOvX7VHwDvPq9Zv4Fftt/a//9AF2BEMH+gkuDPkN3A8GEhkUwxU2F98Y0hq6HDUeKB/PH5cgyCE1I18kDyWgJYcmqSeAKM8o6Sg5KdIpdyrrKg4r7irGKs0q6SrHKmEqJCpnKt4q8Sp7KvUpzSnoKekpqCk/KcooTCjDJycnXiZdJT8kMCM0Iigh7x+QHiMduhtTGt0YPxdvFW0TLhGfDtULGwmtBnEEJQLG/4r9bfse+YD27/O/8a7vK+0e6gnnVOTN4Q3fGNxY2RPXLtVt06PRwM/TzRLMocpeyQPIkMZbxaTEQ8Tdw13DAMMKw5fDkMSsxZ3GV8c1yInJKcuUzKjN3s6r0OzSG9UE1/XYUdsh3hbh1eM95o3oKesl7hPxgPON9dv3wvrl/bEABgM7BaAHMwrADBMPFxHnEsAUtBaEGO0ZGxt7HC4e2B8hIRIi6CLCI54kgCVhJhwnrydUKCwp5ikfKvUp9ilgKtoqCCsJKycrTiswK8AqSyoGKuYp1SneKfAp0ylnKdooaigWKK8nICeAJuAlPCWSJOcjISMZIskgWh/6HaIcKht/GbkXAhZtFN8SKhE6DyUN/gquCBIGRQOcAEL+B/ym+SD3ofQ68tfvcu0Z68XoWubn45vhct8d3XLattdS1U7TXtFjz4rNAMywynTJTshOx3PGwMVPxSzFMMUmxRXFLcWHxRTG0MbNxwjJYcrKy1XNC8/R0JDSYdRs1qLY29oM3Uvfp+EU5IXmAumW60fuF/H688f2XvnY+3P+MAHHAwQGCQgXCjsMWQ5wEJkSyhTYFrIYaBr6G1Idfx63HwwhUiJrI2wkZyUzJqkm/CZ7Jywo1yhsKQQqjCrGKq0qkCqnKsgqvSquKtwqKSsuK8kqSSr2KccplCldKSgp3CheKMonUiflJkMmWyVmJJgjyCKsIUUg4x62HZocUBvbGW0YEReOFbkTsRGxD8MNxQunCXcHLQWhAs//BP2L+k/4/vWB8x3x/e7m7IXq5OdV5Qjj3uC23pnci9pt2DDW99Pq0frPEs5ZzATL+cnbyI3HYsanxT7F48SUxIXEycQxxZ3FF8arxmXHZsjTyYfLHc1yztvPutH/0z7WRNhG2orcD9+j4RvkdubR6FfrH+4A8brzQPbH+HL7If6kAAEDZwXrB3kK7gwrDx8R4RKyFLYWtxhiGsAbJh25Hj0gZSE9IgQj1iO0JKQlnCZnJ+QnOSijKBspaSmLKb0pDio7KhIquil+KW0pcCmDKakpwimoKWkpOCkXKdEoSCiVJ9smHCZPJYMkxiMJIz8ieiHNIBkgJR/pHZkcXxsoGsUYLxeXFSQUvxIrEUgPQQ1dC7UJFgg8BgwEpAEr/678Lvqs9y71wPJ48GLuZOxH6vfnp+WW47vhzd+x3aHb1Nkv2GLWXtRl0rTQSM8JzuvM5cvgyt7JDsmXyGLIQMguyFnIyMhGyajJB8qhyp/L+syLziHQoNEg09zU5NYG2RTbGd1M37ThJuR15pzosurY7CzvuvFb9OT2WPnf+3b+6AAXAzsFnAc0Cr8MBw8FEcoScBQeFuUXlBnsGgAcKh2UHv4fFiHmIbIilSNrJBMlkiUAJnMm/SaPJ+sn4yegJ4InpSe/J5AnQicoJ0wnZSc8J98mdyYSJq8lViUBJZQkByRxI+giXiK4IQIhXCDUH0wfoR7DHbkckxtkGjMZ9hefFjkV5RO2EpERSBDJDiwNigvrCUYIjwa/BMYCnQBT/gz83/m994r1SPMZ8RfvK+0t6x/pMed75dHj8uHf39bdAtxG2nfYotb71JvTbNJU0U/QWc9rzo/N4MxizPPLgMsmyw/LMstly5vL/cuvzLHN8c5k0PTRftP21H7WMNj62b7bjd2L37zh+OMm5lfooOr57FfvxPFI9NP2Ufm5+w3+RQBeAm8ElAbNCP8KEg3+DtMQrhKmFKsWiBgZGnQbxBwHHhUf6x+2IJYhbyIiI70jYiQJJY8l8CVMJq8mBic8J1EnRicPJ64mPCbQJW0lEyXKJI4kQyTaI28jKCP7IrwiWyLtIX8h9iA0IDofKB4aHSIcQxtxGpkZtBjRF+0W7RW6FG0TQBJDEUsQHA+sDRMMaAqxCPgGUAW7Ax8CXQB7/pr8zvoH+TL3U/WA87Txze+47Z3rtOkA6ErmZuRz4q3gJt+63Urc29p+2T3YGtcb1jHVN9Qh0xbSR9Gw0B7QfM/3zsLO2c4Sz17P0M950F/Rd9Kq09bU4dXk1hbYh9kI23Lc4d2S35bhwePr5RPoUuqo7P7uPfFi83j1kfex+cz7z/20/5QBkgO/BQwITApWDCkO9Q/pEfUT1BVZF54Y1Bn/Gv0bxxyOHXgefR94IFghHyLNImMj7yN4JOokKyVEJVclayVfJRclpiQ9JPwj5CPnI+8j5SPCI5AjWyMWI6YiESJ+IQMhgiDHH8Aelx2SHNgbTxvAGhAaVBmzGCEYYRdFFugUlxN+EoARXhABD38N/AuGChQJogc9BvAEsgNhAtMA+v7y/PH6Hvlz98T19PML8i7wbe617OvqFulZ59HlbeQD43Phw98a3p7cX9tI2jfZItgc1z3Wd9Wh1LDT1NJE0v/Rz9GL0UjRM9Fd0bvRPtLf0pjTZNRK1VfWf9eu2N/ZL9uw3FLe9N+T4VHjReVm547pneuT7YzvqPHj8xb2HfgB+uv79P0KAA8C9gPIBZQHbAlcC1sNTQ8bEcwSahTqFToXZBiOGcca+hsHHeYdpx5VH/IfhSAVIZshEyKFIvwibSO7I90j4CPWI8QjsCOgI5AjbyM0I+gimyJMIvQhoSFiIS0h3iBcILIf+B4+Hocd3BxCHLIbGhtwGrYZ7RgSGC4XThZ5FZ0UoBN4EjAR2g+FDjsNBQzjCsYJlghFB9MFTQTCAkEB0P9j/ub8Tvul+f33UfaW9M/yG/GR7ybutews65jpBOhv5tvkZOMg4gHh39+u3oXdedx3223abdmd2ALYc9fV1jjWvtVr1SrV9dTd1PbUPtWq1TDWyNZu1yjYBtkL2iXbP9xb3Y7e5d9a4dniWOTa5XDnI+nj6pTsMu7a76fxl/OK9XH3UPkr+/f8s/5qACgC4AOFBR0HtghSCt0LUg3ADjMQpBEEE0UUYRVcFkUXKxgOGdgZfRoWG8MbgRwpHaMd/x1pHvQejx8UIGcghiCKIJIgqyDFIMwguiCbIHYgRyAEILMfbh9IHzcfFx/IHk0evx03HbAcGRxvG8EaIRqMGecYGxgpFzUWYBWsFPYTFxMLEu0Q0g+3Do0NVgwiC/UJwwh/ByoGygRhA+8BeAAH/6X9SPzg+mL51vdF9rb0IfOJ8fvvh+4t7djreuoW6bnnbeYx5QTk6eLl4fXgEOAu30feWN1u3J7b9dpr2vXZktlR2TbZNNk+2VLZf9nN2TraudpB28zbadwn3QzeCt8Q4CThWuKw4wvlVuaV59/oROrG61vt8+588PTxc/MO9bz2W/jh+Wb7BP2w/koAxQEzA6MEEQZvB7cI7AkfC14Mqw3sDgcQ9xDbEcoSwBOkFGkVFxa5FlQX6Bd3GAMZkBkeGqMaEhtrG7sbEBxlHKMcwBzLHNAc0By8HI4cUBwTHOkb1RvLG7UbgxtBGwEbyRqIGigapRkNGXAY2RdGF7EWGRZ9Fd8UNxR+E7QS4hEWEVMQjQ+yDrQNnAx5C1kKOgkUCOQGqwVtBDQDCgLyANX/n/5X/Rb85/qw+VP42fZq9Rr01fJ68Q3wsu6C7XTsa+tZ6kXpP+hO52jmfeWE5IbjneLW4SbhdODD3yrfvd5x3i/e+N3f3e/dH95g3qre/95e38ffOuC34D3h1eGS4nTjZORK5THmL+dK6G3pk+rV6z/tu+4k8G7xqfLj8x/1Yvay9w/5cfrO+yL9Z/6c/88AGAJ5A9cEEQYhBx0IGAkUCgkL8AvMDKcNiw5xD0YQ/xCkEUcS7BKJEx4UtBRNFdYVPhaQFucWUhe+FwwYMhhFGFsYexiaGLQY0hj3GBgZIxkWGQAZ8BjjGMoYlRhAGNwXfRcpF9AWYRbkFXgVMxUHFcQUTBSnE/4SchIBEo0R9xA+EHcPrw7hDf0MBwwVCzYKYgmICKAHrwa8BcAEsgOUAm4BRwAe/+/9uPx++0X6CfnP95/2gfVz9GjzVfJA8TTwMO8q7hjtBewE6yDqTel36JPnr+bl5Url3OSG5Djk7uOs43TjQuMR4+3i4+L84izjY+Oe4+vjVeTS5Fnl6eWT5lznN+gU6fDpy+qp65Dsj+2o7sPvwvCl8YjyffOB9I31pPbH9+f49fn0+uz73/zN/br+p/+OAGgBPwIXA+sDswR6BUYGCwe9B24IOAkPCr8KNAuMC+0LWwzCDB8New3TDSMOcg7GDhkPaw/LDzwQnRDMENsQ+BA1EXMRmhG2EdwRDhJBEmgSchJdEkUSUxKIEsES4BLiEtQSvRKnEpoSihJcEhISyhGcEX4RXxE4EQQRthBPEOQPew8KD5AOHg6tDRgNTQxsC6AK6Qk2CYoI6gc7B1sGUwVLBFgDbgKIAaoAy//e/t791/zL+7z6ufnQ+PH3+/bi9b30qfOr8sHx8PA78Jrv++5Q7pnt4+w77KHrDuuI6inq/en16ffp+un96f3p9ent6fLpDOpJ6rPqPuvG6zjsn+wH7W3t1u1i7iXvBPDO8HTxCfKc8i7zw/Nl9Bf12vWz9pb3Wfjm+Gv5L/ov+yT84fyD/S/+0P45/3f/y/9XAAQBrwFaAhIDywNuBPUEZwXBBQQGQwaKBrYGlAY6BhAGWQbnBmwH7geoCIUJJApZCncK4QqSCzQMhAx0DBgMowtKCxwLBwsmC8kL/QxQDlYPIxDvEHoRYRHTEGcQPhAAEKwPvQ8uEBkQ3A42DcAMkg6vEhMYtBwTHvka1hSdDjUKjwf8BVYFiwXxBeAFdwU3BT8FYQWjBQQG/AXaBJgC3f9D/fn6Ffmv9672xfXZ9AT0PfNX8mPxs/Bh8Brwmu/87mbuue3T7N/rGetp6p3p2ehx6GvofeiN6N/oqunL6gnsW+3B7gPw1/A08U/xUfFD8UXxjPEl8uHymvNx9Jf1AfeA+AL6k/so/Yz+pP+AAC4BowHpAS4CngI6A/MDxgSkBWsGCQeYBysIswgfCYYJ+glfCpkKuQrgCvsK5AqyCp0Kqwq2CrcK0woGCxwLCgsKCzsLbAtqC1ALSwtUC0MLGwvtCq8KVwoQCgUKIQozCkwKoAoVC0oLJQv8ChoLUgtHC+IKVgrTCX0JdAmyCeQJpgntCP8HHgdnBu4FvwW0BYQFEAWCBP4DeQPWAgwCMQFgALz/Rf+3/rn9Uvz1+gX6bvny+Jn4h/iR+D74XPdB9mn1+/Su9BD02vIy8aHvtO6O7uLuRe9y70Xvte7l7SHts+zD7FvtVe5T7/nvSPCA8KvwgvD274/v4u/i8Ovxj/L08nbzMvQa9S32cvfO+BX6KfsA/I383/w7/e79/f4qAEoBcQK3AwYFQgaHB/cIVQoUC/YKaQosCp8KlQuuDKsNdg4RD50POBDOEBMR4hB4EEEQcxDwEHERvxG+EXAR+hCMED0QCxDhD6YPRQ/ODnUOWw5kDl0ORA5FDmQOXQ7pDQUN6AvGCsUJFQnSCNsI5wjQCKcIggg7CKIHvwbSBQoFXQSzAxoDswJrAvIBBgG8/2X+Lv31+5X6Kfn39x73e/bl9UD1XfT+8hnx7+7E7Jbqfegb50znQOkn7OHux/Cv8YHxPPBB7i3sU+qX6NPmKOXL48niH+Lz4YDi4OPq5SXo6em76r7qmOrO6lvr+euW7EXt7O1X7pXu/+7L7+LwKvLH89z1Qvic+rX8qP6nAMYC5ATCBiMI6QgwCUgJiAkYCvEKBgxiDQYPxxB6EjUUKhY/GOwZtBqbGg4afhkkGQsZCRnaGGEY2ReIF2oXQxcJF+UW5hbzFvcW5RaGFqYVchRnE8MSUxLbEWER4hAhEAEPyw3GDNILvQq+CTIJAwnICGsILwgICIEHawY2BVIEmgOxAqIBwQANADj/Rf6m/ZX9rf1j/a387ftR+5T6a/nn91r26PRx89LxEfBL7pjsBeuM6fjn/OWk45Thl+Dn4AnigONK5Xnni+mO6vnpKuju5dvjM+Ie4bPgwODo4PrgFuF14UXio+OZ5eTn/emB63/sMu2j7cjt6e2M7t/vgfH38iz0TPVh9mX3ivgV+gP8Fv4/ALQCdAUYCEQKEQzADTMPEhBjEKUQPhEnEjsTlBRNFiMYpBmwGoQbRhzXHCgddh35HY0ezh6PHgceih09HQ8d2xx/HPYbUxugGsMZoRhQFxQWKxW0FKQUuhR9FJwTTBIDEdEPXQ6VDP4KCApkCXwIUwdwBvYFeQW0BO0DZQPaAg4COwGnAPz/wf49/Ub8E/zf+/f6qPmx+C34hPdm9jb1b/Tw8z7zJ/Lm8KvvYu757JLrNeqS6FzmzeNw4WHfPd0c2x7acdv53mnji+fq6iftlu3v6wHpCuag457h89/u3rHe+t6Y37ngiuLz5NznUesO7z3yD/ST9Iv0ifSY9Kr0+fSx9Zr2Y/cl+EH53frA/MH+EwHxAyoHSAoJDYsP5xHtE1wVMBaFFmAW1RU+FRYVghVKFksXohhOGvUbSB1kHpcf2iDFIQUiqyEBIT4gYh9OHvIcdRsbGvsY9Bf+Fk4W+xXNFZ8VrxUcFl4WwxV1FEkTfxJsEZoPlA0FDJ8KrAhVBogEvAODA3QDpAMVBEwE+ANnA+kCKwLRAD//M/6o/d38dPvo+bP4n/dP9gb1SfQP9Lnz8PIC8lDxt/DZ77XuoO2m7GfrmOl052TlauMw4ZXez9sE2VbWbtSW1J3Xw9xP4i3nTete7irvwOzw5+PiQd9N3Zzc3tzP3f3eJeCM4Z7jdubx6fHtIPKx9dX3cvgZ+Gb3tvZ59jv3DvlE+wn9F/6z/kD/FwChASUEcwcCC4AO8RE0FcEXMRnYGXIaLxt9G+Ua0xkeGREZShmQGToaqxu8Hewf5CF3I2UkjyQzJK0j8iKpIdYfDB7VHBYcQhv3GVQY1hbiFVkVsBSzE/USJRMIFKYUhBQYFMITAhM5EZwOywvmCNUFHgOFAeYAYwDb/ygAlwEaA5sDWAMhA94C0wH3//j9BPyi+ev25PQj9OTzJPMV8qPx0/Gc8Vzwxu7g7aTtJO3K6+Lp7+f75dzjtOGi3zrdANpb1n3TDdKl0SjS0tTm2mjjQ+vY78jw9e7/6nPlvd+g29LZzdng2qzcvt6T4GfiLuVQ6RHuifKO9gv6Hvzg+/D5WPiK+Dj6Tfxg/owAcwJVAy8DFgMZBFIGVwkfDbcRbxYZGlgc/B29Hx0hRCFmIHMfox48HQcbGRmkGKsZcBunHWYgQCNUJUMmaiYCJq0kPSJWH+0cUxspGgcZ5xf+Fl0WwBXLFHUTIRIgEVUQoQ9pD/4PvRCIEFAPJw53DS8McgkvBuQDcAKMACf+u/wJ/RP+z/6X//AAFgIEAh8BcgC8/8X9n/oD+P72pPa59ZT0UPTZ9AL1P/Q685XyB/IK8b/vme6Y7V7s5OqE6U7ozuas5CvirN/Z3PzYSNRK0LLOLdCz1D/cFOaG7wX1CvVD8T3s2ubp4GDba9gM2dHbad7M33PgZeGp4/Xn/O009PP4wPsP/RT9sfux+fj43vq0/ogCGgVzBtsGPwb9BHsEIgYACv0OHxQCGTkdOSArIvEj1CXrJnAmACWdIwQiQx/TG6gZ1RljGwod7h6xIbokryY6JxonoyZhJUgjCyEFH6IcfBlDFvITlxJwEeoPLA7TDHQMAQ2iDZANNQ2zDRoP6w/7DusMAAsjCVgGqgJS/wb9Y/sv+tT5P/qK+nf6Hfsr/WT/JwCg/zj/IP8b/qb73fjD9vX03vIW8X3wn/A28ArvNO5n7vbu6O5G7rTtOO0h7A3qdecL5e/iweAs3ubadNbP0ILLbsmSzCTUgd3A5lfvPPbj+Ev1s+wg4yjc1thc2LDZ79s93g7gj+Fd4wLm7elg79L1mfvv/mX/Ff5q/Ej7VftO/UYB6gUsCesJuwgOBw8Gcwa0CNsMFhI4F60bih+pIokkSyUVJq8nNikVKQwnSiSuIRIfjhw5G+Ib4x0VIDIiYCQGJlImpyVKJW8l1SSoIsAfZx2ZG0oZ9BVAEkMPgA2WDLcLjAq7CR8KkAv4DLcNHw5WDq8NygtbCQIHQQSxAHn9Ffz4+z/7qPkB+S/6sfv2+5/7DPz0/CD9n/xh/BD8avrL90f2mvbO9gn1XPIX8VHx5vC97lrsmes97Jvs4+uc6kjpwOf85XzkPeMU4RLdBNiT0w7QY8z6yAHJdM8126HnxvDL9RD3IfTX7Gbjfttm18/WO9hr2oTcDd5v363hQ+XU6S/vhvUe/KUAFwEs/vP65/kv+779DwH5BIUIOAqzCT8IbwfeB6YJPg3DEsYYKR17H3IhcCSWJ1MpzClEKp8qPSmTJU8hNB4yHKgaMRrBG7ceYyE9IxwlPCd+KPInBSaMI7cghx1zGuMXkBUUE7gQCw8IDiQNCQztCm0KGAusDLUNywxqCqIIYAgqCHcGHgTxAtICAQL5//79w/yQ+zb60/mz+kH7i/op+sz7NP7T/r79dP3C/mj/pv3G+uf49PeR9tH0/fMb9K7zFPJ48N/vku9x7s7s2euQ65rqMOgn5ZniVuBw3cbZ/tU00i7OK8sxzAPTcd0z5zfue/Ne91P30PCL5eXaF9U21OPVS9j02tXd1uDq4wvnVOpc7tjzPfpL/+AAPP++/Gv7afst/An+nwE1BqYJZQoGCX0HXgcuCeAM9hFRF8EbIh8fIq4kxyUzJaokzCWdJ4Yn0CR3ITsflR16G7kZ/BlPHDEfuiEQJOwlSSb5JCEjfCFTH/UbPhirFWAUHBPoEBYOvQujCpgKqgojCncJoQmACq8KZAmYB4IG1QWYBPgC3AEhAcz/3/1x/L770fqB+ST5Zvr7+5j84vz//VH/eP/B/o3+tf6o/Vv74vlG+rL6K/mq9rL1n/ZV92T2ufTB81bzX/Js8MvtueqD5wzlEuS74/zhCN512TrWGdRQ0RbO8c1j1J/gpe2Z9g/6EPmB9LTs9OIO2r/U6tNU1sDZdNxO3nng7ONO6OLsF/La+CQAowT2A57/l/s5+sH6zfuU/c0AkwTnBhIHVAY4BlwHCQq3DvIUqBr/HYwfciFDJDcm/yX2JCQlHSaCJWAibx6tG04asRn4GYQbxR3tHzsiNyX1J5EolyaiIxwhhB6vGugVzxFvD0EOIQ2bCzMKmQnCCfAJ0QkJCiILIwxsC+oIRwZ7BJYCzP9K/ZP87vxq/Mn6ifk0+cf48/fg9/34/vk0+vX6Jv0S/8f+Wv1h/cH+uP4p/Ez5RvhW+Lr3qfaR9of3VfiY+Pr4cvnh+NH2PvQQ8sTvguy36KnlxeM/4l7gRd7225jYz9OYz4PPSNW/3hvonu/Q9U36WPrY89Poad5q2NzWptc22Q/b89yS3g7gM+IV5lbsWvQE/OgAMAIDAcn+1vuE+OD2e/kKAHcGjQhCBlUD6gK9BP8GjwkJDusUJxz8IIIi6SEDITIhBSO+JW4ntCZbJFwiGCHyHjMbDBhrGGEcACGqI2UkvSSLJVUmMCaRJKchUh5+G0UZ2haSE78PegyoCksKcwr2CawIxQdRCKEJ7Qm2CE4HrQb5BQEEJAGF/ln8TPrY+KL4z/jg9yX2bvUl9p/2FvYn9hr4gPpn+337pfxo/nv+oPxJ+7H73fvU+d32pPVp9hz3vvaE9o33H/nP+Rz5S/eU9FTxcO6N7Bzr/OgG5jTjAOFV3t/ZXNS30GrRP9Y53Sjl/u1x9vv6z/hI8fPoCuML3zDb0tf71jvZd9w73mzeMt+q4gPphfDf9or6Z/tj+qf4Gvdt9kb36PnV/ecB6QQKBhoF3QIqARwCaQaODC4STxbEGWQdiiACIvchDCJzI48lria6JQgj7R+9HeAcpxxeHJYcsx7oIkkniilFKe4nySa+JSEkxSEUH5scuhpQGagXJBUGEkQPgA2ZDB8MkwtdClMIXAaeBdwFpQV/BK0DDgRtBBYDKAAF/TL6cvdm9Rv1/vUs9kb1DPVw9v73RvhF+KD5x/va/Kj8nvwJ/Z78EfsE+n/6Mft7+vP4Wvjw+E/5tfjt97L3fPdm9nX0OfLW7xftT+oy6InmwuO03pnYdtQO1C7Wzdgx3GnideuL83X2yPOR7nHplOSM30HbBNmw2OPY1dgI2U7a+dz04O3lI+uh78/yifSj9B3zC/GR8APzYfdH+2L9Qv7j/lX/W/+N/wIBDwQNCFkMthCxFJoXhhmwGwsf1CJRJdclOSVPJOsiuCBeHhMdYB3LHpQgRiKyI+gkOCbgJ6EpyyrQKpspaieQJGwhUR58GysZpRftFosW0hVhFEkS8Q8TDlgNZQ3CDHkKkgfhBX8FtQSlAqMAFQBGAH7/Yf3S+kX4uvUB9An0BvUO9Qf0C/Qd9kn4cPh197P3VPlI+pT5hPhZ+J/4f/hH+J74RPmm+ff5p/pI+/36xfl1+HX3RvZ/9Iry//DB7/Dt0+p95pDhnNwn2GnVcdZ+3AHmLu8U9Yz3sPdz9RrwrehE4jzfJd/y303g89/I3tzcTtv/28rfkOU56z3vFPHq8GvvxO0o7VHuXvHb9YH6XP3y/KT5lfUY81zzYfaG+6gBUweOC4kOCRFYE1sVdhd+GmQekCE5IjIg7RwYGpoYtRhQGv4cMSCFI5MmlSjSKKQnfCaGJn0nJCiYJ/slDyR6Ijshrh9aHc8aPhnwGLoYaxdXFcYTQRM9ExsTlxJTEQ0PUQwHCiUIywX3AtsADwB4/+n9yPsO+p74Affp9Uf2T/dF9zb28/X09mn3O/YF9Yj1w/aK9gL1PvTo9ID1HfXV9K716PZh92n3//cU+bf5jfkH+Vf4M/eM9bvz0vGS7//sjepe6Lbln+FT3NfX9dYD24Xifeoi8az2G/t3/Kv42PAy6RrlQeQy5JHjwuIt4k3h1d/O3uff3eOx6UfvvvKT87rynPH18O/w8vGu9NH4XPwI/WH6H/aA8tjwh/Fw9OP4u/0zAmIGdQrTDfIPnhFpFHsY5xu3HBYbvhjaFlUVCBSNE6MUXxcyG0MfjiJDJIEkXyTDJGwlgiXbJD8kSiSHJOoj9CE+H9McKRvyGd8YORhXGL4YbBgaF50VqBTlE70SgBHjEKUQpA9ODRgKqQZTA50AKP+b/sH9QPw7+2X7hvtI+qj4vvh6+nj7bPq9+PP3ZffV9QD0rfPp9Az2RvZm9vX2MPd09oH1dvVi9pD3f/jw+I34TvfN9YP0GvMt8WDvzu5d7zfvXuyN5qLfZNqi2Dza+91E41XqaPKW+OL5cfaO8TjumexF69/pKukZ6SnoSuWb4aXf/uAN5eXp5u2O8AfycPLM8aPwWPBY8rb2ufsE/0T/0vwM+Xn1SfMG82D0ifYB+bz7a/5MAEwB7QIJB3UNohMZF6YXtRZBFTsTwxDfDqgOQBDGEggVIBbvFVwVtxWgF40adh2nH94g8iD1H3AeMB2xHOMcVR2DHQcdshuEGbsW/hNpEswSthSUFiQXhRaUFZUULhNbEcQP+Q7bDscO+Q3OC1kIyAR6AokB2gD1/87/AgFHAssBvv8Y/v39kf6+/tH+jP+hAAsBbQA3/9/9z/ym/Jr9x/4b/8j+4/6B/3P/Q/5L/QH+2P/JAJz/5/zy+Vv38/R38i3w0O6q7svulu1v6pfmEOQY5PLmR+y98sn3K/nv9hHzJO8060nnvOQa5QLoB+ur6z3pKOXW4RPh+eJh5jLq5u278GvxeO9j7Irq7+rP7FDvTPIX9cz1GfNp7hDrHOvI7TXxw/TL+Lj8t/7a/a77APs2/WIBpQXMCIYK2Qr7CXkIJQfPBgoI1gpMDu4QwREMEekPNw9SD3IQwxLsFdcYSRrSGScYkxb0FSgWhha+FvcWEhdbFl8UwRG8DwYPeQ+ZEMYRMRJMEWgPXw2eCxMK+wjzCOkJuApHCpEINAarA3cBYAC6AKsBCgK7AXQBcAE1AbgAwQDgAZwDGAXSBaAFiQQWAy8CPQLcAskDWgV8B/sIwAhZB1IGMgZBBjAGrwYBCB8J9AimBxAGlwRXA94CvgOiBZAH0QgECcAHMQXRAigCyQKnAuAA1f7C/X/8CfnT8/Pv3e/n8k72//fs91f3OfdL94T2rPQp827zyPSm9K7xk+0n6xPrneta6/LqsOtZ7TruMu0A63DpqelX60rtne4973PvMu8V7g7s2OmB6JDorOkD6/DrR+xj7NTs5O1g7/nwwPL39Ff3A/ls+Rf5Cfms+Yz6NPu8+3j8Z/1D/tn+JP9T/8z/8gCbAgQElASKBJsEBwVyBZsFwgU2Bs8GFAfABgQGZAVWBeYFqgY3B5kHKgjkCEgJFgm0CLkINQnJCTIKbQpwCiYKowkfCb4IhQiICMMI7wjDCGUITAiZCOsIBgk8CfQJ/wq/C8wLQguCCuIJlwmlCeEJGwpQCoMKigouCoYJAQn+CHcJGgqRCrcKlApMCgIK0QnSCR8KswpdC80LyQtNC5oKHwopCpIK7woFC/sK5gqGCrMJ2AicCCMJCgr/CgEM6AxMDQQNcgz2C38L2AomCp8JEgkYCLkGZwVkBIoDuAIPApoBBQHl/zz+fvwk+1/6Evr3+dH5gfn1+BP4yPYx9aDzZ/Kr8V7xSvEp8dLwVfDU71bv0u5l7jfuNe4C7l7tc+ym6yfr1+qS6mjqgerX6ifrMesE6+vqGOt46+XrbOwo7QHuu+4675nvAPB/8B7x3/Gm8k/z3PNo9Pf0bPXL9Uj2DPf+9+D4k/kV+nj62Ppb+wT8tvxd/Qr+wf5m/+L/SACtAAYBSwGgASQCugIvA4kD6gNJBHsEiwS2BAUFOwU1BSgFTwWVBcUF5AUnBp8GIQeHB9oHLQh/CM8IKgmTCQEKbwrrCnML4gsgDEwMmwwUDYUNyQ3zDSsOcg6sDtIO/w5QD8MPQhC0EAgRQBFyEbgRChJCEkoSPRJGEmcSfBJrEjsSChLoEdIRuhGdEYMReBFzEV0RKRHlEKoQdBAmEK8PHA+FDu8NUA2tDBoMpgs/C74KBQoiCTsIaAefBtEFBwVPBJ0D0QLfAdUAyf+7/qr9nvyf+6X6ofmU+Ij3hfaN9ab02PMj83jyy/EQ8UPwY++E7sHtJ+2t7Efs7+uZ6y/rouoF6nTpAOmf6E7oFej35+bn0+e+56znoeef57jn7ecs6Fzogui26AHpVOms6RTqkeoX65Dr9utR7LHsKe2+7V3u8O6B7zLwCPHV8W7y6PJ68zj0B/XL9YX2Pvf296j4VfkB+q76Yvsi/OP8kv0l/qj+Iv+R//n/agDtAIABHQLCAl8D3QM1BH0E0QQzBZgF/QVqBuEGYwfsB3II4wg7CZIJAwqECvQKSAuVC/ELVwyxDPwMRw2dDfoNVg6mDu0OMg+BD9QPIBBlELMQEhF1EcoRDhJJEn4SqRLPEvsSMBNoE6ET4RMjFFoUgBScFLMUvhS7FLkUxRTRFL4UgBQrFNETahPoEksSqhEcEawQWRASELwPQA+nDgoOew31DGYMyAsYC1YKfgmaCL8H+QY9BnYFnQS8A9UC3wHSALr/sf7K/f38OPxw+6L6zPnp+P33C/cb9jP1XPSb8+jyLfJb8X7wre/w7j7uj+3y7HfsF+y260br0upx6irq8+m+6YnpVukq6Qbp7Oja6MrowejI6OboEOk06U7pZumN6cfpEOph6rvqH+uM6/jrYOzI7DftsO0u7q3uL++470Xw0PBV8dLxTfLQ8mPzA/Sm9Ez19/Wj9kD3x/dI+Nf4d/kV+qr6Pvvc+4H8Hv2t/TP+uv5G/9D/UwDOAEwB1QFiAugCZgPnA3EE/ASABfYFZQbSBkIHtQcmCI8I9AhhCdoJUQq5ChYLeAvkC04MrgwKDWwN1A03DowO1A4UD1APig/ED/4PNBBoEJsQ0RADES0RVBGCEbkR7BEMEhsSJhI4EkoSUBJJEjkSKRIeEhUSCxL6EeARvRGOEVMRChG7EHEQLhDvD6sPXg8LD7YOYA7+DYgN/QxsDOALVwvECiQKhAnwCGIIxwcaB2EGpwXsBDAEcQOxAvEBLAFjAJf/yv78/Sv9WPyD+6f6xfnj+AX4KfdJ9mv1mvTW8xrzY/K08Q/xc/Db70fvve467rvtRO3V7GzsAOyV6zfr6+qr6m/qOuoS6vfp4enO6cXpyune6fjpF+o46l7qiuq36ubqF+tU657r8utH7Jzs8+xP7bDtFO5+7u7uY+/a71Pw0PBR8dPxVvLa8mLz6/Nz9Pv0hPUP9pn2I/et9z341Phw+Qr6nvot+7r7R/zT/Fz94v1m/uz+dP/7/34A/wB/Af4BfgL+An8DAgSFBAMFfAXyBWgG3gZTB8UHMwieCAgJcgnbCUEKowoDC2gL0As2DJQM7wxIDZ8N8Q09DogO0Q4VD1EPiA+9D/APHBBFEG4QlxC7EN0QAREqEVERcBGKEaIRvBHREdsR2xHTEcQRrxGTEXMRSxEaEeEQphBpECoQ6Q+nD2UPIg/dDpMORw72DaENSA3sDIkMHAymCycLnwoOCngJ4AhHCKwHDAdqBsoFKwWIBOMDPgObAvcBTQGgAO//PP+D/sP9AP06/G/7n/rM+fj4JfhV94r2yfUR9WH0uPMY84Ly8vFl8d3wXfDn73rvFe+47l/uB+6w7V7tFe3V7J7sb+xJ7C3sGuwT7BfsJew57FXseeyl7NjsDe1F7X/tve0B7knulu7o7j3vmO/171XwtfAX8X7x6vFZ8sryO/Ot8yD0k/QG9Xr18PVn9uD2V/fM90L4uPgv+ab5HPqQ+gP7dvvo+1v8zPw6/aT9DP5y/tn+QP+o/xAAeQDhAEYBqAEIAmgCyAIpA4sD7QNNBKoEBQVgBbgFDwZkBrcGCQdaB6cH8wc+CIsI2QgoCXgJyQkdCnMKxgoWC2MLrQvzCzYMeAy6DPwMOg11DbAN6w0jDlUOgw6xDt0OBQ8oD0kPaA+GD6EPuQ/OD94P6g/vD/AP6g/fD88Puw+kD4cPZA8+DxcP8A7IDp4Ocw5GDhUO3w2fDVQNAg2sDFAM7QuBCw0LkAoMCoIJ8gheCMgHMQeZBgEGagXUBDsEnQP7AlQCqgH9AE8Aof/y/j7+hf3G/AX8RPuB+r/5APlF+I732/Yq9oH13/RF9LTzLfOs8jPywfFX8fPwlPA58OTvmO9T7xXv2+6n7njuT+4v7hjuCO7/7fztAe4N7h/uN+5T7nTum+7F7vTuKO9g75vv1+8V8FXwmPDe8CjxdfHI8R3yc/LI8iDze/Pb8z30oPQC9WX1yfUw9pn2BPdw9973Tfi9+Cv5mPkC+mz61/pD+677Fvx9/OX8Tv23/SD+h/7u/lb/vf8lAI0A9wBhAcgBLQKPAvICVAO3AxgEeQTXBDYFlQXyBU0GpgYAB1sHtwcSCGkIvggQCWEJswkFClcKqQr5CkYLkQvYCx0MYAyhDOAMHQ1WDYwNwg33DSkOVQ58Dp8Owg7iDgAPGQ8wD0MPUg9eD2gPcQ93D3oPeA9wD2QPUw89DyIPAg/cDrMOig5fDjIOAg7NDZQNVw0UDcwMgAwxDOALiQspC8AKUwrjCW4J8ghvCOsHZgffBlMGwgUvBZ0ECQRyA9YCOQKbAfkAUwCq/wH/WP6u/QL9Vvyr+/36UPqk+fz4Wvi79yD3i/b79XH17fRx9P7zkPMn88PyZvIR8sPxevE18fPwtvCA8FHwKPAE8OXvy++376rvo++j76jvse/A79Xv8e8R8DXwXPCH8LXw5PAU8UrxhPHC8QDyPfJ88r7yBPNM85jz5/M29Ib01/Qs9Yb14/U/9pr29/ZW97b3Ffhz+NL4MvmS+fT5Vvq4+hv7e/va+zf8lfzz/FL9sP0N/mz+zP4t/47/7P9KAKgABwFmAcMBHgJ3AtACKAN/A9QDKgSBBNsENAWNBeMFNwaIBtcGJwd4B8kHGQhoCLYIAwlOCZUJ2wkfCmIKowrhCh0LWAuVC9ALBww6DGwMnAzIDPAMFA03DVkNdw2PDaUNuw3RDeYN9Q37DfsN+A31DfIN6w3gDdENvw2pDY4NcA1RDS8NCQ3gDLMMgwxODBMM0QuJCzsL6wqZCkMK6AmFCRwJsAhCCNIHXgfnBmwG7gVsBeUEXATQA0EDrwIbAocB8gBbAMH/Jf+G/uf9Sv2w/Bj8f/vk+kv6tPkf+Y34Afh69/b2c/bz9Xf1A/WX9DH01PN/8y/z4vKb8lryH/Lp8bfxiPFf8TvxHfEG8fPw4/DX8NLw1PDc8Ofw9vAM8SbxQvFi8YXxrPHX8QLyMfJl8p3y2PIU81Hzj/PN8w/0VPSd9On0NfWD9dT1KPaA9tn2MveL9+T3PfiX+PH4S/ml+f/5WPqx+gv7ZfvA+xr8dPzO/Cj9g/3f/T3+mv7z/kv/o//9/1gArwAFAVoBrwEDAlcCqgL9AlEDpAP1A0QEkgTfBCwFeAXDBQ4GVwagBugGLwd2B7oH/Qc+CH8IvQj6CDUJcAmrCeIJGApNCn8KrgrZCgQLLgtUC3ULkwuwC8sL5Av5CwwMHgwtDDgMPwxDDEQMQwxBDDwMMgwiDA8M+gvkC8oLrguOC2sLRwshC/cKyAqWCmEKKQrvCbIJcAksCeUImwhOCP0HqQdTB/kGnAY9BtwFewUXBbIESgTgA3MDAwOQAh4CrAE3AcAARwDO/1T/2P5c/uT9bf30/Hn8/vuE+wz7lvoj+rX5S/nj+Hv4Ffiz91T3+fai9lD2Afa19W31KfXp9K70d/RE9Bb07PPI86fzifNt81fzRvM58zDzLfMx8zjzQfNM813zc/OL86TzvvPf8wX0LfRX9IL0svTl9Bz1UvWI9b71+PU39nn2vPYB90j3jvfU9xv4Zvi1+AX5VPmj+fL5Q/qW+ur6PvuQ++L7NvyL/N78Lv19/c39Hv5x/sP+Ff9k/7H//v9OAKAA8QBAAY4B2wElAm0CtQL+AkcDjAPPAxMEWQSeBOAEIgVkBaYF5QUgBlgGkAbKBgQHPQdzB6YH1gcGCDYIYgiKCLEI2QgBCSUJQwlcCXMJigmgCbYJzAnfCewJ8gn2CfsJAAoCCgEK/gn4CfEJ5wnZCcgJtQmfCYYJbAlQCTIJEQnwCM4IrAiFCFUIIAjuB8EHkgdbBxwH2wadBl8GHgbaBZYFVAUQBcgEegQnBNMDggM1A+kCmQJEAuwBkgE2AdsAgQAqANT/fv8k/8n+cv4g/s79dP0X/cD8cvwj/M37dPsi+9f6jfo/+vP5q/lq+Sv57viw+HL4OPgA+M33ovd991v3NvcQ9+z2zPat9pT2hfZ99nL2YvZX9lz2aPZv9nD2ePaJ9p32sfbG9t/2+/YV9zH3U/d796j31ff/9yb4Tvh++LX47Pgj+V/5nfnT+f/5L/pu+rH67Pok+2X7q/vl+xT8SfyJ/Mj8Av1C/Yv9yv32/Sj+cv7D/vv+Hv9M/5D/0/8HADwAfwC/AO4AGgFRAY4BxgH6ATMCbAKjAucCOQN7A5UDqQPpA0gEgQSFBI8ExwQBBRIFGAVJBZQF1wUsBp4GwQYsBnIF1wWjB2EJcAkMCPEGHQfXBwwIxQe5BxQITwgaCMQHsgfWB+oH3QfeBxkIbgiFCCsIoAdnB60HAwjqB3cHKwc0Bz8HAQekBnYGewZ+BmcGRAYdBu0FwAWrBZ4FcwUkBdQEogSIBGgEKwTVA4MDVANAAxwD0gJ9AkkCLQL8AakBWgEmAfgAtABmAC8ACwDT/3f/F//a/rz+lf5L/vP9s/2U/Xj9Pf3m/JP8Wfww/AX8zvuR+1b7IPvt+r/6lfpp+jr6Evr2+dz5t/mL+Wf5T/kz+Qn55PjX+Nb4wPiR+Gj4Xfhb+E74PvhH+Gb4fPh2+GX4ZPh7+Jv4t/jI+Mz4y/jV+Pj4J/lL+V35b/mX+c75+/kW+jH6XvqW+sL63fr3+iP7XfuS+6z7tPvK+wf8Xfyh/L/8zvzy/DH9c/2n/dT9Cf5E/nr+rf7g/hP/Qf9p/5L/wP/4/zgAegCyAN4ABQExAWgBqAHsASgCVAJ1AqAC3AIcA0sDawOPA78D8gMfBEoEegSlBMcE5wQRBUMFcQWVBbcF1wXxBQcGIgZDBmEGdwaNBqgGvQbBBrwGwwbYBusG8gb0BvoG/gb6Bu8G6QboBuIG0wa+BqwGngaNBnUGVwY8BiIGCAboBcMFogWGBWcFPgUOBeMEwASeBHQERAQRBNkDoANpAzgDCQPTApgCYAIuAvsBwAGDAUkBEwHaAJ8AZwA0AAAAwf95/zb//v7M/pb+WP4X/tb9mP1f/S79/fzG/Iv8Vvwp/Pz7y/uX+2j7PfsS++b6u/qS+mv6Rfoh+v/53fm/+aj5lvmC+Wn5Ufk++TD5IfkR+Qb5//j5+O/45fjk+PD4AfkM+Q75DfkV+ST5NPlE+Vn5dfmS+aj5uvnN+eX5Afok+k/6fvqk+sL64voO+0P7ePuo+9j7CPw2/GH8ivy4/Oz8Jf1d/Y/9vv3z/TH+b/6e/sP+7f4n/2T/lv+8/+P/EwBIAHsAqQDRAPMAFAE4AWIBkAG/AeoBDQIqAkcCbQKYAsMC5QL9Ag8DIQM6A1wDgQOeA7ADvQPNA+ED9gMKBBwEKQQ0BEMEVQRjBGYEZARrBH8EkgSXBJAEiQSHBIcEgwSABIEEggR+BHMEZQRbBFUETgRDBDEEGwQDBOwD2APHA7YDoQOIA2wDUwM8AyIDAwPiAsYCrQKRAnACSwIqAgsC6gHIAaYBhAFeATUBEAHzANcAtACMAGgASgAqAAIA2P+2/5v/ff9Z/zT/Fv/7/t3+u/6Y/nr+Yv5N/jj+H/4B/uH9xP2u/Zz9if1x/Vj9Qf0u/Rv9Bv3y/OL81PzH/Lj8qvye/JX8jvyK/IT8evxt/GH8WvxW/FP8UfxR/FD8T/xN/E78UPxR/FP8V/xd/GP8Zvxq/G/8efyG/JL8nPyj/K38vvzS/OL87Pz3/An9IP04/Uz9X/1w/YH9l/2w/cz94v31/Qr+J/5F/mH+e/6X/rf+1P7t/gb/I/9C/13/d/+V/7b/1v/y/wwAKABEAF0AdgCRAKsAwwDbAPQADwEnAT0BUwFrAX4BjAGaAa4BxQHYAeUB8AH+AQ0CHQIrAjcCQQJNAlwCaQJwAnMCeQKFApIClwKWApMCkwKVApgCnQKiAqQCogKgAp0CmQKSAo0CigKIAoMCfAJ2Am8CZQJYAkoCQAI4Ai8CJAIUAgEC8QHlAdsBzgG9AakBlgGDAXABXwFPATwBJgEQAQAB8wDkAM0AswCaAIYAdQBlAFMAPQAiAAgA8//f/8r/tf+j/5T/g/9s/1L/O/8p/xj/Bv/x/t7+zP66/qX+kf6A/nf+cf5n/lP+Of4l/hz+Gf4R/gH+8v3r/en95P3Z/cj9uv20/bX9tP2o/Zz9m/2m/a39pf2Z/Zn9pP2s/av9qv2y/bz9wP3B/cT9y/3S/df92/3h/e79Av4W/iP+J/4u/j3+Uv5o/nz+jf6Y/p3+p/67/tX+7P4B/xf/Kv8z/zr/T/9x/43/k/+S/6L/xf/k//P/+v8KACYAQgBUAF0AZABxAIgApAC6AMQAyQDUAOgA/gAOARsBLAE+AUkBTAFOAVcBYgFrAXUBhAGRAZQBkwGaAaUBqAGlAa0BwgHKAb4BuQHQAegB4wHSAdcB6AHXAakBlwHAAfMB8gHGAaUBqgHAAcwBywHGAcQBwwG9Aa8BogGhAacBpAGVAYsBiwGFAW4BVAFNAVcBXgFRATABFAEZAT4BWAFAAQsB6QDrAPcAAAELAQ8B9wDSAMkA1ADKAKoApwDGAMgAlQBwAIYAogCIAGIAdgCoAKgAewBoAHIAUgAGAOz/LgB1AGEAFQD0/w4AJAAXAAoAFgAZAP//6v/2////3v+4/77/zv+i/2T/if8EACYAmv8K/zT/2f8eANv/qP/M/87/V//S/s3+Sv/o/1IAUgDn/27/Vf+F/6T/5v+7AKEBWAHM/7/+mf9lAQwCHQELAOj/NgAwABUAegArAX8BVAEjATwBfAGhAZgBdAFlAZsB+AHlAe8Apv90/wIBCANSA14BSv+G/98BygOBA/IBKQGvAV0CWwInAnIC7gLNAv0BOgETAU4BZgEtAdgAsQAGAfUBEgOEAwADXAKNAjIDQAMrA6EE/wYnBoX/dff89en8pwR+BZcBywFHCd8QPhBSCGkB8gDIBB8IJwmyCEoHHgVkA44DXQUyBxAIFgh4B18GtgWaBp4Irgl3CEQGsAWnB2YKiAuaCkMJBQl5CTkJHwicB7UIcAr9Cu8JhQj4BzgIhAieCAQJJQqnC3wMxQvFCdYHOgfqB7gImAiNB0sGYgXrBL0EpgR7BDME5wO6A7ID0gMcBF0EJgRKA0MCtAGRAT0BjwAmAIgATgGGAagABv+b/Xj97P7eAJoBnwBD//L+cP+a/1b/hf8ZAKz/nf1f+9v6G/x//cj9LP2V/Kz8hP2W/gv/lf70/Qn+hf5F/vL8gPvS+rr6pPqG+of6efo5+hn6WPqT+nD6O/o3+t75yPjY9zz4ePm1+Tz4TvZH9Rv1RvX69Vz3kfil+PH3kffO9wn4CfhQ+B758PlM+l76lPoE+4H78vtj/PL8wf3A/pT/DwCpAAIC3QMnBUcF5AQPBQAGJgcVCNIIXAmKCYEJzQnTClgM2g0JD8IP7g+0D5IP7Q+REAQRJxFCEXARaxELEZcQWhBLEEgQbRDGEPcQmRDYD0cPFg/rDo8OSA5IDiIOQg24C0EKcQkfCbsI+wcnB68GnQaBBvMFFwVoBPoDUwMeAsMA7v+o/1v/qf7C/cj8e/vD+Sr4T/cH95j2rvWX9JLzVvKl8Nrul+3q7EPsKOuu6Rzoc+aS5K/iR+GK4OLfct4I3HbZ9Nc12BvaCt1B4OfiP+QQ5NHiQOHg39PeEd6I3S3dKd3h3ZXf/OGF5PLma+nh693tGO/97zbx0PJV9JP13/aB+ED6xPsn/br+bQDRAbMCcwOaBGgG1gjZC1AP2RLvFUcY7BnmGhoboRr8GbcZyBm3GV0ZPBncGRkbaRyRHcIe8h+tIKMgGiB4H60eZB2mG94ZSRi+Fi0V7BM0E5QSWhFtD4ANRwzBC3ULIQvjCsoKjArHCWkIuAYOBZkDQgLJAPr+/PxN+0/69vkM+pz6y/tE/Ub+af4P/tb93/3j/cf9tf21/Zr9Xf1E/Xr96f10/ij/BADeAKQBgQKQA5UERgWrBfwFPgZRBjcG/QV/BYoENgPaAaUAm//n/sf+D/8g/4j+av0A/Bf6Z/cv9AHxCu7S6tTmIuJh3YLZYtdo103ZTNyd34/iSOTY49Xg7due1knSh88xzt/NTs5Pz4TQedEc0tfSD9Sn1VrXXdkP3A3fV+HA4nbkaOe06rTsXu1P7jnwtvGG8fXwjfL39oz8BALeB5gO6hQeGUEbwRwwHt8epB6aHosf5CDlIRYjdSW5KJwroy1kLw0xxzEiMSAw/C+CMJcw7i8/L9AuyC1uK0MoQyWcIsIfmhzMGeMXtxbvFaAV9RV5FkcW5xSYErUPLQzqB2wDif+a/Ff6nvjX90n4avlS+qD6gPoB+v/4s/fO9qP2tvaC9kT2fPYV94v32/eQ+NL5Cfu6+zz8Pf3e/r8AvgIUBcUHUQoeDAENRQ1KDTcN/gygDGEMngxYDSUOrg4fD88PihCdELsPVA7lDD8L6wgQBnIDdwGm/2D9tvop+L/16fJO7zPr7eZW4izdytfJ0gzO+sjRwzzA1r9qwl3Gmsoqz5vTvNUa07zLpsJiu2C3+bVxtv24rL0nw1fHiMlIy7DOCtS+2WLeKuLc5UXpk+sZ7ZHvKPT9+T//TAPHBu4JCwwTDbMOsxLvGN8f9CbdLiw3VT0+P/I9SjxUO9E5WDf4NYc3vDqoPH48IDz2PCI+Xj4LPh8+AD76O5E3ETK7LK4n6CIrHw4dtBuCGd8VpBG5DSIKmAaOA8cBNQHEAIL/iP1n+xj59PW58R7tKOlK5n7k6uPF5NXmY+nY6xbuCfBk8RLykPJk81f0xfSc9Kj0j/UV96f4dvpb/ZUBJAbMCWIMrw4eETgTnhTiFc0XNBomHCsdoh3QHVwdNRw+G2cbbhxCHYcd2R1rHloe1Rx0GoEYKhdVFTMSWg7RCqEHAwTP/+r7H/n+9m70GfGO7e/pb+V538rYp9IwzX7Hc8EevPG37LOHr62sJ64MtFy7S8GhxenIuMmOxYC8grJCrD+rl61EsVO2kb0axnrNgtLa1u3cuOTO63nwr/NF90X7d/4MAQUFYAs5EvgWfRmyG4geFCH0Itwllit1Mxc7T0HVRtJLXE6WTIhHS0JvPs86iDZHMyIzIjUkNgI1zTNSNF41wTRUMlIvkyucJU0dxRQEDhUJEQW2ATT/BP0y+o326/JH8Pruxu4X7zTvoO5x7QnsherL6Bjn6uVM5bLk6uO44wXlyOcp65zuS/JX9lv67P0qAXMEygeyCrkMzA0cDg0OMA4eDxYR/BOmF9cb9x9CI34lMCfLKBkquCrRKrAqCipfKO8lryMXIrcgPR8lHtIdkx1iHDsa7BerFeESdA9JDPoJxweTBF0A8/uu9yrzKu456RLl5OE031vcI9nY1Y/SqM6yyYDEZMBOveO54LWQskCw86wwp+igjZ0InrmgEqW2rDq4hMTbzP/OSMxgx1PC1L4Lv6HEFM/b2zboQ/Kv+PD6gvqv+t79ewNyCSYPtBVYHSokbyhlK1gwkDgyQSJGXUY8RMZBbz5NOQg0BjLiNME6I0DQQuZCiEH4Pk06PDObKwwmUSPnIT4gOx4hHEoZzBSYDkgHbf+z9x3xRuzb6GPmF+VD5TbmteZI5m7lduTi4izgjdzM2IvVLdMh0uzS1dXQ2pPhYund8JP2GfpW/Gb+dgArAu4DFAdKDE4SFhf/GSkckR7NIFQi7iPbJgsrNS+dMrI15zjOO6c9CT7VPBI6IzbHMbYtaSpEKIUn/ycdKUAqBSstK4AqyCjLJVohlRsmFfgOkAnjBAEBcP5T/Zr8yfqQ987zCfD067nnXeRE4mTg2N0620fZQNfg01PP68o8x8/DiMAMvre8Hby3u227TLsQu0O6urj1trK1nLTysaSsaKZ+onui9KVBrUW6Ps1m4g70a/7dAOD8FfXU7DXn9eVK6VDwZ/l3AsEJJA9tFHsb1iMRK/QvzjOSN4c55TetNBE0EjdaOtQ66TgbNs4xnyraIT8bNRnSGhEecyIzKMQtDTCALW4npx8OFtcJSPyC8IHoxeP94NHfZeAt4ivkuuV75gvmSuS24eHewtsj2LLUDNM71EPX3dnB2rTaC9vU22fcfN0z4aHodPKm/H8GDBCCGHwemSGdIgci6R8nHZobZRw8H80jiSojM5I7zUHIRaVImEr0Sr9JukcJRSRBQDypN3M0ijJcMX0weC+jLagq+CZvI7QgDR9tHmceSB5MHdAabRZFED8JdgJX/MD2DvI974ru0u4L75Hv4vDK8XbwGO2o6dXmRuN73i/artex1aDScs8pzpzO485kzljOVc9c0L7QJ9EK0m3SI9FvzmvLRshYxNa/ZLwfu+266bk5uNa3Vrnnuqy7W76CxijUV+Tc9JwEoBB6FAQPTgRV+RPwRel65yntmPi6BLMOChiAIi0s2jHZM6M1nDjEOe01jy5kKMcloSRCIj0f0R2rHcYb2ha3EcMPoxB/EZkR+BJDFsYYuhdUEw0NwASn+Rvt+eGy2fzTBNHE0eHVHdur34XjxeZD6ADn2uOa4NHd79oY2LDWwte42iXeduFP5TXqhe8O9Kj3iPvAAP8GXA3ME9caHyIdKLorRi2SLQ0tMSz5Kz8t/S/WM6s4LD52Q71H0UqaTKhM3UrGR6hDDD4YN4UwDiyLKbMnSSbwJSImGyUfIn8enxsvGS8WwRLXD4UN0Qr/BlcCm/1x+Ub2LPTE8tDxqPGQ8ubzzvRk9Ub2BfdO9tXzyvD97fDqbeeS5EHjwuIm4v7hL+P75M7lgOUb5cLkpOP54R7hYeFk4UXg4d7i3RzcVti303jQos4QzA3I1MQUxNrDFMFKvDC5drkDu8W8TsKez7LihPQ5AFYGmQgZBqH9sPGE57rijuP56Fvykv4nC9sVih5UJhctIjGiMQ4wJS5/K7gmniAxHOMamRq9GOgVjxS/FMQThBC3DS8OzRBaEj0SgxJrEwgSSQzRAw37t/Gm5obb3dPF0FnQkNHb1brdJeZO67jsrexS7F/q1uVS4KXc39uv3JLd595C4gLocu688zP4lP1rBFoLSxHsFj4dmSNVKN0q8is+LLsroSrOKREqjCsbLpExrTUVOnM+V0IiRWZGLUaIRD1BVzymNiUxJSyIJ2Yj0B9qHPsYHBZ2FLET+BJkEsQS2RP4EwASog7/CugGmQGa+432SvNN8SHwPfAJ8v30Nfg4+8D9Yf/m/3v/Qf4o/Hn5zfY19EjxQe427JvrnuuQ6+rrC+0W7j3uLO7L7ljvWO7v64npVefr4+zeLdpS137V0tJlzxLNYcxxy7nIdMXVw2XDOsEOvDi2rLKVsZixarMWuqXHttpM77MAFQsrDeQIVAHn9wjtZ+P932vltfB0/KgGlhG1HuYqZzGxMTUwmzAyMdgtgyYvIKcezx/mHgMbgxd1FqsVoRLSDqANYw9iETQSBxMbFEETIg9eCW8Dqfsk8A7j69gV0zzPS8yhzBvShtrn4czmPeqc7Lbs7+mO5V/hKd4c3NPb/t1L4mnndOzm8X74lv+HBfQJxw68FbcdFSRBKDks/jBwNB404TDrLfMsAC3xLH0tLjBMNW47nECxQ89Es0SMQ6xAmjsBNSUuxSf1Ic0cyBhsFr8VBBb9FbMUVRL5DzoOfwwLCmMH5gX2BUgGZgU0A5MABP5k+7f4cfYJ9cj0Avau+OD7fv5yAFYC1QOjA2UBXf6l++34zPUL84bx0fBI8HXw8/G28wn01/JH8WvvH+xY59fiB+B33hnd7ttv2yvbBNqd16bUqtFLzijKGMaTw8TCBMIbwCm+xL25vSq7mLZXtQ+8cMmy2Bnos/lZDVEdACOXHVYS+Aaj/Q72vvD377j1gQEUEHodxSfWLyg38TxVPvg5SDIwK+UlaSBeGcASog/5D2AQRg7ICnAIfgctBv0DZQIdAtkBegCR/nH8jvif8QPpzuCO2JbOOcTVvdW9JcLMx9bOr9j7463sH/BD75LsR+lq5YfhNt/i373jD+oq8pX7bAWFDpEWVB73JQ4sTS/aMCAzfjYIOc05SjrDOw89pzxIO7Y6DTtHO3o7mDxtPsE/UkC8QGxAnD22NzownCi5IB8Y0A+cCUoGOAVpBUgGcwd2CNcIIAgKBtACJP+i+3r4yPXt8zzzoPPQ9J32mfgL+sf6pPtR/Qz/l/88/6P/WwH1AhEDLQKCARgBDAAY/tT73fmj+Ib4U/kN+hT65vns+R35HfZV8aHs2OgP5bPgBd2O2/Tbddwy3M3bmdto2iLXqdK9zlzLB8f7wd++1774viO8E7hbt8y6w74awsnJptqm8f8FdRIYGX0dhh4jGP0J9/l872XtF/EA96f+zQl/GFAn8jHYNr83+jbcNAswBiinHt8W8xF1Dj8KMgVNAeD/m/9A/r37Rfo9+0/9M/4+/Ub7vvgY9bDvaOiH3/bVZc1Lx7bDA8LEwpnHntAy2znk1+oN8Bn0lfVy8+butere6GXpcesN70X1j/6lCV4Ugh0oJYorczDnM3c2kjgLOr46AzsHO2E62zgMN5Y1bTRhM9wyPjMfNN40azXJNVQ1SzOvL8cqXCRJHIkToQs6BRUAPvxJ+lf6pvt0/YD/hgHTAtwC0gERAH79I/oI94L1t/Wx9vD3H/q5/eEBTAW+B/8Jlww0DzMRLhIUEiQRvQ8IDg0MEwqLCGwHVAZpBVMF+gVNBs8FSgUzBVkEegEu/dv4gvQq7y7pWOSG4cbfPN5y3fTdyt6A3hzdsNs62mPX4NKTzhzMJMoNxhrAf7t/uRq38LFerWKvXLljx4jWq+ft+gYMwxX4FtIRdwjr/JTylewO63vsjPGQ/HkMdRuoJJMpAC9yNeQ37zI2Ki4kJCKSH0cZ5xHBDX4M7glkBDL/1f03/+b/XP/z/yQCHQMAAV/91Pmv9NnrGuEr2HnRmcrYw3fB0sWYzUTU0tn54C/pRe737d/qsOjT53LmbuQr5I/n3e0J9UL8KgTnDCoVmRuKIGYljCoTL48y1DVgOQA8QDxsOgA4sDUpM4cwxi6uLiQwrjK+NYE4DTrsOR44ljQmL+wnix/4Fh8PfggdAwn/wfyg/Or9I/+N/7D/CQD9/6v+XPxP+jL5f/iG93/2H/aZ9pr3AfkR+9/9JAG0BJEIfQzoD5ISuRRPFnMWXxSgENAMAwoJCEUG6ATnBMUGjglyC6QLIQv8ClUKPAedAab7x/bz8frrJeaz4qzhIeE94BHgHuEG4mvh09983iPdfdqH1tnSQdBYzY7I1sKDvrm7/rd/ss+uQbG+uf7EF9Ic4433vAn8EwcWLBNODXMEAPpi8VHtNe5687P8uwgLFUMfHSeoLZUyyTNZMJcqoSXIIU4dBRjgE4kR3w4ECksEWQBb/k/8wfnf+OX6mf0J/nL8D/vO+fD1VO585fndPtf3z8XJ2ceQyjvPPdSk2triYOqf7gzwuvAA8bvvM+2p67rsy++h8034rf6ABi4OdBSYGZcehSN8Jzwq7SyXMIA09jaYN5Q3aTcNNuYyOy+cLP4qxCmKKT0rGi5MMBUxBDETMCQtlCdKIMAYvxFRC40FAAEs/v782vwj/Yz91v3F/WT99vxq/Gf7C/ok+TL5sPni+QH67PrM/Mj+OwCvARkEZAeKChANoA/AEpgVoRaDFW0TiRHMD6oNYAvdCaAJSQotC9oLMQxVDGgM+QsmCsEGrQKT/gT6p/R378friunC5y/mROXG5MTj9uEZ4IHeZ9xF2fLVhNOJ0XfO38kQxSjBZr1auJeyCK8nsJm1wr28yMvXy+k0+tIE4gjmBy0D8fs99DvuS+tf7CLy6PtgBwoSuhquIW8nmivzLBsruifSJF4iJh9rG74YHReUFEMQ0gv3CB0H2wSXAt4BuwJBAwUCqv8u/f351/Tl7Z3mxd8B2YbSx836y9fMi88D1DbaveB25b3nnei36GPnj+Ql4kni4eRW6CjsUfEr+DD/qASUCEYMgBDAFE4YYBvYHjAj9CcxLFcvgTHxMr0z/DPjM3kznTKcMTExXTEzMVAwmi+vL4QvyC3jKjAo1CWnIgketRi2E0oP+ApxBhwCw/7J/Nj7e/uI++n7fvxN/VL+Jv9p/1//if+q/xD/4/1B/c/98f73/2IBNgRaCJoMIBATE6YVcRcUGMkX6xaNFb0TwBHXDwgOTwzfChAK+wkZCrMJ+gjWCB8JGAinBBkARvzD+Nfzpu1o6F7lVuPF4BPerdyq3K7c7tv82jXaxdgJ1tXSANCTzPjG/L+KupO3NLRNrlSpd6vGte/C0c6h27Hs4/6MCrwLtQVM/lv4J/PO7Vbqruw99nMDKg+/F28fBSjsL/IzCTOfLwQtpyvLKIcixBrqFIoRNg5lCYEEuwGGAe8CLQXPB/oJrgq7CUwHogKc+gXw6uVl3jPYE9HuyZ/GQ8ljzxTVhNnU3tPl4Ovg7efrtehS5q7kMOM54uTi++V061HyFPn0/nwEgAqXEGQVbhjiGiEeECJHJaYmgCZLJgwnRCizKFIo0yh6K1QvaDIONAw11DXANQo0iDC0K6wmpCK+H9YcORnjFQkUEhM4EfwNygoICUkICQe7BF4CBQF9ANn/6f5G/jD+Qf4i/tv9b/3t/Of8F/5wAA8DcQUICEILeg50ENQQfhBfEFMQhQ+YDS4LeQk4CQsK4ApHC+sLVw3dDjYPBw4zDLsKnAnNB28EHgBx/Mf5ivaR8Tfssej/5jrltuLq4PHgsuFb4bHfw93g20fZyNVj0sbPRM1OyvnHlMdEyKbHPcV/wwzEkMTqwam9Rb1Vw8DMA9ZC4GnuRv9xDVoU1xMjD8oJQAXrAMr8gvsmACgKMxWFHTUjfyjYLdQwKi/5Ke4k9yFCH38avBQQEToQqg8MDfIILQU6Am3/1fxN+8X6+/lP+Gv2dPTg8I3qJeOE3erZ49WA0J7Mj82d0vvXttuJ30DlOusl7jft4OqS6XDpc+mV6RnrG+969QD9VwQKC34R0BcTHSogNCGQIWYiviMQJTEmkifJKcwskS/sMAYxUDGQMtUzCDSZM4UzfDNCMn0vBSyNKBMlgiEQHvEaJhimFVYT+RBpDsQLQQkNB0kF7APGAsABDwHbAPYA/gCvAPf/+P4H/mD9+PzV/Gf9If/BAVwEUQbYB2sJ4wqfC4MLPAtZC5ULOAv5CS4IewZzBTkFZQWUBfYF9gZhCEkJ4whfB5wF+QO1Aef9GPn09AfyEO8o64HnvOVq5c3kfOOY4oHi/OEI4Erd1tp72HHVNtI90LjPEM9QzczLDMzgzGHLIMdRw1TC6MGrvnq6xLuVxbzTweCt7MD6NwpCFR8XExHLCPoCGwAW/pH8Z/4ABk0RQRsFIS4kfif/KjYstilZJUgiQCEFILkcYRgKFZIS7Q5MCVMD2P7j+6n5cvgd+QD7//sU+zX5CPcz82XsCeT83A3YtdNjzzbNW8/u1Orar9/+43/o++vv7Nnrpupl6tHqzusW7jXyqvej/dYDOgpnEKkVnRmLHA8feyGtI38lTSeOKewrci3SLeUtXS7eLvQuRi/HMEozwTWAN2Q4EzgSNn0y+S0IKQYkhh8fHNQZBxgbFuETixFNDwkNgwr7BxkGFAVHBAsDmwG2AI8AlQAtAE//e/4d/gz+2f2g/S/+GwDyAp0FewetCGcJYQlPCLAGlgV2BcAF1gXOBeoF7wWUBRwFCQVlBcIFsQU9BaoE3wNlAicAxv2k+zj5+fV68pDvIO2l6nzoaecm57bm4eU25Y/kxuJL3wvbF9cm05jOWMozyFXI5si/yP/I0MryzCTNfsuTyqzLk8wAy9fI7sn1zanQPNEA1cLfe+3m93z/vglWF18hUiKQHZ0a2BpiGPgPUwc0Bv0LNBGTEgUVOh3sJ+YtGy3wKRoo1CWEH8UVEw00CHkF+gGI/R36rfj291z20/OL8UDwau8O7t3rWOn+5qnkweED3gLau9aj1HzTIdMD1JrWptp2367kTer079r0lPh0++/99P9TAYkCcgR0B0ALeg9AFLcZaR9LJJonoCk0K3YsySxQLGks/i1bMIEysDRyN/k5zTqpOX432jSGMbItZSpYKCMn5SVQJLciJSH4HpobYRdRE/4PFA04Cr0HHAYpBVwElgMTA8sCdQIEArIBfAEbAYQAIQBvAFwBVgLuAkwDvgMCBHgDJgIUAVIB3ALDBE4GigecCAQJJAhLBpEEjQPkAh0CTwHIAHQA5P/Y/pn9ifxe+1X5bPa188jx2e8o7YvqQ+ne6NLn0eUN5Bbj3+FN39bbwdh11lvUJtJQ0DvPoM4ozuXN1M2izVXNlc3ZzlzQkND+ziDNcsz4y6TJr8YxyKTRsuBb8K/+6AygGtUjlyRxHSgTUgoABAr/p/sc/NgBYwvIFe0eHiZqK/IuMjAMLjMoYCAfGYETwA4hCh0GZwO4ASAAJf4S/E76Cvlj+Ez4M/hH9y31Q/Ly7gvrAubB3zbZ8dPx0BjQ1dBP00DYnN/I55Tu3PIJ9Rz2Z/Z19TTz7PCF8Mny1vaj+1sBlwjOEFwY5x0yIcgiiyNPJFMlUiZLJ+AoaitGLpUwWDIXNOs1eDd5ONM4XzhMN0k2qjW9NJ4yZS/aK0goSiS/HzcbhBcbFdAT9hIPEj0R0hBxEEIP9QwaCmAH+QTKAu0Axf+e/zkA4wBCAZQBGAKEAoUCPwL3AYoBrQCq/1X/MgDYAYED5QQeBtkGXwalBLQCiAELAY4A4/9q/0H/8f4W/gr9X/za+5z6fPhm9tj0DfN98CbuLO3V7InrOelJ5zTm1ORL4mbfaN1U3Drbv9li2GTXTta81N3S2NBtzrHLnMkUyYjJT8ngx7bG/8Ytx+nEhsEawtrJeNal4zjwh/25Cp4TpxSXDssFaf5/+eH1ifMK9bv8JAmzFQ8fniVdKygwUjEqLc0lQx8qG7YXQxMdD44NXw79DtIN3Au6ClMKSwkXB5QEkwKnANT93PlA9WLwNuu75VvgtdtX2MXWP9eH2QXdVuFG5iDreO5B7wLuOOyb6sToxOYV5iXolOzV8T33X/2JBKsLLxFdFM0VsBbLFw8ZOxqrGzMeFyKDJmEqdC1LMGEzlDY2OaM62jqmOq46dDraONM1rTJMMAguDCvjJ8gl+CScJPEjySI/IXAfTx2UGgkXGBN6D3gM1Al0B7IF/ARXBTwG5Ab3Br4GcgapBfcDyAHr/4j+H/2k++j6lftH/Rr/1gDhAhcFbAYpBuEEmwN+AuAAgP4M/HH68/km+pz6U/tc/F39xf1N/e/7uPke9wX1uvNV8hbwrO0o7B/rO+ls5ijkOuOx4nvh/9893yDfmt443WzbWNl01r/SR8/mzBHL3sjVxnbGrcfXx8PEDcDTvdO//sPEyIHQMN6l7w3+pgTjBAUDbAD9+v/xj+mG5z/tePbx/i0H8xGbHoAoKyz6Kp4oXCZsItUbDxWQEZwRaxJIEowSsBRYFwAYhBbfFMcTpxFSDQcIvwN4AJ38r/cD86Lvt+z66OTkI+JX4ZjhPOLQ48nmL+pd7L7s6eth6g/oOuUJ45Li2ONX5vvp9e7t9Pv6aQDmBDoISQpHC4ULUQtAC0UMBg80ExUYZh03IzYphy5kMr00CjbCNu82UTbsNFEzEzIRMdAvgS74LVwuzC6gLkMuJS6/LUEsrCmdJlgjtR+eG1MXWRMuEO0NWwxkCz8L2guzDEcNWw2yDAsLhAh4BRECa/4l+zb5//j3+Xj7d/0mABQDOgUQBhsGEwbRBZUEIgIi/378r/qd+e/4kfjP+Nb5KPv4++/7b/sS+wT7uvpg+en2RfQV8rXvaezz6NLmK+bM5SLl5uSO5UPm/uXS5EnjF+GZ3VnZxNUj0y7QTswyybHI9ck2ylDID8Y1xXvEV8FvvGS6H7+PyXvVbeDt6k/1Zf0HAEn87/SO7uLr+uu97IHusPMe/VMI7xGsGKgdRCI4Jq8ngSUJIQ0d3hpwGVsX5RSoE8QUlhcLGn4afxkiGSIaaxppF2gReAuJBwEEHv4g9ojvtezj68npQeZ65ILmmeqZ7Zzu9e5z7yrvBO1b6ZXl7uIS4h3jeuU66CXr+e4a9Hf5jf0kAEkCvgQdB4wI7whBCdUKCA7BEcIUPheWGocfIiX5KcUtSjHvNNA3cjhYNsEypi/uLcEsHCumKQ8qtCy9LzMxAjFEMGIvrC1iKoolFSBKG8cX/BQVElYP1Q0EDiUPFhAzEIIPXw7wDMsKiAewA4gAwv7t/UH9lfxr/C79pf4YAAwBrwFvAj8DdwOAAp8A6f4q/uT99PxN+yn6PPqM+uH5ofhQ+GL5cvoE+lr48vZB9gz1b/J0753trexf65LpTejm56nnLefH5oTmyOU05CbiDOC/3fvaMdgt1uTUf9Oz0U3QxM//zpLMvsgCxfjBzL5kuwi6pr2QxkTSW97i6bPzlflB+rz24/Bi6n7lgORd5/jrZPHP+HoCAwz2Eo4XphuqH9chuSCaHXYbahtmG40ZQRf5Fg8YlRcQFY8TNhUuGGoZzxiFGAMZAxjEE4wNpAc7Auj7fvSz7RjpwuZa5vPnU+tK74PyufRQ9vL2evWu8S7t1OmV5zvl9uKm4lrltumu7drwQfRa+FD8Iv/bAIMC9QT6B68KuQy3DnIRtxSQF4cZIxs0HfwfSSPgJoMq3y23MMAydDOCMmowMi5aLJsqwSgrJ2MmlSZ4J4goVCmXKQQpKifyIxIgXxzrGHwVmBIQEZ8QAhCsDnwNUg2ODZ8M6QmiBkQEvALqAL7+q/2k/qgABwJfAo0CHgOYA1ADVgJCAWUAhv+N/sn9X/0H/bD8yPxM/VP9L/yb+s351vmE+R74c/a09ZT1a/Sr8fLuxO1V7frr7enf6D3puuls6cHoOehx59fliOPo4NndPdrY1rPUfdOd0ZvOPMwVzNHMh8ulx/LDxMLSwqPBTMBHw9jMudmu5J3rxu998mjzFPHw6nPjoN9g4grpCO8p9B/89AdnE+MZixsEHIodtR7/HLMYOBUFFakWLhdwFuwWoRmMHLgdrx3rHUYecx1LG/kY5xYBFJ4PtApCBr4BZPxF9zj0MfNk8kfxkPFh9OT3S/lS+AH3JPYL9ILvTupH5+rmYud75zjoE+uu7y70avfs+an8Wf/UAOMA3AArAuIEEAgqC2cO3REIFXEXTRktG1AduB92In0lXSivKnQszy2FLjku7Sz1KsUo3SavJUolhiVhJr8n8CgpKWMoBif5JOMhKR6oGogXRxT9EJwOsA2sDacNOg2CDJkLTgpZCM4FUgOPAYMAqP/N/l7+sf52/xAASgBeAHgAcwAbAHn/wv4h/rj9o/27/aT9P/3O/IP8K/yC+7L6KPoD+tH5A/m096T2I/aI9Tb0nvKJ8brwbe+p7R7s8uq/6X7oiufH5qrlMOTx4vXhPOAR3UbZQtb10wvRNM0LygLJMMlnyBbG1cM3wwXERMVHx6fLO9P93Nrmi+4v8p3xCO+v7DbqdeZV4+LkEew39bT88wKGCq8Tfhv5Hm4e5RxeHI4bWxi7EyMRNBIXFXEXJRlKG+Adyx9mIPwf5x4XHa8aFxgoFQYRlwtjBt4CZgAv/RH5Evaq9cb2dPeV94P4Y/pq+zj6e/eG9IPxFu7e6gHpnOjW6Jvp5OsH8J308fcF+iT8p/48AOP/sv7V/icBegRSB7AJxQwYEYUVkBgwGoAbFh2LHoQfMyDdIMohdCPnJRMouCgFKFonZCc7J9clwiOXIukiWSNTIkQgCB9LH4kfLB6zG9cZHRlEGCMWJBOREN4OSQ3/ChIIZgXIAzcD8AI+AkUB2QBsAWUC0gKrAuUC+gPhBBUEiwHN/hv9Pfxh+5X6oPrH+z/9JP6F/hT/9v9sALv/9v27+2X51Pb9823x3u9M7/nup+4J71bwZfEQ8crvtO6W7UDrt+eZ5Pni/+FB4MbdsNtI2qfYU9YW1K3SmNH3z/LNV8xFy9DJSMekxPbDHcYIyunO49Xq39DqgPIK9eTzffHm7tHrQejx5WLnFe2g9IX77QFYCZARXRjoG3Eclht8Gs8Y0xVBEkAQARFME0UVxRYHGTUcrx4UHwAe+BwXHBEaZRZMEvgOCgyGCHwE+QB1/lH8J/q1+ND47/nF+ij7+fsC/Y/84/mL9kD0nvJH8IjtUOyh7SrwBfIw82X1YPl2/Yb/nv+l/7sAGQKjAqsCqgODBmIKzg1uEBoTUBZeGUgb0hufG70b4hy7HmggxiF8I6AlMydsJ7Mm4iVSJd8kUiSnIwsjnCILIvEgfx9WHpQdphxPG/sZ3xhrFyIVhhJ0EPcOZw2KC98JrwiIB/oFTwQTA2UCEQLuAfMBBgL9AagB5QDH/3z+Kv0G/Gb7bfu9++n7Bfxu/BX9Tv2R/Db7IPqb+RH57vdy9lX12/R49GfzuPGC8JPwQfE/8VXwVu9y7vLsiOrp5+rlo+Sa42Xi7eBB33jdy9t22kPZmtdc1ULTvdEw0NzNFsv/yBXIo8f4xhzHj8qR0ind6eYB7qbyPPVx9bPyUO2f5yPlPOeI60Hv7/L/+KkBSgpVEJATgRVtF7UYhReNE2MP/A0/D4YQfBDPEGgTSxe2GaUZyRjVGEYZjBhSFpgTKhHRDt0LDAjWAwgAPv2T+7n6MPrK+Rv6sful/SX+vvzj+rP5avjw9dny6vDj8LXxE/JW8vbzQfe++iL9pv76/+UA4gBhAH0AwQHVAzgGAQl7DCcQ4BIvFOUU3RXSFmsXWBhXGugcEx+uIO4hqiLQIuMiRiO2I8wjdiMGI+Ii+yKMIv0gAB/gHaEd8RxbGw4a9xkWGuEYeBZQFCETMxKmEHwOQAxCCoII8waLBWUE4wNKBAgFGQU9BEMDxwJaAj4Bfv/9/XL9hv0q/Q38LfuS+978vP2B/b38Lfyv+5X6wfj69gv29vXq9QT1R/O/8UnxVvG78GvvbO4O7nHt4eue6VPnW+Wx4ynirOBP30rext2S3RDdtNvO2TjY8tYL1T3Sks8BzivN7MvbyQ3ImsjBzJTTSNtL47LrT/P+92j41vQ876vqMun26RLr1uw08Wz4RgCXBjELYg8BFAcYMRnGFvMSaBBZDygOfgwXDEUO8xHiFBIWWBa7Fi0XExdXFlkVRxQSE4oROg+lCxIHrAJ+/6D9a/xs+yX7dPzs/q4AhwBR/2n+u/1I/M/5Gvct9W30ePS49Cj1Q/Zj+Er7K/4pABYBnAFPAtQCigLTAf4B4gMBB0EKDQ2TDyESghQdFs8WUhdPGIkZlBqhG+0cHB4cH6kg5iKZJLAkmyOFIuohdCGiIGgfbh5HHkQeDx3MGlwZ4RkpG1EbHRquGJMXGhaKE0wQjA0DDGALxQqlCTkILwe+BjEGyAT6Ag8CTgKaAv4BzgDt/5b/KP/+/Xv81PuN/Jf9hP0x/KX6j/ma+Gb3bPZ79mj3+vcm9/r0TPLz7z7u7ezI6wnr4erB6t3pQOil5oLlpOSn43HiKeHr36TeQd3d23faytjV1ifVCdT/0p3RXNDiz8HPxs6RzHrKrcoZzrHTHNpK4UzpwPCm9fj26fTz8LrtCO3c7ZfuR/AZ9V388QI5B1MKww2dEZ4UaBULFDYSNxFmEJwOeQzzC9gN2xA+E5YUmRWUFvMWaBZ6FZIUVxOKEXIPAg2sCaoFdAIYAe4AkgCy/2r/mwBDAmEClwCr/sj9vfwb+rv2wPT99GL2kvdC+Cb5wvqu/Aj+lP7l/pb/nABmAXwBJAF6AW4Drgb/CZkMzA5DEQQUQBYlFx4XvheyGawbTxxHHCsdJR8WIT8iqCKYIlUi+SFuIeYg3CBMIYwhRCGiIIQfpB2BG/wZJxlJGPMWghWDFPATKhPAEQcQew7vDAoLGQmjB7cGMQYDBtMFIAXqA5wCXAEbAPT+Bv5g/Sj9Tv1M/ej8y/xr/f/9Wf1f+wr5H/eD9bvz3PGb8FrwfvAD8JnuC+1e7HDs1+uj6b7mruRy47XhDd+s3Lzb7Nv/21vbgdoR2r/ZxNj21vDUINN60QbQAs97zgfOPM1RzCfMY80G0EvUCdsj5FjtNfQu+LP5yvjp9Yny7u/G7uXvgPM2+Mz8wQGRBwUN0xBbE3YV5hbsFksVcBJXDzcNiwyzDCQNiw7PEQMWlRg/GK4WIxZHFv4U1hG4DhYNEAzyCYIGRAPDAfABZAIpAsQB5gEJAj0Bl/+2/av7afmT98z2ofZC9vn11/br+L76Jvvn+rD7pf0e//v+Lv5G/mr/nwCBAdwCnQWuCcwNuRCJEjoUHRaTF1AYwRgpGZcZWBp1G2scEh3rHSUfbyCwIeoixCMiJFUkJST1IvUgEB+hHVocNBtnGrwZxRiNF3IWiRV+FDETIRLDEX0RGBBzDcoK3ggZBxsFwgO1A0UEXQSfA1MCBgEUADL/Af4J/fv8Uv3q/KD7N/oS+Qv4Cvcq9ov1O/UI9YX0cPP98Z3weO8w7m3slOpf6b3o1edF5q/kvuMu4yribeCL3grdqtvg2bzXzdVe1GfT79Lj0ufSnNIV0s/R/dHY0W7QmM4Ozx3Twdjh3ZTjgOvi8xX5mfnY9mTzafFL8ZPxpPFM8/T3Ef4RA1YGKAnoDKERfhVUFk0U5xGyEJkPJw1LCqkJbAydEHMTmBTRFaMXahgQF7UU3RJ9Eb0PbQ35CpQILAYEBL0ClQLYAqwCagIJA/EDIAM6AHr9dfzn+zH6Jfid94f4K/m9+GP4Z/lX+6T8zvzu/Oz9Iv94/x7/Tv+jAH4CJgTfBXMI5ws6D4cR5xLpE54UBRW/FVEXKRmJGsAbYB0DHxAgzyDBIbsiKCOBIqggbx4bHfIcHx1FHdodwx4WH1se+Rx4GwMalhgkF6QVARQeEhMQRg7aDH8LMAppCR0JdAgLB3wFXwSUA68CaAHc/6L+Gf6l/Xr86fra+VD5jfhE99713vRw9Ev02fPK8nnxf/Dn7wXvW+1d6/HpH+kf6KjmT+WQ5CXkbuMR4jDgKN4+3JDaOtkw2BTXytXB1BTUH9Nv0YLPR84/zjPPiNA30kzVaNoG4O3j3uUz56noA+rb6gXrEOuK7Bvw/fNx9m34wfsLAJ8D5QVEBwwIjAgiCdMJiwqMCyMNRA+fEawT6hRlFZwVxhWYFcQUZBMYEqoRMhLYEtYSNRJfEYcQnw9dDncMNQpmCDwH5wXXA7EBhACRACkBaAHtAB8Auv/c/8j/3P6F/b/8zvz0/IX8x/t8++z7vvyK/TL+0/7Y/8gBYARrBlEHDgiwCaILrgz9DL8NZw9TEfMSXBTpFbAXdRn6GiUcDB3eHaoeKB8CHz8eQx1lHMobhBt6G38bmBvYG/0bhRtOGuQY9xeIF/EW6xX2FFsUgRO+EU0PAA1dCzwKIgnnB9wGLgaJBaMErgPhAhsCLwEOAK3+GP1t+6X5s/ff9Y/0v/Mk87vyyvIs8xnzGvK18I3vS+5l7FrqCukx6Nfm5OQm4//h8+CT3zfebd0O3WzcXdtf2pfZUtgt1u3TftKp0WHQN84pzKbL4szjzpbRnNaY3gbn1uzR71jxh/Hv723tXeti6vnqs+0g8hL3/vv+AAoGxgqNDpQQ7BDMENIQ/A+MDdEKywnJCoQMQA6oEGUUfBjpGtsachkHGJQWYxSaET0P7A1HDYcMXgswCp0JzwlLCmsK7AkOCUEIegf5BUMDJwAL/lj9S/1J/az9C//+ACsCxQGKAMr/3P8MAK3/5/5//u7+o//G/8L/CgEkBPUHNwuvDbsPchFzEnISsRH5EC8RsRLaFHsWLhfAF9kYBhqhGugafhtyHCwd8hycG9AZZximF1MXRBeEFx8Y/hizGagZvRg9F1QVGRPxEDAPpA0hDAYLngp7CvsJ/QjqBzgH0gYQBnkEeQKwAPL+ofzd+X73JfbY9TH2g/Y19l31dvRk833xxe5R7NTqounO56/lUOTZ43/jz+Io4sXhNOEH4H/eDd2j297Z19cm1vjUxdMx0q7Q+8890MnQ8dAu0RTTRNcd3O3fUOOE53vrEe0i7H7qjemk6QDr2O3v8az2XfuB/wAD0wWtB6EInwklCx0Mbwv5CYMJVQpLC/oLXg1XEDgUNxc2GNEXWBfwFpcV/BJQEPQO9g4zD78O7Q3CDW8O8Q5qDiIN7QsxC6IKeQk3B4EErgIfAtIBHQHUAAYCNgStBXsFaASgAysDQQKyAEj/2P53/4kAeAFXAq0DmgWcB0QJkgqZC0cMqAzoDAIN1wyzDGENRw+/EdUTWxXXFoYY9RmKGjkasRmLGWwZlxg3F0sWYxYgF/IXtxieGakaYxtOG2saDxlsF7YVMRTGEiURdg9HDrsNcw0pDd0MnQxcDMkLUQq5B6cE+QHH/4n9Ivsn+Qv4qPeC9xr3G/a49HnzPvIl8P7sBOpD6CTnfeVj4+fhauE64aTgwt/+3lneg91T3ObaVNmV19XVhtTO0z/TUdIP0RHQwc/bzwPQ39D408PZfeAP5hvqHu3M7q3uOO1M67Lpiem866/vtfMn9876W/82BA4IQQqaC0YN9w7cDi8M0Ag7B60HpAiyCRcMfxCBFdkYmRmxGI8XdRafFO4RfQ8/DtcNQw0uDC0LDAv7C28Nuw6PD98Psg/xDjsNTwrvBokEmgNPAwQDIQMvBMQF4gYWB+UGEAebB+QHhQeyBtgFQwXqBH4E8wPwAyIFSgd/CSwLawyWDbYOUg/2DgMOqg2eDhkQ2xDfED0RexLJE1YUkBR4FSwXphgBGXkY2hd0FwAXPxZgFc0UqxSTFAUULBO0EtMSERMYE/wSuBIGEsMQBA/oDKwKtQgjB6wFFgSgArABPAHaADYAS/80/uv8Dfsw+MX09/Er8HLuLOw76pfpu+lt6WroX+ec5pPlp+MG4XnefdzV2hbZStfC1ZPUftNe0oXRQtEb0SPQSc7EzIbMKc1HzurQOdZV3f/jteg368Pr8uqf6TzoGuc655DpmO3m8eL18vlt/jADugdRC5oN8A6jD18P5g3wC6oKxQpMDAwPyBIeF1obkh4zIFsgdR+sHTQbxxgsFzcWEhWUE4ISaRL7ErkTshQRFlQXcxfpFSgTwg/xCzEIagUHBJsDxAONBNEFJAc2CNMItgjnB9AGqgU6BHYC6wAiAPz/DABjAHMBPgNRBVoHMglsCqwKTwrtCX4JuAgDCDQIZgnbCjEM3g1EEP0SShXTFrUXKhhMGOwX1RZrFX4UZxSvFN0UIBXaFeoW0xddGJsYdhizF1cWjhRVErUPLw1ZCywKMglJCLQHhAdmB/IGBQa8BD8DcAH1/s37p/gi9v3zrPFz7wDuTu3D7Cbsi+um6gPpreYJ5DjhIt722jrYLtZv1KnST9H20GLR4dFi0mLT3NT51b7VzdPe0EnOq8ysy6DLSM6s1BfdouQ16pruUPKw9Af1vPM/8uDxuvLo8wj12PYz+i//SAWfCycRYxWkGOEaLBsoGT4WTBTlE2sUYRX3FoAZsRySH0wh/CFbIqYiYSIgISAf7Ry9GmoY/hXxE8EShhIKEwgU9RQZFS4UkxJ+ELoNdQqZB7cFZQQsA0AC3QG7AZkBogH9AW4CpgJ5AswBiQDg/kb9Ifyq+/n7AP1k/tH/RQG+At0DXQSnBEIF7gUaBvUFVQaEB+4IHgpvC2YNxw/qEYoTrBQrFfwUixQ7FPcTrhO+E34UvxUDF+oXWxiJGKcYhRiuFw4WLRSJEvgQEQ/2DF0LvgrUCvIKlAqiCUAIYQauAy4ApfzC+Uf3s/RG8oLwJe+M7bzrJOqt6MrmZeTz4bLfTt102njXCtVS08XR/M9PzjTNP8xrynPHZsS8wubCPMR6xnfKmdCA1yzdyuCZ4ujiRuKh4XThy+E242Xm1upZ77LzdPi//VcDEgk+DqkR/hL7EhESJRDmDeAMEQ5zEZAWgxzeIYwlvCc4KfwpQCn5JookSSO4IiAhIR6PGykbVRwVHbAcVhzzHGwd4BsnGA0U2RAFDuUKJwixBiYGrwU8BfkEZQQMA2MBHAAx/xz+rfw8+0/69/ma+bz46vc8+MX5Yfsw/Jj8af2y/s3/cAAGAQMCQwNbBCEFpgUKBsAGZwjlCj8NwA7hD24RQhOjFFsV3xWNFkMXmxddF7oWPxZdFv0WsRc6GJkY0RjJGFwYWxetFZgTlBGtD4wNQQteCSUIPQdYBnYFZgTUAtMAkf7L+0j4n/Sr8WTvJO3R6uXogecS5u3jBeHL3YvaUddD1K3Rrs8bztfM7ctCy4zKdsnZxyHGKcU7xevFiMeAyzLSldlM3+/iFuXE5dTku+Jb4AffVOCf5Ebqpu/Z9IX6UwCRBesJEQ3tDlYQ5RGDEv8Qrw52DjQRCxV0GE0c1yHoJ60r9ytMKnsohCbGI+YgTx9VH/MfNSAdIA4g1B/rHpgd2xzGHPMbeRlIFnMTeRCbDLcIVQaqBc4FLAa4BjgHCAeoBV8DEgE9/2r9LvtD+Y/4lvgR+On2cfZ19z350/pc/IH+9gB7AmECVgFeALP/UP/P/74BjgQ2B4wJBwx3DiAQ7RCgEbUSthPwE2sT4BLiEloT3hNvFHUV+hZMGLkYTxiZF9cW4RWcFCsTyRGQEEkPiQ1FCx4JjwcvBmAEZwLjAJL/i/2X+m/3l/TP8djuEOzf6QDo0OUu44Lg8N0m2+fXbdQ90ZzOJ8wyyfPFAcSuxE7HZsrfzXrSjdfw2hDbDtha0wbPuMyjzDrOv9HV17LfV+du7efxQ/Uz+Bf7IP0e/Z37vPpk+3P8Yf3G/zIF5wzOFJ4bESHOJE4mriWuIxAhgh7pHO4cTh4PIIUh3CKUJKwmWSjNKEsouicFJ+gk7yB9HPEYGBZ9E8ARhhEyEswSKhNaE8sS6BDqDZsKlgfhBB4CQv8B/QP8wPsY+xP65vkB+zb8VvzC+4r7tvtc+z76UPlk+Tb6KvtV/Bz+PwAlArMDJQViBiYHtwewCCwKvgscDVkOgQ9tEPQQGBEPEUMRAhIkEzcUAhWTFe8V9BWBFYcULBPnEfgQ8A9KDlwM2wqjCf0H9wVXBDwD5AHo/5r9JPsl+JD0F/FH7szrJel95jbkFuKL34zcqNk+1yrV/9Jq0K3NecvyyX7ILcdsxy7KXc5r0ifWptne2+HbSdoP2ILVadOT093WBNxW4Vzme+vS8N/1vfkF/Ln9LQDLApwDdwKyARYDFQbBCXsOcxR5GkQfoiKPJJskBSNYIekgaSG+IZshtyGnIvwjqyRfJCIkEiWJJqgm7CS3Ivwg8h7oG8sYuxaBFWMUWxOJEoMR+A88Dp4MCwt8CQYIdQaQBIICYgDY/e76sfj891L4ufg3+Vj6wftW/Lz71Ppr+lD6Bvqy+c35Wvr5+qT7yfyl/gcBqANIBn0IwAntCXgJ9wixCL4IXwnaCvQM9Q5gEGIRRBLUEsISTxLyEXcRRxB9DuEMxQuiCiwJ8gdqBx8HVwb1BDQDBAFG/ib72/dq9BPxM+6o6wjpduZR5Dbidd983DnaQ9iF1ZnSltE00+rVO9gk2s7bZtzt2mTXxtKEzgHM3svCzQHRPNVB2qvfvuSG6ITqaOuJ7Bju2u5J7sXtu+728NXzzPd4/UQEJwuXEdQWnxnAGa8Y5Bd2FxgXRBe5GKkbqx/JI+ImyyiXKtEsPC6aLbQrLiofKV8nnCTQIfsfdx8SIBAhhyFKIcIg1R+2HRwa0hXaEbQOeAzXCjQJeAdVBg8GjgWyAxABN/+F/tH9Ofwy+q346vd/9xX3xfbh9pj3rPiA+XX5ifiE90r39/fd+JL5ifpW/Ir+/f84AAMAWgBfAZICrAO3BLwFxQbsBwQJjAlhCSwJkAkcCtsJvwjPB6IHkAfGBoMFgQTLA9QCYQGc/4v9EPtm+Pr1zfOZ8VLvLe1E61LpyeZn4+vf4d0E3lvfvuBx4irl3ecy6CPlCuDt2qnWKtOp0BzQOtJb1traq94D4kblL+hx6hzszey16yzpAedE5j/mvuaK6Q7wxfi8AKkGGQttDjgQHRBsDj0MNQtGDJgOvhDYEkAWJRsXIOUjjyZdKFwpqykzKYIn4STTIlYihCIpIsYhjiJFJJEl2iVvJV8kXiKiH9EcORrCF44V9RPvEhASExESECYPGA6XDJYKTQgUBjoE4wLcAbMARf/r/dr8vfth+kv5CPks+df48vcP90f2QvVJ9CX0xvRF9Wb15fXb9kn30PaV9oD3svjZ+DL4+fdn+JT4M/gl+A/5Xfou+3f7qPu++1n7fvqV+cr44/fK9rL1s/SN8wPyPPCp7nXtKuw76ufnQub75Z3mk+dR6YjsjvCB8+nznvE97aLn++GX3WXb1dv23jzkMOoR7wvylPN39Kj0f/MN8Y7uCe0U7N/qKerI6zfwAvbb+7gBZgd3C8kM7QsUCqUH5wQkA7wDegbwCVEN7hD9FLcYDhvpG0QcDR0QHmEeqx2bHNwbVxvOGpIaKRuiHJ8eqSAeIkoiCSECHwYdWhvXGXkYcBfPFlYWlRVEFJUSIBFOEO8Phw/xDmMO3A3dDN4KHgiJBbsDbQIiAQIAcP8Z/0j+Hv1n/Cv8jPtq+sL51vlU+V/3K/VE9Dn0mvN98kXyNPP889vzjvO789nzWPOq8mXyL/Jx8XTw4++a7+fuve3a7Lvs7eyQ7F7r4Omv6IDneuXg4q7hpeMn6AztPvED9cT3hvdy82TtA+hv5MPikOMr50Ts7PC89Hf4w/sa/Uv8IPu/+v/5dff486PxXfHA8rn1l/q/ALQGVwsYDlUO0AvQB38E6wKaAhMDxQT+B+sLMQ8uEUESEhPOEwoUcRNzEukRBhIrEgASERLiEhoUKRUrFlsXIBiXF+oVLhTvEsMRhxAQEA4R4BIsFGUUCRSBE34SnBAdDscLPgqjCZ8J1AlKCkILewwYDYsMRAvfCTwICAbFAzUCLQEGAAf/Iv85ABQBSAG0AZECiQKxACL+Vvz3+tv4c/Zk9fr1zfYR9473yfi++Vb5//fC9pD1n/Pt8GLukuw76wjqOulH6RDqwOqj6uXpAOmc5/vkreG73z3gB+LD4yjmZ+pP7w/ypPGw77btceuH6DrmEebA59Lp5+vp7gLz9fYG+vv8LwD5AbYAeP23+hP5l/fO9tb4/f3UA2wITwxBEC8TyBOkElERNhCtDqwMOAs9C5oMiw69EKkThhc8GzgdWx3wHIUcDhvlF1wUPRKYEUQR4xBIEfgSShUtFyQYPBiIFwIWxhMfEVAOjwtPCScIMQjcCKEJgQqCCy4MBgwYC7QJ9Qf5BRMEbALmAKL/C/8S/zT/af8pACsBWAGGANH/nf+q/lD8Fvqa+Rj6z/nv+A75Nvqz+uL5Efkl+T35fviV91j3PPdS9uD0y/MX8xXysPCL7/DuW+4A7aDq3eet5VfkaeMF43/kkuiO7aHwv/AQ76nsaelh5f3h9eC+4ojmG+tG7+vxhfLK8QzxzPCX8IjwufFG9Df2CvZY9b32+/m9/Lb+wwHHBdkHjwZDBLQDwAQFBp8Hhgp0DucRxBMTFIATmRJ9EU8Qzg+/ELgSnxRuFg0ZGhyyHTIdRhwzHMYbaxncFTQTMRLrEdYRoxIBFVMYBRvzGzEbaxncFlkTLQ9WC7EIZAcpB8cHJgkdC0AN2w5MD4UOyAwJCkAGSwJL/z79cvtZ+kr7Bv6AAKEBYAI2A6sC2/9k/Bj6bfgq9ir0E/RT9dv1cPXY9Zb3z/ge+If2l/UW9d3z7fFS8HPvsu6a7Yrs9OuV68zqhulk6Kvno+a+5Cbjx+Or5p3pHusS7IftV+6/7EfpZOZp5Y7lBeZw54bqRO7B8JzxV/Lv82P1o/Wt9eH2PfjA9xf2DPY6+GH6j/vN/UkCuwaWCI8I/QhTCjAL7QpmCpQKhwutDJ0NeQ6nDzgR6xKXFBYW2hagFlAW8hbqF7EXXBamFR4WQBYBFX8TQBMaFOkUahUwFlAXChjFF64WPRV7EzkR4w5gDfAMwww1DL4L/QuRDLUMcQxTDHMMYAyVC6gJsQbMAy0CfgGSAK//VwBQAkcDPwIeAVsBhAH6/979BP2o/OL6HPhs9iP2ufXw9Eb1H/fB+OL4LfiN96H21vSb8qHw7O5V7RbsQuuD6sfpaOl06ZTppemT6evokOd95ojm2uZM5rjlEuc36svsku3S7a/uG+/E7Zbrhuq76sHqYOo+6zvu/fEZ9R/45fsy/xoA8P6d/ZP8z/rJ+ML4ovu+/2sD/gZNC6wPgBL7ErkRAhCsDpgNeAzgC78M1w4OEUcTTha7GfwbzhxvHeIdVRxaGE8URBKYEQYRJxEhE14WJBmkGhgbqBo+GQQXUxRuEakOjQx7C3ALLwxtDcAOsA/lD04PEQ5MDP0JaAdOBR8EOAPpAf8A2AHhA+AEKQRqA3MDnALB/6L8SPv0+uf5mfih+J753PlO+UT5rPkn+bX3zfaq9uz1OPT18sryjPKF8brw8fA58Zbwku/d7s7tr+tH6bbnwOa25Tjlm+bp6cXt9vDz8k/zxvHW7mDr8+c/5Z7k3ebD6kDu/vAh9G331Pi391f2kvYc9wr2cPS89MD2R/gI+fP6jv4DAjAEAQYPCFIJDgkgCI0HVgdyB10INQpuDMEOSxGlE/wUMBXsFMgU8hRmFcUVlBUiFVgVFhYOFgMVsBQyFhcYfRjHF4MXrRcFF3EVEBQ+E1ASNBGxENYQrRDrD3cPvA+5D5cOGw1TDNYLlwrPCJwHEwdRBk0F3wT5BIkEYQN4AvEBtACY/hL91vyy/Lr71frk+gz7R/o++fD42PgJ+Pf2afbB9R30YPIK8qvyoPLY8b7xcPJN8qfw1u7u7Trt3uth6prpW+lB6cvpb+t+7fvu+e//8LDxHvE67+rs6Opz6ePokOk5637trvDn9KP41/nK+Oz3J/iY90f1rfM59VL4zfkA+tr7ov9+AjwD9QP8BaMHWAc0BuQFSwa3BogHNQkvC+QMvA7/EPwSBRRcFIoUnBRZFL8T/RKGEtkSvRM3FO0T3hPrFGwWMxdKF4AXqRfFFrQUixInEWIQ5A/NDzcQxBAVEUkReREoEd8PCw54DCoLkwneB+sG3gbdBoIGawbQBtkG9gXABLMDQwIRAN79evy4+0P7YfsQ/JX8q/zh/B79R/w++oL4zfe99mX0TfIb8tHyh/Kj8fDxTPPF86PyG/H877nu++xY6zXqW+nt6I7pTuuJ7ePvRfIB9B30mfJT8LDtqeoo6N/nB+rE7Knuu/B09Jr4KfqE+KD23Pag9yn2VvPQ8rH1HPnL+gf8uP4RAjIEIAXlBTAGMgXbA+8DPAUNBj4GvQd3C4IPchFlEXgRrRLNE4sThhJCEgsT+xOZFCsVsxXrFTEWKBdSGGkYTBdRFlwWuxZNFgQV3RPZE+0UyxUtFZYTzBI/EzsTehEvDzcOdA5VDi0NsgurCjcKIQoUCrAJ6Qj7B/EGjgXHA9UB/f+m/kX+i/5z/qj9G/1U/U39Efxq+or5H/kr+Mr2qfWm9Ijz+PJD8znzGvJM8R/yCfOp8b7uMO2M7Xbt1uuq6rXr0O0A7zrvhO/j79Lvsu8x8Obw+fCW8GnwMvBb74nuBO+X8MfxUPKF87f1Ifez9hj2Kvfr+EL5rviM+Qz83/0j/rL+oAA6AiAC8wHXAxMHRAnTCRwK0AojC4YKwwkPCr4LGg4tEKwREROlFNgVDxaqFX4VjxUwFUoUqhP1E+cU1BWZFpIXyBh4GQQZABhvF/kWXRXUEkYRahGXEaEQ7w/yEHYSdRIyEUgQ2Q/EDusMPgsFCsAIXwdNBqIFJgXBBF0ExgP2AgkC0QA2/8D96PxE/Dv7J/qt+Yv5E/lT+Kv3+vYV9l716vQI9ILyUvEc8QPxJPAd7/rukO/47/nv1e+N7yTvBe9P73XvM+9S75/wo/IJ9CL0cfPa8pfyNPJq8bbwxPCS8ZfypvP59Hn22vc8+dL6CPwl/KT7p/sg/AX8cfvn+wf+nwCgAn8E7wZ9CSYLbgtvCrsIYAcWB3kH6AfeCDALUg7gEIsS/hMbFQQV2xPIEi0SVxE1EO4PPBFHE8IUhxVhFo4XMRiCFwkW0hTVE0USZxCYDyoQxxCxEN4Q7BGqEvoRkhCDD3wO1wwUC+IJ9wj8B2gHaQcsBzEGJQWVBPsD2wKiAdMARgCo/+L+1f2Y/Lz7fPsc+w36BvnR+NT4A/ib9p31PPUE9dT0tfRf9LzzPPMK87XyGvK38b3x5PFI8j7zSvSH9Db0bPQc9f/0xPPE8gLzsvOQ88HyifKM8wT12/Ur9iP3Q/lO+7f7x/o6+sL6OfuX+pz5xfly+9n9NQBSAj8E/gVcB/QHhgdQBt0ErwNIAxgE7AXzB+sJhQzKDx0SDhJ9EHEPRA+UDt8MlQsvDEUOTRCMEaESLRSXFb4VlxQ6EzwSGxGjD54Ofw6oDtoOzw+KEYsS+RH2EIIQyg/2Df4LEwvGCkgK8QkWCtkJsAibB18HCwemBekDCgPkApYC0QHZAAYAxf8QAOH/ZP5+/Kf7mfvS+hn5x/eP98H3tveB9zT3wPZi9kX27fX09OPzefOi89Tz5vP48yT0ovSc9Xv2YfZ49ef0EfX+9ObzifI58jjzk/Rx9fn18vad+Dz66Pqj+jH6CPr0+bf5cflz+Sb64vtm/r4AXQLLA4sFygZOBlwEogI8AtkCuAOeBNUF0werCnUNyA5ZDoENdA24DSgN1wsFC5ILKQ3IDrYPKhD6EFoSQhPBElcRKhB8D+UOeQ6GDsIO6w52D4QQCRFMED4P7g7FDsANWQyUCzcLlgoBCvEJyAnuCCEIBgiwByoGWQS3A/YDogN4ApUBnQHoAZIBhgBp/8X+Yf6D/Qn8wvpT+kH6xvkE+Zv4gvg3+Kj3OvcL98n2MvZe9a70dPSO9JL0jvQV9Rr2jvbk9Rj1LPV39dH0qPNn8zb00PSD9Bz0p/Qn9q/3Wfgu+Cj47fjB+YL5dvgE+L/49fkj+3z89f0y/2QA5AEEA8UCpgFBAf4BvgLkAioDTwQyBkoIBArOCtYKNwtaDPQM2wsnCuUJPguLDOUMLA1uDmQQwBF8EeIPaQ4mDnkOAw7LDEsMVg0LDxYQERBtD+EO2A7gDvgNGAy0CtEKdwscC+cJIQkmCS4JqgivB2sGKAVzBGcESwSSA74CmQL9AgwDTAIRAQ8AqP9u/4P+v/wi+6f6+Pr3+lH6w/ny+Y36ufr0+Yb4RvfD9q32TPZ59c705vS+9b/2Mvfr9pD23fZ49zD3uPVR9DH09fRd9Qz14vTT9cr3lPn0+Rr5mvhN+ez57vga95z28feB+UT64PoW/Ln9fP8yAScCvQHJALsAYwFCAS0A1/9fAeoDGwaQB4EIMwnvCZgKRgqKCMcG3QaBCJcJRwkZCXkKlQyfDSkNVQwcDEAM+wsnC20KaQr9CqELFQxcDGcMLwz1C94LjguzCsUJfwnHCcYJFgk7CNkH7AfdBzcHGQYYBZwEbwQZBHwD4AKHAoAClAJFAkABAABz/53/Yf8j/sv8cfzM/NH8RPy++5X7jPta+9f6+/kS+Z74p/iY+Cj4w/fS9xX4IPjw97b3hPd497n3BPjS9yb3tvbw9l/3YPcZ90D3E/gE+Wz5UflC+ZP5/PkR+tH5mfmr+ST6C/sw/Cz96/3T/hIAGwFeASYBJgGAAdEB/AFHAtcCqgPIBAIGzQbmBtYGMwetB4kH0wZtBuYG2Ad/CJ0InAgKCeMJgApWCrcJbAmnCdMJigkgCQ4JVAmnCdIJuwllCQ4J/AgJCb4ICwh6B3oHuweQB9YGFwbABYoF9QQWBIQDfQOZA3EDFwO4AkYCvAFGAdwAIgAi/4P+lP6z/jP+Z/0S/Tz9P/3G/Bj8hPsd++D6q/pC+rT5fPnJ+QX6ofnv+K745fj2+J34RvhJ+If4vPjM+Jn4JfjY9yL4vfjf+G34VPgy+U76jPoC+sP5S/r6+jP7NfuS+178Sf0F/m7+nP4B/+n/4gA1AfwAGwHoAa0C3ALrAnkDTATRBAsFXwXLBQgGGwY7BmAGbQaRBgUHiwexB3cHSQd2B9oHFwjwB4YHTAeaBzIIcggjCMAHxgcBCNcHFwc/BuYFHwZzBmEG5wWDBZsF4gWTBWsEIgOnAvYCHQOKAtABzAFRAlsCggF+ABMAEgDW/0n/1f6q/pv+i/5+/lL+3f1T/Qr98fyb/Pj7ifum+/T73PtP+8f6oPq4+qf6NPqQ+TL5WPm7+dL5h/lL+W75qfmX+Vf5Rflc+VL5Mvld+dv5Svp5+qj69vos+0X7mPs1/Kf8sPzF/FP9Ff6T/uv+d/8TAFkAWgCEAOkANAFZAa4BWwIhA7oDJQR9BL4E1wTBBJMEigTbBFEFegVQBWQFCwa8BskGVwYgBloGfAYlBp8FYgV/BbEFuwWTBV0FSAVTBTsF2QRyBFAERgT9A5YDcwN/A0MDuAJdAmMCVQLrAYQBegFtAegALwDY/97/uP86/8T+nv6k/qb+kf5K/sb9S/0l/Rn9pPze+4D7zfsc/Mb7IPvq+i37Rfvh+lj6/PnH+bn5+/lt+o/6PfoG+l/64frS+jj6zvkE+oz64vri+sD6zvpJ+wT8X/wE/Jb7/vsH/Yb9Ef29/ID9uv4f/6n+jP5p/3wAxABQAAUAgQB6AR8C+gGPAdIB8QL2AwIEbAM4A6kDGgQMBLwDlwPFA0QE6AREBQ0FpwS1BB4FGwVaBHgDKQNwA9wDFwT5A58DfQPfA0IE3APPAiUCRQJdAr0B+gD2AH0BrwFRAegAyADQAMoAigDb/+3+dv7G/iH/vP4E/v/9pf7l/k7+nf13/Yf9RP3M/Hj8RPwi/Er8tvzQ/ED8lPuB+9v73fta++j68vo9+237d/tu+1D7J/sa+yr7LPsh+0b7qfv6+/z75/sY/Hz8uvy8/MD89fxL/ar9Av4o/gj+9f1b/gz/Yv8+/1r/KQAAAQABbgBYAAQBnQGHATMBTQHNAT8CeAKPAo4ChgKtAgMDLQP5AsgCAQNnA3YDLQMHA0ADiAN7AyED1wLsAkcDeAMpA5ECNgI/AlECKALnAbgBjAFvAZABvgFuAaMAMACBANcAZACP/1P/r//P/2r/Bv/p/sn+kf6L/rD+k/4x/gX+J/4W/qP9RP03/ST92vy1/OH8/fzR/Ln86Pz4/Kn8a/yK/JX8KfzG+wD8ePx3/Cr8UPzt/EL9BP25/MT85PzW/NP8Ef1U/Vj9SP14/eD9Mf5A/iz+L/5Z/ov+mf6G/of+w/4l/4H/x//8/x0AMwBiALAA4gDLAKAAugAZAWUBbwFoAZMB7QEyAjcCDgL0AQQCJQIrAhgCDAITAiACMAJDAkcCMgInAkQCVgIcAr0BpgHQAcABSgHpAAQBVAFWAQoB2QDtABABAwG/AGwAPgA+AC4A1v9l/0r/iv+p/2z/Jf8X/xH/4v7A/sz+sv5D/ub9+/0u/vr9iP1Z/XL9eP1p/Yj9vP21/Yf9hv2X/V396/y6/Nn86vzX/Pf8Xv2r/bb9u/3R/b/9i/2N/dD97f3F/cH9F/5s/nL+af6c/tz+6P7h/gH/Jv8i/xr/PP9i/2L/a/+2/wsAEwDm/+X/HgBUAGsAfgCOAIgAgACeANUA7QDhAOIA+gAGAfsA/wAiATcBIQECAQwBMwFCASQB+QDiANsA0gDLANgA5wDZALIAlwCUAIoAbQBaAFwAUwAxABgAEADm/5L/Zf+S/7//i/8z/y7/af9i/wL/vv7N/uT+xv6Z/n7+WP4w/j/+gP6e/nf+VP5X/kb+Df70/Rf+Jf79/fj9Rf58/kj+B/4x/o7+m/5h/lP+if60/rX+u/7S/tX+0P7o/gz/D/8I/yb/Pv8S/9n+Df+f/wAA7v/C/8//8f/1/+j/3P/C/6L/tf8FAE0AYABiAHUAfwByAHIAhABpABoA+/9BAIMAVwAQAEQA0QD9AJQAOgBvAM4AqwATALD/1/8xAFAAMwAmAE0AhgCeAIAAQAADAOH/y/+t/5r/q//K/8X/mv+Q/8r/CAD3/6D/W/9N/0b/F//Y/sD+2v4C/xj/IP8u/0P/R/8j/+3+0/7b/s/+j/5P/mb+0P4o/yD/3f66/uP+I/8s/+f+mv6e/u3+G//h/ov+nP4R/1//Ov/2/gf/Yf+b/4P/QP8K//z+Ff89/0//Qf88/2b/rv/h/+3/5v/U/7L/hv9r/3D/iP+l/73/0P/f//H/DgArAC4AEQDs/+P/BAAsACsA+v/R/9z/BwAbAA8ADQAqAEsAWwBYAEQAHQAAAAwAHADv/5j/hv/g/zcAHgDN/9L/PAB/ADYAoP9D/0z/cP9b/xH/5/4Z/37/r/+B/z3/Nf9c/2L/Kf/f/rr+vv7Y/vj+FP8j/yn/L/8v/yz/Of9d/3P/Uv8S//H++/77/tv+w/7Z/gX/HP8h/zD/Tf9Z/0P/G//5/ub+3v7a/tf+1/7a/tr+3/4B/0L/c/9g/yH/C/9E/4n/hP81/+X+z/7t/iL/WP+C/5n/pP+z/8n/zP+m/1z/FP/5/hj/Uv94/4P/kv+6/+T/7f/Z/8T/s/+S/1v/K/8i/0D/Zv94/3b/fv+j/8v/yP+Z/3L/cv90/0r/E/8R/0L/Wv80/w3/K/96/6X/gv83///+8P70/vb+7P7f/t/+9f4a/zj/OP8a//b+7f4B/xT/Ef8F/wz/Iv8s/yP/E/8F//P+5v7q/v7+Fv8z/1f/ZP9H/yL/Kv9L/zv/8f64/sj+9v4M/xT/NP9c/2X/U/9F/zn/G//2/ub+5f7i/u7+H/9d/3f/ZP9C/yr/H/8h/yL/Af/N/sn+E/9V/zT/3v7R/iX/af9G/+r+sv66/t3+9v7w/tT+0/4N/0//RP/1/sT+3/76/tL+lf6T/sb+6v7q/ub+7v73/vz+/P7v/tn+2P7y/vT+u/59/oz+1v4D//L+5f4R/1P/a/9J/wf/wv6W/pr+wP7Q/q/+mP7U/k7/n/+L/0r/Nv9L/y//s/4n/gj+bv7v/h//CP8L/1D/hv9T/83+c/6L/tf+8/7h/vP+P/9y/0r/8f7G/tf+4v68/or+iP7H/iD/Yv9u/0//I//4/sH+ff5P/k/+b/6a/tv+M/9n/0P/9P7U/tz+pP4Z/sD9Ef68/gn/w/5o/nb+1f4S//j+uv6f/rj+2f7N/pH+W/5e/o3+qv6Z/pD+x/4h/0L/B/+7/rH+2f7V/n3+Hv4b/nr+4P76/s/+r/7R/hv/O/8J/7j+mv7A/u3+6/7P/sr+5P72/ub+xf65/tr+GP8//yn/9f7t/iP/RP8C/3v+Hv4n/mv+pv7F/t7+B/8//2f/VP8C/6b+dv5f/jP+//0J/l/+wP7t/u3+7f7//hf/Gv/p/n7+F/4F/kH+bv5f/ln+m/70/gr/5P7N/tj+zv6X/mb+Y/57/ob+ff5q/l/+c/6x/vn+Fv8D//H+9f7q/rf+f/5x/oT+lf6i/sT+8/4O/wv/+/7u/uP+3/7s/gH/Cv8E/wT/Fv8i/wz/2v6u/qX+vf7b/uv++/4m/2f/iP9j/xr/7/7v/uX+tf6F/on+uP7q/g3/Kv84/yv/DP/v/s7+nf5z/nP+kP6W/nn+cP6e/tX+1v6l/oD+hv6i/rX+sf6S/mf+Uf5j/nz+cP5T/mX+tf75/u7+t/6g/rf+uv6F/kL+M/5h/pv+tP6s/qn+yv4B/xT/3v6E/lP+X/5u/lz+Xf6u/hv/Ov8B/9T+5f7p/pn+M/4u/ov+5f4A/wX/FP8T/+z+v/6u/qn+mv6U/qj+yP7e/vL+Bf/8/s/+rP6z/rv+m/59/qb+AP8s/wj/2/7j/v7+7f6r/mj+Rv5G/lv+ff6n/tD+8P74/uP+vv6h/pD+gP5q/lb+Rf41/jH+S/52/oj+ev50/pX+v/6+/pD+YP5M/kP+MP4b/iP+VP6Z/tD+3f7F/qn+qf7C/s3+rf54/mX+jf7H/tv+xv67/tn+Av8K/+r+wf6k/pT+hv54/nv+pv70/i3/FP/A/o3+rf7c/sP+df5Y/pP+6f4M//j+2v7Q/tP+y/6l/m3+T/5o/pv+sf6j/p7+u/7e/uX+0P6u/pP+jP6Y/pX+a/5A/lT+nP7D/qD+c/5+/qf+qf5//l3+aP6K/pr+hP5d/lf+i/7A/qL+Pv4F/jz+l/6p/nr+Z/6U/sz+5v7u/un+y/6d/oT+hP57/mn+eP60/u7+A/8O/yb/M/8Z//L+3/7V/rv+ov6w/tj+7v7j/tn+4v7s/uz+8v4G/wr/5v6w/pb+of60/rb+r/65/tr+A/8e/x3/BP/g/sX+vf7G/tL+0v7C/rL+uf7d/gL/Cv/1/t7+1f7T/s3+yv7P/sr+tv6r/sT+5/7d/qn+h/6X/qb+gf5O/l/+tf77/vj+1f7V/vP+7/6w/mP+QP5N/mb+bP5m/nz+wP4V/z//Jv/u/sb+rf6F/lH+PP5g/pP+pP6a/qL+xP7b/tD+uf6y/rT+s/65/s/+4P7S/rH+mv6L/m7+Uv5k/qf+5f71/vH+Bf8n/yz/BP/P/qf+j/5//nf+ef6I/rD+7P4j/z//Rf9B/yn/9f69/qP+oP6S/nv+gP6s/tT+3P7f/v/+Jf8l/wP/8f77/vn+yv6H/mP+bv6L/p/+rf7O/gX/Mv82/xH/3f6v/of+Zf5V/l3+dv6T/rP+2v78/gv/CP/6/ub+1P7S/uD+5f7W/sb+zf7f/t7+xv6y/rH+vf7O/uj+B/8Z/yL/Mf8//y3/8/68/q7+uf65/q/+uv7j/hH/L/9D/1r/Z/9V/xr/zf6S/of+ov67/sD+yv75/jj/Uv9A/yn/LP8u/w3/1P6q/pn+lf6c/r3+8f4S/wn/6/7h/uz+7v7Q/qX+kf6k/sf+1/7J/rD+p/6x/rj+tP60/sj+4v7j/sj+q/6j/qT+of6l/sH+5v70/uH+yf6+/rn+r/6n/q3+vP7I/s7+0v7X/uH+7v7x/tz+wP65/sb+xv6v/qP+v/7p/vj+9P4B/x7/IP/6/s7+t/6u/qf+qv6+/tz+/f4m/0v/Tf8k//D+0f6+/qT+kv6o/uH+E/8Y//j+3P7q/hn/Nf8R/8L+jv6X/q/+pv6N/p3+2/4L///+zv6t/rD+uv6l/nT+WP57/sv++/7a/o/+av6B/p7+kf5x/nj+qv7W/tX+uf6y/tP++P7w/rT+c/5g/nP+g/6A/oz+yP4f/1X/S/8c//X+5v7b/sX+s/66/t3+AP8K//7+8v7y/vn++/4A/xb/PP9X/0r/If///vP+4/6+/qf+x/4G/yT/E/8E/xX/JP8P//P+8/7+/uz+x/65/sn+1/7Y/t3+6P7r/ub+7P7z/uT+0v7f/vn+5f6k/n/+m/7B/rn+o/68/vr+Hv8P/+/+2/7T/tD+x/6n/nT+Wv56/q7+uv6r/sX+Ev9G/yj/5f7K/uD+7P7L/pf+gf6a/sj+4v7c/tf++v44/1b/PP8X/xj/KP8V/+D+wv7W/v3+Ef8c/zX/U/9b/0r/N/81/z3/Qv9A/zr/NP81/zb/Kv8N//P+6/7y/v7+Fv9E/2//eP9m/2H/bP9a/xb/z/69/tP+1v69/rz+9f5G/3b/df9Y/zj/Hf8B/9j+p/6Q/qz+3v7w/uD+6v4q/2r/av8//yb/Kv8d/+/+xv6+/sf+zP7R/tv+4/7r/gj/Ov9d/1T/M/8d/xP//P7R/qj+nP6u/sv+3v7l/vL+D/8t/zD/H/8d/zP/QP8q/wX/9f7z/ub+1P7g/hT/S/9k/2T/Zv92/47/nf+W/3f/Uf83/yv/Gv8B/+/+9P4R/0H/fP+s/7j/of+D/27/Tv8Q/87+s/7F/uD+8/4R/0z/hv+U/3H/Qv8o/x7/Df/s/s/+0f71/iX/Q/9I/0T/Sf9S/0//Pv8v/yn/J/8i/xn/Ef8M/wn/CP8G/wH/+/4C/xr/OP9K/0n/PP8r/xv/Cv/5/ub+2f7Z/uj+9v75/vj+Bv8l/0T/Uv9Y/1//Xf9D/xP/5f7K/sH+wf7N/vD+Kv9u/6H/sP+X/2b/MP8A/9r+xP7J/un+F/9C/1//dP+H/5P/jP9y/1H/OP8k/w3/9v7v/gD/G/8q/y3/NP9G/1b/V/9P/07/WP9g/1X/N/8T//v+9f4A/xX/L/9I/1n/Xf9b/13/Y/9j/1f/Sf9A/zP/Gv/8/u3+9/4U/zr/Yv+C/4v/fv9q/13/UP8y/wz/9P71/gT/E/8m/0L/Wf9d/1X/V/9k/2L/QP8S//v+BP8V/x7/Jv84/0v/T/9E/z3/Q/9N/07/Rv87/yz/Ff/4/uL+3/7y/hX/N/9M/1D/Tf9I/zz/JP8M/wb/EP8Y/xj/HP8t/z3/N/8e/wj/AP8B/wj/Hv9F/2f/bf9X/zv/Kf8e/xP/C/8O/xv/KP8s/yv/M/9I/13/Yv9R/zj/If8J/+z+1P7U/vT+IP8//0z/T/9Q/0X/K/8S/w3/H/80/zv/NP8t/yz/L/82/0P/UP9S/0n/Rv9Q/1L/Pf8f/xf/K/9D/07/Vv9m/3L/ZP9B/yX/IP8i/xr/CP/9/v7+B/8S/x7/LP88/0f/Q/8w/xr/Ef8U/xP/Bf/5/gP/Hf80/zz/P/9F/0v/S/9J/0n/Qf8t/x3/IP8u/zH/KP8o/zz/Uf9T/0//Xv97/4j/c/9R/0H/P/8y/w7/5f7U/t7+8P76/gX/IP9I/2H/U/8r/w3/D/8j/yv/HP8K/wv/G/8h/xL/A/8P/zr/bP+P/6X/vP/P/8b/lf9O/xH/7v7g/uP++/4w/3n/vP/e/9n/uv+R/2P/NP8P//7+/v4F/xD/Iv84/0b/TP9W/27/iP+P/33/YP9D/yn/Dv/5/vH++P4I/xr/KP81/0f/Yf97/43/kv+P/4D/X/8v/wT/8P7z/gD/Ev8t/1H/bP9x/2T/Vf9J/z7/Lv8f/xb/Df///vH+7/4A/x3/Ov9O/1P/T/9H/z7/NP8m/xr/F/8a/xb/Bf/z/vL+Bv8h/zf/Sv9h/3T/df9f/z7/Iv8O///+9P72/gn/J/9D/1f/Zv9z/37/gv99/27/WP9C/zH/I/8S/wP//v4K/yX/Qv9d/3P/gv+B/2//UP8r/wr/9v74/gn/HP8p/zX/Qf9I/0L/Nf8u/zL/Ov8+/zr/L/8l/yH/JP8i/xX/Bv8L/yX/QP9M/1D/XP9r/2j/TP8s/yH/K/82/zP/J/8g/yT/Lv84/z//SP9Z/23/df9p/1T/Rv9D/zv/I/8F//f+AP8T/x//KP87/1n/df97/2v/T/85/zH/NP84/zr/QP9Q/2T/Zv9Q/zn/Pv9i/4b/kv+R/5n/qP+h/3n/Rf8o/yv/PP9I/0v/Sv9L/07/U/9X/1j/Xf9p/3L/aP9G/x//CP8F/wn/C/8T/yr/R/9a/1v/VP9Q/1X/W/9W/0b/NP8o/yT/IP8Z/xf/Jf9E/2j/gf+N/47/hP9r/0f/If8H///+Bv8S/x3/J/82/0j/Vf9Y/1L/Sv8//zL/I/8a/xr/HP8Y/xT/G/8u/0L/TP9N/0z/Tf9L/0X/P/9E/1D/WP9R/0L/Nv8w/yr/JP8o/z//Y/+A/4r/g/9z/2L/T/84/yT/HP8k/zL/Nv8o/xj/HP8y/0b/Sv9J/07/Uf9B/x3//P7w/vr+Cv8W/x7/KP80/zz/Ov8t/yL/J/83/z//Mv8a/w3/Ev8e/yn/OP9U/3f/i/+D/2T/QP8l/xX/Df8Q/yP/Qv9f/2X/U/82/x7/D/8I/wr/F/8s/z//R/9F/0D/N/8o/xf/Dv8V/yn/Pf9K/03/S/9K/07/VP9Y/1j/Uv9I/z//O/8+/0b/Vf9s/4T/jv+F/3D/Yv9e/1z/VP9J/0f/TP9P/0f/OP8t/yr/Kv8n/yf/L/87/z//NP8l/x//If8f/xb/E/8e/y3/Mf8p/yD/Gv8V/w//C/8Q/x3/Lf86/0D/PP8x/yT/G/8X/xf/Hf8o/zT/Ov83/yv/Hv8Y/xr/H/8h/yD/JP8t/zP/L/8k/yH/Kf8z/zX/Mv85/0n/U/9K/zb/Lv83/0L/Pv8q/xv/Gv8i/yb/J/8x/0r/Zv91/2//W/9G/zP/Iv8T/wz/Ff8r/0L/UP9Y/2H/av9w/27/Zv9d/1L/RP81/zD/Nf8//0b/S/9U/2D/Z/9l/2H/YP9h/1n/S/8//zr/Nv8v/yn/MP9I/2b/gf+V/6P/rf+r/5j/e/9k/2H/bf95/3r/eP+C/5n/rP+x/67/sv++/8D/sf+a/4z/h/+D/3z/eP99/4f/kf+Z/53/mv+R/4n/g/98/2//Yv9i/2z/df93/3z/if+b/6f/q/+u/7X/v//E/8D/sv+k/53/nP+e/6H/rP/B/9z/8f/5//r/+f/1/+r/1v+9/6b/mf+Y/6D/rf/A/9j/7f/4//j/8//u/+X/0/+7/6b/m/+Y/5n/n/+v/8f/4v/2/wAABQAFAP7/7P/V/8P/wP/M/9//9v8LAB0AKQAuACkAIQAbABsAHQAYAAwA///8/wAAAQD8//n///8NABcAFQAKAP//+v/6//r/+v/6//v//f///wEAAAD+////CgAhADkARgBIAEMAOQAmAA0A+f/6/w0AJwA8AE4AYABxAHcAcQBiAFMAQAAoAA4A/P/6/wgAHgA2AE4AYgBwAHQAcABlAFIAOQAcAAQA+f/6/wQAEQAiADcATgBeAGIAXQBZAFcAUQBEADYAMAAyADMAMwA2AEEATQBQAEwATQBbAGoAcQBvAHAAcQBnAFEAPQA5AD8AQwBGAFEAawCJAJ8AqgCwALQAtACtAJsAggBrAGEAaAB4AIcAkwCgAK8AugC6ALEApQCbAJEAhQB5AHEAdACBAJIAnwCkAKUApwCpAKgAowCgAKUArQCvAKcAowCrALwAywDUANoA4gDnAOUA3wDZANYA0ADJAMMAwgDFAMoA0ADXANwA3ADcAN8A4wDgANQAxAC2AK0ApgClAKwAuwDNANwA6wD5AAcBEAEUARMBDQEDAfsA+gD9AAABBQEQASMBNQFBAUYBSAFJAUUBOgEuASQBHgEZARIBCwEIAQ4BGgEnATEBOgFEAUoBRgE4ASsBJgEjARkBCwEEAQ0BIAEyAUEBUgFoAXkBfQF5AXQBdAF2AXgBegF6AXcBcwF0AX0BiQGSAZIBiwGBAXcBcAFsAWYBWgFMAUIBQAFCAUYBTgFbAWcBbQFuAWwBagFkAVwBWQFeAWUBZgFhAV8BZwF3AYgBmQGqAbgBwAHCAcABuQGvAaQBnAGZAZgBmwGiAa0BtgG6AbsBvgHBAb8BtwGtAaUBngGUAYkBggGCAYcBjgGTAZYBmgGeAaABoAGhAaMBpAGjAaIBoQGhAaABoQGmAbEBvgHHAcsBywHPAdcB4AHiAd0B1gHRAcsBwgG5AbkBvwHFAckBzgHXAd4B3AHUAcwBxgG/AbQBrQGrAawBqwGqAa0BtAG8AcIBygHTAdoB3gHgAeEB4wHlAecB6AHmAeIB4gHnAfAB+QEBAgwCFwIgAiMCIQIcAhcCEwIQAg4CDgINAg0CEQIXAhkCGQIaAiECKwIwAi0CJgIgAhsCGQIaAh8CJgIvAjcCQQJLAlACVAJYAl4CYgJeAlYCUAJSAlgCYQJpAnACdQJ3AncCdwJ2AnECaQJhAloCVgJSAk4CSwJKAk0CUgJaAmICZwJoAmcCZwJnAmYCZAJgAlwCWQJbAmACZgJrAnECegKEAowCkQKXAqECqAKmAp8CmAKXApgCmQKcAqACpQKqAq8CswK4Ar0CwQLCArsCrgKlAqMCowKeApUCkAKSApYCmAKaAqACqQKxArMCsgKwAq8CsAKyArMCsgKuAqoCpwKkAqYCrAK2Ar0CvwLBAsYCywLLAsYCwQLAAsICwwLDAsICwQLAAr8CvwLBAsQCyALIAsYCxALFAsgCywLMAs0CzwLPAs4C0ALUAtoC3wLiAuQC5gLpAu8C9AL3AvcC9wL3AvgC+AL6Av4CAAP+Av0CAQMIAwwDDQMQAxUDFwMTAw0DCwMNAw4DCwMEA/4C+gL7AgADBQMHAwgDCgMKAwcDAAP6AvkC/AIBAwQDBQMFAwgDDwMXAxsDGgMUAxADDwMOAw0DDQMSAxoDHwMeAxwDHQMiAyUDJQMjAx8DGwMWAxMDEAMOAw4DEQMVAxYDEwMOAwwDDQMLAwgDBAMCAwAD/gL8AvsC+wL6AvoC+wL9Av8CAAMAAwEDAwMEAwMDAQMAAwADAAMBAwIDAQMBAwMDBwMKAwkDBwMFAwQDAAP7AvcC9gL4AvkC+gL9Av4C+wL4AvYC9QLzAu0C6QLnAuUC4gLgAuEC5ALlAuUC5gLoAuoC6QLnAugC7ALvAvAC7wLtAuwC7gLwAvIC8gLzAvcC+gL6AvgC9wL6Av8CAAMAAwADAAP/AvoC9gL3AvkC9wLxAusC6ALpAusC7QLvAvEC8QLuAuoC5wLoAuoC7QLuAuwC6gLsAvAC8wLzAvIC8gLzAvQC9QL5Av4CAQMBAwEDAQMBAwEDAAMBAwEDAAP/Av4C/wL/AgEDAgMCAwAD/QL8Av0C+wL4AvUC9QL2AvUC8QLtAusC6gLqAusC7ALuAu4C7QLsAuwC7ALrAukC5wLnAukC6gLtAvMC+QL8AvoC9gL1AvUC9QL1AvMC8gLyAvIC8gLwAu4C6gLlAuAC3wLgAuIC5ALmAugC6QLkAt4C2QLZAtoC2QLVAtIC0ALOAswCygLJAscCxgLFAsgCywLOAs8CzwLPAs4CzALLAskCyALHAsgCywLOAs8CzgLNAs0CzQLNAs4CzgLNAsoCxwLFAsMCwgLBAsECwgLEAsUCxwLIAscCxQLDAsACvAK2ArMCswK1ArcCuQK8ArsCtgKyArACsQKvAqsCqAKoAqgCpgKjAqMCowKiAqECogKkAqMCoQKeApwCnAKcApwCmwKaApgClwKXApUCkgKQApACkAKRAo8CjgKPApECkQKNAoUCgAJ+An4CfgJ+An8CgQKAAn0CegJ6AnoCeAJ3AncCdgJzAnICcgJ1AncCdwJ1AnACagJmAmcCaQJrAmsCawJrAmcCYwJhAmICZAJlAmYCZwJoAmcCZgJmAmkCagJpAmYCYgJhAmECYAJfAl4CXgJfAl4CWwJZAlgCWQJaAlsCWwJZAlgCVwJZAloCWwJbAlkCWAJZAlwCXgJdAloCWQJbAl0CXgJdAl0CXwJgAl8CXQJbAlsCWwJaAlcCVgJXAloCWgJYAlYCVgJWAlUCUwJUAlYCVgJUAlECUAJQAk8CTgJOAk0CTAJKAkkCSwJOAk8CTAJIAkcCRwJIAkcCRgJHAkcCRwJIAkoCTAJMAkkCRwJGAkYCRQJEAkACPAI6AjoCOwI4AjMCMQIyAjQCNQI3AjgCNgIzAi8CLAIpAiYCJAIlAiYCJgIkAiICIAIfAhwCGAIWAhYCGQIcAhoCFgITAhUCFwIXAhMCDwIMAgsCCQIIAgcCCAIJAggCBQICAgECAgICAv8B+wH3AfMB7wHtAewB6wHqAesB7QHsAegB5AHjAeMB4AHcAdkB2AHVAdABzQHMAcoBxgHDAcMBwwHBAb4BvQG9AboBtgGzAbMBtAGzAbEBsAGxAbEBrwGsAaoBqQGpAaYBogGfAZwBmgGYAZcBlQGSAY4BjQGOAY8BjQGJAYYBgwF/AXsBeQF5AXYBcgFwAXIBdQF3AXYBdAFyAXABbgFuAXABcgFxAW4BagFnAWUBZQFlAWUBZwFpAWoBaQFnAWcBagFsAWsBaQFpAWoBaAFlAWQBZgFpAWgBZAFfAV0BXAFbAVoBWwFbAVsBWAFTAU8BTgFPAVEBUQFMAUkBSwFPAVABTgFNAU0BTQFKAUkBSwFOAU0BSQFHAUgBSAFJAUsBTgFPAU0BSgFJAUkBRwFHAUkBSwFJAUYBRAFEAUEBPQE5ATkBOQE5AToBOwE7ATgBNgE1ATYBNwE3ATcBNgE2ATcBOAE3ATQBMQEwATABMgE1ATgBOAE3ATUBNQE3ATcBNgE0ATMBMgEyATMBMwEzATIBMAEtASwBKwErASoBLAEvATABLQEqASkBLAEtASsBKQEpAScBJQEjASQBJgElASMBIwEkASQBIAEeAR0BHQEZARYBFwEaARsBGQEXARgBGAEVARMBEwEUARMBEQERARIBEQEPAQ8BEAERARABDgEOAQ0BCwEKAQsBCwEJAQcBBgEEAQEB/QD9AAABAQH/AP4AAAECAQEB/QD8AP0A/wD9APkA9ADwAO8A7wDvAPAA8ADxAPAA7gDsAOsA6wDsAO4A7gDtAOoA6QDrAO0A7QDpAOcA5QDiAN0A2QDZAN0A3gDaANQA0QDUANcA1QDRAM8AzgDMAMcAwgDCAMIAwQC/AL8AwAC/ALwAugC5ALgAtQCxALAAsQCwAK0AqgCoAKgAqACmAKUApQCnAKkAqQCpAKgApgCiAJ4AnACbAJoAmQCYAJUAkgCQAI8AjwCMAIkAiACIAIYAggB9AHsAfAB9AHwAewB4AHMAcABuAG0AbABtAG4AbwBuAGwAagBoAGUAZABlAGcAaABlAGIAXwBeAF4AXgBdAFoAVwBXAFkAWgBaAFsAWwBaAFcAVQBVAFUAVQBUAFMAUgBQAE8AUQBTAFQAVABSAFAATgBOAFEAUwBTAFIAUQBRAFIAUwBUAFMAUgBSAFYAWQBaAFgAVwBWAFQAUgBTAFYAVgBSAFAAUwBYAFoAWQBWAFUAVABWAFgAWABWAFMAUgBSAFIAUgBTAFQAVABUAFQAVQBWAFYAVwBXAFcAVgBVAFQAVABTAFUAWABZAFgAVgBUAFMAUQBRAFUAWABYAFQAUgBTAFUAVABSAFAATwBOAE0ATQBNAE0ATABLAEkASABIAEkASABEAEEAQQBDAEQAQgA/AD4APwA/AD0AOwA6ADkAOQA5ADkAOAA5ADkAOQA3ADUAMwAzADIAMgAzADUANAAxAC0AKgApACoALAAsACsAKQApACkAJwAjACEAIgAjACIAHwAfACAAHwAcABkAFwAXABkAGwAbABgAFQAYABsAFwAQAA4AEQASAA0ACgAMAA4ACwAGAAYACgAJAAYABgAHAAYAAgABAAUABQABAP3//f////7/+v/3//X/9v/3//j/9v/1//b/9//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zA3oDfAN5A3gDewN+A30DegN5A3sDfQN9A30DfgN+A3wDeQN6A38DhAODA34DfgOEA4kDiAOEA4UDiwOMA4YDgwOGA4sDiwOIA4cDiwOOA40DiQOJA40DkAOQA44DjgOSA5QDlAORA5ADkwOUA5MDkwOWA5wDoAOfA5wDmwOaA5gDmAOZA5sDmwObA5wDnwOiA6QDpQOlA6YDqAOpA6kDqQOqA6oDqgOrA6sDqgOoA6gDqwOtA60DrgOwA7ADqwOoA60DtAO1A7ADrQOyA7YDtAOxA7UDvAO9A7kDuQO+A8IDvgO5A7gDuQO5A7oDvgPCA8ADvQO9A8ADwgPAA8ADwAO/A70DvQPBA8UDxAPAA78DvwPAA8IDxAPIA8sDzAPMA8sDyQPIA8kDywPNA84DzgPOA80DzQPPA9QD2APYA9YD1QPWA9UD0wPUA9gD3QPdA9kD2APbA98D3gPbA9wD3QPbA9gD2gPgA+ED2wPWA9gD3QPfA+AD5APlA+ED3APdA+UD6QPmA+ID5APmA+UD4gPiA+YD6gPqA+oD6gPsA+0D7wPwA/ED8wP0A/YD+AP4A/gD+AP5A/oD+wP+AwEEAgQCBAEEAQQDBAUEBwQKBA4EEAQQBA8EEQQTBBQEEgQQBBIEFgQYBBcEFQQWBBcEGQQdBCQEKgQqBCUEJQQqBC8ELwQuBDEENgQ4BDcEOAQ8BD8EPgQ9BEAERQRIBEgERwRJBEsETQRNBE0ETgRPBFEEUgRTBFUEWARaBFkEWARaBF4EYARfBF4EYQRnBGgEZQRlBGkEbgRrBGYEaARvBHIEbgRrBG4EcwRzBHIEdwR+BH8EegR4BH4EgwSDBIIEgwSEBIEEfQR8BH8EgQR/BH0EfQR/BIMEhgSGBIUEhgSJBIsEigSIBIkEigSKBIkEiQSMBI8EjwSOBI4EjwSPBI8EjgSPBI8EjgSLBIgEiQSNBI4EjQSLBIwEjASMBIsEjQSOBIwEiQSHBIgEiQSHBIQEhASIBIoEiASEBIQEiASKBIoEigSLBIoEiASHBIgEigSLBIsEjASLBIoEiQSLBIwEjASLBI0EkASOBIoEiQSJBIkEiASJBIsEigSGBIUEiQSMBIkEhQSFBIgEiwSKBIkEigSMBIsEiASHBIkEjASNBIoEiQSMBI8EjwSOBJAEkwSSBI8EjgSRBJMEkgSSBJYEmgSYBJUElgSaBJoElwSUBJUElwSYBJgElgSVBJQElwSYBJgElQSUBJUElQSRBI8EkQSTBJMEkQSPBI4EjQSLBIsEjQSNBIkEhwSIBIcEhQSDBIcEiwSLBIcEhgSMBJIEkQSMBIwEjwSRBI8EjwSOBIsEhgSEBIcEiASFBIEEgASBBIAEfwSABIIEgAR8BHgEdgR2BHcEeAR5BHgEdwR2BHUEdAR0BHQEcwRxBG8EbQRsBGoEaARmBGUEYgRgBGAEYgRiBF4EWgRaBFsEWQRVBFMEVQRXBFQEUARQBFAETgRKBEkESwRKBEYERAREBEUEQwRABD8EPwQ+BDsEOAQ4BDcENQQzBDQENAQyBDAELgQtBCsEKgQrBCwEKgQmBCMEIgQhBB8EHQQcBBwEGwQYBBcEFwQVBBIEEAQQBA8EDAQLBAsECQQFBAIEAgQEBAQEAwQDBAQEBQQEBAMEBAQFBAQEAwQEBAYEBQQBBP4D/QP+A/8DAAQABP8D/gP/AwAEAQQBBAEEAQQBBAAE/wP/AwAEAQQABP0D/QMABAMEAAT7A/oDAAQEBAIE/gMABAUEBgQBBPsD+wMABAgEEQQcBC4ERQRnBJIEvQSoBI4EawQ9BDcELAQxBEIETwRRBFkEUgRKBDQEKAQfBB4EDwQIBAYEEwQTBAwE9wPrA+ED3APUA8gDswOlA6EDogOhA50DlgOTA5MDmQOfA6oDrgOyA6oDowOZA5YDlAOYA48DhQN5A3kDbQNeA04DVANKAzMDCwP4As8CiAInAlcC2wIxA1cDggOeA7QDsAO1A74D8gPzA/gD5APpAwQEMgQ/BFMEYASDBIcEeQRrBHwEeARvBGwEdwRtBFIERgRgBHAEZgRXBF0EcQR6BGYEWARUBGYESgQFBOIDHwRwBI0EZgRJBEoESgQJBP4DIgQtBMkDUwNOA4ADAwMKApoB0gH9AdEBqAH8ATMC5QFtAmwC4gA=" type="audio/x-wav" />
+        Your browser does not support the audio element.
+    </audio>
+
+
+
+
+Interactive inference
+---------------------
+
+
+
+.. code:: ipython3
+
+    import gradio as gr
+    import numpy as np
+    from transformers import AutoFeatureExtractor, set_seed
+    
+    
+    title = "Text-to-speech (TTS) with Parler-TTS and OpenVINO"
+    feature_extractor = AutoFeatureExtractor.from_pretrained(repo_id)
+    SAMPLE_RATE = feature_extractor.sampling_rate
+    
+    
+    def infer(prompt, description, seed):
+        set_seed(seed)
+    
+        input_ids = tokenizer(description, return_tensors="pt").input_ids.to("cpu")
+        prompt_input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to("cpu")
+    
+        generation = model.generate(input_ids=input_ids, prompt_input_ids=prompt_input_ids)
+        audio_arr = generation.cpu().numpy().squeeze()
+        sr = SAMPLE_RATE
+    
+        return sr, audio_arr
+    
+    
+    demo = gr.Interface(
+        infer,
+        [
+            gr.Text(label="Prompt"),
+            gr.Text(label="Description"),
+            gr.Slider(
+                label="Seed",
+                value=42,
+                step=1,
+                minimum=0,
+                maximum=np.iinfo(np.int32).max,
+            ),
+        ],
+        gr.Audio(label="Output Audio", type="numpy"),
+        title=title,
+        description=description,
+        examples=[
+            [
+                "Hey, how are you doing today?",
+                "A female speaker with a slightly low-pitched voice delivers her words quite expressively, in a very confined sounding environment with clear audio quality. She speaks very fast.",
+            ],
+            [
+                "'This is the best time of my life, Bartley,' she said happily.",
+                "A female speaker with a slightly low-pitched, quite monotone voice delivers her words at a slightly faster-than-average pace in a confined space with very clear audio.",
+            ],
+            [
+                "Montrose also, after having experienced still more variety of good and bad fortune, threw down his arms, and retired out of the kingdom.	",
+                "A male speaker with a slightly high-pitched voice delivering his words at a slightly slow pace in a small, confined space with a touch of background noise and a quite monotone tone.",
+            ],
+            [
+                "montrose also after having experienced still more variety of good and bad fortune threw down his arms and retired out of the kingdom",
+                "A male speaker with a low-pitched voice delivering his words at a fast pace in a small, confined space with a lot of background noise and an animated tone.",
+            ],
+        ],
+    )
+    
+    try:
+        demo.queue().launch(debug=False)
+    except Exception:
+        demo.queue().launch(share=True, debug=False)
+    # if you are launching remotely, specify server_name and server_port
+    # demo.launch(server_name='your server name', server_port='server port in int')
+    # Read more in the docs: https://gradio.app/docs/
+
+
+.. parsed-literal::
+
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+      warnings.warn(
+
+
+.. parsed-literal::
+
+    Running on local URL:  http://127.0.0.1:7860
+    
+    To create a public link, set `share=True` in `launch()`.
+
+
+
+
+
+
+
diff --git a/docs/notebooks/person-tracking-with-output.rst b/docs/notebooks/person-tracking-with-output.rst
index d4a89a224c0e2e..de154673d33860 100644
--- a/docs/notebooks/person-tracking-with-output.rst
+++ b/docs/notebooks/person-tracking-with-output.rst
@@ -93,8 +93,8 @@ realtime tracking,” in ICIP, 2016, pp. 3464–3468.
 
 .. |deepsort| image:: https://user-images.githubusercontent.com/91237924/221744683-0042eff8-2c41-43b8-b3ad-b5929bafb60b.png
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Imports <#imports>`__
 -  `Download the Model <#download-the-model>`__
diff --git a/docs/notebooks/person-tracking-with-output_files/person-tracking-with-output_25_0.png b/docs/notebooks/person-tracking-with-output_files/person-tracking-with-output_25_0.png
index d4834df21e0fe3..73b9f2260a98ad 100644
--- a/docs/notebooks/person-tracking-with-output_files/person-tracking-with-output_25_0.png
+++ b/docs/notebooks/person-tracking-with-output_files/person-tracking-with-output_25_0.png
@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:760c838d2fd407c95d5c8df5e0b82b7e132ab78fe46ae939e2319655a1865164
-size 219822
+oid sha256:1f4e2da1e186f32a19b00a97f08eaea9a6780a36d507fd485167772887db27a0
+size 219650
diff --git a/docs/notebooks/photo-maker-with-output.rst b/docs/notebooks/photo-maker-with-output.rst
index f68e5df0a7d911..acc4d7fa46e453 100644
--- a/docs/notebooks/photo-maker-with-output.rst
+++ b/docs/notebooks/photo-maker-with-output.rst
@@ -18,8 +18,8 @@ report <https://arxiv.org/pdf/2312.04461.pdf>`__.
 This notebook explores how to speed up PhotoMaker pipeline using
 OpenVINO.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `PhotoMaker pipeline
    introduction <#photomaker-pipeline-introduction>`__
@@ -72,7 +72,7 @@ Clone PhotoMaker repository
 .. code:: ipython3
 
     from pathlib import Path
-
+    
     if not Path("PhotoMaker").exists():
         !git clone https://github.com/TencentARC/PhotoMaker.git
 
@@ -81,10 +81,10 @@ Clone PhotoMaker repository
 
     Cloning into 'PhotoMaker'...
     remote: Enumerating objects: 236, done.
-    remote: Counting objects: 100% (143/143), done.
+    remote: Counting objects: 100% (145/145), done.
     remote: Compressing objects: 100% (96/96), done.
-    remote: Total 236 (delta 113), reused 64 (delta 47), pack-reused 93
-    Receiving objects: 100% (236/236), 9.31 MiB | 25.70 MiB/s, done.
+    remote: Total 236 (delta 114), reused 68 (delta 49), pack-reused 91
+    Receiving objects: 100% (236/236), 9.31 MiB | 20.87 MiB/s, done.
     Resolving deltas: 100% (120/120), done.
 
 
@@ -93,11 +93,13 @@ Install required packages
 .. code:: ipython3
 
     %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu\
-    transformers "torch>=2.1" "diffusers>=0.26" "gradio>=4.19" "openvino>=2024.0.0" torchvision "peft==0.6.2" "nncf>=2.9.0"
+    transformers "torch>=2.1" "diffusers>=0.26" "gradio>=4.19" "openvino>=2024.0.0" torchvision "peft==0.6.2" "nncf>=2.9.0" "protobuf==3.20.3"
 
 
 .. parsed-literal::
 
+    ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
+    descript-audiotools 0.7.2 requires protobuf<3.20,>=3.9.2, but you have protobuf 3.20.3 which is incompatible.
     Note: you may need to restart the kernel to use updated packages.
 
 
@@ -107,7 +109,7 @@ Prepare PyTorch models
 
     adapter_id = "TencentARC/PhotoMaker"
     base_model_id = "SG161222/RealVisXL_V3.0"
-
+    
     TEXT_ENCODER_OV_PATH = Path("model/text_encoder.xml")
     TEXT_ENCODER_2_OV_PATH = Path("model/text_encoder_2.xml")
     UNET_OV_PATH = Path("model/unet.xml")
@@ -135,14 +137,14 @@ PhotoMaker to generate the original PhotoMaker pipeline.
     from PhotoMaker.photomaker.pipeline import PhotoMakerStableDiffusionXLPipeline
     from diffusers import EulerDiscreteScheduler
     import gc
-
+    
     trigger_word = "img"
-
-
+    
+    
     def load_original_pytorch_pipeline_components(photomaker_path: str, base_model_id: str):
         # Load base model
         pipe = PhotoMakerStableDiffusionXLPipeline.from_pretrained(base_model_id, use_safetensors=True).to("cpu")
-
+    
         # Load PhotoMaker checkpoint
         pipe.load_photomaker_adapter(
             os.path.dirname(photomaker_path),
@@ -158,20 +160,18 @@ PhotoMaker to generate the original PhotoMaker pipeline.
 
 .. parsed-literal::
 
-    2024-06-06 01:04:44.222965: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-06-06 01:04:44.256910: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-07-02 01:14:00.526210: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-07-02 01:14:00.560468: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2024-06-06 01:04:44.912971: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/transformers/transformer_2d.py:34: FutureWarning: `Transformer2DModelOutput` is deprecated and will be removed in version 1.0.0. Importing `Transformer2DModelOutput` from `diffusers.models.transformer_2d` is deprecated and this will be removed in a future version. Please use `from diffusers.models.modeling_outputs import Transformer2DModelOutput`, instead.
-      deprecate("Transformer2DModelOutput", "1.0.0", deprecation_message)
+    2024-07-02 01:14:01.215053: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 .. code:: ipython3
 
     from huggingface_hub import hf_hub_download
-
+    
     photomaker_path = hf_hub_download(repo_id=adapter_id, filename="photomaker-v1.bin", repo_type="model")
-
+    
     pipe = load_original_pytorch_pipeline_components(photomaker_path, base_model_id)
 
 
@@ -181,6 +181,11 @@ PhotoMaker to generate the original PhotoMaker pipeline.
     Loading pipeline components...:   0%|          | 0/7 [00:00<?, ?it/s]
 
 
+.. parsed-literal::
+
+    The installed version of bitsandbytes was compiled without GPU support. 8-bit optimizers, 8-bit multiplication, and GPU quantization are unavailable.
+
+
 .. parsed-literal::
 
     Loading PhotoMaker components [1] id_encoder from [/opt/home/k8sworker/.cache/huggingface/hub/models--TencentARC--PhotoMaker/snapshots/d7ec3fc17290263135825194aeb3bc456da67cc5]...
@@ -238,8 +243,8 @@ documentation <https://docs.openvino.ai/2023.3/weight_compression.html>`__.
 
     import openvino as ov
     import nncf
-
-
+    
+    
     def flattenize_inputs(inputs):
         """
         Helper function for resolve nested input structure (e.g. lists or tuples of tensors)
@@ -253,8 +258,8 @@ documentation <https://docs.openvino.ai/2023.3/weight_compression.html>`__.
             else:
                 flatten_inputs.append(input_data)
         return flatten_inputs
-
-
+    
+    
     dtype_mapping = {
         torch.float32: ov.Type.f32,
         torch.float64: ov.Type.f64,
@@ -262,8 +267,8 @@ documentation <https://docs.openvino.ai/2023.3/weight_compression.html>`__.
         torch.int64: ov.Type.i64,
         torch.bool: ov.Type.boolean,
     }
-
-
+    
+    
     def prepare_input_info(input_dict):
         """
         Helper function for preparing input info (shapes and data types) for conversion based on example inputs
@@ -276,8 +281,8 @@ documentation <https://docs.openvino.ai/2023.3/weight_compression.html>`__.
                 updated_shape[1] = -1
             input_info.append((dtype_mapping[input_data.dtype], updated_shape))
         return input_info
-
-
+    
+    
     def convert(model: torch.nn.Module, xml_path: str, example_input, input_info):
         """
         Helper function for converting PyTorch model to OpenVINO IR
@@ -289,7 +294,7 @@ documentation <https://docs.openvino.ai/2023.3/weight_compression.html>`__.
                 ov_model = ov.convert_model(model, example_input=example_input, input=input_info)
             ov_model = nncf.compress_weights(ov_model)
             ov.save_model(ov_model, xml_path)
-
+    
             del ov_model
             torch._C._jit_clear_class_registry()
             torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore()
@@ -314,23 +319,23 @@ output(text embeddings) which will be the input for U-Net model.
 
     id_encoder = pipe.id_encoder
     id_encoder.eval()
-
-
+    
+    
     def create_bool_tensor(*size):
         new_tensor = torch.zeros((size), dtype=torch.bool)
         return new_tensor
-
-
+    
+    
     inputs = {
         "id_pixel_values": torch.randn((1, 1, 3, 224, 224)),
         "prompt_embeds": torch.randn((1, 77, 2048)),
         "class_tokens_mask": create_bool_tensor(1, 77),
     }
-
+    
     input_info = prepare_input_info(inputs)
-
+    
     convert(id_encoder, ID_ENCODER_OV_PATH, inputs, input_info)
-
+    
     del id_encoder
     gc.collect()
 
@@ -343,13 +348,13 @@ output(text embeddings) which will be the input for U-Net model.
 .. parsed-literal::
 
     [ WARNING ]  Please fix your imports. Module %s has been moved to %s. The old module will be deleted in version %s.
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4481: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4371: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
       warnings.warn(
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:276: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:279: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:316: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:319: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/photo-maker/PhotoMaker/photomaker/model.py:84: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/photo-maker/PhotoMaker/photomaker/model.py:84: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       assert class_tokens_mask.sum() == stacked_id_embeds.shape[0], f"{class_tokens_mask.sum()} != {stacked_id_embeds.shape[0]}"
 
 
@@ -370,17 +375,17 @@ output(text embeddings) which will be the input for U-Net model.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -388,7 +393,7 @@ output(text embeddings) which will be the input for U-Net model.
 
 .. parsed-literal::
 
-    19039
+    15594
 
 
 
@@ -409,19 +414,19 @@ sequence of latent text embeddings.
     text_encoder.eval()
     text_encoder_2 = pipe.text_encoder_2
     text_encoder_2.eval()
-
+    
     text_encoder.config.output_hidden_states = True
     text_encoder.config.return_dict = False
     text_encoder_2.config.output_hidden_states = True
     text_encoder_2.config.return_dict = False
-
+    
     inputs = {"input_ids": torch.ones((1, 77), dtype=torch.long)}
-
+    
     input_info = prepare_input_info(inputs)
-
+    
     convert(text_encoder, TEXT_ENCODER_OV_PATH, inputs, input_info)
     convert(text_encoder_2, TEXT_ENCODER_2_OV_PATH, inputs, input_info)
-
+    
     del text_encoder
     del text_encoder_2
     gc.collect()
@@ -429,11 +434,11 @@ sequence of latent text embeddings.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_attn_mask_utils.py:86: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_attn_mask_utils.py:86: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if input_shape[-1] > 1 or self.sliding_window is not None:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_attn_mask_utils.py:162: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_attn_mask_utils.py:162: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if past_key_values_length > 0:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:284: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:287: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if causal_attention_mask.size() != (bsz, 1, tgt_len, src_len):
 
 
@@ -443,7 +448,7 @@ sequence of latent text embeddings.
     ┍━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┑
     │   Num bits (N) │ % all parameters (layers)   │ % ratio-defining parameters (layers)   │
     ┝━━━━━━━━━━━━━━━━┿━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┿━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┥
-    │              8 │ 100% (74 / 74)              │ 100% (74 / 74)                         │
+    │              8 │ 100% (73 / 73)              │ 100% (73 / 73)                         │
     ┕━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┙
 
 
@@ -454,17 +459,17 @@ sequence of latent text embeddings.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -474,7 +479,7 @@ sequence of latent text embeddings.
     ┍━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┑
     │   Num bits (N) │ % all parameters (layers)   │ % ratio-defining parameters (layers)   │
     ┝━━━━━━━━━━━━━━━━┿━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┿━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┥
-    │              8 │ 100% (195 / 195)            │ 100% (195 / 195)                       │
+    │              8 │ 100% (194 / 194)            │ 100% (194 / 194)                       │
     ┕━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┙
 
 
@@ -485,17 +490,17 @@ sequence of latent text embeddings.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -503,7 +508,7 @@ sequence of latent text embeddings.
 
 .. parsed-literal::
 
-    38600
+    32816
 
 
 
@@ -519,13 +524,13 @@ original Stable Diffusion XL model.
 
     unet = pipe.unet
     unet.eval()
-
-
+    
+    
     class UnetWrapper(torch.nn.Module):
         def __init__(self, unet):
             super().__init__()
             self.unet = unet
-
+    
         def forward(
             self,
             sample=None,
@@ -540,8 +545,8 @@ original Stable Diffusion XL model.
                 encoder_hidden_states,
                 added_cond_kwargs={"text_embeds": text_embeds, "time_ids": time_ids},
             )
-
-
+    
+    
     inputs = {
         "sample": torch.rand([2, 4, 128, 128], dtype=torch.float32),
         "timestep": torch.from_numpy(np.array(1, dtype=float)),
@@ -549,27 +554,27 @@ original Stable Diffusion XL model.
         "text_embeds": torch.rand([2, 1280], dtype=torch.float32),
         "time_ids": torch.rand([2, 6], dtype=torch.float32),
     }
-
+    
     input_info = prepare_input_info(inputs)
-
+    
     w_unet = UnetWrapper(unet)
     convert(w_unet, UNET_OV_PATH, inputs, input_info)
-
+    
     del w_unet, unet
     gc.collect()
 
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/unets/unet_2d_condition.py:1114: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/unets/unet_2d_condition.py:1103: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if dim % default_overall_up_factor != 0:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/downsampling.py:136: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/downsampling.py:136: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       assert hidden_states.shape[1] == self.channels
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/downsampling.py:145: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/downsampling.py:145: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       assert hidden_states.shape[1] == self.channels
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/upsampling.py:146: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/upsampling.py:146: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       assert hidden_states.shape[1] == self.channels
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/upsampling.py:162: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/upsampling.py:162: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if hidden_states.shape[0] >= 64:
 
 
@@ -590,17 +595,17 @@ original Stable Diffusion XL model.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -608,7 +613,7 @@ original Stable Diffusion XL model.
 
 .. parsed-literal::
 
-    112933
+    101843
 
 
 
@@ -629,22 +634,22 @@ VAE decoder.
 
     vae_decoder = pipe.vae
     vae_decoder.eval()
-
-
+    
+    
     class VAEDecoderWrapper(torch.nn.Module):
         def __init__(self, vae_decoder):
             super().__init__()
             self.vae = vae_decoder
-
+    
         def forward(self, latents):
             return self.vae.decode(latents)
-
-
+    
+    
     w_vae_decoder = VAEDecoderWrapper(vae_decoder)
     inputs = torch.zeros((1, 4, 128, 128))
-
+    
     convert(w_vae_decoder, VAE_DECODER_OV_PATH, inputs, input_info=[1, 4, 128, 128])
-
+    
     del w_vae_decoder, vae_decoder
     gc.collect()
 
@@ -666,17 +671,17 @@ VAE decoder.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -684,7 +689,7 @@ VAE decoder.
 
 .. parsed-literal::
 
-    6285
+    6017
 
 
 
@@ -705,16 +710,16 @@ Select inference device for Stable Diffusion pipeline
 .. code:: ipython3
 
     import ipywidgets as widgets
-
+    
     core = ov.Core()
-
+    
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
         value="CPU",
         description="Device:",
         disabled=False,
     )
-
+    
     device
 
 
@@ -750,17 +755,17 @@ model objects and inference output must be converted from numpy to
 .. code:: ipython3
 
     from collections import namedtuple
-
-
+    
+    
     class OVIDEncoderWrapper(PhotoMakerIDEncoder):
         dtype = torch.float32  # accessed in the original workflow
-
+    
         def __init__(self, id_encoder, orig_id_encoder):
             super().__init__()
             self.id_encoder = id_encoder
             self.modules = orig_id_encoder.modules  # accessed in the original workflow
             self.config = orig_id_encoder.config  # accessed in the original workflow
-
+    
         def __call__(
             self,
             *args,
@@ -778,21 +783,21 @@ model objects and inference output must be converted from numpy to
 
     class OVTextEncoderWrapper:
         dtype = torch.float32  # accessed in the original workflow
-
+    
         def __init__(self, text_encoder, orig_text_encoder):
             self.text_encoder = text_encoder
             self.modules = orig_text_encoder.modules  # accessed in the original workflow
             self.config = orig_text_encoder.config  # accessed in the original workflow
-
+    
         def __call__(self, input_ids, **kwargs):
             inputs = {"input_ids": input_ids}
             output = self.text_encoder(inputs)
-
+    
             hidden_states = []
             hidden_states_len = len(output)
             for i in range(1, hidden_states_len):
                 hidden_states.append(torch.from_numpy(output[i]))
-
+    
             BaseModelOutputWithPooling = namedtuple("BaseModelOutputWithPooling", "last_hidden_state hidden_states")
             output = BaseModelOutputWithPooling(torch.from_numpy(output[0]), hidden_states)
             return output
@@ -804,7 +809,7 @@ model objects and inference output must be converted from numpy to
             self.unet = unet
             self.config = unet_orig.config  # accessed in the original workflow
             self.add_embedding = unet_orig.add_embedding  # accessed in the original workflow
-
+    
         def __call__(self, *args, **kwargs):
             latent_model_input, t = args
             inputs = {
@@ -814,24 +819,24 @@ model objects and inference output must be converted from numpy to
                 "text_embeds": kwargs["added_cond_kwargs"]["text_embeds"],
                 "time_ids": kwargs["added_cond_kwargs"]["time_ids"],
             }
-
+    
             output = self.unet(inputs)
-
+    
             return [torch.from_numpy(output[0])]
 
 .. code:: ipython3
 
     class OVVAEDecoderWrapper:
         dtype = torch.float32  # accessed in the original workflow
-
+    
         def __init__(self, vae, vae_orig):
             self.vae = vae
             self.config = vae_orig.config  # accessed in the original workflow
-
+    
         def decode(self, latents, return_dict=False):
             output = self.vae(latents)[0]
             output = torch.from_numpy(output)
-
+    
             return [output]
 
 Replace the PyTorch model objects in original pipeline with OpenVINO
@@ -853,22 +858,22 @@ Running Text-to-Image Generation with OpenVINO
 .. code:: ipython3
 
     from diffusers.utils import load_image
-
+    
     prompt = "sci-fi, closeup portrait photo of a man img in Iron man suit, face"
     negative_prompt = "(asymmetry, worst quality, low quality, illustration, 3d, 2d, painting, cartoons, sketch), open mouth"
     generator = torch.Generator("cpu").manual_seed(42)
-
+    
     input_id_images = []
     original_image = load_image("./PhotoMaker/examples/newton_man/newton_0.jpg")
     input_id_images.append(original_image)
-
+    
     ## Parameter setting
     num_steps = 20
     style_strength_ratio = 20
     start_merge_step = int(float(style_strength_ratio) / 100 * num_steps)
     if start_merge_step > 30:
         start_merge_step = 30
-
+    
     images = pipe(
         prompt=prompt,
         input_id_images=input_id_images,
@@ -889,12 +894,12 @@ Running Text-to-Image Generation with OpenVINO
 .. code:: ipython3
 
     import matplotlib.pyplot as plt
-
-
+    
+    
     def visualize_results(orig_img: Image.Image, output_img: Image.Image):
         """
         Helper function for pose estimationresults visualization
-
+    
         Parameters:
            orig_img (Image.Image): original image
            output_img (Image.Image): processed image with PhotoMaker
@@ -928,8 +933,8 @@ Running Text-to-Image Generation with OpenVINO
         fig.subplots_adjust(wspace=0.01 if is_horizontal else 0.00, hspace=0.01 if is_horizontal else 0.1)
         fig.tight_layout()
         return fig
-
-
+    
+    
     fig = visualize_results(original_image, images[0])
 
 
@@ -945,12 +950,12 @@ Interactive Demo
 .. code:: ipython3
 
     import gradio as gr
-
-
+    
+    
     def generate_from_text(text_promt, input_image, neg_prompt, seed, num_steps, style_strength_ratio):
         """
         Helper function for generating result image from prompt text
-
+    
         Parameters:
            text_promt (String): positive prompt
            input_image (Image.Image): original image
@@ -958,7 +963,7 @@ Interactive Demo
            seed (Int):  seed for random generator state initialization
            num_steps (Int): number of sampling steps
            style_strength_ratio (Int):  the percentage of step when merging the ID embedding to text embedding
-
+    
         Returns:
            result (Image.Image): generation result
         """
@@ -976,10 +981,10 @@ Interactive Demo
             height=1024,
             width=1024,
         ).images[0]
-
+    
         return result
-
-
+    
+    
     with gr.Blocks() as demo:
         with gr.Column():
             with gr.Row():
@@ -1030,8 +1035,8 @@ Interactive Demo
                 ],
                 [positive_input, neg_input],
             )
-
-
+    
+    
     demo.queue().launch()
     # if you are launching remotely, specify server_name and server_port
     # demo.launch(server_name='your server name', server_port='server port in int')
@@ -1041,7 +1046,7 @@ Interactive Demo
 .. parsed-literal::
 
     Running on local URL:  http://127.0.0.1:7860
-
+    
     To create a public link, set `share=True` in `launch()`.
 
 
@@ -1055,6 +1060,8 @@ Interactive Demo
 
 
 
+    
+
 
 
 .. code:: ipython3
diff --git a/docs/notebooks/photo-maker-with-output_files/photo-maker-with-output_33_0.png b/docs/notebooks/photo-maker-with-output_files/photo-maker-with-output_33_0.png
index 432b991abd9bfc..1b3e9b593547c2 100644
--- a/docs/notebooks/photo-maker-with-output_files/photo-maker-with-output_33_0.png
+++ b/docs/notebooks/photo-maker-with-output_files/photo-maker-with-output_33_0.png
@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:1c87da41df00051b45c0f4d64d7b6875c6d20586c68ea6f375f02317d38b6522
-size 357510
+oid sha256:d46147027a932e2aca3ae9f0be761084fe464945b43bb86af256881545ba5431
+size 357844
diff --git a/docs/notebooks/pix2struct-docvqa-with-output.rst b/docs/notebooks/pix2struct-docvqa-with-output.rst
index 8a4f622859cd0d..9fabd7713720aa 100644
--- a/docs/notebooks/pix2struct-docvqa-with-output.rst
+++ b/docs/notebooks/pix2struct-docvqa-with-output.rst
@@ -42,8 +42,8 @@ library. To simplify the user experience, the `Hugging Face
 Optimum <https://huggingface.co/docs/optimum>`__ library is used to
 convert the model to OpenVINO™ IR format.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `About Pix2Struct <#about-pix2struct>`__
 -  `Prerequisites <#prerequisites>`__
diff --git a/docs/notebooks/pixart-with-output.rst b/docs/notebooks/pixart-with-output.rst
new file mode 100644
index 00000000000000..7484d01f11d852
--- /dev/null
+++ b/docs/notebooks/pixart-with-output.rst
@@ -0,0 +1,506 @@
+PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis with OpenVINO
+=========================================================================================================
+
+`This paper <https://arxiv.org/abs/2310.00426>`__ introduces
+`PIXART-α <https://github.com/PixArt-alpha/PixArt-alpha>`__, a
+Transformer-based T2I diffusion model whose image generation quality is
+competitive with state-of-the-art image generators, reaching
+near-commercial application standards. Additionally, it supports
+high-resolution image synthesis up to 1024px resolution with low
+training cost. To achieve this goal, three core designs are proposed: 1.
+Training strategy decomposition: We devise three distinct training steps
+that separately optimize pixel dependency, text-image alignment, and
+image aesthetic quality; 2. Efficient T2I Transformer: We incorporate
+cross-attention modules into Diffusion Transformer (DiT) to inject text
+conditions and streamline the computation-intensive class-condition
+branch; 3. High-informative data: We emphasize the significance of
+concept density in text-image pairs and leverage a large Vision-Language
+model to auto-label dense pseudo-captions to assist text-image alignment
+learning.
+
+|image0|
+
+**Table of contents:**
+
+
+-  `Prerequisites <#prerequisites>`__
+-  `Load the original model <#load-the-original-model>`__
+-  `Convert the model to OpenVINO
+   IR <#convert-the-model-to-openvino-ir>`__
+
+   -  `Convert text encoder <#convert-text-encoder>`__
+   -  `Convert transformer <#convert-transformer>`__
+   -  `Convert VAE decoder <#convert-vae-decoder>`__
+
+-  `Compiling models <#compiling-models>`__
+-  `Building the pipeline <#building-the-pipeline>`__
+-  `Interactive inference <#interactive-inference>`__
+
+.. |image0| image:: https://huggingface.co/PixArt-alpha/PixArt-XL-2-1024-MS/resolve/main/asset/images/teaser.png
+
+Prerequisites
+-------------
+
+
+
+.. code:: ipython3
+
+    %pip install -q "diffusers>=0.14.0" sentencepiece "datasets>=2.14.6" "transformers>=4.25.1" "gradio>=4.19" "torch>=2.1" Pillow opencv-python --extra-index-url https://download.pytorch.org/whl/cpu
+    %pip install --pre -Uq openvino --extra-index-url https://storage.openvinotoolkit.org/simple/wheels/nightly
+
+
+.. parsed-literal::
+
+    Note: you may need to restart the kernel to use updated packages.
+    ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
+    openvino-dev 2024.2.0 requires openvino==2024.2.0, but you have openvino 2024.3.0.dev20240627 which is incompatible.
+    Note: you may need to restart the kernel to use updated packages.
+
+
+Load and run the original pipeline
+----------------------------------
+
+
+
+We use
+`PixArt-LCM-XL-2-1024-MS <https://huggingface.co/PixArt-alpha/PixArt-LCM-XL-2-1024-MS>`__
+that uses LCMs. `LCMs <https://arxiv.org/abs/2310.04378>`__ is a
+diffusion distillation method which predict ``PF-ODE's`` solution
+directly in latent space, achieving super fast inference with few steps.
+
+.. code:: ipython3
+
+    import torch
+    from diffusers import PixArtAlphaPipeline
+
+
+    pipe = PixArtAlphaPipeline.from_pretrained("PixArt-alpha/PixArt-LCM-XL-2-1024-MS", use_safetensors=True)
+
+    prompt = "A small cactus with a happy face in the Sahara desert."
+    generator = torch.Generator().manual_seed(42)
+
+    image = pipe(prompt, guidance_scale=0.0, num_inference_steps=4, generator=generator).images[0]
+
+
+.. parsed-literal::
+
+    2024-07-02 01:22:15.056189: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-07-02 01:22:15.090603: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
+    2024-07-02 01:22:15.773162: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+
+
+
+.. parsed-literal::
+
+    Loading pipeline components...:   0%|          | 0/5 [00:00<?, ?it/s]
+
+
+.. parsed-literal::
+
+    Some weights of the model checkpoint were not used when initializing PixArtTransformer2DModel:
+     ['caption_projection.y_embedding']
+    You are using the default legacy behaviour of the <class 'transformers.models.t5.tokenization_t5.T5Tokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565
+
+
+
+.. parsed-literal::
+
+    Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]
+
+
+.. parsed-literal::
+
+    The installed version of bitsandbytes was compiled without GPU support. 8-bit optimizers, 8-bit multiplication, and GPU quantization are unavailable.
+
+
+
+.. parsed-literal::
+
+      0%|          | 0/4 [00:00<?, ?it/s]
+
+
+.. code:: ipython3
+
+    image
+
+
+
+
+.. image:: pixart-with-output_files/pixart-with-output_5_0.png
+
+
+
+Convert the model to OpenVINO IR
+--------------------------------
+
+
+
+Let’s define the conversion function for PyTorch modules. We use
+``ov.convert_model`` function to obtain OpenVINO Intermediate
+Representation object and ``ov.save_model`` function to save it as XML
+file.
+
+.. code:: ipython3
+
+    from pathlib import Path
+
+    import numpy as np
+    import torch
+
+    import openvino as ov
+
+
+    def convert(model: torch.nn.Module, xml_path: str, example_input):
+        xml_path = Path(xml_path)
+        if not xml_path.exists():
+            xml_path.parent.mkdir(parents=True, exist_ok=True)
+            model.eval()
+            with torch.no_grad():
+                converted_model = ov.convert_model(model, example_input=example_input)
+            ov.save_model(converted_model, xml_path)
+
+            # cleanup memory
+            torch._C._jit_clear_class_registry()
+            torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore()
+            torch.jit._state._clear_class_state()
+
+PixArt-α consists of pure transformer blocks for latent diffusion: It
+can directly generate 1024px images from text prompts within a single
+sampling process.
+
+|image01|.
+
+During inference it uses text encoder ``T5EncoderModel``, transformer
+``Transformer2DModel`` and VAE decoder ``AutoencoderKL``. Let’s convert
+the models from the pipeline one by one.
+
+.. |image01| image:: https://huggingface.co/PixArt-alpha/PixArt-XL-2-1024-MS/resolve/main/asset/images/model.png
+
+.. code:: ipython3
+
+    MODEL_DIR = Path("model")
+
+    TEXT_ENCODER_PATH = MODEL_DIR / "text_encoder.xml"
+    TRANSFORMER_OV_PATH = MODEL_DIR / "transformer_ir.xml"
+    VAE_DECODER_PATH = MODEL_DIR / "vae_decoder.xml"
+
+Convert text encoder
+~~~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    example_input = {
+        "input_ids": torch.zeros(1, 120, dtype=torch.int64),
+        "attention_mask": torch.zeros(1, 120, dtype=torch.int64),
+    }
+
+    convert(pipe.text_encoder, TEXT_ENCODER_PATH, example_input)
+
+
+.. parsed-literal::
+
+    WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.base has been moved to tensorflow.python.trackable.base. The old module will be deleted in version 2.11.
+
+
+.. parsed-literal::
+
+    [ WARNING ]  Please fix your imports. Module %s has been moved to %s. The old module will be deleted in version %s.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4371: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
+      warnings.warn(
+
+
+Convert transformer
+~~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    class TransformerWrapper(torch.nn.Module):
+        def __init__(self, transformer):
+            super().__init__()
+            self.transformer = transformer
+
+        def forward(self, hidden_states=None, timestep=None, encoder_hidden_states=None, encoder_attention_mask=None, resolution=None, aspect_ratio=None):
+
+            return self.transformer.forward(
+                hidden_states,
+                timestep=timestep,
+                encoder_hidden_states=encoder_hidden_states,
+                encoder_attention_mask=encoder_attention_mask,
+                added_cond_kwargs={"resolution": resolution, "aspect_ratio": aspect_ratio},
+            )
+
+
+    example_input = {
+        "hidden_states": torch.rand([2, 4, 128, 128], dtype=torch.float32),
+        "timestep": torch.tensor([999, 999]),
+        "encoder_hidden_states": torch.rand([2, 120, 4096], dtype=torch.float32),
+        "encoder_attention_mask": torch.rand([2, 120], dtype=torch.float32),
+        "resolution": torch.tensor([[1024.0, 1024.0], [1024.0, 1024.0]]),
+        "aspect_ratio": torch.tensor([[1.0], [1.0]]),
+    }
+
+
+    w_transformer = TransformerWrapper(pipe.transformer)
+    convert(w_transformer, TRANSFORMER_OV_PATH, example_input)
+
+
+.. parsed-literal::
+
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/embeddings.py:219: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+      if self.height != height or self.width != width:
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/attention_processor.py:682: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+      if current_length != target_length:
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/attention_processor.py:697: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+      if attention_mask.shape[0] < batch_size * head_size:
+
+
+Convert VAE decoder
+~~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    class VAEDecoderWrapper(torch.nn.Module):
+
+        def __init__(self, vae):
+            super().__init__()
+            self.vae = vae
+
+        def forward(self, latents):
+            return self.vae.decode(latents, return_dict=False)
+
+
+    convert(VAEDecoderWrapper(pipe.vae), VAE_DECODER_PATH, (torch.zeros((1, 4, 128, 128))))
+
+
+.. parsed-literal::
+
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/upsampling.py:146: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+      assert hidden_states.shape[1] == self.channels
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/upsampling.py:162: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+      if hidden_states.shape[0] >= 64:
+
+
+Compiling models
+----------------
+
+
+
+Select device from dropdown list for running inference using OpenVINO.
+
+.. code:: ipython3
+
+    import ipywidgets as widgets
+
+    core = ov.Core()
+    device = widgets.Dropdown(
+        options=core.available_devices + ["AUTO"],
+        value="AUTO",
+        description="Device:",
+        disabled=False,
+    )
+
+    device
+
+
+
+
+.. parsed-literal::
+
+    Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
+
+
+
+.. code:: ipython3
+
+    compiled_model = core.compile_model(TRANSFORMER_OV_PATH)
+    compiled_vae = core.compile_model(VAE_DECODER_PATH)
+    compiled_text_encoder = core.compile_model(TEXT_ENCODER_PATH)
+
+Building the pipeline
+---------------------
+
+
+
+Let’s create callable wrapper classes for compiled models to allow
+interaction with original pipelines. Note that all of wrapper classes
+return ``torch.Tensor``\ s instead of ``np.array``\ s.
+
+.. code:: ipython3
+
+    from collections import namedtuple
+
+    EncoderOutput = namedtuple("EncoderOutput", "last_hidden_state")
+
+
+    class TextEncoderWrapper(torch.nn.Module):
+        def __init__(self, text_encoder, dtype):
+            super().__init__()
+            self.text_encoder = text_encoder
+            self.dtype = dtype
+
+        def forward(self, input_ids=None, attention_mask=None):
+            inputs = {
+                "input_ids": input_ids,
+                "attention_mask": attention_mask,
+            }
+            last_hidden_state = self.text_encoder(inputs)[0]
+            return EncoderOutput(torch.from_numpy(last_hidden_state))
+
+.. code:: ipython3
+
+    class TransformerWrapper(torch.nn.Module):
+        def __init__(self, transformer, config):
+            super().__init__()
+            self.transformer = transformer
+            self.config = config
+
+        def forward(
+            self,
+            hidden_states=None,
+            timestep=None,
+            encoder_hidden_states=None,
+            encoder_attention_mask=None,
+            resolution=None,
+            aspect_ratio=None,
+            added_cond_kwargs=None,
+            **kwargs
+        ):
+            inputs = {
+                "hidden_states": hidden_states,
+                "timestep": timestep,
+                "encoder_hidden_states": encoder_hidden_states,
+                "encoder_attention_mask": encoder_attention_mask,
+            }
+            resolution = added_cond_kwargs["resolution"]
+            aspect_ratio = added_cond_kwargs["aspect_ratio"]
+            if resolution is not None:
+                inputs["resolution"] = resolution
+                inputs["aspect_ratio"] = aspect_ratio
+            outputs = self.transformer(inputs)[0]
+
+            return [torch.from_numpy(outputs)]
+
+.. code:: ipython3
+
+    class VAEWrapper(torch.nn.Module):
+        def __init__(self, vae, config):
+            super().__init__()
+            self.vae = vae
+            self.config = config
+
+        def decode(self, latents=None, **kwargs):
+            inputs = {
+                "latents": latents,
+            }
+
+            outs = self.vae(inputs)
+            outs = namedtuple("VAE", "sample")(torch.from_numpy(outs[0]))
+
+            return outs
+
+And insert wrappers instances in the pipeline:
+
+.. code:: ipython3
+
+    pipe.__dict__["_internal_dict"]["_execution_device"] = pipe._execution_device  # this is to avoid some problem that can occur in the pipeline
+
+    pipe.register_modules(
+        text_encoder=TextEncoderWrapper(compiled_text_encoder, pipe.text_encoder.dtype),
+        transformer=TransformerWrapper(compiled_model, pipe.transformer.config),
+        vae=VAEWrapper(compiled_vae, pipe.vae.config),
+    )
+
+.. code:: ipython3
+
+    generator = torch.Generator().manual_seed(42)
+
+    image = pipe(prompt=prompt, guidance_scale=0.0, num_inference_steps=4, generator=generator).images[0]
+
+
+.. parsed-literal::
+
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/configuration_utils.py:140: FutureWarning: Accessing config attribute `_execution_device` directly via 'PixArtAlphaPipeline' object attribute is deprecated. Please access '_execution_device' over 'PixArtAlphaPipeline's config object instead, e.g. 'scheduler.config._execution_device'.
+      deprecate("direct config name access", "1.0.0", deprecation_message, standard_warn=False)
+
+
+
+.. parsed-literal::
+
+      0%|          | 0/4 [00:00<?, ?it/s]
+
+
+.. code:: ipython3
+
+    image
+
+
+
+
+.. image:: pixart-with-output_files/pixart-with-output_26_0.png
+
+
+
+Interactive inference
+---------------------
+
+
+
+.. code:: ipython3
+
+    import gradio as gr
+
+
+    def generate(prompt, seed, negative_prompt, num_inference_steps):
+        generator = torch.Generator().manual_seed(seed)
+        image = pipe(prompt=prompt, negative_prompt=negative_prompt, num_inference_steps=num_inference_steps, generator=generator, guidance_scale=0.0).images[0]
+        return image
+
+
+    demo = gr.Interface(
+        generate,
+        [
+            gr.Textbox(label="Caption"),
+            gr.Slider(0, np.iinfo(np.int32).max, label="Seed"),
+            gr.Textbox(label="Negative prompt"),
+            gr.Slider(2, 20, step=1, label="Number of inference steps", value=4),
+        ],
+        "image",
+        examples=[
+            ["A small cactus with a happy face in the Sahara desert.", 42],
+            ["an astronaut sitting in a diner, eating fries, cinematic, analog film", 42],
+            [
+                "Pirate ship trapped in a cosmic maelstrom nebula, rendered in cosmic beach whirlpool engine, volumetric lighting, spectacular, ambient lights, light pollution, cinematic atmosphere, art nouveau style, illustration art artwork by SenseiJaye, intricate detail.",
+                0,
+            ],
+            ["professional portrait photo of an anthropomorphic cat wearing fancy gentleman hat and jacket walking in autumn forest.", 0],
+        ],
+        allow_flagging="never",
+    )
+    try:
+        demo.queue().launch(debug=False)
+    except Exception:
+        demo.queue().launch(debug=False, share=True)
+    # if you are launching remotely, specify server_name and server_port
+    # demo.launch(server_name='your server name', server_port='server port in int')
+    # Read more in the docs: https://gradio.app/docs/
+
+
+.. parsed-literal::
+
+    Running on local URL:  http://127.0.0.1:7860
+
+    To create a public link, set `share=True` in `launch()`.
+
+
+
+
+
+
+
diff --git a/docs/notebooks/pixart-with-output_files/pixart-with-output_26_0.jpg b/docs/notebooks/pixart-with-output_files/pixart-with-output_26_0.jpg
new file mode 100644
index 00000000000000..2c7f67218b15d7
--- /dev/null
+++ b/docs/notebooks/pixart-with-output_files/pixart-with-output_26_0.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:46f422396a74ced2b8828a12a4dab692a3bfa051535cdee68a45cd2f8a99595f
+size 84920
diff --git a/docs/notebooks/pixart-with-output_files/pixart-with-output_26_0.png b/docs/notebooks/pixart-with-output_files/pixart-with-output_26_0.png
new file mode 100644
index 00000000000000..9723c2a78347d7
--- /dev/null
+++ b/docs/notebooks/pixart-with-output_files/pixart-with-output_26_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c5b0821b8bb0088d3e5703dfb364542bd144d92fbfc422dade48364c7482a8c8
+size 1500160
diff --git a/docs/notebooks/pixart-with-output_files/pixart-with-output_5_0.jpg b/docs/notebooks/pixart-with-output_files/pixart-with-output_5_0.jpg
new file mode 100644
index 00000000000000..52988615bd28db
--- /dev/null
+++ b/docs/notebooks/pixart-with-output_files/pixart-with-output_5_0.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6f49ea520f6d9f025671e460dd9e21ad620467078e3f7163b3bd506cce38194a
+size 84543
diff --git a/docs/notebooks/pixart-with-output_files/pixart-with-output_5_0.png b/docs/notebooks/pixart-with-output_files/pixart-with-output_5_0.png
new file mode 100644
index 00000000000000..563e52e1776e0a
--- /dev/null
+++ b/docs/notebooks/pixart-with-output_files/pixart-with-output_5_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c8b5df5783b64f31e5acdfb81591c913cf334fd912dc74a31ebbdd26fbd0500a
+size 1486698
diff --git a/docs/notebooks/pose-estimation-with-output.rst b/docs/notebooks/pose-estimation-with-output.rst
index 2a20a6a0672850..12bd6f9d228b8b 100644
--- a/docs/notebooks/pose-estimation-with-output.rst
+++ b/docs/notebooks/pose-estimation-with-output.rst
@@ -14,8 +14,8 @@ Additionally, you can also upload a video file.
    work. However, you can still do inference on a video in the final
    step.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Imports <#imports>`__
 -  `The model <#the-model>`__
diff --git a/docs/notebooks/pose-estimation-with-output_files/pose-estimation-with-output_22_0.png b/docs/notebooks/pose-estimation-with-output_files/pose-estimation-with-output_22_0.png
index b8509464268b86..affe01fc786027 100644
--- a/docs/notebooks/pose-estimation-with-output_files/pose-estimation-with-output_22_0.png
+++ b/docs/notebooks/pose-estimation-with-output_files/pose-estimation-with-output_22_0.png
@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:4d3d81a3fef30928eb8d98479cc71a865e7acbdb41cc1aa8e4d0aed7143e81ac
-size 108056
+oid sha256:8c915bf171042e5e98ca4b28a75aece15bdd10e6452fe2c3e09609a807ddb921
+size 107999
diff --git a/docs/notebooks/pyannote-speaker-diarization-with-output.rst b/docs/notebooks/pyannote-speaker-diarization-with-output.rst
deleted file mode 100644
index 44478d7c2e6a51..00000000000000
--- a/docs/notebooks/pyannote-speaker-diarization-with-output.rst
+++ /dev/null
@@ -1,461 +0,0 @@
-Speaker diarization
-===================
-
-Speaker diarization is the process of partitioning an audio stream
-containing human speech into homogeneous segments according to the
-identity of each speaker. It can enhance the readability of an automatic
-speech transcription by structuring the audio stream into speaker turns
-and, when used together with speaker recognition systems, by providing
-the speaker’s true identity. It is used to answer the question “who
-spoke when?”
-
-.. figure:: https://developer-blogs.nvidia.com/wp-content/uploads/2022/09/speaker-diarization.png
-   :alt: image.png
-
-   image.png
-
-With the increasing number of broadcasts, meeting recordings and voice
-mail collected every year, speaker diarization has received much
-attention by the speech community. Speaker diarization is an essential
-feature for a speech recognition system to enrich the transcription with
-speaker labels.
-
-Speaker diarization is used to increase transcript readability and
-better understand what a conversation is about. Speaker diarization can
-help extract important points or action items from the conversation and
-identify who said what. It also helps to identify how many speakers were
-on the audio.
-
-This tutorial considers ways to build speaker diarization pipeline using
-pyannote.audio and OpenVINO. ``pyannote.audio`` is an open-source
-toolkit written in Python for speaker diarization. Based on PyTorch deep
-learning framework, it provides a set of trainable end-to-end neural
-building blocks that can be combined and jointly optimized to build
-speaker diarization pipelines. You can find more information about
-pyannote pre-trained models in `model
-card <https://huggingface.co/pyannote/speaker-diarization>`__,
-`repo <https://github.com/pyannote/pyannote-audio>`__ and
-`paper <https://arxiv.org/abs/1911.01255>`__.
-
-Table of contents:
-^^^^^^^^^^^^^^^^^^
-
--  `Prerequisites <#prerequisites>`__
--  `Prepare pipeline <#prepare-pipeline>`__
--  `Load test audio file <#load-test-audio-file>`__
--  `Run inference pipeline <#run-inference-pipeline>`__
--  `Convert model to OpenVINO Intermediate Representation
-   format <#convert-model-to-openvino-intermediate-representation-format>`__
--  `Select inference device <#select-inference-device>`__
--  `Replace segmentation model with
-   OpenVINO <#replace-segmentation-model-with-openvino>`__
--  `Run speaker diarization with
-   OpenVINO <#run-speaker-diarization-with-openvino>`__
-
-Prerequisites
--------------
-
-
-
-.. code:: ipython3
-
-    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu  "librosa>=0.8.1" "matplotlib<3.8" "ruamel.yaml>=0.17.8,<0.17.29" "torch>=2.1" tqdm torchvision torchaudio "git+https://github.com/eaidova/pyannote-audio.git@hub0.10" "openvino>=2023.1.0"
-
-
-.. parsed-literal::
-
-    WARNING: typer 0.12.3 does not provide the extra 'all'
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
-    googleapis-common-protos 1.63.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0.dev0,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.
-    onnx 1.16.1 requires protobuf>=3.20.2, but you have protobuf 3.20.1 which is incompatible.
-    paddlepaddle 2.6.1 requires protobuf>=3.20.2; platform_system != "Windows", but you have protobuf 3.20.1 which is incompatible.
-    tensorflow 2.12.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 3.20.1 which is incompatible.
-    tensorflow-metadata 1.14.0 requires protobuf<4.21,>=3.20.3, but you have protobuf 3.20.1 which is incompatible.
-    Note: you may need to restart the kernel to use updated packages.
-
-
-Prepare pipeline
-----------------
-
-
-
-Traditional Speaker Diarization systems can be generalized into a
-five-step process:
-
--  **Feature extraction**: transform the raw waveform into audio
-   features like mel spectrogram.
--  **Voice activity detection**: identify the chunks in the audio where
-   some voice activity was observed. As we are not interested in silence
-   and noise, we ignore those irrelevant chunks.
--  **Speaker change detection**: identify the speaker change points in
-   the conversation present in the audio.
--  **Speech turn representation**: encode each subchunk by creating
-   feature representations.
--  **Speech turn clustering**: cluster the subchunks based on their
-   vector representation. Different clustering algorithms may be applied
-   based on the availability of cluster count (k) and the embedding
-   process of the previous step.
-
-The final output will be the clusters of different subchunks from the
-audio stream. Each cluster can be given an anonymous identifier
-(speaker_a, ..) and then it can be mapped with the audio stream to
-create the speaker-aware audio timeline.
-
-On the diagram, you can see a typical speaker diarization pipeline:
-
-.. figure:: http://mohitmayank.com/a_lazy_data_science_guide/imgs/audio_speakerdiarization_intro.png
-   :alt: diarization_pipeline
-
-   diarization_pipeline
-
-From a simplified point of view, speaker diarization is a combination of
-speaker segmentation and speaker clustering. The first aims at finding
-speaker change points in an audio stream. The second aims at grouping
-together speech segments based on speaker characteristics.
-
-For instantiating speaker diarization pipeline with ``pyannote.audio``
-library, we should import ``Pipeline`` class and use ``from_pretrained``
-method by providing a path to the directory with pipeline configuration
-or identification from `HuggingFace
-hub <https://huggingface.co/pyannote/speaker-diarization>`__.
-
-   **Note**: This tutorial uses a non-official version of model
-   ``philschmid/pyannote-speaker-diarization-endpoint``, provided only
-   for demo purposes. The original model
-   (``pyannote/speaker-diarization``) requires you to accept the model
-   license before downloading or using its weights, visit the
-   `pyannote/speaker-diarization <https://huggingface.co/pyannote/speaker-diarization>`__
-   to read accept the license before you proceed. To use this model, you
-   must be a registered user in Hugging Face Hub. You will need to use
-   an access token for the code below to run. For more information on
-   access tokens, please refer to `this section of the
-   documentation <https://huggingface.co/docs/hub/security-tokens>`__.
-   You can log in on HuggingFace Hub in the notebook environment using
-   the following code:
-
-.. code:: python
-
-
-   ## login to huggingfacehub to get access to pre-trained model
-
-   from huggingface_hub import notebook_login, whoami
-
-   try:
-       whoami()
-       print('Authorization token already provided')
-   except OSError:
-       notebook_login()
-
-.. code:: ipython3
-
-    from pyannote.audio import Pipeline
-    
-    pipeline = Pipeline.from_pretrained("philschmid/pyannote-speaker-diarization-endpoint")
-
-
-.. parsed-literal::
-
-    2024-06-06 01:14:35.515596: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-06-06 01:14:35.549509: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
-    To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2024-06-06 01:14:36.287158: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/pyannote/audio/core/io.py:42: UserWarning: torchaudio._backend.set_audio_backend has been deprecated. With dispatcher enabled, this function is no-op. You can remove the function call.
-      torchaudio.set_audio_backend("soundfile")
-
-
-Load test audio file
---------------------
-
-
-
-.. code:: ipython3
-
-    # Fetch `notebook_utils` module
-    import requests
-    
-    r = requests.get(
-        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
-    )
-    
-    open("notebook_utils.py", "w").write(r.text)
-    
-    from notebook_utils import download_file
-    
-    test_data_url = "https://github.com/pyannote/pyannote-audio/raw/develop/tutorials/assets/sample.wav"
-    
-    sample_file = "sample.wav"
-    download_file(test_data_url, "sample.wav")
-    AUDIO_FILE = {"uri": sample_file.replace(".wav", ""), "audio": sample_file}
-
-
-
-.. parsed-literal::
-
-    sample.wav:   0%|          | 0.00/938k [00:00<?, ?B/s]
-
-
-.. code:: ipython3
-
-    import librosa
-    import matplotlib.pyplot as plt
-    import librosa.display
-    import IPython.display as ipd
-    
-    
-    audio, sr = librosa.load(sample_file)
-    plt.figure(figsize=(14, 5))
-    librosa.display.waveshow(audio, sr=sr)
-    
-    ipd.Audio(sample_file)
-
-
-
-
-.. raw:: html
-
-    
-    <audio  controls="controls" >
-        <source 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" type="audio/x-wav" />
-        Your browser does not support the audio element.
-    </audio>
-
-
-
-
-
-.. image:: pyannote-speaker-diarization-with-output_files/pyannote-speaker-diarization-with-output_9_1.png
-
-
-Run inference pipeline
-----------------------
-
-
-
-For running inference, we should provide a path to input audio to the
-pipeline
-
-.. code:: ipython3
-
-    %%capture
-    import time
-    
-    start = time.perf_counter()
-    diarization = pipeline(AUDIO_FILE)
-    end = time.perf_counter()
-
-.. code:: ipython3
-
-    print(f"Diarization pipeline took {end - start:.2f} s")
-
-
-.. parsed-literal::
-
-    Diarization pipeline took 17.20 s
-
-
-The result of running the pipeline can be represented as a diagram
-indicating when each person speaks.
-
-.. code:: ipython3
-
-    diarization
-
-
-
-
-.. image:: pyannote-speaker-diarization-with-output_files/pyannote-speaker-diarization-with-output_14_0.png
-
-
-
-We can also print each time frame and corresponding speaker:
-
-.. code:: ipython3
-
-    for turn, _, speaker in diarization.itertracks(yield_label=True):
-        print(f"start={turn.start:.1f}s stop={turn.end:.1f}s speaker_{speaker}")
-
-
-.. parsed-literal::
-
-    start=6.7s stop=7.1s speaker_SPEAKER_00
-    start=7.6s stop=8.6s speaker_SPEAKER_00
-    start=8.6s stop=10.0s speaker_SPEAKER_02
-    start=9.8s stop=11.0s speaker_SPEAKER_00
-    start=10.6s stop=14.7s speaker_SPEAKER_02
-    start=14.3s stop=17.9s speaker_SPEAKER_01
-    start=17.9s stop=21.5s speaker_SPEAKER_02
-    start=18.3s stop=18.4s speaker_SPEAKER_01
-    start=21.7s stop=28.6s speaker_SPEAKER_01
-    start=27.8s stop=29.5s speaker_SPEAKER_02
-
-
-Convert model to OpenVINO Intermediate Representation format
-------------------------------------------------------------
-
-
-
-For best results with OpenVINO, it is recommended to convert the model
-to OpenVINO IR format. OpenVINO supports PyTorch via ONNX conversion. We
-will use ``torch.onnx.export`` for exporting the ONNX model from
-PyTorch. We need to provide initialized model’s instance and example of
-inputs for shape inference. We will use ``ov.convert_model``
-functionality to convert the ONNX models. The ``mo.convert_model``
-Python function returns an OpenVINO model ready to load on the device
-and start making predictions. We can save it on disk for the next usage
-with ``ov.save_model``.
-
-.. code:: ipython3
-
-    from pathlib import Path
-    import torch
-    import openvino as ov
-    
-    core = ov.Core()
-    
-    ov_speaker_segmentation_path = Path("pyannote-segmentation.xml")
-    
-    if not ov_speaker_segmentation_path.exists():
-        onnx_path = ov_speaker_segmentation_path.with_suffix(".onnx")
-        torch.onnx.export(
-            pipeline._segmentation.model,
-            torch.zeros((1, 1, 80000)),
-            onnx_path,
-            input_names=["chunks"],
-            output_names=["outputs"],
-            dynamic_axes={"chunks": {0: "batch_size", 2: "wave_len"}},
-        )
-        ov_speaker_segmentation = ov.convert_model(onnx_path)
-        ov.save_model(ov_speaker_segmentation, str(ov_speaker_segmentation_path))
-        print(f"Model successfully converted to IR and saved to {ov_speaker_segmentation_path}")
-    else:
-        ov_speaker_segmentation = core.read_model(ov_speaker_segmentation_path)
-        print(f"Model successfully loaded from {ov_speaker_segmentation_path}")
-
-
-.. parsed-literal::
-
-    Model successfully converted to IR and saved to pyannote-segmentation.xml
-
-
-Select inference device
------------------------
-
-
-
-select device from dropdown list for running inference using OpenVINO
-
-.. code:: ipython3
-
-    import ipywidgets as widgets
-    
-    device = widgets.Dropdown(
-        options=core.available_devices + ["AUTO"],
-        value="AUTO",
-        description="Device:",
-        disabled=False,
-    )
-    
-    device
-
-
-
-
-.. parsed-literal::
-
-    Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
-
-
-
-Replace segmentation model with OpenVINO
-----------------------------------------
-
-
-
-.. code:: ipython3
-
-    core = ov.Core()
-    
-    ov_seg_model = core.compile_model(ov_speaker_segmentation, device.value)
-    infer_request = ov_seg_model.create_infer_request()
-    ov_seg_out = ov_seg_model.output(0)
-
-.. code:: ipython3
-
-    import numpy as np
-    
-    
-    def infer_segm(chunks: torch.Tensor) -> np.ndarray:
-        """
-        Inference speaker segmentation mode using OpenVINO
-        Parameters:
-            chunks (torch.Tensor) input audio chunks
-        Return:
-            segments (np.ndarray)
-        """
-        res = ov_seg_model(chunks)
-        return res[ov_seg_out]
-    
-    
-    pipeline._segmentation.infer = infer_segm
-
-Run speaker diarization with OpenVINO
--------------------------------------
-
-
-
-.. code:: ipython3
-
-    start = time.perf_counter()
-    diarization = pipeline(AUDIO_FILE)
-    end = time.perf_counter()
-
-
-.. parsed-literal::
-
-    Model is not converging.  Current: 16444.281598619917 is not greater than 16444.330820454463. Delta is -0.04922183454618789
-    Model is not converging.  Current: 16444.281598619917 is not greater than 16444.330820454463. Delta is -0.04922183454618789
-
-
-.. code:: ipython3
-
-    print(f"Diarization pipeline took {end - start:.2f} s")
-
-
-.. parsed-literal::
-
-    Diarization pipeline took 17.03 s
-
-
-.. code:: ipython3
-
-    diarization
-
-
-
-
-.. image:: pyannote-speaker-diarization-with-output_files/pyannote-speaker-diarization-with-output_27_0.png
-
-
-
-.. code:: ipython3
-
-    for turn, _, speaker in diarization.itertracks(yield_label=True):
-        print(f"start={turn.start:.1f}s stop={turn.end:.1f}s speaker_{speaker}")
-
-
-.. parsed-literal::
-
-    start=6.7s stop=7.1s speaker_SPEAKER_00
-    start=7.6s stop=8.6s speaker_SPEAKER_00
-    start=8.6s stop=10.0s speaker_SPEAKER_02
-    start=9.8s stop=11.0s speaker_SPEAKER_00
-    start=10.6s stop=14.7s speaker_SPEAKER_02
-    start=14.3s stop=17.9s speaker_SPEAKER_01
-    start=17.9s stop=21.5s speaker_SPEAKER_02
-    start=18.3s stop=18.4s speaker_SPEAKER_01
-    start=21.7s stop=28.6s speaker_SPEAKER_01
-    start=27.8s stop=29.5s speaker_SPEAKER_02
-
-
-Nice! As we can see, the result preserves the same level of accuracy!
diff --git a/docs/notebooks/pyannote-speaker-diarization-with-output_files/pyannote-speaker-diarization-with-output_14_0.png b/docs/notebooks/pyannote-speaker-diarization-with-output_files/pyannote-speaker-diarization-with-output_14_0.png
deleted file mode 100644
index b50ecb8ca64bf4..00000000000000
--- a/docs/notebooks/pyannote-speaker-diarization-with-output_files/pyannote-speaker-diarization-with-output_14_0.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:74e686795f39e2a8378fb62b0f66f6088da0a9f49bcbda2ec6ba306c0708675a
-size 7969
diff --git a/docs/notebooks/pyannote-speaker-diarization-with-output_files/pyannote-speaker-diarization-with-output_27_0.png b/docs/notebooks/pyannote-speaker-diarization-with-output_files/pyannote-speaker-diarization-with-output_27_0.png
deleted file mode 100644
index b50ecb8ca64bf4..00000000000000
--- a/docs/notebooks/pyannote-speaker-diarization-with-output_files/pyannote-speaker-diarization-with-output_27_0.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:74e686795f39e2a8378fb62b0f66f6088da0a9f49bcbda2ec6ba306c0708675a
-size 7969
diff --git a/docs/notebooks/pyannote-speaker-diarization-with-output_files/pyannote-speaker-diarization-with-output_9_1.png b/docs/notebooks/pyannote-speaker-diarization-with-output_files/pyannote-speaker-diarization-with-output_9_1.png
deleted file mode 100644
index a5788ec84d8317..00000000000000
--- a/docs/notebooks/pyannote-speaker-diarization-with-output_files/pyannote-speaker-diarization-with-output_9_1.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:e2a9dcbd10f859efd95f9473627e87779603b6982365c0fdf588dab6f3e862eb
-size 43095
diff --git a/docs/notebooks/pytorch-onnx-to-openvino-with-output.rst b/docs/notebooks/pytorch-onnx-to-openvino-with-output.rst
index 25e1f0b50677a3..997f6e6c9a3591 100644
--- a/docs/notebooks/pytorch-onnx-to-openvino-with-output.rst
+++ b/docs/notebooks/pytorch-onnx-to-openvino-with-output.rst
@@ -33,8 +33,8 @@ plant, sheep, sofa, train, tv monitor**
 More information about the model is available in the `torchvision
 documentation <https://pytorch.org/vision/main/models/lraspp.html>`__
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Preparation <#preparation>`__
 
@@ -74,12 +74,6 @@ Table of contents:
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
-    pytorch-lightning 1.6.5 requires protobuf<=3.20.1, but you have protobuf 5.27.0 which is incompatible.
-    tensorflow 2.12.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 5.27.0 which is incompatible.
-    tensorflow-metadata 1.14.0 requires protobuf<4.21,>=3.20.3, but you have protobuf 5.27.0 which is incompatible.
-    tf2onnx 1.16.1 requires protobuf~=3.20, but you have protobuf 5.27.0 which is incompatible.
     Note: you may need to restart the kernel to use updated packages.
 
 
@@ -565,9 +559,9 @@ performance.
 
 .. parsed-literal::
 
-    PyTorch model on CPU: 0.038 seconds per image, FPS: 26.18
-    ONNX model in OpenVINO Runtime/AUTO: 0.018 seconds per image, FPS: 55.48
-    OpenVINO IR model in OpenVINO Runtime/AUTO: 0.028 seconds per image, FPS: 35.39
+    PyTorch model on CPU: 0.039 seconds per image, FPS: 25.86
+    ONNX model in OpenVINO Runtime/AUTO: 0.018 seconds per image, FPS: 54.43
+    OpenVINO IR model in OpenVINO Runtime/AUTO: 0.027 seconds per image, FPS: 36.71
 
 
 **Show Device Information**
diff --git a/docs/notebooks/pytorch-post-training-quantization-nncf-with-output.rst b/docs/notebooks/pytorch-post-training-quantization-nncf-with-output.rst
index 22884444e02a7d..566e2b132b18c1 100644
--- a/docs/notebooks/pytorch-post-training-quantization-nncf-with-output.rst
+++ b/docs/notebooks/pytorch-post-training-quantization-nncf-with-output.rst
@@ -22,8 +22,8 @@ quantization, not demanding the fine-tuning of the model.
    the default binary search path of the OS you are running the
    notebook.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Preparations <#preparations>`__
 
@@ -64,9 +64,7 @@ Preparations
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
 
 
@@ -150,7 +148,7 @@ Settings
 
 .. parsed-literal::
 
-    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/pytorch-post-training-quantization-nncf/model/resnet50_fp32.pth')
+    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/pytorch-post-training-quantization-nncf/model/resnet50_fp32.pth')
 
 
 
@@ -440,15 +438,15 @@ I. Evaluate the loaded model
 
 .. parsed-literal::
 
-    Test: [ 0/79]	Time 0.272 (0.272)	Acc@1 81.25 (81.25)	Acc@5 92.19 (92.19)
-    Test: [10/79]	Time 0.221 (0.228)	Acc@1 56.25 (66.97)	Acc@5 86.72 (87.50)
-    Test: [20/79]	Time 0.224 (0.225)	Acc@1 67.97 (64.29)	Acc@5 85.16 (87.35)
-    Test: [30/79]	Time 0.226 (0.235)	Acc@1 53.12 (62.37)	Acc@5 77.34 (85.33)
-    Test: [40/79]	Time 0.224 (0.233)	Acc@1 67.19 (60.86)	Acc@5 90.62 (84.51)
-    Test: [50/79]	Time 0.246 (0.232)	Acc@1 60.16 (60.80)	Acc@5 88.28 (84.42)
-    Test: [60/79]	Time 0.228 (0.230)	Acc@1 66.41 (60.46)	Acc@5 86.72 (83.79)
-    Test: [70/79]	Time 0.225 (0.229)	Acc@1 52.34 (60.21)	Acc@5 80.47 (83.33)
-     * Acc@1 60.740 Acc@5 83.960 Total time: 17.890
+    Test: [ 0/79]	Time 0.263 (0.263)	Acc@1 81.25 (81.25)	Acc@5 92.19 (92.19)
+    Test: [10/79]	Time 0.244 (0.253)	Acc@1 56.25 (66.97)	Acc@5 86.72 (87.50)
+    Test: [20/79]	Time 0.253 (0.253)	Acc@1 67.97 (64.29)	Acc@5 85.16 (87.35)
+    Test: [30/79]	Time 0.247 (0.252)	Acc@1 53.12 (62.37)	Acc@5 77.34 (85.33)
+    Test: [40/79]	Time 0.247 (0.253)	Acc@1 67.19 (60.86)	Acc@5 90.62 (84.51)
+    Test: [50/79]	Time 0.249 (0.252)	Acc@1 60.16 (60.80)	Acc@5 88.28 (84.42)
+    Test: [60/79]	Time 0.250 (0.252)	Acc@1 66.41 (60.46)	Acc@5 86.72 (83.79)
+    Test: [70/79]	Time 0.244 (0.252)	Acc@1 52.34 (60.21)	Acc@5 80.47 (83.33)
+     * Acc@1 60.740 Acc@5 83.960 Total time: 19.734
     Test accuracy of FP32 model: 60.740
 
 
@@ -491,10 +489,10 @@ Guide <https://docs.openvino.ai/2024/openvino-workflow/model-optimization-guide/
 
 .. parsed-literal::
 
-    2024-06-06 01:16:17.986037: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-06-06 01:16:18.018024: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-07-02 01:28:55.035175: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-07-02 01:28:55.067560: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2024-06-06 01:16:18.549711: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-07-02 01:28:55.595334: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 
@@ -504,17 +502,17 @@ Guide <https://docs.openvino.ai/2024/openvino-workflow/model-optimization-guide/
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -531,17 +529,17 @@ Guide <https://docs.openvino.ai/2024/openvino-workflow/model-optimization-guide/
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -558,16 +556,16 @@ Guide <https://docs.openvino.ai/2024/openvino-workflow/model-optimization-guide/
 
 .. parsed-literal::
 
-    Test: [ 0/79]	Time 0.462 (0.462)	Acc@1 80.47 (80.47)	Acc@5 91.41 (91.41)
-    Test: [10/79]	Time 0.432 (0.436)	Acc@1 53.91 (66.26)	Acc@5 86.72 (87.78)
-    Test: [20/79]	Time 0.433 (0.434)	Acc@1 68.75 (63.73)	Acc@5 85.16 (87.28)
-    Test: [30/79]	Time 0.435 (0.434)	Acc@1 50.78 (62.07)	Acc@5 73.44 (85.16)
-    Test: [40/79]	Time 0.432 (0.434)	Acc@1 68.75 (60.63)	Acc@5 89.84 (84.26)
-    Test: [50/79]	Time 0.432 (0.433)	Acc@1 58.59 (60.54)	Acc@5 87.50 (84.08)
-    Test: [60/79]	Time 0.434 (0.433)	Acc@1 64.84 (60.32)	Acc@5 85.94 (83.49)
-    Test: [70/79]	Time 0.432 (0.433)	Acc@1 52.34 (60.07)	Acc@5 79.69 (83.11)
-     * Acc@1 60.570 Acc@5 83.720 Total time: 33.968
-    Accuracy of initialized INT8 model: 60.570
+    Test: [ 0/79]	Time 0.461 (0.461)	Acc@1 80.47 (80.47)	Acc@5 91.41 (91.41)
+    Test: [10/79]	Time 0.439 (0.436)	Acc@1 53.91 (66.48)	Acc@5 85.94 (87.78)
+    Test: [20/79]	Time 0.432 (0.435)	Acc@1 68.75 (63.80)	Acc@5 85.94 (87.24)
+    Test: [30/79]	Time 0.433 (0.435)	Acc@1 50.78 (62.05)	Acc@5 74.22 (85.11)
+    Test: [40/79]	Time 0.435 (0.434)	Acc@1 68.75 (60.58)	Acc@5 89.06 (84.24)
+    Test: [50/79]	Time 0.435 (0.434)	Acc@1 60.94 (60.60)	Acc@5 87.50 (84.19)
+    Test: [60/79]	Time 0.429 (0.434)	Acc@1 64.84 (60.36)	Acc@5 85.94 (83.59)
+    Test: [70/79]	Time 0.434 (0.434)	Acc@1 52.34 (60.06)	Acc@5 79.69 (83.19)
+     * Acc@1 60.550 Acc@5 83.790 Total time: 34.015
+    Accuracy of initialized INT8 model: 60.550
 
 
 It should be noted that the inference time for the quantized PyTorch
@@ -616,16 +614,16 @@ For more information about model conversion, refer to this
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/torch/quantization/layers.py:339: TracerWarning: Converting a tensor to a Python number might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/torch/quantization/layers.py:340: TracerWarning: Converting a tensor to a Python number might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       return self._level_low.item()
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/torch/quantization/layers.py:347: TracerWarning: Converting a tensor to a Python number might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/torch/quantization/layers.py:348: TracerWarning: Converting a tensor to a Python number might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       return self._level_high.item()
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:1116: TracerWarning: Output nr 1. of the traced function does not match the corresponding output of the Python function. Detailed error:
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:1116: TracerWarning: Output nr 1. of the traced function does not match the corresponding output of the Python function. Detailed error:
     Tensor-likes are not close!
     
-    Mismatched elements: 25561 / 25600 (99.8%)
-    Greatest absolute difference: 0.1566624641418457 at index (61, 14) (up to 1e-05 allowed)
-    Greatest relative difference: 17.040754035113423 at index (122, 158) (up to 1e-05 allowed)
+    Mismatched elements: 25567 / 25600 (99.9%)
+    Greatest absolute difference: 0.18015646934509277 at index (73, 134) (up to 1e-05 allowed)
+    Greatest relative difference: 4.7938387886837335 at index (13, 168) (up to 1e-05 allowed)
       _check_trace(
 
 
@@ -666,15 +664,15 @@ Evaluate the FP32 and INT8 models.
 
 .. parsed-literal::
 
-    Test: [ 0/79]	Time 0.184 (0.184)	Acc@1 81.25 (81.25)	Acc@5 92.19 (92.19)
-    Test: [10/79]	Time 0.140 (0.144)	Acc@1 56.25 (66.97)	Acc@5 86.72 (87.50)
-    Test: [20/79]	Time 0.140 (0.141)	Acc@1 67.97 (64.29)	Acc@5 85.16 (87.35)
-    Test: [30/79]	Time 0.140 (0.141)	Acc@1 53.12 (62.37)	Acc@5 77.34 (85.33)
-    Test: [40/79]	Time 0.138 (0.140)	Acc@1 67.19 (60.86)	Acc@5 90.62 (84.51)
-    Test: [50/79]	Time 0.140 (0.140)	Acc@1 60.16 (60.80)	Acc@5 88.28 (84.42)
-    Test: [60/79]	Time 0.138 (0.140)	Acc@1 66.41 (60.46)	Acc@5 86.72 (83.79)
-    Test: [70/79]	Time 0.139 (0.140)	Acc@1 52.34 (60.21)	Acc@5 80.47 (83.33)
-     * Acc@1 60.740 Acc@5 83.960 Total time: 10.937
+    Test: [ 0/79]	Time 0.186 (0.186)	Acc@1 81.25 (81.25)	Acc@5 92.19 (92.19)
+    Test: [10/79]	Time 0.145 (0.144)	Acc@1 56.25 (66.97)	Acc@5 86.72 (87.50)
+    Test: [20/79]	Time 0.141 (0.142)	Acc@1 67.97 (64.29)	Acc@5 85.16 (87.35)
+    Test: [30/79]	Time 0.140 (0.142)	Acc@1 53.12 (62.37)	Acc@5 77.34 (85.33)
+    Test: [40/79]	Time 0.140 (0.141)	Acc@1 67.19 (60.86)	Acc@5 90.62 (84.51)
+    Test: [50/79]	Time 0.140 (0.141)	Acc@1 60.16 (60.80)	Acc@5 88.28 (84.42)
+    Test: [60/79]	Time 0.142 (0.141)	Acc@1 66.41 (60.46)	Acc@5 86.72 (83.79)
+    Test: [70/79]	Time 0.141 (0.141)	Acc@1 52.34 (60.21)	Acc@5 80.47 (83.33)
+     * Acc@1 60.740 Acc@5 83.960 Total time: 11.001
     Accuracy of FP32 IR model: 60.740
 
 
@@ -687,16 +685,16 @@ Evaluate the FP32 and INT8 models.
 
 .. parsed-literal::
 
-    Test: [ 0/79]	Time 0.143 (0.143)	Acc@1 79.69 (79.69)	Acc@5 91.41 (91.41)
-    Test: [10/79]	Time 0.078 (0.086)	Acc@1 53.12 (66.34)	Acc@5 86.72 (87.86)
-    Test: [20/79]	Time 0.078 (0.083)	Acc@1 70.31 (64.03)	Acc@5 85.16 (87.39)
-    Test: [30/79]	Time 0.077 (0.081)	Acc@1 50.78 (62.25)	Acc@5 75.00 (85.33)
-    Test: [40/79]	Time 0.077 (0.081)	Acc@1 68.75 (60.86)	Acc@5 89.84 (84.45)
-    Test: [50/79]	Time 0.080 (0.080)	Acc@1 60.16 (60.77)	Acc@5 87.50 (84.31)
-    Test: [60/79]	Time 0.079 (0.080)	Acc@1 66.41 (60.46)	Acc@5 85.94 (83.68)
-    Test: [70/79]	Time 0.081 (0.080)	Acc@1 53.12 (60.18)	Acc@5 78.91 (83.27)
-     * Acc@1 60.670 Acc@5 83.880 Total time: 6.246
-    Accuracy of INT8 IR model: 60.670
+    Test: [ 0/79]	Time 0.137 (0.137)	Acc@1 80.47 (80.47)	Acc@5 91.41 (91.41)
+    Test: [10/79]	Time 0.080 (0.086)	Acc@1 51.56 (66.26)	Acc@5 86.72 (87.86)
+    Test: [20/79]	Time 0.080 (0.082)	Acc@1 68.75 (63.69)	Acc@5 84.38 (87.31)
+    Test: [30/79]	Time 0.079 (0.081)	Acc@1 50.78 (62.05)	Acc@5 74.22 (85.23)
+    Test: [40/79]	Time 0.077 (0.080)	Acc@1 68.75 (60.75)	Acc@5 89.84 (84.36)
+    Test: [50/79]	Time 0.078 (0.080)	Acc@1 60.16 (60.65)	Acc@5 87.50 (84.28)
+    Test: [60/79]	Time 0.079 (0.080)	Acc@1 65.62 (60.41)	Acc@5 85.94 (83.68)
+    Test: [70/79]	Time 0.078 (0.080)	Acc@1 52.34 (60.16)	Acc@5 79.69 (83.25)
+     * Acc@1 60.650 Acc@5 83.820 Total time: 6.223
+    Accuracy of INT8 IR model: 60.650
 
 
 IV. Compare performance of INT8 model and FP32 model in OpenVINO
@@ -761,13 +759,13 @@ throughput (frames per second) values.
 .. parsed-literal::
 
     Benchmark FP32 model (OpenVINO IR)
-    [ INFO ] Throughput:   38.20 FPS
+    [ INFO ] Throughput:   38.05 FPS
     Benchmark INT8 model (OpenVINO IR)
-    [ INFO ] Throughput:   157.41 FPS
+    [ INFO ] Throughput:   153.48 FPS
     Benchmark FP32 model (OpenVINO IR) synchronously
-    [ INFO ] Throughput:   39.78 FPS
+    [ INFO ] Throughput:   39.70 FPS
     Benchmark INT8 model (OpenVINO IR) synchronously
-    [ INFO ] Throughput:   134.99 FPS
+    [ INFO ] Throughput:   138.99 FPS
 
 
 Show device Information for reference:
diff --git a/docs/notebooks/pytorch-quantization-aware-training-with-output.rst b/docs/notebooks/pytorch-quantization-aware-training-with-output.rst
index 8701f81e9608f6..343642f9dd4e59 100644
--- a/docs/notebooks/pytorch-quantization-aware-training-with-output.rst
+++ b/docs/notebooks/pytorch-quantization-aware-training-with-output.rst
@@ -34,8 +34,8 @@ hub <https://pytorch.org/hub/pytorch_vision_resnet/>`__.
    tools, including C++. For Linux you can install gcc with your
    distribution’s package manager.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Imports and Settings <#imports-and-settings>`__
 -  `Pre-train Floating-Point Model <#pre-train-floating-point-model>`__
@@ -61,9 +61,7 @@ Table of contents:
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
 
 
@@ -155,7 +153,7 @@ models will be stored.
 
 .. parsed-literal::
 
-    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/pytorch-quantization-aware-training/model/resnet18_fp32.pth')
+    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/pytorch-quantization-aware-training/model/resnet18_fp32.pth')
 
 
 
@@ -460,9 +458,9 @@ section at the top of this notebook.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
       warnings.warn(
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.
       warnings.warn(msg)
 
 
@@ -572,10 +570,10 @@ about supported parameters can be found on this
 
 .. parsed-literal::
 
-    2024-06-06 01:21:39.441995: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-06-06 01:21:39.478111: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-07-02 01:34:23.981872: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-07-02 01:34:24.017289: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2024-06-06 01:21:40.003115: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-07-02 01:34:24.543627: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 
@@ -585,17 +583,17 @@ about supported parameters can be found on this
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -612,17 +610,17 @@ about supported parameters can be found on this
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -639,14 +637,14 @@ demonstrated here.
 
 .. parsed-literal::
 
-    Test: [ 0/79]	Time 0.217 (0.217)	Loss 1.005 (1.005)	Acc@1 78.91 (78.91)	Acc@5 88.28 (88.28)
-    Test: [10/79]	Time 0.176 (0.179)	Loss 1.992 (1.625)	Acc@1 44.53 (60.37)	Acc@5 79.69 (83.66)
-    Test: [20/79]	Time 0.169 (0.176)	Loss 1.814 (1.705)	Acc@1 60.94 (58.04)	Acc@5 80.47 (82.66)
-    Test: [30/79]	Time 0.169 (0.174)	Loss 2.287 (1.795)	Acc@1 50.78 (56.48)	Acc@5 68.75 (80.97)
-    Test: [40/79]	Time 0.171 (0.174)	Loss 1.615 (1.832)	Acc@1 60.94 (55.43)	Acc@5 82.81 (80.43)
-    Test: [50/79]	Time 0.170 (0.173)	Loss 1.952 (1.833)	Acc@1 57.03 (55.51)	Acc@5 75.00 (80.16)
-    Test: [60/79]	Time 0.198 (0.174)	Loss 1.794 (1.856)	Acc@1 57.03 (55.16)	Acc@5 84.38 (79.84)
-    Test: [70/79]	Time 0.173 (0.174)	Loss 2.371 (1.889)	Acc@1 46.88 (54.68)	Acc@5 74.22 (79.14)
+    Test: [ 0/79]	Time 0.231 (0.231)	Loss 1.005 (1.005)	Acc@1 78.91 (78.91)	Acc@5 88.28 (88.28)
+    Test: [10/79]	Time 0.166 (0.174)	Loss 1.992 (1.625)	Acc@1 44.53 (60.37)	Acc@5 79.69 (83.66)
+    Test: [20/79]	Time 0.169 (0.171)	Loss 1.814 (1.705)	Acc@1 60.94 (58.04)	Acc@5 80.47 (82.66)
+    Test: [30/79]	Time 0.169 (0.169)	Loss 2.287 (1.795)	Acc@1 50.78 (56.48)	Acc@5 68.75 (80.97)
+    Test: [40/79]	Time 0.165 (0.168)	Loss 1.615 (1.832)	Acc@1 60.94 (55.43)	Acc@5 82.81 (80.43)
+    Test: [50/79]	Time 0.170 (0.168)	Loss 1.952 (1.833)	Acc@1 57.03 (55.51)	Acc@5 75.00 (80.16)
+    Test: [60/79]	Time 0.163 (0.168)	Loss 1.794 (1.856)	Acc@1 57.03 (55.16)	Acc@5 84.38 (79.84)
+    Test: [70/79]	Time 0.164 (0.167)	Loss 2.371 (1.889)	Acc@1 46.88 (54.68)	Acc@5 74.22 (79.14)
      * Acc@1 55.040 Acc@5 79.730
     Accuracy of initialized INT8 model: 55.040
 
@@ -679,30 +677,30 @@ training pipeline are required. Here is a simple example.
 
 .. parsed-literal::
 
-    Epoch:[0][  0/782]	Time 0.436 (0.436)	Loss 1.029 (1.029)	Acc@1 75.00 (75.00)	Acc@5 90.62 (90.62)
-    Epoch:[0][ 50/782]	Time 0.375 (0.377)	Loss 0.672 (0.823)	Acc@1 87.50 (79.81)	Acc@5 94.53 (93.84)
-    Epoch:[0][100/782]	Time 0.369 (0.376)	Loss 0.661 (0.799)	Acc@1 85.94 (80.31)	Acc@5 98.44 (94.41)
-    Epoch:[0][150/782]	Time 0.374 (0.375)	Loss 0.632 (0.797)	Acc@1 85.94 (80.50)	Acc@5 94.53 (94.24)
-    Epoch:[0][200/782]	Time 0.373 (0.375)	Loss 0.742 (0.790)	Acc@1 81.25 (80.69)	Acc@5 94.53 (94.31)
-    Epoch:[0][250/782]	Time 0.376 (0.375)	Loss 0.815 (0.785)	Acc@1 81.25 (80.80)	Acc@5 93.75 (94.34)
-    Epoch:[0][300/782]	Time 0.376 (0.374)	Loss 0.878 (0.781)	Acc@1 76.56 (80.87)	Acc@5 92.19 (94.37)
-    Epoch:[0][350/782]	Time 0.367 (0.375)	Loss 0.746 (0.774)	Acc@1 82.03 (81.03)	Acc@5 93.75 (94.44)
-    Epoch:[0][400/782]	Time 0.374 (0.374)	Loss 0.766 (0.772)	Acc@1 79.69 (81.12)	Acc@5 96.88 (94.42)
-    Epoch:[0][450/782]	Time 0.375 (0.374)	Loss 0.865 (0.768)	Acc@1 77.34 (81.28)	Acc@5 93.75 (94.48)
-    Epoch:[0][500/782]	Time 0.380 (0.374)	Loss 0.526 (0.765)	Acc@1 89.06 (81.33)	Acc@5 97.66 (94.53)
-    Epoch:[0][550/782]	Time 0.373 (0.374)	Loss 0.826 (0.762)	Acc@1 79.69 (81.39)	Acc@5 92.19 (94.55)
-    Epoch:[0][600/782]	Time 0.376 (0.374)	Loss 0.644 (0.761)	Acc@1 85.94 (81.45)	Acc@5 95.31 (94.55)
-    Epoch:[0][650/782]	Time 0.374 (0.374)	Loss 0.585 (0.757)	Acc@1 81.25 (81.57)	Acc@5 98.44 (94.59)
-    Epoch:[0][700/782]	Time 0.374 (0.374)	Loss 0.578 (0.755)	Acc@1 86.72 (81.65)	Acc@5 96.88 (94.60)
-    Epoch:[0][750/782]	Time 0.379 (0.374)	Loss 0.783 (0.753)	Acc@1 79.69 (81.69)	Acc@5 95.31 (94.63)
-    Test: [ 0/79]	Time 0.151 (0.151)	Loss 1.063 (1.063)	Acc@1 74.22 (74.22)	Acc@5 87.50 (87.50)
-    Test: [10/79]	Time 0.153 (0.152)	Loss 1.785 (1.514)	Acc@1 50.78 (63.21)	Acc@5 81.25 (84.38)
-    Test: [20/79]	Time 0.152 (0.153)	Loss 1.582 (1.588)	Acc@1 64.84 (61.09)	Acc@5 82.03 (84.04)
-    Test: [30/79]	Time 0.153 (0.153)	Loss 2.103 (1.691)	Acc@1 55.47 (59.30)	Acc@5 71.09 (82.41)
-    Test: [40/79]	Time 0.153 (0.153)	Loss 1.597 (1.745)	Acc@1 64.06 (57.89)	Acc@5 83.59 (81.48)
-    Test: [50/79]	Time 0.154 (0.153)	Loss 1.895 (1.751)	Acc@1 53.91 (57.74)	Acc@5 77.34 (81.20)
-    Test: [60/79]	Time 0.154 (0.153)	Loss 1.566 (1.783)	Acc@1 65.62 (57.18)	Acc@5 84.38 (80.75)
-    Test: [70/79]	Time 0.153 (0.153)	Loss 2.457 (1.811)	Acc@1 45.31 (56.65)	Acc@5 73.44 (80.27)
+    Epoch:[0][  0/782]	Time 0.488 (0.488)	Loss 1.029 (1.029)	Acc@1 75.00 (75.00)	Acc@5 90.62 (90.62)
+    Epoch:[0][ 50/782]	Time 0.366 (0.377)	Loss 0.672 (0.823)	Acc@1 87.50 (79.81)	Acc@5 94.53 (93.84)
+    Epoch:[0][100/782]	Time 0.388 (0.374)	Loss 0.661 (0.799)	Acc@1 85.94 (80.31)	Acc@5 98.44 (94.41)
+    Epoch:[0][150/782]	Time 0.379 (0.375)	Loss 0.632 (0.797)	Acc@1 85.94 (80.50)	Acc@5 94.53 (94.24)
+    Epoch:[0][200/782]	Time 0.376 (0.375)	Loss 0.742 (0.790)	Acc@1 81.25 (80.69)	Acc@5 94.53 (94.31)
+    Epoch:[0][250/782]	Time 0.375 (0.376)	Loss 0.815 (0.785)	Acc@1 81.25 (80.80)	Acc@5 93.75 (94.34)
+    Epoch:[0][300/782]	Time 0.387 (0.376)	Loss 0.878 (0.781)	Acc@1 76.56 (80.87)	Acc@5 92.19 (94.37)
+    Epoch:[0][350/782]	Time 0.377 (0.376)	Loss 0.746 (0.774)	Acc@1 82.03 (81.03)	Acc@5 93.75 (94.44)
+    Epoch:[0][400/782]	Time 0.372 (0.376)	Loss 0.766 (0.772)	Acc@1 79.69 (81.12)	Acc@5 96.88 (94.42)
+    Epoch:[0][450/782]	Time 0.370 (0.375)	Loss 0.865 (0.768)	Acc@1 77.34 (81.28)	Acc@5 93.75 (94.48)
+    Epoch:[0][500/782]	Time 0.374 (0.376)	Loss 0.526 (0.765)	Acc@1 89.06 (81.33)	Acc@5 97.66 (94.53)
+    Epoch:[0][550/782]	Time 0.382 (0.376)	Loss 0.826 (0.762)	Acc@1 79.69 (81.39)	Acc@5 92.19 (94.55)
+    Epoch:[0][600/782]	Time 0.367 (0.375)	Loss 0.644 (0.761)	Acc@1 85.94 (81.45)	Acc@5 95.31 (94.55)
+    Epoch:[0][650/782]	Time 0.374 (0.375)	Loss 0.585 (0.757)	Acc@1 81.25 (81.57)	Acc@5 98.44 (94.59)
+    Epoch:[0][700/782]	Time 0.374 (0.375)	Loss 0.578 (0.755)	Acc@1 86.72 (81.65)	Acc@5 96.88 (94.60)
+    Epoch:[0][750/782]	Time 0.366 (0.375)	Loss 0.783 (0.753)	Acc@1 79.69 (81.69)	Acc@5 95.31 (94.63)
+    Test: [ 0/79]	Time 0.149 (0.149)	Loss 1.063 (1.063)	Acc@1 74.22 (74.22)	Acc@5 87.50 (87.50)
+    Test: [10/79]	Time 0.148 (0.149)	Loss 1.785 (1.514)	Acc@1 50.78 (63.21)	Acc@5 81.25 (84.38)
+    Test: [20/79]	Time 0.150 (0.149)	Loss 1.582 (1.588)	Acc@1 64.84 (61.09)	Acc@5 82.03 (84.04)
+    Test: [30/79]	Time 0.146 (0.149)	Loss 2.103 (1.691)	Acc@1 55.47 (59.30)	Acc@5 71.09 (82.41)
+    Test: [40/79]	Time 0.151 (0.149)	Loss 1.597 (1.745)	Acc@1 64.06 (57.89)	Acc@5 83.59 (81.48)
+    Test: [50/79]	Time 0.149 (0.150)	Loss 1.895 (1.751)	Acc@1 53.91 (57.74)	Acc@5 77.34 (81.20)
+    Test: [60/79]	Time 0.150 (0.150)	Loss 1.566 (1.783)	Acc@1 65.62 (57.18)	Acc@5 84.38 (80.75)
+    Test: [70/79]	Time 0.147 (0.149)	Loss 2.457 (1.811)	Acc@1 45.31 (56.65)	Acc@5 73.44 (80.27)
      * Acc@1 57.080 Acc@5 80.940
     Accuracy of tuned INT8 model: 57.080
     Accuracy drop of tuned INT8 model over pre-trained FP32 model: -1.560
@@ -795,9 +793,9 @@ throughput (frames per second) values.
 .. parsed-literal::
 
     Benchmark FP32 model (IR)
-    [ INFO ] Throughput:   2941.82 FPS
+    [ INFO ] Throughput:   2934.02 FPS
     Benchmark INT8 model (IR)
-    [ INFO ] Throughput:   11834.02 FPS
+    [ INFO ] Throughput:   11792.08 FPS
 
 
 Show Device Information for reference.
diff --git a/docs/notebooks/pytorch-quantization-sparsity-aware-training-with-output.rst b/docs/notebooks/pytorch-quantization-sparsity-aware-training-with-output.rst
index 8f1cf84bfcdaab..fc4eda5170527b 100644
--- a/docs/notebooks/pytorch-quantization-sparsity-aware-training-with-output.rst
+++ b/docs/notebooks/pytorch-quantization-sparsity-aware-training-with-output.rst
@@ -22,8 +22,8 @@ This tutorial uses the ResNet-50 model with the ImageNet dataset. The
 dataset must be downloaded separately. To see ResNet models, visit
 `PyTorch hub <https://pytorch.org/hub/pytorch_vision_resnet/>`__.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Imports and Settings <#imports-and-settings>`__
 -  `Pre-train Floating-Point Model <#pre-train-floating-point-model>`__
@@ -49,9 +49,7 @@ Table of contents:
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
 
 
@@ -408,9 +406,9 @@ It can be obtained by tuning from scratch with the code below.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
       warnings.warn(
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.
       warnings.warn(msg)
 
 
@@ -474,10 +472,10 @@ your original training code are minor.
 
 .. parsed-literal::
 
-    2024-06-06 01:28:37.048469: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-06-06 01:28:37.082914: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-07-02 01:41:27.244480: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-07-02 01:41:27.276044: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2024-06-06 01:28:37.678641: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-07-02 01:41:27.882264: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 .. parsed-literal::
@@ -513,14 +511,14 @@ quantization and sparsity.
 
 .. parsed-literal::
 
-    Test: [ 0/79]	Time 0.337 (0.337)	Loss 6.069 (6.069)	Acc@1 0.00 (0.00)	Acc@5 4.69 (4.69)
-    Test: [10/79]	Time 0.138 (0.163)	Loss 5.368 (5.689)	Acc@1 0.78 (0.07)	Acc@5 3.91 (2.41)
-    Test: [20/79]	Time 0.167 (0.157)	Loss 5.921 (5.653)	Acc@1 0.00 (0.56)	Acc@5 2.34 (3.16)
-    Test: [30/79]	Time 0.138 (0.154)	Loss 5.664 (5.670)	Acc@1 0.00 (0.50)	Acc@5 0.78 (2.90)
-    Test: [40/79]	Time 0.150 (0.153)	Loss 5.608 (5.632)	Acc@1 1.56 (0.59)	Acc@5 3.12 (3.09)
-    Test: [50/79]	Time 0.161 (0.152)	Loss 5.170 (5.618)	Acc@1 0.00 (0.72)	Acc@5 2.34 (3.32)
-    Test: [60/79]	Time 0.133 (0.151)	Loss 6.619 (5.634)	Acc@1 0.00 (0.67)	Acc@5 0.00 (3.00)
-    Test: [70/79]	Time 0.133 (0.150)	Loss 5.771 (5.653)	Acc@1 0.00 (0.57)	Acc@5 1.56 (2.77)
+    Test: [ 0/79]	Time 0.355 (0.355)	Loss 6.069 (6.069)	Acc@1 0.00 (0.00)	Acc@5 4.69 (4.69)
+    Test: [10/79]	Time 0.147 (0.163)	Loss 5.368 (5.689)	Acc@1 0.78 (0.07)	Acc@5 3.91 (2.41)
+    Test: [20/79]	Time 0.155 (0.157)	Loss 5.921 (5.653)	Acc@1 0.00 (0.56)	Acc@5 2.34 (3.16)
+    Test: [30/79]	Time 0.150 (0.155)	Loss 5.664 (5.670)	Acc@1 0.00 (0.50)	Acc@5 0.78 (2.90)
+    Test: [40/79]	Time 0.142 (0.155)	Loss 5.608 (5.632)	Acc@1 1.56 (0.59)	Acc@5 3.12 (3.09)
+    Test: [50/79]	Time 0.144 (0.154)	Loss 5.170 (5.618)	Acc@1 0.00 (0.72)	Acc@5 2.34 (3.32)
+    Test: [60/79]	Time 0.145 (0.154)	Loss 6.619 (5.634)	Acc@1 0.00 (0.67)	Acc@5 0.00 (3.00)
+    Test: [70/79]	Time 0.158 (0.153)	Loss 5.771 (5.653)	Acc@1 0.00 (0.57)	Acc@5 1.56 (2.77)
      * Acc@1 0.570 Acc@5 2.770
     Accuracy of initialized sparse INT8 model: 0.570
 
@@ -558,175 +556,175 @@ training pipeline are required. Here is a simple example.
 .. parsed-literal::
 
     Training
-    Epoch:[0][  0/782]	Time 0.549 (0.549)	Loss 5.673 (5.673)	Acc@1 0.78 (0.78)	Acc@5 3.12 (3.12)
-    Epoch:[0][ 50/782]	Time 0.339 (0.341)	Loss 5.643 (5.644)	Acc@1 0.00 (0.78)	Acc@5 2.34 (3.12)
-    Epoch:[0][100/782]	Time 0.330 (0.340)	Loss 5.565 (5.604)	Acc@1 0.78 (0.80)	Acc@5 2.34 (3.23)
-    Epoch:[0][150/782]	Time 0.339 (0.339)	Loss 5.540 (5.559)	Acc@1 0.78 (0.90)	Acc@5 3.91 (3.53)
-    Epoch:[0][200/782]	Time 0.334 (0.343)	Loss 5.273 (5.515)	Acc@1 2.34 (1.07)	Acc@5 7.81 (3.98)
-    Epoch:[0][250/782]	Time 0.336 (0.342)	Loss 5.358 (5.473)	Acc@1 1.56 (1.24)	Acc@5 6.25 (4.52)
-    Epoch:[0][300/782]	Time 0.340 (0.342)	Loss 5.226 (5.431)	Acc@1 1.56 (1.45)	Acc@5 7.03 (5.10)
-    Epoch:[0][350/782]	Time 0.390 (0.342)	Loss 5.104 (5.388)	Acc@1 1.56 (1.67)	Acc@5 10.16 (5.81)
-    Epoch:[0][400/782]	Time 0.339 (0.342)	Loss 5.052 (5.351)	Acc@1 0.78 (1.84)	Acc@5 12.50 (6.42)
-    Epoch:[0][450/782]	Time 0.348 (0.341)	Loss 5.049 (5.312)	Acc@1 3.91 (2.11)	Acc@5 10.94 (7.15)
-    Epoch:[0][500/782]	Time 0.338 (0.341)	Loss 4.855 (5.275)	Acc@1 5.47 (2.38)	Acc@5 13.28 (7.91)
-    Epoch:[0][550/782]	Time 0.337 (0.341)	Loss 4.707 (5.237)	Acc@1 10.16 (2.74)	Acc@5 24.22 (8.75)
-    Epoch:[0][600/782]	Time 0.336 (0.341)	Loss 4.622 (5.197)	Acc@1 7.81 (3.14)	Acc@5 25.00 (9.72)
-    Epoch:[0][650/782]	Time 0.340 (0.341)	Loss 4.615 (5.160)	Acc@1 10.16 (3.55)	Acc@5 22.66 (10.64)
-    Epoch:[0][700/782]	Time 0.342 (0.341)	Loss 4.655 (5.122)	Acc@1 7.03 (3.99)	Acc@5 22.66 (11.62)
-    Epoch:[0][750/782]	Time 0.342 (0.341)	Loss 4.461 (5.084)	Acc@1 15.62 (4.51)	Acc@5 34.38 (12.66)
-    Epoch:[1][  0/782]	Time 0.696 (0.696)	Loss 4.331 (4.331)	Acc@1 15.62 (15.62)	Acc@5 35.16 (35.16)
-    Epoch:[1][ 50/782]	Time 0.337 (0.350)	Loss 4.327 (4.228)	Acc@1 14.06 (16.68)	Acc@5 32.03 (37.44)
-    Epoch:[1][100/782]	Time 0.334 (0.344)	Loss 4.208 (4.187)	Acc@1 17.97 (18.04)	Acc@5 35.94 (38.38)
-    Epoch:[1][150/782]	Time 0.346 (0.343)	Loss 4.060 (4.166)	Acc@1 17.97 (18.56)	Acc@5 42.97 (38.90)
-    Epoch:[1][200/782]	Time 0.354 (0.342)	Loss 4.100 (4.142)	Acc@1 17.97 (18.94)	Acc@5 41.41 (39.69)
-    Epoch:[1][250/782]	Time 0.339 (0.342)	Loss 4.081 (4.119)	Acc@1 21.88 (19.23)	Acc@5 43.75 (40.24)
-    Epoch:[1][300/782]	Time 0.332 (0.342)	Loss 4.199 (4.099)	Acc@1 15.62 (19.49)	Acc@5 37.50 (40.77)
-    Epoch:[1][350/782]	Time 0.337 (0.341)	Loss 3.830 (4.077)	Acc@1 25.78 (19.82)	Acc@5 45.31 (41.33)
-    Epoch:[1][400/782]	Time 0.343 (0.341)	Loss 4.089 (4.054)	Acc@1 21.09 (20.27)	Acc@5 39.06 (41.95)
-    Epoch:[1][450/782]	Time 0.343 (0.342)	Loss 3.782 (4.034)	Acc@1 26.56 (20.62)	Acc@5 44.53 (42.39)
-    Epoch:[1][500/782]	Time 0.329 (0.341)	Loss 3.816 (4.012)	Acc@1 26.56 (21.00)	Acc@5 50.78 (43.00)
-    Epoch:[1][550/782]	Time 0.337 (0.341)	Loss 3.620 (3.989)	Acc@1 26.56 (21.37)	Acc@5 52.34 (43.58)
-    Epoch:[1][600/782]	Time 0.333 (0.341)	Loss 3.694 (3.971)	Acc@1 28.91 (21.63)	Acc@5 47.66 (44.06)
-    Epoch:[1][650/782]	Time 0.344 (0.341)	Loss 3.738 (3.952)	Acc@1 22.66 (21.86)	Acc@5 45.31 (44.52)
-    Epoch:[1][700/782]	Time 0.354 (0.341)	Loss 3.735 (3.936)	Acc@1 25.00 (22.09)	Acc@5 44.53 (44.90)
-    Epoch:[1][750/782]	Time 0.347 (0.341)	Loss 3.630 (3.918)	Acc@1 29.69 (22.32)	Acc@5 53.12 (45.32)
-    Epoch:[2][  0/782]	Time 0.713 (0.713)	Loss 3.419 (3.419)	Acc@1 32.03 (32.03)	Acc@5 57.81 (57.81)
-    Epoch:[2][ 50/782]	Time 0.343 (0.355)	Loss 3.397 (3.466)	Acc@1 32.03 (29.34)	Acc@5 56.25 (54.96)
-    Epoch:[2][100/782]	Time 0.354 (0.351)	Loss 3.293 (3.432)	Acc@1 33.59 (30.02)	Acc@5 59.38 (56.53)
-    Epoch:[2][150/782]	Time 0.347 (0.349)	Loss 3.358 (3.422)	Acc@1 33.59 (30.30)	Acc@5 59.38 (56.64)
-    Epoch:[2][200/782]	Time 0.343 (0.348)	Loss 3.215 (3.410)	Acc@1 34.38 (30.50)	Acc@5 63.28 (56.97)
-    Epoch:[2][250/782]	Time 0.354 (0.347)	Loss 3.369 (3.392)	Acc@1 32.81 (30.82)	Acc@5 57.81 (57.15)
-    Epoch:[2][300/782]	Time 0.344 (0.347)	Loss 3.487 (3.379)	Acc@1 25.78 (30.96)	Acc@5 51.56 (57.35)
-    Epoch:[2][350/782]	Time 0.346 (0.346)	Loss 3.336 (3.370)	Acc@1 34.38 (31.04)	Acc@5 60.94 (57.51)
-    Epoch:[2][400/782]	Time 0.338 (0.346)	Loss 3.434 (3.359)	Acc@1 25.78 (31.16)	Acc@5 59.38 (57.66)
-    Epoch:[2][450/782]	Time 0.354 (0.346)	Loss 3.440 (3.348)	Acc@1 28.12 (31.42)	Acc@5 57.81 (57.85)
-    Epoch:[2][500/782]	Time 0.344 (0.346)	Loss 3.129 (3.336)	Acc@1 35.16 (31.59)	Acc@5 66.41 (58.09)
-    Epoch:[2][550/782]	Time 0.340 (0.346)	Loss 3.388 (3.322)	Acc@1 26.56 (31.77)	Acc@5 52.34 (58.40)
-    Epoch:[2][600/782]	Time 0.350 (0.346)	Loss 3.078 (3.311)	Acc@1 36.72 (31.89)	Acc@5 63.28 (58.57)
-    Epoch:[2][650/782]	Time 0.344 (0.346)	Loss 3.172 (3.300)	Acc@1 36.72 (32.08)	Acc@5 64.84 (58.76)
-    Epoch:[2][700/782]	Time 0.346 (0.346)	Loss 3.152 (3.287)	Acc@1 32.03 (32.23)	Acc@5 58.59 (58.98)
-    Epoch:[2][750/782]	Time 0.355 (0.346)	Loss 3.228 (3.275)	Acc@1 36.72 (32.45)	Acc@5 56.25 (59.21)
-    Epoch:[3][  0/782]	Time 0.696 (0.696)	Loss 3.060 (3.060)	Acc@1 32.03 (32.03)	Acc@5 66.41 (66.41)
-    Epoch:[3][ 50/782]	Time 0.343 (0.353)	Loss 2.926 (2.958)	Acc@1 44.53 (37.94)	Acc@5 62.50 (65.10)
-    Epoch:[3][100/782]	Time 0.341 (0.349)	Loss 3.022 (2.938)	Acc@1 34.38 (38.18)	Acc@5 61.72 (65.66)
-    Epoch:[3][150/782]	Time 0.347 (0.349)	Loss 2.760 (2.934)	Acc@1 40.62 (38.10)	Acc@5 69.53 (65.46)
-    Epoch:[3][200/782]	Time 0.339 (0.348)	Loss 3.039 (2.928)	Acc@1 34.38 (38.21)	Acc@5 60.94 (65.38)
-    Epoch:[3][250/782]	Time 0.344 (0.348)	Loss 2.829 (2.924)	Acc@1 33.59 (38.16)	Acc@5 67.19 (65.41)
-    Epoch:[3][300/782]	Time 0.344 (0.347)	Loss 2.895 (2.919)	Acc@1 43.75 (38.16)	Acc@5 72.66 (65.39)
-    Epoch:[3][350/782]	Time 0.337 (0.346)	Loss 2.767 (2.914)	Acc@1 41.41 (38.23)	Acc@5 68.75 (65.42)
-    Epoch:[3][400/782]	Time 0.334 (0.346)	Loss 3.116 (2.908)	Acc@1 30.47 (38.20)	Acc@5 60.16 (65.48)
-    Epoch:[3][450/782]	Time 0.340 (0.346)	Loss 2.914 (2.903)	Acc@1 35.94 (38.30)	Acc@5 62.50 (65.54)
-    Epoch:[3][500/782]	Time 0.348 (0.348)	Loss 2.719 (2.895)	Acc@1 44.53 (38.36)	Acc@5 67.97 (65.71)
-    Epoch:[3][550/782]	Time 0.341 (0.347)	Loss 3.138 (2.889)	Acc@1 32.81 (38.40)	Acc@5 60.16 (65.79)
-    Epoch:[3][600/782]	Time 0.345 (0.347)	Loss 3.042 (2.884)	Acc@1 32.03 (38.43)	Acc@5 58.59 (65.82)
-    Epoch:[3][650/782]	Time 0.338 (0.347)	Loss 2.931 (2.877)	Acc@1 42.19 (38.54)	Acc@5 67.19 (65.96)
-    Epoch:[3][700/782]	Time 0.347 (0.346)	Loss 2.968 (2.870)	Acc@1 32.81 (38.57)	Acc@5 61.72 (66.06)
-    Epoch:[3][750/782]	Time 0.342 (0.346)	Loss 2.799 (2.864)	Acc@1 37.50 (38.71)	Acc@5 65.62 (66.12)
-    Epoch:[4][  0/782]	Time 0.683 (0.683)	Loss 2.625 (2.625)	Acc@1 46.09 (46.09)	Acc@5 68.75 (68.75)
-    Epoch:[4][ 50/782]	Time 0.335 (0.351)	Loss 2.682 (2.727)	Acc@1 46.09 (40.18)	Acc@5 67.97 (67.98)
-    Epoch:[4][100/782]	Time 0.353 (0.347)	Loss 2.824 (2.699)	Acc@1 33.59 (41.11)	Acc@5 64.84 (68.60)
-    Epoch:[4][150/782]	Time 0.349 (0.345)	Loss 2.703 (2.690)	Acc@1 46.09 (41.44)	Acc@5 64.84 (68.91)
-    Epoch:[4][200/782]	Time 0.341 (0.345)	Loss 2.523 (2.683)	Acc@1 46.88 (41.64)	Acc@5 74.22 (69.03)
-    Epoch:[4][250/782]	Time 0.347 (0.344)	Loss 2.381 (2.677)	Acc@1 49.22 (41.80)	Acc@5 74.22 (69.10)
-    Epoch:[4][300/782]	Time 0.342 (0.344)	Loss 2.633 (2.674)	Acc@1 42.19 (41.82)	Acc@5 65.62 (68.98)
-    Epoch:[4][350/782]	Time 0.337 (0.344)	Loss 2.621 (2.671)	Acc@1 46.09 (41.86)	Acc@5 71.88 (69.01)
-    Epoch:[4][400/782]	Time 0.338 (0.344)	Loss 2.472 (2.662)	Acc@1 42.97 (42.02)	Acc@5 75.00 (69.15)
-    Epoch:[4][450/782]	Time 0.370 (0.344)	Loss 2.529 (2.659)	Acc@1 42.19 (42.03)	Acc@5 75.78 (69.18)
-    Epoch:[4][500/782]	Time 0.341 (0.344)	Loss 2.793 (2.654)	Acc@1 37.50 (42.12)	Acc@5 64.84 (69.27)
-    Epoch:[4][550/782]	Time 0.349 (0.344)	Loss 2.474 (2.646)	Acc@1 45.31 (42.31)	Acc@5 67.97 (69.32)
-    Epoch:[4][600/782]	Time 0.342 (0.344)	Loss 2.383 (2.642)	Acc@1 51.56 (42.36)	Acc@5 73.44 (69.34)
-    Epoch:[4][650/782]	Time 0.339 (0.344)	Loss 2.595 (2.638)	Acc@1 43.75 (42.41)	Acc@5 71.88 (69.35)
-    Epoch:[4][700/782]	Time 0.338 (0.344)	Loss 2.541 (2.634)	Acc@1 39.84 (42.44)	Acc@5 74.22 (69.37)
-    Epoch:[4][750/782]	Time 0.351 (0.344)	Loss 2.408 (2.628)	Acc@1 45.31 (42.52)	Acc@5 75.00 (69.51)
-    Epoch:[5][  0/782]	Time 0.697 (0.697)	Loss 2.310 (2.310)	Acc@1 48.44 (48.44)	Acc@5 75.00 (75.00)
-    Epoch:[5][ 50/782]	Time 0.341 (0.352)	Loss 2.585 (2.521)	Acc@1 42.97 (43.66)	Acc@5 68.75 (71.32)
-    Epoch:[5][100/782]	Time 0.345 (0.349)	Loss 2.263 (2.491)	Acc@1 48.44 (44.46)	Acc@5 74.22 (71.88)
-    Epoch:[5][150/782]	Time 0.348 (0.348)	Loss 2.296 (2.480)	Acc@1 52.34 (44.62)	Acc@5 75.00 (71.90)
-    Epoch:[5][200/782]	Time 0.339 (0.348)	Loss 2.430 (2.479)	Acc@1 48.44 (44.75)	Acc@5 70.31 (71.79)
-    Epoch:[5][250/782]	Time 0.347 (0.347)	Loss 2.566 (2.482)	Acc@1 40.62 (44.74)	Acc@5 69.53 (71.70)
-    Epoch:[5][300/782]	Time 0.348 (0.347)	Loss 2.414 (2.476)	Acc@1 40.62 (44.86)	Acc@5 78.12 (71.78)
-    Epoch:[5][350/782]	Time 0.346 (0.347)	Loss 2.301 (2.477)	Acc@1 50.78 (44.74)	Acc@5 75.78 (71.62)
-    Epoch:[5][400/782]	Time 0.341 (0.347)	Loss 2.414 (2.472)	Acc@1 44.53 (44.87)	Acc@5 72.66 (71.71)
-    Epoch:[5][450/782]	Time 0.346 (0.347)	Loss 2.352 (2.466)	Acc@1 50.78 (44.94)	Acc@5 72.66 (71.85)
-    Epoch:[5][500/782]	Time 0.341 (0.346)	Loss 2.423 (2.464)	Acc@1 47.66 (44.97)	Acc@5 74.22 (71.84)
-    Epoch:[5][550/782]	Time 0.353 (0.347)	Loss 2.407 (2.459)	Acc@1 40.62 (45.03)	Acc@5 71.88 (71.88)
-    Epoch:[5][600/782]	Time 0.347 (0.346)	Loss 2.326 (2.457)	Acc@1 48.44 (45.05)	Acc@5 77.34 (71.91)
-    Epoch:[5][650/782]	Time 0.346 (0.346)	Loss 2.283 (2.452)	Acc@1 47.66 (45.13)	Acc@5 71.88 (72.01)
-    Epoch:[5][700/782]	Time 0.341 (0.346)	Loss 2.217 (2.446)	Acc@1 46.88 (45.21)	Acc@5 72.66 (72.09)
-    Epoch:[5][750/782]	Time 0.334 (0.346)	Loss 2.474 (2.442)	Acc@1 50.78 (45.29)	Acc@5 65.62 (72.12)
-    Epoch:[6][  0/782]	Time 0.700 (0.700)	Loss 2.568 (2.568)	Acc@1 44.53 (44.53)	Acc@5 64.06 (64.06)
-    Epoch:[6][ 50/782]	Time 0.346 (0.351)	Loss 2.411 (2.321)	Acc@1 45.31 (47.50)	Acc@5 68.75 (74.17)
-    Epoch:[6][100/782]	Time 0.338 (0.348)	Loss 2.401 (2.333)	Acc@1 48.44 (47.05)	Acc@5 72.66 (73.89)
-    Epoch:[6][150/782]	Time 0.343 (0.346)	Loss 2.220 (2.331)	Acc@1 46.88 (47.11)	Acc@5 75.78 (73.85)
-    Epoch:[6][200/782]	Time 0.340 (0.345)	Loss 2.330 (2.329)	Acc@1 49.22 (47.21)	Acc@5 73.44 (73.77)
-    Epoch:[6][250/782]	Time 0.341 (0.346)	Loss 2.581 (2.330)	Acc@1 43.75 (47.22)	Acc@5 67.97 (73.84)
-    Epoch:[6][300/782]	Time 0.332 (0.345)	Loss 2.457 (2.321)	Acc@1 42.97 (47.57)	Acc@5 73.44 (74.00)
-    Epoch:[6][350/782]	Time 0.341 (0.345)	Loss 2.332 (2.321)	Acc@1 50.78 (47.49)	Acc@5 73.44 (73.98)
-    Epoch:[6][400/782]	Time 0.350 (0.345)	Loss 2.057 (2.317)	Acc@1 53.91 (47.56)	Acc@5 80.47 (74.01)
-    Epoch:[6][450/782]	Time 0.343 (0.345)	Loss 2.379 (2.316)	Acc@1 45.31 (47.41)	Acc@5 71.09 (74.02)
-    Epoch:[6][500/782]	Time 0.345 (0.345)	Loss 2.337 (2.313)	Acc@1 48.44 (47.44)	Acc@5 71.09 (74.10)
-    Epoch:[6][550/782]	Time 0.341 (0.345)	Loss 2.207 (2.309)	Acc@1 46.88 (47.54)	Acc@5 74.22 (74.18)
-    Epoch:[6][600/782]	Time 0.341 (0.345)	Loss 2.191 (2.305)	Acc@1 57.03 (47.63)	Acc@5 77.34 (74.22)
-    Epoch:[6][650/782]	Time 0.343 (0.344)	Loss 2.120 (2.303)	Acc@1 53.12 (47.62)	Acc@5 77.34 (74.23)
-    Epoch:[6][700/782]	Time 0.337 (0.344)	Loss 2.312 (2.298)	Acc@1 39.84 (47.71)	Acc@5 71.88 (74.30)
-    Epoch:[6][750/782]	Time 0.334 (0.345)	Loss 2.080 (2.295)	Acc@1 53.12 (47.77)	Acc@5 79.69 (74.34)
-    Epoch:[7][  0/782]	Time 0.678 (0.678)	Loss 2.192 (2.192)	Acc@1 44.53 (44.53)	Acc@5 78.12 (78.12)
-    Epoch:[7][ 50/782]	Time 0.345 (0.351)	Loss 2.139 (2.214)	Acc@1 50.78 (48.56)	Acc@5 76.56 (75.32)
-    Epoch:[7][100/782]	Time 0.347 (0.347)	Loss 2.266 (2.213)	Acc@1 57.03 (49.16)	Acc@5 71.88 (75.45)
-    Epoch:[7][150/782]	Time 0.343 (0.346)	Loss 1.987 (2.209)	Acc@1 54.69 (49.10)	Acc@5 82.03 (75.53)
-    Epoch:[7][200/782]	Time 0.340 (0.346)	Loss 2.232 (2.203)	Acc@1 43.75 (49.37)	Acc@5 75.00 (75.62)
-    Epoch:[7][250/782]	Time 0.344 (0.346)	Loss 2.216 (2.203)	Acc@1 48.44 (49.27)	Acc@5 78.91 (75.66)
-    Epoch:[7][300/782]	Time 0.347 (0.345)	Loss 2.393 (2.202)	Acc@1 49.22 (49.30)	Acc@5 71.09 (75.70)
-    Epoch:[7][350/782]	Time 0.339 (0.345)	Loss 2.084 (2.196)	Acc@1 44.53 (49.47)	Acc@5 80.47 (75.84)
-    Epoch:[7][400/782]	Time 0.342 (0.345)	Loss 1.682 (2.194)	Acc@1 65.62 (49.55)	Acc@5 83.59 (75.82)
-    Epoch:[7][450/782]	Time 0.343 (0.345)	Loss 2.193 (2.194)	Acc@1 47.66 (49.62)	Acc@5 75.78 (75.82)
-    Epoch:[7][500/782]	Time 0.345 (0.345)	Loss 2.166 (2.192)	Acc@1 45.31 (49.59)	Acc@5 78.12 (75.81)
-    Epoch:[7][550/782]	Time 0.337 (0.345)	Loss 2.126 (2.187)	Acc@1 47.66 (49.70)	Acc@5 78.91 (75.84)
-    Epoch:[7][600/782]	Time 0.339 (0.345)	Loss 2.222 (2.184)	Acc@1 49.22 (49.73)	Acc@5 73.44 (75.87)
-    Epoch:[7][650/782]	Time 0.339 (0.345)	Loss 2.075 (2.181)	Acc@1 50.00 (49.79)	Acc@5 78.12 (75.89)
-    Epoch:[7][700/782]	Time 0.349 (0.345)	Loss 2.181 (2.179)	Acc@1 47.66 (49.81)	Acc@5 75.78 (75.89)
-    Epoch:[7][750/782]	Time 0.342 (0.345)	Loss 2.071 (2.177)	Acc@1 53.12 (49.82)	Acc@5 75.78 (75.89)
-    Epoch:[8][  0/782]	Time 0.704 (0.704)	Loss 1.829 (1.829)	Acc@1 58.59 (58.59)	Acc@5 82.03 (82.03)
-    Epoch:[8][ 50/782]	Time 0.353 (0.351)	Loss 2.171 (2.096)	Acc@1 50.78 (51.04)	Acc@5 78.91 (77.51)
-    Epoch:[8][100/782]	Time 0.346 (0.347)	Loss 2.207 (2.089)	Acc@1 52.34 (51.26)	Acc@5 74.22 (77.56)
-    Epoch:[8][150/782]	Time 0.343 (0.345)	Loss 2.289 (2.100)	Acc@1 49.22 (51.13)	Acc@5 73.44 (77.32)
-    Epoch:[8][200/782]	Time 0.337 (0.344)	Loss 2.175 (2.101)	Acc@1 46.88 (51.00)	Acc@5 77.34 (77.29)
-    Epoch:[8][250/782]	Time 0.347 (0.344)	Loss 2.239 (2.092)	Acc@1 47.66 (51.30)	Acc@5 71.88 (77.35)
-    Epoch:[8][300/782]	Time 0.338 (0.344)	Loss 2.070 (2.087)	Acc@1 49.22 (51.40)	Acc@5 75.78 (77.41)
-    Epoch:[8][350/782]	Time 0.346 (0.344)	Loss 1.868 (2.083)	Acc@1 52.34 (51.38)	Acc@5 82.81 (77.39)
-    Epoch:[8][400/782]	Time 0.339 (0.345)	Loss 2.345 (2.084)	Acc@1 40.62 (51.47)	Acc@5 71.88 (77.34)
-    Epoch:[8][450/782]	Time 0.344 (0.345)	Loss 1.731 (2.085)	Acc@1 63.28 (51.43)	Acc@5 82.81 (77.32)
-    Epoch:[8][500/782]	Time 0.346 (0.345)	Loss 2.142 (2.082)	Acc@1 46.09 (51.40)	Acc@5 77.34 (77.35)
-    Epoch:[8][550/782]	Time 0.339 (0.344)	Loss 2.173 (2.080)	Acc@1 53.91 (51.45)	Acc@5 73.44 (77.40)
-    Epoch:[8][600/782]	Time 0.346 (0.345)	Loss 2.184 (2.077)	Acc@1 54.69 (51.55)	Acc@5 73.44 (77.43)
-    Epoch:[8][650/782]	Time 0.349 (0.345)	Loss 2.118 (2.075)	Acc@1 49.22 (51.60)	Acc@5 76.56 (77.43)
-    Epoch:[8][700/782]	Time 0.355 (0.345)	Loss 2.254 (2.074)	Acc@1 51.56 (51.61)	Acc@5 72.66 (77.37)
-    Epoch:[8][750/782]	Time 0.336 (0.345)	Loss 2.056 (2.071)	Acc@1 53.91 (51.67)	Acc@5 75.78 (77.41)
-    Epoch:[9][  0/782]	Time 0.705 (0.705)	Loss 1.824 (1.824)	Acc@1 59.38 (59.38)	Acc@5 85.16 (85.16)
-    Epoch:[9][ 50/782]	Time 0.358 (0.352)	Loss 2.063 (1.996)	Acc@1 50.78 (53.09)	Acc@5 80.47 (78.65)
-    Epoch:[9][100/782]	Time 0.340 (0.352)	Loss 1.874 (1.999)	Acc@1 58.59 (53.12)	Acc@5 82.03 (78.38)
-    Epoch:[9][150/782]	Time 0.339 (0.349)	Loss 2.026 (1.994)	Acc@1 50.78 (53.17)	Acc@5 78.91 (78.80)
-    Epoch:[9][200/782]	Time 0.344 (0.348)	Loss 1.877 (1.994)	Acc@1 59.38 (53.10)	Acc@5 82.81 (78.68)
-    Epoch:[9][250/782]	Time 0.369 (0.349)	Loss 2.166 (1.996)	Acc@1 46.09 (53.00)	Acc@5 73.44 (78.60)
-    Epoch:[9][300/782]	Time 0.342 (0.349)	Loss 2.125 (1.997)	Acc@1 51.56 (53.01)	Acc@5 76.56 (78.49)
-    Epoch:[9][350/782]	Time 0.344 (0.349)	Loss 2.210 (1.995)	Acc@1 46.88 (52.89)	Acc@5 75.00 (78.60)
-    Epoch:[9][400/782]	Time 0.344 (0.349)	Loss 1.897 (1.994)	Acc@1 57.81 (52.86)	Acc@5 79.69 (78.56)
-    Epoch:[9][450/782]	Time 0.364 (0.348)	Loss 2.045 (1.989)	Acc@1 50.78 (53.00)	Acc@5 76.56 (78.62)
-    Epoch:[9][500/782]	Time 0.339 (0.348)	Loss 2.300 (1.990)	Acc@1 46.88 (52.97)	Acc@5 72.66 (78.62)
-    Epoch:[9][550/782]	Time 0.341 (0.347)	Loss 1.604 (1.990)	Acc@1 64.06 (53.02)	Acc@5 82.81 (78.61)
-    Epoch:[9][600/782]	Time 0.345 (0.347)	Loss 1.763 (1.987)	Acc@1 54.69 (53.07)	Acc@5 85.16 (78.65)
-    Epoch:[9][650/782]	Time 0.349 (0.347)	Loss 1.664 (1.984)	Acc@1 63.28 (53.11)	Acc@5 82.81 (78.71)
-    Epoch:[9][700/782]	Time 0.338 (0.347)	Loss 2.284 (1.982)	Acc@1 42.97 (53.12)	Acc@5 78.12 (78.76)
-    Epoch:[9][750/782]	Time 0.345 (0.347)	Loss 1.698 (1.983)	Acc@1 59.38 (53.11)	Acc@5 82.03 (78.72)
+    Epoch:[0][  0/782]	Time 0.555 (0.555)	Loss 5.673 (5.673)	Acc@1 0.78 (0.78)	Acc@5 3.12 (3.12)
+    Epoch:[0][ 50/782]	Time 0.336 (0.342)	Loss 5.643 (5.644)	Acc@1 0.00 (0.78)	Acc@5 2.34 (3.12)
+    Epoch:[0][100/782]	Time 0.335 (0.340)	Loss 5.565 (5.604)	Acc@1 0.78 (0.80)	Acc@5 2.34 (3.23)
+    Epoch:[0][150/782]	Time 0.340 (0.340)	Loss 5.540 (5.559)	Acc@1 0.78 (0.90)	Acc@5 3.91 (3.53)
+    Epoch:[0][200/782]	Time 0.338 (0.340)	Loss 5.273 (5.515)	Acc@1 2.34 (1.07)	Acc@5 7.81 (3.98)
+    Epoch:[0][250/782]	Time 0.354 (0.340)	Loss 5.358 (5.473)	Acc@1 1.56 (1.24)	Acc@5 6.25 (4.52)
+    Epoch:[0][300/782]	Time 0.340 (0.340)	Loss 5.226 (5.431)	Acc@1 1.56 (1.45)	Acc@5 7.03 (5.10)
+    Epoch:[0][350/782]	Time 0.349 (0.340)	Loss 5.104 (5.388)	Acc@1 1.56 (1.67)	Acc@5 10.16 (5.81)
+    Epoch:[0][400/782]	Time 0.341 (0.341)	Loss 5.052 (5.351)	Acc@1 0.78 (1.84)	Acc@5 12.50 (6.42)
+    Epoch:[0][450/782]	Time 0.365 (0.341)	Loss 5.049 (5.312)	Acc@1 3.91 (2.11)	Acc@5 10.94 (7.15)
+    Epoch:[0][500/782]	Time 0.334 (0.341)	Loss 4.855 (5.275)	Acc@1 5.47 (2.38)	Acc@5 13.28 (7.91)
+    Epoch:[0][550/782]	Time 0.345 (0.341)	Loss 4.707 (5.237)	Acc@1 10.16 (2.74)	Acc@5 24.22 (8.75)
+    Epoch:[0][600/782]	Time 0.339 (0.342)	Loss 4.622 (5.197)	Acc@1 7.81 (3.14)	Acc@5 25.00 (9.72)
+    Epoch:[0][650/782]	Time 0.340 (0.342)	Loss 4.615 (5.160)	Acc@1 10.16 (3.55)	Acc@5 22.66 (10.64)
+    Epoch:[0][700/782]	Time 0.340 (0.342)	Loss 4.655 (5.122)	Acc@1 7.03 (3.99)	Acc@5 22.66 (11.62)
+    Epoch:[0][750/782]	Time 0.336 (0.342)	Loss 4.461 (5.084)	Acc@1 15.62 (4.51)	Acc@5 34.38 (12.66)
+    Epoch:[1][  0/782]	Time 0.662 (0.662)	Loss 4.331 (4.331)	Acc@1 15.62 (15.62)	Acc@5 35.16 (35.16)
+    Epoch:[1][ 50/782]	Time 0.338 (0.351)	Loss 4.327 (4.228)	Acc@1 14.06 (16.68)	Acc@5 32.03 (37.44)
+    Epoch:[1][100/782]	Time 0.334 (0.346)	Loss 4.208 (4.187)	Acc@1 17.97 (18.04)	Acc@5 35.94 (38.38)
+    Epoch:[1][150/782]	Time 0.344 (0.345)	Loss 4.060 (4.166)	Acc@1 17.97 (18.56)	Acc@5 42.97 (38.90)
+    Epoch:[1][200/782]	Time 0.349 (0.346)	Loss 4.100 (4.142)	Acc@1 17.97 (18.94)	Acc@5 41.41 (39.69)
+    Epoch:[1][250/782]	Time 0.334 (0.346)	Loss 4.081 (4.119)	Acc@1 21.88 (19.23)	Acc@5 43.75 (40.24)
+    Epoch:[1][300/782]	Time 0.337 (0.345)	Loss 4.199 (4.099)	Acc@1 15.62 (19.49)	Acc@5 37.50 (40.77)
+    Epoch:[1][350/782]	Time 0.347 (0.345)	Loss 3.830 (4.077)	Acc@1 25.78 (19.82)	Acc@5 45.31 (41.33)
+    Epoch:[1][400/782]	Time 0.351 (0.345)	Loss 4.089 (4.054)	Acc@1 21.09 (20.27)	Acc@5 39.06 (41.95)
+    Epoch:[1][450/782]	Time 0.346 (0.345)	Loss 3.782 (4.034)	Acc@1 26.56 (20.62)	Acc@5 44.53 (42.39)
+    Epoch:[1][500/782]	Time 0.350 (0.345)	Loss 3.816 (4.012)	Acc@1 26.56 (21.00)	Acc@5 50.78 (43.00)
+    Epoch:[1][550/782]	Time 0.341 (0.345)	Loss 3.620 (3.989)	Acc@1 26.56 (21.37)	Acc@5 52.34 (43.58)
+    Epoch:[1][600/782]	Time 0.349 (0.345)	Loss 3.694 (3.971)	Acc@1 28.91 (21.63)	Acc@5 47.66 (44.06)
+    Epoch:[1][650/782]	Time 0.355 (0.345)	Loss 3.738 (3.952)	Acc@1 22.66 (21.86)	Acc@5 45.31 (44.52)
+    Epoch:[1][700/782]	Time 0.341 (0.346)	Loss 3.735 (3.936)	Acc@1 25.00 (22.09)	Acc@5 44.53 (44.90)
+    Epoch:[1][750/782]	Time 0.353 (0.346)	Loss 3.630 (3.918)	Acc@1 29.69 (22.32)	Acc@5 53.12 (45.32)
+    Epoch:[2][  0/782]	Time 0.684 (0.684)	Loss 3.419 (3.419)	Acc@1 32.03 (32.03)	Acc@5 57.81 (57.81)
+    Epoch:[2][ 50/782]	Time 0.349 (0.356)	Loss 3.397 (3.466)	Acc@1 32.03 (29.34)	Acc@5 56.25 (54.96)
+    Epoch:[2][100/782]	Time 0.343 (0.353)	Loss 3.293 (3.432)	Acc@1 33.59 (30.02)	Acc@5 59.38 (56.53)
+    Epoch:[2][150/782]	Time 0.345 (0.352)	Loss 3.358 (3.422)	Acc@1 33.59 (30.30)	Acc@5 59.38 (56.64)
+    Epoch:[2][200/782]	Time 0.354 (0.352)	Loss 3.215 (3.410)	Acc@1 34.38 (30.50)	Acc@5 63.28 (56.97)
+    Epoch:[2][250/782]	Time 0.348 (0.351)	Loss 3.369 (3.392)	Acc@1 32.81 (30.82)	Acc@5 57.81 (57.15)
+    Epoch:[2][300/782]	Time 0.345 (0.351)	Loss 3.487 (3.379)	Acc@1 25.78 (30.96)	Acc@5 51.56 (57.35)
+    Epoch:[2][350/782]	Time 0.347 (0.352)	Loss 3.336 (3.370)	Acc@1 34.38 (31.04)	Acc@5 60.94 (57.51)
+    Epoch:[2][400/782]	Time 0.342 (0.352)	Loss 3.434 (3.359)	Acc@1 25.78 (31.16)	Acc@5 59.38 (57.66)
+    Epoch:[2][450/782]	Time 0.359 (0.352)	Loss 3.440 (3.348)	Acc@1 28.12 (31.42)	Acc@5 57.81 (57.85)
+    Epoch:[2][500/782]	Time 0.346 (0.352)	Loss 3.129 (3.336)	Acc@1 35.16 (31.59)	Acc@5 66.41 (58.09)
+    Epoch:[2][550/782]	Time 0.339 (0.352)	Loss 3.388 (3.322)	Acc@1 26.56 (31.77)	Acc@5 52.34 (58.40)
+    Epoch:[2][600/782]	Time 0.348 (0.351)	Loss 3.078 (3.311)	Acc@1 36.72 (31.89)	Acc@5 63.28 (58.57)
+    Epoch:[2][650/782]	Time 0.351 (0.351)	Loss 3.172 (3.300)	Acc@1 36.72 (32.08)	Acc@5 64.84 (58.76)
+    Epoch:[2][700/782]	Time 0.344 (0.351)	Loss 3.152 (3.287)	Acc@1 32.03 (32.23)	Acc@5 58.59 (58.98)
+    Epoch:[2][750/782]	Time 0.355 (0.351)	Loss 3.228 (3.275)	Acc@1 36.72 (32.45)	Acc@5 56.25 (59.21)
+    Epoch:[3][  0/782]	Time 0.669 (0.669)	Loss 3.060 (3.060)	Acc@1 32.03 (32.03)	Acc@5 66.41 (66.41)
+    Epoch:[3][ 50/782]	Time 0.348 (0.355)	Loss 2.926 (2.958)	Acc@1 44.53 (37.94)	Acc@5 62.50 (65.10)
+    Epoch:[3][100/782]	Time 0.349 (0.351)	Loss 3.022 (2.938)	Acc@1 34.38 (38.18)	Acc@5 61.72 (65.66)
+    Epoch:[3][150/782]	Time 0.352 (0.350)	Loss 2.760 (2.934)	Acc@1 40.62 (38.10)	Acc@5 69.53 (65.46)
+    Epoch:[3][200/782]	Time 0.351 (0.351)	Loss 3.039 (2.928)	Acc@1 34.38 (38.21)	Acc@5 60.94 (65.38)
+    Epoch:[3][250/782]	Time 0.355 (0.351)	Loss 2.829 (2.924)	Acc@1 33.59 (38.16)	Acc@5 67.19 (65.41)
+    Epoch:[3][300/782]	Time 0.344 (0.351)	Loss 2.895 (2.919)	Acc@1 43.75 (38.16)	Acc@5 72.66 (65.39)
+    Epoch:[3][350/782]	Time 0.348 (0.351)	Loss 2.767 (2.914)	Acc@1 41.41 (38.23)	Acc@5 68.75 (65.42)
+    Epoch:[3][400/782]	Time 0.345 (0.350)	Loss 3.116 (2.908)	Acc@1 30.47 (38.20)	Acc@5 60.16 (65.48)
+    Epoch:[3][450/782]	Time 0.344 (0.349)	Loss 2.914 (2.903)	Acc@1 35.94 (38.30)	Acc@5 62.50 (65.54)
+    Epoch:[3][500/782]	Time 0.345 (0.349)	Loss 2.719 (2.895)	Acc@1 44.53 (38.36)	Acc@5 67.97 (65.71)
+    Epoch:[3][550/782]	Time 0.353 (0.349)	Loss 3.138 (2.889)	Acc@1 32.81 (38.40)	Acc@5 60.16 (65.79)
+    Epoch:[3][600/782]	Time 0.354 (0.349)	Loss 3.042 (2.884)	Acc@1 32.03 (38.43)	Acc@5 58.59 (65.82)
+    Epoch:[3][650/782]	Time 0.345 (0.349)	Loss 2.931 (2.877)	Acc@1 42.19 (38.54)	Acc@5 67.19 (65.96)
+    Epoch:[3][700/782]	Time 0.343 (0.348)	Loss 2.968 (2.870)	Acc@1 32.81 (38.57)	Acc@5 61.72 (66.06)
+    Epoch:[3][750/782]	Time 0.341 (0.348)	Loss 2.799 (2.864)	Acc@1 37.50 (38.71)	Acc@5 65.62 (66.12)
+    Epoch:[4][  0/782]	Time 0.714 (0.714)	Loss 2.625 (2.625)	Acc@1 46.09 (46.09)	Acc@5 68.75 (68.75)
+    Epoch:[4][ 50/782]	Time 0.340 (0.367)	Loss 2.682 (2.727)	Acc@1 46.09 (40.18)	Acc@5 67.97 (67.98)
+    Epoch:[4][100/782]	Time 0.352 (0.355)	Loss 2.824 (2.699)	Acc@1 33.59 (41.11)	Acc@5 64.84 (68.60)
+    Epoch:[4][150/782]	Time 0.345 (0.352)	Loss 2.703 (2.690)	Acc@1 46.09 (41.44)	Acc@5 64.84 (68.91)
+    Epoch:[4][200/782]	Time 0.345 (0.351)	Loss 2.523 (2.683)	Acc@1 46.88 (41.64)	Acc@5 74.22 (69.03)
+    Epoch:[4][250/782]	Time 0.351 (0.350)	Loss 2.381 (2.677)	Acc@1 49.22 (41.80)	Acc@5 74.22 (69.10)
+    Epoch:[4][300/782]	Time 0.356 (0.350)	Loss 2.633 (2.674)	Acc@1 42.19 (41.82)	Acc@5 65.62 (68.98)
+    Epoch:[4][350/782]	Time 0.344 (0.350)	Loss 2.621 (2.671)	Acc@1 46.09 (41.86)	Acc@5 71.88 (69.01)
+    Epoch:[4][400/782]	Time 0.347 (0.349)	Loss 2.472 (2.662)	Acc@1 42.97 (42.02)	Acc@5 75.00 (69.15)
+    Epoch:[4][450/782]	Time 0.355 (0.349)	Loss 2.529 (2.659)	Acc@1 42.19 (42.03)	Acc@5 75.78 (69.18)
+    Epoch:[4][500/782]	Time 0.355 (0.349)	Loss 2.793 (2.654)	Acc@1 37.50 (42.12)	Acc@5 64.84 (69.27)
+    Epoch:[4][550/782]	Time 0.353 (0.349)	Loss 2.474 (2.646)	Acc@1 45.31 (42.31)	Acc@5 67.97 (69.32)
+    Epoch:[4][600/782]	Time 0.345 (0.349)	Loss 2.383 (2.642)	Acc@1 51.56 (42.36)	Acc@5 73.44 (69.34)
+    Epoch:[4][650/782]	Time 0.335 (0.349)	Loss 2.595 (2.638)	Acc@1 43.75 (42.41)	Acc@5 71.88 (69.35)
+    Epoch:[4][700/782]	Time 0.342 (0.349)	Loss 2.541 (2.634)	Acc@1 39.84 (42.44)	Acc@5 74.22 (69.37)
+    Epoch:[4][750/782]	Time 0.348 (0.349)	Loss 2.408 (2.628)	Acc@1 45.31 (42.52)	Acc@5 75.00 (69.51)
+    Epoch:[5][  0/782]	Time 0.690 (0.690)	Loss 2.310 (2.310)	Acc@1 48.44 (48.44)	Acc@5 75.00 (75.00)
+    Epoch:[5][ 50/782]	Time 0.353 (0.358)	Loss 2.585 (2.521)	Acc@1 42.97 (43.66)	Acc@5 68.75 (71.32)
+    Epoch:[5][100/782]	Time 0.352 (0.354)	Loss 2.263 (2.491)	Acc@1 48.44 (44.46)	Acc@5 74.22 (71.88)
+    Epoch:[5][150/782]	Time 0.354 (0.353)	Loss 2.296 (2.480)	Acc@1 52.34 (44.62)	Acc@5 75.00 (71.90)
+    Epoch:[5][200/782]	Time 0.349 (0.352)	Loss 2.430 (2.479)	Acc@1 48.44 (44.75)	Acc@5 70.31 (71.79)
+    Epoch:[5][250/782]	Time 0.347 (0.351)	Loss 2.566 (2.482)	Acc@1 40.62 (44.74)	Acc@5 69.53 (71.70)
+    Epoch:[5][300/782]	Time 0.343 (0.351)	Loss 2.414 (2.476)	Acc@1 40.62 (44.86)	Acc@5 78.12 (71.78)
+    Epoch:[5][350/782]	Time 0.352 (0.351)	Loss 2.301 (2.477)	Acc@1 50.78 (44.74)	Acc@5 75.78 (71.62)
+    Epoch:[5][400/782]	Time 0.354 (0.350)	Loss 2.414 (2.472)	Acc@1 44.53 (44.87)	Acc@5 72.66 (71.71)
+    Epoch:[5][450/782]	Time 0.348 (0.351)	Loss 2.352 (2.466)	Acc@1 50.78 (44.94)	Acc@5 72.66 (71.85)
+    Epoch:[5][500/782]	Time 0.346 (0.351)	Loss 2.423 (2.464)	Acc@1 47.66 (44.97)	Acc@5 74.22 (71.84)
+    Epoch:[5][550/782]	Time 0.353 (0.350)	Loss 2.407 (2.459)	Acc@1 40.62 (45.03)	Acc@5 71.88 (71.88)
+    Epoch:[5][600/782]	Time 0.349 (0.350)	Loss 2.326 (2.457)	Acc@1 48.44 (45.05)	Acc@5 77.34 (71.91)
+    Epoch:[5][650/782]	Time 0.349 (0.350)	Loss 2.283 (2.452)	Acc@1 47.66 (45.13)	Acc@5 71.88 (72.01)
+    Epoch:[5][700/782]	Time 0.348 (0.351)	Loss 2.217 (2.446)	Acc@1 46.88 (45.21)	Acc@5 72.66 (72.09)
+    Epoch:[5][750/782]	Time 0.352 (0.351)	Loss 2.474 (2.442)	Acc@1 50.78 (45.29)	Acc@5 65.62 (72.12)
+    Epoch:[6][  0/782]	Time 0.687 (0.687)	Loss 2.568 (2.568)	Acc@1 44.53 (44.53)	Acc@5 64.06 (64.06)
+    Epoch:[6][ 50/782]	Time 0.336 (0.357)	Loss 2.411 (2.321)	Acc@1 45.31 (47.50)	Acc@5 68.75 (74.17)
+    Epoch:[6][100/782]	Time 0.344 (0.352)	Loss 2.401 (2.333)	Acc@1 48.44 (47.05)	Acc@5 72.66 (73.89)
+    Epoch:[6][150/782]	Time 0.345 (0.352)	Loss 2.220 (2.331)	Acc@1 46.88 (47.11)	Acc@5 75.78 (73.85)
+    Epoch:[6][200/782]	Time 0.351 (0.352)	Loss 2.330 (2.329)	Acc@1 49.22 (47.21)	Acc@5 73.44 (73.77)
+    Epoch:[6][250/782]	Time 0.343 (0.352)	Loss 2.581 (2.330)	Acc@1 43.75 (47.22)	Acc@5 67.97 (73.84)
+    Epoch:[6][300/782]	Time 0.344 (0.351)	Loss 2.457 (2.321)	Acc@1 42.97 (47.57)	Acc@5 73.44 (74.00)
+    Epoch:[6][350/782]	Time 0.336 (0.350)	Loss 2.332 (2.321)	Acc@1 50.78 (47.49)	Acc@5 73.44 (73.98)
+    Epoch:[6][400/782]	Time 0.353 (0.350)	Loss 2.057 (2.317)	Acc@1 53.91 (47.56)	Acc@5 80.47 (74.01)
+    Epoch:[6][450/782]	Time 0.343 (0.350)	Loss 2.379 (2.316)	Acc@1 45.31 (47.41)	Acc@5 71.09 (74.02)
+    Epoch:[6][500/782]	Time 0.348 (0.350)	Loss 2.337 (2.313)	Acc@1 48.44 (47.44)	Acc@5 71.09 (74.10)
+    Epoch:[6][550/782]	Time 0.345 (0.349)	Loss 2.207 (2.309)	Acc@1 46.88 (47.54)	Acc@5 74.22 (74.18)
+    Epoch:[6][600/782]	Time 0.353 (0.349)	Loss 2.191 (2.305)	Acc@1 57.03 (47.63)	Acc@5 77.34 (74.22)
+    Epoch:[6][650/782]	Time 0.348 (0.349)	Loss 2.120 (2.303)	Acc@1 53.12 (47.62)	Acc@5 77.34 (74.23)
+    Epoch:[6][700/782]	Time 0.339 (0.349)	Loss 2.312 (2.298)	Acc@1 39.84 (47.71)	Acc@5 71.88 (74.30)
+    Epoch:[6][750/782]	Time 0.350 (0.349)	Loss 2.080 (2.295)	Acc@1 53.12 (47.77)	Acc@5 79.69 (74.34)
+    Epoch:[7][  0/782]	Time 0.693 (0.693)	Loss 2.192 (2.192)	Acc@1 44.53 (44.53)	Acc@5 78.12 (78.12)
+    Epoch:[7][ 50/782]	Time 0.341 (0.360)	Loss 2.139 (2.214)	Acc@1 50.78 (48.56)	Acc@5 76.56 (75.32)
+    Epoch:[7][100/782]	Time 0.337 (0.354)	Loss 2.266 (2.213)	Acc@1 57.03 (49.16)	Acc@5 71.88 (75.45)
+    Epoch:[7][150/782]	Time 0.352 (0.353)	Loss 1.987 (2.209)	Acc@1 54.69 (49.10)	Acc@5 82.03 (75.53)
+    Epoch:[7][200/782]	Time 0.352 (0.352)	Loss 2.232 (2.203)	Acc@1 43.75 (49.37)	Acc@5 75.00 (75.62)
+    Epoch:[7][250/782]	Time 0.341 (0.354)	Loss 2.216 (2.203)	Acc@1 48.44 (49.27)	Acc@5 78.91 (75.66)
+    Epoch:[7][300/782]	Time 0.346 (0.353)	Loss 2.393 (2.202)	Acc@1 49.22 (49.30)	Acc@5 71.09 (75.70)
+    Epoch:[7][350/782]	Time 0.347 (0.353)	Loss 2.084 (2.196)	Acc@1 44.53 (49.47)	Acc@5 80.47 (75.84)
+    Epoch:[7][400/782]	Time 0.363 (0.353)	Loss 1.682 (2.194)	Acc@1 65.62 (49.55)	Acc@5 83.59 (75.82)
+    Epoch:[7][450/782]	Time 0.348 (0.352)	Loss 2.193 (2.194)	Acc@1 47.66 (49.62)	Acc@5 75.78 (75.82)
+    Epoch:[7][500/782]	Time 0.350 (0.351)	Loss 2.166 (2.192)	Acc@1 45.31 (49.59)	Acc@5 78.12 (75.81)
+    Epoch:[7][550/782]	Time 0.352 (0.351)	Loss 2.126 (2.187)	Acc@1 47.66 (49.70)	Acc@5 78.91 (75.84)
+    Epoch:[7][600/782]	Time 0.352 (0.350)	Loss 2.222 (2.184)	Acc@1 49.22 (49.73)	Acc@5 73.44 (75.87)
+    Epoch:[7][650/782]	Time 0.348 (0.350)	Loss 2.075 (2.181)	Acc@1 50.00 (49.79)	Acc@5 78.12 (75.89)
+    Epoch:[7][700/782]	Time 0.335 (0.350)	Loss 2.181 (2.179)	Acc@1 47.66 (49.81)	Acc@5 75.78 (75.89)
+    Epoch:[7][750/782]	Time 0.346 (0.349)	Loss 2.071 (2.177)	Acc@1 53.12 (49.82)	Acc@5 75.78 (75.89)
+    Epoch:[8][  0/782]	Time 0.696 (0.696)	Loss 1.829 (1.829)	Acc@1 58.59 (58.59)	Acc@5 82.03 (82.03)
+    Epoch:[8][ 50/782]	Time 0.348 (0.357)	Loss 2.171 (2.096)	Acc@1 50.78 (51.04)	Acc@5 78.91 (77.51)
+    Epoch:[8][100/782]	Time 0.357 (0.353)	Loss 2.207 (2.089)	Acc@1 52.34 (51.26)	Acc@5 74.22 (77.56)
+    Epoch:[8][150/782]	Time 0.353 (0.352)	Loss 2.289 (2.100)	Acc@1 49.22 (51.13)	Acc@5 73.44 (77.32)
+    Epoch:[8][200/782]	Time 0.357 (0.352)	Loss 2.175 (2.101)	Acc@1 46.88 (51.00)	Acc@5 77.34 (77.29)
+    Epoch:[8][250/782]	Time 0.350 (0.351)	Loss 2.239 (2.092)	Acc@1 47.66 (51.30)	Acc@5 71.88 (77.35)
+    Epoch:[8][300/782]	Time 0.345 (0.350)	Loss 2.070 (2.087)	Acc@1 49.22 (51.40)	Acc@5 75.78 (77.41)
+    Epoch:[8][350/782]	Time 0.353 (0.350)	Loss 1.868 (2.083)	Acc@1 52.34 (51.38)	Acc@5 82.81 (77.39)
+    Epoch:[8][400/782]	Time 0.347 (0.350)	Loss 2.345 (2.084)	Acc@1 40.62 (51.47)	Acc@5 71.88 (77.34)
+    Epoch:[8][450/782]	Time 0.371 (0.350)	Loss 1.731 (2.085)	Acc@1 63.28 (51.43)	Acc@5 82.81 (77.32)
+    Epoch:[8][500/782]	Time 0.344 (0.349)	Loss 2.142 (2.082)	Acc@1 46.09 (51.40)	Acc@5 77.34 (77.35)
+    Epoch:[8][550/782]	Time 0.346 (0.349)	Loss 2.173 (2.080)	Acc@1 53.91 (51.45)	Acc@5 73.44 (77.40)
+    Epoch:[8][600/782]	Time 0.348 (0.349)	Loss 2.184 (2.077)	Acc@1 54.69 (51.55)	Acc@5 73.44 (77.43)
+    Epoch:[8][650/782]	Time 0.353 (0.349)	Loss 2.118 (2.075)	Acc@1 49.22 (51.60)	Acc@5 76.56 (77.43)
+    Epoch:[8][700/782]	Time 0.352 (0.349)	Loss 2.254 (2.074)	Acc@1 51.56 (51.61)	Acc@5 72.66 (77.37)
+    Epoch:[8][750/782]	Time 0.347 (0.349)	Loss 2.056 (2.071)	Acc@1 53.91 (51.67)	Acc@5 75.78 (77.41)
+    Epoch:[9][  0/782]	Time 0.683 (0.683)	Loss 1.824 (1.824)	Acc@1 59.38 (59.38)	Acc@5 85.16 (85.16)
+    Epoch:[9][ 50/782]	Time 0.346 (0.353)	Loss 2.063 (1.996)	Acc@1 50.78 (53.09)	Acc@5 80.47 (78.65)
+    Epoch:[9][100/782]	Time 0.350 (0.351)	Loss 1.874 (1.999)	Acc@1 58.59 (53.12)	Acc@5 82.03 (78.38)
+    Epoch:[9][150/782]	Time 0.347 (0.350)	Loss 2.026 (1.994)	Acc@1 50.78 (53.17)	Acc@5 78.91 (78.80)
+    Epoch:[9][200/782]	Time 0.350 (0.349)	Loss 1.877 (1.994)	Acc@1 59.38 (53.10)	Acc@5 82.81 (78.68)
+    Epoch:[9][250/782]	Time 0.341 (0.349)	Loss 2.166 (1.996)	Acc@1 46.09 (53.00)	Acc@5 73.44 (78.60)
+    Epoch:[9][300/782]	Time 0.345 (0.349)	Loss 2.125 (1.997)	Acc@1 51.56 (53.01)	Acc@5 76.56 (78.49)
+    Epoch:[9][350/782]	Time 0.345 (0.349)	Loss 2.210 (1.995)	Acc@1 46.88 (52.89)	Acc@5 75.00 (78.60)
+    Epoch:[9][400/782]	Time 0.350 (0.349)	Loss 1.897 (1.994)	Acc@1 57.81 (52.86)	Acc@5 79.69 (78.56)
+    Epoch:[9][450/782]	Time 0.345 (0.349)	Loss 2.045 (1.989)	Acc@1 50.78 (53.00)	Acc@5 76.56 (78.62)
+    Epoch:[9][500/782]	Time 0.336 (0.349)	Loss 2.300 (1.990)	Acc@1 46.88 (52.97)	Acc@5 72.66 (78.62)
+    Epoch:[9][550/782]	Time 0.351 (0.349)	Loss 1.604 (1.990)	Acc@1 64.06 (53.02)	Acc@5 82.81 (78.61)
+    Epoch:[9][600/782]	Time 0.352 (0.349)	Loss 1.763 (1.987)	Acc@1 54.69 (53.07)	Acc@5 85.16 (78.65)
+    Epoch:[9][650/782]	Time 0.344 (0.349)	Loss 1.664 (1.984)	Acc@1 63.28 (53.11)	Acc@5 82.81 (78.71)
+    Epoch:[9][700/782]	Time 0.347 (0.349)	Loss 2.284 (1.982)	Acc@1 42.97 (53.12)	Acc@5 78.12 (78.76)
+    Epoch:[9][750/782]	Time 0.353 (0.349)	Loss 1.698 (1.983)	Acc@1 59.38 (53.11)	Acc@5 82.03 (78.72)
     Validating
     Test: [ 0/79]	Time 0.409 (0.409)	Loss 4.175 (4.175)	Acc@1 7.81 (7.81)	Acc@5 29.69 (29.69)
-    Test: [10/79]	Time 0.137 (0.159)	Loss 5.955 (4.803)	Acc@1 3.12 (7.81)	Acc@5 7.03 (21.02)
-    Test: [20/79]	Time 0.171 (0.147)	Loss 6.302 (5.109)	Acc@1 0.00 (5.21)	Acc@5 3.12 (17.22)
-    Test: [30/79]	Time 0.172 (0.144)	Loss 5.520 (5.327)	Acc@1 1.56 (4.26)	Acc@5 16.41 (14.36)
-    Test: [40/79]	Time 0.137 (0.142)	Loss 5.560 (5.399)	Acc@1 6.25 (4.12)	Acc@5 7.81 (13.34)
-    Test: [50/79]	Time 0.127 (0.140)	Loss 4.887 (5.498)	Acc@1 7.81 (3.92)	Acc@5 21.88 (12.68)
-    Test: [60/79]	Time 0.138 (0.140)	Loss 5.905 (5.512)	Acc@1 0.00 (3.98)	Acc@5 7.03 (12.58)
-    Test: [70/79]	Time 0.140 (0.140)	Loss 4.785 (5.526)	Acc@1 2.34 (3.75)	Acc@5 11.72 (11.99)
+    Test: [10/79]	Time 0.148 (0.163)	Loss 5.955 (4.803)	Acc@1 3.12 (7.81)	Acc@5 7.03 (21.02)
+    Test: [20/79]	Time 0.136 (0.151)	Loss 6.302 (5.109)	Acc@1 0.00 (5.21)	Acc@5 3.12 (17.22)
+    Test: [30/79]	Time 0.173 (0.145)	Loss 5.520 (5.327)	Acc@1 1.56 (4.26)	Acc@5 16.41 (14.36)
+    Test: [40/79]	Time 0.116 (0.142)	Loss 5.560 (5.399)	Acc@1 6.25 (4.12)	Acc@5 7.81 (13.34)
+    Test: [50/79]	Time 0.150 (0.142)	Loss 4.887 (5.498)	Acc@1 7.81 (3.92)	Acc@5 21.88 (12.68)
+    Test: [60/79]	Time 0.139 (0.142)	Loss 5.905 (5.512)	Acc@1 0.00 (3.98)	Acc@5 7.03 (12.58)
+    Test: [70/79]	Time 0.139 (0.142)	Loss 4.785 (5.526)	Acc@1 2.34 (3.75)	Acc@5 11.72 (11.99)
      * Acc@1 5.320 Acc@5 15.300
     Accuracy of tuned INT8 sparse model: 5.320
     Accuracy drop of tuned INT8 sparse model over pre-trained FP32 model: -4.750
@@ -818,7 +816,7 @@ throughput (frames per second) values.
 .. parsed-literal::
 
     Benchmark FP32 model (IR)
-    [ INFO ] Throughput:   2950.81 FPS
+    [ INFO ] Throughput:   2947.28 FPS
     Benchmark INT8 sparse model (IR)
     
 
diff --git a/docs/notebooks/pytorch-to-openvino-with-output.rst b/docs/notebooks/pytorch-to-openvino-with-output.rst
index 01f173a966631a..f117101a5c95df 100644
--- a/docs/notebooks/pytorch-to-openvino-with-output.rst
+++ b/docs/notebooks/pytorch-to-openvino-with-output.rst
@@ -29,8 +29,8 @@ but elevated to the design space level. The RegNet design space provides
 simple and fast networks that work well across a wide range of flop
 regimes.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Load PyTorch Model <#load-pytorch-model>`__
@@ -89,7 +89,6 @@ Install notebook dependencies
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
 
 
@@ -251,7 +250,7 @@ Benchmark PyTorch Model Inference
 
 .. parsed-literal::
 
-    16.4 ms ± 91.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
+    17.2 ms ± 140 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
 
 
 Convert PyTorch Model to OpenVINO Intermediate Representation
@@ -410,7 +409,7 @@ Benchmark OpenVINO Model Inference
 
 .. parsed-literal::
 
-    3.3 ms ± 8.21 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
+    3.18 ms ± 14.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
 
 
 Convert PyTorch Model with Static Input Shape
@@ -540,7 +539,7 @@ Benchmark OpenVINO Model Inference with Static Input Shape
 
 .. parsed-literal::
 
-    2.98 ms ± 23.6 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
+    2.84 ms ± 17.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
 
 
 Convert TorchScript Model to OpenVINO Intermediate Representation
@@ -635,7 +634,7 @@ Benchmark Scripted Model Inference
 
 .. parsed-literal::
 
-    14 ms ± 28.4 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
+    14 ms ± 60.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
 
 
 Convert PyTorch Scripted Model to OpenVINO Intermediate Representation
@@ -694,7 +693,7 @@ Benchmark OpenVINO Model Inference Converted From Scripted Model
 
 .. parsed-literal::
 
-    3.29 ms ± 21.6 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
+    3.16 ms ± 10.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
 
 
 Traced Model
@@ -770,7 +769,7 @@ Benchmark Traced Model Inference
 
 .. parsed-literal::
 
-    14 ms ± 436 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
+    14.3 ms ± 282 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
 
 
 Convert PyTorch Traced Model to OpenVINO Intermediate Representation
@@ -829,5 +828,5 @@ Benchmark OpenVINO Model Inference Converted From Traced Model
 
 .. parsed-literal::
 
-    3.34 ms ± 29.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
+    3.18 ms ± 19.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
 
diff --git a/docs/notebooks/qrcode-monster-with-output.rst b/docs/notebooks/qrcode-monster-with-output.rst
index afdaccf25db342..f122fa489303f7 100644
--- a/docs/notebooks/qrcode-monster-with-output.rst
+++ b/docs/notebooks/qrcode-monster-with-output.rst
@@ -29,8 +29,8 @@ If you want to learn more about ControlNet and particularly on
 conditioning by pose, please refer to this
 `tutorial <controlnet-stable-diffusion-with-output.html>`__
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Instantiating Generation
diff --git a/docs/notebooks/riffusion-text-to-music-with-output.rst b/docs/notebooks/riffusion-text-to-music-with-output.rst
index 425d07849f0fbc..1181590a16ae68 100644
--- a/docs/notebooks/riffusion-text-to-music-with-output.rst
+++ b/docs/notebooks/riffusion-text-to-music-with-output.rst
@@ -74,8 +74,8 @@ The STFT is invertible, so the original audio can be reconstructed from
 a spectrogram. This idea is a behind approach to using Riffusion for
 audio generation.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Stable Diffusion pipeline in Optimum
diff --git a/docs/notebooks/rmbg-background-removal-with-output.rst b/docs/notebooks/rmbg-background-removal-with-output.rst
index 9974e2a12313d5..6e6b6c72d25268 100644
--- a/docs/notebooks/rmbg-background-removal-with-output.rst
+++ b/docs/notebooks/rmbg-background-removal-with-output.rst
@@ -29,8 +29,8 @@ In this tutorial we consider how to convert and run this model using
 OpenVINO.
 
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Load PyTorch model <#load-pytorch-model>`__
@@ -54,7 +54,6 @@ install required dependencies
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
 
 
@@ -102,6 +101,13 @@ it may take some time.
 
     net = AutoModelForImageSegmentation.from_pretrained("briaai/RMBG-1.4", trust_remote_code=True)
 
+
+.. parsed-literal::
+
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+      warnings.warn(
+
+
 Run PyTorch model inference
 ---------------------------
 
@@ -219,7 +225,7 @@ function or directly loading on device using ``core.complie_model``.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4481: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4371: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
       warnings.warn(
 
 
diff --git a/docs/notebooks/s3d-mil-nce-text-to-video-retrieval-with-output.rst b/docs/notebooks/s3d-mil-nce-text-to-video-retrieval-with-output.rst
index ea73ff618b8181..1375a19d41f4c3 100644
--- a/docs/notebooks/s3d-mil-nce-text-to-video-retrieval-with-output.rst
+++ b/docs/notebooks/s3d-mil-nce-text-to-video-retrieval-with-output.rst
@@ -25,8 +25,8 @@ inference.
 
 The tutorial consists of the following steps:
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `The original inference <#the-original-inference>`__
@@ -50,7 +50,7 @@ Prerequisites
 .. code:: ipython3
 
     import platform
-    
+
     %pip install -Uq pip
     %pip install --upgrade --pre openvino-tokenizers openvino --extra-index-url "https://storage.openvinotoolkit.org/simple/wheels/nightly"
     %pip install -q "tensorflow-macos>=2.5; sys_platform == 'darwin' and platform_machine == 'arm64' and python_version > '3.8'" # macOS M1 and M2
@@ -59,7 +59,7 @@ Prerequisites
     %pip install -q "tensorflow>=2.5,<=2.12.0; sys_platform == 'darwin' and platform_machine != 'arm64' and python_version <= '3.8'" # macOS x86
     %pip install -q "tensorflow>=2.5; sys_platform != 'darwin' and python_version > '3.8'"
     %pip install -q "tensorflow>=2.5,<=2.12.0; sys_platform != 'darwin' and python_version <= '3.8'"
-    
+
     %pip install -q tensorflow_hub tf_keras numpy "opencv-python" "nncf>=2.10.0"
     if platform.system() != "Windows":
         %pip install -q "matplotlib>=3.4"
@@ -69,45 +69,21 @@ Prerequisites
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
     Looking in indexes: https://pypi.org/simple, https://storage.openvinotoolkit.org/simple/wheels/nightly
-    Requirement already satisfied: openvino-tokenizers in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (2024.3.0.0.dev20240605)
-    Requirement already satisfied: openvino in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (2024.1.0)
-    Collecting openvino
-      Using cached https://storage.openvinotoolkit.org/wheels/nightly/openvino/openvino-2024.3.0.dev20240605-15599-cp38-cp38-manylinux2014_x86_64.whl (38.9 MB)
-    Requirement already satisfied: numpy<2.0.0,>=1.16.6 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino) (1.23.5)
-    Requirement already satisfied: openvino-telemetry>=2023.2.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino) (2024.1.0)
-    Requirement already satisfied: packaging in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino) (24.0)
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    Installing collected packages: openvino
-      Attempting uninstall: openvino
-        Found existing installation: openvino 2024.1.0
-        Uninstalling openvino-2024.1.0:
-          Successfully uninstalled openvino-2024.1.0
-    ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
-    openvino-dev 2024.1.0 requires openvino==2024.1.0, but you have openvino 2024.3.0.dev20240605 which is incompatible.
-    Successfully installed openvino-2024.3.0.dev20240605
+    Requirement already satisfied: openvino-tokenizers in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (2024.3.0.0.dev20240627)
+    Requirement already satisfied: openvino in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (2024.3.0.dev20240627)
+    Requirement already satisfied: numpy<2.0.0,>=1.16.6 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino) (1.23.5)
+    Requirement already satisfied: openvino-telemetry>=2023.2.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino) (2024.1.0)
+    Requirement already satisfied: packaging in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino) (24.1)
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
-    pytorch-lightning 1.6.5 requires protobuf<=3.20.1, but you have protobuf 4.25.3 which is incompatible.
-    tensorflow-metadata 1.14.0 requires protobuf<4.21,>=3.20.3, but you have protobuf 4.25.3 which is incompatible.
-    tf2onnx 1.16.1 requires protobuf~=3.20, but you have protobuf 4.25.3 which is incompatible.
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
 
 
@@ -115,24 +91,24 @@ Prerequisites
 
     import os
     from pathlib import Path
-    
+
     import tensorflow as tf
     import tensorflow_hub as hub
-    
+
     import numpy as np
     import cv2
     from IPython import display
     import math
-    
+
     os.environ["TFHUB_CACHE_DIR"] = str(Path("./tfhub_modules").resolve())
 
 
 .. parsed-literal::
 
-    2024-06-06 02:17:03.438995: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-06-06 02:17:03.474053: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-07-02 02:30:01.139846: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-07-02 02:30:01.174528: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2024-06-06 02:17:04.054479: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-07-02 02:30:01.808352: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 Download the model
@@ -145,12 +121,12 @@ Download the model
 
 .. parsed-literal::
 
-    2024-06-06 02:17:11.096490: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:266] failed call to cuInit: CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE: forward compatibility was attempted on non supported HW
-    2024-06-06 02:17:11.096529: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:168] retrieving CUDA diagnostic information for host: iotg-dev-workstation-07
-    2024-06-06 02:17:11.096533: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:175] hostname: iotg-dev-workstation-07
-    2024-06-06 02:17:11.096660: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:199] libcuda reported version is: 470.223.2
-    2024-06-06 02:17:11.096676: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:203] kernel reported version is: 470.182.3
-    2024-06-06 02:17:11.096680: E tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:312] kernel version 470.182.3 does not match DSO version 470.223.2 -- cannot find working devices in this configuration
+    2024-07-02 02:30:09.024906: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:266] failed call to cuInit: CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE: forward compatibility was attempted on non supported HW
+    2024-07-02 02:30:09.024940: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:168] retrieving CUDA diagnostic information for host: iotg-dev-workstation-07
+    2024-07-02 02:30:09.024944: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:175] hostname: iotg-dev-workstation-07
+    2024-07-02 02:30:09.025075: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:199] libcuda reported version is: 470.223.2
+    2024-07-02 02:30:09.025091: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:203] kernel reported version is: 470.182.3
+    2024-07-02 02:30:09.025094: E tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:312] kernel version 470.182.3 does not match DSO version 470.223.2 -- cannot find working devices in this configuration
 
 
 The model has 2 signatures, one for generating video embeddings and one
@@ -165,14 +141,14 @@ Below we will define auxiliary functions
         # Input_frames must be normalized in [0, 1] and of the shape Batch x T x H x W x 3
         vision_output = model.signatures["video"](tf.constant(tf.cast(input_frames, dtype=tf.float32)))
         text_output = model.signatures["text"](tf.constant(input_words))
-    
+
         return vision_output["video_embedding"], text_output["text_embedding"]
 
 .. code:: ipython3
 
     # @title Define video loading and visualization functions  { display-mode: "form" }
-    
-    
+
+
     # Utilities to open video files using CV2
     def crop_center_square(frame):
         y, x = frame.shape[0:2]
@@ -180,8 +156,8 @@ Below we will define auxiliary functions
         start_x = (x // 2) - (min_dim // 2)
         start_y = (y // 2) - (min_dim // 2)
         return frame[start_y : start_y + min_dim, start_x : start_x + min_dim]
-    
-    
+
+
     def load_video(video_url, max_frames=32, resize=(224, 224)):
         path = tf.keras.utils.get_file(os.path.basename(video_url)[-128:], video_url)
         cap = cv2.VideoCapture(path)
@@ -195,7 +171,7 @@ Below we will define auxiliary functions
                 frame = cv2.resize(frame, resize)
                 frame = frame[:, :, [2, 1, 0]]
                 frames.append(frame)
-    
+
                 if len(frames) == max_frames:
                     break
         finally:
@@ -206,8 +182,8 @@ Below we will define auxiliary functions
             frames = frames.repeat(n_repeat, axis=0)
         frames = frames[:max_frames]
         return frames / 255.0
-    
-    
+
+
     def display_video(urls):
         html = "<table>"
         html += "<tr><th>Video 1</th><th>Video 2</th><th>Video 3</th></tr><tr>"
@@ -217,8 +193,8 @@ Below we will define auxiliary functions
             html += "</td>"
         html += "</tr></table>"
         return display.HTML(html)
-    
-    
+
+
     def display_query_and_results_video(query, urls, scores):
         """Display a text query and the top result videos and scores."""
         sorted_ix = np.argsort(-scores)
@@ -235,22 +211,22 @@ Below we will define auxiliary functions
             html += '<img src="{}" height="224">'.format(url)
             html += "</td>"
         html += "</tr></table>"
-    
+
         return html
 
 .. code:: ipython3
 
     # @title Load example videos and define text queries  { display-mode: "form" }
-    
+
     video_1_url = "https://upload.wikimedia.org/wikipedia/commons/b/b0/YosriAirTerjun.gif"  # @param {type:"string"}
     video_2_url = "https://upload.wikimedia.org/wikipedia/commons/e/e6/Guitar_solo_gif.gif"  # @param {type:"string"}
     video_3_url = "https://upload.wikimedia.org/wikipedia/commons/3/30/2009-08-16-autodrift-by-RalfR-gif-by-wau.gif"  # @param {type:"string"}
-    
+
     video_1 = load_video(video_1_url)
     video_2 = load_video(video_2_url)
     video_3 = load_video(video_3_url)
     all_videos = [video_1, video_2, video_3]
-    
+
     query_1_video = "waterfall"  # @param {type:"string"}
     query_2_video = "playing guitar"  # @param {type:"string"}
     query_3_video = "car drifting"  # @param {type:"string"}
@@ -276,13 +252,13 @@ The original inference
 
     # Prepare video inputs.
     videos_np = np.stack(all_videos, axis=0)
-    
+
     # Prepare text input.
     words_np = np.array(all_queries_video)
-    
+
     # Generate the video and text embeddings.
     video_embd, text_embd = generate_embeddings(hub_model, videos_np, words_np)
-    
+
     # Scores between video and text is computed by dot products.
     all_scores = np.dot(text_embd, tf.transpose(video_embd))
 
@@ -307,7 +283,7 @@ The original inference
 Convert the model to OpenVINO IR
 --------------------------------
 
- OpenVINO supports TensorFlow
+OpenVINO supports TensorFlow
 models via conversion into Intermediate Representation (IR) format. We
 need to provide a model object, input data for model tracing to
 ``ov.convert_model`` function to obtain OpenVINO ``ov.Model`` object
@@ -318,12 +294,12 @@ instance. Model can be saved on disk for next deployment using
 
     import openvino_tokenizers  # NOQA Need to import conversion and operation extensions
     import openvino as ov
-    
+
     model_path = hub.resolve(hub_handle)
     # infer on random data
     images_data = np.random.rand(3, 32, 224, 224, 3).astype(np.float32)
     words_data = np.array(["First sentence", "Second one", "Abracadabra"], dtype=str)
-    
+
     ov_model = ov.convert_model(model_path, input=[("words", [3]), ("images", [3, 32, 224, 224, 3])])
 
 Compiling models
@@ -336,7 +312,7 @@ Only CPU is supported for this model due to strings as input.
 .. code:: ipython3
 
     core = ov.Core()
-    
+
     compiled_model = core.compile_model(ov_model, device_name="CPU")
 
 Inference
@@ -351,14 +327,14 @@ Inference
         """Generate embeddings from the model from video frames and input words."""
         # Input_frames must be normalized in [0, 1] and of the shape Batch x T x H x W x 3
         output = compiled_model({"words": input_words, "images": tf.cast(input_frames, dtype=tf.float32)})
-    
+
         return output["video_embedding"], output["text_embedding"]
 
 .. code:: ipython3
 
     # Generate the video and text embeddings.
     video_embd, text_embd = generate_embeddings(compiled_model, videos_np, words_np)
-    
+
     # Scores between video and text is computed by dot products.
     all_scores = np.dot(text_embd, tf.transpose(video_embd))
 
@@ -409,7 +385,7 @@ preparing input data in model expected format.
 .. code:: ipython3
 
     import nncf
-    
+
     dataset = nncf.Dataset(((words_np, tf.cast(videos_np, dtype=tf.float32)),))
 
 
@@ -433,10 +409,10 @@ preset, ignored scope etc.) can be provided.
 
     MODEL_DIR = Path("model/")
     MODEL_DIR.mkdir(exist_ok=True)
-    
+
     quantized_model_path = MODEL_DIR / "quantized_model.xml"
-    
-    
+
+
     if not quantized_model_path.exists():
         quantized_model = nncf.quantize(model=ov_model, calibration_dataset=dataset, model_type=nncf.ModelType.TRANSFORMER)
         ov.save_model(quantized_model, quantized_model_path)
@@ -449,17 +425,17 @@ preset, ignored scope etc.) can be provided.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -470,23 +446,23 @@ preset, ignored scope etc.) can be provided.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
 .. parsed-literal::
 
-    INFO:nncf:39 ignored nodes were found by name in the NNCFGraph
+    INFO:nncf:39 ignored nodes were found by names in the NNCFGraph
 
 
 
@@ -496,17 +472,17 @@ preset, ignored scope etc.) can be provided.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -517,17 +493,17 @@ preset, ignored scope etc.) can be provided.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -547,7 +523,7 @@ check the model work on the previously used example.
 
     # Generate the video and text embeddings.
     video_embd, text_embd = generate_embeddings(int8_model, videos_np, words_np)
-    
+
     # Scores between video and text is computed by dot products.
     all_scores = np.dot(text_embd, tf.transpose(video_embd))
 
diff --git a/docs/notebooks/sdxl-turbo-with-output.rst b/docs/notebooks/sdxl-turbo-with-output.rst
index b7471da39a2300..43c30e26444691 100644
--- a/docs/notebooks/sdxl-turbo-with-output.rst
+++ b/docs/notebooks/sdxl-turbo-with-output.rst
@@ -29,8 +29,8 @@ simplify the user experience, the `Hugging Face Optimum
 Intel <https://huggingface.co/docs/optimum/intel/index>`__ library is
 used to convert the models to OpenVINO™ IR format.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Convert model to OpenVINO
@@ -445,7 +445,7 @@ model inputs for calibration we should customize ``CompiledModel``.
         original_unet = pipe.unet.request
         pipe.unet.request = CompiledModelDecorator(original_unet)
     
-        dataset = datasets.load_dataset("conceptual_captions", split="train").shuffle(seed=42)
+        dataset = datasets.load_dataset("google-research-datasets/conceptual_captions", split="train", trust_remote_code=True).shuffle(seed=42)
         disable_progress_bar(pipe)
     
         # Run inference for data collection
@@ -639,7 +639,7 @@ pipelines, we use median inference time on calibration subset.
     import time
     
     validation_size = 7
-    calibration_dataset = datasets.load_dataset("conceptual_captions", split="train")
+    calibration_dataset = datasets.load_dataset("google-research-datasets/conceptual_captions", split="train", trust_remote_code=True)
     validation_data = []
     for batch in calibration_dataset:
         prompt = batch["caption"]
diff --git a/docs/notebooks/segment-anything-with-output.rst b/docs/notebooks/segment-anything-with-output.rst
index 224c95ca99c219..fe6817d9c784be 100644
--- a/docs/notebooks/segment-anything-with-output.rst
+++ b/docs/notebooks/segment-anything-with-output.rst
@@ -1,8 +1,8 @@
 Object masks from prompts with SAM and OpenVINO
 ===============================================
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Background <#background>`__
 -  `Prerequisites <#prerequisites>`__
@@ -1499,6 +1499,8 @@ activations.
 
 
 
+
+
 .. code:: ipython3
 
     ov_encoder_path_int8 = "sam_image_encoder_int8.xml"
@@ -1573,7 +1575,7 @@ Run ``INT8`` model in automatic mask generation mode
 Compare Performance of the Original and Quantized Models
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
- Finally, use the OpenVINO
+Finally, use the OpenVINO
 `Benchmark
 Tool <https://docs.openvino.ai/2024/learn-openvino/openvino-samples/benchmark-tool.html>`__
 to measure the inference performance of the ``FP32`` and ``INT8``
diff --git a/docs/notebooks/segmind-vegart-with-output.rst b/docs/notebooks/segmind-vegart-with-output.rst
index 45266e95e9ea44..cb88111e706700 100644
--- a/docs/notebooks/segmind-vegart-with-output.rst
+++ b/docs/notebooks/segmind-vegart-with-output.rst
@@ -49,8 +49,8 @@ used to convert the models to OpenVINO™ IR format. Additionally, we
 demonstrate how to improve pipeline latency with the quantization UNet
 model using `NNCF <https://github.com/openvinotoolkit/nncf>`__.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Prepare PyTorch model <#prepare-pytorch-model>`__
@@ -438,7 +438,7 @@ model inputs for calibration we should customize ``CompiledModel``.
         original_unet = pipe.unet.request
         pipe.unet.request = CompiledModelDecorator(original_unet)
     
-        dataset = datasets.load_dataset("conceptual_captions", split="train").shuffle(seed=42)
+        dataset = datasets.load_dataset("google-research-datasets/conceptual_captions", split="train", trust_remote_code=True).shuffle(seed=42)
         disable_progress_bar(pipe)
     
         # Run inference for data collection
@@ -598,7 +598,7 @@ pipelines, we use median inference time on the calibration subset.
     import time
     
     validation_size = 7
-    calibration_dataset = datasets.load_dataset("conceptual_captions", split="train")
+    calibration_dataset = datasets.load_dataset("google-research-datasets/conceptual_captions", split="train", trust_remote_code=True)
     validation_data = []
     for idx, batch in enumerate(calibration_dataset):
         if idx >= validation_size:
diff --git a/docs/notebooks/siglip-zero-shot-image-classification-with-output.rst b/docs/notebooks/siglip-zero-shot-image-classification-with-output.rst
index 48078182a33f2f..5d894356d83800 100644
--- a/docs/notebooks/siglip-zero-shot-image-classification-with-output.rst
+++ b/docs/notebooks/siglip-zero-shot-image-classification-with-output.rst
@@ -24,8 +24,8 @@ and labels. In this tutorial, we will use the
 `SigLIP <https://huggingface.co/docs/transformers/main/en/model_doc/siglip>`__
 model to perform zero-shot image classification.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Instantiate model <#instantiate-model>`__
 -  `Run PyTorch model inference <#run-pytorch-model-inference>`__
@@ -101,9 +101,7 @@ tokenizer and preparing the images.
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
 
 
@@ -117,10 +115,14 @@ tokenizer and preparing the images.
 
 .. parsed-literal::
 
-    2024-06-06 02:17:51.607390: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-06-06 02:17:51.641265: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-07-02 02:30:45.282959: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-07-02 02:30:45.316731: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2024-06-06 02:17:52.235582: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-07-02 02:30:45.945105: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+      warnings.warn(
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+      warnings.warn(
 
 
 Run PyTorch model inference
@@ -256,11 +258,11 @@ object ready to load on the device and start making predictions.
 .. parsed-literal::
 
     [ WARNING ]  Please fix your imports. Module %s has been moved to %s. The old module will be deleted in version %s.
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4481: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4371: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
       warnings.warn(
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/siglip/modeling_siglip.py:393: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/siglip/modeling_siglip.py:354: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if attn_weights.size() != (batch_size, self.num_heads, q_len, k_v_seq_len):
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/siglip/modeling_siglip.py:411: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/siglip/modeling_siglip.py:372: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if attn_output.size() != (batch_size, self.num_heads, q_len, self.head_dim):
 
 
@@ -453,7 +455,7 @@ model.
         """
         Prepares a vision-text dataset for quantization.
         """
-        dataset = load_dataset("conceptual_captions", streaming=True)
+        dataset = load_dataset("google-research-datasets/conceptual_captions", streaming=True, trust_remote_code=True)
         train_dataset = dataset["train"].shuffle(seed=42, buffer_size=max_train_samples)
         dataloader = torch.utils.data.DataLoader(train_dataset, collate_fn=collate_fn, batch_size=1)
         calibration_data = prepare_calibration_data(dataloader, opt_init_steps)
@@ -464,14 +466,6 @@ model.
     calibration_data = prepare_dataset()
 
 
-.. parsed-literal::
-
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/datasets/load.py:1491: FutureWarning: The repository for conceptual_captions contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/conceptual_captions
-    You can avoid this message in future by passing the argument `trust_remote_code=True`.
-    Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.
-      warnings.warn(
-
-
 .. parsed-literal::
 
     Fetching 300 for the initialization...
@@ -523,17 +517,17 @@ Create a quantized model from the pre-trained ``FP16`` model.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -544,17 +538,17 @@ Create a quantized model from the pre-trained ``FP16`` model.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -565,17 +559,17 @@ Create a quantized model from the pre-trained ``FP16`` model.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -586,17 +580,17 @@ Create a quantized model from the pre-trained ``FP16`` model.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -643,7 +637,7 @@ model are similar to the PyTorch model.
 
 .. parsed-literal::
 
-    [{'dog': 0.99}, {'horse': 0.0}, {'cat': 0.0}, {'wolf': 0.0}, {'frog': 0.0}]
+    [{'dog': 0.99}, {'cat': 0.0}, {'horse': 0.0}, {'wolf': 0.0}, {'tiger': 0.0}]
 
 
 
@@ -718,7 +712,7 @@ approximately estimate the speed up of the dynamic quantized models.
 
 .. parsed-literal::
 
-    Performance speed up: 2.102
+    Performance speed up: 2.093
 
 
 Interactive inference
diff --git a/docs/notebooks/siglip-zero-shot-image-classification-with-output_files/siglip-zero-shot-image-classification-with-output_24_1.png b/docs/notebooks/siglip-zero-shot-image-classification-with-output_files/siglip-zero-shot-image-classification-with-output_24_1.png
index f3730ab128e8fa..4f4106d0098094 100644
--- a/docs/notebooks/siglip-zero-shot-image-classification-with-output_files/siglip-zero-shot-image-classification-with-output_24_1.png
+++ b/docs/notebooks/siglip-zero-shot-image-classification-with-output_files/siglip-zero-shot-image-classification-with-output_24_1.png
@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:291ca2b749b99731ea77c5f2a2533b969a0bc770e933a0e5eef2d7fae495826b
-size 581003
+oid sha256:6a171655a1f69c15f58fe8db7c91075695a3e504bca94e0bbd76d610c22a96ad
+size 581043
diff --git a/docs/notebooks/sketch-to-image-pix2pix-turbo-with-output.rst b/docs/notebooks/sketch-to-image-pix2pix-turbo-with-output.rst
index 214193e69991ee..ff62c07d349233 100644
--- a/docs/notebooks/sketch-to-image-pix2pix-turbo-with-output.rst
+++ b/docs/notebooks/sketch-to-image-pix2pix-turbo-with-output.rst
@@ -21,8 +21,9 @@ In this tutorial you will learn how to turn sketches to images using
 `Pix2Pix-Turbo <https://github.com/GaParmar/img2img-turbo>`__ and
 OpenVINO.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Load PyTorch model <#load-pytorch-model>`__
@@ -50,7 +51,6 @@ and install required packages.
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
 
 
@@ -95,9 +95,9 @@ and install required packages.
     remote: Counting objects: 100% (70/70), done.
     remote: Compressing objects: 100% (26/26), done.
     remote: Total 205 (delta 53), reused 46 (delta 44), pack-reused 135
-    Receiving objects: 100% (205/205), 31.89 MiB | 24.46 MiB/s, done.
+    Receiving objects: 100% (205/205), 31.89 MiB | 25.06 MiB/s, done.
     Resolving deltas: 100% (96/96), done.
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/sketch-to-image-pix2pix-turbo/img2img-turbo
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/sketch-to-image-pix2pix-turbo/img2img-turbo
 
 
 Load PyTorch model
@@ -358,8 +358,11 @@ diagram indicate trainable layers. Semi-transparent layers are frozen.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
       torch.utils._pytree._register_pytree_node(
+    The installed version of bitsandbytes was compiled without GPU support. 8-bit optimizers, 8-bit multiplication, and GPU quantization are unavailable.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+      warnings.warn(
 
 
 .. code:: ipython3
@@ -376,11 +379,9 @@ diagram indicate trainable layers. Semi-transparent layers are frozen.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
-      warnings.warn(
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
       torch.utils._pytree._register_pytree_node(
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
       warnings.warn(
 
 
@@ -391,7 +392,7 @@ diagram indicate trainable layers. Semi-transparent layers are frozen.
 
 .. parsed-literal::
 
-    100%|██████████| 525M/525M [25:23<00:00, 345kiB/s]
+    100%|██████████| 525M/525M [08:03<00:00, 1.09MiB/s]
 
 
 .. parsed-literal::
@@ -435,36 +436,48 @@ on disk using ``ov.save_model`` in compressed to FP16 format.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4481: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4371: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
       warnings.warn(
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_attn_mask_utils.py:86: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_attn_mask_utils.py:86: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if input_shape[-1] > 1 or self.sliding_window is not None:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_attn_mask_utils.py:162: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_attn_mask_utils.py:162: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if past_key_values_length > 0:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:276: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:279: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:284: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:287: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if causal_attention_mask.size() != (bsz, 1, tgt_len, src_len):
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:316: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:319: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/downsampling.py:135: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/downsampling.py:135: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       assert hidden_states.shape[1] == self.channels
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/downsampling.py:144: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/downsampling.py:144: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       assert hidden_states.shape[1] == self.channels
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/unet_2d_condition.py:915: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/unet_2d_condition.py:915: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if dim % default_overall_up_factor != 0:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/upsampling.py:149: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/upsampling.py:149: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       assert hidden_states.shape[1] == self.channels
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/upsampling.py:165: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/upsampling.py:165: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if hidden_states.shape[0] >= 64:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/schedulers/scheduling_ddpm.py:433: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/schedulers/scheduling_ddpm.py:433: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/schedulers/scheduling_ddpm.py:440: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/schedulers/scheduling_ddpm.py:440: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       alpha_prod_t_prev = self.alphas_cumprod[prev_t] if prev_t >= 0 else self.one
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/schedulers/scheduling_ddpm.py:479: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/schedulers/scheduling_ddpm.py:479: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if t > 0:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/schedulers/scheduling_ddpm.py:330: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/schedulers/scheduling_ddpm.py:330: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       alpha_prod_t_prev = self.alphas_cumprod[prev_t] if prev_t >= 0 else self.one
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:1116: TracerWarning: Trace had nondeterministic nodes. Did you forget call .eval() on your model? Nodes:
+    	%20785 : Float(1, 4, 64, 64, strides=[16384, 4096, 64, 1], requires_grad=0, device=cpu) = aten::randn(%20779, %20780, %20781, %20782, %20783, %20784) # /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/torch_utils.py:80:0
+    	%35917 : Float(1, 4, 64, 64, strides=[16384, 4096, 64, 1], requires_grad=0, device=cpu) = aten::randn(%35911, %35912, %35913, %35914, %35915, %35916) # /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/torch_utils.py:80:0
+    This may cause errors in trace checking. To disable trace checking, pass check_trace=False to torch.jit.trace()
+      _check_trace(
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:1116: TracerWarning: Output nr 1. of the traced function does not match the corresponding output of the repeated trace. Detailed error:
+    Tensor-likes are not close!
+
+    Mismatched elements: 1 / 786432 (0.0%)
+    Greatest absolute difference: 1.3738870620727539e-05 at index (0, 2, 428, 25) (up to 1e-05 allowed)
+    Greatest relative difference: 4.031259487888383e-05 at index (0, 2, 428, 25) (up to 1e-05 allowed)
+      _check_trace(
 
 
 Select inference device
@@ -907,11 +920,11 @@ Download results using download button
 
 .. parsed-literal::
 
-    /tmp/ipykernel_3121211/1555011934.py:259: GradioDeprecationWarning: 'scale' value should be an integer. Using 0.4 will cause issues.
+    /tmp/ipykernel_2816402/1555011934.py:259: GradioDeprecationWarning: 'scale' value should be an integer. Using 0.4 will cause issues.
       with gr.Column(elem_id="column_process", min_width=50, scale=0.4):
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/gradio/utils.py:776: UserWarning: Expected 1 arguments for function <function <lambda> at 0x7f8fcfafe0d0>, received 0.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/gradio/utils.py:776: UserWarning: Expected 1 arguments for function <function <lambda> at 0x7fc8d7400790>, received 0.
       warnings.warn(
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/gradio/utils.py:780: UserWarning: Expected at least 1 arguments for function <function <lambda> at 0x7f8fcfafe0d0>, received 0.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/gradio/utils.py:780: UserWarning: Expected at least 1 arguments for function <function <lambda> at 0x7fc8d7400790>, received 0.
       warnings.warn(
 
 
diff --git a/docs/notebooks/sketch-to-image-pix2pix-turbo-with-output_files/sketch-to-image-pix2pix-turbo-with-output_18_0.jpg b/docs/notebooks/sketch-to-image-pix2pix-turbo-with-output_files/sketch-to-image-pix2pix-turbo-with-output_18_0.jpg
index 403e52205cea43..58f4262a93ee4d 100644
--- a/docs/notebooks/sketch-to-image-pix2pix-turbo-with-output_files/sketch-to-image-pix2pix-turbo-with-output_18_0.jpg
+++ b/docs/notebooks/sketch-to-image-pix2pix-turbo-with-output_files/sketch-to-image-pix2pix-turbo-with-output_18_0.jpg
@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:806557228074e47f4fea588ba1bc1c22d0ba05b61e96bed446e473bd70c72a45
-size 24924
+oid sha256:88ab59888669d154b998ed858ee17d1252e3f0ad0f76c938b34195ac3f7bc2c4
+size 25148
diff --git a/docs/notebooks/sketch-to-image-pix2pix-turbo-with-output_files/sketch-to-image-pix2pix-turbo-with-output_18_0.png b/docs/notebooks/sketch-to-image-pix2pix-turbo-with-output_files/sketch-to-image-pix2pix-turbo-with-output_18_0.png
index 9f732bd67371a3..5706475b2aa31c 100644
--- a/docs/notebooks/sketch-to-image-pix2pix-turbo-with-output_files/sketch-to-image-pix2pix-turbo-with-output_18_0.png
+++ b/docs/notebooks/sketch-to-image-pix2pix-turbo-with-output_files/sketch-to-image-pix2pix-turbo-with-output_18_0.png
@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:77174face1e4d43eb4292d098f22a348a0f8c0d2b6a095e3b0565e7f631b0d06
-size 299731
+oid sha256:23133955b6bbe1a427e068f5f8d1dce8a322c07470c721c49af4148b2129c8c6
+size 299809
diff --git a/docs/notebooks/softvc-voice-conversion-with-output.rst b/docs/notebooks/softvc-voice-conversion-with-output.rst
index c2785403f9c596..cd8348c721735a 100644
--- a/docs/notebooks/softvc-voice-conversion-with-output.rst
+++ b/docs/notebooks/softvc-voice-conversion-with-output.rst
@@ -18,8 +18,8 @@ audio are preserved.
 
 In this tutorial we will use the base model flow.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Use the original model to run an
@@ -35,7 +35,7 @@ Prerequisites
 
 .. code:: ipython3
 
-    %pip install -q --upgrade pip setuptools
+    %pip install -qU "pip<24.1"  # to fix fairseq install problem
     %pip install -q "openvino>=2023.2.0"
     !git clone https://github.com/svc-develop-team/so-vits-svc -b 4.1-Stable
     %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu  tqdm librosa "torch>=2.1.0" "torchaudio>=2.1.0" faiss-cpu "gradio>=4.19" "numpy>=1.23.5" "fairseq==0.12.2" praat-parselmouth
diff --git a/docs/notebooks/sound-generation-audioldm2-with-output.rst b/docs/notebooks/sound-generation-audioldm2-with-output.rst
index 86262e6ed65bc9..75a9a87b5cd796 100644
--- a/docs/notebooks/sound-generation-audioldm2-with-output.rst
+++ b/docs/notebooks/sound-generation-audioldm2-with-output.rst
@@ -19,8 +19,8 @@ music.
 In this tutorial we will try out the pipeline, convert the models
 backing it one by one and will run an interactive app with Gradio!
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Instantiating Generation
diff --git a/docs/notebooks/sparsity-optimization-with-output.rst b/docs/notebooks/sparsity-optimization-with-output.rst
index c7feacdae07deb..9b4126993378e6 100644
--- a/docs/notebooks/sparsity-optimization-with-output.rst
+++ b/docs/notebooks/sparsity-optimization-with-output.rst
@@ -21,8 +21,8 @@ consists of the following steps:
    integration with Hugging Face Optimum.
 -  Compare sparse 8-bit vs. dense 8-bit inference performance.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Imports <#imports>`__
@@ -50,9 +50,7 @@ Prerequisites
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
 
 
@@ -73,16 +71,12 @@ Imports
 
 .. parsed-literal::
 
-    INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino
-
-
-.. parsed-literal::
-
-    2024-06-06 02:53:32.407670: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-06-06 02:53:32.442101: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-07-02 02:46:50.233018: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-07-02 02:46:50.267335: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2024-06-06 02:53:33.039105: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
+    2024-07-02 02:46:50.854680: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    The installed version of bitsandbytes was compiled without GPU support. 8-bit optimizers, 8-bit multiplication, and GPU quantization are unavailable.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
       torch.utils._pytree._register_pytree_node(
 
 
@@ -119,8 +113,6 @@ model card on Hugging Face.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
-      warnings.warn(
     Compiling the model to CPU ...
 
 
@@ -197,18 +189,18 @@ as an example. It is recommended to tune based on your applications.
     [ INFO ] Parsing input parameters
     [Step 2/11] Loading OpenVINO Runtime
     [ INFO ] OpenVINO:
-    [ INFO ] Build ................................. 2024.3.0-15599-de4d00a5970
+    [ INFO ] Build ................................. 2024.3.0-15837-76a668b0662
     [ INFO ] 
     [ INFO ] Device info:
     [ INFO ] CPU
-    [ INFO ] Build ................................. 2024.3.0-15599-de4d00a5970
+    [ INFO ] Build ................................. 2024.3.0-15837-76a668b0662
     [ INFO ] 
     [ INFO ] 
     [Step 3/11] Setting device configuration
     [ WARNING ] Performance hint was not explicitly specified in command line. Device(CPU) performance hint will be set to PerformanceMode.THROUGHPUT.
     [Step 4/11] Reading model files
     [ INFO ] Loading model files
-    [ INFO ] Read model took 72.11 ms
+    [ INFO ] Read model took 72.33 ms
     [ INFO ] Original model I/O parameters:
     [ INFO ] Model inputs:
     [ INFO ]     input_ids (node: input_ids) : i64 / [...] / [?,?]
@@ -219,7 +211,7 @@ as an example. It is recommended to tune based on your applications.
     [Step 5/11] Resizing model to match image sizes and given batch
     [ INFO ] Model batch size: 1
     [ INFO ] Reshaping model: 'input_ids': [1,64], 'attention_mask': [1,64], 'token_type_ids': [1,64]
-    [ INFO ] Reshape model took 30.85 ms
+    [ INFO ] Reshape model took 30.25 ms
     [Step 6/11] Configuring input of the model
     [ INFO ] Model inputs:
     [ INFO ]     input_ids (node: input_ids) : i64 / [...] / [1,64]
@@ -228,7 +220,7 @@ as an example. It is recommended to tune based on your applications.
     [ INFO ] Model outputs:
     [ INFO ]     logits (node: logits) : f32 / [...] / [1,2]
     [Step 7/11] Loading the model to the device
-    [ INFO ] Compile model took 1277.07 ms
+    [ INFO ] Compile model took 1251.41 ms
     [Step 8/11] Querying optimal runtime parameters
     [ INFO ] Model:
     [ INFO ]   NETWORK_NAME: torch_jit
@@ -248,7 +240,7 @@ as an example. It is recommended to tune based on your applications.
     [ INFO ]   CPU_DENORMALS_OPTIMIZATION: False
     [ INFO ]   LOG_LEVEL: Level.NO
     [ INFO ]   CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE: 1.0
-    [ INFO ]   DYNAMIC_QUANTIZATION_GROUP_SIZE: 0
+    [ INFO ]   DYNAMIC_QUANTIZATION_GROUP_SIZE: 32
     [ INFO ]   KV_CACHE_PRECISION: <Type: 'float16'>
     [ INFO ]   AFFINITY: Affinity.CORE
     [Step 9/11] Creating infer requests and preparing input tensors
@@ -260,17 +252,17 @@ as an example. It is recommended to tune based on your applications.
     [ INFO ] Fill input 'token_type_ids' with random values 
     [Step 10/11] Measuring performance (Start inference asynchronously, 4 inference requests, limits: 60000 ms duration)
     [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
-    [ INFO ] First inference took 25.64 ms
+    [ INFO ] First inference took 28.73 ms
     [Step 11/11] Dumping statistics report
     [ INFO ] Execution Devices:['CPU']
-    [ INFO ] Count:            9508 iterations
-    [ INFO ] Duration:         60047.81 ms
+    [ INFO ] Count:            9084 iterations
+    [ INFO ] Duration:         60026.37 ms
     [ INFO ] Latency:
-    [ INFO ]    Median:        25.04 ms
-    [ INFO ]    Average:       25.08 ms
-    [ INFO ]    Min:           23.07 ms
-    [ INFO ]    Max:           36.95 ms
-    [ INFO ] Throughput:   158.34 FPS
+    [ INFO ]    Median:        26.15 ms
+    [ INFO ]    Average:       26.18 ms
+    [ INFO ]    Min:           23.34 ms
+    [ INFO ]    Max:           43.87 ms
+    [ INFO ] Throughput:   151.33 FPS
 
 
 Benchmark quantized sparse inference performance
@@ -292,7 +284,7 @@ for which a layer will be enabled.
         outfile.write(
             """
             {
-                "CPU": {"NUM_STREAMS": 4, "INFERENCE_NUM_THREADS": 4, "CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE": 0.75}
+                "CPU": {"NUM_STREAMS": 4, "INFERENCE_NUM_THREADS": 4, "CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE": "0.75"}
             }
             """
         )
@@ -316,18 +308,18 @@ for which a layer will be enabled.
     [ INFO ] Parsing input parameters
     [Step 2/11] Loading OpenVINO Runtime
     [ INFO ] OpenVINO:
-    [ INFO ] Build ................................. 2024.3.0-15599-de4d00a5970
+    [ INFO ] Build ................................. 2024.3.0-15837-76a668b0662
     [ INFO ] 
     [ INFO ] Device info:
     [ INFO ] CPU
-    [ INFO ] Build ................................. 2024.3.0-15599-de4d00a5970
+    [ INFO ] Build ................................. 2024.3.0-15837-76a668b0662
     [ INFO ] 
     [ INFO ] 
     [Step 3/11] Setting device configuration
     [ WARNING ] Performance hint was not explicitly specified in command line. Device(CPU) performance hint will be set to PerformanceMode.THROUGHPUT.
     [Step 4/11] Reading model files
     [ INFO ] Loading model files
-    [ INFO ] Read model took 63.33 ms
+    [ INFO ] Read model took 71.03 ms
     [ INFO ] Original model I/O parameters:
     [ INFO ] Model inputs:
     [ INFO ]     input_ids (node: input_ids) : i64 / [...] / [?,?]
@@ -338,7 +330,7 @@ for which a layer will be enabled.
     [Step 5/11] Resizing model to match image sizes and given batch
     [ INFO ] Model batch size: 1
     [ INFO ] Reshaping model: 'input_ids': [1,64], 'attention_mask': [1,64], 'token_type_ids': [1,64]
-    [ INFO ] Reshape model took 30.21 ms
+    [ INFO ] Reshape model took 29.86 ms
     [Step 6/11] Configuring input of the model
     [ INFO ] Model inputs:
     [ INFO ]     input_ids (node: input_ids) : i64 / [...] / [1,64]
@@ -347,24 +339,49 @@ for which a layer will be enabled.
     [ INFO ] Model outputs:
     [ INFO ]     logits (node: logits) : f32 / [...] / [1,2]
     [Step 7/11] Loading the model to the device
-    [ ERROR ] Exception from src/inference/src/cpp/core.cpp:104:
-    Exception from src/inference/src/dev/plugin.cpp:53:
-    Exception from src/plugins/intel_cpu/src/config.cpp:226:
-    Wrong value for property key CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE. Expected only float numbers
-    
-    
-    Traceback (most recent call last):
-      File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/openvino/tools/benchmark/main.py", line 408, in main
-        compiled_model = benchmark.core.compile_model(model, benchmark.device, device_config)
-      File "/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/openvino/runtime/ie_api.py", line 543, in compile_model
-        super().compile_model(model, device_name, {} if config is None else config),
-    RuntimeError: Exception from src/inference/src/cpp/core.cpp:104:
-    Exception from src/inference/src/dev/plugin.cpp:53:
-    Exception from src/plugins/intel_cpu/src/config.cpp:226:
-    Wrong value for property key CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE. Expected only float numbers
-    
-    
-    
+    [ INFO ] Compile model took 1231.35 ms
+    [Step 8/11] Querying optimal runtime parameters
+    [ INFO ] Model:
+    [ INFO ]   NETWORK_NAME: torch_jit
+    [ INFO ]   OPTIMAL_NUMBER_OF_INFER_REQUESTS: 4
+    [ INFO ]   NUM_STREAMS: 4
+    [ INFO ]   INFERENCE_NUM_THREADS: 4
+    [ INFO ]   PERF_COUNT: NO
+    [ INFO ]   INFERENCE_PRECISION_HINT: <Type: 'float32'>
+    [ INFO ]   PERFORMANCE_HINT: THROUGHPUT
+    [ INFO ]   EXECUTION_MODE_HINT: ExecutionMode.PERFORMANCE
+    [ INFO ]   PERFORMANCE_HINT_NUM_REQUESTS: 0
+    [ INFO ]   ENABLE_CPU_PINNING: True
+    [ INFO ]   SCHEDULING_CORE_TYPE: SchedulingCoreType.ANY_CORE
+    [ INFO ]   MODEL_DISTRIBUTION_POLICY: set()
+    [ INFO ]   ENABLE_HYPER_THREADING: True
+    [ INFO ]   EXECUTION_DEVICES: ['CPU']
+    [ INFO ]   CPU_DENORMALS_OPTIMIZATION: False
+    [ INFO ]   LOG_LEVEL: Level.NO
+    [ INFO ]   CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE: 0.75
+    [ INFO ]   DYNAMIC_QUANTIZATION_GROUP_SIZE: 32
+    [ INFO ]   KV_CACHE_PRECISION: <Type: 'float16'>
+    [ INFO ]   AFFINITY: Affinity.CORE
+    [Step 9/11] Creating infer requests and preparing input tensors
+    [ WARNING ] No input files were given for input 'input_ids'!. This input will be filled with random values!
+    [ WARNING ] No input files were given for input 'attention_mask'!. This input will be filled with random values!
+    [ WARNING ] No input files were given for input 'token_type_ids'!. This input will be filled with random values!
+    [ INFO ] Fill input 'input_ids' with random values 
+    [ INFO ] Fill input 'attention_mask' with random values 
+    [ INFO ] Fill input 'token_type_ids' with random values 
+    [Step 10/11] Measuring performance (Start inference asynchronously, 4 inference requests, limits: 60000 ms duration)
+    [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
+    [ INFO ] First inference took 41.62 ms
+    [Step 11/11] Dumping statistics report
+    [ INFO ] Execution Devices:['CPU']
+    [ INFO ] Count:            9080 iterations
+    [ INFO ] Duration:         60043.01 ms
+    [ INFO ] Latency:
+    [ INFO ]    Median:        26.12 ms
+    [ INFO ]    Average:       26.19 ms
+    [ INFO ]    Min:           24.88 ms
+    [ INFO ]    Max:           40.25 ms
+    [ INFO ] Throughput:   151.22 FPS
 
 
 When this might be helpful
diff --git a/docs/notebooks/speculative-sampling-with-output.rst b/docs/notebooks/speculative-sampling-with-output.rst
index c1e0e9e154897d..4fec05cf7727b4 100644
--- a/docs/notebooks/speculative-sampling-with-output.rst
+++ b/docs/notebooks/speculative-sampling-with-output.rst
@@ -33,8 +33,8 @@ http://arxiv.org/abs/2211.17192
 Our blog article describing this implementation with OpenVino is
 available at openvino.ai
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 
diff --git a/docs/notebooks/speech-recognition-quantization-data2vec-with-output.rst b/docs/notebooks/speech-recognition-quantization-data2vec-with-output.rst
deleted file mode 100644
index 80358d9f51be98..00000000000000
--- a/docs/notebooks/speech-recognition-quantization-data2vec-with-output.rst
+++ /dev/null
@@ -1,742 +0,0 @@
-Quantize Data2Vec Speech Recognition Model using NNCF PTQ API
-=============================================================
-
-This tutorial demonstrates how to use the NNCF (Neural Network
-Compression Framework) 8-bit quantization in post-training mode (without
-the fine-tuning pipeline) to optimize the speech recognition model,
-known as `Data2Vec <https://arxiv.org/abs/2202.03555>`__ for the
-high-speed inference via OpenVINO™ Toolkit. This notebook uses a
-fine-tuned
-`data2vec-audio-base-960h <https://huggingface.co/facebook/data2vec-audio-base-960h>`__
-`PyTorch <https://pytorch.org/>`__ model trained on the `LibriSpeech ASR
-corpus <https://www.openslr.org/12>`__. The tutorial is designed to be
-extendable to custom models and datasets. It consists of the following
-steps:
-
--  Download and prepare model.
--  Define data loading and accuracy validation functionality.
--  Prepare the model for quantization and quantize.
--  Compare performance of the original and quantized models.
--  Compare Accuracy of the Original and Quantized Models.
-
-Table of contents:
-^^^^^^^^^^^^^^^^^^
-
--  `Download and prepare model <#download-and-prepare-model>`__
-
-   -  `Obtain Pytorch model
-      representation <#obtain-pytorch-model-representation>`__
-   -  `Convert model to OpenVINO Intermediate
-      Representation <#convert-model-to-openvino-intermediate-representation>`__
-   -  `Prepare inference data <#prepare-inference-data>`__
-
--  `Check model inference result <#check-model-inference-result>`__
--  `Validate model accuracy on
-   dataset <#validate-model-accuracy-on-dataset>`__
--  `Quantization <#quantization>`__
--  `Check INT8 model inference
-   result <#check-int8-model-inference-result>`__
--  `Compare Performance of the Original and Quantized
-   Models <#compare-performance-of-the-original-and-quantized-models>`__
--  `Compare Accuracy of the Original and Quantized
-   Models <#compare-accuracy-of-the-original-and-quantized-models>`__
-
-Download and prepare model
---------------------------
-
-
-
-data2vec is a framework for self-supervised representation learning for
-images, speech, and text as described in `data2vec: A General Framework
-for Self-supervised Learning in Speech, Vision and Language (Baevski et
-al.,
-2022) <https://ai.facebook.com/research/data2vec-a-general-framework-for-self-supervised-learning-in-speech-vision-and-language>`__.
-The algorithm uses the same learning mechanism for different modalities.
-
-.. figure:: https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/data2vec.png
-   :alt: pre-trained pipeline
-
-   pre-trained pipeline
-
-In our case, we will use ``data2vec-audio-base-960h`` model, which was
-finetuned on 960 hours of audio from LibriSpeech Automatic Speech
-Recognition corpus and distributed as part of HuggingFace transformers.
-
-Obtain Pytorch model representation
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-For instantiating PyTorch model class, we should use
-``Data2VecAudioForCTC.from_pretrained`` method with providing model ID
-for downloading from HuggingFace hub. Model weights and configuration
-files will be downloaded automatically in first time usage. Keep in mind
-that downloading the files can take several minutes and depends on your
-internet connection.
-
-Additionally, we can create processor class which is responsible for
-model specific pre- and post-processing steps.
-
-.. code:: ipython3
-
-    %pip install -q "openvino>=2023.3.0" "nncf>=2.7"
-    %pip install -q datasets "torchmetrics>=0.11.0" "torch>=2.1.0" --extra-index-url https://download.pytorch.org/whl/cpu
-    %pip install -q soundfile librosa "transformers>=4.36.2" --extra-index-url https://download.pytorch.org/whl/cpu
-
-.. code:: ipython3
-
-    from transformers import Wav2Vec2Processor, Data2VecAudioForCTC
-    
-    processor = Wav2Vec2Processor.from_pretrained("facebook/data2vec-audio-base-960h")
-    model = Data2VecAudioForCTC.from_pretrained("facebook/data2vec-audio-base-960h")
-
-Convert model to OpenVINO Intermediate Representation
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-.. code:: ipython3
-
-    from pathlib import Path
-    
-    # Set model directory
-    MODEL_DIR = Path("model")
-    MODEL_DIR.mkdir(exist_ok=True)
-
-.. code:: ipython3
-
-    import openvino as ov
-    import torch
-    
-    core = ov.Core()
-    
-    BATCH_SIZE = 1
-    MAX_SEQ_LENGTH = 30480
-    
-    ir_model_path = MODEL_DIR / "data2vec-audo-base.xml"
-    
-    if not ir_model_path.exists():
-        ov_model = ov.convert_model(model, example_input=torch.zeros([1, MAX_SEQ_LENGTH], dtype=torch.float))
-        ov.save_model(ov_model, str(ir_model_path))
-        print("IR model saved to {}".format(ir_model_path))
-    else:
-        print("Read IR model from {}".format(ir_model_path))
-        ov_model = core.read_model(ir_model_path)
-
-Prepare inference data
-~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-For demonstration purposes, we will use short dummy version of
-LibriSpeech dataset - ``patrickvonplaten/librispeech_asr_dummy`` to
-speed up model evaluation. Model accuracy can be different from reported
-in the paper. For reproducing original accuracy, use ``librispeech_asr``
-dataset.
-
-.. code:: ipython3
-
-    from datasets import load_dataset
-    
-    ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
-    
-    
-    # define preprocessing function for converting audio to input values for model
-    def map_to_input(batch):
-        preprocessed_signal = processor(
-            batch["audio"]["array"],
-            return_tensors="pt",
-            padding="longest",
-            sampling_rate=batch["audio"]["sampling_rate"],
-        )
-        input_values = preprocessed_signal.input_values
-        batch["input_values"] = input_values
-        return batch
-    
-    
-    # apply preprocessing function to dataset and remove audio column, to save memory as we do not need it anymore
-    dataset = ds.map(map_to_input, batched=False, remove_columns=["audio"])
-    
-    test_sample = ds[0]["audio"]
-
-Check model inference result
-----------------------------
-
-
-
-The code below is used for running model inference on a single sample
-from the dataset. It contains the following steps:
-
--  Get the input_values tensor as model input.
--  Run model inference and obtain logits.
--  Find logits ids with highest probability, using argmax.
--  Decode predicted token ids, using processor.
-
-For reference, see the same function provided for OpenVINO model.
-
-.. code:: ipython3
-
-    import numpy as np
-    
-    
-    # inference function for pytorch
-    def torch_infer(model, sample):
-        logits = model(torch.Tensor(sample["input_values"])).logits
-        # take argmax and decode
-        predicted_ids = torch.argmax(logits, dim=-1)
-        transcription = processor.batch_decode(predicted_ids)
-        return transcription
-    
-    
-    # inference function for openvino
-    def ov_infer(model, sample):
-        output = model.output(0)
-        logits = model(np.array(sample["input_values"]))[output]
-        predicted_ids = np.argmax(logits, axis=-1)
-        transcription = processor.batch_decode(torch.from_numpy(predicted_ids))
-        return transcription
-
-Select inference device for OpenVINO
-
-.. code:: ipython3
-
-    import ipywidgets as widgets
-    
-    core = ov.Core()
-    device = widgets.Dropdown(
-        options=core.available_devices + ["AUTO"],
-        value="AUTO",
-        description="Device:",
-        disabled=False,
-    )
-    
-    device
-
-
-
-
-.. parsed-literal::
-
-    Dropdown(description='Device:', index=4, options=('CPU', 'GPU.0', 'GPU.1', 'GPU.2', 'AUTO'), value='AUTO')
-
-
-
-.. code:: ipython3
-
-    core = ov.Core()
-    
-    pt_transcription = torch_infer(model, dataset[0])
-    compiled_model = core.compile_model(ov_model, device.value)
-    ov_transcription = ov_infer(compiled_model, dataset[0])
-
-.. code:: ipython3
-
-    import IPython.display as ipd
-    
-    print(f"[Reference]:     {dataset[0]['text']}")
-    print(f"[PyTorch]:       {pt_transcription[0]}")
-    print(f"[OpenVINO FP16]: {ov_transcription[0]}")
-    ipd.Audio(test_sample["array"], rate=16000)
-
-
-.. parsed-literal::
-
-    [Reference]:     BECAUSE YOU WERE SLEEPING INSTEAD OF CONQUERING THE LOVELY ROSE PRINCESS HAS BECOME A FIDDLE WITHOUT A BOW WHILE POOR SHAGGY SITS THERE A COOING DOVE
-    [PyTorch]:       BECAUSE YOU WERE SLEEPING INSTEAD OF CONQUERING THE LOVELY RUSE PRINCESS HAS BECOME A FIDDLE WITHOUT A BOW A POOR SHAGGY SITS THERE ACCOOING DOVE
-    [OpenVINO FP16]: BECAUSE YOU WERE SLEEPING INSTEAD OF CONQUERING THE LOVELY RUSE PRINCESS HAS BECOME A FIDDLE WITHOUT A BOW A POOR SHAGGY SITS THERE ACCOOING DOVE
-
-
-
-
-.. raw:: html
-
-    
-    <audio  controls="controls" >
-        <source 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" type="audio/wav" />
-        Your browser does not support the audio element.
-    </audio>
-
-
-
-
-Validate model accuracy on dataset
-----------------------------------
-
-
-
-For model accuracy evaluation, `Word Error
-Rate <https://en.wikipedia.org/wiki/Word_error_rate>`__ metric can be
-used. Word Error Rate or WER is the ratio of errors in a transcript to
-the total words spoken. A lower WER in speech-to-text means better
-accuracy in recognizing speech.
-
-For WER calculation, we will use
-`torchmetrics <https://torchmetrics.readthedocs.io/en/stable/text/word_error_rate.html>`__
-library.
-
-.. code:: ipython3
-
-    from torchmetrics.text import WordErrorRate
-    from tqdm.notebook import tqdm
-    
-    
-    def compute_wer(dataset, model, infer_fn):
-        wer = WordErrorRate()
-        for sample in tqdm(dataset):
-            # run infer function on sample
-            transcription = infer_fn(model, sample)
-            # update metric on sample result
-            wer.update(transcription, [sample["text"]])
-        # finalize metric calculation
-        result = wer.compute()
-        return result
-
-.. code:: ipython3
-
-    pt_result = compute_wer(dataset, model, torch_infer)
-    ov_result = compute_wer(dataset, compiled_model, ov_infer)
-
-
-
-.. parsed-literal::
-
-      0%|          | 0/73 [00:00<?, ?it/s]
-
-
-
-.. parsed-literal::
-
-      0%|          | 0/73 [00:00<?, ?it/s]
-
-
-.. code:: ipython3
-
-    print(f"[PyTorch]   Word Error Rate: {pt_result:.4f}")
-    print(f"[OpenVino]  Word Error Rate: {ov_result:.4f}")
-
-
-.. parsed-literal::
-
-    [PyTorch]   Word Error Rate: 0.0383
-    [OpenVino]  Word Error Rate: 0.0383
-
-
-Quantization
-------------
-
-
-
-`NNCF <https://github.com/openvinotoolkit/nncf>`__ provides a suite of
-advanced algorithms for Neural Networks inference optimization in
-OpenVINO with minimal accuracy drop.
-
-Create a quantized model from the pre-trained ``FP16`` model and the
-calibration dataset. The optimization process contains the following
-steps:
-
-1. Create a Dataset for quantization.
-2. Run ``nncf.quantize`` for getting an optimized model. The
-   ``nncf.quantize`` function provides an interface for model
-   quantization. It requires an instance of the OpenVINO Model and
-   quantization dataset. Optionally, some additional parameters for the
-   configuration quantization process (number of samples for
-   quantization, preset, ignored scope, etc.) can be provided. For more
-   accurate results, we should keep the operation in the postprocessing
-   subgraph in floating point precision, using the ``ignored_scope``
-   parameter. For more information see `Tune quantization
-   parameters <https://docs.openvino.ai/2024/openvino-workflow/model-optimization-guide/quantizing-models-post-training/basic-quantization-flow.html#tune-quantization-parameters>`__.
-3. Serialize OpenVINO IR model using ``ov.save_model`` function.
-
-.. code:: ipython3
-
-    import nncf
-    from nncf.parameters import ModelType
-    
-    
-    def transform_fn(data_item):
-        """
-        Extract the model's input from the data item.
-        The data item here is the data item that is returned from the data source per iteration.
-        This function should be passed when the data item cannot be used as model's input.
-        """
-        return np.array(data_item["input_values"])
-    
-    
-    calibration_dataset = nncf.Dataset(dataset, transform_fn)
-    
-    
-    quantized_model = nncf.quantize(
-        ov_model,
-        calibration_dataset,
-        model_type=ModelType.TRANSFORMER,  # specify additional transformer patterns in the model
-        subset_size=len(dataset),
-        ignored_scope=nncf.IgnoredScope(
-            names=[
-                "__module.data2vec_audio.feature_extractor.conv_layers.0.conv/aten::_convolution/Convolution",
-                "__module.data2vec_audio.feature_extractor.conv_layers.1.conv/aten::_convolution/Convolution",
-            ],
-        ),
-    )
-
-
-.. parsed-literal::
-
-    INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, openvino
-
-
-
-.. parsed-literal::
-
-    Output()
-
-
-
-.. raw:: html
-
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
-
-
-
-
-.. raw:: html
-
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
-
-
-
-
-.. parsed-literal::
-
-    Output()
-
-
-
-.. raw:: html
-
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
-
-
-
-
-.. raw:: html
-
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
-
-
-
-.. parsed-literal::
-
-    INFO:nncf:2 ignored nodes were found by name in the NNCFGraph
-    INFO:nncf:36 ignored nodes were found by name in the NNCFGraph
-    INFO:nncf:50 ignored nodes were found by name in the NNCFGraph
-    INFO:nncf:Not adding activation input quantizer for operation: 3 __module.data2vec_audio.feature_extractor.conv_layers.0.conv/aten::_convolution/Convolution
-    INFO:nncf:Not adding activation input quantizer for operation: 10 __module.data2vec_audio.feature_extractor.conv_layers.1.conv/aten::_convolution/Convolution
-
-
-
-.. parsed-literal::
-
-    Output()
-
-
-
-.. raw:: html
-
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
-
-
-
-
-.. raw:: html
-
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
-
-
-
-
-.. parsed-literal::
-
-    Output()
-
-
-
-.. raw:: html
-
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
-
-
-
-
-.. raw:: html
-
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
-
-
-
-After quantization is finished, compressed model representation can be
-saved using ``ov.save_model`` function.
-
-.. code:: ipython3
-
-    MODEL_NAME = "quantized_data2vec_base"
-    quantized_model_path = Path(f"{MODEL_NAME}_openvino_model/{MODEL_NAME}_quantized.xml")
-    ov.save_model(quantized_model, quantized_model_path)
-
-Check INT8 model inference result
----------------------------------
-
-
-
-``INT8`` model is the same in usage like the original one. We need to
-read it, using the ``core.read_model`` method and load on the device,
-using ``core.compile_model``. After that, we can reuse the same
-``ov_infer`` function for getting model inference result on test sample.
-
-.. code:: ipython3
-
-    int8_compiled_model = core.compile_model(quantized_model, device.value)
-
-.. code:: ipython3
-
-    transcription = ov_infer(int8_compiled_model, dataset[0])
-    print(f"[Reference]:     {dataset[0]['text']}")
-    print(f"[PyTorch]:       {pt_transcription[0]}")
-    print(f"[OpenVINO FP16]: {ov_transcription[0]}")
-    print(f"[OpenVINO INT8]: {transcription[0]}")
-    ipd.Audio(test_sample["array"], rate=16000)
-
-
-.. parsed-literal::
-
-    [Reference]:     BECAUSE YOU WERE SLEEPING INSTEAD OF CONQUERING THE LOVELY ROSE PRINCESS HAS BECOME A FIDDLE WITHOUT A BOW WHILE POOR SHAGGY SITS THERE A COOING DOVE
-    [PyTorch]:       BECAUSE YOU WERE SLEEPING INSTEAD OF CONQUERING THE LOVELY RUSE PRINCESS HAS BECOME A FIDDLE WITHOUT A BOW A POOR SHAGGY SITS THERE ACCOOING DOVE
-    [OpenVINO FP16]: BECAUSE YOU WERE SLEEPING INSTEAD OF CONQUERING THE LOVELY RUSE PRINCESS HAS BECOME A FIDDLE WITHOUT A BOW A POOR SHAGGY SITS THERE ACCOOING DOVE
-    [OpenVINO INT8]: BECAUSE YOU WERE SLEEPING INSTEAD OF CONQUERING THE LOVELY RUSE PRINCESS HAS BECOME A FIDDLE WITHOUT A BOW A POORA SHAGGY SITS THERE ACCOOING DOVE
-
-
-
-
-.. raw:: html
-
-    
-    <audio  controls="controls" >
-        <source 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" type="audio/wav" />
-        Your browser does not support the audio element.
-    </audio>
-
-
-
-
-Compare Performance of the Original and Quantized Models
---------------------------------------------------------
-
- `Benchmark
-Tool <https://docs.openvino.ai/2024/learn-openvino/openvino-samples/benchmark-tool.html>`__
-is used to measure the inference performance of the ``FP16`` and
-``INT8`` models.
-
-   **NOTE**: For more accurate performance, it is recommended to run
-   ``benchmark_app`` in a terminal/command prompt after closing other
-   applications. Run ``benchmark_app -m model.xml -d CPU`` to benchmark
-   async inference on CPU for one minute. Change ``CPU`` to ``GPU`` to
-   benchmark on GPU. Run ``benchmark_app --help`` to see an overview of
-   all command-line options.
-
-.. code:: ipython3
-
-    # Inference FP16 model (OpenVINO IR)
-    ! benchmark_app -m $ir_model_path -shape [1,30480] -d $device.value -api async -t 15
-
-
-.. parsed-literal::
-
-    [Step 1/11] Parsing and validating input arguments
-    [ INFO ] Parsing input parameters
-    [Step 2/11] Loading OpenVINO Runtime
-    [ INFO ] OpenVINO:
-    [ INFO ] Build ................................. 2023.3.0-13775-ceeafaf64f3-releases/2023/3
-    [ INFO ] 
-    [ INFO ] Device info:
-    [ INFO ] AUTO
-    [ INFO ] Build ................................. 2023.3.0-13775-ceeafaf64f3-releases/2023/3
-    [ INFO ] 
-    [ INFO ] 
-    [Step 3/11] Setting device configuration
-    [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.THROUGHPUT.
-    [Step 4/11] Reading model files
-    [ INFO ] Loading model files
-    [ INFO ] Read model took 26.84 ms
-    [ INFO ] Original model I/O parameters:
-    [ INFO ] Model inputs:
-    [ INFO ]     input_values (node: input_values) : f32 / [...] / [?,?]
-    [ INFO ] Model outputs:
-    [ INFO ]     1289 , logits (node: __module.lm_head/aten::linear/Add) : f32 / [...] / [?,?,32]
-    [Step 5/11] Resizing model to match image sizes and given batch
-    [ INFO ] Model batch size: 1
-    [ INFO ] Reshaping model: 'input_values': [1,30480]
-    [ INFO ] Reshape model took 31.21 ms
-    [Step 6/11] Configuring input of the model
-    [ INFO ] Model inputs:
-    [ INFO ]     input_values (node: input_values) : f32 / [...] / [1,30480]
-    [ INFO ] Model outputs:
-    [ INFO ]     1289 , logits (node: __module.lm_head/aten::linear/Add) : f32 / [...] / [1,95,32]
-    [Step 7/11] Loading the model to the device
-    [ INFO ] Compile model took 871.22 ms
-    [Step 8/11] Querying optimal runtime parameters
-    [ INFO ] Model:
-    [ INFO ]   NETWORK_NAME: Model0
-    [ INFO ]   EXECUTION_DEVICES: ['CPU']
-    [ INFO ]   PERFORMANCE_HINT: PerformanceMode.THROUGHPUT
-    [ INFO ]   OPTIMAL_NUMBER_OF_INFER_REQUESTS: 12
-    [ INFO ]   MULTI_DEVICE_PRIORITIES: CPU
-    [ INFO ]   CPU:
-    [ INFO ]     AFFINITY: Affinity.CORE
-    [ INFO ]     CPU_DENORMALS_OPTIMIZATION: False
-    [ INFO ]     CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE: 1.0
-    [ INFO ]     ENABLE_CPU_PINNING: True
-    [ INFO ]     ENABLE_HYPER_THREADING: True
-    [ INFO ]     EXECUTION_DEVICES: ['CPU']
-    [ INFO ]     EXECUTION_MODE_HINT: ExecutionMode.PERFORMANCE
-    [ INFO ]     INFERENCE_NUM_THREADS: 36
-    [ INFO ]     INFERENCE_PRECISION_HINT: <Type: 'float32'>
-    [ INFO ]     NETWORK_NAME: Model0
-    [ INFO ]     NUM_STREAMS: 12
-    [ INFO ]     OPTIMAL_NUMBER_OF_INFER_REQUESTS: 12
-    [ INFO ]     PERFORMANCE_HINT: THROUGHPUT
-    [ INFO ]     PERFORMANCE_HINT_NUM_REQUESTS: 0
-    [ INFO ]     PERF_COUNT: NO
-    [ INFO ]     SCHEDULING_CORE_TYPE: SchedulingCoreType.ANY_CORE
-    [ INFO ]   MODEL_PRIORITY: Priority.MEDIUM
-    [ INFO ]   LOADED_FROM_CACHE: False
-    [Step 9/11] Creating infer requests and preparing input tensors
-    [ WARNING ] No input files were given for input 'input_values'!. This input will be filled with random values!
-    [ INFO ] Fill input 'input_values' with random values 
-    [Step 10/11] Measuring performance (Start inference asynchronously, 12 inference requests, limits: 15000 ms duration)
-    [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
-    [ INFO ] First inference took 90.48 ms
-    [Step 11/11] Dumping statistics report
-    [ INFO ] Execution Devices:['CPU']
-    [ INFO ] Count:            732 iterations
-    [ INFO ] Duration:         15334.79 ms
-    [ INFO ] Latency:
-    [ INFO ]    Median:        250.16 ms
-    [ INFO ]    Average:       250.01 ms
-    [ INFO ]    Min:           193.17 ms
-    [ INFO ]    Max:           277.38 ms
-    [ INFO ] Throughput:   47.73 FPS
-
-
-.. code:: ipython3
-
-    # Inference INT8 model (OpenVINO IR)
-    ! benchmark_app -m $quantized_model_path -shape [1,30480] -d $device.value -api async -t 15
-
-
-.. parsed-literal::
-
-    [Step 1/11] Parsing and validating input arguments
-    [ INFO ] Parsing input parameters
-    [Step 2/11] Loading OpenVINO Runtime
-    [ INFO ] OpenVINO:
-    [ INFO ] Build ................................. 2023.3.0-13775-ceeafaf64f3-releases/2023/3
-    [ INFO ] 
-    [ INFO ] Device info:
-    [ INFO ] AUTO
-    [ INFO ] Build ................................. 2023.3.0-13775-ceeafaf64f3-releases/2023/3
-    [ INFO ] 
-    [ INFO ] 
-    [Step 3/11] Setting device configuration
-    [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.THROUGHPUT.
-    [Step 4/11] Reading model files
-    [ INFO ] Loading model files
-    [ INFO ] Read model took 65.75 ms
-    [ INFO ] Original model I/O parameters:
-    [ INFO ] Model inputs:
-    [ INFO ]     input_values (node: input_values) : f32 / [...] / [?,?]
-    [ INFO ] Model outputs:
-    [ INFO ]     logits , 1289 (node: __module.lm_head/aten::linear/Add) : f32 / [...] / [?,?,32]
-    [Step 5/11] Resizing model to match image sizes and given batch
-    [ INFO ] Model batch size: 1
-    [ INFO ] Reshaping model: 'input_values': [1,30480]
-    [ INFO ] Reshape model took 37.95 ms
-    [Step 6/11] Configuring input of the model
-    [ INFO ] Model inputs:
-    [ INFO ]     input_values (node: input_values) : f32 / [...] / [1,30480]
-    [ INFO ] Model outputs:
-    [ INFO ]     logits , 1289 (node: __module.lm_head/aten::linear/Add) : f32 / [...] / [1,95,32]
-    [Step 7/11] Loading the model to the device
-    [ INFO ] Compile model took 1339.85 ms
-    [Step 8/11] Querying optimal runtime parameters
-    [ INFO ] Model:
-    [ INFO ]   NETWORK_NAME: Model0
-    [ INFO ]   EXECUTION_DEVICES: ['CPU']
-    [ INFO ]   PERFORMANCE_HINT: PerformanceMode.THROUGHPUT
-    [ INFO ]   OPTIMAL_NUMBER_OF_INFER_REQUESTS: 12
-    [ INFO ]   MULTI_DEVICE_PRIORITIES: CPU
-    [ INFO ]   CPU:
-    [ INFO ]     AFFINITY: Affinity.CORE
-    [ INFO ]     CPU_DENORMALS_OPTIMIZATION: False
-    [ INFO ]     CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE: 1.0
-    [ INFO ]     ENABLE_CPU_PINNING: True
-    [ INFO ]     ENABLE_HYPER_THREADING: True
-    [ INFO ]     EXECUTION_DEVICES: ['CPU']
-    [ INFO ]     EXECUTION_MODE_HINT: ExecutionMode.PERFORMANCE
-    [ INFO ]     INFERENCE_NUM_THREADS: 36
-    [ INFO ]     INFERENCE_PRECISION_HINT: <Type: 'float32'>
-    [ INFO ]     NETWORK_NAME: Model0
-    [ INFO ]     NUM_STREAMS: 12
-    [ INFO ]     OPTIMAL_NUMBER_OF_INFER_REQUESTS: 12
-    [ INFO ]     PERFORMANCE_HINT: THROUGHPUT
-    [ INFO ]     PERFORMANCE_HINT_NUM_REQUESTS: 0
-    [ INFO ]     PERF_COUNT: NO
-    [ INFO ]     SCHEDULING_CORE_TYPE: SchedulingCoreType.ANY_CORE
-    [ INFO ]   MODEL_PRIORITY: Priority.MEDIUM
-    [ INFO ]   LOADED_FROM_CACHE: False
-    [Step 9/11] Creating infer requests and preparing input tensors
-    [ WARNING ] No input files were given for input 'input_values'!. This input will be filled with random values!
-    [ INFO ] Fill input 'input_values' with random values 
-    [Step 10/11] Measuring performance (Start inference asynchronously, 12 inference requests, limits: 15000 ms duration)
-    [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
-    [ INFO ] First inference took 68.59 ms
-    [Step 11/11] Dumping statistics report
-    [ INFO ] Execution Devices:['CPU']
-    [ INFO ] Count:            1092 iterations
-    [ INFO ] Duration:         15108.16 ms
-    [ INFO ] Latency:
-    [ INFO ]    Median:        165.80 ms
-    [ INFO ]    Average:       165.38 ms
-    [ INFO ]    Min:           63.30 ms
-    [ INFO ]    Max:           200.85 ms
-    [ INFO ] Throughput:   72.28 FPS
-
-
-Compare Accuracy of the Original and Quantized Models
------------------------------------------------------
-
-
-
-Finally, calculate WER metric for the ``INT8`` model representation and
-compare it with the ``FP16`` result.
-
-.. code:: ipython3
-
-    int8_ov_result = compute_wer(dataset, int8_compiled_model, ov_infer)
-    print(f"[OpenVino FP16] Word Error Rate: {ov_result:.4}")
-    print(f"[OpenVino INT8] Word Error Rate: {int8_ov_result:.4f}")
-
-
-
-.. parsed-literal::
-
-      0%|          | 0/73 [00:00<?, ?it/s]
-
-
-.. parsed-literal::
-
-    [OpenVino FP16] Word Error Rate: 0.03826
-    [OpenVino INT8] Word Error Rate: 0.0400
-
diff --git a/docs/notebooks/speech-recognition-quantization-wav2vec2-with-output.rst b/docs/notebooks/speech-recognition-quantization-wav2vec2-with-output.rst
index fc97da011d9bee..a5a761151634ff 100644
--- a/docs/notebooks/speech-recognition-quantization-wav2vec2-with-output.rst
+++ b/docs/notebooks/speech-recognition-quantization-wav2vec2-with-output.rst
@@ -20,8 +20,8 @@ steps:
    models.
 -  Compare performance of the original and quantized models.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Imports <#imports>`__
 -  `Settings <#settings>`__
@@ -44,52 +44,49 @@ Table of contents:
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
     Looking in indexes: https://pypi.org/simple, https://download.pytorch.org/whl/cpu
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-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
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+    Requirement already satisfied: typing-extensions in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torchmetrics>=0.11.0) (4.12.2)
+    Requirement already satisfied: sympy in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch>=2.1.0) (1.12.1)
+    Requirement already satisfied: networkx in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch>=2.1.0) (3.1)
+    Requirement already satisfied: jinja2 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from torch>=2.1.0) (3.1.4)
+    Requirement already satisfied: aiosignal>=1.1.2 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from aiohttp->datasets) (1.3.1)
+    Requirement already satisfied: attrs>=17.3.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from aiohttp->datasets) (23.2.0)
+    Requirement already satisfied: frozenlist>=1.1.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from aiohttp->datasets) (1.4.1)
+    Requirement already satisfied: multidict<7.0,>=4.5 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from aiohttp->datasets) (6.0.5)
+    Requirement already satisfied: yarl<2.0,>=1.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from aiohttp->datasets) (1.9.4)
+    Requirement already satisfied: async-timeout<5.0,>=4.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from aiohttp->datasets) (4.0.3)
+    Requirement already satisfied: setuptools in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from lightning-utilities>=0.8.0->torchmetrics>=0.11.0) (70.2.0)
+    Requirement already satisfied: charset-normalizer<4,>=2 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from requests>=2.32.2->datasets) (3.3.2)
+    Requirement already satisfied: idna<4,>=2.5 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from requests>=2.32.2->datasets) (3.7)
+    Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from requests>=2.32.2->datasets) (2.2.2)
+    Requirement already satisfied: certifi>=2017.4.17 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from requests>=2.32.2->datasets) (2024.6.2)
+    Requirement already satisfied: MarkupSafe>=2.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from jinja2->torch>=2.1.0) (2.1.5)
+    Requirement already satisfied: python-dateutil>=2.8.2 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from pandas->datasets) (2.9.0.post0)
+    Requirement already satisfied: pytz>=2020.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from pandas->datasets) (2024.1)
+    Requirement already satisfied: tzdata>=2022.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from pandas->datasets) (2024.1)
+    Requirement already satisfied: mpmath<1.4.0,>=1.1.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from sympy->torch>=2.1.0) (1.3.0)
+    Requirement already satisfied: six>=1.5 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.16.0)
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
 
 
@@ -138,6 +135,8 @@ IR).
 
 .. parsed-literal::
 
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+      warnings.warn(
     Some weights of the model checkpoint at facebook/wav2vec2-base-960h were not used when initializing Wav2Vec2ForCTC: ['wav2vec2.encoder.pos_conv_embed.conv.weight_g', 'wav2vec2.encoder.pos_conv_embed.conv.weight_v']
     - This IS expected if you are initializing Wav2Vec2ForCTC from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
     - This IS NOT expected if you are initializing Wav2Vec2ForCTC from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
@@ -157,10 +156,12 @@ IR).
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4481: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4371: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
       warnings.warn(
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:968: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      if attn_output.size() != (bsz, self.num_heads, tgt_len, self.head_dim):
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:588: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+      if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:627: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+      if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
 
 
 Prepare LibriSpeech Dataset
@@ -179,7 +180,7 @@ dataset.
     from datasets import load_dataset
     
     
-    dataset = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
+    dataset = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True)
     test_sample = dataset[0]["audio"]
     
     
@@ -199,15 +200,6 @@ dataset.
     # apply preprocessing function to dataset and remove audio column, to save memory as we do not need it anymore
     dataset = dataset.map(map_to_input, batched=False, remove_columns=["audio"])
 
-
-.. parsed-literal::
-
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/datasets/load.py:1491: FutureWarning: The repository for patrickvonplaten/librispeech_asr_dummy contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/patrickvonplaten/librispeech_asr_dummy
-    You can avoid this message in future by passing the argument `trust_remote_code=True`.
-    Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.
-      warnings.warn(
-
-
 Run Quantization
 ----------------
 
@@ -277,10 +269,10 @@ steps:
 
 .. parsed-literal::
 
-    2024-06-06 02:55:07.536473: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-06-06 02:55:07.571269: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-07-02 02:49:20.638621: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-07-02 02:49:20.670301: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2024-06-06 02:55:08.150522: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-07-02 02:49:21.278581: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 
@@ -290,17 +282,17 @@ steps:
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -311,28 +303,29 @@ steps:
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
 .. parsed-literal::
 
-    INFO:nncf:4 ignored nodes were found by name in the NNCFGraph
-    INFO:nncf:50 ignored nodes were found by name in the NNCFGraph
+    INFO:nncf:4 ignored nodes were found by names in the NNCFGraph
+    INFO:nncf:36 ignored nodes were found by names in the NNCFGraph
+    INFO:nncf:50 ignored nodes were found by names in the NNCFGraph
     INFO:nncf:Not adding activation input quantizer for operation: 2 __module.wav2vec2.feature_extractor.conv_layers.0.conv/aten::_convolution/Convolution
-    INFO:nncf:Not adding activation input quantizer for operation: 9 __module.wav2vec2.feature_extractor.conv_layers.1.conv/aten::_convolution/Convolution
-    INFO:nncf:Not adding activation input quantizer for operation: 11 __module.wav2vec2.feature_extractor.conv_layers.2.conv/aten::_convolution/Convolution
-    INFO:nncf:Not adding activation input quantizer for operation: 13 __module.wav2vec2.feature_extractor.conv_layers.3.conv/aten::_convolution/Convolution
+    INFO:nncf:Not adding activation input quantizer for operation: 134 __module.wav2vec2.feature_extractor.conv_layers.1.conv/aten::_convolution/Convolution
+    INFO:nncf:Not adding activation input quantizer for operation: 160 __module.wav2vec2.feature_extractor.conv_layers.2.conv/aten::_convolution/Convolution
+    INFO:nncf:Not adding activation input quantizer for operation: 186 __module.wav2vec2.feature_extractor.conv_layers.3.conv/aten::_convolution/Convolution
 
 
 
@@ -342,17 +335,17 @@ steps:
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -363,17 +356,17 @@ steps:
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -533,7 +526,7 @@ quantized model.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torchmetrics/utilities/prints.py:62: FutureWarning: Importing `WordErrorRate` from `torchmetrics` was deprecated and will be removed in 2.0. Import `WordErrorRate` from `torchmetrics.text` instead.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torchmetrics/utilities/prints.py:62: FutureWarning: Importing `WordErrorRate` from `torchmetrics` was deprecated and will be removed in 2.0. Import `WordErrorRate` from `torchmetrics.text` instead.
       _future_warning(
 
 
@@ -559,7 +552,7 @@ quantized model.
 
     [PyTorch]       Word Error Rate: 0.0530
     [OpenVino FP16] Word Error Rate: 0.05304
-    [OpenVino INT8] Word Error Rate: 0.0530
+    [OpenVino INT8] Word Error Rate: 0.0548
 
 
 Compare Performance of the Original and Quantized Models
@@ -592,34 +585,34 @@ models.
     [Step 2/11] Loading OpenVINO Runtime
     [ WARNING ] Default duration 120 seconds is used for unknown device AUTO
     [ INFO ] OpenVINO:
-    [ INFO ] Build ................................. 2024.3.0-15599-de4d00a5970
+    [ INFO ] Build ................................. 2024.3.0-15837-76a668b0662
     [ INFO ] 
     [ INFO ] Device info:
     [ INFO ] AUTO
-    [ INFO ] Build ................................. 2024.3.0-15599-de4d00a5970
+    [ INFO ] Build ................................. 2024.3.0-15837-76a668b0662
     [ INFO ] 
     [ INFO ] 
     [Step 3/11] Setting device configuration
     [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.THROUGHPUT.
     [Step 4/11] Reading model files
     [ INFO ] Loading model files
-    [ INFO ] Read model took 17.90 ms
+    [ INFO ] Read model took 19.42 ms
     [ INFO ] Original model I/O parameters:
     [ INFO ] Model inputs:
-    [ INFO ]     45 , input_values (node: input_values) : f32 / [...] / [?,?]
+    [ INFO ]     46 , input_values (node: input_values) : f32 / [...] / [?,?]
     [ INFO ] Model outputs:
-    [ INFO ]     logits (node: __module.lm_head/aten::linear/Add) : f32 / [...] / [?,1..,32]
+    [ INFO ]     logits (node: __module.lm_head/aten::linear/Add) : f32 / [...] / [?,?,32]
     [Step 5/11] Resizing model to match image sizes and given batch
     [ INFO ] Model batch size: 1
-    [ INFO ] Reshaping model: '45': [1,30480]
-    [ INFO ] Reshape model took 4.90 ms
+    [ INFO ] Reshaping model: '46': [1,30480]
+    [ INFO ] Reshape model took 6.94 ms
     [Step 6/11] Configuring input of the model
     [ INFO ] Model inputs:
-    [ INFO ]     45 , input_values (node: input_values) : f32 / [...] / [1,30480]
+    [ INFO ]     46 , input_values (node: input_values) : f32 / [...] / [1,30480]
     [ INFO ] Model outputs:
     [ INFO ]     logits (node: __module.lm_head/aten::linear/Add) : f32 / [...] / [1,95,32]
     [Step 7/11] Loading the model to the device
-    [ INFO ] Compile model took 634.43 ms
+    [ INFO ] Compile model took 533.50 ms
     [Step 8/11] Querying optimal runtime parameters
     [ INFO ] Model:
     [ INFO ]   NETWORK_NAME: Model0
@@ -631,7 +624,7 @@ models.
     [ INFO ]     AFFINITY: Affinity.CORE
     [ INFO ]     CPU_DENORMALS_OPTIMIZATION: False
     [ INFO ]     CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE: 1.0
-    [ INFO ]     DYNAMIC_QUANTIZATION_GROUP_SIZE: 0
+    [ INFO ]     DYNAMIC_QUANTIZATION_GROUP_SIZE: 32
     [ INFO ]     ENABLE_CPU_PINNING: True
     [ INFO ]     ENABLE_HYPER_THREADING: True
     [ INFO ]     EXECUTION_DEVICES: ['CPU']
@@ -652,21 +645,21 @@ models.
     [ INFO ]   LOADED_FROM_CACHE: False
     [ INFO ]   PERF_COUNT: False
     [Step 9/11] Creating infer requests and preparing input tensors
-    [ WARNING ] No input files were given for input '45'!. This input will be filled with random values!
-    [ INFO ] Fill input '45' with random values 
+    [ WARNING ] No input files were given for input '46'!. This input will be filled with random values!
+    [ INFO ] Fill input '46' with random values 
     [Step 10/11] Measuring performance (Start inference asynchronously, 6 inference requests, limits: 120000 ms duration)
     [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
-    [ INFO ] First inference took 71.28 ms
+    [ INFO ] First inference took 75.17 ms
     [Step 11/11] Dumping statistics report
     [ INFO ] Execution Devices:['CPU']
-    [ INFO ] Count:            5574 iterations
-    [ INFO ] Duration:         120209.53 ms
+    [ INFO ] Count:            5484 iterations
+    [ INFO ] Duration:         120155.78 ms
     [ INFO ] Latency:
-    [ INFO ]    Median:        129.28 ms
-    [ INFO ]    Average:       129.22 ms
-    [ INFO ]    Min:           69.48 ms
-    [ INFO ]    Max:           151.86 ms
-    [ INFO ] Throughput:   46.37 FPS
+    [ INFO ]    Median:        131.28 ms
+    [ INFO ]    Average:       131.30 ms
+    [ INFO ]    Min:           64.42 ms
+    [ INFO ]    Max:           152.65 ms
+    [ INFO ] Throughput:   45.64 FPS
 
 
 .. code:: ipython3
@@ -682,34 +675,34 @@ models.
     [Step 2/11] Loading OpenVINO Runtime
     [ WARNING ] Default duration 120 seconds is used for unknown device AUTO
     [ INFO ] OpenVINO:
-    [ INFO ] Build ................................. 2024.3.0-15599-de4d00a5970
+    [ INFO ] Build ................................. 2024.3.0-15837-76a668b0662
     [ INFO ] 
     [ INFO ] Device info:
     [ INFO ] AUTO
-    [ INFO ] Build ................................. 2024.3.0-15599-de4d00a5970
+    [ INFO ] Build ................................. 2024.3.0-15837-76a668b0662
     [ INFO ] 
     [ INFO ] 
     [Step 3/11] Setting device configuration
     [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.THROUGHPUT.
     [Step 4/11] Reading model files
     [ INFO ] Loading model files
-    [ INFO ] Read model took 24.20 ms
+    [ INFO ] Read model took 25.41 ms
     [ INFO ] Original model I/O parameters:
     [ INFO ] Model inputs:
-    [ INFO ]     input_values , 45 (node: input_values) : f32 / [...] / [?,?]
+    [ INFO ]     46 , input_values (node: input_values) : f32 / [...] / [?,?]
     [ INFO ] Model outputs:
-    [ INFO ]     logits (node: __module.lm_head/aten::linear/Add) : f32 / [...] / [?,1..,32]
+    [ INFO ]     logits (node: __module.lm_head/aten::linear/Add) : f32 / [...] / [?,?,32]
     [Step 5/11] Resizing model to match image sizes and given batch
     [ INFO ] Model batch size: 1
-    [ INFO ] Reshaping model: '45': [1,30480]
-    [ INFO ] Reshape model took 6.80 ms
+    [ INFO ] Reshaping model: '46': [1,30480]
+    [ INFO ] Reshape model took 8.80 ms
     [Step 6/11] Configuring input of the model
     [ INFO ] Model inputs:
-    [ INFO ]     input_values , 45 (node: input_values) : f32 / [...] / [1,30480]
+    [ INFO ]     46 , input_values (node: input_values) : f32 / [...] / [1,30480]
     [ INFO ] Model outputs:
     [ INFO ]     logits (node: __module.lm_head/aten::linear/Add) : f32 / [...] / [1,95,32]
     [Step 7/11] Loading the model to the device
-    [ INFO ] Compile model took 1411.04 ms
+    [ INFO ] Compile model took 1233.01 ms
     [Step 8/11] Querying optimal runtime parameters
     [ INFO ] Model:
     [ INFO ]   NETWORK_NAME: Model0
@@ -721,7 +714,7 @@ models.
     [ INFO ]     AFFINITY: Affinity.CORE
     [ INFO ]     CPU_DENORMALS_OPTIMIZATION: False
     [ INFO ]     CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE: 1.0
-    [ INFO ]     DYNAMIC_QUANTIZATION_GROUP_SIZE: 0
+    [ INFO ]     DYNAMIC_QUANTIZATION_GROUP_SIZE: 32
     [ INFO ]     ENABLE_CPU_PINNING: True
     [ INFO ]     ENABLE_HYPER_THREADING: True
     [ INFO ]     EXECUTION_DEVICES: ['CPU']
@@ -742,19 +735,19 @@ models.
     [ INFO ]   LOADED_FROM_CACHE: False
     [ INFO ]   PERF_COUNT: False
     [Step 9/11] Creating infer requests and preparing input tensors
-    [ WARNING ] No input files were given for input '45'!. This input will be filled with random values!
-    [ INFO ] Fill input '45' with random values 
+    [ WARNING ] No input files were given for input '46'!. This input will be filled with random values!
+    [ INFO ] Fill input '46' with random values 
     [Step 10/11] Measuring performance (Start inference asynchronously, 6 inference requests, limits: 120000 ms duration)
     [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
-    [ INFO ] First inference took 55.19 ms
+    [ INFO ] First inference took 56.80 ms
     [Step 11/11] Dumping statistics report
     [ INFO ] Execution Devices:['CPU']
-    [ INFO ] Count:            8256 iterations
-    [ INFO ] Duration:         120140.04 ms
+    [ INFO ] Count:            8280 iterations
+    [ INFO ] Duration:         120142.25 ms
     [ INFO ] Latency:
-    [ INFO ]    Median:        86.98 ms
-    [ INFO ]    Average:       87.17 ms
-    [ INFO ]    Min:           47.41 ms
-    [ INFO ]    Max:           207.01 ms
-    [ INFO ] Throughput:   68.72 FPS
+    [ INFO ]    Median:        86.94 ms
+    [ INFO ]    Average:       86.92 ms
+    [ INFO ]    Min:           72.59 ms
+    [ INFO ]    Max:           111.68 ms
+    [ INFO ] Throughput:   68.92 FPS
 
diff --git a/docs/notebooks/speech-to-text-with-output.rst b/docs/notebooks/speech-to-text-with-output.rst
deleted file mode 100644
index ee560da3736829..00000000000000
--- a/docs/notebooks/speech-to-text-with-output.rst
+++ /dev/null
@@ -1,607 +0,0 @@
-Speech to Text with OpenVINO™
-=============================
-
-This tutorial demonstrates speech-to-text recognition with OpenVINO.
-
-This tutorial uses the `QuartzNet
-15x5 <https://docs.openvino.ai/2024/omz_models_model_quartznet_15x5_en.html>`__
-model. QuartzNet performs automatic speech recognition. Its design is
-based on the Jasper architecture, which is a convolutional model trained
-with Connectionist Temporal Classification (CTC) loss. The model is
-available from `Open Model
-Zoo <https://github.com/openvinotoolkit/open_model_zoo/>`__.
-
-Table of contents:
-^^^^^^^^^^^^^^^^^^
-
--  `Imports <#imports>`__
--  `Settings <#settings>`__
--  `Download and Convert Public
-   Model <#download-and-convert-public-model>`__
-
-   -  `Download Model <#download-model>`__
-
--  `Audio Processing <#audio-processing>`__
-
-   -  `Define constants <#define-constants>`__
-   -  `Available Audio Formats <#available-audio-formats>`__
-   -  `Load Audio File <#load-audio-file>`__
-   -  `Visualize Audio File <#visualize-audio-file>`__
-   -  `Change Type of Data <#change-type-of-data>`__
-   -  `Convert Audio to Mel Spectrum <#convert-audio-to-mel-spectrum>`__
-   -  `Run Conversion from Audio to Mel
-      Format <#run-conversion-from-audio-to-mel-format>`__
-   -  `Visualize Mel Spectrogram <#visualize-mel-spectrogram>`__
-   -  `Adjust Mel scale to Input <#adjust-mel-scale-to-input>`__
-
--  `Load the Model <#load-the-model>`__
-
-   -  `Do Inference <#do-inference>`__
-   -  `Read Output <#read-output>`__
-   -  `Implementation of Decoding <#implementation-of-decoding>`__
-   -  `Run Decoding and Print Output <#run-decoding-and-print-output>`__
-
-Imports
--------
-
-
-
-.. code:: ipython3
-
-    %pip install -q "librosa>=0.8.1" "scipy" "matplotlib<3.8" "openvino-dev>=2024.0.0" "numpy<1.24" "ruamel.yaml" "torch" "tqdm"
-
-.. code:: ipython3
-
-    from pathlib import Path
-    import sys
-    
-    import torch
-    import torch.nn as nn
-    import IPython.display as ipd
-    import matplotlib.pyplot as plt
-    import librosa
-    import librosa.display
-    import numpy as np
-    import scipy
-    import openvino as ov
-    
-    # Fetch `notebook_utils` module
-    import requests
-    
-    r = requests.get(
-        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
-    )
-    
-    open("notebook_utils.py", "w").write(r.text)
-    from notebook_utils import download_file
-
-Settings
---------
-
-
-
-In this part, all variables used in the notebook are set.
-
-.. code:: ipython3
-
-    model_folder = "model"
-    download_folder = "output"
-    data_folder = "data"
-    
-    precision = "FP16"
-    model_name = "quartznet-15x5-en"
-
-Download and Convert Public Model
----------------------------------
-
- If it is your first run, models
-will be downloaded and converted here. It my take a few minutes. Use
-``omz_downloader`` and ``omz_converter``, which are command-line tools
-from the ``openvino-dev`` package.
-
-Download Model
-~~~~~~~~~~~~~~
-
-
-
-The ``omz_downloader`` tool automatically creates a directory structure
-and downloads the selected model. This step is skipped if the model is
-already downloaded. The selected model comes from the public directory,
-which means it must be converted into OpenVINO Intermediate
-Representation (OpenVINO IR).
-
-.. code:: ipython3
-
-    # Check if a model is already downloaded (to the download directory).
-    path_to_model_weights = Path(f"{download_folder}/public/{model_name}/models")
-    downloaded_model_file = list(path_to_model_weights.glob("*.pth"))
-    
-    if not path_to_model_weights.is_dir() or len(downloaded_model_file) == 0:
-        download_command = f"omz_downloader --name {model_name} --output_dir {download_folder} --precision {precision}"
-        ! $download_command
-    
-    sys.path.insert(0, str(path_to_model_weights))
-
-.. code:: ipython3
-
-    def convert_model(model_path: Path, converted_model_path: Path):
-        """
-        helper function for converting QuartzNet model to IR
-        The function accepts path to directory with dowloaded packages, weights and configs using OMZ downloader,
-        initialize model and convert to OpenVINO model and serialize it to IR.
-        Params:
-          model_path: path to model modules, weights and configs downloaded via omz_downloader
-          converted_model_path: path for saving converted model
-        Returns:
-          None
-        """
-        # add model path to PYTHONPATH for access to downloaded modules
-        sys.path.append(str(model_path))
-    
-        # import necessary classes
-        from ruamel.yaml import YAML
-    
-        from nemo.collections.asr import JasperEncoder, JasperDecoderForCTC
-        from nemo.core import NeuralModuleFactory, DeviceType
-    
-        YAML = YAML(typ="safe")
-    
-        # utility fornction fr replacing 1d convolutions to 2d for better efficiency
-        def convert_to_2d(model):
-            for name, l in model.named_children():
-                layer_type = l.__class__.__name__
-                if layer_type == "Conv1d":
-                    new_layer = nn.Conv2d(
-                        l.in_channels,
-                        l.out_channels,
-                        (1, l.kernel_size[0]),
-                        (1, l.stride[0]),
-                        (0, l.padding[0]),
-                        (1, l.dilation[0]),
-                        l.groups,
-                        False if l.bias is None else True,
-                        l.padding_mode,
-                    )
-                    params = l.state_dict()
-                    params["weight"] = params["weight"].unsqueeze(2)
-                    new_layer.load_state_dict(params)
-                    setattr(model, name, new_layer)
-                elif layer_type == "BatchNorm1d":
-                    new_layer = nn.BatchNorm2d(l.num_features, l.eps)
-                    new_layer.load_state_dict(l.state_dict())
-                    new_layer.eval()
-                    setattr(model, name, new_layer)
-                else:
-                    convert_to_2d(l)
-    
-        # model class
-        class QuartzNet(torch.nn.Module):
-            def __init__(self, model_config, encoder_weights, decoder_weights):
-                super().__init__()
-                with open(model_config, "r") as config:
-                    model_args = YAML.load(config)
-                _ = NeuralModuleFactory(placement=DeviceType.CPU)
-    
-                encoder_params = model_args["init_params"]["encoder_params"]["init_params"]
-                self.encoder = JasperEncoder(**encoder_params)
-                self.encoder.load_state_dict(torch.load(encoder_weights, map_location="cpu"))
-    
-                decoder_params = model_args["init_params"]["decoder_params"]["init_params"]
-                self.decoder = JasperDecoderForCTC(**decoder_params)
-                self.decoder.load_state_dict(torch.load(decoder_weights, map_location="cpu"))
-    
-                self.encoder._prepare_for_deployment()
-                self.decoder._prepare_for_deployment()
-                convert_to_2d(self.encoder)
-                convert_to_2d(self.decoder)
-    
-            def forward(self, input_signal):
-                input_signal = input_signal.unsqueeze(axis=2)
-                i_encoded = self.encoder(input_signal)
-                i_log_probs = self.decoder(i_encoded)
-    
-                shape = i_log_probs.shape
-                return i_log_probs.reshape(shape[0], shape[1], shape[3])
-    
-        # path to configs and weights for creating model instane
-        model_config = model_path / ".nemo_tmp/module.yaml"
-        encoder_weights = model_path / ".nemo_tmp/JasperEncoder.pt"
-        decoder_weights = model_path / ".nemo_tmp/JasperDecoderForCTC.pt"
-        # create model instance
-        model = QuartzNet(model_config, encoder_weights, decoder_weights)
-        # turn model to inference mode
-        model.eval()
-        # convert model to OpenVINO Model using model conversion API
-        ov_model = ov.convert_model(model, example_input=torch.zeros([1, 64, 128]))
-        # save model in IR format for next usage
-        ov.save_model(ov_model, converted_model_path)
-
-.. code:: ipython3
-
-    # Check if a model is already converted (in the model directory).
-    path_to_converted_weights = Path(f"{model_folder}/public/{model_name}/{precision}/{model_name}.bin")
-    path_to_converted_model = Path(f"{model_folder}/public/{model_name}/{precision}/{model_name}.xml")
-    
-    if not path_to_converted_weights.is_file():
-        downloaded_model_path = Path("output/public/quartznet-15x5-en/models")
-        convert_model(downloaded_model_path, path_to_converted_model)
-
-
-.. parsed-literal::
-
-    [NeMo W 2023-09-11 15:01:17 jasper:148] Turned off 170 masked convolutions
-
-
-.. parsed-literal::
-
-    INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino
-
-
-.. parsed-literal::
-
-    [NeMo W 2023-09-11 15:01:18 deprecated:66] Function ``local_parameters`` is deprecated. It is going to be removed in the 0.11 version.
-
-
-Audio Processing
-----------------
-
-
-
-Now that the model is converted, load an audio file.
-
-Define constants
-~~~~~~~~~~~~~~~~
-
-
-
-First, locate an audio file and define the alphabet used by the model.
-This tutorial uses the Latin alphabet beginning with a space symbol and
-ending with a blank symbol. In this case it will be ``~``, but that
-could be any other character.
-
-.. code:: ipython3
-
-    audio_file_name = "edge_to_cloud.ogg"
-    alphabet = " abcdefghijklmnopqrstuvwxyz'~"
-
-Available Audio Formats
-~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-There are multiple supported audio formats that can be used with the
-model:
-
-``AIFF``, ``AU``, ``AVR``, ``CAF``, ``FLAC``, ``HTK``, ``SVX``,
-``MAT4``, ``MAT5``, ``MPC2K``, ``OGG``, ``PAF``, ``PVF``, ``RAW``,
-``RF64``, ``SD2``, ``SDS``, ``IRCAM``, ``VOC``, ``W64``, ``WAV``,
-``NIST``, ``WAVEX``, ``WVE``, ``XI``
-
-Load Audio File
-~~~~~~~~~~~~~~~
-
-
-
-Load the file after checking a file extension. Pass ``sr`` (stands for a
-``sampling rate``) as an additional parameter. The model supports files
-with a ``sampling rate`` of 16 kHz.
-
-.. code:: ipython3
-
-    # Download the audio from the openvino_notebooks storage
-    file_name = download_file(
-        "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/audio/" + audio_file_name,
-        directory=data_folder,
-    )
-    
-    audio, sampling_rate = librosa.load(path=str(file_name), sr=16000)
-
-Now, you can play your audio file.
-
-.. code:: ipython3
-
-    ipd.Audio(audio, rate=sampling_rate)
-
-
-
-
-.. raw:: html
-
-    
-    <audio  controls="controls" >
-        <source 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" type="audio/wav" />
-        Your browser does not support the audio element.
-    </audio>
-
-
-
-
-Visualize Audio File
-~~~~~~~~~~~~~~~~~~~~
-
-
-
-You can visualize how your audio file presents on a wave plot and
-spectrogram.
-
-.. code:: ipython3
-
-    plt.figure()
-    librosa.display.waveshow(y=audio, sr=sampling_rate, max_points=50000, x_axis="time", offset=0.0)
-    plt.show()
-    specto_audio = librosa.stft(audio)
-    specto_audio = librosa.amplitude_to_db(np.abs(specto_audio), ref=np.max)
-    print(specto_audio.shape)
-    librosa.display.specshow(specto_audio, sr=sampling_rate, x_axis="time", y_axis="hz");
-
-
-
-.. image:: speech-to-text-with-output_files/speech-to-text-with-output_20_0.png
-
-
-.. parsed-literal::
-
-    (1025, 51)
-
-
-
-.. image:: speech-to-text-with-output_files/speech-to-text-with-output_20_2.png
-
-
-Change Type of Data
-~~~~~~~~~~~~~~~~~~~
-
-
-
-The file loaded in the previous step may contain data in ``float`` type
-with a range of values between -1 and 1. To generate a viable input,
-multiply each value by the max value of ``int16`` and convert it to
-``int16`` type.
-
-.. code:: ipython3
-
-    if max(np.abs(audio)) <= 1:
-        audio = audio * (2**15 - 1)
-    audio = audio.astype(np.int16)
-
-Convert Audio to Mel Spectrum
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-Next, convert the pre-pre-processed audio to `Mel
-Spectrum <https://medium.com/analytics-vidhya/understanding-the-mel-spectrogram-fca2afa2ce53>`__.
-For more information on why it needs to be done, refer to `this
-article <https://towardsdatascience.com/audio-deep-learning-made-simple-part-2-why-mel-spectrograms-perform-better-aad889a93505>`__.
-
-.. code:: ipython3
-
-    def audio_to_mel(audio, sampling_rate):
-        assert sampling_rate == 16000, "Only 16 KHz audio supported"
-        preemph = 0.97
-        preemphased = np.concatenate([audio[:1], audio[1:] - preemph * audio[:-1].astype(np.float32)])
-    
-        # Calculate the window length.
-        win_length = round(sampling_rate * 0.02)
-    
-        # Based on the previously calculated window length, run short-time Fourier transform.
-        spec = np.abs(
-            librosa.core.spectrum.stft(
-                preemphased,
-                n_fft=512,
-                hop_length=round(sampling_rate * 0.01),
-                win_length=win_length,
-                center=True,
-                window=scipy.signal.windows.hann(win_length),
-                pad_mode="reflect",
-            )
-        )
-    
-        # Create mel filter-bank, produce transformation matrix to project current values onto Mel-frequency bins.
-        mel_basis = librosa.filters.mel(sr=sampling_rate, n_fft=512, n_mels=64, fmin=0.0, fmax=8000.0, htk=False)
-        return mel_basis, spec
-    
-    
-    def mel_to_input(mel_basis, spec, padding=16):
-        # Convert to a logarithmic scale.
-        log_melspectrum = np.log(np.dot(mel_basis, np.power(spec, 2)) + 2**-24)
-    
-        # Normalize the output.
-        normalized = (log_melspectrum - log_melspectrum.mean(1)[:, None]) / (log_melspectrum.std(1)[:, None] + 1e-5)
-    
-        # Calculate padding.
-        remainder = normalized.shape[1] % padding
-        if remainder != 0:
-            return np.pad(normalized, ((0, 0), (0, padding - remainder)))[None]
-        return normalized[None]
-
-Run Conversion from Audio to Mel Format
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-In this step, convert a current audio file into `Mel
-scale <https://en.wikipedia.org/wiki/Mel_scale>`__.
-
-.. code:: ipython3
-
-    mel_basis, spec = audio_to_mel(audio=audio.flatten(), sampling_rate=sampling_rate)
-
-Visualize Mel Spectrogram
-~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-For more information about Mel spectrogram, refer to this
-`article <https://towardsdatascience.com/getting-to-know-the-mel-spectrogram-31bca3e2d9d0>`__.
-The first image visualizes Mel frequency spectrogram, the second one
-presents filter bank for converting Hz to Mels.
-
-.. code:: ipython3
-
-    librosa.display.specshow(data=spec, sr=sampling_rate, x_axis="time", y_axis="log")
-    plt.show()
-    librosa.display.specshow(data=mel_basis, sr=sampling_rate, x_axis="linear")
-    plt.ylabel("Mel filter");
-
-
-
-.. image:: speech-to-text-with-output_files/speech-to-text-with-output_28_0.png
-
-
-
-.. image:: speech-to-text-with-output_files/speech-to-text-with-output_28_1.png
-
-
-Adjust Mel scale to Input
-~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-Before reading the network, make sure that the input is ready.
-
-.. code:: ipython3
-
-    audio = mel_to_input(mel_basis=mel_basis, spec=spec)
-
-Load the Model
---------------
-
-
-
-Now, you can read and load the network.
-
-.. code:: ipython3
-
-    core = ov.Core()
-
-You may run the model on multiple devices. By default, it will load the
-model on CPU (you can choose manually CPU, GPU etc.) or let the engine
-choose the best available device (AUTO).
-
-To list all available devices that can be used, run
-``print(core.available_devices)`` command.
-
-.. code:: ipython3
-
-    print(core.available_devices)
-
-
-.. parsed-literal::
-
-    ['CPU', 'GPU']
-
-
-Select device from dropdown list
-
-.. code:: ipython3
-
-    import ipywidgets as widgets
-    
-    device = widgets.Dropdown(
-        options=core.available_devices + ["AUTO"],
-        value="AUTO",
-        description="Device:",
-        disabled=False,
-    )
-    
-    device
-
-
-
-
-.. parsed-literal::
-
-    Dropdown(description='Device:', index=2, options=('CPU', 'GPU', 'AUTO'), value='AUTO')
-
-
-
-.. code:: ipython3
-
-    model = core.read_model(model=f"{model_folder}/public/{model_name}/{precision}/{model_name}.xml")
-    model_input_layer = model.input(0)
-    shape = model_input_layer.partial_shape
-    shape[2] = -1
-    model.reshape({model_input_layer: shape})
-    compiled_model = core.compile_model(model=model, device_name=device.value)
-
-Do Inference
-~~~~~~~~~~~~
-
-
-
-Everything is set up. Now, the only thing that remains is passing input
-to the previously loaded network and running inference.
-
-.. code:: ipython3
-
-    character_probabilities = compiled_model([ov.Tensor(audio)])[0]
-
-Read Output
-~~~~~~~~~~~
-
-
-
-After inference, you need to reach out the output. The default output
-format for ``QuartzNet 15x5`` are per-frame probabilities (after
-LogSoftmax) for every symbol in the alphabet, name - output, shape -
-1x64x29, output data format is ``BxNxC``, where:
-
--  B - batch size
--  N - number of audio frames
--  C - alphabet size, including the Connectionist Temporal
-   Classification (CTC) blank symbol
-
-You need to make it in a more human-readable format. To do this you, use
-a symbol with the highest probability. When you hold a list of indexes
-that are predicted to have the highest probability, due to limitations
-given by `Connectionist Temporal Classification
-Decoding <https://towardsdatascience.com/beam-search-decoding-in-ctc-trained-neural-networks-5a889a3d85a7>`__
-you will remove concurrent symbols and then remove all the blanks.
-
-The last step is getting symbols from corresponding indexes in charlist.
-
-.. code:: ipython3
-
-    # Remove unnececery dimension
-    character_probabilities = np.squeeze(character_probabilities)
-    
-    # Run argmax to pick most possible symbols
-    character_probabilities = np.argmax(character_probabilities, axis=1)
-
-Implementation of Decoding
-~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-To decode previously explained output, you need the `Connectionist
-Temporal Classification (CTC)
-decode <https://towardsdatascience.com/beam-search-decoding-in-ctc-trained-neural-networks-5a889a3d85a7>`__
-function. This solution will remove consecutive letters from the output.
-
-.. code:: ipython3
-
-    def ctc_greedy_decode(predictions):
-        previous_letter_id = blank_id = len(alphabet) - 1
-        transcription = list()
-        for letter_index in predictions:
-            if previous_letter_id != letter_index != blank_id:
-                transcription.append(alphabet[letter_index])
-            previous_letter_id = letter_index
-        return "".join(transcription)
-
-Run Decoding and Print Output
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-.. code:: ipython3
-
-    transcription = ctc_greedy_decode(character_probabilities)
-    print(transcription)
-
-
-.. parsed-literal::
-
-    from the edge to the cloud
-
diff --git a/docs/notebooks/speech-to-text-with-output_files/speech-to-text-with-output_20_0.png b/docs/notebooks/speech-to-text-with-output_files/speech-to-text-with-output_20_0.png
deleted file mode 100644
index 4f676d7b16d948..00000000000000
--- a/docs/notebooks/speech-to-text-with-output_files/speech-to-text-with-output_20_0.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:657c9b26560f22401566023e5eac946ee9f3c6dbc32519f4bb0f3b61a75f21fb
-size 21796
diff --git a/docs/notebooks/speech-to-text-with-output_files/speech-to-text-with-output_20_2.png b/docs/notebooks/speech-to-text-with-output_files/speech-to-text-with-output_20_2.png
deleted file mode 100644
index 6c8b8f4193c9b9..00000000000000
--- a/docs/notebooks/speech-to-text-with-output_files/speech-to-text-with-output_20_2.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:27930a9a6c4312452039729fe8664861399e2b785455d3ca92c6465ad0a0676b
-size 85260
diff --git a/docs/notebooks/speech-to-text-with-output_files/speech-to-text-with-output_28_0.png b/docs/notebooks/speech-to-text-with-output_files/speech-to-text-with-output_28_0.png
deleted file mode 100644
index 168d3c8ebc15c4..00000000000000
--- a/docs/notebooks/speech-to-text-with-output_files/speech-to-text-with-output_28_0.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:5e60e6c532cc6f5207444526b886985020c7593d4394a1fd590bf1b1b34a3cba
-size 50178
diff --git a/docs/notebooks/speech-to-text-with-output_files/speech-to-text-with-output_28_1.png b/docs/notebooks/speech-to-text-with-output_files/speech-to-text-with-output_28_1.png
deleted file mode 100644
index 363c29cf77b5fc..00000000000000
--- a/docs/notebooks/speech-to-text-with-output_files/speech-to-text-with-output_28_1.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:78aafab4432d290ad542ffeb57ed0669c94999753e265c02429d8f705f381947
-size 10083
diff --git a/docs/notebooks/stable-cascade-image-generation-with-output.rst b/docs/notebooks/stable-cascade-image-generation-with-output.rst
index 77cfa3c8ca6781..25516b13d25629 100644
--- a/docs/notebooks/stable-cascade-image-generation-with-output.rst
+++ b/docs/notebooks/stable-cascade-image-generation-with-output.rst
@@ -14,8 +14,8 @@ of 42, meaning that it is possible to encode a 1024x1024 image to 24x24,
 while maintaining crisp reconstructions. The text-conditional model is
 then trained in the highly compressed latent space.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Load the original model <#load-the-original-model>`__
@@ -45,7 +45,6 @@ Prerequisites
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
 
 
@@ -68,12 +67,14 @@ Load and run the original pipeline
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/transformers/transformer_2d.py:34: FutureWarning: `Transformer2DModelOutput` is deprecated and will be removed in version 1.0.0. Importing `Transformer2DModelOutput` from `diffusers.models.transformer_2d` is deprecated and this will be removed in a future version. Please use `from diffusers.models.modeling_outputs import Transformer2DModelOutput`, instead.
-      deprecate("Transformer2DModelOutput", "1.0.0", deprecation_message)
-    2024-06-06 03:01:11.454287: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-06-06 03:01:11.488883: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-07-02 02:55:53.640509: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-07-02 02:55:53.674384: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2024-06-06 03:01:12.140696: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-07-02 02:55:54.330057: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/vq_model.py:20: FutureWarning: `VQEncoderOutput` is deprecated and will be removed in version 0.31. Importing `VQEncoderOutput` from `diffusers.models.vq_model` is deprecated and this will be removed in a future version. Please use `from diffusers.models.autoencoders.vq_model import VQEncoderOutput`, instead.
+      deprecate("VQEncoderOutput", "0.31", deprecation_message)
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/vq_model.py:25: FutureWarning: `VQModel` is deprecated and will be removed in version 0.31. Importing `VQModel` from `diffusers.models.vq_model` is deprecated and this will be removed in a future version. Please use `from diffusers.models.autoencoders.vq_model import VQModel`, instead.
+      deprecate("VQModel", "0.31", deprecation_message)
 
 
 
@@ -82,6 +83,11 @@ Load and run the original pipeline
     Loading pipeline components...:   0%|          | 0/6 [00:00<?, ?it/s]
 
 
+.. parsed-literal::
+
+    The installed version of bitsandbytes was compiled without GPU support. 8-bit optimizers, 8-bit multiplication, and GPU quantization are unavailable.
+
+
 
 .. parsed-literal::
 
@@ -242,19 +248,19 @@ here, we always use fixed shapes in conversion by using an
 .. parsed-literal::
 
     [ WARNING ]  Please fix your imports. Module %s has been moved to %s. The old module will be deleted in version %s.
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4481: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4371: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
       warnings.warn(
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_attn_mask_utils.py:86: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_attn_mask_utils.py:86: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if input_shape[-1] > 1 or self.sliding_window is not None:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_attn_mask_utils.py:162: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_attn_mask_utils.py:162: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if past_key_values_length > 0:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:276: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:279: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:284: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:287: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if causal_attention_mask.size() != (bsz, 1, tgt_len, src_len):
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:293: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:296: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if attention_mask.size() != (bsz, 1, tgt_len, src_len):
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:316: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:319: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
 
 
@@ -275,17 +281,17 @@ here, we always use fixed shapes in conversion by using an
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -311,7 +317,7 @@ here, we always use fixed shapes in conversion by using an
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/unets/unet_stable_cascade.py:550: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/unets/unet_stable_cascade.py:550: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if skip is not None and (x.size(-1) != skip.size(-1) or x.size(-2) != skip.size(-2)):
 
 
@@ -332,17 +338,17 @@ here, we always use fixed shapes in conversion by using an
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -388,17 +394,17 @@ Decoder pipeline consists of 3 parts: decoder, text encoder and VQGAN.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -438,17 +444,17 @@ Decoder pipeline consists of 3 parts: decoder, text encoder and VQGAN.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -493,17 +499,17 @@ Decoder pipeline consists of 3 parts: decoder, text encoder and VQGAN.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -745,6 +751,8 @@ Interactive inference
 
     Running on local URL:  http://127.0.0.1:7860
     
+    Thanks for being a Gradio user! If you have questions or feedback, please join our Discord server and chat with us: https://discord.gg/feTf9x3ZSB
+    
     To create a public link, set `share=True` in `launch()`.
 
 
diff --git a/docs/notebooks/stable-cascade-image-generation-with-output_files/stable-cascade-image-generation-with-output_29_2.jpg b/docs/notebooks/stable-cascade-image-generation-with-output_files/stable-cascade-image-generation-with-output_29_2.jpg
index 6b6d487dce0c12..eaad0f8d5095fc 100644
--- a/docs/notebooks/stable-cascade-image-generation-with-output_files/stable-cascade-image-generation-with-output_29_2.jpg
+++ b/docs/notebooks/stable-cascade-image-generation-with-output_files/stable-cascade-image-generation-with-output_29_2.jpg
@@ -1,3 +1,3 @@
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-size 93475
+oid sha256:83470d8ca50f608439419eea517d5ad8ebd9fc7990c2db52531af7628e9482ba
+size 92512
diff --git a/docs/notebooks/stable-cascade-image-generation-with-output_files/stable-cascade-image-generation-with-output_29_2.png b/docs/notebooks/stable-cascade-image-generation-with-output_files/stable-cascade-image-generation-with-output_29_2.png
index b13bc4725105f0..02781520bd58dd 100644
--- a/docs/notebooks/stable-cascade-image-generation-with-output_files/stable-cascade-image-generation-with-output_29_2.png
+++ b/docs/notebooks/stable-cascade-image-generation-with-output_files/stable-cascade-image-generation-with-output_29_2.png
@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:2b847e7180f10466a36371be59ad1dcb07f98dd71b12496b9e1625915eb3a239
-size 1708534
+oid sha256:4ae849c7b63cb9ad4a9ad572aeceb467873904cccc090de5072973a0a2cef839
+size 1654387
diff --git a/docs/notebooks/stable-diffusion-ip-adapter-with-output.rst b/docs/notebooks/stable-diffusion-ip-adapter-with-output.rst
index d831f5485ee0e0..f5f96e8ed904ef 100644
--- a/docs/notebooks/stable-diffusion-ip-adapter-with-output.rst
+++ b/docs/notebooks/stable-diffusion-ip-adapter-with-output.rst
@@ -19,8 +19,8 @@ speedup generation process we will use
 
 .. |ip-adapter-pipe.png| image:: https://huggingface.co/h94/IP-Adapter/resolve/main/fig1.png
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Prepare Diffusers pipeline <#prepare-diffusers-pipeline>`__
@@ -62,9 +62,7 @@ Prerequisites
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
 
 
@@ -184,12 +182,16 @@ Additionally, LCM requires using LCMScheduler for efficient generation.
 
 .. parsed-literal::
 
-    2024-06-06 03:28:46.543891: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-06-06 03:28:46.578207: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-07-02 03:24:08.726619: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-07-02 03:24:08.762393: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2024-06-06 03:28:47.244174: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/transformers/transformer_2d.py:34: FutureWarning: `Transformer2DModelOutput` is deprecated and will be removed in version 1.0.0. Importing `Transformer2DModelOutput` from `diffusers.models.transformer_2d` is deprecated and this will be removed in a future version. Please use `from diffusers.models.modeling_outputs import Transformer2DModelOutput`, instead.
-      deprecate("Transformer2DModelOutput", "1.0.0", deprecation_message)
+    2024-07-02 03:24:09.392101: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/vq_model.py:20: FutureWarning: `VQEncoderOutput` is deprecated and will be removed in version 0.31. Importing `VQEncoderOutput` from `diffusers.models.vq_model` is deprecated and this will be removed in a future version. Please use `from diffusers.models.autoencoders.vq_model import VQEncoderOutput`, instead.
+      deprecate("VQEncoderOutput", "0.31", deprecation_message)
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/vq_model.py:25: FutureWarning: `VQModel` is deprecated and will be removed in version 0.31. Importing `VQModel` from `diffusers.models.vq_model` is deprecated and this will be removed in a future version. Please use `from diffusers.models.autoencoders.vq_model import VQModel`, instead.
+      deprecate("VQModel", "0.31", deprecation_message)
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+      warnings.warn(
 
 
 
@@ -198,6 +200,11 @@ Additionally, LCM requires using LCMScheduler for efficient generation.
     Loading pipeline components...:   0%|          | 0/7 [00:00<?, ?it/s]
 
 
+.. parsed-literal::
+
+    The installed version of bitsandbytes was compiled without GPU support. 8-bit optimizers, 8-bit multiplication, and GPU quantization are unavailable.
+
+
 Convert PyTorch models
 ----------------------
 
@@ -276,11 +283,11 @@ extractor as input and returns image embeddings.
 .. parsed-literal::
 
     [ WARNING ]  Please fix your imports. Module %s has been moved to %s. The old module will be deleted in version %s.
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4481: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4371: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
       warnings.warn(
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:276: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:279: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:316: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:319: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
 
 
@@ -342,17 +349,17 @@ Model predicts the ``sample`` state for the next step.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/unets/unet_2d_condition.py:1114: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/unets/unet_2d_condition.py:1103: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if dim % default_overall_up_factor != 0:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/embeddings.py:1229: FutureWarning: You have passed a tensor as `image_embeds`.This is deprecated and will be removed in a future release. Please make sure to update your script to pass `image_embeds` as a list of tensors to supress this warning.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/embeddings.py:1257: FutureWarning: You have passed a tensor as `image_embeds`.This is deprecated and will be removed in a future release. Please make sure to update your script to pass `image_embeds` as a list of tensors to supress this warning.
       deprecate("image_embeds not a list", "1.0.0", deprecation_message, standard_warn=False)
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/downsampling.py:136: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/downsampling.py:136: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       assert hidden_states.shape[1] == self.channels
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/downsampling.py:145: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/downsampling.py:145: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       assert hidden_states.shape[1] == self.channels
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/upsampling.py:146: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/upsampling.py:146: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       assert hidden_states.shape[1] == self.channels
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/upsampling.py:162: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/upsampling.py:162: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if hidden_states.shape[0] >= 64:
 
 
@@ -430,16 +437,16 @@ image in pipeline, we can discuss it in inference examples.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:1116: TracerWarning: Trace had nondeterministic nodes. Did you forget call .eval() on your model? Nodes:
-    	%2494 : Float(1, 4, 64, 64, strides=[16384, 4096, 64, 1], requires_grad=0, device=cpu) = aten::randn(%2488, %2489, %2490, %2491, %2492, %2493) # /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/torch_utils.py:81:0
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:1116: TracerWarning: Trace had nondeterministic nodes. Did you forget call .eval() on your model? Nodes:
+    	%2494 : Float(1, 4, 64, 64, strides=[16384, 4096, 64, 1], requires_grad=0, device=cpu) = aten::randn(%2488, %2489, %2490, %2491, %2492, %2493) # /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/utils/torch_utils.py:81:0
     This may cause errors in trace checking. To disable trace checking, pass check_trace=False to torch.jit.trace()
       _check_trace(
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:1116: TracerWarning: Output nr 1. of the traced function does not match the corresponding output of the Python function. Detailed error:
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:1116: TracerWarning: Output nr 1. of the traced function does not match the corresponding output of the Python function. Detailed error:
     Tensor-likes are not close!
     
-    Mismatched elements: 10321 / 16384 (63.0%)
-    Greatest absolute difference: 0.0012267827987670898 at index (0, 2, 0, 5) (up to 1e-05 allowed)
-    Greatest relative difference: 0.0019544895167805058 at index (0, 3, 63, 59) (up to 1e-05 allowed)
+    Mismatched elements: 10283 / 16384 (62.8%)
+    Greatest absolute difference: 0.0013719797134399414 at index (0, 1, 0, 63) (up to 1e-05 allowed)
+    Greatest relative difference: 0.003101014950651226 at index (0, 3, 63, 59) (up to 1e-05 allowed)
       _check_trace(
 
 
@@ -485,11 +492,11 @@ hidden states.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_attn_mask_utils.py:86: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_attn_mask_utils.py:86: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if input_shape[-1] > 1 or self.sliding_window is not None:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_attn_mask_utils.py:162: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_attn_mask_utils.py:162: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if past_key_values_length > 0:
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:284: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:287: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if causal_attention_mask.size() != (bsz, 1, tgt_len, src_len):
 
 
diff --git a/docs/notebooks/stable-diffusion-ip-adapter-with-output_files/stable-diffusion-ip-adapter-with-output_22_1.png b/docs/notebooks/stable-diffusion-ip-adapter-with-output_files/stable-diffusion-ip-adapter-with-output_22_1.png
index 8890c28c3d9af6..5a1276e6dfc6ca 100644
--- a/docs/notebooks/stable-diffusion-ip-adapter-with-output_files/stable-diffusion-ip-adapter-with-output_22_1.png
+++ b/docs/notebooks/stable-diffusion-ip-adapter-with-output_files/stable-diffusion-ip-adapter-with-output_22_1.png
@@ -1,3 +1,3 @@
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-oid sha256:9c6d8ec77997ecb3a8d1aa21e6b4f7043669fa2f7571b2d25deb4dae5626cf7e
-size 969951
+oid sha256:bd189f4242b2e5d635666be52ca91709e7a72e31c97add060176fc83422bc27a
+size 970535
diff --git a/docs/notebooks/stable-diffusion-ip-adapter-with-output_files/stable-diffusion-ip-adapter-with-output_25_0.png b/docs/notebooks/stable-diffusion-ip-adapter-with-output_files/stable-diffusion-ip-adapter-with-output_25_0.png
index 0ebfb9ced21f46..5610a717188a8f 100644
--- a/docs/notebooks/stable-diffusion-ip-adapter-with-output_files/stable-diffusion-ip-adapter-with-output_25_0.png
+++ b/docs/notebooks/stable-diffusion-ip-adapter-with-output_files/stable-diffusion-ip-adapter-with-output_25_0.png
@@ -1,3 +1,3 @@
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-oid sha256:feba9eed205edfef64a8ea873eba2d507aba30fab3ea85ace1ad8d175e9dda0a
-size 931013
+oid sha256:64c64f098d3dc7d89d96fbadaf3fd19ecaad8d8e2264ea3dfb517ab3293eef88
+size 953006
diff --git a/docs/notebooks/stable-diffusion-ip-adapter-with-output_files/stable-diffusion-ip-adapter-with-output_28_0.png b/docs/notebooks/stable-diffusion-ip-adapter-with-output_files/stable-diffusion-ip-adapter-with-output_28_0.png
index 74c6687096ba16..b394557a36e0ab 100644
--- a/docs/notebooks/stable-diffusion-ip-adapter-with-output_files/stable-diffusion-ip-adapter-with-output_28_0.png
+++ b/docs/notebooks/stable-diffusion-ip-adapter-with-output_files/stable-diffusion-ip-adapter-with-output_28_0.png
@@ -1,3 +1,3 @@
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-oid sha256:055ff3c1076e856175e7d41d1a72cb2dc856306ec6ffd58c89bf65164c7d4df7
-size 589334
+oid sha256:64ad50ce4a249bc24ed712f71a4fd6837b2575a3b457dbf2e14513fc1ddc4d34
+size 601715
diff --git a/docs/notebooks/stable-diffusion-text-to-image-with-output.rst b/docs/notebooks/stable-diffusion-text-to-image-with-output.rst
index 4864a818850d0a..a1b7a82715aaf3 100644
--- a/docs/notebooks/stable-diffusion-text-to-image-with-output.rst
+++ b/docs/notebooks/stable-diffusion-text-to-image-with-output.rst
@@ -32,1026 +32,163 @@ text-guided image-to-image generation using Stable Diffusion.
 This notebook demonstrates how to convert and run stable diffusion model
 using OpenVINO.
 
-Notebook contains the following steps:
+**Table of contents:**
 
-1. Create pipeline with PyTorch models.
-2. Convert models to OpenVINO IR format, using model conversion API.
-3. Run Stable Diffusion pipeline with OpenVINO.
-
-Table of contents:
-^^^^^^^^^^^^^^^^^^
 
 -  `Prerequisites <#prerequisites>`__
--  `login to huggingfacehub to get access to pretrained
-   model <#login-to-huggingfacehub-to-get-access-to-pretrained-model>`__
--  `Create PyTorch Models pipeline <#create-pytorch-models-pipeline>`__
--  `Convert models to OpenVINO Intermediate representation (IR)
-   format <#convert-models-to-openvino-intermediate-representation-ir-format>`__
+-  `Prepare Inference Pipelines <#prepare-inference-pipelines>`__
+-  `Text-to-image pipeline <#text-to-image-pipeline>`__
 
-   -  `Text Encoder <#text-encoder>`__
-   -  `U-net <#u-net>`__
-   -  `VAE <#vae>`__
+   -  `Load Stable Diffusion model and create text-to-image
+      pipeline <#load-stable-diffusion-model-and-create-text-to-image-pipeline>`__
+   -  `Text-to-Image generation <#text-to-image-generation>`__
+   -  `Interactive text-to-image
+      demo <#interactive-text-to-image-demo>`__
 
--  `Prepare Inference Pipeline <#prepare-inference-pipeline>`__
--  `Configure Inference Pipeline <#configure-inference-pipeline>`__
+-  `Image-to-Image pipeline <#image-to-image-pipeline>`__
 
-   -  `Text-to-Image generation <#text-to-image-generation>`__
+   -  `Create image-to-Image
+      pipeline <#create-image-to-image-pipeline>`__
    -  `Image-to-Image generation <#image-to-image-generation>`__
-
--  `Interactive demo <#interactive-demo>`__
+   -  `Interactive image-to-image
+      demo <#interactive-image-to-image-demo>`__
 
 Prerequisites
 -------------
 
 
 
-**The following is needed only if you want to use the original model. If
-not, you do not have to do anything. Just run the notebook.**
-
-   **Note**: The original model (for example, ``stable-diffusion-v1-4``)
-   requires you to accept the model license before downloading or using
-   its weights. Visit the `stable-diffusion-v1-4
-   card <https://huggingface.co/CompVis/stable-diffusion-v1-4>`__ to
-   read and accept the license before you proceed. To use this diffusion
-   model, you must be a registered user in Hugging Face Hub. You will
-   need to use an access token for the code below to run. For more
-   information on access tokens, refer to `this section of the
-   documentation <https://huggingface.co/docs/hub/security-tokens>`__.
-   You can login on Hugging Face Hub in notebook environment, using
-   following code:
-
-.. code:: python
-
-
-   # login to huggingfacehub to get access to pretrained model
-   from huggingface_hub import notebook_login, whoami
-
-   try:
-       whoami()
-       print('Authorization token already provided')
-   except OSError:
-       notebook_login()
-
-This tutorial uses a Stable Diffusion model, fine-tuned using images
-from Midjourney v4 (another popular solution for text to image
-generation). You can find more details about this model on the `model
-card <https://huggingface.co/prompthero/openjourney>`__. The same steps
-for conversion and running the pipeline are applicable to other
-solutions based on Stable Diffusion.
-
 .. code:: ipython3
 
-    %pip install -q "openvino>=2023.1.0"
+    %pip install -q "openvino>=2023.1.0" "git+https://github.com/huggingface/optimum-intel.git"
     %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu "diffusers>=0.9.0" "torch>=2.1"
     %pip install -q "huggingface-hub>=0.9.1"
     %pip install -q "gradio>=4.19"
     %pip install -q transformers Pillow opencv-python tqdm
 
-Create PyTorch Models pipeline
-------------------------------
-
-
-
-``StableDiffusionPipeline`` is an end-to-end inference pipeline that you
-can use to generate images from text with just a few lines of code.
-
-First, load the pre-trained weights of all components of the model.
-
-.. code:: ipython3
-
-    from diffusers import StableDiffusionPipeline
-    import gc
-    
-    pipe = StableDiffusionPipeline.from_pretrained("prompthero/openjourney").to("cpu")
-    text_encoder = pipe.text_encoder
-    text_encoder.eval()
-    unet = pipe.unet
-    unet.eval()
-    vae = pipe.vae
-    vae.eval()
-    
-    del pipe
-    gc.collect()
-
-
-.. parsed-literal::
-
-    2023-08-29 12:35:30.891928: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2023-08-29 12:35:30.933110: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
-    To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2023-08-29 12:35:31.755679: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
-
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-
-    `text_config_dict` is provided which will be used to initialize `CLIPTextConfig`. The value `text_config["id2label"]` will be overriden.
-    `text_config_dict` is provided which will be used to initialize `CLIPTextConfig`. The value `text_config["bos_token_id"]` will be overriden.
-    `text_config_dict` is provided which will be used to initialize `CLIPTextConfig`. The value `text_config["eos_token_id"]` will be overriden.
-
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-    33
-
-
-
-Convert models to OpenVINO Intermediate representation (IR) format
-------------------------------------------------------------------
-
-
-
-Staring from 2023.0 release, OpenVINO supports direct conversion PyTorch
-models to OpenVINO IR format. You need to provide a model object and
-input data for model tracing. Optionally, you can declare expected input
-format for model - shapes, data types. To take advantage of advanced
-OpenVINO optimization tools and features, model should be converted to
-IR format using ``ov.convert_model`` and saved on disk (by default in
-compressed to FP16 weights representation) for next deployment using
-``ov.save_model``.
-
-The model consists of three important parts:
-
--  Text Encoder for creation condition to generate image from text
-   prompt.
--  Unet for step by step denoising latent image representation.
--  Autoencoder (VAE) for encoding input image to latent space (if
-   required) and decoding latent space to image back after generation.
-
-Let us convert each part.
-
-Text Encoder
-~~~~~~~~~~~~
-
-
-
-The text-encoder is responsible for transforming the input prompt, for
-example, “a photo of an astronaut riding a horse” into an embedding
-space that can be understood by the U-Net. It is usually a simple
-transformer-based encoder that maps a sequence of input tokens to a
-sequence of latent text embeddings.
-
-Input of the text encoder is the tensor ``input_ids`` which contains
-indexes of tokens from text processed by tokenizer and padded to maximum
-length accepted by model. Model outputs are two tensors:
-``last_hidden_state`` - hidden state from the last MultiHeadAttention
-layer in the model and ``pooler_out`` - Pooled output for whole model
-hidden states.
-
-.. code:: ipython3
-
-    from pathlib import Path
-    import torch
-    import openvino as ov
-    
-    TEXT_ENCODER_OV_PATH = Path("text_encoder.xml")
-    
-    
-    def cleanup_torchscript_cache():
-        """
-        Helper for removing cached model representation
-        """
-        torch._C._jit_clear_class_registry()
-        torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore()
-        torch.jit._state._clear_class_state()
-    
-    
-    def convert_encoder(text_encoder: torch.nn.Module, ir_path: Path):
-        """
-        Convert Text Encoder mode.
-        Function accepts text encoder model, and prepares example inputs for conversion,
-        Parameters:
-            text_encoder (torch.nn.Module): text_encoder model from Stable Diffusion pipeline
-            ir_path (Path): File for storing model
-        Returns:
-            None
-        """
-        input_ids = torch.ones((1, 77), dtype=torch.long)
-        # switch model to inference mode
-        text_encoder.eval()
-    
-        # disable gradients calculation for reducing memory consumption
-        with torch.no_grad():
-            # Export model to IR format
-            ov_model = ov.convert_model(
-                text_encoder,
-                example_input=input_ids,
-                input=[
-                    (1, 77),
-                ],
-            )
-        ov.save_model(ov_model, ir_path)
-        del ov_model
-        cleanup_torchscript_cache()
-        print(f"Text Encoder successfully converted to IR and saved to {ir_path}")
-    
-    
-    if not TEXT_ENCODER_OV_PATH.exists():
-        convert_encoder(text_encoder, TEXT_ENCODER_OV_PATH)
-    else:
-        print(f"Text encoder will be loaded from {TEXT_ENCODER_OV_PATH}")
-    
-    del text_encoder
-    gc.collect()
-
-
-.. parsed-literal::
-
-    WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.base has been moved to tensorflow.python.trackable.base. The old module will be deleted in version 2.11.
-
-
-.. parsed-literal::
-
-    [ WARNING ]  Please fix your imports. Module %s has been moved to %s. The old module will be deleted in version %s.
-    /home/ea/work/ov_venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:286: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
-    /home/ea/work/ov_venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:294: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      if causal_attention_mask.size() != (bsz, 1, tgt_len, src_len):
-    /home/ea/work/ov_venv/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py:326: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
-    /home/ea/work/ov_venv/lib/python3.8/site-packages/torch/jit/annotations.py:310: UserWarning: TorchScript will treat type annotations of Tensor dtype-specific subtypes as if they are normal Tensors. dtype constraints are not enforced in compilation either.
-      warnings.warn("TorchScript will treat type annotations of Tensor "
-
-
-.. parsed-literal::
-
-    Text Encoder successfully converted to IR and saved to text_encoder.xml
-
-
-
-
-.. parsed-literal::
-
-    4202
-
-
-
-U-net
-~~~~~
-
-
-
-Unet model has three inputs:
-
--  ``sample`` - latent image sample from previous step. Generation
-   process has not been started yet, so you will use random noise.
--  ``timestep`` - current scheduler step.
--  ``encoder_hidden_state`` - hidden state of text encoder.
-
-Model predicts the ``sample`` state for the next step.
-
-.. code:: ipython3
-
-    import numpy as np
-    
-    UNET_OV_PATH = Path("unet.xml")
-    
-    dtype_mapping = {torch.float32: ov.Type.f32, torch.float64: ov.Type.f64}
-    
-    
-    def convert_unet(unet: torch.nn.Module, ir_path: Path):
-        """
-        Convert U-net model to IR format.
-        Function accepts unet model, prepares example inputs for conversion,
-        Parameters:
-            unet (StableDiffusionPipeline): unet from Stable Diffusion pipeline
-            ir_path (Path): File for storing model
-        Returns:
-            None
-        """
-        # prepare inputs
-        encoder_hidden_state = torch.ones((2, 77, 768))
-        latents_shape = (2, 4, 512 // 8, 512 // 8)
-        latents = torch.randn(latents_shape)
-        t = torch.from_numpy(np.array(1, dtype=float))
-        dummy_inputs = (latents, t, encoder_hidden_state)
-        input_info = []
-        for input_tensor in dummy_inputs:
-            shape = ov.PartialShape(tuple(input_tensor.shape))
-            element_type = dtype_mapping[input_tensor.dtype]
-            input_info.append((shape, element_type))
-    
-        unet.eval()
-        with torch.no_grad():
-            ov_model = ov.convert_model(unet, example_input=dummy_inputs, input=input_info)
-        ov.save_model(ov_model, ir_path)
-        del ov_model
-        cleanup_torchscript_cache()
-        print(f"Unet successfully converted to IR and saved to {ir_path}")
-    
-    
-    if not UNET_OV_PATH.exists():
-        convert_unet(unet, UNET_OV_PATH)
-        gc.collect()
-    else:
-        print(f"Unet will be loaded from {UNET_OV_PATH}")
-    del unet
-    gc.collect()
-
-
-.. parsed-literal::
-
-    /home/ea/work/diffusers/src/diffusers/models/unet_2d_condition.py:752: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):
-    /home/ea/work/diffusers/src/diffusers/models/resnet.py:214: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      assert hidden_states.shape[1] == self.channels
-    /home/ea/work/diffusers/src/diffusers/models/resnet.py:219: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      assert hidden_states.shape[1] == self.channels
-    /home/ea/work/diffusers/src/diffusers/models/resnet.py:138: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      assert hidden_states.shape[1] == self.channels
-    /home/ea/work/diffusers/src/diffusers/models/resnet.py:151: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
-      if hidden_states.shape[0] >= 64:
-
-
-.. parsed-literal::
-
-    Unet successfully converted to IR and saved to unet.xml
-
-
-
-
-.. parsed-literal::
-
-    0
-
-
-
-VAE
-~~~
-
-
-
-The VAE model has two parts, an encoder and a decoder. The encoder is
-used to convert the image into a low dimensional latent representation,
-which will serve as the input to the U-Net model. The decoder,
-conversely, transforms the latent representation back into an image.
-
-During latent diffusion training, the encoder is used to get the latent
-representations (latents) of the images for the forward diffusion
-process, which applies more and more noise at each step. During
-inference, the denoised latents generated by the reverse diffusion
-process are converted back into images using the VAE decoder. When you
-run inference for text-to-image, there is no initial image as a starting
-point. You can skip this step and directly generate initial random
-noise.
-
-As the encoder and the decoder are used independently in different parts
-of the pipeline, it will be better to convert them to separate models.
-
-.. code:: ipython3
-
-    VAE_ENCODER_OV_PATH = Path("vae_encoder.xml")
-    
-    
-    def convert_vae_encoder(vae: torch.nn.Module, ir_path: Path):
-        """
-        Convert VAE model for encoding to IR format.
-        Function accepts vae model, creates wrapper class for export only necessary for inference part,
-        prepares example inputs for conversion,
-        Parameters:
-            vae (torch.nn.Module): VAE model from StableDiffusio pipeline
-            ir_path (Path): File for storing model
-        Returns:
-            None
-        """
-    
-        class VAEEncoderWrapper(torch.nn.Module):
-            def __init__(self, vae):
-                super().__init__()
-                self.vae = vae
-    
-            def forward(self, image):
-                return self.vae.encode(x=image)["latent_dist"].sample()
-    
-        vae_encoder = VAEEncoderWrapper(vae)
-        vae_encoder.eval()
-        image = torch.zeros((1, 3, 512, 512))
-        with torch.no_grad():
-            ov_model = ov.convert_model(vae_encoder, example_input=image, input=[((1, 3, 512, 512),)])
-        ov.save_model(ov_model, ir_path)
-        del ov_model
-        cleanup_torchscript_cache()
-        print(f"VAE encoder successfully converted to IR and saved to {ir_path}")
-    
-    
-    if not VAE_ENCODER_OV_PATH.exists():
-        convert_vae_encoder(vae, VAE_ENCODER_OV_PATH)
-    else:
-        print(f"VAE encoder will be loaded from {VAE_ENCODER_OV_PATH}")
-    
-    VAE_DECODER_OV_PATH = Path("vae_decoder.xml")
-    
-    
-    def convert_vae_decoder(vae: torch.nn.Module, ir_path: Path):
-        """
-        Convert VAE model for decoding to IR format.
-        Function accepts vae model, creates wrapper class for export only necessary for inference part,
-        prepares example inputs for conversion,
-        Parameters:
-            vae (torch.nn.Module): VAE model frm StableDiffusion pipeline
-            ir_path (Path): File for storing model
-        Returns:
-            None
-        """
-    
-        class VAEDecoderWrapper(torch.nn.Module):
-            def __init__(self, vae):
-                super().__init__()
-                self.vae = vae
-    
-            def forward(self, latents):
-                return self.vae.decode(latents)
-    
-        vae_decoder = VAEDecoderWrapper(vae)
-        latents = torch.zeros((1, 4, 64, 64))
-    
-        vae_decoder.eval()
-        with torch.no_grad():
-            ov_model = ov.convert_model(vae_decoder, example_input=latents, input=[((1, 4, 64, 64),)])
-        ov.save_model(ov_model, ir_path)
-        del ov_model
-        cleanup_torchscript_cache()
-        print(f"VAE decoder successfully converted to IR and saved to {ir_path}")
-    
-    
-    if not VAE_DECODER_OV_PATH.exists():
-        convert_vae_decoder(vae, VAE_DECODER_OV_PATH)
-    else:
-        print(f"VAE decoder will be loaded from {VAE_DECODER_OV_PATH}")
-    
-    del vae
-    gc.collect()
-
-
-.. parsed-literal::
-
-    /home/ea/work/ov_venv/lib/python3.8/site-packages/torch/jit/_trace.py:1084: TracerWarning: Trace had nondeterministic nodes. Did you forget call .eval() on your model? Nodes:
-    	%2493 : Float(1, 4, 64, 64, strides=[16384, 4096, 64, 1], requires_grad=0, device=cpu) = aten::randn(%2487, %2488, %2489, %2490, %2491, %2492) # /home/ea/work/diffusers/src/diffusers/utils/torch_utils.py:79:0
-    This may cause errors in trace checking. To disable trace checking, pass check_trace=False to torch.jit.trace()
-      _check_trace(
-    /home/ea/work/ov_venv/lib/python3.8/site-packages/torch/jit/_trace.py:1084: TracerWarning: Output nr 1. of the traced function does not match the corresponding output of the Python function. Detailed error:
-    Tensor-likes are not close!
-    
-    Mismatched elements: 10371 / 16384 (63.3%)
-    Greatest absolute difference: 0.0014181137084960938 at index (0, 2, 63, 63) (up to 1e-05 allowed)
-    Greatest relative difference: 0.006298586412390911 at index (0, 3, 63, 59) (up to 1e-05 allowed)
-      _check_trace(
-
-
-.. parsed-literal::
-
-    VAE encoder successfully converted to IR and saved to vae_encoder.xml
-    VAE decoder successfully converted to IR and saved to vae_decoder.xml
-
-
-
-
-.. parsed-literal::
-
-    7650
-
-
-
-Prepare Inference Pipeline
---------------------------
-
-
-
-Putting it all together, let us now take a closer look at how the model
-works in inference by illustrating the logical flow.
-
-.. figure:: https://user-images.githubusercontent.com/29454499/260981188-c112dd0a-5752-4515-adca-8b09bea5d14a.png
-   :alt: sd-pipeline
-
-   sd-pipeline
-
-As you can see from the diagram, the only difference between
-Text-to-Image and text-guided Image-to-Image generation in approach is
-how initial latent state is generated. In case of Image-to-Image
-generation, you additionally have an image encoded by VAE encoder mixed
-with the noise produced by using latent seed, while in Text-to-Image you
-use only noise as initial latent state. The stable diffusion model takes
-both a latent image representation of size :math:`64 \times 64` and a
-text prompt is transformed to text embeddings of size
-:math:`77 \times 768` via CLIP’s text encoder as an input.
-
-Next, the U-Net iteratively *denoises* the random latent image
-representations while being conditioned on the text embeddings. The
-output of the U-Net, being the noise residual, is used to compute a
-denoised latent image representation via a scheduler algorithm. Many
-different scheduler algorithms can be used for this computation, each
-having its pros and cons. For Stable Diffusion, it is recommended to use
-one of:
-
--  `PNDM
-   scheduler <https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_pndm.py>`__
--  `DDIM
-   scheduler <https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddim.py>`__
--  `K-LMS
-   scheduler <https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_lms_discrete.py>`__\ (you
-   will use it in your pipeline)
-
-Theory on how the scheduler algorithm function works is out of scope for
-this notebook. Nonetheless, in short, you should remember that you
-compute the predicted denoised image representation from the previous
-noise representation and the predicted noise residual. For more
-information, refer to the recommended `Elucidating the Design Space of
-Diffusion-Based Generative Models <https://arxiv.org/abs/2206.00364>`__
-
-The *denoising* process is repeated given number of times (by default
-50) to step-by-step retrieve better latent image representations. When
-complete, the latent image representation is decoded by the decoder part
-of the variational auto encoder.
-
-.. code:: ipython3
-
-    import inspect
-    from typing import List, Optional, Union, Dict
-    
-    import PIL
-    import cv2
-    
-    from transformers import CLIPTokenizer
-    from diffusers.pipelines.pipeline_utils import DiffusionPipeline
-    from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
-    from openvino.runtime import Model
-    
-    
-    def scale_fit_to_window(dst_width: int, dst_height: int, image_width: int, image_height: int):
-        """
-        Preprocessing helper function for calculating image size for resize with peserving original aspect ratio
-        and fitting image to specific window size
-    
-        Parameters:
-          dst_width (int): destination window width
-          dst_height (int): destination window height
-          image_width (int): source image width
-          image_height (int): source image height
-        Returns:
-          result_width (int): calculated width for resize
-          result_height (int): calculated height for resize
-        """
-        im_scale = min(dst_height / image_height, dst_width / image_width)
-        return int(im_scale * image_width), int(im_scale * image_height)
-    
-    
-    def preprocess(image: PIL.Image.Image):
-        """
-        Image preprocessing function. Takes image in PIL.Image format, resizes it to keep aspect ration and fits to model input window 512x512,
-        then converts it to np.ndarray and adds padding with zeros on right or bottom side of image (depends from aspect ratio), after that
-        converts data to float32 data type and change range of values from [0, 255] to [-1, 1], finally, converts data layout from planar NHWC to NCHW.
-        The function returns preprocessed input tensor and padding size, which can be used in postprocessing.
-    
-        Parameters:
-          image (PIL.Image.Image): input image
-        Returns:
-           image (np.ndarray): preprocessed image tensor
-           meta (Dict): dictionary with preprocessing metadata info
-        """
-        src_width, src_height = image.size
-        dst_width, dst_height = scale_fit_to_window(512, 512, src_width, src_height)
-        image = np.array(image.resize((dst_width, dst_height), resample=PIL.Image.Resampling.LANCZOS))[None, :]
-        pad_width = 512 - dst_width
-        pad_height = 512 - dst_height
-        pad = ((0, 0), (0, pad_height), (0, pad_width), (0, 0))
-        image = np.pad(image, pad, mode="constant")
-        image = image.astype(np.float32) / 255.0
-        image = 2.0 * image - 1.0
-        image = image.transpose(0, 3, 1, 2)
-        return image, {"padding": pad, "src_width": src_width, "src_height": src_height}
-    
-    
-    class OVStableDiffusionPipeline(DiffusionPipeline):
-        def __init__(
-            self,
-            vae_decoder: Model,
-            text_encoder: Model,
-            tokenizer: CLIPTokenizer,
-            unet: Model,
-            scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
-            vae_encoder: Model = None,
-        ):
-            """
-            Pipeline for text-to-image generation using Stable Diffusion.
-            Parameters:
-                vae (Model):
-                    Variational Auto-Encoder (VAE) Model to decode images to and from latent representations.
-                text_encoder (Model):
-                    Frozen text-encoder. Stable Diffusion uses the text portion of
-                    [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
-                    the clip-vit-large-patch14(https://huggingface.co/openai/clip-vit-large-patch14) variant.
-                tokenizer (CLIPTokenizer):
-                    Tokenizer of class CLIPTokenizer(https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
-                unet (Model): Conditional U-Net architecture to denoise the encoded image latents.
-                scheduler (SchedulerMixin):
-                    A scheduler to be used in combination with unet to denoise the encoded image latents. Can be one of
-                    DDIMScheduler, LMSDiscreteScheduler, or PNDMScheduler.
-            """
-            super().__init__()
-            self.scheduler = scheduler
-            self.vae_decoder = vae_decoder
-            self.vae_encoder = vae_encoder
-            self.text_encoder = text_encoder
-            self.unet = unet
-            self._text_encoder_output = text_encoder.output(0)
-            self._unet_output = unet.output(0)
-            self._vae_d_output = vae_decoder.output(0)
-            self._vae_e_output = vae_encoder.output(0) if vae_encoder is not None else None
-            self.height = 512
-            self.width = 512
-            self.tokenizer = tokenizer
-    
-        def __call__(
-            self,
-            prompt: Union[str, List[str]],
-            image: PIL.Image.Image = None,
-            num_inference_steps: Optional[int] = 50,
-            negative_prompt: Union[str, List[str]] = None,
-            guidance_scale: Optional[float] = 7.5,
-            eta: Optional[float] = 0.0,
-            output_type: Optional[str] = "pil",
-            seed: Optional[int] = None,
-            strength: float = 1.0,
-            gif: Optional[bool] = False,
-            **kwargs,
-        ):
-            """
-            Function invoked when calling the pipeline for generation.
-            Parameters:
-                prompt (str or List[str]):
-                    The prompt or prompts to guide the image generation.
-                image (PIL.Image.Image, *optional*, None):
-                     Intinal image for generation.
-                num_inference_steps (int, *optional*, defaults to 50):
-                    The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                    expense of slower inference.
-                negative_prompt (str or List[str]):
-                    The negative prompt or prompts to guide the image generation.
-                guidance_scale (float, *optional*, defaults to 7.5):
-                    Guidance scale as defined in Classifier-Free Diffusion Guidance(https://arxiv.org/abs/2207.12598).
-                    guidance_scale is defined as `w` of equation 2.
-                    Higher guidance scale encourages to generate images that are closely linked to the text prompt,
-                    usually at the expense of lower image quality.
-                eta (float, *optional*, defaults to 0.0):
-                    Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
-                    [DDIMScheduler], will be ignored for others.
-                output_type (`str`, *optional*, defaults to "pil"):
-                    The output format of the generate image. Choose between
-                    [PIL](https://pillow.readthedocs.io/en/stable/): PIL.Image.Image or np.array.
-                seed (int, *optional*, None):
-                    Seed for random generator state initialization.
-                gif (bool, *optional*, False):
-                    Flag for storing all steps results or not.
-            Returns:
-                Dictionary with keys:
-                    sample - the last generated image PIL.Image.Image or np.array
-                    iterations - *optional* (if gif=True) images for all diffusion steps, List of PIL.Image.Image or np.array.
-            """
-            if seed is not None:
-                np.random.seed(seed)
-    
-            img_buffer = []
-            do_classifier_free_guidance = guidance_scale > 1.0
-            # get prompt text embeddings
-            text_embeddings = self._encode_prompt(
-                prompt,
-                do_classifier_free_guidance=do_classifier_free_guidance,
-                negative_prompt=negative_prompt,
-            )
-    
-            # set timesteps
-            accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
-            extra_set_kwargs = {}
-            if accepts_offset:
-                extra_set_kwargs["offset"] = 1
-    
-            self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
-            timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength)
-            latent_timestep = timesteps[:1]
-    
-            # get the initial random noise unless the user supplied it
-            latents, meta = self.prepare_latents(image, latent_timestep)
-    
-            # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
-            # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
-            # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
-            # and should be between [0, 1]
-            accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
-            extra_step_kwargs = {}
-            if accepts_eta:
-                extra_step_kwargs["eta"] = eta
-    
-            for i, t in enumerate(self.progress_bar(timesteps)):
-                # expand the latents if you are doing classifier free guidance
-                latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
-                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-    
-                # predict the noise residual
-                noise_pred = self.unet([latent_model_input, t, text_embeddings])[self._unet_output]
-                # perform guidance
-                if do_classifier_free_guidance:
-                    noise_pred_uncond, noise_pred_text = noise_pred[0], noise_pred[1]
-                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-    
-                # compute the previous noisy sample x_t -> x_t-1
-                latents = self.scheduler.step(
-                    torch.from_numpy(noise_pred),
-                    t,
-                    torch.from_numpy(latents),
-                    **extra_step_kwargs,
-                )["prev_sample"].numpy()
-                if gif:
-                    image = self.vae_decoder(latents * (1 / 0.18215))[self._vae_d_output]
-                    image = self.postprocess_image(image, meta, output_type)
-                    img_buffer.extend(image)
-    
-            # scale and decode the image latents with vae
-            image = self.vae_decoder(latents * (1 / 0.18215))[self._vae_d_output]
-    
-            image = self.postprocess_image(image, meta, output_type)
-            return {"sample": image, "iterations": img_buffer}
-    
-        def _encode_prompt(
-            self,
-            prompt: Union[str, List[str]],
-            num_images_per_prompt: int = 1,
-            do_classifier_free_guidance: bool = True,
-            negative_prompt: Union[str, List[str]] = None,
-        ):
-            """
-            Encodes the prompt into text encoder hidden states.
-    
-            Parameters:
-                prompt (str or list(str)): prompt to be encoded
-                num_images_per_prompt (int): number of images that should be generated per prompt
-                do_classifier_free_guidance (bool): whether to use classifier free guidance or not
-                negative_prompt (str or list(str)): negative prompt to be encoded
-            Returns:
-                text_embeddings (np.ndarray): text encoder hidden states
-            """
-            batch_size = len(prompt) if isinstance(prompt, list) else 1
-    
-            # tokenize input prompts
-            text_inputs = self.tokenizer(
-                prompt,
-                padding="max_length",
-                max_length=self.tokenizer.model_max_length,
-                truncation=True,
-                return_tensors="np",
-            )
-            text_input_ids = text_inputs.input_ids
-    
-            text_embeddings = self.text_encoder(text_input_ids)[self._text_encoder_output]
-    
-            # duplicate text embeddings for each generation per prompt
-            if num_images_per_prompt != 1:
-                bs_embed, seq_len, _ = text_embeddings.shape
-                text_embeddings = np.tile(text_embeddings, (1, num_images_per_prompt, 1))
-                text_embeddings = np.reshape(text_embeddings, (bs_embed * num_images_per_prompt, seq_len, -1))
-    
-            # get unconditional embeddings for classifier free guidance
-            if do_classifier_free_guidance:
-                uncond_tokens: List[str]
-                max_length = text_input_ids.shape[-1]
-                if negative_prompt is None:
-                    uncond_tokens = [""] * batch_size
-                elif isinstance(negative_prompt, str):
-                    uncond_tokens = [negative_prompt]
-                else:
-                    uncond_tokens = negative_prompt
-                uncond_input = self.tokenizer(
-                    uncond_tokens,
-                    padding="max_length",
-                    max_length=max_length,
-                    truncation=True,
-                    return_tensors="np",
-                )
-    
-                uncond_embeddings = self.text_encoder(uncond_input.input_ids)[self._text_encoder_output]
-    
-                # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
-                seq_len = uncond_embeddings.shape[1]
-                uncond_embeddings = np.tile(uncond_embeddings, (1, num_images_per_prompt, 1))
-                uncond_embeddings = np.reshape(uncond_embeddings, (batch_size * num_images_per_prompt, seq_len, -1))
-    
-                # For classifier free guidance, we need to do two forward passes.
-                # Here we concatenate the unconditional and text embeddings into a single batch
-                # to avoid doing two forward passes
-                text_embeddings = np.concatenate([uncond_embeddings, text_embeddings])
-    
-            return text_embeddings
-    
-        def prepare_latents(self, image: PIL.Image.Image = None, latent_timestep: torch.Tensor = None):
-            """
-            Function for getting initial latents for starting generation
-    
-            Parameters:
-                image (PIL.Image.Image, *optional*, None):
-                    Input image for generation, if not provided randon noise will be used as starting point
-                latent_timestep (torch.Tensor, *optional*, None):
-                    Predicted by scheduler initial step for image generation, required for latent image mixing with nosie
-            Returns:
-                latents (np.ndarray):
-                    Image encoded in latent space
-            """
-            latents_shape = (1, 4, self.height // 8, self.width // 8)
-            noise = np.random.randn(*latents_shape).astype(np.float32)
-            if image is None:
-                # if you use LMSDiscreteScheduler, let's make sure latents are multiplied by sigmas
-                if isinstance(self.scheduler, LMSDiscreteScheduler):
-                    noise = noise * self.scheduler.sigmas[0].numpy()
-                    return noise, {}
-            input_image, meta = preprocess(image)
-            latents = self.vae_encoder(input_image)[self._vae_e_output] * 0.18215
-            latents = self.scheduler.add_noise(torch.from_numpy(latents), torch.from_numpy(noise), latent_timestep).numpy()
-            return latents, meta
-    
-        def postprocess_image(self, image: np.ndarray, meta: Dict, output_type: str = "pil"):
-            """
-            Postprocessing for decoded image. Takes generated image decoded by VAE decoder, unpad it to initila image size (if required),
-            normalize and convert to [0, 255] pixels range. Optionally, convertes it from np.ndarray to PIL.Image format
-    
-            Parameters:
-                image (np.ndarray):
-                    Generated image
-                meta (Dict):
-                    Metadata obtained on latents preparing step, can be empty
-                output_type (str, *optional*, pil):
-                    Output format for result, can be pil or numpy
-            Returns:
-                image (List of np.ndarray or PIL.Image.Image):
-                    Postprocessed images
-            """
-            if "padding" in meta:
-                pad = meta["padding"]
-                (_, end_h), (_, end_w) = pad[1:3]
-                h, w = image.shape[2:]
-                unpad_h = h - end_h
-                unpad_w = w - end_w
-                image = image[:, :, :unpad_h, :unpad_w]
-            image = np.clip(image / 2 + 0.5, 0, 1)
-            image = np.transpose(image, (0, 2, 3, 1))
-            # 9. Convert to PIL
-            if output_type == "pil":
-                image = self.numpy_to_pil(image)
-                if "src_height" in meta:
-                    orig_height, orig_width = meta["src_height"], meta["src_width"]
-                    image = [img.resize((orig_width, orig_height), PIL.Image.Resampling.LANCZOS) for img in image]
-            else:
-                if "src_height" in meta:
-                    orig_height, orig_width = meta["src_height"], meta["src_width"]
-                    image = [cv2.resize(img, (orig_width, orig_width)) for img in image]
-            return image
-    
-        def get_timesteps(self, num_inference_steps: int, strength: float):
-            """
-            Helper function for getting scheduler timesteps for generation
-            In case of image-to-image generation, it updates number of steps according to strength
-    
-            Parameters:
-               num_inference_steps (int):
-                  number of inference steps for generation
-               strength (float):
-                   value between 0.0 and 1.0, that controls the amount of noise that is added to the input image.
-                   Values that approach 1.0 enable lots of variations but will also produce images that are not semantically consistent with the input.
-            """
-            # get the original timestep using init_timestep
-            init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
-    
-            t_start = max(num_inference_steps - init_timestep, 0)
-            timesteps = self.scheduler.timesteps[t_start:]
-    
-            return timesteps, num_inference_steps - t_start
-
-Configure Inference Pipeline
-----------------------------
 
+.. parsed-literal::
 
+    Note: you may need to restart the kernel to use updated packages.
+    Note: you may need to restart the kernel to use updated packages.
+    Note: you may need to restart the kernel to use updated packages.
+    Note: you may need to restart the kernel to use updated packages.
+    Note: you may need to restart the kernel to use updated packages.
 
-First, you should create instances of OpenVINO Model.
 
-.. code:: ipython3
+Prepare Inference Pipelines
+---------------------------
 
-    core = ov.Core()
+
+
+Let us now take a closer look at how the model works in inference by
+illustrating the logical flow.
+
+.. figure:: https://user-images.githubusercontent.com/29454499/260981188-c112dd0a-5752-4515-adca-8b09bea5d14a.png
+   :alt: sd-pipeline
+
+   sd-pipeline
+
+As you can see from the diagram, the only difference between
+Text-to-Image and text-guided Image-to-Image generation in approach is
+how initial latent state is generated. In case of Image-to-Image
+generation, you additionally have an image encoded by VAE encoder mixed
+with the noise produced by using latent seed, while in Text-to-Image you
+use only noise as initial latent state. The stable diffusion model takes
+both a latent image representation of size :math:`64 \times 64` and a
+text prompt is transformed to text embeddings of size
+:math:`77 \times 768` via CLIP’s text encoder as an input.
+
+Next, the U-Net iteratively *denoises* the random latent image
+representations while being conditioned on the text embeddings. The
+output of the U-Net, being the noise residual, is used to compute a
+denoised latent image representation via a scheduler algorithm. Many
+different scheduler algorithms can be used for this computation, each
+having its pros and cons. For Stable Diffusion, it is recommended to use
+one of:
+
+-  `PNDM
+   scheduler <https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_pndm.py>`__
+-  `DDIM
+   scheduler <https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddim.py>`__
+-  `K-LMS
+   scheduler <https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_lms_discrete.py>`__\ (you
+   will use it in your pipeline)
+
+Theory on how the scheduler algorithm function works is out of scope for
+this notebook. Nonetheless, in short, you should remember that you
+compute the predicted denoised image representation from the previous
+noise representation and the predicted noise residual. For more
+information, refer to the recommended `Elucidating the Design Space of
+Diffusion-Based Generative Models <https://arxiv.org/abs/2206.00364>`__
+
+The *denoising* process is repeated given number of times (by default
+50) to step-by-step retrieve better latent image representations. When
+complete, the latent image representation is decoded by the decoder part
+of the variational auto encoder.
+
+Text-to-image pipeline
+----------------------
+
+
+
+Load Stable Diffusion model and create text-to-image pipeline
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+We will load optimized Stable Diffusion model from the Hugging Face Hub
+and create pipeline to run an inference with OpenVINO Runtime by
+`Optimum
+Intel <https://huggingface.co/docs/optimum/intel/inference#stable-diffusion>`__.
+
+For running the Stable Diffusion model with Optimum Intel, we will use
+the ``optimum.intel.OVStableDiffusionPipeline`` class, which represents
+the inference pipeline. ``OVStableDiffusionPipeline`` initialized by the
+``from_pretrained`` method. It supports on-the-fly conversion models
+from PyTorch using the ``export=True`` parameter. A converted model can
+be saved on disk using the ``save_pretrained`` method for the next
+running.
+
+When Stable Diffusion models are exported to the OpenVINO format, they
+are decomposed into three components that consist of four models
+combined during inference into the pipeline:
+
+-  The text encoder
+
+   -  The text-encoder is responsible for transforming the input
+      prompt(for example “a photo of an astronaut riding a horse”) into
+      an embedding space that can be understood by the U-Net. It is
+      usually a simple transformer-based encoder that maps a sequence of
+      input tokens to a sequence of latent text embeddings.
+
+-  The U-NET
+
+   -  Model predicts the ``sample`` state for the next step.
+
+-  The VAE encoder
+
+   -  The encoder is used to convert the image into a low dimensional
+      latent representation, which will serve as the input to the U-Net
+      model.
+
+-  The VAE decoder
+
+   -  The decoder transforms the latent representation back into an
+      image.
 
 Select device from dropdown list for running inference using OpenVINO.
 
 .. code:: ipython3
 
     import ipywidgets as widgets
+    import openvino as ov
+    
+    core = ov.Core()
     
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
-        value="CPU",
+        value="AUTO",
         description="Device:",
         disabled=False,
     )
@@ -1063,44 +200,36 @@ Select device from dropdown list for running inference using OpenVINO.
 
 .. parsed-literal::
 
-    Dropdown(description='Device:', options=('CPU', 'GNA', 'AUTO'), value='CPU')
-
-
-
-.. code:: ipython3
-
-    text_enc = core.compile_model(TEXT_ENCODER_OV_PATH, device.value)
+    Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
 
-.. code:: ipython3
 
-    unet_model = core.compile_model(UNET_OV_PATH, device.value)
 
 .. code:: ipython3
 
-    ov_config = {"INFERENCE_PRECISION_HINT": "f32"} if device.value != "CPU" else {}
+    from optimum.intel.openvino import OVStableDiffusionPipeline
+    from pathlib import Path
+    
+    DEVICE = device.value
     
-    vae_decoder = core.compile_model(VAE_DECODER_OV_PATH, device.value, ov_config)
-    vae_encoder = core.compile_model(VAE_ENCODER_OV_PATH, device.value, ov_config)
+    MODEL_ID = "prompthero/openjourney"
+    MODEL_DIR = Path("diffusion_pipeline")
+    
+    if not MODEL_DIR.exists():
+        ov_pipe = OVStableDiffusionPipeline.from_pretrained(MODEL_ID, export=True, device=DEVICE, compile=False)
+        ov_pipe.save_pretrained(MODEL_DIR)
+    else:
+        ov_pipe = OVStableDiffusionPipeline.from_pretrained(MODEL_DIR, device=DEVICE, compile=False)
+    
+    ov_pipe.compile()
 
-Model tokenizer and scheduler are also important parts of the pipeline.
-Let us define them and put all components together
 
-.. code:: ipython3
+.. parsed-literal::
+
+    Compiling the vae_decoder to CPU ...
+    Compiling the unet to CPU ...
+    Compiling the text_encoder to CPU ...
+    Compiling the vae_encoder to CPU ...
 
-    from transformers import CLIPTokenizer
-    from diffusers.schedulers import LMSDiscreteScheduler
-    
-    lms = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
-    tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
-    
-    ov_pipe = OVStableDiffusionPipeline(
-        tokenizer=tokenizer,
-        text_encoder=text_enc,
-        unet=unet_model,
-        vae_encoder=vae_encoder,
-        vae_decoder=vae_decoder,
-        scheduler=lms,
-    )
 
 Text-to-Image generation
 ~~~~~~~~~~~~~~~~~~~~~~~~
@@ -1108,16 +237,13 @@ Text-to-Image generation
 
 
 Now, you can define a text prompt for image generation and run inference
-pipeline. Optionally, you can also change the random generator seed for
-latent state initialization and number of steps.
+pipeline.
 
    **Note**: Consider increasing ``steps`` to get more precise results.
    A suggested value is ``50``, but it will take longer time to process.
 
 .. code:: ipython3
 
-    import ipywidgets as widgets
-    
     sample_text = (
         "cyberpunk cityscape like Tokyo New York  with tall buildings at dusk golden hour cinematic lighting, epic composition. "
         "A golden daylight, hyper-realistic environment. "
@@ -1129,7 +255,7 @@ latent state initialization and number of steps.
     text_prompt = widgets.Text(value=sample_text, description="your text")
     num_steps = widgets.IntSlider(min=1, max=50, value=20, description="steps:")
     seed = widgets.IntSlider(min=0, max=10000000, description="seed: ", value=42)
-    widgets.VBox([text_prompt, seed, num_steps])
+    widgets.VBox([text_prompt, num_steps, seed])
 
 
 
@@ -1156,43 +282,33 @@ latent state initialization and number of steps.
     Number of steps: 20
 
 
+Let’s generate an image and save the generation results. The pipeline
+returns one or several results: ``images`` contains final generated
+image. To get more than one result, you can set the
+``num_images_per_prompt`` parameter.
+
 .. code:: ipython3
 
-    result = ov_pipe(text_prompt.value, num_inference_steps=num_steps.value, seed=seed.value)
+    import numpy as np
+    
+    np.random.seed(seed.value)
+    
+    result = ov_pipe(text_prompt.value, num_inference_steps=num_steps.value)
+    
+    final_image = result["images"][0]
+    final_image.save("result.png")
 
 
 
 .. parsed-literal::
 
-      0%|          | 0/20 [00:00<?, ?it/s]
-
-
-Finally, let us save generation results. The pipeline returns several
-results: ``sample`` contains final generated image, ``iterations``
-contains list of intermediate results for each step.
-
-.. code:: ipython3
+      0%|          | 0/21 [00:00<?, ?it/s]
 
-    final_image = result["sample"][0]
-    if result["iterations"]:
-        all_frames = result["iterations"]
-        img = next(iter(all_frames))
-        img.save(
-            fp="result.gif",
-            format="GIF",
-            append_images=iter(all_frames),
-            save_all=True,
-            duration=len(all_frames) * 5,
-            loop=0,
-        )
-    final_image.save("result.png")
 
 Now is show time!
 
 .. code:: ipython3
 
-    import ipywidgets as widgets
-    
     text = "\n\t".join(text_prompt.value.split("."))
     print("Input text:")
     print("\t" + text)
@@ -1211,11 +327,110 @@ Now is show time!
 
 
 
-.. image:: stable-diffusion-text-to-image-with-output_files/stable-diffusion-text-to-image-with-output_33_1.png
+.. image:: stable-diffusion-text-to-image-with-output_files/stable-diffusion-text-to-image-with-output_16_1.png
 
 
 Nice. As you can see, the picture has quite a high definition 🔥.
 
+Interactive text-to-image demo
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    import gradio as gr
+    
+    
+    def generate_from_text(text, seed, num_steps, _=gr.Progress(track_tqdm=True)):
+        np.random.seed(seed)
+        result = ov_pipe(text, num_inference_steps=num_steps)
+        return result["images"][0]
+    
+    
+    with gr.Blocks() as demo:
+        with gr.Tab("Text-to-Image generation"):
+            with gr.Row():
+                with gr.Column():
+                    text_input = gr.Textbox(lines=3, label="Text")
+                    seed_input = gr.Slider(0, 10000000, value=42, step=1, label="Seed")
+                    steps_input = gr.Slider(1, 50, value=20, step=1, label="Steps")
+                out = gr.Image(label="Result", type="pil")
+            btn = gr.Button()
+            btn.click(generate_from_text, [text_input, seed_input, steps_input], out)
+            gr.Examples([[sample_text, 42, 20]], [text_input, seed_input, steps_input])
+    try:
+        demo.queue().launch()
+    except Exception:
+        demo.queue().launch(share=True)
+    # if you are launching remotely, specify server_name and server_port
+    # demo.launch(server_name='your server name', server_port='server port in int')
+    # Read more in the docs: https://gradio.app/docs/
+
+.. code:: ipython3
+
+    demo.close()
+    del ov_pipe
+    np.random.seed(None)
+
+Image-to-Image pipeline
+-----------------------
+
+
+
+Create image-to-Image pipeline
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+For running the Stable Diffusion model with Optimum Intel, we will use
+the ``optimum.intel.OVStableDiffusionImg2ImgPipeline`` class, which
+represents the inference pipeline. We will use the same model as for
+text-to-image pipeline. The model has already been downloaded from the
+Hugging Face Hub and converted to OpenVINO IR format on previous steps,
+so we can just load it.
+
+.. code:: ipython3
+
+    core = ov.Core()
+    
+    device = widgets.Dropdown(
+        options=core.available_devices + ["AUTO"],
+        value="AUTO",
+        description="Device:",
+        disabled=False,
+    )
+    
+    device
+
+
+
+
+.. parsed-literal::
+
+    Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
+
+
+
+.. code:: ipython3
+
+    from optimum.intel.openvino import OVStableDiffusionImg2ImgPipeline
+    from pathlib import Path
+    
+    DEVICE = device.value
+    
+    ov_pipe_i2i = OVStableDiffusionImg2ImgPipeline.from_pretrained(MODEL_DIR, device=DEVICE, compile=False)
+    ov_pipe_i2i.compile()
+
+
+.. parsed-literal::
+
+    Compiling the vae_decoder to CPU ...
+    Compiling the unet to CPU ...
+    Compiling the text_encoder to CPU ...
+    Compiling the vae_encoder to CPU ...
+
+
 Image-to-Image generation
 ~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -1266,6 +481,7 @@ semantically consistent with the input.
 .. code:: ipython3
 
     import io
+    import PIL
     
     default_image_path = download_file(
         "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/coco.jpg",
@@ -1281,49 +497,107 @@ semantically consistent with the input.
     print(f"Strength: {strength.value}")
     print("Input image:")
     display(image)
-    processed_image = ov_pipe(
-        text_prompt_i2i.value,
-        image,
-        num_inference_steps=num_steps_i2i.value,
-        seed=seed_i2i.value,
-        strength=strength.value,
-    )
 
 
 .. parsed-literal::
 
+    'coco.jpg' already exists.
     Pipeline settings
     Input text: amazing watercolor painting
     Seed: 42
-    Number of steps: 10
-    Strength: 0.5
+    Number of steps: 20
+    Strength: 0.4
     Input image:
 
 
 
-.. image:: stable-diffusion-text-to-image-with-output_files/stable-diffusion-text-to-image-with-output_38_1.png
+.. image:: stable-diffusion-text-to-image-with-output_files/stable-diffusion-text-to-image-with-output_27_1.png
+
+
+.. code:: ipython3
+
+    import PIL
+    import numpy as np
+    
+    
+    def scale_fit_to_window(dst_width: int, dst_height: int, image_width: int, image_height: int):
+        """
+        Preprocessing helper function for calculating image size for resize with peserving original aspect ratio
+        and fitting image to specific window size
+    
+        Parameters:
+          dst_width (int): destination window width
+          dst_height (int): destination window height
+          image_width (int): source image width
+          image_height (int): source image height
+        Returns:
+          result_width (int): calculated width for resize
+          result_height (int): calculated height for resize
+        """
+        im_scale = min(dst_height / image_height, dst_width / image_width)
+        return int(im_scale * image_width), int(im_scale * image_height)
+    
+    
+    def preprocess(image: PIL.Image.Image):
+        """
+        Image preprocessing function. Takes image in PIL.Image format, resizes it to keep aspect ration and fits to model input window 512x512,
+        then converts it to np.ndarray and adds padding with zeros on right or bottom side of image (depends from aspect ratio), after that
+        converts data to float32 data type and change range of values from [0, 255] to [-1, 1].
+        The function returns preprocessed input tensor and padding size, which can be used in postprocessing.
+    
+        Parameters:
+          image (PIL.Image.Image): input image
+        Returns:
+           image (np.ndarray): preprocessed image tensor
+           meta (Dict): dictionary with preprocessing metadata info
+        """
+        src_width, src_height = image.size
+        dst_width, dst_height = scale_fit_to_window(512, 512, src_width, src_height)
+        image = np.array(image.resize((dst_width, dst_height), resample=PIL.Image.Resampling.LANCZOS))[None, :]
+        pad_width = 512 - dst_width
+        pad_height = 512 - dst_height
+        pad = ((0, 0), (0, pad_height), (0, pad_width), (0, 0))
+        image = np.pad(image, pad, mode="constant")
+        image = image.astype(np.float32) / 255.0
+        image = 2.0 * image - 1.0
+        return image, {"padding": pad, "src_width": src_width, "src_height": src_height}
+    
+    
+    def postprocess(image: PIL.Image.Image, orig_width: int, orig_height: int):
+        """
+        Image postprocessing function. Takes image in PIL.Image format and metrics of original image. Image is cropped and resized to restore initial size.
+    
+        Parameters:
+          image (PIL.Image.Image): input image
+          orig_width (int): original image width
+          orig_height (int): original image height
+        Returns:
+           image (PIL.Image.Image): postprocess image
+        """
+        src_width, src_height = image.size
+        dst_width, dst_height = scale_fit_to_window(src_width, src_height, orig_width, orig_height)
+        image = image.crop((0, 0, dst_width, dst_height))
+        image = image.resize((orig_width, orig_height))
+        return image
+
+.. code:: ipython3
+
+    preprocessed_image, meta_data = preprocess(image)
+    
+    np.random.seed(seed_i2i.value)
+    
+    processed_image = ov_pipe_i2i(text_prompt_i2i.value, preprocessed_image, num_inference_steps=num_steps_i2i.value, strength=strength.value)
 
 
 
 .. parsed-literal::
 
-      0%|          | 0/5 [00:00<?, ?it/s]
+      0%|          | 0/9 [00:00<?, ?steps/s]
 
 
 .. code:: ipython3
 
-    final_image_i2i = processed_image["sample"][0]
-    if processed_image["iterations"]:
-        all_frames_i2i = processed_image["iterations"]
-        img = next(iter(all_frames_i2i))
-        img.save(
-            fp="result_i2i.gif",
-            format="GIF",
-            append_images=iter(all_frames_i2i),
-            save_all=True,
-            duration=len(all_frames_i2i) * 5,
-            loop=0,
-        )
+    final_image_i2i = postprocess(processed_image["images"][0], meta_data["src_width"], meta_data["src_height"])
     final_image_i2i.save("result_i2i.png")
 
 .. code:: ipython3
@@ -1341,11 +615,11 @@ semantically consistent with the input.
 
 
 
-.. image:: stable-diffusion-text-to-image-with-output_files/stable-diffusion-text-to-image-with-output_40_1.png
+.. image:: stable-diffusion-text-to-image-with-output_files/stable-diffusion-text-to-image-with-output_31_1.png
 
 
-Interactive demo
-----------------
+Interactive image-to-image demo
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 
 
@@ -1354,33 +628,21 @@ Interactive demo
     import gradio as gr
     
     
-    def generate_from_text(text, seed, num_steps, _=gr.Progress(track_tqdm=True)):
-        result = ov_pipe(text, num_inference_steps=num_steps, seed=seed)
-        return result["sample"][0]
-    
-    
     def generate_from_image(img, text, seed, num_steps, strength, _=gr.Progress(track_tqdm=True)):
-        result = ov_pipe(text, img, num_inference_steps=num_steps, seed=seed, strength=strength)
-        return result["sample"][0]
+        preprocessed_img, meta_data = preprocess(img)
+        np.random.seed(seed)
+        result = ov_pipe_i2i(text, preprocessed_img, num_inference_steps=num_steps, strength=strength)
+        result_img = postprocess(result["images"][0], meta_data["src_width"], meta_data["src_height"])
+        return result_img
     
     
     with gr.Blocks() as demo:
-        with gr.Tab("Text-to-Image generation"):
-            with gr.Row():
-                with gr.Column():
-                    text_input = gr.Textbox(lines=3, label="Text")
-                    seed_input = gr.Slider(0, 10000000, value=42, label="Seed")
-                    steps_input = gr.Slider(1, 50, value=20, step=1, label="Steps")
-                out = gr.Image(label="Result", type="pil")
-            btn = gr.Button()
-            btn.click(generate_from_text, [text_input, seed_input, steps_input], out)
-            gr.Examples([[sample_text, 42, 20]], [text_input, seed_input, steps_input])
         with gr.Tab("Image-to-Image generation"):
             with gr.Row():
                 with gr.Column():
                     i2i_input = gr.Image(label="Image", type="pil")
                     i2i_text_input = gr.Textbox(lines=3, label="Text")
-                    i2i_seed_input = gr.Slider(0, 1024, value=42, label="Seed")
+                    i2i_seed_input = gr.Slider(0, 1024, value=42, step=1, label="Seed")
                     i2i_steps_input = gr.Slider(1, 50, value=10, step=1, label="Steps")
                     strength_input = gr.Slider(0, 1, value=0.5, label="Strength")
                 i2i_out = gr.Image(label="Result")
@@ -1409,23 +671,13 @@ Interactive demo
             )
     
     try:
-        demo.queue().launch(debug=False)
+        demo.queue().launch()
     except Exception:
-        demo.queue().launch(share=True, debug=False)
+        demo.queue().launch(share=True)
     # if you are launching remotely, specify server_name and server_port
     # demo.launch(server_name='your server name', server_port='server port in int')
     # Read more in the docs: https://gradio.app/docs/
 
+.. code:: ipython3
 
-.. parsed-literal::
-
-    Running on local URL:  http://127.0.0.1:7860
-    
-    To create a public link, set `share=True` in `launch()`.
-
-
-
-
-
-
-
+    demo.close()
diff --git a/docs/notebooks/stable-diffusion-text-to-image-with-output_files/stable-diffusion-text-to-image-with-output_16_1.jpg b/docs/notebooks/stable-diffusion-text-to-image-with-output_files/stable-diffusion-text-to-image-with-output_16_1.jpg
new file mode 100644
index 00000000000000..8f0e6f362a87bb
--- /dev/null
+++ b/docs/notebooks/stable-diffusion-text-to-image-with-output_files/stable-diffusion-text-to-image-with-output_16_1.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bf6344ca9a69d5f44e3bb81bdd09edaadef8156e8605aeec9ab6df7c903f6328
+size 44901
diff --git a/docs/notebooks/stable-diffusion-text-to-image-with-output_files/stable-diffusion-text-to-image-with-output_16_1.png b/docs/notebooks/stable-diffusion-text-to-image-with-output_files/stable-diffusion-text-to-image-with-output_16_1.png
new file mode 100644
index 00000000000000..0a8415528dcb46
--- /dev/null
+++ b/docs/notebooks/stable-diffusion-text-to-image-with-output_files/stable-diffusion-text-to-image-with-output_16_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f6bbb33801d98216f57fe4e9530e5e506db4d37ec3657c86d97c9611be027b1b
+size 458609
diff --git a/docs/notebooks/stable-diffusion-text-to-image-with-output_files/stable-diffusion-text-to-image-with-output_27_1.jpg b/docs/notebooks/stable-diffusion-text-to-image-with-output_files/stable-diffusion-text-to-image-with-output_27_1.jpg
new file mode 100644
index 00000000000000..abe8cb45ca2fd7
--- /dev/null
+++ b/docs/notebooks/stable-diffusion-text-to-image-with-output_files/stable-diffusion-text-to-image-with-output_27_1.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5a58493845ebd3e98186df7a1ea042b20545bb3c2a5b4a326163da6c9eb5e7d9
+size 121563
diff --git a/docs/notebooks/stable-diffusion-text-to-image-with-output_files/stable-diffusion-text-to-image-with-output_27_1.png b/docs/notebooks/stable-diffusion-text-to-image-with-output_files/stable-diffusion-text-to-image-with-output_27_1.png
new file mode 100644
index 00000000000000..30f6673bf72850
--- /dev/null
+++ b/docs/notebooks/stable-diffusion-text-to-image-with-output_files/stable-diffusion-text-to-image-with-output_27_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:84112f4f04be0a3445174f4dcf7e300fc12dbd50fd8a5e2d98b4082402dda6de
+size 869661
diff --git a/docs/notebooks/stable-diffusion-text-to-image-with-output_files/stable-diffusion-text-to-image-with-output_31_1.jpg b/docs/notebooks/stable-diffusion-text-to-image-with-output_files/stable-diffusion-text-to-image-with-output_31_1.jpg
new file mode 100644
index 00000000000000..ae0deb903c06af
--- /dev/null
+++ b/docs/notebooks/stable-diffusion-text-to-image-with-output_files/stable-diffusion-text-to-image-with-output_31_1.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4a52ed9efeb6375a3994c8fbd3be1f84950a2333940a901976a13085aadf27f5
+size 92165
diff --git a/docs/notebooks/stable-diffusion-text-to-image-with-output_files/stable-diffusion-text-to-image-with-output_31_1.png b/docs/notebooks/stable-diffusion-text-to-image-with-output_files/stable-diffusion-text-to-image-with-output_31_1.png
new file mode 100644
index 00000000000000..61dc72736bff81
--- /dev/null
+++ b/docs/notebooks/stable-diffusion-text-to-image-with-output_files/stable-diffusion-text-to-image-with-output_31_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:de4759f505d3390f89968c972619b3bdc1e545ffc4a46b22a67a1df3817c6ccc
+size 833090
diff --git a/docs/notebooks/stable-diffusion-text-to-image-with-output_files/stable-diffusion-text-to-image-with-output_33_1.png b/docs/notebooks/stable-diffusion-text-to-image-with-output_files/stable-diffusion-text-to-image-with-output_33_1.png
deleted file mode 100644
index be83c18365ed0d..00000000000000
--- a/docs/notebooks/stable-diffusion-text-to-image-with-output_files/stable-diffusion-text-to-image-with-output_33_1.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:56f816275df653fed42eb4d8565dde858c60540f5f4c4efcd45fdd395dea7acc
-size 372482
diff --git a/docs/notebooks/stable-diffusion-text-to-image-with-output_files/stable-diffusion-text-to-image-with-output_38_1.png b/docs/notebooks/stable-diffusion-text-to-image-with-output_files/stable-diffusion-text-to-image-with-output_38_1.png
deleted file mode 100644
index 64c4abb841c93b..00000000000000
--- a/docs/notebooks/stable-diffusion-text-to-image-with-output_files/stable-diffusion-text-to-image-with-output_38_1.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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-size 928958
diff --git a/docs/notebooks/stable-diffusion-text-to-image-with-output_files/stable-diffusion-text-to-image-with-output_40_1.png b/docs/notebooks/stable-diffusion-text-to-image-with-output_files/stable-diffusion-text-to-image-with-output_40_1.png
deleted file mode 100644
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+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/docs/notebooks/stable-diffusion-torchdynamo-backend-with-output.rst b/docs/notebooks/stable-diffusion-torchdynamo-backend-with-output.rst
index 08f11faa0702a7..1e0007df8c8876 100644
--- a/docs/notebooks/stable-diffusion-torchdynamo-backend-with-output.rst
+++ b/docs/notebooks/stable-diffusion-torchdynamo-backend-with-output.rst
@@ -30,8 +30,8 @@ Notebook contains the following steps:
 2. Add OpenVINO optimization using OpenVINO TorchDynamo backend.
 3. Run Stable Diffusion pipeline with OpenVINO.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Stable Diffusion with Diffusers
@@ -57,9 +57,7 @@ Prerequisites
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
 
 
@@ -76,12 +74,10 @@ Prerequisites
 
 .. parsed-literal::
 
-    2024-06-06 03:31:47.318537: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-06-06 03:31:47.352773: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-07-02 03:26:56.106749: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-07-02 03:26:56.140600: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2024-06-06 03:31:47.873530: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/transformers/transformer_2d.py:34: FutureWarning: `Transformer2DModelOutput` is deprecated and will be removed in version 1.0.0. Importing `Transformer2DModelOutput` from `diffusers.models.transformer_2d` is deprecated and this will be removed in a future version. Please use `from diffusers.models.modeling_outputs import Transformer2DModelOutput`, instead.
-      deprecate("Transformer2DModelOutput", "1.0.0", deprecation_message)
+    2024-07-02 03:26:56.686548: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 Stable Diffusion with Diffusers library
@@ -114,6 +110,11 @@ The code below demonstrates how to create the
     Loading pipeline components...:   0%|          | 0/6 [00:00<?, ?it/s]
 
 
+.. parsed-literal::
+
+    The installed version of bitsandbytes was compiled without GPU support. 8-bit optimizers, 8-bit multiplication, and GPU quantization are unavailable.
+
+
 OpenVINO TorchDynamo backend
 ----------------------------
 
diff --git a/docs/notebooks/stable-diffusion-torchdynamo-backend-with-output_files/stable-diffusion-torchdynamo-backend-with-output_14_1.jpg b/docs/notebooks/stable-diffusion-torchdynamo-backend-with-output_files/stable-diffusion-torchdynamo-backend-with-output_14_1.jpg
index ddb162c97780b5..420c88460c0657 100644
--- a/docs/notebooks/stable-diffusion-torchdynamo-backend-with-output_files/stable-diffusion-torchdynamo-backend-with-output_14_1.jpg
+++ b/docs/notebooks/stable-diffusion-torchdynamo-backend-with-output_files/stable-diffusion-torchdynamo-backend-with-output_14_1.jpg
@@ -1,3 +1,3 @@
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-oid sha256:f85011141133bf7c7c79f046e88c61d72138c6f515d674bfe9ba458ebcd71764
-size 32126
+oid sha256:cc3e1508e62f8e66686ad8c11f8cf4076fc8c73b9eff28a4aa28d192ee2bad78
+size 32915
diff --git a/docs/notebooks/stable-diffusion-torchdynamo-backend-with-output_files/stable-diffusion-torchdynamo-backend-with-output_14_1.png b/docs/notebooks/stable-diffusion-torchdynamo-backend-with-output_files/stable-diffusion-torchdynamo-backend-with-output_14_1.png
index a392b78241785c..b5ff3ba435416e 100644
--- a/docs/notebooks/stable-diffusion-torchdynamo-backend-with-output_files/stable-diffusion-torchdynamo-backend-with-output_14_1.png
+++ b/docs/notebooks/stable-diffusion-torchdynamo-backend-with-output_files/stable-diffusion-torchdynamo-backend-with-output_14_1.png
@@ -1,3 +1,3 @@
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-oid sha256:d1598c0730abd6cfc34f1c3fa64abec279d94ac75b13e545e8f7409fe85caebe
-size 383430
+oid sha256:ab8f25f3a1c965a4d976281ccff55dde6d93dff67a6063b6372c090156ef421a
+size 420807
diff --git a/docs/notebooks/stable-diffusion-v2-infinite-zoom-with-output.rst b/docs/notebooks/stable-diffusion-v2-infinite-zoom-with-output.rst
index b0612ef64da765..c5994bf8ecbaee 100644
--- a/docs/notebooks/stable-diffusion-v2-infinite-zoom-with-output.rst
+++ b/docs/notebooks/stable-diffusion-v2-infinite-zoom-with-output.rst
@@ -61,18 +61,14 @@ are:
    previous model. This text-guided inpainting makes switching out parts
    in the image easier than before.
 
-This notebook demonstrates how to convert and run Stable Diffusion v2
-model using OpenVINO.
+This notebook demonstrates how to download the model from the Hugging
+Face Hub and converted to OpenVINO IR format with `Optimum
+Intel <https://huggingface.co/docs/optimum/intel/inference#stable-diffusion>`__.
+And how to use the model to generate sequence of images for infinite
+zoom video effect.
 
-Notebook contains the following steps:
+**Table of contents:**
 
-1. Create pipeline with PyTorch models using Diffusers library.
-2. Convert models to OpenVINO IR format, using model conversion API.
-3. Run Stable Diffusion v2 inpainting pipeline for generation infinity
-   zoom video
-
-Table of contents:
-^^^^^^^^^^^^^^^^^^
 
 -  `Stable Diffusion v2 Infinite Zoom
    Showcase <#stable-diffusion-v2-infinite-zoom-showcase>`__
@@ -81,17 +77,11 @@ Table of contents:
       Inpainting <#stable-diffusion-text-guided-inpainting>`__
 
 -  `Prerequisites <#prerequisites>`__
-
-   -  `Stable Diffusion in Diffusers
-      library <#stable-diffusion-in-diffusers-library>`__
-   -  `Convert models to OpenVINO Intermediate representation (IR)
-      format <#convert-models-to-openvino-intermediate-representation-ir-format>`__
-   -  `Prepare Inference pipeline <#prepare-inference-pipeline>`__
-   -  `Zoom Video Generation <#zoom-video-generation>`__
-   -  `Configure Inference Pipeline <#configure-inference-pipeline>`__
-   -  `Select inference device <#select-inference-device>`__
-   -  `Run Infinite Zoom video
-      generation <#run-infinite-zoom-video-generation>`__
+-  `Load Stable Diffusion Inpaint pipeline using Optimum
+   Intel <#load-stable-diffusion-inpaint-pipeline-using-optimum-intel>`__
+-  `Zoom Video Generation <#zoom-video-generation>`__
+-  `Run Infinite Zoom video
+   generation <#run-infinite-zoom-video-generation>`__
 
 Stable Diffusion v2 Infinite Zoom Showcase
 ------------------------------------------
@@ -149,713 +139,72 @@ install required packages
 
 .. code:: ipython3
 
-    %pip install -q "diffusers>=0.14.0" "transformers>=4.25.1" "gradio>=4.19" "openvino>=2023.1.0" "torch>=2.1" Pillow opencv-python --extra-index-url https://download.pytorch.org/whl/cpu
-
-Stable Diffusion in Diffusers library
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-To work with Stable Diffusion v2, we will use Hugging Face
-`Diffusers <https://github.com/huggingface/diffusers>`__ library. To
-experiment with Stable Diffusion models for Inpainting use case,
-Diffusers exposes the
-`StableDiffusionInpaintPipeline <https://huggingface.co/docs/diffusers/using-diffusers/conditional_image_generation>`__
-similar to the `other Diffusers
-pipelines <https://huggingface.co/docs/diffusers/api/pipelines/overview>`__.
-The code below demonstrates how to create
-``StableDiffusionInpaintPipeline`` using
-``stable-diffusion-2-inpainting``:
-
-.. code:: ipython3
-
-    from diffusers import StableDiffusionInpaintPipeline, DPMSolverMultistepScheduler
+    %pip install -q "diffusers>=0.14.0" "transformers>=4.25.1" "gradio>=4.19" "openvino>=2024.2.0" "torch>=2.1" Pillow opencv-python "git+https://github.com/huggingface/optimum-intel.git" --extra-index-url https://download.pytorch.org/whl/cpu
 
-    model_id_inpaint = "stabilityai/stable-diffusion-2-inpainting"
+Load Stable Diffusion Inpaint pipeline using Optimum Intel
+----------------------------------------------------------
 
-    pipe_inpaint = StableDiffusionInpaintPipeline.from_pretrained(model_id_inpaint)
-    scheduler_inpaint = DPMSolverMultistepScheduler.from_config(pipe_inpaint.scheduler.config)
 
 
-.. parsed-literal::
+We will load optimized Stable Diffusion model from the Hugging Face Hub
+and create pipeline to run an inference with OpenVINO Runtime by
+`Optimum
+Intel <https://huggingface.co/docs/optimum/intel/inference#stable-diffusion>`__.
 
-    2023-09-25 12:14:32.810031: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2023-09-25 12:14:32.851215: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
-    To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2023-09-25 12:14:33.562760: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
-
-
-
-.. parsed-literal::
-
-    Loading pipeline components...:   0%|          | 0/6 [00:00<?, ?it/s]
+For running the Stable Diffusion model with Optimum Intel, we will use
+the optimum.intel.OVStableDiffusionInpaintPipeline class, which
+represents the inference pipeline. OVStableDiffusionInpaintPipeline
+initialized by the from_pretrained method. It supports on-the-fly
+conversion models from PyTorch using the export=True parameter. A
+converted model can be saved on disk using the save_pretrained method
+for the next running.
 
+Select device from dropdown list for running inference using OpenVINO.
 
 .. code:: ipython3
 
-    import gc
-
-    text_encoder_inpaint = pipe_inpaint.text_encoder
-    text_encoder_inpaint.eval()
-    unet_inpaint = pipe_inpaint.unet
-    unet_inpaint.eval()
-    vae_inpaint = pipe_inpaint.vae
-    vae_inpaint.eval()
-
-    del pipe_inpaint
-    gc.collect();
-
-Convert models to OpenVINO Intermediate representation (IR) format
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-Conversion part of model stayed remain as in `Text-to-Image generation
-notebook <./stable-diffusion-v2-text-to-image.ipynb>`__. Except U-Net
-now has 9 channels, which now calculated like 4 for U-Net generated
-latents channels + 4 for latent representation of masked image + 1
-channel resized mask.
-
-.. code:: ipython3
-
-    from pathlib import Path
-    import torch
-    import numpy as np
+    import ipywidgets as widgets
     import openvino as ov
-
-    sd2_inpainting_model_dir = Path("sd2_inpainting")
-    sd2_inpainting_model_dir.mkdir(exist_ok=True)
-
-.. code:: ipython3
-
-    def cleanup_torchscript_cache():
-        """
-        Helper for removing cached model representation
-        """
-        torch._C._jit_clear_class_registry()
-        torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore()
-        torch.jit._state._clear_class_state()
-
-
-    def convert_encoder(text_encoder: torch.nn.Module, ir_path: Path):
-        """
-        Convert Text Encoder model to IR.
-        Function accepts pipeline, prepares example inputs for conversion
-        Parameters:
-            text_encoder (torch.nn.Module): text encoder PyTorch model
-            ir_path (Path): File for storing model
-        Returns:
-            None
-        """
-        if not ir_path.exists():
-            input_ids = torch.ones((1, 77), dtype=torch.long)
-            # switch model to inference mode
-            text_encoder.eval()
-
-            # disable gradients calculation for reducing memory consumption
-            with torch.no_grad():
-                # export model
-                ov_model = ov.convert_model(
-                    text_encoder,  # model instance
-                    example_input=input_ids,  # example inputs for model tracing
-                    input=([1, 77],),  # input shape for conversion
-                )
-                ov.save_model(ov_model, ir_path)
-                del ov_model
-                cleanup_torchscript_cache()
-            print("Text Encoder successfully converted to IR")
-
-
-    def convert_unet(
-        unet: torch.nn.Module,
-        ir_path: Path,
-        num_channels: int = 4,
-        width: int = 64,
-        height: int = 64,
-    ):
-        """
-        Convert Unet model to IR format.
-        Function accepts pipeline, prepares example inputs for conversion
-        Parameters:
-            unet (torch.nn.Module): UNet PyTorch model
-            ir_path (Path): File for storing model
-            num_channels (int, optional, 4): number of input channels
-            width (int, optional, 64): input width
-            height (int, optional, 64): input height
-        Returns:
-            None
-        """
-        dtype_mapping = {torch.float32: ov.Type.f32, torch.float64: ov.Type.f64}
-        if not ir_path.exists():
-            # prepare inputs
-            encoder_hidden_state = torch.ones((2, 77, 1024))
-            latents_shape = (2, num_channels, width, height)
-            latents = torch.randn(latents_shape)
-            t = torch.from_numpy(np.array(1, dtype=np.float32))
-            unet.eval()
-            dummy_inputs = (latents, t, encoder_hidden_state)
-            input_info = []
-            for input_tensor in dummy_inputs:
-                shape = ov.PartialShape(tuple(input_tensor.shape))
-                element_type = dtype_mapping[input_tensor.dtype]
-                input_info.append((shape, element_type))
-
-            with torch.no_grad():
-                ov_model = ov.convert_model(unet, example_input=dummy_inputs, input=input_info)
-            ov.save_model(ov_model, ir_path)
-            del ov_model
-            cleanup_torchscript_cache()
-            print("U-Net successfully converted to IR")
-
-
-    def convert_vae_encoder(vae: torch.nn.Module, ir_path: Path, width: int = 512, height: int = 512):
-        """
-        Convert VAE model to IR format.
-        VAE model, creates wrapper class for export only necessary for inference part,
-        prepares example inputs for onversion
-        Parameters:
-            vae (torch.nn.Module): VAE PyTorch model
-            ir_path (Path): File for storing model
-            width (int, optional, 512): input width
-            height (int, optional, 512): input height
-        Returns:
-            None
-        """
-
-        class VAEEncoderWrapper(torch.nn.Module):
-            def __init__(self, vae):
-                super().__init__()
-                self.vae = vae
-
-            def forward(self, image):
-                return self.vae.encode(x=image)["latent_dist"].sample()
-
-        if not ir_path.exists():
-            vae_encoder = VAEEncoderWrapper(vae)
-            vae_encoder.eval()
-            image = torch.zeros((1, 3, width, height))
-            with torch.no_grad():
-                ov_model = ov.convert_model(vae_encoder, example_input=image, input=([1, 3, width, height],))
-            ov.save_model(ov_model, ir_path)
-            del ov_model
-            cleanup_torchscript_cache()
-            print("VAE encoder successfully converted to IR")
-
-
-    def convert_vae_decoder(vae: torch.nn.Module, ir_path: Path, width: int = 64, height: int = 64):
-        """
-        Convert VAE decoder model to IR format.
-        Function accepts VAE model, creates wrapper class for export only necessary for inference part,
-        prepares example inputs for conversion
-        Parameters:
-            vae (torch.nn.Module): VAE model
-            ir_path (Path): File for storing model
-            width (int, optional, 64): input width
-            height (int, optional, 64): input height
-        Returns:
-            None
-        """
-
-        class VAEDecoderWrapper(torch.nn.Module):
-            def __init__(self, vae):
-                super().__init__()
-                self.vae = vae
-
-            def forward(self, latents):
-                return self.vae.decode(latents)
-
-        if not ir_path.exists():
-            vae_decoder = VAEDecoderWrapper(vae)
-            latents = torch.zeros((1, 4, width, height))
-
-            vae_decoder.eval()
-            with torch.no_grad():
-                ov_model = ov.convert_model(vae_decoder, example_input=latents, input=([1, 4, width, height],))
-            ov.save_model(ov_model, ir_path)
-            del ov_model
-            cleanup_torchscript_cache()
-            print("VAE decoder successfully converted to IR")
-
-.. code:: ipython3
-
-    TEXT_ENCODER_OV_PATH_INPAINT = sd2_inpainting_model_dir / "text_encoder.xml"
-
-    if not TEXT_ENCODER_OV_PATH_INPAINT.exists():
-        convert_encoder(text_encoder_inpaint, TEXT_ENCODER_OV_PATH_INPAINT)
-    else:
-        print(f"Text encoder will be loaded from {TEXT_ENCODER_OV_PATH_INPAINT}")
-
-    del text_encoder_inpaint
-    gc.collect();
-
-
-.. parsed-literal::
-
-    Text encoder will be loaded from sd2_inpainting/text_encoder.xml
-
-
-.. code:: ipython3
-
-    UNET_OV_PATH_INPAINT = sd2_inpainting_model_dir / "unet.xml"
-    if not UNET_OV_PATH_INPAINT.exists():
-        convert_unet(unet_inpaint, UNET_OV_PATH_INPAINT, num_channels=9, width=64, height=64)
-        del unet_inpaint
-        gc.collect()
-    else:
-        del unet_inpaint
-        print(f"U-Net will be loaded from {UNET_OV_PATH_INPAINT}")
-    gc.collect();
-
-
-.. parsed-literal::
-
-    U-Net will be loaded from sd2_inpainting/unet.xml
-
+    
+    core = ov.Core()
+    
+    device = widgets.Dropdown(
+        options=core.available_devices + ["AUTO"],
+        value="AUTO",
+        description="Device:",
+        disabled=False,
+    )
+    
+    device
 
 .. code:: ipython3
 
-    VAE_ENCODER_OV_PATH_INPAINT = sd2_inpainting_model_dir / "vae_encoder.xml"
-
-    if not VAE_ENCODER_OV_PATH_INPAINT.exists():
-        convert_vae_encoder(vae_inpaint, VAE_ENCODER_OV_PATH_INPAINT, 512, 512)
-    else:
-        print(f"VAE encoder will be loaded from {VAE_ENCODER_OV_PATH_INPAINT}")
-
-    VAE_DECODER_OV_PATH_INPAINT = sd2_inpainting_model_dir / "vae_decoder.xml"
-    if not VAE_DECODER_OV_PATH_INPAINT.exists():
-        convert_vae_decoder(vae_inpaint, VAE_DECODER_OV_PATH_INPAINT, 64, 64)
+    from optimum.intel.openvino import OVStableDiffusionInpaintPipeline
+    from pathlib import Path
+    
+    DEVICE = device.value
+    
+    MODEL_ID = "stabilityai/stable-diffusion-2-inpainting"
+    MODEL_DIR = Path("sd2_inpainting")
+    
+    if not MODEL_DIR.exists():
+        ov_pipe = OVStableDiffusionInpaintPipeline.from_pretrained(MODEL_ID, export=True, device=DEVICE, compile=False)
+        ov_pipe.save_pretrained(MODEL_DIR)
     else:
-        print(f"VAE decoder will be loaded from {VAE_DECODER_OV_PATH_INPAINT}")
-
-    del vae_inpaint
-    gc.collect();
-
-
-.. parsed-literal::
-
-    VAE encoder will be loaded from sd2_inpainting/vae_encoder.xml
-    VAE decoder will be loaded from sd2_inpainting/vae_decoder.xml
-
-
-Prepare Inference pipeline
-~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-As it was discussed previously, Inpainting inference pipeline is based
-on Text-to-Image inference pipeline with addition mask processing step.
-We will reuse ``OVStableDiffusionPipeline`` basic utilities in
-``OVStableDiffusionInpaintingPipeline`` class.
-
-.. code:: ipython3
-
-    import inspect
-    from typing import List, Optional, Union, Dict
-
-    import PIL
-    import cv2
-
-    from transformers import CLIPTokenizer
-    from diffusers import DiffusionPipeline
-    from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
-
-
-    def prepare_mask_and_masked_image(image: PIL.Image.Image, mask: PIL.Image.Image):
-        """
-        Prepares a pair (image, mask) to be consumed by the Stable Diffusion pipeline. This means that those inputs will be
-        converted to ``np.array`` with shapes ``batch x channels x height x width`` where ``channels`` is ``3`` for the
-        ``image`` and ``1`` for the ``mask``.
-
-        The ``image`` will be converted to ``np.float32`` and normalized to be in ``[-1, 1]``. The ``mask`` will be
-        binarized (``mask > 0.5``) and cast to ``np.float32`` too.
-
-        Args:
-            image (Union[np.array, PIL.Image]): The image to inpaint.
-                It can be a ``PIL.Image``, or a ``height x width x 3`` ``np.array``
-            mask (_type_): The mask to apply to the image, i.e. regions to inpaint.
-                It can be a ``PIL.Image``, or a ``height x width`` ``np.array``.
-
-        Returns:
-            tuple[np.array]: The pair (mask, masked_image) as ``torch.Tensor`` with 4
-                dimensions: ``batch x channels x height x width``.
-        """
-        if isinstance(image, (PIL.Image.Image, np.ndarray)):
-            image = [image]
-
-        if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):
-            image = [np.array(i.convert("RGB"))[None, :] for i in image]
-            image = np.concatenate(image, axis=0)
-        elif isinstance(image, list) and isinstance(image[0], np.ndarray):
-            image = np.concatenate([i[None, :] for i in image], axis=0)
-
-        image = image.transpose(0, 3, 1, 2)
-        image = image.astype(np.float32) / 127.5 - 1.0
-
-        # preprocess mask
-        if isinstance(mask, (PIL.Image.Image, np.ndarray)):
-            mask = [mask]
-
-        if isinstance(mask, list) and isinstance(mask[0], PIL.Image.Image):
-            mask = np.concatenate([np.array(m.convert("L"))[None, None, :] for m in mask], axis=0)
-            mask = mask.astype(np.float32) / 255.0
-        elif isinstance(mask, list) and isinstance(mask[0], np.ndarray):
-            mask = np.concatenate([m[None, None, :] for m in mask], axis=0)
-
-        mask[mask < 0.5] = 0
-        mask[mask >= 0.5] = 1
-
-        masked_image = image * (mask < 0.5)
-
-        return mask, masked_image
-
-
-.. parsed-literal::
-
-    /tmp/ipykernel_1292073/2055396221.py:8: FutureWarning: Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.
-      from diffusers import DiffusionPipeline
-
-
-.. code:: ipython3
-
-    class OVStableDiffusionInpaintingPipeline(DiffusionPipeline):
-        def __init__(
-            self,
-            vae_decoder: ov.Model,
-            text_encoder: ov.Model,
-            tokenizer: CLIPTokenizer,
-            unet: ov.Model,
-            scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
-            vae_encoder: ov.Model = None,
-        ):
-            """
-            Pipeline for text-to-image generation using Stable Diffusion.
-            Parameters:
-                vae_decoder (Model):
-                    Variational Auto-Encoder (VAE) Model to decode images to and from latent representations.
-                text_encoder (Model):
-                    Frozen text-encoder. Stable Diffusion uses the text portion of
-                    [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
-                    the clip-vit-large-patch14(https://huggingface.co/openai/clip-vit-large-patch14) variant.
-                tokenizer (CLIPTokenizer):
-                    Tokenizer of class CLIPTokenizer(https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
-                unet (Model): Conditional U-Net architecture to denoise the encoded image latents.
-                vae_encoder (Model):
-                    Variational Auto-Encoder (VAE) Model to encode images to latent representation.
-                scheduler (SchedulerMixin):
-                    A scheduler to be used in combination with unet to denoise the encoded image latents. Can be one of
-                    DDIMScheduler, LMSDiscreteScheduler, or PNDMScheduler.
-            """
-            super().__init__()
-            self.scheduler = scheduler
-            self.vae_decoder = vae_decoder
-            self.vae_encoder = vae_encoder
-            self.text_encoder = text_encoder
-            self.unet = unet
-            self._text_encoder_output = text_encoder.output(0)
-            self._unet_output = unet.output(0)
-            self._vae_d_output = vae_decoder.output(0)
-            self._vae_e_output = vae_encoder.output(0) if vae_encoder is not None else None
-            self.height = self.unet.input(0).shape[2] * 8
-            self.width = self.unet.input(0).shape[3] * 8
-            self.tokenizer = tokenizer
-            self.register_to_config(_progress_bar_config={})
-
-        def prepare_mask_latents(
-            self,
-            mask,
-            masked_image,
-            height=512,
-            width=512,
-            do_classifier_free_guidance=True,
-        ):
-            """
-            Prepare mask as Unet nput and encode input masked image to latent space using vae encoder
-
-            Parameters:
-              mask (np.array): input mask array
-              masked_image (np.array): masked input image tensor
-              heigh (int, *optional*, 512): generated image height
-              width (int, *optional*, 512): generated image width
-              do_classifier_free_guidance (bool, *optional*, True): whether to use classifier free guidance or not
-            Returns:
-              mask (np.array): resized mask tensor
-              masked_image_latents (np.array): masked image encoded into latent space using VAE
-            """
-            mask = torch.nn.functional.interpolate(torch.from_numpy(mask), size=(height // 8, width // 8))
-            mask = mask.numpy()
-
-            # encode the mask image into latents space so we can concatenate it to the latents
-            latents = self.vae_encoder(masked_image)[self._vae_e_output]
-            masked_image_latents = latents * 0.18215
-
-            mask = np.concatenate([mask] * 2) if do_classifier_free_guidance else mask
-            masked_image_latents = np.concatenate([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents
-            return mask, masked_image_latents
-
-        def __call__(
-            self,
-            prompt: Union[str, List[str]],
-            image: PIL.Image.Image,
-            mask_image: PIL.Image.Image,
-            negative_prompt: Union[str, List[str]] = None,
-            num_inference_steps: Optional[int] = 50,
-            guidance_scale: Optional[float] = 7.5,
-            eta: Optional[float] = 0,
-            output_type: Optional[str] = "pil",
-            seed: Optional[int] = None,
-        ):
-            """
-            Function invoked when calling the pipeline for generation.
-            Parameters:
-                prompt (str or List[str]):
-                    The prompt or prompts to guide the image generation.
-                image (PIL.Image.Image):
-                     Source image for inpainting.
-                mask_image (PIL.Image.Image):
-                     Mask area for inpainting
-                negative_prompt (str or List[str]):
-                    The negative prompt or prompts to guide the image generation.
-                num_inference_steps (int, *optional*, defaults to 50):
-                    The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                    expense of slower inference.
-                guidance_scale (float, *optional*, defaults to 7.5):
-                    Guidance scale as defined in Classifier-Free Diffusion Guidance(https://arxiv.org/abs/2207.12598).
-                    guidance_scale is defined as `w` of equation 2.
-                    Higher guidance scale encourages to generate images that are closely linked to the text prompt,
-                    usually at the expense of lower image quality.
-                eta (float, *optional*, defaults to 0.0):
-                    Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
-                    [DDIMScheduler], will be ignored for others.
-                output_type (`str`, *optional*, defaults to "pil"):
-                    The output format of the generate image. Choose between
-                    [PIL](https://pillow.readthedocs.io/en/stable/): PIL.Image.Image or np.array.
-                seed (int, *optional*, None):
-                    Seed for random generator state initialization.
-            Returns:
-                Dictionary with keys:
-                    sample - the last generated image PIL.Image.Image or np.array
-            """
-            if seed is not None:
-                np.random.seed(seed)
-            # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-            # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
-            # corresponds to doing no classifier free guidance.
-            do_classifier_free_guidance = guidance_scale > 1.0
-            # get prompt text embeddings
-            text_embeddings = self._encode_prompt(
-                prompt,
-                do_classifier_free_guidance=do_classifier_free_guidance,
-                negative_prompt=negative_prompt,
-            )
-            # prepare mask
-            mask, masked_image = prepare_mask_and_masked_image(image, mask_image)
-            # set timesteps
-            accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
-            extra_set_kwargs = {}
-            if accepts_offset:
-                extra_set_kwargs["offset"] = 1
-
-            self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
-            timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, 1)
-            latent_timestep = timesteps[:1]
-
-            # get the initial random noise unless the user supplied it
-            latents, meta = self.prepare_latents(latent_timestep)
-            mask, masked_image_latents = self.prepare_mask_latents(
-                mask,
-                masked_image,
-                do_classifier_free_guidance=do_classifier_free_guidance,
-            )
-
-            # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
-            # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
-            # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
-            # and should be between [0, 1]
-            accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
-            extra_step_kwargs = {}
-            if accepts_eta:
-                extra_step_kwargs["eta"] = eta
-
-            for t in self.progress_bar(timesteps):
-                # expand the latents if we are doing classifier free guidance
-                latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
-                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-                latent_model_input = np.concatenate([latent_model_input, mask, masked_image_latents], axis=1)
-                # predict the noise residual
-                noise_pred = self.unet([latent_model_input, np.array(t, dtype=np.float32), text_embeddings])[self._unet_output]
-                # perform guidance
-                if do_classifier_free_guidance:
-                    noise_pred_uncond, noise_pred_text = noise_pred[0], noise_pred[1]
-                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-                # compute the previous noisy sample x_t -> x_t-1
-                latents = self.scheduler.step(
-                    torch.from_numpy(noise_pred),
-                    t,
-                    torch.from_numpy(latents),
-                    **extra_step_kwargs,
-                )["prev_sample"].numpy()
-            # scale and decode the image latents with vae
-            image = self.vae_decoder(latents * (1 / 0.18215))[self._vae_d_output]
-
-            image = self.postprocess_image(image, meta, output_type)
-            return {"sample": image}
-
-        def _encode_prompt(
-            self,
-            prompt: Union[str, List[str]],
-            num_images_per_prompt: int = 1,
-            do_classifier_free_guidance: bool = True,
-            negative_prompt: Union[str, List[str]] = None,
-        ):
-            """
-            Encodes the prompt into text encoder hidden states.
-
-            Parameters:
-                prompt (str or list(str)): prompt to be encoded
-                num_images_per_prompt (int): number of images that should be generated per prompt
-                do_classifier_free_guidance (bool): whether to use classifier free guidance or not
-                negative_prompt (str or list(str)): negative prompt to be encoded
-            Returns:
-                text_embeddings (np.ndarray): text encoder hidden states
-            """
-            batch_size = len(prompt) if isinstance(prompt, list) else 1
-
-            # tokenize input prompts
-            text_inputs = self.tokenizer(
-                prompt,
-                padding="max_length",
-                max_length=self.tokenizer.model_max_length,
-                truncation=True,
-                return_tensors="np",
-            )
-            text_input_ids = text_inputs.input_ids
-
-            text_embeddings = self.text_encoder(text_input_ids)[self._text_encoder_output]
-
-            # duplicate text embeddings for each generation per prompt
-            if num_images_per_prompt != 1:
-                bs_embed, seq_len, _ = text_embeddings.shape
-                text_embeddings = np.tile(text_embeddings, (1, num_images_per_prompt, 1))
-                text_embeddings = np.reshape(text_embeddings, (bs_embed * num_images_per_prompt, seq_len, -1))
-
-            # get unconditional embeddings for classifier free guidance
-            if do_classifier_free_guidance:
-                uncond_tokens: List[str]
-                max_length = text_input_ids.shape[-1]
-                if negative_prompt is None:
-                    uncond_tokens = [""] * batch_size
-                elif isinstance(negative_prompt, str):
-                    uncond_tokens = [negative_prompt]
-                else:
-                    uncond_tokens = negative_prompt
-                uncond_input = self.tokenizer(
-                    uncond_tokens,
-                    padding="max_length",
-                    max_length=max_length,
-                    truncation=True,
-                    return_tensors="np",
-                )
-
-                uncond_embeddings = self.text_encoder(uncond_input.input_ids)[self._text_encoder_output]
-
-                # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
-                seq_len = uncond_embeddings.shape[1]
-                uncond_embeddings = np.tile(uncond_embeddings, (1, num_images_per_prompt, 1))
-                uncond_embeddings = np.reshape(uncond_embeddings, (batch_size * num_images_per_prompt, seq_len, -1))
-
-                # For classifier free guidance, we need to do two forward passes.
-                # Here we concatenate the unconditional and text embeddings into a single batch
-                # to avoid doing two forward passes
-                text_embeddings = np.concatenate([uncond_embeddings, text_embeddings])
-
-            return text_embeddings
-
-        def prepare_latents(self, latent_timestep: torch.Tensor = None):
-            """
-            Function for getting initial latents for starting generation
-
-            Parameters:
-                latent_timestep (torch.Tensor, *optional*, None):
-                    Predicted by scheduler initial step for image generation, required for latent image mixing with nosie
-            Returns:
-                latents (np.ndarray):
-                    Image encoded in latent space
-            """
-            latents_shape = (1, 4, self.height // 8, self.width // 8)
-            noise = np.random.randn(*latents_shape).astype(np.float32)
-            # if we use LMSDiscreteScheduler, let's make sure latents are mulitplied by sigmas
-            if isinstance(self.scheduler, LMSDiscreteScheduler):
-                noise = noise * self.scheduler.sigmas[0].numpy()
-            return noise, {}
-
-        def postprocess_image(self, image: np.ndarray, meta: Dict, output_type: str = "pil"):
-            """
-            Postprocessing for decoded image. Takes generated image decoded by VAE decoder, unpad it to initila image size (if required),
-            normalize and convert to [0, 255] pixels range. Optionally, convertes it from np.ndarray to PIL.Image format
-
-            Parameters:
-                image (np.ndarray):
-                    Generated image
-                meta (Dict):
-                    Metadata obtained on latents preparing step, can be empty
-                output_type (str, *optional*, pil):
-                    Output format for result, can be pil or numpy
-            Returns:
-                image (List of np.ndarray or PIL.Image.Image):
-                    Postprocessed images
-            """
-            if "padding" in meta:
-                pad = meta["padding"]
-                (_, end_h), (_, end_w) = pad[1:3]
-                h, w = image.shape[2:]
-                unpad_h = h - end_h
-                unpad_w = w - end_w
-                image = image[:, :, :unpad_h, :unpad_w]
-            image = np.clip(image / 2 + 0.5, 0, 1)
-            image = np.transpose(image, (0, 2, 3, 1))
-            # 9. Convert to PIL
-            if output_type == "pil":
-                image = self.numpy_to_pil(image)
-                if "src_height" in meta:
-                    orig_height, orig_width = meta["src_height"], meta["src_width"]
-                    image = [img.resize((orig_width, orig_height), PIL.Image.Resampling.LANCZOS) for img in image]
-            else:
-                if "src_height" in meta:
-                    orig_height, orig_width = meta["src_height"], meta["src_width"]
-                    image = [cv2.resize(img, (orig_width, orig_width)) for img in image]
-            return image
-
-        def get_timesteps(self, num_inference_steps: int, strength: float):
-            """
-            Helper function for getting scheduler timesteps for generation
-            In case of image-to-image generation, it updates number of steps according to strength
-
-            Parameters:
-               num_inference_steps (int):
-                  number of inference steps for generation
-               strength (float):
-                   value between 0.0 and 1.0, that controls the amount of noise that is added to the input image.
-                   Values that approach 1.0 allow for lots of variations but will also produce images that are not semantically consistent with the input.
-            """
-            # get the original timestep using init_timestep
-            init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
-
-            t_start = max(num_inference_steps - init_timestep, 0)
-            timesteps = self.scheduler.timesteps[t_start:]
-
-            return timesteps, num_inference_steps - t_start
+        ov_pipe = OVStableDiffusionInpaintPipeline.from_pretrained(MODEL_DIR, device=DEVICE, compile=False)
+    
+    ov_pipe.compile()
 
 Zoom Video Generation
-~~~~~~~~~~~~~~~~~~~~~
+---------------------
 
 
 
 For achieving zoom effect, we will use inpainting to expand images
 beyond their original borders. We run our
-``OVStableDiffusionInpaintingPipeline`` in the loop, where each next
-frame will add edges to previous. The frame generation process
-illustrated on diagram below:
+``OVStableDiffusionInpaintPipeline`` in the loop, where each next frame
+will add edges to previous. The frame generation process illustrated on
+diagram below:
 
 .. figure:: https://user-images.githubusercontent.com/29454499/228739686-436f2759-4c79-42a2-a70f-959fb226834c.png
    :alt: frame generation)
@@ -877,11 +226,16 @@ generation is finished, we record frames in reversed order.
 
 .. code:: ipython3
 
+    from typing import List, Union
+    
+    import PIL
+    import cv2
     from tqdm import trange
-
-
+    import numpy as np
+    
+    
     def generate_video(
-        pipe: OVStableDiffusionInpaintingPipeline,
+        pipe,
         prompt: Union[str, List[str]],
         negative_prompt: Union[str, List[str]],
         guidance_scale: float = 7.5,
@@ -893,7 +247,7 @@ generation is finished, we record frames in reversed order.
     ):
         """
         Zoom video generation function
-
+    
         Parameters:
           pipe (OVStableDiffusionInpaintingPipeline): inpainting pipeline.
           prompt (str or List[str]): The prompt or prompts to guide the image generation.
@@ -906,36 +260,32 @@ generation is finished, we record frames in reversed order.
           num_inference_steps (int, *optional*, defaults to 50): The number of denoising steps for each frame. More denoising steps usually lead to a higher quality image at the expense of slower inference.
           num_frames (int, *optional*, 20): number frames for video.
           mask_width (int, *optional*, 128): size of border mask for inpainting on each step.
-          seed (int, *optional*, None): Seed for random generator state initialization.
           zoom_in (bool, *optional*, False): zoom mode Zoom In or Zoom Out.
         Returns:
           output_path (str): Path where generated video loacated.
         """
-
+    
         height = 512
         width = height
-
+    
         current_image = PIL.Image.new(mode="RGBA", size=(height, width))
         mask_image = np.array(current_image)[:, :, 3]
         mask_image = PIL.Image.fromarray(255 - mask_image).convert("RGB")
         current_image = current_image.convert("RGB")
-        pipe.set_progress_bar_config(desc="Generating initial image...")
         init_images = pipe(
             prompt=prompt,
             negative_prompt=negative_prompt,
             image=current_image,
             guidance_scale=guidance_scale,
             mask_image=mask_image,
-            seed=seed,
             num_inference_steps=num_inference_steps,
-        )["sample"]
-        pipe.set_progress_bar_config()
-
+        ).images
+    
         image_grid(init_images, rows=1, cols=1)
-
+    
         num_outpainting_steps = num_frames
         num_interpol_frames = 30
-
+    
         current_image = init_images[0]
         all_frames = []
         all_frames.append(current_image)
@@ -944,15 +294,15 @@ generation is finished, we record frames in reversed order.
             desc=f"Generating {num_outpainting_steps} additional images...",
         ):
             prev_image_fix = current_image
-
+    
             prev_image = shrink_and_paste_on_blank(current_image, mask_width)
-
+    
             current_image = prev_image
-
+    
             # create mask (black image with white mask_width width edges)
             mask_image = np.array(current_image)[:, :, 3]
             mask_image = PIL.Image.fromarray(255 - mask_image).convert("RGB")
-
+    
             # inpainting step
             current_image = current_image.convert("RGB")
             images = pipe(
@@ -961,12 +311,11 @@ generation is finished, we record frames in reversed order.
                 image=current_image,
                 guidance_scale=guidance_scale,
                 mask_image=mask_image,
-                seed=seed,
                 num_inference_steps=num_inference_steps,
-            )["sample"]
+            ).images
             current_image = images[0]
             current_image.paste(prev_image, mask=prev_image)
-
+    
             # interpolation steps bewteen 2 inpainted images (=sequential zoom and crop)
             for j in range(num_interpol_frames - 1):
                 interpol_image = current_image
@@ -979,16 +328,16 @@ generation is finished, we record frames in reversed order.
                         height - interpol_width,
                     )
                 )
-
+    
                 interpol_image = interpol_image.resize((height, width))
-
+    
                 # paste the higher resolution previous image in the middle to avoid drop in quality caused by zooming
                 interpol_width2 = round((1 - (height - 2 * mask_width) / (height - 2 * interpol_width)) / 2 * height)
                 prev_image_fix_crop = shrink_and_paste_on_blank(prev_image_fix, interpol_width2)
                 interpol_image.paste(prev_image_fix_crop, mask=prev_image_fix_crop)
                 all_frames.append(interpol_image)
             all_frames.append(current_image)
-
+    
         video_file_name = f"infinite_zoom_{'in' if zoom_in else 'out'}"
         fps = 30
         save_path = video_file_name + ".mp4"
@@ -1002,37 +351,37 @@ generation is finished, we record frames in reversed order.
         Decreases size of current_image by mask_width pixels from each side,
         then adds a mask_width width transparent frame,
         so that the image the function returns is the same size as the input.
-
+    
         Parameters:
             current_image (PIL.Image): input image to transform
             mask_width (int): width in pixels to shrink from each side
         Returns:
            prev_image (PIL.Image): resized image with extended borders
         """
-
+    
         height = current_image.height
         width = current_image.width
-
+    
         # shrink down by mask_width
         prev_image = current_image.resize((height - 2 * mask_width, width - 2 * mask_width))
         prev_image = prev_image.convert("RGBA")
         prev_image = np.array(prev_image)
-
+    
         # create blank non-transparent image
         blank_image = np.array(current_image.convert("RGBA")) * 0
         blank_image[:, :, 3] = 1
-
+    
         # paste shrinked onto blank
         blank_image[mask_width : height - mask_width, mask_width : width - mask_width, :] = prev_image
         prev_image = PIL.Image.fromarray(blank_image)
-
+    
         return prev_image
-
-
+    
+    
     def image_grid(imgs: List[PIL.Image.Image], rows: int, cols: int):
         """
         Insert images to grid
-
+    
         Parameters:
             imgs (List[PIL.Image.Image]): list of images for making grid
             rows (int): number of rows in grid
@@ -1041,15 +390,15 @@ generation is finished, we record frames in reversed order.
             grid (PIL.Image): image with input images collage
         """
         assert len(imgs) == rows * cols
-
+    
         w, h = imgs[0].size
         grid = PIL.Image.new("RGB", size=(cols * w, rows * h))
-
+    
         for i, img in enumerate(imgs):
             grid.paste(img, box=(i % cols * w, i // cols * h))
         return grid
-
-
+    
+    
     def write_video(
         file_path: str,
         frames: List[PIL.Image.Image],
@@ -1059,7 +408,7 @@ generation is finished, we record frames in reversed order.
     ):
         """
         Writes frames to an mp4 video file and optionaly to gif
-
+    
         Parameters:
             file_path (str): Path to output video, must end with .mp4
             frames (List of PIL.Image): list of frames
@@ -1071,17 +420,16 @@ generation is finished, we record frames in reversed order.
         """
         if reversed_order:
             frames.reverse()
-
+    
         w, h = frames[0].size
         fourcc = cv2.VideoWriter_fourcc("m", "p", "4", "v")
-        # fourcc = cv2.VideoWriter_fourcc(*'avc1')
         writer = cv2.VideoWriter(file_path, fourcc, fps, (w, h))
-
+    
         for frame in frames:
             np_frame = np.array(frame.convert("RGB"))
             cv_frame = cv2.cvtColor(np_frame, cv2.COLOR_RGB2BGR)
             writer.write(cv_frame)
-
+    
         writer.release()
         if gif:
             frames[0].save(
@@ -1092,80 +440,16 @@ generation is finished, we record frames in reversed order.
                 loop=0,
             )
 
-Configure Inference Pipeline
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-Configuration steps: 1. Load models on device 2. Configure tokenizer and
-scheduler 3. Create instance of ``OVStableDiffusionInpaintingPipeline``
-class
-
-.. code:: ipython3
-
-    core = ov.Core()
-
-    tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
-
-Select inference device
-~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-select device from dropdown list for running inference using OpenVINO
-
-.. code:: ipython3
-
-    import ipywidgets as widgets
-
-    device = widgets.Dropdown(
-        options=core.available_devices + ["AUTO"],
-        value="AUTO",
-        description="Device:",
-        disabled=False,
-    )
-
-    device
-
-
-
-
-.. parsed-literal::
-
-    Dropdown(description='Device:', index=2, options=('CPU', 'GNA', 'AUTO'), value='AUTO')
-
-
-
-.. code:: ipython3
-
-    ov_config = {"INFERENCE_PRECISION_HINT": "f32"} if device.value != "CPU" else {}
-
-
-    text_enc_inpaint = core.compile_model(TEXT_ENCODER_OV_PATH_INPAINT, device.value)
-    unet_model_inpaint = core.compile_model(UNET_OV_PATH_INPAINT, device.value)
-    vae_decoder_inpaint = core.compile_model(VAE_DECODER_OV_PATH_INPAINT, device.value, ov_config)
-    vae_encoder_inpaint = core.compile_model(VAE_ENCODER_OV_PATH_INPAINT, device.value, ov_config)
-
-    ov_pipe_inpaint = OVStableDiffusionInpaintingPipeline(
-        tokenizer=tokenizer,
-        text_encoder=text_enc_inpaint,
-        unet=unet_model_inpaint,
-        vae_encoder=vae_encoder_inpaint,
-        vae_decoder=vae_decoder_inpaint,
-        scheduler=scheduler_inpaint,
-    )
-
 Run Infinite Zoom video generation
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+----------------------------------
 
 
 
 .. code:: ipython3
 
     import gradio as gr
-    from socket import gethostbyname, gethostname
-
-
+    
+    
     def generate(
         prompt,
         negative_prompt,
@@ -1176,19 +460,21 @@ Run Infinite Zoom video generation
         zoom_in,
         progress=gr.Progress(track_tqdm=True),
     ):
+        np.random.seed(seed)
         video_path = generate_video(
-            ov_pipe_inpaint,
+            ov_pipe,
             prompt,
             negative_prompt,
             num_inference_steps=steps,
             num_frames=frames,
             mask_width=edge_size,
-            seed=seed,
             zoom_in=zoom_in,
         )
+        np.random.seed(None)
+    
         return video_path.replace(".mp4", ".gif")
-
-
+    
+    
     gr.close_all()
     demo = gr.Interface(
         generate,
@@ -1198,7 +484,7 @@ Run Infinite Zoom video generation
                 label="Prompt",
             ),
             gr.Textbox("lurry, bad art, blurred, text, watermark", label="Negative prompt"),
-            gr.Slider(value=9999, label="Seed", maximum=10000000),
+            gr.Slider(value=9999, label="Seed", step=1, maximum=10000000),
             gr.Slider(value=20, label="Steps", minimum=1, maximum=50),
             gr.Slider(value=3, label="Frames", minimum=1, maximum=50),
             gr.Slider(value=128, label="Edge size", minimum=32, maximum=256),
@@ -1206,26 +492,11 @@ Run Infinite Zoom video generation
         ],
         "image",
     )
-    ipaddr = gethostbyname(gethostname())
-    demo.queue().launch(share=True)
-
-
-.. parsed-literal::
-
-    Running on local URL:  http://127.0.0.1:7860
-    Running on public URL: https://372deef95f8b1d0168.gradio.live
-
-    This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)
-
-
-
-
-
-
-
-
-
-
-
-
-
+    
+    try:
+        demo.queue().launch()
+    except Exception:
+        demo.queue().launch(share=True)
+    # if you are launching remotely, specify server_name and server_port
+    # demo.launch(server_name='your server name', server_port='server port in int')
+    # Read more in the docs: https://gradio.app/docs/
diff --git a/docs/notebooks/stable-diffusion-v2-optimum-demo-with-output.rst b/docs/notebooks/stable-diffusion-v2-optimum-demo-with-output.rst
index 1a113eeee37fc8..2daf3371ef18e6 100644
--- a/docs/notebooks/stable-diffusion-v2-optimum-demo-with-output.rst
+++ b/docs/notebooks/stable-diffusion-v2-optimum-demo-with-output.rst
@@ -8,8 +8,8 @@ running multiple times.
 
 |image0|
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Showing Info Available Devices <#showing-info-available-devices>`__
 -  `Configure Inference Pipeline <#configure-inference-pipeline>`__
diff --git a/docs/notebooks/stable-diffusion-v2-text-to-image-demo-with-output.rst b/docs/notebooks/stable-diffusion-v2-text-to-image-demo-with-output.rst
index c5b9893462efea..266b1cbd4d32a7 100644
--- a/docs/notebooks/stable-diffusion-v2-text-to-image-demo-with-output.rst
+++ b/docs/notebooks/stable-diffusion-v2-text-to-image-demo-with-output.rst
@@ -21,8 +21,8 @@ like to see the full implementation of stable diffusion for text to
 image, please visit
 `stable-diffusion-v2-text-to-image <stable-diffusion-v2-with-output.html>`__.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Step 0: Install and import
    prerequisites <#step-0-install-and-import-prerequisites>`__
diff --git a/docs/notebooks/stable-diffusion-v2-text-to-image-with-output.rst b/docs/notebooks/stable-diffusion-v2-text-to-image-with-output.rst
index dbece89e181caf..7855e902ac972a 100644
--- a/docs/notebooks/stable-diffusion-v2-text-to-image-with-output.rst
+++ b/docs/notebooks/stable-diffusion-v2-text-to-image-with-output.rst
@@ -78,8 +78,8 @@ implementation. If you would like to get started and run the notebook
 quickly, check out `stable-diffusion-v2-text-to-image-demo
 notebook <stable-diffusion-v2-with-output.html>`__.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Stable Diffusion v2 for Text-to-Image
@@ -142,7 +142,7 @@ and original model
 Stable Diffusion in Diffusers library
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
- To work with Stable Diffusion
+To work with Stable Diffusion
 v2, we will use Hugging Face
 `Diffusers <https://github.com/huggingface/diffusers>`__ library. To
 experiment with Stable Diffusion models, Diffusers exposes the
@@ -155,9 +155,9 @@ using ``stable-diffusion-2-1``:
 .. code:: ipython3
 
     from diffusers import StableDiffusionPipeline
-    
+
     pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base").to("cpu")
-    
+
     # for reducing memory consumption get all components from pipeline independently
     text_encoder = pipe.text_encoder
     text_encoder.eval()
@@ -165,9 +165,9 @@ using ``stable-diffusion-2-1``:
     unet.eval()
     vae = pipe.vae
     vae.eval()
-    
+
     conf = pipe.scheduler.config
-    
+
     del pipe
 
 
@@ -218,7 +218,7 @@ hidden states.
 .. code:: ipython3
 
     from pathlib import Path
-    
+
     sd2_1_model_dir = Path("sd2.1")
     sd2_1_model_dir.mkdir(exist_ok=True)
 
@@ -227,10 +227,10 @@ hidden states.
     import gc
     import torch
     import openvino as ov
-    
+
     TEXT_ENCODER_OV_PATH = sd2_1_model_dir / "text_encoder.xml"
-    
-    
+
+
     def cleanup_torchscript_cache():
         """
         Helper for removing cached model representation
@@ -238,8 +238,8 @@ hidden states.
         torch._C._jit_clear_class_registry()
         torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore()
         torch.jit._state._clear_class_state()
-    
-    
+
+
     def convert_encoder(text_encoder: torch.nn.Module, ir_path: Path):
         """
         Convert Text Encoder model to IR.
@@ -254,7 +254,7 @@ hidden states.
             input_ids = torch.ones((1, 77), dtype=torch.long)
             # switch model to inference mode
             text_encoder.eval()
-    
+
             # disable gradients calculation for reducing memory consumption
             with torch.no_grad():
                 # export model
@@ -267,13 +267,13 @@ hidden states.
                 del ov_model
                 cleanup_torchscript_cache()
             print("Text Encoder successfully converted to IR")
-    
-    
+
+
     if not TEXT_ENCODER_OV_PATH.exists():
         convert_encoder(text_encoder, TEXT_ENCODER_OV_PATH)
     else:
         print(f"Text encoder will be loaded from {TEXT_ENCODER_OV_PATH}")
-    
+
     del text_encoder
     gc.collect();
 
@@ -312,10 +312,10 @@ such use cases required to modify number of input channels.
 .. code:: ipython3
 
     import numpy as np
-    
+
     UNET_OV_PATH = sd2_1_model_dir / "unet.xml"
-    
-    
+
+
     def convert_unet(
         unet: torch.nn.Module,
         ir_path: Path,
@@ -349,15 +349,15 @@ such use cases required to modify number of input channels.
                 shape = ov.PartialShape(tuple(input_tensor.shape))
                 element_type = dtype_mapping[input_tensor.dtype]
                 input_info.append((shape, element_type))
-    
+
             with torch.no_grad():
                 ov_model = ov.convert_model(unet, example_input=dummy_inputs, input=input_info)
             ov.save_model(ov_model, ir_path)
             del ov_model
             cleanup_torchscript_cache()
             print("U-Net successfully converted to IR")
-    
-    
+
+
     if not UNET_OV_PATH.exists():
         convert_unet(unet, UNET_OV_PATH, width=96, height=96)
         del unet
@@ -395,8 +395,8 @@ RAM (recommended at least 32GB).
 .. code:: ipython3
 
     VAE_ENCODER_OV_PATH = sd2_1_model_dir / "vae_encoder.xml"
-    
-    
+
+
     def convert_vae_encoder(vae: torch.nn.Module, ir_path: Path, width: int = 512, height: int = 512):
         """
         Convert VAE model to IR format.
@@ -410,15 +410,15 @@ RAM (recommended at least 32GB).
         Returns:
             None
         """
-    
+
         class VAEEncoderWrapper(torch.nn.Module):
             def __init__(self, vae):
                 super().__init__()
                 self.vae = vae
-    
+
             def forward(self, image):
                 return self.vae.encode(x=image)["latent_dist"].sample()
-    
+
         if not ir_path.exists():
             vae_encoder = VAEEncoderWrapper(vae)
             vae_encoder.eval()
@@ -429,8 +429,8 @@ RAM (recommended at least 32GB).
             del ov_model
             cleanup_torchscript_cache()
             print("VAE encoder successfully converted to IR")
-    
-    
+
+
     def convert_vae_decoder(vae: torch.nn.Module, ir_path: Path, width: int = 64, height: int = 64):
         """
         Convert VAE decoder model to IR format.
@@ -444,19 +444,19 @@ RAM (recommended at least 32GB).
         Returns:
             None
         """
-    
+
         class VAEDecoderWrapper(torch.nn.Module):
             def __init__(self, vae):
                 super().__init__()
                 self.vae = vae
-    
+
             def forward(self, latents):
                 return self.vae.decode(latents)
-    
+
         if not ir_path.exists():
             vae_decoder = VAEDecoderWrapper(vae)
             latents = torch.zeros((1, 4, width, height))
-    
+
             vae_decoder.eval()
             with torch.no_grad():
                 ov_model = ov.convert_model(vae_decoder, example_input=latents, input=([1, 4, width, height],))
@@ -464,20 +464,20 @@ RAM (recommended at least 32GB).
             del ov_model
             cleanup_torchscript_cache()
             print("VAE decoder successfully converted to IR")
-    
-    
+
+
     if not VAE_ENCODER_OV_PATH.exists():
         convert_vae_encoder(vae, VAE_ENCODER_OV_PATH, 768, 768)
     else:
         print(f"VAE encoder will be loaded from {VAE_ENCODER_OV_PATH}")
-    
+
     VAE_DECODER_OV_PATH = sd2_1_model_dir / "vae_decoder.xml"
-    
+
     if not VAE_DECODER_OV_PATH.exists():
         convert_vae_decoder(vae, VAE_DECODER_OV_PATH, 96, 96)
     else:
         print(f"VAE decoder will be loaded from {VAE_DECODER_OV_PATH}")
-    
+
     del vae
     gc.collect();
 
@@ -552,21 +552,21 @@ but there is some small difference in details:
 
     import inspect
     from typing import List, Optional, Union, Dict
-    
+
     import PIL
     import cv2
     import torch
-    
+
     from transformers import CLIPTokenizer
     from diffusers import DiffusionPipeline
     from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
-    
-    
+
+
     def scale_fit_to_window(dst_width: int, dst_height: int, image_width: int, image_height: int):
         """
         Preprocessing helper function for calculating image size for resize with peserving original aspect ratio
         and fitting image to specific window size
-    
+
         Parameters:
           dst_width (int): destination window width
           dst_height (int): destination window height
@@ -578,15 +578,15 @@ but there is some small difference in details:
         """
         im_scale = min(dst_height / image_height, dst_width / image_width)
         return int(im_scale * image_width), int(im_scale * image_height)
-    
-    
+
+
     def preprocess(image: PIL.Image.Image):
         """
         Image preprocessing function. Takes image in PIL.Image format, resizes it to keep aspect ration and fits to model input window 512x512,
         then converts it to np.ndarray and adds padding with zeros on right or bottom side of image (depends from aspect ratio), after that
         converts data to float32 data type and change range of values from [0, 255] to [-1, 1], finally, converts data layout from planar NHWC to NCHW.
         The function returns preprocessed input tensor and padding size, which can be used in postprocessing.
-    
+
         Parameters:
           image (PIL.Image.Image): input image
         Returns:
@@ -604,8 +604,8 @@ but there is some small difference in details:
         image = 2.0 * image - 1.0
         image = image.transpose(0, 3, 1, 2)
         return image, {"padding": pad, "src_width": src_width, "src_height": src_height}
-    
-    
+
+
     class OVStableDiffusionPipeline(DiffusionPipeline):
         def __init__(
             self,
@@ -648,7 +648,7 @@ but there is some small difference in details:
             self.height = self.unet.input(0).shape[2] * 8
             self.width = self.unet.input(0).shape[3] * 8
             self.tokenizer = tokenizer
-    
+
         def __call__(
             self,
             prompt: Union[str, List[str]],
@@ -709,14 +709,14 @@ but there is some small difference in details:
             extra_set_kwargs = {}
             if accepts_offset:
                 extra_set_kwargs["offset"] = 1
-    
+
             self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
             timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength)
             latent_timestep = timesteps[:1]
-    
+
             # get the initial random noise unless the user supplied it
             latents, meta = self.prepare_latents(image, latent_timestep)
-    
+
             # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
             # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
             # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
@@ -725,27 +725,27 @@ but there is some small difference in details:
             extra_step_kwargs = {}
             if accepts_eta:
                 extra_step_kwargs["eta"] = eta
-    
+
             for t in self.progress_bar(timesteps):
                 # expand the latents if we are doing classifier free guidance
                 latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
                 latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-    
+
                 # predict the noise residual
                 noise_pred = self.unet([latent_model_input, np.array(t, dtype=np.float32), text_embeddings])[self._unet_output]
                 # perform guidance
                 if do_classifier_free_guidance:
                     noise_pred_uncond, noise_pred_text = noise_pred[0], noise_pred[1]
                     noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-    
+
                 # compute the previous noisy sample x_t -> x_t-1
                 latents = self.scheduler.step(torch.from_numpy(noise_pred), t, torch.from_numpy(latents), **extra_step_kwargs)["prev_sample"].numpy()
             # scale and decode the image latents with vae
             image = self.vae_decoder(latents * (1 / 0.18215))[self._vae_d_output]
-    
+
             image = self.postprocess_image(image, meta, output_type)
             return {"sample": image}
-    
+
         def _encode_prompt(
             self,
             prompt: Union[str, List[str]],
@@ -755,7 +755,7 @@ but there is some small difference in details:
         ):
             """
             Encodes the prompt into text encoder hidden states.
-    
+
             Parameters:
                 prompt (str or list(str)): prompt to be encoded
                 num_images_per_prompt (int): number of images that should be generated per prompt
@@ -765,7 +765,7 @@ but there is some small difference in details:
                 text_embeddings (np.ndarray): text encoder hidden states
             """
             batch_size = len(prompt) if isinstance(prompt, list) else 1
-    
+
             # tokenize input prompts
             text_inputs = self.tokenizer(
                 prompt,
@@ -775,15 +775,15 @@ but there is some small difference in details:
                 return_tensors="np",
             )
             text_input_ids = text_inputs.input_ids
-    
+
             text_embeddings = self.text_encoder(text_input_ids)[self._text_encoder_output]
-    
+
             # duplicate text embeddings for each generation per prompt
             if num_images_per_prompt != 1:
                 bs_embed, seq_len, _ = text_embeddings.shape
                 text_embeddings = np.tile(text_embeddings, (1, num_images_per_prompt, 1))
                 text_embeddings = np.reshape(text_embeddings, (bs_embed * num_images_per_prompt, seq_len, -1))
-    
+
             # get unconditional embeddings for classifier free guidance
             if do_classifier_free_guidance:
                 uncond_tokens: List[str]
@@ -801,25 +801,25 @@ but there is some small difference in details:
                     truncation=True,
                     return_tensors="np",
                 )
-    
+
                 uncond_embeddings = self.text_encoder(uncond_input.input_ids)[self._text_encoder_output]
-    
+
                 # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                 seq_len = uncond_embeddings.shape[1]
                 uncond_embeddings = np.tile(uncond_embeddings, (1, num_images_per_prompt, 1))
                 uncond_embeddings = np.reshape(uncond_embeddings, (batch_size * num_images_per_prompt, seq_len, -1))
-    
+
                 # For classifier free guidance, we need to do two forward passes.
                 # Here we concatenate the unconditional and text embeddings into a single batch
                 # to avoid doing two forward passes
                 text_embeddings = np.concatenate([uncond_embeddings, text_embeddings])
-    
+
             return text_embeddings
-    
+
         def prepare_latents(self, image: PIL.Image.Image = None, latent_timestep: torch.Tensor = None):
             """
             Function for getting initial latents for starting generation
-    
+
             Parameters:
                 image (PIL.Image.Image, *optional*, None):
                     Input image for generation, if not provided randon noise will be used as starting point
@@ -841,12 +841,12 @@ but there is some small difference in details:
             latents = latents * 0.18215
             latents = self.scheduler.add_noise(torch.from_numpy(latents), torch.from_numpy(noise), latent_timestep).numpy()
             return latents, meta
-    
+
         def postprocess_image(self, image: np.ndarray, meta: Dict, output_type: str = "pil"):
             """
             Postprocessing for decoded image. Takes generated image decoded by VAE decoder, unpad it to initila image size (if required),
             normalize and convert to [0, 255] pixels range. Optionally, convertes it from np.ndarray to PIL.Image format
-    
+
             Parameters:
                 image (np.ndarray):
                     Generated image
@@ -878,12 +878,12 @@ but there is some small difference in details:
                     orig_height, orig_width = meta["src_height"], meta["src_width"]
                     image = [cv2.resize(img, (orig_width, orig_width)) for img in image]
             return image
-    
+
         def get_timesteps(self, num_inference_steps: int, strength: float):
             """
             Helper function for getting scheduler timesteps for generation
             In case of image-to-image generation, it updates number of steps according to strength
-    
+
             Parameters:
                num_inference_steps (int):
                   number of inference steps for generation
@@ -893,10 +893,10 @@ but there is some small difference in details:
             """
             # get the original timestep using init_timestep
             init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
-    
+
             t_start = max(num_inference_steps - init_timestep, 0)
             timesteps = self.scheduler.timesteps[t_start:]
-    
+
             return timesteps, num_inference_steps - t_start
 
 Configure Inference Pipeline
@@ -909,7 +909,7 @@ First, you should create instances of OpenVINO Model.
 .. code:: ipython3
 
     import ipywidgets as widgets
-    
+
     core = ov.Core()
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
@@ -917,7 +917,7 @@ First, you should create instances of OpenVINO Model.
         description="Device:",
         disabled=False,
     )
-    
+
     device
 
 
@@ -932,7 +932,7 @@ First, you should create instances of OpenVINO Model.
 .. code:: ipython3
 
     ov_config = {"INFERENCE_PRECISION_HINT": "f32"} if device.value != "CPU" else {}
-    
+
     text_enc = core.compile_model(TEXT_ENCODER_OV_PATH, device.value)
     unet_model = core.compile_model(UNET_OV_PATH, device.value)
     vae_decoder = core.compile_model(VAE_DECODER_OV_PATH, device.value, ov_config)
@@ -944,10 +944,10 @@ Let us define them and put all components together.
 .. code:: ipython3
 
     from transformers import CLIPTokenizer
-    
+
     scheduler = DDIMScheduler.from_config(conf)  # DDIMScheduler is used because UNet quantization produces better results with it
     tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
-    
+
     ov_pipe = OVStableDiffusionPipeline(
         tokenizer=tokenizer,
         text_encoder=text_enc,
@@ -1001,7 +1001,7 @@ improve model inference speed.
         description="Quantization",
         disabled=False,
     )
-    
+
     to_quantize
 
 
@@ -1017,14 +1017,14 @@ improve model inference speed.
 
     # Fetch `skip_kernel_extension` module
     import requests
-    
+
     r = requests.get(
         url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/skip_kernel_extension.py",
     )
     open("skip_kernel_extension.py", "w").write(r.text)
-    
+
     int8_ov_pipe = None
-    
+
     %load_ext skip_kernel_extension
 
 Prepare calibration dataset
@@ -1033,47 +1033,47 @@ Prepare calibration dataset
 
 
 We use a portion of
-`conceptual_captions <https://huggingface.co/datasets/conceptual_captions>`__
+`conceptual_captions <https://huggingface.co/datasets/google-research-datasets/conceptual_captions>`__
 dataset from Hugging Face as calibration data. To collect intermediate
 model inputs for calibration we should customize ``CompiledModel``.
 
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     import datasets
     import numpy as np
     from tqdm.notebook import tqdm
     from typing import Any, Dict, List
-    
-    
+
+
     def disable_progress_bar(pipeline, disable=True):
         if not hasattr(pipeline, "_progress_bar_config"):
             pipeline._progress_bar_config = {'disable': disable}
         else:
             pipeline._progress_bar_config['disable'] = disable
-    
-    
+
+
     class CompiledModelDecorator(ov.CompiledModel):
         def __init__(self, compiled_model: ov.CompiledModel, data_cache: List[Any] = None, keep_prob: float = 0.5):
             super().__init__(compiled_model)
             self.data_cache = data_cache if data_cache is not None else []
             self.keep_prob = keep_prob
-    
+
         def __call__(self, *args, **kwargs):
             if np.random.rand() <= self.keep_prob:
                 self.data_cache.append(*args)
             return super().__call__(*args, **kwargs)
-    
-    
+
+
     def collect_calibration_data(ov_pipe, calibration_dataset_size: int, num_inference_steps: int) -> List[Dict]:
         original_unet = ov_pipe.unet
         calibration_data = []
         ov_pipe.unet = CompiledModelDecorator(original_unet, calibration_data, keep_prob=0.7)
         disable_progress_bar(ov_pipe)
-    
-        dataset = datasets.load_dataset("conceptual_captions", split="train").shuffle(seed=42)
-    
+
+        dataset = datasets.load_dataset("google-research-datasets/conceptual_captions", split="train", trust_remote_code=True).shuffle(seed=42)
+
         # Run inference for data collection
         pbar = tqdm(total=calibration_dataset_size)
         for batch in dataset:
@@ -1084,7 +1084,7 @@ model inputs for calibration we should customize ``CompiledModel``.
             pbar.update(len(calibration_data) - pbar.n)
             if pbar.n >= calibration_dataset_size:
                 break
-    
+
         disable_progress_bar(ov_pipe, disable=False)
         ov_pipe.unet = original_unet
         return calibration_data
@@ -1097,17 +1097,17 @@ Run Hybrid Model Quantization
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     from collections import deque
     from transformers import set_seed
     import nncf
-    
+
     def get_operation_const_op(operation, const_port_id: int):
         node = operation.input_value(const_port_id).get_node()
         queue = deque([node])
         constant_node = None
         allowed_propagation_types_list = ["Convert", "FakeQuantize", "Reshape"]
-    
+
         while len(queue) != 0:
             curr_node = queue.popleft()
             if curr_node.get_type_name() == "Constant":
@@ -1117,10 +1117,10 @@ Run Hybrid Model Quantization
                 break
             if curr_node.get_type_name() in allowed_propagation_types_list:
                 queue.append(curr_node.input_value(0).get_node())
-    
+
         return constant_node
-    
-    
+
+
     def is_embedding(node) -> bool:
         allowed_types_list = ["f16", "f32", "f64"]
         const_port_id = 0
@@ -1129,10 +1129,10 @@ Run Hybrid Model Quantization
             const_node = get_operation_const_op(node, const_port_id)
             if const_node is not None:
                 return True
-    
+
         return False
-    
-    
+
+
     def collect_ops_with_weights(model):
         ops_with_weights = []
         for op in model.get_ops():
@@ -1143,9 +1143,9 @@ Run Hybrid Model Quantization
                     ops_with_weights.append(op.get_friendly_name())
             if op.get_type_name() == "Gather" and is_embedding(op):
                 ops_with_weights.append(op.get_friendly_name())
-    
+
         return ops_with_weights
-    
+
     UNET_INT8_OV_PATH = sd2_1_model_dir / 'unet_optimized.xml'
     if not UNET_INT8_OV_PATH.exists():
         calibration_dataset_size = 300
@@ -1153,15 +1153,15 @@ Run Hybrid Model Quantization
         unet_calibration_data = collect_calibration_data(ov_pipe,
                                                          calibration_dataset_size=calibration_dataset_size,
                                                          num_inference_steps=50)
-    
+
         unet = core.read_model(UNET_OV_PATH)
-        
+
         # Collect operations which weights will be compressed
         unet_ignored_scope = collect_ops_with_weights(unet)
-        
+
         # Compress model weights
         compressed_unet = nncf.compress_weights(unet, ignored_scope=nncf.IgnoredScope(types=['Convolution']))
-        
+
         # Quantize both weights and activations of Convolution layers
         quantized_unet = nncf.quantize(
             model=compressed_unet,
@@ -1171,7 +1171,7 @@ Run Hybrid Model Quantization
             ignored_scope=nncf.IgnoredScope(names=unet_ignored_scope),
             advanced_parameters=nncf.AdvancedQuantizationParameters(smooth_quant_alpha=-1)
         )
-        
+
         ov.save_model(quantized_unet, UNET_INT8_OV_PATH)
 
 
@@ -1183,7 +1183,7 @@ Run Hybrid Model Quantization
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     int8_unet_model = core.compile_model(UNET_INT8_OV_PATH, device.value)
     int8_ov_pipe = OVStableDiffusionPipeline(
         tokenizer=tokenizer,
@@ -1200,10 +1200,10 @@ Compare UNet file size
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     fp16_ir_model_size = UNET_OV_PATH.with_suffix(".bin").stat().st_size / 1024
     quantized_model_size = UNET_INT8_OV_PATH.with_suffix(".bin").stat().st_size / 1024
-    
+
     print(f"FP16 model size: {fp16_ir_model_size:.2f} KB")
     print(f"INT8 model size: {quantized_model_size:.2f} KB")
     print(f"Model compression rate: {fp16_ir_model_size / quantized_model_size:.3f}")
@@ -1231,9 +1231,9 @@ pipelines, we use median inference time on calibration subset.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     import time
-    
+
     def calculate_inference_time(pipeline, validation_data):
         inference_time = []
         pipeline.set_progress_bar_config(disable=True)
@@ -1248,11 +1248,11 @@ pipelines, we use median inference time on calibration subset.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     validation_size = 10
-    validation_dataset = datasets.load_dataset("conceptual_captions", split="train", streaming=True).take(validation_size)
+    validation_dataset = datasets.load_dataset("google-research-datasets/conceptual_captions", split="train", streaming=True, trust_remote_code=True).take(validation_size)
     validation_data = [batch["caption"] for batch in validation_dataset]
-    
+
     fp_latency = calculate_inference_time(ov_pipe, validation_data)
     int8_latency = calculate_inference_time(int8_ov_pipe, validation_data)
     print(f"Performance speed-up: {fp_latency / int8_latency:.3f}")
@@ -1289,13 +1289,13 @@ launch the interactive demo.
 .. code:: ipython3
 
     quantized_model_present = int8_ov_pipe is not None
-    
+
     use_quantized_model = widgets.Checkbox(
         value=True if quantized_model_present else False,
         description="Use quantized model",
         disabled=not quantized_model_present,
     )
-    
+
     use_quantized_model
 
 
@@ -1310,11 +1310,11 @@ launch the interactive demo.
 .. code:: ipython3
 
     import gradio as gr
-    
-    
+
+
     pipeline = int8_ov_pipe if use_quantized_model.value else ov_pipe
-    
-    
+
+
     def generate(prompt, negative_prompt, seed, num_steps, _=gr.Progress(track_tqdm=True)):
         result = pipeline(
             prompt,
@@ -1323,8 +1323,8 @@ launch the interactive demo.
             seed=seed,
         )
         return result["sample"][0]
-    
-    
+
+
     gr.close_all()
     demo = gr.Interface(
         generate,
@@ -1342,7 +1342,7 @@ launch the interactive demo.
         ],
         "image",
     )
-    
+
     try:
         demo.queue().launch()
     except Exception:
diff --git a/docs/notebooks/stable-diffusion-v3-with-output.rst b/docs/notebooks/stable-diffusion-v3-with-output.rst
new file mode 100644
index 00000000000000..462b13a0c42c93
--- /dev/null
+++ b/docs/notebooks/stable-diffusion-v3-with-output.rst
@@ -0,0 +1,1324 @@
+Image generation with Stable Diffusion v3 and OpenVINO
+======================================================
+
+Stable Diffusion V3 is next generation of latent diffusion image Stable
+Diffusion models family that outperforms state-of-the-art text-to-image
+generation systems in typography and prompt adherence, based on human
+preference evaluations. In comparison with previous versions, it based
+on Multimodal Diffusion Transformer (MMDiT) text-to-image model that
+features greatly improved performance in image quality, typography,
+complex prompt understanding, and resource-efficiency.
+
+.. figure:: https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/dd079427-89f2-4d28-a10e-c80792d750bf
+   :alt: mmdit.png
+
+   mmdit.png
+
+More details about model can be found in `model
+card <https://huggingface.co/stabilityai/stable-diffusion-3-medium>`__,
+`research
+paper <https://stability.ai/news/stable-diffusion-3-research-paper>`__
+and `Stability.AI blog
+post <https://stability.ai/news/stable-diffusion-3-medium>`__. In this
+tutorial, we will consider how to convert and optimize Stable Diffusion
+v3 for running with OpenVINO. If you want to run previous Stable
+Diffusion versions, please check our other notebooks:
+
+-  `Stable Diffusion <../stable-diffusion-text-to-image>`__
+-  `Stable Diffusion v2 <../stable-diffusion-v2>`__
+-  `Stable Diffusion XL <../stable-diffusion-xl>`__
+-  `LCM Stable
+   Diffusion <../latent-consistency-models-image-generation>`__
+-  `Turbo SDXL <../sdxl-turbo>`__
+-  `Turbo SD <../sketch-to-image-pix2pix-turbo>`__
+
+**Table of contents:**
+
+
+-  `Prerequisites <#prerequisites>`__
+-  `Build PyTorch pipeline <#build-pytorch-pipeline>`__
+-  `Convert and Optimize models with OpenVINO and
+   NNCF <#convert-and-optimize-models-with-openvino-and-nncf>`__
+
+   -  `Transformer <#transformer>`__
+   -  `T5 Text Encoder <#t5-text-encoder>`__
+   -  `Clip text encoders <#clip-text-encoders>`__
+   -  `VAE <#vae>`__
+
+-  `Prepare OpenVINO inference
+   pipeline <#prepare-openvino-inference-pipeline>`__
+-  `Run OpenVINO model <#run-openvino-model>`__
+-  `Interactive demo <#interactive-demo>`__
+
+Prerequisites
+-------------
+
+
+
+.. code:: ipython3
+
+    %pip install -q "git+https://github.com/initml/diffusers.git@clement/feature/flash_sd3" "gradio>=4.19" "torch>=2.1"  "transformers" "nncf>=2.11.0" "opencv-python" "pillow" "peft>=0.7.0" --extra-index-url https://download.pytorch.org/whl/cpu
+    %pip install -qU --pre  "openvino" --extra-index-url https://storage.openvinotoolkit.org/simple/wheels/nightly
+
+Build PyTorch pipeline
+----------------------
+
+
+
+   **Note**: run model with notebook, you will need to accept license
+   agreement. You must be a registered user in Hugging Face Hub.
+   Please visit `HuggingFace model
+   card <https://huggingface.co/stabilityai/stable-diffusion-3-medium-diffusers>`__,
+   carefully read terms of usage and click accept button. You will need
+   to use an access token for the code below to run. For more
+   information on access tokens, refer to `this section of the
+   documentation <https://huggingface.co/docs/hub/security-tokens>`__.
+   You can login on Hugging Face Hub in notebook environment, using
+   following code:
+
+.. code:: ipython3
+
+    # uncomment these lines to login to huggingfacehub to get access to pretrained model
+    
+    # from huggingface_hub import notebook_login, whoami
+    
+    # try:
+    #     whoami()
+    #     print('Authorization token already provided')
+    # except OSError:
+    #     notebook_login()
+
+We will use
+`Diffusers <https://huggingface.co/docs/diffusers/main/en/index>`__
+library integration for running Stable Diffusion v3 model. You can find
+more details in Diffusers
+`documentation <https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/stable_diffusion_3>`__.
+Additionally, we can apply optimization for pipeline performance and
+memory consumption:
+
+-  **Use flash SD3**. Flash Diffusion is a diffusion distillation method
+   proposed in `Flash Diffusion: Accelerating Any Conditional Diffusion
+   Model for Few Steps Image
+   Generation <http://arxiv.org/abs/2406.02347>`__. The model
+   represented as a 90.4M LoRA distilled version of SD3 model that is
+   able to generate 1024x1024 images in 4 steps. If you want disable it,
+   you can unset checkbox **Use flash SD3**
+-  **Remove T5 text encoder**. Removing the memory-intensive 4.7B
+   parameter T5-XXL text encoder during inference can significantly
+   decrease the memory requirements for SD3 with only a slight loss in
+   performance. If you want to use this model in pipeline, please set
+   **use t5 text encoder** checkbox.
+
+.. code:: ipython3
+
+    import ipywidgets as widgets
+    
+    use_flash_lora = widgets.Checkbox(
+        value=True,
+        description="Use flash SD3",
+        disabled=False,
+    )
+    
+    load_t5 = widgets.Checkbox(
+        value=False,
+        description="Use t5 text encoder",
+        disabled=False,
+    )
+    
+    pt_pipeline_options = widgets.VBox([use_flash_lora, load_t5])
+    display(pt_pipeline_options)
+
+
+
+.. parsed-literal::
+
+    VBox(children=(Checkbox(value=True, description='Use flash SD3'), Checkbox(value=False, description='Use t5 te…
+
+
+.. code:: ipython3
+
+    from pathlib import Path
+    import torch
+    from diffusers import StableDiffusion3Pipeline, SD3Transformer2DModel
+    from peft import PeftModel
+    
+    
+    MODEL_DIR = Path("stable-diffusion-3")
+    MODEL_DIR.mkdir(exist_ok=True)
+    
+    TRANSFORMER_PATH = MODEL_DIR / "transformer.xml"
+    VAE_DECODER_PATH = MODEL_DIR / "vae_decoder.xml"
+    TEXT_ENCODER_PATH = MODEL_DIR / "text_encoder.xml"
+    TEXT_ENCODER_2_PATH = MODEL_DIR / "text_encoder_2.xml"
+    TEXT_ENCODER_3_PATH = MODEL_DIR / "text_encoder_3.xml"
+    
+    conversion_statuses = [TRANSFORMER_PATH.exists(), VAE_DECODER_PATH.exists(), TEXT_ENCODER_PATH.exists(), TEXT_ENCODER_2_PATH.exists()]
+    
+    if load_t5.value:
+        conversion_statuses.append(TEXT_ENCODER_3_PATH.exists())
+    
+    requires_conversion = not all(conversion_statuses)
+    
+    transformer, vae, text_encoder, text_encoder_2, text_encoder_3 = None, None, None, None, None
+    
+    
+    def get_pipeline_components():
+        pipe_kwargs = {}
+        if use_flash_lora.value:
+            # Load LoRA
+            transformer = SD3Transformer2DModel.from_pretrained(
+                "stabilityai/stable-diffusion-3-medium-diffusers",
+                subfolder="transformer",
+            )
+            transformer = PeftModel.from_pretrained(transformer, "jasperai/flash-sd3")
+            pipe_kwargs["transformer"] = transformer
+        if not load_t5.value:
+            pipe_kwargs.update({"text_encoder_3": None, "tokenizer_3": None})
+        pipe = StableDiffusion3Pipeline.from_pretrained("stabilityai/stable-diffusion-3-medium-diffusers", **pipe_kwargs)
+        pipe.tokenizer.save_pretrained(MODEL_DIR / "tokenizer")
+        pipe.tokenizer_2.save_pretrained(MODEL_DIR / "tokenizer_2")
+        if load_t5.value:
+            pipe.tokenizer_3.save_pretrained(MODEL_DIR / "tokenizer_3")
+        pipe.scheduler.save_pretrained(MODEL_DIR / "scheduler")
+        transformer, vae, text_encoder, text_encoder_2, text_encoder_3 = None, None, None, None, None
+        if not TRANSFORMER_PATH.exists():
+            transformer = pipe.transformer
+            transformer.eval()
+        if not VAE_DECODER_PATH.exists():
+            vae = pipe.vae
+            vae.eval()
+        if not TEXT_ENCODER_PATH.exists():
+            text_encoder = pipe.text_encoder
+            text_encoder.eval()
+        if not TEXT_ENCODER_2_PATH.exists():
+            text_encoder_2 = pipe.text_encoder_2
+            text_encoder_2.eval()
+        if not TEXT_ENCODER_3_PATH.exists() and load_t5.value:
+            text_encoder_3 = pipe.text_encoder_3
+            text_encoder_3.eval()
+        return transformer, vae, text_encoder, text_encoder_2, text_encoder_3
+    
+    
+    if requires_conversion:
+        transformer, vae, text_encoder, text_encoder_2, text_encoder_3 = get_pipeline_components()
+
+
+.. parsed-literal::
+
+    /home/ea/work/notebooks_env/lib/python3.8/site-packages/diffusers/models/transformers/transformer_2d.py:34: FutureWarning: `Transformer2DModelOutput` is deprecated and will be removed in version 1.0.0. Importing `Transformer2DModelOutput` from `diffusers.models.transformer_2d` is deprecated and this will be removed in a future version. Please use `from diffusers.models.modeling_outputs import Transformer2DModelOutput`, instead.
+      deprecate("Transformer2DModelOutput", "1.0.0", deprecation_message)
+
+
+Convert and Optimize models with OpenVINO and NNCF
+--------------------------------------------------
+
+
+
+Starting from 2023.0 release, OpenVINO supports PyTorch models directly
+via Model Conversion API. ``ov.convert_model`` function accepts instance
+of PyTorch model and example inputs for tracing and returns object of
+``ov.Model`` class, ready to use or save on disk using ``ov.save_model``
+function.
+
+The pipeline consists of four important parts:
+
+-  Clip and T5 Text Encoders to create condition to generate an image
+   from a text prompt.
+-  Transformer for step-by-step denoising latent image representation.
+-  Autoencoder (VAE) for decoding latent space to image.
+
+For reducing model memory consumption and improving performance we will
+use weights compression. The `Weights
+Compression <https://docs.openvino.ai/2024/openvino-workflow/model-optimization-guide/weight-compression.html>`__
+algorithm is aimed at compressing the weights of the models and can be
+used to optimize the model footprint and performance of large models
+where the size of weights is relatively larger than the size of
+activations, for example, Large Language Models (LLM). Compared to INT8
+compression, INT4 compression improves performance even more, but
+introduces a minor drop in prediction quality.
+
+Let us convert and optimize each part:
+
+Transformer
+~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    import openvino as ov
+    from functools import partial
+    import gc
+    
+    
+    def cleanup_torchscript_cache():
+        """
+        Helper for removing cached model representation
+        """
+        torch._C._jit_clear_class_registry()
+        torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore()
+        torch.jit._state._clear_class_state()
+    
+    
+    class TransformerWrapper(torch.nn.Module):
+        def __init__(self, model):
+            super().__init__()
+            self.model = model
+    
+        def forward(self, hidden_states, encoder_hidden_states, pooled_projections, timestep, return_dict=False):
+            return self.model(
+                hidden_states=hidden_states,
+                encoder_hidden_states=encoder_hidden_states,
+                pooled_projections=pooled_projections,
+                timestep=timestep,
+                return_dict=return_dict,
+            )
+    
+    
+    if not TRANSFORMER_PATH.exists():
+        if isinstance(transformer, PeftModel):
+            transformer = TransformerWrapper(transformer)
+        transformer.forward = partial(transformer.forward, return_dict=False)
+    
+        with torch.no_grad():
+            ov_model = ov.convert_model(
+                transformer,
+                example_input={
+                    "hidden_states": torch.zeros((2, 16, 64, 64)),
+                    "timestep": torch.tensor([1, 1]),
+                    "encoder_hidden_states": torch.ones([2, 154, 4096]),
+                    "pooled_projections": torch.ones([2, 2048]),
+                },
+            )
+        ov.save_model(ov_model, TRANSFORMER_PATH)
+        del ov_model
+        cleanup_torchscript_cache()
+    
+    del transformer
+    gc.collect()
+
+
+
+
+.. parsed-literal::
+
+    20
+
+
+
+.. code:: ipython3
+
+    import ipywidgets as widgets
+    
+    to_compress_weights = widgets.Checkbox(
+        value=True,
+        description="Weights Compression",
+        disabled=False,
+    )
+    
+    to_compress_weights
+
+
+
+
+.. parsed-literal::
+
+    Checkbox(value=True, description='Weights Compression')
+
+
+
+.. code:: ipython3
+
+    import nncf
+    
+    core = ov.Core()
+    
+    TRANSFORMER_INT4_PATH = MODEL_DIR / "transformer_int4.xml"
+    
+    if to_compress_weights.value and not TRANSFORMER_INT4_PATH.exists():
+        transformer = core.read_model(TRANSFORMER_PATH)
+        compressed_transformer = nncf.compress_weights(transformer, mode=nncf.CompressWeightsMode.INT4_SYM, ratio=0.8, group_size=64)
+        ov.save_model(compressed_transformer, TRANSFORMER_INT4_PATH)
+        del compressed_transformer
+        del transformer
+        gc.collect()
+    
+    if TRANSFORMER_INT4_PATH.exists():
+        fp16_ir_model_size = TRANSFORMER_PATH.with_suffix(".bin").stat().st_size / 1024
+        compressed_model_size = TRANSFORMER_INT4_PATH.with_suffix(".bin").stat().st_size / 1024
+    
+        print(f"FP16 model size: {fp16_ir_model_size:.2f} KB")
+        print(f"INT8 model size: {compressed_model_size:.2f} KB")
+        print(f"Model compression rate: {fp16_ir_model_size / compressed_model_size:.3f}")
+
+
+.. parsed-literal::
+
+    INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, onnx, openvino
+    FP16 model size: 4243354.63 KB
+    INT8 model size: 1411706.74 KB
+    Model compression rate: 3.006
+
+
+T5 Text Encoder
+~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    if not TEXT_ENCODER_3_PATH.exists() and load_t5.value:
+        with torch.no_grad():
+            ov_model = ov.convert_model(text_encoder_3, example_input=torch.ones([1, 77], dtype=torch.long))
+        ov.save_model(ov_model, TEXT_ENCODER_3_PATH)
+        del ov_model
+        cleanup_torchscript_cache()
+    
+    del text_encoder_3
+    gc.collect()
+
+
+
+
+.. parsed-literal::
+
+    11
+
+
+
+.. code:: ipython3
+
+    if load_t5.value:
+        display(to_compress_weights)
+
+.. code:: ipython3
+
+    TEXT_ENCODER_3_INT4_PATH = MODEL_DIR / "text_encoder_3_int4.xml"
+    
+    if load_t5.value and to_compress_weights.value and not TEXT_ENCODER_3_INT4_PATH.exists():
+        encoder = core.read_model(TEXT_ENCODER_3_PATH)
+        compressed_encoder = nncf.compress_weights(encoder, mode=nncf.CompressWeightsMode.INT4_SYM, ratio=0.8, group_size=64)
+        ov.save_model(compressed_encoder, TEXT_ENCODER_3_INT4_PATH)
+        del compressed_encoder
+        del encoder
+        gc.collect()
+    
+    if TEXT_ENCODER_3_INT4_PATH.exists():
+        fp16_ir_model_size = TEXT_ENCODER_3_PATH.with_suffix(".bin").stat().st_size / 1024
+        compressed_model_size = TEXT_ENCODER_3_INT4_PATH.with_suffix(".bin").stat().st_size / 1024
+    
+        print(f"FP16 model size: {fp16_ir_model_size:.2f} KB")
+        print(f"INT8 model size: {compressed_model_size:.2f} KB")
+        print(f"Model compression rate: {fp16_ir_model_size / compressed_model_size:.3f}")
+
+Clip text encoders
+~~~~~~~~~~~~~~~~~~
+
+
+
+.. code:: ipython3
+
+    if not TEXT_ENCODER_PATH.exists():
+        with torch.no_grad():
+            text_encoder.forward = partial(text_encoder.forward, output_hidden_states=True, return_dict=False)
+            ov_model = ov.convert_model(text_encoder, example_input=torch.ones([1, 77], dtype=torch.long))
+        ov.save_model(ov_model, TEXT_ENCODER_PATH)
+        del ov_model
+        cleanup_torchscript_cache()
+    
+    del text_encoder
+    gc.collect()
+
+
+
+
+.. parsed-literal::
+
+    0
+
+
+
+.. code:: ipython3
+
+    if not TEXT_ENCODER_2_PATH.exists():
+        with torch.no_grad():
+            text_encoder_2.forward = partial(text_encoder_2.forward, output_hidden_states=True, return_dict=False)
+            ov_model = ov.convert_model(text_encoder_2, example_input=torch.ones([1, 77], dtype=torch.long))
+        ov.save_model(ov_model, TEXT_ENCODER_2_PATH)
+        del ov_model
+        cleanup_torchscript_cache()
+    
+    del text_encoder_2
+    gc.collect()
+
+
+
+
+.. parsed-literal::
+
+    0
+
+
+
+VAE
+~~~
+
+
+
+.. code:: ipython3
+
+    if not VAE_DECODER_PATH.exists():
+        with torch.no_grad():
+            vae.forward = vae.decode
+            ov_model = ov.convert_model(vae, example_input=torch.ones([1, 16, 64, 64]))
+        ov.save_model(ov_model, VAE_DECODER_PATH)
+    
+    del vae
+    gc.collect()
+
+
+
+
+.. parsed-literal::
+
+    0
+
+
+
+Prepare OpenVINO inference pipeline
+-----------------------------------
+
+
+
+.. code:: ipython3
+
+    import inspect
+    from typing import Callable, Dict, List, Optional, Union
+    
+    import torch
+    from transformers import (
+        CLIPTextModelWithProjection,
+        CLIPTokenizer,
+        T5EncoderModel,
+        T5TokenizerFast,
+    )
+    
+    from diffusers.image_processor import VaeImageProcessor
+    from diffusers.models.autoencoders import AutoencoderKL
+    from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
+    from diffusers.utils import (
+        logging,
+    )
+    from diffusers.utils.torch_utils import randn_tensor
+    from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+    from diffusers.pipelines.stable_diffusion_3.pipeline_output import StableDiffusion3PipelineOutput
+    
+    
+    logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+    
+    
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
+    def retrieve_timesteps(
+        scheduler,
+        num_inference_steps: Optional[int] = None,
+        device: Optional[Union[str, torch.device]] = None,
+        timesteps: Optional[List[int]] = None,
+        sigmas: Optional[List[float]] = None,
+        **kwargs,
+    ):
+        """
+        Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
+        custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
+    
+        Args:
+            scheduler (`SchedulerMixin`):
+                The scheduler to get timesteps from.
+            num_inference_steps (`int`):
+                The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
+                must be `None`.
+            device (`str` or `torch.device`, *optional*):
+                The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+            timesteps (`List[int]`, *optional*):
+                Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
+                `num_inference_steps` and `sigmas` must be `None`.
+            sigmas (`List[float]`, *optional*):
+                Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
+                `num_inference_steps` and `timesteps` must be `None`.
+    
+        Returns:
+            `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
+            second element is the number of inference steps.
+        """
+        if timesteps is not None and sigmas is not None:
+            raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
+        if timesteps is not None:
+            accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+            if not accepts_timesteps:
+                raise ValueError(
+                    f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                    f" timestep schedules. Please check whether you are using the correct scheduler."
+                )
+            scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
+            timesteps = scheduler.timesteps
+            num_inference_steps = len(timesteps)
+        elif sigmas is not None:
+            accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+            if not accept_sigmas:
+                raise ValueError(
+                    f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                    f" sigmas schedules. Please check whether you are using the correct scheduler."
+                )
+            scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
+            timesteps = scheduler.timesteps
+            num_inference_steps = len(timesteps)
+        else:
+            scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+            timesteps = scheduler.timesteps
+        return timesteps, num_inference_steps
+    
+    
+    class OVStableDiffusion3Pipeline(DiffusionPipeline):
+        r"""
+        Args:
+            transformer ([`SD3Transformer2DModel`]):
+                Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
+            scheduler ([`FlowMatchEulerDiscreteScheduler`]):
+                A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
+            vae ([`AutoencoderKL`]):
+                Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+            text_encoder ([`CLIPTextModelWithProjection`]):
+                [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
+                specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant,
+                with an additional added projection layer that is initialized with a diagonal matrix with the `hidden_size`
+                as its dimension.
+            text_encoder_2 ([`CLIPTextModelWithProjection`]):
+                [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
+                specifically the
+                [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)
+                variant.
+            text_encoder_3 ([`T5EncoderModel`]):
+                Frozen text-encoder. Stable Diffusion 3 uses
+                [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel), specifically the
+                [t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant.
+            tokenizer (`CLIPTokenizer`):
+                Tokenizer of class
+                [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+            tokenizer_2 (`CLIPTokenizer`):
+                Second Tokenizer of class
+                [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+            tokenizer_3 (`T5TokenizerFast`):
+                Tokenizer of class
+                [T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).
+        """
+    
+        _optional_components = []
+        _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds", "negative_pooled_prompt_embeds"]
+    
+        def __init__(
+            self,
+            transformer: SD3Transformer2DModel,
+            scheduler: FlowMatchEulerDiscreteScheduler,
+            vae: AutoencoderKL,
+            text_encoder: CLIPTextModelWithProjection,
+            tokenizer: CLIPTokenizer,
+            text_encoder_2: CLIPTextModelWithProjection,
+            tokenizer_2: CLIPTokenizer,
+            text_encoder_3: T5EncoderModel,
+            tokenizer_3: T5TokenizerFast,
+        ):
+            super().__init__()
+    
+            self.register_modules(
+                vae=vae,
+                text_encoder=text_encoder,
+                text_encoder_2=text_encoder_2,
+                text_encoder_3=text_encoder_3,
+                tokenizer=tokenizer,
+                tokenizer_2=tokenizer_2,
+                tokenizer_3=tokenizer_3,
+                transformer=transformer,
+                scheduler=scheduler,
+            )
+            self.vae_scale_factor = 2**3
+            self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
+            self.tokenizer_max_length = self.tokenizer.model_max_length if hasattr(self, "tokenizer") and self.tokenizer is not None else 77
+            self.vae_scaling_factor = 1.5305
+            self.vae_shift_factor = 0.0609
+            self.default_sample_size = 64
+    
+        def _get_t5_prompt_embeds(
+            self,
+            prompt: Union[str, List[str]] = None,
+            num_images_per_prompt: int = 1,
+        ):
+            prompt = [prompt] if isinstance(prompt, str) else prompt
+            batch_size = len(prompt)
+    
+            if self.text_encoder_3 is None:
+                return torch.zeros(
+                    (batch_size, self.tokenizer_max_length, 4096),
+                )
+    
+            text_inputs = self.tokenizer_3(
+                prompt,
+                padding="max_length",
+                max_length=self.tokenizer_max_length,
+                truncation=True,
+                add_special_tokens=True,
+                return_tensors="pt",
+            )
+            text_input_ids = text_inputs.input_ids
+            prompt_embeds = torch.from_numpy(self.text_encoder_3(text_input_ids)[0])
+            _, seq_len, _ = prompt_embeds.shape
+            prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+            prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+    
+            return prompt_embeds
+    
+        def _get_clip_prompt_embeds(
+            self,
+            prompt: Union[str, List[str]],
+            num_images_per_prompt: int = 1,
+            clip_skip: Optional[int] = None,
+            clip_model_index: int = 0,
+        ):
+            clip_tokenizers = [self.tokenizer, self.tokenizer_2]
+            clip_text_encoders = [self.text_encoder, self.text_encoder_2]
+    
+            tokenizer = clip_tokenizers[clip_model_index]
+            text_encoder = clip_text_encoders[clip_model_index]
+    
+            prompt = [prompt] if isinstance(prompt, str) else prompt
+            batch_size = len(prompt)
+    
+            text_inputs = tokenizer(prompt, padding="max_length", max_length=self.tokenizer_max_length, truncation=True, return_tensors="pt")
+    
+            text_input_ids = text_inputs.input_ids
+            prompt_embeds = text_encoder(text_input_ids)
+            pooled_prompt_embeds = torch.from_numpy(prompt_embeds[0])
+            hidden_states = list(prompt_embeds.values())[1:]
+    
+            if clip_skip is None:
+                prompt_embeds = torch.from_numpy(hidden_states[-2])
+            else:
+                prompt_embeds = torch.from_numpy(hidden_states[-(clip_skip + 2)])
+    
+            _, seq_len, _ = prompt_embeds.shape
+            prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+            prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+    
+            pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt, 1)
+            pooled_prompt_embeds = pooled_prompt_embeds.view(batch_size * num_images_per_prompt, -1)
+    
+            return prompt_embeds, pooled_prompt_embeds
+    
+        def encode_prompt(
+            self,
+            prompt: Union[str, List[str]],
+            prompt_2: Union[str, List[str]],
+            prompt_3: Union[str, List[str]],
+            num_images_per_prompt: int = 1,
+            do_classifier_free_guidance: bool = True,
+            negative_prompt: Optional[Union[str, List[str]]] = None,
+            negative_prompt_2: Optional[Union[str, List[str]]] = None,
+            negative_prompt_3: Optional[Union[str, List[str]]] = None,
+            prompt_embeds: Optional[torch.FloatTensor] = None,
+            negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+            pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+            negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+            clip_skip: Optional[int] = None,
+        ):
+            prompt = [prompt] if isinstance(prompt, str) else prompt
+            if prompt is not None:
+                batch_size = len(prompt)
+            else:
+                batch_size = prompt_embeds.shape[0]
+    
+            if prompt_embeds is None:
+                prompt_2 = prompt_2 or prompt
+                prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2
+    
+                prompt_3 = prompt_3 or prompt
+                prompt_3 = [prompt_3] if isinstance(prompt_3, str) else prompt_3
+    
+                prompt_embed, pooled_prompt_embed = self._get_clip_prompt_embeds(
+                    prompt=prompt,
+                    num_images_per_prompt=num_images_per_prompt,
+                    clip_skip=clip_skip,
+                    clip_model_index=0,
+                )
+                prompt_2_embed, pooled_prompt_2_embed = self._get_clip_prompt_embeds(
+                    prompt=prompt_2,
+                    num_images_per_prompt=num_images_per_prompt,
+                    clip_skip=clip_skip,
+                    clip_model_index=1,
+                )
+                clip_prompt_embeds = torch.cat([prompt_embed, prompt_2_embed], dim=-1)
+    
+                t5_prompt_embed = self._get_t5_prompt_embeds(
+                    prompt=prompt_3,
+                    num_images_per_prompt=num_images_per_prompt,
+                )
+    
+                clip_prompt_embeds = torch.nn.functional.pad(clip_prompt_embeds, (0, t5_prompt_embed.shape[-1] - clip_prompt_embeds.shape[-1]))
+    
+                prompt_embeds = torch.cat([clip_prompt_embeds, t5_prompt_embed], dim=-2)
+                pooled_prompt_embeds = torch.cat([pooled_prompt_embed, pooled_prompt_2_embed], dim=-1)
+    
+            if do_classifier_free_guidance and negative_prompt_embeds is None:
+                negative_prompt = negative_prompt or ""
+                negative_prompt_2 = negative_prompt_2 or negative_prompt
+                negative_prompt_3 = negative_prompt_3 or negative_prompt
+    
+                # normalize str to list
+                negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
+                negative_prompt_2 = batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2
+                negative_prompt_3 = batch_size * [negative_prompt_3] if isinstance(negative_prompt_3, str) else negative_prompt_3
+    
+                if prompt is not None and type(prompt) is not type(negative_prompt):
+                    raise TypeError(f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}.")
+                elif batch_size != len(negative_prompt):
+                    raise ValueError(
+                        f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                        f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                        " the batch size of `prompt`."
+                    )
+    
+                negative_prompt_embed, negative_pooled_prompt_embed = self._get_clip_prompt_embeds(
+                    negative_prompt,
+                    num_images_per_prompt=num_images_per_prompt,
+                    clip_skip=None,
+                    clip_model_index=0,
+                )
+                negative_prompt_2_embed, negative_pooled_prompt_2_embed = self._get_clip_prompt_embeds(
+                    negative_prompt_2,
+                    num_images_per_prompt=num_images_per_prompt,
+                    clip_skip=None,
+                    clip_model_index=1,
+                )
+                negative_clip_prompt_embeds = torch.cat([negative_prompt_embed, negative_prompt_2_embed], dim=-1)
+    
+                t5_negative_prompt_embed = self._get_t5_prompt_embeds(prompt=negative_prompt_3, num_images_per_prompt=num_images_per_prompt)
+    
+                negative_clip_prompt_embeds = torch.nn.functional.pad(
+                    negative_clip_prompt_embeds,
+                    (0, t5_negative_prompt_embed.shape[-1] - negative_clip_prompt_embeds.shape[-1]),
+                )
+    
+                negative_prompt_embeds = torch.cat([negative_clip_prompt_embeds, t5_negative_prompt_embed], dim=-2)
+                negative_pooled_prompt_embeds = torch.cat([negative_pooled_prompt_embed, negative_pooled_prompt_2_embed], dim=-1)
+    
+            return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
+    
+        def check_inputs(
+            self,
+            prompt,
+            prompt_2,
+            prompt_3,
+            height,
+            width,
+            negative_prompt=None,
+            negative_prompt_2=None,
+            negative_prompt_3=None,
+            prompt_embeds=None,
+            negative_prompt_embeds=None,
+            pooled_prompt_embeds=None,
+            negative_pooled_prompt_embeds=None,
+            callback_on_step_end_tensor_inputs=None,
+        ):
+            if height % 8 != 0 or width % 8 != 0:
+                raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+    
+            if callback_on_step_end_tensor_inputs is not None and not all(k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs):
+                raise ValueError(
+                    f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
+                )
+    
+            if prompt is not None and prompt_embeds is not None:
+                raise ValueError(
+                    f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two."
+                )
+            elif prompt_2 is not None and prompt_embeds is not None:
+                raise ValueError(
+                    f"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two."
+                )
+            elif prompt_3 is not None and prompt_embeds is not None:
+                raise ValueError(
+                    f"Cannot forward both `prompt_3`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two."
+                )
+            elif prompt is None and prompt_embeds is None:
+                raise ValueError("Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.")
+            elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+                raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+            elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):
+                raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}")
+            elif prompt_3 is not None and (not isinstance(prompt_3, str) and not isinstance(prompt_3, list)):
+                raise ValueError(f"`prompt_3` has to be of type `str` or `list` but is {type(prompt_3)}")
+    
+            if negative_prompt is not None and negative_prompt_embeds is not None:
+                raise ValueError(
+                    f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
+                    f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+                )
+            elif negative_prompt_2 is not None and negative_prompt_embeds is not None:
+                raise ValueError(
+                    f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:"
+                    f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+                )
+            elif negative_prompt_3 is not None and negative_prompt_embeds is not None:
+                raise ValueError(
+                    f"Cannot forward both `negative_prompt_3`: {negative_prompt_3} and `negative_prompt_embeds`:"
+                    f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+                )
+    
+            if prompt_embeds is not None and negative_prompt_embeds is not None:
+                if prompt_embeds.shape != negative_prompt_embeds.shape:
+                    raise ValueError(
+                        "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
+                        f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
+                        f" {negative_prompt_embeds.shape}."
+                    )
+    
+            if prompt_embeds is not None and pooled_prompt_embeds is None:
+                raise ValueError(
+                    "If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`."
+                )
+    
+            if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:
+                raise ValueError(
+                    "If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`."
+                )
+    
+        def prepare_latents(self, batch_size, num_channels_latents, height, width, generator, latents=None):
+            if latents is not None:
+                return latents
+    
+            shape = (batch_size, num_channels_latents, int(height) // self.vae_scale_factor, int(width) // self.vae_scale_factor)
+    
+            if isinstance(generator, list) and len(generator) != batch_size:
+                raise ValueError(
+                    f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                    f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+                )
+    
+            latents = randn_tensor(shape, generator=generator, device=torch.device("cpu"), dtype=torch.float32)
+    
+            return latents
+    
+        @property
+        def guidance_scale(self):
+            return self._guidance_scale
+    
+        @property
+        def clip_skip(self):
+            return self._clip_skip
+    
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        @property
+        def do_classifier_free_guidance(self):
+            return self._guidance_scale > 1
+    
+        @property
+        def joint_attention_kwargs(self):
+            return self._joint_attention_kwargs
+    
+        @property
+        def num_timesteps(self):
+            return self._num_timesteps
+    
+        @property
+        def interrupt(self):
+            return self._interrupt
+    
+        @torch.no_grad()
+        def __call__(
+            self,
+            prompt: Union[str, List[str]] = None,
+            prompt_2: Optional[Union[str, List[str]]] = None,
+            prompt_3: Optional[Union[str, List[str]]] = None,
+            height: Optional[int] = None,
+            width: Optional[int] = None,
+            num_inference_steps: int = 28,
+            timesteps: List[int] = None,
+            guidance_scale: float = 7.0,
+            negative_prompt: Optional[Union[str, List[str]]] = None,
+            negative_prompt_2: Optional[Union[str, List[str]]] = None,
+            negative_prompt_3: Optional[Union[str, List[str]]] = None,
+            num_images_per_prompt: Optional[int] = 1,
+            generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+            latents: Optional[torch.FloatTensor] = None,
+            prompt_embeds: Optional[torch.FloatTensor] = None,
+            negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+            pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+            negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+            output_type: Optional[str] = "pil",
+            return_dict: bool = True,
+            clip_skip: Optional[int] = None,
+            callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+            callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        ):
+            height = height or self.default_sample_size * self.vae_scale_factor
+            width = width or self.default_sample_size * self.vae_scale_factor
+    
+            # 1. Check inputs. Raise error if not correct
+            self.check_inputs(
+                prompt,
+                prompt_2,
+                prompt_3,
+                height,
+                width,
+                negative_prompt=negative_prompt,
+                negative_prompt_2=negative_prompt_2,
+                negative_prompt_3=negative_prompt_3,
+                prompt_embeds=prompt_embeds,
+                negative_prompt_embeds=negative_prompt_embeds,
+                pooled_prompt_embeds=pooled_prompt_embeds,
+                negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
+                callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
+            )
+    
+            self._guidance_scale = guidance_scale
+            self._clip_skip = clip_skip
+            self._interrupt = False
+    
+            # 2. Define call parameters
+            if prompt is not None and isinstance(prompt, str):
+                batch_size = 1
+            elif prompt is not None and isinstance(prompt, list):
+                batch_size = len(prompt)
+            else:
+                batch_size = prompt_embeds.shape[0]
+            results = self.encode_prompt(
+                prompt=prompt,
+                prompt_2=prompt_2,
+                prompt_3=prompt_3,
+                negative_prompt=negative_prompt,
+                negative_prompt_2=negative_prompt_2,
+                negative_prompt_3=negative_prompt_3,
+                do_classifier_free_guidance=self.do_classifier_free_guidance,
+                prompt_embeds=prompt_embeds,
+                negative_prompt_embeds=negative_prompt_embeds,
+                pooled_prompt_embeds=pooled_prompt_embeds,
+                negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
+                clip_skip=self.clip_skip,
+                num_images_per_prompt=num_images_per_prompt,
+            )
+    
+            (prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds) = results
+    
+            if self.do_classifier_free_guidance:
+                prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
+                pooled_prompt_embeds = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds], dim=0)
+    
+            # 4. Prepare timesteps
+            timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, timesteps)
+            num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+            self._num_timesteps = len(timesteps)
+    
+            # 5. Prepare latent variables
+            num_channels_latents = 16
+            latents = self.prepare_latents(batch_size * num_images_per_prompt, num_channels_latents, height, width, generator, latents)
+    
+            # 6. Denoising loop
+            with self.progress_bar(total=num_inference_steps) as progress_bar:
+                for i, t in enumerate(timesteps):
+                    if self.interrupt:
+                        continue
+    
+                    # expand the latents if we are doing classifier free guidance
+                    latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
+                    # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+                    timestep = t.expand(latent_model_input.shape[0])
+    
+                    noise_pred = self.transformer([latent_model_input, prompt_embeds, pooled_prompt_embeds, timestep])[0]
+    
+                    noise_pred = torch.from_numpy(noise_pred)
+    
+                    # perform guidance
+                    if self.do_classifier_free_guidance:
+                        noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                        noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
+    
+                    # compute the previous noisy sample x_t -> x_t-1
+                    latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+    
+                    if callback_on_step_end is not None:
+                        callback_kwargs = {}
+                        for k in callback_on_step_end_tensor_inputs:
+                            callback_kwargs[k] = locals()[k]
+                        callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+    
+                        latents = callback_outputs.pop("latents", latents)
+                        prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+                        negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
+                        negative_pooled_prompt_embeds = callback_outputs.pop("negative_pooled_prompt_embeds", negative_pooled_prompt_embeds)
+    
+                    # call the callback, if provided
+                    if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                        progress_bar.update()
+    
+            if output_type == "latent":
+                image = latents
+    
+            else:
+                latents = (latents / self.vae_scaling_factor) + self.vae_shift_factor
+    
+                image = torch.from_numpy(self.vae(latents)[0])
+                image = self.image_processor.postprocess(image, output_type=output_type)
+    
+            if not return_dict:
+                return (image,)
+    
+            return StableDiffusion3PipelineOutput(images=image)
+
+Run OpenVINO model
+------------------
+
+
+
+.. code:: ipython3
+
+    device = widgets.Dropdown(
+        options=core.available_devices + ["AUTO"],
+        value="CPU",
+        description="Device:",
+        disabled=False,
+    )
+    
+    device
+
+
+
+
+.. parsed-literal::
+
+    Dropdown(description='Device:', options=('CPU', 'GPU.0', 'GPU.1', 'AUTO'), value='CPU')
+
+
+
+.. code:: ipython3
+
+    use_int4_transformer = widgets.Checkbox(value=TRANSFORMER_INT4_PATH.exists(), description="INT4 transformer", disabled=not TRANSFORMER_INT4_PATH.exists())
+    
+    use_int4_t5 = widgets.Checkbox(value=TEXT_ENCODER_3_INT4_PATH.exists(), description="INT4 t5 text encoder", disabled=not TEXT_ENCODER_3_INT4_PATH.exists())
+    
+    v_box_widgets = []
+    if TRANSFORMER_INT4_PATH.exists():
+        v_box_widgets.append(use_int4_transformer)
+    
+    if load_t5.value and TEXT_ENCODER_3_INT4_PATH.exists():
+        v_box_widgets.append(use_int4_t5)
+    
+    if v_box_widgets:
+        model_options = widgets.VBox(v_box_widgets)
+        display(model_options)
+
+
+
+.. parsed-literal::
+
+    VBox(children=(Checkbox(value=True, description='INT4 transformer'),))
+
+
+.. code:: ipython3
+
+    ov_config = {}
+    if "GPU" in device.value:
+        ov_config["INFERENCE_PRECISION_HINT"] = "f32"
+    
+    transformer = core.compile_model(TRANSFORMER_PATH if not use_int4_transformer.value else TRANSFORMER_INT4_PATH, device.value)
+    text_encoder_3 = (
+        core.compile_model(TEXT_ENCODER_3_PATH if not use_int4_t5.value else TEXT_ENCODER_3_INT4_PATH, device.value, ov_config) if load_t5.value else None
+    )
+    text_encoder = core.compile_model(TEXT_ENCODER_PATH, device.value, ov_config)
+    text_encoder_2 = core.compile_model(TEXT_ENCODER_2_PATH, device.value, ov_config)
+    vae = core.compile_model(VAE_DECODER_PATH, device.value)
+
+.. code:: ipython3
+
+    from diffusers.schedulers import FlowMatchEulerDiscreteScheduler, FlashFlowMatchEulerDiscreteScheduler
+    from transformers import AutoTokenizer
+    
+    scheduler = (
+        FlowMatchEulerDiscreteScheduler.from_pretrained(MODEL_DIR / "scheduler")
+        if not use_flash_lora.value
+        else FlashFlowMatchEulerDiscreteScheduler.from_pretrained(MODEL_DIR / "scheduler")
+    )
+    
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_DIR / "tokenizer")
+    tokenizer_2 = AutoTokenizer.from_pretrained(MODEL_DIR / "tokenizer_2")
+    tokenizer_3 = AutoTokenizer.from_pretrained(MODEL_DIR / "tokenizer_3") if load_t5.value else None
+
+.. code:: ipython3
+
+    ov_pipe = OVStableDiffusion3Pipeline(transformer, scheduler, vae, text_encoder, tokenizer, text_encoder_2, tokenizer_2, text_encoder_3, tokenizer_3)
+
+.. code:: ipython3
+
+    image = ov_pipe(
+        "A raccoon trapped inside a glass jar full of colorful candies, the background is steamy with vivid colors",
+        negative_prompt="",
+        num_inference_steps=28 if not use_flash_lora.value else 4,
+        guidance_scale=5 if not use_flash_lora.value else 0,
+        height=512,
+        width=512,
+        generator=torch.Generator().manual_seed(141),
+    ).images[0]
+    image
+
+
+
+.. parsed-literal::
+
+      0%|          | 0/4 [00:00<?, ?it/s]
+
+
+
+
+.. image:: stable-diffusion-v3-with-output_files/stable-diffusion-v3-with-output_30_1.png
+
+
+
+Interactive demo
+----------------
+
+
+
+.. code:: ipython3
+
+    import gradio as gr
+    import numpy as np
+    import random
+    
+    MAX_SEED = np.iinfo(np.int32).max
+    MAX_IMAGE_SIZE = 1344
+    
+    
+    def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
+        if randomize_seed:
+            seed = random.randint(0, MAX_SEED)
+    
+        generator = torch.Generator().manual_seed(seed)
+    
+        image = ov_pipe(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            guidance_scale=guidance_scale,
+            num_inference_steps=num_inference_steps,
+            width=width,
+            height=height,
+            generator=generator,
+        ).images[0]
+    
+        return image, seed
+    
+    
+    examples = [
+        "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
+        "An astronaut riding a green horse",
+        "A delicious ceviche cheesecake slice",
+        "A panda reading a book in a lush forest.",
+        "A 3d render of a futuristic city with a giant robot in the middle full of neon lights, pink and blue colors",
+        'a wizard kitten holding a sign saying "openvino" with a magic wand.',
+        "photo of a huge red cat with green eyes sitting on a cloud in the sky, looking at the camera",
+        "Pirate ship sailing on a sea with the milky way galaxy in the sky and purple glow lights",
+    ]
+    
+    css = """
+    #col-container {
+        margin: 0 auto;
+        max-width: 580px;
+    }
+    """
+    
+    with gr.Blocks(css=css) as demo:
+        with gr.Column(elem_id="col-container"):
+            gr.Markdown(
+                """
+            # Demo [Stable Diffusion 3 Medium](https://huggingface.co/stabilityai/stable-diffusion-3-medium) with OpenVINO
+            """
+            )
+    
+            with gr.Row():
+                prompt = gr.Text(
+                    label="Prompt",
+                    show_label=False,
+                    max_lines=1,
+                    placeholder="Enter your prompt",
+                    container=False,
+                )
+    
+                run_button = gr.Button("Run", scale=0)
+    
+            result = gr.Image(label="Result", show_label=False)
+    
+            with gr.Accordion("Advanced Settings", open=False):
+                negative_prompt = gr.Text(
+                    label="Negative prompt",
+                    max_lines=1,
+                    placeholder="Enter a negative prompt",
+                )
+    
+                seed = gr.Slider(
+                    label="Seed",
+                    minimum=0,
+                    maximum=MAX_SEED,
+                    step=1,
+                    value=0,
+                )
+    
+                randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+    
+                with gr.Row():
+                    width = gr.Slider(
+                        label="Width",
+                        minimum=256,
+                        maximum=MAX_IMAGE_SIZE,
+                        step=64,
+                        value=512,
+                    )
+    
+                    height = gr.Slider(
+                        label="Height",
+                        minimum=256,
+                        maximum=MAX_IMAGE_SIZE,
+                        step=64,
+                        value=512,
+                    )
+    
+                with gr.Row():
+                    guidance_scale = gr.Slider(
+                        label="Guidance scale",
+                        minimum=0.0,
+                        maximum=10.0 if not use_flash_lora.value else 2,
+                        step=0.1,
+                        value=5.0 if not use_flash_lora.value else 0,
+                    )
+    
+                    num_inference_steps = gr.Slider(
+                        label="Number of inference steps",
+                        minimum=1,
+                        maximum=50,
+                        step=1,
+                        value=28 if not use_flash_lora.value else 4,
+                    )
+    
+            gr.Examples(examples=examples, inputs=[prompt])
+        gr.on(
+            triggers=[run_button.click, prompt.submit, negative_prompt.submit],
+            fn=infer,
+            inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+            outputs=[result, seed],
+        )
+    
+    # if you are launching remotely, specify server_name and server_port
+    #  demo.launch(server_name='your server name', server_port='server port in int')
+    # if you have any issue to launch on your platform, you can pass share=True to launch method:
+    # demo.launch(share=True)
+    # it creates a publicly shareable link for the interface. Read more in the docs: https://gradio.app/docs/
+    try:
+        demo.launch(debug=False)
+    except Exception:
+        demo.launch(debug=False, share=True)
diff --git a/docs/notebooks/stable-diffusion-v3-with-output_files/stable-diffusion-v3-with-output_30_1.jpg b/docs/notebooks/stable-diffusion-v3-with-output_files/stable-diffusion-v3-with-output_30_1.jpg
new file mode 100644
index 00000000000000..b1475036fbed41
--- /dev/null
+++ b/docs/notebooks/stable-diffusion-v3-with-output_files/stable-diffusion-v3-with-output_30_1.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5cc71854976b01cbab6f4c2bfddc0212818ea2f71b568b5ff277951a0cae806a
+size 49907
diff --git a/docs/notebooks/stable-diffusion-v3-with-output_files/stable-diffusion-v3-with-output_30_1.png b/docs/notebooks/stable-diffusion-v3-with-output_files/stable-diffusion-v3-with-output_30_1.png
new file mode 100644
index 00000000000000..129bbaf37186af
--- /dev/null
+++ b/docs/notebooks/stable-diffusion-v3-with-output_files/stable-diffusion-v3-with-output_30_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:76618dcde6706e2998c9907b453203debee9052d66bc54406697c29c6f8b73de
+size 504297
diff --git a/docs/notebooks/stable-diffusion-xl-with-output.rst b/docs/notebooks/stable-diffusion-xl-with-output.rst
index e0a6a6fa333075..58577f4460ec95 100644
--- a/docs/notebooks/stable-diffusion-xl-with-output.rst
+++ b/docs/notebooks/stable-diffusion-xl-with-output.rst
@@ -65,8 +65,8 @@ The tutorial consists of the following steps:
    **Note**: Some demonstrated models can require at least 64GB RAM for
    conversion and running.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Install prerequisites <#install-prerequisites>`__
 -  `SDXL Base model <#sdxl-base-model>`__
@@ -234,17 +234,17 @@ compression parameters.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -266,17 +266,17 @@ compression parameters.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -298,17 +298,17 @@ compression parameters.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -330,17 +330,17 @@ compression parameters.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -362,17 +362,17 @@ compression parameters.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
diff --git a/docs/notebooks/stable-video-diffusion-with-output.rst b/docs/notebooks/stable-video-diffusion-with-output.rst
index 11c526c37ec68d..3f0064b3217db4 100644
--- a/docs/notebooks/stable-video-diffusion-with-output.rst
+++ b/docs/notebooks/stable-video-diffusion-with-output.rst
@@ -11,8 +11,8 @@ apply `AnimateLCM <https://arxiv.org/abs/2402.00769>`__ LoRA weights and
 run optimization with
 `NNCF <https://github.com/openvinotoolkit/nncf/>`__.
 
-Table of contents:
-------------------
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Download PyTorch Model <#download-pytorch-model>`__
@@ -75,27 +75,27 @@ apply Consistency Distilled AnimateLCM weights.
     from safetensors import safe_open
     import gc
     import requests
-    
+
     lcm_scheduler_url = "https://huggingface.co/spaces/wangfuyun/AnimateLCM-SVD/raw/main/lcm_scheduler.py"
-    
+
     r = requests.get(lcm_scheduler_url)
-    
+
     with open("lcm_scheduler.py", "w") as f:
         f.write(r.text)
-    
+
     from lcm_scheduler import AnimateLCMSVDStochasticIterativeScheduler
     from huggingface_hub import hf_hub_download
-    
+
     MODEL_DIR = Path("model")
-    
+
     IMAGE_ENCODER_PATH = MODEL_DIR / "image_encoder.xml"
     VAE_ENCODER_PATH = MODEL_DIR / "vae_encoder.xml"
     VAE_DECODER_PATH = MODEL_DIR / "vae_decoder.xml"
     UNET_PATH = MODEL_DIR / "unet.xml"
-    
-    
+
+
     load_pt_pipeline = not (VAE_ENCODER_PATH.exists() and VAE_DECODER_PATH.exists() and UNET_PATH.exists() and IMAGE_ENCODER_PATH.exists())
-    
+
     unet, vae, image_encoder = None, None, None
     if load_pt_pipeline:
         noise_scheduler = AnimateLCMSVDStochasticIterativeScheduler(
@@ -126,7 +126,7 @@ apply Consistency Distilled AnimateLCM weights.
             for key in f.keys():
                 state_dict[key] = f.get_tensor(key)
         missing, unexpected = pipe.unet.load_state_dict(state_dict, strict=True)
-    
+
         pipe.scheduler.save_pretrained(MODEL_DIR / "scheduler")
         pipe.feature_extractor.save_pretrained(MODEL_DIR / "feature_extractor")
         unet = pipe.unet
@@ -137,7 +137,7 @@ apply Consistency Distilled AnimateLCM weights.
         image_encoder.eval()
         del pipe
         gc.collect()
-    
+
     # Load the conditioning image
     image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/svd/rocket.png?download=true")
     image = image.resize((512, 256))
@@ -170,8 +170,8 @@ Image Encoder
 .. code:: ipython3
 
     import openvino as ov
-    
-    
+
+
     def cleanup_torchscript_cache():
         """
         Helper for removing cached model representation
@@ -179,8 +179,8 @@ Image Encoder
         torch._C._jit_clear_class_registry()
         torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore()
         torch.jit._state._clear_class_state()
-    
-    
+
+
     if not IMAGE_ENCODER_PATH.exists():
         with torch.no_grad():
             ov_model = ov.convert_model(
@@ -215,7 +215,7 @@ U-net
         del ov_model
         cleanup_torchscript_cache()
         print(f"UNet successfully converted to IR and saved to {UNET_PATH}")
-    
+
     del unet
     gc.collect();
 
@@ -235,20 +235,20 @@ Encoder and Decoder.
         def __init__(self, vae):
             super().__init__()
             self.vae = vae
-    
+
         def forward(self, image):
             return self.vae.encode(x=image)["latent_dist"].sample()
-    
-    
+
+
     class VAEDecoderWrapper(torch.nn.Module):
         def __init__(self, vae):
             super().__init__()
             self.vae = vae
-    
+
         def forward(self, latents, num_frames: int):
             return self.vae.decode(latents, num_frames=num_frames)
-    
-    
+
+
     if not VAE_ENCODER_PATH.exists():
         vae_encoder = VAEEncoderWrapper(vae)
         with torch.no_grad():
@@ -258,7 +258,7 @@ Encoder and Decoder.
         print(f"VAE Encoder successfully converted to IR and saved to {VAE_ENCODER_PATH}")
         del vae_encoder
         gc.collect()
-    
+
     if not VAE_DECODER_PATH.exists():
         vae_decoder = VAEDecoderWrapper(vae)
         with torch.no_grad():
@@ -268,7 +268,7 @@ Encoder and Decoder.
         print(f"VAE Decoder successfully converted to IR and saved to {VAE_ENCODER_PATH}")
         del vae_decoder
         gc.collect()
-    
+
     del vae
     gc.collect();
 
@@ -312,38 +312,38 @@ frames.
     from diffusers.pipelines.stable_video_diffusion import (
         StableVideoDiffusionPipelineOutput,
     )
-    
-    
+
+
     def _append_dims(x, target_dims):
         """Appends dimensions to the end of a tensor until it has target_dims dimensions."""
         dims_to_append = target_dims - x.ndim
         if dims_to_append < 0:
             raise ValueError(f"input has {x.ndim} dims but target_dims is {target_dims}, which is less")
         return x[(...,) + (None,) * dims_to_append]
-    
-    
+
+
     def tensor2vid(video: torch.Tensor, processor, output_type="np"):
         # Based on:
         # https://github.com/modelscope/modelscope/blob/1509fdb973e5871f37148a4b5e5964cafd43e64d/modelscope/pipelines/multi_modal/text_to_video_synthesis_pipeline.py#L78
-    
+
         batch_size, channels, num_frames, height, width = video.shape
         outputs = []
         for batch_idx in range(batch_size):
             batch_vid = video[batch_idx].permute(1, 0, 2, 3)
             batch_output = processor.postprocess(batch_vid, output_type)
-    
+
             outputs.append(batch_output)
-    
+
         return outputs
-    
-    
+
+
     class OVStableVideoDiffusionPipeline(DiffusionPipeline):
         r"""
         Pipeline to generate video from an input image using Stable Video Diffusion.
-    
+
         This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
         implemented for all pipelines (downloading, saving, running on a particular device, etc.).
-    
+
         Args:
             vae ([`AutoencoderKL`]):
                 Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
@@ -356,7 +356,7 @@ frames.
             feature_extractor ([`~transformers.CLIPImageProcessor`]):
                 A `CLIPImageProcessor` to extract features from generated images.
         """
-    
+
         def __init__(
             self,
             vae_encoder,
@@ -375,20 +375,20 @@ frames.
             self.feature_extractor = feature_extractor
             self.vae_scale_factor = 2 ** (4 - 1)
             self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
-    
+
         def _encode_image(self, image, device, num_videos_per_prompt, do_classifier_free_guidance):
             dtype = torch.float32
-    
+
             if not isinstance(image, torch.Tensor):
                 image = self.image_processor.pil_to_numpy(image)
                 image = self.image_processor.numpy_to_pt(image)
-    
+
                 # We normalize the image before resizing to match with the original implementation.
                 # Then we unnormalize it after resizing.
                 image = image * 2.0 - 1.0
                 image = _resize_with_antialiasing(image, (224, 224))
                 image = (image + 1.0) / 2.0
-    
+
                 # Normalize the image with for CLIP input
                 image = self.feature_extractor(
                     images=image,
@@ -398,25 +398,25 @@ frames.
                     do_rescale=False,
                     return_tensors="pt",
                 ).pixel_values
-    
+
             image = image.to(device=device, dtype=dtype)
             image_embeddings = torch.from_numpy(self.image_encoder(image)[0])
             image_embeddings = image_embeddings.unsqueeze(1)
-    
+
             # duplicate image embeddings for each generation per prompt, using mps friendly method
             bs_embed, seq_len, _ = image_embeddings.shape
             image_embeddings = image_embeddings.repeat(1, num_videos_per_prompt, 1)
             image_embeddings = image_embeddings.view(bs_embed * num_videos_per_prompt, seq_len, -1)
-    
+
             if do_classifier_free_guidance:
                 negative_image_embeddings = torch.zeros_like(image_embeddings)
-    
+
                 # For classifier free guidance, we need to do two forward passes.
                 # Here we concatenate the unconditional and text embeddings into a single batch
                 # to avoid doing two forward passes
                 image_embeddings = torch.cat([negative_image_embeddings, image_embeddings])
             return image_embeddings
-    
+
         def _encode_vae_image(
             self,
             image: torch.Tensor,
@@ -425,20 +425,20 @@ frames.
             do_classifier_free_guidance,
         ):
             image_latents = torch.from_numpy(self.vae_encoder(image)[0])
-    
+
             if do_classifier_free_guidance:
                 negative_image_latents = torch.zeros_like(image_latents)
-    
+
                 # For classifier free guidance, we need to do two forward passes.
                 # Here we concatenate the unconditional and text embeddings into a single batch
                 # to avoid doing two forward passes
                 image_latents = torch.cat([negative_image_latents, image_latents])
-    
+
             # duplicate image_latents for each generation per prompt, using mps friendly method
             image_latents = image_latents.repeat(num_videos_per_prompt, 1, 1, 1)
-    
+
             return image_latents
-    
+
         def _get_add_time_ids(
             self,
             fps,
@@ -450,50 +450,50 @@ frames.
             do_classifier_free_guidance,
         ):
             add_time_ids = [fps, motion_bucket_id, noise_aug_strength]
-    
+
             passed_add_embed_dim = 256 * len(add_time_ids)
             expected_add_embed_dim = 3 * 256
-    
+
             if expected_add_embed_dim != passed_add_embed_dim:
                 raise ValueError(
                     f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`."
                 )
-    
+
             add_time_ids = torch.tensor([add_time_ids], dtype=dtype)
             add_time_ids = add_time_ids.repeat(batch_size * num_videos_per_prompt, 1)
-    
+
             if do_classifier_free_guidance:
                 add_time_ids = torch.cat([add_time_ids, add_time_ids])
-    
+
             return add_time_ids
-    
+
         def decode_latents(self, latents, num_frames, decode_chunk_size=14):
             # [batch, frames, channels, height, width] -> [batch*frames, channels, height, width]
             latents = latents.flatten(0, 1)
-    
+
             latents = 1 / 0.18215 * latents
-    
+
             # decode decode_chunk_size frames at a time to avoid OOM
             frames = []
             for i in range(0, latents.shape[0], decode_chunk_size):
                 frame = torch.from_numpy(self.vae_decoder([latents[i : i + decode_chunk_size], num_frames])[0])
                 frames.append(frame)
             frames = torch.cat(frames, dim=0)
-    
+
             # [batch*frames, channels, height, width] -> [batch, channels, frames, height, width]
             frames = frames.reshape(-1, num_frames, *frames.shape[1:]).permute(0, 2, 1, 3, 4)
-    
+
             # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
             frames = frames.float()
             return frames
-    
+
         def check_inputs(self, image, height, width):
             if not isinstance(image, torch.Tensor) and not isinstance(image, PIL.Image.Image) and not isinstance(image, list):
                 raise ValueError("`image` has to be of type `torch.FloatTensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is" f" {type(image)}")
-    
+
             if height % 8 != 0 or width % 8 != 0:
                 raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
-    
+
         def prepare_latents(
             self,
             batch_size,
@@ -518,16 +518,16 @@ frames.
                     f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
                     f" size of {batch_size}. Make sure the batch size matches the length of the generators."
                 )
-    
+
             if latents is None:
                 latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
             else:
                 latents = latents.to(device)
-    
+
             # scale the initial noise by the standard deviation required by the scheduler
             latents = latents * self.scheduler.init_noise_sigma
             return latents
-    
+
         @torch.no_grad()
         def __call__(
             self,
@@ -552,7 +552,7 @@ frames.
         ):
             r"""
             The call function to the pipeline for generation.
-    
+
             Args:discussed
                 image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.FloatTensor`):
                     Image or images to guide image generation. If you provide a tensor, it needs to be compatible with
@@ -564,8 +564,8 @@ frames.
                 num_frames (`int`, *optional*):
                     The number of video frames to generate. Defaults to 14 for `stable-video-diffusion-img2vid` and to 25 for `stable-video-diffusion-img2vid-xt`
                 num_inference_steps (`int`, *optional*, defaults to 25):
-    
-    
+
+
                     The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                     expense of slower inference. This parameter is modulated by `strength`.
                 min_guidance_scale (`float`, *optional*, defaults to 1.0):
@@ -606,24 +606,24 @@ frames.
                 return_dict (`bool`, *optional*, defaults to `True`):
                     Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
                     plain tuple.
-    
+
             Returns:
                 [`~pipelines.stable_diffusion.StableVideoDiffusionPipelineOutput`] or `tuple`:
                     If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableVideoDiffusionPipelineOutput`] is returned,
                     otherwise a `tuple` is returned where the first element is a list of list with the generated frames.
-    
+
             Examples:
-    
+
             ```py
             from diffusers import StableVideoDiffusionPipeline
             from diffusers.utils import load_image, export_to_video
-    
+
             pipe = StableVideoDiffusionPipeline.from_pretrained("stabilityai/stable-video-diffusion-img2vid-xt", torch_dtype=torch.float16, variant="fp16")
             pipe.to("cuda")
-    
+
             image = load_image("https://lh3.googleusercontent.com/y-iFOHfLTwkuQSUegpwDdgKmOjRSTvPxat63dQLB25xkTs4lhIbRUFeNBWZzYf370g=s1200")
             image = image.resize((1024, 576))
-    
+
             frames = pipe(image, num_frames=25, decode_chunk_size=8).frames[0]
             export_to_video(frames, "generated.mp4", fps=7)
             ```
@@ -631,13 +631,13 @@ frames.
             # 0. Default height and width to unet
             height = height or 96 * self.vae_scale_factor
             width = width or 96 * self.vae_scale_factor
-    
+
             num_frames = num_frames if num_frames is not None else 25
             decode_chunk_size = decode_chunk_size if decode_chunk_size is not None else num_frames
-    
+
             # 1. Check inputs. Raise error if not correct
             self.check_inputs(image, height, width)
-    
+
             # 2. Define call parameters
             if isinstance(image, PIL.Image.Image):
                 batch_size = 1
@@ -646,32 +646,32 @@ frames.
             else:
                 batch_size = image.shape[0]
             device = torch.device("cpu")
-    
+
             # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
             # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
             # corresponds to doing no classifier free guidance.
             do_classifier_free_guidance = max_guidance_scale > 1.0
-    
+
             # 3. Encode input image
             image_embeddings = self._encode_image(image, device, num_videos_per_prompt, do_classifier_free_guidance)
-    
+
             # NOTE: Stable Diffusion Video was conditioned on fps - 1, which
             # is why it is reduced here.
             # See: https://github.com/Stability-AI/generative-models/blob/ed0997173f98eaf8f4edf7ba5fe8f15c6b877fd3/scripts/sampling/simple_video_sample.py#L188
             fps = fps - 1
-    
+
             # 4. Encode input image using VAE
             image = self.image_processor.preprocess(image, height=height, width=width)
             noise = randn_tensor(image.shape, generator=generator, device=image.device, dtype=image.dtype)
             image = image + noise_aug_strength * noise
-    
+
             image_latents = self._encode_vae_image(image, device, num_videos_per_prompt, do_classifier_free_guidance)
             image_latents = image_latents.to(image_embeddings.dtype)
-    
+
             # Repeat the image latents for each frame so we can concatenate them with the noise
             # image_latents [batch, channels, height, width] ->[batch, num_frames, channels, height, width]
             image_latents = image_latents.unsqueeze(1).repeat(1, num_frames, 1, 1, 1)
-    
+
             # 5. Get Added Time IDs
             added_time_ids = self._get_add_time_ids(
                 fps,
@@ -683,7 +683,7 @@ frames.
                 do_classifier_free_guidance,
             )
             added_time_ids = added_time_ids
-    
+
             # 4. Prepare timesteps
             self.scheduler.set_timesteps(num_inference_steps, device=device)
             timesteps = self.scheduler.timesteps
@@ -700,13 +700,13 @@ frames.
                 generator,
                 latents,
             )
-    
+
             # 7. Prepare guidance scale
             guidance_scale = torch.linspace(min_guidance_scale, max_guidance_scale, num_frames).unsqueeze(0)
             guidance_scale = guidance_scale.to(device, latents.dtype)
             guidance_scale = guidance_scale.repeat(batch_size * num_videos_per_prompt, 1)
             guidance_scale = _append_dims(guidance_scale, latents.ndim)
-    
+
             # 8. Denoising loop
             num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
             num_timesteps = len(timesteps)
@@ -715,7 +715,7 @@ frames.
                     # expand the latents if we are doing classifier free guidance
                     latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
                     latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-    
+
                     # Concatenate image_latents over channels dimention
                     latent_model_input = torch.cat([latent_model_input, image_latents], dim=2)
                     # predict the noise residual
@@ -733,38 +733,38 @@ frames.
                     if do_classifier_free_guidance:
                         noise_pred_uncond, noise_pred_cond = noise_pred.chunk(2)
                         noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond)
-    
+
                     # compute the previous noisy sample x_t -> x_t-1
                     latents = self.scheduler.step(noise_pred, t, latents).prev_sample
-    
+
                     if callback_on_step_end is not None:
                         callback_kwargs = {}
                         for k in callback_on_step_end_tensor_inputs:
                             callback_kwargs[k] = locals()[k]
                         callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
-    
+
                         latents = callback_outputs.pop("latents", latents)
-    
+
                     if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                         progress_bar.update()
-    
+
             if not output_type == "latent":
                 frames = self.decode_latents(latents, num_frames, decode_chunk_size)
                 frames = tensor2vid(frames, self.image_processor, output_type=output_type)
             else:
                 frames = latents
-    
+
             if not return_dict:
                 return frames
-    
+
             return StableVideoDiffusionPipelineOutput(frames=frames)
-    
-    
+
+
     # resizing utils
     def _resize_with_antialiasing(input, size, interpolation="bicubic", align_corners=True):
         h, w = input.shape[-2:]
         factors = (h / size[0], w / size[1])
-    
+
         # First, we have to determine sigma
         # Taken from skimage: https://github.com/scikit-image/scikit-image/blob/v0.19.2/skimage/transform/_warps.py#L171
         sigmas = (
@@ -775,21 +775,21 @@ frames.
         # https://github.com/python-pillow/Pillow/blob/master/src/libImaging/Resample.c#L206
         # But they do it in the 2 passes, which gives better results. Let's try 2 sigmas for now
         ks = int(max(2.0 * 2 * sigmas[0], 3)), int(max(2.0 * 2 * sigmas[1], 3))
-    
+
         # Make sure it is odd
         if (ks[0] % 2) == 0:
             ks = ks[0] + 1, ks[1]
-    
+
         if (ks[1] % 2) == 0:
-    
+
             ks = ks[0], ks[1] + 1
-    
+
         input = _gaussian_blur2d(input, ks, sigmas)
-    
+
         output = torch.nn.functional.interpolate(input, size=size, mode=interpolation, align_corners=align_corners)
         return output
-    
-    
+
+
     def _compute_padding(kernel_size):
         """Compute padding tuple."""
         # 4 or 6 ints:  (padding_left, padding_right,padding_top,padding_bottom)
@@ -797,75 +797,75 @@ frames.
         if len(kernel_size) < 2:
             raise AssertionError(kernel_size)
         computed = [k - 1 for k in kernel_size]
-    
+
         # for even kernels we need to do asymmetric padding :(
         out_padding = 2 * len(kernel_size) * [0]
-    
+
         for i in range(len(kernel_size)):
             computed_tmp = computed[-(i + 1)]
-    
+
             pad_front = computed_tmp // 2
             pad_rear = computed_tmp - pad_front
-    
+
             out_padding[2 * i + 0] = pad_front
             out_padding[2 * i + 1] = pad_rear
-    
+
         return out_padding
-    
-    
+
+
     def _filter2d(input, kernel):
         # prepare kernel
         b, c, h, w = input.shape
         tmp_kernel = kernel[:, None, ...].to(device=input.device, dtype=input.dtype)
-    
+
         tmp_kernel = tmp_kernel.expand(-1, c, -1, -1)
-    
+
         height, width = tmp_kernel.shape[-2:]
-    
+
         padding_shape: list[int] = _compute_padding([height, width])
         input = torch.nn.functional.pad(input, padding_shape, mode="reflect")
-    
+
         # kernel and input tensor reshape to align element-wise or batch-wise params
         tmp_kernel = tmp_kernel.reshape(-1, 1, height, width)
         input = input.view(-1, tmp_kernel.size(0), input.size(-2), input.size(-1))
-    
+
         # convolve the tensor with the kernel.
         output = torch.nn.functional.conv2d(input, tmp_kernel, groups=tmp_kernel.size(0), padding=0, stride=1)
-    
+
         out = output.view(b, c, h, w)
         return out
-    
-    
+
+
     def _gaussian(window_size: int, sigma):
         if isinstance(sigma, float):
             sigma = torch.tensor([[sigma]])
-    
+
         batch_size = sigma.shape[0]
-    
+
         x = (torch.arange(window_size, device=sigma.device, dtype=sigma.dtype) - window_size // 2).expand(batch_size, -1)
-    
+
         if window_size % 2 == 0:
-    
+
             x = x + 0.5
-    
+
         gauss = torch.exp(-x.pow(2.0) / (2 * sigma.pow(2.0)))
-    
+
         return gauss / gauss.sum(-1, keepdim=True)
-    
-    
+
+
     def _gaussian_blur2d(input, kernel_size, sigma):
         if isinstance(sigma, tuple):
             sigma = torch.tensor([sigma], dtype=input.dtype)
         else:
             sigma = sigma.to(dtype=input.dtype)
-    
+
         ky, kx = int(kernel_size[0]), int(kernel_size[1])
         bs = sigma.shape[0]
         kernel_x = _gaussian(kx, sigma[:, 1].view(bs, 1))
         kernel_y = _gaussian(ky, sigma[:, 0].view(bs, 1))
         out_x = _filter2d(input, kernel_x[..., None, :])
         out = _filter2d(out_x, kernel_y[..., None])
-    
+
         return out
 
 Run Video Generation
@@ -881,16 +881,16 @@ Select Inference Device
 .. code:: ipython3
 
     import ipywidgets as widgets
-    
+
     core = ov.Core()
-    
+
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
         value="AUTO",
         description="Device:",
         disabled=False,
     )
-    
+
     device
 
 
@@ -905,8 +905,8 @@ Select Inference Device
 .. code:: ipython3
 
     from transformers import CLIPImageProcessor
-    
-    
+
+
     vae_encoder = core.compile_model(VAE_ENCODER_PATH, device.value)
     image_encoder = core.compile_model(IMAGE_ENCODER_PATH, device.value)
     unet = core.compile_model(UNET_PATH, device.value)
@@ -959,7 +959,7 @@ parameters into pipeline.
 .. code:: ipython3
 
     out_path = Path("generated.mp4")
-    
+
     export_to_video(frames, str(out_path), fps=7)
     frames[0].save(
         "generated.gif",
@@ -973,7 +973,7 @@ parameters into pipeline.
 .. code:: ipython3
 
     from IPython.display import HTML
-    
+
     HTML('<img src="generated.gif">')
 
 
@@ -1031,7 +1031,7 @@ improve model inference speed.
         description="Quantization",
         disabled=False,
     )
-    
+
     to_quantize
 
 
@@ -1047,17 +1047,17 @@ improve model inference speed.
 
     # Fetch `skip_kernel_extension` module
     import requests
-    
+
     r = requests.get(
         url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/skip_kernel_extension.py",
     )
     open("skip_kernel_extension.py", "w").write(r.text)
-    
+
     ov_int8_pipeline = None
     OV_INT8_UNET_PATH = MODEL_DIR / "unet_int8.xml"
     OV_INT8_VAE_ENCODER_PATH = MODEL_DIR / "vae_encoder_int8.xml"
     OV_INT8_VAE_DECODER_PATH = MODEL_DIR / "vae_decoder_int8.xml"
-    
+
     %load_ext skip_kernel_extension
 
 Prepare calibration dataset
@@ -1074,38 +1074,38 @@ model inputs for UNet optimization we should customize
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     from typing import Any
-    
+
     import datasets
     import numpy as np
     from tqdm.notebook import tqdm
     from IPython.utils import io
-    
-    
+
+
     class CompiledModelDecorator(ov.CompiledModel):
         def __init__(self, compiled_model: ov.CompiledModel, data_cache: List[Any] = None, keep_prob: float = 0.5):
             super().__init__(compiled_model)
             self.data_cache = data_cache if data_cache is not None else []
             self.keep_prob = keep_prob
-    
+
         def __call__(self, *args, **kwargs):
             if np.random.rand() <= self.keep_prob:
                 self.data_cache.append(*args)
             return super().__call__(*args, **kwargs)
-    
-    
+
+
     def collect_calibration_data(ov_pipe, calibration_dataset_size: int, num_inference_steps: int = 50) -> List[Dict]:
         original_unet = ov_pipe.unet
         calibration_data = []
         ov_pipe.unet = CompiledModelDecorator(original_unet, calibration_data, keep_prob=1)
-    
+
         dataset = datasets.load_dataset("fusing/instructpix2pix-1000-samples", split="train", streaming=False).shuffle(seed=42)
         # Run inference for data collection
         pbar = tqdm(total=calibration_dataset_size)
         for batch in dataset:
             image = batch["input_image"]
-    
+
             with io.capture_output() as captured:
                 ov_pipe(
                     image,
@@ -1119,14 +1119,14 @@ model inputs for UNet optimization we should customize
             pbar.update(len(calibration_data) - pbar.n)
             if len(calibration_data) >= calibration_dataset_size:
                 break
-    
+
         ov_pipe.unet = original_unet
         return calibration_data[:calibration_dataset_size]
 
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     if not OV_INT8_UNET_PATH.exists():
         subset_size = 200
         calibration_data = collect_calibration_data(ov_pipe, calibration_dataset_size=subset_size)
@@ -1139,15 +1139,15 @@ Run Hybrid Model Quantization
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     from collections import deque
-    
+
     def get_operation_const_op(operation, const_port_id: int):
         node = operation.input_value(const_port_id).get_node()
         queue = deque([node])
         constant_node = None
         allowed_propagation_types_list = ["Convert", "FakeQuantize", "Reshape"]
-    
+
         while len(queue) != 0:
             curr_node = queue.popleft()
             if curr_node.get_type_name() == "Constant":
@@ -1157,10 +1157,10 @@ Run Hybrid Model Quantization
                 break
             if curr_node.get_type_name() in allowed_propagation_types_list:
                 queue.append(curr_node.input_value(0).get_node())
-    
+
         return constant_node
-    
-    
+
+
     def is_embedding(node) -> bool:
         allowed_types_list = ["f16", "f32", "f64"]
         const_port_id = 0
@@ -1169,10 +1169,10 @@ Run Hybrid Model Quantization
             const_node = get_operation_const_op(node, const_port_id)
             if const_node is not None:
                 return True
-    
+
         return False
-    
-    
+
+
     def collect_ops_with_weights(model):
         ops_with_weights = []
         for op in model.get_ops():
@@ -1183,19 +1183,19 @@ Run Hybrid Model Quantization
                     ops_with_weights.append(op.get_friendly_name())
             if op.get_type_name() == "Gather" and is_embedding(op):
                 ops_with_weights.append(op.get_friendly_name())
-    
+
         return ops_with_weights
 
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     import nncf
     import logging
     from nncf.quantization.advanced_parameters import AdvancedSmoothQuantParameters
-    
+
     nncf.set_log_level(logging.ERROR)
-    
+
     if not OV_INT8_UNET_PATH.exists():
         diffusion_model = core.read_model(UNET_PATH)
         unet_ignored_scope = collect_ops_with_weights(diffusion_model)
@@ -1224,14 +1224,14 @@ applied for footprint reduction.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     nncf.set_log_level(logging.INFO)
-    
+
     if not OV_INT8_VAE_ENCODER_PATH.exists():
         text_encoder_model = core.read_model(VAE_ENCODER_PATH)
         compressed_text_encoder_model = nncf.compress_weights(text_encoder_model, mode=nncf.CompressWeightsMode.INT4_SYM, group_size=64)
         ov.save_model(compressed_text_encoder_model, OV_INT8_VAE_ENCODER_PATH)
-    
+
     if not OV_INT8_VAE_DECODER_PATH.exists():
         decoder_model = core.read_model(VAE_DECODER_PATH)
         compressed_decoder_model = nncf.compress_weights(decoder_model, mode=nncf.CompressWeightsMode.INT4_SYM, group_size=64)
@@ -1257,17 +1257,17 @@ applied for footprint reduction.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -1290,17 +1290,17 @@ applied for footprint reduction.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -1310,15 +1310,15 @@ pipelines.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     ov_int8_vae_encoder = core.compile_model(OV_INT8_VAE_ENCODER_PATH, device.value)
     ov_int8_unet = core.compile_model(OV_INT8_UNET_PATH, device.value)
     ov_int8_decoder = core.compile_model(OV_INT8_VAE_DECODER_PATH, device.value)
-    
+
     ov_int8_pipeline = OVStableVideoDiffusionPipeline(
         ov_int8_vae_encoder, image_encoder, ov_int8_unet, ov_int8_decoder, scheduler, feature_extractor
     )
-    
+
     int8_frames = ov_int8_pipeline(
         image,
         num_inference_steps=4,
@@ -1357,7 +1357,7 @@ pipelines.
 .. code:: ipython3
 
     int8_out_path = Path("generated_int8.mp4")
-    
+
     export_to_video(frames, str(out_path), fps=7)
     int8_frames[0].save(
         "generated_int8.gif",
@@ -1386,10 +1386,10 @@ Compare model file sizes
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     fp16_model_paths = [VAE_ENCODER_PATH, UNET_PATH, VAE_DECODER_PATH]
     int8_model_paths = [OV_INT8_VAE_ENCODER_PATH, OV_INT8_UNET_PATH, OV_INT8_VAE_DECODER_PATH]
-    
+
     for fp16_path, int8_path in zip(fp16_model_paths, int8_model_paths):
         fp16_ir_model_size = fp16_path.with_suffix(".bin").stat().st_size
         int8_model_size = int8_path.with_suffix(".bin").stat().st_size
@@ -1418,9 +1418,9 @@ pipelines, we use median inference time on calibration subset.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     import time
-    
+
     def calculate_inference_time(pipeline, validation_data):
         inference_time = []
         for prompt in validation_data:
@@ -1443,11 +1443,11 @@ pipelines, we use median inference time on calibration subset.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     validation_size = 3
     validation_dataset = datasets.load_dataset("fusing/instructpix2pix-1000-samples", split="train", streaming=True).shuffle(seed=42).take(validation_size)
     validation_data = [data["input_image"] for data in validation_dataset]
-    
+
     fp_latency = calculate_inference_time(ov_pipe, validation_data)
     int8_latency = calculate_inference_time(ov_int8_pipeline, validation_data)
     print(f"Performance speed-up: {fp_latency / int8_latency:.3f}")
@@ -1469,13 +1469,13 @@ launch the interactive demo.
 .. code:: ipython3
 
     quantized_model_present = ov_int8_pipeline is not None
-    
+
     use_quantized_model = widgets.Checkbox(
         value=quantized_model_present,
         description="Use quantized model",
         disabled=not quantized_model_present,
     )
-    
+
     use_quantized_model
 
 
@@ -1491,10 +1491,10 @@ launch the interactive demo.
 
     import gradio as gr
     import random
-    
+
     max_64_bit_int = 2**63 - 1
     pipeline = ov_int8_pipeline if use_quantized_model.value else ov_pipe
-    
+
     example_images_urls = [
         "https://huggingface.co/spaces/wangfuyun/AnimateLCM-SVD/resolve/main/test_imgs/ship-7833921_1280.jpg?download=true",
         "https://huggingface.co/spaces/wangfuyun/AnimateLCM-SVD/resolve/main/test_imgs/ai-generated-8476858_1280.png?download=true",
@@ -1502,18 +1502,18 @@ launch the interactive demo.
         "https://huggingface.co/spaces/wangfuyun/AnimateLCM-SVD/resolve/main/test_imgs/dog-7396912_1280.jpg?download=true",
         "https://huggingface.co/spaces/wangfuyun/AnimateLCM-SVD/resolve/main/test_imgs/cupcakes-380178_1280.jpg?download=true",
     ]
-    
+
     example_images_dir = Path("example_images")
     example_images_dir.mkdir(exist_ok=True)
     example_imgs = []
-    
+
     for image_id, url in enumerate(example_images_urls):
         img = load_image(url)
         image_path = example_images_dir / f"{image_id}.png"
         img.save(image_path)
         example_imgs.append([image_path])
-    
-    
+
+
     def sample(
         image: PIL.Image,
         seed: Optional[int] = 42,
@@ -1530,16 +1530,16 @@ launch the interactive demo.
     ):
         if image.mode == "RGBA":
             image = image.convert("RGB")
-    
+
         if randomize_seed:
             seed = random.randint(0, max_64_bit_int)
         generator = torch.manual_seed(seed)
-    
+
         output_folder = Path(output_folder)
         output_folder.mkdir(exist_ok=True)
         base_count = len(list(output_folder.glob("*.mp4")))
         video_path = output_folder / f"{base_count:06d}.mp4"
-    
+
         frames = pipeline(
             image,
             decode_chunk_size=decoding_t,
@@ -1552,15 +1552,15 @@ launch the interactive demo.
             min_guidance_scale=min_guidance_scale,
         ).frames[0]
         export_to_video(frames, str(video_path), fps=fps_id)
-    
+
         return video_path, seed
-    
-    
+
+
     def resize_image(image, output_size=(512, 320)):
         # Calculate aspect ratios
         target_aspect = output_size[0] / output_size[1]  # Aspect ratio of the desired size
         image_aspect = image.width / image.height  # Aspect ratio of the original image
-    
+
         # Resize then crop if the original image is larger
         if image_aspect > target_aspect:
             # Resize the image to match the target height, maintaining aspect ratio
@@ -1582,12 +1582,12 @@ launch the interactive demo.
             top = (new_height - output_size[1]) / 2
             right = output_size[0]
             bottom = (new_height + output_size[1]) / 2
-    
+
         # Crop the image
         cropped_image = resized_image.crop((left, top, right, bottom))
         return cropped_image
-    
-    
+
+
     with gr.Blocks() as demo:
         gr.Markdown(
             """# Stable Video Diffusion: Image to Video Generation with OpenVINO.
@@ -1644,7 +1644,7 @@ launch the interactive demo.
             inputs=[image_in],
             outputs=[video, seed],
         )
-    
+
         image_in.upload(fn=resize_image, inputs=image_in, outputs=image_in)
         generate_btn.click(
             fn=sample,
@@ -1662,8 +1662,8 @@ launch the interactive demo.
             outputs=[video, seed],
             api_name="video",
         )
-    
-    
+
+
     try:
         demo.queue().launch(debug=False)
     except Exception:
diff --git a/docs/notebooks/stable-zephyr-3b-chatbot-with-output.rst b/docs/notebooks/stable-zephyr-3b-chatbot-with-output.rst
index e49b78e0ebb4a1..b7885fc6bf474c 100644
--- a/docs/notebooks/stable-zephyr-3b-chatbot-with-output.rst
+++ b/docs/notebooks/stable-zephyr-3b-chatbot-with-output.rst
@@ -38,8 +38,8 @@ estimate performance for Pytorch and OpenVINO models using almost the
 same code. We also demonstrate how to make model stateful, that provides
 opportunity for processing model cache state.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Convert model to OpenVINO Intermediate Representation (IR) and
diff --git a/docs/notebooks/style-transfer-with-output.rst b/docs/notebooks/style-transfer-with-output.rst
index c317c37d623720..f164b760aea483 100644
--- a/docs/notebooks/style-transfer-with-output.rst
+++ b/docs/notebooks/style-transfer-with-output.rst
@@ -28,8 +28,8 @@ Additionally, you can also upload a video file.
    server, the webcam will not work but you can run inference, using a
    video file.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Preparation <#preparation>`__
 
@@ -64,22 +64,20 @@ Install requirements
 
     %pip install -q "openvino>=2023.1.0"
     %pip install -q opencv-python requests tqdm
-    
+
     # Fetch `notebook_utils` module
     import requests
-    
+
     r = requests.get(
         url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
-    
+
     open("notebook_utils.py", "w").write(r.text)
 
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
 
 
@@ -87,7 +85,7 @@ Install requirements
 
 .. parsed-literal::
 
-    21503
+    23215
 
 
 
@@ -100,14 +98,14 @@ Imports
 
     import collections
     import time
-    
+
     import cv2
     import numpy as np
     from pathlib import Path
     import ipywidgets as widgets
     from IPython.display import display, clear_output, Image
     import openvino as ov
-    
+
     import notebook_utils as utils
 
 Select one of the styles below: Mosaic, Rain Princess, Candy, Udnie, and
@@ -123,17 +121,17 @@ Pointilism to do the style transfer.
         disabled=False,
         style={"description_width": "initial"},  # Adjust the width as needed
     )
-    
-    
+
+
     # Function to handle changes in dropdown and print the selected style
     def print_style(change):
         if change["type"] == "change" and change["name"] == "value":
             print(f"Selected style {change['new']}")
-    
-    
+
+
     # Observe changes in the dropdown value
     style_dropdown.observe(print_style, names="value")
-    
+
     # Display the dropdown
     display(style_dropdown)
 
@@ -166,10 +164,10 @@ OpenVINO Intermediate Representation (IR) with ``FP16`` precision.
     # Directory to download the model from ONNX model zoo
     base_model_dir = "model"
     base_url = "https://github.com/onnx/models/raw/69d69010b7ed6ba9438c392943d2715026792d40/archive/vision/style_transfer/fast_neural_style/model"
-    
+
     # Selected ONNX model will be downloaded in the path
     model_path = Path(f"{style_dropdown.value.lower()}-9.onnx")
-    
+
     style_url = f"{base_url}/{model_path}"
     utils.download_file(style_url, directory=base_model_dir)
 
@@ -184,7 +182,7 @@ OpenVINO Intermediate Representation (IR) with ``FP16`` precision.
 
 .. parsed-literal::
 
-    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/style-transfer-webcam/model/mosaic-9.onnx')
+    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/style-transfer-webcam/model/mosaic-9.onnx')
 
 
 
@@ -207,7 +205,7 @@ this step.
 .. code:: ipython3
 
     # Construct the command for model conversion API.
-    
+
     ov_model = ov.convert_model(f"model/{style_dropdown.value.lower()}-9.onnx")
     ov.save_model(ov_model, f"model/{style_dropdown.value.lower()}-9.xml")
 
@@ -246,25 +244,25 @@ results.
 
     # Initialize OpenVINO Runtime.
     core = ov.Core()
-    
+
     # Read the network and corresponding weights from ONNX Model.
     # model = ie_core.read_model(model=onnx_path)
-    
+
     # Read the network and corresponding weights from IR Model.
     model = core.read_model(model=ir_path)
 
 .. code:: ipython3
 
     import ipywidgets as widgets
-    
+
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
         value="AUTO",
         description="Device:",
         disabled=False,
     )
-    
-    
+
+
     # Compile the model for CPU (or change to GPU, etc. for other devices)
     # or let OpenVINO select the best available device with AUTO.
     device
@@ -281,7 +279,7 @@ results.
 .. code:: ipython3
 
     compiled_model = core.compile_model(model=model, device_name=device.value)
-    
+
     # Get the input and output nodes.
     input_layer = compiled_model.input(0)
     output_layer = compiled_model.output(0)
@@ -295,7 +293,7 @@ respectively. For *fast-neural-style-mosaic-onnx*, there is 1 input and
     print(input_layer.any_name, output_layer.any_name)
     print(input_layer.shape)
     print(output_layer.shape)
-    
+
     # Get the input size.
     N, C, H, W = list(input_layer.shape)
 
@@ -310,7 +308,7 @@ respectively. For *fast-neural-style-mosaic-onnx*, there is 1 input and
 Preprocess the image
 ~~~~~~~~~~~~~~~~~~~~
 
- Preprocess the input image
+Preprocess the input image
 before running the model. Prepare the dimensions and channel order for
 the image to match the original image with the input tensor
 
@@ -323,7 +321,7 @@ the image to match the original image with the input tensor
     def preprocess_images(frame, H, W):
         """
         Preprocess input image to align with network size
-    
+
         Parameters:
             :param frame:  input frame
             :param H:  height of the frame to style transfer model
@@ -353,7 +351,7 @@ shape .
     def convert_result_to_image(frame, stylized_image) -> np.ndarray:
         """
         Postprocess stylized image for visualization
-    
+
         Parameters:
             :param frame:  input frame
             :param stylized_image:  stylized image with specific style applied
@@ -398,7 +396,7 @@ either using a webcam or a video file.
             if use_popup:
                 title = "Press ESC to Exit"
                 cv2.namedWindow(winname=title, flags=cv2.WINDOW_GUI_NORMAL | cv2.WINDOW_AUTOSIZE)
-    
+
             processing_times = collections.deque()
             while True:
                 # Grab the frame.
@@ -417,24 +415,24 @@ either using a webcam or a video file.
                         interpolation=cv2.INTER_AREA,
                     )
                 # Preprocess the input image.
-    
+
                 image = preprocess_images(frame, H, W)
-    
+
                 # Measure processing time for the input image.
                 start_time = time.time()
                 # Perform the inference step.
                 stylized_image = compiled_model([image])[output_layer]
                 stop_time = time.time()
-    
+
                 # Postprocessing for stylized image.
                 result_image = convert_result_to_image(frame, stylized_image)
-    
+
                 processing_times.append(stop_time - start_time)
                 # Use processing times from last 200 frames.
                 if len(processing_times) > 200:
                     processing_times.popleft()
                 processing_time_det = np.mean(processing_times) * 1000
-    
+
                 # Visualize the results.
                 f_height, f_width = frame.shape[:2]
                 fps = 1000 / processing_time_det
@@ -448,7 +446,7 @@ either using a webcam or a video file.
                     thickness=1,
                     lineType=cv2.LINE_AA,
                 )
-    
+
                 # Use this workaround if there is flickering.
                 if use_popup:
                     cv2.imshow(title, result_image)
@@ -501,12 +499,12 @@ OpenCV <https://docs.opencv.org/4.5.1/dd/d43/tutorial_py_video_display.html>`__
 .. code:: ipython3
 
     USE_WEBCAM = False
-    
+
     cam_id = 0
     video_file = "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/video/Coco%20Walking%20in%20Berkeley.mp4"
-    
+
     source = cam_id if USE_WEBCAM else video_file
-    
+
     run_style_transfer(source=source, flip=isinstance(source, int), use_popup=False)
 
 
diff --git a/docs/notebooks/style-transfer-with-output_files/style-transfer-with-output_25_0.png b/docs/notebooks/style-transfer-with-output_files/style-transfer-with-output_25_0.png
index 651bd46b220ded..4126ff51d9e754 100644
--- a/docs/notebooks/style-transfer-with-output_files/style-transfer-with-output_25_0.png
+++ b/docs/notebooks/style-transfer-with-output_files/style-transfer-with-output_25_0.png
@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:9df74f6472d730508d502804f10e45985303be50c45dfba53f1ef613a862e09a
-size 82036
+oid sha256:c3d4c8235a99ace18d03a8bd0d0f02bfa63782f05fdf908ec6f7bcdf62178402
+size 81834
diff --git a/docs/notebooks/table-question-answering-with-output.rst b/docs/notebooks/table-question-answering-with-output.rst
index b2b6400ca0b1f3..7f75c038e5538b 100644
--- a/docs/notebooks/table-question-answering-with-output.rst
+++ b/docs/notebooks/table-question-answering-with-output.rst
@@ -28,8 +28,8 @@ architecture to encode tables as input, initializes from an effective
 joint pre-training of text segments and tables crawled from Wikipedia,
 and is trained end-to-end.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Use the original model to run an
@@ -54,9 +54,7 @@ Prerequisites
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
 
 
@@ -71,10 +69,10 @@ Prerequisites
 
 .. parsed-literal::
 
-    2024-06-06 03:34:48.995636: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-06-06 03:34:49.030539: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-07-02 03:29:39.469445: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-07-02 03:29:39.504710: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2024-06-06 03:34:49.574086: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-07-02 03:29:40.041573: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 Use ``TapasForQuestionAnswering.from_pretrained`` to download a
@@ -95,6 +93,12 @@ tokenizer.
     table
 
 
+.. parsed-literal::
+
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+      warnings.warn(
+
+
 
 
 .. raw:: html
@@ -210,57 +214,57 @@ function to serialize the result of conversion.
 .. parsed-literal::
 
     [ WARNING ]  Please fix your imports. Module %s has been moved to %s. The old module will be deleted in version %s.
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4481: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4371: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
       warnings.warn(
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1571: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1570: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
       self.indices = torch.as_tensor(indices)
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1572: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1571: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
       self.num_segments = torch.as_tensor(num_segments, device=indices.device)
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1674: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1673: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
       batch_size = torch.prod(torch.tensor(list(index.batch_shape())))
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1750: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1749: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
       [torch.as_tensor([-1], dtype=torch.long), torch.as_tensor(vector_shape, dtype=torch.long)], dim=0
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1753: TracerWarning: Converting a tensor to a Python list might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1752: TracerWarning: Converting a tensor to a Python list might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       flat_values = values.reshape(flattened_shape.tolist())
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1755: TracerWarning: Converting a tensor to a Python integer might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1754: TracerWarning: Converting a tensor to a Python integer might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       out = torch.zeros(int(flat_index.num_segments), dtype=torch.float, device=flat_values.device)
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1763: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1762: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
       torch.as_tensor(index.batch_shape(), dtype=torch.long),
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1764: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1763: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
       torch.as_tensor([index.num_segments], dtype=torch.long),
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1765: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1764: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
       torch.as_tensor(vector_shape, dtype=torch.long),
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1770: TracerWarning: Converting a tensor to a Python list might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1769: TracerWarning: Converting a tensor to a Python list might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       output_values = segment_means.clone().view(new_shape.tolist()).to(values.dtype)
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1701: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1700: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
       batch_shape = torch.as_tensor(
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1705: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1704: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
       num_segments = torch.as_tensor(num_segments)  # create a rank 0 tensor (scalar) containing num_segments (e.g. 64)
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1716: TracerWarning: Converting a tensor to a Python list might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1715: TracerWarning: Converting a tensor to a Python list might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       new_shape = [int(x) for x in new_tensor.tolist()]
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1719: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1718: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
       multiples = torch.cat([batch_shape, torch.as_tensor([1])], dim=0)
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1720: TracerWarning: Converting a tensor to a Python list might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1719: TracerWarning: Converting a tensor to a Python list might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       indices = indices.repeat(multiples.tolist())
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:283: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:286: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
       torch.as_tensor(self.config.max_position_embeddings - 1, device=device), position - first_position
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1231: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1230: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
       indices=torch.min(row_ids, torch.as_tensor(self.config.max_num_rows - 1, device=row_ids.device)),
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1236: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1235: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
       indices=torch.min(column_ids, torch.as_tensor(self.config.max_num_columns - 1, device=column_ids.device)),
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1928: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1927: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
       column_logits += CLOSE_ENOUGH_TO_LOG_ZERO * torch.as_tensor(
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1933: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1932: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
       column_logits += CLOSE_ENOUGH_TO_LOG_ZERO * torch.as_tensor(
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1969: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1968: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
       labels_per_column, _ = reduce_sum(torch.as_tensor(labels, dtype=torch.float32, device=labels.device), col_index)
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1992: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1991: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
       torch.as_tensor(labels, dtype=torch.long, device=labels.device), cell_index
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1999: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:1998: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
       column_mask = torch.as_tensor(
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:2024: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:2023: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
       selected_column_id = torch.as_tensor(
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:2029: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/tapas/modeling_tapas.py:2028: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
       selected_column_mask = torch.as_tensor(
 
 
@@ -375,7 +379,7 @@ attributes of original model class to be integrated into the pipeline.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
       warnings.warn(
 
 
@@ -466,8 +470,6 @@ Interactive inference
 
     Running on local URL:  http://127.0.0.1:7860
     
-    Thanks for being a Gradio user! If you have questions or feedback, please join our Discord server and chat with us: https://discord.gg/feTf9x3ZSB
-    
     To create a public link, set `share=True` in `launch()`.
 
 
diff --git a/docs/notebooks/tensorflow-bit-image-classification-nncf-quantization-with-output.rst b/docs/notebooks/tensorflow-bit-image-classification-nncf-quantization-with-output.rst
index ebc3560408485b..b483140d430021 100644
--- a/docs/notebooks/tensorflow-bit-image-classification-nncf-quantization-with-output.rst
+++ b/docs/notebooks/tensorflow-bit-image-classification-nncf-quantization-with-output.rst
@@ -13,8 +13,8 @@ achieves excellent performance on a wide variety of tasks, even when
 using very few labeled examples from the target dataset. This tutorial
 uses OpenVINO backend for performing model quantization in NNCF.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prepare Dataset <#prepare-dataset>`__
 -  `Plotting data samples <#plotting-data-samples>`__
@@ -137,8 +137,8 @@ Prepare Dataset
 
 .. parsed-literal::
 
-    2024-06-05 23:30:23.049720: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:266] failed call to cuInit: CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE: forward compatibility was attempted on non supported HW
-    2024-06-05 23:30:23.049944: E tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:312] kernel version 470.182.3 does not match DSO version 470.223.2 -- cannot find working devices in this configuration
+    2024-07-01 23:28:13.057215: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:266] failed call to cuInit: CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE: forward compatibility was attempted on non supported HW
+    2024-07-01 23:28:13.057429: E tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:312] kernel version 470.182.3 does not match DSO version 470.223.2 -- cannot find working devices in this configuration
 
 
 .. code:: ipython3
@@ -274,7 +274,7 @@ Model Fine-tuning
 
 .. parsed-literal::
 
-    101/101 [==============================] - 966s 9s/step - loss: 0.6241 - accuracy: 0.8605 - val_loss: 0.0783 - val_accuracy: 0.9720
+    101/101 [==============================] - 964s 9s/step - loss: 0.5942 - accuracy: 0.8622 - val_loss: 0.0841 - val_accuracy: 0.9720
 
 
 .. parsed-literal::
@@ -423,17 +423,17 @@ Model Quantization using NNCF
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -444,17 +444,17 @@ Model Quantization using NNCF
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -504,8 +504,8 @@ Compare FP32 and INT8 accuracy
 
     Accuracy of the tensorflow model (fp32):  97.20%
     Accuracy of the OpenVINO optimized model (fp32):  97.20%
-    Accuracy of the OpenVINO quantized model (int8):  96.60%
-    Accuracy drop between OV FP32 and INT8 model: 0.6% 
+    Accuracy of the OpenVINO quantized model (int8):  96.20%
+    Accuracy drop between OV FP32 and INT8 model: 1.0% 
 
 
 Compare inference results on one picture
@@ -573,9 +573,9 @@ Compare inference results on one picture
 
 .. parsed-literal::
 
-    Predicted label for the sample picture by float (fp32) model: church
+    Predicted label for the sample picture by float (fp32) model: gas pump
     
-    Predicted label for the sample picture by qunatized (int8) model: church
+    Predicted label for the sample picture by qunatized (int8) model: gas pump
     
 
 
diff --git a/docs/notebooks/tensorflow-classification-to-openvino-with-output.rst b/docs/notebooks/tensorflow-classification-to-openvino-with-output.rst
index e2b9a72aac8565..8e9ce8997e6c1f 100644
--- a/docs/notebooks/tensorflow-classification-to-openvino-with-output.rst
+++ b/docs/notebooks/tensorflow-classification-to-openvino-with-output.rst
@@ -11,8 +11,8 @@ After creating the OpenVINO IR, load the model in `OpenVINO
 Runtime <https://docs.openvino.ai/2024/openvino-workflow/running-inference.html>`__
 and do inference with a sample image.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Imports <#imports>`__
 -  `Settings <#settings>`__
@@ -57,23 +57,14 @@ Table of contents:
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
 
 
@@ -145,8 +136,8 @@ and save it to the disk.
 
 .. parsed-literal::
 
-    2024-06-06 03:35:45.398097: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:266] failed call to cuInit: CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE: forward compatibility was attempted on non supported HW
-    2024-06-06 03:35:45.398280: E tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:312] kernel version 470.182.3 does not match DSO version 470.223.2 -- cannot find working devices in this configuration
+    2024-07-02 03:30:20.896494: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:266] failed call to cuInit: CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE: forward compatibility was attempted on non supported HW
+    2024-07-02 03:30:20.896675: E tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:312] kernel version 470.182.3 does not match DSO version 470.223.2 -- cannot find working devices in this configuration
 
 
 .. parsed-literal::
@@ -370,5 +361,5 @@ performance.
 
 .. parsed-literal::
 
-    IR model in OpenVINO Runtime/CPU: 0.0010 seconds per image, FPS: 955.14
+    IR model in OpenVINO Runtime/CPU: 0.0010 seconds per image, FPS: 958.30
 
diff --git a/docs/notebooks/tensorflow-hub-with-output.rst b/docs/notebooks/tensorflow-hub-with-output.rst
index 2eff9daf1bf53e..7c1d3c41233769 100644
--- a/docs/notebooks/tensorflow-hub-with-output.rst
+++ b/docs/notebooks/tensorflow-hub-with-output.rst
@@ -23,8 +23,8 @@ You have the flexibility to run this tutorial notebook in its entirety
 or selectively execute specific sections, as each section operates
 independently.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Install required packages <#install-required-packages>`__
 -  `Image classification <#image-classification>`__
@@ -59,15 +59,15 @@ Install required packages
 .. code:: ipython3
 
     import platform
-    
+
     %pip install -q pillow numpy
     %pip install -q "openvino>=2023.2.0" "opencv-python"
-    
+
     if platform.system() != "Windows":
         %pip install -q "matplotlib>=3.4"
     else:
         %pip install -q "matplotlib>=3.4,<3.7"
-    
+
     %pip install -q "tensorflow-macos>=2.5; sys_platform == 'darwin' and platform_machine == 'arm64' and python_version > '3.8'" # macOS M1 and M2
     %pip install -q "tensorflow-macos>=2.5,<=2.12.0; sys_platform == 'darwin' and platform_machine == 'arm64' and python_version <= '3.8'" # macOS M1 and M2
     %pip install -q "tensorflow>=2.5; sys_platform == 'darwin' and platform_machine != 'arm64' and python_version > '3.8'" # macOS x86
@@ -79,25 +79,15 @@ Install required packages
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
 
 
@@ -134,19 +124,19 @@ Import libraries
     from pathlib import Path
     import os
     import requests
-    
+
     os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
     os.environ["TF_USE_LEGACY_KERAS"] = "1"
     os.environ["TFHUB_CACHE_DIR"] = str(Path("./tfhub_modules").resolve())
-    
+
     import tensorflow_hub as hub
     import tensorflow as tf
     import PIL
     import numpy as np
     import matplotlib.pyplot as plt
-    
+
     import openvino as ov
-    
+
     tf.get_logger().setLevel("ERROR")
 
 .. code:: ipython3
@@ -164,7 +154,7 @@ Import libraries
 Download the classifier
 ~~~~~~~~~~~~~~~~~~~~~~~
 
- Select a MobileNetV2
+Select a MobileNetV2
 pre-trained model `from TensorFlow
 Hub <https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/5>`__
 and wrap it as a Keras layer with ``hub.KerasLayer``.
@@ -176,14 +166,14 @@ and wrap it as a Keras layer with ``hub.KerasLayer``.
 
 .. parsed-literal::
 
-    2024-06-06 03:36:40.329272: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:266] failed call to cuInit: CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE: forward compatibility was attempted on non supported HW
-    2024-06-06 03:36:40.329452: E tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:312] kernel version 470.182.3 does not match DSO version 470.223.2 -- cannot find working devices in this configuration
+    2024-07-02 03:30:59.980732: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:266] failed call to cuInit: CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE: forward compatibility was attempted on non supported HW
+    2024-07-02 03:30:59.980903: E tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:312] kernel version 470.182.3 does not match DSO version 470.223.2 -- cannot find working devices in this configuration
 
 
 Download a single image to try the model on
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
- The input ``images`` are
+The input ``images`` are
 expected to have color values in the range [0,1], following the `common
 image input
 conventions <https://www.tensorflow.org/hub/common_signatures/images#input>`__.
@@ -193,7 +183,7 @@ For this model, the size of the input images is fixed to ``height`` x
 .. code:: ipython3
 
     Path(IMAGE_PATH).parent.mkdir(parents=True, exist_ok=True)
-    
+
     r = requests.get(IMAGE_URL)
     with Path(IMAGE_PATH).open("wb") as f:
         f.write(r.content)
@@ -249,16 +239,16 @@ select device from dropdown list for running inference using OpenVINO
 .. code:: ipython3
 
     import ipywidgets as widgets
-    
+
     core = ov.Core()
-    
+
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
         value="AUTO",
         description="Device:",
         disabled=False,
     )
-    
+
     device
 
 
@@ -369,15 +359,15 @@ Hub <https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2>`__.
 .. code:: ipython3
 
     import os
-    
+
     os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
     os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
     os.environ["TF_USE_LEGACY_KERAS"] = "1"
     os.environ["TFHUB_CACHE_DIR"] = str(Path("./tfhub_modules").resolve())
     from pathlib import Path
-    
+
     import openvino as ov
-    
+
     import tensorflow_hub as hub
     import tensorflow as tf
     import cv2
@@ -388,10 +378,10 @@ Hub <https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2>`__.
 
     CONTENT_IMAGE_URL = "https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/525babb8-1289-45f8-a3a5-e248f74dfb24"
     CONTENT_IMAGE_PATH = "./data/YellowLabradorLooking_new.jpg"
-    
+
     STYLE_IMAGE_URL = "https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/c212233d-9a33-4979-b8f9-2a94a529026e"
     STYLE_IMAGE_PATH = "./data/Vassily_Kandinsky%2C_1913_-_Composition_7.jpg"
-    
+
     MODEL_URL = "https://www.kaggle.com/models/google/arbitrary-image-stylization-v1/frameworks/tensorFlow1/variations/256/versions/2"
     MODEL_PATH = "./models/arbitrary-image-stylization-v1-256.xml"
 
@@ -442,16 +432,16 @@ select device from dropdown list for running inference using OpenVINO
 .. code:: ipython3
 
     import ipywidgets as widgets
-    
+
     core = ov.Core()
-    
+
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
         value="AUTO",
         description="Device:",
         disabled=False,
     )
-    
+
     device
 
 
@@ -482,8 +472,8 @@ Inference
         r = requests.get(CONTENT_IMAGE_URL)
         with open(CONTENT_IMAGE_PATH, "wb") as f:
             f.write(r.content)
-    
-    
+
+
     def load_image(dst):
         image = cv2.imread(dst)
         image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)  # Convert image color to RGB space
diff --git a/docs/notebooks/tensorflow-hub-with-output_files/tensorflow-hub-with-output_43_0.png b/docs/notebooks/tensorflow-hub-with-output_files/tensorflow-hub-with-output_43_0.png
index 77b2f70419d467..043906486c65ba 100644
--- a/docs/notebooks/tensorflow-hub-with-output_files/tensorflow-hub-with-output_43_0.png
+++ b/docs/notebooks/tensorflow-hub-with-output_files/tensorflow-hub-with-output_43_0.png
@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:2f4b0fb5d082511d93c8d2d5247da30c6c756c4222a6ac501083058857d5d519
+oid sha256:df4c51055f6433a18be84ac96510c599147f2e775174fe854a8a1789d0a595a5
 size 538743
diff --git a/docs/notebooks/tensorflow-instance-segmentation-to-openvino-with-output.rst b/docs/notebooks/tensorflow-instance-segmentation-to-openvino-with-output.rst
index 05e27085dd2303..469d6dad97e265 100644
--- a/docs/notebooks/tensorflow-instance-segmentation-to-openvino-with-output.rst
+++ b/docs/notebooks/tensorflow-instance-segmentation-to-openvino-with-output.rst
@@ -24,8 +24,8 @@ After creating the OpenVINO IR, load the model in `OpenVINO
 Runtime <https://docs.openvino.ai/2024/openvino-workflow/running-inference.html>`__
 and do inference with a sample image.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Imports <#imports>`__
@@ -61,14 +61,14 @@ Install required packages:
 .. code:: ipython3
 
     import platform
-    
+
     %pip install -q "openvino>=2023.1.0" "numpy>=1.21.0" "opencv-python" "tqdm"
-    
+
     if platform.system() != "Windows":
         %pip install -q "matplotlib>=3.4"
     else:
         %pip install -q "matplotlib>=3.4,<3.7"
-    
+
     %pip install -q "tensorflow-macos>=2.5; sys_platform == 'darwin' and platform_machine == 'arm64' and python_version > '3.8'" # macOS M1 and M2
     %pip install -q "tensorflow-macos>=2.5,<=2.12.0; sys_platform == 'darwin' and platform_machine == 'arm64' and python_version <= '3.8'" # macOS M1 and M2
     %pip install -q "tensorflow>=2.5; sys_platform == 'darwin' and platform_machine != 'arm64' and python_version > '3.8'" # macOS x86
@@ -79,21 +79,13 @@ Install required packages:
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
 
 
@@ -104,11 +96,11 @@ The notebook uses utility functions. The cell below will download the
 
     # Fetch the notebook utils script from the openvino_notebooks repo
     import requests
-    
+
     r = requests.get(
         url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
-    
+
     open("notebook_utils.py", "w").write(r.text)
 
 
@@ -116,7 +108,7 @@ The notebook uses utility functions. The cell below will download the
 
 .. parsed-literal::
 
-    21503
+    23215
 
 
 
@@ -129,15 +121,15 @@ Imports
 
     # Standard python modules
     from pathlib import Path
-    
+
     # External modules and dependencies
     import cv2
     import matplotlib.pyplot as plt
     import numpy as np
-    
+
     # Notebook utils module
     from notebook_utils import download_file
-    
+
     # OpenVINO modules
     import openvino as ov
 
@@ -153,23 +145,23 @@ Define model related variables and create corresponding directories:
     # Create directories for models files
     model_dir = Path("model")
     model_dir.mkdir(exist_ok=True)
-    
+
     # Create directory for TensorFlow model
     tf_model_dir = model_dir / "tf"
     tf_model_dir.mkdir(exist_ok=True)
-    
+
     # Create directory for OpenVINO IR model
     ir_model_dir = model_dir / "ir"
     ir_model_dir.mkdir(exist_ok=True)
-    
+
     model_name = "mask_rcnn_inception_resnet_v2_1024x1024"
-    
+
     openvino_ir_path = ir_model_dir / f"{model_name}.xml"
-    
+
     tf_model_url = (
         "https://www.kaggle.com/models/tensorflow/mask-rcnn-inception-resnet-v2/frameworks/tensorFlow2/variations/1024x1024/versions/1?tf-hub-format=compressed"
     )
-    
+
     tf_model_archive_filename = f"{model_name}.tar.gz"
 
 Download Model from TensorFlow Hub
@@ -198,7 +190,7 @@ archive:
 .. code:: ipython3
 
     import tarfile
-    
+
     with tarfile.open(tf_model_dir / tf_model_archive_filename) as file:
         file.extractall(path=tf_model_dir)
 
@@ -226,7 +218,7 @@ when the model is run in the future.
 .. code:: ipython3
 
     ov_model = ov.convert_model(tf_model_dir)
-    
+
     # Save converted OpenVINO IR model to the corresponding directory
     ov.save_model(ov_model, openvino_ir_path)
 
@@ -245,7 +237,7 @@ select device from dropdown list for running inference using OpenVINO
 .. code:: ipython3
 
     import ipywidgets as widgets
-    
+
     core = ov.Core()
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
@@ -253,7 +245,7 @@ select device from dropdown list for running inference using OpenVINO
         description="Device:",
         disabled=False,
     )
-    
+
     device
 
 
@@ -309,12 +301,12 @@ the first (and highest) detection score.
 
     model_inputs = compiled_model.inputs
     model_outputs = compiled_model.outputs
-    
+
     print("Model inputs count:", len(model_inputs))
     print("Model inputs:")
     for _input in model_inputs:
         print("  ", _input)
-    
+
     print("Model outputs count:", len(model_outputs))
     print("Model outputs:")
     for output in model_outputs:
@@ -363,7 +355,7 @@ Load and save an image:
 .. code:: ipython3
 
     image_path = Path("./data/coco_bike.jpg")
-    
+
     download_file(
         url="https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/coco_bike.jpg",
         filename=image_path.name,
@@ -383,16 +375,16 @@ Read the image, resize and convert it to the input shape of the network:
 
     # Read the image
     image = cv2.imread(filename=str(image_path))
-    
+
     # The network expects images in RGB format
     image = cv2.cvtColor(image, code=cv2.COLOR_BGR2RGB)
-    
+
     # Resize the image to the network input shape
     resized_image = cv2.resize(src=image, dsize=(255, 255))
-    
+
     # Add batch dimension to image
     network_input_image = np.expand_dims(resized_image, 0)
-    
+
     # Show the image
     plt.imshow(image)
 
@@ -401,7 +393,7 @@ Read the image, resize and convert it to the input shape of the network:
 
 .. parsed-literal::
 
-    <matplotlib.image.AxesImage at 0x7f7d7bcdfc40>
+    <matplotlib.image.AxesImage at 0x7fa4edd15f70>
 
 
 
@@ -428,23 +420,23 @@ be extracted from the result. For further model result visualization
     detection_boxes = compiled_model.output("detection_boxes")
     image_detection_boxes = inference_result[detection_boxes]
     print("image_detection_boxes:", image_detection_boxes.shape)
-    
+
     detection_masks = compiled_model.output("detection_masks")
     image_detection_masks = inference_result[detection_masks]
     print("image_detection_masks:", image_detection_masks.shape)
-    
+
     detection_classes = compiled_model.output("detection_classes")
     image_detection_classes = inference_result[detection_classes]
     print("image_detection_classes:", image_detection_classes.shape)
-    
+
     detection_scores = compiled_model.output("detection_scores")
     image_detection_scores = inference_result[detection_scores]
     print("image_detection_scores:", image_detection_scores.shape)
-    
+
     num_detections = compiled_model.output("num_detections")
     image_num_detections = inference_result[num_detections]
     print("image_detections_num:", image_num_detections)
-    
+
     # Alternatively, inference result data can be extracted by model output name with `.get()` method
     assert (inference_result[detection_boxes] == inference_result.get("detection_boxes")).all(), "extracted inference result data should be equal"
 
@@ -469,12 +461,12 @@ Define utility functions to visualize the inference results
 
     import random
     from typing import Optional
-    
-    
+
+
     def add_detection_box(box: np.ndarray, image: np.ndarray, mask: np.ndarray, label: Optional[str] = None) -> np.ndarray:
         """
         Helper function for adding single bounding box to the image
-    
+
         Parameters
         ----------
         box : np.ndarray
@@ -485,18 +477,18 @@ Define utility functions to visualize the inference results
             Segmentation mask in format (H, W)
         label : str, optional
             Detection box label string, if not provided will not be added to result image (default is None)
-    
+
         Returns
         -------
         np.ndarray
             NumPy array including image, detection box, and segmentation mask
-    
+
         """
         ymin, xmin, ymax, xmax = box
         point1, point2 = (int(xmin), int(ymin)), (int(xmax), int(ymax))
         box_color = [random.randint(0, 255) for _ in range(3)]
         line_thickness = round(0.002 * (image.shape[0] + image.shape[1]) / 2) + 1
-    
+
         result = cv2.rectangle(
             img=image,
             pt1=point1,
@@ -505,7 +497,7 @@ Define utility functions to visualize the inference results
             thickness=line_thickness,
             lineType=cv2.LINE_AA,
         )
-    
+
         if label:
             font_thickness = max(line_thickness - 1, 1)
             font_face = 0
@@ -551,12 +543,12 @@ Define utility functions to visualize the inference results
     def get_mask_frame(box, frame, mask):
         """
         Transform a binary mask to fit within a specified bounding box in a frame using perspective transformation.
-    
+
         Args:
             box (tuple): A bounding box represented as a tuple (y_min, x_min, y_max, x_max).
             frame (numpy.ndarray): The larger frame or image where the mask will be placed.
             mask (numpy.ndarray): A binary mask image to be transformed.
-    
+
         Returns:
             numpy.ndarray: A transformed mask image that fits within the specified bounding box in the frame.
         """
@@ -584,10 +576,10 @@ Define utility functions to visualize the inference results
 .. code:: ipython3
 
     from typing import Dict
-    
+
     from openvino.runtime.utils.data_helpers import OVDict
-    
-    
+
+
     def visualize_inference_result(
         inference_result: OVDict,
         image: np.ndarray,
@@ -596,7 +588,7 @@ Define utility functions to visualize the inference results
     ):
         """
         Helper function for visualizing inference result on the image
-    
+
         Parameters
         ----------
         inference_result : OVDict
@@ -613,9 +605,9 @@ Define utility functions to visualize the inference results
         detection_scores = inference_result.get("detection_scores")
         num_detections = inference_result.get("num_detections")
         detection_masks = inference_result.get("detection_masks")
-    
+
         detections_limit = int(min(detections_limit, num_detections[0]) if detections_limit is not None else num_detections[0])
-    
+
         # Normalize detection boxes coordinates to original image size
         original_image_height, original_image_width, _ = image.shape
         normalized_detection_boxes = detection_boxes[0, :detections_limit] * [
@@ -638,7 +630,7 @@ Define utility functions to visualize the inference results
                 mask=mask_reframed,
                 label=label,
             )
-    
+
         plt.imshow(result)
 
 TensorFlow Instance Segmentation model
@@ -654,7 +646,7 @@ Zoo <https://github.com/openvinotoolkit/open_model_zoo/>`__:
 .. code:: ipython3
 
     coco_labels_file_path = Path("./data/coco_91cl.txt")
-    
+
     download_file(
         url="https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/data/dataset_classes/coco_91cl.txt",
         filename=coco_labels_file_path.name,
@@ -677,7 +669,7 @@ file:
     with open(coco_labels_file_path, "r") as file:
         coco_labels = file.read().strip().split("\n")
         coco_labels_map = dict(enumerate(coco_labels, 1))
-    
+
     print(coco_labels_map)
 
 
@@ -714,7 +706,7 @@ performance of your application using OpenVINO.
 Async inference pipeline
 ~~~~~~~~~~~~~~~~~~~~~~~~
 
- The key advantage of the Async
+The key advantage of the Async
 API is that when a device is busy with inference, the application can
 perform other tasks in parallel (for example, populating inputs or
 scheduling other requests) rather than wait for the current inference to
diff --git a/docs/notebooks/tensorflow-instance-segmentation-to-openvino-with-output_files/tensorflow-instance-segmentation-to-openvino-with-output_39_0.png b/docs/notebooks/tensorflow-instance-segmentation-to-openvino-with-output_files/tensorflow-instance-segmentation-to-openvino-with-output_39_0.png
index a95bec1d2e75bf..c4cf3ccac57829 100644
--- a/docs/notebooks/tensorflow-instance-segmentation-to-openvino-with-output_files/tensorflow-instance-segmentation-to-openvino-with-output_39_0.png
+++ b/docs/notebooks/tensorflow-instance-segmentation-to-openvino-with-output_files/tensorflow-instance-segmentation-to-openvino-with-output_39_0.png
@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:7d3af0bdc1070a5e163ac3e673ec920040f25e377c10ac1fd4d1ce9f62a7b908
-size 393298
+oid sha256:c7ba57623156867d58d26b430308cdd2885f5019f3beb3ab13f07d4f9ec440f4
+size 393495
diff --git a/docs/notebooks/tensorflow-object-detection-to-openvino-with-output.rst b/docs/notebooks/tensorflow-object-detection-to-openvino-with-output.rst
index f68b97b1d4862b..6a1f8d32ae0456 100644
--- a/docs/notebooks/tensorflow-object-detection-to-openvino-with-output.rst
+++ b/docs/notebooks/tensorflow-object-detection-to-openvino-with-output.rst
@@ -23,8 +23,8 @@ IR, load the model in `OpenVINO
 Runtime <https://docs.openvino.ai/2024/openvino-workflow/running-inference.html>`__
 and do inference with a sample image.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Imports <#imports>`__
@@ -60,9 +60,9 @@ Install required packages:
 .. code:: ipython3
 
     import platform
-    
+
     %pip install -q "openvino>=2023.1.0" "numpy>=1.21.0" "opencv-python" "tqdm"
-    
+
     if platform.system() != "Windows":
         %pip install -q "matplotlib>=3.4"
     else:
@@ -77,21 +77,13 @@ Install required packages:
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
 
 
@@ -102,11 +94,11 @@ The notebook uses utility functions. The cell below will download the
 
     # Fetch the notebook utils script from the openvino_notebooks repo
     import requests
-    
+
     r = requests.get(
         url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
-    
+
     open("notebook_utils.py", "w").write(r.text)
 
 
@@ -114,7 +106,7 @@ The notebook uses utility functions. The cell below will download the
 
 .. parsed-literal::
 
-    21503
+    23215
 
 
 
@@ -127,15 +119,15 @@ Imports
 
     # Standard python modules
     from pathlib import Path
-    
+
     # External modules and dependencies
     import cv2
     import matplotlib.pyplot as plt
     import numpy as np
-    
+
     # OpenVINO import
     import openvino as ov
-    
+
     # Notebook utils module
     from notebook_utils import download_file
 
@@ -151,21 +143,21 @@ Define model related variables and create corresponding directories:
     # Create directories for models files
     model_dir = Path("model")
     model_dir.mkdir(exist_ok=True)
-    
+
     # Create directory for TensorFlow model
     tf_model_dir = model_dir / "tf"
     tf_model_dir.mkdir(exist_ok=True)
-    
+
     # Create directory for OpenVINO IR model
     ir_model_dir = model_dir / "ir"
     ir_model_dir.mkdir(exist_ok=True)
-    
+
     model_name = "faster_rcnn_resnet50_v1_640x640"
-    
+
     openvino_ir_path = ir_model_dir / f"{model_name}.xml"
-    
+
     tf_model_url = "https://www.kaggle.com/models/tensorflow/faster-rcnn-resnet-v1/frameworks/tensorFlow2/variations/faster-rcnn-resnet50-v1-640x640/versions/1?tf-hub-format=compressed"
-    
+
     tf_model_archive_filename = f"{model_name}.tar.gz"
 
 Download Model from TensorFlow Hub
@@ -192,7 +184,7 @@ from TensorFlow Hub:
 
 .. parsed-literal::
 
-    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/tensorflow-object-detection-to-openvino/model/tf/faster_rcnn_resnet50_v1_640x640.tar.gz')
+    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/tensorflow-object-detection-to-openvino/model/tf/faster_rcnn_resnet50_v1_640x640.tar.gz')
 
 
 
@@ -201,7 +193,7 @@ Extract TensorFlow Object Detection model from the downloaded archive:
 .. code:: ipython3
 
     import tarfile
-    
+
     with tarfile.open(tf_model_dir / tf_model_archive_filename) as file:
         file.extractall(path=tf_model_dir)
 
@@ -231,7 +223,7 @@ support <https://docs.openvino.ai/2024/openvino-workflow/model-preparation/conve
 .. code:: ipython3
 
     ov_model = ov.convert_model(tf_model_dir)
-    
+
     # Save converted OpenVINO IR model to the corresponding directory
     ov.save_model(ov_model, openvino_ir_path)
 
@@ -250,7 +242,7 @@ select device from dropdown list for running inference using OpenVINO
 .. code:: ipython3
 
     import ipywidgets as widgets
-    
+
     core = ov.Core()
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
@@ -258,7 +250,7 @@ select device from dropdown list for running inference using OpenVINO
         description="Device:",
         disabled=False,
     )
-    
+
     device
 
 
@@ -325,10 +317,10 @@ for more information about model inputs, outputs and their formats.
     model_inputs = compiled_model.inputs
     model_input = compiled_model.input(0)
     model_outputs = compiled_model.outputs
-    
+
     print("Model inputs count:", len(model_inputs))
     print("Model input:", model_input)
-    
+
     print("Model outputs count:", len(model_outputs))
     print("Model outputs:")
     for output in model_outputs:
@@ -361,7 +353,7 @@ Load and save an image:
 .. code:: ipython3
 
     image_path = Path("./data/coco_bike.jpg")
-    
+
     download_file(
         url="https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/coco_bike.jpg",
         filename=image_path.name,
@@ -378,7 +370,7 @@ Load and save an image:
 
 .. parsed-literal::
 
-    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/tensorflow-object-detection-to-openvino/data/coco_bike.jpg')
+    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/tensorflow-object-detection-to-openvino/data/coco_bike.jpg')
 
 
 
@@ -388,16 +380,16 @@ Read the image, resize and convert it to the input shape of the network:
 
     # Read the image
     image = cv2.imread(filename=str(image_path))
-    
+
     # The network expects images in RGB format
     image = cv2.cvtColor(image, code=cv2.COLOR_BGR2RGB)
-    
+
     # Resize the image to the network input shape
     resized_image = cv2.resize(src=image, dsize=(255, 255))
-    
+
     # Transpose the image to the network input shape
     network_input_image = np.expand_dims(resized_image, 0)
-    
+
     # Show the image
     plt.imshow(image)
 
@@ -406,7 +398,7 @@ Read the image, resize and convert it to the input shape of the network:
 
 .. parsed-literal::
 
-    <matplotlib.image.AxesImage at 0x7fe4cb740eb0>
+    <matplotlib.image.AxesImage at 0x7f03f00436a0>
 
 
 
@@ -440,19 +432,19 @@ outputs will be used.
         _,
         _,
     ) = model_outputs
-    
+
     image_detection_boxes = inference_result[detection_boxes]
     print("image_detection_boxes:", image_detection_boxes)
-    
+
     image_detection_classes = inference_result[detection_classes]
     print("image_detection_classes:", image_detection_classes)
-    
+
     image_detection_scores = inference_result[detection_scores]
     print("image_detection_scores:", image_detection_scores)
-    
+
     image_num_detections = inference_result[num_detections]
     print("image_detections_num:", image_num_detections)
-    
+
     # Alternatively, inference result data can be extracted by model output name with `.get()` method
     assert (inference_result[detection_boxes] == inference_result.get("detection_boxes")).all(), "extracted inference result data should be equal"
 
@@ -547,12 +539,12 @@ Define utility functions to visualize the inference results
 
     import random
     from typing import Optional
-    
-    
+
+
     def add_detection_box(box: np.ndarray, image: np.ndarray, label: Optional[str] = None) -> np.ndarray:
         """
         Helper function for adding single bounding box to the image
-    
+
         Parameters
         ----------
         box : np.ndarray
@@ -561,18 +553,18 @@ Define utility functions to visualize the inference results
             The image to which detection box is added
         label : str, optional
             Detection box label string, if not provided will not be added to result image (default is None)
-    
+
         Returns
         -------
         np.ndarray
             NumPy array including both image and detection box
-    
+
         """
         ymin, xmin, ymax, xmax = box
         point1, point2 = (int(xmin), int(ymin)), (int(xmax), int(ymax))
         box_color = [random.randint(0, 255) for _ in range(3)]
         line_thickness = round(0.002 * (image.shape[0] + image.shape[1]) / 2) + 1
-    
+
         cv2.rectangle(
             img=image,
             pt1=point1,
@@ -581,7 +573,7 @@ Define utility functions to visualize the inference results
             thickness=line_thickness,
             lineType=cv2.LINE_AA,
         )
-    
+
         if label:
             font_thickness = max(line_thickness - 1, 1)
             font_face = 0
@@ -623,10 +615,10 @@ Define utility functions to visualize the inference results
 .. code:: ipython3
 
     from typing import Dict
-    
+
     from openvino.runtime.utils.data_helpers import OVDict
-    
-    
+
+
     def visualize_inference_result(
         inference_result: OVDict,
         image: np.ndarray,
@@ -635,7 +627,7 @@ Define utility functions to visualize the inference results
     ):
         """
         Helper function for visualizing inference result on the image
-    
+
         Parameters
         ----------
         inference_result : OVDict
@@ -651,9 +643,9 @@ Define utility functions to visualize the inference results
         detection_classes: np.ndarray = inference_result.get("detection_classes")
         detection_scores: np.ndarray = inference_result.get("detection_scores")
         num_detections: np.ndarray = inference_result.get("num_detections")
-    
+
         detections_limit = int(min(detections_limit, num_detections[0]) if detections_limit is not None else num_detections[0])
-    
+
         # Normalize detection boxes coordinates to original image size
         original_image_height, original_image_width, _ = image.shape
         normalized_detection_boxex = detection_boxes[::] * [
@@ -662,9 +654,9 @@ Define utility functions to visualize the inference results
             original_image_height,
             original_image_width,
         ]
-    
+
         image_with_detection_boxex = np.copy(image)
-    
+
         for i in range(detections_limit):
             detected_class_name = labels_map[int(detection_classes[0, i])]
             score = detection_scores[0, i]
@@ -674,7 +666,7 @@ Define utility functions to visualize the inference results
                 image=image_with_detection_boxex,
                 label=label,
             )
-    
+
         plt.imshow(image_with_detection_boxex)
 
 TensorFlow Object Detection model
@@ -690,7 +682,7 @@ Zoo <https://github.com/openvinotoolkit/open_model_zoo/>`__:
 .. code:: ipython3
 
     coco_labels_file_path = Path("./data/coco_91cl.txt")
-    
+
     download_file(
         url="https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/master/data/dataset_classes/coco_91cl.txt",
         filename=coco_labels_file_path.name,
@@ -708,7 +700,7 @@ Zoo <https://github.com/openvinotoolkit/open_model_zoo/>`__:
 
 .. parsed-literal::
 
-    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/tensorflow-object-detection-to-openvino/data/coco_91cl.txt')
+    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/tensorflow-object-detection-to-openvino/data/coco_91cl.txt')
 
 
 
@@ -721,7 +713,7 @@ file:
     with open(coco_labels_file_path, "r") as file:
         coco_labels = file.read().strip().split("\n")
         coco_labels_map = dict(enumerate(coco_labels, 1))
-    
+
     print(coco_labels_map)
 
 
@@ -758,7 +750,7 @@ performance of your application using OpenVINO.
 Async inference pipeline
 ~~~~~~~~~~~~~~~~~~~~~~~~
 
- The key advantage of the Async
+The key advantage of the Async
 API is that when a device is busy with inference, the application can
 perform other tasks in parallel (for example, populating inputs or
 scheduling other requests) rather than wait for the current inference to
diff --git a/docs/notebooks/tensorflow-object-detection-to-openvino-with-output_files/tensorflow-object-detection-to-openvino-with-output_38_0.png b/docs/notebooks/tensorflow-object-detection-to-openvino-with-output_files/tensorflow-object-detection-to-openvino-with-output_38_0.png
index 7e312532ea778d..b17ad1ebec2c2c 100644
--- a/docs/notebooks/tensorflow-object-detection-to-openvino-with-output_files/tensorflow-object-detection-to-openvino-with-output_38_0.png
+++ b/docs/notebooks/tensorflow-object-detection-to-openvino-with-output_files/tensorflow-object-detection-to-openvino-with-output_38_0.png
@@ -1,3 +1,3 @@
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-oid sha256:6cf761b0e07653398ada637a877a308cba550f08ec5d4e05eb50d41091d5a8bc
-size 391214
+oid sha256:432404053368af09825c576a05277b1d92b4a3dd19241b3fc799d103307e6756
+size 391883
diff --git a/docs/notebooks/tensorflow-quantization-aware-training-with-output.rst b/docs/notebooks/tensorflow-quantization-aware-training-with-output.rst
index 4d4d08456cc98d..b253a7b3ed732d 100644
--- a/docs/notebooks/tensorflow-quantization-aware-training-with-output.rst
+++ b/docs/notebooks/tensorflow-quantization-aware-training-with-output.rst
@@ -21,8 +21,8 @@ Imagenette is a subset of 10 easily classified classes from the ImageNet
 dataset. Using the smaller model and dataset will speed up training and
 download time.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Imports and Settings <#imports-and-settings>`__
 -  `Dataset Preprocessing <#dataset-preprocessing>`__
@@ -64,17 +64,11 @@ models will be stored.
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
 
 
@@ -82,39 +76,39 @@ models will be stored.
 
     from pathlib import Path
     import logging
-    
+
     import tensorflow as tf
     import tensorflow_datasets as tfds
-    
+
     from nncf import NNCFConfig
     from nncf.tensorflow.helpers.model_creation import create_compressed_model
     from nncf.tensorflow.initialization import register_default_init_args
     from nncf.common.logging.logger import set_log_level
     import openvino as ov
-    
+
     set_log_level(logging.ERROR)
-    
+
     MODEL_DIR = Path("model")
     OUTPUT_DIR = Path("output")
     MODEL_DIR.mkdir(exist_ok=True)
     OUTPUT_DIR.mkdir(exist_ok=True)
-    
+
     BASE_MODEL_NAME = "ResNet-18"
-    
+
     fp32_h5_path = Path(MODEL_DIR / (BASE_MODEL_NAME + "_fp32")).with_suffix(".h5")
     fp32_ir_path = Path(OUTPUT_DIR / "saved_model").with_suffix(".xml")
     int8_pb_path = Path(OUTPUT_DIR / (BASE_MODEL_NAME + "_int8")).with_suffix(".pb")
     int8_ir_path = int8_pb_path.with_suffix(".xml")
-    
+
     BATCH_SIZE = 128
     IMG_SIZE = (64, 64)  # Default Imagenet image size
     NUM_CLASSES = 10  # For Imagenette dataset
-    
+
     LR = 1e-5
-    
+
     MEAN_RGB = (0.485 * 255, 0.456 * 255, 0.406 * 255)  # From Imagenet dataset
     STDDEV_RGB = (0.229 * 255, 0.224 * 255, 0.225 * 255)  # From Imagenet dataset
-    
+
     fp32_pth_url = "https://storage.openvinotoolkit.org/repositories/nncf/openvino_notebook_ckpts/305_resnet18_imagenette_fp32_v1.h5"
     _ = tf.keras.utils.get_file(fp32_h5_path.resolve(), fp32_pth_url)
     print(f"Absolute path where the model weights are saved:\n {fp32_h5_path.resolve()}")
@@ -122,19 +116,20 @@ models will be stored.
 
 .. parsed-literal::
 
-    2024-06-06 03:39:02.561572: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-06-06 03:39:02.596425: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-07-02 03:32:47.128177: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-07-02 03:32:47.162892: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2024-06-06 03:39:03.197901: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-07-02 03:32:47.759379: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 .. parsed-literal::
 
     INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino
+    WARNING:nncf:NNCF provides best results with torch==2.15.*, while current torch version is 2.12.0. If you encounter issues, consider switching to torch==2.15.*
     Downloading data from https://storage.openvinotoolkit.org/repositories/nncf/openvino_notebook_ckpts/305_resnet18_imagenette_fp32_v1.h5
     134604992/134604992 [==============================] - 4s 0us/step
     Absolute path where the model weights are saved:
-     /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/tensorflow-quantization-aware-training/model/ResNet-18_fp32.h5
+     /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/tensorflow-quantization-aware-training/model/ResNet-18_fp32.h5
 
 
 Dataset Preprocessing
@@ -142,8 +137,10 @@ Dataset Preprocessing
 
 
 
-Download and prepare Imagenette 160px dataset. - Number of classes: 10 -
-Download size: 94.18 MiB
+Download and prepare Imagenette 160px dataset.
+
+- Number of classes: 10
+- Download size: 94.18 MiB
 
 ::
 
@@ -167,17 +164,17 @@ Download size: 94.18 MiB
 
 .. parsed-literal::
 
-    2024-06-06 03:39:11.942470: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:266] failed call to cuInit: CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE: forward compatibility was attempted on non supported HW
-    2024-06-06 03:39:11.942504: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:168] retrieving CUDA diagnostic information for host: iotg-dev-workstation-07
-    2024-06-06 03:39:11.942508: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:175] hostname: iotg-dev-workstation-07
-    2024-06-06 03:39:11.942662: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:199] libcuda reported version is: 470.223.2
-    2024-06-06 03:39:11.942676: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:203] kernel reported version is: 470.182.3
-    2024-06-06 03:39:11.942680: E tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:312] kernel version 470.182.3 does not match DSO version 470.223.2 -- cannot find working devices in this configuration
-    2024-06-06 03:39:12.051672: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_3' with dtype int64 and shape [1]
-    	 [[{{node Placeholder/_3}}]]
-    2024-06-06 03:39:12.051996: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_3' with dtype int64 and shape [1]
-    	 [[{{node Placeholder/_3}}]]
-    2024-06-06 03:39:12.122902: W tensorflow/core/kernels/data/cache_dataset_ops.cc:856] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset  will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.
+    2024-07-02 03:32:56.484704: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:266] failed call to cuInit: CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE: forward compatibility was attempted on non supported HW
+    2024-07-02 03:32:56.484739: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:168] retrieving CUDA diagnostic information for host: iotg-dev-workstation-07
+    2024-07-02 03:32:56.484744: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:175] hostname: iotg-dev-workstation-07
+    2024-07-02 03:32:56.484894: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:199] libcuda reported version is: 470.223.2
+    2024-07-02 03:32:56.484911: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:203] kernel reported version is: 470.182.3
+    2024-07-02 03:32:56.484914: E tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:312] kernel version 470.182.3 does not match DSO version 470.223.2 -- cannot find working devices in this configuration
+    2024-07-02 03:32:56.630091: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_2' with dtype string and shape [1]
+    	 [[{{node Placeholder/_2}}]]
+    2024-07-02 03:32:56.630419: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_1' with dtype string and shape [1]
+    	 [[{{node Placeholder/_1}}]]
+    2024-07-02 03:32:56.717922: W tensorflow/core/kernels/data/cache_dataset_ops.cc:856] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset  will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.
 
 
 
@@ -192,10 +189,10 @@ Download size: 94.18 MiB
         image = image / STDDEV_RGB
         label = tf.one_hot(label, NUM_CLASSES)
         return image, label
-    
-    
+
+
     train_dataset = train_dataset.map(preprocessing, num_parallel_calls=tf.data.experimental.AUTOTUNE).batch(BATCH_SIZE).prefetch(tf.data.experimental.AUTOTUNE)
-    
+
     validation_dataset = (
         validation_dataset.map(preprocessing, num_parallel_calls=tf.data.experimental.AUTOTUNE).batch(BATCH_SIZE).prefetch(tf.data.experimental.AUTOTUNE)
     )
@@ -211,7 +208,7 @@ Define a Floating-Point Model
         def layer(input_tensor):
             x = tf.keras.layers.BatchNormalization(epsilon=2e-5)(input_tensor)
             x = tf.keras.layers.Activation("relu")(x)
-    
+
             # Defining shortcut connection.
             if cut == "pre":
                 shortcut = input_tensor
@@ -223,7 +220,7 @@ Define a Floating-Point Model
                     kernel_initializer="he_uniform",
                     use_bias=False,
                 )(x)
-    
+
             # Continue with convolution layers.
             x = tf.keras.layers.ZeroPadding2D(padding=(1, 1))(x)
             x = tf.keras.layers.Conv2D(
@@ -233,23 +230,23 @@ Define a Floating-Point Model
                 kernel_initializer="he_uniform",
                 use_bias=False,
             )(x)
-    
+
             x = tf.keras.layers.BatchNormalization(epsilon=2e-5)(x)
             x = tf.keras.layers.Activation("relu")(x)
             x = tf.keras.layers.ZeroPadding2D(padding=(1, 1))(x)
             x = tf.keras.layers.Conv2D(filters, (3, 3), kernel_initializer="he_uniform", use_bias=False)(x)
-    
+
             # Add residual connection.
             x = tf.keras.layers.Add()([x, shortcut])
             return x
-    
+
         return layer
-    
-    
+
+
     def ResNet18(input_shape=None):
         """Instantiates the ResNet18 architecture."""
         img_input = tf.keras.layers.Input(shape=input_shape, name="data")
-    
+
         # ResNet18 bottom
         x = tf.keras.layers.BatchNormalization(epsilon=2e-5, scale=False)(img_input)
         x = tf.keras.layers.ZeroPadding2D(padding=(3, 3))(x)
@@ -258,7 +255,7 @@ Define a Floating-Point Model
         x = tf.keras.layers.Activation("relu")(x)
         x = tf.keras.layers.ZeroPadding2D(padding=(1, 1))(x)
         x = tf.keras.layers.MaxPooling2D((3, 3), strides=(2, 2), padding="valid")(x)
-    
+
         # ResNet18 body
         repetitions = (2, 2, 2, 2)
         for stage, rep in enumerate(repetitions):
@@ -272,15 +269,15 @@ Define a Floating-Point Model
                     x = residual_conv_block(filters, stage, block, strides=(1, 1), cut="pre")(x)
         x = tf.keras.layers.BatchNormalization(epsilon=2e-5)(x)
         x = tf.keras.layers.Activation("relu")(x)
-    
+
         # ResNet18 top
         x = tf.keras.layers.GlobalAveragePooling2D()(x)
         x = tf.keras.layers.Dense(NUM_CLASSES)(x)
         x = tf.keras.layers.Activation("softmax")(x)
-    
+
         # Create the model.
         model = tf.keras.models.Model(img_input, x)
-    
+
         return model
 
 .. code:: ipython3
@@ -304,13 +301,13 @@ model and a training pipeline.
 
     # Load the floating-point weights.
     fp32_model.load_weights(fp32_h5_path)
-    
+
     # Compile the floating-point model.
     fp32_model.compile(
         loss=tf.keras.losses.CategoricalCrossentropy(label_smoothing=0.1),
         metrics=[tf.keras.metrics.CategoricalAccuracy(name="acc@1")],
     )
-    
+
     # Validate the floating-point model.
     test_loss, acc_fp32 = fp32_model.evaluate(
         validation_dataset,
@@ -321,16 +318,16 @@ model and a training pipeline.
 
 .. parsed-literal::
 
-    2024-06-06 03:39:13.292630: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype string and shape [1]
-    	 [[{{node Placeholder/_0}}]]
-    2024-06-06 03:39:13.293394: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_3' with dtype int64 and shape [1]
-    	 [[{{node Placeholder/_3}}]]
+    2024-07-02 03:32:57.655358: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int64 and shape [1]
+    	 [[{{node Placeholder/_4}}]]
+    2024-07-02 03:32:57.656345: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_1' with dtype string and shape [1]
+    	 [[{{node Placeholder/_1}}]]
 
 
 .. parsed-literal::
 
-    4/4 [==============================] - 1s 243ms/sample - loss: 0.9807 - acc@1: 0.8220
-    
+    4/4 [==============================] - 1s 298ms/sample - loss: 0.9807 - acc@1: 0.8220
+
     Accuracy of FP32 model: 0.822
 
 
@@ -375,13 +372,13 @@ scenario and requires only 3 modifications.
 
 .. parsed-literal::
 
-    2024-06-06 03:39:15.881712: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_1' with dtype string and shape [1]
+    2024-07-02 03:33:00.528798: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_1' with dtype string and shape [1]
     	 [[{{node Placeholder/_1}}]]
-    2024-06-06 03:39:15.882101: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_1' with dtype string and shape [1]
-    	 [[{{node Placeholder/_1}}]]
-    2024-06-06 03:39:16.811397: W tensorflow/core/kernels/data/cache_dataset_ops.cc:856] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset  will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.
-    2024-06-06 03:39:17.409491: W tensorflow/core/kernels/data/cache_dataset_ops.cc:856] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset  will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.
-    2024-06-06 03:39:25.687275: W tensorflow/core/kernels/data/cache_dataset_ops.cc:856] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset  will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.
+    2024-07-02 03:33:00.529172: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int64 and shape [1]
+    	 [[{{node Placeholder/_4}}]]
+    2024-07-02 03:33:01.425324: W tensorflow/core/kernels/data/cache_dataset_ops.cc:856] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset  will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.
+    2024-07-02 03:33:02.059991: W tensorflow/core/kernels/data/cache_dataset_ops.cc:856] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset  will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.
+    2024-07-02 03:33:10.084482: W tensorflow/core/kernels/data/cache_dataset_ops.cc:856] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset  will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.
 
 
 Evaluate the new model on the validation set after initialization of
@@ -397,7 +394,7 @@ demonstrated here.
         loss=tf.keras.losses.CategoricalCrossentropy(label_smoothing=0.1),
         metrics=[tf.keras.metrics.CategoricalAccuracy(name="acc@1")],
     )
-    
+
     # Validate the INT8 model.
     test_loss, test_acc = int8_model.evaluate(
         validation_dataset,
@@ -424,10 +421,10 @@ training pipeline are required. Here is a simple example.
 .. code:: ipython3
 
     print(f"\nAccuracy of INT8 model after initialization: {test_acc:.3f}")
-    
+
     # Train the INT8 model.
     int8_model.fit(train_dataset, epochs=2)
-    
+
     # Validate the INT8 model.
     test_loss, acc_int8 = int8_model.evaluate(
         validation_dataset,
@@ -439,16 +436,16 @@ training pipeline are required. Here is a simple example.
 
 .. parsed-literal::
 
-    
+
     Accuracy of INT8 model after initialization: 0.812
     Epoch 1/2
-    101/101 [==============================] - 49s 418ms/step - loss: 0.7134 - acc@1: 0.9299
+    101/101 [==============================] - 49s 416ms/step - loss: 0.7134 - acc@1: 0.9299
     Epoch 2/2
-    101/101 [==============================] - 42s 417ms/step - loss: 0.6807 - acc@1: 0.9489
-    4/4 [==============================] - 1s 143ms/sample - loss: 0.9760 - acc@1: 0.8160
-    
+    101/101 [==============================] - 42s 419ms/step - loss: 0.6807 - acc@1: 0.9489
+    4/4 [==============================] - 1s 147ms/sample - loss: 0.9760 - acc@1: 0.8160
+
     Accuracy of INT8 model after fine-tuning: 0.816
-    
+
     Accuracy drop of tuned INT8 model over pre-trained FP32 model: 0.006
 
 
@@ -515,7 +512,7 @@ Please select a benchmarking device using the dropdown list:
 .. code:: ipython3
 
     import ipywidgets as widgets
-    
+
     # Initialize OpenVINO runtime
     core = ov.Core()
     device = widgets.Dropdown(
@@ -524,7 +521,7 @@ Please select a benchmarking device using the dropdown list:
         description="Device:",
         disabled=False,
     )
-    
+
     device
 
 
@@ -541,12 +538,12 @@ Please select a benchmarking device using the dropdown list:
     def parse_benchmark_output(benchmark_output):
         parsed_output = [line for line in benchmark_output if "FPS" in line]
         print(*parsed_output, sep="\n")
-    
-    
+
+
     print("Benchmark FP32 model (IR)")
     benchmark_output = ! benchmark_app -m $fp32_ir_path -d $device.value -api async -t 15 -shape [1,64,64,3]
     parse_benchmark_output(benchmark_output)
-    
+
     print("\nBenchmark INT8 model (IR)")
     benchmark_output = ! benchmark_app -m $int8_ir_path -d $device.value -api async -t 15 -shape [1,64,64,3]
     parse_benchmark_output(benchmark_output)
@@ -555,10 +552,10 @@ Please select a benchmarking device using the dropdown list:
 .. parsed-literal::
 
     Benchmark FP32 model (IR)
-    [ INFO ] Throughput:   2843.86 FPS
-    
+    [ INFO ] Throughput:   2831.00 FPS
+
     Benchmark INT8 model (IR)
-    [ INFO ] Throughput:   11417.45 FPS
+    [ INFO ] Throughput:   11136.52 FPS
 
 
 Show Device Information for reference.
diff --git a/docs/notebooks/text-prediction-with-output.rst b/docs/notebooks/text-prediction-with-output.rst
deleted file mode 100644
index b0a53752be6fdc..00000000000000
--- a/docs/notebooks/text-prediction-with-output.rst
+++ /dev/null
@@ -1,732 +0,0 @@
-Text Prediction with OpenVINO™
-==============================
-
-This notebook shows text prediction with OpenVINO. This notebook can
-work in two different modes, Text Generation and Conversation, which the
-user can select via selecting the model in the Model Selection Section.
-We use three models
-`GPT-2 <https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf>`__,
-`GPT-Neo <https://zenodo.org/record/5297715#.ZAmpsXZBztU>`__, and
-`PersonaGPT <https://arxiv.org/abs/2110.12949v1>`__, which are a part of
-the Generative Pre-trained Transformer (GPT) family. GPT-2 and GPT-Neo
-can be used for text generation, whereas PersonaGPT is trained for the
-downstream task of conversation.
-
-GPT-2 and GPT-Neo are pre-trained on a large corpus of English text
-using unsupervised training. They both display a broad set of
-capabilities, including the ability to generate conditional synthetic
-text samples of unprecedented quality, where we prime the model with an
-input and have it generate a lengthy continuation.
-
-More details about the models are provided on their HuggingFace cards:
-
--  `GPT-2 <https://huggingface.co/gpt2>`__
--  `GPT-Neo <https://huggingface.co/EleutherAI/gpt-neo-125M>`__
-
-PersonaGPT is an open-domain conversational agent that can decode
-*personalized* and *controlled* responses based on user input. It is
-built on the pretrained
-`DialoGPT-medium <https://github.com/microsoft/DialoGPT>`__ model,
-following the `GPT-2 <https://github.com/openai/gpt-2>`__ architecture.
-PersonaGPT is fine-tuned on the
-`Persona-Chat <https://arxiv.org/pdf/1801.07243>`__ dataset. The model
-is available from
-`HuggingFace <https://huggingface.co/af1tang/personaGPT>`__. PersonaGPT
-displays a broad set of capabilities, including the ability to take on
-personas, where we prime the model with few facts and have it generate
-based upon that, it can also be used for creating a chatbot on a
-knowledge base.
-
-The following image illustrates the complete demo pipeline used for text
-generation:
-
-.. figure:: https://user-images.githubusercontent.com/91228207/163990722-d2713ede-921e-4594-8b00-8b5c1a4d73b5.jpeg
-   :alt: image2
-
-   image2
-
-This is a demonstration in which the user can type the beginning of the
-text and the network will generate a further. This procedure can be
-repeated as many times as the user desires.
-
-For Text Generation, The model input is tokenized text, which serves as
-the initial condition for text generation. Then, logits from the models’
-inference results are obtained, and the token with the highest
-probability is selected using the top-k sampling strategy and joined to
-the input sequence. This procedure repeats until the end of the sequence
-token is received or the specified maximum length is reached. After
-that, tokenized IDs are decoded to text.
-
-The following image illustrates the demo pipeline for conversation:
-
-.. figure:: https://user-images.githubusercontent.com/95569637/226101538-e204aebd-a34f-4c8b-b90c-5363ba41c080.jpeg
-   :alt: image2
-
-   image2
-
-For Conversation, User Input is tokenized with ``eos_token``
-concatenated in the end. Then, the text gets generated as detailed
-above. The Generated response is added to the history with the
-``eos_token`` at the end. Additional user input is added to the history,
-and the sequence is passed back into the model.
-
-Table of contents:
-^^^^^^^^^^^^^^^^^^
-
--  `Model Selection <#model-selection>`__
--  `Load Model <#load-model>`__
--  `Convert Pytorch Model to OpenVINO
-   IR <#convert-pytorch-model-to-openvino-ir>`__
-
-   -  `Load the model <#load-the-model>`__
-
-      -  `Select inference device <#select-inference-device>`__
-
--  `Pre-Processing <#pre-processing>`__
--  `Define tokenization <#define-tokenization>`__
-
-   -  `Define Softmax layer <#define-softmax-layer>`__
-   -  `Set the minimum sequence
-      length <#set-the-minimum-sequence-length>`__
-   -  `Top-K sampling <#top-k-sampling>`__
-   -  `Main Processing Function <#main-processing-function>`__
-
--  `Inference with GPT-Neo/GPT-2 <#inference-with-gpt-neogpt-2>`__
--  `Conversation with PersonaGPT using
-   OpenVINO <#conversation-with-personagpt-using-openvino>`__
--  `Converse Function <#converse-function>`__
--  `Conversation Class <#conversation-class>`__
--  `Conversation with PersonaGPT <#conversation-with-personagpt>`__
-
-Model Selection
----------------
-
-
-
-Select the Model to be used for text generation, GPT-2 and GPT-Neo are
-used for text generation whereas PersonaGPT is used for Conversation.
-
-.. code:: ipython3
-
-    %pip install -q "openvino>=2023.1.0"
-    %pip install -q "gradio>=4.19"
-    %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu transformers "torch>=2.1"
-
-
-.. parsed-literal::
-
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    Note: you may need to restart the kernel to use updated packages.
-
-
-.. code:: ipython3
-
-    import ipywidgets as widgets
-    
-    style = {"description_width": "initial"}
-    model_name = widgets.Select(
-        options=["PersonaGPT (Converastional)", "GPT-2", "GPT-Neo"],
-        value="PersonaGPT (Converastional)",
-        description="Select Model:",
-        disabled=False,
-    )
-    
-    widgets.VBox([model_name])
-
-
-
-
-.. parsed-literal::
-
-    VBox(children=(Select(description='Select Model:', options=('PersonaGPT (Converastional)', 'GPT-2', 'GPT-Neo')…
-
-
-
-Load Model
-----------
-
-
-
-Download the Selected Model and Tokenizer from HuggingFace
-
-.. code:: ipython3
-
-    from transformers import (
-        GPTNeoForCausalLM,
-        GPT2TokenizerFast,
-        GPT2Tokenizer,
-        GPT2LMHeadModel,
-    )
-    
-    if model_name.value == "PersonaGPT (Converastional)":
-        pt_model = GPT2LMHeadModel.from_pretrained("af1tang/personaGPT")
-        tokenizer = GPT2Tokenizer.from_pretrained("af1tang/personaGPT")
-    elif model_name.value == "GPT-2":
-        pt_model = GPT2LMHeadModel.from_pretrained("gpt2")
-        tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
-    elif model_name.value == "GPT-Neo":
-        pt_model = GPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-125M")
-        tokenizer = GPT2TokenizerFast.from_pretrained("EleutherAI/gpt-neo-125M")
-
-Convert Pytorch Model to OpenVINO IR
-------------------------------------
-
-
-
-For starting work with GPT-Neo model using OpenVINO, a model should be
-converted to OpenVINO Intermediate Representation (IR) format.
-HuggingFace provides a GPT-Neo model in PyTorch format, which is
-supported in OpenVINO via Model Conversion API. The ``ov.convert_model``
-Python function of `model conversion
-API <https://docs.openvino.ai/2024/openvino-workflow/model-preparation.html>`__
-can be used for converting the model. The function returns instance of
-OpenVINO Model class, which is ready to use in Python interface. The
-Model can also be save on device in OpenVINO IR format for future
-execution using ``ov.save_model``. In our case dynamic input shapes with
-a possible shape range (from 1 token to a maximum length defined in our
-processing function) are specified for optimization of memory
-consumption.
-
-.. code:: ipython3
-
-    from pathlib import Path
-    import torch
-    
-    import openvino as ov
-    
-    # define path for saving openvino model
-    model_path = Path("model/text_generator.xml")
-    
-    example_input = {
-        "input_ids": torch.ones((1, 10), dtype=torch.long),
-        "attention_mask": torch.ones((1, 10), dtype=torch.long),
-    }
-    pt_model.config.torchscript = True
-    
-    # convert model to openvino
-    if model_name.value == "PersonaGPT (Converastional)":
-        ov_model = ov.convert_model(
-            pt_model,
-            example_input=example_input,
-            input=[
-                ("input_ids", [1, -1], ov.Type.i64),
-                ("attention_mask", [1, -1], ov.Type.i64),
-            ],
-        )
-    else:
-        ov_model = ov.convert_model(
-            pt_model,
-            example_input=example_input,
-            input=[
-                ("input_ids", [1, ov.Dimension(1, 128)], ov.Type.i64),
-                ("attention_mask", [1, ov.Dimension(1, 128)], ov.Type.i64),
-            ],
-        )
-    
-    # serialize openvino model
-    ov.save_model(ov_model, str(model_path))
-
-
-.. parsed-literal::
-
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4481: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
-      warnings.warn(
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:1116: TracerWarning: Output nr 1. of the traced function does not match the corresponding output of the Python function. Detailed error:
-    Tensor-likes are not close!
-    
-    Mismatched elements: 22 / 502630 (0.0%)
-    Greatest absolute difference: 1.3828277587890625e-05 at index (0, 1, 1287) (up to 1e-05 allowed)
-    Greatest relative difference: 0.006159087930808544 at index (0, 5, 8109) (up to 1e-05 allowed)
-      _check_trace(
-
-
-Load the model
-~~~~~~~~~~~~~~
-
-
-
-We start by building an OpenVINO Core object. Then we read the network
-architecture and model weights from the ``.xml`` and ``.bin`` files,
-respectively. Finally, we compile the model for the desired device.
-
-Select inference device
-^^^^^^^^^^^^^^^^^^^^^^^
-
-
-
-select device from dropdown list for running inference using OpenVINO
-
-.. code:: ipython3
-
-    import ipywidgets as widgets
-    
-    # initialize openvino core
-    core = ov.Core()
-    
-    device = widgets.Dropdown(
-        options=core.available_devices + ["AUTO"],
-        value="AUTO",
-        description="Device:",
-        disabled=False,
-    )
-    
-    device
-
-
-
-
-.. parsed-literal::
-
-    Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
-
-
-
-.. code:: ipython3
-
-    # read the model and corresponding weights from file
-    model = core.read_model(model_path)
-
-.. code:: ipython3
-
-    # compile the model for CPU devices
-    compiled_model = core.compile_model(model=model, device_name=device.value)
-    
-    # get output tensors
-    output_key = compiled_model.output(0)
-
-Input keys are the names of the input nodes and output keys contain
-names of the output nodes of the network. In the case of GPT-Neo, we
-have ``batch size`` and ``sequence length`` as inputs and
-``batch size``, ``sequence length`` and ``vocab size`` as outputs.
-
-Pre-Processing
---------------
-
-
-
-NLP models often take a list of tokens as a standard input. A token is a
-word or a part of a word mapped to an integer. To provide the proper
-input, we use a vocabulary file to handle the mapping. So first let’s
-load the vocabulary file.
-
-Define tokenization
--------------------
-
-
-
-.. code:: ipython3
-
-    from typing import List, Tuple
-    
-    
-    # this function converts text to tokens
-    def tokenize(text: str) -> Tuple[List[int], List[int]]:
-        """
-        tokenize input text using GPT2 tokenizer
-    
-        Parameters:
-          text, str - input text
-        Returns:
-          input_ids - np.array with input token ids
-          attention_mask - np.array with 0 in place, where should be padding and 1 for places where original tokens are located, represents attention mask for model
-        """
-    
-        inputs = tokenizer(text, return_tensors="np")
-        return inputs["input_ids"], inputs["attention_mask"]
-
-``eos_token`` is special token, which means that generation is finished.
-We store the index of this token in order to use this index as padding
-at later stage.
-
-.. code:: ipython3
-
-    eos_token_id = tokenizer.eos_token_id
-    eos_token = tokenizer.decode(eos_token_id)
-
-
-.. parsed-literal::
-
-    2024-06-06 03:42:05.179279: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-06-06 03:42:05.214993: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
-    To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2024-06-06 03:42:05.744388: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
-
-
-Define Softmax layer
-~~~~~~~~~~~~~~~~~~~~
-
- A softmax function is used to
-convert top-k logits into a probability distribution.
-
-.. code:: ipython3
-
-    import numpy as np
-    
-    
-    def softmax(x: np.array) -> np.array:
-        e_x = np.exp(x - np.max(x, axis=-1, keepdims=True))
-        summation = e_x.sum(axis=-1, keepdims=True)
-        return e_x / summation
-
-Set the minimum sequence length
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-If the minimum sequence length is not reached, the following code will
-reduce the probability of the ``eos`` token occurring. This continues
-the process of generating the next words.
-
-.. code:: ipython3
-
-    def process_logits(cur_length: int, scores: np.array, eos_token_id: int, min_length: int = 0) -> np.array:
-        """
-        Reduce probability for padded indices.
-    
-        Parameters:
-          cur_length: Current length of input sequence.
-          scores: Model output logits.
-          eos_token_id: Index of end of string token in model vocab.
-          min_length: Minimum length for applying postprocessing.
-    
-        Returns:
-          Processed logits with reduced probability for padded indices.
-        """
-        if cur_length < min_length:
-            scores[:, eos_token_id] = -float("inf")
-        return scores
-
-Top-K sampling
-~~~~~~~~~~~~~~
-
-
-
-In Top-K sampling, we filter the K most likely next words and
-redistribute the probability mass among only those K next words.
-
-.. code:: ipython3
-
-    def get_top_k_logits(scores: np.array, top_k: int) -> np.array:
-        """
-        Perform top-k sampling on the logits scores.
-    
-        Parameters:
-          scores: np.array, model output logits.
-          top_k: int, number of elements with the highest probability to select.
-    
-        Returns:
-          np.array, shape (batch_size, sequence_length, vocab_size),
-            filtered logits scores where only the top-k elements with the highest
-            probability are kept and the rest are replaced with -inf
-        """
-        filter_value = -float("inf")
-        top_k = min(max(top_k, 1), scores.shape[-1])
-        top_k_scores = -np.sort(-scores)[:, :top_k]
-        indices_to_remove = scores < np.min(top_k_scores)
-        filtred_scores = np.ma.array(scores, mask=indices_to_remove, fill_value=filter_value).filled()
-        return filtred_scores
-
-Main Processing Function
-~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-Generating the predicted sequence.
-
-.. code:: ipython3
-
-    def generate_sequence(
-        input_ids: List[int],
-        attention_mask: List[int],
-        max_sequence_length: int = 128,
-        eos_token_id: int = eos_token_id,
-        dynamic_shapes: bool = True,
-    ) -> List[int]:
-        """
-        Generates a sequence of tokens using a pre-trained language model.
-    
-        Parameters:
-          input_ids: np.array, tokenized input ids for model
-          attention_mask: np.array, attention mask for model
-          max_sequence_length: int, maximum sequence length for stopping iteration
-          eos_token_id: int, index of the end-of-sequence token in the model's vocabulary
-          dynamic_shapes: bool, whether to use dynamic shapes for inference or pad model input to max_sequence_length
-    
-        Returns:
-          np.array, the predicted sequence of token ids
-        """
-        while True:
-            cur_input_len = len(input_ids[0])
-            if not dynamic_shapes:
-                pad_len = max_sequence_length - cur_input_len
-                model_input_ids = np.concatenate((input_ids, [[eos_token_id] * pad_len]), axis=-1)
-                model_input_attention_mask = np.concatenate((attention_mask, [[0] * pad_len]), axis=-1)
-            else:
-                model_input_ids = input_ids
-                model_input_attention_mask = attention_mask
-            outputs = compiled_model({"input_ids": model_input_ids, "attention_mask": model_input_attention_mask})[output_key]
-            next_token_logits = outputs[:, cur_input_len - 1, :]
-            # pre-process distribution
-            next_token_scores = process_logits(cur_input_len, next_token_logits, eos_token_id)
-            top_k = 20
-            next_token_scores = get_top_k_logits(next_token_scores, top_k)
-            # get next token id
-            probs = softmax(next_token_scores)
-            next_tokens = np.random.choice(probs.shape[-1], 1, p=probs[0], replace=True)
-            # break the loop if max length or end of text token is reached
-            if cur_input_len == max_sequence_length or next_tokens[0] == eos_token_id:
-                break
-            else:
-                input_ids = np.concatenate((input_ids, [next_tokens]), axis=-1)
-                attention_mask = np.concatenate((attention_mask, [[1] * len(next_tokens)]), axis=-1)
-        return input_ids
-
-Inference with GPT-Neo/GPT-2
-----------------------------
-
-
-
-The ``text`` variable below is the input used to generate a predicted
-sequence.
-
-.. code:: ipython3
-
-    import time
-    
-    if not model_name.value == "PersonaGPT (Converastional)":
-        text = "Deep learning is a type of machine learning that uses neural networks"
-        input_ids, attention_mask = tokenize(text)
-    
-        start = time.perf_counter()
-        output_ids = generate_sequence(input_ids, attention_mask)
-        end = time.perf_counter()
-        output_text = " "
-        # Convert IDs to words and make the sentence from it
-        for i in output_ids[0]:
-            output_text += tokenizer.batch_decode([i])[0]
-        print(f"Generation took {end - start:.3f} s")
-        print(f"Input Text:  {text}")
-        print()
-        print(f"{model_name.value}: {output_text}")
-    else:
-        print("Selected Model is PersonaGPT. Please select GPT-Neo or GPT-2 in the first cell to generate text sequences")
-
-
-.. parsed-literal::
-
-    Selected Model is PersonaGPT. Please select GPT-Neo or GPT-2 in the first cell to generate text sequences
-
-
-Conversation with PersonaGPT using OpenVINO
-===========================================
-
-
-
-User Input is tokenized with ``eos_token`` concatenated in the end.
-Model input is tokenized text, which serves as initial condition for
-generation, then logits from model inference result should be obtained
-and token with the highest probability is selected using top-k sampling
-strategy and joined to input sequence. The procedure repeats until end
-of sequence token will be received or specified maximum length is
-reached. After that, decoding token ids to text using tokenized should
-be applied.
-
-The Generated response is added to the history with the ``eos_token`` at
-the end. Further User Input is added to it and again passed into the
-model.
-
-Converse Function
------------------
-
-
-
-Wrapper on generate sequence function to support conversation
-
-.. code:: ipython3
-
-    def converse(
-        input: str,
-        history: List[int],
-        eos_token: str = eos_token,
-        eos_token_id: int = eos_token_id,
-    ) -> Tuple[str, List[int]]:
-        """
-        Converse with the Model.
-    
-        Parameters:
-          input: Text input given by the User
-          history: Chat History, ids of tokens of chat occured so far
-          eos_token: end of sequence string
-          eos_token_id: end of sequence index from vocab
-        Returns:
-          response: Text Response generated by the model
-          history: Chat History, Ids of the tokens of chat occured so far,including the tokens of generated response
-        """
-    
-        # Get Input Ids of the User Input
-        new_user_input_ids, _ = tokenize(input + eos_token)
-    
-        # append the new user input tokens to the chat history, if history exists
-        if len(history) == 0:
-            bot_input_ids = new_user_input_ids
-        else:
-            bot_input_ids = np.concatenate([history, new_user_input_ids[0]])
-            bot_input_ids = np.expand_dims(bot_input_ids, axis=0)
-    
-        # Create Attention Mask
-        bot_attention_mask = np.ones_like(bot_input_ids)
-    
-        # Generate Response from the model
-        history = generate_sequence(bot_input_ids, bot_attention_mask, max_sequence_length=1000)
-    
-        # Add the eos_token to mark end of sequence
-        history = np.append(history[0], eos_token_id)
-    
-        # convert the tokens to text, and then split the responses into lines and retrieve the response from the Model
-        response = "".join(tokenizer.batch_decode(history)).split(eos_token)[-2]
-        return response, history
-
-Conversation Class
-------------------
-
-
-
-.. code:: ipython3
-
-    class Conversation:
-        def __init__(self):
-            # Initialize Empty History
-            self.history = []
-            self.messages = []
-    
-        def chat(self, input_text):
-            """
-            Wrapper Over Converse Function.
-            Parameters:
-                input_text: Text input given by the User
-            Returns:
-                response: Text Response generated by the model
-            """
-            response, self.history = converse(input_text, self.history)
-            self.messages.append(f"Person: {input_text}")
-            self.messages.append(f"PersonaGPT: {response}")
-            return response
-
-Conversation with PersonaGPT
-----------------------------
-
-
-
-This notebook provides two styles of inference, Plain and Interactive.
-The style of inference can be selected in the next cell.
-
-.. code:: ipython3
-
-    style = {"description_width": "initial"}
-    interactive_mode = widgets.Select(
-        options=["Plain", "Interactive"],
-        value="Plain",
-        description="Inference Style:",
-        disabled=False,
-    )
-    
-    widgets.VBox([interactive_mode])
-
-
-
-
-.. parsed-literal::
-
-    VBox(children=(Select(description='Inference Style:', options=('Plain', 'Interactive'), value='Plain'),))
-
-
-
-.. code:: ipython3
-
-    import gradio as gr
-    
-    if model_name.value == "PersonaGPT (Converastional)":
-        if interactive_mode.value == "Plain":
-            conversation = Conversation()
-            user_prompt = None
-            pre_written_prompts = [
-                "Hi,How are you?",
-                "What are you doing?",
-                "I like to dance,do you?",
-                "Can you recommend me some books?",
-            ]
-            # Number of responses generated by model
-            n_prompts = 10
-            for i in range(n_prompts):
-                # Uncomment for taking User Input
-                # user_prompt = input()
-                if not user_prompt:
-                    user_prompt = pre_written_prompts[i % len(pre_written_prompts)]
-                conversation.chat(user_prompt)
-                print(conversation.messages[-2])
-                print(conversation.messages[-1])
-                user_prompt = None
-        else:
-    
-            def add_text(history, text):
-                history = history + [(text, None)]
-                return history, ""
-    
-            conversation = Conversation()
-    
-            def bot(history):
-                conversation.chat(history[-1][0])
-                response = conversation.messages[-1]
-                history[-1][1] = response
-                return history
-    
-            with gr.Blocks() as demo:
-                chatbot = gr.Chatbot([], elem_id="chatbot")
-    
-                with gr.Row():
-                    with gr.Column():
-                        txt = gr.Textbox(
-                            show_label=False,
-                            placeholder="Enter text and press enter, or upload an image",
-                            container=False,
-                        )
-    
-                txt.submit(add_text, [chatbot, txt], [chatbot, txt]).then(bot, chatbot, chatbot)
-            try:
-                demo.launch(debug=False)
-            except Exception:
-                demo.launch(debug=False, share=True)
-            # if you are launching remotely, specify server_name and server_port
-            # demo.launch(server_name='your server name', server_port='server port in int')
-            # Read more in the docs: https://gradio.app/docs/
-    else:
-        print("Selected Model is not PersonaGPT, Please select PersonaGPT in the first cell to have a conversation")
-
-
-.. parsed-literal::
-
-    Person: Hi,How are you?
-    PersonaGPT: i am doing very well, you?
-    Person: What are you doing?
-    PersonaGPT: i am trying to figure out what to make for dinner
-    Person: I like to dance,do you?
-    PersonaGPT: not really, i don't know the recipes
-    Person: Can you recommend me some books?
-    PersonaGPT: of course i can. what do you like to eat?
-    Person: Hi,How are you?
-    PersonaGPT: i am great thank you. what are some foods you like to cook?
-    Person: What are you doing?
-    PersonaGPT: i am cooking spaghetti right now.
-    Person: I like to dance,do you?
-    PersonaGPT: i don't really do anything but listen to music
-    Person: Can you recommend me some books?
-    PersonaGPT: of course. what kind of music do you like?
-    Person: Hi,How are you?
-    PersonaGPT: i'm doing very well, you?
-    Person: What are you doing?
-    PersonaGPT: i'm cooking a feast right now.
-
diff --git a/docs/notebooks/tflite-selfie-segmentation-with-output.rst b/docs/notebooks/tflite-selfie-segmentation-with-output.rst
index 71c579b50857f7..d7fff5030c8408 100644
--- a/docs/notebooks/tflite-selfie-segmentation-with-output.rst
+++ b/docs/notebooks/tflite-selfie-segmentation-with-output.rst
@@ -34,8 +34,8 @@ The tutorial consists of following steps:
 2. Run inference on the image.
 3. Run interactive background blurring demo on video.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 
@@ -83,9 +83,7 @@ Install required dependencies
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
 
 
@@ -104,7 +102,7 @@ Install required dependencies
 
 .. parsed-literal::
 
-    21503
+    23215
 
 
 
@@ -134,7 +132,7 @@ Download pretrained model and test image
 
 .. parsed-literal::
 
-    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/tflite-selfie-segmentation/selfie_multiclass_256x256.tflite')
+    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/tflite-selfie-segmentation/selfie_multiclass_256x256.tflite')
 
 
 
diff --git a/docs/notebooks/tflite-selfie-segmentation-with-output_files/tflite-selfie-segmentation-with-output_33_0.png b/docs/notebooks/tflite-selfie-segmentation-with-output_files/tflite-selfie-segmentation-with-output_33_0.png
index 3080920583f1d6..7ec3f06143135c 100644
--- a/docs/notebooks/tflite-selfie-segmentation-with-output_files/tflite-selfie-segmentation-with-output_33_0.png
+++ b/docs/notebooks/tflite-selfie-segmentation-with-output_files/tflite-selfie-segmentation-with-output_33_0.png
@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:12cfaa9fb67f90059deaeac689f3304d20a2e818f33f346c65a19b5facb2746d
-size 14229
+oid sha256:1a720c92c7acfa0cff9a3280f9126edd189116bb8b03df08de36c6008d4b7dda
+size 14324
diff --git a/docs/notebooks/tflite-to-openvino-with-output.rst b/docs/notebooks/tflite-to-openvino-with-output.rst
index e8d07b9e51b04a..a6904559d32251 100644
--- a/docs/notebooks/tflite-to-openvino-with-output.rst
+++ b/docs/notebooks/tflite-to-openvino-with-output.rst
@@ -14,8 +14,8 @@ IR, load the model in `OpenVINO
 Runtime <https://docs.openvino.ai/2024/openvino-workflow/running-inference.html>`__
 and do inference with a sample image.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Preparation <#preparation>`__
 
@@ -47,22 +47,20 @@ Install requirements
 
     %pip install -q "openvino>=2023.1.0"
     %pip install -q opencv-python requests tqdm
-    
+
     # Fetch `notebook_utils` module
     import requests
-    
+
     r = requests.get(
         url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
-    
+
     open("notebook_utils.py", "w").write(r.text)
 
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
 
 
@@ -70,7 +68,7 @@ Install requirements
 
 .. parsed-literal::
 
-    21503
+    23215
 
 
 
@@ -85,7 +83,7 @@ Imports
     import numpy as np
     from PIL import Image
     import openvino as ov
-    
+
     from notebook_utils import download_file, load_image
 
 Download TFLite model
@@ -97,10 +95,10 @@ Download TFLite model
 
     model_dir = Path("model")
     tflite_model_path = model_dir / "efficientnet_lite0_fp32_2.tflite"
-    
+
     ov_model_path = tflite_model_path.with_suffix(".xml")
     model_url = "https://www.kaggle.com/models/tensorflow/efficientnet/frameworks/tfLite/variations/lite0-fp32/versions/2?lite-format=tflite"
-    
+
     download_file(model_url, tflite_model_path.name, model_dir)
 
 
@@ -114,7 +112,7 @@ Download TFLite model
 
 .. parsed-literal::
 
-    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/tflite-to-openvino/model/efficientnet_lite0_fp32_2.tflite')
+    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-716/.workspace/scm/ov-notebook/notebooks/tflite-to-openvino/model/efficientnet_lite0_fp32_2.tflite')
 
 
 
@@ -161,7 +159,7 @@ this `tutorial <../openvino-api>`__.
 .. code:: ipython3
 
     core = ov.Core()
-    
+
     ov_model = core.read_model(tflite_model_path)
 
 Run OpenVINO model inference
@@ -191,14 +189,14 @@ select device from dropdown list for running inference using OpenVINO
 .. code:: ipython3
 
     import ipywidgets as widgets
-    
+
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
         value="AUTO",
         description="Device:",
         disabled=False,
     )
-    
+
     device
 
 
@@ -219,11 +217,11 @@ select device from dropdown list for running inference using OpenVINO
 
     imagenet_classes_file_path = download_file("https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/datasets/imagenet/imagenet_2012.txt")
     imagenet_classes = open(imagenet_classes_file_path).read().splitlines()
-    
+
     top1_predicted_cls_id = np.argmax(predicted_scores)
     top1_predicted_score = predicted_scores[0][top1_predicted_cls_id]
     predicted_label = imagenet_classes[top1_predicted_cls_id]
-    
+
     display(image.resize((640, 512)))
     print(f"Predicted label: {predicted_label} with probability {top1_predicted_score :2f}")
 
@@ -246,7 +244,7 @@ select device from dropdown list for running inference using OpenVINO
 Estimate Model Performance
 --------------------------
 
- `Benchmark
+`Benchmark
 Tool <https://docs.openvino.ai/2024/learn-openvino/openvino-samples/benchmark-tool.html>`__
 is used to measure the inference performance of the model on CPU and
 GPU.
@@ -271,18 +269,18 @@ GPU.
     [ INFO ] Parsing input parameters
     [Step 2/11] Loading OpenVINO Runtime
     [ INFO ] OpenVINO:
-    [ INFO ] Build ................................. 2024.3.0-15599-de4d00a5970
-    [ INFO ] 
+    [ INFO ] Build ................................. 2024.3.0-15837-76a668b0662
+    [ INFO ]
     [ INFO ] Device info:
     [ INFO ] AUTO
-    [ INFO ] Build ................................. 2024.3.0-15599-de4d00a5970
-    [ INFO ] 
-    [ INFO ] 
+    [ INFO ] Build ................................. 2024.3.0-15837-76a668b0662
+    [ INFO ]
+    [ INFO ]
     [Step 3/11] Setting device configuration
     [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.THROUGHPUT.
     [Step 4/11] Reading model files
     [ INFO ] Loading model files
-    [ INFO ] Read model took 9.51 ms
+    [ INFO ] Read model took 9.20 ms
     [ INFO ] Original model I/O parameters:
     [ INFO ] Model inputs:
     [ INFO ]     images (node: images) : f32 / [...] / [1,224,224,3]
@@ -296,7 +294,7 @@ GPU.
     [ INFO ] Model outputs:
     [ INFO ]     Softmax (node: 61) : f32 / [...] / [1,1000]
     [Step 7/11] Loading the model to the device
-    [ INFO ] Compile model took 210.51 ms
+    [ INFO ] Compile model took 189.13 ms
     [Step 8/11] Querying optimal runtime parameters
     [ INFO ] Model:
     [ INFO ]   NETWORK_NAME: TensorFlow_Lite_Frontend_IR
@@ -308,7 +306,7 @@ GPU.
     [ INFO ]     AFFINITY: Affinity.CORE
     [ INFO ]     CPU_DENORMALS_OPTIMIZATION: False
     [ INFO ]     CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE: 1.0
-    [ INFO ]     DYNAMIC_QUANTIZATION_GROUP_SIZE: 0
+    [ INFO ]     DYNAMIC_QUANTIZATION_GROUP_SIZE: 32
     [ INFO ]     ENABLE_CPU_PINNING: True
     [ INFO ]     ENABLE_HYPER_THREADING: True
     [ INFO ]     EXECUTION_DEVICES: ['CPU']
@@ -330,18 +328,18 @@ GPU.
     [ INFO ]   PERF_COUNT: False
     [Step 9/11] Creating infer requests and preparing input tensors
     [ WARNING ] No input files were given for input 'images'!. This input will be filled with random values!
-    [ INFO ] Fill input 'images' with random values 
+    [ INFO ] Fill input 'images' with random values
     [Step 10/11] Measuring performance (Start inference asynchronously, 6 inference requests, limits: 15000 ms duration)
     [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
-    [ INFO ] First inference took 6.71 ms
+    [ INFO ] First inference took 7.71 ms
     [Step 11/11] Dumping statistics report
     [ INFO ] Execution Devices:['CPU']
-    [ INFO ] Count:            17454 iterations
-    [ INFO ] Duration:         15007.91 ms
+    [ INFO ] Count:            17322 iterations
+    [ INFO ] Duration:         15007.88 ms
     [ INFO ] Latency:
-    [ INFO ]    Median:        5.02 ms
-    [ INFO ]    Average:       5.02 ms
-    [ INFO ]    Min:           3.03 ms
-    [ INFO ]    Max:           13.38 ms
-    [ INFO ] Throughput:   1162.99 FPS
+    [ INFO ]    Median:        5.05 ms
+    [ INFO ]    Average:       5.06 ms
+    [ INFO ]    Min:           3.09 ms
+    [ INFO ]    Max:           13.62 ms
+    [ INFO ] Throughput:   1154.19 FPS
 
diff --git a/docs/notebooks/tiny-sd-image-generation-with-output.rst b/docs/notebooks/tiny-sd-image-generation-with-output.rst
index 640df7f15b082a..c12982f5d513b6 100644
--- a/docs/notebooks/tiny-sd-image-generation-with-output.rst
+++ b/docs/notebooks/tiny-sd-image-generation-with-output.rst
@@ -39,8 +39,8 @@ The notebook contains the following steps:
 3. Run Inference pipeline with OpenVINO.
 4. Run Interactive demo for Tiny-SD model
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Create PyTorch Models pipeline <#create-pytorch-models-pipeline>`__
diff --git a/docs/notebooks/triposr-3d-reconstruction-with-output.rst b/docs/notebooks/triposr-3d-reconstruction-with-output.rst
index 0b0c4e86a1e037..64d55e6e1e96a4 100644
--- a/docs/notebooks/triposr-3d-reconstruction-with-output.rst
+++ b/docs/notebooks/triposr-3d-reconstruction-with-output.rst
@@ -18,8 +18,8 @@ from a Single Image <https://arxiv.org/abs/2403.02151>`__.
 
    Teaser Video
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Get the original model <#get-the-original-model>`__
@@ -36,24 +36,19 @@ Prerequisites
 
 .. code:: ipython3
 
-    %pip install -q wheel setuptools pip --upgrade
     %pip install -q "gradio>=4.19" "torch==2.2.2" rembg trimesh einops "omegaconf>=2.3.0" "transformers>=4.35.0" "openvino>=2024.0.0" --extra-index-url https://download.pytorch.org/whl/cpu
     %pip install -q "git+https://github.com/tatsy/torchmcubes.git"
 
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
+    descript-audiotools 0.7.2 requires protobuf<3.20,>=3.9.2, but you have protobuf 3.20.3 which is incompatible.
     mobileclip 0.1.0 requires torch==1.13.1, but you have torch 2.2.2+cpu which is incompatible.
     mobileclip 0.1.0 requires torchvision==0.14.1, but you have torchvision 0.18.1+cpu which is incompatible.
-    pytorch-lightning 1.6.5 requires protobuf<=3.20.1, but you have protobuf 3.20.3 which is incompatible.
     torchaudio 2.3.1+cpu requires torch==2.3.1, but you have torch 2.2.2+cpu which is incompatible.
     torchvision 0.18.1+cpu requires torch==2.3.1, but you have torch 2.2.2+cpu which is incompatible.
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
 
 
@@ -75,7 +70,7 @@ Prerequisites
     remote: Counting objects: 100% (113/113), done.
     remote: Compressing objects: 100% (111/111), done.
     remote: Total 117 (delta 36), reused 0 (delta 0), pack-reused 4 (from 1)
-    Receiving objects: 100% (117/117), 569.16 KiB | 7.49 MiB/s, done.
+    Receiving objects: 100% (117/117), 569.16 KiB | 2.03 MiB/s, done.
     Resolving deltas: 100% (36/36), done.
 
 
@@ -114,8 +109,8 @@ Get the original model
           (encoder): ViTEncoder(
             (layer): ModuleList(
               (0-11): 12 x ViTLayer(
-                (attention): ViTSdpaAttention(
-                  (attention): ViTSdpaSelfAttention(
+                (attention): ViTAttention(
+                  (attention): ViTSelfAttention(
                     (query): Linear(in_features=768, out_features=768, bias=True)
                     (key): Linear(in_features=768, out_features=768, bias=True)
                     (value): Linear(in_features=768, out_features=768, bias=True)
@@ -283,7 +278,7 @@ models one by one.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/vit/modeling_vit.py:164: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/vit/modeling_vit.py:167: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       if num_channels != self.num_channels:
 
 
diff --git a/docs/notebooks/typo-detector-with-output.rst b/docs/notebooks/typo-detector-with-output.rst
index a0ec71f05a70cd..f181aae604af45 100644
--- a/docs/notebooks/typo-detector-with-output.rst
+++ b/docs/notebooks/typo-detector-with-output.rst
@@ -19,8 +19,11 @@ Transformers <https://huggingface.co/docs/transformers/index>`__ library
 in the OpenVINO environment to perform the above task.
 
 The model detects typos in a given text with a high accuracy,
-performances of which are listed below, - Precision score of 0.9923 -
-Recall score of 0.9859 - f1-score of 0.9891
+performances of which are listed below,
+
+- Precision score of 0.9923
+- Recall score of 0.9859
+- f1-score of 0.9891
 
 `Source for above
 metrics <https://huggingface.co/m3hrdadfi/typo-detector-distilbert-en>`__
@@ -32,8 +35,8 @@ positives and false negatives.
 The model has been pretrained on the
 `NeuSpell <https://github.com/neuspell/neuspell>`__ dataset.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Imports <#imports>`__
 -  `Methods <#methods>`__
@@ -68,9 +71,7 @@ Table of contents:
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
 
 
@@ -96,10 +97,10 @@ Imports
 
 .. parsed-literal::
 
-    2024-06-06 03:46:19.563930: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-06-06 03:46:19.598566: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-07-02 03:38:30.791330: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-07-02 03:38:30.826343: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2024-06-06 03:46:20.196134: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-07-02 03:38:31.425204: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 Methods
@@ -167,16 +168,16 @@ select device from dropdown list for running inference using OpenVINO
 
     import ipywidgets as widgets
     import openvino as ov
-    
+
     core = ov.Core()
-    
+
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
         value="AUTO",
         description="Device:",
         disabled=False,
     )
-    
+
     device
 
 
@@ -214,13 +215,7 @@ Import required model class
 
 .. parsed-literal::
 
-    INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino
-
-
-.. parsed-literal::
-
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/transformers/transformer_2d.py:34: FutureWarning: `Transformer2DModelOutput` is deprecated and will be removed in version 1.0.0. Importing `Transformer2DModelOutput` from `diffusers.models.transformer_2d` is deprecated and this will be removed in a future version. Please use `from diffusers.models.modeling_outputs import Transformer2DModelOutput`, instead.
-      deprecate("Transformer2DModelOutput", "1.0.0", deprecation_message)
+    The installed version of bitsandbytes was compiled without GPU support. 8-bit optimizers, 8-bit multiplication, and GPU quantization are unavailable.
 
 
 Load the model
@@ -237,9 +232,9 @@ your model.
 
     # The pretrained model we are using
     model_id = "m3hrdadfi/typo-detector-distilbert-en"
-    
+
     model_dir = Path("optimum_model")
-    
+
     # Save the model to the path if not existing
     if model_dir.exists():
         model = OVModelForTokenClassification.from_pretrained(model_dir, device=device.value)
@@ -250,8 +245,6 @@ your model.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
-      warnings.warn(
     Framework not specified. Using pt to export the model.
     Using framework PyTorch: 2.2.2+cpu
 
@@ -264,9 +257,7 @@ your model.
 .. parsed-literal::
 
     [ WARNING ]  Please fix your imports. Module %s has been moved to %s. The old module will be deleted in version %s.
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:4481: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead
-      warnings.warn(
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/torch/dynamic_graph/wrappers.py:81: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/nncf/torch/dynamic_graph/wrappers.py:86: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
       op1 = operator(\*args, \*\*kwargs)
     Compiling the model to AUTO ...
 
@@ -312,17 +303,17 @@ Function to find typos in a sentence and write them to the terminal
         """
         Detect typos from the given sentence.
         Writes both the original input and typo-tagged version to the terminal.
-    
+
         Arguments:
         sentence -- Sentence to be evaluated (string)
         """
-    
+
         typos = [sentence[r["start"] : r["end"]] for r in nlp(sentence)]
-    
+
         detected = sentence
         for typo in typos:
             detected = detected.replace(typo, f"<i>{typo}</i>")
-    
+
         print("[Input]: ", sentence)
         print("[Detected]: ", detected)
         print("-" * 130)
@@ -343,12 +334,12 @@ Let’s run a demo using the Hugging Face Optimum API.
         "My freind and I went campign in the forest last weekend and saw a beutiful sunst that was so amzing it took our breth away.",
         "I  have been stuying for my math exam all week, but I'm stil not very confidet that I will pass it, because there are so many formuals to remeber.",
     ]
-    
+
     start = time.time()
-    
+
     for sentence in sentences:
         show_typos(sentence)
-    
+
     print(f"Time elapsed: {time.time() - start}")
 
 
@@ -384,7 +375,7 @@ Let’s run a demo using the Hugging Face Optimum API.
     [Input]:  I  have been stuying for my math exam all week, but I'm stil not very confidet that I will pass it, because there are so many formuals to remeber.
     [Detected]:  I  have been <i>stuying</i> for my math exam all week, but I'm <i>stil</i> not very <i>confidet</i> that I will pass it, because there are so many formuals to <i>remeber</i>.
     ----------------------------------------------------------------------------------------------------------------------------------
-    Time elapsed: 0.1713876724243164
+    Time elapsed: 0.1586296558380127
 
 
 2. Converting the model to OpenVINO IR
@@ -404,10 +395,10 @@ pytorch model.
 
     model_id = "m3hrdadfi/typo-detector-distilbert-en"
     model_dir = Path("pytorch_model")
-    
+
     tokenizer = AutoTokenizer.from_pretrained(model_id)
     config = AutoConfig.from_pretrained(model_id)
-    
+
     # Save the model to the path if not existing
     if model_dir.exists():
         model = AutoModelForTokenClassification.from_pretrained(model_dir)
@@ -423,7 +414,7 @@ Converting to OpenVINO IR
 .. code:: ipython3
 
     ov_model_path = Path(model_dir) / "typo_detect.xml"
-    
+
     dummy_model_input = tokenizer("This is a sample", return_tensors="pt")
     ov_model = ov.convert_model(model, example_input=dict(dummy_model_input))
     ov.save_model(ov_model, ov_model_path)
@@ -456,14 +447,14 @@ Helper Functions
         """
         Maps the list of tokens to words in the original text.
         Built on the feature that tokens starting with '##' is attached to the previous token as tokens derived from the same word.
-    
+
         Arguments:
         tokens -- List of tokens
-    
+
         Returns:
         map_to_words -- Dictionary mapping tokens to words in original text
         """
-    
+
         word_count = -1
         map_to_words = {}
         for token in tokens:
@@ -479,14 +470,14 @@ Helper Functions
     def infer(input_text: str) -> Dict[np.ndarray, np.ndarray]:
         """
         Creating a generic inference function to read the input and infer the result
-    
+
         Arguments:
         input_text -- The text to be infered (String)
-    
+
         Returns:
         result -- Resulting list from inference
         """
-    
+
         tokens = tokenizer(
             input_text,
             return_tensors="np",
@@ -504,15 +495,15 @@ Helper Functions
     ) -> List[int]:
         """
         Given results from the inference and tokens-map-to-words, identifies the indexes of the words with typos.
-    
+
         Arguments:
         result -- Result from inference (tensor)
         map_to_words -- Dictionary mapping tokens to words (Dictionary)
-    
+
         Results:
         wrong_words -- List of indexes of words with typos
         """
-    
+
         wrong_words = []
         c = 0
         result_list = result[0][1:-1]
@@ -529,14 +520,14 @@ Helper Functions
     def sentence_split(sentence: str) -> List[str]:
         """
         Split the sentence into words and characters
-    
+
         Arguments:
         sentence - Sentence to be split (string)
-    
+
         Returns:
         splitted -- List of words and characters
         """
-    
+
         splitted = re.split("([',. ])", sentence)
         splitted = [x for x in splitted if x != " " and x != ""]
         return splitted
@@ -547,24 +538,24 @@ Helper Functions
         """
         Detect typos from the given sentence.
         Writes both the original input and typo-tagged version to the terminal.
-    
+
         Arguments:
         sentence -- Sentence to be evaluated (string)
         """
-    
+
         tokens = tokenizer.tokenize(sentence)
         map_to_words = token_to_words(tokens)
         result = infer(sentence)
         typo_indexes = get_typo_indexes(result, map_to_words, tokens)
-    
+
         sentence_words = sentence_split(sentence)
-    
+
         typos = [sentence_words[i] for i in typo_indexes]
-    
+
         detected = sentence
         for typo in typos:
             detected = detected.replace(typo, f"<i>{typo}</i>")
-    
+
         print("   [Input]: ", sentence)
         print("[Detected]: ", detected)
         print("-" * 130)
@@ -585,12 +576,12 @@ Let’s run a demo using the converted OpenVINO IR model.
         "My freind and I went campign in the forest last weekend and saw a beutiful sunst that was so amzing it took our breth away.",
         "I  have been stuying for my math exam all week, but I'm stil not very confidet that I will pass it, because there are so many formuals to remeber.",
     ]
-    
+
     start = time.time()
-    
+
     for sentence in sentences:
         show_typos(sentence)
-    
+
     print(f"Time elapsed: {time.time() - start}")
 
 
@@ -626,5 +617,5 @@ Let’s run a demo using the converted OpenVINO IR model.
        [Input]:  I  have been stuying for my math exam all week, but I'm stil not very confidet that I will pass it, because there are so many formuals to remeber.
     [Detected]:  I  have been <i>stuying</i> for my math exam all week, but I'm <i>stil</i> not very <i>confidet</i> that I will pass it, because there are so many formuals to <i>remeber</i>.
     ----------------------------------------------------------------------------------------------------------------------------------
-    Time elapsed: 0.10121345520019531
+    Time elapsed: 0.09734940528869629
 
diff --git a/docs/notebooks/vehicle-detection-and-recognition-with-output.rst b/docs/notebooks/vehicle-detection-and-recognition-with-output.rst
index 7666df4b2e576c..a5d5635a47da0a 100644
--- a/docs/notebooks/vehicle-detection-and-recognition-with-output.rst
+++ b/docs/notebooks/vehicle-detection-and-recognition-with-output.rst
@@ -17,8 +17,8 @@ As a result, you can get:
 
    result
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Imports <#imports>`__
 -  `Download Models <#download-models>`__
@@ -61,9 +61,7 @@ Import the required modules.
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
 
 
diff --git a/docs/notebooks/videollava-multimodal-chatbot-with-output.rst b/docs/notebooks/videollava-multimodal-chatbot-with-output.rst
index a1f4cb07d7f9d9..e0448a8df688c9 100644
--- a/docs/notebooks/videollava-multimodal-chatbot-with-output.rst
+++ b/docs/notebooks/videollava-multimodal-chatbot-with-output.rst
@@ -27,8 +27,8 @@ The tutorial consists from following steps:
 -  Prepare OpenVINO-based inference pipeline
 -  Run OpenVINO model
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `About model <#about-model>`__
 -  `Prerequisites <#prerequisites>`__
@@ -40,11 +40,11 @@ Table of contents:
    -  `Convert and Optimize Model <#convert-and-optimize-model>`__
 
       -  `Instantiate PyTorch model
-          <#instantiate-pytorch-model-uparrow#table-of-content>`__
+          <#instantiate-pytorch-model>`__
       -  `Compress Model weights to 4 and 8 bits using NNCF
-          <#compress-model-weights-to-4-and-8-bits-using-nncf-uparrow#table-of-content>`__
+          <#compress-model-weights-to-4-and-8-bits-using-nncf>`__
       -  `Convert model to OpenVINO IR format
-          <#convert-model-to-openvino-ir-format-uparrow#table-of-content>`__
+          <#convert-model-to-openvino-ir-format>`__
 
 -  `Prepare OpenVINO based inference
    pipeline <#prepare-openvino-based-inference-pipeline>`__
@@ -90,12 +90,12 @@ Install required dependencies
 
     from pathlib import Path
     import sys
-    
+
     repo_dir = Path("Video-LLaVA")
-    
+
     if not repo_dir.exists():
         !git clone https://github.com/PKU-YuanGroup/Video-LLaVA.git
-    
+
     sys.path.insert(0, str(repo_dir.resolve()))
 
 .. container:: alert alert-block alert-warning
@@ -106,7 +106,7 @@ Install required dependencies
 .. code:: ipython3
 
     import gc
-    
+
     import transformers
     from videollava.model import LlavaLlamaForCausalLM
     from videollava.constants import (
@@ -118,13 +118,13 @@ Install required dependencies
         DEFAULT_VID_END_TOKEN,
         DEFAULT_IMAGE_TOKEN,
     )
-    
+
     transformers.logging.set_verbosity_error()
-    
+
     model_id = "LanguageBind/Video-LLaVA-7B"
-    
+
     config = transformers.AutoConfig.from_pretrained(model_id)
-    
+
     tokenizer = transformers.AutoTokenizer.from_pretrained(model_id)
     model = LlavaLlamaForCausalLM.from_pretrained(model_id)
     image_tower = model.get_image_tower()
@@ -142,7 +142,7 @@ Install required dependencies
         tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)
         tokenizer.add_tokens([DEFAULT_VID_START_TOKEN, DEFAULT_VID_END_TOKEN], special_tokens=True)
     preprocess_fn = model.prepare_inputs_labels_for_multimodal
-    
+
     del model
     gc.collect()
 
@@ -247,13 +247,13 @@ on disk using ``ov.save_model``.
     import openvino as ov
     import nncf
     from typing import Optional, Tuple, List
-    
-    
+
+
     class ModelWrapper(torch.nn.Module):
         def __init__(self, model):
             super().__init__()
             self.model = model
-    
+
         def forward(
             self,
             input_ids: torch.LongTensor = None,
@@ -272,13 +272,13 @@ on disk using ``ov.save_model``.
                 output_hidden_states=False,
                 return_dict=True,
             )
-    
+
             hidden_states = outputs[0]
             logits = self.model.lm_head(hidden_states)
-    
+
             return (logits, outputs.past_key_values)
-    
-    
+
+
     def set_node_names(ov_model, input_names=None, output_names=None):
         if input_names is not None:
             for inp, name in zip(ov_model.inputs, input_names):
@@ -286,10 +286,10 @@ on disk using ``ov.save_model``.
         if output_names is not None:
             for out, name in zip(ov_model.outputs, output_names):
                 out.get_tensor().set_names({name})
-    
+
         ov_model.validate_nodes_and_infer_types()
-    
-    
+
+
     def cleanup_torchscript_cache():
         """
         Helper for removing cached model representation
@@ -297,8 +297,8 @@ on disk using ``ov.save_model``.
         torch._C._jit_clear_class_registry()
         torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore()
         torch.jit._state._clear_class_state()
-    
-    
+
+
     def convert_videollava(
         pt_model: torch.nn.Module,
         model_path: Path,
@@ -306,7 +306,7 @@ on disk using ``ov.save_model``.
     ):
         """
         Video-LLaVA model conversion function
-    
+
         Params:
           pt_model: PyTorch model
           model_path: path for saving model
@@ -320,7 +320,7 @@ on disk using ``ov.save_model``.
         wrapped = ModelWrapper(pt_model)
         first_stage_model_path = ov_out_path / "videollava_input_embed.xml"
         second_stage_model_path = ov_out_path / "videollava_with_past.xml"
-    
+
         if first_stage_model_path.exists() and second_stage_model_path.exists():
             print("Video-LLaVA model successfully converted")
             del pt_model
@@ -335,7 +335,7 @@ on disk using ``ov.save_model``.
         for idx in range(len(outs[1])):
             input_names.extend([f"past_key_values.{idx}.key", f"past_key_values.{idx}.value"])
             output_names.extend([f"present.{idx}.key", f"present.{idx}.value"])
-    
+
         if not first_stage_model_path.exists():
             ov_model = ov.convert_model(wrapped, example_input=example_input_first_stage)
             set_node_names(ov_model, output_names=output_names)
@@ -346,7 +346,7 @@ on disk using ``ov.save_model``.
             cleanup_torchscript_cache()
             del ov_model
             gc.collect()
-    
+
         if not second_stage_model_path.exists():
             example_input_second_stage = {
                 "input_ids": torch.ones((1, 1), dtype=torch.long),
@@ -355,7 +355,7 @@ on disk using ``ov.save_model``.
             }
             ov_model = ov.convert_model(wrapped, example_input=example_input_second_stage)
             set_node_names(ov_model, input_names, output_names)
-    
+
             if videollava_wc_parameters is not None:
                 print("Applying weight compression to second stage Video-LLaVA model")
                 ov_model = nncf.compress_weights(ov_model, **videollava_wc_parameters)
@@ -384,7 +384,7 @@ Convert model to OpenVINO format and save it on disk.
 
 Let’s consider each step more deeply.
 
-Instantiate PyTorch model `:math:`\Uparrow` <#table-of-content>`__
+Instantiate PyTorch model
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 
@@ -395,7 +395,7 @@ from `HuggingFace hub <https://huggingface.co/models>`__ during first
 run. It may takes some time and requires at least 13 Gb free space on
 disk.
 
-Compress Model weights to 4 and 8 bits using NNCF `:math:`\Uparrow` <#table-of-content>`__
+Compress Model weights to 4 and 8 bits using NNCF
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 
@@ -438,7 +438,7 @@ documentation <https://docs.openvino.ai/2024/openvino-workflow/model-optimizatio
 
    **Note**: There is no speedup for INT4 compressed models on dGPU.
 
-Convert model to OpenVINO IR format `:math:`\Uparrow` <#table-of-content>`__
+Convert model to OpenVINO IR format
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 
@@ -452,14 +452,14 @@ compression instead of INT8 weight compression.
 .. code:: ipython3
 
     import ipywidgets as widgets
-    
+
     compression_mode = widgets.Dropdown(
         options=["INT4", "INT8"],
         value="INT4",
         description="Compression mode:",
         disabled=False,
     )
-    
+
     compression_mode
 
 
@@ -479,12 +479,12 @@ compression instead of INT8 weight compression.
     else:
         compressed_model_dir = Path("videollava/INT8_compressed_weights")
         videollava_wc_parameters = dict(mode=nncf.CompressWeightsMode.INT8)
-    
+
     if not compressed_model_dir.exists():
         compressed_model_dir.mkdir(exist_ok=True, parents=True)
         model = LlavaLlamaForCausalLM.from_pretrained(model_id)
         model.resize_token_embeddings(len(tokenizer))
-    
+
         if hasattr(config, "max_sequence_length"):
             context_len = config.max_sequence_length
         else:
@@ -495,7 +495,7 @@ compression instead of INT8 weight compression.
         video_tower = model.get_video_tower()
         if not video_tower.is_loaded:
             video_tower.load_model()
-    
+
         model.eval()
         with torch.no_grad():
             convert_videollava(
@@ -526,17 +526,17 @@ compression instead of INT8 weight compression.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -559,17 +559,17 @@ compression instead of INT8 weight compression.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -585,17 +585,17 @@ compression instead of INT8 weight compression.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -618,17 +618,17 @@ compression instead of INT8 weight compression.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -656,8 +656,8 @@ documentation <https://huggingface.co/docs/transformers/main_classes/text_genera
     from transformers.modeling_outputs import CausalLMOutputWithPast
     import numpy as np
     import torch
-    
-    
+
+
     class OVLlavaLlamaForCausalLM(GenerationMixin):
         def __init__(self, core, model_dir, device):
             self.model = core.read_model(model_dir / "videollava_with_past.xml")
@@ -673,11 +673,11 @@ documentation <https://huggingface.co/docs/transformers/main_classes/text_genera
             self.main_input_name = "input_ids"
             self.device = torch.device("cpu")
             self.num_pkv = 2
-    
+
         def can_generate(self):
             """Returns True to validate the check that the model using `GenerationMixin.generate()` can indeed generate."""
             return True
-    
+
         def __call__(
             self,
             input_ids: torch.LongTensor,
@@ -688,7 +688,7 @@ documentation <https://huggingface.co/docs/transformers/main_classes/text_genera
             **kwargs,
         ) -> CausalLMOutputWithPast:
             return self.forward(input_ids, images, attention_mask, prefix_mask, past_key_values)
-    
+
         def forward(
             self,
             input_ids: torch.LongTensor,
@@ -709,27 +709,27 @@ documentation <https://huggingface.co/docs/transformers/main_classes/text_genera
                 past_key_values = (past_key_value for pkv_per_layer in past_key_values for past_key_value in pkv_per_layer)
                 # Add the past_key_values to the decoder inputs
                 inputs = dict(zip(self.key_value_input_names, past_key_values))
-    
+
             else:
                 return self.forward_with_image(input_ids, images, attention_mask)
             inputs["input_ids"] = np.array(input_ids)
-    
+
             if "attention_mask" in self.input_names:
                 inputs["attention_mask"] = np.array(attention_mask)
-    
+
             # Run inference
             self.request.start_async(inputs, share_inputs=True)
             self.request.wait()
-    
+
             logits = torch.from_numpy(self.request.get_tensor("logits").data)
-    
+
             # Tuple of length equal to : number of layer * number of past_key_value per decoder layer (2 corresponds to the self-attention layer)
             past_key_values = tuple(self.request.get_tensor(key).data for key in self.key_value_output_names)
             # Tuple of tuple of length `n_layers`, with each tuple of length equal to 2 (k/v of self-attention)
-    
+
             past_key_values = tuple(past_key_values[i : i + self.num_pkv] for i in range(0, len(past_key_values), self.num_pkv))
             return CausalLMOutputWithPast(logits=logits, past_key_values=past_key_values)
-    
+
         def forward_with_image(self, input_ids, images, attention_mask):
             """First step inference method, that resolves multimodal data"""
             _, _, attention_mask, _, input_embeds, _ = preprocess_fn(
@@ -745,7 +745,7 @@ documentation <https://huggingface.co/docs/transformers/main_classes/text_genera
             pkv = list(outs.values())[1:]
             pkv = tuple(pkv[i : i + self.num_pkv] for i in range(0, len(pkv), self.num_pkv))
             return CausalLMOutputWithPast(logits=torch.from_numpy(logits), past_key_values=pkv)
-    
+
         def prepare_inputs_for_generation(self, input_ids, past_key_values=None, **kwargs):
             """
             This function is used during running GenerationMixin.generate for preparing model specific inputs for
@@ -770,14 +770,14 @@ documentation <https://huggingface.co/docs/transformers/main_classes/text_genera
                 "past_key_values": past_key_values,
                 "images": kwargs.get("images", None),
             }
-    
+
         def _reorder_cache(self, past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor) -> Tuple[Tuple[torch.Tensor]]:
             """
             This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
             [`~PreTrainedModel.beam_sample`] is called.
             This is required to match `past_key_values` with the correct beam_idx at every generation step.
             """
-    
+
             # from transformers.models.gpt2.modeling_gpt2.GPT2LMHeadModel._reorder_cache
             return tuple(tuple(np.take(past_state, beam_idx, 0) for past_state in layer_past) for layer_past in past_key_values)
 
@@ -801,16 +801,16 @@ Select device from dropdown list for running inference using OpenVINO.
 .. code:: ipython3
 
     import ipywidgets as widgets
-    
+
     core = ov.Core()
-    
+
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
         value="AUTO",
         description="Device:",
         disabled=False,
     )
-    
+
     device
 
 
@@ -843,17 +843,17 @@ PyTorch implementation we will use PyTorch tensors as input.
 .. code:: ipython3
 
     from IPython.display import display, Video, Image
-    
-    
+
+
     examples_dir = Path("Video-LLaVA/videollava/serve/examples")
     video_file = examples_dir / "sample_demo_22.mp4"
     image_file = examples_dir / "sample_img_22.png"
-    
-    
+
+
     video_tensor = video_processor.preprocess(str(video_file), return_tensors="pt")["pixel_values"][0]
     image_tensor = image_processor.preprocess(str(image_file), return_tensors="pt")["pixel_values"][0]
     images_tensor = [video_tensor, image_tensor]
-    
+
     text_message = "Are the instruments in the pictures used in the video?"
     print(f"Question: {text_message}")
     display(Video(video_file, embed=True))
@@ -906,31 +906,31 @@ accumulating history of provided messages and images.
     from videollava.constants import IMAGE_TOKEN_INDEX
     from transformers import TextStreamer
     from videollava.conversation import conv_templates, SeparatorStyle
-    
+
     # Prepare
     streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
     conv_mode = "llava_v1"
-    
+
     conv = conv_templates[conv_mode].copy()
     roles = ("user", "assistant")
-    
+
     if mm_use_im_start_end:
         inp = DEFAULT_VIDEO_START_TOKEN + DEFAULT_IMAGE_TOKEN * 8 + DEFAULT_VIDEO_END_TOKEN + "\n" + text_message
     else:
         inp = DEFAULT_IMAGE_TOKEN * 8 + "\n" + text_message
     conv.append_message(conv.roles[0], inp)
     conv.append_message(conv.roles[1], None)
-    
+
     prompt = conv.get_prompt()
     input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0)
-    
-    
+
+
     stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
     keywords = [stop_str]
     stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
     streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
     print("Answer:")
-    
+
     output_ids = ov_model.generate(
         input_ids,
         images=images_tensor,
@@ -959,11 +959,11 @@ Interactive demo
 
     import torch
     import gradio as gr
-    
+
     from videollava.constants import DEFAULT_IMAGE_TOKEN, IMAGE_TOKEN_INDEX
     from videollava.conversation import conv_templates, SeparatorStyle
-    
-    
+
+
     def generate(image, video, textbox_in):
         if video is not None:
             textbox_in = DEFAULT_IMAGE_TOKEN * 8 + "\n" + textbox_in
@@ -971,7 +971,7 @@ Interactive demo
                 textbox_in += "\n" + DEFAULT_IMAGE_TOKEN
         elif image is not None:
             textbox_in = DEFAULT_IMAGE_TOKEN + "\n" + textbox_in
-    
+
         conv_mode = "llava_v1"
         conv = conv_templates[conv_mode].copy()
         conv.append_message(conv.roles[0], textbox_in)
@@ -983,11 +983,11 @@ Interactive demo
         if video is not None:
             images_tensor.append(video_processor(video, return_tensors="pt")["pixel_values"][0])
         input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0)
-    
+
         stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
         keywords = [stop_str]
         stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
-    
+
         generate_kwargs = dict(
             input_ids=input_ids,
             images=images_tensor,
@@ -997,19 +997,19 @@ Interactive demo
             use_cache=True,
             stopping_criteria=[stopping_criteria],
         )
-    
+
         output_ids = ov_model.generate(**generate_kwargs)
-    
+
         input_token_len = input_ids.shape[1]
         outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0]
         outputs = outputs.strip()
         if outputs.endswith(stop_str):
             outputs = outputs[: -len(stop_str)]
         outputs = outputs.strip()
-    
+
         return outputs
-    
-    
+
+
     demo = gr.Interface(
         generate,
         [
diff --git a/docs/notebooks/vision-background-removal-with-output.rst b/docs/notebooks/vision-background-removal-with-output.rst
index a4fbb37e36c40e..9868d7bf5f9f09 100644
--- a/docs/notebooks/vision-background-removal-with-output.rst
+++ b/docs/notebooks/vision-background-removal-with-output.rst
@@ -14,8 +14,8 @@ The PyTorch U\ :math:`^2`-Net model is converted to OpenVINO IR format.
 The model source is available
 `here <https://github.com/xuebinqin/U-2-Net>`__.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Preparation <#preparation>`__
 
@@ -51,11 +51,11 @@ Install requirements
 .. code:: ipython3
 
     import platform
-
+    
     %pip install -q "openvino>=2023.1.0"
     %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu "torch>=2.1" opencv-python
     %pip install -q "gdown<4.6.4"
-
+    
     if platform.system() != "Windows":
         %pip install -q "matplotlib>=3.4"
     else:
@@ -64,13 +64,9 @@ Install requirements
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
 
 
@@ -85,7 +81,7 @@ Import the PyTorch Library and U\ :math:`^2`-Net
     import time
     from collections import namedtuple
     from pathlib import Path
-
+    
     import cv2
     import matplotlib.pyplot as plt
     import numpy as np
@@ -97,18 +93,18 @@ Import the PyTorch Library and U\ :math:`^2`-Net
 
     # Import local modules
     import requests
-
+    
     if not Path("./notebook_utils.py").exists():
         # Fetch `notebook_utils` module
-
+    
         r = requests.get(
             url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
         )
-
+    
         open("notebook_utils.py", "w").write(r.text)
-
+    
     from notebook_utils import load_image, download_file
-
+    
     if not Path("./model/u2net.py").exists():
         download_file(
             url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/notebooks/vision-background-removal/model/u2net.py", directory="model"
@@ -128,7 +124,7 @@ detection and human segmentation.
 .. code:: ipython3
 
     model_config = namedtuple("ModelConfig", ["name", "url", "model", "model_args"])
-
+    
     u2net_lite = model_config(
         name="u2net_lite",
         url="https://drive.google.com/uc?id=1W8E4FHIlTVstfRkYmNOjbr0VDXTZm0jD",
@@ -147,7 +143,7 @@ detection and human segmentation.
         model=U2NET,
         model_args=(3, 1),
     )
-
+    
     # Set u2net_model to one of the three configurations listed above.
     u2net_model = u2net_lite
 
@@ -170,7 +166,7 @@ next cell loads the model and the pre-trained weights.
 
     if not model_path.exists():
         import gdown
-
+    
         os.makedirs(name=model_path.parent, exist_ok=True)
         print("Start downloading model weights file... ")
         with open(model_path, "wb") as model_file:
@@ -180,7 +176,7 @@ next cell loads the model and the pre-trained weights.
 
 .. parsed-literal::
 
-    Start downloading model weights file...
+    Start downloading model weights file... 
 
 
 .. parsed-literal::
@@ -188,7 +184,7 @@ next cell loads the model and the pre-trained weights.
     Downloading...
     From: https://drive.google.com/uc?id=1W8E4FHIlTVstfRkYmNOjbr0VDXTZm0jD
     To: <_io.BufferedWriter name='model/u2net_lite/u2net_lite.pth'>
-    100%|██████████| 4.68M/4.68M [00:00<00:00, 29.0MB/s]
+    100%|██████████| 4.68M/4.68M [00:00<00:00, 24.4MB/s]
 
 .. parsed-literal::
 
@@ -197,13 +193,15 @@ next cell loads the model and the pre-trained weights.
 
 
 
+    
+
 
 .. code:: ipython3
 
     # Load the model.
     net = u2net_model.model(*u2net_model.model_args)
     net.eval()
-
+    
     # Load the weights.
     print(f"Loading model weights from: '{model_path}'")
     net.load_state_dict(state_dict=torch.load(model_path, map_location="cpu"))
@@ -237,7 +235,7 @@ OpenVINO IR format. Executing the following command may take a while.
 
 .. parsed-literal::
 
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/functional.py:3782: UserWarning: nn.functional.upsample is deprecated. Use nn.functional.interpolate instead.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/nn/functional.py:3782: UserWarning: nn.functional.upsample is deprecated. Use nn.functional.interpolate instead.
       warnings.warn("nn.functional.upsample is deprecated. Use nn.functional.interpolate instead.")
 
 
@@ -263,20 +261,20 @@ repository <https://github.com/xuebinqin/U-2-Net/>`__ and multiplied by
 .. code:: ipython3
 
     IMAGE_URI = "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/coco_hollywood.jpg"
-
+    
     input_mean = np.array([123.675, 116.28, 103.53]).reshape(1, 3, 1, 1)
     input_scale = np.array([58.395, 57.12, 57.375]).reshape(1, 3, 1, 1)
-
+    
     image = cv2.cvtColor(
         src=load_image(IMAGE_URI),
         code=cv2.COLOR_BGR2RGB,
     )
-
+    
     resized_image = cv2.resize(src=image, dsize=(512, 512))
     # Convert the image shape to a shape and a data type expected by the network
     # for OpenVINO IR model: (1, 3, 512, 512).
     input_image = np.expand_dims(np.transpose(resized_image, (2, 0, 1)), 0)
-
+    
     input_image = (input_image - input_mean) / input_scale
 
 Select inference device
@@ -289,7 +287,7 @@ select device from dropdown list for running inference using OpenVINO
 .. code:: ipython3
 
     import ipywidgets as widgets
-
+    
     core = ov.Core()
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
@@ -297,7 +295,7 @@ select device from dropdown list for running inference using OpenVINO
         description="Device:",
         disabled=False,
     )
-
+    
     device
 
 
@@ -324,7 +322,7 @@ Load the OpenVINO IR model to OpenVINO Runtime and do inference.
     # Get the names of input and output layers.
     input_layer_ir = compiled_model_ir.input(0)
     output_layer_ir = compiled_model_ir.output(0)
-
+    
     # Do inference on the input image.
     start_time = time.perf_counter()
     result = compiled_model_ir([input_image])[output_layer_ir]
@@ -334,7 +332,7 @@ Load the OpenVINO IR model to OpenVINO Runtime and do inference.
 
 .. parsed-literal::
 
-    Inference finished. Inference time: 0.113 seconds, FPS: 8.88.
+    Inference finished. Inference time: 2.104 seconds, FPS: 0.48.
 
 
 Visualize Results
@@ -351,11 +349,11 @@ with the background removed.
     # to 0 (background) and 1 (foreground).
     # The network result has (1,1,512,512) shape. The `np.squeeze` function converts this to (512, 512).
     resized_result = np.rint(cv2.resize(src=np.squeeze(result), dsize=(image.shape[1], image.shape[0]))).astype(np.uint8)
-
+    
     # Create a copy of the image and set all background values to 255 (white).
     bg_removed_result = image.copy()
     bg_removed_result[resized_result == 0] = 255
-
+    
     fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(20, 7))
     ax[0].imshow(image)
     ax[1].imshow(resized_result, cmap="gray")
@@ -389,21 +387,21 @@ background pixels a value of 0. Replace the background image as follows:
 
     BACKGROUND_FILE = "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/wall.jpg"
     OUTPUT_DIR = "output"
-
+    
     os.makedirs(name=OUTPUT_DIR, exist_ok=True)
-
+    
     background_image = cv2.cvtColor(src=load_image(BACKGROUND_FILE), code=cv2.COLOR_BGR2RGB)
     background_image = cv2.resize(src=background_image, dsize=(image.shape[1], image.shape[0]))
-
+    
     # Set all the foreground pixels from the result to 0
     # in the background image and add the image with the background removed.
     background_image[resized_result == 1] = 0
     new_image = background_image + bg_removed_result
-
+    
     # Save the generated image.
     new_image_path = Path(f"{OUTPUT_DIR}/{Path(IMAGE_URI).stem}-{Path(BACKGROUND_FILE).stem}.jpg")
     cv2.imwrite(filename=str(new_image_path), img=cv2.cvtColor(new_image, cv2.COLOR_RGB2BGR))
-
+    
     # Display the original image and the image with the new background side by side
     fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(18, 7))
     ax[0].imshow(image)
@@ -411,7 +409,7 @@ background pixels a value of 0. Replace the background image as follows:
     for a in ax:
         a.axis("off")
     plt.show()
-
+    
     # Create a link to download the image.
     image_link = FileLink(new_image_path)
     image_link.html_link_str = "<a href='%s' download>%s</a>"
diff --git a/docs/notebooks/vision-image-colorization-with-output.rst b/docs/notebooks/vision-image-colorization-with-output.rst
deleted file mode 100644
index f28079601a798d..00000000000000
--- a/docs/notebooks/vision-image-colorization-with-output.rst
+++ /dev/null
@@ -1,460 +0,0 @@
-Image Colorization with OpenVINO
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-This notebook demonstrates how to colorize images with OpenVINO using
-the Colorization model
-`colorization-v2 <https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/public/colorization-v2/README.md>`__
-or
-`colorization-siggraph <https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/colorization-siggraph>`__
-from `Open Model
-Zoo <https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/public/index.md>`__
-based on the paper `Colorful Image
-Colorization <https://arxiv.org/abs/1603.08511>`__ models from Open
-Model Zoo.
-
-.. figure:: https://user-images.githubusercontent.com/18904157/180923280-9caefaf1-742b-4d2f-8943-5d4a6126e2fc.png
-   :alt: Let there be color
-
-   Let there be color
-
-Given a grayscale image as input, the model generates colorized version
-of the image as the output.
-
-About Colorization-v2
-^^^^^^^^^^^^^^^^^^^^^
-
--  The colorization-v2 model is one of the colorization group of models
-   designed to perform image colorization.
--  Model trained on the ImageNet dataset.
--  Model consumes L-channel of LAB-image as input and produces predict
-   A- and B-channels of LAB-image as output.
-
-About Colorization-siggraph
-^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
--  The colorization-siggraph model is one of the colorization group of
-   models designed to real-time user-guided image colorization.
--  Model trained on the ImageNet dataset with synthetically generated
-   user interaction.
--  Model consumes L-channel of LAB-image as input and produces predict
-   A- and B-channels of LAB-image as output.
-
-See the `colorization <https://github.com/richzhang/colorization>`__
-repository for more details.
-
-Table of contents:
-^^^^^^^^^^^^^^^^^^
-
--  `Imports <#imports>`__
--  `Configurations <#configurations>`__
-
-   -  `Select inference device <#select-inference-device>`__
-
--  `Download the model <#download-the-model>`__
--  `Convert the model to OpenVINO
-   IR <#convert-the-model-to-openvino-ir>`__
--  `Loading the Model <#loading-the-model>`__
--  `Utility Functions <#utility-functions>`__
--  `Load the Image <#load-the-image>`__
--  `Display Colorized Image <#display-colorized-image>`__
-
-.. code:: ipython3
-
-    import platform
-    
-    %pip install "openvino-dev>=2024.0.0" opencv-python tqdm
-    
-    if platform.system() != "Windows":
-        %pip install -q "matplotlib>=3.4"
-    else:
-        %pip install -q "matplotlib>=3.4,<3.7"
-
-
-.. parsed-literal::
-
-    Requirement already satisfied: openvino-dev>=2024.0.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (2024.1.0)
-    Requirement already satisfied: opencv-python in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (4.10.0.82)
-    Requirement already satisfied: tqdm in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (4.66.4)
-    Requirement already satisfied: defusedxml>=0.7.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino-dev>=2024.0.0) (0.7.1)
-    Requirement already satisfied: networkx<=3.1.0 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino-dev>=2024.0.0) (3.1)
-    Requirement already satisfied: numpy<2.0.0,>=1.16.6 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino-dev>=2024.0.0) (1.23.5)
-    Requirement already satisfied: openvino-telemetry>=2023.2.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino-dev>=2024.0.0) (2024.1.0)
-    Requirement already satisfied: packaging in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino-dev>=2024.0.0) (24.0)
-    Requirement already satisfied: pyyaml>=5.4.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino-dev>=2024.0.0) (6.0.1)
-    Requirement already satisfied: requests>=2.25.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from openvino-dev>=2024.0.0) (2.32.3)
-    Collecting openvino==2024.1.0 (from openvino-dev>=2024.0.0)
-      Using cached openvino-2024.1.0-15008-cp38-cp38-manylinux2014_x86_64.whl.metadata (8.8 kB)
-    Requirement already satisfied: charset-normalizer<4,>=2 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from requests>=2.25.1->openvino-dev>=2024.0.0) (3.3.2)
-    Requirement already satisfied: idna<4,>=2.5 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from requests>=2.25.1->openvino-dev>=2024.0.0) (3.7)
-    Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from requests>=2.25.1->openvino-dev>=2024.0.0) (2.2.1)
-    Requirement already satisfied: certifi>=2017.4.17 in /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages (from requests>=2.25.1->openvino-dev>=2024.0.0) (2024.6.2)
-    Using cached openvino-2024.1.0-15008-cp38-cp38-manylinux2014_x86_64.whl (38.7 MB)
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    Installing collected packages: openvino
-      Attempting uninstall: openvino
-        Found existing installation: openvino 2024.3.0.dev20240605
-        Uninstalling openvino-2024.3.0.dev20240605:
-          Successfully uninstalled openvino-2024.3.0.dev20240605
-    ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
-    openvino-tokenizers 2024.3.0.0.dev20240605 requires openvino~=2024.3.0.0.dev, but you have openvino 2024.1.0 which is incompatible.
-    Successfully installed openvino-2024.1.0
-    Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
-    Note: you may need to restart the kernel to use updated packages.
-
-
-Imports
--------
-
-
-
-.. code:: ipython3
-
-    import os
-    from pathlib import Path
-    
-    import cv2
-    import matplotlib.pyplot as plt
-    import numpy as np
-    import openvino as ov
-    
-    # Fetch `notebook_utils` module
-    import requests
-    
-    r = requests.get(
-        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
-    )
-    
-    open("notebook_utils.py", "w").write(r.text)
-    
-    import notebook_utils as utils
-
-Configurations
---------------
-
-
-
--  ``PRECISION`` - {FP16, FP32}, default: FP16.
--  ``MODEL_DIR`` - directory where the model is to be stored, default:
-   public.
--  ``MODEL_NAME`` - name of the model used for inference, default:
-   colorization-v2.
--  ``DATA_DIR`` - directory where test images are stored, default: data.
-
-.. code:: ipython3
-
-    PRECISION = "FP16"
-    MODEL_DIR = "models"
-    MODEL_NAME = "colorization-v2"
-    # MODEL_NAME="colorization-siggraph"
-    MODEL_PATH = f"{MODEL_DIR}/public/{MODEL_NAME}/{PRECISION}/{MODEL_NAME}.xml"
-    DATA_DIR = "data"
-
-Select inference device
-~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-select device from dropdown list for running inference using OpenVINO
-
-.. code:: ipython3
-
-    import ipywidgets as widgets
-    
-    core = ov.Core()
-    
-    device = widgets.Dropdown(
-        options=core.available_devices + ["AUTO"],
-        value="AUTO",
-        description="Device:",
-        disabled=False,
-    )
-    
-    device
-
-
-
-
-.. parsed-literal::
-
-    Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
-
-
-
-Download the model
-------------------
-
-
-
-``omz_downloader`` downloads model files from online sources and, if
-necessary, patches them to make them more usable with Model Converter.
-
-In this case, ``omz_downloader`` downloads the checkpoint and pytorch
-model of
-`colorization-v2 <https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/public/colorization-v2/README.md>`__
-or
-`colorization-siggraph <https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/colorization-siggraph>`__
-from `Open Model
-Zoo <https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/public/index.md>`__
-and saves it under ``MODEL_DIR``, as specified in the configuration
-above.
-
-.. code:: ipython3
-
-    download_command = f"omz_downloader " f"--name {MODEL_NAME} " f"--output_dir {MODEL_DIR} " f"--cache_dir {MODEL_DIR}"
-    ! $download_command
-
-
-.. parsed-literal::
-
-    ################|| Downloading colorization-v2 ||################
-    
-    ========== Downloading models/public/colorization-v2/ckpt/colorization-v2-eccv16.pth
-    
-    
-    ========== Downloading models/public/colorization-v2/model/__init__.py
-    
-    
-    ========== Downloading models/public/colorization-v2/model/base_color.py
-    
-    
-    ========== Downloading models/public/colorization-v2/model/eccv16.py
-    
-    
-    ========== Replacing text in models/public/colorization-v2/model/__init__.py
-    ========== Replacing text in models/public/colorization-v2/model/__init__.py
-    ========== Replacing text in models/public/colorization-v2/model/eccv16.py
-    
-
-
-Convert the model to OpenVINO IR
---------------------------------
-
-
-
-``omz_converter`` converts the models that are not in the OpenVINO™ IR
-format into that format using model conversion API.
-
-The downloaded pytorch model is not in OpenVINO IR format which is
-required for inference with OpenVINO runtime. ``omz_converter`` is used
-to convert the downloaded pytorch model into ONNX and OpenVINO IR format
-respectively
-
-.. code:: ipython3
-
-    if not os.path.exists(MODEL_PATH):
-        convert_command = f"omz_converter " f"--name {MODEL_NAME} " f"--download_dir {MODEL_DIR} " f"--precisions {PRECISION}"
-        ! $convert_command
-
-
-.. parsed-literal::
-
-    ========== Converting colorization-v2 to ONNX
-    Conversion to ONNX command: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/bin/python -- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/omz_tools/internal_scripts/pytorch_to_onnx.py --model-path=models/public/colorization-v2 --model-name=ECCVGenerator --weights=models/public/colorization-v2/ckpt/colorization-v2-eccv16.pth --import-module=model --input-shape=1,1,256,256 --output-file=models/public/colorization-v2/colorization-v2-eccv16.onnx --input-names=data_l --output-names=color_ab
-    
-    ONNX check passed successfully.
-    
-    ========== Converting colorization-v2 to IR (FP16)
-    Conversion command: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/bin/python -- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/.venv/bin/mo --framework=onnx --output_dir=models/public/colorization-v2/FP16 --model_name=colorization-v2 --input=data_l --output=color_ab --input_model=models/public/colorization-v2/colorization-v2-eccv16.onnx '--layout=data_l(NCHW)' '--input_shape=[1, 1, 256, 256]' --compress_to_fp16=True
-    
-    [ INFO ] Generated IR will be compressed to FP16. If you get lower accuracy, please consider disabling compression explicitly by adding argument --compress_to_fp16=False.
-    Find more information about compression to FP16 at https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_FP16_Compression.html
-    [ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release. Please use OpenVINO Model Converter (OVC). OVC represents a lightweight alternative of MO and provides simplified model conversion API. 
-    Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
-    [ SUCCESS ] Generated IR version 11 model.
-    [ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/vision-image-colorization/models/public/colorization-v2/FP16/colorization-v2.xml
-    [ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/vision-image-colorization/models/public/colorization-v2/FP16/colorization-v2.bin
-    
-
-
-Loading the Model
------------------
-
-
-
-Load the model in OpenVINO Runtime with ``ie.read_model`` and compile it
-for the specified device with ``ie.compile_model``.
-
-.. code:: ipython3
-
-    core = ov.Core()
-    model = core.read_model(model=MODEL_PATH)
-    compiled_model = core.compile_model(model=model, device_name=device.value)
-    input_layer = compiled_model.input(0)
-    output_layer = compiled_model.output(0)
-    N, C, H, W = list(input_layer.shape)
-
-Utility Functions
------------------
-
-
-
-.. code:: ipython3
-
-    def read_image(impath: str) -> np.ndarray:
-        """
-        Returns an image as ndarra, given path to an image reads the
-        (BGR) image using opencv's imread() API.
-    
-            Parameter:
-                impath (string): Path of the image to be read and returned.
-    
-            Returns:
-                image (ndarray): Numpy array representing the read image.
-        """
-    
-        raw_image = cv2.imread(impath)
-        if raw_image.shape[2] > 1:
-            image = cv2.cvtColor(cv2.cvtColor(raw_image, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2RGB)
-        else:
-            image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
-    
-        return image
-    
-    
-    def plot_image(image: np.ndarray, title: str = "") -> None:
-        """
-        Given a image as ndarray and title as string, display it using
-        matplotlib.
-    
-            Parameters:
-                image (ndarray): Numpy array representing the image to be
-                                 displayed.
-                title (string): String representing the title of the plot.
-    
-            Returns:
-                None
-    
-        """
-    
-        plt.imshow(image)
-        plt.title(title)
-        plt.axis("off")
-        plt.show()
-    
-    
-    def plot_output(gray_img: np.ndarray, color_img: np.ndarray) -> None:
-        """
-        Plots the original (bw or grayscale) image and colorized image
-        on different column axes for comparing side by side.
-    
-            Parameters:
-                gray_image (ndarray): Numpy array representing the original image.
-                color_image (ndarray): Numpy array representing the model output.
-    
-            Returns:
-                None
-        """
-    
-        fig = plt.figure(figsize=(12, 12))
-    
-        ax1 = fig.add_subplot(1, 2, 1)
-        plt.title("Input", fontsize=20)
-        ax1.axis("off")
-    
-        ax2 = fig.add_subplot(1, 2, 2)
-        plt.title("Colorized", fontsize=20)
-        ax2.axis("off")
-    
-        ax1.imshow(gray_img)
-        ax2.imshow(color_img)
-    
-        plt.show()
-
-Load the Image
---------------
-
-
-
-.. code:: ipython3
-
-    img_url_0 = "https://user-images.githubusercontent.com/18904157/180923287-20339d01-b1bf-493f-9a0d-55eff997aff1.jpg"
-    img_url_1 = "https://user-images.githubusercontent.com/18904157/180923289-0bb71e09-25e1-46a6-aaf1-e8f666b62d26.jpg"
-    
-    image_file_0 = utils.download_file(
-        img_url_0,
-        filename="test_0.jpg",
-        directory="data",
-        show_progress=False,
-        silent=True,
-        timeout=30,
-    )
-    assert Path(image_file_0).exists()
-    
-    image_file_1 = utils.download_file(
-        img_url_1,
-        filename="test_1.jpg",
-        directory="data",
-        show_progress=False,
-        silent=True,
-        timeout=30,
-    )
-    assert Path(image_file_1).exists()
-    
-    test_img_0 = read_image("data/test_0.jpg")
-    test_img_1 = read_image("data/test_1.jpg")
-
-.. code:: ipython3
-
-    def colorize(gray_img: np.ndarray) -> np.ndarray:
-        """
-        Given an image as ndarray for inference convert the image into LAB image,
-        the model consumes as input L-Channel of LAB image and provides output
-        A & B - Channels of LAB image. i.e returns a colorized image
-    
-            Parameters:
-                gray_img (ndarray): Numpy array representing the original
-                                    image.
-    
-            Returns:
-                colorize_image (ndarray): Numpy arrray depicting the
-                                          colorized version of the original
-                                          image.
-        """
-    
-        # Preprocess
-        h_in, w_in, _ = gray_img.shape
-        img_rgb = gray_img.astype(np.float32) / 255
-        img_lab = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2Lab)
-        img_l_rs = cv2.resize(img_lab.copy(), (W, H))[:, :, 0]
-    
-        # Inference
-        inputs = np.expand_dims(img_l_rs, axis=[0, 1])
-        res = compiled_model([inputs])[output_layer]
-        update_res = np.squeeze(res)
-    
-        # Post-process
-        out = update_res.transpose((1, 2, 0))
-        out = cv2.resize(out, (w_in, h_in))
-        img_lab_out = np.concatenate((img_lab[:, :, 0][:, :, np.newaxis], out), axis=2)
-        img_bgr_out = np.clip(cv2.cvtColor(img_lab_out, cv2.COLOR_Lab2RGB), 0, 1)
-        colorized_image = (cv2.resize(img_bgr_out, (w_in, h_in)) * 255).astype(np.uint8)
-        return colorized_image
-
-.. code:: ipython3
-
-    color_img_0 = colorize(test_img_0)
-    color_img_1 = colorize(test_img_1)
-
-Display Colorized Image
------------------------
-
-
-
-.. code:: ipython3
-
-    plot_output(test_img_0, color_img_0)
-
-
-
-.. image:: vision-image-colorization-with-output_files/vision-image-colorization-with-output_21_0.png
-
-
-.. code:: ipython3
-
-    plot_output(test_img_1, color_img_1)
-
-
-
-.. image:: vision-image-colorization-with-output_files/vision-image-colorization-with-output_22_0.png
-
diff --git a/docs/notebooks/vision-image-colorization-with-output_files/vision-image-colorization-with-output_21_0.png b/docs/notebooks/vision-image-colorization-with-output_files/vision-image-colorization-with-output_21_0.png
deleted file mode 100644
index 4b8608e6f20611..00000000000000
--- a/docs/notebooks/vision-image-colorization-with-output_files/vision-image-colorization-with-output_21_0.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:5c278cf1e55ecf1ea7593ae094cbafee4fccef56951ba7b4de2b20d3bb6cb364
-size 415784
diff --git a/docs/notebooks/vision-image-colorization-with-output_files/vision-image-colorization-with-output_22_0.png b/docs/notebooks/vision-image-colorization-with-output_files/vision-image-colorization-with-output_22_0.png
deleted file mode 100644
index 019f11ffd29f0e..00000000000000
--- a/docs/notebooks/vision-image-colorization-with-output_files/vision-image-colorization-with-output_22_0.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ff4ab92d3df015fc56a10173b2192cb2026d64c12c03c734d775aa5189a85ebe
-size 284968
diff --git a/docs/notebooks/vision-monodepth-with-output.rst b/docs/notebooks/vision-monodepth-with-output.rst
index 563b13a682feab..b91951bc93614b 100644
--- a/docs/notebooks/vision-monodepth-with-output.rst
+++ b/docs/notebooks/vision-monodepth-with-output.rst
@@ -28,8 +28,8 @@ Transfer,” <https://ieeexplore.ieee.org/document/9178977>`__ in IEEE
 Transactions on Pattern Analysis and Machine Intelligence, doi:
 ``10.1109/TPAMI.2020.3019967``.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Preparation <#preparation>`__
 
@@ -89,11 +89,8 @@ Install requirements
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
 
 
@@ -101,7 +98,7 @@ Install requirements
 
 .. parsed-literal::
 
-    21503
+    23215
 
 
 
@@ -306,7 +303,7 @@ original image shape.
 
 .. parsed-literal::
 
-    /tmp/ipykernel_3139661/2076527990.py:15: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.
+    /tmp/ipykernel_2831079/2076527990.py:15: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.
       cmap = matplotlib.cm.get_cmap(colormap)
 
 
@@ -505,7 +502,7 @@ Do Inference on a Video and Create Monodepth Video
 
 .. parsed-literal::
 
-    Processed 60 frames in 27.26 seconds. Total FPS (including video processing): 2.20.Inference FPS: 42.67 
+    Processed 60 frames in 25.62 seconds. Total FPS (including video processing): 2.34.Inference FPS: 43.97 
     Monodepth Video saved to 'output/Coco%20Walking%20in%20Berkeley_monodepth.mp4'.
 
 
@@ -532,7 +529,7 @@ Display Monodepth Video
 .. parsed-literal::
 
     Showing monodepth video saved at
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/vision-monodepth/output/Coco%20Walking%20in%20Berkeley_monodepth.mp4
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/vision-monodepth/output/Coco%20Walking%20in%20Berkeley_monodepth.mp4
     If you cannot see the video in your browser, please click on the following link to download the video 
 
 
diff --git a/docs/notebooks/vision-paddlegan-anime-with-output.rst b/docs/notebooks/vision-paddlegan-anime-with-output.rst
index 6cd783f45f8583..715d22413b49a3 100644
--- a/docs/notebooks/vision-paddlegan-anime-with-output.rst
+++ b/docs/notebooks/vision-paddlegan-anime-with-output.rst
@@ -14,8 +14,8 @@ documentation <https://github.com/PaddlePaddle/PaddleGAN/blob/develop/docs/en_US
 
    anime
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Preparation <#preparation>`__
 
@@ -64,7 +64,7 @@ Install requirements
     
     %pip install -q "openvino>=2023.1.0"
     
-    %pip install -q "paddlepaddle>=2.5.1" "paddle2onnx>=0.6"
+    %pip install -q "paddlepaddle>=2.5.1" "paddle2onnx>=0.6,<1.2.4"
     %pip install -q "git+https://github.com/PaddlePaddle/PaddleGAN.git" --no-deps
     
     if platform.system() != "Windows":
@@ -73,26 +73,21 @@ Install requirements
         %pip install -q "matplotlib>=3.4,<3.7"
     
     %pip install -q opencv-python scikit-learn "scikit-image>=0.19.2"
-    %pip install -q "imageio==2.9.0" "imageio-ffmpeg" "numba>=0.53.1" easydict munch natsort
+    %pip install -q "imageio==2.9.0" "imageio-ffmpeg" "numba>=0.53.1" easydict munch natsort tqdm
 
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
     paddleclas 2.5.2 requires gast==0.3.3, but you have gast 0.4.0 which is incompatible.
-    paddleclas 2.5.2 requires opencv-python==4.6.0.66, but you have opencv-python 4.10.0.82 which is incompatible.
-    ppgan 2.1.0 requires librosa==0.8.1, but you have librosa 0.9.2 which is incompatible.
-    ppgan 2.1.0 requires opencv-python<=4.6.0.66, but you have opencv-python 4.10.0.82 which is incompatible.
+    paddleclas 2.5.2 requires opencv-python==4.6.0.66, but you have opencv-python 4.10.0.84 which is incompatible.
+    ppgan 2.1.0 requires librosa==0.8.1, but you have librosa 0.10.2.post1 which is incompatible.
+    ppgan 2.1.0 requires opencv-python<=4.6.0.66, but you have opencv-python 4.10.0.84 which is incompatible.
     scikit-image 0.21.0 requires imageio>=2.27, but you have imageio 2.9.0 which is incompatible.
     Note: you may need to restart the kernel to use updated packages.
 
@@ -197,7 +192,7 @@ source of the function.
 
 .. parsed-literal::
 
-    [06/06 03:48:51] ppgan INFO: Found /opt/home/k8sworker/.cache/ppgan/animeganv2_hayao.pdparams
+    [07/02 03:40:15] ppgan INFO: Found /opt/home/k8sworker/.cache/ppgan/animeganv2_hayao.pdparams
 
 
 .. code:: ipython3
@@ -340,19 +335,19 @@ succeeds, the output of the next cell will include
 
 .. parsed-literal::
 
-    2024-06-06 03:48:59 [INFO]	Static PaddlePaddle model saved in model/paddle_model_static_onnx_temp_dir.
+    2024-07-02 03:40:24 [INFO]	Static PaddlePaddle model saved in model/paddle_model_static_onnx_temp_dir.
     [Paddle2ONNX] Start to parse PaddlePaddle model...
     [Paddle2ONNX] Model file path: model/paddle_model_static_onnx_temp_dir/model.pdmodel
     [Paddle2ONNX] Parameters file path: model/paddle_model_static_onnx_temp_dir/model.pdiparams
     [Paddle2ONNX] Start to parsing Paddle model...
     [Paddle2ONNX] Use opset_version = 11 for ONNX export.
     [Paddle2ONNX] PaddlePaddle model is exported as ONNX format now.
-    2024-06-06 03:48:59 [INFO]	ONNX model saved in model/paddlegan_anime.onnx.
+    2024-07-02 03:40:24 [INFO]	ONNX model saved in model/paddlegan_anime.onnx.
 
 
 .. parsed-literal::
 
-    I0606 03:48:59.878971 3139950 program_interpreter.cc:212] New Executor is Running.
+    I0702 03:40:24.038077 2831363 program_interpreter.cc:212] New Executor is Running.
 
 
 Convert to OpenVINO IR
@@ -628,8 +623,8 @@ measure inference on one image. For more accurate benchmarking, use
 
 .. parsed-literal::
 
-    OpenVINO IR model in OpenVINO Runtime/CPU: 0.423 seconds per image, FPS: 2.37
-    PaddleGAN model on CPU: 6.059 seconds per image, FPS: 0.17
+    OpenVINO IR model in OpenVINO Runtime/CPU: 0.402 seconds per image, FPS: 2.49
+    PaddleGAN model on CPU: 6.143 seconds per image, FPS: 0.16
 
 
 References
diff --git a/docs/notebooks/vision-paddlegan-superresolution-with-output.rst b/docs/notebooks/vision-paddlegan-superresolution-with-output.rst
index e8435ef19b5f16..0aa6abd349e763 100644
--- a/docs/notebooks/vision-paddlegan-superresolution-with-output.rst
+++ b/docs/notebooks/vision-paddlegan-superresolution-with-output.rst
@@ -16,8 +16,8 @@ from CVPR 2020.
 
 This notebook works best with small images (up to 800x600 resolution).
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Imports <#imports>`__
 -  `Settings <#settings>`__
@@ -55,7 +55,7 @@ Imports
     
     %pip install -q "openvino>=2023.1.0"
     
-    %pip install -q "paddlepaddle>=2.5.1" "paddle2onnx>=0.6"
+    %pip install -q "paddlepaddle>=2.5.1" "paddle2onnx>=0.6,<1.2.4"
     
     %pip install -q "imageio==2.9.0" "imageio" "numba>=0.53.1" "easydict" "munch" "natsort" Pillow tqdm
     %pip install -q "git+https://github.com/PaddlePaddle/PaddleGAN.git" --no-deps
@@ -69,20 +69,15 @@ Imports
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
-    ppgan 2.1.0 requires imageio==2.9.0, but you have imageio 2.34.1 which is incompatible.
-    ppgan 2.1.0 requires librosa==0.8.1, but you have librosa 0.9.2 which is incompatible.
-    ppgan 2.1.0 requires opencv-python<=4.6.0.66, but you have opencv-python 4.10.0.82 which is incompatible.
+    ppgan 2.1.0 requires imageio==2.9.0, but you have imageio 2.34.2 which is incompatible.
+    ppgan 2.1.0 requires librosa==0.8.1, but you have librosa 0.10.2.post1 which is incompatible.
+    ppgan 2.1.0 requires opencv-python<=4.6.0.66, but you have opencv-python 4.10.0.84 which is incompatible.
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
 
 
@@ -156,7 +151,7 @@ source code.
 
 .. parsed-literal::
 
-    [06/06 03:49:33] ppgan INFO: Found /opt/home/k8sworker/.cache/ppgan/DF2K_JPEG.pdparams
+    [07/02 03:40:50] ppgan INFO: Found /opt/home/k8sworker/.cache/ppgan/DF2K_JPEG.pdparams
 
 
 .. code:: ipython3
@@ -282,12 +277,12 @@ Convert PaddlePaddle Model to ONNX
 
 .. parsed-literal::
 
-    2024-06-06 03:49:40 [INFO]	Static PaddlePaddle model saved in model/paddle_model_static_onnx_temp_dir.
+    2024-07-02 03:40:57 [INFO]	Static PaddlePaddle model saved in model/paddle_model_static_onnx_temp_dir.
 
 
 .. parsed-literal::
 
-    I0606 03:49:40.817200 3140277 program_interpreter.cc:212] New Executor is Running.
+    I0702 03:40:57.163997 2831841 program_interpreter.cc:212] New Executor is Running.
 
 
 .. parsed-literal::
@@ -298,7 +293,7 @@ Convert PaddlePaddle Model to ONNX
     [Paddle2ONNX] Start to parsing Paddle model...
     [Paddle2ONNX] Use opset_version = 13 for ONNX export.
     [Paddle2ONNX] PaddlePaddle model is exported as ONNX format now.
-    2024-06-06 03:49:44 [INFO]	ONNX model saved in model/paddlegan_sr.onnx.
+    2024-07-02 03:41:00 [INFO]	ONNX model saved in model/paddlegan_sr.onnx.
 
 
 Convert ONNX Model to OpenVINO IR with `Model Conversion Python API <https://docs.openvino.ai/2024/openvino-workflow/model-preparation.html>`__
@@ -380,7 +375,7 @@ select device from dropdown list for running inference using OpenVINO
 
 .. parsed-literal::
 
-    <matplotlib.image.AxesImage at 0x7f5d265b8580>
+    <matplotlib.image.AxesImage at 0x7f2006f19850>
 
 
 
@@ -432,7 +427,7 @@ select device from dropdown list for running inference using OpenVINO
 
 .. parsed-literal::
 
-    <matplotlib.image.AxesImage at 0x7f5d10058df0>
+    <matplotlib.image.AxesImage at 0x7f1febdfe910>
 
 
 
diff --git a/docs/notebooks/whisper-convert-with-output.rst b/docs/notebooks/whisper-convert-with-output.rst
deleted file mode 100644
index 9dd34a97e95ef3..00000000000000
--- a/docs/notebooks/whisper-convert-with-output.rst
+++ /dev/null
@@ -1,595 +0,0 @@
-Video Subtitle Generation using Whisper and OpenVINO™
-=====================================================
-
-`Whisper <https://openai.com/blog/whisper/>`__ is an automatic speech
-recognition (ASR) system trained on 680,000 hours of multilingual and
-multitask supervised data collected from the web. It is a multi-task
-model that can perform multilingual speech recognition as well as speech
-translation and language identification.
-
-.. figure:: https://user-images.githubusercontent.com/29454499/204536347-28976978-9a07-416c-acff-fc1214bbfbe0.svg
-   :alt: asr-training-data-desktop.svg
-
-   asr-training-data-desktop.svg
-
-You can find more information about this model in the `research
-paper <https://cdn.openai.com/papers/whisper.pdf>`__, `OpenAI
-blog <https://openai.com/blog/whisper/>`__, `model
-card <https://github.com/openai/whisper/blob/main/model-card.md>`__ and
-GitHub `repository <https://github.com/openai/whisper>`__.
-
-In this notebook, we will use Whisper with OpenVINO to generate
-subtitles in a sample video. Notebook contains the following steps: 1.
-Download the model. 2. Instantiate the PyTorch model pipeline. 3.
-Convert model to OpenVINO IR, using model conversion API. 4. Run the
-Whisper pipeline with OpenVINO models.
-
-Table of contents:
-^^^^^^^^^^^^^^^^^^
-
--  `Prerequisites <#prerequisites>`__
--  `Instantiate model <#instantiate-model>`__
-
-   -  `Convert model to OpenVINO Intermediate Representation (IR)
-      format. <#convert-model-to-openvino-intermediate-representation-ir-format->`__
-   -  `Convert Whisper Encoder to OpenVINO
-      IR <#convert-whisper-encoder-to-openvino-ir>`__
-   -  `Convert Whisper decoder to OpenVINO
-      IR <#convert-whisper-decoder-to-openvino-ir>`__
-
--  `Prepare inference pipeline <#prepare-inference-pipeline>`__
-
-   -  `Select inference device <#select-inference-device>`__
-
--  `Run video transcription
-   pipeline <#run-video-transcription-pipeline>`__
--  `Interactive demo <#interactive-demo>`__
-
-Prerequisites
--------------
-
-
-
-Install dependencies.
-
-.. code:: ipython3
-
-    %pip install -q "openvino>=2023.1.0"
-    %pip install -q "python-ffmpeg<=1.0.16" moviepy transformers --extra-index-url https://download.pytorch.org/whl/cpu
-    %pip install -q "git+https://github.com/garywu007/pytube.git"
-    %pip install -q  "gradio>=4.19"
-    %pip install -q "openai-whisper==20231117" --extra-index-url https://download.pytorch.org/whl/cpu
-
-Instantiate model
------------------
-
-
-
-Whisper is a Transformer based encoder-decoder model, also referred to
-as a sequence-to-sequence model. It maps a sequence of audio spectrogram
-features to a sequence of text tokens. First, the raw audio inputs are
-converted to a log-Mel spectrogram by action of the feature extractor.
-Then, the Transformer encoder encodes the spectrogram to form a sequence
-of encoder hidden states. Finally, the decoder autoregressively predicts
-text tokens, conditional on both the previous tokens and the encoder
-hidden states.
-
-You can see the model architecture in the diagram below:
-
-.. figure:: https://user-images.githubusercontent.com/29454499/204536571-8f6d8d77-5fbd-4c6d-8e29-14e734837860.svg
-   :alt: whisper_architecture.svg
-
-   whisper_architecture.svg
-
-There are several models of different sizes and capabilities trained by
-the authors of the model. In this tutorial, we will use the ``base``
-model, but the same actions are also applicable to other models from
-Whisper family.
-
-.. code:: ipython3
-
-    from whisper import _MODELS
-    import ipywidgets as widgets
-    
-    model_id = widgets.Dropdown(
-        options=list(_MODELS),
-        value="large-v2",
-        description="Model:",
-        disabled=False,
-    )
-    
-    model_id
-
-
-
-
-.. parsed-literal::
-
-    Dropdown(description='Model:', index=9, options=('tiny.en', 'tiny', 'base.en', 'base', 'small.en', 'small', 'm…
-
-
-
-.. code:: ipython3
-
-    import whisper
-    
-    model = whisper.load_model(model_id.value, "cpu")
-    model.eval()
-    pass
-
-Convert model to OpenVINO Intermediate Representation (IR) format.
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-For best results with OpenVINO, it is recommended to convert the model
-to OpenVINO IR format. We need to provide initialized model object and
-example of inputs for shape inference. We will use ``ov.convert_model``
-functionality to convert models. The ``ov.convert_model`` Python
-function returns an OpenVINO model ready to load on device and start
-making predictions. We can save it on disk for next usage with
-``ov.save_model``.
-
-Convert Whisper Encoder to OpenVINO IR
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-.. code:: ipython3
-
-    from pathlib import Path
-    
-    WHISPER_ENCODER_OV = Path(f"whisper_{model_id.value}_encoder.xml")
-    WHISPER_DECODER_OV = Path(f"whisper_{model_id.value}_decoder.xml")
-
-.. code:: ipython3
-
-    import torch
-    import openvino as ov
-    
-    mel = torch.zeros((1, 80 if "v3" not in model_id.value else 128, 3000))
-    audio_features = model.encoder(mel)
-    if not WHISPER_ENCODER_OV.exists():
-        encoder_model = ov.convert_model(model.encoder, example_input=mel)
-        ov.save_model(encoder_model, WHISPER_ENCODER_OV)
-
-Convert Whisper decoder to OpenVINO IR
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-To reduce computational complexity, the decoder uses cached key/value
-projections in attention modules from the previous steps. We need to
-modify this process for correct tracing.
-
-.. code:: ipython3
-
-    import torch
-    from typing import Optional, Tuple
-    from functools import partial
-    
-    
-    def attention_forward(
-        attention_module,
-        x: torch.Tensor,
-        xa: Optional[torch.Tensor] = None,
-        mask: Optional[torch.Tensor] = None,
-        kv_cache: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-    ):
-        """
-        Override for forward method of decoder attention module with storing cache values explicitly.
-        Parameters:
-          attention_module: current attention module
-          x: input token ids.
-          xa: input audio features (Optional).
-          mask: mask for applying attention (Optional).
-          kv_cache: dictionary with cached key values for attention modules.
-          idx: idx for search in kv_cache.
-        Returns:
-          attention module output tensor
-          updated kv_cache
-        """
-        q = attention_module.query(x)
-    
-        if xa is None:
-            # hooks, if installed (i.e. kv_cache is not None), will prepend the cached kv tensors;
-            # otherwise, perform key/value projections for self- or cross-attention as usual.
-            k = attention_module.key(x)
-            v = attention_module.value(x)
-            if kv_cache is not None:
-                k = torch.cat((kv_cache[0], k), dim=1)
-                v = torch.cat((kv_cache[1], v), dim=1)
-            kv_cache_new = (k, v)
-        else:
-            # for cross-attention, calculate keys and values once and reuse in subsequent calls.
-            k = attention_module.key(xa)
-            v = attention_module.value(xa)
-            kv_cache_new = (None, None)
-    
-        wv, qk = attention_module.qkv_attention(q, k, v, mask)
-        return attention_module.out(wv), kv_cache_new
-    
-    
-    def block_forward(
-        residual_block,
-        x: torch.Tensor,
-        xa: Optional[torch.Tensor] = None,
-        mask: Optional[torch.Tensor] = None,
-        kv_cache: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-    ):
-        """
-        Override for residual block forward method for providing kv_cache to attention module.
-          Parameters:
-            residual_block: current residual block.
-            x: input token_ids.
-            xa: input audio features (Optional).
-            mask: attention mask (Optional).
-            kv_cache: cache for storing attention key values.
-          Returns:
-            x: residual block output
-            kv_cache: updated kv_cache
-    
-        """
-        x0, kv_cache = residual_block.attn(residual_block.attn_ln(x), mask=mask, kv_cache=kv_cache)
-        x = x + x0
-        if residual_block.cross_attn:
-            x1, _ = residual_block.cross_attn(residual_block.cross_attn_ln(x), xa)
-            x = x + x1
-        x = x + residual_block.mlp(residual_block.mlp_ln(x))
-        return x, kv_cache
-    
-    
-    # update forward functions
-    for idx, block in enumerate(model.decoder.blocks):
-        block.forward = partial(block_forward, block)
-        block.attn.forward = partial(attention_forward, block.attn)
-        if block.cross_attn:
-            block.cross_attn.forward = partial(attention_forward, block.cross_attn)
-    
-    
-    def decoder_forward(
-        decoder,
-        x: torch.Tensor,
-        xa: torch.Tensor,
-        kv_cache: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor]]] = None,
-    ):
-        """
-        Override for decoder forward method.
-        Parameters:
-          x: torch.LongTensor, shape = (batch_size, <= n_ctx) the text tokens
-          xa: torch.Tensor, shape = (batch_size, n_mels, n_audio_ctx)
-               the encoded audio features to be attended on
-          kv_cache: Dict[str, torch.Tensor], attention modules hidden states cache from previous steps
-        """
-        if kv_cache is not None:
-            offset = kv_cache[0][0].shape[1]
-        else:
-            offset = 0
-            kv_cache = [None for _ in range(len(decoder.blocks))]
-        x = decoder.token_embedding(x) + decoder.positional_embedding[offset : offset + x.shape[-1]]
-        x = x.to(xa.dtype)
-        kv_cache_upd = []
-    
-        for block, kv_block_cache in zip(decoder.blocks, kv_cache):
-            x, kv_block_cache_upd = block(x, xa, mask=decoder.mask, kv_cache=kv_block_cache)
-            kv_cache_upd.append(tuple(kv_block_cache_upd))
-    
-        x = decoder.ln(x)
-        logits = (x @ torch.transpose(decoder.token_embedding.weight.to(x.dtype), 1, 0)).float()
-    
-        return logits, tuple(kv_cache_upd)
-    
-    
-    # override decoder forward
-    model.decoder.forward = partial(decoder_forward, model.decoder)
-
-.. code:: ipython3
-
-    tokens = torch.ones((5, 3), dtype=torch.int64)
-    logits, kv_cache = model.decoder(tokens, audio_features, kv_cache=None)
-    
-    tokens = torch.ones((5, 1), dtype=torch.int64)
-    
-    if not WHISPER_DECODER_OV.exists():
-        decoder_model = ov.convert_model(model.decoder, example_input=(tokens, audio_features, kv_cache))
-        ov.save_model(decoder_model, WHISPER_DECODER_OV)
-
-The decoder model autoregressively predicts the next token guided by
-encoder hidden states and previously predicted sequence. This means that
-the shape of inputs which depends on the previous step (inputs for
-tokens and attention hidden states from previous step) are dynamic. For
-efficient utilization of memory, you define an upper bound for dynamic
-input shapes.
-
-Prepare inference pipeline
---------------------------
-
-
-
-The image below illustrates the pipeline of video transcribing using the
-Whisper model.
-
-.. figure:: https://user-images.githubusercontent.com/29454499/204536733-1f4342f7-2328-476a-a431-cb596df69854.png
-   :alt: whisper_pipeline.png
-
-   whisper_pipeline.png
-
-To run the PyTorch Whisper model, we just need to call the
-``model.transcribe(audio, **parameters)`` function. We will try to reuse
-original model pipeline for audio transcribing after replacing the
-original models with OpenVINO IR versions.
-
-Select inference device
-~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-select device from dropdown list for running inference using OpenVINO
-
-.. code:: ipython3
-
-    core = ov.Core()
-
-.. code:: ipython3
-
-    import ipywidgets as widgets
-    
-    device = widgets.Dropdown(
-        options=core.available_devices + ["AUTO"],
-        value="AUTO",
-        description="Device:",
-        disabled=False,
-    )
-    
-    device
-
-
-
-
-.. parsed-literal::
-
-    Dropdown(description='Device:', index=2, options=('CPU', 'GPU', 'AUTO'), value='AUTO')
-
-
-
-.. code:: ipython3
-
-    # Fetch `notebook_utils` module
-    import requests
-    
-    r = requests.get(
-        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
-    )
-    open("notebook_utils.py", "w").write(r.text)
-    from notebook_utils import download_file
-    
-    if not Path("./utils.py").exists():
-        download_file(url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/notebooks/whisper-subtitles-generation/utils.py")
-    
-    from utils import (
-        patch_whisper_for_ov_inference,
-        OpenVINOAudioEncoder,
-        OpenVINOTextDecoder,
-    )
-    
-    patch_whisper_for_ov_inference(model)
-    
-    model.encoder = OpenVINOAudioEncoder(core, WHISPER_ENCODER_OV, device=device.value)
-    model.decoder = OpenVINOTextDecoder(core, WHISPER_DECODER_OV, device=device.value)
-
-Run video transcription pipeline
---------------------------------
-
-
-
-Now, we are ready to start transcription. We select a video from YouTube
-that we want to transcribe. Be patient, as downloading the video may
-take some time.
-
-.. code:: ipython3
-
-    import ipywidgets as widgets
-    
-    VIDEO_LINK = "https://youtu.be/kgL5LBM-hFI"
-    link = widgets.Text(
-        value=VIDEO_LINK,
-        placeholder="Type link for video",
-        description="Video:",
-        disabled=False,
-    )
-    
-    link
-
-
-
-
-.. parsed-literal::
-
-    Text(value='https://youtu.be/kgL5LBM-hFI', description='Video:', placeholder='Type link for video')
-
-
-
-.. code:: ipython3
-
-    from pytube import YouTube
-    
-    print(f"Downloading video {link.value} started")
-    
-    output_file = Path("downloaded_video.mp4")
-    yt = YouTube(link.value)
-    yt.streams.get_highest_resolution().download(filename=output_file)
-    print(f"Video saved to {output_file}")
-
-
-.. parsed-literal::
-
-    Downloading video https://youtu.be/kgL5LBM-hFI started
-    Video saved to downloaded_video.mp4
-
-
-.. code:: ipython3
-
-    from utils import get_audio
-    
-    audio, duration = get_audio(output_file)
-
-Select the task for the model:
-
--  **transcribe** - generate audio transcription in the source language
-   (automatically detected).
--  **translate** - generate audio transcription with translation to
-   English language.
-
-.. code:: ipython3
-
-    task = widgets.Select(
-        options=["transcribe", "translate"],
-        value="translate",
-        description="Select task:",
-        disabled=False,
-    )
-    task
-
-
-
-
-.. parsed-literal::
-
-    Select(description='Select task:', index=1, options=('transcribe', 'translate'), value='translate')
-
-
-
-.. code:: ipython3
-
-    transcription = model.transcribe(audio, task=task.value)
-
-"The results will be saved in the ``downloaded_video.srt`` file. SRT is
-one of the most popular formats for storing subtitles and is compatible
-with many modern video players. This file can be used to embed
-transcription into videos during playback or by injecting them directly
-into video files using ``ffmpeg``.
-
-.. code:: ipython3
-
-    from utils import prepare_srt
-    
-    srt_lines = prepare_srt(transcription, filter_duration=duration)
-    # save transcription
-    with output_file.with_suffix(".srt").open("w") as f:
-        f.writelines(srt_lines)
-
-Now let us see the results.
-
-.. code:: ipython3
-
-    widgets.Video.from_file(output_file, loop=False, width=800, height=800)
-
-
-
-
-.. parsed-literal::
-
-    Video(value=b"\x00\x00\x00\x18ftypmp42\x00\x00\x00\x00isommp42\x00\x00:'moov\x00\x00\x00lmvhd...", height='800…
-
-
-
-.. code:: ipython3
-
-    print("".join(srt_lines))
-
-
-.. parsed-literal::
-
-    1
-    00:00:00,000 --> 00:00:05,000
-     What's that?
-    
-    2
-    00:00:05,000 --> 00:00:07,000
-     Wow.
-    
-    3
-    00:00:07,000 --> 00:00:10,000
-     Hello, humans.
-    
-    4
-    00:00:10,000 --> 00:00:15,000
-     Focus on me.
-    
-    5
-    00:00:15,000 --> 00:00:16,000
-     Focus on the guard.
-    
-    6
-    00:00:16,000 --> 00:00:20,000
-     Don't tell anyone what you've seen in here.
-    
-    7
-    00:00:20,000 --> 00:00:24,000
-     Have you seen what's in there?
-    
-    8
-    00:00:24,000 --> 00:00:30,000
-     Intel. This is where it all changes.
-    
-    
-
-
-Interactive demo
-----------------
-
-
-
-.. code:: ipython3
-
-    import gradio as gr
-    
-    
-    def transcribe(url, task):
-        output_file = Path("downloaded_video.mp4")
-        yt = YouTube(url)
-        yt.streams.get_highest_resolution().download(filename=output_file)
-        audio, duration = get_audio(output_file)
-        transcription = model.transcribe(audio, task=task.lower())
-        srt_lines = prepare_srt(transcription, duration)
-        with output_file.with_suffix(".srt").open("w") as f:
-            f.writelines(srt_lines)
-        return [str(output_file), str(output_file.with_suffix(".srt"))]
-    
-    
-    demo = gr.Interface(
-        transcribe,
-        [
-            gr.Textbox(label="YouTube URL"),
-            gr.Radio(["Transcribe", "Translate"], value="Transcribe"),
-        ],
-        "video",
-        examples=[["https://youtu.be/kgL5LBM-hFI", "Transcribe"]],
-        allow_flagging="never",
-    )
-    try:
-        demo.launch(debug=False)
-    except Exception:
-        demo.launch(share=True, debug=False)
-    # if you are launching remotely, specify server_name and server_port
-    # demo.launch(server_name='your server name', server_port='server port in int')
-    # Read more in the docs: https://gradio.app/docs/
-
-
-.. parsed-literal::
-
-    Running on local URL:  http://127.0.0.1:7862
-    
-    To create a public link, set `share=True` in `launch()`.
-
-
-
-
-
-
-
-
-.. parsed-literal::
-
-    Keyboard interruption in main thread... closing server.
-
diff --git a/docs/notebooks/whisper-nncf-quantize-with-output.rst b/docs/notebooks/whisper-nncf-quantize-with-output.rst
deleted file mode 100644
index f313360bdb966f..00000000000000
--- a/docs/notebooks/whisper-nncf-quantize-with-output.rst
+++ /dev/null
@@ -1,706 +0,0 @@
-Post-Training Quantization of OpenAI Whisper model with NNCF
-============================================================
-
-The goal of this tutorial is to demonstrate how to speed up the model by
-applying 8-bit post-training quantization from
-`NNCF <https://github.com/openvinotoolkit/nncf/>`__ (Neural Network
-Compression Framework) and infer quantized model via OpenVINO™ Toolkit.
-The optimization process contains the following steps:
-
-1. Quantize the converted OpenVINO model from `whisper-convert
-   notebook <whisper-convert.ipynb>`__ with NNCF.
-2. Check model result for the demo video.
-3. Compare model size, performance and accuracy of FP32 and quantized
-   INT8 models.
-
-..
-
-   **NOTE**: you should run `whisper-convert <whisper-convert.ipynb>`__
-   notebook first to generate OpenVINO IR model that is used for
-   quantization.
-
-Table of contents:
-^^^^^^^^^^^^^^^^^^
-
--  `Prerequisites <#prerequisites>`__
--  `Create and initialize
-   quantization <#create-and-initialize-quantization>`__
-
-   -  `Prepare calibration datasets <#prepare-calibration-datasets>`__
-   -  `Quantize Whisper encoder and decoder
-      models <#quantize-whisper-encoder-and-decoder-models>`__
-
--  `Transcribe video with quantized OpenVINO
-   model <#transcribe-video-with-quantized-openvino-model>`__
--  `Compare performance and accuracy of the FP32 and INT8
-   IRs <#compare-performance-and-accuracy-of-the-fp32-and-int8-irs>`__
-
-Prerequisites
--------------
-
-
-
-Install dependencies.
-
-.. code:: ipython3
-
-    %pip install -q "openvino>=2023.1.0"
-    %pip install -q "nncf>=2.6.0"
-    %pip install -q datasets librosa soundfile
-    %pip install -q evaluate jiwer
-
-Select model for quantization
-
-.. code:: ipython3
-
-    from pathlib import Path
-    import ipywidgets as widgets
-
-
-    def get_model_id(model_path):
-        return model_path.name.replace("whisper_", "").replace("encoder.xml", "").replace("_", "")
-
-
-    model_list = [get_model_id(model_path) for model_path in Path(".").glob("whisper_*encoder.xml")]
-    model_list = [model_name for model_name in model_list if model_name]
-
-    if not model_list:
-        raise RuntimeError("Please run conversion notebook first")
-
-    model_id = widgets.Dropdown(
-        options=model_list,
-        value=model_list[0],
-        description="Model:",
-        disabled=False,
-    )
-
-    model_id
-
-
-
-
-.. parsed-literal::
-
-    Dropdown(description='Model:', options=('large-v2', 'large-v3'), value='large-v2')
-
-
-
-Select device from dropdown list for running inference using OpenVINO.
-
-.. code:: ipython3
-
-    import ipywidgets as widgets
-
-    from openvino import Core
-
-    core = Core()
-
-    device = widgets.Dropdown(
-        options=core.available_devices + ["AUTO"],
-        value="AUTO",
-        description="Device:",
-        disabled=False,
-    )
-
-    device
-
-
-
-
-.. parsed-literal::
-
-    Dropdown(description='Device:', index=2, options=('CPU', 'GPU', 'AUTO'), value='AUTO')
-
-
-
-Select the task for the model:
-
--  **transcribe** - generate audio transcription in the source language
-   (automatically detected).
--  **translate** - generate audio transcription with translation to
-   English language.
-
-.. code:: ipython3
-
-    task = widgets.Select(
-        options=["transcribe", "translate"],
-        value="translate",
-        description="Select task:",
-        disabled=False,
-    )
-    task
-
-
-
-
-.. parsed-literal::
-
-    Select(description='Select task:', index=1, options=('transcribe', 'translate'), value='translate')
-
-
-
-Create and initialize quantization
-----------------------------------
-
-
-
-`NNCF <https://github.com/openvinotoolkit/nncf/>`__ enables
-post-training quantization by adding the quantization layers into the
-model graph and then using a subset of the training dataset to
-initialize the parameters of these additional quantization layers. The
-framework is designed so that modifications to your original training
-code are minor. Quantization is the simplest scenario and requires a few
-modifications.
-
-The optimization process contains the following steps:
-
-1. Create a calibration dataset for quantization.
-2. Run ``nncf.quantize`` to obtain quantized models.
-3. Serialize the ``INT8`` model using ``openvino.runtime.serialize``
-   function.
-
-Set paths to the model converted in
-`whisper-convert <whisper-convert.ipynb>`__ notebook and the paths where
-quantized models will be saved.
-
-.. code:: ipython3
-
-    from pathlib import Path
-
-    WHISPER_ENCODER_OV = Path(f"whisper_{model_id.value}_encoder.xml")
-    WHISPER_DECODER_OV = Path(f"whisper_{model_id.value}_decoder.xml")
-
-    WHISPER_ENCODER_OV_INT8 = Path(f"whisper_{model_id.value}_encoder_int8.xml")
-    WHISPER_DECODER_OV_INT8 = Path(f"whisper_{model_id.value}_decoder_int8.xml")
-
-Load FP32 model IR.
-
-.. code:: ipython3
-
-    import whisper
-
-    # Fetch `notebook_utils` module
-    import requests
-
-    r = requests.get(
-        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
-    )
-    open("notebook_utils.py", "w").write(r.text)
-    from notebook_utils import download_file
-
-    if not Path("./utils.py").exists():
-        download_file(url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/notebooks/whisper-subtitles-generation/utils.py")
-
-    from utils import (
-        patch_whisper_for_ov_inference,
-        OpenVINOAudioEncoder,
-        OpenVINOTextDecoder,
-    )
-
-    model_fp32 = whisper.load_model(model_id.value, "cpu").eval()
-    patch_whisper_for_ov_inference(model_fp32)
-
-    model_fp32.encoder = OpenVINOAudioEncoder(core, WHISPER_ENCODER_OV, device=device.value)
-    model_fp32.decoder = OpenVINOTextDecoder(core, WHISPER_DECODER_OV, device=device.value)
-
-Prepare calibration datasets
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-Whisper consists of an encoder and a decoder models. We need to collect
-calibration data for both of them.
-
-Below we overwrite encoder/decoder forward methods in order to collect
-calibration samples.
-
-.. code:: ipython3
-
-    from contextlib import contextmanager
-    from functools import partial
-    import openvino as ov
-    from typing import Optional
-    import torch
-
-    COLLECT_CALIBRATION_DATA = False
-    encoder_calibration_data = []
-    decoder_calibration_data = []
-
-
-    @contextmanager
-    def calibration_data_collection():
-        global COLLECT_CALIBRATION_DATA
-        try:
-            COLLECT_CALIBRATION_DATA = True
-            yield
-        finally:
-            COLLECT_CALIBRATION_DATA = False
-
-
-    def encoder_forward(self, mel: torch.Tensor):
-        if COLLECT_CALIBRATION_DATA:
-            encoder_calibration_data.append(mel)
-        return torch.from_numpy(self.compiled_model(mel)[self.output_blob])
-
-
-    def decoder_forward(self, x: torch.Tensor, xa: torch.Tensor, kv_cache: Optional[dict] = None):
-        feed_dict = {"x": ov.Tensor(x.numpy()), "xa": ov.Tensor(xa.numpy())}
-        feed_dict = self.preprocess_kv_cache_inputs(feed_dict, kv_cache)
-        if COLLECT_CALIBRATION_DATA:
-            decoder_calibration_data.append(feed_dict)
-        res = self.compiled_model(feed_dict)
-        return self.postprocess_outputs(res)
-
-
-    model_fp32.encoder.forward = partial(encoder_forward, model_fp32.encoder)
-    model_fp32.decoder.forward = partial(decoder_forward, model_fp32.decoder)
-
-We use a portion of validation
-`librispeech_asr <https://huggingface.co/datasets/librispeech_asr>`__
-dataset from Hugging Face as calibration data.
-
-.. code:: ipython3
-
-    from datasets import load_dataset
-    from tqdm.notebook import tqdm
-
-    CALIBRATION_DATASET_SIZE = 30
-
-    calibration_dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True).take(CALIBRATION_DATASET_SIZE)
-
-    with calibration_data_collection():
-        for data_item in tqdm(
-            calibration_dataset,
-            desc="Collecting calibration data",
-            total=CALIBRATION_DATASET_SIZE,
-        ):
-            model_fp32.transcribe(data_item["audio"]["array"].astype("float32"), task=task.value)
-
-
-
-.. parsed-literal::
-
-    Collecting calibration data:   0%|          | 0/30 [00:00<?, ?it/s]
-
-
-Quantize Whisper encoder and decoder models
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-
-
-Quantize both encoder and decoder models using ``nncf.quantize()`` API
-and save the quantized IRs after that.
-
-.. code:: ipython3
-
-    import nncf
-    from openvino.runtime import serialize
-
-    print("Quantizing encoder...")
-    quantized_encoder = nncf.quantize(
-        model=model_fp32.encoder.model,
-        calibration_dataset=nncf.Dataset(encoder_calibration_data),
-        subset_size=len(encoder_calibration_data),
-        model_type=nncf.ModelType.TRANSFORMER,
-        advanced_parameters=nncf.AdvancedQuantizationParameters(
-            smooth_quant_alpha=0.5  # Smooth Quant algorithm reduces activation quantization error; optimal alpha value was obtained through grid search
-        ),
-    )
-    serialize(quantized_encoder, WHISPER_ENCODER_OV_INT8)
-    print(f"Saved quantized encoder at ./{WHISPER_ENCODER_OV_INT8}")
-
-    print("Quantizing decoder...")
-    quantized_decoder = nncf.quantize(
-        model=model_fp32.decoder.model,
-        calibration_dataset=nncf.Dataset(decoder_calibration_data),
-        subset_size=len(decoder_calibration_data),
-        model_type=nncf.ModelType.TRANSFORMER,
-        advanced_parameters=nncf.AdvancedQuantizationParameters(
-            smooth_quant_alpha=0.95  # Smooth Quant algorithm reduces activation quantization error; optimal alpha value was obtained through grid search
-        ),
-    )
-    serialize(quantized_decoder, WHISPER_DECODER_OV_INT8)
-    print(f"Saved quantized decoder at ./{WHISPER_DECODER_OV_INT8}")
-
-
-.. parsed-literal::
-
-    INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, onnx, openvino
-    Quantizing encoder...
-
-
-.. parsed-literal::
-
-    Statistics collection: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 60/60 [01:42<00:00,  1.72s/it]
-    Applying Smooth Quant: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 128/128 [00:13<00:00,  9.71it/s]
-
-
-.. parsed-literal::
-
-    INFO:nncf:96 ignored nodes was found by name in the NNCFGraph
-
-
-.. parsed-literal::
-
-    Statistics collection: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 60/60 [03:17<00:00,  3.29s/it]
-    Applying Fast Bias correction: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 162/162 [03:09<00:00,  1.17s/it]
-
-
-.. parsed-literal::
-
-    Saved quantized encoder at ./whisper_large-v2_encoder_int8.xml
-    Quantizing decoder...
-
-
-.. parsed-literal::
-
-    Statistics collection: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 669/669 [03:20<00:00,  3.33it/s]
-    Applying Smooth Quant: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 194/194 [00:23<00:00,  8.41it/s]
-
-
-.. parsed-literal::
-
-    INFO:nncf:192 ignored nodes was found by name in the NNCFGraph
-
-
-.. parsed-literal::
-
-    Statistics collection: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 669/669 [07:22<00:00,  1.51it/s]
-    Applying Fast Bias correction: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 256/256 [04:01<00:00,  1.06it/s]
-
-.. parsed-literal::
-
-    Saved quantized decoder at ./whisper_large-v2_decoder_int8.xml
-
-
-
-
-
-Transcribe video with quantized OpenVINO model
-----------------------------------------------
-
-
-
-Load ``INT8`` models saved above into a new instance of Whisper model.
-
-.. code:: ipython3
-
-    model_int8 = whisper.load_model(model_id.value, device="cpu").eval()
-    patch_whisper_for_ov_inference(model_int8)
-
-    model_int8.encoder = OpenVINOAudioEncoder(core, WHISPER_ENCODER_OV_INT8, device=device.value)
-    model_int8.decoder = OpenVINOTextDecoder(core, WHISPER_DECODER_OV_INT8, device=device.value)
-
-Select a video for transcription as in
-`whisper-convert <whisper-convert.ipynb>`__ notebook.
-
-.. code:: ipython3
-
-    VIDEO_LINK = "https://youtu.be/kgL5LBM-hFI"
-    link = widgets.Text(
-        value=VIDEO_LINK,
-        placeholder="Type link for video",
-        description="Video:",
-        disabled=False,
-    )
-    link
-
-
-
-
-.. parsed-literal::
-
-    Text(value='https://youtu.be/kgL5LBM-hFI', description='Video:', placeholder='Type link for video')
-
-
-
-.. code:: ipython3
-
-    from pytube import YouTube
-
-    print(f"Downloading video {link.value} started")
-
-    output_file = Path("downloaded_video.mp4")
-    yt = YouTube(link.value)
-    yt.streams.get_highest_resolution().download(filename=output_file)
-    print(f"Video saved to {output_file}")
-
-
-.. parsed-literal::
-
-    Downloading video https://youtu.be/kgL5LBM-hFI started
-    Video saved to downloaded_video.mp4
-
-
-.. code:: ipython3
-
-    from utils import get_audio
-
-    audio, duration = get_audio(output_file)
-
-Run transcription by the quantized model.
-
-.. code:: ipython3
-
-    transcription = model_int8.transcribe(audio, task=task.value)
-
-.. code:: ipython3
-
-    from utils import prepare_srt
-
-    srt_lines = prepare_srt(transcription, duration)
-    # save transcription
-    with output_file.with_suffix(".srt").open("w") as f:
-        f.writelines(srt_lines)
-
-Now let us see the results.
-
-.. code:: ipython3
-
-    widgets.Video.from_file(output_file, loop=False, width=800, height=800)
-
-
-
-
-.. parsed-literal::
-
-    Video(value=b"\x00\x00\x00\x18ftypmp42\x00\x00\x00\x00isommp42\x00\x00:'moov\x00\x00\x00lmvhd...", height='800…
-
-
-
-.. code:: ipython3
-
-    print("".join(srt_lines))
-
-
-.. parsed-literal::
-
-    1
-    00:00:00,000 --> 00:00:05,000
-     What's that?
-
-    2
-    00:00:05,000 --> 00:00:07,000
-     Oh, wow.
-
-    3
-    00:00:09,000 --> 00:00:11,000
-     Hello, humans.
-
-    4
-    00:00:13,000 --> 00:00:15,000
-     Focus on me.
-
-    5
-    00:00:15,000 --> 00:00:17,000
-     Focus on the guard.
-
-    6
-    00:00:17,000 --> 00:00:20,000
-     Don't tell anyone what you see in here.
-
-    7
-    00:00:22,000 --> 00:00:24,000
-     Have you seen what's in there?
-
-    8
-    00:00:24,000 --> 00:00:25,000
-     They have...
-
-    9
-    00:00:25,000 --> 00:00:27,000
-     Intel. This is where it all changes.
-
-
-
-
-As you can see the result is almost the same.
-
-Compare performance and accuracy of the FP32 and INT8 IRs
----------------------------------------------------------
-
-
-
-Compare model file size.
-
-.. code:: ipython3
-
-    def calculate_compression_rate(model_path_ov, model_path_ov_int8):
-        model_size_fp32 = model_path_ov.with_suffix(".bin").stat().st_size / 1024
-        model_size_int8 = model_path_ov_int8.with_suffix(".bin").stat().st_size / 1024
-        print(f"Model: {model_path_ov.stem}")
-        print(f"    * FP32 IR model size: {model_size_fp32:.2f} KB")
-        print(f"    * INT8 IR model size: {model_size_int8:.2f} KB")
-        print(f"    * Model compression rate: {model_size_fp32 / model_size_int8:.3f}")
-
-
-    calculate_compression_rate(WHISPER_ENCODER_OV, WHISPER_ENCODER_OV_INT8)
-    calculate_compression_rate(WHISPER_DECODER_OV, WHISPER_DECODER_OV_INT8)
-
-
-.. parsed-literal::
-
-    Model: whisper_large-v2_encoder
-        * FP32 IR model size: 1244080.07 KB
-        * INT8 IR model size: 626971.58 KB
-        * Model compression rate: 1.984
-    Model: whisper_large-v2_decoder
-        * FP32 IR model size: 1900607.09 KB
-        * INT8 IR model size: 955679.81 KB
-        * Model compression rate: 1.989
-
-
-To measure the inference performance of the ``FP32`` and ``INT8``
-encoder/decoder models, we use median inference time on calibration
-dataset. So we can approximately estimate the speed-up of the dynamic
-quantized models.
-
-   **NOTE**: For the most accurate performance estimation, it is
-   recommended to run ``benchmark_app`` with static shapes in a
-   terminal/command prompt after closing other applications.
-
-.. code:: ipython3
-
-    import time
-    import numpy as np
-
-
-    def calculate_call_inference_time(model, dataset):
-        inference_time = []
-        for data_item in tqdm(dataset[:100], desc="Measuring performance"):
-            start = time.perf_counter()
-            model(data_item)
-            end = time.perf_counter()
-            delta = end - start
-            inference_time.append(delta)
-        return np.median(inference_time)
-
-
-    encoder_time_fp32 = calculate_call_inference_time(model_fp32.encoder.compiled_model, encoder_calibration_data)
-    encoder_time_int8 = calculate_call_inference_time(model_int8.encoder.compiled_model, encoder_calibration_data)
-    print(f"Encoder performance speedup: {encoder_time_fp32 / encoder_time_int8:.3f}")
-
-    decoder_time_fp32 = calculate_call_inference_time(model_fp32.decoder.compiled_model, decoder_calibration_data)
-    decoder_time_int8 = calculate_call_inference_time(model_int8.decoder.compiled_model, decoder_calibration_data)
-    print(f"Decoder performance speedup: {decoder_time_fp32 / decoder_time_int8:.3f}")
-
-
-
-.. parsed-literal::
-
-    Measuring performance:   0%|          | 0/60 [00:00<?, ?it/s]
-
-
-
-.. parsed-literal::
-
-    Measuring performance:   0%|          | 0/60 [00:00<?, ?it/s]
-
-
-.. parsed-literal::
-
-    Encoder performance speedup: 1.763
-
-
-
-.. parsed-literal::
-
-    Measuring performance:   0%|          | 0/100 [00:00<?, ?it/s]
-
-
-
-.. parsed-literal::
-
-    Measuring performance:   0%|          | 0/100 [00:00<?, ?it/s]
-
-
-.. parsed-literal::
-
-    Decoder performance speedup: 2.022
-
-
-We measure the whole transcription performance separately, because a
-single Whisper ``transcribe()`` call triggers multiple encoder and
-decoder inference calls. And the number of these calls is dynamic
-depending on the model accuracy. In this experiment we use the mean time
-instead of the median because the model transcription time is less
-uniform.
-
-We also compare accuracy values of the ``FP32`` and ``INT8`` models on a
-subset of
-`librispeech_asr <https://huggingface.co/datasets/librispeech_asr>`__
-test dataset. We rely on the Word Error Rate (WER) metric and compute
-accuracy as ``(1 - WER)``.
-
-.. code:: ipython3
-
-    from evaluate import load
-    from transformers import WhisperProcessor
-
-    wer = load("wer")
-
-    TEST_DATASET_SIZE = 100
-    test_dataset = load_dataset("librispeech_asr", "clean", split="test", streaming=True).take(TEST_DATASET_SIZE)
-
-
-    def calculate_transcription_time_and_accuracy(model, dataset):
-        processor = WhisperProcessor.from_pretrained("openai/whisper-large")
-
-        ground_truths = []
-        predictions = []
-        inference_time = []
-        for data_item in tqdm(dataset, desc="Measuring performance and accuracy", total=TEST_DATASET_SIZE):
-            audio = data_item["audio"]["array"].astype("float32")
-
-            start_time = time.perf_counter()
-            transcription = model.transcribe(audio, task=task.value)
-            end_time = time.perf_counter()
-            delta_time = end_time - start_time
-
-            reference = processor.tokenizer._normalize(data_item["text"])
-            prediction = processor.tokenizer._normalize(transcription["text"])
-            ground_truths.append(reference)
-            predictions.append(prediction)
-            inference_time.append(delta_time)
-
-        word_accuracy = (1 - wer.compute(references=ground_truths, predictions=predictions)) * 100
-        mean_inference_time = np.mean(inference_time)
-        return mean_inference_time, word_accuracy
-
-
-    transcription_time_fp32, accuracy_fp32 = calculate_transcription_time_and_accuracy(model_fp32, test_dataset)
-    transcription_time_int8, accuracy_int8 = calculate_transcription_time_and_accuracy(model_int8, test_dataset)
-    print(f"Whisper transcription performance speedup: {transcription_time_fp32 / transcription_time_int8:.3f}")
-    print(f"Whisper transcription word accuracy. FP32: {accuracy_fp32:.2f}%. INT8: {accuracy_int8:.2f}%. Accuracy drop :{accuracy_fp32 - accuracy_int8:.2f}%.")
-
-
-.. parsed-literal::
-
-    Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
-
-
-
-.. parsed-literal::
-
-    Measuring performance and accuracy:   0%|          | 0/100 [00:00<?, ?it/s]
-
-
-.. parsed-literal::
-
-    Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
-
-
-
-.. parsed-literal::
-
-    Measuring performance and accuracy:   0%|          | 0/100 [00:00<?, ?it/s]
-
-
-.. parsed-literal::
-
-    Whisper transcription performance speedup: 1.799
-    Whisper transcription word accuracy. FP32: 98.41%. INT8: 97.51%. Accuracy drop :0.90%.
-
-
-   **NOTE**: Accuracy drop can generally be improved by increasing
-   calibration dataset size.
diff --git a/docs/notebooks/whisper-subtitles-generation-with-output.rst b/docs/notebooks/whisper-subtitles-generation-with-output.rst
new file mode 100644
index 00000000000000..448e92dce16431
--- /dev/null
+++ b/docs/notebooks/whisper-subtitles-generation-with-output.rst
@@ -0,0 +1,1139 @@
+Video Subtitle Generation using Whisper and OpenVINO™
+=====================================================
+
+`Whisper <https://openai.com/blog/whisper/>`__ is an automatic speech
+recognition (ASR) system trained on 680,000 hours of multilingual and
+multitask supervised data collected from the web. It is a multi-task
+model that can perform multilingual speech recognition as well as speech
+translation and language identification.
+
+.. figure:: https://user-images.githubusercontent.com/29454499/204536347-28976978-9a07-416c-acff-fc1214bbfbe0.svg
+   :alt: asr-training-data-desktop.svg
+
+   asr-training-data-desktop.svg
+
+You can find more information about this model in the `research
+paper <https://cdn.openai.com/papers/whisper.pdf>`__, `OpenAI
+blog <https://openai.com/blog/whisper/>`__, `model
+card <https://github.com/openai/whisper/blob/main/model-card.md>`__ and
+GitHub `repository <https://github.com/openai/whisper>`__.
+
+In this notebook, we will use Whisper with OpenVINO to generate
+subtitles in a sample video. Additionally, we will use
+`NNCF <https://github.com/openvinotoolkit/nncf>`__ improving model
+performance by INT8 quantization. Notebook contains the following steps:
+1. Download the model. 2. Instantiate the PyTorch model pipeline. 3.
+Convert model to OpenVINO IR, using model conversion API. 4. Run the
+Whisper pipeline with OpenVINO models. 5. Quantize the OpenVINO model
+with NNCF. 6. Check quantized model result for the demo video. 7.
+Compare model size, performance and accuracy of FP32 and quantized INT8
+models. 8. Launch Interactive demo for video subtitles generation.
+
+**Table of contents:**
+
+
+-  `Prerequisites <#prerequisites>`__
+-  `Instantiate model <#instantiate-model>`__
+
+   -  `Convert model to OpenVINO Intermediate Representation (IR)
+      format. <#convert-model-to-openvino-intermediate-representation-ir-format->`__
+
+-  `Prepare inference pipeline <#prepare-inference-pipeline>`__
+
+   -  `Select inference device <#select-inference-device>`__
+
+-  `Run video transcription
+   pipeline <#run-video-transcription-pipeline>`__
+-  `Quantization <#quantization>`__
+
+   -  `Prepare calibration datasets <#prepare-calibration-datasets>`__
+   -  `Quantize Whisper encoder and decoder
+      models <#quantize-whisper-encoder-and-decoder-models>`__
+   -  `Run quantized model inference <#run-quantized-model-inference>`__
+   -  `Compare performance and accuracy of the original and quantized
+      models <#compare-performance-and-accuracy-of-the-original-and-quantized-models>`__
+
+-  `Interactive demo <#interactive-demo>`__
+
+Prerequisites
+-------------
+
+
+
+Install dependencies.
+
+.. code:: ipython3
+
+    %pip install -q "openvino>=2024.1.0" "nncf>=2.10.0"
+    %pip install -q "python-ffmpeg<=1.0.16" moviepy transformers onnx "git+https://github.com/huggingface/optimum-intel.git" "peft==0.6.2" --extra-index-url https://download.pytorch.org/whl/cpu
+    %pip install -q "git+https://github.com/garywu007/pytube.git" soundfile librosa jiwer
+    %pip install -q  "gradio>=4.19"
+
+Instantiate model
+-----------------
+
+
+
+Whisper is a Transformer based encoder-decoder model, also referred to
+as a sequence-to-sequence model. It maps a sequence of audio spectrogram
+features to a sequence of text tokens. First, the raw audio inputs are
+converted to a log-Mel spectrogram by action of the feature extractor.
+Then, the Transformer encoder encodes the spectrogram to form a sequence
+of encoder hidden states. Finally, the decoder autoregressively predicts
+text tokens, conditional on both the previous tokens and the encoder
+hidden states.
+
+You can see the model architecture in the diagram below:
+
+.. figure:: https://user-images.githubusercontent.com/29454499/204536571-8f6d8d77-5fbd-4c6d-8e29-14e734837860.svg
+   :alt: whisper_architecture.svg
+
+   whisper_architecture.svg
+
+There are several models of different sizes and capabilities trained by
+the authors of the model. In this tutorial, we will use the ``tiny``
+model, but the same actions are also applicable to other models from
+Whisper family.
+
+.. code:: ipython3
+
+    import ipywidgets as widgets
+    
+    MODELS = [
+        "openai/whisper-large-v3",
+        "openai/whisper-large-v2",
+        "openai/whisper-large",
+        "openai/whisper-medium",
+        "openai/whisper-small",
+        "openai/whisper-base",
+        "openai/whisper-tiny",
+    ]
+    
+    model_id = widgets.Dropdown(
+        options=list(MODELS),
+        value="openai/whisper-tiny",
+        description="Model:",
+        disabled=False,
+    )
+    
+    model_id
+
+
+
+
+.. parsed-literal::
+
+    Dropdown(description='Model:', index=6, options=('openai/whisper-large-v3', 'openai/whisper-large-v2', 'openai…
+
+
+
+Convert model to OpenVINO Intermediate Representation (IR) format using Optimum-Intel.
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+The Hugging Face Optimum API is a high-level API that enables us to
+convert and quantize models from the Hugging Face Transformers library
+to the OpenVINO™ IR format. For more details, refer to the `Hugging Face
+Optimum
+documentation <https://huggingface.co/docs/optimum/intel/inference>`__.
+
+Optimum Intel can be used to load optimized models from the `Hugging
+Face Hub <https://huggingface.co/docs/optimum/intel/hf.co/models>`__ and
+create pipelines to run an inference with OpenVINO Runtime using Hugging
+Face APIs. The Optimum Inference models are API compatible with Hugging
+Face Transformers models. This means we just need to replace the
+``AutoModelForXxx`` class with the corresponding ``OVModelForXxx``
+class.
+
+Below is an example of the whisper-tiny model
+
+.. code:: diff
+
+   -from transformers import AutoModelForSpeechSeq2Seq
+   +from optimum.intel.openvino import OVModelForSpeechSeq2Seq
+   from transformers import AutoTokenizer, pipeline
+
+   model_id = "openai/whisper-tiny"
+   -model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id)
+   +model = OVModelForSpeechSeq2Seq.from_pretrained(model_id, export=True)
+
+Model class initialization starts with calling the ``from_pretrained``
+method. When downloading and converting the Transformers model, the
+parameter ``export=True`` should be added. We can save the converted
+model for the next usage with the ``save_pretrained`` method.
+Alternatively, model conversion can be performed using Optimum-CLI
+interface. You can find more details about Optimum-Intel and Optimum CLI
+usage in this `tutorial <hugging-face-hub-with-output.html>`__.
+The command bellow illustrates how to convert whisper using optimum cli.
+
+.. code:: ipython3
+
+    from pathlib import Path
+    
+    model_dir = model_id.value.split("/")[-1]
+    
+    if not Path(model_dir).exists():
+        !optimum-cli export openvino -m {model_id.value} {model_dir} --weight-format fp16
+
+Prepare inference pipeline
+--------------------------
+
+
+
+The image below illustrates the pipeline of video transcribing using the
+Whisper model.
+
+.. figure:: https://user-images.githubusercontent.com/29454499/204536733-1f4342f7-2328-476a-a431-cb596df69854.png
+   :alt: whisper_pipeline.png
+
+   whisper_pipeline.png
+
+Preprocessing and post-processing are important in this model use.
+``transformers.AutoProcessor`` class used for initialization
+``WhisperProcessor`` is responsible for preparing audio input data for
+the PyTorch model, converting it to Mel-spectrogram and decoding
+predicted output token_ids into string using tokenizer. Tokenizers and
+Processors are distributed with models also compatible with the OpenVINO
+model.
+
+Like the original PyTorch model, the OpenVINO model is also compatible
+with HuggingFace
+`pipeline <https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline>`__
+interface for ``automatic-speech-recognition``. Pipeline can be used for
+long audio transcription. Distil-Whisper uses a chunked algorithm to
+transcribe long-form audio files. In practice, this chunked long-form
+algorithm is 9x faster than the sequential algorithm proposed by OpenAI
+in the Whisper paper. To enable chunking, pass the chunk_length_s
+parameter to the pipeline. For Distil-Whisper, a chunk length of 15
+seconds is optimal. To activate batching, pass the argument batch_size.
+
+Select inference device
+~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+select device from dropdown list for running inference using OpenVINO
+
+.. code:: ipython3
+
+    import openvino as ov
+    
+    core = ov.Core()
+
+.. code:: ipython3
+
+    import ipywidgets as widgets
+    
+    device = widgets.Dropdown(
+        options=core.available_devices + ["AUTO"],
+        value="AUTO",
+        description="Device:",
+        disabled=False,
+    )
+    
+    device
+
+
+
+
+.. parsed-literal::
+
+    Dropdown(description='Device:', index=3, options=('CPU', 'GPU.0', 'GPU.1', 'AUTO'), value='AUTO')
+
+
+
+.. code:: ipython3
+
+    from optimum.intel.openvino import OVModelForSpeechSeq2Seq
+    from transformers import AutoProcessor, pipeline
+    
+    ov_model = OVModelForSpeechSeq2Seq.from_pretrained(model_dir, device=device.value)
+    
+    processor = AutoProcessor.from_pretrained(model_dir)
+    
+    pipe = pipeline(
+        "automatic-speech-recognition",
+        model=ov_model,
+        chunk_length_s=30,
+        tokenizer=processor.tokenizer,
+        feature_extractor=processor.feature_extractor,
+    )
+
+
+.. parsed-literal::
+
+    2024-06-10 09:43:58.190233: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-06-10 09:43:58.192258: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
+    2024-06-10 09:43:58.228701: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
+    2024-06-10 09:43:58.903562: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    WARNING[XFORMERS]: xFormers can't load C++/CUDA extensions. xFormers was built for:
+        PyTorch 2.0.1+cu118 with CUDA 1108 (you have 2.3.0+cu121)
+        Python  3.8.18 (you have 3.8.10)
+      Please reinstall xformers (see https://github.com/facebookresearch/xformers#installing-xformers)
+      Memory-efficient attention, SwiGLU, sparse and more won't be available.
+      Set XFORMERS_MORE_DETAILS=1 for more details
+    /home/ea/work/my_optimum_intel/optimum_env/lib/python3.8/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
+      torch.utils._pytree._register_pytree_node(
+    Compiling the encoder to AUTO ...
+    Compiling the decoder to AUTO ...
+    Compiling the decoder to AUTO ...
+    Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
+
+
+Run video transcription pipeline
+--------------------------------
+
+
+
+Now, we are ready to start transcription. We select a video from YouTube
+that we want to transcribe. Be patient, as downloading the video may
+take some time.
+
+.. code:: ipython3
+
+    import ipywidgets as widgets
+    
+    VIDEO_LINK = "https://youtu.be/kgL5LBM-hFI"
+    link = widgets.Text(
+        value=VIDEO_LINK,
+        placeholder="Type link for video",
+        description="Video:",
+        disabled=False,
+    )
+    
+    link
+
+
+
+
+.. parsed-literal::
+
+    Text(value='https://youtu.be/kgL5LBM-hFI', description='Video:', placeholder='Type link for video')
+
+
+
+.. code:: ipython3
+
+    from pathlib import Path
+    from pytube import YouTube
+    
+    print(f"Downloading video {link.value} started")
+    
+    output_file = Path("downloaded_video.mp4")
+    yt = YouTube(link.value)
+    yt.streams.get_highest_resolution().download(filename=output_file)
+    print(f"Video saved to {output_file}")
+
+
+.. parsed-literal::
+
+    Downloading video https://youtu.be/kgL5LBM-hFI started
+    Video saved to downloaded_video.mp4
+
+
+Select the task for the model:
+
+-  **transcribe** - generate audio transcription in the source language
+   (automatically detected).
+-  **translate** - generate audio transcription with translation to
+   English language.
+
+.. code:: ipython3
+
+    task = widgets.Select(
+        options=["transcribe", "translate"],
+        value="translate",
+        description="Select task:",
+        disabled=False,
+    )
+    task
+
+
+
+
+.. parsed-literal::
+
+    Select(description='Select task:', index=1, options=('transcribe', 'translate'), value='translate')
+
+
+
+.. code:: ipython3
+
+    from moviepy.editor import VideoFileClip
+    from transformers.pipelines.audio_utils import ffmpeg_read
+    
+    
+    def get_audio(video_file):
+        """
+        Extract audio signal from a given video file, then convert it to float,
+        then mono-channel format and resample it to the expected sample rate
+    
+        Parameters:
+            video_file: path to input video file
+        Returns:
+          resampled_audio: mono-channel float audio signal with 16000 Hz sample rate
+                           extracted from video
+          duration: duration of video fragment in seconds
+        """
+        input_video = VideoFileClip(str(video_file))
+        duration = input_video.duration
+        audio_file = video_file.stem + ".wav"
+        input_video.audio.write_audiofile(audio_file, verbose=False, logger=None)
+        with open(audio_file, "rb") as f:
+            inputs = f.read()
+        audio = ffmpeg_read(inputs, pipe.feature_extractor.sampling_rate)
+        return {"raw": audio, "sampling_rate": pipe.feature_extractor.sampling_rate}, duration
+
+.. code:: ipython3
+
+    inputs, duration = get_audio(output_file)
+    
+    transcription = pipe(inputs, generate_kwargs={"task": task.value}, return_timestamps=True)["chunks"]
+
+.. code:: ipython3
+
+    import math
+    
+    
+    def format_timestamp(seconds: float):
+        """
+        format time in srt-file expected format
+        """
+        assert seconds >= 0, "non-negative timestamp expected"
+        milliseconds = round(seconds * 1000.0)
+    
+        hours = milliseconds // 3_600_000
+        milliseconds -= hours * 3_600_000
+    
+        minutes = milliseconds // 60_000
+        milliseconds -= minutes * 60_000
+    
+        seconds = milliseconds // 1_000
+        milliseconds -= seconds * 1_000
+    
+        return (f"{hours}:" if hours > 0 else "00:") + f"{minutes:02d}:{seconds:02d},{milliseconds:03d}"
+    
+    
+    def prepare_srt(transcription, filter_duration=None):
+        """
+        Format transcription into srt file format
+        """
+        segment_lines = []
+        for idx, segment in enumerate(transcription):
+            # for the case where the model could not predict an ending timestamp, which can happen if audio is cut off in the middle of a word.
+            if segment["timestamp"][1] is None:
+                segment["timestamp"] = (segment["timestamp"][0], filter_duration)
+    
+            if filter_duration is not None and (segment["timestamp"][0] >= math.floor(filter_duration) or segment["timestamp"][1] > math.ceil(filter_duration) + 1):
+                break
+            segment_lines.append(str(idx + 1) + "\n")
+            time_start = format_timestamp(segment["timestamp"][0])
+            time_end = format_timestamp(segment["timestamp"][1])
+            time_str = f"{time_start} --> {time_end}\n"
+            segment_lines.append(time_str)
+            segment_lines.append(segment["text"] + "\n\n")
+        return segment_lines
+
+"The results will be saved in the ``downloaded_video.srt`` file. SRT is
+one of the most popular formats for storing subtitles and is compatible
+with many modern video players. This file can be used to embed
+transcription into videos during playback or by injecting them directly
+into video files using ``ffmpeg``.
+
+.. code:: ipython3
+
+    srt_lines = prepare_srt(transcription, filter_duration=duration)
+    # save transcription
+    with output_file.with_suffix(".srt").open("w") as f:
+        f.writelines(srt_lines)
+
+Now let us see the results.
+
+.. code:: ipython3
+
+    widgets.Video.from_file(output_file, loop=False, width=800, height=800)
+
+
+
+
+.. parsed-literal::
+
+    Video(value=b"\x00\x00\x00\x18ftypmp42\x00\x00\x00\x00isommp42\x00\x00:'moov\x00\x00\x00lmvhd...", height='800…
+
+
+
+.. code:: ipython3
+
+    print("".join(srt_lines))
+
+
+.. parsed-literal::
+
+    1
+    00:00:00,000 --> 00:00:05,000
+     Oh, what's that?
+    
+    2
+    00:00:05,000 --> 00:00:08,000
+     Oh, wow.
+    
+    3
+    00:00:08,000 --> 00:00:10,000
+     Hello, humans.
+    
+    4
+    00:00:13,000 --> 00:00:15,000
+     Focus on me.
+    
+    5
+    00:00:15,000 --> 00:00:17,000
+     Focus on the guard.
+    
+    6
+    00:00:17,000 --> 00:00:20,000
+     Don't tell anyone what you're seeing in here.
+    
+    7
+    00:00:22,000 --> 00:00:24,000
+     Have you seen what's in there?
+    
+    8
+    00:00:24,000 --> 00:00:25,000
+     They have intel.
+    
+    9
+    00:00:25,000 --> 00:00:27,000
+     This is where it all changes.
+    
+    
+
+
+Quantization
+------------
+
+
+
+`NNCF <https://github.com/openvinotoolkit/nncf/>`__ enables
+post-training quantization by adding the quantization layers into the
+model graph and then using a subset of the training dataset to
+initialize the parameters of these additional quantization layers. The
+framework is designed so that modifications to your original training
+code are minor.
+
+The optimization process contains the following steps:
+
+1. Create a calibration dataset for quantization.
+2. Run ``nncf.quantize`` to obtain quantized encoder and decoder models.
+3. Serialize the ``INT8`` model using ``openvino.save_model`` function.
+
+..
+
+   **Note**: Quantization is time and memory consuming operation.
+   Running quantization code below may take some time.
+
+Please select below whether you would like to run Whisper quantization.
+
+.. code:: ipython3
+
+    to_quantize = widgets.Checkbox(
+        value=True,
+        description="Quantization",
+        disabled=False,
+    )
+    
+    to_quantize
+
+
+
+
+.. parsed-literal::
+
+    Checkbox(value=True, description='Quantization')
+
+
+
+.. code:: ipython3
+
+    # Fetch `skip_kernel_extension` module
+    import requests
+    
+    r = requests.get(
+        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/skip_kernel_extension.py",
+    )
+    open("skip_kernel_extension.py", "w").write(r.text)
+    
+    ov_quantized_model = None
+    
+    %load_ext skip_kernel_extension
+
+Prepare calibration datasets
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+First step is to prepare calibration datasets for quantization. Since we
+quantize whisper encoder and decoder separately, we need to prepare a
+calibration dataset for each of the models. We import an
+``InferRequestWrapper`` class that will intercept model inputs and
+collect them to a list. Then we run model inference on some small amount
+of audio samples. Generally, increasing the calibration dataset size
+improves quantization quality.
+
+.. code:: ipython3
+
+    %%skip not $to_quantize.value
+    
+    from itertools import islice
+    from optimum.intel.openvino.quantization import InferRequestWrapper
+    
+    
+    def collect_calibration_dataset(ov_model: OVModelForSpeechSeq2Seq, calibration_dataset_size: int):
+        # Overwrite model request properties, saving the original ones for restoring later
+        encoder_calibration_data = []
+        decoder_calibration_data = []
+        ov_model.encoder.request = InferRequestWrapper(ov_model.encoder.request, encoder_calibration_data, apply_caching=True)
+        ov_model.decoder_with_past.request = InferRequestWrapper(ov_model.decoder_with_past.request,
+                                                                 decoder_calibration_data,
+                                                                 apply_caching=True)
+    
+        pipe = pipeline(
+          "automatic-speech-recognition",
+          model=ov_model,
+          chunk_length_s=30,
+          tokenizer=processor.tokenizer,
+          feature_extractor=processor.feature_extractor)
+        try:
+            calibration_dataset = dataset = load_dataset("openslr/librispeech_asr", "clean", split="validation", streaming=True, trust_remote_code=True)
+            for sample in tqdm(islice(calibration_dataset, calibration_dataset_size), desc="Collecting calibration data",
+                               total=calibration_dataset_size):
+                pipe(sample["audio"], generate_kwargs={"task": task.value}, return_timestamps=True)
+        finally:
+            ov_model.encoder.request = ov_model.encoder.request.request
+            ov_model.decoder_with_past.request = ov_model.decoder_with_past.request.request
+    
+        return encoder_calibration_data, decoder_calibration_data
+
+Quantize Whisper encoder and decoder models
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+Below we run the ``quantize`` function which calls ``nncf.quantize`` on
+Whisper encoder and decoder-with-past models. We don’t quantize
+first-step-decoder because its share in whole inference time is
+negligible.
+
+.. code:: ipython3
+
+    %%skip not $to_quantize.value
+    
+    import gc
+    import shutil
+    import nncf
+    from datasets import load_dataset
+    from tqdm.notebook import tqdm
+    
+    def extract_input_features(sample):
+        input_features = processor(
+            sample["audio"]["array"],
+            sampling_rate=sample["audio"]["sampling_rate"],
+            return_tensors="pt",
+        ).input_features
+        return input_features
+    
+    
+    
+    CALIBRATION_DATASET_SIZE = 50
+    quantized_model_path = Path(f"{model_dir}_quantized")
+    
+    
+    def quantize(ov_model: OVModelForSpeechSeq2Seq, calibration_dataset_size: int):
+        if not quantized_model_path.exists():
+            encoder_calibration_data, decoder_calibration_data = collect_calibration_dataset(
+                ov_model, calibration_dataset_size
+            )
+            print("Quantizing encoder")
+            quantized_encoder = nncf.quantize(
+                ov_model.encoder.model,
+                nncf.Dataset(encoder_calibration_data),
+                subset_size=len(encoder_calibration_data),
+                model_type=nncf.ModelType.TRANSFORMER,
+                # Smooth Quant algorithm reduces activation quantization error; optimal alpha value was obtained through grid search
+                advanced_parameters=nncf.AdvancedQuantizationParameters(smooth_quant_alpha=0.50)
+            )
+            ov.save_model(quantized_encoder, quantized_model_path / "openvino_encoder_model.xml")
+            del quantized_encoder
+            del encoder_calibration_data
+            gc.collect()
+    
+            print("Quantizing decoder with past")
+            quantized_decoder_with_past = nncf.quantize(
+                ov_model.decoder_with_past.model,
+                nncf.Dataset(decoder_calibration_data),
+                subset_size=len(decoder_calibration_data),
+                model_type=nncf.ModelType.TRANSFORMER,
+                # Smooth Quant algorithm reduces activation quantization error; optimal alpha value was obtained through grid search
+                advanced_parameters=nncf.AdvancedQuantizationParameters(smooth_quant_alpha=0.96)
+            )
+            ov.save_model(quantized_decoder_with_past, quantized_model_path / "openvino_decoder_with_past_model.xml")
+            del quantized_decoder_with_past
+            del decoder_calibration_data
+            gc.collect()
+    
+            # Copy the config file and the first-step-decoder manually
+            model_path = Path(model_dir)
+            shutil.copy(model_path / "config.json", quantized_model_path / "config.json")
+            shutil.copy(model_path / "generation_config.json", quantized_model_path / "generation_config.json")
+            shutil.copy(model_path / "openvino_decoder_model.xml", quantized_model_path / "openvino_decoder_model.xml")
+            shutil.copy(model_path / "openvino_decoder_model.bin", quantized_model_path / "openvino_decoder_model.bin")
+    
+        quantized_ov_model = OVModelForSpeechSeq2Seq.from_pretrained(quantized_model_path, compile=False)
+        quantized_ov_model.to(device.value)
+        quantized_ov_model.compile()
+        return quantized_ov_model
+    
+    
+    ov_quantized_model = quantize(ov_model, CALIBRATION_DATASET_SIZE)
+
+
+
+.. parsed-literal::
+
+    Collecting calibration data:   0%|          | 0/50 [00:00<?, ?it/s]
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+.. parsed-literal::
+
+    Quantizing encoder
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+.. parsed-literal::
+
+    INFO:nncf:12 ignored nodes were found by name in the NNCFGraph
+    INFO:nncf:16 ignored nodes were found by name in the NNCFGraph
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+.. parsed-literal::
+
+    Quantizing decoder with past
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+.. parsed-literal::
+
+    INFO:nncf:24 ignored nodes were found by name in the NNCFGraph
+    INFO:nncf:24 ignored nodes were found by name in the NNCFGraph
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+.. parsed-literal::
+
+    Output()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+.. parsed-literal::
+
+    Compiling the encoder to AUTO ...
+    Compiling the decoder to AUTO ...
+    Compiling the decoder to AUTO ...
+
+
+Run quantized model inference
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+Let’s compare the transcription results for original and quantized
+models.
+
+.. code:: ipython3
+
+    if ov_quantized_model is not None:
+        int8_pipe = pipeline(
+            "automatic-speech-recognition",
+            model=ov_quantized_model,
+            chunk_length_s=30,
+            tokenizer=processor.tokenizer,
+            feature_extractor=processor.feature_extractor,
+        )
+        inputs, duration = get_audio(output_file)
+        transcription = int8_pipe(inputs, generate_kwargs={"task": task.value}, return_timestamps=True)["chunks"]
+        srt_lines = prepare_srt(transcription, filter_duration=duration)
+        print("".join(srt_lines))
+        widgets.Video.from_file(output_file, loop=False, width=800, height=800)
+
+
+.. parsed-literal::
+
+    1
+    00:00:00,000 --> 00:00:05,000
+     What's that?
+    
+    2
+    00:00:05,000 --> 00:00:07,000
+     Oh, wow.
+    
+    3
+    00:00:09,000 --> 00:00:11,000
+     Hello humans.
+    
+    4
+    00:00:14,000 --> 00:00:15,000
+     Focus on me.
+    
+    5
+    00:00:15,000 --> 00:00:16,000
+     Focus on the guard.
+    
+    6
+    00:00:18,000 --> 00:00:20,000
+     Don't tell anyone what you're seen in here.
+    
+    7
+    00:00:22,000 --> 00:00:24,000
+     Have you seen what's in there?
+    
+    8
+    00:00:24,000 --> 00:00:25,000
+     They have intel.
+    
+    9
+    00:00:25,000 --> 00:00:27,000
+     This is where it all changes.
+    
+    
+
+
+Compare performance and accuracy of the original and quantized models
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+Finally, we compare original and quantized Whisper models from accuracy
+and performance stand-points.
+
+To measure accuracy, we use ``1 - WER`` as a metric, where WER stands
+for Word Error Rate.
+
+When measuring inference time, we do it separately for encoder and
+decoder-with-past model forwards, and for the whole model inference too.
+
+.. code:: ipython3
+
+    %%skip not $to_quantize.value
+    
+    import time
+    from contextlib import contextmanager
+    from jiwer import wer, wer_standardize
+    
+    
+    TEST_DATASET_SIZE = 50
+    MEASURE_TIME = False
+    
+    @contextmanager
+    def time_measurement():
+        global MEASURE_TIME
+        try:
+            MEASURE_TIME = True
+            yield
+        finally:
+            MEASURE_TIME = False
+    
+    def time_fn(obj, fn_name, time_list):
+        original_fn = getattr(obj, fn_name)
+    
+        def wrapper(*args, **kwargs):
+            if not MEASURE_TIME:
+                return original_fn(\*args, \*\*kwargs)
+            start_time = time.perf_counter()
+            result = original_fn(\*args, \*\*kwargs)
+            end_time = time.perf_counter()
+            time_list.append(end_time - start_time)
+            return result
+    
+        setattr(obj, fn_name, wrapper)
+    
+    def calculate_transcription_time_and_accuracy(ov_model, test_samples):
+        encoder_infer_times = []
+        decoder_with_past_infer_times = []
+        whole_infer_times = []
+        time_fn(ov_model, "generate", whole_infer_times)
+        time_fn(ov_model.encoder, "forward", encoder_infer_times)
+        time_fn(ov_model.decoder_with_past, "forward", decoder_with_past_infer_times)
+    
+        ground_truths = []
+        predictions = []
+        for data_item in tqdm(test_samples, desc="Measuring performance and accuracy"):
+            input_features = extract_input_features(data_item)
+    
+            with time_measurement():
+                predicted_ids = ov_model.generate(input_features)
+            transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
+    
+            ground_truths.append(data_item["text"])
+            predictions.append(transcription[0])
+    
+        word_accuracy = (1 - wer(ground_truths, predictions, reference_transform=wer_standardize,
+                                 hypothesis_transform=wer_standardize)) * 100
+        mean_whole_infer_time = sum(whole_infer_times)
+        mean_encoder_infer_time = sum(encoder_infer_times)
+        mean_decoder_with_time_infer_time = sum(decoder_with_past_infer_times)
+        return word_accuracy, (mean_whole_infer_time, mean_encoder_infer_time, mean_decoder_with_time_infer_time)
+    
+    test_dataset = load_dataset("openslr/librispeech_asr", "clean", split="validation", streaming=True, trust_remote_code=True)
+    test_dataset = test_dataset.shuffle(seed=42).take(TEST_DATASET_SIZE)
+    test_samples = [sample for sample in test_dataset]
+    
+    accuracy_original, times_original = calculate_transcription_time_and_accuracy(ov_model, test_samples)
+    accuracy_quantized, times_quantized = calculate_transcription_time_and_accuracy(ov_quantized_model, test_samples)
+    print(f"Encoder performance speedup: {times_original[1] / times_quantized[1]:.3f}")
+    print(f"Decoder with past performance speedup: {times_original[2] / times_quantized[2]:.3f}")
+    print(f"Whole pipeline performance speedup: {times_original[0] / times_quantized[0]:.3f}")
+    print(f"Whisper transcription word accuracy. Original model: {accuracy_original:.2f}%. Quantized model: {accuracy_quantized:.2f}%.")
+    print(f"Accuracy drop: {accuracy_original - accuracy_quantized:.2f}%.")
+
+
+
+.. parsed-literal::
+
+    Measuring performance and accuracy:   0%|          | 0/50 [00:00<?, ?it/s]
+
+
+
+.. parsed-literal::
+
+    Measuring performance and accuracy:   0%|          | 0/50 [00:00<?, ?it/s]
+
+
+.. parsed-literal::
+
+    Encoder performance speedup: 1.352
+    Decoder with past performance speedup: 1.342
+    Whole pipeline performance speedup: 1.350
+    Whisper transcription word accuracy. Original model: 81.67%. Quantized model: 83.67%.
+    Accuracy drop: -1.99%.
+
+
+Interactive demo
+----------------
+
+
+
+.. code:: ipython3
+
+    import gradio as gr
+    
+    
+    def transcribe(url, task, use_int8):
+        output_file = Path("downloaded_video.mp4")
+        yt = YouTube(url)
+        yt.streams.get_highest_resolution().download(filename=output_file)
+        inputs, duration = get_audio(output_file)
+        m_pipe = int8_pipe if use_int8 else pipe
+        transcription = m_pipe(inputs, generate_kwargs={"task": task.lower()}, return_timestamps=True)["chunks"]
+        srt_lines = prepare_srt(transcription, duration)
+        with output_file.with_suffix(".srt").open("w") as f:
+            f.writelines(srt_lines)
+        return [str(output_file), str(output_file.with_suffix(".srt"))]
+    
+    
+    demo = gr.Interface(
+        transcribe,
+        [
+            gr.Textbox(label="YouTube URL"),
+            gr.Radio(["Transcribe", "Translate"], value="Transcribe"),
+            gr.Checkbox(value=ov_quantized_model is not None, visible=ov_quantized_model is not None, label="Use INT8"),
+        ],
+        "video",
+        examples=[["https://youtu.be/kgL5LBM-hFI", "Transcribe"]],
+        allow_flagging="never",
+    )
+    try:
+        demo.launch(debug=False)
+    except Exception:
+        demo.launch(share=True, debug=False)
+    # if you are launching remotely, specify server_name and server_port
+    # demo.launch(server_name='your server name', server_port='server port in int')
+    # Read more in the docs: https://gradio.app/docs/
+
+
+.. parsed-literal::
+
+    Running on local URL:  http://127.0.0.1:7860
+    
+    To create a public link, set `share=True` in `launch()`.
+
+
+
+
+
+
+
+
+.. parsed-literal::
+
+    Keyboard interruption in main thread... closing server.
+
diff --git a/docs/notebooks/wuerstchen-image-generation-with-output.rst b/docs/notebooks/wuerstchen-image-generation-with-output.rst
index ce3f1a2d35306d..785f3630e256b0 100644
--- a/docs/notebooks/wuerstchen-image-generation-with-output.rst
+++ b/docs/notebooks/wuerstchen-image-generation-with-output.rst
@@ -24,8 +24,8 @@ top-performing models, allowing also cheaper and faster inference.
 We will use PyTorch version of Würstchen `model from HuggingFace
 Hub <https://huggingface.co/warp-ai/wuerstchen>`__.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Load the original model <#load-the-original-model>`__
@@ -59,12 +59,12 @@ Prerequisites
 .. code:: ipython3
 
     import platform
-    
+
     if platform.system() != "Windows":
         %pip install -q "matplotlib>=3.4"
     else:
         %pip install -q "matplotlib>=3.4,<3.7"
-    
+
     %pip install -q  "diffusers>=0.21.0" "torch>=2.1" transformers accelerate "gradio>=4.19" "openvino>=2023.2.0" "peft==0.6.2" --extra-index-url https://download.pytorch.org/whl/cpu
     %pip install -q datasets "nncf>=2.7.0"
 
@@ -79,13 +79,13 @@ Prerequisites
     from pathlib import Path
     from collections import namedtuple
     import gc
-    
+
     import diffusers
     import torch
     import matplotlib.pyplot as plt
     import gradio as gr
     import numpy as np
-    
+
     import openvino as ov
 
 .. code:: ipython3
@@ -96,7 +96,7 @@ Prerequisites
     DECODER_PATH = MODELS_DIR / "decoder.xml"
     TEXT_ENCODER_PATH = MODELS_DIR / "text_encoder.xml"
     VQGAN_PATH = MODELS_DIR / "vqgan.xml"
-    
+
     MODELS_DIR.mkdir(parents=True, exist_ok=True)
 
 .. code:: ipython3
@@ -174,7 +174,7 @@ The pipeline consists of 2 sub-pipelines: Prior pipeline accessed by
     # Prior pipeline
     pipeline.prior_text_encoder.eval()
     pipeline.prior_prior.eval()
-    
+
     # Decoder pipeline
     pipeline.decoder.eval()
     pipeline.text_encoder.eval()
@@ -192,12 +192,12 @@ file.
             converted_model = ov.convert_model(model, **convert_kwargs)
             ov.save_model(converted_model, xml_path, compress_to_fp16=False)
             del converted_model
-    
+
             # Clean torch jit cache
             torch._C._jit_clear_class_registry()
             torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore()
             torch.jit._state._clear_class_state()
-    
+
             gc.collect()
 
 Prior pipeline
@@ -209,10 +209,12 @@ This pipeline consists of text encoder and prior diffusion model. From
 here, we always use fixed shapes in conversion by using an ``input``
 parameter to generate a less memory-demanding model.
 
-Text encoder model has 2 inputs: - ``input_ids``: vector of tokenized
-input sentence. Default tokenizer vector length is 77. -
-``attention_mask``: vector of same length as ``input_ids`` describing
-the attention mask.
+Text encoder model has 2 inputs:
+
+- ``input_ids``: vector of tokenized
+  input sentence. Default tokenizer vector length is 77.
+- ``attention_mask``: vector of same length as ``input_ids`` describing
+  the attention mask.
 
 .. code:: ipython3
 
@@ -274,9 +276,12 @@ Decoder pipeline
 
 Decoder pipeline consists of 3 parts: decoder, text encoder and VQGAN.
 
-Decoder model is the WuerstchenDiffNeXt UNet decoder. Inputs are: -
-``x``: sample - ``r``: timestep - ``effnet``: interpolation block -
-``clip``: encoder hidden states
+Decoder model is the WuerstchenDiffNeXt UNet decoder. Inputs are:
+
+- ``x``: sample
+- ``r``: timestep
+- ``effnet``: interpolation block
+- ``clip``: encoder hidden states
 
 .. code:: ipython3
 
@@ -346,7 +351,7 @@ decoder takes as input 4x256x256 latent image.
         def __init__(self, vqgan):
             super().__init__()
             self.vqgan = vqgan
-    
+
         def forward(self, h):
             return self.vqgan.decode(h)
 
@@ -384,14 +389,14 @@ Select device from dropdown list for running inference using OpenVINO.
 .. code:: ipython3
 
     import ipywidgets as widgets
-    
+
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
         value="AUTO",
         description="Device:",
         disabled=False,
     )
-    
+
     device
 
 
@@ -437,10 +442,10 @@ that all of wrapper classes return ``torch.Tensor``\ s instead of
 
     class TextEncoderWrapper:
         dtype = torch.float32  # accessed in the original workflow
-    
+
         def __init__(self, text_encoder):
             self.text_encoder = text_encoder
-    
+
         def __call__(self, input_ids, attention_mask):
             output = self.text_encoder({"input_ids": input_ids, "attention_mask": attention_mask})["last_hidden_state"]
             output = torch.tensor(output)
@@ -450,10 +455,10 @@ that all of wrapper classes return ``torch.Tensor``\ s instead of
 
     class PriorPriorWrapper:
         config = namedtuple("PriorPriorWrapperConfig", "c_in")(16)  # accessed in the original workflow
-    
+
         def __init__(self, prior):
             self.prior = prior
-    
+
         def __call__(self, x, r, c):
             output = self.prior([x, r, c])[0]
             return torch.tensor(output)
@@ -462,10 +467,10 @@ that all of wrapper classes return ``torch.Tensor``\ s instead of
 
     class DecoderWrapper:
         dtype = torch.float32  # accessed in the original workflow
-    
+
         def __init__(self, decoder):
             self.decoder = decoder
-    
+
         def __call__(self, x, r, effnet, clip):
             output = self.decoder({"x": x, "r": r, "effnet": effnet, "clip": clip})[0]
             output = torch.tensor(output)
@@ -475,10 +480,10 @@ that all of wrapper classes return ``torch.Tensor``\ s instead of
 
     class VqganWrapper:
         config = namedtuple("VqganWrapperConfig", "scale_factor")(0.3764)  # accessed in the original workflow
-    
+
         def __init__(self, vqgan):
             self.vqgan = vqgan
-    
+
         def decode(self, h):
             output = self.vqgan(h)[0]
             output = torch.tensor(output)
@@ -490,7 +495,7 @@ And insert wrappers instances in the pipeline:
 
     pipeline.prior_pipe.text_encoder = TextEncoderWrapper(ov_prior_text_encoder)
     pipeline.prior_pipe.prior = PriorPriorWrapper(ov_prior_prior)
-    
+
     pipeline.decoder_pipe.decoder = DecoderWrapper(ov_decoder)
     pipeline.decoder_pipe.text_encoder = TextEncoderWrapper(ov_text_encoder)
     pipeline.decoder_pipe.vqgan = VqganWrapper(ov_vqgan)
@@ -505,7 +510,7 @@ Inference
     caption = "Anthropomorphic cat dressed as a fire fighter"
     negative_prompt = ""
     generator = torch.Generator().manual_seed(1)
-    
+
     output = pipeline(
         prompt=caption,
         height=1024,
@@ -567,7 +572,7 @@ improve model inference speed.
         description="Quantization",
         disabled=False,
     )
-    
+
     to_quantize
 
 Let’s load ``skip magic`` extension to skip quantization if
@@ -577,14 +582,14 @@ Let’s load ``skip magic`` extension to skip quantization if
 
     # Fetch `skip_kernel_extension` module
     import requests
-    
+
     r = requests.get(
         url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/skip_kernel_extension.py",
     )
     open("skip_kernel_extension.py", "w").write(r.text)
-    
+
     int8_pipeline = None
-    
+
     %load_ext skip_kernel_extension
 
 Prepare calibration datasets
@@ -593,19 +598,19 @@ Prepare calibration datasets
 
 
 We use a portion of
-`conceptual_captions <https://huggingface.co/datasets/conceptual_captions>`__
+`conceptual_captions <https://huggingface.co/datasets/google-research-datasets/conceptual_captions>`__
 dataset from Hugging Face as calibration data. To collect intermediate
 model inputs for calibration we should customize ``CompiledModel``.
 
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     class CompiledModelDecorator(ov.CompiledModel):
         def __init__(self, compiled_model):
             super().__init__(compiled_model)
             self.data_cache = []
-    
+
         def __call__(self, *args, **kwargs):
             self.data_cache.append(*args)
             return super().__call__(*args, **kwargs)
@@ -613,22 +618,22 @@ model inputs for calibration we should customize ``CompiledModel``.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     import datasets
     from tqdm.notebook import tqdm
     from transformers import set_seed
-    
+
     set_seed(1)
-    
+
     def collect_calibration_data(pipeline, subset_size):
         pipeline.set_progress_bar_config(disable=True)
-    
+
         original_prior = pipeline.prior_pipe.prior.prior
         original_decoder = pipeline.decoder_pipe.decoder.decoder
         pipeline.prior_pipe.prior.prior = CompiledModelDecorator(original_prior)
         pipeline.decoder_pipe.decoder.decoder = CompiledModelDecorator(original_decoder)
-    
-        dataset = datasets.load_dataset("conceptual_captions", split="train").shuffle(seed=42)
+
+        dataset = datasets.load_dataset("google-research-datasets/conceptual_captions", split="train", trust_remote_code=True).shuffle(seed=42)
         pbar = tqdm(total=subset_size)
         diff = 0
         for batch in dataset:
@@ -650,7 +655,7 @@ model inputs for calibration we should customize ``CompiledModel``.
                 break
             pbar.update(collected_subset_size - diff)
             diff = collected_subset_size
-    
+
         prior_calibration_dataset = pipeline.prior_pipe.prior.prior.data_cache
         decoder_calibration_dataset = pipeline.decoder_pipe.decoder.decoder.data_cache
         pipeline.prior_pipe.prior.prior = original_prior
@@ -661,10 +666,10 @@ model inputs for calibration we should customize ``CompiledModel``.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     PRIOR_PRIOR_INT8_PATH = MODELS_DIR / "prior_prior_int8.xml"
     DECODER_INT8_PATH = MODELS_DIR / "decoder_int8.xml"
-    
+
     if not (PRIOR_PRIOR_INT8_PATH.exists() and DECODER_INT8_PATH.exists()):
         subset_size = 300
         prior_calibration_dataset, decoder_calibration_dataset = collect_calibration_data(pipeline, subset_size=subset_size)
@@ -693,10 +698,10 @@ precision.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     import nncf
     from nncf.scopes import IgnoredScope
-    
+
     if not PRIOR_PRIOR_INT8_PATH.exists():
         prior_model = core.read_model(PRIOR_PRIOR_PATH)
         quantized_prior_prior = nncf.quantize(
@@ -717,7 +722,7 @@ precision.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     if not DECODER_INT8_PATH.exists():
         decoder_model = core.read_model(DECODER_PATH)
         quantized_decoder = nncf.quantize(
@@ -737,14 +742,14 @@ pipelines.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     import matplotlib.pyplot as plt
     from PIL import Image
-    
+
     def visualize_results(orig_img:Image.Image, optimized_img:Image.Image):
         """
         Helper function for results visualization
-    
+
         Parameters:
            orig_img (Image.Image): generated image using FP16 models
            optimized_img (Image.Image): generated image using quantized models
@@ -766,7 +771,7 @@ pipelines.
         list_axes[1].imshow(np.array(optimized_img))
         list_axes[0].set_title(orig_title, fontsize=15)
         list_axes[1].set_title(control_title, fontsize=15)
-    
+
         fig.subplots_adjust(wspace=0.01, hspace=0.01)
         fig.tight_layout()
         return fig
@@ -774,18 +779,18 @@ pipelines.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     caption = "Anthropomorphic cat dressed as a fire fighter"
     negative_prompt = ""
-    
+
     int8_pipeline = diffusers.AutoPipelineForText2Image.from_pretrained("warp-diffusion/wuerstchen")
-    
+
     int8_prior_prior = core.compile_model(PRIOR_PRIOR_INT8_PATH)
     int8_pipeline.prior_pipe.prior = PriorPriorWrapper(int8_prior_prior)
-    
+
     int8_decoder = core.compile_model(DECODER_INT8_PATH)
     int8_pipeline.decoder_pipe.decoder = DecoderWrapper(int8_decoder)
-    
+
     int8_pipeline.prior_pipe.text_encoder = TextEncoderWrapper(ov_prior_text_encoder)
     int8_pipeline.decoder_pipe.text_encoder = TextEncoderWrapper(ov_text_encoder)
     int8_pipeline.decoder_pipe.vqgan = VqganWrapper(ov_vqgan)
@@ -793,7 +798,7 @@ pipelines.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     generator = torch.Generator().manual_seed(1)
     int8_output = int8_pipeline(
         prompt=caption,
@@ -809,7 +814,7 @@ pipelines.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     fig = visualize_results(output[0], int8_output[0])
 
 
@@ -825,10 +830,10 @@ Compare model file sizes
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     fp16_ir_model_size = PRIOR_PRIOR_PATH.with_suffix(".bin").stat().st_size / 2**20
     quantized_model_size = PRIOR_PRIOR_INT8_PATH.with_suffix(".bin").stat().st_size / 2**20
-    
+
     print(f"FP16 Prior size: {fp16_ir_model_size:.2f} MB")
     print(f"INT8 Prior size: {quantized_model_size:.2f} MB")
     print(f"Prior compression rate: {fp16_ir_model_size / quantized_model_size:.3f}")
@@ -844,10 +849,10 @@ Compare model file sizes
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     fp16_ir_model_size = DECODER_PATH.with_suffix(".bin").stat().st_size / 2**20
     quantized_model_size = DECODER_INT8_PATH.with_suffix(".bin").stat().st_size / 2**20
-    
+
     print(f"FP16 Decoder size: {fp16_ir_model_size:.2f} MB")
     print(f"INT8 Decoder size: {quantized_model_size:.2f} MB")
     print(f"Decoder compression rate: {fp16_ir_model_size / quantized_model_size:.3f}")
@@ -875,9 +880,9 @@ pipelines, we use mean inference time on 3 samples.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     import time
-    
+
     def calculate_inference_time(pipeline):
         inference_time = []
         pipeline.set_progress_bar_config(disable=True)
@@ -901,7 +906,7 @@ pipelines, we use mean inference time on 3 samples.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     fp_latency = calculate_inference_time(pipeline)
     print(f"FP16 pipeline: {fp_latency:.3f} seconds")
     int8_latency = calculate_inference_time(int8_pipeline)
@@ -927,20 +932,20 @@ launch the interactive demo.
 .. code:: ipython3
 
     quantized_model_present = int8_pipeline is not None
-    
+
     use_quantized_model = widgets.Checkbox(
         value=quantized_model_present,
         description="Use quantized model",
         disabled=not quantized_model_present,
     )
-    
+
     use_quantized_model
 
 .. code:: ipython3
 
     pipe = int8_pipeline if use_quantized_model.value else pipeline
-    
-    
+
+
     def generate(caption, negative_prompt, prior_guidance_scale, seed):
         generator = torch.Generator().manual_seed(seed)
         image = pipe(
diff --git a/docs/notebooks/yolov10-optimization-with-output.rst b/docs/notebooks/yolov10-optimization-with-output.rst
index d6e440ef8bb573..608cc82bdb1e6a 100644
--- a/docs/notebooks/yolov10-optimization-with-output.rst
+++ b/docs/notebooks/yolov10-optimization-with-output.rst
@@ -44,8 +44,8 @@ The tutorial consists of the following steps:
 -  Run optimized model inference on video
 -  Launch interactive Gradio demo
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Download PyTorch model <#download-pytorch-model>`__
@@ -91,8 +91,7 @@ Prerequisites
     
     os.environ["GIT_CLONE_PROTECTION_ACTIVE"] = "false"
     
-    %pip install -Uq pip
-    %pip install -q "git+https://github.com/openvinotoolkit/nncf.git"
+    %pip install -q "nncf>=2.11.0"
     %pip install --pre -Uq openvino --extra-index-url https://storage.openvinotoolkit.org/simple/wheels/nightly
     %pip install -q "git+https://github.com/THU-MIG/yolov10.git" --extra-index-url https://download.pytorch.org/whl/cpu
     %pip install -q "torch>=2.1" "torchvision>=0.16" tqdm opencv-python "gradio>=4.19" --extra-index-url https://download.pytorch.org/whl/cpu
diff --git a/docs/notebooks/yolov7-optimization-with-output.rst b/docs/notebooks/yolov7-optimization-with-output.rst
index 7ba70daa96ccc5..300b58dca3b86d 100644
--- a/docs/notebooks/yolov7-optimization-with-output.rst
+++ b/docs/notebooks/yolov7-optimization-with-output.rst
@@ -38,8 +38,8 @@ The tutorial consists of the following steps:
 -  Compare accuracy of the FP32 and quantized models.
 -  Compare performance of the FP32 and quantized models.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Get Pytorch model <#get-pytorch-model>`__
 -  `Prerequisites <#prerequisites>`__
@@ -96,9 +96,9 @@ Prerequisites
 .. code:: ipython3
 
     import platform
-    
+
     %pip install -q "openvino>=2023.1.0" "nncf>=2.5.0" "opencv-python" "seaborn" "onnx" "Pillow" "pandas" "scikit-learn" "torch" "torchvision"  "PyYAML>=5.3.1" "tqdm" --extra-index-url https://download.pytorch.org/whl/cpu
-    
+
     if platform.system() != "Windows":
         %pip install -q "matplotlib>=3.4"
     else:
@@ -107,13 +107,11 @@ Prerequisites
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
+    descript-audiotools 0.7.2 requires protobuf<3.20,>=3.9.2, but you have protobuf 3.20.3 which is incompatible.
     mobileclip 0.1.0 requires torch==1.13.1, but you have torch 2.3.1+cpu which is incompatible.
     mobileclip 0.1.0 requires torchvision==0.14.1, but you have torchvision 0.18.1+cpu which is incompatible.
-    pytorch-lightning 1.6.5 requires protobuf<=3.20.1, but you have protobuf 3.20.3 which is incompatible.
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
 
 
@@ -121,11 +119,11 @@ Prerequisites
 
     # Fetch `notebook_utils` module
     import requests
-    
+
     r = requests.get(
         url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
-    
+
     open("notebook_utils.py", "w").write(r.text)
     from notebook_utils import download_file
 
@@ -133,7 +131,7 @@ Prerequisites
 
     # Clone YOLOv7 repo
     from pathlib import Path
-    
+
     if not Path("yolov7").exists():
         !git clone https://github.com/WongKinYiu/yolov7
     %cd yolov7
@@ -144,9 +142,9 @@ Prerequisites
     Cloning into 'yolov7'...
     remote: Enumerating objects: 1197, done.
     remote: Total 1197 (delta 0), reused 0 (delta 0), pack-reused 1197
-    Receiving objects: 100% (1197/1197), 74.23 MiB | 31.20 MiB/s, done.
+    Receiving objects: 100% (1197/1197), 74.23 MiB | 28.87 MiB/s, done.
     Resolving deltas: 100% (519/519), done.
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/yolov7-optimization/yolov7
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/yolov7-optimization/yolov7
 
 
 .. code:: ipython3
@@ -157,7 +155,7 @@ Prerequisites
     MODEL_DIR = Path("model/")
     MODEL_DIR.mkdir(exist_ok=True)
     DATA_DIR.mkdir(exist_ok=True)
-    
+
     download_file(MODEL_LINK, directory=MODEL_DIR, show_progress=True)
 
 
@@ -171,7 +169,7 @@ Prerequisites
 
 .. parsed-literal::
 
-    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/yolov7-optimization/yolov7/model/yolov7-tiny.pt')
+    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/yolov7-optimization/yolov7/model/yolov7-tiny.pt')
 
 
 
@@ -192,22 +190,22 @@ result,
 
     Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.25, device='', exist_ok=False, img_size=640, iou_thres=0.45, name='exp', no_trace=False, nosave=False, project='runs/detect', save_conf=False, save_txt=False, source='inference/images/horses.jpg', update=False, view_img=False, weights=['model/yolov7-tiny.pt'])
     YOLOR 🚀 v0.1-128-ga207844 torch 2.3.1+cpu CPU
-    
-    Fusing layers... 
+
+    Fusing layers...
     Model Summary: 200 layers, 6219709 parameters, 229245 gradients, 13.7 GFLOPS
-     Convert model to Traced-model... 
-     traced_script_module saved! 
-     model is traced! 
-    
-    5 horses, Done. (74.7ms) Inference, (0.9ms) NMS
+     Convert model to Traced-model...
+     traced_script_module saved!
+     model is traced!
+
+    5 horses, Done. (74.1ms) Inference, (0.9ms) NMS
      The image with the result is saved in: runs/detect/exp/horses.jpg
-    Done. (0.082s)
+    Done. (0.081s)
 
 
 .. code:: ipython3
 
     from PIL import Image
-    
+
     # visualize prediction result
     Image.open("runs/detect/exp/horses.jpg")
 
@@ -240,7 +238,7 @@ Let us check its arguments.
                      [--iou-thres IOU_THRES] [--conf-thres CONF_THRES]
                      [--device DEVICE] [--simplify] [--include-nms] [--fp16]
                      [--int8]
-    
+
     optional arguments:
       -h, --help            show this help message and exit
       --weights WEIGHTS     weights path
@@ -299,27 +297,27 @@ an end2end ONNX model, you can check this
     Import onnx_graphsurgeon failure: No module named 'onnx_graphsurgeon'
     Namespace(batch_size=1, conf_thres=0.25, device='cpu', dynamic=False, dynamic_batch=False, end2end=False, fp16=False, grid=True, img_size=[640, 640], include_nms=False, int8=False, iou_thres=0.45, max_wh=None, simplify=False, topk_all=100, weights='model/yolov7-tiny.pt')
     YOLOR 🚀 v0.1-128-ga207844 torch 2.3.1+cpu CPU
-    
-    Fusing layers... 
+
+    Fusing layers...
     Model Summary: 200 layers, 6219709 parameters, 6219709 gradients, 13.7 GFLOPS
-    
+
     Starting TorchScript export with torch 2.3.1+cpu...
     TorchScript export success, saved as model/yolov7-tiny.torchscript.pt
     CoreML export failure: No module named 'coremltools'
-    
+
     Starting TorchScript-Lite export with torch 2.3.1+cpu...
     TorchScript-Lite export success, saved as model/yolov7-tiny.torchscript.ptl
-    
+
     Starting ONNX export with onnx 1.16.1...
     ONNX export success, saved as model/yolov7-tiny.onnx
-    
+
     Export complete (2.59s). Visualize with https://github.com/lutzroeder/netron.
 
 
 Convert ONNX Model to OpenVINO Intermediate Representation (IR)
 ---------------------------------------------------------------
 
- While ONNX models are directly
+While ONNX models are directly
 supported by OpenVINO runtime, it can be useful to convert them to IR
 format to take the advantage of OpenVINO model conversion API features.
 The ``ov.convert_model`` python function of `model conversion
@@ -332,7 +330,7 @@ However, it can also be save on device in OpenVINO IR format using
 .. code:: ipython3
 
     import openvino as ov
-    
+
     model = ov.convert_model("model/yolov7-tiny.onnx")
     # serialize model for saving IR
     ov.save_model(model, "model/yolov7-tiny.xml")
@@ -373,13 +371,13 @@ To keep specific shape, preprocessing automatically enables padding.
     from PIL import Image
     from utils.datasets import letterbox
     from utils.plots import plot_one_box
-    
-    
+
+
     def preprocess_image(img0: np.ndarray):
         """
         Preprocess image according to YOLOv7 input requirements.
         Takes image in np.array format, resizes it to specific size using letterbox resize, converts color space from BGR (default in OpenCV) to RGB and changes data layout from HWC to CHW.
-    
+
         Parameters:
           img0 (np.ndarray): image for preprocessing
         Returns:
@@ -388,18 +386,18 @@ To keep specific shape, preprocessing automatically enables padding.
         """
         # resize
         img = letterbox(img0, auto=False)[0]
-    
+
         # Convert
         img = img.transpose(2, 0, 1)
         img = np.ascontiguousarray(img)
         return img, img0
-    
-    
+
+
     def prepare_input_tensor(image: np.ndarray):
         """
         Converts preprocessed image to tensor format according to YOLOv7 input requirements.
         Takes image in np.array format with unit8 data in [0, 255] range and converts it to torch.Tensor object with float data in [0, 1] range
-    
+
         Parameters:
           image (np.ndarray): image for conversion to tensor
         Returns:
@@ -407,12 +405,12 @@ To keep specific shape, preprocessing automatically enables padding.
         """
         input_tensor = image.astype(np.float32)  # uint8 to fp16/32
         input_tensor /= 255.0  # 0 - 255 to 0.0 - 1.0
-    
+
         if input_tensor.ndim == 3:
             input_tensor = np.expand_dims(input_tensor, 0)
         return input_tensor
-    
-    
+
+
     # label names for visualization
     DEFAULT_NAMES = [
         "person",
@@ -496,16 +494,16 @@ To keep specific shape, preprocessing automatically enables padding.
         "hair drier",
         "toothbrush",
     ]
-    
+
     # obtain class names from model checkpoint
     state_dict = torch.load("model/yolov7-tiny.pt", map_location="cpu")
     if hasattr(state_dict["model"], "module"):
         NAMES = getattr(state_dict["model"].module, "names", DEFAULT_NAMES)
     else:
         NAMES = getattr(state_dict["model"], "names", DEFAULT_NAMES)
-    
+
     del state_dict
-    
+
     # colors for visualization
     COLORS = {name: [np.random.randint(0, 255) for _ in range(3)] for i, name in enumerate(NAMES)}
 
@@ -536,8 +534,8 @@ algorithm and rescale boxes coordinates to original image size.
 
     from typing import List, Tuple, Dict
     from utils.general import scale_coords, non_max_suppression
-    
-    
+
+
     def detect(
         model: ov.Model,
         image_path: Path,
@@ -567,8 +565,8 @@ algorithm and rescale boxes coordinates to original image size.
         predictions = torch.from_numpy(model(input_tensor)[output_blob])
         pred = non_max_suppression(predictions, conf_thres, iou_thres, classes=classes, agnostic=agnostic_nms)
         return pred, orig_img, input_tensor.shape
-    
-    
+
+
     def draw_boxes(
         predictions: np.ndarray,
         input_shape: Tuple[int],
@@ -590,7 +588,7 @@ algorithm and rescale boxes coordinates to original image size.
             return image
         # Rescale boxes from input size to original image size
         predictions[:, :4] = scale_coords(input_shape[2:], predictions[:, :4], image.shape).round()
-    
+
         # Write results
         for *xyxy, conf, cls in reversed(predictions):
             label = f"{names[int(cls)]} {conf:.2f}"
@@ -613,14 +611,14 @@ select device from dropdown list for running inference using OpenVINO
 .. code:: ipython3
 
     import ipywidgets as widgets
-    
+
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
         value="AUTO",
         description="Device:",
         disabled=False,
     )
-    
+
     device
 
 
@@ -670,15 +668,15 @@ the original model evaluation scripts.
 .. code:: ipython3
 
     from zipfile import ZipFile
-    
+
     DATA_URL = "http://images.cocodataset.org/zips/val2017.zip"
     LABELS_URL = "https://github.com/ultralytics/yolov5/releases/download/v1.0/coco2017labels-segments.zip"
-    
+
     OUT_DIR = Path(".")
-    
+
     download_file(DATA_URL, directory=OUT_DIR, show_progress=True)
     download_file(LABELS_URL, directory=OUT_DIR, show_progress=True)
-    
+
     if not (OUT_DIR / "coco/labels").exists():
         with ZipFile("coco2017labels-segments.zip", "r") as zip_ref:
             zip_ref.extractall(OUT_DIR)
@@ -709,12 +707,12 @@ Create dataloader
     import yaml
     from utils.datasets import create_dataloader
     from utils.general import check_dataset, box_iou, xywh2xyxy, colorstr
-    
+
     # read dataset config
     DATA_CONFIG = "data/coco.yaml"
     with open(DATA_CONFIG) as f:
         data = yaml.load(f, Loader=yaml.SafeLoader)
-    
+
     # Dataloader
     TASK = "val"  # path to train/val/test images
     Option = namedtuple("Options", ["single_cls"])  # imitation of commandline provided options for single class evaluation
@@ -724,7 +722,7 @@ Create dataloader
 
 .. parsed-literal::
 
-    val: Scanning 'coco/val2017' images and labels... 4952 found, 48 missing, 0 empty, 0 corrupted: 100%|██████████| 5000/5000 [00:02<00:00, 2473.28it/s]
+    val: Scanning 'coco/val2017' images and labels... 4952 found, 48 missing, 0 empty, 0 corrupted: 100%|██████████| 5000/5000 [00:01<00:00, 2506.92it/s]
 
 
 Define validation function
@@ -742,8 +740,8 @@ evaluation procedure can be found in this
     import numpy as np
     from tqdm.notebook import tqdm
     from utils.metrics import ap_per_class
-    
-    
+
+
     def test(
         data,
         model: ov.Model,
@@ -757,7 +755,7 @@ evaluation procedure can be found in this
     ):
         """
         YOLOv7 accuracy evaluation. Processes validation dataset and compites metrics.
-    
+
         Parameters:
             model (ov.Model): OpenVINO compiled model.
             dataloader (torch.utils.DataLoader): validation dataset.
@@ -776,16 +774,16 @@ evaluation procedure can be found in this
             seen (int): number of evaluated images
             labels (int): number of labels
         """
-    
+
         model_output = model.output(0)
         check_dataset(data)  # check
         nc = 1 if single_cls else int(data["nc"])  # number of classes
         iouv = torch.linspace(0.5, 0.95, 10)  # iou vector for mAP@0.5:0.95
         niou = iouv.numel()
-    
+
         if v5_metric:
             print("Testing with YOLOv5 AP metric...")
-    
+
         seen = 0
         p, r, mp, mr, map50, map = 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
         stats, ap, ap_class = [], [], []
@@ -795,13 +793,13 @@ evaluation procedure can be found in this
             img = prepare_input_tensor(img.numpy())
             targets = targets
             height, width = img.shape[2:]
-    
+
             with torch.no_grad():
                 # Run model
                 out = torch.from_numpy(model(ov.Tensor(img))[model_output])  # inference output
                 # Run NMS
                 targets[:, 2:] *= torch.Tensor([width, height, width, height])  # to pixels
-    
+
                 out = non_max_suppression(
                     out,
                     conf_thres=conf_thres,
@@ -815,7 +813,7 @@ evaluation procedure can be found in this
                 nl = len(labels)
                 tcls = labels[:, 0].tolist() if nl else []  # target class
                 seen += 1
-    
+
                 if len(pred) == 0:
                     if nl:
                         stats.append(
@@ -945,8 +943,8 @@ transformation function for getting only input tensors.
 .. code:: ipython3
 
     import nncf  # noqa: F811
-    
-    
+
+
     def transform_fn(data_item):
         """
         Quantization transform function. Extracts and preprocess input data from dataloader item for quantization.
@@ -958,8 +956,8 @@ transformation function for getting only input tensors.
         img = data_item[0].numpy()
         input_tensor = prepare_input_tensor(img)
         return input_tensor
-    
-    
+
+
     quantization_dataset = nncf.Dataset(dataloader, transform_fn)
 
 
@@ -981,16 +979,16 @@ asymmetric quantization of activations.
 .. code:: ipython3
 
     quantized_model = nncf.quantize(model, quantization_dataset, preset=nncf.QuantizationPreset.MIXED)
-    
+
     ov.save_model(quantized_model, "model/yolov7-tiny_int8.xml")
 
 
 .. parsed-literal::
 
-    2024-06-06 03:53:34.859213: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-06-06 03:53:34.891040: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-07-02 03:44:42.740146: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-07-02 03:44:42.772348: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2024-06-06 03:53:35.475627: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-07-02 03:44:43.355364: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 
@@ -1000,17 +998,17 @@ asymmetric quantization of activations.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -1021,17 +1019,17 @@ asymmetric quantization of activations.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -1154,18 +1152,18 @@ models.
     [Step 2/11] Loading OpenVINO Runtime
     [ WARNING ] Default duration 120 seconds is used for unknown device AUTO
     [ INFO ] OpenVINO:
-    [ INFO ] Build ................................. 2024.1.0-15008-f4afc983258-releases/2024/1
-    [ INFO ] 
+    [ INFO ] Build ................................. 2024.3.0-15837-76a668b0662
+    [ INFO ]
     [ INFO ] Device info:
     [ INFO ] AUTO
-    [ INFO ] Build ................................. 2024.1.0-15008-f4afc983258-releases/2024/1
-    [ INFO ] 
-    [ INFO ] 
+    [ INFO ] Build ................................. 2024.3.0-15837-76a668b0662
+    [ INFO ]
+    [ INFO ]
     [Step 3/11] Setting device configuration
     [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.THROUGHPUT.
     [Step 4/11] Reading model files
     [ INFO ] Loading model files
-    [ INFO ] Read model took 13.13 ms
+    [ INFO ] Read model took 12.95 ms
     [ INFO ] Original model I/O parameters:
     [ INFO ] Model inputs:
     [ INFO ]     images (node: images) : f32 / [...] / [1,3,640,640]
@@ -1179,7 +1177,7 @@ models.
     [ INFO ] Model outputs:
     [ INFO ]     output (node: output) : f32 / [...] / [1,25200,85]
     [Step 7/11] Loading the model to the device
-    [ INFO ] Compile model took 265.04 ms
+    [ INFO ] Compile model took 265.12 ms
     [Step 8/11] Querying optimal runtime parameters
     [ INFO ] Model:
     [ INFO ]   NETWORK_NAME: main_graph
@@ -1191,7 +1189,7 @@ models.
     [ INFO ]     AFFINITY: Affinity.CORE
     [ INFO ]     CPU_DENORMALS_OPTIMIZATION: False
     [ INFO ]     CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE: 1.0
-    [ INFO ]     DYNAMIC_QUANTIZATION_GROUP_SIZE: 0
+    [ INFO ]     DYNAMIC_QUANTIZATION_GROUP_SIZE: 32
     [ INFO ]     ENABLE_CPU_PINNING: True
     [ INFO ]     ENABLE_HYPER_THREADING: True
     [ INFO ]     EXECUTION_DEVICES: ['CPU']
@@ -1213,20 +1211,20 @@ models.
     [ INFO ]   PERF_COUNT: False
     [Step 9/11] Creating infer requests and preparing input tensors
     [ WARNING ] No input files were given for input 'images'!. This input will be filled with random values!
-    [ INFO ] Fill input 'images' with random values 
+    [ INFO ] Fill input 'images' with random values
     [Step 10/11] Measuring performance (Start inference asynchronously, 6 inference requests, limits: 120000 ms duration)
     [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
-    [ INFO ] First inference took 45.52 ms
+    [ INFO ] First inference took 44.77 ms
     [Step 11/11] Dumping statistics report
     [ INFO ] Execution Devices:['CPU']
-    [ INFO ] Count:            11772 iterations
-    [ INFO ] Duration:         120088.10 ms
+    [ INFO ] Count:            11700 iterations
+    [ INFO ] Duration:         120095.00 ms
     [ INFO ] Latency:
-    [ INFO ]    Median:        60.93 ms
-    [ INFO ]    Average:       61.07 ms
-    [ INFO ]    Min:           45.87 ms
-    [ INFO ]    Max:           84.07 ms
-    [ INFO ] Throughput:   98.03 FPS
+    [ INFO ]    Median:        61.32 ms
+    [ INFO ]    Average:       61.44 ms
+    [ INFO ]    Min:           35.90 ms
+    [ INFO ]    Max:           81.91 ms
+    [ INFO ] Throughput:   97.42 FPS
 
 
 .. code:: ipython3
@@ -1242,18 +1240,18 @@ models.
     [Step 2/11] Loading OpenVINO Runtime
     [ WARNING ] Default duration 120 seconds is used for unknown device AUTO
     [ INFO ] OpenVINO:
-    [ INFO ] Build ................................. 2024.1.0-15008-f4afc983258-releases/2024/1
-    [ INFO ] 
+    [ INFO ] Build ................................. 2024.3.0-15837-76a668b0662
+    [ INFO ]
     [ INFO ] Device info:
     [ INFO ] AUTO
-    [ INFO ] Build ................................. 2024.1.0-15008-f4afc983258-releases/2024/1
-    [ INFO ] 
-    [ INFO ] 
+    [ INFO ] Build ................................. 2024.3.0-15837-76a668b0662
+    [ INFO ]
+    [ INFO ]
     [Step 3/11] Setting device configuration
     [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.THROUGHPUT.
     [Step 4/11] Reading model files
     [ INFO ] Loading model files
-    [ INFO ] Read model took 18.95 ms
+    [ INFO ] Read model took 18.92 ms
     [ INFO ] Original model I/O parameters:
     [ INFO ] Model inputs:
     [ INFO ]     images (node: images) : f32 / [...] / [1,3,640,640]
@@ -1267,7 +1265,7 @@ models.
     [ INFO ] Model outputs:
     [ INFO ]     output (node: output) : f32 / [...] / [1,25200,85]
     [Step 7/11] Loading the model to the device
-    [ INFO ] Compile model took 495.11 ms
+    [ INFO ] Compile model took 488.21 ms
     [Step 8/11] Querying optimal runtime parameters
     [ INFO ] Model:
     [ INFO ]   NETWORK_NAME: main_graph
@@ -1279,7 +1277,7 @@ models.
     [ INFO ]     AFFINITY: Affinity.CORE
     [ INFO ]     CPU_DENORMALS_OPTIMIZATION: False
     [ INFO ]     CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE: 1.0
-    [ INFO ]     DYNAMIC_QUANTIZATION_GROUP_SIZE: 0
+    [ INFO ]     DYNAMIC_QUANTIZATION_GROUP_SIZE: 32
     [ INFO ]     ENABLE_CPU_PINNING: True
     [ INFO ]     ENABLE_HYPER_THREADING: True
     [ INFO ]     EXECUTION_DEVICES: ['CPU']
@@ -1301,18 +1299,18 @@ models.
     [ INFO ]   PERF_COUNT: False
     [Step 9/11] Creating infer requests and preparing input tensors
     [ WARNING ] No input files were given for input 'images'!. This input will be filled with random values!
-    [ INFO ] Fill input 'images' with random values 
+    [ INFO ] Fill input 'images' with random values
     [Step 10/11] Measuring performance (Start inference asynchronously, 6 inference requests, limits: 120000 ms duration)
     [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
-    [ INFO ] First inference took 23.46 ms
+    [ INFO ] First inference took 23.94 ms
     [Step 11/11] Dumping statistics report
     [ INFO ] Execution Devices:['CPU']
-    [ INFO ] Count:            34698 iterations
-    [ INFO ] Duration:         120019.34 ms
+    [ INFO ] Count:            34572 iterations
+    [ INFO ] Duration:         120027.52 ms
     [ INFO ] Latency:
-    [ INFO ]    Median:        20.56 ms
-    [ INFO ]    Average:       20.64 ms
-    [ INFO ]    Min:           14.43 ms
-    [ INFO ]    Max:           39.46 ms
-    [ INFO ] Throughput:   289.10 FPS
+    [ INFO ]    Median:        20.63 ms
+    [ INFO ]    Average:       20.72 ms
+    [ INFO ]    Min:           14.83 ms
+    [ INFO ]    Max:           39.17 ms
+    [ INFO ] Throughput:   288.03 FPS
 
diff --git a/docs/notebooks/yolov7-optimization-with-output_files/yolov7-optimization-with-output_10_0.jpg b/docs/notebooks/yolov7-optimization-with-output_files/yolov7-optimization-with-output_10_0.jpg
index 31e3af621f0b67..f7646cc89d7975 100644
--- a/docs/notebooks/yolov7-optimization-with-output_files/yolov7-optimization-with-output_10_0.jpg
+++ b/docs/notebooks/yolov7-optimization-with-output_files/yolov7-optimization-with-output_10_0.jpg
@@ -1,3 +1,3 @@
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-oid sha256:b60e7cc107a0da5edc446cf5b7b6c823b16bf9062228ec707bb56a1ea8165144
-size 64280
+oid sha256:39d754e52150761ef69215275356f5707f3e719bed0d8600d70c2552fd035834
+size 63712
diff --git a/docs/notebooks/yolov7-optimization-with-output_files/yolov7-optimization-with-output_10_0.png b/docs/notebooks/yolov7-optimization-with-output_files/yolov7-optimization-with-output_10_0.png
index 119a17012da12e..d85ca3dc216c75 100644
--- a/docs/notebooks/yolov7-optimization-with-output_files/yolov7-optimization-with-output_10_0.png
+++ b/docs/notebooks/yolov7-optimization-with-output_files/yolov7-optimization-with-output_10_0.png
@@ -1,3 +1,3 @@
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-oid sha256:854989a048ca7af03153e2832641480aa9763a57acd71d093b77bf9342a02317
-size 563966
+oid sha256:bbeb23e96689fe8d7c262a2f5b314399df65dd842af3ef16484582bd59012cf6
+size 568429
diff --git a/docs/notebooks/yolov7-optimization-with-output_files/yolov7-optimization-with-output_27_0.jpg b/docs/notebooks/yolov7-optimization-with-output_files/yolov7-optimization-with-output_27_0.jpg
index 225690d4c7a17f..47959953819c2e 100644
--- a/docs/notebooks/yolov7-optimization-with-output_files/yolov7-optimization-with-output_27_0.jpg
+++ b/docs/notebooks/yolov7-optimization-with-output_files/yolov7-optimization-with-output_27_0.jpg
@@ -1,3 +1,3 @@
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-oid sha256:75e080aa28c1b424d134bdf1704599ee5b8b1b06057de731bee946ed77a07541
-size 64840
+oid sha256:6532955102711985a79d02b6f7b11803f847e791b71c795e2b0740e988881dd2
+size 64416
diff --git a/docs/notebooks/yolov7-optimization-with-output_files/yolov7-optimization-with-output_27_0.png b/docs/notebooks/yolov7-optimization-with-output_files/yolov7-optimization-with-output_27_0.png
index ebda42978c2980..47faf2e92e0490 100644
--- a/docs/notebooks/yolov7-optimization-with-output_files/yolov7-optimization-with-output_27_0.png
+++ b/docs/notebooks/yolov7-optimization-with-output_files/yolov7-optimization-with-output_27_0.png
@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:f6601955d39530322aba35d867d2901b3ad698c00a3f6f970f9fab32ac2c56c3
-size 574061
+oid sha256:611e1757749932c940f30dff9cc6d700bd91396add19c73eca588c5c2b9082c5
+size 574924
diff --git a/docs/notebooks/yolov7-optimization-with-output_files/yolov7-optimization-with-output_44_0.jpg b/docs/notebooks/yolov7-optimization-with-output_files/yolov7-optimization-with-output_44_0.jpg
index b2a9803eff9944..b7c84e462e465f 100644
--- a/docs/notebooks/yolov7-optimization-with-output_files/yolov7-optimization-with-output_44_0.jpg
+++ b/docs/notebooks/yolov7-optimization-with-output_files/yolov7-optimization-with-output_44_0.jpg
@@ -1,3 +1,3 @@
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-size 64930
+oid sha256:6d5c7dfddde4a120bc34fd13c630be56fa6270855c53247a5dfd57783c0320a8
+size 64445
diff --git a/docs/notebooks/yolov7-optimization-with-output_files/yolov7-optimization-with-output_44_0.png b/docs/notebooks/yolov7-optimization-with-output_files/yolov7-optimization-with-output_44_0.png
index 83fb12a961ba54..dcc8b1b4e6e180 100644
--- a/docs/notebooks/yolov7-optimization-with-output_files/yolov7-optimization-with-output_44_0.png
+++ b/docs/notebooks/yolov7-optimization-with-output_files/yolov7-optimization-with-output_44_0.png
@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:efa20c5447bd094e03624eb07fad15f08176fce3636c5dcd96cd95b548879317
-size 574122
+oid sha256:dd98f170f0ae14a44ed2bd00831d74cc1ccbb487296d0095681719459413e904
+size 574803
diff --git a/docs/notebooks/yolov8-instance-segmentation-with-output.rst b/docs/notebooks/yolov8-instance-segmentation-with-output.rst
index ef01405528e04f..abbda33e14e597 100644
--- a/docs/notebooks/yolov8-instance-segmentation-with-output.rst
+++ b/docs/notebooks/yolov8-instance-segmentation-with-output.rst
@@ -20,8 +20,8 @@ model. - Prepare and run optimization pipeline. - Compare performance of
 the FP32 and quantized models. - Compare accuracy of the FP32 and
 quantized models. - Live demo
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Get PyTorch model <#get-pytorch-model>`__
 
@@ -102,14 +102,14 @@ Import required utility functions. The lower cell will download the
 .. code:: ipython3
 
     from pathlib import Path
-    
+
     # Fetch `notebook_utils` module
     import requests
-    
+
     r = requests.get(
         url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
-    
+
     open("notebook_utils.py", "w").write(r.text)
     from notebook_utils import download_file, VideoPlayer
 
@@ -164,12 +164,12 @@ Let us consider the examples:
 
     from PIL import Image
     from ultralytics import YOLO
-    
+
     SEG_MODEL_NAME = "yolov8n-seg"
-    
+
     seg_model = YOLO(models_dir / f"{SEG_MODEL_NAME}.pt")
     label_map = seg_model.model.names
-    
+
     res = seg_model(IMAGE_PATH)
     Image.fromarray(res[0].plot()[:, :, ::-1])
 
@@ -186,7 +186,7 @@ Let us consider the examples:
 
 .. parsed-literal::
 
-    
+
     image 1/1 /home/maleksandr/test_notebooks/update_ultralytics/openvino_notebooks/notebooks/yolov8-optimization/data/coco_bike.jpg: 480x640 1 bicycle, 2 cars, 1 dog, 55.6ms
     Speed: 1.8ms preprocess, 55.6ms inference, 2.0ms postprocess per image at shape (1, 3, 480, 640)
 
@@ -218,16 +218,16 @@ preserve dynamic shapes in the model.
 .. parsed-literal::
 
     Ultralytics YOLOv8.1.42 🚀 Python-3.10.12 torch-2.2.2+cpu CPU (Intel Core(TM) i9-10980XE 3.00GHz)
-    
+
     PyTorch: starting from 'models/yolov8n-seg.pt' with input shape (1, 3, 640, 640) BCHW and output shape(s) ((1, 116, 8400), (1, 32, 160, 160)) (6.7 MB)
-    
+
     OpenVINO: starting export with openvino 2024.0.0-14509-34caeefd078-releases/2024/0...
     OpenVINO: export success ✅ 1.8s, saved as 'models/yolov8n-seg_openvino_model/' (6.9 MB)
-    
+
     Export complete (3.0s)
     Results saved to /home/maleksandr/test_notebooks/update_ultralytics/openvino_notebooks/notebooks/yolov8-optimization/models
-    Predict:         yolo predict task=segment model=models/yolov8n-seg_openvino_model imgsz=640 half 
-    Validate:        yolo val task=segment model=models/yolov8n-seg_openvino_model imgsz=640 data=coco.yaml half 
+    Predict:         yolo predict task=segment model=models/yolov8n-seg_openvino_model imgsz=640 half
+    Validate:        yolo val task=segment model=models/yolov8n-seg_openvino_model imgsz=640 data=coco.yaml half
     Visualize:       https://netron.app
 
 
@@ -251,16 +251,16 @@ Select device from dropdown list for running inference using OpenVINO
 
     import ipywidgets as widgets
     import openvino as ov
-    
+
     core = ov.Core()
-    
+
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
         value="AUTO",
         description="Device:",
         disabled=False,
     )
-    
+
     device
 
 
@@ -281,7 +281,7 @@ Test on single image
 
     core = ov.Core()
     seg_ov_model = core.read_model(seg_model_path)
-    
+
     ov_config = {}
     if device.value != "CPU":
         seg_ov_model.reshape({0: [1, 3, 640, 640]})
@@ -292,13 +292,13 @@ Test on single image
 .. code:: ipython3
 
     import torch
-    
-    
+
+
     def infer(*args):
         result = seg_compiled_model(args)
         return torch.from_numpy(result[0]), torch.from_numpy(result[1])
-    
-    
+
+
     seg_model.predictor.inference = infer
     seg_model.predictor.model.pt = False
 
@@ -310,7 +310,7 @@ Test on single image
 
 .. parsed-literal::
 
-    
+
     image 1/1 /home/maleksandr/test_notebooks/update_ultralytics/openvino_notebooks/notebooks/yolov8-optimization/data/coco_bike.jpg: 640x640 1 bicycle, 2 cars, 1 dog, 27.6ms
     Speed: 3.5ms preprocess, 27.6ms inference, 4.5ms postprocess per image at shape (1, 3, 640, 640)
 
@@ -350,24 +350,24 @@ evaluation function.
 .. code:: ipython3
 
     from zipfile import ZipFile
-    
+
     from ultralytics.data.utils import DATASETS_DIR
-    
-    
+
+
     DATA_URL = "http://images.cocodataset.org/zips/val2017.zip"
     LABELS_URL = "https://github.com/ultralytics/yolov5/releases/download/v1.0/coco2017labels-segments.zip"
     CFG_URL = "https://raw.githubusercontent.com/ultralytics/ultralytics/v8.1.0/ultralytics/cfg/datasets/coco.yaml"
-    
+
     OUT_DIR = DATASETS_DIR
-    
+
     DATA_PATH = OUT_DIR / "val2017.zip"
     LABELS_PATH = OUT_DIR / "coco2017labels-segments.zip"
     CFG_PATH = OUT_DIR / "coco.yaml"
-    
+
     download_file(DATA_URL, DATA_PATH.name, DATA_PATH.parent)
     download_file(LABELS_URL, LABELS_PATH.name, LABELS_PATH.parent)
     download_file(CFG_URL, CFG_PATH.name, CFG_PATH.parent)
-    
+
     if not (OUT_DIR / "coco/labels").exists():
         with ZipFile(LABELS_PATH, "r") as zip_ref:
             zip_ref.extractall(OUT_DIR)
@@ -384,8 +384,8 @@ Define validation function
     import numpy as np
     from tqdm.notebook import tqdm
     from ultralytics.utils.metrics import ConfusionMatrix
-    
-    
+
+
     def test(
         model: ov.Model,
         core: ov.Core,
@@ -427,8 +427,8 @@ Define validation function
             validator.update_metrics(preds, batch)
         stats = validator.get_stats()
         return stats
-    
-    
+
+
     def print_stats(stats: np.ndarray, total_images: int, total_objects: int):
         """
         Helper function for printing accuracy statistic
@@ -504,7 +504,7 @@ validator class instance.
     from ultralytics.data.converter import coco80_to_coco91_class
     from ultralytics.data.utils import check_det_dataset
     from ultralytics.utils import ops
-    
+
     args = get_cfg(cfg=DEFAULT_CFG)
     args.data = str(CFG_PATH)
 
@@ -514,7 +514,7 @@ validator class instance.
     seg_validator.data = check_det_dataset(args.data)
     seg_validator.stride = 32
     seg_data_loader = seg_validator.get_dataloader(OUT_DIR / "coco/", 1)
-    
+
     seg_validator.is_coco = True
     seg_validator.class_map = coco80_to_coco91_class()
     seg_validator.names = seg_model.model.names
@@ -608,15 +608,15 @@ improve model inference speed.
 .. code:: ipython3
 
     import ipywidgets as widgets
-    
+
     int8_model_seg_path = models_dir / f"{SEG_MODEL_NAME}_openvino_int8_model/{SEG_MODEL_NAME}.xml"
-    
+
     to_quantize = widgets.Checkbox(
         value=True,
         description="Quantization",
         disabled=False,
     )
-    
+
     to_quantize
 
 
@@ -635,12 +635,12 @@ Let’s load ``skip magic`` extension to skip quantization if
 
     # Fetch skip_kernel_extension module
     import requests
-    
+
     r = requests.get(
         url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/skip_kernel_extension.py",
     )
     open("skip_kernel_extension.py", "w").write(r.text)
-    
+
     %load_ext skip_kernel_extension
 
 Reuse validation dataloader in accuracy testing for quantization. For
@@ -650,12 +650,12 @@ transformation function for getting only input tensors.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
-    
+
+
     import nncf
     from typing import Dict
-    
-    
+
+
     def transform_fn(data_item:Dict):
         """
         Quantization transform function. Extracts and preprocess input data from dataloader item for quantization.
@@ -666,8 +666,8 @@ transformation function for getting only input tensors.
         """
         input_tensor = seg_validator.preprocess(data_item)['img'].numpy()
         return input_tensor
-    
-    
+
+
     quantization_dataset = nncf.Dataset(seg_data_loader, transform_fn)
 
 
@@ -694,7 +694,7 @@ point precision, using the ``ignored_scope`` parameter.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     ignored_scope = nncf.IgnoredScope(
         names=[
             "__module.model.22.cv3.0.0.conv/aten::_convolution/Convolution",  # in the post-processing subgraph
@@ -722,7 +722,7 @@ point precision, using the ``ignored_scope`` parameter.
         	"__module.model.0.conv/aten::_convolution/Convolution"
         ]
     )
-    
+
     # Segmentation model
     quantized_seg_model = nncf.quantize(
         seg_ov_model,
@@ -738,81 +738,81 @@ point precision, using the ``ignored_scope`` parameter.
     INFO:nncf:Not adding activation input quantizer for operation: 1 __module.model.0.conv/aten::_convolution/Convolution
     2 __module.model.0.conv/aten::_convolution/Add
     3 __module.model.22.cv4.2.1.act/aten::silu_/Swish
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 25 __module.model.4.cv1.conv/aten::_convolution/Convolution
     26 __module.model.4.cv1.conv/aten::_convolution/Add
     27 __module.model.22.cv4.2.1.act/aten::silu_/Swish_7
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 43 __module.model.5.conv/aten::_convolution/Convolution
     47 __module.model.5.conv/aten::_convolution/Add
     51 __module.model.22.cv4.2.1.act/aten::silu_/Swish_13
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 54 __module.model.6.cv1.conv/aten::_convolution/Convolution
     56 __module.model.6.cv1.conv/aten::_convolution/Add
     59 __module.model.22.cv4.2.1.act/aten::silu_/Swish_14
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 98 __module.model.7.conv/aten::_convolution/Convolution
     107 __module.model.7.conv/aten::_convolution/Add
     116 __module.model.22.cv4.2.1.act/aten::silu_/Swish_20
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 106 __module.model.12.cv1.conv/aten::_convolution/Convolution
     115 __module.model.12.cv1.conv/aten::_convolution/Add
     123 __module.model.22.cv4.2.1.act/aten::silu_/Swish_27
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 46 __module.model.15.cv1.conv/aten::_convolution/Convolution
     50 __module.model.15.cv1.conv/aten::_convolution/Add
     53 __module.model.22.cv4.2.1.act/aten::silu_/Swish_31
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 74 __module.model.16.conv/aten::_convolution/Convolution
     83 __module.model.16.conv/aten::_convolution/Add
     92 __module.model.22.cv4.2.1.act/aten::silu_/Swish_42
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 75 __module.model.22.cv2.0.0.conv/aten::_convolution/Convolution
     84 __module.model.22.cv2.0.0.conv/aten::_convolution/Add
     93 __module.model.22.cv4.2.1.act/aten::silu_/Swish_38
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 102 __module.model.22.cv2.0.1.conv/aten::_convolution/Convolution
     111 __module.model.22.cv2.0.1.conv/aten::_convolution/Add
     119 __module.model.22.cv4.2.1.act/aten::silu_/Swish_39
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 76 __module.model.22.cv3.0.0.conv/aten::_convolution/Convolution
     85 __module.model.22.cv3.0.0.conv/aten::_convolution/Add
     94 __module.model.22.cv4.2.1.act/aten::silu_/Swish_40
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 127 __module.model.22.cv3.0.2/aten::_convolution/Convolution
     134 __module.model.22.cv3.0.2/aten::_convolution/Add
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 77 __module.model.22.cv4.0.0.conv/aten::_convolution/Convolution
     86 __module.model.22.cv4.0.0.conv/aten::_convolution/Add
     95 __module.model.22.cv4.2.1.act/aten::silu_/Swish_60
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 78 __module.model.22.proto.cv1.conv/aten::_convolution/Convolution
     87 __module.model.22.proto.cv1.conv/aten::_convolution/Add
     96 __module.model.22.cv4.2.1.act/aten::silu_/Swish_35
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 234 __module.model.22.cv3.1.1.conv/aten::_convolution/Convolution
     247 __module.model.22.cv3.1.1.conv/aten::_convolution/Add
     258 __module.model.22.cv4.2.1.act/aten::silu_/Swish_50
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 289 __module.model.21.m.0.cv1.conv/aten::_convolution/Convolution
     296 __module.model.21.m.0.cv1.conv/aten::_convolution/Add
     301 __module.model.22.cv4.2.1.act/aten::silu_/Swish_53
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 295 __module.model.21.cv2.conv/aten::_convolution/Convolution
     300 __module.model.21.cv2.conv/aten::_convolution/Add
     304 __module.model.22.cv4.2.1.act/aten::silu_/Swish_55
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 331 __module.model.22.cv2.2.1.conv/aten::_convolution/Convolution
     339 __module.model.22.cv2.2.1.conv/aten::_convolution/Add
     344 __module.model.22.cv4.2.1.act/aten::silu_/Swish_57
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 333 __module.model.22.cv4.2.1.conv/aten::_convolution/Convolution
     341 __module.model.22.cv4.2.1.conv/aten::_convolution/Add
     346 __module.model.22.cv4.2.1.act/aten::silu_/Swish_65
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 349 __module.model.22.cv3.2.2/aten::_convolution/Convolution
     353 __module.model.22.cv3.2.2/aten::_convolution/Add
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 243 __module.model.22.dfl.conv/aten::_convolution/Convolution
     INFO:nncf:Not adding activation input quantizer for operation: 263 __module.model.22/aten::sub/Subtract
     INFO:nncf:Not adding activation input quantizer for operation: 264 __module.model.22/aten::add/Add_6
@@ -825,17 +825,17 @@ point precision, using the ``ignored_scope`` parameter.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -852,24 +852,24 @@ point precision, using the ``ignored_scope`` parameter.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     print(f"Quantized segmentation model will be saved to {int8_model_seg_path}")
     ov.save_model(quantized_seg_model, str(int8_model_seg_path))
 
@@ -894,43 +894,43 @@ on the image.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     device
 
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     ov_config = {}
     if device.value != "CPU":
         quantized_seg_model.reshape({0: [1, 3, 640, 640]})
     if "GPU" in device.value or ("AUTO" in device.value and "GPU" in core.available_devices):
         ov_config = {"GPU_DISABLE_WINOGRAD_CONVOLUTION": "YES"}
-    
+
     quantized_seg_compiled_model = core.compile_model(quantized_seg_model, device.value, ov_config)
 
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
-    
+
+
     def infer(*args):
         result = quantized_seg_compiled_model(args)
         return torch.from_numpy(result[0]), torch.from_numpy(result[1])
-    
+
     seg_model.predictor.inference = infer
 
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     res = seg_model(IMAGE_PATH)
     display(Image.fromarray(res[0].plot()[:, :, ::-1]))
 
 
 .. parsed-literal::
 
-    
+
     image 1/1 /home/maleksandr/test_notebooks/update_ultralytics/openvino_notebooks/notebooks/yolov8-optimization/data/coco_bike.jpg: 640x640 1 bicycle, 2 cars, 2 dogs, 26.8ms
     Speed: 2.8ms preprocess, 26.8ms inference, 3.4ms postprocess per image at shape (1, 3, 640, 640)
 
@@ -947,7 +947,7 @@ Compare the Original and Quantized Models
 Compare performance of the Original and Quantized Models
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
- Finally, use the OpenVINO
+Finally, use the OpenVINO
 `Benchmark
 Tool <https://docs.openvino.ai/2024/learn-openvino/openvino-samples/benchmark-tool.html>`__
 to measure the inference performance of the ``FP32`` and ``INT8``
@@ -965,7 +965,7 @@ models.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     device
 
 .. code:: ipython3
@@ -981,12 +981,12 @@ models.
     [Step 2/11] Loading OpenVINO Runtime
     [ INFO ] OpenVINO:
     [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
-    [ INFO ] 
+    [ INFO ]
     [ INFO ] Device info:
     [ INFO ] AUTO
     [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
-    [ INFO ] 
-    [ INFO ] 
+    [ INFO ]
+    [ INFO ]
     [Step 3/11] Setting device configuration
     [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.THROUGHPUT.
     [Step 4/11] Reading model files
@@ -1041,7 +1041,7 @@ models.
     [ INFO ]   LOADED_FROM_CACHE: False
     [Step 9/11] Creating infer requests and preparing input tensors
     [ WARNING ] No input files were given for input 'x'!. This input will be filled with random values!
-    [ INFO ] Fill input 'x' with random values 
+    [ INFO ] Fill input 'x' with random values
     [Step 10/11] Measuring performance (Start inference asynchronously, 12 inference requests, limits: 15000 ms duration)
     [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
     [ INFO ] First inference took 50.67 ms
@@ -1070,12 +1070,12 @@ models.
     [Step 2/11] Loading OpenVINO Runtime
     [ INFO ] OpenVINO:
     [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
-    [ INFO ] 
+    [ INFO ]
     [ INFO ] Device info:
     [ INFO ] AUTO
     [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
-    [ INFO ] 
-    [ INFO ] 
+    [ INFO ]
+    [ INFO ]
     [Step 3/11] Setting device configuration
     [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.THROUGHPUT.
     [Step 4/11] Reading model files
@@ -1130,7 +1130,7 @@ models.
     [ INFO ]   LOADED_FROM_CACHE: False
     [Step 9/11] Creating infer requests and preparing input tensors
     [ WARNING ] No input files were given for input 'x'!. This input will be filled with random values!
-    [ INFO ] Fill input 'x' with random values 
+    [ INFO ] Fill input 'x' with random values
     [Step 10/11] Measuring performance (Start inference asynchronously, 12 inference requests, limits: 15000 ms duration)
     [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
     [ INFO ] First inference took 41.26 ms
@@ -1159,7 +1159,7 @@ accuracy on a dataset.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     int8_seg_stats = test(quantized_seg_model, core, seg_data_loader, seg_validator, num_samples=NUM_TEST_SAMPLES)
 
 
@@ -1172,10 +1172,10 @@ accuracy on a dataset.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     print("FP32 model accuracy")
     print_stats(fp_seg_stats, seg_validator.seen, seg_validator.nt_per_class.sum())
-    
+
     print("INT8 model accuracy")
     print_stats(int8_seg_stats, seg_validator.seen, seg_validator.nt_per_class.sum())
 
@@ -1245,8 +1245,8 @@ The following code runs model inference on a video:
     import time
     import cv2
     from IPython import display
-    
-    
+
+
     def run_instance_segmentation(
         source=0,
         flip=False,
@@ -1256,20 +1256,20 @@ The following code runs model inference on a video:
         device=device.value,
     ):
         player = None
-    
+
         ov_config = {}
         if device != "CPU":
             model.reshape({0: [1, 3, 640, 640]})
         if "GPU" in device or ("AUTO" in device and "GPU" in core.available_devices):
             ov_config = {"GPU_DISABLE_WINOGRAD_CONVOLUTION": "YES"}
         compiled_model = core.compile_model(model, device, ov_config)
-    
+
         def infer(*args):
             result = compiled_model(args)
             return torch.from_numpy(result[0]), torch.from_numpy(result[1])
-    
+
         seg_model.predictor.inference = infer
-    
+
         try:
             # Create a video player to play with target fps.
             player = VideoPlayer(source=source, flip=flip, fps=30, skip_first_frames=skip_first_frames)
@@ -1278,7 +1278,7 @@ The following code runs model inference on a video:
             if use_popup:
                 title = "Press ESC to Exit"
                 cv2.namedWindow(winname=title, flags=cv2.WINDOW_GUI_NORMAL | cv2.WINDOW_AUTOSIZE)
-    
+
             processing_times = collections.deque()
             while True:
                 # Grab the frame.
@@ -1298,17 +1298,17 @@ The following code runs model inference on a video:
                     )
                 # Get the results.
                 input_image = np.array(frame)
-    
+
                 start_time = time.time()
                 detections = seg_model(input_image)
                 stop_time = time.time()
                 frame = detections[0].plot()
-    
+
                 processing_times.append(stop_time - start_time)
                 # Use processing times from last 200 frames.
                 if len(processing_times) > 200:
                     processing_times.popleft()
-    
+
                 _, f_width = frame.shape[:2]
                 # Mean processing time [ms].
                 processing_time = np.mean(processing_times) * 1000
@@ -1373,7 +1373,7 @@ set \ ``use_popup=True``.
 .. code:: ipython3
 
     WEBCAM_INFERENCE = False
-    
+
     if WEBCAM_INFERENCE:
         VIDEO_SOURCE = 0  # Webcam
     else:
diff --git a/docs/notebooks/yolov8-keypoint-detection-with-output.rst b/docs/notebooks/yolov8-keypoint-detection-with-output.rst
index 30f6571b768124..5e176348e441b9 100644
--- a/docs/notebooks/yolov8-keypoint-detection-with-output.rst
+++ b/docs/notebooks/yolov8-keypoint-detection-with-output.rst
@@ -19,8 +19,8 @@ model. - Prepare and run optimization pipeline. - Compare performance of
 the FP32 and quantized models. - Compare accuracy of the FP32 and
 quantized models. - Live demo
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Get PyTorch model <#get-pytorch-model>`__
 
@@ -101,14 +101,14 @@ Import required utility functions. The lower cell will download the
 .. code:: ipython3
 
     from pathlib import Path
-    
+
     # Fetch `notebook_utils` module
     import requests
-    
+
     r = requests.get(
         url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
-    
+
     open("notebook_utils.py", "w").write(r.text)
     from notebook_utils import download_file, VideoPlayer
 
@@ -162,12 +162,12 @@ Let us consider the examples:
 
     from PIL import Image
     from ultralytics import YOLO
-    
+
     POSE_MODEL_NAME = "yolov8n-pose"
-    
+
     pose_model = YOLO(models_dir / f"{POSE_MODEL_NAME}.pt")
     label_map = pose_model.model.names
-    
+
     res = pose_model(IMAGE_PATH)
     Image.fromarray(res[0].plot()[:, :, ::-1])
 
@@ -184,7 +184,7 @@ Let us consider the examples:
 
 .. parsed-literal::
 
-    
+
     image 1/1 /home/maleksandr/test_notebooks/update_ultralytics/openvino_notebooks/notebooks/yolov8-optimization/data/intel_rnb.jpg: 480x640 1 person, 45.3ms
     Speed: 1.5ms preprocess, 45.3ms inference, 1.0ms postprocess per image at shape (1, 3, 480, 640)
 
@@ -216,16 +216,16 @@ preserve dynamic shapes in the model.
 .. parsed-literal::
 
     Ultralytics YOLOv8.1.42 🚀 Python-3.10.12 torch-2.2.2+cpu CPU (Intel Core(TM) i9-10980XE 3.00GHz)
-    
+
     PyTorch: starting from 'models/yolov8n-pose.pt' with input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 56, 8400) (6.5 MB)
-    
+
     OpenVINO: starting export with openvino 2024.0.0-14509-34caeefd078-releases/2024/0...
     OpenVINO: export success ✅ 1.7s, saved as 'models/yolov8n-pose_openvino_model/' (6.7 MB)
-    
+
     Export complete (3.0s)
     Results saved to /home/maleksandr/test_notebooks/update_ultralytics/openvino_notebooks/notebooks/yolov8-optimization/models
-    Predict:         yolo predict task=pose model=models/yolov8n-pose_openvino_model imgsz=640 half 
-    Validate:        yolo val task=pose model=models/yolov8n-pose_openvino_model imgsz=640 data=/usr/src/app/ultralytics/datasets/coco-pose.yaml half 
+    Predict:         yolo predict task=pose model=models/yolov8n-pose_openvino_model imgsz=640 half
+    Validate:        yolo val task=pose model=models/yolov8n-pose_openvino_model imgsz=640 data=/usr/src/app/ultralytics/datasets/coco-pose.yaml half
     Visualize:       https://netron.app
 
 
@@ -249,16 +249,16 @@ Select device from dropdown list for running inference using OpenVINO
 
     import ipywidgets as widgets
     import openvino as ov
-    
+
     core = ov.Core()
-    
+
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
         value="AUTO",
         description="Device:",
         disabled=False,
     )
-    
+
     device
 
 
@@ -281,33 +281,33 @@ ready to check model prediction.
 .. code:: ipython3
 
     import torch
-    
+
     core = ov.Core()
     pose_ov_model = core.read_model(pose_model_path)
-    
+
     ov_config = {}
     if device.value != "CPU":
         pose_ov_model.reshape({0: [1, 3, 640, 640]})
     if "GPU" in device.value or ("AUTO" in device.value and "GPU" in core.available_devices):
         ov_config = {"GPU_DISABLE_WINOGRAD_CONVOLUTION": "YES"}
     pose_compiled_model = core.compile_model(pose_ov_model, device.value, ov_config)
-    
-    
+
+
     def infer(*args):
         result = pose_compiled_model(args)
         return torch.from_numpy(result[0])
-    
-    
+
+
     pose_model.predictor.inference = infer
     pose_model.predictor.model.pt = False
-    
+
     res = pose_model(IMAGE_PATH)
     Image.fromarray(res[0].plot()[:, :, ::-1])
 
 
 .. parsed-literal::
 
-    
+
     image 1/1 /home/maleksandr/test_notebooks/update_ultralytics/openvino_notebooks/notebooks/yolov8-optimization/data/intel_rnb.jpg: 640x640 2 persons, 23.7ms
     Speed: 5.0ms preprocess, 23.7ms inference, 2.2ms postprocess per image at shape (1, 3, 640, 640)
 
@@ -347,21 +347,21 @@ evaluation function.
 .. code:: ipython3
 
     from zipfile import ZipFile
-    
+
     from ultralytics.data.utils import DATASETS_DIR
-    
-    
+
+
     DATA_URL = "https://ultralytics.com/assets/coco8-pose.zip"
     CFG_URL = "https://raw.githubusercontent.com/ultralytics/ultralytics/v8.1.0/ultralytics/cfg/datasets/coco8-pose.yaml"
-    
+
     OUT_DIR = DATASETS_DIR
-    
+
     DATA_PATH = OUT_DIR / "val2017.zip"
     CFG_PATH = OUT_DIR / "coco8-pose.yaml"
-    
+
     download_file(DATA_URL, DATA_PATH.name, DATA_PATH.parent)
     download_file(CFG_URL, CFG_PATH.name, CFG_PATH.parent)
-    
+
     if not (OUT_DIR / "coco8-pose/labels").exists():
         with ZipFile(DATA_PATH, "r") as zip_ref:
             zip_ref.extractall(OUT_DIR)
@@ -389,8 +389,8 @@ Define validation function
     import numpy as np
     from tqdm.notebook import tqdm
     from ultralytics.utils.metrics import ConfusionMatrix
-    
-    
+
+
     def test(
         model: ov.Model,
         core: ov.Core,
@@ -425,8 +425,8 @@ Define validation function
             validator.update_metrics(preds, batch)
         stats = validator.get_stats()
         return stats
-    
-    
+
+
     def print_stats(stats: np.ndarray, total_images: int, total_objects: int):
         """
         Helper function for printing accuracy statistic
@@ -500,7 +500,7 @@ validator class instance.
     from ultralytics.utils import DEFAULT_CFG
     from ultralytics.cfg import get_cfg
     from ultralytics.data.utils import check_det_dataset
-    
+
     args = get_cfg(cfg=DEFAULT_CFG)
     args.data = "coco8-pose.yaml"
     args.model = "yolov8n-pose.pt"
@@ -508,7 +508,7 @@ validator class instance.
 .. code:: ipython3
 
     from ultralytics.models.yolo.pose import PoseValidator
-    
+
     pose_validator = PoseValidator(args=args)
 
 .. code:: ipython3
@@ -526,7 +526,7 @@ validator class instance.
 .. code:: ipython3
 
     from ultralytics.utils.metrics import OKS_SIGMA
-    
+
     pose_validator.is_coco = True
     pose_validator.names = pose_model.model.names
     pose_validator.metrics.names = pose_validator.names
@@ -608,15 +608,15 @@ improve model inference speed.
 .. code:: ipython3
 
     import ipywidgets as widgets
-    
+
     int8_model_pose_path = models_dir / f"{POSE_MODEL_NAME}_openvino_int8_model/{POSE_MODEL_NAME}.xml"
-    
+
     to_quantize = widgets.Checkbox(
         value=True,
         description="Quantization",
         disabled=False,
     )
-    
+
     to_quantize
 
 
@@ -638,7 +638,7 @@ Let’s load ``skip magic`` extension to skip quantization if
         url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/skip_kernel_extension.py",
     )
     open("skip_kernel_extension.py", "w").write(r.text)
-    
+
     %load_ext skip_kernel_extension
 
 Reuse validation dataloader in accuracy testing for quantization. For
@@ -648,11 +648,11 @@ transformation function for getting only input tensors.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     import nncf
     from typing import Dict
-    
-    
+
+
     def transform_fn(data_item:Dict):
         """
         Quantization transform function. Extracts and preprocess input data from dataloader item for quantization.
@@ -663,8 +663,8 @@ transformation function for getting only input tensors.
         """
         input_tensor = pose_validator.preprocess(data_item)['img'].numpy()
         return input_tensor
-    
-    
+
+
     quantization_dataset = nncf.Dataset(pose_data_loader, transform_fn)
 
 
@@ -691,7 +691,7 @@ point precision, using the ``ignored_scope`` parameter.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     ignored_scope = nncf.IgnoredScope(
         names=[
             "__module.model.22.cv3.0.0.conv/aten::_convolution/Convolution",  # in the post-processing subgraph
@@ -718,8 +718,8 @@ point precision, using the ``ignored_scope`` parameter.
         	"__module.model.0.conv/aten::_convolution/Convolution"
         ]
     )
-    
-    
+
+
     # Detection model
     quantized_pose_model = nncf.quantize(
         pose_ov_model,
@@ -735,77 +735,77 @@ point precision, using the ``ignored_scope`` parameter.
     INFO:nncf:Not adding activation input quantizer for operation: 1 __module.model.0.conv/aten::_convolution/Convolution
     2 __module.model.0.conv/aten::_convolution/Add
     3 __module.model.22.cv4.2.1.act/aten::silu_/Swish
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 25 __module.model.4.cv1.conv/aten::_convolution/Convolution
     26 __module.model.4.cv1.conv/aten::_convolution/Add
     27 __module.model.22.cv4.2.1.act/aten::silu_/Swish_7
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 43 __module.model.5.conv/aten::_convolution/Convolution
     47 __module.model.5.conv/aten::_convolution/Add
     51 __module.model.22.cv4.2.1.act/aten::silu_/Swish_13
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 54 __module.model.6.cv1.conv/aten::_convolution/Convolution
     56 __module.model.6.cv1.conv/aten::_convolution/Add
     59 __module.model.22.cv4.2.1.act/aten::silu_/Swish_14
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 99 __module.model.7.conv/aten::_convolution/Convolution
     112 __module.model.7.conv/aten::_convolution/Add
     123 __module.model.22.cv4.2.1.act/aten::silu_/Swish_20
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 111 __module.model.12.cv1.conv/aten::_convolution/Convolution
     122 __module.model.12.cv1.conv/aten::_convolution/Add
     131 __module.model.22.cv4.2.1.act/aten::silu_/Swish_27
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 46 __module.model.15.cv1.conv/aten::_convolution/Convolution
     50 __module.model.15.cv1.conv/aten::_convolution/Add
     53 __module.model.22.cv4.2.1.act/aten::silu_/Swish_31
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 74 __module.model.16.conv/aten::_convolution/Convolution
     83 __module.model.16.conv/aten::_convolution/Add
     92 __module.model.22.cv4.2.1.act/aten::silu_/Swish_39
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 75 __module.model.22.cv2.0.0.conv/aten::_convolution/Convolution
     84 __module.model.22.cv2.0.0.conv/aten::_convolution/Add
     93 __module.model.22.cv4.2.1.act/aten::silu_/Swish_35
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 103 __module.model.22.cv2.0.1.conv/aten::_convolution/Convolution
     116 __module.model.22.cv2.0.1.conv/aten::_convolution/Add
     126 __module.model.22.cv4.2.1.act/aten::silu_/Swish_36
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 76 __module.model.22.cv3.0.0.conv/aten::_convolution/Convolution
     85 __module.model.22.cv3.0.0.conv/aten::_convolution/Add
     94 __module.model.22.cv4.2.1.act/aten::silu_/Swish_37
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 135 __module.model.22.cv3.0.2/aten::_convolution/Convolution
     143 __module.model.22.cv3.0.2/aten::_convolution/Add
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 77 __module.model.22.cv4.0.0.conv/aten::_convolution/Convolution
     86 __module.model.22.cv4.0.0.conv/aten::_convolution/Add
     95 __module.model.22.cv4.2.1.act/aten::silu_/Swish_57
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 234 __module.model.22.cv3.1.1.conv/aten::_convolution/Convolution
     247 __module.model.22.cv3.1.1.conv/aten::_convolution/Add
     258 __module.model.22.cv4.2.1.act/aten::silu_/Swish_47
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 289 __module.model.21.m.0.cv1.conv/aten::_convolution/Convolution
     296 __module.model.21.m.0.cv1.conv/aten::_convolution/Add
     302 __module.model.22.cv4.2.1.act/aten::silu_/Swish_50
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 295 __module.model.21.cv2.conv/aten::_convolution/Convolution
     301 __module.model.21.cv2.conv/aten::_convolution/Add
     305 __module.model.22.cv4.2.1.act/aten::silu_/Swish_52
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 332 __module.model.22.cv2.2.1.conv/aten::_convolution/Convolution
     340 __module.model.22.cv2.2.1.conv/aten::_convolution/Add
     345 __module.model.22.cv4.2.1.act/aten::silu_/Swish_54
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 334 __module.model.22.cv4.2.1.conv/aten::_convolution/Convolution
     342 __module.model.22.cv4.2.1.conv/aten::_convolution/Add
     347 __module.model.22.cv4.2.1.act/aten::silu_/Swish_62
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 350 __module.model.22.cv3.2.2/aten::_convolution/Convolution
     354 __module.model.22.cv3.2.2/aten::_convolution/Add
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 243 __module.model.22.dfl.conv/aten::_convolution/Convolution
     INFO:nncf:Not adding activation input quantizer for operation: 263 __module.model.22/aten::sub/Subtract
     INFO:nncf:Not adding activation input quantizer for operation: 264 __module.model.22/aten::add/Add_6
@@ -818,17 +818,17 @@ point precision, using the ``ignored_scope`` parameter.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -845,24 +845,24 @@ point precision, using the ``ignored_scope`` parameter.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     print(f"Quantized keypoint detection model will be saved to {int8_model_pose_path}")
     ov.save_model(quantized_pose_model, str(int8_model_pose_path))
 
@@ -887,33 +887,33 @@ on the image.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     device
 
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     ov_config = {}
     if device.value != "CPU":
         quantized_pose_model.reshape({0: [1, 3, 640, 640]})
     if "GPU" in device.value or ("AUTO" in device.value and "GPU" in core.available_devices):
         ov_config = {"GPU_DISABLE_WINOGRAD_CONVOLUTION": "YES"}
     quantized_pose_compiled_model = core.compile_model(quantized_pose_model, device.value, ov_config)
-    
+
     def infer(*args):
         result = quantized_pose_compiled_model(args)
         return torch.from_numpy(result[0])
-    
+
     pose_model.predictor.inference = infer
-    
+
     res = pose_model(IMAGE_PATH)
     display(Image.fromarray(res[0].plot()[:, :, ::-1]))
 
 
 .. parsed-literal::
 
-    
+
     image 1/1 /home/maleksandr/test_notebooks/update_ultralytics/openvino_notebooks/notebooks/yolov8-optimization/data/intel_rnb.jpg: 640x640 2 persons, 20.0ms
     Speed: 3.5ms preprocess, 20.0ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)
 
@@ -930,7 +930,7 @@ Compare the Original and Quantized Models
 Compare performance of the Original and Quantized Models
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
- Finally, use the OpenVINO
+Finally, use the OpenVINO
 `Benchmark
 Tool <https://docs.openvino.ai/2024/learn-openvino/openvino-samples/benchmark-tool.html>`__
 to measure the inference performance of the ``FP32`` and ``INT8``
@@ -948,7 +948,7 @@ models.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     device
 
 .. code:: ipython3
@@ -966,12 +966,12 @@ models.
     [ WARNING ] Default duration 120 seconds is used for unknown device AUTO
     [ INFO ] OpenVINO:
     [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
-    [ INFO ] 
+    [ INFO ]
     [ INFO ] Device info:
     [ INFO ] AUTO
     [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
-    [ INFO ] 
-    [ INFO ] 
+    [ INFO ]
+    [ INFO ]
     [Step 3/11] Setting device configuration
     [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.THROUGHPUT.
     [Step 4/11] Reading model files
@@ -1024,7 +1024,7 @@ models.
     [ INFO ]   LOADED_FROM_CACHE: False
     [Step 9/11] Creating infer requests and preparing input tensors
     [ WARNING ] No input files were given for input 'x'!. This input will be filled with random values!
-    [ INFO ] Fill input 'x' with random values 
+    [ INFO ] Fill input 'x' with random values
     [Step 10/11] Measuring performance (Start inference asynchronously, 12 inference requests, limits: 120000 ms duration)
     [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
     [ INFO ] First inference took 42.40 ms
@@ -1054,12 +1054,12 @@ models.
     [Step 2/11] Loading OpenVINO Runtime
     [ INFO ] OpenVINO:
     [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
-    [ INFO ] 
+    [ INFO ]
     [ INFO ] Device info:
     [ INFO ] AUTO
     [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
-    [ INFO ] 
-    [ INFO ] 
+    [ INFO ]
+    [ INFO ]
     [Step 3/11] Setting device configuration
     [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.THROUGHPUT.
     [Step 4/11] Reading model files
@@ -1112,7 +1112,7 @@ models.
     [ INFO ]   LOADED_FROM_CACHE: False
     [Step 9/11] Creating infer requests and preparing input tensors
     [ WARNING ] No input files were given for input 'x'!. This input will be filled with random values!
-    [ INFO ] Fill input 'x' with random values 
+    [ INFO ] Fill input 'x' with random values
     [Step 10/11] Measuring performance (Start inference asynchronously, 12 inference requests, limits: 15000 ms duration)
     [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
     [ INFO ] First inference took 34.70 ms
@@ -1141,7 +1141,7 @@ accuracy on a dataset.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     int8_pose_stats = test(quantized_pose_model, core, pose_data_loader, pose_validator, num_samples=NUM_TEST_SAMPLES)
 
 
@@ -1154,10 +1154,10 @@ accuracy on a dataset.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     print("FP32 model accuracy")
     print_stats(fp_pose_stats, pose_validator.seen, pose_validator.nt_per_class.sum())
-    
+
     print("INT8 model accuracy")
     print_stats(int8_pose_stats, pose_validator.seen, pose_validator.nt_per_class.sum())
 
@@ -1221,8 +1221,8 @@ The following code runs model inference on a video:
     import time
     from IPython import display
     import cv2
-    
-    
+
+
     def run_keypoint_detection(
         source=0,
         flip=False,
@@ -1232,20 +1232,20 @@ The following code runs model inference on a video:
         device=device.value,
     ):
         player = None
-    
+
         ov_config = {}
         if device != "CPU":
             model.reshape({0: [1, 3, 640, 640]})
         if "GPU" in device or ("AUTO" in device and "GPU" in core.available_devices):
             ov_config = {"GPU_DISABLE_WINOGRAD_CONVOLUTION": "YES"}
         compiled_model = core.compile_model(model, device, ov_config)
-    
+
         def infer(*args):
             result = compiled_model(args)
             return torch.from_numpy(result[0])
-    
+
         pose_model.predictor.inference = infer
-    
+
         try:
             # Create a video player to play with target fps.
             player = VideoPlayer(source=source, flip=flip, fps=30, skip_first_frames=skip_first_frames)
@@ -1254,7 +1254,7 @@ The following code runs model inference on a video:
             if use_popup:
                 title = "Press ESC to Exit"
                 cv2.namedWindow(winname=title, flags=cv2.WINDOW_GUI_NORMAL | cv2.WINDOW_AUTOSIZE)
-    
+
             processing_times = collections.deque()
             while True:
                 # Grab the frame.
@@ -1274,18 +1274,18 @@ The following code runs model inference on a video:
                     )
                 # Get the results
                 input_image = np.array(frame)
-    
+
                 start_time = time.time()
-    
+
                 detections = pose_model(input_image)
                 stop_time = time.time()
                 frame = detections[0].plot()
-    
+
                 processing_times.append(stop_time - start_time)
                 # Use processing times from last 200 frames.
                 if len(processing_times) > 200:
                     processing_times.popleft()
-    
+
                 _, f_width = frame.shape[:2]
                 # Mean processing time [ms].
                 processing_time = np.mean(processing_times) * 1000
diff --git a/docs/notebooks/yolov8-obb-with-output.rst b/docs/notebooks/yolov8-obb-with-output.rst
index 0e60ac807efca5..c0435e0de09f6a 100644
--- a/docs/notebooks/yolov8-obb-with-output.rst
+++ b/docs/notebooks/yolov8-obb-with-output.rst
@@ -13,8 +13,8 @@ labels and confidence scores for each box. Object detection is a good
 choice when you need to identify objects of interest in a scene, but
 don’t need to know exactly where the object is or its exact shape.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Get PyTorch model <#get-pytorch-model>`__
@@ -171,6 +171,7 @@ instance.
 
 
 
+
 Run inference
 ~~~~~~~~~~~~~
 
@@ -456,17 +457,17 @@ Create a quantized model from the pre-trained converted OpenVINO model.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -477,17 +478,17 @@ Create a quantized model from the pre-trained converted OpenVINO model.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
diff --git a/docs/notebooks/yolov8-object-detection-with-output.rst b/docs/notebooks/yolov8-object-detection-with-output.rst
index dbaebcfe99cbac..81590e2353e35b 100644
--- a/docs/notebooks/yolov8-object-detection-with-output.rst
+++ b/docs/notebooks/yolov8-object-detection-with-output.rst
@@ -10,16 +10,21 @@ This tutorial demonstrates step-by-step instructions on how to run and
 optimize PyTorch YOLOv8 with OpenVINO. We consider the steps required
 for object detection scenario.
 
-The tutorial consists of the following steps: - Prepare the PyTorch
-model. - Download and prepare a dataset. - Validate the original model.
-- Convert the PyTorch model to OpenVINO IR. - Validate the converted
-model. - Prepare and run optimization pipeline. - Compare performance of
-the FP32 and quantized models. - Compare accuracy of the FP32 and
-quantized models. - Other optimization possibilities with OpenVINO api -
-Live demo
+The tutorial consists of the following steps:
+
+- Prepare the PyTorch model.
+- Download and prepare a dataset.
+- Validate the original model.
+- Convert the PyTorch model to OpenVINO IR.
+- Validate the converted model.
+- Prepare and run optimization pipeline.
+- Compare performance of the FP32 and quantized models.
+- Compare accuracy of the FP32 and quantized models.
+- Other optimization possibilities with OpenVINO api
+- Live demo
+
+**Table of contents:**
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
 
 -  `Get PyTorch model <#get-pytorch-model>`__
 
@@ -113,16 +118,16 @@ Import required utility functions. The lower cell will download the
 .. code:: ipython3
 
     from pathlib import Path
-    
+
     # Fetch `notebook_utils` module
     import requests
-    
+
     r = requests.get(
         url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
-    
+
     open("notebook_utils.py", "w").write(r.text)
-    
+
     from notebook_utils import download_file, VideoPlayer
 
 .. code:: ipython3
@@ -163,12 +168,12 @@ Let us consider the examples:
 
     from PIL import Image
     from ultralytics import YOLO
-    
+
     DET_MODEL_NAME = "yolov8n"
-    
+
     det_model = YOLO(models_dir / f"{DET_MODEL_NAME}.pt")
     label_map = det_model.model.names
-    
+
     res = det_model(IMAGE_PATH)
     Image.fromarray(res[0].plot()[:, :, ::-1])
 
@@ -185,7 +190,7 @@ Let us consider the examples:
 
 .. parsed-literal::
 
-    
+
     image 1/1 /home/maleksandr/test_notebooks/update_ultralytics/openvino_notebooks/notebooks/yolov8-optimization/data/coco_bike.jpg: 480x640 2 bicycles, 2 cars, 1 dog, 43.2ms
     Speed: 1.9ms preprocess, 43.2ms inference, 0.9ms postprocess per image at shape (1, 3, 480, 640)
 
@@ -217,16 +222,16 @@ preserve dynamic shapes in the model.
 .. parsed-literal::
 
     Ultralytics YOLOv8.1.42 🚀 Python-3.10.12 torch-2.2.2+cpu CPU (Intel Core(TM) i9-10980XE 3.00GHz)
-    
+
     PyTorch: starting from 'models/yolov8n.pt' with input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 84, 8400) (6.2 MB)
-    
+
     OpenVINO: starting export with openvino 2024.0.0-14509-34caeefd078-releases/2024/0...
     OpenVINO: export success ✅ 1.8s, saved as 'models/yolov8n_openvino_model/' (6.4 MB)
-    
+
     Export complete (3.0s)
     Results saved to /home/maleksandr/test_notebooks/update_ultralytics/openvino_notebooks/notebooks/yolov8-optimization/models
-    Predict:         yolo predict task=detect model=models/yolov8n_openvino_model imgsz=640 half 
-    Validate:        yolo val task=detect model=models/yolov8n_openvino_model imgsz=640 data=coco.yaml half 
+    Predict:         yolo predict task=detect model=models/yolov8n_openvino_model imgsz=640 half
+    Validate:        yolo val task=detect model=models/yolov8n_openvino_model imgsz=640 data=coco.yaml half
     Visualize:       https://netron.app
 
 
@@ -250,16 +255,16 @@ Select device from dropdown list for running inference using OpenVINO
 
     import ipywidgets as widgets
     import openvino as ov
-    
+
     core = ov.Core()
-    
+
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
         value="AUTO",
         description="Device:",
         disabled=False,
     )
-    
+
     device
 
 
@@ -282,34 +287,34 @@ ready to check model prediction for object detection.
 .. code:: ipython3
 
     import torch
-    
+
     core = ov.Core()
-    
+
     det_ov_model = core.read_model(det_model_path)
-    
+
     ov_config = {}
     if device.value != "CPU":
         det_ov_model.reshape({0: [1, 3, 640, 640]})
     if "GPU" in device.value or ("AUTO" in device.value and "GPU" in core.available_devices):
         ov_config = {"GPU_DISABLE_WINOGRAD_CONVOLUTION": "YES"}
     det_compiled_model = core.compile_model(det_ov_model, device.value, ov_config)
-    
-    
+
+
     def infer(*args):
         result = det_compiled_model(args)
         return torch.from_numpy(result[0])
-    
-    
+
+
     det_model.predictor.inference = infer
     det_model.predictor.model.pt = False
-    
+
     res = det_model(IMAGE_PATH)
     Image.fromarray(res[0].plot()[:, :, ::-1])
 
 
 .. parsed-literal::
 
-    
+
     image 1/1 /home/maleksandr/test_notebooks/update_ultralytics/openvino_notebooks/notebooks/yolov8-optimization/data/coco_bike.jpg: 640x640 2 bicycles, 2 cars, 1 dog, 27.5ms
     Speed: 3.2ms preprocess, 27.5ms inference, 1.2ms postprocess per image at shape (1, 3, 640, 640)
 
@@ -347,24 +352,24 @@ evaluation function.
 .. code:: ipython3
 
     from zipfile import ZipFile
-    
+
     from ultralytics.data.utils import DATASETS_DIR
-    
-    
+
+
     DATA_URL = "http://images.cocodataset.org/zips/val2017.zip"
     LABELS_URL = "https://github.com/ultralytics/yolov5/releases/download/v1.0/coco2017labels-segments.zip"
     CFG_URL = "https://raw.githubusercontent.com/ultralytics/ultralytics/v8.1.0/ultralytics/cfg/datasets/coco.yaml"
-    
+
     OUT_DIR = DATASETS_DIR
-    
+
     DATA_PATH = OUT_DIR / "val2017.zip"
     LABELS_PATH = OUT_DIR / "coco2017labels-segments.zip"
     CFG_PATH = OUT_DIR / "coco.yaml"
-    
+
     download_file(DATA_URL, DATA_PATH.name, DATA_PATH.parent)
     download_file(LABELS_URL, LABELS_PATH.name, LABELS_PATH.parent)
     download_file(CFG_URL, CFG_PATH.name, CFG_PATH.parent)
-    
+
     if not (OUT_DIR / "coco/labels").exists():
         with ZipFile(LABELS_PATH, "r") as zip_ref:
             zip_ref.extractall(OUT_DIR)
@@ -379,11 +384,11 @@ Define validation function
 .. code:: ipython3
 
     import numpy as np
-    
+
     from tqdm.notebook import tqdm
     from ultralytics.utils.metrics import ConfusionMatrix
-    
-    
+
+
     def test(
         model: ov.Model,
         core: ov.Core,
@@ -418,8 +423,8 @@ Define validation function
             validator.update_metrics(preds, batch)
         stats = validator.get_stats()
         return stats
-    
-    
+
+
     def print_stats(stats: np.ndarray, total_images: int, total_objects: int):
         """
         Helper function for printing accuracy statistic
@@ -494,7 +499,7 @@ validator class instance.
     from ultralytics.cfg import get_cfg
     from ultralytics.data.converter import coco80_to_coco91_class
     from ultralytics.data.utils import check_det_dataset
-    
+
     args = get_cfg(cfg=DEFAULT_CFG)
     args.data = str(CFG_PATH)
 
@@ -597,15 +602,15 @@ improve model inference speed.
 .. code:: ipython3
 
     import ipywidgets as widgets
-    
+
     int8_model_det_path = models_dir / f"{DET_MODEL_NAME}_openvino_int8_model/{DET_MODEL_NAME}.xml"
-    
+
     to_quantize = widgets.Checkbox(
         value=True,
         description="Quantization",
         disabled=False,
     )
-    
+
     to_quantize
 
 
@@ -627,7 +632,7 @@ Let’s load ``skip magic`` extension to skip quantization if
         url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/skip_kernel_extension.py",
     )
     open("skip_kernel_extension.py", "w").write(r.text)
-    
+
     %load_ext skip_kernel_extension
 
 Reuse validation dataloader in accuracy testing for quantization. For
@@ -637,11 +642,11 @@ transformation function for getting only input tensors.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     import nncf
     from typing import Dict
-    
-    
+
+
     def transform_fn(data_item:Dict):
         """
         Quantization transform function. Extracts and preprocess input data from dataloader item for quantization.
@@ -652,8 +657,8 @@ transformation function for getting only input tensors.
         """
         input_tensor = det_validator.preprocess(data_item)['img'].numpy()
         return input_tensor
-    
-    
+
+
     quantization_dataset = nncf.Dataset(det_data_loader, transform_fn)
 
 
@@ -680,8 +685,8 @@ point precision, using the ``ignored_scope`` parameter.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
-    
+
+
     ignored_scope = nncf.IgnoredScope(
         names=[
             "__module.model.22.cv3.0.0.conv/aten::_convolution/Convolution",  # in the post-processing subgraph
@@ -706,8 +711,8 @@ point precision, using the ``ignored_scope`` parameter.
         	"__module.model.0.conv/aten::_convolution/Convolution"
         ]
     )
-    
-    
+
+
     # Detection model
     quantized_det_model = nncf.quantize(
         det_ov_model,
@@ -723,69 +728,69 @@ point precision, using the ``ignored_scope`` parameter.
     INFO:nncf:Not adding activation input quantizer for operation: 1 __module.model.0.conv/aten::_convolution/Convolution
     2 __module.model.0.conv/aten::_convolution/Add
     3 __module.model.22.cv3.2.1.act/aten::silu_/Swish
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 25 __module.model.4.cv1.conv/aten::_convolution/Convolution
     26 __module.model.4.cv1.conv/aten::_convolution/Add
     27 __module.model.22.cv3.2.1.act/aten::silu_/Swish_7
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 43 __module.model.5.conv/aten::_convolution/Convolution
     47 __module.model.5.conv/aten::_convolution/Add
     51 __module.model.22.cv3.2.1.act/aten::silu_/Swish_13
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 54 __module.model.6.cv1.conv/aten::_convolution/Convolution
     56 __module.model.6.cv1.conv/aten::_convolution/Add
     59 __module.model.22.cv3.2.1.act/aten::silu_/Swish_14
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 92 __module.model.7.conv/aten::_convolution/Convolution
     99 __module.model.7.conv/aten::_convolution/Add
     106 __module.model.22.cv3.2.1.act/aten::silu_/Swish_20
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 98 __module.model.12.cv1.conv/aten::_convolution/Convolution
     105 __module.model.12.cv1.conv/aten::_convolution/Add
     111 __module.model.22.cv3.2.1.act/aten::silu_/Swish_27
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 46 __module.model.15.cv1.conv/aten::_convolution/Convolution
     50 __module.model.15.cv1.conv/aten::_convolution/Add
     53 __module.model.22.cv3.2.1.act/aten::silu_/Swish_31
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 74 __module.model.16.conv/aten::_convolution/Convolution
     81 __module.model.16.conv/aten::_convolution/Add
     88 __module.model.22.cv3.2.1.act/aten::silu_/Swish_39
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 75 __module.model.22.cv2.0.0.conv/aten::_convolution/Convolution
     82 __module.model.22.cv2.0.0.conv/aten::_convolution/Add
     89 __module.model.22.cv3.2.1.act/aten::silu_/Swish_35
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 76 __module.model.22.cv3.0.0.conv/aten::_convolution/Convolution
     83 __module.model.22.cv3.0.0.conv/aten::_convolution/Add
     90 __module.model.22.cv3.2.1.act/aten::silu_/Swish_37
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 96 __module.model.22.cv2.0.1.conv/aten::_convolution/Convolution
     103 __module.model.22.cv2.0.1.conv/aten::_convolution/Add
     109 __module.model.22.cv3.2.1.act/aten::silu_/Swish_36
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 115 __module.model.22.cv3.0.2/aten::_convolution/Convolution
     120 __module.model.22.cv3.0.2/aten::_convolution/Add
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 204 __module.model.22.cv3.1.1.conv/aten::_convolution/Convolution
     216 __module.model.22.cv3.1.1.conv/aten::_convolution/Add
     226 __module.model.22.cv3.2.1.act/aten::silu_/Swish_47
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 254 __module.model.21.m.0.cv1.conv/aten::_convolution/Convolution
     261 __module.model.21.m.0.cv1.conv/aten::_convolution/Add
     266 __module.model.22.cv3.2.1.act/aten::silu_/Swish_50
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 260 __module.model.21.cv2.conv/aten::_convolution/Convolution
     265 __module.model.21.cv2.conv/aten::_convolution/Add
     269 __module.model.22.cv3.2.1.act/aten::silu_/Swish_52
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 293 __module.model.22.cv2.2.1.conv/aten::_convolution/Convolution
     300 __module.model.22.cv2.2.1.conv/aten::_convolution/Add
     304 __module.model.22.cv3.2.1.act/aten::silu_/Swish_54
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 308 __module.model.22.cv3.2.2/aten::_convolution/Convolution
     311 __module.model.22.cv3.2.2/aten::_convolution/Add
-    
+
     INFO:nncf:Not adding activation input quantizer for operation: 212 __module.model.22.dfl.conv/aten::_convolution/Convolution
     INFO:nncf:Not adding activation input quantizer for operation: 230 __module.model.22/aten::sub/Subtract
     INFO:nncf:Not adding activation input quantizer for operation: 231 __module.model.22/aten::add/Add_6
@@ -798,17 +803,17 @@ point precision, using the ``ignored_scope`` parameter.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -825,24 +830,24 @@ point precision, using the ``ignored_scope`` parameter.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     print(f"Quantized detection model will be saved to {int8_model_det_path}")
     ov.save_model(quantized_det_model, str(int8_model_det_path))
 
@@ -867,34 +872,34 @@ on the image.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     device
 
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     ov_config = {}
     if device.value != "CPU":
         quantized_det_model.reshape({0: [1, 3, 640, 640]})
     if "GPU" in device.value or ("AUTO" in device.value and "GPU" in core.available_devices):
         ov_config = {"GPU_DISABLE_WINOGRAD_CONVOLUTION": "YES"}
     quantized_det_compiled_model = core.compile_model(quantized_det_model, device.value, ov_config)
-    
-    
+
+
     def infer(*args):
         result = quantized_det_compiled_model(args)
         return torch.from_numpy(result[0])
-    
+
     det_model.predictor.inference = infer
-    
+
     res = det_model(IMAGE_PATH)
     display(Image.fromarray(res[0].plot()[:, :, ::-1]))
 
 
 .. parsed-literal::
 
-    
+
     image 1/1 /home/maleksandr/test_notebooks/update_ultralytics/openvino_notebooks/notebooks/yolov8-optimization/data/coco_bike.jpg: 640x640 2 bicycles, 2 cars, 1 dog, 18.4ms
     Speed: 2.1ms preprocess, 18.4ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)
 
@@ -930,7 +935,7 @@ models.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     device
 
 .. code:: ipython3
@@ -948,12 +953,12 @@ models.
     [ WARNING ] Default duration 120 seconds is used for unknown device AUTO
     [ INFO ] OpenVINO:
     [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
-    [ INFO ] 
+    [ INFO ]
     [ INFO ] Device info:
     [ INFO ] AUTO
     [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
-    [ INFO ] 
-    [ INFO ] 
+    [ INFO ]
+    [ INFO ]
     [Step 3/11] Setting device configuration
     [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.THROUGHPUT.
     [Step 4/11] Reading model files
@@ -1006,7 +1011,7 @@ models.
     [ INFO ]   LOADED_FROM_CACHE: False
     [Step 9/11] Creating infer requests and preparing input tensors
     [ WARNING ] No input files were given for input 'x'!. This input will be filled with random values!
-    [ INFO ] Fill input 'x' with random values 
+    [ INFO ] Fill input 'x' with random values
     [Step 10/11] Measuring performance (Start inference asynchronously, 12 inference requests, limits: 120000 ms duration)
     [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
     [ INFO ] First inference took 41.00 ms
@@ -1036,12 +1041,12 @@ models.
     [Step 2/11] Loading OpenVINO Runtime
     [ INFO ] OpenVINO:
     [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
-    [ INFO ] 
+    [ INFO ]
     [ INFO ] Device info:
     [ INFO ] AUTO
     [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
-    [ INFO ] 
-    [ INFO ] 
+    [ INFO ]
+    [ INFO ]
     [Step 3/11] Setting device configuration
     [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.THROUGHPUT.
     [Step 4/11] Reading model files
@@ -1094,7 +1099,7 @@ models.
     [ INFO ]   LOADED_FROM_CACHE: False
     [Step 9/11] Creating infer requests and preparing input tensors
     [ WARNING ] No input files were given for input 'x'!. This input will be filled with random values!
-    [ INFO ] Fill input 'x' with random values 
+    [ INFO ] Fill input 'x' with random values
     [Step 10/11] Measuring performance (Start inference asynchronously, 12 inference requests, limits: 15000 ms duration)
     [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
     [ INFO ] First inference took 35.17 ms
@@ -1123,7 +1128,7 @@ accuracy on a dataset.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     int8_det_stats = test(quantized_det_model, core, det_data_loader, det_validator, num_samples=NUM_TEST_SAMPLES)
 
 
@@ -1136,10 +1141,10 @@ accuracy on a dataset.
 .. code:: ipython3
 
     %%skip not $to_quantize.value
-    
+
     print("FP32 model accuracy")
     print_stats(fp_det_stats, det_validator.seen, det_validator.nt_per_class.sum())
-    
+
     print("INT8 model accuracy")
     print_stats(int8_det_stats, det_validator.seen, det_validator.nt_per_class.sum())
 
@@ -1164,14 +1169,14 @@ meets passing criteria.
 Next steps
 ----------
 
- This section contains
+This section contains
 suggestions on how to additionally improve the performance of your
 application using OpenVINO.
 
 Async inference pipeline
 ~~~~~~~~~~~~~~~~~~~~~~~~
 
- The key advantage of the Async
+The key advantage of the Async
 API is that when a device is busy with inference, the application can
 perform other tasks in parallel (for example, populating inputs or
 scheduling other requests) rather than wait for the current inference to
@@ -1212,7 +1217,7 @@ preprocessing and postprocessing steps for a model.
 .. code:: ipython3
 
     from openvino.preprocess import PrePostProcessor
-    
+
     ppp = PrePostProcessor(quantized_det_model)
 
 Define input data format
@@ -1252,7 +1257,7 @@ preprocessing steps:
 .. code:: ipython3
 
     ppp.input(0).preprocess().convert_element_type(ov.Type.f32).convert_layout(ov.Layout("NCHW")).scale([255.0, 255.0, 255.0])
-    
+
     print(ppp)
 
 
@@ -1265,7 +1270,7 @@ preprocessing steps:
           convert type (f32): ([1,640,640,3], [N,H,W,C], u8) -> ([1,640,640,3], [N,H,W,C], f32)
           convert layout [N,C,H,W]: ([1,640,640,3], [N,H,W,C], f32) -> ([1,3,640,640], [N,C,H,W], f32)
           scale (255,255,255): ([1,3,640,640], [N,C,H,W], f32) -> ([1,3,640,640], [N,C,H,W], f32)
-    
+
 
 
 Integrating Steps into a Model
@@ -1294,8 +1299,8 @@ device.
     import cv2
     import numpy as np
     from ultralytics.utils.plotting import colors
-    
-    
+
+
     def plot_one_box(
         box: np.ndarray,
         img: np.ndarray,
@@ -1332,10 +1337,10 @@ device.
                 thickness=tf,
                 lineType=cv2.LINE_AA,
             )
-    
+
         return img
-    
-    
+
+
     def draw_results(results: Dict, source_image: np.ndarray, label_map: Dict):
         """
         Helper function for drawing bounding boxes on image
@@ -1377,8 +1382,8 @@ classes.
     from ultralytics.utils import ops
     import torch
     import numpy as np
-    
-    
+
+
     def letterbox(
         img: np.ndarray,
         new_shape: Tuple[int, int] = (640, 640),
@@ -1391,7 +1396,7 @@ classes.
         """
         Resize image and padding for detection. Takes image as input,
         resizes image to fit into new shape with saving original aspect ratio and pads it to meet stride-multiple constraints
-    
+
         Parameters:
           img (np.ndarray): image for preprocessing
           new_shape (Tuple(int, int)): image size after preprocessing in format [height, width]
@@ -1404,19 +1409,19 @@ classes.
           img (np.ndarray): image after preprocessing
           ratio (Tuple(float, float)): hight and width scaling ratio
           padding_size (Tuple(int, int)): height and width padding size
-    
-    
+
+
         """
         # Resize and pad image while meeting stride-multiple constraints
         shape = img.shape[:2]  # current shape [height, width]
         if isinstance(new_shape, int):
             new_shape = (new_shape, new_shape)
-    
+
         # Scale ratio (new / old)
         r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
         if not scaleup:  # only scale down, do not scale up (for better test mAP)
             r = min(r, 1.0)
-    
+
         # Compute padding
         ratio = r, r  # width, height ratios
         new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
@@ -1427,18 +1432,18 @@ classes.
             dw, dh = 0.0, 0.0
             new_unpad = (new_shape[1], new_shape[0])
             ratio = new_shape[1] / shape[1], new_shape[0] / shape[0]  # width, height ratios
-    
+
         dw /= 2  # divide padding into 2 sides
         dh /= 2
-    
+
         if shape[::-1] != new_unpad:  # resize
             img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
         top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
         left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
         img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
         return img, ratio, (dw, dh)
-    
-    
+
+
     def postprocess(
         pred_boxes: np.ndarray,
         input_hw: Tuple[int, int],
@@ -1463,7 +1468,7 @@ classes.
         """
         nms_kwargs = {"agnostic": agnosting_nms, "max_det": max_detections}
         preds = ops.non_max_suppression(torch.from_numpy(pred_boxes), min_conf_threshold, nms_iou_threshold, nc=80, **nms_kwargs)
-    
+
         results = []
         for i, pred in enumerate(preds):
             shape = orig_img[i].shape if isinstance(orig_img, list) else orig_img.shape
@@ -1472,7 +1477,7 @@ classes.
                 continue
             pred[:, :4] = ops.scale_boxes(input_hw, pred[:, :4], shape).round()
             results.append({"det": pred})
-    
+
         return results
 
 Now, we can skip these preprocessing steps in detect function:
@@ -1495,13 +1500,13 @@ Now, we can skip these preprocessing steps in detect function:
         result = model(input_tensor)[output_layer]
         detections = postprocess(result, input_hw, image)
         return detections
-    
-    
+
+
     compiled_model = core.compile_model(quantized_model_with_preprocess, device.value)
     input_image = np.array(Image.open(IMAGE_PATH))
     detections = detect_without_preprocess(input_image, compiled_model)[0]
     image_with_boxes = draw_results(detections, input_image, label_map)
-    
+
     Image.fromarray(image_with_boxes)
 
 
@@ -1523,8 +1528,8 @@ The following code runs model inference on a video:
     import collections
     import time
     from IPython import display
-    
-    
+
+
     # Main processing function to run object detection.
     def run_object_detection(
         source=0,
@@ -1541,13 +1546,13 @@ The following code runs model inference on a video:
         if "GPU" in device or ("AUTO" in device and "GPU" in core.available_devices):
             ov_config = {"GPU_DISABLE_WINOGRAD_CONVOLUTION": "YES"}
         compiled_model = core.compile_model(model, device, ov_config)
-    
+
         def infer(*args):
             result = compiled_model(args)
             return torch.from_numpy(result[0])
-    
+
         det_model.predictor.inference = infer
-    
+
         try:
             # Create a video player to play with target fps.
             player = VideoPlayer(source=source, flip=flip, fps=30, skip_first_frames=skip_first_frames)
@@ -1556,7 +1561,7 @@ The following code runs model inference on a video:
             if use_popup:
                 title = "Press ESC to Exit"
                 cv2.namedWindow(winname=title, flags=cv2.WINDOW_GUI_NORMAL | cv2.WINDOW_AUTOSIZE)
-    
+
             processing_times = collections.deque()
             while True:
                 # Grab the frame.
@@ -1576,17 +1581,17 @@ The following code runs model inference on a video:
                     )
                 # Get the results.
                 input_image = np.array(frame)
-    
+
                 start_time = time.time()
                 detections = det_model(input_image)
                 stop_time = time.time()
                 frame = detections[0].plot()
-    
+
                 processing_times.append(stop_time - start_time)
                 # Use processing times from last 200 frames.
                 if len(processing_times) > 200:
                     processing_times.popleft()
-    
+
                 _, f_width = frame.shape[:2]
                 # Mean processing time [ms].
                 processing_time = np.mean(processing_times) * 1000
@@ -1653,7 +1658,7 @@ Run the object detection:
 .. code:: ipython3
 
     WEBCAM_INFERENCE = False
-    
+
     if WEBCAM_INFERENCE:
         VIDEO_SOURCE = 0  # Webcam
     else:
diff --git a/docs/notebooks/yolov8-quantization-with-accuracy-control-with-output.rst b/docs/notebooks/yolov8-quantization-with-accuracy-control-with-output.rst
index 0606f49713e854..82a4c35be70cf6 100644
--- a/docs/notebooks/yolov8-quantization-with-accuracy-control-with-output.rst
+++ b/docs/notebooks/yolov8-quantization-with-accuracy-control-with-output.rst
@@ -38,8 +38,8 @@ and has the following differences:
 The steps for the quantization with accuracy control are described
 below.
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Prerequisites <#prerequisites>`__
 -  `Get Pytorch model and OpenVINO IR
@@ -95,7 +95,7 @@ we do not need to do these steps manually.
 
     import os
     from pathlib import Path
-    
+
     from ultralytics import YOLO
     from ultralytics.cfg import get_cfg
     from ultralytics.data.utils import check_det_dataset
@@ -103,11 +103,11 @@ we do not need to do these steps manually.
     from ultralytics.utils import DEFAULT_CFG
     from ultralytics.utils import ops
     from ultralytics.utils.metrics import ConfusionMatrix
-    
+
     ROOT = os.path.abspath("")
-    
+
     MODEL_NAME = "yolov8n-seg"
-    
+
     model = YOLO(f"{ROOT}/{MODEL_NAME}.pt")
     args = get_cfg(cfg=DEFAULT_CFG)
     args.data = "coco128-seg.yaml"
@@ -116,32 +116,32 @@ we do not need to do these steps manually.
 
     # Fetch the notebook utils script from the openvino_notebooks repo
     import requests
-    
+
     r = requests.get(
         url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
     )
-    
+
     open("notebook_utils.py", "w").write(r.text)
-    
+
     from notebook_utils import download_file
 
 .. code:: ipython3
 
     from zipfile import ZipFile
-    
+
     from ultralytics.data.utils import DATASETS_DIR
-    
+
     DATA_URL = "https://www.ultralytics.com/assets/coco128-seg.zip"
     CFG_URL = "https://raw.githubusercontent.com/ultralytics/ultralytics/8ebe94d1e928687feaa1fee6d5668987df5e43be/ultralytics/datasets/coco128-seg.yaml"  # last compatible format with ultralytics 8.0.43
-    
+
     OUT_DIR = DATASETS_DIR
-    
+
     DATA_PATH = OUT_DIR / "coco128-seg.zip"
     CFG_PATH = OUT_DIR / "coco128-seg.yaml"
-    
+
     download_file(DATA_URL, DATA_PATH.name, DATA_PATH.parent)
     download_file(CFG_URL, CFG_PATH.name, CFG_PATH.parent)
-    
+
     if not (OUT_DIR / "coco128/labels").exists():
         with ZipFile(DATA_PATH, "r") as zip_ref:
             zip_ref.extractall(OUT_DIR)
@@ -163,12 +163,12 @@ Load model.
 .. code:: ipython3
 
     import openvino as ov
-    
-    
+
+
     model_path = Path(f"{ROOT}/{MODEL_NAME}_openvino_model/{MODEL_NAME}.xml")
     if not model_path.exists():
         model.export(format="openvino", dynamic=True, half=False)
-    
+
     ov_model = ov.Core().read_model(model_path)
 
 Define validator and data loader
@@ -189,13 +189,13 @@ validator class instance.
 .. code:: ipython3
 
     from ultralytics.data.converter import coco80_to_coco91_class
-    
-    
+
+
     validator = model.task_map[model.task]["validator"](args=args)
     validator.data = check_det_dataset(args.data)
     validator.stride = 3
     data_loader = validator.get_dataloader(OUT_DIR / "coco128-seg", 1)
-    
+
     validator.is_coco = True
     validator.class_map = coco80_to_coco91_class()
     validator.names = model.model.names
@@ -216,15 +216,15 @@ We can use one dataset as calibration and validation datasets. Name it
 .. code:: ipython3
 
     from typing import Dict
-    
+
     import nncf
-    
-    
+
+
     def transform_fn(data_item: Dict):
         input_tensor = validator.preprocess(data_item)["img"].numpy()
         return input_tensor
-    
-    
+
+
     quantization_dataset = nncf.Dataset(data_loader, transform_fn)
 
 
@@ -241,11 +241,11 @@ Prepare validation function
 .. code:: ipython3
 
     from functools import partial
-    
+
     import torch
     from nncf.quantization.advanced_parameters import AdvancedAccuracyRestorerParameters
-    
-    
+
+
     def validation_ac(
         compiled_model: ov.CompiledModel,
         validation_loader: torch.utils.data.DataLoader,
@@ -259,7 +259,7 @@ Prepare validation function
         validator.batch_i = 1
         validator.confusion_matrix = ConfusionMatrix(nc=validator.nc)
         num_outputs = len(compiled_model.outputs)
-    
+
         counter = 0
         for batch_i, batch in enumerate(validation_loader):
             if num_samples is not None and batch_i == num_samples:
@@ -283,10 +283,10 @@ Prepare validation function
             stats_metrics = stats["metrics/mAP50-95(M)"]
         if log:
             print(f"Validate: dataset length = {counter}, metric value = {stats_metrics:.3f}")
-    
+
         return stats_metrics
-    
-    
+
+
     validation_fn = partial(validation_ac, validator=validator, log=False)
 
 Run quantization with accuracy control
@@ -295,17 +295,20 @@ Run quantization with accuracy control
 
 
 You should provide the calibration dataset and the validation dataset.
-It can be the same dataset. - parameter ``max_drop`` defines the
-accuracy drop threshold. The quantization process stops when the
-degradation of accuracy metric on the validation dataset is less than
-the ``max_drop``. The default value is 0.01. NNCF will stop the
-quantization and report an error if the ``max_drop`` value can’t be
-reached. - ``drop_type`` defines how the accuracy drop will be
-calculated: ABSOLUTE (used by default) or RELATIVE. -
-``ranking_subset_size`` - size of a subset that is used to rank layers
-by their contribution to the accuracy drop. Default value is 300, and
-the more samples it has the better ranking, potentially. Here we use the
-value 25 to speed up the execution.
+It can be the same dataset.
+
+- parameter ``max_drop`` defines the
+  accuracy drop threshold. The quantization process stops when the
+  degradation of accuracy metric on the validation dataset is less than
+  the ``max_drop``. The default value is 0.01. NNCF will stop the
+  quantization and report an error if the ``max_drop`` value can’t be
+  reached.
+- ``drop_type`` defines how the accuracy drop will be
+  calculated: ABSOLUTE (used by default) or RELATIVE.
+- ``ranking_subset_size`` - size of a subset that is used to rank layers
+  by their contribution to the accuracy drop. Default value is 300, and
+  the more samples it has the better ranking, potentially. Here we use the
+  value 25 to speed up the execution.
 
    **NOTE**: Execution can take tens of minutes and requires up to 15 GB
    of free memory
@@ -331,17 +334,17 @@ value 25 to speed up the execution.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -358,17 +361,17 @@ value 25 to speed up the execution.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -400,17 +403,17 @@ value 25 to speed up the execution.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -418,10 +421,10 @@ value 25 to speed up the execution.
 
     INFO:nncf:Elapsed Time: 00:01:38
     INFO:nncf:Changing the scope of quantizer nodes was started
-    INFO:nncf:Reverted 1 operations to the floating-point precision: 
+    INFO:nncf:Reverted 1 operations to the floating-point precision:
     	__module.model.4.m.0.cv2.conv/aten::_convolution/Convolution
     INFO:nncf:Accuracy drop with the new quantization scope is 0.023408466397916217 (absolute)
-    INFO:nncf:Reverted 1 operations to the floating-point precision: 
+    INFO:nncf:Reverted 1 operations to the floating-point precision:
     	__module.model.18.m.0.cv2.conv/aten::_convolution/Convolution
     INFO:nncf:Accuracy drop with the new quantization scope is 0.024749654890442174 (absolute)
     INFO:nncf:Re-calculating ranking scores for remaining groups
@@ -434,27 +437,27 @@ value 25 to speed up the execution.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
 .. parsed-literal::
 
     INFO:nncf:Elapsed Time: 00:01:36
-    INFO:nncf:Reverted 1 operations to the floating-point precision: 
+    INFO:nncf:Reverted 1 operations to the floating-point precision:
     	__module.model.22.proto.cv3.conv/aten::_convolution/Convolution
     INFO:nncf:Accuracy drop with the new quantization scope is 0.023229513575966754 (absolute)
-    INFO:nncf:Reverted 2 operations to the floating-point precision: 
+    INFO:nncf:Reverted 2 operations to the floating-point precision:
     	__module.model.22/aten::add/Add_6
     	__module.model.22/aten::sub/Subtract
     INFO:nncf:Accuracy drop with the new quantization scope is 0.02425608378963906 (absolute)
@@ -468,35 +471,35 @@ value 25 to speed up the execution.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
 .. parsed-literal::
 
     INFO:nncf:Elapsed Time: 00:01:35
-    INFO:nncf:Reverted 1 operations to the floating-point precision: 
+    INFO:nncf:Reverted 1 operations to the floating-point precision:
     	__module.model.6.m.0.cv2.conv/aten::_convolution/Convolution
     INFO:nncf:Accuracy drop with the new quantization scope is 0.023297881500256024 (absolute)
-    INFO:nncf:Reverted 2 operations to the floating-point precision: 
+    INFO:nncf:Reverted 2 operations to the floating-point precision:
     	__module.model.12.cv2.conv/aten::_convolution/Convolution
     	__module.model.12.m.0.cv1.conv/aten::_convolution/Convolution
     INFO:nncf:Accuracy drop with the new quantization scope is 0.021779128052922092 (absolute)
-    INFO:nncf:Reverted 2 operations to the floating-point precision: 
+    INFO:nncf:Reverted 2 operations to the floating-point precision:
     	__module.model.7.conv/aten::_convolution/Convolution
     	__module.model.12.cv1.conv/aten::_convolution/Convolution
     INFO:nncf:Accuracy drop with the new quantization scope is 0.01696486517685941 (absolute)
-    INFO:nncf:Reverted 2 operations to the floating-point precision: 
+    INFO:nncf:Reverted 2 operations to the floating-point precision:
     	__module.model.22/aten::add/Add_7
     	__module.model.22/aten::sub/Subtract_1
     INFO:nncf:Algorithm completed: achieved required accuracy drop 0.005923437521415831 (absolute)
@@ -524,16 +527,16 @@ is not exceeded.
 .. code:: ipython3
 
     import ipywidgets as widgets
-    
+
     core = ov.Core()
-    
+
     device = widgets.Dropdown(
         options=core.available_devices + ["AUTO"],
         value="AUTO",
         description="Device:",
         disabled=False,
     )
-    
+
     device
 
 
@@ -555,11 +558,11 @@ is not exceeded.
         ov_config = {"GPU_DISABLE_WINOGRAD_CONVOLUTION": "YES"}
     quantized_compiled_model = core.compile_model(quantized_model, device.value, ov_config)
     compiled_ov_model = core.compile_model(ov_model, device.value, ov_config)
-    
+
     pt_result = validation_ac(compiled_ov_model, data_loader, validator)
     quantized_result = validation_ac(quantized_compiled_model, data_loader, validator)
-    
-    
+
+
     print(f"[Original OpenVINO]: {pt_result:.4f}")
     print(f"[Quantized OpenVINO]: {quantized_result:.4f}")
 
@@ -577,14 +580,14 @@ And compare performance.
 .. code:: ipython3
 
     from pathlib import Path
-    
+
     # Set model directory
     MODEL_DIR = Path("model")
     MODEL_DIR.mkdir(exist_ok=True)
-    
+
     ir_model_path = MODEL_DIR / "ir_model.xml"
     quantized_model_path = MODEL_DIR / "quantized_model.xml"
-    
+
     # Save models to use them in the commandline banchmark app
     ov.save_model(ov_model, ir_model_path, compress_to_fp16=False)
     ov.save_model(quantized_model, quantized_model_path, compress_to_fp16=False)
@@ -603,12 +606,12 @@ And compare performance.
     [ WARNING ] Default duration 120 seconds is used for unknown device AUTO
     [ INFO ] OpenVINO:
     [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
-    [ INFO ] 
+    [ INFO ]
     [ INFO ] Device info:
     [ INFO ] AUTO
     [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
-    [ INFO ] 
-    [ INFO ] 
+    [ INFO ]
+    [ INFO ]
     [Step 3/11] Setting device configuration
     [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.THROUGHPUT.
     [Step 4/11] Reading model files
@@ -663,7 +666,7 @@ And compare performance.
     [ INFO ]   LOADED_FROM_CACHE: False
     [Step 9/11] Creating infer requests and preparing input tensors
     [ WARNING ] No input files were given for input 'x'!. This input will be filled with random values!
-    [ INFO ] Fill input 'x' with random values 
+    [ INFO ] Fill input 'x' with random values
     [Step 10/11] Measuring performance (Start inference asynchronously, 12 inference requests, limits: 120000 ms duration)
     [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
     [ INFO ] First inference took 46.51 ms
@@ -693,12 +696,12 @@ And compare performance.
     [ WARNING ] Default duration 120 seconds is used for unknown device AUTO
     [ INFO ] OpenVINO:
     [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
-    [ INFO ] 
+    [ INFO ]
     [ INFO ] Device info:
     [ INFO ] AUTO
     [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
-    [ INFO ] 
-    [ INFO ] 
+    [ INFO ]
+    [ INFO ]
     [Step 3/11] Setting device configuration
     [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.THROUGHPUT.
     [Step 4/11] Reading model files
@@ -753,7 +756,7 @@ And compare performance.
     [ INFO ]   LOADED_FROM_CACHE: False
     [Step 9/11] Creating infer requests and preparing input tensors
     [ WARNING ] No input files were given for input 'x'!. This input will be filled with random values!
-    [ INFO ] Fill input 'x' with random values 
+    [ INFO ] Fill input 'x' with random values
     [Step 10/11] Measuring performance (Start inference asynchronously, 12 inference requests, limits: 120000 ms duration)
     [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
     [ INFO ] First inference took 35.64 ms
diff --git a/docs/notebooks/yolov9-optimization-with-output.rst b/docs/notebooks/yolov9-optimization-with-output.rst
index f0f0674b13d583..0ebedc06b2399a 100644
--- a/docs/notebooks/yolov9-optimization-with-output.rst
+++ b/docs/notebooks/yolov9-optimization-with-output.rst
@@ -19,11 +19,7 @@ The tutorial consists of the following steps:
 -  Run model inference with OpenVINO
 -  Prepare and run optimization pipeline
 -  Compare performance of the FP32 and quantized models.
--  Run optimized model inference on video
-
-
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+-  Run optimized model inference on video #### Table of contents:
 
 -  `Prerequisites <#prerequisites>`__
 -  `Get PyTorch model <#get-pytorch-model>`__
@@ -64,9 +60,7 @@ Prerequisites
 
 .. parsed-literal::
 
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
-    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
     Note: you may need to restart the kernel to use updated packages.
 
 
@@ -92,13 +86,11 @@ Prerequisites
 .. parsed-literal::
 
     Cloning into 'yolov9'...
-    remote: Enumerating objects: 741, done.
-    remote: Counting objects: 100% (367/367), done.
-    remote: Compressing objects: 100% (146/146), done.
-    remote: Total 741 (delta 268), reused 235 (delta 214), pack-reused 374
-    Receiving objects: 100% (741/741), 3.24 MiB | 18.56 MiB/s, done.
-    Resolving deltas: 100% (313/313), done.
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/yolov9-optimization/yolov9
+    remote: Enumerating objects: 781, done.
+    remote: Total 781 (delta 0), reused 0 (delta 0), pack-reused 781
+    Receiving objects: 100% (781/781), 3.27 MiB | 16.92 MiB/s, done.
+    Resolving deltas: 100% (330/330), done.
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/yolov9-optimization/yolov9
 
 
 Get PyTorch model
@@ -136,7 +128,7 @@ applicable for other models from YOLO V9 family.
 
 .. parsed-literal::
 
-    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/yolov9-optimization/yolov9/model/gelan-c.pt')
+    PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/yolov9-optimization/yolov9/model/gelan-c.pt')
 
 
 
@@ -193,7 +185,7 @@ using ``ov.save_model``.
 
     Fusing layers...
     Model summary: 387 layers, 25288768 parameters, 0 gradients, 102.1 GFLOPs
-    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-697/.workspace/scm/ov-notebook/notebooks/yolov9-optimization/yolov9/models/yolo.py:108: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+    /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-717/.workspace/scm/ov-notebook/notebooks/yolov9-optimization/yolov9/models/yolo.py:108: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
       elif self.dynamic or self.shape != shape:
 
 
@@ -581,10 +573,10 @@ asymmetric quantization of activations.
 
 .. parsed-literal::
 
-    2024-06-06 04:01:06.470924: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-    2024-06-06 04:01:06.505182: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+    2024-07-02 03:52:09.485390: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+    2024-07-02 03:52:09.519990: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
     To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-    2024-06-06 04:01:07.273953: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+    2024-07-02 03:52:10.108825: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 
 
 
@@ -594,17 +586,17 @@ asymmetric quantization of activations.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -615,17 +607,17 @@ asymmetric quantization of activations.
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"></pre>
 
 
 
 
-.. raw:: html
 
-    <pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">
-    </pre>
+
+
+
+
+
 
 
 
@@ -688,18 +680,18 @@ models.
     [ INFO ] Parsing input parameters
     [Step 2/11] Loading OpenVINO Runtime
     [ INFO ] OpenVINO:
-    [ INFO ] Build ................................. 2024.1.0-15008-f4afc983258-releases/2024/1
+    [ INFO ] Build ................................. 2024.3.0-15837-76a668b0662
     [ INFO ]
     [ INFO ] Device info:
     [ INFO ] AUTO
-    [ INFO ] Build ................................. 2024.1.0-15008-f4afc983258-releases/2024/1
+    [ INFO ] Build ................................. 2024.3.0-15837-76a668b0662
     [ INFO ]
     [ INFO ]
     [Step 3/11] Setting device configuration
     [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.THROUGHPUT.
     [Step 4/11] Reading model files
     [ INFO ] Loading model files
-    [ INFO ] Read model took 27.02 ms
+    [ INFO ] Read model took 25.63 ms
     [ INFO ] Original model I/O parameters:
     [ INFO ] Model inputs:
     [ INFO ]     images (node: x) : f32 / [...] / [?,3,?,?]
@@ -711,7 +703,7 @@ models.
     [Step 5/11] Resizing model to match image sizes and given batch
     [ INFO ] Model batch size: 1
     [ INFO ] Reshaping model: 'images': [1,3,640,640]
-    [ INFO ] Reshape model took 8.63 ms
+    [ INFO ] Reshape model took 7.89 ms
     [Step 6/11] Configuring input of the model
     [ INFO ] Model inputs:
     [ INFO ]     images (node: x) : u8 / [N,C,H,W] / [1,3,640,640]
@@ -721,7 +713,7 @@ models.
     [ INFO ]     xi.3 (node: __module.model.22/aten::cat/Concat_1) : f32 / [...] / [1,144,40,40]
     [ INFO ]     xi (node: __module.model.22/aten::cat/Concat) : f32 / [...] / [1,144,20,20]
     [Step 7/11] Loading the model to the device
-    [ INFO ] Compile model took 594.09 ms
+    [ INFO ] Compile model took 507.38 ms
     [Step 8/11] Querying optimal runtime parameters
     [ INFO ] Model:
     [ INFO ]   NETWORK_NAME: Model0
@@ -733,7 +725,7 @@ models.
     [ INFO ]     AFFINITY: Affinity.CORE
     [ INFO ]     CPU_DENORMALS_OPTIMIZATION: False
     [ INFO ]     CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE: 1.0
-    [ INFO ]     DYNAMIC_QUANTIZATION_GROUP_SIZE: 0
+    [ INFO ]     DYNAMIC_QUANTIZATION_GROUP_SIZE: 32
     [ INFO ]     ENABLE_CPU_PINNING: True
     [ INFO ]     ENABLE_HYPER_THREADING: True
     [ INFO ]     EXECUTION_DEVICES: ['CPU']
@@ -758,17 +750,17 @@ models.
     [ INFO ] Fill input 'images' with random values
     [Step 10/11] Measuring performance (Start inference asynchronously, 6 inference requests, limits: 15000 ms duration)
     [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
-    [ INFO ] First inference took 185.44 ms
+    [ INFO ] First inference took 185.08 ms
     [Step 11/11] Dumping statistics report
     [ INFO ] Execution Devices:['CPU']
-    [ INFO ] Count:            222 iterations
-    [ INFO ] Duration:         15376.10 ms
+    [ INFO ] Count:            228 iterations
+    [ INFO ] Duration:         15726.00 ms
     [ INFO ] Latency:
-    [ INFO ]    Median:        412.61 ms
-    [ INFO ]    Average:       412.57 ms
-    [ INFO ]    Min:           353.60 ms
-    [ INFO ]    Max:           494.63 ms
-    [ INFO ] Throughput:   14.44 FPS
+    [ INFO ]    Median:        412.72 ms
+    [ INFO ]    Average:       410.65 ms
+    [ INFO ]    Min:           305.83 ms
+    [ INFO ]    Max:           431.66 ms
+    [ INFO ] Throughput:   14.50 FPS
 
 
 .. code:: ipython3
@@ -782,18 +774,18 @@ models.
     [ INFO ] Parsing input parameters
     [Step 2/11] Loading OpenVINO Runtime
     [ INFO ] OpenVINO:
-    [ INFO ] Build ................................. 2024.1.0-15008-f4afc983258-releases/2024/1
+    [ INFO ] Build ................................. 2024.3.0-15837-76a668b0662
     [ INFO ]
     [ INFO ] Device info:
     [ INFO ] AUTO
-    [ INFO ] Build ................................. 2024.1.0-15008-f4afc983258-releases/2024/1
+    [ INFO ] Build ................................. 2024.3.0-15837-76a668b0662
     [ INFO ]
     [ INFO ]
     [Step 3/11] Setting device configuration
     [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.THROUGHPUT.
     [Step 4/11] Reading model files
     [ INFO ] Loading model files
-    [ INFO ] Read model took 41.38 ms
+    [ INFO ] Read model took 40.32 ms
     [ INFO ] Original model I/O parameters:
     [ INFO ] Model inputs:
     [ INFO ]     images (node: x) : f32 / [...] / [1,3,640,640]
@@ -815,7 +807,7 @@ models.
     [ INFO ]     xi.3 (node: __module.model.22/aten::cat/Concat_1) : f32 / [...] / [1,144,40,40]
     [ INFO ]     xi (node: __module.model.22/aten::cat/Concat) : f32 / [...] / [1,144,20,20]
     [Step 7/11] Loading the model to the device
-    [ INFO ] Compile model took 1210.12 ms
+    [ INFO ] Compile model took 1102.75 ms
     [Step 8/11] Querying optimal runtime parameters
     [ INFO ] Model:
     [ INFO ]   NETWORK_NAME: Model0
@@ -827,7 +819,7 @@ models.
     [ INFO ]     AFFINITY: Affinity.CORE
     [ INFO ]     CPU_DENORMALS_OPTIMIZATION: False
     [ INFO ]     CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE: 1.0
-    [ INFO ]     DYNAMIC_QUANTIZATION_GROUP_SIZE: 0
+    [ INFO ]     DYNAMIC_QUANTIZATION_GROUP_SIZE: 32
     [ INFO ]     ENABLE_CPU_PINNING: True
     [ INFO ]     ENABLE_HYPER_THREADING: True
     [ INFO ]     EXECUTION_DEVICES: ['CPU']
@@ -852,17 +844,17 @@ models.
     [ INFO ] Fill input 'images' with random values
     [Step 10/11] Measuring performance (Start inference asynchronously, 6 inference requests, limits: 15000 ms duration)
     [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
-    [ INFO ] First inference took 76.70 ms
+    [ INFO ] First inference took 73.96 ms
     [Step 11/11] Dumping statistics report
     [ INFO ] Execution Devices:['CPU']
-    [ INFO ] Count:            756 iterations
-    [ INFO ] Duration:         15142.55 ms
+    [ INFO ] Count:            750 iterations
+    [ INFO ] Duration:         15151.23 ms
     [ INFO ] Latency:
-    [ INFO ]    Median:        120.12 ms
-    [ INFO ]    Average:       119.83 ms
-    [ INFO ]    Min:           84.47 ms
-    [ INFO ]    Max:           133.40 ms
-    [ INFO ] Throughput:   49.93 FPS
+    [ INFO ]    Median:        121.36 ms
+    [ INFO ]    Average:       120.92 ms
+    [ INFO ]    Min:           69.51 ms
+    [ INFO ]    Max:           134.06 ms
+    [ INFO ] Throughput:   49.50 FPS
 
 
 Run Live Object Detection
diff --git a/docs/notebooks/yolov9-optimization-with-output_files/yolov9-optimization-with-output_36_0.png b/docs/notebooks/yolov9-optimization-with-output_files/yolov9-optimization-with-output_36_0.png
index 3869212ef5f23e..c890d1144c0dc9 100644
--- a/docs/notebooks/yolov9-optimization-with-output_files/yolov9-optimization-with-output_36_0.png
+++ b/docs/notebooks/yolov9-optimization-with-output_files/yolov9-optimization-with-output_36_0.png
@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:1fb96b6c567f59252f699809edb7501993276c99383e9f0209cc2cf72b68f179
-size 496564
+oid sha256:ad22f872d9541c0ada6f2fabb8e6fc5567d8861e49842bcfa2f4e37159816785
+size 496537
diff --git a/docs/notebooks/zeroscope-text2video-with-output.rst b/docs/notebooks/zeroscope-text2video-with-output.rst
index 28e28d504150bd..aa0be8bfe9de73 100644
--- a/docs/notebooks/zeroscope-text2video-with-output.rst
+++ b/docs/notebooks/zeroscope-text2video-with-output.rst
@@ -1,8 +1,8 @@
 Video generation with ZeroScope and OpenVINO
 ============================================
 
-Table of contents:
-^^^^^^^^^^^^^^^^^^
+**Table of contents:**
+
 
 -  `Install and import required
    packages <#install-and-import-required-packages>`__
@@ -48,10 +48,10 @@ various videos, from simple animations to complex scenes. It is still
 under development, but it has the potential to revolutionize the way we
 create and consume video content.
 
-Both versions of the ZeroScope model are available on Hugging Face: -
-`ZeroScope_v2
-576w <https://huggingface.co/cerspense/zeroscope_v2_576w>`__ -
-`ZeroScope_v2 XL <https://huggingface.co/cerspense/zeroscope_v2_XL>`__
+Both versions of the ZeroScope model are available on Hugging Face:
+
+- `ZeroScope_v2 576w <https://huggingface.co/cerspense/zeroscope_v2_576w>`__
+- `ZeroScope_v2 XL <https://huggingface.co/cerspense/zeroscope_v2_XL>`__
 
 We will use the first one.
 
@@ -81,7 +81,7 @@ provides already pretrained model from ``cerspense``.
     import base64
     import tempfile
     import warnings
-    
+
     import diffusers
     import transformers
     import numpy as np
@@ -90,7 +90,7 @@ provides already pretrained model from ``cerspense``.
     import torch
     import PIL
     import gradio as gr
-    
+
     import openvino as ov
 
 
@@ -188,8 +188,8 @@ function to serialize the result of conversion.
 .. code:: ipython3
 
     from pathlib import Path
-    
-    
+
+
     def convert(model: torch.nn.Module, xml_path: str, **convert_kwargs) -> Path:
         xml_path = Path(xml_path)
         if not xml_path.exists():
@@ -248,7 +248,7 @@ inference, we need only decoder part.
         def __init__(self, vae):
             super().__init__()
             self.vae = vae
-    
+
         def forward(self, z: torch.FloatTensor):
             return self.vae.decode(z)
 
@@ -310,8 +310,8 @@ Build a pipeline
         from diffusers.utils import randn_tensor
     except ImportError:
         from diffusers.utils.torch_utils import randn_tensor
-    
-    
+
+
     class OVTextToVideoSDPipeline(diffusers.DiffusionPipeline):
         def __init__(
             self,
@@ -322,7 +322,7 @@ Build a pipeline
             scheduler: diffusers.schedulers.DDIMScheduler,
         ):
             super().__init__()
-    
+
             self.vae_decoder = vae_decoder
             self.text_encoder = text_encoder
             self.tokenizer = tokenizer
@@ -333,7 +333,7 @@ Build a pipeline
             self.width = WIDTH
             self.height = HEIGHT
             self.num_frames = NUM_FRAMES
-    
+
         def __call__(
             self,
             prompt: Union[str, List[str]] = None,
@@ -352,7 +352,7 @@ Build a pipeline
         ):
             r"""
             Function invoked when calling the pipeline for generation.
-    
+
             Args:
                 prompt (`str` or `List[str]`, *optional*):
                     The prompt or prompts to guide the video generation. If not defined, one has to pass `prompt_embeds`.
@@ -399,13 +399,13 @@ Build a pipeline
                 callback_steps (`int`, *optional*, defaults to 1):
                     The frequency at which the `callback` function will be called. If not specified, the callback will be
                     called at every step.
-    
+
             Returns:
                 `List[np.ndarray]`: generated video frames
             """
-    
+
             num_images_per_prompt = 1
-    
+
             # 1. Check inputs. Raise error if not correct
             self.check_inputs(
                 prompt,
@@ -414,7 +414,7 @@ Build a pipeline
                 prompt_embeds,
                 negative_prompt_embeds,
             )
-    
+
             # 2. Define call parameters
             if prompt is not None and isinstance(prompt, str):
                 batch_size = 1
@@ -422,12 +422,12 @@ Build a pipeline
                 batch_size = len(prompt)
             else:
                 batch_size = prompt_embeds.shape[0]
-    
+
             # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
             # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
             # corresponds to doing no classifier free guidance.
             do_classifier_free_guidance = guidance_scale > 1.0
-    
+
             # 3. Encode input prompt
             prompt_embeds = self._encode_prompt(
                 prompt,
@@ -437,11 +437,11 @@ Build a pipeline
                 prompt_embeds=prompt_embeds,
                 negative_prompt_embeds=negative_prompt_embeds,
             )
-    
+
             # 4. Prepare timesteps
             self.scheduler.set_timesteps(num_inference_steps)
             timesteps = self.scheduler.timesteps
-    
+
             # 5. Prepare latent variables
             num_channels_latents = self.unet_in_channels
             latents = self.prepare_latents(
@@ -451,10 +451,10 @@ Build a pipeline
                 generator,
                 latents,
             )
-    
+
             # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
             extra_step_kwargs = {"generator": generator, "eta": eta}
-    
+
             # 7. Denoising loop
             num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
             with self.progress_bar(total=num_inference_steps) as progress_bar:
@@ -462,7 +462,7 @@ Build a pipeline
                     # expand the latents if we are doing classifier free guidance
                     latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
                     latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-    
+
                     # predict the noise residual
                     noise_pred = self.unet(
                         {
@@ -472,41 +472,41 @@ Build a pipeline
                         }
                     )[0]
                     noise_pred = torch.tensor(noise_pred)
-    
+
                     # perform guidance
                     if do_classifier_free_guidance:
                         noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                         noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-    
+
                     # reshape latents
                     bsz, channel, frames, width, height = latents.shape
                     latents = latents.permute(0, 2, 1, 3, 4).reshape(bsz * frames, channel, width, height)
                     noise_pred = noise_pred.permute(0, 2, 1, 3, 4).reshape(bsz * frames, channel, width, height)
-    
+
                     # compute the previous noisy sample x_t -> x_t-1
                     latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
-    
+
                     # reshape latents back
                     latents = latents[None, :].reshape(bsz, frames, channel, width, height).permute(0, 2, 1, 3, 4)
-    
+
                     # call the callback, if provided
                     if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                         progress_bar.update()
                         if callback is not None and i % callback_steps == 0:
                             callback(i, t, latents)
-    
+
             video_tensor = self.decode_latents(latents)
-    
+
             if output_type == "pt":
                 video = video_tensor
             else:
                 video = tensor2vid(video_tensor)
-    
+
             if not return_dict:
                 return (video,)
-    
+
             return {"frames": video}
-    
+
         # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
         def _encode_prompt(
             self,
@@ -519,7 +519,7 @@ Build a pipeline
         ):
             r"""
             Encodes the prompt into text encoder hidden states.
-    
+
             Args:
                  prompt (`str` or `List[str]`, *optional*):
                     prompt to be encoded
@@ -545,7 +545,7 @@ Build a pipeline
                 batch_size = len(prompt)
             else:
                 batch_size = prompt_embeds.shape[0]
-    
+
             if prompt_embeds is None:
                 text_inputs = self.tokenizer(
                     prompt,
@@ -556,23 +556,23 @@ Build a pipeline
                 )
                 text_input_ids = text_inputs.input_ids
                 untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
-    
+
                 if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
                     removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
                     print(
                         "The following part of your input was truncated because CLIP can only handle sequences up to"
                         f" {self.tokenizer.model_max_length} tokens: {removed_text}"
                     )
-    
+
                 prompt_embeds = self.text_encoder(text_input_ids)
                 prompt_embeds = prompt_embeds[0]
                 prompt_embeds = torch.tensor(prompt_embeds)
-    
+
             bs_embed, seq_len, _ = prompt_embeds.shape
             # duplicate text embeddings for each generation per prompt, using mps friendly method
             prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
             prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
-    
+
             # get unconditional embeddings for classifier free guidance
             if do_classifier_free_guidance and negative_prompt_embeds is None:
                 uncond_tokens: List[str]
@@ -590,7 +590,7 @@ Build a pipeline
                     )
                 else:
                     uncond_tokens = negative_prompt
-    
+
                 max_length = prompt_embeds.shape[1]
                 uncond_input = self.tokenizer(
                     uncond_tokens,
@@ -599,25 +599,25 @@ Build a pipeline
                     truncation=True,
                     return_tensors="pt",
                 )
-    
+
                 negative_prompt_embeds = self.text_encoder(uncond_input.input_ids)
                 negative_prompt_embeds = negative_prompt_embeds[0]
                 negative_prompt_embeds = torch.tensor(negative_prompt_embeds)
-    
+
             if do_classifier_free_guidance:
                 # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                 seq_len = negative_prompt_embeds.shape[1]
-    
+
                 negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
                 negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
-    
+
                 # For classifier free guidance, we need to do two forward passes.
                 # Here we concatenate the unconditional and text embeddings into a single batch
                 # to avoid doing two forward passes
                 prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
-    
+
             return prompt_embeds
-    
+
         def prepare_latents(
             self,
             batch_size,
@@ -638,14 +638,14 @@ Build a pipeline
                     f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
                     f" size of {batch_size}. Make sure the batch size matches the length of the generators."
                 )
-    
+
             if latents is None:
                 latents = randn_tensor(shape, generator=generator, dtype=dtype)
-    
+
             # scale the initial noise by the standard deviation required by the scheduler
             latents = latents * self.scheduler.init_noise_sigma
             return latents
-    
+
         def check_inputs(
             self,
             prompt,
@@ -656,10 +656,10 @@ Build a pipeline
         ):
             if self.height % 8 != 0 or self.width % 8 != 0:
                 raise ValueError(f"`height` and `width` have to be divisible by 8 but are {self.height} and {self.width}.")
-    
+
             if (callback_steps is None) or (callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)):
                 raise ValueError(f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}.")
-    
+
             if prompt is not None and prompt_embeds is not None:
                 raise ValueError(
                     f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two."
@@ -668,13 +668,13 @@ Build a pipeline
                 raise ValueError("Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.")
             elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
                 raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-    
+
             if negative_prompt is not None and negative_prompt_embeds is not None:
                 raise ValueError(
                     f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
                     f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
                 )
-    
+
             if prompt_embeds is not None and negative_prompt_embeds is not None:
                 if prompt_embeds.shape != negative_prompt_embeds.shape:
                     raise ValueError(
@@ -682,11 +682,11 @@ Build a pipeline
                         f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
                         f" {negative_prompt_embeds.shape}."
                     )
-    
+
         def decode_latents(self, latents):
             scale_factor = 0.18215
             latents = 1 / scale_factor * latents
-    
+
             batch_size, channels, num_frames, height, width = latents.shape
             latents = latents.permute(0, 2, 1, 3, 4).reshape(batch_size * num_frames, channels, height, width)
             image = self.vae_decoder(latents)[0]
@@ -731,7 +731,7 @@ select device from dropdown list for running inference using OpenVINO
         description="Device:",
         disabled=False,
     )
-    
+
     device
 
 
@@ -851,8 +851,8 @@ Interactive demo
         images = [PIL.Image.fromarray(frame) for frame in frames]
         images[0].save(out_file, save_all=True, append_images=images[1:], duration=125, loop=0)
         return out_file.name
-    
-    
+
+
     demo = gr.Interface(
         generate,
         [
@@ -868,7 +868,7 @@ Interactive demo
         ],
         allow_flagging="never",
     )
-    
+
     try:
         demo.queue().launch(debug=False)
     except Exception:
@@ -881,7 +881,7 @@ Interactive demo
 .. parsed-literal::
 
     Running on local URL:  http://127.0.0.1:7860
-    
+
     To create a public link, set `share=True` in `launch()`.
 
 
diff --git a/docs/openvino_custom_sphinx_sitemap/openvino_custom_sphinx_sitemap/__init__.py b/docs/openvino_custom_sphinx_sitemap/openvino_custom_sphinx_sitemap/__init__.py
index 25b1c954043e9d..3d44a04e5b462c 100644
--- a/docs/openvino_custom_sphinx_sitemap/openvino_custom_sphinx_sitemap/__init__.py
+++ b/docs/openvino_custom_sphinx_sitemap/openvino_custom_sphinx_sitemap/__init__.py
@@ -27,6 +27,9 @@ def setup(app):
     app.connect("builder-inited", record_builder_type)
     app.connect("html-page-context", add_html_link)
     app.connect('build-finished', create_sitemap)
+    app.parallel_safe = True
+    app.parallel_read_safe = True
+    app.parallel_write_safe = True
     return setup
 
 
@@ -61,18 +64,23 @@ def create_sitemap(app, exception):
         for item in urlset:
             root.set(*item)
 
-    locales = get_locales(app, exception)
+    locales = get_locales(app)
 
     if app.builder.config.version:
         version = app.builder.config.version + '/'
     else:
         version = ""
 
-    for link in app.sitemap_links:
+    while True:
+        try:
+            link = app.env.app.sitemap_links.get_nowait()  # type: ignore
+        except queue.Empty:
+            break
+
         url = ET.SubElement(root, "url")
         scheme = app.config.sitemap_url_scheme
         if app.builder.config.language:
-            lang = app.builder.config.language + '/'
+            lang = app.builder.config.language + "/"
         else:
             lang = ""
 
@@ -87,18 +95,15 @@ def create_sitemap(app, exception):
                 for tag_name, tag_value in values.items():
                     ET.SubElement(namespace_element, tag_name).text = tag_value
 
-        if len(app.locales) > 0:
-            for lang in locales:
-                lang = lang + '/'
-                linktag = ET.SubElement(
-                    url,
-                    "{http://www.w3.org/1999/xhtml}link"
-                )
-                linktag.set("rel", "alternate")
-                linktag.set("hreflang",  hreflang_formatter(lang.rstrip('/')))
-                linktag.set("href", site_url + scheme.format(
-                    lang=lang, version=version, link=link
-                ))
+        for lang in locales:
+            lang = lang + "/"
+            ET.SubElement(
+                url,
+                "{http://www.w3.org/1999/xhtml}link",
+                rel="alternate",
+                hreflang=hreflang_formatter(lang.rstrip("/")),
+                href=site_url + scheme.format(lang=lang, version=version, link=link),
+            )
 
     filename = Path(app.outdir) / app.config.sitemap_filename
     ET.ElementTree(root).write(filename,
diff --git a/docs/openvino_sphinx_theme/openvino_sphinx_theme/directives/code.py b/docs/openvino_sphinx_theme/openvino_sphinx_theme/directives/code.py
index 788d7ea35d2736..b91b0d599ee28a 100644
--- a/docs/openvino_sphinx_theme/openvino_sphinx_theme/directives/code.py
+++ b/docs/openvino_sphinx_theme/openvino_sphinx_theme/directives/code.py
@@ -136,117 +136,3 @@ def run(self):
         if self.content:
             self.state.nested_parse(self.content, self.content_offset, node)
         return [node]
-
-def visit_showcase(self, node):
-    attrs = {}
-    notebook_file = ("../../notebooks/" + node["title"] + "-with-output.html") if 'title' in node is not None else ""
-    link_title = (node["title"]) if 'title' in node is not None else "OpenVINO Interactive Tutorial"
-
-    if "height" or "width" in node:
-        attrs["style"] = (
-            (("height:" + "".join(c for c in str(node["height"]) if c.isdigit()) + "px!important; " ) if "height" in node is not None else "")
-            + (("width:" + "".join(c for c in str(node["width"]) if c.isdigit()) ) if "width" in node is not None else "")
-            + (("px; " if node["width"].find("px") != -1 else "%;") if "width" in node is not None else "")
-        )
-    self.body.append("<div class='showcase-wrap'>")
-    self.body.append(self.starttag(node, "div", **attrs))
-    self.body.append("<div class='showcase-img-placeholder'>")
-    self.body.append("<a href='" + notebook_file + "' title='" + link_title + "'>")
-    self.body.append("<img " + (" class='" + (node["img-class"] + " showcase-img' ") if 'img-class' in node is not None else " class='showcase-img'"))
-    self.body.append("src='" + node["img"] + "' alt='"+os.path.basename(node["img"]) + "' /></a></div>") if "img" in node is not None else ""
-
-    self.body.append("<div class='showcase-content'><div class='showcase-content-container'>")
-
-
-def depart_showcase(self, node):
-    notebooks_repo = "https://github.com/openvinotoolkit/openvino_notebooks/blob/main/"
-    notebooks_binder = "https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?filepath="
-    notebooks_colab = "https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/"
-    git_badge = "<img class='showcase-badge' src='https://badgen.net/badge/icon/github?icon=github&amp;label' alt='Github'>"
-    binder_badge = "<img class='showcase-badge' src='https://mybinder.org/badge_logo.svg' alt='Binder'>"
-    colab_badge = "<img class='showcase-badge' src='https://colab.research.google.com/assets/colab-badge.svg' alt='Colab'>"
-    binder_list_file = Path('../../../docs/notebooks/notebooks_with_binder_buttons.txt').resolve(strict=True)
-    colab_list_file = Path('../../../docs/notebooks/notebooks_with_colab_buttons.txt').resolve(strict=True)
-    openvino_notebooks_repo_listing = Path('../../../docs/notebooks/all_notebooks_paths.txt').resolve(strict=True)
-    with open(binder_list_file, 'r+', encoding='cp437') as file:
-        binder_buttons_list = file.read().splitlines()
-    with open(colab_list_file, 'r+', encoding='cp437') as file:
-        colab_buttons_list = file.read().splitlines()
-    if not os.path.exists(openvino_notebooks_repo_listing):
-        raise FileNotFoundError("all_notebooks_paths.txt is not found")
-    else:
-        paths_list = open(openvino_notebooks_repo_listing, 'r').readlines()
-        ipynb_list = [x for x in paths_list if re.match("notebooks/[0-9]{3}.*\.ipynb$", x)]
-        notebook_with_ext = node["title"] + ".ipynb"
-        matched_notebook = [match for match in ipynb_list if notebook_with_ext in match]
-
-
-    notebook_file = ("../../notebooks/" + node["title"] + "-with-output.html") if 'title' in node is not None else ""
-    link_title = (node["title"]) if 'title' in node is not None else "OpenVINO Interactive Tutorial"
-
-    self.body.append("<a href='" + notebook_file + "' title='" + link_title + "'>")
-    self.body.append("<h2 class='showcase-title'>" + node["title"] + "</h2></a>") if 'title' in node is not None else ""
-
-    if matched_notebook is not None:
-        for n in matched_notebook:
-            self.body.append(("<a href='" + notebooks_repo + n + "' target='_blank'>" + git_badge + "</a>") if 'title' in node is not None else "")
-            if node["title"] in binder_buttons_list:
-                self.body.append(("<a href='" + notebooks_binder + n + "' target='_blank'>" + binder_badge + "</a>"
-                                  ) if 'title' in node is not None else "")
-            if node["title"] in colab_buttons_list:
-                self.body.append(("<a href='" + notebooks_colab + n + "' target='_blank'>" + colab_badge + "</a>"
-                                  ) if 'title' in node is not None else "")
-
-    self.body.append("</div><button class='showcase-button' type='button' title='" + link_title +
-                     "' onclick=\"location.href='" + notebook_file + "'\">Read more</a></div></div></div>\n")
-
-
-class Nodeshowcase(nodes.container):
-    def create_showcase_component(
-        rawtext: str = "",
-        **attributes,
-    ) -> nodes.container:
-        node = nodes.container(rawtext, is_div=True, **attributes)
-        return node
-
-
-class Showcase(Directive):
-    has_content = True
-    required_arguments = 0
-    optional_arguments = 1
-    final_argument_whitespace = True
-    option_spec = {
-        'class': directives.class_option,
-        'name': directives.unchanged,
-        'width': directives.length_or_percentage_or_unitless,
-        'height': directives.length_or_percentage_or_unitless,
-        'style': directives.unchanged,
-        'img': directives.unchanged,
-        'img-class': directives.unchanged,
-        'title': directives.unchanged,
-        'git': directives.unchanged,
-    }
-
-    has_content = True
-
-    def run(self):
-
-        classes = ['showcase']
-        node = Nodeshowcase("div", rawtext="\n".join(self.content), classes=classes)
-        if 'height' in self.options:
-            node['height'] = self.options['height']
-        if 'width' in self.options:
-            node['width'] = self.options['width']
-        if 'img' in self.options:
-            node['img'] = self.options['img']
-        if 'img-class' in self.options:
-            node['img-class'] = self.options['img-class']
-        if 'title' in self.options:
-            node['title'] = self.options['title']
-        if 'git' in self.options:
-            node['git'] = self.options['git']
-        node['classes'] += self.options.get('class', [])
-        self.add_name(node)
-        if self.content:
-            self.state.nested_parse(self.content, self.content_offset, node)
-        return [node]
\ No newline at end of file
diff --git a/docs/openvino_sphinx_theme/openvino_sphinx_theme/templates/navbar-nav.html b/docs/openvino_sphinx_theme/openvino_sphinx_theme/templates/navbar-nav.html
index faba6fc1538a62..8d428571f18b12 100644
--- a/docs/openvino_sphinx_theme/openvino_sphinx_theme/templates/navbar-nav.html
+++ b/docs/openvino_sphinx_theme/openvino_sphinx_theme/templates/navbar-nav.html
@@ -31,6 +31,13 @@
             </a>
           </li>
 
+          <li class="toctree-l1 nav-item">
+            <a class="reference external nav-link"
+              href="https://docs.openvino.ai/2024/about-openvino/performance-benchmarks.html">
+              Performance Benchmarks
+            </a>
+          </li>
+
           <li class="toctree-l1 nav-item">
             <a class="reference external nav-link" href="https://github.com/openvinotoolkit">
               GitHub
diff --git a/docs/requirements.txt b/docs/requirements.txt
index 20fec19af7a351..98328772f48c60 100644
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -1,4 +1,4 @@
-alabaster==0.7.12
+alabaster==0.7.14
 atomicwrites==1.4.0
 attrs==22.1.0
 Babel==2.11.0
@@ -33,11 +33,11 @@ requests==2.32.0
 six==1.15.0
 snowballstemmer==2.1.0
 soupsieve==2.2.1
-sphinx==7.2.6
+sphinx==7.3.7
 sphinx-copybutton==0.5.2
 sphinx-design==0.5.0
 sphinx-inline-tabs==2023.4.21
-sphinx-sitemap==2.2.0
+sphinx-sitemap==2.6.0
 sphinxcontrib-applehelp==1.0.2
 sphinxcontrib-devhelp==1.0.2
 sphinxcontrib-htmlhelp==2.0.0
diff --git a/docs/sphinx_setup/_static/benchmarks_files/OV-2024.2-Performance-Data.xlsx b/docs/sphinx_setup/_static/benchmarks_files/OV-2024.2-Performance-Data.xlsx
index 7dc43dbd697e8c..adb9eb19ac7f8e 100644
Binary files a/docs/sphinx_setup/_static/benchmarks_files/OV-2024.2-Performance-Data.xlsx and b/docs/sphinx_setup/_static/benchmarks_files/OV-2024.2-Performance-Data.xlsx differ
diff --git a/docs/sphinx_setup/_static/benchmarks_files/OV-2024.2-platform_list.pdf b/docs/sphinx_setup/_static/benchmarks_files/OV-2024.2-platform_list.pdf
index 29bfac35179b91..7ad90a9611675a 100644
Binary files a/docs/sphinx_setup/_static/benchmarks_files/OV-2024.2-platform_list.pdf and b/docs/sphinx_setup/_static/benchmarks_files/OV-2024.2-platform_list.pdf differ
diff --git a/docs/sphinx_setup/_static/benchmarks_files/OV-benchmark-data.csv b/docs/sphinx_setup/_static/benchmarks_files/OV-benchmark-data.csv
index 384581d6849f97..12624bf541f602 100644
--- a/docs/sphinx_setup/_static/benchmarks_files/OV-benchmark-data.csv
+++ b/docs/sphinx_setup/_static/benchmarks_files/OV-benchmark-data.csv
@@ -541,8 +541,11 @@ chatglm2-6b,OV-2024.2,xeon,Intel® Xeon® Platinum 8380 CPU-only,21.1,12.6,,,,18
 chatglm2-6b,OV-2024.2,xeon,Intel® Xeon® Platinum 8490H CPU-only,25.1,16.2,,,,34000,700,2,17000,350,39.82,tokens/sec,tokens/sec/$,tokens/sec/TDP,msec/token,61.83,,30.99,32.3,,
 chatglm2-6b,OV-2024.2,xeon,Intel® Xeon® Platinum 8580 CPU-only,29.0,19.0,,,,21420,700,2,10710,350,34.49,tokens/sec,tokens/sec/$,tokens/sec/TDP,msec/token,52.75,,26.55,37.7,,
 chatglm2-6b,OV-2024.2,core,Intel® Core™ Ultra 7-165H CPU-only,6.1,4.7,,,,460,28,1,460,28,162.77,tokens/sec,tokens/sec/$,tokens/sec/TDP,msec/token,211.71,,152.39,6.6,,
+chatglm2-6b,OV-2024.2,core-iGPU,Intel® Core™ Ultra 7-165H iGPU-only,8.8,,,,,,,,,,113.9,tokens/sec,tokens/sec/$,tokens/sec/TDP,msec/token,,,87.9,11.4,,
 chatglm2-6b,OV-2024.2,accel,Intel® Data Center GPU Flex 170 dGPU,31.7,,,,,2000,150,1,2000,150,31.59,tokens/sec,tokens/sec/$,tokens/sec/TDP,msec/token,,,26.93,37.1,,
 chatglm2-6b,OV-2024.2,accel,Intel® Arc™ A770M,27.6,,,,,321,150,1,321,150,36.29,tokens/sec,tokens/sec/$,tokens/sec/TDP,msec/token,,,31.25,32.0,,
+chatglm2-6b,OV-2024.2,accel,Intel® Arc™ A570M,12.5,,,,,,,,,,80.1,tokens/sec,tokens/sec/$,tokens/sec/TDP,msec/token,,,48.1,20.8,,
+chatglm2-6b,OV-2024.2,accel,Intel® Arc™ A530M,12.5,,,,,,,,,,80.3,tokens/sec,tokens/sec/$,tokens/sec/TDP,msec/token,,,58.7,17.0,,
 chatglm2-6b,OV-2024.2,accel,Intel® Data Center GPU Flex 140 dGPU,,,,,,1900,75,1,1900,75,,tokens/sec,tokens/sec/$,tokens/sec/TDP,msec/token,,,79.41,12.6,,
 end_rec,,,,,,,,,,,,,,,,,,,,,,,,
 begin_rec, , , ,,,,,,,,,,,,tokens/sec,tokens/sec/$,tokens/sec/TDP,msec/token,,,,,,
@@ -553,7 +556,9 @@ llama-2-7b-chat,OV-2024.2,xeon,Intel® Xeon® Platinum 8580 CPU-only,23.6,15.8,,
 llama-2-7b-chat,OV-2024.2,core,Intel® Core™ Ultra 7-165H CPU-only,5.4,,,,,460,28,1,460,28,185.54,tokens/sec,tokens/sec/$,tokens/sec/TDP,msec/token,,,175.01,5.7,,
 llama-2-7b-chat,OV-2024.2,accel,Intel® Data Center GPU Flex 170 dGPU,27.4,,,,,2000,150,1,2000,150,36.43,tokens/sec,tokens/sec/$,tokens/sec/TDP,msec/token,,,32.54,30.7,,
 llama-2-7b-chat,OV-2024.2,accel,Intel® Arc™ A770M,24.5,,,,,321,150,1,321,150,40.74,tokens/sec,tokens/sec/$,tokens/sec/TDP,msec/token,,,34.51,29.0,,
-llama-2-7b-chat,OV-2024.2,core-iGPU,Intel® Core™ Ultra 7-165H iGPU-only,,,,,,460,28,1,460,28,109.77,tokens/sec,tokens/sec/$,tokens/sec/TDP,msec/token,189.04,,77.32,12.9,,
+llama-2-7b-chat,OV-2024.2,accel,Intel® Arc™ A570M,3.7,,,,,,,,,,272.8,tokens/sec,tokens/sec/$,tokens/sec/TDP,msec/token,,,57.2,17.5,,
+llama-2-7b-chat,OV-2024.2,accel,Intel® Arc™ A530M,3.8,,,,,,,,,,264.6,tokens/sec,tokens/sec/$,tokens/sec/TDP,msec/token,,,70.7,14.1,,
+llama-2-7b-chat,OV-2024.2,core-iGPU,Intel® Core™ Ultra 7-165H iGPU-only,9.1,,,,,460,28,1,460,28,109.77,tokens/sec,tokens/sec/$,tokens/sec/TDP,msec/token,189.04,,77.32,12.9,,
 end_rec,,,,,,,,,,,,,,,,,,,,,,,,
 begin_rec, , , ,,,,,,,,,,,,tokens/sec,tokens/sec/$,tokens/sec/TDP,msec/token,,,,,,
 mistral-7b-v0.1,OV-2024.2,core,Intel® Core™ i9-13900K CPU-only,8.5,4.4,,,,599,125,1,599,125,117.63,tokens/sec,tokens/sec/$,tokens/sec/TDP,msec/token,227.08,,72.45,13.8,,
@@ -562,7 +567,10 @@ mistral-7b-v0.1,OV-2024.2,xeon,Intel® Xeon® Platinum 8490H CPU-only,19.2,12.7,
 mistral-7b-v0.1,OV-2024.2,xeon,Intel® Xeon® Platinum 8580 CPU-only,22.5,15.1,,,,21420,700,2,10710,350,44.5,tokens/sec,tokens/sec/$,tokens/sec/TDP,msec/token,66.37,,32.15,31.1,,
 mistral-7b-v0.1,OV-2024.2,core,Intel® Core™ Ultra 7-165H CPU-only,5.2,,,,,460,28,1,460,28,191.52,tokens/sec,tokens/sec/$,tokens/sec/TDP,msec/token,,,181.29,5.5,,
 mistral-7b-v0.1,OV-2024.2,accel,Intel® Data Center GPU Flex 170 dGPU,14.7,,,,,2000,150,1,2000,150,68.25,tokens/sec,tokens/sec/$,tokens/sec/TDP,msec/token,,,61.0,16.4,,
+mistral-7b-v0.1,OV-2024.2,core-iGPU,Intel® Core™ Ultra 7-165H iGPU-only,9.4,,,,,,,,,,106.9,tokens/sec,tokens/sec/$,tokens/sec/TDP,msec/token,,,70.2,14.2,,
 mistral-7b-v0.1,OV-2024.2,accel,Intel® Arc™ A770M,18.1,,,,,321,150,1,321,150,55.28,tokens/sec,tokens/sec/$,tokens/sec/TDP,msec/token,,,47.44,21.1,,
+mistral-7b-v0.1,OV-2024.2,accel,Intel® Arc™ A570M,7.1,,,,,,,,,,140.2,tokens/sec,tokens/sec/$,tokens/sec/TDP,msec/token,,,66.11,15.1,,
+mistral-7b-v0.1,OV-2024.2,accel,Intel® Arc™ A530M,7.2,,,,,,,,,,138.4,tokens/sec,tokens/sec/$,tokens/sec/TDP,msec/token,,,75.6,13.2,,
 mistral-7b-v0.1,OV-2024.2,accel,Intel® Data Center GPU Flex 140 dGPU,,,,,,1900,75,1,1900,75,,tokens/sec,tokens/sec/$,tokens/sec/TDP,msec/token,,,194.94,5.1,,
 end_rec,,,,,,,,,,,,,,,,,,,,,,,,
 begin_rec, , , ,,,,,,,,,,,,tokens/sec,tokens/sec/$,tokens/sec/TDP,msec/token,,,,,,
@@ -573,15 +581,20 @@ falcon-7b-instruct,OV-2024.2,xeon,Intel® Xeon® Platinum 8580 CPU-only,25.0,,,,
 falcon-7b-instruct,OV-2024.2,core,Intel® Core™ Ultra 7-165H CPU-only,5.8,,,,,460,28,1,460,28,171.8,tokens/sec,tokens/sec/$,tokens/sec/TDP,msec/token,,,167.1,6.0,,
 falcon-7b-instruct,OV-2024.2,accel,Intel® Data Center GPU Flex 170 dGPU,25.2,,,,,2000,150,1,2000,150,39.6,tokens/sec,tokens/sec/$,tokens/sec/TDP,msec/token,,,36.9,27.1,,
 falcon-7b-instruct,OV-2024.2,accel,Intel® Arc™ A770M,22.1,,,,,321,150,1,321,150,45.3,tokens/sec,tokens/sec/$,tokens/sec/TDP,msec/token,,,36.6,27.3,,
+falcon-7b-instruct,OV-2024.2,accel,Intel® Arc™ A570M,7.4,,,,,,,,,,134.5,tokens/sec,tokens/sec/$,tokens/sec/TDP,msec/token,,,51.9,19.3,,
+falcon-7b-instruct,OV-2024.2,accel,Intel® Arc™ A530M,7.4,,,,,,,,,,134.9,tokens/sec,tokens/sec/$,tokens/sec/TDP,msec/token,,,71.8,13.9,,
 falcon-7b-instruct,OV-2024.2,accel,Intel® Data Center GPU Flex 140 dGPU,,,,,,1900,75,1,1900,75, ,tokens/sec,tokens/sec/$,tokens/sec/TDP,msec/token,,,84.2,11.9,,
-falcon-7b-instruct,OV-2024.2,core,Intel® Core™ Ultra 7-165H iGPU-only,9.6,4.9,,,,460,28,1,460,28,104.2,tokens/sec,tokens/sec/$,tokens/sec/TDP,msec/token,206.1,,68.9,14.5,,
+falcon-7b-instruct,OV-2024.2,core-iGPU,Intel® Core™ Ultra 7-165H iGPU-only,9.6,4.9,,,,460,28,1,460,28,104.2,tokens/sec,tokens/sec/$,tokens/sec/TDP,msec/token,206.1,,68.9,14.5,,
 end_rec,,,,,,,,,,,,,,,,,,,,,,,,
 begin_rec, , , ,,,,,,,,,,,,"Generation time, sec.",Generation time/$,Generation time/TDP,"Generation time, sec.",,,,,,
 stable-diffusion-v2-1,OV-2024.2,core,Intel® Core™ i9-13900K CPU-only,,,,,,599,125,1,599,125,40.47,"Generation time, sec.",Generation time/$,Generation time/TDP,"Generation time, sec.",40.48,,,,,
+stable-diffusion-v2-1,OV-2024.2,core-iGPU,Intel® Core™ Ultra 7-165H iGPU-only,,,,,,,,,,,15.1,"Generation time, sec.",Generation time/$,Generation time/TDP,"Generation time, sec.",14.5,,,,,
 stable-diffusion-v2-1,OV-2024.2,xeon,Intel® Xeon® Platinum 8380 CPU-only,,,,,,18718,540,2,9359,270,15.82,"Generation time, sec.",Generation time/$,Generation time/TDP,"Generation time, sec.",15.1,,,,,
 stable-diffusion-v2-1,OV-2024.2,xeon,Intel® Xeon® Platinum 8490H CPU-only,,,,,,34000,700,2,17000,350,3.71,"Generation time, sec.",Generation time/$,Generation time/TDP,"Generation time, sec.",3.76,,,,,
 stable-diffusion-v2-1,OV-2024.2,xeon,Intel® Xeon® Platinum 8580 CPU-only,,,,,,21420,700,2,10710,350,3.53,"Generation time, sec.",Generation time/$,Generation time/TDP,"Generation time, sec.",3.63,,,,,
 stable-diffusion-v2-1,OV-2024.2,accel,Intel® Data Center GPU Flex 170 dGPU,,,,,,2000,150,1,2000,150,,"Generation time, sec.",Generation time/$,Generation time/TDP,"Generation time, sec.",2.51,,,,,
 stable-diffusion-v2-1,OV-2024.2,accel,Intel® Arc™ A770M,,,,,,321,150,1,321,150,,"Generation time, sec.",Generation time/$,Generation time/TDP,"Generation time, sec.",2.91,,,,,
+stable-diffusion-v2-1,OV-2024.2,accel,Intel® Arc™ A570M,,,,,,,,,,,,"Generation time, sec.",Generation time/$,Generation time/TDP,"Generation time, sec.",11.9,,,,,
+stable-diffusion-v2-1,OV-2024.2,accel,Intel® Arc™ A530M,,,,,,,,,,,,"Generation time, sec.",Generation time/$,Generation time/TDP,"Generation time, sec.",10.6,,,,,
 stable-diffusion-v2-1,OV-2024.2,accel,Intel® Data Center GPU Flex 140 dGPU,,,,,,1900,75,1,1900,75,,"Generation time, sec.",Generation time/$,Generation time/TDP,"Generation time, sec.",25.36,,,,,
 end_rec
\ No newline at end of file
diff --git a/docs/sphinx_setup/_static/benchmarks_files/OVMS-benchmark-data.csv b/docs/sphinx_setup/_static/benchmarks_files/OVMS-benchmark-data.csv
index 1c8178b5270dea..3ca1c7cc313491 100644
--- a/docs/sphinx_setup/_static/benchmarks_files/OVMS-benchmark-data.csv
+++ b/docs/sphinx_setup/_static/benchmarks_files/OVMS-benchmark-data.csv
@@ -1,67 +1,78 @@
-Network model,Release,IE-Type,Platform name,Throughput-OVMS-INT8,Throughput-OV-INT8,Throughput-OVMS-FP32,Throughput-OV-FP32,UOM_T
+Network model,Release,IE-Type,Platform name,Throughput-OVMS-INT8,Throughput-OV-INT8,Throughput-OVMS-FP32,Throughput-OV-FP32,
 begin_rec,,,,,,,,
-bert-base-cased,OV-2024.1,xeon,Intel®  Xeon® 8260M CPU-only,476.413,492.202,181.088,181.699,FPS
-bert-base-cased,OV-2024.1,xeon,Intel®  Xeon® Gold 6238M CPU-only,426.087,444.057,161.123,163.048,FPS
-bert-base-cased,OV-2024.1,core,Intel® Core™  i9-10920X CPU-only,162.953,177.975,53.133,58.18,FPS
-bert-base-cased,OV-2024.1,core,Intel® Core™  i3-10100 CPU-only,26.038,26.159,17.201,17.357,FPS
+bert-base-cased,OV-2024.2,xeon,Intel®  Xeon® 8260M CPU-only,,484.765,486.962,181.829,179.94
+bert-base-cased,OV-2024.2,xeon,Intel®  Xeon® Gold 6238M CPU-only,,430.151,434.276,157.835,158.277
+bert-base-cased,OV-2024.2,core,Intel® Core™  i9-11900K CPU-only,,101.044,102.838,35.727,36.57
+bert-base-cased,OV-2024.2,core,Intel® Core™  i7-11700K CPU-only,,100.741,103.322,35.046,36.607
+bert-base-cased,OV-2024.2,core,Intel® Core™  i3-10100 CPU-only,,26.124,26.329,17.155,17.387
 end_rec,,,,,,,,
 begin_rec,,,,,,,,
-bert-large-uncased,OV-2024.1,xeon,Intel®  Xeon® 8260M CPU-only,41.919,42.236,14.896,14.33,FPS
-bert-large-uncased,OV-2024.1,xeon,Intel®  Xeon® Gold 6238M CPU-only,37.289,37.704,13.063,12.897,FPS
-bert-large-uncased,OV-2024.1,core,Intel® Core™  i9-10920X CPU-only,14.046,14.276,5.033,4.96,FPS
-bert-large-uncased,OV-2024.1,core,Intel® Core™  i3-10100 CPU-only,2.428,2.401,1.446,1.432,FPS
+bert-large-uncased,OV-2024.2,xeon,Intel®  Xeon® 8260M CPU-only,,41.302,41.994,14.937,14.482
+bert-large-uncased,OV-2024.2,xeon,Intel®  Xeon® Gold 6238M CPU-only,,36.595,37.1,13.114,13.03
+bert-large-uncased,OV-2024.2,core,Intel® Core™  i9-11900K CPU-only,,10.076,10.145,3.267,3.246
+bert-large-uncased,OV-2024.2,core,Intel® Core™  i7-11700K CPU-only,,10.161,10.203,3.287,3.26
+bert-large-uncased,OV-2024.2,core,Intel® Core™  i3-10100 CPU-only,,2.422,2.424,1.447,1.427
 end_rec,,,,,,,,
 begin_rec,,,,,,,,
-Efficientdet-D0,OV-2024.1,xeon,Intel®  Xeon® 8260M CPU-only,423.3,466.485,284.666,287.182,FPS
-Efficientdet-D0,OV-2024.1,xeon,Intel®  Xeon® Gold 6238M CPU-only,352.495,389.158,210.714,202.001,FPS
-Efficientdet-D0,OV-2024.1,core,Intel® Core™  i9-10920X CPU-only,151.702,178.273,58.861,61.524,FPS
-Efficientdet-D0,OV-2024.1,core,Intel® Core™  i3-10100 CPU-only,46.985,49.806,27.872,28.841,FPS
+Efficientdet-D0,OV-2024.2,xeon,Intel®  Xeon® 8260M CPU-only,,433.166,479.055,285.65,287.547
+Efficientdet-D0,OV-2024.2,xeon,Intel®  Xeon® Gold 6238M CPU-only,,367.395,407,251.401,257.516
+Efficientdet-D0,OV-2024.2,core,Intel® Core™  i9-11900K CPU-only,,132.153,149.424,57.682,61.811
+Efficientdet-D0,OV-2024.2,core,Intel® Core™  i7-11700K CPU-only,,124.984,142.514,50.265,53.089
+Efficientdet-D0,OV-2024.2,core,Intel® Core™  i3-10100 CPU-only,,47.048,50.328,30.026,31.473
 end_rec,,,,,,,,
 begin_rec,,,,,,,,
-mask_rcnn_resnet50_atrous_coco,OV-2024.1,xeon,Intel®  Xeon® 8260M CPU-only,6.472,6.468,1.97,1.889,FPS
-mask_rcnn_resnet50_atrous_coco,OV-2024.1,xeon,Intel®  Xeon® Gold 6238M CPU-only,5.443,5.46,1.743,1.62,FPS
-mask_rcnn_resnet50_atrous_coco,OV-2024.1,core,Intel® Core™  i9-10920X CPU-only,2.035,2.023,0.555,0.531,FPS
-mask_rcnn_resnet50_atrous_coco,OV-2024.1,core,Intel® Core™  i3-10100 CPU-only,0.379,0.351,0.249,0.215,FPS
+mask_rcnn_resnet50_atrous_coco,OV-2024.2,xeon,Intel®  Xeon® 8260M CPU-only,,6.323,6.488,1.978,1.868
+mask_rcnn_resnet50_atrous_coco,OV-2024.2,xeon,Intel®  Xeon® Gold 6238M CPU-only,,5.637,5.742,1.715,1.633
+mask_rcnn_resnet50_atrous_coco,OV-2024.2,core,Intel® Core™  i9-11900K CPU-only,,1.302,1.276,0.396,0.373
+mask_rcnn_resnet50_atrous_coco,OV-2024.2,core,Intel® Core™  i7-11700K CPU-only,,1.307,1.28,0.374,0.36
+mask_rcnn_resnet50_atrous_coco,OV-2024.2,core,Intel® Core™  i3-10100 CPU-only,,0.381,0.35,0.181,0.15
 end_rec,,,,,,,,
 begin_rec,,,,,,,,
-Mobilenet-V2 ,OV-2024.1,xeon,Intel®  Xeon® 8260M CPU-only,7586.42,12368.284,3342.938,3909.837,FPS
-Mobilenet-V2 ,OV-2024.1,xeon,Intel®  Xeon® Gold 6238M CPU-only,7371.605,10620.061,2651.393,3168.019,FPS
-Mobilenet-V2 ,OV-2024.1,core,Intel® Core™  i9-10920X CPU-only,1888.454,4239.691,877.888,1072.947,FPS
-Mobilenet-V2 ,OV-2024.1,core,Intel® Core™  i3-10100 CPU-only,626.817,715.776,435.043,512.516,FPS
+Mobilenet-V2 ,OV-2024.2,xeon,Intel®  Xeon® 8260M CPU-only,,7578.115,12346.3,3354.203,3938.523
+Mobilenet-V2 ,OV-2024.2,xeon,Intel®  Xeon® Gold 6238M CPU-only,,7513.034,10367.947,2915.906,3349.306
+Mobilenet-V2 ,OV-2024.2,core,Intel® Core™  i9-11900K CPU-only,,2152.015,2740.691,745.81,882.839
+Mobilenet-V2 ,OV-2024.2,core,Intel® Core™  i7-11700K CPU-only,,2093.311,2822.613,667.391,795.616
+Mobilenet-V2 ,OV-2024.2,core,Intel® Core™  i3-10100 CPU-only,,615.392,719.715,381.686,454.574
 end_rec,,,,,,,,
 begin_rec,,,,,,,,
-Resnet-50,OV-2024.1,xeon,Intel®  Xeon® 8260M CPU-only,2353.23,2487.25,639.118,645.669,FPS
-Resnet-50,OV-2024.1,xeon,Intel®  Xeon® Gold 6238M CPU-only,2084.709,2248.889,558.128,546.481,FPS
-Resnet-50,OV-2024.1,core,Intel® Core™  i9-10920X CPU-only,794.725,940.482,205.072,199.384,FPS
-Resnet-50,OV-2024.1,core,Intel® Core™  i3-10100 CPU-only,114.636,118.435,59.034,60.112,FPS
+Resnet-50,OV-2024.2,xeon,Intel®  Xeon® 8260M CPU-only,,2354.173,2482.832,639.577,645.443
+Resnet-50,OV-2024.2,xeon,Intel®  Xeon® Gold 6238M CPU-only,,2070.726,2177.751,571.252,575.778
+Resnet-50,OV-2024.2,core,Intel® Core™  i9-11900K CPU-only,,440.402,458.622,114.169,116.577
+Resnet-50,OV-2024.2,core,Intel® Core™  i7-11700K CPU-only,,448.464,470.586,111.785,114.628
+Resnet-50,OV-2024.2,core,Intel® Core™  i3-10100 CPU-only,,114.267,118.502,57.29,58.233
 end_rec,,,,,,,,
 begin_rec,,,,,,,,
-SSD-Resnet34-1200 ,OV-2024.1,xeon,Intel®  Xeon® 8260M CPU-only,44.428,47.308,12.103,12.229,FPS
-SSD-Resnet34-1200 ,OV-2024.1,xeon,Intel®  Xeon® Gold 6238M CPU-only,38.586,40.72,10.507,10.619,FPS
-SSD-Resnet34-1200 ,OV-2024.1,core,Intel® Core™  i9-10920X CPU-only,16.863,17.476,4.217,4.271,FPS
-SSD-Resnet34-1200 ,OV-2024.1,core,Intel® Core™  i3-10100 CPU-only,1.955,1.935,1.142,1.121,FPS
+SSD-Resnet34-1200 ,OV-2024.2,xeon,Intel®  Xeon® 8260M CPU-only,,44.587,47.293,12.111,12.248
+SSD-Resnet34-1200 ,OV-2024.2,xeon,Intel®  Xeon® Gold 6238M CPU-only,,38.784,40.602,10.521,10.613
+SSD-Resnet34-1200 ,OV-2024.2,core,Intel® Core™  i9-11900K CPU-only,,7.736,7.821,2.034,2.011
+SSD-Resnet34-1200 ,OV-2024.2,core,Intel® Core™  i7-11700K CPU-only,,7.953,8.033,2.083,2.058
+SSD-Resnet34-1200 ,OV-2024.2,core,Intel® Core™  i3-10100 CPU-only,,1.951,1.936,1.04,1.014
 end_rec,,,,,,,,
 begin_rec,,,,,,,,
-SSD_Mobilenet_V1_Coco,OV-2024.1,xeon,Intel®  Xeon® 8260M CPU-only,4720.423,4991.46,1364.141,1392.376,FPS
-SSD_Mobilenet_V1_Coco,OV-2024.1,xeon,Intel®  Xeon® Gold 6238M CPU-only,4067.035,4228.271,1096.362,1112.343,FPS
-SSD_Mobilenet_V1_Coco,OV-2024.1,core,Intel® Core™  i9-10920X CPU-only,1519.287,1785.937,408.959,442.492,FPS
-SSD_Mobilenet_V1_Coco,OV-2024.1,core,Intel® Core™  i3-10100 CPU-only,256.837,265.931,135.537,139.551,FPS
+SSD_Mobilenet_V1_Coco,OV-2024.2,xeon,Intel®  Xeon® 8260M CPU-only,,4753.674,4933.241,1370.423,1379.026
+SSD_Mobilenet_V1_Coco,OV-2024.2,xeon,Intel®  Xeon® Gold 6238M CPU-only,,4165.318,4276.949,1197.151,1222.112
+SSD_Mobilenet_V1_Coco,OV-2024.2,core,Intel® Core™  i9-11900K CPU-only,,920.957,1001.56,270.597,281.293
+SSD_Mobilenet_V1_Coco,OV-2024.2,core,Intel® Core™  i7-11700K CPU-only,,941.323,1030.464,256.649,266.715
+SSD_Mobilenet_V1_Coco,OV-2024.2,core,Intel® Core™  i3-10100 CPU-only,,256.291,266.245,129.84,135.453
 end_rec,,,,,,,,
 begin_rec,,,,,,,,
-Unet-Camvid--0001 ,OV-2024.1,xeon,Intel®  Xeon® 8260M CPU-only,71.937,78.1,17.787,18.034,FPS
-Unet-Camvid--0001 ,OV-2024.1,xeon,Intel®  Xeon® Gold 6238M CPU-only,62.515,66.299,15.47,15.6,FPS
-Unet-Camvid--0001 ,OV-2024.1,core,Intel® Core™  i9-10920X CPU-only,26.386,27.506,5.861,5.932,FPS
-Unet-Camvid--0001 ,OV-2024.1,core,Intel® Core™  i3-10100 CPU-only,2.958,2.941,1.844,1.825,FPS
+Unet-Camvid--0001 ,OV-2024.2,xeon,Intel®  Xeon® 8260M CPU-only,,73.871,78.016,18.23,18.374
+Unet-Camvid--0001 ,OV-2024.2,xeon,Intel®  Xeon® Gold 6238M CPU-only,,64.573,67.713,15.815,16.023
+Unet-Camvid--0001 ,OV-2024.2,core,Intel® Core™  i9-11900K CPU-only,,12.572,12.669,3.28,3.254
+Unet-Camvid--0001 ,OV-2024.2,core,Intel® Core™  i7-11700K CPU-only,,12.779,12.894,3.315,3.299
+Unet-Camvid--0001 ,OV-2024.2,core,Intel® Core™  i3-10100 CPU-only,,2.99,2.971,1.549,1.542
 end_rec,,,,,,,,
 begin_rec,,,,,,,,
-Yolo_V3_Tiny,OV-2024.1,xeon,Intel®  Xeon® 8260M CPU-only,1864.709,2323.356,776.774,786.171,FPS
-Yolo_V3_Tiny,OV-2024.1,xeon,Intel®  Xeon® Gold 6238M CPU-only,1536.778,2031.428,594.33,602.848,FPS
-Yolo_V3_Tiny,OV-2024.1,core,Intel® Core™  i9-10920X CPU-only,480.953,863.418,211.699,248.475,FPS
-Yolo_V3_Tiny,OV-2024.1,core,Intel® Core™  i3-10100 CPU-only,119.656,133.278,70.525,75.039,FPS
+Yolo_V3_Tiny,OV-2024.2,xeon,Intel®  Xeon® 8260M CPU-only,,1863.229,2344.128,775.001,786.09
+Yolo_V3_Tiny,OV-2024.2,xeon,Intel®  Xeon® Gold 6238M CPU-only,,1669.35,2066.437,675.625,703.203
+Yolo_V3_Tiny,OV-2024.2,core,Intel® Core™  i9-11900K CPU-only,,430.441,505.532,146.284,151.09
+Yolo_V3_Tiny,OV-2024.2,core,Intel® Core™  i7-11700K CPU-only,,419.347,513.112,142.009,148.117
+Yolo_V3_Tiny,OV-2024.2,core,Intel® Core™  i3-10100 CPU-only,,117.889,133.312,63.598,69.377
 end_rec,,,,,,,,
 begin_rec,,,,,,,,
-Yolo_V8n,OV-2024.1,xeon,Intel®  Xeon® 8260M CPU-only,676.683,836.562,315.786,388.673,FPS
-Yolo_V8n,OV-2024.1,xeon,Intel®  Xeon® Gold 6238M CPU-only,500.88,702.198,264.887,318.088,FPS
-Yolo_V8n,OV-2024.1,core,Intel® Core™  i9-10920X CPU-only,176.708,269.617,91.142,112.079,FPS
-Yolo_V8n,OV-2024.1,core,Intel® Core™  i3-10100 CPU-only,57.891,67.784,37.688,40.711,FPS
+Yolo_V8n,OV-2024.2,xeon,Intel®  Xeon® 8260M CPU-only,,705.714,845.484,316.319,388.763
+Yolo_V8n,OV-2024.2,xeon,Intel®  Xeon® Gold 6238M CPU-only,,641.815,746.965,278.824,338.806
+Yolo_V8n,OV-2024.2,core,Intel® Core™  i9-11900K CPU-only,,154.54,205.294,67.744,75.634
+Yolo_V8n,OV-2024.2,core,Intel® Core™  i7-11700K CPU-only,,149.289,199.997,65.243,72.677
+Yolo_V8n,OV-2024.2,core,Intel® Core™  i3-10100 CPU-only,,55.999,68.155,34.454,38.544
 end_rec,,,,,,,,
\ No newline at end of file
diff --git a/docs/sphinx_setup/_static/css/custom.css b/docs/sphinx_setup/_static/css/custom.css
index ee94f21b0ee851..8cf6521fe05a8a 100644
--- a/docs/sphinx_setup/_static/css/custom.css
+++ b/docs/sphinx_setup/_static/css/custom.css
@@ -460,7 +460,7 @@ div.highlight {
     .container-md,
     .container-sm,
     .container-xl {
-        max-width: 1900px;
+        max-width: 100%;
     }
 }
 
diff --git a/docs/sphinx_setup/_static/llm_models.csv b/docs/sphinx_setup/_static/download/llm_models.csv
similarity index 92%
rename from docs/sphinx_setup/_static/llm_models.csv
rename to docs/sphinx_setup/_static/download/llm_models.csv
index 28b730c6c7dd1c..28ac9f2c79ec83 100644
--- a/docs/sphinx_setup/_static/llm_models.csv
+++ b/docs/sphinx_setup/_static/download/llm_models.csv
@@ -19,5 +19,4 @@ Mistral-7b-v0.1,10.5,4462,7242,1007,128,INT4,1,1,PT,
 Zephyr-7b-beta,10.5,4500,7039,1024,128,INT4,1,1,PT,
 Qwen1.5-7b-chat,9.9,4318,7034,1024,128,INT4,1,1,PT,
 Baichuan2-7b-chat,9.8,4668,6724,1024,128,INT4,1,1,PT,
-Qwen-7b-chat,9.0,5141,6996,1024,128,INT4,1,1,PT,
-Vicuna-7b-v1.5,0.0,3982,7022,1024,128,INT4,1,1,PT,
\ No newline at end of file
+Qwen-7b-chat,9.0,5141,6996,1024,128,INT4,1,1,PT,
\ No newline at end of file
diff --git a/docs/sphinx_setup/_static/download/supported_models.csv b/docs/sphinx_setup/_static/download/supported_models.csv
new file mode 100644
index 00000000000000..798c37467b67bc
--- /dev/null
+++ b/docs/sphinx_setup/_static/download/supported_models.csv
@@ -0,0 +1,1210 @@
+Model,Framework,Precision,AI PC CPU,AI PC GPU,AI PC NPU
+Denoise,tf,FP16,v,,
+Denoise,tf,FP32,v,,
+Denoise,tf,FP16,,v,v
+Denoise,tf,FP32,,,v
+GPT-2,onnx,FP16,v,,
+GPT-2,onnx,FP32,v,,
+GPT-2,onnx,FP16,,v,
+GPT-2,onnx,FP32,,v,
+Sharpen-LensBlur,tf,FP16,v,,
+Sharpen-LensBlur,tf,FP32,v,,
+Sharpen-LensBlur,tf,FP16,,v,v
+Sharpen-LensBlur,tf,FP16-INT8,,v,
+Sharpen-LensBlur,tf,FP32,,v,v
+Sharpen-MotionBlur,tf,FP16,v,,
+Sharpen-MotionBlur,tf,FP16-INT8,v,,
+Sharpen-MotionBlur,tf,FP32,v,,
+Sharpen-MotionBlur,tf,FP16,,v,v
+Sharpen-MotionBlur,tf,FP16-INT8,,v,v
+Sharpen-MotionBlur,tf,FP32,,v,v
+Sharpen-Sharpen,tf,FP16,v,,
+Sharpen-Sharpen,tf,FP16-INT8,v,,
+Sharpen-Sharpen,tf,FP32,v,,
+Sharpen-Sharpen,tf,FP16,,v,v
+Sharpen-Sharpen,tf,FP16-INT8,,v,
+Sharpen-Sharpen,tf,FP32,,v,v
+Sphereface,caffe,FP16,v,,
+Sphereface,caffe,FP32,v,,
+Sphereface,caffe,FP16,,v,v
+Sphereface,caffe,FP32,,v,v
+aclnet,onnx,FP16,v,,
+aclnet,onnx,FP16-INT8,v,,
+aclnet,onnx,FP32,v,,
+aclnet,onnx,FP16,,v,v
+aclnet,onnx,FP16-INT8,,v,v
+aclnet,onnx,FP32,,v,v
+aclnet-int8,onnx,FP16-INT8,,,v
+alexnet-caffe,caffe,FP16,v,,
+alexnet-caffe,caffe,FP32,v,,
+alexnet-caffe,caffe,FP16,,v,
+alexnet-caffe,caffe,FP32,,v,
+alexnet-onnx,onnx,FP16,v,,
+alexnet-onnx,onnx,FP16-INT8,v,,
+alexnet-onnx,onnx,FP32,v,,
+alexnet-onnx,onnx,FP16,,v,v
+alexnet-onnx,onnx,FP16-INT8,,v,
+alexnet-onnx,onnx,FP32,,v,v
+began,onnx,FP16,v,,
+began,onnx,FP32,v,,
+began,onnx,FP16,,v,v
+began,onnx,FP32,,v,v
+bert-base-cased-onnx,onnx,FP16,v,,
+bert-base-cased-onnx,onnx,FP16-INT8,v,,
+bert-base-cased-onnx,onnx,FP32,v,,
+bert-base-cased-onnx,onnx,FP16,,v,v
+bert-base-cased-onnx,onnx,FP16-INT8,,v,v
+bert-base-cased-onnx,onnx,FP32,,,v
+bert-base-cased-paddle,paddle,FP16,v,,
+bert-base-cased-paddle,paddle,FP16-INT8,v,,
+bert-base-cased-paddle,paddle,FP32,v,,
+bert-base-cased-paddle,paddle,FP16,,v,
+bert-base-cased-paddle,paddle,FP16-INT8,,v,
+bert-base-cased-squad2,onnx,FP16,v,,
+bert-base-cased-squad2,onnx,FP32,v,,
+bert-base-cased-squad2,onnx,FP16,,v,
+bert-base-chinese-xnli-zh,tf,FP16,,,v
+bert-base-chinese-xnli-zh,tf,FP32,,,v
+bert-base-ner,onnx,FP16,v,,
+bert-base-ner,onnx,FP16-INT8,v,,
+bert-base-ner,onnx,FP32,v,,
+bert-base-ner,onnx,FP16,,v,v
+bert-base-ner,onnx,FP16-INT8,,v,v
+bert-base-uncased-mrpc,onnx,FP16,,,v
+bert-base-uncased-mrpc,onnx,FP16-INT8,,,v
+bert-base-uncased-mrpc,onnx,FP32,,,v
+bert-large-uncased-whole-word-masking-squad-0001,onnx,FP16,v,,
+bert-large-uncased-whole-word-masking-squad-0001,onnx,FP32,v,,
+bert-large-uncased-whole-word-masking-squad-0001,onnx,FP16,,v,v
+bert-large-uncased-whole-word-masking-squad-0001,onnx,FP32,,v,v
+bert-large-uncased-whole-word-masking-squad-int8-0001,onnx,FP16-INT8,v,,
+bert-large-uncased-whole-word-masking-squad-int8-0001,onnx,FP16-INT8,,v,v
+bisenet-v2,paddle,FP16,v,,
+bisenet-v2,paddle,FP32,v,,
+bisenet-v2,paddle,FP16,,v,v
+brain-tumor-segmentation-0002,onnx,FP16,v,,
+brain-tumor-segmentation-0002,onnx,FP16-INT8,v,,
+colorization-siggraph,onnx,FP16,v,,
+colorization-siggraph,onnx,FP32,v,,
+colorization-siggraph,onnx,FP16,,v,v
+colorization-siggraph,onnx,FP32,,,v
+colorization-v2,onnx,FP16-INT8,,,v
+common-sign-language-0001,onnx,FP16,v,,
+common-sign-language-0001,onnx,FP32,v,,
+common-sign-language-0001,onnx,FP16,,v,v
+common-sign-language-0001,onnx,FP32,,v,v
+crnn-tf,tf,FP16,v,,
+crnn-tf,tf,FP32,v,,
+crnn-tf,tf,FP16,,v,
+darknet19,tf,FP16,v,,
+darknet19,tf,FP16-INT8,v,,
+darknet19,tf,FP32,v,,
+darknet19,tf,FP16,,v,v
+darknet19,tf,FP16-INT8,,v,v
+darknet19,tf,FP32,,v,
+deeplab-v3p-resnet50-os8,paddle,FP16,v,,
+deeplab-v3p-resnet50-os8,paddle,FP16-INT8,v,,
+deeplab-v3p-resnet50-os8,paddle,FP32,v,,
+deeplab-v3p-resnet50-os8,paddle,FP16,,v,
+deeplab-v3p-resnet50-os8,paddle,FP16-INT8,,v,
+deeplab-v3p-resnet50-os8,paddle,FP32,,v,
+deeplabv3,tf,FP16,v,,
+deeplabv3,tf,FP16-INT8,v,,
+deeplabv3,tf,FP32,v,,
+deeplabv3,tf,FP16,,v,
+deeplabv3,tf,FP16-INT8,,v,
+deeplabv3,tf,FP32,,v,
+densenet-121-caffe,caffe,FP16,v,,
+densenet-121-caffe,caffe,FP16,,v,v
+densenet-121-caffe,caffe,FP32,,,v
+densenet-121-onnx,onnx,FP16,v,,
+densenet-121-onnx,onnx,FP16-INT8,v,,
+densenet-121-onnx,onnx,FP32,v,,
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+Llama-2-7b-chat-hf,pytorch,intel-optimum default,,v,
+Llama-2-7b-hf,pytorch,intel-optimum default,,v,
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+Magicoder-DS-6.7B,pytorch,intel-optimum default,,v,
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+Magicoder-S-DS-6.7B,pytorch,intel-optimum default,,v,
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+Meta-Llama-Guard-2-8B,pytorch,intel-optimum default,,v,
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diff --git a/docs/sphinx_setup/_static/html/footer.html b/docs/sphinx_setup/_static/html/footer.html
index da0353dd09698e..4fc60089d51d4e 100644
--- a/docs/sphinx_setup/_static/html/footer.html
+++ b/docs/sphinx_setup/_static/html/footer.html
@@ -3,7 +3,7 @@
         margin: 0;
     }
 
-    .container {
+    .footer .container {
         padding-right: 30px;
         padding-left: 30px;
         margin-right: auto;
@@ -19,16 +19,12 @@
     }
 
     .footer-link-list a:nth-child(1) {
-        margin-right: 50px;
-    }
-
-    .footer-link-list a:nth-child(2) {
-        clear: both;
+        margin-right: 80px;
     }
 
     .footer-contents {
         border-top: 1px solid lightgray;
-        font-size: 14px;
+        font-size: 12px;
         box-sizing: border-box;
         font-family: sans-serif;
         padding-top: 10px;
@@ -39,10 +35,12 @@
         margin-right: 0px;
     }
 
-    .footer-contents p {
+    .footer-item p {
         margin-top: 0;
         margin-bottom: 0;
         line-height: 1.65;
+        padding: 10px 10px 0 30px !important;
+        font-size: 11px !important;
     }
 
     .footer-contents .footer-link-list a {
@@ -51,23 +49,22 @@
 
     .newsletter-btn,
     .newsletter-btn:active {
-        background-color: #0054AE;
-        border: 1px solid #0054AE;
+        background-color: white;
+        border: 1px solid lightgray !important;
         border-radius: none;
-        color: white;
-        margin-top: 5px;
-        cursor: pointer;
+        color: #0054AE !important;
         display: inline-block;
         font-weight: 400;
         font-size: .8rem;
         line-height: 2.0;
-        padding: .2rem .5rem;
+        padding: 0.5rem 0.65625rem 0.5rem 0.96875rem;
         text-align: center;
         text-decoration: none;
         transition: color .15s ease-in-out, background-color .15s ease-in-out, border-color .15s ease-in-out, box-shadow .15s ease-in-out;
         vertical-align: middle;
         align-self: flex-start;
     }
+
     ul {
         margin-bottom: 0px;
     }
@@ -77,8 +74,8 @@
     }
 
     .newsletter-btn:hover {
-        background-color: rgb(240, 240, 240);
-        color: hsl(211, 100%, 34%);
+        background-color: rgba(var(--ost-color-sst-dropdown-background), 1) !important;
+        color: rgb(240, 240, 240) !important;
     }
 
     .footer-list {
@@ -95,36 +92,50 @@
 
     /* cookie wap requirement */
     li#wap_dns {
+        display: none;        
+    }
+
+    li#wap_nac {
         display: none;
     }
 
     .reference:hover {
         text-decoration: underline;
     }
-
 </style>
 
 <div class="container footer-contents">
     <div class="footer-item">
         <div class="footer-list">
             <div class="footer-link-list">
-                <a href="https://www.intel.com/content/www/us/en/homepage.html" alt="Intel" style="color: #000;"
-                    target="" _blank>©2024 Intel Corporation</a>
                 <ul class="no-bullets">
-                    <li ><a class="reference" href="https://www.intel.com/content/www/us/en/legal/terms-of-use.html" alt="terms of use" target="_blank">Terms of Use</a></li>
-                    <li><a class="reference" href="https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" alt="cookies policy" target="_blank" data-cookie-notice="true" >Cookies</a></li>
-                    <li><a class="reference" href="https://www.intel.com/content/www/us/en/privacy/intel-privacy-notice.html" alt="privacy" target="_blank">Privacy</a></li>
+                    <lh>©2024 Intel Corporation</lh>
+                </ul>
+                <ul class="no-bullets">
+                    <lh>OpenVino</lh>
+                    <li><a href="https://docs.openvino.ai/2024/about-openvino/additional-resources/telemetry.html" alt="telemetry">OpenVINO™ Telemetry</a></li>
+                    <li><a href="https://docs.openvino.ai/2024/about-openvino/additional-resources/glossary.html" alt="glossary">Glossary</a></li>
+                    <li><a href="https://www.intel.com/content/www/us/en/internet-of-things/ai-in-production/success-stories.html"  alt="stories" target="_blank">Case Studies</a></li>
                 </ul>
-
                 <ul class="no-bullets">
-                    <li><a class="reference" href="https://www.intel.com/content/www/us/en/privacy/intel-privacy-notice.html" alt="legal and responsible ai information" target="_blank">Legal and Responsible AI Information</a></li>
-                    <li><a class="reference" href="https://www.intel.com/content/www/us/en/artificial-intelligence/responsible-ai.html" alt="responsible ai" target="_blank">Responsible AI</a></li>
-                    <li data-wap_ref="dns" id="wap_dns"><a class="reference" href="https://www.intel.com/content/www/us/en/privacy/intel-cookienotice.html" alt="terms of use" target="_parent">Do Not Share My Personal Information</a></li>
+                    <lh>Legal</lh>
+                    <li><a href="https://docs.openvino.ai/2024/about-openvino/additional-resources/legal-information.html" alt="the intel">Terms of Use</a></li>
+                    <li><a href="https://www.intel.com/content/www/us/en/privacy/intel-privacy-notice.html" alt="legal and responsible ai information" target="_blank">Legal and Responsible AI
+                            Information</a></li>
+                    <li><a href="https://www.intel.com/content/www/us/en/artificial-intelligence/responsible-ai.html" alt="responsible ai" target="_blank">Responsible AI</a></li>
+                </ul>
+                <ul class="no-bullets">
+                    <lh>Privacy</lh>
+                    <li><a href="https://www.intel.com/content/www/us/en/privacy/intel-cookie-notice.html" alt="cookies policy" target="_blank" data-cookie-notice="true">Cookies</a></li>
+                    <li><a href="https://www.intel.com/content/www/us/en/privacy/intel-privacy-notice.html" alt="privacy" target="_blank">Privacy</a></li>
+                    <li data-wap_ref="dns" id="wap_dns"><a href="/#" alt="your privacy choice" target="_parent">Your Privacy Choices</a></li>
+                    <li id="footer_it_problem_fix_do_not_remove"></li>
+                    <li data-wap_ref="nac" id="wap_nac"><a href="https://www.intel.com/content/www/us/en/privacy/privacy-residents-certain-states.html"
+                            alt="notice at collection" target="_parent">Notice at Collection</a></li>
                 </ul>
             </div>
             <a id="newsletterTrigger" class="newsletter-btn" href="#" alt="Newsletter"
                 onclick="return false;">Newsletter</a>
-
         </div>
         <p style="font-size: 0.9em">Intel, the Intel logo, and other Intel marks are trademarks of Intel Corporation or
             its subsidiaries. Other names and brands may be claimed as the property of others.</p>
diff --git a/docs/sphinx_setup/_static/models.csv b/docs/sphinx_setup/_static/models.csv
deleted file mode 100644
index 4f1278817729ea..00000000000000
--- a/docs/sphinx_setup/_static/models.csv
+++ /dev/null
@@ -1,14 +0,0 @@
-Topology,Source framework,Precision,4th GEN Xeon Scalable Procesor,3rd GEN Xeon Scalable Processor,Fles Series discrete GP,Arc Series Discrete GPU
-aclnet,onnx,BF16,accurate,,,
-aclnet,onnx,FP16,accurate,accurate,accurate,accurate
-aclnet,onnx,FP32,accurate,accurate,accurate,accurate
-aclnet-int8 ,onnx,FP16-INT8,accurate,accurate,accurate,accurate
-aclnet-int8,onnx,FP32,completes,,,
-action-recognition-0001 ,null,BF16,accurate,,,
-action-recognition-0001,null,BF16,accurate,accurate,accurate,accurate
-action-recognition-0001,null,FP16-INT8,accurate,accurate,accurate,accurate
-action-recognition-0001,null,FP32 ,accurate,accurate,accurate,accurate
-age-gender-recognition-retail-0013,null,BF16,accurate,,,
-age-gender-recognition-retail-0013,null,BF16,accurate,accurate,accurate,accurate
-age-gender-recognition-retail-0013,null,FP16-INT8,accurate,accurate,completes,completes
-age-gender-recognition-retail-0013,null,FP32 ,completes,completes,completes,completes
\ No newline at end of file
diff --git a/docs/sphinx_setup/_static/selector-tool/assets/selector-6bf35a6f.css b/docs/sphinx_setup/_static/selector-tool/assets/selector-6bf35a6f.css
deleted file mode 100644
index f7b9dbfdeed4b6..00000000000000
--- a/docs/sphinx_setup/_static/selector-tool/assets/selector-6bf35a6f.css
+++ /dev/null
@@ -1 +0,0 @@
-:root{--spark-font-intel-one-text: IntelOneText, var(--global-font-apple-system), var(--global-font-blink-mac-system-font), var(--global-font-segoe-ui), var(--global-font-roboto), var(--global-font-helvetica), var(--global-font-arial), var(--global-font-sans-serif), var(--global-font-apple-color-emoji), var(--global-font-segoe-ui-emoji), var(--global-font-segoe-ui-symbol);--spark-font-intel-one-display: IntelOneDisplay, var(--global-font-apple-system), var(--global-font-blink-mac-system-font), var(--global-font-segoe-ui), var(--global-font-roboto), var(--global-font-helvetica), var(--global-font-arial), var(--global-font-sans-serif), var(--global-font-apple-color-emoji), var(--global-font-segoe-ui-emoji), var(--global-font-segoe-ui-symbol)}:root{--global-font-apple-system: -apple-system;--global-font-blink-mac-system-font: BlinkMacSystemFont;--global-font-segoe-ui: "Segoe UI";--global-font-roboto: Roboto;--global-font-helvetica: Helvetica;--global-font-arial: Arial;--global-font-sans-serif: sans-serif;--global-font-apple-color-emoji: "Apple Color Emoji";--global-font-segoe-ui-emoji: "Segoe UI Emoji";--global-font-segoe-ui-symbol: "Segoe UI Symbol"}:root{--spark-font-size-25: .6875rem;--spark-font-size-50: .75rem;--spark-font-size-75: .875rem;--spark-font-size-100: 1rem;--spark-font-size-200: 1.25rem;--spark-font-size-300: 1.5rem;--spark-font-size-400: 2.25rem;--spark-font-size-500: 3rem;--spark-font-size-600: 3.75rem;--spark-font-size-700: 4.5rem}:root{--spark-line-height-text: 1.5;--spark-line-height-heading: 1.3}:root{--spark-letter-spacing-small: .015625rem;--spark-letter-spacing-zero: 0rem}body{font-size:var(--spark-font-size-100, 1rem);font-family:var(--spark-font-intel-one-text);font-weight:400;line-height:var(--spark-line-height-text, 1.5);letter-spacing:var(--spark-letter-spacing-zero, 0rem)}:root{--spark-color-transparent: transparent;--spark-color-theme-light-gray50: #ffffff;--spark-color-theme-light-gray75: #f9f9f9;--spark-color-theme-light-gray100: #f4f5f5;--spark-color-theme-light-gray200: #e9eaeb;--spark-color-theme-light-gray300: #e2e2e4;--spark-color-theme-light-gray400: #c9cace;--spark-color-theme-light-gray500: #b2b3b9;--spark-color-theme-light-gray600: #8b8e97;--spark-color-theme-light-gray700: #6a6d75;--spark-color-theme-light-gray800: #494b51;--spark-color-theme-light-gray900: #2b2c30;--spark-color-theme-dark-gray50: #242528;--spark-color-theme-dark-gray75: #2e2f32;--spark-color-theme-dark-gray100: #313236;--spark-color-theme-dark-gray200: #3c3e42;--spark-color-theme-dark-gray300: #484a50;--spark-color-theme-dark-gray400: #585a62;--spark-color-theme-dark-gray500: #6b6e76;--spark-color-theme-dark-gray600: #8e9099;--spark-color-theme-dark-gray700: #b7b9be;--spark-color-theme-dark-gray800: #e3e3e5;--spark-color-theme-dark-gray900: #ffffff;--spark-color-classic-blue-tint2: #6cc4f5;--spark-color-classic-blue-tint1: #0099ec;--spark-color-classic-blue: #0054ae;--spark-color-classic-blue-shade1: #00377c;--spark-color-classic-blue-shade2: #001e50;--spark-color-energy-blue-tint2: #a0ebff;--spark-color-energy-blue-tint1: #6ddcff;--spark-color-energy-blue: #00c7fd;--spark-color-energy-blue-shade1: #0095ca;--spark-color-energy-blue-shade2: #005b85;--spark-color-carbon-tint2: #e9e9e9;--spark-color-carbon-tint1: #aeaeae;--spark-color-carbon: #808080;--spark-color-carbon-shade1: #525252;--spark-color-carbon-shade2: #262626;--spark-color-blue-steel-tint2: #b9d6e5;--spark-color-blue-steel-tint1: #86b3ca;--spark-color-blue-steel: #548fad;--spark-color-blue-steel-shade1: #41728a;--spark-color-blue-steel-shade2: #183544;--spark-color-geode-tint2: #eec3f7;--spark-color-geode-tint1: #cc94da;--spark-color-geode: #8f5da2;--spark-color-geode-shade1: #653171;--spark-color-moss-tint2: #d7f3a2;--spark-color-moss-tint1: #b1d272;--spark-color-moss: #8bae46;--spark-color-moss-shade1: #708541;--spark-color-moss-shade2: #515a3d;--spark-color-rust-tint2: #ffc599;--spark-color-rust-tint1: #ff8f51;--spark-color-rust: #e96115;--spark-color-rust-shade1: #b24501;--spark-color-cobalt-tint2: #98a1ff;--spark-color-cobalt-tint1: #5b69ff;--spark-color-cobalt: #1e2eb8;--spark-color-cobalt-shade1: #000f8a;--spark-color-cobalt-shade2: #000864;--spark-color-cobalt-shade3: #040e35;--spark-color-coral-tint2: #ffb6b9;--spark-color-coral-tint1: #ff848a;--spark-color-coral: #ff5662;--spark-color-coral-shade1: #c81326;--spark-color-daisy-tint1: #ffe17a;--spark-color-daisy: #fec91b;--spark-color-daisy-shade1: #edb200;--spark-color-daisy-shade2: #c98f00;--spark-color-white: #ffffff;--spark-color-black: #000000}.spark-visually-hidden:not(:focus):not(:active){clip:rect(0 0 0 0);clip-path:inset(50%);height:.0625rem;overflow:hidden;position:absolute;white-space:nowrap;width:.0625rem}.spark-focus-snap{box-shadow:var(--spark-focus-box-shadow-x) var(--spark-focus-box-shadow-y) var(--spark-focus-box-shadow-blur-radius) calc(var(--spark-focus-outline-width-final-primary) + var(--spark-focus-outline-width-init-backup) + var(--spark-focus-outline-width-final-extra)) transparent;transition:box-shadow var(--spark-focus-snap-transition-duration) var(--spark-focus-snap-transition-timing-function),outline var(--spark-focus-snap-transition-duration) var(--spark-focus-snap-transition-timing-function),outline-offset var(--spark-focus-snap-transition-duration) var(--spark-focus-snap-transition-timing-function),background-color var(--spark-focus-snap-transition-duration) var(--spark-focus-snap-transition-timing-function);outline-color:transparent;outline-style:solid;outline-width:var(--spark-focus-outline-width-init-primary);outline-offset:var(--spark-focus-outline-width-init-backup);-webkit-transform:translate3d(0,0,0)}.spark-focus.spark-focus-suppress:focus{outline:var(--spark-focus-custom-focus-suppress-outline) solid transparent;box-shadow:none}.spark-focus.spark-focus-self:focus{z-index:1;position:relative;box-shadow:var(--spark-focus-box-shadow-x) var(--spark-focus-box-shadow-y) var(--spark-focus-box-shadow-blur-radius) calc(var(--spark-focus-outline-width-final-primary) + var(--spark-focus-outline-width-final-backup) + var(--spark-focus-outline-width-final-extra)) var(--spark-focus-color-focus-backup);outline-color:var(--spark-focus-color-focus-primary);outline-style:solid;outline-width:var(--spark-focus-outline-width-final-primary);outline-offset:var(--spark-focus-outline-width-final-backup)}.spark-focus.spark-focus-self.spark-focus-snap:not(:focus){transition-delay:0s;transition-duration:0s}.spark-focus.spark-focus-self.spark-focus-background:focus{color:var(--spark-focus-color-focus-foreground);background-color:var(--spark-focus-color-focus-background)}.spark-focus.spark-focus-within:focus-within{z-index:1;position:relative;box-shadow:var(--spark-focus-box-shadow-x) var(--spark-focus-box-shadow-y) var(--spark-focus-box-shadow-blur-radius) calc(var(--spark-focus-outline-width-final-primary) + var(--spark-focus-outline-width-final-backup) + var(--spark-focus-outline-width-final-extra)) var(--spark-focus-color-focus-backup);outline-color:var(--spark-focus-color-focus-primary);outline-style:solid;outline-width:var(--spark-focus-outline-width-final-primary);outline-offset:var(--spark-focus-outline-width-final-backup)}.spark-focus.spark-focus-within.spark-focus-snap:not(:focus-within){transition-delay:0s;transition-duration:0s}.spark-focus.spark-focus-within.spark-focus-background:focus-within{color:var(--spark-focus-color-focus-foreground);background-color:var(--spark-focus-color-focus-background)}.spark-focus.spark-focus-adjacent:focus+.spark-focus{z-index:1;position:relative;box-shadow:var(--spark-focus-box-shadow-x) var(--spark-focus-box-shadow-y) var(--spark-focus-box-shadow-blur-radius) calc(var(--spark-focus-outline-width-final-primary) + var(--spark-focus-outline-width-final-backup) + var(--spark-focus-outline-width-final-extra)) var(--spark-focus-color-focus-backup);outline-color:var(--spark-focus-color-focus-primary);outline-style:solid;outline-width:var(--spark-focus-outline-width-final-primary);outline-offset:var(--spark-focus-outline-width-final-backup)}.spark-focus.spark-focus-adjacent:not(:focus)+.spark-focus.spark-focus-snap{transition-delay:0s;transition-duration:0s}.spark-focus.spark-focus-adjacent:focus+.spark-focus.spark-focus-background{color:var(--spark-focus-color-focus-foreground);background-color:var(--spark-focus-color-focus-background)}.spark-focus:where(.spark-focus-slider):focus::-webkit-slider-thumb{z-index:1;position:relative;box-shadow:var(--spark-focus-box-shadow-x) var(--spark-focus-box-shadow-y) var(--spark-focus-box-shadow-blur-radius) calc(var(--spark-focus-outline-width-final-primary) + var(--spark-focus-outline-width-final-backup) + var(--spark-focus-outline-width-final-extra)) var(--spark-focus-color-focus-backup);outline-color:var(--spark-focus-color-focus-primary);outline-style:solid;outline-width:var(--spark-focus-outline-width-final-primary);outline-offset:var(--spark-focus-outline-width-final-backup)}.spark-focus:where(.spark-focus-slider.spark-focus-snap)::-webkit-slider-thumb{box-shadow:var(--spark-focus-box-shadow-x) var(--spark-focus-box-shadow-y) var(--spark-focus-box-shadow-blur-radius) calc(var(--spark-focus-outline-width-final-primary) + var(--spark-focus-outline-width-init-backup) + var(--spark-focus-outline-width-final-extra)) transparent;transition:box-shadow var(--spark-focus-snap-transition-duration) var(--spark-focus-snap-transition-timing-function),outline var(--spark-focus-snap-transition-duration) var(--spark-focus-snap-transition-timing-function),outline-offset var(--spark-focus-snap-transition-duration) var(--spark-focus-snap-transition-timing-function),background-color var(--spark-focus-snap-transition-duration) var(--spark-focus-snap-transition-timing-function);outline-color:transparent;outline-style:solid;outline-width:var(--spark-focus-outline-width-init-primary);outline-offset:var(--spark-focus-outline-width-init-backup);-webkit-transform:translate3d(0,0,0)}.spark-focus:where(.spark-focus-slider.spark-focus-snap):not(:focus)::-webkit-slider-thumb{transition-delay:0s;transition-duration:0s}.spark-focus:where(.spark-focus-slider.spark-focus-background):focus::-webkit-slider-thumb{color:var(--spark-focus-color-focus-foreground);background-color:var(--spark-focus-color-focus-background)}:root{--spark-focus-outline-width-final-extra: 0rem;--spark-focus-outline-width-final-primary: .1875rem;--spark-focus-outline-width-final-backup: .1875rem;--spark-focus-outline-width-init-primary: .75rem;--spark-focus-outline-width-init-backup: .1875rem;--spark-focus-snap-transition-duration: 1.5s;--spark-focus-snap-transition-timing-function: cubic-bezier(.06, .98, .2, .99);--spark-focus-custom-focus-suppress-outline: .125rem;--spark-focus-box-shadow-x: 0rem;--spark-focus-box-shadow-y: 0rem;--spark-focus-box-shadow-blur-radius: 0rem}:root,[data-theme=light]{--spark-focus-color-focus-primary: var(--spark-color-classic-blue-shade1, #00377c);--spark-focus-color-focus-backup: var(--spark-color-theme-light-gray50, #ffffff);--spark-focus-color-focus-background: var(--spark-color-theme-light-gray50, #ffffff);--spark-focus-color-focus-foreground: var(--spark-color-carbon-shade2, #262626)}[data-theme=dark]{--spark-focus-color-focus-primary: var(--spark-color-energy-blue-tint1, #6ddcff);--spark-focus-color-focus-backup: var(--spark-color-theme-dark-gray50, #242528);--spark-focus-color-focus-background: var(--spark-color-theme-dark-gray50, #242528);--spark-focus-color-focus-foreground: var(--spark-color-theme-dark-gray900, #ffffff)}.spark-focus-visible-snap{box-shadow:var(--spark-focus-box-shadow-x) var(--spark-focus-box-shadow-y) var(--spark-focus-box-shadow-blur-radius) calc(var(--spark-focus-outline-width-final-primary) + var(--spark-focus-outline-width-init-backup) + var(--spark-focus-outline-width-final-extra)) transparent;transition:box-shadow var(--spark-focus-snap-transition-duration) var(--spark-focus-snap-transition-timing-function),outline var(--spark-focus-snap-transition-duration) var(--spark-focus-snap-transition-timing-function),outline-offset var(--spark-focus-snap-transition-duration) var(--spark-focus-snap-transition-timing-function),background-color var(--spark-focus-snap-transition-duration) var(--spark-focus-snap-transition-timing-function);outline-color:transparent;outline-style:solid;outline-width:var(--spark-focus-outline-width-init-primary);outline-offset:var(--spark-focus-outline-width-init-backup);-webkit-transform:translate3d(0,0,0)}.spark-focus-visible.spark-focus-visible-suppress:focus{outline:var(--spark-focus-custom-focus-suppress-outline) solid transparent;box-shadow:none}.spark-focus-visible.spark-focus-visible-self:focus{z-index:1;position:relative;box-shadow:var(--spark-focus-box-shadow-x) var(--spark-focus-box-shadow-y) var(--spark-focus-box-shadow-blur-radius) calc(var(--spark-focus-outline-width-final-primary) + var(--spark-focus-outline-width-final-backup) + var(--spark-focus-outline-width-final-extra)) var(--spark-focus-color-focus-backup);outline-color:var(--spark-focus-color-focus-primary);outline-style:solid;outline-width:var(--spark-focus-outline-width-final-primary);outline-offset:var(--spark-focus-outline-width-final-backup)}.spark-focus-visible.spark-focus-visible-self:focus:not(:focus-visible){z-index:revert;position:revert;box-shadow:revert;outline-color:revert;outline-style:revert;outline-width:revert;outline-offset:revert}.spark-focus-visible.spark-focus-visible-self:focus-visible{z-index:1;position:relative;box-shadow:var(--spark-focus-box-shadow-x) var(--spark-focus-box-shadow-y) var(--spark-focus-box-shadow-blur-radius) calc(var(--spark-focus-outline-width-final-primary) + var(--spark-focus-outline-width-final-backup) + var(--spark-focus-outline-width-final-extra)) var(--spark-focus-color-focus-backup);outline-color:var(--spark-focus-color-focus-primary);outline-style:solid;outline-width:var(--spark-focus-outline-width-final-primary);outline-offset:var(--spark-focus-outline-width-final-backup)}.spark-focus-visible.spark-focus-visible-self.spark-focus-visible-snap:not(:focus-visible){transition-delay:0s;transition-duration:0s}.spark-focus-visible.spark-focus-visible-self.spark-focus-visible-background:focus{color:var(--spark-focus-color-focus-foreground);background-color:var(--spark-focus-color-focus-background)}.spark-focus-visible.spark-focus-visible-self.spark-focus-visible-background:focus:not(:focus-visible){color:revert;background-color:revert}.spark-focus-visible.spark-focus-visible-self.spark-focus-visible-background:focus-visible{color:var(--spark-focus-color-focus-foreground);background-color:var(--spark-focus-color-focus-background)}.spark-focus-visible.spark-focus-visible-within:has(:focus-visible){z-index:1;position:relative;box-shadow:var(--spark-focus-box-shadow-x) var(--spark-focus-box-shadow-y) var(--spark-focus-box-shadow-blur-radius) calc(var(--spark-focus-outline-width-final-primary) + var(--spark-focus-outline-width-final-backup) + var(--spark-focus-outline-width-final-extra)) var(--spark-focus-color-focus-backup);outline-color:var(--spark-focus-color-focus-primary);outline-style:solid;outline-width:var(--spark-focus-outline-width-final-primary);outline-offset:var(--spark-focus-outline-width-final-backup)}.spark-focus-visible.spark-focus-visible-within.spark-focus-visible-snap:not(:has(:focus-visible)){transition-delay:0s;transition-duration:0s}.spark-focus-visible.spark-focus-visible-within.spark-focus-visible-background:has(:focus-visible){color:var(--spark-focus-color-focus-foreground);background-color:var(--spark-focus-color-focus-background)}.spark-focus-visible.spark-focus-visible-adjacent:focus+.spark-focus-visible{z-index:1;position:relative;box-shadow:var(--spark-focus-box-shadow-x) var(--spark-focus-box-shadow-y) var(--spark-focus-box-shadow-blur-radius) calc(var(--spark-focus-outline-width-final-primary) + var(--spark-focus-outline-width-final-backup) + var(--spark-focus-outline-width-final-extra)) var(--spark-focus-color-focus-backup);outline-color:var(--spark-focus-color-focus-primary);outline-style:solid;outline-width:var(--spark-focus-outline-width-final-primary);outline-offset:var(--spark-focus-outline-width-final-backup)}.spark-focus-visible.spark-focus-visible-adjacent:focus:not(:focus-visible)+.spark-focus-visible{z-index:revert;position:revert;box-shadow:revert;outline-color:revert;outline-style:revert;outline-width:revert;outline-offset:revert}.spark-focus-visible.spark-focus-visible-adjacent:focus-visible+.spark-focus-visible{z-index:1;position:relative;box-shadow:var(--spark-focus-box-shadow-x) var(--spark-focus-box-shadow-y) var(--spark-focus-box-shadow-blur-radius) calc(var(--spark-focus-outline-width-final-primary) + var(--spark-focus-outline-width-final-backup) + var(--spark-focus-outline-width-final-extra)) var(--spark-focus-color-focus-backup);outline-color:var(--spark-focus-color-focus-primary);outline-style:solid;outline-width:var(--spark-focus-outline-width-final-primary);outline-offset:var(--spark-focus-outline-width-final-backup)}.spark-focus-visible.spark-focus-visible-adjacent:not(:focus-visible)+.spark-focus-visible.spark-focus-visible-snap{transition-delay:0s;transition-duration:0s}.spark-focus-visible.spark-focus-visible-adjacent:focus+.spark-focus-visible.spark-focus-visible-background{color:var(--spark-focus-color-focus-foreground);background-color:var(--spark-focus-color-focus-background)}.spark-focus-visible.spark-focus-visible-adjacent:focus:not(:focus-visible)+.spark-focus-visible.spark-focus-visible-background{color:revert;background-color:revert}.spark-focus-visible.spark-focus-visible-adjacent:focus-visible+.spark-focus-visible.spark-focus-visible-background{color:var(--spark-focus-color-focus-foreground);background-color:var(--spark-focus-color-focus-background)}.spark-focus-visible:where(.spark-focus-visible-slider):focus::-webkit-slider-thumb{z-index:1;position:relative;box-shadow:var(--spark-focus-box-shadow-x) var(--spark-focus-box-shadow-y) var(--spark-focus-box-shadow-blur-radius) calc(var(--spark-focus-outline-width-final-primary) + var(--spark-focus-outline-width-final-backup) + var(--spark-focus-outline-width-final-extra)) var(--spark-focus-color-focus-backup);outline-color:var(--spark-focus-color-focus-primary);outline-style:solid;outline-width:var(--spark-focus-outline-width-final-primary);outline-offset:var(--spark-focus-outline-width-final-backup)}.spark-focus-visible:where(.spark-focus-visible-slider):focus:not(:focus-visible)::-webkit-slider-thumb{z-index:revert;position:revert;box-shadow:revert;outline-color:revert;outline-style:revert;outline-width:revert;outline-offset:revert}.spark-focus-visible:where(.spark-focus-visible-slider):focus-visible::-webkit-slider-thumb{z-index:1;position:relative;box-shadow:var(--spark-focus-box-shadow-x) var(--spark-focus-box-shadow-y) var(--spark-focus-box-shadow-blur-radius) calc(var(--spark-focus-outline-width-final-primary) + var(--spark-focus-outline-width-final-backup) + var(--spark-focus-outline-width-final-extra)) var(--spark-focus-color-focus-backup);outline-color:var(--spark-focus-color-focus-primary);outline-style:solid;outline-width:var(--spark-focus-outline-width-final-primary);outline-offset:var(--spark-focus-outline-width-final-backup)}.spark-focus-visible:where(.spark-focus-visible-slider.spark-focus-visible-snap)::-webkit-slider-thumb{box-shadow:var(--spark-focus-box-shadow-x) var(--spark-focus-box-shadow-y) var(--spark-focus-box-shadow-blur-radius) calc(var(--spark-focus-outline-width-final-primary) + var(--spark-focus-outline-width-init-backup) + var(--spark-focus-outline-width-final-extra)) transparent;transition:box-shadow var(--spark-focus-snap-transition-duration) var(--spark-focus-snap-transition-timing-function),outline var(--spark-focus-snap-transition-duration) var(--spark-focus-snap-transition-timing-function),outline-offset var(--spark-focus-snap-transition-duration) var(--spark-focus-snap-transition-timing-function),background-color var(--spark-focus-snap-transition-duration) var(--spark-focus-snap-transition-timing-function);outline-color:transparent;outline-style:solid;outline-width:var(--spark-focus-outline-width-init-primary);outline-offset:var(--spark-focus-outline-width-init-backup);-webkit-transform:translate3d(0,0,0)}.spark-focus-visible:where(.spark-focus-visible-slider.spark-focus-visible-snap):not(:focus-visible)::-webkit-slider-thumb{transition-delay:0s;transition-duration:0s}.spark-focus-visible:where(.spark-focus-visible-slider.spark-focus-visible-background):focus::-webkit-slider-thumb{color:var(--spark-focus-color-focus-foreground);background-color:var(--spark-focus-color-focus-background)}.spark-focus-visible:where(.spark-focus-visible-slider.spark-focus-visible-background):focus:not(:focus-visible)::-webkit-slider-thumb{color:revert;background-color:revert}.spark-focus-visible:where(.spark-focus-visible-slider.spark-focus-visible-background):focus-visible::-webkit-slider-thumb{color:var(--spark-focus-color-focus-foreground);background-color:var(--spark-focus-color-focus-background)}.svg-icon{display:block}body{margin:0}.st-responsive-container{padding:var(--spark-button-group-xl-gap);margin:0 auto;box-sizing:border-box;overflow-x:hidden;background-color:var(--spark-color-theme-light-gray75)}.spark-code-snippet-line-numbering{padding-inline-start:var(--spark-code-snippet-line-numbering-padding-inline-start)}.spark-code-snippet-pre{margin:var(--spark-code-snippet-zero-margin);display:grid;padding:var(--spark-code-snippet-zero-padding);counter-reset:count 0;grid-auto-rows:auto;grid-template-columns:min-content 1fr}.spark-code-snippet-size-l .spark-code-snippet-animate-l{animation:keyframes-spark-code-snippet-tooltip-animation-l;animation-duration:4s}.spark-code-snippet-size-m .spark-code-snippet-animate-m{animation:keyframes-spark-code-snippet-tooltip-animation-m;animation-duration:4s}.spark-code-snippet-size-s .spark-code-snippet-animate-s{animation:keyframes-spark-code-snippet-tooltip-animation-s;animation-duration:4s}.spark-code-snippet-check-icon{color:#fff}.spark-code-snippet-copy-icon{display:none!important}.spark-code-snippet-is-visible{display:block!important}.spark-code-snippet-single-copy-icon{padding:var(--spark-code-snippet-single-padding);z-index:999;position:absolute!important;-webkit-appearance:none!important;-moz-appearance:none!important;appearance:none!important;inset-inline-end:var(--spark-code-snippet-single-inset-inline-end-copy-icon)!important;inset-block-start:var(--spark-code-snippet-single-inset-block-start-copy-icon)!important}.spark-code-snippet-single-scrollbar-y-is-hidden{overflow-y:hidden}.spark-code-snippet-multiline-copy-icon{z-index:999;position:absolute;font-size:var(--spark-code-snippet-copy-icon-font-size);inset-inline-end:var(--spark-code-snippet-multiline-inset-inline-end-copy-icon)!important;inset-block-start:var(--spark-code-snippet-multiline-inset-block-start-copy-icon)!important}.spark-code-snippet-scrollbar-is-hidden{overflow-x:hidden!important;overflow-y:hidden!important}.spark-code-snippet-tooltip-multiline-is-opened{opacity:var(--spark-code-snippet-opened-opacity);visibility:visible;-pointer-events:auto}.spark-code-snippet-tooltip-multiline{opacity:var(--spark-code-snippet-closed-opacity);visibility:hidden;-pointer-events:auto}.spark-code-snippet-tooltip-l{gap:var(--spark-code-snippet-multiline-l-gap);padding:var(--spark-code-snippet-multiline-l-tooltip-top) var(--spark-code-snippet-multiline-l-tooltip-top);block-size:var(--spark-code-snippet-single-l-block-size)!important;align-items:center!important}.spark-code-snippet-tooltip-m{block-size:var(--spark-code-snippet-single-m-block-size)!important;align-items:center!important}.spark-code-snippet-tooltip-s{block-size:var(--spark-code-snippet-single-s-block-size)!important;align-items:center!important}.spark-code-snippet-line-count{display:grid;text-align:end}.spark-code-snippet{color:var(--spark-code-snippet-color);position:relative;background-color:var(--spark-code-snippet-background-color)}.spark-code-snippet .spark-code-snippet-line-count:before{color:var(--spark-code-snippet-numbering-color);content:counter(count);white-space:pre;margin-right:var(--spark-code-snippet-line-numbering-margin-right);border-inline-end:var(--spark-code-snippet-line-numbering-border-inline-end) solid var(--spark-code-snippet-border-color);counter-increment:count;padding-inline-end:var(--spark-code-snippet-line-numbering-padding-inline-end);padding-inline-start:var(--spark-code-snippet-line-numbering-padding-inline-start)}.spark-code-snippet.spark-code-snippet-inline{display:inline-block;align-items:flex-start}.spark-code-snippet.spark-code-snippet-multiline{align-items:flex-start}.spark-code-snippet.spark-code-snippet-multiline.spark-code-snippet-size-l{font-size:var(--spark-code-snippet-multiline-l-font-size);block-size:var(--spark-code-snippet-multiline-l-block-size);padding-block-start:var(--spark-code-snippet-multiline-l-padding-block-start);padding-inline-start:var(--spark-code-snippet-multiline-l-padding-inline-start)}.spark-code-snippet.spark-code-snippet-multiline.spark-code-snippet-size-m{font-size:var(--spark-code-snippet-multiline-m-font-size);block-size:var(--spark-code-snippet-multiline-m-block-size);padding-block-start:var(--spark-code-snippet-multiline-m-padding-block-start);padding-inline-start:var(--spark-code-snippet-multiline-m-padding-inline-start)}.spark-code-snippet.spark-code-snippet-multiline.spark-code-snippet-size-s{font-size:var(--spark-code-snippet-multiline-s-font-size);block-size:var(--spark-code-snippet-multiline-s-block-size);padding-block-start:var(--spark-code-snippet-multiline-s-padding-block-start);padding-inline-start:var(--spark-code-snippet-multiline-s-padding-inline-start)}.spark-code-snippet.spark-code-snippet-single pre{margin:var(--spark-code-snippet-pre-margin);font-family:var(--spark-code-snippet-font-family)}.spark-code-snippet.spark-code-snippet-single code{font-family:var(--spark-code-snippet-font-family)}.spark-code-snippet.spark-code-snippet-single.spark-code-snippet-size-l{font-size:var(--spark-code-snippet-single-l-font-size);block-size:var(--spark-code-snippet-single-l-block-size);line-height:var(--spark-code-snippet-single-l-line-height);padding-inline-start:var(--spark-code-snippet-single-l-padding-inline-start)}.spark-code-snippet.spark-code-snippet-single.spark-code-snippet-size-m{font-size:var(--spark-code-snippet-single-m-font-size);block-size:var(--spark-code-snippet-single-m-block-size);line-height:var(--spark-code-snippet-single-m-line-height);padding-inline-start:var(--spark-code-snippet-single-m-padding-inline-start)}.spark-code-snippet.spark-code-snippet-single.spark-code-snippet-size-s{font-size:var(--spark-code-snippet-single-s-font-size);block-size:var(--spark-code-snippet-single-s-block-size);line-height:var(--spark-code-snippet-single-s-line-height);padding-inline-start:var(--spark-code-snippet-single-s-padding-inline-start)}.spark-code-snippet.spark-code-snippet-inline.spark-code-snippet-size-l{font-size:var(--spark-code-snippet-inline-l-font-size);block-size:var(--spark-code-snippet-inline-l-block-size);inline-size:var(--spark-code-snippet-inline-l-width);padding-inline-end:var(--spark-code-snippet-inline-l-padding-inline);padding-inline-start:var(--spark-code-snippet-inline-l-padding-inline)}.spark-code-snippet.spark-code-snippet-inline.spark-code-snippet-size-m{font-size:var(--spark-code-snippet-inline-m-font-size);block-size:var(--spark-code-snippet-inline-m-block-size);inline-size:var(--spark-code-snippet-inline-m-width);padding-inline-end:var(--spark-code-snippet-inline-m-padding-inline);padding-inline-start:var(--spark-code-snippet-inline-m-padding-inline)}.spark-code-snippet.spark-code-snippet-inline.spark-code-snippet-size-s{font-size:var(--spark-code-snippet-inline-s-font-size);block-size:var(--spark-code-snippet-inline-s-block-size);inline-size:var(--spark-code-snippet-inline-s-width);padding-inline-end:var(--spark-code-snippet-inline-s-padding-inline);padding-inline-start:var(--spark-code-snippet-inline-s-padding-inline)}@keyframes 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1rem;--spark-code-snippet-single-l-line-height: 2.5rem;--spark-code-snippet-single-l-inline-tooltip-size: 12.5rem;--spark-code-snippet-single-m-block-size: 2rem;--spark-code-snippet-single-m-font-size: .875rem;--spark-code-snippet-single-m-padding-inline-start: .75rem;--spark-code-snippet-single-m-line-height: 2rem;--spark-code-snippet-single-m-inline-tooltip-size: 10rem;--spark-code-snippet-single-s-block-size: 1.5rem;--spark-code-snippet-single-s-font-size: .75rem;--spark-code-snippet-single-s-padding-inline-start: .5rem;--spark-code-snippet-single-s-line-height: 1.5rem;--spark-code-snippet-single-s-inline-tooltip-size: 9.0625rem;--spark-code-snippet-multiline-inset-block-start-copy-icon: 0rem;--spark-code-snippet-multiline-inset-inline-end-copy-icon: .5rem;--spark-code-snippet-multiline-l-font-size: 1rem;--spark-code-snippet-multiline-l-block-size: 12.25rem;--spark-code-snippet-multiline-l-padding-inline-start: .5rem;--spark-code-snippet-multiline-l-padding-block-start: .5rem;--spark-code-snippet-multiline-l-gap: 1rem;--spark-code-snippet-multiline-l-tooltip-top: .75rem;--spark-code-snippet-multiline-l-tooltip-right: 1rem;--spark-code-snippet-multiline-m-font-size: .875rem;--spark-code-snippet-multiline-m-block-size: 11.25rem;--spark-code-snippet-multiline-m-padding-inline-start: .5rem;--spark-code-snippet-multiline-m-padding-block-start: .5rem;--spark-code-snippet-multiline-m-gap: .75rem;--spark-code-snippet-multiline-s-font-size: .75rem;--spark-code-snippet-multiline-s-block-size: 10rem;--spark-code-snippet-multiline-s-padding-inline-start: .5rem;--spark-code-snippet-multiline-s-padding-block-start: .5rem;--spark-code-snippet-multiline-s-gap: .5rem;--spark-code-snippet-copy-icon-font-size: 1rem;--spark-code-snippet-copy-icon-flex-shrink: 0;--spark-code-snippet-copy-icon-margin-inline-end: 0;--spark-code-snippet-copy-icon-padding-inline-end: 0rem;--spark-code-snippet-line-numbering-padding-inline-start: .5rem;--spark-code-snippet-line-numbering-padding-inline-end: .3125rem;--spark-code-snippet-line-numbering-padding-inline-top: .5rem;--spark-code-snippet-line-numbering-border-inline-end: .0625rem;--spark-code-snippet-line-numbering-margin-right: .625rem}:root,[data-theme=light]{--spark-code-snippet-color: var(--spark-color-theme-light-gray900, #2b2c30);--spark-code-snippet-background-color: var(--spark-color-theme-light-gray200, #e9eaeb);--spark-code-snippet-border-color: var(--spark-color-theme-light-gray400, #c9cace);--spark-code-snippet-numbering-color: var(--spark-color-theme-light-gray900, #2b2c30)}[data-theme=dark]{--spark-code-snippet-color: var(--spark-color-theme-dark-gray900, #ffffff);--spark-code-snippet-background-color: var(--spark-color-theme-dark-gray200, #3c3e42);--spark-code-snippet-border-color: var(--spark-color-theme-dark-gray400, #585a62);--spark-code-snippet-numbering-color: var(--spark-color-theme-dark-gray900, #ffffff)}.st-code-snippet{padding:var(--spark-code-snippet-multiline-m-padding-block-start);background-color:var(--spark-code-snippet-background-color);color:var(--spark-code-snippet-color);font-size:var(--spark-code-snippet-multiline-m-font-size);display:flex;flex-direction:column;flex-grow:1;position:relative}.st-code-snippet .st-code-snippet-comment{margin:auto 0;white-space:pre-line;color:var(--spark-color-moss-shade1);font-style:italic;padding-right:calc(var(--spark-button-m-block-size) - var(--spark-code-snippet-multiline-m-padding-block-start))}.st-code-snippet .st-code-snippet-content{white-space:pre-line;word-break:break-word}.st-code-snippet .copy-button{position:absolute!important;top:var(--spark-code-snippet-multiline-m-padding-block-start);right:var(--spark-code-snippet-multiline-m-padding-block-start);-webkit-user-select:none;user-select:none;padding:0;border:none;outline:none;background-color:transparent;cursor:pointer}.st-code-snippet-multi-line{padding:var(--spark-code-snippet-multiline-m-padding-block-start) 0 var(--spark-code-snippet-multiline-m-padding-block-start) var(--spark-code-snippet-multiline-m-padding-block-start);background-color:var(--spark-code-snippet-background-color);color:var(--spark-code-snippet-color);font-size:var(--spark-code-snippet-multiline-m-font-size);display:flex;flex-direction:column;flex-grow:1;position:relative}.st-code-snippet-multi-line .st-code-snippet-comment{margin:auto 0;white-space:pre-line;color:var(--spark-color-moss-shade1);font-style:italic;padding-right:calc(var(--spark-button-m-block-size) - var(--spark-code-snippet-multiline-m-padding-block-start))}.st-code-snippet-multi-line .st-code-snippet{padding-left:0}.st-distribution-links{display:flex;flex-wrap:wrap;column-gap:var(--spark-button-group-xl-gap)}.nncf-link{margin-top:.5rem}.nncf-link a{white-space:nowrap}.spark-hyperlink-is-disabled{pointer-events:none}.spark-hyperlink-primary{color:var(--spark-hyperlink-color-primary)}.spark-hyperlink-primary a{color:var(--spark-hyperlink-color-secondary)!important;text-decoration:none!important}.spark-hyperlink-secondary{color:var(--spark-hyperlink-color-secondary)}.spark-hyperlink-secondary a{color:var(--spark-hyperlink-color-secondary)!important;text-decoration:none!important}.spark-hyperlink-standard{text-decoration:underline}.spark-hyperlink-quiet{text-decoration:none}.spark-hyperlink-quiet:hover{text-decoration:underline}.spark-hyperlink:visited,.spark-hyperlink.visited{color:var(--spark-hyperlink-color-visited)}:root,[data-theme=light]{--spark-hyperlink-color-primary: var(--spark-color-classic-blue, #0054ae);--spark-hyperlink-color-secondary: inherit;--spark-hyperlink-color-visited: var(--spark-color-geode, #8f5da2)}[data-theme=dark]{--spark-hyperlink-color-primary: var(--spark-color-energy-blue, #00c7fd);--spark-hyperlink-color-secondary: inherit;--spark-hyperlink-color-visited: var(--spark-color-geode-tint1, #cc94da)}.spark-hyperlink:visited,.spark-hyperlink.visited{color:var(--spark-hyperlink-color-primary)}.st-section{border-left:2px solid var(--spark-color-theme-light-gray500);display:flex;align-items:center}.st-section:not(:last-child){margin-bottom:var(--spark-button-group-l-gap)}.st-section .st-section-title{font-weight:500;padding:0 var(--spark-button-group-l-gap);width:150px;flex-shrink:0;height:var(--spark-button-l-block-size);display:flex;align-items:center;align-self:flex-start}.st-section .spark-button{max-inline-size:none}.st-section .st-section-content{display:flex;flex-direction:column;flex-grow:1}.st-section .st-section-content .st-section-content-row{display:inline-flex;align-items:center;min-height:var(--spark-button-l-block-size);gap:var(--spark-button-group-l-gap)}.st-section .st-section-content .st-section-content-row:not(:last-child){margin-bottom:var(--spark-button-group-l-gap)}.st-section .st-section-content .st-section-content-row:not(:first-child):last-child{min-height:auto}.st-section-accent{border-left-color:var(--spark-color-classic-blue)}@media (max-width: 550px){.st-section{flex-direction:column;border-left:none;align-items:start}.st-section:not(:last-child){margin-bottom:var(--spark-button-group-xl-gap)}.st-section .st-section-title{border-left:2px solid var(--spark-color-theme-light-gray500)}.st-section .st-section-content{margin-top:var(--spark-button-group-l-gap);width:100%}}.links-section{display:flex;flex-direction:column;align-items:flex-start}.spark-tooltip-tip{color:var(--spark-tooltip-tip-color);position:absolute;block-size:var(--spark-tooltip-tip-block-size);inline-size:var(--spark-tooltip-tip-inline-size);border-style:solid;border-width:var(--spark-tooltip-tip-border-width);border-block-end-color:transparent;border-inline-end-color:transparent;border-inline-start-color:transparent}.spark-tooltip-toggle{width:fit-content;display:flex;position:relative}.spark-tooltip-size-m{padding:var(--spark-tooltip-m-padding-top-bottom) var(--spark-tooltip-m-padding-right-left)}.spark-tooltip-size-m .spark-tooltip-label{width:max-content;display:flex;font-size:var(--spark-tooltip-m-font-size);font-weight:var(--spark-tooltip-m-label-font-weight);line-height:var(--spark-tooltip-m-label-line-height)}.spark-tooltip-size-m.spark-tooltip-top .spark-tooltip-tip{inset-block-start:100%;margin-inline-start:calc(50% - var(--spark-tooltip-m-gap-size))}.spark-tooltip-size-m.spark-tooltip-bottom .spark-tooltip-tip{margin-inline-start:calc(50% - var(--spark-tooltip-m-gap-size))}.spark-tooltip-size-m.spark-tooltip-bottom.spark-tooltip-left .spark-tooltip-tip,.spark-tooltip-size-m.spark-tooltip-top.spark-tooltip-left .spark-tooltip-tip,.spark-tooltip-size-m.spark-tooltip-top.spark-tooltip-right .spark-tooltip-tip{margin-inline-start:calc(-1 * var(--spark-tooltip-m-gap-size))}.spark-tooltip-size-m .spark-icon{line-height:var(--spark-tooltip-m-icon-line-height);margin-inline-end:var(--spark-tooltip-margin-inline-end)}.spark-tooltip-size-s{padding:var(--spark-tooltip-s-padding-top-bottom) var(--spark-tooltip-s-padding-right-left)}.spark-tooltip-size-s .spark-tooltip-label{width:max-content;display:flex;font-size:var(--spark-tooltip-s-font-size);font-weight:var(--spark-tooltip-s-label-font-weight);line-height:var(--spark-tooltip-s-label-line-height)}.spark-tooltip-size-s.spark-tooltip-top .spark-tooltip-tip{inset-block-start:100%;margin-inline-start:calc(50% - var(--spark-tooltip-s-gap-size))}.spark-tooltip-size-s.spark-tooltip-bottom .spark-tooltip-tip{margin-inline-start:calc(50% - var(--spark-tooltip-s-gap-size))}.spark-tooltip-size-s.spark-tooltip-bottom.spark-tooltip-left .spark-tooltip-tip,.spark-tooltip-size-s.spark-tooltip-top.spark-tooltip-left .spark-tooltip-tip,.spark-tooltip-size-s.spark-tooltip-top.spark-tooltip-right .spark-tooltip-tip{margin-inline-start:calc(-1 * var(--spark-tooltip-s-gap-size))}.spark-tooltip-size-s .spark-icon{line-height:var(--spark-tooltip-s-icon-line-height);margin-inline-end:var(--spark-tooltip-margin-inline-end)}.spark-tooltip{top:var(--spark-tooltip-top);left:var(--spark-tooltip-left);color:var(--spark-tooltip-color);display:inline-flex;z-index:var(--spark-tooltip-z-index);position:absolute;box-sizing:border-box;visibility:visible;word-break:break-word;align-items:flex-start;inline-size:auto;flex-direction:row;pointer-events:auto;vertical-align:top;max-inline-size:var(--spark-tooltip-max-inline-size);background-color:var(--spark-tooltip-background-color);inset-block-start:var(--spark-tooltip-inset-block-start);inset-inline-start:var(--spark-tooltip-inset-inline-start)}.spark-tooltip.spark-tooltip-top .spark-tooltip-tip{inset-block-start:100%}.spark-tooltip.spark-tooltip-right-side .spark-tooltip-tip{transform:rotate(90deg);inset-block-end:calc(50% - var(--spark-tooltip-mid-tooltip-size));inset-inline-end:100%}.spark-tooltip.spark-tooltip-bottom .spark-tooltip-tip{transform:rotate(-180deg);inset-block-end:100%}.spark-tooltip.spark-tooltip-left-side .spark-tooltip-tip{transform:rotate(-90deg);inset-block-end:calc(50% - var(--spark-tooltip-mid-tooltip-size));inset-inline-start:100%}.spark-tooltip.spark-tooltip-bottom.spark-tooltip-left .spark-tooltip-tip{transform:rotate(-180deg);inset-inline-start:100%}.spark-tooltip.spark-tooltip-bottom.spark-tooltip-right .spark-tooltip-tip{transform:rotate(-180deg);inset-inline-start:0%;margin-inline-start:var(--spark-tooltip-tip-margin-inline-start)}.spark-tooltip.spark-tooltip-top.spark-tooltip-left .spark-tooltip-tip{transform:rotate(0);inset-inline-start:100%}.spark-tooltip.spark-tooltip-top.spark-tooltip-right .spark-tooltip-tip{transform:rotate(0);inset-inline-start:0%;margin-inline-start:var(--spark-tooltip-tip-margin-inline-start)}.spark-tooltip.spark-tooltip-right.spark-tooltip-end .spark-tooltip-tip{transform:rotate(90deg);inset-block-end:var(--spark-tooltip-tip-inset-block-end);inset-inline-end:100%}.spark-tooltip.spark-tooltip-right.spark-tooltip-start .spark-tooltip-tip{transform:rotate(90deg);inset-inline-end:100%;inset-block-start:var(--spark-tooltip-tip-inset-block-start)}.spark-tooltip.spark-tooltip-left.spark-tooltip-end .spark-tooltip-tip{transform:rotate(-90deg);inset-block-end:var(--spark-tooltip-tip-inset-block-end);inset-inline-start:100%}.spark-tooltip.spark-tooltip-left.spark-tooltip-start .spark-tooltip-tip{transform:rotate(-90deg);inset-block-start:var(--spark-tooltip-tip-inset-block-start);inset-inline-start:100%}:root{--spark-tooltip-inset-inline-start: 0rem;--spark-tooltip-inset-block-start: 0rem;--spark-tooltip-z-index: 999;--spark-tooltip-top: 0rem;--spark-tooltip-left: 0rem;--spark-tooltip-tip-block-size: 0rem;--spark-tooltip-tip-inline-size: 0rem;--spark-tooltip-tip-border-width: .3125rem;--spark-tooltip-margin-inline-end: .5rem;--spark-tooltip-gap-size: 1.125rem;--spark-tooltip-inset-block-end: .75rem;--spark-tooltip-tip-margin-inline-start: .25rem;--spark-tooltip-tip-inset-block-start: .25rem;--spark-tooltip-tip-inset-block-end: .25rem;--spark-tooltip-max-inline-size: 30ch;--spark-tooltip-mid-tooltip-size: .3125rem;--spark-tooltip-m-font-size: .75rem;--spark-tooltip-m-label-font-weight: 400;--spark-tooltip-m-label-line-height: 1rem;--spark-tooltip-m-icon-line-height: 1rem;--spark-tooltip-m-padding-top-bottom: .5rem;--spark-tooltip-m-padding-right-left: .75rem;--spark-tooltip-m-gap-size: 1.0625rem;--spark-tooltip-m-tooltip-gap: .5rem;--spark-tooltip-m-diff-size-gap: 0rem;--spark-tooltip-s-font-size: .6875rem;--spark-tooltip-s-label-font-weight: 400;--spark-tooltip-s-label-line-height: 1rem;--spark-tooltip-s-icon-line-height: 1rem;--spark-tooltip-s-padding-top-bottom: .25rem;--spark-tooltip-s-padding-right-left: .5rem;--spark-tooltip-s-gap-size: .8125rem;--spark-tooltip-s-tooltip-gap: .8125rem;--spark-tooltip-s-diff-size-gap: .25rem}:root,[data-theme=light]{--spark-tooltip-background-color: var(--spark-color-theme-dark-gray200, #3c3e42);--spark-tooltip-color: var(--spark-color-theme-light-gray50, #ffffff);--spark-tooltip-tip-color: var(--spark-color-theme-dark-gray200, #3c3e42)}[data-theme=dark]{--spark-tooltip-background-color: var(--spark-color-theme-dark-gray900, #ffffff);--spark-tooltip-color: var(--spark-color-theme-light-gray900, #2b2c30);--spark-tooltip-tip-color: var(--spark-color-theme-dark-gray900, #ffffff)}.help-tooltip{display:flex;justify-content:center}.help-tooltip .help-tooltip-icon{cursor:pointer}.help-tooltip .help-tooltip-icon .svg-icon{width:20px;height:20px}.help-tooltip .spark-tooltip-toggle{justify-content:center}.help-tooltip .spark-tooltip-toggle .help-tooltip-popover{top:calc(100% + var(--spark-tooltip-m-padding-top-bottom));left:auto}.help-tooltip .spark-tooltip-toggle .help-tooltip-popover .help-tooltip-popover-content{white-space:pre-line}.spark-toggle-switch-action .spark-toggle-switch-selector{cursor:pointer;display:block;padding:var(--spark-toggle-switch-selector-space);background:var(--spark-toggle-switch-color-off);block-size:var(--spark-toggle-switch-action-block-size);box-sizing:border-box;inline-size:var(--spark-toggle-switch-action-inline-size)}.spark-toggle-switch-action input{cursor:pointer}.spark-toggle-switch-action input:checked+.spark-toggle-switch-selector{background:var(--spark-toggle-switch-color-on)}.spark-toggle-switch-action input:disabled{cursor:initial}.spark-toggle-switch-action.spark-toggle-switch-size-s .spark-toggle-switch-selector~*{font-size:var(--spark-toggle-switch-size-s-ghost-indicator-font-size);margin-inline-start:.25rem}.spark-toggle-switch-action.spark-toggle-switch-size-m .spark-toggle-switch-selector~*{font-size:var(--spark-toggle-switch-size-m-ghost-indicator-font-size);margin-inline-start:.25rem}.spark-toggle-switch-action.spark-toggle-switch-size-l .spark-toggle-switch-selector~*{font-size:var(--spark-toggle-switch-size-l-ghost-indicator-font-size);margin-inline-start:.25rem}.spark-toggle-switch-action input:disabled~*{color:var(--spark-toggle-switch-color-disabled);cursor:initial}.spark-toggle-switch-action input:disabled+.spark-toggle-switch-selector{background:var(--spark-toggle-switch-color-disabled)}.spark-toggle-switch-action input:disabled+.spark-toggle-switch-selector:after{background:var(--spark-toggle-switch-selector-color-disabled)}.spark-toggle-switch-action input:checked+.spark-toggle-switch-selector:after{margin-inline-start:auto}.spark-toggle-switch-action input~*{cursor:pointer}.spark-toggle-switch-action .spark-toggle-switch-selector:after{content:"";display:block;background:var(--spark-toggle-switch-selector-color);block-size:100%;inline-size:50%}.spark-toggle-switch-ghost .spark-toggle-switch-selector{cursor:pointer;display:flex;align-items:center}.spark-toggle-switch-ghost input:disabled+.spark-toggle-switch-selector{cursor:initial}.spark-toggle-switch-ghost input:disabled+.spark-toggle-switch-selector:before{color:var(--spark-toggle-switch-color-disabled);border-color:var(--spark-toggle-switch-color-disabled)}.spark-toggle-switch-ghost input:disabled+.spark-toggle-switch-selector:after{color:var(--spark-toggle-switch-color-disabled)}.spark-toggle-switch-ghost input:checked:not(:disabled)+.spark-toggle-switch-selector:before{border-color:var(--spark-toggle-switch-color-on)}.spark-toggle-switch-ghost.spark-toggle-switch-size-s .spark-toggle-switch-selector{font-size:var(--spark-toggle-switch-size-s-ghost-indicator-font-size)}.spark-toggle-switch-ghost.spark-toggle-switch-size-s input:checked+.spark-toggle-switch-selector:before{inline-size:calc(var(--spark-toggle-switch-size-s-ghost-indicator-inline-size) + var(--spark-toggle-switch-size-s-ghost-selector-thin))}.spark-toggle-switch-ghost.spark-toggle-switch-size-s input:checked+.spark-toggle-switch-selector:after{inline-size:var(--spark-toggle-switch-inline-size)}.spark-toggle-switch-ghost.spark-toggle-switch-size-s .spark-toggle-switch-selector:after{block-size:var(--spark-toggle-switch-size-s-ghost-selector-block-size);inline-size:var(--spark-toggle-switch-size-s-ghost-indicator-inline-size)}.spark-toggle-switch-ghost.spark-toggle-switch-size-s .spark-toggle-switch-selector:before{block-size:var(--spark-toggle-switch-size-s-ghost-selector-block-size);inline-size:var(--spark-toggle-switch-size-s-ghost-selector-thin);border-inline-end-width:var(--spark-toggle-switch-size-s-ghost-selector-thin)}.spark-toggle-switch-ghost.spark-toggle-switch-size-m .spark-toggle-switch-selector{font-size:var(--spark-toggle-switch-size-m-ghost-indicator-font-size)}.spark-toggle-switch-ghost.spark-toggle-switch-size-m input:checked+.spark-toggle-switch-selector:before{inline-size:calc(var(--spark-toggle-switch-size-m-ghost-indicator-inline-size) + var(--spark-toggle-switch-size-m-ghost-selector-thin))}.spark-toggle-switch-ghost.spark-toggle-switch-size-m input:checked+.spark-toggle-switch-selector:after{inline-size:var(--spark-toggle-switch-inline-size)}.spark-toggle-switch-ghost.spark-toggle-switch-size-m .spark-toggle-switch-selector:after{block-size:var(--spark-toggle-switch-size-m-ghost-selector-block-size);inline-size:var(--spark-toggle-switch-size-m-ghost-indicator-inline-size)}.spark-toggle-switch-ghost.spark-toggle-switch-size-m .spark-toggle-switch-selector:before{block-size:var(--spark-toggle-switch-size-m-ghost-selector-block-size);inline-size:var(--spark-toggle-switch-size-m-ghost-selector-thin);border-inline-end-width:var(--spark-toggle-switch-size-m-ghost-selector-thin)}.spark-toggle-switch-ghost.spark-toggle-switch-size-l .spark-toggle-switch-selector{font-size:var(--spark-toggle-switch-size-l-ghost-indicator-font-size)}.spark-toggle-switch-ghost.spark-toggle-switch-size-l input:checked+.spark-toggle-switch-selector:before{inline-size:calc(var(--spark-toggle-switch-size-l-ghost-indicator-inline-size) + var(--spark-toggle-switch-size-l-ghost-selector-thin))}.spark-toggle-switch-ghost.spark-toggle-switch-size-l input:checked+.spark-toggle-switch-selector:after{inline-size:var(--spark-toggle-switch-inline-size)}.spark-toggle-switch-ghost.spark-toggle-switch-size-l .spark-toggle-switch-selector:after{block-size:var(--spark-toggle-switch-size-l-ghost-selector-block-size);inline-size:var(--spark-toggle-switch-size-l-ghost-indicator-inline-size)}.spark-toggle-switch-ghost.spark-toggle-switch-size-l .spark-toggle-switch-selector:before{block-size:var(--spark-toggle-switch-size-l-ghost-selector-block-size);inline-size:var(--spark-toggle-switch-size-l-ghost-selector-thin);border-inline-end-width:var(--spark-toggle-switch-size-l-ghost-selector-thin)}.spark-toggle-switch-ghost .spark-toggle-switch-selector:after{content:"Off";display:flex;overflow:hidden;box-sizing:border-box;align-items:center;justify-content:center}.spark-toggle-switch-ghost .spark-toggle-switch-selector:before{content:"On";display:flex;overflow:hidden;box-sizing:border-box;align-items:center;border-color:var(--spark-toggle-switch-color-off);border-style:solid;border-width:var(--spark-toggle-switch-border-width);justify-content:center}.spark-toggle-switch{display:flex!important;box-sizing:border-box;align-items:center;inline-size:fit-content!important}.spark-toggle-switch input{border:none;margin:var(--spark-toggle-switch-margin);opacity:var(--spark-toggle-switch-opacity);outline:none;padding:var(--spark-toggle-switch-padding);block-size:var(--spark-toggle-switch-block-size);inline-size:var(--spark-toggle-switch-inline-size)}:root{--spark-toggle-switch-selector-space: .125rem;--spark-toggle-switch-action-inline-size: 1.75rem;--spark-toggle-switch-action-block-size: 1rem;--spark-toggle-switch-opacity: 0rem;--spark-toggle-switch-padding: 0rem;--spark-toggle-switch-margin: 0rem;--spark-toggle-switch-inline-size: 0rem;--spark-toggle-switch-block-size: 0rem;--spark-toggle-switch-border-width: 0rem;--spark-toggle-switch-size-l-ghost-selector-thin: .5rem;--spark-toggle-switch-size-l-ghost-selector-block-size: 1.5rem;--spark-toggle-switch-size-l-ghost-indicator-inline-size: 2rem;--spark-toggle-switch-size-l-ghost-indicator-font-size: 1rem;--spark-toggle-switch-size-m-ghost-selector-thin: .25rem;--spark-toggle-switch-size-m-ghost-selector-block-size: 1.25rem;--spark-toggle-switch-size-m-ghost-indicator-inline-size: 1.75rem;--spark-toggle-switch-size-m-ghost-indicator-font-size: .875rem;--spark-toggle-switch-size-s-ghost-selector-thin: .25rem;--spark-toggle-switch-size-s-ghost-selector-block-size: 1rem;--spark-toggle-switch-size-s-ghost-indicator-inline-size: 1.5rem;--spark-toggle-switch-size-s-ghost-indicator-font-size: .75rem}:root,[data-theme=light]{--spark-toggle-switch-color-on: var(--spark-color-classic-blue, #0054ae);--spark-toggle-switch-color-off: var(--spark-color-theme-light-gray600, #8b8e97);--spark-toggle-switch-color-disabled: var(--spark-color-theme-light-gray500, #b2b3b9);--spark-toggle-switch-selector-color: var(--spark-color-theme-light-gray50, #ffffff);--spark-toggle-switch-selector-color-disabled: var(--spark-color-theme-light-gray300, #e2e2e4)}[data-theme=dark]{--spark-toggle-switch-color-on: var(--spark-color-energy-blue, #00c7fd);--spark-toggle-switch-color-off: var(--spark-color-theme-dark-gray600, #8e9099);--spark-toggle-switch-color-disabled: var(--spark-color-theme-dark-gray500, #6b6e76);--spark-toggle-switch-selector-color: var(--spark-color-theme-dark-gray50, #242528);--spark-toggle-switch-selector-color-disabled: var(--spark-color-theme-dark-gray300, #484a50)}.spark-toggle-button-clicked-ghost{color:var(--spark-toggle-button-ghost-color);border-color:var(--spark-toggle-button-ghost-border-color);background-color:var(--spark-toggle-button-ghost-bg-color)}:root,[data-theme=light]{--spark-toggle-button-ghost-color: var(--spark-color-theme-light-gray900, #2b2c30);--spark-toggle-button-ghost-bg-color: var(--spark-color-theme-light-gray400, #c9cace);--spark-toggle-button-ghost-border-color: var(--spark-color-transparent, transparent)}[data-theme=dark]{--spark-toggle-button-ghost-color: var(--spark-color-theme-dark-gray900, #ffffff);--spark-toggle-button-ghost-bg-color: var(--spark-color-theme-dark-gray400, #585a62);--spark-toggle-button-ghost-border-color: var(--spark-color-transparent, transparent)}.spark-button-start-slot{font-size:var(--spark-button-start-slot-font-size);flex-shrink:var(--spark-button-start-slot-flex-shrink)}.spark-button-end-slot{font-size:var(--spark-button-end-slot-font-size);flex-shrink:var(--spark-button-end-slot-flex-shrink)}.spark-button-disabled{pointer-events:none}.spark-button-content{display:flex;overflow:hidden;align-items:center;text-overflow:ellipsis;justify-content:center}.spark-button-size-l{font-size:var(--spark-button-l-font-size);block-size:var(--spark-button-l-block-size);line-height:var(--spark-button-l-line-height);min-inline-size:var(--spark-button-l-block-size);padding-block-end:var(--spark-button-l-padding-block);padding-inline-end:var(--spark-button-l-padding-inline);padding-block-start:var(--spark-button-l-padding-block);padding-inline-start:var(--spark-button-l-padding-inline)}.spark-button-size-l.spark-button-icon-only{font-size:var(--spark-button-icon-only-l-font-size);max-inline-size:var(--spark-button-l-block-size);min-inline-size:var(--spark-button-l-block-size);padding-inline-end:var(--spark-button-icon-only-l-padding-inline);padding-inline-start:var(--spark-button-icon-only-l-padding-inline)}.spark-button-size-l:not(.spark-button-icon-only) .spark-button-start-slot{padding-inline-end:var(--spark-button-l-icon-gap)}.spark-button-size-l:not(.spark-button-icon-only) .spark-button-end-slot{padding-inline-start:var(--spark-button-l-icon-gap)}.spark-button-size-m{font-size:var(--spark-button-m-font-size);block-size:var(--spark-button-m-block-size);line-height:var(--spark-button-m-line-height);min-inline-size:var(--spark-button-m-block-size);padding-block-end:var(--spark-button-m-padding-block);padding-inline-end:var(--spark-button-m-padding-inline);padding-block-start:var(--spark-button-m-padding-block);padding-inline-start:var(--spark-button-m-padding-inline)}.spark-button-size-m.spark-button-icon-only{font-size:var(--spark-button-icon-only-m-font-size);max-inline-size:var(--spark-button-m-block-size);min-inline-size:var(--spark-button-m-block-size);padding-inline-end:var(--spark-button-icon-only-m-padding-inline);padding-inline-start:var(--spark-button-icon-only-m-padding-inline)}.spark-button-size-m:not(.spark-button-icon-only) .spark-button-start-slot{padding-inline-end:var(--spark-button-m-icon-gap)}.spark-button-size-m:not(.spark-button-icon-only) .spark-button-end-slot{padding-inline-start:var(--spark-button-m-icon-gap)}.spark-button-size-s{font-size:var(--spark-button-s-font-size);block-size:var(--spark-button-s-block-size);line-height:var(--spark-button-s-line-height);min-inline-size:var(--spark-button-s-block-size);padding-block-end:var(--spark-button-s-padding-block);padding-inline-end:var(--spark-button-s-padding-inline);padding-block-start:var(--spark-button-s-padding-block);padding-inline-start:var(--spark-button-s-padding-inline)}.spark-button-size-s.spark-button-icon-only{font-size:var(--spark-button-icon-only-s-font-size);max-inline-size:var(--spark-button-s-block-size);min-inline-size:var(--spark-button-s-block-size);padding-inline-end:var(--spark-button-icon-only-s-padding-inline);padding-inline-start:var(--spark-button-icon-only-s-padding-inline)}.spark-button-size-s:not(.spark-button-icon-only) .spark-button-start-slot{padding-inline-end:var(--spark-button-s-icon-gap)}.spark-button-size-s:not(.spark-button-icon-only) .spark-button-end-slot{padding-inline-start:var(--spark-button-s-icon-gap)}.spark-button-action{color:var(--spark-button-action-color);border-color:transparent;background-color:var(--spark-button-action-bg-color)}.spark-button-action:hover,.spark-button-action .spark-button-hovered{background-color:var(--spark-button-action-bg-color-hover)}.spark-button-action:visited{color:var(--spark-button-action-color)}.spark-button-action:active,.spark-button-action.spark-button-active{background-color:var(--spark-button-action-bg-color-active)}.spark-button-action:disabled,.spark-button-action.spark-button-disabled{color:var(--spark-button-disabled-color);cursor:default;border-color:var(--spark-button-disabled-border-color);background-color:var(--spark-button-disabled-bg-color)}.spark-button-action .spark-button-content a{color:var(--spark-button-action-color);text-decoration:none}.spark-button-action:disabled a,.spark-button-action.spark-button-disabled .spark-button-content a{color:var(--spark-button-disabled-color);cursor:default;border-color:transparent;background-color:transparent}.spark-button-primary{color:var(--spark-button-primary-color);border-color:var(--spark-button-primary-color);background-color:var(--spark-button-primary-bg-color)}.spark-button-primary:hover,.spark-button-primary .spark-button-hovered{background-color:var(--spark-button-primary-bg-color-hover)}.spark-button-primary:visited{color:var(--spark-button-primary-color)}.spark-button-primary:active,.spark-button-primary.spark-button-active{background-color:var(--spark-button-primary-bg-color-active)}.spark-button-primary:disabled,.spark-button-primary.spark-button-disabled{color:var(--spark-button-disabled-color);cursor:default;border-color:var(--spark-button-disabled-border-color);background-color:var(--spark-button-disabled-bg-color)}.spark-button-primary .spark-button-content a{color:var(--spark-button-primary-color);text-decoration:none}.spark-button-primary:disabled a,.spark-button-primary.spark-button-disabled .spark-button-content 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a{color:var(--spark-button-secondary-color);text-decoration:none}.spark-button-secondary:disabled a,.spark-button-secondary.spark-button-disabled .spark-button-content a{color:var(--spark-button-disabled-color);cursor:default;border-color:transparent;background-color:transparent}.spark-button-ghost{color:var(--spark-button-ghost-color);border-color:var(--spark-button-ghost-border-color);background-color:var(--spark-button-ghost-bg-color)}.spark-button-ghost:not(.spark-button-icon-only){min-inline-size:auto!important}.spark-button-ghost:hover,.spark-button-ghost .spark-button-hovered{background-color:var(--spark-button-ghost-bg-color-hover)}.spark-button-ghost:visited{color:var(--spark-button-ghost-color)}.spark-button-ghost:active,.spark-button-ghost.spark-button-active{background-color:var(--spark-button-ghost-bg-color-active)}.spark-button-ghost:disabled,.spark-button-ghost.spark-button-disabled{color:var(--spark-button-disabled-color);cursor:default;border-color:transparent;background-color:transparent}.spark-button-ghost .spark-button-content a{color:var(--spark-button-ghost-color);text-decoration:none}.spark-button-ghost:disabled a,.spark-button-ghost.spark-button-disabled .spark-button-content a{color:var(--spark-button-disabled-color);cursor:default;border-color:transparent;background-color:transparent}.spark-button-alert{color:var(--spark-button-alert-color);border-color:var(--spark-button-alert-border-color);background-color:var(--spark-button-alert-bg-color)}.spark-button-alert:hover,.spark-button-alert .spark-button-hovered{background-color:var(--spark-button-alert-bg-color-hover)}.spark-button-alert:visited{color:var(--spark-button-alert-color)}.spark-button-alert:active,.spark-button-alert.spark-button-active{background-color:var(--spark-button-alert-bg-color-active)}.spark-button-alert:disabled,.spark-button-alert.spark-button-disabled{color:var(--spark-button-disabled-color);cursor:default;border-color:var(--spark-button-disabled-border-color);background-color:var(--spark-button-disabled-bg-color)}.spark-button-alert .spark-button-content a{color:var(--spark-button-alert-color);text-decoration:none}.spark-button-alert:disabled a,.spark-button-alert.spark-button-disabled .spark-button-content a{color:var(--spark-button-disabled-color);cursor:default;border-color:transparent;background-color:transparent}.spark-button-alert-ghost{color:var(--spark-button-alert-ghost-color);border-color:var(--spark-button-alert-ghost-border-color);background-color:var(--spark-button-alert-ghost-bg-color)}.spark-button-alert-ghost:not(.spark-button-icon-only){min-inline-size:auto!important}.spark-button-alert-ghost:hover,.spark-button-alert-ghost .spark-button-hovered{background-color:var(--spark-button-alert-ghost-bg-color-hover)}.spark-button-alert-ghost:visited{color:var(--spark-button-alert-ghost-color)}.spark-button-alert-ghost:active,.spark-button-alert-ghost.spark-button-active{background-color:var(--spark-button-alert-ghost-bg-color-active)}.spark-button-alert-ghost:disabled,.spark-button-alert-ghost.spark-button-disabled{color:var(--spark-button-disabled-color);cursor:default;border-color:transparent;background-color:transparent}.spark-button-alert-ghost .spark-button-content a{color:var(--spark-button-alert-ghost-color);text-decoration:none}.spark-button-alert-ghost:disabled a,.spark-button-alert-ghost.spark-button-disabled .spark-button-content a{color:var(--spark-button-disabled-color);cursor:default;border-color:transparent;background-color:transparent}.spark-button{cursor:pointer;display:flex;box-sizing:border-box;align-items:center;font-family:var(--spark-button-font-family);font-weight:var(--spark-button-font-weight);inline-size:max-content;white-space:nowrap;border-style:solid;border-width:var(--spark-button-border-width);justify-content:center;max-inline-size:var(--spark-button-max-inline-size);text-decoration:none}:root{--spark-button-font-weight: 500;--spark-button-font-family: inherit;--spark-button-max-inline-size: 37.5rem;--spark-button-border-width: .0625rem;--spark-button-l-block-size: 2.5rem;--spark-button-l-font-size: 1rem;--spark-button-l-line-height: 1.375rem;--spark-button-l-padding-block: .75rem;--spark-button-l-padding-inline: 1.25rem;--spark-button-l-icon-gap: .5rem;--spark-button-m-block-size: 2rem;--spark-button-m-font-size: .875rem;--spark-button-m-line-height: 1.125rem;--spark-button-m-padding-block: .5rem;--spark-button-m-padding-inline: 1rem;--spark-button-m-icon-gap: .25rem;--spark-button-s-block-size: 1.5rem;--spark-button-s-font-size: .75rem;--spark-button-s-line-height: .875rem;--spark-button-s-padding-block: .25rem;--spark-button-s-padding-inline: .75rem;--spark-button-s-icon-gap: .25rem;--spark-button-start-slot-font-size: 1rem;--spark-button-start-slot-flex-shrink: 0;--spark-button-end-slot-font-size: 1rem;--spark-button-end-slot-flex-shrink: 0;--spark-button-icon-only-l-font-size: 1rem;--spark-button-icon-only-l-padding-inline: .5rem;--spark-button-icon-only-m-font-size: 1rem;--spark-button-icon-only-m-padding-inline: .5rem;--spark-button-icon-only-s-font-size: 1rem;--spark-button-icon-only-s-padding-inline: .25rem}:root,[data-theme=light]{--spark-button-action-color: var(--spark-color-theme-light-gray50, #ffffff);--spark-button-action-bg-color: var(--spark-color-classic-blue, #0054ae);--spark-button-action-bg-color-hover: var(--spark-color-classic-blue-shade1, #00377c);--spark-button-action-bg-color-active: var(--spark-color-classic-blue-shade2, #001e50);--spark-button-primary-color: var(--spark-color-theme-light-gray900, #2b2c30);--spark-button-primary-bg-color: transparent;--spark-button-primary-bg-color-hover: var(--spark-color-theme-light-gray200, #e9eaeb);--spark-button-primary-bg-color-active: var(--spark-color-theme-light-gray400, #c9cace);--spark-button-secondary-color: var(--spark-color-theme-light-gray900, #2b2c30);--spark-button-secondary-bg-color: transparent;--spark-button-secondary-bg-color-hover: var(--spark-color-theme-light-gray200, #e9eaeb);--spark-button-secondary-bg-color-active: var(--spark-color-theme-light-gray400, #c9cace);--spark-button-secondary-border-color: var(--spark-color-theme-light-gray400, #c9cace);--spark-button-ghost-color: var(--spark-color-theme-light-gray900, #2b2c30);--spark-button-ghost-bg-color: transparent;--spark-button-ghost-bg-color-hover: var(--spark-color-theme-light-gray200, #e9eaeb);--spark-button-ghost-bg-color-active: var(--spark-color-theme-light-gray400, #c9cace);--spark-button-ghost-border-color: transparent;--spark-button-alert-color: var(--spark-color-coral-shade1, #c81326);--spark-button-alert-bg-color: transparent;--spark-button-alert-bg-color-hover: #fee9e9;--spark-button-alert-bg-color-active: var(--spark-color-coral-tint2, #ffb6b9);--spark-button-alert-border-color: var(--spark-color-coral-shade1, #c81326);--spark-button-alert-ghost-color: var(--spark-color-coral-shade1, #c81326);--spark-button-alert-ghost-bg-color: transparent;--spark-button-alert-ghost-bg-color-hover: #fee9e9;--spark-button-alert-ghost-bg-color-active: var(--spark-color-coral-tint2, #ffb6b9);--spark-button-alert-ghost-border-color: transparent;--spark-button-disabled-color: var(--spark-color-theme-light-gray500, #b2b3b9);--spark-button-disabled-bg-color: var(--spark-color-theme-light-gray200, #e9eaeb);--spark-button-disabled-border-color: var(--spark-color-theme-light-gray200, #e9eaeb)}[data-theme=dark]{--spark-button-action-color: var(--spark-color-theme-dark-gray50, #242528);--spark-button-action-bg-color: var(--spark-color-energy-blue, #00c7fd);--spark-button-action-bg-color-hover: var(--spark-color-energy-blue-tint1, #6ddcff);--spark-button-action-bg-color-active: var(--spark-color-energy-blue-tint2, #a0ebff);--spark-button-primary-color: var(--spark-color-theme-dark-gray900, #ffffff);--spark-button-primary-bg-color: transparent;--spark-button-primary-bg-color-hover: var(--spark-color-theme-dark-gray200, #3c3e42);--spark-button-primary-bg-color-active: var(--spark-color-theme-dark-gray400, #585a62);--spark-button-secondary-color: var(--spark-color-theme-dark-gray900, #ffffff);--spark-button-secondary-bg-color: transparent;--spark-button-secondary-bg-color-hover: var(--spark-color-theme-dark-gray200, #3c3e42);--spark-button-secondary-bg-color-active: var(--spark-color-theme-dark-gray400, #585a62);--spark-button-secondary-border-color: var(--spark-color-theme-dark-gray400, #585a62);--spark-button-ghost-color: var(--spark-color-theme-dark-gray900, #ffffff);--spark-button-ghost-bg-color: transparent;--spark-button-ghost-bg-color-hover: var(--spark-color-theme-dark-gray200, #3c3e42);--spark-button-ghost-bg-color-active: var(--spark-color-theme-dark-gray400, #585a62);--spark-button-ghost-border-color: transparent;--spark-button-alert-color: var(--spark-color-coral, #ff5662);--spark-button-alert-bg-color: transparent;--spark-button-alert-bg-color-hover: #3a2325;--spark-button-alert-bg-color-active: #321a1b;--spark-button-alert-border-color: var(--spark-color-coral, #ff5662);--spark-button-alert-ghost-color: var(--spark-color-coral, #ff5662);--spark-button-alert-ghost-bg-color: transparent;--spark-button-alert-ghost-bg-color-hover: #3a2325;--spark-button-alert-ghost-bg-color-active: #321a1b;--spark-button-alert-ghost-border-color: transparent;--spark-button-disabled-color: var(--spark-color-theme-dark-gray500, #6b6e76);--spark-button-disabled-bg-color: var(--spark-color-theme-dark-gray200, #3c3e42);--spark-button-disabled-border-color: var(--spark-color-theme-dark-gray200, #3c3e42)}.spark-button-group-spacing-none{gap:var(--spark-button-group-none-gap)}.spark-button-group-spacing-s{gap:var(--spark-button-group-s-gap)}.spark-button-group-spacing-m{gap:var(--spark-button-group-m-gap)}.spark-button-group-spacing-l{gap:var(--spark-button-group-l-gap)}.spark-button-group-spacing-xl{gap:var(--spark-button-group-xl-gap)}.spark-button-group{display:inline-flex;position:relative;align-items:flex-start}.spark-button-group.spark-button-group-orientation-vertical{display:inline-flex;flex-wrap:wrap;flex-direction:column}.spark-button-group.spark-button-group-orientation-horizontal{display:inline-flex;flex-wrap:wrap;flex-direction:row}.spark-button-group.spark-button-group-align-end{align-items:flex-end}.spark-button-group.spark-button-group-align-start{align-items:flex-start}.spark-button-group.spark-button-group-align-center{align-items:center}.spark-button-group.spark-button-group-is-disabled>:not(.spark-tooltip-toggle){pointer-events:none}:root{--spark-button-group-none-gap: 0rem;--spark-button-group-s-gap: .125rem;--spark-button-group-m-gap: .25rem;--spark-button-group-l-gap: .5rem;--spark-button-group-xl-gap: .75rem}:root,[data-theme=light]{--spark-button-group-disabled-color: var(--spark-color-theme-light-gray500, #b2b3b9);--spark-button-group-disabled-bg-color: var(--spark-color-theme-light-gray200, #e9eaeb);--spark-button-group-disabled-border-color: var(--spark-color-theme-light-gray200, #e9eaeb)}[data-theme=dark]{--spark-button-group-disabled-color: var(--spark-color-theme-dark-gray500, #6b6e76);--spark-button-group-disabled-bg-color: var(--spark-color-theme-dark-gray200, #3c3e42);--spark-button-group-disabled-border-color: var(--spark-color-theme-dark-gray200, #3c3e42)}.option-button-group{flex-grow:1;display:flex}.option-button-group .spark-button{flex-grow:1;block-size:3rem}.option-button-group .spark-button .spark-button-content{flex-direction:column}.option-button-group .spark-button .spark-button-content .subtitle{font-size:var(--spark-font-size-25);font-weight:400;line-height:.75rem;letter-spacing:var(--spark-letter-spacing-small);white-space:pre-line}@media (max-width: 550px){.option-button-group{flex-direction:column}.option-button-group .spark-button{width:100%}}
diff --git a/docs/sphinx_setup/_static/selector-tool/assets/selector-8f39cd2e.js b/docs/sphinx_setup/_static/selector-tool/assets/selector-8f39cd2e.js
deleted file mode 100644
index 8ff2ed756634d5..00000000000000
--- a/docs/sphinx_setup/_static/selector-tool/assets/selector-8f39cd2e.js
+++ /dev/null
@@ -1,61 +0,0 @@
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- * react.production.min.js
- *
- * Copyright (c) Facebook, Inc. and its affiliates.
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- * This source code is licensed under the MIT license found in the
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this.getResource(o,i)!==void 0}},{key:"getResourceBundle",value:function(o,i){return i||(i=this.options.defaultNS),this.options.compatibilityAPI==="v1"?io(io({},{}),this.getResource(o,i)):this.getResource(o,i)}},{key:"getDataByLanguage",value:function(o){return this.data[o]}},{key:"hasLanguageSomeTranslations",value:function(o){var i=this.getDataByLanguage(o),s=i&&Object.keys(i)||[];return!!s.find(function(l){return i[l]&&Object.keys(i[l]).length>0})}},{key:"toJSON",value:function(){return this.data}}]),n}(tn),Cc={processors:{},addPostProcessor:function(t){this.processors[t.name]=t},handle:function(t,n,r,o,i){var s=this;return t.forEach(function(l){s.processors[l]&&(n=s.processors[l].process(n,r,o,i))}),n}};function Ra(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter(function(o){return Object.getOwnPropertyDescriptor(e,o).enumerable})),n.push.apply(n,r)}return n}function ke(e){for(var t=1;t<arguments.length;t++){var n=arguments[t]!=null?arguments[t]:{};t%2?Ra(Object(n),!0).forEach(function(r){Je(e,r,n[r])}):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):Ra(Object(n)).forEach(function(r){Object.defineProperty(e,r,Object.getOwnPropertyDescriptor(n,r))})}return e}function Mf(e){var t=zf();return function(){var r=Ot(e),o;if(t){var i=Ot(this).constructor;o=Reflect.construct(r,arguments,i)}else o=r.apply(this,arguments);return Qr(this,o)}}function zf(){if(typeof Reflect>"u"||!Reflect.construct||Reflect.construct.sham)return!1;if(typeof Proxy=="function")return!0;try{return Boolean.prototype.valueOf.call(Reflect.construct(Boolean,[],function(){})),!0}catch{return!1}}var ja={},Fa=function(e){gi(n,e);var t=Mf(n);function n(r){var o,i=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};return ct(this,n),o=t.call(this),mi&&tn.call(Wt(o)),Lf(["resourceStore","languageUtils","pluralResolver","interpolator","backendConnector","i18nFormat","utils"],r,Wt(o)),o.options=i,o.options.keySeparator===void 0&&(o.options.keySeparator="."),o.logger=yt.create("translator"),o}return dt(n,[{key:"changeLanguage",value:function(o){o&&(this.language=o)}},{key:"exists",value:function(o){var i=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{interpolation:{}};if(o==null)return!1;var s=this.resolve(o,i);return s&&s.res!==void 0}},{key:"extractFromKey",value:function(o,i){var s=i.nsSeparator!==void 0?i.nsSeparator:this.options.nsSeparator;s===void 0&&(s=":");var l=i.keySeparator!==void 0?i.keySeparator:this.options.keySeparator,a=i.ns||this.options.defaultNS||[],u=s&&o.indexOf(s)>-1,f=!this.options.userDefinedKeySeparator&&!i.keySeparator&&!this.options.userDefinedNsSeparator&&!i.nsSeparator&&!Ff(o,s,l);if(u&&!f){var d=o.match(this.interpolator.nestingRegexp);if(d&&d.length>0)return{key:o,namespaces:a};var h=o.split(s);(s!==l||s===l&&this.options.ns.indexOf(h[0])>-1)&&(a=h.shift()),o=h.join(l)}return typeof a=="string"&&(a=[a]),{key:o,namespaces:a}}},{key:"translate",value:function(o,i,s){var l=this;if(de(i)!=="object"&&this.options.overloadTranslationOptionHandler&&(i=this.options.overloadTranslationOptionHandler(arguments)),i||(i={}),o==null)return"";Array.isArray(o)||(o=[String(o)]);var a=i.returnDetails!==void 0?i.returnDetails:this.options.returnDetails,u=i.keySeparator!==void 0?i.keySeparator:this.options.keySeparator,f=this.extractFromKey(o[o.length-1],i),d=f.key,h=f.namespaces,m=h[h.length-1],v=i.lng||this.language,w=i.appendNamespaceToCIMode||this.options.appendNamespaceToCIMode;if(v&&v.toLowerCase()==="cimode"){if(w){var O=i.nsSeparator||this.options.nsSeparator;return a?{res:"".concat(m).concat(O).concat(d),usedKey:d,exactUsedKey:d,usedLng:v,usedNS:m}:"".concat(m).concat(O).concat(d)}return a?{res:d,usedKey:d,exactUsedKey:d,usedLng:v,usedNS:m}:d}var p=this.resolve(o,i),c=p&&p.res,g=p&&p.usedKey||d,k=p&&p.exactUsedKey||d,_=Object.prototype.toString.apply(c),S=["[object Number]","[object Function]","[object RegExp]"],x=i.joinArrays!==void 0?i.joinArrays:this.options.joinArrays,P=!this.i18nFormat||this.i18nFormat.handleAsObject,R=typeof c!="string"&&typeof c!="boolean"&&typeof c!="number";if(P&&c&&R&&S.indexOf(_)<0&&!(typeof x=="string"&&_==="[object Array]")){if(!i.returnObjects&&!this.options.returnObjects){this.options.returnedObjectHandler||this.logger.warn("accessing an object - but returnObjects options is not enabled!");var E=this.options.returnedObjectHandler?this.options.returnedObjectHandler(g,c,ke(ke({},i),{},{ns:h})):"key '".concat(d," (").concat(this.language,")' returned an object instead of string.");return a?(p.res=E,p):E}if(u){var H=_==="[object Array]",Pe=H?[]:{},Nt=H?k:g;for(var et in c)if(Object.prototype.hasOwnProperty.call(c,et)){var Nn="".concat(Nt).concat(u).concat(et);Pe[et]=this.translate(Nn,ke(ke({},i),{joinArrays:!1,ns:h})),Pe[et]===Nn&&(Pe[et]=c[et])}c=Pe}}else if(P&&typeof x=="string"&&_==="[object Array]")c=c.join(x),c&&(c=this.extendTranslation(c,o,i,s));else{var ft=!1,tt=!1,C=i.count!==void 0&&typeof i.count!="string",I=n.hasDefaultValue(i),j=C?this.pluralResolver.getSuffix(v,i.count,i):"",M=i["defaultValue".concat(j)]||i.defaultValue;!this.isValidLookup(c)&&I&&(ft=!0,c=M),this.isValidLookup(c)||(tt=!0,c=d);var Q=i.missingKeyNoValueFallbackToKey||this.options.missingKeyNoValueFallbackToKey,xt=Q&&tt?void 0:c,Fe=I&&M!==c&&this.options.updateMissing;if(tt||ft||Fe){if(this.logger.log(Fe?"updateKey":"missingKey",v,m,d,Fe?M:c),u){var xn=this.resolve(d,ke(ke({},i),{},{keySeparator:!1}));xn&&xn.res&&this.logger.warn("Seems the loaded translations were in flat JSON format instead of nested. Either set keySeparator: false on init or make sure your translations are published in nested format.")}var De=[],Pt=this.languageUtils.getFallbackCodes(this.options.fallbackLng,i.lng||this.language);if(this.options.saveMissingTo==="fallback"&&Pt&&Pt[0])for(var Ii=0;Ii<Pt.length;Ii++)De.push(Pt[Ii]);else this.options.saveMissingTo==="all"?De=this.languageUtils.toResolveHierarchy(i.lng||this.language):De.push(i.lng||this.language);var ka=function(Pn,ji,wa){var Sa=I&&wa!==c?wa:xt;l.options.missingKeyHandler?l.options.missingKeyHandler(Pn,m,ji,Sa,Fe,i):l.backendConnector&&l.backendConnector.saveMissing&&l.backendConnector.saveMissing(Pn,m,ji,Sa,Fe,i),l.emit("missingKey",Pn,m,ji,c)};this.options.saveMissing&&(this.options.saveMissingPlurals&&C?De.forEach(function(Ri){l.pluralResolver.getSuffixes(Ri,i).forEach(function(Pn){ka([Ri],d+Pn,i["defaultValue".concat(Pn)]||M)})}):ka(De,d,M))}c=this.extendTranslation(c,o,i,p,s),tt&&c===d&&this.options.appendNamespaceToMissingKey&&(c="".concat(m,":").concat(d)),(tt||ft)&&this.options.parseMissingKeyHandler&&(this.options.compatibilityAPI!=="v1"?c=this.options.parseMissingKeyHandler(this.options.appendNamespaceToMissingKey?"".concat(m,":").concat(d):d,ft?c:void 0):c=this.options.parseMissingKeyHandler(c))}return a?(p.res=c,p):c}},{key:"extendTranslation",value:function(o,i,s,l,a){var u=this;if(this.i18nFormat&&this.i18nFormat.parse)o=this.i18nFormat.parse(o,ke(ke({},this.options.interpolation.defaultVariables),s),l.usedLng,l.usedNS,l.usedKey,{resolved:l});else if(!s.skipInterpolation){s.interpolation&&this.interpolator.init(ke(ke({},s),{interpolation:ke(ke({},this.options.interpolation),s.interpolation)}));var f=typeof o=="string"&&(s&&s.interpolation&&s.interpolation.skipOnVariables!==void 0?s.interpolation.skipOnVariables:this.options.interpolation.skipOnVariables),d;if(f){var h=o.match(this.interpolator.nestingRegexp);d=h&&h.length}var m=s.replace&&typeof s.replace!="string"?s.replace:s;if(this.options.interpolation.defaultVariables&&(m=ke(ke({},this.options.interpolation.defaultVariables),m)),o=this.interpolator.interpolate(o,m,s.lng||this.language,s),f){var v=o.match(this.interpolator.nestingRegexp),w=v&&v.length;d<w&&(s.nest=!1)}s.nest!==!1&&(o=this.interpolator.nest(o,function(){for(var c=arguments.length,g=new Array(c),k=0;k<c;k++)g[k]=arguments[k];return a&&a[0]===g[0]&&!s.context?(u.logger.warn("It seems you are nesting recursively key: ".concat(g[0]," in key: ").concat(i[0])),null):u.translate.apply(u,g.concat([i]))},s)),s.interpolation&&this.interpolator.reset()}var O=s.postProcess||this.options.postProcess,p=typeof O=="string"?[O]:O;return o!=null&&p&&p.length&&s.applyPostProcessor!==!1&&(o=Cc.handle(p,o,i,this.options&&this.options.postProcessPassResolved?ke({i18nResolved:l},s):s,this)),o}},{key:"resolve",value:function(o){var i=this,s=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{},l,a,u,f,d;return typeof o=="string"&&(o=[o]),o.forEach(function(h){if(!i.isValidLookup(l)){var m=i.extractFromKey(h,s),v=m.key;a=v;var w=m.namespaces;i.options.fallbackNS&&(w=w.concat(i.options.fallbackNS));var O=s.count!==void 0&&typeof s.count!="string",p=O&&!s.ordinal&&s.count===0&&i.pluralResolver.shouldUseIntlApi(),c=s.context!==void 0&&(typeof s.context=="string"||typeof s.context=="number")&&s.context!=="",g=s.lngs?s.lngs:i.languageUtils.toResolveHierarchy(s.lng||i.language,s.fallbackLng);w.forEach(function(k){i.isValidLookup(l)||(d=k,!ja["".concat(g[0],"-").concat(k)]&&i.utils&&i.utils.hasLoadedNamespace&&!i.utils.hasLoadedNamespace(d)&&(ja["".concat(g[0],"-").concat(k)]=!0,i.logger.warn('key "'.concat(a,'" for languages "').concat(g.join(", "),`" won't get resolved as namespace "`).concat(d,'" was not yet loaded'),"This means something IS WRONG in your setup. You access the t function before i18next.init / i18next.loadNamespace / i18next.changeLanguage was done. Wait for the callback or Promise to resolve before accessing it!!!")),g.forEach(function(_){if(!i.isValidLookup(l)){f=_;var S=[v];if(i.i18nFormat&&i.i18nFormat.addLookupKeys)i.i18nFormat.addLookupKeys(S,v,_,k,s);else{var x;O&&(x=i.pluralResolver.getSuffix(_,s.count,s));var P="".concat(i.options.pluralSeparator,"zero");if(O&&(S.push(v+x),p&&S.push(v+P)),c){var R="".concat(v).concat(i.options.contextSeparator).concat(s.context);S.push(R),O&&(S.push(R+x),p&&S.push(R+P))}}for(var E;E=S.pop();)i.isValidLookup(l)||(u=E,l=i.getResource(_,k,E,s))}}))})}}),{res:l,usedKey:a,exactUsedKey:u,usedLng:f,usedNS:d}}},{key:"isValidLookup",value:function(o){return o!==void 0&&!(!this.options.returnNull&&o===null)&&!(!this.options.returnEmptyString&&o==="")}},{key:"getResource",value:function(o,i,s){var l=arguments.length>3&&arguments[3]!==void 0?arguments[3]:{};return this.i18nFormat&&this.i18nFormat.getResource?this.i18nFormat.getResource(o,i,s,l):this.resourceStore.getResource(o,i,s,l)}}],[{key:"hasDefaultValue",value:function(o){var i="defaultValue";for(var s in o)if(Object.prototype.hasOwnProperty.call(o,s)&&i===s.substring(0,i.length)&&o[s]!==void 0)return!0;return!1}}]),n}(tn);function Di(e){return e.charAt(0).toUpperCase()+e.slice(1)}var Da=function(){function e(t){ct(this,e),this.options=t,this.supportedLngs=this.options.supportedLngs||!1,this.logger=yt.create("languageUtils")}return dt(e,[{key:"getScriptPartFromCode",value:function(n){if(!n||n.indexOf("-")<0)return null;var r=n.split("-");return r.length===2||(r.pop(),r[r.length-1].toLowerCase()==="x")?null:this.formatLanguageCode(r.join("-"))}},{key:"getLanguagePartFromCode",value:function(n){if(!n||n.indexOf("-")<0)return n;var r=n.split("-");return this.formatLanguageCode(r[0])}},{key:"formatLanguageCode",value:function(n){if(typeof n=="string"&&n.indexOf("-")>-1){var r=["hans","hant","latn","cyrl","cans","mong","arab"],o=n.split("-");return this.options.lowerCaseLng?o=o.map(function(i){return i.toLowerCase()}):o.length===2?(o[0]=o[0].toLowerCase(),o[1]=o[1].toUpperCase(),r.indexOf(o[1].toLowerCase())>-1&&(o[1]=Di(o[1].toLowerCase()))):o.length===3&&(o[0]=o[0].toLowerCase(),o[1].length===2&&(o[1]=o[1].toUpperCase()),o[0]!=="sgn"&&o[2].length===2&&(o[2]=o[2].toUpperCase()),r.indexOf(o[1].toLowerCase())>-1&&(o[1]=Di(o[1].toLowerCase())),r.indexOf(o[2].toLowerCase())>-1&&(o[2]=Di(o[2].toLowerCase()))),o.join("-")}return this.options.cleanCode||this.options.lowerCaseLng?n.toLowerCase():n}},{key:"isSupportedCode",value:function(n){return(this.options.load==="languageOnly"||this.options.nonExplicitSupportedLngs)&&(n=this.getLanguagePartFromCode(n)),!this.supportedLngs||!this.supportedLngs.length||this.supportedLngs.indexOf(n)>-1}},{key:"getBestMatchFromCodes",value:function(n){var r=this;if(!n)return null;var o;return n.forEach(function(i){if(!o){var s=r.formatLanguageCode(i);(!r.options.supportedLngs||r.isSupportedCode(s))&&(o=s)}}),!o&&this.options.supportedLngs&&n.forEach(function(i){if(!o){var s=r.getLanguagePartFromCode(i);if(r.isSupportedCode(s))return o=s;o=r.options.supportedLngs.find(function(l){if(l.indexOf(s)===0)return l})}}),o||(o=this.getFallbackCodes(this.options.fallbackLng)[0]),o}},{key:"getFallbackCodes",value:function(n,r){if(!n)return[];if(typeof n=="function"&&(n=n(r)),typeof n=="string"&&(n=[n]),Object.prototype.toString.apply(n)==="[object Array]")return n;if(!r)return n.default||[];var o=n[r];return o||(o=n[this.getScriptPartFromCode(r)]),o||(o=n[this.formatLanguageCode(r)]),o||(o=n[this.getLanguagePartFromCode(r)]),o||(o=n.default),o||[]}},{key:"toResolveHierarchy",value:function(n,r){var o=this,i=this.getFallbackCodes(r||this.options.fallbackLng||[],n),s=[],l=function(u){u&&(o.isSupportedCode(u)?s.push(u):o.logger.warn("rejecting language code not found in supportedLngs: ".concat(u)))};return typeof n=="string"&&n.indexOf("-")>-1?(this.options.load!=="languageOnly"&&l(this.formatLanguageCode(n)),this.options.load!=="languageOnly"&&this.options.load!=="currentOnly"&&l(this.getScriptPartFromCode(n)),this.options.load!=="currentOnly"&&l(this.getLanguagePartFromCode(n))):typeof n=="string"&&l(this.formatLanguageCode(n)),i.forEach(function(a){s.indexOf(a)<0&&l(o.formatLanguageCode(a))}),s}}]),e}(),Vf=[{lngs:["ach","ak","am","arn","br","fil","gun","ln","mfe","mg","mi","oc","pt","pt-BR","tg","tl","ti","tr","uz","wa"],nr:[1,2],fc:1},{lngs:["af","an","ast","az","bg","bn","ca","da","de","dev","el","en","eo","es","et","eu","fi","fo","fur","fy","gl","gu","ha","hi","hu","hy","ia","it","kk","kn","ku","lb","mai","ml","mn","mr","nah","nap","nb","ne","nl","nn","no","nso","pa","pap","pms","ps","pt-PT","rm","sco","se","si","so","son","sq","sv","sw","ta","te","tk","ur","yo"],nr:[1,2],fc:2},{lngs:["ay","bo","cgg","fa","ht","id","ja","jbo","ka","km","ko","ky","lo","ms","sah","su","th","tt","ug","vi","wo","zh"],nr:[1],fc:3},{lngs:["be","bs","cnr","dz","hr","ru","sr","uk"],nr:[1,2,5],fc:4},{lngs:["ar"],nr:[0,1,2,3,11,100],fc:5},{lngs:["cs","sk"],nr:[1,2,5],fc:6},{lngs:["csb","pl"],nr:[1,2,5],fc:7},{lngs:["cy"],nr:[1,2,3,8],fc:8},{lngs:["fr"],nr:[1,2],fc:9},{lngs:["ga"],nr:[1,2,3,7,11],fc:10},{lngs:["gd"],nr:[1,2,3,20],fc:11},{lngs:["is"],nr:[1,2],fc:12},{lngs:["jv"],nr:[0,1],fc:13},{lngs:["kw"],nr:[1,2,3,4],fc:14},{lngs:["lt"],nr:[1,2,10],fc:15},{lngs:["lv"],nr:[1,2,0],fc:16},{lngs:["mk"],nr:[1,2],fc:17},{lngs:["mnk"],nr:[0,1,2],fc:18},{lngs:["mt"],nr:[1,2,11,20],fc:19},{lngs:["or"],nr:[2,1],fc:2},{lngs:["ro"],nr:[1,2,20],fc:20},{lngs:["sl"],nr:[5,1,2,3],fc:21},{lngs:["he","iw"],nr:[1,2,20,21],fc:22}],$f={1:function(t){return+(t>1)},2:function(t){return+(t!=1)},3:function(t){return 0},4:function(t){return t%10==1&&t%100!=11?0:t%10>=2&&t%10<=4&&(t%100<10||t%100>=20)?1:2},5:function(t){return t==0?0:t==1?1:t==2?2:t%100>=3&&t%100<=10?3:t%100>=11?4:5},6:function(t){return t==1?0:t>=2&&t<=4?1:2},7:function(t){return t==1?0:t%10>=2&&t%10<=4&&(t%100<10||t%100>=20)?1:2},8:function(t){return t==1?0:t==2?1:t!=8&&t!=11?2:3},9:function(t){return+(t>=2)},10:function(t){return t==1?0:t==2?1:t<7?2:t<11?3:4},11:function(t){return t==1||t==11?0:t==2||t==12?1:t>2&&t<20?2:3},12:function(t){return+(t%10!=1||t%100==11)},13:function(t){return+(t!==0)},14:function(t){return t==1?0:t==2?1:t==3?2:3},15:function(t){return t%10==1&&t%100!=11?0:t%10>=2&&(t%100<10||t%100>=20)?1:2},16:function(t){return t%10==1&&t%100!=11?0:t!==0?1:2},17:function(t){return t==1||t%10==1&&t%100!=11?0:1},18:function(t){return t==0?0:t==1?1:2},19:function(t){return t==1?0:t==0||t%100>1&&t%100<11?1:t%100>10&&t%100<20?2:3},20:function(t){return t==1?0:t==0||t%100>0&&t%100<20?1:2},21:function(t){return t%100==1?1:t%100==2?2:t%100==3||t%100==4?3:0},22:function(t){return t==1?0:t==2?1:(t<0||t>10)&&t%10==0?2:3}},Bf=["v1","v2","v3"],Aa={zero:0,one:1,two:2,few:3,many:4,other:5};function Kf(){var e={};return Vf.forEach(function(t){t.lngs.forEach(function(n){e[n]={numbers:t.nr,plurals:$f[t.fc]}})}),e}var Hf=function(){function e(t){var n=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};ct(this,e),this.languageUtils=t,this.options=n,this.logger=yt.create("pluralResolver"),(!this.options.compatibilityJSON||this.options.compatibilityJSON==="v4")&&(typeof Intl>"u"||!Intl.PluralRules)&&(this.options.compatibilityJSON="v3",this.logger.error("Your environment seems not to be Intl API compatible, use an Intl.PluralRules polyfill. 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$a(){return{debug:!1,initImmediate:!0,ns:["translation"],defaultNS:["translation"],fallbackLng:["dev"],fallbackNS:!1,supportedLngs:!1,nonExplicitSupportedLngs:!1,load:"all",preload:!1,simplifyPluralSuffix:!0,keySeparator:".",nsSeparator:":",pluralSeparator:"_",contextSeparator:"_",partialBundledLanguages:!1,saveMissing:!1,updateMissing:!1,saveMissingTo:"fallback",saveMissingPlurals:!0,missingKeyHandler:!1,missingInterpolationHandler:!1,postProcess:!1,postProcessPassResolved:!1,returnNull:!0,returnEmptyString:!0,returnObjects:!1,joinArrays:!1,returnedObjectHandler:!1,parseMissingKeyHandler:!1,appendNamespaceToMissingKey:!1,appendNamespaceToCIMode:!1,overloadTranslationOptionHandler:function(t){var n={};if(de(t[1])==="object"&&(n=t[1]),typeof t[1]=="string"&&(n.defaultValue=t[1]),typeof t[2]=="string"&&(n.tDescription=t[2]),de(t[2])==="object"||de(t[3])==="object"){var r=t[3]||t[2];Object.keys(r).forEach(function(o){n[o]=r[o]})}return 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p?typeof p=="function"?new p:p:null}if(!this.options.isClone){this.modules.logger?yt.init(a(this.modules.logger),this.options):yt.init(null,this.options);var u;this.modules.formatter?u=this.modules.formatter:typeof Intl<"u"&&(u=Gf);var f=new Da(this.options);this.store=new Uf(this.options.resources,this.options);var d=this.services;d.logger=yt,d.resourceStore=this.store,d.languageUtils=f,d.pluralResolver=new Hf(f,{prepend:this.options.pluralSeparator,compatibilityJSON:this.options.compatibilityJSON,simplifyPluralSuffix:this.options.simplifyPluralSuffix}),u&&(!this.options.interpolation.format||this.options.interpolation.format===l.interpolation.format)&&(d.formatter=a(u),d.formatter.init(d,this.options),this.options.interpolation.format=d.formatter.format.bind(d.formatter)),d.interpolator=new Wf(this.options),d.utils={hasLoadedNamespace:this.hasLoadedNamespace.bind(this)},d.backendConnector=new Zf(a(this.modules.backend),d.resourceStore,d,this.options),d.backendConnector.on("*",function(p){for(var c=arguments.length,g=new Array(c>1?c-1:0),k=1;k<c;k++)g[k-1]=arguments[k];o.emit.apply(o,[p].concat(g))}),this.modules.languageDetector&&(d.languageDetector=a(this.modules.languageDetector),d.languageDetector.init&&d.languageDetector.init(d,this.options.detection,this.options)),this.modules.i18nFormat&&(d.i18nFormat=a(this.modules.i18nFormat),d.i18nFormat.init&&d.i18nFormat.init(this)),this.translator=new Fa(this.services,this.options),this.translator.on("*",function(p){for(var c=arguments.length,g=new Array(c>1?c-1:0),k=1;k<c;k++)g[k-1]=arguments[k];o.emit.apply(o,[p].concat(g))}),this.modules.external.forEach(function(p){p.init&&p.init(o)})}if(this.format=this.options.interpolation.format,s||(s=so),this.options.fallbackLng&&!this.services.languageDetector&&!this.options.lng){var h=this.services.languageUtils.getFallbackCodes(this.options.fallbackLng);h.length>0&&h[0]!=="dev"&&(this.options.lng=h[0])}!this.services.languageDetector&&!this.options.lng&&this.logger.warn("init: no languageDetector is used and no lng is defined");var m=["getResource","hasResourceBundle","getResourceBundle","getDataByLanguage"];m.forEach(function(p){o[p]=function(){var c;return(c=o.store)[p].apply(c,arguments)}});var v=["addResource","addResources","addResourceBundle","removeResourceBundle"];v.forEach(function(p){o[p]=function(){var c;return(c=o.store)[p].apply(c,arguments),o}});var w=lr(),O=function(){var c=function(k,_){o.isInitialized&&!o.initializedStoreOnce&&o.logger.warn("init: i18next is already initialized. 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-`):this.metadata.subtitle=n,this}}class fa extends Ze{constructor(t){super({level:L.DISTRIBUTION,key:U.APT,metadata:{title:"APT"}}),this._data=t}get data(){return{...this._data,commands:gv}}cApiOnly(){return this.metadata.subtitle=ee("distributions.CAPIOnly"),this}}const pl="GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB",ic=`https://apt.repos.intel.com/intel-gpg-keys/${pl}`,sc={[St.UBUNTU_18]:18,[St.UBUNTU_20]:20,[St.UBUNTU_22]:22},dv={alias:{22:"jammy",20:"focal",18:"bionic"},numberName:{22:"ubuntu22",20:"ubuntu20",18:"ubuntu18"}},pv=new Set([Ut.v_2022_3_2]),fv=(e,t)=>{const n=pv.has(e.key)?"alias":"numberName";return dv[n][t]},hv=(e,t)=>{const n=fv(e,t);return`echo "deb https://apt.repos.intel.com/openvino/${e.metadata.aptYear} ${n} main" | sudo tee /etc/apt/sources.list.d/intel-openvino-${e.metadata.aptYear}.list`},gv={keyFilename:pl,keyHref:ic,downloadKeyCommand:`wget ${ic}`,addKeyCommand:`sudo apt-key add ${pl}`,getAddRepositoryCommands:(e,t)=>t.map(n=>({ubuntuVersionNumber:sc[n],command:hv(e,sc[n])})),updatePackagesCommand:"sudo apt update",verifyAptCacheCommand:"apt-cache search openvino",getInstallCommand:e=>`sudo apt install openvino-${e.metadata.aptVersion}`};class qe extends Ze{constructor(t){super({level:L.DISTRIBUTION,key:U.ARCHIVE,metadata:{title:ee("distributions.openVINOArchives")}}),this.data=t}}class Up extends Ze{constructor(t){super({level:L.DISTRIBUTION,key:U.BREW,metadata:{title:"Homebrew"}}),this._data=t}get data(){return{...this._data,commands:mv}}}const mv={install:"brew install openvino"};class ir extends Ze{constructor(t){super({level:L.DISTRIBUTION,key:U.CONAN,metadata:{title:"Conan"}}),this._data=t}get data(){return{...this._data,commands:yv}}}const lc="conanfile.txt",ac="conan_toolchain.cmake",vv=e=>`[requires]
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-[generators]
-CMakeDeps
-CMakeToolchain
-[layout]
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-[OpenVINO]
-name=Intel(R) Distribution of OpenVINO ${e.metadata.yumYear}
-baseurl=https://yum.repos.intel.com/openvino/${e.metadata.yumYear}
-enabled=1
-gpgcheck=1
-repo_gpgcheck=1
-gpgkey=https://yum.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
-EOF`,getMoveRepoFileCommand:e=>`sudo mv /tmp/openvino-${e.metadata.yumYear}.repo ${uc}`,verifyRepoCommand:"yum repolist | grep -i openvino",getInstallCommand:e=>`sudo yum install openvino-${e.metadata.yumVersion}`},jv={title:ee("versions.titles.nightlyBuild"),compactTitle:ee("versions.titles.nightlyBuild"),pipVersion:"",githubVersion:"master",giteeVersion:"master",systemRequirementsLink:"https://docs.openvino.ai/nightly/about-openvino/release-notes-openvino/system-requirements.html"};class Fv extends ce{constructor(t){super({level:L.VERSION,key:Ut.NIGHTLY,metadata:jv},t)}}const Dv={title:"2024.1",subtitle:ee("versions.titles.recommended"),compactTitle:"2024.1",compactSubtitle:ee("versions.titles.recommended"),pipVersion:"2024.1.0",githubVersion:"2024.1.0",giteeVersion:"2024.1.0",aptYear:2024,aptVersion:"2024.1.0",yumYear:2024,yumVersion:"2024.1.0",condaVersion:"2024.1.0",conanVersion:"2024.1.0",npmVersion:"2024.1.0",systemRequirementsLink:"https://docs.openvino.ai/2024/about-openvino/system-requirements.html"};class Av extends ce{constructor(t){super({level:L.VERSION,key:Ut.v_2024_1_0,metadata:Dv},t)}}const Uv={title:`2023.3 ${ee("versions.titles.LTS")}`,subtitle:"",compactTitle:`2023.3 ${ee("versions.titles.LTS")}`,compactSubtitle:"",pipVersion:"2023.3.0",githubVersion:"2023.3.0",giteeVersion:"2023.3.0",aptYear:2023,aptVersion:"2023.3.0",yumYear:2023,yumVersion:"2023.3.0",condaVersion:"2023.3.0",conanVersion:"2023.3.0",systemRequirementsLink:"https://docs.openvino.ai/2023.3/system_requirements.html"};class Mv extends ce{constructor(t){super({level:L.VERSION,key:Ut.v_2023_3_0,metadata:Uv},t)}}const zv={title:`2022.3.2 ${ee("versions.titles.LTS")}`,subtitle:ee("versions.titles.hddlSupport"),compactTitle:`2022.3.2 ${ee("versions.titles.LTS")}`,compactSubtitle:ee("versions.titles.hddlSupport"),pipVersion:"2022.3.2",githubVersion:"2022.3.2",giteeVersion:"2022.3.2",aptYear:2022,aptVersion:"2022.3.2",yumYear:2022,yumVersion:"2022.3.2",systemRequirementsLink:"https://docs.openvino.ai/systemrequirements"};class zp extends ce{constructor(t){super({level:L.VERSION,key:Ut.v_2022_3_2,metadata:zv},t)}}class va extends ce{constructor(t,n,r){super({level:L.OP_SYSTEM,key:t,metadata:n,childrenSelector:Pv},r),this._setDefaultOS()}_setDefaultOS(){const t=this._detectOS()||Ye.WINDOWS;this.key===t&&this.default()}_detectOS(){const{userAgent:t}=navigator,n={windows:/(Windows|Win)/g,macOS:/(Macintosh|Mac)/g,linux:/(Linux|X11)/g};return 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ye({linksSet:{installation:"https://docs.openvino.ai/nightly/get-started/install-openvino/install-openvino-pip.html",getStarted:"https://docs.openvino.ai/nightly/get-started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/nightly/get-started/troubleshooting-install-config.html"},frameworks:[]},{pythonAPI:!0}).includesNPUPlugin(),new xe({}),new Ne({})]),$v=new no([new qe({linksSet:{installation:"https://docs.openvino.ai/nightly/get-started/install-openvino/install-openvino-archive-macos.html",getStarted:"https://docs.openvino.ai/nightly/get-started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/nightly/get-started/troubleshooting-install-config.html"},downloadLink:"https://storage.openvinotoolkit.org/repositories/openvino/packages/nightly/latest"}).default(),new ye({linksSet:{installation:"https://docs.openvino.ai/nightly/get-started/install-openvino/install-openvino-pip.html",getStarted:"https://docs.openvino.ai/nightly/get-started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/nightly/get-started/troubleshooting-install-config.html"},frameworks:[]},{pythonAPI:!0}),new xe({}),new Ne({})]),Bv=new to([new qe({linksSet:{installation:"https://docs.openvino.ai/nightly/get-started/install-openvino/install-openvino-archive-windows.html",getStarted:"https://docs.openvino.ai/nightly/get-started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/nightly/get-started/troubleshooting-install-config.html"},downloadLink:"https://storage.openvinotoolkit.org/repositories/openvino/packages/nightly/latest"}).default(),new ye({linksSet:{installation:"https://docs.openvino.ai/nightly/get-started/install-openvino/install-openvino-pip.html",getStarted:"https://docs.openvino.ai/nightly/get-started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/nightly/get-started/troubleshooting-install-config.html"},frameworks:[]},{pythonAPI:!0}).includesNPUPlugin(),new xe({}),new Ne({})]),Vp=new Fv([Bv,$v,Vv]);class Kv extends ce{constructor(t){super({level:L.ENVIRONMENT,key:eo.RUNTIME,metadata:{title:ee("environment.runtime.title"),subtitle:ee("environment.runtime.subtitle")}},t)}}class Hv extends ce{constructor(t){super({level:L.ENVIRONMENT,key:eo.DEV_TOOLS,metadata:{title:ee("environment.devTools.title"),subtitle:ee("environment.devTools.subtitle")}},t)}}const Wv=new ro([new 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ye({linksSet:{installation:"https://docs.openvino.ai/2022.3/openvino_docs_install_guides_install_dev_tools.html",getStarted:"https://docs.openvino.ai/2022.3/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2022.3/openvino_docs_get_started_guide_troubleshooting_steps.html"},systemRequirements:ds,frameworks:[]},{hasFrameworks:!0}).addFootnote(L.OP_SYSTEM).default(),new xe({systemRequirements:ds}).addFootnote(L.OP_SYSTEM),new Ne({systemRequirements:ds}).addFootnote(L.OP_SYSTEM)]),Gv=new to([new ye({linksSet:{installation:"https://docs.openvino.ai/2022.3/openvino_docs_install_guides_install_dev_tools.html",getStarted:"https://docs.openvino.ai/2022.3/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2022.3/openvino_docs_get_started_guide_troubleshooting_steps.html"},frameworks:[]},{hasFrameworks:!0}).default(),new xe({}),new Ne({})]),Qv=new Hv([Gv,Yv,Wv]),Xv=new ro([new qe({linksSet:{installation:"https://docs.openvino.ai/2022.3/openvino_docs_install_guides_installing_openvino_from_archive_linux.html",getStarted:"https://docs.openvino.ai/2022.3/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2022.3/openvino_docs_get_started_guide_troubleshooting_steps.html"},downloadLink:"https://storage.openvinotoolkit.org/repositories/openvino/packages/2022.3.2/linux"}).default(),new ye({linksSet:{installation:"https://docs.openvino.ai/2022.3/openvino_docs_install_guides_installing_openvino_pip.html",getStarted:"https://docs.openvino.ai/2022.3/get_started.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2022.3/openvino_docs_get_started_guide_troubleshooting_steps.html"},frameworks:[]},{pythonAPI:!0}),new 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On({linksSet:{installation:"https://docs.openvino.ai/2022.3/openvino_docs_install_guides_installing_openvino_docker_linux.html",getStarted:"https://docs.openvino.ai/2022.3/openvino_docs_get_started_get_started_demos.html",notebooks:"https://github.com/openvinotoolkit/openvino_notebooks",troubleshooting:"https://docs.openvino.ai/2022.3/openvino_docs_get_started_guide_troubleshooting_steps.html"},downloadLink:"https://docs.openvino.ai/2022.3/openvino_docs_install_guides_installing_openvino_docker_linux.html"})]),No={note:Ap},Jv=new no([new 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0}})),r=e.OP_SYSTEM.nodes,o=e.DISTRIBUTION.nodes,i=(a=e.FRAMEWORK)==null?void 0:a.nodes;F.useEffect(()=>Re.combinationView(),[e]);const{t:s}=B("translation",{keyPrefix:"selectorForm.titles"});return T(zo,{children:[y(Po,{title:s("version"),options:t,level:L.VERSION}),n&&y(Po,{title:s("envinronment"),options:n,level:L.ENVIRONMENT}),y(Po,{title:s("os"),options:r,level:L.OP_SYSTEM}),y(Po,{title:s("distribution"),options:o,level:L.DISTRIBUTION}),i&&y(zy,{title:s("frameworks"),options:i,level:L.FRAMEWORK})]})}const{CompactSelectorContext:ya,useCompactSelector:$y}=gy();Re.initialize(window.parent);function By(){const[e,t]=$y();return y("div",{className:"st-responsive-container",children:T(ya.Provider,{value:t,children:[y(Vy,{state:e}),y(Ay,{state:e})]})})}ys.createRoot(document.getElementById("root")).render(y(vf.StrictMode,{children:y(By,{})}));
diff --git a/docs/sphinx_setup/_static/selector-tool/assets/selector-b0e2854e.css b/docs/sphinx_setup/_static/selector-tool/assets/selector-b0e2854e.css
new file mode 100644
index 00000000000000..37302d9aa13eef
--- /dev/null
+++ b/docs/sphinx_setup/_static/selector-tool/assets/selector-b0e2854e.css
@@ -0,0 +1 @@
+:root{--spark-font-intel-one-text: IntelOneText, var(--global-font-apple-system), var(--global-font-blink-mac-system-font), var(--global-font-segoe-ui), var(--global-font-roboto), var(--global-font-helvetica), var(--global-font-arial), var(--global-font-sans-serif), var(--global-font-apple-color-emoji), var(--global-font-segoe-ui-emoji), var(--global-font-segoe-ui-symbol);--spark-font-intel-one-display: IntelOneDisplay, var(--global-font-apple-system), var(--global-font-blink-mac-system-font), var(--global-font-segoe-ui), var(--global-font-roboto), var(--global-font-helvetica), var(--global-font-arial), var(--global-font-sans-serif), var(--global-font-apple-color-emoji), var(--global-font-segoe-ui-emoji), var(--global-font-segoe-ui-symbol)}:root{--global-font-apple-system: -apple-system;--global-font-blink-mac-system-font: BlinkMacSystemFont;--global-font-segoe-ui: "Segoe UI";--global-font-roboto: Roboto;--global-font-helvetica: Helvetica;--global-font-arial: Arial;--global-font-sans-serif: sans-serif;--global-font-apple-color-emoji: "Apple Color Emoji";--global-font-segoe-ui-emoji: "Segoe UI Emoji";--global-font-segoe-ui-symbol: "Segoe UI Symbol"}:root{--spark-font-size-25: .6875rem;--spark-font-size-50: .75rem;--spark-font-size-75: .875rem;--spark-font-size-100: 1rem;--spark-font-size-200: 1.25rem;--spark-font-size-300: 1.5rem;--spark-font-size-400: 2.25rem;--spark-font-size-500: 3rem;--spark-font-size-600: 3.75rem;--spark-font-size-700: 4.5rem}:root{--spark-line-height-text: 1.5;--spark-line-height-heading: 1.3}:root{--spark-letter-spacing-small: .015625rem;--spark-letter-spacing-zero: 0rem}body{font-size:var(--spark-font-size-100, 1rem);font-family:var(--spark-font-intel-one-text);font-weight:400;line-height:var(--spark-line-height-text, 1.5);letter-spacing:var(--spark-letter-spacing-zero, 0rem)}:root{--spark-color-transparent: transparent;--spark-color-theme-light-gray50: #ffffff;--spark-color-theme-light-gray75: #f9f9f9;--spark-color-theme-light-gray100: #f4f5f5;--spark-color-theme-light-gray200: #e9eaeb;--spark-color-theme-light-gray300: #e2e2e4;--spark-color-theme-light-gray400: #c9cace;--spark-color-theme-light-gray500: #b2b3b9;--spark-color-theme-light-gray600: #8b8e97;--spark-color-theme-light-gray700: #6a6d75;--spark-color-theme-light-gray800: #494b51;--spark-color-theme-light-gray900: #2b2c30;--spark-color-theme-dark-gray50: #242528;--spark-color-theme-dark-gray75: #2e2f32;--spark-color-theme-dark-gray100: #313236;--spark-color-theme-dark-gray200: #3c3e42;--spark-color-theme-dark-gray300: #484a50;--spark-color-theme-dark-gray400: #585a62;--spark-color-theme-dark-gray500: #6b6e76;--spark-color-theme-dark-gray600: #8e9099;--spark-color-theme-dark-gray700: #b7b9be;--spark-color-theme-dark-gray800: #e3e3e5;--spark-color-theme-dark-gray900: #ffffff;--spark-color-classic-blue-tint2: #6cc4f5;--spark-color-classic-blue-tint1: #0099ec;--spark-color-classic-blue: #0054ae;--spark-color-classic-blue-shade1: #00377c;--spark-color-classic-blue-shade2: #001e50;--spark-color-energy-blue-tint2: #a0ebff;--spark-color-energy-blue-tint1: #6ddcff;--spark-color-energy-blue: #00c7fd;--spark-color-energy-blue-shade1: #0095ca;--spark-color-energy-blue-shade2: #005b85;--spark-color-carbon-tint2: #e9e9e9;--spark-color-carbon-tint1: #aeaeae;--spark-color-carbon: #808080;--spark-color-carbon-shade1: #525252;--spark-color-carbon-shade2: #262626;--spark-color-blue-steel-tint2: #b9d6e5;--spark-color-blue-steel-tint1: #86b3ca;--spark-color-blue-steel: #548fad;--spark-color-blue-steel-shade1: #41728a;--spark-color-blue-steel-shade2: #183544;--spark-color-geode-tint2: #eec3f7;--spark-color-geode-tint1: #cc94da;--spark-color-geode: #8f5da2;--spark-color-geode-shade1: #653171;--spark-color-moss-tint2: #d7f3a2;--spark-color-moss-tint1: #b1d272;--spark-color-moss: #8bae46;--spark-color-moss-shade1: #708541;--spark-color-moss-shade2: #515a3d;--spark-color-rust-tint2: #ffc599;--spark-color-rust-tint1: #ff8f51;--spark-color-rust: #e96115;--spark-color-rust-shade1: #b24501;--spark-color-cobalt-tint2: #98a1ff;--spark-color-cobalt-tint1: #5b69ff;--spark-color-cobalt: #1e2eb8;--spark-color-cobalt-shade1: #000f8a;--spark-color-cobalt-shade2: #000864;--spark-color-cobalt-shade3: #040e35;--spark-color-coral-tint2: #ffb6b9;--spark-color-coral-tint1: #ff848a;--spark-color-coral: #ff5662;--spark-color-coral-shade1: #c81326;--spark-color-daisy-tint1: #ffe17a;--spark-color-daisy: #fec91b;--spark-color-daisy-shade1: #edb200;--spark-color-daisy-shade2: #c98f00;--spark-color-white: #ffffff;--spark-color-black: #000000}.spark-visually-hidden:not(:focus):not(:active){clip:rect(0 0 0 0);clip-path:inset(50%);height:.0625rem;overflow:hidden;position:absolute;white-space:nowrap;width:.0625rem}.spark-focus-snap{box-shadow:var(--spark-focus-box-shadow-x) var(--spark-focus-box-shadow-y) var(--spark-focus-box-shadow-blur-radius) calc(var(--spark-focus-outline-width-final-primary) + var(--spark-focus-outline-width-init-backup) + var(--spark-focus-outline-width-final-extra)) transparent;transition:box-shadow var(--spark-focus-snap-transition-duration) var(--spark-focus-snap-transition-timing-function),outline var(--spark-focus-snap-transition-duration) var(--spark-focus-snap-transition-timing-function),outline-offset var(--spark-focus-snap-transition-duration) var(--spark-focus-snap-transition-timing-function),background-color var(--spark-focus-snap-transition-duration) var(--spark-focus-snap-transition-timing-function);outline-color:transparent;outline-style:solid;outline-width:var(--spark-focus-outline-width-init-primary);outline-offset:var(--spark-focus-outline-width-init-backup);-webkit-transform:translate3d(0,0,0)}.spark-focus.spark-focus-suppress:focus{outline:var(--spark-focus-custom-focus-suppress-outline) solid transparent;box-shadow:none}.spark-focus.spark-focus-self:focus{z-index:1;position:relative;box-shadow:var(--spark-focus-box-shadow-x) var(--spark-focus-box-shadow-y) var(--spark-focus-box-shadow-blur-radius) calc(var(--spark-focus-outline-width-final-primary) + var(--spark-focus-outline-width-final-backup) + var(--spark-focus-outline-width-final-extra)) var(--spark-focus-color-focus-backup);outline-color:var(--spark-focus-color-focus-primary);outline-style:solid;outline-width:var(--spark-focus-outline-width-final-primary);outline-offset:var(--spark-focus-outline-width-final-backup)}.spark-focus.spark-focus-self.spark-focus-snap:not(:focus){transition-delay:0s;transition-duration:0s}.spark-focus.spark-focus-self.spark-focus-background:focus{color:var(--spark-focus-color-focus-foreground);background-color:var(--spark-focus-color-focus-background)}.spark-focus.spark-focus-within:focus-within{z-index:1;position:relative;box-shadow:var(--spark-focus-box-shadow-x) var(--spark-focus-box-shadow-y) var(--spark-focus-box-shadow-blur-radius) calc(var(--spark-focus-outline-width-final-primary) + var(--spark-focus-outline-width-final-backup) + var(--spark-focus-outline-width-final-extra)) var(--spark-focus-color-focus-backup);outline-color:var(--spark-focus-color-focus-primary);outline-style:solid;outline-width:var(--spark-focus-outline-width-final-primary);outline-offset:var(--spark-focus-outline-width-final-backup)}.spark-focus.spark-focus-within.spark-focus-snap:not(:focus-within){transition-delay:0s;transition-duration:0s}.spark-focus.spark-focus-within.spark-focus-background:focus-within{color:var(--spark-focus-color-focus-foreground);background-color:var(--spark-focus-color-focus-background)}.spark-focus.spark-focus-adjacent:focus+.spark-focus{z-index:1;position:relative;box-shadow:var(--spark-focus-box-shadow-x) var(--spark-focus-box-shadow-y) var(--spark-focus-box-shadow-blur-radius) calc(var(--spark-focus-outline-width-final-primary) + var(--spark-focus-outline-width-final-backup) + var(--spark-focus-outline-width-final-extra)) var(--spark-focus-color-focus-backup);outline-color:var(--spark-focus-color-focus-primary);outline-style:solid;outline-width:var(--spark-focus-outline-width-final-primary);outline-offset:var(--spark-focus-outline-width-final-backup)}.spark-focus.spark-focus-adjacent:not(:focus)+.spark-focus.spark-focus-snap{transition-delay:0s;transition-duration:0s}.spark-focus.spark-focus-adjacent:focus+.spark-focus.spark-focus-background{color:var(--spark-focus-color-focus-foreground);background-color:var(--spark-focus-color-focus-background)}.spark-focus:where(.spark-focus-slider):focus::-webkit-slider-thumb{z-index:1;position:relative;box-shadow:var(--spark-focus-box-shadow-x) var(--spark-focus-box-shadow-y) var(--spark-focus-box-shadow-blur-radius) calc(var(--spark-focus-outline-width-final-primary) + var(--spark-focus-outline-width-final-backup) + var(--spark-focus-outline-width-final-extra)) var(--spark-focus-color-focus-backup);outline-color:var(--spark-focus-color-focus-primary);outline-style:solid;outline-width:var(--spark-focus-outline-width-final-primary);outline-offset:var(--spark-focus-outline-width-final-backup)}.spark-focus:where(.spark-focus-slider.spark-focus-snap)::-webkit-slider-thumb{box-shadow:var(--spark-focus-box-shadow-x) var(--spark-focus-box-shadow-y) var(--spark-focus-box-shadow-blur-radius) calc(var(--spark-focus-outline-width-final-primary) + var(--spark-focus-outline-width-init-backup) + var(--spark-focus-outline-width-final-extra)) transparent;transition:box-shadow var(--spark-focus-snap-transition-duration) var(--spark-focus-snap-transition-timing-function),outline var(--spark-focus-snap-transition-duration) var(--spark-focus-snap-transition-timing-function),outline-offset var(--spark-focus-snap-transition-duration) var(--spark-focus-snap-transition-timing-function),background-color var(--spark-focus-snap-transition-duration) var(--spark-focus-snap-transition-timing-function);outline-color:transparent;outline-style:solid;outline-width:var(--spark-focus-outline-width-init-primary);outline-offset:var(--spark-focus-outline-width-init-backup);-webkit-transform:translate3d(0,0,0)}.spark-focus:where(.spark-focus-slider.spark-focus-snap):not(:focus)::-webkit-slider-thumb{transition-delay:0s;transition-duration:0s}.spark-focus:where(.spark-focus-slider.spark-focus-background):focus::-webkit-slider-thumb{color:var(--spark-focus-color-focus-foreground);background-color:var(--spark-focus-color-focus-background)}:root{--spark-focus-outline-width-final-extra: 0rem;--spark-focus-outline-width-final-primary: .1875rem;--spark-focus-outline-width-final-backup: .1875rem;--spark-focus-outline-width-init-primary: .75rem;--spark-focus-outline-width-init-backup: .1875rem;--spark-focus-snap-transition-duration: 1.5s;--spark-focus-snap-transition-timing-function: cubic-bezier(.06, .98, .2, .99);--spark-focus-custom-focus-suppress-outline: .125rem;--spark-focus-box-shadow-x: 0rem;--spark-focus-box-shadow-y: 0rem;--spark-focus-box-shadow-blur-radius: 0rem}:root,[data-theme=light]{--spark-focus-color-focus-primary: var(--spark-color-classic-blue-shade1, #00377c);--spark-focus-color-focus-backup: var(--spark-color-theme-light-gray50, #ffffff);--spark-focus-color-focus-background: var(--spark-color-theme-light-gray50, #ffffff);--spark-focus-color-focus-foreground: var(--spark-color-carbon-shade2, #262626)}[data-theme=dark]{--spark-focus-color-focus-primary: var(--spark-color-energy-blue-tint1, #6ddcff);--spark-focus-color-focus-backup: var(--spark-color-theme-dark-gray50, #242528);--spark-focus-color-focus-background: var(--spark-color-theme-dark-gray50, #242528);--spark-focus-color-focus-foreground: var(--spark-color-theme-dark-gray900, #ffffff)}.spark-focus-visible-snap{box-shadow:var(--spark-focus-box-shadow-x) var(--spark-focus-box-shadow-y) var(--spark-focus-box-shadow-blur-radius) calc(var(--spark-focus-outline-width-final-primary) + var(--spark-focus-outline-width-init-backup) + var(--spark-focus-outline-width-final-extra)) transparent;transition:box-shadow var(--spark-focus-snap-transition-duration) var(--spark-focus-snap-transition-timing-function),outline var(--spark-focus-snap-transition-duration) var(--spark-focus-snap-transition-timing-function),outline-offset var(--spark-focus-snap-transition-duration) var(--spark-focus-snap-transition-timing-function),background-color var(--spark-focus-snap-transition-duration) var(--spark-focus-snap-transition-timing-function);outline-color:transparent;outline-style:solid;outline-width:var(--spark-focus-outline-width-init-primary);outline-offset:var(--spark-focus-outline-width-init-backup);-webkit-transform:translate3d(0,0,0)}.spark-focus-visible.spark-focus-visible-suppress:focus{outline:var(--spark-focus-custom-focus-suppress-outline) solid transparent;box-shadow:none}.spark-focus-visible.spark-focus-visible-self:focus{z-index:1;position:relative;box-shadow:var(--spark-focus-box-shadow-x) var(--spark-focus-box-shadow-y) var(--spark-focus-box-shadow-blur-radius) calc(var(--spark-focus-outline-width-final-primary) + var(--spark-focus-outline-width-final-backup) + var(--spark-focus-outline-width-final-extra)) var(--spark-focus-color-focus-backup);outline-color:var(--spark-focus-color-focus-primary);outline-style:solid;outline-width:var(--spark-focus-outline-width-final-primary);outline-offset:var(--spark-focus-outline-width-final-backup)}.spark-focus-visible.spark-focus-visible-self:focus:not(:focus-visible){z-index:revert;position:revert;box-shadow:revert;outline-color:revert;outline-style:revert;outline-width:revert;outline-offset:revert}.spark-focus-visible.spark-focus-visible-self:focus-visible{z-index:1;position:relative;box-shadow:var(--spark-focus-box-shadow-x) var(--spark-focus-box-shadow-y) var(--spark-focus-box-shadow-blur-radius) calc(var(--spark-focus-outline-width-final-primary) + var(--spark-focus-outline-width-final-backup) + var(--spark-focus-outline-width-final-extra)) var(--spark-focus-color-focus-backup);outline-color:var(--spark-focus-color-focus-primary);outline-style:solid;outline-width:var(--spark-focus-outline-width-final-primary);outline-offset:var(--spark-focus-outline-width-final-backup)}.spark-focus-visible.spark-focus-visible-self.spark-focus-visible-snap:not(:focus-visible){transition-delay:0s;transition-duration:0s}.spark-focus-visible.spark-focus-visible-self.spark-focus-visible-background:focus{color:var(--spark-focus-color-focus-foreground);background-color:var(--spark-focus-color-focus-background)}.spark-focus-visible.spark-focus-visible-self.spark-focus-visible-background:focus:not(:focus-visible){color:revert;background-color:revert}.spark-focus-visible.spark-focus-visible-self.spark-focus-visible-background:focus-visible{color:var(--spark-focus-color-focus-foreground);background-color:var(--spark-focus-color-focus-background)}.spark-focus-visible.spark-focus-visible-within:has(:focus-visible){z-index:1;position:relative;box-shadow:var(--spark-focus-box-shadow-x) var(--spark-focus-box-shadow-y) var(--spark-focus-box-shadow-blur-radius) calc(var(--spark-focus-outline-width-final-primary) + var(--spark-focus-outline-width-final-backup) + var(--spark-focus-outline-width-final-extra)) var(--spark-focus-color-focus-backup);outline-color:var(--spark-focus-color-focus-primary);outline-style:solid;outline-width:var(--spark-focus-outline-width-final-primary);outline-offset:var(--spark-focus-outline-width-final-backup)}.spark-focus-visible.spark-focus-visible-within.spark-focus-visible-snap:not(:has(:focus-visible)){transition-delay:0s;transition-duration:0s}.spark-focus-visible.spark-focus-visible-within.spark-focus-visible-background:has(:focus-visible){color:var(--spark-focus-color-focus-foreground);background-color:var(--spark-focus-color-focus-background)}.spark-focus-visible.spark-focus-visible-adjacent:focus+.spark-focus-visible{z-index:1;position:relative;box-shadow:var(--spark-focus-box-shadow-x) var(--spark-focus-box-shadow-y) var(--spark-focus-box-shadow-blur-radius) calc(var(--spark-focus-outline-width-final-primary) + var(--spark-focus-outline-width-final-backup) + var(--spark-focus-outline-width-final-extra)) var(--spark-focus-color-focus-backup);outline-color:var(--spark-focus-color-focus-primary);outline-style:solid;outline-width:var(--spark-focus-outline-width-final-primary);outline-offset:var(--spark-focus-outline-width-final-backup)}.spark-focus-visible.spark-focus-visible-adjacent:focus:not(:focus-visible)+.spark-focus-visible{z-index:revert;position:revert;box-shadow:revert;outline-color:revert;outline-style:revert;outline-width:revert;outline-offset:revert}.spark-focus-visible.spark-focus-visible-adjacent:focus-visible+.spark-focus-visible{z-index:1;position:relative;box-shadow:var(--spark-focus-box-shadow-x) var(--spark-focus-box-shadow-y) var(--spark-focus-box-shadow-blur-radius) calc(var(--spark-focus-outline-width-final-primary) + var(--spark-focus-outline-width-final-backup) + var(--spark-focus-outline-width-final-extra)) var(--spark-focus-color-focus-backup);outline-color:var(--spark-focus-color-focus-primary);outline-style:solid;outline-width:var(--spark-focus-outline-width-final-primary);outline-offset:var(--spark-focus-outline-width-final-backup)}.spark-focus-visible.spark-focus-visible-adjacent:not(:focus-visible)+.spark-focus-visible.spark-focus-visible-snap{transition-delay:0s;transition-duration:0s}.spark-focus-visible.spark-focus-visible-adjacent:focus+.spark-focus-visible.spark-focus-visible-background{color:var(--spark-focus-color-focus-foreground);background-color:var(--spark-focus-color-focus-background)}.spark-focus-visible.spark-focus-visible-adjacent:focus:not(:focus-visible)+.spark-focus-visible.spark-focus-visible-background{color:revert;background-color:revert}.spark-focus-visible.spark-focus-visible-adjacent:focus-visible+.spark-focus-visible.spark-focus-visible-background{color:var(--spark-focus-color-focus-foreground);background-color:var(--spark-focus-color-focus-background)}.spark-focus-visible:where(.spark-focus-visible-slider):focus::-webkit-slider-thumb{z-index:1;position:relative;box-shadow:var(--spark-focus-box-shadow-x) var(--spark-focus-box-shadow-y) var(--spark-focus-box-shadow-blur-radius) calc(var(--spark-focus-outline-width-final-primary) + var(--spark-focus-outline-width-final-backup) + var(--spark-focus-outline-width-final-extra)) var(--spark-focus-color-focus-backup);outline-color:var(--spark-focus-color-focus-primary);outline-style:solid;outline-width:var(--spark-focus-outline-width-final-primary);outline-offset:var(--spark-focus-outline-width-final-backup)}.spark-focus-visible:where(.spark-focus-visible-slider):focus:not(:focus-visible)::-webkit-slider-thumb{z-index:revert;position:revert;box-shadow:revert;outline-color:revert;outline-style:revert;outline-width:revert;outline-offset:revert}.spark-focus-visible:where(.spark-focus-visible-slider):focus-visible::-webkit-slider-thumb{z-index:1;position:relative;box-shadow:var(--spark-focus-box-shadow-x) var(--spark-focus-box-shadow-y) var(--spark-focus-box-shadow-blur-radius) calc(var(--spark-focus-outline-width-final-primary) + var(--spark-focus-outline-width-final-backup) + var(--spark-focus-outline-width-final-extra)) var(--spark-focus-color-focus-backup);outline-color:var(--spark-focus-color-focus-primary);outline-style:solid;outline-width:var(--spark-focus-outline-width-final-primary);outline-offset:var(--spark-focus-outline-width-final-backup)}.spark-focus-visible:where(.spark-focus-visible-slider.spark-focus-visible-snap)::-webkit-slider-thumb{box-shadow:var(--spark-focus-box-shadow-x) var(--spark-focus-box-shadow-y) var(--spark-focus-box-shadow-blur-radius) calc(var(--spark-focus-outline-width-final-primary) + var(--spark-focus-outline-width-init-backup) + var(--spark-focus-outline-width-final-extra)) transparent;transition:box-shadow var(--spark-focus-snap-transition-duration) var(--spark-focus-snap-transition-timing-function),outline var(--spark-focus-snap-transition-duration) var(--spark-focus-snap-transition-timing-function),outline-offset var(--spark-focus-snap-transition-duration) var(--spark-focus-snap-transition-timing-function),background-color var(--spark-focus-snap-transition-duration) var(--spark-focus-snap-transition-timing-function);outline-color:transparent;outline-style:solid;outline-width:var(--spark-focus-outline-width-init-primary);outline-offset:var(--spark-focus-outline-width-init-backup);-webkit-transform:translate3d(0,0,0)}.spark-focus-visible:where(.spark-focus-visible-slider.spark-focus-visible-snap):not(:focus-visible)::-webkit-slider-thumb{transition-delay:0s;transition-duration:0s}.spark-focus-visible:where(.spark-focus-visible-slider.spark-focus-visible-background):focus::-webkit-slider-thumb{color:var(--spark-focus-color-focus-foreground);background-color:var(--spark-focus-color-focus-background)}.spark-focus-visible:where(.spark-focus-visible-slider.spark-focus-visible-background):focus:not(:focus-visible)::-webkit-slider-thumb{color:revert;background-color:revert}.spark-focus-visible:where(.spark-focus-visible-slider.spark-focus-visible-background):focus-visible::-webkit-slider-thumb{color:var(--spark-focus-color-focus-foreground);background-color:var(--spark-focus-color-focus-background)}.svg-icon{display:block}body{margin:0}.st-responsive-container{padding:var(--spark-button-group-xl-gap);margin:0 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var(--spark-code-snippet-multiline-l-tooltip-top);block-size:var(--spark-code-snippet-single-l-block-size)!important;align-items:center!important}.spark-code-snippet-tooltip-m{block-size:var(--spark-code-snippet-single-m-block-size)!important;align-items:center!important}.spark-code-snippet-tooltip-s{block-size:var(--spark-code-snippet-single-s-block-size)!important;align-items:center!important}.spark-code-snippet-line-count{display:grid;text-align:end}.spark-code-snippet{color:var(--spark-code-snippet-color);position:relative;background-color:var(--spark-code-snippet-background-color)}.spark-code-snippet .spark-code-snippet-line-count:before{color:var(--spark-code-snippet-numbering-color);content:counter(count);white-space:pre;margin-right:var(--spark-code-snippet-line-numbering-margin-right);border-inline-end:var(--spark-code-snippet-line-numbering-border-inline-end) solid var(--spark-code-snippet-border-color);counter-increment:count;padding-inline-end:var(--spark-code-snippet-line-numbering-padding-inline-end);padding-inline-start:var(--spark-code-snippet-line-numbering-padding-inline-start)}.spark-code-snippet.spark-code-snippet-inline{display:inline-block;align-items:flex-start}.spark-code-snippet.spark-code-snippet-multiline{align-items:flex-start}.spark-code-snippet.spark-code-snippet-multiline.spark-code-snippet-size-l{font-size:var(--spark-code-snippet-multiline-l-font-size);block-size:var(--spark-code-snippet-multiline-l-block-size);padding-block-start:var(--spark-code-snippet-multiline-l-padding-block-start);padding-inline-start:var(--spark-code-snippet-multiline-l-padding-inline-start)}.spark-code-snippet.spark-code-snippet-multiline.spark-code-snippet-size-m{font-size:var(--spark-code-snippet-multiline-m-font-size);block-size:var(--spark-code-snippet-multiline-m-block-size);padding-block-start:var(--spark-code-snippet-multiline-m-padding-block-start);padding-inline-start:var(--spark-code-snippet-multiline-m-padding-inline-start)}.spark-code-snippet.spark-code-snippet-multiline.spark-code-snippet-size-s{font-size:var(--spark-code-snippet-multiline-s-font-size);block-size:var(--spark-code-snippet-multiline-s-block-size);padding-block-start:var(--spark-code-snippet-multiline-s-padding-block-start);padding-inline-start:var(--spark-code-snippet-multiline-s-padding-inline-start)}.spark-code-snippet.spark-code-snippet-single 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.5rem;--spark-code-snippet-multiline-l-gap: 1rem;--spark-code-snippet-multiline-l-tooltip-top: .75rem;--spark-code-snippet-multiline-l-tooltip-right: 1rem;--spark-code-snippet-multiline-m-font-size: .875rem;--spark-code-snippet-multiline-m-block-size: 11.25rem;--spark-code-snippet-multiline-m-padding-inline-start: .5rem;--spark-code-snippet-multiline-m-padding-block-start: .5rem;--spark-code-snippet-multiline-m-gap: .75rem;--spark-code-snippet-multiline-s-font-size: .75rem;--spark-code-snippet-multiline-s-block-size: 10rem;--spark-code-snippet-multiline-s-padding-inline-start: .5rem;--spark-code-snippet-multiline-s-padding-block-start: .5rem;--spark-code-snippet-multiline-s-gap: .5rem;--spark-code-snippet-copy-icon-font-size: 1rem;--spark-code-snippet-copy-icon-flex-shrink: 0;--spark-code-snippet-copy-icon-margin-inline-end: 0;--spark-code-snippet-copy-icon-padding-inline-end: 0rem;--spark-code-snippet-line-numbering-padding-inline-start: .5rem;--spark-code-snippet-line-numbering-padding-inline-end: .3125rem;--spark-code-snippet-line-numbering-padding-inline-top: .5rem;--spark-code-snippet-line-numbering-border-inline-end: .0625rem;--spark-code-snippet-line-numbering-margin-right: .625rem}:root,[data-theme=light]{--spark-code-snippet-color: var(--spark-color-theme-light-gray900, #2b2c30);--spark-code-snippet-background-color: var(--spark-color-theme-light-gray200, #e9eaeb);--spark-code-snippet-border-color: var(--spark-color-theme-light-gray400, #c9cace);--spark-code-snippet-numbering-color: var(--spark-color-theme-light-gray900, #2b2c30)}[data-theme=dark]{--spark-code-snippet-color: var(--spark-color-theme-dark-gray900, #ffffff);--spark-code-snippet-background-color: var(--spark-color-theme-dark-gray200, #3c3e42);--spark-code-snippet-border-color: var(--spark-color-theme-dark-gray400, #585a62);--spark-code-snippet-numbering-color: var(--spark-color-theme-dark-gray900, #ffffff)}.st-code-snippet{padding:var(--spark-code-snippet-multiline-m-padding-block-start);background-color:var(--spark-code-snippet-background-color);color:var(--spark-code-snippet-color);font-size:var(--spark-code-snippet-multiline-m-font-size);display:flex;flex-direction:column;flex-grow:1;position:relative}.st-code-snippet .st-code-snippet-comment{margin:auto 0;white-space:pre-line;color:var(--spark-color-moss-shade1);font-style:italic;padding-right:calc(var(--spark-button-m-block-size) - var(--spark-code-snippet-multiline-m-padding-block-start))}.st-code-snippet .st-code-snippet-content{white-space:pre-line;word-break:break-word}.st-code-snippet .copy-button{position:absolute!important;top:var(--spark-code-snippet-multiline-m-padding-block-start);right:var(--spark-code-snippet-multiline-m-padding-block-start);-webkit-user-select:none;user-select:none;padding:0;border:none;outline:none;background-color:transparent;cursor:pointer}.st-code-snippet-multi-line{padding:var(--spark-code-snippet-multiline-m-padding-block-start) 0 var(--spark-code-snippet-multiline-m-padding-block-start) var(--spark-code-snippet-multiline-m-padding-block-start);background-color:var(--spark-code-snippet-background-color);color:var(--spark-code-snippet-color);font-size:var(--spark-code-snippet-multiline-m-font-size);display:flex;flex-direction:column;flex-grow:1;position:relative}.st-code-snippet-multi-line .st-code-snippet-comment{margin:auto 0;white-space:pre-line;color:var(--spark-color-moss-shade1);font-style:italic;padding-right:calc(var(--spark-button-m-block-size) - var(--spark-code-snippet-multiline-m-padding-block-start))}.st-code-snippet-multi-line .st-code-snippet{padding-left:0}.spark-hyperlink-is-disabled{pointer-events:none}.spark-hyperlink-primary{color:var(--spark-hyperlink-color-primary)}.spark-hyperlink-primary a{color:var(--spark-hyperlink-color-secondary)!important;text-decoration:none!important}.spark-hyperlink-secondary{color:var(--spark-hyperlink-color-secondary)}.spark-hyperlink-secondary a{color:var(--spark-hyperlink-color-secondary)!important;text-decoration:none!important}.spark-hyperlink-standard{text-decoration:underline}.spark-hyperlink-quiet{text-decoration:none}.spark-hyperlink-quiet:hover{text-decoration:underline}.spark-hyperlink:visited,.spark-hyperlink.visited{color:var(--spark-hyperlink-color-visited)}:root,[data-theme=light]{--spark-hyperlink-color-primary: var(--spark-color-classic-blue, #0054ae);--spark-hyperlink-color-secondary: inherit;--spark-hyperlink-color-visited: var(--spark-color-geode, #8f5da2)}[data-theme=dark]{--spark-hyperlink-color-primary: var(--spark-color-energy-blue, #00c7fd);--spark-hyperlink-color-secondary: inherit;--spark-hyperlink-color-visited: var(--spark-color-geode-tint1, #cc94da)}.spark-hyperlink:visited,.spark-hyperlink.visited{color:var(--spark-hyperlink-color-primary)}.st-section{border-left:2px solid var(--spark-color-theme-light-gray500);display:flex;align-items:center}.st-section:not(:last-child){margin-bottom:var(--spark-button-group-l-gap)}.st-section .st-section-title{font-weight:500;padding:0 var(--spark-button-group-l-gap);width:150px;flex-shrink:0;height:var(--spark-button-l-block-size);display:flex;align-items:center;align-self:flex-start}.st-section .spark-button{max-inline-size:none}.st-section .st-section-content{display:flex;flex-direction:column;flex-grow:1}.st-section .st-section-content .st-section-content-row{display:inline-flex;align-items:center;min-height:var(--spark-button-l-block-size);gap:var(--spark-button-group-l-gap)}.st-section .st-section-content .st-section-content-row:not(:last-child){margin-bottom:var(--spark-button-group-l-gap)}.st-section .st-section-content .st-section-content-row:not(:first-child):last-child{min-height:auto}.st-section-accent{border-left-color:var(--spark-color-classic-blue)}.st-section-dashed{border-left-style:dashed}@media (max-width: 550px){.st-section{flex-direction:column;border-left:none;align-items:start}.st-section:not(:last-child){margin-bottom:var(--spark-button-group-xl-gap)}.st-section .st-section-title{border-left:2px solid var(--spark-color-theme-light-gray500)}.st-section .st-section-content{margin-top:var(--spark-button-group-l-gap);width:100%}}.st-related-tools-links{display:flex;flex-direction:column}.st-related-tools-links>*{height:var(--spark-button-l-block-size);display:flex;align-items:center;white-space:pre}.st-resources-links>div{display:flex;flex-wrap:wrap;column-gap:var(--spark-button-group-xl-gap);line-height:var(--spark-button-l-block-size)}.links-section{display:flex;flex-direction:column;align-items:flex-start}.spark-tooltip-tip{color:var(--spark-tooltip-tip-color);position:absolute;block-size:var(--spark-tooltip-tip-block-size);inline-size:var(--spark-tooltip-tip-inline-size);border-style:solid;border-width:var(--spark-tooltip-tip-border-width);border-block-end-color:transparent;border-inline-end-color:transparent;border-inline-start-color:transparent}.spark-tooltip-toggle{width:fit-content;display:flex;position:relative}.spark-tooltip-size-m{padding:var(--spark-tooltip-m-padding-top-bottom) var(--spark-tooltip-m-padding-right-left)}.spark-tooltip-size-m .spark-tooltip-label{width:max-content;display:flex;font-size:var(--spark-tooltip-m-font-size);font-weight:var(--spark-tooltip-m-label-font-weight);line-height:var(--spark-tooltip-m-label-line-height)}.spark-tooltip-size-m.spark-tooltip-top .spark-tooltip-tip{inset-block-start:100%;margin-inline-start:calc(50% - var(--spark-tooltip-m-gap-size))}.spark-tooltip-size-m.spark-tooltip-bottom .spark-tooltip-tip{margin-inline-start:calc(50% - var(--spark-tooltip-m-gap-size))}.spark-tooltip-size-m.spark-tooltip-bottom.spark-tooltip-left .spark-tooltip-tip,.spark-tooltip-size-m.spark-tooltip-top.spark-tooltip-left .spark-tooltip-tip,.spark-tooltip-size-m.spark-tooltip-top.spark-tooltip-right .spark-tooltip-tip{margin-inline-start:calc(-1 * var(--spark-tooltip-m-gap-size))}.spark-tooltip-size-m .spark-icon{line-height:var(--spark-tooltip-m-icon-line-height);margin-inline-end:var(--spark-tooltip-margin-inline-end)}.spark-tooltip-size-s{padding:var(--spark-tooltip-s-padding-top-bottom) var(--spark-tooltip-s-padding-right-left)}.spark-tooltip-size-s .spark-tooltip-label{width:max-content;display:flex;font-size:var(--spark-tooltip-s-font-size);font-weight:var(--spark-tooltip-s-label-font-weight);line-height:var(--spark-tooltip-s-label-line-height)}.spark-tooltip-size-s.spark-tooltip-top .spark-tooltip-tip{inset-block-start:100%;margin-inline-start:calc(50% - var(--spark-tooltip-s-gap-size))}.spark-tooltip-size-s.spark-tooltip-bottom .spark-tooltip-tip{margin-inline-start:calc(50% - var(--spark-tooltip-s-gap-size))}.spark-tooltip-size-s.spark-tooltip-bottom.spark-tooltip-left .spark-tooltip-tip,.spark-tooltip-size-s.spark-tooltip-top.spark-tooltip-left .spark-tooltip-tip,.spark-tooltip-size-s.spark-tooltip-top.spark-tooltip-right .spark-tooltip-tip{margin-inline-start:calc(-1 * var(--spark-tooltip-s-gap-size))}.spark-tooltip-size-s .spark-icon{line-height:var(--spark-tooltip-s-icon-line-height);margin-inline-end:var(--spark-tooltip-margin-inline-end)}.spark-tooltip{top:var(--spark-tooltip-top);left:var(--spark-tooltip-left);color:var(--spark-tooltip-color);display:inline-flex;z-index:var(--spark-tooltip-z-index);position:absolute;box-sizing:border-box;visibility:visible;word-break:break-word;align-items:flex-start;inline-size:auto;flex-direction:row;pointer-events:auto;vertical-align:top;max-inline-size:var(--spark-tooltip-max-inline-size);background-color:var(--spark-tooltip-background-color);inset-block-start:var(--spark-tooltip-inset-block-start);inset-inline-start:var(--spark-tooltip-inset-inline-start)}.spark-tooltip.spark-tooltip-top .spark-tooltip-tip{inset-block-start:100%}.spark-tooltip.spark-tooltip-right-side .spark-tooltip-tip{transform:rotate(90deg);inset-block-end:calc(50% - var(--spark-tooltip-mid-tooltip-size));inset-inline-end:100%}.spark-tooltip.spark-tooltip-bottom .spark-tooltip-tip{transform:rotate(-180deg);inset-block-end:100%}.spark-tooltip.spark-tooltip-left-side .spark-tooltip-tip{transform:rotate(-90deg);inset-block-end:calc(50% - var(--spark-tooltip-mid-tooltip-size));inset-inline-start:100%}.spark-tooltip.spark-tooltip-bottom.spark-tooltip-left .spark-tooltip-tip{transform:rotate(-180deg);inset-inline-start:100%}.spark-tooltip.spark-tooltip-bottom.spark-tooltip-right .spark-tooltip-tip{transform:rotate(-180deg);inset-inline-start:0%;margin-inline-start:var(--spark-tooltip-tip-margin-inline-start)}.spark-tooltip.spark-tooltip-top.spark-tooltip-left .spark-tooltip-tip{transform:rotate(0);inset-inline-start:100%}.spark-tooltip.spark-tooltip-top.spark-tooltip-right .spark-tooltip-tip{transform:rotate(0);inset-inline-start:0%;margin-inline-start:var(--spark-tooltip-tip-margin-inline-start)}.spark-tooltip.spark-tooltip-right.spark-tooltip-end .spark-tooltip-tip{transform:rotate(90deg);inset-block-end:var(--spark-tooltip-tip-inset-block-end);inset-inline-end:100%}.spark-tooltip.spark-tooltip-right.spark-tooltip-start .spark-tooltip-tip{transform:rotate(90deg);inset-inline-end:100%;inset-block-start:var(--spark-tooltip-tip-inset-block-start)}.spark-tooltip.spark-tooltip-left.spark-tooltip-end .spark-tooltip-tip{transform:rotate(-90deg);inset-block-end:var(--spark-tooltip-tip-inset-block-end);inset-inline-start:100%}.spark-tooltip.spark-tooltip-left.spark-tooltip-start .spark-tooltip-tip{transform:rotate(-90deg);inset-block-start:var(--spark-tooltip-tip-inset-block-start);inset-inline-start:100%}:root{--spark-tooltip-inset-inline-start: 0rem;--spark-tooltip-inset-block-start: 0rem;--spark-tooltip-z-index: 999;--spark-tooltip-top: 0rem;--spark-tooltip-left: 0rem;--spark-tooltip-tip-block-size: 0rem;--spark-tooltip-tip-inline-size: 0rem;--spark-tooltip-tip-border-width: .3125rem;--spark-tooltip-margin-inline-end: .5rem;--spark-tooltip-gap-size: 1.125rem;--spark-tooltip-inset-block-end: .75rem;--spark-tooltip-tip-margin-inline-start: .25rem;--spark-tooltip-tip-inset-block-start: .25rem;--spark-tooltip-tip-inset-block-end: .25rem;--spark-tooltip-max-inline-size: 30ch;--spark-tooltip-mid-tooltip-size: .3125rem;--spark-tooltip-m-font-size: .75rem;--spark-tooltip-m-label-font-weight: 400;--spark-tooltip-m-label-line-height: 1rem;--spark-tooltip-m-icon-line-height: 1rem;--spark-tooltip-m-padding-top-bottom: .5rem;--spark-tooltip-m-padding-right-left: .75rem;--spark-tooltip-m-gap-size: 1.0625rem;--spark-tooltip-m-tooltip-gap: .5rem;--spark-tooltip-m-diff-size-gap: 0rem;--spark-tooltip-s-font-size: .6875rem;--spark-tooltip-s-label-font-weight: 400;--spark-tooltip-s-label-line-height: 1rem;--spark-tooltip-s-icon-line-height: 1rem;--spark-tooltip-s-padding-top-bottom: .25rem;--spark-tooltip-s-padding-right-left: .5rem;--spark-tooltip-s-gap-size: .8125rem;--spark-tooltip-s-tooltip-gap: .8125rem;--spark-tooltip-s-diff-size-gap: .25rem}:root,[data-theme=light]{--spark-tooltip-background-color: var(--spark-color-theme-dark-gray200, #3c3e42);--spark-tooltip-color: var(--spark-color-theme-light-gray50, #ffffff);--spark-tooltip-tip-color: var(--spark-color-theme-dark-gray200, #3c3e42)}[data-theme=dark]{--spark-tooltip-background-color: var(--spark-color-theme-dark-gray900, #ffffff);--spark-tooltip-color: var(--spark-color-theme-light-gray900, #2b2c30);--spark-tooltip-tip-color: var(--spark-color-theme-dark-gray900, #ffffff)}.help-tooltip{display:flex;justify-content:center}.help-tooltip .help-tooltip-icon{cursor:pointer}.help-tooltip .help-tooltip-icon .svg-icon{width:20px;height:20px}.help-tooltip .spark-tooltip-toggle{justify-content:center}.help-tooltip .spark-tooltip-toggle .help-tooltip-popover{top:calc(100% + var(--spark-tooltip-m-padding-top-bottom));left:auto}.help-tooltip .spark-tooltip-toggle .help-tooltip-popover .help-tooltip-popover-content{white-space:pre-line}.spark-toggle-switch-action .spark-toggle-switch-selector{cursor:pointer;display:block;padding:var(--spark-toggle-switch-selector-space);background:var(--spark-toggle-switch-color-off);block-size:var(--spark-toggle-switch-action-block-size);box-sizing:border-box;inline-size:var(--spark-toggle-switch-action-inline-size)}.spark-toggle-switch-action input{cursor:pointer}.spark-toggle-switch-action input:checked+.spark-toggle-switch-selector{background:var(--spark-toggle-switch-color-on)}.spark-toggle-switch-action input:disabled{cursor:initial}.spark-toggle-switch-action.spark-toggle-switch-size-s .spark-toggle-switch-selector~*{font-size:var(--spark-toggle-switch-size-s-ghost-indicator-font-size);margin-inline-start:.25rem}.spark-toggle-switch-action.spark-toggle-switch-size-m .spark-toggle-switch-selector~*{font-size:var(--spark-toggle-switch-size-m-ghost-indicator-font-size);margin-inline-start:.25rem}.spark-toggle-switch-action.spark-toggle-switch-size-l .spark-toggle-switch-selector~*{font-size:var(--spark-toggle-switch-size-l-ghost-indicator-font-size);margin-inline-start:.25rem}.spark-toggle-switch-action input:disabled~*{color:var(--spark-toggle-switch-color-disabled);cursor:initial}.spark-toggle-switch-action input:disabled+.spark-toggle-switch-selector{background:var(--spark-toggle-switch-color-disabled)}.spark-toggle-switch-action input:disabled+.spark-toggle-switch-selector:after{background:var(--spark-toggle-switch-selector-color-disabled)}.spark-toggle-switch-action input:checked+.spark-toggle-switch-selector:after{margin-inline-start:auto}.spark-toggle-switch-action input~*{cursor:pointer}.spark-toggle-switch-action .spark-toggle-switch-selector:after{content:"";display:block;background:var(--spark-toggle-switch-selector-color);block-size:100%;inline-size:50%}.spark-toggle-switch-ghost .spark-toggle-switch-selector{cursor:pointer;display:flex;align-items:center}.spark-toggle-switch-ghost input:disabled+.spark-toggle-switch-selector{cursor:initial}.spark-toggle-switch-ghost input:disabled+.spark-toggle-switch-selector:before{color:var(--spark-toggle-switch-color-disabled);border-color:var(--spark-toggle-switch-color-disabled)}.spark-toggle-switch-ghost input:disabled+.spark-toggle-switch-selector:after{color:var(--spark-toggle-switch-color-disabled)}.spark-toggle-switch-ghost input:checked:not(:disabled)+.spark-toggle-switch-selector:before{border-color:var(--spark-toggle-switch-color-on)}.spark-toggle-switch-ghost.spark-toggle-switch-size-s .spark-toggle-switch-selector{font-size:var(--spark-toggle-switch-size-s-ghost-indicator-font-size)}.spark-toggle-switch-ghost.spark-toggle-switch-size-s input:checked+.spark-toggle-switch-selector:before{inline-size:calc(var(--spark-toggle-switch-size-s-ghost-indicator-inline-size) + var(--spark-toggle-switch-size-s-ghost-selector-thin))}.spark-toggle-switch-ghost.spark-toggle-switch-size-s input:checked+.spark-toggle-switch-selector:after{inline-size:var(--spark-toggle-switch-inline-size)}.spark-toggle-switch-ghost.spark-toggle-switch-size-s .spark-toggle-switch-selector:after{block-size:var(--spark-toggle-switch-size-s-ghost-selector-block-size);inline-size:var(--spark-toggle-switch-size-s-ghost-indicator-inline-size)}.spark-toggle-switch-ghost.spark-toggle-switch-size-s .spark-toggle-switch-selector:before{block-size:var(--spark-toggle-switch-size-s-ghost-selector-block-size);inline-size:var(--spark-toggle-switch-size-s-ghost-selector-thin);border-inline-end-width:var(--spark-toggle-switch-size-s-ghost-selector-thin)}.spark-toggle-switch-ghost.spark-toggle-switch-size-m .spark-toggle-switch-selector{font-size:var(--spark-toggle-switch-size-m-ghost-indicator-font-size)}.spark-toggle-switch-ghost.spark-toggle-switch-size-m input:checked+.spark-toggle-switch-selector:before{inline-size:calc(var(--spark-toggle-switch-size-m-ghost-indicator-inline-size) + var(--spark-toggle-switch-size-m-ghost-selector-thin))}.spark-toggle-switch-ghost.spark-toggle-switch-size-m input:checked+.spark-toggle-switch-selector:after{inline-size:var(--spark-toggle-switch-inline-size)}.spark-toggle-switch-ghost.spark-toggle-switch-size-m .spark-toggle-switch-selector:after{block-size:var(--spark-toggle-switch-size-m-ghost-selector-block-size);inline-size:var(--spark-toggle-switch-size-m-ghost-indicator-inline-size)}.spark-toggle-switch-ghost.spark-toggle-switch-size-m .spark-toggle-switch-selector:before{block-size:var(--spark-toggle-switch-size-m-ghost-selector-block-size);inline-size:var(--spark-toggle-switch-size-m-ghost-selector-thin);border-inline-end-width:var(--spark-toggle-switch-size-m-ghost-selector-thin)}.spark-toggle-switch-ghost.spark-toggle-switch-size-l .spark-toggle-switch-selector{font-size:var(--spark-toggle-switch-size-l-ghost-indicator-font-size)}.spark-toggle-switch-ghost.spark-toggle-switch-size-l input:checked+.spark-toggle-switch-selector:before{inline-size:calc(var(--spark-toggle-switch-size-l-ghost-indicator-inline-size) + var(--spark-toggle-switch-size-l-ghost-selector-thin))}.spark-toggle-switch-ghost.spark-toggle-switch-size-l input:checked+.spark-toggle-switch-selector:after{inline-size:var(--spark-toggle-switch-inline-size)}.spark-toggle-switch-ghost.spark-toggle-switch-size-l .spark-toggle-switch-selector:after{block-size:var(--spark-toggle-switch-size-l-ghost-selector-block-size);inline-size:var(--spark-toggle-switch-size-l-ghost-indicator-inline-size)}.spark-toggle-switch-ghost.spark-toggle-switch-size-l .spark-toggle-switch-selector:before{block-size:var(--spark-toggle-switch-size-l-ghost-selector-block-size);inline-size:var(--spark-toggle-switch-size-l-ghost-selector-thin);border-inline-end-width:var(--spark-toggle-switch-size-l-ghost-selector-thin)}.spark-toggle-switch-ghost .spark-toggle-switch-selector:after{content:"Off";display:flex;overflow:hidden;box-sizing:border-box;align-items:center;justify-content:center}.spark-toggle-switch-ghost .spark-toggle-switch-selector:before{content:"On";display:flex;overflow:hidden;box-sizing:border-box;align-items:center;border-color:var(--spark-toggle-switch-color-off);border-style:solid;border-width:var(--spark-toggle-switch-border-width);justify-content:center}.spark-toggle-switch{display:flex!important;box-sizing:border-box;align-items:center;inline-size:fit-content!important}.spark-toggle-switch input{border:none;margin:var(--spark-toggle-switch-margin);opacity:var(--spark-toggle-switch-opacity);outline:none;padding:var(--spark-toggle-switch-padding);block-size:var(--spark-toggle-switch-block-size);inline-size:var(--spark-toggle-switch-inline-size)}:root{--spark-toggle-switch-selector-space: .125rem;--spark-toggle-switch-action-inline-size: 1.75rem;--spark-toggle-switch-action-block-size: 1rem;--spark-toggle-switch-opacity: 0rem;--spark-toggle-switch-padding: 0rem;--spark-toggle-switch-margin: 0rem;--spark-toggle-switch-inline-size: 0rem;--spark-toggle-switch-block-size: 0rem;--spark-toggle-switch-border-width: 0rem;--spark-toggle-switch-size-l-ghost-selector-thin: .5rem;--spark-toggle-switch-size-l-ghost-selector-block-size: 1.5rem;--spark-toggle-switch-size-l-ghost-indicator-inline-size: 2rem;--spark-toggle-switch-size-l-ghost-indicator-font-size: 1rem;--spark-toggle-switch-size-m-ghost-selector-thin: .25rem;--spark-toggle-switch-size-m-ghost-selector-block-size: 1.25rem;--spark-toggle-switch-size-m-ghost-indicator-inline-size: 1.75rem;--spark-toggle-switch-size-m-ghost-indicator-font-size: .875rem;--spark-toggle-switch-size-s-ghost-selector-thin: .25rem;--spark-toggle-switch-size-s-ghost-selector-block-size: 1rem;--spark-toggle-switch-size-s-ghost-indicator-inline-size: 1.5rem;--spark-toggle-switch-size-s-ghost-indicator-font-size: .75rem}:root,[data-theme=light]{--spark-toggle-switch-color-on: var(--spark-color-classic-blue, #0054ae);--spark-toggle-switch-color-off: var(--spark-color-theme-light-gray600, #8b8e97);--spark-toggle-switch-color-disabled: var(--spark-color-theme-light-gray500, #b2b3b9);--spark-toggle-switch-selector-color: var(--spark-color-theme-light-gray50, #ffffff);--spark-toggle-switch-selector-color-disabled: var(--spark-color-theme-light-gray300, #e2e2e4)}[data-theme=dark]{--spark-toggle-switch-color-on: var(--spark-color-energy-blue, #00c7fd);--spark-toggle-switch-color-off: var(--spark-color-theme-dark-gray600, #8e9099);--spark-toggle-switch-color-disabled: var(--spark-color-theme-dark-gray500, #6b6e76);--spark-toggle-switch-selector-color: var(--spark-color-theme-dark-gray50, #242528);--spark-toggle-switch-selector-color-disabled: var(--spark-color-theme-dark-gray300, #484a50)}.spark-toggle-button-clicked-ghost{color:var(--spark-toggle-button-ghost-color);border-color:var(--spark-toggle-button-ghost-border-color);background-color:var(--spark-toggle-button-ghost-bg-color)}:root,[data-theme=light]{--spark-toggle-button-ghost-color: var(--spark-color-theme-light-gray900, #2b2c30);--spark-toggle-button-ghost-bg-color: var(--spark-color-theme-light-gray400, #c9cace);--spark-toggle-button-ghost-border-color: var(--spark-color-transparent, transparent)}[data-theme=dark]{--spark-toggle-button-ghost-color: var(--spark-color-theme-dark-gray900, #ffffff);--spark-toggle-button-ghost-bg-color: var(--spark-color-theme-dark-gray400, #585a62);--spark-toggle-button-ghost-border-color: var(--spark-color-transparent, transparent)}.spark-button-start-slot{font-size:var(--spark-button-start-slot-font-size);flex-shrink:var(--spark-button-start-slot-flex-shrink)}.spark-button-end-slot{font-size:var(--spark-button-end-slot-font-size);flex-shrink:var(--spark-button-end-slot-flex-shrink)}.spark-button-disabled{pointer-events:none}.spark-button-content{display:flex;overflow:hidden;align-items:center;text-overflow:ellipsis;justify-content:center}.spark-button-size-l{font-size:var(--spark-button-l-font-size);block-size:var(--spark-button-l-block-size);line-height:var(--spark-button-l-line-height);min-inline-size:var(--spark-button-l-block-size);padding-block-end:var(--spark-button-l-padding-block);padding-inline-end:var(--spark-button-l-padding-inline);padding-block-start:var(--spark-button-l-padding-block);padding-inline-start:var(--spark-button-l-padding-inline)}.spark-button-size-l.spark-button-icon-only{font-size:var(--spark-button-icon-only-l-font-size);max-inline-size:var(--spark-button-l-block-size);min-inline-size:var(--spark-button-l-block-size);padding-inline-end:var(--spark-button-icon-only-l-padding-inline);padding-inline-start:var(--spark-button-icon-only-l-padding-inline)}.spark-button-size-l:not(.spark-button-icon-only) .spark-button-start-slot{padding-inline-end:var(--spark-button-l-icon-gap)}.spark-button-size-l:not(.spark-button-icon-only) .spark-button-end-slot{padding-inline-start:var(--spark-button-l-icon-gap)}.spark-button-size-m{font-size:var(--spark-button-m-font-size);block-size:var(--spark-button-m-block-size);line-height:var(--spark-button-m-line-height);min-inline-size:var(--spark-button-m-block-size);padding-block-end:var(--spark-button-m-padding-block);padding-inline-end:var(--spark-button-m-padding-inline);padding-block-start:var(--spark-button-m-padding-block);padding-inline-start:var(--spark-button-m-padding-inline)}.spark-button-size-m.spark-button-icon-only{font-size:var(--spark-button-icon-only-m-font-size);max-inline-size:var(--spark-button-m-block-size);min-inline-size:var(--spark-button-m-block-size);padding-inline-end:var(--spark-button-icon-only-m-padding-inline);padding-inline-start:var(--spark-button-icon-only-m-padding-inline)}.spark-button-size-m:not(.spark-button-icon-only) .spark-button-start-slot{padding-inline-end:var(--spark-button-m-icon-gap)}.spark-button-size-m:not(.spark-button-icon-only) .spark-button-end-slot{padding-inline-start:var(--spark-button-m-icon-gap)}.spark-button-size-s{font-size:var(--spark-button-s-font-size);block-size:var(--spark-button-s-block-size);line-height:var(--spark-button-s-line-height);min-inline-size:var(--spark-button-s-block-size);padding-block-end:var(--spark-button-s-padding-block);padding-inline-end:var(--spark-button-s-padding-inline);padding-block-start:var(--spark-button-s-padding-block);padding-inline-start:var(--spark-button-s-padding-inline)}.spark-button-size-s.spark-button-icon-only{font-size:var(--spark-button-icon-only-s-font-size);max-inline-size:var(--spark-button-s-block-size);min-inline-size:var(--spark-button-s-block-size);padding-inline-end:var(--spark-button-icon-only-s-padding-inline);padding-inline-start:var(--spark-button-icon-only-s-padding-inline)}.spark-button-size-s:not(.spark-button-icon-only) .spark-button-start-slot{padding-inline-end:var(--spark-button-s-icon-gap)}.spark-button-size-s:not(.spark-button-icon-only) .spark-button-end-slot{padding-inline-start:var(--spark-button-s-icon-gap)}.spark-button-action{color:var(--spark-button-action-color);border-color:transparent;background-color:var(--spark-button-action-bg-color)}.spark-button-action:hover,.spark-button-action .spark-button-hovered{background-color:var(--spark-button-action-bg-color-hover)}.spark-button-action:visited{color:var(--spark-button-action-color)}.spark-button-action:active,.spark-button-action.spark-button-active{background-color:var(--spark-button-action-bg-color-active)}.spark-button-action:disabled,.spark-button-action.spark-button-disabled{color:var(--spark-button-disabled-color);cursor:default;border-color:var(--spark-button-disabled-border-color);background-color:var(--spark-button-disabled-bg-color)}.spark-button-action .spark-button-content a{color:var(--spark-button-action-color);text-decoration:none}.spark-button-action:disabled a,.spark-button-action.spark-button-disabled .spark-button-content a{color:var(--spark-button-disabled-color);cursor:default;border-color:transparent;background-color:transparent}.spark-button-primary{color:var(--spark-button-primary-color);border-color:var(--spark-button-primary-color);background-color:var(--spark-button-primary-bg-color)}.spark-button-primary:hover,.spark-button-primary .spark-button-hovered{background-color:var(--spark-button-primary-bg-color-hover)}.spark-button-primary:visited{color:var(--spark-button-primary-color)}.spark-button-primary:active,.spark-button-primary.spark-button-active{background-color:var(--spark-button-primary-bg-color-active)}.spark-button-primary:disabled,.spark-button-primary.spark-button-disabled{color:var(--spark-button-disabled-color);cursor:default;border-color:var(--spark-button-disabled-border-color);background-color:var(--spark-button-disabled-bg-color)}.spark-button-primary .spark-button-content a{color:var(--spark-button-primary-color);text-decoration:none}.spark-button-primary:disabled a,.spark-button-primary.spark-button-disabled .spark-button-content a{color:var(--spark-button-disabled-color);cursor:default;border-color:transparent;background-color:transparent}.spark-button-secondary{color:var(--spark-button-secondary-color);border-color:var(--spark-button-secondary-border-color);background-color:var(--spark-button-secondary-bg-color)}.spark-button-secondary:hover,.spark-button-secondary .spark-button-hovered{background-color:var(--spark-button-secondary-bg-color-hover)}.spark-button-secondary:visited{color:var(--spark-button-secondary-color)}.spark-button-secondary:active,.spark-button-secondary.spark-button-active{background-color:var(--spark-button-secondary-bg-color-active)}.spark-button-secondary:disabled,.spark-button-secondary.spark-button-disabled{color:var(--spark-button-disabled-color);cursor:default;border-color:var(--spark-button-disabled-border-color);background-color:var(--spark-button-disabled-bg-color)}.spark-button-secondary .spark-button-content a{color:var(--spark-button-secondary-color);text-decoration:none}.spark-button-secondary:disabled a,.spark-button-secondary.spark-button-disabled .spark-button-content a{color:var(--spark-button-disabled-color);cursor:default;border-color:transparent;background-color:transparent}.spark-button-ghost{color:var(--spark-button-ghost-color);border-color:var(--spark-button-ghost-border-color);background-color:var(--spark-button-ghost-bg-color)}.spark-button-ghost:not(.spark-button-icon-only){min-inline-size:auto!important}.spark-button-ghost:hover,.spark-button-ghost .spark-button-hovered{background-color:var(--spark-button-ghost-bg-color-hover)}.spark-button-ghost:visited{color:var(--spark-button-ghost-color)}.spark-button-ghost:active,.spark-button-ghost.spark-button-active{background-color:var(--spark-button-ghost-bg-color-active)}.spark-button-ghost:disabled,.spark-button-ghost.spark-button-disabled{color:var(--spark-button-disabled-color);cursor:default;border-color:transparent;background-color:transparent}.spark-button-ghost .spark-button-content a{color:var(--spark-button-ghost-color);text-decoration:none}.spark-button-ghost:disabled a,.spark-button-ghost.spark-button-disabled .spark-button-content a{color:var(--spark-button-disabled-color);cursor:default;border-color:transparent;background-color:transparent}.spark-button-alert{color:var(--spark-button-alert-color);border-color:var(--spark-button-alert-border-color);background-color:var(--spark-button-alert-bg-color)}.spark-button-alert:hover,.spark-button-alert .spark-button-hovered{background-color:var(--spark-button-alert-bg-color-hover)}.spark-button-alert:visited{color:var(--spark-button-alert-color)}.spark-button-alert:active,.spark-button-alert.spark-button-active{background-color:var(--spark-button-alert-bg-color-active)}.spark-button-alert:disabled,.spark-button-alert.spark-button-disabled{color:var(--spark-button-disabled-color);cursor:default;border-color:var(--spark-button-disabled-border-color);background-color:var(--spark-button-disabled-bg-color)}.spark-button-alert .spark-button-content a{color:var(--spark-button-alert-color);text-decoration:none}.spark-button-alert:disabled a,.spark-button-alert.spark-button-disabled .spark-button-content a{color:var(--spark-button-disabled-color);cursor:default;border-color:transparent;background-color:transparent}.spark-button-alert-ghost{color:var(--spark-button-alert-ghost-color);border-color:var(--spark-button-alert-ghost-border-color);background-color:var(--spark-button-alert-ghost-bg-color)}.spark-button-alert-ghost:not(.spark-button-icon-only){min-inline-size:auto!important}.spark-button-alert-ghost:hover,.spark-button-alert-ghost .spark-button-hovered{background-color:var(--spark-button-alert-ghost-bg-color-hover)}.spark-button-alert-ghost:visited{color:var(--spark-button-alert-ghost-color)}.spark-button-alert-ghost:active,.spark-button-alert-ghost.spark-button-active{background-color:var(--spark-button-alert-ghost-bg-color-active)}.spark-button-alert-ghost:disabled,.spark-button-alert-ghost.spark-button-disabled{color:var(--spark-button-disabled-color);cursor:default;border-color:transparent;background-color:transparent}.spark-button-alert-ghost .spark-button-content a{color:var(--spark-button-alert-ghost-color);text-decoration:none}.spark-button-alert-ghost:disabled a,.spark-button-alert-ghost.spark-button-disabled .spark-button-content a{color:var(--spark-button-disabled-color);cursor:default;border-color:transparent;background-color:transparent}.spark-button{cursor:pointer;display:flex;box-sizing:border-box;align-items:center;font-family:var(--spark-button-font-family);font-weight:var(--spark-button-font-weight);inline-size:max-content;white-space:nowrap;border-style:solid;border-width:var(--spark-button-border-width);justify-content:center;max-inline-size:var(--spark-button-max-inline-size);text-decoration:none}:root{--spark-button-font-weight: 500;--spark-button-font-family: inherit;--spark-button-max-inline-size: 37.5rem;--spark-button-border-width: .0625rem;--spark-button-l-block-size: 2.5rem;--spark-button-l-font-size: 1rem;--spark-button-l-line-height: 1.375rem;--spark-button-l-padding-block: .75rem;--spark-button-l-padding-inline: 1.25rem;--spark-button-l-icon-gap: .5rem;--spark-button-m-block-size: 2rem;--spark-button-m-font-size: .875rem;--spark-button-m-line-height: 1.125rem;--spark-button-m-padding-block: .5rem;--spark-button-m-padding-inline: 1rem;--spark-button-m-icon-gap: .25rem;--spark-button-s-block-size: 1.5rem;--spark-button-s-font-size: .75rem;--spark-button-s-line-height: .875rem;--spark-button-s-padding-block: .25rem;--spark-button-s-padding-inline: .75rem;--spark-button-s-icon-gap: .25rem;--spark-button-start-slot-font-size: 1rem;--spark-button-start-slot-flex-shrink: 0;--spark-button-end-slot-font-size: 1rem;--spark-button-end-slot-flex-shrink: 0;--spark-button-icon-only-l-font-size: 1rem;--spark-button-icon-only-l-padding-inline: .5rem;--spark-button-icon-only-m-font-size: 1rem;--spark-button-icon-only-m-padding-inline: .5rem;--spark-button-icon-only-s-font-size: 1rem;--spark-button-icon-only-s-padding-inline: .25rem}:root,[data-theme=light]{--spark-button-action-color: var(--spark-color-theme-light-gray50, #ffffff);--spark-button-action-bg-color: var(--spark-color-classic-blue, #0054ae);--spark-button-action-bg-color-hover: var(--spark-color-classic-blue-shade1, #00377c);--spark-button-action-bg-color-active: var(--spark-color-classic-blue-shade2, #001e50);--spark-button-primary-color: var(--spark-color-theme-light-gray900, #2b2c30);--spark-button-primary-bg-color: transparent;--spark-button-primary-bg-color-hover: var(--spark-color-theme-light-gray200, #e9eaeb);--spark-button-primary-bg-color-active: var(--spark-color-theme-light-gray400, #c9cace);--spark-button-secondary-color: var(--spark-color-theme-light-gray900, #2b2c30);--spark-button-secondary-bg-color: transparent;--spark-button-secondary-bg-color-hover: var(--spark-color-theme-light-gray200, #e9eaeb);--spark-button-secondary-bg-color-active: var(--spark-color-theme-light-gray400, #c9cace);--spark-button-secondary-border-color: var(--spark-color-theme-light-gray400, #c9cace);--spark-button-ghost-color: var(--spark-color-theme-light-gray900, #2b2c30);--spark-button-ghost-bg-color: transparent;--spark-button-ghost-bg-color-hover: var(--spark-color-theme-light-gray200, #e9eaeb);--spark-button-ghost-bg-color-active: var(--spark-color-theme-light-gray400, #c9cace);--spark-button-ghost-border-color: transparent;--spark-button-alert-color: var(--spark-color-coral-shade1, #c81326);--spark-button-alert-bg-color: transparent;--spark-button-alert-bg-color-hover: #fee9e9;--spark-button-alert-bg-color-active: var(--spark-color-coral-tint2, #ffb6b9);--spark-button-alert-border-color: var(--spark-color-coral-shade1, #c81326);--spark-button-alert-ghost-color: var(--spark-color-coral-shade1, #c81326);--spark-button-alert-ghost-bg-color: transparent;--spark-button-alert-ghost-bg-color-hover: #fee9e9;--spark-button-alert-ghost-bg-color-active: var(--spark-color-coral-tint2, #ffb6b9);--spark-button-alert-ghost-border-color: transparent;--spark-button-disabled-color: var(--spark-color-theme-light-gray500, #b2b3b9);--spark-button-disabled-bg-color: var(--spark-color-theme-light-gray200, #e9eaeb);--spark-button-disabled-border-color: var(--spark-color-theme-light-gray200, #e9eaeb)}[data-theme=dark]{--spark-button-action-color: var(--spark-color-theme-dark-gray50, #242528);--spark-button-action-bg-color: var(--spark-color-energy-blue, #00c7fd);--spark-button-action-bg-color-hover: var(--spark-color-energy-blue-tint1, #6ddcff);--spark-button-action-bg-color-active: var(--spark-color-energy-blue-tint2, #a0ebff);--spark-button-primary-color: var(--spark-color-theme-dark-gray900, #ffffff);--spark-button-primary-bg-color: transparent;--spark-button-primary-bg-color-hover: var(--spark-color-theme-dark-gray200, #3c3e42);--spark-button-primary-bg-color-active: var(--spark-color-theme-dark-gray400, #585a62);--spark-button-secondary-color: var(--spark-color-theme-dark-gray900, #ffffff);--spark-button-secondary-bg-color: transparent;--spark-button-secondary-bg-color-hover: var(--spark-color-theme-dark-gray200, #3c3e42);--spark-button-secondary-bg-color-active: var(--spark-color-theme-dark-gray400, #585a62);--spark-button-secondary-border-color: var(--spark-color-theme-dark-gray400, #585a62);--spark-button-ghost-color: var(--spark-color-theme-dark-gray900, #ffffff);--spark-button-ghost-bg-color: transparent;--spark-button-ghost-bg-color-hover: var(--spark-color-theme-dark-gray200, #3c3e42);--spark-button-ghost-bg-color-active: var(--spark-color-theme-dark-gray400, #585a62);--spark-button-ghost-border-color: transparent;--spark-button-alert-color: var(--spark-color-coral, #ff5662);--spark-button-alert-bg-color: transparent;--spark-button-alert-bg-color-hover: #3a2325;--spark-button-alert-bg-color-active: #321a1b;--spark-button-alert-border-color: var(--spark-color-coral, #ff5662);--spark-button-alert-ghost-color: var(--spark-color-coral, #ff5662);--spark-button-alert-ghost-bg-color: transparent;--spark-button-alert-ghost-bg-color-hover: #3a2325;--spark-button-alert-ghost-bg-color-active: #321a1b;--spark-button-alert-ghost-border-color: transparent;--spark-button-disabled-color: var(--spark-color-theme-dark-gray500, #6b6e76);--spark-button-disabled-bg-color: var(--spark-color-theme-dark-gray200, #3c3e42);--spark-button-disabled-border-color: var(--spark-color-theme-dark-gray200, #3c3e42)}.spark-button-group-spacing-none{gap:var(--spark-button-group-none-gap)}.spark-button-group-spacing-s{gap:var(--spark-button-group-s-gap)}.spark-button-group-spacing-m{gap:var(--spark-button-group-m-gap)}.spark-button-group-spacing-l{gap:var(--spark-button-group-l-gap)}.spark-button-group-spacing-xl{gap:var(--spark-button-group-xl-gap)}.spark-button-group{display:inline-flex;position:relative;align-items:flex-start}.spark-button-group.spark-button-group-orientation-vertical{display:inline-flex;flex-wrap:wrap;flex-direction:column}.spark-button-group.spark-button-group-orientation-horizontal{display:inline-flex;flex-wrap:wrap;flex-direction:row}.spark-button-group.spark-button-group-align-end{align-items:flex-end}.spark-button-group.spark-button-group-align-start{align-items:flex-start}.spark-button-group.spark-button-group-align-center{align-items:center}.spark-button-group.spark-button-group-is-disabled>:not(.spark-tooltip-toggle){pointer-events:none}:root{--spark-button-group-none-gap: 0rem;--spark-button-group-s-gap: .125rem;--spark-button-group-m-gap: .25rem;--spark-button-group-l-gap: .5rem;--spark-button-group-xl-gap: .75rem}:root,[data-theme=light]{--spark-button-group-disabled-color: var(--spark-color-theme-light-gray500, #b2b3b9);--spark-button-group-disabled-bg-color: var(--spark-color-theme-light-gray200, #e9eaeb);--spark-button-group-disabled-border-color: var(--spark-color-theme-light-gray200, #e9eaeb)}[data-theme=dark]{--spark-button-group-disabled-color: var(--spark-color-theme-dark-gray500, #6b6e76);--spark-button-group-disabled-bg-color: var(--spark-color-theme-dark-gray200, #3c3e42);--spark-button-group-disabled-border-color: var(--spark-color-theme-dark-gray200, #3c3e42)}.option-button-group{flex-grow:1;display:flex}.option-button-group .spark-button{flex-grow:1;block-size:3rem}.option-button-group .spark-button .spark-button-content{flex-direction:column}.option-button-group .spark-button .spark-button-content .subtitle{font-size:var(--spark-font-size-25);font-weight:400;line-height:.75rem;letter-spacing:var(--spark-letter-spacing-small);white-space:pre-line}@media (max-width: 550px){.option-button-group{flex-direction:column}.option-button-group .spark-button{width:100%}}
diff --git a/docs/sphinx_setup/_static/selector-tool/assets/selector-d6eabe1c.js b/docs/sphinx_setup/_static/selector-tool/assets/selector-d6eabe1c.js
new file mode 100644
index 00000000000000..f61119f330c64e
--- /dev/null
+++ b/docs/sphinx_setup/_static/selector-tool/assets/selector-d6eabe1c.js
@@ -0,0 +1,61 @@
+var nf=Object.defineProperty;var of=(e,t,n)=>t in e?nf(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n;var Re=(e,t,n)=>(of(e,typeof t!="symbol"?t+"":t,n),n);function fc(e){return e&&e.__esModule&&Object.prototype.hasOwnProperty.call(e,"default")?e.default:e}var hc={exports:{}},hi={},gc={exports:{}},D={};/**
+ * @license React
+ * react.production.min.js
+ *
+ * Copyright (c) Facebook, Inc. and its affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ */var er=Symbol.for("react.element"),rf=Symbol.for("react.portal"),sf=Symbol.for("react.fragment"),lf=Symbol.for("react.strict_mode"),af=Symbol.for("react.profiler"),uf=Symbol.for("react.provider"),cf=Symbol.for("react.context"),df=Symbol.for("react.forward_ref"),pf=Symbol.for("react.suspense"),ff=Symbol.for("react.memo"),hf=Symbol.for("react.lazy"),Pa=Symbol.iterator;function gf(e){return e===null||typeof e!="object"?null:(e=Pa&&e[Pa]||e["@@iterator"],typeof e=="function"?e:null)}var mc={isMounted:function(){return!1},enqueueForceUpdate:function(){},enqueueReplaceState:function(){},enqueueSetState:function(){}},vc=Object.assign,yc={};function oo(e,t,n){this.props=e,this.context=t,this.refs=yc,this.updater=n||mc}oo.prototype.isReactComponent={};oo.prototype.setState=function(e,t){if(typeof e!="object"&&typeof e!="function"&&e!=null)throw Error("setState(...): takes an object of state variables to update or a function which returns an object of state variables.");this.updater.enqueueSetState(this,e,t,"setState")};oo.prototype.forceUpdate=function(e){this.updater.enqueueForceUpdate(this,e,"forceUpdate")};function kc(){}kc.prototype=oo.prototype;function ml(e,t,n){this.props=e,this.context=t,this.refs=yc,this.updater=n||mc}var vl=ml.prototype=new kc;vl.constructor=ml;vc(vl,oo.prototype);vl.isPureReactComponent=!0;var ba=Array.isArray,_c=Object.prototype.hasOwnProperty,yl={current:null},Sc={key:!0,ref:!0,__self:!0,__source:!0};function wc(e,t,n){var o,r={},i=null,s=null;if(t!=null)for(o in t.ref!==void 0&&(s=t.ref),t.key!==void 0&&(i=""+t.key),t)_c.call(t,o)&&!Sc.hasOwnProperty(o)&&(r[o]=t[o]);var l=arguments.length-2;if(l===1)r.children=n;else if(1<l){for(var a=Array(l),u=0;u<l;u++)a[u]=arguments[u+2];r.children=a}if(e&&e.defaultProps)for(o in l=e.defaultProps,l)r[o]===void 0&&(r[o]=l[o]);return{$$typeof:er,type:e,key:i,ref:s,props:r,_owner:yl.current}}function mf(e,t){return{$$typeof:er,type:e.type,key:t,ref:e.ref,props:e.props,_owner:e._owner}}function kl(e){return typeof e=="object"&&e!==null&&e.$$typeof===er}function vf(e){var t={"=":"=0",":":"=2"};return"$"+e.replace(/[=:]/g,function(n){return t[n]})}var xa=/\/+/g;function Ai(e,t){return typeof e=="object"&&e!==null&&e.key!=null?vf(""+e.key):t.toString(36)}function Er(e,t,n,o,r){var i=typeof e;(i==="undefined"||i==="boolean")&&(e=null);var s=!1;if(e===null)s=!0;else switch(i){case"string":case"number":s=!0;break;case"object":switch(e.$$typeof){case er:case rf:s=!0}}if(s)return s=e,r=r(s),e=o===""?"."+Ai(s,0):o,ba(r)?(n="",e!=null&&(n=e.replace(xa,"$&/")+"/"),Er(r,t,n,"",function(u){return u})):r!=null&&(kl(r)&&(r=mf(r,n+(!r.key||s&&s.key===r.key?"":(""+r.key).replace(xa,"$&/")+"/")+e)),t.push(r)),1;if(s=0,o=o===""?".":o+":",ba(e))for(var l=0;l<e.length;l++){i=e[l];var a=o+Ai(i,l);s+=Er(i,t,n,a,r)}else if(a=gf(e),typeof a=="function")for(e=a.call(e),l=0;!(i=e.next()).done;)i=i.value,a=o+Ai(i,l++),s+=Er(i,t,n,a,r);else if(i==="object")throw t=String(e),Error("Objects are not valid as a React child (found: "+(t==="[object Object]"?"object with keys {"+Object.keys(e).join(", ")+"}":t)+"). 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+ * @license React
+ * react-jsx-runtime.production.min.js
+ *
+ * Copyright (c) Facebook, Inc. and its affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
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Either set keySeparator: false on init or make sure your translations are published in nested format.")}var Ue=[],Pt=this.languageUtils.getFallbackCodes(this.options.fallbackLng,i.lng||this.language);if(this.options.saveMissingTo==="fallback"&&Pt&&Pt[0])for(var Li=0;Li<Pt.length;Li++)Ue.push(Pt[Li]);else this.options.saveMissingTo==="all"?Ue=this.languageUtils.toResolveHierarchy(i.lng||this.language):Ue.push(i.lng||this.language);var wa=function(bn,ji,Oa){var Na=T&&Oa!==c?Oa:Nt;l.options.missingKeyHandler?l.options.missingKeyHandler(bn,m,ji,Na,ze,i):l.backendConnector&&l.backendConnector.saveMissing&&l.backendConnector.saveMissing(bn,m,ji,Na,ze,i),l.emit("missingKey",bn,m,ji,c)};this.options.saveMissing&&(this.options.saveMissingPlurals&&E?Ue.forEach(function(Fi){l.pluralResolver.getSuffixes(Fi,i).forEach(function(bn){wa([Fi],d+bn,i["defaultValue".concat(bn)]||$)})}):wa(Ue,d,$))}c=this.extendTranslation(c,r,i,p,s),nt&&c===d&&this.options.appendNamespaceToMissingKey&&(c="".concat(m,":").concat(d)),(nt||ft)&&this.options.parseMissingKeyHandler&&(this.options.compatibilityAPI!=="v1"?c=this.options.parseMissingKeyHandler(this.options.appendNamespaceToMissingKey?"".concat(m,":").concat(d):d,ft?c:void 0):c=this.options.parseMissingKeyHandler(c))}return a?(p.res=c,p):c}},{key:"extendTranslation",value:function(r,i,s,l,a){var u=this;if(this.i18nFormat&&this.i18nFormat.parse)r=this.i18nFormat.parse(r,_e(_e({},this.options.interpolation.defaultVariables),s),l.usedLng,l.usedNS,l.usedKey,{resolved:l});else if(!s.skipInterpolation){s.interpolation&&this.interpolator.init(_e(_e({},s),{interpolation:_e(_e({},this.options.interpolation),s.interpolation)}));var f=typeof r=="string"&&(s&&s.interpolation&&s.interpolation.skipOnVariables!==void 0?s.interpolation.skipOnVariables:this.options.interpolation.skipOnVariables),d;if(f){var h=r.match(this.interpolator.nestingRegexp);d=h&&h.length}var m=s.replace&&typeof s.replace!="string"?s.replace:s;if(this.options.interpolation.defaultVariables&&(m=_e(_e({},this.options.interpolation.defaultVariables),m)),r=this.interpolator.interpolate(r,m,s.lng||this.language,s),f){var y=r.match(this.interpolator.nestingRegexp),_=y&&y.length;d<_&&(s.nest=!1)}s.nest!==!1&&(r=this.interpolator.nest(r,function(){for(var c=arguments.length,g=new Array(c),k=0;k<c;k++)g[k]=arguments[k];return a&&a[0]===g[0]&&!s.context?(u.logger.warn("It seems you are nesting recursively key: ".concat(g[0]," in key: ").concat(i[0])),null):u.translate.apply(u,g.concat([i]))},s)),s.interpolation&&this.interpolator.reset()}var O=s.postProcess||this.options.postProcess,p=typeof O=="string"?[O]:O;return r!=null&&p&&p.length&&s.applyPostProcessor!==!1&&(r=Ic.handle(p,r,i,this.options&&this.options.postProcessPassResolved?_e({i18nResolved:l},s):s,this)),r}},{key:"resolve",value:function(r){var i=this,s=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{},l,a,u,f,d;return typeof r=="string"&&(r=[r]),r.forEach(function(h){if(!i.isValidLookup(l)){var m=i.extractFromKey(h,s),y=m.key;a=y;var _=m.namespaces;i.options.fallbackNS&&(_=_.concat(i.options.fallbackNS));var O=s.count!==void 0&&typeof s.count!="string",p=O&&!s.ordinal&&s.count===0&&i.pluralResolver.shouldUseIntlApi(),c=s.context!==void 0&&(typeof s.context=="string"||typeof s.context=="number")&&s.context!=="",g=s.lngs?s.lngs:i.languageUtils.toResolveHierarchy(s.lng||i.language,s.fallbackLng);_.forEach(function(k){i.isValidLookup(l)||(d=k,!Da["".concat(g[0],"-").concat(k)]&&i.utils&&i.utils.hasLoadedNamespace&&!i.utils.hasLoadedNamespace(d)&&(Da["".concat(g[0],"-").concat(k)]=!0,i.logger.warn('key "'.concat(a,'" for languages "').concat(g.join(", "),`" won't get resolved as namespace "`).concat(d,'" was not yet loaded'),"This means something IS WRONG in your setup. You access the t function before i18next.init / i18next.loadNamespace / i18next.changeLanguage was done. Wait for the callback or Promise to resolve before accessing it!!!")),g.forEach(function(w){if(!i.isValidLookup(l)){f=w;var S=[y];if(i.i18nFormat&&i.i18nFormat.addLookupKeys)i.i18nFormat.addLookupKeys(S,y,w,k,s);else{var P;O&&(P=i.pluralResolver.getSuffix(w,s.count,s));var b="".concat(i.options.pluralSeparator,"zero");if(O&&(S.push(y+P),p&&S.push(y+b)),c){var L="".concat(y).concat(i.options.contextSeparator).concat(s.context);S.push(L),O&&(S.push(L+P),p&&S.push(L+b))}}for(var x;x=S.pop();)i.isValidLookup(l)||(u=x,l=i.getResource(w,k,x,s))}}))})}}),{res:l,usedKey:a,exactUsedKey:u,usedLng:f,usedNS:d}}},{key:"isValidLookup",value:function(r){return r!==void 0&&!(!this.options.returnNull&&r===null)&&!(!this.options.returnEmptyString&&r==="")}},{key:"getResource",value:function(r,i,s){var l=arguments.length>3&&arguments[3]!==void 0?arguments[3]:{};return this.i18nFormat&&this.i18nFormat.getResource?this.i18nFormat.getResource(r,i,s,l):this.resourceStore.getResource(r,i,s,l)}}],[{key:"hasDefaultValue",value:function(r){var i="defaultValue";for(var s in r)if(Object.prototype.hasOwnProperty.call(r,s)&&i===s.substring(0,i.length)&&r[s]!==void 0)return!0;return!1}}]),n}(en);function Di(e){return e.charAt(0).toUpperCase()+e.slice(1)}var Ua=function(){function e(t){dt(this,e),this.options=t,this.supportedLngs=this.options.supportedLngs||!1,this.logger=yt.create("languageUtils")}return pt(e,[{key:"getScriptPartFromCode",value:function(n){if(!n||n.indexOf("-")<0)return null;var o=n.split("-");return o.length===2||(o.pop(),o[o.length-1].toLowerCase()==="x")?null:this.formatLanguageCode(o.join("-"))}},{key:"getLanguagePartFromCode",value:function(n){if(!n||n.indexOf("-")<0)return n;var o=n.split("-");return this.formatLanguageCode(o[0])}},{key:"formatLanguageCode",value:function(n){if(typeof n=="string"&&n.indexOf("-")>-1){var o=["hans","hant","latn","cyrl","cans","mong","arab"],r=n.split("-");return this.options.lowerCaseLng?r=r.map(function(i){return i.toLowerCase()}):r.length===2?(r[0]=r[0].toLowerCase(),r[1]=r[1].toUpperCase(),o.indexOf(r[1].toLowerCase())>-1&&(r[1]=Di(r[1].toLowerCase()))):r.length===3&&(r[0]=r[0].toLowerCase(),r[1].length===2&&(r[1]=r[1].toUpperCase()),r[0]!=="sgn"&&r[2].length===2&&(r[2]=r[2].toUpperCase()),o.indexOf(r[1].toLowerCase())>-1&&(r[1]=Di(r[1].toLowerCase())),o.indexOf(r[2].toLowerCase())>-1&&(r[2]=Di(r[2].toLowerCase()))),r.join("-")}return this.options.cleanCode||this.options.lowerCaseLng?n.toLowerCase():n}},{key:"isSupportedCode",value:function(n){return(this.options.load==="languageOnly"||this.options.nonExplicitSupportedLngs)&&(n=this.getLanguagePartFromCode(n)),!this.supportedLngs||!this.supportedLngs.length||this.supportedLngs.indexOf(n)>-1}},{key:"getBestMatchFromCodes",value:function(n){var o=this;if(!n)return null;var r;return n.forEach(function(i){if(!r){var s=o.formatLanguageCode(i);(!o.options.supportedLngs||o.isSupportedCode(s))&&(r=s)}}),!r&&this.options.supportedLngs&&n.forEach(function(i){if(!r){var s=o.getLanguagePartFromCode(i);if(o.isSupportedCode(s))return r=s;r=o.options.supportedLngs.find(function(l){if(l.indexOf(s)===0)return l})}}),r||(r=this.getFallbackCodes(this.options.fallbackLng)[0]),r}},{key:"getFallbackCodes",value:function(n,o){if(!n)return[];if(typeof n=="function"&&(n=n(o)),typeof n=="string"&&(n=[n]),Object.prototype.toString.apply(n)==="[object Array]")return n;if(!o)return n.default||[];var r=n[o];return r||(r=n[this.getScriptPartFromCode(o)]),r||(r=n[this.formatLanguageCode(o)]),r||(r=n[this.getLanguagePartFromCode(o)]),r||(r=n.default),r||[]}},{key:"toResolveHierarchy",value:function(n,o){var r=this,i=this.getFallbackCodes(o||this.options.fallbackLng||[],n),s=[],l=function(u){u&&(r.isSupportedCode(u)?s.push(u):r.logger.warn("rejecting language code not found in supportedLngs: ".concat(u)))};return typeof n=="string"&&n.indexOf("-")>-1?(this.options.load!=="languageOnly"&&l(this.formatLanguageCode(n)),this.options.load!=="languageOnly"&&this.options.load!=="currentOnly"&&l(this.getScriptPartFromCode(n)),this.options.load!=="currentOnly"&&l(this.getLanguagePartFromCode(n))):typeof n=="string"&&l(this.formatLanguageCode(n)),i.forEach(function(a){s.indexOf(a)<0&&l(r.formatLanguageCode(a))}),s}}]),e}(),Kf=[{lngs:["ach","ak","am","arn","br","fil","gun","ln","mfe","mg","mi","oc","pt","pt-BR","tg","tl","ti","tr","uz","wa"],nr:[1,2],fc:1},{lngs:["af","an","ast","az","bg","bn","ca","da","de","dev","el","en","eo","es","et","eu","fi","fo","fur","fy","gl","gu","ha","hi","hu","hy","ia","it","kk","kn","ku","lb","mai","ml","mn","mr","nah","nap","nb","ne","nl","nn","no","nso","pa","pap","pms","ps","pt-PT","rm","sco","se","si","so","son","sq","sv","sw","ta","te","tk","ur","yo"],nr:[1,2],fc:2},{lngs:["ay","bo","cgg","fa","ht","id","ja","jbo","ka","km","ko","ky","lo","ms","sah","su","th","tt","ug","vi","wo","zh"],nr:[1],fc:3},{lngs:["be","bs","cnr","dz","hr","ru","sr","uk"],nr:[1,2,5],fc:4},{lngs:["ar"],nr:[0,1,2,3,11,100],fc:5},{lngs:["cs","sk"],nr:[1,2,5],fc:6},{lngs:["csb","pl"],nr:[1,2,5],fc:7},{lngs:["cy"],nr:[1,2,3,8],fc:8},{lngs:["fr"],nr:[1,2],fc:9},{lngs:["ga"],nr:[1,2,3,7,11],fc:10},{lngs:["gd"],nr:[1,2,3,20],fc:11},{lngs:["is"],nr:[1,2],fc:12},{lngs:["jv"],nr:[0,1],fc:13},{lngs:["kw"],nr:[1,2,3,4],fc:14},{lngs:["lt"],nr:[1,2,10],fc:15},{lngs:["lv"],nr:[1,2,0],fc:16},{lngs:["mk"],nr:[1,2],fc:17},{lngs:["mnk"],nr:[0,1,2],fc:18},{lngs:["mt"],nr:[1,2,11,20],fc:19},{lngs:["or"],nr:[2,1],fc:2},{lngs:["ro"],nr:[1,2,20],fc:20},{lngs:["sl"],nr:[5,1,2,3],fc:21},{lngs:["he","iw"],nr:[1,2,20,21],fc:22}],Hf={1:function(t){return+(t>1)},2:function(t){return+(t!=1)},3:function(t){return 0},4:function(t){return t%10==1&&t%100!=11?0:t%10>=2&&t%10<=4&&(t%100<10||t%100>=20)?1:2},5:function(t){return t==0?0:t==1?1:t==2?2:t%100>=3&&t%100<=10?3:t%100>=11?4:5},6:function(t){return t==1?0:t>=2&&t<=4?1:2},7:function(t){return t==1?0:t%10>=2&&t%10<=4&&(t%100<10||t%100>=20)?1:2},8:function(t){return t==1?0:t==2?1:t!=8&&t!=11?2:3},9:function(t){return+(t>=2)},10:function(t){return t==1?0:t==2?1:t<7?2:t<11?3:4},11:function(t){return t==1||t==11?0:t==2||t==12?1:t>2&&t<20?2:3},12:function(t){return+(t%10!=1||t%100==11)},13:function(t){return+(t!==0)},14:function(t){return t==1?0:t==2?1:t==3?2:3},15:function(t){return t%10==1&&t%100!=11?0:t%10>=2&&(t%100<10||t%100>=20)?1:2},16:function(t){return t%10==1&&t%100!=11?0:t!==0?1:2},17:function(t){return t==1||t%10==1&&t%100!=11?0:1},18:function(t){return t==0?0:t==1?1:2},19:function(t){return t==1?0:t==0||t%100>1&&t%100<11?1:t%100>10&&t%100<20?2:3},20:function(t){return t==1?0:t==0||t%100>0&&t%100<20?1:2},21:function(t){return t%100==1?1:t%100==2?2:t%100==3||t%100==4?3:0},22:function(t){return t==1?0:t==2?1:(t<0||t>10)&&t%10==0?2:3}},Wf=["v1","v2","v3"],Va={zero:0,one:1,two:2,few:3,many:4,other:5};function Gf(){var e={};return Kf.forEach(function(t){t.lngs.forEach(function(n){e[n]={numbers:t.nr,plurals:Hf[t.fc]}})}),e}var Yf=function(){function e(t){var n=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};dt(this,e),this.languageUtils=t,this.options=n,this.logger=yt.create("pluralResolver"),(!this.options.compatibilityJSON||this.options.compatibilityJSON==="v4")&&(typeof Intl>"u"||!Intl.PluralRules)&&(this.options.compatibilityJSON="v3",this.logger.error("Your environment seems not to be Intl API compatible, use an Intl.PluralRules polyfill. Will fallback to the compatibilityJSON v3 format handling.")),this.rules=Gf()}return pt(e,[{key:"addRule",value:function(n,o){this.rules[n]=o}},{key:"getRule",value:function(n){var o=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};if(this.shouldUseIntlApi())try{return new Intl.PluralRules(n,{type:o.ordinal?"ordinal":"cardinal"})}catch{return}return this.rules[n]||this.rules[this.languageUtils.getLanguagePartFromCode(n)]}},{key:"needsPlural",value:function(n){var o=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{},r=this.getRule(n,o);return this.shouldUseIntlApi()?r&&r.resolvedOptions().pluralCategories.length>1:r&&r.numbers.length>1}},{key:"getPluralFormsOfKey",value:function(n,o){var r=arguments.length>2&&arguments[2]!==void 0?arguments[2]:{};return this.getSuffixes(n,r).map(function(i){return"".concat(o).concat(i)})}},{key:"getSuffixes",value:function(n){var o=this,r=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{},i=this.getRule(n,r);return i?this.shouldUseIntlApi()?i.resolvedOptions().pluralCategories.sort(function(s,l){return Va[s]-Va[l]}).map(function(s){return"".concat(o.options.prepend).concat(s)}):i.numbers.map(function(s){return o.getSuffix(n,s,r)}):[]}},{key:"getSuffix",value:function(n,o){var r=arguments.length>2&&arguments[2]!==void 0?arguments[2]:{},i=this.getRule(n,r);return i?this.shouldUseIntlApi()?"".concat(this.options.prepend).concat(i.select(o)):this.getSuffixRetroCompatible(i,o):(this.logger.warn("no plural rule found for: ".concat(n)),"")}},{key:"getSuffixRetroCompatible",value:function(n,o){var r=this,i=n.noAbs?n.plurals(o):n.plurals(Math.abs(o)),s=n.numbers[i];this.options.simplifyPluralSuffix&&n.numbers.length===2&&n.numbers[0]===1&&(s===2?s="plural":s===1&&(s=""));var l=function(){return r.options.prepend&&s.toString()?r.options.prepend+s.toString():s.toString()};return this.options.compatibilityJSON==="v1"?s===1?"":typeof s=="number"?"_plural_".concat(s.toString()):l():this.options.compatibilityJSON==="v2"||this.options.simplifyPluralSuffix&&n.numbers.length===2&&n.numbers[0]===1?l():this.options.prepend&&i.toString()?this.options.prepend+i.toString():i.toString()}},{key:"shouldUseIntlApi",value:function(){return!Wf.includes(this.options.compatibilityJSON)}}]),e}();function Ma(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var o=Object.getOwnPropertySymbols(e);t&&(o=o.filter(function(r){return Object.getOwnPropertyDescriptor(e,r).enumerable})),n.push.apply(n,o)}return n}function ot(e){for(var t=1;t<arguments.length;t++){var n=arguments[t]!=null?arguments[t]:{};t%2?Ma(Object(n),!0).forEach(function(o){et(e,o,n[o])}):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):Ma(Object(n)).forEach(function(o){Object.defineProperty(e,o,Object.getOwnPropertyDescriptor(n,o))})}return e}var Qf=function(){function e(){var t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{};dt(this,e),this.logger=yt.create("interpolator"),this.options=t,this.format=t.interpolation&&t.interpolation.format||function(n){return n},this.init(t)}return pt(e,[{key:"init",value:function(){var n=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{};n.interpolation||(n.interpolation={escapeValue:!0});var o=n.interpolation;this.escape=o.escape!==void 0?o.escape:Af,this.escapeValue=o.escapeValue!==void 0?o.escapeValue:!0,this.useRawValueToEscape=o.useRawValueToEscape!==void 0?o.useRawValueToEscape:!1,this.prefix=o.prefix?xn(o.prefix):o.prefixEscaped||"{{",this.suffix=o.suffix?xn(o.suffix):o.suffixEscaped||"}}",this.formatSeparator=o.formatSeparator?o.formatSeparator:o.formatSeparator||",",this.unescapePrefix=o.unescapeSuffix?"":o.unescapePrefix||"-",this.unescapeSuffix=this.unescapePrefix?"":o.unescapeSuffix||"",this.nestingPrefix=o.nestingPrefix?xn(o.nestingPrefix):o.nestingPrefixEscaped||xn("$t("),this.nestingSuffix=o.nestingSuffix?xn(o.nestingSuffix):o.nestingSuffixEscaped||xn(")"),this.nestingOptionsSeparator=o.nestingOptionsSeparator?o.nestingOptionsSeparator:o.nestingOptionsSeparator||",",this.maxReplaces=o.maxReplaces?o.maxReplaces:1e3,this.alwaysFormat=o.alwaysFormat!==void 0?o.alwaysFormat:!1,this.resetRegExp()}},{key:"reset",value:function(){this.options&&this.init(this.options)}},{key:"resetRegExp",value:function(){var n="".concat(this.prefix,"(.+?)").concat(this.suffix);this.regexp=new RegExp(n,"g");var o="".concat(this.prefix).concat(this.unescapePrefix,"(.+?)").concat(this.unescapeSuffix).concat(this.suffix);this.regexpUnescape=new RegExp(o,"g");var r="".concat(this.nestingPrefix,"(.+?)").concat(this.nestingSuffix);this.nestingRegexp=new RegExp(r,"g")}},{key:"interpolate",value:function(n,o,r,i){var s=this,l,a,u,f=this.options&&this.options.interpolation&&this.options.interpolation.defaultVariables||{};function d(O){return O.replace(/\$/g,"$$$$")}var h=function(p){if(p.indexOf(s.formatSeparator)<0){var c=Fa(o,f,p);return s.alwaysFormat?s.format(c,void 0,r,ot(ot(ot({},i),o),{},{interpolationkey:p})):c}var g=p.split(s.formatSeparator),k=g.shift().trim(),w=g.join(s.formatSeparator).trim();return s.format(Fa(o,f,k),w,r,ot(ot(ot({},i),o),{},{interpolationkey:k}))};this.resetRegExp();var m=i&&i.missingInterpolationHandler||this.options.missingInterpolationHandler,y=i&&i.interpolation&&i.interpolation.skipOnVariables!==void 0?i.interpolation.skipOnVariables:this.options.interpolation.skipOnVariables,_=[{regex:this.regexpUnescape,safeValue:function(p){return d(p)}},{regex:this.regexp,safeValue:function(p){return s.escapeValue?d(s.escape(p)):d(p)}}];return _.forEach(function(O){for(u=0;l=O.regex.exec(n);){var p=l[1].trim();if(a=h(p),a===void 0)if(typeof m=="function"){var c=m(n,l,i);a=typeof c=="string"?c:""}else if(i&&Object.prototype.hasOwnProperty.call(i,p))a="";else if(y){a=l[0];continue}else s.logger.warn("missed to pass in variable ".concat(p," for interpolating ").concat(n)),a="";else typeof a!="string"&&!s.useRawValueToEscape&&(a=Ta(a));var g=O.safeValue(a);if(n=n.replace(l[0],g),y?(O.regex.lastIndex+=a.length,O.regex.lastIndex-=l[0].length):O.regex.lastIndex=0,u++,u>=s.maxReplaces)break}}),n}},{key:"nest",value:function(n,o){var r=this,i=arguments.length>2&&arguments[2]!==void 0?arguments[2]:{},s,l,a;function u(m,y){var _=this.nestingOptionsSeparator;if(m.indexOf(_)<0)return m;var O=m.split(new 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r.forEach(function(m){var y=!0;i.forEach(function(_){var O="".concat(m,"|").concat(_);!s.reload&&a.store.hasResourceBundle(m,_)?a.state[O]=2:a.state[O]<0||(a.state[O]===1?f[O]===void 0&&(f[O]=!0):(a.state[O]=1,y=!1,f[O]===void 0&&(f[O]=!0),u[O]===void 0&&(u[O]=!0),h[_]===void 0&&(h[_]=!0)))}),y||(d[m]=!0)}),(Object.keys(u).length||Object.keys(f).length)&&this.queue.push({pending:f,pendingCount:Object.keys(f).length,loaded:{},errors:[],callback:l}),{toLoad:Object.keys(u),pending:Object.keys(f),toLoadLanguages:Object.keys(d),toLoadNamespaces:Object.keys(h)}}},{key:"loaded",value:function(r,i,s){var l=r.split("|"),a=l[0],u=l[1];i&&this.emit("failedLoading",a,u,i),s&&this.store.addResourceBundle(a,u,s),this.state[r]=i?-1:2;var f={};this.queue.forEach(function(d){Ff(d.loaded,[a],u),eh(d,r),i&&d.errors.push(i),d.pendingCount===0&&!d.done&&(Object.keys(d.loaded).forEach(function(h){f[h]||(f[h]={});var m=d.loaded[h];m.length&&m.forEach(function(y){f[h][y]===void 0&&(f[h][y]=!0)})}),d.done=!0,d.errors.length?d.callback(d.errors):d.callback())}),this.emit("loaded",f),this.queue=this.queue.filter(function(d){return!d.done})}},{key:"read",value:function(r,i,s){var l=this,a=arguments.length>3&&arguments[3]!==void 0?arguments[3]:0,u=arguments.length>4&&arguments[4]!==void 0?arguments[4]:this.retryTimeout,f=arguments.length>5?arguments[5]:void 0;if(!r.length)return f(null,{});if(this.readingCalls>=this.maxParallelReads){this.waitingReads.push({lng:r,ns:i,fcName:s,tried:a,wait:u,callback:f});return}this.readingCalls++;var d=function(_,O){if(l.readingCalls--,l.waitingReads.length>0){var p=l.waitingReads.shift();l.read(p.lng,p.ns,p.fcName,p.tried,p.wait,p.callback)}if(_&&O&&a<l.maxRetries){setTimeout(function(){l.read.call(l,r,i,s,a+1,u*2,f)},u);return}f(_,O)},h=this.backend[s].bind(this.backend);if(h.length===2){try{var m=h(r,i);m&&typeof m.then=="function"?m.then(function(y){return d(null,y)}).catch(d):d(null,m)}catch(y){d(y)}return}return 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Ha(){return{debug:!1,initImmediate:!0,ns:["translation"],defaultNS:["translation"],fallbackLng:["dev"],fallbackNS:!1,supportedLngs:!1,nonExplicitSupportedLngs:!1,load:"all",preload:!1,simplifyPluralSuffix:!0,keySeparator:".",nsSeparator:":",pluralSeparator:"_",contextSeparator:"_",partialBundledLanguages:!1,saveMissing:!1,updateMissing:!1,saveMissingTo:"fallback",saveMissingPlurals:!0,missingKeyHandler:!1,missingInterpolationHandler:!1,postProcess:!1,postProcessPassResolved:!1,returnNull:!0,returnEmptyString:!0,returnObjects:!1,joinArrays:!1,returnedObjectHandler:!1,parseMissingKeyHandler:!1,appendNamespaceToMissingKey:!1,appendNamespaceToCIMode:!1,overloadTranslationOptionHandler:function(t){var n={};if(pe(t[1])==="object"&&(n=t[1]),typeof t[1]=="string"&&(n.defaultValue=t[1]),typeof t[2]=="string"&&(n.tDescription=t[2]),pe(t[2])==="object"||pe(t[3])==="object"){var o=t[3]||t[2];Object.keys(o).forEach(function(r){n[r]=o[r]})}return 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p?typeof p=="function"?new p:p:null}if(!this.options.isClone){this.modules.logger?yt.init(a(this.modules.logger),this.options):yt.init(null,this.options);var u;this.modules.formatter?u=this.modules.formatter:typeof Intl<"u"&&(u=Jf);var f=new Ua(this.options);this.store=new Mf(this.options.resources,this.options);var d=this.services;d.logger=yt,d.resourceStore=this.store,d.languageUtils=f,d.pluralResolver=new Yf(f,{prepend:this.options.pluralSeparator,compatibilityJSON:this.options.compatibilityJSON,simplifyPluralSuffix:this.options.simplifyPluralSuffix}),u&&(!this.options.interpolation.format||this.options.interpolation.format===l.interpolation.format)&&(d.formatter=a(u),d.formatter.init(d,this.options),this.options.interpolation.format=d.formatter.format.bind(d.formatter)),d.interpolator=new Qf(this.options),d.utils={hasLoadedNamespace:this.hasLoadedNamespace.bind(this)},d.backendConnector=new th(a(this.modules.backend),d.resourceStore,d,this.options),d.backendConnector.on("*",function(p){for(var c=arguments.length,g=new Array(c>1?c-1:0),k=1;k<c;k++)g[k-1]=arguments[k];r.emit.apply(r,[p].concat(g))}),this.modules.languageDetector&&(d.languageDetector=a(this.modules.languageDetector),d.languageDetector.init&&d.languageDetector.init(d,this.options.detection,this.options)),this.modules.i18nFormat&&(d.i18nFormat=a(this.modules.i18nFormat),d.i18nFormat.init&&d.i18nFormat.init(this)),this.translator=new za(this.services,this.options),this.translator.on("*",function(p){for(var c=arguments.length,g=new Array(c>1?c-1:0),k=1;k<c;k++)g[k-1]=arguments[k];r.emit.apply(r,[p].concat(g))}),this.modules.external.forEach(function(p){p.init&&p.init(r)})}if(this.format=this.options.interpolation.format,s||(s=ur),this.options.fallbackLng&&!this.services.languageDetector&&!this.options.lng){var h=this.services.languageUtils.getFallbackCodes(this.options.fallbackLng);h.length>0&&h[0]!=="dev"&&(this.options.lng=h[0])}!this.services.languageDetector&&!this.options.lng&&this.logger.warn("init: no languageDetector is used and no lng is defined");var m=["getResource","hasResourceBundle","getResourceBundle","getDataByLanguage"];m.forEach(function(p){r[p]=function(){var c;return(c=r.store)[p].apply(c,arguments)}});var y=["addResource","addResources","addResourceBundle","removeResourceBundle"];y.forEach(function(p){r[p]=function(){var c;return(c=r.store)[p].apply(c,arguments),r}});var _=po(),O=function(){var c=function(k,w){r.isInitialized&&!r.initializedStoreOnce&&r.logger.warn("init: i18next is already initialized. 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gh=/&(?:amp|#38|lt|#60|gt|#62|apos|#39|quot|#34|nbsp|#160|copy|#169|reg|#174|hellip|#8230|#x2F|#47);/g,mh={"&amp;":"&","&#38;":"&","&lt;":"<","&#60;":"<","&gt;":">","&#62;":">","&apos;":"'","&#39;":"'","&quot;":'"',"&#34;":'"',"&nbsp;":" ","&#160;":" ","&copy;":"©","&#169;":"©","&reg;":"®","&#174;":"®","&hellip;":"…","&#8230;":"…","&#x2F;":"/","&#47;":"/"},vh=function(t){return mh[t]},yh=function(t){return t.replace(gh,vh)};function Ja(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var o=Object.getOwnPropertySymbols(e);t&&(o=o.filter(function(r){return Object.getOwnPropertyDescriptor(e,r).enumerable})),n.push.apply(n,o)}return n}function Za(e){for(var t=1;t<arguments.length;t++){var n=arguments[t]!=null?arguments[t]:{};t%2?Ja(Object(n),!0).forEach(function(o){et(e,o,n[o])}):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):Ja(Object(n)).forEach(function(o){Object.defineProperty(e,o,Object.getOwnPropertyDescriptor(n,o))})}return 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+ * scheduler.production.min.js
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+ *
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+`):this.metadata.subtitle=n,this}}class ma extends De{constructor(t){super({level:C.DISTRIBUTION,key:j.APT,metadata:{title:"APT"}}),this._data=t}get data(){return{...this._data,commands:_v}}cApiOnly(){return this.metadata.subtitle=K("distributions.CAPIOnly"),this}}const gl="GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB",ac=`https://apt.repos.intel.com/intel-gpg-keys/${gl}`,uc={[St.UBUNTU_18]:18,[St.UBUNTU_20]:20,[St.UBUNTU_22]:22},mv={alias:{22:"jammy",20:"focal",18:"bionic"},numberName:{22:"ubuntu22",20:"ubuntu20",18:"ubuntu18"}},vv=new Set([Dt.v_2022_3_2]),yv=(e,t)=>{const n=vv.has(e.key)?"alias":"numberName";return mv[n][t]},kv=(e,t)=>{const n=yv(e,t);return`echo "deb https://apt.repos.intel.com/openvino/${e.metadata.aptYear} ${n} main" | sudo tee /etc/apt/sources.list.d/intel-openvino-${e.metadata.aptYear}.list`},_v={keyFilename:gl,keyHref:ac,downloadKeyCommand:`wget ${ac}`,addKeyCommand:`sudo apt-key add ${gl}`,getAddRepositoryCommands:(e,t)=>t.map(n=>({ubuntuVersionNumber:uc[n],command:kv(e,uc[n])})),updatePackagesCommand:"sudo apt update",verifyAptCacheCommand:"apt-cache search openvino",getInstallCommand:e=>`sudo apt install openvino-${e.metadata.aptVersion}`};class be extends De{constructor(t){super({level:C.DISTRIBUTION,key:j.ARCHIVE,metadata:{title:K("distributions.openVINOArchives")}}),this.data=t}}class Vp extends De{constructor(t){super({level:C.DISTRIBUTION,key:j.BREW,metadata:{title:"Homebrew"}}),this._data=t}get data(){return{...this._data,commands:Sv}}}const Sv={install:"brew install openvino"};class so extends De{constructor(t){super({level:C.DISTRIBUTION,key:j.CONAN,metadata:{title:"Conan"}}),this._data=t}get data(){return{...this._data,commands:Ov}}}const cc="conanfile.txt",dc="conan_toolchain.cmake",wv=e=>`[requires]
+openvino/${e.metadata.conanVersion}
+[generators]
+CMakeDeps
+CMakeToolchain
+[layout]
+cmake_layout`,Ov={conanTXTContent:wv,txtFilename:cc,cmakeFilename:dc,install:`conan install ${cc} --build=missing`,compile:`cmake -DCMAKE_TOOLCHAIN_FILE=<path to ${dc}> -DCMAKE_BUILD_TYPE=Release -S <path to CMakeLists.txt of your project> -B <build dir>
+cmake --build <build dir> --parallel`};class lo extends De{constructor(t){super({level:C.DISTRIBUTION,key:j.CONDA,metadata:{title:"Conda"}}),this._data=t}get data(){return{...this._data,commands:Nv}}}const Nv={createEnv:"conda create --name py310 python=3.10",activateEnv:"conda activate py310",update:"conda update --all",getInstall:({metadata:e})=>`conda install -c conda-forge openvino=${e.condaVersion}`};class On extends De{constructor(t){super({level:C.DISTRIBUTION,key:j.DOCKER,metadata:{title:"Docker"}}),this.data=t}}class xe extends De{constructor(t){super({level:C.DISTRIBUTION,key:j.GITEE,metadata:{title:"Gitee",subtitle:K("distributions.source")}}),this._data=t}get data(){return{...this._data,commands:Pv,links:bv}}}const Pv={getCloneCommand(e){return`git clone -b ${e.metadata.giteeVersion} https://gitee.com/openvinotoolkit-prc/openvino.git`}},bv={getBuildInstructionsLink:e=>`https://github.com/openvinotoolkit/openvino/blob/${e.metadata.giteeVersion}/docs/dev/build.md`,getRepositoryLink:e=>`https://gitee.com/openvinotoolkit-prc/openvino/tree/${e.metadata.giteeVersion}`};class Ee extends De{constructor(t){super({level:C.DISTRIBUTION,key:j.GITHUB,metadata:{title:"GitHub",subtitle:K("distributions.source")}}),this._data=t}get data(){return{...this._data,commands:xv,links:Ev}}}const xv={getCloneCommand(e){return`git clone -b ${e.metadata.githubVersion} https://github.com/openvinotoolkit/openvino.git`}},Ev={getBuildInstructionsLink:e=>`https://github.com/openvinotoolkit/openvino/blob/${e.metadata.githubVersion}/docs/dev/build.md`,getRepositoryLink:e=>`https://github.com/openvinotoolkit/openvino/tree/${e.metadata.githubVersion}`};class va extends De{constructor(t){super({level:C.DISTRIBUTION,key:j.NPM,metadata:{title:"npm",subtitle:K("distributions.jsAPIOnly")}}),this._data=t}get data(){return{...this._data,commands:Cv}}}const Cv={getInstall:({metadata:e})=>`npm install openvino-node@${e.npmVersion}`},Iv=(e,t)=>{e.forEach(n=>{n.key===Me.TENSORFLOW_1&&n.checked&&t===Me.TENSORFLOW_2||n.key===Me.TENSORFLOW_2&&n.checked&&t===Me.TENSORFLOW_1?n.checked=!1:t===n.key&&(n.checked=!n.checked)})},Mp=(e,t)=>{e.forEach(n=>{n.checked=n.key===t,n.key===j.PIP&&n.checked&&n.children.forEach(o=>o.checked=o.isDefault)})},Rv=()=>[new we({level:C.FRAMEWORK,key:Me.CAFFE,metadata:{title:"Caffe",pipKey:"caffe"}}),new we({level:C.FRAMEWORK,key:Me.KALDI,metadata:{title:"Kaldi",pipKey:"kaldi"}}),new we({level:C.FRAMEWORK,key:Me.MXNET,metadata:{title:"Apache MXNet",pipKey:"mxnet"}}),new we({level:C.FRAMEWORK,key:Me.ONNX,metadata:{title:"Open Neural Network Exchange (ONNX)",pipKey:"ONNX"}}),new we({level:C.FRAMEWORK,key:Me.PYTORCH,metadata:{title:"PyTorch",pipKey:"pytorch"}}),new we({level:C.FRAMEWORK,key:Me.TENSORFLOW_1,metadata:{title:"TensorFlow 1.x",pipKey:"tensorflow"}}),new we({level:C.FRAMEWORK,key:Me.TENSORFLOW_2,metadata:{title:"TensorFlow 2.x",pipKey:"tensorflow2"}})];class se extends De{constructor(t,{hasFrameworks:n,pythonAPI:o}={}){super({level:C.DISTRIBUTION,key:j.PIP,metadata:{title:"PIP",subtitle:o?K("distributions.pythonAPIOnly"):void 0},childrenSelector:Iv},n?Rv():[]),this._data=t}get data(){return{...this._data,commands:jv,frameworks:this.children.filter(({checked:t})=>t).map(t=>t.toOption())}}}function $p(e){return e.key===Dt.NIGHTLY?"openvino_nightly_env":"openvino_env"}function Tv(e,t){const n=$p(t);return e.key===Xe.WINDOWS?`python -m venv ${n}`:`python3 -m venv ${n}`}function Lv(e,t){const n=$p(t);return e.key===Xe.WINDOWS?`${n}\\Scripts\\activate`:`source ${n}/bin/activate`}function Fv({ovPackage:e,environment:t,os:n,version:o,distribution:r}){const i=r.data.frameworks.map(u=>u.metadata.pipKey),s=i.length?`[${i.join(",")}]`:"";if(o.key===Dt.NIGHTLY)return"pip install --pre -U openvino --extra-index-url https://storage.openvinotoolkit.org/simple/wheels/nightly";let a=`${(t==null?void 0:t.key)===sr.DEV_TOOLS?"openvino-dev":e.key===wn.OPENVINO_GENAI?"openvino-genai":"openvino"}${s}==${o.metadata.pipVersion}`;return n.key===Xe.MACOS&&(a=`'${a}'`),`pip install ${a}`}const jv={getCreateVenvCommand:Tv,getActivateVenvCommand:Lv,upgradeCommand:"python -m pip install --upgrade pip",getInstallCommand:Fv};class Av extends De{constructor(t){super({level:C.DISTRIBUTION,key:j.SNAP,metadata:{title:"Snap",subtitle:K("distributions.CAPIOnly")}}),this.data=t}}class ya extends De{constructor(t){super({level:C.DISTRIBUTION,key:j.VCPKG,metadata:{title:"vcpkg",subtitle:K("distributions.source")}}),this._data=t}get data(){return{...this._data,commands:Dv}}}const Dv={install:"vcpkg install openvino"};class ka extends De{constructor(t){super({level:C.DISTRIBUTION,key:j.YUM,metadata:{title:"YUM",subtitle:K("distributions.CAPIOnly")}}),this._data=t}get data(){return{...this._data,commands:zv}}}const pc="/etc/yum.repos.d",zv={directory:pc,getCreateRepoCommand:e=>`tee > /tmp/openvino-${e.metadata.yumYear}.repo << EOF
+[OpenVINO]
+name=Intel(R) Distribution of OpenVINO ${e.metadata.yumYear}
+baseurl=https://yum.repos.intel.com/openvino/${e.metadata.yumYear}
+enabled=1
+gpgcheck=1
+repo_gpgcheck=1
+gpgkey=https://yum.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
+EOF`,getMoveRepoFileCommand:e=>`sudo mv /tmp/openvino-${e.metadata.yumYear}.repo ${pc}`,verifyRepoCommand:"yum repolist | grep -i openvino",getInstallCommand:e=>`sudo yum install openvino-${e.metadata.yumVersion}`};class Uv extends De{constructor(t){super({level:C.DISTRIBUTION,key:j.ZYPPER,metadata:{title:"ZYPPER",subtitle:K("distributions.CAPIOnly")}}),this._data=t}get data(){return{...this._data,commands:Vv}}}const Vv={addRepo:"sudo zypper addrepo https://download.opensuse.org/repositories/science/openSUSE_Tumbleweed/science.repo",refresh:"sudo zypper refresh",getInstallCommand:({metadata:e})=>`sudo zypper install openvino-devel-${e.zypperVersion} openvino-sample-${e.zypperVersion}`};class Bp extends we{constructor(t,n,o){super({level:C.PACKAGE,key:t,metadata:n,childrenSelector:Mp},o),this._setDefaultPackage()}_setDefaultPackage(){const t=wn.OPENVINO_BASE;this.key===t&&this.default()}}class Mv extends Bp{constructor(t){super(wn.OPENVINO_BASE,{title:K("package.base.title"),subtitle:K("package.base.subtitle")},t)}}class $v extends Bp{constructor(t){super(wn.OPENVINO_GENAI,{title:K("package.genai.title"),subtitle:K("package.genai.subtitle")},t)}}class Ti extends we{constructor(t,n,o){super({level:C.VERSION,key:t,metadata:n},o)}}const Bv={title:K("versions.titles.nightlyBuild"),compactTitle:K("versions.titles.nightlyBuild"),pipVersion:"",githubVersion:"master",giteeVersion:"master",systemRequirementsLink:"https://docs.openvino.ai/nightly/about-openvino/release-notes-openvino/system-requirements.html"};class Kv extends Ti{constructor(t){super(Dt.NIGHTLY,Bv,t)}}const 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diff --git a/docs/sphinx_setup/_static/selector-tool/selector-1c16038.html b/docs/sphinx_setup/_static/selector-tool/selector-0fa99c3.html
similarity index 67%
rename from docs/sphinx_setup/_static/selector-tool/selector-1c16038.html
rename to docs/sphinx_setup/_static/selector-tool/selector-0fa99c3.html
index 19961f29937d12..f6b16bd8a6c2b5 100644
--- a/docs/sphinx_setup/_static/selector-tool/selector-1c16038.html
+++ b/docs/sphinx_setup/_static/selector-tool/selector-0fa99c3.html
@@ -1,7 +1,7 @@
 <!DOCTYPE html>
 <html lang="en">
   <head>
-    <meta name="version" content="v_2022_3_2-1c16038" />
+    <meta name="version" content="v_2024_2_0-0fa99c3" />
     <meta charset="UTF-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
     <title>Download Intel® Distribution of OpenVINO™ Toolkit</title>
@@ -9,11 +9,11 @@
       name="description"
       content="Download a version of the Intel® Distribution of OpenVINO™ toolkit for Linux, Windows, or macOS."
     />
-    <script type="module" crossorigin src="./assets/selector-8f39cd2e.js"></script>
-    <link rel="stylesheet" href="./assets/selector-6bf35a6f.css">
+    <script type="module" crossorigin src="./assets/selector-d6eabe1c.js"></script>
+    <link rel="stylesheet" href="./assets/selector-b0e2854e.css">
   </head>
   <body>
     <div id="root"></div>
-
+    
   </body>
 </html>
diff --git a/docs/sphinx_setup/api/nodejs_api/openvino-node/interfaces/CompiledModel.rst b/docs/sphinx_setup/api/nodejs_api/openvino-node/interfaces/CompiledModel.rst
index cd479a1a8591b1..0368d73e4d36db 100644
--- a/docs/sphinx_setup/api/nodejs_api/openvino-node/interfaces/CompiledModel.rst
+++ b/docs/sphinx_setup/api/nodejs_api/openvino-node/interfaces/CompiledModel.rst
@@ -165,7 +165,7 @@ Methods
      A compiled model output.
 
    * **Defined in:**
-   `addon.ts:294 <https://github.com/openvinotoolkit/openvino/blob/master/src/bindings/js/node/lib/addon.ts#L294>`__
+     `addon.ts:294 <https://github.com/openvinotoolkit/openvino/blob/master/src/bindings/js/node/lib/addon.ts#L294>`__
 
 
    .. code-block:: ts
@@ -189,7 +189,7 @@ Methods
      A compiled model output.
 
    * **Defined in:**
-   `addon.ts:300 <https://github.com/openvinotoolkit/openvino/blob/master/src/bindings/js/node/lib/addon.ts#L300>`__
+     `addon.ts:300 <https://github.com/openvinotoolkit/openvino/blob/master/src/bindings/js/node/lib/addon.ts#L300>`__
 
 
    .. code-block:: ts
@@ -213,5 +213,5 @@ Methods
      A compiled model output.
 
    * **Defined in:**
-   `addon.ts:306 <https://github.com/openvinotoolkit/openvino/blob/master/src/bindings/js/node/lib/addon.ts#L306>`__
+     `addon.ts:306 <https://github.com/openvinotoolkit/openvino/blob/master/src/bindings/js/node/lib/addon.ts#L306>`__
 
diff --git a/docs/sphinx_setup/api/nodejs_api/openvino-node/interfaces/Core.rst b/docs/sphinx_setup/api/nodejs_api/openvino-node/interfaces/Core.rst
index 2b654648428711..bf5743cf20ee6a 100644
--- a/docs/sphinx_setup/api/nodejs_api/openvino-node/interfaces/Core.rst
+++ b/docs/sphinx_setup/api/nodejs_api/openvino-node/interfaces/Core.rst
@@ -63,6 +63,7 @@ Methods
 
 
 .. rubric:: compileModel
+   :name: compileModel
 
 .. container:: m-4
 
diff --git a/docs/sphinx_setup/api/nodejs_api/openvino-node/interfaces/TensorConstructor.rst b/docs/sphinx_setup/api/nodejs_api/openvino-node/interfaces/TensorConstructor.rst
index 456a3337d86251..a0e2894d181504 100644
--- a/docs/sphinx_setup/api/nodejs_api/openvino-node/interfaces/TensorConstructor.rst
+++ b/docs/sphinx_setup/api/nodejs_api/openvino-node/interfaces/TensorConstructor.rst
@@ -76,3 +76,17 @@ Constructors
    * **Defined in:**
      `addon.ts:392 <https://github.com/openvinotoolkit/openvino/blob/master/src/bindings/js/node/lib/addon.ts#L392>`__
 
+
+   .. code-block:: ts
+
+       new TensorConstructor(tensorData: string[]): Tensor;
+
+   It constructs a string tensor. The strings from
+   the array are used to fill tensor data. Each element of a string tensor
+   is a string of arbitrary length.
+
+   * **Returns:**  :doc:`Tensor <Tensor>`
+
+   * **Defined in:**
+     `addon.ts:399 <https://github.com/openvinotoolkit/openvino/blob/master/src/bindings/js/node/lib/addon.ts#L399>`__
+
diff --git a/docs/sphinx_setup/index.rst b/docs/sphinx_setup/index.rst
index f3e06b3f956f13..8ab774d7b5689a 100644
--- a/docs/sphinx_setup/index.rst
+++ b/docs/sphinx_setup/index.rst
@@ -14,6 +14,7 @@ and on-device, in the browser or in the cloud.
 Check out the `OpenVINO Cheat Sheet. <https://docs.openvino.ai/2024/_static/download/OpenVINO_Quick_Start_Guide.pdf>`__
 
 
+
 .. container::
    :name: ov-homepage-banner
 
@@ -24,31 +25,21 @@ Check out the `OpenVINO Cheat Sheet. <https://docs.openvino.ai/2024/_static/down
          <section class="splide" aria-label="Splide Banner Carousel">
            <div class="splide__track">
          		<ul class="splide__list">
-                  <li id="ov-homepage-slide1" id class="splide__slide">
-                     <p class="ov-homepage-slide-title">An open-source toolkit for optimizing and deploying deep learning models.</p>
-                     <p class="ov-homepage-slide-subtitle">Boost your AI deep-learning inference performance!</p>
-                     <a class="ov-homepage-banner-btn" href="https://docs.openvino.ai/2024/get-started.html">Learn more</a>
+                  <li id="ov-homepage-slide1" class="splide__slide">
+                  <p class="ov-homepage-slide-title">New Generative AI API</p>
+                  <p class="ov-homepage-slide-subtitle">Generate text with LLMs in only a few lines of code!</p>
+                  <a class="ov-homepage-banner-btn" href="https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide/genai-guide.html">Check out our guide</a>
                   </li>
                   <li id="ov-homepage-slide2" class="splide__slide">
-                  <p class="ov-homepage-slide-title">Better OpenVINO integration with PyTorch!</p>
-                  <p class="ov-homepage-slide-subtitle">Use PyTorch models directly, without converting them first.</p>
-                     <a class="ov-homepage-banner-btn" href="https://docs.openvino.ai/2024/openvino-workflow/model-preparation/convert-model-pytorch.html">Learn more</a>
+                  <p class="ov-homepage-slide-title">Improved model serving</p>
+                  <p class="ov-homepage-slide-subtitle">OpenVINO Model Server has improved parallel inferencing!</p>
+                  <a class="ov-homepage-banner-btn" href="https://docs.openvino.ai/2024/ovms_what_is_openvino_model_server.html">Learn more</a>
                   </li>
                   <li id="ov-homepage-slide3" class="splide__slide">
                   <p class="ov-homepage-slide-title">OpenVINO via PyTorch 2.0 torch.compile()</p>
                   <p class="ov-homepage-slide-subtitle">Use OpenVINO directly in PyTorch-native applications!</p>
                   <a class="ov-homepage-banner-btn" href="https://docs.openvino.ai/2024/openvino-workflow/torch-compile.html">Learn more</a>
                   </li>
-                  <li id="ov-homepage-slide4" class="splide__slide">
-                  <p class="ov-homepage-slide-title">Do you like Generative AI?</p>
-                  <p class="ov-homepage-slide-subtitle">You will love how it performs with OpenVINO!</p>
-                  <a class="ov-homepage-banner-btn" href="https://docs.openvino.ai/2024/learn-openvino/interactive-tutorials-python.html">Check out our new notebooks</a>
-                  </li>
-                  <li id="ov-homepage-slide5" id class="splide__slide">
-                     <p class="ov-homepage-slide-title">Boost your AI deep learning interface performance.</p>
-                     <p class="ov-homepage-slide-subtitle">Use Intel's open-source OpenVino toolkit for optimizing and deploying deep learning models.</p>
-                     <a class="ov-homepage-banner-btn" href="https://docs.openvino.ai/2024/openvino-workflow/model-preparation/convert-model-pytorch.html">Learn more</a>
-                  </li>
             </ul>
            </div>
          </section>
@@ -115,7 +106,7 @@ Places to Begin
 
       Get started in just a few lines of code.
 
-      .. button-link:: openvino-workflow/running-inference.html
+      .. button-link:: openvino-workflow/running-inference/integrate-openvino-with-your-application.html
          :color: primary
          :outline:
 
diff --git a/samples/cpp/benchmark_app/inputs_filling.cpp b/samples/cpp/benchmark_app/inputs_filling.cpp
index 59c567d7fc42be..d77dafaa803aa6 100644
--- a/samples/cpp/benchmark_app/inputs_filling.cpp
+++ b/samples/cpp/benchmark_app/inputs_filling.cpp
@@ -115,6 +115,7 @@ ov::Tensor create_tensor_from_numpy(const std::vector<std::string>& files,
                    << slog::endl;
     }
 
+    tensor_size = tensor_size / numpy_batch_size;
     for (size_t b = 0; b < numpy_batch_size; ++b) {
         auto inputIndex = (inputId + b) % files.size();
         if (filenames_used) {
diff --git a/samples/js/node/README.md b/samples/js/node/README.md
index f0b18b15dd4352..59fb381f460abc 100644
--- a/samples/js/node/README.md
+++ b/samples/js/node/README.md
@@ -21,10 +21,18 @@ Use [Node.js Notebooks (REPL)](https://marketplace.visualstudio.com/items?itemNa
 VSCode extension to run these notebook samples
 
 - ./notebooks
-  - 001-hello-world.nnb
-  - 003-hello-segmentation.nnb
-  - 004-hello-detection.nnb
-  - 213-question-answering.nnb
+  - hello-world.nnb
+  - hello-segmentation.nnb
+  - hello-detection.nnb
+  - question-answering.nnb
+  - pose-estimation.nnb
+
+## Live Sample
+
+You can run this sample in the browser; no installation is required.
+[Codesandbox](https://codesandbox.io/) is a free online service with limited resources. For optimal performance and more control,  it is recommended to run the sample locally.
+
+- [hello-classification-sample](https://codesandbox.io/p/devbox/openvino-node-hello-classification-sample-djl893)
 
 ## See Also
 
diff --git a/samples/js/node/notebooks/004-hello-detection.nnb b/samples/js/node/notebooks/hello-detection.nnb
similarity index 100%
rename from samples/js/node/notebooks/004-hello-detection.nnb
rename to samples/js/node/notebooks/hello-detection.nnb
diff --git a/samples/js/node/notebooks/003-hello-segmentation.nnb b/samples/js/node/notebooks/hello-segmentation.nnb
similarity index 100%
rename from samples/js/node/notebooks/003-hello-segmentation.nnb
rename to samples/js/node/notebooks/hello-segmentation.nnb
diff --git a/samples/js/node/notebooks/001-hello-world.nnb b/samples/js/node/notebooks/hello-world.nnb
similarity index 100%
rename from samples/js/node/notebooks/001-hello-world.nnb
rename to samples/js/node/notebooks/hello-world.nnb
diff --git a/samples/js/node/notebooks/pose-estimation.nnb b/samples/js/node/notebooks/pose-estimation.nnb
new file mode 100644
index 00000000000000..6ca358ed2b0cf4
--- /dev/null
+++ b/samples/js/node/notebooks/pose-estimation.nnb
@@ -0,0 +1,133 @@
+{
+    "cells": [
+        {
+            "language": "markdown",
+            "source": [
+                "# Human Pose Estimation with OpenVINO™\n\nThis notebook demonstrates live pose estimation with OpenVINO, using the OpenPose human-pose-estimation-0001 model"
+            ],
+            "outputs": []
+        },
+        {
+            "language": "markdown",
+            "source": [
+                "## Imports"
+            ],
+            "outputs": []
+        },
+        {
+            "language": "javascript",
+            "source": [
+                "const path = require('node:path');\nconst { cv } = require('opencv-wasm');\nconst { display } = require('node-kernel');\nconst tf = require('@tensorflow/tfjs-node');\nconst {\n  getImageData,\n  displayArrayAsImage,\n  arrayToImageData,\n  getImageBuffer,\n  transform,\n  downloadFile,\n} = require('../helpers.js');\n\nconst { addon: ov } = require('openvino-node');\n"
+            ],
+            "outputs": []
+        },
+        {
+            "language": "markdown",
+            "source": [
+                "## Download the Model"
+            ],
+            "outputs": []
+        },
+        {
+            "language": "javascript",
+            "source": [
+                "const baseArtifactsDir = '../../assets/models';\n\nconst modelName = 'human-pose-estimation-0001';\nconst modelXMLName = `${modelName}.xml`;\nconst modelBINName = `${modelName}.bin`;\n\nconst modelXMLPath = baseArtifactsDir + '/' + modelXMLName;\n\nconst baseURL = `https://storage.openvinotoolkit.org/repositories/open_model_zoo/2022.1/models_bin/3/${modelName}/FP16-INT8/`;\n\nawait downloadFile(baseURL + modelXMLName, modelXMLName, baseArtifactsDir);\nawait downloadFile(baseURL + modelBINName, modelBINName, baseArtifactsDir);\n\nconst imgUrl = 'https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/intel_rnb.jpg';\nconst imageFilename = path.parse(imgUrl).base;\n\nawait downloadFile(imgUrl, imageFilename, '../../assets/images');\n\nconst imagePath = `../../assets/images/${imageFilename}`\n"
+            ],
+            "outputs": [
+                {
+                    "items": [
+                        {
+                            "mime": "application/vnd.code.notebook.stdout",
+                            "value": [
+                                "Proxy agent configured using: 'http://proxy-dmz.intel.com:911'",
+                                "Proxy agent configured using: 'http://proxy-dmz.intel.com:911'",
+                                "Proxy agent configured using: 'http://proxy-dmz.intel.com:911'",
+                                "File successfully stored at '/home/nvishnya/Code/wasm-openvino/samples/js/assets/images/intel_rnb.jpg'",
+                                ""
+                            ]
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "language": "markdown",
+            "source": [
+                "## Load the Model"
+            ],
+            "outputs": []
+        },
+        {
+            "language": "typescript",
+            "source": [
+                "const core = new ov.Core();\nconst model = await core.readModel(modelXMLPath);\nconst compiledModel = await core.compileModel(model, 'CPU', { PERFORMANCE_HINT: 'LATENCY' });\n\nconst inputLayer = compiledModel.inputs[0];\nconst outputLayers = compiledModel.outputs;\n\nconst [height, width] = inputLayer.shape.slice(2);\n\nconst heatmapsOutputKey = 'Mconv7_stage2_L2';\n\nconst THRESHOLD = 0.3;\nconst COLOR = [0, 255, 0, 255];"
+            ],
+            "outputs": []
+        },
+        {
+            "language": "markdown",
+            "source": [
+                "## Load an Image"
+            ],
+            "outputs": []
+        },
+        {
+            "language": "typescript",
+            "source": [
+                "const imgData = await getImageData(imagePath);\n\nconst originalImage = cv.matFromImageData(imgData);\nconst { cols: originalWidth, rows: originalHeight } = originalImage;\n\nconst image = new cv.Mat();\ncv.cvtColor(originalImage, image, cv.COLOR_RGBA2RGB);\ncv.cvtColor(image, image, cv.COLOR_BGR2RGB);\ncv.resize(image, image, new cv.Size(width, height), cv.INTER_AREA);\n\n// NHWC to NCHW\nconst inputImage = transform(image.data, { width, height }, [0, 1, 2]);"
+            ],
+            "outputs": []
+        },
+        {
+            "language": "markdown",
+            "source": [
+                "## Define postprocessing functions"
+            ],
+            "outputs": []
+        },
+        {
+            "language": "typescript",
+            "source": [
+                "\n// Utility function to get the coordinates of the maximum value in a tensor\nfunction getCoords(tensor) {\n  const { values, indices } = tf.topk(tensor.flatten(), 1);\n  const [maxVal] = values.dataSync();\n  const [maxIndex] = indices.dataSync();\n  const x = maxIndex % tensor.shape[1];\n  const y = Math.floor(maxIndex / tensor.shape[1]);\n\n  return { x, y, confidence: maxVal };\n}\n\n// Draw keypoints on the input image\nfunction drawKeypoints(image, keypoints) {\n  keypoints.forEach(keypoint => {\n    if (keypoint.confidence > THRESHOLD) {\n      cv.circle(image,\n        new cv.Point(keypoint.x, keypoint.y),\n        2,\n        COLOR,\n        3,\n      );\n    }\n  });\n}\n\n// Draw skeleton (lines between keypoints) on the input image\nfunction drawSkeleton(image, keypoints) {\n  const skeleton = [\n    [1, 2], [1, 5], [2, 3], [3, 4], [5, 6], [6, 7], [1, 8], [8, 9], [9, 10],\n    [1, 11], [11, 12], [12, 13], [1, 0], [0, 14], [14, 16], [0, 15], [15, 17],\n  ];\n\n  skeleton.forEach(([start, end]) => {\n    const startPoint = keypoints[start];\n    const endPoint = keypoints[end];\n\n    if (startPoint.confidence > THRESHOLD\n      && endPoint.confidence > THRESHOLD) {\n      cv.line(image,\n        new cv.Point(startPoint.x, startPoint.y),\n        new cv.Point(endPoint.x, endPoint.y),\n        COLOR,\n        2,\n      );\n    }\n  });\n}\n\nfunction toTFTensor(ovTensor) {\n  return tf.tensor(ovTensor.data, ovTensor.getShape());\n}"
+            ],
+            "outputs": []
+        },
+        {
+            "language": "markdown",
+            "source": [
+                "## Do Inference"
+            ],
+            "outputs": []
+        },
+        {
+            "language": "typescript",
+            "source": [
+                "const tensorData = new Float32Array(inputImage);\nconst tensor = new ov.Tensor(ov.element.f32, inputLayer.shape, tensorData);\n\nconst inferRequest = compiledModel.createInferRequest();\ninferRequest.setInputTensor(tensor);\nconst outputs = inferRequest.infer();\n\nconst heatmaps = outputs[heatmapsOutputKey];"
+            ],
+            "outputs": []
+        },
+        {
+            "language": "markdown",
+            "source": [
+                "## Process result"
+            ],
+            "outputs": []
+        },
+        {
+            "language": "typescript",
+            "source": [
+                "try {\n  const outputHeatmapShape = heatmaps.getShape();\n\n  // Define constants\n  const numKeypoints = 18;\n  const heatmapWidth = outputHeatmapShape[3];\n  const heatmapHeight = outputHeatmapShape[2];\n\n  // Extract keypoints from heatmaps\n  let keypoints = [];\n  const heatmapsTFTensors = toTFTensor(heatmaps);\n  const xCoef = originalWidth / heatmapWidth;\n  const yCoef = originalHeight / heatmapHeight;\n\n  for (let i = 0; i < numKeypoints; i++) {\n    const heatmap = heatmapsTFTensors.slice(\n      [0, i, 0, 0],\n      [1, 1, heatmapHeight, heatmapWidth]).squeeze();\n\n    const { x, y, confidence } = getCoords(heatmap);\n\n    keypoints.push({\n      x: x * xCoef,\n      y: y * yCoef,\n      confidence,\n    });\n  }\n\n  drawKeypoints(originalImage, keypoints);\n  drawSkeleton(originalImage, keypoints);\n\n  displayArrayAsImage(originalImage.data, originalWidth, originalHeight, display);\n} catch(e) {\n  console.log(e);\n}"
+            ],
+            "outputs": [
+                {
+                    "items": [
+                        {
+                            "mime": "image/jpeg",
+                            "value": 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"
+                        }
+                    ]
+                }
+            ]
+        }
+    ]
+}
\ No newline at end of file
diff --git a/samples/js/node/notebooks/213-question-answering.nnb b/samples/js/node/notebooks/question-answering.nnb
similarity index 100%
rename from samples/js/node/notebooks/213-question-answering.nnb
rename to samples/js/node/notebooks/question-answering.nnb
diff --git a/samples/js/node/package-lock.json b/samples/js/node/package-lock.json
index a3a71bd28141ba..df77a1d2fe54a7 100644
--- a/samples/js/node/package-lock.json
+++ b/samples/js/node/package-lock.json
@@ -9,6 +9,7 @@
       "version": "1.0.0",
       "license": "Apache-2.0",
       "devDependencies": {
+        "@tensorflow/tfjs-node": "^4.19.0",
         "args": "^5.0.3",
         "canvas": "^2.11.2",
         "eslint": "^8.39.0",
@@ -22,7 +23,7 @@
     },
     "../../../src/bindings/js/node": {
       "name": "openvino-node",
-      "version": "2024.1.0",
+      "version": "2024.2.0",
       "dev": true,
       "hasInstallScript": true,
       "license": "Apache-2.0",
@@ -71,7 +72,7 @@
       }
     },
     "../../../src/bindings/js/node/node_modules/@eslint-community/regexpp": {
-      "version": "4.8.0",
+      "version": "4.8.1",
       "dev": true,
       "license": "MIT",
       "engines": {
@@ -101,7 +102,7 @@
       }
     },
     "../../../src/bindings/js/node/node_modules/@eslint/js": {
-      "version": "8.48.0",
+      "version": "8.49.0",
       "dev": true,
       "license": "MIT",
       "engines": {
@@ -109,7 +110,7 @@
       }
     },
     "../../../src/bindings/js/node/node_modules/@humanwhocodes/config-array": {
-      "version": "0.11.10",
+      "version": "0.11.11",
       "dev": true,
       "license": "Apache-2.0",
       "dependencies": {
@@ -181,36 +182,37 @@
       "license": "MIT"
     },
     "../../../src/bindings/js/node/node_modules/@types/semver": {
-      "version": "7.5.1",
+      "version": "7.5.2",
       "dev": true,
       "license": "MIT"
     },
     "../../../src/bindings/js/node/node_modules/@typescript-eslint/eslint-plugin": {
-      "version": "5.62.0",
+      "version": "6.7.0",
       "dev": true,
       "license": "MIT",
       "dependencies": {
-        "@eslint-community/regexpp": "^4.4.0",
-        "@typescript-eslint/scope-manager": "5.62.0",
-        "@typescript-eslint/type-utils": "5.62.0",
-        "@typescript-eslint/utils": "5.62.0",
+        "@eslint-community/regexpp": "^4.5.1",
+        "@typescript-eslint/scope-manager": "6.7.0",
+        "@typescript-eslint/type-utils": "6.7.0",
+        "@typescript-eslint/utils": "6.7.0",
+        "@typescript-eslint/visitor-keys": "6.7.0",
         "debug": "^4.3.4",
         "graphemer": "^1.4.0",
-        "ignore": "^5.2.0",
-        "natural-compare-lite": "^1.4.0",
-        "semver": "^7.3.7",
-        "tsutils": "^3.21.0"
+        "ignore": "^5.2.4",
+        "natural-compare": "^1.4.0",
+        "semver": "^7.5.4",
+        "ts-api-utils": "^1.0.1"
       },
       "engines": {
-        "node": "^12.22.0 || ^14.17.0 || >=16.0.0"
+        "node": "^16.0.0 || >=18.0.0"
       },
       "funding": {
         "type": "opencollective",
         "url": "https://opencollective.com/typescript-eslint"
       },
       "peerDependencies": {
-        "@typescript-eslint/parser": "^5.0.0",
-        "eslint": "^6.0.0 || ^7.0.0 || ^8.0.0"
+        "@typescript-eslint/parser": "^6.0.0 || ^6.0.0-alpha",
+        "eslint": "^7.0.0 || ^8.0.0"
       },
       "peerDependenciesMeta": {
         "typescript": {
@@ -219,24 +221,25 @@
       }
     },
     "../../../src/bindings/js/node/node_modules/@typescript-eslint/parser": {
-      "version": "5.62.0",
+      "version": "6.7.0",
       "dev": true,
       "license": "BSD-2-Clause",
       "dependencies": {
-        "@typescript-eslint/scope-manager": "5.62.0",
-        "@typescript-eslint/types": "5.62.0",
-        "@typescript-eslint/typescript-estree": "5.62.0",
+        "@typescript-eslint/scope-manager": "6.7.0",
+        "@typescript-eslint/types": "6.7.0",
+        "@typescript-eslint/typescript-estree": "6.7.0",
+        "@typescript-eslint/visitor-keys": "6.7.0",
         "debug": "^4.3.4"
       },
       "engines": {
-        "node": "^12.22.0 || ^14.17.0 || >=16.0.0"
+        "node": "^16.0.0 || >=18.0.0"
       },
       "funding": {
         "type": "opencollective",
         "url": "https://opencollective.com/typescript-eslint"
       },
       "peerDependencies": {
-        "eslint": "^6.0.0 || ^7.0.0 || ^8.0.0"
+        "eslint": "^7.0.0 || ^8.0.0"
       },
       "peerDependenciesMeta": {
         "typescript": {
@@ -245,15 +248,15 @@
       }
     },
     "../../../src/bindings/js/node/node_modules/@typescript-eslint/scope-manager": {
-      "version": "5.62.0",
+      "version": "6.7.0",
       "dev": true,
       "license": "MIT",
       "dependencies": {
-        "@typescript-eslint/types": "5.62.0",
-        "@typescript-eslint/visitor-keys": "5.62.0"
+        "@typescript-eslint/types": "6.7.0",
+        "@typescript-eslint/visitor-keys": "6.7.0"
       },
       "engines": {
-        "node": "^12.22.0 || ^14.17.0 || >=16.0.0"
+        "node": "^16.0.0 || >=18.0.0"
       },
       "funding": {
         "type": "opencollective",
@@ -261,24 +264,24 @@
       }
     },
     "../../../src/bindings/js/node/node_modules/@typescript-eslint/type-utils": {
-      "version": "5.62.0",
+      "version": "6.7.0",
       "dev": true,
       "license": "MIT",
       "dependencies": {
-        "@typescript-eslint/typescript-estree": "5.62.0",
-        "@typescript-eslint/utils": "5.62.0",
+        "@typescript-eslint/typescript-estree": "6.7.0",
+        "@typescript-eslint/utils": "6.7.0",
         "debug": "^4.3.4",
-        "tsutils": "^3.21.0"
+        "ts-api-utils": "^1.0.1"
       },
       "engines": {
-        "node": "^12.22.0 || ^14.17.0 || >=16.0.0"
+        "node": "^16.0.0 || >=18.0.0"
       },
       "funding": {
         "type": "opencollective",
         "url": "https://opencollective.com/typescript-eslint"
       },
       "peerDependencies": {
-        "eslint": "*"
+        "eslint": "^7.0.0 || ^8.0.0"
       },
       "peerDependenciesMeta": {
         "typescript": {
@@ -287,11 +290,11 @@
       }
     },
     "../../../src/bindings/js/node/node_modules/@typescript-eslint/types": {
-      "version": "5.62.0",
+      "version": "6.7.0",
       "dev": true,
       "license": "MIT",
       "engines": {
-        "node": "^12.22.0 || ^14.17.0 || >=16.0.0"
+        "node": "^16.0.0 || >=18.0.0"
       },
       "funding": {
         "type": "opencollective",
@@ -299,20 +302,20 @@
       }
     },
     "../../../src/bindings/js/node/node_modules/@typescript-eslint/typescript-estree": {
-      "version": "5.62.0",
+      "version": "6.7.0",
       "dev": true,
       "license": "BSD-2-Clause",
       "dependencies": {
-        "@typescript-eslint/types": "5.62.0",
-        "@typescript-eslint/visitor-keys": "5.62.0",
+        "@typescript-eslint/types": "6.7.0",
+        "@typescript-eslint/visitor-keys": "6.7.0",
         "debug": "^4.3.4",
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@@ -434,6 +447,11 @@
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@@ -460,6 +477,19 @@
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@@ -560,6 +595,25 @@
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@@ -1013,11 +1045,18 @@
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@@ -1030,6 +1069,11 @@
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@@ -1241,6 +1290,16 @@
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@@ -1260,6 +1319,39 @@
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@@ -1287,6 +1379,11 @@
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@@ -1295,6 +1392,20 @@
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@@ -1400,6 +1516,28 @@
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@@ -1433,16 +1571,44 @@
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-        "flatted": "^3.1.0",
+        "flatted": "^3.2.9",
+        "keyv": "^4.5.3",
         "rimraf": "^3.0.2"
       },
       "engines": {
@@ -2266,10 +2918,24 @@
       }
     },
     "node_modules/flatted": {
-      "version": "3.2.7",
+      "version": "3.3.1",
       "dev": true,
       "license": "ISC"
     },
+    "node_modules/form-data": {
+      "version": "4.0.0",
+      "resolved": "https://registry.npmjs.org/form-data/-/form-data-4.0.0.tgz",
+      "integrity": "sha512-ETEklSGi5t0QMZuiXoA/Q6vcnxcLQP5vdugSpuAyi6SVGi2clPPp+xgEhuMaHC+zGgn31Kd235W35f7Hykkaww==",
+      "dev": true,
+      "dependencies": {
+        "asynckit": "^0.4.0",
+        "combined-stream": "^1.0.8",
+        "mime-types": "^2.1.12"
+      },
+      "engines": {
+        "node": ">= 6"
+      }
+    },
     "node_modules/fs-minipass": {
       "version": "2.1.0",
       "dev": true,
@@ -2316,6 +2982,15 @@
         "node": ">=10"
       }
     },
+    "node_modules/get-caller-file": {
+      "version": "2.0.5",
+      "resolved": "https://registry.npmjs.org/get-caller-file/-/get-caller-file-2.0.5.tgz",
+      "integrity": "sha512-DyFP3BM/3YHTQOCUL/w0OZHR0lpKeGrxotcHWcqNEdnltqFwXVfhEBQ94eIo34AfQpo0rGki4cyIiftY06h2Fg==",
+      "dev": true,
+      "engines": {
+        "node": "6.* || 8.* || >= 10.*"
+      }
+    },
     "node_modules/glob": {
       "version": "7.2.3",
       "dev": true,
@@ -2347,7 +3022,7 @@
       }
     },
     "node_modules/globals": {
-      "version": "13.20.0",
+      "version": "13.24.0",
       "dev": true,
       "license": "MIT",
       "dependencies": {
@@ -2360,17 +3035,23 @@
         "url": "https://github.com/sponsors/sindresorhus"
       }
     },
-    "node_modules/grapheme-splitter": {
-      "version": "1.0.4",
+    "node_modules/google-protobuf": {
+      "version": "3.21.2",
+      "resolved": "https://registry.npmjs.org/google-protobuf/-/google-protobuf-3.21.2.tgz",
+      "integrity": "sha512-3MSOYFO5U9mPGikIYCzK0SaThypfGgS6bHqrUGXG3DPHCrb+txNqeEcns1W0lkGfk0rCyNXm7xB9rMxnCiZOoA==",
+      "dev": true
+    },
+    "node_modules/graphemer": {
+      "version": "1.4.0",
       "dev": true,
       "license": "MIT"
     },
     "node_modules/has-flag": {
-      "version": "4.0.0",
+      "version": "3.0.0",
       "dev": true,
       "license": "MIT",
       "engines": {
-        "node": ">=8"
+        "node": ">=4"
       }
     },
     "node_modules/has-unicode": {
@@ -2379,7 +3060,7 @@
       "license": "ISC"
     },
     "node_modules/https-proxy-agent": {
-      "version": "7.0.2",
+      "version": "7.0.4",
       "dev": true,
       "license": "MIT",
       "dependencies": {
@@ -2391,7 +3072,7 @@
       }
     },
     "node_modules/ignore": {
-      "version": "5.2.4",
+      "version": "5.3.1",
       "dev": true,
       "license": "MIT",
       "engines": {
@@ -2475,15 +3156,6 @@
       "dev": true,
       "license": "ISC"
     },
-    "node_modules/js-sdsl": {
-      "version": "4.4.0",
-      "dev": true,
-      "license": "MIT",
-      "funding": {
-        "type": "opencollective",
-        "url": "https://opencollective.com/js-sdsl"
-      }
-    },
     "node_modules/js-yaml": {
       "version": "4.1.0",
       "dev": true,
@@ -2495,6 +3167,11 @@
         "js-yaml": "bin/js-yaml.js"
       }
     },
+    "node_modules/json-buffer": {
+      "version": "3.0.1",
+      "dev": true,
+      "license": "MIT"
+    },
     "node_modules/json-schema-traverse": {
       "version": "0.4.1",
       "dev": true,
@@ -2505,6 +3182,14 @@
       "dev": true,
       "license": "MIT"
     },
+    "node_modules/keyv": {
+      "version": "4.5.4",
+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "json-buffer": "3.0.1"
+      }
+    },
     "node_modules/leven": {
       "version": "2.1.0",
       "dev": true,
@@ -2544,16 +3229,11 @@
       "dev": true,
       "license": "MIT"
     },
-    "node_modules/lru-cache": {
-      "version": "6.0.0",
-      "dev": true,
-      "license": "ISC",
-      "dependencies": {
-        "yallist": "^4.0.0"
-      },
-      "engines": {
-        "node": ">=10"
-      }
+    "node_modules/long": {
+      "version": "4.0.0",
+      "resolved": "https://registry.npmjs.org/long/-/long-4.0.0.tgz",
+      "integrity": "sha512-XsP+KhQif4bjX1kbuSiySJFNAehNxgLb6hPRGJ9QsUr8ajHkuXGdrHmFUTUUXhDwVX2R5bY4JNZEwbUiMhV+MA==",
+      "dev": true
     },
     "node_modules/make-dir": {
       "version": "3.1.0",
@@ -2577,6 +3257,27 @@
         "semver": "bin/semver.js"
       }
     },
+    "node_modules/mime-db": {
+      "version": "1.52.0",
+      "resolved": "https://registry.npmjs.org/mime-db/-/mime-db-1.52.0.tgz",
+      "integrity": "sha512-sPU4uV7dYlvtWJxwwxHD0PuihVNiE7TyAbQ5SWxDCB9mUYvOgroQOwYQQOKPJ8CIbE+1ETVlOoK1UC2nU3gYvg==",
+      "dev": true,
+      "engines": {
+        "node": ">= 0.6"
+      }
+    },
+    "node_modules/mime-types": {
+      "version": "2.1.35",
+      "resolved": "https://registry.npmjs.org/mime-types/-/mime-types-2.1.35.tgz",
+      "integrity": "sha512-ZDY+bPm5zTTF+YpCrAU9nK0UgICYPT0QtT1NZWFv4s++TNkcgVaT0g6+4R2uI4MjQjzysHB1zxuWL50hzaeXiw==",
+      "dev": true,
+      "dependencies": {
+        "mime-db": "1.52.0"
+      },
+      "engines": {
+        "node": ">= 0.6"
+      }
+    },
     "node_modules/mimic-response": {
       "version": "2.1.0",
       "dev": true,
@@ -2655,7 +3356,7 @@
       "license": "MIT"
     },
     "node_modules/nan": {
-      "version": "2.17.0",
+      "version": "2.20.0",
       "dev": true,
       "license": "MIT"
     },
@@ -2665,7 +3366,7 @@
       "license": "MIT"
     },
     "node_modules/node-fetch": {
-      "version": "2.6.11",
+      "version": "2.7.0",
       "dev": true,
       "license": "MIT",
       "dependencies": {
@@ -2726,25 +3427,24 @@
     },
     "node_modules/opencv-wasm": {
       "version": "4.3.0-10",
-      "resolved": "https://registry.npmjs.org/opencv-wasm/-/opencv-wasm-4.3.0-10.tgz",
-      "integrity": "sha512-EWmWLUzp2suoc6N44Y4ouWT85QwvShx23Q430R+lp6NyS828bjQn6mCgA3NJ6Z/S59aaTeeu+RhqPQIJIYld1w==",
-      "dev": true
+      "dev": true,
+      "license": "BSD-3-Clause"
     },
     "node_modules/openvino-node": {
       "resolved": "../../../src/bindings/js/node",
       "link": true
     },
     "node_modules/optionator": {
-      "version": "0.9.3",
+      "version": "0.9.4",
       "dev": true,
       "license": "MIT",
       "dependencies": {
-        "@aashutoshrathi/word-wrap": "^1.2.3",
         "deep-is": "^0.1.3",
         "fast-levenshtein": "^2.0.6",
         "levn": "^0.4.1",
         "prelude-ls": "^1.2.1",
-        "type-check": "^0.4.0"
+        "type-check": "^0.4.0",
+        "word-wrap": "^1.2.5"
       },
       "engines": {
         "node": ">= 0.8.0"
@@ -2821,8 +3521,17 @@
         "node": ">= 0.8.0"
       }
     },
+    "node_modules/progress": {
+      "version": "2.0.3",
+      "resolved": "https://registry.npmjs.org/progress/-/progress-2.0.3.tgz",
+      "integrity": "sha512-7PiHtLll5LdnKIMw100I+8xJXR5gW2QwWYkT6iJva0bXitZKa/XMrSbdmg3r2Xnaidz9Qumd0VPaMrZlF9V9sA==",
+      "dev": true,
+      "engines": {
+        "node": ">=0.4.0"
+      }
+    },
     "node_modules/punycode": {
-      "version": "2.3.0",
+      "version": "2.3.1",
       "dev": true,
       "license": "MIT",
       "engines": {
@@ -2861,6 +3570,21 @@
         "node": ">= 6"
       }
     },
+    "node_modules/regenerator-runtime": {
+      "version": "0.13.11",
+      "resolved": "https://registry.npmjs.org/regenerator-runtime/-/regenerator-runtime-0.13.11.tgz",
+      "integrity": "sha512-kY1AZVr2Ra+t+piVaJ4gxaFaReZVH40AKNo7UCX6W+dEwBo/2oZJzqfuN1qLq1oL45o56cPaTXELwrTh8Fpggg==",
+      "dev": true
+    },
+    "node_modules/require-directory": {
+      "version": "2.1.1",
+      "resolved": "https://registry.npmjs.org/require-directory/-/require-directory-2.1.1.tgz",
+      "integrity": "sha512-fGxEI7+wsG9xrvdjsrlmL22OMTTiHRwAMroiEeMgq8gzoLC/PQr7RsRDSTLUg/bZAZtF+TVIkHc6/4RIKrui+Q==",
+      "dev": true,
+      "engines": {
+        "node": ">=0.10.0"
+      }
+    },
     "node_modules/resolve-from": {
       "version": "4.0.0",
       "dev": true,
@@ -2933,13 +3657,16 @@
       ],
       "license": "MIT"
     },
+    "node_modules/seedrandom": {
+      "version": "3.0.5",
+      "resolved": "https://registry.npmjs.org/seedrandom/-/seedrandom-3.0.5.tgz",
+      "integrity": "sha512-8OwmbklUNzwezjGInmZ+2clQmExQPvomqjL7LFqOYqtmuxRgQYqOD3mHaU+MvZn5FLUeVxVfQjwLZW/n/JFuqg==",
+      "dev": true
+    },
     "node_modules/semver": {
-      "version": "7.5.4",
+      "version": "7.6.2",
       "dev": true,
       "license": "ISC",
-      "dependencies": {
-        "lru-cache": "^6.0.0"
-      },
       "bin": {
         "semver": "bin/semver.js"
       },
@@ -3005,6 +3732,12 @@
         "simple-concat": "^1.0.0"
       }
     },
+    "node_modules/sprintf-js": {
+      "version": "1.0.3",
+      "resolved": "https://registry.npmjs.org/sprintf-js/-/sprintf-js-1.0.3.tgz",
+      "integrity": "sha512-D9cPgkvLlV3t3IzL0D0YLvGA9Ahk4PcvVwUbN0dSGr1aP0Nrt4AEnTUbuGvquEC0mA64Gqt1fzirlRs5ibXx8g==",
+      "dev": true
+    },
     "node_modules/string_decoder": {
       "version": "1.3.0",
       "dev": true,
@@ -3049,21 +3782,20 @@
       }
     },
     "node_modules/supports-color": {
-      "version": "7.2.0",
+      "version": "5.5.0",
       "dev": true,
       "license": "MIT",
       "dependencies": {
-        "has-flag": "^4.0.0"
+        "has-flag": "^3.0.0"
       },
       "engines": {
-        "node": ">=8"
+        "node": ">=4"
       }
     },
     "node_modules/tar": {
       "version": "6.2.1",
-      "resolved": "https://registry.npmjs.org/tar/-/tar-6.2.1.tgz",
-      "integrity": "sha512-DZ4yORTwrbTj/7MZYq2w+/ZFdI6OZ/f9SFHR+71gIVUZhOQPHzVCLpvRnPgyaMpfWxxk/4ONva3GQSyNIKRv6A==",
       "dev": true,
+      "license": "ISC",
       "dependencies": {
         "chownr": "^2.0.0",
         "fs-minipass": "^2.0.0",
@@ -3108,6 +3840,12 @@
         "url": "https://github.com/sponsors/sindresorhus"
       }
     },
+    "node_modules/undici-types": {
+      "version": "5.26.5",
+      "resolved": "https://registry.npmjs.org/undici-types/-/undici-types-5.26.5.tgz",
+      "integrity": "sha512-JlCMO+ehdEIKqlFxk6IfVoAUVmgz7cU7zD/h9XZ0qzeosSHmUJVOzSQvvYSYWXkFXC+IfLKSIffhv0sVZup6pA==",
+      "dev": true
+    },
     "node_modules/uri-js": {
       "version": "4.4.1",
       "dev": true,
@@ -3157,16 +3895,110 @@
         "string-width": "^1.0.2 || 2 || 3 || 4"
       }
     },
+    "node_modules/word-wrap": {
+      "version": "1.2.5",
+      "dev": true,
+      "license": "MIT",
+      "engines": {
+        "node": ">=0.10.0"
+      }
+    },
+    "node_modules/wrap-ansi": {
+      "version": "7.0.0",
+      "resolved": "https://registry.npmjs.org/wrap-ansi/-/wrap-ansi-7.0.0.tgz",
+      "integrity": "sha512-YVGIj2kamLSTxw6NsZjoBxfSwsn0ycdesmc4p+Q21c5zPuZ1pl+NfxVdxPtdHvmNVOQ6XSYG4AUtyt/Fi7D16Q==",
+      "dev": true,
+      "dependencies": {
+        "ansi-styles": "^4.0.0",
+        "string-width": "^4.1.0",
+        "strip-ansi": "^6.0.0"
+      },
+      "engines": {
+        "node": ">=10"
+      },
+      "funding": {
+        "url": "https://github.com/chalk/wrap-ansi?sponsor=1"
+      }
+    },
+    "node_modules/wrap-ansi/node_modules/ansi-styles": {
+      "version": "4.3.0",
+      "resolved": "https://registry.npmjs.org/ansi-styles/-/ansi-styles-4.3.0.tgz",
+      "integrity": "sha512-zbB9rCJAT1rbjiVDb2hqKFHNYLxgtk8NURxZ3IZwD3F6NtxbXZQCnnSi1Lkx+IDohdPlFp222wVALIheZJQSEg==",
+      "dev": true,
+      "dependencies": {
+        "color-convert": "^2.0.1"
+      },
+      "engines": {
+        "node": ">=8"
+      },
+      "funding": {
+        "url": "https://github.com/chalk/ansi-styles?sponsor=1"
+      }
+    },
+    "node_modules/wrap-ansi/node_modules/color-convert": {
+      "version": "2.0.1",
+      "resolved": "https://registry.npmjs.org/color-convert/-/color-convert-2.0.1.tgz",
+      "integrity": "sha512-RRECPsj7iu/xb5oKYcsFHSppFNnsj/52OVTRKb4zP5onXwVF3zVmmToNcOfGC+CRDpfK/U584fMg38ZHCaElKQ==",
+      "dev": true,
+      "dependencies": {
+        "color-name": "~1.1.4"
+      },
+      "engines": {
+        "node": ">=7.0.0"
+      }
+    },
+    "node_modules/wrap-ansi/node_modules/color-name": {
+      "version": "1.1.4",
+      "resolved": "https://registry.npmjs.org/color-name/-/color-name-1.1.4.tgz",
+      "integrity": "sha512-dOy+3AuW3a2wNbZHIuMZpTcgjGuLU/uBL/ubcZF9OXbDo8ff4O8yVp5Bf0efS8uEoYo5q4Fx7dY9OgQGXgAsQA==",
+      "dev": true
+    },
     "node_modules/wrappy": {
       "version": "1.0.2",
       "dev": true,
       "license": "ISC"
     },
+    "node_modules/y18n": {
+      "version": "5.0.8",
+      "resolved": "https://registry.npmjs.org/y18n/-/y18n-5.0.8.tgz",
+      "integrity": "sha512-0pfFzegeDWJHJIAmTLRP2DwHjdF5s7jo9tuztdQxAhINCdvS+3nGINqPd00AphqJR/0LhANUS6/+7SCb98YOfA==",
+      "dev": true,
+      "engines": {
+        "node": ">=10"
+      }
+    },
     "node_modules/yallist": {
       "version": "4.0.0",
       "dev": true,
       "license": "ISC"
     },
+    "node_modules/yargs": {
+      "version": "16.2.0",
+      "resolved": "https://registry.npmjs.org/yargs/-/yargs-16.2.0.tgz",
+      "integrity": "sha512-D1mvvtDG0L5ft/jGWkLpG1+m0eQxOfaBvTNELraWj22wSVUMWxZUvYgJYcKh6jGGIkJFhH4IZPQhR4TKpc8mBw==",
+      "dev": true,
+      "dependencies": {
+        "cliui": "^7.0.2",
+        "escalade": "^3.1.1",
+        "get-caller-file": "^2.0.5",
+        "require-directory": "^2.1.1",
+        "string-width": "^4.2.0",
+        "y18n": "^5.0.5",
+        "yargs-parser": "^20.2.2"
+      },
+      "engines": {
+        "node": ">=10"
+      }
+    },
+    "node_modules/yargs-parser": {
+      "version": "20.2.9",
+      "resolved": "https://registry.npmjs.org/yargs-parser/-/yargs-parser-20.2.9.tgz",
+      "integrity": "sha512-y11nGElTIV+CT3Zv9t7VKl+Q3hTQoT9a1Qzezhhl6Rp21gJ/IVTW7Z3y9EWXhuUBC2Shnf+DX0antecpAwSP8w==",
+      "dev": true,
+      "engines": {
+        "node": ">=10"
+      }
+    },
     "node_modules/yocto-queue": {
       "version": "0.1.0",
       "dev": true,
diff --git a/samples/js/node/package.json b/samples/js/node/package.json
index 719c9f1e22ae89..07e8f48143242a 100644
--- a/samples/js/node/package.json
+++ b/samples/js/node/package.json
@@ -8,6 +8,7 @@
     "eslint": "^8.39.0",
     "https-proxy-agent": "^7.0.2",
     "opencv-wasm": "^4.3.0-10",
+    "@tensorflow/tfjs-node": "^4.19.0",
     "openvino-node": "file:../../../src/bindings/js/node/"
   },
   "scripts": {
diff --git a/scripts/setupvars/setupvars.bat b/scripts/setupvars/setupvars.bat
index 1e401696382c42..5b436551ee0e3b 100644
--- a/scripts/setupvars/setupvars.bat
+++ b/scripts/setupvars/setupvars.bat
@@ -36,7 +36,7 @@ if exist "%INTEL_OPENVINO_DIR%\extras\opencv\setupvars.bat" (
 set "OpenVINO_DIR=%INTEL_OPENVINO_DIR%\runtime\cmake"
 if exist "%OpenVINO_DIR%\OpenVINOGenAIConfig.cmake" (
    :: If GenAI is installed, export it as well.
-   set OpenVINOGenAI_DIR=%OpenVINO_DIR%
+   set "OpenVINOGenAI_DIR=%OpenVINO_DIR%"
 )
 set "OPENVINO_LIB_PATHS=%INTEL_OPENVINO_DIR%\runtime\bin\intel64\Release;%INTEL_OPENVINO_DIR%\runtime\bin\intel64\Debug;%OPENVINO_LIB_PATHS%"
 
diff --git a/src/bindings/c/docs/api_overview.md b/src/bindings/c/docs/api_overview.md
index 6649582d9ebc78..a4f113ea00715b 100644
--- a/src/bindings/c/docs/api_overview.md
+++ b/src/bindings/c/docs/api_overview.md
@@ -207,6 +207,8 @@ typedef enum {
 
     F4E2M1,          //!< f4e2m1 element type
 
+    F8E8M0,          //!< f8e8m0 element type
+
 } ov_element_type_e;
 ```
 
diff --git a/src/bindings/c/include/openvino/c/ov_common.h b/src/bindings/c/include/openvino/c/ov_common.h
index 8c42e35247b62b..321dca8eb2023d 100644
--- a/src/bindings/c/include/openvino/c/ov_common.h
+++ b/src/bindings/c/include/openvino/c/ov_common.h
@@ -195,6 +195,7 @@ typedef enum {
     F8E5M3,          //!< f8e5m2 element type
     STRING,          //!< string element type
     F4E2M1,          //!< f4e2m1 element type
+    F8E8M0,          //!< f8e8m0 element type
 } ov_element_type_e;
 
 /**
diff --git a/src/bindings/c/src/CMakeLists.txt b/src/bindings/c/src/CMakeLists.txt
index 7e966f0e697db8..106106ed7eaced 100644
--- a/src/bindings/c/src/CMakeLists.txt
+++ b/src/bindings/c/src/CMakeLists.txt
@@ -50,11 +50,17 @@ export(TARGETS ${TARGET_NAME} NAMESPACE openvino::
 ov_cpack_add_component(${OV_CPACK_COMP_CORE_C} HIDDEN)
 ov_cpack_add_component(${OV_CPACK_COMP_CORE_C_DEV} HIDDEN)
 
+if(BUILD_SHARED_LIBS)
+    set(archive_comp CORE_C_DEV)
+else()
+    set(archive_comp CORE_C)
+endif()
+
 install(TARGETS ${TARGET_NAME} EXPORT OpenVINOTargets
         RUNTIME DESTINATION ${OV_CPACK_RUNTIMEDIR} COMPONENT ${OV_CPACK_COMP_CORE_C} ${OV_CPACK_COMP_CORE_C_EXCLUDE_ALL}
-        ARCHIVE DESTINATION ${OV_CPACK_ARCHIVEDIR} COMPONENT ${OV_CPACK_COMP_CORE_C} ${OV_CPACK_COMP_CORE_C_EXCLUDE_ALL}
+        ARCHIVE DESTINATION ${OV_CPACK_ARCHIVEDIR} COMPONENT ${OV_CPACK_COMP_${archive_comp}} ${OV_CPACK_COMP_${archive_comp}_EXCLUDE_ALL}
         LIBRARY DESTINATION ${OV_CPACK_LIBRARYDIR} COMPONENT ${OV_CPACK_COMP_CORE_C} ${OV_CPACK_COMP_CORE_C_EXCLUDE_ALL}
-        NAMELINK_COMPONENT ${OV_CPACK_COMP_CORE_C_DEV}
+        NAMELINK_COMPONENT ${OV_CPACK_COMP_LINKS} ${OV_CPACK_COMP_LINKS_EXCLUDE_ALL}
         INCLUDES DESTINATION ${OV_CPACK_INCLUDEDIR})
 
 install(DIRECTORY ${OpenVINO_C_API_SOURCE_DIR}/include/openvino/
diff --git a/src/bindings/c/src/ov_tensor.cpp b/src/bindings/c/src/ov_tensor.cpp
index 729f911cdbb74b..547b29ac3a6bf3 100644
--- a/src/bindings/c/src/ov_tensor.cpp
+++ b/src/bindings/c/src/ov_tensor.cpp
@@ -31,7 +31,9 @@ const std::map<ov_element_type_e, ov::element::Type> element_type_map = {
     {ov_element_type_e::F8E4M3, ov::element::f8e4m3},
     {ov_element_type_e::F8E5M3, ov::element::f8e5m2},
     {ov_element_type_e::STRING, ov::element::string},
-    {ov_element_type_e::F4E2M1, ov::element::f4e2m1}};
+    {ov_element_type_e::F4E2M1, ov::element::f4e2m1},
+    {ov_element_type_e::F8E8M0, ov::element::f8e8m0},
+};
 
 ov_element_type_e find_ov_element_type_e(ov::element::Type type) {
     for (auto iter = element_type_map.begin(); iter != element_type_map.end(); iter++) {
diff --git a/src/bindings/js/node/CMakeLists.txt b/src/bindings/js/node/CMakeLists.txt
index 0741e57901d209..e4730ad5920a19 100644
--- a/src/bindings/js/node/CMakeLists.txt
+++ b/src/bindings/js/node/CMakeLists.txt
@@ -13,6 +13,7 @@ if(CMAKE_COMPILER_IS_GNUCXX AND LINUX AND CMAKE_CXX_COMPILER_VERSION VERSION_LES
 endif()
 
 if(WIN32)
+    set(CMAKE_SHARED_LINKER_FLAGS /DELAYLOAD:NODE.EXE)
     set(CMAKE_JS_LIB ${CMAKE_CURRENT_SOURCE_DIR}/thirdparty/node.lib)
     set(CMAKE_JS_SRC ${CMAKE_CURRENT_SOURCE_DIR}/thirdparty/win_delay_load_hook.cc)
 
@@ -77,7 +78,7 @@ target_include_directories(${PROJECT_NAME} PRIVATE
     "${CMAKE_CURRENT_SOURCE_DIR}/.."
 )
 
-target_link_libraries(${PROJECT_NAME} PRIVATE openvino::runtime ${DELAYIMP_LIB} ${CMAKE_JS_LIB})
+target_link_libraries(${PROJECT_NAME} PRIVATE openvino::runtime openvino::util ${DELAYIMP_LIB} ${CMAKE_JS_LIB})
 
 if(MSVC AND CMAKE_JS_NODELIB_DEF AND CMAKE_JS_NODELIB_TARGET)
     # Generate node.lib
diff --git a/src/bindings/js/node/README.md b/src/bindings/js/node/README.md
index 426bda134779f6..577b4a5cf861eb 100644
--- a/src/bindings/js/node/README.md
+++ b/src/bindings/js/node/README.md
@@ -23,6 +23,13 @@ For more details, refer to the [OpenVINO™ JavaScript API Developer Documentati
 - [OpenVINO™ Node.js API](https://docs.openvino.ai/2024/api/nodejs_api/nodejs_api.html)
 - [OpenVINO™ Node.js Bindings Examples of Usage](https://github.com/openvinotoolkit/openvino/blob/master/samples/js/node/README.md)
 
+## Live Sample
+
+You can run this sample in the browser; no installation is required.
+[Codesandbox](https://codesandbox.io/) is a free online service with limited resources. For optimal performance and more control,  it is recommended to run the sample locally.
+
+- [hello-classification-sample](https://codesandbox.io/p/devbox/openvino-node-hello-classification-sample-djl893)
+
 ## See Also
 
 * [OpenVINO™ README](https://github.com/openvinotoolkit/openvino/blob/master/README.md)
diff --git a/src/bindings/js/node/include/helper.hpp b/src/bindings/js/node/include/helper.hpp
index fab5aa9ea395f3..15132cbe5455f4 100644
--- a/src/bindings/js/node/include/helper.hpp
+++ b/src/bindings/js/node/include/helper.hpp
@@ -126,6 +126,11 @@ ov::Tensor cast_to_tensor(const Napi::CallbackInfo& info, int index);
 /** @brief Creates ov::tensor from TypedArray using given shape and element type*/
 ov::Tensor cast_to_tensor(const Napi::TypedArray& data, const ov::Shape& shape, const ov::element::Type_t& type);
 
+void fill_tensor_from_strings(ov::Tensor& tensor, const Napi::Array& arr);
+
+/** @brief Helper method to create ov::tensor of ov::element::string type */
+ov::Tensor cast_to_tensor(const Napi::Array& array);
+
 /** @brief A helper function to create a ov::Tensor from Napi::Value.
  * @param value a Napi::Value that can be either a TypedArray or a TensorWrap Object.
  * @param infer_request The reference to InferRequest.
@@ -138,9 +143,9 @@ ov::Tensor value_to_tensor(const Napi::Value& value, ov::InferRequest& infer_req
         const auto input = get_request_tensor(infer_request, key);
         const auto& shape = input.get_shape();
         const auto& type = input.get_element_type();
-        const auto data = value.As<Napi::TypedArray>();
-        return cast_to_tensor(data, shape, type);
-
+        return cast_to_tensor(value.As<Napi::TypedArray>(), shape, type);
+    } else if (value.IsArray()) {
+        return cast_to_tensor(value.As<Napi::Array>());
     } else {
         return cast_to_tensor(value.As<Napi::Value>());
     }
@@ -153,3 +158,5 @@ Napi::Value any_to_js(const Napi::CallbackInfo& info, ov::Any value);
 bool is_napi_value_int(const Napi::Env& env, const Napi::Value& num);
 
 ov::AnyMap to_anyMap(const Napi::Env&, const Napi::Value&);
+
+std::string buffer_to_string(const Napi::Value& value);
diff --git a/src/bindings/js/node/include/type_validation.hpp b/src/bindings/js/node/include/type_validation.hpp
index 2e699f1f96b8ad..4d5e4d3ba988b0 100644
--- a/src/bindings/js/node/include/type_validation.hpp
+++ b/src/bindings/js/node/include/type_validation.hpp
@@ -5,7 +5,100 @@
 #include <napi.h>
 
 #include "node/include/addon.hpp"
+#include "node/include/model_wrap.hpp"
+#include "node/include/tensor.hpp"
 #include "openvino/openvino.hpp"
+#include "openvino/util/common_util.hpp"
 
-/** @brief Checks if Napi::Value is a TensorWrap.*/
-bool is_tensor(const Napi::Env& env, const Napi::Value& value);
+namespace ov {
+namespace js {
+namespace NapiArg {
+const char* get_type_name(napi_valuetype type);
+}  // namespace NapiArg
+
+std::string get_current_signature(const Napi::CallbackInfo& info);
+
+template <typename T>
+const char* get_attr_type() {
+    OPENVINO_THROW("get_attr_type is not implemented for passed type!");
+};
+
+template <>
+const char* get_attr_type<Napi::String>();
+
+template <>
+const char* get_attr_type<Napi::Object>();
+
+template <>
+const char* get_attr_type<Napi::Buffer<uint8_t>>();
+
+template <>
+const char* get_attr_type<int>();
+
+template <>
+const char* get_attr_type<ModelWrap>();
+
+template <>
+const char* get_attr_type<TensorWrap>();
+
+template <typename T>
+bool validate_value(const Napi::Env& env, const Napi::Value& arg) {
+    OPENVINO_THROW("Validation for this type is not implemented!");
+};
+
+template <>
+bool validate_value<Napi::String>(const Napi::Env& env, const Napi::Value& value);
+
+template <>
+bool validate_value<Napi::Object>(const Napi::Env& env, const Napi::Value& value);
+
+template <>
+bool validate_value<Napi::Buffer<uint8_t>>(const Napi::Env& env, const Napi::Value& value);
+
+template <>
+bool validate_value<int>(const Napi::Env& env, const Napi::Value& value);
+
+template <>
+bool validate_value<ModelWrap>(const Napi::Env& env, const Napi::Value& value);
+
+/** @brief Checks if Napi::Value is a Tensor.*/
+template <>
+bool validate_value<TensorWrap>(const Napi::Env& env, const Napi::Value& value);
+
+template <typename... Ts>
+std::string get_signature() {
+    if constexpr (sizeof...(Ts) == 0) {
+        return "()";
+    } else {
+        std::string signature = "(";
+        (signature.append(get_attr_type<Ts>()).append(", "), ...);
+        signature.pop_back();
+        signature.back() = ')';
+
+        return signature;
+    }
+};
+
+template <class... Ts>
+struct InputParameters {
+    template <size_t... Is>
+    static auto validate(const Napi::CallbackInfo& info, std::index_sequence<Is...>) {
+        return sizeof...(Ts) == info.Length() && (... && (validate_value<Ts>(info.Env(), info[Is])));
+    }
+};
+
+template <typename... Ts>
+bool validate(const Napi::CallbackInfo& info) {
+    return InputParameters<Ts...>::validate(info, std::index_sequence_for<Ts...>{});
+};
+
+template <typename... Ts>
+bool validate(const Napi::CallbackInfo& info, std::vector<std::string>& allowed_signatures) {
+    allowed_signatures.push_back(get_signature<Ts...>());
+
+    return InputParameters<Ts...>::validate(info, std::index_sequence_for<Ts...>{});
+};
+
+std::string get_parameters_error_msg(const Napi::CallbackInfo& info, std::vector<std::string>& allowed_signatures);
+}  // namespace js
+}  // namespace ov
diff --git a/src/bindings/js/node/lib/addon.ts b/src/bindings/js/node/lib/addon.ts
index 38ff1d43d7d3fb..00ab7a92771ad0 100644
--- a/src/bindings/js/node/lib/addon.ts
+++ b/src/bindings/js/node/lib/addon.ts
@@ -391,6 +391,12 @@ interface TensorConstructor {
    */
   new(type: element | elementTypeString, shape: number[],
     tensorData: SupportedTypedArray): Tensor;
+  /**
+   * It constructs a tensor using the element type and shape. The strings from 
+   * the array are used to fill the new tensor. Each element of a string tensor
+   * is a string of arbitrary length, including an empty string.
+   */
+  new(tensorData: string[]): Tensor;
 }
 
 /**
diff --git a/src/bindings/js/node/package-lock.json b/src/bindings/js/node/package-lock.json
index 75451ab5167af4..abc18c2060e234 100644
--- a/src/bindings/js/node/package-lock.json
+++ b/src/bindings/js/node/package-lock.json
@@ -1,12 +1,12 @@
 {
   "name": "openvino-node",
-  "version": "2024.1.0",
+  "version": "2024.2.0",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
     "": {
       "name": "openvino-node",
-      "version": "2024.1.0",
+      "version": "2024.2.0",
       "hasInstallScript": true,
       "license": "Apache-2.0",
       "os": [
diff --git a/src/bindings/js/node/package.json b/src/bindings/js/node/package.json
index da7db2a36382ba..3e1f745655d99e 100644
--- a/src/bindings/js/node/package.json
+++ b/src/bindings/js/node/package.json
@@ -1,6 +1,6 @@
 {
   "name": "openvino-node",
-  "version": "2024.1.0",
+  "version": "2024.2.0",
   "description": "OpenVINO™ utils for using from Node.js environment",
   "repository": {
     "url": "https://github.com/openvinotoolkit/openvino.git",
diff --git a/src/bindings/js/node/src/core_wrap.cpp b/src/bindings/js/node/src/core_wrap.cpp
index 6a96eb8fa9b4c9..33350056fc443c 100644
--- a/src/bindings/js/node/src/core_wrap.cpp
+++ b/src/bindings/js/node/src/core_wrap.cpp
@@ -10,6 +10,8 @@
 #include "node/include/helper.hpp"
 #include "node/include/model_wrap.hpp"
 #include "node/include/read_model_args.hpp"
+#include "node/include/type_validation.hpp"
+#include "openvino/util/common_util.hpp"
 
 void validate_set_property_args(const Napi::CallbackInfo& info) {
     const size_t args_length = info.Length();
@@ -58,12 +60,34 @@ Napi::Function CoreWrap::get_class(Napi::Env env) {
 }
 
 Napi::Value CoreWrap::read_model_sync(const Napi::CallbackInfo& info) {
+    std::vector<std::string> allowed_signatures;
+
     try {
-        ReadModelArgs* args;
-        args = new ReadModelArgs(info);
-        auto model = args->model_str.empty() ? _core.read_model(args->model_path, args->bin_path)
-                                             : _core.read_model(args->model_str, args->weight_tensor);
-        delete args;
+        std::shared_ptr<ov::Model> model;
+
+        if (ov::js::validate<Napi::String, Napi::String>(info, allowed_signatures)) {
+            model = _core.read_model(info[0].ToString(), info[1].ToString());
+        } else if (ov::js::validate<Napi::Buffer<uint8_t>, Napi::Buffer<uint8_t>>(info, allowed_signatures)) {
+            std::string model_str = buffer_to_string(info[0]);
+
+            Napi::Buffer<uint8_t> weights = info[1].As<Napi::Buffer<uint8_t>>();
+            const uint8_t* bin = reinterpret_cast<const uint8_t*>(weights.Data());
+
+            size_t bin_size = weights.Length();
+            ov::Tensor weight_tensor = ov::Tensor(ov::element::Type_t::u8, {bin_size});
+            std::memcpy(weight_tensor.data(), bin, bin_size);
+
+            model = _core.read_model(model_str, weight_tensor);
+        } else if (ov::js::validate<Napi::Buffer<uint8_t>>(info, allowed_signatures)) {
+            std::string model_str = buffer_to_string(info[0]);
+            ov::Tensor weight_tensor = ov::Tensor(ov::element::Type_t::u8, {0});
+
+            model = _core.read_model(model_str, weight_tensor);
+        } else if (ov::js::validate<Napi::String>(info, allowed_signatures)) {
+            model = _core.read_model(info[0].ToString());
+        } else {
+            OPENVINO_THROW("'readModelSync'", ov::js::get_parameters_error_msg(info, allowed_signatures));
+        }
 
         return ModelWrap::wrap(info.Env(), model);
     } catch (std::runtime_error& err) {
@@ -126,26 +150,27 @@ Napi::Value CoreWrap::compile_model_sync(const Napi::CallbackInfo& info,
 }
 
 Napi::Value CoreWrap::compile_model_sync_dispatch(const Napi::CallbackInfo& info) {
+    std::vector<std::string> allowed_signatures;
+
     try {
-        if (info.Length() == 2 && info[0].IsString() && info[1].IsString()) {
+        if (ov::js::validate<Napi::String, Napi::String>(info, allowed_signatures)) {
             return compile_model_sync(info, info[0].ToString(), info[1].ToString());
-        } else if (info.Length() == 2 && info[0].IsObject() && info[1].IsString()) {
+        } else if (ov::js::validate<ModelWrap, Napi::String>(info, allowed_signatures)) {
             return compile_model_sync(info, info[0].ToObject(), info[1].ToString());
-        } else if (info.Length() == 3 && info[0].IsString() && info[1].IsString()) {
+        } else if (ov::js::validate<Napi::String, Napi::String, Napi::Object>(info, allowed_signatures)) {
             const auto& config = js_to_cpp<std::map<std::string, ov::Any>>(info, 2);
+
             return compile_model_sync(info, info[0].ToString(), info[1].ToString(), config);
-        } else if (info.Length() == 3 && info[0].IsObject() && info[1].IsString()) {
+        } else if (ov::js::validate<ModelWrap, Napi::String, Napi::Object>(info, allowed_signatures)) {
             const auto& config = js_to_cpp<std::map<std::string, ov::Any>>(info, 2);
+
             return compile_model_sync(info, info[0].ToObject(), info[1].ToString(), config);
-        } else if (info.Length() < 2 || info.Length() > 3) {
-            reportError(info.Env(), "Invalid number of arguments -> " + std::to_string(info.Length()));
-            return info.Env().Undefined();
-        } else {
-            reportError(info.Env(), "Error while compiling model.");
-            return info.Env().Undefined();
         }
+        OPENVINO_THROW("'compileModelSync'", ov::js::get_parameters_error_msg(info, allowed_signatures));
+
     } catch (std::exception& e) {
         reportError(info.Env(), e.what());
+
         return info.Env().Undefined();
     }
 }
diff --git a/src/bindings/js/node/src/helper.cpp b/src/bindings/js/node/src/helper.cpp
index 32ca962010b94a..01474a49c4ae3c 100644
--- a/src/bindings/js/node/src/helper.cpp
+++ b/src/bindings/js/node/src/helper.cpp
@@ -294,7 +294,7 @@ ov::Tensor cast_to_tensor(const Napi::Value& value) {
 }
 
 ov::Tensor cast_to_tensor(const Napi::CallbackInfo& info, int index) {
-    if (!is_tensor(info.Env(), info[index])) {
+    if (!ov::js::validate_value<TensorWrap>(info.Env(), info[index])) {
         OPENVINO_THROW(std::string("Argument #" + std::to_string(index) + " must be a Tensor."));
     }
     const auto tensor_wrap = Napi::ObjectWrap<TensorWrap>::Unwrap(info[index].ToObject());
@@ -317,6 +317,23 @@ ov::Tensor cast_to_tensor(const Napi::TypedArray& typed_array,
     return tensor;
 }
 
+void fill_tensor_from_strings(ov::Tensor& tensor, const Napi::Array& arr) {
+    if (tensor.get_size() != static_cast<size_t>(arr.Length())) {
+        OPENVINO_THROW("Passed array must have the same size (number of elements) as the Tensor!");
+    }
+    const auto data = tensor.data<std::string>();
+    for (uint32_t i = 0; i < tensor.get_size(); ++i) {
+        OPENVINO_ASSERT(arr[i].IsString(), "The array passed to create string tensor must contain only strings.");
+        data[i] = arr[i].ToString().Utf8Value();
+    }
+}
+
+ov::Tensor cast_to_tensor(const Napi::Array& array) {
+    auto tensor = ov::Tensor(ov::element::string, ov::Shape{array.Length()});
+    fill_tensor_from_strings(tensor, array);
+    return tensor;
+}
+
 /**
  * @brief  Template function to convert C++ map into Javascript Object. Map key must be std::string.
  * @tparam MapElementType C++ data type of map elements.
@@ -521,3 +538,9 @@ ov::AnyMap to_anyMap(const Napi::Env& env, const Napi::Value& val) {
 
     return properties;
 }
+
+std::string buffer_to_string(const Napi::Value& value) {
+    Napi::Buffer<uint8_t> model_data = value.As<Napi::Buffer<uint8_t>>();
+
+    return std::string(reinterpret_cast<char*>(model_data.Data()), model_data.Length());
+}
diff --git a/src/bindings/js/node/src/preprocess/input_tensor_info.cpp b/src/bindings/js/node/src/preprocess/input_tensor_info.cpp
index 487920baeb2480..8787e18e7add38 100644
--- a/src/bindings/js/node/src/preprocess/input_tensor_info.cpp
+++ b/src/bindings/js/node/src/preprocess/input_tensor_info.cpp
@@ -52,8 +52,6 @@ Napi::Value InputTensorInfo::set_element_type(const Napi::CallbackInfo& info) {
 
         const auto type = js_to_cpp<ov::element::Type_t>(info, 0);
 
-        OPENVINO_ASSERT(type != ov::element::string, "String tensors are not supported in JS API.");
-
         _tensor_info->set_element_type(type);
     } catch (std::exception& e) {
         reportError(info.Env(), e.what());
diff --git a/src/bindings/js/node/src/preprocess/output_tensor_info.cpp b/src/bindings/js/node/src/preprocess/output_tensor_info.cpp
index e45d67b2054062..67fad6124c78b8 100644
--- a/src/bindings/js/node/src/preprocess/output_tensor_info.cpp
+++ b/src/bindings/js/node/src/preprocess/output_tensor_info.cpp
@@ -37,8 +37,6 @@ Napi::Value OutputTensorInfo::set_element_type(const Napi::CallbackInfo& info) {
 
         const auto type = js_to_cpp<ov::element::Type_t>(info, 0);
 
-        OPENVINO_ASSERT(type != ov::element::string, "String tensors are not supported in JS API.");
-
         _tensor_info->set_element_type(type);
     } catch (std::exception& e) {
         reportError(info.Env(), e.what());
diff --git a/src/bindings/js/node/src/tensor.cpp b/src/bindings/js/node/src/tensor.cpp
index b745eb90dc1df2..78785a10b2275b 100644
--- a/src/bindings/js/node/src/tensor.cpp
+++ b/src/bindings/js/node/src/tensor.cpp
@@ -13,29 +13,21 @@ TensorWrap::TensorWrap(const Napi::CallbackInfo& info) : Napi::ObjectWrap<Tensor
     if (info.Length() == 0) {
         return;
     }
-
-    if (info.Length() == 1 || info.Length() > 3) {
-        reportError(info.Env(), "Invalid number of arguments for Tensor constructor.");
-        return;
-    }
-
     try {
-        const auto type = js_to_cpp<ov::element::Type_t>(info, 0);
-
-        OPENVINO_ASSERT(type != ov::element::string, "String tensors are not supported in JS API.");
-
-        const auto& shape = js_to_cpp<ov::Shape>(info, 1);
-
-        if (info.Length() == 2) {
-            this->_tensor = ov::Tensor(type, shape);
-        } else if (info.Length() == 3) {
-            if (!info[2].IsTypedArray()) {
-                reportError(info.Env(), "Third argument of a tensor must be of type TypedArray.");
-                return;
+        OPENVINO_ASSERT(info.Length() <= 3, "Invalid number of arguments for Tensor constructor.");
+        if (info.Length() == 1 && info[0].IsArray()) {
+            this->_tensor = cast_to_tensor(info[0].As<Napi::Array>());
+        } else {
+            const auto type = js_to_cpp<ov::element::Type_t>(info, 0);
+            const auto& shape = js_to_cpp<ov::Shape>(info, 1);
+
+            if (info.Length() == 2) {
+                this->_tensor = ov::Tensor(type, shape);
+            } else if (info.Length() == 3 && info[2].IsTypedArray()) {
+                this->_tensor = cast_to_tensor(info[2].As<Napi::TypedArray>(), shape, type);
+            } else {
+                OPENVINO_THROW("Third argument of a tensor must be TypedArray.");
             }
-
-            const auto data = info[2].As<Napi::TypedArray>();
-            this->_tensor = cast_to_tensor(data, shape, type);
         }
     } catch (std::invalid_argument& e) {
         reportError(info.Env(), std::string("Invalid tensor argument. ") + e.what());
@@ -76,8 +68,6 @@ Napi::Object TensorWrap::wrap(Napi::Env env, ov::Tensor tensor) {
 Napi::Value TensorWrap::get_data(const Napi::CallbackInfo& info) {
     auto type = _tensor.get_element_type();
 
-    OPENVINO_ASSERT(type != ov::element::string, "String tensors are not supported in JS API.");
-
     switch (type) {
     case ov::element::Type_t::i8: {
         auto arr = Napi::Int8Array::New(info.Env(), _tensor.get_size());
@@ -129,6 +119,14 @@ Napi::Value TensorWrap::get_data(const Napi::CallbackInfo& info) {
         std::memcpy(arr.Data(), _tensor.data(), _tensor.get_byte_size());
         return arr;
     }
+    case ov::element::string: {
+        auto str_data = Napi::Array::New(info.Env(), _tensor.get_size());
+        const auto data = _tensor.data<std::string>();
+        for (uint32_t i = 0; i < _tensor.get_size(); ++i) {
+            str_data[i] = Napi::String::New(info.Env(), data[i]);
+        }
+        return str_data;
+    }
     default: {
         reportError(info.Env(), "Failed to return tensor data.");
         return info.Env().Null();
@@ -138,16 +136,19 @@ Napi::Value TensorWrap::get_data(const Napi::CallbackInfo& info) {
 
 void TensorWrap::set_data(const Napi::CallbackInfo& info, const Napi::Value& value) {
     try {
-        if (!value.IsTypedArray()) {
-            OPENVINO_THROW("Passed argument must be a TypedArray.");
-        }
-        const auto buf = value.As<Napi::TypedArray>();
+        if (value.IsTypedArray()) {
+            const auto buf = value.As<Napi::TypedArray>();
 
-        if (_tensor.get_byte_size() != buf.ByteLength()) {
-            OPENVINO_THROW("Passed array must have the same size as the Tensor!");
+            if (_tensor.get_byte_size() != buf.ByteLength()) {
+                OPENVINO_THROW("Passed array must have the same size as the Tensor!");
+            }
+            const auto napi_type = buf.TypedArrayType();
+            std::memcpy(_tensor.data(get_ov_type(napi_type)), buf.ArrayBuffer().Data(), _tensor.get_byte_size());
+        } else if (value.IsArray()) {
+            fill_tensor_from_strings(_tensor, value.As<Napi::Array>());
+        } else {
+            OPENVINO_THROW("Passed argument must be TypedArray, or Array if the tensor type is string.");
         }
-        const auto napi_type = buf.TypedArrayType();
-        std::memcpy(_tensor.data(get_ov_type(napi_type)), buf.ArrayBuffer().Data(), _tensor.get_byte_size());
     } catch (std::exception& e) {
         reportError(info.Env(), e.what());
     }
diff --git a/src/bindings/js/node/src/type_validation.cpp b/src/bindings/js/node/src/type_validation.cpp
index 08c6c0787130ee..4852e0e4051c2a 100644
--- a/src/bindings/js/node/src/type_validation.cpp
+++ b/src/bindings/js/node/src/type_validation.cpp
@@ -3,7 +3,152 @@
 
 #include "node/include/type_validation.hpp"
 
-bool is_tensor(const Napi::Env& env, const Napi::Value& value) {
+namespace ov {
+namespace js {
+namespace NapiTypename {
+static const char UNDEFINED_STR[] = "undefined";
+static const char NULL_STR[] = "null";
+static const char BOOLEAN_STR[] = "boolean";
+static const char NUMBER_STR[] = "number";
+static const char STIRNG_STR[] = "string";
+static const char SYMBOL_STR[] = "symbol";
+static const char OBJECT_STR[] = "object";
+static const char FUNCTION_STR[] = "function";
+static const char EXTERNAL_STR[] = "external";
+static const char BIGINT_STR[] = "bigint";
+static const char UNKNOWN_STR[] = "unknown";
+}  // namespace NapiTypename
+
+namespace BindingTypename {
+static const char INT[] = "Integer";
+static const char MODEL[] = "Model";
+static const char TENSOR[] = "Tensor";
+static const char BUFFER[] = "Buffer";
+}  // namespace BindingTypename
+namespace NapiArg {
+const char* get_type_name(napi_valuetype type) {
+    switch (type) {
+    case napi_undefined:
+        return NapiTypename::UNDEFINED_STR;
+
+    case napi_null:
+        return NapiTypename::NULL_STR;
+
+    case napi_boolean:
+        return NapiTypename::BOOLEAN_STR;
+
+    case napi_number:
+        return NapiTypename::NUMBER_STR;
+
+    case napi_string:
+        return NapiTypename::STIRNG_STR;
+
+    case napi_symbol:
+        return NapiTypename::SYMBOL_STR;
+
+    case napi_object:
+        return NapiTypename::OBJECT_STR;
+
+    case napi_function:
+        return NapiTypename::FUNCTION_STR;
+
+    case napi_external:
+        return NapiTypename::EXTERNAL_STR;
+
+    case napi_bigint:
+        return NapiTypename::BIGINT_STR;
+
+    default:
+        return NapiTypename::UNKNOWN_STR;
+    }
+}
+}  // namespace NapiArg
+
+std::string get_current_signature(const Napi::CallbackInfo& info) {
+    std::vector<std::string> signature_attributes;
+    size_t attrs_length = info.Length();
+
+    for (size_t i = 0; i < attrs_length; ++i) {
+        signature_attributes.push_back(NapiArg::get_type_name(info[i].Type()));
+    }
+
+    return std::string("(" + ov::util::join(signature_attributes) + ")");
+};
+
+template <>
+const char* get_attr_type<Napi::String>() {
+    return NapiArg::get_type_name(napi_string);
+}
+
+template <>
+const char* get_attr_type<Napi::Object>() {
+    return NapiArg::get_type_name(napi_object);
+}
+
+template <>
+const char* get_attr_type<Napi::Buffer<uint8_t>>() {
+    return BindingTypename::BUFFER;
+}
+
+template <>
+const char* get_attr_type<int>() {
+    return BindingTypename::INT;
+}
+
+template <>
+const char* get_attr_type<ModelWrap>() {
+    return BindingTypename::MODEL;
+}
+
+template <>
+const char* get_attr_type<TensorWrap>() {
+    return BindingTypename::TENSOR;
+}
+
+template <>
+bool validate_value<Napi::String>(const Napi::Env& env, const Napi::Value& value) {
+    return napi_string == value.Type();
+}
+
+template <>
+bool validate_value<Napi::Object>(const Napi::Env& env, const Napi::Value& value) {
+    return napi_object == value.Type();
+}
+
+template <>
+bool validate_value<Napi::Buffer<uint8_t>>(const Napi::Env& env, const Napi::Value& value) {
+    return value.IsBuffer();
+}
+
+template <>
+bool validate_value<int>(const Napi::Env& env, const Napi::Value& value) {
+    return value.IsNumber() && env.Global()
+                                   .Get("Number")
+                                   .ToObject()
+                                   .Get("isInteger")
+                                   .As<Napi::Function>()
+                                   .Call({value.ToNumber()})
+                                   .ToBoolean()
+                                   .Value();
+}
+
+template <>
+bool validate_value<ModelWrap>(const Napi::Env& env, const Napi::Value& value) {
+    const auto& prototype = env.GetInstanceData<AddonData>()->model;
+
+    return value.ToObject().InstanceOf(prototype.Value().As<Napi::Function>());
+}
+
+template <>
+bool validate_value<TensorWrap>(const Napi::Env& env, const Napi::Value& value) {
     const auto& prototype = env.GetInstanceData<AddonData>()->tensor;
+
     return value.ToObject().InstanceOf(prototype.Value().As<Napi::Function>());
 }
+
+std::string get_parameters_error_msg(const Napi::CallbackInfo& info, std::vector<std::string>& allowed_signatures) {
+    return " method called with incorrect parameters.\nProvided signature: " + js::get_current_signature(info) +
+           " \nAllowed signatures:\n- " + ov::util::join(allowed_signatures, "\n- ");
+}
+}  // namespace js
+}  // namespace ov
diff --git a/src/bindings/js/node/tests/basic.test.js b/src/bindings/js/node/tests/basic.test.js
index e50cc4c1815962..ba3e585e40ce7d 100644
--- a/src/bindings/js/node/tests/basic.test.js
+++ b/src/bindings/js/node/tests/basic.test.js
@@ -22,7 +22,7 @@ it('Core.getAvailableDevices()', () => {
 
 describe('Core.getVersions()', () => {
 
-  it('getVersions(validDeviceName: string)', () => {    
+  it('getVersions(validDeviceName: string)', () => {
     const deviceVersion = core.getVersions('CPU');
     assert.strictEqual(typeof deviceVersion, 'object');
     assert.strictEqual(typeof deviceVersion.CPU, 'object');
@@ -33,14 +33,14 @@ describe('Core.getVersions()', () => {
   it('getVersions() throws if no arguments are passed into the function', () => {
     assert.throws(
       () => core.getVersions(),
-      {message: 'getVersions() method expects 1 argument of string type.'}
+      { message: 'getVersions() method expects 1 argument of string type.' }
     );
   });
 
   it('getVersions() throws if non string coercable arguments are passed into the function', () => {
     assert.throws(
       () => core.getVersions({ deviceName: 'CPU' }),
-      {message: 'The argument in getVersions() method must be a string or convertible to a string.'}
+      { message: 'The argument in getVersions() method must be a string or convertible to a string.' }
     );
   });
 
@@ -75,9 +75,17 @@ describe('Core.compileModelSync()', () => {
   });
 
   it('compileModelSync(model, device, config) throws when config is a string', () => {
+    const expectedMsg = ("'compileModelSync' method called with incorrect parameters.\n" +
+      'Provided signature: (object, string, string) \n' +
+      'Allowed signatures:\n' +
+      '- (string, string)\n' +
+      '- (Model, string)\n' +
+      '- (string, string, object)\n' +
+      '- (Model, string, object)\n').replace(/[()]/g, '\\$&');
+
     assert.throws(
       () => core.compileModelSync(model, 'CPU', 'string'),
-      "Argument #3 must be an Object."
+      new RegExp(expectedMsg),
     );
   });
 
@@ -89,12 +97,19 @@ describe('Core.compileModelSync()', () => {
   });
 
   it('compileModelSync(model) throws if the number of arguments is invalid', () => {
+    const expectedMsg = ("'compileModelSync' method called with incorrect parameters.\n" +
+      'Provided signature: (object) \n' +
+      'Allowed signatures:\n' +
+      '- (string, string)\n' +
+      '- (Model, string)\n' +
+      '- (string, string, object)\n' +
+      '- (Model, string, object)\n').replace(/[()]/g, '\\$&');
+
     assert.throws(
       () => core.compileModelSync(model),
-      /Invalid number of arguments/
+      new RegExp(expectedMsg),
     );
   });
-
 });
 
 describe('Core.compileModel()', () => {
@@ -268,7 +283,7 @@ describe('Test exportModel()/importModel()', () => {
   it('Test importModel(stream, device, config: unsupported property) throws', () => {
     const tmpDir = '/tmp';
     assert.throws(
-      () => core.importModelSync(userStream, 'CPU', {'CACHE_DIR': tmpDir}),
+      () => core.importModelSync(userStream, 'CPU', { 'CACHE_DIR': tmpDir }),
       /Unsupported property CACHE_DIR by CPU plugin./
     );
   });
diff --git a/src/bindings/js/node/tests/read_model.test.js b/src/bindings/js/node/tests/read_model.test.js
index 407627c5ae9cb0..731001fbb861b7 100644
--- a/src/bindings/js/node/tests/read_model.test.js
+++ b/src/bindings/js/node/tests/read_model.test.js
@@ -27,6 +27,13 @@ describe('Core.readModeSync', () => {
     assert.equal(model.inputs.length, 1);
   });
 
+  it('readModeSync throws', () => {
+    assert.throws(
+      () => core.readModelSync(core),
+      /'readModelSync' method called with incorrect parameters./,
+    )
+  });
+
   it('readModeSync(modelUint8ArrayBuffer, weightsUint8ArrayBuffer) ', () => {
     const model = core.readModelSync(
       new Uint8Array(modelFile.buffer),
diff --git a/src/bindings/js/node/tests/tensor.test.js b/src/bindings/js/node/tests/tensor.test.js
index 47bb2da672157d..2e14546b95ad67 100644
--- a/src/bindings/js/node/tests/tensor.test.js
+++ b/src/bindings/js/node/tests/tensor.test.js
@@ -24,8 +24,8 @@ const params = [
 ];
 
 test('Test for number of arguments in tensor', () => {
-  assert.throws( () => new ov.Tensor(ov.element.f32),
-    {message: 'Invalid number of arguments for Tensor constructor.'});
+  assert.throws( () => new ov.Tensor(ov.element.f32, shape, data, params),
+    {message: /Invalid number of arguments for Tensor constructor./});
 });
 
 describe('Tensor without data parameters', () => {
@@ -44,6 +44,32 @@ describe('Tensor data', () => {
     });
   });
 
+  it('Create string tensor', () => {
+    const str_arr = ['text', 'more text', 'even more text'];
+    const tensor = new ov.Tensor(str_arr);
+    assert.deepStrictEqual(tensor.data, str_arr);
+  });
+
+  it('Create string tensor', () => {
+    const str_arr = ['text', 'more text', 'even more text'];
+    const tensor = new ov.Tensor(str_arr);
+    assert.deepStrictEqual(tensor.data, str_arr);
+  });
+
+  it('String tensor - passed array does not contain string elements', () => {
+    const str_arr = ['text', true];
+    assert.throws(() => { new ov.Tensor(str_arr);},
+      /The array passed to create string tensor must contain only strings./
+    );
+  });
+
+  it('Set string tensor data', () => {
+    const str_arr = ['H', 'e', 'l', 'l', 'o'];
+    const tensor = new ov.Tensor(ov.element.string, [1, 1, 1, 5]);
+    tensor.data = str_arr;
+    assert.deepStrictEqual(tensor.data, str_arr);
+  });
+
   it('Test tensor getData()', () => {
     const tensor = new ov.Tensor(ov.element.f32, shape, data);
     assert.deepStrictEqual(tensor.getData(), data);
@@ -67,12 +93,12 @@ describe('Tensor data', () => {
     );
   });
 
-  it('Test tensor.data setter - not TypedArray arg throws', () => {
+  it('Test tensor.data setter', () => {
     const testString = 'test';
     const tensor = new ov.Tensor(ov.element.f64, [1, 2]);
     assert.throws(() => {
       tensor.data = testString;},
-    /Passed argument must be a TypedArray./
+    /Passed argument must be TypedArray, or Array if the tensor type is string./
     );
   });
 
@@ -103,13 +129,13 @@ describe('Tensor data', () => {
   it('Third argument of a tensor cannot be an ArrayBuffer', () => {
     assert.throws(
       () => new ov.Tensor(ov.element.f32, shape, new ArrayBuffer(1234)),
-      {message: 'Third argument of a tensor must be of type TypedArray.'});
+      {message: /Third argument of a tensor must be TypedArray./});
   });
 
   it('Third argument of a tensor cannot be an array object', () => {
     assert.throws(
       () => new ov.Tensor(ov.element.f32, shape, [1, 2, 3, 4]),
-      {message: 'Third argument of a tensor must be of type TypedArray.'});
+      {message: /Third argument of a tensor must be TypedArray./});
   });
 });
 
@@ -178,7 +204,6 @@ describe('Tensor element type', () => {
   });
 });
 
-
 describe('Tensor getSize', () => {
 
   it('getSize returns the correct total number of elements', () => {
@@ -222,4 +247,3 @@ describe('Tensor getSize for various shapes', () => {
     assert.strictEqual(tensor.getSize(), expectedSize);
   });
 });
-
diff --git a/src/bindings/python/CMakeLists.txt b/src/bindings/python/CMakeLists.txt
index f075dc21250e3a..e9250fbba6a61a 100644
--- a/src/bindings/python/CMakeLists.txt
+++ b/src/bindings/python/CMakeLists.txt
@@ -35,28 +35,6 @@ if(NOT DEFINED OpenVINODeveloperPackage_RELOCATABLE)
     set(OpenVINODeveloperPackage_RELOCATABLE OFF)
 endif()
 
-#
-# Settings for FindPython3.cmake
-#
-
-if(NOT DEFINED Python3_USE_STATIC_LIBS)
-    set(Python3_USE_STATIC_LIBS OFF)
-endif()
-
-if(NOT DEFINED Python3_FIND_VIRTUALENV)
-    set(Python3_FIND_VIRTUALENV FIRST)
-endif()
-
-if(NOT DEFINED Python3_FIND_IMPLEMENTATIONS)
-    set(Python3_FIND_IMPLEMENTATIONS CPython PyPy)
-endif()
-
-if(CMAKE_VERSION VERSION_GREATER_EQUAL 3.18)
-    set(python3_development_component Development.Module)
-else()
-    set(python3_development_component Development)
-endif()
-
 #
 # Check python requirements
 #
diff --git a/src/bindings/python/requirements_test.txt b/src/bindings/python/requirements_test.txt
index 300cbf2e818416..3bac1845592ea3 100644
--- a/src/bindings/python/requirements_test.txt
+++ b/src/bindings/python/requirements_test.txt
@@ -2,12 +2,12 @@
 numpy
 bandit
 black
-flake8<=7.0.0
+flake8<=7.1.0
 flake8-annotations-complexity<=0.0.8
 flake8-broken-line<=1.0.0
 flake8-bugbear<=24.4.26
 flake8-class-attributes-order<=0.1.3
-flake8-comprehensions<=3.14.0
+flake8-comprehensions<=3.15.0
 flake8-debugger<=4.1.2
 flake8-docstrings<=1.7.0
 flake8-eradicate<=1.5.0
diff --git a/src/bindings/python/src/openvino/frontend/jax/__init__.py b/src/bindings/python/src/openvino/frontend/jax/__init__.py
new file mode 100644
index 00000000000000..b6c7d15f4a0e50
--- /dev/null
+++ b/src/bindings/python/src/openvino/frontend/jax/__init__.py
@@ -0,0 +1,15 @@
+# Copyright (C) 2018-2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+"""
+Package: openvino
+Low level wrappers for the FrontEnd C++ API.
+"""
+
+# flake8: noqa
+
+try:
+    from openvino.frontend.jax.py_jax_frontend import _FrontEndJaxDecoder as Decoder
+except ImportError as err:
+    raise ImportError("OpenVINO JAX frontend is not available, please make sure the frontend is built."
+                      "{}".format(err))
\ No newline at end of file
diff --git a/src/bindings/python/src/openvino/frontend/jax/jaxpr_decoder.py b/src/bindings/python/src/openvino/frontend/jax/jaxpr_decoder.py
new file mode 100644
index 00000000000000..56dfe21297d5b8
--- /dev/null
+++ b/src/bindings/python/src/openvino/frontend/jax/jaxpr_decoder.py
@@ -0,0 +1,283 @@
+# Copyright (C) 2018-2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+# flake8: noqa
+# mypy: ignore-errors
+
+import jax.core
+from openvino.frontend.jax.py_jax_frontend import _FrontEndJaxDecoder as Decoder
+from openvino.runtime import PartialShape, Type as OVType, OVAny
+from openvino.frontend.jax.utils import jax_array_to_ov_const, get_ov_type_for_value, \
+    ivalue_to_constant
+
+import jax
+import numpy as np
+
+from typing import List
+import logging
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.WARNING)
+
+class JaxprPythonDecoder (Decoder):
+    '''
+    The jaxpr decoder uses Jaxpr to get graph information from a jax module.
+    It takes use of the following parts.
+    
+    - `ClosedJaxpr`: the jaxpr object that contains the jaxpr and literals.
+        - `Jaxpr`: the jaxpr object that contains the invars, outvars, and eqns.
+            - `JaxEqns`: A list of jaxpr equations, which contains the information of the operation.
+                - `Primitive`: the operation that is used in the equation.
+                - `invars`: the input variables of the equation.
+                    - `aval`: the abstract value.
+                - `outvars`: the output variables of the equation.
+                    - `aval`: the abstract value.
+                - `params`: the named params of this equation.
+            - `invars`: the inputs of the model (traced graph).
+                - `aval`: the abstract value.
+            - `outvars`: the outputs of the model (traced graph).
+                - `aval`: the abstract value.
+            - `constvars`: the constant variables used in this model.
+                - `aval`: the abstract value.
+        - `Literal`: the literal object that contains the value of the constants.
+    '''
+    
+    def __init__(self, jaxpr, name=None, literals=None):
+        '''
+        Inputs: 
+            - jaxpr: for users, `ClosedJaxpr` is expected here. See https://github.com/google/jax/blob/jaxlib-v0.4.29/jax/_src/core.py#L197
+            - name: the name for the model.
+            - literals: the literals (constants) that are used in the model.
+        '''
+        Decoder.__init__(self)
+        
+        if isinstance(jaxpr, (jax.core.JaxprEqn, jax.core.Jaxpr)):
+            self.jaxpr = jaxpr
+        elif isinstance(jaxpr, jax.core.ClosedJaxpr):
+            # Take the `Jaxpr` from `ClosedJaxpr`, see https://github.com/google/jax/blob/jaxlib-v0.4.29/jax/_src/core.py#L85
+            self.jaxpr = jaxpr.jaxpr
+            # Literal should be a `Jax.core.Var`, see https://github.com/google/jax/blob/jaxlib-v0.4.29/jax/_src/core.py#L85
+            self.literals = jaxpr.literals
+        else:
+            raise ValueError(f"Unexpected type of jaxpr: {type(jaxpr)}")
+        self.name = name
+        if self.name is None:
+            self.name = "jax_module"
+        if literals is not None:
+            self.literals = literals
+            
+        self.params = {}
+        if hasattr(self.jaxpr, 'params') and isinstance(self.jaxpr.params, dict):
+            for k in self.jaxpr.params.keys():
+                self.params[k] = self.convert_param_to_constant_node(self.jaxpr, k)
+                
+        # TODO: this implementation may lead to memory increasing. Any better solution?
+        self.m_decoders = []
+        
+    def inputs(self) -> List[int]:
+        if isinstance(self.jaxpr, jax.core.JaxprEqn):
+            idx = 0
+            res = []
+            for inp in self.jaxpr.invars:
+                if isinstance(inp, jax.core.Literal):
+                    res.append(self.literals[idx].output(0))
+                    idx += 1
+                else:
+                    res.append(id(inp))
+            return res
+        else:
+            return [id(v) for v in self.jaxpr.invars]
+    
+    def input(self, idx: int) -> int:
+        return id(self.jaxpr.invars[idx])
+    
+    def get_input_shape(self, index):
+        return PartialShape(self.jaxpr.invars[index].aval.shape)
+    
+    def get_input_signature_name(self, index) -> str:
+        return "jaxpr_invar_" + str(index)
+    
+    def get_input_type(self, index) -> OVType:
+        return get_ov_type_for_value(self.jaxpr.invars[index])
+        
+    def get_named_param(self, name):
+        '''
+        Get the object id of the named parameter by the name.
+        '''
+        return self.params[name].output(0)
+    
+    def get_named_param_as_constant(self, name):
+        '''
+        The named parameter in JAX is a python object but we want to use its value in cpp.
+        Therefore this API is used to get the named parameter as a constant, which can be used
+        to extract the value of it in cpp-level.
+        '''
+        return self.params[name].as_constant()
+    
+    def get_param_names(self):
+        '''
+        In JAX, the named parameters may exist in `params` attribute of `JaxEqn`.
+        For example, the `jax.lax.cat` operation has a named parameter `dim`, 
+        which is used to indicate the dimension to concatenate the tensors.
+        
+        Here we return the names of all the named params that appear in the model for the current `JaxEqn`.
+        '''
+        return list(self.params.keys())
+    
+    def get_output_type(self, index) -> OVType:
+        return get_ov_type_for_value(self.jaxpr.outvars[index])
+        
+    def get_output_name(self, index) -> str:
+        return "jaxpr_outvar_" + str(index)
+    
+    def get_output_shape(self, index):
+        return PartialShape(self.jaxpr.outvars[index].aval.shape)
+    
+    def visit_subgraph(self, node_visitor) -> None:
+        if isinstance(self.jaxpr, jax.core.JaxprEqn):
+            return
+        for _, decoder in self.params.items():
+            self.m_decoders.append(decoder)
+            node_visitor(decoder)
+        for idx, node in enumerate(self.jaxpr.constvars):
+            decoder = self.convert_literal_to_constant_node(
+                literal=self.literals[idx], 
+                name=self.name + "/" + f"const({id(node)})", 
+                output_id=id(node)
+            )
+            self.m_decoders.append(decoder)
+            node_visitor(decoder)
+        # Visit every `JaxEqn` in the jaxpr, see https://github.com/google/jax/blob/jaxlib-v0.4.29/jax/_src/core.py#L285
+        for node in self.jaxpr.eqns:
+            literal_decoders = []
+            for inp in node.invars:
+                if isinstance(inp, jax.core.Literal):
+                    literal_decoder = self.convert_literal_to_constant_node(inp)
+                    literal_decoders.append(literal_decoder)
+                    node_visitor(literal_decoder)
+            decoder = JaxprPythonDecoder(node, name=self.name + "/" + node.primitive.name, literals=literal_decoders)
+            self.m_decoders.append(decoder)
+            node_visitor(decoder)
+            
+    def get_op_type(self) -> str:
+        if isinstance(self.jaxpr, jax.core.JaxprEqn):
+            return self.jaxpr.primitive.name
+        else:
+            return "root"
+    
+    def outputs(self) -> List[int]:
+        return [id(v) for v in self.jaxpr.outvars]
+    
+    def output(self, idx: int) -> int:
+        return id(self.jaxpr.outvars[idx])
+    
+    def num_inputs(self) -> int:
+        return len(self.jaxpr.invars)
+    
+    def num_outputs(self) -> int:
+        return len(self.jaxpr.outvars)
+    
+    def as_constant(self):
+        if self.get_op_type() == 'constant':
+            value = self.literals
+            # TODO: dig out how to share the memory.
+            # Currently, using shared_memory will raise `ValueError: array is not writeable``
+            ov_const = jax_array_to_ov_const(value, shared_memory=False)
+            return ov_const.outputs()
+        else:
+            raise ValueError("This is not a constant node so it cannot be converted to a constant.")
+        
+    @staticmethod
+    def convert_param_to_constant_node(jaxpr, param):
+        assert hasattr(jaxpr, 'params'), "The jaxpr does not have params."
+        constant = ivalue_to_constant(jaxpr.params[param], shared_memory=False)
+        return _JaxprPythonConstantDecoder(constant=constant)
+    
+    @staticmethod
+    def convert_literal_to_constant_node(literal, name=None, output_id=None):
+        if isinstance(literal, jax.core.Literal):
+            constant = ivalue_to_constant(literal.val, shared_memory=False)
+        elif isinstance(literal, (jax.Array, np.ndarray)):
+            constant = ivalue_to_constant(literal, shared_memory=False)
+        else:
+            raise TypeError( f"The input should be a literal or jax array, but got {type(literal)}.")
+        return _JaxprPythonConstantDecoder(constant=constant, name=name, output_id=output_id)
+        
+class _JaxprPythonConstantDecoder (Decoder):
+    def __init__(self, name=None, constant=None, output_id=None):
+        '''
+        A decoder specially for constants and named parameters.
+        
+        Inputs:
+            - name: the name for the model.
+            - literals: the literals (constants) that are used in the model.
+            - output_id: the id specified for this decoder's output. If none, use `id(self.constant)`.
+        '''
+        Decoder.__init__(self)
+        
+        self.name = name
+        self.constant = constant
+        self.output_id = id(self.constant) if output_id is None else output_id
+        
+    def inputs(self) -> List[int]:
+        return []
+    
+    def input(self, idx: int) -> int:
+        raise ValueError("This is a constant node so it does not have input.")
+    
+    def get_input_shape(self, index):
+        raise ValueError("This is a constant node so it does not have input shape.")
+    
+    def get_input_signature_name(self, index) -> str:
+        raise ValueError("This is a constant node so it does not have input signature name.")
+    
+    def get_input_type(self, index) -> OVType:
+        raise ValueError("This is a constant node so it does not have input type.")
+        
+    def get_named_param(self, name):
+        raise ValueError("This is a constant node so it does not have named param.")
+    
+    def get_named_param_as_constant(self, name):
+        raise ValueError("This is a constant node so it does not have named param.")
+    
+    def get_param_names(self):
+        '''
+        In JAX, the named parameters may exist in `params` attribute of `JaxEqn`.
+        For example, the `jax.lax.cat` operation has a named parameter `dim`, 
+        which is used to indicate the dimension to concatenate the tensors.
+        
+        However, `_JaxprPythonConstantDecoder` is already a named param or a constant.
+        So it will never have a named param.
+        '''
+        return []
+    
+    def get_output_type(self, index) -> OVType:
+        assert len(self.constant) == 1
+        return OVAny(self.constant[0].element_type)
+        
+    def get_output_name(self, index) -> str:
+        return "jaxpr_outvar_" + str(index)
+    
+    def get_output_shape(self, index):
+        assert len(self.constant) == 1
+        return PartialShape(self.constant[0].shape)
+    
+    def visit_subgraph(self, node_visitor) -> None:
+        return
+            
+    def get_op_type(self) -> str:
+        return "constant"
+    
+    def outputs(self) -> List[int]:
+        return [self.output_id]
+    
+    def output(self, idx: int) -> int:
+        return self.output_id
+    
+    def num_inputs(self) -> int:
+        return 0
+    
+    def num_outputs(self) -> int:
+        return 1
+    
+    def as_constant(self):
+        return self.constant
\ No newline at end of file
diff --git a/src/bindings/python/src/openvino/frontend/jax/utils.py b/src/bindings/python/src/openvino/frontend/jax/utils.py
new file mode 100644
index 00000000000000..09fa8487e523c7
--- /dev/null
+++ b/src/bindings/python/src/openvino/frontend/jax/utils.py
@@ -0,0 +1,129 @@
+# Copyright (C) 2018-2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+# flake8: noqa
+# mypy: ignore-errors
+
+import jax
+import numpy as np
+import jax.numpy as jnp
+
+from openvino.runtime import op, Type as OVType, Shape, OVAny
+
+numpy_to_ov_type_map = {
+    np.float32: OVType.f32,
+    bool: OVType.boolean,
+    jax.dtypes.bfloat16: OVType.bf16, # TODO: check this
+    np.float16: OVType.f16,
+    np.float32: OVType.f32,
+    np.float64: OVType.f64,
+    np.uint8: OVType.u8,
+    np.int8: OVType.i8,
+    np.int16: OVType.i16,
+    np.int32: OVType.i32,
+    np.int64: OVType.i64,
+}
+
+jax_to_ov_type_map = {
+    jnp.float32: OVType.f32,
+    jnp.bfloat16: OVType.bf16, # TODO: check this
+    jnp.float16: OVType.f16,
+    jnp.float64: OVType.f64,
+    jnp.uint8: OVType.u8,
+    jnp.int8: OVType.i8,
+    jnp.int16: OVType.i16,
+    jnp.int32: OVType.i32,
+    jnp.int64: OVType.i64,
+}
+
+try:
+    jax_to_ov_type_map[jnp.bool] = OVType.boolean
+except:
+    pass
+
+basic_to_ov_type_map = {
+    int: OVType.i64,
+    float: OVType.f32,
+    bool: OVType.boolean,
+}
+
+ov_type_to_int_map = {
+    OVType.u8: 0,
+    OVType.i8: 1,
+    OVType.i16: 2,
+    OVType.i32: 3,
+    OVType.i64: 4,
+    OVType.f16: 5,
+    OVType.f32: 6,
+    OVType.f64: 7,
+    OVType.boolean: 11,
+    OVType.bf16: 15,
+}
+
+def get_type_from_py_type(value):
+    if isinstance(value, float):
+        return OVType.f32
+    if isinstance(value, bool):
+        return OVType.boolean
+    if isinstance(value, int):
+        return OVType.i64
+    return OVType.dynamic
+
+def get_ov_type_for_value(value):
+    if isinstance(value, (jax.core.Var, jax.core.Literal)):
+        if value.aval.dtype in jax_to_ov_type_map:
+            return OVAny(jax_to_ov_type_map[value.aval.dtype])
+        for k, v in numpy_to_ov_type_map.items():
+            if value.aval.dtype == k:
+                return OVAny(v)
+        for k, v in basic_to_ov_type_map.items():
+            if isinstance(value.aval.dtype, k):
+                return OVAny(v)
+    elif isinstance(value, (int, float, bool)):
+        return OVAny(jax_to_ov_type_map[type(value)])
+    else:
+        raise NotImplementedError(f"dtype for {value} of type {type(value)} has not been supported yet.")
+    
+def get_ov_type_from_jax_type(dtype):
+    if dtype in jax_to_ov_type_map:
+        return OVAny(jax_to_ov_type_map[dtype])
+    for k, v in numpy_to_ov_type_map.items():
+        if dtype == k:
+            return OVAny(v)
+    for k, v in basic_to_ov_type_map.items():
+        if isinstance(dtype, k):
+            return OVAny(v)
+    return None
+
+def jax_array_to_ov_const(arr: np.ndarray, shared_memory=True):
+    # TODO: deal with bfloat16 dtype here.
+    if isinstance(arr, np.ndarray):
+        return op.Constant(arr, shared_memory=shared_memory)
+    elif isinstance(arr, jax.Array):
+        return op.Constant(np.array(jax.device_get(arr)), shared_memory=shared_memory)
+    else:
+        raise ValueError(f"Constant is expected to be a numpy array or jax array but got {type(arr)}")
+
+def ivalue_to_constant(ivalue, shared_memory=True):
+    '''
+    Convert a python object to an openvino constant.
+    '''
+    ov_type = get_type_from_py_type(ivalue)
+    if ov_type.is_static():
+        return op.Constant(ov_type, Shape([]), [ivalue]).outputs()
+
+    if isinstance(ivalue, (list, tuple)):
+        assert len(ivalue) > 0, "Can't deduce type for empty list"
+        ov_type = get_type_from_py_type(ivalue[0])
+        assert ov_type.is_static(), "Can't deduce type for list"
+        return op.Constant(ov_type, Shape([len(ivalue)]), ivalue).outputs()
+
+    if isinstance(ivalue, (jax.Array, np.ndarray)):
+        return jax_array_to_ov_const(ivalue, shared_memory=shared_memory).outputs()
+            
+    ov_dtype_value = get_ov_type_from_jax_type(ivalue)
+    if ov_dtype_value is not None:
+        return op.Constant(OVType.i64, Shape([]), [ov_type_to_int_map[ov_dtype_value]]).outputs()
+    
+    print(f"[WARNING][JAX FE] Cannot get constant from value {ivalue}")
+    return None
\ No newline at end of file
diff --git a/src/bindings/python/src/openvino/frontend/pytorch/fx_decoder.py b/src/bindings/python/src/openvino/frontend/pytorch/fx_decoder.py
index 596c4415868910..e8ca651ccae918 100644
--- a/src/bindings/python/src/openvino/frontend/pytorch/fx_decoder.py
+++ b/src/bindings/python/src/openvino/frontend/pytorch/fx_decoder.py
@@ -30,6 +30,7 @@ def __init__(self, pt_module, fx_gm=None, nodes=None, mark_node_callback=None, i
         self.input_shapes = input_shapes
 
         self._input_signature = []
+        self._example_input = None
 
         if issubclass(type(pt_module), torch.fx.graph_module.GraphModule):
 
diff --git a/src/bindings/python/src/openvino/frontend/pytorch/patch_model.py b/src/bindings/python/src/openvino/frontend/pytorch/patch_model.py
index 8245a69ecd7f3f..56ee32fa8ca0c0 100644
--- a/src/bindings/python/src/openvino/frontend/pytorch/patch_model.py
+++ b/src/bindings/python/src/openvino/frontend/pytorch/patch_model.py
@@ -30,6 +30,7 @@ def module_patcher(m, name):
 
         if extension:
             # The Trampoline class is instantiated for every module replacement, so we can use class members individually for each module.
+
             class Trampoline(torch.autograd.Function):
                 target_extension = extension
                 original_module = m
@@ -83,14 +84,32 @@ def unpatch_model(model, orig_forward_name):
 
 
 def __make_16bit_traceable(model: torch.nn.Module):
-    # Replace torch.nn.Linear with ModuleExtension and move other modules to fp32
-    extensions = {torch.nn.Linear: ModuleExtension(
-        torch.nn.Linear,
-        "aten::linear",
-        evaluate=lambda module, *args, **kwargs: torch.ones(
-            list(args[0].shape[:-1]) + [module.out_features], dtype=torch.float32) * 0.5,
-        convert=lambda module, target_op, *args, **kwargs: target_op(args[0], module.weight, module.bias))
+    """
+    Prepare a 16-bit PyTorch model for tracing with OpenVINO.
+     - Replace known list of modules with ModuleExtension.
+     - Convert other modules with weights to FP32.
+    """
+    extensions = {
+        torch.nn.Linear: ModuleExtension(
+            torch.nn.Linear, "ov_ext::linear",
+            evaluate=lambda module, *args, **kwargs: torch.full(
+                list(args[0].shape[:-1]) + [module.out_features], 0.5, dtype=torch.float32),
+            convert=lambda module, target_op, *args, **kwargs: target_op(args[0], module.weight, module.bias)),
+        torch.nn.Embedding: ModuleExtension(
+            torch.nn.Embedding, "ov_ext::embedding",
+            evaluate=lambda module, *args, **kwargs: torch.full(
+                list(args[0].shape) + [module.embedding_dim], 0.5, dtype=torch.float32),
+            convert=lambda module, target_op, *args, **kwargs: target_op(module.weight, args[0], module.padding_idx, module.scale_grad_by_freq, module.sparse)),
     }
+    try:
+        from transformers.pytorch_utils import Conv1D
+        extensions[Conv1D] = ModuleExtension(
+            Conv1D, "ov_ext::conv1d",
+            evaluate=lambda module, *args, **kwargs: torch.full(
+                list(args[0].shape[:-1]) + [module.nf], 0.5, dtype=torch.float32),
+            convert=lambda module, target_op, *args, **kwargs: target_op(args[0], module.weight, module.bias))
+    except:
+        pass
     patch_model(model, extensions,
                 "_openvino_module_extension_patch_orig_forward")
     for _, module in model.named_modules():
diff --git a/src/bindings/python/src/openvino/frontend/pytorch/ts_decoder.py b/src/bindings/python/src/openvino/frontend/pytorch/ts_decoder.py
index 29b09c6e55e04f..4fa45850d78f8b 100644
--- a/src/bindings/python/src/openvino/frontend/pytorch/ts_decoder.py
+++ b/src/bindings/python/src/openvino/frontend/pytorch/ts_decoder.py
@@ -96,6 +96,7 @@ def _get_scripted_model(self, pt_module, example_inputs=None, skip_freeze=False)
         if isinstance(pt_module, torch.nn.Module):
             pt_module.eval()
         input_signature = None
+        input_parameters = None
         if isinstance(pt_module, torch.nn.Module) and not isinstance(pt_module, (torch.jit._trace.TopLevelTracedModule, torch.jit._script.RecursiveScriptModule)):
             # input params is dictionary contains input names and their signature values (type hints and default values if any)
             input_params = inspect.signature(pt_module.forward if hasattr(
@@ -150,8 +151,10 @@ def _get_scripted_model(self, pt_module, example_inputs=None, skip_freeze=False)
                     scripted, preserved_attrs=preserved_attrs)
             else:
                 f_model = scripted
+            self._example_input = input_parameters["example_inputs"] if input_parameters else None
         else:
             f_model = pt_module
+            self._example_input = example_inputs
 
         self._input_signature = input_signature
         return f_model
diff --git a/src/bindings/python/src/openvino/runtime/opset13/ops.py b/src/bindings/python/src/openvino/runtime/opset13/ops.py
index 9dc4dc538279cd..ecd16fac797588 100644
--- a/src/bindings/python/src/openvino/runtime/opset13/ops.py
+++ b/src/bindings/python/src/openvino/runtime/opset13/ops.py
@@ -315,7 +315,7 @@ def display_shared_memory_warning(warning_message: str) -> None:
         if dtype in [Type.u1, Type.i4, Type.u4, Type.nf4, Type.f4e2m1]:
             display_shared_memory_warning(f"Constant initialized with packed type of {dtype}")
             return Constant(dtype, Shape(_value.shape), _value.flatten().tolist())
-        elif dtype in [Type.bf16]:
+        elif dtype in [Type.bf16, Type.f8e8m0, Type.f8e4m3, Type.f8e5m2]:
             display_shared_memory_warning(f"Constant initialized with OpenVINO custom {dtype}")
             return Constant(dtype, Shape(_value.shape), _value.flatten().tolist())
         # General use-case for all other types:
diff --git a/src/bindings/python/src/openvino/runtime/opset15/__init__.py b/src/bindings/python/src/openvino/runtime/opset15/__init__.py
index 3cecb6e5e03540..d7040d974c1ab9 100644
--- a/src/bindings/python/src/openvino/runtime/opset15/__init__.py
+++ b/src/bindings/python/src/openvino/runtime/opset15/__init__.py
@@ -7,4 +7,6 @@
 # TODO (ticket 138273): Add previous opset operators at the end of opset15 development
 from openvino.runtime.opset1.ops import parameter
 from openvino.runtime.opset15.ops import col2im
+from openvino.runtime.opset15.ops import embedding_bag_offsets
+from openvino.runtime.opset15.ops import embedding_bag_packed
 from openvino.runtime.opset15.ops import scatter_nd_update
diff --git a/src/bindings/python/src/openvino/runtime/opset15/ops.py b/src/bindings/python/src/openvino/runtime/opset15/ops.py
index 2b7406099c71ba..5613de6bd8267a 100644
--- a/src/bindings/python/src/openvino/runtime/opset15/ops.py
+++ b/src/bindings/python/src/openvino/runtime/opset15/ops.py
@@ -4,9 +4,12 @@
 
 """Factory functions for ops added to openvino opset15."""
 from functools import partial
-from typing import Optional, Literal, List
+from typing import List, Literal, Optional
 
+import numpy as np
 from openvino.runtime import Node, Type
+from openvino.runtime.opset1 import convert_like
+from openvino.runtime.opset14 import constant
 from openvino.runtime.opset_utils import _get_node_factory
 from openvino.runtime.utils.decorators import nameable_op
 from openvino.runtime.utils.types import NodeInput, as_nodes
@@ -83,3 +86,61 @@ def col2im(
             "pads_end": pads_end,
         },
     )
+
+
+@nameable_op
+def embedding_bag_offsets(
+    emb_table: NodeInput,
+    indices: NodeInput,
+    offsets: NodeInput,
+    default_index: Optional[NodeInput] = None,
+    per_sample_weights: Optional[NodeInput] = None,
+    reduction: Literal["sum", "mean"] = "sum",
+    name: Optional[str] = None,
+) -> Node:
+    """Return a node which performs sums or means of bags of embeddings without the intermediate embeddings.
+
+    :param emb_table: Tensor containing the embedding lookup table.
+    :param indices: 1D Tensor with indices.
+    :param offsets: 1D Tensor containing the starting index positions of each bag in indices.
+    :param per_sample_weights: Tensor with weights for each sample.
+    :param default_index: Scalar containing default index in embedding table to fill empty bags.
+                          If unset or set to -1, empty bags will be filled with 0.
+                          Reverse indexing using negative indices is not supported.
+    :param reduction: String to select algorithm used to perform reduction of elements in bag.
+    :param name: Optional name for output node.
+    :return: The new node performing EmbeddingBagOffsets operation.
+    """
+    inputs = [emb_table, indices, offsets]
+    if default_index is not None:
+        inputs.append(default_index)
+    elif per_sample_weights is not None:
+        inputs.append(convert_like(constant(np.array(-1, np.int32)), inputs[1]))
+    if per_sample_weights is not None:
+        inputs.append(per_sample_weights)
+
+    return _get_node_factory_opset15().create("EmbeddingBagOffsets", as_nodes(*inputs, name=name), {"reduction": reduction})
+
+
+@nameable_op
+def embedding_bag_packed(
+    emb_table: NodeInput,
+    indices: NodeInput,
+    per_sample_weights: Optional[NodeInput] = None,
+    reduction: Literal["sum", "mean"] = "sum",
+    name: Optional[str] = None,
+) -> Node:
+    """Return a node which performs sums or means of "bags" of embeddings, without the intermediate embeddings.
+
+    :param emb_table: Tensor containing the embedding lookup table.
+    :param indices: 2D Tensor of shape [batch, indices_per_bag] with indices.
+    :param per_sample_weights: Tensor of weights to be multiplied with embedding table with same shape as indices.
+    :param reduction: Operator to perform reduction of elements in bag.
+    :param name: Optional name for output node.
+    :return: The new node performing EmbeddingBagPacked operation.
+    """
+    inputs = [emb_table, indices]
+    if per_sample_weights is not None:
+        inputs.append(per_sample_weights)
+
+    return _get_node_factory_opset15().create("EmbeddingBagPacked", as_nodes(*inputs, name=name), {"reduction": reduction})
diff --git a/src/bindings/python/src/openvino/runtime/opset8/ops.py b/src/bindings/python/src/openvino/runtime/opset8/ops.py
index 025ba55442411e..d0025ca4a6bbe2 100644
--- a/src/bindings/python/src/openvino/runtime/opset8/ops.py
+++ b/src/bindings/python/src/openvino/runtime/opset8/ops.py
@@ -336,6 +336,7 @@ def random_uniform(
     output_type: str,
     global_seed: int = 0,
     op_seed: int = 0,
+    alignment: str = "tensorflow",
     name: Optional[str] = None,
 ) -> Node:
     """Return a node which generates sequence of random values from uniform distribution.
@@ -347,6 +348,8 @@ def random_uniform(
                                 'i64', 'i32', 'f64', 'f32', 'f16', 'bf16'.
     :param global_seed: Specifies global seed value. Required to be a positive integer or 0.
     :param op_seed: Specifies operational seed value. Required to be a positive integer or 0.
+    :param alignment: Specifies alignment of the randomly generated numbers to a given framework.
+                                Possible values: 'tensorflow', 'pytorch'. Default is 'tensorflow'.
     :param name: Optional output node name.
 
     :return: The new node which performs generation of random values from uniform distribution.
@@ -363,6 +366,7 @@ def random_uniform(
         "output_type": output_type,
         "global_seed": global_seed,
         "op_seed": op_seed,
+        "alignment": alignment.lower()
     }
     return _get_node_factory_opset8().create("RandomUniform", inputs, attributes)
 
diff --git a/src/bindings/python/src/openvino/utils.py b/src/bindings/python/src/openvino/utils.py
index 988728d149ec78..9890ae9b3e6460 100644
--- a/src/bindings/python/src/openvino/utils.py
+++ b/src/bindings/python/src/openvino/utils.py
@@ -6,6 +6,7 @@
 import sys
 from functools import wraps
 from typing import Callable, Any
+from pathlib import Path
 
 
 def _add_openvino_libs_to_search_path() -> None:
@@ -38,6 +39,22 @@ def _add_openvino_libs_to_search_path() -> None:
                 os.add_dll_directory(os.path.abspath(lib_path))
 
 
+def get_cmake_path() -> str:
+    """Searches for the directory containing CMake files within the package install directory.
+
+    :return: The path to the directory containing CMake files, if found. Otherwise, returns empty string.
+    :rtype: str
+    """
+    package_path = Path(__file__).parent
+    cmake_file = "OpenVINOConfig.cmake"
+
+    for dirpath, _, filenames in os.walk(package_path):
+        if cmake_file in filenames:
+            return dirpath
+
+    return ""
+
+
 def deprecated(name: Any = None, version: str = "", message: str = "", stacklevel: int = 2) -> Callable[..., Any]:
     """Prints deprecation warning "{function_name} is deprecated and will be removed in version {version}. {message}" and runs the function.
 
diff --git a/src/bindings/python/src/pyopenvino/CMakeLists.txt b/src/bindings/python/src/pyopenvino/CMakeLists.txt
index 086cd622643e02..99592f4552be25 100644
--- a/src/bindings/python/src/pyopenvino/CMakeLists.txt
+++ b/src/bindings/python/src/pyopenvino/CMakeLists.txt
@@ -50,10 +50,14 @@ if(TARGET openvino::frontend::pytorch)
     add_subdirectory(frontend/pytorch)
 endif()
 
+if(TARGET openvino::frontend::jax)
+    add_subdirectory(frontend/jax)
+endif()
+
 # create target
 
 file(GLOB_RECURSE SOURCES core/*.cpp graph/*.cpp frontend/*.cpp utils/*.cpp pyopenvino.cpp)
-list(FILTER SOURCES EXCLUDE REGEX ".*(frontend/(onnx|tensorflow|paddle|pytorch))/*")
+list(FILTER SOURCES EXCLUDE REGEX ".*(frontend/(onnx|tensorflow|paddle|pytorch|jax))/*")
 
 pybind11_add_module(${PROJECT_NAME} MODULE NO_EXTRAS ${SOURCES})
 
diff --git a/src/bindings/python/src/pyopenvino/core/common.cpp b/src/bindings/python/src/pyopenvino/core/common.cpp
index 90f80453016978..5d44bb74a11e35 100644
--- a/src/bindings/python/src/pyopenvino/core/common.cpp
+++ b/src/bindings/python/src/pyopenvino/core/common.cpp
@@ -29,7 +29,7 @@ const std::map<ov::element::Type, py::dtype>& ov_type_to_dtype() {
         {ov::element::u4, py::dtype("uint8")},     {ov::element::nf4, py::dtype("uint8")},
         {ov::element::i4, py::dtype("int8")},      {ov::element::f8e4m3, py::dtype("uint8")},
         {ov::element::f8e5m2, py::dtype("uint8")}, {ov::element::string, py::dtype("bytes_")},
-        {ov::element::f4e2m1, py::dtype("uint8")},
+        {ov::element::f4e2m1, py::dtype("uint8")}, {ov::element::f8e8m0, py::dtype("uint8")},
     };
     return ov_type_to_dtype_mapping;
 }
@@ -366,39 +366,26 @@ py::array array_from_constant_view(ov::op::v0::Constant&& c) {
 };  // namespace array_helpers
 
 namespace constant_helpers {
+
+std::vector<size_t> _get_byte_strides(const ov::Shape& s, const size_t element_byte_size) {
+    auto byte_strides = ov::row_major_strides(s);
+    for (auto&& stride : byte_strides) {
+        stride *= element_byte_size;
+    }
+    return byte_strides;
+}
+
 std::vector<size_t> _get_strides(const ov::op::v0::Constant& self) {
-    auto element_type = self.get_element_type();
-    auto shape = self.get_shape();
-    if (element_type == ov::element::boolean) {
-        return _get_byte_strides<char>(shape);
-    } else if (element_type == ov::element::f16 || element_type == ov::element::bf16) {
-        // WA for bf16, returned as f16 array
-        return _get_byte_strides<ov::float16>(shape);
-    } else if (element_type == ov::element::f32) {
-        return _get_byte_strides<float>(shape);
-    } else if (element_type == ov::element::f64) {
-        return _get_byte_strides<double>(shape);
-    } else if (element_type == ov::element::i8 || element_type == ov::element::i4) {
-        // WA for i4, returned as int8 array
-        return _get_byte_strides<int8_t>(shape);
-    } else if (element_type == ov::element::i16) {
-        return _get_byte_strides<int16_t>(shape);
-    } else if (element_type == ov::element::i32) {
-        return _get_byte_strides<int32_t>(shape);
-    } else if (element_type == ov::element::i64) {
-        return _get_byte_strides<int64_t>(shape);
-    } else if (element_type == ov::element::u8 || element_type == ov::element::u1 || element_type == ov::element::u4 ||
-               element_type == ov::element::nf4 || element_type == ov::element::f4e2m1) {
-        // WA for u1, u4, nf4, all returned as packed uint8 arrays
-        return _get_byte_strides<uint8_t>(shape);
-    } else if (element_type == ov::element::u16) {
-        return _get_byte_strides<uint16_t>(shape);
-    } else if (element_type == ov::element::u32) {
-        return _get_byte_strides<uint32_t>(shape);
-    } else if (element_type == ov::element::u64) {
-        return _get_byte_strides<uint64_t>(shape);
-    } else {
-        throw std::runtime_error("Unsupported data type!");
+    using namespace ov::element;
+    switch (self.get_element_type()) {
+    case i4:
+    case u1:
+    case u4:
+    case nf4:
+    case f4e2m1:
+        return _get_byte_strides(self.get_shape(), 8);
+    default:
+        return self.get_strides();
     }
 }
 };  // namespace constant_helpers
diff --git a/src/bindings/python/src/pyopenvino/core/common.hpp b/src/bindings/python/src/pyopenvino/core/common.hpp
index 2311855a8ad34e..a1fc1df7837f79 100644
--- a/src/bindings/python/src/pyopenvino/core/common.hpp
+++ b/src/bindings/python/src/pyopenvino/core/common.hpp
@@ -113,13 +113,11 @@ py::array array_from_constant_view(ov::op::v0::Constant&& c);
 }; // namespace array_helpers
 
 namespace constant_helpers {
+std::vector<size_t> _get_byte_strides(const ov::Shape& s, size_t element_byte_size);
+
 template <typename T>
 std::vector<size_t> _get_byte_strides(const ov::Shape& s) {
-    auto byte_strides = ov::row_major_strides(s);
-    for (auto&& stride : byte_strides) {
-        stride *= sizeof(T);
-     }
-    return byte_strides;
+    return _get_byte_strides(s, sizeof(T));
 }
 
 std::vector<size_t> _get_strides(const ov::op::v0::Constant& self);
diff --git a/src/bindings/python/src/pyopenvino/frontend/frontend.cpp b/src/bindings/python/src/pyopenvino/frontend/frontend.cpp
index c643ae7a4d16ad..afc9e0af361c52 100644
--- a/src/bindings/python/src/pyopenvino/frontend/frontend.cpp
+++ b/src/bindings/python/src/pyopenvino/frontend/frontend.cpp
@@ -12,6 +12,7 @@
 #include "openvino/frontend/exception.hpp"
 #include "openvino/frontend/extension/telemetry.hpp"
 #include "openvino/frontend/manager.hpp"
+#include "openvino/util/file_util.hpp"
 #include "pyopenvino/graph/model.hpp"
 #include "pyopenvino/utils/utils.hpp"
 
@@ -42,6 +43,16 @@ void regclass_frontend_FrontEnd(py::module m) {
                 py::isinstance<py::str>(py_obj) || py::isinstance<py::bytes>(py_obj)) {
                 // check if model path is either a string/pathlib.Path/bytes
                 std::string model_path = Common::utils::convert_path_to_string(py_obj);
+                if (py::isinstance(py_obj, py::module_::import("pathlib").attr("Path")) ||
+                    py::isinstance<py::str>(py_obj)) {
+
+                // Fix unicode path
+#if defined(OPENVINO_ENABLE_UNICODE_PATH_SUPPORT) && defined(_WIN32)
+                    return self.load(ov::util::string_to_wstring(model_path.c_str()));
+#else
+                    return self.load(model_path.c_str());
+#endif
+                }
                 return self.load(model_path, enable_mmap);
             } else if (py::isinstance(py_obj, pybind11::module::import("io").attr("BytesIO"))) {
                 // support of BytesIO
@@ -70,6 +81,21 @@ void regclass_frontend_FrontEnd(py::module m) {
     fem.def(
         "supported",
         [](FrontEnd& self, const py::object& model) {
+            if (py::isinstance(model, py::module_::import("pathlib").attr("Path")) || py::isinstance<py::str>(model) ||
+                py::isinstance<py::bytes>(model)) {
+                // check if model path is either a string/pathlib.Path/bytes
+                std::string model_path = Common::utils::convert_path_to_string(model);
+                if (py::isinstance(model, py::module_::import("pathlib").attr("Path")) ||
+                    py::isinstance<py::str>(model)) {
+
+                // Fix unicode path
+#if defined(OPENVINO_ENABLE_UNICODE_PATH_SUPPORT) && defined(_WIN32)
+                    return self.supported(ov::util::string_to_wstring(model_path.c_str()));
+#else
+                    return self.supported(model_path.c_str());
+#endif
+                }
+            }
             return self.supported({Common::utils::py_object_to_any(model)});
         },
         py::arg("model"),
@@ -157,7 +183,7 @@ void regclass_frontend_FrontEnd(py::module m) {
     fem.def("add_extension",
             static_cast<void (FrontEnd::*)(const std::shared_ptr<ov::Extension>& extension)>(&FrontEnd::add_extension),
             R"(
-                Add extension defined by an object inheriting from Extension 
+                Add extension defined by an object inheriting from Extension
                 used in order to extend capabilities of Frontend.
 
                 :param extension: Provided extension object.
@@ -168,7 +194,7 @@ void regclass_frontend_FrontEnd(py::module m) {
             static_cast<void (FrontEnd::*)(const std::vector<std::shared_ptr<ov::Extension>>& extension)>(
                 &FrontEnd::add_extension),
             R"(
-                Add extensions defined by objects inheriting from Extension 
+                Add extensions defined by objects inheriting from Extension
                 used in order to extend capabilities of Frontend.
 
                 :param extension: Provided extension objects.
@@ -181,7 +207,7 @@ void regclass_frontend_FrontEnd(py::module m) {
             return self.add_extension(Common::utils::convert_path_to_string(extension_path));
         },
         R"(
-                Add extension defined in external library indicated by a extension_path 
+                Add extension defined in external library indicated by a extension_path
                 used in order to extend capabilities of Frontend.
 
                 :param extension_path: A path to extension.
diff --git a/src/bindings/python/src/pyopenvino/frontend/input_model.cpp b/src/bindings/python/src/pyopenvino/frontend/input_model.cpp
index 20315a74338316..66a41c2ed4ee44 100644
--- a/src/bindings/python/src/pyopenvino/frontend/input_model.cpp
+++ b/src/bindings/python/src/pyopenvino/frontend/input_model.cpp
@@ -33,6 +33,18 @@ void regclass_frontend_InputModel(py::module m) {
                 :rtype: openvino.frontend.Place
              )");
 
+    im.def("get_place_by_input_index",
+           &ov::frontend::InputModel::get_place_by_input_index,
+           py::arg("input_idx"),
+           R"(
+                Returns a tensor place by an input index.
+
+                :param input_idx: Index of model input.
+                :type input_idx: int
+                :return: Tensor place corresponding to specified input index or nullptr.
+                :rtype: openvino.frontend.Place
+             )");
+
     im.def("get_place_by_operation_name",
            &ov::frontend::InputModel::get_place_by_operation_name,
            py::arg("operation_name"),
diff --git a/src/bindings/python/src/pyopenvino/frontend/jax/CMakeLists.txt b/src/bindings/python/src/pyopenvino/frontend/jax/CMakeLists.txt
new file mode 100644
index 00000000000000..5bf4b9504a117b
--- /dev/null
+++ b/src/bindings/python/src/pyopenvino/frontend/jax/CMakeLists.txt
@@ -0,0 +1,6 @@
+# Copyright (C) 2018-2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+#
+
+include(${pyopenvino_SOURCE_DIR}/frontend/frontend_module.cmake)
+frontend_module(py_jax_frontend jax ${OV_CPACK_COMP_PYTHON_OPENVINO}_${pyversion})
diff --git a/src/bindings/python/src/pyopenvino/frontend/jax/decoder.cpp b/src/bindings/python/src/pyopenvino/frontend/jax/decoder.cpp
new file mode 100644
index 00000000000000..c84a31cf44a78f
--- /dev/null
+++ b/src/bindings/python/src/pyopenvino/frontend/jax/decoder.cpp
@@ -0,0 +1,22 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <pybind11/functional.h>
+#include <pybind11/pybind11.h>
+#include <pybind11/stl.h>
+#include <pybind11/stl_bind.h>
+
+#include "decoder.hpp"
+
+#include "openvino/frontend/decoder.hpp"
+
+namespace py = pybind11;
+
+using namespace ov::frontend;
+
+
+void regclass_frontend_jax_decoder(py::module m) {
+    py::class_<jax::JaxDecoder, IDecoder, PyDecoder, std::shared_ptr<jax::JaxDecoder>>(m, "_FrontEndJaxDecoder")
+        .def(py::init<>());
+}
diff --git a/src/bindings/python/src/pyopenvino/frontend/jax/decoder.hpp b/src/bindings/python/src/pyopenvino/frontend/jax/decoder.hpp
new file mode 100644
index 00000000000000..ed9b9706c17f6f
--- /dev/null
+++ b/src/bindings/python/src/pyopenvino/frontend/jax/decoder.hpp
@@ -0,0 +1,93 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <pybind11/pybind11.h>
+
+#include <cstddef>
+#include <string>
+#include <vector>
+
+#include "openvino/core/any.hpp"
+#include "openvino/core/node_vector.hpp"
+#include "openvino/core/partial_shape.hpp"
+#include "openvino/frontend/jax/decoder.hpp"
+
+namespace py = pybind11;
+
+/// Trampoline class to support inheritence from JaxDecoder in Python
+class PyDecoder : public ov::frontend::jax::JaxDecoder {
+    using ov::frontend::jax::JaxDecoder::JaxDecoder;
+
+    ov::OutputVector as_constant() const override {
+        PYBIND11_OVERRIDE_PURE(ov::OutputVector, JaxDecoder, as_constant);
+    }
+
+    const std::string get_op_type() const override {
+        PYBIND11_OVERRIDE_PURE(std::string, JaxDecoder, get_op_type);
+    }
+
+    const std::string& get_input_signature_name(size_t index) const override {
+        PYBIND11_OVERRIDE_PURE(const std::string&, JaxDecoder, get_input_signature_name, index);
+    }
+
+    ov::PartialShape get_input_shape(size_t index) const override {
+        PYBIND11_OVERRIDE_PURE(ov::PartialShape, JaxDecoder, get_input_shape, index);
+    }
+
+    ov::PartialShape get_output_shape(size_t index) const override {
+        PYBIND11_OVERRIDE_PURE(ov::PartialShape, JaxDecoder, get_output_shape, index);
+    }
+
+    ov::Any get_input_type(size_t index) const override {
+        PYBIND11_OVERRIDE_PURE(ov::Any, JaxDecoder, get_input_type, index);
+    }
+
+    size_t get_named_param(const std::string& name) const override {
+        PYBIND11_OVERRIDE_PURE(size_t, JaxDecoder, get_named_param, name);
+    }
+
+    ov::OutputVector get_named_param_as_constant(const std::string& name) const override {
+        PYBIND11_OVERRIDE_PURE(ov::OutputVector, JaxDecoder, get_named_param_as_constant, name);
+    }
+
+    const std::vector<std::string>& get_param_names() const override {
+        PYBIND11_OVERRIDE_PURE(const std::vector<std::string>&, JaxDecoder, get_param_names);
+    }
+
+    const std::string& get_output_name(size_t index) const override {
+        PYBIND11_OVERRIDE_PURE(const std::string&, JaxDecoder, get_output_name, index);
+    }
+
+    ov::Any get_output_type(size_t index) const override {
+        PYBIND11_OVERRIDE_PURE(ov::Any, JaxDecoder, get_output_type, index);
+    }
+
+    const std::vector<size_t>& inputs() const override {
+        PYBIND11_OVERRIDE_PURE(std::vector<size_t>&, JaxDecoder, inputs);
+    }
+
+    size_t num_inputs() const override {
+        PYBIND11_OVERRIDE_PURE(size_t, JaxDecoder, num_inputs);
+    }
+
+    size_t num_outputs() const override {
+        PYBIND11_OVERRIDE_PURE(size_t, JaxDecoder, num_outputs);
+    }
+
+    size_t output(size_t index) const override {
+        PYBIND11_OVERRIDE_PURE(size_t, JaxDecoder, output, index);
+    }
+
+    const std::vector<size_t>& outputs() const override {
+        PYBIND11_OVERRIDE_PURE(std::vector<size_t>&, JaxDecoder, outputs);
+    }
+
+    void visit_subgraph(std::function<void(std::shared_ptr<JaxDecoder>)> node_visitor) const override {
+        PYBIND11_OVERRIDE_PURE(void, JaxDecoder, visit_subgraph, node_visitor);
+    }
+};
+
+void regclass_frontend_jax_decoder(py::module m);
diff --git a/src/bindings/python/src/pyopenvino/frontend/jax/py_module.cpp b/src/bindings/python/src/pyopenvino/frontend/jax/py_module.cpp
new file mode 100644
index 00000000000000..8a217d8d3bb8ae
--- /dev/null
+++ b/src/bindings/python/src/pyopenvino/frontend/jax/py_module.cpp
@@ -0,0 +1,14 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <pybind11/pybind11.h>
+
+#include "decoder.hpp"
+// #include "extension.hpp"
+
+namespace py = pybind11;
+
+PYBIND11_MODULE(py_jax_frontend, m) {
+    regclass_frontend_jax_decoder(m);
+}
diff --git a/src/bindings/python/src/pyopenvino/frontend/manager.cpp b/src/bindings/python/src/pyopenvino/frontend/manager.cpp
index a824e9473bc181..408bb224f7679b 100644
--- a/src/bindings/python/src/pyopenvino/frontend/manager.cpp
+++ b/src/bindings/python/src/pyopenvino/frontend/manager.cpp
@@ -9,6 +9,7 @@
 #include <pybind11/stl_bind.h>
 
 #include "openvino/frontend/exception.hpp"
+#include "openvino/util/file_util.hpp"
 #include "pyopenvino/frontend/manager.hpp"
 #include "pyopenvino/utils/utils.hpp"
 
@@ -51,10 +52,10 @@ void regclass_frontend_FrontEndManager(py::module m) {
         py::arg("library_path"),
         R"(
                 Register frontend with name and factory loaded from provided library.
-            
+
                 :param name: Name of front end.
                 :type name: str
-                
+
                 :param library_path: Path (absolute or relative) or name of a frontend library. If name is
                 provided, depending on platform, it will be wrapped with shared library suffix and prefix
                 to identify library full name.
@@ -78,9 +79,16 @@ void regclass_frontend_FrontEndManager(py::module m) {
     fem.def(
         "load_by_model",
         [](const std::shared_ptr<ov::frontend::FrontEndManager>& fem, const py::object& model) {
-            if (py::isinstance(model, py::module_::import("pathlib").attr("Path"))) {
+            if (py::isinstance(model, py::module_::import("pathlib").attr("Path")) || py::isinstance<py::str>(model)) {
                 std::string model_path = Common::utils::convert_path_to_string(model);
-                return fem->load_by_model(model_path);
+
+            // Fix unicode path
+#if defined(OPENVINO_ENABLE_UNICODE_PATH_SUPPORT) && defined(_WIN32)
+                return fem->load_by_model(ov::util::string_to_wstring(model_path.c_str()));
+#else
+                std::string model_path_str = model_path;
+                return fem->load_by_model(model_path_str);
+#endif
             }
             return fem->load_by_model({Common::utils::py_object_to_any(model)});
         },
diff --git a/src/bindings/python/src/pyopenvino/graph/attribute_visitor.cpp b/src/bindings/python/src/pyopenvino/graph/attribute_visitor.cpp
new file mode 100644
index 00000000000000..587d3906b02607
--- /dev/null
+++ b/src/bindings/python/src/pyopenvino/graph/attribute_visitor.cpp
@@ -0,0 +1,95 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "pyopenvino/graph/attribute_visitor.hpp"
+
+#include <pybind11/numpy.h>
+#include <pybind11/pybind11.h>
+#include <pybind11/stl.h>
+
+#include "openvino/core/attribute_adapter.hpp"
+#include "openvino/core/attribute_visitor.hpp"
+#include "openvino/core/except.hpp"
+#include "pyopenvino/utils/utils.hpp"
+
+namespace py = pybind11;
+
+template <typename AT>
+void visit_attribute(py::dict& attributes,
+                     const std::pair<py::handle, py::handle>& attribute,
+                     ov::AttributeVisitor* visitor) {
+    auto attr_casted = attribute.second.cast<AT>();
+    visitor->on_attribute<AT>(attribute.first.cast<std::string>(), attr_casted);
+    attributes[attribute.first] = std::move(attr_casted);
+
+    return;
+};
+
+void regclass_graph_AttributeVisitor(py::module m) {
+    using PY_TYPE = Common::utils::PY_TYPE;
+
+    py::class_<ov::AttributeVisitor, std::shared_ptr<ov::AttributeVisitor>> visitor(m, "AttributeVisitor");
+
+    visitor.def(
+        "on_attributes",
+        [](ov::AttributeVisitor* self, py::dict& attributes) {
+            py::object float_32_type = py::module_::import("numpy").attr("float32");
+            for (const auto& attribute : attributes) {
+                if (py::isinstance<py::bool_>(attribute.second)) {
+                    visit_attribute<bool>(attributes, attribute, self);
+                } else if (py::isinstance<py::str>(attribute.second)) {
+                    visit_attribute<std::string>(attributes, attribute, self);
+                } else if (py::isinstance<py::int_>(attribute.second)) {
+                    visit_attribute<int64_t>(attributes, attribute, self);
+                } else if (py::isinstance<py::float_>(attribute.second)) {
+                    visit_attribute<double>(attributes, attribute, self);
+                } else if (py::isinstance(attribute.second, float_32_type)) {
+                    visit_attribute<float>(attributes, attribute, self);
+                } else if (py::isinstance<ov::Model>(attribute.second)) {
+                    visit_attribute<std::shared_ptr<ov::Model>>(attributes, attribute, self);
+                } else if (py::isinstance<ov::Dimension>(attribute.second)) {
+                    visit_attribute<ov::Dimension>(attributes, attribute, self);
+                } else if (py::isinstance<ov::PartialShape>(attribute.second)) {
+                    visit_attribute<ov::PartialShape>(attributes, attribute, self);
+                } else if (py::isinstance<py::array>(attribute.second)) {
+                    // numpy arrays
+                    auto _array = attribute.second.cast<py::array>();
+                    if (py::isinstance<py::array_t<float>>(_array)) {
+                        visit_attribute<std::vector<float>>(attributes, std::make_pair(attribute.first, _array), self);
+                    } else {
+                        throw py::type_error("Unsupported NumPy array dtype: " + std::string(py::str(_array.dtype())));
+                    }
+                } else if (py::isinstance<py::list>(attribute.second)) {
+                    // python list
+                    auto _list = attribute.second.cast<py::list>();
+
+                    OPENVINO_ASSERT(!_list.empty(), "Attributes list is empty.");
+
+                    PY_TYPE detected_type = Common::utils::check_list_element_type(_list);
+
+                    switch (detected_type) {
+                    case PY_TYPE::STR:
+                        visit_attribute<std::vector<std::string>>(attributes,
+                                                                  std::make_pair(attribute.first, _list),
+                                                                  self);
+                        break;
+                    case PY_TYPE::FLOAT:
+                        // python float is float64 in C++
+                        visit_attribute<std::vector<double>>(attributes, std::make_pair(attribute.first, _list), self);
+                        break;
+                    case PY_TYPE::INT:
+                        visit_attribute<std::vector<int64_t>>(attributes, std::make_pair(attribute.first, _list), self);
+                        break;
+                    default:
+                        throw py::type_error("Unsupported attribute type in provided list: " +
+                                             std::string(py::str(_list[0].get_type())));
+                    }
+                } else {
+                    throw py::type_error("Unsupported attribute type: " +
+                                         std::string(py::str(attribute.second.get_type())));
+                }
+            }
+        },
+        py::return_value_policy::reference_internal);
+}
diff --git a/src/bindings/python/src/pyopenvino/graph/attribute_visitor.hpp b/src/bindings/python/src/pyopenvino/graph/attribute_visitor.hpp
new file mode 100644
index 00000000000000..866212e19d24a6
--- /dev/null
+++ b/src/bindings/python/src/pyopenvino/graph/attribute_visitor.hpp
@@ -0,0 +1,11 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <pybind11/pybind11.h>
+
+namespace py = pybind11;
+
+void regclass_graph_AttributeVisitor(py::module m);
diff --git a/src/bindings/python/src/pyopenvino/graph/descriptors/tensor.cpp b/src/bindings/python/src/pyopenvino/graph/descriptors/tensor.cpp
index 284aba75ff6989..2975edfb39693b 100644
--- a/src/bindings/python/src/pyopenvino/graph/descriptors/tensor.cpp
+++ b/src/bindings/python/src/pyopenvino/graph/descriptors/tensor.cpp
@@ -107,6 +107,63 @@ void regclass_graph_descriptor_Tensor(py::module m) {
                 :rtype: string
              )");
 
+    tensor.def("get_lower_value",
+               &ov::descriptor::Tensor::get_lower_value,
+               R"(
+                Returns the lower bound of the tensor.
+
+                :return: Lower bound.
+                :rtype: openvino.runtime.Tensor
+             )");
+
+    tensor.def("set_lower_value",
+               &ov::descriptor::Tensor::set_lower_value,
+               py::arg("lower_bound"),
+               R"(
+                Sets the lower bound of the tensor.
+
+                :param lower_bound: The lower bound value.
+                :type lower_bound: openvino.runtime.Tensor
+             )");
+
+    tensor.def("get_upper_value",
+               &ov::descriptor::Tensor::get_upper_value,
+               R"(
+                Returns the upper bound of the tensor.
+
+                :return: Upper bound.
+                :rtype: openvino.runtime.Tensor
+             )");
+
+    tensor.def("set_upper_value",
+               &ov::descriptor::Tensor::set_upper_value,
+               py::arg("upper_bound"),
+               R"(
+                Sets the upper bound of the tensor.
+
+                :param upper_bound: Sets the upper bound value.
+                :type upper_bound: openvino.runtime.Tensor
+             )");
+
+    tensor.def("get_value_symbol",
+               &ov::descriptor::Tensor::get_value_symbol,
+               R"(
+                Returns the List of symbols.
+
+                :return: List of Symbols.
+                :rtype: List[openvino.runtime.Symbol]
+             )");
+
+    tensor.def("set_value_symbol",
+               &ov::descriptor::Tensor::set_value_symbol,
+               py::arg("value_symbol"),
+               R"(
+                Sets the value symbol of the tensor.
+
+                :param value_symbol: List of Symbols
+                :type value_symbol: List[openvino.runtime.Symbol]
+             )");
+
     tensor.def_property_readonly("shape", &ov::descriptor::Tensor::get_shape);
 
     tensor.def_property_readonly("rt_info",
diff --git a/src/bindings/python/src/pyopenvino/graph/model.cpp b/src/bindings/python/src/pyopenvino/graph/model.cpp
index 498ee992173457..2842d30810f5b4 100644
--- a/src/bindings/python/src/pyopenvino/graph/model.cpp
+++ b/src/bindings/python/src/pyopenvino/graph/model.cpp
@@ -84,6 +84,50 @@ static void set_correct_variables_for_assign_ops(const std::shared_ptr<ov::Model
     }
 }
 
+static ov::Output<ov::Node> output_from_handle(ov::Model& model, const py::handle& handle) {
+    if (py::isinstance<py::int_>(handle)) {
+        return model.input(handle.cast<size_t>());
+    } else if (py::isinstance<py::str>(handle)) {
+        return model.input(handle.cast<std::string>());
+    } else if (py::isinstance<ov::Output<ov::Node>>(handle)) {
+        return handle.cast<ov::Output<ov::Node>>();
+    } else {
+        throw py::type_error("Incorrect key type " + std::string(py::str(handle.get_type())) +
+                             " to reshape a model, expected keys as openvino.runtime.Output, int or str.");
+    }
+}
+
+static ov::PartialShape partial_shape_from_handle(const py::handle& handle) {
+    if (py::isinstance<ov::PartialShape>(handle)) {
+        return handle.cast<ov::PartialShape>();
+    } else if (py::isinstance<py::list>(handle) || py::isinstance<py::tuple>(handle)) {
+        return Common::partial_shape_from_list(handle.cast<py::list>());
+    } else if (py::isinstance<py::str>(handle)) {
+        return ov::PartialShape(handle.cast<std::string>());
+    } else {
+        throw py::type_error(
+            "Incorrect value type " + std::string(py::str(handle.get_type())) +
+            " to reshape a model, expected values as openvino.runtime.PartialShape, str, list or tuple.");
+    }
+}
+
+static std::string string_from_handle(const py::handle& handle) {
+    if (py::isinstance<py::str>(handle)) {
+        return handle.cast<std::string>();
+    } else {
+        throw py::type_error("Incorrect key type " + std::string(py::str(handle.get_type())) +
+                             " to reshape a model, expected values as str.");
+    }
+}
+
+static std::unordered_map<std::string, ov::PartialShape> get_variables_shapes(const py::dict& variables_shapes) {
+    std::unordered_map<std::string, ov::PartialShape> variables_shape_map;
+    for (const auto& item : variables_shapes) {
+        variables_shape_map.emplace(string_from_handle(item.first), partial_shape_from_handle(item.second));
+    }
+    return variables_shape_map;
+}
+
 void regclass_graph_Model(py::module m) {
     py::class_<ov::Model, std::shared_ptr<ov::Model>> model(m, "Model", py::module_local());
     model.doc() = "openvino.runtime.Model wraps ov::Model";
@@ -309,109 +353,186 @@ void regclass_graph_Model(py::module m) {
 
     model.def(
         "reshape",
-        [](ov::Model& self, const ov::PartialShape& partial_shape) {
-            self.reshape(partial_shape);
+        [](ov::Model& self, const ov::PartialShape& partial_shape, const py::dict& variables_shapes) {
+            const auto new_variable_shapes = get_variables_shapes(variables_shapes);
+            py::gil_scoped_release release;
+            self.reshape(partial_shape, new_variable_shapes);
         },
-        py::call_guard<py::gil_scoped_release>(),
         py::arg("partial_shape"),
+        py::arg("variables_shapes") = py::dict(),
         R"(
                 Reshape model input.
 
+                The allowed types of keys in the `variables_shapes` dictionary is `str`.
+                The allowed types of values in the `variables_shapes` are:
+
+                (1) `openvino.runtime.PartialShape`
+                (2) `list` consisting of dimensions
+                (3) `tuple` consisting of dimensions
+                (4) `str`, string representation of `openvino.runtime.PartialShape`
+
+                When list or tuple are used to describe dimensions, each dimension can be written in form:
+
+                (1) non-negative `int` which means static value for the dimension
+                (2) `[min, max]`, dynamic dimension where `min` specifies lower bound and `max` specifies upper bound;
+                the range includes both `min` and `max`; using `-1` for `min` or `max` means no known bound (3) `(min,
+                max)`, the same as above (4) `-1` is a dynamic dimension without known bounds (4)
+                `openvino.runtime.Dimension` (5) `str` using next syntax:
+                    '?' - to define fully dynamic dimension
+                    '1' - to define dimension which length is 1
+                    '1..10' - to define bounded dimension
+                    '..10' or '1..' to define dimension with only lower or only upper limit
+
                 GIL is released while running this function.
 
                 :param partial_shape: New shape.
                 :type partial_shape: openvino.runtime.PartialShape
+                :param variables_shapes: New shapes for variables
+                :type variables_shapes: Dict[keys, values]
                 :return : void
              )");
 
     model.def(
         "reshape",
-        [](ov::Model& self, const py::list& partial_shape) {
-            ov::PartialShape new_shape(Common::partial_shape_from_list(partial_shape));
+        [](ov::Model& self, const py::list& partial_shape, const py::dict& variables_shapes) {
+            const auto new_shape = Common::partial_shape_from_list(partial_shape);
+            const auto new_variables_shapes = get_variables_shapes(variables_shapes);
             py::gil_scoped_release release;
-            self.reshape(new_shape);
+            self.reshape(new_shape, new_variables_shapes);
         },
         py::arg("partial_shape"),
+        py::arg("variables_shapes") = py::dict(),
         R"(
                 Reshape model input.
 
+                The allowed types of keys in the `variables_shapes` dictionary is `str`.
+                The allowed types of values in the `variables_shapes` are:
+
+                (1) `openvino.runtime.PartialShape`
+                (2) `list` consisting of dimensions
+                (3) `tuple` consisting of dimensions
+                (4) `str`, string representation of `openvino.runtime.PartialShape`
+
+                When list or tuple are used to describe dimensions, each dimension can be written in form:
+
+                (1) non-negative `int` which means static value for the dimension
+                (2) `[min, max]`, dynamic dimension where `min` specifies lower bound and `max` specifies upper bound;
+                the range includes both `min` and `max`; using `-1` for `min` or `max` means no known bound (3) `(min,
+                max)`, the same as above (4) `-1` is a dynamic dimension without known bounds (4)
+                `openvino.runtime.Dimension` (5) `str` using next syntax:
+                    '?' - to define fully dynamic dimension
+                    '1' - to define dimension which length is 1
+                    '1..10' - to define bounded dimension
+                    '..10' or '1..' to define dimension with only lower or only upper limit
+
                 GIL is released while running this function.
 
                 :param partial_shape: New shape.
                 :type partial_shape: list
+                :param variables_shapes: New shapes for variables
+                :type variables_shapes: Dict[keys, values]
                 :return : void
              )");
 
     model.def(
         "reshape",
-        [](ov::Model& self, const py::tuple& partial_shape) {
-            ov::PartialShape new_shape(Common::partial_shape_from_list(partial_shape.cast<py::list>()));
+        [](ov::Model& self, const py::tuple& partial_shape, const py::dict& variables_shapes) {
+            const auto new_shape = Common::partial_shape_from_list(partial_shape.cast<py::list>());
+            const auto new_variables_shapes = get_variables_shapes(variables_shapes);
             py::gil_scoped_release release;
-            self.reshape(new_shape);
+            self.reshape(new_shape, new_variables_shapes);
         },
         py::arg("partial_shape"),
+        py::arg("variables_shapes") = py::dict(),
         R"(
                 Reshape model input.
 
+                The allowed types of keys in the `variables_shapes` dictionary is `str`.
+                The allowed types of values in the `variables_shapes` are:
+
+                (1) `openvino.runtime.PartialShape`
+                (2) `list` consisting of dimensions
+                (3) `tuple` consisting of dimensions
+                (4) `str`, string representation of `openvino.runtime.PartialShape`
+
+                When list or tuple are used to describe dimensions, each dimension can be written in form:
+
+                (1) non-negative `int` which means static value for the dimension
+                (2) `[min, max]`, dynamic dimension where `min` specifies lower bound and `max` specifies upper bound;
+                the range includes both `min` and `max`; using `-1` for `min` or `max` means no known bound (3) `(min,
+                max)`, the same as above (4) `-1` is a dynamic dimension without known bounds (4)
+                `openvino.runtime.Dimension` (5) `str` using next syntax:
+                    '?' - to define fully dynamic dimension
+                    '1' - to define dimension which length is 1
+                    '1..10' - to define bounded dimension
+                    '..10' or '1..' to define dimension with only lower or only upper limit
+
                 GIL is released while running this function.
 
                 :param partial_shape: New shape.
                 :type partial_shape: tuple
+                :param variables_shapes: New shapes for variables
+                :type variables_shapes: Dict[keys, values]
                 :return : void
              )");
 
     model.def(
         "reshape",
-        [](ov::Model& self, const std::string& partial_shape) {
-            self.reshape(ov::PartialShape(partial_shape));
+        [](ov::Model& self, const std::string& partial_shape, const py::dict& variables_shapes) {
+            const auto new_variables_shape = get_variables_shapes(variables_shapes);
+            py::gil_scoped_release release;
+            self.reshape(ov::PartialShape(partial_shape), new_variables_shape);
         },
-        py::call_guard<py::gil_scoped_release>(),
         py::arg("partial_shape"),
+        py::arg("variables_shapes") = py::dict(),
         R"(
                 Reshape model input.
 
+                The allowed types of keys in the `variables_shapes` dictionary is `str`.
+                The allowed types of values in the `variables_shapes` are:
+
+                (1) `openvino.runtime.PartialShape`
+                (2) `list` consisting of dimensions
+                (3) `tuple` consisting of dimensions
+                (4) `str`, string representation of `openvino.runtime.PartialShape`
+
+                When list or tuple are used to describe dimensions, each dimension can be written in form:
+
+                (1) non-negative `int` which means static value for the dimension
+                (2) `[min, max]`, dynamic dimension where `min` specifies lower bound and `max` specifies upper bound;
+                the range includes both `min` and `max`; using `-1` for `min` or `max` means no known bound (3) `(min,
+                max)`, the same as above (4) `-1` is a dynamic dimension without known bounds (4)
+                `openvino.runtime.Dimension` (5) `str` using next syntax:
+                    '?' - to define fully dynamic dimension
+                    '1' - to define dimension which length is 1
+                    '1..10' - to define bounded dimension
+                    '..10' or '1..' to define dimension with only lower or only upper limit
+
+
                 GIL is released while running this function.
 
                 :param partial_shape: New shape.
                 :type partial_shape: str
+                :param variables_shapes: New shapes for variables
+                :type variables_shapes: Dict[keys, values]
                 :return : void
              )");
 
     model.def(
         "reshape",
-        [](ov::Model& self, const py::dict& partial_shapes) {
+        [](ov::Model& self, const py::dict& partial_shapes, const py::dict& variables_shapes) {
             std::map<ov::Output<ov::Node>, ov::PartialShape> new_shapes;
             for (const auto& item : partial_shapes) {
-                std::pair<ov::Output<ov::Node>, ov::PartialShape> new_shape;
-                // check keys
-                if (py::isinstance<py::int_>(item.first)) {
-                    new_shape.first = self.input(item.first.cast<size_t>());
-                } else if (py::isinstance<py::str>(item.first)) {
-                    new_shape.first = self.input(item.first.cast<std::string>());
-                } else if (py::isinstance<ov::Output<ov::Node>>(item.first)) {
-                    new_shape.first = item.first.cast<ov::Output<ov::Node>>();
-                } else {
-                    throw py::type_error("Incorrect key type " + std::string(py::str(item.first.get_type())) +
-                                         " to reshape a model, expected keys as openvino.runtime.Output, int or str.");
-                }
-                // check values
-                if (py::isinstance<ov::PartialShape>(item.second)) {
-                    new_shape.second = item.second.cast<ov::PartialShape>();
-                } else if (py::isinstance<py::list>(item.second) || py::isinstance<py::tuple>(item.second)) {
-                    new_shape.second = Common::partial_shape_from_list(item.second.cast<py::list>());
-                } else if (py::isinstance<py::str>(item.second)) {
-                    new_shape.second = ov::PartialShape(item.second.cast<std::string>());
-                } else {
-                    throw py::type_error(
-                        "Incorrect value type " + std::string(py::str(item.second.get_type())) +
-                        " to reshape a model, expected values as openvino.runtime.PartialShape, str, list or tuple.");
-                }
-                new_shapes.insert(new_shape);
+                new_shapes.emplace_hint(new_shapes.end(),
+                                        output_from_handle(self, item.first),
+                                        partial_shape_from_handle(item.second));
             }
+            const auto new_variables_shapes = get_variables_shapes(variables_shapes);
             py::gil_scoped_release release;
-            self.reshape(new_shapes);
+            self.reshape(new_shapes, new_variables_shapes);
         },
         py::arg("partial_shapes"),
+        py::arg("variables_shapes") = py::dict(),
         R"( Reshape model inputs.
 
             The allowed types of keys in the `partial_shapes` dictionary are:
@@ -440,12 +561,34 @@ void regclass_graph_Model(py::module m) {
                 '1..10' - to define bounded dimension
                 '..10' or '1..' to define dimension with only lower or only upper limit
 
-            Reshape model input.
+            The allowed types of keys in the `variables_shapes` dictionary is `str`.
+            The allowed types of values in the `variables_shapes` are:
+
+            (1) `openvino.runtime.PartialShape`
+            (2) `list` consisting of dimensions
+            (3) `tuple` consisting of dimensions
+            (4) `str`, string representation of `openvino.runtime.PartialShape`
+
+            When list or tuple are used to describe dimensions, each dimension can be written in form:
+
+            (1) non-negative `int` which means static value for the dimension
+            (2) `[min, max]`, dynamic dimension where `min` specifies lower bound and `max` specifies upper bound;
+            the range includes both `min` and `max`; using `-1` for `min` or `max` means no known bound (3) `(min,
+            max)`, the same as above (4) `-1` is a dynamic dimension without known bounds (4)
+            `openvino.runtime.Dimension` (5) `str` using next syntax:
+                '?' - to define fully dynamic dimension
+                '1' - to define dimension which length is 1
+                '1..10' - to define bounded dimension
+                '..10' or '1..' to define dimension with only lower or only upper limit
+
+            Reshape model inputs.
 
             GIL is released while running this function.
 
             :param partial_shapes: New shapes.
             :type partial_shapes: Dict[keys, values]
+            :param variables_shapes: New shapes for variables
+            :type variables_shapes: Dict[keys, values]
         )");
 
     model.def("get_output_size",
@@ -590,6 +733,22 @@ void regclass_graph_Model(py::module m) {
                     :return: Index for value referencing it.
                     :rtype: int
                  )");
+    model.def(
+        "get_result_index",
+        [](const ov::Model& model, const ov::op::v0::Result& result) {
+            return model.get_result_index(result.get_default_output());
+        },
+        py::arg("result"),
+        R"(
+                Return index of result.
+
+                Return -1 if `result` not matched.
+
+                :param result: Result operation
+                :type result: op.Result
+                :return: Index for result referencing it.
+                :rtype: int
+             )");
 
     model.def("get_name",
               &ov::Model::get_name,
@@ -963,10 +1122,7 @@ void regclass_graph_Model(py::module m) {
     });
 
     model.def("__copy__", [](ov::Model& self) {
-        auto error_message =
-            py::detail::c_str(std::string("cannot copy 'openvino.runtime.Model. Please, use deepcopy instead."));
-        PyErr_SetString(PyExc_TypeError, error_message);
-        throw py::error_already_set();
+        throw py::type_error("Cannot copy 'openvino.runtime.Model. Please, use deepcopy instead.");
     });
 
     model.def("get_rt_info",
diff --git a/src/bindings/python/src/pyopenvino/graph/node_output.hpp b/src/bindings/python/src/pyopenvino/graph/node_output.hpp
index 0e9429cc1981c2..141b449c87caec 100644
--- a/src/bindings/python/src/pyopenvino/graph/node_output.hpp
+++ b/src/bindings/python/src/pyopenvino/graph/node_output.hpp
@@ -59,10 +59,7 @@ void regclass_graph_Output(py::module m, std::string typestring)
     });
 
     output.def("__deepcopy__", [typestring](ov::Output<VT>& self, py::dict& memo) {
-        auto error_message = py::detail::c_str(std::string("cannot deepcopy 'openvino.runtime.")
-                                                + typestring + std::string("Output' object."));
-        PyErr_SetString(PyExc_TypeError, error_message);
-        throw py::error_already_set();
+        throw py::type_error("Cannot deepcopy 'openvino.runtime." + typestring + "Output' object.");
     });
 
     output.def("get_node",
diff --git a/src/bindings/python/src/pyopenvino/graph/op.cpp b/src/bindings/python/src/pyopenvino/graph/op.cpp
index b4d2f700eaa389..3b1e22b589e03b 100644
--- a/src/bindings/python/src/pyopenvino/graph/op.cpp
+++ b/src/bindings/python/src/pyopenvino/graph/op.cpp
@@ -28,11 +28,14 @@ class PyOp : public ov::op::Op {
         PYBIND11_OVERRIDE(void, ov::op::Op, validate_and_infer_types);
     }
 
-    bool visit_attributes(ov::AttributeVisitor& visitor) override {
-        // PYBIND11_OVERRIDE_PURE(bool, Op, visit_attributes, visitor);
-        //  Requires binding for visitor
-        //  Now works only for operations without attributes
-        return true;
+    bool visit_attributes(ov::AttributeVisitor& value) override {
+        pybind11::gil_scoped_acquire gil;  // Acquire the GIL while in this scope.
+        // Try to look up the overridden method on the Python side.
+        pybind11::function overrided_py_method = pybind11::get_override(this, "visit_attributes");
+        if (overrided_py_method) {                                       // method is found
+            return static_cast<py::bool_>(overrided_py_method(&value));  // Call the Python function.
+        }
+        return false;
     }
 
     std::shared_ptr<Node> clone_with_new_inputs(const ov::OutputVector& new_args) const override {
@@ -56,8 +59,9 @@ class PyOp : public ov::op::Op {
 };
 
 void regclass_graph_Op(py::module m) {
-    py::class_<ov::op::Op, std::shared_ptr<ov::op::Op>, PyOp, ov::Node>(m, "Op").def(
-        py::init([](const py::object& py_obj) {
-            return PyOp(py_obj);
-        }));
+    py::class_<ov::op::Op, std::shared_ptr<ov::op::Op>, PyOp, ov::Node> op(m, "Op");
+
+    op.def(py::init([](const py::object& py_obj) {
+        return PyOp(py_obj);
+    }));
 }
diff --git a/src/bindings/python/src/pyopenvino/graph/symbol.cpp b/src/bindings/python/src/pyopenvino/graph/symbol.cpp
index 85cbdc23e3da7b..5ed06c3506177d 100644
--- a/src/bindings/python/src/pyopenvino/graph/symbol.cpp
+++ b/src/bindings/python/src/pyopenvino/graph/symbol.cpp
@@ -27,8 +27,13 @@ void regclass_graph_Symbol(py::module m) {
 
     symbol.def(
         "__bool__",
-        [](const std::shared_ptr<ov::Symbol>& a) -> bool {
-            return a != nullptr;
+        [](const std::shared_ptr<ov::Symbol>& self) -> bool {
+            return self != nullptr;
         },
         "Check whether the symbol is meaningful");
+
+    symbol.def("__hash__", [](const std::shared_ptr<ov::Symbol>& self) {
+        auto ancestor = ov::symbol::ancestor_of(self);
+        return std::hash<std::shared_ptr<ov::Symbol>>{}(ancestor);
+    });
 }
diff --git a/src/bindings/python/src/pyopenvino/graph/types/element_type.cpp b/src/bindings/python/src/pyopenvino/graph/types/element_type.cpp
index bc1f19a70a18d4..970ada766bab4f 100644
--- a/src/bindings/python/src/pyopenvino/graph/types/element_type.cpp
+++ b/src/bindings/python/src/pyopenvino/graph/types/element_type.cpp
@@ -54,6 +54,7 @@ void regclass_graph_Type(py::module m) {
     type.attr("f8e5m2") = ov::element::f8e5m2;
     type.attr("string") = ov::element::string;
     type.attr("f4e2m1") = ov::element::f4e2m1;
+    type.attr("f8e8m0") = ov::element::f8e8m0;
 
     type.def("__hash__", &ov::element::Type::hash);
     type.def("__repr__", [](const ov::element::Type& self) {
diff --git a/src/bindings/python/src/pyopenvino/pyopenvino.cpp b/src/bindings/python/src/pyopenvino/pyopenvino.cpp
index bc67efc984beca..f293f57d002613 100644
--- a/src/bindings/python/src/pyopenvino/pyopenvino.cpp
+++ b/src/bindings/python/src/pyopenvino/pyopenvino.cpp
@@ -47,6 +47,7 @@
 #include "pyopenvino/graph/descriptors/tensor.hpp"
 #include "pyopenvino/graph/dimension.hpp"
 #include "pyopenvino/graph/discrete_type_info.hpp"
+#include "pyopenvino/graph/attribute_visitor.hpp"
 #include "pyopenvino/graph/layout.hpp"
 #include "pyopenvino/graph/layout_helpers.hpp"
 #include "pyopenvino/graph/ops/assign.hpp"
@@ -234,6 +235,7 @@ PYBIND11_MODULE(_pyopenvino, m) {
     regclass_graph_Coordinate(m);
     regclass_graph_descriptor_Tensor(m);
     regclass_graph_DiscreteTypeInfo(m);
+    regclass_graph_AttributeVisitor(m);
     py::module m_op = m.def_submodule("op", "Package ngraph.impl.op that wraps ov::op");  // TODO(!)
     regclass_graph_op_Assign(m_op);
     regclass_graph_op_Constant(m_op);
diff --git a/src/bindings/python/src/pyopenvino/utils/utils.cpp b/src/bindings/python/src/pyopenvino/utils/utils.cpp
index feeac2d7a02a73..d216c805c5f4aa 100644
--- a/src/bindings/python/src/pyopenvino/utils/utils.cpp
+++ b/src/bindings/python/src/pyopenvino/utils/utils.cpp
@@ -23,6 +23,35 @@ using Version = ov::pass::Serialize::Version;
 namespace Common {
 namespace utils {
 
+PY_TYPE check_list_element_type(const py::list& list) {
+    PY_TYPE detected_type = PY_TYPE::UNKNOWN;
+
+    auto check_type = [&](PY_TYPE type) {
+        if (detected_type == PY_TYPE::UNKNOWN || detected_type == type) {
+            detected_type = type;
+            return;
+        }
+        OPENVINO_THROW("Incorrect attribute. Mixed types in the list are not allowed.");
+    };
+
+    for (const auto& it : list) {
+        // Check the type of elements in the list
+        if (py::isinstance<py::str>(it)) {
+            check_type(PY_TYPE::STR);
+        } else if (py::isinstance<py::int_>(it)) {
+            check_type(PY_TYPE::INT);
+        } else if (py::isinstance<py::float_>(it)) {
+            check_type(PY_TYPE::FLOAT);
+        } else if (py::isinstance<py::bool_>(it)) {
+            check_type(PY_TYPE::BOOL);
+        } else if (py::isinstance<ov::PartialShape>(it)) {
+            check_type(PY_TYPE::PARTIAL_SHAPE);
+        }
+    }
+
+    return detected_type;
+};
+
 // For complex structure if an element isn't map, then just cast it to OVAny
 py::object from_ov_any_no_leaves(const ov::Any& any) {
     if (any.is<std::shared_ptr<ov::Meta>>() || any.is<ov::AnyMap>()) {
@@ -323,28 +352,8 @@ ov::Any py_object_to_any(const py::object& py_obj) {
         return {};
     } else if (py::isinstance<py::list>(py_obj)) {
         auto _list = py_obj.cast<py::list>();
-        enum class PY_TYPE : int { UNKNOWN = 0, STR, INT, FLOAT, BOOL, PARTIAL_SHAPE };
-        PY_TYPE detected_type = PY_TYPE::UNKNOWN;
-        for (const auto& it : _list) {
-            auto check_type = [&](PY_TYPE type) {
-                if (detected_type == PY_TYPE::UNKNOWN || detected_type == type) {
-                    detected_type = type;
-                    return;
-                }
-                OPENVINO_THROW("Incorrect attribute. Mixed types in the list are not allowed.");
-            };
-            if (py::isinstance<py::str>(it)) {
-                check_type(PY_TYPE::STR);
-            } else if (py::isinstance<py::int_>(it)) {
-                check_type(PY_TYPE::INT);
-            } else if (py::isinstance<py::float_>(it)) {
-                check_type(PY_TYPE::FLOAT);
-            } else if (py::isinstance<py::bool_>(it)) {
-                check_type(PY_TYPE::BOOL);
-            } else if (py::isinstance<ov::PartialShape>(it)) {
-                check_type(PY_TYPE::PARTIAL_SHAPE);
-            }
-        }
+
+        PY_TYPE detected_type = check_list_element_type(_list);
 
         if (_list.empty())
             return ov::Any(EmptyList());
diff --git a/src/bindings/python/src/pyopenvino/utils/utils.hpp b/src/bindings/python/src/pyopenvino/utils/utils.hpp
index e4048b3f52feb3..2a376cc27b456a 100644
--- a/src/bindings/python/src/pyopenvino/utils/utils.hpp
+++ b/src/bindings/python/src/pyopenvino/utils/utils.hpp
@@ -32,8 +32,12 @@ namespace py = pybind11;
 
 namespace Common {
 namespace utils {
+    enum class PY_TYPE : int { UNKNOWN = 0, STR, INT, FLOAT, BOOL, PARTIAL_SHAPE };
+
     struct EmptyList {};
 
+    PY_TYPE check_list_element_type(const py::list& list);
+
     py::object from_ov_any_no_leaves(const ov::Any& any);
 
     py::object from_ov_any_map_no_leaves(const ov::Any& almost_map);
diff --git a/src/bindings/python/tests/test_graph/test_constant.py b/src/bindings/python/tests/test_graph/test_constant.py
index 8d9ec4f183397e..bc5b6f87a09659 100644
--- a/src/bindings/python/tests/test_graph/test_constant.py
+++ b/src/bindings/python/tests/test_graph/test_constant.py
@@ -3,6 +3,7 @@
 # SPDX-License-Identifier: Apache-2.0
 
 import numpy as np
+import openvino as ov
 
 import openvino.runtime.opset13 as ops
 from openvino import Type, PartialShape, Model, compile_model
@@ -413,13 +414,18 @@ def test_memory_sharing(shared_flag):
         assert not np.shares_memory(arr, ov_const.data)
 
 
-@pytest.mark.parametrize(("ov_type", "numpy_dtype"), [
-    (Type.f32, np.float32),
-    (Type.f16, np.float16),
-])
-def test_float_to_f8e5m2_constant(ov_type, numpy_dtype):
-    from openvino.runtime import opset12 as opset
-    import openvino as ov
+OPSETS = [ov.runtime.opset12, ov.runtime.opset13]
+
+
+@pytest.mark.parametrize(("opset"), OPSETS)
+@pytest.mark.parametrize(
+    ("ov_type", "numpy_dtype"),
+    [
+        (Type.f32, np.float32),
+        (Type.f16, np.float16),
+    ],
+)
+def test_float_to_f8e5m2_constant(ov_type, numpy_dtype, opset):
     data = np.array([4.75, 4.5, -5.25, 0.0, 0.1, 0.2, 0.3, 0.4, 0.5,
                      0.6, 0.7, 0.8, 0.9, 1, -0.0, -0.1, -0.2, -0.3,
                      -0.4, -0.5, -0.6, -0.7, -0.8, -0.9, -1.0, 0.0000152587890625, 448, 500, 512, 57344], dtype=numpy_dtype)
@@ -443,13 +449,15 @@ def test_float_to_f8e5m2_constant(ov_type, numpy_dtype):
     assert np.allclose(result, target)
 
 
-@pytest.mark.parametrize(("ov_type", "numpy_dtype"), [
-    (Type.f32, np.float32),
-    (Type.f16, np.float16),
-])
-def test_float_to_f8e4m3_constant(ov_type, numpy_dtype):
-    from openvino.runtime import opset12 as opset
-    import openvino as ov
+@pytest.mark.parametrize(("opset"), OPSETS)
+@pytest.mark.parametrize(
+    ("ov_type", "numpy_dtype"),
+    [
+        (Type.f32, np.float32),
+        (Type.f16, np.float16),
+    ],
+)
+def test_float_to_f8e4m3_constant(ov_type, numpy_dtype, opset):
     data = np.array([4.75, 4.5, -5.25, 0.0, 0.1, 0.2, 0.3, 0.4, 0.5,
                      0.6, 0.7, 0.8, 0.9, 1, -0.0, -0.1, -0.2, -0.3,
                      -0.4, -0.5, -0.6, -0.7, -0.8, -0.9, -1, 448, 512], dtype=numpy_dtype)
@@ -473,13 +481,47 @@ def test_float_to_f8e4m3_constant(ov_type, numpy_dtype):
     assert np.allclose(result, target, equal_nan=True)
 
 
-@pytest.mark.parametrize(("ov_type", "numpy_dtype"), [
-    (Type.f32, np.float32),
-    (Type.f16, np.float16),
-])
-def test_float_to_f8e5m2_convert(ov_type, numpy_dtype):
-    from openvino.runtime import opset12 as opset
-    import openvino as ov
+@pytest.mark.parametrize(("opset"), OPSETS)
+@pytest.mark.parametrize(
+    ("ov_type", "numpy_dtype"),
+    [
+        (Type.f32, np.float32),
+        (Type.f16, np.float16),
+    ],
+)
+def test_float_to_f8e8m0_constant(ov_type, numpy_dtype, opset):
+    data = np.array([4.75, 4.5, 5.25, 0.0, 0.1, 0.2, 0.3, 0.4, 0.5,
+                     0.6, 0.7, 0.8, 0.9, 1, -0.0, 1.1, 1.2, 1.3,
+                     1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0, 448, 512, np.nan], dtype=numpy_dtype)
+
+    compressed_const = opset.constant(data, dtype=ov.Type.f8e8m0, name="f8e8m0_constant")
+    convert = opset.convert(compressed_const, data.dtype)
+    parameter = opset.parameter(ov.PartialShape([-1]), ov_type)
+    add_op = opset.add(parameter, convert)
+    model = ov.Model([add_op], [parameter])
+
+    compiled = ov.compile_model(model)
+    tensor = np.zeros(data.shape, dtype=numpy_dtype)
+    result = compiled(tensor)[0]
+
+    target = [4.0, 4.0, 4.0, 0.0, 0.125, 0.25, 0.25,
+              0.5, 0.5, 0.5, 0.5, 1.0, 1.0, 1.0,
+              0.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0,
+              2.0, 2.0, 2.0, 2.0, 512, 512, np.nan]
+    target = np.array(target, dtype=numpy_dtype)
+
+    assert np.allclose(result, target, equal_nan=True)
+
+
+@pytest.mark.parametrize(("opset"), OPSETS)
+@pytest.mark.parametrize(
+    ("ov_type", "numpy_dtype"),
+    [
+        (Type.f32, np.float32),
+        (Type.f16, np.float16),
+    ],
+)
+def test_float_to_f8e5m2_convert(ov_type, numpy_dtype, opset):
     data = np.array([4.75, 4.5, -5.25, 0.0, 0.1, 0.2, 0.3, 0.4, 0.5,
                      0.6, 0.7, 0.8, 0.9, 1, -0.0, -0.1, -0.2, -0.3,
                      -0.4, -0.5, -0.6, -0.7, -0.8, -0.9, -1.0, 0.0000152587890625, 448, 500, 512, 57344], dtype=numpy_dtype)
@@ -504,13 +546,15 @@ def test_float_to_f8e5m2_convert(ov_type, numpy_dtype):
     assert np.allclose(result, target)
 
 
-@pytest.mark.parametrize(("ov_type", "numpy_dtype"), [
-    (Type.f32, np.float32),
-    (Type.f16, np.float16),
-])
-def test_float_to_f8e4m3_convert(ov_type, numpy_dtype):
-    from openvino.runtime import opset12 as opset
-    import openvino as ov
+@pytest.mark.parametrize(("opset"), OPSETS)
+@pytest.mark.parametrize(
+    ("ov_type", "numpy_dtype"),
+    [
+        (Type.f32, np.float32),
+        (Type.f16, np.float16),
+    ],
+)
+def test_float_to_f8e4m3_convert(ov_type, numpy_dtype, opset):
     data = np.array([4.75, 4.5, -5.25, 0.0, 0.1, 0.2, 0.3, 0.4, 0.5,
                      0.6, 0.7, 0.8, 0.9, 1, -0.0, -0.1, -0.2, -0.3,
                      -0.4, -0.5, -0.6, -0.7, -0.8, -0.9, -1, 448, 512], dtype=numpy_dtype)
@@ -535,6 +579,39 @@ def test_float_to_f8e4m3_convert(ov_type, numpy_dtype):
     assert np.allclose(result, target, equal_nan=True)
 
 
+@pytest.mark.parametrize(("opset"), OPSETS)
+@pytest.mark.parametrize(
+    ("ov_type", "numpy_dtype"),
+    [
+        (Type.f32, np.float32),
+        (Type.f16, np.float16),
+    ],
+)
+def test_float_to_f8e8m0_convert(ov_type, numpy_dtype, opset):
+    data = np.array([4.75, 4.5, 5.25, 0.0, 0.1, 0.2, 0.3, 0.4, 0.5,
+                     0.6, 0.7, 0.8, 0.9, 1, -0.0, 1.1, 1.2, 1.3,
+                     1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0, 448, 512, np.nan], dtype=numpy_dtype)
+
+    compressed_const = opset.constant(data, dtype=ov_type, name="fx_constant")
+    convert_to_fp8 = opset.convert(compressed_const, Type.f8e8m0)
+    convert_back = opset.convert(convert_to_fp8, ov_type)
+    parameter = opset.parameter(ov.PartialShape([-1]), ov_type)
+    add_op = opset.add(parameter, convert_back)
+    model = ov.Model([add_op], [parameter])
+
+    compiled = ov.compile_model(model)
+    tensor = np.zeros(data.shape, dtype=numpy_dtype)
+    result = compiled(tensor)[0]
+
+    target = [4.0, 4.0, 4.0, 0.0, 0.125, 0.25, 0.25,
+              0.5, 0.5, 0.5, 0.5, 1.0, 1.0, 1.0,
+              0.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0,
+              2.0, 2.0, 2.0, 2.0, 512, 512, np.nan]
+    target = np.array(target, dtype=numpy_dtype)
+
+    assert np.allclose(result, target, equal_nan=True)
+
+
 @pytest.mark.parametrize(
     ("src_dtype"),
     [
diff --git a/src/bindings/python/tests/test_graph/test_op.py b/src/bindings/python/tests/test_graph/test_custom_op.py
similarity index 52%
rename from src/bindings/python/tests/test_graph/test_op.py
rename to src/bindings/python/tests/test_graph/test_custom_op.py
index 5a8abdc55ea86c..5a2e33c7ee5783 100644
--- a/src/bindings/python/tests/test_graph/test_op.py
+++ b/src/bindings/python/tests/test_graph/test_custom_op.py
@@ -2,12 +2,18 @@
 # Copyright (C) 2018-2024 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
+import os
+import pytest
 import numpy as np
+from contextlib import nullcontext as does_not_raise
 
 from openvino import Op
-from openvino.runtime import CompiledModel, DiscreteTypeInfo, Model, Shape, compile_model, Tensor
+from openvino import CompiledModel, Model, Dimension, Shape, Tensor, compile_model, serialize
+from openvino.runtime import DiscreteTypeInfo
 import openvino.runtime.opset14 as ops
 
+from tests.utils.helpers import create_filename_for_test
+
 
 class CustomOp(Op):
     class_type_info = DiscreteTypeInfo("Custom", "extension")
@@ -76,6 +82,79 @@ def create_add_model():
     return Model(res, [param1, param2], "AddModel")
 
 
+class CustomOpWithAttribute(Op):
+    class_type_info = DiscreteTypeInfo("CustomOpWithAttribute", "extension")
+
+    def __init__(self, inputs, attrs):
+        super().__init__(self)
+        self._attrs = attrs
+        self.set_arguments(inputs)
+        self.constructor_validate_and_infer_types()
+
+    def validate_and_infer_types(self):
+        self.set_output_type(0, self.get_input_element_type(0), self.get_input_partial_shape(0))
+
+    def clone_with_new_inputs(self, new_inputs):
+        return CustomOpWithAttribute(new_inputs)
+
+    def get_type_info(self):
+        return CustomOpWithAttribute.class_type_info
+
+    def visit_attributes(self, visitor):
+        visitor.on_attributes(self._attrs)
+        return True
+
+
+# request - https://docs.pytest.org/en/7.1.x/reference/reference.html#request
+@pytest.fixture
+def prepared_paths(request, tmp_path):
+    xml_path, bin_path = create_filename_for_test(request.node.name, tmp_path)
+
+    yield xml_path, bin_path
+
+    os.remove(xml_path)
+    os.remove(bin_path)
+
+
+@pytest.mark.parametrize(("attributes", "expectation", "raise_msg"), [
+    ({"axis": 0}, does_not_raise(), ""),
+    ({"value_str": "test_attribute"}, does_not_raise(), ""),
+    ({"value_float": 0.25}, does_not_raise(), ""),
+    ({"value_bool": True}, does_not_raise(), ""),
+    ({"list_str": ["one", "two"]}, does_not_raise(), ""),
+    ({"list_int": [1, 2]}, does_not_raise(), ""),
+    ({"list_float": np.array([1.5, 2.5], dtype="float32")}, does_not_raise(), ""),
+    ({"axis": 0, "list_int": [1, 2]}, does_not_raise(), ""),
+    ({"body": Model(ops.constant([1]), [])}, does_not_raise(), ""),
+    ({"dim": Dimension(2)}, does_not_raise(), ""),
+    ({"wrong_list": [{}]}, pytest.raises(TypeError), "Unsupported attribute type in provided list: <class 'dict'>"),
+    ({"wrong_np": np.array([1.5, 2.5], dtype="complex128")}, pytest.raises(TypeError), "Unsupported NumPy array dtype: complex128"),
+    ({"wrong": {}}, pytest.raises(TypeError), "Unsupported attribute type: <class 'dict'>")
+])
+def test_visit_attributes_custom_op(prepared_paths, attributes, expectation, raise_msg):
+    input_shape = [2, 1]
+
+    param1 = ops.parameter(Shape(input_shape), dtype=np.float32, name="data1")
+    param2 = ops.parameter(Shape(input_shape), dtype=np.float32, name="data2")
+    custom = CustomOpWithAttribute(inputs=[param1, param2], attrs=attributes)
+    res = ops.result(custom, name="result")
+    model_with_op_attr = Model(res, [param1, param2], "CustomModel")
+
+    xml_path, bin_path = prepared_paths
+
+    with expectation as e:
+        serialize(model_with_op_attr, xml_path, bin_path)
+        ordered_ops = model_with_op_attr.get_ordered_ops()
+        ops_dict = {op.get_type_name(): op for op in ordered_ops}
+        attrs = ops_dict["CustomOpWithAttribute"].get_attributes()
+        for key, value in attrs.items():
+            assert key in attributes
+            assert attributes[key] == value
+
+    if e is not None:
+        assert raise_msg in str(e.value)
+
+
 def test_custom_add_op():
     data1 = np.array([1, 2, 3])
     data2 = np.array([4, 5, 6])
diff --git a/src/bindings/python/tests/test_graph/test_ops_embedding.py b/src/bindings/python/tests/test_graph/test_ops_embedding.py
new file mode 100644
index 00000000000000..4560506df4f3b7
--- /dev/null
+++ b/src/bindings/python/tests/test_graph/test_ops_embedding.py
@@ -0,0 +1,96 @@
+# -*- coding: utf-8 -*-
+# Copyright (C) 2018-2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np
+import pytest
+
+from openvino import Type
+from openvino.runtime import opset15
+
+
+def test_embedding_bag_offsets_15():
+    emb_table = opset15.parameter([5, 2], name="emb_table", dtype=np.float32)
+    indices = opset15.parameter([4], name="indices", dtype=np.int64)
+    offsets = opset15.parameter([3], name="offsets", dtype=np.int64)
+
+    node = opset15.embedding_bag_offsets(emb_table, indices, offsets)
+
+    assert node.get_type_name() == "EmbeddingBagOffsets"
+    assert node.get_output_size() == 1
+    assert list(node.get_output_shape(0)) == [3, 2]
+    assert node.get_output_element_type(0) == Type.f32
+    assert node.get_attributes()["reduction"] == "sum"
+
+
+def test_embedding_bag_offsets_15_default_index():
+    emb_table = opset15.parameter([5, 2], name="emb_table", dtype=np.float32)
+    indices = opset15.parameter([4], name="indices", dtype=np.int64)
+    offsets = opset15.parameter([3], name="offsets", dtype=np.int64)
+    default_index = opset15.parameter([], name="default_index", dtype=np.int64)
+
+    node = opset15.embedding_bag_offsets(emb_table, indices, offsets, default_index, reduction="MeAn")
+
+    assert node.get_type_name() == "EmbeddingBagOffsets"
+    assert node.get_output_size() == 1
+    assert list(node.get_output_shape(0)) == [3, 2]
+    assert node.get_output_element_type(0) == Type.f32
+    assert node.get_attributes()["reduction"] == "mean"
+
+
+def test_embedding_bag_offsets_15_per_sample_weights():
+    emb_table = opset15.parameter([5, 2], name="emb_table", dtype=np.float32)
+    indices = opset15.parameter([4], name="indices", dtype=np.int64)
+    offsets = opset15.parameter([3], name="offsets", dtype=np.int64)
+    per_sample_weights = opset15.parameter([4], name="per_sample_weights", dtype=np.float32)
+
+    node = opset15.embedding_bag_offsets(emb_table, indices, offsets, per_sample_weights=per_sample_weights, reduction="SUM")
+
+    assert node.get_type_name() == "EmbeddingBagOffsets"
+    assert node.get_output_size() == 1
+    assert list(node.get_output_shape(0)) == [3, 2]
+    assert node.get_output_element_type(0) == Type.f32
+    assert node.get_attributes()["reduction"] == "sum"
+
+
+def test_embedding_bag_offsets_15_default_index_per_sample_weights():
+    emb_table = opset15.parameter([5, 2], name="emb_table", dtype=np.float32)
+    indices = opset15.parameter([4], name="indices", dtype=np.int64)
+    offsets = opset15.parameter([3], name="offsets", dtype=np.int64)
+    default_index = opset15.parameter([], name="default_index", dtype=np.int64)
+    per_sample_weights = opset15.parameter([4], name="per_sample_weights", dtype=np.float32)
+
+    node = opset15.embedding_bag_offsets(emb_table, indices, offsets, default_index, per_sample_weights, "sum")
+
+    assert node.get_type_name() == "EmbeddingBagOffsets"
+    assert node.get_output_size() == 1
+    assert list(node.get_output_shape(0)) == [3, 2]
+    assert node.get_output_element_type(0) == Type.f32
+    assert node.get_attributes()["reduction"] == "sum"
+
+
+def test_embedding_bag_packed_15():
+    emb_table = opset15.parameter([5, 2], name="emb_table", dtype=np.float32)
+    indices = opset15.parameter([3, 3], name="indices", dtype=np.int64)
+
+    node = opset15.embedding_bag_packed(emb_table, indices, reduction="mEaN")
+
+    assert node.get_type_name() == "EmbeddingBagPacked"
+    assert node.get_output_size() == 1
+    assert list(node.get_output_shape(0)) == [3, 2]
+    assert node.get_output_element_type(0) == Type.f32
+    assert node.get_attributes()["reduction"] == "mean"
+
+
+def test_embedding_bag_packed_15_per_sample_weights():
+    emb_table = opset15.parameter([5, 2], name="emb_table", dtype=np.float32)
+    indices = opset15.parameter([3, 3], name="indices", dtype=np.int64)
+    per_sample_weights = opset15.parameter([3, 3], name="per_sample_weights", dtype=np.float32)
+
+    node = opset15.embedding_bag_packed(emb_table, indices, per_sample_weights)
+
+    assert node.get_type_name() == "EmbeddingBagPacked"
+    assert node.get_output_size() == 1
+    assert list(node.get_output_shape(0)) == [3, 2]
+    assert node.get_output_element_type(0) == Type.f32
+    assert node.get_attributes()["reduction"] == "sum"
diff --git a/src/bindings/python/tests/test_runtime/test_dimension.py b/src/bindings/python/tests/test_runtime/test_dimension.py
index 4a618a123093c0..cd20accc14bc33 100644
--- a/src/bindings/python/tests/test_runtime/test_dimension.py
+++ b/src/bindings/python/tests/test_runtime/test_dimension.py
@@ -78,3 +78,19 @@ def test_symbol():
 
     new_dimension.set_symbol(dimension.get_symbol())
     assert dimension.get_symbol() == new_dimension.get_symbol(), "Check: Two symbols are equal: Symbol.__eq__"
+
+
+def test_symbol_hash():
+    symbol = Symbol()
+    assert isinstance(hash(symbol), int)
+
+    hash1 = hash(symbol)
+    hash2 = hash(symbol)
+    assert hash1 == hash2
+
+    symbol1 = Symbol()
+    symbol2 = Symbol()
+    assert hash(symbol1) != hash(symbol2)
+
+    symbols = {symbol1: "symbol1", symbol2: "symbol2"}
+    assert symbols[symbol1] == "symbol1"
diff --git a/src/bindings/python/tests/test_runtime/test_input_node.py b/src/bindings/python/tests/test_runtime/test_input_node.py
index 9b5beb587d1e94..5e083051934afb 100644
--- a/src/bindings/python/tests/test_runtime/test_input_node.py
+++ b/src/bindings/python/tests/test_runtime/test_input_node.py
@@ -3,12 +3,12 @@
 # SPDX-License-Identifier: Apache-2.0
 
 import os
-
+import numpy as np
 from openvino.runtime import Input, RTMap
 from openvino._pyopenvino import DescriptorTensor
 import openvino.runtime.opset13 as ops
 
-from openvino import Core, OVAny, Shape, PartialShape, Type
+from openvino import Core, OVAny, Shape, PartialShape, Type, Tensor, Symbol
 from tests.utils.helpers import get_relu_model
 
 
@@ -133,3 +133,36 @@ def test_input_update_rt_info(device):
     for key, value in input_node.get_rt_info().items():
         assert key == "test12345"
         assert isinstance(value, OVAny)
+
+
+def test_tensor_bounds_in_model(device):
+    core = Core()
+    model = get_relu_model()
+    compiled_model = core.compile_model(model, device)
+    tensor = compiled_model.output(0).get_tensor()
+    partial_shape = tensor.get_partial_shape().to_shape()
+    tensor_size = np.prod(partial_shape)
+
+    lower_value = np.zeros(tensor_size, dtype=np.float32)
+    lower_value_tensor = Tensor(lower_value.reshape(partial_shape))
+    upper_value = np.ones(tensor_size, dtype=np.float32)
+    upper_value_tensor = Tensor(upper_value.reshape(partial_shape))
+
+    tensor.set_lower_value(lower_value_tensor)
+    retrieved_lower_value = tensor.get_lower_value().data
+    tensor.set_upper_value(upper_value_tensor)
+    retrieved_upper_value = tensor.get_upper_value().data
+    assert np.array_equal(retrieved_lower_value, lower_value_tensor.data)
+    assert np.array_equal(retrieved_upper_value, upper_value_tensor.data)
+
+
+def test_value_symbol_in_model(device):
+    core = Core()
+    model = get_relu_model()
+    compiled_model = core.compile_model(model, device)
+    tensor = compiled_model.output(0).get_tensor()
+    partial_shape = tensor.get_partial_shape().to_shape()
+    tensor_size = np.prod(partial_shape)
+    values = [Symbol() for _ in range(tensor_size)]
+    tensor.set_value_symbol(values)
+    assert tensor.get_value_symbol() == values
diff --git a/src/bindings/python/tests/test_runtime/test_model.py b/src/bindings/python/tests/test_runtime/test_model.py
index 65bef9a2ac0f6c..3db2a870acf5d5 100644
--- a/src/bindings/python/tests/test_runtime/test_model.py
+++ b/src/bindings/python/tests/test_runtime/test_model.py
@@ -27,7 +27,18 @@
 from openvino.runtime import Output
 from openvino.runtime.op.util import VariableInfo, Variable
 
-from tests.utils.helpers import generate_add_model, generate_model_with_memory, create_filename_for_test
+from tests.utils.helpers import (
+    generate_add_model,
+    generate_model_with_memory,
+    create_filename_for_test,
+)
+
+
+def make_add_with_variable_model(shape, variable_id: str, dtype=np.float32) -> Model:
+    param1 = ops.parameter(Shape(shape), dtype=dtype, name="data1")
+    param2 = ops.parameter(Shape(shape), dtype=dtype, name="data2")
+    read_value = ops.read_value(param1, variable_id, dtype, shape)
+    return Model(ops.add(read_value, param2), [param1, param2])
 
 
 def test_descriptor_tensor():
@@ -146,22 +157,37 @@ def test_get_result_index_invalid():
     assert model.get_result_index(invalid_output) == -1
 
 
-def test_parameter_index():
-    input_shape = PartialShape([1])
-    param = ops.parameter(input_shape, dtype=np.float32, name="data")
-    relu = ops.relu(param, name="relu")
-    model = Model(relu, [param], "TestModel")
-    assert model.get_parameter_index(param) == 0
-
-
-def test_parameter_index_invalid():
-    shape1 = PartialShape([1])
-    param1 = ops.parameter(shape1, dtype=np.float32, name="data1")
+@pytest.mark.parametrize(("shapes", "relu_names", "model_name", "expected_outputs_length", "is_invalid", "expected_result_index"), [
+    ([PartialShape([1])], ["relu"], "TestModel", 1, False, 0),
+    ([PartialShape([1]), PartialShape([4])], ["relu1", "relu2"], "TestModel1", 1, True, -1)
+])
+def test_result_index(shapes, relu_names, model_name, expected_outputs_length, is_invalid, expected_result_index):
+    params = [ops.parameter(shape, dtype=np.float32, name=f"data{i+1}") for i, shape in enumerate(shapes)]
+    relus = [ops.relu(param, name=relu_name) for param, relu_name in zip(params, relu_names)]
+
+    model = Model(relus[0], [params[0]], model_name)
+    assert len(model.outputs) == expected_outputs_length
+    if is_invalid:
+        invalid_result_node = ops.result(relus[1].outputs()[0])
+        assert model.get_result_index(invalid_result_node) == expected_result_index
+    else:
+        assert model.get_result_index(model.get_results()[0]) == expected_result_index
+
+
+@pytest.mark.parametrize(("shapes", "param_names", "model_name", "expected_index", "is_invalid"), [
+    ([PartialShape([1]), None], ["data", None], "TestModel", 0, False),
+    ([PartialShape([1]), PartialShape([2])], ["data1", "data2"], "TestModel", -1, True)
+])
+def test_parameter_index(shapes, param_names, model_name, expected_index, is_invalid):
+    param1 = ops.parameter(shapes[0], dtype=np.float32, name=param_names[0])
     relu = ops.relu(param1, name="relu")
-    model = Model(relu, [param1], "TestModel")
-    shape2 = PartialShape([2])
-    param2 = ops.parameter(shape2, dtype=np.float32, name="data2")
-    assert model.get_parameter_index(param2) == -1
+    model = Model(relu, [param1], model_name)
+
+    if is_invalid:
+        param2 = ops.parameter(shapes[1], dtype=np.float32, name=param_names[1])
+        assert model.get_parameter_index(param2) == expected_index
+    else:
+        assert model.get_parameter_index(param1) == expected_index
 
 
 def test_replace_parameter():
@@ -288,6 +314,55 @@ def test_reshape(device):
     assert compiled_model.input().partial_shape == ref_shape
 
 
+def test_reshape_with_ports_and_variable():
+    new_shape = PartialShape([46, 1])
+    var_id = "ID1"
+    model = make_add_with_variable_model([1, 5], var_id)
+    for model_input in model.inputs:
+        assert isinstance(model_input, Output)
+        model.reshape({model_input: new_shape}, {var_id: new_shape})
+        assert model_input.partial_shape == new_shape
+
+
+def test_reshape_with_indexes_and_variable():
+    var_id = "ID1"
+    model = make_add_with_variable_model([1, 5], var_id)
+    new_shape = PartialShape([1, 4])
+    model.reshape(
+        {i: new_shape for i, model_input in enumerate(model.inputs)},
+        {var_id: new_shape},
+    )
+    for model_input in model.inputs:
+        assert model_input.partial_shape == new_shape
+
+
+def test_reshape_with_names_and_variables():
+    var_id = "ID1"
+    model = make_add_with_variable_model([1, 25], var_id)
+    new_shape = PartialShape([4, 1])
+    model.reshape(
+        {model_input.any_name: new_shape for model_input in model.inputs},
+        {var_id: new_shape},
+    )
+    for model_input in model.inputs:
+        assert model_input.partial_shape == new_shape
+
+
+def test_reshape_with_variable(device):
+    shape = PartialShape([1, 10])
+
+    param = ops.parameter(shape, dtype=np.float32)
+    read_value = ops.read_value(param, "MyVar", Type.f32, shape)
+    model = Model(ops.relu(read_value), [param])
+
+    ref_shape = model.input().partial_shape
+    ref_shape[0] = 3
+    model.reshape(ref_shape, {"MyVar": ref_shape})
+    core = Core()
+    compiled_model = core.compile_model(model, device)
+    assert compiled_model.input().partial_shape == ref_shape
+
+
 def test_reshape_with_python_types(device):
     model = generate_add_model()
 
@@ -366,6 +441,85 @@ def check_shape(new_shape):
     )
 
 
+def test_reshape_with_python_types_for_variable(device):
+    var_id = "ID1"
+    model = make_add_with_variable_model([1, 2, 5], var_id)
+
+    def check_shape(new_shape):
+        for model_input in model.inputs:
+            assert model_input.partial_shape == new_shape
+
+    shape1 = [1, 4]
+    new_shapes = {input: PartialShape(shape1) for input in model.inputs}
+    model.reshape(new_shapes, {var_id: shape1})
+    check_shape(PartialShape(shape1))
+
+    shape2 = [1, 6]
+    new_shapes = {input.any_name: PartialShape(shape2) for input in model.inputs}
+    model.reshape(new_shapes, {var_id: shape2})
+    check_shape(PartialShape(shape2))
+
+    shape3 = [1, 8]
+    new_shapes = {i: PartialShape(shape3) for i, _ in enumerate(model.inputs)}
+    model.reshape(new_shapes, {var_id: shape3})
+    check_shape(PartialShape(shape3))
+
+    shape4 = [1, -1]
+    new_shapes = {input: PartialShape(shape4) for input in model.inputs}
+    model.reshape(new_shapes, {var_id: shape4})
+    check_shape(PartialShape([Dimension(1), Dimension(-1)]))
+
+    shape5 = [1, (1, 10)]
+    new_shapes = {input: PartialShape(shape5) for input in model.inputs}
+    model.reshape(new_shapes, {var_id: shape5})
+    check_shape(PartialShape([Dimension(1), Dimension(1, 10)]))
+
+    shape6 = [Dimension(3), Dimension(3, 10)]
+    new_shapes = {input: PartialShape(shape6) for input in model.inputs}
+    model.reshape(new_shapes, {var_id: shape6})
+    check_shape(PartialShape(shape6))
+
+    shape7 = "[1..10, ?]"
+    new_shapes = {input: PartialShape(shape7) for input in model.inputs}
+    model.reshape(new_shapes, {var_id: shape7})
+    check_shape(PartialShape(shape7))
+
+    # reshape mixed keys
+    shape8 = [(1, 20), -1]
+    new_shapes = {"data1": PartialShape(shape8), 1: shape8}
+    model.reshape(new_shapes, {var_id: shape8})
+    check_shape(PartialShape([Dimension(1, 20), Dimension(-1)]))
+
+    # reshape with one input
+    param = ops.parameter([1, 3, 28, 28])
+    read_value = ops.read_value(param, var_id, Type.f32, param.get_partial_shape())
+    model = Model(ops.relu(read_value), [param])
+
+    shape9 = [-1, 3, (28, 56), (28, 56)]
+    model.reshape(shape9, {var_id: shape9})
+    check_shape(PartialShape([Dimension(-1), Dimension(3), Dimension(28, 56), Dimension(28, 56)]))
+
+    shape10 = "[?,3,..224,..224]"
+    model.reshape(shape10, {var_id: shape10})
+    check_shape(PartialShape([Dimension(-1), Dimension(3), Dimension(-1, 224), Dimension(-1, 224)]))
+
+    # check exceptions
+    shape10 = [1, 1, 1, 1]
+    with pytest.raises(TypeError) as e:
+        model.reshape({0: shape10}, {0: shape10})
+    assert (
+        "Incorrect key type <class 'int'> to reshape a model, expected values as str." in str(e.value)
+    )
+
+    with pytest.raises(TypeError) as e:
+        model.reshape({0: shape10}, {var_id: range(1, 9)})
+    assert (
+        "Incorrect value type <class 'range'> to reshape a model, "
+        "expected values as openvino.runtime.PartialShape, str, list or tuple."
+        in str(e.value)
+    )
+
+
 # request - https://docs.pytest.org/en/7.1.x/reference/reference.html#request
 def test_serialize_rt_info(request, tmp_path):
     version = "TestVersion"
@@ -580,4 +734,4 @@ def test_copy_failed():
     model = generate_add_model()
     with pytest.raises(TypeError) as e:
         copy(model)
-    assert "cannot copy 'openvino.runtime.Model. Please, use deepcopy instead." in str(e.value)
+    assert "Cannot copy 'openvino.runtime.Model. Please, use deepcopy instead." in str(e.value)
diff --git a/src/bindings/python/tests/test_runtime/test_output_const_node.py b/src/bindings/python/tests/test_runtime/test_output_const_node.py
index e5037a46aa6b50..61c5bfcff44b09 100644
--- a/src/bindings/python/tests/test_runtime/test_output_const_node.py
+++ b/src/bindings/python/tests/test_runtime/test_output_const_node.py
@@ -156,4 +156,4 @@ def test_deepcopy():
     output_node = node.outputs()[0]
     with pytest.raises(TypeError) as e:
         deepcopy(output_node)
-    assert "cannot deepcopy 'openvino.runtime.Output' object." in str(e)
+    assert "Cannot deepcopy 'openvino.runtime.Output' object." in str(e)
diff --git a/src/bindings/python/tests/test_runtime/test_output_node.py b/src/bindings/python/tests/test_runtime/test_output_node.py
index f87109a3fdd167..c0e0025e1d9b8b 100644
--- a/src/bindings/python/tests/test_runtime/test_output_node.py
+++ b/src/bindings/python/tests/test_runtime/test_output_node.py
@@ -4,7 +4,8 @@
 
 
 import openvino.runtime.opset13 as ops
-from openvino import Type
+from openvino import Type, Tensor, Symbol
+import numpy as np
 
 
 def test_output_replace(device):
@@ -31,3 +32,22 @@ def test_output_names():
     more_names = {"yet_another_name", "input1"}
     param.output(0).add_names(more_names)
     assert param.output(0).get_names() == names.union(more_names)
+
+
+def test_tensor_bounds():
+    param = ops.parameter([1, 64], Type.f32)
+    tensor = param.output(0).get_tensor()
+    values = Tensor(np.zeros([1, 64], dtype=np.float32))
+    tensor.set_lower_value(values)
+    tensor.set_upper_value(values)
+    assert np.array_equal(tensor.get_lower_value().data, values.data)
+    assert np.array_equal(tensor.get_upper_value().data, values.data)
+
+
+def test_output_symbol():
+    param = ops.parameter([1, 64], Type.f32)
+    tensor = param.output(0).get_tensor()
+    values = [Symbol() for i in range(64)]
+    tensor.set_value_symbol(values)
+    gotten_values = tensor.get_value_symbol()
+    assert gotten_values == values
diff --git a/src/bindings/python/tests/test_runtime/test_sync_infer_request.py b/src/bindings/python/tests/test_runtime/test_sync_infer_request.py
index 75be00cc7a07f6..df8ae33c52335f 100644
--- a/src/bindings/python/tests/test_runtime/test_sync_infer_request.py
+++ b/src/bindings/python/tests/test_runtime/test_sync_infer_request.py
@@ -630,7 +630,7 @@ def test_convert_infer_request(device):
     res = request.infer(inputs)
     with pytest.raises(TypeError) as e:
         deepcopy(res)
-    assert "cannot deepcopy 'openvino.runtime.ConstOutput' object." in str(e)
+    assert "Cannot deepcopy 'openvino.runtime.ConstOutput' object." in str(e)
 
 
 @pytest.mark.parametrize("share_inputs", [True, False])
diff --git a/src/bindings/python/tests/test_utils/test_utils.py b/src/bindings/python/tests/test_utils/test_utils.py
index cc263f80eb7da0..6f650d4e644644 100644
--- a/src/bindings/python/tests/test_utils/test_utils.py
+++ b/src/bindings/python/tests/test_utils/test_utils.py
@@ -3,7 +3,9 @@
 # SPDX-License-Identifier: Apache-2.0
 
 import pytest
-from openvino.utils import deprecated
+import os
+from pathlib import Path
+from openvino.utils import deprecated, get_cmake_path
 from tests.utils.helpers import compare_models, get_relu_model
 
 
@@ -58,3 +60,37 @@ def deprecated_function4():
         deprecated_function3()
     with pytest.warns(DeprecationWarning, match="deprecated_function4 is deprecated and will be removed in version 2025.4. Use another function instead"):
         deprecated_function4()
+
+
+def test_cmake_file_found(monkeypatch):
+    fake_package_path = "/fake/site-packages/openvino"
+
+    def mock_walk(path):
+        return [
+            ("/fake/site-packages/openvino/dir1", ("subdir",), ("OpenVINOConfig.cmake", "otherfile.txt")),
+            ("/fake/site-packages/openvino/dir2", ("subdir",), ("otherfile.txt",)),
+        ]
+
+    monkeypatch.setattr(Path, "parent", fake_package_path)
+    monkeypatch.setattr(os, "walk", mock_walk)
+
+    result = get_cmake_path()
+
+    assert result == f"{fake_package_path}/dir1"
+
+
+def test_cmake_file_not_found(monkeypatch):
+    fake_package_path = "/fake/site-packages/openvino"
+
+    def mock_walk(path):
+        return [
+            ("/fake/site-packages/openvino/dir1", ("subdir",), ("otherfile.txt", "OpenVINOConfig")),
+            ("/fake/site-packages/openvino/dir2", ("subdir",), ("otherfile.txt", "OpenVINO.cmake")),
+        ]
+
+    monkeypatch.setattr(Path, "parent", fake_package_path)
+    monkeypatch.setattr(os, "walk", mock_walk)
+
+    result = get_cmake_path()
+
+    assert result == ""
diff --git a/src/bindings/python/wheel/CMakeLists.txt b/src/bindings/python/wheel/CMakeLists.txt
index 1097fa852bd403..f896ef5107b20a 100644
--- a/src/bindings/python/wheel/CMakeLists.txt
+++ b/src/bindings/python/wheel/CMakeLists.txt
@@ -100,7 +100,7 @@ if(pip_version VERSION_GREATER_EQUAL 22.0)
         ${Python3_EXECUTABLE} -m pip wheel
             --no-deps
             --wheel-dir ${openvino_wheels_output_dir}
-            # --verbose
+            --verbose
             --build-option --build-number=${WHEEL_BUILD}
             --build-option --plat-name=${PLATFORM_TAG}
             "${CMAKE_CURRENT_SOURCE_DIR}")
@@ -115,6 +115,11 @@ else()
             --plat-name=${PLATFORM_TAG})
 endif()
 
+# dev target includes c headers and we need to include c bindings
+if(TARGET openvino_c)
+    list(APPEND ov_setup_py_deps openvino_c)
+endif()
+
 add_custom_command(OUTPUT ${openvino_wheel_path}
     COMMAND ${setup_py_env}
         ${wheel_build_command}
diff --git a/src/bindings/python/wheel/setup.py b/src/bindings/python/wheel/setup.py
index 7a516d6e1f0983..610c4e744e32e3 100644
--- a/src/bindings/python/wheel/setup.py
+++ b/src/bindings/python/wheel/setup.py
@@ -14,7 +14,7 @@
 import logging as log
 from fnmatch import fnmatchcase
 from pathlib import Path
-from shutil import copyfile, rmtree
+from shutil import copyfile, rmtree, move
 from setuptools import setup, find_namespace_packages, Extension, Command
 from setuptools.command.build_ext import build_ext
 from setuptools.command.build_clib import build_clib
@@ -22,8 +22,8 @@
 from setuptools.command.build import build
 from setuptools.errors import SetupError
 
-
-WHEEL_LIBS_INSTALL_DIR = os.path.join("openvino", "libs")
+WHEEL_PACKAGE_DIR = "openvino"
+WHEEL_LIBS_INSTALL_DIR = f"{WHEEL_PACKAGE_DIR}/libs"
 WHEEL_LIBS_PACKAGE = "openvino.libs"
 PYTHON_VERSION = f"python{sys.version_info.major}.{sys.version_info.minor}"
 
@@ -66,6 +66,13 @@
         "rpath": LIBS_RPATH,
         "binary_dir": OPENVINO_BINARY_DIR,
     },
+    "ie_libs_с": {
+        "name": "core_c",
+        "prefix": f"{BUILD_BASE}/libs.core_с",
+        "install_dir": OV_RUNTIME_LIBS_DIR,
+        "rpath": LIBS_RPATH,
+        "binary_dir": OPENVINO_BINARY_DIR,
+    },
     "hetero_plugin": {
         "name": "hetero",
         "prefix": f"{BUILD_BASE}/libs.hetero",
@@ -128,6 +135,13 @@
         "rpath": LIBS_RPATH,
         "binary_dir": OPENVINO_BINARY_DIR,
     },
+    "jax_libs": {
+        "name": "jax",
+        "prefix": f"{BUILD_BASE}/libs.jax",
+        "install_dir": OV_RUNTIME_LIBS_DIR,
+        "rpath": LIBS_RPATH,
+        "binary_dir": OPENVINO_BINARY_DIR,
+    },
     "paddle_libs": {
         "name": "paddle",
         "prefix": f"{BUILD_BASE}/libs.paddle",
@@ -162,7 +176,24 @@
         "install_dir": OV_RUNTIME_LIBS_DIR,
         "rpath": LIBS_RPATH,
         "binary_dir": OPENVINO_BINARY_DIR,
+    }
+}
+
+DATA_INSTALL_CFG = {
+    "core_dev": {
+        "name": "core_dev",
+        "prefix": f"{BUILD_BASE}/libs.core.dev",
+        "install_dir": "runtime",
+        "binary_dir": OPENVINO_BINARY_DIR,
+        "source_dir": OPENVINO_SOURCE_DIR
     },
+    "core_c_dev": {
+        "name": "core_c_dev",
+        "prefix": f"{BUILD_BASE}/libs.core_c.dev",
+        "install_dir": "runtime",
+        "binary_dir": OPENVINO_BINARY_DIR,
+        "source_dir": OPENVINO_SOURCE_DIR
+    }
 }
 
 PY_INSTALL_CFG = {
@@ -196,7 +227,7 @@
         "source_dir": f"{OPENVINO_SOURCE_DIR}/tools/benchmark_tool",
         "install_dir": PY_PACKAGES_DIR,
         "binary_dir": "benchmark_app",
-    },
+    }
 }
 
 
@@ -242,12 +273,11 @@ def cmake_build_and_install(self, install_cfg):
         plat_specifier = ".{0}-{1}.{2}".format(self.plat_name, *sys.version_info[:2])
         self.build_temp = os.path.join(self.build_base, "temp" + plat_specifier)
         self.announce(f"Create build directory: {self.build_temp}", level=3)
-
         # build some components which have not been built yet
         for comp, comp_data in install_cfg.items():
             cpack_comp_name = comp_data.get("name")
-            source_dir = comp_data.get("source_dir")
-            binary_dir = comp_data.get("binary_dir")
+            source_dir = comp_data.get("source_dir", OPENVINO_SOURCE_DIR)
+            binary_dir = comp_data.get("binary_dir", OPENVINO_BINARY_DIR)
             install_dir = comp_data.get("install_dir")
             prefix = comp_data.get("prefix")
 
@@ -258,17 +288,19 @@ def cmake_build_and_install(self, install_cfg):
 
                 # even perform a build in case of binary directory does not exist
                 binary_dir = binary_dir if os.path.isabs(binary_dir) else os.path.join(self.build_temp, binary_dir)
+
                 if not os.path.exists(binary_dir):
                     binary_dir = os.path.join(self.build_temp, binary_dir)
                     self.announce(f"Configuring {comp} cmake project", level=3)
-                    self.spawn(["cmake", f"-DOpenVINODeveloperPackage_DIR={OPENVINO_BINARY_DIR}",
-                                         f"-DPython3_EXECUTABLE={sys.executable}",
-                                         f"-DCPACK_GENERATOR={CPACK_GENERATOR}",
-                                         f"-DCMAKE_BUILD_TYPE={CONFIG}",
-                                         "-DENABLE_WHEEL=OFF",
-                                         self.cmake_args,
-                                         "-S", source_dir,
-                                         "-B", binary_dir])
+                    self.spawn(["cmake",
+                                f"-DOpenVINODeveloperPackage_DIR={OPENVINO_BINARY_DIR}",
+                                f"-DPython3_EXECUTABLE={sys.executable}",
+                                f"-DCMAKE_BUILD_TYPE={CONFIG}",
+                                f"-DCPACK_GENERATOR={CPACK_GENERATOR}",
+                                "-DENABLE_WHEEL=OFF",
+                                self.cmake_args,
+                                "-S", source_dir,
+                                "-B", binary_dir])
 
                     self.announce(f"Building {comp} project", level=3)
                     self.spawn(["cmake", "--build", binary_dir,
@@ -282,9 +314,11 @@ def cmake_build_and_install(self, install_cfg):
                                      "--component", cpack_comp_name])
 
     def run(self):
-        # build and install clib into temporary directories
         if not PYTHON_EXTENSIONS_ONLY:
+            # build and install clib into temporary directories
             self.cmake_build_and_install(LIB_INSTALL_CFG)
+            # build and install additional files into temporary directories
+            self.cmake_build_and_install(DATA_INSTALL_CFG)
 
         # install python code into a temporary directory (site-packages)
         self.cmake_build_and_install(PY_INSTALL_CFG)
@@ -315,7 +349,10 @@ def run(self):
         # remove symlink to avoid copying it, set RPATH
         self.post_install(LIB_INSTALL_CFG)
         # copy clib to package data (to WHEEL_LIBS_INSTALL_DIR)
-        self.copy_package_data(get_install_dirs_list(LIB_INSTALL_CFG))
+        self.copy_package_libs(get_install_dirs_list(LIB_INSTALL_CFG))
+        # copy package data (everything except python or libraries)
+        if CPACK_GENERATOR in ["7Z", "TBZ2", "TGZ", "TXZ", "TZ", "TZST", "ZIP"]:
+            self.copy_package_data(get_install_dirs_list(DATA_INSTALL_CFG))
 
     def post_install(self, install_cfg):
         """Install prebuilt libraries to the temp directories, set rpath."""
@@ -391,7 +428,7 @@ def resolve_symlinks(self, local_base_dir: Path):
             os.rename(real_name, symlink)
             self.announce(f"Resolved symlink {symlink} as {real_name}", level=3)
 
-    def copy_package_data(self, src_dirs):
+    def copy_package_libs(self, src_dirs):
         """Collect package data files (clibs and other plugin support files) from preinstalled dirs and put all runtime libraries to the subpackage."""
         package_clibs_dir = os.path.join(PACKAGE_DIR, WHEEL_LIBS_INSTALL_DIR)
         os.makedirs(package_clibs_dir, exist_ok=True)
@@ -399,7 +436,7 @@ def copy_package_data(self, src_dirs):
         for src_dir in src_dirs:
             # additional blacklist filter, just to fix cmake install issues
             blacklist_patterns = [  # static libraries and PBD files
-                                    "^.*\\.a$", "^.*\\.lib$", "^.*\\.pdb$",
+                                    "^.*\\.a$", "^.*\\.pdb$",
                                     # TBB debug libraries
                                     "^.*_debug\\.dll$", "^.*_debug\\.\\d*\\.dylib$", "^.*_debug\\.so\\.\\d*$",
                                     # hwloc static libs on Windows
@@ -426,6 +463,42 @@ def copy_package_data(self, src_dirs):
             packages.append(WHEEL_LIBS_PACKAGE)
             package_data.update({WHEEL_LIBS_PACKAGE: ["*"]})
 
+    def copy_package_data(self, src_dirs):
+        """Collect package data files from preinstalled dirs and put to the subpackage."""
+        package_dir = os.path.join(PACKAGE_DIR, WHEEL_PACKAGE_DIR)
+        os.makedirs(package_dir, exist_ok=True)
+        package_clibs_dir = os.path.join(PACKAGE_DIR, WHEEL_LIBS_INSTALL_DIR)
+        os.makedirs(package_clibs_dir, exist_ok=True)
+
+        replacements = {
+            # change the path where the libraries are installed (runtime/lib/intel64/Release -> openvino/libs)
+            r"({_IMPORT_PREFIX})\/(.*)\/(.*.[lib|dylib|so|dll])": rf"\1/{WHEEL_LIBS_INSTALL_DIR}/\3",
+            # change the path where the include files are installed (runtime/include -> openvino/include)
+            r"({_IMPORT_PREFIX})\/(.*)\/(include)": rf"\1/{WHEEL_PACKAGE_DIR}/\3",
+            # change the libs versions (so.2024.3.0 -> so.2430 or 2024.3.0.dylib -> 2430.dylib)
+            r"(.so)?.(\d\d)(\d\d).(\d+).(\d+)(.dylib)?": r"\1.\3\4\5\6",
+        }
+
+        for src_dir in src_dirs:
+            src, dst = Path(src_dir), Path(package_dir)
+
+            # move the static libs to the directory with the shared libraries
+            for file_path in Path(src).rglob("*.lib"):
+                file_name = os.path.basename(file_path)
+                if file_path.is_file():
+                    dst_file = os.path.join(package_clibs_dir, file_name)
+                    move(file_path, dst_file)
+                    self.announce(f"Move {file_path} to {dst_file}", level=3)
+
+            if os.path.isdir(src) and os.listdir(src):
+                # copy the rest of the files to the package directly
+                shutil.copytree(src, dst, dirs_exist_ok=True)
+
+                # patch cmake configurations
+                for file_path in Path(dst).rglob("*.cmake"):
+                    if file_path.is_file():
+                        replace_strings_in_file(file_path, replacements)
+
 
 def copy_file(src, dst, verbose=False, dry_run=False):
     """Custom file copy."""
@@ -437,6 +510,23 @@ def copy_file(src, dst, verbose=False, dry_run=False):
             log.info(f"Copied '{src}' to '{dst}'")
 
 
+def replace_strings_in_file(file_path, replacements):
+    """Replace strings in a text file.
+
+    :param file_path: Path to the source file.
+    :param replacements: A dictionary where keys are strings or regex patterns to be replaced and values are the new strings.
+    """
+    # Read the content of the file
+    with open(file_path, "r", encoding="utf-8") as file:
+        content = file.read()
+
+    for pattern, new_string in replacements.items():
+        content = re.sub(pattern, new_string, content)
+
+    with open(file_path, "w", encoding="utf-8") as file:
+        file.write(content)
+
+
 class CopyExt(build_ext):
     """Copy extension files to the build directory."""
     def run(self):
diff --git a/src/cmake/openvino.cmake b/src/cmake/openvino.cmake
index c6822f0253c1a3..affaaed44226a0 100644
--- a/src/cmake/openvino.cmake
+++ b/src/cmake/openvino.cmake
@@ -100,13 +100,13 @@ install(TARGETS ${TARGET_NAME} EXPORT OpenVINOTargets
         RUNTIME DESTINATION ${OV_CPACK_RUNTIMEDIR} COMPONENT ${OV_CPACK_COMP_CORE} ${OV_CPACK_COMP_CORE_EXCLUDE_ALL}
         ARCHIVE DESTINATION ${OV_CPACK_ARCHIVEDIR} COMPONENT ${OV_CPACK_COMP_${archive_comp}} ${OV_CPACK_COMP_${archive_comp}_EXCLUDE_ALL}
         LIBRARY DESTINATION ${OV_CPACK_LIBRARYDIR} COMPONENT ${OV_CPACK_COMP_CORE} ${OV_CPACK_COMP_CORE_EXCLUDE_ALL}
-        NAMELINK_COMPONENT ${OV_CPACK_COMP_CORE_DEV}
+        NAMELINK_COMPONENT ${OV_CPACK_COMP_LINKS} ${OV_CPACK_COMP_LINKS_EXCLUDE_ALL}
         INCLUDES DESTINATION ${OV_CPACK_INCLUDEDIR})
 
 # OpenVINO runtime library dev
 
 #
-# Add openvino::runtine::dev target
+# Add openvino::runtime::dev target
 #
 
 add_library(openvino_runtime_dev INTERFACE)
@@ -146,6 +146,10 @@ ov_cpack_add_component(${OV_CPACK_COMP_CORE_DEV}
                        HIDDEN
                        DEPENDS ${OV_CPACK_COMP_CORE} ${core_dev_components})
 
+ov_cpack_add_component(${OV_CPACK_COMP_LINKS}
+                       HIDDEN
+                       DEPENDS ${OV_CPACK_COMP_CORE_DEV})
+
 if(ENABLE_PLUGINS_XML)
     install(FILES $<TARGET_FILE_DIR:${TARGET_NAME}>/plugins.xml
             DESTINATION ${OV_CPACK_PLUGINSDIR}
@@ -216,6 +220,11 @@ install(FILES "${CMAKE_BINARY_DIR}/share/OpenVINOConfig.cmake"
 #
 
 if(ENABLE_PKGCONFIG_GEN)
+
+    ov_cpack_add_component(${OV_CPACK_COMP_PKG_CONFIG}
+                        HIDDEN
+                        DEPENDS ${OV_CPACK_COMP_CORE_DEV})
+
     # fill in PKGCONFIG_OpenVINO_DEFINITIONS
     get_target_property(openvino_defs openvino INTERFACE_COMPILE_DEFINITIONS)
     foreach(openvino_def IN LISTS openvino_defs)
@@ -290,6 +299,6 @@ if(ENABLE_PKGCONFIG_GEN)
 
     install(FILES "${pkgconfig_out}"
             DESTINATION "${OV_CPACK_RUNTIMEDIR}/pkgconfig"
-            COMPONENT ${OV_CPACK_COMP_CORE_DEV}
-            ${OV_CPACK_COMP_CORE_DEV_EXCLUDE_ALL})
+            COMPONENT ${OV_CPACK_COMP_PKG_CONFIG}
+            ${OV_CPACK_COMP_PKG_CONFIG_EXCLUDE_ALL})
 endif()
diff --git a/src/common/snippets/include/snippets/kernel_executor_table.hpp b/src/common/snippets/include/snippets/kernel_executor_table.hpp
index 757e51f015a8f3..bfff0d9d4f778d 100644
--- a/src/common/snippets/include/snippets/kernel_executor_table.hpp
+++ b/src/common/snippets/include/snippets/kernel_executor_table.hpp
@@ -4,8 +4,10 @@
 
 #pragma once
 
-#include "snippets/lowered/expression.hpp"
-
+#include "snippets/lowered/linear_ir.hpp"
+#if defined(SNIPPETS_DEBUG_CAPS) && !defined(_WIN32)
+#include <cxxabi.h>
+#endif
 namespace ov {
 namespace snippets {
 
@@ -23,8 +25,38 @@ class KernelExecutorBase {
          * while dynamic kernels will be completed only in runtime, when all the shapes are known.
         */
         virtual bool is_completed() const = 0;
+
+        /*** Return deep copy of the config */
+        virtual std::shared_ptr<GenericConfig> clone() const = 0;
+
+        /*** Compute hash for fast comparison operations or caching support */
+        virtual size_t hash() const = 0;
+
+        bool operator==(const GenericConfig& rhs) const { return hash() == rhs.hash(); }
+        bool operator!=(const GenericConfig& rhs) const { return hash() != rhs.hash(); }
+
         virtual ~GenericConfig() = default;
+        /** serialize config for debug purposes */
+#ifdef SNIPPETS_DEBUG_CAPS
+        virtual std::string to_string() const = 0;
+#endif
     };
+    /**
+    * @brief Update current kernel config in accordance with the passed expression. Corresponding kernel is recompiled if necessary.
+     * This method should be called to update KernelExecutor based on runtime info (e.g. shapes) available through expression ptr
+    */
+    virtual void update_by_expression(const ov::snippets::lowered::ExpressionPtr& expr) = 0;
+    /**
+    * @brief Replace current kernel config with the provided value. Corresponding kernel is recompiled if necessary.
+     * This method should be called to restore a saved state of the executor, that was configured using update_by_expression().
+    */
+    virtual void update_by_config(const std::shared_ptr<const GenericConfig>& new_config) = 0;
+
+    virtual std::shared_ptr<const GenericConfig> get_config() const = 0;
+    /** serialize for debug purposes */
+#ifdef SNIPPETS_DEBUG_CAPS
+    virtual std::string to_string() const = 0;
+#endif
     virtual ~KernelExecutorBase() = default;
 
 private:
@@ -38,17 +70,47 @@ template<typename Conf, typename KernelType,
 class KernelExecutor : public snippets::KernelExecutorBase {
 public:
     explicit KernelExecutor(std::shared_ptr<Conf> c) : KernelExecutorBase(), m_config{std::move(c)} {}
-    /**
-    * @brief check current config and recompile kernel if necessary. Use kernel caching to avoid redundant recompilations.
-     * This method must be called only for complete configs. It's the user responsibility to check is_completed() before calling.
-    */
-    virtual void update_kernel()  = 0;
+
+    // Note: override when final is redundant, but needed to avoid warnings on some compilers
+    void update_by_expression(const ov::snippets::lowered::ExpressionPtr& expr) override final { // NOLINT
+        m_config = std::static_pointer_cast<Conf>(m_config->clone());
+        update_config(expr, m_config);
+        OPENVINO_ASSERT(m_config && m_config->is_completed(), "Failed to update kernel config in update_by_expression");
+        update_kernel(m_config, m_kernel);
+        OPENVINO_ASSERT(m_kernel, "Failed to compile kernel executor");
+    }
+    void update_by_config(const std::shared_ptr<const GenericConfig>& new_config) override final { // NOLINT
+        if (*m_config == *new_config)
+            return;
+        m_config = std::static_pointer_cast<Conf>(std::const_pointer_cast<GenericConfig>(new_config));
+        OPENVINO_ASSERT(m_config && m_config->is_completed(), "Failed to update kernel config in get_config");
+        update_kernel(m_config, m_kernel);
+        OPENVINO_ASSERT(m_kernel, "Failed to compile kernel executor");
+    }
+    std::shared_ptr<const GenericConfig> get_config() const override { return m_config; }
+    std::shared_ptr<const KernelType> get_kernel() const { return m_kernel; }
+#ifdef SNIPPETS_DEBUG_CAPS
+    std::string to_string() const override {
+        std::string type_name = typeid(KernelType).name();
+#ifndef _WIN32
+        int status;
+        std::unique_ptr<char, void (*)(void*)> demangled_name(
+                abi::__cxa_demangle(type_name.c_str(), nullptr, nullptr, &status),
+                std::free);
+        type_name = demangled_name.get();
+#endif
+        return  "KernelExecutorType: " + std::string(type_name) + " KernelConfig: " + m_config->to_string();
+    }
+#endif
+
 protected:
-    /**
-    * @brief Takes shared_ptr to compilation config, returns shared_ptr to compiled kernel.
-     * Should be called only if actual compilation is required. Kernel caching must be implemented in update_kernel().
-    */
-    virtual std::shared_ptr<KernelType> compile_kernel(const std::shared_ptr<Conf>& c) const = 0;
+    /*** Updates stored kernel config based on runtime info from expression (e.g. new input shapes). */
+    virtual void update_config(const ov::snippets::lowered::ExpressionPtr& expr, std::shared_ptr<Conf>& config) const = 0;
+    /*** Updates stored kernel in accordance with the passed config. Recompilation of the kernel is
+     * performed only if necessary, otherwise an appropriate kernel is retrieved from cache. */
+    virtual void update_kernel(const std::shared_ptr<const Conf>& c, std::shared_ptr<KernelType>& kernel) const = 0;
+
+private:
     /** Contains all the necessary information to compile a desired kernel*/
     std::shared_ptr<Conf> m_config = nullptr;
     /** Stores pointer to compiled kernel since the last update_kernel() call */
@@ -57,6 +119,7 @@ class KernelExecutor : public snippets::KernelExecutorBase {
 
 class KernelExecutorTable {
 public:
+    /*** Register KernelExecutor in the KernelExecutorTable so it can be later updated in runtime. */
     template<typename T, class ...C,
             typename std::enable_if<std::is_base_of<KernelExecutorBase, T>::value, bool>::type = true>
     std::shared_ptr<T> register_kernel(const snippets::lowered::ExpressionPtr& expr, C... args) {
@@ -69,10 +132,37 @@ class KernelExecutorTable {
         OPENVINO_ASSERT(m_table.count(expr), "This expression doesn't have a registered kernel executor");
         return m_table.at(expr);
     }
+    /*** Updates every registered KernelExecutor in accordance with the corresponding expression */
+    void update_state() const {
+        for (const auto& record : m_table)
+            record.second->update_by_expression(record.first);
+    }
+
+    /*** Returns lambda function that contains current state of the table, and restores this state when called  */
+    std::function<void()> get_state_reset() {
+        auto current_state = get_state();
+        return [=]() { reset_state(current_state); };
+    }
+
+    /**
+    * @brief Replace originally registered ExpressionPtr with a new value.
+     * Note that code emission is performed on a copy of LIR, so all expression pointers visible from emitters won't
+     * be accessible from RuntimeConfigurator. In order to replace these cloned ExpressionPtrs with the original ones,
+     * we need to call this method.
+    */
+    void replace_key_expression(const snippets::lowered::ExpressionPtr& from, const snippets::lowered::ExpressionPtr& to);
+
     virtual ~KernelExecutorTable() = default;
 
 protected:
     std::unordered_map<snippets::lowered::ExpressionPtr, std::shared_ptr<KernelExecutorBase>> m_table{};
+    typedef std::vector<std::pair<snippets::lowered::ExpressionPtr, std::shared_ptr<const KernelExecutorBase::GenericConfig>>> ExecTableState;
+
+    /*** Restore the table state previously obtained by get_state() */
+    void reset_state(const ExecTableState& state);
+
+    /*** Return cumulative state of all the executors in the table. The returned ExecTableState object can be passed to reset_state */
+    ExecTableState get_state() const;
 };
 
 using KernelExecutorTablePtr = std::shared_ptr<KernelExecutorTable>;
diff --git a/src/common/snippets/include/snippets/lowered/linear_ir_builder.hpp b/src/common/snippets/include/snippets/lowered/linear_ir_builder.hpp
index 969bf21cd27480..afd778047c9279 100644
--- a/src/common/snippets/include/snippets/lowered/linear_ir_builder.hpp
+++ b/src/common/snippets/include/snippets/lowered/linear_ir_builder.hpp
@@ -29,9 +29,14 @@ class LinearIRBuilder {
     /**
      * @brief Make a full copy of LinearIR by rules described in `m_config`
      * @param linear_ir Linear IR
+     * @param expression_map expression map
      * @return clone of `linear_ir`
      */
-    std::shared_ptr<LinearIR> clone(const std::shared_ptr<LinearIR>& linear_ir) const;
+    std::shared_ptr<LinearIR> clone(const std::shared_ptr<LinearIR>& linear_ir,  ExpressionMap& expression_map) const;
+    inline std::shared_ptr<LinearIR> clone(const std::shared_ptr<LinearIR>& linear_ir) const {
+        ExpressionMap expression_map;
+        return clone(linear_ir, expression_map);
+    }
     /**
      * @brief Make a copy of LinearIR range by rules described in `m_config`
      * @param begin begin iterator of the target range of LinearIR
diff --git a/src/common/snippets/include/snippets/lowered/loop_info.hpp b/src/common/snippets/include/snippets/lowered/loop_info.hpp
index 8878affdc0fb35..e763f2244d76c6 100644
--- a/src/common/snippets/include/snippets/lowered/loop_info.hpp
+++ b/src/common/snippets/include/snippets/lowered/loop_info.hpp
@@ -156,6 +156,16 @@ class LoopInfo {
     const char* get_type_name() const {
         return get_type_info().name;
     }
+    /**
+     * @brief Return true if expression port is a loop port
+     * @param expr_port - expression port to check
+     */
+    bool is_loop_port(const ExpressionPort& expr_port);
+    /**
+     * @brief Return loop port of an expression port
+     * @param expr_port - expression port.
+     */
+    const LoopPort& get_loop_port(const ExpressionPort& expr_port);
 
 protected:
     /**
@@ -324,6 +334,15 @@ class UnifiedLoopInfo : public LoopInfo {
      */
     void replace_with_new_ports(const ExpressionPort& actual_port, const std::vector<ExpressionPort>& target_ports) override;
 
+    /**
+     * @brief Remove remove_ports and add add_ports to the current LoopPort.
+     *        This function removes ports directly and adds ports at the end of current LoopPort, caller is responsible to
+     *        sort the LoopPort after LoopPort being updated according to execution order of the expressions.
+     *        Note: all port in remove_ports and add_ports should have the same type.
+     * @param remove_ports need to be removed
+     * @param add_ports need to be added
+     */
+    void update_loop_ports(const std::vector<ExpressionPort>& remove_ports, const std::vector<ExpressionPort>& add_ports);
     /**
      * @brief Iterates through all LoopPortDesc and call `caller` for each of them
      * @param caller - function that called for each LoopPortDesc
@@ -374,6 +393,23 @@ class UnifiedLoopInfo : public LoopInfo {
      *         - Consistency of ports and descriptors
      */
     void validate() const;
+    /**
+     * @brief Remove the current LoopPort that contains ExpressionPort.
+     *        Note: If there is no LoopPort with ExpressionPort `ports`, does nothing.
+     *        This function removes ports directly, caller is responsible to sort the LoopPort after updated
+     *        according to execution order of the expressions.
+     *        Note: all port in ports should have the same type.
+     * @param ports need to be removed
+     */
+    void remove_loop_ports(const std::vector<ExpressionPort>& ports);
+    /**
+     * @brief Add ports to the current LoopPort.
+     *        This function adds ports in end of current LoopPort vector, caller is responsible to
+     *        sort the LoopPort after updated according to execution order of the expressions.
+     *        Note: all port in ports should have the same type.
+     * @param ports need to be added
+     */
+    void add_loop_ports(const std::vector<ExpressionPort>& ports);
 
     SpecificIterationHandlers m_handlers = {};
     std::vector<LoopPortDesc> m_input_port_descs = {};
diff --git a/src/common/snippets/include/snippets/lowered/loop_manager.hpp b/src/common/snippets/include/snippets/lowered/loop_manager.hpp
index f0718107ca30a2..854c367fa4010f 100644
--- a/src/common/snippets/include/snippets/lowered/loop_manager.hpp
+++ b/src/common/snippets/include/snippets/lowered/loop_manager.hpp
@@ -3,7 +3,6 @@
 //
 
 #pragma once
-
 #include <openvino/core/node.hpp>
 #include <openvino/opsets/opset1.hpp>
 
@@ -102,7 +101,7 @@ class LoopManager {
                      bool set_default_handlers = true,
                      bool is_work_amount_const = false) {
         const auto normalized_increment = utils::is_dynamic_value(work_amount) || work_amount == 0 ? increment : std::min(increment, work_amount);
-        const auto handlers = set_default_handlers
+        const auto& handlers = set_default_handlers
                                   ? SpecificIterationHandlers(work_amount, normalized_increment)
                                   : SpecificIterationHandlers();
         const auto loop_info = std::make_shared<UnifiedLoopInfo>(work_amount, normalized_increment, entries, exits, handlers, is_work_amount_const);
@@ -218,7 +217,7 @@ class LoopManager {
      * @param loop_end_pos the next iterator after the last expression
      * @param loop_id target Loop ID
      */
-    void sort_loop_ports(LinearIR::constExprIt& loop_begin_pos, LinearIR::constExprIt& loop_end_pos, size_t loop_id);
+    void sort_loop_ports(const LinearIR::constExprIt& loop_begin_pos, const LinearIR::constExprIt& loop_end_pos, size_t loop_id);
     /**
      * @brief When the previous expression was replaced with new expressions (decomposition), the method updates the corresponding Loop.
      *        If ports of decomposed expression were the Loop ports, these Loop ports may be updated by parameters `entries` and `exits`
@@ -276,7 +275,6 @@ class LoopManager {
      */
     bool reorder_identifiers(const std::map<size_t, size_t>& loop_id_map);
 
-private:
     /**
      * @brief Add new Loop Info to the map
      * @param loop target loop info
@@ -288,6 +286,8 @@ class LoopManager {
      * @param index the target index of Loop
      */
     void remove_loop_info(size_t index);
+
+private:
     /**
      * @brief Find expression ports in bounds that are connected to consumers or parent that aren't in these bounds
      * @param loop_begin_pos the first expression iterator of the Loop
diff --git a/src/common/snippets/include/snippets/lowered/pass/extract_loop_invariants.hpp b/src/common/snippets/include/snippets/lowered/pass/extract_loop_invariants.hpp
new file mode 100644
index 00000000000000..d4fb19f8d0c6a4
--- /dev/null
+++ b/src/common/snippets/include/snippets/lowered/pass/extract_loop_invariants.hpp
@@ -0,0 +1,31 @@
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "pass.hpp"
+#include "snippets/lowered/loop_manager.hpp"
+
+namespace ov {
+namespace snippets {
+namespace lowered {
+namespace pass {
+
+/**
+ * @interface ExtractLoopInvariants
+ * @brief Extracts expressions that produce identical result on every loop iteration outside of the loop's body.
+ *        This extraction is to remove repeated computation, not cover constant subgraph extraction.
+ * @ingroup snippets
+ */
+class ExtractLoopInvariants : public RangedPass {
+public:
+    OPENVINO_RTTI("ExtractLoopInvariants", "RangedPass")
+    ExtractLoopInvariants() = default;
+    bool run(LinearIR& linear_ir, lowered::LinearIR::constExprIt begin, lowered::LinearIR::constExprIt end) override;
+};
+
+} // namespace pass
+} // namespace lowered
+} // namespace snippets
+} // namespace ov
diff --git a/src/common/snippets/include/snippets/op/brgemm.hpp b/src/common/snippets/include/snippets/op/brgemm.hpp
index a170b02b15346e..d7b179246366e9 100644
--- a/src/common/snippets/include/snippets/op/brgemm.hpp
+++ b/src/common/snippets/include/snippets/op/brgemm.hpp
@@ -55,9 +55,9 @@ class Brgemm : virtual public modifier::MemoryAccess, public ov::op::Op {
 protected:
     ov::element::Type get_output_type() const;
     std::vector<ov::PartialShape> get_planar_input_shapes(const std::vector<ov::Input<ov::Node>>& inputs) const;
-    ov::PartialShape get_output_partial_shape(const std::vector<ov::PartialShape>& input_shapes) const;
+    ov::PartialShape infer_output_partial_shape(const std::vector<ov::PartialShape>& input_shapes) const;
     ov::PartialShape get_planar_output_shape(const ov::PartialShape& output_shape) const;
-    void compute_block_size_values(size_t blk_size_m, size_t blk_size_k, size_t blk_size_n);
+    void set_block_size_values(size_t blk_size_m, size_t blk_size_k, size_t blk_size_n);
     size_t m_M_blk = 0;
     size_t m_K_blk = 0;
     size_t m_N_blk = 0;
@@ -65,7 +65,6 @@ class Brgemm : virtual public modifier::MemoryAccess, public ov::op::Op {
 
 private:
     void custom_constructor_validate_and_infer_types(std::vector<size_t> layout_a, std::vector<size_t> layout_b, std::vector<size_t> layout_c);
-    void validate_inputs() const;
 };
 
 } // namespace op
diff --git a/src/common/snippets/include/snippets/pass/tokenization.hpp b/src/common/snippets/include/snippets/pass/tokenization.hpp
index 55fc8f9e97d807..aeee72160c85fe 100644
--- a/src/common/snippets/include/snippets/pass/tokenization.hpp
+++ b/src/common/snippets/include/snippets/pass/tokenization.hpp
@@ -66,52 +66,62 @@ class SnippetsTokenization : public ov::pass::ModelPass {
      * @ingroup snippets
      */
     struct Config {
-        Config(size_t concurrency, size_t data_ptr_gpr_count, bool split_m_dimension, bool enable_transpose_on_output, std::set<size_t> mha_transpose_ranks)
-            : concurrency(concurrency), data_ptr_gpr_count(data_ptr_gpr_count), split_m_dimension(split_m_dimension),
-              mha_token_enable_transpose_on_output(enable_transpose_on_output), mha_supported_transpose_ranks(std::move(mha_transpose_ranks)) {
+        Config(size_t concurrency, size_t data_ptr_gpr_count, bool split_m_dimension, bool enable_transpose_on_output,
+               bool dyn_mha_token, std::set<size_t> mha_transpose_ranks)
+            : m_concurrency(concurrency), m_data_ptr_gpr_count(data_ptr_gpr_count), m_split_m_dimension(split_m_dimension),
+              m_mha_token_enable_transpose_on_output(enable_transpose_on_output), m_is_dynamic_mha_token_enabled(dyn_mha_token),
+              m_mha_supported_transpose_ranks(std::move(mha_transpose_ranks)) {
             OPENVINO_ASSERT(concurrency > 0, "Concurrency should be greater than 0");
             OPENVINO_ASSERT(data_ptr_gpr_count > 0, "data_ptr_gpr_count should be greater than 0");
         }
 
         void set_concurrency(size_t concur) {
-            concurrency = concur;
+            m_concurrency = concur;
         }
 
         size_t get_concurrency() const {
-            return concurrency;
+            return m_concurrency;
         }
 
         size_t get_data_ptr_gpr_count() const {
-            return data_ptr_gpr_count;
+            return m_data_ptr_gpr_count;
         }
 
         bool get_split_m_dimension() const {
-            return split_m_dimension;
+            return m_split_m_dimension;
         }
 
         bool get_mha_token_enable_transpose_on_output() const {
-            return mha_token_enable_transpose_on_output;
+            return m_mha_token_enable_transpose_on_output;
+        }
+
+        bool is_dynamic_mha_token_enabled() const {
+            return m_is_dynamic_mha_token_enabled;
         }
 
         std::set<size_t> get_mha_supported_transpose_ranks() const {
-            return mha_supported_transpose_ranks;
+            return m_mha_supported_transpose_ranks;
         }
 
     private:
-        size_t concurrency = 0;
+        size_t m_concurrency = 0;
         // The number of gpr that can be used as data pointers for data nodes (Parameter (and non-Scalar Constants),
         // Result, Buffers with the same ID)
-        size_t data_ptr_gpr_count = 0;
+        size_t m_data_ptr_gpr_count = 0;
         // True if "SplitDimensionM" optimization is enabled. Otherwise, it's disabled.
-        bool split_m_dimension = true;
+        bool m_split_m_dimension = true;
         // False if Transpose on output isn't tokenized in MHA Tokenization.
         // Otherwise, it may be fused into Subgraph if possible
         // TODO [111813]: Remove please when the ticket 111813 is implemented
-        bool mha_token_enable_transpose_on_output = true;
+        bool m_mha_token_enable_transpose_on_output = true;
+        // If True, MHA pattern with dynamic nodes will be tokenized
+        // Otherwise dynamic MHA won't be tokenized
+        // Currently, the flag can be set to `True` only for testing purposes.
+        bool m_is_dynamic_mha_token_enabled = true;
         // Set of supported Transpose shape ranks for tokenization in MHATokenization pass.
         // Note that in general Snippets support Transpose of any ranks.
         // But at the moment Transpose is used only in MHA pattern where 3D and 4D tensors are supported.
-        std::set<size_t> mha_supported_transpose_ranks = { 3, 4 };
+        std::set<size_t> m_mha_supported_transpose_ranks = { 3, 4 };
     };
 
     OPENVINO_RTTI("SnippetsTokenization", "0");
diff --git a/src/common/snippets/include/snippets/runtime_configurator.hpp b/src/common/snippets/include/snippets/runtime_configurator.hpp
index 10a2c26b1d843a..059771d961df82 100644
--- a/src/common/snippets/include/snippets/runtime_configurator.hpp
+++ b/src/common/snippets/include/snippets/runtime_configurator.hpp
@@ -5,6 +5,7 @@
 #pragma once
 
 #include "snippets/lowered/linear_ir.hpp"
+#include "snippets/kernel_executor_table.hpp"
 #include "snippets/lowered/pass/pass.hpp"
 
 namespace ov {
@@ -42,7 +43,8 @@ class RuntimeConfig {
     ov::snippets::VectorDims master_shape = {};
 
     size_t buffer_scratchpad_size = 0;
-    std::vector<size_t> buffer_cluster_offsets;
+    std::vector<size_t> buffer_cluster_offsets {};
+    KernelExecutorTablePtr kernel_executor_table = std::make_shared<ov::snippets::KernelExecutorTable>();
 };
 
 /**
@@ -60,6 +62,8 @@ class RuntimeConfigurator {
      * @return updated config
      */
     const std::shared_ptr<RuntimeConfig>& get_updated_config(const std::shared_ptr<lowered::LinearIR>& linear_ir);
+    /*** Returns pointer to KernelExecutorTable owned by the config */
+    const std::shared_ptr<KernelExecutorTable>& get_kernel_executor_table() const { return m_config->kernel_executor_table; }
 
 protected:
     /**
diff --git a/src/common/snippets/include/snippets/target_machine.hpp b/src/common/snippets/include/snippets/target_machine.hpp
index f514e1a944ca3e..d9d89264fe1926 100644
--- a/src/common/snippets/include/snippets/target_machine.hpp
+++ b/src/common/snippets/include/snippets/target_machine.hpp
@@ -10,7 +10,6 @@
 
 #include "emitter.hpp"
 #include "snippets/lowered/expression.hpp"
-#include "kernel_executor_table.hpp"
 
 namespace ov {
 namespace snippets {
@@ -94,7 +93,6 @@ class TargetMachine {
 
 protected:
     std::map<const ov::DiscreteTypeInfo, jitters_value> jitters;
-    std::shared_ptr<KernelExecutorTable> kernel_executor_table;
     std::shared_ptr<RuntimeConfigurator> configurator;
 };
 
diff --git a/src/common/snippets/include/snippets/utils.hpp b/src/common/snippets/include/snippets/utils.hpp
index 8485b87bb63066..268bb838df92f7 100644
--- a/src/common/snippets/include/snippets/utils.hpp
+++ b/src/common/snippets/include/snippets/utils.hpp
@@ -131,6 +131,10 @@ inline size_t get_output_dim_idx(const std::vector<size_t>& layout, size_t dim_i
 // dim_idx starts from the layout end
 size_t get_dim_idx(const lowered::ExpressionPort& port, size_t dim_idx);
 
+// get stride on dimenison of dim_idx
+// given shape [a,b,c,d], the stride is [b*c*d, c*d, d, 1]
+int64_t get_stride(size_t dim_idx, const VectorDims& shape);
+
 /* ----- Shape `getters` ----- */
 /**
  * @brief Returns a dense shape after applying the order.
@@ -243,6 +247,44 @@ std::shared_ptr<ov::Node> get_leaf_node_of_first_child_shape_infer_seq(const std
  */
 std::shared_ptr<ov::Node> get_leaf_node_of_first_parent_shape_infer_seq(const std::shared_ptr<ov::Node>& start_node);
 
+/**
+ * @brief Calculate leading dimension of the shape that should be read according to the layout
+ * @param shape original (not reordered) input shape
+ * @param layout specifies the order in what dimensions of in the input shape should be read
+ * @return stride of the dimension idx = layout[layout.size() - 2] in the original shape
+   Example:
+         Original shape (shape) = [1, 49, 2, 23]
+         Layout (transpose order) = [2, 0, 1, 3]
+
+         dim_idx = layout.size() - 2 = 2
+         // Since layout specifies the order of dimensions in which the shape should be read
+         dim = layout[dim_idx] = 1
+         stride(shape[1]) = shape[2] * shape[3] = 2 * 23
+ */
+size_t get_in_leading_dim(const VectorDims& shape, const std::vector<size_t>& layout);
+inline size_t get_in_leading_dim(const lowered::PortDescriptorPtr& pd) {
+    return get_in_leading_dim(pd->get_shape(), pd->get_layout());
+}
+/**
+ *
+ * @param shape reordered input shape that is stored according to the layout
+ * @param layout specifies the order in what the dimensions of the input shape are stored
+ * @return
+     Output shape is already transposed, we need to correctly write the data with original shape by the order
+     Example:
+          Original transposed shape (shape) = [49, 2, 7, 39]
+          Layout (transpose order) = [2, 0, 1, 3]
+
+          dim_idx = layout.size() - 2 = 2
+          // Since the shape dimensions are already reordered according to the layout
+          dim = /find dim_idx index in layout/ = 0
+          stride(shape[0]) = shape[1] * shape[2] * shape[3] = 2 * 7 * 39
+ */
+size_t get_out_leading_dim(const VectorDims& shape, const std::vector<size_t>& layout);
+inline size_t get_out_leading_dim(const lowered::PortDescriptorPtr& pd) {
+    return get_out_leading_dim(pd->get_shape(), pd->get_layout());
+}
+
 } // namespace utils
 } // namespace snippets
 } // namespace ov
diff --git a/src/common/snippets/src/kernel_executor_table.cpp b/src/common/snippets/src/kernel_executor_table.cpp
new file mode 100644
index 00000000000000..97c30af8e3c56d
--- /dev/null
+++ b/src/common/snippets/src/kernel_executor_table.cpp
@@ -0,0 +1,39 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "snippets/kernel_executor_table.hpp"
+
+namespace ov {
+namespace snippets {
+
+void KernelExecutorTable::replace_key_expression(const snippets::lowered::ExpressionPtr& from, const snippets::lowered::ExpressionPtr& to) {
+    const auto& found = m_table.find(from);
+    if (found != m_table.end()) {
+        OPENVINO_ASSERT(m_table.count(to) == 0, "Attempt to replace a value that is already in the KernelExecutorTable");
+        m_table.insert({to, found->second});
+        m_table.erase(found);
+    }
+}
+
+void KernelExecutorTable::reset_state(const ExecTableState& state) {
+    OPENVINO_ASSERT(state.size() == m_table.size(), "Invalid state in restore_state: size mismatch");
+    auto state_it = state.begin();
+    for (const auto& table_record : m_table) {
+        const auto& state_record = *state_it++;
+        OPENVINO_ASSERT(table_record.first == state_record.first, "Invalid state in restore_state: expressions mismatch");
+        table_record.second->update_by_config(state_record.second);
+    }
+}
+
+KernelExecutorTable::ExecTableState KernelExecutorTable::get_state() const {
+    ExecTableState result;
+    // Note: we need to clone configs when saving the state, since the configs still stored in the table can
+    // be modified e.g. by calling update_by_expression();
+    for (const auto& record : m_table)
+        result.emplace_back(std::make_pair(record.first, record.second->get_config()->clone()));
+    return result;
+}
+
+}// namespace snippets
+}// namespace ov
diff --git a/src/common/snippets/src/lowered/linear_ir_builder.cpp b/src/common/snippets/src/lowered/linear_ir_builder.cpp
index 56b87eb2856d14..e5d28375db05f6 100644
--- a/src/common/snippets/src/lowered/linear_ir_builder.cpp
+++ b/src/common/snippets/src/lowered/linear_ir_builder.cpp
@@ -65,11 +65,10 @@ std::vector<std::shared_ptr<ov::Node>> clone_nodes(const std::vector<std::shared
 }
 }  // namespace
 
-std::shared_ptr<LinearIR> LinearIRBuilder::clone(const std::shared_ptr<LinearIR>& linear_ir) const {
+std::shared_ptr<LinearIR> LinearIRBuilder::clone(const std::shared_ptr<LinearIR>& linear_ir, ExpressionMap& expression_map) const {
     auto cloned = std::make_shared<LinearIR>();
     cloned->m_config = linear_ir->m_config;
 
-    ExpressionMap expression_map;
     cloned->m_expressions = clone_range(linear_ir->m_expressions.cbegin(), linear_ir->m_expressions.cend(), expression_map);
     for (const auto& expr : cloned->m_expressions) {
         cloned->register_expression(expr, true);
diff --git a/src/common/snippets/src/lowered/loop_info.cpp b/src/common/snippets/src/lowered/loop_info.cpp
index 5564a65bd81d91..f2e1b9c6a3c2e2 100644
--- a/src/common/snippets/src/lowered/loop_info.cpp
+++ b/src/common/snippets/src/lowered/loop_info.cpp
@@ -98,7 +98,7 @@ std::vector<LoopPort>::iterator LoopInfo::find_loop_port(const LoopPort& loop_po
     auto& ports = loop_port.expr_port->get_type() == ExpressionPort::Input ? m_input_ports : m_output_ports;
     const auto it = std::find_if(ports.begin(), ports.end(),
                                  [&loop_port](const LoopPort& port) { return port == loop_port; });
-    OPENVINO_ASSERT(it != ports.end(), "Failed update_loop_port: existing loop port has not been found");
+    OPENVINO_ASSERT(it != ports.end(), "Failed find_loop_port: existing loop port has not been found");
     return it;
 }
 
@@ -110,6 +110,17 @@ std::vector<LoopPort>::iterator LoopInfo::find_loop_port(const ExpressionPort& e
     return it;
 }
 
+bool LoopInfo::is_loop_port(const ExpressionPort& expr_port) {
+    const auto& loop_port_it = find_loop_port(expr_port);
+    const auto& ports = expr_port.get_type() == ExpressionPort::Input ? m_input_ports : m_output_ports;
+    return loop_port_it != ports.end();
+}
+
+const LoopPort& LoopInfo::get_loop_port(const ExpressionPort& expr_port) {
+    OPENVINO_ASSERT(is_loop_port(expr_port), "Failed get_loop_port: expr_port is not a loop port");
+    return *find_loop_port(expr_port);
+}
+
 void LoopInfo::replace_with_new_ports(const LoopPort& actual_port, const std::vector<LoopPort>& target_ports) {
     auto& ports = actual_port.expr_port->get_type() == ExpressionPort::Input ? m_input_ports : m_output_ports;
     auto port_it = find_loop_port(actual_port);
@@ -259,7 +270,7 @@ void order(const std::vector<size_t>& new_order, std::vector<T>& values) {
                     "Failed to sort values: `new_order` must contain new indexes for ALL values");
     std::vector<T> ordered_values(values.size());
     for (size_t i = 0; i < values.size(); ++i) {
-        ordered_values[new_order[i]] = values[i];
+        ordered_values[i] = values[new_order[i]];
     }
     values = std::move(ordered_values);
 }
@@ -314,6 +325,51 @@ void UnifiedLoopInfo::replace_with_new_ports(const ExpressionPort& actual_port,
     validate();
 }
 
+void UnifiedLoopInfo::update_loop_ports(const std::vector<ExpressionPort>& actual_ports, const std::vector<ExpressionPort>& target_ports) {
+    add_loop_ports(target_ports);
+    remove_loop_ports(actual_ports);
+    validate();
+}
+
+void UnifiedLoopInfo::remove_loop_ports(const std::vector<ExpressionPort>& ports) {
+    if (ports.empty())
+        return;
+    bool is_input = ports[0].get_type() == ExpressionPort::Input;
+    auto& loop_ports = is_input ? m_input_ports : m_output_ports;
+    auto& loop_ports_desc = is_input ? m_input_port_descs : m_output_port_descs;
+    for (size_t i = 0; i < ports.size(); i++) {
+        OPENVINO_ASSERT(is_input ? (ports[i].get_type() == ExpressionPort::Input) : (ports[i].get_type() == ExpressionPort::Output),
+            "ports in remove_loop_ports have different type.");
+        auto port_it = find_loop_port(ports[i]);
+        // if not in loop ports, skip
+        if (port_it == loop_ports.end())
+            continue;
+
+        loop_ports.erase(port_it);
+        auto dist = std::distance(loop_ports.begin(), port_it);
+        loop_ports_desc.erase(loop_ports_desc.begin() + dist);
+    }
+}
+
+void UnifiedLoopInfo::add_loop_ports(const std::vector<ExpressionPort>& ports) {
+    if (ports.empty())
+        return;
+    bool is_input = ports[0].get_type() == ExpressionPort::Input;
+    auto& loop_ports = is_input ? m_input_ports : m_output_ports;
+    auto& loop_ports_desc = is_input ? m_input_port_descs : m_output_port_descs;
+    size_t loop_dim_idx = get_dim_idx();
+    for (size_t i = 0; i < ports.size(); i++) {
+        OPENVINO_ASSERT(is_input ? (ports[i].get_type() == ExpressionPort::Input) : (ports[i].get_type() == ExpressionPort::Output),
+            "ports in add_loop_ports have different type.");
+        // if already in loop ports, skip
+        auto loop_port = find_loop_port(ports[i]);
+        if (loop_port != loop_ports.end())
+            continue;
+        loop_ports.push_back(LoopPort(ports[i], true, loop_dim_idx));
+        loop_ports_desc.push_back(LoopPortDesc());
+    }
+}
+
 ExpandedLoopInfo::ExpandedLoopInfo(size_t work_amount, size_t increment,
                                    const std::vector<LoopPort>& entries, const std::vector<LoopPort>& exits,
                                    std::vector<int64_t> ptr_increments, std::vector<int64_t> final_offsets, std::vector<int64_t> data_sizes,
diff --git a/src/common/snippets/src/lowered/loop_manager.cpp b/src/common/snippets/src/lowered/loop_manager.cpp
index 224e1add666948..bbf3971792ad20 100644
--- a/src/common/snippets/src/lowered/loop_manager.cpp
+++ b/src/common/snippets/src/lowered/loop_manager.cpp
@@ -123,15 +123,8 @@ std::pair<LinearIR::constExprIt, LinearIR::constExprIt> LoopManager::get_loop_bo
 }
 
 LoopPort LoopManager::get_loop_port_by_expr_port(const ExpressionPort& expr_port, const size_t loop_id) {
-    auto get_loop_port = [&](const std::vector<LoopPort>& ports) {
-        auto it = std::find_if(ports.cbegin(), ports.cend(), [&](const LoopPort& p) { return *p.expr_port == expr_port; });
-        if (it == ports.cend())
-            OPENVINO_THROW("Expression has not been found among loop ports. Loop id: " + std::to_string(loop_id));
-        return *it;
-    };
     const auto& loop_info = get_loop_info(loop_id);
-    return expr_port.get_type() == ExpressionPort::Input ? get_loop_port(loop_info->get_input_ports())
-                                                         : get_loop_port(loop_info->get_output_ports());
+    return loop_info->get_loop_port(expr_port);
 }
 
 void LoopManager::get_io_loop_ports(LinearIR::constExprIt loop_begin_pos,
@@ -272,7 +265,7 @@ void LoopManager::fuse_loops(LinearIR::constExprIt loop_begin_target, LinearIR::
     auto input_ports_upper = loop_info_upper->get_input_ports();
     auto output_ports_upper = loop_info_upper->get_output_ports();
     auto input_ports_lower = loop_info_lower->get_input_ports();
-    auto output_ports_lower = loop_info_lower->get_output_ports();
+    const auto& output_ports_lower = loop_info_lower->get_output_ports();
     fuse_loop_ports(output_ports_upper, input_ports_lower, loop_id_upper);
 
     const auto& from = fuse_into_upper ? loop_id_lower : loop_id_upper;
@@ -285,9 +278,9 @@ void LoopManager::fuse_loops(LinearIR::constExprIt loop_begin_target, LinearIR::
     const auto handlers = SpecificIterationHandlers::merge_handlers(loop_info_upper->get_handlers(), loop_info_lower->get_handlers());
     const auto is_work_amount_const = loop_info_upper->is_work_amount_const() || loop_info_lower->is_work_amount_const();
 
-    auto new_entries = input_ports_upper;
+    auto new_entries = std::move(input_ports_upper);
     new_entries.insert(new_entries.end(), input_ports_lower.begin(), input_ports_lower.end());
-    auto new_exits = output_ports_upper;
+    auto new_exits = std::move(output_ports_upper);
     new_exits.insert(new_exits.end(), output_ports_lower.begin(), output_ports_lower.end());
 
     m_map[to] = std::make_shared<UnifiedLoopInfo>(work_amount, increment, new_entries, new_exits, handlers, is_work_amount_const);
@@ -397,7 +390,7 @@ void LoopManager::expression_replacement(LinearIR::constExprIt new_expr_begin, L
     }
 }
 
-void LoopManager::sort_loop_ports(LinearIR::constExprIt& loop_begin_pos, LinearIR::constExprIt& loop_end_pos, size_t loop_id) {
+void LoopManager::sort_loop_ports(const LinearIR::constExprIt& loop_begin_pos, const LinearIR::constExprIt& loop_end_pos, size_t loop_id) {
     // [113536] Update this logic please, when expression numeration will be implemented
     const auto& loop_info = get_loop_info<UnifiedLoopInfo>(loop_id);
     const auto& loop_entries = loop_info->get_input_ports();
diff --git a/src/common/snippets/src/lowered/pass/define_buffer_clusters.cpp b/src/common/snippets/src/lowered/pass/define_buffer_clusters.cpp
index dc65918a96ecd0..d4e42c8179f6c1 100644
--- a/src/common/snippets/src/lowered/pass/define_buffer_clusters.cpp
+++ b/src/common/snippets/src/lowered/pass/define_buffer_clusters.cpp
@@ -320,8 +320,6 @@ bool DefineBufferClusters::are_buffer_neighbours(const ExpressionPtr& up, const
 
 void DefineBufferClusters::parse_memory_access_op(const ExpressionPtr& expr) {
     const auto ma = std::dynamic_pointer_cast<modifier::MemoryAccess>(expr->get_node());
-    if (!ma->is_full_memory_access_op(expr->get_node()))
-        return;
     // TODO: Some full MemoryAccess ops can have inplace inputs and outputs in general.
     //       Need to add mechanism of inplace ports using MemoryAccess::PortDescriptor::inplace
     for (const auto& input : expr->get_input_port_connectors()) {
diff --git a/src/common/snippets/src/lowered/pass/extract_loop_invariants.cpp b/src/common/snippets/src/lowered/pass/extract_loop_invariants.cpp
new file mode 100644
index 00000000000000..12c3f63f23b779
--- /dev/null
+++ b/src/common/snippets/src/lowered/pass/extract_loop_invariants.cpp
@@ -0,0 +1,198 @@
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "snippets/lowered/pass/extract_loop_invariants.hpp"
+
+#include "snippets/itt.hpp"
+#include "snippets/lowered/linear_ir.hpp"
+#include "snippets/snippets_isa.hpp"
+#include "snippets/utils.hpp"
+
+namespace ov {
+namespace snippets {
+namespace lowered {
+namespace pass {
+namespace {
+void remove_last_loop_id(const std::shared_ptr<Expression>& expr) {
+    auto loop_ids = expr->get_loop_ids();
+    OPENVINO_ASSERT(!loop_ids.empty(), "Expr loop_ids should not be empty when remove last loop id.");
+    loop_ids.pop_back();
+    expr->set_loop_ids(loop_ids);
+}
+
+int64_t get_stride_after_move_outer(const LoopPort& loop_port) {
+    const auto& expr_port = loop_port.expr_port;
+    const auto& shape = expr_port->get_descriptor_ptr()->get_shape();
+    size_t shape_dim_idx = utils::get_dim_idx(*expr_port, loop_port.dim_idx);
+    int64_t stride = utils::get_stride(shape_dim_idx, shape);
+    if (utils::is_dynamic_value(stride) || utils::is_dynamic_value(shape[shape_dim_idx])) {
+        return utils::get_dynamic_value<int64_t>();
+    } else {
+        return stride * static_cast<int64_t>(shape[shape_dim_idx]);
+    }
+}
+
+bool is_extraction_applicable(const ExpressionPtr& expr, const UnifiedLoopInfoPtr& inner_loop_info) {
+    const auto& expr_input_ports = expr->get_input_ports();
+    const auto& input_port_size = expr_input_ports.size();
+    if (input_port_size == 0)
+        return false;
+
+    for (size_t i = 0; i < input_port_size; ++i) {
+        const auto& parent = expr->get_input_port_connector(i)->get_source().get_expr();
+        bool parent_scalar_with_single_consumer = ov::is_type<snippets::op::Scalar>(parent->get_node()) &&
+                                                  parent->get_output_port_connector(0)->get_consumers().size() == 1;
+        const auto& is_loop_port = inner_loop_info->is_loop_port(expr_input_ports[i]);
+        // If expr input port is not a loop input port, then should not extract. In this case expr depend on result of another expr in inner loop,
+        // i.e. move expr to top(outside) of inner loop does not keep data dependency.
+        // If expr has parent scalar which has single consumer, expr and parent scalar could be extracted together. If parent scalar has multiple
+        // consumers, the scalar has chance to move with other consumers, which maybe break data dependency as well.
+        if (!is_loop_port && !parent_scalar_with_single_consumer) {
+            return false;
+        }
+        if (is_loop_port) {
+            // stride is not 1 after move to outside, then should not extract.
+            const auto& loop_port = inner_loop_info->get_loop_port(expr_input_ports[i]);
+            if (get_stride_after_move_outer(loop_port) != 1) {
+                return false;
+            }
+        }
+    }
+    return true;
+}
+
+void extract_expr(const ExpressionPtr& expr, LinearIR& linear_ir,
+                  LinearIR::constExprIt& inner_loop_begin_pos, const LinearIR::constExprIt& inner_loop_end_pos) {
+    // update expr loop id
+    remove_last_loop_id(expr);
+    // move if it is not the first
+    if (expr != *inner_loop_begin_pos) {
+        auto port_expr_iter = std::find(inner_loop_begin_pos, inner_loop_end_pos, expr);
+        OPENVINO_ASSERT(port_expr_iter != inner_loop_end_pos, "Identified extractable expr is not found in loop.");
+        linear_ir.move(port_expr_iter, inner_loop_begin_pos);
+    } else {
+        inner_loop_begin_pos++;
+    }
+}
+
+void update_loop_ports(const ExpressionPtr& expr, const LoopManagerPtr& loop_manager, size_t inner_loop_id,
+                       const LinearIR::constExprIt& inner_loop_begin_pos, const LinearIR::constExprIt& inner_loop_end_pos) {
+    const auto& inner_loop_info = loop_manager->get_loop_info<UnifiedLoopInfo>(inner_loop_id);
+    // delete expr input ports from loop input points, add expr output ports' consumers if
+    // consumed in inner loop to loop input ports.
+    std::vector<ExpressionPort> new_loop_input_ports;
+    for (size_t i = 0; i < expr->get_output_count(); i++) {
+        const auto& consumers = expr->get_output_port_connector(i)->get_consumers();
+        for (const auto& consumer : consumers) {
+            const auto& loop_ids = consumer.get_expr()->get_loop_ids();
+            if (std::find(loop_ids.cbegin(), loop_ids.cend(), inner_loop_id) != loop_ids.cend()) {
+                new_loop_input_ports.push_back(consumer);
+            }
+        }
+    }
+    const auto& expr_input_ports = expr->get_input_ports();
+    inner_loop_info->update_loop_ports(expr_input_ports, new_loop_input_ports);
+
+    // delete expr out ports from loop out ports directly if it's in loop output ports
+    std::vector<ExpressionPort> out_ports_to_delete;
+    for (size_t i = 0; i < expr->get_output_count(); ++i) {
+        const auto& out_port = expr->get_output_port(i);
+        if (inner_loop_info->is_loop_port(out_port)) {
+            out_ports_to_delete.push_back(out_port);
+        }
+    }
+    if (!out_ports_to_delete.empty()) {
+        std::vector<ExpressionPort> new_ports;
+        inner_loop_info->update_loop_ports(out_ports_to_delete, new_ports);
+    }
+    // TODO: 142990.
+    // Need sort after update loop ports. There are possibility that all exprs are moved to outer loop.
+    if (!inner_loop_info->get_input_ports().empty() && !inner_loop_info->get_output_ports().empty()) {
+        loop_manager->sort_loop_ports(inner_loop_begin_pos, inner_loop_end_pos, inner_loop_id);
+    }
+}
+
+std::vector<ExpressionPtr> get_loop_input_exprs(const std::vector<LoopPort>& loop_in_ports) {
+    std::vector<ExpressionPtr> input_exprs;
+    std::unordered_set<ExpressionPtr> seen_exprs;
+    for (size_t port_num = 0; port_num < loop_in_ports.size(); ++port_num) {
+        const auto& expr = loop_in_ports[port_num].expr_port->get_expr();
+        if (seen_exprs.count(expr) == 0) {
+            input_exprs.push_back(expr);
+            seen_exprs.insert(expr);
+        }
+    }
+    return input_exprs;
+}
+
+bool extract_from_loop(const size_t& inner_loop_id, LinearIR& linear_ir) {
+    const auto& loop_manager = linear_ir.get_loop_manager();
+    bool status = false;
+    bool continue_to_extract = true;
+    const auto& inner_loop_info = loop_manager->get_loop_info<UnifiedLoopInfo>(inner_loop_id);
+    while (continue_to_extract) {
+        const auto& inner_loop_input_ports = inner_loop_info->get_input_ports();
+        const auto& potential_extractable_exprs = get_loop_input_exprs(inner_loop_input_ports);
+        bool expr_extracted = false;
+        for (const auto& port_expr : potential_extractable_exprs) {
+            if (is_extraction_applicable(port_expr, inner_loop_info)) {
+                status = true;
+                LinearIR::constExprIt inner_loop_begin_pos, inner_loop_end_pos;
+                std::tie(inner_loop_begin_pos, inner_loop_end_pos) =
+                    loop_manager->get_loop_bounds(linear_ir, inner_loop_id);
+                // extract scalar on inputs if there are
+                for (size_t i = 0; i < port_expr->get_input_count(); ++i) {
+                    auto parent = port_expr->get_input_port_connector(i)->get_source().get_expr();
+                    if (ov::is_type<snippets::op::Scalar>(parent->get_node())) {
+                        extract_expr(parent, linear_ir, inner_loop_begin_pos, inner_loop_end_pos);
+                    }
+                }
+                extract_expr(port_expr, linear_ir, inner_loop_begin_pos, inner_loop_end_pos);
+                update_loop_ports(port_expr, loop_manager, inner_loop_id, inner_loop_begin_pos, inner_loop_end_pos);
+                expr_extracted = true;
+                break;  // extracted and refreshed loop_input_ports. break potential_extractable_exprs loop, and go while() to start again.
+            }
+        }
+        if (inner_loop_input_ports.size() == 0 && inner_loop_info->get_output_ports().size() == 0) {
+            // If the loop becomes empty (inner_loop_input_ports is ref) after extraction, remove it from loop_manager
+            loop_manager->remove_loop_info(inner_loop_id);
+            break;
+        }
+        // no more extractable expr in this loop after go through all potential_extractable_exprs, done for this loop.
+        if (!expr_extracted)
+            continue_to_extract = false;
+    }
+    return status;
+}
+}  // namespace
+
+bool ExtractLoopInvariants::run(LinearIR& linear_ir, lowered::LinearIR::constExprIt begin, lowered::LinearIR::constExprIt end) {
+    OV_ITT_SCOPED_TASK(ov::pass::itt::domains::SnippetsTransform, "Snippets::ExtractLoopInvariants")
+    bool modified = false;
+
+    const auto& loop_depth = linear_ir.get_config().m_loop_depth;
+    std::vector<std::set<size_t>> loop_ids_need_extract(loop_depth);
+    const auto& loop_map = linear_ir.get_loop_manager()->get_map();
+    for (const auto& loop : loop_map) {
+        const auto& loop_dim = loop.second->get_dim_idx();
+        if (loop_dim != LoopInfo::UNDEFINED_DIM_IDX) {
+            OPENVINO_ASSERT(loop_dim < loop_depth, "dim_idx of loop should be smaller than loop_depth");
+            loop_ids_need_extract[loop_dim].insert(loop.first);
+        }
+    }
+    // move invariant expr to top(outside) of current loop
+    for (size_t d = 0; d < loop_depth; d++) {
+        const auto& loops_in_this_depth = loop_ids_need_extract[d];
+        for (const auto& loop_id : loops_in_this_depth) {
+            modified |= extract_from_loop(loop_id, linear_ir);
+        }
+    }
+
+    return modified;
+}
+
+}  // namespace pass
+}  // namespace lowered
+}  // namespace snippets
+}  // namespace ov
diff --git a/src/common/snippets/src/lowered/pass/init_loops.cpp b/src/common/snippets/src/lowered/pass/init_loops.cpp
index 3b9aa3f5109f23..32c44729bc04c9 100644
--- a/src/common/snippets/src/lowered/pass/init_loops.cpp
+++ b/src/common/snippets/src/lowered/pass/init_loops.cpp
@@ -20,17 +20,6 @@ namespace pass {
 using MemoryAccess = ov::snippets::modifier::MemoryAccess;
 
 namespace {
-inline int64_t get_stride(size_t dim, const VectorDims& shape) {
-    int64_t stride = 1;
-    for (size_t i = dim + 1; i < shape.size(); ++i) {
-        if (utils::is_dynamic_value(shape[i])) {
-            return utils::get_dynamic_value<int64_t>();
-        }
-        stride *= static_cast<int64_t>(shape[i]);
-    }
-    return stride;
-}
-
 inline void init_is_incremented(LoopPort& port, size_t loop_id) {
     const auto& expr = port.expr_port->get_expr();
     const auto& expr_loops = expr->get_loop_ids();
@@ -93,7 +82,7 @@ inline int64_t get_ptr_increment(const LoopPort& loop_port, size_t work_amount,
     if (utils::is_dynamic_value(shape[dim]) && port_count > 1) {
         return utils::get_dynamic_value<int64_t>();
     } else if (!(shape[dim] == 1 && work_amount != 1)) {
-        return get_stride(dim, shape);
+        return utils::get_stride(dim, shape);
     }
     return 0;
 }
diff --git a/src/common/snippets/src/lowered/pass/insert_load_store.cpp b/src/common/snippets/src/lowered/pass/insert_load_store.cpp
index 27a2b6e2d36174..309708966de01d 100644
--- a/src/common/snippets/src/lowered/pass/insert_load_store.cpp
+++ b/src/common/snippets/src/lowered/pass/insert_load_store.cpp
@@ -58,7 +58,9 @@ bool InsertLoadStore::insert_store(LinearIR& linear_ir, const LinearIR::constExp
 
     const auto& loop_ids = parent_expr->get_loop_ids();
     const auto store = std::make_shared<op::Store>(parent->output(port), get_count(data_expr->get_input_port(0)));
-    const auto& insertion_pos = linear_ir.find_after(std::reverse_iterator<LinearIR::constExprIt>(data_expr_it), parent_expr).base();
+    const auto insertion_it =
+        linear_ir.find_after(std::reverse_iterator<LinearIR::constExprIt>(data_expr_it), parent_expr);
+    const auto& insertion_pos = insertion_it.base();
     linear_ir.insert_node(store, std::vector<ExpressionPort>{ parent_output }, loop_ids, true, insertion_pos, { data_expr->get_input_port(0) });
     return true;
 }
diff --git a/src/common/snippets/src/lowered/pass/load_movebroadcast_to_broadcastload.cpp b/src/common/snippets/src/lowered/pass/load_movebroadcast_to_broadcastload.cpp
index 5ead7be79fca83..27232f1605ea0e 100644
--- a/src/common/snippets/src/lowered/pass/load_movebroadcast_to_broadcastload.cpp
+++ b/src/common/snippets/src/lowered/pass/load_movebroadcast_to_broadcastload.cpp
@@ -31,9 +31,6 @@ bool LoadMoveBroadcastToBroadcastLoad::run(LinearIR& linear_ir, lowered::LinearI
                  load_expr->get_input_port_descriptor(0)->get_shape() != load_expr->get_output_port_descriptor(0)->get_shape())
                  continue;
 
-            OPENVINO_ASSERT(expr->get_loop_ids() == load_expr->get_loop_ids(),
-                            "The pair of Load and MoveBroadcast expressions must be in the same loops!");
-
             // Cannot rewrite Broadcast + Load if load has more than 1 user
             // or more than one input, or if Broadcast has several inputs
             const auto load_consumers_inputs = interm_connector->get_consumers();
@@ -50,7 +47,10 @@ bool LoadMoveBroadcastToBroadcastLoad::run(LinearIR& linear_ir, lowered::LinearI
             const auto& load_parent_node = load_expr->get_input_port_connector(0)->get_source().get_expr()->get_node();
             const auto& outshape = move_broadcast->get_output_partial_shape(0);
             const auto broadcastload = std::make_shared<snippets::op::BroadcastLoad>(load_parent_node, *outshape.rbegin(), load->get_offset());
-            expr_it = linear_ir.replace_with_node({ load_expr, expr }, broadcastload);
+            // insert at position of load_expr. As BroadcastMove and Load will be removed, preserve expr_it.
+            expr_it = std::next(expr_it);
+            linear_ir.replace_with_node({ load_expr, expr }, broadcastload, load_expr->get_loop_ids(), linear_ir.find(load_expr));
+            expr_it = std::prev(expr_it);
             modified |= true;
         }
     }
diff --git a/src/common/snippets/src/lowered/pass/validate_expanded_loops.cpp b/src/common/snippets/src/lowered/pass/validate_expanded_loops.cpp
index 2205b60ea9bacf..cebe581dbda443 100644
--- a/src/common/snippets/src/lowered/pass/validate_expanded_loops.cpp
+++ b/src/common/snippets/src/lowered/pass/validate_expanded_loops.cpp
@@ -52,7 +52,10 @@ void ValidateExpandedLoops::validate_loop_information(const LinearIR& linear_ir)
         const auto& expanded_loop_info = ov::as_type_ptr<ExpandedLoopInfo>(p.second);
         INFORMATIVE_ASSERT(expanded_loop_info, "expects only ExpandedLoopInfo in LoopManager");
 
-        if (expanded_loop_info->get_unified_loop_info() != current_unified_loop_info) {
+        const auto& unified_loop_info = expanded_loop_info->get_unified_loop_info();
+        INFORMATIVE_ASSERT(unified_loop_info, "expects non nullptr UnifiedLoopInfo in ExpandedLoopInfo");
+
+        if (unified_loop_info != current_unified_loop_info) {
             // If there is `current_unified_loop_info` - the previos loop is finished and need to validate total information
             if (current_unified_loop_info) {
                 INFORMATIVE_ASSERT(current_work_amount == current_unified_loop_info->get_work_amount(),
@@ -61,7 +64,7 @@ void ValidateExpandedLoops::validate_loop_information(const LinearIR& linear_ir)
                                    "total finalization offsets are not equal to finalization offsets of undefined loop");
             }
 
-            current_unified_loop_info = expanded_loop_info->get_unified_loop_info();
+            current_unified_loop_info = unified_loop_info;
 
             INFORMATIVE_ASSERT(current_unified_loop_info->get_input_count() == expanded_loop_info->get_input_count() &&
                                current_unified_loop_info->get_output_count() == expanded_loop_info->get_output_count(),
@@ -74,6 +77,7 @@ void ValidateExpandedLoops::validate_loop_information(const LinearIR& linear_ir)
         }
 
         current_work_amount = utils::dynamic_safe_add(current_work_amount, expanded_loop_info->get_work_amount());
+        INFORMATIVE_ASSERT(current_unified_loop_info, "expects non nullptr current UnifiedLoopInfo");
         INFORMATIVE_ASSERT(current_unified_loop_info->get_ptr_increments() == expanded_loop_info->get_ptr_increments(),
                            "incompatible pointer increments with UnifiedLoopInfo");
 
diff --git a/src/common/snippets/src/op/brgemm.cpp b/src/common/snippets/src/op/brgemm.cpp
index 43af058e87d82d..c69d4193a6d943 100644
--- a/src/common/snippets/src/op/brgemm.cpp
+++ b/src/common/snippets/src/op/brgemm.cpp
@@ -39,7 +39,7 @@ Brgemm::Brgemm(const Output<Node>& A, const Output<Node>& B,
     set_input_offset(offset_a, 0);
     set_input_offset(offset_b, 1);
     set_output_offset(offset_c, 0);
-    compute_block_size_values(blk_size_m, blk_size_k, blk_size_n);
+    set_block_size_values(blk_size_m, blk_size_k, blk_size_n);
     custom_constructor_validate_and_infer_types(std::move(layout_a), std::move(layout_b), std::move(layout_c));
 }
 
@@ -49,43 +49,33 @@ Brgemm::Brgemm(const Output<Node>& A, const Output<Node>& B,
                const size_t blk_size_m, const size_t blk_size_k, const size_t blk_size_n)
     : MemoryAccess(PortMap{{0, desc_a}, {1, desc_b}}, PortMap{{0, desc_c}}), Op({A, B}) {
     set_output_size(1);
-    compute_block_size_values(blk_size_m, blk_size_k, blk_size_n);
+    set_block_size_values(blk_size_m, blk_size_k, blk_size_n);
     custom_constructor_validate_and_infer_types(std::move(layout_a), std::move(layout_b), std::move(layout_c));
 }
 
-void Brgemm::compute_block_size_values(const size_t blk_size_m, const size_t blk_size_k, const size_t blk_size_n) {
-    const auto input_shape_0 = snippets::utils::get_planar_pshape(input(0)).get_shape();
-    const auto input_shape_1 = snippets::utils::get_planar_pshape(input(1)).get_shape();
-    m_M_blk = blk_size_m != 0 ? blk_size_m : *++input_shape_0.rbegin();
-    m_K_blk = blk_size_k != 0 ? blk_size_k : *input_shape_0.rbegin();
-    m_N_blk = blk_size_n != 0 ? blk_size_n : *input_shape_1.rbegin();
+void Brgemm::set_block_size_values(const size_t blk_size_m, const size_t blk_size_k, const size_t blk_size_n) {
+    m_M_blk = blk_size_m;
+    m_K_blk = blk_size_k;
+    m_N_blk = blk_size_n;
 }
 
 void Brgemm::custom_constructor_validate_and_infer_types(std::vector<size_t> layout_a, std::vector<size_t> layout_b, std::vector<size_t> layout_c) {
     INTERNAL_OP_SCOPE(BrgemmCPU_constructor_validate_and_infer_types);
-    validate_inputs();
 
     // During ctor call, Brgemm doesn't know his port descriptors.
     // So we use explicit layouts from parameters
     const auto planar_input_shapes =
             std::vector<ov::PartialShape>{ ov::snippets::utils::get_planar_pshape(get_input_partial_shape(0), layout_a),
                                            ov::snippets::utils::get_planar_pshape(get_input_partial_shape(1), layout_b) };
-    auto output_shape = get_output_partial_shape(planar_input_shapes);
+    auto output_shape = infer_output_partial_shape(planar_input_shapes);
     set_output_type(0, get_output_type(), ov::snippets::utils::get_planar_pshape(output_shape, layout_c));
 }
 
-void Brgemm::validate_inputs() const {
-    // If no leading dimensions are provided, assume dense row-major inputs-outputs
-    NODE_VALIDATION_CHECK(this, get_input_partial_shape(0).is_static() && get_input_partial_shape(1).is_static(),
-                          "Brgemm currently supports only static shapes.");
-}
-
 void Brgemm::validate_and_infer_types() {
     INTERNAL_OP_SCOPE(Brgemm_validate_and_infer_types);
-    validate_inputs();
 
     const auto planar_input_shapes = get_planar_input_shapes(inputs());
-    auto output_shape = get_output_partial_shape(planar_input_shapes);
+    auto output_shape = infer_output_partial_shape(planar_input_shapes);
     set_output_type(0, get_output_type(), get_planar_output_shape(output_shape));
 }
 
@@ -146,7 +136,7 @@ ov::PartialShape Brgemm::get_planar_output_shape(const ov::PartialShape& output_
     return output_shape;
 }
 
-ov::PartialShape Brgemm::get_output_partial_shape(const std::vector<ov::PartialShape>& input_shapes) const {
+ov::PartialShape Brgemm::infer_output_partial_shape(const std::vector<ov::PartialShape>& input_shapes) const {
     OPENVINO_ASSERT(input_shapes.size() == 2, "BRGEMM expects 2 input shapes for shape inference");
 
     // Note: All majors checks are missed because Brgemm is transformed from MatMul with whole shape infer support
diff --git a/src/common/snippets/src/op/subgraph.cpp b/src/common/snippets/src/op/subgraph.cpp
index ab793c722d1e3e..003db9a8527249 100644
--- a/src/common/snippets/src/op/subgraph.cpp
+++ b/src/common/snippets/src/op/subgraph.cpp
@@ -54,6 +54,7 @@
 #include "snippets/lowered/pass/normalize_loop_ids.hpp"
 #include "snippets/lowered/pass/validate_expanded_loops.hpp"
 #include "snippets/lowered/pass/set_load_store_scalar.hpp"
+#include "snippets/lowered/pass/extract_loop_invariants.hpp"
 
 #include "transformations/utils/utils.hpp"
 
@@ -459,6 +460,7 @@ void Subgraph::control_flow_transformations(size_t min_parallel_work_amount, siz
     pipeline.register_pass<lowered::pass::MoveScalarToConsumer>();
     pipeline.register_pass<lowered::pass::InsertBroadcastMove>();
     pipeline.register_pass<lowered::pass::LoadMoveBroadcastToBroadcastLoad>();
+    pipeline.register_pass<lowered::pass::ExtractLoopInvariants>();
     pipeline.register_pass<lowered::pass::ValidateShapes>();
     pipeline.register_pass<lowered::pass::ValidateUnifiedLoops>();
     pipeline.register_pass<lowered::pass::InitLoops>();
@@ -530,7 +532,8 @@ snippets::Schedule Subgraph::generate(const void* compile_params) const {
     // actual code emission
     // Note: to not corrupt the lowered linear IR for the shape-dependent passes, we have to make a copy
     OPENVINO_ASSERT(m_linear_ir, "Attempt to call generate, when linear IR was not initialized");
-    auto linear_ir = *lowered::LinearIRBuilder().clone(m_linear_ir);
+    ov::snippets::lowered::ExpressionMap expression_map;
+    auto linear_ir = *lowered::LinearIRBuilder().clone(m_linear_ir, expression_map);
 
     if (is_dynamic()) {
         ov::snippets::lowered::pass::PassPipeline shape_dependent_pipeline;
@@ -542,6 +545,14 @@ snippets::Schedule Subgraph::generate(const void* compile_params) const {
 
     auto lowering_result = m_generator->generate(linear_ir, compile_params);
 
+    // Note: Since the code emission is performed on a copy of LIR, but RuntimeConfigurator works with the initial instance,
+    //  we need to replace cloned expression pointers to original ones in the KernelExecutorTable
+    const auto& exec_table = m_generator->get_target_machine()->get_runtime_configurator()->get_kernel_executor_table();
+    for (const auto& expr : *m_linear_ir)
+        exec_table->replace_key_expression(expression_map.at(expr.get()), expr);
+    // Some kernel executors might've been registered during code emission.
+    //  We need to update them, so appropriate kernels will be compiled.
+    exec_table->update_state();
     return {std::move(lowering_result)};
 }
 
diff --git a/src/common/snippets/src/pass/collapse_subgraph.cpp b/src/common/snippets/src/pass/collapse_subgraph.cpp
index b63b9612f818d4..82675ba946e6a5 100644
--- a/src/common/snippets/src/pass/collapse_subgraph.cpp
+++ b/src/common/snippets/src/pass/collapse_subgraph.cpp
@@ -47,8 +47,8 @@ auto is_supported_op(const std::shared_ptr<const Node> &n) -> bool {
     OV_ITT_SCOPED_TASK(ov::pass::itt::domains::SnippetsTransform, "Snippets::is_supported_op")
     auto is_supported_matmul = [](const std::shared_ptr<const Node>& n) -> bool {
         const auto& matmul = ov::as_type_ptr<const opset1::MatMul>(n);
-        const auto& out_shape = n->get_output_partial_shape(0);
-        if (!matmul || out_shape.is_dynamic() || out_shape.size() != 4)
+        const auto& out_rank = n->get_output_partial_shape(0).rank();
+        if (!matmul || out_rank.is_dynamic() || out_rank.get_length() != 4)
             return false;
         const auto intype_0 = matmul->get_input_element_type(0);
         const auto intype_1 = matmul->get_input_element_type(1);
@@ -124,6 +124,7 @@ auto is_supported_op(const std::shared_ptr<const Node> &n) -> bool {
             || ov::is_type<ov::op::v0::Erf>(n)
             || ov::is_type<ov::op::v0::Exp>(n)
             || ov::is_type<ov::op::v1::LogicalNot>(n)
+            || ov::is_type<ov::op::v4::Mish>(n)
             || ov::is_type<ov::op::v0::Negative>(n)
             || ov::is_type<ov::op::v0::Relu>(n)
             || ov::is_type<ov::op::v5::Round>(n)
diff --git a/src/common/snippets/src/pass/matmul_to_brgemm.cpp b/src/common/snippets/src/pass/matmul_to_brgemm.cpp
index 22be569125ecf9..37fda8306ed04f 100644
--- a/src/common/snippets/src/pass/matmul_to_brgemm.cpp
+++ b/src/common/snippets/src/pass/matmul_to_brgemm.cpp
@@ -6,6 +6,7 @@
 
 #include "snippets/itt.hpp"
 #include "snippets/snippets_isa.hpp"
+#include "snippets/utils.hpp"
 #include "snippets/lowered/port_descriptor.hpp"
 
 #include "openvino/core/rt_info.hpp"
@@ -17,16 +18,16 @@ namespace snippets {
 namespace pass {
 
 void MatMulToBrgemm::init_ports(const std::shared_ptr<op::Brgemm>& brgemm) const {
-    auto get_subtensor = [](const ov::Shape& shape) {
+    auto get_subtensor = []() {
         return std::vector<size_t>{ lowered::PortDescriptor::ServiceDimensions::FULL_DIM, lowered::PortDescriptor::ServiceDimensions::FULL_DIM };
     };
     for (const auto& input : brgemm->inputs()) {
-        const auto tensor = input.get_shape();
-        const auto subtensor = get_subtensor(tensor);
+        const auto& tensor = utils::pshape_to_vdims(input.get_partial_shape());
+        const auto& subtensor = get_subtensor();
         lowered::PortDescriptorUtils::set_port_descriptor_ptr(input, std::make_shared<lowered::PortDescriptor>(tensor, subtensor));
     }
-    const auto tensor = brgemm->get_output_shape(0);
-    const auto subtensor = get_subtensor(tensor);
+    const auto& tensor = utils::pshape_to_vdims(brgemm->get_output_partial_shape(0));
+    const auto& subtensor = get_subtensor();
     lowered::PortDescriptorUtils::set_port_descriptor_ptr(brgemm->output(0), std::make_shared<lowered::PortDescriptor>(tensor, subtensor));
 }
 
diff --git a/src/common/snippets/src/pass/mha_tokenization.cpp b/src/common/snippets/src/pass/mha_tokenization.cpp
index d928cdd1d33eba..7483151f4dfd05 100644
--- a/src/common/snippets/src/pass/mha_tokenization.cpp
+++ b/src/common/snippets/src/pass/mha_tokenization.cpp
@@ -468,7 +468,7 @@ ov::snippets::pass::TokenizeMHASnippets::TokenizeMHASnippets(const SnippetsToken
 
         /* ================================ */
 
-        /* ====== Subgraph creation ======= */
+        /* ======= Support checks ========= */
 
         // TODO [75567]: move this plugin-specific constraint to the plugin callback
         const auto last_node = ordered_ops.back();
@@ -476,6 +476,16 @@ ov::snippets::pass::TokenizeMHASnippets::TokenizeMHASnippets(const SnippetsToken
             return false;
         }
 
+        // If backend doesn't enable dynamic MHA tokenization, return false
+        if (!config.is_dynamic_mha_token_enabled()) {
+            if (std::any_of(ordered_ops.cbegin(), ordered_ops.cend(), [](const std::shared_ptr<ov::Node>& op) { return op->is_dynamic(); }))
+                return false;
+        }
+
+        /* ================================ */
+
+        /* ====== Subgraph creation ======= */
+
         ov::OutputVector body_inputs, subgraph_inputs;
         ov::ParameterVector body_parameters;
         ov::ResultVector body_results;
diff --git a/src/common/snippets/src/runtime_configurator.cpp b/src/common/snippets/src/runtime_configurator.cpp
index 7681e78f1ebb49..f790ec747cd7bf 100644
--- a/src/common/snippets/src/runtime_configurator.cpp
+++ b/src/common/snippets/src/runtime_configurator.cpp
@@ -30,7 +30,8 @@ void init_data_ptr_shifts(const lowered::UnifiedLoopInfoPtr& unified_loop_info,
 }
 }  // namespace
 
-RuntimeConfigurator::RuntimeConfigurator(std::shared_ptr<RuntimeConfig> c) : m_config(std::move(c)) {
+RuntimeConfigurator::RuntimeConfigurator(std::shared_ptr<RuntimeConfig> c) :
+    m_config(std::move(c)) {
     OPENVINO_ASSERT(m_config, "Runtime config is nullptr!");
 }
 
diff --git a/src/common/snippets/src/utils.cpp b/src/common/snippets/src/utils.cpp
index a7f00bbfebcb9c..2204d00e181fb0 100644
--- a/src/common/snippets/src/utils.cpp
+++ b/src/common/snippets/src/utils.cpp
@@ -142,6 +142,18 @@ size_t get_dim_idx(const lowered::ExpressionPort& port, size_t dim_idx) {
     return 0;
 }
 
+int64_t get_stride(size_t dim_idx, const VectorDims& shape) {
+    OPENVINO_ASSERT(dim_idx < shape.size(), "dim_idx should be in range of [0, shape.size()) in get_stride");
+    int64_t stride = 1;
+    for (size_t i = dim_idx + 1; i < shape.size(); ++i) {
+        if (utils::is_dynamic_value(shape[i])) {
+            return utils::get_dynamic_value<int64_t>();
+        }
+        stride *= static_cast<int64_t>(shape[i]);
+    }
+    return stride;
+}
+
 ov::PartialShape get_planar_pshape(const ov::PartialShape& shape, const std::vector<size_t>& order) {
     return get_pshape(shape, order, true);
 }
@@ -279,6 +291,24 @@ std::shared_ptr<ov::Node> get_leaf_node_of_first_parent_shape_infer_seq(const st
     return leaf_node;
 }
 
+size_t get_in_leading_dim(const VectorDims& shape, const std::vector<size_t>& layout) {
+    if (layout.empty())
+        return shape.back();
+    OPENVINO_ASSERT(layout.back() == layout.size() - 1 && layout.size() == shape.size(),
+                              "detected invalid layout values: check that this shape + layout combination is schedulable");
+    const auto idx = static_cast<VectorDims::difference_type>(layout[layout.size() - 2]);
+    return std::accumulate(shape.cbegin() + idx + 1, shape.end(), 1ull, std::multiplies<size_t>());
+}
+size_t get_out_leading_dim(const VectorDims& shape, const std::vector<size_t>& layout) {
+    if (layout.empty())
+        return shape.back();
+    OPENVINO_ASSERT(layout.back() == layout.size() - 1 && layout.size() == shape.size(),
+                              "detected invalid layout values: check that this shape + layout combination is schedulable");
+    const auto idx = layout.size() - 2;
+    const auto dim = std::distance(layout.cbegin(), std::find(layout.cbegin(), layout.cend(), idx));
+    return std::accumulate(shape.cbegin() + dim + 1, shape.cend(), 1ull, std::multiplies<size_t>());
+}
+
 } // namespace utils
 } // namespace snippets
 } // namespace ov
diff --git a/src/common/snippets/tests/include/lir_test_utils.hpp b/src/common/snippets/tests/include/lir_test_utils.hpp
index 62ff366eb54c9b..2f687f6e1412d1 100644
--- a/src/common/snippets/tests/include/lir_test_utils.hpp
+++ b/src/common/snippets/tests/include/lir_test_utils.hpp
@@ -58,6 +58,24 @@ void create_and_add_unified_loop_info(const std::shared_ptr<ov::snippets::lowere
                                       const std::vector<ov::snippets::lowered::LoopPort>& entries,
                                       const std::vector<ov::snippets::lowered::LoopPort>& exits,
                                       bool add_default_handlers = true);
+/**
+ * @brief Creates unified loop info based on provided entry and exit points, and adds it to the linear_ir's loops map.
+ * Meanwhile set loop id to expr range [loop_begin_pos, loop_end_pos).
+ * @attention This helper wraps LoopManager::mark_loop method, which also marks expressions with the corresponding loop info
+ * @param linear_ir linear_ir in which loop info should be added
+ * @param loop_begin_pos begin expr postion in this loop
+ * @param loop_end_pos end expr postion in this loop
+ * @param entries entry points of loop
+ * @param exits exit points of loop
+ */
+void create_and_add_unified_loop_info(const std::shared_ptr<ov::snippets::lowered::LinearIR>& linear_ir,
+                                      ov::snippets::lowered::LinearIR::constExprIt loop_begin_pos,
+                                      ov::snippets::lowered::LinearIR::constExprIt loop_end_pos,
+                                      size_t work_amount,
+                                      size_t increment,
+                                      const std::vector<ov::snippets::lowered::LoopPort>& entries,
+                                      const std::vector<ov::snippets::lowered::LoopPort>& exits,
+                                      bool add_default_handlers = true);
 }  // namespace snippets
 }  // namespace test
 }  // namespace ov
diff --git a/src/common/snippets/tests/include/utils.hpp b/src/common/snippets/tests/include/utils.hpp
index 3ac4b8301d0e74..6190e1f6ca4291 100644
--- a/src/common/snippets/tests/include/utils.hpp
+++ b/src/common/snippets/tests/include/utils.hpp
@@ -11,7 +11,7 @@ namespace test {
 namespace snippets {
 
 static ov::snippets::pass::SnippetsTokenization::Config get_default_tokenization_config() {
-    return { 1, std::numeric_limits<size_t>::max(), true, true, { 3, 4 } };
+    return { 1, std::numeric_limits<size_t>::max(), true, true, true, { 3, 4 } };
 }
 
 }  // namespace snippets
diff --git a/src/common/snippets/tests/src/lir_comparator.cpp b/src/common/snippets/tests/src/lir_comparator.cpp
index ed20853a0ea425..3552c81cdf40a5 100644
--- a/src/common/snippets/tests/src/lir_comparator.cpp
+++ b/src/common/snippets/tests/src/lir_comparator.cpp
@@ -96,8 +96,9 @@ LIRComparator::Result LIRComparator::compare(const LinearIRPtr& linear_ir,
     for (auto result_it = results.begin(), result_it_ref = results_ref.begin(); result_it != results.end(); ++result_it, ++result_it_ref)
         PROPAGATE_ERROR("", run_comparison(result_it, result_it_ref));
 
-    if (should_compare(LIRCmpValues::LOOP_MANAGER))
+    if (should_compare(LIRCmpValues::LOOP_MANAGER)) {
         PROPAGATE_ERROR("Loop managers", compare_loop_managers(linear_ir->get_loop_manager(), linear_ir_ref->get_loop_manager()));
+    }
     return Result::ok();
 }
 
diff --git a/src/common/snippets/tests/src/lir_test_utils.cpp b/src/common/snippets/tests/src/lir_test_utils.cpp
index 31a2092acc3020..71cfadc9c3a442 100644
--- a/src/common/snippets/tests/src/lir_test_utils.cpp
+++ b/src/common/snippets/tests/src/lir_test_utils.cpp
@@ -91,9 +91,20 @@ void create_and_add_unified_loop_info(const LinearIRPtr& linear_ir,
                                       const std::vector<LoopPort>& entries,
                                       const std::vector<LoopPort>& exits,
                                       bool set_default_handlers) {
-    const auto& loop_manager = linear_ir->get_loop_manager();
     // Equal begin and end iterators are set to avoid expressions marking with new loop id
-    loop_manager->mark_loop(linear_ir->begin(), linear_ir->begin(), work_amount, increment, entries, exits, set_default_handlers);
+    create_and_add_unified_loop_info(linear_ir, linear_ir->begin(), linear_ir->begin(), work_amount, increment, entries, exits, set_default_handlers);
+}
+
+void create_and_add_unified_loop_info(const LinearIRPtr& linear_ir,
+                                      ov::snippets::lowered::LinearIR::constExprIt loop_begin_pos,
+                                      ov::snippets::lowered::LinearIR::constExprIt loop_end_pos,
+                                      size_t work_amount,
+                                      size_t increment,
+                                      const std::vector<LoopPort>& entries,
+                                      const std::vector<LoopPort>& exits,
+                                      bool set_default_handlers) {
+    const auto& loop_manager = linear_ir->get_loop_manager();
+    loop_manager->mark_loop(loop_begin_pos, loop_end_pos, work_amount, increment, entries, exits, set_default_handlers);
 }
 
 }  // namespace snippets
diff --git a/src/common/snippets/tests/src/lowered/pass/extracted_loop_invariants.cpp b/src/common/snippets/tests/src/lowered/pass/extracted_loop_invariants.cpp
new file mode 100644
index 00000000000000..c3f4f5ea7f6877
--- /dev/null
+++ b/src/common/snippets/tests/src/lowered/pass/extracted_loop_invariants.cpp
@@ -0,0 +1,423 @@
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "lir_test_utils.hpp"
+
+#include "openvino/opsets/opset10.hpp"
+#include "snippets/lowered/pass/extract_loop_invariants.hpp"
+#include "snippets/lowered/pass/normalize_loop_ids.hpp"
+#include "snippets/op/broadcastmove.hpp"
+#include "snippets/op/scalar.hpp"
+#include "snippets/op/vector_buffer.hpp"
+#include "snippets/op/horizon_max.hpp"
+#include "snippets/op/horizon_sum.hpp"
+#include "snippets/op/powerstatic.hpp"
+
+namespace ov {
+namespace test {
+namespace snippets {
+
+using namespace ov::snippets::lowered;
+using namespace ov::snippets::lowered::pass;
+
+class ExtractLoopInvariantsTest : public LoweredPassTestsF {
+public:
+    ExtractLoopInvariantsTest() : LoweredPassTestsF() {
+        comparator.enable(LIRComparator::LIRCmpValues::LOOP_INDICES);
+        comparator.enable(LIRComparator::LIRCmpValues::PORT_DESCRIPTORS);
+        comparator.enable(LIRComparator::LIRCmpValues::PORT_CONNECTORS);
+        comparator.enable(LIRComparator::LIRCmpValues::LOOP_MANAGER);
+    }
+
+    void SetUp() override {
+        pipeline.register_pass<ExtractLoopInvariants>();
+    }
+};
+
+TEST_F(ExtractLoopInvariantsTest, ExtractedLoopInvariantsWithParams) {
+    size_t vector_size = 16;
+    const auto input_precision = ov::element::f32;
+    const ov::Shape input_shape0{1};
+    const ov::Shape input_shape1{512};
+    const std::vector<ov::snippets::VectorDims> layout_1d{{0}, {0}, {0}};
+    const std::vector<ov::snippets::VectorDims> mul_subtensor{{1}, {1}, {1}};
+    const std::vector<ov::snippets::VectorDims> sub_subtensor{{512}, {1}, {512}};
+    /*
+     *      Param00    Param01
+     *         \       /
+     *          Multiply(loopBegin)
+     *              |
+     *          Broadcast     Param1
+     *               \         /
+     *                Substract(loopBeginRef)
+     *                    |
+     *                  Result(LoopEnd and LoopEndRef)
+    */
+    {
+        auto param00 = linear_ir->push_node<ov::opset10::Parameter>(input_precision, input_shape0);
+        auto param01 = linear_ir->push_node<ov::opset10::Parameter>(input_precision, input_shape0);
+        auto param1 = linear_ir->push_node<ov::opset10::Parameter>(input_precision, input_shape1);
+        auto multiply = linear_ir->push_node<ov::opset10::Multiply>(param00.second, param01.second);
+        init_expr_descriptors(*multiply.first, mul_subtensor, layout_1d);
+        auto broadcastmove = linear_ir->push_node<ov::snippets::op::BroadcastMove>(multiply.second, 512);
+        auto sub = linear_ir->push_node<ov::opset10::Subtract>(param1.second, broadcastmove.second);
+        init_expr_descriptors(*sub.first, sub_subtensor, layout_1d);
+        auto result = linear_ir->push_node<ov::opset10::Result>(sub.second);
+        auto begin = multiply.first;
+        auto end = result.first;
+        create_and_add_unified_loop_info(linear_ir, begin, end, 512, vector_size,
+                                        {LoopPort((*multiply.first)->get_input_port(0)),
+                                         LoopPort((*multiply.first)->get_input_port(1)),
+                                         LoopPort((*sub.first)->get_input_port(0))},
+                                        {LoopPort((*sub.first)->get_output_port(0))});
+        linear_ir->set_loop_depth(1);
+    }
+    {
+        auto param00 = linear_ir_ref->push_node<ov::opset10::Parameter>(input_precision, input_shape0);
+        auto param01 = linear_ir_ref->push_node<ov::opset10::Parameter>(input_precision, input_shape0);
+        auto param1 = linear_ir_ref->push_node<ov::opset10::Parameter>(input_precision, input_shape1);
+        auto multiply = linear_ir_ref->push_node<ov::opset10::Multiply>(param00.second, param01.second);
+        init_expr_descriptors(*multiply.first, mul_subtensor, layout_1d);
+        auto broadcastmove = linear_ir_ref->push_node<ov::snippets::op::BroadcastMove>(multiply.second, 512);
+        auto sub = linear_ir_ref->push_node<ov::opset10::Subtract>(param1.second, broadcastmove.second);
+        init_expr_descriptors(*sub.first, sub_subtensor, layout_1d);
+        auto result = linear_ir_ref->push_node<ov::opset10::Result>(sub.second);
+        auto begin = sub.first;
+        auto end = result.first;
+        create_and_add_unified_loop_info(linear_ir_ref, begin, end, 512, vector_size,
+                                        {LoopPort((*sub.first)->get_input_port(0)),
+                                         LoopPort((*sub.first)->get_input_port(1))},
+                                        {LoopPort((*sub.first)->get_output_port(0))});
+    }
+}
+
+TEST_F(ExtractLoopInvariantsTest, ExtractedLoopInvariantsWithScalar) {
+    size_t vector_size = 16;
+    const auto input_precision = ov::element::f32;
+    const ov::Shape scalar_shape{1};
+    const ov::Shape input_shape0{1};
+    const ov::Shape input_shape1{512};
+    const std::vector<ov::snippets::VectorDims> layout_1d{{0}, {0}, {0}};
+    const std::vector<ov::snippets::VectorDims> mul_subtensor{{1}, {1}, {1}};
+    const std::vector<ov::snippets::VectorDims> sub_subtensor{{512}, {1}, {512}};
+    /*
+     *      Param0    Scalar(loopBegin)
+     *         \       /
+     *          Multiply
+     *              |
+     *          Broadcast     Param1
+     *               \         /
+     *                Substract(loopBeginRef)
+     *                    |
+     *                  Result(LoopEnd and LoopEndRef)
+    */
+    {
+        auto param0 = linear_ir->push_node<ov::opset10::Parameter>(input_precision, input_shape0);
+        auto param1 = linear_ir->push_node<ov::opset10::Parameter>(input_precision, input_shape1);
+        auto scalar = linear_ir->push_node<ov::snippets::op::Scalar>(input_precision, scalar_shape, 3.8f);
+        auto multiply = linear_ir->push_node<ov::opset10::Multiply>(param0.second, scalar.second);
+        init_expr_descriptors(*multiply.first, mul_subtensor, layout_1d);
+        auto broadcastmove = linear_ir->push_node<ov::snippets::op::BroadcastMove>(multiply.second, 512);
+        auto sub = linear_ir->push_node<ov::opset10::Subtract>(param1.second, broadcastmove.second);
+        init_expr_descriptors(*sub.first, sub_subtensor, layout_1d);
+        auto result = linear_ir->push_node<ov::opset10::Result>(sub.second);
+        auto begin = scalar.first;
+        auto end = result.first;
+        create_and_add_unified_loop_info(linear_ir, begin, end, 512, vector_size,
+                                        {LoopPort((*multiply.first)->get_input_port(0)),
+                                         LoopPort((*sub.first)->get_input_port(0))},
+                                        {LoopPort((*sub.first)->get_output_port(0))});
+        linear_ir->set_loop_depth(1);
+    }
+    {
+        auto param0 = linear_ir_ref->push_node<ov::opset10::Parameter>(input_precision, input_shape0);
+        auto param1 = linear_ir_ref->push_node<ov::opset10::Parameter>(input_precision, input_shape1);
+        auto scalar = linear_ir_ref->push_node<ov::snippets::op::Scalar>(input_precision, scalar_shape, 3.8f);
+        auto multiply = linear_ir_ref->push_node<ov::opset10::Multiply>(param0.second, scalar.second);
+        init_expr_descriptors(*multiply.first, mul_subtensor, layout_1d);
+        auto broadcastmove = linear_ir_ref->push_node<ov::snippets::op::BroadcastMove>(multiply.second, 512);
+        auto sub = linear_ir_ref->push_node<ov::opset10::Subtract>(param1.second, broadcastmove.second);
+        init_expr_descriptors(*sub.first, sub_subtensor, layout_1d);
+        auto result = linear_ir_ref->push_node<ov::opset10::Result>(sub.second);
+        auto begin = sub.first;
+        auto end = result.first;
+        create_and_add_unified_loop_info(linear_ir_ref, begin, end, 512, vector_size,
+                                        {LoopPort((*sub.first)->get_input_port(0)),
+                                         LoopPort((*sub.first)->get_input_port(1))},
+                                        {LoopPort((*sub.first)->get_output_port(0))});
+    }
+}
+
+TEST_F(ExtractLoopInvariantsTest, ExtractedLoopInvariantsOutputLoopUpdateNotNeed) {
+    size_t vector_size = 16;
+    const auto input_precision = ov::element::f32;
+    const ov::Shape input_shape_a{3, 1};
+    const ov::Shape input_shape_b{3, 16};
+    const std::vector<ov::snippets::VectorDims> layout{{0, 1}, {0, 1}, {0, 1}};
+    const std::vector<ov::snippets::VectorDims> subtensor_mul{{3, 1}, {3, 1}, {3, 1}};
+    const std::vector<ov::snippets::VectorDims> subtensor_add{{3, 16}, {3, 16}, {3, 16}};
+    /*
+     *  Before: Param0, Param1, Param2, [[Multiply, Broadcast, Add, Sub]], Result0, Result1
+     *  After:  Param0, Param1, Param2, [Multiply, Broadcast, [Add, Sub]], Result0, Result1
+     *      Param0(3,1)    Param1(3,1)
+     *             \       /
+     *              Multiply
+     *                 |
+     *             Broadcast  Param2(3,16)
+     *                    \   /
+     *                     Add --- Result0
+     *                      |
+     *    Param3(3,16) --- Sub
+     *                      |
+     *                     Result1
+    */
+    {
+        auto param0 = linear_ir->push_node<ov::opset10::Parameter>(input_precision, input_shape_a);
+        auto param1 = linear_ir->push_node<ov::opset10::Parameter>(input_precision, input_shape_a);
+        auto param2 = linear_ir->push_node<ov::opset10::Parameter>(input_precision, input_shape_b);
+        auto param3 = linear_ir->push_node<ov::opset10::Parameter>(input_precision, input_shape_b);
+        auto multiply = linear_ir->push_node<ov::opset10::Multiply>(param0.second, param1.second);
+        init_expr_descriptors(*multiply.first, subtensor_mul, layout);
+        auto broadcastmove = linear_ir->push_node<ov::snippets::op::BroadcastMove>(multiply.second, 16);
+        auto add = linear_ir->push_node<ov::opset10::Add>(param2.second, broadcastmove.second);
+        init_expr_descriptors(*add.first, subtensor_add, layout);
+        auto sub = linear_ir->push_node<ov::opset10::Subtract>(param3.second, add.second);
+        auto result0 = linear_ir->push_node<ov::opset10::Result>(add.second);
+        auto result1 = linear_ir->push_node<ov::opset10::Result>(sub.second);
+        auto begin = multiply.first;
+        auto end = result1.first;
+        create_and_add_unified_loop_info(linear_ir, begin, end, 16, vector_size,
+                                        {LoopPort((*multiply.first)->get_input_port(0), true, 0),
+                                         LoopPort((*multiply.first)->get_input_port(1), true, 0),
+                                         LoopPort((*add.first)->get_input_port(0), true, 0),
+                                         LoopPort((*sub.first)->get_input_port(0), true, 0)},
+                                        {LoopPort((*add.first)->get_output_port(0), true, 0),
+                                         LoopPort((*sub.first)->get_output_port(0), true, 0)});
+        create_and_add_unified_loop_info(linear_ir, begin, end, 3, 1,
+                                        {LoopPort((*multiply.first)->get_input_port(0), true, 1),
+                                         LoopPort((*multiply.first)->get_input_port(1), true, 1),
+                                         LoopPort((*add.first)->get_input_port(0), true, 1),
+                                         LoopPort((*sub.first)->get_input_port(0), true, 1)},
+                                        {LoopPort((*add.first)->get_output_port(0), true, 1),
+                                         LoopPort((*sub.first)->get_output_port(0), true, 1)});
+        linear_ir->set_loop_depth(2);
+    }
+    {
+        auto param0 = linear_ir_ref->push_node<ov::opset10::Parameter>(input_precision, input_shape_a);
+        auto param1 = linear_ir_ref->push_node<ov::opset10::Parameter>(input_precision, input_shape_a);
+        auto param2 = linear_ir_ref->push_node<ov::opset10::Parameter>(input_precision, input_shape_b);
+        auto param3 = linear_ir_ref->push_node<ov::opset10::Parameter>(input_precision, input_shape_b);
+        auto multiply = linear_ir_ref->push_node<ov::opset10::Multiply>(param0.second, param1.second);
+        init_expr_descriptors(*multiply.first, subtensor_mul, layout);
+        auto broadcastmove = linear_ir_ref->push_node<ov::snippets::op::BroadcastMove>(multiply.second, 16);
+        auto add = linear_ir_ref->push_node<ov::opset10::Add>(param2.second, broadcastmove.second);
+        init_expr_descriptors(*add.first, subtensor_add, layout);
+        auto sub = linear_ir_ref->push_node<ov::opset10::Subtract>(param3.second, add.second);
+        auto result0 = linear_ir_ref->push_node<ov::opset10::Result>(add.second);
+        auto result1 = linear_ir_ref->push_node<ov::opset10::Result>(sub.second);
+        auto begin_inner = add.first;
+        auto end_inner = result1.first;
+        create_and_add_unified_loop_info(linear_ir_ref, begin_inner, end_inner, 16, vector_size,
+                                        {LoopPort((*add.first)->get_input_port(0), true, 0),
+                                         LoopPort((*add.first)->get_input_port(1), true, 0),
+                                         LoopPort((*sub.first)->get_input_port(0), true, 0)},
+                                        {LoopPort((*add.first)->get_output_port(0), true, 0),
+                                         LoopPort((*sub.first)->get_output_port(0), true, 0)});
+        auto begin_outer = multiply.first;
+        auto end_outer = result1.first;
+        create_and_add_unified_loop_info(linear_ir_ref, begin_outer, end_outer, 3, 1,
+                                        {LoopPort((*multiply.first)->get_input_port(0), true, 1),
+                                         LoopPort((*multiply.first)->get_input_port(1), true, 1),
+                                         LoopPort((*add.first)->get_input_port(0), true, 1),
+                                         LoopPort((*sub.first)->get_input_port(0), true, 1)},
+                                        {LoopPort((*add.first)->get_output_port(0), true, 1),
+                                         LoopPort((*sub.first)->get_output_port(0), true, 1)});
+    }
+}
+
+TEST_F(ExtractLoopInvariantsTest, ExtractedLoopInvariantsFromInnermostToLoopOutside) {
+    size_t vector_size = 16;
+    const auto input_precision = ov::element::f32;
+    const ov::Shape input_shape_0{3, 512};
+    const ov::Shape input_shape_1{1, 1};
+    ov::snippets::VectorDims layout{0, 1};
+    ov::snippets::VectorDims subtensor{3, 512};
+    /*
+     * before:       Param0, Param1, [[Broadcast, Add]], Result
+     * intermediate: Param0, Param1, [Broadcast, [Add]], Result
+     * after:        Param0, Param1, Broadcast, [[Add]], Result
+     *      Param0(3,512)    Param1(1,1)
+     *              \         /
+     *               \    Broadcast
+     *                \     /
+     *                  Add
+     *                   |
+     *                 Result
+    */
+    {
+        auto param_0 = linear_ir->push_node<ov::opset10::Parameter>(input_precision, input_shape_0);
+        auto param_1 = linear_ir->push_node<ov::opset10::Parameter>(input_precision, input_shape_1);
+        auto broadcastmove = linear_ir->push_node<ov::snippets::op::BroadcastMove>(param_1.second, 512);
+        init_expr_descriptors(*broadcastmove.first, {{1, 1}, subtensor}, {layout, layout});
+        auto add = linear_ir->push_node<ov::opset10::Add>(param_0.second, broadcastmove.second);
+        init_expr_descriptors(*add.first, {subtensor, subtensor, subtensor}, {layout, layout, layout});
+        auto result = linear_ir->push_node<ov::opset10::Result>(add.second);
+        create_and_add_unified_loop_info(linear_ir, broadcastmove.first, result.first, 3, 1,
+                                        {LoopPort((*broadcastmove.first)->get_input_port(0), true, 1),
+                                         LoopPort((*add.first)->get_input_port(0), true, 1)},
+                                        {LoopPort((*add.first)->get_output_port(0), true, 1)});
+        create_and_add_unified_loop_info(linear_ir, broadcastmove.first, result.first, 512, vector_size,
+                                        {LoopPort((*broadcastmove.first)->get_input_port(0), true, 0),
+                                         LoopPort((*add.first)->get_input_port(0), true, 0)},
+                                        {LoopPort((*add.first)->get_output_port(0), true, 0)});
+        linear_ir->set_loop_depth(2);
+    }
+    {
+        auto param_0 = linear_ir_ref->push_node<ov::opset10::Parameter>(input_precision, input_shape_0);
+        auto param_1 = linear_ir_ref->push_node<ov::opset10::Parameter>(input_precision, input_shape_1);
+        auto broadcastmove = linear_ir_ref->push_node<ov::snippets::op::BroadcastMove>(param_1.second, 512);
+        init_expr_descriptors(*broadcastmove.first, {{1, 1}, subtensor}, {layout, layout});
+        auto add = linear_ir_ref->push_node<ov::opset10::Add>(param_0.second, broadcastmove.second);
+        init_expr_descriptors(*add.first, {subtensor, subtensor, subtensor}, {layout, layout, layout});
+        auto result = linear_ir_ref->push_node<ov::opset10::Result>(add.second);
+        create_and_add_unified_loop_info(linear_ir_ref, add.first, result.first, 3, 1,
+                                        {LoopPort((*add.first)->get_input_port(0), true, 1),
+                                         LoopPort((*add.first)->get_input_port(1), true, 1)},
+                                        {LoopPort((*add.first)->get_output_port(0), true, 1)});
+        create_and_add_unified_loop_info(linear_ir_ref, add.first, result.first, 512, vector_size,
+                                        {LoopPort((*add.first)->get_input_port(0), true, 0),
+                                         LoopPort((*add.first)->get_input_port(1), true, 0)},
+                                        {LoopPort((*add.first)->get_output_port(0), true, 0)});
+    }
+}
+
+class ExtractLoopInvariantsRemoveLoopsTest : public LoweredPassTestsF {
+public:
+    ExtractLoopInvariantsRemoveLoopsTest() : LoweredPassTestsF() {
+        comparator.enable(LIRComparator::LIRCmpValues::LOOP_INDICES);
+        comparator.enable(LIRComparator::LIRCmpValues::PORT_DESCRIPTORS);
+        comparator.enable(LIRComparator::LIRCmpValues::PORT_CONNECTORS);
+        comparator.enable(LIRComparator::LIRCmpValues::LOOP_MANAGER);
+    }
+
+    void SetUp() override {
+        pipeline.register_pass<ExtractLoopInvariants>();
+        pipeline.register_pass<NormalizeLoopIDs>(); // loop could be removed and loop index could be different, normalize it
+    }
+};
+
+// softmax with shape of 1 for innermost dimension.
+// Cover multiple(all) exprs are extracted, and inner loops are removed.
+TEST_F(ExtractLoopInvariantsRemoveLoopsTest, ExtractedLoopInvariantsAllExprsInLoopExtracted) {
+    size_t vector_size = 16;
+    const auto input_precision = ov::element::f32;
+    const ov::Shape input_shape{10, 1};
+    ov::snippets::VectorDims layout{0, 1};
+    ov::snippets::VectorDims subtensor{10, 1};
+    /*
+     *       Param  Vector
+     *       |   |   |
+     *       |  Maximum
+     *       |   |
+     *       | HorizonMax
+     *       |   |
+     *        Sub
+     *         |
+     * Vector Exp
+     *   |   |   |
+     *    Add    |
+     *     |     |
+     *    HAdd   |
+     *     |     |
+     *   Power   |
+     *     |     |
+     *    Multiply
+     *       |
+     *     Result
+    */
+    {
+        auto param = linear_ir->push_node<ov::opset10::Parameter>(input_precision, input_shape);
+        auto vector_max = linear_ir->push_node<ov::snippets::op::VectorBuffer>(input_precision);
+        auto vector_sum = linear_ir->push_node<ov::snippets::op::VectorBuffer>(input_precision);
+        auto max = linear_ir->push_node<ov::opset10::Maximum>(param.second, vector_max.second);
+        init_expr_descriptors(*max.first, {{subtensor}, {1}, {subtensor}}, {layout, {0}, layout});
+        auto hmax = linear_ir->push_node<ov::snippets::op::HorizonMax>(max.second);
+        auto sub = linear_ir->push_node<ov::opset10::Subtract>(param.second, hmax.second);
+        init_expr_descriptors(*sub.first, {subtensor, subtensor, subtensor}, {layout, layout, layout});
+        auto exp = linear_ir->push_node<ov::opset10::Exp>(sub.second);
+        init_expr_descriptors(*exp.first, {subtensor, subtensor}, {layout, layout});
+        auto add = linear_ir->push_node<ov::opset10::Add>(exp.second, vector_sum.second);
+        init_expr_descriptors(*add.first, {subtensor, {1}, subtensor}, {layout, {0}, layout});
+        auto hsum = linear_ir->push_node<ov::snippets::op::HorizonSum>(add.second);
+        auto power_static = linear_ir->push_node<ov::snippets::op::PowerStatic>(hsum.second, -1.0f);
+        init_expr_descriptors(*power_static.first, {subtensor, subtensor}, {layout, layout});
+        auto multiply = linear_ir->push_node<ov::opset10::Multiply>(exp.second, power_static.second);
+        init_expr_descriptors(*multiply.first, {subtensor, subtensor, subtensor}, {layout, layout, layout});
+        auto result = linear_ir->push_node<ov::opset10::Result>(multiply.second);
+        // 3 inner loop
+        create_and_add_unified_loop_info(linear_ir, max.first, hmax.first, 1, vector_size,
+                                        {LoopPort((*max.first)->get_input_port(0), true, 0),
+                                         LoopPort((*max.first)->get_input_port(1), true, 0)},
+                                        {LoopPort((*max.first)->get_output_port(0), true, 0)});
+        create_and_add_unified_loop_info(linear_ir, sub.first, hsum.first, 1, vector_size,
+                                        {LoopPort((*sub.first)->get_input_port(0), true, 0),
+                                         LoopPort((*sub.first)->get_input_port(1), true, 0),
+                                         LoopPort((*add.first)->get_input_port(1), true, 0)},
+                                        {LoopPort((*exp.first)->get_output_port(0), true, 0),
+                                         LoopPort((*add.first)->get_output_port(0), true, 0)});
+        create_and_add_unified_loop_info(linear_ir, multiply.first, result.first, 1, vector_size,
+                                        {LoopPort((*multiply.first)->get_input_port(0), true, 0),
+                                         LoopPort((*multiply.first)->get_input_port(1), true, 0)},
+                                        {LoopPort((*multiply.first)->get_output_port(0), true, 0)});
+        // outer loop info
+        const auto loop_begin = std::make_shared<ov::snippets::op::LoopBegin>();
+        auto loop_begin_expr = linear_ir->insert_node(loop_begin, std::vector<PortConnectorPtr>{}, {}, false, max.first);
+        const auto loop_end = std::make_shared<ov::snippets::op::LoopEnd>();
+        std::vector<PortConnectorPtr> loop_end_inputs{(*loop_begin_expr)->get_output_port_connector(0)};
+        auto loop_end_expr = linear_ir->insert_node(loop_end, loop_end_inputs, {}, false, result.first);
+        create_and_add_unified_loop_info(linear_ir, loop_begin_expr, result.first, 10, 1,
+                                        {LoopPort((*max.first)->get_input_port(0), true, 1),
+                                         LoopPort((*max.first)->get_input_port(1), true, 0),
+                                         LoopPort((*add.first)->get_input_port(1), true, 0)},
+                                        {LoopPort((*multiply.first)->get_output_port(0), true, 1)});
+        loop_end->set_id((*loop_end_expr)->get_loop_ids().back());
+        linear_ir->set_loop_depth(2);
+    }
+    {
+        auto param = linear_ir_ref->push_node<ov::opset10::Parameter>(input_precision, input_shape);
+        auto vector_max = linear_ir_ref->push_node<ov::snippets::op::VectorBuffer>(input_precision);
+        auto vector_sum = linear_ir_ref->push_node<ov::snippets::op::VectorBuffer>(input_precision);
+        auto max = linear_ir_ref->push_node<ov::opset10::Maximum>(param.second, vector_max.second);
+        init_expr_descriptors(*max.first, {{subtensor}, {1}, {subtensor}}, {layout, {0}, layout});
+        auto hmax = linear_ir_ref->push_node<ov::snippets::op::HorizonMax>(max.second);
+        auto sub = linear_ir_ref->push_node<ov::opset10::Subtract>(param.second, hmax.second);
+        init_expr_descriptors(*sub.first, {subtensor, subtensor, subtensor}, {layout, layout, layout});
+        auto exp = linear_ir_ref->push_node<ov::opset10::Exp>(sub.second);
+        init_expr_descriptors(*exp.first, {subtensor, subtensor}, {layout, layout});
+        auto add = linear_ir_ref->push_node<ov::opset10::Add>(exp.second, vector_sum.second);
+        init_expr_descriptors(*add.first, {subtensor, {1}, subtensor}, {layout, {0}, layout});
+        auto hsum = linear_ir_ref->push_node<ov::snippets::op::HorizonSum>(add.second);
+        auto power_static = linear_ir_ref->push_node<ov::snippets::op::PowerStatic>(hsum.second, -1.0f);
+        init_expr_descriptors(*power_static.first, {subtensor, subtensor}, {layout, layout});
+        auto multiply = linear_ir_ref->push_node<ov::opset10::Multiply>(exp.second, power_static.second);
+        init_expr_descriptors(*multiply.first, {subtensor, subtensor, subtensor}, {layout, layout, layout});
+        auto result = linear_ir_ref->push_node<ov::opset10::Result>(multiply.second);
+        // outer loop
+        const auto loop_begin = std::make_shared<ov::snippets::op::LoopBegin>();
+        auto loop_begin_expr = linear_ir_ref->insert_node(loop_begin, std::vector<PortConnectorPtr>{}, {}, false, max.first);
+        const auto loop_end = std::make_shared<ov::snippets::op::LoopEnd>();
+        std::vector<PortConnectorPtr> loop_end_inputs{(*loop_begin_expr)->get_output_port_connector(0)};
+        auto loop_end_expr = linear_ir_ref->insert_node(loop_end, loop_end_inputs, {}, false, result.first);
+        create_and_add_unified_loop_info(linear_ir_ref, loop_begin_expr, result.first, 10, 1,
+                                        {LoopPort((*max.first)->get_input_port(0), true, 1),
+                                         LoopPort((*max.first)->get_input_port(1), true, 0),
+                                         LoopPort((*add.first)->get_input_port(1), true, 0)},
+                                        {LoopPort((*multiply.first)->get_output_port(0), true, 1)});
+        loop_end->set_id((*loop_end_expr)->get_loop_ids().back());
+    }
+}
+
+}  // namespace snippets
+}  // namespace test
+}  // namespace ov
diff --git a/src/common/snippets/tests/src/pass/mha_tokenization.cpp b/src/common/snippets/tests/src/pass/mha_tokenization.cpp
index b411aace066203..3a97b94925a2c0 100644
--- a/src/common/snippets/tests/src/pass/mha_tokenization.cpp
+++ b/src/common/snippets/tests/src/pass/mha_tokenization.cpp
@@ -47,6 +47,22 @@ TEST_F(TokenizeMHASnippetsTests, smoke_Snippets_MHA_4D_Dynamic) {
     run();
 }
 
+TEST_F(TokenizeMHASnippetsTests, smoke_Snippets_MHA_4D_Partially_Dynamic) {
+    const auto &f = MHAFunction(std::vector<PartialShape>{{1, 128, 12, 64}, {1, 128, 12, 64}, {1, 1, -1, 1}, {1, 128, 12, 64}},
+                                std::vector<ov::element::Type>({ov::element::f32, ov::element::f32, ov::element::f32, ov::element::f32}), true, false);
+    model = f.getOriginal();
+    model_ref = f.getReference();
+    run();
+}
+
+TEST_F(TokenizeMHASnippetsTests, smoke_Snippets_MHA_4D_Partially_Dynamic_Disabled_Dynamic) {
+    const auto &f = MHAFunction(std::vector<PartialShape>{{1, 128, 12, 64}, {1, 128, 12, 64}, {1, 1, -1, 1}, {1, 128, 12, 64}},
+                                std::vector<ov::element::Type>({ov::element::f32, ov::element::f32, ov::element::f32, ov::element::f32}), true, false);
+    model = f.getOriginal();
+    config = ov::snippets::pass::SnippetsTokenization::Config{1, std::numeric_limits<size_t>::max(), true, true, false, { 3, 4 }};
+    run();
+}
+
 TEST_F(TokenizeMHASnippetsTests, smoke_Snippets_MHA_4D_Dynamic_M) {
     const auto &f = MHAFunction(std::vector<PartialShape>{{1, -1, 12, 64}, {1, 128, 12, 64}, {1, 12, -1, 128}, {1, 128, 12, 64}},
                                 std::vector<ov::element::Type>({ov::element::f32, ov::element::f32, ov::element::f32, ov::element::f32}), true, false);
@@ -145,7 +161,7 @@ TEST_F(TokenizeMHASnippetsTests, smoke_Snippets_MHA_Transpose_fusion) {
     run();
 }
 
-TEST_F(TokenizeMHASnippetsTests, smoke_Snippets_MHA_Dyanmic_Transpose_fusion) {
+TEST_F(TokenizeMHASnippetsTests, smoke_Snippets_MHA_Dynamic_Transpose_fusion) {
     const auto& f = MHATransposedInputFunction(std::vector<PartialShape>{{-1, -1, -1, -1}, {-1, -1, -1, -1}, {-1, -1, -1, -1}}, false,
                                                std::vector<int64_t>{0, 2, 1, 3});
     model = f.getOriginal();
diff --git a/src/common/transformations/include/transformations/op_conversions/convert_ti_to_sequences.hpp b/src/common/transformations/include/transformations/op_conversions/convert_ti_to_sequences.hpp
index 4d8199c28709d2..e729d735c652d1 100644
--- a/src/common/transformations/include/transformations/op_conversions/convert_ti_to_sequences.hpp
+++ b/src/common/transformations/include/transformations/op_conversions/convert_ti_to_sequences.hpp
@@ -18,6 +18,8 @@ class TRANSFORMATIONS_API ConvertTensorIteratorToRNNSequence;
 class TRANSFORMATIONS_API ConvertTensorIteratorToGRUSequence;
 class TRANSFORMATIONS_API ConvertTensorIteratorToSequence;
 
+class TRANSFORMATIONS_API ConvertLoopWithSlicedInputConcatOutputToLSTMSequence;
+class TRANSFORMATIONS_API ConvertLoopWithScatterUpdateToLSTMSequence;
 class TRANSFORMATIONS_API ConvertLoopToLSTMSequence;
 class TRANSFORMATIONS_API FuseReverseLSTMSequence;
 
@@ -68,14 +70,29 @@ class ov::pass::ConvertTensorIteratorToSequence : public GraphRewrite {
     ConvertTensorIteratorToSequence();
 };
 
+class ov::pass::ConvertLoopWithSlicedInputConcatOutputToLSTMSequence : public ov::pass::MatcherPass {
+public:
+    OPENVINO_RTTI("ConvertLoopWithSlicedInputConcatOutputToLSTMSequence", "0");
+    ConvertLoopWithSlicedInputConcatOutputToLSTMSequence();
+};
+
+class ov::pass::ConvertLoopWithScatterUpdateToLSTMSequence : public ov::pass::MatcherPass {
+public:
+    OPENVINO_RTTI("ConvertLoopWithScatterUpdateToLSTMSequence", "0");
+    ConvertLoopWithScatterUpdateToLSTMSequence();
+};
+
 /**
  * @ingroup ov_transformation_common_api
  * @brief Replaces Loop with LSTMCell inside to LSTMSequence
  */
-class ov::pass::ConvertLoopToLSTMSequence : public ov::pass::MatcherPass {
+class ov::pass::ConvertLoopToLSTMSequence : public ov::pass::GraphRewrite {
 public:
     OPENVINO_RTTI("ConvertLoopToLSTMSequence", "0");
-    ConvertLoopToLSTMSequence();
+    ConvertLoopToLSTMSequence() {
+        add_matcher<ov::pass::ConvertLoopWithScatterUpdateToLSTMSequence>();
+        add_matcher<ov::pass::ConvertLoopWithSlicedInputConcatOutputToLSTMSequence>();
+    }
 };
 
 /**
diff --git a/src/common/transformations/include/transformations/utils/utils.hpp b/src/common/transformations/include/transformations/utils/utils.hpp
index a8818c7440e63c..a6114e8e07172a 100644
--- a/src/common/transformations/include/transformations/utils/utils.hpp
+++ b/src/common/transformations/include/transformations/utils/utils.hpp
@@ -114,6 +114,17 @@ inline std::string get_ie_output_name(const Output<Node>& output) {
  */
 float cast_eps_to_float(double eps_d);
 
+template <typename T>
+bool get_constant_value(const std::shared_ptr<ov::Node>& node, T& value) {
+    auto constant = ov::as_type_ptr<ov::op::v0::Constant>(node);
+    if (!constant)
+        return false;
+    if (shape_size(constant->get_shape()) != 1)
+        return false;
+    value = constant->cast_vector<T>()[0];
+    return true;
+}
+
 template <typename T>
 bool has_constant_value(const std::shared_ptr<Node>& node,
                         const T value,
diff --git a/src/common/transformations/src/transformations/common_optimizations/common_optimizations.cpp b/src/common/transformations/src/transformations/common_optimizations/common_optimizations.cpp
index c49974bdbcff85..9da4340c2423f4 100644
--- a/src/common/transformations/src/transformations/common_optimizations/common_optimizations.cpp
+++ b/src/common/transformations/src/transformations/common_optimizations/common_optimizations.cpp
@@ -213,9 +213,9 @@ bool ov::pass::CommonOptimizations::run_on_model(const std::shared_ptr<ov::Model
     REGISTER_PASS(manager, ConvertDeformableConv8To1)
     REGISTER_PASS(manager, ConvertSoftMax8ToSoftMax1)
     REGISTER_DISABLED_PASS(manager, ConvertSoftMax1ToSoftMax8)
+    REGISTER_PASS(manager, ConvertMaxPool14ToMaxPool8)
     REGISTER_PASS(manager, ConvertMaxPool8ToMaxPool1)
     REGISTER_DISABLED_PASS(manager, ConvertMaxPool1ToMaxPool8)
-    REGISTER_PASS(manager, ConvertMaxPool14ToMaxPool8)
     REGISTER_PASS(manager, ConvertPriorBox8To0)
     REGISTER_DISABLED_PASS(manager, ConvertDetectionOutput1ToDetectionOutput8)
     REGISTER_PASS(manager, ConvertDetectionOutput8ToDetectionOutput1)
@@ -251,7 +251,6 @@ bool ov::pass::CommonOptimizations::run_on_model(const std::shared_ptr<ov::Model
     // because we cannot insert any MaxPools since they may prevent
     // other optimizations
     REGISTER_PASS(manager, StridesOptimization)
-    REGISTER_PASS(manager, SymbolicOptimizations)
     REGISTER_PASS(manager, Validate)
     manager.run_passes(f);
 
diff --git a/src/common/transformations/src/transformations/op_conversions/convert_maxpool_downgrade.cpp b/src/common/transformations/src/transformations/op_conversions/convert_maxpool_downgrade.cpp
index 8d35bc53421c99..0edff11b8429ed 100644
--- a/src/common/transformations/src/transformations/op_conversions/convert_maxpool_downgrade.cpp
+++ b/src/common/transformations/src/transformations/op_conversions/convert_maxpool_downgrade.cpp
@@ -72,7 +72,7 @@ ov::pass::ConvertMaxPool14ToMaxPool8::ConvertMaxPool14ToMaxPool8() {
     MATCHER_SCOPE(ConvertMaxPool14ToMaxPool8);
     const auto max_pool_v14_pattern = pattern::wrap_type<ov::op::v14::MaxPool>();
 
-    const matcher_pass_callback callback = [](pattern::Matcher& m) {
+    const matcher_pass_callback callback = [OV_CAPTURE_CPY_AND_THIS](pattern::Matcher& m) {
         using ov::op::v0::Constant;
         using ov::op::v0::Concat;
         using ov::op::v1::Subtract;
@@ -87,6 +87,9 @@ ov::pass::ConvertMaxPool14ToMaxPool8::ConvertMaxPool14ToMaxPool8() {
         using ov::op::v12::Pad;
 
         const auto max_pool_v14 = std::dynamic_pointer_cast<ov::op::v14::MaxPool>(m.get_match_root());
+        if (!max_pool_v14 || transformation_callback(max_pool_v14)) {
+            return false;
+        }
         const auto rounding_type_v14 = max_pool_v14->get_rounding_type();
         std::shared_ptr<ov::op::v8::MaxPool> max_pool_v8;
         NodeRegistry node_registry;
diff --git a/src/common/transformations/src/transformations/op_conversions/convert_ti_to_sequences.cpp b/src/common/transformations/src/transformations/op_conversions/convert_ti_to_sequences.cpp
index d2cd621ab24c89..1888cfd22c2d0c 100644
--- a/src/common/transformations/src/transformations/op_conversions/convert_ti_to_sequences.cpp
+++ b/src/common/transformations/src/transformations/op_conversions/convert_ti_to_sequences.cpp
@@ -35,6 +35,7 @@
 #include "openvino/op/scatter_update.hpp"
 #include "openvino/op/shape_of.hpp"
 #include "openvino/op/squeeze.hpp"
+#include "openvino/op/strided_slice.hpp"
 #include "openvino/op/tensor_iterator.hpp"
 #include "openvino/op/transpose.hpp"
 #include "openvino/op/unsqueeze.hpp"
@@ -43,6 +44,10 @@
 #include "openvino/pass/pattern/op/wrap_type.hpp"
 #include "transformations/utils/utils.hpp"
 
+using namespace ov;
+using namespace ov::pass;
+using namespace ov::op::util;
+
 namespace {
 bool convertTensorIteratorToSequence(const std::shared_ptr<ov::op::v0::TensorIterator>& ti,
                                      const std::shared_ptr<ov::op::util::RNNCellBase>& found_cell,
@@ -242,6 +247,223 @@ bool convertTensorIteratorToSequence(const std::shared_ptr<ov::op::v0::TensorIte
     copy_runtime_info(ti, new_nodes);
     return true;
 }
+
+bool check_condition_increment_pattern(
+    const std::shared_ptr<op::v0::Result>& cond_result,
+    const std::shared_ptr<op::v5::Loop>& loop,
+    const ov::ParameterVector& body_params,
+    const ov::ResultVector& body_results,
+    const ov::op::util::MultiSubGraphOp::MultiSubgraphInputDescriptionVector& input_descriptions,
+    Output<Node>& num_iters_output) {
+    // return a number of iterations if pattern matches
+    // pattern for condition sub-graph in Loop operarion
+    auto num_iter_const_label = pattern::wrap_type<op::v0::Constant>();
+    auto num_iter_param_label = pattern::wrap_type<op::v0::Parameter>();
+    auto num_iterations_label =
+        std::make_shared<pattern::op::Or>(OutputVector{num_iter_const_label, num_iter_param_label});
+    auto counter_label = pattern::wrap_type<op::v0::Parameter>();
+    auto counter_step_label = pattern::wrap_type<op::v0::Constant>();
+    auto updated_counter_label = pattern::wrap_type<op::v1::Add>({counter_label, counter_step_label});
+    auto less_label = pattern::wrap_type<op::v1::Less>({updated_counter_label, num_iterations_label});
+    auto condition_label = pattern::wrap_type<op::v0::Result>({less_label});
+
+    // check a pattern of condition graph and get a number of iterations
+    ov::pass::pattern::Matcher condition_matcher(condition_label);
+    if (!condition_matcher.match(cond_result->output(0))) {
+        return false;
+    }
+    const auto& condition_map = condition_matcher.get_pattern_value_map();
+    int64_t counter_step = -1;
+    if (!get_constant_value(condition_map.at(counter_step_label).get_node_shared_ptr(), counter_step) ||
+        counter_step != 1) {
+        return false;
+    }
+    // get initial value of counter
+    int64_t initial_counter = -1;
+    auto counter_param = ov::as_type_ptr<ov::op::v0::Parameter>(condition_map.at(counter_label).get_node_shared_ptr());
+    if (!counter_param) {
+        return false;
+    }
+    for (const auto& input_desc : input_descriptions) {
+        auto body_param_idx = input_desc->m_body_parameter_index;
+        auto input_idx = input_desc->m_input_index;
+        if (body_params[body_param_idx] != counter_param) {
+            continue;
+        }
+        // it must be merged input and incremented each iteration
+        auto merged_desc = ov::as_type_ptr<ov::op::util::MultiSubGraphOp::MergedInputDescription>(input_desc);
+        if (!merged_desc) {
+            return false;
+        }
+        auto result_idx = merged_desc->m_body_value_index;
+        auto update_counter = body_results[result_idx]->input_value(0).get_node_shared_ptr();
+        if (update_counter != condition_map.at(updated_counter_label).get_node_shared_ptr()) {
+            return false;
+        }
+        // get initial value of counter
+        if (!get_constant_value(loop->input_value(input_idx).get_node_shared_ptr(), initial_counter)) {
+            return false;
+        }
+        // suitable counter-parameter is found and checked
+        break;
+    }
+    if (initial_counter != 0) {
+        return false;
+    }
+    // retrieve a number of iterations
+    int64_t num_iters = -1;
+    if (condition_map.count(num_iter_param_label)) {
+        auto num_iter_param = condition_map.at(num_iter_param_label).get_node_shared_ptr();
+        for (const auto& input_desc : input_descriptions) {
+            auto body_param_idx = input_desc->m_body_parameter_index;
+            auto input_idx = input_desc->m_input_index;
+            if (body_params[body_param_idx] != num_iter_param) {
+                continue;
+            }
+            num_iters_output = loop->input_value(input_idx);
+            break;
+        }
+    } else if (condition_map.count(num_iter_const_label)) {
+        if (!get_constant_value(condition_map.at(num_iter_const_label).get_node_shared_ptr(), num_iters) ||
+            num_iters < 1) {
+            return false;
+        }
+        num_iters_output = std::make_shared<ov::op::v0::Constant>(element::i64, Shape{}, num_iters);
+    } else {
+        return false;
+    }
+
+    return true;
+}
+
+bool check_condition_true_pattern(const std::shared_ptr<op::v0::Result>& cond_result,
+                                  const std::shared_ptr<op::v5::Loop>& loop,
+                                  Output<Node>& num_iters_output) {
+    // return a number of iterations if pattern matches
+    auto cond_const_label = pattern::wrap_type<op::v0::Constant>();
+    auto condition_label = pattern::wrap_type<op::v0::Result>({cond_const_label});
+
+    // check a pattern of condition graph and get a number of iterations
+    ov::pass::pattern::Matcher condition_matcher(condition_label);
+    if (!condition_matcher.match(cond_result->output(0))) {
+        return false;
+    }
+    const auto& condition_map = condition_matcher.get_pattern_value_map();
+    const auto& cond_const =
+        ov::as_type_ptr<op::v0::Constant>(condition_map.at(cond_const_label).get_node_shared_ptr());
+    bool cond_value = false;
+    if (!ov::op::util::get_constant_value(cond_const, cond_value) || !cond_value) {
+        return false;
+    }
+    // number of iteration is retrieve from the first input port
+    num_iters_output = loop->input_value(0);
+
+    return true;
+}
+
+bool check_lstm_cell_pattern(
+    const std::shared_ptr<op::v5::Loop>& loop,
+    const ov::ParameterVector& body_params,
+    const ov::ResultVector& body_results,
+    const ov::op::util::MultiSubGraphOp::MultiSubgraphInputDescriptionVector& input_descriptions,
+    ov::Output<Node>& init_hidden_state,
+    ov::Output<Node>& init_cell_state,
+    ov::Output<Node>& x,
+    ov::Output<Node>& W,
+    ov::Output<Node>& R,
+    ov::Output<Node>& B,
+    std::shared_ptr<op::v0::Result>& result_hidden_state,
+    std::shared_ptr<ov::op::v4::LSTMCell>& lstm_cell_node) {
+    // check pattern with LSMCell and return key points
+    // required for fusion to LSTMSequence
+    // pattern for LSTMCell in the body
+    auto xi_label = pattern::wrap_type<op::v0::Parameter>();
+    auto squeeze_axis_label = pattern::wrap_type<op::v0::Constant>();
+    auto xi_reshape_label = pattern::wrap_type<op::v0::Squeeze>({xi_label, squeeze_axis_label});
+    auto init_hidden_state_i_label = pattern::wrap_type<op::v0::Parameter>();
+    auto init_cell_state_i_label = pattern::wrap_type<op::v0::Parameter>();
+    auto W_label = pattern::wrap_type<op::v0::Constant>();
+    auto R_label = pattern::wrap_type<op::v0::Constant>();
+    auto B_label = pattern::wrap_type<op::v0::Constant>();
+
+    auto lstm_cell_label = pattern::wrap_type<op::v4::LSTMCell>(
+        {xi_reshape_label, init_hidden_state_i_label, init_cell_state_i_label, W_label, R_label, B_label});
+    auto unsqueeze_axis_label = pattern::wrap_type<op::v0::Constant>();
+    auto unsqueeze_hidden_state_label = pattern::wrap_type<op::v0::Unsqueeze>({lstm_cell_label, unsqueeze_axis_label});
+    auto result_hidden_state_label = pattern::wrap_type<op::v0::Result>({unsqueeze_hidden_state_label});
+
+    // check that body-graph contains a pattern corresponding LSTMCell
+    ov::pass::pattern::Matcher lstm_cell_matcher(result_hidden_state_label);
+    int64_t unsqueeze_hidden_state_result_idx = -1;
+    for (int64_t result_idx = 0; result_idx < static_cast<int64_t>(body_results.size()); ++result_idx) {
+        if (lstm_cell_matcher.match(body_results[result_idx]->output(0))) {
+            unsqueeze_hidden_state_result_idx = result_idx;
+            auto reshape_node =
+                lstm_cell_matcher.get_pattern_value_map()[unsqueeze_hidden_state_label].get_node_shared_ptr();
+            break;
+        }
+    }
+    if (unsqueeze_hidden_state_result_idx < 0) {
+        // not found LSTMCell inside Loop operation
+        return false;
+    }
+    const auto& lstm_cell_map = lstm_cell_matcher.get_pattern_value_map();
+
+    // check that Results with hidden and cell states connected with corresponding
+    // Parameter nodes with back edges and found initial hidden and cell states
+    lstm_cell_node = ov::as_type_ptr<op::v4::LSTMCell>(lstm_cell_map.at(lstm_cell_label).get_node_shared_ptr());
+    if (!lstm_cell_node) {
+        return false;
+    }
+    result_hidden_state =
+        ov::as_type_ptr<op::v0::Result>(lstm_cell_map.at(result_hidden_state_label).get_node_shared_ptr());
+    if (!result_hidden_state) {
+        return false;
+    }
+    for (const auto& input_desc : input_descriptions) {
+        auto param_idx = input_desc->m_body_parameter_index;
+        auto input_idx = input_desc->m_input_index;
+        if (body_params[param_idx] == lstm_cell_map.at(init_hidden_state_i_label).get_node_shared_ptr()) {
+            // hidden state Parameter node
+            auto merged_desc = ov::as_type_ptr<ov::op::util::MultiSubGraphOp::MergedInputDescription>(input_desc);
+            if (!merged_desc) {
+                return false;
+            }
+            const auto& hidden_state_result = body_results[merged_desc->m_body_value_index];
+            if ((hidden_state_result->get_input_node_shared_ptr(0) != lstm_cell_node) ||
+                (hidden_state_result->input_value(0).get_index() != 0)) {
+                return false;
+            }
+            init_hidden_state = loop->input_value(input_idx);
+        } else if (body_params[param_idx] == lstm_cell_map.at(init_cell_state_i_label).get_node_shared_ptr()) {
+            // cell state Parameter node
+            auto merged_desc = ov::as_type_ptr<ov::op::util::MultiSubGraphOp::MergedInputDescription>(input_desc);
+            if (!merged_desc) {
+                return false;
+            }
+            const auto& cell_state_result = body_results[merged_desc->m_body_value_index];
+            if ((cell_state_result->get_input_node_shared_ptr(0) != lstm_cell_node) ||
+                (cell_state_result->input_value(0).get_index() != 1)) {
+                return false;
+            }
+            init_cell_state = loop->input_value(input_idx);
+        } else if (body_params[param_idx] == lstm_cell_map.at(xi_label).get_node_shared_ptr()) {
+            // input data Parameter node
+            auto sliced_desc = ov::as_type_ptr<ov::op::util::MultiSubGraphOp::SliceInputDescription>(input_desc);
+            if (!sliced_desc || (sliced_desc->m_axis != 0) || (sliced_desc->m_start != 0) ||
+                (sliced_desc->m_stride != 1) || (sliced_desc->m_end != -1)) {
+                return false;
+            }
+            x = loop->input_value(input_idx);
+        }
+    }
+
+    W = lstm_cell_map.at(W_label);
+    R = lstm_cell_map.at(R_label);
+    B = lstm_cell_map.at(B_label);
+
+    return true;
+}
 }  // namespace
 
 ov::pass::ConvertTensorIteratorToLSTMSequence::ConvertTensorIteratorToLSTMSequence() {
@@ -423,19 +645,6 @@ ov::pass::ConvertTensorIteratorToGRUSequence::ConvertTensorIteratorToGRUSequence
     register_matcher(m, callback);
 }
 
-static bool get_scalar_constant_value(const ov::Output<ov::Node>& node, int64_t& output_value) {
-    auto constant = ov::as_type<ov::op::v0::Constant>(node.get_node());
-    if (!constant)
-        return false;
-    if (ov::shape_size(constant->get_shape()) != 1)
-        return false;
-    const auto& type = constant->get_output_element_type(0);
-    if (type != ov::element::i32 && type != ov::element::i64)
-        return false;
-    output_value = constant->cast_vector<int64_t>()[0];
-    return true;
-}
-
 // clang-format off
 /*
 
@@ -511,8 +720,8 @@ static bool get_scalar_constant_value(const ov::Output<ov::Node>& node, int64_t&
 */
 // clang-format on
 
-ov::pass::ConvertLoopToLSTMSequence::ConvertLoopToLSTMSequence() {
-    MATCHER_SCOPE(ConvertLoopToLSTMSequence);
+ov::pass::ConvertLoopWithScatterUpdateToLSTMSequence::ConvertLoopWithScatterUpdateToLSTMSequence() {
+    MATCHER_SCOPE(ConvertLoopWithScatterUpdateToLSTMSequence);
     auto input_label = pattern::any_input(pattern::rank_equals(3));
     auto input_transpose_const_label = pattern::wrap_type<op::v0::Constant>();
     auto input_transpose_label =
@@ -612,28 +821,34 @@ ov::pass::ConvertLoopToLSTMSequence::ConvertLoopToLSTMSequence() {
         const auto& loop_output_map = loop_output_matcher.get_pattern_value_map();
 
         int64_t iteration_counter_step = -1;
-        if (!get_scalar_constant_value(loop_condition_map.at(iteration_counter_step_label), iteration_counter_step) ||
+        if (!get_constant_value(loop_condition_map.at(iteration_counter_step_label).get_node_shared_ptr(),
+                                iteration_counter_step) ||
             iteration_counter_step != 1)
             return false;
         int64_t sequence_index_step = -1;
-        if (!get_scalar_constant_value(loop_condition_map.at(sequence_index_step_label), sequence_index_step) ||
+        if (!get_constant_value(loop_condition_map.at(sequence_index_step_label).get_node_shared_ptr(),
+                                sequence_index_step) ||
             sequence_index_step != 1)
             return false;
 
         int64_t iteration_counter_limit = -1;
-        if (!get_scalar_constant_value(loop_condition_map.at(iteration_counter_limit_label), iteration_counter_limit))
+        if (!get_constant_value(loop_condition_map.at(iteration_counter_limit_label).get_node_shared_ptr(),
+                                iteration_counter_limit))
             return false;
         int64_t sequence_index_limit = -1;
-        if (!get_scalar_constant_value(loop_condition_map.at(sequence_index_limit_label), sequence_index_limit))
+        if (!get_constant_value(loop_condition_map.at(sequence_index_limit_label).get_node_shared_ptr(),
+                                sequence_index_limit))
             return false;
         if (iteration_counter_limit != sequence_index_limit)
             return false;
 
         int64_t gather_axis = -1;
-        if (!get_scalar_constant_value(loop_output_map.at(gather_axis_label), gather_axis) || gather_axis != 0)
+        if (!get_constant_value(loop_output_map.at(gather_axis_label).get_node_shared_ptr(), gather_axis) ||
+            gather_axis != 0)
             return false;
         int64_t scatter_axis = -1;
-        if (!get_scalar_constant_value(loop_output_map.at(scatter_axis_label), scatter_axis) || scatter_axis != 0)
+        if (!get_constant_value(loop_output_map.at(scatter_axis_label).get_node_shared_ptr(), scatter_axis) ||
+            scatter_axis != 0)
             return false;
 
         const auto& sequence_index = loop_condition_map.at(sequence_index_label).get_node_shared_ptr();
@@ -830,6 +1045,219 @@ ov::pass::ConvertLoopToLSTMSequence::ConvertLoopToLSTMSequence() {
     register_matcher(m, callback);
 }
 
+// clang-format off
+/*
+
+   Following subgraph in Loop is fused into LSTMSequence
+
+   +------------------------------+
+   |              X               |                          +------+
+   |         (sliced input)       |                          | axis |
+   |     [1, batch, input_size]   |                          | {0}  |
+   +--------------+---------------+                          +--+---+
+                  |                                             |
+                  |                                             |
+                  +---+                                         |
+                      |             +---------------------------+
+                      |             |
+                      |             |
+                      v             v        +----------------------+    +----------------------+
+                  +---+------+------+---+    |          H           |    |          C           |
+                  |        Squeeze      |    | (merged with H_out)  |    | (merged with C_out)  |    +-----+    +-----+    +-----+
+                  | [batch, input_size] |    | [batch, hidden_size] |    | [batch, hidden_size] |    |  W  |    |  R  |    |  B  |
+                  +----------+----------+    +----------+-----------+    +----------+-----------+    +--+--+    +--+--+    +--+--+
+                             |                          |                           |                   |          |          |
+                             |                          |           +---------------+                   |          |          |
+                             |                          |           |                                   |          |          |
+                             |                          |           |    +------------------------------+          |          |
+                             |                          |           |    |                                         |          |
+                             |                          +------+    |    |    +------------------------------------+          |
+                             +----------------------------+    |    |    |    |    +------------------------------------------+
+                                                          |    |    |    |    |    |
+                                                          v    v    v    v    v    v
+                                                      +---+----+----+----+----+----+---+
+                                                      |            LSTMCell            |
+                                                      +--------+-------------------+---+
+                                                               |                   |
+                                                               |                   |
+                                                    +----------+---------------+   |
+                                                    |                          |   +---------------------+
+                                                    |                          |                         |
+                                                    v                          v                         v
+                                       +------------+------------+   +---------+------------+   +--------+--------+
+                                       |   Unsqueeze, axis=0     |   |         H_out        |   |      C_out      |
+                                       | [batch, 1, hidden_size] |   |   (merged with H)    |   | (merged with C) |
+                                       |   (concat output)       |   | [batch, hidden_size] |   |                 |
+                                       +-------------------------+   +----------------------+   +-----------------+
+
+*/
+// clang-format on
+
+ov::pass::ConvertLoopWithSlicedInputConcatOutputToLSTMSequence::ConvertLoopWithSlicedInputConcatOutputToLSTMSequence() {
+    MATCHER_SCOPE(ConvertLoopWithSlicedInputConcatOutputToLSTMSequence);
+
+    auto loop_label = pattern::wrap_type<ov::op::v5::Loop>();
+
+    matcher_pass_callback callback = [=](pattern::Matcher& m) {
+        auto loop = ov::as_type_ptr<ov::op::v5::Loop>(m.get_match_root());
+        if (!loop) {
+            return false;
+        }
+
+        const auto& body_graph = loop->get_function();
+        const auto& body_params = body_graph->get_parameters();
+        const auto& body_results = body_graph->get_results();
+        const auto& special_body_ports = loop->get_special_body_ports();
+        const auto& input_descriptions = loop->get_input_descriptions();
+        const auto& output_descriptions = loop->get_output_descriptions();
+        if (special_body_ports.body_condition_output_idx < 0) {
+            return false;
+        }
+
+        // check condition pattern and retrieve a number of iterations
+        ov::Output<ov::Node> num_iters_output;
+        if (!check_condition_increment_pattern(body_results[special_body_ports.body_condition_output_idx],
+                                               loop,
+                                               body_params,
+                                               body_results,
+                                               input_descriptions,
+                                               num_iters_output) &&
+            !check_condition_true_pattern(body_results[special_body_ports.body_condition_output_idx],
+                                          loop,
+                                          num_iters_output)) {
+            return false;
+        }
+
+        // check body pattern with LSTMCell
+        // and extract key points for LSTMSequence creation
+        ov::Output<Node> init_hidden_state, init_cell_state, x, W, R, B;
+        std::shared_ptr<op::v0::Result> result_hidden_state;
+        std::shared_ptr<ov::op::v4::LSTMCell> lstm_cell_node;
+        if (!check_lstm_cell_pattern(loop,
+                                     body_params,
+                                     body_results,
+                                     input_descriptions,
+                                     init_hidden_state,
+                                     init_cell_state,
+                                     x,
+                                     W,
+                                     R,
+                                     B,
+                                     result_hidden_state,
+                                     lstm_cell_node)) {
+            return false;
+        }
+
+        // check that only output with concatenated hidden states from Loop is needed
+        // or get hidden state from last iteration
+        std::vector<uint64_t> last_iter_hidden_state_output_ids;
+        std::vector<uint64_t> all_hidden_states_output_ids;
+        for (const auto& output_desc : output_descriptions) {
+            auto result_idx = output_desc->m_body_value_index;
+            auto output_idx = output_desc->m_output_index;
+            if (body_results[result_idx] != result_hidden_state) {
+                if (loop->get_output_target_inputs(output_idx).size() > 0) {
+                    // some other Result node value from body graph is required by real consumers
+                    return false;
+                }
+                continue;
+            }
+            auto concat_desc = ov::as_type_ptr<ov::op::util::MultiSubGraphOp::ConcatOutputDescription>(output_desc);
+            if (concat_desc) {
+                // all hidden states are concatenated
+                if ((concat_desc->m_axis != 0) || (concat_desc->m_start != 0) || (concat_desc->m_end != -1) ||
+                    (concat_desc->m_stride != 1)) {
+                    return false;
+                }
+                all_hidden_states_output_ids.push_back(output_idx);
+                continue;
+            }
+            last_iter_hidden_state_output_ids.push_back(output_idx);
+        }
+
+        // all input and output ports are almost ready
+        // preparation of the right format of inputs and outputs is remained before reconnection
+        NodeRegistry rg;
+        // x is in a format [seq_len, batch_size, input_size]
+        auto tr_order = rg.make<ov::op::v0::Constant>(element::i32, Shape{3}, std::vector<int32_t>{1, 0, 2});
+        auto x_prep = rg.make<ov::op::v1::Transpose>(x, tr_order);
+        // initial hidden and cell states are of shape [batch_size, hidden_size]
+        // needs to add num_directions size
+        auto unsqueeze_axis = rg.make<ov::op::v0::Constant>(element::i32, Shape{1}, std::vector<int32_t>{1});
+        auto init_hidden_state_prep = rg.make<ov::op::v0::Unsqueeze>(init_hidden_state, unsqueeze_axis);
+        auto init_cell_state_prep = rg.make<ov::op::v0::Unsqueeze>(init_cell_state, unsqueeze_axis);
+        // sequence length is currently scalar and it needs broadcasting to shape [batch_size]
+        auto x_shape = rg.make<ov::op::v3::ShapeOf>(x, element::i64);
+        auto ss_start = rg.make<ov::op::v0::Constant>(element::i64, Shape{1}, 1);
+        auto ss_stop = rg.make<ov::op::v0::Constant>(element::i64, Shape{1}, 2);
+        auto ss_step = rg.make<ov::op::v0::Constant>(element::i64, Shape{1}, 1);
+        auto batch_size = rg.make<ov::op::v1::StridedSlice>(x_shape,
+                                                            ss_start,
+                                                            ss_stop,
+                                                            ss_step,
+                                                            std::vector<int64_t>{0},
+                                                            std::vector<int64_t>{0});
+        auto seq_lens_prep = rg.make<ov::op::v1::Broadcast>(num_iters_output, batch_size);
+        // prepare W, R, B to add num_directions dimension
+        unsqueeze_axis = rg.make<ov::op::v0::Constant>(element::i32, Shape{1}, std::vector<int32_t>{0});
+        auto W_prep = rg.make<ov::op::v0::Unsqueeze>(W, unsqueeze_axis);
+        auto R_prep = rg.make<ov::op::v0::Unsqueeze>(R, unsqueeze_axis);
+        auto B_prep = rg.make<ov::op::v0::Unsqueeze>(B, unsqueeze_axis);
+
+        // create LSTMSequence operation since all inputs are prepared
+        // extract hidden size that should be static as a requirement of OpenVINO LSTMCell operation
+        int64_t hidden_size = static_cast<int64_t>(lstm_cell_node->get_hidden_size());
+        auto lstm_sequence = rg.make<ov::op::v5::LSTMSequence>(x_prep,
+                                                               init_hidden_state_prep,
+                                                               init_cell_state_prep,
+                                                               seq_lens_prep,
+                                                               W_prep,
+                                                               R_prep,
+                                                               B_prep,
+                                                               hidden_size,
+                                                               ov::op::RecurrentSequenceDirection::FORWARD,
+                                                               lstm_cell_node->get_activations_alpha(),
+                                                               lstm_cell_node->get_activations_beta(),
+                                                               lstm_cell_node->get_activations(),
+                                                               lstm_cell_node->get_clip());
+
+        if (transformation_callback(lstm_sequence))
+            return false;
+
+        // prepare outputs of LSTMSequence
+        // output with concatenated hidden states must be in a format [seq_len, batch_size, hidden_size]
+        // LSTMSequence generates it in a format [batch_size, num_directions, seq_len, hidden_size]
+        auto squeeze_axis = rg.make<ov::op::v0::Constant>(element::i32, Shape{1}, std::vector<int32_t>{1});
+        ov::Output<Node> all_hidden_states_prep = rg.make<ov::op::v0::Squeeze>(lstm_sequence->output(0), squeeze_axis);
+        all_hidden_states_prep = rg.make<ov::op::v1::Transpose>(all_hidden_states_prep, tr_order);
+        // prepare output with last hidden state
+        // LSTMSequence operation outputs it in a format [batch_size, num_directions, hidden_size]
+        auto tr_order2 = rg.make<ov::op::v0::Constant>(element::i32, Shape{3}, std::vector<int32_t>{1, 0, 2});
+        ov::Output<ov::Node> last_hidden_state_prep =
+            rg.make<ov::op::v1::Transpose>(lstm_sequence->output(1), tr_order2);
+
+        // reconnect all consumers for hidden states
+        for (auto output_idx : all_hidden_states_output_ids) {
+            loop->output(output_idx).replace(all_hidden_states_prep);
+        }
+        for (auto output_idx : last_iter_hidden_state_output_ids) {
+            loop->output(output_idx).replace(last_hidden_state_prep);
+        }
+
+        copy_runtime_info(m.get_matched_nodes(), rg.get());
+        if ((all_hidden_states_output_ids.size() > 0) && (last_iter_hidden_state_output_ids.size() == 0)) {
+            all_hidden_states_prep.get_node_shared_ptr()->set_friendly_name(loop->get_friendly_name());
+        } else if ((all_hidden_states_output_ids.size() == 0) && (last_iter_hidden_state_output_ids.size() > 0)) {
+            last_hidden_state_prep.get_node_shared_ptr()->set_friendly_name(loop->get_friendly_name());
+        }
+
+        return true;
+    };
+
+    auto m = std::make_shared<pattern::Matcher>(loop_label, matcher_name);
+    register_matcher(m, callback);
+}
+
 class EliminateGatherWithRange : public ov::pass::MatcherPass {
 public:
     EliminateGatherWithRange() {
@@ -868,9 +1296,9 @@ class EliminateGatherWithRange : public ov::pass::MatcherPass {
             const auto shapeof_gather2 = pattern_map.at(shapeof_gather2_label).get_node_shared_ptr();
             int64_t shapeof_gather2_index = -1;
             int64_t shapeof_gather2_axis = -1;
-            if (!get_scalar_constant_value(shapeof_gather2->get_input_node_shared_ptr(1), shapeof_gather2_index))
+            if (!get_constant_value(shapeof_gather2->get_input_node_shared_ptr(1), shapeof_gather2_index))
                 return false;
-            if (!get_scalar_constant_value(shapeof_gather2->get_input_node_shared_ptr(2), shapeof_gather2_axis) ||
+            if (!get_constant_value(shapeof_gather2->get_input_node_shared_ptr(2), shapeof_gather2_axis) ||
                 shapeof_gather2_axis != 0)
                 return false;
             const auto reshape = pattern_map.at(reshape_label).get_node_shared_ptr();
@@ -880,13 +1308,13 @@ class EliminateGatherWithRange : public ov::pass::MatcherPass {
             const auto range = pattern_map.at(range_label).get_node_shared_ptr();
             int64_t range_start = -1;
             int64_t range_step = -1;
-            if (!get_scalar_constant_value(range->get_input_node_shared_ptr(0), range_start) || range_start != 0)
+            if (!get_constant_value(range->get_input_node_shared_ptr(0), range_start) || range_start != 0)
                 return false;
-            if (!get_scalar_constant_value(range->get_input_node_shared_ptr(2), range_step) || range_step != 1)
+            if (!get_constant_value(range->get_input_node_shared_ptr(2), range_step) || range_step != 1)
                 return false;
 
             int64_t gather_axis = -1;
-            if (!get_scalar_constant_value(gather->get_input_node_shared_ptr(2), gather_axis) ||
+            if (!get_constant_value(gather->get_input_node_shared_ptr(2), gather_axis) ||
                 gather_axis != shapeof_gather_indexes[shapeof_gather2_index])
                 return false;
 
@@ -1005,7 +1433,7 @@ ov::pass::FuseReverseLSTMSequence::FuseReverseLSTMSequence() {
         if (squeeze->input_value(0) != lstm->output(0))
             return false;
         int64_t squeeze_axis = -1;
-        if (!get_scalar_constant_value(squeeze->get_input_node_shared_ptr(1), squeeze_axis) || squeeze_axis != 1)
+        if (!get_constant_value(squeeze->get_input_node_shared_ptr(1), squeeze_axis) || squeeze_axis != 1)
             return false;
         auto new_squeeze = node_registry.make<op::v0::Squeeze>(new_lstm->output(0), squeeze->input_value(1));
         const auto match_root = m.get_match_root();
@@ -1122,7 +1550,7 @@ ov::pass::FuseLSTMSequencesToBidirectionalLSTMSequence::FuseLSTMSequencesToBidir
         if (squeeze_forward->input_value(0) != lstm_forward->output(0))
             return false;
         int64_t squeeze_forward_axis = -1;
-        if (!get_scalar_constant_value(squeeze_forward->get_input_node_shared_ptr(1), squeeze_forward_axis) ||
+        if (!get_constant_value(squeeze_forward->get_input_node_shared_ptr(1), squeeze_forward_axis) ||
             squeeze_forward_axis != 1)
             return false;
 
@@ -1130,7 +1558,7 @@ ov::pass::FuseLSTMSequencesToBidirectionalLSTMSequence::FuseLSTMSequencesToBidir
         if (squeeze_reverse->input_value(0) != lstm_reverse->output(0))
             return false;
         int64_t squeeze_reverse_axis = -1;
-        if (!get_scalar_constant_value(squeeze_reverse->get_input_node_shared_ptr(1), squeeze_reverse_axis) ||
+        if (!get_constant_value(squeeze_reverse->get_input_node_shared_ptr(1), squeeze_reverse_axis) ||
             squeeze_reverse_axis != 1)
             return false;
 
@@ -1180,21 +1608,17 @@ ov::pass::FuseLSTMSequencesToBidirectionalLSTMSequence::FuseLSTMSequencesToBidir
             auto gather_forward = pattern_map.at(gather_forward_label);
             int64_t gather_index = -1;
             int64_t gather_axis = -1;
-            if (!get_scalar_constant_value(gather_forward->get_input_node_shared_ptr(1), gather_index) ||
-                gather_index != 0)
+            if (!get_constant_value(gather_forward->get_input_node_shared_ptr(1), gather_index) || gather_index != 0)
                 return false;
-            if (!get_scalar_constant_value(gather_forward->get_input_node_shared_ptr(2), gather_axis) ||
-                gather_axis != 0)
+            if (!get_constant_value(gather_forward->get_input_node_shared_ptr(2), gather_axis) || gather_axis != 0)
                 return false;
 
             auto gather_reverse = pattern_map.at(gather_reverse_label);
             gather_index = -1;
             gather_axis = -1;
-            if (!get_scalar_constant_value(gather_reverse->get_input_node_shared_ptr(1), gather_index) ||
-                gather_index != 0)
+            if (!get_constant_value(gather_reverse->get_input_node_shared_ptr(1), gather_index) || gather_index != 0)
                 return false;
-            if (!get_scalar_constant_value(gather_reverse->get_input_node_shared_ptr(2), gather_axis) ||
-                gather_axis != 0)
+            if (!get_constant_value(gather_reverse->get_input_node_shared_ptr(2), gather_axis) || gather_axis != 0)
                 return false;
 
             from.push_back(max_sequence_len_forward);
diff --git a/src/common/transformations/src/transformations/symbolic_transformations/symbol_optimization.cpp b/src/common/transformations/src/transformations/symbolic_transformations/symbol_optimization.cpp
index 3bf315bebf4467..469150c02a857c 100644
--- a/src/common/transformations/src/transformations/symbolic_transformations/symbol_optimization.cpp
+++ b/src/common/transformations/src/transformations/symbolic_transformations/symbol_optimization.cpp
@@ -315,18 +315,43 @@ std::vector<std::shared_ptr<ov::Node>> topological_order(const std::shared_ptr<o
 }
 
 void save_shape_sources(const std::shared_ptr<ov::Node>& op, STS_map& symbol_shape_source) {
-    if (!ov::is_type<ov::op::v0::ShapeOf>(op) && !ov::is_type<ov::op::v3::ShapeOf>(op))
-        return;
-    const auto& output = op->input_value(0);
-    for (const auto& d : output.get_partial_shape()) {
-        if (d.is_static())
-            continue;
-        auto symbol = d.get_symbol();
-        if (symbol == nullptr)
-            continue;
-        if (symbol_shape_source.count(symbol))
-            continue;
-        symbol_shape_source[symbol] = output;
+    if (ov::is_type<ov::op::v0::ShapeOf>(op) || ov::is_type<ov::op::v3::ShapeOf>(op)) {
+        const auto& output = op->input_value(0);
+        if (output.get_partial_shape().rank().is_dynamic())
+            return;
+        for (const auto& d : output.get_partial_shape()) {
+            if (d.is_static())
+                continue;
+            auto symbol = d.get_symbol();
+            if (symbol == nullptr)
+                continue;
+            if (symbol_shape_source.count(symbol))
+                continue;
+            symbol_shape_source[symbol] = output;
+        }
+    } else if (const auto concat = ov::as_type_ptr<ov::op::v0::Concat>(op)) {
+        // we agreed to detach ShapeOf sourcing output axis dimension from Concat operations for smoother matching
+        // this code allows to find places to reconnect ShapeOfs
+        const auto rank = concat->get_output_partial_shape(0).rank();
+        auto axis = concat->get_axis();
+        if (axis < 0) {
+            if (rank.is_dynamic())
+                return;
+            axis += rank.get_length();
+        }
+        for (const auto& input : concat->input_values()) {
+            if (input.get_partial_shape().rank().is_dynamic())
+                continue;
+            const auto dimension = input.get_partial_shape()[axis];
+            if (dimension.is_static())
+                continue;
+            auto symbol = dimension.get_symbol();
+            if (symbol == nullptr)
+                continue;
+            if (symbol_shape_source.count(symbol))
+                continue;
+            symbol_shape_source[symbol] = input;
+        }
     }
 }
 }  // namespace
diff --git a/src/common/transformations/tests/op_conversions/convert_ti_to_sequences_test.cpp b/src/common/transformations/tests/op_conversions/convert_ti_to_sequences_test.cpp
index e03f821c55584a..0115abc6779091 100644
--- a/src/common/transformations/tests/op_conversions/convert_ti_to_sequences_test.cpp
+++ b/src/common/transformations/tests/op_conversions/convert_ti_to_sequences_test.cpp
@@ -7,6 +7,7 @@
 #include <gtest/gtest.h>
 
 #include <memory>
+#include <random>
 #include <string>
 
 #include "common_test_utils/ov_test_utils.hpp"
@@ -1265,3 +1266,197 @@ TEST_P(FuseLSTMSequencesToBidirectionalLSTMSequenceTest, FusionTest) {
 INSTANTIATE_TEST_SUITE_P(FuseLSTMSequencesToBidirectionalLSTMSequence,
                          FuseLSTMSequencesToBidirectionalLSTMSequenceTest,
                          testing::Combine(testing::Values(false, true), testing::Values(false, true)));
+
+using LoopWithLSTMCellToLSTMSequenceFusionParam = std::tuple<std::string,  // f activation function
+                                                             std::string,  // g activation function
+                                                             std::string,  // h activation function
+                                                             size_t,       // input size
+                                                             size_t>;      // hidden size
+
+class LoopWithLSTMCellToLSTMSequenceFusionTest
+    : public testing::WithParamInterface<LoopWithLSTMCellToLSTMSequenceFusionParam>,
+      public TransformationTestsF {};
+
+namespace {
+void generate_weights_value(std::vector<float>& weights_value, const Shape& weights_shape) {
+    weights_value.resize(shape_size(weights_shape));
+    std::mt19937 rng(9812);
+    std::uniform_real_distribution<float> distribution(-300, 300);
+    for (size_t i = 0; i < weights_value.size(); ++i) {
+        weights_value[i] = distribution(rng);
+    }
+}
+}  // namespace
+
+TEST_P(LoopWithLSTMCellToLSTMSequenceFusionTest, FusionTest) {
+    const auto& param = GetParam();
+    const std::string& f_activation = std::get<0>(param);
+    const std::string& g_activation = std::get<1>(param);
+    const std::string& h_activation = std::get<2>(param);
+    size_t input_size = std::get<3>(param);
+    size_t hidden_size = std::get<4>(param);
+    size_t batch_size = 2;
+    size_t time_len = 10;
+
+    // generate weights values
+    // w must be of a shape [input_size, hidden_size]
+    // r must be of a shape [hidden_size, hidden_size]
+    // b must be of a shape [hidden_size]
+    Shape w_shape({4 * hidden_size, input_size});
+    Shape r_shape({4 * hidden_size, hidden_size});
+    Shape b_shape({4 * hidden_size});
+    std::vector<float> w, r, b;
+    generate_weights_value(w, w_shape);
+    generate_weights_value(r, r_shape);
+    generate_weights_value(b, b_shape);
+
+    {
+        // create body graph with LSTMCell
+        auto xi = std::make_shared<op::v0::Parameter>(element::f32, Shape{1, batch_size, input_size});
+        auto squeeze_axis = std::make_shared<op::v0::Constant>(element::i64, Shape{}, 0);
+        auto xi_squeeze = std::make_shared<op::v0::Squeeze>(xi, squeeze_axis);
+        auto init_hidden_state = std::make_shared<op::v0::Parameter>(element::f32, Shape{batch_size, hidden_size});
+        auto init_cell_state = std::make_shared<op::v0::Parameter>(element::f32, Shape{batch_size, hidden_size});
+        auto w_const = op::v0::Constant::create(element::f32, w_shape, w);
+        auto r_const = op::v0::Constant::create(element::f32, r_shape, r);
+        auto b_const = op::v0::Constant::create(element::f32, b_shape, b);
+        auto lstm_cell =
+            std::make_shared<op::v4::LSTMCell>(xi_squeeze,
+                                               init_hidden_state,
+                                               init_cell_state,
+                                               w_const,
+                                               r_const,
+                                               b_const,
+                                               hidden_size,
+                                               std::vector<std::string>{f_activation, g_activation, h_activation});
+
+        auto hidden_state_res = std::make_shared<op::v0::Result>(lstm_cell->output(0));
+        auto cell_state_res = std::make_shared<op::v0::Result>(lstm_cell->output(1));
+        auto unsqueeze_axis = std::make_shared<op::v0::Constant>(element::i64, Shape{}, 0);
+        auto unsqueeze_hidden_state = std::make_shared<op::v0::Unsqueeze>(lstm_cell->output(0), unsqueeze_axis);
+        auto unsqueeze_hidden_state_res = std::make_shared<op::v0::Result>(unsqueeze_hidden_state);
+
+        // conditional graph
+        auto num_iters = std::make_shared<op::v0::Parameter>(element::i32, Shape{1});
+        auto counter = std::make_shared<op::v0::Parameter>(element::i32, Shape{1});
+        auto increment = std::make_shared<op::v0::Constant>(element::i32, Shape{}, 1);
+        auto add = std::make_shared<op::v1::Add>(counter, increment);
+        auto updated_counter = std::make_shared<op::v0::Result>(add);
+        auto less = std::make_shared<op::v1::Less>(add, num_iters);
+        auto less_res = std::make_shared<op::v0::Result>(less);
+
+        auto body_graph = std::make_shared<Model>(
+            ResultVector{hidden_state_res, cell_state_res, unsqueeze_hidden_state_res, less_res, updated_counter},
+            ParameterVector{xi, init_hidden_state, init_cell_state, num_iters, counter});
+
+        // create main graph with Loop
+        auto x = std::make_shared<op::v0::Parameter>(element::f32, Shape{time_len, batch_size, input_size});
+        auto h_init = std::make_shared<op::v0::Parameter>(element::f32, Shape{batch_size, hidden_size});
+        auto c_init = std::make_shared<op::v0::Parameter>(element::f32, Shape{batch_size, hidden_size});
+        auto execution_cond = std::make_shared<op::v0::Constant>(ov::element::boolean, ov::Shape{}, true);
+        auto max_iter = std::make_shared<op::v0::Constant>(ov::element::i32, ov::Shape{1}, -1);
+        auto num_iter_const =
+            std::make_shared<op::v0::Constant>(ov::element::i32, ov::Shape{1}, static_cast<int32_t>(time_len));
+        auto counter_const = std::make_shared<op::v0::Constant>(ov::element::i32, ov::Shape{1}, 0);
+
+        auto loop_node = std::make_shared<op::v5::Loop>(max_iter, execution_cond);
+
+        loop_node->set_function(body_graph);
+        loop_node->set_special_body_ports(ov::op::v5::Loop::SpecialBodyPorts{-1, 3});
+
+        // set inputs for Loop
+        // x input will be sliced for each time step
+        loop_node->set_sliced_input(xi, x, 0, 1, 1, -1, 0);
+        // set back edges for cell and hidden states
+        // since they are changing through timeline
+        loop_node->set_merged_input(init_hidden_state, h_init, hidden_state_res);
+        loop_node->set_merged_input(init_cell_state, c_init, cell_state_res);
+        loop_node->set_invariant_input(num_iters, num_iter_const);
+        loop_node->set_merged_input(counter, counter_const, updated_counter);
+
+        // set external outputs for Loop node
+        // concatenated cell and hidden states from all time steps
+        auto hs = loop_node->get_concatenated_slices(unsqueeze_hidden_state_res, 0, 1, 1, -1, 0);
+        auto hs_res = std::make_shared<op::v0::Result>(hs);
+
+        model = std::make_shared<Model>(ResultVector{hs_res}, ParameterVector{x, h_init, c_init});
+        manager.register_pass<ov::pass::ConvertLoopWithSlicedInputConcatOutputToLSTMSequence>();
+    }
+
+    {
+        auto x = std::make_shared<op::v0::Parameter>(element::f32, Shape{time_len, batch_size, input_size});
+        auto h_init = std::make_shared<op::v0::Parameter>(element::f32, Shape{batch_size, hidden_size});
+        auto c_init = std::make_shared<op::v0::Parameter>(element::f32, Shape{batch_size, hidden_size});
+
+        // transpose x since LSTMSequence expects x in a format [batch_size, time_len, input_size]
+        auto tr_order =
+            std::make_shared<op::v0::Constant>(ov::element::i32, ov::Shape{3}, std::vector<int32_t>{1, 0, 2});
+        auto tr_x = std::make_shared<op::v1::Transpose>(x, tr_order);
+        // prepare init hidden and cell states to have a format [batch_size, num_directions, hidden_size]
+        // where num_directions equals one
+        auto unsqueeze_axis =
+            std::make_shared<op::v0::Constant>(ov::element::i32, ov::Shape{1}, std::vector<int32_t>{1});
+        auto h_init_unsqueeze = std::make_shared<op::v0::Unsqueeze>(h_init, unsqueeze_axis);
+        auto c_init_unsqueeze = std::make_shared<op::v0::Unsqueeze>(c_init, unsqueeze_axis);
+        // prepare seq_lens
+        auto batch_size = std::make_shared<op::v3::ShapeOf>(x, element::i64)->output(0);
+        auto begin = std::make_shared<op::v0::Constant>(ov::element::i64, ov::Shape{1}, std::vector<int32_t>{1});
+        auto end = std::make_shared<op::v0::Constant>(ov::element::i64, ov::Shape{1}, std::vector<int32_t>{2});
+        auto stride = std::make_shared<op::v0::Constant>(ov::element::i64, ov::Shape{1}, std::vector<int32_t>{1});
+        batch_size = std::make_shared<op::v1::StridedSlice>(batch_size,
+                                                            begin,
+                                                            end,
+                                                            stride,
+                                                            std::vector<int64_t>{0},
+                                                            std::vector<int64_t>{0});
+        auto num_iter_const =
+            std::make_shared<op::v0::Constant>(ov::element::i32, ov::Shape{1}, static_cast<int32_t>(time_len));
+        auto seq_lens = std::make_shared<op::v1::Broadcast>(num_iter_const, batch_size);
+        // prepare W, R, B weights to a format with num_directions dimension
+        auto w_const = op::v0::Constant::create(element::f32, w_shape, w);
+        auto r_const = op::v0::Constant::create(element::f32, r_shape, r);
+        auto b_const = op::v0::Constant::create(element::f32, b_shape, b);
+        auto unsqueeze_axis2 =
+            std::make_shared<op::v0::Constant>(ov::element::i32, ov::Shape{1}, std::vector<int32_t>{0});
+        auto w = std::make_shared<op::v0::Unsqueeze>(w_const, unsqueeze_axis2);
+        auto r = std::make_shared<op::v0::Unsqueeze>(r_const, unsqueeze_axis2);
+        auto b = std::make_shared<op::v0::Unsqueeze>(b_const, unsqueeze_axis2);
+
+        // create LSTMSequence
+        auto lstm_sequence = std::make_shared<ov::op::v5::LSTMSequence>(
+            tr_x,
+            h_init_unsqueeze,
+            c_init_unsqueeze,
+            seq_lens,
+            w,
+            r,
+            b,
+            hidden_size,
+            ov::op::RecurrentSequenceDirection::FORWARD,
+            std::vector<float>{},
+            std::vector<float>{},
+            std::vector<std::string>{f_activation, g_activation, h_activation},
+            0.0f);
+
+        // prepare output
+        auto squeeze_axis = std::make_shared<op::v0::Constant>(ov::element::i32, ov::Shape{1}, 1);
+        auto squeeze_output_hs = std::make_shared<op::v0::Squeeze>(lstm_sequence->output(0), squeeze_axis);
+        auto tr_order2 =
+            std::make_shared<op::v0::Constant>(ov::element::i32, ov::Shape{3}, std::vector<int32_t>{1, 0, 2});
+        auto tr_squeeze_output_hs = std::make_shared<op::v1::Transpose>(squeeze_output_hs, tr_order2);
+        auto output_hs_res = std::make_shared<op::v0::Result>(tr_squeeze_output_hs);
+        model_ref = std::make_shared<Model>(ResultVector{output_hs_res}, ParameterVector{x, h_init, c_init});
+    }
+
+    comparator.enable(FunctionsComparator::CmpValues::CONST_VALUES);
+    comparator.enable(FunctionsComparator::CmpValues::ATTRIBUTES);
+    comparator.enable(FunctionsComparator::CmpValues::ACCURACY);
+}
+
+INSTANTIATE_TEST_SUITE_P(LoopWithLSTMCellToLSTMSequenceFusion,
+                         LoopWithLSTMCellToLSTMSequenceFusionTest,
+                         testing::Combine(testing::Values("sigmoid", "tanh"),
+                                          testing::Values("sigmoid", "relu"),
+                                          testing::Values("tanh", "relu"),
+                                          testing::Values(2, 3),
+                                          testing::Values(3, 4)));
diff --git a/src/common/transformations/tests/symbolic_transformations/label_optimization.cpp b/src/common/transformations/tests/symbolic_transformations/symbol_optimization.cpp
similarity index 83%
rename from src/common/transformations/tests/symbolic_transformations/label_optimization.cpp
rename to src/common/transformations/tests/symbolic_transformations/symbol_optimization.cpp
index eb108e4c6591ba..590290fb19d84b 100644
--- a/src/common/transformations/tests/symbolic_transformations/label_optimization.cpp
+++ b/src/common/transformations/tests/symbolic_transformations/symbol_optimization.cpp
@@ -2,6 +2,8 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
+#include "transformations/symbolic_transformations/symbol_optimization.hpp"
+
 #include <gtest/gtest.h>
 
 #include "common_test_utils/ov_test_utils.hpp"
@@ -12,7 +14,6 @@
 #include "openvino/op/shape_of.hpp"
 #include "openvino/pass/manager.hpp"
 #include "openvino/pass/visualize_tree.hpp"
-#include "transformations/symbolic_transformations/symbol_optimization.hpp"
 #include "transformations/symbolic_transformations/symbolic_optimizations.hpp"
 #include "transformations/symbolic_transformations/utils.hpp"
 
@@ -75,16 +76,22 @@ TEST_F(TransformationTestsF, ApplySymbolEquivalence_Concat_Values) {
         auto input_2 = make_shared<v0::Parameter>(element::f32, PartialShape::dynamic(4));
         auto concat = make_shared<v0::Concat>(OutputVector{input_1, input_2}, -1);
 
-        auto shape = make_shared<v3::ShapeOf>(concat);
-        auto gather = make_shared<v8::Gather>(shape,
-                                              v0::Constant::create(element::i64, {1}, {-1}),
-                                              v0::Constant::create(element::i64, {}, {0}));
+        auto shape_1 = make_shared<v3::ShapeOf>(input_1);
+        auto gather_1 = make_shared<v8::Gather>(shape_1,
+                                                v0::Constant::create(element::i64, {1}, {3}),
+                                                v0::Constant::create(element::i64, {}, {0}));
+
+        auto shape_2 = make_shared<v3::ShapeOf>(input_2);
+        auto gather_2 = make_shared<v8::Gather>(shape_2,
+                                                v0::Constant::create(element::i64, {1}, {3}),
+                                                v0::Constant::create(element::i64, {}, {0}));
+
+        auto sum = make_shared<v1::Add>(gather_1, gather_2);
 
         auto reshape = make_shared<v1::Reshape>(
             concat,
-            make_shared<v0::Concat>(OutputVector{gather, v0::Constant::create(element::i64, {1}, {-1})}, 0),
+            make_shared<v0::Concat>(OutputVector{sum, v0::Constant::create(element::i64, {1}, {-1})}, 0),
             false);
-
         model_ref = make_shared<Model>(NodeVector{reshape}, ParameterVector{input_2, input_1});
     }
 }
diff --git a/src/common/util/include/openvino/util/file_util.hpp b/src/common/util/include/openvino/util/file_util.hpp
index 96d0b62865d59d..88b9110abeced8 100644
--- a/src/common/util/include/openvino/util/file_util.hpp
+++ b/src/common/util/include/openvino/util/file_util.hpp
@@ -77,6 +77,17 @@ struct FileTraits<wchar_t> {
     }
 };
 
+/**
+ * @brief Convert path as char string to to a single-byte chain.
+ * @param path Path as char string.
+ * @return Reference to input path (no conversion).
+ */
+template <class Path,
+          typename std::enable_if<std::is_same<typename std::decay<Path>::type, std::string>::value>::type* = nullptr>
+const std::string& path_to_string(const Path& path) {
+    return path;
+}
+
 #ifdef OPENVINO_ENABLE_UNICODE_PATH_SUPPORT
 /**
  * @brief Conversion from wide character string to a single-byte chain.
@@ -91,6 +102,17 @@ std::string wstring_to_string(const std::wstring& wstr);
  */
 std::wstring string_to_wstring(const std::string& str);
 
+/**
+ * @brief Convert path as wide character string to a single-byte chain.
+ * @param path  Path as wide-char string.
+ * @return A char string
+ */
+template <class Path,
+          typename std::enable_if<std::is_same<typename std::decay<Path>::type, std::wstring>::value>::type* = nullptr>
+std::string path_to_string(const Path& path) {
+    return ov::util::wstring_to_string(path);
+}
+
 #endif
 
 /// \brief Remove path components which would allow traversing up a directory tree.
diff --git a/src/core/include/openvino/core/model.hpp b/src/core/include/openvino/core/model.hpp
index 3f82b205d0c9fc..3a17b354364387 100644
--- a/src/core/include/openvino/core/model.hpp
+++ b/src/core/include/openvino/core/model.hpp
@@ -140,10 +140,14 @@ class OPENVINO_API Model : public std::enable_shared_from_this<Model> {
     ov::Output<ov::Node> add_output(const std::string& op_name, size_t output_idx);
     ov::Output<ov::Node> add_output(const ov::Output<ov::Node>& port);
 
-    void reshape(const ov::PartialShape& partial_shape);
-    void reshape(const std::map<size_t, ov::PartialShape>& partial_shapes);
-    void reshape(const std::map<std::string, ov::PartialShape>& partial_shapes);
-    void reshape(const std::map<ov::Output<ov::Node>, ov::PartialShape>& partial_shapes);
+    void reshape(const ov::PartialShape& partial_shape,
+                 const std::unordered_map<std::string, ov::PartialShape>& variable_shapes = {});
+    void reshape(const std::map<size_t, ov::PartialShape>& partial_shapes,
+                 const std::unordered_map<std::string, ov::PartialShape>& variable_shapes = {});
+    void reshape(const std::map<std::string, ov::PartialShape>& partial_shapes,
+                 const std::unordered_map<std::string, ov::PartialShape>& variable_shapes = {});
+    void reshape(const std::map<ov::Output<ov::Node>, ov::PartialShape>& partial_shapes,
+                 const std::unordered_map<std::string, ov::PartialShape>& variable_shapes = {});
 
     /// Return the element type of output i
     const ov::element::Type& get_output_element_type(size_t i) const;
@@ -414,7 +418,7 @@ class OPENVINO_API Model : public std::enable_shared_from_this<Model> {
      */
     template <class T, class... Args>
     void set_rt_info(const T& argument, Args... args) {
-        ov::Any& arg = get_rt_arg<Args...>(m_rt_info, args...);
+        ov::Any& arg = get_rt_arg<Args...>(m_rt_info, std::move(args)...);
         arg = argument;
     }
 
diff --git a/src/core/include/openvino/core/node_input.hpp b/src/core/include/openvino/core/node_input.hpp
index b151a92a7353f0..884ff85bebb9d7 100644
--- a/src/core/include/openvino/core/node_input.hpp
+++ b/src/core/include/openvino/core/node_input.hpp
@@ -38,15 +38,15 @@ class OPENVINO_API Input<Node> {
     /// \return The index of the input referred to by this input handle.
     size_t get_index() const;
     /// \return The element type of the input referred to by this input handle.
-    const element::Type& get_element_type() const;
+    OV_NO_DANGLING const element::Type& get_element_type() const;
     /// \return The shape of the input referred to by this input handle.
-    const Shape& get_shape() const;
+    OV_NO_DANGLING const Shape& get_shape() const;
     /// \return The partial shape of the input referred to by this input handle.
-    const PartialShape& get_partial_shape() const;
+    OV_NO_DANGLING const PartialShape& get_partial_shape() const;
     /// \return A handle to the output that is connected to this input.
     Output<Node> get_source_output() const;
     /// \return A reference to the tensor descriptor for this input.
-    descriptor::Tensor& get_tensor() const;
+    OV_NO_DANGLING descriptor::Tensor& get_tensor() const;
     /// \return A shared pointer to the tensor descriptor for this input.
     std::shared_ptr<descriptor::Tensor> get_tensor_ptr() const;
     /// \return true if this input is relevant to its node's output shapes; else false.
@@ -61,7 +61,7 @@ class OPENVINO_API Input<Node> {
     /// \return The reference to runtime info map
     RTMap& get_rt_info();
     /// \return The constant reference to runtime info map
-    const RTMap& get_rt_info() const;
+    OV_NO_DANGLING const RTMap& get_rt_info() const;
 
     bool operator==(const Input& other) const;
     bool operator!=(const Input& other) const;
@@ -90,15 +90,15 @@ class OPENVINO_API Input<const Node> {
     /// \return The index of the input referred to by this input handle.
     size_t get_index() const;
     /// \return The element type of the input referred to by this input handle.
-    const element::Type& get_element_type() const;
+    OV_NO_DANGLING const element::Type& get_element_type() const;
     /// \return The shape of the input referred to by this input handle.
-    const Shape& get_shape() const;
+    OV_NO_DANGLING const Shape& get_shape() const;
     /// \return The partial shape of the input referred to by this input handle.
-    const PartialShape& get_partial_shape() const;
+    OV_NO_DANGLING const PartialShape& get_partial_shape() const;
     /// \return A handle to the output that is connected to this input.
     Output<Node> get_source_output() const;
     /// \return A reference to the tensor descriptor for this input.
-    descriptor::Tensor& get_tensor() const;
+    OV_NO_DANGLING descriptor::Tensor& get_tensor() const;
     /// \return A shared pointer to the tensor descriptor for this input.
     std::shared_ptr<descriptor::Tensor> get_tensor_ptr() const;
     /// \return true if this input is relevant to its node's output shapes; else false.
@@ -107,7 +107,7 @@ class OPENVINO_API Input<const Node> {
     bool get_is_relevant_to_values() const;
 
     /// \return The constant reference to runtime info map
-    const RTMap& get_rt_info() const;
+    OV_NO_DANGLING const RTMap& get_rt_info() const;
 
     bool operator==(const Input& other) const;
     bool operator!=(const Input& other) const;
diff --git a/src/core/include/openvino/core/node_output.hpp b/src/core/include/openvino/core/node_output.hpp
index d377d6a424cac2..90722efa5b57bc 100644
--- a/src/core/include/openvino/core/node_output.hpp
+++ b/src/core/include/openvino/core/node_output.hpp
@@ -61,25 +61,25 @@ class OPENVINO_API Output<Node> {
     /// \return The index of the output referred to by this output handle.
     size_t get_index() const;
     /// \return A reference to the tensor descriptor for this output.
-    descriptor::Tensor& get_tensor() const;
+    OV_NO_DANGLING descriptor::Tensor& get_tensor() const;
     /// \return A shared point to the tensor ptr for this output.
     std::shared_ptr<descriptor::Tensor> get_tensor_ptr() const;
     /// \return Set new tensor desc shared pointer to this output
     void set_tensor_ptr(std::shared_ptr<descriptor::Tensor> tensor_ptr);
     /// \return The element type of the output referred to by this output handle.
-    const element::Type& get_element_type() const;
+    OV_NO_DANGLING const element::Type& get_element_type() const;
     /// \return The shape of the output referred to by this output handle.
-    const Shape& get_shape() const;
+    OV_NO_DANGLING const Shape& get_shape() const;
     /// \return The partial shape of the output referred to by this output handle.
-    const PartialShape& get_partial_shape() const;
+    OV_NO_DANGLING const PartialShape& get_partial_shape() const;
 
     /// \return The reference to runtime info map
     RTMap& get_rt_info();
     /// \return The constant reference to runtime info map
-    const RTMap& get_rt_info() const;
+    OV_NO_DANGLING const RTMap& get_rt_info() const;
 
     /// \return The tensor names associated with this output
-    const std::unordered_set<std::string>& get_names() const;
+    OV_NO_DANGLING const std::unordered_set<std::string>& get_names() const;
     /// \return Any tensor names associated with this output
     std::string get_any_name() const;
     /// \return Set tensor names associated with this output
@@ -149,20 +149,20 @@ class OPENVINO_API Output<const Node> {
     /// \return The index of the output referred to by this output handle.
     size_t get_index() const;
     /// \return A reference to the tensor descriptor for this output.
-    descriptor::Tensor& get_tensor() const;
+    OV_NO_DANGLING descriptor::Tensor& get_tensor() const;
     /// \return A shared point to the tensor ptr for this output.
     std::shared_ptr<descriptor::Tensor> get_tensor_ptr() const;
     /// \return The element type of the output referred to by this output handle.
-    const element::Type& get_element_type() const;
+    OV_NO_DANGLING const element::Type& get_element_type() const;
     /// \return The shape of the output referred to by this output handle.
-    const Shape& get_shape() const;
+    OV_NO_DANGLING const Shape& get_shape() const;
     /// \return The partial shape of the output referred to by this output handle.
-    const PartialShape& get_partial_shape() const;
+    OV_NO_DANGLING const PartialShape& get_partial_shape() const;
 
     /// \return The constant reference to runtime info map
-    const RTMap& get_rt_info() const;
+    OV_NO_DANGLING const RTMap& get_rt_info() const;
     /// \return The tensor names associated with this output
-    const std::unordered_set<std::string>& get_names() const;
+    OV_NO_DANGLING const std::unordered_set<std::string>& get_names() const;
     /// \return Any tensor name associated with this output
     std::string get_any_name() const;
     /// \return A set containing handles for all inputs targeted by the output referenced by
diff --git a/src/core/include/openvino/core/type/element_type.hpp b/src/core/include/openvino/core/type/element_type.hpp
index 52a285c6c82904..e579e8ba83210b 100644
--- a/src/core/include/openvino/core/type/element_type.hpp
+++ b/src/core/include/openvino/core/type/element_type.hpp
@@ -23,6 +23,7 @@
 #include "openvino/core/type/float4_e2m1.hpp"
 #include "openvino/core/type/float8_e4m3.hpp"
 #include "openvino/core/type/float8_e5m2.hpp"
+#include "openvino/core/type/float8_e8m0.hpp"
 
 /**
  * @defgroup ov_element_cpp_api Element types
@@ -61,7 +62,8 @@ enum class Type_t {
     f8e4m3,     //!< f8e4m3 element type
     f8e5m2,     //!< f8e5m2 element type
     string,     //!< string element type
-    f4e2m1      //!< f4e2m1 element type
+    f4e2m1,     //!< f4e2m1 element type
+    f8e8m0,     //!< f8e8m0 element type
 };
 
 /// \brief Base class to define element type
@@ -214,6 +216,9 @@ constexpr Type string(Type_t::string);
 /// \brief f4e2m1 element type
 /// \ingroup ov_element_cpp_api
 constexpr Type f4e2m1(Type_t::f4e2m1);
+/// \brief f8e8m0 element type
+/// \ingroup ov_element_cpp_api
+constexpr Type f8e8m0(Type_t::f8e8m0);
 
 template <typename T>
 Type from() {
@@ -255,6 +260,8 @@ template <>
 OPENVINO_API Type from<std::string>();
 template <>
 OPENVINO_API Type from<ov::float4_e2m1>();
+template <>
+OPENVINO_API Type from<ov::float8_e8m0>();
 
 OPENVINO_API Type fundamental_type_for(const Type& type);
 
diff --git a/src/core/include/openvino/core/type/element_type_traits.hpp b/src/core/include/openvino/core/type/element_type_traits.hpp
index 4938c79bfd2bea..26457e9a441f72 100644
--- a/src/core/include/openvino/core/type/element_type_traits.hpp
+++ b/src/core/include/openvino/core/type/element_type_traits.hpp
@@ -132,4 +132,10 @@ template <>
 struct element_type_traits<element::Type_t::f4e2m1> {
     using value_type = ov::float4_e2m1;
 };
+
+template <>
+struct element_type_traits<element::Type_t::f8e8m0> {
+    using value_type = ov::float8_e8m0;
+};
+
 }  // namespace ov
diff --git a/src/core/include/openvino/core/type/float8_e8m0.hpp b/src/core/include/openvino/core/type/float8_e8m0.hpp
new file mode 100644
index 00000000000000..f5be23f2010721
--- /dev/null
+++ b/src/core/include/openvino/core/type/float8_e8m0.hpp
@@ -0,0 +1,218 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <cmath>
+#include <iostream>
+#include <limits>
+#include <memory>
+#include <string>
+#include <vector>
+
+#include "openvino/core/core_visibility.hpp"
+
+namespace ov {
+
+/**
+ * @brief Class to represent the f8e8m0 type.
+ */
+class OPENVINO_API float8_e8m0 {
+public:
+    float8_e8m0() = default;
+    float8_e8m0(const float value);
+
+    template <typename I>
+    explicit float8_e8m0(I value) : float8_e8m0{static_cast<float>(value)} {}
+
+    template <typename T>
+    bool operator==(const T& other) const;
+    template <typename T>
+    bool operator!=(const T& other) const {
+        return !(*this == other);
+    }
+
+    template <typename T>
+    bool operator<(const T& other) const;
+    template <typename T>
+    bool operator<=(const T& other) const;
+    template <typename T>
+    bool operator>(const T& other) const;
+    template <typename T>
+    bool operator>=(const T& other) const;
+    template <typename T>
+    float8_e8m0 operator+(const T& other) const;
+    template <typename T>
+    float8_e8m0 operator+=(const T& other);
+    template <typename T>
+    float8_e8m0 operator-(const T& other) const;
+    template <typename T>
+    float8_e8m0 operator-=(const T& other);
+    template <typename T>
+    float8_e8m0 operator*(const T& other) const;
+    template <typename T>
+    float8_e8m0 operator*=(const T& other);
+    template <typename T>
+    float8_e8m0 operator/(const T& other) const;
+    template <typename T>
+    float8_e8m0 operator/=(const T& other);
+
+    operator float() const;
+
+    static constexpr float8_e8m0 from_bits(uint8_t bits) {
+        return float8_e8m0(bits, true);
+    }
+
+    uint8_t to_bits() const;
+
+    friend std::ostream& operator<<(std::ostream& out, const float8_e8m0& obj) {
+        out << static_cast<float>(obj);
+        return out;
+    }
+
+private:
+    constexpr float8_e8m0(const uint8_t x, bool) : m_value{x} {}
+
+    uint8_t m_value{};
+};
+
+#if defined(_MSC_VER)
+#    pragma warning(push)
+#    pragma warning(disable : 4756)
+#endif
+template <typename T>
+bool float8_e8m0::operator==(const T& other) const {
+#if defined(__GNUC__)
+#    pragma GCC diagnostic push
+#    pragma GCC diagnostic ignored "-Wfloat-equal"
+#endif
+    return (static_cast<float>(*this) == static_cast<float>(other));
+#if defined(__GNUC__)
+#    pragma GCC diagnostic pop
+#endif
+}
+
+template <typename T>
+bool float8_e8m0::operator<(const T& other) const {
+    return (static_cast<float>(*this) < static_cast<float>(other));
+}
+
+template <typename T>
+bool float8_e8m0::operator<=(const T& other) const {
+    return (static_cast<float>(*this) <= static_cast<float>(other));
+}
+
+template <typename T>
+bool float8_e8m0::operator>(const T& other) const {
+    return (static_cast<float>(*this) > static_cast<float>(other));
+}
+
+template <typename T>
+bool float8_e8m0::operator>=(const T& other) const {
+    return (static_cast<float>(*this) >= static_cast<float>(other));
+}
+
+template <typename T>
+float8_e8m0 float8_e8m0::operator+(const T& other) const {
+    return {static_cast<float>(*this) + static_cast<float>(other)};
+}
+
+template <typename T>
+float8_e8m0 float8_e8m0::operator+=(const T& other) {
+    return *this = *this + other;
+}
+
+template <typename T>
+float8_e8m0 float8_e8m0::operator-(const T& other) const {
+    return {static_cast<float>(*this) - static_cast<float>(other)};
+}
+
+template <typename T>
+float8_e8m0 float8_e8m0::operator-=(const T& other) {
+    return *this = *this - other;
+}
+
+template <typename T>
+float8_e8m0 float8_e8m0::operator*(const T& other) const {
+    return {static_cast<float>(*this) * static_cast<float>(other)};
+}
+
+template <typename T>
+float8_e8m0 float8_e8m0::operator*=(const T& other) {
+    return *this = *this * other;
+}
+
+template <typename T>
+float8_e8m0 float8_e8m0::operator/(const T& other) const {
+    return {static_cast<float>(*this) / static_cast<float>(other)};
+}
+
+template <typename T>
+float8_e8m0 float8_e8m0::operator/=(const T& other) {
+    return *this = *this / other;
+}
+#if defined(_MSC_VER)
+#    pragma warning(pop)
+#endif
+}  // namespace ov
+
+namespace std {
+template <>
+class numeric_limits<ov::float8_e8m0> {
+public:
+    static constexpr bool is_specialized = true;
+    static constexpr ov::float8_e8m0 min() noexcept {
+        return ov::float8_e8m0::from_bits(0b00000000u);
+    }
+    static constexpr ov::float8_e8m0 max() noexcept {
+        return ov::float8_e8m0::from_bits(0b11111110u);
+    }
+    static constexpr ov::float8_e8m0 lowest() noexcept {
+        return ov::float8_e8m0::from_bits(0b00000000u);
+    }
+    static constexpr int digits = 1;
+    static constexpr int digits10 = 0;
+
+    static constexpr bool is_signed = false;
+    static constexpr bool is_integer = false;
+    static constexpr bool is_exact = false;
+
+    static constexpr int radix = 2;
+
+    static constexpr ov::float8_e8m0 epsilon() noexcept {
+        return ov::float8_e8m0::from_bits(0b00000001u);
+    }
+    static constexpr ov::float8_e8m0 round_error() noexcept {
+        return ov::float8_e8m0::from_bits(0b01111110u);
+    }
+
+    static constexpr int min_exponent = -126;
+    static constexpr int min_exponent10 = -38;
+    static constexpr int max_exponent = 128;
+    static constexpr int max_exponent10 = 38;
+
+    static constexpr bool has_infinity = false;
+    static constexpr bool has_quiet_NaN = true;
+    static constexpr bool has_signaling_NaN = false;
+
+    static constexpr float_denorm_style has_denorm = denorm_absent;
+    static constexpr bool has_denorm_loss = false;
+
+    static constexpr ov::float8_e8m0 infinity() noexcept {
+        return ov::float8_e8m0::from_bits(0);  // no infinity
+    }
+    static constexpr ov::float8_e8m0 quiet_NaN() noexcept {
+        return ov::float8_e8m0::from_bits(0b11111111);
+    }
+    static constexpr ov::float8_e8m0 signaling_NaN() noexcept {
+        return ov::float8_e8m0::from_bits(0);  // no signaling NaN
+    }
+    static constexpr bool is_iec559 = false;
+    static constexpr bool is_bounded = false;
+    static constexpr bool is_modulo = false;
+    static constexpr bool traps = false;
+    static constexpr bool tinyness_before = false;
+    static constexpr float_round_style round_style = round_to_nearest;
+};
+}  // namespace std
diff --git a/src/core/include/openvino/core/visibility.hpp b/src/core/include/openvino/core/visibility.hpp
index dd1723a98b5948..bcce8a0736bdc1 100644
--- a/src/core/include/openvino/core/visibility.hpp
+++ b/src/core/include/openvino/core/visibility.hpp
@@ -67,3 +67,13 @@
 #    define OPENVINO_ARCH_RISCV64
 #    define OPENVINO_ARCH_64_BIT
 #endif
+
+/**
+ * @brief Define no dangling attribute.
+ * Use it as C++ attribute e.g. OV_NO_DANGLING void my_func();
+ */
+#if defined(__GNUC__) && (__GNUC__ >= 14)
+#    define OV_NO_DANGLING [[gnu::no_dangling]]
+#else
+#    define OV_NO_DANGLING
+#endif
diff --git a/src/core/include/openvino/op/constant.hpp b/src/core/include/openvino/op/constant.hpp
index eea446647fda53..6f7500e69a3a95 100644
--- a/src/core/include/openvino/op/constant.hpp
+++ b/src/core/include/openvino/op/constant.hpp
@@ -159,6 +159,9 @@ class OPENVINO_API Constant : public Op {
         case Type_t::f4e2m1:
             fill_lp_data<Type_t::f4e2m1>(value);
             break;
+        case Type_t::f8e8m0:
+            fill_data<Type_t::f8e8m0>(value);
+            break;
         case Type_t::undefined:
         case Type_t::dynamic:
             OPENVINO_THROW("unsupported type");
@@ -369,6 +372,9 @@ class OPENVINO_API Constant : public Op {
         case Type_t::f4e2m1:
             cast_lp_vector<Type_t::f4e2m1>(rc, num_elements_to_cast);
             break;
+        case Type_t::f8e8m0:
+            cast_vector<Type_t::f8e8m0>(rc, num_elements_to_cast);
+            break;
         default:
             OPENVINO_THROW("unsupported type");
         }
@@ -399,6 +405,13 @@ class OPENVINO_API Constant : public Op {
      */
     void alloc_buffer_on_visit_attributes(bool val);
 
+    /// @brief Get view on constant data as tensor.
+    /// @return OV::Tensor with constant data.
+    const Tensor get_tensor_view() const;
+
+    /// @return Constant's strides in bytes.
+    const Strides& get_strides() const;
+
 private:
     Constant(bool memset_allocation, const element::Type& type, const Shape& shape);
 
@@ -720,6 +733,9 @@ class OPENVINO_API Constant : public Op {
         case Type_t::f4e2m1:
             write_lp_buffer<Type_t::f4e2m1>(source);
             break;
+        case Type_t::f8e8m0:
+            write_buffer<Type_t::f8e8m0>(source);
+            break;
         case Type_t::undefined:
         case Type_t::dynamic:
             OPENVINO_THROW("unsupported type");
@@ -763,6 +779,7 @@ class OPENVINO_API Constant : public Op {
 
     element::Type m_element_type{};
     Shape m_shape{};
+    Strides m_byte_strides{};
     std::shared_ptr<ov::AlignedBuffer> m_data{};
     mutable std::atomic_bool m_all_elements_bitwise_identical{false};
     mutable std::atomic_bool m_all_elements_bitwise_identical_checked{false};
@@ -855,6 +872,7 @@ CONSTANT_FILL_DATA_SPECIALIZATION(u1, long long)
 CONSTANT_FILL_DATA_SPECIALIZATION(u1, unsigned long long)
 CONSTANT_FILL_DATA_SPECIALIZATION(u1, float8_e4m3)
 CONSTANT_FILL_DATA_SPECIALIZATION(u1, float8_e5m2)
+CONSTANT_FILL_DATA_SPECIALIZATION(u1, float8_e8m0)
 CONSTANT_FILL_DATA_SPECIALIZATION(u1, float16)
 CONSTANT_FILL_DATA_SPECIALIZATION(u1, bfloat16)
 CONSTANT_FILL_DATA_SPECIALIZATION(u1, float)
@@ -874,6 +892,7 @@ CONSTANT_FILL_DATA_SPECIALIZATION(u2, long long)
 CONSTANT_FILL_DATA_SPECIALIZATION(u2, unsigned long long)
 CONSTANT_FILL_DATA_SPECIALIZATION(u2, float8_e4m3)
 CONSTANT_FILL_DATA_SPECIALIZATION(u2, float8_e5m2)
+CONSTANT_FILL_DATA_SPECIALIZATION(u2, float8_e8m0)
 CONSTANT_FILL_DATA_SPECIALIZATION(u2, float16)
 CONSTANT_FILL_DATA_SPECIALIZATION(u2, bfloat16)
 CONSTANT_FILL_DATA_SPECIALIZATION(u2, float)
@@ -893,6 +912,7 @@ CONSTANT_FILL_DATA_SPECIALIZATION(u3, long long)
 CONSTANT_FILL_DATA_SPECIALIZATION(u3, unsigned long long)
 CONSTANT_FILL_DATA_SPECIALIZATION(u3, float8_e4m3)
 CONSTANT_FILL_DATA_SPECIALIZATION(u3, float8_e5m2)
+CONSTANT_FILL_DATA_SPECIALIZATION(u3, float8_e8m0)
 CONSTANT_FILL_DATA_SPECIALIZATION(u3, float16)
 CONSTANT_FILL_DATA_SPECIALIZATION(u3, bfloat16)
 CONSTANT_FILL_DATA_SPECIALIZATION(u3, float)
@@ -912,6 +932,7 @@ CONSTANT_FILL_DATA_SPECIALIZATION(u4, long long)
 CONSTANT_FILL_DATA_SPECIALIZATION(u4, unsigned long long)
 CONSTANT_FILL_DATA_SPECIALIZATION(u4, float8_e4m3)
 CONSTANT_FILL_DATA_SPECIALIZATION(u4, float8_e5m2)
+CONSTANT_FILL_DATA_SPECIALIZATION(u4, float8_e8m0)
 CONSTANT_FILL_DATA_SPECIALIZATION(u4, float16)
 CONSTANT_FILL_DATA_SPECIALIZATION(u4, bfloat16)
 CONSTANT_FILL_DATA_SPECIALIZATION(u4, float)
@@ -931,6 +952,7 @@ CONSTANT_FILL_DATA_SPECIALIZATION(u6, long long)
 CONSTANT_FILL_DATA_SPECIALIZATION(u6, unsigned long long)
 CONSTANT_FILL_DATA_SPECIALIZATION(u6, float8_e4m3)
 CONSTANT_FILL_DATA_SPECIALIZATION(u6, float8_e5m2)
+CONSTANT_FILL_DATA_SPECIALIZATION(u6, float8_e8m0)
 CONSTANT_FILL_DATA_SPECIALIZATION(u6, float16)
 CONSTANT_FILL_DATA_SPECIALIZATION(u6, bfloat16)
 CONSTANT_FILL_DATA_SPECIALIZATION(u6, float)
@@ -950,6 +972,7 @@ CONSTANT_FILL_DATA_SPECIALIZATION(i4, long long)
 CONSTANT_FILL_DATA_SPECIALIZATION(i4, unsigned long long)
 CONSTANT_FILL_DATA_SPECIALIZATION(i4, float8_e4m3)
 CONSTANT_FILL_DATA_SPECIALIZATION(i4, float8_e5m2)
+CONSTANT_FILL_DATA_SPECIALIZATION(i4, float8_e8m0)
 CONSTANT_FILL_DATA_SPECIALIZATION(i4, float16)
 CONSTANT_FILL_DATA_SPECIALIZATION(i4, bfloat16)
 CONSTANT_FILL_DATA_SPECIALIZATION(i4, float)
@@ -969,6 +992,7 @@ CONSTANT_FILL_DATA_SPECIALIZATION(nf4, long long)
 CONSTANT_FILL_DATA_SPECIALIZATION(nf4, unsigned long long)
 CONSTANT_FILL_DATA_SPECIALIZATION(nf4, float8_e4m3)
 CONSTANT_FILL_DATA_SPECIALIZATION(nf4, float8_e5m2)
+CONSTANT_FILL_DATA_SPECIALIZATION(nf4, float8_e8m0)
 CONSTANT_FILL_DATA_SPECIALIZATION(nf4, float16)
 CONSTANT_FILL_DATA_SPECIALIZATION(nf4, bfloat16)
 CONSTANT_FILL_DATA_SPECIALIZATION(nf4, float)
@@ -988,6 +1012,7 @@ CONSTANT_FILL_DATA_SPECIALIZATION(f4e2m1, long long)
 CONSTANT_FILL_DATA_SPECIALIZATION(f4e2m1, unsigned long long)
 CONSTANT_FILL_DATA_SPECIALIZATION(f4e2m1, float8_e4m3)
 CONSTANT_FILL_DATA_SPECIALIZATION(f4e2m1, float8_e5m2)
+CONSTANT_FILL_DATA_SPECIALIZATION(f4e2m1, float8_e8m0)
 CONSTANT_FILL_DATA_SPECIALIZATION(f4e2m1, float16)
 CONSTANT_FILL_DATA_SPECIALIZATION(f4e2m1, bfloat16)
 CONSTANT_FILL_DATA_SPECIALIZATION(f4e2m1, float)
@@ -1138,6 +1163,7 @@ CONSTANT_WRITE_BUFFER_SPECIALIZATION(u1, long long)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(u1, unsigned long long)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(u1, float8_e4m3)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(u1, float8_e5m2)
+CONSTANT_WRITE_BUFFER_SPECIALIZATION(u1, float8_e8m0)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(u1, float16)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(u1, bfloat16)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(u1, float)
@@ -1157,6 +1183,7 @@ CONSTANT_WRITE_BUFFER_SPECIALIZATION(u2, long long)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(u2, unsigned long long)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(u2, float8_e4m3)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(u2, float8_e5m2)
+CONSTANT_WRITE_BUFFER_SPECIALIZATION(u2, float8_e8m0)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(u2, float16)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(u2, bfloat16)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(u2, float)
@@ -1176,6 +1203,7 @@ CONSTANT_WRITE_BUFFER_SPECIALIZATION(u3, long long)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(u3, unsigned long long)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(u3, float8_e4m3)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(u3, float8_e5m2)
+CONSTANT_WRITE_BUFFER_SPECIALIZATION(u3, float8_e8m0)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(u3, float16)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(u3, bfloat16)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(u3, float)
@@ -1195,6 +1223,7 @@ CONSTANT_WRITE_BUFFER_SPECIALIZATION(u4, long long)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(u4, unsigned long long)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(u4, float8_e4m3)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(u4, float8_e5m2)
+CONSTANT_WRITE_BUFFER_SPECIALIZATION(u4, float8_e8m0)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(u4, float16)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(u4, bfloat16)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(u4, float)
@@ -1214,6 +1243,7 @@ CONSTANT_WRITE_BUFFER_SPECIALIZATION(u6, long long)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(u6, unsigned long long)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(u6, float8_e4m3)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(u6, float8_e5m2)
+CONSTANT_WRITE_BUFFER_SPECIALIZATION(u6, float8_e8m0)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(u6, float16)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(u6, bfloat16)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(u6, float)
@@ -1233,6 +1263,7 @@ CONSTANT_WRITE_BUFFER_SPECIALIZATION(i4, long long)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(i4, unsigned long long)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(i4, float8_e4m3)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(i4, float8_e5m2)
+CONSTANT_WRITE_BUFFER_SPECIALIZATION(i4, float8_e8m0)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(i4, float16)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(i4, bfloat16)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(i4, float)
@@ -1252,6 +1283,7 @@ CONSTANT_WRITE_BUFFER_SPECIALIZATION(nf4, long long)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(nf4, unsigned long long)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(nf4, float8_e4m3)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(nf4, float8_e5m2)
+CONSTANT_WRITE_BUFFER_SPECIALIZATION(nf4, float8_e8m0)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(nf4, float16)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(nf4, bfloat16)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(nf4, float)
@@ -1271,6 +1303,7 @@ CONSTANT_WRITE_BUFFER_SPECIALIZATION(f4e2m1, long long)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(f4e2m1, unsigned long long)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(f4e2m1, float8_e4m3)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(f4e2m1, float8_e5m2)
+CONSTANT_WRITE_BUFFER_SPECIALIZATION(f4e2m1, float8_e8m0)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(f4e2m1, float16)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(f4e2m1, bfloat16)
 CONSTANT_WRITE_BUFFER_SPECIALIZATION(f4e2m1, float)
diff --git a/src/core/include/openvino/op/embeddingbag_offsets.hpp b/src/core/include/openvino/op/embeddingbag_offsets.hpp
index 82bc074c8594ed..9b7765ac08c55e 100644
--- a/src/core/include/openvino/op/embeddingbag_offsets.hpp
+++ b/src/core/include/openvino/op/embeddingbag_offsets.hpp
@@ -30,12 +30,12 @@ class OPENVINO_API EmbeddingBagOffsets : public util::EmbeddingBagOffsetsBase {
     /// \param offsets tensor of shape [batch] and of type T_IND containing the starting
     /// index positions of each "bag" in indices. Required.
     /// \param default_index scalar of type T_IND containing default index in embedding
-    /// table to fill empty "bags". If not provided empty "bags"
-    /// are filled with zeros. Optional.
-    /// \param per_sample_weigths tensor of the same shape as indices and of type T.
+    /// table to fill empty "bags". If set to value -1 or not provided, empty "bags"
+    /// are filled with zeros. Reverse indexing using negative values is not supported. Optional.
+    /// \param per_sample_weights tensor of the same shape as indices and of type T.
     /// Each value in this tensor are multiplied with each
     /// value pooled from embedding table for each index. Optional.
-
+    /// \param reduction enum to select algorithm used to perform reduction of elements in bag. Optional.
     EmbeddingBagOffsets(const Output<Node>& emb_table,
                         const Output<Node>& indices,
                         const Output<Node>& offsets,
diff --git a/src/core/include/openvino/op/embeddingbag_offsets_sum.hpp b/src/core/include/openvino/op/embeddingbag_offsets_sum.hpp
index e916fa4a638b97..dda2326f0c0c3f 100644
--- a/src/core/include/openvino/op/embeddingbag_offsets_sum.hpp
+++ b/src/core/include/openvino/op/embeddingbag_offsets_sum.hpp
@@ -30,8 +30,8 @@ class OPENVINO_API EmbeddingBagOffsetsSum : public util::EmbeddingBagOffsetsBase
     /// \param offsets tensor of shape [batch] and of type T_IND containing the starting
     /// index positions of each "bag" in indices. Required.
     /// \param default_index scalar of type T_IND containing default index in embedding
-    /// table to fill empty "bags". If not provided empty "bags"
-    /// are filled with zeros. Optional.
+    /// table to fill empty "bags". If set to value -1 or not provided, empty "bags"
+    /// are filled with zeros. Reverse indexing using negative values is not supported. Optional.
     /// \param per_sample_weights tensor of the same shape as indices and of type T.
     /// Each value in this tensor are multiplied with each
     /// value pooled from embedding table for each index. Optional.
diff --git a/src/core/include/openvino/op/embeddingbag_packed.hpp b/src/core/include/openvino/op/embeddingbag_packed.hpp
index 13288e0e462f9a..3982f1154e8600 100644
--- a/src/core/include/openvino/op/embeddingbag_packed.hpp
+++ b/src/core/include/openvino/op/embeddingbag_packed.hpp
@@ -28,10 +28,10 @@ class OPENVINO_API EmbeddingBagPacked : public util::EmbeddingBagPackedBase {
     /// shape [num_emb, emb_dim1, emb_dim2, ...] and  of type T
     /// \param  indices Tensor of shape `[batch, indices_per_bag]` and of type *T_IND*.
     /// Required.
-    /// \param per_sample_weigths tensor of the same shape as indices and of type T.
+    /// \param per_sample_weights tensor of the same shape as indices and of type T.
     /// Each value in this tensor are multiplied with each
     /// value pooled from embedding table for each index. Optional.
-
+    /// \param reduction enum to select algorithm used to perform reduction of elements in bag. Optional.
     EmbeddingBagPacked(const Output<Node>& emb_table,
                        const Output<Node>& indices,
                        const Output<Node>& per_sample_weights,
diff --git a/src/core/include/openvino/op/random_uniform.hpp b/src/core/include/openvino/op/random_uniform.hpp
index d784e5d8cebab9..f0e9cb1c174dec 100644
--- a/src/core/include/openvino/op/random_uniform.hpp
+++ b/src/core/include/openvino/op/random_uniform.hpp
@@ -5,6 +5,7 @@
 #pragma once
 
 #include "openvino/op/op.hpp"
+#include "openvino/op/util/attr_types.hpp"
 
 namespace ov {
 namespace op {
@@ -17,7 +18,6 @@ class OPENVINO_API RandomUniform : public Op {
 
     RandomUniform() = default;
 
-    ///
     /// \brief      Constructs a RandomUniform operation.
     ///
     /// \param      out_shape         Node producing the tensor with output shape.
@@ -26,12 +26,14 @@ class OPENVINO_API RandomUniform : public Op {
     /// \param      out_type          Output type of the tensor.
     /// \param      global_seed       Global seed value.
     /// \param      op_seed           Operational seed value.
+    /// \param      alignment         Alignment of numbers generated by Phillox algorithm based on provided seed.
     RandomUniform(const Output<Node>& out_shape,
                   const Output<Node>& min_val,
                   const Output<Node>& max_val,
                   const ov::element::Type& out_type,
                   uint64_t global_seed = 0,
-                  uint64_t op_seed = 0);
+                  uint64_t op_seed = 0,
+                  ov::op::PhilloxAlignment alignment = ov::op::PhilloxAlignment::TENSORFLOW);
 
     void validate_and_infer_types() override;
 
@@ -40,38 +42,30 @@ class OPENVINO_API RandomUniform : public Op {
     std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
 
     /// \return Turns off constant folding for RandomUniform operation.
-    bool constant_fold(OutputVector& output_values, const OutputVector& inputs_values) override {
-        return false;
-    }
+    bool constant_fold(OutputVector& output_values, const OutputVector& inputs_values) override;
 
     /// \return The output tensor type.
-    const ov::element::Type& get_out_type() const {
-        return m_output_type;
-    }
-    void set_out_type(const ov::element::Type& output_type) {
-        m_output_type = output_type;
-    }
+    const ov::element::Type& get_out_type() const;
+
+    void set_out_type(const ov::element::Type& output_type);
 
     /// \return The global seed value.
-    uint64_t get_global_seed() const {
-        return m_global_seed;
-    }
-    void set_global_seed(uint64_t seed) {
-        m_global_seed = seed;
-    }
+    uint64_t get_global_seed() const;
+
+    void set_global_seed(uint64_t seed);
 
     /// \return The operational seed value.
-    uint64_t get_op_seed() const {
-        return m_op_seed;
-    }
-    void set_op_seed(uint64_t seed2) {
-        m_op_seed = seed2;
-    }
+    uint64_t get_op_seed() const;
+
+    void set_op_seed(uint64_t seed2);
 
     /// \return The state value.
-    std::pair<uint64_t, uint64_t> get_state() const {
-        return m_state;
-    }
+    std::pair<uint64_t, uint64_t> get_state() const;
+
+    /// \return The alignment mode.
+    ov::op::PhilloxAlignment get_alignment() const;
+
+    void set_alignment(ov::op::PhilloxAlignment alignmnent);
 
     bool evaluate(TensorVector& outputs, const TensorVector& inputs) const override;
     bool has_evaluate() const override;
@@ -80,6 +74,7 @@ class OPENVINO_API RandomUniform : public Op {
     ov::element::Type m_output_type;
     uint64_t m_global_seed;
     uint64_t m_op_seed;
+    ov::op::PhilloxAlignment m_alignment = ov::op::PhilloxAlignment::TENSORFLOW;
 
     mutable std::pair<uint64_t, uint64_t> m_state;
 };
diff --git a/src/core/include/openvino/op/roi_align_rotated.hpp b/src/core/include/openvino/op/roi_align_rotated.hpp
index 5e7bf95fa68bcc..80366e7b9878ef 100644
--- a/src/core/include/openvino/op/roi_align_rotated.hpp
+++ b/src/core/include/openvino/op/roi_align_rotated.hpp
@@ -55,7 +55,7 @@ class OPENVINO_API ROIAlignRotated : public util::ROIAlignBase {
     }
 
 private:
-    bool m_clockwise_mode;
+    bool m_clockwise_mode{};
 };
 }  // namespace v15
 }  // namespace op
diff --git a/src/core/include/openvino/op/util/attr_types.hpp b/src/core/include/openvino/op/util/attr_types.hpp
index cfbb43fcfe7c2f..ea0a067661e73b 100644
--- a/src/core/include/openvino/op/util/attr_types.hpp
+++ b/src/core/include/openvino/op/util/attr_types.hpp
@@ -154,6 +154,11 @@ enum class TopKMode {
 OPENVINO_API
 std::ostream& operator<<(std::ostream& s, const TopKMode& type);
 
+enum class PhilloxAlignment { TENSORFLOW, PYTORCH, MOCK };
+
+OPENVINO_API
+std::ostream& operator<<(std::ostream& s, const PhilloxAlignment& alignment);
+
 /// \brief Implicit broadcast specification
 struct OPENVINO_API AutoBroadcastSpec {
     AutoBroadcastSpec() : m_type(AutoBroadcastType::NONE), m_axis(0) {}
@@ -270,6 +275,14 @@ class OPENVINO_API AttributeAdapter<op::TopKMode> : public EnumAttributeAdapterB
     OPENVINO_RTTI("AttributeAdapter<TopKMode>");
 };
 
+template <>
+class OPENVINO_API AttributeAdapter<op::PhilloxAlignment> : public EnumAttributeAdapterBase<op::PhilloxAlignment> {
+public:
+    AttributeAdapter(op::PhilloxAlignment& value) : EnumAttributeAdapterBase<op::PhilloxAlignment>(value) {}
+
+    OPENVINO_RTTI("AttributeAdapter<PhilloxAlignment>");
+};
+
 template <>
 class OPENVINO_API AttributeAdapter<op::AutoBroadcastSpec> : public VisitorAdapter {
 public:
diff --git a/src/core/include/openvino/op/util/embeddingbag_offsets_base.hpp b/src/core/include/openvino/op/util/embeddingbag_offsets_base.hpp
index bf2b7ab1c34ac4..eac03e913c2771 100644
--- a/src/core/include/openvino/op/util/embeddingbag_offsets_base.hpp
+++ b/src/core/include/openvino/op/util/embeddingbag_offsets_base.hpp
@@ -22,21 +22,20 @@ class OPENVINO_API EmbeddingBagOffsetsBase : public Op {
     /// \brief Constructs a EmbeddingBagOffsetsBase operation.
     ///
     /// EmbeddingBagOffsetsBase constructs an output tensor by replacing every index in
-    /// a
-    /// given
-    /// input tensor with a row (from the weights matrix) at that index
+    /// a given input tensor with a row (from the weights matrix) at that index
     ///
     /// \param emb_table tensor containing the embedding lookup table of the module of
     /// shape [num_emb, emb_dim1, emb_dim2, ...] and  of type T
     /// \param indices tensor of shape [num_indices] and of type T_IND. Required
     /// \param offsets tensor of shape [batch] and of type T_IND containing the starting
     /// index positions of each "bag" in indices. Required.
+    /// \param default_index scalar of type T_IND containing default index in embedding
+    /// table to fill empty "bags". If set to value -1 or not provided, empty "bags"
+    /// are filled with zeros. Reverse indexing using negative values is not supported. Optional.
     /// \param per_sample_weights tensor of the same shape as indices and of type T.
     /// Each value in this tensor are multiplied with each
     /// value pooled from embedding table for each index. Optional.
-    /// \param default_index scalar of type T_IND containing default index in embedding
-    /// table to fill empty "bags". If not provided empty "bags"
-    /// are filled with zeros. Optional.
+    /// \param reduction enum to select algorithm used to perform reduction of elements in bag. Optional.
     EmbeddingBagOffsetsBase(const Output<Node>& emb_table,
                             const Output<Node>& indices,
                             const Output<Node>& offsets,
diff --git a/src/core/include/openvino/op/util/embeddingbag_packed_base.hpp b/src/core/include/openvino/op/util/embeddingbag_packed_base.hpp
index b5ab30e2fde539..c33afb4ad2bbbb 100644
--- a/src/core/include/openvino/op/util/embeddingbag_packed_base.hpp
+++ b/src/core/include/openvino/op/util/embeddingbag_packed_base.hpp
@@ -30,16 +30,17 @@ class OPENVINO_API EmbeddingBagPackedBase : public Op {
     /// \param per_sample_weights tensor of the same shape as indices and of type T.
     /// Each value in this tensor are multiplied with each
     /// value pooled from embedding table for each index. Optional.
+    /// \param reduction enum to select algorithm used to perform reduction of elements in bag. Optional.
     EmbeddingBagPackedBase(const Output<Node>& emb_table,
                            const Output<Node>& indices,
-                           const Output<Node>& per_sample_weights);
-
-    EmbeddingBagPackedBase(const Output<Node>& emb_table, const Output<Node>& indices);
+                           const Output<Node>& per_sample_weights,
+                           const Reduction& reduction);
 
     EmbeddingBagPackedBase(const Output<Node>& emb_table,
                            const Output<Node>& indices,
-                           const Output<Node>& per_sample_weights,
-                           const Reduction& reduction);
+                           const Output<Node>& per_sample_weights);
+
+    EmbeddingBagPackedBase(const Output<Node>& emb_table, const Output<Node>& indices);
 
     EmbeddingBagPackedBase(const Output<Node>& emb_table, const Output<Node>& indices, const Reduction& reduction);
 
diff --git a/src/core/include/openvino/pass/pattern/matcher.hpp b/src/core/include/openvino/pass/pattern/matcher.hpp
index 9613180377af84..bbd7e32b0a1802 100644
--- a/src/core/include/openvino/pass/pattern/matcher.hpp
+++ b/src/core/include/openvino/pass/pattern/matcher.hpp
@@ -16,7 +16,6 @@
 #include "openvino/pass/pattern/op/any_of.hpp"
 #include "openvino/pass/pattern/op/any_output.hpp"
 #include "openvino/pass/pattern/op/label.hpp"
-#include "openvino/pass/pattern/op/skip.hpp"
 
 namespace ov {
 namespace pass {
diff --git a/src/core/include/openvino/pass/pattern/op/branch.hpp b/src/core/include/openvino/pass/pattern/op/branch.hpp
deleted file mode 100644
index 7b9a2c6323fd11..00000000000000
--- a/src/core/include/openvino/pass/pattern/op/branch.hpp
+++ /dev/null
@@ -1,52 +0,0 @@
-// Copyright (C) 2018-2024 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#pragma once
-
-#include "openvino/core/node.hpp"
-#include "openvino/pass/pattern/op/pattern.hpp"
-
-namespace ov {
-namespace pass {
-namespace pattern {
-namespace op {
-/// A branch adds a loop to the pattern. The branch match is successful if the
-/// destination node pattern matches the graph value. The destination node is a node in
-/// the pattern graph that will not have been created some time after the Branch node is
-/// created; use set_destination to add it.
-///
-/// The branch destination is not stored as a shared pointer to prevent reference
-/// cycles. Thus the destination node must be referenced in some other way to prevent it
-/// from being deleted.
-class OPENVINO_API Branch : public Pattern {
-public:
-    OPENVINO_RTTI("patternBranch");
-    /// \brief Creates a Branch pattern
-    Branch() : Pattern(OutputVector{}) {
-        set_output_type(0, element::f32, Shape{});
-    }
-
-    void set_destination(const Output<Node>& destination) {
-        m_destination_node = destination.get_node();
-        m_destination_index = destination.get_index();
-    }
-
-    Output<Node> get_destination() const {
-        return m_destination_node == nullptr
-                   ? Output<Node>()
-                   : Output<Node>{m_destination_node->shared_from_this(), m_destination_index};
-    }
-
-    bool match_value(pattern::Matcher* matcher,
-                     const Output<Node>& pattern_value,
-                     const Output<Node>& graph_value) override;
-
-protected:
-    Node* m_destination_node{nullptr};
-    size_t m_destination_index{0};
-};
-}  // namespace op
-}  // namespace pattern
-}  // namespace pass
-}  // namespace ov
diff --git a/src/core/include/openvino/pass/pattern/op/capture.hpp b/src/core/include/openvino/pass/pattern/op/capture.hpp
deleted file mode 100644
index 0d1d7aeecb431d..00000000000000
--- a/src/core/include/openvino/pass/pattern/op/capture.hpp
+++ /dev/null
@@ -1,43 +0,0 @@
-// Copyright (C) 2018-2024 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#pragma once
-
-#include "openvino/core/node.hpp"
-#include "openvino/pass/pattern/op/pattern.hpp"
-
-namespace ov {
-namespace pass {
-namespace pattern {
-namespace op {
-/// Experimental for support of recurrent matches.
-///
-/// Capture adds the pattern value map to a list of pattern value maps and resets
-/// matches for pattern nodes not in the static node list. The match always succeeds.
-class OPENVINO_API Capture : public Pattern {
-public:
-    OPENVINO_RTTI("patternCapture");
-    Capture(const Output<Node>& arg) : Pattern({arg}) {
-        set_output_type(0, arg.get_element_type(), arg.get_partial_shape());
-    }
-
-    /// \brief static nodes are retained after a capture. All other nodes are dropped
-    std::set<Node*> get_static_nodes() {
-        return m_static_nodes;
-    }
-    void set_static_nodes(const std::set<Node*>& static_nodes) {
-        m_static_nodes = static_nodes;
-    }
-
-    bool match_value(pattern::Matcher* matcher,
-                     const Output<Node>& pattern_value,
-                     const Output<Node>& graph_value) override;
-
-protected:
-    std::set<Node*> m_static_nodes;
-};
-}  // namespace op
-}  // namespace pattern
-}  // namespace pass
-}  // namespace ov
diff --git a/src/core/include/openvino/pass/pattern/op/label.hpp b/src/core/include/openvino/pass/pattern/op/label.hpp
index 82fa50a6a5067c..bbfa626abc0a76 100644
--- a/src/core/include/openvino/pass/pattern/op/label.hpp
+++ b/src/core/include/openvino/pass/pattern/op/label.hpp
@@ -54,13 +54,13 @@ class OPENVINO_API Label : public Pattern {
               OutputVector()) {}
 
     Label(const element::Type& type, const PartialShape& s, ValuePredicate pred)
-        : Label(type, s, pred, OutputVector{}) {}
+        : Label(type, s, std::move(pred), OutputVector{}) {}
 
     Label(const element::Type& type, const PartialShape& s, NodePredicate pred)
-        : Label(type, s, as_value_predicate(pred), OutputVector{}) {}
+        : Label(type, s, as_value_predicate(std::move(pred)), OutputVector{}) {}
 
-    Label(const element::Type& type, const PartialShape& s, const NodePredicate pred, const NodeVector& wrapped_values)
-        : Label(type, s, as_value_predicate(pred), as_output_vector(wrapped_values)) {}
+    Label(const element::Type& type, const PartialShape& s, NodePredicate pred, const NodeVector& wrapped_values)
+        : Label(type, s, as_value_predicate(std::move(pred)), as_output_vector(wrapped_values)) {}
 
     /// \brief creates a Label node containing a sub-pattern described by the type and
     ///        shape of \sa node.
diff --git a/src/core/include/openvino/pass/pattern/op/pattern.hpp b/src/core/include/openvino/pass/pattern/op/pattern.hpp
index 9af075f07e140b..ff78b5e93d4dd1 100644
--- a/src/core/include/openvino/pass/pattern/op/pattern.hpp
+++ b/src/core/include/openvino/pass/pattern/op/pattern.hpp
@@ -85,13 +85,7 @@ class OPENVINO_API Pattern : public Node {
 public:
     /// \brief \p a base class for \sa Skip and \sa Label
     ///
-    Pattern(const OutputVector& patterns, ValuePredicate pred) : Node(patterns), m_predicate(pred) {
-        if (!m_predicate) {
-            m_predicate = [](const Output<Node>&) {
-                return true;
-            };
-        }
-    }
+    Pattern(const OutputVector& patterns, ValuePredicate pred);
 
     Pattern(const OutputVector& patterns) : Pattern(patterns, nullptr) {}
 
diff --git a/src/core/include/openvino/pass/pattern/op/skip.hpp b/src/core/include/openvino/pass/pattern/op/skip.hpp
deleted file mode 100644
index 350c7d2b81e146..00000000000000
--- a/src/core/include/openvino/pass/pattern/op/skip.hpp
+++ /dev/null
@@ -1,43 +0,0 @@
-// Copyright (C) 2018-2024 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#pragma once
-
-#include "openvino/core/node.hpp"
-#include "openvino/pass/pattern/op/pattern.hpp"
-
-namespace ov {
-namespace pass {
-namespace pattern {
-namespace op {
-/// The graph value is added to the matched value list. If the predicate is true, the
-/// match succeeds if the arguments match; if the predicate is false, the match succeeds
-/// if the pattern input matches the graph value.
-class OPENVINO_API Skip : public Pattern {
-public:
-    OPENVINO_RTTI("patternSkip");
-    Skip(const Output<Node>& arg, ValuePredicate pred) : Pattern({arg}, pred) {
-        set_output_type(0, arg.get_element_type(), arg.get_partial_shape());
-    }
-
-    Skip(const Output<Node>& arg, NodePredicate pred = nullptr) : Pattern({arg}, as_value_predicate(pred)) {
-        set_output_type(0, arg.get_element_type(), arg.get_partial_shape());
-    }
-
-    Skip(const OutputVector& args, ValuePredicate pred) : Pattern(args, pred) {
-        set_output_type(0, args.at(0).get_element_type(), args.at(0).get_partial_shape());
-    }
-
-    Skip(const OutputVector& args, NodePredicate pred = nullptr) : Pattern(args, as_value_predicate(pred)) {
-        set_output_type(0, args.at(0).get_element_type(), args.at(0).get_partial_shape());
-    }
-
-    bool match_value(pattern::Matcher* matcher,
-                     const Output<Node>& pattern_value,
-                     const Output<Node>& graph_value) override;
-};
-}  // namespace op
-}  // namespace pattern
-}  // namespace pass
-}  // namespace ov
diff --git a/src/core/reference/include/openvino/reference/ctc_loss.hpp b/src/core/reference/include/openvino/reference/ctc_loss.hpp
index 3a253ee1745964..26591e9de4f6bb 100644
--- a/src/core/reference/include/openvino/reference/ctc_loss.hpp
+++ b/src/core/reference/include/openvino/reference/ctc_loss.hpp
@@ -121,12 +121,12 @@ void CTCLoss(const T* logits,
                     for (size_t pos = start; pos < end; pos++) {
                         newLogProb = prevLogProb;
                         for (size_t bl = start; bl < pos; bl++) {
-                            newLogProb += logProbabilities[bl].find(blankIndex)->second;
+                            newLogProb += logProbabilities[bl][blankIndex];
                         }
-                        newLogProb += logProbabilities[pos].find(targetD[targetIdx])->second;
+                        newLogProb += logProbabilities[pos][targetD[targetIdx]];
                         if (end == static_cast<size_t>(actualLogitLen)) {
                             for (size_t ble = pos + 1; ble < static_cast<size_t>(actualLogitLen); ble++) {
-                                newLogProb += logProbabilities[ble].find(blankIndex)->second;
+                                newLogProb += logProbabilities[ble][blankIndex];
                             }
                         }
                         findPaths(nextIdx, pos + 1, end + 1, newLogProb);
@@ -136,21 +136,21 @@ void CTCLoss(const T* logits,
                         newLogProb = prevLogProb;
                         size_t next_start = pos + 1;
                         for (size_t bl = start; bl < pos; bl++) {
-                            newLogProb += logProbabilities[bl].find(blankIndex)->second;
+                            newLogProb += logProbabilities[bl][blankIndex];
                         }
                         if (end == static_cast<size_t>(actualLogitLen)) {
                             for (int64_t ble = pos + 1; ble < actualLogitLen; ble++) {
-                                newLogProb += logProbabilities[ble].find(blankIndex)->second;
+                                newLogProb += logProbabilities[ble][blankIndex];
                             }
                         }
                         if (targetIdx < decodedTargetLen - 1 && targetD[targetIdx] == targetD[targetIdx + 1]) {
-                            newLogProb += logProbabilities[next_start++].find(blankIndex)->second;
+                            newLogProb += logProbabilities[next_start++][blankIndex];
                         }
                         for (int64_t bl = pos; bl >= st64; bl--) {
-                            newLogProb += logProbabilities[bl].find(targetD[targetIdx])->second;
+                            newLogProb += logProbabilities[bl][targetD[targetIdx]];
                             findPaths(nextIdx, next_start, end + 1, newLogProb);
                             if (bl > 0)
-                                newLogProb -= logProbabilities[bl - 1].find(blankIndex)->second;
+                                newLogProb -= logProbabilities[bl - 1][blankIndex];
                         }
                     }
                 }
diff --git a/src/core/reference/include/openvino/reference/detection_output.hpp b/src/core/reference/include/openvino/reference/detection_output.hpp
index f0160880e4ef01..fc5f8890465357 100644
--- a/src/core/reference/include/openvino/reference/detection_output.hpp
+++ b/src/core/reference/include/openvino/reference/detection_output.hpp
@@ -256,7 +256,7 @@ class referenceDetectionOutput {
                 if (attrs.background_label_id > -1 && label == attrs.background_label_id) {
                     continue;
                 }
-                const std::vector<NormalizedBBox>& labelLocPreds = locPreds[i].find(label)->second;
+                const auto& labelLocPreds = locPreds[i].at(label);
                 DecodeBBoxes(currPrBbox, currPrVar, labelLocPreds, decodeBboxesImage[label]);
             }
         }
@@ -277,10 +277,10 @@ class referenceDetectionOutput {
                 if (attrs.background_label_id > -1 && label == attrs.background_label_id) {
                     continue;
                 }
-                const std::vector<NormalizedBBox>& labelArmLocPreds = armLocPreds[i].find(label)->second;
+                const auto& labelArmLocPreds = armLocPreds[i].at(label);
                 std::vector<NormalizedBBox> decodePriorBboxes;
                 DecodeBBoxes(currPrBbox, currPrVar, labelArmLocPreds, decodePriorBboxes);
-                const std::vector<NormalizedBBox>& labelLocPreds = locPreds[i].find(label)->second;
+                const auto& labelLocPreds = locPreds[i].at(label);
                 DecodeBBoxes(decodePriorBboxes, currPrVar, labelLocPreds, decodeBboxesImage[label]);
             }
         }
@@ -491,14 +491,16 @@ class referenceDetectionOutput {
                     if (c == attrs.background_label_id) {
                         continue;
                     }
-                    if (confScores.find(c) == confScores.end())
+                    const auto conf_score = confScores.find(c);
+                    if (conf_score == confScores.end())
                         continue;
-                    const std::vector<dataType>& scores = confScores.find(c)->second;
+                    const std::vector<dataType>& scores = conf_score->second;
 
                     int label = attrs.share_location ? -1 : c;
-                    if (decodeBboxesImage.find(label) == decodeBboxesImage.end())
+                    const auto decode_bboxes = decodeBboxesImage.find(label);
+                    if (decode_bboxes == decodeBboxesImage.end())
                         continue;
-                    const std::vector<NormalizedBBox>& bboxes = decodeBboxesImage.find(label)->second;
+                    const std::vector<NormalizedBBox>& bboxes = decode_bboxes->second;
                     caffeNMS(bboxes, scores, indices[c]);
                     numDet += static_cast<int>(indices[c].size());
                 }
@@ -513,9 +515,10 @@ class referenceDetectionOutput {
                 for (auto it = indices.begin(); it != indices.end(); ++it) {
                     int label = it->first;
                     const std::vector<int>& labelIndices = it->second;
-                    if (confScores.find(label) == confScores.end())
+                    const auto conf_score = confScores.find(label);
+                    if (conf_score == confScores.end())
                         continue;
-                    const std::vector<dataType>& scores = confScores.find(label)->second;
+                    const std::vector<dataType>& scores = conf_score->second;
                     for (size_t j = 0; j < labelIndices.size(); ++j) {
                         int idx = labelIndices[j];
                         scoreIndexPairs.push_back(std::make_pair(scores[idx], std::make_pair(label, idx)));
@@ -547,11 +550,12 @@ class referenceDetectionOutput {
             const LabelBBox& decodeBboxesImage = decodeBboxes[i];
             for (auto it = allIndices[i].begin(); it != allIndices[i].end(); ++it) {
                 int label = it->first;
-                const std::vector<dataType>& scores = confScores.find(label)->second;
+                const std::vector<dataType>& scores = confScores.at(label);
                 int loc_label = attrs.share_location ? -1 : label;
-                if (decodeBboxesImage.find(loc_label) == decodeBboxesImage.end())
+                const auto decode_bboxes = decodeBboxesImage.find(loc_label);
+                if (decode_bboxes == decodeBboxesImage.end())
                     continue;
-                const std::vector<NormalizedBBox>& bboxes = decodeBboxesImage.find(loc_label)->second;
+                const std::vector<NormalizedBBox>& bboxes = decode_bboxes->second;
                 std::vector<int>& indices = it->second;
                 for (size_t j = 0; j < indices.size(); ++j) {
                     int idx = indices[j];
diff --git a/src/core/reference/include/openvino/reference/embedding_bag_offsets.hpp b/src/core/reference/include/openvino/reference/embedding_bag_offsets.hpp
index 0a42464102953c..48af992096db7f 100644
--- a/src/core/reference/include/openvino/reference/embedding_bag_offsets.hpp
+++ b/src/core/reference/include/openvino/reference/embedding_bag_offsets.hpp
@@ -53,7 +53,7 @@ void embeddingBagOffsets(const T* emb_table,
             } else {
                 // Empty or default bag
                 with_weights = false;
-                if (default_indices.size() == 1lu) {
+                if (default_indices.size() == 1lu && *default_indices.data() >= 0) {
                     indices_ref = default_indices.data();
                     indices_num = 1lu;
                 }
diff --git a/src/core/reference/include/openvino/reference/fake_quantize.hpp b/src/core/reference/include/openvino/reference/fake_quantize.hpp
index 95d9c086917559..ac6df15f9a0cd2 100644
--- a/src/core/reference/include/openvino/reference/fake_quantize.hpp
+++ b/src/core/reference/include/openvino/reference/fake_quantize.hpp
@@ -37,6 +37,32 @@ static inline T quantize(const T arg,
                           out_low);
 }
 
+template <>
+inline ov::bfloat16 quantize(const ov::bfloat16 arg,
+                             const ov::bfloat16 in_low,
+                             const ov::bfloat16 in_high,
+                             const ov::bfloat16 out_low,
+                             const ov::bfloat16 out_high,
+                             const ov::bfloat16 levels_minus_one) {
+    if (arg <= std::min(in_low, in_high)) {
+        return out_low;
+    } else if (arg > std::max(in_low, in_high)) {
+        return out_high;
+    }
+
+    // make explicit convertion bf16->float to prevent implicit conversion bf16->float->bf16 on every operation
+    const float arg_f = arg;
+    const float in_low_f = in_low;
+    const float in_high_f = in_high;
+    const float out_low_f = out_low;
+    const float out_high_f = out_high;
+    const float levels_minus_one_f = levels_minus_one;
+
+    return static_cast<ov::bfloat16>(std::nearbyint((arg_f - in_low_f) / (in_high_f - in_low_f) * levels_minus_one_f) /
+                                         levels_minus_one_f * (out_high_f - out_low_f) +
+                                     out_low_f);
+}
+
 static std::vector<size_t> compute_strides(const ov::Shape& out_shape, const ov::Shape& shape);
 
 static std::tuple<size_t, size_t> get_inner_stride(size_t num_output_elements,
diff --git a/src/core/reference/include/openvino/reference/random_uniform.hpp b/src/core/reference/include/openvino/reference/random_uniform.hpp
index 2be89160f7eac2..223152b6beb598 100644
--- a/src/core/reference/include/openvino/reference/random_uniform.hpp
+++ b/src/core/reference/include/openvino/reference/random_uniform.hpp
@@ -4,25 +4,14 @@
 
 #pragma once
 
-#include <ctime>
-
 #include "openvino/core/shape.hpp"
 #include "openvino/core/type/element_type.hpp"
+#include "openvino/op/random_uniform.hpp"
+#include "openvino/op/util/attr_types.hpp"
 
 namespace ov {
 namespace reference {
 
-// Helper struct for converting between types
-struct convert_types {
-    union {
-        uint64_t ui64;
-        double d;
-        float f;
-        float16 f16;
-        bfloat16 bf16;
-    };
-};
-
 std::pair<uint64_t, uint64_t> random_uniform(const uint64_t* out_shape,
                                              const char* min_val,
                                              const char* max_val,
@@ -31,19 +20,8 @@ std::pair<uint64_t, uint64_t> random_uniform(const uint64_t* out_shape,
                                              const element::Type& elem_type,
                                              uint64_t seed,
                                              uint64_t seed2,
-                                             std::pair<uint64_t, uint64_t> prev_state);
-
-// Following const values are taken from the original paper:
-// https://www.thesalmons.org/john/random123/papers/random123sc11.pdf
-const uint32_t crush_resistance_const_lower_value = 0x9E3779B9;
-const uint32_t crush_resistance_const_upper_value = 0xBB67AE85;
-const uint64_t statistic_maximizing_multiplier_n = 0xD2511F53;
-const uint64_t statistic_maximizing_multiplier_counter = 0xCD9E8D57;
-const size_t rounds_number = 10;
-
-// Determines how many sequence elements of RNG sequence are skipped between runs.
-// Can be any positive value, 256 is chosen for parity with Tensorflow.
-const uint64_t skip_const = 256;
+                                             std::pair<uint64_t, uint64_t> prev_state,
+                                             ov::op::PhilloxAlignment alignment = ov::op::PhilloxAlignment::TENSORFLOW);
 
 }  // namespace reference
 }  // namespace ov
diff --git a/src/core/reference/include/openvino/reference/utils/phillox_converter.hpp b/src/core/reference/include/openvino/reference/utils/phillox_converter.hpp
new file mode 100644
index 00000000000000..521a23b240a9f3
--- /dev/null
+++ b/src/core/reference/include/openvino/reference/utils/phillox_converter.hpp
@@ -0,0 +1,120 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <cstdint>
+#include <vector>
+
+#include "openvino/core/type/element_type.hpp"
+#include "openvino/op/util/attr_types.hpp"
+#include "openvino/reference/utils/phillox_generator.hpp"
+
+namespace ov {
+namespace reference {
+namespace phillox {
+
+class PhilloxConverter {
+public:
+    PhilloxConverter() = delete;
+
+    virtual ~PhilloxConverter(){};
+
+    /// \brief Returns the number of generated elements per execution
+    /// based on the requested data type.
+    virtual size_t get_converted_elements_count() const = 0;
+
+    /// \brief Converts the given array (PhilloxOutput) to the target dtype and assigns them at the k-th index of the
+    /// output array.
+    virtual void convert(PhilloxOutput result, size_t k) = 0;
+
+protected:
+    PhilloxConverter(char* out,
+                     const element::Type& elem_type,
+                     const size_t elem_count,
+                     const char* min_val,
+                     const char* max_val)
+        : m_out(out),
+          m_elem_count(elem_count),
+          m_elem_type(elem_type),
+          m_min_val(min_val),
+          m_max_val(max_val) {}
+
+    char* m_out;
+    const size_t m_elem_count;
+    const element::Type m_elem_type;
+    const char* m_min_val;
+    const char* m_max_val;
+};
+
+class MockPhilloxConverter : public PhilloxConverter {
+public:
+    MockPhilloxConverter(char* out,
+                         const element::Type& elem_type,
+                         const size_t elem_count,
+                         const char* min_val,
+                         const char* max_val);
+
+    /// \brief Returns the number of generated elements per execution
+    /// based on the requested data type.
+    size_t get_converted_elements_count() const override;
+
+    /// \brief Converts the given array (PhilloxOutput) to the target dtype and assigns them at the k-th index of the
+    /// output array.
+    void convert(PhilloxOutput result, size_t idx) override;
+};
+
+class TensorflowPhilloxConverter : public PhilloxConverter {
+public:
+    TensorflowPhilloxConverter(char* out,
+                               const element::Type& elem_type,
+                               const size_t elem_count,
+                               const char* min_val,
+                               const char* max_val);
+
+    /// \brief Returns the number of generated elements per execution
+    /// based on the requested data type.
+    size_t get_converted_elements_count() const override;
+
+    /// \brief Converts the given array (PhilloxOutput) to the target dtype and assigns them at the k-th index of the
+    /// output array.
+    void convert(PhilloxOutput result, size_t idx) override;
+};
+
+class PyTorchPhilloxConverter : public PhilloxConverter {
+public:
+    PyTorchPhilloxConverter() = delete;
+
+    PyTorchPhilloxConverter(char* out,
+                            const element::Type& elem_type,
+                            const size_t elem_count,
+                            const char* min_val,
+                            const char* max_val);
+
+    /// \brief Returns the number of generated elements per execution
+    /// based on the requested data type.
+    size_t get_converted_elements_count() const override;
+
+    /// \brief Converts the given array (PhilloxOutput) to the target dtype and assigns them at the k-th index of the
+    /// output array.
+    void convert(PhilloxOutput result, size_t idx) override;
+
+private:
+    // Determines whether the ghenerator uses optimized execution
+    // when generating 64-bit numbers.
+    // This occurs when the minimum and maximum are below the threshold of uint32_t.
+    bool m_optimization_enabled = false;
+};
+
+/// \brief Constructs and returns a shared pointer to the converter matching to the provided gtenerator.
+std::shared_ptr<PhilloxConverter> make_phillox_converter(char* out,
+                                                         const element::Type& elem_type,
+                                                         const size_t elem_count,
+                                                         const char* min_val,
+                                                         const char* max_val,
+                                                         const op::PhilloxAlignment alignment);
+
+}  // namespace phillox
+}  // namespace reference
+}  // namespace ov
diff --git a/src/core/reference/include/openvino/reference/utils/phillox_generator.hpp b/src/core/reference/include/openvino/reference/utils/phillox_generator.hpp
new file mode 100644
index 00000000000000..dd309db7f15eba
--- /dev/null
+++ b/src/core/reference/include/openvino/reference/utils/phillox_generator.hpp
@@ -0,0 +1,188 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <array>
+#include <cstdint>
+#include <utility>
+#include <vector>
+
+#include "openvino/core/type/element_type.hpp"
+#include "openvino/op/util/attr_types.hpp"
+
+namespace ov {
+namespace reference {
+namespace phillox {
+
+typedef std::vector<uint32_t> PhilloxOutput;
+
+// All generators are aligned to output exactly 4 uint32_t values.
+// This value is shared between generators and converters.
+static constexpr size_t ELEMENTS_PER_EXECUTION = 4;
+
+/// \brief Generator of random numbers based on the Phillox algorithm.
+///        Abstract base class for various specializations
+///        used to match outputs based on input seed(s)
+///        for supported frameworks
+class PhilloxGenerator {
+public:
+    PhilloxGenerator() = delete;
+
+    virtual ~PhilloxGenerator(){};
+
+    /// @brief Get a set of 4 random 32-bit unsigned integers based on the seed(s).
+    /// @return A vector with a random set of 4 32-bit unsigned integers.
+    virtual PhilloxOutput random() = 0;
+
+    /// \brief Returns the modified state to feed to the next execution.
+    /// @return A pair of uint64s that represent the output state to be fed to the next generator execution.
+    virtual std::pair<uint64_t, uint64_t> get_next_state() = 0;
+
+    // ========================================================
+
+    /// \brief Returns the global seed of the generator.
+    uint64_t get_global_seed() const;
+
+    /// \brief Returns the operator seed of the generator.
+    uint64_t get_operator_seed() const;
+
+    /// \brief Returns the alignment mode of the generator.
+    ov::op::PhilloxAlignment get_alignment() const;
+
+    /// \brief Returns the input (previous execution) state of the generator.
+    std::pair<uint64_t, uint64_t> get_previous_state() const;
+
+    /// \brief Setter for the global seed
+    /// \param global_seed The new global seed for the Phillox algorithm
+    void set_global_seed(const uint64_t global_seed);
+
+    /// \brief Setter for the operator seed
+    /// \param op_seed The new operator seed for the Phillox algorithm
+    void set_operator_seed(const uint64_t op_seed);
+
+protected:
+    /// \brief Constructor for the PhilloxGenerator class.
+    /// \param alignment The alignment mode of the generator.
+    /// \param global_seed The global seed for the Phillox algorithm.
+    /// \param operator_seed The operator seed for the Phillox algorithm.
+    /// \param previous_state The previous state (if any) of the algorithm.
+    /// \param generated_elements_count The amount of elements generated per execution of random().
+    PhilloxGenerator(const ov::op::PhilloxAlignment alignment,
+                     const uint64_t global_seed,
+                     const uint64_t operator_seed,
+                     const std::pair<uint64_t, uint64_t> previous_state);
+
+private:
+    const ov::op::PhilloxAlignment m_alignment;
+    uint64_t m_global_seed;
+    uint64_t m_operator_seed;
+
+protected:
+    const std::pair<uint64_t, uint64_t> m_previous_state;
+};
+
+/// \brief Mock specialization of the PhilloxGenerator class (in case of unknown alignment).
+class MockPhilloxGenerator : public PhilloxGenerator {
+public:
+    MockPhilloxGenerator();
+
+    /// @brief Get a set of 4 32-bit unsigned integers (zeros).
+    /// @return A structure with a set of 32-bit zeros.
+    PhilloxOutput random() override;
+
+    /// \brief Returns the modified state to feed to the next execution.
+    /// @return A pair of uint64s that represent the output state to be fed to the next generator execution.
+    std::pair<uint64_t, uint64_t> get_next_state() override;
+};
+
+/// \brief OpenVINO specialization of the PhilloxGenerator class.
+class TensorflowPhilloxGenerator : public PhilloxGenerator {
+public:
+    TensorflowPhilloxGenerator() = delete;
+
+    /// \brief Constructor for the TensorflowPhilloxGenerator class.
+    /// \param global_seed The global seed for the Phillox algorithm
+    /// \param operator_seed The operator seed for the Phillox algorithm
+    /// \param previous_state The state returned from the previous execution of the generator
+    TensorflowPhilloxGenerator(const uint64_t global_seed,
+                               const uint64_t operator_seed,
+                               const std::pair<uint64_t, uint64_t> previous_state);
+
+    /// @brief Get a set of 4 random 32-bit unsigned integers based on the seeds.
+    /// @return A structure with a random set of 32-bit unsigned integers.
+    PhilloxOutput random() override;
+
+    /// \brief Returns the modified state to feed to the next execution.
+    /// @return A pair of uint64s that represent the output state to be fed to the next generator execution.
+    std::pair<uint64_t, uint64_t> get_next_state() override;
+
+private:
+    // ====== Mersenne Twister paper variables ======
+    // Following const values are taken from the original paper:
+    // https://www.thesalmons.org/john/random123/papers/random123sc11.pdf
+    //
+    // Values for OpenVINO, Tensorflow
+    static constexpr uint32_t CRUSH_RESISTANCE_LOWER_VALUE = 0x9E3779B9;
+    static constexpr uint32_t CRUSH_RESISTANCE_UPPER_VALUE = 0xBB67AE85;
+    static constexpr uint64_t STATISTIC_MAXIMIZING_MULTIPLIER_N = 0xD2511F53;
+    static constexpr uint64_t STATISTIC_MAXIMIZING_MULTIPLIER_COUNTER = 0xCD9E8D57;
+    static constexpr uint16_t SHUFFLE_ROUNDS = 10;
+
+    void execute_single_round();
+    void increment_key();
+
+    uint64_t m_n64;
+    uint64_t m_key64;
+    uint64_t m_counter64;
+    std::pair<uint32_t, uint32_t> m_n;
+    std::pair<uint32_t, uint32_t> m_key;
+    std::pair<uint32_t, uint32_t> m_counter;
+
+    size_t m_total_generated_elements;
+};
+
+/// \brief PyTorch specialization of the PhilloxGenerator class.
+class PytorchPhilloxGenerator : public PhilloxGenerator {
+public:
+    PytorchPhilloxGenerator() = delete;
+
+    /// \brief Constructor for the PytorchPhilloxGenerator class.
+    /// \param global_seed The operator seed for the Phillox algorithm
+    /// \note This version of the Phillox algorithm does NOT use operator seed, and.
+    /// does not support the use of the previous/next state.
+    PytorchPhilloxGenerator(const uint64_t global_seed);
+
+    /// @brief Get a set of random 32-bit unsigned integers based on the seed(s).
+    /// @return A structure with a random set of 32-bit unsigned integers and their count.
+    PhilloxOutput random() override;
+
+    /// \brief Returns the INPUT state to feed to the next execution, as the algorithm does not use this to compute
+    /// results.
+    std::pair<uint64_t, uint64_t> get_next_state() override;
+
+private:
+    // Values for PyTorch
+    static constexpr int MERSENNE_STATE_N = 624;
+    static constexpr int MERSENNE_STATE_M = 397;
+
+    void next_mersenne_state();
+
+    uint32_t m_left;
+    uint32_t m_next;
+    std::array<uint32_t, MERSENNE_STATE_N> m_mersenne_state;
+};
+
+/// \brief Constructs and returns a shared pointer to the generator chosen by alignment.
+std::shared_ptr<PhilloxGenerator> make_phillox_generator(const uint64_t seed,
+                                                         const uint64_t seed2,
+                                                         const std::pair<uint64_t, uint64_t> prev_state,
+                                                         const size_t elem_count,
+                                                         const op::PhilloxAlignment alignment);
+
+}  // namespace phillox
+
+}  // namespace reference
+
+}  // namespace ov
diff --git a/src/core/reference/include/openvino/reference/utils/type_util.hpp b/src/core/reference/include/openvino/reference/utils/type_util.hpp
index 878a7e9db712d9..e368edd3c83ed1 100644
--- a/src/core/reference/include/openvino/reference/utils/type_util.hpp
+++ b/src/core/reference/include/openvino/reference/utils/type_util.hpp
@@ -21,6 +21,6 @@ constexpr bool is_floating_point() {
     using U = typename std::decay<T>::type;
     return std::is_floating_point<U>::value || std::is_same<float16, U>::value || std::is_same<bfloat16, U>::value ||
            std::is_same<float8_e4m3, U>::value || std::is_same<float8_e5m2, U>::value ||
-           std::is_same<float4_e2m1, U>::value;
+           std::is_same<float4_e2m1, U>::value || std::is_same<float8_e8m0, U>::value;
 }
 }  // namespace ov
diff --git a/src/core/reference/src/op/random_uniform.cpp b/src/core/reference/src/op/random_uniform.cpp
index 37d3a437e2aee1..3d28c798e7306d 100644
--- a/src/core/reference/src/op/random_uniform.cpp
+++ b/src/core/reference/src/op/random_uniform.cpp
@@ -5,179 +5,15 @@
 #include "openvino/reference/random_uniform.hpp"
 
 #include <ctime>
+#include <memory>
 
 #include "openvino/core/except.hpp"
 #include "openvino/core/shape.hpp"
+#include "openvino/reference/utils/phillox_converter.hpp"
+#include "openvino/reference/utils/phillox_generator.hpp"
 
 namespace ov {
 namespace reference {
-namespace {
-
-// Splits uint64 value into two uint32 values with right and left part of original value.
-std::pair<uint32_t, uint32_t> split_high_low(uint64_t value) {
-    uint32_t low = static_cast<uint32_t>(value);
-    uint32_t high = static_cast<uint32_t>(value >> 32);
-    return {low, high};
-}
-
-// Concatenates two uint32 values into single uint64 values.
-uint64_t unite_high_low(uint32_t high, uint32_t low) {
-    return (static_cast<uint64_t>(high) << 32) + low;
-}
-
-// Runs single "round" of Philox algorithm.
-void calculate_round(uint64_t key, uint64_t& counter, uint64_t& n) {
-    // Split key, counter and n into two uint32 values.
-    auto counter_lr = split_high_low(counter);
-    auto key_lr = split_high_low(key);
-    auto n_lr = split_high_low(n);
-
-    // Each round performs following updating for n and counter:
-    // left uint32 part = mullo(R, M)
-    // right uint32 part  = mulhi(R, M) xor k xor L
-    // mulhi(a, b) = floor((a * b) / 2^32)
-    // mullo(a, b) = (a * b) mod 2^32,
-    // where M - statistic_maximizing_multiplier const
-    auto prod0 = split_high_low(statistic_maximizing_multiplier_n * n_lr.first);
-    auto prod1 = split_high_low(statistic_maximizing_multiplier_counter * counter_lr.first);
-    n_lr.first = prod1.second ^ n_lr.second ^ key_lr.first;
-    n_lr.second = prod1.first;
-    counter_lr.first = prod0.second ^ counter_lr.second ^ key_lr.second;
-    counter_lr.second = prod0.first;
-
-    // Unite counter and n into uint64 values.
-    counter = unite_high_low(counter_lr.second, counter_lr.first);
-    n = unite_high_low(n_lr.second, n_lr.first);
-}
-
-// Increases key value.
-void raise_key(uint64_t& key) {
-    auto key_lr = split_high_low(key);
-    key_lr.first += crush_resistance_const_lower_value;
-    key_lr.second += crush_resistance_const_upper_value;
-    key = unite_high_low(key_lr.second, key_lr.first);
-}
-
-// Helper function for converting uint32 values to float32. Sets fractional part of
-// floating value with bits from uint32 value. Resulting value is in interval [0,1).
-float uint32_to_float(uint32_t x) {
-    // float32 is formatted as follows: sign(1 bit) exponent(8 bits) mantissa(23 bits). The value is interpreted
-    // The value is interpreted using following formula:
-    // (-1)^sign * 1, mantissa * 2 ^ (exponent - 127)
-    // Here we set the following values:
-    // sign = 0
-    // exponent = 127, for obtaining a zero exponent.
-    // mantissa = 23 right bits from generated uint32 random value.
-
-    convert_types out_val = {(static_cast<uint32_t>(127) << 23) | (x & 0x7fffffu)};
-    return out_val.f - 1.0f;
-}
-
-// Helper function for converting uint32 values to float16.Sets fractional part of
-// floating value with bits from uint32 value. Resulting value is in interval [0,1).
-float16 uint32_to_float16(uint32_t x) {
-    // float16 is formatted as follows: sign(1 bit) exponent(5 bits) mantissa(10 bits). The value is interpreted
-    // The value is interpreted using following formula:
-    // (-1)^sign * 1, mantissa * 2 ^ (exponent - 15)
-    // Here we set the following values:
-    // sign = 0
-    // exponent = 15, for obtaining a zero exponent.
-    // mantissa = 10 right bits from generated uint32 random value.
-
-    uint16_t x_uint16 = static_cast<uint16_t>(x);
-    convert_types out_val = {(static_cast<uint16_t>(15) << 10) | (x_uint16 & 0x3ffu)};
-    return out_val.f16 - static_cast<float16>(1);
-}
-
-// Helper function for converting uint32 values to double. Sets fractional part of
-// floating double with bits from uint32 values. Resulting value is in interval [0,1).
-double uint32_to_double(uint32_t x1, uint32_t x2) {
-    // float64 is formatted as follows: sign(1 bit) exponent(11 bits) mantissa(52 bits). The value is interpreted
-    // The value is interpreted using following formula:
-    // (-1)^sign * 1, mantissa * 2 ^ (exponent - 1023)
-    // Here we set the following values:
-    // sign = 0
-    // exponent = 1023, for obtaining a zero exponent.
-    // mantissa = 52 right bits from two concatenated uint32 values from random integer generator.
-
-    uint64_t significant = ((static_cast<uint64_t>(x1) & 0xfffffu) << 32) | static_cast<uint64_t>(x2);
-    convert_types out_val = {((static_cast<uint64_t>(1023) << 52) | significant)};
-    return out_val.d - 1.0;
-}
-
-// Helper function for converting uint32 values to bfloat16. Sets fractional part of
-// floating value with bits from uint32 value. Resulting value is in interval [0,1).
-bfloat16 uint32_to_bfloat16(uint32_t x) {
-    // bfloat16 is formatted as follows: sign(1 bit) exponent(8 bits) mantissa(7 bits). The value is interpreted
-    // The value is interpreted using following formula:
-    // (-1)^sign * 1, mantissa * 2 ^ (exponent - 127)
-    // Here we set the following values:
-    // sign = 0
-    // exponent = 127, for obtaining a zero exponent.
-    // mantissa = 7 right bits from generated uint32 random value.
-
-    uint16_t x_uint16 = static_cast<uint16_t>(x);
-    convert_types out_val = {(static_cast<uint16_t>(127) << 7) | (x_uint16 & 0x7fu)};
-    return out_val.bf16 - static_cast<bfloat16>(1);
-}
-
-// Runs Philox algorithm.
-void run_philox(uint64_t key, uint64_t counter, uint64_t n, size_t n_rounds, std::vector<uint32_t>& res) {
-    for (size_t i = 0; i < n_rounds; i++) {
-        calculate_round(key, counter, n);
-        if (i < n_rounds - 1)
-            raise_key(key);
-    }
-    auto res1 = split_high_low(n);
-    auto res2 = split_high_low(counter);
-    res[0] = res1.first;
-    res[1] = res1.second;
-    res[2] = res2.first;
-    res[3] = res2.second;
-}
-
-// Converts uint32 values to destination type and normalizes to required range
-template <typename T>
-void convert_to_output_type(const std::vector<uint32_t>& res,
-                            size_t step,
-                            const element::Type& elem_type,
-                            const char* min_val,
-                            const char* max_val,
-                            char* out,
-                            size_t k,
-                            size_t elem_count,
-                            T (*convert_single_input)(uint32_t) = nullptr,
-                            T (*convert_two_inputs)(uint32_t, uint32_t, T, T) = nullptr,
-                            T (*mod_func)(uint32_t, T, T) = nullptr) {
-    // Get min and max values
-    T mn[1];
-    T mx[1];
-    memcpy(mn, min_val, elem_type.size());
-    memcpy(mx, max_val, elem_type.size());
-
-    std::vector<T> res_out_type(step);
-    if (elem_type.size() > 4) {
-        // Each element of resulting sequence is formed using two uint32 values
-        res_out_type[0] = convert_two_inputs(res[0], res[1], mn[0], mx[0]);
-        res_out_type[1] = convert_two_inputs(res[2], res[3], mn[0], mx[0]);
-    } else {
-        // Each element of resulting sequence is formed using single uint32 value
-        std::transform(res.data(),
-                       res.data() + step,
-                       res_out_type.data(),
-                       [&mn, &mx, &convert_single_input, &mod_func](uint32_t elem) {
-                           if (convert_single_input != nullptr) {
-                               return convert_single_input(elem) * (mx[0] - mn[0]) + mn[0];
-                           } else {
-                               return mod_func(elem, mn[0], mx[0]);
-                           }
-                       });
-    }
-
-    memcpy(out + k * elem_type.size(), res_out_type.data(), std::min(step, elem_count - k) * elem_type.size());
-}
-
-}  // namespace
 
 // Implementation of RandomUniform that uses Philox algorithm as inner random unsigned integer generator.
 std::pair<uint64_t, uint64_t> random_uniform(const uint64_t* out_shape,
@@ -188,7 +24,8 @@ std::pair<uint64_t, uint64_t> random_uniform(const uint64_t* out_shape,
                                              const element::Type& elem_type,
                                              uint64_t seed,
                                              uint64_t seed2,
-                                             std::pair<uint64_t, uint64_t> prev_state) {
+                                             std::pair<uint64_t, uint64_t> prev_state,
+                                             ov::op::PhilloxAlignment alignment) {
     // When both seed values are equal to zero RandomUniform should generate non-deterministic sequence.
     // Implementation in plugins may differ for this case.
     if (seed == 0 && seed2 == 0) {
@@ -196,15 +33,6 @@ std::pair<uint64_t, uint64_t> random_uniform(const uint64_t* out_shape,
         seed = std::rand();
     }
 
-    // Get previous counter state
-    uint64_t n_state = prev_state.first;
-    uint64_t counter_state = prev_state.second;
-
-    // Initialize Philox key and counters
-    uint64_t key = seed;
-    uint64_t counter = counter_state > 0 ? counter_state : seed2;
-    uint64_t n = n_state;
-
     // Calculate total element count for generation
     size_t shape_count = shape_size(out_shape_shape);
     size_t elem_count = 1;
@@ -212,111 +40,21 @@ std::pair<uint64_t, uint64_t> random_uniform(const uint64_t* out_shape,
         elem_count *= out_shape[i];
     }
 
-    // Philox algorithm returns 4 elements of RNG sequence per each invocation
-    const size_t philox_output_size = 4;
-
-    // Each run of Philox algorithm generates 4 uint32 values.
-    // If output_type is int32, f32, bf16, or f16 each value is converted to
-    // corresponding type so we have 4 result values. For f64 and i64 we use
-    // a pair of values for conversion, so we have 2 result values.
-    // Step indicates how many values we generate in one iteration.
-    const size_t step = elem_type.size() > 4 ? 2 : 4;
-
-    for (size_t k = 0; k < elem_count; k += step) {
-        // generate 4 random uint32 values using Philox algorithm
-        std::vector<uint32_t> res(philox_output_size);
-        run_philox(key, counter, n, rounds_number, res);
-
-        // convert values to corresponding output_type
-        switch (elem_type) {
-        case element::Type_t::f32: {
-            convert_to_output_type<float>(res, step, elem_type, min_val, max_val, out, k, elem_count, uint32_to_float);
-            break;
-        }
-        case element::Type_t::f16: {
-            convert_to_output_type<float16>(res,
-                                            step,
-                                            elem_type,
-                                            min_val,
-                                            max_val,
-                                            out,
-                                            k,
-                                            elem_count,
-                                            uint32_to_float16);
-            break;
-        }
-        case element::Type_t::bf16: {
-            convert_to_output_type<bfloat16>(res,
-                                             step,
-                                             elem_type,
-                                             min_val,
-                                             max_val,
-                                             out,
-                                             k,
-                                             elem_count,
-                                             uint32_to_bfloat16);
-            break;
-        }
-        case element::Type_t::f64: {
-            convert_to_output_type<double>(res,
-                                           step,
-                                           elem_type,
-                                           min_val,
-                                           max_val,
-                                           out,
-                                           k,
-                                           elem_count,
-                                           nullptr,
-                                           [](uint32_t a, uint32_t b, double mn, double mx) {
-                                               return uint32_to_double(a, b) * (mx - mn) + mn;
-                                           });
-            break;
-        }
-        case element::Type_t::i32: {
-            convert_to_output_type<int>(res,
-                                        step,
-                                        elem_type,
-                                        min_val,
-                                        max_val,
-                                        out,
-                                        k,
-                                        elem_count,
-                                        nullptr,
-                                        nullptr,
-                                        [](uint32_t x, int mn, int mx) {
-                                            return static_cast<int>(x % (mx - mn) + mn);
-                                        });
-            break;
-        }
-        case element::Type_t::i64: {
-            convert_to_output_type<int64_t>(res,
-                                            step,
-                                            elem_type,
-                                            min_val,
-                                            max_val,
-                                            out,
-                                            k,
-                                            elem_count,
-                                            nullptr,
-                                            [](uint32_t a, uint32_t b, int64_t mn, int64_t mx) {
-                                                return static_cast<int64_t>(unite_high_low(b, a) % (mx - mn) + mn);
-                                            });
-            break;
-        }
-        default:
-            OPENVINO_THROW("Unsupported type of RandomUniform: ", elem_type.to_string());
-        }
-        if (++n == 0)
-            ++counter;
+    // Sets up the generator of random numbers and matching converter
+    std::shared_ptr<phillox::PhilloxGenerator> generator =
+        phillox::make_phillox_generator(seed, seed2, prev_state, elem_count, alignment);
+    std::shared_ptr<phillox::PhilloxConverter> converter =
+        phillox::make_phillox_converter(out, elem_type, elem_count, min_val, max_val, alignment);
+
+    // Generate randon numbers and convert them until the output array is full
+    const size_t step = converter->get_converted_elements_count();
+    for (size_t i = 0; i < elem_count; i += step) {
+        phillox::PhilloxOutput result = generator->random();
+        converter->convert(result, i);
     }
 
-    // Calculate counter values for next RandomUniform run
-    uint64_t skip_count = elem_count * skip_const;
-    n_state += skip_count;
-    if (n_state < skip_count)
-        counter_state++;
-
-    return {n_state, counter_state};
+    // Return the next state to feed into the generator
+    return generator->get_next_state();
 }
 
 }  // namespace reference
diff --git a/src/core/reference/src/utils/phillox_converter.cpp b/src/core/reference/src/utils/phillox_converter.cpp
new file mode 100644
index 00000000000000..2e84073140bce8
--- /dev/null
+++ b/src/core/reference/src/utils/phillox_converter.cpp
@@ -0,0 +1,489 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "openvino/reference/utils/phillox_converter.hpp"
+
+namespace ov {
+
+namespace reference {
+
+namespace phillox {
+
+// Utils functions
+namespace {
+
+/// ================ PYTORCH ======================
+
+// Helper struct for converting between types in PyTorch
+template <typename T>
+struct PytorchDistributionAccumulatorType {};
+template <>
+struct PytorchDistributionAccumulatorType<ov::bfloat16> {
+    using type = float;
+};
+template <>
+struct PytorchDistributionAccumulatorType<ov::float16> {
+    using type = float;
+};
+template <>
+struct PytorchDistributionAccumulatorType<float> {
+    using type = float;
+};
+template <>
+struct PytorchDistributionAccumulatorType<double> {
+    using type = double;
+};
+
+template <typename T>
+using PytorchAccumulatorType = typename PytorchDistributionAccumulatorType<T>::type;
+
+// Helper function for converting uint values to any T type float, using PyTorch
+// conversion method. Sets fractional part of floating value with bits from
+// uint32 value. Resulting value is in interval [0,1).
+template <typename T, typename V>
+PytorchAccumulatorType<T> pytorch_uint_to_float(V x) {
+    const auto mask = static_cast<V>((uint64_t(1) << std::numeric_limits<T>::digits) - 1);
+    const auto divisor = static_cast<PytorchAccumulatorType<T>>(1) / (uint64_t(1) << std::numeric_limits<T>::digits);
+    PytorchAccumulatorType<T> ret = (x & mask) * divisor;
+    return ret;
+}
+
+float pytorch_uint_to_bfloat(uint32_t x) {
+    const auto mask = static_cast<uint32_t>((1UL << 8) - 1);
+    const auto divisor = static_cast<PytorchAccumulatorType<ov::bfloat16>>(1) / (1UL << 8);
+    PytorchAccumulatorType<ov::bfloat16> ret = (x & mask) * divisor;
+    return ret;
+}
+
+/// ================ TENSORFLOW ======================
+
+// Helper struct for converting between types in Tensorflow
+struct convert_types {
+    union {
+        uint64_t ui64;
+        double d;
+        float f;
+        float16 f16;
+        bfloat16 bf16;
+    };
+};
+
+// Concatenates two uint32 values into single uint64 values.
+uint64_t unite_high_low(uint32_t high, uint32_t low) {
+    return (static_cast<uint64_t>(high) << 32) + low;
+}
+
+// Helper function for converting uint32 values to float32. Sets fractional part of
+// floating value with bits from uint32 value. Resulting value is in interval [0,1).
+float uint32_to_float(uint32_t x) {
+    // float32 is formatted as follows: sign(1 bit) exponent(8 bits) mantissa(23 bits). The value is interpreted
+    // The value is interpreted using following formula:
+    // (-1)^sign * 1, mantissa * 2 ^ (exponent - 127)
+    // Here we set the following values:
+    // sign = 0
+    // exponent = 127, for obtaining a zero exponent.
+    // mantissa = 23 right bits from generated uint32 random value.
+
+    convert_types out_val = {(static_cast<uint32_t>(127) << 23) | (x & 0x7fffffu)};
+    return out_val.f - 1.0f;
+}
+
+// Helper function for converting uint32 values to float16.Sets fractional part of
+// floating value with bits from uint32 value. Resulting value is in interval [0,1).
+float16 uint32_to_float16(uint32_t x) {
+    // float16 is formatted as follows: sign(1 bit) exponent(5 bits) mantissa(10 bits). The value is interpreted
+    // The value is interpreted using following formula:
+    // (-1)^sign * 1, mantissa * 2 ^ (exponent - 15)
+    // Here we set the following values:
+    // sign = 0
+    // exponent = 15, for obtaining a zero exponent.
+    // mantissa = 10 right bits from generated uint32 random value.
+
+    uint16_t x_uint16 = static_cast<uint16_t>(x);
+    convert_types out_val = {(static_cast<uint16_t>(15) << 10) | (x_uint16 & 0x3ffu)};
+    return out_val.f16 - static_cast<float16>(1);
+}
+
+// Helper function for converting uint32 values to double. Sets fractional part of
+// floating double with bits from uint32 values. Resulting value is in interval [0,1).
+double uint32_to_double(uint32_t x1, uint32_t x2) {
+    // float64 is formatted as follows: sign(1 bit) exponent(11 bits) mantissa(52 bits). The value is interpreted
+    // The value is interpreted using following formula:
+    // (-1)^sign * 1, mantissa * 2 ^ (exponent - 1023)
+    // Here we set the following values:
+    // sign = 0
+    // exponent = 1023, for obtaining a zero exponent.
+    // mantissa = 52 right bits from two concatenated uint32 values from random integer generator.
+
+    uint64_t significant = ((static_cast<uint64_t>(x1) & 0xfffffu) << 32) | static_cast<uint64_t>(x2);
+    convert_types out_val = {((static_cast<uint64_t>(1023) << 52) | significant)};
+    return out_val.d - 1.0;
+}
+
+// Helper function for converting uint32 values to bfloat16. Sets fractional part of
+// floating value with bits from uint32 value. Resulting value is in interval [0,1).
+bfloat16 uint32_to_bfloat16(uint32_t x) {
+    // bfloat16 is formatted as follows: sign(1 bit) exponent(8 bits) mantissa(7 bits). The value is interpreted
+    // The value is interpreted using following formula:
+    // (-1)^sign * 1, mantissa * 2 ^ (exponent - 127)
+    // Here we set the following values:
+    // sign = 0
+    // exponent = 127, for obtaining a zero exponent.
+    // mantissa = 7 right bits from generated uint32 random value.
+
+    auto x_uint16 = static_cast<uint16_t>(x);
+    convert_types out_val = {(static_cast<uint16_t>(127) << 7) | (x_uint16 & 0x7fu)};
+    return out_val.bf16 - static_cast<bfloat16>(1);
+}
+
+/// \brief General util function used to convert uint32 values to destination type and normalize to required range.
+/// \param generated_numbers The set of uint32s generated from a generator
+/// \param converted_output_values_per_exec_count The amount of values a converter outputs when given the set of
+/// generated numbers \param min_val The pointer to the minimum value \param max_val The pointer to the maximum value
+/// \param out The pointer to the output location to place the converted values
+/// \param idx The current starting index to place the values at in the 'out' array
+/// \param elem_count The total number of elements (size of 'out' array)
+/// \param convert_single_input A lambda to convert one uint32 to a given dtype (auto normalization)
+/// \param convert_two_inputs A lambda to convert two uint32s to a given dtype (normalization required)
+/// \param mod_func A lambda to convert one uint32 to a given dtype (normalization required)
+template <typename T>
+void convert_to_output_type(const std::vector<uint32_t>& generated_numbers,
+                            size_t converted_output_values_per_exec_count,
+                            const char* min_val,
+                            const char* max_val,
+                            char* out,
+                            size_t idx,
+                            size_t elem_count,
+                            T (*convert_single_input)(uint32_t) = nullptr,
+                            T (*convert_two_inputs)(uint32_t, uint32_t, T, T) = nullptr,
+                            T (*mod_func)(uint32_t, T, T) = nullptr) {
+    // Get min and max values
+    const auto mn = *reinterpret_cast<const T*>(min_val);
+    const auto mx = *reinterpret_cast<const T*>(max_val);
+
+    std::vector<T> output_elements_buffer(converted_output_values_per_exec_count);
+
+    if (converted_output_values_per_exec_count == ELEMENTS_PER_EXECUTION) {
+        // Each element of resulting sequence is formed using single uint32 value,
+        // therefore the number of output elements is equal to number of generated uint32s.
+        std::transform(generated_numbers.data(),
+                       generated_numbers.data() + ELEMENTS_PER_EXECUTION,
+                       output_elements_buffer.data(),
+                       [&mn, &mx, &convert_single_input, &mod_func](uint32_t elem) {
+                           if (convert_single_input != nullptr) {
+                               return convert_single_input(elem) * (mx - mn) + mn;
+                           } else {
+                               return mod_func(elem, mn, mx);
+                           }
+                       });
+    } else if (converted_output_values_per_exec_count == ELEMENTS_PER_EXECUTION / 2) {
+        // Each element of resulting sequence is formed using two uint32 values,
+        // therefore the number of output elements is half of ELEMENTS_PER_EXECUTION.
+        for (size_t i = 0; i < converted_output_values_per_exec_count; ++i) {
+            output_elements_buffer[i] =
+                convert_two_inputs(generated_numbers[2 * i], generated_numbers[2 * i + 1], mn, mx);
+        }
+    } else {
+        // This behavior might change one day, if 128 bit numbers are possible
+        OPENVINO_THROW("The converter has requested an incorrect number of output values: ",
+                       converted_output_values_per_exec_count,
+                       " (possible ",
+                       ELEMENTS_PER_EXECUTION / 2,
+                       " or ",
+                       ELEMENTS_PER_EXECUTION,
+                       ")");
+    }
+
+    memcpy(out + idx * sizeof(T),
+           output_elements_buffer.data(),
+           std::min(converted_output_values_per_exec_count, elem_count - idx) * sizeof(T));
+}
+}  // namespace
+
+// ======= MockPhilloxConverter functions =======
+MockPhilloxConverter::MockPhilloxConverter(char* out,
+                                           const element::Type& elem_type,
+                                           const size_t elem_count,
+                                           const char* min_val,
+                                           const char* max_val)
+    : PhilloxConverter(out, elem_type, elem_count, min_val, max_val) {}
+
+void MockPhilloxConverter::convert(PhilloxOutput result, size_t idx) {
+    // Mock converter does nothing
+    return;
+}
+
+size_t MockPhilloxConverter::get_converted_elements_count() const {
+    return ELEMENTS_PER_EXECUTION;
+}
+
+// ======= TensorflowPhilloxConverter functions =======
+
+TensorflowPhilloxConverter::TensorflowPhilloxConverter(char* out,
+                                                       const element::Type& elem_type,
+                                                       const size_t elem_count,
+                                                       const char* min_val,
+                                                       const char* max_val)
+    : PhilloxConverter(out, elem_type, elem_count, min_val, max_val) {}
+
+size_t TensorflowPhilloxConverter::get_converted_elements_count() const {
+    // Each run of Philox algorithm generates 4 uint32 values.
+    // If output_type is int32, f32, bf16, or f16 each value is converted to
+    // corresponding type so we have 4 result values.
+    // For f64 and i64 we use a pair of values for conversion (2 values),
+    // so we have 2 result values.
+    return m_elem_type.size() > 4 ? ELEMENTS_PER_EXECUTION / 2 : ELEMENTS_PER_EXECUTION;
+}
+
+void TensorflowPhilloxConverter::convert(PhilloxOutput result, size_t idx) {
+    // convert values to corresponding output_type
+    switch (m_elem_type) {
+    case element::Type_t::f32: {
+        convert_to_output_type<float>(result,
+                                      get_converted_elements_count(),
+                                      m_min_val,
+                                      m_max_val,
+                                      m_out,
+                                      idx,
+                                      m_elem_count,
+                                      uint32_to_float);
+        break;
+    }
+    case element::Type_t::f16: {
+        convert_to_output_type<float16>(result,
+                                        get_converted_elements_count(),
+                                        m_min_val,
+                                        m_max_val,
+                                        m_out,
+                                        idx,
+                                        m_elem_count,
+                                        uint32_to_float16);
+        break;
+    }
+    case element::Type_t::bf16: {
+        convert_to_output_type<bfloat16>(result,
+                                         get_converted_elements_count(),
+                                         m_min_val,
+                                         m_max_val,
+                                         m_out,
+                                         idx,
+                                         m_elem_count,
+                                         uint32_to_bfloat16);
+        break;
+    }
+    case element::Type_t::f64: {
+        convert_to_output_type<double>(result,
+                                       get_converted_elements_count(),
+                                       m_min_val,
+                                       m_max_val,
+                                       m_out,
+                                       idx,
+                                       m_elem_count,
+                                       nullptr,
+                                       [](uint32_t a, uint32_t b, double mn, double mx) {
+                                           return uint32_to_double(a, b) * (mx - mn) + mn;
+                                       });
+        break;
+    }
+    case element::Type_t::i32: {
+        convert_to_output_type<int>(result,
+                                    get_converted_elements_count(),
+                                    m_min_val,
+                                    m_max_val,
+                                    m_out,
+                                    idx,
+                                    m_elem_count,
+                                    nullptr,
+                                    nullptr,
+                                    [](uint32_t x, int mn, int mx) {
+                                        return static_cast<int>(x % (mx - mn) + mn);
+                                    });
+        break;
+    }
+    case element::Type_t::i64: {
+        convert_to_output_type<int64_t>(result,
+                                        get_converted_elements_count(),
+                                        m_min_val,
+                                        m_max_val,
+                                        m_out,
+                                        idx,
+                                        m_elem_count,
+                                        nullptr,
+                                        [](uint32_t a, uint32_t b, int64_t mn, int64_t mx) {
+                                            return static_cast<int64_t>(unite_high_low(b, a) % (mx - mn) + mn);
+                                        });
+        break;
+    }
+    default:
+        OPENVINO_THROW("Unsupported type of RandomUniform: ", m_elem_type.to_string());
+    }
+}
+
+// ======= PyTorchPhilloxConverter functions =======
+
+PyTorchPhilloxConverter::PyTorchPhilloxConverter(char* out,
+                                                 const element::Type& elem_type,
+                                                 const size_t elem_count,
+                                                 const char* min_val,
+                                                 const char* max_val)
+    : PhilloxConverter(out, elem_type, elem_count, min_val, max_val) {
+    // Check for optimization conditions for int64_t.
+    // If both min and max fall below the maximum value of uint32_t,
+    // PyTorch generates 64-bit numbers by casting
+    // a single 32 bit random number to 64 bit,
+    // instead of using 2 32 bit numbers.
+    if (elem_type == element::i64) {
+        int64_t mn, mx;
+        memcpy(&mn, min_val, elem_type.size());
+        memcpy(&mx, max_val, elem_type.size());
+        m_optimization_enabled =
+            mn <= std::numeric_limits<uint32_t>::max() && mx <= std::numeric_limits<uint32_t>::max();
+    } else {
+        m_optimization_enabled = false;
+    }
+}
+
+size_t PyTorchPhilloxConverter::get_converted_elements_count() const {
+    // PyTorch uses one uint32_t value per generated output for 32 bit random numbers
+    // and either one or two values (if optimization is off) to generate one 64 bit random number.
+    // Therefore, the only case where 2 output values are generated is when optimization is OFF and the dtype is 64 bit
+    // (size > 4)
+    return m_elem_type.size() > 4 && !m_optimization_enabled ? ELEMENTS_PER_EXECUTION / 2 : ELEMENTS_PER_EXECUTION;
+}
+
+void PyTorchPhilloxConverter::convert(PhilloxOutput result, size_t idx) {
+    // convert values to corresponding output_type
+    switch (m_elem_type) {
+    case element::Type_t::f32: {
+        convert_to_output_type<float>(result,
+                                      get_converted_elements_count(),
+                                      m_min_val,
+                                      m_max_val,
+                                      m_out,
+                                      idx,
+                                      m_elem_count,
+                                      pytorch_uint_to_float<float, uint32_t>);
+        break;
+    }
+    case element::Type_t::f16: {
+        convert_to_output_type<float16>(result,
+                                        get_converted_elements_count(),
+                                        m_min_val,
+                                        m_max_val,
+                                        m_out,
+                                        idx,
+                                        m_elem_count,
+                                        nullptr,
+                                        nullptr,
+                                        [](uint32_t x, float16 mn, float16 mx) {
+                                            auto x_conv = pytorch_uint_to_float<float16, uint32_t>(x);
+                                            return static_cast<float16>(x_conv * (mx - mn) + mn);
+                                        });
+        break;
+    }
+    case element::Type_t::bf16: {
+        convert_to_output_type<bfloat16>(result,
+                                         get_converted_elements_count(),
+                                         m_min_val,
+                                         m_max_val,
+                                         m_out,
+                                         idx,
+                                         m_elem_count,
+                                         nullptr,
+                                         nullptr,
+                                         [](uint32_t x, bfloat16 mn, bfloat16 mx) {
+                                             auto x_conv = pytorch_uint_to_bfloat(x);
+                                             return bfloat16(x_conv * (mx - mn) + mn);
+                                         });
+        break;
+    }
+    case element::Type_t::f64: {
+        convert_to_output_type<double>(result,
+                                       get_converted_elements_count(),
+                                       m_min_val,
+                                       m_max_val,
+                                       m_out,
+                                       idx,
+                                       m_elem_count,
+                                       nullptr,
+                                       [](uint32_t a, uint32_t b, double mn, double mx) {
+                                           uint64_t val = unite_high_low(a, b);
+                                           return pytorch_uint_to_float<double, uint64_t>(val) * (mx - mn) + mn;
+                                       });
+        break;
+    }
+    case element::Type_t::i32: {
+        convert_to_output_type<int>(result,
+                                    get_converted_elements_count(),
+                                    m_min_val,
+                                    m_max_val,
+                                    m_out,
+                                    idx,
+                                    m_elem_count,
+                                    nullptr,
+                                    nullptr,
+                                    [](uint32_t x, int mn, int mx) {
+                                        return static_cast<int>((x) % (mx - mn) + mn);
+                                    });
+        break;
+    }
+    case element::Type_t::i64: {
+        if (get_converted_elements_count() == 4) {
+            convert_to_output_type<int64_t>(result,
+                                            get_converted_elements_count(),
+                                            m_min_val,
+                                            m_max_val,
+                                            m_out,
+                                            idx,
+                                            m_elem_count,
+                                            nullptr,
+                                            nullptr,
+                                            [](uint32_t x, int64_t mn, int64_t mx) {
+                                                return (static_cast<int64_t>(x) % (mx - mn)) + mn;
+                                            });
+        } else {
+            convert_to_output_type<int64_t>(result,
+                                            get_converted_elements_count(),
+                                            m_min_val,
+                                            m_max_val,
+                                            m_out,
+                                            idx,
+                                            m_elem_count,
+                                            nullptr,
+                                            [](uint32_t a, uint32_t b, int64_t mn, int64_t mx) {
+                                                auto val = unite_high_low(a, b);
+                                                return static_cast<int64_t>(val % (mx - mn)) + mn;
+                                            });
+        }
+        break;
+    }
+    default:
+        OPENVINO_THROW("Unsupported type of RandomUniform: ", m_elem_type.to_string());
+    }
+}
+
+// ====== General selector function to construct a desired converter for a generator ======
+
+std::shared_ptr<PhilloxConverter> make_phillox_converter(char* out,
+                                                         const element::Type& elem_type,
+                                                         const size_t elem_count,
+                                                         const char* min_val,
+                                                         const char* max_val,
+                                                         const op::PhilloxAlignment alignment) {
+    switch (alignment) {
+    case op::PhilloxAlignment::TENSORFLOW:
+        return std::make_shared<TensorflowPhilloxConverter>(out, elem_type, elem_count, min_val, max_val);
+    case op::PhilloxAlignment::PYTORCH:
+        return std::make_shared<PyTorchPhilloxConverter>(out, elem_type, elem_count, min_val, max_val);
+    default:
+        // Mock conversion (no conversion)
+        return std::make_shared<MockPhilloxConverter>(out, elem_type, elem_count, min_val, max_val);
+    }
+}
+
+}  // namespace phillox
+
+}  // namespace reference
+
+}  // namespace ov
diff --git a/src/core/reference/src/utils/phillox_generator.cpp b/src/core/reference/src/utils/phillox_generator.cpp
new file mode 100644
index 00000000000000..065c51fce89ae2
--- /dev/null
+++ b/src/core/reference/src/utils/phillox_generator.cpp
@@ -0,0 +1,221 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "openvino/reference/utils/phillox_generator.hpp"
+
+namespace ov {
+namespace reference {
+namespace phillox {
+
+// ====== Utils functions ======
+namespace {
+
+// Splits uint64 value into two uint32 values with right and left part of original value.
+std::pair<uint32_t, uint32_t> split_high_low(uint64_t value) {
+    uint32_t low = static_cast<uint32_t>(value);
+    uint32_t high = static_cast<uint32_t>(value >> 32);
+    return {low, high};
+}
+
+uint32_t mix_bits(uint32_t u, uint32_t v) {
+    return (u & 0x80000000) | (v & 0x7fffffff);
+}
+
+uint32_t twist(uint32_t u, uint32_t v) {
+    return (mix_bits(u, v) >> 1) ^ (v & 1 ? 0x9908b0df : 0);
+}
+
+}  // namespace
+
+// ====== PhilloxGenerator base class functions ======
+
+PhilloxGenerator::PhilloxGenerator(const op::PhilloxAlignment alignment,
+                                   const uint64_t global_seed,
+                                   const uint64_t operator_seed,
+                                   const std::pair<uint64_t, uint64_t> previous_state)
+    : m_alignment(alignment),
+      m_global_seed(global_seed),
+      m_operator_seed(operator_seed),
+      m_previous_state(previous_state) {}
+
+uint64_t PhilloxGenerator::get_global_seed() const {
+    return m_global_seed;
+}
+
+uint64_t PhilloxGenerator::get_operator_seed() const {
+    return m_operator_seed;
+}
+
+std::pair<uint64_t, uint64_t> PhilloxGenerator::get_previous_state() const {
+    return m_previous_state;
+}
+
+op::PhilloxAlignment PhilloxGenerator::get_alignment() const {
+    return m_alignment;
+}
+
+void PhilloxGenerator::set_global_seed(const uint64_t global_seed) {
+    m_global_seed = global_seed;
+}
+
+void PhilloxGenerator::set_operator_seed(const uint64_t op_seed) {
+    m_operator_seed = op_seed;
+}
+
+// ====== MockPhilloxGenerator functions ======
+MockPhilloxGenerator::MockPhilloxGenerator() : PhilloxGenerator(op::PhilloxAlignment::MOCK, 0, 0, {0, 0}){};
+
+std::pair<uint64_t, uint64_t> MockPhilloxGenerator::get_next_state() {
+    return get_previous_state();
+}
+
+PhilloxOutput MockPhilloxGenerator::random() {
+    PhilloxOutput result(ELEMENTS_PER_EXECUTION, 0);
+    return result;
+}
+
+// ====== OpenvinoPhilloxGenerator functions ======
+TensorflowPhilloxGenerator::TensorflowPhilloxGenerator(const uint64_t global_seed,
+                                                       const uint64_t operator_seed,
+                                                       const std::pair<uint64_t, uint64_t> previous_state)
+    : PhilloxGenerator(op::PhilloxAlignment::TENSORFLOW,
+                       global_seed,
+                       previous_state.second > 0 ? previous_state.second : operator_seed,
+                       previous_state),
+      m_n64(previous_state.first),
+      m_key64(global_seed),
+      m_counter64(previous_state.second > 0 ? previous_state.second : operator_seed),
+      m_n(split_high_low(m_n64)),
+      m_key(split_high_low(m_key64)),
+      m_counter(split_high_low(m_counter64)),
+      m_total_generated_elements(0) {}
+
+void TensorflowPhilloxGenerator::increment_key() {
+    m_key.first += CRUSH_RESISTANCE_LOWER_VALUE;
+    m_key.second += CRUSH_RESISTANCE_UPPER_VALUE;
+}
+
+void TensorflowPhilloxGenerator::execute_single_round() {
+    // Each round performs following updating for n and counter:
+    // left uint32 part = mullo(R, M)
+    // right uint32 part  = mulhi(R, M) xor k xor L
+    // mulhi(a, b) = floor((a * b) / 2^32)
+    // mullo(a, b) = (a * b) mod 2^32,
+    // where M - statistic_maximizing_multiplier const
+    auto prod0 = split_high_low(STATISTIC_MAXIMIZING_MULTIPLIER_N * m_n.first);
+    auto prod1 = split_high_low(STATISTIC_MAXIMIZING_MULTIPLIER_COUNTER * m_counter.first);
+    m_n.first = prod1.second ^ m_n.second ^ m_key.first;
+    m_n.second = prod1.first;
+    m_counter.first = prod0.second ^ m_counter.second ^ m_key.second;
+    m_counter.second = prod0.first;
+}
+
+std::pair<uint64_t, uint64_t> TensorflowPhilloxGenerator::get_next_state() {
+    // 256 is chosen to match Tensorflow's 'Reserve128Samples()' behavior
+    const auto previous_state = get_previous_state();
+    uint64_t skip_count = m_total_generated_elements * 256;
+    uint64_t new_n = previous_state.first + skip_count;
+    uint64_t new_op_seed = previous_state.second + (new_n < skip_count ? 1 : 0);
+
+    return {new_n, new_op_seed};
+}
+
+PhilloxOutput TensorflowPhilloxGenerator::random() {
+    PhilloxOutput result(ELEMENTS_PER_EXECUTION);
+
+    // There are 10 rounds in the execution,
+    // but the final one does not perform key increment
+    for (uint16_t i = 0; i < SHUFFLE_ROUNDS - 1; ++i) {
+        execute_single_round();
+        increment_key();
+    }
+    execute_single_round();
+
+    result[0] = m_n.first;
+    result[1] = m_n.second;
+    result[2] = m_counter.first;
+    result[3] = m_counter.second;
+
+    if (++m_n64 == 0) {
+        ++m_counter64;
+    }
+
+    m_n = split_high_low(m_n64);
+    m_key = split_high_low(m_key64);
+    m_counter = split_high_low(m_counter64);
+
+    ++m_total_generated_elements;
+
+    return result;
+}
+
+// ====== PytorchPhilloxGenerator functions ======
+PytorchPhilloxGenerator::PytorchPhilloxGenerator(const uint64_t global_seed)
+    : PhilloxGenerator(op::PhilloxAlignment::PYTORCH, global_seed, 0UL, {0UL, 0UL}),
+      m_left(1),
+      m_next(0) {
+    m_mersenne_state[0] = global_seed & 0xffffffff;
+    for (uint32_t j = 1; j < MERSENNE_STATE_N; ++j) {
+        m_mersenne_state[j] = (1812433253 * (m_mersenne_state[j - 1] ^ (m_mersenne_state[j - 1] >> 30)) + j);
+    }
+}
+
+std::pair<uint64_t, uint64_t> PytorchPhilloxGenerator::get_next_state() {
+    return get_previous_state();
+}
+
+void PytorchPhilloxGenerator::next_mersenne_state() {
+    uint32_t* p = m_mersenne_state.data();
+    m_left = MERSENNE_STATE_N;
+    m_next = 0;
+
+    for (int j = MERSENNE_STATE_N - MERSENNE_STATE_M + 1; --j; p++) {
+        *p = p[MERSENNE_STATE_M] ^ twist(p[0], p[1]);
+    }
+
+    for (int j = MERSENNE_STATE_M; --j; p++) {
+        *p = p[MERSENNE_STATE_M - MERSENNE_STATE_N] ^ twist(p[0], p[1]);
+    }
+
+    *p = p[MERSENNE_STATE_M - MERSENNE_STATE_N] ^ twist(p[0], m_mersenne_state[0]);
+}
+
+PhilloxOutput PytorchPhilloxGenerator::random() {
+    PhilloxOutput result(ELEMENTS_PER_EXECUTION);
+
+    for (size_t i = 0; i < ELEMENTS_PER_EXECUTION; ++i) {
+        if (--m_left == 0) {
+            next_mersenne_state();
+        }
+
+        result[i] = *(m_mersenne_state.data() + m_next++);
+        result[i] ^= (result[i] >> 11);
+        result[i] ^= (result[i] << 7) & 0x9d2c5680;
+        result[i] ^= (result[i] << 15) & 0xefc60000;
+        result[i] ^= (result[i] >> 18);
+    }
+
+    return result;
+}
+
+// ====== General selector function to construct a desired generator  ======
+
+std::shared_ptr<PhilloxGenerator> make_phillox_generator(uint64_t seed,
+                                                         uint64_t seed2,
+                                                         std::pair<uint64_t, uint64_t> prev_state,
+                                                         size_t elem_count,
+                                                         op::PhilloxAlignment alignment) {
+    switch (alignment) {
+    case op::PhilloxAlignment::TENSORFLOW:
+        return std::make_shared<TensorflowPhilloxGenerator>(seed, seed2, prev_state);
+    case op::PhilloxAlignment::PYTORCH:
+        return std::make_shared<PytorchPhilloxGenerator>(seed);
+    default:
+        return std::make_shared<MockPhilloxGenerator>();
+    }
+}
+
+}  // namespace phillox
+}  // namespace reference
+}  // namespace ov
diff --git a/src/core/shape_inference/include/batch_norm_shape_inference.hpp b/src/core/shape_inference/include/batch_norm_shape_inference.hpp
index 8c48920db9ad9e..e4e6ed6897c468 100644
--- a/src/core/shape_inference/include/batch_norm_shape_inference.hpp
+++ b/src/core/shape_inference/include/batch_norm_shape_inference.hpp
@@ -60,7 +60,7 @@ std::vector<TRShape> infer_shape(const Node* node, const std::vector<TShape>& in
 
     auto& output_shape = outputs_shapes[0];
     if (data_input_rank.is_static()) {
-        output_shape[1] = channel_dim;
+        output_shape[1] = std::move(channel_dim);
     }
 
     return outputs_shapes;
diff --git a/src/core/shape_inference/include/concat_shape_inference.hpp b/src/core/shape_inference/include/concat_shape_inference.hpp
index 9978a79871111b..1a7ad8c7b05a22 100644
--- a/src/core/shape_inference/include/concat_shape_inference.hpp
+++ b/src/core/shape_inference/include/concat_shape_inference.hpp
@@ -17,7 +17,7 @@ std::vector<TRShape> shape_infer(const Concat* op, const std::vector<T>& input_s
     NODE_VALIDATION_CHECK(op, !input_shapes.empty());
     using DimType = typename T::value_type;
 
-    auto concat_axis = op->get_axis();
+    size_t concat_axis{0};
     const auto empty_dim = DimType{};
 
     auto concat_dim = DimType{0};
@@ -28,14 +28,14 @@ std::vector<TRShape> shape_infer(const Concat* op, const std::vector<T>& input_s
         output_shape = PartialShape::dynamic();
     } else {
         output_shape = input_shapes.front();
-        concat_axis = ov::util::normalize_axis(op, concat_axis, output_shape.rank());
+        concat_axis = static_cast<size_t>(ov::util::normalize_axis(op, op->get_axis(), output_shape.rank()));
         output_shape[concat_axis] = empty_dim;
     }
 
     for (auto& input : input_shapes) {
         const auto& input_rank = input.rank();
         if (input_rank.is_static()) {
-            concat_axis = ov::util::normalize_axis(op, concat_axis, input_rank);
+            concat_axis = static_cast<size_t>(ov::util::normalize_axis(op, op->get_axis(), input_rank));
             auto in_copy = TRShape(input);
             concat_dim += in_copy[concat_axis];
             in_copy[concat_axis] = empty_dim;
@@ -53,7 +53,7 @@ std::vector<TRShape> shape_infer(const Concat* op, const std::vector<T>& input_s
     }
 
     if (output_shape.rank().is_static()) {
-        output_shape[concat_axis] = concat_dim;
+        output_shape[concat_axis] = std::move(concat_dim);
     }
     return output_shapes;
 }
diff --git a/src/core/shape_inference/include/convolution_shape_inference.hpp b/src/core/shape_inference/include/convolution_shape_inference.hpp
index 03addf2b3470ee..fc6bde1552e9f4 100644
--- a/src/core/shape_inference/include/convolution_shape_inference.hpp
+++ b/src/core/shape_inference/include/convolution_shape_inference.hpp
@@ -24,7 +24,8 @@ std::vector<TRShape> shape_infer(const TOp* op,
 
     const auto num_spatial = convolution::calculate_num_spatial(op, input_shapes);
 
-    TRShape output_shape;
+    auto output_shapes = std::vector<TRShape>(1);
+    auto& output_shape = output_shapes[0];
     if (num_spatial != util::num_spatial_undefined) {
         const auto& data_shape = input_shapes[0];
         const auto& filters_shape = input_shapes[1];
@@ -47,7 +48,7 @@ std::vector<TRShape> shape_infer(const TOp* op,
         output_shape = PartialShape::dynamic();
     }
 
-    return {output_shape};
+    return output_shapes;
 }
 }  // namespace v1
 }  // namespace op
diff --git a/src/core/shape_inference/include/ctc_greedy_decoder_seq_len_shape_inference.hpp b/src/core/shape_inference/include/ctc_greedy_decoder_seq_len_shape_inference.hpp
index b7768742f36f39..18d18020d447a1 100644
--- a/src/core/shape_inference/include/ctc_greedy_decoder_seq_len_shape_inference.hpp
+++ b/src/core/shape_inference/include/ctc_greedy_decoder_seq_len_shape_inference.hpp
@@ -42,7 +42,7 @@ std::vector<TRShape> shape_infer(const CTCGreedyDecoderSeqLen* op, const std::ve
                               "The first dimensions of input tensors must match.");
     }
 
-    return {TRShape{batch_size, time_size}, TRShape{batch_size}};
+    return {TRShape{batch_size, std::move(time_size)}, TRShape{batch_size}};
 }
 }  // namespace v6
 }  // namespace op
diff --git a/src/core/shape_inference/include/ctc_loss_shape_inference.hpp b/src/core/shape_inference/include/ctc_loss_shape_inference.hpp
index 65a5fee89ef4fb..277843dd347cef 100644
--- a/src/core/shape_inference/include/ctc_loss_shape_inference.hpp
+++ b/src/core/shape_inference/include/ctc_loss_shape_inference.hpp
@@ -91,7 +91,7 @@ std::vector<TRShape> shape_infer(const CTCLoss* op, const std::vector<TShape>& i
         " and: ",
         batch_size);
 
-    return {TRShape{batch_size}};
+    return {TRShape{std::move(batch_size)}};
 }
 }  // namespace v4
 }  // namespace op
diff --git a/src/core/shape_inference/include/deformable_convolution_shape_inference.hpp b/src/core/shape_inference/include/deformable_convolution_shape_inference.hpp
index 0840a0f727192b..efd70c4dd561ca 100644
--- a/src/core/shape_inference/include/deformable_convolution_shape_inference.hpp
+++ b/src/core/shape_inference/include/deformable_convolution_shape_inference.hpp
@@ -74,7 +74,8 @@ std::vector<TRShape> shape_infer(const DeformableConvolutionBase* op,
 
     const auto num_spatial = deformable_conv::calculate_num_spatial(op, input_shapes);
 
-    TRShape output_shape;
+    auto output_shapes = std::vector<TRShape>(1);
+    auto& output_shape = output_shapes[0];
     if (num_spatial != convolution::num_spatial_undefined) {
         const auto& data_shape = input_shapes[0];
         const auto& offsets_shape = input_shapes[1];
@@ -162,7 +163,7 @@ std::vector<TRShape> shape_infer(const DeformableConvolutionBase* op,
         output_shape = PartialShape::dynamic();
     }
 
-    return {output_shape};
+    return output_shapes;
 }
 }  // namespace util
 
@@ -196,7 +197,7 @@ std::vector<TRShape> shape_infer(const DeformableConvolution* op,
     const auto offsets_rank = offsets_shape.rank();
 
     if (has_mask_shape) {
-        const auto mask_shape = input_shapes[3];
+        const auto& mask_shape = input_shapes[3];
         if (mask_shape.rank().is_static()) {
             if (filters_rank.is_static()) {
                 auto offsets_channels = filters_shape[2] * filters_shape[3] * op->get_deformable_group();
diff --git a/src/core/shape_inference/include/depth_to_space_shape_inference.hpp b/src/core/shape_inference/include/depth_to_space_shape_inference.hpp
index b36053ce0985ba..081d8dce57ada0 100644
--- a/src/core/shape_inference/include/depth_to_space_shape_inference.hpp
+++ b/src/core/shape_inference/include/depth_to_space_shape_inference.hpp
@@ -20,6 +20,7 @@ std::vector<TRShape> shape_infer(const DepthToSpace* op, const std::vector<TShap
     NODE_VALIDATION_CHECK(op, input_shapes.size() == 1);
 
     const auto& data_shape = input_shapes[0];
+    auto output_shapes = std::vector<TRShape>{data_shape};
 
     if (data_shape.rank().is_static()) {
         static constexpr size_t spatial_dim_offset = 2;
@@ -33,16 +34,15 @@ std::vector<TRShape> shape_infer(const DepthToSpace* op, const std::vector<TShap
         const auto divisor = static_cast<TVal>(std::pow(block_size, data_shape.size() - spatial_dim_offset));
         NODE_VALIDATION_CHECK(op, divisor != 0, "DepthToSpace: The divisor must not be 0");
 
-        auto out_shape = TRShape(data_shape);
+        auto& out_shape = output_shapes[0];
         out_shape[1] /= divisor;
         check_divided_result(op, out_shape[1], data_shape[1], divisor);
         std::for_each(out_shape.begin() + spatial_dim_offset, out_shape.end(), [&block_size](TDim& d) {
             d *= static_cast<TVal>(block_size);
         });
-        return {out_shape};
-    } else {
-        return {PartialShape::dynamic()};
     }
+
+    return output_shapes;
 }
 }  // namespace v0
 }  // namespace op
diff --git a/src/core/shape_inference/include/einsum_shape_inference.hpp b/src/core/shape_inference/include/einsum_shape_inference.hpp
index 94f902509c3f68..eb84482af0f052 100644
--- a/src/core/shape_inference/include/einsum_shape_inference.hpp
+++ b/src/core/shape_inference/include/einsum_shape_inference.hpp
@@ -48,7 +48,7 @@ std::vector<TRShape> shape_infer(const Einsum* op, const std::vector<T>& input_s
                     auto current_sub_pshape = T(std::vector<DimType>(pshape.begin() + dim_ind,
                                                                      pshape.begin() + dim_ind + num_broadcasted_dims));
                     if (label_to_shape.find(label) == label_to_shape.end()) {
-                        label_to_shape[label] = current_sub_pshape;
+                        label_to_shape[label] = std::move(current_sub_pshape);
                     } else {
                         bool is_broadcast_success = TRShape::broadcast_merge_into(label_to_shape[label],
                                                                                   current_sub_pshape,
diff --git a/src/core/shape_inference/include/embedding_segments_sum_shape_inference.hpp b/src/core/shape_inference/include/embedding_segments_sum_shape_inference.hpp
index 44671e4272bd8c..8bc78d10fe776c 100644
--- a/src/core/shape_inference/include/embedding_segments_sum_shape_inference.hpp
+++ b/src/core/shape_inference/include/embedding_segments_sum_shape_inference.hpp
@@ -48,7 +48,8 @@ std::vector<TRShape> shape_infer(const EmbeddingSegmentsSum* op,
                               "INDICES and PER_SAMPLE_WEIGHTS shape must be same.");
     }
     const auto& emb_table_shape = input_shapes[EMB_TABLE];
-    TRShape result_shape = emb_table_shape;
+    auto output_shapes = std::vector<TRShape>{emb_table_shape};
+    auto& result_shape = output_shapes[0];
     if (emb_table_shape.rank().is_static()) {
         NODE_VALIDATION_CHECK(op, emb_table_shape.size() > 0, "EMB_TABLE can't be a scalar.");
         if (auto segments_value = get_input_const_data_as_shape<TRShape>(op, NUM_SEGMENTS, ta)) {
@@ -57,7 +58,7 @@ std::vector<TRShape> shape_infer(const EmbeddingSegmentsSum* op,
             result_shape[0] = Dimension::dynamic();
         }
     }
-    return {result_shape};
+    return output_shapes;
 }
 }  // namespace v3
 }  // namespace op
diff --git a/src/core/shape_inference/include/embeddingbag_packed_shape_inference.hpp b/src/core/shape_inference/include/embeddingbag_packed_shape_inference.hpp
index b4dde64e4cbae2..f008a7d79056a5 100644
--- a/src/core/shape_inference/include/embeddingbag_packed_shape_inference.hpp
+++ b/src/core/shape_inference/include/embeddingbag_packed_shape_inference.hpp
@@ -34,7 +34,7 @@ std::vector<TRShape> shape_infer(const ov::op::util::EmbeddingBagPackedBase* op,
                               TRShape::merge_into(indices_shape, input_shapes[PER_SAMPLE_WEIGHTS]),
                               "INDICES and PER_SAMPLE_WEIGHTS shape must be same.");
     }
-    return {embedding::out_shape_infer(op, input_shapes[EMB_TABLE], TShape(indices_shape))};
+    return {embedding::out_shape_infer(op, input_shapes[EMB_TABLE], TShape(std::move(indices_shape)))};
 }
 }  // namespace util
 }  // namespace op
diff --git a/src/core/shape_inference/include/experimental_detectron_generate_proposals_shape_inference.hpp b/src/core/shape_inference/include/experimental_detectron_generate_proposals_shape_inference.hpp
index 79956f72ce5253..17faafa8635723 100644
--- a/src/core/shape_inference/include/experimental_detectron_generate_proposals_shape_inference.hpp
+++ b/src/core/shape_inference/include/experimental_detectron_generate_proposals_shape_inference.hpp
@@ -70,7 +70,7 @@ std::vector<TRShape> shape_infer(const ExperimentalDetectronGenerateProposalsSin
     }
 
     auto post_nms_count = static_cast<typename TShape::value_type>(op->get_attrs().post_nms_count);
-    auto output_shapes = std::vector<TRShape>(2, TRShape{post_nms_count});
+    auto output_shapes = std::vector<TRShape>(2, TRShape{std::move(post_nms_count)});
     output_shapes[0].push_back(4);
     return output_shapes;
 }
diff --git a/src/core/shape_inference/include/experimental_detectron_prior_grid_generator_shape_inference.hpp b/src/core/shape_inference/include/experimental_detectron_prior_grid_generator_shape_inference.hpp
index 2dc8743d3d9cb9..733da6a85c3db3 100644
--- a/src/core/shape_inference/include/experimental_detectron_prior_grid_generator_shape_inference.hpp
+++ b/src/core/shape_inference/include/experimental_detectron_prior_grid_generator_shape_inference.hpp
@@ -21,7 +21,8 @@ std::vector<TRShape> shape_infer(const ExperimentalDetectronPriorGridGenerator*
     const auto is_flatten = op->get_attrs().flatten;
     const size_t output_size = is_flatten ? 2 : 4;
 
-    TRShape output_shape;
+    auto output_shapes = std::vector<TRShape>(1);
+    auto& output_shape = output_shapes[0];
     output_shape.resize(output_size);
     output_shape[output_size - 1] = 4;
 
@@ -69,7 +70,7 @@ std::vector<TRShape> shape_infer(const ExperimentalDetectronPriorGridGenerator*
         }
     }
 
-    return {output_shape};
+    return output_shapes;
 }
 }  // namespace v6
 }  // namespace op
diff --git a/src/core/shape_inference/include/eye_shape_inference.hpp b/src/core/shape_inference/include/eye_shape_inference.hpp
index 8bc05da89ab704..cfd6a4721b5b94 100644
--- a/src/core/shape_inference/include/eye_shape_inference.hpp
+++ b/src/core/shape_inference/include/eye_shape_inference.hpp
@@ -47,7 +47,8 @@ std::vector<TRShape> shape_infer(const Eye* op,
                                  const ITensorAccessor& ta = make_tensor_accessor()) {
     const auto& inputs_count = input_shapes.size();
     NODE_VALIDATION_CHECK(op, (inputs_count == 3 || inputs_count == 4));
-    TRShape output_shape;
+    auto output_shapes = std::vector<TRShape>(1);
+    auto& output_shape = output_shapes[0];
 
     for (size_t i = 0; i < 3; ++i) {
         util::check_1D_or_scalar_shape(op, input_shapes[i], eye::shape_names[i]);
@@ -66,7 +67,8 @@ std::vector<TRShape> shape_infer(const Eye* op,
                 output_shape = PartialShape::dynamic(batch_shape[0].get_length());
             }
         } else {
-            return {ov::PartialShape::dynamic()};
+            output_shape = PartialShape::dynamic();
+            return output_shapes;
         }
     }
 
@@ -86,7 +88,7 @@ std::vector<TRShape> shape_infer(const Eye* op,
         }
     }
 
-    return {output_shape};
+    return output_shapes;
 }
 }  // namespace v9
 }  // namespace op
diff --git a/src/core/shape_inference/include/fake_convert_shape_inference.hpp b/src/core/shape_inference/include/fake_convert_shape_inference.hpp
index 6f8d432daca654..40df5fd59f15c4 100644
--- a/src/core/shape_inference/include/fake_convert_shape_inference.hpp
+++ b/src/core/shape_inference/include/fake_convert_shape_inference.hpp
@@ -22,7 +22,8 @@ std::vector<TRShape> shape_infer(const FakeConvert* op, const std::vector<T>& in
                                scales_shape.compatible(shifts_shape),
                                "FakeConvert scale shape is not compatible with shift shape.");
     }
-    TRShape output_pshape = input_shapes[0];
+    auto output_shapes = std::vector<TRShape>{input_shapes[0]};
+    auto& output_pshape = output_shapes[0];
     NODE_SHAPE_INFER_CHECK(op,
                            input_shapes,
                            T::broadcast_merge_into(output_pshape, scales_shape, op::AutoBroadcastType::NUMPY),
@@ -31,8 +32,8 @@ std::vector<TRShape> shape_infer(const FakeConvert* op, const std::vector<T>& in
         op,
         input_shapes,
         input_shapes[0].compatible(output_pshape),
-        "FakeConvert support only unidirectional broadcasting, inputs cannot be broadcastd into data.");
-    return {output_pshape};
+        "FakeConvert support only unidirectional broadcasting, inputs cannot be broadcast into data.");
+    return output_shapes;
 }
 }  // namespace v13
 }  // namespace op
diff --git a/src/core/shape_inference/include/fft_base_shape_inference.hpp b/src/core/shape_inference/include/fft_base_shape_inference.hpp
index 4c3b19db17c0ad..f7f98286a7c5a5 100644
--- a/src/core/shape_inference/include/fft_base_shape_inference.hpp
+++ b/src/core/shape_inference/include/fft_base_shape_inference.hpp
@@ -25,7 +25,7 @@ void apply_dims_from_sizes(const util::FFTBase* op,
     if (const auto output_bounds = get_input_bounds<TRShape, int64_t>(op, 2, ta)) {
         const auto minus_one_bound = std::make_pair(dim::inf_bound, dim::inf_bound);
         const auto num_of_axes = axes.size();
-        const auto symbols =
+        const auto& symbols =
             op->get_input_size() > 2 ? op->get_input_source_output(2).get_tensor().get_value_symbol() : TensorSymbol();
         const bool propagate_symbols = num_of_axes <= symbols.size();
         for (size_t i = 0; i < num_of_axes; ++i) {
diff --git a/src/core/shape_inference/include/gather_elements_shape_inference.hpp b/src/core/shape_inference/include/gather_elements_shape_inference.hpp
index 4dfbb7c9b37699..54ce5a0bbb2b3c 100644
--- a/src/core/shape_inference/include/gather_elements_shape_inference.hpp
+++ b/src/core/shape_inference/include/gather_elements_shape_inference.hpp
@@ -19,32 +19,25 @@ std::vector<TRShape> shape_infer(const GatherElements* op, const std::vector<T>&
     const auto& indices_pshape = input_shapes[1];
     auto data_rank = data_pshape.rank();
     auto indices_rank = indices_pshape.rank();
-    auto output_shapes = std::vector<TRShape>(1);
-    auto& output_shape = output_shapes[0];
-
-    int64_t axis = op->get_axis();
-    if (data_rank.is_static()) {
-        axis = ov::util::normalize_axis(op, axis, data_rank);
-    }
+    auto output_shapes = std::vector<TRShape>();
 
-    NODE_VALIDATION_CHECK(op, data_rank.is_dynamic() || data_rank.get_length() >= 1, "data rank must be >= 1.");
     NODE_VALIDATION_CHECK(op,
                           indices_rank.is_dynamic() || indices_rank.get_length() >= 1,
                           "indices rank must be >= 1.");
-
-    if (data_rank.is_static() && indices_rank.is_dynamic()) {
-        // output has the same rank of data
-        output_shape = data_pshape;
-        output_shape[axis] = DimType();
+    if (data_rank.is_dynamic()) {
+        output_shapes.push_back(indices_pshape);
         return output_shapes;
+    } else {
+        output_shapes.push_back(data_pshape);
     }
+    auto& output_shape = output_shapes[0];
 
-    if (data_rank.is_dynamic()) {
-        if (indices_rank.is_dynamic()) {
-            output_shape = PartialShape::dynamic();
-            return output_shapes;
-        }
-        output_shape = indices_pshape;
+    NODE_VALIDATION_CHECK(op, data_rank.get_length() >= 1, "data rank must be >= 1.");
+    const auto axis = static_cast<size_t>(ov::util::normalize_axis(op, op->get_axis(), data_rank));
+
+    if (indices_rank.is_dynamic()) {
+        // output has the same rank of data
+        output_shape[axis] = DimType();
         return output_shapes;
     }
 
@@ -59,7 +52,6 @@ std::vector<TRShape> shape_infer(const GatherElements* op, const std::vector<T>&
     // if size of the current dimension of indices is unknown it will be retrieved from data
     // e.g., if data_shape = {4, 4, ?}, indices_shape = {1, ?, 5} and axis = 0
     // (and if intervals intersect) then output_pshape will be {1, 4, 5}
-    output_shape = data_pshape;
     output_shape[axis] = indices_pshape[axis];
     NODE_VALIDATION_CHECK(op,
                           output_shape.merge_into(output_shape, indices_pshape),
diff --git a/src/core/shape_inference/include/gather_shape_inference.hpp b/src/core/shape_inference/include/gather_shape_inference.hpp
index 7a256a571858ef..cd036091dafc4a 100644
--- a/src/core/shape_inference/include/gather_shape_inference.hpp
+++ b/src/core/shape_inference/include/gather_shape_inference.hpp
@@ -106,7 +106,7 @@ std::vector<TRShape> shape_infer(const util::GatherBase* op,
         auto out_rank = data_rank + indices_rank - 1 - batch_dims;
         if (batch_dims < 0)
             out_rank = out_rank - indices_rank.get_max_length();
-        output_pshape = PartialShape::dynamic(out_rank);
+        output_pshape = PartialShape::dynamic(std::move(out_rank));
     }
     return output_shapes;
 }
diff --git a/src/core/shape_inference/include/gather_tree_shape_inference.hpp b/src/core/shape_inference/include/gather_tree_shape_inference.hpp
index eea63daed11b61..3365443d03161f 100644
--- a/src/core/shape_inference/include/gather_tree_shape_inference.hpp
+++ b/src/core/shape_inference/include/gather_tree_shape_inference.hpp
@@ -19,7 +19,9 @@ std::vector<TRShape> shape_infer(const GatherTree* op, const std::vector<TShape>
     const auto& parent_idx_shape = input_shapes[1];
     const auto& max_seq_len_pshape = input_shapes[2];
     const auto& end_token_pshape = input_shapes[3];
-    TRShape result_shape = step_ids_shape;
+
+    auto output_shapes = std::vector<TRShape>{step_ids_shape};
+    auto& result_shape = output_shapes[0];
     NODE_VALIDATION_CHECK(op,
                           TRShape::merge_into(result_shape, parent_idx_shape) && result_shape.rank().compatible(3),
                           "step_ids and parent_idx inputs must have the same shape with rank 3. Got: ",
@@ -48,7 +50,7 @@ std::vector<TRShape> shape_infer(const GatherTree* op, const std::vector<TShape>
                           end_token_pshape.rank().compatible(0),
                           "end_token input must be scalar. Got: ",
                           end_token_pshape);
-    return {result_shape};
+    return output_shapes;
 }
 }  // namespace v1
 }  // namespace op
diff --git a/src/core/shape_inference/include/generate_proposals_shape_inference.hpp b/src/core/shape_inference/include/generate_proposals_shape_inference.hpp
index 6e075b54b627e2..e827385473c0f7 100644
--- a/src/core/shape_inference/include/generate_proposals_shape_inference.hpp
+++ b/src/core/shape_inference/include/generate_proposals_shape_inference.hpp
@@ -139,7 +139,7 @@ std::vector<TRShape> shape_infer(const GenerateProposals* op, const std::vector<
     }
 
     auto num_rois = Dimension(0, (num_batches * op->get_attrs().post_nms_count).get_max_length());
-    return {TRShape{num_rois, 4}, TRShape{num_rois}, TRShape{num_batches}};
+    return {TRShape{num_rois, 4}, TRShape{num_rois}, TRShape{std::move(num_batches)}};
 }
 
 }  // namespace v9
diff --git a/src/core/shape_inference/include/group_convolution_backprop_shape_inference.hpp b/src/core/shape_inference/include/group_convolution_backprop_shape_inference.hpp
index a133debad2d52c..ed17daf4e2fc36 100644
--- a/src/core/shape_inference/include/group_convolution_backprop_shape_inference.hpp
+++ b/src/core/shape_inference/include/group_convolution_backprop_shape_inference.hpp
@@ -53,7 +53,8 @@ std::vector<TRShape> shape_infer(const GroupConvolutionBackpropData* op,
     }
     const auto num_spatial = convolution::calculate_num_spatial(op, input_shapes, *out_spatial_shape);
 
-    TRShape output_shape;
+    auto output_shapes = std::vector<TRShape>(1);
+    auto& output_shape = output_shapes[0];
     if (num_spatial != util::num_spatial_undefined) {
         const auto& data_shape = input_shapes[0];
         const auto& filters_shape = input_shapes[1];
@@ -111,7 +112,7 @@ std::vector<TRShape> shape_infer(const GroupConvolutionBackpropData* op,
         output_shape = PartialShape::dynamic();
     }
 
-    return {output_shape};
+    return output_shapes;
 }
 }  // namespace v1
 }  // namespace op
diff --git a/src/core/shape_inference/include/group_convolution_shape_inference.hpp b/src/core/shape_inference/include/group_convolution_shape_inference.hpp
index 756cf6e0b8c2c4..5d60c8fd89f2a9 100644
--- a/src/core/shape_inference/include/group_convolution_shape_inference.hpp
+++ b/src/core/shape_inference/include/group_convolution_shape_inference.hpp
@@ -32,7 +32,8 @@ std::vector<TRShape> shape_infer(const GroupConvolution* op,
 
     const auto num_spatial = convolution::calculate_num_spatial(op, input_shapes);
 
-    TRShape output_shape;
+    auto output_shapes = std::vector<TRShape>(1);
+    auto& output_shape = output_shapes[0];
     if (num_spatial != convolution::num_spatial_undefined) {
         const auto& data_shape = input_shapes[0];
         const auto& filters_shape = input_shapes[1];
@@ -79,7 +80,7 @@ std::vector<TRShape> shape_infer(const GroupConvolution* op,
         output_shape = PartialShape::dynamic();
     }
 
-    return {output_shape};
+    return output_shapes;
 }
 }  // namespace v1
 }  // namespace op
diff --git a/src/core/shape_inference/include/matmul_shape_inference.hpp b/src/core/shape_inference/include/matmul_shape_inference.hpp
index 1e5a0a70037986..6e4b7f68649538 100644
--- a/src/core/shape_inference/include/matmul_shape_inference.hpp
+++ b/src/core/shape_inference/include/matmul_shape_inference.hpp
@@ -12,7 +12,8 @@ template <class T, class TRShape = result_shape_t<T>>
 std::vector<TRShape> shape_infer(const MatMul* op, const std::vector<T>& input_shapes) {
     NODE_VALIDATION_CHECK(op, input_shapes.size() == 2);
 
-    auto arg0_shape = input_shapes[0], arg1_shape = input_shapes[1];
+    const auto& arg0_shape = input_shapes[0];
+    const auto& arg1_shape = input_shapes[1];
     if (arg0_shape.rank().is_dynamic() || arg1_shape.rank().is_dynamic()) {
         return {ov::PartialShape::dynamic()};
     }
diff --git a/src/core/shape_inference/include/matrix_nms_shape_inference.hpp b/src/core/shape_inference/include/matrix_nms_shape_inference.hpp
index c1daf9cf52ac45..9811ae552015ea 100644
--- a/src/core/shape_inference/include/matrix_nms_shape_inference.hpp
+++ b/src/core/shape_inference/include/matrix_nms_shape_inference.hpp
@@ -56,7 +56,7 @@ std::vector<TRShape> shape_infer(const MatrixNms* op, const std::vector<TShape>&
             }
             num_boxes *= scores_shape[0];
             const auto& selected_boxes =
-                std::is_same<TRShape, PartialShape>::value ? TDim(0, num_boxes.get_max_length()) : num_boxes;
+                std::is_same<TRShape, PartialShape>::value ? TDim(0, num_boxes.get_max_length()) : std::move(num_boxes);
 
             std::for_each(output_shapes.begin(), output_shapes.begin() + 2, [&selected_boxes](TRShape& s) {
                 s[0] = selected_boxes;
diff --git a/src/core/shape_inference/include/region_yolo_shape_inference.hpp b/src/core/shape_inference/include/region_yolo_shape_inference.hpp
index 844fda2a78f733..d2b173b4644407 100644
--- a/src/core/shape_inference/include/region_yolo_shape_inference.hpp
+++ b/src/core/shape_inference/include/region_yolo_shape_inference.hpp
@@ -35,7 +35,7 @@ std::vector<TRShape> shape_infer(const RegionYolo* op, const std::vector<TShape>
         output_shape.reserve(out_rank);
 
         if (op->get_do_softmax()) {
-            const auto axis = ov::util::normalize_axis(op, op->get_axis(), input_rank);
+            const auto axis = static_cast<size_t>(ov::util::normalize_axis(op, op->get_axis(), input_rank));
             const auto end_axis = ov::util::normalize_axis(op, op->get_end_axis(), input_rank);
 
             auto input_it = input_shape.cbegin();
diff --git a/src/core/shape_inference/include/reverse_sequence_shape_inference.hpp b/src/core/shape_inference/include/reverse_sequence_shape_inference.hpp
index 76c9d7752a75fc..577376d7f43338 100644
--- a/src/core/shape_inference/include/reverse_sequence_shape_inference.hpp
+++ b/src/core/shape_inference/include/reverse_sequence_shape_inference.hpp
@@ -29,9 +29,11 @@ std::vector<TRShape> shape_infer(const ReverseSequence* op, const std::vector<TS
                           seq_lengths_rank.compatible(1),
                           "Sequence lengths rank must be equal to 1. Got: ",
                           seq_lengths_pshape);
-    TRShape output_pshape = data_pshape;
+    auto output_shapes = std::vector<TRShape>{data_pshape};
+    auto& output_pshape = output_shapes[0];
     if (data_rank.is_static() && seq_lengths_rank.is_static()) {
-        const auto normalized_batch_axis = ov::util::normalize_axis(op, op->get_origin_batch_axis(), data_rank);
+        const auto normalized_batch_axis =
+            static_cast<size_t>(ov::util::normalize_axis(op, op->get_origin_batch_axis(), data_rank));
         DimType merged_sequence_length;
         NODE_VALIDATION_CHECK(
             op,
@@ -48,7 +50,7 @@ std::vector<TRShape> shape_infer(const ReverseSequence* op, const std::vector<TS
         output_pshape[normalized_batch_axis] = merged_sequence_length;
     }
 
-    return {output_pshape};
+    return output_shapes;
 }
 }  // namespace v0
 }  // namespace op
diff --git a/src/core/shape_inference/include/rnn_base_shape_inference.hpp b/src/core/shape_inference/include/rnn_base_shape_inference.hpp
index d9d0347b8eafa5..34f5e6ad90af2c 100644
--- a/src/core/shape_inference/include/rnn_base_shape_inference.hpp
+++ b/src/core/shape_inference/include/rnn_base_shape_inference.hpp
@@ -54,7 +54,11 @@ std::vector<TRShape> cell_base_shape_infer(const op::util::RNNCellBase* op,
     using DimType = typename TShape::value_type;
 
     // Merge batch_size dimension across all inputs to evaluate output[0] dimension
-    DimType merged_batch_size = x_pshape.rank().is_static() ? x_pshape[0] : DimType();
+    auto output_shapes = std::vector<TRShape>(1);
+    output_shapes[0].push_back(x_pshape.rank().is_static() ? x_pshape[0] : DimType());
+    output_shapes[0].push_back(ht_pshape.rank().is_static() ? ht_pshape[1] : DimType());
+    auto& merged_batch_size = output_shapes[0][0];
+    auto& merged_hidden_size = output_shapes[0][1];
     for (size_t i = 1; i <= num_state_nodes; ++i) {
         NODE_VALIDATION_CHECK(op,
                               DimType::merge(merged_batch_size,
@@ -65,7 +69,6 @@ std::vector<TRShape> cell_base_shape_infer(const op::util::RNNCellBase* op,
 
     // Merge hidden_size dimension across all inputs to evaluate output dimension
     // `hidden_size` attribute is not used for backward compatibility
-    DimType merged_hidden_size = ht_pshape.rank().is_static() ? ht_pshape[1] : DimType();
     for (size_t i = 2; i <= num_state_nodes; ++i) {
         if (input_shapes[i].rank().is_static()) {
             NODE_VALIDATION_CHECK(op,
@@ -124,7 +127,8 @@ std::vector<TRShape> cell_base_shape_infer(const op::util::RNNCellBase* op,
         }
     }
 
-    return {num_state_nodes, TRShape{merged_batch_size, merged_hidden_size}};
+    output_shapes.resize(num_state_nodes, output_shapes[0]);
+    return output_shapes;
 }
 
 // Output shapes layout:
@@ -140,9 +144,6 @@ std::vector<TRShape> seq_base_shape_infer(const op::util::RNNCellBase* op,
     const auto num_inputs = 5 + num_state_nodes;
     NODE_VALIDATION_CHECK(op, input_shapes.size() >= num_inputs, "Incorrect number of shapes has been provided.");
 
-    std::vector<TRShape> output_shapes;
-    output_shapes.reserve(1 + num_state_nodes);
-
     std::vector<Rank> expected_in_ranks;
     expected_in_ranks.reserve(num_inputs);
     expected_in_ranks.insert(expected_in_ranks.end(), 1 + num_state_nodes, Rank(3));
@@ -158,9 +159,14 @@ std::vector<TRShape> seq_base_shape_infer(const op::util::RNNCellBase* op,
     const auto& b_pshape = input_shapes[4 + num_state_nodes];
 
     using DimType = typename TShape::value_type;
+    // Y output
+    auto output_shapes = std::vector<TRShape>{{x_pshape.rank().is_static() ? x_pshape[0] : DimType(),
+                                               DimType(),
+                                               x_pshape.rank().is_static() ? x_pshape[1] : DimType(),
+                                               ht_pshape.rank().is_static() ? ht_pshape[2] : DimType()}};
 
     // Merge batch_size dimension across all inputs to evaluate output[0] dimension
-    DimType merged_batch_size = x_pshape.rank().is_static() ? x_pshape[0] : DimType();
+    auto& merged_batch_size = output_shapes[0][0];
     for (size_t i = 1; i <= 1 + num_state_nodes; ++i) {
         if (input_shapes[i].rank().is_static()) {
             NODE_VALIDATION_CHECK(op,
@@ -171,7 +177,7 @@ std::vector<TRShape> seq_base_shape_infer(const op::util::RNNCellBase* op,
 
     // Merge hidden_size dimension across all inputs to evaluate output dimension
     // `hidden_size` attribute is not used for backward compatibility
-    DimType merged_hidden_size = ht_pshape.rank().is_static() ? ht_pshape[2] : DimType();
+    auto& merged_hidden_size = output_shapes[0][3];
     for (size_t i = 2; i <= num_state_nodes; ++i) {
         if (input_shapes[i].rank().is_static()) {
             NODE_VALIDATION_CHECK(op,
@@ -187,19 +193,18 @@ std::vector<TRShape> seq_base_shape_infer(const op::util::RNNCellBase* op,
     }
 
     // Validate num_directions dimension across all inputs
-    size_t valid_num_directions;
+    auto& merged_num_directions = output_shapes[0][1];
     const auto m_direction = direction;
     if (m_direction == op::RecurrentSequenceDirection::FORWARD ||
         m_direction == op::RecurrentSequenceDirection::REVERSE) {
-        valid_num_directions = 1;
+        merged_num_directions = 1;
     } else if (m_direction == op::RecurrentSequenceDirection::BIDIRECTIONAL) {
-        valid_num_directions = 2;
+        merged_num_directions = 2;
     } else {
         NODE_VALIDATION_CHECK(op, false, "Attribute direction must be FORWARD or REVERSE or BIDIRECTIONAL.");
     }
 
     bool is_num_dir_valid = true;
-    DimType merged_num_directions = DimType(valid_num_directions);
     for (size_t i = 1; i <= num_state_nodes; ++i) {
         is_num_dir_valid &= DimType::merge(merged_num_directions,
                                            merged_num_directions,
@@ -260,11 +265,6 @@ std::vector<TRShape> seq_base_shape_infer(const op::util::RNNCellBase* op,
         }
     }
 
-    // Y output
-    output_shapes.push_back(TRShape{merged_batch_size,
-                                    merged_num_directions,
-                                    x_pshape.rank().is_static() ? x_pshape[1] : DimType(),
-                                    merged_hidden_size});
     // Ho, Co outputs
     output_shapes.insert(output_shapes.end(),
                          num_state_nodes,
diff --git a/src/core/shape_inference/include/scaled_dot_product_attention_shape_inference.hpp b/src/core/shape_inference/include/scaled_dot_product_attention_shape_inference.hpp
index b6b60cfad3aea2..d6783292b7472e 100644
--- a/src/core/shape_inference/include/scaled_dot_product_attention_shape_inference.hpp
+++ b/src/core/shape_inference/include/scaled_dot_product_attention_shape_inference.hpp
@@ -24,7 +24,8 @@ std::vector<TRShape> shape_infer(const ScaledDotProductAttention* op,
     DimType s_dim{};
     DimType ev_dim{};
 
-    TRShape n_dims = input_shapes[0];
+    auto output_shapes = std::vector<TRShape>{input_shapes[0]};
+    auto& n_dims = output_shapes[0];
     const auto& n_dims_rank = n_dims.rank();
     if (n_dims_rank.is_static()) {
         NODE_SHAPE_INFER_CHECK(op,
@@ -107,7 +108,7 @@ std::vector<TRShape> shape_infer(const ScaledDotProductAttention* op,
         n_dims.push_back(l_dim);
         n_dims.push_back(ev_dim);
     }
-    return {n_dims};
+    return output_shapes;
 }
 }  // namespace v13
 }  // namespace op
diff --git a/src/core/shape_inference/include/select_shape_inference.hpp b/src/core/shape_inference/include/select_shape_inference.hpp
index ca4f46c3db30e4..49549fc89cda20 100644
--- a/src/core/shape_inference/include/select_shape_inference.hpp
+++ b/src/core/shape_inference/include/select_shape_inference.hpp
@@ -17,9 +17,10 @@ std::vector<TRShape> shape_infer(const Select* op, const std::vector<TShape>& in
     NODE_VALIDATION_CHECK(op, input_shapes.size() == 3);
 
     const auto& broadcast_spec = op->get_auto_broadcast();
-    TRShape result_shape;
+    auto output_shapes = std::vector<TRShape>();
     if (broadcast_spec.m_type == op::AutoBroadcastType::PDPD) {
-        result_shape = input_shapes[1];  // 'then' tensor
+        output_shapes.push_back(input_shapes[1]);
+        auto& result_shape = output_shapes[0];
         // in PDPD type, Broadcast-merging 'else' into 'then' one way not each other.
         NODE_VALIDATION_CHECK(op,
                               TRShape::broadcast_merge_into(result_shape, input_shapes[2], broadcast_spec),
@@ -28,7 +29,8 @@ std::vector<TRShape> shape_infer(const Select* op, const std::vector<TShape>& in
                               TRShape::broadcast_merge_into(result_shape, input_shapes[0], broadcast_spec),
                               "'Cond' tensor shape is not broadcastable.");
     } else {
-        result_shape = input_shapes[2];
+        output_shapes.push_back(input_shapes[2]);
+        auto& result_shape = output_shapes[0];
         for (int input_port = 1; input_port >= 0; input_port--) {
             if (broadcast_spec.m_type == op::AutoBroadcastType::NONE) {
                 NODE_VALIDATION_CHECK(op,
@@ -45,7 +47,7 @@ std::vector<TRShape> shape_infer(const Select* op, const std::vector<TShape>& in
         }
     }
 
-    return {result_shape};
+    return output_shapes;
 }
 
 }  // namespace v1
diff --git a/src/core/shape_inference/include/shuffle_channels_shape_inference.hpp b/src/core/shape_inference/include/shuffle_channels_shape_inference.hpp
index e007a00fcbdeb1..4814d67682d4ae 100644
--- a/src/core/shape_inference/include/shuffle_channels_shape_inference.hpp
+++ b/src/core/shape_inference/include/shuffle_channels_shape_inference.hpp
@@ -33,7 +33,7 @@ std::vector<TRShape> shape_infer(const ShuffleChannels* op, const std::vector<TS
                               "The channel dimension size has to be a multiple of the groups parameter value.");
 
         if (std::is_same<TShape, PartialShape>::value) {
-            // overwrite channel dimension to loose label
+            // overwrite channel dimension to loose symbol
             using TDim = typename TShape::value_type;
             output_shapes.front()[axis_zb] = TDim{channel_dim.get_min_length(), channel_dim.get_max_length()};
         }
diff --git a/src/core/shape_inference/include/space_to_batch_shape_inference.hpp b/src/core/shape_inference/include/space_to_batch_shape_inference.hpp
index 6995d40c6cc170..3ed1f907bec117 100644
--- a/src/core/shape_inference/include/space_to_batch_shape_inference.hpp
+++ b/src/core/shape_inference/include/space_to_batch_shape_inference.hpp
@@ -43,6 +43,7 @@ std::vector<TRShape> shape_infer(const SpaceToBatch* op,
                           "block_shape and pads inputs must have rank 1. Got: ",
                           inputs_same_ps.rank());
 
+    auto output_shapes = std::vector<TRShape>{data_shape};
     if (data_shape.rank().is_static()) {
         constexpr size_t spatial_dim_offset = 1;
         const auto data_rank_size = data_shape.size();
@@ -52,8 +53,8 @@ std::vector<TRShape> shape_infer(const SpaceToBatch* op,
                               data_rank_size,
                               ")");
 
-        TRShape out_shape;
-        out_shape.reserve(data_rank_size);
+        auto& out_shape = output_shapes[0];
+        out_shape.resize(0);
 
         auto blocks = get_input_const_data_as<TShape, int64_t>(op, 1, ta);
         if (blocks) {
@@ -83,11 +84,9 @@ std::vector<TRShape> shape_infer(const SpaceToBatch* op,
         } else {
             out_shape.insert(out_shape.end(), data_rank_size - spatial_dim_offset, dim::inf_bound);
         }
-
-        return {out_shape};
-    } else {
-        return {PartialShape::dynamic()};
     }
+
+    return output_shapes;
 }
 }  // namespace v1
 }  // namespace op
diff --git a/src/core/shape_inference/include/space_to_depth_shape_inference.hpp b/src/core/shape_inference/include/space_to_depth_shape_inference.hpp
index 274f3b8ce87278..af19c61452d059 100644
--- a/src/core/shape_inference/include/space_to_depth_shape_inference.hpp
+++ b/src/core/shape_inference/include/space_to_depth_shape_inference.hpp
@@ -19,6 +19,7 @@ std::vector<TRShape> shape_infer(const ov::op::v0::SpaceToDepth* op, const std::
     NODE_VALIDATION_CHECK(op, input_shapes.size() == 1);
 
     const auto& data_shape = input_shapes[0];
+    auto output_shapes = std::vector<TRShape>{data_shape};
     if (data_shape.rank().is_static()) {
         static constexpr size_t spatial_dim_offset = 2;
         NODE_VALIDATION_CHECK(op,
@@ -29,8 +30,7 @@ std::vector<TRShape> shape_infer(const ov::op::v0::SpaceToDepth* op, const std::
 
         const auto& block_size = op->get_block_size();
         NODE_VALIDATION_CHECK(op, block_size > 0, "The block size must be greater than 0 ", block_size);
-
-        TRShape out_shape = data_shape;
+        auto& out_shape = output_shapes[0];
         out_shape[1] *= static_cast<TVal>(std::pow(block_size, data_shape.size() - spatial_dim_offset));
         const auto divisor = static_cast<TVal>(block_size);
 
@@ -38,10 +38,8 @@ std::vector<TRShape> shape_infer(const ov::op::v0::SpaceToDepth* op, const std::
             out_shape[i] /= divisor;
             check_divided_result(op, out_shape[i], data_shape[i], divisor);
         }
-        return {out_shape};
-    } else {
-        return {PartialShape::dynamic()};
     }
+    return output_shapes;
 }
 }  // namespace v0
 }  // namespace op
diff --git a/src/core/shape_inference/include/split_shape_inference.hpp b/src/core/shape_inference/include/split_shape_inference.hpp
index aeccd3d788b5c5..52a7c666cbbb67 100644
--- a/src/core/shape_inference/include/split_shape_inference.hpp
+++ b/src/core/shape_inference/include/split_shape_inference.hpp
@@ -47,7 +47,7 @@ std::vector<TRShape> shape_infer(const Split* op,
                               axes_values->size(),
                               " axes");
 
-        auto axis = ov::util::normalize_axis(op, (*axes_values)[0], data_rank);
+        auto axis = static_cast<size_t>(ov::util::normalize_axis(op, (*axes_values)[0], data_rank));
 
         if (data_ps[axis].is_static()) {
             const auto dimension_at_axis = data_ps[axis].get_length();
diff --git a/src/core/shape_inference/include/topk_shape_inference.hpp b/src/core/shape_inference/include/topk_shape_inference.hpp
index 85753ccf82f447..996f4e3e86ddae 100644
--- a/src/core/shape_inference/include/topk_shape_inference.hpp
+++ b/src/core/shape_inference/include/topk_shape_inference.hpp
@@ -68,7 +68,8 @@ std::vector<TRShape> shape_infer(const util::TopKBase* op,
 
     TRShape output_shape = input_shape;
     if (input_shape.rank().is_static()) {
-        const auto normalized_axis = ov::util::normalize_axis(op, op->get_provided_axis(), input_shape.rank());
+        const auto normalized_axis =
+            static_cast<size_t>(ov::util::normalize_axis(op, op->get_provided_axis(), input_shape.rank()));
         auto& dim_axis = output_shape[normalized_axis];
 
         if (auto k_as_shape =
diff --git a/src/core/shape_inference/include/variadic_split_shape_inference.hpp b/src/core/shape_inference/include/variadic_split_shape_inference.hpp
index 2694ee5e4594c6..dd08d46e818bc8 100644
--- a/src/core/shape_inference/include/variadic_split_shape_inference.hpp
+++ b/src/core/shape_inference/include/variadic_split_shape_inference.hpp
@@ -48,9 +48,8 @@ std::vector<TRShape> shape_infer(const VariadicSplit* op,
                                   "a scalar axis value is expected. Got: ",
                                   axis_values->size(),
                                   " axes");
-            const auto axis_val = (*axis_values)[0];
             // Adjust split axis in case of negatives
-            const int64_t axis = ov::util::normalize_axis(op, axis_val, data_shape.rank());
+            const auto axis = static_cast<size_t>(ov::util::normalize_axis(op, (*axis_values)[0], data_shape.rank()));
 
             if (auto split_lengths = get_input_const_data_as<TRShape, int64_t>(op, 2, ta)) {
                 // Adjust split lengths in case of negatives
diff --git a/src/core/src/axis_vector.cpp b/src/core/src/axis_vector.cpp
index 753af77926ef4b..39694bf3d3f271 100644
--- a/src/core/src/axis_vector.cpp
+++ b/src/core/src/axis_vector.cpp
@@ -31,6 +31,6 @@ ov::AxisVector& ov::AxisVector::operator=(const AxisVector& v) {
 }
 
 ov::AxisVector& ov::AxisVector::operator=(AxisVector&& v) noexcept {
-    static_cast<std::vector<size_t>*>(this)->operator=(v);
+    static_cast<std::vector<size_t>*>(this)->operator=(std::move(v));
     return *this;
 }
diff --git a/src/core/src/constant_fold_utils.cpp b/src/core/src/constant_fold_utils.cpp
index f8da2e3b53e5c6..f8d81bd287631e 100644
--- a/src/core/src/constant_fold_utils.cpp
+++ b/src/core/src/constant_fold_utils.cpp
@@ -179,16 +179,17 @@ std::shared_ptr<ov::Node> ov::util::convert_to_supported_precision(Node* const n
             type_relaxed->set_origin_input_type(origin_input_types[i], i);
         }
 
-        auto cloned_type_relaxed = std::dynamic_pointer_cast<op::TypeRelaxedBase>(cloned_node);
-        // Override TypeRelaxed types
-        for (size_t i = 0; i < num_inputs; i++) {
-            if (ov::util::is_type_unsupported(cloned_type_relaxed->get_origin_input_type(i))) {
-                cloned_type_relaxed->set_origin_input_type(cloned_node->get_input_element_type(i), i);
+        if (auto cloned_type_relaxed = std::dynamic_pointer_cast<op::TypeRelaxedBase>(cloned_node)) {
+            // Override TypeRelaxed types
+            for (size_t i = 0; i < num_inputs; i++) {
+                if (ov::util::is_type_unsupported(cloned_type_relaxed->get_origin_input_type(i))) {
+                    cloned_type_relaxed->set_origin_input_type(cloned_node->get_input_element_type(i), i);
+                }
             }
-        }
-        for (size_t i = 0; i < cloned_node->get_output_size(); i++) {
-            if (ov::util::is_type_unsupported(cloned_node->get_output_element_type(i))) {
-                cloned_type_relaxed->set_overridden_output_type(element::f32, i);
+            for (size_t i = 0; i < cloned_node->get_output_size(); i++) {
+                if (ov::util::is_type_unsupported(cloned_node->get_output_element_type(i))) {
+                    cloned_type_relaxed->set_overridden_output_type(element::f32, i);
+                }
             }
         }
         cloned_node->validate_and_infer_types();
diff --git a/src/core/src/graph_util.cpp b/src/core/src/graph_util.cpp
index dd46ad5151e9ad..5078f9cf3b073b 100644
--- a/src/core/src/graph_util.cpp
+++ b/src/core/src/graph_util.cpp
@@ -341,7 +341,7 @@ void save_model(const std::shared_ptr<const ov::Model>& m, const std::string& ou
     ov::pass::Manager manager;
     manager.register_pass<ov::pass::FusedNamesCleanup>();
     manager.register_pass<ov::pass::Serialize>(output_model, "");
-    manager.run_passes(cloned);
+    manager.run_passes(std::move(cloned));
 }
 
 bool is_used(Node* node);
diff --git a/src/core/src/model.cpp b/src/core/src/model.cpp
index ac2413318f22d1..88609222885f05 100644
--- a/src/core/src/model.cpp
+++ b/src/core/src/model.cpp
@@ -88,6 +88,43 @@ const std::shared_ptr<ov::Node>& verify_node(const std::shared_ptr<ov::Node>& no
     return node;
 }
 
+std::map<ov::Output<ov::Node>, ov::PartialShape> port_shapes_to_node_shapes(
+    ov::Model* model,
+    const std::map<size_t, ov::PartialShape>& partial_shapes) {
+    std::map<ov::Output<ov::Node>, ov::PartialShape> node_shapes;
+    for (const auto& it : partial_shapes) {
+        const auto port = model->input(it.first);
+        node_shapes[port] = it.second;
+    }
+    return node_shapes;
+}
+
+std::map<ov::Output<ov::Node>, ov::PartialShape> tensor_names_shapes_to_node_shapes(
+    ov::Model* model,
+    const std::map<std::string, ov::PartialShape>& partial_shapes) {
+    std::map<ov::Output<ov::Node>, ov::PartialShape> const_pshape;
+    std::unordered_map<ov::Node*, std::string> port_tensor_map;
+    for (const auto& it : partial_shapes) {
+        const auto port = model->input(it.first);
+        if (port_tensor_map.find(port.get_node()) != port_tensor_map.end()) {
+            OPENVINO_ASSERT(it.second == const_pshape.at(port),
+                            "Tensor with names {'",
+                            it.first,
+                            "', '",
+                            port_tensor_map[port.get_node()],
+                            "'} has "
+                            "conflicting shapes ",
+                            it.second,
+                            " and ",
+                            const_pshape.at(port),
+                            ", but they define the same tensor");
+        }
+        port_tensor_map[port.get_node()] = it.first;
+        const_pshape[port] = it.second;
+    }
+    return const_pshape;
+}
+
 }  // namespace
 
 ov::Model::Model(const ResultVector& results, const ov::ParameterVector& parameters, const std::string& name)
@@ -740,50 +777,25 @@ ov::Output<ov::Node> ov::Model::input(const std::string& tensor_name) {
     OPENVINO_THROW("Input for tensor name '", tensor_name, "' is not found.");
 }
 
-void ov::Model::reshape(const ov::PartialShape& partial_shape) {
-    OPENVINO_ASSERT(m_parameters.size() == 1,
-                    "reshape(const ov::PartialShape&) must be called on a Model with exactly one parameter.");
-    std::map<size_t, ov::PartialShape> shapes;
-    shapes[0] = partial_shape;
-    reshape(shapes);
+void ov::Model::reshape(const ov::PartialShape& partial_shape,
+                        const std::unordered_map<std::string, ov::PartialShape>& variable_shapes) {
+    OPENVINO_ASSERT(m_parameters.size() == 1, "must be called on a Model with exactly one parameter.");
+    std::map<size_t, ov::PartialShape> shapes{{0, partial_shape}};
+    reshape(shapes, variable_shapes);
 }
 
-void ov::Model::reshape(const std::map<size_t, ov::PartialShape>& partial_shapes) {
-    std::map<ov::Output<ov::Node>, ov::PartialShape> const_pshape;
-    std::unordered_map<ov::Node*, std::string> port_tensor_map;
-    for (const auto& it : partial_shapes) {
-        const auto port = input(it.first);
-        port_tensor_map[port.get_node()] = std::to_string(it.first);
-        const_pshape[port] = it.second;
-    }
-    reshape(const_pshape);
+void ov::Model::reshape(const std::map<size_t, ov::PartialShape>& partial_shapes,
+                        const std::unordered_map<std::string, ov::PartialShape>& variable_shapes) {
+    reshape(port_shapes_to_node_shapes(this, partial_shapes), variable_shapes);
 }
 
-void ov::Model::reshape(const std::map<std::string, ov::PartialShape>& partial_shapes) {
-    std::map<ov::Output<ov::Node>, ov::PartialShape> const_pshape;
-    std::unordered_map<ov::Node*, std::string> port_tensor_map;
-    for (const auto& it : partial_shapes) {
-        const auto port = input(it.first);
-        if (port_tensor_map.find(port.get_node()) != port_tensor_map.end()) {
-            OPENVINO_ASSERT(it.second == const_pshape.at(port),
-                            "Tensor with names {'",
-                            it.first,
-                            "', '",
-                            port_tensor_map[port.get_node()],
-                            "'} has "
-                            "conflicting shapes ",
-                            it.second,
-                            " and ",
-                            const_pshape.at(port),
-                            ", but they define the same tensor");
-        }
-        port_tensor_map[port.get_node()] = it.first;
-        const_pshape[port] = it.second;
-    }
-    reshape(const_pshape);
+void ov::Model::reshape(const std::map<std::string, ov::PartialShape>& partial_shapes,
+                        const std::unordered_map<std::string, ov::PartialShape>& variable_shapes) {
+    reshape(tensor_names_shapes_to_node_shapes(this, partial_shapes), variable_shapes);
 }
 
-void ov::Model::reshape(const std::map<ov::Output<ov::Node>, ov::PartialShape>& partial_shapes) {
+void ov::Model::reshape(const std::map<ov::Output<ov::Node>, ov::PartialShape>& partial_shapes,
+                        const std::unordered_map<std::string, ov::PartialShape>& variables_shapes) {
     if (partial_shapes.empty())
         return;
 
@@ -818,17 +830,42 @@ void ov::Model::reshape(const std::map<ov::Output<ov::Node>, ov::PartialShape>&
     if (!need_reshape)
         return;
 
+    std::unordered_map<op::util::Variable*, PartialShape> new_vars_shapes;
+    std::unordered_map<op::util::Variable*, PartialShape> original_vars_shapes;
+    for (const auto& variable : get_variables()) {
+        const auto& var_info = variable->get_info();
+
+        for (const auto& var_id_new_shape : variables_shapes) {
+            const auto& variable_id = var_id_new_shape.first;
+            const auto& new_shape = var_id_new_shape.second;
+            if (variable_id == var_info.variable_id && new_shape != var_info.data_shape) {
+                original_vars_shapes[variable.get()] = var_info.data_shape;
+                new_vars_shapes[variable.get()] = new_shape;
+            }
+        }
+    }
+
     // save original parameters shape
     std::unordered_map<ov::op::v0::Parameter*, ov::PartialShape> original_input_shapes;
     for (const auto& param : params) {
         original_input_shapes[param.get()] = param->get_output_partial_shape(0);
     }
 
-    auto reshape_only = [&](const std::unordered_map<ov::op::v0::Parameter*, ov::PartialShape>& pshapes) {
+    std::unordered_map<op::util::Variable*, PartialShape> original_var_shapes;
+    for (const auto& v : get_variables()) {
+        original_var_shapes[v.get()] = v->get_info().data_shape;
+    }
+
+    auto reshape_only = [this](const std::unordered_map<op::v0::Parameter*, PartialShape>& pshapes,
+                               const std::unordered_map<op::util::Variable*, PartialShape>& vars_shapes) {
         for (const auto& pshape : pshapes) {
             pshape.first->set_partial_shape(pshape.second);
         }
 
+        for (const auto& shape : vars_shapes) {
+            shape.first->update_data_shape(shape.second);
+        }
+
         validate_nodes_and_infer_types();
     };
 
@@ -837,10 +874,10 @@ void ov::Model::reshape(const std::map<ov::Output<ov::Node>, ov::PartialShape>&
         ssr_manager.register_pass<ov::pass::SmartReshape>();
         ssr_manager.run_passes(shared_from_this());
 
-        reshape_only(new_param_shapes);
+        reshape_only(new_param_shapes, new_vars_shapes);
     } catch (...) {
         // restore shapes to original ones
-        reshape_only(original_input_shapes);
+        reshape_only(original_input_shapes, original_vars_shapes);
         throw;
     }
 }
diff --git a/src/core/src/op/assign.cpp b/src/core/src/op/assign.cpp
index da79244127c317..bf6e55c11b1d39 100644
--- a/src/core/src/op/assign.cpp
+++ b/src/core/src/op/assign.cpp
@@ -29,7 +29,7 @@ void Assign::validate_and_infer_types() {
         for (const auto& input : inputs()) {
             start_nodes.push_back(input.get_source_output().get_node_shared_ptr());
         }
-        auto nodes = topological_sort(start_nodes);
+        auto nodes = topological_sort(std::move(start_nodes));
         for (const auto& node : nodes) {
             if (auto read_value = ov::as_type_ptr<v3::ReadValue>(node)) {
                 if (read_value->get_variable_id() == m_variable_id)
@@ -109,15 +109,15 @@ bool Assign::evaluate(TensorVector& outputs,
 
     auto& variable_context = const_cast<util::VariableContext&>(found_context->second.as<util::VariableContext>());
 
-    const auto& variable_values = variable_context.get_variable_values();
+    auto var_value = variable_context.get_variable_value(m_variable);
 
     // automatically allocate memory if not provided by user
-    if (variable_values.find(m_variable) == variable_values.end()) {
+    if (!var_value) {
         auto tensor = Tensor(m_variable->get_info().data_type, m_variable->get_info().data_shape.to_shape());
-        variable_context.set_variable_value(m_variable, std::make_shared<util::VariableValue>(tensor));
+        var_value = std::make_shared<util::VariableValue>(tensor);
+        variable_context.set_variable_value(m_variable, var_value);
     }
 
-    const auto var_value = variable_values.find(m_variable)->second;
     var_value->set_reset(false);
     auto memory_buffer = var_value->get_state();
     memory_buffer.set_shape(inputs[0].get_shape());
diff --git a/src/core/src/op/concat.cpp b/src/core/src/op/concat.cpp
index de24245fa28458..607848b0ef0555 100644
--- a/src/core/src/op/concat.cpp
+++ b/src/core/src/op/concat.cpp
@@ -39,9 +39,9 @@ void Concat::validate_and_infer_types() {
         input_shapes.push_back(get_input_partial_shape(i));
     }
 
-    const auto output_shape = shape_infer(this, input_shapes).front();
+    const auto output_shapes = shape_infer(this, input_shapes);
 
-    set_output_type(0, inputs_et, output_shape);
+    set_output_type(0, inputs_et, output_shapes[0]);
 }
 
 std::shared_ptr<Node> Concat::clone_with_new_inputs(const OutputVector& new_args) const {
diff --git a/src/core/src/op/constant.cpp b/src/core/src/op/constant.cpp
index 732d91561965f5..e84cad9341b9c9 100644
--- a/src/core/src/op/constant.cpp
+++ b/src/core/src/op/constant.cpp
@@ -12,6 +12,7 @@
 #include "compare.hpp"
 #include "element_visitor.hpp"
 #include "itt.hpp"
+#include "openvino/core/tensor_util.hpp"
 #include "openvino/core/type/element_iterator.hpp"
 #include "openvino/core/type/float16.hpp"
 #include "openvino/core/type/nf4.hpp"
@@ -147,6 +148,20 @@ template <element::Type_t ET, class T>
 void write_buffer(const std::vector<T>& source, void* buffer) {
     std::transform(source.begin(), source.end(), element::iterator<ET>(buffer), convert_if_in_element_range<ET, T>);
 }
+
+Strides calc_byte_strides(const Shape& shape, const element::Type& et) {
+    Strides strides;
+    if (!shape.empty() && et.bitwidth() >= 8) {
+        strides.resize(shape.size());
+        strides.back() = et.size();
+        std::transform(shape.crbegin(),
+                       shape.crend() - 1,
+                       strides.rbegin(),
+                       strides.rbegin() + 1,
+                       std::multiplies<size_t>());
+    }
+    return strides;
+}
 }  // namespace
 
 namespace v0 {
@@ -154,6 +169,7 @@ namespace v0 {
 Constant::Constant(const Tensor& tensor)
     : m_element_type{tensor.get_element_type()},
       m_shape{tensor.get_shape()},
+      m_byte_strides{m_element_type.bitwidth() >= 8 ? tensor.get_strides() : Strides{}},
       m_data{
           std::make_shared<SharedBuffer<Tensor>>(static_cast<char*>(tensor.data()), tensor.get_byte_size(), tensor)} {
     constructor_validate_and_infer_types();
@@ -191,7 +207,8 @@ Constant::Constant(const element::Type& type, const Shape& shape) : Constant(tru
 
 Constant::Constant(bool memset_allocation, const element::Type& type, const Shape& shape)
     : m_element_type(type),
-      m_shape(shape) {
+      m_shape(shape),
+      m_byte_strides{calc_byte_strides(m_shape, m_element_type)} {
     allocate_buffer(memset_allocation);
     constructor_validate_and_infer_types();
 }
@@ -225,6 +242,7 @@ Constant::Constant(const element::Type& type, const Shape& shape, const void* da
 Constant::Constant(const element::Type& type, const Shape& shape, const std::shared_ptr<ov::AlignedBuffer>& data)
     : m_element_type(type),
       m_shape(shape),
+      m_byte_strides(calc_byte_strides(m_shape, m_element_type)),
       m_data(data) {
     constructor_validate_and_infer_types();
 }
@@ -232,6 +250,7 @@ Constant::Constant(const element::Type& type, const Shape& shape, const std::sha
 Constant::Constant(const Constant& other)
     : m_element_type{other.m_element_type},
       m_shape{other.m_shape},
+      m_byte_strides{other.m_byte_strides},
       m_data{other.m_data},
       m_all_elements_bitwise_identical{other.m_all_elements_bitwise_identical.load()},
       m_all_elements_bitwise_identical_checked{other.m_all_elements_bitwise_identical_checked.load()} {
@@ -241,6 +260,7 @@ Constant::Constant(const Constant& other)
 Constant::Constant(const Constant& other, const Shape& new_shape)
     : m_element_type{other.m_element_type},
       m_shape{new_shape},
+      m_byte_strides{calc_byte_strides(m_shape, m_element_type)},
       m_data{other.m_data},
       m_all_elements_bitwise_identical{other.m_all_elements_bitwise_identical.load()},
       m_all_elements_bitwise_identical_checked{other.m_all_elements_bitwise_identical_checked.load()} {
@@ -314,7 +334,8 @@ std::string Constant::convert_value_to_string(size_t index) const {
                     f8e4m3,
                     f8e5m2,
                     string,
-                    f4e2m1>::apply<ValueToString>(get_element_type(), get_data_ptr(), index);
+                    f4e2m1,
+                    f8e8m0>::apply<ValueToString>(get_element_type(), get_data_ptr(), index);
 }
 
 size_t Constant::get_byte_size() const {
@@ -390,7 +411,8 @@ std::vector<std::string> Constant::get_value_strings() const {
              u64,
              nf4,
              string,
-             f4e2m1>::apply<ValuesToString>(get_element_type(), get_data_ptr(), shape_size(m_shape), out);
+             f4e2m1,
+             f8e8m0>::apply<ValuesToString>(get_element_type(), get_data_ptr(), shape_size(m_shape), out);
     return out;
 }
 
@@ -566,6 +588,17 @@ bool Constant::constant_fold(OutputVector&, const OutputVector&) {
     return false;
 }
 
+const Tensor Constant::get_tensor_view() const {
+    return m_data ? Tensor{m_element_type, m_shape, m_data->get_ptr(), m_byte_strides} : Tensor{};
+}
+
+const Strides& Constant::get_strides() const {
+    OPENVINO_ASSERT(m_element_type.bitwidth() >= 8,
+                    "Could not get strides for types with bit widths less then 8 bit. Type: ",
+                    m_element_type);
+    return m_byte_strides;
+}
+
 template <>
 Constant::LPBuffer<element::u1>::LPBuffer(void* ptr)
     : iter{std::make_shared<lp_iter>(reinterpret_cast<ov::fundamental_type_for<element::u1>*>(ptr))} {}
@@ -746,6 +779,7 @@ CONSTANT_FILL_DATA(u1, long long)
 CONSTANT_FILL_DATA(u1, unsigned long long)
 CONSTANT_FILL_DATA(u1, float8_e4m3)
 CONSTANT_FILL_DATA(u1, float8_e5m2)
+CONSTANT_FILL_DATA(u1, float8_e8m0)
 CONSTANT_FILL_DATA(u1, float16)
 CONSTANT_FILL_DATA(u1, bfloat16)
 CONSTANT_FILL_DATA(u1, float)
@@ -765,6 +799,7 @@ CONSTANT_FILL_DATA(u2, long long)
 CONSTANT_FILL_DATA(u2, unsigned long long)
 CONSTANT_FILL_DATA(u2, float8_e4m3)
 CONSTANT_FILL_DATA(u2, float8_e5m2)
+CONSTANT_FILL_DATA(u2, float8_e8m0)
 CONSTANT_FILL_DATA(u2, float16)
 CONSTANT_FILL_DATA(u2, bfloat16)
 CONSTANT_FILL_DATA(u2, float)
@@ -784,6 +819,7 @@ CONSTANT_FILL_DATA(u3, long long)
 CONSTANT_FILL_DATA(u3, unsigned long long)
 CONSTANT_FILL_DATA(u3, float8_e4m3)
 CONSTANT_FILL_DATA(u3, float8_e5m2)
+CONSTANT_FILL_DATA(u3, float8_e8m0)
 CONSTANT_FILL_DATA(u3, float16)
 CONSTANT_FILL_DATA(u3, bfloat16)
 CONSTANT_FILL_DATA(u3, float)
@@ -803,6 +839,7 @@ CONSTANT_FILL_DATA(u4, long long)
 CONSTANT_FILL_DATA(u4, unsigned long long)
 CONSTANT_FILL_DATA(u4, float8_e4m3)
 CONSTANT_FILL_DATA(u4, float8_e5m2)
+CONSTANT_FILL_DATA(u4, float8_e8m0)
 CONSTANT_FILL_DATA(u4, float16)
 CONSTANT_FILL_DATA(u4, bfloat16)
 CONSTANT_FILL_DATA(u4, float)
@@ -822,6 +859,7 @@ CONSTANT_FILL_DATA(u6, long long)
 CONSTANT_FILL_DATA(u6, unsigned long long)
 CONSTANT_FILL_DATA(u6, float8_e4m3)
 CONSTANT_FILL_DATA(u6, float8_e5m2)
+CONSTANT_FILL_DATA(u6, float8_e8m0)
 CONSTANT_FILL_DATA(u6, float16)
 CONSTANT_FILL_DATA(u6, bfloat16)
 CONSTANT_FILL_DATA(u6, float)
@@ -841,6 +879,7 @@ CONSTANT_FILL_DATA(i4, long long)
 CONSTANT_FILL_DATA(i4, unsigned long long)
 CONSTANT_FILL_DATA(i4, float8_e4m3)
 CONSTANT_FILL_DATA(i4, float8_e5m2)
+CONSTANT_FILL_DATA(i4, float8_e8m0)
 CONSTANT_FILL_DATA(i4, float16)
 CONSTANT_FILL_DATA(i4, bfloat16)
 CONSTANT_FILL_DATA(i4, float)
@@ -860,6 +899,7 @@ CONSTANT_FILL_DATA(nf4, long long)
 CONSTANT_FILL_DATA(nf4, unsigned long long)
 CONSTANT_FILL_DATA(nf4, float8_e4m3)
 CONSTANT_FILL_DATA(nf4, float8_e5m2)
+CONSTANT_FILL_DATA(nf4, float8_e8m0)
 CONSTANT_FILL_DATA(nf4, float16)
 CONSTANT_FILL_DATA(nf4, bfloat16)
 CONSTANT_FILL_DATA(nf4, float)
@@ -879,6 +919,7 @@ CONSTANT_FILL_DATA(f4e2m1, long long)
 CONSTANT_FILL_DATA(f4e2m1, unsigned long long)
 CONSTANT_FILL_DATA(f4e2m1, float8_e4m3)
 CONSTANT_FILL_DATA(f4e2m1, float8_e5m2)
+CONSTANT_FILL_DATA(f4e2m1, float8_e8m0)
 CONSTANT_FILL_DATA(f4e2m1, float16)
 CONSTANT_FILL_DATA(f4e2m1, bfloat16)
 CONSTANT_FILL_DATA(f4e2m1, float)
@@ -1034,6 +1075,7 @@ CONSTANT_WRITE_BUFFER(u1, long long)
 CONSTANT_WRITE_BUFFER(u1, unsigned long long)
 CONSTANT_WRITE_BUFFER(u1, float8_e4m3)
 CONSTANT_WRITE_BUFFER(u1, float8_e5m2)
+CONSTANT_WRITE_BUFFER(u1, float8_e8m0)
 CONSTANT_WRITE_BUFFER(u1, float16)
 CONSTANT_WRITE_BUFFER(u1, bfloat16)
 CONSTANT_WRITE_BUFFER(u1, float)
@@ -1053,6 +1095,7 @@ CONSTANT_WRITE_BUFFER(u2, long long)
 CONSTANT_WRITE_BUFFER(u2, unsigned long long)
 CONSTANT_WRITE_BUFFER(u2, float8_e4m3)
 CONSTANT_WRITE_BUFFER(u2, float8_e5m2)
+CONSTANT_WRITE_BUFFER(u2, float8_e8m0)
 CONSTANT_WRITE_BUFFER(u2, float16)
 CONSTANT_WRITE_BUFFER(u2, bfloat16)
 CONSTANT_WRITE_BUFFER(u2, float)
@@ -1072,6 +1115,7 @@ CONSTANT_WRITE_BUFFER(u3, long long)
 CONSTANT_WRITE_BUFFER(u3, unsigned long long)
 CONSTANT_WRITE_BUFFER(u3, float8_e4m3)
 CONSTANT_WRITE_BUFFER(u3, float8_e5m2)
+CONSTANT_WRITE_BUFFER(u3, float8_e8m0)
 CONSTANT_WRITE_BUFFER(u3, float16)
 CONSTANT_WRITE_BUFFER(u3, bfloat16)
 CONSTANT_WRITE_BUFFER(u3, float)
@@ -1091,6 +1135,7 @@ CONSTANT_WRITE_BUFFER(u4, long long)
 CONSTANT_WRITE_BUFFER(u4, unsigned long long)
 CONSTANT_WRITE_BUFFER(u4, float8_e4m3)
 CONSTANT_WRITE_BUFFER(u4, float8_e5m2)
+CONSTANT_WRITE_BUFFER(u4, float8_e8m0)
 CONSTANT_WRITE_BUFFER(u4, float16)
 CONSTANT_WRITE_BUFFER(u4, bfloat16)
 CONSTANT_WRITE_BUFFER(u4, float)
@@ -1110,6 +1155,7 @@ CONSTANT_WRITE_BUFFER(u6, long long)
 CONSTANT_WRITE_BUFFER(u6, unsigned long long)
 CONSTANT_WRITE_BUFFER(u6, float8_e4m3)
 CONSTANT_WRITE_BUFFER(u6, float8_e5m2)
+CONSTANT_WRITE_BUFFER(u6, float8_e8m0)
 CONSTANT_WRITE_BUFFER(u6, float16)
 CONSTANT_WRITE_BUFFER(u6, bfloat16)
 CONSTANT_WRITE_BUFFER(u6, float)
@@ -1129,6 +1175,7 @@ CONSTANT_WRITE_BUFFER(i4, long long)
 CONSTANT_WRITE_BUFFER(i4, unsigned long long)
 CONSTANT_WRITE_BUFFER(i4, float8_e4m3)
 CONSTANT_WRITE_BUFFER(i4, float8_e5m2)
+CONSTANT_WRITE_BUFFER(i4, float8_e8m0)
 CONSTANT_WRITE_BUFFER(i4, float16)
 CONSTANT_WRITE_BUFFER(i4, bfloat16)
 CONSTANT_WRITE_BUFFER(i4, float)
@@ -1148,6 +1195,7 @@ CONSTANT_WRITE_BUFFER(nf4, long long)
 CONSTANT_WRITE_BUFFER(nf4, unsigned long long)
 CONSTANT_WRITE_BUFFER(nf4, float8_e4m3)
 CONSTANT_WRITE_BUFFER(nf4, float8_e5m2)
+CONSTANT_WRITE_BUFFER(nf4, float8_e8m0)
 CONSTANT_WRITE_BUFFER(nf4, float16)
 CONSTANT_WRITE_BUFFER(nf4, bfloat16)
 CONSTANT_WRITE_BUFFER(nf4, float)
@@ -1167,6 +1215,7 @@ CONSTANT_WRITE_BUFFER(f4e2m1, long long)
 CONSTANT_WRITE_BUFFER(f4e2m1, unsigned long long)
 CONSTANT_WRITE_BUFFER(f4e2m1, float8_e4m3)
 CONSTANT_WRITE_BUFFER(f4e2m1, float8_e5m2)
+CONSTANT_WRITE_BUFFER(f4e2m1, float8_e8m0)
 CONSTANT_WRITE_BUFFER(f4e2m1, float16)
 CONSTANT_WRITE_BUFFER(f4e2m1, bfloat16)
 CONSTANT_WRITE_BUFFER(f4e2m1, float)
diff --git a/src/core/src/op/convert.cpp b/src/core/src/op/convert.cpp
index 3a62dcfed32e7c..8dd0675fe6a6ed 100644
--- a/src/core/src/op/convert.cpp
+++ b/src/core/src/op/convert.cpp
@@ -18,10 +18,11 @@ namespace convert {
 
 #define CONVERT_ET_LIST                                                                                              \
     boolean, bf16, f16, f32, f64, i4, i8, i16, i32, i64, u1, u2, u3, u4, u6, u8, u16, u32, u64, nf4, f8e4m3, f8e5m2, \
-        f4e2m1
+        f4e2m1, f8e8m0
 
-#define CONVERT_TO_ANY_NO_NF4 \
-    boolean, bf16, f16, f32, f64, i4, i8, i16, i32, i64, u1, u2, u3, u4, u6, u8, u16, u32, u64, f8e4m3, f8e5m2, f4e2m1
+#define CONVERT_TO_ANY_NO_NF4                                                                                   \
+    boolean, bf16, f16, f32, f64, i4, i8, i16, i32, i64, u1, u2, u3, u4, u6, u8, u16, u32, u64, f8e4m3, f8e5m2, \
+        f4e2m1, f8e8m0
 
 #define CONVERT_TO_ANY_NO_F4 \
     boolean, bf16, f16, f32, f64, i4, i8, i16, i32, i64, u1, u2, u3, u4, u6, u8, u16, u32, u64, f8e4m3, f8e5m2
@@ -29,11 +30,12 @@ namespace convert {
 struct Evaluate : public element::NoAction<bool> {
     using element::NoAction<bool>::visit;
 
-    // convert from any (except F16, bf16, f32, NF4) to any except NF4
+    // convert from any (except F16, bf16, f32, NF4, f8e8m0) to any except NF4
     template <element::Type_t ET_IN,
               class TI = fundamental_type_for<ET_IN>,
               typename std::enable_if<ET_IN != element::f16 && ET_IN != element::bf16 && ET_IN != element::f32 &&
-                                      ET_IN != element::nf4 && ET_IN != element::f4e2m1>::type* = nullptr>
+                                      ET_IN != element::nf4 && ET_IN != element::f4e2m1 &&
+                                      ET_IN != element::f8e8m0>::type* = nullptr>
     static result_type visit(const Tensor& arg, Tensor& out, const size_t count) {
         using namespace ov::element;
         return IF_TYPE_OF(Convert_out,
@@ -90,7 +92,7 @@ struct Evaluate : public element::NoAction<bool> {
                           count);
     }
 
-    // convert to F4E2M1
+    // convert from F4E2M1
     template <element::Type_t ET_IN,
               class TI = fundamental_type_for<ET_IN>,
               typename std::enable_if<ET_IN == element::f4e2m1>::type* = nullptr>
@@ -105,6 +107,21 @@ struct Evaluate : public element::NoAction<bool> {
                           count);
     }
 
+    // convert from F8E8M0
+    template <element::Type_t ET_IN,
+              class TI = fundamental_type_for<ET_IN>,
+              typename std::enable_if<ET_IN == element::f8e8m0>::type* = nullptr>
+    static result_type visit(const Tensor& arg, Tensor& out, const size_t count) {
+        using namespace ov::element;
+        return IF_TYPE_OF(Convert_out,
+                          OV_PP_ET_LIST(f16, bf16, f32, f8e8m0),
+                          EvalByOutputType,
+                          out.get_element_type(),
+                          iterator<ET_IN>(reinterpret_cast<const TI*>(arg.data())),
+                          out,
+                          count);
+    }
+
 private:
     struct EvalByOutputType : public element::NoAction<bool> {
         using element::NoAction<bool>::visit;
@@ -145,8 +162,8 @@ bool evaluate_bound(const Node* const node, TensorVector& output_values, const T
             return false;
 
         // dynamic values translation
-        auto input_dynamic_mask = Tensor(element::boolean, in_bound_shape);
-        auto outputs = TensorVector{input_dynamic_mask};
+        auto outputs = TensorVector{{element::boolean, in_bound_shape}};
+        const auto& input_dynamic_mask = outputs[0];
 
         return v1::Equal().evaluate(outputs, {input_bound, input_max}) &&
                v1::Select().evaluate(output_values, {input_dynamic_mask, output_max, output_values[0]});
@@ -208,8 +225,16 @@ bool Convert::has_evaluate() const {
         return (from == element::nf4) && (to == element::f16 || to == element::f32 || to == element::nf4);
     };
 
-    const auto can_convert_f4e2m1 = [](const element::Type& et) {
-        return et == element::f16 || et == element::bf16 || et == element::f32 || et == element::f4e2m1;
+    const auto can_convert_f16_bf16_f32 = [](const element::Type& et) {
+        return et == element::f16 || et == element::bf16 || et == element::f32;
+    };
+
+    const auto can_convert_f4e2m1 = [&](const element::Type& et) {
+        return can_convert_f16_bf16_f32(et) || et == element::f4e2m1;
+    };
+
+    const auto can_convert_f8e8m0 = [&](const element::Type& et) {
+        return can_convert_f16_bf16_f32(et) || et == element::f8e8m0;
     };
 
     const auto is_valid_type = [](const element::Type& et) -> bool {
@@ -242,7 +267,8 @@ bool Convert::has_evaluate() const {
     const auto& output_et = get_output_element_type(0);
 
     return (is_valid_type(input_et) && is_valid_type(output_et)) || is_to_nf4_supported(input_et, output_et) ||
-           (can_convert_f4e2m1(input_et) && can_convert_f4e2m1(output_et));
+           (can_convert_f4e2m1(input_et) && can_convert_f4e2m1(output_et)) ||
+           (can_convert_f8e8m0(input_et) && can_convert_f8e8m0(output_et));
 }
 
 bool Convert::evaluate_lower(TensorVector& output_values) const {
diff --git a/src/core/src/op/eye.cpp b/src/core/src/op/eye.cpp
index 1230ce9e4d8f9f..7d4505d52816bc 100644
--- a/src/core/src/op/eye.cpp
+++ b/src/core/src/op/eye.cpp
@@ -57,8 +57,8 @@ void Eye::validate_and_infer_types() {
                               input_et);
     }
 
-    const auto output_shape = shape_infer(this, ov::util::get_node_input_partial_shapes(*this)).front();
-    set_output_type(0, get_out_type(), output_shape);
+    const auto output_shapes = shape_infer(this, ov::util::get_node_input_partial_shapes(*this));
+    set_output_type(0, get_out_type(), output_shapes[0]);
 }
 
 bool Eye::visit_attributes(ov::AttributeVisitor& visitor) {
diff --git a/src/core/src/op/if.cpp b/src/core/src/op/if.cpp
index cf393e06aba859..d8ac09096e714f 100644
--- a/src/core/src/op/if.cpp
+++ b/src/core/src/op/if.cpp
@@ -40,9 +40,9 @@ static ov::PartialShape resolve_shape(const ov::PartialShape& then_pshape, const
             return ov::PartialShape::dynamic();
         }
     }
-    std::vector<ov::Dimension> new_dims;
+    ov::PartialShape new_dims;
 
-    // If rangs are equal each dimesion of then_body output is union with each dimension of
+    // If ranges are equal each dimension of then_body output is union with each dimension of
     // else_body
     for (auto then_it = then_pshape.cbegin(), else_it = else_pshape.cbegin(); then_it != then_pshape.cend();
          then_it++, else_it++) {
@@ -57,7 +57,7 @@ static ov::PartialShape resolve_shape(const ov::PartialShape& then_pshape, const
         }
     }
 
-    return ov::PartialShape(new_dims);
+    return new_dims;
 }
 
 bool ov::op::v8::If::visit_attributes(AttributeVisitor& visitor) {
diff --git a/src/core/src/op/loop.cpp b/src/core/src/op/loop.cpp
index 2d2ed726cea339..8c734e894a8e35 100644
--- a/src/core/src/op/loop.cpp
+++ b/src/core/src/op/loop.cpp
@@ -179,8 +179,8 @@ void Loop::validate_and_infer_types() {
                        as_type_ptr<op::v0::TensorIterator::InvariantInputDescription>(input_description)) {
             auto body_parameter = m_bodies[0]->get_parameters().at(invariant_input_description->m_body_parameter_index);
 
-            auto input_partial_shape = input(index).get_partial_shape();
-            auto input_type = input(index).get_element_type();
+            const auto& input_partial_shape = input(index).get_partial_shape();
+            const auto& input_type = input(index).get_element_type();
 
             body_parameter->set_partial_shape(input_partial_shape);
             body_parameter->set_element_type(input_type);
diff --git a/src/core/src/op/max_pool.cpp b/src/core/src/op/max_pool.cpp
index 78df2bf78b8664..741f57105efb4d 100644
--- a/src/core/src/op/max_pool.cpp
+++ b/src/core/src/op/max_pool.cpp
@@ -204,9 +204,9 @@ bool MaxPool::evaluate(TensorVector& outputs, const TensorVector& inputs) const
     const auto input_shapes = std::vector<PartialShape>{inputs[0].get_shape()};
     auto pads_begin = m_pads_begin;
     auto pads_end = m_pads_end;
-    const auto output_shape = shape_infer(this, input_shapes, pads_begin, pads_end).front();
+    const auto output_shapes = shape_infer(this, input_shapes, pads_begin, pads_end);
 
-    outputs[0].set_shape(output_shape.get_shape());
+    outputs[0].set_shape(output_shapes[0].get_shape());
     using namespace ov::element;
     return IF_TYPE_OF_CONVERT_TENSORS(v1_MaxPool_evaluate,
                                       this,
diff --git a/src/core/src/op/mod.cpp b/src/core/src/op/mod.cpp
index e741ddfe8b9efd..1d15ffb60d4971 100644
--- a/src/core/src/op/mod.cpp
+++ b/src/core/src/op/mod.cpp
@@ -110,8 +110,8 @@ Tensor evaluate_undefined_result_mask(const TensorVector& bounds) {
 
     const auto& in_et = bounds.front().get_element_type();
 
-    auto zero_t = ov::util::make_tensor_of_value(in_et, 0);
-    auto max_t = ov::util::make_tensor_of_max_value(in_et);
+    const auto zero_t = ov::util::make_tensor_of_value(in_et, 0);
+    const auto max_t = ov::util::make_tensor_of_max_value(in_et);
 
     const auto& v_ub = bounds[1];
     const auto& m_lb = bounds[2];
@@ -159,7 +159,7 @@ TensorVector get_bounds_with_valid_values(const TensorVector& bounds, const Tens
 
     auto m_bounds = TensorVector();
     m_bounds.reserve(bounds.size());
-    std::transform(bounds.cbegin(), bounds.cend(), std::back_inserter(m_bounds), [&](const Tensor& b) {
+    std::transform(bounds.cbegin(), bounds.cend(), std::back_inserter(m_bounds), [&](const Tensor& b) -> ov::Tensor {
         auto tmp = TensorVector{{b.get_element_type(), mask.get_shape()}};
         return select_op.evaluate(tmp, {mask, one_t, b}) ? tmp.front() : Tensor{};
     });
@@ -205,7 +205,7 @@ bool evaluate_bound(const Node* const op, TensorVector& outputs, bool is_lower)
         }
         // Set undefined bound value for results which cannot be calculated.
         const auto select_op = v1::Select();
-        const auto undefined_bound =
+        const auto& undefined_bound =
             is_lower ? ov::util::make_tensor_of_value(in_et, 0) : ov::util::make_tensor_of_max_value(in_et);
         return select_op.evaluate(outputs, {undefined_result_mask, undefined_bound, outputs.front()});
     } else {
diff --git a/src/core/src/op/multiply.cpp b/src/core/src/op/multiply.cpp
index 88dbd347d46edf..fa3ef518c03202 100644
--- a/src/core/src/op/multiply.cpp
+++ b/src/core/src/op/multiply.cpp
@@ -51,7 +51,7 @@ bool Multiply::evaluate(TensorVector& outputs, const TensorVector& inputs) const
                                       this,
                                       outputs,
                                       inputs,
-                                      OV_PP_ET_LIST(f32, f64, i8, i32, i64, u8, u32, u64),
+                                      OV_PP_ET_LIST(f32, f64, i32, i64, u32, u64),
                                       multiply::Evaluate,
                                       inputs[0].get_element_type(),
                                       inputs[0],
diff --git a/src/core/src/op/non_zero.cpp b/src/core/src/op/non_zero.cpp
index 7f8169354b70c0..83b98f19a60d4b 100644
--- a/src/core/src/op/non_zero.cpp
+++ b/src/core/src/op/non_zero.cpp
@@ -80,22 +80,18 @@ void NonZero::validate_and_infer_types() {
     if (input_shape.rank().compatible(0)) {
         set_output_type(0, m_output_type, PartialShape::dynamic(2));
     } else {
-        auto dim = Dimension{0, 1};
+        auto output_shape = PartialShape{input_shape.rank(), {0, 1}};
+        auto& dim = output_shape[1];
         for (auto&& d : input_shape)
             dim *= d;
-        set_output_type(0, m_output_type, PartialShape{input_shape.rank(), dim});
+        set_output_type(0, m_output_type, output_shape);
     }
 
     set_input_is_relevant_to_shape(0);
 
     if (const auto input_constant = ov::util::get_constant_from_source(input_value(0))) {
         // input_value is available to calculate output shape
-
-        // const_cast of Constant data is needed to avoid obsolete copy of this data into the Tensor.
-        // It's safe here as evaluate() method doesn't modify input Tensors.
-        const auto inputs = TensorVector{{input_constant->get_element_type(),
-                                          input_constant->get_shape(),
-                                          const_cast<void*>(input_constant->get_data_ptr())}};
+        const auto inputs = TensorVector{input_constant->get_tensor_view()};
         auto outputs = TensorVector{{m_output_type, {}}};
         if (!evaluate(outputs, inputs))
             return;
diff --git a/src/core/src/op/parameter.cpp b/src/core/src/op/parameter.cpp
index 16cd315f9dff0e..705c11f31196b5 100644
--- a/src/core/src/op/parameter.cpp
+++ b/src/core/src/op/parameter.cpp
@@ -84,7 +84,7 @@ bool AttributeAdapter<ParameterVector>::visit_attributes(AttributeVisitor& visit
         }
         visitor.on_attribute(index.str(), id);
         if (!m_ref[i]) {
-            m_ref[i] = ov::as_type_ptr<op::v0::Parameter>(visitor.get_registered_node(id));
+            m_ref[i] = ov::as_type_ptr<op::v0::Parameter>(visitor.get_registered_node(std::move(id)));
         }
     }
     return true;
diff --git a/src/core/src/op/random_uniform.cpp b/src/core/src/op/random_uniform.cpp
index 932d8cefa780fb..616f272c3c591d 100644
--- a/src/core/src/op/random_uniform.cpp
+++ b/src/core/src/op/random_uniform.cpp
@@ -19,6 +19,10 @@ inline bool shape_et(const element::Type& et) {
 inline bool out_et(const element::Type& et) {
     return et.is_real() || shape_et(et);
 }
+
+inline bool alignment(const PhilloxAlignment& alignment) {
+    return alignment == PhilloxAlignment::TENSORFLOW || alignment == PhilloxAlignment::PYTORCH;
+}
 }  // namespace validate
 
 RandomUniform::RandomUniform(const Output<Node>& out_shape,
@@ -26,11 +30,13 @@ RandomUniform::RandomUniform(const Output<Node>& out_shape,
                              const Output<Node>& max_val,
                              const ov::element::Type& out_type,
                              uint64_t global_seed,
-                             uint64_t op_seed)
+                             uint64_t op_seed,
+                             PhilloxAlignment alignment)
     : Op({out_shape, min_val, max_val}),
       m_output_type(out_type),
       m_global_seed(global_seed),
-      m_op_seed(op_seed) {
+      m_op_seed(op_seed),
+      m_alignment(alignment) {
     constructor_validate_and_infer_types();
 }
 
@@ -44,10 +50,13 @@ void RandomUniform::validate_and_infer_types() {
     const auto& min_et = get_input_element_type(1);
     const auto& max_et = get_input_element_type(2);
     const auto& out_et = get_out_type();
+    const auto& alignment = get_alignment();
+
     NODE_VALIDATION_CHECK(this, min_et == max_et, "'min_val' should have the same type as 'max_val'.");
     NODE_VALIDATION_CHECK(this,
                           validate::out_et(out_et) && (out_et == min_et),
                           "'min_val' and 'max_val' should have the same type as 'out_type' attribute.");
+    NODE_VALIDATION_CHECK(this, validate::alignment(alignment), "Unknown alignment mode provided to RandomUniform.");
 
     const auto input_shapes = ov::util::get_node_input_partial_shapes(*this);
     const auto output_shapes = shape_infer(this, input_shapes);
@@ -60,6 +69,7 @@ bool RandomUniform::visit_attributes(AttributeVisitor& visitor) {
     visitor.on_attribute("output_type", m_output_type);
     visitor.on_attribute("op_seed", m_op_seed);
     visitor.on_attribute("global_seed", m_global_seed);
+    visitor.on_attribute("alignment", m_alignment);
     return true;
 }
 
@@ -71,11 +81,52 @@ std::shared_ptr<Node> RandomUniform::clone_with_new_inputs(const OutputVector& n
                                                        new_args.at(2),
                                                        m_output_type,
                                                        m_global_seed,
-                                                       m_op_seed);
+                                                       m_op_seed,
+                                                       m_alignment);
     ru_copy->m_state = this->m_state;
     return ru_copy;
 }
 
+/// \return Turns off constant folding for RandomUniform operation.
+bool RandomUniform::constant_fold(OutputVector& output_values, const OutputVector& inputs_values) {
+    return false;
+}
+
+/// \return The output tensor type.
+const ov::element::Type& RandomUniform::get_out_type() const {
+    return m_output_type;
+}
+
+void RandomUniform::set_out_type(const ov::element::Type& output_type) {
+    m_output_type = output_type;
+}
+
+/// \return The global seed value.
+uint64_t RandomUniform::get_global_seed() const {
+    return m_global_seed;
+}
+void RandomUniform::set_global_seed(uint64_t seed) {
+    m_global_seed = seed;
+}
+
+/// \return The operational seed value.
+uint64_t RandomUniform::get_op_seed() const {
+    return m_op_seed;
+}
+void RandomUniform::set_op_seed(uint64_t seed2) {
+    m_op_seed = seed2;
+}
+
+/// \return The state value.
+std::pair<uint64_t, uint64_t> RandomUniform::get_state() const {
+    return m_state;
+}
+
+/// \return The alignment mode.
+PhilloxAlignment RandomUniform::get_alignment() const {
+    return m_alignment;
+}
+
 bool RandomUniform::evaluate(TensorVector& outputs, const TensorVector& inputs) const {
     OV_OP_SCOPE(v8_RandomUniform_evaluate);
 
@@ -100,7 +151,8 @@ bool RandomUniform::evaluate(TensorVector& outputs, const TensorVector& inputs)
                                             get_out_type(),
                                             get_global_seed(),
                                             get_op_seed(),
-                                            m_state);
+                                            m_state,
+                                            m_alignment);
     return true;
 }
 
diff --git a/src/core/src/op/result.cpp b/src/core/src/op/result.cpp
index b500ae13100ad2..3667e5ff22b422 100644
--- a/src/core/src/op/result.cpp
+++ b/src/core/src/op/result.cpp
@@ -99,7 +99,7 @@ bool AttributeAdapter<ResultVector>::visit_attributes(AttributeVisitor& visitor)
         }
         visitor.on_attribute(index.str(), id);
         if (!m_ref[i]) {
-            m_ref[i] = as_type_ptr<op::v0::Result>(visitor.get_registered_node(id));
+            m_ref[i] = as_type_ptr<op::v0::Result>(visitor.get_registered_node(std::move(id)));
         }
     }
     return true;
diff --git a/src/core/src/op/space_to_batch.cpp b/src/core/src/op/space_to_batch.cpp
index a641586c6879aa..7d7880f937eba2 100644
--- a/src/core/src/op/space_to_batch.cpp
+++ b/src/core/src/op/space_to_batch.cpp
@@ -113,7 +113,7 @@ bool evaluate(TensorVector& outputs, const TensorVector& inputs) {
                    pads_begin_vec,
                    pads_end_vec,
                    op::PadMode::CONSTANT);
-    data_shape = padded_shape;
+    data_shape = std::move(padded_shape);
 
     Shape dispersed_shape(block_values_size + 1);
     std::vector<size_t> axes_order(block_values_size + 1);
diff --git a/src/core/src/op/type_relaxed.cpp b/src/core/src/op/type_relaxed.cpp
index 09e4d37cfe0a5f..67a44b410cde57 100644
--- a/src/core/src/op/type_relaxed.cpp
+++ b/src/core/src/op/type_relaxed.cpp
@@ -56,9 +56,10 @@ void TypeRelaxedBase::restore_input_data_types(Node& node, const element::TypeVe
     }
 }
 
-TemporaryReplaceOutputType::TemporaryReplaceOutputType(Output<Node> output, element::Type tmp_type) : m_output(output) {
+TemporaryReplaceOutputType::TemporaryReplaceOutputType(Output<Node> output, element::Type tmp_type)
+    : m_output(std::move(output)),
+      orig_type(m_output.get_element_type()) {
     // save original element type in order to restore it in the destructor
-    orig_type = m_output.get_element_type();
     ov::descriptor::set_element_type(m_output.get_tensor(), tmp_type);
 }
 
diff --git a/src/core/src/op/unique.cpp b/src/core/src/op/unique.cpp
index 8088f1fa7098d4..b00f207f820ae6 100644
--- a/src/core/src/op/unique.cpp
+++ b/src/core/src/op/unique.cpp
@@ -163,11 +163,9 @@ void op::v10::Unique::validate_and_infer_types() {
                     rev_idx_size = Dimension{dim_at_axis.get_max_length()};
                 }
 
-                auto output_shape = input_shape;
-                output_shape[normalized_axis] = output_dim_at_axis;
-                output_shapes[0] = output_shape;
-
-                output_shapes[2] = PartialShape{rev_idx_size};
+                output_shapes[0] = input_shape;
+                output_shapes[0][normalized_axis] = std::move(output_dim_at_axis);
+                output_shapes[2] = PartialShape{std::move(rev_idx_size)};
             }
         } else {
             // no axis => flattened input tensor
diff --git a/src/core/src/op/util/attr_types.cpp b/src/core/src/op/util/attr_types.cpp
index 56d01820b386d0..8cf4696f7a4f06 100644
--- a/src/core/src/op/util/attr_types.cpp
+++ b/src/core/src/op/util/attr_types.cpp
@@ -85,6 +85,15 @@ OPENVINO_API EnumNames<ov::op::TopKMode>& EnumNames<ov::op::TopKMode>::get() {
     return enum_names;
 }
 
+template <>
+OPENVINO_API EnumNames<ov::op::PhilloxAlignment>& EnumNames<ov::op::PhilloxAlignment>::get() {
+    static auto enum_names = EnumNames<ov::op::PhilloxAlignment>("ov::op::PhilloxAlignment",
+                                                                 {{"pytorch", ov::op::PhilloxAlignment::PYTORCH},
+                                                                  {"tensorflow", ov::op::PhilloxAlignment::TENSORFLOW},
+                                                                  {"mock", ov::op::PhilloxAlignment::MOCK}});
+    return enum_names;
+}
+
 bool AttributeAdapter<ov::op::AutoBroadcastSpec>::visit_attributes(AttributeVisitor& visitor) {
     // Maintain back-compatibility
     std::string name = visitor.finish_structure();
@@ -151,6 +160,10 @@ std::ostream& op::operator<<(std::ostream& s, const ov::op::TopKMode& type) {
     return s << as_string(type);
 }
 
+std::ostream& op::operator<<(std::ostream& s, const ov::op::PhilloxAlignment& type) {
+    return s << as_string(type);
+}
+
 op::AutoBroadcastType op::AutoBroadcastSpec::type_from_string(const std::string& type) const {
     auto lowercase_type = type;
     std::transform(lowercase_type.begin(), lowercase_type.end(), lowercase_type.begin(), [](char c) {
diff --git a/src/core/src/op/util/broadcast_base.cpp b/src/core/src/op/util/broadcast_base.cpp
index 1d4f7c1541ae1f..59154e45e2b37a 100644
--- a/src/core/src/op/util/broadcast_base.cpp
+++ b/src/core/src/op/util/broadcast_base.cpp
@@ -197,17 +197,16 @@ void ov::op::util::BroadcastBase::validate_and_infer_types() {
 
         if (!output_shape_defined && concat->get_output_partial_shape(0).is_static() &&
             concat->get_shape().size() == 1 && concat_inputs.size() == shape_size(concat->get_shape())) {
-            auto output_partial_shape = std::vector<Dimension>{};
+            output_shape.resize(0);
             for (const auto& concat_input : concat_inputs) {
                 auto source_node_ptr = concat_input.get_source_output().get_node_shared_ptr();
                 if (auto source_const_ptr = ov::as_type_ptr<ov::op::v0::Constant>(source_node_ptr)) {
-                    output_partial_shape.emplace_back(source_const_ptr->get_axis_vector_val()[0]);
+                    output_shape.emplace_back(source_const_ptr->get_axis_vector_val()[0]);
                 } else {
-                    output_partial_shape.push_back(Dimension::dynamic());
+                    output_shape.push_back(Dimension::dynamic());
                 }
             }
             output_shape_defined = true;
-            output_shape = PartialShape(output_partial_shape);
         }
     }
 
diff --git a/src/core/src/op/util/framework_node.cpp b/src/core/src/op/util/framework_node.cpp
index 9a3389a020b8a7..a7a71203c3d1c0 100644
--- a/src/core/src/op/util/framework_node.cpp
+++ b/src/core/src/op/util/framework_node.cpp
@@ -94,7 +94,7 @@ void ov::op::util::FrameworkNode::validate_and_infer_types() {
                 pshape = shape_map.at(output_index);
             }
             if (PartialShape::merge_into(pshape, node_result.get_partial_shape())) {
-                shape_map[output_index] = pshape;
+                shape_map[output_index] = std::move(pshape);
             } else {
                 shape_map[output_index] = PartialShape::dynamic();
             }
diff --git a/src/core/src/op/util/index_reduction.cpp b/src/core/src/op/util/index_reduction.cpp
index dac49b32c7738b..1c40af33785e73 100644
--- a/src/core/src/op/util/index_reduction.cpp
+++ b/src/core/src/op/util/index_reduction.cpp
@@ -70,7 +70,7 @@ void ov::op::util::IndexReduction::validate_and_infer_types() {
             output_dims[i] = arg_shape[j++];
         }
 
-        output_shape = PartialShape(output_dims);
+        output_shape = PartialShape(std::move(output_dims));
     }
 
     set_output_type(0, m_index_element_type, output_shape);
diff --git a/src/core/src/opsets/opset.cpp b/src/core/src/opsets/opset.cpp
index 590cf12e7c170b..1013a4f353fb80 100644
--- a/src/core/src/opsets/opset.cpp
+++ b/src/core/src/opsets/opset.cpp
@@ -41,7 +41,7 @@ ov::Node* ov::OpSet::create(const std::string& name) const {
         return nullptr;
     }
     REGISTER_OP(m_name, name);
-    return m_factory_registry.find(type_info_it->second)->second();
+    return m_factory_registry.at(type_info_it->second)();
 }
 
 ov::Node* ov::OpSet::create_insensitive(const std::string& name) const {
@@ -51,7 +51,7 @@ ov::Node* ov::OpSet::create_insensitive(const std::string& name) const {
         return nullptr;
     }
     REGISTER_OP(m_name, name);
-    return m_factory_registry.find(type_info_it->second)->second();
+    return m_factory_registry.at(type_info_it->second)();
 }
 
 bool ov::OpSet::contains_type(const ov::NodeTypeInfo& type_info) const {
diff --git a/src/core/src/pass/graph_rewrite.cpp b/src/core/src/pass/graph_rewrite.cpp
index 7c1942a7f25664..4d7de2c7e60a93 100644
--- a/src/core/src/pass/graph_rewrite.cpp
+++ b/src/core/src/pass/graph_rewrite.cpp
@@ -150,7 +150,7 @@ bool ov::pass::GraphRewrite::apply_matcher_passes(std::shared_ptr<Model> f,
 
         // Apply MatcherPass. In case if it returns true no other MatcherPasses will apply
         // to this node
-        bool status = m_pass->apply(node);
+        bool status = m_pass->apply(std::move(node));
 
         // In case if MatcherPass registered nodes they will be added to the beginning of execution
         // queue
diff --git a/src/core/src/pass/low_latency.cpp b/src/core/src/pass/low_latency.cpp
index 2dd75aa072e4fb..d3ccd79769e59c 100644
--- a/src/core/src/pass/low_latency.cpp
+++ b/src/core/src/pass/low_latency.cpp
@@ -74,7 +74,7 @@ void unroll_single_iteration(const std::shared_ptr<ov::op::util::SubGraphOp>& su
     }
     outer_f->add_sinks(sub_graph_op->get_function()->get_sinks());
     ov::copy_runtime_info(sub_graph_op, sub_graph_op->get_function()->get_ops());
-    ov::copy_runtime_info(sub_graph_op, new_ops);
+    ov::copy_runtime_info(sub_graph_op, std::move(new_ops));
 }
 
 ov::Output<ov::Node> create_init_subgraph(const ov::Output<ov::Node>& in_node, ov::pass::NodeRegistry& to) {
diff --git a/src/core/src/pass/make_stateful.cpp b/src/core/src/pass/make_stateful.cpp
index 8a39e667e8d19a..4b6c26e59c8a8e 100644
--- a/src/core/src/pass/make_stateful.cpp
+++ b/src/core/src/pass/make_stateful.cpp
@@ -100,7 +100,7 @@ bool ov::pass::MakeStateful::run_on_model(const std::shared_ptr<ov::Model>& f) {
         const auto& res = m_param_res_pairs[i].second;
 
         // Create Variable
-        std::string var_name = variable_names[i];
+        const auto& var_name = variable_names[i];
         auto variable = std::make_shared<ov::op::util::Variable>(
             ov::op::util::VariableInfo{param->get_partial_shape(), param->get_element_type(), var_name});
         variables.push_back(variable);
diff --git a/src/core/src/pass/pass.cpp b/src/core/src/pass/pass.cpp
index 629accb04ead17..3c816f9cad41c5 100644
--- a/src/core/src/pass/pass.cpp
+++ b/src/core/src/pass/pass.cpp
@@ -14,7 +14,7 @@
 
 using namespace std;
 
-ov::pass::PassBase::PassBase() : m_property(), m_pass_config(std::make_shared<PassConfig>()) {}
+ov::pass::PassBase::PassBase() : m_property(), m_name(), m_pass_config(std::make_shared<PassConfig>()) {}
 
 bool ov::pass::PassBase::get_property(const PassPropertyMask& prop) const {
     return m_property.is_set(prop);
diff --git a/src/core/src/pass/sdpa_to_paged_attention.cpp b/src/core/src/pass/sdpa_to_paged_attention.cpp
index 0d71c6a4b0d8dc..ace48f53f68a55 100644
--- a/src/core/src/pass/sdpa_to_paged_attention.cpp
+++ b/src/core/src/pass/sdpa_to_paged_attention.cpp
@@ -139,7 +139,7 @@ bool ov::pass::SDPAToPagedAttention::run_on_model(const std::shared_ptr<ov::Mode
 
     model->add_parameters(kv_parameters);
     model->add_parameters(model_remaining_params);
-    model->add_parameters({max_context_len});
+    model->add_parameters({std::move(max_context_len)});
     model->validate_nodes_and_infer_types();
     return true;
 }
diff --git a/src/core/src/pass/serialize.cpp b/src/core/src/pass/serialize.cpp
index 19e2a8031b3708..b4ae8f09c98d82 100644
--- a/src/core/src/pass/serialize.cpp
+++ b/src/core/src/pass/serialize.cpp
@@ -769,6 +769,8 @@ std::string get_precision_name(const ov::element::Type& elem_type) {
         return "STRING";
     case ::ov::element::Type_t::f4e2m1:
         return "F4E2M1";
+    case ::ov::element::Type_t::f8e8m0:
+        return "F8E8M0";
     default:
         OPENVINO_THROW("Unsupported precision: ", elem_type);
     }
@@ -941,7 +943,7 @@ void ngfunction_2_ir(pugi::xml_node& netXml,
         for (const auto& res : model.get_results()) {
             result.emplace_back(res);
         }
-        sorted_ops = result;
+        sorted_ops = std::move(result);
     }
 
     for (const auto& n : sorted_ops) {
diff --git a/src/core/src/pass/visualize_tree.cpp b/src/core/src/pass/visualize_tree.cpp
index 4d1184397c2ea0..a0d0b4a05e3094 100644
--- a/src/core/src/pass/visualize_tree.cpp
+++ b/src/core/src/pass/visualize_tree.cpp
@@ -458,6 +458,7 @@ static std::string get_value(const std::shared_ptr<ov::op::v0::Constant>& consta
     case ov::element::Type_t::f8e4m3:
     case ov::element::Type_t::f8e5m2:
     case ov::element::Type_t::f4e2m1:
+    case ov::element::Type_t::f8e8m0:
         ss << constant->get_output_element_type(0).get_type_name() << " value";
         break;
     case ov::element::Type_t::bf16:
diff --git a/src/core/src/pattern/op/branch.cpp b/src/core/src/pattern/op/branch.cpp
deleted file mode 100644
index 2215ca3976aac0..00000000000000
--- a/src/core/src/pattern/op/branch.cpp
+++ /dev/null
@@ -1,13 +0,0 @@
-// Copyright (C) 2018-2024 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include "openvino/pass/pattern/op/branch.hpp"
-
-#include "openvino/pass/pattern/matcher.hpp"
-
-bool ov::pass::pattern::op::Branch::match_value(Matcher* matcher,
-                                                const Output<Node>& pattern_value,
-                                                const Output<Node>& graph_value) {
-    return matcher->match_value(get_destination(), graph_value);
-}
diff --git a/src/core/src/pattern/op/capture.cpp b/src/core/src/pattern/op/capture.cpp
deleted file mode 100644
index 27f05c1e666c3f..00000000000000
--- a/src/core/src/pattern/op/capture.cpp
+++ /dev/null
@@ -1,14 +0,0 @@
-// Copyright (C) 2018-2024 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include "openvino/pass/pattern/op/capture.hpp"
-
-#include "openvino/pass/pattern/matcher.hpp"
-
-bool ov::pass::pattern::op::Capture::match_value(Matcher* matcher,
-                                                 const Output<Node>& pattern_value,
-                                                 const Output<Node>& graph_value) {
-    matcher->capture(m_static_nodes);
-    return true;
-}
diff --git a/src/core/src/pattern/op/pattern.cpp b/src/core/src/pattern/op/pattern.cpp
index e0e0810deb5e59..93187026f5677c 100644
--- a/src/core/src/pattern/op/pattern.cpp
+++ b/src/core/src/pattern/op/pattern.cpp
@@ -11,19 +11,34 @@ namespace ov {
 namespace pass {
 namespace pattern {
 namespace op {
+namespace {
+constexpr bool node_value_true_predicate(const Output<Node>&) {
+    return true;
+}
+}  // namespace
+
+struct NodeValuePredicate {
+    bool operator()(const Output<Node>& value) const {
+        return pred(value.get_node_shared_ptr());
+    }
+
+    NodePredicate pred;
+};
+
+Pattern::Pattern(const OutputVector& patterns, ValuePredicate pred)
+    : Node(patterns),
+      m_predicate(pred ? std::move(pred) : node_value_true_predicate) {}
+
 // The symbols are required to be in cpp file to workaround RTTI issue on Android LLVM
 ValuePredicate Pattern::get_predicate() const {
     return m_predicate;
 }
+
 ValuePredicate as_value_predicate(NodePredicate pred) {
-    if (pred == nullptr) {
-        return [](const Output<Node>&) {
-            return true;
-        };
+    if (pred) {
+        return NodeValuePredicate{std::move(pred)};
     } else {
-        return [pred](const Output<Node>& value) {
-            return pred(value.get_node_shared_ptr());
-        };
+        return node_value_true_predicate;
     }
 }
 }  // namespace op
diff --git a/src/core/src/pattern/op/skip.cpp b/src/core/src/pattern/op/skip.cpp
deleted file mode 100644
index 7d653a9711dd96..00000000000000
--- a/src/core/src/pattern/op/skip.cpp
+++ /dev/null
@@ -1,16 +0,0 @@
-// Copyright (C) 2018-2024 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include "openvino/pass/pattern/op/skip.hpp"
-
-#include "openvino/pass/pattern/matcher.hpp"
-
-bool ov::pass::pattern::op::Skip::match_value(Matcher* matcher,
-                                              const Output<Node>& pattern_value,
-                                              const Output<Node>& graph_value) {
-    matcher->add_node(graph_value);
-    return m_predicate(graph_value)
-               ? matcher->match_arguments(pattern_value.get_node(), graph_value.get_node_shared_ptr())
-               : matcher->match_value(input_value(0), graph_value);
-}
diff --git a/src/core/src/preprocess/preprocess_impls.cpp b/src/core/src/preprocess/preprocess_impls.cpp
index a3a3c36291fc22..13a4c6f1353312 100644
--- a/src/core/src/preprocess/preprocess_impls.cpp
+++ b/src/core/src/preprocess/preprocess_impls.cpp
@@ -69,7 +69,7 @@ InputInfo::InputInfoImpl::InputInfoData InputInfo::InputInfoImpl::create_new_par
                            [&](int64_t v) -> const Dimension& {
                                return new_param_shape[v];
                            });
-            new_param_shape = PartialShape(dims);
+            new_param_shape = PartialShape(std::move(dims));
         }
     } else {
         Layout new_layout;
@@ -77,7 +77,7 @@ InputInfo::InputInfoImpl::InputInfoData InputInfo::InputInfoImpl::create_new_par
             get_preprocess()->calculate_param_shape(new_param_shape, res.m_model_layout);
         if (res.m_tensor_layout.empty()) {
             // Reusing param's layout according to converted calculated layout
-            res.m_tensor_layout = new_layout;
+            res.m_tensor_layout = std::move(new_layout);
         }
     }
 
diff --git a/src/core/src/preprocess/preprocess_steps_impl.cpp b/src/core/src/preprocess/preprocess_steps_impl.cpp
index 522e545b714509..e494b1112d9fd9 100644
--- a/src/core/src/preprocess/preprocess_steps_impl.cpp
+++ b/src/core/src/preprocess/preprocess_steps_impl.cpp
@@ -349,7 +349,7 @@ void PreStepsList::add_convert_layout_impl(const Layout& layout) {
             auto perm_constant =
                 op::v0::Constant::create<int64_t>(element::i64, Shape{permutation.size()}, permutation);
             auto transpose = std::make_shared<op::v1::Transpose>(node, perm_constant);
-            context.layout() = dst_layout;  // Update context's current layout
+            context.layout() = std::move(dst_layout);  // Update context's current layout
             // return false to avoid excess function revalidations as layout conversion
             // doesn't require shape or type propagation.
             return std::make_tuple(std::vector<Output<Node>>{transpose}, false);
@@ -552,7 +552,7 @@ void PreStepsList::add_convert_color_impl(const ColorFormat& dst_format) {
                                                                  ov::Strides(weights_shape.size() - 2, 1));
 
                 if (is_converted) {
-                    // Round values according to OpenCV rule before converting to integral values
+                    // Roundp values according to OpenCV rule before converting to integral values
                     auto round_val =
                         std::make_shared<ov::op::v5::Round>(node, ov::op::v5::Round::RoundMode::HALF_TO_EVEN);
                     node = std::make_shared<op::v0::Convert>(round_val, elem_type);
@@ -565,7 +565,7 @@ void PreStepsList::add_convert_color_impl(const ColorFormat& dst_format) {
                     node = std::make_shared<op::v1::Transpose>(node, perm_constant);
                 }
                 context.color_format() = dst_format;
-                return std::make_tuple(std::vector<Output<Node>>{node}, true);
+                return std::make_tuple(std::vector<Output<Node>>{std::move(node)}, true);
             }
             if (context.color_format() == ColorFormat::RGBX) {
                 if (dst_format == ColorFormat::RGB) {
@@ -722,7 +722,7 @@ void PostStepsList::add_convert_layout_impl(const Layout& layout) {
             auto perm_constant =
                 op::v0::Constant::create<int64_t>(element::i64, Shape{permutation.size()}, permutation);
             auto transpose = std::make_shared<op::v1::Transpose>(node, perm_constant);
-            context.layout() = dst_layout;  // Update context's current layout
+            context.layout() = std::move(dst_layout);  // Update context's current layout
             return std::make_tuple(Output<Node>(transpose), true);
         },
         "convert layout " + layout.to_string());
diff --git a/src/core/src/runtime/itensor.cpp b/src/core/src/runtime/itensor.cpp
index e7ce33c28dfd19..203297c671d401 100644
--- a/src/core/src/runtime/itensor.cpp
+++ b/src/core/src/runtime/itensor.cpp
@@ -147,8 +147,8 @@ void ITensor::copy_to(const std::shared_ptr<ov::ITensor>& dst) const {
             max_pos[inverted_idx] = shape[inverted_idx];
             cur_pos[inverted_idx] = 0;
         }
-        src_strides = src_str;
-        dst_strides = dst_str;
+        src_strides = std::move(src_str);
+        dst_strides = std::move(dst_str);
     }
 
     const auto update_index = [](const ov::Shape& pos, const ov::Shape& shape, const ov::Strides& strides) {
diff --git a/src/core/src/strides.cpp b/src/core/src/strides.cpp
index c9e92b18fef74f..e09b99c3cb5e9e 100644
--- a/src/core/src/strides.cpp
+++ b/src/core/src/strides.cpp
@@ -29,6 +29,6 @@ ov::Strides& ov::Strides::operator=(const Strides& v) {
 }
 
 ov::Strides& ov::Strides::operator=(Strides&& v) noexcept {
-    static_cast<std::vector<size_t>*>(this)->operator=(v);
+    static_cast<std::vector<size_t>*>(this)->operator=(std::move(v));
     return *this;
 }
diff --git a/src/core/src/symbol.cpp b/src/core/src/symbol.cpp
index a17fc62d5d2045..48c913bfb53881 100644
--- a/src/core/src/symbol.cpp
+++ b/src/core/src/symbol.cpp
@@ -26,5 +26,5 @@ void ov::symbol::set_equal(const std::shared_ptr<Symbol>& lhs, const std::shared
     auto lhs_root = ov::symbol::ancestor_of(lhs), rhs_root = ov::symbol::ancestor_of(rhs);
     if (lhs_root.get() == rhs_root.get())
         return;  // already are equal
-    lhs_root->m_parent = rhs_root;
+    lhs_root->m_parent = std::move(rhs_root);
 }
diff --git a/src/core/src/tensor_util.cpp b/src/core/src/tensor_util.cpp
index a0cb1909f26fed..394f342f8af994 100644
--- a/src/core/src/tensor_util.cpp
+++ b/src/core/src/tensor_util.cpp
@@ -10,10 +10,9 @@
 ov::Tensor ov::util::greater_equal(const ov::Tensor& lhs, const ov::Tensor& rhs) {
     if (!lhs || !rhs)
         return {};
-    Tensor result(element::boolean, {});
-    TensorVector outputs = {result};
+    TensorVector outputs{{element::boolean, {}}};
     if (ov::op::v1::GreaterEqual().evaluate(outputs, {lhs, rhs}))
-        return outputs[0];
+        return std::move(outputs[0]);
     else
         return {};
 }
@@ -25,7 +24,7 @@ bool ov::util::reduce_and(const ov::Tensor& t) {
     auto outputs = TensorVector{{element::boolean, Shape{}}};
     auto axes = Tensor(element::i64, Shape{t.get_shape().size()});
     std::iota(axes.data<int64_t>(), axes.data<int64_t>() + t.get_shape().size(), 0);
-    if (!ov::op::v1::ReduceLogicalAnd().evaluate(outputs, {t, axes}))
+    if (!ov::op::v1::ReduceLogicalAnd().evaluate(outputs, {t, std::move(axes)}))
         return false;
     return outputs[0].data<char>();
 }
diff --git a/src/core/src/type/element_type.cpp b/src/core/src/type/element_type.cpp
index 3d8e6413a86a11..c0fad90285e3f6 100644
--- a/src/core/src/type/element_type.cpp
+++ b/src/core/src/type/element_type.cpp
@@ -85,6 +85,8 @@ inline TypeInfo get_type_info(ov::element::Type_t type) {
         return {8 * sizeof(std::string), false, false, false, "string", "string"};
     case ov::element::Type_t::f4e2m1:
         return {4, true, true, true, "f4e2m1", "f4e2m1"};
+    case ov::element::Type_t::f8e8m0:
+        return {8, true, true, true, "f8e8m0", "f8e8m0"};
     default:
         OPENVINO_THROW("ov::element::Type_t not supported: ", type);
     }
@@ -143,6 +145,8 @@ ov::element::Type type_from_string(const std::string& type) {
         return ::ov::element::Type(::ov::element::Type_t::f8e5m2);
     } else if (type == "f4e2m1" || type == "F4E2M1") {
         return ::ov::element::Type(::ov::element::Type_t::f4e2m1);
+    } else if (type == "f8e8m0" || type == "F8E8M0") {
+        return ::ov::element::Type(::ov::element::Type_t::f8e8m0);
     } else {
         OPENVINO_THROW("Incorrect type: ", type);
     }
@@ -155,7 +159,8 @@ std::vector<const ov::element::Type*> ov::element::Type::get_known_types() {
         &ov::element::f64,     &ov::element::i4,      &ov::element::i8,     &ov::element::i16,    &ov::element::i32,
         &ov::element::i64,     &ov::element::u1,      &ov::element::u2,     &ov::element::u3,     &ov::element::u4,
         &ov::element::u6,      &ov::element::u8,      &ov::element::u16,    &ov::element::u32,    &ov::element::u64,
-        &ov::element::nf4,     &ov::element::f8e4m3,  &ov::element::f8e5m2, &ov::element::string, &ov::element::f4e2m1};
+        &ov::element::nf4,     &ov::element::f8e4m3,  &ov::element::f8e5m2, &ov::element::string, &ov::element::f4e2m1,
+        &ov::element::f8e8m0};
     return rc;
 }
 
@@ -191,7 +196,8 @@ ov::element::Type::Type(size_t bitwidth,
         {ov::element::Type_t::f8e4m3, {8, true, true, true, "f8e4m3", "f8e4m3"}},
         {ov::element::Type_t::f8e5m2, {8, true, true, true, "f8e5m2", "f8e5m2"}},
         {ov::element::Type_t::string, {8 * sizeof(std::string), false, false, false, "string", "string"}},
-        {ov::element::Type_t::f8e5m2, {4, true, true, true, "f4e2m1", "f4e2m1"}},
+        {ov::element::Type_t::f4e2m1, {4, true, true, true, "f4e2m1", "f4e2m1"}},
+        {ov::element::Type_t::f8e8m0, {4, true, true, true, "f8e8m0", "f8e8m0"}},
     };
     for (const auto& t : elements_map) {
         const TypeInfo& info = t.second;
@@ -299,6 +305,10 @@ template <>
 Type from<ov::float4_e2m1>() {
     return Type_t::f4e2m1;
 }
+template <>
+Type from<ov::float8_e8m0>() {
+    return Type_t::f8e8m0;
+}
 
 Type fundamental_type_for(const Type& type) {
     switch (type) {
@@ -350,6 +360,8 @@ Type fundamental_type_for(const Type& type) {
         return from<element_type_traits<Type_t::string>::value_type>();
     case Type_t::f4e2m1:
         return from<element_type_traits<Type_t::f4e2m1>::value_type>();
+    case Type_t::f8e8m0:
+        return from<element_type_traits<Type_t::f8e8m0>::value_type>();
     default:
         OPENVINO_THROW("Unsupported Data type: ", type);
     }
@@ -370,7 +382,8 @@ std::istream& ov::element::operator>>(std::istream& in, ov::element::Type& obj)
         {"U16", ov::element::u16},       {"U32", ov::element::u32},       {"U64", ov::element::u64},
         {"FP32", ov::element::f32},      {"FP64", ov::element::f64},      {"FP16", ov::element::f16},
         {"BIN", ov::element::u1},        {"NF4", ov::element::nf4},       {"F8E4M3", ov::element::f8e4m3},
-        {"F8E5M2", ov::element::f8e5m2}, {"STRING", ov::element::string}, {"F4E2M1", ov::element::f4e2m1}};
+        {"F8E5M2", ov::element::f8e5m2}, {"STRING", ov::element::string}, {"F4E2M1", ov::element::f4e2m1},
+        {"F8E8M0", ov::element::f8e8m0}};
     std::string str;
     in >> str;
     auto it_legacy = legacy.find(str);
@@ -459,6 +472,7 @@ inline size_t compiler_byte_size(ov::element::Type_t et) {
         ET_CASE(f8e5m2);
         ET_CASE(string);
         ET_CASE(f4e2m1);
+        ET_CASE(f8e8m0);
 #undef ET_CASE
     case ov::element::Type_t::undefined:
         return 0;
@@ -498,7 +512,8 @@ OPENVINO_API EnumNames<element::Type_t>& EnumNames<element::Type_t>::get() {
                                                          {"f8e4m3", element::Type_t::f8e4m3},
                                                          {"f8e5m2", element::Type_t::f8e5m2},
                                                          {"string", element::Type_t::string},
-                                                         {"f4e2m1", element::Type_t::f4e2m1}});
+                                                         {"f4e2m1", element::Type_t::f4e2m1},
+                                                         {"f8e8m0", element::Type_t::f8e8m0}});
     return enum_names;
 }
 
diff --git a/src/core/src/type/float8_e8m0.cpp b/src/core/src/type/float8_e8m0.cpp
new file mode 100644
index 00000000000000..ee998780061c96
--- /dev/null
+++ b/src/core/src/type/float8_e8m0.cpp
@@ -0,0 +1,71 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "openvino/core/type/float8_e8m0.hpp"
+
+#include <cmath>
+#include <limits>
+
+#include "openvino/reference/fake_convert.hpp"
+
+namespace ov {
+static_assert(sizeof(ov::float8_e8m0) == 1, "class f8e8m0 must be exactly 1 byte");
+static_assert(std::is_trivially_constructible<ov::float8_e8m0, ov::float8_e8m0>::value,
+              "should be trivially constructible");
+static_assert(std::is_trivially_copyable<ov::float8_e8m0>::value, "must be trivially copyable");
+static_assert(std::is_trivially_destructible<ov::float8_e8m0>::value, "must be trivially destructible");
+static_assert(std::numeric_limits<ov::float8_e8m0>::is_specialized, "numeric_limits must be specialized");
+static_assert(!std::numeric_limits<ov::float8_e8m0>::is_integer, "numeric_limits::is_integer must be false");
+
+namespace {
+
+union f32_t {
+    uint32_t bits;
+    float value;
+};
+
+constexpr uint8_t f32_mantissa_bits{23u};
+constexpr uint32_t f32_exponent_bits_mask{0x7f800000u};
+constexpr uint32_t f32_mantissa_bits_mask{0x007fffffu};
+constexpr uint32_t round_even{0x00400000u};
+
+uint8_t f32_to_f8e8m0_bits(const float value) {
+    f32_t input{};
+    input.value = value;
+
+    const auto input_exponent_bits = static_cast<uint8_t>((input.bits & f32_exponent_bits_mask) >> f32_mantissa_bits);
+
+    if (std::signbit(value)) {
+        return 0b00000000;
+    } else if (input_exponent_bits >= 0b11111110) {
+        return input_exponent_bits - static_cast<uint8_t>(std::isinf(value));
+    } else {
+        // normal values
+        const auto input_mantissa_bits = input.bits & f32_mantissa_bits_mask;
+        return input_exponent_bits + static_cast<uint8_t>((input_mantissa_bits > round_even) ||    // round to nearest
+                                                          ((input_mantissa_bits == round_even) &&  // round to even
+                                                           (input_exponent_bits & 0x1)));
+    }
+}
+}  // namespace
+
+float8_e8m0::float8_e8m0(const float value) : m_value(f32_to_f8e8m0_bits(value)){};
+
+float8_e8m0::operator float() const {
+    constexpr auto f8e8m0_2_power_negative_127 = std::numeric_limits<ov::float8_e8m0>::min();
+    constexpr auto float_2_power_negative_127 = std::numeric_limits<float>::min() / 2;
+
+    if (to_bits() == std::numeric_limits<ov::float8_e8m0>::quiet_NaN().to_bits()) {
+        return std::numeric_limits<float>::quiet_NaN();
+    } else if (to_bits() == f8e8m0_2_power_negative_127.to_bits()) {
+        return float_2_power_negative_127;
+    }
+
+    return f32_t{static_cast<uint32_t>(m_value) << f32_mantissa_bits}.value;
+}
+
+uint8_t float8_e8m0::to_bits() const {
+    return m_value;
+}
+}  // namespace ov
diff --git a/src/core/src/validation_util.cpp b/src/core/src/validation_util.cpp
index 777f03e82b587e..6410f019f3edbf 100644
--- a/src/core/src/validation_util.cpp
+++ b/src/core/src/validation_util.cpp
@@ -287,7 +287,7 @@ bool evaluate_as_partial_shape(const ov::Output<ov::Node>& output, ov::PartialSh
         const TensorSymbol& symbols = output.get_tensor().get_value_symbol();
         OPENVINO_ASSERT(symbols.empty() || lower_bound.size() == symbols.size());
 
-        std::vector<Dimension> resulting_pshape(lower_bound.size());
+        pshape.resize(lower_bound.size());
         for (size_t i = 0; i < lower_bound.size(); ++i) {
             auto low = lower_bound[i], up = upper_bound[i];
             OPENVINO_ASSERT(low >= 0 && up >= 0, "Value for partial shape evaluation can't be lower than zero.");
@@ -297,11 +297,10 @@ bool evaluate_as_partial_shape(const ov::Output<ov::Node>& output, ov::PartialSh
                 if (low == std::numeric_limits<std::int32_t>::max())
                     low = std::numeric_limits<std::int64_t>::max();
             }
-            resulting_pshape[i] = {low, up};
+            pshape[i] = {low, up};
             if (!symbols.empty())
-                resulting_pshape[i].set_symbol(symbols[i]);
+                pshape[i].set_symbol(symbols[i]);
         }
-        pshape = ov::PartialShape(resulting_pshape);
         shape_defined = true;
     }
     return shape_defined;
diff --git a/src/core/tests/constant.cpp b/src/core/tests/constant.cpp
index 1014415eb5c875..11eb25188ee92c 100644
--- a/src/core/tests/constant.cpp
+++ b/src/core/tests/constant.cpp
@@ -69,6 +69,8 @@ TEST(constant, boolean_string) {
     for (unsigned i = 0; i != input.size(); ++i) {
         EXPECT_EQ(input[i], c.convert_value_to_string(i));
     }
+
+    EXPECT_EQ(c.get_strides(), Strides({1}));
 }
 
 TEST(constant, boolean_string_broadcast) {
@@ -148,6 +150,8 @@ TEST(constant, float_string) {
     for (unsigned i = 0; i != input.size(); ++i) {
         EXPECT_EQ(input[i], c.convert_value_to_string(i));
     }
+
+    EXPECT_EQ(c.get_strides(), Strides({4}));
 }
 
 TEST(constant, float_string_broadcast) {
@@ -227,6 +231,7 @@ TEST(constant, double_string) {
     for (unsigned i = 0; i != input.size(); ++i) {
         EXPECT_EQ(input[i], c.convert_value_to_string(i));
     }
+    EXPECT_EQ(c.get_strides(), Strides({element::f64.size()}));
 }
 
 TEST(constant, double_string_broadcast) {
@@ -303,6 +308,7 @@ TEST(constant, int4_string) {
     for (unsigned i = 0; i != input.size(); ++i) {
         EXPECT_EQ(input[i], c.convert_value_to_string(i));
     }
+    EXPECT_THROW(c.get_strides(), Exception);
 }
 
 TEST(constant, int4_string_broadcast_negative_number) {
@@ -446,6 +452,7 @@ TEST(constant, int8_string) {
     for (unsigned i = 0; i != input.size(); ++i) {
         EXPECT_EQ(input[i], c.convert_value_to_string(i));
     }
+    EXPECT_EQ(c.get_strides(), Strides({element::i8.size()}));
 }
 
 TEST(constant, int8_string_broadcast) {
@@ -527,6 +534,7 @@ TEST(constant, int16_string) {
     for (unsigned i = 0; i != input.size(); ++i) {
         EXPECT_EQ(input[i], c.convert_value_to_string(i));
     }
+    EXPECT_EQ(c.get_strides(), Strides({element::i16.size()}));
 }
 
 TEST(constant, int16_string_broadcast) {
@@ -784,6 +792,7 @@ TEST(constant, uint1_string) {
     for (unsigned i = 0; i != input.size(); ++i) {
         EXPECT_EQ(input[i], c.convert_value_to_string(i));
     }
+    EXPECT_THROW(c.get_strides(), Exception);
 }
 
 TEST(constant, uint1_string_broadcast) {
@@ -870,6 +879,7 @@ TEST(constant, uint2_string) {
 
     EXPECT_EQ(c.convert_value_to_string(1), "0");
     EXPECT_THAT(c.get_value_strings(), ElementsAre("3", "0", "1", "2"));
+    EXPECT_THROW(c.get_strides(), Exception);
 }
 
 TEST(constant, uint2_string_broadcast) {
@@ -980,6 +990,7 @@ TEST(constant, uint3_string) {
 
     EXPECT_EQ(c.convert_value_to_string(6), "5");
     EXPECT_THAT(c.get_value_strings(), ElementsAre("3", "0", "1", "2", "4", "7", "5", "6"));
+    EXPECT_THROW(c.get_strides(), Exception);
 }
 
 TEST(constant, uint3_string_broadcast) {
@@ -1104,6 +1115,7 @@ TEST(constant, uint4_string) {
     for (unsigned i = 0; i != input.size(); ++i) {
         EXPECT_EQ(input[i], c.convert_value_to_string(i));
     }
+    EXPECT_THROW(c.get_strides(), Exception);
 }
 
 TEST(constant, uint4_string_broadcast) {
@@ -1198,6 +1210,7 @@ TEST(constant, uint6_string) {
 
     EXPECT_EQ(c.convert_value_to_string(2), "15");
     EXPECT_THAT(c.get_value_strings(), ElementsAre("4", "9", "15", "16"));
+    EXPECT_THROW(c.get_strides(), Exception);
 }
 
 TEST(constant, uint6_string_broadcast) {
@@ -1324,6 +1337,7 @@ TEST(constant, uint8_string) {
     for (unsigned i = 0; i != input.size(); ++i) {
         EXPECT_EQ(input[i], c.convert_value_to_string(i));
     }
+    EXPECT_EQ(c.get_strides(), Strides({element::u8.size()}));
 }
 
 TEST(constant, uint8_string_broadcast) {
@@ -1403,6 +1417,7 @@ TEST(constant, uint16_string) {
     for (unsigned i = 0; i != input.size(); ++i) {
         EXPECT_EQ(input[i], c.convert_value_to_string(i));
     }
+    EXPECT_EQ(c.get_strides(), Strides({element::u16.size()}));
 }
 
 TEST(constant, uint16_string_broadcast) {
@@ -1482,6 +1497,7 @@ TEST(constant, uint32_string) {
     for (unsigned i = 0; i != input.size(); ++i) {
         EXPECT_EQ(input[i], c.convert_value_to_string(i));
     }
+    EXPECT_EQ(c.get_strides(), Strides({element::u32.size()}));
 }
 
 TEST(constant, uint32_string_broadcast) {
@@ -1561,6 +1577,7 @@ TEST(constant, uint64_string) {
     for (unsigned i = 0; i != input.size(); ++i) {
         EXPECT_EQ(input[i], c.convert_value_to_string(i));
     }
+    EXPECT_EQ(c.get_strides(), Strides({element::u64.size()}));
 }
 
 TEST(constant, uint64_string_broadcast) {
@@ -1651,6 +1668,7 @@ TEST(constant, nf4_write_custom_type) {
 
     EXPECT_EQ(p[0], 0x20);
     EXPECT_EQ(p[1] & 0x0f, 0x0f);
+    EXPECT_THROW(c.get_strides(), Exception);
 }
 
 //
@@ -1679,6 +1697,7 @@ TEST(constant, bfloat16_string) {
     for (unsigned i = 0; i != input.size(); ++i) {
         EXPECT_EQ(input[i], c.convert_value_to_string(i));
     }
+    EXPECT_EQ(c.get_strides(), Strides({element::bf16.size()}));
 }
 
 TEST(constant, bfloat16_string_broadcast) {
@@ -1758,6 +1777,7 @@ TEST(constant, float16_string) {
     for (unsigned i = 0; i != input.size(); ++i) {
         EXPECT_EQ(input[i], c.convert_value_to_string(i));
     }
+    EXPECT_EQ(c.get_strides(), Strides({element::f16.size()}));
 }
 
 TEST(constant, float16_string_broadcast) {
@@ -1840,6 +1860,29 @@ TEST(constant, float8_e5m3_vector) {
     EXPECT_EQ(data_vec, const_op_from_ptr.get_vector<ov::float8_e5m2>());
 }
 
+TEST(constant, float8_e8m0_vector) {
+    const auto data_vec = std::vector<ov::float8_e8m0>{std::numeric_limits<ov::float8_e8m0>::lowest(),
+                                                       -std::numeric_limits<ov::float8_e8m0>::min(),
+                                                       std::numeric_limits<ov::float8_e8m0>::min(),
+                                                       std::numeric_limits<ov::float8_e8m0>::max(),
+                                                       std::numeric_limits<ov::float8_e8m0>::min(),
+                                                       -1.5f,
+                                                       -1.f,
+                                                       -0.5f,
+                                                       0.f,
+                                                       0.5f,
+                                                       1.f,
+                                                       1.5f};
+    Shape data_shape{data_vec.size()};
+    EXPECT_EQ(data_vec.size(), shape_size(data_shape));
+
+    ov::op::v0::Constant const_op_from_vec(ov::element::f8e8m0, data_shape, data_vec);
+    EXPECT_EQ(data_vec, const_op_from_vec.get_vector<ov::float8_e8m0>());
+
+    ov::op::v0::Constant const_op_from_ptr(ov::element::f8e8m0, data_shape, data_vec.data());
+    EXPECT_EQ(data_vec, const_op_from_ptr.get_vector<ov::float8_e8m0>());
+}
+
 TEST(constant, float16_vector_broadcast) {
     Shape shape{4};
     ov::op::v0::Constant c(element::f16, shape, vector<float16>{1});
@@ -1892,6 +1935,7 @@ TEST(constant, ov_string) {
     for (unsigned i = 0; i != input.size(); ++i) {
         EXPECT_EQ(input[i], c.convert_value_to_string(i));
     }
+    EXPECT_EQ(c.get_strides(), Strides({element::string.size()}));
 }
 
 TEST(constant, ov_string_broadcast) {
@@ -2007,10 +2051,97 @@ TEST(constant, f4e2m1_write_then_cast_custom_type) {
     EXPECT_EQ(v, input);
 }
 
+//
+// f8e8m0
+//
+TEST(constant, f8e8m0_string) {
+    vector<string> input{"1", "0", "4", "0.5"};
+    vector<string> output{"1", "5.87747e-39", "4", "0.5"};
+    ov::op::v0::Constant c(element::f8e8m0, Shape{4}, input);
+    auto v = c.cast_vector<float8_e8m0>();
+    ASSERT_EQ(v.size(), shape_size(c.get_shape()));
+    EXPECT_THAT(v, ElementsAre(1.0f, std::numeric_limits<float>::min() / 2, 4.0f, 0.5f));
+
+    const auto p = c.get_data_ptr<uint8_t>();
+    EXPECT_EQ(p[0], 0x7f);
+    EXPECT_EQ(p[1], 0x00);
+    EXPECT_EQ(p[2], 0x81);
+    EXPECT_EQ(p[3], 0x7e);
+
+    EXPECT_EQ(output, c.get_value_strings());
+
+    for (unsigned i = 0; i != input.size(); ++i) {
+        EXPECT_EQ(output[i], c.convert_value_to_string(i));
+    }
+}
+
+TEST(constant, f8e8m0_string_broadcast) {
+    Shape shape{4};
+    op::v0::Constant c(element::f8e8m0, shape, std::vector<string>{"1.5"});
+    auto v = c.cast_vector<float>();
+    ASSERT_EQ(v.size(), shape_size(shape));
+    EXPECT_THAT(v, Each(2.0f));
+
+    const auto p = c.get_data_ptr<uint8_t>();
+    EXPECT_EQ(p[0], 0x80);
+    EXPECT_EQ(p[1], 0x80);
+    EXPECT_EQ(p[2], 0x80);
+    EXPECT_EQ(p[3], 0x80);
+}
+
+TEST(constant, f8e8m0_vector) {
+    op::v0::Constant c(element::f8e8m0, Shape{5}, std::vector<float8_e8m0>{-1.5f, 4.0f, 2.0f, 1.5f, 3.0f});
+    auto v = c.cast_vector<float>();
+    EXPECT_THAT(v, ElementsAre(std::numeric_limits<float>::min() / 2, 4.0f, 2.0f, 2.0f, 2.0f));
+
+    const auto p = c.get_data_ptr<uint8_t>();
+    EXPECT_EQ(p[0], 0x00);
+    EXPECT_EQ(p[1], 0x81);
+    EXPECT_EQ(p[2], 0x80);
+    EXPECT_EQ(p[3], 0x80);
+    EXPECT_EQ(p[4], 0x80);
+}
+
+TEST(constant, f8e8m0_from_float_vector) {
+    op::v0::Constant c(element::f8e8m0, Shape{5}, std::vector<float>{-1.5f, 4.0f, 2.0f, 1.5f, 2.0f});
+    auto v = c.cast_vector<float>();
+    EXPECT_THAT(v, ElementsAre(std::numeric_limits<float>::min() / 2, 4.0f, 2.0f, 2.0f, 2.0f));
+
+    const auto p = c.get_data_ptr<uint8_t>();
+    EXPECT_EQ(p[0], 0x00);
+    EXPECT_EQ(p[1], 0x81);
+    EXPECT_EQ(p[2], 0x80);
+    EXPECT_EQ(p[3], 0x80);
+    EXPECT_EQ(p[4], 0x80);
+}
+
+TEST(constant, f8e8m0_vector_broadcast) {
+    Shape shape{3};
+    op::v0::Constant c(element::f8e8m0, shape, std::vector<float8_e8m0>{1.5f});
+    auto v = c.cast_vector<float>();
+    ASSERT_EQ(v.size(), shape_size(shape));
+    EXPECT_THAT(v, Each(2.0f));
+
+    const auto p = c.get_data_ptr<uint8_t>();
+    EXPECT_EQ(0x80, p[0]);
+    EXPECT_EQ(0x80, p[1]);
+}
+
+TEST(constant, f8e8m0_write_then_cast_custom_type) {
+    Shape shape{3};
+    std::vector<TestDType> input{1.5f, 3.0f, 6.0f};
+    std::vector<TestDType> expected{2.0f, 2.0f, 8.0f};
+    op::v0::Constant c(element::f8e8m0, shape, input);
+
+    auto v = c.cast_vector<TestDType>();
+
+    ASSERT_EQ(v.size(), shape_size(shape));
+    EXPECT_EQ(v, expected);
+}
+
 template <typename T1, typename T2>
-::testing::AssertionResult test_convert() {
+::testing::AssertionResult test_convert(vector<T1> expected = {1, 2, 3, 4, 6}) {
     Shape shape{5};
-    vector<T1> expected{1, 2, 3, 4, 6};
     auto c1 = std::make_shared<ov::op::v0::Constant>(ov::element::from<T2>(), shape, expected);
     vector<T1> actual = c1->template cast_vector<T1>();
     ::testing::AssertionResult rc =
@@ -2042,6 +2173,7 @@ TEST(constant, convert_input) {
     EXPECT_TRUE((test_convert<float, uint32_t>()));
     EXPECT_TRUE((test_convert<float, uint64_t>()));
     EXPECT_TRUE((test_convert<float, float4_e2m1>()));
+    EXPECT_TRUE((test_convert<float, float8_e8m0>({1, 2, 4, 8, 16})));
 
     EXPECT_TRUE((test_convert<double, float>()));
     EXPECT_TRUE((test_convert<double, double>()));
@@ -2056,6 +2188,7 @@ TEST(constant, convert_input) {
     EXPECT_TRUE((test_convert<double, uint32_t>()));
     EXPECT_TRUE((test_convert<double, uint64_t>()));
     EXPECT_TRUE((test_convert<double, float4_e2m1>()));
+    EXPECT_TRUE((test_convert<double, float8_e8m0>({1, 2, 4, 8, 16})));
 
     EXPECT_TRUE((test_convert<float16, float>()));
     EXPECT_TRUE((test_convert<float16, double>()));
@@ -2070,6 +2203,7 @@ TEST(constant, convert_input) {
     EXPECT_TRUE((test_convert<float16, uint32_t>()));
     EXPECT_TRUE((test_convert<float16, uint64_t>()));
     EXPECT_TRUE((test_convert<float16, float4_e2m1>()));
+    EXPECT_TRUE((test_convert<float16, float8_e8m0>({1, 2, 4, 8, 16})));
 
     EXPECT_TRUE((test_convert<bfloat16, float>()));
     EXPECT_TRUE((test_convert<bfloat16, double>()));
@@ -2084,6 +2218,7 @@ TEST(constant, convert_input) {
     EXPECT_TRUE((test_convert<bfloat16, uint32_t>()));
     EXPECT_TRUE((test_convert<bfloat16, uint64_t>()));
     EXPECT_TRUE((test_convert<bfloat16, float4_e2m1>()));
+    EXPECT_TRUE((test_convert<bfloat16, float8_e8m0>({1, 2, 4, 8, 16})));
 
     EXPECT_TRUE((test_convert<int8_t, float>()));
     EXPECT_TRUE((test_convert<int8_t, double>()));
@@ -2216,6 +2351,7 @@ TEST(constant, construct_uniform) {
     EXPECT_TRUE((test_uniform_ctor<float, uint32_t>()));
     EXPECT_TRUE((test_uniform_ctor<float, uint64_t>()));
     EXPECT_TRUE((test_uniform_ctor<float, float4_e2m1>()));
+    EXPECT_TRUE((test_uniform_ctor<float, float8_e8m0>()));
 
     EXPECT_TRUE((test_uniform_ctor<double, float>()));
     EXPECT_TRUE((test_uniform_ctor<double, double>()));
@@ -2230,6 +2366,7 @@ TEST(constant, construct_uniform) {
     EXPECT_TRUE((test_uniform_ctor<double, uint32_t>()));
     EXPECT_TRUE((test_uniform_ctor<double, uint64_t>()));
     EXPECT_TRUE((test_uniform_ctor<double, float4_e2m1>()));
+    EXPECT_TRUE((test_uniform_ctor<double, float8_e8m0>()));
 
     EXPECT_TRUE((test_uniform_ctor<float16, float>()));
     EXPECT_TRUE((test_uniform_ctor<float16, double>()));
@@ -2244,6 +2381,7 @@ TEST(constant, construct_uniform) {
     EXPECT_TRUE((test_uniform_ctor<float16, uint32_t>()));
     EXPECT_TRUE((test_uniform_ctor<float16, uint64_t>()));
     EXPECT_TRUE((test_uniform_ctor<float16, float4_e2m1>()));
+    EXPECT_TRUE((test_uniform_ctor<float16, float8_e8m0>()));
 
     EXPECT_TRUE((test_uniform_ctor<bfloat16, float>()));
     EXPECT_TRUE((test_uniform_ctor<bfloat16, double>()));
@@ -2258,6 +2396,7 @@ TEST(constant, construct_uniform) {
     EXPECT_TRUE((test_uniform_ctor<bfloat16, uint32_t>()));
     EXPECT_TRUE((test_uniform_ctor<bfloat16, uint64_t>()));
     EXPECT_TRUE((test_uniform_ctor<bfloat16, float4_e2m1>()));
+    EXPECT_TRUE((test_uniform_ctor<bfloat16, float8_e8m0>()));
 
     EXPECT_TRUE((test_uniform_ctor<int8_t, float>()));
     EXPECT_TRUE((test_uniform_ctor<int8_t, double>()));
@@ -2409,6 +2548,8 @@ TEST(constant, hold_tensor) {
     }
     const void* constDataPtr = constOp->get_data_ptr();
     ASSERT_EQ(constDataPtr, hostDataPtr);
+    EXPECT_EQ(constOp->get_strides(), Strides({72, 36, 12, 4}));
+    EXPECT_EQ(constOp->get_tensor_view().get_strides(), Strides({72, 36, 12, 4}));
 }
 
 TEST(constant, hold_tensor_ov_string) {
@@ -2424,6 +2565,46 @@ TEST(constant, hold_tensor_ov_string) {
     ASSERT_EQ(constDataPtr, hostDataPtr);
 }
 
+TEST(constant, hold_tensor_custom_strides) {
+    const auto shape = Shape{1, 2, 3, 4};
+    const auto strides = Strides{192, 96, 32, 8};
+    void* shared_data_ptr = nullptr;
+    std::shared_ptr<op::v0::Constant> const_op;
+    auto storage = std::vector<float>(shape_size(shape));
+    {
+        auto strided_view = Tensor(element::f32, shape, storage.data(), strides);
+        shared_data_ptr = strided_view.data();
+        const_op = std::make_shared<op::v0::Constant>(strided_view);
+    }
+
+    ASSERT_EQ(const_op->get_data_ptr(), shared_data_ptr);
+    EXPECT_EQ(const_op->get_strides(), strides);
+    EXPECT_EQ(const_op->get_tensor_view().get_strides(), strides);
+}
+
+TEST(constant, hold_tensor_custom_strides_revalidate) {
+    const auto shape = Shape{1, 2, 3, 4};
+    const auto strides = Strides{192, 96, 32, 8};
+    void* shared_data_ptr = nullptr;
+    std::shared_ptr<op::v0::Constant> const_op;
+    auto storage = std::vector<float>(shape_size(shape));
+    {
+        auto strided_view = Tensor(element::f32, shape, storage.data(), strides);
+        shared_data_ptr = strided_view.data();
+        const_op = std::make_shared<op::v0::Constant>(strided_view);
+    }
+
+    ASSERT_EQ(const_op->get_data_ptr(), shared_data_ptr);
+    EXPECT_EQ(const_op->get_strides(), strides);
+    EXPECT_EQ(const_op->get_tensor_view().get_strides(), strides);
+
+    const_op->revalidate_and_infer_types();
+
+    ASSERT_EQ(const_op->get_data_ptr(), shared_data_ptr);
+    EXPECT_EQ(const_op->get_strides(), strides);
+    EXPECT_EQ(const_op->get_tensor_view().get_strides(), strides);
+}
+
 // Test verifies 2 things:
 // a) Checks that bitwise comparison happens on first call of 'get_all_data_elements_bitwise_identical'
 // b) Next call of 'get_all_data_elements_bitwise_identical' takes already calculated value
@@ -2484,10 +2665,10 @@ TEST(constant, lazy_bitwise_identical) {
 }
 
 TEST(constant, cast_vector) {
-    std::vector<element::Type_t> types = {element::boolean, element::bf16, element::f16, element::f32, element::f64,
-                                          element::i4,      element::i8,   element::i16, element::i32, element::i64,
-                                          element::u1,      element::u2,   element::u3,  element::u4,  element::u6,
-                                          element::u8,      element::u16,  element::u32, element::u64, element::f4e2m1};
+    std::vector<element::Type_t> types = {
+        element::boolean, element::bf16, element::f16, element::f32, element::f64, element::i4,     element::i8,
+        element::i16,     element::i32,  element::i64, element::u1,  element::u2,  element::u3,     element::u4,
+        element::u6,      element::u8,   element::u16, element::u32, element::u64, element::f4e2m1, element::f8e8m0};
     std::vector<int64_t> data = {0, 1, 0, 0, 1, 1, 0, 1};
     std::vector<int64_t> expected_partial_data = {0, 1, 0, 0, 1, 1};
 
diff --git a/src/core/tests/element_type.cpp b/src/core/tests/element_type.cpp
index 407056cb81095f..af99362a89d706 100644
--- a/src/core/tests/element_type.cpp
+++ b/src/core/tests/element_type.cpp
@@ -73,8 +73,10 @@ TEST(element_type, from_string) {
     EXPECT_EQ(element::Type("F8E5M2"), element::f8e5m2);
     EXPECT_EQ(element::Type("string"), element::string);
     EXPECT_EQ(element::Type("STRING"), element::string);
+    EXPECT_EQ(element::Type("f4e2m1"), element::f4e2m1);
     EXPECT_EQ(element::Type("F4E2M1"), element::f4e2m1);
-    EXPECT_EQ(element::Type("F4E2M1"), element::f4e2m1);
+    EXPECT_EQ(element::Type("f8e8m0"), element::f8e8m0);
+    EXPECT_EQ(element::Type("F8E8M0"), element::f8e8m0);
 
     EXPECT_EQ(element::Type("undefined"), element::undefined);
     EXPECT_EQ(element::Type("UNSPECIFIED"), element::undefined);
diff --git a/src/core/tests/float8_e8m0.cpp b/src/core/tests/float8_e8m0.cpp
new file mode 100644
index 00000000000000..506a64244789d7
--- /dev/null
+++ b/src/core/tests/float8_e8m0.cpp
@@ -0,0 +1,378 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "openvino/core/type/float8_e8m0.hpp"
+
+#include <gtest/gtest.h>
+
+#include <climits>
+#include <tuple>
+
+#include "common_test_utils/float_util.hpp"
+
+namespace ov {
+namespace test {
+
+TEST(F8E8M0Test, stream_operator) {
+    std::stringstream s;
+    s << ov::float8_e8m0(2.0f);
+
+    EXPECT_EQ(s.str(), "2");
+}
+
+TEST(F8E8M0Test, to_string) {
+    const auto f8 = ov::float8_e8m0::from_bits(0b10010111);
+
+    EXPECT_EQ(std::to_string(f8), "16777216.000000");
+}
+
+TEST(F8E8M0Test, to_float) {
+    const auto f8 = ov::float8_e8m0::from_bits(0b00000001);
+
+    EXPECT_EQ(static_cast<float>(f8), 1.1754944e-38f);
+}
+
+TEST(F8E8M0Test, f32_inf) {
+    const auto f8 = ov::float8_e8m0(std::numeric_limits<float>::infinity());
+
+    EXPECT_EQ(f8.to_bits(), 0b11111110);
+}
+
+TEST(F8E8M0Test, f32_minus_inf) {
+    const auto f8 = ov::float8_e8m0(-std::numeric_limits<float>::infinity());
+
+    EXPECT_EQ(f8.to_bits(), 0b00000000);
+}
+
+TEST(F8E8M0Test, f32_nan_quiet) {
+    const auto f8 = ov::float8_e8m0(std::numeric_limits<float>::quiet_NaN());
+
+    EXPECT_EQ(f8.to_bits(), 0b11111111);
+}
+
+TEST(F8E8M0Test, f32_nan_signaling) {
+    const auto f8 = ov::float8_e8m0(std::numeric_limits<float>::signaling_NaN());
+
+    EXPECT_EQ(f8.to_bits(), 0b11111111);
+}
+
+TEST(F8E8M0Test, f32_zero) {
+    const auto f8 = ov::float8_e8m0(0.0f);
+
+    EXPECT_EQ(f8.to_bits(), 0b00000000);
+}
+
+TEST(F8E8M0Test, f32_negative_zero) {
+    const auto f8 = ov::float8_e8m0(-0.0f);
+
+    EXPECT_EQ(f8.to_bits(), 0b00000000);
+}
+
+TEST(F8E8M0Test, f32_negative_one) {
+    const auto f8 = ov::float8_e8m0(-1.0f);
+
+    EXPECT_EQ(f8.to_bits(), 0b00000000);
+}
+
+TEST(F8E8M0Test, F8E8M0_nan) {
+    const auto f8 = static_cast<float>(std::numeric_limits<ov::float8_e8m0>::quiet_NaN());
+
+    EXPECT_TRUE(std::isnan(static_cast<float>(f8)));
+}
+
+TEST(F8E8M0Test, f8e8m0_num_limits_is_specialized) {
+    const auto val = std::numeric_limits<ov::float8_e8m0>::is_specialized;
+    EXPECT_TRUE(val);
+}
+
+TEST(F8E8M0Test, f8e8m0_num_limits_is_signed) {
+    const auto val = std::numeric_limits<ov::float8_e8m0>::is_signed;
+    EXPECT_FALSE(val);
+}
+
+TEST(F8E8M0Test, f8e8m0_num_limits_is_integer) {
+    const auto val = std::numeric_limits<ov::float8_e8m0>::is_integer;
+    EXPECT_FALSE(val);
+}
+
+TEST(F8E8M0Test, f8e8m0_num_limits_is_exact) {
+    const auto val = std::numeric_limits<ov::float8_e8m0>::is_exact;
+    EXPECT_FALSE(val);
+}
+
+TEST(F8E8M0Test, f8e8m0_num_limits_radix) {
+    const auto val = std::numeric_limits<ov::float8_e8m0>::radix;
+    EXPECT_EQ(val, 2);
+}
+
+TEST(F8E8M0Test, f8e8m0_num_limits_digits) {
+    const auto val = std::numeric_limits<ov::float8_e8m0>::digits;
+    EXPECT_EQ(val, 1);
+}
+
+TEST(F8E8M0Test, f8e8m0_num_limits_digits10) {
+    const auto f8_dig = std::numeric_limits<ov::float8_e8m0>::digits;
+    const auto f8_dig10 = std::numeric_limits<ov::float8_e8m0>::digits10;
+
+    EXPECT_EQ(f8_dig10, static_cast<int>((f8_dig - 1) * std::log10(2)));
+    EXPECT_EQ(f8_dig10, 0);
+}
+
+TEST(F8E8M0Test, f8e8m0_num_limits_epsilon) {
+    const auto f8_1 = ov::float8_e8m0(1.f);
+    const auto f8_1_bits = f8_1.to_bits();
+    const auto f8_1_next_bits = f8_1_bits + 1u;
+
+    const auto f8_eps = ov::float8_e8m0::from_bits(f8_1_next_bits - f8_1_bits);
+
+    EXPECT_EQ(f8_eps, std::numeric_limits<ov::float8_e8m0>::epsilon());
+    EXPECT_EQ(f8_eps.to_bits(), std::numeric_limits<ov::float8_e8m0>::epsilon().to_bits());
+}
+
+TEST(F8E8M0Test, f8e8m0_num_limits_round_error) {
+    const auto f8 = ov::float8_e8m0(0.5f);
+
+    EXPECT_EQ(f8, std::numeric_limits<ov::float8_e8m0>::round_error());
+    EXPECT_EQ(f8.to_bits(), std::numeric_limits<ov::float8_e8m0>::round_error().to_bits());
+}
+
+TEST(F8E8M0Test, f8e8m0_quiet_nan) {
+    const auto f8 = ov::float8_e8m0(std::numeric_limits<ov::float8_e8m0>::quiet_NaN());
+    const auto has_quiet_nan = std::numeric_limits<ov::float8_e8m0>::has_quiet_NaN;
+
+    EXPECT_TRUE(has_quiet_nan);
+    EXPECT_TRUE(std::isnan(f8));
+    EXPECT_EQ(f8.to_bits(), 0b11111111);
+    EXPECT_EQ(std::numeric_limits<ov::float8_e8m0>::quiet_NaN().to_bits(), 0b11111111);
+}
+
+TEST(F8E8M0Test, f8e8m0_sig_nan) {
+    const auto val = std::numeric_limits<ov::float8_e8m0>::has_signaling_NaN;
+    EXPECT_FALSE(val);
+}
+
+TEST(F8E8M0Test, f16_quiet_nan) {
+    const auto f8 = ov::float8_e8m0(std::numeric_limits<ov::float16>::quiet_NaN());
+
+    EXPECT_TRUE(std::isnan(f8));
+    EXPECT_EQ(f8.to_bits(), 0b11111111);
+    EXPECT_EQ(std::numeric_limits<ov::float8_e8m0>::quiet_NaN().to_bits(), 0b11111111);
+}
+
+TEST(F8E8M0Test, f16_sig_nan) {
+    const auto f8 = ov::float8_e8m0(std::numeric_limits<ov::float16>::signaling_NaN());
+
+    EXPECT_TRUE(std::isnan(f8));
+    EXPECT_EQ(f8.to_bits(), 0b11111111);
+}
+
+TEST(F8E8M0Test, f32_quiet_nan) {
+    const auto f8 = ov::float8_e8m0(std::numeric_limits<float>::quiet_NaN());
+
+    EXPECT_TRUE(std::isnan(f8));
+    EXPECT_EQ(f8.to_bits(), 0b11111111);
+}
+
+TEST(F8E8M0Test, f32_sig_nan) {
+    const auto f8 = ov::float8_e8m0(std::numeric_limits<float>::signaling_NaN());
+
+    EXPECT_TRUE(std::isnan(f8));
+    EXPECT_EQ(f8.to_bits(), 0b11111111);
+}
+
+TEST(F8E8M0Test, f8e8m0_min_normalized) {
+    const auto f8 = ov::float8_e8m0(5.877471754111437e-39f);
+
+    EXPECT_EQ(f8.to_bits(), 0b00000000);
+    EXPECT_EQ(f8.to_bits(), std::numeric_limits<ov::float8_e8m0>::min().to_bits());
+}
+
+TEST(F8E8M0Test, f8e8m0_max_normalized) {
+    const auto f8 = ov::float8_e8m0(1.7014118346046923e+38f);
+
+    EXPECT_EQ(f8.to_bits(), 0b11111110);
+    EXPECT_EQ(f8.to_bits(), std::numeric_limits<ov::float8_e8m0>::max().to_bits());
+}
+
+TEST(F8E8M0Test, f8e8m0_lowest_normalized) {
+    const auto f8 = ov::float8_e8m0(5.877471754111437e-39f);
+
+    EXPECT_EQ(f8.to_bits(), 0b00000000);
+    EXPECT_EQ(f8.to_bits(), std::numeric_limits<ov::float8_e8m0>::lowest().to_bits());
+}
+
+TEST(F8E8M0Test, f32_ge_f8_round_to_max) {
+    const auto f8 = ov::float8_e8m0(3.402823466385288598117042E+38);
+
+    EXPECT_EQ(f8.to_bits(), 0b11111110);
+}
+
+TEST(F8E8M0Test, f8e8m0_num_limits_exp) {
+    const auto min_exp = std::numeric_limits<ov::float8_e8m0>::min_exponent;
+    const auto min_exp10 = std::numeric_limits<ov::float8_e8m0>::min_exponent10;
+    const auto max_exp = std::numeric_limits<ov::float8_e8m0>::max_exponent;
+    const auto max_exp10 = std::numeric_limits<ov::float8_e8m0>::max_exponent10;
+
+    EXPECT_EQ(min_exp, -126);
+    EXPECT_EQ(min_exp10, -38);
+    EXPECT_EQ(max_exp, 128);
+    EXPECT_EQ(max_exp10, 38);
+}
+
+template <class TContainer>
+std::vector<std::tuple<uint8_t, typename TContainer::value_type>> enumerate(const TContainer& values) {
+    std::vector<std::tuple<uint8_t, typename TContainer::value_type>> enum_values;
+    uint8_t i = 0;
+    for (const auto& v : values) {
+        enum_values.emplace_back(i++, v);
+    }
+    return enum_values;
+}
+
+constexpr auto f32_nan = std::numeric_limits<float>::quiet_NaN();
+constexpr float two_power_127 = 1.7014118346046923e+38f;
+constexpr float two_power_negative_127 = 5.877471754111437e-39f;
+
+// clang-format off
+const auto exp_floats = std::vector<float>{
+    two_power_negative_127,     1.1754943508222875e-38f,    2.350988701644575e-38f,     4.70197740328915e-38f,
+    9.4039548065783e-38f,       1.88079096131566e-37f,      3.76158192263132e-37f,      7.52316384526264e-37f,
+    1.504632769052528e-36f,     3.009265538105056e-36f,     6.018531076210112e-36f,     1.2037062152420224e-35f,
+    2.407412430484045e-35f,     4.81482486096809e-35f,      9.62964972193618e-35f,      1.925929944387236e-34f,
+    3.851859888774472e-34f,     7.703719777548943e-34f,     1.5407439555097887e-33f,    3.0814879110195774e-33f,
+    6.162975822039155e-33f,     1.232595164407831e-32f,     2.465190328815662e-32f,     4.930380657631324e-32f,
+    9.860761315262648e-32f,     1.9721522630525295e-31f,    3.944304526105059e-31f,     7.888609052210118e-31f,
+    1.5777218104420236e-30f,    3.1554436208840472e-30f,    6.310887241768095e-30f,     1.262177448353619e-29f,
+    2.524354896707238e-29f,     5.048709793414476e-29f,     1.0097419586828951e-28f,    2.0194839173657902e-28f,
+    4.0389678347315804e-28f,    8.077935669463161e-28f,     1.6155871338926322e-27f,    3.2311742677852644e-27f,
+    6.462348535570529e-27f,     1.2924697071141057e-26f,    2.5849394142282115e-26f,    5.169878828456423e-26f,
+    1.0339757656912846e-25f,    2.0679515313825692e-25f,    4.1359030627651384e-25f,    8.271806125530277e-25f,
+    1.6543612251060553e-24f,    3.308722450212111e-24f,     6.617444900424222e-24f,     1.3234889800848443e-23f,
+    2.6469779601696886e-23f,    5.293955920339377e-23f,     1.0587911840678754e-22f,    2.117582368135751e-22f,
+    4.235164736271502e-22f,     8.470329472543003e-22f,     1.6940658945086007e-21f,    3.3881317890172014e-21f,
+    6.776263578034403e-21f,     1.3552527156068805e-20f,    2.710505431213761e-20f,     5.421010862427522e-20f,
+    1.0842021724855044e-19f,    2.168404344971009e-19f,     4.336808689942018e-19f,     8.673617379884035e-19f,
+    1.734723475976807e-18f,     3.469446951953614e-18f,     6.938893903907228e-18f,     1.3877787807814457e-17f,
+    2.7755575615628914e-17f,    5.551115123125783e-17f,     1.1102230246251565e-16f,    2.220446049250313e-16f,
+    4.440892098500626e-16f,     8.881784197001252e-16f,     1.7763568394002505e-15f,    3.552713678800501e-15f,
+    7.105427357601002e-15f,     1.4210854715202004e-14f,    2.842170943040401e-14f,     5.684341886080802e-14f,
+    1.1368683772161603e-13f,    2.2737367544323206e-13f,    4.547473508864641e-13f,     9.094947017729282e-13f,
+    1.8189894035458565e-12f,    3.637978807091713e-12f,     7.275957614183426e-12f,     1.4551915228366852e-11f,
+    2.9103830456733704e-11f,    5.820766091346741e-11f,     1.1641532182693481e-10f,    2.3283064365386963e-10f,
+    4.656612873077393e-10f,     9.313225746154785e-10f,     1.862645149230957e-09f,     3.725290298461914e-09f,
+    7.450580596923828e-09f,     1.4901161193847656e-08f,    2.9802322387695312e-08f,    5.960464477539063e-08f,
+    1.1920928955078125e-07f,    2.384185791015625e-07f,     4.76837158203125e-07f,      9.5367431640625e-07f,
+    1.9073486328125e-06f,       3.814697265625e-06f,        7.62939453125e-06f,         1.52587890625e-05f,
+    3.0517578125e-05f,          6.103515625e-05f,           0.0001220703125f,           0.000244140625f,
+    0.00048828125f,             0.0009765625f,              0.001953125f,               0.00390625f,
+    0.0078125f,                 0.015625f,                  0.03125f,                   0.0625f,
+    0.125f,                     0.25f,                      0.5f,                       1.0f,
+    2.0f,                       4.0f,                       8.0f,                       16.0f,
+    32.0f,                      64.0f,                      128.0f,                     256.0f,
+    512.0f,                     1024.0f,                    2048.0f,                    4096.0f,
+    8192.0f,                    16384.0f,                   32768.0f,                   65536.0f,
+    131072.0f,                  262144.0f,                  524288.0f,                  1048576.0f,
+    2097152.0f,                 4194304.0f,                 8388608.0f,                 16777216.0f,
+    33554432.0f,                67108864.0f,                134217728.0f,               268435456.0f,
+    536870912.0f,               1073741824.0f,              2147483648.0f,              4294967296.0f,
+    8589934592.0f,              17179869184.0f,             34359738368.0f,             68719476736.0f,
+    137438953472.0f,            274877906944.0f,            549755813888.0f,            1099511627776.0f,
+    2199023255552.0f,           4398046511104.0f,           8796093022208.0f,           17592186044416.0f,
+    35184372088832.0f,          70368744177664.0f,          140737488355328.0f,         281474976710656.0f,
+    562949953421312.0f,         1125899906842624.0f,        2251799813685248.0f,        4503599627370496.0f,
+    9007199254740992.0f,        1.8014398509481984e+16f,    3.602879701896397e+16f,     7.205759403792794e+16f,
+    1.4411518807585587e+17f,    2.8823037615171174e+17f,    5.764607523034235e+17f,     1.152921504606847e+18f,
+    2.305843009213694e+18f,     4.611686018427388e+18f,     9.223372036854776e+18f,     1.8446744073709552e+19f,
+    3.6893488147419103e+19f,    7.378697629483821e+19f,     1.4757395258967641e+20f,    2.9514790517935283e+20f,
+    5.902958103587057e+20f,     1.1805916207174113e+21f,    2.3611832414348226e+21f,    4.722366482869645e+21f,
+    9.44473296573929e+21f,      1.888946593147858e+22f,     3.777893186295716e+22f,     7.555786372591432e+22f,
+    1.5111572745182865e+23f,    3.022314549036573e+23f,     6.044629098073146e+23f,     1.2089258196146292e+24f,
+    2.4178516392292583e+24f,    4.835703278458517e+24f,     9.671406556917033e+24f,     1.9342813113834067e+25f,
+    3.8685626227668134e+25f,    7.737125245533627e+25f,     1.5474250491067253e+26f,    3.094850098213451e+26f,
+    6.189700196426902e+26f,     1.2379400392853803e+27f,    2.4758800785707605e+27f,    4.951760157141521e+27f,
+    9.903520314283042e+27f,     1.9807040628566084e+28f,    3.961408125713217e+28f,     7.922816251426434e+28f,
+    1.5845632502852868e+29f,    3.1691265005705735e+29f,    6.338253001141147e+29f,     1.2676506002282294e+30f,
+    2.535301200456459e+30f,     5.070602400912918e+30f,     1.0141204801825835e+31f,    2.028240960365167e+31f,
+    4.056481920730334e+31f,     8.112963841460668e+31f,     1.6225927682921336e+32f,    3.2451855365842673e+32f,
+    6.490371073168535e+32f,     1.298074214633707e+33f,     2.596148429267414e+33f,     5.192296858534828e+33f,
+    1.0384593717069655e+34f,    2.076918743413931e+34f,     4.153837486827862e+34f,     8.307674973655724e+34f,
+    1.661534994731145e+35f,     3.32306998946229e+35f,      6.64613997892458e+35f,      1.329227995784916e+36f,
+    2.658455991569832e+36f,     5.316911983139664e+36f,     1.0633823966279327e+37f,    2.1267647932558654e+37f,
+    4.253529586511731e+37f,     8.507059173023462e+37f,     two_power_127,              f32_nan,
+};
+// clang-format on
+
+using f8m8e0_params = std::tuple<uint8_t, float>;
+class F8E8M0PTest : public testing::TestWithParam<f8m8e0_params> {};
+
+INSTANTIATE_TEST_SUITE_P(convert,
+                         F8E8M0PTest,
+                         testing::ValuesIn(enumerate(exp_floats)),
+                         testing::PrintToStringParamName());
+
+TEST_P(F8E8M0PTest, f8_bits_to_f32) {
+    const auto& params = GetParam();
+    const auto& exp_value = std::get<1>(params);
+    const auto f8 = ov::float8_e8m0::from_bits(std::get<0>(params));
+
+    if (std::isnan(exp_value)) {
+        EXPECT_TRUE(std::isnan(static_cast<float>(f8)));
+    } else {
+        EXPECT_EQ(static_cast<float>(f8), exp_value);
+    }
+}
+
+TEST_P(F8E8M0PTest, f32_to_f8_bits) {
+    const auto& params = GetParam();
+    const auto& value = std::get<1>(params);
+    const auto& exp_value = std::get<0>(params);
+    const auto f8 = ov::float8_e8m0(value);
+    const auto f8_bits = f8.to_bits();
+
+    if (exp_value == 0xFF) {
+        EXPECT_TRUE(std::isnan(f8));
+    } else {
+        EXPECT_EQ(f8_bits, exp_value);
+    }
+}
+
+// clang-format off
+using f8m8e0_params_cast = std::tuple<float, float>;
+const auto test_values = testing::Values<f8m8e0_params_cast>(
+    std::make_tuple(-std::numeric_limits<float>::infinity(),                                two_power_negative_127),
+    std::make_tuple(-15466.5486f,                                                           two_power_negative_127),
+    std::make_tuple(-1.f,                                                                   two_power_negative_127),
+    std::make_tuple(0.f,                                                                    two_power_negative_127),
+    std::make_tuple(std::nextafter(two_power_127, std::numeric_limits<float>::infinity()),  two_power_127),
+    std::make_tuple(std::numeric_limits<float>::max(),                                      two_power_127),
+    std::make_tuple(std::numeric_limits<float>::infinity(),                                 two_power_127),
+    std::make_tuple(std::numeric_limits<float>::signaling_NaN(),                            f32_nan),
+    std::make_tuple(1.f, 1.f),      std::make_tuple(1.49f, 1.f),    std::make_tuple(1.5f, 2.f),     std::make_tuple(1.51f, 2.f),
+    std::make_tuple(2.f, 2.f),      std::make_tuple(2.5f, 2.f),     std::make_tuple(3.f, 2.f),      std::make_tuple(3.5f, 4.f),
+    std::make_tuple(4.f, 4.f),      std::make_tuple(5.f, 4.f),      std::make_tuple(5.5f, 4.f),     std::make_tuple(5.99f, 4.f),
+    std::make_tuple(6.f, 8.f),      std::make_tuple(7.f, 8.f),      std::make_tuple(8.f, 8.f),      std::make_tuple(9.f, 8.f),
+    std::make_tuple(10.f, 8.f),     std::make_tuple(11.f, 8.f),     std::make_tuple(12.f, 8.f),     std::make_tuple(13.f, 16.f),
+    std::make_tuple(14.f, 16.f),    std::make_tuple(15.f, 16.f),    std::make_tuple(16.f, 16.f),    std::make_tuple(24.f, 32.f),
+    std::make_tuple(32.f, 32.f),    std::make_tuple(32.1f, 32.f),   std::make_tuple(63.9f, 64.f),   std::make_tuple(64.f, 64.f));
+// clang-format on
+
+class F8E8M0PTestCast : public testing::TestWithParam<f8m8e0_params_cast> {};
+
+INSTANTIATE_TEST_SUITE_P(cast, F8E8M0PTestCast, test_values, testing::PrintToStringParamName());
+
+TEST_P(F8E8M0PTestCast, f32_to_f8_to_f32) {
+    const auto& params = GetParam();
+
+    const auto f8 = ov::float8_e8m0(std::get<0>(params));
+    const auto& exp_value = std::get<1>(params);
+
+    if (std::isnan(exp_value)) {
+        EXPECT_TRUE(std::isnan(static_cast<float>(f8)));
+    } else {
+        EXPECT_EQ(static_cast<float>(f8), exp_value);
+    }
+}
+
+}  // namespace test
+}  // namespace ov
diff --git a/src/core/tests/frontend/frontend_manager.cpp b/src/core/tests/frontend/frontend_manager.cpp
index 62545ea439f6b1..8a2b8c87849527 100644
--- a/src/core/tests/frontend/frontend_manager.cpp
+++ b/src/core/tests/frontend/frontend_manager.cpp
@@ -117,7 +117,7 @@ TEST(FrontEndManagerTest, testDefaultFrontEnd) {
     FrontEndManager fem;
     fem.register_front_end("mock1", mock_fe_path());
     FrontEnd::Ptr fe;
-    ASSERT_NO_THROW(fe = fem.load_by_model(""));
+    ASSERT_NO_THROW(fe = fem.load_by_model());
     ASSERT_EQ(nullptr, fe);
 
     class MockFrontEnd : public FrontEnd {};
diff --git a/src/core/tests/graph_rewrite.cpp b/src/core/tests/graph_rewrite.cpp
index 587fc5bf3d114f..02369738741dcc 100644
--- a/src/core/tests/graph_rewrite.cpp
+++ b/src/core/tests/graph_rewrite.cpp
@@ -410,7 +410,7 @@ class CheckConsumers : public ov::pass::MatcherPass {
              * 4. Some GraphRewrite facilities
              */
             auto cnt = consumers(node.get());
-            if (node.use_count() != cnt + 7) {
+            if (node.use_count() != cnt + 6) {
                 OPENVINO_THROW("Wrong number of consumers");
             }
 
@@ -440,5 +440,5 @@ TEST(GraphRewriteTest, nodes_use_count) {
     auto f = get_model();
     pass::Manager m;
     m.register_pass<CheckConsumers>();
-    ASSERT_NO_THROW(m.run_passes(f));
+    OV_ASSERT_NO_THROW(m.run_passes(f));
 }
diff --git a/src/core/tests/model.cpp b/src/core/tests/model.cpp
index 2b566cd23237c1..e2cd95e8389735 100644
--- a/src/core/tests/model.cpp
+++ b/src/core/tests/model.cpp
@@ -7,13 +7,18 @@
 #include <gtest/gtest.h>
 
 #include <memory>
-#include <shared_node_info.hpp>
 
 #include "common_test_utils/graph_comparator.hpp"
 #include "common_test_utils/test_common.hpp"
 #include "openvino/core/except.hpp"
 #include "openvino/core/partial_shape.hpp"
 #include "openvino/opsets/opset8.hpp"
+#include "shared_node_info.hpp"
+
+using ov::op::util::Variable;
+using ov::op::util::VariableInfo;
+using ov::op::v0::Parameter;
+using ov::op::v0::Result;
 
 TEST(model, get_input_by_tensor_name) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1});
@@ -932,7 +937,7 @@ TEST(model_reshape, ReshapeBatchReLUWithOneInput) {
     EXPECT_EQ(model->get_results()[0]->get_shape(), ov::Shape({2, 3, 22, 22}));
 }
 
-TEST(model_reshape, IncoreectReshapeBatchWithMultipleInputs) {
+TEST(model_reshape, IncorrectReshapeBatchWithMultipleInputs) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1, 3, 3, 3});
     arg0->set_friendly_name("data");
     arg0->get_output_tensor(0).set_names({"input1"});
@@ -957,6 +962,161 @@ TEST(model_reshape, IncoreectReshapeBatchWithMultipleInputs) {
     EXPECT_THROW(f->reshape(shape), ov::Exception);
 }
 
+TEST(model_reshape, ReshapeWithStaticVariableSingleInput) {
+    auto arg0 = std::make_shared<Parameter>(ov::element::f32, ov::PartialShape{1, 3, 4, 5});
+
+    auto variable = std::make_shared<Variable>(VariableInfo{arg0->get_output_partial_shape(0), ov::element::f32, "ID"});
+    auto read_value = std::make_shared<ov::op::v6::ReadValue>(arg0, variable);
+    auto assign = std::make_shared<ov::op::v6::Assign>(read_value, variable);
+
+    auto result1 = std::make_shared<Result>(assign);
+
+    auto shape_of = std::make_shared<ov::opset8::ShapeOf>(arg0);
+    auto result2 = std::make_shared<Result>(shape_of);
+    auto model = std::make_shared<ov::Model>(ov::ResultVector{result1, result2}, ov::ParameterVector{arg0});
+
+    model->validate_nodes_and_infer_types();
+
+    EXPECT_EQ(model->get_parameters()[0]->get_shape(), ov::Shape({1, 3, 4, 5}));
+    EXPECT_EQ(model->get_results()[0]->get_shape(), ov::Shape({1, 3, 4, 5}));
+    EXPECT_EQ(model->get_results()[1]->get_shape(), ov::Shape({4}));
+
+    {
+        ov::PartialShape shape({1, 4, 3, 3});
+        model->reshape(shape, {{"ID", shape}});
+    }
+
+    EXPECT_EQ(model->get_parameters()[0]->get_shape(), ov::Shape({1, 4, 3, 3}));
+    EXPECT_EQ(model->get_results()[0]->get_shape(), ov::Shape({1, 4, 3, 3}));
+    EXPECT_EQ(model->get_results()[1]->get_shape(), ov::Shape({4}));
+}
+
+TEST(model_reshape, ReshapeWithStaticVariablesSingleInput) {
+    auto arg0 = std::make_shared<Parameter>(ov::element::f32, ov::PartialShape{1, 3, 4, 5});
+
+    auto variable =
+        std::make_shared<Variable>(VariableInfo{arg0->get_output_partial_shape(0), ov::element::f32, "ID1"});
+    auto read_value = std::make_shared<ov::op::v6::ReadValue>(arg0, variable);
+    auto assign = std::make_shared<ov::op::v6::Assign>(read_value, variable);
+
+    auto add = std::make_shared<ov::op::v1::Add>(read_value, read_value);
+    auto var_add = std::make_shared<Variable>(VariableInfo{add->get_output_partial_shape(0), ov::element::f32, "ID2"});
+    auto read_value_add = std::make_shared<ov::op::v6::ReadValue>(add, var_add);
+    auto assign_add = std::make_shared<ov::op::v6::Assign>(read_value_add, var_add);
+
+    auto result1 = std::make_shared<Result>(assign);
+
+    auto shape_of = std::make_shared<ov::opset8::ShapeOf>(add);
+    auto result2 = std::make_shared<Result>(shape_of);
+    auto model = std::make_shared<ov::Model>(ov::ResultVector{result1, result2}, ov::ParameterVector{arg0});
+
+    model->validate_nodes_and_infer_types();
+
+    EXPECT_EQ(model->get_parameters()[0]->get_shape(), ov::Shape({1, 3, 4, 5}));
+    EXPECT_EQ(model->get_results()[0]->get_shape(), ov::Shape({1, 3, 4, 5}));
+    EXPECT_EQ(model->get_results()[1]->get_shape(), ov::Shape({4}));
+
+    {
+        ov::PartialShape shape({1, 4, 3, 3});
+        model->reshape(shape, {{"ID2", shape}, {"ID1", shape}});
+    }
+
+    EXPECT_EQ(model->get_parameters()[0]->get_shape(), ov::Shape({1, 4, 3, 3}));
+    EXPECT_EQ(model->get_results()[0]->get_shape(), ov::Shape({1, 4, 3, 3}));
+    EXPECT_EQ(model->get_results()[1]->get_shape(), ov::Shape({4}));
+}
+
+TEST(model_reshape, ReshapeWithDynamicVariableSingleInput) {
+    auto arg0 = std::make_shared<Parameter>(ov::element::f32, ov::PartialShape{1, 3, 4, 5});
+    arg0->get_output_tensor(0).set_names({"input"});
+
+    auto variable = std::make_shared<Variable>(VariableInfo{ov::PartialShape::dynamic(4), ov::element::f32, "ID"});
+    auto read_value = std::make_shared<ov::op::v6::ReadValue>(arg0, variable);
+    auto assign = std::make_shared<ov::op::v6::Assign>(read_value, variable);
+
+    auto result1 = std::make_shared<Result>(assign);
+
+    auto shape_of = std::make_shared<ov::opset8::ShapeOf>(arg0);
+    auto result2 = std::make_shared<Result>(shape_of);
+    auto model = std::make_shared<ov::Model>(ov::ResultVector{result1, result2}, ov::ParameterVector{arg0});
+
+    model->validate_nodes_and_infer_types();
+
+    EXPECT_EQ(model->get_parameters()[0]->get_shape(), ov::Shape({1, 3, 4, 5}));
+    EXPECT_EQ(model->get_results()[0]->get_output_partial_shape(0), ov::PartialShape::dynamic(4));
+    EXPECT_EQ(model->get_results()[1]->get_output_partial_shape(0), ov::Shape({4}));
+
+    model->reshape({{"input", ov::PartialShape{1, 4, 8, 9}}});
+
+    EXPECT_EQ(model->get_parameters()[0]->get_shape(), ov::Shape({1, 4, 8, 9}));
+    EXPECT_EQ(model->get_results()[0]->get_output_partial_shape(0), ov::PartialShape::dynamic(4));
+    EXPECT_EQ(model->get_results()[1]->get_shape(), ov::Shape({4}));
+}
+
+TEST(model_reshape, ReshapeStaticWithVariableMultipleInputs) {
+    auto arg0 = std::make_shared<Parameter>(ov::element::f32, ov::PartialShape{1, 3, 4, 5});
+    auto arg1 = std::make_shared<Parameter>(ov::element::f32, ov::PartialShape{1, 2, 4, 5});
+    auto concat = std::make_shared<ov::op::v0::Concat>(ov::NodeVector{arg0, arg1}, 1);
+
+    auto variable =
+        std::make_shared<Variable>(VariableInfo{concat->get_output_partial_shape(0), ov::element::f32, "ID"});
+    auto read_value = std::make_shared<ov::op::v6::ReadValue>(concat, variable);
+    auto assign = std::make_shared<ov::op::v6::Assign>(read_value, variable);
+
+    auto result1 = std::make_shared<Result>(assign);
+
+    auto shape_of = std::make_shared<ov::opset8::ShapeOf>(concat);
+    auto result2 = std::make_shared<Result>(shape_of);
+    auto model = std::make_shared<ov::Model>(ov::ResultVector{result1, result2}, ov::ParameterVector{arg0, arg1});
+
+    model->validate_nodes_and_infer_types();
+
+    EXPECT_EQ(model->get_parameters()[0]->get_shape(), ov::Shape({1, 3, 4, 5}));
+    EXPECT_EQ(model->get_parameters()[1]->get_shape(), ov::Shape({1, 2, 4, 5}));
+    EXPECT_EQ(model->get_results()[0]->get_shape(), ov::Shape({1, 5, 4, 5}));
+    EXPECT_EQ(model->get_results()[1]->get_shape(), ov::Shape({4}));
+
+    {
+        ov::PartialShape shape({1, 14, 4, 5});
+        model->reshape({{0, shape}, {1, shape}}, {{"ID", {1, 28, 4, 5}}});
+    }
+
+    EXPECT_EQ(model->get_parameters()[0]->get_shape(), ov::Shape({1, 14, 4, 5}));
+    EXPECT_EQ(model->get_parameters()[1]->get_shape(), ov::Shape({1, 14, 4, 5}));
+    EXPECT_EQ(model->get_results()[0]->get_shape(), ov::Shape({1, 28, 4, 5}));
+    EXPECT_EQ(model->get_results()[1]->get_shape(), ov::Shape({4}));
+}
+
+TEST(model_reshape, ReshapeWithStaticVariableIncorrectVariable) {
+    auto arg0 = std::make_shared<Parameter>(ov::element::f32, ov::PartialShape{1, 3, 4, 5});
+    auto arg1 = std::make_shared<Parameter>(ov::element::f32, ov::PartialShape{1, 2, 4, 5});
+    auto concat = std::make_shared<ov::op::v0::Concat>(ov::NodeVector{arg0, arg1}, 1);
+
+    auto variable =
+        std::make_shared<Variable>(VariableInfo{concat->get_output_partial_shape(0), ov::element::f32, "ID"});
+    auto read_value = std::make_shared<ov::op::v6::ReadValue>(concat, variable);
+    auto assign = std::make_shared<ov::op::v6::Assign>(read_value, variable);
+
+    auto result1 = std::make_shared<Result>(assign);
+
+    auto shape_of = std::make_shared<ov::opset8::ShapeOf>(concat);
+    auto result2 = std::make_shared<Result>(shape_of);
+    auto model = std::make_shared<ov::Model>(ov::ResultVector{result1, result2}, ov::ParameterVector{arg0, arg1});
+
+    model->validate_nodes_and_infer_types();
+
+    EXPECT_EQ(model->get_parameters()[0]->get_shape(), ov::Shape({1, 3, 4, 5}));
+    EXPECT_EQ(model->get_parameters()[1]->get_shape(), ov::Shape({1, 2, 4, 5}));
+    EXPECT_EQ(model->get_results()[0]->get_shape(), ov::Shape({1, 5, 4, 5}));
+    EXPECT_EQ(model->get_results()[1]->get_shape(), ov::Shape({4}));
+
+    ov::PartialShape shape({1, 14, 4, 5});
+    ov::PartialShape wrong_var_shape({1, 20, 4, 5});
+    EXPECT_THROW(model->reshape({{0, shape}, {1, shape}}), ov::AssertFailure);
+    EXPECT_THROW(model->reshape({{0, shape}, {1, shape}}, {{"WRONG_ID", {1, 28, 4, 5}}}), ov::AssertFailure);
+    EXPECT_THROW(model->reshape({{0, shape}, {1, shape}}, {{"ID", wrong_var_shape}}), ov::AssertFailure);
+}
+
 TEST(model, add_output_tensor_name) {
     auto arg0 = std::make_shared<ov::opset8::Parameter>(ov::element::f32, ov::PartialShape{1});
     arg0->set_friendly_name("data");
diff --git a/src/core/tests/pattern.cpp b/src/core/tests/pattern.cpp
index b7eedbadc33581..0e037fdd15ac25 100644
--- a/src/core/tests/pattern.cpp
+++ b/src/core/tests/pattern.cpp
@@ -38,7 +38,6 @@
 #include "openvino/pass/graph_rewrite.hpp"
 #include "openvino/pass/manager.hpp"
 #include "openvino/pass/pattern/matcher.hpp"
-#include "openvino/pass/pattern/op/branch.hpp"
 #include "openvino/pass/pattern/op/label.hpp"
 #include "openvino/pass/pattern/op/optional.hpp"
 #include "openvino/pass/pattern/op/or.hpp"
@@ -310,16 +309,10 @@ TEST(pattern, matcher) {
     ASSERT_EQ(n.get_matched_nodes(), (NodeVector{a}));
 
     auto abs = make_shared<op::v0::Abs>(a);
-    auto any = std::make_shared<pattern::op::Skip>(a);
-    ASSERT_TRUE(n.match(any, abs));
-    ASSERT_EQ(n.get_matched_nodes(), (NodeVector{abs, a}));
 
     auto false_pred = [](std::shared_ptr<Node> /* no */) {
         return false;
     };
-    auto any_false = std::make_shared<pattern::op::Skip>(a, false_pred);
-    ASSERT_TRUE(n.match(any_false, a));
-    ASSERT_EQ(n.get_matched_nodes(), (NodeVector{a, a}));
 
     auto pattern = std::make_shared<pattern::op::Label>(a);
     ASSERT_TRUE(n.match(pattern, a));
@@ -371,39 +364,6 @@ TEST(pattern, matcher) {
     ASSERT_FALSE(n.match(std::make_shared<op::v1::Add>(abs, b), std::make_shared<op::v1::Add>(b, b)));
     ASSERT_EQ(n.get_matched_nodes(), (NodeVector{}));
 
-    auto add_absb = std::make_shared<op::v1::Add>(abs, b);
-    ASSERT_TRUE(n.match(std::make_shared<op::v1::Add>(any, b), add_absb));
-    ASSERT_EQ(n.get_matched_nodes(), (NodeVector{add_absb, abs, a, b}));
-
-    ASSERT_TRUE(n.match(std::make_shared<op::v1::Add>(pattern, b), add_absb));
-    ASSERT_EQ(n.get_pattern_map()[pattern], abs);
-    ASSERT_EQ(n.get_matched_nodes(), (NodeVector{add_absb, abs, b}));
-
-    ASSERT_TRUE(n.match(std::make_shared<op::v1::Add>(b, pattern), add_absb));
-    ASSERT_EQ(n.get_pattern_map()[pattern], abs);
-    ASSERT_EQ(n.get_matched_nodes(), (NodeVector{add_absb, abs, b}));
-
-    auto c = make_shared<op::v0::Parameter>(element::i32, shape);
-    auto mul_add_absb = std::make_shared<op::v1::Multiply>(c, add_absb);
-    ASSERT_TRUE(
-        n.match(std::make_shared<op::v1::Multiply>(c, std::make_shared<op::v1::Add>(b, pattern)), mul_add_absb));
-    ASSERT_EQ(n.get_pattern_map()[pattern], abs);
-    ASSERT_EQ(n.get_matched_nodes(), (NodeVector{mul_add_absb, c, add_absb, abs, b}));
-
-    ASSERT_TRUE(n.match(std::make_shared<op::v1::Multiply>(c, std::make_shared<op::v1::Add>(any, b)),
-                        mul_add_absb));  // nested any
-    ASSERT_EQ(n.get_matched_nodes(), (NodeVector{mul_add_absb, c, add_absb, abs, a, b}));
-    ASSERT_TRUE(n.match(std::make_shared<op::v1::Multiply>(c, std::make_shared<op::v1::Add>(any, b)),
-                        std::make_shared<op::v1::Multiply>(std::make_shared<op::v1::Add>(b, abs),
-                                                           c)));  // permutations w/ any
-    auto mul_c_add_ab = make_shared<op::v1::Multiply>(c, add_ab);
-    ASSERT_TRUE(n.match(std::make_shared<op::v1::Multiply>(c, std::make_shared<op::v1::Add>(any_false, b)),
-                        std::make_shared<op::v1::Multiply>(c, std::make_shared<op::v1::Add>(a, b))));  //
-    // nested any
-    ASSERT_TRUE(n.match(std::make_shared<op::v1::Multiply>(c, std::make_shared<op::v1::Add>(any_false, b)),
-                        mul_c_add_ab));  // permutations w/ any_false
-    ASSERT_EQ(n.get_matched_nodes(), (NodeVector{mul_c_add_ab, c, add_ab, a, a, b}));
-
     auto iconst1_0 = construct_constant_node(1);
     auto iconst1_1 = construct_constant_node(1);
     ASSERT_TRUE(n.match(make_shared<op::v1::Multiply>(pattern, iconst1_0),
@@ -462,18 +422,6 @@ TEST(pattern, matcher) {
                                                                        std::make_shared<op::v1::Subtract>(a, b)}),
                         std::make_shared<op::v1::Subtract>(a, b)));
 
-    // Branch
-    {
-        auto branch = std::make_shared<pattern::op::Branch>();
-        auto star = std::make_shared<pattern::op::Or>(OutputVector{branch, std::make_shared<pattern::op::True>()});
-        auto pattern = std::make_shared<op::v1::Add>(star, star);
-        branch->set_destination(pattern);
-        auto arg =
-            std::make_shared<op::v1::Add>(std::make_shared<op::v1::Add>(a, b), std::make_shared<op::v1::Add>(b, a));
-        ASSERT_TRUE(n.match(pattern, std::make_shared<op::v1::Add>(arg, a)));
-        ASSERT_EQ(n.get_matched_nodes().size(), 4);
-    }
-
     // strict mode
     {
         TestMatcher sm(Output<Node>{}, "TestMatcher", true);
@@ -959,47 +907,6 @@ TEST(pattern, test_sort) {
     }
 }
 
-TEST(pattern, label_on_skip) {
-    const auto zero = std::string{"0"};
-    const auto is_zero = [&zero](const Output<Node>& node) {
-        if (const auto c = as_type_ptr<op::v0::Constant>(node.get_node_shared_ptr())) {
-            return (c->get_all_data_elements_bitwise_identical() && c->convert_value_to_string(0) == zero);
-        } else {
-            return false;
-        }
-    };
-
-    Shape shape{2, 2};
-    auto a = make_shared<op::v0::Parameter>(element::i32, shape);
-    auto b = make_shared<op::v0::Parameter>(element::i32, Shape{});
-    auto iconst = op::v0::Constant::create(element::i32, Shape{}, {0.0f});
-    auto label = std::make_shared<pattern::op::Label>(iconst);
-    auto const_label = std::make_shared<pattern::op::Label>(iconst, is_zero, NodeVector{iconst});
-
-    auto bcst_pred = [](std::shared_ptr<Node> n) {
-        return ov::as_type_ptr<op::v1::Broadcast>(n) != nullptr;
-    };
-
-    auto shape_const = ov::op::v0::Constant::create(element::u64, Shape{shape.size()}, shape);
-    auto axes_const = ov::op::v0::Constant::create(element::u8, Shape{}, {0});
-    auto bcst = std::make_shared<pattern::op::Skip>(OutputVector{const_label, shape_const, axes_const}, bcst_pred);
-    auto bcst_label = std::make_shared<pattern::op::Label>(bcst, nullptr, NodeVector{bcst});
-    auto matcher =
-        std::make_shared<pattern::Matcher>(std::make_shared<op::v1::Multiply>(label, bcst_label), "label_on_skip");
-
-    auto const_broadcast = make_shared<op::v1::Broadcast>(iconst, shape_const);
-    std::shared_ptr<Node> mul = std::make_shared<op::v1::Multiply>(a, const_broadcast);
-    std::shared_ptr<Node> mul_scalar = std::make_shared<op::v1::Multiply>(b, iconst);
-    ASSERT_TRUE(matcher->match(mul));
-    ASSERT_EQ(matcher->get_pattern_map()[bcst_label], const_broadcast);
-    ASSERT_EQ(matcher->get_pattern_map()[const_label], iconst);
-    ASSERT_EQ(matcher->get_pattern_map()[label], a);
-    ASSERT_TRUE(matcher->match(mul_scalar));
-    ASSERT_EQ(matcher->get_pattern_map()[bcst_label], iconst);
-    ASSERT_EQ(matcher->get_pattern_map()[const_label], iconst);
-    ASSERT_EQ(matcher->get_pattern_map()[label], b);
-}
-
 TEST(pattern, is_contained_match) {
     Shape shape{};
     auto a = make_shared<op::v0::Parameter>(element::i32, shape);
diff --git a/src/core/tests/type_prop/fake_convert.cpp b/src/core/tests/type_prop/fake_convert.cpp
index b96a99467ff7c0..629d575eefb169 100644
--- a/src/core/tests/type_prop/fake_convert.cpp
+++ b/src/core/tests/type_prop/fake_convert.cpp
@@ -157,11 +157,10 @@ TEST(type_prop, fake_convert_basic_unsupported_shape_not_unidirectional_broadcas
     const auto scale = std::make_shared<Parameter>(element::f32, PartialShape{1, 1, 3, 1});
     const auto shift = std::make_shared<Parameter>(element::f32, PartialShape{1, 1, 3, 1});
 
-    OV_EXPECT_THROW(
-        const auto op = std::make_shared<op::v13::FakeConvert>(data, scale, shift),
-        AssertFailure,
-        testing::HasSubstr(
-            "FakeConvert support only unidirectional broadcasting, inputs cannot be broadcastd into data."));
+    OV_EXPECT_THROW(const auto op = std::make_shared<op::v13::FakeConvert>(data, scale, shift),
+                    AssertFailure,
+                    testing::HasSubstr(
+                        "FakeConvert support only unidirectional broadcasting, inputs cannot be broadcast into data."));
 }
 TEST(type_prop, fake_convert_basic_unsupported_mixed_types) {
     const auto data = std::make_shared<Parameter>(element::f32, PartialShape{2, 3, 8, 6});
diff --git a/src/core/tests/type_prop/gather_elements.cpp b/src/core/tests/type_prop/gather_elements.cpp
index d3ad38e2eb0305..0e7df1f40cf22e 100644
--- a/src/core/tests/type_prop/gather_elements.cpp
+++ b/src/core/tests/type_prop/gather_elements.cpp
@@ -4,6 +4,7 @@
 
 #include <gtest/gtest.h>
 
+#include "common_test_utils/test_assertions.hpp"
 #include "common_test_utils/type_prop.hpp"
 #include "openvino/op/ops.hpp"
 
@@ -327,3 +328,13 @@ TEST(type_prop, gather_elements_incosistent_interval_shapes) {
         FAIL() << "Shape inconsistency check for dynamic PartialShape failed for unexpected reason";
     }
 }
+
+TEST(type_prop, gather_elements_data_rank_dynamic_indicies_incorrect_rank) {
+    constexpr int64_t axis = 0;
+    const auto data = std::make_shared<v0::Parameter>(element::i8, PartialShape::dynamic());
+    const auto indices = std::make_shared<v0::Parameter>(element::i64, PartialShape{});
+
+    OV_EXPECT_THROW(std::ignore = std::make_shared<v6::GatherElements>(data, indices, axis),
+                    NodeValidationFailure,
+                    HasSubstr("indices rank must be >= 1"));
+}
diff --git a/src/core/tests/type_prop/interpolate.cpp b/src/core/tests/type_prop/interpolate.cpp
index 50c4cf789e5b2d..95cdbfc265e835 100644
--- a/src/core/tests/type_prop/interpolate.cpp
+++ b/src/core/tests/type_prop/interpolate.cpp
@@ -119,8 +119,8 @@ TEST(type_prop, interpolate_v4_default_ctor) {
     attrs.coordinate_transformation_mode = CoordinateTransformMode::HALF_PIXEL;
     attrs.nearest_mode = Nearest_mode::ROUND_PREFER_FLOOR;
     attrs.antialias = false;
-    attrs.pads_begin = {0, 0, 0, 0};
-    attrs.pads_end = {0, 0, 0, 0};
+    attrs.pads_begin = std::vector<size_t>{0, 0, 0, 0};
+    attrs.pads_end = std::vector<size_t>{0, 0, 0, 0};
     attrs.cube_coeff = -0.75;
 
     auto interp = std::make_shared<op::v4::Interpolate>();
diff --git a/src/core/tests/type_prop/scaled_dot_product_attention.cpp b/src/core/tests/type_prop/scaled_dot_product_attention.cpp
index 890a8b52d45b09..c4448e48c1a51f 100644
--- a/src/core/tests/type_prop/scaled_dot_product_attention.cpp
+++ b/src/core/tests/type_prop/scaled_dot_product_attention.cpp
@@ -297,6 +297,34 @@ TEST(type_prop, scaled_dot_product_attention_type_infer_4_inputs_bool_attention)
     EXPECT_EQ(op->get_output_partial_shape(0), (PartialShape{2, 3, 6}));
 }
 
+TEST(type_prop, scaled_dot_product_attention_static_broadcast_attention_L) {
+    const auto query = std::make_shared<opset13::Parameter>(element::f32, PartialShape{2, 8, 16, 32});
+    const auto key = std::make_shared<opset13::Parameter>(element::f32, PartialShape{2, 8, 48, 32});
+    const auto value = std::make_shared<opset13::Parameter>(element::f32, PartialShape{2, 8, 48, 64});
+    const auto attention_mask = std::make_shared<opset13::Parameter>(element::f32, PartialShape{2, 8, 1, 48});
+    const auto scale = std::make_shared<opset13::Parameter>(element::f32, PartialShape{1});
+    auto causal = false;
+
+    const auto op =
+        std::make_shared<opset13::ScaledDotProductAttention>(query, key, value, attention_mask, scale, causal);
+    EXPECT_EQ(op->get_output_element_type(0), element::f32);
+    EXPECT_EQ(op->get_output_partial_shape(0), (PartialShape{2, 8, 16, 64}));
+}
+
+TEST(type_prop, scaled_dot_product_attention_dyn_broadcast_attention_L) {
+    const auto query = std::make_shared<opset13::Parameter>(element::f32, PartialShape{2, 8, {8, -1}, 32});
+    const auto key = std::make_shared<opset13::Parameter>(element::f32, PartialShape{2, 8, -1, 32});
+    const auto value = std::make_shared<opset13::Parameter>(element::f32, PartialShape{2, 8, -1, 64});
+    const auto attention_mask = std::make_shared<opset13::Parameter>(element::f32, PartialShape{2, 8, 1, -1});
+    const auto scale = std::make_shared<opset13::Parameter>(element::f32, PartialShape{1});
+    auto causal = false;
+
+    const auto op =
+        std::make_shared<opset13::ScaledDotProductAttention>(query, key, value, attention_mask, scale, causal);
+    EXPECT_EQ(op->get_output_element_type(0), element::f32);
+    EXPECT_EQ(op->get_output_partial_shape(0), (PartialShape{2, 8, {8, -1}, 64}));
+}
+
 TEST(type_prop, scaled_dot_product_unsupported_key_shape) {
     const auto query = std::make_shared<opset13::Parameter>(element::f32, PartialShape{2, 3, 4});
     const auto key = std::make_shared<opset13::Parameter>(element::f32, PartialShape{3, 5, 4});
diff --git a/src/core/tests/visitors/op/random_uniform.cpp b/src/core/tests/visitors/op/random_uniform.cpp
index 88662cd49ea2f7..8cdb360f0b8258 100644
--- a/src/core/tests/visitors/op/random_uniform.cpp
+++ b/src/core/tests/visitors/op/random_uniform.cpp
@@ -19,14 +19,20 @@ TEST(attributes, random_uniform_op) {
     auto min_val = make_shared<ov::op::v0::Constant>(element::f32, Shape{}, 0);
     auto max_val = make_shared<ov::op::v0::Constant>(element::f32, Shape{}, 1);
 
-    const auto random_uniform =
-        make_shared<ov::op::v8::RandomUniform>(out_shape, min_val, max_val, element::Type_t::f32, 150, 10);
+    const auto random_uniform = make_shared<ov::op::v8::RandomUniform>(out_shape,
+                                                                       min_val,
+                                                                       max_val,
+                                                                       element::Type_t::f32,
+                                                                       150,
+                                                                       10,
+                                                                       ov::op::PhilloxAlignment::TENSORFLOW);
     NodeBuilder builder(random_uniform, {out_shape, min_val, max_val});
     auto g_random_uniform = ov::as_type_ptr<ov::op::v8::RandomUniform>(builder.create());
 
-    const auto expected_attr_count = 3;
+    const auto expected_attr_count = 4;
     EXPECT_EQ(builder.get_value_map_size(), expected_attr_count);
     EXPECT_EQ(g_random_uniform->get_global_seed(), random_uniform->get_global_seed());
     EXPECT_EQ(g_random_uniform->get_op_seed(), random_uniform->get_op_seed());
     EXPECT_EQ(g_random_uniform->get_out_type(), random_uniform->get_out_type());
+    EXPECT_EQ(g_random_uniform->get_alignment(), random_uniform->get_alignment());
 }
diff --git a/src/plugins/intel_cpu/docs/performance_analysis_ITT_counters.md b/src/docs/performance_analysis_ITT_counters.md
similarity index 59%
rename from src/plugins/intel_cpu/docs/performance_analysis_ITT_counters.md
rename to src/docs/performance_analysis_ITT_counters.md
index 1cb302b9ab2f9f..64a92200cc8bcb 100644
--- a/src/plugins/intel_cpu/docs/performance_analysis_ITT_counters.md
+++ b/src/docs/performance_analysis_ITT_counters.md
@@ -15,25 +15,10 @@ OpenVINO has a powerful capabilities for performance analysis of the key stages,
 For performance analysis, follow the steps below:
 1. Run the CMake tool with the following option: `-DENABLE_PROFILING_ITT=ON` and build OpenVINO.
 2. Choose the tool for statistics collection using ITT counters.
-    1. [Intel SEAPI](https://github.com/vladislav-volkov/IntelSEAPI) should be built from sources. See the [Readme](https://github.com/vladislav-volkov/IntelSEAPI/blob/master/README.txt) file for details.
-    2. [Intel Vtune Profiler](https://software.intel.com/content/www/us/en/develop/tools/oneapi/components/vtune-profiler.html)
-3. Run OpenVINO project with performance analysis tool.
 
-### Intel SEAPI
+    1. [Intel Vtune Profiler](https://software.intel.com/content/www/us/en/develop/tools/oneapi/components/vtune-profiler.html)
 
-#### Example of running the tool:
-
-```sh
-python ~/tools/IntelSEAPI/runtool/sea_runtool.py -o trace -f gt ! ./benchmark_app -niter 1 -nireq 1 -nstreams 1 -api sync -m ./resnet-50-pytorch/resnest-50-pytorch.xml
-```
-
-#### Mandatory parameters:
-* -o trace – output file name
-* -f gt - statistics type to be generated (Google traces)
-
-#### Generated artifacts:
-`trace.pid-21725-0.json`
-Generated file can be opened with google chrome using "chrome://tracing" URL.
+3. Run an OpenVINO project with the performance analysis tool.
 
 ### Intel Vtune Profiler
 
@@ -56,8 +41,10 @@ Use API defined in [openvino/itt](https://docs.openvinotoolkit.org/latest/itt_2i
 
 ## See also
 
- * [OpenVINO™ README](../../../../README.md)
- * [OpenVINO Core Components](../../../README.md)
- * [OpenVINO Plugins](../../README.md)
- * [OpenVINO GPU Plugin](../README.md)
- * [Developer documentation](../../../../docs/dev/index.md)
+ * [OpenVINO™ README](../../README.md)
+ * [OpenVINO Core Components](../README.md)
+ * [OpenVINO Plugins](../plugins/README.md)
+ * [OpenVINO GPU Plugin](../plugins/intel_gpu/README.md)
+ * [OpenVINO CPU Plugin](../plugins/intel_cpu/README.md)
+ * [OpenVINO NPU Plugin](../plugins/intel_npu/README.md)
+ * [Developer documentation](../../docs/dev/index.md)
diff --git a/src/frontends/CMakeLists.txt b/src/frontends/CMakeLists.txt
index 038fb146a1a125..541770886a0879 100644
--- a/src/frontends/CMakeLists.txt
+++ b/src/frontends/CMakeLists.txt
@@ -12,6 +12,10 @@ if(ENABLE_OV_IR_FRONTEND)
     add_subdirectory(ir)
 endif()
 
+if(ENABLE_OV_JAX_FRONTEND)
+    add_subdirectory(jax)
+endif()
+
 if(ENABLE_OV_ONNX_FRONTEND)
     add_subdirectory(onnx)
 endif()
diff --git a/src/frontends/common/include/openvino/frontend/frontend.hpp b/src/frontends/common/include/openvino/frontend/frontend.hpp
index 8e6020891cab71..0035382fe20c5f 100644
--- a/src/frontends/common/include/openvino/frontend/frontend.hpp
+++ b/src/frontends/common/include/openvino/frontend/frontend.hpp
@@ -152,6 +152,11 @@ class FRONTEND_API FrontEnd {
 
     virtual InputModel::Ptr load_impl(const std::vector<ov::Any>& variants) const;
 
+    void validate_path(const std::string& path) const;
+#if defined(OPENVINO_ENABLE_UNICODE_PATH_SUPPORT) && defined(_WIN32)
+    void validate_path(const std::wstring& path) const;
+#endif
+
     std::vector<ov::Extension::Ptr> m_extensions;
 
 private:
diff --git a/src/frontends/common/include/openvino/frontend/input_model.hpp b/src/frontends/common/include/openvino/frontend/input_model.hpp
index fab6d3d3e4f40b..268bfd8625747b 100644
--- a/src/frontends/common/include/openvino/frontend/input_model.hpp
+++ b/src/frontends/common/include/openvino/frontend/input_model.hpp
@@ -82,6 +82,11 @@ class FRONTEND_API InputModel {
     /// \return Tensor place corresponding to specified tensor name or nullptr if not exists
     virtual Place::Ptr get_place_by_tensor_name(const std::string& tensor_name) const;
 
+    /// \brief Returns a tensor place by an input index.
+    /// \param input_idx Index of model input
+    /// \return Tensor place corresponding to specified input index or nullptr
+    virtual Place::Ptr get_place_by_input_index(size_t input_idx) const;
+
     /// \brief Returns an operation place by an operation name following framework
     /// conventions, or nullptr if an operation with this name doesn't exist.
     /// \param operation_name Name of operation
diff --git a/src/frontends/common/src/frontend.cpp b/src/frontends/common/src/frontend.cpp
index f952e0e98de0c9..4c236c4ee2b4b9 100644
--- a/src/frontends/common/src/frontend.cpp
+++ b/src/frontends/common/src/frontend.cpp
@@ -108,3 +108,17 @@ std::string FrontEnd::get_name() const {
     }
     FRONTEND_RETURN_STATEMENT("Getting frontend name", m_actual->get_name();)
 }
+
+void FrontEnd::validate_path(const std::string& path) const {
+    FRONT_END_GENERAL_CHECK(util::directory_exists(path) || util::file_exists(path),
+                            get_name(),
+                            ": Could not open the file: \"",
+                            path,
+                            '"');
+}
+
+#if defined(OPENVINO_ENABLE_UNICODE_PATH_SUPPORT) && defined(_WIN32)
+void FrontEnd::validate_path(const std::wstring& path) const {
+    validate_path(ov::util::wstring_to_string(path));
+}
+#endif
diff --git a/src/frontends/common/src/input_model.cpp b/src/frontends/common/src/input_model.cpp
index f725eca0f31da0..e373593d26a3d5 100644
--- a/src/frontends/common/src/input_model.cpp
+++ b/src/frontends/common/src/input_model.cpp
@@ -33,6 +33,13 @@ Place::Ptr InputModel::get_place_by_tensor_name(const std::string& tensor_name)
     FRONTEND_RETURN_STATEMENT("get_place_by_tensor_name", m_actual->get_place_by_tensor_name(tensor_name))
 }
 
+Place::Ptr InputModel::get_place_by_input_index(size_t input_idx) const {
+    if (!m_actual) {
+        return {};
+    }
+    FRONTEND_RETURN_STATEMENT("get_place_by_input_index", m_actual->get_place_by_input_index(input_idx))
+}
+
 Place::Ptr InputModel::get_place_by_operation_name(const std::string& operation_name) const {
     if (!m_actual) {
         return {};
diff --git a/src/frontends/common/src/manager.cpp b/src/frontends/common/src/manager.cpp
index d48ad0e52fc601..0e75ffde3ddba7 100644
--- a/src/frontends/common/src/manager.cpp
+++ b/src/frontends/common/src/manager.cpp
@@ -34,14 +34,13 @@ class FrontEndManager::Impl {
 
     FrontEnd::Ptr load_by_framework(const std::string& framework) {
         // Mapping of default FE name to file name (without prefix and suffix)
-        static const std::map<std::string, std::string> predefined_frontends = {
-            {"ir", "ir"},
-            {"onnx", "onnx"},
-            {"tf", "tensorflow"},
-            {"tflite", "tensorflow_lite"},
-            {"paddle", "paddle"},
-            {"pytorch", "pytorch"},
-        };
+        static const std::map<std::string, std::string> predefined_frontends = {{"ir", "ir"},
+                                                                                {"onnx", "onnx"},
+                                                                                {"tf", "tensorflow"},
+                                                                                {"tflite", "tensorflow_lite"},
+                                                                                {"paddle", "paddle"},
+                                                                                {"pytorch", "pytorch"},
+                                                                                {"jax", "jax"}};
         auto it = predefined_frontends.find(framework);
         std::lock_guard<std::mutex> guard(m_loading_mutex);
         if (it != predefined_frontends.end()) {
@@ -155,7 +154,8 @@ class FrontEndManager::Impl {
                                                   {"tf", "tensorflow"},
                                                   {"tflite", "tensorflow_lite"},
                                                   {"paddle", "paddle"},
-                                                  {"pytorch", "pytorch"}};
+                                                  {"pytorch", "pytorch"},
+                                                  {"jax", "jax"}};
         if (variants.empty()) {
             return nullptr;
         }
diff --git a/src/frontends/ir/src/frontend.cpp b/src/frontends/ir/src/frontend.cpp
index 278a5782aca3fb..74553520410cda 100644
--- a/src/frontends/ir/src/frontend.cpp
+++ b/src/frontends/ir/src/frontend.cpp
@@ -74,10 +74,12 @@ bool FrontEnd::supported_impl(const std::vector<ov::Any>& variants) const {
     const auto& model_variant = variants[0];
     if (model_variant.is<std::string>()) {
         const auto& path = model_variant.as<std::string>();
+        validate_path(path);
         local_model_stream.open(path, std::ios::in | std::ifstream::binary);
 #if defined(OPENVINO_ENABLE_UNICODE_PATH_SUPPORT) && defined(_WIN32)
     } else if (model_variant.is<std::wstring>()) {
         const auto& path = model_variant.as<std::wstring>();
+        validate_path(path);
         local_model_stream.open(path.c_str(), std::ios::in | std::ifstream::binary);
 #endif
     } else if (model_variant.is<std::istream*>()) {
@@ -149,6 +151,7 @@ InputModel::Ptr FrontEnd::load_impl(const std::vector<ov::Any>& variants) const
 
     if (model_variant.is<std::string>()) {
         const auto& tmp_path = model_variant.as<std::string>();
+        validate_path(tmp_path);
 #if defined(OPENVINO_ENABLE_UNICODE_PATH_SUPPORT) && defined(_WIN32)
         model_path = ov::util::string_to_wstring(tmp_path.c_str());
 #else
@@ -158,6 +161,7 @@ InputModel::Ptr FrontEnd::load_impl(const std::vector<ov::Any>& variants) const
 #if defined(OPENVINO_ENABLE_UNICODE_PATH_SUPPORT) && defined(_WIN32)
     } else if (model_variant.is<std::wstring>()) {
         model_path = model_variant.as<std::wstring>();
+        validate_path(model_path);
         local_model_stream.open(model_path.c_str(), std::ios::in | std::ifstream::binary);
 #endif
     } else if (model_variant.is<std::istream*>()) {
diff --git a/src/frontends/ir/tests/CMakeLists.txt b/src/frontends/ir/tests/CMakeLists.txt
index 5965ba0dfc0593..df390faab4c7da 100644
--- a/src/frontends/ir/tests/CMakeLists.txt
+++ b/src/frontends/ir/tests/CMakeLists.txt
@@ -14,6 +14,7 @@ ov_add_test_target(
             gtest_main
             openvino::runtime::dev
             common_test_utils
+            frontend_shared_test_classes
         INCLUDES
             "${CMAKE_CURRENT_SOURCE_DIR}/../include"
         ADD_CLANG_FORMAT
diff --git a/src/frontends/ir/tests/frontend_test_basic.cpp b/src/frontends/ir/tests/frontend_test_basic.cpp
index 10a9fd586ba77a..aab8fe610d49f3 100644
--- a/src/frontends/ir/tests/frontend_test_basic.cpp
+++ b/src/frontends/ir/tests/frontend_test_basic.cpp
@@ -2,10 +2,12 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
+#include "common_test_utils/test_assertions.hpp"
 #include "frontend_test.hpp"
 #include "openvino/opsets/opset1.hpp"
 #include "openvino/opsets/opset3.hpp"
 #include "openvino/opsets/opset6.hpp"
+#include "utils.hpp"
 
 class IRFrontendTests : public ::testing::Test, public IRFrontendTestsImpl {
 protected:
@@ -1400,3 +1402,38 @@ TEST_F(IRFrontendTests, DetectionOutput) {
     ASSERT_NO_THROW(model = getWithIRFrontend(testModel));
     ASSERT_TRUE(!!model);
 }
+
+TEST_F(IRFrontendTests, load_model_not_exists_at_path) {
+    const auto model_name = "not_existing_model";
+    auto error_msg = std::string("Could not open the file: ");
+    auto model_file_path = FrontEndTestUtils::make_model_path(model_name);
+    error_msg += '"' + model_file_path + '"';
+
+    auto fem = ov::frontend::FrontEndManager();
+    auto fe = fem.load_by_framework("ir");
+
+    OV_EXPECT_THROW(fe->supported({model_file_path}), ov::Exception, testing::HasSubstr(error_msg));
+    OV_EXPECT_THROW(fe->load(model_file_path), ov::Exception, testing::HasSubstr(error_msg));
+}
+
+TEST_F(IRFrontendTests, load_model_weights_not_exist_at_path) {
+    const auto error_msg = std::string(" cannot be opened");
+    const auto name_prefix = ov::test::utils::generateTestFilePrefix();
+    const auto model_file_path = name_prefix + "existing_model.xml";
+    const auto weights_file_path = name_prefix + "not_existing_weights.bin";
+
+    {
+        std::ofstream model_file;
+        model_file.open(model_file_path);
+        model_file.close();
+    }
+
+    auto fem = ov::frontend::FrontEndManager();
+    auto fe = fem.load_by_framework("ir");
+
+    OV_EXPECT_THROW(fe->load(model_file_path, weights_file_path),
+                    ov::Exception,
+                    testing::HasSubstr(weights_file_path + error_msg));
+
+    std::remove(model_file_path.c_str());
+}
diff --git a/src/frontends/jax/CMakeLists.txt b/src/frontends/jax/CMakeLists.txt
new file mode 100644
index 00000000000000..161a22b9055a74
--- /dev/null
+++ b/src/frontends/jax/CMakeLists.txt
@@ -0,0 +1,5 @@
+# Copyright (C) 2018-2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+#
+
+add_subdirectory(src)
diff --git a/src/frontends/jax/include/openvino/frontend/jax/decoder.hpp b/src/frontends/jax/include/openvino/frontend/jax/decoder.hpp
new file mode 100644
index 00000000000000..2581c61eb69643
--- /dev/null
+++ b/src/frontends/jax/include/openvino/frontend/jax/decoder.hpp
@@ -0,0 +1,71 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <cstddef>
+#include <string>
+#include <vector>
+
+#include "openvino/core/node.hpp"
+#include "openvino/core/node_vector.hpp"
+#include "openvino/frontend/decoder.hpp"
+
+namespace ov {
+namespace frontend {
+namespace jax {
+
+class JaxDecoder : public IDecoder {
+public:
+    virtual OutputVector as_constant() const = 0;
+
+    virtual const std::string get_op_type() const = 0;
+
+    virtual const std::vector<size_t>& inputs() const = 0;
+
+    // Return signature name of the input tensor
+    virtual const std::string& get_input_signature_name(size_t index) const = 0;
+
+    // Return the input shape
+    virtual PartialShape get_input_shape(size_t index) const = 0;
+
+    virtual Any get_input_type(size_t index) const = 0;
+
+    virtual size_t get_named_param(const std::string& name) const = 0;
+
+    virtual OutputVector get_named_param_as_constant(const std::string& name) const = 0;
+
+    virtual const std::vector<std::string>& get_param_names() const = 0;
+
+    // Return name of the output tensor
+    virtual const std::string& get_output_name(size_t index) const = 0;
+
+    // Return the output shape
+    virtual PartialShape get_output_shape(size_t index) const = 0;
+
+    // Return element::Type when it the original type can be represented, otherwise returns PT-specific data type object
+    // (see custom_type.hpp)
+    virtual Any get_output_type(size_t index) const = 0;
+
+    // Get the inputs size. Note that jaxpr flattens the inputs in python. Therefore we do not need to deal with nested
+    // inputs here.
+    virtual size_t num_inputs() const = 0;
+
+    // Get the outputs size.
+    virtual size_t num_outputs() const = 0;
+
+    // Return a vector of output IDs
+    virtual const std::vector<size_t>& outputs() const = 0;
+
+    virtual size_t output(size_t index) const = 0;
+
+    /// \brief Returns subgraph converted on demand by the first access
+    /// If there is no query for specific sub-graph it shouldn't be converted
+    // node_visitor is a function that will be fed by nodes in subgraph for all nodes in graph
+    virtual void visit_subgraph(std::function<void(std::shared_ptr<JaxDecoder>)> node_visitor) const = 0;
+};
+
+}  // namespace jax
+}  // namespace frontend
+}  // namespace ov
diff --git a/src/frontends/jax/include/openvino/frontend/jax/extension/conversion.hpp b/src/frontends/jax/include/openvino/frontend/jax/extension/conversion.hpp
new file mode 100644
index 00000000000000..490177dde53e5d
--- /dev/null
+++ b/src/frontends/jax/include/openvino/frontend/jax/extension/conversion.hpp
@@ -0,0 +1,38 @@
+// Copyright (C) 2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "openvino/frontend/extension/conversion.hpp"
+#include "openvino/frontend/frontend.hpp"
+#include "openvino/frontend/jax/node_context.hpp"
+#include "openvino/frontend/jax/visibility.hpp"
+
+namespace ov {
+namespace frontend {
+namespace jax {
+
+class JAX_API ConversionExtension : public ConversionExtensionBase {
+public:
+    using Ptr = std::shared_ptr<ConversionExtension>;
+
+    ConversionExtension() = delete;
+
+    ConversionExtension(const std::string& op_type, const ov::frontend::CreatorFunction& converter)
+        : ConversionExtensionBase(op_type),
+          m_converter(converter) {}
+
+    const ov::frontend::CreatorFunction& get_converter() const {
+        return m_converter;
+    }
+
+    ~ConversionExtension() override;
+
+private:
+    ov::frontend::CreatorFunction m_converter;
+};
+
+}  // namespace jax
+}  // namespace frontend
+}  // namespace ov
diff --git a/src/frontends/jax/include/openvino/frontend/jax/frontend.hpp b/src/frontends/jax/include/openvino/frontend/jax/frontend.hpp
new file mode 100644
index 00000000000000..5a32d6c0b23b78
--- /dev/null
+++ b/src/frontends/jax/include/openvino/frontend/jax/frontend.hpp
@@ -0,0 +1,69 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "openvino/frontend/extension/conversion.hpp"
+#include "openvino/frontend/extension/telemetry.hpp"
+#include "openvino/frontend/frontend.hpp"
+#include "openvino/frontend/jax/node_context.hpp"
+#include "openvino/frontend/jax/visibility.hpp"
+
+namespace ov {
+namespace frontend {
+namespace jax {
+
+class JAX_API FrontEnd : public ov::frontend::FrontEnd {
+public:
+    using Ptr = std::shared_ptr<FrontEnd>;
+    FrontEnd();
+
+    /// \brief Completely convert and normalize entire Model, throws if it is not possible
+    /// \param model Input model
+    /// \return fully converted OV Model
+    std::shared_ptr<Model> convert(const ov::frontend::InputModel::Ptr& model) const override;
+
+    /// \brief Completely convert the remaining, not converted part of a Model.
+    /// \param partiallyConverted partially converted OV Model
+    void convert(const std::shared_ptr<Model>& partiallyConverted) const override;
+
+    /// \brief Convert only those parts of the model that can be converted leaving others
+    /// as-is. Converted parts are not normalized by additional transformations; normalize
+    /// function or another form of convert function should be called to finalize the
+    /// conversion process.
+    /// \param model Input model
+    /// \return partially converted OV Model
+    std::shared_ptr<Model> convert_partially(const InputModel::Ptr& model) const override;
+
+    /// \brief Convert operations with one-to-one mapping with decoding nodes.
+    /// Each decoding node is an OV node representing a single FW operation node with
+    /// all attributes represented in FW-independent way.
+    /// \param model Input model
+    /// \return OV Model after decoding
+    std::shared_ptr<Model> decode(const InputModel::Ptr& model) const override;
+
+    /// \brief Gets name of this FrontEnd. Can be used by clients
+    /// if frontend is selected automatically by FrontEndManager::load_by_model
+    /// \return Paddle frontend name.
+    std::string get_name() const override {
+        return "jax";
+    }
+
+    /// \brief Register base extension in the FrontEnd
+    /// \param extension base extension
+    void add_extension(const std::shared_ptr<ov::Extension>& extension) override;
+
+protected:
+    bool supported_impl(const std::vector<ov::Any>& variants) const override;
+    ov::frontend::InputModel::Ptr load_impl(const std::vector<ov::Any>& variants) const override;
+    std::map<std::string, CreatorFunction> get_supported_ops(const ov::frontend::InputModel::Ptr& model) const;
+
+    std::map<std::string, CreatorFunction> m_op_extension_translators;
+    std::vector<ConversionExtensionBase::Ptr> m_conversion_extensions;
+    TelemetryExtension::Ptr m_telemetry;
+};
+
+}  // namespace jax
+}  // namespace frontend
+}  // namespace ov
diff --git a/src/frontends/jax/include/openvino/frontend/jax/node_context.hpp b/src/frontends/jax/include/openvino/frontend/jax/node_context.hpp
new file mode 100644
index 00000000000000..12ef68f4bfdbc1
--- /dev/null
+++ b/src/frontends/jax/include/openvino/frontend/jax/node_context.hpp
@@ -0,0 +1,147 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <functional>
+#include <string>
+#include <unordered_map>
+
+#include "openvino/frontend/jax/decoder.hpp"
+#include "openvino/frontend/node_context.hpp"
+
+namespace ov {
+namespace frontend {
+namespace jax {
+
+class TranslateSession;
+
+typedef std::unordered_map<size_t, Output<Node>> TensorMap;
+
+class NodeContext : public frontend::NodeContext {
+public:
+    NodeContext(std::shared_ptr<JaxDecoder> decoder,
+                std::shared_ptr<TensorMap> tensor_map,
+                std::shared_ptr<ParameterVector> external_parameters,
+                std::unordered_map<std::string, size_t> param_name_to_id,
+                TranslateSession* translate_session)
+        : frontend::NodeContext(decoder->get_op_type()),
+          m_decoder(decoder),
+          m_tensor_map(tensor_map),
+          m_external_parameters(external_parameters),
+          m_translate_session(translate_session),
+          m_decoder_inputs(decoder->inputs()),
+          m_decoder_outputs(decoder->outputs()),
+          m_param_name_to_id(param_name_to_id) {
+        FRONT_END_GENERAL_CHECK(m_tensor_map != nullptr && m_external_parameters != nullptr &&
+                                m_translate_session != nullptr);
+    }
+
+    // Do not search for input in tensor map; try to access it as a constant of
+    // specified type T and return its value
+    template <typename T>
+    T const_input(size_t index) const;
+
+    template <typename T>
+    T const_named_param(const std::string& name) const;
+
+    size_t get_input_size() const override {
+        return m_decoder_inputs.size();
+    };
+
+    // Search for input in tensor map and return an output port for already
+    // converted op
+    // TODO: int due to base class uses it, but naturally it should be size_t for
+    // PT
+    Output<Node> get_input(int index) const override {
+        auto input = m_decoder_inputs.at(index);
+        FRONT_END_GENERAL_CHECK(m_tensor_map->count(input), "No tensor corresponding input: ", input, " exist.");
+        return m_tensor_map->at(input);
+    }
+
+    Output<Node> get_input(const std::string& name) const override {
+        FRONT_END_GENERAL_CHECK(has_attribute(name), "Input with name ", name, " doesn't exist");
+        auto attr = get_attribute_as_any(name);
+        if (attr.is<Output<Node>>()) {
+            // Case when input is constant value
+            return attr.as<Output<Node>>();
+        } else if (attr.is<type::PyNone>()) {
+            FRONT_END_THROW("Got a none input in the JAX frontend.");
+        }
+        FRONT_END_GENERAL_CHECK(false, "Input has type which can't be converted to ov::Node.");
+    }
+
+    Any get_values_from_const_input(int index) const override;
+
+    // TODO: upstream to base class
+    OutputVector inputs() const {
+        OutputVector res;
+        for (auto input : m_decoder_inputs) {
+            FRONT_END_GENERAL_CHECK(m_tensor_map->count(input), "No tensor corresponding index: ", input, " exist.");
+            res.push_back(m_tensor_map->at(input));
+        }
+        return res;
+    }
+
+    Any get_input_type(size_t index) const {
+        return m_decoder->get_input_type(index);
+    }
+
+    Any get_output_type(size_t index) const {
+        return m_decoder->get_output_type(index);
+    }
+
+    size_t get_output_size() const {
+        return m_decoder_outputs.size();
+    }
+
+    Output<Node> get_param(const std::string& name) const {
+        auto id = m_param_name_to_id.at(name);
+        FRONT_END_GENERAL_CHECK(m_tensor_map->count(id), "No tensor corresponding param id: ", id, " exist.");
+        return m_tensor_map->at(id);
+    }
+
+    std::vector<size_t> outputs() const {
+        return m_decoder_outputs;
+    }
+
+    // Convert the resulting value of this node to ov Constant; works correctly
+    // only for nodes that produce constant value, naturally for prim::Constant
+    OutputVector as_constant() const;
+
+    Any get_attribute_as_any(const std::string& name) const override {
+        FRONT_END_THROW("Attribute is not expected to appear in JAX. Implement it if it does appear.");
+    }
+
+    std::shared_ptr<JaxDecoder> get_decoder() const {
+        return m_decoder;
+    }
+
+    TranslateSession* get_session() const {
+        return m_translate_session;
+    }
+
+    Output<Node> get_tensor_from_model(size_t index) const {
+        if (m_tensor_map->find(index) != m_tensor_map->end()) {
+            return m_tensor_map->at(index);
+        } else {
+            return Output<Node>();
+        }
+    }
+
+private:
+    std::shared_ptr<JaxDecoder> m_decoder;
+    std::shared_ptr<TensorMap> m_tensor_map;
+    std::shared_ptr<ParameterVector> m_external_parameters;
+    TranslateSession* m_translate_session = nullptr;
+    const std::vector<size_t> m_decoder_inputs;
+    const std::vector<size_t> m_decoder_outputs;
+    std::unordered_map<std::string, size_t> m_param_name_to_id;
+};
+
+using CreatorFunction = std::function<ov::OutputVector(const ov::frontend::jax::NodeContext&)>;
+
+}  // namespace jax
+}  // namespace frontend
+}  // namespace ov
\ No newline at end of file
diff --git a/src/frontends/jax/include/openvino/frontend/jax/visibility.hpp b/src/frontends/jax/include/openvino/frontend/jax/visibility.hpp
new file mode 100644
index 00000000000000..61449476b1cfa8
--- /dev/null
+++ b/src/frontends/jax/include/openvino/frontend/jax/visibility.hpp
@@ -0,0 +1,20 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "openvino/frontend/visibility.hpp"
+
+#ifdef OPENVINO_STATIC_LIBRARY
+#    define JAX_API
+#    define JAX_C_API
+#else
+#    ifdef openvino_jax_frontend_EXPORTS
+#        define JAX_API   OPENVINO_CORE_EXPORTS
+#        define JAX_C_API OPENVINO_EXTERN_C OPENVINO_CORE_EXPORTS
+#    else
+#        define JAX_API   OPENVINO_CORE_IMPORTS
+#        define JAX_C_API OPENVINO_EXTERN_C OPENVINO_CORE_IMPORTS
+#    endif  // openvino_jax_frontend_EXPORTS
+#endif      // OPENVINO_STATIC_LIBRARY
diff --git a/src/frontends/jax/src/CMakeLists.txt b/src/frontends/jax/src/CMakeLists.txt
new file mode 100644
index 00000000000000..d31c81f1e19659
--- /dev/null
+++ b/src/frontends/jax/src/CMakeLists.txt
@@ -0,0 +1,12 @@
+# Copyright (C) 2018-2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+#
+
+if((CMAKE_COMPILER_IS_GNUCXX OR OV_COMPILER_IS_CLANG) AND CMAKE_CXX_STANDARD GREATER_EQUAL 20)
+    set(CMAKE_CXX_FLAGS "-Wno-error=deprecated ${CMAKE_CXX_FLAGS}")
+endif()
+
+ov_add_frontend(NAME jax
+                LINKABLE_FRONTEND
+                FILEDESCRIPTION "FrontEnd to load and convert JAX/Flax models"
+                LINK_LIBRARIES openvino::util openvino::core::dev)
diff --git a/src/frontends/jax/src/extensions.cpp b/src/frontends/jax/src/extensions.cpp
new file mode 100644
index 00000000000000..6f23debdff3603
--- /dev/null
+++ b/src/frontends/jax/src/extensions.cpp
@@ -0,0 +1,7 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "openvino/frontend/jax/extension/conversion.hpp"
+
+ov::frontend::jax::ConversionExtension::~ConversionExtension() = default;
diff --git a/src/frontends/jax/src/frontend.cpp b/src/frontends/jax/src/frontend.cpp
new file mode 100644
index 00000000000000..b1471098368a7a
--- /dev/null
+++ b/src/frontends/jax/src/frontend.cpp
@@ -0,0 +1,187 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "openvino/frontend/jax/frontend.hpp"
+
+#include "input_model.hpp"
+#include "jax_framework_node.hpp"
+#include "op_table.hpp"
+#include "openvino/core/so_extension.hpp"
+#include "openvino/frontend/jax/extension/conversion.hpp"
+#include "openvino/op/util/multi_subgraph_base.hpp"
+#include "openvino/util/log.hpp"
+#include "translate_session.hpp"
+
+namespace ov {
+namespace frontend {
+namespace jax {
+
+namespace {
+std::map<std::string, std::string> get_unconverted_types_from_model(const std::shared_ptr<Model>& model) {
+    std::map<std::string, std::string> unconverted_ops_types;
+    for (const auto& node : model->get_ordered_ops()) {
+        if (const auto& fw_node = ov::as_type_ptr<JaxFrameworkNode>(node)) {
+            const auto& attrs = fw_node->get_attrs();
+            FRONT_END_GENERAL_CHECK(attrs.find(JaxFrameworkNode::op_type_key) != attrs.end(),
+                                    "FrameworkNode attributes do not contain operation type.");
+            std::string exception_msg;
+            if (attrs.find(JaxFrameworkNode::failed_conversion_key) != attrs.end()) {
+                exception_msg = attrs.at(JaxFrameworkNode::failed_conversion_key);
+            }
+            if (!unconverted_ops_types.count(attrs.at(JaxFrameworkNode::op_type_key))) {
+                unconverted_ops_types[attrs.at(JaxFrameworkNode::op_type_key)] = exception_msg;
+            }
+        }
+        if (const auto& fw_node = ov::as_type_ptr<ov::op::util::MultiSubGraphOp>(node)) {
+            for (size_t i = 0; i < fw_node->get_internal_subgraphs_size(); i++) {
+                const auto& internal_types = get_unconverted_types_from_model(fw_node->get_function(i));
+                unconverted_ops_types.insert(internal_types.begin(), internal_types.end());
+            }
+        }
+    }
+    return unconverted_ops_types;
+}
+
+std::string pack_detailed_failure_report(const std::map<std::string, std::string>& unconverted_ops,
+                                         const std::string& additional_error = "") {
+    std::stringstream error_msg;
+    std::stringstream unconverted_ops_msg;
+    std::stringstream failed_ops_msg;
+    std::stringstream failed_ops_short;
+    error_msg << "Model wasn't fully converted.";
+    unconverted_ops_msg << "-- No conversion rule found for operations: ";
+    failed_ops_msg << " Failed operations detailed log:";
+    failed_ops_short << "-- Conversion is failed for: ";
+    bool at_least_one = false;
+    bool at_least_one_except = false;
+    for (auto&& op : unconverted_ops) {
+        if (op.second.empty()) {
+            if (at_least_one)
+                unconverted_ops_msg << ", ";
+            unconverted_ops_msg << op.first;
+            at_least_one = true;
+        } else {
+            if (at_least_one_except)
+                failed_ops_short << ", ";
+            failed_ops_short << op.first;
+            failed_ops_msg << "\n-- " << op.first << " with a message:\n" << op.second;
+            at_least_one_except = true;
+        }
+    }
+    if (at_least_one_except)
+        error_msg << failed_ops_msg.str();
+    error_msg << "\nSummary:" << additional_error;
+    if (at_least_one)
+        error_msg << '\n' << unconverted_ops_msg.str();
+    if (at_least_one_except)
+        error_msg << '\n' << failed_ops_short.str();
+    return error_msg.str();
+}
+}  // namespace
+
+FrontEnd::FrontEnd() {}
+
+std::shared_ptr<Model> FrontEnd::convert(const ov::frontend::InputModel::Ptr& model) const {
+    FRONT_END_GENERAL_CHECK(std::dynamic_pointer_cast<jax::InputModel>(model), "Invalid input model");
+    std::map<std::string, CreatorFunction> supported_ops = get_supported_ops(model);
+    std::shared_ptr<Model> converted_model;
+    {
+        TranslateSession translate_session(model, supported_ops, m_telemetry);
+        converted_model = translate_session.get_converted_model();
+    }
+
+    const auto& unconverted_ops = get_unconverted_types_from_model(converted_model);
+    for (auto&& op : unconverted_ops) {
+        if (m_telemetry) {
+            m_telemetry->send_event("error_cause", "jax_" + op.first);
+        }
+    }
+    bool is_conversion_successful = unconverted_ops.size() == 0;
+    FRONT_END_OP_CONVERSION_CHECK(is_conversion_successful, pack_detailed_failure_report(unconverted_ops));
+    return converted_model;
+}
+
+void FrontEnd::convert(const std::shared_ptr<Model>& partiallyConverted) const {
+    FRONT_END_NOT_IMPLEMENTED(convert);
+}
+
+std::shared_ptr<Model> FrontEnd::convert_partially(const ov::frontend::InputModel::Ptr& model) const {
+    FRONT_END_GENERAL_CHECK(std::dynamic_pointer_cast<jax::InputModel>(model), "Invalid input model");
+    std::map<std::string, CreatorFunction> supported_ops = get_supported_ops(model);
+    std::shared_ptr<Model> partial_model;
+    {
+        TranslateSession translate_session(model, supported_ops, m_telemetry);
+        partial_model = translate_session.get_converted_model();
+    }
+    return partial_model;
+}
+
+std::shared_ptr<Model> FrontEnd::decode(const InputModel::Ptr& model) const {
+    FRONT_END_NOT_IMPLEMENTED(decode);
+}
+
+void FrontEnd::add_extension(const std::shared_ptr<ov::Extension>& extension) {
+    if (auto conv_ext = std::dynamic_pointer_cast<ov::frontend::ConversionExtension>(extension)) {
+        m_conversion_extensions.push_back(conv_ext);
+        m_op_extension_translators[conv_ext->get_op_type()] = [=](const NodeContext& context) {
+            return conv_ext->get_converter()(context);
+        };
+    } else if (auto conv_ext = std::dynamic_pointer_cast<ov::frontend::jax::ConversionExtension>(extension)) {
+        m_conversion_extensions.push_back(conv_ext);
+        m_op_extension_translators[conv_ext->get_op_type()] = [=](const NodeContext& context) {
+            return conv_ext->get_converter()(context);
+        };
+    } else if (const auto& so_ext = std::dynamic_pointer_cast<ov::detail::SOExtension>(extension)) {
+        add_extension(so_ext->extension());
+        m_extensions.push_back(so_ext);
+    } else if (const auto& telemetry = std::dynamic_pointer_cast<TelemetryExtension>(extension)) {
+        m_telemetry = telemetry;
+    } else if (auto op_base_ext = std::dynamic_pointer_cast<ov::BaseOpExtension>(extension)) {
+        for (const auto& attached_ext : op_base_ext->get_attached_extensions()) {
+            add_extension(attached_ext);
+        }
+    }
+}
+
+bool FrontEnd::supported_impl(const std::vector<ov::Any>& variants) const {
+    // Last boolean flag in `variants` (if presented) is reserved for FE
+    // configuration
+    size_t extra_variants_num = variants.size() > 0 && variants[variants.size() - 1].is<bool>() ? 1 : 0;
+    // Currently Jax FrontEnd only support JaxDecoder as input
+    if (variants.size() != 1 + extra_variants_num || !variants[0].is<std::shared_ptr<IDecoder>>())
+        return false;
+    auto decoder = variants[0].as<std::shared_ptr<IDecoder>>();
+    return decoder && std::dynamic_pointer_cast<JaxDecoder>(decoder);
+}
+
+ov::frontend::InputModel::Ptr FrontEnd::load_impl(const std::vector<ov::Any>& variants) const {
+    // Last boolean flag in `variants` (if presented) is reserved for FE
+    // configuration
+    size_t extra_variants_num = variants.size() > 0 && variants[variants.size() - 1].is<bool>() ? 1 : 0;
+    FRONT_END_GENERAL_CHECK(variants.size() == 1 + extra_variants_num,
+                            "Jax Frontend supports exactly one parameter in "
+                            "model representation, got ",
+                            std::to_string(variants.size()),
+                            " instead.");
+    FRONT_END_GENERAL_CHECK(variants[0].is<std::shared_ptr<IDecoder>>(),
+                            "Jax Frontend doesn't support provided model type. "
+                            "Please provide supported model "
+                            "object using Python API.");
+    auto decoder = variants[0].as<std::shared_ptr<IDecoder>>();
+    auto tdecoder = std::dynamic_pointer_cast<JaxDecoder>(decoder);
+    FRONT_END_GENERAL_CHECK(tdecoder, "Couldn't cast ov::Any to JaxDecoder");
+    return std::make_shared<jax::InputModel>(tdecoder);
+}
+
+std::map<std::string, CreatorFunction> FrontEnd::get_supported_ops(const ov::frontend::InputModel::Ptr& model) const {
+    std::map<std::string, CreatorFunction> supported_ops;
+    supported_ops = get_supported_ops_jaxpr();
+    for (auto i = m_op_extension_translators.begin(); i != m_op_extension_translators.end(); i++)
+        supported_ops[i->first] = i->second;
+    return supported_ops;
+}
+
+}  // namespace jax
+}  // namespace frontend
+}  // namespace ov
diff --git a/src/frontends/jax/src/input_model.cpp b/src/frontends/jax/src/input_model.cpp
new file mode 100644
index 00000000000000..44855266bbdfd8
--- /dev/null
+++ b/src/frontends/jax/src/input_model.cpp
@@ -0,0 +1,117 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "input_model.hpp"
+
+#include "openvino/frontend/exception.hpp"
+#include "place.hpp"
+#include "utils.hpp"
+
+namespace ov {
+namespace frontend {
+namespace jax {
+
+InputModel::InputModel(const std::shared_ptr<JaxDecoder>& model_decoder) : m_model_decoder(model_decoder) {
+    const auto& inputs = m_model_decoder->inputs();
+    for (size_t i = 0; i < inputs.size(); ++i) {
+        auto in_place = std::make_shared<jax::Place>(*this, inputs[i]);
+        m_name_to_place.emplace(std::to_string(inputs[i]), std::dynamic_pointer_cast<frontend::Place>(in_place));
+        for (const auto& name : in_place->get_names()) {
+            m_name_to_place.emplace(name, std::dynamic_pointer_cast<frontend::Place>(in_place));
+        }
+        m_inputs.push_back(in_place);
+    }
+    const auto& outputs = m_model_decoder->outputs();
+    for (size_t i = 0; i < outputs.size(); ++i) {
+        auto out_place = std::make_shared<jax::Place>(*this, outputs[i]);
+        m_name_to_place.emplace(std::to_string(outputs[i]), std::dynamic_pointer_cast<frontend::Place>(out_place));
+        for (const auto& name : out_place->get_names()) {
+            m_name_to_place.emplace(name, std::dynamic_pointer_cast<frontend::Place>(out_place));
+        }
+        m_outputs.push_back(out_place);
+    }
+}
+
+std::vector<ov::frontend::Place::Ptr> InputModel::get_inputs() const {
+    return m_inputs;
+}
+
+std::vector<ov::frontend::Place::Ptr> InputModel::get_outputs() const {
+    return m_outputs;
+}
+
+Place::Ptr InputModel::get_place_by_tensor_name(const std::string& tensor_name) const {
+    auto place_it = m_name_to_place.find(tensor_name);
+    if (place_it != m_name_to_place.end()) {
+        return place_it->second;
+    }
+    return nullptr;
+}
+
+void InputModel::set_partial_shape(const Place::Ptr& place, const ov::PartialShape& shape) {
+    FRONT_END_GENERAL_CHECK(place && place->is_input(),
+                            "Provided place is invalid, only inputs are supported for setting shape.");
+    auto jax_place = std::dynamic_pointer_cast<jax::Place>(place);
+    FRONT_END_GENERAL_CHECK(jax_place, "Only place produced by Jax Frontend is supported");
+    jax_place->m_pshape = shape;
+}
+
+ov::PartialShape InputModel::get_partial_shape(const Place::Ptr& place) const {
+    auto jax_place = std::dynamic_pointer_cast<jax::Place>(place);
+    FRONT_END_GENERAL_CHECK(jax_place,
+                            "Provided place is invalid. Only place of input or output is supported by Jax Frontend.");
+    return jax_place->get_partial_shape();
+}
+
+void InputModel::set_element_type(const Place::Ptr& place, const ov::element::Type& type) {
+    FRONT_END_GENERAL_CHECK(place && place->is_input(),
+                            "Provided place is invalid, only inputs are supported for setting element type.");
+    auto jax_place = std::dynamic_pointer_cast<jax::Place>(place);
+    FRONT_END_GENERAL_CHECK(jax_place, "Only place produced by Jax Frontend is supported");
+    jax_place->m_type = type;
+}
+
+ov::element::Type InputModel::get_element_type(const Place::Ptr& place) const {
+    auto jax_place = std::dynamic_pointer_cast<jax::Place>(place);
+    FRONT_END_GENERAL_CHECK(jax_place,
+                            "Provided place is invalid. Only place of input or output is supported by Jax Frontend.");
+    return jax_place->get_element_type();
+}
+
+void InputModel::set_tensor_value(const Place::Ptr& place, const void* value) {
+    FRONT_END_NOT_IMPLEMENTED("set_tensor_value");
+}
+
+void InputModel::override_all_outputs(const std::vector<Place::Ptr>& outputs) {
+    // Topology modification is not allowed, but order can be changed
+    auto all_outputs = std::all_of(outputs.cbegin(), outputs.cend(), [](const Place::Ptr& p) {
+        return p->is_output();
+    });
+    FRONT_END_GENERAL_CHECK(all_outputs, "Only initial outputs are supported by override_all_inputs.");
+    // Number of outputs can be lower then in original model
+    m_outputs = outputs;
+}
+
+void InputModel::override_all_inputs(const std::vector<Place::Ptr>& inputs) {
+    // Topology modification is not allowed, but order can be changed
+    auto all_inputs = std::all_of(inputs.cbegin(), inputs.cend(), [](const Place::Ptr& p) {
+        return p->is_input();
+    });
+    FRONT_END_GENERAL_CHECK(all_inputs, "Only initial inputs are supported by override_all_inputs.");
+    FRONT_END_GENERAL_CHECK(inputs.size() == m_inputs.size(),
+                            "Number of inputs provided is incorrect. Graph modification is not supported for "
+                            "this model. Expected number of inputs: ",
+                            m_inputs.size(),
+                            " received ",
+                            inputs.size());
+    m_inputs = inputs;
+}
+
+std::shared_ptr<JaxDecoder> InputModel::get_decoder() const {
+    return m_model_decoder;
+}
+
+}  // namespace jax
+}  // namespace frontend
+}  // namespace ov
diff --git a/src/frontends/jax/src/input_model.hpp b/src/frontends/jax/src/input_model.hpp
new file mode 100644
index 00000000000000..3c9cf818faad99
--- /dev/null
+++ b/src/frontends/jax/src/input_model.hpp
@@ -0,0 +1,45 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "openvino/frontend/input_model.hpp"
+#include "openvino/frontend/place.hpp"
+
+namespace ov {
+namespace frontend {
+namespace jax {
+
+class TranslateSession;
+class Place;
+class JaxDecoder;
+
+class InputModel : public ov::frontend::InputModel {
+    friend class ::ov::frontend::jax::TranslateSession;
+
+public:
+    explicit InputModel(const std::shared_ptr<JaxDecoder>& model_decoder);
+
+    std::vector<frontend::Place::Ptr> get_inputs() const override;
+    std::vector<frontend::Place::Ptr> get_outputs() const override;
+    frontend::Place::Ptr get_place_by_tensor_name(const std::string& tensor_name) const override;
+    void set_partial_shape(const frontend::Place::Ptr& place, const ov::PartialShape& shape) override;
+    ov::PartialShape get_partial_shape(const frontend::Place::Ptr& place) const override;
+    void set_element_type(const frontend::Place::Ptr& place, const ov::element::Type& type) override;
+    ov::element::Type get_element_type(const frontend::Place::Ptr& place) const override;
+    void set_tensor_value(const frontend::Place::Ptr& place, const void* value) override;
+    void override_all_outputs(const std::vector<frontend::Place::Ptr>& outputs) override;
+    void override_all_inputs(const std::vector<frontend::Place::Ptr>& inputs) override;
+    std::shared_ptr<JaxDecoder> get_decoder() const;
+
+private:
+    std::shared_ptr<JaxDecoder> m_model_decoder;
+    std::unordered_map<std::string, std::shared_ptr<frontend::Place>> m_name_to_place;
+    std::vector<std::shared_ptr<frontend::Place>> m_inputs;
+    std::vector<std::shared_ptr<frontend::Place>> m_outputs;
+};
+
+}  // namespace jax
+}  // namespace frontend
+}  // namespace ov
diff --git a/src/frontends/jax/src/jax.cpp b/src/frontends/jax/src/jax.cpp
new file mode 100644
index 00000000000000..df7d194c2e670c
--- /dev/null
+++ b/src/frontends/jax/src/jax.cpp
@@ -0,0 +1,20 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "openvino/frontend/jax/frontend.hpp"
+#include "openvino/frontend/jax/visibility.hpp"
+#include "openvino/frontend/manager.hpp"
+
+JAX_C_API ov::frontend::FrontEndVersion get_api_version() {
+    return OV_FRONTEND_API_VERSION;
+}
+
+JAX_C_API void* get_front_end_data() {
+    auto res = new ov::frontend::FrontEndPluginInfo();
+    res->m_name = "jax";
+    res->m_creator = []() {
+        return std::make_shared<ov::frontend::jax::FrontEnd>();
+    };
+    return res;
+}
diff --git a/src/frontends/jax/src/jax_framework_node.hpp b/src/frontends/jax/src/jax_framework_node.hpp
new file mode 100644
index 00000000000000..82b2b15e9405d6
--- /dev/null
+++ b/src/frontends/jax/src/jax_framework_node.hpp
@@ -0,0 +1,79 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "openvino/op/util/framework_node.hpp"
+#include "utils.hpp"
+
+#pragma once
+
+namespace ov {
+namespace frontend {
+namespace jax {
+class JaxFrameworkNode : public ov::op::util::FrameworkNode {
+public:
+    OPENVINO_OP("JaxFrameworkNode", "util", ::ov::op::util::FrameworkNode);
+    static constexpr const char* op_type_key = "JaxTypeName";
+    static constexpr const char* failed_conversion_key = "JaxException";
+
+    JaxFrameworkNode(const std::shared_ptr<JaxDecoder>& decoder, const OutputVector& inputs, size_t output_size)
+        : ov::op::util::FrameworkNode(inputs, output_size, 0),
+          m_decoder(decoder) {
+        ov::op::util::FrameworkNodeAttrs attrs;
+        attrs.set_type_name("JaxFrameworkNode");
+        attrs[op_type_key] = m_decoder->get_op_type();
+        set_attrs(attrs);
+
+        // Set output shapes and types if recognized
+        for (size_t i = 0; i < output_size; ++i) {
+            PartialShape ps;
+            auto type = element::dynamic;
+            if (i < decoder->num_outputs()) {
+                try {
+                    ps = m_decoder->get_output_shape(i);
+                    auto dec_type = decoder->get_output_type(i);
+                    if (dec_type.is<element::Type>())
+                        type = dec_type.as<element::Type>();
+                } catch (...) {
+                    // nothing, means the info cannot be queried and remains unknown
+                }
+            }
+            set_output_type(i, type, ps);
+        }
+    }
+
+    JaxFrameworkNode(const std::shared_ptr<JaxDecoder>& decoder, const OutputVector& inputs)
+        : JaxFrameworkNode(decoder, inputs, decoder->num_outputs()) {}
+
+    std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& inputs) const override {
+        auto op = std::make_shared<JaxFrameworkNode>(m_decoder, inputs, get_output_size());
+
+        for (size_t body_index = 0; body_index < m_bodies.size(); ++body_index) {
+            op->set_function(static_cast<int>(body_index), get_function(static_cast<int>(body_index))->clone());
+            for (const auto& m_input_descr : m_input_descriptions[body_index]) {
+                op->m_input_descriptions[body_index].push_back(m_input_descr->copy());
+            }
+            for (const auto& m_output_descr : m_output_descriptions[body_index]) {
+                op->m_output_descriptions[body_index].push_back(m_output_descr->copy());
+            }
+        }
+        op->validate_and_infer_types();
+
+        return op;
+    }
+
+    std::string get_op_type() const {
+        return m_decoder->get_op_type();
+    }
+
+    std::shared_ptr<JaxDecoder> get_decoder() const {
+        return m_decoder;
+    }
+
+private:
+    std::shared_ptr<JaxDecoder> m_decoder;
+};
+
+}  // namespace jax
+}  // namespace frontend
+}  // namespace ov
diff --git a/src/frontends/jax/src/node_context.cpp b/src/frontends/jax/src/node_context.cpp
new file mode 100644
index 00000000000000..2ac43a15f37d8c
--- /dev/null
+++ b/src/frontends/jax/src/node_context.cpp
@@ -0,0 +1,196 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "openvino/frontend/jax/node_context.hpp"
+
+#include <string>
+
+#include "jax_framework_node.hpp"
+#include "openvino/core/except.hpp"
+#include "openvino/core/validation_util.hpp"
+#include "openvino/frontend/exception.hpp"
+#include "openvino/frontend/jax/decoder.hpp"
+#include "openvino/op/constant.hpp"
+#include "openvino/op/parameter.hpp"
+#include "openvino/util/log.hpp"
+#include "translate_session.hpp"
+#include "utils.hpp"
+
+namespace ov {
+namespace frontend {
+namespace jax {
+
+using namespace ov::op;
+
+OutputVector NodeContext::as_constant() const {
+    auto dtype = m_decoder->get_output_type(0);
+    if (dtype.is<type::Str>()) {
+        OPENVINO_THROW("Dtype string is not supported in JAX yet.");
+    } else if (dtype.is<type::PyNone>()) {
+        // Cannot represent None as Constant, creating FrameworkNode
+        auto fw_node = std::make_shared<JaxFrameworkNode>(m_decoder, OutputVector{});
+        auto attrs = fw_node->get_attrs();
+        attrs["none_value"] = "";
+        attrs[JaxFrameworkNode::failed_conversion_key] =
+            "None constant cannot be converted to OpenVINO opset and should be "
+            "removed by consuming operation.";
+        fw_node->set_attrs(attrs);
+        return {fw_node};
+    } else {
+        auto c_outs = m_decoder->as_constant();
+        FRONT_END_OP_CONVERSION_CHECK(c_outs.size() == 1, "Constant must have exactly one output.");
+        return c_outs;
+    }
+}
+
+namespace {
+std::shared_ptr<v0::Constant> get_constant_at_input(const NodeContext& ctx, size_t index, bool allow_empty = true) {
+    auto input_val = ctx.get_input(static_cast<int>(index));
+    if (ctx.get_input_type(index).is<type::List>()) {
+        FRONT_END_THROW("Taking list as constant has not been supported in JAX frontend yet.");
+    }
+    auto constant = ov::util::get_constant_from_source(input_val);
+    FRONT_END_GENERAL_CHECK(constant, "Input with index ", index, " cannot be interpreted as Constant: ", input_val);
+    return constant;
+}
+
+std::shared_ptr<v0::Constant> get_constant_from_params(const NodeContext& ctx, const std::string& name) {
+    auto param_val = ctx.get_param(name);
+    auto constant = ov::util::get_constant_from_source(param_val);
+    FRONT_END_GENERAL_CHECK(constant, "Param with name ", name, " cannot be interpreted as Constant: ", param_val);
+    return constant;
+}
+}  // namespace
+
+template <>
+std::vector<int64_t> NodeContext::const_input<std::vector<int64_t>>(size_t index) const {
+    auto c = get_constant_at_input(*this, index);
+    if (c)
+        return c->cast_vector<int64_t>();
+    else
+        return {};
+}
+
+template <>
+Strides NodeContext::const_input<Strides>(size_t index) const {
+    auto c = get_constant_at_input(*this, index);
+    if (c)
+        return c->cast_vector<Strides::value_type>();
+    else
+        return {};
+}
+
+template <>
+CoordinateDiff NodeContext::const_input<CoordinateDiff>(size_t index) const {
+    auto c = get_constant_at_input(*this, index);
+    if (c)
+        return c->cast_vector<CoordinateDiff::value_type>();
+    else
+        return {};
+}
+
+template <>
+Shape NodeContext::const_input<Shape>(size_t index) const {
+    auto c = get_constant_at_input(*this, index);
+    if (c)
+        return c->cast_vector<Shape::value_type>();
+    else
+        return {};
+}
+
+template <>
+int32_t NodeContext::const_input<int32_t>(size_t index) const {
+    return get_constant_at_input(*this, index, false)->cast_vector<int32_t>()[0];
+}
+
+template <>
+int64_t NodeContext::const_input<int64_t>(size_t index) const {
+    return get_constant_at_input(*this, index, false)->cast_vector<int64_t>()[0];
+}
+
+template <>
+bool NodeContext::const_input<bool>(size_t index) const {
+    return get_constant_at_input(*this, index, false)->cast_vector<bool>()[0];
+}
+
+template <>
+double NodeContext::const_input<double>(size_t index) const {
+    return get_constant_at_input(*this, index, false)->cast_vector<double>()[0];
+}
+
+template <>
+float NodeContext::const_input<float>(size_t index) const {
+    return get_constant_at_input(*this, index, false)->cast_vector<float>()[0];
+}
+
+template <>
+int64_t NodeContext::const_named_param<int64_t>(const std::string& name) const {
+    return get_constant_from_params(*this, name)->cast_vector<int64_t>()[0];
+}
+
+namespace {
+template <typename T>
+Any get_constant_data(const std::shared_ptr<v0::Constant>& constant) {
+    const T* ptr = reinterpret_cast<const T*>(constant->get_data_ptr());
+    const auto& shape = constant->get_shape();
+    if (is_scalar(shape)) {
+        return ptr[0];
+    }
+    return std::vector<T>(ptr, ptr + shape_size(shape));
+}
+}  // namespace
+
+Any NodeContext::get_values_from_const_input(int index) const {
+    FRONT_END_GENERAL_CHECK(static_cast<size_t>(index) < get_input_size(),
+                            "Input with index: ",
+                            index,
+                            " does not exist.");
+    auto input_val = get_input(index);
+    if (auto input = std::dynamic_pointer_cast<JaxFrameworkNode>(input_val.get_node_shared_ptr())) {
+        const auto& attrs = input->get_attrs();
+        if (attrs.find("none_value") != attrs.end()) {
+            return {};
+        }
+        auto it = attrs.find("string_value");
+        if (it != attrs.end()) {
+            return it->second;
+        }
+    }
+    auto constant = get_constant_at_input(*this, index);
+    if (constant) {
+        switch (constant->get_element_type()) {
+        case element::f32:
+            return get_constant_data<float>(constant);
+        case element::f64:
+            return get_constant_data<double>(constant);
+        case element::i32:
+            return get_constant_data<int32_t>(constant);
+        case element::u32:
+            return get_constant_data<uint32_t>(constant);
+        case element::i64:
+            return get_constant_data<int64_t>(constant);
+        case element::u64:
+            return get_constant_data<uint64_t>(constant);
+        case element::i8:
+            return get_constant_data<int8_t>(constant);
+        case element::u8:
+            return get_constant_data<uint8_t>(constant);
+        case element::i16:
+            return get_constant_data<int16_t>(constant);
+        case element::u16:
+            return get_constant_data<uint16_t>(constant);
+        case element::boolean:
+            return get_constant_data<bool>(constant);
+        default:
+            FRONT_END_GENERAL_CHECK(false, "Input with index: ", index, " has unsupported type.");
+        }
+    }
+    FRONT_END_GENERAL_CHECK(false, "Input node with index ", index, " cannot be interpreted as constant", input_val);
+
+    return 0;
+}
+
+}  // namespace jax
+}  // namespace frontend
+}  // namespace ov
diff --git a/src/frontends/jax/src/op/constant.cpp b/src/frontends/jax/src/op/constant.cpp
new file mode 100644
index 00000000000000..6a9c5262e1d8ee
--- /dev/null
+++ b/src/frontends/jax/src/op/constant.cpp
@@ -0,0 +1,19 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "openvino/frontend/jax/node_context.hpp"
+
+namespace ov {
+namespace frontend {
+namespace jax {
+namespace op {
+
+OutputVector translate_constant(const NodeContext& context) {
+    return context.as_constant();
+};
+
+}  // namespace op
+}  // namespace jax
+}  // namespace frontend
+}  // namespace ov
\ No newline at end of file
diff --git a/src/frontends/jax/src/op/convert.cpp b/src/frontends/jax/src/op/convert.cpp
new file mode 100644
index 00000000000000..005c809e929f35
--- /dev/null
+++ b/src/frontends/jax/src/op/convert.cpp
@@ -0,0 +1,33 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "openvino/op/convert.hpp"
+
+#include <cstdint>
+
+#include "openvino/core/node.hpp"
+#include "openvino/core/node_output.hpp"
+#include "openvino/frontend/jax/node_context.hpp"
+#include "openvino/op/convert.hpp"
+#include "utils.hpp"
+
+namespace ov {
+namespace frontend {
+namespace jax {
+namespace op {
+
+using namespace ov::op;
+
+OutputVector translate_convert(const NodeContext& context) {
+    num_inputs_check(context, 1, 1);
+    Output<Node> src = context.get_input(0);
+    auto dtype = convert_dtype(context.const_named_param<int64_t>("new_dtype"));
+    Output<Node> res = std::make_shared<v0::Convert>(src, dtype);
+    return {res};
+};
+
+}  // namespace op
+}  // namespace jax
+}  // namespace frontend
+}  // namespace ov
diff --git a/src/frontends/jax/src/op_table.cpp b/src/frontends/jax/src/op_table.cpp
new file mode 100644
index 00000000000000..52b1820983ec3d
--- /dev/null
+++ b/src/frontends/jax/src/op_table.cpp
@@ -0,0 +1,34 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "op_table.hpp"
+
+#include "openvino/opsets/opset14.hpp"
+#include "utils.hpp"
+
+namespace ov {
+namespace frontend {
+namespace jax {
+namespace op {
+
+#define OP_CONVERTER(op) OutputVector op(const NodeContext& node)
+
+OP_CONVERTER(translate_constant);
+OP_CONVERTER(translate_convert);
+
+}  // namespace op
+
+// Supported ops for Jaxpr
+const std::map<std::string, CreatorFunction> get_supported_ops_jaxpr() {
+    return {{"add", op::translate_1to1_match_2_inputs<opset14::Add>},
+            {"sub", op::translate_1to1_match_2_inputs<opset14::Subtract>},
+            {"mul", op::translate_1to1_match_2_inputs<opset14::Multiply>},
+            {"div", op::translate_1to1_match_2_inputs<opset14::Divide>},
+            {"constant", op::translate_constant},
+            {"convert_element_type", op::translate_convert}};
+};
+
+}  // namespace jax
+}  // namespace frontend
+}  // namespace ov
diff --git a/src/frontends/jax/src/op_table.hpp b/src/frontends/jax/src/op_table.hpp
new file mode 100644
index 00000000000000..593568ab90e0b3
--- /dev/null
+++ b/src/frontends/jax/src/op_table.hpp
@@ -0,0 +1,17 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "openvino/frontend/jax/node_context.hpp"
+
+namespace ov {
+namespace frontend {
+namespace jax {
+
+const std::map<std::string, CreatorFunction> get_supported_ops_jaxpr();
+
+}  // namespace jax
+}  // namespace frontend
+}  // namespace ov
diff --git a/src/frontends/jax/src/place.cpp b/src/frontends/jax/src/place.cpp
new file mode 100644
index 00000000000000..83f5364edf26fa
--- /dev/null
+++ b/src/frontends/jax/src/place.cpp
@@ -0,0 +1,59 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "place.hpp"
+
+#include "input_model.hpp"
+#include "openvino/frontend/exception.hpp"
+#include "openvino/util/log.hpp"
+#include "utils.hpp"
+
+namespace ov {
+namespace frontend {
+namespace jax {
+
+Place::Place(const ov::frontend::InputModel& input_model, size_t tensor_index)
+    : m_input_model(input_model),
+      m_tensor_index(tensor_index) {
+    const auto im = dynamic_cast<const ov::frontend::jax::InputModel*>(&m_input_model);
+    FRONT_END_GENERAL_CHECK(im, "Jax Place requires Jax InputModel class.");
+    auto decoder = im->get_decoder();
+    const auto& inputs = decoder->inputs();
+    const auto& outputs = decoder->outputs();
+    auto in_it = std::find(inputs.begin(), inputs.end(), tensor_index);
+    if (in_it != inputs.end()) {
+        m_is_input = true;
+        auto idx = std::distance(inputs.begin(), in_it);
+        const auto& signature_name = decoder->get_input_signature_name(idx);
+        m_names.push_back(signature_name);
+
+        auto type_any = decoder->get_input_type(idx);
+        if (type_any.is<element::Type>()) {
+            m_type = type_any.as<element::Type>();
+        }
+        m_pshape = decoder->get_input_shape(idx);
+    }
+    auto out_it = std::find(outputs.begin(), outputs.end(), tensor_index);
+    if (out_it != outputs.end()) {
+        m_is_output = true;
+        if (!m_is_input) {
+            auto idx = std::distance(outputs.begin(), out_it);
+            const auto& debug_name = decoder->get_output_name(idx);
+            m_names.push_back(debug_name);
+
+            auto type_any = decoder->get_output_type(idx);
+            if (type_any.is<element::Type>()) {
+                m_type = type_any.as<element::Type>();
+            }
+            m_pshape = decoder->get_output_shape(idx);
+        }
+    }
+    if (m_is_input && m_is_output) {
+        OPENVINO_DEBUG << "[WARNING] Place " << tensor_index << " is input and output at a same time.";
+    }
+}
+
+}  // namespace jax
+}  // namespace frontend
+}  // namespace ov
diff --git a/src/frontends/jax/src/place.hpp b/src/frontends/jax/src/place.hpp
new file mode 100644
index 00000000000000..8ffe3338d7d74a
--- /dev/null
+++ b/src/frontends/jax/src/place.hpp
@@ -0,0 +1,66 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "openvino/frontend/input_model.hpp"
+#include "openvino/frontend/place.hpp"
+
+namespace ov {
+namespace frontend {
+namespace jax {
+
+class InputModel;
+
+// Place represents a tensor in the model
+class Place : public ov::frontend::Place {
+    friend class ::ov::frontend::jax::InputModel;
+
+public:
+    Place(const ov::frontend::InputModel& input_model, size_t tensor_index);
+
+    ~Place() override = default;
+
+    bool is_input() const override {
+        return m_is_input;
+    }
+    bool is_output() const override {
+        return m_is_output;
+    }
+    bool is_equal(const Ptr& another) const override {
+        return this == another.get();
+    }
+    std::vector<std::string> get_names() const override {
+        return m_names;
+    }
+    size_t get_tensor_index() const {
+        return m_tensor_index;
+    }
+    element::Type get_element_type() const {
+        return m_type;
+    }
+    PartialShape get_partial_shape() const {
+        return m_pshape;
+    }
+    bool is_equal_data(const Ptr& another) const override {
+        const auto another_pt = dynamic_cast<ov::frontend::jax::Place*>(another.get());
+        if (!another_pt) {
+            return false;
+        }
+        return m_tensor_index == another_pt->get_tensor_index();
+    }
+
+private:
+    const ov::frontend::InputModel& m_input_model;
+    const size_t m_tensor_index;
+    std::vector<std::string> m_names;
+    element::Type m_type = element::dynamic;
+    PartialShape m_pshape;
+    bool m_is_input = false;
+    bool m_is_output = false;
+};
+
+}  // namespace jax
+}  // namespace frontend
+}  // namespace ov
diff --git a/src/frontends/jax/src/translate_session.cpp b/src/frontends/jax/src/translate_session.cpp
new file mode 100644
index 00000000000000..d94eb59fd15077
--- /dev/null
+++ b/src/frontends/jax/src/translate_session.cpp
@@ -0,0 +1,252 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "translate_session.hpp"
+
+#include <cstddef>
+#include <string>
+#include <unordered_map>
+
+#include "input_model.hpp"
+#include "openvino/frontend/exception.hpp"
+#include "openvino/op/constant.hpp"
+#include "openvino/util/common_util.hpp"
+#include "openvino/util/log.hpp"
+#include "place.hpp"
+#include "utils.hpp"
+
+namespace ov {
+namespace frontend {
+namespace jax {
+
+using namespace ov::op;
+
+TranslateSession::TranslateSession(const ov::frontend::InputModel::Ptr& input_model,
+                                   const std::map<std::string, CreatorFunction>& translator_map,
+                                   const std::shared_ptr<TelemetryExtension>& telemetry)
+    : m_input_model(input_model),
+      m_translator_map(translator_map),
+      m_telemetry(telemetry),
+      m_ov_model(nullptr) {}
+
+TranslateSession::~TranslateSession() {
+    if (m_telemetry) {
+        // Send statistics
+        for (const auto& op : m_op_statistics) {
+            m_telemetry->send_event("op_count", "jax_" + op.first, static_cast<int>(op.second));
+        }
+    }
+}
+
+std::shared_ptr<ov::Model> TranslateSession::get_converted_model() {
+    if (m_ov_model) {
+        return m_ov_model;
+    }
+    m_ov_model = translate_graph(m_input_model);
+    return m_ov_model;
+}
+
+std::shared_ptr<ov::Model> TranslateSession::translate_graph(const ov::frontend::InputModel::Ptr& input_model) {
+    auto jax_model = std::dynamic_pointer_cast<jax::InputModel>(input_model);
+    FRONT_END_GENERAL_CHECK(jax_model != nullptr, "Invalid input model");
+    auto model = convert_jax_model(jax_model->m_model_decoder, jax_model);
+    // First delete tensor indexes from outputs then resolve input names,
+    // otherwise Parameter->Result will fail
+    for (auto& result : model->get_results()) {
+        auto tensor_desc = result->input_value(0);
+        auto names = tensor_desc.get_names();
+        if (!names.empty()) {
+            auto tensor_idx = decode_tensor_name(tensor_desc);
+            if (names.erase(std::to_string(tensor_idx))) {
+                tensor_desc.set_names(names);
+            }
+        }
+    }
+    // Set input tensor names to be equal to signature name saved in friendly name
+    for (auto& param : model->get_parameters()) {
+        if (param->get_friendly_name() != param->get_name()) {
+            // get_name is autogenerated name, we need to make sure that this
+            // parameter was named by frontend
+            param->output(0).set_names({param->get_friendly_name()});
+        }
+    }
+    return model;
+}
+
+std::shared_ptr<Model> TranslateSession::convert_jax_model(std::shared_ptr<JaxDecoder> jax_model,
+                                                           const std::shared_ptr<jax::InputModel>& input_model) {
+    std::shared_ptr<Model> resulting_model;  // define here to make a conversion in a nested scope
+    {
+        auto parameters = std::make_shared<ParameterVector>();
+        auto tensor_map = std::make_shared<TensorMap>();  // tensor map of the current context
+
+        FRONT_END_GENERAL_CHECK(input_model,
+                                "Got null input model in JAX frontend TranslateSession::convert_jax_model.");
+
+        // When we have input model we should use its inputs order to create
+        // Parameters We use m_inputs instead of get_inputs() because latter
+        // doesn't have "self" input
+        for (auto& input_p : input_model->get_inputs()) {
+            auto jax_place = std::dynamic_pointer_cast<jax::Place>(input_p);
+            FRONT_END_GENERAL_CHECK(jax_place, "Only place produced by Jax Frontend is supported.");
+            auto tensor_id = jax_place->get_tensor_index();
+            element::Type type = jax_place->get_element_type();
+            PartialShape pshape = jax_place->get_partial_shape();
+            auto parameter = std::make_shared<v0::Parameter>(type, pshape);
+            if (jax_place->get_names().size() > 0)
+                parameter->set_friendly_name(jax_place->get_names().at(0));
+            encode_tensor_name(parameter->output(0), tensor_id);
+            parameters->push_back(parameter);
+            (*tensor_map)[tensor_id] = parameter;
+        }
+
+        auto node_visitor = [&](std::shared_ptr<JaxDecoder> node) {
+            // Get named params, if there's any.
+            std::unordered_map<std::string, size_t> param_name_to_id;
+            auto param_names = node->get_param_names();
+            for (auto name : param_names) {
+                auto param_tensor_id = node->get_named_param(name);
+                if (tensor_map->find(param_tensor_id) == tensor_map->end()) {
+                    auto constant = node->get_named_param_as_constant(name);
+                    // TODO: maybe allow list constant here if needed?
+                    FRONT_END_GENERAL_CHECK(constant.size() == 1, "Constant should have only one value yet.");
+                    (*tensor_map)[param_tensor_id] = constant[0];
+                }
+                param_name_to_id[name] = param_tensor_id;
+            }
+
+            auto context = NodeContext(node, tensor_map, parameters, param_name_to_id, this);
+            // Add op type in the statistics
+            m_op_statistics[context.get_op_type()]++;
+            auto converted_outputs = convert_node(context);
+
+            const auto& fw_outputs = node->outputs();
+            // Ops with subgraphs or with mutated inputs may have more outputs after
+            // conversion compared to jax ones
+            FRONT_END_OP_CONVERSION_CHECK(fw_outputs.size() <= converted_outputs.size(),
+                                          "Number of ",
+                                          context.get_op_type(),
+                                          " outputs greater than number of converted outputs, which are",
+                                          fw_outputs.size(),
+                                          " and ",
+                                          converted_outputs.size(),
+                                          " respectively.");
+
+            for (size_t i = 0; i < fw_outputs.size(); ++i) {
+                size_t fw_tensor_id = node->output(i);
+                FRONT_END_GENERAL_CHECK(tensor_map->find(fw_tensor_id) == tensor_map->end(),
+                                        "Duplicated producer for Jax value with unique ID: ",
+                                        fw_tensor_id);
+                auto out_type = context.get_output_type(i);
+                if (out_type.is<element::Type>()) {
+                    if (!converted_outputs[i].get_element_type().compatible(out_type.as<element::Type>())) {
+                        OPENVINO_DEBUG << "[WARNING] Produced output type for operation " << context.get_op_type()
+                                       << " for tensor id: " << fw_tensor_id << " is incompatible: produced "
+                                       << converted_outputs[i].get_element_type() << " vs "
+                                       << out_type.as<element::Type>();
+                    }
+                }
+                (*tensor_map)[fw_tensor_id] = converted_outputs[i];
+                encode_tensor_name(converted_outputs[i], fw_tensor_id, {node->get_output_name(i)});
+            }
+        };
+
+        jax_model->visit_subgraph(node_visitor);
+
+        ResultVector results;
+        // For the case when we have InputModel we need to have same order as its
+        // outputs
+        for (auto& output_p : input_model->get_outputs()) {
+            auto jax_place = std::dynamic_pointer_cast<jax::Place>(output_p);
+            FRONT_END_GENERAL_CHECK(jax_place, "Only place produced by Jax Frontend is supported.");
+            auto tensor_id = jax_place->get_tensor_index();
+            auto ov_output = tensor_map->at(tensor_id);
+            FRONT_END_GENERAL_CHECK(ov_output.get_names().size() > 0,
+                                    "Tensor doesn't have name, while it should have name: ",
+                                    tensor_id);
+            auto result = std::make_shared<v0::Result>(ov_output);
+            results.push_back(result);
+        }
+
+        resulting_model = std::make_shared<Model>(results, *parameters);
+    }
+
+    return resulting_model;
+}
+
+OutputVector TranslateSession::convert_node(const NodeContext& context) {
+    std::string exception;
+    try {
+        auto it = m_translator_map.find(context.get_op_type());
+        if (it != m_translator_map.end()) {
+            return it->second(context);
+        }
+        OPENVINO_DEBUG << "No translator found for: " << context.get_op_type() << "\n";
+    } catch (std::exception& e) {
+        exception = e.what();
+        if (m_telemetry) {
+            auto cropped_message = ov::util::filter_lines_by_prefix(exception, get_jax_prefix());
+            if (cropped_message.size()) {
+                m_telemetry->send_event("error_info", cropped_message);
+            }
+        }
+    } catch (...) {
+        exception = "Unknown exception type.";
+    }
+    exception += "Failed to convert operation: " + context.get_op_type() + ". Reason: ";
+    FRONT_END_THROW(exception);
+}
+
+void TranslateSession::encode_tensor_name(Output<Node> output,
+                                          size_t tensor_idx,
+                                          std::vector<std::string> additional_names) {
+    if (!output.get_names().empty()) {
+        OPENVINO_DEBUG << "Tensor names already exist: " << output.get_any_name() << ". Will not be rewritten with "
+                       << tensor_idx << ". This is likely a mutated tensor.";
+        return;
+    }
+    auto name = std::to_string(tensor_idx);
+    std::unordered_set<std::string> names;
+    names.insert(name);
+    if (additional_names.size() > 0) {
+        names.insert(additional_names.begin(), additional_names.end());
+    }
+
+    if (m_counter_map.count(tensor_idx)) {
+        auto&& pair = m_counter_map[tensor_idx];
+        auto new_name = name + '_' + std::to_string(++pair.first);
+        pair.second.set_names({new_name});
+        pair.second = output;
+        output.set_names(names);
+    } else {
+        m_counter_map[tensor_idx] = {0, output};
+        output.set_names(names);
+    }
+}
+
+namespace {
+bool is_number(const std::string& s) {
+    std::string::const_iterator it = s.begin();
+    while (it != s.end() && std::isdigit(*it))
+        ++it;
+    return !s.empty() && it == s.end();
+}
+}  // namespace
+
+size_t TranslateSession::decode_tensor_name(const Output<Node>& output) {
+    // any_name should always return numerical value even if there is a word value
+    // exist in names
+    auto name = output.get_any_name();
+    auto pos = name.find("_");
+    if (pos != std::string::npos) {
+        name = name.substr(0, pos);
+    }
+    // numbers after "_" will be ignored by stoll function
+    FRONT_END_GENERAL_CHECK(is_number(name), "Tensor name is not a number: ", name);
+    return static_cast<size_t>(std::stoll(name));
+}
+
+}  // namespace jax
+}  // namespace frontend
+}  // namespace ov
\ No newline at end of file
diff --git a/src/frontends/jax/src/translate_session.hpp b/src/frontends/jax/src/translate_session.hpp
new file mode 100644
index 00000000000000..3372a05d4a1211
--- /dev/null
+++ b/src/frontends/jax/src/translate_session.hpp
@@ -0,0 +1,55 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "input_model.hpp"
+#include "openvino/frontend/extension/telemetry.hpp"
+#include "openvino/frontend/jax/node_context.hpp"
+
+namespace ov {
+namespace frontend {
+namespace jax {
+
+class TranslateSession {
+public:
+    TranslateSession(const frontend::InputModel::Ptr& input_model,
+                     const std::map<std::string, CreatorFunction>& translator_map,
+                     const std::shared_ptr<TelemetryExtension>& telemetry);
+    ~TranslateSession();
+    std::shared_ptr<Model> get_converted_model();
+    std::shared_ptr<Model> translate_graph(const frontend::InputModel::Ptr& input_model);
+
+    /// \brief Completely convert jax_model, creates JaxFrameworkNode if not possible to convert node
+    /// \param jax_model Input model
+    /// \param external_tensor_map Is used for recursive calls of convert_jax_model and represent the external
+    /// context which is visible from nested model. Empty external_tensor_map is used as an indication that this is a
+    /// main body conversion.
+    /// \return fully converted OV Model
+    std::shared_ptr<Model> convert_jax_model(std::shared_ptr<JaxDecoder> jax_model,
+                                             const std::shared_ptr<jax::InputModel>& input_model = nullptr);
+
+    /// \brief Writes jax tensor index into openvino tensor
+    void encode_tensor_name(Output<Node> tensor_desc,
+                            size_t tensor_idx,
+                            std::vector<std::string> additional_names = {});
+
+    /// \brief Gets jax tensor index from openvino tensor
+    size_t decode_tensor_name(const Output<Node>& tensor_desc);
+
+private:
+    OutputVector convert_node(const NodeContext& context);
+
+    const frontend::InputModel::Ptr m_input_model;
+    const std::map<std::string, CreatorFunction>& m_translator_map;
+    std::shared_ptr<TelemetryExtension> m_telemetry;
+    std::shared_ptr<Model> m_ov_model;
+
+    std::map<size_t, std::pair<size_t, Output<Node>>> m_counter_map;
+    std::map<std::string, uint64_t> m_op_statistics;
+};
+
+}  // namespace jax
+}  // namespace frontend
+}  // namespace ov
diff --git a/src/frontends/jax/src/utils.cpp b/src/frontends/jax/src/utils.cpp
new file mode 100644
index 00000000000000..08959e79bf0138
--- /dev/null
+++ b/src/frontends/jax/src/utils.cpp
@@ -0,0 +1,66 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "utils.hpp"
+
+#include "jax_framework_node.hpp"
+#include "openvino/core/rt_info.hpp"
+#include "openvino/op/convert_promote_types.hpp"
+#include "openvino/opsets/opset10.hpp"
+
+namespace ov {
+namespace frontend {
+namespace jax {
+
+void num_inputs_check(const NodeContext& context, size_t min_inputs, size_t max_inputs) {
+    auto inputs = context.inputs();
+    FRONT_END_OP_CONVERSION_CHECK(inputs.size() >= min_inputs, "Got less inputs than expected");
+}
+
+const std::string& get_jax_prefix() {
+    return jax_prefix;
+}
+
+bool is_python_scalar_input(const NodeContext& context, size_t index) {
+    return context.get_input_type(index).is<type::PyScalar>();
+}
+
+void add_exception_to_fw_node(std::shared_ptr<Node> node, const std::string& msg) {
+    if (auto fw_node = ov::as_type_ptr<JaxFrameworkNode>(node)) {
+        auto attrs = fw_node->get_attrs();
+        attrs[JaxFrameworkNode::failed_conversion_key] = msg;
+        fw_node->set_attrs(attrs);
+    }
+}
+
+namespace {
+/*
+ * Please change the corresponding map in `src/bindings/python/src/openvino/frontend/jax/utils.py`
+ * if you want to change this map.
+ */
+const std::unordered_map<int64_t, element::Type> JAX_TO_OV_TYPE{
+    {0, element::u8},
+    {1, element::i8},
+    {2, element::i16},
+    {3, element::i32},
+    {4, element::i64},
+    {5, element::f16},
+    {6, element::f32},
+    {7, element::f64},
+    {11, element::boolean},
+    {12, element::i8},   // quantized i8
+    {13, element::u8},   // quantized u8
+    {14, element::i32},  // quantized i32
+    {15, element::bf16},
+};
+}  // namespace
+
+element::Type convert_dtype(int64_t jax_type) {
+    FRONT_END_OP_CONVERSION_CHECK(JAX_TO_OV_TYPE.count(jax_type), "Unknown type: ", jax_type);
+    return JAX_TO_OV_TYPE.at(jax_type);
+};
+
+}  // namespace jax
+}  // namespace frontend
+}  // namespace ov
diff --git a/src/frontends/jax/src/utils.hpp b/src/frontends/jax/src/utils.hpp
new file mode 100644
index 00000000000000..bcdfbf67d8ba94
--- /dev/null
+++ b/src/frontends/jax/src/utils.hpp
@@ -0,0 +1,55 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "openvino/frontend/jax/node_context.hpp"
+
+namespace ov {
+
+namespace op {
+namespace util {
+class FrameworkNode;
+}  // namespace util
+}  // namespace op
+
+namespace frontend {
+namespace jax {
+
+const std::string jax_prefix = "[JAX Frontend] ";
+
+const std::string& get_jax_prefix();
+
+/// \brief Macro to check whether a boolean condition holds.
+/// \param COND Condition to check
+/// \param ... Additional error message info to be added to the error message via the `<<`
+///            stream-insertion operator. Note that the expressions here will be evaluated lazily,
+///            i.e., only if the `cond` evalutes to `false`.
+/// \throws ::ov::frontend::OpConversionFailure if `cond` is false.
+#ifndef JAX_OP_CONVERSION_CHECK
+#    define JAX_OP_CONVERSION_CHECK(COND, ...) \
+        OPENVINO_ASSERT_HELPER(::ov::frontend::OpConversionFailure, "", (COND), get_jax_prefix(), __VA_ARGS__)
+#endif
+
+void num_inputs_check(const NodeContext& context, size_t min_inputs, size_t max_inputs);
+
+void add_exception_to_fw_node(std::shared_ptr<Node> node, const std::string& msg);
+
+bool is_python_scalar_input(const NodeContext& context, size_t index);
+
+element::Type convert_dtype(int64_t pt_type);
+
+namespace op {
+
+template <typename T>
+OutputVector translate_1to1_match_2_inputs(const NodeContext& context) {
+    num_inputs_check(context, 2, 2);
+    return {std::make_shared<T>(context.get_input(0), context.get_input(1))};
+}
+
+}  // namespace op
+
+}  // namespace jax
+}  // namespace frontend
+}  // namespace ov
diff --git a/src/frontends/onnx/frontend/src/core/tensor.cpp b/src/frontends/onnx/frontend/src/core/tensor.cpp
index 75d95f0dd56db6..90159cc5d8ce32 100644
--- a/src/frontends/onnx/frontend/src/core/tensor.cpp
+++ b/src/frontends/onnx/frontend/src/core/tensor.cpp
@@ -202,6 +202,20 @@ std::vector<char> Tensor::get_data() const {
     ONNX_INVALID_DATA_TYPE(m_tensor_proto->data_type(), "BOOL, raw data");
 }
 
+template <>
+std::vector<std::string> Tensor::get_data() const {
+    if (has_external_data()) {
+        FRONT_END_THROW("External strings are not supported");
+    }
+    if (m_tensor_proto->has_raw_data()) {
+        FRONT_END_THROW("Loading strings from raw data isn't supported");
+    }
+    if (m_tensor_proto->data_type() == TensorProto_DataType::TensorProto_DataType_STRING) {
+        return detail::__get_data<std::string>(m_tensor_proto->string_data());
+    }
+    ONNX_INVALID_DATA_TYPE(m_tensor_proto->data_type(), "STRING");
+}
+
 }  // namespace onnx
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/onnx/frontend/src/core/tensor.hpp b/src/frontends/onnx/frontend/src/core/tensor.hpp
index 67f5fbbb1a08f0..9c93e40c3b73d3 100644
--- a/src/frontends/onnx/frontend/src/core/tensor.hpp
+++ b/src/frontends/onnx/frontend/src/core/tensor.hpp
@@ -157,12 +157,14 @@ class Tensor {
             return ov::element::u64;
         case TensorProto_DataType::TensorProto_DataType_BFLOAT16:
             return ov::element::bf16;
+        case TensorProto_DataType::TensorProto_DataType_STRING:
+            return ov::element::string;
         case TensorProto_DataType::TensorProto_DataType_UNDEFINED:
             FRONT_END_THROW("Data type is Undefined");
         default:
-            ONNX_UNSUPPORTED_DATA_TYPE(
-                m_tensor_proto->data_type(),
-                "BOOL, BFLOAT16, FLOAT, FLOAT16, DOUBLE, INT8, INT16, INT32, INT64, UINT8, UINT16, UINT32, UINT64");
+            ONNX_UNSUPPORTED_DATA_TYPE(m_tensor_proto->data_type(),
+                                       "BOOL, BFLOAT16, FLOAT, FLOAT16, DOUBLE, INT8, INT16, INT32, INT64, UINT8, "
+                                       "UINT16, UINT32, UINT64, STRING");
         }
     }
 
@@ -201,10 +203,12 @@ class Tensor {
             return make_ov_constant<uint64_t>(ov::element::u64);
         case TensorProto_DataType::TensorProto_DataType_BFLOAT16:
             return make_ov_constant<ov::bfloat16>(ov::element::bf16);
+        case TensorProto_DataType::TensorProto_DataType_STRING:
+            return make_ov_constant<std::string>(ov::element::string);
         default:
-            ONNX_UNSUPPORTED_DATA_TYPE(
-                m_tensor_proto->data_type(),
-                "BOOL, BFLOAT16, FLOAT, FLOAT16, DOUBLE, INT8, INT16, INT32, INT64, UINT8, UINT16, UINT32, UINT64");
+            ONNX_UNSUPPORTED_DATA_TYPE(m_tensor_proto->data_type(),
+                                       "BOOL, BFLOAT16, FLOAT, FLOAT16, DOUBLE, INT8, INT16, INT32, INT64, UINT8, "
+                                       "UINT16, UINT32, UINT64, STRING");
         }
     }
 
@@ -320,8 +324,10 @@ class Tensor {
             return m_tensor_proto->uint64_data_size();
         case TensorProto_DataType::TensorProto_DataType_DOUBLE:
             return m_tensor_proto->double_data_size();
+        case TensorProto_DataType::TensorProto_DataType_STRING:
+            return m_tensor_proto->string_data_size();
         }
-        ONNX_INVALID_DATA_TYPE(m_tensor_proto->data_type(), "FLOAT, INT32, INT64, UINT64, DOUBLE");
+        ONNX_INVALID_DATA_TYPE(m_tensor_proto->data_type(), "FLOAT, INT32, INT64, UINT64, DOUBLE, STRING");
     }
 
     const TensorProto* m_tensor_proto;
@@ -373,6 +379,9 @@ std::vector<uint64_t> Tensor::get_data() const;
 template <>
 std::vector<char> Tensor::get_data() const;
 
+template <>
+std::vector<std::string> Tensor::get_data() const;
+
 }  // namespace onnx
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/onnx/frontend/src/frontend.cpp b/src/frontends/onnx/frontend/src/frontend.cpp
index f773527e353f46..529869a7d936d0 100644
--- a/src/frontends/onnx/frontend/src/frontend.cpp
+++ b/src/frontends/onnx/frontend/src/frontend.cpp
@@ -192,11 +192,13 @@ bool FrontEnd::supported_impl(const std::vector<ov::Any>& variants) const {
     std::ifstream model_stream;
     if (variants[0].is<std::string>()) {
         const auto path = variants[0].as<std::string>();
+        validate_path(path);
         model_stream.open(path, std::ios::in | std::ifstream::binary);
     }
 #if defined(OPENVINO_ENABLE_UNICODE_PATH_SUPPORT) && defined(_WIN32)
     else if (variants[0].is<std::wstring>()) {
         const auto path = variants[0].as<std::wstring>();
+        validate_path(path);
         model_stream.open(path.c_str(), std::ios::in | std::ifstream::binary);
     }
 #endif
diff --git a/src/frontends/onnx/frontend/src/input_model.cpp b/src/frontends/onnx/frontend/src/input_model.cpp
index 9a7c6e62f5d9d2..e7cd7b6efc1d49 100644
--- a/src/frontends/onnx/frontend/src/input_model.cpp
+++ b/src/frontends/onnx/frontend/src/input_model.cpp
@@ -64,6 +64,10 @@ ov::frontend::Place::Ptr InputModel::get_place_by_tensor_name(const std::string&
     return nullptr;
 }
 
+ov::frontend::Place::Ptr InputModel::get_place_by_input_index(size_t input_idx) const {
+    FRONT_END_NOT_IMPLEMENTED(get_place_by_input_index);
+}
+
 ov::frontend::Place::Ptr InputModel::get_place_by_operation_name(const std::string& operation_name) const {
     if (m_editor->is_correct_and_unambiguous_node(operation_name)) {
         const auto node_index = m_editor->get_node_index(EditorNode{operation_name});
diff --git a/src/frontends/onnx/frontend/src/input_model.hpp b/src/frontends/onnx/frontend/src/input_model.hpp
index 54d3694f0e1106..9bf44a5672fb28 100644
--- a/src/frontends/onnx/frontend/src/input_model.hpp
+++ b/src/frontends/onnx/frontend/src/input_model.hpp
@@ -37,6 +37,7 @@ class InputModel : public ov::frontend::InputModel {
     std::vector<ov::frontend::Place::Ptr> get_inputs() const override;
     std::vector<ov::frontend::Place::Ptr> get_outputs() const override;
     ov::frontend::Place::Ptr get_place_by_tensor_name(const std::string& tensor_name) const override;
+    ov::frontend::Place::Ptr get_place_by_input_index(size_t input_idx) const override;
     ov::frontend::Place::Ptr get_place_by_operation_name(const std::string& operation_name) const override;
     ov::frontend::Place::Ptr get_place_by_operation_name_and_input_port(const std::string& operation_name,
                                                                         int input_port_index) override;
diff --git a/src/frontends/onnx/frontend/src/op/trilu.cpp b/src/frontends/onnx/frontend/src/op/trilu.cpp
index ec9929a1d40bf5..6d24b61645bb52 100644
--- a/src/frontends/onnx/frontend/src/op/trilu.cpp
+++ b/src/frontends/onnx/frontend/src/op/trilu.cpp
@@ -58,7 +58,7 @@ ov::OutputVector trilu(const ov::frontend::onnx::Node& node) {
     const auto one = v0::Constant::create(ov::element::i64, ov::Shape{}, {1});
 
     // The approach here is to create a mask, that later can be used in Select operator
-    // to choose appropiate values from the input
+    // to choose appropriate values from the input
     //
     // For example N = 4, M = 5, k = -1, upper = false
     // horizontal_range = [[0, 1, 2, 3, 4]]
diff --git a/src/frontends/onnx/frontend/src/utils/common.cpp b/src/frontends/onnx/frontend/src/utils/common.cpp
index 7059f5aa684f67..5ce7a03e2be026 100644
--- a/src/frontends/onnx/frontend/src/utils/common.cpp
+++ b/src/frontends/onnx/frontend/src/utils/common.cpp
@@ -6,6 +6,7 @@
 
 #include <onnx/onnx_pb.h>  // onnx types
 
+#include "core/tensor.hpp"
 #include "onnx_framework_node.hpp"
 #include "openvino/frontend/exception.hpp"
 #include "openvino/op/add.hpp"
@@ -60,8 +61,12 @@ const ov::element::Type& get_ov_element_type(int64_t onnx_type) {
         return ov::element::dynamic;
     case TensorProto_DataType::TensorProto_DataType_BFLOAT16:
         return ov::element::bf16;
+    case TensorProto_DataType::TensorProto_DataType_STRING:
+        return ov::element::string;
     }
-    OPENVINO_THROW("unsupported element type");
+    ONNX_UNSUPPORTED_DATA_TYPE(onnx_type,
+                               "BOOL, BFLOAT16, FLOAT, FLOAT16, DOUBLE, INT8, INT16, INT32, INT64, UINT8, UINT16, "
+                               "UINT32, UINT64, STRING, UNDEFINED");
 }
 
 std::shared_ptr<ov::Node> get_monotonic_range_along_node_rank(const ov::Output<ov::Node>& value,
diff --git a/src/frontends/onnx/onnx_common/src/parser.cpp b/src/frontends/onnx/onnx_common/src/parser.cpp
index e56f617eb32926..2b2ab1debc6bca 100644
--- a/src/frontends/onnx/onnx_common/src/parser.cpp
+++ b/src/frontends/onnx/onnx_common/src/parser.cpp
@@ -21,7 +21,7 @@ ModelProto parse_from_file(const std::string& file_path) {
     std::ifstream file_stream{file_path.c_str(), std::ios::in | std::ios::binary};
 
     if (!file_stream.is_open()) {
-        OPENVINO_THROW("Could not open the file: " + file_path);
+        OPENVINO_THROW("Could not open the file: \"" + file_path, '"');
     };
 
     auto model_proto = parse_from_istream(file_stream);
@@ -34,7 +34,7 @@ ModelProto parse_from_file(const std::wstring& file_path) {
     std::ifstream file_stream{file_path.c_str(), std::ios::in | std::ios::binary};
 
     if (!file_stream.is_open()) {
-        OPENVINO_THROW("Could not open the file: " + ov::util::wstring_to_string(file_path));
+        OPENVINO_THROW("Could not open the file: \"", ov::util::wstring_to_string(file_path), '"');
     };
 
     auto model_proto = parse_from_istream(file_stream);
diff --git a/src/frontends/onnx/onnx_common/src/utils.cpp b/src/frontends/onnx/onnx_common/src/utils.cpp
index ff390c944f6227..5dfcdf3df759c4 100644
--- a/src/frontends/onnx/onnx_common/src/utils.cpp
+++ b/src/frontends/onnx/onnx_common/src/utils.cpp
@@ -60,7 +60,8 @@ const std::map<ov::element::Type_t, TensorProto_DataType> OV_2_ONNX_TYPES = {
     {ov::element::Type_t::u16, TensorProto_DataType::TensorProto_DataType_UINT16},
     {ov::element::Type_t::u32, TensorProto_DataType::TensorProto_DataType_UINT32},
     {ov::element::Type_t::u64, TensorProto_DataType::TensorProto_DataType_UINT64},
-    {ov::element::Type_t::boolean, TensorProto_DataType::TensorProto_DataType_BOOL}};
+    {ov::element::Type_t::boolean, TensorProto_DataType::TensorProto_DataType_BOOL},
+    {ov::element::Type_t::string, TensorProto_DataType::TensorProto_DataType_STRING}};
 
 ov::element::Type_t onnx_to_ov_data_type(const TensorProto_DataType& onnx_type) {
     const auto result = std::find_if(OV_2_ONNX_TYPES.begin(),
diff --git a/src/frontends/onnx/tests/load_from.cpp b/src/frontends/onnx/tests/load_from.cpp
index 1cf7c5bb2500c9..660ff3d96df7b7 100644
--- a/src/frontends/onnx/tests/load_from.cpp
+++ b/src/frontends/onnx/tests/load_from.cpp
@@ -7,6 +7,7 @@
 
 #include <fstream>
 
+#include "common_test_utils/test_assertions.hpp"
 #include "onnx_utils.hpp"
 #include "utils.hpp"
 
@@ -43,6 +44,19 @@ TEST_P(FrontEndLoadFromTest, testLoadFromStreamAndPassPath) {
     ASSERT_NE(function, nullptr);
 }
 
+TEST_P(FrontEndLoadFromTest, load_model_not_exists_at_path) {
+    const auto model_name = "not_existing_model";
+    auto error_msg = std::string("Could not open the file: ");
+    auto model_file_path = FrontEndTestUtils::make_model_path(model_name);
+    error_msg += '"' + model_file_path + '"';
+
+    auto fem = ov::frontend::FrontEndManager();
+    auto fe = fem.load_by_framework("onnx");
+
+    OV_EXPECT_THROW(fe->supported({model_file_path}), ov::Exception, testing::HasSubstr(error_msg));
+    OV_EXPECT_THROW(fe->load(model_file_path), ov::Exception, testing::HasSubstr(error_msg));
+}
+
 INSTANTIATE_TEST_SUITE_P(ONNXLoadTest,
                          FrontEndLoadFromTest,
                          ::testing::Values(getTestData()),
diff --git a/src/frontends/onnx/tests/models/string_constant.prototxt b/src/frontends/onnx/tests/models/string_constant.prototxt
new file mode 100644
index 00000000000000..1d0b22f04b09ff
--- /dev/null
+++ b/src/frontends/onnx/tests/models/string_constant.prototxt
@@ -0,0 +1,59 @@
+ir_version: 9
+producer_name: "OpenVINO ONNX Frontend"
+graph {
+  name: "test"
+  node {
+    output: "Y"
+    op_type: "Constant"
+    attribute {
+      name: "value"
+      t {
+        dims: 2
+        data_type: 8
+        string_data: "string1"
+        string_data: "string2"
+        name: "str_const"
+      }
+      type: TENSOR
+    }
+  }
+  node {
+    input: "Y"
+    output: "O"
+    op_type: "Shape"
+  }
+  node {
+    input: "Y"
+    output: "V"
+    op_type: "Identity"
+  }
+  output {
+    name: "O"
+    type {
+      tensor_type {
+        elem_type: 7
+        shape {
+          dim {
+            dim_value: 1
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "V"
+    type {
+      tensor_type {
+        elem_type: 8
+        shape {
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  version: 20
+}
diff --git a/src/frontends/onnx/tests/models/string_input.prototxt b/src/frontends/onnx/tests/models/string_input.prototxt
new file mode 100644
index 00000000000000..3afa2b65567612
--- /dev/null
+++ b/src/frontends/onnx/tests/models/string_input.prototxt
@@ -0,0 +1,57 @@
+ir_version: 9
+producer_name: "OpenVINO ONNX Frontend"
+graph {
+  name: "test"
+  node {
+    input: "I"
+    output: "O"
+    op_type: "Shape"
+  }
+  node {
+    input: "I"
+    output: "V"
+    op_type: "Identity"
+  }
+  input {
+    name: "I"
+    type {
+      tensor_type {
+        elem_type: 8
+        shape {
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "O"
+    type {
+      tensor_type {
+        elem_type: 7
+        shape {
+          dim {
+            dim_value: 1
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "V"
+    type {
+      tensor_type {
+        elem_type: 8
+        shape {
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  version: 20
+}
diff --git a/src/frontends/onnx/tests/onnx_import.in.cpp b/src/frontends/onnx/tests/onnx_import.in.cpp
index e15e4e768b6505..9bfa370cac3828 100644
--- a/src/frontends/onnx/tests/onnx_import.in.cpp
+++ b/src/frontends/onnx/tests/onnx_import.in.cpp
@@ -6736,6 +6736,25 @@ OPENVINO_TEST(${BACKEND_NAME}, onnx_model_reduce_min_20_boolean) {
     test_case.run();
 }
 
+OPENVINO_TEST(${BACKEND_NAME}, onnx_string_input) {
+    const auto model = convert_model("string_input.onnx");
+    auto test_case = test::TestCase(model);
+    test_case.add_input<std::string>({"strinpt1", "strinpt2"});
+    test_case.add_expected_output<int64_t>({2});
+    test_case.add_expected_output<std::string>({"strinpt1", "strinpt2"});
+
+    test_case.run();
+}
+
+OPENVINO_TEST(${BACKEND_NAME}, onnx_string_constant) {
+    const auto model = convert_model("string_constant.onnx");
+    auto test_case = test::TestCase(model);
+    test_case.add_expected_output<int64_t>({2});
+    test_case.add_expected_output<std::string>({"string1", "string2"});
+
+    test_case.run();
+}
+
 OPENVINO_TEST(${BACKEND_NAME}, onnx_model_multinomial_7) {
     auto model = convert_model("multinomial.onnx");
 
diff --git a/src/frontends/onnx/tests/tests_python/test_frontend_onnx.py b/src/frontends/onnx/tests/tests_python/test_frontend_onnx.py
index b3625b620710cd..0ebf86717d45c8 100644
--- a/src/frontends/onnx/tests/tests_python/test_frontend_onnx.py
+++ b/src/frontends/onnx/tests/tests_python/test_frontend_onnx.py
@@ -318,8 +318,15 @@ def test_load_by_model():
     decoded_function = fe.decode(model)
     assert decoded_function
 
-    assert not fem.load_by_model("test.xx")
-    assert not fem.load_by_model("onnx.yy")
+    with pytest.raises(Exception) as e:
+        fem.load_by_model("test.xx")
+
+    assert e.match(r'Could not open the file: "test.xx"')
+
+    with pytest.raises(Exception) as e:
+        fem.load_by_model("onnx.yy")
+
+    assert e.match(r'Could not open the file: "onnx.yy"')
 
 
 def test_onnx_conversion_extension_check_attributes():
diff --git a/src/frontends/paddle/src/frontend.cpp b/src/frontends/paddle/src/frontend.cpp
index 416ec152ce399f..0b6ab0d4eef331 100644
--- a/src/frontends/paddle/src/frontend.cpp
+++ b/src/frontends/paddle/src/frontend.cpp
@@ -378,6 +378,7 @@ bool FrontEnd::supported_impl(const std::vector<ov::Any>& variants) const {
     if (variants[0].is<std::string>()) {
         std::string suffix = ".pdmodel";
         std::string model_path = variants[0].as<std::string>();
+        FRONT_END_GENERAL_CHECK(util::file_exists(model_path), "Could not open the file: \"", model_path, '"');
         if (!ov::util::ends_with(model_path, suffix)) {
             model_path += paddle::get_path_sep<char>() + "__model__";
         }
@@ -390,6 +391,10 @@ bool FrontEnd::supported_impl(const std::vector<ov::Any>& variants) const {
     else if (variants[0].is<std::wstring>()) {
         std::wstring suffix = L".pdmodel";
         std::wstring model_path = variants[0].as<std::wstring>();
+        FRONT_END_GENERAL_CHECK(util::file_exists(model_path),
+                                "Could not open the file: \"",
+                                util::path_to_string(model_path),
+                                '"');
         if (!ov::util::ends_with(model_path, suffix)) {
             model_path += paddle::get_path_sep<wchar_t>() + L"__model__";
         }
diff --git a/src/frontends/paddle/src/input_model.cpp b/src/frontends/paddle/src/input_model.cpp
index 51deb0637ba257..53dd627ec42774 100644
--- a/src/frontends/paddle/src/input_model.cpp
+++ b/src/frontends/paddle/src/input_model.cpp
@@ -399,7 +399,10 @@ InputModel::InputModelImpl::InputModelImpl(const std::basic_string<T>& path,
     std::ifstream weights_stream;
     std::ifstream pb_stream(get_model_path<T>(path, &weights_stream).c_str(), std::ios::in | std::ifstream::binary);
 
-    FRONT_END_GENERAL_CHECK(pb_stream && pb_stream.is_open(), "Model file doesn't exist");
+    FRONT_END_GENERAL_CHECK(pb_stream && pb_stream.is_open(),
+                            "Could not open the file: \"",
+                            util::path_to_string(path),
+                            '"');
     FRONT_END_GENERAL_CHECK(m_fw_ptr->ParseFromIstream(&pb_stream), "Model can't be parsed");
     // According to Paddle, the saved model has the framework version
     // For example Paddle 2.1.0 is encoded as 2001000. 0 means the latest framework.
@@ -612,6 +615,10 @@ Place::Ptr InputModel::get_place_by_tensor_name(const std::string& tensorName) c
     return _impl->get_place_by_tensor_name(tensorName);
 }
 
+Place::Ptr InputModel::get_place_by_input_index(size_t input_idx) const {
+    FRONT_END_NOT_IMPLEMENTED(get_place_by_input_index);
+}
+
 void InputModel::override_all_outputs(const std::vector<Place::Ptr>& outputs) {
     _impl->override_all_outputs(outputs);
 }
@@ -646,4 +653,4 @@ void InputModel::set_tensor_value(const Place::Ptr& place, const void* value) {
 
 }  // namespace paddle
 }  // namespace frontend
-}  // namespace ov
\ No newline at end of file
+}  // namespace ov
diff --git a/src/frontends/paddle/src/input_model.hpp b/src/frontends/paddle/src/input_model.hpp
index 6eedb6759a70dd..e401e84416455f 100644
--- a/src/frontends/paddle/src/input_model.hpp
+++ b/src/frontends/paddle/src/input_model.hpp
@@ -25,6 +25,7 @@ class InputModel : public ov::frontend::InputModel {
     std::vector<Place::Ptr> get_inputs() const override;
     std::vector<Place::Ptr> get_outputs() const override;
     Place::Ptr get_place_by_tensor_name(const std::string& tensorName) const override;
+    Place::Ptr get_place_by_input_index(size_t input_idx) const override;
     void override_all_outputs(const std::vector<Place::Ptr>& outputs) override;
     void override_all_inputs(const std::vector<Place::Ptr>& inputs) override;
     void extract_subgraph(const std::vector<Place::Ptr>& inputs, const std::vector<Place::Ptr>& outputs) override;
diff --git a/src/frontends/pytorch/src/frontend.cpp b/src/frontends/pytorch/src/frontend.cpp
index 4763ee15c91942..c54d37e28fb037 100644
--- a/src/frontends/pytorch/src/frontend.cpp
+++ b/src/frontends/pytorch/src/frontend.cpp
@@ -11,6 +11,7 @@
 #include "openvino/op/util/multi_subgraph_base.hpp"
 #include "openvino/pass/constant_folding.hpp"
 #include "openvino/util/log.hpp"
+#include "place.hpp"
 #include "pt_framework_node.hpp"
 #include "transformations/common_optimizations/push_constant_to_subgraph.hpp"
 #include "transformations/common_optimizations/remove_multi_subgraph_op_dangling_params.hpp"
@@ -113,15 +114,37 @@ std::string pack_detailed_failure_report(const std::map<std::string, std::string
         error_msg << '\n' << failed_ops_short.str();
     return error_msg.str();
 }
+
+void update_parameter_info(std::shared_ptr<ov::op::v0::Parameter>& param,
+                           const Place::Ptr& place,
+                           const std::shared_ptr<Model> ov_model) {
+    const auto& pt_place = std::dynamic_pointer_cast<pytorch::Place>(place);
+    FRONT_END_GENERAL_CHECK(pt_place, "Wrong place type.");
+    if (pt_place->get_element_type().is_static())
+        param->set_element_type(pt_place->get_element_type());
+    auto pshape = param->get_partial_shape();
+    FRONT_END_GENERAL_CHECK(PartialShape::merge_into(pshape, pt_place->get_partial_shape()),
+                            "Incompatible shape was requested for input #",
+                            ov_model->get_parameter_index(param),
+                            ", ",
+                            param,
+                            "\nOriginal shape: ",
+                            pshape,
+                            " update shape: ",
+                            pt_place->get_partial_shape());
+    param->set_partial_shape(pshape);
+}
 }  // namespace
 
 FrontEnd::FrontEnd() {}
 
 std::shared_ptr<Model> FrontEnd::convert(const ov::frontend::InputModel::Ptr& model) const {
-    FRONT_END_GENERAL_CHECK(std::dynamic_pointer_cast<pytorch::InputModel>(model), "Invalid input model");
+    auto pt_model = std::dynamic_pointer_cast<pytorch::InputModel>(model);
+    FRONT_END_GENERAL_CHECK(pt_model, "Invalid input model");
     std::map<std::string, CreatorFunction> supported_ops = get_supported_ops(model);
     std::shared_ptr<Model> converted_model;
     {
+        pt_model->flush_places();
         TranslateSession translate_session(model, supported_ops, m_telemetry);
         converted_model = translate_session.get_converted_model();
     }
@@ -141,6 +164,50 @@ std::shared_ptr<Model> FrontEnd::convert(const ov::frontend::InputModel::Ptr& mo
     }
     bool is_conversion_successful = unconverted_ops.size() == 0 && norm_err.empty();
     FRONT_END_OP_CONVERSION_CHECK(is_conversion_successful, pack_detailed_failure_report(unconverted_ops, norm_err));
+
+    if (pt_model->m_requested_places.size() != 0) {
+        // Fake tensors mean that types were set to non-existent before conversion inputs.
+        // Here we resolve this. If input doesn't exist after conversion exception is raised.
+        auto parameters = converted_model->get_parameters();
+        std::set<std::string> input_names;
+        for (const auto& param : parameters)
+            for (const auto& name : param->get_output_tensor(0).get_names())
+                input_names.insert(name);
+        const auto& inputs = pt_model->get_inputs();
+        for (size_t i = 0; i < inputs.size(); i++) {
+            auto place = inputs[i];
+            if (place->get_names().size() != 0 && input_names.find(place->get_names().at(0)) != input_names.end()) {
+                auto input = converted_model->input(place->get_names().at(0));
+                auto param = std::dynamic_pointer_cast<ov::op::v0::Parameter>(input.get_node_shared_ptr());
+                FRONT_END_GENERAL_CHECK(param, "Input is not a Parameter.");
+                update_parameter_info(param, place, converted_model);
+            } else {
+                FRONT_END_OP_CONVERSION_CHECK(i < parameters.size(),
+                                              "Type/shape was set to non-existent input. Converted model:\n",
+                                              converted_model);
+                update_parameter_info(parameters[i], inputs[i], converted_model);
+            }
+        }
+        for (const auto& fplace : pt_model->m_requested_places) {
+            const auto& pt_place = std::dynamic_pointer_cast<pytorch::Place>(fplace);
+            FRONT_END_GENERAL_CHECK(pt_place, "Wrong place type.");
+            if (fplace->get_names().size() == 0) {
+                // Fake place was set by input index
+                auto idx = pt_place->get_input_index();
+                FRONT_END_GENERAL_CHECK(idx < parameters.size(),
+                                        "Type/shape was set to non-existent input. Converted model:\n",
+                                        converted_model);
+                update_parameter_info(parameters[idx], fplace, converted_model);
+            } else {
+                auto input = converted_model->input(fplace->get_names().at(0));
+                auto param = std::dynamic_pointer_cast<ov::op::v0::Parameter>(input.get_node_shared_ptr());
+                FRONT_END_GENERAL_CHECK(param, "Input is not a Parameter.");
+                update_parameter_info(param, fplace, converted_model);
+            }
+        }
+        converted_model->validate_nodes_and_infer_types();
+    }
+
     return converted_model;
 }
 
@@ -149,10 +216,12 @@ void FrontEnd::convert(const std::shared_ptr<Model>& partiallyConverted) const {
 }
 
 std::shared_ptr<Model> FrontEnd::convert_partially(const ov::frontend::InputModel::Ptr& model) const {
-    FRONT_END_GENERAL_CHECK(std::dynamic_pointer_cast<pytorch::InputModel>(model), "Invalid input model");
+    auto pt_model = std::dynamic_pointer_cast<pytorch::InputModel>(model);
+    FRONT_END_GENERAL_CHECK(pt_model, "Invalid input model");
     std::map<std::string, CreatorFunction> supported_ops = get_supported_ops(model);
     std::shared_ptr<Model> partial_model;
     {
+        pt_model->flush_places();
         TranslateSession translate_session(model, supported_ops, m_telemetry);
         partial_model = translate_session.get_converted_model();
     }
diff --git a/src/frontends/pytorch/src/input_model.cpp b/src/frontends/pytorch/src/input_model.cpp
index 4adfa1669b974c..72140881c89698 100644
--- a/src/frontends/pytorch/src/input_model.cpp
+++ b/src/frontends/pytorch/src/input_model.cpp
@@ -49,11 +49,22 @@ std::vector<ov::frontend::Place::Ptr> InputModel::get_outputs() const {
 }
 
 Place::Ptr InputModel::get_place_by_tensor_name(const std::string& tensor_name) const {
+    if (tensor_name.empty())
+        return {};
     auto place_it = m_name_to_place.find(tensor_name);
     if (place_it != m_name_to_place.end()) {
         return place_it->second;
+    } else {
+        // Return fake place that can be used to change shape or type of inputs that will exist after conversion
+        auto place = std::make_shared<pytorch::Place>(*this, tensor_name, 0);
+        return place;
     }
-    return nullptr;
+}
+
+Place::Ptr InputModel::get_place_by_input_index(size_t input_idx) const {
+    // Return place that can be used to change shape or type of inputs that will exist after conversion
+    auto place = std::make_shared<pytorch::Place>(*this, "", input_idx);
+    return place;
 }
 
 void InputModel::set_partial_shape(const Place::Ptr& place, const ov::PartialShape& shape) {
@@ -61,6 +72,18 @@ void InputModel::set_partial_shape(const Place::Ptr& place, const ov::PartialSha
                             "Provided place is invalid, only inputs are supported for setting shape.");
     auto pytorch_place = std::dynamic_pointer_cast<pytorch::Place>(place);
     FRONT_END_GENERAL_CHECK(pytorch_place, "Only place produced by PyTorch Frontend is supported");
+    if (pytorch_place->m_is_fake) {
+        bool is_new = true;
+        for (auto& p : m_requested_places) {
+            if (p->is_equal(pytorch_place)) {
+                is_new = false;
+                pytorch_place = std::dynamic_pointer_cast<pytorch::Place>(p);
+                FRONT_END_GENERAL_CHECK(pytorch_place, "Only place produced by PyTorch Frontend is supported");
+            }
+        }
+        if (is_new)
+            m_requested_places.push_back(place);
+    }
     pytorch_place->m_pshape = shape;
 }
 
@@ -77,6 +100,18 @@ void InputModel::set_element_type(const Place::Ptr& place, const ov::element::Ty
                             "Provided place is invalid, only inputs are supported for setting element type.");
     auto pytorch_place = std::dynamic_pointer_cast<pytorch::Place>(place);
     FRONT_END_GENERAL_CHECK(pytorch_place, "Only place produced by PyTorch Frontend is supported");
+    if (pytorch_place->m_is_fake) {
+        bool is_new = true;
+        for (auto& p : m_requested_places) {
+            if (p->is_equal(pytorch_place)) {
+                is_new = false;
+                pytorch_place = std::dynamic_pointer_cast<pytorch::Place>(p);
+                FRONT_END_GENERAL_CHECK(pytorch_place, "Only place produced by PyTorch Frontend is supported");
+            }
+        }
+        if (is_new)
+            m_requested_places.push_back(place);
+    }
     pytorch_place->m_type = type;
 }
 
@@ -171,6 +206,25 @@ std::shared_ptr<TorchDecoder> InputModel::get_decoder() const {
     return m_model_decoder;
 }
 
+void InputModel::flush_places() {
+    auto input_places = get_inputs();
+    if (m_requested_places.size() > input_places.size())
+        return;
+    for (auto place : m_requested_places) {
+        auto pt_place = std::dynamic_pointer_cast<pytorch::Place>(place);
+        if (!pt_place || pt_place->get_input_index() >= input_places.size() || pt_place->m_names.size() != 0)
+            return;
+        auto to_update_place = std::dynamic_pointer_cast<pytorch::Place>(input_places[pt_place->get_input_index()]);
+        if (!to_update_place || !to_update_place->m_type.is_dynamic() || !to_update_place->m_pshape.rank().is_dynamic())
+            return;
+        if (pt_place->m_type.is_static())
+            to_update_place->m_type = pt_place->m_type;
+        if (pt_place->m_pshape.rank().is_static())
+            to_update_place->m_pshape = pt_place->m_pshape;
+    }
+    m_requested_places = {};
+}
+
 }  // namespace pytorch
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/pytorch/src/input_model.hpp b/src/frontends/pytorch/src/input_model.hpp
index 58d91eae5764ca..b647da29465660 100644
--- a/src/frontends/pytorch/src/input_model.hpp
+++ b/src/frontends/pytorch/src/input_model.hpp
@@ -12,6 +12,7 @@ namespace ov {
 namespace frontend {
 namespace pytorch {
 
+class FrontEnd;
 class TranslateSession;
 class Place;
 class TorchDecoder;
@@ -23,6 +24,7 @@ struct PlaceDesc {
 
 class InputModel : public ov::frontend::InputModel {
     friend class ::ov::frontend::pytorch::TranslateSession;
+    friend class ::ov::frontend::pytorch::FrontEnd;
 
 public:
     explicit InputModel(const std::shared_ptr<TorchDecoder>& model_decoder);
@@ -30,6 +32,7 @@ class InputModel : public ov::frontend::InputModel {
     std::vector<frontend::Place::Ptr> get_inputs() const override;
     std::vector<frontend::Place::Ptr> get_outputs() const override;
     frontend::Place::Ptr get_place_by_tensor_name(const std::string& tensor_name) const override;
+    frontend::Place::Ptr get_place_by_input_index(size_t input_idx) const override;
     void set_partial_shape(const frontend::Place::Ptr& place, const ov::PartialShape& shape) override;
     ov::PartialShape get_partial_shape(const frontend::Place::Ptr& place) const override;
     void set_element_type(const frontend::Place::Ptr& place, const ov::element::Type& type) override;
@@ -39,12 +42,15 @@ class InputModel : public ov::frontend::InputModel {
     void override_all_inputs(const std::vector<frontend::Place::Ptr>& inputs) override;
     const std::string& decoder_type_name() const;
     std::shared_ptr<TorchDecoder> get_decoder() const;
+    // update input places and erase requested places if possible
+    void flush_places();
 
 private:
     std::shared_ptr<TorchDecoder> m_model_decoder;
     std::unordered_map<std::string, std::shared_ptr<frontend::Place>> m_name_to_place;
     std::vector<std::shared_ptr<frontend::Place>> m_inputs;
     std::vector<std::shared_ptr<frontend::Place>> m_outputs;
+    std::vector<std::shared_ptr<frontend::Place>> m_requested_places;
     std::unordered_map<size_t, PlaceDesc> m_descriptors;
 };
 
diff --git a/src/frontends/pytorch/src/op/addmm.cpp b/src/frontends/pytorch/src/op/addmm.cpp
index 2f43079a037405..522c5237ecd11b 100644
--- a/src/frontends/pytorch/src/op/addmm.cpp
+++ b/src/frontends/pytorch/src/op/addmm.cpp
@@ -4,9 +4,14 @@
 
 #include "openvino/frontend/pytorch/node_context.hpp"
 #include "openvino/op/add.hpp"
+#include "openvino/op/concat.hpp"
 #include "openvino/op/convert_like.hpp"
+#include "openvino/op/gather.hpp"
 #include "openvino/op/matmul.hpp"
 #include "openvino/op/multiply.hpp"
+#include "openvino/op/reshape.hpp"
+#include "openvino/op/scatter_elements_update.hpp"
+#include "openvino/op/shape_of.hpp"
 #include "utils.hpp"
 
 namespace ov {
@@ -58,6 +63,29 @@ OutputVector translate_addmm_fx(const NodeContext& context) {
     return {translate_addmm_common(context, beta, alpha)};
 };
 
+OutputVector translate_conv1d_ext(const NodeContext& context) {
+    // not really a convolution, implemented based on
+    // https://github.com/huggingface/transformers/blob/0ed3ffcb4461a244b87781a24e5ebd0a78f98142/src/transformers/pytorch_utils.py#L84
+    num_inputs_check(context, 3, 3);
+    auto x = context.get_input(0);
+    auto weight = context.get_input(1);
+    weight = context.mark_node(std::make_shared<ov::op::v1::ConvertLike>(weight, x));
+    auto bias = context.get_input(2);
+    bias = context.mark_node(std::make_shared<ov::op::v1::ConvertLike>(bias, x));
+
+    auto neg_one = context.mark_node(v0::Constant::create(element::i32, Shape{1}, {-1}));
+    auto zero = context.mark_node(v0::Constant::create(element::i32, Shape{1}, {0}));
+    auto shape_x = context.mark_node(std::make_shared<v3::ShapeOf>(x, element::i32));
+    auto x_last_dim = context.mark_node(std::make_shared<v8::Gather>(shape_x, neg_one, zero));
+    auto x_new_shape = context.mark_node(std::make_shared<v0::Concat>(OutputVector{neg_one, x_last_dim}, 0));
+
+    auto x_new = context.mark_node(std::make_shared<v1::Reshape>(x, x_new_shape, false));
+    auto mm = context.mark_node(std::make_shared<v0::MatMul>(x_new, weight));
+    auto addmm = context.mark_node(std::make_shared<v1::Add>(bias, mm));
+    auto size_out = context.mark_node(std::make_shared<v12::ScatterElementsUpdate>(shape_x, neg_one, neg_one, zero));
+    return {context.mark_node(std::make_shared<v1::Reshape>(addmm, size_out, false))};
+};
+
 }  // namespace op
 }  // namespace pytorch
 }  // namespace frontend
diff --git a/src/frontends/pytorch/src/op/cat.cpp b/src/frontends/pytorch/src/op/cat.cpp
index c0ee57854aeb9b..b8bd2d2487944e 100644
--- a/src/frontends/pytorch/src/op/cat.cpp
+++ b/src/frontends/pytorch/src/op/cat.cpp
@@ -4,6 +4,8 @@
 
 #include "openvino/frontend/pytorch/node_context.hpp"
 #include "openvino/op/concat.hpp"
+#include "openvino/op/convert_like.hpp"
+#include "openvino/op/convert_promote_types.hpp"
 #include "openvino/op/parameter.hpp"
 #include "openvino/op/reshape.hpp"
 #include "openvino/op/scatter_elements_update.hpp"
@@ -56,6 +58,36 @@ OutputVector translate_cat_common(const NodeContext& context,
             context.mark_node(std::make_shared<v12::ScatterElementsUpdate>(shape_sliced, axis_const, neg_1, zero));
         return {context.mark_node(std::make_shared<v1::Reshape>(tensor, new_shape, false))};
     }
+
+    const auto first_in_type = list_elems.front().get_element_type();
+    const bool is_mixed_type =
+        list_elems.size() > 1 && (std::any_of(std::next(list_elems.begin()),
+                                              list_elems.end(),
+                                              [&first_in_type](const ov::Output<ov::Node>& input) {
+                                                  return input.get_element_type() != first_in_type ||
+                                                         input.get_element_type() == ov::element::dynamic;
+                                              }));
+    if (is_mixed_type) {
+        auto node_of_type = list_elems[0];
+        for (size_t i = 1; i < list_elems.size(); ++i) {
+            node_of_type = std::make_shared<v14::ConvertPromoteTypes>(node_of_type, list_elems[i], true)->output(0);
+            context.mark_node(node_of_type.get_node_shared_ptr());
+        }
+
+        const auto unified_type = node_of_type.get_element_type();
+        auto inputs_vec = OutputVector(list_elems.begin(), list_elems.end());
+        for (size_t i = 0; i < inputs_vec.size(); ++i) {
+            if (inputs_vec[i].get_element_type() != unified_type ||
+                inputs_vec[i].get_element_type() == ov::element::dynamic) {
+                inputs_vec[i] =
+                    std::make_shared<v1::ConvertLike>(list_elems[i].get_node_shared_ptr(), node_of_type)->output(0);
+                context.mark_node(inputs_vec[i].get_node_shared_ptr());
+            }
+        }
+        auto concat = std::make_shared<v0::Concat>(inputs_vec, axis);
+        return {context.mark_node(concat)};
+    }
+
     auto concat = std::make_shared<v0::Concat>(OutputVector(list_elems.begin(), list_elems.end()), axis);
     return {context.mark_node(concat)};
 }
diff --git a/src/frontends/pytorch/src/op/embedding.cpp b/src/frontends/pytorch/src/op/embedding.cpp
index 0bde94b432649e..8f80699ec007a9 100644
--- a/src/frontends/pytorch/src/op/embedding.cpp
+++ b/src/frontends/pytorch/src/op/embedding.cpp
@@ -20,7 +20,7 @@ OutputVector translate_embedding(const NodeContext& context) {
     auto data = context.get_input(0);
     auto indices = context.get_input(1);
     indices = context.mark_node(std::make_shared<ov::op::v0::Convert>(indices, element::i32));
-    // skip parameters 2, 3, 4 used only during trainig:
+    // skip parameters 2, 3, 4 used only during training:
     // padding_idx - if specified, the entries at padding_idx do not contribute to the gradient
     // scale_grad_by_freq - if given, this will scale gradients by the inverse of frequency of
     //                      the words in the mini-batch.
@@ -29,6 +29,17 @@ OutputVector translate_embedding(const NodeContext& context) {
     return {context.mark_node(std::make_shared<ov::op::v8::Gather>(data, indices, axis_0))};
 };
 
+OutputVector translate_embedding_ext(const NodeContext& context) {
+    // used in 16-bit patching
+    num_inputs_check(context, 2, 5);
+    auto data = context.get_input(0);
+    data = context.mark_node(std::make_shared<ov::op::v0::Convert>(data, element::f32));
+    auto indices = context.get_input(1);
+    indices = context.mark_node(std::make_shared<ov::op::v0::Convert>(indices, element::i32));
+    auto axis_0 = context.mark_node(ov::op::v0::Constant::create(element::i32, Shape{}, {0}));
+    return {context.mark_node(std::make_shared<ov::op::v8::Gather>(data, indices, axis_0))};
+};
+
 }  // namespace op
 }  // namespace pytorch
 }  // namespace frontend
diff --git a/src/frontends/pytorch/src/op/embedding_bag.cpp b/src/frontends/pytorch/src/op/embedding_bag.cpp
index b4d424d4e2c22e..4e69b22f9e3144 100644
--- a/src/frontends/pytorch/src/op/embedding_bag.cpp
+++ b/src/frontends/pytorch/src/op/embedding_bag.cpp
@@ -58,10 +58,11 @@ OutputVector translate_embedding_bag_common(const NodeContext& context) {
         } else {
             auto per_sample_weight = context.get_input(6);
             per_sample_weight = context.mark_node(std::make_shared<ov::op::v1::ConvertLike>(per_sample_weight, weight));
+            auto neg_one = context.mark_node(ov::op::v0::Constant::create(element::i32, Shape{}, {-1}));
             result = context.mark_node(std::make_shared<ov::op::v15::EmbeddingBagOffsets>(weight,
                                                                                           indices,
                                                                                           offsets,
-                                                                                          zero,
+                                                                                          neg_one,
                                                                                           per_sample_weight,
                                                                                           reduction));
         }
diff --git a/src/frontends/pytorch/src/op/im2col.cpp b/src/frontends/pytorch/src/op/im2col.cpp
index 4884c813794be3..1347a3d72d15ad 100644
--- a/src/frontends/pytorch/src/op/im2col.cpp
+++ b/src/frontends/pytorch/src/op/im2col.cpp
@@ -60,32 +60,31 @@ OutputVector translate_im2col(const NodeContext& context) {
     num_inputs_check(context, 5, 5);
     auto input = context.get_input(0);
     auto kernel_size = context.const_input<std::vector<int64_t>>(1);
-    PYTORCH_OP_CONVERSION_CHECK(kernel_size.size() == 2, "kernel size should contains 2 elements");
+    PYTORCH_OP_CONVERSION_CHECK(kernel_size.size() == 2, "kernel size should contain 2 elements");
     auto dilation = context.const_input<std::vector<int64_t>>(2);
-    PYTORCH_OP_CONVERSION_CHECK(kernel_size.size() == 2, "dilation should contains 2 elements");
+    PYTORCH_OP_CONVERSION_CHECK(dilation.size() == 2, "dilation should contain 2 elements");
     auto padding = context.const_input<std::vector<int64_t>>(3);
-    PYTORCH_OP_CONVERSION_CHECK(kernel_size.size() == 2, "padding should contains 2 elements");
+    PYTORCH_OP_CONVERSION_CHECK(padding.size() == 2, "padding should contain 2 elements");
     auto stride = context.const_input<std::vector<int64_t>>(4);
-    PYTORCH_OP_CONVERSION_CHECK(kernel_size.size() == 2, "stride should contains 2 elements");
+    PYTORCH_OP_CONVERSION_CHECK(stride.size() == 2, "stride should contain 2 elements");
     auto zero = context.mark_node(v0::Constant::create(element::i32, Shape{}, {0}));
     auto input_shape = context.mark_node(std::make_shared<v3::ShapeOf>(input, element::i32));
-    auto zero_f = context.mark_node(v0::Constant::create(element::f32, Shape{}, {0}));
     auto minus_one = context.mark_node(v0::Constant::create(element::i32, Shape{1}, {-1}));
     auto two = context.mark_node(v0::Constant::create(element::i32, Shape{}, {2}));
     auto four = context.mark_node(v0::Constant::create(element::i32, Shape{}, {4}));
     auto input_shape_split = context.mark_node(std::make_shared<v1::Split>(input_shape, zero, 4));
-    auto input_b = input_shape_split->output(0);
-    auto input_c = input_shape_split->output(1);
-    auto input_h = input_shape_split->output(2);
-    auto input_w = input_shape_split->output(3);
-    auto stride_h = stride[0];
-    auto stride_w = stride[1];
-    auto padding_h = padding[0];
-    auto padding_w = padding[1];
-    auto dilation_h = dilation[0];
-    auto dilation_w = dilation[1];
-    auto kernel_h = kernel_size[0];
-    auto kernel_w = kernel_size[1];
+    const auto& input_b = input_shape_split->output(0);
+    const auto& input_c = input_shape_split->output(1);
+    const auto& input_h = input_shape_split->output(2);
+    const auto& input_w = input_shape_split->output(3);
+    const auto& stride_h = stride[0];
+    const auto& stride_w = stride[1];
+    const auto& padding_h = padding[0];
+    const auto& padding_w = padding[1];
+    const auto& dilation_h = dilation[0];
+    const auto& dilation_w = dilation[1];
+    const auto& kernel_h = kernel_size[0];
+    const auto& kernel_w = kernel_size[1];
     auto blocks_row_indices = get_im2col_indices_along_dim(context, input_h, kernel_h, dilation_h, padding_h, stride_h);
     auto blocks_col_indices = get_im2col_indices_along_dim(context, input_w, kernel_w, dilation_w, padding_w, stride_w);
     auto kernel_window = context.mark_node(v0::Constant::create(element::i32, Shape{}, {kernel_h * kernel_w}));
@@ -96,6 +95,7 @@ OutputVector translate_im2col(const NodeContext& context) {
         std::make_shared<v0::Concat>(OutputVector{input_b, channel_unfolded_unsqueezed, minus_one}, 0));
     auto pads = context.mark_node(
         v0::Constant::create(element::i32, Shape{4}, std::vector<int64_t>{0, 0, padding_h, padding_w}));
+    auto zero_f = context.mark_node(std::make_shared<v1::ConvertLike>(zero, input));
     auto padded_input =
         context.mark_node(std::make_shared<v1::Pad>(input, pads, pads, zero_f, ov::op::PadMode::CONSTANT));
     auto output = context.mark_node(std::make_shared<v8::Gather>(padded_input, blocks_row_indices, two));
diff --git a/src/frontends/pytorch/src/op/max_poolnd.cpp b/src/frontends/pytorch/src/op/max_poolnd.cpp
index f31b1c108ad48b..b6a01af1a7c2df 100644
--- a/src/frontends/pytorch/src/op/max_poolnd.cpp
+++ b/src/frontends/pytorch/src/op/max_poolnd.cpp
@@ -32,8 +32,7 @@ OutputVector translate_max_poolnd(const NodeContext& context) {
     if (!context.input_is_none(2)) {
         strides = context.const_input<Strides>(2);
     }
-    const bool use_kernel = context.input_is_none(2) || (strides.size() == 0);
-    if (use_kernel) {
+    if (context.input_is_none(2) || strides.size() == 0) {
         // In case strides are not provided default is kernel
         strides = kernel;
     }
@@ -51,68 +50,19 @@ OutputVector translate_max_poolnd(const NodeContext& context) {
     if (context.input_is_none(5)) {
         rounding_type = RoundingType::FLOOR;
     } else {
-        rounding_type = context.const_input<bool>(5) ? RoundingType::CEIL : RoundingType::FLOOR;
+        rounding_type = context.const_input<bool>(5) ? RoundingType::CEIL_TORCH : RoundingType::FLOOR;
     }
 
-    auto input = context.get_input(0);
-    if (rounding_type == RoundingType::CEIL) {
-        // The corner case of Max Pooling with ceil_mode on
-        // PyTorch allows sliding window go off bound, which leads to this accommodation.
-        // More detail on https://github.com/pytorch/pytorch/issues/57178
-        const auto zero = context.mark_node(v0::Constant::create(element::i32, Shape{}, {0}));
-        const auto one = context.mark_node(v0::Constant::create(element::i32, Shape{}, {1}));
-        const auto two = context.mark_node(v0::Constant::create(element::i32, Shape{}, {2}));
-
-        const auto& padding =
-            context.input_is_none(3)
-                ? context.mark_node(std::make_shared<v0::Constant>(element::i32, Shape{pads.size()}, 0))->output(0)
-                : get_input_as_i32(context, 3);
-        const auto pads_len = context.mark_node(v0::Constant::create(element::i32, Shape{}, {pads.size()}));
-        const auto pads_remaining = context.mark_node(v0::Constant::create(element::i32, Shape{2}, {0, 0}));
-
-        // gather input spatial dims and prepare for compare as values (in_dim + pad)
-        const auto input_shape_rank = get_shape_rank(context, input);
-        const auto end = context.mark_node(v0::Constant::create(element::i32, Shape{}, {pads.size() + 2}));
-        const auto dim_idxs = context.mark_node(std::make_shared<v4::Range>(two, end, one, element::i32));
-        const auto gth_in_dims =
-            context.mark_node(std::make_shared<v8::Gather>(std::get<0>(input_shape_rank), dim_idxs, zero));
-        const auto in_left_padded = context.mark_node(std::make_shared<v1::Add>(gth_in_dims, padding));
-
-        // gather output spatial dims and prepare it for compare as values (out_dim - 1) * stride
-        const auto mp = context.mark_node(
-            std::make_shared<v8::MaxPool>(input, strides, dilations, pads, pads, kernel, rounding_type));
-        const auto shape_of_mp = context.mark_node(std::make_shared<v3::ShapeOf>(mp, element::i32));
-        const auto gth_out_dims = context.mark_node(std::make_shared<v8::Gather>(shape_of_mp, dim_idxs, zero));
-        const auto out_sub_one = context.mark_node(std::make_shared<v1::Subtract>(gth_out_dims, one));
-        const auto& stride_node = use_kernel ? context.get_input(1) : context.get_input(2);
-        const auto stride_node_i32 = context.mark_node(std::make_shared<v0::Convert>(stride_node, element::i32));
-        const auto out_mul_stride = context.mark_node(std::make_shared<v1::Multiply>(out_sub_one, stride_node_i32));
-
-        // if (in_dim + pad) > ((out_dim - 1) * stride) sliding window in bound use end padding.
-        const auto in_gt_out = context.mark_node(std::make_shared<v1::Greater>(in_left_padded, out_mul_stride));
-        const auto selected_pads = context.mark_node(std::make_shared<v1::Select>(in_gt_out, padding, zero));
-
-        // apply padding on input clear pads attribute
-        const auto pb = context.mark_node(std::make_shared<v0::Concat>(OutputVector{pads_remaining, padding}, 0));
-        const auto& pe =
-            context.mark_node(std::make_shared<v0::Concat>(OutputVector{pads_remaining, selected_pads}, 0));
-        auto minus_inf =
-            context.mark_node(v0::Constant::create(element::f32, Shape{}, {-std::numeric_limits<float>::infinity()}));
-        minus_inf = context.mark_node(std::make_shared<v1::ConvertLike>(minus_inf, input));
-        input = context.mark_node(std::make_shared<v12::Pad>(input, pb, pe, minus_inf, op::PadMode::CONSTANT));
-        std::fill_n(pads.begin(), pads.size(), 0);
-    }
-
-    auto res = context.mark_node(std::make_shared<v8::MaxPool>(input,
-                                                               strides,
-                                                               dilations,
-                                                               pads,
-                                                               pads,
-                                                               kernel,
-                                                               rounding_type,
-                                                               PadType::EXPLICIT,
-                                                               element::i64,
-                                                               2));
+    auto res = context.mark_node(std::make_shared<v14::MaxPool>(context.get_input(0),
+                                                                strides,
+                                                                dilations,
+                                                                pads,
+                                                                pads,
+                                                                kernel,
+                                                                rounding_type,
+                                                                PadType::EXPLICIT,
+                                                                element::i64,
+                                                                2));
     if (context.get_output_size() == 2) {
         auto out1 = res->output(0);
         auto out2 = res->output(1);
diff --git a/src/frontends/pytorch/src/op_table.cpp b/src/frontends/pytorch/src/op_table.cpp
index 9cc73f854bbf6a..5e00395614f31d 100644
--- a/src/frontends/pytorch/src/op_table.cpp
+++ b/src/frontends/pytorch/src/op_table.cpp
@@ -57,6 +57,7 @@ OP_CONVERTER(translate_channel_shuffle);
 OP_CONVERTER(translate_clamp);
 OP_CONVERTER(translate_constant);
 OP_CONVERTER(translate_conv_transposend);
+OP_CONVERTER(translate_conv1d_ext);
 OP_CONVERTER(translate_convnd);
 OP_CONVERTER(translate_convolution);
 OP_CONVERTER(translate_convolution_mode);
@@ -72,6 +73,7 @@ OP_CONVERTER(translate_dot);
 OP_CONVERTER(translate_elu);
 OP_CONVERTER(translate_embedding);
 OP_CONVERTER(translate_embedding_bag);
+OP_CONVERTER(translate_embedding_ext);
 OP_CONVERTER(translate_empty);
 OP_CONVERTER(translate_empty_like);
 OP_CONVERTER(translate_erf);
@@ -702,6 +704,9 @@ const std::map<std::string, CreatorFunction> get_supported_ops_ts() {
         {"aten::zero_", op::inplace_op<op::translate_zeros_like>},
         {"aten::zeros", op::translate_zeros},
         {"aten::zeros_like", op::translate_zeros_like},
+        {"ov_ext::embedding", op::translate_embedding_ext},
+        {"ov_ext::conv1d", op::translate_conv1d_ext},
+        {"ov_ext::linear", op::translate_linear},
         {"prim::Constant", op::translate_constant},
         {"prim::device", op::translate_constant},
         // prim::DictConstruct - Supported in limited set of patterns
diff --git a/src/frontends/pytorch/src/place.cpp b/src/frontends/pytorch/src/place.cpp
index 968cad19988dd6..6a6886b1b97210 100644
--- a/src/frontends/pytorch/src/place.cpp
+++ b/src/frontends/pytorch/src/place.cpp
@@ -25,6 +25,7 @@ Place::Place(const ov::frontend::InputModel& input_model, size_t tensor_index)
     auto in_it = std::find(inputs.begin(), inputs.end(), tensor_index);
     if (in_it != inputs.end()) {
         m_is_input = true;
+        m_input_index = std::distance(inputs.begin(), in_it);
         auto idx = std::distance(inputs.begin(), in_it);
         const auto& signature_name = decoder->get_input_signature_name(idx);
         m_names.push_back(signature_name);
@@ -55,6 +56,31 @@ Place::Place(const ov::frontend::InputModel& input_model, size_t tensor_index)
     }
 }
 
+Place::Place(const ov::frontend::InputModel& input_model, const std::string& name, size_t input_index)
+    : m_input_model(input_model),
+      m_tensor_index(0),
+      m_is_fake(true),
+      m_input_index(input_index),
+      m_pshape(PartialShape::dynamic()),
+      m_type(element::dynamic),
+      m_is_input(true) {
+    if (!name.empty())
+        m_names = {name};
+}
+
+bool Place::is_equal(const Ptr& another) const {
+    const auto& pt_place = std::dynamic_pointer_cast<pytorch::Place>(another);
+    if (!pt_place)
+        return this == another.get();
+    if (m_is_fake || pt_place->m_is_fake) {
+        if ((m_is_fake && m_names.size() != 0) || (pt_place->m_is_fake && pt_place->m_names.size() != 0))
+            // named fake place can only be equal to itself
+            return this == another.get();
+        return m_input_index == pt_place->get_input_index();
+    }
+    return this == another.get();
+}
+
 }  // namespace pytorch
 }  // namespace frontend
 }  // namespace ov
diff --git a/src/frontends/pytorch/src/place.hpp b/src/frontends/pytorch/src/place.hpp
index c823a126516aa6..e286861fc2d4fd 100644
--- a/src/frontends/pytorch/src/place.hpp
+++ b/src/frontends/pytorch/src/place.hpp
@@ -19,6 +19,8 @@ class Place : public ov::frontend::Place {
 
 public:
     Place(const ov::frontend::InputModel& input_model, size_t tensor_index);
+    // Fake place of the input. If name is non-empty it is used as id of the input, otherwise input_index is used
+    Place(const ov::frontend::InputModel& input_model, const std::string& name, size_t input_index);
 
     ~Place() override = default;
 
@@ -28,9 +30,7 @@ class Place : public ov::frontend::Place {
     bool is_output() const override {
         return m_is_output;
     }
-    bool is_equal(const Ptr& another) const override {
-        return this == another.get();
-    }
+    bool is_equal(const Ptr& another) const override;
     std::vector<std::string> get_names() const override {
         return m_names;
     }
@@ -50,13 +50,18 @@ class Place : public ov::frontend::Place {
         }
         return m_tensor_index == another_pt->get_tensor_index();
     }
+    size_t get_input_index() const {
+        return m_input_index;
+    }
 
 private:
     const ov::frontend::InputModel& m_input_model;
     const size_t m_tensor_index;
     std::vector<std::string> m_names;
-    element::Type m_type = element::dynamic;
+    bool m_is_fake = false;
+    size_t m_input_index = 0;  // Only used for fake place
     PartialShape m_pshape;
+    element::Type m_type = element::dynamic;
     bool m_is_input = false;
     bool m_is_output = false;
 };
diff --git a/src/frontends/pytorch/src/translate_session.cpp b/src/frontends/pytorch/src/translate_session.cpp
index 60410f97c33df4..e2c41e70a0254a 100644
--- a/src/frontends/pytorch/src/translate_session.cpp
+++ b/src/frontends/pytorch/src/translate_session.cpp
@@ -82,9 +82,10 @@ std::shared_ptr<Model> TranslateSession::convert_pytorch_model(
         auto mutated_tensors = std::make_shared<std::set<size_t>>();
         std::vector<size_t> inserted_params;
 
-        if (input_model) {
+        if (input_model && input_model->m_requested_places.size() == 0) {
             // When we have input model we should use its inputs order to create Parameters
             // We use m_inputs instead of get_inputs() because latter doesn't have "self" input
+            // If there are fake places we don't need to use model inputs in that case
             for (auto& input_p : input_model->m_inputs) {
                 auto pytorch_place = std::dynamic_pointer_cast<pytorch::Place>(input_p);
                 FRONT_END_GENERAL_CHECK(pytorch_place, "Only place produced by PyTorch Frontend is supported.");
@@ -98,10 +99,6 @@ std::shared_ptr<Model> TranslateSession::convert_pytorch_model(
                 parameters->push_back(parameter);
                 (*tensor_map)[tensor_id] = parameter;
             }
-            // Add all tensors that were frozen
-            for (auto& desc : input_model->m_descriptors) {
-                (*tensor_map)[desc.first] = desc.second.m_value;
-            }
         } else {
             // Go over all pytorch_model inputs and register them in the tensor map:
             auto inputs = pytorch_model->inputs();
@@ -120,6 +117,12 @@ std::shared_ptr<Model> TranslateSession::convert_pytorch_model(
                 (*tensor_map)[inputs.at(i)] = parameter;
             }
         }
+        if (input_model) {
+            // Add all tensors that were frozen
+            for (auto& desc : input_model->m_descriptors) {
+                (*tensor_map)[desc.first] = desc.second.m_value;
+            }
+        }
 
         auto node_visitor = [&](std::shared_ptr<TorchDecoder> node) {
             // Explore all inputs of node. Node may refer to input value that hasn't been created in the current scope.
diff --git a/src/frontends/tensorflow/src/frontend.cpp b/src/frontends/tensorflow/src/frontend.cpp
index dfc7e98db5e6b2..e2e49355e914f0 100644
--- a/src/frontends/tensorflow/src/frontend.cpp
+++ b/src/frontends/tensorflow/src/frontend.cpp
@@ -13,6 +13,7 @@
 #include "helper_transforms/embedding_segments_feature_fusing.hpp"
 #include "helper_transforms/saved_model_unused_remover.hpp"
 #include "helper_transforms/tensor_array_v3_replacer.hpp"
+#include "helper_transforms/tensor_list_ops_resolver.hpp"
 #include "input_model.hpp"
 #include "op_table.hpp"
 #include "openvino/core/so_extension.hpp"
@@ -26,6 +27,7 @@
 #include "openvino/util/file_util.hpp"
 #include "openvino/util/log.hpp"
 #include "tf_framework_node.hpp"
+#include "transformations/common_optimizations/eliminate_loop_inputs_outputs.hpp"
 #include "transformations/common_optimizations/remove_concat_zero_dim_input.hpp"
 #include "transformations/common_optimizations/reverse_shape_and_type_infer.hpp"
 #include "transformations/control_flow/unroll_if.hpp"
@@ -280,9 +282,8 @@ ov::frontend::InputModel::Ptr FrontEnd::load_impl(const std::vector<ov::Any>& va
                                                 HashTableKeysValuesMap{},
                                                 graph_iterator->get_checkpoint_v1_reader(),
                                                 false);
-        }
-        auto saved_model_tags = paths[1];
-        if (GraphIteratorSavedModel::is_supported(model_path)) {
+        } else if (GraphIteratorSavedModel::is_supported(model_path)) {
+            auto saved_model_tags = paths[1];
             std::shared_ptr<GraphIteratorSavedModel> graph_iterator;
             graph_iterator = std::make_shared<GraphIteratorSavedModel>(model_path, saved_model_tags, mmap_enabled);
             return std::make_shared<InputModel>(graph_iterator,
@@ -567,6 +568,16 @@ void FrontEnd::normalize(const std::shared_ptr<ov::Model>& model) const {
     manager.register_pass<pass::TensorArrayV3Replacer>();
     manager.register_pass<pass::ConstToResultRemover>();
     manager.register_pass<pass::SwitchMergeResolver>();
+
+    // apply EliminateLoopInputsOutputs to avoid extra Results
+    // that output the same value as receiving on input
+    // it is needed for applying TensorListInLoopOptimization
+    manager.register_pass<ov::pass::EliminateLoopInputsOutputs>();
+    manager.register_pass<pass::TensorListReplacer>();
+    manager.register_pass<pass::TensorListInLoopOptimization>();
+    manager.register_pass<pass::TensorListSetItemReplacer>();
+    manager.register_pass<pass::TensorListGetItemReplacer>();
+
     manager.register_pass<ov::pass::UnrollIf>();
     manager.register_pass<ov::pass::RemoveConcatZeroDimInput>();
     manager.register_pass<ov::pass::TransposeSinkingGeneral>();
diff --git a/src/frontends/tensorflow/src/graph_iterator_meta.hpp b/src/frontends/tensorflow/src/graph_iterator_meta.hpp
index 6a2a7b30013203..b5f79292edc388 100644
--- a/src/frontends/tensorflow/src/graph_iterator_meta.hpp
+++ b/src/frontends/tensorflow/src/graph_iterator_meta.hpp
@@ -45,6 +45,10 @@ class GraphIteratorMeta : public GraphIteratorProto {
 
     template <typename T>
     static bool is_supported(const std::basic_string<T>& path) {
+        FRONT_END_GENERAL_CHECK(util::directory_exists(path) || util::file_exists(path),
+                                "Could not open the file: \"",
+                                util::path_to_string(path),
+                                '"');
         try {
             std::ifstream mg_stream(path.c_str(), std::ios::in | std::ifstream::binary);
             auto metagraph_def = std::make_shared<::tensorflow::MetaGraphDef>();
diff --git a/src/frontends/tensorflow/src/graph_iterator_proto.hpp b/src/frontends/tensorflow/src/graph_iterator_proto.hpp
index 9559f620cb87ef..5d0c717af87e8f 100644
--- a/src/frontends/tensorflow/src/graph_iterator_proto.hpp
+++ b/src/frontends/tensorflow/src/graph_iterator_proto.hpp
@@ -16,6 +16,7 @@
 #include "openvino/frontend/exception.hpp"
 #include "openvino/frontend/graph_iterator.hpp"
 #include "openvino/frontend/tensorflow/decoder.hpp"
+#include "openvino/util/file_util.hpp"
 #include "ov_tensorflow/graph.pb.h"
 
 namespace ov {
@@ -141,6 +142,10 @@ class GraphIteratorProto : public GraphIterator {
     /// \brief Check if the input file is supported
     template <typename T>
     static bool is_supported(const std::basic_string<T>& path) {
+        FRONT_END_GENERAL_CHECK(util::directory_exists(path) || util::file_exists(path),
+                                "Could not open the file: \"",
+                                util::path_to_string(path),
+                                '"');
         try {
 #if defined(__MINGW32__) || defined(__MINGW64__)
             std::ifstream pb_stream(std::filesystem::path(path), std::ios::in | std::ifstream::binary);
diff --git a/src/frontends/tensorflow/src/graph_iterator_proto_txt.hpp b/src/frontends/tensorflow/src/graph_iterator_proto_txt.hpp
index a469b9ba577bc4..bd3383afd93253 100644
--- a/src/frontends/tensorflow/src/graph_iterator_proto_txt.hpp
+++ b/src/frontends/tensorflow/src/graph_iterator_proto_txt.hpp
@@ -13,6 +13,7 @@
 #include "google/protobuf/text_format.h"
 #include "graph_iterator_proto.hpp"
 #include "openvino/frontend/exception.hpp"
+#include "openvino/util/file_util.hpp"
 
 namespace ov {
 namespace frontend {
@@ -59,6 +60,10 @@ class GraphIteratorProtoTxt : public GraphIteratorProto {
     /// \brief Check if the input file is supported
     template <typename T>
     static bool is_supported(const std::basic_string<T>& path) {
+        FRONT_END_GENERAL_CHECK(util::directory_exists(path) || util::file_exists(path),
+                                "Could not open the file: \"",
+                                util::path_to_string(path),
+                                '"');
         try {
             std::ifstream pbtxt_stream(path.c_str(), std::ios::in);
             bool model_exists = (pbtxt_stream && pbtxt_stream.is_open());
diff --git a/src/frontends/tensorflow/src/graph_iterator_saved_model.cpp b/src/frontends/tensorflow/src/graph_iterator_saved_model.cpp
index cba31db4c5e3c2..61a074cf110418 100644
--- a/src/frontends/tensorflow/src/graph_iterator_saved_model.cpp
+++ b/src/frontends/tensorflow/src/graph_iterator_saved_model.cpp
@@ -30,7 +30,15 @@ bool GraphIteratorSavedModel::is_valid_signature(const ::tensorflow::SignatureDe
 }
 
 bool GraphIteratorSavedModel::is_supported(const std::string& path) {
-    return ov::util::directory_exists(path) && ov::util::file_exists(ov::util::path_join({path, "saved_model.pb"}));
+    if (ov::util::directory_exists(path)) {
+        FRONT_END_GENERAL_CHECK(util::file_exists(ov::util::path_join({path, "saved_model.pb"})),
+                                "Could not open the file: \"",
+                                util::path_to_string(ov::util::path_join({path, "saved_model.pb"})),
+                                '"');
+        return true;
+    } else {
+        return false;
+    }
 }
 
 #if defined(OPENVINO_ENABLE_UNICODE_PATH_SUPPORT) && defined(_WIN32)
diff --git a/src/frontends/tensorflow/src/input_model.cpp b/src/frontends/tensorflow/src/input_model.cpp
index 14474eec64125a..5f0a8acf9e64e4 100644
--- a/src/frontends/tensorflow/src/input_model.cpp
+++ b/src/frontends/tensorflow/src/input_model.cpp
@@ -824,6 +824,10 @@ ov::frontend::Place::Ptr InputModel::get_place_by_tensor_name(const std::string&
     return _impl->get_place_by_tensor_name(tensorName);
 }
 
+ov::frontend::Place::Ptr InputModel::get_place_by_input_index(size_t input_idx) const {
+    FRONT_END_NOT_IMPLEMENTED(get_place_by_input_index);
+}
+
 void InputModel::override_all_outputs(const std::vector<ov::frontend::Place::Ptr>& outputs) {
     _impl->override_all_outputs(outputs);
 }
diff --git a/src/frontends/tensorflow/src/input_model.hpp b/src/frontends/tensorflow/src/input_model.hpp
index 11f4033dcf4384..39847c12d5fd88 100644
--- a/src/frontends/tensorflow/src/input_model.hpp
+++ b/src/frontends/tensorflow/src/input_model.hpp
@@ -44,6 +44,7 @@ class InputModel : public ov::frontend::InputModel {
     std::vector<ov::frontend::Place::Ptr> get_inputs() const override;
     std::vector<ov::frontend::Place::Ptr> get_outputs() const override;
     ov::frontend::Place::Ptr get_place_by_tensor_name(const std::string& tensorName) const override;
+    ov::frontend::Place::Ptr get_place_by_input_index(size_t input_idx) const override;
     void override_all_outputs(const std::vector<ov::frontend::Place::Ptr>& outputs) override;
     void override_all_inputs(const std::vector<ov::frontend::Place::Ptr>& inputs) override;
     void extract_subgraph(const std::vector<ov::frontend::Place::Ptr>& inputs,
diff --git a/src/frontends/tensorflow/src/op/block_lstm.cpp b/src/frontends/tensorflow/src/op/block_lstm.cpp
index 663f50794476c5..7498f6e2702316 100644
--- a/src/frontends/tensorflow/src/op/block_lstm.cpp
+++ b/src/frontends/tensorflow/src/op/block_lstm.cpp
@@ -71,7 +71,7 @@ void create_decomposed_block_lstm(const Output<Node>& x,
     auto squeeze_axis = std::make_shared<v0::Constant>(element::i32, Shape{1}, 0);
     auto xi = std::make_shared<v0::Squeeze>(xi_param, squeeze_axis);
 
-    auto lstm_cell = std::make_shared<v0::LSTMCell>(xi,
+    auto lstm_cell = std::make_shared<v4::LSTMCell>(xi,
                                                     h_prev_param,
                                                     c_prev_param,
                                                     w_param,
diff --git a/src/frontends/tensorflow/src/op_table.cpp b/src/frontends/tensorflow/src/op_table.cpp
index 9c97debd0b89c0..f62d55a05fc520 100644
--- a/src/frontends/tensorflow/src/op_table.cpp
+++ b/src/frontends/tensorflow/src/op_table.cpp
@@ -247,7 +247,7 @@ const std::map<std::string, CreatorFunction> get_supported_ops() {
         {"DynamicPartition", CreatorFunction(translate_dynamic_partition_op)},
         {"Einsum", CreatorFunction(translate_einsum_op)},
         {"Elu", CreatorFunction(translate_elu_op)},
-        {"EmptyTensorList", CreatorFunction(translate_tensor_list_reserve_op)},
+        {"EmptyTensorList", CreatorFunction(translate_empty_tensor_list_op)},
         {"EnsureShape", CreatorFunction(translate_identity_op)},
         {"ExpandDims", CreatorFunction(translate_expand_dims_op)},
         {"ExtractImagePatches", CreatorFunction(translate_extract_image_patches_op)},
diff --git a/src/frontends/tensorflow/src/variables_index.cpp b/src/frontends/tensorflow/src/variables_index.cpp
index 746604e1567c06..778f8b2f94bb7c 100644
--- a/src/frontends/tensorflow/src/variables_index.cpp
+++ b/src/frontends/tensorflow/src/variables_index.cpp
@@ -190,7 +190,7 @@ bool VariablesIndex::read_variables(std::ifstream& vi_stream, const std::string&
     read_variables_index(vi_stream, m_variables_index);
     read_bundle_header();
 
-    std::vector<char> suffix(20);
+    std::vector<char> suffix(32);
     for (int32_t shard = 0; shard < m_total_shards; ++shard) {
         std::snprintf(suffix.data(), suffix.size(), "data-%05d-of-%05d", shard, m_total_shards);
         std::string fullPath;
diff --git a/src/frontends/tensorflow/tests/convert_tricky_models.cpp b/src/frontends/tensorflow/tests/convert_tricky_models.cpp
index c4e93ce4656981..73320450ddca04 100644
--- a/src/frontends/tensorflow/tests/convert_tricky_models.cpp
+++ b/src/frontends/tensorflow/tests/convert_tricky_models.cpp
@@ -420,20 +420,10 @@ TEST_F(FrontEndConversionWithReferenceTestsF, ModelWithEmptyTensorListAndPushBac
     { model = convert_model("empty_tensor_list/empty_tensor_list.pb"); }
     {
         auto x = make_shared<v0::Parameter>(f32, Shape{2, 3, 5});
-        auto minus_one_const = make_shared<v0::Constant>(i32, Shape{1}, -1);
-        auto x_flatten = make_shared<v1::Reshape>(x, minus_one_const, false);
-        auto zero_const = make_shared<v0::Constant>(i32, Shape{1}, 0);
-        auto x_unsqueeze_flatten = make_shared<v0::Unsqueeze>(x_flatten, zero_const);
-        auto list_push_back = make_shared<v0::Concat>(OutputVector{x_unsqueeze_flatten}, 0);
-        auto list_push_back_shape = make_shared<v3::ShapeOf>(list_push_back, element::i32);
-        auto start = make_shared<v0::Constant>(i32, Shape{1}, 0);
-        auto stop = make_shared<v0::Constant>(i32, Shape{1}, 1);
-        auto step = make_shared<v0::Constant>(i32, Shape{1}, 1);
-        auto batch = make_shared<v8::Slice>(list_push_back_shape, start, stop, step);
-        auto shape_without_batch = make_shared<v0::Constant>(i32, Shape{3}, vector<int32_t>{2, 3, 5});
-        auto recover_item_shape = make_shared<v0::Concat>(OutputVector{batch, shape_without_batch}, 0);
-        auto recover_item = make_shared<v1::Reshape>(list_push_back, recover_item_shape, false);
-        model_ref = make_shared<Model>(OutputVector{recover_item}, ParameterVector{x});
+        auto axes = make_shared<v0::Constant>(i32, Shape{1}, 0);
+        auto x_unsqueeze = make_shared<v0::Unsqueeze>(x, axes);
+        auto list_push_back = make_shared<v0::Concat>(OutputVector{x_unsqueeze}, 0);
+        model_ref = make_shared<Model>(OutputVector{list_push_back}, ParameterVector{x});
     }
     comparator.disable(FunctionsComparator::CmpValues::ATTRIBUTES);
 }
diff --git a/src/frontends/tensorflow_common/include/common_op_table.hpp b/src/frontends/tensorflow_common/include/common_op_table.hpp
index 784cd332794224..e617aae601bf58 100644
--- a/src/frontends/tensorflow_common/include/common_op_table.hpp
+++ b/src/frontends/tensorflow_common/include/common_op_table.hpp
@@ -160,6 +160,7 @@ OP_CONVERTER(translate_square_op);
 OP_CONVERTER(translate_squeeze_op);
 OP_CONVERTER(translate_strided_slice_op);
 OP_CONVERTER(translate_sqrt_op);
+OP_CONVERTER(translate_empty_tensor_list_op);
 OP_CONVERTER(translate_tensor_list_from_tensor_op);
 OP_CONVERTER(translate_tensor_list_get_item_op);
 OP_CONVERTER(translate_tensor_list_length_op);
diff --git a/src/frontends/tensorflow_common/include/helper_ops/tensor_list_ops.hpp b/src/frontends/tensorflow_common/include/helper_ops/tensor_list_ops.hpp
new file mode 100644
index 00000000000000..71837180c5b21a
--- /dev/null
+++ b/src/frontends/tensorflow_common/include/helper_ops/tensor_list_ops.hpp
@@ -0,0 +1,185 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <vector>
+
+#include "internal_operation.hpp"
+#include "openvino/op/constant.hpp"
+
+namespace ov {
+namespace frontend {
+namespace tensorflow {
+
+// Internal operation for TensorList that represents a initial state of tensor list container
+class TensorList : public InternalOperation {
+public:
+    OPENVINO_OP("TensorList", "ov::frontend::tensorflow", InternalOperation);
+
+    TensorList(const ov::Output<ov::Node>& num_elements,
+               const ov::Rank& element_rank,
+               const element::Type& element_dtype,
+               const std::shared_ptr<DecoderBase>& decoder = std::make_shared<DecoderFake>())
+        : InternalOperation(decoder, OutputVector{num_elements}, 1, "TensorList"),
+          m_num_elements(num_elements),
+          m_element_rank(element_rank),
+          m_element_dtype(element_dtype) {
+        validate_and_infer_types();
+    }
+
+    void validate_and_infer_types() override {
+        if (m_element_rank.is_static()) {
+            auto element_rank = m_element_rank.get_length();
+            auto output_shape = ov::PartialShape::dynamic(element_rank + 1);
+            set_output_type(0, m_element_dtype, output_shape);
+        }
+
+        set_output_type(0, m_element_dtype, ov::PartialShape::dynamic());
+    }
+
+    ov::element::Type get_element_type() const {
+        return m_element_dtype;
+    }
+
+    std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& inputs) const override {
+        FRONT_END_OP_CONVERSION_CHECK(inputs.size() == 1,
+                                      "[TensorFlow Frontend] internal error: TensorList expects no inputs");
+        auto tensor_list_node = std::make_shared<TensorList>(inputs[0], m_element_rank, m_element_dtype, m_decoder);
+        tensor_list_node->set_attrs(get_attrs());
+        return tensor_list_node;
+    }
+
+    ov::Rank get_element_rank() const {
+        return m_element_rank;
+    }
+
+    void set_element_rank(const ov::Rank& element_rank) {
+        m_element_rank = element_rank;
+    }
+
+    ov::Output<ov::Node> get_num_elements() const {
+        return m_num_elements;
+    }
+
+private:
+    ov::Output<ov::Node> m_num_elements;
+    ov::Rank m_element_rank;
+    ov::element::Type m_element_dtype;
+};
+
+// Internal operation for TensorListGetItem
+// it gets an element (Tensor) in tensor list by index
+class TensorListGetItem : public InternalOperation {
+public:
+    OPENVINO_OP("TensorListGetItem", "ov::frontend::tensorflow", InternalOperation);
+
+    TensorListGetItem(const Output<Node>& input_handle,
+                      const Output<Node>& index,
+                      const Output<Node>& element_shape,
+                      const ov::element::Type& element_type,
+                      const std::shared_ptr<DecoderBase>& decoder = std::make_shared<DecoderFake>())
+        : InternalOperation(decoder, OutputVector{input_handle, index, element_shape}, 1, "TensorListGetItem"),
+          m_element_type(element_type) {
+        validate_and_infer_types();
+    }
+
+    void validate_and_infer_types() override {
+        // deduce an element (Tensor) shape
+        ov::PartialShape comp_element_shape = ov::PartialShape::dynamic();
+        if (const auto& const_element_shape =
+                ov::as_type_ptr<ov::op::v0::Constant>(input_value(2).get_node_shared_ptr())) {
+            auto element_shape_value = const_element_shape->get_vector<int32_t>();
+            comp_element_shape = ov::PartialShape::dynamic(static_cast<int64_t>(element_shape_value.size()));
+            for (size_t idx = 0; idx < element_shape_value.size(); ++idx) {
+                comp_element_shape[idx] = (element_shape_value[idx] >= 0)
+                                              ? static_cast<int64_t>(element_shape_value[idx])
+                                              : ov::Dimension::dynamic();
+            }
+        } else if (input_value(0).get_partial_shape().rank().is_static()) {
+            // the second try to deduce element shape if it is still of dynamic rank
+            auto tensor_list_rank = input_value(0).get_partial_shape().rank().get_length();
+            OPENVINO_ASSERT(
+                tensor_list_rank > 0,
+                "[TensorFlow Frontend] internal error or inconsistent model: tensor list rank must be greater than 0");
+            // exclude tensor dimension (or batch)
+            comp_element_shape = ov::PartialShape::dynamic(tensor_list_rank - 1);
+            for (int64_t idx = 1; idx < tensor_list_rank; ++idx) {
+                comp_element_shape[idx - 1] = input_value(0).get_partial_shape()[idx];
+            }
+        }
+
+        // deduce an element (Tensor) type
+        if (m_element_type.is_dynamic() && input_value(0).get_element_type().is_static()) {
+            m_element_type = input_value(0).get_element_type();
+        }
+
+        set_output_type(0, m_element_type, comp_element_shape);
+    }
+
+    ov::element::Type get_element_type() const {
+        return m_element_type;
+    }
+
+    std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& inputs) const override {
+        FRONT_END_OP_CONVERSION_CHECK(inputs.size() == 3,
+                                      "[TensorFlow Frontend] internal error: TensorListGetItem expects three inputs");
+        auto tensor_list_get_item =
+            std::make_shared<TensorListGetItem>(inputs[0], inputs[1], inputs[2], m_element_type, m_decoder);
+        tensor_list_get_item->set_attrs(get_attrs());
+        return tensor_list_get_item;
+    }
+
+private:
+    ov::element::Type m_element_type;
+};
+
+// Internal operation for TensorListSetItem
+// it inserts tensor to tensor list by index
+class TensorListSetItem : public InternalOperation {
+public:
+    OPENVINO_OP("TensorListSetItem", "ov::frontend::tensorflow", InternalOperation);
+
+    TensorListSetItem(const Output<Node>& input_handle,
+                      const Output<Node>& index,
+                      const Output<Node>& item,
+                      const std::shared_ptr<DecoderBase>& decoder = std::make_shared<DecoderFake>())
+        : InternalOperation(decoder, OutputVector{input_handle, index, item}, 1, "TensorListSetItem") {
+        validate_and_infer_types();
+    }
+
+    void validate_and_infer_types() override {
+        // deduce a type of elements in tensor list
+        ov::element::Type element_type = ov::element::dynamic;
+        if (input_value(0).get_element_type().is_static()) {
+            element_type = input_value(0).get_element_type();
+        } else if (input_value(2).get_element_type().is_static()) {
+            element_type = input_value(2).get_element_type();
+        }
+
+        // deduce a shape of tensor list [num_tensors, <tensor shape>]
+        ov::PartialShape tensor_list_shape = ov::PartialShape::dynamic();
+        if (input_value(2).get_partial_shape().rank().is_static()) {
+            auto element_rank = input_value(2).get_partial_shape().rank().get_length();
+            tensor_list_shape = ov::PartialShape::dynamic(element_rank + 1);
+            for (int64_t idx = 0; idx < element_rank; ++idx) {
+                tensor_list_shape[idx + 1] = input_value(2).get_partial_shape()[idx];
+            }
+        }
+
+        set_output_type(0, element_type, tensor_list_shape);
+    }
+
+    std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& inputs) const override {
+        FRONT_END_OP_CONVERSION_CHECK(inputs.size() == 3,
+                                      "[TensorFlow Frontend] internal error: TensorListSetItem expects three inputs");
+        auto tensor_list_set_item = std::make_shared<TensorListSetItem>(inputs[0], inputs[1], inputs[2], m_decoder);
+        tensor_list_set_item->set_attrs(get_attrs());
+        return tensor_list_set_item;
+    }
+};
+
+}  // namespace tensorflow
+}  // namespace frontend
+}  // namespace ov
diff --git a/src/frontends/tensorflow_common/include/helper_transforms/tensor_list_ops_resolver.hpp b/src/frontends/tensorflow_common/include/helper_transforms/tensor_list_ops_resolver.hpp
new file mode 100644
index 00000000000000..2ef83a6c3682f5
--- /dev/null
+++ b/src/frontends/tensorflow_common/include/helper_transforms/tensor_list_ops_resolver.hpp
@@ -0,0 +1,47 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "openvino/pass/graph_rewrite.hpp"
+#include "openvino/pass/pass.hpp"
+
+namespace ov {
+namespace frontend {
+namespace tensorflow {
+namespace pass {
+
+// Replace internal operation TensorListReserve with a sub-graph producing initial container
+class TensorListReplacer : public ov::pass::MatcherPass {
+public:
+    OPENVINO_RTTI("ov::frontend::tensorflow::pass::TensorListReplacer");
+    TensorListReplacer();
+};
+
+// Replace internal operation TensorListSetItem with a sub-graph that inserts a new tensor into container
+class TensorListSetItemReplacer : public ov::pass::MatcherPass {
+public:
+    OPENVINO_RTTI("ov::frontend::tensorflow::pass::TensorListSetItemReplacer");
+    TensorListSetItemReplacer();
+};
+
+// Replace internal operation TensorListGetItem with a sub-graph that gets a tensor from container by index
+class TensorListGetItemReplacer : public ov::pass::MatcherPass {
+public:
+    OPENVINO_RTTI("ov::frontend::tensorflow::pass::TensorListGetItemReplacer");
+    TensorListGetItemReplacer();
+};
+
+// Optimize sub-graphs with TensorList operations in Loop body graph
+// Replace TensorListSetItem and TensorListGetItem with ConcatOutput and SlicedInput
+class TensorListInLoopOptimization : public ov::pass::MatcherPass {
+public:
+    OPENVINO_RTTI("ov::frontend::tensorflow::pass::TensorListInLoopOptimization");
+    TensorListInLoopOptimization();
+};
+
+}  // namespace pass
+}  // namespace tensorflow
+}  // namespace frontend
+}  // namespace ov
diff --git a/src/frontends/tensorflow_common/src/helper_transforms/tensor_list_ops_resolver.cpp b/src/frontends/tensorflow_common/src/helper_transforms/tensor_list_ops_resolver.cpp
new file mode 100644
index 00000000000000..a185b7abee4372
--- /dev/null
+++ b/src/frontends/tensorflow_common/src/helper_transforms/tensor_list_ops_resolver.cpp
@@ -0,0 +1,632 @@
+// Copyright (C) 2023-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "helper_transforms/tensor_list_ops_resolver.hpp"
+
+#include "helper_ops/tensor_list_ops.hpp"
+#include "openvino/op/add.hpp"
+#include "openvino/op/broadcast.hpp"
+#include "openvino/op/concat.hpp"
+#include "openvino/op/constant.hpp"
+#include "openvino/op/convert.hpp"
+#include "openvino/op/gather.hpp"
+#include "openvino/op/less.hpp"
+#include "openvino/op/loop.hpp"
+#include "openvino/op/parameter.hpp"
+#include "openvino/op/reshape.hpp"
+#include "openvino/op/scatter_update.hpp"
+#include "openvino/op/shape_of.hpp"
+#include "openvino/op/slice.hpp"
+#include "openvino/op/squeeze.hpp"
+#include "openvino/op/unsqueeze.hpp"
+#include "openvino/op/util/multi_subgraph_base.hpp"
+#include "openvino/pass/pattern/op/wrap_type.hpp"
+#include "transformations/utils/utils.hpp"
+
+using namespace std;
+using namespace ov;
+using namespace ov::pass;
+using namespace ov::op;
+using namespace ov::frontend;
+using namespace ov::op::util;
+
+using InvariantD = ov::op::util::MultiSubGraphOp::InvariantInputDescription;
+using SlicedD = ov::op::util::MultiSubGraphOp::SliceInputDescription;
+using MergedD = ov::op::util::MultiSubGraphOp::MergedInputDescription;
+using OutputD = ov::op::util::MultiSubGraphOp::BodyOutputDescription;
+using ConcatD = ov::op::util::MultiSubGraphOp::ConcatOutputDescription;
+
+namespace {
+ov::Rank find_element_rank(const ov::Input<ov::Node>& target_input) {
+    const auto& curr_node = target_input.get_node()->shared_from_this();
+    if (const auto& tensor_list_set_item = ov::as_type_ptr<tensorflow::TensorListSetItem>(curr_node)) {
+        // the third argument to TensorListSetItem is new element to be inserted
+        const auto& item = tensor_list_set_item->input_value(2);
+        return item.get_partial_shape().rank();
+    } else if (const auto& multi_sub_graph_op = ov::as_type_ptr<ov::op::util::MultiSubGraphOp>(curr_node)) {
+        auto input_port_idx = static_cast<uint64_t>(target_input.get_index());
+        int num_body_graphs = static_cast<int>(multi_sub_graph_op->get_functions().size());
+        for (int body_idx = 0; body_idx < num_body_graphs; ++body_idx) {
+            const auto& body_graph = multi_sub_graph_op->get_function(body_idx);
+            const auto& input_desc = multi_sub_graph_op->get_input_descriptions(body_idx);
+
+            // find parameter index that corresponds to external input target_input
+            std::shared_ptr<v0::Parameter> param = nullptr;
+            for (const auto& desc : input_desc) {
+                if (desc && desc->m_input_index == input_port_idx) {
+                    param = body_graph->get_parameters()[desc->m_body_parameter_index];
+                    break;
+                }
+            }
+            if (!param) {
+                continue;
+            }
+
+            // walk through all consumer inputs and try to deduce element rank
+            for (const auto& target_input : param->get_output_target_inputs(0)) {
+                auto element_rank = find_element_rank(target_input);
+                if (element_rank.is_static()) {
+                    return element_rank;
+                }
+            }
+        }
+    }
+
+    return ov::Rank::dynamic();
+}
+
+bool find_input_description(const ov::op::util::InputDescriptionVector& input_descriptions,
+                            uint64_t param_idx,
+                            ov::op::util::SubGraphOp::InputDescription::Ptr& found_desc) {
+    for (const auto& input_desc : input_descriptions) {
+        if (input_desc->m_body_parameter_index == param_idx) {
+            found_desc = input_desc;
+            return true;
+        }
+    }
+
+    return false;
+}
+
+void update_parameter_to_slice_input(const std::shared_ptr<ov::Node>& node,
+                                     const std::shared_ptr<ov::Model>& body_graph,
+                                     const ov::op::util::InputDescriptionVector& input_descriptions,
+                                     std::vector<uint64_t>& update_param_ids) {
+    // select only TensorListGetItem that accepts a tensor list from Parameter node
+    // value of Parameter node is unchanged from one iteration to another one in Loop
+    auto tensor_list_get_item = std::dynamic_pointer_cast<ov::frontend::tensorflow::TensorListGetItem>(node);
+    if (!tensor_list_get_item) {
+        return;
+    }
+    auto tensor_list = ov::as_type_ptr<v0::Parameter>(tensor_list_get_item->get_input_node_shared_ptr(0));
+    if (!tensor_list) {
+        return;
+    }
+    if (tensor_list->get_output_target_inputs(0).size() != 1) {
+        return;
+    }
+
+    // tensor list must be invariant through iterations
+    int64_t param_idx = body_graph->get_parameter_index(tensor_list);
+    if (param_idx < 0) {
+        return;
+    }
+    ov::op::util::SubGraphOp::InputDescription::Ptr input_desc = nullptr;
+    if (!find_input_description(input_descriptions, static_cast<uint64_t>(param_idx), input_desc) || !input_desc) {
+        return;
+    }
+    auto invariant_input_desc = ov::as_type_ptr<InvariantD>(input_desc);
+    if (!invariant_input_desc) {
+        return;
+    }
+
+    update_param_ids.push_back(static_cast<uint64_t>(param_idx));
+}
+
+void update_result_to_concat_output(const std::shared_ptr<ov::Node>& node,
+                                    const std::shared_ptr<ov::Model>& body_graph,
+                                    const ov::ResultVector& results,
+                                    const ov::op::util::InputDescriptionVector& input_descriptions,
+                                    std::vector<uint64_t>& update_result_ids,
+                                    std::vector<uint64_t>& remove_param_ids) {
+    // select only TensorListSetItem that accepts a tensor list from Parameter node
+    // output of TensorListSetItem goes to Result that is connected with the tensor list by a back edge
+    auto tensor_list_set_item = std::dynamic_pointer_cast<ov::frontend::tensorflow::TensorListSetItem>(node);
+    if (!tensor_list_set_item) {
+        return;
+    }
+    auto tensor_list = ov::as_type_ptr<v0::Parameter>(tensor_list_set_item->get_input_node_shared_ptr(0));
+    if (!tensor_list) {
+        return;
+    }
+    if (tensor_list->get_output_target_inputs(0).size() != 1) {
+        return;
+    }
+
+    int64_t param_idx = body_graph->get_parameter_index(tensor_list);
+    if (param_idx < 0) {
+        return;
+    }
+    ov::op::util::SubGraphOp::InputDescription::Ptr input_desc = nullptr;
+    if (!find_input_description(input_descriptions, static_cast<uint64_t>(param_idx), input_desc) || !input_desc) {
+        return;
+    }
+    auto merged_input_desc = ov::as_type_ptr<MergedD>(input_desc);
+    if (!merged_input_desc) {
+        return;
+    }
+
+    uint64_t result_idx = merged_input_desc->m_body_value_index;
+    if (results[result_idx]->get_input_node_shared_ptr(0) != tensor_list_set_item) {
+        return;
+    }
+
+    update_result_ids.push_back(result_idx);
+    remove_param_ids.push_back(static_cast<uint64_t>(param_idx));
+}
+
+uint64_t get_new_param_idx(const std::vector<uint64_t>& remove_parameter_idxs, uint64_t old_idx) {
+    // compute a number of Parameters nodes standing before old_idx that will be removed
+    uint64_t num_removed = 0;
+    for (auto remove_idx : remove_parameter_idxs) {
+        FRONT_END_GENERAL_CHECK(old_idx != remove_idx,
+                                "[TensorFlow Frontend] internal error: incorrect old_idx for "
+                                "TensorListInLoopOptimization transformation");
+        if (remove_idx < old_idx) {
+            ++num_removed;
+        }
+    }
+
+    // compute shifted index
+    FRONT_END_GENERAL_CHECK(num_removed <= old_idx,
+                            "[TensorFlow Frontend] internal error: incorrect new parameter index computation "
+                            "TensorListInLoopOptimization transformation");
+    return old_idx - num_removed;
+}
+}  // namespace
+
+ov::frontend::tensorflow::pass::TensorListReplacer::TensorListReplacer() {
+    auto tensor_list_label = pattern::wrap_type<TensorList>();
+
+    matcher_pass_callback callback = [=](pattern::Matcher& m) {
+        NodeRegistry rg;
+
+        auto tensor_list = std::dynamic_pointer_cast<TensorList>(m.get_match_root());
+        if (!tensor_list) {
+            return false;
+        }
+
+        auto num_elements = tensor_list->get_num_elements();
+        auto element_dtype = tensor_list->get_element_type();
+
+        if (tensor_list->get_element_rank().is_dynamic()) {
+            // try to deduce element rank using successive operations
+            ov::Rank element_rank = ov::Rank::dynamic();
+            for (const auto& target_input : tensor_list->get_output_target_inputs(0)) {
+                element_rank = find_element_rank(target_input);
+                if (element_rank.is_static()) {
+                    break;
+                }
+            }
+            tensor_list->set_element_rank(element_rank);
+        }
+
+        if (tensor_list->get_element_rank().is_static()) {
+            size_t element_rank = static_cast<size_t>(tensor_list->get_element_rank().get_length());
+            auto initial_element_shape = rg.make<v0::Constant>(element::i32, Shape{element_rank}, 1);
+
+            auto initial_tensor_list_shape = rg.make<v0::Concat>(OutputVector{num_elements, initial_element_shape}, 0);
+            auto one_element = rg.make<v0::Constant>(element_dtype, Shape{}, 0);
+
+            // create initial container of tensors with zeros and a shape equal to [num_elements, 1, ..., 1]
+            auto initial_tensor_list = rg.make<v1::Broadcast>(one_element, initial_tensor_list_shape);
+
+            // preserve names of the node and the output tensor
+            initial_tensor_list->set_friendly_name(tensor_list->get_friendly_name());
+            copy_runtime_info(tensor_list, rg.get());
+
+            ov::replace_node(tensor_list, ov::OutputVector{initial_tensor_list->output(0)});
+            return true;
+        }
+
+        return true;
+    };
+
+    auto m =
+        std::make_shared<pattern::Matcher>(tensor_list_label, "ov::frontend::tensorflow::pass::TensorListReplacer");
+    register_matcher(m, callback);
+}
+
+ov::frontend::tensorflow::pass::TensorListSetItemReplacer::TensorListSetItemReplacer() {
+    auto tensor_list_set_item_label = pattern::wrap_type<TensorListSetItem>();
+
+    matcher_pass_callback callback = [=](pattern::Matcher& m) {
+        NodeRegistry rg;
+
+        auto tensor_list_set_item = std::dynamic_pointer_cast<TensorListSetItem>(m.get_match_root());
+        if (!tensor_list_set_item) {
+            return false;
+        }
+
+        auto input_handle = tensor_list_set_item->input_value(0);
+        auto index = tensor_list_set_item->input_value(1);
+        auto item = tensor_list_set_item->input_value(2);
+
+        // make index be of a shape [1]
+        if (index.get_partial_shape() != PartialShape{1}) {
+            auto new_index_shape = rg.make<v0::Constant>(element::i32, Shape{1}, vector<int32_t>{1});
+            index = rg.make<v1::Reshape>(index, new_index_shape, false);
+        }
+
+        // compute the current length of the list
+        Output<Node> list_length = rg.make<v3::ShapeOf>(input_handle, element::i32);
+        auto zero_const = rg.make<v0::Constant>(element::i32, Shape{1}, 0);
+        auto one_const = rg.make<v0::Constant>(element::i32, Shape{1}, 1);
+        list_length = rg.make<v8::Slice>(list_length, zero_const, one_const, one_const);
+
+        // compute element shape of real elements to be inserted into the list
+        auto item_shape = rg.make<v3::ShapeOf>(item, element::i32);
+
+        // broadcast tensor list container to the real shape
+        // since the initial state has shape [num_elements, 1, ..., 1]
+        auto target_shape = rg.make<v0::Concat>(OutputVector{list_length, item_shape}, 0);
+        input_handle = rg.make<v1::Broadcast>(input_handle, target_shape);
+
+        // reshape item before insertion to source tensor
+        item = rg.make<v0::Unsqueeze>(item, zero_const);
+
+        // update the resulted tensor using ScatterUpdate
+        auto scatter_update = rg.make<v3::ScatterUpdate>(input_handle, index, item, zero_const);
+
+        // preserve names of the node and the output tensor
+        scatter_update->set_friendly_name(tensor_list_set_item->get_friendly_name());
+        copy_runtime_info(tensor_list_set_item, rg.get());
+
+        ov::replace_node(tensor_list_set_item, ov::OutputVector{scatter_update->output(0)});
+        return true;
+    };
+
+    auto m = std::make_shared<pattern::Matcher>(tensor_list_set_item_label,
+                                                "ov::frontend::tensorflow::pass::TensorListSetItemReplacer");
+    register_matcher(m, callback);
+}
+
+ov::frontend::tensorflow::pass::TensorListGetItemReplacer::TensorListGetItemReplacer() {
+    auto tensor_list_get_item_label = pattern::wrap_type<TensorListGetItem>();
+
+    matcher_pass_callback callback = [=](pattern::Matcher& m) {
+        NodeRegistry rg;
+
+        auto tensor_list_get_item = std::dynamic_pointer_cast<TensorListGetItem>(m.get_match_root());
+        if (!tensor_list_get_item) {
+            return false;
+        }
+
+        auto input_handle = tensor_list_get_item->input_value(0);
+        auto index = tensor_list_get_item->input_value(1);
+        auto element_dtype = tensor_list_get_item->get_element_type();
+        // make index be a scalar
+        if (index.get_partial_shape() != PartialShape{}) {
+            auto new_index_shape = rg.make<v0::Constant>(element::i32, Shape{0}, vector<int32_t>{});
+            index = rg.make<v1::Reshape>(index, new_index_shape, false);
+        }
+
+        // gather tensor element by the required position
+        auto gather_axis = rg.make<v0::Constant>(element::i32, Shape{1}, 0);
+        Output<Node> tensor_element = rg.make<v8::Gather>(input_handle, index, gather_axis);
+        tensor_element = rg.make<v0::Convert>(tensor_element, element_dtype);
+
+        // preserve names of the node and the output tensor
+        tensor_element.get_node_shared_ptr()->set_friendly_name(tensor_list_get_item->get_friendly_name());
+        copy_runtime_info(tensor_list_get_item, rg.get());
+
+        ov::replace_node(tensor_list_get_item, ov::OutputVector{tensor_element});
+        return true;
+    };
+
+    auto m = std::make_shared<pattern::Matcher>(tensor_list_get_item_label,
+                                                "ov::frontend::tensorflow::pass::TensorListGetItemReplacer");
+    register_matcher(m, callback);
+}
+
+ov::frontend::tensorflow::pass::TensorListInLoopOptimization::TensorListInLoopOptimization() {
+    auto loop_label = pattern::wrap_type<v5::Loop>();
+
+    // pattern for condition sub-graph in Loop operarion
+    auto num_iterations_label = pattern::wrap_type<op::v0::Parameter>();
+    auto counter_label = pattern::wrap_type<op::v0::Parameter>();
+    auto counter_step_label = pattern::wrap_type<op::v0::Constant>();
+    auto updated_counter_label = pattern::wrap_type<op::v1::Add>({counter_label, counter_step_label});
+    auto less_label = pattern::wrap_type<op::v1::Less>({updated_counter_label, num_iterations_label});
+    auto condition_label = pattern::wrap_type<op::v0::Result>({less_label});
+
+    matcher_pass_callback callback = [=](pattern::Matcher& m) {
+        NodeRegistry rg;
+
+        auto loop = ov::as_type_ptr<v5::Loop>(m.get_match_root());
+        if (!loop) {
+            return false;
+        }
+
+        // check that condition sub-graph of the required form:
+        // counter with zero initial value and increments each iteration
+        // loop continues until counter is less than given number
+        ov::pass::pattern::Matcher condition_matcher(condition_label);
+
+        const auto& body = loop->get_function();
+        const auto& body_params = body->get_parameters();
+        const auto& body_results = body->get_results();
+        const auto& special_body_ports = loop->get_special_body_ports();
+        const auto& input_descriptions = loop->get_input_descriptions();
+        const auto& output_descriptions = loop->get_output_descriptions();
+
+        if (!condition_matcher.match(body_results[special_body_ports.body_condition_output_idx]->output(0))) {
+            return false;
+        }
+
+        const auto& condition_map = condition_matcher.get_pattern_value_map();
+        int64_t counter_step = -1;
+        if (!get_constant_value(condition_map.at(counter_step_label).get_node_shared_ptr(), counter_step) ||
+            counter_step != 1) {
+            return false;
+        }
+
+        // get initial value of counter
+        int64_t initial_counter = 0;
+        auto counter_param = ov::as_type_ptr<v0::Parameter>(condition_map.at(counter_label).get_node_shared_ptr());
+        if (!counter_param) {
+            return false;
+        }
+
+        for (const auto& input_desc : loop->get_input_descriptions()) {
+            auto body_param_idx = input_desc->m_body_parameter_index;
+            auto input_idx = input_desc->m_input_index;
+            if (body_params[body_param_idx] != counter_param) {
+                continue;
+            }
+
+            // it must be merged input and incremented each iteration
+            auto merged_desc = ov::as_type_ptr<MergedD>(input_desc);
+            if (!merged_desc) {
+                return false;
+            }
+            auto result_idx = merged_desc->m_body_value_index;
+            auto update_counter = body_results[result_idx]->input_value(0).get_node_shared_ptr();
+            if (update_counter != condition_map.at(updated_counter_label).get_node_shared_ptr()) {
+                return false;
+            }
+
+            // get initial value of counter
+            if (!get_constant_value(loop->get_input_node_shared_ptr(input_idx), initial_counter)) {
+                return false;
+            }
+
+            // suitable counter-parameter is found and checked
+            break;
+        }
+
+        if (initial_counter != 0) {
+            return false;
+        }
+
+        // collect vector of updated Parameter indices (they will be converted to SlicedInput parameters)
+        // and Result nodes (they will be converted to ConcatOutput results)
+        // also, some parameters and results can be removed and they are directly connected
+        // to updated Parameter/Result nodes
+        std::vector<uint64_t> remove_parameter_ids;
+        std::vector<uint64_t> update_parameter_ids, update_result_ids;
+        for (const auto& target_input : counter_param->get_output_target_inputs(0)) {
+            update_parameter_to_slice_input(target_input.get_node()->shared_from_this(),
+                                            body,
+                                            input_descriptions,
+                                            update_parameter_ids);
+            update_result_to_concat_output(target_input.get_node()->shared_from_this(),
+                                           body,
+                                           body_results,
+                                           input_descriptions,
+                                           update_result_ids,
+                                           remove_parameter_ids);
+        }
+
+        // avoid TensorListSetItem that overrides tensor list with data computed for each iteration
+        // by index zero that is equivalent to Loop returning data only from last iteration
+        // TensorListSetItem accepts constant index equal to zero
+        // TensorListSetItem is not connected with counter node
+        std::vector<uint64_t> update_result_last_iter_ids;
+        for (uint64_t result_idx = 0; result_idx < body_results.size(); ++result_idx) {
+            const auto& result = body_results[result_idx];
+            auto tensor_list_set_item =
+                std::dynamic_pointer_cast<TensorListSetItem>(result->get_input_node_shared_ptr(0));
+            if (!tensor_list_set_item) {
+                continue;
+            }
+            int64_t index_value = -1;
+            if (!get_constant_value(tensor_list_set_item->get_input_node_shared_ptr(1), index_value) ||
+                (index_value != 0)) {
+                continue;
+            }
+
+            auto tensor_list = ov::as_type_ptr<v0::Parameter>(tensor_list_set_item->get_input_node_shared_ptr(0));
+            if (!tensor_list) {
+                continue;
+            }
+            int64_t param_idx = body->get_parameter_index(tensor_list);
+            if (param_idx < 0) {
+                continue;
+            }
+
+            update_result_last_iter_ids.push_back(result_idx);
+            remove_parameter_ids.push_back(static_cast<uint64_t>(param_idx));
+        }
+
+        // nothing to update
+        if (update_parameter_ids.size() == 0 && update_result_ids.size() == 0 &&
+            update_result_last_iter_ids.size() == 0) {
+            return false;
+        }
+
+        // build a new body_graph
+        auto new_body_results = body_results;
+        std::vector<uint64_t> all_update_result_ids = update_result_ids;
+        all_update_result_ids.insert(all_update_result_ids.end(),
+                                     update_result_last_iter_ids.begin(),
+                                     update_result_last_iter_ids.end());
+        for (auto update_result_idx : all_update_result_ids) {
+            const auto& body_result = body_results[update_result_idx];
+            auto tensor_list_set_item =
+                std::dynamic_pointer_cast<TensorListSetItem>(body_result->get_input_node_shared_ptr(0));
+            FRONT_END_GENERAL_CHECK(tensor_list_set_item,
+                                    "[TensorFlow Frontend] internal error: tensor_list_set_item is nullptr in "
+                                    "TensorListInLoopOptimization");
+            // unsqueeze newly generated data at this iteration
+            // that will be concatenated
+            auto new_data = tensor_list_set_item->input_value(2);
+            auto axis = std::make_shared<v0::Constant>(ov::element::i32, ov::Shape{1}, 0);
+            auto unsqueeze_new_data = std::make_shared<v0::Unsqueeze>(new_data, axis);
+            auto new_body_result = std::make_shared<v0::Result>(unsqueeze_new_data);
+            new_body_results[update_result_idx] = new_body_result;
+        }
+        auto new_body_params = ParameterVector{};
+        for (uint64_t param_idx = 0; param_idx < static_cast<uint64_t>(body_params.size()); ++param_idx) {
+            // skip Parameter nodes from remove_parameter_ids list
+            if (std::find(remove_parameter_ids.begin(), remove_parameter_ids.end(), param_idx) !=
+                remove_parameter_ids.end()) {
+                continue;
+            }
+
+            // use updated Parameter node if needed
+            if (std::find(update_parameter_ids.begin(), update_parameter_ids.end(), param_idx) !=
+                update_parameter_ids.end()) {
+                const auto& body_param = body_params[param_idx];
+                FRONT_END_GENERAL_CHECK(body_param->get_output_target_inputs(0).size() == 1,
+                                        "[TensorFlow Frontend] internal error: tensor list must have only consumer "
+                                        "TensorListGetItem operation in TensorListInLoopOptimization");
+                auto target_input = *(body_param->get_output_target_inputs(0).begin());
+                auto tensor_list_get_item =
+                    std::dynamic_pointer_cast<TensorListGetItem>(target_input.get_node()->shared_from_this());
+                FRONT_END_GENERAL_CHECK(tensor_list_get_item,
+                                        "[TensorFlow Frontend] internal error: tensor list must have only consumer "
+                                        "TensorListGetItem operation in TensorListInLoopOptimization");
+
+                auto new_shape = body_param->get_output_partial_shape(0);
+                if (new_shape.rank().is_static() && new_shape.rank().get_length() > 0) {
+                    // set a static dimension equal to 1 since it is sliced by axis 0
+                    new_shape[0] = 1;
+                }
+                auto new_param = std::make_shared<v0::Parameter>(body_param->get_output_element_type(0), new_shape);
+                new_param->set_friendly_name(body_param->get_friendly_name());
+
+                // adjust new_param since it comes after slicing and sliced input needs to be squeezed
+                auto squeeze_axis = std::make_shared<v0::Constant>(element::i32, Shape{1}, 0);
+                auto squeeze_param = std::make_shared<v0::Squeeze>(new_param, squeeze_axis);
+
+                // replace data producer for all consumers of TensorListGetItem
+                tensor_list_get_item->output(0).replace(squeeze_param->output(0));
+                new_body_params.push_back(new_param);
+                continue;
+            }
+
+            new_body_params.push_back(body_params[param_idx]);
+        }
+        auto new_body_graph = std::make_shared<ov::Model>(new_body_results, new_body_params);
+
+        // eventually, only some Parameter nodes can be removed
+        // so indices of Parameters can be changed
+        // a number of Result nodes and their indices leave unchanged
+        // create new Loop operation and set input and output descriptions
+        const auto& trip_count = loop->input_value(0);
+        const auto& exec_cond = loop->input_value(1);
+        auto new_loop = rg.make<v5::Loop>(trip_count, exec_cond);
+        new_loop->set_special_body_ports(special_body_ports);
+        new_loop->set_function(new_body_graph);
+
+        // update current_iteration_input_idx since some Parameters can be removed
+        // Result nodes are not removed so body_condition_output_idx leaves unchanged
+        auto current_iteration_input_idx = special_body_ports.current_iteration_input_idx;
+        if (current_iteration_input_idx > 0) {
+            auto new_idx = get_new_param_idx(remove_parameter_ids, static_cast<uint64_t>(current_iteration_input_idx));
+            auto new_special_body_ports = special_body_ports;
+            new_special_body_ports.current_iteration_input_idx = static_cast<int64_t>(new_idx);
+            new_loop->set_special_body_ports(new_special_body_ports);
+        }
+
+        // set inputs for new Loop operation
+        for (const auto& input_desc : input_descriptions) {
+            // skip already removed body Parameters
+            auto param_idx = input_desc->m_body_parameter_index;
+            auto input_index = input_desc->m_input_index;
+            if (std::find(remove_parameter_ids.begin(), remove_parameter_ids.end(), param_idx) !=
+                remove_parameter_ids.end()) {
+                continue;
+            }
+
+            auto new_param_idx = get_new_param_idx(remove_parameter_ids, param_idx);
+            const auto& new_body_param = new_body_params[new_param_idx];
+            const auto& init_value = loop->input_value(input_index);
+            if (std::find(update_parameter_ids.begin(), update_parameter_ids.end(), param_idx) !=
+                update_parameter_ids.end()) {
+                // set this input as sliced input
+                new_loop->set_sliced_input(new_body_param, init_value, initial_counter, counter_step, 1, -1, 0);
+            } else if (const auto& invariant_input = ov::as_type_ptr<InvariantD>(input_desc)) {
+                new_loop->set_invariant_input(new_body_param, init_value);
+            } else if (const auto& merged_input = ov::as_type_ptr<MergedD>(input_desc)) {
+                const auto& body_res = new_body_results[merged_input->m_body_value_index];
+                new_loop->set_merged_input(new_body_param, init_value, body_res);
+            } else if (const auto& sliced_input = ov::as_type_ptr<SlicedD>(input_desc)) {
+                new_loop->set_sliced_input(new_body_param,
+                                           init_value,
+                                           sliced_input->m_start,
+                                           sliced_input->m_stride,
+                                           sliced_input->m_part_size,
+                                           sliced_input->m_end,
+                                           sliced_input->m_axis);
+            } else {
+                // unknown type of input
+                // transformation is not applicable
+                return false;
+            }
+        }
+
+        // set outputs for new Loop operation
+        std::unordered_map<uint64_t, Output<Node>> idx_to_new_output;
+        for (const auto& output_desc : output_descriptions) {
+            auto result_idx = output_desc->m_body_value_index;
+            auto output_index = output_desc->m_output_index;
+            auto new_body_result = new_body_results[result_idx];
+
+            if (std::find(update_result_ids.begin(), update_result_ids.end(), result_idx) != update_result_ids.end()) {
+                idx_to_new_output[output_index] =
+                    new_loop->get_concatenated_slices(new_body_result, initial_counter, counter_step, 1, -1, 0);
+            } else if (std::find(update_result_last_iter_ids.begin(), update_result_last_iter_ids.end(), result_idx) !=
+                       update_result_last_iter_ids.end()) {
+                idx_to_new_output[output_index] = new_loop->get_iter_value(new_body_result, -1);
+            } else if (const auto& concat_output = ov::as_type_ptr<ConcatD>(output_desc)) {
+                idx_to_new_output[output_index] = new_loop->get_concatenated_slices(new_body_result,
+                                                                                    concat_output->m_start,
+                                                                                    concat_output->m_stride,
+                                                                                    concat_output->m_part_size,
+                                                                                    concat_output->m_end,
+                                                                                    concat_output->m_axis);
+            } else if (const auto& iter_output = ov::as_type_ptr<OutputD>(output_desc)) {
+                idx_to_new_output[output_index] = new_loop->get_iter_value(new_body_result, iter_output->m_iteration);
+            } else {
+                // unknown type of output
+                return false;
+            }
+        }
+
+        auto loop_outputs = loop->outputs();
+        for (size_t i = 0; i < loop_outputs.size(); ++i) {
+            loop_outputs[i].replace(idx_to_new_output[i]);
+        }
+        copy_runtime_info(loop, rg.get());
+        new_loop->set_friendly_name(loop->get_friendly_name());
+
+        return true;
+    };
+
+    auto m =
+        std::make_shared<pattern::Matcher>(loop_label, "ov::frontend::tensorflow::pass::TensorListInLoopOptimization");
+    register_matcher(m, callback);
+}
diff --git a/src/frontends/tensorflow_common/src/op/tensor_list_operations.cpp b/src/frontends/tensorflow_common/src/op/tensor_list_operations.cpp
index a733ac2eac1c8a..ffbdfaf4a40b50 100644
--- a/src/frontends/tensorflow_common/src/op/tensor_list_operations.cpp
+++ b/src/frontends/tensorflow_common/src/op/tensor_list_operations.cpp
@@ -5,6 +5,8 @@
 #include <climits>
 
 #include "common_op_table.hpp"
+#include "helper_ops/tensor_list_ops.hpp"
+#include "openvino/core/validation_util.hpp"
 #include "openvino/op/add.hpp"
 #include "openvino/op/broadcast.hpp"
 #include "openvino/op/concat.hpp"
@@ -30,17 +32,71 @@ namespace ov {
 namespace frontend {
 namespace tensorflow {
 namespace op {
+
+namespace {
+Output<Node> create_initial_tensor_list(const NodeContext& node,
+                                        const Output<Node>& num_elements,
+                                        const Output<Node>& element_shape,
+                                        const element::Type& element_dtype) {
+    // handle tensor elements of dynamic rank
+    if (element_shape.get_partial_shape().is_dynamic() || element_shape.get_shape().size() != 1) {
+        auto tensor_list = make_shared<TensorList>(num_elements, ov::Rank::dynamic(), element_dtype);
+        return {tensor_list};
+    }
+
+    TENSORFLOW_OP_VALIDATION(node,
+                             element_shape.get_partial_shape().is_static(),
+                             "[TensorFlow Frontend] internal error: element_shape must be of static shape");
+
+    TENSORFLOW_OP_VALIDATION(node,
+                             element_shape.get_shape().size() == 1,
+                             "[TensorFlow Frontend] inconsistent model: element_shape is not 1D vector");
+
+    // create initial shape of elements
+    size_t element_rank = static_cast<size_t>(element_shape.get_shape()[0]);
+    auto initial_element_shape = make_shared<v0::Constant>(element::i32, Shape{element_rank}, 1);
+
+    auto initial_tensor_list_shape = make_shared<v0::Concat>(OutputVector{num_elements, initial_element_shape}, 0);
+    auto one_element = make_shared<v0::Constant>(element_dtype, Shape{}, 0);
+
+    // create initial container of tensors with zeros and a shape equal to [num_elements, 1, ..., 1]
+    auto initial_tensor_list = make_shared<v1::Broadcast>(one_element, initial_tensor_list_shape);
+
+    return initial_tensor_list;
+}
+}  // namespace
+
 OutputVector translate_tensor_list_reserve_op(const NodeContext& node) {
-    default_op_checks(node, 2, {"TensorListReserve", "EmptyTensorList"});
+    default_op_checks(node, 2, {"TensorListReserve"});
+    auto element_shape = node.get_input(0);
+    // num_elements cannot be negative and define a number of elements in tensor list
+    // trying setting element by out-of-bound index leads to failure
+    auto num_elements = node.get_input(1);
     auto element_dtype = node.get_attribute<element::Type>("element_dtype");
 
-    // always reserve an empty constant of rank equal to two
-    // all tensor elements will be saved in the flatten form in the list
-    // because we want to cover a case of dynamic rank tensor list
-    // the real shape of the tensor elements will be restored by TensorListStack operations
-    auto empty_constant = make_shared<v0::Constant>(element_dtype, Shape{0, 0});
-    set_node_name(node.get_name(), empty_constant);
-    return {empty_constant};
+    // provide num_elements of a shape [1]
+    if (num_elements.get_partial_shape() != ov::PartialShape{1}) {
+        auto new_num_elements_shape = make_shared<v0::Constant>(element::i32, Shape{1}, 1);
+        num_elements = make_shared<v1::Reshape>(num_elements, new_num_elements_shape, false);
+    }
+
+    auto initial_tensor_list = create_initial_tensor_list(node, num_elements, element_shape, element_dtype);
+
+    set_node_name(node.get_name(), initial_tensor_list.get_node_shared_ptr());
+    return {initial_tensor_list};
+}
+
+OutputVector translate_empty_tensor_list_op(const NodeContext& node) {
+    default_op_checks(node, 2, {"EmptyTensorList"});
+    auto element_shape = node.get_input(0);
+    auto element_dtype = node.get_attribute<element::Type>("element_dtype");
+
+    // a number of elements must be zero
+    auto num_elements = make_shared<v0::Constant>(element::i32, Shape{1}, 0);
+    auto initial_tensor_list = create_initial_tensor_list(node, num_elements, element_shape, element_dtype);
+
+    set_node_name(node.get_name(), initial_tensor_list.get_node_shared_ptr());
+    return {initial_tensor_list};
 }
 
 OutputVector translate_tensor_list_from_tensor_op(const NodeContext& node) {
@@ -54,20 +110,9 @@ OutputVector translate_tensor_list_from_tensor_op(const NodeContext& node) {
 OutputVector translate_tensor_list_stack_op(const NodeContext& node) {
     default_op_checks(node, 2, {"TensorListStack"});
     auto input_handle = node.get_input(0);
-    auto element_shape = node.get_input(1);
 
-    // compute number of tensor elements in the list
-    Output<Node> num_elements = make_shared<v3::ShapeOf>(input_handle, element::i32);
-    auto zero_const = make_shared<v0::Constant>(element::i32, Shape{1}, 0);
-    auto one_const = make_shared<v0::Constant>(element::i32, Shape{1}, 1);
-    num_elements = make_shared<v8::Slice>(num_elements, zero_const, one_const, one_const);
-
-    // restore the real shape of tensor elements
-    auto new_shape = make_shared<v0::Concat>(OutputVector{num_elements, element_shape}, 0);
-    auto reshape = make_shared<v1::Reshape>(input_handle, new_shape, false);
-
-    set_node_name(node.get_name(), reshape);
-    return {reshape};
+    // nothing to do, the input tensor will simulate the tensor list
+    return {input_handle};
 }
 
 OutputVector translate_tensor_list_get_item_op(const NodeContext& node) {
@@ -77,16 +122,9 @@ OutputVector translate_tensor_list_get_item_op(const NodeContext& node) {
     auto element_shape = node.get_input(2);
     auto element_dtype = node.get_attribute<element::Type>("element_dtype");
 
-    // squeeze index tensor to have a scalar
-    index = make_shared<v0::Squeeze>(index);
-
-    // gather tensor element by the required position
-    auto gather_axis = make_shared<v0::Constant>(element::i32, Shape{1}, 0);
-    Output<Node> tensor_element = make_shared<v8::Gather>(input_handle, index, gather_axis);
-    tensor_element = make_shared<v0::Convert>(tensor_element, element_dtype);
-
-    set_node_name(node.get_name(), tensor_element.get_node_shared_ptr());
-    return {tensor_element};
+    auto tensor_list_get_item = make_shared<TensorListGetItem>(input_handle, index, element_shape, element_dtype);
+    set_node_name(node.get_name(), tensor_list_get_item);
+    return {tensor_list_get_item};
 }
 
 OutputVector translate_tensor_list_set_item_op(const NodeContext& node) {
@@ -94,49 +132,10 @@ OutputVector translate_tensor_list_set_item_op(const NodeContext& node) {
     auto input_handle = node.get_input(0);
     auto index = node.get_input(1);
     auto item = node.get_input(2);
+    auto tensor_list_set_item = make_shared<TensorListSetItem>(input_handle, index, item);
 
-    // squeeze index tensor to have a scalar
-    index = make_shared<v0::Squeeze>(index);
-
-    // flatten item to be inserted since
-    // the tensor list saves elements in the flatten form
-    auto new_item_shape = make_shared<v0::Constant>(element::i32, Shape{1}, -1);
-    item = make_shared<v1::Reshape>(item, new_item_shape, false);
-    auto item_shape = make_shared<v3::ShapeOf>(item, element::i32);
-
-    // reshape the tensor list to the shape [num_elements, -1]
-    // that is because in the first iteration we have empty constant of a shape [0,0]
-    auto minus_one = make_shared<v0::Constant>(element::i32, Shape{1}, -1);
-    auto new_input_handle_shape = make_shared<v0::Concat>(OutputVector{minus_one, item_shape}, 0);
-    input_handle = make_shared<v1::Reshape>(input_handle, new_input_handle_shape, false);
-    input_handle = make_shared<v1::ConvertLike>(input_handle, item);
-
-    // compute the current length of the list
-    Output<Node> list_length = make_shared<v3::ShapeOf>(input_handle, element::i32);
-    auto zero_const = make_shared<v0::Constant>(element::i32, Shape{1}, 0);
-    auto one_const = make_shared<v0::Constant>(element::i32, Shape{1}, 1);
-    list_length = make_shared<v8::Slice>(list_length, zero_const, one_const, one_const);
-
-    // compute a size of the dummy tensor that serves to fill holes in the list
-    // if no tensor is inserted at this position
-    auto one_const_scalar = make_shared<v0::Constant>(element::i32, Shape{1}, 1);
-    auto index_plus_one = make_shared<v1::Add>(index, one_const_scalar);
-    Output<Node> max_length = make_shared<v1::Maximum>(list_length, index_plus_one);
-    Output<Node> dummy_tensor_size = make_shared<v1::Subtract>(max_length, list_length);
-
-    // create dummy tensor and concatenate it
-    auto zero_element = create_same_type_const_scalar<int32_t>(item, 0);
-    auto dummy_tensor_shape = make_shared<v0::Concat>(OutputVector{dummy_tensor_size, item_shape}, 0);
-    auto dummy_tensor = make_shared<v3::Broadcast>(zero_element, dummy_tensor_shape);
-    input_handle = make_shared<v0::Concat>(OutputVector{input_handle, dummy_tensor}, 0);
-
-    // update the resulted tensor using ScatterUpdate
-    index = make_shared<v0::Unsqueeze>(index, zero_const);
-    item = make_shared<v0::Unsqueeze>(item, zero_const);
-    auto scatter_update = make_shared<v3::ScatterUpdate>(input_handle, index, item, zero_const);
-
-    set_node_name(node.get_name(), scatter_update);
-    return {scatter_update};
+    set_node_name(node.get_name(), tensor_list_set_item);
+    return {tensor_list_set_item};
 }
 
 OutputVector translate_tensor_list_push_back_op(const NodeContext& node) {
@@ -144,22 +143,16 @@ OutputVector translate_tensor_list_push_back_op(const NodeContext& node) {
     auto input_handle = node.get_input(0);
     auto tensor = node.get_input(1);
 
-    // flatten item to be inserted since
-    // the tensor list saves elements in the flatten form
-    // because we want to cover a case of dynamic rank tensor list
-    // the real shape of the tensor elements will be restored by TensorListStack operations
-    auto new_tensor_shape = make_shared<v0::Constant>(element::i32, Shape{1}, -1);
-    tensor = make_shared<v1::Reshape>(tensor, new_tensor_shape, false);
+    // compute tensor shape to be inserted
     auto tensor_shape = make_shared<v3::ShapeOf>(tensor, element::i32);
 
-    // reshape the tensor list to the shape [num_elements, -1]
-    // that is because in the first iteration we have empty constant of a shape [0,0]
+    // broadcast the tensor list to the shape [num_elements, <tensor_shape>]
     Output<Node> num_elements = make_shared<v3::ShapeOf>(input_handle, element::i32);
     auto zero_const = make_shared<v0::Constant>(element::i32, Shape{1}, 0);
     auto one_const = make_shared<v0::Constant>(element::i32, Shape{1}, 1);
     num_elements = make_shared<v8::Slice>(num_elements, zero_const, one_const, one_const);
     auto new_input_handle_shape = make_shared<v0::Concat>(OutputVector{num_elements, tensor_shape}, 0);
-    input_handle = make_shared<v1::Reshape>(input_handle, new_input_handle_shape, false);
+    input_handle = make_shared<v1::Broadcast>(input_handle, new_input_handle_shape);
 
     // unsqueeze tensor to be inserted into the list
     tensor = make_shared<v0::Unsqueeze>(tensor, zero_const);
@@ -240,13 +233,13 @@ OutputVector translate_tensor_list_concat_v2_op(const NodeContext& node) {
                              as_type_ptr<v0::Constant>(node.get_input(1).get_node_shared_ptr()),
                              "TensorListConcatV2 is not supported with non-constant shape input");
 
-    std::vector<int64_t> list_elememt_shape;
-    get_const_input(node, 1, &list_elememt_shape);
+    std::vector<int64_t> list_element_shape;
+    get_const_input(node, 1, &list_element_shape);
 
-    list_elememt_shape[0] = list_elememt_shape[0] * input_handle.get_partial_shape()[0].get_max_length();
+    list_element_shape[0] = list_element_shape[0] * input_handle.get_partial_shape()[0].get_max_length();
     auto out = make_shared<v1::Reshape>(
         input_handle,
-        make_shared<v0::Constant>(element::i64, Shape{list_elememt_shape.size()}, list_elememt_shape),
+        make_shared<v0::Constant>(element::i64, Shape{list_element_shape.size()}, list_element_shape),
         false);
 
     set_node_name(node.get_name(), out);
diff --git a/src/frontends/tensorflow_lite/src/graph_iterator_flatbuffer.hpp b/src/frontends/tensorflow_lite/src/graph_iterator_flatbuffer.hpp
index 31a76d62f20029..9bf5cabbf91c7f 100644
--- a/src/frontends/tensorflow_lite/src/graph_iterator_flatbuffer.hpp
+++ b/src/frontends/tensorflow_lite/src/graph_iterator_flatbuffer.hpp
@@ -30,6 +30,7 @@ template <typename T>
 std::basic_string<T> get_model_extension() {}
 template <>
 std::basic_string<char> get_model_extension<char>();
+
 #if defined(OPENVINO_ENABLE_UNICODE_PATH_SUPPORT) && defined(_WIN32)
 template <>
 std::basic_string<wchar_t> get_model_extension<wchar_t>();
@@ -58,6 +59,10 @@ class GraphIteratorFlatBuffer : public GraphIterator {
     /// Verifies file is supported
     template <typename T>
     static bool is_supported(const std::basic_string<T>& path) {
+        FRONT_END_GENERAL_CHECK(util::file_exists(path),
+                                "Could not open the file: \"",
+                                util::path_to_string(path),
+                                '"');
         try {
             if (!ov::util::ends_with<T>(path, get_model_extension<T>())) {
                 return false;
diff --git a/src/frontends/tensorflow_lite/src/input_model.cpp b/src/frontends/tensorflow_lite/src/input_model.cpp
index 7f2ad296e9b38d..d120d3898945b2 100644
--- a/src/frontends/tensorflow_lite/src/input_model.cpp
+++ b/src/frontends/tensorflow_lite/src/input_model.cpp
@@ -406,6 +406,10 @@ ov::frontend::Place::Ptr InputModel::get_place_by_tensor_name(const std::string&
     return _impl->get_place_by_tensor_name(tensorName);
 }
 
+ov::frontend::Place::Ptr InputModel::get_place_by_input_index(size_t input_idx) const {
+    FRONT_END_NOT_IMPLEMENTED(get_place_by_input_index);
+}
+
 void InputModel::set_partial_shape(const Place::Ptr& place, const PartialShape& shape) {
     _impl->set_partial_shape(place, shape);
 }
diff --git a/src/frontends/tensorflow_lite/src/input_model.hpp b/src/frontends/tensorflow_lite/src/input_model.hpp
index 4ac655628f80d2..b1112fb14d6c9e 100644
--- a/src/frontends/tensorflow_lite/src/input_model.hpp
+++ b/src/frontends/tensorflow_lite/src/input_model.hpp
@@ -35,6 +35,7 @@ class InputModel : public ov::frontend::InputModel {
     std::vector<ov::frontend::Place::Ptr> get_inputs() const override;
     std::vector<ov::frontend::Place::Ptr> get_outputs() const override;
     ov::frontend::Place::Ptr get_place_by_tensor_name(const std::string& tensorName) const override;
+    ov::frontend::Place::Ptr get_place_by_input_index(size_t input_idx) const override;
 
     ///// Naming and annotation  /////
     void set_name_for_tensor(const Place::Ptr& tensor, const std::string& new_name) override;
diff --git a/src/frontends/tensorflow_lite/tests/convert_unsupported.cpp b/src/frontends/tensorflow_lite/tests/convert_unsupported.cpp
index 00bf9f866e012f..c28194ffa58b39 100644
--- a/src/frontends/tensorflow_lite/tests/convert_unsupported.cpp
+++ b/src/frontends/tensorflow_lite/tests/convert_unsupported.cpp
@@ -2,6 +2,7 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
+#include "common_test_utils/test_assertions.hpp"
 #include "convert_model.hpp"
 #include "tf_utils.hpp"
 #include "utils.hpp"
@@ -16,8 +17,8 @@ TEST(FrontEndConvertModelTest, test_zerolen) {
     InputModel::Ptr inputModel;
     ASSERT_NO_THROW(frontEnd = fem.load_by_framework(TF_LITE_FE));
     ASSERT_NE(frontEnd, nullptr);
-    auto model_filename =
-        FrontEndTestUtils::make_model_path(string(TEST_TENSORFLOW_LITE_MODELS_DIRNAME) + string("zerolen.tflite"));
+    auto model_filename = FrontEndTestUtils::make_model_path(string(TEST_TENSORFLOW_LITE_MODELS_DIRNAME) +
+                                                             string("bad_header/zerolen.tflite"));
     ASSERT_NO_THROW(inputModel = frontEnd->load(model_filename));
     ASSERT_NE(inputModel, nullptr);
     shared_ptr<ov::Model> model;
@@ -44,8 +45,8 @@ TEST(FrontEndConvertModelTest, test_wrong_pos) {
     InputModel::Ptr inputModel;
     ASSERT_NO_THROW(frontEnd = fem.load_by_framework(TF_LITE_FE));
     ASSERT_NE(frontEnd, nullptr);
-    auto model_filename =
-        FrontEndTestUtils::make_model_path(string(TEST_TENSORFLOW_LITE_MODELS_DIRNAME) + string("wrong_pos.tflite"));
+    auto model_filename = FrontEndTestUtils::make_model_path(string(TEST_TENSORFLOW_LITE_MODELS_DIRNAME) +
+                                                             string("bad_header/wrong_pos.tflite"));
     ASSERT_NO_THROW(inputModel = frontEnd->load(model_filename));
     ASSERT_NE(inputModel, nullptr);
     shared_ptr<ov::Model> model;
diff --git a/src/frontends/tests/frontend/shared/src/basic_api.cpp b/src/frontends/tests/frontend/shared/src/basic_api.cpp
index b0384173403ed4..ef9f911d4c0c86 100644
--- a/src/frontends/tests/frontend/shared/src/basic_api.cpp
+++ b/src/frontends/tests/frontend/shared/src/basic_api.cpp
@@ -4,13 +4,14 @@
 
 #include "basic_api.hpp"
 
+#include "common_test_utils/test_assertions.hpp"
 #include "utils.hpp"
 
 using namespace ov::frontend;
 
 std::string FrontEndBasicTest::getTestCaseName(const testing::TestParamInfo<BasicTestParam>& obj) {
-    std::string fe, path, fileName;
-    std::tie(fe, path, fileName) = obj.param;
+    const auto& fe = std::get<0>(obj.param);
+    const auto& fileName = std::get<2>(obj.param);
     return fe + "_" + FrontEndTestUtils::fileToTestName(fileName);
 }
 
@@ -173,3 +174,16 @@ TEST_P(FrontEndBasicTest, testInputModel_overrideAll_empty) {
             EXPECT_FALSE(m_inputModel->get_place_by_tensor_name(name)->is_input());
         });
 }
+
+TEST_P(FrontEndBasicTest, load_model_not_exists_at_path) {
+    const auto model_name = "not_existing_model.tflite";
+    auto error_msg = std::string("Could not open the file: \"");
+    const auto model_file_path = FrontEndTestUtils::make_model_path(model_name);
+    error_msg += model_file_path;
+
+    auto fem = ov::frontend::FrontEndManager();
+    auto fe = fem.load_by_framework(m_feName);
+
+    OV_EXPECT_THROW(fe->supported({model_file_path}), ov::Exception, testing::HasSubstr(error_msg));
+    OV_EXPECT_THROW(fe->load(model_file_path), ov::Exception, testing::HasSubstr(error_msg));
+}
diff --git a/src/frontends/tests/frontend/shared/test_builtin_extensions/CMakeLists.txt b/src/frontends/tests/frontend/shared/test_builtin_extensions/CMakeLists.txt
index e43f53031044c1..daf41cde458715 100644
--- a/src/frontends/tests/frontend/shared/test_builtin_extensions/CMakeLists.txt
+++ b/src/frontends/tests/frontend/shared/test_builtin_extensions/CMakeLists.txt
@@ -40,6 +40,11 @@ if (ENABLE_OV_PYTORCH_FRONTEND)
     list(APPEND DEFINITIONS ENABLE_OV_PYTORCH_FRONTEND)
 endif()
 
+if (ENABLE_OV_JAX_FRONTEND)
+    list(APPEND DEPENDENCIES openvino::frontend::jax)
+    list(APPEND DEFINITIONS ENABLE_OV_JAX_FRONTEND)
+endif()
+
 # Create library
 
 add_library(${TARGET_NAME} MODULE ${LIBRARY_SRC} ${LIBRARY_HEADERS})
diff --git a/src/inference/dev_api/openvino/runtime/system_conf.hpp b/src/inference/dev_api/openvino/runtime/system_conf.hpp
index c3d3364c550f1a..b6f22ad4f8daa7 100644
--- a/src/inference/dev_api/openvino/runtime/system_conf.hpp
+++ b/src/inference/dev_api/openvino/runtime/system_conf.hpp
@@ -146,6 +146,13 @@ OPENVINO_RUNTIME_API bool with_cpu_x86_avx512_core_amx_int8();
  */
 OPENVINO_RUNTIME_API bool with_cpu_x86_avx512_core_amx_bf16();
 
+/**
+ * @brief      Checks whether CPU supports AMX fp16 capability
+ * @ingroup    ov_dev_api_system_conf
+ * @return     `True` is tAMX_FP16 instructions are available, `false` otherwise
+ */
+OPENVINO_RUNTIME_API bool with_cpu_x86_avx512_core_amx_fp16();
+
 /**
  * @brief      Checks whether CPU supports AMX capability
  * @ingroup    ov_dev_api_system_conf
diff --git a/src/inference/dev_api/openvino/runtime/threading/cpu_streams_executor.hpp b/src/inference/dev_api/openvino/runtime/threading/cpu_streams_executor.hpp
index 1f694fe9b70836..29cb893a9d8587 100644
--- a/src/inference/dev_api/openvino/runtime/threading/cpu_streams_executor.hpp
+++ b/src/inference/dev_api/openvino/runtime/threading/cpu_streams_executor.hpp
@@ -49,6 +49,8 @@ class OPENVINO_RUNTIME_API CPUStreamsExecutor : public IStreamsExecutor {
 
     int get_stream_id() override;
 
+    int get_streams_num();
+
     int get_numa_node_id() override;
 
     int get_socket_id() override;
diff --git a/src/inference/src/cache_guard.cpp b/src/inference/src/cache_guard.cpp
index bf2eff78ee46ea..2da3aa4e5e4e75 100644
--- a/src/inference/src/cache_guard.cpp
+++ b/src/inference/src/cache_guard.cpp
@@ -12,7 +12,7 @@ CacheGuardEntry::CacheGuardEntry(CacheGuard& cacheGuard,
                                  std::atomic_int& refCount)
     : m_cacheGuard(cacheGuard),
       m_hash(hash),
-      m_mutex(m),
+      m_mutex(std::move(m)),
       m_refCount(refCount) {
     // Don't lock mutex right here for exception-safe considerations
     m_refCount++;
@@ -31,21 +31,22 @@ void CacheGuardEntry::perform_lock() {
 //////////////////////////////////////////////////////
 
 std::unique_ptr<CacheGuardEntry> CacheGuard::get_hash_lock(const std::string& hash) {
-    std::unique_lock<std::mutex> lock(m_tableMutex);
-    auto& data = m_table[hash];
     std::unique_ptr<CacheGuardEntry> res;
-    try {
-        // TODO: use std::make_unique when migrated to C++14
-        res =
-            std::unique_ptr<CacheGuardEntry>(new CacheGuardEntry(*this, hash, data.m_mutexPtr, data.m_itemRefCounter));
-    } catch (...) {
-        // In case of exception, we shall remove hash entry if it is not used
-        if (data.m_itemRefCounter == 0) {
-            m_table.erase(hash);
+    {
+        std::unique_lock<std::mutex> lock(m_tableMutex);
+        auto& data = m_table[hash];
+        try {
+            // TODO: use std::make_unique when migrated to C++14
+            res = std::unique_ptr<CacheGuardEntry>(
+                new CacheGuardEntry(*this, hash, data.m_mutexPtr, data.m_itemRefCounter));
+        } catch (...) {
+            // In case of exception, we shall remove hash entry if it is not used
+            if (data.m_itemRefCounter == 0) {
+                m_table.erase(hash);
+            }
+            throw;
         }
-        throw;
     }
-    lock.unlock();        // can unlock table lock here, as refCounter is positive and nobody can remove entry
     res->perform_lock();  // in case of exception, 'res' will be destroyed and item will be cleaned up from table
     return res;
 }
diff --git a/src/inference/src/dev/threading/cpu_streams_executor.cpp b/src/inference/src/dev/threading/cpu_streams_executor.cpp
index ebfe445e1d0d7a..1270c39c7fd95a 100644
--- a/src/inference/src/dev/threading/cpu_streams_executor.cpp
+++ b/src/inference/src/dev/threading/cpu_streams_executor.cpp
@@ -502,6 +502,10 @@ int CPUStreamsExecutor::get_stream_id() {
     return stream->_streamId;
 }
 
+int CPUStreamsExecutor::get_streams_num() {
+    return _impl->_config.get_streams();
+}
+
 int CPUStreamsExecutor::get_numa_node_id() {
     auto stream = _impl->_streams.local();
     return stream->_numaNodeId;
diff --git a/src/inference/src/system_conf.cpp b/src/inference/src/system_conf.cpp
index 4987e72355a5a6..fd8f97d93addd5 100644
--- a/src/inference/src/system_conf.cpp
+++ b/src/inference/src/system_conf.cpp
@@ -92,6 +92,10 @@ bool with_cpu_x86_avx512_core_amx_bf16() {
     return get_cpu_info().has(Xbyak::util::Cpu::tAMX_BF16);
 }
 
+bool with_cpu_x86_avx512_core_amx_fp16() {
+    return get_cpu_info().has(Xbyak::util::Cpu::tAMX_FP16);
+}
+
 bool with_cpu_x86_avx512_core_amx() {
     return with_cpu_x86_avx512_core_amx_int8() || with_cpu_x86_avx512_core_amx_bf16();
 }
@@ -131,6 +135,9 @@ bool with_cpu_x86_avx512_core_amx_int8() {
 bool with_cpu_x86_avx512_core_amx_bf16() {
     return false;
 }
+bool with_cpu_x86_avx512_core_amx_fp16() {
+    return false;
+}
 bool with_cpu_x86_avx512_core_amx() {
     return false;
 }
diff --git a/src/inference/tests/unit/core.cpp b/src/inference/tests/unit/core.cpp
index 9a9c859020235e..e60922a367cd84 100644
--- a/src/inference/tests/unit/core.cpp
+++ b/src/inference/tests/unit/core.cpp
@@ -16,7 +16,6 @@
 #include "openvino/runtime/device_id_parser.hpp"
 #include "openvino/util/file_util.hpp"
 
-using namespace testing;
 using namespace ov::util;
 
 TEST(CoreTests, Throw_on_register_plugin_twice) {
diff --git a/src/plugins/auto/src/auto_schedule.cpp b/src/plugins/auto/src/auto_schedule.cpp
index 3de0cc9f00b871..613287af14534b 100644
--- a/src/plugins/auto/src/auto_schedule.cpp
+++ b/src/plugins/auto/src/auto_schedule.cpp
@@ -155,9 +155,13 @@ void AutoSchedule::init() {
                         const auto properties =
                             m_context->m_ov_core->create_compile_config(ov::DeviceIDParser(device).get_device_name(),
                                                                         device_config);
-                        auto blobId =
-                            ov::ModelCache::compute_hash(std::const_pointer_cast<const ov::Model>(m_context->m_model),
-                                                         properties);
+                        std::string blobId;
+                        if (m_context->m_model)
+                            blobId = ov::ModelCache::compute_hash(
+                                std::const_pointer_cast<const ov::Model>(m_context->m_model),
+                                properties);
+                        else
+                            blobId = ov::ModelCache::compute_hash(m_context->m_model_path, properties);
                         std::string cached_model_path = ov::util::make_path(cache_dir, blobId + ".blob");
                         m_compile_context[CPU].m_is_enabled = !ov::util::file_exists(cached_model_path);
                         LOG_DEBUG_TAG("device: %s %s cached blob: %s ",
diff --git a/src/plugins/auto/tests/functional/behavior/caching_test.cpp b/src/plugins/auto/tests/functional/behavior/caching_test.cpp
index 1b2df23f9c0d1c..1ef107cd59991f 100644
--- a/src/plugins/auto/tests/functional/behavior/caching_test.cpp
+++ b/src/plugins/auto/tests/functional/behavior/caching_test.cpp
@@ -4,6 +4,7 @@
 
 #include "auto_func_test.hpp"
 #include "common_test_utils/include/common_test_utils/file_utils.hpp"
+#include "openvino/pass/serialize.hpp"
 
 using namespace ov::auto_plugin::tests;
 
@@ -74,6 +75,53 @@ TEST_F(AutoFuncTests, load_cached_model_to_actual_device_and_disable_CPU_acceler
     core.set_property(ov::cache_dir(""));
 }
 
+TEST_F(AutoFuncTests, load_model_path_to_actual_device_and_disable_CPU_accelerating_default_startup_fallback) {
+    std::string filePrefix = ov::test::utils::generateTestFilePrefix();
+    auto m_xml_path = filePrefix + ".xml";
+    auto m_bin_path = filePrefix + ".bin";
+    ov::pass::Serialize(m_xml_path, m_bin_path).run_on_model(model_cannot_batch);
+    core.set_property(ov::cache_dir(cache_path));
+    core.set_property("MOCK_GPU", ov::device::id("test"));  // device id for cache property distinguish with MOCK_CPU
+    {
+        auto compiled_model = core.compile_model(m_xml_path,
+                                                 "AUTO",
+                                                 {ov::device::priorities("MOCK_GPU", "MOCK_CPU"),
+                                                  ov::hint::performance_mode(ov::hint::PerformanceMode::THROUGHPUT)});
+    }
+    // No cached model for actual device
+    // will cache model for both actual device and CPU plugin
+    ASSERT_EQ(ov::test::utils::listFilesWithExt(cache_path, "blob").size(), 2);
+    ov::test::utils::removeFilesWithExt(cache_path, "blob");
+    {
+        auto compiled_model = core.compile_model(
+            m_xml_path,
+            "AUTO",
+            {ov::device::priorities("MOCK_GPU"), ov::hint::performance_mode(ov::hint::PerformanceMode::THROUGHPUT)});
+    }
+    {
+        auto compiled_model = core.compile_model(m_xml_path,
+                                                 "AUTO",
+                                                 {ov::device::priorities("MOCK_GPU", "MOCK_CPU"),
+                                                  ov::hint::performance_mode(ov::hint::PerformanceMode::THROUGHPUT)});
+    }
+    // cached model exists for actual device
+    // will reuse cached model for actual device without CPU accelerating(No cached model for CPU)
+    ASSERT_EQ(ov::test::utils::listFilesWithExt(cache_path, "blob").size(), 1);
+
+    core.set_property("MOCK_GPU", ov::device::id("test_regenerate"));
+    {
+        auto compiled_model = core.compile_model(m_xml_path,
+                                                 "AUTO",
+                                                 {ov::device::priorities("MOCK_GPU", "MOCK_CPU"),
+                                                  ov::hint::performance_mode(ov::hint::PerformanceMode::THROUGHPUT)});
+    }
+    // model hash id changed for actual device
+    // will cache model for both actual device and CPU as accelerator
+    ASSERT_EQ(ov::test::utils::listFilesWithExt(cache_path, "blob").size(), 3);
+    core.set_property(ov::cache_dir(""));
+    ov::test::utils::removeIRFiles(m_xml_path, m_bin_path);
+}
+
 TEST_F(AutoFuncTests, load_cached_model_to_actual_device_and_disable_CPU_accelerating_set_startup_fallback) {
     core.set_property(ov::cache_dir(cache_path));
     core.set_property("MOCK_GPU", ov::device::id("test"));  // device id for cache property distinguish with MOCK_CPU
diff --git a/src/plugins/auto/tests/unit/compile_model_metric_test.cpp b/src/plugins/auto/tests/unit/compile_model_metric_test.cpp
index c5b0ddedc0d32b..48dc4c8057f049 100644
--- a/src/plugins/auto/tests/unit/compile_model_metric_test.cpp
+++ b/src/plugins/auto/tests/unit/compile_model_metric_test.cpp
@@ -172,7 +172,7 @@ TEST_P(ExecNetworkget_propertyOptimalNumInferReq, OPTIMAL_NUMBER_OF_INFER_REQUES
             .WillByDefault(RETURN_MOCK_VALUE(actualOptimalNum));
         ON_CALL(
             *core,
-            get_property(StrEq(ov::test::utils::DEVICE_KEEMBAY), StrEq(ov::optimal_number_of_infer_requests.name()), _))
+            get_property(StrEq(ov::test::utils::DEVICE_NPU), StrEq(ov::optimal_number_of_infer_requests.name()), _))
             .WillByDefault(RETURN_MOCK_VALUE(actualOptimalNum));
     }
     if (isSupportNumRequests)
@@ -196,7 +196,7 @@ TEST_P(ExecNetworkget_propertyOptimalNumInferReq, OPTIMAL_NUMBER_OF_INFER_REQUES
         std::tuple<unsigned int, unsigned int> rangeOfStreams = std::make_tuple<unsigned int, unsigned int>(1, 3);
         ON_CALL(*core, get_property(StrEq(ov::test::utils::DEVICE_GPU), StrEq(ov::optimal_batch_size.name()), _))
             .WillByDefault(RETURN_MOCK_VALUE(gpuOptimalBatchNum));
-        ON_CALL(*core, get_property(StrEq(ov::test::utils::DEVICE_KEEMBAY), StrEq(ov::optimal_batch_size.name()), _))
+        ON_CALL(*core, get_property(StrEq(ov::test::utils::DEVICE_NPU), StrEq(ov::optimal_batch_size.name()), _))
             .WillByDefault(RETURN_MOCK_VALUE(npuOptimalBatchNum));
         ON_CALL(*core, get_property(_, StrEq(ov::range_for_streams.name()), _))
             .WillByDefault(RETURN_MOCK_VALUE(rangeOfStreams));
@@ -330,28 +330,28 @@ const std::vector<ConfigParams> testConfigs = {
     ConfigParams{false, 3, 5, false, 0, 5, true, ov::test::utils::DEVICE_GPU, 1, 6, true, true},
     ConfigParams{true, 3, 5, false, 6, 5, true, ov::test::utils::DEVICE_GPU, 6, 6, true, true},
     ConfigParams{false, 3, 5, false, 6, 5, true, ov::test::utils::DEVICE_GPU, 6, 6, true, true},
-    ConfigParams{false, 3, -1, false, 2, -1, true, ov::test::utils::DEVICE_KEEMBAY, 1, 0, false, true},
-    ConfigParams{true, 3, -1, false, 2, -1, true, ov::test::utils::DEVICE_KEEMBAY, 8, 0, false, true},
-    ConfigParams{true, 3, -1, false, 2, -1, true, ov::test::utils::DEVICE_KEEMBAY, 8, 0, false, false},
-    ConfigParams{true, 3, -1, false, 0, -1, true, ov::test::utils::DEVICE_KEEMBAY, 8, 0, true, false},
-    ConfigParams{true, 3, -1, false, 2, -1, true, ov::test::utils::DEVICE_KEEMBAY, 2, 0, true, false},
-    ConfigParams{true, 3, -1, false, 2, -1, true, ov::test::utils::DEVICE_KEEMBAY, 2, 1, true, false},
-    ConfigParams{true, 3, -1, false, 2, -1, true, ov::test::utils::DEVICE_KEEMBAY, 6, 6, false, false},
-    ConfigParams{true, 3, -1, false, 2, -1, true, ov::test::utils::DEVICE_KEEMBAY, 8, 10, false, false},
-    ConfigParams{true, 3, -1, false, 4, -1, true, ov::test::utils::DEVICE_KEEMBAY, 4, 6, true, true},
-    ConfigParams{true, 3, -1, false, 4, -1, true, ov::test::utils::DEVICE_KEEMBAY, 2, 2, true, true},
-    ConfigParams{true, 3, -1, false, 0, -1, true, ov::test::utils::DEVICE_KEEMBAY, 8, 10, true, true},
-    ConfigParams{true, 3, -1, false, 0, -1, true, ov::test::utils::DEVICE_KEEMBAY, 6, 6, true, true},
-    ConfigParams{false, 3, -1, false, 0, -1, true, ov::test::utils::DEVICE_KEEMBAY, 1, 0, true, true},
-    ConfigParams{true, 3, -1, false, 0, -1, true, ov::test::utils::DEVICE_KEEMBAY, 8, 0, true, true},
-    ConfigParams{false, 3, -1, false, 2, -1, true, ov::test::utils::DEVICE_KEEMBAY, 2, 0, true, true},
-    ConfigParams{true, 3, -1, false, 2, -1, true, ov::test::utils::DEVICE_KEEMBAY, 2, 0, true, true},
-    ConfigParams{false, 3, -1, true, 2, -1, false, ov::test::utils::DEVICE_KEEMBAY, 2, 0, false, true},
-    ConfigParams{true, 3, -1, true, 2, -1, false, ov::test::utils::DEVICE_KEEMBAY, 2, 0, false, true},
-    ConfigParams{false, 3, 5, false, 2, 5, true, ov::test::utils::DEVICE_KEEMBAY, 1, 0, false, true},
-    ConfigParams{true, 3, 5, false, 2, 5, true, ov::test::utils::DEVICE_KEEMBAY, 8, 0, false, true},
-    ConfigParams{false, 3, 5, true, 2, 5, false, ov::test::utils::DEVICE_KEEMBAY, 2, 0, false, true},
-    ConfigParams{true, 3, 5, true, 2, 5, false, ov::test::utils::DEVICE_KEEMBAY, 2, 0, false, true},
+    ConfigParams{false, 3, -1, false, 2, -1, true, ov::test::utils::DEVICE_NPU, 1, 0, false, true},
+    ConfigParams{true, 3, -1, false, 2, -1, true, ov::test::utils::DEVICE_NPU, 8, 0, false, true},
+    ConfigParams{true, 3, -1, false, 2, -1, true, ov::test::utils::DEVICE_NPU, 8, 0, false, false},
+    ConfigParams{true, 3, -1, false, 0, -1, true, ov::test::utils::DEVICE_NPU, 8, 0, true, false},
+    ConfigParams{true, 3, -1, false, 2, -1, true, ov::test::utils::DEVICE_NPU, 2, 0, true, false},
+    ConfigParams{true, 3, -1, false, 2, -1, true, ov::test::utils::DEVICE_NPU, 2, 1, true, false},
+    ConfigParams{true, 3, -1, false, 2, -1, true, ov::test::utils::DEVICE_NPU, 6, 6, false, false},
+    ConfigParams{true, 3, -1, false, 2, -1, true, ov::test::utils::DEVICE_NPU, 8, 10, false, false},
+    ConfigParams{true, 3, -1, false, 4, -1, true, ov::test::utils::DEVICE_NPU, 4, 6, true, true},
+    ConfigParams{true, 3, -1, false, 4, -1, true, ov::test::utils::DEVICE_NPU, 2, 2, true, true},
+    ConfigParams{true, 3, -1, false, 0, -1, true, ov::test::utils::DEVICE_NPU, 8, 10, true, true},
+    ConfigParams{true, 3, -1, false, 0, -1, true, ov::test::utils::DEVICE_NPU, 6, 6, true, true},
+    ConfigParams{false, 3, -1, false, 0, -1, true, ov::test::utils::DEVICE_NPU, 1, 0, true, true},
+    ConfigParams{true, 3, -1, false, 0, -1, true, ov::test::utils::DEVICE_NPU, 8, 0, true, true},
+    ConfigParams{false, 3, -1, false, 2, -1, true, ov::test::utils::DEVICE_NPU, 2, 0, true, true},
+    ConfigParams{true, 3, -1, false, 2, -1, true, ov::test::utils::DEVICE_NPU, 2, 0, true, true},
+    ConfigParams{false, 3, -1, true, 2, -1, false, ov::test::utils::DEVICE_NPU, 2, 0, false, true},
+    ConfigParams{true, 3, -1, true, 2, -1, false, ov::test::utils::DEVICE_NPU, 2, 0, false, true},
+    ConfigParams{false, 3, 5, false, 2, 5, true, ov::test::utils::DEVICE_NPU, 1, 0, false, true},
+    ConfigParams{true, 3, 5, false, 2, 5, true, ov::test::utils::DEVICE_NPU, 8, 0, false, true},
+    ConfigParams{false, 3, 5, true, 2, 5, false, ov::test::utils::DEVICE_NPU, 2, 0, false, true},
+    ConfigParams{true, 3, 5, true, 2, 5, false, ov::test::utils::DEVICE_NPU, 2, 0, false, true},
 };
 
 INSTANTIATE_TEST_SUITE_P(smoke_Auto_BehaviorTests,
diff --git a/src/plugins/intel_cpu/src/compiled_model.cpp b/src/plugins/intel_cpu/src/compiled_model.cpp
index 81a2e7e700e4e3..ce36f2df1fc04a 100644
--- a/src/plugins/intel_cpu/src/compiled_model.cpp
+++ b/src/plugins/intel_cpu/src/compiled_model.cpp
@@ -165,17 +165,10 @@ CompiledModel::GraphGuard::Lock CompiledModel::get_graph() const {
                 GraphContext::Ptr ctx;
                 {
                     std::lock_guard<std::mutex> lock{*m_mutex.get()};
-                    // disable weights caching if graph was created only once
-                    auto weightsCache = m_cfg.streamExecutorConfig.get_streams() != 1 ? m_socketWeights[socketId] : nullptr;
                     auto isQuantizedFlag =
                         (m_cfg.lpTransformsMode == Config::On) &&
                         ov::pass::low_precision::LowPrecision::isFunctionQuantized(m_model);
-
-                    ctx = std::make_shared<GraphContext>(m_cfg,
-                                                         weightsCache,
-                                                         isQuantizedFlag,
-                                                         streamsExecutor,
-                                                         m_sub_memory_manager);
+                    ctx = std::make_shared<GraphContext>(m_cfg, m_socketWeights[socketId], isQuantizedFlag, streamsExecutor);
                 }
                 const std::shared_ptr<const ov::Model> model = m_model;
                 graphLock._graph.CreateGraph(model, ctx);
diff --git a/src/plugins/intel_cpu/src/config.cpp b/src/plugins/intel_cpu/src/config.cpp
index b1a57cedde5a22..3a383a04c48a28 100644
--- a/src/plugins/intel_cpu/src/config.cpp
+++ b/src/plugins/intel_cpu/src/config.cpp
@@ -236,6 +236,7 @@ void Config::readProperties(const ov::AnyMap& prop, const ModelType modelType) {
             }
         } else if (key == ov::hint::dynamic_quantization_group_size.name()) {
             try {
+                fcDynamicQuantizationGroupSizeSetExplicitly = true;
                 fcDynamicQuantizationGroupSize = val.as<uint64_t>();
             } catch (const ov::Exception&) {
                 OPENVINO_THROW("Wrong value for property key ",
@@ -390,6 +391,12 @@ void Config::readProperties(const ov::AnyMap& prop, const ModelType modelType) {
             inferencePrecision = ov::element::undefined;
         }
     }
+    // disable dynamic quantization and kv quantization for best accuracy
+    if (executionMode == ov::hint::ExecutionMode::ACCURACY) {
+        if (!fcDynamicQuantizationGroupSizeSetExplicitly) {
+            fcDynamicQuantizationGroupSize = 0;
+        }
+    }
 
     if (!prop.empty())
         _config.clear();
diff --git a/src/plugins/intel_cpu/src/config.h b/src/plugins/intel_cpu/src/config.h
index 8f97183a326eb4..98559ce434513b 100644
--- a/src/plugins/intel_cpu/src/config.h
+++ b/src/plugins/intel_cpu/src/config.h
@@ -50,8 +50,9 @@ struct Config {
     std::string dumpToDot = {};
     std::string device_id = {};
     float fcSparseWeiDecompressionRate = 1.0f;
-    uint64_t fcDynamicQuantizationGroupSize = 0;
+    uint64_t fcDynamicQuantizationGroupSize = 32;
     ov::element::Type kvCachePrecision = ov::element::f16;
+    bool fcDynamicQuantizationGroupSizeSetExplicitly = false;
 #if defined(OPENVINO_ARCH_X86_64)
     size_t rtCacheCapacity = 5000ul;
 #else
diff --git a/src/plugins/intel_cpu/src/cpu_types.cpp b/src/plugins/intel_cpu/src/cpu_types.cpp
index 130fca6d6eb916..cd6021a40a5a8c 100644
--- a/src/plugins/intel_cpu/src/cpu_types.cpp
+++ b/src/plugins/intel_cpu/src/cpu_types.cpp
@@ -142,6 +142,7 @@ static const TypeToNameMap& get_type_to_name_tbl() {
         {"NV12toBGR", Type::ColorConvert},
         {"I420toRGB", Type::ColorConvert},
         {"I420toBGR", Type::ColorConvert},
+        {"Col2Im", Type::Col2Im},
         {"MVN", Type::MVN},
         {"NormalizeL2", Type::NormalizeL2},
         {"ScatterUpdate", Type::ScatterUpdate},
@@ -305,6 +306,7 @@ std::string NameFromType(const Type type) {
         CASE(MVN);
         CASE(TensorIterator);
         CASE(Convert);
+        CASE(Col2Im);
         CASE(ColorConvert);
         CASE(NormalizeL2);
         CASE(ScatterUpdate);
diff --git a/src/plugins/intel_cpu/src/cpu_types.h b/src/plugins/intel_cpu/src/cpu_types.h
index bfb411446c2bd9..6834225c1f2515 100644
--- a/src/plugins/intel_cpu/src/cpu_types.h
+++ b/src/plugins/intel_cpu/src/cpu_types.h
@@ -61,6 +61,7 @@ enum class Type {
     TensorIterator,
     Convert,
     ColorConvert,
+    Col2Im,
     MVN,
     NormalizeL2,
     ScatterUpdate,
diff --git a/src/plugins/intel_cpu/src/emitters/plugin/aarch64/jit_eltwise_emitters.cpp b/src/plugins/intel_cpu/src/emitters/plugin/aarch64/jit_eltwise_emitters.cpp
index 8953489de208ae..85867ac5d04ec9 100644
--- a/src/plugins/intel_cpu/src/emitters/plugin/aarch64/jit_eltwise_emitters.cpp
+++ b/src/plugins/intel_cpu/src/emitters/plugin/aarch64/jit_eltwise_emitters.cpp
@@ -3,6 +3,7 @@
 //
 
 #include "jit_eltwise_emitters.hpp"
+#include "transformations/cpu_opset/common/op/swish_cpu.hpp"
 
 #include <memory>
 #include "common/utils.hpp"
@@ -900,6 +901,69 @@ void jit_is_inf_emitter::register_table_entries() {
     push_arg_entry_of("inf_neg", 0xFF800000, true);
 }
 
+/// IS_NAN ///
+jit_is_nan_emitter::jit_is_nan_emitter(dnnl::impl::cpu::aarch64::jit_generator* host,
+                                   dnnl::impl::cpu::aarch64::cpu_isa_t host_isa,
+                                   const std::shared_ptr<ov::Node>& node)
+                                   : jit_emitter(host, host_isa, node, get_arithmetic_binary_exec_precision(node)) {
+    auto isNaN = ov::as_type_ptr<ov::op::v10::IsNaN>(node);
+    if (isNaN == nullptr) {
+        OV_CPU_JIT_EMITTER_THROW("Can't cast to ov::op::v10::IsNaN");
+    }
+
+    prepare_table();
+}
+
+jit_is_nan_emitter::jit_is_nan_emitter(dnnl::impl::cpu::aarch64::jit_generator* host,
+                                   dnnl::impl::cpu::aarch64::cpu_isa_t host_isa,
+                                   const ov::element::Type exec_prc)
+                                   : jit_emitter(host, host_isa, exec_prc) {
+    prepare_table();
+}
+
+size_t jit_is_nan_emitter::get_inputs_count() const { return 1; }
+
+size_t jit_is_nan_emitter::get_aux_vecs_count() const { return 1; }
+
+size_t jit_is_nan_emitter::get_aux_gprs_count() const { return 1; }
+
+std::set<std::vector<element::Type>> jit_is_nan_emitter::get_supported_precisions(const std::shared_ptr<ov::Node>& node) {
+    return {{element::f32}};
+}
+
+void jit_is_nan_emitter::emit_impl(const std::vector<size_t>& in_vec_idxs, const std::vector<size_t>& out_vec_idxs) const {
+    if (host_isa_ == dnnl::impl::cpu::aarch64::asimd) {
+        emit_isa<dnnl::impl::cpu::aarch64::asimd>(in_vec_idxs, out_vec_idxs);
+    } else {
+        OV_CPU_JIT_EMITTER_THROW("Can't create jit eltwise kernel");
+    }
+}
+
+template <dnnl::impl::cpu::aarch64::cpu_isa_t isa>
+void jit_is_nan_emitter::emit_isa(const std::vector<size_t> &in_vec_idxs, const std::vector<size_t> &out_vec_idxs) const {
+    OV_CPU_JIT_EMITTER_ASSERT(exec_prc_ == ov::element::f32, "unsupported precision: " + exec_prc_.to_string());
+
+    using TReg = typename dnnl::impl::cpu::aarch64::cpu_isa_traits<isa>::TReg;
+
+    TReg src = TReg(in_vec_idxs[0]);
+    TReg dst = TReg(out_vec_idxs[0]);
+    TReg aux = TReg(aux_vec_idxs[0]);
+
+    // According to the IEEE standard, NaN values have the odd property that comparisons involving them are always false.
+    h->fcmeq(dst.s, src.s, src.s);
+    h->ld1r(aux.s, table_val2("zero"));
+    h->fcmeq(dst.s, dst.s, aux.s);
+    // Sets elements in 'dst' to 1.0 where the comparison was true.
+    h->ld1r(aux.s, table_val2("one"));
+    h->and_(dst.b16, dst.b16, aux.b16);
+}
+
+void jit_is_nan_emitter::register_table_entries() {
+    // Registers constant values that comply with the IEEE 754 standard.
+    push_arg_entry_of("one", 0x3f800000, true);
+    push_arg_entry_of("zero", 0x00000000, true);
+}
+
 /// MAX ///
 jit_maximum_emitter::jit_maximum_emitter(dnnl::impl::cpu::aarch64::jit_generator* host,
                                          dnnl::impl::cpu::aarch64::cpu_isa_t host_isa,
@@ -935,7 +999,7 @@ void jit_maximum_emitter::emit_isa(const std::vector<size_t> &in_vec_idxs, const
 }
 
 std::set<std::vector<element::Type>> jit_maximum_emitter::get_supported_precisions(const std::shared_ptr<ov::Node>& node) {
-    return {{element::f32}, {element::f32}};
+    return {{element::f32, element::f32}};
 }
 
 /// MIN ///
@@ -973,7 +1037,7 @@ void jit_minimum_emitter::emit_isa(const std::vector<size_t> &in_vec_idxs, const
 }
 
 std::set<std::vector<element::Type>> jit_minimum_emitter::get_supported_precisions(const std::shared_ptr<ov::Node>& node) {
-    return {{element::f32}, {element::f32}};
+    return {{element::f32, element::f32}};
 }
 
 /// MISH ///
@@ -1618,6 +1682,12 @@ jit_swish_emitter::jit_swish_emitter(dnnl::impl::cpu::aarch64::jit_generator* ho
                                      dnnl::impl::cpu::aarch64::cpu_isa_t host_isa,
                                      const std::shared_ptr<ov::Node>& node)
         : jit_emitter(host, host_isa, node, get_arithmetic_binary_exec_precision(node)) {
+    const auto swish = std::dynamic_pointer_cast<SwishNode>(node);
+    if (swish == nullptr) {
+        OV_CPU_JIT_EMITTER_THROW("Can't cast to SwishNode");
+    }
+    beta = static_cast<float>(swish->get_alpha());
+
     prepare_table();
     sigmoid_emitter = std::make_unique<jit_sigmoid_emitter>(h, host_isa, node);
 }
diff --git a/src/plugins/intel_cpu/src/emitters/plugin/aarch64/jit_eltwise_emitters.hpp b/src/plugins/intel_cpu/src/emitters/plugin/aarch64/jit_eltwise_emitters.hpp
index ba107907d7b967..8dda7f3e91c7d4 100644
--- a/src/plugins/intel_cpu/src/emitters/plugin/aarch64/jit_eltwise_emitters.hpp
+++ b/src/plugins/intel_cpu/src/emitters/plugin/aarch64/jit_eltwise_emitters.hpp
@@ -305,6 +305,33 @@ class jit_hswish_emitter : public jit_emitter {
     void emit_isa(const std::vector<size_t> &in_vec_idxs, const std::vector<size_t> &out_vec_idxs) const;
 };
 
+class jit_is_nan_emitter : public jit_emitter {
+public:
+    jit_is_nan_emitter(dnnl::impl::cpu::aarch64::jit_generator *host,
+                       dnnl::impl::cpu::aarch64::cpu_isa_t host_isa,
+                       const ov::element::Type exec_prc = ov::element::f32);
+
+    jit_is_nan_emitter(dnnl::impl::cpu::aarch64::jit_generator *host,
+                       dnnl::impl::cpu::aarch64::cpu_isa_t host_isa,
+                       const std::shared_ptr<ov::Node>& node);
+
+    size_t get_inputs_count() const override;
+
+    size_t get_aux_vecs_count() const override;
+
+    size_t get_aux_gprs_count() const override;
+
+    static std::set<std::vector<element::Type>> get_supported_precisions(const std::shared_ptr<ov::Node>& node = nullptr);
+
+private:
+    void emit_impl(const std::vector<size_t> &in_vec_idxs, const std::vector<size_t> &out_vec_idxs) const override;
+
+    template <dnnl::impl::cpu::aarch64::cpu_isa_t isa>
+    void emit_isa(const std::vector<size_t> &in_vec_idxs, const std::vector<size_t> &out_vec_idxs) const;
+
+    void register_table_entries() override;
+};
+
 class jit_maximum_emitter : public jit_emitter {
 public:
     jit_maximum_emitter(dnnl::impl::cpu::aarch64::jit_generator *host,
diff --git a/src/plugins/intel_cpu/src/emitters/plugin/x64/jit_load_store_emitters.cpp b/src/plugins/intel_cpu/src/emitters/plugin/x64/jit_load_store_emitters.cpp
index 73b3f827018783..dd764c66348d44 100644
--- a/src/plugins/intel_cpu/src/emitters/plugin/x64/jit_load_store_emitters.cpp
+++ b/src/plugins/intel_cpu/src/emitters/plugin/x64/jit_load_store_emitters.cpp
@@ -61,13 +61,21 @@ size_t store_emitter_params::hash() const {
     return seed;
 }
 
-static int get_aux_regs_as_temp(const int elem_count, const int data_size, bool is_pure_move,
+static int get_aux_regs_as_temp(const int elem_count, const int data_size, bool is_pure_move, bool is_store_as_real16,
                                 const int avx512_threshold_for_mask = 0, const bool is_fill = false) {
     if (mayiuse(cpu::x64::avx512_core) && is_fill)
         return 1;
-
+    // for pure move, there are direct no-mask instructions to move on full xmm/ymm/zmm, so aux_gpr is not needed.
+    // for move+convert:
+    // there are direct no-mask instructions to load i8/u8/i16/u16/bf16/fp16 to full xmm/ymm/zmm as f32/i32, so aux_gpr is not needed.
+    // there are direct no-mask instructions to store i32 on full xmm/ymm/zmm to i8/u8/i16/u16, so aux_gpr is not needed.
+    // store f32 on full xmm/ymm/zmm to bf16/fp16, need convert to bf16/fp16 on vmm, then store vmm to memory, use store_dword_to_word/byte_base condition.
+    // store_num == 16, vector: 16 * f32 -> 16 * bf16 -> ymm(256bit) -> store
+    // store_num == 8,  vector:  8 * f32 ->  8 * bf16 -> xmm(128bit)  -> store
+    // store_num == 4,  vector:  4 * f32 ->  4 * bf16 ->       64bit  -> masked instruction with aux_gpr needed
+    // f32<->i32 is on full vmm, so aux_gpr is not needed.
     const int byte_size = elem_count * data_size;
-    if ((is_pure_move && one_of(byte_size, 16, 32, 64)) || (!is_pure_move && one_of(elem_count, 4, 8, 16)))
+    if ((is_pure_move && one_of(byte_size, 16, 32, 64)) || (!is_pure_move && one_of(elem_count, 4, 8, 16) && !is_store_as_real16))
         return 0;
     if ((mayiuse(cpu::x64::avx512_core) && (byte_size > avx512_threshold_for_mask)) || (one_of(byte_size % 16, 1, 2, 3)))
         return 1;
@@ -92,7 +100,7 @@ size_t jit_load_emitter::aux_gprs_count() const {
     const auto is_pure_load = (src_prc_ == dst_prc_) ||
                                 (one_of(src_prc_, ov::element::f32, ov::element::i32) &&
                                  one_of(dst_prc_, ov::element::f32, ov::element::i32));
-    int count = get_aux_regs_as_temp(load_num_, static_cast<int>(src_prc_.size()), is_pure_load, threshold_for_mask_emu_load, is_fill_);
+    int count = get_aux_regs_as_temp(load_num_, static_cast<int>(src_prc_.size()), is_pure_load, false, threshold_for_mask_emu_load, is_fill_);
 
     // 1 for table address
     if (is_fill_)
@@ -630,7 +638,8 @@ size_t jit_store_emitter::aux_gprs_count() const {
     const auto is_pure_store = (src_prc_ == dst_prc_) ||
                                 (one_of(src_prc_, ov::element::f32, ov::element::i32) &&
                                  one_of(dst_prc_, ov::element::f32, ov::element::i32));
-    int count = get_aux_regs_as_temp(store_num_, static_cast<int>(dst_prc_.size()), is_pure_store, threshold_for_mask_emu_store);
+    const auto is_store_as_real16 = one_of(dst_prc_, ov::element::bf16, ov::element::f16);
+    int count = get_aux_regs_as_temp(store_num_, static_cast<int>(dst_prc_.size()), is_pure_store, is_store_as_real16, threshold_for_mask_emu_store);
 
     // for table value in truncation arithmetic mode
     if (is_truncation_emulation())
@@ -695,7 +704,8 @@ void jit_store_emitter::emit_isa(const int in_vec_idx, const Xbyak::Reg64 &reg_d
 
     data_idx = in_vec_idx;
     data_reg_updated = false;
-    aux_src_idx = aux_vec_idxs.back(); // for avoid src pollution
+    if (!aux_vec_idxs.empty())
+        aux_src_idx = aux_vec_idxs.back(); // to avoid src pollution
     if (src_prc_ != dst_prc_) {
         switch (src_prc_) {
             case ov::element::f32:
diff --git a/src/plugins/intel_cpu/src/emitters/snippets/aarch64/cpu_generator.cpp b/src/plugins/intel_cpu/src/emitters/snippets/aarch64/cpu_generator.cpp
index 4d9b807dee79ad..685ace977f7415 100644
--- a/src/plugins/intel_cpu/src/emitters/snippets/aarch64/cpu_generator.cpp
+++ b/src/plugins/intel_cpu/src/emitters/snippets/aarch64/cpu_generator.cpp
@@ -14,6 +14,7 @@
 #include "emitters/snippets/aarch64/jit_fill_emitter.hpp"
 
 #include "transformations/snippets/common/op/fused_mul_add.hpp"
+#include "transformations/cpu_opset/common/op/swish_cpu.hpp"
 
 #include "openvino/opsets/opset13.hpp"
 
@@ -37,6 +38,38 @@ namespace ov {
     } \
 }
 
+#define CREATE_GELU_V7_EMITTER(e_type_erf, e_type_tanh) { \
+    [this](const snippets::lowered::ExpressionPtr& expr) -> std::shared_ptr<snippets::Emitter> { \
+        const auto& n = expr->get_node(); \
+        const auto& gelu = std::dynamic_pointer_cast<ov::op::v7::Gelu>(n); \
+        if (gelu == nullptr) { \
+            OPENVINO_THROW("Can't cast to ov::op::v7::Gelu"); \
+        } \
+        const auto approximationMode = gelu->get_approximation_mode(); \
+        if (approximationMode == ov::op::GeluApproximationMode::ERF) { \
+            return std::make_shared<e_type_erf>(h.get(), isa, n); \
+        } else if (approximationMode == ov::op::GeluApproximationMode::TANH) { \
+            return std::make_shared<e_type_tanh>(h.get(), isa, n); \
+        } else { \
+            OPENVINO_THROW("Unsupported Gelu approximation mode"); \
+        } \
+    }, \
+    [](const std::shared_ptr<ov::Node>& n) -> std::set<std::vector<element::Type>> { \
+        const auto& gelu = std::dynamic_pointer_cast<ov::op::v7::Gelu>(n); \
+        if (gelu == nullptr) { \
+            OPENVINO_THROW("Can't cast to ov::op::v7::Gelu"); \
+        } \
+        const auto approximationMode = gelu->get_approximation_mode(); \
+        if (approximationMode == ov::op::GeluApproximationMode::ERF) { \
+            return e_type_erf::get_supported_precisions(n); \
+        } else if (approximationMode == ov::op::GeluApproximationMode::TANH) { \
+            return e_type_tanh::get_supported_precisions(n); \
+        } else { \
+            OPENVINO_THROW("Unsupported Gelu approximation mode"); \
+        } \
+    } \
+}
+
 class jit_snippet : public dnnl::impl::cpu::aarch64::jit_generator {
 public:
     DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_snippet)
@@ -87,11 +120,25 @@ CPUTargetMachine::CPUTargetMachine(dnnl::impl::cpu::aarch64::cpu_isa_t host_isa)
     // binary
     jitters[op::v1::Add::get_type_info_static()] = CREATE_CPU_EMITTER(jit_add_emitter);
     jitters[op::v1::Divide::get_type_info_static()] = CREATE_CPU_EMITTER(jit_divide_emitter);
+    jitters[op::v1::Maximum::get_type_info_static()] = CREATE_CPU_EMITTER(jit_maximum_emitter);
+    jitters[op::v1::Minimum::get_type_info_static()] = CREATE_CPU_EMITTER(jit_minimum_emitter);
+    jitters[op::v1::Mod::get_type_info_static()] = CREATE_CPU_EMITTER(jit_mod_emitter);
     jitters[op::v1::Multiply::get_type_info_static()] = CREATE_CPU_EMITTER(jit_multiply_emitter);
+    jitters[op::v1::Subtract::get_type_info_static()] = CREATE_CPU_EMITTER(jit_subtract_emitter);
 
     // unary
+    jitters[ov::op::v0::Abs::get_type_info_static()] = CREATE_CPU_EMITTER(jit_abs_emitter);
+    jitters[ov::op::v0::Clamp::get_type_info_static()] = CREATE_CPU_EMITTER(jit_clamp_emitter);
+    jitters[ov::op::v0::Elu::get_type_info_static()] = CREATE_CPU_EMITTER(jit_elu_emitter);
     jitters[ov::op::v0::Exp::get_type_info_static()] = CREATE_CPU_EMITTER(jit_exp_emitter);
+    jitters[ov::op::v0::Floor::get_type_info_static()] = CREATE_CPU_EMITTER(jit_floor_emitter);
+    jitters[ov::op::v0::Gelu::get_type_info_static()] = CREATE_CPU_EMITTER(jit_gelu_erf_emitter);
+    jitters[ov::op::v7::Gelu::get_type_info_static()] = CREATE_GELU_V7_EMITTER(jit_gelu_erf_emitter, jit_gelu_tanh_emitter);
+    jitters[ov::op::v4::HSwish::get_type_info_static()] = CREATE_CPU_EMITTER(jit_hswish_emitter);
+    jitters[ov::op::v4::Mish::get_type_info_static()] = CREATE_CPU_EMITTER(jit_mish_emitter);
     jitters[ov::op::v0::Relu::get_type_info_static()] = CREATE_CPU_EMITTER(jit_relu_emitter);
+    jitters[ov::op::v0::Sigmoid::get_type_info_static()] = CREATE_CPU_EMITTER(jit_sigmoid_emitter);
+    jitters[ov::intel_cpu::SwishNode::get_type_info_static()] = CREATE_CPU_EMITTER(jit_swish_emitter);
     jitters[ov::op::v0::Tanh::get_type_info_static()] = CREATE_CPU_EMITTER(jit_tanh_emitter);
 
     // control flow
@@ -150,7 +197,8 @@ std::shared_ptr<snippets::Generator> CPUGenerator::clone() const {
 
 ov::snippets::RegType CPUGenerator::get_specific_op_out_reg_type(const ov::Output<ov::Node>& out) const {
     const auto op = out.get_node_shared_ptr();
-    if (std::dynamic_pointer_cast<intel_cpu::FusedMulAdd>(op))
+    if (std::dynamic_pointer_cast<intel_cpu::FusedMulAdd>(op) ||
+        std::dynamic_pointer_cast<intel_cpu::SwishNode>(op))
         return ov::snippets::RegType::vec;
     else
         return ov::snippets::RegType::undefined;
diff --git a/src/plugins/intel_cpu/src/emitters/snippets/cpu_kernel_executor_table.hpp b/src/plugins/intel_cpu/src/emitters/snippets/cpu_kernel_executor_table.hpp
index 14154a79fe67f0..09781e014df946 100644
--- a/src/plugins/intel_cpu/src/emitters/snippets/cpu_kernel_executor_table.hpp
+++ b/src/plugins/intel_cpu/src/emitters/snippets/cpu_kernel_executor_table.hpp
@@ -16,28 +16,24 @@ class CPUKernelExecutor : public snippets::KernelExecutor<Conf, KernelType> {
      CPUKernelExecutor(ov::intel_cpu::MultiCacheWeakPtr kernel_cache, std::shared_ptr<Conf> c) :
                        snippets::KernelExecutor<Conf, KernelType>(c), m_kernel_cache(std::move(kernel_cache)) {}
      struct Key {
-         explicit Key(std::shared_ptr<Conf> c) : config{std::move(c)} {}
-         const std::shared_ptr<Conf> config;
+         explicit Key(const std::shared_ptr<const Conf>& c) : config{c} {}
+         const std::shared_ptr<const  Conf> config;
          size_t hash() const { return config->hash(); }
          bool operator==(const Key& rhs) const { return *config == *rhs.config; }
      };
-    void update_kernel() override {
-        OPENVINO_ASSERT(m_config && m_config->is_completed(), "Update kernel was called with invalid config");
+    void update_kernel(const std::shared_ptr<const Conf>& config, std::shared_ptr<KernelType>& kernel) const override final { // NOLINT
         const auto& cache = m_kernel_cache.lock();
         OPENVINO_ASSERT(cache, "Invalid kernel cache pointer in CPUKernelExecutor::update_kernel()");
-        const auto& lookup_result = cache->getOrCreate(Key(m_config),
+        const auto& lookup_result = cache->getOrCreate(Key(config),
                                                        [this](const Key& k) {
                                                             return compile_kernel(k.config);
                                                        });
-        m_kernel = lookup_result.first;
-        OPENVINO_ASSERT(m_kernel, "Failed to compile kernel executor");
+        kernel = lookup_result.first;
     }
 
 protected:
-    // Note: this usings are needed because non-dependent names are not looked up in dependent base classes
-    using snippets::KernelExecutor<Conf, KernelType>::m_config;
-    using snippets::KernelExecutor<Conf, KernelType>::m_kernel;
-    using snippets::KernelExecutor<Conf, KernelType>::compile_kernel;
+    /** Compile kernel managed by KernelExecutor instance. Will be called only if Kernel is not found in the cache */
+    virtual std::shared_ptr<KernelType> compile_kernel(const std::shared_ptr<const Conf>& c) const = 0;
     /** CPU plugin cache implementation is used to avoid redundant recompilations */
     ov::intel_cpu::MultiCacheWeakPtr m_kernel_cache;
 };
diff --git a/src/plugins/intel_cpu/src/emitters/snippets/cpu_runtime_configurator.cpp b/src/plugins/intel_cpu/src/emitters/snippets/cpu_runtime_configurator.cpp
index 920e2f1720f3a4..b92d70136ab4d5 100644
--- a/src/plugins/intel_cpu/src/emitters/snippets/cpu_runtime_configurator.cpp
+++ b/src/plugins/intel_cpu/src/emitters/snippets/cpu_runtime_configurator.cpp
@@ -11,13 +11,16 @@
 namespace ov {
 namespace intel_cpu {
 
-CPURuntimeConfigurator::CPURuntimeConfigurator() : ov::snippets::RuntimeConfigurator(std::make_shared<CPURuntimeConfig>()) {}
+CPURuntimeConfigurator::CPURuntimeConfigurator() : ov::snippets::RuntimeConfigurator(std::make_shared<CPURuntimeConfig>()) {
+}
 
 void CPURuntimeConfigurator::update(const std::shared_ptr<ov::snippets::lowered::LinearIR>& linear_ir) {
     RuntimeConfigurator::update(linear_ir);
 
-    if (linear_ir->is_dynamic())
+    if (linear_ir->is_dynamic()) {
+        get_kernel_executor_table()->update_state();
         update_loop_args(linear_ir);
+    }
 }
 
 void CPURuntimeConfigurator::init_tensor_rank(const std::shared_ptr<ov::snippets::lowered::LinearIR>& linear_ir) const {
diff --git a/src/plugins/intel_cpu/src/emitters/snippets/x64/cpu_generator.cpp b/src/plugins/intel_cpu/src/emitters/snippets/x64/cpu_generator.cpp
index e444d65bab774b..4b000ee1521d43 100644
--- a/src/plugins/intel_cpu/src/emitters/snippets/x64/cpu_generator.cpp
+++ b/src/plugins/intel_cpu/src/emitters/snippets/x64/cpu_generator.cpp
@@ -156,7 +156,6 @@ class jit_snippet : public dnnl::impl::cpu::x64::jit_generator {
 intel_cpu::CPUTargetMachine::CPUTargetMachine(dnnl::impl::cpu::x64::cpu_isa_t host_isa,
                                               ov::intel_cpu::MultiCacheWeakPtr cache)
    : TargetMachine(std::make_shared<CPURuntimeConfigurator>()), h(new jit_snippet()), isa(host_isa), compiled_kernel_cache(std::move(cache)) {
-    kernel_executor_table = std::make_shared<ov::snippets::KernelExecutorTable>();
     // data movement
     jitters[op::v0::Parameter::get_type_info_static()] = CREATE_SNIPPETS_EMITTER(intel_cpu::jit_nop_emitter);
     jitters[op::v0::Result::get_type_info_static()] = CREATE_SNIPPETS_EMITTER(intel_cpu::jit_nop_emitter);
@@ -241,7 +240,7 @@ intel_cpu::CPUTargetMachine::CPUTargetMachine(dnnl::impl::cpu::x64::cpu_isa_t ho
     jitters[snippets::op::LoopEnd::get_type_info_static()] = CREATE_SNIPPETS_EMITTER(intel_cpu::jit_loop_end_emitter);
     // Note: jit_brgemm_emitter supports runtime recompilation, so its constructor takes additional arguments
     jitters[intel_cpu::BrgemmCPU::get_type_info_static()] = CREATE_SNIPPETS_EMITTER(intel_cpu::jit_brgemm_emitter,
-                                                                                    kernel_executor_table,
+                                                                                    configurator->get_kernel_executor_table(),
                                                                                     compiled_kernel_cache);
     jitters[intel_cpu::BrgemmCopyB::get_type_info_static()] = CREATE_SNIPPETS_EMITTER(intel_cpu::jit_brgemm_copy_b_emitter);
     jitters[snippets::op::ReduceMax::get_type_info_static()] = CREATE_UNDEFINED_EMITTER({{ov::element::f32}});
diff --git a/src/plugins/intel_cpu/src/emitters/snippets/x64/jit_brgemm_copy_b_emitter.cpp b/src/plugins/intel_cpu/src/emitters/snippets/x64/jit_brgemm_copy_b_emitter.cpp
index 0fec3269efae9b..759eadfe4d0747 100644
--- a/src/plugins/intel_cpu/src/emitters/snippets/x64/jit_brgemm_copy_b_emitter.cpp
+++ b/src/plugins/intel_cpu/src/emitters/snippets/x64/jit_brgemm_copy_b_emitter.cpp
@@ -43,7 +43,7 @@ jit_brgemm_copy_b_emitter::jit_brgemm_copy_b_emitter(jit_generator* h, cpu_isa_t
     size_t leading_dimension = *(original_shape.rbegin());
     if (!layout.empty()) {
         transposed_shape = snippets::utils::get_planar_vdims(original_shape, layout);
-        leading_dimension = jit_brgemm_emitter::get_in_leading_dim(original_shape, layout);
+        leading_dimension = ov::snippets::utils::get_in_leading_dim(original_shape, layout);
     }
 
     const auto& in_subtensor = in_desc->get_subtensor();
diff --git a/src/plugins/intel_cpu/src/emitters/snippets/x64/jit_brgemm_emitter.cpp b/src/plugins/intel_cpu/src/emitters/snippets/x64/jit_brgemm_emitter.cpp
index 95fd0e522bfff4..a638da7c2e1a3f 100644
--- a/src/plugins/intel_cpu/src/emitters/snippets/x64/jit_brgemm_emitter.cpp
+++ b/src/plugins/intel_cpu/src/emitters/snippets/x64/jit_brgemm_emitter.cpp
@@ -7,6 +7,7 @@
 #include "transformations/snippets/x64/op/brgemm_cpu.hpp"
 #include <cpu/x64/brgemm/brgemm.hpp>
 #include <cpu/x64/amx_tile_configure.hpp>
+#include "snippets/utils.hpp"
 
 using namespace Xbyak;
 using namespace dnnl::impl;
@@ -15,33 +16,6 @@ using namespace dnnl::impl::cpu::x64;
 namespace ov {
 namespace intel_cpu {
 
-size_t jit_brgemm_emitter::get_in_leading_dim(const VectorDims& shape, const std::vector<size_t>& layout) {
-    // Input shape is original, so we need to correctly read this data by order
-    // Example:
-    //      Original shape (shape) = [1, 49, 2, 23]
-    //      Layout (transpose order) = [2, 0, 1, 3]
-    //      Transposed shape = [2, 1, 49, 23]
-    //      The leading dimension is equal to stride of shape[layout[3]] = 2 x 23
-    OV_CPU_JIT_EMITTER_ASSERT(layout.back() == layout.size() - 1 && layout.size() == shape.size(),
-                              "detected invalid layout values: check that this shape + layout combination is schedulable");
-    const auto idx = layout[layout.size() - 2];  // `1` in example
-    return std::accumulate(shape.cbegin() + idx + 1, shape.end(), 1, std::multiplies<size_t>());
-}
-size_t jit_brgemm_emitter::get_out_leading_dim(const VectorDims& shape, const std::vector<size_t>& layout) {
-    // Output shape is already transposed, we need to correctly write the data with original shape by the order
-    // Example:
-    //      Original transposed shape (shape) = [49, 2, 7, 39]
-    //      Layout (transpose order) = [2, 0, 1, 3]
-    //      Before leading dimension with index 3 there is dimension with index 2 in planar layout.
-    //      Since we have non-planar layout, we have to find this before LD dim in transposed order.
-    //      In layout 2nd idx is first element, it means, that the leading dimension is equal to stride of shape[0]
-    OV_CPU_JIT_EMITTER_ASSERT(layout.back() == layout.size() - 1 && layout.size() == shape.size(),
-                              "detected invalid layout values: check that this shape + layout combination is schedulable");
-    const auto idx = layout.size() - 2; // 2 in the example
-    const auto dim = std::distance(layout.cbegin(), std::find(layout.cbegin(), layout.cend(), idx)); // 0 in the example: shape[0] = 49
-    return std::accumulate(shape.cbegin() + dim + 1, shape.cend(), 1, std::multiplies<size_t>()); // shape[1] x shape[2] x shape[3] = 2 x 7 x 39
-}
-
 jit_brgemm_emitter::jit_brgemm_emitter(jit_generator* h, cpu_isa_t isa,
                                        const ov::snippets::lowered::ExpressionPtr& expr,
                                        const snippets::KernelExecutorTablePtr& kernel_table,
@@ -49,67 +23,24 @@ jit_brgemm_emitter::jit_brgemm_emitter(jit_generator* h, cpu_isa_t isa,
                                        jit_emitter(h, isa) {
     in_out_type_ = emitter_in_out_map::gpr_to_gpr;
     const auto& brgemm_node = as_type_ptr<ov::intel_cpu::BrgemmCPU>(expr->get_node());
-    OV_CPU_JIT_EMITTER_ASSERT(!brgemm_node->is_dynamic(), "Snippets don't support code generation for dynamic Brgemm");
-
-    std::vector<size_t> leading_dimensions;
-     auto get_layout = [](const std::vector<size_t>& layout, const snippets::VectorDims& io_shape) {
-        if (!layout.empty()) return layout;
-        std::vector<size_t> default_layout(io_shape.size());
-        std::iota(default_layout.begin(), default_layout.end(), 0);
-        return default_layout;
-    };
 
-    auto init_in_scheduling_params = [&](const snippets::lowered::PortDescriptorPtr& input) {
-        const auto& layout = get_layout(input->get_layout(), input->get_shape());
-        leading_dimensions.push_back(get_in_leading_dim(input->get_shape(), layout));
-    };
-    auto init_out_scheduling_params = [&](const snippets::lowered::PortDescriptorPtr& output) {
-        const auto& layout = get_layout(output->get_layout(), output->get_shape());
-        leading_dimensions.push_back(get_out_leading_dim(output->get_shape(), layout));
-    };
-
-    const auto& input_0_desc = expr->get_input_port_descriptor(0);
-    const auto& input_1_desc = expr->get_input_port_descriptor(1);
-    const auto& output_desc = expr->get_output_port_descriptor(0);
-
-    init_in_scheduling_params(input_0_desc);
-    if (brgemm_node->is_with_data_repacking()) {
-        const auto repacking_buffer_shape = brgemm_node->get_brgemm_copy()->get_repacking_buffer_shape();
-        OV_CPU_JIT_EMITTER_ASSERT(!repacking_buffer_shape.empty(), "Repacking buffer shape mustn't be empty");
-        leading_dimensions.push_back(repacking_buffer_shape.back());
-    } else {
-        init_in_scheduling_params(input_1_desc);
-    }
-    init_out_scheduling_params(output_desc);
+    m_with_scratch = brgemm_node->is_with_scratchpad();
 
     const auto& brg0Prc = brgemm_node->get_input_element_type(0);
     const auto& brg1Prc = brgemm_node->get_input_element_type(1);
-
-    m_with_scratch = brgemm_node->is_with_scratchpad();
-
-    const auto& output_subtensor = output_desc->get_subtensor();
-    const auto& input_0_subtensor = input_0_desc->get_subtensor();
-    const auto& input_1_subtensor = input_1_desc->get_subtensor();
-
-    OV_CPU_JIT_EMITTER_ASSERT(*(output_subtensor.rbegin() + 1) == *(input_0_subtensor.rbegin() + 1),
-                              "Brgemm has different M dimension subtensors on input0 and output");
-    OV_CPU_JIT_EMITTER_ASSERT(*output_subtensor.rbegin() == *input_1_subtensor.rbegin(),
-                              "Brgemm has different N dimension subtensors on input1 and output");
-    OV_CPU_JIT_EMITTER_ASSERT(*input_0_subtensor.rbegin() == *(input_1_subtensor.rbegin() + 1),
-                              "Brgemm has different K dimension subtensors on input0 and input1");
-
     auto kernel_config = std::make_shared<BrgemmKernelConfig>(brg0Prc, brg1Prc,
                                                             brgemm_node->get_beta(),
                                                             brgemm_node->is_amx(),
                                                             brgemm_node->is_with_compensations());
-
-    m_kernel_executor = kernel_table->register_kernel<BrgemmKernelExecutor>(expr, compiled_kernel_cache, kernel_config);
-    m_kernel_executor->update(*(output_subtensor.rbegin() + 1),
-                              *output_subtensor.rbegin(),
-                              *input_0_subtensor.rbegin(),
-                              leading_dimensions[0],
-                              leading_dimensions[1],
-                              leading_dimensions[2]);
+    m_kernel_executor = kernel_table->register_kernel<BrgemmKernelExecutor>(expr,
+                                                                            compiled_kernel_cache,
+                                                                            kernel_config);
+    // Note: even if the Brgemm node is dynamic, the first shapeInfer and RuntimeConfigurator::update()
+    // are performed before the BrgemmKernelExecutor registration. So we have to trigger update() manually
+    // for both static and the 1st dynamic shapes.
+    OV_CPU_JIT_EMITTER_ASSERT(!snippets::utils::is_dynamic_vdims(expr->get_input_port_descriptor(0)->get_shape()) &&
+                              !snippets::utils::is_dynamic_vdims(expr->get_input_port_descriptor(1)->get_shape()),
+                              "Jit emitter is called when the shapes are unknown");
 
     m_load_offset_a = brgemm_node->get_offset_a();
     m_load_offset_b = brgemm_node->get_offset_b();
diff --git a/src/plugins/intel_cpu/src/emitters/snippets/x64/jit_brgemm_emitter.hpp b/src/plugins/intel_cpu/src/emitters/snippets/x64/jit_brgemm_emitter.hpp
index 1950f15f5b4c95..ecadb7105271b3 100644
--- a/src/plugins/intel_cpu/src/emitters/snippets/x64/jit_brgemm_emitter.hpp
+++ b/src/plugins/intel_cpu/src/emitters/snippets/x64/jit_brgemm_emitter.hpp
@@ -20,9 +20,6 @@ class jit_brgemm_emitter : public jit_emitter {
     size_t get_inputs_num() const override { return m_with_scratch ? 3 : 2; }
     static std::set<std::vector<element::Type>> get_supported_precisions(const std::shared_ptr<ov::Node>& node = nullptr);
 
-    static size_t get_in_leading_dim(const VectorDims& shape, const std::vector<size_t>& layout);
-    static size_t get_out_leading_dim(const VectorDims& shape, const std::vector<size_t>& layout);
-
 private:
     void validate_arguments(const std::vector<size_t> &in, const std::vector<size_t> &out) const override;
     void emit_impl(const std::vector<size_t>& in, const std::vector<size_t>& out) const override;
diff --git a/src/plugins/intel_cpu/src/emitters/snippets/x64/kernel_executors/brgemm.cpp b/src/plugins/intel_cpu/src/emitters/snippets/x64/kernel_executors/brgemm.cpp
index 82770b7688f3f6..367b9f4506a980 100644
--- a/src/plugins/intel_cpu/src/emitters/snippets/x64/kernel_executors/brgemm.cpp
+++ b/src/plugins/intel_cpu/src/emitters/snippets/x64/kernel_executors/brgemm.cpp
@@ -7,6 +7,7 @@
 #include <cpu/x64/amx_tile_configure.hpp>
 #include "common/utils.hpp"
 #include "dnnl_extension_utils.h"
+#include "transformations/snippets/x64/op/brgemm_cpu.hpp"
 
 #define DIM_CAST(X) static_cast<dnnl_dim_t>(X)
 #define DTYPE_CAST(X) static_cast<dnnl_data_type_t>(DnnlExtensionUtils::ElementTypeToDataType(X))
@@ -20,84 +21,83 @@ namespace intel_cpu {
 BrgemmKernelConfig::BrgemmKernelConfig(const element::Type& in0_dtype, const element::Type& in1_dtype, float beta,
                                        bool is_with_amx, bool is_with_comp,
                                        size_t M, size_t N, size_t K, size_t LDA, size_t LDB, size_t LDC) :
-                     dt_in0(DTYPE_CAST(in0_dtype)), dt_in1(DTYPE_CAST(in1_dtype)),
-                     is_with_amx(is_with_amx), is_with_comp(is_with_comp), beta(beta),
-                     M(DIM_CAST(M)), N(DIM_CAST(N)), K(DIM_CAST(K)),
-                     LDA(DIM_CAST(LDA)), LDB(DIM_CAST(LDB)), LDC(DIM_CAST(LDC)) {
-    bool is_int8 = utils::one_of(dt_in0, data_type::u8, data_type::s8) &&
-                   utils::one_of(dt_in1, data_type::u8, data_type::s8);
-    isa = is_with_amx ?
+                     m_dt_in0(DTYPE_CAST(in0_dtype)), m_dt_in1(DTYPE_CAST(in1_dtype)),
+                     m_is_with_amx(is_with_amx), m_is_with_comp(is_with_comp), m_beta(beta),
+                     m_M(DIM_CAST(M)), m_N(DIM_CAST(N)), m_K(DIM_CAST(K)),
+                     m_LDA(DIM_CAST(LDA)), m_LDB(DIM_CAST(LDB)), m_LDC(DIM_CAST(LDC)) {
+    bool is_int8 = utils::one_of(m_dt_in0, data_type::u8, data_type::s8) &&
+                   utils::one_of(m_dt_in1, data_type::u8, data_type::s8);
+    m_isa = is_with_amx ?
             cpu::x64::avx512_core_amx :
-            dt_in0 == dnnl_data_type_t::dnnl_bf16 ?
+            m_dt_in0 == dnnl_data_type_t::dnnl_bf16 ?
                 cpu::x64::avx512_core_bf16 :
                 is_int8 ?
                     cpu::x64::avx512_core_vnni :
                     cpu::x64::avx512_core;
+    m_hash = compute_hash();
 }
 
 bool BrgemmKernelConfig::is_completed() const {
-    return !utils::one_of(0, M, N, K, LDA, LDB, LDC);
+    return !utils::one_of(0, m_M, m_N, m_K, m_LDA, m_LDB, m_LDC);
 }
 
-size_t BrgemmKernelConfig::hash() const {
+void BrgemmKernelConfig::update(dnnl_dim_t M, dnnl_dim_t N, dnnl_dim_t K, dnnl_dim_t LDA, dnnl_dim_t LDB, dnnl_dim_t LDC) {
+    m_M = M; m_N = N; m_K = K;
+    m_LDA = LDA; m_LDB = LDB; m_LDC = LDC;
+    m_hash = compute_hash();
+}
+
+BrgemmKernelConfig::operator amx_tile_config_t() const {
+    amx_tile_config_t res;
+    res.M = m_M; res.N = m_N; res.K = m_K;
+    return  res;
+}
+
+size_t BrgemmKernelConfig::compute_hash() const {
     size_t seed = 0;
 #define HASH(X) seed = hash_combine(seed, X)
-    HASH(dt_in0); HASH(dt_in1);
-    HASH(is_with_amx); HASH(is_with_comp);
-    HASH(beta); HASH(isa);
-    HASH(M); HASH(N); HASH(K);
-    HASH(LDA); HASH(LDB); HASH(LDC);
+    HASH(m_dt_in0); HASH(m_dt_in1);
+    HASH(m_is_with_amx); HASH(m_is_with_comp);
+    HASH(m_beta); HASH(m_isa);
+    HASH(m_M); HASH(m_N); HASH(m_K);
+    HASH(m_LDA); HASH(m_LDB); HASH(m_LDC);
 #undef HASH
     return seed;
 }
-bool BrgemmKernelConfig::operator==(const BrgemmKernelConfig& rhs) const {
-#define EQUAL(X) X == rhs.X
-    return EQUAL(dt_in0) && EQUAL(dt_in1) &&
-           EQUAL(is_with_amx)  && EQUAL(is_with_comp) &&
-           EQUAL(beta) && EQUAL(isa) &&
-           EQUAL(M) && EQUAL(N) && EQUAL(K) &&
-           EQUAL(LDA) && EQUAL(LDB) && EQUAL(LDC);
-#undef EQUAL
-}
-bool BrgemmKernelConfig::operator!=(const BrgemmKernelConfig& rhs) const {
-    return !(*this == rhs);
-}
 
 #ifdef SNIPPETS_DEBUG_CAPS
 std::string BrgemmKernelConfig::to_string() const {
     std::stringstream ss;
 #define PRINT(X) ss << #X  << " = " << X << "\n"
-    PRINT(dt_in0); PRINT(dt_in1);
-    PRINT(is_with_amx); PRINT(is_with_comp);
-    PRINT(beta); PRINT(isa);
-    PRINT(M); PRINT(N); PRINT(K);
-    PRINT(LDA); PRINT(LDB); PRINT(LDC);
+    PRINT(m_dt_in0); PRINT(m_dt_in1);
+    PRINT(m_is_with_amx); PRINT(m_is_with_comp);
+    PRINT(m_beta); PRINT(m_isa);
+    PRINT(m_M); PRINT(m_N); PRINT(m_K);
+    PRINT(m_LDA); PRINT(m_LDB); PRINT(m_LDC);
 #undef PRINT
     return ss.str();
 }
 #endif
 
-BrgemmKernelExecutor::BrgemmKernelExecutor(ov::intel_cpu::MultiCacheWeakPtr kernel_cache, const std::shared_ptr<BrgemmKernelConfig>& config) :
-        CPUKernelExecutor<BrgemmKernelConfig, BrgemmCompiledKernel>(std::move(kernel_cache), config) {
-    if (config->is_completed())
-        update_kernel();
-}
+BrgemmKernelExecutor::BrgemmKernelExecutor(ov::intel_cpu::MultiCacheWeakPtr kernel_cache,
+                                           const std::shared_ptr<BrgemmKernelConfig>& config) :
+        CPUKernelExecutor<BrgemmKernelConfig, BrgemmCompiledKernel>(std::move(kernel_cache), config) { }
 
 
-std::shared_ptr<BrgemmCompiledKernel> BrgemmKernelExecutor::compile_kernel(const std::shared_ptr<BrgemmKernelConfig>& config) const {
+std::shared_ptr<BrgemmCompiledKernel> BrgemmKernelExecutor::compile_kernel(const std::shared_ptr<const BrgemmKernelConfig>& config) const {
     OV_CPU_JIT_EMITTER_ASSERT(config, "Invalid config provided for BrgemmKernelDesc::compile_kernel");
     cpu::x64::brgemm_t desc;
-    auto status = brgemm_desc_init(&desc, config->isa, cpu::x64::brgemm_strd,
-                                   config->dt_in0, config->dt_in1,
+    auto status = brgemm_desc_init(&desc, config->get_isa(), cpu::x64::brgemm_strd,
+                                   config->get_dt_in0(), config->get_dt_in1(),
                                    false, false, cpu::x64::brgemm_row_major, 1.f,
-                                   config->beta,
-                                   config->LDA, config->LDB, config->LDC,
-                                   config->M, config->N, config->K, nullptr);
+                                   config->get_beta(),
+                                   config->get_LDA(), config->get_LDB(), config->get_LDC(),
+                                   config->get_M(), config->get_N(), config->get_K(), nullptr);
 
     std::shared_ptr<BrgemmCompiledKernel> compiled_kernel = std::make_shared<BrgemmCompiledKernel>();
 
     OV_CPU_JIT_EMITTER_ASSERT(status == dnnl_success, "Cannot initialize brgemm descriptor due to invalid params");
-    if (config->is_with_amx) {
+    if (config->is_with_amx()) {
         status = brgemm_init_tiles(desc, compiled_kernel->palette);
         OV_CPU_JIT_EMITTER_ASSERT(status == dnnl_success, "Cannot initialize brgemm tiles due to invalid params");
     }
@@ -110,19 +110,55 @@ std::shared_ptr<BrgemmCompiledKernel> BrgemmKernelExecutor::compile_kernel(const
     return compiled_kernel;
 }
 
-void BrgemmKernelExecutor::execute(const BrgemmKernelExecutor* desc, call_args* args) {
-    const auto& kernel = desc->m_kernel;
-    OV_CPU_JIT_EMITTER_ASSERT(kernel, "has nullptr compiler kernel");
-
-    const auto& config = desc->m_config;
-    if (config->is_with_amx) {
-        const auto& amx_tile_config = args->amx_tile_config;
-        if (config->M != amx_tile_config->M || config->K != amx_tile_config->K || config->N != amx_tile_config->N) {
-            amx_tile_config->M = config->M;
-            amx_tile_config->K = config->K;
-            amx_tile_config->N = config->N;
-            cpu::x64::amx_tile_configure(kernel->palette);
+void BrgemmKernelExecutor::update_config(const ov::snippets::lowered::ExpressionPtr& expr, std::shared_ptr<BrgemmKernelConfig>& config) const {
+    auto get_projected_input_subtensor = [](const snippets::lowered::PortDescriptorPtr& desc) {
+        // Note: for output shape you will need get_preordered_vdims()
+        auto shape = snippets::utils::get_planar_vdims(desc->get_shape(), desc->get_layout());
+        auto subtensor = desc->get_subtensor();
+        OV_CPU_JIT_EMITTER_ASSERT(subtensor.size() <= shape.size() && subtensor.size() == 2,
+                                  "Invalid subtensor + shape combination");
+        auto shape_it = shape.rbegin();
+        for (auto sub_it = subtensor.rbegin(); sub_it != subtensor.rend(); sub_it++, shape_it++) {
+            *sub_it = std::min(*sub_it, *shape_it);
         }
+        return subtensor;
+    };
+    const auto& input_pds = expr->get_input_port_descriptors();
+    const auto& output_pds = expr->get_output_port_descriptors();
+    OV_CPU_JIT_EMITTER_ASSERT((input_pds.size() == 2 || input_pds.size() == 3) && output_pds.size() == 1,
+                              "Invalid number of in/out port descriptors");
+    // Update runtime-defined config fields:
+    // Matrix A (first input)
+    const auto LDA = DIM_CAST(snippets::utils::get_in_leading_dim(input_pds[0]));
+    const auto& in0_subtensor = get_projected_input_subtensor(input_pds[0]);
+    const auto K = DIM_CAST(*in0_subtensor.rbegin());
+    const auto M = DIM_CAST(*++in0_subtensor.rbegin());
+    // Matrix B (second input)
+    // Non float input 1 => with data repacking
+    auto LDB = DIM_CAST(snippets::utils::get_in_leading_dim(input_pds[1]));
+
+    const auto& brgemm_node = as_type_ptr<ov::intel_cpu::BrgemmCPU>(expr->get_node());
+    OV_CPU_JIT_EMITTER_ASSERT(brgemm_node, "Got invalid node type in update_config");
+    if (brgemm_node->is_with_data_repacking()) {
+        const auto repacking_buffer_shape = brgemm_node->get_brgemm_copy()->get_repacking_buffer_shape();
+        OV_CPU_JIT_EMITTER_ASSERT(!repacking_buffer_shape.empty(), "Repacking buffer shape mustn't be empty");
+        LDB = DIM_CAST(repacking_buffer_shape.back());
+    }
+    const auto N = DIM_CAST(*get_projected_input_subtensor(input_pds[1]).rbegin());
+    // Matrix C (output)
+    const auto LDC = DIM_CAST(snippets::utils::get_out_leading_dim(output_pds[0]));
+    config->update(M, N, K, LDA, LDB, LDC);
+}
+
+void BrgemmKernelExecutor::execute(const BrgemmKernelExecutor* executor, call_args* args) {
+    const auto& kernel = executor->get_kernel();
+    const auto& config = std::static_pointer_cast<const BrgemmKernelConfig>(executor->get_config());
+    OV_CPU_JIT_EMITTER_ASSERT(kernel && config, "has nullptr compiler kernel or invalid config");
+
+    const auto tile_config = args->amx_tile_config;
+    if (config->is_with_amx() && tile_config && !config->compatible(tile_config)) {
+        *tile_config = static_cast<amx_tile_config_t>(*config);
+        cpu::x64::amx_tile_configure(kernel->palette);
     }
 
     cpu::x64::brgemm_kernel_params_t brgemm_p;
@@ -134,28 +170,13 @@ void BrgemmKernelExecutor::execute(const BrgemmKernelExecutor* desc, call_args*
     brgemm_p.ptr_D = args->C;
     brgemm_p.ptr_buf = args->scratch;
     brgemm_p.ptr_bias = nullptr;
-    brgemm_p.do_post_ops = static_cast<size_t>(config->is_with_comp);
-    brgemm_p.do_apply_comp = static_cast<size_t>(config->is_with_comp);
+    brgemm_p.do_post_ops = static_cast<size_t>(config->is_with_comp());
+    brgemm_p.do_apply_comp = static_cast<size_t>(config->is_with_comp());
     brgemm_p.skip_accm = 0;
     brgemm_p.BS = 1;  // default value
     OV_CPU_JIT_EMITTER_ASSERT(kernel->compiled_kernel, "has nullptr kernel");
     (*kernel->compiled_kernel)(&brgemm_p);
 }
 
-void BrgemmKernelExecutor::update(size_t M, size_t N, size_t K, size_t LDA, size_t LDB, size_t LDC) {
-    OV_CPU_JIT_EMITTER_ASSERT(m_config, "update is called for empty kernel config");
-#define CAST(X) m_config->X = DIM_CAST(X)
-    CAST(M); CAST(N); CAST(K);
-    CAST(LDA); CAST(LDB); CAST(LDC);
-#undef CAST
-    update_kernel();
-}
-
-#ifdef SNIPPETS_DEBUG_CAPS
-std::string BrgemmKernelExecutor::config_to_string() const {
-    return m_config ? m_config->to_string() : "";
-}
-#endif
-
 }   // namespace intel_cpu
 }   // namespace ov
diff --git a/src/plugins/intel_cpu/src/emitters/snippets/x64/kernel_executors/brgemm.hpp b/src/plugins/intel_cpu/src/emitters/snippets/x64/kernel_executors/brgemm.hpp
index 8c426e4ecf86b4..b0fbb468db12f2 100644
--- a/src/plugins/intel_cpu/src/emitters/snippets/x64/kernel_executors/brgemm.hpp
+++ b/src/plugins/intel_cpu/src/emitters/snippets/x64/kernel_executors/brgemm.hpp
@@ -11,11 +11,7 @@
 
 namespace ov {
 namespace intel_cpu {
-class BrgemmKernelExecutor;
-#define GET_OFF_BRGEMM_ARGS(field) offsetof(BrgemmKernelExecutor::call_args, field)
-
 struct BrgemmKernelConfig : public snippets::KernelExecutorBase::GenericConfig {
-    friend BrgemmKernelExecutor;
 public:
     BrgemmKernelConfig(const element::Type& in0_dtype, const element::Type& in1_dtype, float beta,
                        bool is_with_amx, bool is_with_comp,
@@ -23,19 +19,46 @@ struct BrgemmKernelConfig : public snippets::KernelExecutorBase::GenericConfig {
                        size_t LDA = 0, size_t LDB = 0, size_t LDC = 0);
     BrgemmKernelConfig() = default;
     bool is_completed() const override;
-    size_t hash() const;
-    bool operator==(const BrgemmKernelConfig& rhs) const;
-    bool operator!=(const BrgemmKernelConfig& rhs) const;
+    size_t hash() const override { return m_hash; }
+    std::shared_ptr<GenericConfig> clone() const override {
+        return std::make_shared<BrgemmKernelConfig>(*this);
+    }
+    void update(dnnl_dim_t M, dnnl_dim_t N, dnnl_dim_t K, dnnl_dim_t LDA, dnnl_dim_t LDB, dnnl_dim_t LDC);
+
+    dnnl_data_type_t get_dt_in0() const { return m_dt_in0; }
+    dnnl_data_type_t get_dt_in1() const { return m_dt_in1; }
+
+    dnnl::impl::cpu::x64::cpu_isa_t get_isa() const { return m_isa; }
+    bool is_with_amx() const {return m_is_with_amx; }
+    bool is_with_comp() const { return m_is_with_comp; }
+    float get_beta() const { return m_beta; }
+
+    dnnl_dim_t get_M() const { return m_M; }
+    dnnl_dim_t get_N() const { return m_N; }
+    dnnl_dim_t get_K() const { return m_K; }
+
+    dnnl_dim_t get_LDA() const { return m_LDA; }
+    dnnl_dim_t get_LDB() const { return m_LDB; }
+    dnnl_dim_t get_LDC() const { return m_LDC; }
+
+    explicit operator amx_tile_config_t() const;
+    inline bool compatible(amx_tile_config_t* rhs) const {
+        return rhs && rhs->M == m_M && rhs->N == m_N && rhs->K == m_K;
+    }
+
 #ifdef SNIPPETS_DEBUG_CAPS
-    std::string to_string() const;
+    std::string to_string() const override;
 #endif
+
 private:
-    dnnl_data_type_t dt_in0 {dnnl_f32}, dt_in1 {dnnl_f32};
-    bool is_with_amx {false};
-    bool is_with_comp {false};
-    float beta {0};
-    dnnl::impl::cpu::x64::cpu_isa_t isa {dnnl::impl::cpu::x64::isa_undef};
-    dnnl_dim_t M {0}, N {0}, K {0}, LDA {0}, LDB {0}, LDC {0};
+    size_t compute_hash() const;
+    dnnl_data_type_t m_dt_in0 {dnnl_f32}, m_dt_in1 {dnnl_f32};
+    bool m_is_with_amx {false};
+    bool m_is_with_comp {false};
+    float m_beta {0};
+    dnnl::impl::cpu::x64::cpu_isa_t m_isa {dnnl::impl::cpu::x64::isa_undef};
+    dnnl_dim_t m_M {0}, m_N {0}, m_K {0}, m_LDA {0}, m_LDB {0}, m_LDC {0};
+    size_t m_hash {SIZE_MAX};
 };
 
 struct BrgemmCompiledKernel {
@@ -54,20 +77,16 @@ class BrgemmKernelExecutor : public CPUKernelExecutor<BrgemmKernelConfig, Brgemm
         void* scratch = nullptr;
         amx_tile_config_t* amx_tile_config = nullptr;
     };
-    BrgemmKernelExecutor(ov::intel_cpu::MultiCacheWeakPtr kernel_cache, const std::shared_ptr<BrgemmKernelConfig>& config);
+    BrgemmKernelExecutor(ov::intel_cpu::MultiCacheWeakPtr kernel_cache,
+                         const std::shared_ptr<BrgemmKernelConfig>& config);
 
     /** Function that will be called in runtime to execute the kernel */
-    static void execute(const BrgemmKernelExecutor* desc, call_args* args);
-
-    /** Update kernel config using the arguments passed, and recompile the kernel */
-    void update(size_t M, size_t N, size_t K, size_t LDA, size_t LDB, size_t LDC);
+    static void execute(const BrgemmKernelExecutor* executor, call_args* args);
 
-    /** print current kernel config for debug purposes */
-#ifdef SNIPPETS_DEBUG_CAPS
-    std::string config_to_string() const;
-#endif
 protected:
-    std::shared_ptr<BrgemmCompiledKernel> compile_kernel(const std::shared_ptr<BrgemmKernelConfig>& c) const override;
+    std::shared_ptr<BrgemmCompiledKernel> compile_kernel(const std::shared_ptr<const BrgemmKernelConfig>& c) const override;
+    void update_config(const ov::snippets::lowered::ExpressionPtr& expr, std::shared_ptr<BrgemmKernelConfig>& config) const override;
 };
+#define GET_OFF_BRGEMM_ARGS(field) offsetof(BrgemmKernelExecutor::call_args, field)
 }   // namespace intel_cpu
 }   // namespace ov
diff --git a/src/plugins/intel_cpu/src/emitters/snippets/x64/verbose.cpp b/src/plugins/intel_cpu/src/emitters/snippets/x64/verbose.cpp
index d9c87dbf8b3ae3..8f7e58048e92b9 100644
--- a/src/plugins/intel_cpu/src/emitters/snippets/x64/verbose.cpp
+++ b/src/plugins/intel_cpu/src/emitters/snippets/x64/verbose.cpp
@@ -87,7 +87,7 @@ static std::string init_info_jit_store_memory_emitter(const jit_store_memory_emi
 std::string init_info_jit_brgemm_emitter(const jit_brgemm_emitter *emitter) {
     std::stringstream ss;
     ss << "Emitter_type_name:jit_brgemm_emitter"
-       <<  emitter->m_kernel_executor->config_to_string()
+       <<  emitter->m_kernel_executor->to_string()
        << " m_load_offset_a:" << emitter->m_load_offset_a
        << " m_load_offset_b:" << emitter->m_load_offset_b
        << " m_load_offset_scratch:" << emitter->m_load_offset_scratch
diff --git a/src/plugins/intel_cpu/src/graph.cpp b/src/plugins/intel_cpu/src/graph.cpp
index 0ad5976730729d..63e857890c7dab 100644
--- a/src/plugins/intel_cpu/src/graph.cpp
+++ b/src/plugins/intel_cpu/src/graph.cpp
@@ -290,7 +290,8 @@ static std::tuple<std::vector<NodePtr>, std::vector<size_t>> ExtractExecutableNo
     std::vector<NodePtr> executableGraphNodes;
     for (size_t i = 0; i < graphNodes.size(); i++) {
         const auto& graphNode = graphNodes[i];
-        if ((!graphNode->isConstant() && CPU_DEBUG_CAPS_ALWAYS_TRUE(graphNode->isExecutable())) || graphNode->isDynamicNode()) {
+        if ((!graphNode->isConstant() && CPU_DEBUG_CAPS_ALWAYS_TRUE(graphNode->isExecutable())) || // non-constant executable or
+            (graphNode->isDynamicNode() && !one_of(graphNode->getType(), Type::Input, Type::Output))) { // dynamic, except inputs / outputs
             /* @todo
              * Revise implementation.
              * With current way it is possible that with debug_caps enabled
@@ -1844,7 +1845,7 @@ void Graph::EnforceInferencePrecision() {
 
         for (size_t i = 0; i < node->getOriginalInputsNumber(); i++) {
             auto keepOriginalInputPrecisionAtPort = [](const NodePtr& node, const size_t inPort) {
-                // keep non-float precisions
+                // keep non-float32 precisions
                 if (node->getOriginalInputPrecisionAtPort(inPort) != ov::element::f32)
                     return true;
 
@@ -1888,7 +1889,7 @@ void Graph::EnforceInferencePrecision() {
         }
 
         for (size_t i = 0; i < node->getOriginalOutputsNumber(); i++) {
-            // keep non-float precisions
+            // keep non-float32 precisions
             if (node->getOriginalOutputPrecisionAtPort(i) != ov::element::f32)
                 continue;
 
diff --git a/src/plugins/intel_cpu/src/node.cpp b/src/plugins/intel_cpu/src/node.cpp
index 3bd885efb16743..60f6206818783b 100644
--- a/src/plugins/intel_cpu/src/node.cpp
+++ b/src/plugins/intel_cpu/src/node.cpp
@@ -895,7 +895,7 @@ MemoryPtr Node::prepareWeightMemory(DnnlMemoryDescPtr dstWeightDesc, DnnlMemoryD
     MemoryPtr ptr;
     const auto& format = dstWeightDesc->serializeFormat();
 
-    assert(privateWeightCache);
+    OPENVINO_ASSERT(privateWeightCache, "privateWeightCache is nullptr");
 
     auto itr = privateWeightCache->find(format);
     if (privateWeightCache->end() != itr) {
diff --git a/src/plugins/intel_cpu/src/nodes/col2im.cpp b/src/plugins/intel_cpu/src/nodes/col2im.cpp
new file mode 100644
index 00000000000000..0ec2d8af23e125
--- /dev/null
+++ b/src/plugins/intel_cpu/src/nodes/col2im.cpp
@@ -0,0 +1,110 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "col2im.h"
+#include "openvino/reference/col2im.hpp"
+#include "openvino/op/col2im.hpp"
+
+namespace ov {
+namespace intel_cpu {
+namespace node {
+Col2Im::Col2Im(const std::shared_ptr<ov::Node>& op, const GraphContext::CPtr context)
+    : Node(op, context, NgraphShapeInferFactory(op, EMPTY_PORT_MASK)) {
+    std::string errorMessage;
+    if (!isSupportedOperation(op, errorMessage)) {
+        OPENVINO_THROW_NOT_IMPLEMENTED(errorMessage);
+    }
+    const auto col2Im = ov::as_type_ptr<const ov::op::v15::Col2Im>(op);
+    strides = col2Im->get_strides();
+    dilations = col2Im->get_dilations();
+    padsBegin = col2Im->get_pads_begin();
+    padsEnd = col2Im->get_pads_end();
+}
+
+bool Col2Im::isSupportedOperation(const std::shared_ptr<const ov::Node>& op, std::string& errorMessage) noexcept {
+    try {
+        if (!ov::is_type<ov::op::v15::Col2Im>(op)) {
+            errorMessage = "Only opset15 Col2Im operation is supported";
+            return false;
+        }
+    } catch (...) {
+        return false;
+    }
+    return true;
+}
+
+void Col2Im::getSupportedDescriptors() {
+    // Validation is already done in the ov::opset15::Col2Im.
+}
+
+void Col2Im::initSupportedPrimitiveDescriptors() {
+    if (!supportedPrimitiveDescriptors.empty())
+        return;
+    ov::element::Type dataPrecision = getOriginalInputPrecisionAtPort(0);
+    addSupportedPrimDesc(
+        {{LayoutType::ncsp, dataPrecision}, {LayoutType::ncsp, ov::element::i32}, {LayoutType::ncsp, ov::element::i32}},
+        {{LayoutType::ncsp, dataPrecision}},
+        impl_desc_type::ref);
+}
+
+bool Col2Im::created() const {
+    return getType() == Type::Col2Im;
+}
+
+bool Col2Im::needPrepareParams() const {
+    return false;
+}
+
+void Col2Im::executeDynamicImpl(dnnl::stream strm) {
+    execute(strm);
+}
+
+template <class T, class T_idx>
+void Col2Im::executeImpl() {
+    ov::reference::col2im<T, T_idx>(
+        getSrcDataAtPortAs<const T>(0),
+        ov::Shape{getSrcMemoryAtPort(0)->getStaticDims()},
+        getSrcDataAtPortAs<const T_idx>(1),
+        getSrcDataAtPortAs<const T_idx>(2),
+        getDstDataAtPortAs<T>(0),
+        strides,
+        dilations,
+        padsBegin,
+        padsEnd);
+}
+
+namespace {
+struct Col2ImContext {
+    Col2Im &node;
+};
+}
+
+template<typename T>
+struct Col2Im::Col2ImExecute {
+    using TData = typename std::tuple_element<0, T>::type;
+    using TIndex = typename std::tuple_element<1, T>::type;
+
+    void operator()(Col2ImContext & ctx) {
+            ctx.node.executeImpl<TData, TIndex>();
+        }
+};
+void Col2Im::execute(dnnl::stream strm) {
+    auto dataPrecision = getParentEdgeAt(0)->getMemory().getDesc().getPrecision();
+    auto indexPrecision = getParentEdgeAt(1)->getMemory().getDesc().getPrecision();
+
+    Col2ImContext ctx = {
+            *this
+    };
+
+    OV_SWITCH(intel_cpu, Col2ImExecute, ctx, std::tie(dataPrecision, indexPrecision),
+              OV_CASE2(ov::element::f32, ov::element::i32, float, int32_t),
+              OV_CASE2(ov::element::f16, ov::element::i32, ov::float16, int32_t),
+              OV_CASE2(ov::element::bf16, ov::element::i32, ov::bfloat16, int32_t),
+              OV_CASE2(ov::element::i32, ov::element::i32, int32_t, int32_t),
+              OV_CASE2(ov::element::i8, ov::element::i32, int8_t, int32_t),
+              OV_CASE2(ov::element::u8, ov::element::i32, uint8_t, int32_t))
+}
+}  // namespace node
+}  // namespace intel_cpu
+}  // namespace ov
diff --git a/src/plugins/intel_cpu/src/nodes/col2im.h b/src/plugins/intel_cpu/src/nodes/col2im.h
new file mode 100644
index 00000000000000..9904689e53be0f
--- /dev/null
+++ b/src/plugins/intel_cpu/src/nodes/col2im.h
@@ -0,0 +1,40 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "node.h"
+
+namespace ov {
+namespace intel_cpu {
+namespace node {
+
+class Col2Im : public Node {
+public:
+    Col2Im(const std::shared_ptr<ov::Node>& op, const GraphContext::CPtr context);
+
+    static bool isSupportedOperation(const std::shared_ptr<const ov::Node>& op, std::string& errorMessage) noexcept;
+    void getSupportedDescriptors() override;
+    void initSupportedPrimitiveDescriptors() override;
+    void execute(dnnl::stream strm) override;
+    bool created() const override;
+    bool needPrepareParams() const override;
+    void executeDynamicImpl(dnnl::stream strm) override;
+
+private:
+    template <class OV_DATA_TYPE, class OV_INDEX_TYPE>
+    void executeImpl();
+
+    template<typename T>
+    struct Col2ImExecute;
+
+    ov::Strides strides;
+    ov::Strides dilations;
+    ov::Shape padsBegin;
+    ov::Shape padsEnd;
+};
+
+}  // namespace node
+}  // namespace intel_cpu
+}  // namespace ov
diff --git a/src/plugins/intel_cpu/src/nodes/conv.cpp b/src/plugins/intel_cpu/src/nodes/conv.cpp
index 5fb5e6ddc6813e..e22a36af852a14 100644
--- a/src/plugins/intel_cpu/src/nodes/conv.cpp
+++ b/src/plugins/intel_cpu/src/nodes/conv.cpp
@@ -531,8 +531,9 @@ void Convolution::getSupportedDescriptors() {
         auto dt = memory::data_type::f32;
 
         // supported lower precisions: bf16, f16
-        if (one_of(originalDT, memory::data_type::bf16, memory::data_type::f16))
+        if (one_of(originalDT, memory::data_type::bf16, memory::data_type::f16) && hasHardwareSupport(originalPrec)) {
             dt = originalDT;
+        }
 
         // fallback to f32 on special case for performance reasons
         if (isDepthWise() && ndims == 5)
diff --git a/src/plugins/intel_cpu/src/nodes/embedding_bag_offsets.cpp b/src/plugins/intel_cpu/src/nodes/embedding_bag_offsets.cpp
index 4ab297df11c552..dc69892dabb2e4 100644
--- a/src/plugins/intel_cpu/src/nodes/embedding_bag_offsets.cpp
+++ b/src/plugins/intel_cpu/src/nodes/embedding_bag_offsets.cpp
@@ -98,7 +98,7 @@ void EmbeddingBagOffset::initFromInputs() {
     indicesData_ = getSrcDataAtPortAs<const int>(INDICES_IDX);
     offsetsData_ = getSrcDataAtPortAs<const int>(OFFSETS_IDX);
 
-    if (getParentEdges().size() > DEFAULT_INDEX_IDX) {
+    if (getParentEdges().size() > DEFAULT_INDEX_IDX && *getSrcDataAtPortAs<const int>(DEFAULT_INDEX_IDX) != -1) {
         defaultIndices_ = getSrcDataAtPortAs<const int>(DEFAULT_INDEX_IDX);
     }
 }
diff --git a/src/plugins/intel_cpu/src/nodes/executors/aarch64/jit_eltwise.cpp b/src/plugins/intel_cpu/src/nodes/executors/aarch64/jit_eltwise.cpp
index 01cbb57f49a32c..3097824c07ff47 100644
--- a/src/plugins/intel_cpu/src/nodes/executors/aarch64/jit_eltwise.cpp
+++ b/src/plugins/intel_cpu/src/nodes/executors/aarch64/jit_eltwise.cpp
@@ -30,6 +30,7 @@ bool JitEltwiseExecutor::isSupported(
                                      Algorithm::EltwiseGeluTanh,
                                      Algorithm::EltwiseHswish,
                                      Algorithm::EltwiseIsInf,
+                                     Algorithm::EltwiseIsNaN,
                                      Algorithm::EltwiseMaximum,
                                      Algorithm::EltwiseMinimum,
                                      Algorithm::EltwiseMish,
diff --git a/src/plugins/intel_cpu/src/nodes/executors/dnnl/dnnl_fullyconnected_primitive.cpp b/src/plugins/intel_cpu/src/nodes/executors/dnnl/dnnl_fullyconnected_primitive.cpp
index 432a78419e9497..ef1e127f44f636 100644
--- a/src/plugins/intel_cpu/src/nodes/executors/dnnl/dnnl_fullyconnected_primitive.cpp
+++ b/src/plugins/intel_cpu/src/nodes/executors/dnnl/dnnl_fullyconnected_primitive.cpp
@@ -122,7 +122,7 @@ bool DnnlFCPrimitive::useWeightsDecompressionImpl(const ov::element::Type inputT
             // f16c kernel saves memory footprint with additional decompression computational overhead
             // which is only meaningful on LLM with small batch-size.
             // TODO: fall-back to use f32 weights on large batch-size
-            if (inputType == f32 && weightsType == f16)
+            if (inputType == f32 && one_of(weightsType, f16, bf16))
                 return true;
         }
     }
diff --git a/src/plugins/intel_cpu/src/nodes/executors/dnnl/dnnl_utils.cpp b/src/plugins/intel_cpu/src/nodes/executors/dnnl/dnnl_utils.cpp
index e04356887ea8bf..d9cbd05a847231 100644
--- a/src/plugins/intel_cpu/src/nodes/executors/dnnl/dnnl_utils.cpp
+++ b/src/plugins/intel_cpu/src/nodes/executors/dnnl/dnnl_utils.cpp
@@ -32,6 +32,7 @@ MemoryPtr prepareWeightsMemory(const DnnlMemoryDescPtr srcWeightDesc,
     const auto& format = dstWeightDesc->serializeFormat();
 
     const auto privateWeightCache = context->getPrivateWeighCache();
+    OPENVINO_ASSERT(privateWeightCache, "privateWeightCache is nullptr");
     if (privateWeightCache) {
         auto itr = privateWeightCache->find(format);
         if (privateWeightCache->end() != itr) {
@@ -86,7 +87,7 @@ MemoryPtr prepareWeightsMemory(const DnnlMemoryDescPtr srcWeightDesc,
     if (globalWeightCache &&
         dnnl::memory::format_kind::blocked == dstWeightDesc->getDnnlDesc().get_format_kind()) {
         const std::string string_hash = format + "_" + std::to_string(weightsMem->getSize()) + "_" +
-                                        std::to_string(*weightsMem->getDataAs<uint64_t>());
+                                        std::to_string(reinterpret_cast<uint64_t>(weightsMem->getData()));
         ptr = *globalWeightCache->findOrCreate(string_hash, create);
     } else {
         ptr = create();
diff --git a/src/plugins/intel_cpu/src/nodes/executors/mlas/mlas_gemm.cpp b/src/plugins/intel_cpu/src/nodes/executors/mlas/mlas_gemm.cpp
index f5d5d18006192f..a03bfe2649413a 100644
--- a/src/plugins/intel_cpu/src/nodes/executors/mlas/mlas_gemm.cpp
+++ b/src/plugins/intel_cpu/src/nodes/executors/mlas/mlas_gemm.cpp
@@ -51,7 +51,7 @@ static MemoryPtr prepareWeightMemory(const MemoryPtr weightsMemory,
     if (weightCache != nullptr) {
         std::string format = "gemm_mlas_" + std::to_string(N) + "_" + std::to_string(K);
         const std::string string_hash = format + "_" + std::to_string(weightsMemory->getSize()) + "_" +
-            std::to_string(*weightsMemory->getDataAs<uint64_t>());
+                                        std::to_string(reinterpret_cast<uint64_t>(weightsMemory->getData()));
         DEBUG_LOG("MlasGemmExecutor: findOrCreate, string_hash: ", string_hash);
         return *weightCache->findOrCreate(string_hash, create);
     }
diff --git a/src/plugins/intel_cpu/src/nodes/executors/type_mask.hpp b/src/plugins/intel_cpu/src/nodes/executors/type_mask.hpp
index 7a1ace29623036..d492bd6b6f368a 100644
--- a/src/plugins/intel_cpu/src/nodes/executors/type_mask.hpp
+++ b/src/plugins/intel_cpu/src/nodes/executors/type_mask.hpp
@@ -35,7 +35,8 @@ struct TypeMask {
         _f8e4m3    = 1 << 19,
         _f8e5m2    = 1 << 20,
         _string    = 1 << 21,
-        _f4e2m1    = 1 << 22
+        _f4e2m1    = 1 << 22,
+        _f8e8m0    = 1 << 23,
     };
 
     TypeMask(const ov::element::Type precision) : value(generateMask(precision)), precision(precision) {}
@@ -83,6 +84,7 @@ struct TypeMask {
             CASE(f8e5m2)
             CASE(string)
             CASE(f4e2m1)
+            CASE(f8e8m0)
         default:
             return _undefined;
         }
@@ -117,6 +119,7 @@ DEFINE_TYPE_ALIAS(_f8e4m3);
 DEFINE_TYPE_ALIAS(_f8e5m2);
 DEFINE_TYPE_ALIAS(_string);
 DEFINE_TYPE_ALIAS(_f4e2m1);
+DEFINE_TYPE_ALIAS(_f8e8m0);
 constexpr auto _any_float = _f64 | _f32 | _f16 | _bf16;
 constexpr auto _half_float = _f16 | _bf16;
 constexpr auto _quant = _u8 | _i8;
diff --git a/src/plugins/intel_cpu/src/nodes/input.cpp b/src/plugins/intel_cpu/src/nodes/input.cpp
index 03054af7054e1a..72a22132aba175 100644
--- a/src/plugins/intel_cpu/src/nodes/input.cpp
+++ b/src/plugins/intel_cpu/src/nodes/input.cpp
@@ -373,18 +373,20 @@ void Input::cloneBlobIfRequired() {
                 + "_" + ptr;
     };
 
-    auto weightCache = context->getWeightsCache();
-
-    if (weightCache) {
-        MemoryPtr ptr = *weightCache->findOrCreate(blobKey(), cloneBlob);
-        memoryPtr = std::const_pointer_cast<const IMemory>(ptr);
-    // IRs already have all subnormals flushed to zero, but in
-    // read_model scenario with directly loaded original model still can have subnormals
-    } else if (prec != element::string && isBlobAligned() && (!needFlushDenormalsToZero || !hasSubnormals()) && !isWA()) {
-        memoryPtr = std::make_shared<Memory>(getEngine(), memDesc, constOp->get_data_ptr());
-    } else {
-        memoryPtr = std::const_pointer_cast<const IMemory>(cloneBlob());
-    }
+    const auto weightCache = context->getWeightsCache();
+    const bool clone_is_not_needed =
+        prec != element::string && !isWA() &&
+        // IRs already have all subnormals flushed to zero, but in
+        // read_model scenario with directly loaded original model still can have subnormals
+        isBlobAligned() && (!needFlushDenormalsToZero || !hasSubnormals()) &&
+        // Blob should be cloned in cache only if original weights are stored on other numa node.
+        // This is possible only in multistream case on multisocket machine.
+        // TODO: don't clone blob for multisocket + multistream case if current stream is run on the numa node where original weights are stored.
+        (!weightCache || context->getNumNumaNodes() == 1 || context->getCPUStreamExecutor()->get_streams_num() == 1);
+
+    memoryPtr = clone_is_not_needed ? std::make_shared<Memory>(getEngine(), memDesc, constOp->get_data_ptr())
+                                    : std::const_pointer_cast<const IMemory>(
+                                          weightCache ? *weightCache->findOrCreate(blobKey(), cloneBlob) : cloneBlob());
 }
 
 static std::vector<Shape> createInputShapes(const Shape& shape,
diff --git a/src/plugins/intel_cpu/src/nodes/kernels/aarch64/jit_uni_eltwise_generic.cpp b/src/plugins/intel_cpu/src/nodes/kernels/aarch64/jit_uni_eltwise_generic.cpp
index 1d0368b1a318c5..eee26497593474 100644
--- a/src/plugins/intel_cpu/src/nodes/kernels/aarch64/jit_uni_eltwise_generic.cpp
+++ b/src/plugins/intel_cpu/src/nodes/kernels/aarch64/jit_uni_eltwise_generic.cpp
@@ -650,6 +650,7 @@ std::shared_ptr<jit_emitter> jit_uni_eltwise_generic<isa>::create_eltwise_emitte
     OV_CASE(Algorithm::EltwiseFloor, ov::intel_cpu::aarch64::jit_floor_emitter),
     OV_CASE(Algorithm::EltwiseHswish, ov::intel_cpu::aarch64::jit_hswish_emitter),
     OV_CASE(Algorithm::EltwiseIsInf, ov::intel_cpu::aarch64::jit_is_inf_emitter),
+    OV_CASE(Algorithm::EltwiseIsNaN, ov::intel_cpu::aarch64::jit_is_nan_emitter),
     OV_CASE(Algorithm::EltwiseMaximum, ov::intel_cpu::aarch64::jit_maximum_emitter),
     OV_CASE(Algorithm::EltwiseMinimum, ov::intel_cpu::aarch64::jit_minimum_emitter),
     OV_CASE(Algorithm::EltwiseMish, ov::intel_cpu::aarch64::jit_mish_emitter),
@@ -823,6 +824,7 @@ std::set<std::vector<element::Type>> eltwise_precision_helper::get_supported_pre
         OV_CASE(Algorithm::EltwiseGeluTanh, jit_gelu_tanh_emitter),
         OV_CASE(Algorithm::EltwiseHswish, jit_hswish_emitter),
         OV_CASE(Algorithm::EltwiseIsInf, jit_is_inf_emitter),
+        OV_CASE(Algorithm::EltwiseIsNaN, jit_is_nan_emitter),
         OV_CASE(Algorithm::EltwiseMaximum, jit_maximum_emitter),
         OV_CASE(Algorithm::EltwiseMinimum, jit_minimum_emitter),
         OV_CASE(Algorithm::EltwiseMish, jit_mish_emitter),
diff --git a/src/plugins/intel_cpu/src/nodes/kernels/scaled_attn/attn_quant.cpp b/src/plugins/intel_cpu/src/nodes/kernels/scaled_attn/attn_quant.cpp
index c4c92da96193b1..a9998e88402ca7 100644
--- a/src/plugins/intel_cpu/src/nodes/kernels/scaled_attn/attn_quant.cpp
+++ b/src/plugins/intel_cpu/src/nodes/kernels/scaled_attn/attn_quant.cpp
@@ -132,6 +132,8 @@ static void quant_u8(const T* src, uint8_t* dst, size_t n, float& scale, float&
         min = std::min(min, tmp);
     }
     scale = (max - min) / 255;
+    if (scale == 0)
+        scale = 0.0001f;
     zp = -min / scale;
 
     i = 0;
diff --git a/src/plugins/intel_cpu/src/nodes/random_uniform.cpp b/src/plugins/intel_cpu/src/nodes/random_uniform.cpp
index 544b3cf24716ec..b48a7b9ca6d4b9 100644
--- a/src/plugins/intel_cpu/src/nodes/random_uniform.cpp
+++ b/src/plugins/intel_cpu/src/nodes/random_uniform.cpp
@@ -18,6 +18,10 @@ bool RandomUniform::isSupportedOperation(const std::shared_ptr<const ov::Node>&
             errorMessage = "Only RandomUniform operation from the opset8 is supported by the CPU plugin.";
             return false;
         }
+        if (as_type_ptr<const op::v8::RandomUniform>(op)->get_alignment() != op::PhilloxAlignment::TENSORFLOW) {
+            errorMessage = "Only TENSORFLOW alignment mode is supported by the CPU plugin.";
+            return false;
+        }
     } catch (...) {
         return false;
     }
@@ -39,7 +43,6 @@ RandomUniform::RandomUniform(const std::shared_ptr<ov::Node>& op, const GraphCon
     auto rnd_op = as_type_ptr<op::v8::RandomUniform>(op);
     m_global_seed = rnd_op->get_global_seed();
     m_op_seed = rnd_op->get_op_seed();
-
     m_output_prc = op->get_output_element_type(0);
 
     for (size_t i = 0lu; i < op->get_input_size(); i++) {
diff --git a/src/plugins/intel_cpu/src/nodes/reduce.cpp b/src/plugins/intel_cpu/src/nodes/reduce.cpp
index e09062a3f58d6a..63675b364d4425 100644
--- a/src/plugins/intel_cpu/src/nodes/reduce.cpp
+++ b/src/plugins/intel_cpu/src/nodes/reduce.cpp
@@ -135,6 +135,7 @@ struct jit_uni_reduce_kernel_f32 : public jit_uni_reduce_kernel, public jit_gene
         this->preamble();
 
         planar_layout = jcp_.layout == ReduceLayoutType::reduce_ncsp || jcp_.layout == ReduceLayoutType::reduce_nspc;
+        support_intermediate_int = jcp_.reduce_mode == Algorithm::ReduceProd;
 
         mov(reg_src, ptr[reg_params + GET_OFF(src)]);
         mov(reg_dst, ptr[reg_params + GET_OFF(dst)]);
@@ -176,6 +177,7 @@ struct jit_uni_reduce_kernel_f32 : public jit_uni_reduce_kernel, public jit_gene
             Xbyak::Ymm, Xbyak::Zmm>::type;
     size_t vlen = cpu_isa_traits<isa>::vlen;
     bool planar_layout = false;
+    bool support_intermediate_int = false;
 
     Xbyak::Address table_val(int index) { return ptr[reg_table + index * vlen]; }
 
@@ -317,8 +319,14 @@ struct jit_uni_reduce_kernel_f32 : public jit_uni_reduce_kernel, public jit_gene
                     mov(reg_divisor, ptr[reg_params + GET_OFF(divisor)]);
                     uni_vbroadcastss(vmm_aux, ptr[reg_divisor]);
                     uni_vdivps(vmm_dst, vmm_dst, vmm_aux);
+                    if (!isFloatCompatible(jcp_.dst_dt)) {
+                        uni_vroundps(vmm_dst, vmm_dst, 3); // rounding to zero
+                    }
                     if (isa == cpu::x64::sse41) {
                         uni_vdivps(vmm_dst_aux, vmm_dst_aux, vmm_aux);
+                        if (!isFloatCompatible(jcp_.dst_dt)) {
+                            uni_vroundps(vmm_dst_aux, vmm_dst_aux, 3);
+                        }
                     }
                 }
                 L(reduce_divide_end_label);
@@ -337,9 +345,14 @@ struct jit_uni_reduce_kernel_f32 : public jit_uni_reduce_kernel, public jit_gene
             // init dst, dst loading is embedded in horiz_reduce_store
             switch (jcp_.reduce_mode) {
                 case Algorithm::ReduceAnd:
-                case Algorithm::ReduceProd:
                     uni_vmovups(vmm_dst, table_val(0));
                     break;
+                case Algorithm::ReduceProd:
+                    if (isFloatCompatible(jcp_.src_dt))
+                        uni_vmovups(vmm_dst, table_val(0));
+                    else
+                        uni_vmovups(vmm_dst, table_val(6));
+                    break;
                 case Algorithm::ReduceL1:
                     uni_vmovups(vmm_aux, table_val(1));
                     uni_vpxor(vmm_dst, vmm_dst, vmm_dst);
@@ -577,7 +590,9 @@ struct jit_uni_reduce_kernel_f32 : public jit_uni_reduce_kernel, public jit_gene
                         vgatherdps(vmm_src | k_mask, ptr[reg_src + offset + vmm_idx]);
                     } else {
                         vpgatherdd(vmm_src | k_mask, ptr[reg_src + offset + vmm_idx]);
-                        uni_vcvtdq2ps(vmm_src, vmm_src);
+                        if (!support_intermediate_int) {
+                            uni_vcvtdq2ps(vmm_src, vmm_src);
+                        }
                     }
                 } else if (isa == cpu::x64::avx2) {
                     uni_vpcmpeqd(vmm_mask, vmm_mask, vmm_mask);
@@ -585,7 +600,9 @@ struct jit_uni_reduce_kernel_f32 : public jit_uni_reduce_kernel, public jit_gene
                         vgatherdps(vmm_src, ptr[reg_src + offset + vmm_idx], vmm_mask);
                     } else {
                         vpgatherdd(vmm_src, ptr[reg_src + offset + vmm_idx], vmm_mask);
-                        uni_vcvtdq2ps(vmm_src, vmm_src);
+                        if (!support_intermediate_int) {
+                            uni_vcvtdq2ps(vmm_src, vmm_src);
+                        }
                     }
                 } else {
                     pack_gathered_vector(vmm_src, vmm_idx, offset, jcp_.src_dt);
@@ -653,7 +670,7 @@ struct jit_uni_reduce_kernel_f32 : public jit_uni_reduce_kernel, public jit_gene
                 assert(!"unknown src_dt");
         }
 
-        if (!isFloatCompatible(src_dt))
+        if (convert_i32_to_f32(src_dt))
             uni_vcvtdq2ps(vmm_val, vmm_val);
         add(rsp, vlen);
     }
@@ -818,7 +835,11 @@ struct jit_uni_reduce_kernel_f32 : public jit_uni_reduce_kernel, public jit_gene
                 uni_vorps(vmm_dst, vmm_dst, vmm_src);
                 break;
             case Algorithm::ReduceProd:
-                uni_vmulps(vmm_dst, vmm_dst, vmm_src);
+                if (isFloatCompatible(jcp_.src_dt)) {
+                    uni_vmulps(vmm_dst, vmm_dst, vmm_src);
+                } else {
+                    uni_vpmulld(vmm_dst, vmm_dst, vmm_src);
+                }
                 break;
             default:
                 assert(!"unsupported reduce mode");
@@ -859,7 +880,11 @@ struct jit_uni_reduce_kernel_f32 : public jit_uni_reduce_kernel, public jit_gene
                 uni_vorps(xmm_dst, xmm_dst, xmm_src);
                 break;
             case Algorithm::ReduceProd:
-                uni_vmulps(xmm_dst, xmm_dst, xmm_src);
+                if (isFloatCompatible(jcp_.src_dt)) {
+                    uni_vmulps(xmm_dst, xmm_dst, xmm_src);
+                } else {
+                    uni_vpmulld(xmm_dst, xmm_dst, xmm_src);
+                }
                 break;
             default:
                 assert(!"unsupported reduce mode");
@@ -910,7 +935,7 @@ struct jit_uni_reduce_kernel_f32 : public jit_uni_reduce_kernel, public jit_gene
                 assert(!"unknown src_dt");
         }
 
-        if (!isFloatCompatible(src_dt))
+        if (convert_i32_to_f32(src_dt))
             uni_vcvtdq2ps(vmm_src, vmm_src);
     }
 
@@ -939,7 +964,7 @@ struct jit_uni_reduce_kernel_f32 : public jit_uni_reduce_kernel, public jit_gene
                 assert(!"unknown src_dt");
         }
 
-        if (!isFloatCompatible(src_dt)) {
+        if (convert_i32_to_f32(src_dt)) {
             uni_vcvtdq2ps(xmm_src, xmm_src);
         }
     }
@@ -948,7 +973,7 @@ struct jit_uni_reduce_kernel_f32 : public jit_uni_reduce_kernel, public jit_gene
         Xmm xmm_dst = Xmm(vmm_dst.getIdx());
         Ymm ymm_dst = Ymm(vmm_dst.getIdx());
 
-        if (!isFloatCompatible(dst_dt)) {
+        if (convert_f32_to_i32(dst_dt)) {
             uni_vcvtps2dq(vmm_dst, vmm_dst);
         }
 
@@ -999,7 +1024,7 @@ struct jit_uni_reduce_kernel_f32 : public jit_uni_reduce_kernel, public jit_gene
     }
 
     inline void store_scalar(const Xbyak::Address &op, Xmm xmm_dst, memory::data_type dst_dt) {
-        if (!isFloatCompatible(dst_dt)) {
+        if (convert_f32_to_i32(dst_dt)) {
             uni_vcvtps2dq(xmm_dst, xmm_dst);
         }
 
@@ -1092,13 +1117,24 @@ struct jit_uni_reduce_kernel_f32 : public jit_uni_reduce_kernel, public jit_gene
                 uni_vorps(xmm, xmm, op);
                 break;
             case Algorithm::ReduceProd:
-                uni_vmulps(xmm, xmm, op);
+                if (isFloatCompatible(jcp_.src_dt))
+                    uni_vmulps(xmm, xmm, op);
+                else
+                    uni_vpmulld(xmm, xmm, op);
                 break;
             default:
                 assert(!"unsupported reduce mode");
         }
     }
 
+    inline bool convert_i32_to_f32(memory::data_type src_dt) {
+        return !isFloatCompatible(src_dt) && !support_intermediate_int;
+    }
+
+    inline bool convert_f32_to_i32(memory::data_type dst_dt) {
+        return !isFloatCompatible(dst_dt) && !support_intermediate_int;
+    }
+
     void prepare_aux_table() {
         auto broadcast_int = [&](int val) {
             for (size_t d = 0; d < vlen / sizeof(float); ++d) {
@@ -1115,6 +1151,7 @@ struct jit_uni_reduce_kernel_f32 : public jit_uni_reduce_kernel, public jit_gene
         broadcast_int(aux_vals.float_max);
         broadcast_int(aux_vals.int32_min);
         broadcast_int(aux_vals.int32_max);
+        broadcast_int(aux_vals.int32_one);
     }
 
     const struct aux_vals_type {
@@ -1124,6 +1161,7 @@ struct jit_uni_reduce_kernel_f32 : public jit_uni_reduce_kernel, public jit_gene
         int float_max = 0x7f7fffff; // float maximum
         int int32_min = 0xcf000000; // -2^31 presented in float
         int int32_max = 0x4effffff; // 2^31-1 presented in float
+        int int32_one = 0x00000001; // integer 1
     } aux_vals;
 };
 
@@ -1342,14 +1380,14 @@ struct jit_uni_reduce_post_kernel_f32 : public jit_uni_reduce_post_kernel, publi
                     jl(reduce_loop_end_label, T_NEAR);
 
                     wrap_load_vector(vmm_dst, 0);
-                    reduce_map_kernel(vmm_dst);
+                    reduce_map_kernel(vmm_dst, jcp_.dst_dt);
                     if (post_ops_fusing)
                         apply_post_ops(jcp_.dst_dt, jcp_.fuse_broadcast);
                     store_vector(ptr[reg_dst], vmm_dst, jcp_.dst_dt);
 
                     if (isa == cpu::x64::sse41) {
                         wrap_load_vector(vmm_dst, 4);
-                        reduce_map_kernel(vmm_dst);
+                        reduce_map_kernel(vmm_dst, jcp_.dst_dt);
                         if (post_ops_fusing) {
                             if (jcp_.layout != ReduceLayoutType::reduce_ncsp)
                                 add(reg_oc_off, 4 * sizeof(float));
@@ -1430,7 +1468,7 @@ struct jit_uni_reduce_post_kernel_f32 : public jit_uni_reduce_post_kernel, publi
                 wrap_load_scalar(xmm_dst, 0);
 
                 // reduce
-                reduce_map_kernel_scalar(xmm_dst);
+                reduce_map_kernel_scalar(xmm_dst, jcp_.dst_dt);
 
                 // store
                 if (post_ops_fusing)
@@ -1522,22 +1560,30 @@ struct jit_uni_reduce_post_kernel_f32 : public jit_uni_reduce_post_kernel, publi
         }
     }
 
-    inline void reduce_map_kernel(Vmm vmm_dst) {
-        if (jcp_.reduce_mode == Algorithm::ReduceMean)
+    inline void reduce_map_kernel(Vmm vmm_dst, memory::data_type dst_dt) {
+        if (jcp_.reduce_mode == Algorithm::ReduceMean) {
             uni_vdivps(vmm_dst, vmm_dst, vmm_aux);
-        else if (jcp_.reduce_mode == Algorithm::ReduceL2)
+            if (!isFloatCompatible(dst_dt)) {
+                uni_vroundps(vmm_dst, vmm_dst, 3);
+            }
+        } else if (jcp_.reduce_mode == Algorithm::ReduceL2) {
             uni_vsqrtps(vmm_dst, vmm_dst);
-        else if (jcp_.reduce_mode == Algorithm::ReduceLogSum || jcp_.reduce_mode == Algorithm::ReduceLogSumExp)
+        } else if (jcp_.reduce_mode == Algorithm::ReduceLogSum || jcp_.reduce_mode == Algorithm::ReduceLogSumExp) {
             log_injector->compute_vector_range(vmm_dst.getIdx(), vmm_dst.getIdx() + 1);
+        }
     }
 
-    inline void reduce_map_kernel_scalar(Xmm xmm_dst) {
-        if (jcp_.reduce_mode == Algorithm::ReduceMean)
+    inline void reduce_map_kernel_scalar(Xmm xmm_dst, memory::data_type dst_dt) {
+        if (jcp_.reduce_mode == Algorithm::ReduceMean) {
             uni_vdivps(xmm_dst, xmm_dst, xmm_aux);
-        else if (jcp_.reduce_mode == Algorithm::ReduceL2)
+            if (!isFloatCompatible(dst_dt)) {
+                uni_vroundps(xmm_dst, xmm_dst, 3);
+            }
+        } else if (jcp_.reduce_mode == Algorithm::ReduceL2) {
             uni_vsqrtps(xmm_dst, xmm_dst);
-        else if (jcp_.reduce_mode == Algorithm::ReduceLogSum || jcp_.reduce_mode == Algorithm::ReduceLogSumExp)
+        } else if (jcp_.reduce_mode == Algorithm::ReduceLogSum || jcp_.reduce_mode == Algorithm::ReduceLogSumExp) {
             log_injector->compute_vector_range(xmm_dst.getIdx(), xmm_dst.getIdx() + 1);
+        }
     }
 
     inline void wrap_load_vector(Vmm vmm_val, size_t offset) {
diff --git a/src/plugins/intel_cpu/src/nodes/rope.cpp b/src/plugins/intel_cpu/src/nodes/rope.cpp
index 1f62931cb4bc1a..ac95b0f31213de 100644
--- a/src/plugins/intel_cpu/src/nodes/rope.cpp
+++ b/src/plugins/intel_cpu/src/nodes/rope.cpp
@@ -342,14 +342,18 @@ void RoPE::initSupportedPrimitiveDescriptors() {
     bool can_inplace = false;
 
     if (m_config.is_qwen) {
-        if (rtPrecision == ov::element::bf16) {
+        if (rtPrecision == ov::element::f16) {
+            m_executor = std::make_shared<RoPEExecutorQwen<ov::float16>>(m_config);
+        } else if (rtPrecision == ov::element::bf16) {
             m_executor = std::make_shared<RoPEExecutorQwen<ov::bfloat16>>(m_config);
         } else {
             m_executor = std::make_shared<RoPEExecutorQwen<float>>(m_config);
             rtPrecision = ov::element::f32;
         }
     } else if (m_config.is_chatglm) {
-        if (rtPrecision == ov::element::bf16) {
+        if (rtPrecision == ov::element::f16) {
+            m_executor = std::make_shared<RoPEExecutorChatGLM<ov::float16>>(m_config);
+        } else if (rtPrecision == ov::element::bf16) {
             m_executor = std::make_shared<RoPEExecutorChatGLM<ov::bfloat16>>(m_config);
         } else {
             m_executor = std::make_shared<RoPEExecutorChatGLM<float>>(m_config);
@@ -360,7 +364,9 @@ void RoPE::initSupportedPrimitiveDescriptors() {
         OPENVINO_ASSERT(m_config.slice_start == 0);
         OPENVINO_ASSERT(m_config.slice_stop == 0);
         OPENVINO_ASSERT(m_config.gather_position_arg_id == 0);
-        if (rtPrecision == ov::element::bf16) {
+        if (rtPrecision == ov::element::f16) {
+            m_executor = std::make_shared<RoPEExecutorInterleaved<ov::float16>>(m_config);
+        } else if (rtPrecision == ov::element::bf16) {
             m_executor = std::make_shared<RoPEExecutorInterleaved<ov::bfloat16>>(m_config);
         } else {
             m_executor = std::make_shared<RoPEExecutorInterleaved<float>>(m_config);
@@ -368,7 +374,9 @@ void RoPE::initSupportedPrimitiveDescriptors() {
         }
     } else {
         can_inplace = true;
-        if (rtPrecision == ov::element::bf16) {
+        if (rtPrecision == ov::element::f16) {
+            m_executor = std::make_shared<RoPEExecutorRotateHalf<ov::float16>>(m_config);
+        } else if (rtPrecision == ov::element::bf16) {
             m_executor = std::make_shared<RoPEExecutorRotateHalf<ov::bfloat16>>(m_config);
         } else {
             m_executor = std::make_shared<RoPEExecutorRotateHalf<float>>(m_config);
diff --git a/src/plugins/intel_cpu/src/nodes/subgraph.cpp b/src/plugins/intel_cpu/src/nodes/subgraph.cpp
index 25f5ed5dc4718b..eac50bf04dbd82 100644
--- a/src/plugins/intel_cpu/src/nodes/subgraph.cpp
+++ b/src/plugins/intel_cpu/src/nodes/subgraph.cpp
@@ -127,6 +127,10 @@ class SubgraphDynamicSpecializedExecutor : public Subgraph::SubgraphExecutor {
         OPENVINO_ASSERT(data_offsets.size() == inMemPtrs.size() + outMemPtrs.size(), "Incorrect data offset count!");
         OPENVINO_ASSERT(data_offsets.front().size() == m_parallel_exec_domain.size(), "Data offsets with invalid ranks detected");
 
+        // Note: we need to reset KernelExecutorTable to the state that was recorded in the SubgraphDynamicSpecializedExecutor
+        // constructor because the table might've been used for other shapes
+        reset_exec_table_state();
+
         std::vector<const uint8_t*> src_ptrs;
         std::vector<uint8_t*> dst_ptrs;
         init_original_ptrs(inMemPtrs, outMemPtrs, src_ptrs, dst_ptrs);
@@ -195,11 +199,13 @@ class SubgraphDynamicSpecializedExecutor : public Subgraph::SubgraphExecutor {
         buffer_offsets = snippet_config->buffer_cluster_offsets;
         data_offsets = snippet_config->io_data_offsets;
         loop_args = snippet_config->loop_args;
-    }
+        reset_exec_table_state = snippet_config->kernel_executor_table->get_state_reset();
+    };
 
     std::vector<size_t> buffer_offsets = {};
     std::vector<std::vector<size_t>> data_offsets = {};
     std::vector<jit_snippets_call_args::loop_args_t> loop_args = {};
+    std::function<void()> reset_exec_table_state;
 };
 
 struct SubgraphKey {
@@ -631,7 +637,7 @@ Subgraph::DataFlowPasses Subgraph::getDataFlowPasses() const {
 #    define SNIPPETS_REGISTER_PASS_RELATIVE_X86_64(PASS_PLACE, TARGET_PASS, PASS, ...)
 #endif  // OPENVINO_ARCH_X86_64
 
-    SNIPPETS_REGISTER_PASS_ABSOLUTE_X86_64(Place::PipelineStart, ConvertToSwishCPU);
+    SNIPPETS_REGISTER_PASS_ABSOLUTE_COMMON(Place::PipelineStart, ConvertToSwishCPU);
     if (context->getConfig().inferencePrecision == ov::element::bf16 && subgraph_attrs->snippet->has_domain_sensitive_ops()) {
         // enforce BF16 precisions to supported operations
         // MatMul has to be decomposed to Brgemm operations before enforcement
diff --git a/src/plugins/intel_cpu/src/nodes_factory.cpp b/src/plugins/intel_cpu/src/nodes_factory.cpp
index e45c9104e30e72..9012c37f5ac23b 100644
--- a/src/plugins/intel_cpu/src/nodes_factory.cpp
+++ b/src/plugins/intel_cpu/src/nodes_factory.cpp
@@ -7,6 +7,7 @@
 #include "nodes/bin_conv.h"
 #include "nodes/broadcast.h"
 #include "nodes/bucketize.h"
+#include "nodes/col2im.h"
 #include "nodes/color_convert.h"
 #include "nodes/concat.h"
 #include "nodes/conv.h"
@@ -160,6 +161,7 @@ Node::NodesFactory::NodesFactory() : Factory("NodesFactory") {
     INTEL_CPU_NODE(Math, Type::Math);
     INTEL_CPU_NODE(MultiClassNms, Type::MulticlassNms);
     INTEL_CPU_NODE(Convert, Type::Convert);
+    INTEL_CPU_NODE(Col2Im, Type::Col2Im);
     INTEL_CPU_NODE(ColorConvert, Type::ColorConvert);
     INTEL_CPU_NODE(EmbeddingBagOffset, Type::EmbeddingBagOffsetsSum);
     INTEL_CPU_NODE(EmbeddingBagOffset, Type::EmbeddingBagOffsets);
diff --git a/src/plugins/intel_cpu/src/transformations/snippets/aarch64/pass/snippets_mark_skipped.cpp b/src/plugins/intel_cpu/src/transformations/snippets/aarch64/pass/snippets_mark_skipped.cpp
index b9605dbfa67d3f..9923dae15e4e6b 100644
--- a/src/plugins/intel_cpu/src/transformations/snippets/aarch64/pass/snippets_mark_skipped.cpp
+++ b/src/plugins/intel_cpu/src/transformations/snippets/aarch64/pass/snippets_mark_skipped.cpp
@@ -73,10 +73,14 @@ bool isFullyConnected(const std::shared_ptr<const ov::Node>& node) {
 }
 
 bool SupportsFusingWithConvolution_Simple(const std::shared_ptr<const Node> &node) {
-    // Note: some other operations support this fusing (Abs, Clamp, Elu, Sigmoid, SoftPlus, Sqrt).
+    // Note: some other operations support this fusing (SoftPlus, Sqrt).
     // Skip them here, when they are supported by Snippets ARM. Ticket: 141170.
-    return ov::is_type<ov::op::v0::Tanh>(node) ||
-           ov::is_type<ov::op::v0::Relu>(node);
+    return ov::is_type<ov::op::v0::Abs>(node) ||
+           ov::is_type<ov::op::v0::Clamp>(node) ||
+           ov::is_type<ov::op::v0::Elu>(node) ||
+           ov::is_type<ov::op::v0::Relu>(node) ||
+           ov::is_type<ov::op::v0::Sigmoid>(node) ||
+           ov::is_type<ov::op::v0::Tanh>(node);
 }
 // Convolution is a special case, since it supports peculiar fusings
 bool isSuitableConvolutionParent(const std::shared_ptr<const Node> &node) {
diff --git a/src/plugins/intel_cpu/src/transformations/snippets/aarch64/shape_inference.cpp b/src/plugins/intel_cpu/src/transformations/snippets/aarch64/shape_inference.cpp
index c5dabb2efa63a9..967afe946e0793 100644
--- a/src/plugins/intel_cpu/src/transformations/snippets/aarch64/shape_inference.cpp
+++ b/src/plugins/intel_cpu/src/transformations/snippets/aarch64/shape_inference.cpp
@@ -5,6 +5,7 @@
 #include "shape_inference.hpp"
 #include "snippets/shape_inference/shape_infer_instances.hpp"
 #include "transformations/snippets/common/op/fused_mul_add.hpp"
+#include "transformations/cpu_opset/common/op/swish_cpu.hpp"
 
 namespace ov {
 namespace snippets {
@@ -26,7 +27,8 @@ ShapeInferPtr CPUShapeInferSnippetsFactory::get_specific_op_shape_infer(const ov
     { OP::get_type_info_static(), [](const std::shared_ptr<ov::Node>& n) { return std::make_shared<InferType>(n);} }
 
 const CPUShapeInferSnippetsFactory::TRegistry CPUShapeInferSnippetsFactory::specific_ops_registry {
-    SHAPE_INFER_PREDEFINED(ov::intel_cpu::FusedMulAdd, NumpyBroadcastShapeInfer)
+    SHAPE_INFER_PREDEFINED(ov::intel_cpu::FusedMulAdd, NumpyBroadcastShapeInfer),
+    SHAPE_INFER_PREDEFINED(ov::intel_cpu::SwishNode, PassThroughShapeInfer),
 };
 #undef SHAPE_INFER_OP_SPECIFIC
 #undef SHAPE_INFER_PREDEFINED
diff --git a/src/plugins/intel_cpu/src/transformations/snippets/x64/op/brgemm_cpu.cpp b/src/plugins/intel_cpu/src/transformations/snippets/x64/op/brgemm_cpu.cpp
index d0265b8606d286..d701495ce7b372 100644
--- a/src/plugins/intel_cpu/src/transformations/snippets/x64/op/brgemm_cpu.cpp
+++ b/src/plugins/intel_cpu/src/transformations/snippets/x64/op/brgemm_cpu.cpp
@@ -26,7 +26,7 @@ BrgemmCPU::BrgemmCPU(const Output<Node>& A, const Output<Node>& B, const Type ty
     set_input_port_descriptor({0, offset_a}, 0);
     set_input_port_descriptor({0, offset_b}, 1);
     set_output_port_descriptor({0, offset_c}, 0);
-    compute_block_size_values(blk_size_m, blk_size_k, blk_size_n);
+    set_block_size_values(blk_size_m, blk_size_k, blk_size_n);
     custom_constructor_validate_and_infer_types(std::move(layout_a), std::move(layout_b), std::move(layout_c));
     set_beta(beta);
 }
@@ -43,7 +43,7 @@ BrgemmCPU::BrgemmCPU(const Output<Node>& A, const Output<Node>& B, const Output<
     set_input_port_descriptor({0, offset_b}, 1);
     set_output_port_descriptor({0, offset_c}, 0);
     set_input_port_descriptor({0, offset_scratch}, 2);
-    compute_block_size_values(blk_size_m, blk_size_k, blk_size_n);
+    set_block_size_values(blk_size_m, blk_size_k, blk_size_n);
     custom_constructor_validate_and_infer_types(std::move(layout_a), std::move(layout_b), std::move(layout_c));
     set_beta(beta);
 }
@@ -57,7 +57,7 @@ BrgemmCPU::BrgemmCPU(const Output<Node>& A, const Output<Node>& B, const Type ty
     set_output_size(1);
     m_input_ports = {{0, desc_a}, {1, desc_b}};
     m_output_ports = {{0, desc_c}};
-    compute_block_size_values(blk_size_m, blk_size_k, blk_size_n);
+    set_block_size_values(blk_size_m, blk_size_k, blk_size_n);
     custom_constructor_validate_and_infer_types(std::move(layout_a), std::move(layout_b), std::move(layout_c));
     set_beta(beta);
 }
@@ -71,7 +71,7 @@ BrgemmCPU::BrgemmCPU(const Output<Node>& A, const Output<Node>& B, const Output<
     set_output_size(1);
     m_input_ports = {{0, desc_a}, {1, desc_b}, {2, desc_scratch}};
     m_output_ports = {{0, desc_c}};
-    compute_block_size_values(blk_size_m, blk_size_k, blk_size_n);
+    set_block_size_values(blk_size_m, blk_size_k, blk_size_n);
     custom_constructor_validate_and_infer_types(std::move(layout_a), std::move(layout_b), std::move(layout_c));
     set_beta(beta);
 }
@@ -87,7 +87,7 @@ void BrgemmCPU::custom_constructor_validate_and_infer_types(std::vector<size_t>
         std::vector<ov::PartialShape>{ snippets::utils::get_planar_pshape(get_input_partial_shape(0), layout_a),
                                        brgemm_copy ? snippets::utils::get_planar_pshape(brgemm_copy->input(0))
                                                    : snippets::utils::get_planar_pshape(get_input_partial_shape(1), layout_b) };
-    auto output_shape = get_output_partial_shape(planar_input_shapes);
+    auto output_shape = infer_output_partial_shape(planar_input_shapes);
     set_output_type(0, get_output_type(), snippets::utils::get_planar_pshape(output_shape, layout_c));
 
     // Additional check for 3rd input
@@ -100,7 +100,7 @@ void BrgemmCPU::validate_and_infer_types() {
 
     const auto brgemm_copy = is_with_data_repacking() ? get_brgemm_copy() : nullptr;
     const auto planar_input_shapes = get_planar_input_shapes({input(0), brgemm_copy ? brgemm_copy->input(0) : input(1)});
-    auto output_shape = get_output_partial_shape(planar_input_shapes);
+    auto output_shape = infer_output_partial_shape(planar_input_shapes);
     set_output_type(0, get_output_type(), get_planar_output_shape(output_shape));
 
     // Additional check for 3rd input
@@ -119,9 +119,6 @@ void BrgemmCPU::validate_with_scratchpad() const {
 }
 
 void BrgemmCPU::validate_inputs() const {
-    // If no leading dimensions are provided, assume dense row-major inputs-outputs
-    NODE_VALIDATION_CHECK(this, get_input_partial_shape(0).is_static() && get_input_partial_shape(1).is_static(),
-                          "BrgemmCPU currently supports only static shapes.");
     OPENVINO_ASSERT(implication(one_of(m_type, Type::Floating, Type::WithDataRepacking), get_input_size() == 2),
                     "BrgemmCPU expects 2 inputs in cases, when input precisions are f32|f32, u8|i8 or bf16|bf16 (non-AMX system)");
     OPENVINO_ASSERT(implication(one_of(m_type, Type::WithCompensations, Type::AMX), get_input_size() == 3),
diff --git a/src/plugins/intel_cpu/src/transformations/snippets/x64/pass/brgemm_to_brgemm_cpu.cpp b/src/plugins/intel_cpu/src/transformations/snippets/x64/pass/brgemm_to_brgemm_cpu.cpp
index 80fde9c733ba18..81a9fa0a525013 100644
--- a/src/plugins/intel_cpu/src/transformations/snippets/x64/pass/brgemm_to_brgemm_cpu.cpp
+++ b/src/plugins/intel_cpu/src/transformations/snippets/x64/pass/brgemm_to_brgemm_cpu.cpp
@@ -58,16 +58,12 @@ pass::BrgemmToBrgemmCPU::BrgemmToBrgemmCPU() {
         if (!brgemm || brgemm_plugin)
             OPENVINO_THROW("BrgemmCPU cannot be in body before BrgemmToBrgemmCPU pass");
 
-        if (brgemm->is_dynamic()) {
-            return false;
-        }
-
         const auto& brgemm_in0_desc = PortDescriptorUtils::get_port_descriptor_ptr(brgemm->input(0));
         const auto& brgemm_in1_desc = PortDescriptorUtils::get_port_descriptor_ptr(brgemm->input(1));
         const auto& brgemm_out_desc = PortDescriptorUtils::get_port_descriptor_ptr(brgemm->output(0));
 
-        const auto dimsMatMulIn0 = snippets::utils::get_planar_pshape(brgemm->input(0)).get_shape();
-        const auto dimsMatMulIn1 = snippets::utils::get_planar_pshape(brgemm->input(1)).get_shape();
+        const auto dimsMatMulIn0 = snippets::utils::get_planar_pshape(brgemm->input(0));
+        const auto dimsMatMulIn1 = snippets::utils::get_planar_pshape(brgemm->input(1));
 
         const auto K = *dimsMatMulIn0.rbegin();
         const auto N = *dimsMatMulIn1.rbegin();
@@ -75,7 +71,9 @@ pass::BrgemmToBrgemmCPU::BrgemmToBrgemmCPU() {
         const auto element_type_a = brgemm->get_input_element_type(0);
         const auto brgemmVNNIFactor = 4 / element_type_a.size();
         const bool isAMXSupported = dnnl::impl::cpu::x64::mayiuse(dnnl::impl::cpu::x64::avx512_core_amx);
-        const bool with_amx = isAMXSupported && element_type_a != ov::element::f32 && (K % brgemmVNNIFactor == 0) && (N % brgemmVNNIFactor == 0);
+        const bool with_amx = isAMXSupported && element_type_a != ov::element::f32 &&
+                              K.is_static() && K.get_length() % brgemmVNNIFactor == 0 &&
+                              N.is_static() && N.get_length() % brgemmVNNIFactor == 0;
         const bool with_comp = element_type_a == ov::element::i8 && !with_amx;
 
         const auto offset_a = brgemm->get_offset_a();
diff --git a/src/plugins/intel_cpu/src/transformations/snippets/x64/pass/set_brgemm_cpu_blocking_params.cpp b/src/plugins/intel_cpu/src/transformations/snippets/x64/pass/set_brgemm_cpu_blocking_params.cpp
index 63e33cd42635f1..c682c2eae85a14 100644
--- a/src/plugins/intel_cpu/src/transformations/snippets/x64/pass/set_brgemm_cpu_blocking_params.cpp
+++ b/src/plugins/intel_cpu/src/transformations/snippets/x64/pass/set_brgemm_cpu_blocking_params.cpp
@@ -10,12 +10,9 @@
 #include "transformations/snippets/x64/op/brgemm_copy_b.hpp"
 #include "transformations/snippets/x64/op/brgemm_cpu.hpp"
 
-#include "openvino/core/rt_info.hpp"
 #include "openvino/pass/pattern/op/wrap_type.hpp"
 #include "openvino/pass/pattern/matcher.hpp"
 
-#include "cpu/x64/cpu_isa_traits.hpp"
-
 #include "cpu_shape.h"
 #include "utils/general_utils.h"
 
@@ -30,27 +27,32 @@ pass::SetBrgemmCPUBlockingParams::SetBrgemmCPUBlockingParams() {
         OV_ITT_SCOPED_TASK(ov::pass::itt::domains::SnippetsTransform, "ov::intel_cpu::pass::SetBrgemmCPUBlockingParams")
         const auto node = m.get_match_root();
         auto brgemm = ov::as_type_ptr<BrgemmCPU>(node);
-        if (brgemm->is_dynamic()) {
-            return false;
-        }
 
         const auto& input_1_precision = brgemm->get_input_element_type(1);
         // Ticket: 113745
         // TODO: extend block size selection heuristics
-        auto get_block_size_m = [&](const size_t M) {
+        auto get_block_size_m = [&](const ov::Dimension& M_dim) -> size_t {
             return 32;
         };
-        auto get_block_size_k = [&](const size_t K) {
+        auto get_block_size_k = [&](const ov::Dimension& K_dim) -> size_t {
+            // K blocking is disabled in dynamism by default
+            if (K_dim.is_dynamic())
+                return snippets::utils::get_dynamic_value<size_t>();
+
+            const auto K = K_dim.get_length();
             if (input_1_precision != ov::element::f32)
                 return K;
             return K > 1024 ? 1024 : K > 512 ? 512 : K;
         };
-        auto get_block_size_n = [&](const size_t N) {
-            return input_1_precision != ov::element::f32 ? N : 64;
+        auto get_block_size_n = [&](const ov::Dimension& N_dim) -> size_t {
+            // N blocking is disabled in dynamism by default
+            if (N_dim.is_dynamic())
+                return snippets::utils::get_dynamic_value<size_t>();
+            return input_1_precision == ov::element::f32 ? 64 : N_dim.get_length();
         };
 
-        const auto brgemm_in0_dims = snippets::utils::get_planar_pshape(brgemm->input(0)).get_shape();
-        const auto brgemm_in1_dims = snippets::utils::get_planar_pshape(brgemm->input(1)).get_shape();
+        const auto brgemm_in0_dims = snippets::utils::get_planar_pshape(brgemm->input(0));
+        const auto brgemm_in1_dims = snippets::utils::get_planar_pshape(brgemm->input(1));
         const auto& M = *++brgemm_in0_dims.rbegin();
         const auto& K = *brgemm_in0_dims.rbegin();
         const auto& N = *brgemm_in1_dims.rbegin();
@@ -61,7 +63,7 @@ pass::SetBrgemmCPUBlockingParams::SetBrgemmCPUBlockingParams() {
         if (brgemm->is_with_data_repacking()) {
             const auto brgemm_copy_b = brgemm->get_brgemm_copy();
             const auto brgemmVNNIFactor = brgemm_copy_b->get_brgemm_vnni_factor();
-            OPENVINO_ASSERT(k_blk == K || k_blk % brgemmVNNIFactor == 0,
+            OPENVINO_ASSERT(K.is_dynamic() || k_blk == static_cast<size_t>(K.get_length()) || k_blk % brgemmVNNIFactor == 0,
                             "K Block size (",
                             k_blk,
                             "), which is not divisible by brgemmVNNIFactor (",
diff --git a/src/plugins/intel_cpu/src/transformations/transformation_pipeline.cpp b/src/plugins/intel_cpu/src/transformations/transformation_pipeline.cpp
index f84b0ba70b0e54..bd3397245f5a26 100644
--- a/src/plugins/intel_cpu/src/transformations/transformation_pipeline.cpp
+++ b/src/plugins/intel_cpu/src/transformations/transformation_pipeline.cpp
@@ -47,6 +47,7 @@
 #include "transformations/op_conversions/convert_gelu.hpp"
 #include "transformations/op_conversions/convert_interpolate1_to_interpolate4.hpp"
 #include "transformations/op_conversions/convert_matrix_nms_to_matrix_nms_ie.hpp"
+#include "transformations/op_conversions/convert_maxpool_downgrade.hpp"
 #include "transformations/op_conversions/convert_minimum_to_power_and_max.hpp"
 #include "transformations/op_conversions/convert_mod.hpp"
 #include "transformations/op_conversions/convert_multiclass_nms_to_multiclass_nms_ie.hpp"
@@ -355,7 +356,8 @@ void Transformations::PreLpt(const std::vector<ov::element::Type>& defaultPrecis
         // @todo should we always convert to f32 regardless of hardware support, as it is done for f16?
         if (!hasHardwareSupport(ov::element::bf16))
             map.insert({ov::element::bf16, ov::element::f32});
-        if (!one_of(inferencePrecision, element::f16, element::undefined)) {
+        // TODO: Remove 'hasHardwareSupport' when all nodes are able to handle f16 properly.
+        if (!one_of(inferencePrecision, element::f16, element::undefined) || !hasHardwareSupport(element::f16)) {
             map.insert({ov::element::f16, ov::element::f32});
         }
         return map;
@@ -462,6 +464,14 @@ void Transformations::PreLpt(const std::vector<ov::element::Type>& defaultPrecis
         },
         ov::pass::ConvertBatchToSpace, ov::pass::ConvertSpaceToBatch);
 
+    CPU_SET_CALLBACK_COMMON(
+        manager,
+        [](const_node_ptr& node) -> bool {
+            const auto maxpool = std::dynamic_pointer_cast<const ov::op::v14::MaxPool>(node);
+            return !maxpool ||  maxpool->get_rounding_type() == ov::op::RoundingType::CEIL_TORCH;
+        },
+        ov::pass::ConvertMaxPool14ToMaxPool8);
+
     CPU_SET_CALLBACK_COMMON(manager,
         [](const_node_ptr &node) -> bool {
             std::string msg;
@@ -785,6 +795,7 @@ void Transformations::PostLpt() {
     CPU_REGISTER_PASS_X64(postLPTPassManager, ConvertFqRnnToQuantizedRnn);
 
     CPU_REGISTER_PASS_X64(postLPTPassManager, ov::pass::RoPEFusion);
+    CPU_REGISTER_PASS_ARM64(postLPTPassManager, ov::pass::RoPEFusion);
     CPU_REGISTER_PASS_X64(postLPTPassManager, CausalMaskPreprocessFusion);
 
     // MLP & QKV fusion optimizations is focused on throughput, only enabled on AMX-bf16 & LLM serving use cases.
@@ -817,8 +828,7 @@ void Transformations::PostLpt() {
             }
         }
     }
-
-    CPU_REGISTER_PASS_X64(postLPTPassManager, StatefulSDPAFusion);
+    CPU_REGISTER_PASS_COMMON(postLPTPassManager, StatefulSDPAFusion);
 
     // Should be before Snippets pipeline because Ngram pattern contains eltwise nodes that can be tokenized by Snippets.
     auto symbolic_pipeline = CPU_REGISTER_PASS_COMMON(postLPTPassManager, ov::pass::SymbolicOptimizations, false);
@@ -865,10 +875,13 @@ void Transformations::MainSnippets(void) {
     // The optimization "SplitDimensionM" depends on target machine (thread count).
     // To avoid uncontrolled behavior in tests, we disabled the optimization when there is Config::SnippetsMode::IgnoreCallback
     bool split_m_dimension = !ignoreCallback;
+    // [113198] Add dynamic Subgraph with MHA pattern inside execution support
+    bool is_dynamic_mha_token_enabled = false;
     // [122706] Some 3D MHA Patterns have perf regressions when Transpose op is tokenized
     std::set<size_t> mha_supported_transpose_ranks = { 4 };
-    ov::snippets::pass::SnippetsTokenization::Config tokenization_config(concurrency, data_ptr_gpr_count, split_m_dimension,
-                                                     mha_token_enable_transpose_on_output, mha_supported_transpose_ranks);
+    snippets::pass::SnippetsTokenization::Config tokenization_config(concurrency, data_ptr_gpr_count, split_m_dimension,
+                                                                     mha_token_enable_transpose_on_output, is_dynamic_mha_token_enabled,
+                                                                     mha_supported_transpose_ranks);
 
     ov::pass::Manager snippetsManager;
     snippetsManager.set_per_pass_validation(false);
@@ -945,17 +958,33 @@ void Transformations::MainSnippets(void) {
 
     auto is_supported_op = [](const std::shared_ptr<const ov::Node> &n) -> bool {
 #if defined(OPENVINO_ARCH_ARM64)
-        return (ov::is_type<ov::op::v1::Add>(n) ||
+        return (ov::is_type<ov::op::v0::Abs>(n) ||
+                ov::is_type<ov::op::v1::Add>(n) ||
+                ov::is_type<ov::op::v0::Clamp>(n) ||
                 ov::is_type<ov::op::v1::Divide>(n) ||
-                ov::is_type<ov::op::v1::Multiply>(n) ||
+                ov::is_type<ov::op::v0::Elu>(n) ||
                 ov::is_type<ov::op::v0::Exp>(n) ||
+                ov::is_type<ov::op::v0::Floor>(n) ||
+                ov::is_type<ov::op::v0::Gelu>(n) ||
+                ov::is_type<ov::op::v7::Gelu>(n) ||
+                ov::is_type<ov::op::v4::HSwish>(n) ||
+                ov::is_type<ov::op::v1::Maximum>(n) ||
+                ov::is_type<ov::op::v1::Minimum>(n) ||
+                ov::is_type<ov::op::v4::Mish>(n) ||
+                ov::is_type<ov::op::v1::Mod>(n) ||
+                ov::is_type<ov::op::v1::Multiply>(n) ||
                 ov::is_type<ov::op::v0::Relu>(n) ||
+                ov::is_type<ov::op::v0::Sigmoid>(n) ||
+                ov::is_type<ov::op::v1::Subtract>(n) ||
+                ov::is_type<ov::op::v4::Swish>(n) ||
                 ov::is_type<ov::op::v0::Tanh>(n));
 #else
-        // CPU Plugin support Swish in Subgraph via conversion to SwichCPU which assumes second input to be constant
-        auto is_unsupported_swish = [](const std::shared_ptr<const ov::Node> &n) {
-            return ov::is_type<const ov::op::v4::Swish>(n) && n->inputs().size() > 1 &&
-                   !ov::is_type<const ov::op::v0::Constant>(n->get_input_node_shared_ptr(1));
+        // CPU Plugin support Swish in Subgraph via conversion to SwichCPU which assumes second input to be constant,
+        // and CPU Plugin does not support Mish for x64
+        auto is_unsupported = [](const std::shared_ptr<const ov::Node> &n) {
+            return (ov::is_type<const ov::op::v4::Swish>(n) && n->inputs().size() > 1 &&
+                   !ov::is_type<const ov::op::v0::Constant>(n->get_input_node_shared_ptr(1))) ||
+                   ov::is_type<const ov::op::v4::Mish>(n);
         };
         // todo: general tokenization flow is not currently supported for these operations.
         // they can be tokenized only as a part of complex patterns
@@ -969,7 +998,7 @@ void Transformations::MainSnippets(void) {
                     ov::is_type<const ov::op::v1::ReduceMax>(n) ||
                     ov::is_type<const ov::op::v1::ReduceSum>(n));
         };
-        return !is_unsupported_swish(n) && !is_unsupported_by_common_tokenization(n);
+        return !is_unsupported(n) && !is_unsupported_by_common_tokenization(n);
 #endif
     };
 
diff --git a/src/plugins/intel_cpu/src/utils/profiler.cpp b/src/plugins/intel_cpu/src/utils/profiler.cpp
index 85a27c303a9285..87cdf3fd0516f4 100644
--- a/src/plugins/intel_cpu/src/utils/profiler.cpp
+++ b/src/plugins/intel_cpu/src/utils/profiler.cpp
@@ -1,3 +1,6 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
 #include "profiler.hpp"
 
 #include <algorithm>
@@ -156,5 +159,5 @@ ProfilerManager::~ProfilerManager() {
         finalizer.finalize();
 }
 
-}  // namespace profiler 
+}  // namespace profiler
 #endif
diff --git a/src/plugins/intel_cpu/src/utils/profiler.hpp b/src/plugins/intel_cpu/src/utils/profiler.hpp
index 0923d3599877d5..6f799178995cc9 100644
--- a/src/plugins/intel_cpu/src/utils/profiler.hpp
+++ b/src/plugins/intel_cpu/src/utils/profiler.hpp
@@ -1,3 +1,6 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
 #pragma once
 #ifndef CPU_WITH_PROFILER
 #define CPU_WITH_PROFILER
diff --git a/src/plugins/intel_cpu/tests/functional/custom/behavior/ov_executable_network/properties.cpp b/src/plugins/intel_cpu/tests/functional/custom/behavior/ov_executable_network/properties.cpp
index cef9e809bf2a62..5cf5a668f7512e 100644
--- a/src/plugins/intel_cpu/tests/functional/custom/behavior/ov_executable_network/properties.cpp
+++ b/src/plugins/intel_cpu/tests/functional/custom/behavior/ov_executable_network/properties.cpp
@@ -183,6 +183,17 @@ TEST_F(OVClassConfigTestCPU, smoke_CpuExecNetworkCheckKVCachePrecision) {
     ASSERT_EQ(kv_cache_precision_value, ov::element::f32);
 }
 
+TEST_F(OVClassConfigTestCPU, smoke_CpuExecNetworkCheckAccuracyModeDynamicQuantizationGroupSize) {
+    ov::Core core;
+
+    ASSERT_NO_THROW(core.set_property(deviceName, ov::hint::execution_mode(ov::hint::ExecutionMode::ACCURACY)));
+    ov::CompiledModel compiledModel = core.compile_model(model, deviceName);
+
+    size_t groupSize = 0;
+    ASSERT_NO_THROW(groupSize = compiledModel.get_property(ov::hint::dynamic_quantization_group_size));
+    ASSERT_EQ(groupSize, 0);
+}
+
 const auto bf16_if_can_be_emulated = ov::with_cpu_x86_avx512_core() ? ov::element::bf16 : ov::element::f32;
 
 TEST_F(OVClassConfigTestCPU, smoke_CpuExecNetworkCheckExecutionModeIsAvailableInCoreAndModel) {
diff --git a/src/plugins/intel_cpu/tests/functional/custom/behavior/ov_plugin/properties.cpp b/src/plugins/intel_cpu/tests/functional/custom/behavior/ov_plugin/properties.cpp
index 92d800839b1fe2..38d4f1a38a1516 100644
--- a/src/plugins/intel_cpu/tests/functional/custom/behavior/ov_plugin/properties.cpp
+++ b/src/plugins/intel_cpu/tests/functional/custom/behavior/ov_plugin/properties.cpp
@@ -162,11 +162,11 @@ TEST_F(OVClassConfigTestCPU, smoke_PluginSetConfigAffinity) {
 #    else
     auto defaultBindThreadParameter = ov::Affinity::CORE;
 #    endif
+#endif
     auto coreTypes = ov::get_available_cores_types();
     if (coreTypes.size() > 1) {
         defaultBindThreadParameter = ov::Affinity::HYBRID_AWARE;
     }
-#endif
     ASSERT_NO_THROW(value = ie.get_property("CPU", ov::affinity));
     ASSERT_EQ(defaultBindThreadParameter, value);
 
diff --git a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/activation.cpp b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/activation.cpp
index b9bb07a3bb9324..876ee3e5e22d72 100644
--- a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/activation.cpp
+++ b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/activation.cpp
@@ -4,6 +4,7 @@
 
 #include "activation.hpp"
 #include "gtest/gtest.h"
+#include "internal_properties.hpp"
 #include "utils/cpu_test_utils.hpp"
 #include "common_test_utils/node_builders/activation.hpp"
 #include "shared_test_classes/single_op/activation.hpp"
@@ -19,7 +20,9 @@ std::string ActivationLayerCPUTest::getTestCaseName(const testing::TestParamInfo
     std::pair<utils::ActivationTypes, std::vector<float>> activationTypeAndConstValue;
     ov::element::Type netPrecision, inPrecision, outPrecision;
     CPUTestUtils::CPUSpecificParams cpuParams;
-    std::tie(inputShapes, activationShapes, activationTypeAndConstValue, netPrecision, inPrecision, outPrecision, cpuParams) = obj.param;
+    bool enforceSnippets;
+    std::tie(inputShapes, activationShapes, activationTypeAndConstValue, netPrecision, inPrecision, outPrecision, cpuParams, enforceSnippets) =
+             obj.param;
 
     std::ostringstream result;
     result << activationNames[activationTypeAndConstValue.first] << "_";
@@ -43,6 +46,7 @@ std::string ActivationLayerCPUTest::getTestCaseName(const testing::TestParamInfo
     result << "inPRC=" << inPrecision.to_string() << "_";
     result << "outPRC=" << outPrecision.to_string() << "_";
     result << CPUTestUtils::CPUTestsBase::getTestCaseName(cpuParams);
+    result << "_enforceSnippets=" << enforceSnippets;
 
     return result.str();
 }
@@ -107,7 +111,9 @@ void ActivationLayerCPUTest::SetUp() {
     std::pair<utils::ActivationTypes, std::vector<float>> activationTypeAndConstValue;
     ov::element::Type inPrecision, outPrecision;
     CPUTestUtils::CPUSpecificParams cpuParams;
-    std::tie(inputShapes, activationShapes, activationTypeAndConstValue, netPrecision, inPrecision, outPrecision, cpuParams) = this->GetParam();
+    bool enforceSnippets;
+    std::tie(inputShapes, activationShapes, activationTypeAndConstValue, netPrecision, inPrecision, outPrecision, cpuParams, enforceSnippets) =
+             this->GetParam();
     std::tie(inFmts, outFmts, priority, selectedType) = cpuParams;
     activationType = activationTypeAndConstValue.first;
     auto constantsValue = activationTypeAndConstValue.second;
@@ -136,6 +142,12 @@ void ActivationLayerCPUTest::SetUp() {
 
     init_input_shapes(inputShapes);
 
+    if (enforceSnippets) {
+        configuration.insert(ov::intel_cpu::snippets_mode(ov::intel_cpu::SnippetsMode::IGNORE_CALLBACK));
+    } else {
+        configuration.insert(ov::intel_cpu::snippets_mode(ov::intel_cpu::SnippetsMode::DISABLE));
+    }
+
     auto params = std::make_shared<ov::op::v0::Parameter>(netPrecision, inputDynamicShapes.front());
     auto activation = utils::make_activation(params, netPrecision, activationType, activationShapes, constantsValue);
     activation->get_rt_info() = getCPUInfo();
@@ -163,6 +175,7 @@ std::string ActivationLayerCPUTest::getPrimitiveType(const utils::ActivationType
         (activation_type == utils::ActivationTypes::HSwish) ||
         (activation_type == utils::ActivationTypes::IsInf) ||
         (activation_type == utils::ActivationTypes::HardSigmoid) ||
+        (activation_type == utils::ActivationTypes::IsNaN) ||
         (activation_type == utils::ActivationTypes::Mish) ||
         (activation_type == utils::ActivationTypes::GeluErf) ||
         (activation_type == utils::ActivationTypes::GeluTanh) ||
@@ -178,7 +191,8 @@ std::string ActivationLayerCPUTest::getPrimitiveType(const utils::ActivationType
         return "";
     }
 #endif
-    if (activation_type == utils::ActivationTypes::Floor) {
+    if ((activation_type == utils::ActivationTypes::Floor) ||
+       (activation_type == utils::ActivationTypes::IsNaN)) {
         return "ref";
     }
     return "acl";
@@ -215,6 +229,29 @@ const std::map<utils::ActivationTypes, std::vector<std::vector<float>>>& activat
         {GeluTanh,    {{}}},
         {SoftSign,    {{}}},
         {SoftPlus,    {{}}},
+        {IsNaN,    {{}}},
+    };
+
+    return activationTypes;
+}
+
+const std::map<utils::ActivationTypes, std::vector<std::vector<float>>>& activationTypesSnippets() {
+    static const std::map<utils::ActivationTypes, std::vector<std::vector<float>>> activationTypes {
+        {Abs,         {{}}},
+        {Exp,         {{}}},
+        {Clamp,       {{-2.0f, 2.0f}}},
+        {Elu,         {{0.1f}}},
+        {Floor,       {{}}},
+        {GeluErf,     {{}}},
+        {GeluTanh,    {{}}},
+        {Relu,        {{}}},
+        {HSwish,      {{}}},
+#if defined(OPENVINO_ARCH_ARM64)
+        {Mish,        {{}}},
+#endif
+        {Sigmoid,     {{}}},
+        {Swish,       {{0.1f}}},
+        {Tanh,        {{}}},
     };
 
     return activationTypes;
diff --git a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/activation.hpp b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/activation.hpp
index 578f71d6bf4e30..817cc2155f2212 100644
--- a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/activation.hpp
+++ b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/activation.hpp
@@ -22,7 +22,8 @@ using ActivationLayerCPUTestParamSet =
                ov::element::Type,                                      // Net precision
                ov::element::Type,                                      // Input precision
                ov::element::Type,                                      // Output precision
-               CPUTestUtils::CPUSpecificParams>;
+               CPUTestUtils::CPUSpecificParams,
+               bool>;
 
 class ActivationLayerCPUTest : public testing::WithParamInterface<ActivationLayerCPUTestParamSet>,
                                virtual public ov::test::SubgraphBaseTest,
@@ -49,6 +50,7 @@ namespace Activation {
 const std::vector<size_t> activationShapes();
 
 const std::map<utils::ActivationTypes, std::vector<std::vector<float>>>& activationTypes();
+const std::map<utils::ActivationTypes, std::vector<std::vector<float>>>& activationTypesSnippets();
 
 const std::vector<ov::element::Type>& netPrc();
 
diff --git a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/col2im.cpp b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/col2im.cpp
new file mode 100644
index 00000000000000..3bb0ff903a9ba8
--- /dev/null
+++ b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/col2im.cpp
@@ -0,0 +1,250 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "col2im.hpp"
+#include "utils/cpu_test_utils.hpp"
+#include "common_test_utils/ov_tensor_utils.hpp"
+#include "shared_test_classes/base/ov_subgraph.hpp"
+#include "utils/precision_support.h"
+
+using namespace CPUTestUtils;
+
+namespace ov {
+namespace test {
+namespace Col2Im {
+std::string Col2ImLayerCPUTest::getTestCaseName(testing::TestParamInfo<Col2ImLayerCPUTestParamsSet> obj) {
+        Col2ImLayerTestParams basicParamsSet;
+        CPUSpecificParams cpuParams;
+        std::tie(basicParamsSet, cpuParams) = obj.param;
+        std::string td;
+        ElementType netPrecision;
+        ElementType indexPrecision;
+        Col2ImSpecificParams col2ImPar;
+        std::tie(col2ImPar, netPrecision, indexPrecision, td) = basicParamsSet;
+
+        InputShape inputShape;
+        std::vector<int64_t> outputSize;
+        std::vector<int64_t> kernelSize;
+        ov::Strides strides;
+        ov::Strides dilations;
+        ov::Shape pads_begin;
+        ov::Shape pads_end;
+        std::tie(inputShape, outputSize, kernelSize, strides, dilations, pads_begin, pads_end) = col2ImPar;
+        std::ostringstream result;
+
+        result << netPrecision << "_IS=";
+        result << ov::test::utils::partialShape2str({ inputShape.first }) << "_";
+        result << "TS=";
+        result << "(";
+        for (const auto& targetShape : inputShape.second) {
+            result << ov::test::utils::vec2str(targetShape) << "_";
+        }
+        result << ")_";
+        result << "outputSize=" << ov::test::utils::vec2str(outputSize) << "_";
+        result << "kernelSize=" << ov::test::utils::vec2str(kernelSize) << "_";
+        result << "strides=" << strides << "_";
+        result << "dilations=" << dilations << "_";
+        result << "padsBegin=" << pads_begin << "_";
+        result << "padsEnd=" << pads_end << "_";
+        result << "dataPrecision=" << netPrecision << "_";
+        result << "indexPrecision=" << indexPrecision;
+        result << CPUTestsBase::getTestCaseName(cpuParams);
+
+        return result.str();
+    }
+
+void Col2ImLayerCPUTest::generate_inputs(const std::vector<ov::Shape>& targetInputStaticShapes) {
+        inputs.clear();
+        const auto& funcInputs = function->inputs();
+
+        ov::Tensor data_tensor;
+        const auto& dataPrecision = funcInputs[0].get_element_type();
+        const auto& dataShape = targetInputStaticShapes.front();
+        ov::test::utils::InputGenerateData in_data;
+        in_data.start_from = 0;
+        in_data.range = 10;
+        in_data.resolution = 1000;
+        data_tensor = ov::test::utils::create_and_fill_tensor(dataPrecision, dataShape, in_data);
+        inputs.insert({ funcInputs[0].get_node_shared_ptr(), data_tensor });
+    }
+
+void Col2ImLayerCPUTest::SetUp() {
+        Col2ImLayerTestParams basicParamsSet;
+        CPUSpecificParams cpuParams;
+        std::tie(basicParamsSet, cpuParams) = this->GetParam();
+        std::tie(inFmts, outFmts, priority, selectedType) = cpuParams;
+
+        Col2ImSpecificParams Col2ImParams;
+        ElementType inputPrecision;
+        ElementType indexPrecision;
+        std::tie(Col2ImParams, inputPrecision, indexPrecision, targetDevice) = basicParamsSet;
+
+        InputShape dataInputShape;
+        std::vector<int64_t> outputSize;
+        std::vector<int64_t> kernelSize;
+        ov::Strides strides;
+        ov::Strides dilations;
+        ov::Shape pads_begin;
+        ov::Shape pads_end;
+        std::tie(dataInputShape, outputSize, kernelSize, strides, dilations, pads_begin, pads_end) = Col2ImParams;
+
+        auto image2DSpatialShape = InputShape{{}, {{2}}};
+        init_input_shapes({dataInputShape, image2DSpatialShape, image2DSpatialShape});
+        auto dataParameter = std::make_shared<ov::op::v0::Parameter>(inputPrecision, inputDynamicShapes[0]);
+        auto outputSizeConst = std::make_shared<ov::op::v0::Constant>(indexPrecision, ov::Shape{ 2 }, outputSize);
+        auto kernelSizeConst = std::make_shared<ov::op::v0::Constant>(indexPrecision, ov::Shape{ 2 }, kernelSize);
+
+        auto Col2Im = std::make_shared<ov::op::v15::Col2Im>(dataParameter,
+                                                            outputSizeConst,
+                                                            kernelSizeConst,
+                                                            strides,
+                                                            dilations,
+                                                            pads_begin,
+                                                            pads_end);
+
+        ov::ParameterVector params{ dataParameter };
+        function = makeNgraphFunction(inputPrecision, params, Col2Im, "Col2Im");
+
+        if (inputPrecision == ov::element::bf16) {
+            rel_threshold = 0.02;
+        }
+}
+
+TEST_P(Col2ImLayerCPUTest, CompareWithRefs) {
+    run();
+    CheckPluginRelatedResults(compiledModel, "Col2Im");
+}
+
+const std::vector<Col2ImSpecificParams> col2ImParamsVector = {
+    Col2ImSpecificParams {
+        InputShape{{}, {{1, 12, 9}}},
+        std::vector<int64_t>{4, 4},
+        std::vector<int64_t>{2, 2},
+        ov::Strides{1, 1},
+        ov::Strides{1, 1},
+        ov::Shape{0, 0},
+        ov::Shape{0, 0}
+    },
+    Col2ImSpecificParams {
+        InputShape{{}, {{3, 12, 81}}},
+        std::vector<int64_t>{16, 16},
+        std::vector<int64_t>{2, 2},
+        ov::Strides{2, 2},
+        ov::Strides{2, 2},
+        ov::Shape{2, 2},
+        ov::Shape{2, 2}
+    },
+    Col2ImSpecificParams {
+        InputShape{{}, {{12, 81}}},
+        std::vector<int64_t>{16, 16},
+        std::vector<int64_t>{2, 2},
+        ov::Strides{2, 2},
+        ov::Strides{2, 2},
+        ov::Shape{2, 2},
+        ov::Shape{2, 2}
+    },
+    Col2ImSpecificParams {
+        InputShape{{}, {{3, 12, 225}}},
+        std::vector<int64_t>{16, 16},
+        std::vector<int64_t>{2, 2},
+        ov::Strides{1, 1},
+        ov::Strides{1, 1},
+        ov::Shape{0, 0},
+        ov::Shape{0, 0}
+    },
+    Col2ImSpecificParams {
+        InputShape{{}, {{1, 27, 49}}},
+        std::vector<int64_t>{16, 16},
+        std::vector<int64_t>{3, 3},
+        ov::Strides{2, 2},
+        ov::Strides{2, 2},
+        ov::Shape{1, 1},
+        ov::Shape{1, 1}
+    },
+    Col2ImSpecificParams {
+        InputShape{{}, {{1, 18, 104}}},
+        std::vector<int64_t>{16, 16},
+        std::vector<int64_t>{2, 3},
+        ov::Strides{2, 1},
+        ov::Strides{2, 2},
+        ov::Shape{1, 0},
+        ov::Shape{0, 1}
+    },
+    Col2ImSpecificParams {
+        InputShape{{-1, -1, -1}, {{1, 12, 120}, {3, 12, 120}}},
+        std::vector<int64_t>{16, 16},
+        std::vector<int64_t>{2, 2},
+        ov::Strides{2, 1},
+        ov::Strides{2, 2},
+        ov::Shape{1, 0},
+        ov::Shape{0, 1}
+    },
+    Col2ImSpecificParams {
+        InputShape{{}, {{12, 12, 324}}},
+        std::vector<int64_t>{32, 32},
+        std::vector<int64_t>{2, 2},
+        ov::Strides{2, 2},
+        ov::Strides{2, 2},
+        ov::Shape{3, 3},
+        ov::Shape{3, 3}
+    },
+    Col2ImSpecificParams {
+        InputShape{{-1, 12, 324}, {{12, 12, 324}}},
+        std::vector<int64_t>{32, 32},
+        std::vector<int64_t>{2, 2},
+        ov::Strides{2, 2},
+        ov::Strides{2, 2},
+        ov::Shape{3, 3},
+        ov::Shape{3, 3}
+    },
+    Col2ImSpecificParams {
+        InputShape{{-1, -1, -1}, {{12, 12, 324}}},
+        std::vector<int64_t>{32, 32},
+        std::vector<int64_t>{2, 2},
+        ov::Strides{2, 2},
+        ov::Strides{2, 2},
+        ov::Shape{3, 3},
+        ov::Shape{3, 3}
+    },
+    Col2ImSpecificParams {
+        InputShape{{12, -1, -1}, {{12, 12, 324}}},
+        std::vector<int64_t>{32, 32},
+        std::vector<int64_t>{2, 2},
+        ov::Strides{2, 2},
+        ov::Strides{2, 2},
+        ov::Shape{3, 3},
+        ov::Shape{3, 3}
+    },
+    Col2ImSpecificParams {
+        InputShape{{12, 12, -1}, {{12, 12, 324}}},
+        std::vector<int64_t>{32, 32},
+        std::vector<int64_t>{2, 2},
+        ov::Strides{2, 2},
+        ov::Strides{2, 2},
+        ov::Shape{3, 3},
+        ov::Shape{3, 3}
+    },
+    Col2ImSpecificParams {
+        InputShape{{12, -1, 324}, {{12, 12, 324}}},
+        std::vector<int64_t>{32, 32},
+        std::vector<int64_t>{2, 2},
+        ov::Strides{2, 2},
+        ov::Strides{2, 2},
+        ov::Shape{3, 3},
+        ov::Shape{3, 3}
+    },
+    Col2ImSpecificParams {
+        InputShape{{-1, -1}, {{12, 324}}},
+        std::vector<int64_t>{32, 32},
+        std::vector<int64_t>{2, 2},
+        ov::Strides{2, 2},
+        ov::Strides{2, 2},
+        ov::Shape{3, 3},
+        ov::Shape{3, 3}
+    }
+};
+
+}  // namespace Col2Im
+}  // namespace test
+}  // namespace ov
diff --git a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/col2im.hpp b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/col2im.hpp
new file mode 100644
index 00000000000000..4444addf244023
--- /dev/null
+++ b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/col2im.hpp
@@ -0,0 +1,56 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "shared_test_classes/base/ov_subgraph.hpp"
+#include "common_test_utils/ov_tensor_utils.hpp"
+#include "utils/fusing_test_utils.hpp"
+#include "utils/cpu_test_utils.hpp"
+#include "gtest/gtest.h"
+
+using namespace CPUTestUtils;
+
+namespace ov {
+namespace test {
+namespace Col2Im {
+using Col2ImSpecificParams =  std::tuple<
+        InputShape,                                         // data shape
+        std::vector<int64_t>,                               // output size values
+        std::vector<int64_t>,                               // kernel size values
+        ov::Strides,                                        // strides
+        ov::Strides,                                        // dilations
+        ov::Shape,                                          // pads_begin
+        ov::Shape                                           // pads_end
+>;
+
+using Col2ImLayerTestParams = std::tuple<
+        Col2ImSpecificParams,
+        ElementType,                                        // data precision
+        ElementType,                                        // index precision
+        ov::test::TargetDevice                              // device name
+>;
+
+using Col2ImLayerCPUTestParamsSet = std::tuple<
+        Col2ImLayerTestParams,
+        CPUSpecificParams>;
+
+class Col2ImLayerCPUTest : public testing::WithParamInterface<Col2ImLayerCPUTestParamsSet>,
+                             public SubgraphBaseTest, public CPUTestsBase {
+public:
+   static std::string getTestCaseName(testing::TestParamInfo<Col2ImLayerCPUTestParamsSet> obj);
+protected:
+   void SetUp() override;
+   void generate_inputs(const std::vector<ov::Shape>& targetInputStaticShapes) override;
+};
+
+const std::vector<ElementType> indexPrecisions = {
+        ElementType::i32,
+        ElementType::i64
+};
+
+extern const std::vector<Col2ImSpecificParams> col2ImParamsVector;
+}  // namespace Col2Im
+}  // namespace test
+}  // namespace ov
diff --git a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/convolution.cpp b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/convolution.cpp
index 7f294b3fc7fc75..207e522c32f823 100644
--- a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/convolution.cpp
+++ b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/convolution.cpp
@@ -160,11 +160,16 @@ void ConvolutionLayerCPUTest::SetUp() {
     init_input_shapes({inputShape});
 
     auto it = configuration.find(ov::hint::inference_precision.name());
-    if (it != configuration.end() && it->second.as<ov::element::Type>() == ov::element::bf16) {
+    ov::element::Type inference_precision = (it != configuration.end()) ?
+                                            it->second.as<ov::element::Type>() : ov::element::undefined;
+    if (inference_precision == ov::element::bf16) {
         selectedType += "_BF16";
         rel_threshold = 1e-2f;
         if (selectedType == "jit_gemm_BF16")
             rel_threshold = 0.05f;
+    } else if (inference_precision == ov::element::f16) {
+            selectedType +=  "_FP16";
+            rel_threshold = 0.00125f;
     } else {
         selectedType = makeSelectedTypeStr(selectedType, netType);
     }
diff --git a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/convolution_backprop_data.cpp b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/convolution_backprop_data.cpp
index 93f1b11cf117a2..415515ef7f40a2 100755
--- a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/convolution_backprop_data.cpp
+++ b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/convolution_backprop_data.cpp
@@ -196,9 +196,15 @@ void DeconvolutionLayerCPUTest::SetUp() {
 
     std::tie(kernel, stride, padBegin, padEnd, dilation, convOutChannels, padType, outPadding) = basicParamsSet;
 
-    if (additionalConfig[ov::hint::inference_precision.name()] == ov::element::bf16) {
+    auto it = configuration.find(ov::hint::inference_precision.name());
+    ov::element::Type inference_precision = (it != configuration.end()) ?
+        it->second.as<ov::element::Type>() : ov::element::undefined;
+    if (inference_precision == ov::element::bf16) {
         inType = outType = prec = ElementType::bf16;
         rel_threshold = 1e-2f;
+    } else if (inference_precision == ov::element::f16) {
+        inType = outType = prec = ElementType::f16;
+        rel_threshold = 0.00125f;
     } else {
         inType = outType = prec;
     }
diff --git a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/eltwise.cpp b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/eltwise.cpp
index 53ce67d890abb6..2378bf82ede20e 100644
--- a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/eltwise.cpp
+++ b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/eltwise.cpp
@@ -314,6 +314,15 @@ const std::vector<utils::EltwiseTypes>& eltwiseOpTypesBinInp() {
     return eltwiseOpTypesBinInp;
 }
 
+const std::vector<utils::EltwiseTypes>& eltwiseOpTypesBinInpSnippets() {
+    static const std::vector<utils::EltwiseTypes> eltwiseOpTypesBinInp = {
+        utils::EltwiseTypes::ADD,
+        utils::EltwiseTypes::MULTIPLY,
+        utils::EltwiseTypes::MOD,
+    };
+    return eltwiseOpTypesBinInp;
+}
+
 const std::vector<utils::EltwiseTypes>& eltwiseOpTypesDiffInp() {
     static const std::vector<utils::EltwiseTypes> eltwiseOpTypesDiffInp = { // Different number of input nodes depending on optimizations
         utils::EltwiseTypes::POWER,
diff --git a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/eltwise.hpp b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/eltwise.hpp
index a80754f9294c73..7c2592e6f23deb 100644
--- a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/eltwise.hpp
+++ b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/eltwise.hpp
@@ -51,6 +51,7 @@ const std::vector<utils::EltwiseTypes>& eltwiseOpTypesBinInp();
 const std::vector<utils::InputLayerType>& secondaryInputTypes();
 
 const std::vector<utils::EltwiseTypes>& eltwiseOpTypesBinInp();
+const std::vector<utils::EltwiseTypes>& eltwiseOpTypesBinInpSnippets();
 const std::vector<utils::EltwiseTypes>& eltwiseOpTypesDiffInp();
 const std::vector<utils::EltwiseTypes>& eltwiseOpTypesBinDyn();
 
diff --git a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/extremum.cpp b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/extremum.cpp
new file mode 100644
index 00000000000000..2cc3b2dd1d91ff
--- /dev/null
+++ b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/extremum.cpp
@@ -0,0 +1,134 @@
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "extremum.hpp"
+#include "internal_properties.hpp"
+#include "common_test_utils/node_builders/extremum.hpp"
+#include "shared_test_classes/single_op/minimum_maximum.hpp"
+
+namespace ov {
+namespace test {
+
+using namespace CPUTestUtils;
+using namespace ov::test::utils;
+
+std::string ExtremumLayerCPUTest::getTestCaseName(const testing::TestParamInfo<ExtremumLayerCPUTestParamSet> &obj) {
+    std::vector<ov::test::InputShape> inputShapes;
+    utils::MinMaxOpType extremumType;
+    ov::element::Type netPrecision, inPrecision, outPrecision;
+    CPUTestUtils::CPUSpecificParams cpuParams;
+    bool enforceSnippets;
+    std::tie(inputShapes, extremumType, netPrecision, inPrecision, outPrecision, cpuParams, enforceSnippets) = obj.param;
+    std::ostringstream result;
+    result << extremumNames[extremumType] << "_";
+    if (inputShapes.front().first.size() != 0) {
+        result << "IS=(";
+        for (const auto &shape : inputShapes) {
+            result << ov::test::utils::partialShape2str({shape.first}) << "_";
+        }
+        result.seekp(-1, result.cur);
+        result << ")_";
+    }
+    result << "TS=";
+    for (const auto& shape : inputShapes) {
+        for (const auto& item : shape.second) {
+            result << ov::test::utils::vec2str(item) << "_";
+        }
+    }
+    result << "netPRC=" << netPrecision.to_string() << "_";
+    result << "inPRC=" << inPrecision.to_string() << "_";
+    result << "outPRC=" << outPrecision.to_string() << "_";
+    result << CPUTestUtils::CPUTestsBase::getTestCaseName(cpuParams);
+    result << "_enforceSnippets=" << enforceSnippets;
+
+    return result.str();
+}
+
+void ExtremumLayerCPUTest::SetUp() {
+    targetDevice = ov::test::utils::DEVICE_CPU;
+
+    std::vector<ov::test::InputShape> inputShapes;
+    utils::MinMaxOpType extremumType;
+    ov::element::Type netPrecision, inPrecision, outPrecision;
+    CPUTestUtils::CPUSpecificParams cpuParams;
+    bool enforceSnippets;
+    std::tie(inputShapes, extremumType, netPrecision, inPrecision, outPrecision, cpuParams, enforceSnippets) = this->GetParam();
+    std::tie(inFmts, outFmts, priority, selectedType) = cpuParams;
+
+    inType  = inPrecision;
+    outType = outPrecision;
+    const auto primitiveType = getPrimitiveType();
+    selectedType = primitiveType.empty() ? "" : primitiveType + "_" + netPrecision.to_string();
+
+    init_input_shapes(inputShapes);
+
+    if (enforceSnippets) {
+        configuration.insert(ov::intel_cpu::snippets_mode(ov::intel_cpu::SnippetsMode::IGNORE_CALLBACK));
+    } else {
+        configuration.insert(ov::intel_cpu::snippets_mode(ov::intel_cpu::SnippetsMode::DISABLE));
+    }
+
+    auto param1 = std::make_shared<ov::op::v0::Parameter>(netPrecision, inputDynamicShapes[0]);
+    auto param2 = std::make_shared<ov::op::v0::Parameter>(netPrecision, inputDynamicShapes[1]);
+    auto extremum = utils::make_extremum(param1, param2, extremumType);
+    extremum->get_rt_info() = getCPUInfo();
+    function = std::make_shared<ov::Model>(ov::NodeVector{extremum}, ov::ParameterVector{param1, param2}, "Extremum");
+}
+
+std::string ExtremumLayerCPUTest::getPrimitiveType() {
+#if defined(OV_CPU_WITH_ACL)
+#if defined(OPENVINO_ARCH_ARM64)
+    return "jit";
+#endif
+    return "acl";
+#else
+    return CPUTestsBase::getPrimitiveType();
+#endif
+}
+
+TEST_P(ExtremumLayerCPUTest, CompareWithRefs) {
+    run();
+    CheckPluginRelatedResults(compiledModel, "Eltwise");
+}
+
+namespace extremum {
+
+const std::vector<std::vector<ov::Shape>>& inputShape() {
+    static const std::vector<std::vector<ov::Shape>> shape {
+        {{2, 4, 4, 1}, {2, 4, 4, 1}},
+        {{2, 17, 5, 4}, {2, 17, 5, 4}},
+    };
+
+    return shape;
+}
+
+const std::vector<utils::MinMaxOpType>& extremumTypes() {
+    static std::vector<utils::MinMaxOpType> types {
+        MINIMUM,
+        MAXIMUM,
+    };
+
+    return types;
+}
+
+const std::vector<ov::element::Type>& netPrecisions() {
+    static const std::vector<ov::element::Type> netPrecisions {
+        ov::element::f32
+    };
+
+    return netPrecisions;
+}
+
+const std::vector<CPUSpecificParams>& cpuParams4D() {
+    static const std::vector<CPUSpecificParams> cpuParams4D {
+        CPUSpecificParams({nhwc}, {nhwc}, {}, {}),
+        CPUSpecificParams({nchw}, {nchw}, {}, {})
+    };
+
+    return cpuParams4D;
+}
+
+}  // namespace extremum
+}  // namespace test
+}  // namespace ov
diff --git a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/extremum.hpp b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/extremum.hpp
new file mode 100644
index 00000000000000..4d0be1c72b8966
--- /dev/null
+++ b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/extremum.hpp
@@ -0,0 +1,49 @@
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "shared_test_classes/base/ov_subgraph.hpp"
+#include "utils/cpu_test_utils.hpp"
+#include "gtest/gtest.h"
+#include "common_test_utils/test_enums.hpp"
+
+namespace ov {
+namespace test {
+
+using ExtremumLayerCPUTestParamSet =
+    std::tuple<std::vector<InputShape>,           // Input shapes
+               utils::MinMaxOpType,               // Extremum type
+               ov::element::Type,                 // Net precision
+               ov::element::Type,                 // Input precision
+               ov::element::Type,                 // Output precision
+               CPUTestUtils::CPUSpecificParams,
+               bool>;
+
+class ExtremumLayerCPUTest : public testing::WithParamInterface<ExtremumLayerCPUTestParamSet>,
+                             virtual public ov::test::SubgraphBaseTest,
+                             public CPUTestUtils::CPUTestsBase {
+public:
+    static std::string getTestCaseName(const testing::TestParamInfo<ExtremumLayerCPUTestParamSet> &obj);
+
+protected:
+    void SetUp() override;
+
+private:
+    std::string getPrimitiveType();
+};
+
+namespace extremum {
+
+const std::vector<std::vector<ov::Shape>>& inputShape();
+
+const std::vector<utils::MinMaxOpType>& extremumTypes();
+
+const std::vector<ov::element::Type>& netPrecisions();
+
+const std::vector<CPUTestUtils::CPUSpecificParams>& cpuParams4D();
+
+}  // namespace extremum
+}  // namespace test
+}  // namespace ov
diff --git a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/matmul.cpp b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/matmul.cpp
index 3e55d3368cefb3..dd58da28c8ad05 100644
--- a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/matmul.cpp
+++ b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/matmul.cpp
@@ -118,9 +118,14 @@ void MatMulLayerCPUTest::SetUp() {
     configuration.insert(additionalConfig.begin(), additionalConfig.end());
 
     auto it = additionalConfig.find(ov::hint::inference_precision.name());
-    if (it != additionalConfig.end() && it->second.as<ov::element::Type>() == ov::element::bf16) {
+    ov::element::Type inference_precision = (it != additionalConfig.end()) ?
+                                            it->second.as<ov::element::Type>() : ov::element::undefined;
+    if (inference_precision == ov::element::bf16) {
         inType = outType = netType = ElementType::bf16;
         rel_threshold = abs_threshold = 1e-2f;
+    } else if (inference_precision == ov::element::f16) {
+        inType = outType = netType = ElementType::f16;
+        rel_threshold = abs_threshold = 1e-4f;
     } else {
         inType = outType = netType;
         rel_threshold = 1e-4f;
@@ -128,7 +133,7 @@ void MatMulLayerCPUTest::SetUp() {
     }
 
     cpuNodeType = nodeType == MatMulNodeType::MatMul ? "MatMul" : "FullyConnected";
-    selectedType = makeSelectedTypeStr(selectedType, outType);
+    selectedType = makeSelectedTypeStr(selectedType, deduce_expected_precision(outType, configuration));
 
     ov::ParameterVector params{std::make_shared<ov::op::v0::Parameter>(netType, inShapeA)};
 
diff --git a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/mvn.cpp b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/mvn.cpp
index b0c9486a06bae4..598447f8ec8363 100644
--- a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/mvn.cpp
+++ b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/mvn.cpp
@@ -5,8 +5,10 @@
 #include "mvn.hpp"
 #include "gtest/gtest.h"
 #include "utils/cpu_test_utils.hpp"
+#include "utils/general_utils.h"
 
 using namespace CPUTestUtils;
+using namespace ov::intel_cpu;
 
 namespace ov {
 namespace test {
@@ -116,11 +118,10 @@ void MvnLayerCPUTest::SetUp() {
         mvn->set_reduction_axes(axes);
     }
 
-    rel_threshold = 0.015f;
-    if (additionalConfig[ov::hint::inference_precision.name()] == ov::element::f16) {
-        //FIXME: ref and acl mvn implementation has accuracy issues on fp16 (#116344)
-        abs_threshold = .05f;
-        rel_threshold = 250.f;
+    rel_threshold = 5e-4;
+    if (one_of(additionalConfig[ov::hint::inference_precision.name()], ov::element::f16, ov::element::bf16)) {
+        rel_threshold = 1e-2;
+        abs_threshold = .03f;
     }
     configuration.insert(additionalConfig.begin(), additionalConfig.end());
     updateSelectedType(getPrimitiveType(), netPrecision, configuration);
diff --git a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/pooling.cpp b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/pooling.cpp
index 22bf0994deb37e..65f48713185f59 100644
--- a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/pooling.cpp
+++ b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/pooling.cpp
@@ -19,7 +19,8 @@ std::string PoolingLayerCPUTest::getTestCaseName(const testing::TestParamInfo<po
     bool isInt8;
     CPUSpecificParams cpuParams;
     fusingSpecificParams fusingParams;
-    std::tie(basicParamsSet, inputShapes, inPrc, isInt8, cpuParams, fusingParams) = obj.param;
+    ov::AnyMap additionalConfig;
+    std::tie(basicParamsSet, inputShapes, inPrc, isInt8, cpuParams, fusingParams, additionalConfig) = obj.param;
 
     utils::PoolingTypes poolType;
     std::vector<size_t> kernel, stride;
@@ -53,6 +54,12 @@ std::string PoolingLayerCPUTest::getTestCaseName(const testing::TestParamInfo<po
     results << "Rounding=" << roundingType << "_";
     results << "AutoPad=" << padType << "_";
     results << "INT8=" << isInt8 << "_";
+    if (!additionalConfig.empty()) {
+        results << "_PluginConf";
+            for (auto& item : additionalConfig) {
+                results << "_" << item.first << "=" << item.second.as<std::string>();
+            }
+        }
 
     results << CPUTestsBase::getTestCaseName(cpuParams);
     results << CpuTestWithFusing::getTestCaseName(fusingParams);
@@ -68,7 +75,9 @@ void PoolingLayerCPUTest::SetUp() {
     bool isInt8;
     CPUSpecificParams cpuParams;
     fusingSpecificParams fusingParams;
-    std::tie(basicParamsSet, inputShapes, inPrc, isInt8, cpuParams, fusingParams) = this->GetParam();
+    ov::AnyMap additionalConfig;
+    std::tie(basicParamsSet, inputShapes, inPrc, isInt8, cpuParams, fusingParams, additionalConfig) = this->GetParam();
+    configuration.insert(additionalConfig.begin(), additionalConfig.end());
 
     utils::PoolingTypes poolType;
     std::vector<size_t> kernel, stride;
@@ -87,7 +96,7 @@ void PoolingLayerCPUTest::SetUp() {
     if (isInt8)
         selectedType = selectedType + "_I8";
     else
-        selectedType = makeSelectedTypeStr(selectedType, inPrc);
+        selectedType = makeSelectedTypeStr(selectedType, deduce_expected_precision(inPrc, configuration));
 
     init_input_shapes({inputShapes});
 
@@ -119,7 +128,8 @@ std::string MaxPoolingV8LayerCPUTest::getTestCaseName(
     InputShape inputShapes;
     ElementType inPrc;
     CPUSpecificParams cpuParams;
-    std::tie(basicParamsSet, inputShapes, inPrc, cpuParams) = obj.param;
+    ov::AnyMap additionalConfig;
+    std::tie(basicParamsSet, inputShapes, inPrc, cpuParams, additionalConfig) = obj.param;
 
     std::vector<size_t> kernel, stride, dilation;
     std::vector<size_t> padBegin, padEnd;
@@ -146,6 +156,12 @@ std::string MaxPoolingV8LayerCPUTest::getTestCaseName(
     results << "PE" << ov::test::utils::vec2str(padEnd) << "_";
     results << "Rounding=" << roundingType << "_";
     results << "AutoPad=" << padType << "_";
+    if (!additionalConfig.empty()) {
+        results << "_PluginConf";
+        for (auto& item : additionalConfig) {
+            results << "_" << item.first << "=" << item.second.as<std::string>();
+        }
+    }
 
     results << CPUTestsBase::getTestCaseName(cpuParams);
     return results.str();
@@ -158,7 +174,9 @@ void MaxPoolingV8LayerCPUTest::SetUp() {
     InputShape inputShapes;
     ElementType inPrc;
     CPUSpecificParams cpuParams;
-    std::tie(basicParamsSet, inputShapes, inPrc, cpuParams) = this->GetParam();
+    ov::AnyMap additionalConfig;
+    std::tie(basicParamsSet, inputShapes, inPrc, cpuParams, additionalConfig) = this->GetParam();
+    configuration.insert(additionalConfig.begin(), additionalConfig.end());
 
     std::vector<size_t> kernel, stride, dilation;
     std::vector<size_t> padBegin, padEnd;
@@ -172,7 +190,7 @@ void MaxPoolingV8LayerCPUTest::SetUp() {
     if (selectedType.empty()) {
         selectedType = getPrimitiveType();
     }
-    selectedType = makeSelectedTypeStr(selectedType, inPrc);
+    selectedType = makeSelectedTypeStr(selectedType, deduce_expected_precision(inPrc, configuration));
 
     init_input_shapes({inputShapes});
 
diff --git a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/pooling.hpp b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/pooling.hpp
index a2add35826accd..a1d04c633e94f1 100644
--- a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/pooling.hpp
+++ b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/pooling.hpp
@@ -19,12 +19,15 @@ using poolLayerCpuTestParamsSet = std::tuple<poolSpecificParams,
                                              ElementType, //inPrc
                                              bool, // isInt8
                                              CPUSpecificParams,
-                                             fusingSpecificParams>;
+                                             fusingSpecificParams,
+                                             ov::AnyMap>;
+
 
 using maxPoolV8LayerCpuTestParamsSet = std::tuple<maxPoolV8SpecificParams,
         InputShape,
         ElementType,
-        CPUSpecificParams>;
+        CPUSpecificParams,
+        ov::AnyMap>;
 
 class PoolingLayerCPUTest : public testing::WithParamInterface<poolLayerCpuTestParamsSet>,
                             virtual public SubgraphBaseTest, public CpuTestWithFusing {
diff --git a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/reduce.cpp b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/reduce.cpp
index 06dd6a56e50f93..892e347c6a2a63 100644
--- a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/reduce.cpp
+++ b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/reduce.cpp
@@ -279,6 +279,13 @@ const std::vector<ov::test::utils::ReductionType>& reductionTypesInt32() {
     return reductionTypesInt32;
 }
 
+const std::vector<ov::test::utils::ReductionType>& reductionTypesNativeInt32() {
+    static const std::vector<ov::test::utils::ReductionType> reductionTypesNativeInt32 = {
+            ov::test::utils::ReductionType::Prod,
+    };
+    return reductionTypesNativeInt32;
+}
+
 }  // namespace Reduce
 }  // namespace test
 }  // namespace ov
diff --git a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/reduce.hpp b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/reduce.hpp
index 3634e35fb0a882..b3e5fff2ba0b7b 100644
--- a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/reduce.hpp
+++ b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/reduce.hpp
@@ -56,6 +56,7 @@ const std::vector<ElementType>& inpOutPrc();
 const std::vector<std::map<std::string, ov::element::Type>> additionalConfig();
 const std::vector<std::map<std::string, ov::element::Type>> additionalConfigFP32();
 const std::vector<utils::ReductionType>& reductionTypesInt32();
+const std::vector<utils::ReductionType>& reductionTypesNativeInt32();
 
 }  // namespace Reduce
 }  // namespace test
diff --git a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/softmax.cpp b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/softmax.cpp
index fe95030df307ae..bf2cc9617d8835 100644
--- a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/softmax.cpp
+++ b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/softmax.cpp
@@ -16,7 +16,8 @@ std::string SoftMaxLayerCPUTest::getTestCaseName(const testing::TestParamInfo<so
     ElementType inType;
     SoftMaxConfig config;
     std::string targetDevice;
-    std::tie(inType, config, targetDevice, cpuParams) = obj.param;
+    ov::AnyMap additionalConfig;
+    std::tie(inType, config, targetDevice, cpuParams, additionalConfig) = obj.param;
 
     std::ostringstream result;
     result << "netPRC=" << inType << "_";
@@ -30,6 +31,12 @@ std::string SoftMaxLayerCPUTest::getTestCaseName(const testing::TestParamInfo<so
     result << "axis=" << config.axis << "_";
     result << "trgDev=" << targetDevice;
     result << CPUTestsBase::getTestCaseName(cpuParams);
+    if (!additionalConfig.empty()) {
+        result << "_PluginConf";
+        for (auto& item : additionalConfig) {
+            result << "_" << item.first << "=" << item.second.as<std::string>();
+        }
+    }
 
     return result.str();
 }
@@ -38,7 +45,9 @@ void SoftMaxLayerCPUTest::SetUp() {
     ElementType inType;
     SoftMaxConfig config;
     CPUSpecificParams cpuParams;
-    std::tie(inType, config, targetDevice, cpuParams) = this->GetParam();
+    ov::AnyMap additionalConfig;
+    std::tie(inType, config, targetDevice, cpuParams, additionalConfig) = this->GetParam();
+    configuration.insert(additionalConfig.begin(), additionalConfig.end());
 
     std::tie(inFmts, outFmts, priority, selectedType) = cpuParams;
     if (selectedType.empty()) {
@@ -47,8 +56,10 @@ void SoftMaxLayerCPUTest::SetUp() {
 
     if (inType == ElementType::bf16) {
         rel_threshold = 2e-2f;
+    } else if (inType == ElementType::f16) {
+        rel_threshold = 0.0025f;
     }
-    selectedType = makeSelectedTypeStr(selectedType, inType);
+    selectedType = makeSelectedTypeStr(selectedType, deduce_expected_precision(inType, configuration));
     init_input_shapes({config.inputShape});
     ov::ParameterVector params;
     for (auto&& shape : inputDynamicShapes)
diff --git a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/softmax.hpp b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/softmax.hpp
index 1949a4ec37bf34..5ee5d318a51d17 100644
--- a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/softmax.hpp
+++ b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/classes/softmax.hpp
@@ -18,7 +18,8 @@ struct SoftMaxConfig {
 typedef std::tuple<ElementType,    // netPrecision
                    SoftMaxConfig,  // softmaxTestConfig
                    std::string,    // targetDevice
-                   CPUSpecificParams>
+                   CPUSpecificParams,
+                   ov::AnyMap> //device_config
     softmaxCPUTestParams;
 
 class SoftMaxLayerCPUTest : public testing::WithParamInterface<softmaxCPUTestParams>,
diff --git a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/common/activation.cpp b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/common/activation.cpp
index 7fae7516eff95f..4112969810e65a 100644
--- a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/common/activation.cpp
+++ b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/common/activation.cpp
@@ -19,11 +19,25 @@ const auto basicCases3D = ::testing::Combine(
     ::testing::ValuesIn(netPrc()),
     ::testing::Values(ov::element::f32),
     ::testing::Values(ov::element::f32),
-    ::testing::ValuesIn(filterCPUSpecificParams(cpuParams3D()))
+    ::testing::ValuesIn(filterCPUSpecificParams(cpuParams3D())),
+    ::testing::Values(false)
 );
 
 INSTANTIATE_TEST_SUITE_P(smoke_Activation3D_Eltwise_CPU, ActivationLayerCPUTest, basicCases3D, ActivationLayerCPUTest::getTestCaseName);
 
+const auto basicCasesSnippets3D = ::testing::Combine(
+    ::testing::ValuesIn(static_shapes_to_test_representation(basic3D())),
+    ::testing::Values(activationShapes()),
+    ::testing::ValuesIn(ov::test::utils::combineParams(activationTypesSnippets())),
+    ::testing::ValuesIn(netPrc()),
+    ::testing::Values(ov::element::f32),
+    ::testing::Values(ov::element::f32),
+    ::testing::ValuesIn(filterCPUSpecificParams(cpuParams3D())),
+    ::testing::Values(true)
+);
+
+INSTANTIATE_TEST_SUITE_P(smoke_Activation3D_Snippets_CPU, ActivationLayerCPUTest, basicCasesSnippets3D, ActivationLayerCPUTest::getTestCaseName);
+
 /* ============= Activation (2D) ============= */
 const auto basicCases4D = ::testing::Combine(
     ::testing::ValuesIn(static_shapes_to_test_representation(basic4D())),
@@ -32,11 +46,25 @@ const auto basicCases4D = ::testing::Combine(
     ::testing::ValuesIn(netPrc()),
     ::testing::Values(ov::element::f32),
     ::testing::Values(ov::element::f32),
-    ::testing::ValuesIn(filterCPUSpecificParams(cpuParams4D()))
+    ::testing::ValuesIn(filterCPUSpecificParams(cpuParams4D())),
+    ::testing::Values(false)
 );
 
 INSTANTIATE_TEST_SUITE_P(smoke_Activation4D_Eltwise_CPU, ActivationLayerCPUTest, basicCases4D, ActivationLayerCPUTest::getTestCaseName);
 
+const auto basicCasesSnippets4D = ::testing::Combine(
+    ::testing::ValuesIn(static_shapes_to_test_representation(basic4D())),
+    ::testing::Values(activationShapes()),
+    ::testing::ValuesIn(ov::test::utils::combineParams(activationTypesSnippets())),
+    ::testing::ValuesIn(netPrc()),
+    ::testing::Values(ov::element::f32),
+    ::testing::Values(ov::element::f32),
+    ::testing::ValuesIn(filterCPUSpecificParams(cpuParams4D())),
+    ::testing::Values(true)
+);
+
+INSTANTIATE_TEST_SUITE_P(smoke_Activation4D_Snippets_CPU, ActivationLayerCPUTest, basicCasesSnippets4D, ActivationLayerCPUTest::getTestCaseName);
+
 /* ============= Activation (5D) ============= */
 const auto basicCases5D = ::testing::Combine(
     ::testing::ValuesIn(static_shapes_to_test_representation(basic5D())),
@@ -45,11 +73,25 @@ const auto basicCases5D = ::testing::Combine(
     ::testing::ValuesIn(netPrc()),
     ::testing::Values(ov::element::f32),
     ::testing::Values(ov::element::f32),
-    ::testing::ValuesIn(filterCPUSpecificParams(cpuParams5D()))
+    ::testing::ValuesIn(filterCPUSpecificParams(cpuParams5D())),
+    ::testing::Values(false)
 );
 
 INSTANTIATE_TEST_SUITE_P(smoke_Activation5D_Eltwise_CPU, ActivationLayerCPUTest, basicCases5D, ActivationLayerCPUTest::getTestCaseName);
 
+const auto basicCasesSnippets5D = ::testing::Combine(
+    ::testing::ValuesIn(static_shapes_to_test_representation(basic5D())),
+    ::testing::Values(activationShapes()),
+    ::testing::ValuesIn(ov::test::utils::combineParams(activationTypesSnippets())),
+    ::testing::ValuesIn(netPrc()),
+    ::testing::Values(ov::element::f32),
+    ::testing::Values(ov::element::f32),
+    ::testing::ValuesIn(filterCPUSpecificParams(cpuParams5D())),
+    ::testing::Values(true)
+);
+
+INSTANTIATE_TEST_SUITE_P(smoke_Activation5D_Snippets_CPU, ActivationLayerCPUTest, basicCasesSnippets5D, ActivationLayerCPUTest::getTestCaseName);
+
 const auto dynamicMathBasicCases = ::testing::Combine(
     ::testing::ValuesIn(dynamicMathBasic()),
     ::testing::Values(activationShapes()),
@@ -57,7 +99,8 @@ const auto dynamicMathBasicCases = ::testing::Combine(
     ::testing::ValuesIn(netPrecisions()),
     ::testing::Values(ov::element::f32),
     ::testing::Values(ov::element::f32),
-    ::testing::ValuesIn(cpuParamsDynamicMath())
+    ::testing::ValuesIn(cpuParamsDynamicMath()),
+    ::testing::Values(false)
 );
 
 INSTANTIATE_TEST_SUITE_P(smoke_Activation5D_dynamicMath_CPU, ActivationLayerCPUTest, dynamicMathBasicCases, ActivationLayerCPUTest::getTestCaseName);
diff --git a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/common/col2im.cpp b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/common/col2im.cpp
new file mode 100644
index 00000000000000..fa07b89fc317c8
--- /dev/null
+++ b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/common/col2im.cpp
@@ -0,0 +1,45 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "custom/single_layer_tests/classes/col2im.hpp"
+#include "utils/cpu_test_utils.hpp"
+#include "utils/fusing_test_utils.hpp"
+
+using namespace CPUTestUtils;
+
+namespace ov {
+namespace test {
+namespace Col2Im {
+INSTANTIATE_TEST_SUITE_P(smoke_Col2ImLayoutTestF32, Col2ImLayerCPUTest,
+        ::testing::Combine(
+                ::testing::Combine(
+                        ::testing::ValuesIn(col2ImParamsVector),
+                        ::testing::ValuesIn(std::vector<ElementType>{ElementType::f32, ElementType::f16}),
+                        ::testing::ValuesIn(indexPrecisions),
+                        ::testing::Values(ov::test::utils::DEVICE_CPU)),
+                ::testing::Values(CPUSpecificParams{{}, {}, {}, "ref_f32"})),
+                Col2ImLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_Col2ImLayoutTestI32, Col2ImLayerCPUTest,
+        ::testing::Combine(
+                ::testing::Combine(
+                        ::testing::ValuesIn(col2ImParamsVector),
+                        ::testing::Values(ElementType::i32),
+                        ::testing::ValuesIn(indexPrecisions),
+                        ::testing::Values(ov::test::utils::DEVICE_CPU)),
+                ::testing::Values(CPUSpecificParams{{}, {}, {}, "ref_i32"})),
+                Col2ImLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_Col2ImLayoutTestUI8, Col2ImLayerCPUTest,
+        ::testing::Combine(
+                ::testing::Combine(
+                        ::testing::ValuesIn(col2ImParamsVector),
+                        ::testing::Values(ElementType::u8, ElementType::i8),
+                        ::testing::ValuesIn(indexPrecisions),
+                        ::testing::Values(ov::test::utils::DEVICE_CPU)),
+                ::testing::Values(CPUSpecificParams{{}, {}, {}, "ref_i8"})),
+                Col2ImLayerCPUTest::getTestCaseName);
+}  // namespace Col2Im
+}  // namespace test
+}  // namespace ov
diff --git a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/common/eltwise.cpp b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/common/eltwise.cpp
index e6e8168da5c7ed..da82b9a12a8011 100644
--- a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/common/eltwise.cpp
+++ b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/common/eltwise.cpp
@@ -29,6 +29,23 @@ const auto params_4D = ::testing::Combine(
 
 INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_4D_MemOrder, EltwiseLayerCPUTest, params_4D, EltwiseLayerCPUTest::getTestCaseName);
 
+const auto params_4D_Snippets = ::testing::Combine(
+        ::testing::Combine(
+                ::testing::ValuesIn(static_shapes_to_test_representation(inShapes_4D())),
+                ::testing::ValuesIn(eltwiseOpTypesBinInpSnippets()),
+                ::testing::ValuesIn(secondaryInputTypes()),
+                ::testing::ValuesIn(opTypes()),
+                ::testing::ValuesIn(netType()),
+                ::testing::Values(ov::element::undefined),
+                ::testing::Values(ov::element::undefined),
+                ::testing::Values(ov::test::utils::DEVICE_CPU),
+                ::testing::Values(additional_config()[0])),
+        ::testing::ValuesIn(filterCPUSpecificParams(cpuParams_4D())),
+        ::testing::Values(emptyFusingSpec),
+        ::testing::Values(true));
+
+INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs_4D_MemOrder_Snippets, EltwiseLayerCPUTest, params_4D_Snippets, EltwiseLayerCPUTest::getTestCaseName);
+
 const auto params_4D_emptyCPUSpec = ::testing::Combine(
         ::testing::Combine(
                 ::testing::ValuesIn(static_shapes_to_test_representation(inShapes_4D())),
diff --git a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/common/extremum.cpp b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/common/extremum.cpp
new file mode 100644
index 00000000000000..6263dd933bb04f
--- /dev/null
+++ b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/common/extremum.cpp
@@ -0,0 +1,28 @@
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "custom/single_layer_tests/classes/extremum.hpp"
+#include "utils/cpu_test_utils.hpp"
+
+using namespace CPUTestUtils;
+
+namespace ov {
+namespace test {
+namespace extremum {
+
+const auto basicCasesSnippets = ::testing::Combine(
+    ::testing::ValuesIn(static_shapes_to_test_representation(inputShape())),
+    ::testing::ValuesIn(extremumTypes()),
+    ::testing::ValuesIn(netPrecisions()),
+    ::testing::Values(ov::element::f32),
+    ::testing::Values(ov::element::f32),
+    ::testing::ValuesIn(filterCPUSpecificParams(cpuParams4D())),
+    ::testing::Values(true)
+);
+
+INSTANTIATE_TEST_SUITE_P(smoke_Extremum_Snippets_CPU, ExtremumLayerCPUTest, basicCasesSnippets, ExtremumLayerCPUTest::getTestCaseName);
+
+}  // namespace extremum
+}  // namespace test
+}  // namespace ov
diff --git a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/common/pooling.cpp b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/common/pooling.cpp
index 98827bcb77829c..8e8deeef20dbee 100644
--- a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/common/pooling.cpp
+++ b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/common/pooling.cpp
@@ -27,7 +27,8 @@ INSTANTIATE_TEST_SUITE_P(smoke_MaxPool_CPU_3D, PoolingLayerCPUTest,
                                  ::testing::ValuesIn((inpOutPrecision())),
                                  ::testing::Values(false),
                                  ::testing::ValuesIn(vecCpuConfigs),
-                                 ::testing::Values(emptyFusingSpec)),
+                                 ::testing::Values(emptyFusingSpec),
+                                 ::testing::Values(CPUTestUtils::empty_plugin_config)),
                          PoolingLayerCPUTest::getTestCaseName);
 
 INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_3D, PoolingLayerCPUTest,
@@ -37,7 +38,8 @@ INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_3D, PoolingLayerCPUTest,
                                  ::testing::ValuesIn((inpOutPrecision())),
                                  ::testing::Values(false),
                                  ::testing::ValuesIn(vecCpuConfigs),
-                                 ::testing::Values(emptyFusingSpec)),
+                                 ::testing::Values(emptyFusingSpec),
+                                 ::testing::Values(CPUTestUtils::empty_plugin_config)),
                          PoolingLayerCPUTest::getTestCaseName);
 
 INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_3D_NotOptimized, PoolingLayerCPUTest,
@@ -47,7 +49,8 @@ INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_3D_NotOptimized, PoolingLayerCPUTest,
                                  ::testing::ValuesIn((inpOutPrecision())),
                                  ::testing::Values(false),
                                  ::testing::Values(expectedCpuConfigAnyLayout()),
-                                 ::testing::Values(emptyFusingSpec)),
+                                 ::testing::Values(emptyFusingSpec),
+                                 ::testing::Values(CPUTestUtils::empty_plugin_config)),
                          PoolingLayerCPUTest::getTestCaseName);
 
 const std::vector<poolSpecificParams> paramsAvg4D_RefOnly = {
@@ -62,7 +65,8 @@ INSTANTIATE_TEST_SUITE_P(smoke_MaxPool_CPU_4D, PoolingLayerCPUTest,
                             ::testing::ValuesIn((inpOutPrecision())),
                             ::testing::Values(false),
                             ::testing::ValuesIn(filterCPUInfo(vecCpuConfigsFusing_4D())),
-                            ::testing::Values(emptyFusingSpec)),
+                            ::testing::Values(emptyFusingSpec),
+                            ::testing::Values(CPUTestUtils::empty_plugin_config)),
                         PoolingLayerCPUTest::getTestCaseName);
 
 INSTANTIATE_TEST_SUITE_P(smoke_MaxPoolV8_CPU_4D, MaxPoolingV8LayerCPUTest,
@@ -70,7 +74,8 @@ INSTANTIATE_TEST_SUITE_P(smoke_MaxPoolV8_CPU_4D, MaxPoolingV8LayerCPUTest,
                                  ::testing::ValuesIn(paramsMaxV84D()),
                                  ::testing::ValuesIn(inputShapes4D()),
                                  ::testing::ValuesIn((inpOutPrecision())),
-                                 ::testing::ValuesIn(filterCPUInfo(vecCpuConfigsFusing_4D()))),
+                                 ::testing::ValuesIn(filterCPUInfo(vecCpuConfigsFusing_4D())),
+                                 ::testing::Values(CPUTestUtils::empty_plugin_config)),
                          MaxPoolingV8LayerCPUTest::getTestCaseName);
 
 INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_4D, PoolingLayerCPUTest,
@@ -80,7 +85,8 @@ INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_4D, PoolingLayerCPUTest,
                             ::testing::ValuesIn((inpOutPrecision())),
                             ::testing::Values(false),
                             ::testing::ValuesIn(vecCpuConfigs),
-                            ::testing::Values(emptyFusingSpec)),
+                            ::testing::Values(emptyFusingSpec),
+                            ::testing::Values(CPUTestUtils::empty_plugin_config)),
                         PoolingLayerCPUTest::getTestCaseName);
 
 INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_4D_NotOptimized, PoolingLayerCPUTest,
@@ -90,7 +96,8 @@ INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_4D_NotOptimized, PoolingLayerCPUTest,
                             ::testing::ValuesIn((inpOutPrecision())),
                             ::testing::Values(false),
                             ::testing::Values(expectedCpuConfigAnyLayout()),
-                            ::testing::Values(emptyFusingSpec)),
+                            ::testing::Values(emptyFusingSpec),
+                            ::testing::Values(CPUTestUtils::empty_plugin_config)),
                         PoolingLayerCPUTest::getTestCaseName);
 
 INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_Large, PoolingLayerCPUTest,
@@ -100,7 +107,8 @@ INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_Large, PoolingLayerCPUTest,
                             ::testing::ValuesIn((inpOutPrecision())),
                             ::testing::Values(false),
                             ::testing::ValuesIn(vecCpuConfigs),
-                            ::testing::Values(emptyFusingSpec)),
+                            ::testing::Values(emptyFusingSpec),
+                            ::testing::Values(CPUTestUtils::empty_plugin_config)),
                         PoolingLayerCPUTest::getTestCaseName);
 
 const std::vector<maxPoolV8SpecificParams> paramsMaxV85D_ref = {
@@ -129,7 +137,8 @@ INSTANTIATE_TEST_SUITE_P(smoke_MaxPool_CPU_5D, PoolingLayerCPUTest,
                              ::testing::ValuesIn((inpOutPrecision())),
                              ::testing::Values(false),
                              ::testing::ValuesIn(vecCpuConfigs),
-                             ::testing::Values(emptyFusingSpec)),
+                             ::testing::Values(emptyFusingSpec),
+                             ::testing::Values(CPUTestUtils::empty_plugin_config)),
                          PoolingLayerCPUTest::getTestCaseName);
 
 INSTANTIATE_TEST_SUITE_P(smoke_MaxPoolV8_CPU_5D, MaxPoolingV8LayerCPUTest,
@@ -137,7 +146,8 @@ INSTANTIATE_TEST_SUITE_P(smoke_MaxPoolV8_CPU_5D, MaxPoolingV8LayerCPUTest,
                                  ::testing::ValuesIn(paramsMaxV85D()),
                                  ::testing::ValuesIn(inputShapes5D()),
                                  ::testing::ValuesIn((inpOutPrecision())),
-                                 ::testing::ValuesIn(vecCpuConfigs)),
+                                 ::testing::ValuesIn(vecCpuConfigs),
+                                 ::testing::Values(CPUTestUtils::empty_plugin_config)),
                          MaxPoolingV8LayerCPUTest::getTestCaseName);
 
 INSTANTIATE_TEST_SUITE_P(smoke_MaxPoolV8_CPU_5D_ref, MaxPoolingV8LayerCPUTest,
@@ -145,7 +155,8 @@ INSTANTIATE_TEST_SUITE_P(smoke_MaxPoolV8_CPU_5D_ref, MaxPoolingV8LayerCPUTest,
                                  ::testing::ValuesIn(paramsMaxV85D_ref),
                                  ::testing::ValuesIn(inputShapes5D()),
                                  ::testing::ValuesIn((inpOutPrecision())),
-                                 ::testing::Values(expectedCpuConfigAnyLayout())),
+                                 ::testing::Values(expectedCpuConfigAnyLayout()),
+                                 ::testing::Values(CPUTestUtils::empty_plugin_config)),
                          MaxPoolingV8LayerCPUTest::getTestCaseName);
 
 INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_5D, PoolingLayerCPUTest,
@@ -155,7 +166,8 @@ INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_5D, PoolingLayerCPUTest,
                               ::testing::ValuesIn((inpOutPrecision())),
                               ::testing::Values(false),
                               ::testing::ValuesIn(vecCpuConfigs),
-                              ::testing::Values(emptyFusingSpec)),
+                              ::testing::Values(emptyFusingSpec),
+                              ::testing::Values(CPUTestUtils::empty_plugin_config)),
                           PoolingLayerCPUTest::getTestCaseName);
 
 INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_5D_NotOptimized, PoolingLayerCPUTest,
@@ -165,7 +177,8 @@ INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_5D_NotOptimized, PoolingLayerCPUTest,
                               ::testing::ValuesIn((inpOutPrecision())),
                               ::testing::Values(false),
                               ::testing::Values(expectedCpuConfigAnyLayout()),
-                              ::testing::Values(emptyFusingSpec)),
+                              ::testing::Values(emptyFusingSpec),
+                              ::testing::Values(CPUTestUtils::empty_plugin_config)),
                           PoolingLayerCPUTest::getTestCaseName);
 #endif
 }  // namespace Pooling
diff --git a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/common/scaled_attn.cpp b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/common/scaled_attn.cpp
index ad48de2765ad2c..3cd3a11f78c045 100644
--- a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/common/scaled_attn.cpp
+++ b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/common/scaled_attn.cpp
@@ -17,15 +17,18 @@ const std::vector<std::vector<InputShape>> shapes{
     {
         // q shape
         {ov::test::InputShape{ov::PartialShape{-1, 8, -1, 64},
-            {ov::Shape{1, 8, 100, 64}, ov::Shape{1, 8, 1, 64}, ov::Shape{2, 8, 10, 64}}}
+            {ov::Shape{1, 8, 100, 64}, ov::Shape{1, 8, 1, 64},
+             ov::Shape{2, 8, 10, 64}, ov::Shape{2, 8, 10, 64}}}
         },
         // kv shape
         {ov::test::InputShape{ov::PartialShape{-1, 8, -1, 64},
-            {ov::Shape{1, 8, 100, 64}, ov::Shape{1, 8, 1, 64}, ov::Shape{2, 8, 10, 64}}}
+            {ov::Shape{1, 8, 100, 64}, ov::Shape{1, 8, 1, 64},
+             ov::Shape{2, 8, 10, 64}, ov::Shape{2, 8, 10, 64}}}
         },
         // attn shape: [B, 1, -1, L0+L1]
         {ov::test::InputShape{ov::PartialShape{-1, 1, -1, -1},
-            {ov::Shape{1, 1, 100, 100}, ov::Shape{1, 1, 1, 1}, ov::Shape{2, 1, 10, 10}}}
+            {ov::Shape{1, 1, 1, 100}, ov::Shape{1, 1, 1, 1},
+             ov::Shape{2, 1, 1, 10}, ov::Shape{2, 1, 10, 10}}}
         },
     },
     // heads number of kv is 1, attn mask: [B, H, L1, L0+L1]
@@ -58,6 +61,21 @@ const std::vector<std::vector<InputShape>> shapes{
             {ov::Shape{8, 100, 100}, ov::Shape{8, 1, 1}, ov::Shape{8, 10, 10}}}
         },
     },
+    // More attention mask broadcast cases
+    {
+        // q shape
+        {ov::test::InputShape{ov::PartialShape{-1, -1, -1, -1},
+            {ov::Shape{2, 8, 16, 32}, ov::Shape{2, 8, 16, 32}, ov::Shape{2, 8, 16, 32}}}
+        },
+        // kv shape
+        {ov::test::InputShape{ov::PartialShape{-1, -1, -1, -1},
+            {ov::Shape{1, 8, 48, 32}, ov::Shape{1, 8, 48, 32}, ov::Shape{1, 8, 48, 32}}}
+        },
+        // attn shape
+        {ov::test::InputShape{ov::PartialShape{-1, -1},
+           {ov::Shape{16, 48},  ov::Shape{16, 1}, ov::Shape{1, 48}}}
+        },
+    },
 };
 
 const auto params = testing::Combine(testing::Values(ElementType::f32, ElementType::bf16),
diff --git a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/common/softmax.cpp b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/common/softmax.cpp
index f7ca27086bace0..678395ef29631c 100644
--- a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/common/softmax.cpp
+++ b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/common/softmax.cpp
@@ -114,11 +114,11 @@ const std::vector<SoftMaxConfig> unsupportedConfigsFP32{
      4},
 };
 
-
 const auto OptimizedParams = testing::Combine(testing::Values(ElementType::f32, ElementType::bf16),
                                               testing::ValuesIn(optimizedConfigsFP32),
                                               testing::Values(ov::test::utils::DEVICE_CPU),
-                                              testing::Values(notOptimizedCPUSpec));
+                                              testing::Values(notOptimizedCPUSpec),
+                                              testing::Values(CPUTestUtils::empty_plugin_config));
 
 INSTANTIATE_TEST_SUITE_P(smoke_SoftMax_Optimized_CPU,
                          SoftMaxLayerCPUTest,
@@ -128,7 +128,8 @@ INSTANTIATE_TEST_SUITE_P(smoke_SoftMax_Optimized_CPU,
 const auto NotOptimizedParams = testing::Combine(testing::Values(ElementType::f32, ElementType::bf16),
                                                  testing::ValuesIn(notOptimizedConfigsFP32),
                                                  testing::Values(ov::test::utils::DEVICE_CPU),
-                                                 testing::Values(notOptimizedCPUSpec));
+                                                 testing::Values(notOptimizedCPUSpec),
+                                                 testing::Values(CPUTestUtils::empty_plugin_config));
 
 INSTANTIATE_TEST_SUITE_P(smoke_SoftMax_CPU,
                          SoftMaxLayerCPUTest,
@@ -138,7 +139,8 @@ INSTANTIATE_TEST_SUITE_P(smoke_SoftMax_CPU,
 const auto UnsupportedParams = testing::Combine(testing::Values(ElementType::f32, ElementType::bf16),
                                                 testing::ValuesIn(unsupportedConfigsFP32),
                                                 testing::Values(ov::test::utils::DEVICE_CPU),
-                                                testing::Values(notOptimizedCPUSpec));
+                                                testing::Values(notOptimizedCPUSpec),
+                                                testing::Values(CPUTestUtils::empty_plugin_config));
 
 INSTANTIATE_TEST_SUITE_P(smoke_SoftMax_Unsupported_CPU,
                          SoftMaxLayerCPUTest,
@@ -146,4 +148,4 @@ INSTANTIATE_TEST_SUITE_P(smoke_SoftMax_Unsupported_CPU,
                          SoftMaxLayerCPUTest::getTestCaseName);
 }  // namespace SoftMax
 }  // namespace test
-}  // namespace ov
\ No newline at end of file
+}  // namespace ov
diff --git a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/x64/activation.cpp b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/x64/activation.cpp
index 35581758847904..7c7558f7c03d9f 100644
--- a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/x64/activation.cpp
+++ b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/x64/activation.cpp
@@ -47,7 +47,8 @@ const auto blockedCases3D = ::testing::Combine(
     ::testing::ValuesIn(netPrc()),
     ::testing::Values(ov::element::f32),
     ::testing::Values(ov::element::f32),
-    ::testing::ValuesIn(filterCPUSpecificParams(cpuParams3Dblocked()))
+    ::testing::ValuesIn(filterCPUSpecificParams(cpuParams3Dblocked())),
+    ::testing::Values(false)
 );
 
 INSTANTIATE_TEST_SUITE_P(smoke_Activation3D_Eltwise_CPU_Blocked, ActivationLayerCPUTest, blockedCases3D, ActivationLayerCPUTest::getTestCaseName);
@@ -68,7 +69,8 @@ const auto basicCases4D = ::testing::Combine(
     ::testing::ValuesIn(netPrc()),
     ::testing::Values(ov::element::f32),
     ::testing::Values(ov::element::f32),
-    ::testing::ValuesIn(filterCPUSpecificParams(cpuParams4Dblocked()))
+    ::testing::ValuesIn(filterCPUSpecificParams(cpuParams4Dblocked())),
+    ::testing::Values(false)
 );
 
 INSTANTIATE_TEST_SUITE_P(smoke_Activation4D_Eltwise_CPU_Blocked, ActivationLayerCPUTest, basicCases4D, ActivationLayerCPUTest::getTestCaseName);
@@ -89,7 +91,8 @@ const auto basicCases5D = ::testing::Combine(
     ::testing::ValuesIn(netPrc()),
     ::testing::Values(ov::element::f32),
     ::testing::Values(ov::element::f32),
-    ::testing::ValuesIn(filterCPUSpecificParams(cpuParams5Dblocked()))
+    ::testing::ValuesIn(filterCPUSpecificParams(cpuParams5Dblocked())),
+    ::testing::Values(false)
 );
 
 INSTANTIATE_TEST_SUITE_P(smoke_Activation5D_Eltwise_CPU_Blocked, ActivationLayerCPUTest, basicCases5D, ActivationLayerCPUTest::getTestCaseName);
diff --git a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/x64/col2im.cpp b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/x64/col2im.cpp
new file mode 100644
index 00000000000000..2d84fc1164c8a3
--- /dev/null
+++ b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/x64/col2im.cpp
@@ -0,0 +1,25 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "custom/single_layer_tests/classes/col2im.hpp"
+#include "utils/cpu_test_utils.hpp"
+#include "utils/fusing_test_utils.hpp"
+
+using namespace CPUTestUtils;
+
+namespace ov {
+namespace test {
+namespace Col2Im {
+INSTANTIATE_TEST_SUITE_P(smoke_Col2ImLayoutTestBF16, Col2ImLayerCPUTest,
+        ::testing::Combine(
+                ::testing::Combine(
+                        ::testing::ValuesIn(col2ImParamsVector),
+                        ::testing::Values(ElementType::bf16),
+                        ::testing::ValuesIn(indexPrecisions),
+                        ::testing::Values(ov::test::utils::DEVICE_CPU)),
+                ::testing::Values(CPUSpecificParams{{}, {}, {}, "ref_bf16"})),
+                Col2ImLayerCPUTest::getTestCaseName);
+}  // namespace Col2Im
+}  // namespace test
+}  // namespace ov
diff --git a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/x64/convolution.cpp b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/x64/convolution.cpp
index ae48f67eec269a..741e12031c680c 100644
--- a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/x64/convolution.cpp
+++ b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/x64/convolution.cpp
@@ -57,6 +57,7 @@ const std::vector<fusingSpecificParams> fusingParamsSetBF16{
         fusingAddPerChannel
 };
 
+const std::vector<fusingSpecificParams> fusingParamsSetFP16 = fusingParamsSetBF16;
 /* ============= Convolution (Gemm 1D) ============= */
 INSTANTIATE_TEST_SUITE_P(smoke_Conv_1D_GEMM_FP32, ConvolutionLayerCPUTest,
                          ::testing::Combine(
@@ -393,6 +394,21 @@ INSTANTIATE_TEST_SUITE_P(smoke_Conv_1D_BF16, ConvolutionLayerCPUTest,
                                  ::testing::Values(cpu_bf16_plugin_config)),
                          ConvolutionLayerCPUTest::getTestCaseName);
 
+INSTANTIATE_TEST_SUITE_P(smoke_Conv_1D_FP16, ConvolutionLayerCPUTest,
+                         ::testing::Combine(
+                                 ::testing::Combine(
+                                         convParams_ExplicitPadding_1D,
+                                         ::testing::Values(ElementType::f32),
+                                         ::testing::Values(ElementType::undefined),
+                                         ::testing::Values(ElementType::undefined),
+                                         ::testing::ValuesIn(inputShapes1d()),
+                                         ::testing::Values(ov::test::utils::DEVICE_CPU)),
+                                 ::testing::ValuesIn(filterCPUInfoForDeviceWithFP16({conv_avx512_1D_nspc_brgconv,
+                                        conv_avx512_1D_nspc_brgconv_amx})),
+                                 ::testing::ValuesIn(fusingParamsSetFP16),
+                                 ::testing::Values(cpu_f16_plugin_config)),
+                         ConvolutionLayerCPUTest::getTestCaseName);
+
 INSTANTIATE_TEST_SUITE_P(smoke_Conv_1D_I8, ConvolutionLayerCPUTest,
                          ::testing::Combine(
                                  ::testing::Combine(
@@ -478,6 +494,21 @@ INSTANTIATE_TEST_SUITE_P(smoke_Conv_2D_BF16, ConvolutionLayerCPUTest,
                                  ::testing::Values(cpu_bf16_plugin_config)),
                          ConvolutionLayerCPUTest::getTestCaseName);
 
+INSTANTIATE_TEST_SUITE_P(smoke_Conv_2D_FP16, ConvolutionLayerCPUTest,
+                         ::testing::Combine(
+                                 ::testing::Combine(
+                                         convParams_ExplicitPadding_2D(),
+                                         ::testing::Values(ElementType::f32),
+                                         ::testing::Values(ElementType::undefined),
+                                         ::testing::Values(ElementType::undefined),
+                                         ::testing::ValuesIn(inputShapes2d()),
+                                         ::testing::Values(ov::test::utils::DEVICE_CPU)),
+                                 ::testing::ValuesIn(filterCPUInfoForDeviceWithFP16({conv_avx512_2D_nspc_brgconv,
+                                        conv_avx512_2D_nspc_brgconv_amx})),
+                                 ::testing::ValuesIn(fusingParamsSetFP16),
+                                 ::testing::Values(cpu_f16_plugin_config)),
+                         ConvolutionLayerCPUTest::getTestCaseName);
+
 INSTANTIATE_TEST_SUITE_P(Conv_2D_BF16_dilated, ConvolutionLayerCPUTest,
                          ::testing::Combine(
                                  ::testing::Combine(
@@ -493,6 +524,21 @@ INSTANTIATE_TEST_SUITE_P(Conv_2D_BF16_dilated, ConvolutionLayerCPUTest,
                                  ::testing::Values(cpu_bf16_plugin_config)),
                          ConvolutionLayerCPUTest::getTestCaseName);
 
+INSTANTIATE_TEST_SUITE_P(Conv_2D_FP16_dilated, ConvolutionLayerCPUTest,
+                         ::testing::Combine(
+                                 ::testing::Combine(
+                                         convParams_ExplicitPadding_2D_dilated(),
+                                         ::testing::Values(ElementType::f32),
+                                         ::testing::Values(ElementType::undefined),
+                                         ::testing::Values(ElementType::undefined),
+                                         ::testing::ValuesIn(inputShapes2d()),
+                                         ::testing::Values(ov::test::utils::DEVICE_CPU)),
+                                 ::testing::ValuesIn(filterCPUInfoForDeviceWithFP16({conv_avx512_2D_nspc_brgconv,
+                                         conv_avx512_2D_nspc_brgconv_amx})),
+                                 ::testing::ValuesIn(fusingParamsSetFP16),
+                                 ::testing::Values(cpu_f16_plugin_config)),
+                         ConvolutionLayerCPUTest::getTestCaseName);
+
 INSTANTIATE_TEST_SUITE_P(smoke_Conv_2D_PlainToBlocked_BF16, ConvolutionLayerCPUTest,
                          ::testing::Combine(
                                  ::testing::Combine(
@@ -536,6 +582,21 @@ INSTANTIATE_TEST_SUITE_P(smoke_Conv_1D_1x1_BF16, ConvolutionLayerCPUTest,
                                  ::testing::Values(cpu_bf16_plugin_config)),
                          ConvolutionLayerCPUTest::getTestCaseName);
 
+INSTANTIATE_TEST_SUITE_P(smoke_Conv_1D_1x1_FP16, ConvolutionLayerCPUTest,
+                         ::testing::Combine(
+                                 ::testing::Combine(
+                                         convParams_ExplicitPadding_1x1_1D(),
+                                         ::testing::Values(ElementType::f32),
+                                         ::testing::Values(ElementType::undefined),
+                                         ::testing::Values(ElementType::undefined),
+                                         ::testing::ValuesIn(inputShapes1d()),
+                                         ::testing::Values(ov::test::utils::DEVICE_CPU)),
+                                 ::testing::ValuesIn(filterCPUInfoForDeviceWithFP16({conv_avx512_1D_1x1_nspc_brgconv,
+                                         conv_avx512_1D_1x1_nspc_brgconv_amx})),
+                                 ::testing::ValuesIn(fusingParamsSetFP16),
+                                 ::testing::Values(cpu_f16_plugin_config)),
+                         ConvolutionLayerCPUTest::getTestCaseName);
+
 INSTANTIATE_TEST_SUITE_P(smoke_Conv_2D_1x1_BF16, ConvolutionLayerCPUTest,
                          ::testing::Combine(
                                  ::testing::Combine(
@@ -551,6 +612,21 @@ INSTANTIATE_TEST_SUITE_P(smoke_Conv_2D_1x1_BF16, ConvolutionLayerCPUTest,
                                  ::testing::Values(cpu_bf16_plugin_config)),
                          ConvolutionLayerCPUTest::getTestCaseName);
 
+INSTANTIATE_TEST_SUITE_P(smoke_Conv_2D_1x1_FP16, ConvolutionLayerCPUTest,
+                         ::testing::Combine(
+                                 ::testing::Combine(
+                                         convParams_ExplicitPadding_1x1_2D(),
+                                         ::testing::Values(ElementType::f32),
+                                         ::testing::Values(ElementType::undefined),
+                                         ::testing::Values(ElementType::undefined),
+                                         ::testing::ValuesIn(inputShapes2d()),
+                                         ::testing::Values(ov::test::utils::DEVICE_CPU)),
+                                 ::testing::ValuesIn(filterCPUInfoForDeviceWithFP16({conv_avx512_2D_1x1_nspc_brgconv,
+                                         conv_avx512_2D_1x1_nspc_brgconv_amx})),
+                                 ::testing::ValuesIn(fusingParamsSetFP16),
+                                 ::testing::Values(cpu_f16_plugin_config)),
+                         ConvolutionLayerCPUTest::getTestCaseName);
+
 /* ============= Jit Planar ============= */
 /* ============= Convolution planar params (2D) ============= */
 const std::vector<CPUSpecificParams> CPUParams_Jit_Planar_2D = {
@@ -762,6 +838,21 @@ INSTANTIATE_TEST_SUITE_P(smoke_Conv_3D_BF16, ConvolutionLayerCPUTest,
                                  ::testing::Values(cpu_bf16_plugin_config)),
                          ConvolutionLayerCPUTest::getTestCaseName);
 
+INSTANTIATE_TEST_SUITE_P(smoke_Conv_3D_FP16, ConvolutionLayerCPUTest,
+                         ::testing::Combine(
+                                 ::testing::Combine(
+                                         convParams_ExplicitPadding_3D(),
+                                         ::testing::Values(ElementType::f32),
+                                         ::testing::Values(ElementType::undefined),
+                                         ::testing::Values(ElementType::undefined),
+                                         ::testing::ValuesIn(inputShapes3d()),
+                                         ::testing::Values(ov::test::utils::DEVICE_CPU)),
+                                 ::testing::ValuesIn(filterCPUInfoForDeviceWithFP16({conv_avx512_3D_nspc_brgconv,
+                                         conv_avx512_3D_nspc_brgconv_amx})),
+                                 ::testing::ValuesIn(fusingParamsSetFP16),
+                                 ::testing::Values(cpu_f16_plugin_config)),
+                         ConvolutionLayerCPUTest::getTestCaseName);
+
 INSTANTIATE_TEST_SUITE_P(Conv_3D_BF16_dilated, ConvolutionLayerCPUTest,
                          ::testing::Combine(
                                  ::testing::Combine(
@@ -777,6 +868,21 @@ INSTANTIATE_TEST_SUITE_P(Conv_3D_BF16_dilated, ConvolutionLayerCPUTest,
                                  ::testing::Values(cpu_bf16_plugin_config)),
                          ConvolutionLayerCPUTest::getTestCaseName);
 
+INSTANTIATE_TEST_SUITE_P(Conv_3D_FP16_dilated, ConvolutionLayerCPUTest,
+                         ::testing::Combine(
+                                 ::testing::Combine(
+                                         convParams_ExplicitPadding_3D_dilated(),
+                                         ::testing::Values(ElementType::f32),
+                                         ::testing::Values(ElementType::undefined),
+                                         ::testing::Values(ElementType::undefined),
+                                         ::testing::ValuesIn(inputShapes3d()),
+                                         ::testing::Values(ov::test::utils::DEVICE_CPU)),
+                                 ::testing::ValuesIn(filterCPUInfoForDeviceWithFP16({conv_avx512_3D_nspc_brgconv,
+                                         conv_avx512_3D_nspc_brgconv_amx})),
+                                 ::testing::ValuesIn(fusingParamsSetFP16),
+                                 ::testing::Values(cpu_f16_plugin_config)),
+                         ConvolutionLayerCPUTest::getTestCaseName);
+
 INSTANTIATE_TEST_SUITE_P(smoke_Conv_3D_Jit_Planar_FP32, ConvolutionLayerCPUTest,
                          ::testing::Combine(
                                  ::testing::Combine(
@@ -808,4 +914,4 @@ INSTANTIATE_TEST_SUITE_P(Conv_3D_Jit_Planar_FP32_dilated, ConvolutionLayerCPUTes
 }  // namespace
 }  // namespace Convolution
 }  // namespace test
-}  // namespace ov
\ No newline at end of file
+}  // namespace ov
diff --git a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/x64/convolution_backprop_data.cpp b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/x64/convolution_backprop_data.cpp
index 43f668413ff82b..3263396b52521d 100755
--- a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/x64/convolution_backprop_data.cpp
+++ b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/x64/convolution_backprop_data.cpp
@@ -11,6 +11,9 @@
 using namespace CPUTestUtils;
 using namespace ov::test;
 
+namespace ov {
+namespace test {
+namespace DeConvolution {
 /* COMMON PARAMS */
 const std::vector<fusingSpecificParams> brgDeconvFusingParamsSet{
     emptyFusingSpec,
@@ -207,6 +210,16 @@ INSTANTIATE_TEST_SUITE_P(smoke_Deconv_2D_NSPC_BF16_AMX,
                            ::testing::Values(cpu_bf16_plugin_config)),
         DeconvolutionLayerCPUTest::getTestCaseName);
 
+INSTANTIATE_TEST_SUITE_P(smoke_Deconv_2D_NSPC_FP16_AMX,
+        DeconvolutionLayerCPUTest,
+        ::testing::Combine(convParams_ExplicitPadding_NSPC_2D,
+                           ::testing::ValuesIn(smoke_2D_inputs),
+                           ::testing::Values(ElementType::f32),
+                           ::testing::ValuesIn(brgDeconvFusingParamsSet),
+                           ::testing::ValuesIn(filterCPUInfoForDeviceWithFP16({conv_avx512_2D_nspc_brgconv_amx})),
+                           ::testing::Values(cpu_f16_plugin_config)),
+        DeconvolutionLayerCPUTest::getTestCaseName);
+
 INSTANTIATE_TEST_SUITE_P(smoke_Deconv_2D_NSPC_BF16_AMX_1x1,
         DeconvolutionLayerCPUTest,
         ::testing::Combine(convParams_ExplicitPadding_NSPC_2D_1x1,
@@ -217,6 +230,15 @@ INSTANTIATE_TEST_SUITE_P(smoke_Deconv_2D_NSPC_BF16_AMX_1x1,
                            ::testing::Values(cpu_bf16_plugin_config)),
         DeconvolutionLayerCPUTest::getTestCaseName);
 
+INSTANTIATE_TEST_SUITE_P(smoke_Deconv_2D_NSPC_FP16_AMX_1x1,
+        DeconvolutionLayerCPUTest,
+        ::testing::Combine(convParams_ExplicitPadding_NSPC_2D_1x1,
+                           ::testing::ValuesIn(smoke_2D_inputs),
+                           ::testing::Values(ElementType::f32),
+                           ::testing::ValuesIn(brgDeconvFusingParamsSet),
+                           ::testing::ValuesIn(filterCPUInfoForDeviceWithFP16({conv_avx512_2D_1x1_nspc_brgconv_amx})),
+                           ::testing::Values(cpu_f16_plugin_config)),
+        DeconvolutionLayerCPUTest::getTestCaseName);
 
 INSTANTIATE_TEST_SUITE_P(smoke_Deconv_2D_NSPC_BRG,
         DeconvolutionLayerCPUTest,
@@ -338,6 +360,16 @@ INSTANTIATE_TEST_SUITE_P(smoke_Deconv_3D_NSPC_BF16_AMX,
                            ::testing::Values(cpu_bf16_plugin_config)),
         DeconvolutionLayerCPUTest::getTestCaseName);
 
+INSTANTIATE_TEST_SUITE_P(smoke_Deconv_3D_NSPC_FP16_AMX,
+        DeconvolutionLayerCPUTest,
+        ::testing::Combine(convParams_ExplicitPadding_NSPC_3D,
+                           ::testing::ValuesIn(smoke_3D_inputs),
+                           ::testing::Values(ElementType::f32),
+                           ::testing::ValuesIn(brgDeconvFusingParamsSet),
+                           ::testing::ValuesIn(filterCPUInfoForDeviceWithFP16({conv_avx512_3D_nspc_brgconv_amx})),
+                           ::testing::Values(cpu_f16_plugin_config)),
+        DeconvolutionLayerCPUTest::getTestCaseName);
+
 INSTANTIATE_TEST_SUITE_P(smoke_Deconv_3D_NSPC_BF16_AMX_1x1,
         DeconvolutionLayerCPUTest,
         ::testing::Combine(convParams_ExplicitPadding_NSPC_3D_1x1,
@@ -348,6 +380,16 @@ INSTANTIATE_TEST_SUITE_P(smoke_Deconv_3D_NSPC_BF16_AMX_1x1,
                            ::testing::Values(cpu_bf16_plugin_config)),
         DeconvolutionLayerCPUTest::getTestCaseName);
 
+INSTANTIATE_TEST_SUITE_P(smoke_Deconv_3D_NSPC_FP16_AMX_1x1,
+        DeconvolutionLayerCPUTest,
+        ::testing::Combine(convParams_ExplicitPadding_NSPC_3D_1x1,
+                           ::testing::ValuesIn(smoke_3D_inputs),
+                           ::testing::Values(ElementType::f32),
+                           ::testing::ValuesIn(brgDeconvFusingParamsSet),
+                           ::testing::ValuesIn(filterCPUInfoForDeviceWithFP16({conv_avx512_3D_1x1_nspc_brgconv_amx})),
+                           ::testing::Values(cpu_f16_plugin_config)),
+        DeconvolutionLayerCPUTest::getTestCaseName);
+
 INSTANTIATE_TEST_SUITE_P(smoke_Deconv_3D_NSPC_BRG,
         DeconvolutionLayerCPUTest,
         ::testing::Combine(convParams_ExplicitPadding_NSPC_3D,
@@ -525,3 +567,18 @@ INSTANTIATE_TEST_SUITE_P(
                            ::testing::ValuesIn(filterCPUInfoForDevice({conv_avx512_2D_nspc_brgconv_amx})),
                            ::testing::Values(cpu_bf16_plugin_config)),
         DeconvolutionLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(
+        smoke_Deconv_2D_AutoPadding_AMX_FP16,
+        DeconvolutionLayerCPUTest,
+        ::testing::Combine(deconvParams_AutoPadding_2D_AMX,
+                           ::testing::Values(inputs_2D_AutoPadding_AMX),
+                           ::testing::Values(ElementType::f32),
+                           ::testing::Values(emptyFusingSpec),
+                           ::testing::ValuesIn(filterCPUInfoForDeviceWithFP16({conv_avx512_2D_nspc_brgconv_amx})),
+                           ::testing::Values(cpu_f16_plugin_config)),
+        DeconvolutionLayerCPUTest::getTestCaseName);
+
+}  // namespace DeConvolution
+}  // namespace test
+}  // namespace ov
diff --git a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/x64/matmul.cpp b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/x64/matmul.cpp
index 108a063dfbcab0..3daa819cd4854d 100644
--- a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/x64/matmul.cpp
+++ b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/x64/matmul.cpp
@@ -13,6 +13,15 @@ namespace ov {
 namespace test {
 namespace MatMul {
 namespace {
+
+const std::vector<CPUSpecificParams> specificParams_FP16 {
+    CPUSpecificParams{{}, {}, {"brgemm_avx512"}, "brgemm_avx512"}
+};
+
+const std::vector<CPUSpecificParams> specificParams_FP16_AMX {
+    CPUSpecificParams{{}, {}, {"brgemm_avx512_amx"}, "brgemm_avx512_amx"}
+};
+
 const std::vector<ShapeRelatedParams> IS_x64 = {
     {static_shapes_to_test_representation({{7, 32, 120}, {3, 7, 120, 50}}), {false, false}},
     {static_shapes_to_test_representation({{7, 32, 120}, {3, 7, 120, 50}}), {true, false}},
@@ -47,6 +56,7 @@ std::vector<fusingSpecificParams> fusingParamsSet2DBF16 {
         fusingPReluPerTensor,
 };
 
+std::vector<fusingSpecificParams> fusingParamsSet2DFP16 = fusingParamsSet2DBF16;
 const std::vector<fusingSpecificParams> matmulFusingParamsNightly {
         emptyFusingSpec,
         fusingElu,
@@ -71,6 +81,21 @@ const auto testParams_Static_IS_x64 = ::testing::Combine(matMulParams_x64,
 
 INSTANTIATE_TEST_SUITE_P(smoke_MM_Static_IS_x64, MatMulLayerCPUTest, testParams_Static_IS_x64, MatMulLayerCPUTest::getTestCaseName);
 
+const auto matMulParams_x64_FP16 = ::testing::Combine(::testing::ValuesIn(IS_x64),
+                                             ::testing::Values(ov::element::f32),
+                                             ::testing::Values(ElementType::undefined),
+                                             ::testing::Values(ElementType::undefined),
+                                             ::testing::Values(utils::InputLayerType::PARAMETER),
+                                             ::testing::Values(ov::test::utils::DEVICE_CPU),
+                                             ::testing::Values(cpu_f16_plugin_config));
+
+const auto testParams_Static_IS_x64_FP16 = ::testing::Combine(matMulParams_x64_FP16,
+                                           ::testing::Values(MatMulNodeType::MatMul),
+                                           ::testing::ValuesIn(matmulFusingParams()),
+                                           ::testing::ValuesIn(filterCPUInfoForDeviceWithFP16(specificParams_FP16)));
+
+INSTANTIATE_TEST_SUITE_P(smoke_MM_Static_IS_x64_FP16, MatMulLayerCPUTest, testParams_Static_IS_x64_FP16, MatMulLayerCPUTest::getTestCaseName);
+
 const auto testParams2D_smoke = ::testing::Combine(::testing::Combine(::testing::ValuesIn(IS2D_smoke()),
                                                                 ::testing::Values(ElementType::f32),
                                                                 ::testing::Values(ElementType::undefined),
@@ -93,8 +118,20 @@ const auto testParams2DBF16_smoke = ::testing::Combine(::testing::Combine(::test
                                                  ::testing::ValuesIn(fusingParamsSet2DBF16),
                                                  ::testing::ValuesIn(filterCPUInfo(filterSpecificParams())));
 
+const auto testParams2DFP16_smoke = ::testing::Combine(::testing::Combine(::testing::ValuesIn(IS2D_smoke()),
+                                                                    ::testing::Values(ov::element::f32),
+                                                                    ::testing::Values(ElementType::undefined),
+                                                                    ::testing::Values(ElementType::undefined),
+                                                                    ::testing::Values(utils::InputLayerType::CONSTANT),
+                                                                    ::testing::Values(ov::test::utils::DEVICE_CPU),
+                                                                    ::testing::Values(cpu_f16_plugin_config)),
+                                                 ::testing::Values(MatMulNodeType::FullyConnected),
+                                                 ::testing::ValuesIn(fusingParamsSet2DFP16),
+                                                 ::testing::ValuesIn(filterCPUInfoForDeviceWithFP16(specificParams_FP16)));
+
 INSTANTIATE_TEST_SUITE_P(smoke_FC_2D, MatMulLayerCPUTest, testParams2D_smoke, MatMulLayerCPUTest::getTestCaseName);
 INSTANTIATE_TEST_SUITE_P(smoke_FC_2D_BF16, MatMulLayerCPUTest, testParams2DBF16_smoke, MatMulLayerCPUTest::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_FC_2D_FP16, MatMulLayerCPUTest, testParams2DFP16_smoke, MatMulLayerCPUTest::getTestCaseName);
 
 const auto testParams2D_nightly = ::testing::Combine(::testing::Combine(::testing::ValuesIn(IS2D_nightly()),
                                                                 ::testing::Values(ElementType::f32),
@@ -212,12 +249,26 @@ const auto fullyConnectedParams2D_Brgemm_smoke = ::testing::Combine(::testing::V
                                                        ::testing::Values(ov::test::utils::DEVICE_CPU),
                                                        ::testing::ValuesIn(filterAdditionalConfig_Brgemm()));
 
+const auto fullyConnectedParams2D_Brgemm_FP16_smoke = ::testing::Combine(::testing::ValuesIn(IS2D_Brgemm_smoke),
+                                                       ::testing::Values(ElementType::f32),
+                                                       ::testing::Values(ElementType::undefined),
+                                                       ::testing::Values(ElementType::undefined),
+                                                       ::testing::Values(utils::InputLayerType::CONSTANT),
+                                                       ::testing::Values(ov::test::utils::DEVICE_CPU),
+                                                       ::testing::Values(cpu_f16_plugin_config));
+
 const auto testParams2D_Brgemm_smoke = ::testing::Combine(fullyConnectedParams2D_Brgemm_smoke,
                                              ::testing::Values(MatMulNodeType::FullyConnected),
                                              ::testing::ValuesIn(fusingParamsSet2D_Brgemm_smoke),
                                              ::testing::ValuesIn(filterSpecificParams_Brgemm(true)));
 
+const auto testParams2D_Brgemm_FP16_smoke = ::testing::Combine(fullyConnectedParams2D_Brgemm_FP16_smoke,
+                                             ::testing::Values(MatMulNodeType::FullyConnected),
+                                             ::testing::ValuesIn(fusingParamsSet2D_Brgemm_smoke),
+                                             ::testing::ValuesIn(filterCPUInfoForDeviceWithFP16(specificParams_FP16)));
+
 INSTANTIATE_TEST_SUITE_P(smoke_FC_2D_Brgemm, MatMulLayerCPUTest, testParams2D_Brgemm_smoke, MatMulLayerCPUTest::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_FC_2D_Brgemm_FP16, MatMulLayerCPUTest, testParams2D_Brgemm_FP16_smoke, MatMulLayerCPUTest::getTestCaseName);
 
 const std::vector<ShapeRelatedParams> IS_brgemm_smoke = {
         {static_shapes_to_test_representation({{1, 2, 32, 120}, {120, 5}}), {false, false}},
@@ -248,6 +299,21 @@ const auto testBrgemmParams_smoke = ::testing::Combine(matMulBrgemmParams_smoke,
 
 INSTANTIATE_TEST_SUITE_P(smoke_MM_Brgemm_Static, MatMulLayerCPUTest, testBrgemmParams_smoke, MatMulLayerCPUTest::getTestCaseName);
 
+const auto matMulBrgemmParams_FP16_smoke = ::testing::Combine(::testing::ValuesIn(IS_brgemm_smoke),
+                                                         ::testing::Values(ElementType::f32),
+                                                         ::testing::Values(ElementType::undefined),
+                                                         ::testing::Values(ElementType::undefined),
+                                                         ::testing::Values(utils::InputLayerType::PARAMETER),
+                                                         ::testing::Values(ov::test::utils::DEVICE_CPU),
+                                                         ::testing::Values(cpu_f16_plugin_config));
+
+const auto testBrgemmParams_FP16_smoke = ::testing::Combine(matMulBrgemmParams_FP16_smoke,
+                                                       ::testing::Values(MatMulNodeType::MatMul),
+                                                       ::testing::ValuesIn(matmulFusingParams()),
+                                                       ::testing::ValuesIn(filterCPUInfoForDeviceWithFP16(specificParams_FP16)));
+
+INSTANTIATE_TEST_SUITE_P(smoke_MM_Brgemm_Static_FP16, MatMulLayerCPUTest, testBrgemmParams_FP16_smoke, MatMulLayerCPUTest::getTestCaseName);
+
 const std::vector<ShapeRelatedParams> IS_brgemm_nightly = {
         {static_shapes_to_test_representation({{1, 2, 32, 120}, {120, 5}}), {false, true}},
         {static_shapes_to_test_representation({{1, 2, 32, 120}, {120, 5}}), {true, true}},
@@ -277,6 +343,21 @@ const auto testBrgemmParams_nightly = ::testing::Combine(matMulBrgemmParams_nigh
 
 INSTANTIATE_TEST_SUITE_P(nightly_MM_Brgemm_Static, MatMulLayerCPUTest, testBrgemmParams_nightly, MatMulLayerCPUTest::getTestCaseName);
 
+const auto matMulBrgemmParams_FP16_nightly = ::testing::Combine(::testing::ValuesIn(IS_brgemm_nightly),
+                                                         ::testing::Values(ElementType::f32),
+                                                         ::testing::Values(ElementType::undefined),
+                                                         ::testing::Values(ElementType::undefined),
+                                                         ::testing::Values(utils::InputLayerType::PARAMETER),
+                                                         ::testing::Values(ov::test::utils::DEVICE_CPU),
+                                                         ::testing::Values(cpu_f16_plugin_config));
+
+const auto testBrgemmParams_FP16_nightly = ::testing::Combine(matMulBrgemmParams_FP16_nightly,
+                                                       ::testing::Values(MatMulNodeType::MatMul),
+                                                       ::testing::ValuesIn(matmulFusingParams()),
+                                                       ::testing::ValuesIn(filterCPUInfoForDeviceWithFP16(specificParams_FP16)));
+
+INSTANTIATE_TEST_SUITE_P(nightly_MM_Brgemm_Static_FP16, MatMulLayerCPUTest, testBrgemmParams_FP16_nightly, MatMulLayerCPUTest::getTestCaseName);
+
 const std::vector<ShapeRelatedParams> IS_Brgemm_Dynamic = {
         {
                 {
@@ -351,6 +432,21 @@ const auto testBrgemmParamsDynamic = ::testing::Combine(matMulBrgemmParamsDynami
 
 INSTANTIATE_TEST_SUITE_P(smoke_MM_Brgemm_Dynamic, MatMulLayerCPUTest, testBrgemmParamsDynamic, MatMulLayerCPUTest::getTestCaseName);
 
+const auto matMulBrgemmParamsDynamic_FP16 = ::testing::Combine(::testing::ValuesIn(IS_Brgemm_Dynamic),
+                                                          ::testing::Values(ElementType::f32),
+                                                          ::testing::Values(ElementType::undefined),
+                                                          ::testing::Values(ElementType::undefined),
+                                                          ::testing::Values(utils::InputLayerType::PARAMETER),
+                                                          ::testing::Values(ov::test::utils::DEVICE_CPU),
+                                                          ::testing::Values(cpu_f16_plugin_config));
+
+const auto testBrgemmParamsDynamic_FP16 = ::testing::Combine(matMulBrgemmParamsDynamic_FP16,
+                                                        ::testing::Values(MatMulNodeType::MatMul),
+                                                        ::testing::Values(emptyFusingSpec),
+                                                        ::testing::ValuesIn(filterCPUInfoForDeviceWithFP16(specificParams_FP16)));
+
+INSTANTIATE_TEST_SUITE_P(smoke_MM_Brgemm_Dynamic_FP16, MatMulLayerCPUTest, testBrgemmParamsDynamic_FP16, MatMulLayerCPUTest::getTestCaseName);
+
 const std::vector<ShapeRelatedParams> IS_Dynamic_Fusing = {
     {
         { //dynamic case description each pair per each input has {{dynamic shape}, {{static shape case1}, {static shape case2}, ...}
@@ -415,6 +511,21 @@ const auto testParamsDynamicFusing = ::testing::Combine(matMulParamsDynamicFusin
 
 INSTANTIATE_TEST_SUITE_P(smoke_MM_Dynamic_Fusing, MatMulLayerCPUTest, testParamsDynamicFusing, MatMulLayerCPUTest::getTestCaseName);
 
+const auto matMulParamsDynamicFusing_FP16 = ::testing::Combine(::testing::ValuesIn(IS_Dynamic_Fusing),
+                                                        ::testing::Values(ov::element::f32),
+                                                        ::testing::Values(ElementType::undefined),
+                                                        ::testing::Values(ElementType::undefined),
+                                                        ::testing::Values(utils::InputLayerType::PARAMETER),
+                                                        ::testing::Values(ov::test::utils::DEVICE_CPU),
+                                                        ::testing::Values(cpu_f16_plugin_config));
+
+const auto testParamsDynamicFusing_FP16 = ::testing::Combine(matMulParamsDynamicFusing_FP16,
+                                                  ::testing::Values(MatMulNodeType::MatMul),
+                                                  ::testing::ValuesIn(matmulFusingParams()),
+                                                  ::testing::ValuesIn(filterCPUInfoForDeviceWithFP16(specificParams_FP16)));
+
+INSTANTIATE_TEST_SUITE_P(smoke_MM_Dynamic_Fusing_FP16, MatMulLayerCPUTest, testParamsDynamicFusing_FP16, MatMulLayerCPUTest::getTestCaseName);
+
 const auto matMulParamsBrgemmDynamicFusing = ::testing::Combine(::testing::ValuesIn(IS_Dynamic_Fusing),
                                                                 ::testing::Values(ElementType::f32),
                                                                 ::testing::Values(ElementType::undefined),
@@ -430,6 +541,21 @@ const auto testParamsBrgemmDynamicFusing = ::testing::Combine(matMulParamsBrgemm
 
 INSTANTIATE_TEST_SUITE_P(smoke_MM_Brgemm_Dynamic_Fusing, MatMulLayerCPUTest, testParamsBrgemmDynamicFusing, MatMulLayerCPUTest::getTestCaseName);
 
+const auto matMulParamsBrgemmDynamicFusing_FP16 = ::testing::Combine(::testing::ValuesIn(IS_Dynamic_Fusing),
+                                                                ::testing::Values(ElementType::f32),
+                                                                ::testing::Values(ElementType::undefined),
+                                                                ::testing::Values(ElementType::undefined),
+                                                                ::testing::Values(utils::InputLayerType::PARAMETER),
+                                                                ::testing::Values(ov::test::utils::DEVICE_CPU),
+                                                                ::testing::Values(cpu_f16_plugin_config));
+
+const auto testParamsBrgemmDynamicFusing_FP16 = ::testing::Combine(matMulParamsBrgemmDynamicFusing_FP16,
+                                                              ::testing::Values(MatMulNodeType::MatMul),
+                                                              ::testing::ValuesIn(matmulFusingParams()),
+                                                              ::testing::ValuesIn(filterCPUInfoForDeviceWithFP16(specificParams_FP16)));
+
+INSTANTIATE_TEST_SUITE_P(smoke_MM_Brgemm_Dynamic_Fusing_FP16, MatMulLayerCPUTest, testParamsBrgemmDynamicFusing_FP16, MatMulLayerCPUTest::getTestCaseName);
+
 std::vector<ov::AnyMap> filterAdditionalConfig_BrgemmAmx() {
     std::vector<ov::AnyMap> additionalConfig;
     if (with_cpu_x86_bfloat16()) {
@@ -485,6 +611,21 @@ const auto testBrgemmAmxParams_smoke = ::testing::Combine(matMulBrgemmAmxParams_
 
 INSTANTIATE_TEST_SUITE_P(smoke_MM_Brgemm_Amx_Static, MatMulLayerCPUTest, testBrgemmAmxParams_smoke, MatMulLayerCPUTest::getTestCaseName);
 
+const auto matMulBrgemmAmxParams_FP16_smoke = ::testing::Combine(::testing::ValuesIn(IS_brgemm_Amx_smoke),
+                                                         ::testing::Values(ElementType::f32),
+                                                         ::testing::Values(ElementType::undefined),
+                                                         ::testing::Values(ElementType::undefined),
+                                                         ::testing::Values(utils::InputLayerType::PARAMETER),
+                                                         ::testing::Values(ov::test::utils::DEVICE_CPU),
+                                                         ::testing::Values(cpu_f16_plugin_config));
+
+const auto testBrgemmAmxParams_FP16_smoke = ::testing::Combine(matMulBrgemmAmxParams_FP16_smoke,
+                                                       ::testing::Values(MatMulNodeType::MatMul),
+                                                       ::testing::ValuesIn(matmulBrgemmAmxFusingParams),
+                                                       ::testing::ValuesIn(filterCPUInfoForDeviceWithFP16(specificParams_FP16_AMX)));
+
+INSTANTIATE_TEST_SUITE_P(smoke_MM_Brgemm_Amx_Static_FP16, MatMulLayerCPUTest, testBrgemmAmxParams_FP16_smoke, MatMulLayerCPUTest::getTestCaseName);
+
 const auto matMulBrgemmAmxParams_nightly = ::testing::Combine(::testing::ValuesIn(IS_brgemm_Amx_smoke),
                                                          ::testing::Values(ElementType::f32),
                                                          ::testing::Values(ElementType::undefined),
@@ -500,6 +641,21 @@ const auto testBrgemmAmxParams_nightly = ::testing::Combine(matMulBrgemmAmxParam
 
 INSTANTIATE_TEST_SUITE_P(nightly_MM_Brgemm_Amx_Static, MatMulLayerCPUTest, testBrgemmAmxParams_nightly, MatMulLayerCPUTest::getTestCaseName);
 
+const auto matMulBrgemmAmxParams_FP16_nightly = ::testing::Combine(::testing::ValuesIn(IS_brgemm_Amx_smoke),
+                                                         ::testing::Values(ElementType::f32),
+                                                         ::testing::Values(ElementType::undefined),
+                                                         ::testing::Values(ElementType::undefined),
+                                                         ::testing::Values(utils::InputLayerType::PARAMETER),
+                                                         ::testing::Values(ov::test::utils::DEVICE_CPU),
+                                                         ::testing::Values(cpu_f16_plugin_config));
+
+const auto testBrgemmAmxParams_FP16_nightly = ::testing::Combine(matMulBrgemmAmxParams_FP16_nightly,
+                                                       ::testing::Values(MatMulNodeType::MatMul),
+                                                       ::testing::ValuesIn(matmulBrgemmAmxFusingParams),
+                                                       ::testing::ValuesIn(filterCPUInfoForDeviceWithFP16(specificParams_FP16_AMX)));
+
+INSTANTIATE_TEST_SUITE_P(nightly_MM_Brgemm_Amx_Static_FP16, MatMulLayerCPUTest, testBrgemmAmxParams_FP16_nightly, MatMulLayerCPUTest::getTestCaseName);
+
 const auto matMulBrgemmAmxParamsDynamic = ::testing::Combine(::testing::ValuesIn(IS_Brgemm_Dynamic),
                                                           ::testing::Values(ElementType::f32),
                                                           ::testing::Values(ElementType::undefined),
@@ -515,6 +671,21 @@ const auto testBrgemmAmxParamsDynamic = ::testing::Combine(matMulBrgemmAmxParams
 
 INSTANTIATE_TEST_SUITE_P(smoke_MM_Brgemm_Amx_Dynamic, MatMulLayerCPUTest, testBrgemmAmxParamsDynamic, MatMulLayerCPUTest::getTestCaseName);
 
+const auto matMulBrgemmAmxParamsDynamic_FP16 = ::testing::Combine(::testing::ValuesIn(IS_Brgemm_Dynamic),
+                                                          ::testing::Values(ElementType::f32),
+                                                          ::testing::Values(ElementType::undefined),
+                                                          ::testing::Values(ElementType::undefined),
+                                                          ::testing::Values(utils::InputLayerType::PARAMETER),
+                                                          ::testing::Values(ov::test::utils::DEVICE_CPU),
+                                                          ::testing::Values(cpu_f16_plugin_config));
+
+const auto testBrgemmAmxParamsDynamic_FP16 = ::testing::Combine(matMulBrgemmAmxParamsDynamic_FP16,
+                                                        ::testing::Values(MatMulNodeType::MatMul),
+                                                        ::testing::Values(emptyFusingSpec),
+                                                        ::testing::ValuesIn(filterCPUInfoForDeviceWithFP16(specificParams_FP16_AMX)));
+
+INSTANTIATE_TEST_SUITE_P(smoke_MM_Brgemm_Amx_Dynamic_FP16, MatMulLayerCPUTest, testBrgemmAmxParamsDynamic_FP16, MatMulLayerCPUTest::getTestCaseName);
+
 const std::vector<ShapeRelatedParams> IS2D_Brgemm_Amx_smoke = {
     {static_shapes_to_test_representation({{59, 16}, {16, 120}}), {true, false}},
     {static_shapes_to_test_representation({{59, 16}, {16, 120}}), {true, true}},
@@ -630,6 +801,21 @@ const auto testParams2D_Brgemm_Amx_smoke = ::testing::Combine(fullyConnectedPara
 
 INSTANTIATE_TEST_SUITE_P(smoke_FC_2D_Brgemm_Amx, MatMulLayerCPUTest, testParams2D_Brgemm_Amx_smoke, MatMulLayerCPUTest::getTestCaseName);
 
+const auto fullyConnectedParams2D_FP16_Brgemm_Amx_smoke = ::testing::Combine(::testing::ValuesIn(IS2D_Brgemm_Amx_smoke),
+                                                       ::testing::Values(ElementType::f32),
+                                                       ::testing::Values(ElementType::undefined),
+                                                       ::testing::Values(ElementType::undefined),
+                                                       ::testing::Values(utils::InputLayerType::CONSTANT),
+                                                       ::testing::Values(ov::test::utils::DEVICE_CPU),
+                                                       ::testing::Values(cpu_f16_plugin_config));
+
+const auto testParams2D_FP16_Brgemm_Amx_smoke = ::testing::Combine(fullyConnectedParams2D_FP16_Brgemm_Amx_smoke,
+                                             ::testing::Values(MatMulNodeType::FullyConnected),
+                                             ::testing::ValuesIn(fusingParamsSet2D_Brgemm_smoke),
+                                             ::testing::ValuesIn(filterCPUInfoForDeviceWithFP16(specificParams_FP16_AMX)));
+
+INSTANTIATE_TEST_SUITE_P(smoke_FC_2D_Brgemm_Amx_FP16, MatMulLayerCPUTest, testParams2D_FP16_Brgemm_Amx_smoke, MatMulLayerCPUTest::getTestCaseName);
+
 const std::vector<ShapeRelatedParams> IS2D_Brgemm_nightly = {
     {static_shapes_to_test_representation({{59, 16}, {16, 120}}), {false, false}},
     {static_shapes_to_test_representation({{59, 16}, {16, 120}}), {false, true}},
@@ -668,6 +854,21 @@ const auto testParams2D_Brgemm_nightly = ::testing::Combine(fullyConnectedParams
 
 INSTANTIATE_TEST_SUITE_P(nightly_FC_2D_Brgemm, MatMulLayerCPUTest, testParams2D_Brgemm_nightly, MatMulLayerCPUTest::getTestCaseName);
 
+const auto fullyConnectedParams2D_FP16_Brgemm_nightly = ::testing::Combine(::testing::ValuesIn(IS2D_Brgemm_nightly),
+                                                       ::testing::Values(ElementType::f32),
+                                                       ::testing::Values(ElementType::undefined),
+                                                       ::testing::Values(ElementType::undefined),
+                                                       ::testing::Values(utils::InputLayerType::CONSTANT),
+                                                       ::testing::Values(ov::test::utils::DEVICE_CPU),
+                                                       ::testing::Values(cpu_f16_plugin_config));
+
+const auto testParams2D_FP16_Brgemm_nightly = ::testing::Combine(fullyConnectedParams2D_FP16_Brgemm_nightly,
+                                             ::testing::Values(MatMulNodeType::FullyConnected),
+                                             ::testing::ValuesIn(fusingParamsSet2D_nightly),
+                                             ::testing::ValuesIn(filterCPUInfoForDeviceWithFP16(specificParams_FP16)));
+
+INSTANTIATE_TEST_SUITE_P(nightly_FC_2D_Brgemm_FP16, MatMulLayerCPUTest, testParams2D_FP16_Brgemm_nightly, MatMulLayerCPUTest::getTestCaseName);
+
 const auto fullyConnectedParams2D_Brgemm_Amx_nightly = ::testing::Combine(::testing::ValuesIn(IS2D_Brgemm_nightly),
                                                        ::testing::Values(ElementType::f32),
                                                        ::testing::Values(ElementType::undefined),
@@ -683,6 +884,21 @@ const auto testParams2D_Brgemm_Amx_nightly = ::testing::Combine(fullyConnectedPa
 
 INSTANTIATE_TEST_SUITE_P(nightly_FC_2D_Brgemm_Amx, MatMulLayerCPUTest, testParams2D_Brgemm_Amx_nightly, MatMulLayerCPUTest::getTestCaseName);
 
+const auto fullyConnectedParams2D_FP16_Brgemm_Amx_nightly = ::testing::Combine(::testing::ValuesIn(IS2D_Brgemm_nightly),
+                                                       ::testing::Values(ElementType::f32),
+                                                       ::testing::Values(ElementType::undefined),
+                                                       ::testing::Values(ElementType::undefined),
+                                                       ::testing::Values(utils::InputLayerType::CONSTANT),
+                                                       ::testing::Values(ov::test::utils::DEVICE_CPU),
+                                                       ::testing::Values(cpu_f16_plugin_config));
+
+const auto testParams2D_FP16_Brgemm_Amx_nightly = ::testing::Combine(fullyConnectedParams2D_FP16_Brgemm_Amx_nightly,
+                                             ::testing::Values(MatMulNodeType::FullyConnected),
+                                             ::testing::ValuesIn(fusingParamsSet2D_nightly),
+                                             ::testing::ValuesIn(filterCPUInfoForDeviceWithFP16(specificParams_FP16_AMX)));
+
+INSTANTIATE_TEST_SUITE_P(nightly_FC_2D_Brgemm_Amx_FP16, MatMulLayerCPUTest, testParams2D_FP16_Brgemm_Amx_nightly, MatMulLayerCPUTest::getTestCaseName);
+
 const auto testParams2DBF16_nightly = ::testing::Combine(::testing::Combine(::testing::ValuesIn(IS2D_nightly()),
                                                                     ::testing::ValuesIn(netPRCs()),
                                                                     ::testing::Values(ElementType::undefined),
@@ -694,8 +910,20 @@ const auto testParams2DBF16_nightly = ::testing::Combine(::testing::Combine(::te
                                                  ::testing::ValuesIn(fusingParamsSet2DBF16),
                                                  ::testing::ValuesIn(filterCPUInfo(filterSpecificParams())));
 
+const auto testParams2DFP16_nightly = ::testing::Combine(::testing::Combine(::testing::ValuesIn(IS2D_nightly()),
+                                                                    ::testing::Values(ov::element::f32),
+                                                                    ::testing::Values(ElementType::undefined),
+                                                                    ::testing::Values(ElementType::undefined),
+                                                                    ::testing::Values(utils::InputLayerType::CONSTANT),
+                                                                    ::testing::Values(ov::test::utils::DEVICE_CPU),
+                                                                    ::testing::Values(cpu_f16_plugin_config)),
+                                                 ::testing::Values(MatMulNodeType::FullyConnected),
+                                                 ::testing::ValuesIn(fusingParamsSet2DFP16),
+                                                 ::testing::ValuesIn(filterCPUInfoForDeviceWithFP16(specificParams_FP16)));
+
 INSTANTIATE_TEST_SUITE_P(nightly_FC_2D, MatMulLayerCPUTest, testParams2D_nightly, MatMulLayerCPUTest::getTestCaseName);
 INSTANTIATE_TEST_SUITE_P(nightly_FC_2D_BF16, MatMulLayerCPUTest, testParams2DBF16_nightly, MatMulLayerCPUTest::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(nightly_FC_2D_FP16, MatMulLayerCPUTest, testParams2DFP16_nightly, MatMulLayerCPUTest::getTestCaseName);
 
 std::vector<fusingSpecificParams> fusingParamsSet3D_smoke {
 // The following three patterns are convered by MLAS test
@@ -714,6 +942,12 @@ std::vector<fusingSpecificParams> fusingParamsSet3DBF16 {
         fusingMultiplyPerChannel,
 };
 
+std::vector<fusingSpecificParams> fusingParamsSet3DFP16 {
+        emptyFusingSpec,
+        fusingBias,
+        fusingMultiplyPerChannel,
+};
+
 const auto fullyConnectedParams3DBF16_smoke = ::testing::Combine(::testing::ValuesIn(IS3D_smoke()),
                                                            ::testing::ValuesIn(netPRCs()),
                                                            ::testing::Values(ElementType::undefined),
@@ -722,12 +956,26 @@ const auto fullyConnectedParams3DBF16_smoke = ::testing::Combine(::testing::Valu
                                                            ::testing::Values(ov::test::utils::DEVICE_CPU),
                                                            ::testing::ValuesIn(additionalConfig()));
 
+const auto fullyConnectedParams3DFP16_smoke = ::testing::Combine(::testing::ValuesIn(IS3D_smoke()),
+                                                           ::testing::Values(ov::element::f32),
+                                                           ::testing::Values(ElementType::undefined),
+                                                           ::testing::Values(ElementType::undefined),
+                                                           ::testing::Values(utils::InputLayerType::CONSTANT),
+                                                           ::testing::Values(ov::test::utils::DEVICE_CPU),
+                                                           ::testing::Values(cpu_f16_plugin_config));
+
 const auto testParams3DBF16_smoke = ::testing::Combine(fullyConnectedParams3DBF16_smoke,
                                                  ::testing::Values(MatMulNodeType::FullyConnected),
                                                  ::testing::ValuesIn(fusingParamsSet3DBF16),
                                                  ::testing::ValuesIn(filterCPUInfo(filterSpecificParams())));
 
+const auto testParams3DFP16_smoke = ::testing::Combine(fullyConnectedParams3DFP16_smoke,
+                                                 ::testing::Values(MatMulNodeType::FullyConnected),
+                                                 ::testing::ValuesIn(fusingParamsSet3DFP16),
+                                                 ::testing::ValuesIn(filterCPUInfoForDeviceWithFP16(specificParams_FP16)));
+
 INSTANTIATE_TEST_SUITE_P(smoke_FC_3D_BF16, MatMulLayerCPUTest, testParams3DBF16_smoke, MatMulLayerCPUTest::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_FC_3D_FP16, MatMulLayerCPUTest, testParams3DFP16_smoke, MatMulLayerCPUTest::getTestCaseName);
 
 const auto fullyConnectedParams3D_smoke = ::testing::Combine(::testing::ValuesIn(IS3D_smoke()),
                                                        ::testing::Values(ElementType::f32),
@@ -794,19 +1042,32 @@ const auto fullyConnectedParams3DBF16_nightly = ::testing::Combine(::testing::Va
                                                            ::testing::Values(ov::test::utils::DEVICE_CPU),
                                                            ::testing::ValuesIn(additionalConfig()));
 
-const auto testParams3DBF16_nightly = ::testing::Combine(fullyConnectedParams3DBF16_nightly,
-                                                 ::testing::Values(MatMulNodeType::FullyConnected),
-                                                 ::testing::ValuesIn(fusingParamsSet3DBF16),
-                                                 ::testing::ValuesIn(filterCPUInfo(filterSpecificParams())));
-
-INSTANTIATE_TEST_SUITE_P(nightly_FC_3D_BF16, MatMulLayerCPUTest, testParams3DBF16_nightly, MatMulLayerCPUTest::getTestCaseName);
+const auto fullyConnectedParams3DFP16_nightly = ::testing::Combine(::testing::ValuesIn(IS3D_nightly),
+                                                           ::testing::Values(ov::element::f32),
+                                                           ::testing::Values(ElementType::undefined),
+                                                           ::testing::Values(ElementType::undefined),
+                                                           ::testing::Values(utils::InputLayerType::CONSTANT),
+                                                           ::testing::Values(ov::test::utils::DEVICE_CPU),
+                                                           ::testing::Values(cpu_f16_plugin_config));
 
 const auto testParams3D_nightly = ::testing::Combine(fullyConnectedParams3D_nightly,
                                              ::testing::Values(MatMulNodeType::FullyConnected),
                                              ::testing::ValuesIn(fusingParamsSet3D_nightly),
                                              ::testing::ValuesIn(filterCPUInfo(filterSpecificParams())));
 
+const auto testParams3DBF16_nightly = ::testing::Combine(fullyConnectedParams3DBF16_nightly,
+                                                 ::testing::Values(MatMulNodeType::FullyConnected),
+                                                 ::testing::ValuesIn(fusingParamsSet3DBF16),
+                                                 ::testing::ValuesIn(filterCPUInfo(filterSpecificParams())));
+
+const auto testParams3DFP16_nightly = ::testing::Combine(fullyConnectedParams3DFP16_nightly,
+                                                 ::testing::Values(MatMulNodeType::FullyConnected),
+                                                 ::testing::ValuesIn(fusingParamsSet3DFP16),
+                                                 ::testing::ValuesIn(filterCPUInfoForDeviceWithFP16(specificParams_FP16)));
+
 INSTANTIATE_TEST_SUITE_P(nightly_FC_3D, MatMulLayerCPUTest, testParams3D_nightly, MatMulLayerCPUTest::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(nightly_FC_3D_BF16, MatMulLayerCPUTest, testParams3DBF16_nightly, MatMulLayerCPUTest::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(nightly_FC_3D_FP16, MatMulLayerCPUTest, testParams3DFP16_nightly, MatMulLayerCPUTest::getTestCaseName);
 
 class MatMulLayerCPUTestUndefShapes : public MatMulLayerCPUTest {
 };
diff --git a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/x64/pooling.cpp b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/x64/pooling.cpp
index 753b671dd7bace..4127d0c9deddd8 100644
--- a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/x64/pooling.cpp
+++ b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/x64/pooling.cpp
@@ -38,7 +38,8 @@ INSTANTIATE_TEST_SUITE_P(smoke_MaxPoolV8_CPU_4D_ref, MaxPoolingV8LayerCPUTest,
                                  ::testing::ValuesIn(paramsMaxV84D_ref),
                                  ::testing::ValuesIn(inputShapes4D()),
                                  ::testing::ValuesIn((inpOutPrecision())),
-                                 ::testing::Values(ref)),
+                                 ::testing::Values(ref),
+                                 ::testing::Values(CPUTestUtils::empty_plugin_config)),
                          MaxPoolingV8LayerCPUTest::getTestCaseName);
 
 const auto avx512_nwc = CPUSpecificParams{{nwc}, {nwc}, {"jit_avx512"}, "jit_avx512"};
@@ -100,7 +101,8 @@ INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_4D_I8, PoolingLayerCPUTest,
                               ::testing::Values(ElementType::f32),
                               ::testing::Values(true),
                               ::testing::ValuesIn(filterCPUInfoForDevice(vecCpuConfigsFusing_4D)),
-                              ::testing::ValuesIn(fusingParamsSet)),
+                              ::testing::ValuesIn(fusingParamsSet),
+                              ::testing::Values(CPUTestUtils::empty_plugin_config)),
                           PoolingLayerCPUTest::getTestCaseName);
 
 const std::vector<InputShape> inputShapes5D_int8 = {
@@ -140,9 +142,116 @@ INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_5D_I8, PoolingLayerCPUTest,
                               ::testing::Values(ElementType::f32),
                               ::testing::Values(true),
                               ::testing::ValuesIn(filterCPUInfoForDevice(vecCpuConfigsFusing_5D)),
-                              ::testing::ValuesIn(fusingParamsSet)),
+                              ::testing::ValuesIn(fusingParamsSet),
+                              ::testing::Values(CPUTestUtils::empty_plugin_config)),
                           PoolingLayerCPUTest::getTestCaseName);
 
+INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_4D_I8_FP16, PoolingLayerCPUTest,
+                         ::testing::Combine(
+                              ::testing::ValuesIn(paramsAvg4D()),
+                              ::testing::ValuesIn(inputShapes4D_int8),
+                              ::testing::Values(ElementType::f32),
+                              ::testing::Values(true),
+                              ::testing::ValuesIn(filterCPUInfoForDeviceWithFP16(vecCpuConfigsFusing_4D)),
+                              ::testing::ValuesIn(fusingParamsSet),
+                              ::testing::Values(cpu_f16_plugin_config)),
+                          PoolingLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_5D_I8_FP16, PoolingLayerCPUTest,
+                         ::testing::Combine(
+                              ::testing::ValuesIn(paramsAvg5D()),
+                              ::testing::ValuesIn(inputShapes5D_int8),
+                              ::testing::Values(ElementType::f32),
+                              ::testing::Values(true),
+                              ::testing::ValuesIn(filterCPUInfoForDeviceWithFP16(vecCpuConfigsFusing_5D)),
+                              ::testing::ValuesIn(fusingParamsSet),
+                              ::testing::Values(cpu_f16_plugin_config)),
+                          PoolingLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_MaxPool_CPU_3D_FP16, PoolingLayerCPUTest,
+                         ::testing::Combine(
+                                 ::testing::ValuesIn(paramsMax3D()),
+                                 ::testing::ValuesIn(inputShapes3D()),
+                                 ::testing::ValuesIn(inpOutPrecision()),
+                                 ::testing::Values(false),
+                                 ::testing::ValuesIn(filterCPUInfoForDeviceWithFP16(vecCpuConfigs)),
+                                 ::testing::Values(emptyFusingSpec),
+                                 ::testing::Values(cpu_f16_plugin_config)),
+                         PoolingLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_3D_FP16, PoolingLayerCPUTest,
+                         ::testing::Combine(
+                                 ::testing::ValuesIn(paramsAvg3D()),
+                                 ::testing::ValuesIn(inputShapes3D()),
+                                 ::testing::ValuesIn(inpOutPrecision()),
+                                 ::testing::Values(false),
+                                 ::testing::ValuesIn(filterCPUInfoForDeviceWithFP16(vecCpuConfigs)),
+                                 ::testing::Values(emptyFusingSpec),
+                                 ::testing::Values(cpu_f16_plugin_config)),
+                         PoolingLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_MaxPool_CPU_4D_FP16, PoolingLayerCPUTest,
+                            ::testing::Combine(
+                            ::testing::ValuesIn(paramsMax4D()),
+                            ::testing::ValuesIn(inputShapes4D()),
+                            ::testing::ValuesIn(inpOutPrecision()),
+                            ::testing::Values(false),
+                            ::testing::ValuesIn(filterCPUInfoForDeviceWithFP16(vecCpuConfigs)),
+                            ::testing::Values(emptyFusingSpec),
+                            ::testing::Values(cpu_f16_plugin_config)),
+                        PoolingLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_MaxPoolV8_CPU_4D_FP16, MaxPoolingV8LayerCPUTest,
+                         ::testing::Combine(
+                                 ::testing::ValuesIn(paramsMaxV84D()),
+                                 ::testing::ValuesIn(inputShapes4D()),
+                                 ::testing::ValuesIn(inpOutPrecision()),
+                                 ::testing::ValuesIn(filterCPUInfoForDeviceWithFP16(vecCpuConfigs)),
+                                 ::testing::Values(cpu_f16_plugin_config)),
+                         MaxPoolingV8LayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_4D_FP16, PoolingLayerCPUTest,
+                        ::testing::Combine(
+                            ::testing::ValuesIn(paramsAvg4D()),
+                            ::testing::ValuesIn(inputShapes4D()),
+                            ::testing::ValuesIn(inpOutPrecision()),
+                            ::testing::Values(false),
+                            ::testing::ValuesIn(filterCPUInfoForDeviceWithFP16(vecCpuConfigs)),
+                            ::testing::Values(emptyFusingSpec),
+                            ::testing::Values(cpu_f16_plugin_config)),
+                        PoolingLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_Large_FP16, PoolingLayerCPUTest,
+                        ::testing::Combine(
+                            ::testing::ValuesIn(paramsAvg4D_Large()),
+                            ::testing::ValuesIn(inputShapes4D_Large()),
+                            ::testing::ValuesIn(inpOutPrecision()),
+                            ::testing::Values(false),
+                            ::testing::ValuesIn(filterCPUInfoForDeviceWithFP16(vecCpuConfigs)),
+                            ::testing::Values(emptyFusingSpec),
+                            ::testing::Values(cpu_f16_plugin_config)),
+                        PoolingLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_MaxPool_CPU_5D_FP16, PoolingLayerCPUTest,
+                         ::testing::Combine(
+                             ::testing::ValuesIn(paramsMax5D()),
+                             ::testing::ValuesIn(inputShapes5D()),
+                             ::testing::ValuesIn(inpOutPrecision()),
+                             ::testing::Values(false),
+                             ::testing::ValuesIn(filterCPUInfoForDeviceWithFP16(vecCpuConfigs)),
+                             ::testing::Values(emptyFusingSpec),
+                             ::testing::Values(cpu_f16_plugin_config)),
+                         PoolingLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_MaxPoolV8_CPU_5D_FP16, MaxPoolingV8LayerCPUTest,
+                         ::testing::Combine(
+                                 ::testing::ValuesIn(paramsMaxV85D()),
+                                 ::testing::ValuesIn(inputShapes5D()),
+                                 ::testing::ValuesIn(inpOutPrecision()),
+                                 ::testing::ValuesIn(filterCPUInfoForDeviceWithFP16(vecCpuConfigs)),
+                                 ::testing::Values(cpu_f16_plugin_config)),
+                         MaxPoolingV8LayerCPUTest::getTestCaseName);
+
 INSTANTIATE_TEST_SUITE_P(smoke_MaxPool_CPU_3D, PoolingLayerCPUTest,
                          ::testing::Combine(
                                  ::testing::ValuesIn(paramsMax3D()),
@@ -150,7 +259,8 @@ INSTANTIATE_TEST_SUITE_P(smoke_MaxPool_CPU_3D, PoolingLayerCPUTest,
                                  ::testing::ValuesIn((inpOutPrecision())),
                                  ::testing::Values(false),
                                  ::testing::ValuesIn(filterCPUInfoForDevice(vecCpuConfigsFusing_3D)),
-                                 ::testing::Values(emptyFusingSpec)),
+                                 ::testing::Values(emptyFusingSpec),
+                                 ::testing::Values(CPUTestUtils::empty_plugin_config)),
                          PoolingLayerCPUTest::getTestCaseName);
 
 INSTANTIATE_TEST_SUITE_P(smoke_MaxPoolV8_CPU_3D, MaxPoolingV8LayerCPUTest,
@@ -158,7 +268,8 @@ INSTANTIATE_TEST_SUITE_P(smoke_MaxPoolV8_CPU_3D, MaxPoolingV8LayerCPUTest,
                                  ::testing::ValuesIn(paramsMaxV83D()),
                                  ::testing::ValuesIn(inputShapes3D()),
                                  ::testing::ValuesIn((inpOutPrecision())),
-                                 ::testing::ValuesIn(filterCPUInfoForDevice(vecCpuConfigsFusing_3D))),
+                                 ::testing::ValuesIn(filterCPUInfoForDevice(vecCpuConfigsFusing_3D)),
+                                 ::testing::Values(CPUTestUtils::empty_plugin_config)),
                          MaxPoolingV8LayerCPUTest::getTestCaseName);
 
 INSTANTIATE_TEST_SUITE_P(smoke_MaxPool_CPU_5D, PoolingLayerCPUTest,
@@ -168,7 +279,8 @@ INSTANTIATE_TEST_SUITE_P(smoke_MaxPool_CPU_5D, PoolingLayerCPUTest,
                              ::testing::ValuesIn((inpOutPrecision())),
                              ::testing::Values(false),
                              ::testing::ValuesIn(filterCPUInfoForDevice(vecCpuConfigsFusing_5D)),
-                             ::testing::Values(emptyFusingSpec)),
+                             ::testing::Values(emptyFusingSpec),
+                             ::testing::Values(CPUTestUtils::empty_plugin_config)),
                          PoolingLayerCPUTest::getTestCaseName);
 
 INSTANTIATE_TEST_SUITE_P(smoke_MaxPoolV8_CPU_5D, MaxPoolingV8LayerCPUTest,
@@ -176,7 +288,8 @@ INSTANTIATE_TEST_SUITE_P(smoke_MaxPoolV8_CPU_5D, MaxPoolingV8LayerCPUTest,
                                  ::testing::ValuesIn(paramsMaxV85D()),
                                  ::testing::ValuesIn(inputShapes5D()),
                                  ::testing::ValuesIn((inpOutPrecision())),
-                                 ::testing::ValuesIn(filterCPUInfoForDevice(vecCpuConfigsFusing_5D))),
+                                 ::testing::ValuesIn(filterCPUInfoForDevice(vecCpuConfigsFusing_5D)),
+                                 ::testing::Values(CPUTestUtils::empty_plugin_config)),
                          MaxPoolingV8LayerCPUTest::getTestCaseName);
 }  // namespace
 }  // namespace Pooling
diff --git a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/x64/reduce.cpp b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/x64/reduce.cpp
index 233920c76b78a3..1a18dbbb015ede 100644
--- a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/x64/reduce.cpp
+++ b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/x64/reduce.cpp
@@ -35,6 +35,18 @@ std::vector<std::vector<ov::test::InputShape>> inputShapes_Int32_dyn = {
     {{{{1, 5}, 19, {1, 5}, {1, 10}}, {{2, 19, 2, 2}, {2, 19, 2, 3}}}},
 };
 
+std::vector<std::vector<ov::test::InputShape>> inputShapes_NativeInt32_dyn = {
+    {{{{1, 5}, 2, {1, 5}, {1, 10}}, {{2, 2, 2, 2}, {2, 2, 2, 3}}}},
+};
+
+std::vector<std::vector<ov::test::InputShape>> inputShapes_NativeInt32Gather_dyn = {
+    {{{{1, 5}, 6, {1, 5}, {1, 10}}, {{1, 6, 4, 3}, {1, 6, 4, 4}}}},
+};
+
+std::vector<std::vector<ov::test::InputShape>> inputShapes_Rounding_dyn = {
+    {{{{1, 5}, 3, {1, 5}, {1, 5}}, {{1, 3, 3, 1}, {1, 3, 3, 3}}}},
+};
+
 std::vector<std::vector<ov::test::InputShape>> inputShapes_SmallChannel_dyn = {
     {{{{1, 5}, 3, {1, 5}, {1, 10}}, {{2, 3, 2, 2}, {2, 3, 2, 9}}}},
 };
@@ -85,6 +97,10 @@ const std::vector<std::vector<int>> axesHW = {
         {2, 3}
 };
 
+const std::vector<std::vector<int>> axesGather = {
+        {3}
+};
+
 std::vector<CPUSpecificParams> cpuParams_5D = {
         CPUSpecificParams({nCdhw16c}, {nCdhw16c}, {}, {}),
         CPUSpecificParams({ndhwc}, {ndhwc}, {}, {}),
@@ -255,6 +271,48 @@ const auto params_Int32 = testing::Combine(
         testing::Values(emptyFusingSpec),
         testing::ValuesIn(additionalConfigFP32()));
 
+const auto params_NativeInt32 = testing::Combine(
+        testing::Combine(
+            testing::ValuesIn(axes()),
+            testing::Values(ov::test::utils::OpType::VECTOR),
+            testing::ValuesIn(keepDims()),
+            testing::ValuesIn(reductionTypesNativeInt32()),
+            testing::Values(ElementType::i32),
+            testing::Values(ElementType::undefined),
+            testing::Values(ElementType::undefined),
+            testing::ValuesIn(inputShapes_NativeInt32_dyn)),
+        testing::Values(emptyCPUSpec),
+        testing::Values(emptyFusingSpec),
+        testing::ValuesIn(additionalConfigFP32()));
+
+const auto params_NativeInt32Gather = testing::Combine(
+        testing::Combine(
+            testing::ValuesIn(axesGather),
+            testing::Values(ov::test::utils::OpType::VECTOR),
+            testing::ValuesIn(keepDims()),
+            testing::ValuesIn(reductionTypesNativeInt32()),
+            testing::Values(ElementType::i32),
+            testing::Values(ElementType::undefined),
+            testing::Values(ElementType::undefined),
+            testing::ValuesIn(inputShapes_NativeInt32Gather_dyn)),
+        testing::Values(emptyCPUSpec),
+        testing::Values(emptyFusingSpec),
+        testing::ValuesIn(additionalConfigFP32()));
+
+const auto params_Rounding = testing::Combine(
+        testing::Combine(
+            testing::Values(axesND()[3]),
+            testing::Values(ov::test::utils::OpType::VECTOR),
+            testing::Values(keepDims()[1]),
+            testing::Values(reductionTypes()[0]),
+            testing::Values(ElementType::i32),
+            testing::Values(ElementType::undefined),
+            testing::Values(ElementType::undefined),
+            testing::ValuesIn(inputShapes_Rounding_dyn)),
+        testing::Values(emptyCPUSpec),
+        testing::Values(emptyFusingSpec),
+        testing::ValuesIn(additionalConfigFP32()));
+
 const auto params_NHWC_SmallChannel = testing::Combine(
         testing::Combine(
                 testing::ValuesIn(axesHW),
@@ -332,6 +390,27 @@ INSTANTIATE_TEST_SUITE_P(
         ReduceCPULayerTest::getTestCaseName
 );
 
+INSTANTIATE_TEST_SUITE_P(
+        smoke_Reduce_NativeInt32_CPU,
+        ReduceCPULayerTest,
+        params_NativeInt32,
+        ReduceCPULayerTest::getTestCaseName
+);
+
+INSTANTIATE_TEST_SUITE_P(
+        smoke_Reduce_NativeInt32Gather_CPU,
+        ReduceCPULayerTest,
+        params_NativeInt32Gather,
+        ReduceCPULayerTest::getTestCaseName
+);
+
+INSTANTIATE_TEST_SUITE_P(
+        smoke_Reduce_Rounding_CPU,
+        ReduceCPULayerTest,
+        params_Rounding,
+        ReduceCPULayerTest::getTestCaseName
+);
+
 INSTANTIATE_TEST_SUITE_P(
         smoke_Reduce_NHWC_SmallChannel_CPU,
         ReduceCPULayerTest,
diff --git a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/x64/softmax.cpp b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/x64/softmax.cpp
new file mode 100644
index 00000000000000..2a22f629c29661
--- /dev/null
+++ b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/x64/softmax.cpp
@@ -0,0 +1,94 @@
+// Copyright (C) 2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "custom/single_layer_tests/classes/softmax.hpp"
+#include <vector>
+#include "utils/cpu_test_utils.hpp"
+#include "utils/filter_cpu_info.hpp"
+#include <vector>
+
+using namespace CPUTestUtils;
+
+namespace ov {
+namespace test {
+namespace SoftMax {
+namespace {
+const auto optimizedCPUSpec = []()-> std::vector<CPUSpecificParams>{
+    const auto avx512 = CPUSpecificParams{{}, {}, {"jit"}, "jit_avx512"};
+    const auto avx2 = CPUSpecificParams{{}, {}, {"jit"}, "jit_avx2"};
+    const auto sse42 = CPUSpecificParams{{}, {}, {"jit"}, "jit_sse42"};
+    const std::vector<CPUSpecificParams> vecCpuConfigs = {avx512, avx2, sse42};
+    auto supportConfigure = CPUTestUtils::filterCPUInfoForDevice(vecCpuConfigs);
+    // only the MAX ISA of vecCpuConfigs will be tested
+    if (supportConfigure.size() > 0) {
+         return std::vector<CPUSpecificParams>{supportConfigure[0]};
+     } else {
+         return std::vector<CPUSpecificParams>{};
+     }
+};
+
+const std::vector<SoftMaxConfig> optimizedConfigsFP32 = {
+    // Static shapes
+    {ov::test::InputShape{ov::PartialShape{1, 100}, {ov::Shape{1, 100}}}, 1},
+    {ov::test::InputShape{ov::PartialShape{10, 10}, {ov::Shape{10, 10}}}, 1},
+    {ov::test::InputShape{ov::PartialShape{100, 1}, {ov::Shape{100, 1}}}, 0},
+    {ov::test::InputShape{ov::PartialShape{100, 1}, {ov::Shape{100, 1}}}, 1},
+    {ov::test::InputShape{ov::PartialShape{5, 5, 1}, {ov::Shape{5, 5, 1}}}, 1},
+    {ov::test::InputShape{ov::PartialShape{5, 5, 5}, {ov::Shape{5, 5, 5}}}, 2},
+    {ov::test::InputShape{ov::PartialShape{5, 5, 5, 5}, {ov::Shape{5, 5, 5, 5}}}, 0},
+    {ov::test::InputShape{ov::PartialShape{5, 5, 1, 1}, {ov::Shape{5, 5, 1, 1}}}, 1},
+    {ov::test::InputShape{ov::PartialShape{5, 5, 5, 5}, {ov::Shape{5, 5, 5, 5}}}, 1},
+    {ov::test::InputShape{ov::PartialShape{5, 5, 5, 1}, {ov::Shape{5, 5, 5, 1}}}, 2},
+    {ov::test::InputShape{ov::PartialShape{5, 5, 5, 5}, {ov::Shape{5, 5, 5, 5}}}, 2},
+    {ov::test::InputShape{ov::PartialShape{5, 5, 5, 5}, {ov::Shape{5, 5, 5, 5}}}, 3},
+    {ov::test::InputShape{ov::PartialShape{5, 5, 5, 5, 5}, {ov::Shape{5, 5, 5, 5, 5}}}, 0},
+    {ov::test::InputShape{ov::PartialShape{5, 5, 1, 1, 1}, {ov::Shape{5, 5, 1, 1, 1}}}, 1},
+    {ov::test::InputShape{ov::PartialShape{5, 5, 5, 5, 5}, {ov::Shape{5, 5, 5, 5, 5}}}, 1},
+    {ov::test::InputShape{ov::PartialShape{5, 5, 5, 1, 1}, {ov::Shape{5, 5, 5, 1, 1}}}, 2},
+    {ov::test::InputShape{ov::PartialShape{5, 5, 5, 5, 5}, {ov::Shape{5, 5, 5, 5, 5}}}, 2},
+    {ov::test::InputShape{ov::PartialShape{5, 5, 5, 1, 1}, {ov::Shape{5, 5, 5, 1, 1}}}, 3},
+    {ov::test::InputShape{ov::PartialShape{5, 5, 5, 5, 5}, {ov::Shape{5, 5, 5, 5, 5}}}, 3},
+    {ov::test::InputShape{ov::PartialShape{5, 5, 5, 5, 1}, {ov::Shape{5, 5, 5, 5, 1}}}, 4},
+    {ov::test::InputShape{ov::PartialShape{5, 5, 5, 5, 5}, {ov::Shape{5, 5, 5, 5, 5}}}, 4},
+    // Dynamic shapes
+    {ov::test::InputShape{// dynamic shape
+                          ov::PartialShape{-1, -1},
+                          {// target static shapes
+                           ov::Shape{10, 10},
+                           ov::Shape{15, 15},
+                           ov::Shape{10, 10},
+                           ov::Shape{10, 5}}},
+     1},
+    {ov::test::InputShape{// dynamic shape
+                          ov::PartialShape{-1, -1, 1, 1, 1},
+                          {// target static shapes
+                           ov::Shape{5, 5, 1, 1, 1},
+                           ov::Shape{10, 7, 1, 1, 1},
+                           ov::Shape{5, 5, 1, 1, 1}}},
+     1},
+    {ov::test::InputShape{// dynamic shape
+                          ov::PartialShape{{1, 10}, 10},
+                          {// target static shapes
+                           ov::Shape{10, 10},
+                           ov::Shape{5, 10}}},
+     1},
+};
+
+const auto OptimizedParams = testing::Combine(testing::Values(ElementType::f32, ElementType::bf16),
+                                              testing::ValuesIn(optimizedConfigsFP32),
+                                              testing::Values(ov::test::utils::DEVICE_CPU),
+                                              testing::ValuesIn(optimizedCPUSpec()),
+                                              testing::Values(CPUTestUtils::empty_plugin_config));
+
+INSTANTIATE_TEST_SUITE_P(smoke_SoftMax_Optimized_CPU,
+                         SoftMaxLayerCPUTest,
+                         OptimizedParams,
+                         SoftMaxLayerCPUTest::getTestCaseName);
+
+//TODO CVS-143812
+
+}  // namespace
+}  // namespace SoftMax
+}  // namespace test
+}  // namespace ov
diff --git a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/pad.cpp b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/pad.cpp
index f3b8a603f86e1b..7d35fe6b822cb2 100644
--- a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/pad.cpp
+++ b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/pad.cpp
@@ -2,6 +2,7 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
+#include "openvino/runtime/system_conf.hpp"
 
 #include "common_test_utils/ov_tensor_utils.hpp"
 #include "common_test_utils/test_enums.hpp"
@@ -23,7 +24,8 @@ using PadLayerCPUTestParamSet = std::tuple<
         std::vector<int64_t>,                           // padsEnd
         float,                                          // argPadValue
         ov::op::PadMode,                                // padMode
-        CPUSpecificParams
+        CPUSpecificParams,
+        ov::AnyMap                                      //  cpu device config
 >;
 
 class PadLayerCPUTest : public testing::WithParamInterface<PadLayerCPUTestParamSet>,
@@ -37,7 +39,8 @@ class PadLayerCPUTest : public testing::WithParamInterface<PadLayerCPUTestParamS
         ov::op::PadMode padMode;
         float argPadValue;
         CPUSpecificParams cpuParams;
-        std::tie(shapes, secondaryInputType, elementType, padsBegin, padsEnd, argPadValue, padMode, cpuParams) = obj.param;
+        ov::AnyMap additionalConfig;
+        std::tie(shapes, secondaryInputType, elementType, padsBegin, padsEnd, argPadValue, padMode, cpuParams, additionalConfig) = obj.param;
 
         std::ostringstream results;
         results << "IS=" << ov::test::utils::partialShape2str({shapes.first}) << "_";
@@ -54,6 +57,12 @@ class PadLayerCPUTest : public testing::WithParamInterface<PadLayerCPUTestParamS
         }
         results << "PadMode=" << padMode << "_";
         results << CPUTestsBase::getTestCaseName(cpuParams);
+        if (!additionalConfig.empty()) {
+            results << "_PluginConf";
+            for (auto& item : additionalConfig) {
+                results << "_" << item.first << "=" << item.second.as<std::string>();
+            }
+        }
 
         return results.str();
     }
@@ -87,10 +96,12 @@ class PadLayerCPUTest : public testing::WithParamInterface<PadLayerCPUTestParamS
         ov::op::PadMode padMode;
         ElementType dataType;
         CPUSpecificParams cpuParams;
-        std::tie(shapes, secondaryInputType, dataType, padsBegin, padsEnd, padValue, padMode, cpuParams) = this->GetParam();
-
+        ov::AnyMap additionalConfig;
+        std::tie(shapes, secondaryInputType, dataType, padsBegin, padsEnd, padValue, padMode, cpuParams, additionalConfig) = this->GetParam();
+        configuration.insert(additionalConfig.begin(), additionalConfig.end());
         std::tie(inFmts, outFmts, priority, selectedType) = cpuParams;
-        selectedType = makeSelectedTypeStr("ref", dataType);
+
+        selectedType = makeSelectedTypeStr("ref", deduce_expected_precision(dataType, configuration));
         targetDevice = ov::test::utils::DEVICE_CPU;
         init_input_shapes({shapes});
         for (auto& targetShapes : targetStaticShapes) {
@@ -159,6 +170,11 @@ const std::vector<ElementType> inputPrecisions = {
         ElementType::i8
 };
 
+const std::vector<ov::AnyMap> deviceConfig = {
+    CPUTestUtils::empty_plugin_config,
+    cpu_f16_plugin_config
+};
+
 const std::vector<ov::test::utils::InputLayerType> inputLayerTypes = {
         ov::test::utils::InputLayerType::CONSTANT,
         ov::test::utils::InputLayerType::PARAMETER
@@ -198,7 +214,7 @@ const std::vector<std::vector<int64_t>> padsEnd4D_Full   = {{0, 0, -2, 0}, {0, 0
 
 const std::vector<CPUSpecificParams> CPUParams4DBlocked = {
         cpuParams_nChw16c,
-        cpuParams_nChw8c,
+        cpuParams_nChw8c
 };
 
 INSTANTIATE_TEST_SUITE_P(
@@ -212,7 +228,8 @@ INSTANTIATE_TEST_SUITE_P(
                 ::testing::ValuesIn(padsEnd4DConstBlocked_Smoke),
                 ::testing::ValuesIn(argPadValue),
                 ::testing::Values(ov::op::PadMode::CONSTANT),
-                ::testing::ValuesIn(CPUParams4DBlocked)),
+                ::testing::ValuesIn(CPUParams4DBlocked),
+                ::testing::ValuesIn(deviceConfig)),
         PadLayerCPUTest::getTestCaseName
 );
 
@@ -227,7 +244,8 @@ INSTANTIATE_TEST_SUITE_P(
                 ::testing::ValuesIn(padsEnd4D_Smoke),
                 ::testing::ValuesIn(argPadValue),
                 ::testing::Values(ov::op::PadMode::CONSTANT),
-                ::testing::Values(cpuParams_nhwc)),
+                ::testing::Values(cpuParams_nhwc),
+                ::testing::ValuesIn(deviceConfig)),
         PadLayerCPUTest::getTestCaseName
 );
 
@@ -242,7 +260,8 @@ INSTANTIATE_TEST_SUITE_P(
                 ::testing::ValuesIn(padsEnd4DBlocked_Smoke),
                 ::testing::Values(0),
                 ::testing::ValuesIn(padMode),
-                ::testing::ValuesIn(CPUParams4DBlocked)),
+                ::testing::ValuesIn(CPUParams4DBlocked),
+                ::testing::ValuesIn(deviceConfig)),
         PadLayerCPUTest::getTestCaseName
 );
 
@@ -257,7 +276,8 @@ INSTANTIATE_TEST_SUITE_P(
                 ::testing::ValuesIn(padsEnd4DBlocked_Smoke),
                 ::testing::Values(0),
                 ::testing::ValuesIn(padMode),
-                ::testing::Values(cpuParams_nhwc)),
+                ::testing::Values(cpuParams_nhwc),
+                ::testing::ValuesIn(deviceConfig)),
         PadLayerCPUTest::getTestCaseName
 );
 
@@ -272,7 +292,8 @@ INSTANTIATE_TEST_SUITE_P(
                 ::testing::ValuesIn(padsEnd4DConstBlocked_Full),
                 ::testing::ValuesIn(argPadValue),
                 ::testing::Values(ov::op::PadMode::CONSTANT),
-                ::testing::ValuesIn(CPUParams4DBlocked)),
+                ::testing::ValuesIn(CPUParams4DBlocked),
+                ::testing::ValuesIn(deviceConfig)),
         PadLayerCPUTest::getTestCaseName
 );
 
@@ -287,7 +308,8 @@ INSTANTIATE_TEST_SUITE_P(
                 ::testing::ValuesIn(padsEnd4D_Full),
                 ::testing::ValuesIn(argPadValue),
                 ::testing::Values(ov::op::PadMode::CONSTANT),
-                ::testing::Values(cpuParams_nhwc)),
+                ::testing::Values(cpuParams_nhwc),
+                ::testing::ValuesIn(deviceConfig)),
         PadLayerCPUTest::getTestCaseName
 );
 
@@ -302,7 +324,8 @@ INSTANTIATE_TEST_SUITE_P(
                 ::testing::ValuesIn(padsEnd4DBlocked_Full),
                 ::testing::Values(0),
                 ::testing::ValuesIn(padMode),
-                ::testing::ValuesIn(CPUParams4DBlocked)),
+                ::testing::ValuesIn(CPUParams4DBlocked),
+                ::testing::ValuesIn(deviceConfig)),
         PadLayerCPUTest::getTestCaseName
 );
 
@@ -317,7 +340,8 @@ INSTANTIATE_TEST_SUITE_P(
                 ::testing::ValuesIn(padsEnd4DBlocked_Full),
                 ::testing::Values(0),
                 ::testing::ValuesIn(padMode),
-                ::testing::Values(cpuParams_nhwc)),
+                ::testing::Values(cpuParams_nhwc),
+                ::testing::ValuesIn(deviceConfig)),
         PadLayerCPUTest::getTestCaseName
 );
 
@@ -352,7 +376,8 @@ INSTANTIATE_TEST_SUITE_P(
                 ::testing::ValuesIn(padsEnd4D_Smoke),
                 ::testing::ValuesIn(argPadValue),
                 ::testing::Values(ov::op::PadMode::CONSTANT),
-                ::testing::ValuesIn(CPUParams4DDynamic)),
+                ::testing::ValuesIn(CPUParams4DDynamic),
+                ::testing::ValuesIn(deviceConfig)),
         PadLayerCPUTest::getTestCaseName
 );
 
@@ -367,7 +392,8 @@ INSTANTIATE_TEST_SUITE_P(
                 ::testing::ValuesIn(padsEnd4DConstBlocked_Smoke),
                 ::testing::ValuesIn(argPadValue),
                 ::testing::Values(ov::op::PadMode::CONSTANT),
-                ::testing::ValuesIn(CPUParams4DBlocked)),
+                ::testing::ValuesIn(CPUParams4DBlocked),
+                ::testing::ValuesIn(deviceConfig)),
         PadLayerCPUTest::getTestCaseName
 );
 
@@ -382,7 +408,8 @@ INSTANTIATE_TEST_SUITE_P(
                 ::testing::ValuesIn(padsEnd4D_Smoke),
                 ::testing::Values(0),
                 ::testing::ValuesIn(padMode),
-                ::testing::ValuesIn(CPUParams4DDynamic)),
+                ::testing::ValuesIn(CPUParams4DDynamic),
+                ::testing::ValuesIn(deviceConfig)),
         PadLayerCPUTest::getTestCaseName
 );
 
@@ -397,7 +424,8 @@ INSTANTIATE_TEST_SUITE_P(
                 ::testing::ValuesIn(padsEnd4DBlocked_Smoke),
                 ::testing::Values(0),
                 ::testing::ValuesIn(padMode),
-                ::testing::ValuesIn(CPUParams4DBlocked)),
+                ::testing::ValuesIn(CPUParams4DBlocked),
+                ::testing::ValuesIn(deviceConfig)),
         PadLayerCPUTest::getTestCaseName
 );
 
@@ -412,7 +440,8 @@ INSTANTIATE_TEST_SUITE_P(
                 ::testing::ValuesIn(padsEnd4D_Full),
                 ::testing::ValuesIn(argPadValue),
                 ::testing::Values(ov::op::PadMode::CONSTANT),
-                ::testing::ValuesIn(CPUParams4DDynamic)),
+                ::testing::ValuesIn(CPUParams4DDynamic),
+                ::testing::ValuesIn(deviceConfig)),
         PadLayerCPUTest::getTestCaseName
 );
 
@@ -427,7 +456,8 @@ INSTANTIATE_TEST_SUITE_P(
                 ::testing::ValuesIn(padsEnd4DConstBlocked_Full),
                 ::testing::ValuesIn(argPadValue),
                 ::testing::Values(ov::op::PadMode::CONSTANT),
-                ::testing::ValuesIn(CPUParams4DBlocked)),
+                ::testing::ValuesIn(CPUParams4DBlocked),
+                ::testing::ValuesIn(deviceConfig)),
         PadLayerCPUTest::getTestCaseName
 );
 
@@ -442,7 +472,8 @@ INSTANTIATE_TEST_SUITE_P(
                 ::testing::ValuesIn(padsEnd4D_Full),
                 ::testing::Values(0),
                 ::testing::ValuesIn(padMode),
-                ::testing::ValuesIn(CPUParams4DDynamic)),
+                ::testing::ValuesIn(CPUParams4DDynamic),
+                ::testing::ValuesIn(deviceConfig)),
         PadLayerCPUTest::getTestCaseName
 );
 
@@ -457,7 +488,8 @@ INSTANTIATE_TEST_SUITE_P(
                 ::testing::ValuesIn(padsEnd4DBlocked_Full),
                 ::testing::Values(0),
                 ::testing::ValuesIn(padMode),
-                ::testing::ValuesIn(CPUParams4DBlocked)),
+                ::testing::ValuesIn(CPUParams4DBlocked),
+                ::testing::ValuesIn(deviceConfig)),
         PadLayerCPUTest::getTestCaseName
 );
 
@@ -499,7 +531,8 @@ INSTANTIATE_TEST_SUITE_P(
                 ::testing::ValuesIn(padsEnd5DConstBlocked_Smoke),
                 ::testing::ValuesIn(argPadValue),
                 ::testing::Values(ov::op::PadMode::CONSTANT),
-                ::testing::ValuesIn(CPUParams5DBlocked)),
+                ::testing::ValuesIn(CPUParams5DBlocked),
+                ::testing::ValuesIn(deviceConfig)),
         PadLayerCPUTest::getTestCaseName
 );
 
@@ -514,7 +547,8 @@ INSTANTIATE_TEST_SUITE_P(
                 ::testing::ValuesIn(padsEnd5D_Smoke),
                 ::testing::ValuesIn(argPadValue),
                 ::testing::Values(ov::op::PadMode::CONSTANT),
-                ::testing::Values(cpuParams_ndhwc)),
+                ::testing::Values(cpuParams_ndhwc),
+                ::testing::ValuesIn(deviceConfig)),
         PadLayerCPUTest::getTestCaseName
 );
 
@@ -529,7 +563,8 @@ INSTANTIATE_TEST_SUITE_P(
                 ::testing::ValuesIn(padsEnd5DBlocked_Smoke),
                 ::testing::Values(0),
                 ::testing::ValuesIn(padMode),
-                ::testing::ValuesIn(CPUParams5DBlocked)),
+                ::testing::ValuesIn(CPUParams5DBlocked),
+                ::testing::ValuesIn(deviceConfig)),
         PadLayerCPUTest::getTestCaseName
 );
 
@@ -544,7 +579,8 @@ INSTANTIATE_TEST_SUITE_P(
                 ::testing::ValuesIn(padsEnd5D_Smoke),
                 ::testing::Values(0),
                 ::testing::ValuesIn(padMode),
-                ::testing::Values(cpuParams_ndhwc)),
+                ::testing::Values(cpuParams_ndhwc),
+                ::testing::ValuesIn(deviceConfig)),
         PadLayerCPUTest::getTestCaseName
 );
 
@@ -559,7 +595,8 @@ INSTANTIATE_TEST_SUITE_P(
                 ::testing::ValuesIn(padsEnd5DConstBlocked_Full),
                 ::testing::ValuesIn(argPadValue),
                 ::testing::Values(ov::op::PadMode::CONSTANT),
-                ::testing::ValuesIn(CPUParams5DBlocked)),
+                ::testing::ValuesIn(CPUParams5DBlocked),
+                ::testing::ValuesIn(deviceConfig)),
         PadLayerCPUTest::getTestCaseName
 );
 
@@ -574,7 +611,8 @@ INSTANTIATE_TEST_SUITE_P(
                 ::testing::ValuesIn(padsEnd5D_Full),
                 ::testing::ValuesIn(argPadValue),
                 ::testing::Values(ov::op::PadMode::CONSTANT),
-                ::testing::Values(cpuParams_ndhwc)),
+                ::testing::Values(cpuParams_ndhwc),
+                ::testing::ValuesIn(deviceConfig)),
         PadLayerCPUTest::getTestCaseName
 );
 
@@ -589,7 +627,8 @@ INSTANTIATE_TEST_SUITE_P(
                 ::testing::ValuesIn(padsEnd5DBlocked_Full),
                 ::testing::Values(0),
                 ::testing::ValuesIn(padMode),
-                ::testing::ValuesIn(CPUParams5DBlocked)),
+                ::testing::ValuesIn(CPUParams5DBlocked),
+                ::testing::ValuesIn(deviceConfig)),
         PadLayerCPUTest::getTestCaseName
 );
 
@@ -604,7 +643,8 @@ INSTANTIATE_TEST_SUITE_P(
                 ::testing::ValuesIn(padsEnd5D_Full),
                 ::testing::Values(0),
                 ::testing::ValuesIn(padMode),
-                ::testing::Values(cpuParams_ndhwc)),
+                ::testing::Values(cpuParams_ndhwc),
+                ::testing::ValuesIn(deviceConfig)),
         PadLayerCPUTest::getTestCaseName
 );
 
@@ -639,7 +679,8 @@ INSTANTIATE_TEST_SUITE_P(
                 ::testing::ValuesIn(padsEnd5D_Smoke),
                 ::testing::ValuesIn(argPadValue),
                 ::testing::Values(ov::op::PadMode::CONSTANT),
-                ::testing::ValuesIn(CPUParams5DDynamic)),
+                ::testing::ValuesIn(CPUParams5DDynamic),
+                ::testing::ValuesIn(deviceConfig)),
         PadLayerCPUTest::getTestCaseName
 );
 
@@ -654,7 +695,8 @@ INSTANTIATE_TEST_SUITE_P(
                 ::testing::ValuesIn(padsEnd5DConstBlocked_Smoke),
                 ::testing::ValuesIn(argPadValue),
                 ::testing::Values(ov::op::PadMode::CONSTANT),
-                ::testing::ValuesIn(CPUParams5DBlocked)),
+                ::testing::ValuesIn(CPUParams5DBlocked),
+                ::testing::ValuesIn(deviceConfig)),
         PadLayerCPUTest::getTestCaseName
 );
 
@@ -669,7 +711,8 @@ INSTANTIATE_TEST_SUITE_P(
                 ::testing::ValuesIn(padsEnd5D_Smoke),
                 ::testing::Values(0),
                 ::testing::ValuesIn(padMode),
-                ::testing::ValuesIn(CPUParams5DDynamic)),
+                ::testing::ValuesIn(CPUParams5DDynamic),
+                ::testing::ValuesIn(deviceConfig)),
         PadLayerCPUTest::getTestCaseName
 );
 
@@ -684,7 +727,8 @@ INSTANTIATE_TEST_SUITE_P(
                 ::testing::ValuesIn(padsEnd5DBlocked_Smoke),
                 ::testing::Values(0),
                 ::testing::ValuesIn(padMode),
-                ::testing::ValuesIn(CPUParams5DBlocked)),
+                ::testing::ValuesIn(CPUParams5DBlocked),
+                ::testing::ValuesIn(deviceConfig)),
         PadLayerCPUTest::getTestCaseName
 );
 
@@ -699,7 +743,8 @@ INSTANTIATE_TEST_SUITE_P(
                 ::testing::ValuesIn(padsEnd5D_Full),
                 ::testing::ValuesIn(argPadValue),
                 ::testing::Values(ov::op::PadMode::CONSTANT),
-                ::testing::ValuesIn(CPUParams5DDynamic)),
+                ::testing::ValuesIn(CPUParams5DDynamic),
+                ::testing::ValuesIn(deviceConfig)),
         PadLayerCPUTest::getTestCaseName
 );
 
@@ -714,7 +759,8 @@ INSTANTIATE_TEST_SUITE_P(
                 ::testing::ValuesIn(padsEnd5DConstBlocked_Full),
                 ::testing::ValuesIn(argPadValue),
                 ::testing::Values(ov::op::PadMode::CONSTANT),
-                ::testing::ValuesIn(CPUParams5DBlocked)),
+                ::testing::ValuesIn(CPUParams5DBlocked),
+                ::testing::ValuesIn(deviceConfig)),
         PadLayerCPUTest::getTestCaseName
 );
 
@@ -729,7 +775,8 @@ INSTANTIATE_TEST_SUITE_P(
                 ::testing::ValuesIn(padsEnd5D_Full),
                 ::testing::Values(0),
                 ::testing::ValuesIn(padMode),
-                ::testing::ValuesIn(CPUParams5DDynamic)),
+                ::testing::ValuesIn(CPUParams5DDynamic),
+                ::testing::ValuesIn(deviceConfig)),
         PadLayerCPUTest::getTestCaseName
 );
 
@@ -744,7 +791,8 @@ INSTANTIATE_TEST_SUITE_P(
                 ::testing::ValuesIn(padsEnd5DBlocked_Full),
                 ::testing::Values(0),
                 ::testing::ValuesIn(padMode),
-                ::testing::ValuesIn(CPUParams5DBlocked)),
+                ::testing::ValuesIn(CPUParams5DBlocked),
+                ::testing::ValuesIn(deviceConfig)),
         PadLayerCPUTest::getTestCaseName
 );
 
diff --git a/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/classes/undefined_et.cpp b/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/classes/undefined_et.cpp
index 40b3277c8708b3..f232a2d641eb55 100644
--- a/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/classes/undefined_et.cpp
+++ b/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/classes/undefined_et.cpp
@@ -26,6 +26,7 @@
 //       -----------
 
 #include "custom/subgraph_tests/include/undefined_et.hpp"
+#include "utils/precision_support.h"
 
 namespace ov {
 namespace test {
@@ -74,6 +75,12 @@ void UndefinedEtSubgraphTest::SetUp() {
     auto logical_not = std::make_shared<op::v1::LogicalNot>(cvt_f32);
 
     function = std::make_shared<ov::Model>(OutputVector{logical_not->output(0)}, ParameterVector{param_0, param_1, param_2}, "UndefinedET");
+
+    // TODO: Need to remove when the hardware checking for f16 will be eliminated in the Transformations pipeline.
+    if (m_data_et == element::f16 && !ov::intel_cpu::hasHardwareSupport(m_data_et)) {
+        abs_threshold = 1.f;
+        rel_threshold = 0.1f;
+    }
 }
 
 template<typename TD, typename TS>
@@ -146,6 +153,10 @@ TEST_P(UndefinedEtSubgraphTest, CompareWithRefs) {
 
     size_t rnd_unfm_counter = 0lu;
     size_t logical_not_counter = 0lu;
+    auto expected_dt = m_data_et;
+    if (!ov::intel_cpu::hasHardwareSupport(expected_dt)) {
+        expected_dt = element::f32;
+    }
     for (const auto& node : compiledModel.get_runtime_model()->get_ops()) {
         auto rt_info = node->get_rt_info();
         auto it = rt_info.find(exec_model_info::LAYER_TYPE);
@@ -153,7 +164,7 @@ TEST_P(UndefinedEtSubgraphTest, CompareWithRefs) {
         auto op_name = it->second.as<std::string>();
 
         if (op_name == "RandomUniform") {
-            ASSERT_EQ(node->get_output_element_type(0), m_data_et);
+            ASSERT_EQ(node->get_output_element_type(0), expected_dt);
             rnd_unfm_counter++;
         }
         if (op_name == "Eltwise") {
diff --git a/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/concat_multiple_query_sdp.cpp b/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/common/concat_multiple_query_sdp.cpp
similarity index 100%
rename from src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/concat_multiple_query_sdp.cpp
rename to src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/common/concat_multiple_query_sdp.cpp
diff --git a/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/concat_transpose_sdp_transpose.cpp b/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/common/concat_transpose_sdp_transpose.cpp
similarity index 100%
rename from src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/concat_transpose_sdp_transpose.cpp
rename to src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/common/concat_transpose_sdp_transpose.cpp
diff --git a/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/sdpa_group_beam_search.cpp b/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/common/sdpa_group_beam_search.cpp
similarity index 100%
rename from src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/sdpa_group_beam_search.cpp
rename to src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/common/sdpa_group_beam_search.cpp
diff --git a/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/concat_const_inplace.cpp b/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/concat_const_inplace.cpp
index ff3f0bad3f302a..1ae0d661746987 100644
--- a/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/concat_const_inplace.cpp
+++ b/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/concat_const_inplace.cpp
@@ -2,6 +2,7 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
+#include "openvino/runtime/system_conf.hpp"
 #include "shared_test_classes/base/ov_subgraph.hpp"
 #include "utils/cpu_test_utils.hpp"
 #include "common_test_utils/ov_tensor_utils.hpp"
@@ -37,8 +38,13 @@ class ConcatConstantInPlaceTest : public testing::WithParamInterface<ov::element
 
     void SetUp() override {
         targetDevice = ov::test::utils::DEVICE_CPU;
-        if (ov::element::bf16 == (inType = outType = this->GetParam()))
-            configuration.insert({ov::hint::inference_precision.name(), ov::element::bf16});
+        if (ov::element::bf16 == (inType = outType = this->GetParam())) {
+            configuration.insert({ov::hint::inference_precision(ov::element::bf16)});
+        } else if (ov::element::f16 == (inType = outType = this->GetParam())) {
+            configuration.insert({ov::hint::inference_precision(ov::element::f16)});
+        } else {
+            configuration.insert({ov::hint::inference_precision(ov::element::f32)});
+        }
 
         const ov::Shape inputShape = {1, 3, 3, 11};
         ov::ParameterVector inputParams{std::make_shared<ov::op::v0::Parameter>(ov::element::f32, inputShape)};
@@ -79,11 +85,11 @@ TEST_P(ConcatConstantInPlaceTest, smoke_ConcatConstantInPlaceTest_CPU) {
         CheckNumberOfNodesWithType(compiledModel, "Reorder", 2);
 }
 
+
 INSTANTIATE_TEST_SUITE_P(smoke_ConcatConstantInPlaceTest_CPU,
                          ConcatConstantInPlaceTest,
-                         testing::Values(ov::element::f32, ov::element::bf16),
+                         testing::Values(ov::element::f32, ov::element::bf16, ov::element::f16),
                          ConcatConstantInPlaceTest::getTestCaseName);
-
 }  // namespace
 }  // namespace test
 }  // namespace ov
diff --git a/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/conv_sum_broadcast.cpp b/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/conv_sum_broadcast.cpp
index 4ac54dcd19b009..002ca921c2afcf 100644
--- a/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/conv_sum_broadcast.cpp
+++ b/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/conv_sum_broadcast.cpp
@@ -118,8 +118,12 @@ class ConvSumInPlaceTest : public testing::WithParamInterface<convSumBroadcastPa
 
         runtimeType = getNetType();
         auto it = configuration.find(ov::hint::inference_precision.name());
-        if (it != configuration.end() && it->second.as<ov::element::Type>() == ov::element::bf16) {
+        ov::element::Type inference_precision = (it != configuration.end()) ?
+                                                it->second.as<ov::element::Type>() : ov::element::undefined;
+        if (inference_precision == ov::element::bf16) {
             runtimeType = ov::element::Type_t::bf16;
+        } else if (inference_precision == ov::element::f16) {
+            runtimeType = ov::element::Type_t::f16;
         }
 
         if (inputParams.front()->get_element_type() == ov::element::i8 || inputParams.front()->get_element_type() == ov::element::u8) {
@@ -138,12 +142,25 @@ class ConvSumInPlaceTest : public testing::WithParamInterface<convSumBroadcastPa
 
 protected:
     bool primTypeCheck(std::string primType) const override {
-        auto isaType = getISA(runtimeType == ov::element::Type_t::f32);
+        bool skip_amx = false;
+        switch (runtimeType) {
+            case ov::element::Type_t::f32:
+                skip_amx = true;
+                break;
+            case ov::element::Type_t::f16:
+                skip_amx = ov::with_cpu_x86_avx512_core_amx_fp16() ? false : true;
+                break;
+            case ov::element::Type_t::bf16:
+                skip_amx = false;
+                break;
+            default:
+                skip_amx = false;
+        }
+        auto isaType = getISA(skip_amx);
         const std::regex jit_case_regex(makeSelectedTypeStr(std::string("jit_") + isaType, runtimeType),
                                         std::regex_constants::icase);
         const std::regex brgconv_case_regex(makeSelectedTypeStr(std::string("brgconv_") + isaType, runtimeType),
                                             std::regex_constants::icase);
-
         if (isaType == "")
             return primType == "ref";
         else
@@ -415,6 +432,8 @@ const std::vector<fusingSpecificParams> fusingParamsSetBF16{
         fusingReluScaleShift
 };
 
+const std::vector<fusingSpecificParams> fusingParamsSetFP16 = fusingParamsSetBF16;
+
 InputShape convInpShape = {
         //dynamic shapes
         {-1, 32, -1, -1},
@@ -465,6 +484,16 @@ INSTANTIATE_TEST_SUITE_P(smoke_Conv_Sum_Broadcast_BF16,
                                             ::testing::Values(cpu_bf16_plugin_config)),
                          ConvSumInPlaceTest::getTestCaseName);
 
+INSTANTIATE_TEST_SUITE_P(smoke_Conv_Sum_Broadcast_FP16,
+                         ConvSumInPlaceTest,
+                         ::testing::Combine(
+                                 ::testing::Values(convInpShape),
+                                 ::testing::ValuesIn(secondInp),
+                                 ::testing::Values(true, false),
+                                 ::testing::ValuesIn(fusingParamsSetFP16),
+                                 ::testing::Values(cpu_f16_plugin_config)),
+                         ConvSumInPlaceTest::getTestCaseName);
+
 INSTANTIATE_TEST_SUITE_P(smoke_Conv_Sum_Broadcast_INT8, ConvSumInPlaceTestInt8,
                          ::testing::Combine(
                                  ::testing::Values(convInpShape),
diff --git a/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/fullyconnected_strided_inputs_outputs.cpp b/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/fullyconnected_strided_inputs_outputs.cpp
index b0a5855fd4de5c..1a81e6019fc6c1 100644
--- a/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/fullyconnected_strided_inputs_outputs.cpp
+++ b/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/fullyconnected_strided_inputs_outputs.cpp
@@ -102,6 +102,16 @@ TEST_P(FullyConnectedStridedInputsOutputsTest, CompareWithRefs) {
     run();
 }
 
+using FullyConnectedStridedInputsOutputsTest_FP16 = FullyConnectedStridedInputsOutputsTest;
+TEST_P(FullyConnectedStridedInputsOutputsTest_FP16, CompareWithRefs) {
+    if (!(ov::with_cpu_x86_avx512_core_fp16())) {
+        GTEST_SKIP() << "Skipping test, platform don't support precision f16";
+    }
+    configuration.insert({ov::hint::inference_precision.name(), ov::element::f16});
+
+    run();
+}
+
 namespace {
 
 INSTANTIATE_TEST_SUITE_P(smoke_Check,
@@ -110,6 +120,12 @@ INSTANTIATE_TEST_SUITE_P(smoke_Check,
                                             ::testing::Values(2, 3)),
                          FullyConnectedStridedInputsOutputsTest::getTestCaseName);
 
+INSTANTIATE_TEST_SUITE_P(smoke_Check,
+                         FullyConnectedStridedInputsOutputsTest_FP16,
+                         ::testing::Combine(::testing::Values(ov::element::f32),
+                                            ::testing::Values(2, 3)),
+                         FullyConnectedStridedInputsOutputsTest::getTestCaseName);
+
 }  // namespace
 
 }  // namespace test
diff --git a/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/index_add_scatter_elements_update.cpp b/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/index_add_scatter_elements_update.cpp
index 4aa422fa263b18..0b8086b48a1110 100644
--- a/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/index_add_scatter_elements_update.cpp
+++ b/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/index_add_scatter_elements_update.cpp
@@ -98,7 +98,6 @@ class IndexAddTest : public testing::WithParamInterface<IndexAddTestParams>,
 
         if (ov::element::bf16 == data_type || ov::element::f16 == data_type) {
             configuration.insert({ov::hint::inference_precision.name(), data_type});
-            inType = outType = data_type;
             abs_threshold = 0.01f;
             rel_threshold = 0.01f;
         }
@@ -209,13 +208,13 @@ class IndexAddTest : public testing::WithParamInterface<IndexAddTestParams>,
                 // All index values are expected to be within bounds [-d, d - 1] along dimension d pointed by axis.
                 auto d = dataShape[normalized_axis];
                 in_data.start_from = -1.0 * static_cast<int64_t>(d);
-                in_data.range = d-1;
+                in_data.range = static_cast<uint32_t>(d-1 - in_data.start_from);
                 in_data.resolution = 1;
                 tensor = shape_size(indicesShape) == 0 ? ov::Tensor(funcInput.get_element_type(), indicesShape) :
                                             ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), indicesShape, in_data);
             } else if (i == 2) {  // "updates"
                 in_data.start_from = -50;
-                in_data.range = 50;
+                in_data.range = 100;
                 in_data.resolution = 1;
                 tensor = shape_size(updateShape) == 0 ? ov::Tensor(funcInput.get_element_type(), updateShape) :
                             ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), updateShape, in_data);
diff --git a/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/inplace_edge.cpp b/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/inplace_edge.cpp
index 8f7f6780048827..feae1769e0e439 100644
--- a/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/inplace_edge.cpp
+++ b/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/inplace_edge.cpp
@@ -37,7 +37,7 @@ class NonInputInPlaceTest : public testing::WithParamInterface<ElementType>, vir
 
     void SetUp() override {
         targetDevice = utils::DEVICE_CPU;
-        configuration.insert({ov::hint::inference_precision.name(), ov::element::f16.to_string()});
+        configuration.insert({ov::hint::inference_precision.name(), ov::element::f16});
         const ov::Shape inputShape = {1, 11, 3, 3};
         targetStaticShapes = {{inputShape, inputShape}};
         ElementType prc = this->GetParam();
diff --git a/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/input_noreorder_eltwise_bf16.cpp b/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/input_noreorder_eltwise_bf16.cpp
index 9cfa14baa8a277..a947c765b009bb 100644
--- a/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/input_noreorder_eltwise_bf16.cpp
+++ b/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/input_noreorder_eltwise_bf16.cpp
@@ -14,12 +14,15 @@ namespace test {
 
 class InputNoReorderEltwiseBF16 : virtual public SubgraphBaseStaticTest, public CPUTestsBase {
 protected:
-    void SetUp() override {
-        auto netPrecision = inType = ov::element::f32;
+    virtual void set_output_type_and_config() {
         outType = ov::element::bf16;
-        targetDevice = ov::test::utils::DEVICE_CPU;
         ov::AnyMap additional_config{ov::hint::inference_precision(ov::element::bf16)};
         configuration.insert(additional_config.begin(), additional_config.end());
+    }
+    void SetUp() override {
+        auto netPrecision = inType = ov::element::f32;
+        set_output_type_and_config();
+        targetDevice = ov::test::utils::DEVICE_CPU;
 
         ov::Shape inputShape{2, 4, 4, 1};
         auto eltwiseType = ov::test::utils::EltwiseTypes::ADD;
@@ -56,5 +59,27 @@ TEST_F(InputNoReorderEltwiseBF16, smoke_CompareWithRefs) {
     CheckNumberOfNodesWithType(compiledModel, "Convert", 0);
     CheckNumberOfNodesWithTypes(compiledModel, {"Eltwise", "Subgraph"}, 1);
 }
+
+class InputNoReorderEltwiseFP16 : public InputNoReorderEltwiseBF16 {
+protected:
+    void set_output_type_and_config() override {
+        outType = ov::element::f16;
+        ov::AnyMap additional_config{ov::hint::inference_precision(ov::element::f16)};
+        configuration.insert(additional_config.begin(), additional_config.end());
+    }
+};
+
+TEST_F(InputNoReorderEltwiseFP16, smoke_CompareWithRefs) {
+    if (!(ov::with_cpu_x86_avx512_core_fp16() || ov::with_cpu_x86_avx512_core_amx_fp16())) {
+        GTEST_SKIP() << "Skipping test, platform don't support precision f16";
+    }
+
+    run();
+
+    CheckNumberOfNodesWithType(compiledModel, "Reorder", 0);
+    CheckNumberOfNodesWithType(compiledModel, "Convert", 0);
+    CheckNumberOfNodesWithTypes(compiledModel, {"Eltwise", "Subgraph"}, 1);
+}
+
 }  // namespace test
 }  // namespace ov
diff --git a/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/interaction.cpp b/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/interaction.cpp
index b670b565a3903a..1be7035c08285f 100644
--- a/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/interaction.cpp
+++ b/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/interaction.cpp
@@ -171,8 +171,20 @@ TEST_P(IntertactionCPUTest, CompareWithRefs) {
     CheckNumberOfNodesWithType(compiledModel, "Interaction", 1);
 }
 
+using IntertactionCPUTest_FP16 = IntertactionCPUTest;
+TEST_P(IntertactionCPUTest_FP16, CompareWithRefs) {
+    if (!(ov::with_cpu_x86_avx512_core_fp16())) {
+        GTEST_SKIP() << "Skipping test, platform don't support precision f16";
+    }
+    configuration.insert({ov::hint::inference_precision.name(), ov::element::f16});
+
+    run();
+    CheckNumberOfNodesWithType(compiledModel, "Interaction", 1);
+}
+
 namespace {
 const std::vector<ElementType> inPrecisions = {ElementType::f32, ElementType::bf16, ElementType::i32, ElementType::i8};
+const std::vector<ElementType> inPrecisions_FP16 = {ElementType::f32, ElementType::i32};
 // the model has 27 inputs with same shape
 const std::vector<InputShape> input_shapes = {
     // temporarily disable dynamic shape for performance issue
@@ -194,4 +206,8 @@ INSTANTIATE_TEST_SUITE_P(smoke_Interaction,
                          IntertactionCPUTest,
                          ::testing::Combine(::testing::ValuesIn(inPrecisions), ::testing::ValuesIn(input_shapes)),
                          IntertactionCPUTest::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_Interaction,
+                         IntertactionCPUTest_FP16,
+                         ::testing::Combine(::testing::ValuesIn(inPrecisions_FP16), ::testing::ValuesIn(input_shapes)),
+                         IntertactionCPUTest::getTestCaseName);
 }  // namespace
diff --git a/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/matmul_strided_inputs_outputs.cpp b/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/matmul_strided_inputs_outputs.cpp
index d30c4378921553..7ce57dbfe28ee6 100644
--- a/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/matmul_strided_inputs_outputs.cpp
+++ b/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/matmul_strided_inputs_outputs.cpp
@@ -89,6 +89,16 @@ TEST_P(MatmulStridedInputsOutputsTest, CompareWithRefs) {
     run();
 }
 
+using MatmulStridedInputsOutputsTest_FP16 = MatmulStridedInputsOutputsTest;
+TEST_P(MatmulStridedInputsOutputsTest_FP16, CompareWithRefs) {
+    if (!(ov::with_cpu_x86_avx512_core_fp16())) {
+        GTEST_SKIP() << "Skipping test, platform don't support precision f16";
+    }
+    configuration.insert({ov::hint::inference_precision.name(), ov::element::f16});
+
+    run();
+}
+
 namespace {
 
 INSTANTIATE_TEST_SUITE_P(smoke_Check,
@@ -96,6 +106,11 @@ INSTANTIATE_TEST_SUITE_P(smoke_Check,
                          ::testing::Values(ov::element::f32, ov::element::bf16),
                          MatmulStridedInputsOutputsTest::getTestCaseName);
 
+INSTANTIATE_TEST_SUITE_P(smoke_Check,
+                         MatmulStridedInputsOutputsTest_FP16,
+                         ::testing::Values(ov::element::f32),
+                         MatmulStridedInputsOutputsTest::getTestCaseName);
+
 }  // namespace
 
 }  // namespace test
diff --git a/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/matmul_weights_decompression.cpp b/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/matmul_weights_decompression.cpp
index 29196b5bdb95b5..0059d8a983d725 100644
--- a/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/matmul_weights_decompression.cpp
+++ b/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/matmul_weights_decompression.cpp
@@ -199,13 +199,13 @@ TEST_P(MatmulWeightsDecompression, CompareWithRefs) {
 namespace {
 
 std::vector<ov::AnyMap> filter_additional_config_basic() {
-    std::vector<ov::AnyMap> additional_config = {CPUTestUtils::empty_plugin_config};
+    std::vector<ov::AnyMap> additional_config = {{ov::hint::dynamic_quantization_group_size(0)}};
     return additional_config;
 }
 std::vector<ov::AnyMap> filter_additional_config_amx() {
     std::vector<ov::AnyMap> additional_config = {};
     if (ov::with_cpu_x86_avx512_core_amx())
-        additional_config.push_back({{ov::hint::inference_precision(ov::element::bf16)}});
+        additional_config.push_back({{ov::hint::dynamic_quantization_group_size(0), ov::hint::inference_precision(ov::element::bf16)}});
     return additional_config;
 }
 
diff --git a/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/remove_convert.cpp b/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/remove_convert.cpp
index 1f62e678802328..b2d57bc64b138f 100644
--- a/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/remove_convert.cpp
+++ b/src/plugins/intel_cpu/tests/functional/custom/subgraph_tests/src/remove_convert.cpp
@@ -12,7 +12,7 @@ using namespace CPUTestUtils;
 
 namespace ov {
 namespace test {
-using RemoveConvertCPUTestParams = std::tuple<ElementType, InputShape>;
+using RemoveConvertCPUTestParams = std::tuple<ElementType, InputShape, ov::AnyMap>;
 
 class RemoveUselessBF16ConvertCPUTest : public testing::WithParamInterface<RemoveConvertCPUTestParams>,
                                         virtual public SubgraphBaseTest,
@@ -21,21 +21,27 @@ class RemoveUselessBF16ConvertCPUTest : public testing::WithParamInterface<Remov
     static std::string getTestCaseName(const testing::TestParamInfo<RemoveConvertCPUTestParams>& obj) {
         ElementType inType;
         InputShape inputShape;
-        std::tie(inType, inputShape) = obj.param;
+        ov::AnyMap additionalConfig;
+        std::tie(inType, inputShape, additionalConfig) = obj.param;
         std::ostringstream result;
         result << "IS=" << inputShape << "_";
         result << "Prc=" << inType;
+        if (!additionalConfig.empty()) {
+            result << "_PluginConf";
+            for (auto& item : additionalConfig) {
+                result << "_" << item.first << "=" << item.second.as<std::string>();
+            }
+        }
         return result.str();
     }
 
     void SetUp() override {
         ElementType inType;
         InputShape inputShape;
-        std::tie(inType, inputShape) = this->GetParam();
+        ov::AnyMap additionalConfig;
+        std::tie(inType, inputShape, additionalConfig) = this->GetParam();
+        configuration.insert(additionalConfig.begin(), additionalConfig.end());
         targetDevice = ov::test::utils::DEVICE_CPU;
-        if (inType == ElementType::bf16) {
-            configuration.insert({ov::hint::inference_precision(ov::element::bf16)});
-        }
         std::tie(inFmts, outFmts, priority, selectedType) =
             CPUSpecificParams{{}, {}, {}, makeSelectedTypeStr("ref", inType)};
         init_input_shapes({inputShape});
@@ -65,17 +71,25 @@ class RemoveUselessConvertCPUTest : public testing::WithParamInterface<RemoveCon
     static std::string getTestCaseName(const testing::TestParamInfo<RemoveConvertCPUTestParams>& obj) {
         ElementType inType;
         InputShape inputShape;
-        std::tie(inType, inputShape) = obj.param;
+        ov::AnyMap additionalConfig;
+        std::tie(inType, inputShape, additionalConfig) = obj.param;
         std::ostringstream result;
         result << "IS=" << inputShape << "_";
         result << "Prc=" << inType;
+        if (!additionalConfig.empty()) {
+            result << "_PluginConf";
+            for (auto& item : additionalConfig) {
+                result << "_" << item.first << "=" << item.second.as<std::string>();
+            }
+        }
         return result.str();
     }
 
     void SetUp() override {
         ElementType inType;
         InputShape inputShape;
-        std::tie(inType, inputShape) = this->GetParam();
+        ov::AnyMap additionalConfig;
+        std::tie(inType, inputShape, additionalConfig) = this->GetParam();
         targetDevice = ov::test::utils::DEVICE_CPU;
 
         init_input_shapes({inputShape});
@@ -110,6 +124,15 @@ TEST_P(RemoveUselessConvertCPUTest, CompareWithRefs) {
     CheckNumberOfNodesWithType(compiledModel, "Convert", 0);
 }
 
+using RemoveUselessFP16ConvertCPUTest = RemoveUselessBF16ConvertCPUTest;
+TEST_P(RemoveUselessFP16ConvertCPUTest, CompareWithRefs) {
+    auto implType = deduce_expected_precision(ov::element::f16, configuration);
+    selectedType = makeSelectedTypeStr("ref", implType);
+    run();
+    CheckNumberOfNodesWithTypes(compiledModel, {"Convert", "Subgraph"}, 0);
+    CheckPluginRelatedResults(compiledModel, "StridedSlice");
+}
+
 namespace {
 const std::vector<InputShape> inputShapes = {
     // dynamic batch
@@ -121,12 +144,20 @@ const std::vector<InputShape> inputShapes = {
 
 INSTANTIATE_TEST_SUITE_P(smoke_RemoveConvert,
                          RemoveUselessBF16ConvertCPUTest,
-                         ::testing::Combine(::testing::Values(ElementType::bf16), ::testing::ValuesIn(inputShapes)),
+                         ::testing::Combine(::testing::Values(ElementType::bf16), ::testing::ValuesIn(inputShapes),
+                             ::testing::Values(cpu_bf16_plugin_config)),
                          RemoveUselessBF16ConvertCPUTest::getTestCaseName);
 
+INSTANTIATE_TEST_SUITE_P(smoke_RemoveConvert,
+                         RemoveUselessFP16ConvertCPUTest,
+                         ::testing::Combine(::testing::Values(ElementType::f16), ::testing::ValuesIn(inputShapes),
+                             ::testing::Values(cpu_f16_plugin_config)),
+                         RemoveUselessFP16ConvertCPUTest::getTestCaseName);
+
 INSTANTIATE_TEST_SUITE_P(smoke_RemoveConvert,
                          RemoveUselessConvertCPUTest,
-                         ::testing::Combine(::testing::Values(ElementType::f32), ::testing::Values(inputShapes[0])),
+                         ::testing::Combine(::testing::Values(ElementType::f32), ::testing::Values(inputShapes[0]),
+                             ::testing::Values(empty_plugin_config)),
                          RemoveUselessConvertCPUTest::getTestCaseName);
 }  // namespace
 }  // namespace test
diff --git a/src/plugins/intel_cpu/tests/functional/shared_tests_instances/single_layer_tests/activation.cpp b/src/plugins/intel_cpu/tests/functional/shared_tests_instances/single_layer_tests/activation.cpp
index 83b648779bbb49..5b6f0c276799b1 100644
--- a/src/plugins/intel_cpu/tests/functional/shared_tests_instances/single_layer_tests/activation.cpp
+++ b/src/plugins/intel_cpu/tests/functional/shared_tests_instances/single_layer_tests/activation.cpp
@@ -55,7 +55,8 @@ const std::map<ActivationTypes, std::vector<std::vector<float>>> activationTypes
         {ActivationTypes::GeluErf,               {}},
         {ActivationTypes::GeluTanh,              {}},
         {ActivationTypes::Swish,                 {{0.4f}}},
-        {ActivationTypes::IsInf,                 {}}
+        {ActivationTypes::IsInf,                 {}},
+        {ActivationTypes::IsNaN,                 {{}}},
 };
 
 // List of operations that should be tested also with integer precision
diff --git a/src/plugins/intel_cpu/tests/functional/shared_tests_instances/skip_tests_config.cpp b/src/plugins/intel_cpu/tests/functional/shared_tests_instances/skip_tests_config.cpp
index 94b9331b6bbe3d..70dd06ecfb44d9 100644
--- a/src/plugins/intel_cpu/tests/functional/shared_tests_instances/skip_tests_config.cpp
+++ b/src/plugins/intel_cpu/tests/functional/shared_tests_instances/skip_tests_config.cpp
@@ -118,9 +118,7 @@ std::vector<std::string> disabledTestPatterns() {
         R"(smoke_ConversionLayerTest/ConversionLayerTest.CompareWithRefs.*UNSPECIFIED.*)",
         // Issue:
         // New API tensor tests
-        R"(.*OVInferRequestCheckTensorPrecision.*type=i4.*)",
         R"(.*OVInferRequestCheckTensorPrecision.*type=u1.*)",
-        R"(.*OVInferRequestCheckTensorPrecision.*type=u4.*)",
         // Issue: 77390
         R"(.*LoopLayerCPUTest.*exec_cond=0.*)",
         R"(.*LoopLayerCPUTest.*trip_count=0.*)",
@@ -314,6 +312,8 @@ std::vector<std::string> disabledTestPatterns() {
         R"(.*smoke_LoopForCommon/LoopLayerCPUTest.CompareWithRefs/.*trip_count=5_exec_cond=1_netType=i8.*)",
         R"(.*smoke_LoopForCommon/LoopLayerCPUTest.CompareWithRefs/Input0_IS=\[\?.1.\?\]_TS=\(10.1.10\)_\(1.1.1\)_\(1.1.1\)_\(5.1.3\)_Input1_IS=\[\?.\?.\?\]_TS=.*_Input2_IS=\[\?.1.\?\]_.*_types=0_0_1_trip_count_type=.*_trip_count=(1|5)_exec_cond=1_netType=i8.*)",
         R"(.*smoke_LoopForCommon/LoopLayerCPUTest.CompareWithRefs/Input0_IS=\[1..10.1.1..10\]_.*_Input1_IS=\[1..8.1.1..8\]_.*_Input2_IS=\[1..10.\?.1..10\]_TS=.*_types=0_0_1_trip_count_type=.*_trip_count=(1|5)_exec_cond=1_netType=i8.*)",
+        // Issue: 142997
+        R"(.*smoke_TestsROIAlign.*)",
     };
 
     // fp32 floor for bf16 models: conversion issue
@@ -331,13 +331,6 @@ std::vector<std::string> disabledTestPatterns() {
         // Issue: 123321
         retVector.emplace_back(
             R"(.*smoke_RNNSequenceCommonZeroClip/RNNSequenceTest.Inference.*hidden_size=1.*relu.*direction=reverse.*)");
-        // Ticket: 122769
-        retVector.emplace_back(R"(.*smoke_nonzero/NonZeroLayerTest.Inference/IS.*)");
-        retVector.emplace_back(R"(.*smoke_NormalizeL2_.*)");
-        retVector.emplace_back(R"(.*Extension.XmlModelWithExtensionFromDSO.*)");
-        retVector.emplace_back(R"(.*Extension.OnnxModelWithExtensionFromDSO.*)");
-        retVector.emplace_back(R"(.*ONNXQuantizedModels/QuantizedModelsTests.MaxPool.*)");
-        retVector.emplace_back(R"(.*ONNXQuantizedModels/QuantizedModelsTests.Convolution.*)");
         // Ticket: 134601
         retVector.emplace_back(R"(.*smoke_GroupNormalization.*)");
         // by calc abs_threshold with expected value
@@ -349,15 +342,13 @@ std::vector<std::string> disabledTestPatterns() {
         retVector.emplace_back(R"(.*smoke_LogSoftmax4D/LogSoftmaxLayerTest.Inference/.*TS=\{\(2.3.4.5\)\}_modelType=f32_axis=(-4|0).*)");
         retVector.emplace_back(R"(.*smoke_Interpolate_Basic/InterpolateLayerTest.Inference/.*InterpolateMode=cubic_ShapeCalcMode=sizes_CoordinateTransformMode=tf_half_pixel.*PB=\(0.0.0.0\)_PE=\(0.0.1.1\)_.*netType=f32.*)");
         retVector.emplace_back(R"(.*smoke_CompareWithRefs_4D_Bitwise.*/EltwiseLayerCPUTest.*_eltwise_op_type=Bitwise.*_model_type=i32_.*)");
+        // Ticket: 144845
+        retVector.emplace_back(R"(.*LSTMCellFusion/LSTMCellFusionWithSplitWeights.SubgraphFusedToLSTMCell/(1|8|15))");
     }
     // invalid test: checks u8 precision for runtime graph, while it should be f32
     retVector.emplace_back(R"(smoke_NegativeQuantizedMatMulMultiplyFusion.*)");
     // int8 specific
     retVector.emplace_back(R"(smoke_Quantized.*)");
-
-    // Issue: 123019
-    retVector.emplace_back(R"(smoke_staticShapes4D.*INFERENCE_PRECISION_HINT=f16.*)");
-    retVector.emplace_back(R"(smoke_dynamicShapes4D.*INFERENCE_PRECISION_HINT=f16.*)");
     // Issue: 124309
     retVector.emplace_back(R"(.*InferRequestPreprocessConversionTest.*oLT=NHWC.*)");
     retVector.emplace_back(R"(.*smoke_NoReshape/OVCompiledModelGraphUniqueNodeNamesTest.CheckUniqueNodeNames.*)");
@@ -370,11 +361,12 @@ std::vector<std::string> disabledTestPatterns() {
     retVector.emplace_back(R"(smoke_VariableState/OVInferRequestVariableStateTest.*)");
     // Issue: 141705
     retVector.emplace_back(R"(.*smoke_arm_Deconv_2D_Planar_FP16/DeconvolutionLayerCPUTest.*INFERENCE_PRECISION_HINT=f16.*)");
-
-    retVector.emplace_back(R"(.*smoke_RoPETest.*)");
 #endif
 
 #if defined(OPENVINO_ARCH_ARM)
+    // Issue: 144998
+    retVector.emplace_back(R"(.*smoke_CachingSupportCase_CPU.*_(i8|u8).*)");
+    retVector.emplace_back(R"(.*smoke_Hetero_CachingSupportCase.*_(i8|u8).*)");
     // TODO: rounding errors
     retVector.emplace_back(R"(.*iv_secondaryInputType=PARAMETER_opType=VECTOR_NetType=i32.*)");
     // not supported
@@ -388,6 +380,8 @@ std::vector<std::string> disabledTestPatterns() {
     retVector.emplace_back(R"(.*smoke_EltwiseChain_MergeConvert_int8/.*InPRC0=i32.*Conversion=i8.*)");
     // by calc abs_threshold with expected value
     retVector.emplace_back(R"(.*smoke_CompareWithRefs_static/EltwiseLayerTest.*_eltwise_op_type=Div_.*_model_type=i32_.*)");
+
+    retVector.emplace_back(R"(.*smoke_RoPETest.*)");
 #endif
 
 #if !defined(OPENVINO_ARCH_X86_64)
@@ -539,6 +533,13 @@ std::vector<std::string> disabledTestPatterns() {
         // Issue: 142465
         retVector.emplace_back(R"(smoke_Reduce_MultiAxis_4D_fusing_CPU/ReduceCPULayerTest.CompareWithRefs.*INFERENCE_PRECISION_HINT=f16.*)");
         retVector.emplace_back(R"(smoke_Reduce_MultiAxis_5D_fusing_CPU/ReduceCPULayerTest.CompareWithRefs.*INFERENCE_PRECISION_HINT=f16.*)");
+        // Issue: 143852
+        retVector.emplace_back(R"((smoke|nightly)_FC_3D_FP16/.*_Fused=Multiply\(PerChannel\).*)");
+        retVector.emplace_back(R"((smoke|nightly)_MM_Brgemm_Static_FP16.*TS=\(\(55\.12\)\).*_Fused=Multiply\(PerChannel\).*)");
+        retVector.emplace_back(R"(smoke_MM_Dynamic_Fusing_FP16/.*TS=\(\(16\.12\)_\(33\.7\)_\(16\.12\)\).*_Fused=Multiply\(PerChannel\).*)");
+        retVector.emplace_back(R"(smoke_MM_Brgemm_Dynamic_Fusing_FP16/.*TS=\(\(16\.12\)_\(33\.7\)_\(16\.12\)\).*_Fused=Multiply\(PerChannel\).*)");
+        retVector.emplace_back(R"(smoke_Conv_.*_FP16/.*_Fused=PRelu1D\.Multiply\(PerChannel\)\.Add\(PerChannel\).*)");
+        retVector.emplace_back(R"(smoke_Conv_Sum_Broadcast_FP16/ConvSumInPlaceTest.*Relu\.Multiply\(PerChannel\)\.Add\(PerChannel\).*)");
     }
 
     return retVector;
diff --git a/src/plugins/intel_cpu/tests/functional/shared_tests_instances/snippets/matmul.cpp b/src/plugins/intel_cpu/tests/functional/shared_tests_instances/snippets/matmul.cpp
index 55c6a2aa095cda..96733959205ca7 100644
--- a/src/plugins/intel_cpu/tests/functional/shared_tests_instances/snippets/matmul.cpp
+++ b/src/plugins/intel_cpu/tests/functional/shared_tests_instances/snippets/matmul.cpp
@@ -11,19 +11,21 @@ namespace ov {
 namespace test {
 namespace snippets {
 
+#define STATIC_SHAPE(...) {{}, {{__VA_ARGS__}}}
 
 namespace {
-std::vector<std::vector<ov::PartialShape>> input_shapes{
-        {{2, 1, 3, 5}, {1, 3, 5, 3}},
-        {{3, 1, 32, 14}, {1, 2, 14, 32}},
-        {{1, 2, 37, 23}, {2, 1, 23, 37}},
-        {{1, 1, 37, 23}, {1, 2, 23, 33}},
-        {{1, 1, 32, 23}, {1, 1, 23, 68}},
-        {{1, 16, 384, 64}, {1, 16, 64, 384}},
-        {{1, 1, 100, 700}, {1, 1, 700, 100}},
-        {{1, 1, 100, 1024}, {1, 1, 1024, 100}},
-        {{1, 1, 100, 2500}, {1, 1, 2500, 100}},
-        {{1, 1, 100, 4500}, {1, 1, 4500, 100}},
+std::vector<std::vector<ov::test::InputShape>> input_shapes{
+        {STATIC_SHAPE(2, 1, 3, 5), STATIC_SHAPE(1, 3, 5, 3)},
+        {STATIC_SHAPE(3, 1, 32, 14), STATIC_SHAPE(1, 2, 14, 32)},
+        {STATIC_SHAPE(1, 2, 37, 23), STATIC_SHAPE(2, 1, 23, 37)},
+        {STATIC_SHAPE(1, 1, 37, 23), STATIC_SHAPE(1, 2, 23, 33)},
+
+        {STATIC_SHAPE(1, 1, 32, 23), STATIC_SHAPE(1, 1, 23, 68)},
+        {STATIC_SHAPE(1, 16, 384, 64), STATIC_SHAPE(1, 16, 64, 384)},
+        {STATIC_SHAPE(1, 1, 100, 700), STATIC_SHAPE(1, 1, 700, 100)},
+        {STATIC_SHAPE(1, 1, 100, 1024), STATIC_SHAPE(1, 1, 1024, 100)},
+        {STATIC_SHAPE(1, 1, 100, 2500), STATIC_SHAPE(1, 1, 2500, 100)},
+        {STATIC_SHAPE(1, 1, 100, 4500), STATIC_SHAPE(1, 1, 4500, 100)},
 };
 
 static inline std::vector<std::vector<element::Type>> quantized_precisions() {
@@ -63,6 +65,25 @@ INSTANTIATE_TEST_SUITE_P(smoke_Snippets_MatMult, MatMul,
                              ::testing::Values(ov::test::utils::DEVICE_CPU)),
                          MatMul::getTestCaseName);
 
+
+std::vector<std::vector<ov::test::InputShape>> input_shapes_dynamic{
+        {
+            {PartialShape{-1, -1, -1, -1}, {{2, 1, 32, 64}, {2, 2, 10, 20}, {2, 2, 100, 80},
+                                                           {2, 2, 10, 20}, {2, 1, 32, 64}}},
+            {PartialShape{-1, -1, -1, -1}, {{1, 3, 64, 128}, {2, 2, 20, 30}, {2, 2, 80, 120},
+                                                           {2, 2, 20, 30}, {1, 3, 64, 128}}}
+        },
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_Snippets_DynMatMult, MatMul,
+                         ::testing::Combine(
+                                 ::testing::ValuesIn(input_shapes_dynamic),
+                                 ::testing::ValuesIn(precisions(true)),
+                                 ::testing::Values(1), // MatMul
+                                 ::testing::Values(1), // Tokenized MatMul
+                                 ::testing::Values(ov::test::utils::DEVICE_CPU)),
+                         MatMul::getTestCaseName);
+
 INSTANTIATE_TEST_SUITE_P(smoke_Snippets_MatMulFQ, MatMulFQ,
                          ::testing::Combine(
                                  ::testing::ValuesIn(input_shapes),
@@ -74,8 +95,12 @@ INSTANTIATE_TEST_SUITE_P(smoke_Snippets_MatMulFQ, MatMulFQ,
 
 INSTANTIATE_TEST_SUITE_P(smoke_Snippets_MatMulBias, MatMulBias,
                          ::testing::Combine(
-                                 ::testing::Values(std::vector<ov::PartialShape>{{1, 2, 69, 43}, {2, 1, 43, 49}, {1, 1, 69, 49}},
-                                                   std::vector<ov::PartialShape>{{1, 2, 95, 1023}, {1, 2, 1023, 255}, {1, 2, 95, 255}}),
+                                 ::testing::Values(std::vector<ov::test::InputShape>{STATIC_SHAPE(1, 2, 69, 43),
+                                                                                     STATIC_SHAPE(2, 1, 43, 49),
+                                                                                     STATIC_SHAPE(1, 1, 69, 49)},
+                                                   std::vector<ov::test::InputShape>{STATIC_SHAPE(1, 2, 95, 1023),
+                                                                                     STATIC_SHAPE(1, 2, 1023, 255),
+                                                                                     STATIC_SHAPE(1, 2, 95, 255)}),
                                  ::testing::ValuesIn(precisions(false)),
                                  ::testing::Values(1), // Subgraph;
                                  ::testing::Values(1), // Tokenized MatMul+Bias
@@ -84,9 +109,13 @@ INSTANTIATE_TEST_SUITE_P(smoke_Snippets_MatMulBias, MatMulBias,
 
 INSTANTIATE_TEST_SUITE_P(smoke_Snippets_MatMulBiasQuantized, MatMulBiasQuantized,
                          ::testing::Combine(
-                                 ::testing::ValuesIn(std::vector<std::vector<ov::PartialShape>>{
-                                        std::vector<ov::PartialShape>{{1, 2, 69, 43}, {2, 1, 43, 49}, {1, 2, 1, 1}},
-                                        std::vector<ov::PartialShape>{{1, 2, 69, 43}, {2, 1, 43, 49}, {1, 2, 69, 49}}}),
+                                 ::testing::ValuesIn(std::vector<std::vector<ov::test::InputShape>>{
+                                        std::vector<ov::test::InputShape>{STATIC_SHAPE(1, 2, 69, 43),
+                                                                          STATIC_SHAPE(2, 1, 43, 49),
+                                                                          STATIC_SHAPE(1, 2, 1, 1)},
+                                        std::vector<ov::test::InputShape>{STATIC_SHAPE(1, 2, 69, 43),
+                                                                          STATIC_SHAPE(2, 1, 43, 49),
+                                                                          STATIC_SHAPE(1, 2, 69, 49)}}),
                                  ::testing::ValuesIn(quantized_precisions()),
                                  ::testing::Values(1), // Subgraph
                                  ::testing::Values(1), // Tokenized MatMul+Bias
@@ -95,7 +124,9 @@ INSTANTIATE_TEST_SUITE_P(smoke_Snippets_MatMulBiasQuantized, MatMulBiasQuantized
 
 INSTANTIATE_TEST_SUITE_P(smoke_Snippets_MatMulQuantized, MatMulQuantized,
                          ::testing::Combine(
-                                 ::testing::Values(std::vector<ov::PartialShape>{{1, 16, 128, 64}, {1, 16, 64, 128}, {128, 64}}),
+                                 ::testing::Values(std::vector<ov::test::InputShape>{STATIC_SHAPE(1, 16, 128, 64),
+                                                                                      STATIC_SHAPE(1, 16, 64, 128),
+                                                                                      STATIC_SHAPE(128, 64)}),
                                  ::testing::ValuesIn(quantized_precisions()),
                                  ::testing::Values(3), // Subgraph + Reshape + Subgraph
                                  ::testing::Values(2), // Tokenized [MatMul+FQ+Matmul] and [FQ]
@@ -104,7 +135,9 @@ INSTANTIATE_TEST_SUITE_P(smoke_Snippets_MatMulQuantized, MatMulQuantized,
 
 INSTANTIATE_TEST_SUITE_P(smoke_Snippets_MatMulQuantizedSoftmax, MatMulQuantizedSoftmax,
                          ::testing::Combine(
-                                 ::testing::Values(std::vector<ov::PartialShape>{{1, 16, 128, 64}, {1, 16, 64, 128}, {128, 64}}),
+                                 ::testing::Values(std::vector<ov::test::InputShape>{STATIC_SHAPE(1, 16, 128, 64),
+                                                                                     STATIC_SHAPE(1, 16, 64, 128),
+                                                                                     STATIC_SHAPE(128, 64)}),
                                  ::testing::ValuesIn(quantized_precisions()),
                                  ::testing::Values(3), // Subgraph + Reshape + Subgraph
                                  ::testing::Values(2), // Tokenized [MatMul+FQ+Matmul] and [FQ]
diff --git a/src/plugins/intel_cpu/tests/functional/utils/arm/filter_cpu_info.cpp b/src/plugins/intel_cpu/tests/functional/utils/arm/filter_cpu_info.cpp
index 3bb42fe47e0615..2d348b323219dd 100644
--- a/src/plugins/intel_cpu/tests/functional/utils/arm/filter_cpu_info.cpp
+++ b/src/plugins/intel_cpu/tests/functional/utils/arm/filter_cpu_info.cpp
@@ -38,4 +38,8 @@ std::vector<CPUSpecificParams> filterCPUInfoForDevice(const std::vector<CPUSpeci
     return CPUParams;
 }
 
+std::vector<CPUSpecificParams> filterCPUInfoForDeviceWithFP16(const std::vector<CPUSpecificParams>& allParams) {
+    std::vector<CPUSpecificParams> resCPUParams;
+    return resCPUParams;
+}
 } // namespace CPUTestUtils
diff --git a/src/plugins/intel_cpu/tests/functional/utils/cpu_test_utils.cpp b/src/plugins/intel_cpu/tests/functional/utils/cpu_test_utils.cpp
index f3fde176331885..fab8365bc28e49 100644
--- a/src/plugins/intel_cpu/tests/functional/utils/cpu_test_utils.cpp
+++ b/src/plugins/intel_cpu/tests/functional/utils/cpu_test_utils.cpp
@@ -8,6 +8,7 @@
 #include "cpu_test_utils.hpp"
 
 #include "openvino/core/type/element_type.hpp"
+#include "openvino/runtime/system_conf.hpp"
 #include "transformations/rt_info/primitives_priority_attribute.hpp"
 #include "utils/general_utils.h"
 #include "utils/rt_info/memory_formats_attribute.hpp"
@@ -471,4 +472,74 @@ void CheckNumberOfNodesWithType(const ov::CompiledModel& compiledModel,
                                 size_t expectedCount) {
     CheckNumberOfNodesWithTypes(compiledModel, {nodeType}, expectedCount);
 }
+
+
+// deduce the actual precision of the operation given the ngraph level operation precision and the plugin config
+ov::element::Type
+CPUTestsBase::deduce_expected_precision(const ov::element::Type& opPrecision,
+                                        const ov::AnyMap& configuration) {
+#if defined(OPENVINO_ARCH_ARM) || defined(OPENVINO_ARCH_ARM64)
+    return opPrecision;
+#endif
+#if defined(OPENVINO_ARCH_RISCV64)
+    return opPrecision;
+#endif
+#if defined(OPENVINO_ARCH_X86_64)
+    // if is not float
+    if (!opPrecision.is_real()) {
+        return opPrecision;
+    }
+    ov::element::Type inferencePrecision = ov::element::f32;
+    bool inferencePrecisionSetExplicitly = false;
+    const std::string precisionKey = ov::hint::inference_precision.name();
+    const auto& it = configuration.find(precisionKey);
+    if (it != configuration.end()) {
+        auto inferencePrecisionConfig = it->second.as<ov::element::Type>();
+        inferencePrecisionSetExplicitly = true;
+        // TODO also need to check (dnnl::impl::cpu::x64::avx2_vnni_2)
+        if ((inferencePrecisionConfig == ov::element::bf16 && ov::with_cpu_x86_avx512_core())
+                || (inferencePrecisionConfig == ov::element::f16 && ov::with_cpu_x86_avx512_core_fp16())
+                || (inferencePrecisionConfig == ov::element::f32)
+                || (inferencePrecisionConfig == ov::element::undefined)) {
+            inferencePrecision = inferencePrecisionConfig;
+        }
+    }
+    if (!inferencePrecisionSetExplicitly) {
+        const std::string executionModeKey = ov::hint::execution_mode.name();
+        const auto& configIt = configuration.find(executionModeKey);
+        if (configIt != configuration.end() && configIt->second.as<ov::hint::ExecutionMode>() == ov::hint::ExecutionMode::PERFORMANCE) {
+            inferencePrecision = ov::element::f32;
+            if (ov::with_cpu_x86_bfloat16()) {
+                inferencePrecision = ov::element::bf16;
+            }
+        } else {
+            inferencePrecision = ov::element::undefined;
+        }
+    }
+
+    ov::element::Type deducedType = opPrecision;
+    // enforceInferPrecision stage
+    if (inferencePrecision == ov::element::bf16) {
+        deducedType = ov::with_cpu_x86_avx512_core() ? ov::element::bf16 : ov::element::f32;
+    }
+
+    // ngraph transform pipeline stage
+    if (inferencePrecision == ov::element::f16) {
+        if (deducedType == ov::element::f32) {
+            deducedType = ov::element::f16;
+        }
+    }
+    if (deducedType == ov::element::bf16) {
+        deducedType = ov::with_cpu_x86_avx512_core() ? ov::element::bf16 : ov::element::f32;
+    } else if (deducedType == ov::element::f16) {
+        if (inferencePrecision != ov::element::f16 && inferencePrecision != ov::element::undefined) {
+            deducedType = ov::element::f32;
+        }
+    } else {
+        deducedType = ov::element::f32;
+    }
+
+    return deducedType;
+#endif
+}
 }  // namespace CPUTestUtils
diff --git a/src/plugins/intel_cpu/tests/functional/utils/cpu_test_utils.hpp b/src/plugins/intel_cpu/tests/functional/utils/cpu_test_utils.hpp
index 2a15f89c72ad85..792e84a050f095 100644
--- a/src/plugins/intel_cpu/tests/functional/utils/cpu_test_utils.hpp
+++ b/src/plugins/intel_cpu/tests/functional/utils/cpu_test_utils.hpp
@@ -4,6 +4,7 @@
 
 #pragma once
 
+#include "openvino/core/any.hpp"
 #include "openvino/runtime/compiled_model.hpp"
 #include "openvino/runtime/exec_model_info.hpp"
 #include "openvino/runtime/system_conf.hpp"
@@ -128,6 +129,8 @@ class CPUTestsBase {
                                const std::vector<std::string>& priority);
     // TODO: change to setter method
     static std::string makeSelectedTypeStr(std::string implString, ov::element::Type_t elType);
+    static ov::element::Type deduce_expected_precision(const ov::element::Type& opPrecision,
+                                                       const ov::AnyMap& configuration);
     void updateSelectedType(const std::string& primitiveType,
                             const ov::element::Type netType,
                             const ov::AnyMap& config);
@@ -171,8 +174,9 @@ class CPUTestsBase {
 // common parameters
 const auto emptyCPUSpec = CPUSpecificParams{{}, {}, {}, {}};
 const ov::AnyMap empty_plugin_config{};
-const ov::AnyMap cpu_bf16_plugin_config = {{ov::hint::inference_precision(ov::element::bf16)}};
 const ov::AnyMap cpu_f16_plugin_config = {{ov::hint::inference_precision(ov::element::f16)}};
+const ov::AnyMap cpu_bf16_plugin_config = {{ov::hint::inference_precision(ov::element::bf16)}};
+const ov::AnyMap cpu_f32_plugin_config = {{ov::hint::inference_precision(ov::element::f32)}};
 
 // utility functions
 std::vector<CPUSpecificParams> filterCPUSpecificParams(const std::vector<CPUSpecificParams>& paramsVector);
diff --git a/src/plugins/intel_cpu/tests/functional/utils/filter_cpu_info.hpp b/src/plugins/intel_cpu/tests/functional/utils/filter_cpu_info.hpp
index 8a92dea80575ab..d8f9f6ea1b3470 100644
--- a/src/plugins/intel_cpu/tests/functional/utils/filter_cpu_info.hpp
+++ b/src/plugins/intel_cpu/tests/functional/utils/filter_cpu_info.hpp
@@ -11,4 +11,5 @@ namespace CPUTestUtils {
 std::vector<CPUSpecificParams> filterCPUInfo(const std::vector<CPUSpecificParams>& CPUParams);
 std::vector<CPUSpecificParams> filterCPUInfoForArch(const std::vector<CPUSpecificParams>& CPUParams);
 std::vector<CPUSpecificParams> filterCPUInfoForDevice(const std::vector<CPUSpecificParams>& CPUParams);
+std::vector<CPUSpecificParams> filterCPUInfoForDeviceWithFP16(const std::vector<CPUSpecificParams>& allParams);
 } // namespace CPUTestUtils
diff --git a/src/plugins/intel_cpu/tests/functional/utils/x64/filter_cpu_info.cpp b/src/plugins/intel_cpu/tests/functional/utils/x64/filter_cpu_info.cpp
index cd30f756db66db..f0c663375cee5b 100644
--- a/src/plugins/intel_cpu/tests/functional/utils/x64/filter_cpu_info.cpp
+++ b/src/plugins/intel_cpu/tests/functional/utils/x64/filter_cpu_info.cpp
@@ -63,4 +63,22 @@ std::vector<CPUSpecificParams> filterCPUInfoForDevice(const std::vector<CPUSpeci
     return resCPUParams;
 }
 
+std::vector<CPUSpecificParams> filterCPUInfoForDeviceWithFP16(const std::vector<CPUSpecificParams>& allParams) {
+    std::vector<CPUSpecificParams> specificParams;
+    if (!ov::with_cpu_x86_avx512_core_fp16()) {
+        return specificParams;
+    }
+    std::copy_if(allParams.begin(), allParams.end(), std::back_inserter(specificParams), [](const CPUSpecificParams& item) {
+        const auto &selected = std::get<3>(item);
+        bool isValid = false;
+        if (selected.find("avx512") != std::string::npos) {
+            isValid = true;
+        }
+        if ((!ov::with_cpu_x86_avx512_core_amx_fp16()) && selected.find("amx") != std::string::npos) {
+            isValid = false;
+        }
+        return isValid;
+    });
+    return specificParams;
+}
 } // namespace CPUTestUtils
diff --git a/src/plugins/intel_cpu/tests/unit/jit_kernel_test.cpp b/src/plugins/intel_cpu/tests/unit/jit_kernel_test.cpp
index a4de792a305227..b6c177049666ef 100644
--- a/src/plugins/intel_cpu/tests/unit/jit_kernel_test.cpp
+++ b/src/plugins/intel_cpu/tests/unit/jit_kernel_test.cpp
@@ -269,19 +269,18 @@ struct jit_variable_load_store_test_kernel {
     struct Params {
         const SrcT *src;
         DstT *dst;
-        size_t size;
     };
 
     template<size_t N, size_t M, bool is_src>
     void test() {
-        kernel_impl<N, is_src> kernel;
+        kernel_impl<N, M, is_src> kernel;
         kernel.init();
         ASSERT_GE(N, M);
 
         std::array<SrcT, N> src {};
         std::array<DstT, N> result {};
 
-        Params args = { src.data(), result.data(), M };
+        Params args = { src.data(), result.data()};
 
         src.fill(static_cast<SrcT>(42));
         for (size_t i = 0; i < M; ++i) {
@@ -300,7 +299,7 @@ struct jit_variable_load_store_test_kernel {
     }
 
 private:
-    template<size_t N, bool is_src>
+    template<size_t N, size_t M, bool is_src>
     class kernel_impl : public jit_test_kernel<Params> {
     public:
         void generate() override {
@@ -308,12 +307,11 @@ struct jit_variable_load_store_test_kernel {
 
             auto src_ptr = jit_kernel::arg(&Params::src);
             auto dst_ptr = jit_kernel::arg(&Params::dst);
-            auto size = jit_kernel::arg(&Params::size);
 
             auto interm = jit_kernel::var<typename std::conditional<is_src, SrcT[N], DstT[N]>::type>();
 
-            jit_kernel::load(interm, src_ptr, size);
-            jit_kernel::store(dst_ptr, interm, size);
+            jit_kernel::load(interm, src_ptr, M);
+            jit_kernel::store(dst_ptr, interm, M);
 
             jit_kernel::postamble();
         }
@@ -360,6 +358,7 @@ TEST(JitKernel, variable_load_and_store) {
     {
         jit_variable_load_store_test_kernel<float, bfloat16_t> kernel;
         if (mayiuse(cpu_isa_t::avx512_core)) {
+            kernel.test<16, 4, true>();
             kernel.test<16, 11, true>();
         }
         if (mayiuse(cpu_isa_t::avx2)) {
diff --git a/src/plugins/intel_cpu/tests/unit/shape_inference_test/scaled_dot_product_attention_shape_inference_test.cpp b/src/plugins/intel_cpu/tests/unit/shape_inference_test/scaled_dot_product_attention_shape_inference_test.cpp
index bb2e85ad34b3fb..7ee414710c06de 100644
--- a/src/plugins/intel_cpu/tests/unit/shape_inference_test/scaled_dot_product_attention_shape_inference_test.cpp
+++ b/src/plugins/intel_cpu/tests/unit/shape_inference_test/scaled_dot_product_attention_shape_inference_test.cpp
@@ -68,3 +68,35 @@ TEST_F(ScaledDotProductAttentionV13StaticShapeInferenceTest, mixed_shapes) {
     EXPECT_EQ(output_shapes.size(), 1);
     EXPECT_EQ(output_shapes.front(), StaticShape({2, 3, 6}));
 }
+
+TEST_F(ScaledDotProductAttentionV13StaticShapeInferenceTest, attention_L_broadcast) {
+    const auto query = std::make_shared<opset13::Parameter>(element::f32, PartialShape::dynamic());
+    const auto key = std::make_shared<opset13::Parameter>(element::f32, PartialShape::dynamic());
+    const auto value = std::make_shared<opset13::Parameter>(element::f32, PartialShape::dynamic());
+    const auto attention_mask = std::make_shared<opset13::Parameter>(element::f32, PartialShape::dynamic());
+    const auto scale = std::make_shared<opset13::Parameter>(element::f32, PartialShape::dynamic());
+    auto causal = false;
+
+    op = make_op(query, key, value, attention_mask, scale, causal);
+
+    input_shapes = ShapeVector{{2, 8, 16, 32}, {2, 8, 24, 32}, {2, 8, 24, 48}, {1, 1, 24}, {}};
+    output_shapes = shape_inference(op.get(), input_shapes);
+    EXPECT_EQ(output_shapes.size(), 1);
+    EXPECT_EQ(output_shapes.front(), StaticShape({2, 8, 16, 48}));
+}
+
+TEST_F(ScaledDotProductAttentionV13StaticShapeInferenceTest, attention_S_broadcast) {
+    const auto query = std::make_shared<opset13::Parameter>(element::f32, PartialShape::dynamic());
+    const auto key = std::make_shared<opset13::Parameter>(element::f32, PartialShape::dynamic());
+    const auto value = std::make_shared<opset13::Parameter>(element::f32, PartialShape::dynamic());
+    const auto attention_mask = std::make_shared<opset13::Parameter>(element::f32, PartialShape::dynamic());
+    const auto scale = std::make_shared<opset13::Parameter>(element::f32, PartialShape::dynamic());
+    auto causal = false;
+
+    op = make_op(query, key, value, attention_mask, scale, causal);
+
+    input_shapes = ShapeVector{{2, 8, 16, 32}, {2, 8, 24, 32}, {2, 8, 24, 48}, {1, 16, 1}, {}};
+    output_shapes = shape_inference(op.get(), input_shapes);
+    EXPECT_EQ(output_shapes.size(), 1);
+    EXPECT_EQ(output_shapes.front(), StaticShape({2, 8, 16, 48}));
+}
diff --git a/src/plugins/intel_cpu/tests/unit/snippets_transformations/arm/snipptes_mark_skipped.cpp b/src/plugins/intel_cpu/tests/unit/snippets_transformations/arm/snipptes_mark_skipped.cpp
index 1bd07a76531365..180f810cedb05a 100644
--- a/src/plugins/intel_cpu/tests/unit/snippets_transformations/arm/snipptes_mark_skipped.cpp
+++ b/src/plugins/intel_cpu/tests/unit/snippets_transformations/arm/snipptes_mark_skipped.cpp
@@ -16,7 +16,7 @@ class SnippetsMarkSkippedTests : public TransformationTestsF {
 public:
     void run() {
         ASSERT_TRUE(model);
-        ov::snippets::pass::SnippetsTokenization::Config config = { 1, 23, true, true, { 3, 4 } };
+        ov::snippets::pass::SnippetsTokenization::Config config = { 1, 23, true, true, true, { 3, 4 } };
         manager.register_pass<ov::intel_cpu::SnippetsMarkSkipped>();
         manager.register_pass<ov::snippets::pass::EnumerateNodes>();
         manager.register_pass<ov::snippets::pass::TokenizeSnippets>(config);
diff --git a/src/plugins/intel_cpu/tests/unit/snippets_transformations/x64/fake_quantize_tokenization_test.cpp b/src/plugins/intel_cpu/tests/unit/snippets_transformations/x64/fake_quantize_tokenization_test.cpp
index 275f9d5371f69a..e8f07cbaf64b87 100644
--- a/src/plugins/intel_cpu/tests/unit/snippets_transformations/x64/fake_quantize_tokenization_test.cpp
+++ b/src/plugins/intel_cpu/tests/unit/snippets_transformations/x64/fake_quantize_tokenization_test.cpp
@@ -20,7 +20,7 @@ namespace snippets {
 class FakeQuantizeTokenizationTest : public TransformationTestsF {
 public:
     void register_passes() {
-        ov::snippets::pass::SnippetsTokenization::Config config = { 1, std::numeric_limits<size_t>::max(), true, true, { 3, 4 } };
+        ov::snippets::pass::SnippetsTokenization::Config config = { 1, std::numeric_limits<size_t>::max(), true, true, true, { 3, 4 } };
         manager.register_pass<ov::intel_cpu::SnippetsMarkSkipped>();
         manager.register_pass<ov::snippets::pass::EnumerateNodes>();
         manager.register_pass<ov::snippets::pass::TokenizeSnippets>(config);
diff --git a/src/plugins/intel_cpu/tests/unit/snippets_transformations/x64/lowered/brgemm_blocking.cpp b/src/plugins/intel_cpu/tests/unit/snippets_transformations/x64/lowered/brgemm_blocking.cpp
index 436da71327f96f..7a276d2fe9ef95 100644
--- a/src/plugins/intel_cpu/tests/unit/snippets_transformations/x64/lowered/brgemm_blocking.cpp
+++ b/src/plugins/intel_cpu/tests/unit/snippets_transformations/x64/lowered/brgemm_blocking.cpp
@@ -131,18 +131,21 @@ TEST_F(BrgemmBlockingTest, BlockingIsNotNeeded) {
     const ov::PartialShape input_shape_a{1, 16, m, k};
     const ov::PartialShape input_shape_b{1, 16, k, n};
     const auto precision = ov::element::f32;
+    const std::vector<size_t> layout{};
 
     {
         auto data_a = linear_ir->push_node<ov::opset10::Parameter>(precision, input_shape_a);
         auto data_b = linear_ir->push_node<ov::opset10::Parameter>(precision, input_shape_b);
-        auto brgemm = linear_ir->push_node<BrgemmCPU>(data_a.second, data_b.second, BrgemmCPU::Type::Floating);
+        auto brgemm = linear_ir->push_node<BrgemmCPU>(data_a.second, data_b.second, BrgemmCPU::Type::Floating,
+                                                                 0, 0, 0, layout, layout, layout, m, k, n);
         init_expr_descriptors(*brgemm.first);
         auto result = linear_ir->push_node<ov::opset10::Result>(brgemm.second);
     }
     {
         auto data_a = linear_ir_ref->push_node<ov::opset10::Parameter>(precision, input_shape_a);
         auto data_b = linear_ir_ref->push_node<ov::opset10::Parameter>(precision, input_shape_b);
-        auto brgemm = linear_ir_ref->push_node<BrgemmCPU>(data_a.second, data_b.second, BrgemmCPU::Type::Floating);
+        auto brgemm = linear_ir_ref->push_node<BrgemmCPU>(data_a.second, data_b.second, BrgemmCPU::Type::Floating,
+                                                                     0, 0, 0, layout, layout, layout, m, k, n);
         brgemm.second->set_beta(0.f);
         init_expr_descriptors(*brgemm.first, {{m, k}, {k, n}, {m, n}});
         auto result = linear_ir_ref->push_node<ov::opset10::Result>(brgemm.second);
diff --git a/src/plugins/intel_cpu/tests/unit/snippets_transformations/x64/snipptes_mark_skipped.cpp b/src/plugins/intel_cpu/tests/unit/snippets_transformations/x64/snipptes_mark_skipped.cpp
index 6729b97c45d3a7..9bde0ea244cce1 100644
--- a/src/plugins/intel_cpu/tests/unit/snippets_transformations/x64/snipptes_mark_skipped.cpp
+++ b/src/plugins/intel_cpu/tests/unit/snippets_transformations/x64/snipptes_mark_skipped.cpp
@@ -17,7 +17,7 @@ class SnippetsMarkSkippedTests : public TransformationTestsF {
 public:
     void run() {
         ASSERT_TRUE(model);
-        ov::snippets::pass::SnippetsTokenization::Config config = { 1, 11, true, true, { 3, 4 } };
+        ov::snippets::pass::SnippetsTokenization::Config config = { 1, 11, true, true, true, { 3, 4 } };
         manager.register_pass<ov::intel_cpu::SnippetsMarkSkipped>();
         manager.register_pass<ov::snippets::pass::EnumerateNodes>();
         manager.register_pass<ov::snippets::pass::TokenizeSnippets>(config);
diff --git a/src/plugins/intel_cpu/thirdparty/CMakeLists.txt b/src/plugins/intel_cpu/thirdparty/CMakeLists.txt
index 3c5ff3b878caf4..7f4f1905c45f15 100644
--- a/src/plugins/intel_cpu/thirdparty/CMakeLists.txt
+++ b/src/plugins/intel_cpu/thirdparty/CMakeLists.txt
@@ -80,6 +80,7 @@ function(ov_add_onednn)
     endif()
 
     if(CMAKE_COMPILER_IS_GNUCXX OR OV_COMPILER_IS_CLANG OR OV_COMPILER_IS_INTEL_LLVM)
+        ov_add_compiler_flags(-Wno-error)
         ov_add_compiler_flags(-Wno-undef)
         ov_add_compiler_flags(-Wno-missing-declarations)
         if(ARM OR AARCH64)
@@ -111,13 +112,6 @@ function(ov_add_onednn)
         ov_add_compiler_flags(/wd4334)
     endif()
 
-    # remove CMAKE_COMPILE_WARNING_AS_ERROR for thirdparty
-    if(CMAKE_CROSSCOMPILING AND CMAKE_COMPILE_WARNING_AS_ERROR)
-        string(REPLACE " -Werror " "  " CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
-        string(REPLACE " -Werror " "  "  CMAKE_C_FLAGS "${CMAKE_C_FLAGS}")
-        set(CMAKE_COMPILE_WARNING_AS_ERROR "OFF")
-    endif()
-
     if(SUGGEST_OVERRIDE_SUPPORTED)
         # xbyak compilation fails
         set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-suggest-override")
diff --git a/src/plugins/intel_cpu/thirdparty/ComputeLibrary b/src/plugins/intel_cpu/thirdparty/ComputeLibrary
index 4fda7a803eaadf..505adb91d40e05 160000
--- a/src/plugins/intel_cpu/thirdparty/ComputeLibrary
+++ b/src/plugins/intel_cpu/thirdparty/ComputeLibrary
@@ -1 +1 @@
-Subproject commit 4fda7a803eaadf00ba36bd532481a33c18952089
+Subproject commit 505adb91d40e05b3f80a075a4467a78a253395e1
diff --git a/src/plugins/intel_cpu/thirdparty/onednn b/src/plugins/intel_cpu/thirdparty/onednn
index a320d02d6e733c..d19b5d85880ba9 160000
--- a/src/plugins/intel_cpu/thirdparty/onednn
+++ b/src/plugins/intel_cpu/thirdparty/onednn
@@ -1 +1 @@
-Subproject commit a320d02d6e733c775724901675cbc8944391459d
+Subproject commit d19b5d85880ba917552e57183670f7a3e2fcd086
diff --git a/src/plugins/intel_cpu/tools/commit_slider/tests/commit_slider_test.py b/src/plugins/intel_cpu/tools/commit_slider/tests/commit_slider_test.py
index ce4abfcdc277e5..2a5125870ef4dd 100644
--- a/src/plugins/intel_cpu/tools/commit_slider/tests/commit_slider_test.py
+++ b/src/plugins/intel_cpu/tools/commit_slider/tests/commit_slider_test.py
@@ -142,7 +142,7 @@ def testBmSteppedBreak2(self):
         )
 
     @skip_commit_slider_devtest
-    def testForsubstitutionRule(self):
+    def testForMapSubstitutionRule(self):
         from utils.helpers import applySubstitutionRules
         cfg = {
                 "serviceConfig": {
@@ -228,6 +228,74 @@ def applyByRef(cfg: map, rules: list, substitution: str):
             "{commitHash1} is unchanged"
         )
 
+    @skip_commit_slider_devtest
+    def testForStaticSubstitutionRule(self):
+        from utils.helpers import applySubstitutionRules
+        cfg = {
+                "serviceConfig": {
+                    "previousKey": "previousValue"
+                },
+                "wrongDst": "{pathOne} is unchanged",
+                "dst": {
+                    "complex": {
+                        "path": [
+                            "{pathOne} is natural number",
+                            "{pathTwo} is natural number",
+                            "{pathOne} is not {pathTwo}"
+                        ]
+                    }
+                },
+                "src": {
+                    "complex": {
+                        "path": {
+                            "one": "1",
+                            "two": "2"
+                        }
+                    }
+                }
+        }
+        rules = [
+            {
+                "name": "testRule1",
+                "enabled": True,
+                "type": "static",
+                "placeholder": "pathOne",
+                "from": "$.src.complex.path.one",
+                "to": "$.dst.complex.path"
+            },
+            {
+                "name": "testRule2",
+                "enabled": True,
+                "type": "static",
+                "placeholder": "pathTwo",
+                "from": "$.src.complex.path.two",
+                "to": "$.dst.complex.path"
+            }
+        ]
+        def applyByRef(cfg: map, rules: list, substitution: str):
+            applySubstitutionRules(cfg, rules, substitution)
+
+        applyByRef(cfg, rules, "mustBeIgnored")
+
+        # assert substitutions
+        self.assertEqual(
+            cfg["dst"]["complex"]["path"][0],
+            "1 is natural number"
+        )
+
+        self.assertEqual(
+            cfg["dst"]["complex"]["path"][1],
+            "2 is natural number"
+        )
+        self.assertEqual(
+            cfg["dst"]["complex"]["path"][2],
+            "1 is not 2"
+        )
+        self.assertEqual(
+            cfg["wrongDst"],
+            "{pathOne} is unchanged"
+        )
+
     @skip_commit_slider_devtest
     def testForDeepUpdate(self):
         from utils.helpers import deepMapUpdate
diff --git a/src/plugins/intel_cpu/tools/commit_slider/utils/cfg_manager.py b/src/plugins/intel_cpu/tools/commit_slider/utils/cfg_manager.py
index 5982cda733ea88..85d1b7754946bc 100644
--- a/src/plugins/intel_cpu/tools/commit_slider/utils/cfg_manager.py
+++ b/src/plugins/intel_cpu/tools/commit_slider/utils/cfg_manager.py
@@ -9,14 +9,26 @@ class CfgManager():
     def __init__(self, cfg) -> None:
         self.cfg = cfg
 
+    @staticmethod
+    def multistepStrFormat(input: str, placeholder: str, substitution: str):
+        return input.replace(
+            '{}{}{}'.format('{', placeholder, '}'),
+            substitution
+        )
+
     def applyTemplate(self):
         if not "template" in self.cfg:
             return self.cfg
         logPath = self.cfg["logPath"]
         tmplName = self.cfg["template"]["name"]
         fullCfg = {}
+        # todo: generalize tmplcfg generator
         if tmplName == "bm_simple":
             fullCfg = self.generatebmSimpleTemplate()
+        elif tmplName == "e2e":
+            fullCfg = self.generateE2ETemplate()
+        elif tmplName == "bm_functional":
+            fullCfg = self.generatebmFunctionalTemplate()
         else:
             raise Exception(
                 "Unknown template '{}'".format(tmplName)
@@ -32,6 +44,45 @@ def readJsonTmpl(self, tmplFileName: str):
             tmplJSON = json.load(cfgFile)
             return tmplJSON
 
+    def generateE2ETemplate(self):
+        tmpl = self.cfg["template"]
+        tmpJSON = self.readJsonTmpl("e2e_for_CI.json")
+
+        if "errorPattern" in tmpl and\
+            "precommitPath" in tmpl and\
+            "testCmd" in tmpl:
+            tmpJSON["runConfig"]["stopPattern"] = tmpl["errorPattern"]
+            tmpJSON["dlbConfig"]["commonPath"] = tmpl["precommitPath"]
+            tmpJSON["cachedPathConfig"]["commonPath"] = tmpl["precommitPath"]
+            tmpJSON["appCmd"] = CfgManager.multistepStrFormat(
+                tmpJSON["appCmd"],
+                tmpl["appCmd"],
+                "testCmd"
+            )
+        else:
+            raise("Template is incomplete.")
+        subPath = "private_linux_manylinux2014_release/"
+        if "subPath" in tmpl:
+            subPath = tmpl["subPath"]
+        tmpJSON["dlbConfig"]["subPath"] = subPath
+        tmpJSON["cachedPathConfig"]["subPath"] = subPath
+
+        return tmpJSON
+
+    def generatebmFunctionalTemplate(self):
+        tmpl = self.cfg["template"]
+        tmpJSON = self.readJsonTmpl("bm_output.json")
+        stopPattern = "stopPattern"
+        if "appCmd" in tmpl:
+            tmpJSON["appCmd"] = tmpl["appCmd"]
+        else:
+            raise("No 'appcmd' in template")
+        if stopPattern in tmpl:
+            tmpJSON["runConfig"][stopPattern] = tmpl[stopPattern]
+        else:
+            raise("No 'stopPattern' in template")
+        return tmpJSON
+
     def generatebmSimpleTemplate(self):
         tmpl = self.cfg["template"]
         tmpJSON = self.readJsonTmpl("bm_perf_for_CI.json")
diff --git a/src/plugins/intel_cpu/tools/commit_slider/utils/cfg_samples/AC_for_CI.json b/src/plugins/intel_cpu/tools/commit_slider/utils/cfg_samples/AC_for_CI.json
new file mode 100644
index 00000000000000..a0562532c7736d
--- /dev/null
+++ b/src/plugins/intel_cpu/tools/commit_slider/utils/cfg_samples/AC_for_CI.json
@@ -0,0 +1,83 @@
+{
+    "appCmd":"source {venvName}/bin/activate && cd {appPath} && {py} -m pip install --upgrade pip && {py} -m pip install openvino-dev[caffe,kaldi,mxnet,onnx,pytorch,tensorflow2]=={wheelVersion} --find-links={precommitPath}wheels/ && {py} -m pip install -r requirements_dev.txt && {testCmd}",
+    "venvCfg":{
+       "venvEnabled":true,
+       "venvDir":"{workPath}/venv/",
+       "venvName":"tempVenv"
+    },
+    "commandList":[
+ 
+    ],
+    "runConfig":{
+       "mode":"checkOutput",
+       "traversal":"firstFailedVersion",
+       "stopPattern":"{ErrorPattern}"
+    },
+    "dlbConfig":{
+       "launchedAsJob":true,
+       "toolName":"ac",
+       "wheelVersionsMap":{
+ 
+       },
+       "commonPath":"{precommitPath}",
+       "subPath":"{subPath}",
+       "appPath":"",
+       "appCmd":""
+    },
+    "cachedPathConfig":{
+       "enabled":true,
+       "scheme":"mandatory",
+       "passCmdList":false,
+       "changeAppPath":false,
+       "commonPath":"{precommitPath}",
+       "subPath":"{subPath}",
+       "cashMap":{
+ 
+       }
+    },
+    "substitutionRules":[
+       {
+          "name":"precommitPath",
+          "enabled":true,
+          "type":"map",
+          "placeholder":"precommitPath",
+          "from":"$.cachedPathConfig.cashMap",
+          "to":"$.appCmd"
+       },
+       {
+          "name":"wheelVersion",
+          "enabled":true,
+          "type":"map",
+          "placeholder":"wheelVersion",
+          "from":"$.dlbConfig.wheelVersionsMap",
+          "to":"$.appCmd"
+       },
+       {
+        "name":"command line for client application",
+        "enabled":true,
+        "type":"static",
+        "placeholder":"appCmd",
+        "from":"$.dlbConfig.appCmd",
+        "to":"$.appCmd"
+        },
+        {
+        "name":"path to AC requirements",
+        "enabled":true,
+        "type":"static",
+        "placeholder":"toolPath",
+        "from":"$.dlbConfig.toolPath",
+        "to":"$.appCmd"
+        }
+    ],
+    "subscriptions":[
+       {
+          "name":"wheelPathsMap",
+          "enabled":true
+       },
+       {
+          "name":"wheelVersionsMap",
+          "enabled":true
+       }
+    ],
+    "verboseOutput":false
+ }
\ No newline at end of file
diff --git a/src/plugins/intel_cpu/tools/commit_slider/utils/cfg_samples/bm_output.json b/src/plugins/intel_cpu/tools/commit_slider/utils/cfg_samples/bm_output.json
index 3ec3d96d5963d8..99fce43d90d2ad 100644
--- a/src/plugins/intel_cpu/tools/commit_slider/utils/cfg_samples/bm_output.json
+++ b/src/plugins/intel_cpu/tools/commit_slider/utils/cfg_samples/bm_output.json
@@ -1,5 +1,5 @@
 {
-    "appCmd" : "./benchmark_app -m <model_path> -d CPU -t 10",
+    "appCmd" : "{appCmd}",
     "makeCmd" : "cmake -DCMAKE_BUILD_TYPE=Release -DENABLE_PYTHON=OFF -DTHREADING=TBB -DENABLE_INTEL_GPU=OFF -DENABLE_SAMPLES=ON -DENABLE_TESTS=OFF -DENABLE_HETERO=OFF -DENABLE_TEMPLATE=OFF -DENABLE_CPU_DEBUG_CAPS=OFF -DENABLE_DEBUG_CAPS=OFF -DENABLE_OPENVINO_DEBUG=OFF -DCMAKE_CXX_FLAGS=-Wno-deprecated -DCMAKE_C_FLAGS=-Wno-deprecated -DCMAKE_CXX_FLAGS=-Wno-deprecated-declarations -DCMAKE_C_FLAGS=-Wno-deprecated-declarations ..",
     "runConfig" : {
         "commitList" : {
@@ -7,6 +7,7 @@
         },
         "mode" : "checkOutput",
         "traversal" : "firstFailedVersion",
-        "stopPattern" : "(.)*<bm_error_message>(.)*"
-    }
+        "stopPattern" : "(.)*{bm_error_message}(.)*"
+    },
+    "extendBuildCommand":true
 }
\ No newline at end of file
diff --git a/src/plugins/intel_cpu/tools/commit_slider/utils/cfg_samples/e2e_for_CI.json b/src/plugins/intel_cpu/tools/commit_slider/utils/cfg_samples/e2e_for_CI.json
new file mode 100644
index 00000000000000..484e80cabe81dc
--- /dev/null
+++ b/src/plugins/intel_cpu/tools/commit_slider/utils/cfg_samples/e2e_for_CI.json
@@ -0,0 +1,67 @@
+{
+    "appCmd":"source {venvName}/bin/activate && cd {appPath} && {py} -m pip install --upgrade pip && {py} -m pip install openvino-dev[caffe,kaldi,mxnet,onnx,pytorch,tensorflow2]=={wheelVersion} --find-links={precommitPath}wheels/ && {py} -m pip install -r requirements_dev.txt && {testCmd}",
+    "venvCfg":{
+       "venvEnabled":true,
+       "venvDir":"{workPath}/venv/",
+       "venvName":"tempVenv"
+    },
+    "commandList":[
+ 
+    ],
+    "runConfig":{
+       "mode":"checkOutput",
+       "traversal":"firstFailedVersion",
+       "stopPattern":"{ErrorPattern}"
+    },
+    "dlbConfig":{
+       "launchedAsJob":true,
+       "toolName":"e2e",
+       "wheelVersionsMap":{
+ 
+       },
+       "commonPath":"{precommitPath}",
+       "subPath":"{subPath}",
+       "appPath":"",
+       "appCmd":""
+    },
+    "cachedPathConfig":{
+       "enabled":true,
+       "scheme":"mandatory",
+       "passCmdList":false,
+       "changeAppPath":false,
+       "commonPath":"{precommitPath}",
+       "subPath":"{subPath}",
+       "cashMap":{
+ 
+       }
+    },
+    "substitutionRules":[
+       {
+          "name":"precommitPath",
+          "enabled":true,
+          "type":"map",
+          "placeholder":"precommitPath",
+          "from":"$.cachedPathConfig.cashMap",
+          "to":"$.appCmd"
+       },
+       {
+          "name":"wheelVersion",
+          "enabled":true,
+          "type":"map",
+          "placeholder":"wheelVersion",
+          "from":"$.dlbConfig.wheelVersionsMap",
+          "to":"$.appCmd"
+       }
+    ],
+    "subscriptions":[
+       {
+          "name":"wheelPathsMap",
+          "enabled":true
+       },
+       {
+          "name":"wheelVersionsMap",
+          "enabled":true
+       }
+    ],
+    "verboseOutput":false
+ }
\ No newline at end of file
diff --git a/src/plugins/intel_cpu/tools/commit_slider/utils/helpers.py b/src/plugins/intel_cpu/tools/commit_slider/utils/helpers.py
index c705e8b362c56c..8185946c1ae54a 100644
--- a/src/plugins/intel_cpu/tools/commit_slider/utils/helpers.py
+++ b/src/plugins/intel_cpu/tools/commit_slider/utils/helpers.py
@@ -290,12 +290,9 @@ def fetchAppOutput(cfg, commit):
         for item in [
                 {"src": cfg["venvCfg"]["venvName"], "dst": "venvName"},
                 {"src": cfg["appPath"], "dst": "appPath"},
-                {"src": sys.executable, "dst": "py"},
-                # for AC case
-                {"src": cfg["dlbConfig"]["appCmd"], "dst": "appCmd"},
-                {"src": cfg["dlbConfig"]["toolPath"], "dst": "toolPath"}
+                {"src": sys.executable, "dst": "py"}
                 ]:
-            appCmd = multistepStrFormat(
+            appCmd = CfgManager.multistepStrFormat(
                 appCmd,
                 item["dst"],
                 item["src"]
@@ -646,7 +643,8 @@ def formatJSON(content, formatLambda):
     return content
 
 def applySubstitutionRules(cfg: map, rules: list, commit: str=None):
-    # if commit is None, the rule is considered as static,
+    # if commit is None or rule['type'] == 'static',
+    # the rule is considered as static,
     # substitution proceeds as simple string replacing
 
     serviceCfg = cfg["serviceConfig"]
@@ -682,14 +680,15 @@ def applySubstitutionRules(cfg: map, rules: list, commit: str=None):
             srcPos = srcPos[item]
         for item in pathToDst:
             dstPos = dstPos[item]
+        ruleIsStatic = True if rule["type"] == "static" else False
         dstPos = formatJSON(
             dstPos,
             lambda content:
-            multistepStrFormat(
+            CfgManager.multistepStrFormat(
                 content,
                 rule["placeholder"],
                 getMapValueByShortHash(srcPos, commit)\
-                    if commit is not None\
+                    if commit is not None and not ruleIsStatic\
                     else srcPos
             )
         )
@@ -704,12 +703,6 @@ def getMapValueByShortHash(map: dict, commit: str):
         commit, map.keys()
     ))
 
-def multistepStrFormat(input: str, placeholder: str, substitution: str):
-    return input.replace(
-        '{}{}{}'.format('{', placeholder, '}'),
-        substitution
-    )
-
 def deepMapUpdate(content: map, path: list, substitution):
     if not path:
         return substitution
diff --git a/src/plugins/intel_gpu/include/intel_gpu/graph/kernel_impl_params.hpp b/src/plugins/intel_gpu/include/intel_gpu/graph/kernel_impl_params.hpp
index 6bb1f5da35929d..8779467ce0a23b 100644
--- a/src/plugins/intel_gpu/include/intel_gpu/graph/kernel_impl_params.hpp
+++ b/src/plugins/intel_gpu/include/intel_gpu/graph/kernel_impl_params.hpp
@@ -44,6 +44,7 @@ struct kernel_impl_params final {
     std::vector<cldnn::fused_primitive_desc> fused_desc;
 #ifdef ENABLE_ONEDNN_FOR_GPU
     std::vector<cldnn::fused_primitive_desc_onednn> fused_desc_onednn;
+    std::shared_ptr<dnnl::primitive_attr> attrs_onednn;
 #endif // ENABLE_ONEDNN_FOR_GPU
 
     optional_layout weights_layout = optional_layout();
diff --git a/src/plugins/intel_gpu/include/intel_gpu/graph/program.hpp b/src/plugins/intel_gpu/include/intel_gpu/graph/program.hpp
index 309e606080a38a..bc3c438f579055 100644
--- a/src/plugins/intel_gpu/include/intel_gpu/graph/program.hpp
+++ b/src/plugins/intel_gpu/include/intel_gpu/graph/program.hpp
@@ -203,7 +203,7 @@ struct program {
                           bool connect_int_node_with_old_dep = true,
                           bool move_usrs_of_prev_to_node = false);
 
-    void add_connection(program_node& prev, program_node& next);
+    void add_connection(program_node& prev, program_node& next, int32_t port_idx = -1);
 
     // removes a node from the graph and deletes it afterwards,
     // prereq: node cannot be marked as output and has to have exactly one dependency
diff --git a/src/plugins/intel_gpu/include/intel_gpu/graph/serialization/cl_kernel_data_serializer.hpp b/src/plugins/intel_gpu/include/intel_gpu/graph/serialization/cl_kernel_data_serializer.hpp
index a35b2f5905c079..543b88b9295299 100644
--- a/src/plugins/intel_gpu/include/intel_gpu/graph/serialization/cl_kernel_data_serializer.hpp
+++ b/src/plugins/intel_gpu/include/intel_gpu/graph/serialization/cl_kernel_data_serializer.hpp
@@ -6,9 +6,8 @@
 
 #include <type_traits>
 #include "buffer.hpp"
-#include "helpers.hpp"
 #include "kernel_selector_common.h"
-#include "intel_gpu/runtime/kernel_args.hpp"
+
 
 namespace cldnn {
 
@@ -16,17 +15,7 @@ template <typename BufferType>
 class Serializer<BufferType, kernel_selector::clKernelData, typename std::enable_if<std::is_base_of<OutputBuffer<BufferType>, BufferType>::value>::type> {
 public:
     static void save(BufferType& buffer, const kernel_selector::clKernelData& data) {
-        const auto& params = data.params;
-        buffer(params.workGroups.global, params.workGroups.local);
-        buffer << params.arguments.size();
-        for (const auto& arg : params.arguments) {
-            buffer << make_data(&arg.t, sizeof(argument_desc::Types)) << arg.index;
-        }
-        buffer << params.scalars.size();
-        for (const auto& scalar : params.scalars) {
-            buffer << make_data(&scalar.t, sizeof(scalar_desc::Types)) << make_data(&scalar.v, sizeof(scalar_desc::ValueT));
-        }
-        buffer << params.layerID;
+        data.save(buffer);
     }
 };
 
@@ -34,24 +23,7 @@ template <typename BufferType>
 class Serializer<BufferType, kernel_selector::clKernelData, typename std::enable_if<std::is_base_of<InputBuffer<BufferType>, BufferType>::value>::type> {
 public:
     static void load(BufferType& buffer, kernel_selector::clKernelData& data) {
-        auto& params = data.params;
-        buffer(params.workGroups.global, params.workGroups.local);
-
-        typename arguments_desc::size_type arguments_desc_size = 0UL;
-        buffer >> arguments_desc_size;
-        params.arguments.resize(arguments_desc_size);
-        for (auto& arg : params.arguments) {
-            buffer >> make_data(&arg.t, sizeof(argument_desc::Types)) >> arg.index;
-        }
-
-        typename scalars_desc::size_type scalars_desc_size = 0UL;
-        buffer >> scalars_desc_size;
-        params.scalars.resize(scalars_desc_size);
-        for (auto& scalar : params.scalars) {
-                buffer >> make_data(&scalar.t, sizeof(scalar_desc::Types)) >> make_data(&scalar.v, sizeof(scalar_desc::ValueT));
-        }
-
-        buffer >> params.layerID;
+        data.load(buffer);
     }
 };
 
diff --git a/src/plugins/intel_gpu/include/intel_gpu/op/rank_constant.hpp b/src/plugins/intel_gpu/include/intel_gpu/op/rank_constant.hpp
deleted file mode 100644
index 3fab2a825fe9a4..00000000000000
--- a/src/plugins/intel_gpu/include/intel_gpu/op/rank_constant.hpp
+++ /dev/null
@@ -1,37 +0,0 @@
-// Copyright (C) 2018-2024 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#pragma once
-
-#include "openvino/core/node.hpp"
-#include "openvino/op/constant.hpp"
-#include "openvino/op/op.hpp"
-#include "util.hpp"
-
-namespace ov {
-namespace intel_gpu {
-namespace op {
-class RankConstant : public ov::op::v0::Constant {
-public:
-    OPENVINO_OP("FullyConnected", "gpu_opset");
-
-    RankConstant() = default;
-    //RankConstant(const element::Type& type, const Shape& shape, const void* data, size_t rank);
-    RankConstant(const Constant& constant,
-                 size_t rank);
-
-    bool visit_attributes(ov::AttributeVisitor &visitor) override;
-
-    void validate_and_infer_types() override;
-
-    std::shared_ptr<Node> clone_with_new_inputs(const ov::OutputVector& new_args) const override;
-
-protected:
-    size_t m_rank;
-    TP_MODE m_tp_mode = TP_MODE::ALL_GATHERH;
-};
-
-}   // namespace op
-}   // namespace intel_gpu
-}   // namespace ov
\ No newline at end of file
diff --git a/src/plugins/intel_gpu/include/intel_gpu/op/swiglu.hpp b/src/plugins/intel_gpu/include/intel_gpu/op/swiglu.hpp
index ca7e465a679898..ddfea93ea598c2 100644
--- a/src/plugins/intel_gpu/include/intel_gpu/op/swiglu.hpp
+++ b/src/plugins/intel_gpu/include/intel_gpu/op/swiglu.hpp
@@ -11,21 +11,31 @@ namespace intel_gpu {
 namespace op {
 
 /// \brief Operator performing Swish Gated Linear Unit Activation
-/// This operation performs gated linear unit activation that combines swish activation function
+/// This operation performs gated linear unit activation that combines swish or gelu activation function
 class SwiGLU : public ov::op::Op {
 public:
     OPENVINO_OP("SwiGLU", "gpu_opset");
 
+    enum GluType {
+        Swish = 0,
+        Gelu,
+        Gelu_Tanh
+    };
+
     SwiGLU() = default;
     /// \brief Constructs an SwiGLU operation.
     ///
     /// \param data Input tensor with data
     /// \param axis The index of an axis in "data" along which to perform the split
     /// \param split_lenghts A list containing the sizes of each output tensor along the split "axis"
+    /// \param glu_type GLU type, one of Swish, Gelu and Gelu_Tanh
+    /// \param split_to_glu_idx Output index of variadic split, which is connected to GLU
     /// \param output_type Output element type
     SwiGLU(const Output<Node>& data,
            int64_t axis,
            int64_t split_lengths,
+           const GluType glu_type,
+           const size_t split_to_glu_idx,
            const ov::element::Type output_type = ov::element::undefined);
 
     bool visit_attributes(ov::AttributeVisitor& visitor) override;
@@ -36,13 +46,19 @@ class SwiGLU : public ov::op::Op {
 
     int64_t get_axis() const { return m_axis; }
     int64_t get_split_lengths() const { return m_split_lengths; }
+    GluType get_glu_type() const { return m_glu_type; }
+    size_t get_split_to_glu_idx() const { return m_split_to_glu_idx; }
 
     void set_axis(int64_t axis) { m_axis = axis; }
     void set_split_lengths(int64_t split_lengths) { m_split_lengths = split_lengths; }
+    void set_glu_type(GluType glu_type) { m_glu_type = glu_type; }
+    void set_split_to_glu_idx(size_t split_to_glu_idx) { m_split_to_glu_idx = split_to_glu_idx; }
 
 private:
     int64_t m_axis = 0;
     int64_t m_split_lengths = 0;
+    GluType m_glu_type = GluType::Swish;
+    size_t m_split_to_glu_idx = 0;
     ov::element::Type m_output_type;
 };
 
diff --git a/src/plugins/intel_gpu/include/intel_gpu/plugin/plugin.hpp b/src/plugins/intel_gpu/include/intel_gpu/plugin/plugin.hpp
index 24d272f7d57ad9..5da1378763c1a9 100644
--- a/src/plugins/intel_gpu/include/intel_gpu/plugin/plugin.hpp
+++ b/src/plugins/intel_gpu/include/intel_gpu/plugin/plugin.hpp
@@ -27,8 +27,10 @@ class Plugin : public ov::IPlugin {
 
     std::map<std::string, std::shared_ptr<RemoteContextImpl>> get_default_contexts() const;
 
-    std::shared_ptr<ov::Model> clone_and_transform_model(const std::shared_ptr<const ov::Model>& network, const ExecutionConfig& config) const;
-    void transform_model(std::shared_ptr<ov::Model>& model, const ExecutionConfig& config) const;
+    std::shared_ptr<ov::Model> clone_and_transform_model(const std::shared_ptr<const ov::Model>& network,
+                                                         const ExecutionConfig& config,
+                                                         const std::shared_ptr<RemoteContextImpl>& context) const;
+    void transform_model(std::shared_ptr<ov::Model>& model, const ExecutionConfig& config, const std::shared_ptr<RemoteContextImpl>& context) const;
     void register_primitives() const;
     std::string get_device_id_from_config(const ov::AnyMap& config) const;
     std::string get_device_id(const ov::AnyMap& config) const;
diff --git a/src/plugins/intel_gpu/include/intel_gpu/plugin/transformations_pipeline.hpp b/src/plugins/intel_gpu/include/intel_gpu/plugin/transformations_pipeline.hpp
index f0c0361ce72077..365deb66ad2a4d 100644
--- a/src/plugins/intel_gpu/include/intel_gpu/plugin/transformations_pipeline.hpp
+++ b/src/plugins/intel_gpu/include/intel_gpu/plugin/transformations_pipeline.hpp
@@ -6,6 +6,7 @@
 
 #include <memory>
 
+#include "intel_gpu/plugin/remote_context.hpp"
 #include "openvino/core/model.hpp"
 
 #include "intel_gpu/runtime/execution_config.hpp"
@@ -16,12 +17,13 @@ namespace intel_gpu {
 
 class TransformationsPipeline {
 public:
-    explicit TransformationsPipeline(const ExecutionConfig &conf, const cldnn::device_info &device_info)
-        : config(conf), device_info(device_info) {}
+    explicit TransformationsPipeline(const ExecutionConfig &conf, const std::shared_ptr<RemoteContextImpl>& context)
+        : config(conf), m_context(context), device_info(context->get_engine().get_device_info()) {}
     void apply(std::shared_ptr<ov::Model> func);
 
 private:
     const ExecutionConfig& config;
+    std::shared_ptr<RemoteContextImpl> m_context;
     cldnn::device_info device_info;
 };
 
diff --git a/src/plugins/intel_gpu/include/intel_gpu/primitives/crop.hpp b/src/plugins/intel_gpu/include/intel_gpu/primitives/crop.hpp
index 909f4c8a83f1f0..abe6d287bd41a9 100644
--- a/src/plugins/intel_gpu/include/intel_gpu/primitives/crop.hpp
+++ b/src/plugins/intel_gpu/include/intel_gpu/primitives/crop.hpp
@@ -104,6 +104,7 @@ struct crop : public primitive_base<crop> {
     /// @param reference_input Reference input tensor with the required dimensions.
     /// @param offsets Input offsets.
     /// @param output_idx Output data index of splited output.
+    /// @param axis Axis along data to split.
     /// @param num_splits The number of pieces that the data tensor should be split into.
     crop(const primitive_id& id,
          const std::vector<input_info>& inputs,
@@ -111,10 +112,11 @@ struct crop : public primitive_base<crop> {
          const tensor& offsets,
          const crop_ngraph_op_mode op_mode,
          const int output_idx,
+         const int64_t axis = -1,
          const size_t num_splits = 1,
          const padding& output_padding = padding())
-        : primitive_base(id, inputs, {output_padding}), reference_input(reference_input),
-            offsets(offsets), output_idx(output_idx), num_splits(num_splits), op_mode(op_mode) {}
+        : primitive_base(id, inputs, {output_padding}), reference_input(reference_input), offsets(offsets),
+            output_idx(output_idx), axis(axis), num_splits(num_splits), op_mode(op_mode) {}
 
     /// @brief Reference input tensor with the required dimensions.
     tensor reference_input;
@@ -122,6 +124,8 @@ struct crop : public primitive_base<crop> {
     tensor offsets;
     /// @brief data index of splited output.
     int output_idx = 0;
+    /// @brief axis along data to split
+    int64_t axis = -1;
     /// @brief num_splits which Split has number of split as property
     size_t num_splits = 1;
     /// @brief original ngraph operation type
@@ -132,6 +136,7 @@ struct crop : public primitive_base<crop> {
         seed = hash_combine(seed, reference_input.hash());
         seed = hash_combine(seed, offsets.hash());
         seed = hash_combine(seed, output_idx);
+        seed = hash_combine(seed, axis);
         seed = hash_combine(seed, num_splits);
         seed = hash_combine(seed, op_mode);
         return seed;
@@ -146,6 +151,7 @@ struct crop : public primitive_base<crop> {
         return reference_input == rhs_casted.reference_input &&
                offsets == rhs_casted.offsets &&
                output_idx == rhs_casted.output_idx &&
+               axis == rhs_casted.axis &&
                num_splits == rhs_casted.num_splits &&
                op_mode == rhs_casted.op_mode;
     }
@@ -155,6 +161,7 @@ struct crop : public primitive_base<crop> {
         ob << reference_input;
         ob << offsets;
         ob << output_idx;
+        ob << axis;
         ob << num_splits;
         ob << make_data(&op_mode, sizeof(crop_ngraph_op_mode));
     }
@@ -164,6 +171,7 @@ struct crop : public primitive_base<crop> {
         ib >> reference_input;
         ib >> offsets;
         ib >> output_idx;
+        ib >> axis;
         ib >> num_splits;
         ib >> make_data(&op_mode, sizeof(crop_ngraph_op_mode));
     }
diff --git a/src/plugins/intel_gpu/include/intel_gpu/primitives/swiglu.hpp b/src/plugins/intel_gpu/include/intel_gpu/primitives/swiglu.hpp
index 975f6677a8493b..0a79f7a9324532 100644
--- a/src/plugins/intel_gpu/include/intel_gpu/primitives/swiglu.hpp
+++ b/src/plugins/intel_gpu/include/intel_gpu/primitives/swiglu.hpp
@@ -4,11 +4,12 @@
 
 #pragma once
 #include "primitive.hpp"
+#include "intel_gpu/op/swiglu.hpp"
 
 namespace cldnn {
 
 /// @brief Swish Gated Linear Unit Activation primitive
-/// @details Performs gated linear unit activation that combines swish activation function
+/// @details Performs gated linear unit activation that combines swish or gelu activation function
 struct swiglu : public primitive_base<swiglu> {
     CLDNN_DECLARE_PRIMITIVE(swiglu);
 
@@ -24,21 +25,29 @@ struct swiglu : public primitive_base<swiglu> {
            const input_info& input,
            const int64_t& axis,
            const int64_t& split_lengths,
+           const ov::intel_gpu::op::SwiGLU::GluType glu_type,
+           const size_t split_to_glu_idx,
            const tensor output_size,
            const padding& output_padding = padding())
            : primitive_base(id, {input}, {output_padding}),
              axis(axis),
              split_lengths(split_lengths),
+             glu_type(glu_type),
+             split_to_glu_idx(split_to_glu_idx),
              output_size(output_size) {}
 
     int64_t axis = 0;
     int64_t split_lengths = 0;
+    ov::intel_gpu::op::SwiGLU::GluType glu_type = ov::intel_gpu::op::SwiGLU::GluType::Swish;
+    size_t split_to_glu_idx = 0;
     tensor output_size;
 
     size_t hash() const override {
         size_t seed = primitive::hash();
         seed = hash_combine(seed, axis);
         seed = hash_combine(seed, split_lengths);
+        seed = hash_combine(seed, glu_type);
+        seed = hash_combine(seed, split_to_glu_idx);
         return seed;
     }
 
@@ -47,7 +56,8 @@ struct swiglu : public primitive_base<swiglu> {
             return false;
 
         auto rhs_casted = downcast<const swiglu>(rhs);
-        return axis == rhs_casted.axis && split_lengths == rhs_casted.split_lengths;
+        return axis == rhs_casted.axis && split_lengths == rhs_casted.split_lengths &&
+               glu_type == rhs_casted.glu_type && split_to_glu_idx == rhs_casted.split_to_glu_idx;
     }
 
     void save(BinaryOutputBuffer& ob) const override {
@@ -55,6 +65,8 @@ struct swiglu : public primitive_base<swiglu> {
         ob << axis;
         ob << split_lengths;
         ob << output_size;
+        ob << make_data(&glu_type, sizeof(glu_type));
+        ob << split_to_glu_idx;
     }
 
     void load(BinaryInputBuffer& ib) override {
@@ -62,6 +74,8 @@ struct swiglu : public primitive_base<swiglu> {
         ib >> axis;
         ib >> split_lengths;
         ib >> output_size;
+        ib >> make_data(&glu_type, sizeof(glu_type));
+        ib >> split_to_glu_idx;
     }
 };
 }  // namespace cldnn
diff --git a/src/plugins/intel_gpu/include/intel_gpu/runtime/debug_configuration.hpp b/src/plugins/intel_gpu/include/intel_gpu/runtime/debug_configuration.hpp
index d86bdab75785c8..d9a30e3244b4ae 100644
--- a/src/plugins/intel_gpu/include/intel_gpu/runtime/debug_configuration.hpp
+++ b/src/plugins/intel_gpu/include/intel_gpu/runtime/debug_configuration.hpp
@@ -48,7 +48,7 @@ enum class LogLevel : int8_t {
 #else
 #define SEPARATE '/'
 #endif
-#define __FILENAME__ (strrchr(__FILE__, SEPARATE) ? strrchr(__FILE__, SEPARATE) + 1 : __FILE__)
+#define GPU_FILENAME (strrchr(__FILE__, SEPARATE) ? strrchr(__FILE__, SEPARATE) + 1 : __FILE__)
 #define GPU_DEBUG_IF(cond) if (cond)
 #define GPU_DEBUG_CODE(...) __VA_ARGS__
 #define GPU_DEBUG_DEFINE_MEM_LOGGER(stage) \
@@ -62,9 +62,9 @@ enum class LogLevel : int8_t {
 #define GPU_DEBUG_LOG_RAW_INT(min_verbose_level) if (cldnn::debug_configuration::get_instance()->verbose >= min_verbose_level) \
     ((cldnn::debug_configuration::get_instance()->verbose_color == 0) ? GPU_DEBUG_LOG_PREFIX : GPU_DEBUG_LOG_COLOR_PREFIX)
 #define GPU_DEBUG_LOG_RAW(min_verbose_level) GPU_DEBUG_LOG_RAW_INT(static_cast<std::underlying_type<ov::intel_gpu::LogLevel>::type>(min_verbose_level))
-#define GPU_DEBUG_LOG_PREFIX    std::cout << cldnn::debug_configuration::prefix << __FILENAME__ << ":" <<__LINE__ << ":" << __func__ << ": "
+#define GPU_DEBUG_LOG_PREFIX    std::cout << cldnn::debug_configuration::prefix << GPU_FILENAME << ":" <<__LINE__ << ":" << __func__ << ": "
 #define GPU_DEBUG_LOG_COLOR_PREFIX  std::cout << DARK_GRAY << cldnn::debug_configuration::prefix << \
-    BLUE << __FILENAME__ << ":" << PURPLE <<  __LINE__ << ":" << CYAN << __func__ << ": " << RESET
+    BLUE << GPU_FILENAME << ":" << PURPLE <<  __LINE__ << ":" << CYAN << __func__ << ": " << RESET
 #define DARK_GRAY   "\033[1;30m"
 #define BLUE        "\033[1;34m"
 #define PURPLE      "\033[1;35m"
@@ -140,6 +140,7 @@ class debug_configuration {
     int disable_runtime_skip_reorder;                           // Disable runtime skip reorder
     int disable_primitive_fusing;                               // Disable primitive fusing
     int disable_fake_alignment;                                 // Disable fake alignment
+    int enable_dynamic_quantize;                                // Enable Dynamic quantization for fully connected primitive
     std::set<int64_t> dump_iteration;                           // Dump n-th execution of network.
     std::vector<std::string> load_layers_raw_dump;              // List of layers to load dumped raw binary and filenames
     static const debug_configuration *get_instance();
diff --git a/src/plugins/intel_gpu/include/intel_gpu/runtime/device_info.hpp b/src/plugins/intel_gpu/include/intel_gpu/runtime/device_info.hpp
index 7a18cd311e1fd4..0c83877851b48b 100644
--- a/src/plugins/intel_gpu/include/intel_gpu/runtime/device_info.hpp
+++ b/src/plugins/intel_gpu/include/intel_gpu/runtime/device_info.hpp
@@ -10,7 +10,6 @@
 #include <string>
 #include <vector>
 #include <tuple>
-#include <array>
 
 namespace cldnn {
 /// @addtogroup cpp_api C++ API
@@ -25,6 +24,17 @@ enum class device_type {
     discrete_gpu = 1
 };
 
+enum class gpu_arch {
+    unknown = 0,
+    gen9 = 1,
+    gen11 = 2,
+    xe_lp = 3,
+    xe_hp = 4,
+    xe_hpg = 5,
+    xe_hpc = 6,
+    xe2 = 7,
+};
+
 /// @brief Defines version of GFX IP
 struct gfx_version {
     uint16_t major;
@@ -77,6 +87,8 @@ struct device_info {
     device_type dev_type;                       ///< Defines type of current GPU device (integrated or discrete)
 
     gfx_version gfx_ver;                        ///< Defines GFX IP version
+    gpu_arch arch;                              ///< Defines arch human readable name
+    uint32_t ip_version;                        ///< Defines raw GFX IP version
     uint32_t device_id;                         ///< ID of current GPU
     uint32_t num_slices;                        ///< Number of slices
     uint32_t num_sub_slices_per_slice;          ///< Number of subslices in a slice
diff --git a/src/plugins/intel_gpu/include/intel_gpu/runtime/kernel_args.hpp b/src/plugins/intel_gpu/include/intel_gpu/runtime/kernel_args.hpp
index 6bce66e64ef808..09dfcf68f05725 100644
--- a/src/plugins/intel_gpu/include/intel_gpu/runtime/kernel_args.hpp
+++ b/src/plugins/intel_gpu/include/intel_gpu/runtime/kernel_args.hpp
@@ -121,8 +121,9 @@ struct kernel_string {
     std::string options;
     std::string entry_point;
     bool batch_compilation;
+    bool has_microkernels;
 
-    kernel_string() : str(""), jit(""), undefs(""), options(""), entry_point(""), batch_compilation(false) {}
+    kernel_string() : str(""), jit(""), undefs(""), options(""), entry_point(""), batch_compilation(false), has_microkernels(false) {}
 
     std::string get_str() const { return str + jit + undefs + options + entry_point; }
     size_t get_hash() const { return std::hash<std::string>()(get_str()); }
diff --git a/src/plugins/intel_gpu/src/graph/crop.cpp b/src/plugins/intel_gpu/src/graph/crop.cpp
index ade00e32152eb9..1a95f56b91ded9 100644
--- a/src/plugins/intel_gpu/src/graph/crop.cpp
+++ b/src/plugins/intel_gpu/src/graph/crop.cpp
@@ -45,7 +45,7 @@ std::vector<layout> crop_inst::calc_output_layouts(const crop_node& /*node*/, co
            "Output data type forcing is not supported for crop_node!");
 
     auto desc = impl_param.typed_desc<crop>();
-    const auto in_layout = impl_param.get_input_layout();
+    const auto in_layout = impl_param.get_input_layout(0);
     std::vector<ShapeType> output_shapes = {ShapeType()};
     std::vector<ShapeType> input_shapes = {
         impl_param.input_layouts[0].get<ShapeType>(),
@@ -53,16 +53,21 @@ std::vector<layout> crop_inst::calc_output_layouts(const crop_node& /*node*/, co
     for (size_t i = 1; i < impl_param.input_layouts.size(); ++i) {
         input_shapes.push_back(impl_param.input_layouts[i].get<ShapeType>());
     }
+    int64_t axis = desc->axis;
 
     // TODO: calling shape_infer for all cropped outpus is redundant... Need to optimize.
     if (desc->op_mode == cldnn::crop_ngraph_op_mode::variadic_split) {
         std::unordered_map<size_t, ov::Tensor> const_data;
 
-        OPENVINO_ASSERT(impl_param.memory_deps.count(1) > 0, "[GPU] Can't find Crop(ngraph VariadicSplit op mode) axis values memory dependency");
-        auto axis_values_mem = impl_param.memory_deps.at(1);
-        cldnn::mem_lock<uint8_t, mem_lock_type::read> axis_values_mem_lock(axis_values_mem, impl_param.get_stream());
-        const_data.emplace(1, make_tensor(axis_values_mem->get_layout(), axis_values_mem_lock.data()));
-
+        // If axis is negative value, it's not nomralized axis value, so it's non-constant input
+        if (axis < 0) {
+            OPENVINO_ASSERT(impl_param.memory_deps.count(1) > 0, "[GPU] Can't find Crop(ngraph VariadicSplit op mode) axis values memory dependency");
+            auto axis_values_mem = impl_param.memory_deps.at(1);
+            cldnn::mem_lock<uint8_t, mem_lock_type::read> axis_values_mem_lock(axis_values_mem, impl_param.get_stream());
+            const_data.emplace(1, make_tensor(axis_values_mem->get_layout(), axis_values_mem_lock.data()));
+        } else {
+            const_data.emplace(1, ov::Tensor(ov::element::i64, ov::Shape{}, static_cast<void*>(&axis)));
+        }
         if (impl_param.memory_deps.count(2) > 0) {
             auto split_length_mem = impl_param.memory_deps.at(2);
             cldnn::mem_lock<uint8_t, mem_lock_type::read> split_length_mem_lock(split_length_mem, impl_param.get_stream());
@@ -79,10 +84,15 @@ std::vector<layout> crop_inst::calc_output_layouts(const crop_node& /*node*/, co
     } else if (desc->op_mode == cldnn::crop_ngraph_op_mode::split) {
         std::unordered_map<size_t, ov::Tensor> const_data;
 
-        OPENVINO_ASSERT(impl_param.memory_deps.count(1) > 0, "[GPU] Can't find Crop(ngraph Split op mode) axis values memory dependency");
-        auto axis_values_mem = impl_param.memory_deps.at(1);
-        cldnn::mem_lock<uint8_t, mem_lock_type::read> axis_values_mem_lock(axis_values_mem, impl_param.get_stream());
-        const_data.emplace(1, make_tensor(axis_values_mem->get_layout(), axis_values_mem_lock.data()));
+        // If axis is negative value, it's not nomralized axis value, so it's non-constant input
+        if (axis < 0) {
+            OPENVINO_ASSERT(impl_param.memory_deps.count(1) > 0, "[GPU] Can't find Crop(ngraph Split op mode) axis values memory dependency");
+            auto axis_values_mem = impl_param.memory_deps.at(1);
+            cldnn::mem_lock<uint8_t, mem_lock_type::read> axis_values_mem_lock(axis_values_mem, impl_param.get_stream());
+            const_data.emplace(1, make_tensor(axis_values_mem->get_layout(), axis_values_mem_lock.data()));
+        } else {
+            const_data.emplace(1, ov::Tensor(ov::element::i64, ov::Shape{}, static_cast<void*>(&axis)));
+        }
 
         ov::op::v1::Split op;
         op.set_friendly_name(desc->id);
diff --git a/src/plugins/intel_gpu/src/graph/graph_optimizer/add_onednn_optimization_attributes.cpp b/src/plugins/intel_gpu/src/graph/graph_optimizer/add_onednn_optimization_attributes.cpp
index 54a688f39037f0..ac599eda6c4bb6 100644
--- a/src/plugins/intel_gpu/src/graph/graph_optimizer/add_onednn_optimization_attributes.cpp
+++ b/src/plugins/intel_gpu/src/graph/graph_optimizer/add_onednn_optimization_attributes.cpp
@@ -15,7 +15,8 @@ using namespace cldnn;
 void add_onednn_optimization_attributes::run(program& p) {
 #ifdef ENABLE_ONEDNN_FOR_GPU
     for (auto& node : p.get_processing_order()) {
-        if (node->get_preferred_impl_type() == impl_types::onednn) {
+        if (node->get_preferred_impl_type() == impl_types::onednn
+            && !node->is_dynamic()) {
             node->init_onednn_primitive_attributes();
         }
     }
diff --git a/src/plugins/intel_gpu/src/graph/graph_optimizer/compile_graph.cpp b/src/plugins/intel_gpu/src/graph/graph_optimizer/compile_graph.cpp
index 0e369974d95e42..f71857791e0a1c 100644
--- a/src/plugins/intel_gpu/src/graph/graph_optimizer/compile_graph.cpp
+++ b/src/plugins/intel_gpu/src/graph/graph_optimizer/compile_graph.cpp
@@ -45,7 +45,6 @@ void compile_graph::run(program& p) {
     GPU_DEBUG_IF(debug_config->disable_onednn_permute_fusion == 1)
         disable_permute_fuse_onednn_gemm = true;
 
-
     for (size_t idx = 0; idx < proc_order.size(); idx++) {
         auto& node = *(std::next(proc_order.begin(), idx));
         const bool use_shape_agnostic_impl = !p.get_config().get_property(ov::intel_gpu::use_only_static_kernels_for_dynamic_shape);
@@ -70,6 +69,14 @@ void compile_graph::run(program& p) {
                         change_initial_impl = false;
                 }
             }
+            if (node->is_type<convolution>()) {
+                auto w_layout = node->as<convolution>().weights().get_output_layout();
+                // Convolution_fsv16_1x1 is only available shape agnostic kernel for onednn convolution which uses the block format.(fsv16)
+                // Onednn convolution doesn't support input padding but most of cldnn optimized convolution require input padding except fsv16_1x1.
+                if (w_layout.spatial(0) != 1 || w_layout.spatial(1) != 1) {
+                    change_initial_impl = false;
+                }
+            }
         }
 
         if (change_initial_impl)
@@ -93,6 +100,10 @@ void compile_graph::run(program& p) {
         if (node->is_type<fully_connected>() && node->is_dynamic() && node->get_output_pshape().size() > 3)
             can_select_impl = false;
 
+        // onednn impls do not support shape agnostic kernel currently.
+        if (node->get_preferred_impl_type() == impl_types::onednn && node->is_dynamic())
+            can_select_impl = false;
+
         // TODO: Remove this WA once we have shape agnostic arg_max_min_axis kernel with non-const k input
         if (node->is_type<arg_max_min>() && node->is_dynamic() && node->as<arg_max_min>().get_primitive()->top_k == 0) {
             can_select_impl = false;
@@ -100,8 +111,10 @@ void compile_graph::run(program& p) {
 
         bool is_planar = format::is_default_format(node->get_output_layout().format);
 
-        if (node->is_dynamic() && !is_planar)
+        if ((node->is_dynamic() && !is_planar &&
+            (!node->is_type<convolution>() || (node->is_type<convolution>() && node->get_output_layout().format != cldnn::format::b_fs_yx_fsv16)))) {
             can_select_impl = false;
+        }
 
         if (node->is_type<condition>() || node->is_type<loop>() || node->is_type<proposal>())
             can_select_impl = true;
diff --git a/src/plugins/intel_gpu/src/graph/graph_optimizer/post_optimize_weights.cpp b/src/plugins/intel_gpu/src/graph/graph_optimizer/post_optimize_weights.cpp
index 9b184485a1da66..f7b4db99afa092 100644
--- a/src/plugins/intel_gpu/src/graph/graph_optimizer/post_optimize_weights.cpp
+++ b/src/plugins/intel_gpu/src/graph/graph_optimizer/post_optimize_weights.cpp
@@ -10,7 +10,9 @@
 #include "deconvolution_inst.h"
 #include "fully_connected_inst.h"
 #include "intel_gpu/runtime/format.hpp"
-
+#ifdef ENABLE_ONEDNN_FOR_GPU
+#include "graph/impls/onednn/utils.hpp"
+#endif // ENABLE_ONEDNN_FOR_GPU
 namespace cldnn {
 
 post_optimize_weights::post_optimize_weights(reorder_factory& rf_ref)
@@ -90,6 +92,15 @@ void post_optimize_weights::optimize_weights(T& node, program& p) {
                 updated_input_layout.format = from_weights_layout(to_weights_layout(input_fmt, false));
 
                 weights_reorder_params->set_input_layout(updated_input_layout);
+#ifdef ENABLE_ONEDNN_FOR_GPU
+                // Need to update WeightsReorderParamsOneDNN of fc onednn imple when input layout data_type/format is different
+                auto onednn_weights_params = std::dynamic_pointer_cast<onednn::WeightsReorderParamsOneDNN>(weights_reorder_params);
+                if (onednn_weights_params &&
+                   (updated_input_layout.format != onednn::find_data_format(onednn_weights_params->_in_desc) ||
+                    onednn::convert_data_type(updated_input_layout.data_type) != onednn_weights_params->_in_desc.get_data_type())) {
+                    onednn_weights_params->_in_desc = onednn::layout_to_memory_desc(updated_input_layout);
+                }
+#endif // ENABLE_ONEDNN_FOR_GPU
                 auto weights_reorder = _rf.get_weights_reorder(prev_node.get_primitive()->input[0].pid,
                                                                weights_reorder_params);
                 auto& weights_reorder_node = p.get_or_create(weights_reorder.first);
diff --git a/src/plugins/intel_gpu/src/graph/graph_optimizer/prepare_buffer_fusing.cpp b/src/plugins/intel_gpu/src/graph/graph_optimizer/prepare_buffer_fusing.cpp
index 1a235f1293f382..5f60d3739a41f5 100644
--- a/src/plugins/intel_gpu/src/graph/graph_optimizer/prepare_buffer_fusing.cpp
+++ b/src/plugins/intel_gpu/src/graph/graph_optimizer/prepare_buffer_fusing.cpp
@@ -71,8 +71,8 @@ auto available_pred = [](const program_node& input) {
 };
 
 bool concat_in_place_optimization::match(const program_node& concat_node,
-                                         kernel_impl_params concat_params,
-                                         std::vector<kernel_impl_params> pred_params,
+                                         kernel_impl_params& concat_params,
+                                         std::vector<kernel_impl_params>& pred_params,
                                          bool is_runtime) {
     if (concat_node.is_output() || concat_params.fused_desc.size() > 0 || concat_node.is_in_shape_of_subgraph())
         return false;
@@ -96,8 +96,8 @@ bool concat_in_place_optimization::match(const program_node& concat_node,
         }
     }
 
-    auto pred_nodes = concat_node.get_dependencies();
-    for (auto p : pred_nodes) {
+    const auto& pred_nodes = concat_node.get_dependencies();
+    for (const auto& p : pred_nodes) {
         // TODO : In dynamic shape only one user is allowed for optimzied concat
         // It is mainly because of the limited flexibility of current exec order
         // For now, we are doing shape_infer for all pred nodes and concats when executing one of the predecessors for runtime buffer fusing
@@ -119,15 +119,15 @@ bool concat_in_place_optimization::match(const program_node& concat_node,
     // For in place concatenation input layouts and data types must match.
     // Also, it checks whether data along f-axis is aligned properly for implicit concat.
     // Otherwise, use explicit concat instead.
-    auto output_format = concat_params.get_output_layout().format;
-    auto output_datatype = concat_params.get_output_layout().data_type;
+    const auto& output_format = concat_params.get_output_layout().format;
+    const auto& output_datatype = concat_params.get_output_layout().data_type;
 
     auto lower_padd_in_axis = concat_params.get_output_layout().data_padding.lower_size().sizes(def_fmt)[concat_axis];
     lower_padd_in_axis = std::max(lower_padd_in_axis,
                                   pred_params[0].get_output_layout().data_padding.lower_size().sizes(def_fmt)[concat_axis]);
 
     size_t idx = 0;
-    for (auto pred : pred_nodes) {
+    for (const auto& pred : pred_nodes) {
         if (!available_pred(*pred.first))
             return false;
         if (pred.first->is_output())
@@ -136,7 +136,7 @@ bool concat_in_place_optimization::match(const program_node& concat_node,
         // which would affect a form of its output (unless debug flag is set),
         // we also need to restrict input types to those which support padding on all axis
         if (!pred.first->is_dynamic() || is_runtime) {
-            if (!pred.first->is_padding_supported(concat_axis, lower_padd_in_axis))
+            if (!pred.first->is_padding_supported(static_cast<int>(concat_axis), lower_padd_in_axis))
                 return false;
         }
         // TODO: handle optimized reshape
@@ -159,7 +159,7 @@ bool concat_in_place_optimization::match(const program_node& concat_node,
             return false;
 
         size_t concat_users = 0;
-        for (auto& user : pred.first->get_users())
+        for (const auto& user : pred.first->get_users())
             if (user->is_type<concatenation>())
                 concat_users += 1;
 
@@ -167,7 +167,7 @@ bool concat_in_place_optimization::match(const program_node& concat_node,
         if (concat_users != 1)
             return false;
 
-        layout pred_l = pred_params[idx].get_output_layout();
+        const layout& pred_l = pred_params[idx].get_output_layout();
         if (output_format != pred_l.format || output_datatype != pred_l.data_type)
             return false;
         if (pred_l.format.block_sizes().size() > 1)
@@ -210,7 +210,7 @@ bool concat_in_place_optimization::match(const program_node& concat_node,
                 }
             }
         }
-        auto input_padd = pred.first->get_output_layout().data_padding;
+        const auto& input_padd = pred.first->get_output_layout().data_padding;
 
         // Check that there isn't already some padding between inputs in concat axis.
         // If node has already been optimized we skip this check - this is just cascade adjustment.
@@ -228,7 +228,7 @@ bool concat_in_place_optimization::match(const program_node& concat_node,
     // Implicit concat for onednn only when use_usm and batch 1.
     if (is_onednn_impl) {
         bool use_usm = concat_node.get_program().get_engine().use_unified_shared_memory();
-        layout concat_out_l = concat_params.get_output_layout();
+        const layout& concat_out_l = concat_params.get_output_layout();
         if (!use_usm)
             return false;
         if (concat_node.is_dynamic() && !is_runtime) {
diff --git a/src/plugins/intel_gpu/src/graph/graph_optimizer/prepare_buffer_fusing.h b/src/plugins/intel_gpu/src/graph/graph_optimizer/prepare_buffer_fusing.h
index b3d9bd4577a9cc..78ac1ff64f99b5 100644
--- a/src/plugins/intel_gpu/src/graph/graph_optimizer/prepare_buffer_fusing.h
+++ b/src/plugins/intel_gpu/src/graph/graph_optimizer/prepare_buffer_fusing.h
@@ -40,8 +40,8 @@ struct concat_in_place_optimization : pattern_match_optimization_typed<concat_in
                                  bool is_runtime);
     bool match(concatenation_node& node);
     static bool match(const program_node& concat_node,
-                      kernel_impl_params concat_params,
-                      std::vector<kernel_impl_params> pred_params,
+                      kernel_impl_params& concat_params,
+                      std::vector<kernel_impl_params>& pred_params,
                       bool is_runtime = false);
     bool optimize(concatenation_node& node) {
         std::list<concatenation_node*> need_reopt;
diff --git a/src/plugins/intel_gpu/src/graph/graph_optimizer/prepare_primitive_fusing.cpp b/src/plugins/intel_gpu/src/graph/graph_optimizer/prepare_primitive_fusing.cpp
index c4f8d1acdd440c..6c6a41a948f212 100644
--- a/src/plugins/intel_gpu/src/graph/graph_optimizer/prepare_primitive_fusing.cpp
+++ b/src/plugins/intel_gpu/src/graph/graph_optimizer/prepare_primitive_fusing.cpp
@@ -196,26 +196,30 @@ void prepare_primitive_fusing::fuse_bias(program &p) {
 
 
         if (node->get_output_layout().is_dynamic()) {
-            auto broadcast_type = eltw_node.get_primitive()->broadcast_spec.m_type;
-            if (!eltw_node.get_dependency(non_const_dep_idx).is_type<fully_connected>())
-                continue;
-            if (broadcast_type != ov::op::AutoBroadcastType::NUMPY && broadcast_type != ov::op::AutoBroadcastType::NONE)
-                continue;
-            // Numpy broadcast rule requires the dimension size which is not one to be same as the corresponding dimension of the other operand.
-            // So we can ensure that the feature size is same for this broadcasting rule, thereby being considered as bias.
-            auto const_shape = eltw_node.get_dependency(const_dep_idx).get_output_layout().get_shape();
-            int32_t count_elements_not_one = 0;
-            int32_t idx_element_not_one = -1;
-            for (size_t i = 0; i < const_shape.size(); ++i) {
-                if (const_shape[i] != 1) {
-                    count_elements_not_one++;
-                    idx_element_not_one = static_cast<int32_t>(i);
+            if (eltw_node.get_dependency(non_const_dep_idx).is_type<fully_connected>()) {
+                auto broadcast_type = eltw_node.get_primitive()->broadcast_spec.m_type;
+                if (broadcast_type != ov::op::AutoBroadcastType::NUMPY && broadcast_type != ov::op::AutoBroadcastType::NONE)
+                    continue;
+
+                // Numpy broadcast rule requires the dimension size which is not one to be same as the corresponding dimension of the other operand.
+                // So we can ensure that the feature size is same for this broadcasting rule, thereby being considered as bias.
+                auto const_shape = eltw_node.get_dependency(const_dep_idx).get_output_layout().get_shape();
+                int32_t count_elements_not_one = 0;
+                int32_t idx_element_not_one = -1;
+                for (size_t i = 0; i < const_shape.size(); ++i) {
+                    if (const_shape[i] != 1) {
+                        count_elements_not_one++;
+                        idx_element_not_one = static_cast<int32_t>(i);
+                    }
+                    if (count_elements_not_one > 1)
+                        break;
                 }
-                if (count_elements_not_one > 1)
-                    break;
-            }
-            if (count_elements_not_one != 1 ||
-                (idx_element_not_one != (static_cast<int32_t>(const_shape.size()) - 1))) {
+
+                if (count_elements_not_one != 1 ||
+                    (idx_element_not_one != (static_cast<int32_t>(const_shape.size()) - 1))) {
+                    continue;
+                }
+            } else if (!eltw_node.get_dependency(non_const_dep_idx).is_type<convolution>()) {
                 continue;
             }
         } else {
@@ -994,10 +998,11 @@ void prepare_primitive_fusing::fuse_simple_primitives(program &p) {
 
             auto fused_node = parents[fused_idx].first;
             auto peer_node = parents[peer_idx].first;
-
             if (_lo.get_optimization_attributes().use_onednn_impls && _lo.is_primitive_implemented_for_onednn(*fused_node)) {
                 auto eltw_in_size = peer_node->get_output_layout();
-                if (eltw_in_size.is_dynamic())
+                if (eltw_in_size.is_dynamic()
+                    // this whitelist condition is temporarily and to be relaxed soon.
+                    && !fused_node->is_type<fully_connected>())
                     return;
             }
             if (parent1.first->is_type<convolution>() && !conv_supports_fusings(parent1.first->as<convolution>()))
diff --git a/src/plugins/intel_gpu/src/graph/graph_optimizer/remove_redundant_reorders.cpp b/src/plugins/intel_gpu/src/graph/graph_optimizer/remove_redundant_reorders.cpp
index 3c669d2d7beb9e..534b6ec857943d 100644
--- a/src/plugins/intel_gpu/src/graph/graph_optimizer/remove_redundant_reorders.cpp
+++ b/src/plugins/intel_gpu/src/graph/graph_optimizer/remove_redundant_reorders.cpp
@@ -11,6 +11,10 @@
 #include "convert_color_inst.h"
 #include "one_hot_inst.h"
 #include "shape_of_inst.h"
+#include "gather_inst.h"
+#include "select_inst.h"
+#include "eltwise_inst.h"
+#include "broadcast_inst.h"
 #include "permute_inst.h"
 #include "depth_to_space_inst.h"
 #include "concatenation_inst.h"
@@ -410,8 +414,11 @@ void remove_redundant_reorders::run(program& p) {
                 continue;
 
             bool same_data_type = input.get_output_layout().data_type == output_layout.data_type;
-            bool allowed_dt_conversion_fuse = (input.is_type<one_hot>() || input.is_type<permute>() || input.is_type<mvn>() || input.is_type<concatenation>() ||
-                                               input.is_type<depth_to_space>() || input.is_type<region_yolo>() || input.is_type<detection_output>());
+            bool allowed_dt_conversion_fuse =
+                (input.is_type<one_hot>() || input.is_type<permute>() || input.is_type<mvn>() ||
+                 input.is_type<concatenation>() || input.is_type<depth_to_space>() || input.is_type<region_yolo>() ||
+                 input.is_type<detection_output>() || input.is_type<gather>() || input.is_type<broadcast>() ||
+                 input.is_type<select>() || input.is_type<eltwise>());
             if (!same_data_type && !allowed_dt_conversion_fuse)
                 continue;
 
@@ -426,8 +433,10 @@ void remove_redundant_reorders::run(program& p) {
             auto old_output_layout_of_input = input.get_output_layout();
             input.set_output_layout(output_layout, false);
             if (input.type()->does_possible_implementation_exist(input)) {
-                // Add fused_primitive_desc of reorder to the previous node which propagates original output layout during shape inference
-                if (input.is_type<mvn>() || input.is_type<concatenation>()) {
+                // Add fused_primitive_desc of reorder to the previous node which propagates original output layout
+                // during shape inference
+                if (input.is_type<mvn>() || input.is_type<concatenation>() || input.is_type<gather>() ||
+                    input.is_type<broadcast>() || input.is_type<select>() || input.is_type<eltwise>()) {
                     fused_primitive_desc local_desc(node.get_primitive());
                     local_desc.f_param = node.get_fuse_params();
                     local_desc.total_num_deps = node.get_dependencies().size();
diff --git a/src/plugins/intel_gpu/src/graph/impls/cpu/sync_tensor.cpp b/src/plugins/intel_gpu/src/graph/impls/cpu/sync_tensor.cpp
index 7f8a83962478d4..605e43bd462893 100644
--- a/src/plugins/intel_gpu/src/graph/impls/cpu/sync_tensor.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/cpu/sync_tensor.cpp
@@ -73,7 +73,7 @@ struct sync_tensor_impl : public typed_primitive_impl<sync_tensor> {
         sub_mem_mgr->_memorys_table[id][w_rank].flag = true;
         std::vector<int> wait_list(w_size, 1);
         wait_list[w_rank] = 0; // no need to wait for itself
-         while (true) {
+        while (true) {
             int wait_size = 0;
             for (int idx = 0; idx < static_cast<int>(w_size); idx++) {
                 if (idx != w_rank && wait_list[idx] > 0 && sub_mem_mgr->_memorys_table[id][idx].flag) {
diff --git a/src/plugins/intel_gpu/src/graph/impls/ocl/activation.cpp b/src/plugins/intel_gpu/src/graph/impls/ocl/activation.cpp
index d58d19e2b1018a..0cda4e14905d06 100644
--- a/src/plugins/intel_gpu/src/graph/impls/ocl/activation.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/ocl/activation.cpp
@@ -73,8 +73,13 @@ struct activation_impl : typed_primitive_impl_ocl<activation> {
     }
 
     void update_dispatch_data(const kernel_impl_params& impl_param) override {
-        auto kernel_params = get_kernel_params(impl_param, true);
-        (_kernel_data.update_dispatch_data_func)(kernel_params, _kernel_data);
+        // If model loaded from cache, params are not initialized, so we create a new object and reuse it in the future
+        if (_kernel_data.params == nullptr) {
+            _kernel_data.params = std::make_shared<kernel_params_t>(get_kernel_params(impl_param, true));
+        }
+
+        update_shapes(*_kernel_data.params, impl_param);
+        (_kernel_data.update_dispatch_data_func)(*_kernel_data.params, _kernel_data);
     }
 };
 
diff --git a/src/plugins/intel_gpu/src/graph/impls/ocl/arg_max_min.cpp b/src/plugins/intel_gpu/src/graph/impls/ocl/arg_max_min.cpp
index d7750e85828e99..a1a869f131b3f4 100644
--- a/src/plugins/intel_gpu/src/graph/impls/ocl/arg_max_min.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/ocl/arg_max_min.cpp
@@ -119,8 +119,13 @@ struct arg_max_min_impl : typed_primitive_impl_ocl<arg_max_min> {
     }
 
     void update_dispatch_data(const kernel_impl_params& impl_param) override {
-        auto kernel_params = get_kernel_params(impl_param, true);
-        (_kernel_data.update_dispatch_data_func)(kernel_params, _kernel_data);
+        // If model loaded from cache, params are not initialized, so we create a new object and reuse it in the future
+        if (_kernel_data.params == nullptr) {
+            _kernel_data.params = std::make_shared<kernel_params_t>(get_kernel_params(impl_param, true));
+        }
+
+        update_shapes(*_kernel_data.params, impl_param);
+        (_kernel_data.update_dispatch_data_func)(*_kernel_data.params, _kernel_data);
     }
 };
 
diff --git a/src/plugins/intel_gpu/src/graph/impls/ocl/border.cpp b/src/plugins/intel_gpu/src/graph/impls/ocl/border.cpp
index b8f341f7442ac8..5efc6fdab36b62 100644
--- a/src/plugins/intel_gpu/src/graph/impls/ocl/border.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/ocl/border.cpp
@@ -105,8 +105,13 @@ struct border_impl : typed_primitive_impl_ocl<border> {
     }
 
     void update_dispatch_data(const kernel_impl_params& impl_param) override {
-        auto kernel_params = get_kernel_params(impl_param, true);
-        (_kernel_data.update_dispatch_data_func)(kernel_params, _kernel_data);
+        // If model loaded from cache, params are not initialized, so we create a new object and reuse it in the future
+        if (_kernel_data.params == nullptr) {
+            _kernel_data.params = std::make_shared<kernel_params_t>(get_kernel_params(impl_param, true));
+        }
+
+        update_shapes(*_kernel_data.params, impl_param);
+        (_kernel_data.update_dispatch_data_func)(*_kernel_data.params, _kernel_data);
     }
 
     void save(BinaryOutputBuffer& ob) const override {
diff --git a/src/plugins/intel_gpu/src/graph/impls/ocl/concatenation.cpp b/src/plugins/intel_gpu/src/graph/impls/ocl/concatenation.cpp
index 50e48f66df5f05..811a6d968bef31 100644
--- a/src/plugins/intel_gpu/src/graph/impls/ocl/concatenation.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/ocl/concatenation.cpp
@@ -82,8 +82,13 @@ struct concatenation_impl : typed_primitive_impl_ocl<concatenation> {
     }
 
     void update_dispatch_data(const kernel_impl_params& impl_param) override {
-        auto kernel_params = get_kernel_params(impl_param, true);
-        (_kernel_data.update_dispatch_data_func)(kernel_params, _kernel_data);
+        // If model loaded from cache, params are not initialized, so we create a new object and reuse it in the future
+        if (_kernel_data.params == nullptr) {
+            _kernel_data.params = std::make_shared<kernel_params_t>(get_kernel_params(impl_param, true));
+        }
+
+        update_shapes(*_kernel_data.params, impl_param);
+        (_kernel_data.update_dispatch_data_func)(*_kernel_data.params, _kernel_data);
     }
 };
 
diff --git a/src/plugins/intel_gpu/src/graph/impls/ocl/convolution.cpp b/src/plugins/intel_gpu/src/graph/impls/ocl/convolution.cpp
index 27b1a40fcf370e..86b1071ad7d077 100644
--- a/src/plugins/intel_gpu/src/graph/impls/ocl/convolution.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/ocl/convolution.cpp
@@ -261,8 +261,13 @@ struct convolution_impl : typed_primitive_impl_ocl<convolution> {
     }
 
     void update_dispatch_data(const kernel_impl_params& impl_param) override {
-       auto kernel_params = get_kernel_params(impl_param, true);
-       (_kernel_data.update_dispatch_data_func)(kernel_params, _kernel_data);
+        // If model loaded from cache, params are not initialized, so we create a new object and reuse it in the future
+        if (_kernel_data.params == nullptr) {
+            _kernel_data.params = std::make_shared<kernel_params_t>(get_kernel_params(impl_param, true));
+        }
+
+        update_shapes(*_kernel_data.params, impl_param);
+        (_kernel_data.update_dispatch_data_func)(*_kernel_data.params, _kernel_data);
     }
 };
 
@@ -356,7 +361,8 @@ attach_convolution_impl::attach_convolution_impl() {
     };
     auto dyn_formats = {
         format::bfyx,
-        format::bfzyx
+        format::bfzyx,
+        format::b_fs_yx_fsv16
     };
 
     implementation_map<convolution>::add(impl_types::ocl,
diff --git a/src/plugins/intel_gpu/src/graph/impls/ocl/crop.cpp b/src/plugins/intel_gpu/src/graph/impls/ocl/crop.cpp
index c4b11b4ababd3c..6fc4d5dd0dfa9e 100644
--- a/src/plugins/intel_gpu/src/graph/impls/ocl/crop.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/ocl/crop.cpp
@@ -47,16 +47,22 @@ struct crop_impl : typed_primitive_impl_ocl<crop> {
         }
         return params;
     }
-        void update_dispatch_data(const kernel_impl_params& impl_param) override {
-            auto kernel_params = get_kernel_params(impl_param, true);
-            auto runtime_offset = convert_data_tensor(impl_param.get_input_layout(), impl_param.input_offsets[0]).GetFirstElementOffset();
-            kernel_selector::ScalarDescriptor s;
-            s.t = kernel_selector::ScalarDescriptor::Types::UINT32;
-            s.v.u32 = static_cast<uint32_t>(runtime_offset);
-            OPENVINO_ASSERT(_kernel_data.kernels[0].params.scalars.size() == 1,
-                    "[GPU] Scalar field for runtime offset is not added for crop shape agnostic impl");
-            _kernel_data.kernels[0].params.scalars[0] = s;
-            (_kernel_data.update_dispatch_data_func)(kernel_params, _kernel_data);
+
+    void update_dispatch_data(const kernel_impl_params& impl_param) override {
+        // If model loaded from cache, params are not initialized, so we create a new object and reuse it in the future
+        if (_kernel_data.params == nullptr) {
+            _kernel_data.params = std::make_shared<kernel_params_t>(get_kernel_params(impl_param, true));
+        }
+
+        update_shapes(*_kernel_data.params, impl_param);
+        auto runtime_offset = convert_data_tensor(impl_param.get_input_layout(), impl_param.input_offsets[0]).GetFirstElementOffset();
+        kernel_selector::ScalarDescriptor s;
+        s.t = kernel_selector::ScalarDescriptor::Types::UINT32;
+        s.v.u32 = static_cast<uint32_t>(runtime_offset);
+        OPENVINO_ASSERT(_kernel_data.kernels[0].params.scalars.size() == 1,
+                "[GPU] Scalar field for runtime offset is not added for crop shape agnostic impl");
+        _kernel_data.kernels[0].params.scalars[0] = s;
+        (_kernel_data.update_dispatch_data_func)(*_kernel_data.params, _kernel_data);
     }
 };
 
diff --git a/src/plugins/intel_gpu/src/graph/impls/ocl/cum_sum.cpp b/src/plugins/intel_gpu/src/graph/impls/ocl/cum_sum.cpp
index 414d70e3b03c2f..c806a785c530f2 100644
--- a/src/plugins/intel_gpu/src/graph/impls/ocl/cum_sum.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/ocl/cum_sum.cpp
@@ -78,8 +78,13 @@ struct cum_sum_impl : typed_primitive_impl_ocl<cum_sum> {
     }
 
     void update_dispatch_data(const kernel_impl_params& impl_param) override {
-        auto kernel_params = get_kernel_params(impl_param, true);
-        (_kernel_data.update_dispatch_data_func)(kernel_params, _kernel_data);
+        // If model loaded from cache, params are not initialized, so we create a new object and reuse it in the future
+        if (_kernel_data.params == nullptr) {
+            _kernel_data.params = std::make_shared<kernel_params_t>(get_kernel_params(impl_param, true));
+        }
+
+        update_shapes(*_kernel_data.params, impl_param);
+        (_kernel_data.update_dispatch_data_func)(*_kernel_data.params, _kernel_data);
     }
 };
 
diff --git a/src/plugins/intel_gpu/src/graph/impls/ocl/fully_connected.cpp b/src/plugins/intel_gpu/src/graph/impls/ocl/fully_connected.cpp
index 1161c8a595ed4f..f5f619bbb6c21a 100644
--- a/src/plugins/intel_gpu/src/graph/impls/ocl/fully_connected.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/ocl/fully_connected.cpp
@@ -73,7 +73,7 @@ struct fully_connected_impl : typed_primitive_impl_ocl<fully_connected> {
     }
 
 public:
-    static kernel_params_t get_kernel_params(const kernel_impl_params& impl_param, bool is_shape_agnostic = false) {
+    static kernel_impl_params update_impl_params(const kernel_impl_params& impl_param) {
         const auto& primitive = impl_param.typed_desc<fully_connected>();
 
         auto get_fc_input_layouts = [primitive](const std::vector<layout>& input_layouts, bool allow_new_shape_infer) {
@@ -147,12 +147,19 @@ struct fully_connected_impl : typed_primitive_impl_ocl<fully_connected> {
         auto updated_impl_param = impl_param;
 
         const auto input_layouts = get_fc_input_layouts(impl_param.input_layouts, allow_new_shape_infer);
-        updated_impl_param.input_layouts[0] = input_layouts[0];
-        updated_impl_param.input_layouts[1] = input_layouts[1];
+        for (size_t i = 0; i < input_layouts.size(); ++i) {
+            updated_impl_param.input_layouts[i] = input_layouts[i];
+        }
         updated_impl_param.weights_layout = input_layouts[1];
 
         updated_impl_param.output_layouts[0] = get_fc_output_layout(input_layouts, impl_param.get_output_layout());
 
+        return updated_impl_param;
+    }
+
+    static kernel_params_t get_kernel_params(const kernel_impl_params& impl_param, bool is_shape_agnostic = false) {
+        const auto& primitive = impl_param.typed_desc<fully_connected>();
+        auto updated_impl_param = update_impl_params(impl_param);
         auto params = get_weights_bias_default_params<kernel_selector::fully_connected_params>(updated_impl_param, false, is_shape_agnostic);
         params.allowInputReordering = true;
 
@@ -160,10 +167,10 @@ struct fully_connected_impl : typed_primitive_impl_ocl<fully_connected> {
         bool with_zp = !primitive->decompression_zero_point.empty();
         if (commpressed) {
             params.compressed = true;
-            params.decompression_scale = convert_data_tensor(input_layouts[2]);
+            params.decompression_scale = convert_data_tensor(updated_impl_param.input_layouts[2]);
             if (with_zp) {
                 params.has_decompression_zp = true;
-                params.decompression_zero_point = convert_data_tensor(input_layouts[3]);
+                params.decompression_zero_point = convert_data_tensor(updated_impl_param.input_layouts[3]);
             } else if (primitive->decompression_zero_point_scalar.has_value()) {
                 params.has_decompression_zp = true;
                 params.scalar_zp = true;
@@ -184,12 +191,26 @@ struct fully_connected_impl : typed_primitive_impl_ocl<fully_connected> {
             params.quantization = kernel_selector::QuantizationType::NONE;
         }
 
+        params.dynamic_quantization_group_size = impl_param.get_program().get_config().get_property(ov::hint::dynamic_quantization_group_size);
+
         return params;
     }
 
     void update_dispatch_data(const kernel_impl_params& impl_param) override {
-        auto kernel_params = get_kernel_params(impl_param, true);
-        (_kernel_data.update_dispatch_data_func)(kernel_params, _kernel_data);
+        // If model loaded from cache, params are not initialized, so we create a new object and reuse it in the future
+        if (_kernel_data.params == nullptr) {
+            _kernel_data.params = std::make_shared<kernel_params_t>(get_kernel_params(impl_param, true));
+        }
+
+        auto& params = static_cast<kernel_params_t&>(*_kernel_data.params);
+        auto updated_impl_param = update_impl_params(impl_param);
+        update_shapes(params, updated_impl_param);
+
+        if (impl_param.typed_desc<fully_connected>()->input_size != 3) {
+            params.outputs = { params.outputs[0].FlattenFeatureAndSpatials() };
+        }
+
+        (_kernel_data.update_dispatch_data_func)(*_kernel_data.params, _kernel_data);
     }
 };
 namespace detail {
diff --git a/src/plugins/intel_gpu/src/graph/impls/ocl/gather.cpp b/src/plugins/intel_gpu/src/graph/impls/ocl/gather.cpp
index af69439d7aa51a..0d20294c6bc33b 100644
--- a/src/plugins/intel_gpu/src/graph/impls/ocl/gather.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/ocl/gather.cpp
@@ -167,8 +167,13 @@ struct gather_impl : typed_primitive_impl_ocl<gather> {
     }
 
     void update_dispatch_data(const kernel_impl_params& impl_param) override {
-        auto kernel_params = get_kernel_params(impl_param, true);
-        (_kernel_data.update_dispatch_data_func)(kernel_params, _kernel_data);
+        // If model loaded from cache, params are not initialized, so we create a new object and reuse it in the future
+        if (_kernel_data.params == nullptr) {
+            _kernel_data.params = std::make_shared<kernel_params_t>(get_kernel_params(impl_param, true));
+        }
+
+        update_shapes(*_kernel_data.params, impl_param);
+        (_kernel_data.update_dispatch_data_func)(*_kernel_data.params, _kernel_data);
     }
 };
 
diff --git a/src/plugins/intel_gpu/src/graph/impls/ocl/gather_elements.cpp b/src/plugins/intel_gpu/src/graph/impls/ocl/gather_elements.cpp
index 317db10b1463b3..6a683090a7f4ac 100644
--- a/src/plugins/intel_gpu/src/graph/impls/ocl/gather_elements.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/ocl/gather_elements.cpp
@@ -75,8 +75,13 @@ struct gather_elements_impl : typed_primitive_impl_ocl<gather_elements> {
     }
 
     void update_dispatch_data(const kernel_impl_params& impl_param) override {
-       auto kernel_params = get_kernel_params(impl_param, true);
-       (_kernel_data.update_dispatch_data_func)(kernel_params, _kernel_data);
+        // If model loaded from cache, params are not initialized, so we create a new object and reuse it in the future
+        if (_kernel_data.params == nullptr) {
+            _kernel_data.params = std::make_shared<kernel_params_t>(get_kernel_params(impl_param, true));
+        }
+
+        update_shapes(*_kernel_data.params, impl_param);
+        (_kernel_data.update_dispatch_data_func)(*_kernel_data.params, _kernel_data);
     }
 };
 
diff --git a/src/plugins/intel_gpu/src/graph/impls/ocl/gather_nd.cpp b/src/plugins/intel_gpu/src/graph/impls/ocl/gather_nd.cpp
index ac7c8da1ac15eb..8ea57b56614cc9 100644
--- a/src/plugins/intel_gpu/src/graph/impls/ocl/gather_nd.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/ocl/gather_nd.cpp
@@ -45,8 +45,13 @@ struct gather_nd_impl : typed_primitive_impl_ocl<gather_nd> {
     }
 
     void update_dispatch_data(const kernel_impl_params& impl_param) override {
-        auto kernel_params = get_kernel_params(impl_param);
-        (_kernel_data.update_dispatch_data_func)(kernel_params, _kernel_data);
+        // If model loaded from cache, params are not initialized, so we create a new object and reuse it in the future
+        if (_kernel_data.params == nullptr) {
+            _kernel_data.params = std::make_shared<kernel_params_t>(get_kernel_params(impl_param));
+        }
+
+        update_shapes(*_kernel_data.params, impl_param);
+        (_kernel_data.update_dispatch_data_func)(*_kernel_data.params, _kernel_data);
     }
 };
 
diff --git a/src/plugins/intel_gpu/src/graph/impls/ocl/gemm.cpp b/src/plugins/intel_gpu/src/graph/impls/ocl/gemm.cpp
index f7220dad387348..979d1efb8cd76c 100644
--- a/src/plugins/intel_gpu/src/graph/impls/ocl/gemm.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/ocl/gemm.cpp
@@ -303,12 +303,19 @@ struct gemm_impl : multi_stage_primitive<gemm> {
     }
 
     void update_dispatch_data(const kernel_impl_params& impl_param) override {
-        auto kernel_params = get_kernel_params(impl_param, true, false);
-        (_kernels_data[default_gemm].update_dispatch_data_func)(kernel_params, _kernels_data[default_gemm]);
+        // If model loaded from cache, params are not initialized, so we create a new object and reuse it in the future
+        if (_kernels_data[default_gemm].params == nullptr) {
+            _kernels_data[default_gemm].params = std::make_shared<kernel_params_t>(get_kernel_params(impl_param, true, false));
+        }
+        update_shapes(*_kernels_data[default_gemm].params, impl_param);
+        (_kernels_data[default_gemm].update_dispatch_data_func)(*_kernels_data[default_gemm].params, _kernels_data[default_gemm]);
 
         if (_kernels_data.size() == 2) {
-            auto kernel_params = get_kernel_params(impl_param, true, true);
-            (_kernels_data[indirect_gemm].update_dispatch_data_func)(kernel_params, _kernels_data[indirect_gemm]);
+            if (_kernels_data[indirect_gemm].params == nullptr) {
+                _kernels_data[indirect_gemm].params = std::make_shared<kernel_params_t>(get_kernel_params(impl_param, true, true));
+            }
+            update_shapes(*_kernels_data[indirect_gemm].params, impl_param);
+            (_kernels_data[indirect_gemm].update_dispatch_data_func)(*_kernels_data[indirect_gemm].params, _kernels_data[indirect_gemm]);
         }
     }
 };
diff --git a/src/plugins/intel_gpu/src/graph/impls/ocl/group_normalization.cpp b/src/plugins/intel_gpu/src/graph/impls/ocl/group_normalization.cpp
index 827f05c2d2e20f..d79e47e8a114e0 100644
--- a/src/plugins/intel_gpu/src/graph/impls/ocl/group_normalization.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/ocl/group_normalization.cpp
@@ -32,8 +32,13 @@ struct group_normalization_impl : typed_primitive_impl_ocl<group_normalization>
     }
 
     void update_dispatch_data(const kernel_impl_params& impl_param) override {
-        auto kernel_params = get_kernel_params(impl_param, true);
-        (_kernel_data.update_dispatch_data_func)(kernel_params, _kernel_data);
+        // If model loaded from cache, params are not initialized, so we create a new object and reuse it in the future
+        if (_kernel_data.params == nullptr) {
+            _kernel_data.params = std::make_shared<kernel_params_t>(get_kernel_params(impl_param, true));
+        }
+
+        update_shapes(*_kernel_data.params, impl_param);
+        (_kernel_data.update_dispatch_data_func)(*_kernel_data.params, _kernel_data);
     }
 };
 
diff --git a/src/plugins/intel_gpu/src/graph/impls/ocl/kernel_selector_helper.cpp b/src/plugins/intel_gpu/src/graph/impls/ocl/kernel_selector_helper.cpp
index 6e77ddd63d35c5..1f492e14c9fc7e 100644
--- a/src/plugins/intel_gpu/src/graph/impls/ocl/kernel_selector_helper.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/ocl/kernel_selector_helper.cpp
@@ -5,6 +5,7 @@
 #include "intel_gpu/graph/program.hpp"
 
 #include "kernel_selector_helper.h"
+#include "intel_gpu/runtime/device_info.hpp"
 #include "kernel_selector_params.h"
 #include "to_string_utils.h"
 #include "program_node.h"
@@ -32,7 +33,6 @@
 #include "intel_gpu/primitives/extract_image_patches.hpp"
 
 #include "activation_inst.h"
-#include "depth_to_space_inst.h"
 #include "eltwise_inst.h"
 #include "quantize_inst.h"
 #include "reorder_inst.h"
@@ -44,9 +44,9 @@
 #include "kernel_selector/kernels/reorder/reorder_kernel_base.h"
 
 #include "runtime/kernels_cache.hpp"
-#include "kernel_base.h"
 
 #include <string>
+#include <type_traits>
 #include <vector>
 
 namespace {
@@ -119,6 +119,48 @@ bool query_local_block_io_supported(engine& e, const ExecutionConfig& config) {
 
 namespace cldnn {
 
+bool query_microkernels_supported(cldnn::engine& e, const cldnn::ExecutionConfig& config) {
+    auto device = e.get_device().get();
+
+    static std::mutex m;
+    std::lock_guard<std::mutex> lock(m);
+    static std::map<cldnn::device*, bool> cache;
+    if (cache.find(device) != cache.end()) {
+        return cache.at(device);
+    }
+
+    std::shared_ptr<kernel_selector::KernelString> kernel_string = std::make_shared<kernel_selector::KernelString>();
+    // This program check that all required vISA features are supported by current IGC version
+    const char* kernel_code = R""""(
+        kernel void igc_check() {
+            __asm__ volatile(
+                    ".decl AA0 v_type=G type=ud num_elts=1\n"
+                    ".decl AA1 v_type=G type=ud num_elts=1\n"
+                    ".implicit_PSEUDO_INPUT AA0 offset=256 size=4\n"
+                    ".implicit_PSEUDO_INPUT AA1 offset=256 size=4\n"
+                    "mov (M1_NM,1) AA0(0,0)<1> AA1(0,0)<0;1,0>\n"
+            );
+        }
+        )"""";
+
+    kernel_string->str = kernel_code;
+    kernel_string->options = "";
+    kernel_string->entry_point = "igc_check";
+    kernel_string->batch_compilation = true;
+
+    try {
+        cldnn::kernel_impl_params dummy_params;
+        auto _kernels_cache_device_query = std::unique_ptr<cldnn::kernels_cache>(new cldnn::kernels_cache(e, config, 0));
+        _kernels_cache_device_query->add_kernels_source(dummy_params, {kernel_string}, false);
+        _kernels_cache_device_query->build_all();
+        cache[device] = true;
+    } catch (std::exception&) {
+        cache[device] = false;
+    }
+
+    return cache.at(device);
+}
+
 kernel_selector::data_type to_data_type(data_types dt) {
     switch (dt) {
         case cldnn::data_types::i4:
@@ -137,6 +179,8 @@ kernel_selector::data_type to_data_type(data_types dt) {
             return kernel_selector::data_type::F16;
         case cldnn::data_types::f32:
             return kernel_selector::data_type::F32;
+        case cldnn::data_types::bf16:
+            return kernel_selector::data_type::BF16;
         default:
             OPENVINO_THROW("[GPU] Unable to convert cldnn data type ", dt, " to kernel_selector data type");
     }
@@ -181,6 +225,8 @@ kernel_selector::weights_type to_weights_type(data_types dt) {
             return kernel_selector::weights_type::F32;
         case cldnn::data_types::i32:
             return kernel_selector::weights_type::INT32;
+        case cldnn::data_types::bf16:
+            return kernel_selector::weights_type::BF16;
         default:
             OPENVINO_THROW("[GPU] Unable to convert cldnn data type ", dt, " to kernel_selector weights type");
     }
@@ -1077,6 +1123,7 @@ void set_params(const kernel_impl_params& param_info, kernel_selector::params& p
     params.engineInfo.bOptHintsSupport = false;
 
     params.engineInfo.bLocalBlockIOSupport = query_local_block_io_supported(engine, config);
+    params.engineInfo.supports_microkernels = query_microkernels_supported(engine, config);
     params.engineInfo.deviceType = get_device_type(device_info.dev_type);
     params.engineInfo.maxWorkGroupSize = device_info.max_work_group_size;
     params.engineInfo.maxLocalMemSize = device_info.max_local_mem_size;
@@ -1088,6 +1135,8 @@ void set_params(const kernel_impl_params& param_info, kernel_selector::params& p
     params.engineInfo.driverVersion = device_info.driver_version;
     params.engineInfo.supportedSimdSizes = device_info.supported_simd_sizes;
     params.engineInfo.vendor_id = device_info.vendor_id;
+    params.engineInfo.ip_version = device_info.ip_version;
+    params.engineInfo.arch = kernel_selector::gpu_arch(static_cast<std::underlying_type<gpu_arch>::type>(device_info.arch));
 
     auto impl_forcing = config.get_property(ov::intel_gpu::force_implementations);
 
diff --git a/src/plugins/intel_gpu/src/graph/impls/ocl/kernel_selector_helper.h b/src/plugins/intel_gpu/src/graph/impls/ocl/kernel_selector_helper.h
index de746e94661ff7..67e2127a064001 100644
--- a/src/plugins/intel_gpu/src/graph/impls/ocl/kernel_selector_helper.h
+++ b/src/plugins/intel_gpu/src/graph/impls/ocl/kernel_selector_helper.h
@@ -294,4 +294,6 @@ inline void update_shapes(kernel_selector::Params& p, const kernel_impl_params&
     }
 }
 
+bool query_microkernels_supported(cldnn::engine& e, const cldnn::ExecutionConfig& config);
+
 }  // namespace cldnn
diff --git a/src/plugins/intel_gpu/src/graph/impls/ocl/multinomial.cpp b/src/plugins/intel_gpu/src/graph/impls/ocl/multinomial.cpp
index bcc2fa15be20b3..45607326ff2925 100644
--- a/src/plugins/intel_gpu/src/graph/impls/ocl/multinomial.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/ocl/multinomial.cpp
@@ -32,8 +32,13 @@ struct multinomial_impl : typed_primitive_impl_ocl<multinomial> {
     }
 
     void update_dispatch_data(const kernel_impl_params& impl_param) override {
-        auto kernel_params = get_kernel_params(impl_param, true);
-        (_kernel_data.update_dispatch_data_func)(kernel_params, _kernel_data);
+        // If model loaded from cache, params are not initialized, so we create a new object and reuse it in the future
+        if (_kernel_data.params == nullptr) {
+            _kernel_data.params = std::make_shared<kernel_params_t>(get_kernel_params(impl_param, true));
+        }
+
+        update_shapes(*_kernel_data.params, impl_param);
+        (_kernel_data.update_dispatch_data_func)(*_kernel_data.params, _kernel_data);
     }
 };
 
diff --git a/src/plugins/intel_gpu/src/graph/impls/ocl/mvn.cpp b/src/plugins/intel_gpu/src/graph/impls/ocl/mvn.cpp
index c370bd8dff4c40..a3de617405fbad 100644
--- a/src/plugins/intel_gpu/src/graph/impls/ocl/mvn.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/ocl/mvn.cpp
@@ -88,8 +88,13 @@ struct mvn_impl : typed_primitive_impl_ocl<mvn> {
     }
 
     void update_dispatch_data(const kernel_impl_params& impl_param) override {
-        auto kernel_params = get_kernel_params(impl_param, true);
-        (_kernel_data.update_dispatch_data_func)(kernel_params, _kernel_data);
+        // If model loaded from cache, params are not initialized, so we create a new object and reuse it in the future
+        if (_kernel_data.params == nullptr) {
+            _kernel_data.params = std::make_shared<kernel_params_t>(get_kernel_params(impl_param, true));
+        }
+
+        update_shapes(*_kernel_data.params, impl_param);
+        (_kernel_data.update_dispatch_data_func)(*_kernel_data.params, _kernel_data);
     }
 };
 
diff --git a/src/plugins/intel_gpu/src/graph/impls/ocl/non_max_suppression.cpp b/src/plugins/intel_gpu/src/graph/impls/ocl/non_max_suppression.cpp
index 81194cb8ba9c5a..65bfa94173bf11 100644
--- a/src/plugins/intel_gpu/src/graph/impls/ocl/non_max_suppression.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/ocl/non_max_suppression.cpp
@@ -60,8 +60,9 @@ struct non_max_suppression_impl : typed_primitive_impl_ocl<non_max_suppression>
     }
 
 public:
-    static std::unique_ptr<primitive_impl> create(const non_max_suppression_node& arg, const kernel_impl_params& impl_param) {
+static kernel_params_t get_kernel_params(const kernel_impl_params& impl_param, bool is_shape_agnostic = false) {
         const auto& primitive = impl_param.typed_desc<non_max_suppression>();
+        const auto& arg = impl_param.prog->get_node(impl_param.desc->id).as<non_max_suppression>();
         auto params = get_default_params<kernel_selector::non_max_suppression_params>(impl_param);
 
         const auto input_scores_idx = 1;
@@ -154,11 +155,7 @@ struct non_max_suppression_impl : typed_primitive_impl_ocl<non_max_suppression>
             params.reuse_internal_buffer = true;
         }
 
-        params.set_dynamic_shape_offsets();
-        auto& kernel_selector = kernel_selector::non_max_suppression_kernel_selector::Instance();
-        auto best_kernel = kernel_selector.get_best_kernel(params);
-
-        return make_unique<non_max_suppression_impl>(best_kernel);
+        return params;
     }
 
 private:
@@ -200,7 +197,7 @@ namespace detail {
 
 attach_non_max_suppression_impl::attach_non_max_suppression_impl() {
     implementation_map<non_max_suppression>::add(impl_types::ocl,
-                                                 non_max_suppression_impl::create,
+                                                 typed_primitive_impl_ocl<non_max_suppression>::create<non_max_suppression_impl>,
                                                  {
                                                      std::make_tuple(data_types::i32, format::bfyx),
 
diff --git a/src/plugins/intel_gpu/src/graph/impls/ocl/non_zero.cpp b/src/plugins/intel_gpu/src/graph/impls/ocl/non_zero.cpp
index c8f0a5cbae10c0..d8f0e45c25146f 100644
--- a/src/plugins/intel_gpu/src/graph/impls/ocl/non_zero.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/ocl/non_zero.cpp
@@ -48,8 +48,13 @@ struct count_nonzero_impl : typed_primitive_impl_ocl<count_nonzero> {
     }
 
     void update_dispatch_data(const kernel_impl_params& impl_param) override {
-        auto kernel_params = get_kernel_params(impl_param, true);
-        (_kernel_data.update_dispatch_data_func)(kernel_params, _kernel_data);
+        // If model loaded from cache, params are not initialized, so we create a new object and reuse it in the future
+        if (_kernel_data.params == nullptr) {
+            _kernel_data.params = std::make_shared<kernel_params_t>(get_kernel_params(impl_param, true));
+        }
+
+        update_shapes(*_kernel_data.params, impl_param);
+        (_kernel_data.update_dispatch_data_func)(*_kernel_data.params, _kernel_data);
     }
 };
 
@@ -82,8 +87,13 @@ struct gather_nonzero_impl : typed_primitive_impl_ocl<gather_nonzero> {
     }
 
     void update_dispatch_data(const kernel_impl_params& impl_param) override {
-        auto kernel_params = get_kernel_params(impl_param, true);
-        (_kernel_data.update_dispatch_data_func)(kernel_params, _kernel_data);
+        // If model loaded from cache, params are not initialized, so we create a new object and reuse it in the future
+        if (_kernel_data.params == nullptr) {
+            _kernel_data.params = std::make_shared<kernel_params_t>(get_kernel_params(impl_param, true));
+        }
+
+        update_shapes(*_kernel_data.params, impl_param);
+        (_kernel_data.update_dispatch_data_func)(*_kernel_data.params, _kernel_data);
     }
 };
 
diff --git a/src/plugins/intel_gpu/src/graph/impls/ocl/permute.cpp b/src/plugins/intel_gpu/src/graph/impls/ocl/permute.cpp
index 06ea0a215d12cf..e253fef751b091 100644
--- a/src/plugins/intel_gpu/src/graph/impls/ocl/permute.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/ocl/permute.cpp
@@ -71,8 +71,13 @@ struct permute_impl : typed_primitive_impl_ocl<permute> {
     }
 
     void update_dispatch_data(const kernel_impl_params& impl_param) override {
-        auto kernel_params = get_kernel_params(impl_param, true);
-        (_kernel_data.update_dispatch_data_func)(kernel_params, _kernel_data);
+        // If model loaded from cache, params are not initialized, so we create a new object and reuse it in the future
+        if (_kernel_data.params == nullptr) {
+            _kernel_data.params = std::make_shared<kernel_params_t>(get_kernel_params(impl_param, true));
+        }
+
+        update_shapes(*_kernel_data.params, impl_param);
+        (_kernel_data.update_dispatch_data_func)(*_kernel_data.params, _kernel_data);
     }
 };
 
diff --git a/src/plugins/intel_gpu/src/graph/impls/ocl/quantize.cpp b/src/plugins/intel_gpu/src/graph/impls/ocl/quantize.cpp
index f1517b13824c42..73a668966ceeb5 100644
--- a/src/plugins/intel_gpu/src/graph/impls/ocl/quantize.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/ocl/quantize.cpp
@@ -54,8 +54,9 @@ struct quantize_impl : typed_primitive_impl_ocl<quantize> {
     }
 
 public:
-    static std::unique_ptr<primitive_impl> create(const quantize_node& arg, const kernel_impl_params& impl_param) {
+    static kernel_params_t get_kernel_params(const kernel_impl_params& impl_param, bool is_shape_agnostic = false) {
         auto quantize_params = get_default_params<kernel_selector::quantize_params>(impl_param);
+        const auto& arg = impl_param.prog->get_node(impl_param.desc->id).as<quantize>();
 
         quantize_params.levels = arg.get_levels();
         quantize_params.scale_shift_opt = arg.get_scale_shift_opt();
@@ -86,17 +87,17 @@ struct quantize_impl : typed_primitive_impl_ocl<quantize> {
             quantize_params.inputs.push_back(convert_data_tensor(impl_param.input_layouts[i]));
         }
 
-        quantize_params.is_shape_agnostic = impl_param.is_dynamic();
-        quantize_params.set_dynamic_shape_offsets();
-        auto& kernel_selector = kernel_selector::quantize_kernel_selector::Instance();
-        auto best_kernel = kernel_selector.get_best_kernel(quantize_params);
-
-        return make_unique<quantize_impl>(best_kernel);
+        return quantize_params;
     }
 
     void update_dispatch_data(const kernel_impl_params& impl_param) override {
-        auto quantize_params = get_default_params<kernel_selector::quantize_params>(impl_param);
-        (_kernel_data.update_dispatch_data_func)(quantize_params, _kernel_data);
+        // If model loaded from cache, params are not initialized, so we create a new object and reuse it in the future
+        if (_kernel_data.params == nullptr) {
+            _kernel_data.params = std::make_shared<kernel_params_t>(get_kernel_params(impl_param, true));
+        }
+
+        update_shapes(*_kernel_data.params, impl_param);
+        (_kernel_data.update_dispatch_data_func)(*_kernel_data.params, _kernel_data);
     }
 };
 
@@ -147,9 +148,16 @@ attach_quantize_impl::attach_quantize_impl() {
     keys.emplace(data_types::f16, format::yxfb);
     keys.emplace(data_types::f32, format::yxfb);
 
-    implementation_map<quantize>::add(impl_types::ocl, shape_types::static_shape, quantize_impl::create, keys);
+    implementation_map<quantize>::add(impl_types::ocl,
+                                      shape_types::static_shape,
+                                      typed_primitive_impl_ocl<quantize>::create<quantize_impl>,
+                                      keys);
 
-    implementation_map<quantize>::add(impl_types::ocl, shape_types::dynamic_shape, quantize_impl::create, types, dyn_formats);
+    implementation_map<quantize>::add(impl_types::ocl,
+                                      shape_types::dynamic_shape,
+                                      typed_primitive_impl_ocl<quantize>::create<quantize_impl>,
+                                      types,
+                                      dyn_formats);
 }
 
 }  // namespace detail
diff --git a/src/plugins/intel_gpu/src/graph/impls/ocl/reduce.cpp b/src/plugins/intel_gpu/src/graph/impls/ocl/reduce.cpp
index c004b63c4f23fd..d937d7fc8a190c 100644
--- a/src/plugins/intel_gpu/src/graph/impls/ocl/reduce.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/ocl/reduce.cpp
@@ -90,8 +90,13 @@ struct reduce_impl : typed_primitive_impl_ocl<reduce> {
     }
 
     void update_dispatch_data(const kernel_impl_params& impl_param) override {
-        auto kernel_params = get_kernel_params(impl_param, true);
-        (_kernel_data.update_dispatch_data_func)(kernel_params, _kernel_data);
+        // If model loaded from cache, params are not initialized, so we create a new object and reuse it in the future
+        if (_kernel_data.params == nullptr) {
+            _kernel_data.params = std::make_shared<kernel_params_t>(get_kernel_params(impl_param, true));
+        }
+
+        update_shapes(*_kernel_data.params, impl_param);
+        (_kernel_data.update_dispatch_data_func)(*_kernel_data.params, _kernel_data);
     }
 };
 
diff --git a/src/plugins/intel_gpu/src/graph/impls/ocl/reorder.cpp b/src/plugins/intel_gpu/src/graph/impls/ocl/reorder.cpp
index 02d2e9e19827ec..aa030af71dfc3d 100644
--- a/src/plugins/intel_gpu/src/graph/impls/ocl/reorder.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/ocl/reorder.cpp
@@ -119,8 +119,13 @@ struct reorder_impl : typed_primitive_impl_ocl<reorder> {
     }
 
     void update_dispatch_data(const kernel_impl_params& impl_param) override {
-        auto kernel_params = get_kernel_params(impl_param, true);
-        (_kernel_data.update_dispatch_data_func)(kernel_params, _kernel_data);
+        // If model loaded from cache, params are not initialized, so we create a new object and reuse it in the future
+        if (_kernel_data.params == nullptr) {
+            _kernel_data.params = std::make_shared<kernel_params_t>(get_kernel_params(impl_param, true));
+        }
+
+        update_shapes(*_kernel_data.params, impl_param);
+        (_kernel_data.update_dispatch_data_func)(*_kernel_data.params, _kernel_data);
     }
 
     static std::unique_ptr<primitive_impl> create(const reorder_node& arg, const kernel_impl_params& impl_param) {
diff --git a/src/plugins/intel_gpu/src/graph/impls/ocl/rms.cpp b/src/plugins/intel_gpu/src/graph/impls/ocl/rms.cpp
index 946334f7c59d90..34e379564ee10e 100644
--- a/src/plugins/intel_gpu/src/graph/impls/ocl/rms.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/ocl/rms.cpp
@@ -43,8 +43,13 @@ struct rms_impl : typed_primitive_impl_ocl<rms> {
     }
 
     void update_dispatch_data(const kernel_impl_params& impl_param) override {
-        auto kernel_params = get_kernel_params(impl_param, true);
-        (_kernel_data.update_dispatch_data_func)(kernel_params, _kernel_data);
+        // If model loaded from cache, params are not initialized, so we create a new object and reuse it in the future
+        if (_kernel_data.params == nullptr) {
+            _kernel_data.params = std::make_shared<kernel_params_t>(get_kernel_params(impl_param, true));
+        }
+
+        update_shapes(*_kernel_data.params, impl_param);
+        (_kernel_data.update_dispatch_data_func)(*_kernel_data.params, _kernel_data);
     }
 
     static kernel_impl_params static_canonicalize_shapes(const kernel_impl_params& impl_params) {
diff --git a/src/plugins/intel_gpu/src/graph/impls/ocl/rope.cpp b/src/plugins/intel_gpu/src/graph/impls/ocl/rope.cpp
index 4706227e35cff2..f65768b8e6eb20 100644
--- a/src/plugins/intel_gpu/src/graph/impls/ocl/rope.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/ocl/rope.cpp
@@ -91,8 +91,13 @@ struct rope_impl : typed_primitive_impl_ocl<rope> {
     }
 
     void update_dispatch_data(const kernel_impl_params& impl_param) override {
-        auto kernel_params = get_kernel_params(impl_param, true);
-        (_kernel_data.update_dispatch_data_func)(kernel_params, _kernel_data);
+        // If model loaded from cache, params are not initialized, so we create a new object and reuse it in the future
+        if (_kernel_data.params == nullptr) {
+            _kernel_data.params = std::make_shared<kernel_params_t>(get_kernel_params(impl_param, true));
+        }
+
+        update_shapes(*_kernel_data.params, impl_param);
+        (_kernel_data.update_dispatch_data_func)(*_kernel_data.params, _kernel_data);
     }
 };
 
diff --git a/src/plugins/intel_gpu/src/graph/impls/ocl/scaled_dot_product_attention.cpp b/src/plugins/intel_gpu/src/graph/impls/ocl/scaled_dot_product_attention.cpp
index 681237b2bcd02b..fd2270fa366ec6 100644
--- a/src/plugins/intel_gpu/src/graph/impls/ocl/scaled_dot_product_attention.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/ocl/scaled_dot_product_attention.cpp
@@ -58,7 +58,7 @@ struct scaled_dot_product_attention_impl : multi_stage_primitive<scaled_dot_prod
         // buffers number and its' sizes (since update_dispatch_data is called for both kernels too), and
         // do not double memory allocations during reallocate_if_needed() function call
         std::vector<layout> layouts;
-        if (_kernels_data.size() > 0) {
+        if (_kernels_data.size() > 0 && !_kernels_data[0].internalBufferSizes.empty()) {
             auto dtype = from_data_type(_kernels_data[0].internalBufferDataType);
             const auto bpp = data_type_traits::size_of(dtype);
             for (auto size : _kernels_data[0].internalBufferSizes) {
@@ -275,12 +275,19 @@ struct scaled_dot_product_attention_impl : multi_stage_primitive<scaled_dot_prod
     }
 
     void update_dispatch_data(const kernel_impl_params& impl_param) override {
-       auto kernel_params = get_kernel_params(impl_param, true);
-       (_kernels_data[default_sdpa].update_dispatch_data_func)(kernel_params, _kernels_data[default_sdpa]);
+        // If model loaded from cache, params are not initialized, so we create a new object and reuse it in the future
+        if (_kernels_data[default_sdpa].params == nullptr) {
+            _kernels_data[default_sdpa].params = std::make_shared<kernel_params_t>(get_kernel_params(impl_param, true));
+        }
+        update_shapes(*_kernels_data[default_sdpa].params, impl_param);
+        (_kernels_data[default_sdpa].update_dispatch_data_func)(*_kernels_data[default_sdpa].params, _kernels_data[default_sdpa]);
 
         if (_kernels_data.size() == 2) {
-            auto kernel_params = get_kernel_params(impl_param, true);
-            (_kernels_data[indirect_sdpa].update_dispatch_data_func)(kernel_params, _kernels_data[indirect_sdpa]);
+            if (_kernels_data[indirect_sdpa].params == nullptr) {
+                _kernels_data[indirect_sdpa].params = std::make_shared<kernel_params_t>(get_kernel_params(impl_param, true));
+            }
+            update_shapes(*_kernels_data[indirect_sdpa].params, impl_param);
+            (_kernels_data[indirect_sdpa].update_dispatch_data_func)(*_kernels_data[indirect_sdpa].params, _kernels_data[indirect_sdpa]);
         }
     }
 };
diff --git a/src/plugins/intel_gpu/src/graph/impls/ocl/scatter_nd_update.cpp b/src/plugins/intel_gpu/src/graph/impls/ocl/scatter_nd_update.cpp
index 8709b8be75c0ea..e5f1ebd04b1d4a 100644
--- a/src/plugins/intel_gpu/src/graph/impls/ocl/scatter_nd_update.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/ocl/scatter_nd_update.cpp
@@ -45,8 +45,13 @@ struct scatter_nd_update_impl : typed_primitive_impl_ocl<scatter_nd_update> {
     }
 
     void update_dispatch_data(const kernel_impl_params& impl_param) override {
-       auto kernel_params = get_kernel_params(impl_param, true);
-       (_kernel_data.update_dispatch_data_func)(kernel_params, _kernel_data);
+        // If model loaded from cache, params are not initialized, so we create a new object and reuse it in the future
+        if (_kernel_data.params == nullptr) {
+            _kernel_data.params = std::make_shared<kernel_params_t>(get_kernel_params(impl_param, true));
+        }
+
+        update_shapes(*_kernel_data.params, impl_param);
+        (_kernel_data.update_dispatch_data_func)(*_kernel_data.params, _kernel_data);
     }
 };
 
diff --git a/src/plugins/intel_gpu/src/graph/impls/ocl/scatter_update.cpp b/src/plugins/intel_gpu/src/graph/impls/ocl/scatter_update.cpp
index 3e59ecbf63691e..af1029aacb2036 100644
--- a/src/plugins/intel_gpu/src/graph/impls/ocl/scatter_update.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/ocl/scatter_update.cpp
@@ -70,8 +70,13 @@ struct scatter_update_impl : typed_primitive_impl_ocl<scatter_update> {
     }
 
     void update_dispatch_data(const kernel_impl_params& impl_param) override {
-       auto kernel_params = get_kernel_params(impl_param, true);
-       (_kernel_data.update_dispatch_data_func)(kernel_params, _kernel_data);
+        // If model loaded from cache, params are not initialized, so we create a new object and reuse it in the future
+        if (_kernel_data.params == nullptr) {
+            _kernel_data.params = std::make_shared<kernel_params_t>(get_kernel_params(impl_param, true));
+        }
+
+        update_shapes(*_kernel_data.params, impl_param);
+        (_kernel_data.update_dispatch_data_func)(*_kernel_data.params, _kernel_data);
     }
 };
 
diff --git a/src/plugins/intel_gpu/src/graph/impls/ocl/select.cpp b/src/plugins/intel_gpu/src/graph/impls/ocl/select.cpp
index 90a369006715d7..0f69ab377ed3fe 100644
--- a/src/plugins/intel_gpu/src/graph/impls/ocl/select.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/ocl/select.cpp
@@ -61,8 +61,13 @@ struct select_impl : typed_primitive_impl_ocl<select> {
     }
 
     void update_dispatch_data(const kernel_impl_params& impl_param) override {
-        auto kernel_params = get_kernel_params(impl_param, true);
-        (_kernel_data.update_dispatch_data_func)(kernel_params, _kernel_data);
+        // If model loaded from cache, params are not initialized, so we create a new object and reuse it in the future
+        if (_kernel_data.params == nullptr) {
+            _kernel_data.params = std::make_shared<kernel_params_t>(get_kernel_params(impl_param, true));
+        }
+
+        update_shapes(*_kernel_data.params, impl_param);
+        (_kernel_data.update_dispatch_data_func)(*_kernel_data.params, _kernel_data);
     }
 };
 
diff --git a/src/plugins/intel_gpu/src/graph/impls/ocl/shape_of.cpp b/src/plugins/intel_gpu/src/graph/impls/ocl/shape_of.cpp
index ee17337d39044f..70e20b1a01ed53 100644
--- a/src/plugins/intel_gpu/src/graph/impls/ocl/shape_of.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/ocl/shape_of.cpp
@@ -59,8 +59,13 @@ struct shape_of_impl : typed_primitive_impl_ocl<shape_of> {
     }
 
     void update_dispatch_data(const kernel_impl_params& impl_param) override {
-        auto kernel_params = get_kernel_params(impl_param, true);
-        (_kernel_data.update_dispatch_data_func)(kernel_params, _kernel_data);
+        // If model loaded from cache, params are not initialized, so we create a new object and reuse it in the future
+        if (_kernel_data.params == nullptr) {
+            _kernel_data.params = std::make_shared<kernel_params_t>(get_kernel_params(impl_param, true));
+        }
+
+        update_shapes(*_kernel_data.params, impl_param);
+        (_kernel_data.update_dispatch_data_func)(*_kernel_data.params, _kernel_data);
     }
 };
 
diff --git a/src/plugins/intel_gpu/src/graph/impls/ocl/slice.cpp b/src/plugins/intel_gpu/src/graph/impls/ocl/slice.cpp
index 175e26b1a3c5d8..8a7225cb3c64ce 100644
--- a/src/plugins/intel_gpu/src/graph/impls/ocl/slice.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/ocl/slice.cpp
@@ -88,9 +88,10 @@ struct slice_impl : typed_primitive_impl_ocl<slice> {
         return args;
     }
 
-    static std::unique_ptr<primitive_impl> create(const slice_node& arg, const kernel_impl_params& impl_param) {
-        auto params = get_default_params<kernel_selector::slice_params>(impl_param, impl_param.is_dynamic());
+    static kernel_params_t get_kernel_params(const kernel_impl_params& impl_param, bool is_shape_agnostic = false) {
+        auto params = get_default_params<kernel_selector::slice_params>(impl_param, is_shape_agnostic);
         const auto input_rank = params.inputs[0].Dimentions();
+        const auto& arg = impl_param.prog->get_node(impl_param.desc->id);
 
         if (!PrepareInput(arg,
                           SliceKernelRefNeededInputs::kStart,
@@ -135,15 +136,18 @@ struct slice_impl : typed_primitive_impl_ocl<slice> {
         }
 
         params.set_dynamic_shape_offsets();
-        auto& kernel_selector = kernel_selector::slice_kernel_selector::Instance();
-        auto best_kernel = kernel_selector.get_best_kernel(params);
 
-        return make_unique<slice_impl>(best_kernel);
+        return params;
     }
 
     void update_dispatch_data(const kernel_impl_params& impl_param) override {
-        auto kernel_params = get_default_params<kernel_selector::slice_params>(impl_param, true);
-        (_kernel_data.update_dispatch_data_func)(kernel_params, _kernel_data);
+        // If model loaded from cache, params are not initialized, so we create a new object and reuse it in the future
+        if (_kernel_data.params == nullptr) {
+            _kernel_data.params = std::make_shared<kernel_params_t>(get_kernel_params(impl_param, true));
+        }
+
+        update_shapes(*_kernel_data.params, impl_param);
+        (_kernel_data.update_dispatch_data_func)(*_kernel_data.params, _kernel_data);
     }
 
 private:
@@ -177,18 +181,28 @@ struct slice_impl : typed_primitive_impl_ocl<slice> {
 namespace detail {
 
 attach_slice_impl::attach_slice_impl() {
-    auto types = {data_types::f32, data_types::f16, data_types::i8, data_types::u8, data_types::i32, data_types::i64};
+    auto types = {
+        data_types::f32,
+        data_types::f16,
+        data_types::i8,
+        data_types::u8,
+        data_types::i32,
+        data_types::i64
+    };
 
     auto formats = {
         format::bfyx,
         format::bfzyx,
     };
 
-    implementation_map<slice>::add(impl_types::ocl, shape_types::any, slice_impl::create, types, formats);
+    implementation_map<slice>::add(impl_types::ocl,
+                                   shape_types::any,
+                                   typed_primitive_impl_ocl<slice>::create<slice_impl>,
+                                   types,
+                                   formats);
 }
 
 }  // namespace detail
-
 }  // namespace ocl
 }  // namespace cldnn
 
diff --git a/src/plugins/intel_gpu/src/graph/impls/ocl/space_to_batch.cpp b/src/plugins/intel_gpu/src/graph/impls/ocl/space_to_batch.cpp
index c9800619849b90..75568cc8984bd3 100644
--- a/src/plugins/intel_gpu/src/graph/impls/ocl/space_to_batch.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/ocl/space_to_batch.cpp
@@ -59,8 +59,13 @@ struct space_to_batch_impl : typed_primitive_impl_ocl<space_to_batch> {
     }
 
     void update_dispatch_data(const kernel_impl_params& impl_param) override {
-        auto kernel_params = get_kernel_params(impl_param, true);
-        (_kernel_data.update_dispatch_data_func)(kernel_params, _kernel_data);
+        // If model loaded from cache, params are not initialized, so we create a new object and reuse it in the future
+        if (_kernel_data.params == nullptr) {
+            _kernel_data.params = std::make_shared<kernel_params_t>(get_kernel_params(impl_param, true));
+        }
+
+        update_shapes(*_kernel_data.params, impl_param);
+        (_kernel_data.update_dispatch_data_func)(*_kernel_data.params, _kernel_data);
     }
 };
 
diff --git a/src/plugins/intel_gpu/src/graph/impls/ocl/swiglu.cpp b/src/plugins/intel_gpu/src/graph/impls/ocl/swiglu.cpp
index ba3ca79ce6c2fb..8f96f7afa31556 100644
--- a/src/plugins/intel_gpu/src/graph/impls/ocl/swiglu.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/ocl/swiglu.cpp
@@ -39,13 +39,20 @@ struct swiglu_impl : typed_primitive_impl_ocl<swiglu> {
         auto rank = impl_param.get_input_layout(0).get_partial_shape().rank();
         params.axis = ov::util::normalize(primitive->axis, rank.get_length());
         params.split_length = primitive->split_lengths;
+        params.glu_type = primitive->glu_type;
+        params.split_to_glu_idx = static_cast<int32_t>(primitive->split_to_glu_idx);
 
         return params;
     }
 
     void update_dispatch_data(const kernel_impl_params& impl_param) override {
-        auto kernel_params = get_kernel_params(impl_param, true);
-        (_kernel_data.update_dispatch_data_func)(kernel_params, _kernel_data);
+        // If model loaded from cache, params are not initialized, so we create a new object and reuse it in the future
+        if (_kernel_data.params == nullptr) {
+            _kernel_data.params = std::make_shared<kernel_params_t>(get_kernel_params(impl_param, true));
+        }
+
+        update_shapes(*_kernel_data.params, impl_param);
+        (_kernel_data.update_dispatch_data_func)(*_kernel_data.params, _kernel_data);
     }
 };
 
diff --git a/src/plugins/intel_gpu/src/graph/impls/ocl/tile.cpp b/src/plugins/intel_gpu/src/graph/impls/ocl/tile.cpp
index 0dd1636213e7e8..6d2268de0730e7 100644
--- a/src/plugins/intel_gpu/src/graph/impls/ocl/tile.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/ocl/tile.cpp
@@ -52,8 +52,13 @@ struct tile_impl : typed_primitive_impl_ocl<tile> {
     }
 
     void update_dispatch_data(const kernel_impl_params& impl_param) override {
-        auto kernel_params = get_kernel_params(impl_param, true);
-        (_kernel_data.update_dispatch_data_func)(kernel_params, _kernel_data);
+        // If model loaded from cache, params are not initialized, so we create a new object and reuse it in the future
+        if (_kernel_data.params == nullptr) {
+            _kernel_data.params = std::make_shared<kernel_params_t>(get_kernel_params(impl_param, true));
+        }
+
+        update_shapes(*_kernel_data.params, impl_param);
+        (_kernel_data.update_dispatch_data_func)(*_kernel_data.params, _kernel_data);
     }
 };
 
diff --git a/src/plugins/intel_gpu/src/graph/impls/ocl/unique.cpp b/src/plugins/intel_gpu/src/graph/impls/ocl/unique.cpp
index ce1e32944a760c..d90580de3c35fd 100644
--- a/src/plugins/intel_gpu/src/graph/impls/ocl/unique.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/ocl/unique.cpp
@@ -42,8 +42,13 @@ struct unique_count_impl : typed_primitive_impl_ocl<unique_count> {
     }
 
     void update_dispatch_data(const kernel_impl_params& impl_param) override {
-        auto kernel_params = get_kernel_params(impl_param, true);
-        (_kernel_data.update_dispatch_data_func)(kernel_params, _kernel_data);
+        // If model loaded from cache, params are not initialized, so we create a new object and reuse it in the future
+        if (_kernel_data.params == nullptr) {
+            _kernel_data.params = std::make_shared<kernel_params_t>(get_kernel_params(impl_param, true));
+        }
+
+        update_shapes(*_kernel_data.params, impl_param);
+        (_kernel_data.update_dispatch_data_func)(*_kernel_data.params, _kernel_data);
     }
 };
 
@@ -128,8 +133,13 @@ struct unique_gather_impl : typed_primitive_impl_ocl<unique_gather> {
     }
 
     void update_dispatch_data(const kernel_impl_params& impl_param) override {
-        auto kernel_params = get_kernel_params(impl_param, true);
-        (_kernel_data.update_dispatch_data_func)(kernel_params, _kernel_data);
+        // If model loaded from cache, params are not initialized, so we create a new object and reuse it in the future
+        if (_kernel_data.params == nullptr) {
+            _kernel_data.params = std::make_shared<kernel_params_t>(get_kernel_params(impl_param, true));
+        }
+
+        update_shapes(*_kernel_data.params, impl_param);
+        (_kernel_data.update_dispatch_data_func)(*_kernel_data.params, _kernel_data);
     }
 };
 
diff --git a/src/plugins/intel_gpu/src/graph/impls/onednn/concatenation_onednn.cpp b/src/plugins/intel_gpu/src/graph/impls/onednn/concatenation_onednn.cpp
index c2283932486d71..da002d28128313 100644
--- a/src/plugins/intel_gpu/src/graph/impls/onednn/concatenation_onednn.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/onednn/concatenation_onednn.cpp
@@ -120,7 +120,7 @@ struct concatenation_onednn : typed_primitive_onednn_impl<concatenation, dnnl::c
         if (impl_params.can_be_optimized())
             return make_unique<concatenation_onednn>(engine, config);
         auto prim = impl_params.typed_desc<concatenation>();
-        auto attr = arg.get_onednn_primitive_attributes();
+        auto attr = impl_params.attrs_onednn;
         auto prim_desc = get_concatenation_primitive_descriptor(impl_params, impl_params.prog->get_engine(), *attr, prim->axis);
 
         return cldnn::make_unique<concatenation_onednn>(engine, config, attr, *prim_desc);
diff --git a/src/plugins/intel_gpu/src/graph/impls/onednn/convolution_onednn.cpp b/src/plugins/intel_gpu/src/graph/impls/onednn/convolution_onednn.cpp
index 749f5c9cd6ccc1..f2642de7a8aace 100644
--- a/src/plugins/intel_gpu/src/graph/impls/onednn/convolution_onednn.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/onednn/convolution_onednn.cpp
@@ -110,8 +110,9 @@ struct convolution_onednn : typed_primitive_onednn_impl<convolution> {
     }
 
     static std::shared_ptr<dnnl::primitive_attr> get_primitive_attributes(const typed_program_node<convolution>& arg,
-                                                                          int& zero_point_mask) {
-        auto attrs = arg.get_onednn_primitive_attributes();
+                                                                            const kernel_impl_params& impl_params,
+                                                                            int& zero_point_mask) {
+        auto attrs = impl_params.attrs_onednn;
 
         if (arg.activations_zero_points_term()) {
             auto& a_zp = arg.activations_zero_points();
@@ -247,7 +248,7 @@ struct convolution_onednn : typed_primitive_onednn_impl<convolution> {
         auto& engine = impl_params.prog->get_engine();
         auto& config = impl_params.prog->get_config();
         int zero_point_mask = -1;
-        auto attr = get_primitive_attributes(arg, zero_point_mask);
+        auto attr = get_primitive_attributes(arg, impl_params, zero_point_mask);
 
         auto prim_desc = get_convolution_primitive_descriptor(impl_params, *attr);
 
diff --git a/src/plugins/intel_gpu/src/graph/impls/onednn/deconvolution_onednn.cpp b/src/plugins/intel_gpu/src/graph/impls/onednn/deconvolution_onednn.cpp
index f77f9a443f446b..4ede876d9098af 100644
--- a/src/plugins/intel_gpu/src/graph/impls/onednn/deconvolution_onednn.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/onednn/deconvolution_onednn.cpp
@@ -50,10 +50,6 @@ struct deconvolution_onednn : typed_primitive_onednn_impl<deconvolution> {
         return args;
     }
 
-    static std::shared_ptr<dnnl::primitive_attr> get_primitive_attributes(const typed_program_node<deconvolution>& arg) {
-        return arg.get_onednn_primitive_attributes();
-    }
-
     static std::shared_ptr<WeightsReorderParams> get_weights_reorder(const kernel_impl_params& impl_params, const dnnl::primitive_desc& pd) {
         auto cldnn_prim = impl_params.typed_desc<deconvolution>();
 
@@ -153,7 +149,7 @@ struct deconvolution_onednn : typed_primitive_onednn_impl<deconvolution> {
     static std::unique_ptr<primitive_impl> create(const deconvolution_node& arg, const kernel_impl_params& impl_params) {
         auto& engine = impl_params.prog->get_engine();
         auto& config = impl_params.prog->get_config();
-        auto attr = get_primitive_attributes(arg);
+        auto attr = impl_params.attrs_onednn;
         auto prim_desc = get_deconvolution_primitive_descriptor(impl_params, *attr);
 
         return cldnn::make_unique<deconvolution_onednn>(engine, config, attr, *prim_desc, get_weights_reorder(impl_params, *prim_desc));
diff --git a/src/plugins/intel_gpu/src/graph/impls/onednn/fully_connected_onednn.cpp b/src/plugins/intel_gpu/src/graph/impls/onednn/fully_connected_onednn.cpp
index 2b0f593ccb6963..1227579953589b 100644
--- a/src/plugins/intel_gpu/src/graph/impls/onednn/fully_connected_onednn.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/onednn/fully_connected_onednn.cpp
@@ -421,7 +421,7 @@ struct fully_connected_onednn : typed_primitive_onednn_impl<fully_connected> {
     static std::unique_ptr<primitive_impl> create(const fully_connected_node& arg, const kernel_impl_params& impl_params) {
         auto& engine = impl_params.prog->get_engine();
         auto& config = impl_params.prog->get_config();
-        auto attr = arg.get_onednn_primitive_attributes();
+        auto attr = impl_params.attrs_onednn;
         auto prim = impl_params.typed_desc<fully_connected>();
         memory::ptr zp_mem(nullptr);
         int group_size = 0;
diff --git a/src/plugins/intel_gpu/src/graph/impls/onednn/gemm_onednn.cpp b/src/plugins/intel_gpu/src/graph/impls/onednn/gemm_onednn.cpp
index a9d0e93d879f95..3e4036964b6c18 100644
--- a/src/plugins/intel_gpu/src/graph/impls/onednn/gemm_onednn.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/onednn/gemm_onednn.cpp
@@ -114,18 +114,17 @@ struct gemm_onednn : typed_primitive_onednn_impl<gemm> {
 
         if (in0_l.data_padding) {
             dnnl::memory::dims in0_padded_dims = onednn::convert_gemm_tensor(in0_l.get_buffer_size(), rank, batched_dims_can_be_removed);
+            in0_strides = onednn::get_strides(in0_padded_dims);
             if (prim->transpose_input0) {
-                std::swap(in0_padded_dims[in0_padded_dims.size() - 1], in0_padded_dims[in0_padded_dims.size() - 2]);
+                std::swap(in0_strides[in0_strides.size() - 1], in0_strides[in0_strides.size() - 2]);
             }
-            in0_strides = onednn::get_strides(in0_padded_dims);
         }
 
         if (in1_l.data_padding) {
             dnnl::memory::dims in1_padded_dims = onednn::convert_gemm_tensor(in1_l.get_buffer_size(), rank, batched_dims_can_be_removed);
-            if (prim->transpose_input1) {
-                std::swap(in1_padded_dims[in1_padded_dims.size() - 1], in1_padded_dims[in1_padded_dims.size() - 2]);
-            }
             in1_strides = onednn::get_strides(in1_padded_dims);
+            if (prim->transpose_input1)
+                std::swap(in1_strides[in1_strides.size() - 1], in1_strides[in1_strides.size() - 2]);
         }
 
         // Check whether transpose_order increase sequential or not.
@@ -139,56 +138,79 @@ struct gemm_onednn : typed_primitive_onednn_impl<gemm> {
             return false;
         };
 
-        if (batched_dims_can_be_removed) {
-            if (has_transpose_order(prim->input0_transpose_order)) {
+        // Check whether transpose_order has transpose only to the last two elements.
+        // Return true when transpose_order is 0, 1, 3, 2.
+        auto has_transpose_order_xy_only = [](std::vector<int64_t> transpose_order) {
+            for (size_t i = 0; i < transpose_order.size() - 2; i++) {
+                if (i != static_cast<size_t>(transpose_order[i])) {
+                    return false;
+                }
+            }
+            size_t last_idx = transpose_order.size() - 1;
+            if (static_cast<size_t>(transpose_order[last_idx]) != last_idx - 1)
+                return false;
+            if (static_cast<size_t>(transpose_order[last_idx - 1]) != last_idx)
+                return false;
+            return true;
+        };
+
+        auto transpose_dims_and_format_tag = [](std::vector<int64_t> transpose_order,
+                                                dnnl::memory::dims& dims,
+                                                dnnl::memory::format_tag& tag,
+                                                bool is_input = true) {
+            std::vector<size_t> order(std::begin(transpose_order), std::end(transpose_order));
+            if (dims.size() > order.size()) {
+                size_t orders_to_add = dims.size() - order.size();
+                for (size_t i = 0; i < orders_to_add; ++i)
+                    order.insert(order.begin(), i);
+                for (size_t i = orders_to_add; i < order.size(); ++i)
+                    order[i] = order[i] + orders_to_add;
+            }
+
+            bool ret = false;
+            format transposed_format = format::bfyx;
+            if (is_input) {
+                ret = gemm_inst::is_fusable_permute_input_order_onednn(order, transposed_format);
+            } else {
+                ret = gemm_inst::is_fusable_permute_output_order_onednn(order, transposed_format);
+            }
+
+            if (ret) {
+                tag = convert_data_format(transposed_format);
+                dnnl::memory::dims original_dims = dims;
+                for (size_t i = 0; i < original_dims.size(); ++i) {
+                    dims[i] = original_dims[order[i]];
+                }
+            } else {
+                std::ostringstream ostream;
+                std::copy(order.begin(), order.end(), std::ostream_iterator<size_t>(ostream, ", "));
+                OPENVINO_ASSERT(false, "[GPU] Can't find fusable transpose format: ", ostream.str());
+            }
+        };
+
+        if (has_transpose_order(prim->input0_transpose_order)) {
+            if (has_transpose_order_xy_only(prim->input0_transpose_order)) {
                 in0_fmt = transpose_format(in0_fmt);
                 std::swap(in0_dims[in0_dims.size() - 1], in0_dims[in0_dims.size() - 2]);
+            } else {
+                transpose_dims_and_format_tag(prim->input0_transpose_order, in0_dims, in0_fmt);
             }
-            if (has_transpose_order(prim->input1_transpose_order)) {
+        }
+        if (has_transpose_order(prim->input1_transpose_order)) {
+            if (has_transpose_order_xy_only(prim->input1_transpose_order)) {
                 in1_fmt = transpose_format(in1_fmt);
                 std::swap(in1_dims[in1_dims.size() - 1], in1_dims[in1_dims.size() - 2]);
+            } else {
+                transpose_dims_and_format_tag(prim->input1_transpose_order, in1_dims, in1_fmt);
             }
-            if (has_transpose_order(prim->output_transpose_order)) {
+        }
+        if (has_transpose_order(prim->output_transpose_order)) {
+            if (has_transpose_order_xy_only(prim->output_transpose_order)) {
                 out_fmt = transpose_format(out_fmt);
                 std::swap(out_dims[out_dims.size() - 1], out_dims[out_dims.size() - 2]);
+            } else {
+                transpose_dims_and_format_tag(prim->output_transpose_order, out_dims, out_fmt, false);
             }
-        } else {
-            auto transpose_dims_and_format_tag = [](std::vector<int64_t> transpose_order,
-                                                    dnnl::memory::dims& dims,
-                                                    dnnl::memory::format_tag& tag,
-                                                    bool is_input = true) {
-                std::vector<size_t> order(std::begin(transpose_order), std::end(transpose_order));
-                if (dims.size() > order.size()) {
-                    size_t orders_to_add = dims.size() - order.size();
-                    for (size_t i = 0; i < orders_to_add; ++i)
-                        order.insert(order.begin(), i);
-                    for (size_t i = orders_to_add; i < order.size(); ++i)
-                        order[i] = order[i] + orders_to_add;
-                }
-
-                bool ret = false;
-                format transposed_format = format::bfyx;
-                if (is_input) {
-                    ret = gemm_inst::is_fusable_permute_input_order_onednn(order, transposed_format);
-                } else {
-                    ret = gemm_inst::is_fusable_permute_output_order_onednn(order, transposed_format);
-                }
-
-                if (ret) {
-                    tag = convert_data_format(transposed_format);
-                    dnnl::memory::dims original_dims = dims;
-                    for (size_t i = 0; i < original_dims.size(); ++i) {
-                        dims[i] = original_dims[order[i]];
-                    }
-                } else {
-                    std::ostringstream ostream;
-                    std::copy(order.begin(), order.end() - 1, std::ostream_iterator<size_t>(ostream, ", "));
-                    OPENVINO_ASSERT(false, "[GPU] Can't find fusable transpoed format: ", ostream.str());
-                }
-            };
-            if (has_transpose_order(prim->input0_transpose_order)) transpose_dims_and_format_tag(prim->input0_transpose_order, in0_dims, in0_fmt);
-            if (has_transpose_order(prim->input1_transpose_order)) transpose_dims_and_format_tag(prim->input1_transpose_order, in1_dims, in1_fmt);
-            if (has_transpose_order(prim->output_transpose_order)) transpose_dims_and_format_tag(prim->output_transpose_order, out_dims, out_fmt, false);
         }
 
         if (gemm_with_bias) {
@@ -408,7 +430,7 @@ struct gemm_onednn : typed_primitive_onednn_impl<gemm> {
     static std::unique_ptr<primitive_impl> create(const gemm_node& arg, const kernel_impl_params& impl_params) {
         auto& engine = impl_params.prog->get_engine();
         auto& config = impl_params.prog->get_config();
-        auto attr = arg.get_onednn_primitive_attributes();
+        auto attr = impl_params.attrs_onednn;
         auto prim_desc = get_gemm_primitive_descriptor(impl_params, *attr);
 
         return cldnn::make_unique<gemm_onednn>(engine, config, attr, *prim_desc);
diff --git a/src/plugins/intel_gpu/src/graph/impls/onednn/pooling_onednn.cpp b/src/plugins/intel_gpu/src/graph/impls/onednn/pooling_onednn.cpp
index 27323ee6600ffd..345e1c4401b4fd 100644
--- a/src/plugins/intel_gpu/src/graph/impls/onednn/pooling_onednn.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/onednn/pooling_onednn.cpp
@@ -153,7 +153,7 @@ struct pooling_onednn : typed_primitive_onednn_impl<pooling> {
     static std::unique_ptr<primitive_impl> create(const pooling_node& arg, const kernel_impl_params& impl_params) {
         auto& engine = impl_params.prog->get_engine();
         auto& config = impl_params.prog->get_config();
-        auto attr = arg.get_onednn_primitive_attributes();
+        auto attr = impl_params.attrs_onednn;
         auto prim_desc = get_pooling_primitive_descriptor(impl_params, *attr);
 
         return cldnn::make_unique<pooling_onednn>(engine, config, attr, *prim_desc);
diff --git a/src/plugins/intel_gpu/src/graph/impls/onednn/reduction_onednn.cpp b/src/plugins/intel_gpu/src/graph/impls/onednn/reduction_onednn.cpp
index 635d465fc58f8d..107fcb1f4ecc6e 100644
--- a/src/plugins/intel_gpu/src/graph/impls/onednn/reduction_onednn.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/onednn/reduction_onednn.cpp
@@ -152,7 +152,7 @@ struct reduction_onednn : typed_primitive_onednn_impl<reduce> {
     static std::unique_ptr<primitive_impl> create(const reduce_node& arg, const kernel_impl_params& impl_params) {
         auto& engine = impl_params.prog->get_engine();
         auto& config = impl_params.prog->get_config();
-        auto attr = arg.get_onednn_primitive_attributes();
+        auto attr = impl_params.attrs_onednn;
         auto prim_desc = get_reduction_primitive_descriptor(impl_params, *attr);
 
         return cldnn::make_unique<reduction_onednn>(engine, config, attr, *prim_desc);
diff --git a/src/plugins/intel_gpu/src/graph/impls/onednn/reorder_onednn.cpp b/src/plugins/intel_gpu/src/graph/impls/onednn/reorder_onednn.cpp
index d8a5e5403dcf2b..899d4ab29a87de 100644
--- a/src/plugins/intel_gpu/src/graph/impls/onednn/reorder_onednn.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/onednn/reorder_onednn.cpp
@@ -103,8 +103,10 @@ struct reorder_onednn : typed_primitive_onednn_impl<reorder, dnnl::reorder::prim
         ib >> prim_cache;
 
         _scratchpad_md = _pd.scratchpad_desc();
-
-        _prim = dnnl::reorder(_pd, prim_cache);
+        if (prim_cache.size() > 0)
+            _prim = dnnl::reorder(_pd, prim_cache);
+        else
+            _prim = dnnl::reorder(_pd);
 #endif
     }
 
@@ -116,7 +118,7 @@ struct reorder_onednn : typed_primitive_onednn_impl<reorder, dnnl::reorder::prim
         } else {
             auto& engine = impl_params.prog->get_engine();
             auto& config = impl_params.prog->get_config();
-            auto attr = arg.get_onednn_primitive_attributes();
+            auto attr = impl_params.attrs_onednn;
             auto prim_desc = get_reorder_primitive_descriptor(impl_params, *attr);
             return cldnn::make_unique<reorder_onednn>(engine, config, attr, *prim_desc);
         }
diff --git a/src/plugins/intel_gpu/src/graph/impls/onednn/utils.cpp b/src/plugins/intel_gpu/src/graph/impls/onednn/utils.cpp
index a8ba49a96256ea..e7c9ce750c7ae1 100644
--- a/src/plugins/intel_gpu/src/graph/impls/onednn/utils.cpp
+++ b/src/plugins/intel_gpu/src/graph/impls/onednn/utils.cpp
@@ -204,13 +204,13 @@ dnnl::memory::format_tag convert_data_format(cldnn::format fmt) {
 }
 
 void combine_bf_with_first_spatial_dim(cldnn::layout& l) {
-    auto pshape = l.get_shape();
+    auto pshape = l.get_partial_shape();
     ov::Shape new_shape{1, 1};
     for (size_t i = 0; i < pshape.size(); ++i) {
         if (i < 2) {
-            new_shape[0] *= pshape[i];
+            new_shape[0] *= pshape[i].get_length();
         } else {
-            new_shape[1] *= pshape[i];
+            new_shape[1] *= pshape[i].get_length();
         }
     }
     l.set_partial_shape(new_shape);
diff --git a/src/plugins/intel_gpu/src/graph/include/primitive_inst.h b/src/plugins/intel_gpu/src/graph/include/primitive_inst.h
index 62dab243ddaed8..22941d23b45b56 100644
--- a/src/plugins/intel_gpu/src/graph/include/primitive_inst.h
+++ b/src/plugins/intel_gpu/src/graph/include/primitive_inst.h
@@ -58,7 +58,7 @@ struct primitive_impl {
     virtual void set_arguments(primitive_inst& instance) = 0;
     virtual void set_arguments(primitive_inst& instance, kernel_arguments_data& args) = 0;
     virtual event::ptr execute(const std::vector<event::ptr>& events, primitive_inst& instance) = 0;
-    std::string get_kernel_name() const { return _kernel_name; }
+    const std::string& get_kernel_name() const { return _kernel_name; }
 
     // class typed_primitive_gpu_impl override this with return false;
     virtual bool is_cpu() const { return true; }
@@ -166,8 +166,8 @@ class primitive_inst {
         return true;
     }
     primitive_type_id type() const { return _type; }
-    primitive_id id() const { return _id; }
-    primitive_id org_id() const { return _org_id; }
+    const primitive_id& id() const { return _id; }
+    const primitive_id& org_id() const { return _org_id; }
     bool can_be_optimized() const { return _can_be_optimized; }
     void set_can_be_optimized(bool optimized) {
         // TODO: consolidate to _impl_param in the future
diff --git a/src/plugins/intel_gpu/src/graph/include/program_node.h b/src/plugins/intel_gpu/src/graph/include/program_node.h
index d717f972d1a6cd..2edee6a201a9cc 100644
--- a/src/plugins/intel_gpu/src/graph/include/program_node.h
+++ b/src/plugins/intel_gpu/src/graph/include/program_node.h
@@ -142,7 +142,8 @@ struct program_node {
             }
         }
 #ifdef ENABLE_ONEDNN_FOR_GPU
-        params->fused_desc_onednn = get_fused_primitives_onednn();
+        params->fused_desc_onednn   = get_fused_primitives_onednn();
+        params->attrs_onednn        = get_onednn_primitive_attributes();
 #endif // ENABLE_ONEDNN_FOR_GPU
         return params;
     }
@@ -402,6 +403,11 @@ struct program_node {
     std::vector<fused_primitive_desc_onednn>& get_fused_primitives_onednn() { return fused_prims_onednn; }
 
     void init_onednn_primitive_attributes();
+    void create_onednn_primitive_attributes(
+                                const std::vector<fused_primitive_desc>& cldnn_post_ops,
+                                std::shared_ptr<dnnl::primitive_attr>& attrs,
+                                std::vector<fused_primitive_desc_onednn>& fused_ops,
+                                kernel_impl_params* impl_params) const;
 #endif // ENABLE_ONEDNN_FOR_GPU
 
     size_t get_fused_inputs_count() const {
@@ -521,7 +527,9 @@ struct program_node {
         onednn_attrs = attrs;
     }
 
-    dnnl::post_ops try_optimize_post_ops(dnnl::post_ops& p_ops, const std::shared_ptr<dnnl::primitive_attr>& attr, bool& optimization_is_completed);
+    dnnl::post_ops try_optimize_post_ops(std::vector<fused_primitive_desc_onednn>& cur_post_ops,
+                                                    dnnl::post_ops& p_ops, const std::shared_ptr<dnnl::primitive_attr>& attr,
+                                                    bool& optimization_is_completed) const;
 
 #endif // ENABLE_ONEDNN_FOR_GPU
     size_t num_outputs = 1;
diff --git a/src/plugins/intel_gpu/src/graph/layout_optimizer.cpp b/src/plugins/intel_gpu/src/graph/layout_optimizer.cpp
index b7d4b7230247cd..8c5930e4595065 100644
--- a/src/plugins/intel_gpu/src/graph/layout_optimizer.cpp
+++ b/src/plugins/intel_gpu/src/graph/layout_optimizer.cpp
@@ -13,6 +13,7 @@
 #include "reshape_inst.h"
 #include "arg_max_min_inst.h"
 #include "shape_of_inst.h"
+#include "select_inst.h"
 #include "condition_inst.h"
 #include "strided_slice_inst.h"
 #include <sstream>
@@ -33,6 +34,7 @@
 #include "prior_box_inst.h"
 #include "scatter_nd_update_inst.h"
 #include "gather_inst.h"
+#include "broadcast_inst.h"
 #include "loop_inst.h"
 #include "dft_inst.h"
 #include "to_string_utils.h"
@@ -428,10 +430,18 @@ bool layout_optimizer::can_fuse_reorder_to_prev(program_node& prev, reorder_node
     bool allow_new_shape_infer = node.get_program().is_new_shape_infer();
     // Because mvn and concatenation kernel can work cross-layout, if reorder only performs type conversion,
     // fusing reorder to the previous node can be done even if it is a dynamic shape case
-    if ((prev.is_type<mvn>() || prev.is_type<concatenation>()) &&
+    if ((prev.is_type<mvn>() || prev.is_type<concatenation>() || prev.is_type<gather>() || prev.is_type<broadcast>() ||
+         prev.is_type<select>() || prev.is_type<eltwise>()) &&
         !prev.is_in_shape_of_subgraph() && node.is_type_conversion_only() &&
-        (format::is_simple_data_format(fmt_prev) && format::is_simple_data_format(fmt_next)))
+        (format::is_simple_data_format(fmt_prev) && format::is_simple_data_format(fmt_next)) &&
+        // If the prev node is backedge of the loop, the type will be changed by fusing reorder.
+        // We can void only that case if we can check whether the current node is backedge of the network.
+        // However no such handle is existing yet. (To be done in the future when we need to optimize out the type converting
+        // reorders in the body network)
+        !node.get_program().is_body_program() &&
+        prev.get_preferred_impl_type() != cldnn::impl_types::cpu) {
         return true;
+    }
 
     if (prev.is_dynamic() || (!node.get_users().empty() && node.get_users().front()->is_dynamic()))
         return false;
@@ -924,6 +934,10 @@ static bool is_node_for_onednn(fully_connected_node const& node) {
             auto decompression_zp_dt = node.get_input_layout(decompression_zp_idx).data_type;
             if (weights_dt != decompression_zp_dt)
                 return false;
+
+            auto input_dt = node.get_input_layout(0).data_type;
+            if (input_dt == data_types::f32)
+                return false;
         }
     }
 
diff --git a/src/plugins/intel_gpu/src/graph/primitive_inst.cpp b/src/plugins/intel_gpu/src/graph/primitive_inst.cpp
index 1ec47e9c8385f8..9b67615c14187f 100644
--- a/src/plugins/intel_gpu/src/graph/primitive_inst.cpp
+++ b/src/plugins/intel_gpu/src/graph/primitive_inst.cpp
@@ -20,6 +20,7 @@
 #include "eltwise_inst.h"
 #include "loop_inst.h"
 #include "deconvolution_inst.h"
+#include "sync_tensor_inst.h"
 #include "shape_of_inst.h"
 #include "softmax_inst.h"
 #include "strided_slice_inst.h"
@@ -199,10 +200,21 @@ kernel_impl_params primitive_impl::static_canonicalize_shapes(const kernel_impl_
 
 uint32_t primitive_inst::get_network_id() const { return _network.get_id(); }
 
-void primitive_inst::check_memory_to_set(const memory& mem, const layout& layout) const {
-    OPENVINO_ASSERT((mem.get_layout() == layout) || layout.is_dynamic(), "[GPU] Unexpected layout of input memory for ", id(), " node!\n",
-                     "Node layout: ", layout.to_short_string(), "\n",
-                     "Memory layout: ", mem.get_layout().to_short_string());
+void primitive_inst::check_memory_to_set(const memory& mem, const layout& l) const {
+    // The layout with empty tensor (scalar) is regarded as 1 dimension with value 1
+    bool single_value_layout = false;
+    if (!l.is_dynamic()) {
+        auto layout_ps = l.get_partial_shape();
+        single_value_layout = (layout_ps.size() == 1 && layout_ps[0] == 1);
+    }
+
+    auto mem_layout = mem.get_layout();
+    OPENVINO_ASSERT((mem_layout == l)
+                    || l.is_dynamic()
+                    || (mem_layout.get_partial_shape().size() == 0 && single_value_layout),
+                    "[GPU] Unexpected layout of input memory for ", id(), " node!\n",
+                    "Node layout: ", l.to_short_string(), "\n",
+                    "Memory layout: ", mem_layout.to_short_string());
 
     // check shared image/buffer compatibility, if applicable
     auto params = mem.get_internal_params();
@@ -218,11 +230,11 @@ void primitive_inst::check_memory_to_set(const memory& mem, const layout& layout
         switch (params.mem_type) {
         case shared_mem_type::shared_mem_vasurface:
         case shared_mem_type::shared_mem_image:
-            OPENVINO_ASSERT(layout.format.is_image_2d(), "Attempt to set user-supplied input or output image instead of a buffer");
+            OPENVINO_ASSERT(l.format.is_image_2d(), "Attempt to set user-supplied input or output image instead of a buffer");
             break;
         case shared_mem_type::shared_mem_buffer:
         case shared_mem_type::shared_mem_dxbuffer:
-            OPENVINO_ASSERT(!layout.format.is_image_2d(), "Attempt to set user-supplied input or output buffer instead of an image");
+            OPENVINO_ASSERT(!l.format.is_image_2d(), "Attempt to set user-supplied input or output buffer instead of an image");
             break;
         case shared_mem_type::shared_mem_usm:
             break;
@@ -319,6 +331,8 @@ void primitive_inst::update_shape() {
 
     if (input_shape_changed)
         set_shape_change();
+    else
+        reset_shape_change();
 
     // We assume that tensor ranks are static, thus shape_of doesn't need to update anything even if input shape is dynamic
     if (_node->is_type<shape_of>() && !input_shape_changed) {
@@ -522,7 +536,7 @@ event::ptr primitive_inst::realloc_if_needed() {
     auto dt_size = ov::element::Type(actual_layout.data_type).bitwidth();
     // read_value/assign nodes are supposed to always use variable memory
     if (auto stateful_prim = dynamic_cast<memory_state::variable*>(this)) {
-        std::string variable_id = stateful_prim->variable_id();
+        auto& variable_id = stateful_prim->variable_id();
         auto& variable = get_network().get_variable(variable_id);
         if (_node->is_type<kv_cache>()) {
             // Reuse state memory as output for kv cache if possible
@@ -561,7 +575,28 @@ event::ptr primitive_inst::realloc_if_needed() {
 
     // Update output layout with respect to FC's fake alignment
     auto updated_layout = actual_layout;
-    for (auto user : get_user_insts()) {
+    std::vector<cldnn::primitive_inst *> user_insts;
+    {
+        auto user_insts_origin = get_user_insts();
+        for (auto& user : user_insts_origin) {
+            auto uid = user->id();
+            if (user->get_node().is_type<fully_connected>() && user->is_dynamic() && user->_deps[0].first == this
+                && std::find_if(user_insts_origin.begin(), user_insts_origin.end(), [&](cldnn::primitive_inst * uu){
+                    for (auto dep_inst : uu->_deps) {
+                        if (dep_inst.first->id() == uid)
+                            return true;
+                    }
+                    return false;
+                }) != user_insts_origin.end()) {
+                    user_insts.insert(user_insts.begin(), user);
+            } else {
+                user_insts.push_back(user);
+            }
+        }
+        OPENVINO_ASSERT(user_insts.size() == user_insts_origin.size(), "Should have same size between ",
+                        user_insts.size(), " and ", user_insts_origin.size());
+    }
+    for (auto user : user_insts) {
         // Since fake alignment is applicable for input tensor as well, make sure we allocate enough memory
         // to prevent reading beyond the allocated memory bounds
         if (user->get_node().is_type<fully_connected>() && user->is_dynamic() && user->_deps[0].first == this) {
@@ -624,7 +659,7 @@ event::ptr primitive_inst::realloc_if_needed() {
     }
     prealloc_info = sp.predict_preallocation_shape(id(), updated_layout, can_reuse_buffer, tmp_prealloc_count);
 
-    if (prealloc_info.first && sp.can_preallocate(ov::shape_size(prealloc_info.second) * dt_size)) {
+    if (prealloc_info.first && sp.can_preallocate(ov::shape_size(prealloc_info.second) * (dt_size / 8))) {
         auto new_layout = updated_layout;
         new_layout.set_partial_shape(prealloc_info.second);
         updated_params.output_layouts[0] = new_layout;
@@ -652,7 +687,7 @@ event::ptr primitive_inst::realloc_if_needed() {
         GPU_DEBUG_CODE(std::string memalloc_info = "");
         GPU_DEBUG_CODE(for (size_t out_idx = 0; out_idx < _outputs.size(); ++out_idx) {
             memalloc_info += (((_outputs.size() > 1) ? ("o" + to_string(out_idx) + ":") : "") +
-                              (_outputs[out_idx]->from_memory_pool ? "from_pool" : "new_alloc"));
+                              (_outputs[out_idx] ? (_outputs[out_idx]->from_memory_pool ? "from_pool" : "new_alloc") : "not allocated"));
         })
         GPU_DEBUG_PROFILED_STAGE_MEMALLOC_INFO(memalloc_info);
 
@@ -845,6 +880,23 @@ bool primitive_inst::update_impl() {
     }
 
     if (!_node->is_type<data>() && !(_node->is_type<mutable_data>() && _node->get_dependencies().empty())) {
+#ifdef ENABLE_ONEDNN_FOR_GPU
+        if (get_node().get_preferred_impl_type() == impl_types::onednn) {
+            auto attrs_onednn = std::make_shared<dnnl::primitive_attr>();
+            std::vector<cldnn::fused_primitive_desc_onednn> fused_desc_onednn;
+            get_node().create_onednn_primitive_attributes(_impl_params->fused_desc,
+                                                            attrs_onednn,
+                                                            fused_desc_onednn,
+                                                            _impl_params.get());
+            _impl_params->attrs_onednn = attrs_onednn;
+            {
+                auto& fused_prims_onednn = _impl_params->fused_desc_onednn;
+                fused_prims_onednn.erase(fused_prims_onednn.begin(), fused_prims_onednn.end());
+                fused_prims_onednn.insert(fused_prims_onednn.end(), fused_desc_onednn.begin(), fused_desc_onednn.end());
+            }
+        }
+#endif
+
         // Update param if fake_alignment is available
         auto updated_params = get_fake_aligned_params_if_possible(*_impl_params);
         // Change weights layout of `updated_params` to original one to have valid information
@@ -1043,9 +1095,8 @@ void primitive_inst::do_runtime_in_place_kv_cache() {
     auto& present_layout = _impl_params->output_layouts[0];
     const auto& sequence_axis = desc->concat_axis;
     const auto& gather_axis = desc->gather_axis;
-
-    const auto& prev_batch_size = static_cast<size_t>(past_layout.get_shape()[gather_axis]);
-    const auto& beam_size = static_cast<size_t>(present_layout.get_shape()[gather_axis]);
+    const auto prev_batch_size = static_cast<size_t>(past_layout.get_shape()[gather_axis]);
+    const auto beam_size = static_cast<size_t>(present_layout.get_shape()[gather_axis]);
     if (prev_batch_size != beam_size) {
         // If the previous batch size is not same as beam size, need explicit concat
         _impl_params->_can_be_optimized = false;
@@ -1277,11 +1328,14 @@ void primitive_inst::do_runtime_in_place_concat() {
 
     std::vector<kernel_impl_params> pred_params;
     std::vector<layout> preds_layouts;
-    for (auto& pred : concat_inst->_deps) {
+    for (const auto& pred : concat_inst->_deps) {
         pred_params.push_back(*pred.first->_impl_params);
         preds_layouts.push_back(pred.first->_impl_params->get_output_layout());
     }
 
+    if (!concat_inst->shape_changed())
+        return;
+
     if (!concat_in_place_optimization::match(concat_inst->get_node(), *concat_inst->_impl_params, pred_params, true)) {
         concat_inst->set_can_be_optimized(false);
         GPU_DEBUG_TRACE_DETAIL << "[In place concat] " << concat_inst->id() << " cannot be optimized " << std::endl;
@@ -1311,13 +1365,11 @@ bool primitive_inst::has_inner_networks() const {
 
 event::ptr primitive_inst::execute(const std::vector<event::ptr>& events) {
     OV_ITT_SCOPED_TASK(ov::intel_gpu::itt::domains::intel_gpu_plugin, openvino::itt::handle("primitive_inst::execute: " + id()));
-    const auto primitive_id = id();
+    const auto& primitive_id = id();
     OPENVINO_ASSERT(_has_valid_input, primitive_id, " has invalid/unset input");
     GPU_DEBUG_GET_INSTANCE(debug_config);
     GPU_DEBUG_TRACE_DETAIL << "-----------------------------------------------------------------" << std::endl;
     GPU_DEBUG_TRACE_DETAIL << "Execute " << id() << " (type: " << _impl_params->desc->type_string() << ") " << std::endl;
-    if (id() == "synctensor:__module.model.layers.0.self_attn.v_proj/aten::linear/MatMul_TP")
-        std::cout << "break" << std::endl;
     for (size_t i = 0; i < _deps.size(); ++i) {
         GPU_DEBUG_TRACE_DETAIL << "- inputs[" << i << "] : " <<  _deps[i].first->id() << std::endl;
     }
@@ -1456,7 +1508,7 @@ event::ptr primitive_inst::execute(const std::vector<event::ptr>& events) {
         if (out_of_order_queue || (_impl->is_cpu() && !can_be_optimized()) || (can_be_optimized() && needs_completion_event() && !is_output())) {
             dependencies.reserve(dependencies.size() + _exec_deps.size());
             for (auto& input : _exec_deps) {
-                auto id = input->id();
+                auto& id = input->id();
                 try {
                     // if the requested event does not exists it means that it has not been executed, so the processing_order is
                     // wrong or synchronization failed.
@@ -1944,7 +1996,15 @@ std::vector<memory::ptr> primitive_inst::allocate_outputs(kernel_impl_params* up
     auto impl_params = updated_params != nullptr ? *updated_params : *_impl_params;
     auto& out_layouts = impl_params.output_layouts;
     for (size_t i = 0; i < get_node().get_outputs_count() ; ++i) {
-        if (out_layouts[i].is_dynamic() && !out_layouts[i].has_upper_bound()) {
+        // skip mem alloc for current rank, as it will share in on_execute
+        auto skip_alloc = [&](int index) {
+            if (out_layouts[index].is_dynamic() && !out_layouts[index].has_upper_bound())
+                return true;
+            else if (_node->is_type<sync_tensor>() && static_cast<int>(index) == updated_params->w_rank)
+                return true;
+            return false;
+        };
+        if (skip_alloc(i)) {
             outputs.push_back(memory::ptr());
         } else {
             auto current_memory_ptr = _outputs.size() > i ? output_memory_ptr(i).get() : nullptr;
diff --git a/src/plugins/intel_gpu/src/graph/program.cpp b/src/plugins/intel_gpu/src/graph/program.cpp
index 0a76bfa7582818..a914b94cd4636b 100644
--- a/src/plugins/intel_gpu/src/graph/program.cpp
+++ b/src/plugins/intel_gpu/src/graph/program.cpp
@@ -224,7 +224,7 @@ void program::init_program() {
     if (_task_executor == nullptr)
         _task_executor = program::make_task_executor(_config);
     _kernels_cache = std::unique_ptr<kernels_cache>(new kernels_cache(_engine, _config, prog_id, _task_executor,
-                                                                      kernel_selector::KernelBase::get_db().get_batch_header_str()));
+                                                                      kernel_selector::KernelBase::get_db().get_batch_headers()));
 
     if (!_compilation_context)
         _compilation_context = program::make_compilation_context(_config);
@@ -518,7 +518,10 @@ void program::init_graph() {
     apply_opt_pass<graph_initializations>();
 
     apply_opt_pass<mark_nodes>();
-
+    for (auto& node : processing_order) {
+        if (!node->is_type<data>())
+            node->get_output_layouts();
+    }
     // Perform initial shape_of subgraphs markup
     apply_opt_pass<mark_shape_of_subgraphs>();
 }
@@ -534,10 +537,6 @@ void program::pre_optimize_graph(bool is_internal) {
     processing_order.calculate_BFS_processing_order();  // this method makes sense only for OOOQ (out of order execution queue)
 
     bool output_size_handling_enabled = analyze_output_size_handling_need();
-    for (auto& node : processing_order) {
-        if (!node->is_type<data>())
-            node->get_output_layouts();
-    }
 
     bool optimize_data = _config.get_property(ov::intel_gpu::optimize_data);
     if (optimize_data) {
@@ -978,9 +977,12 @@ void program::add_intermediate(program_node& node,
     add_intermediate(node, next, idx, connect_int_node_with_old_dep, move_usrs_of_prev_to_node);
 }
 
-void program::add_connection(program_node& prev, program_node& next) {
+void program::add_connection(program_node& prev, program_node& next, int32_t port_idx) {
     prev.users.push_back(&next);
-    auto port_idx = next.get_port_from_deps(prev.id());
+    // When this function is called from program::replace, we need to keep the port number as it was
+    if (port_idx < 0)
+        port_idx = next.get_port_from_deps(prev.id());
+
     next.dependencies.push_back({&prev, port_idx});
 }
 
@@ -1062,7 +1064,7 @@ void program::replace(program_node& old_node, program_node& new_node) {
     // copy old's dependencies
     // First copy them from old node to new node
     for (auto& dependency : old_node.dependencies) {
-        add_connection(*dependency.first, new_node);
+        add_connection(*dependency.first, new_node, dependency.second);
     }
     // Second delete them from old node
     while (!old_node.dependencies.empty()) {
diff --git a/src/plugins/intel_gpu/src/graph/program_dump_graph.cpp b/src/plugins/intel_gpu/src/graph/program_dump_graph.cpp
index b931e7c17772db..bff45cd81f9900 100644
--- a/src/plugins/intel_gpu/src/graph/program_dump_graph.cpp
+++ b/src/plugins/intel_gpu/src/graph/program_dump_graph.cpp
@@ -262,14 +262,22 @@ void dump_graph_init(std::ofstream& graph,
         }
         graph << "];\n";
 
+        // To print duplicated connection port between two nodes.
+        // <user_node, user's input port>
+        std::set<std::pair<program_node *, int>> marked_connection;
+
         for (auto& user : node->get_users()) {
             bool doubled = true;
             auto it = user->get_dependencies().begin();
             while (it != user->get_dependencies().end()) {
-                if (it->first == node)
+                int input_port = it - user->get_dependencies().begin();
+                if (it->first == node && marked_connection.find({node, input_port}) == marked_connection.end()) {
+                    marked_connection.emplace(user, input_port);
                     break;
+                }
                 ++it;
             }
+
             if (it == user->get_dependencies().end())
                 doubled = false;
             graph << "    " << get_node_id(node) << " -> " << get_node_id(user)
diff --git a/src/plugins/intel_gpu/src/graph/program_node.cpp b/src/plugins/intel_gpu/src/graph/program_node.cpp
index a8ab867c185035..95c5a70250d0bf 100644
--- a/src/plugins/intel_gpu/src/graph/program_node.cpp
+++ b/src/plugins/intel_gpu/src/graph/program_node.cpp
@@ -997,13 +997,18 @@ void program_node::load(cldnn::BinaryInputBuffer& ib) {
 
 #ifdef ENABLE_ONEDNN_FOR_GPU
 
-dnnl::post_ops program_node::try_optimize_post_ops(dnnl::post_ops& p_ops, const std::shared_ptr<dnnl::primitive_attr>& attr,
-                                                   bool& optimization_is_completed) {
+dnnl::post_ops program_node::try_optimize_post_ops(std::vector<fused_primitive_desc_onednn>& cur_post_ops,
+                                                    dnnl::post_ops& p_ops, const std::shared_ptr<dnnl::primitive_attr>& attr,
+                                                    bool& optimization_is_completed) const {
     // Create new dnnl::post_ops object which will be filled inside the optimization process
     dnnl::post_ops optimized_p_ops;
 
     // Add new post-op into optimized_p_ops structure
     auto add_post_op = [&](onednn_post_op_type type, const dnnl::post_ops& cur_p_ops, dnnl::post_ops& new_p_ops, int idx) {
+        GPU_DEBUG_TRACE << "add_post_op: " << type
+                        << ", idx: " << idx
+                        << ", cur_p_ops: " << cur_p_ops.len()
+                        << ", new_p_ops: " << new_p_ops.len() << std::endl;
         switch (type) {
             case onednn_post_op_type::eltwise_act:
             case onednn_post_op_type::eltwise_clip:
@@ -1137,14 +1142,17 @@ dnnl::post_ops program_node::try_optimize_post_ops(dnnl::post_ops& p_ops, const
         }
     };
 
-    auto& cur_post_ops = get_fused_primitives_onednn();
-
     int64_t cur_post_op_idx = 1;
     int64_t prev_post_op_idx = 0;
     bool optimization_done = false;
 
     GPU_DEBUG_TRACE << "================================================" << std::endl;
     GPU_DEBUG_TRACE << " " << id() << ", num of post_ops " << p_ops.len() << std::endl;
+    GPU_DEBUG_TRACE << " * p_ops: " << std::endl;
+    for (int i = 0; i < p_ops.len(); i++)
+        GPU_DEBUG_TRACE << "    " << i << ": " << static_cast<int>(p_ops.kind(i)) << std::endl;
+
+    GPU_DEBUG_TRACE << " * cur_post_ops: " << std::endl;
     for (size_t i = 0; i < cur_post_ops.size(); i++)
         GPU_DEBUG_TRACE << "    " << i << ": " << cur_post_ops[i].op_type << std::endl;
 
@@ -1152,6 +1160,10 @@ dnnl::post_ops program_node::try_optimize_post_ops(dnnl::post_ops& p_ops, const
 
     GPU_DEBUG_TRACE << "remove optimized prefix ------------------------" << std::endl;
     GPU_DEBUG_TRACE << " " << id() << ", num of post_ops " << p_ops.len() << std::endl;
+    GPU_DEBUG_TRACE << " * p_ops: " << std::endl;
+    for (int i = 0; i < p_ops.len(); i++)
+        GPU_DEBUG_TRACE << "    " << i << ": " << static_cast<int>(p_ops.kind(i)) << std::endl;
+    GPU_DEBUG_TRACE << " * cur_post_ops: " << std::endl;
     for (size_t i = 0; i < cur_post_ops.size(); i++)
         GPU_DEBUG_TRACE << "    " << i << ": " << cur_post_ops[i].op_type << std::endl;
     GPU_DEBUG_TRACE << "----------------------------------->>>>>>>>>>>>>" << std::endl;
@@ -1400,21 +1412,17 @@ dnnl::post_ops program_node::try_optimize_post_ops(dnnl::post_ops& p_ops, const
         GPU_DEBUG_TRACE << "    " << i << ": " << cur_post_ops[i].op_type << std::endl;
     GPU_DEBUG_TRACE << "------------------------------------------------" << std::endl;
 
-    add_onednn_fused_primitives(cur_post_ops);
-
     return optimized_p_ops;
 }
 
-
-void program_node::init_onednn_primitive_attributes() {
-    const std::vector<fused_primitive_desc>& cldnn_post_ops = get_fused_primitives();
-    auto attrs = std::make_shared<dnnl::primitive_attr>();
+void program_node::create_onednn_primitive_attributes(
+        const std::vector<fused_primitive_desc>& cldnn_post_ops,
+        std::shared_ptr<dnnl::primitive_attr>& attrs,
+        std::vector<fused_primitive_desc_onednn>& fused_ops,
+        kernel_impl_params* impl_params = nullptr) const {
     dnnl::post_ops post_ops;
     size_t memory_offset = 0;
 
-    // Create onednn post-ops list related to the current node
-    std::vector<fused_primitive_desc_onednn> fused_ops;
-
     // Added this for debug purposes only
     size_t empty_mem = 0xff;
 
@@ -1422,6 +1430,14 @@ void program_node::init_onednn_primitive_attributes() {
     if (attrs->get_scratchpad_mode() == dnnl::scratchpad_mode::library)
         attrs->set_scratchpad_mode(dnnl::scratchpad_mode::user);
 
+    const auto& get_input_layout = [&](int32_t idx) -> cldnn::layout {
+        if (impl_params != nullptr) {
+            return impl_params->get_input_layout(idx);
+        } else {
+            return get_dependency(idx).get_output_layout();;
+        }
+    };
+
     // Add information about post-operation into the list, update indices
     auto update_onednn_post_op_list = [&](onednn_post_op_type type, size_t m_dep,
                                           dnnl::memory::format_tag tag = dnnl::memory::format_tag::undef,
@@ -1488,11 +1504,11 @@ void program_node::init_onednn_primitive_attributes() {
             }
         } else if (desc.is_type<eltwise>()) {
             auto dep_idx = desc.outer_dep_start_idx;
-            auto in = get_dependency(dep_idx).get_output_layout();
+            auto in = get_input_layout(dep_idx);
+            auto in_origin = in;
             auto set_binary_op = [&](dnnl::algorithm alg, onednn_post_op_type op_type) {
                 if (is_type<fully_connected>()) {
-                    std::unique_ptr<const kernel_impl_params> impl_params = get_kernel_impl_params();
-                    auto prim = impl_params->typed_desc<fully_connected>();
+                    auto prim = this->as<fully_connected>().get_primitive();
                     if (prim->input_size == 3) {
                         cldnn::onednn::combine_bf_with_first_spatial_dim(in);
                     }
@@ -1536,7 +1552,7 @@ void program_node::init_onednn_primitive_attributes() {
             } else {
                 std::stringstream error_msg;
                 error_msg << "Unsupported eltwise mode: " << static_cast<int>(desc.typed_desc<eltwise>()->mode) << ". ";
-                error_msg << desc.desc->id << " is fused node of " + this->id() + ".";
+                error_msg << desc.desc->id << " is fused node of " + id() + ".";
                 OPENVINO_ASSERT(false, error_msg.str());
             }
         } else if (desc.is_type<quantize>()) {
@@ -1555,7 +1571,7 @@ void program_node::init_onednn_primitive_attributes() {
                             post_ops.append_eltwise(dnnl::algorithm::eltwise_linear, q_param->_in_scale, 0.0f);
                             update_onednn_post_op_list(onednn_post_op_type::eltwise_linear, empty_mem);
                         } else {
-                            auto in_scale = get_dependency(dep_idx++).get_output_layout();
+                            auto in_scale = get_input_layout(dep_idx++);
                             dnnl::memory::desc in_scale_desc = onednn::layout_to_memory_desc(in_scale, dnnl::memory::format_tag::ab, true);
                             post_ops.append_binary(dnnl::algorithm::binary_mul, in_scale_desc);
                             update_onednn_post_op_list(onednn_post_op_type::binary_mul, dep_idx - 1, dnnl::memory::format_tag::ab, true,
@@ -1567,7 +1583,7 @@ void program_node::init_onednn_primitive_attributes() {
                                 post_ops.append_eltwise(dnnl::algorithm::eltwise_linear, 1.0f, q_param->_in_shift);
                                 update_onednn_post_op_list(onednn_post_op_type::eltwise_linear, empty_mem);
                             } else {
-                                auto in_shift = get_dependency(dep_idx++).get_output_layout();
+                                auto in_shift = get_input_layout(dep_idx++);
                                 dnnl::memory::desc in_shift_desc = onednn::layout_to_memory_desc(in_shift, dnnl::memory::format_tag::ab, true);
                                 post_ops.append_binary(dnnl::algorithm::binary_add, in_shift_desc);
                                 update_onednn_post_op_list(onednn_post_op_type::binary_add, dep_idx - 1, dnnl::memory::format_tag::ab, true,
@@ -1599,7 +1615,7 @@ void program_node::init_onednn_primitive_attributes() {
                                 post_ops.append_eltwise(dnnl::algorithm::eltwise_linear, q_param->_out_scale, 0.0f);
                                 update_onednn_post_op_list(onednn_post_op_type::eltwise_linear, empty_mem);
                             } else {
-                                auto out_scale = get_dependency(dep_idx++).get_output_layout();
+                                auto out_scale = get_input_layout(dep_idx++);
                                 dnnl::memory::desc out_scale_desc = onednn::layout_to_memory_desc(out_scale, dnnl::memory::format_tag::ab, true);
                                 post_ops.append_binary(dnnl::algorithm::binary_mul, out_scale_desc);
                                 update_onednn_post_op_list(onednn_post_op_type::binary_mul, dep_idx - 1, dnnl::memory::format_tag::ab, true,
@@ -1612,7 +1628,7 @@ void program_node::init_onednn_primitive_attributes() {
                                 post_ops.append_eltwise(dnnl::algorithm::eltwise_linear, 1.0f, q_param->_out_shift);
                                 update_onednn_post_op_list(onednn_post_op_type::eltwise_linear, empty_mem);
                             } else {
-                                auto out_shift = get_dependency(dep_idx++).get_output_layout();
+                                auto out_shift = get_input_layout(dep_idx++);
                                 dnnl::memory::desc out_shift_desc = onednn::layout_to_memory_desc(out_shift, dnnl::memory::format_tag::ab, true);
                                 post_ops.append_binary(dnnl::algorithm::binary_add, out_shift_desc);
                                 update_onednn_post_op_list(onednn_post_op_type::binary_add, dep_idx - 1, dnnl::memory::format_tag::ab, true,
@@ -1636,8 +1652,8 @@ void program_node::init_onednn_primitive_attributes() {
                 // 1. clamp
                 {
                     if (q_param->_need_clamp) {
-                        auto in_lo = get_dependency(dep_idx++).get_output_layout();
-                        auto in_hi = get_dependency(dep_idx++).get_output_layout();
+                        auto in_lo = get_input_layout(dep_idx++);
+                        auto in_hi = get_input_layout(dep_idx++);
                         dnnl::algorithm clamp_max = dnnl::algorithm::binary_max;
                         dnnl::algorithm clamp_min = dnnl::algorithm::binary_min;
                         dnnl::memory::desc in_lo_desc = onednn::layout_to_memory_desc(in_lo, dnnl::memory::format_tag::ab, true);
@@ -1662,7 +1678,7 @@ void program_node::init_onednn_primitive_attributes() {
                             post_ops.append_eltwise(dnnl::algorithm::eltwise_linear, q_param->_in_scale, 0.0f);
                             update_onednn_post_op_list(onednn_post_op_type::eltwise_linear, empty_mem);
                         } else {
-                            auto in_scale = get_dependency(dep_idx++).get_output_layout();
+                            auto in_scale = get_input_layout(dep_idx++);
                             dnnl::memory::desc in_scale_desc = onednn::layout_to_memory_desc(in_scale, dnnl::memory::format_tag::ab, true);
                             post_ops.append_binary(dnnl::algorithm::binary_mul, in_scale_desc);
                             update_onednn_post_op_list(onednn_post_op_type::binary_mul, dep_idx - 1, dnnl::memory::format_tag::ab, true,
@@ -1674,7 +1690,7 @@ void program_node::init_onednn_primitive_attributes() {
                                 post_ops.append_eltwise(dnnl::algorithm::eltwise_linear, 1.0f, q_param->_in_shift);
                                 update_onednn_post_op_list(onednn_post_op_type::eltwise_linear, empty_mem);
                             } else {
-                                auto in_shift = get_dependency(dep_idx++).get_output_layout();
+                                auto in_shift = get_input_layout(dep_idx++);
                                 dnnl::memory::desc in_shift_desc = onednn::layout_to_memory_desc(in_shift, dnnl::memory::format_tag::ab, true);
                                 post_ops.append_binary(dnnl::algorithm::binary_add, in_shift_desc);
                                 update_onednn_post_op_list(onednn_post_op_type::binary_add, dep_idx - 1, dnnl::memory::format_tag::ab, true,
@@ -1702,7 +1718,7 @@ void program_node::init_onednn_primitive_attributes() {
                                 post_ops.append_eltwise(dnnl::algorithm::eltwise_linear, q_param->_out_scale, 0.0f);
                                 update_onednn_post_op_list(onednn_post_op_type::eltwise_linear, empty_mem);
                             } else {
-                                auto out_scale = get_dependency(dep_idx++).get_output_layout();
+                                auto out_scale = get_input_layout(dep_idx++);
                                 dnnl::memory::desc out_scale_desc = onednn::layout_to_memory_desc(out_scale, dnnl::memory::format_tag::ab, true);
                                 post_ops.append_binary(dnnl::algorithm::binary_mul, out_scale_desc);
                                 update_onednn_post_op_list(onednn_post_op_type::binary_mul, dep_idx - 1, dnnl::memory::format_tag::ab, true,
@@ -1715,7 +1731,7 @@ void program_node::init_onednn_primitive_attributes() {
                                 post_ops.append_eltwise(dnnl::algorithm::eltwise_linear, 1.0f, q_param->_out_shift);
                                 update_onednn_post_op_list(onednn_post_op_type::eltwise_linear, empty_mem);
                             } else {
-                                auto out_shift = get_dependency(dep_idx++).get_output_layout();
+                                auto out_shift = get_input_layout(dep_idx++);
                                 dnnl::memory::desc out_shift_desc = onednn::layout_to_memory_desc(out_shift, dnnl::memory::format_tag::ab, true);
                                 post_ops.append_binary(dnnl::algorithm::binary_add, out_shift_desc);
                                 update_onednn_post_op_list(onednn_post_op_type::binary_add, dep_idx - 1, dnnl::memory::format_tag::ab, true,
@@ -1737,26 +1753,33 @@ void program_node::init_onednn_primitive_attributes() {
         dnnl::post_ops optimized_post_ops = post_ops;
         bool optimization_is_finished = false;
 
-        add_onednn_fused_primitives(fused_ops);
-
         // Trying to combine multiplications and additions which are placed one after another.
         // We do it in the cycle because some optimization cases can be simplified again from time to time
         do {
             GPU_DEBUG_GET_INSTANCE(debug_config);
             GPU_DEBUG_IF(debug_config->disable_onednn_opt_post_ops)
                 break;
-            optimized_post_ops = try_optimize_post_ops(optimized_post_ops, attrs, optimization_is_finished);
+            optimized_post_ops = try_optimize_post_ops(fused_ops, optimized_post_ops, attrs, optimization_is_finished);
         } while (!optimization_is_finished);
 
         attrs->set_post_ops(optimized_post_ops);
     } else {
         // Set post-ops without any optimizations
-        add_onednn_fused_primitives(fused_ops);
         attrs->set_post_ops(post_ops);
     }
-
-    add_onednn_attrs(attrs);
 }
 
+void program_node::init_onednn_primitive_attributes() {
+    auto attrs = std::make_shared<dnnl::primitive_attr>();
+    // Create onednn post-ops list related to the current node
+    std::vector<fused_primitive_desc_onednn> fused_ops;
+    // Can't create onednn attirbute on dynamic shape
+    if (!is_dynamic()) {
+        const std::vector<fused_primitive_desc>& cldnn_post_ops = get_fused_primitives();
+        create_onednn_primitive_attributes(cldnn_post_ops, attrs, fused_ops, nullptr);
+    }
 
+    add_onednn_fused_primitives(fused_ops);
+    add_onednn_attrs(attrs);
+}
 #endif // ENABLE_ONEDNN_FOR_GPU
diff --git a/src/plugins/intel_gpu/src/graph/reorder.cpp b/src/plugins/intel_gpu/src/graph/reorder.cpp
index 18c00d949b857c..e322baeee95e52 100644
--- a/src/plugins/intel_gpu/src/graph/reorder.cpp
+++ b/src/plugins/intel_gpu/src/graph/reorder.cpp
@@ -270,7 +270,9 @@ void reorder_inst::update_output_memory() {
     if (!can_be_optimized())
         return;
 
-    if (static_cast<bool>(_outputs[0]) && _network.get_engine().is_the_same_buffer(output_memory(), input_memory()))
+    if (static_cast<bool>(_outputs[0])
+        && _network.get_engine().is_the_same_buffer(output_memory(), input_memory())
+        && output_memory().get_layout().identical(get_output_layout()))
         return;
 
     if (_node != nullptr)
diff --git a/src/plugins/intel_gpu/src/graph/reshape.cpp b/src/plugins/intel_gpu/src/graph/reshape.cpp
index 08447c6bdd2d0f..eed87ed759211d 100644
--- a/src/plugins/intel_gpu/src/graph/reshape.cpp
+++ b/src/plugins/intel_gpu/src/graph/reshape.cpp
@@ -179,11 +179,14 @@ std::vector<layout> reshape_inst::calc_output_layouts(reshape_node const& node,
     auto run_shape_infer = [&](reshape::reshape_mode mode) {
          switch (mode) {
             case reshape::reshape_mode::base: {
-                OPENVINO_ASSERT(!input_layout.has_dynamic_pad());
                 ov::op::v1::Reshape op;
                 op.set_special_zero(prim->special_zero);
                 op.set_friendly_name(prim->id.c_str());
                 output_shapes = ov::op::v1::shape_infer(&op, input_shapes, ta);
+                // If the reshape is base mode, it is currently not set as can_be_optimized at prepare_buffer_fusing.
+                // So we can just run the reshape kernel
+                // TODO: allow propagatable reshapes
+                out_pad = padding();
                 break;
             }
             case reshape::reshape_mode::squeeze: {
diff --git a/src/plugins/intel_gpu/src/graph/rope.cpp b/src/plugins/intel_gpu/src/graph/rope.cpp
index 8a2307e180bfe7..ea904916d4cf41 100644
--- a/src/plugins/intel_gpu/src/graph/rope.cpp
+++ b/src/plugins/intel_gpu/src/graph/rope.cpp
@@ -58,6 +58,7 @@ std::string rope_inst::to_string(rope_node const& node) {
 
     json_composite rope_info;
     rope_info.add("gather_position_arg_id", desc->config.gather_position_arg_id);
+    rope_info.add("gather_rank", desc->gather_rank);
     rope_info.add("head_cnt", desc->config.head_cnt);
     rope_info.add("head_size", desc->config.head_size);
     rope_info.add("input_trans0213", desc->config.input_trans0213);
diff --git a/src/plugins/intel_gpu/src/graph/select.cpp b/src/plugins/intel_gpu/src/graph/select.cpp
index d3e91aaa2b3753..9e0902e1f2ad4e 100644
--- a/src/plugins/intel_gpu/src/graph/select.cpp
+++ b/src/plugins/intel_gpu/src/graph/select.cpp
@@ -39,6 +39,9 @@ std::vector<layout> select_inst::calc_output_layouts(const select_node& /*node*/
 
     auto desc = impl_param.typed_desc<select>();
     auto dt = desc->output_data_types[0].value_or(input1_layout.data_type);
+    if (impl_param.has_fused_primitives()) {
+        dt = impl_param.get_output_element_type();
+    }
 
     ov::op::v1::Select op;
     op.set_auto_broadcast(desc->broadcast_spec);
diff --git a/src/plugins/intel_gpu/src/kernel_selector/CMakeLists.txt b/src/plugins/intel_gpu/src/kernel_selector/CMakeLists.txt
index 52c3fb052ff093..993ef596a21256 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/CMakeLists.txt
+++ b/src/plugins/intel_gpu/src/kernel_selector/CMakeLists.txt
@@ -67,6 +67,10 @@ target_include_directories(${TARGET_NAME} PUBLIC $<BUILD_INTERFACE:${INCLUDE_DIR
 target_compile_options(${TARGET_NAME} PRIVATE
   $<$<CONFIG:Release>:$<IF:$<CXX_COMPILER_ID:MSVC>,/Os,-Os>>)
 
+if (ENABLE_ONEDNN_FOR_GPU)
+    target_link_libraries(${TARGET_NAME} PRIVATE onednn_gpu_tgt)
+endif()
+
 if(COMMAND add_cpplint_target)
   add_cpplint_target(${TARGET_NAME}_cpplint FOR_TARGETS ${TARGET_NAME})
 endif()
diff --git a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/broadcast_gpu_ref.cl b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/broadcast_gpu_ref.cl
index 3f2314e1a38ed4..3195ffe2f3e023 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/broadcast_gpu_ref.cl
+++ b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/broadcast_gpu_ref.cl
@@ -253,6 +253,7 @@ inline uint FUNC(get_idx_pos)(OPTIONAL_SHAPE_INFO_ARG uint out_b, uint out_f, ui
 #define VLOAD CAT(vload, VEC_SIZE)
 #define VSTORE CAT(vstore,VEC_SIZE)
 #define INPUT0_VTYPE MAKE_VECTOR_TYPE(INPUT0_TYPE, VEC_SIZE)
+#define OUTPUT_VTYPE MAKE_VECTOR_TYPE(OUTPUT_TYPE, VEC_SIZE)
 
 KERNEL(broadcast_gpu_ref)(
     OPTIONAL_SHAPE_INFO_ARG
@@ -322,7 +323,7 @@ KERNEL(broadcast_gpu_ref)(
             uint output_idx = out_pos;
             unroll_for(uint j = 0; j < y_nums; j++) {
                 unroll_for(uint i = 0; i < x_stride; i++) {
-                    output[output_idx + i] = input[idx_pos + i];
+                    output[output_idx + i] = TO_OUTPUT_TYPE(input[idx_pos + i]);
                 }
                 output_idx += OUTPUT_SIZE_X;
             }
@@ -330,7 +331,10 @@ KERNEL(broadcast_gpu_ref)(
             uint output_idx = out_pos;
             INPUT0_VTYPE input_vec = VLOAD(0, &input[idx_pos]);
             unroll_for(uint i = 0; i < y_nums; i++) {
-                VSTORE(input_vec, 0, &output[output_idx]);
+                OUTPUT_VTYPE out_v;
+                for (int j = 0; j < VEC_SIZE; ++j)
+                    out_v[j] = TO_OUTPUT_TYPE(input_vec[j]);
+                VSTORE(out_v, 0, &output[output_idx]);
                 output_idx += OUTPUT_SIZE_X;
             }
 
@@ -339,7 +343,7 @@ KERNEL(broadcast_gpu_ref)(
 
                 output_idx = out_pos;
                 unroll_for(uint i = 0; i < y_nums; i++) {
-                    output[output_idx + x_stride] = input_val;
+                    output[output_idx + x_stride] = TO_OUTPUT_TYPE(input_val);
                     output_idx += OUTPUT_SIZE_X;
                 }
             }
@@ -375,7 +379,7 @@ KERNEL(broadcast_gpu_ref)(
         const uint out_pos = OUTPUT_GET_INDEX(out_b, out_f, out_y, out_x);
         const uint idx_pos = FUNC_CALL(get_idx_pos)(OPTIONAL_SHAPE_INFO_TENSOR out_b, out_f, out_y, out_x);
 #endif
-        output[out_pos] = input[idx_pos];
+        output[out_pos] = TO_OUTPUT_TYPE(input[idx_pos]);
     }
 }
 
diff --git a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/convolution_gpu_bfyx_f16_1x1.cl b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/convolution_gpu_bfyx_f16_1x1.cl
index ae3731a962244b..542fa69ebc241b 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/convolution_gpu_bfyx_f16_1x1.cl
+++ b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/convolution_gpu_bfyx_f16_1x1.cl
@@ -26,6 +26,7 @@
 REQD_SUB_GROUP_SIZE(SUB_GROUP_SIZE)
 __attribute__((reqd_work_group_size(1, SUB_GROUP_SIZE * SLM_DIV_FACTOR, 1)))
 KERNEL(convolution_b_fs_yx_fsv16_1x1)(
+    OPTIONAL_SHAPE_INFO_ARG
     __global INPUT0_TYPE* input,
     __global OUTPUT_TYPE* output,
     __global FILTER_TYPE* weights
diff --git a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/fully_connected_gpu_bf_tiled.cl b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/fully_connected_gpu_bf_tiled.cl
index 9263421ccee6d0..f22c1ee136c004 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/fully_connected_gpu_bf_tiled.cl
+++ b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/fully_connected_gpu_bf_tiled.cl
@@ -17,6 +17,39 @@
 // DISPATCH_FSV - output coordinates for each sub-group are calculated from linearized coordinates
 // DISPATCH_BSV   as if they laid in bs_fs_bsv_fsv format, these macros describe fsv and bsv factors;
 
+#define INPUT_LOAD_SIZE                     4
+
+#if FC_KERNEL_DYNAMIC_QUANTIZE
+KERNEL(quantize_input)(
+    const __global INPUT0_TYPE* input,
+    __global char* quantized_input,
+    __global INPUT0_TYPE* de_quan_scale) {
+    const uint offset = get_global_id(0);
+
+    uint input_offset = offset * QUANTIZE_GROUP_SIZE;
+    half4 input_0[8];
+    char4 quantized_value[8];
+    half  max[8];
+
+    unroll_for (uint i = 0 ; i < 8 ; ++i) {
+        input_0[i] = vload4(0, &input[input_offset + i * 4]);
+        max[i] = fmax(fmax(fabs(input_0[i][0]), fabs(input_0[i][1])), fmax(fabs(input_0[i][2]), fabs(input_0[i][3])));
+    }
+
+    half max_value = fmax(fmax(fmax(max[0], max[1]), fmax(max[2], max[3])),
+                            fmax(fmax(max[4], max[5]), fmax(max[6], max[7])));
+
+    half quan_scale = max_value / 128;
+
+    unroll_for (uint i = 0 ; i < 8 ; ++i) {
+        quantized_value[i] = CAT(convert_, MAKE_VECTOR_TYPE(char, INPUT_LOAD_SIZE))(input_0[i] / (half4)quan_scale);
+        vstore4(quantized_value[i], 0, &quantized_input[input_offset + i * 4]);
+    }
+
+    de_quan_scale[offset] = quan_scale;
+}
+#else  // !FC_KERNEL_DYNAMIC_QUANTIZE
+
 // Verify JIT parameters.
 #if SIMD != 8 && SIMD != 16
 #   error "fully_connected_gpu_bf_tiled.cl - SIMD must be one of {8, 16}"
@@ -50,8 +83,10 @@
 // Data stored in memory : f0k0k1|f16k0k1|f0k2k3|f16k2k3
 // => unpack as f0k0k1|f0k2k3|f16k0k1|f16k2k3 so that the weight access order is preserved 
 #define UNPACK_INT4 UNPACK_INT4x2_OSV32_ISV2
+#define UNPACK_TRANSPOSED_INT4 UNPACK_INT4x2_OSV32_ISV2
 #else
 #define UNPACK_INT4 UNPACK_INT4x2
+#define UNPACK_TRANSPOSED_INT4 UNPACK_TRANSPOSED_INT4x2
 #endif
 // Macros for vectorized types.
 #define INPUT_VEC_TYPE             MAKE_VECTOR_TYPE(INPUT0_TYPE, TILE_IFM)
@@ -79,6 +114,7 @@
 // Check alignment restrictions for using block writes on output.
 #define USE_BLOCK_WRITE ((OUTPUT_TYPE_SIZE * TILE_OUT_B_PITCH) % 16 == 0 && (OUTPUT_TYPE_SIZE * OUTPUT_OFFSET) % 16 == 0)
 
+
 #if !REALIGN_FP16_OFFSET
 #   if OUTPUT_3D
 #       define MAIN_LOOP_ELEMENTS_COUNT  INPUT0_SIZE_Y
@@ -645,27 +681,353 @@ inline void FUNC(fc_bf_tiled_kernel_default)(
         #undef WRITE_OUTPUT
     } else {
         output_offset += sglid;
+        for (uint bi = 0; bi < TILE_B; ++bi) {
+            for (uint fi = 0; fi < TILE_OFM; ++fi) {
+                const bool should_write =
+#if IS_DYNAMIC
+                    bi + out_b < BATCH_SIZE &&
+#endif
+                    (TILE_OUT_F_NUM % (TILE_OFM * SIMD) == 0 ||
+                    out_f + fi * SIMD + sglid < TILE_OUT_F_NUM);
+                if (should_write) {
+                    output[output_offset] = ((OUTPUT_TYPE*)(&result[bi]))[fi];
+                }
+                output_offset += SIMD;
+            }
+            output_offset += TILE_OUT_B_PITCH - TILE_OFM * SIMD;
+        }
+    }
+    // =====================================================================================================================================
+}
+
+// Dyc Quantize
+#if USE_SLM && DYNAMIC_QUANTIZE
+#define PACKED_DQ_TYPE                      int
+#define DQ_VEC_TYPE                         MAKE_VECTOR_TYPE(DQ_TYPE, TILE_IFM)
+#define DQ_SLM_FILTER_VEC                   MAKE_VECTOR_TYPE(DQ_TYPE, 4)
+#define DQ_SLM_FILTER_PACKED_VEC            MAKE_VECTOR_TYPE(FILTER_TYPE, FILTER_LOAD_BLOCK_SIZE)
+#define DQ_SLM_FILTER_UNPACKED_VEC          MAKE_VECTOR_TYPE(DQ_TYPE, FILTER_ELEMENTS_PER_LOAD)
+#define DQ_FILTER_VEC_TYPE                  MAKE_VECTOR_TYPE(DQ_TYPE, TILE_K_OFM)
+
+#define TO_DQ_TYPE(x)                       CAT(CAT(convert_, DQ_TYPE),_sat)(x)
+#define TO_DQ_VEC_TYPE(x)                   CAT(convert_, DQ_VEC_TYPE)(x)
+#define TO_DQ_SLM_FILTER_UNPACKED_VEC(x)  CAT(convert_, DQ_SLM_FILTER_UNPACKED_VEC)(x)
+#define TO_DQ_FILTER_VEC_TYPE(x)            CAT(convert_, DQ_FILTER_VEC_TYPE)(x)
+
+#define AS_TYPE_N_(type, n, x)  as_##type##n(x)
+#define AS_TYPE_N(type, n, x)   AS_TYPE_N_(type, n, x)
+#define AS_DQ_TYPE_4(x)         AS_TYPE_N(DQ_TYPE, INPUT_LOAD_SIZE, x)
+
+inline void FUNC(fc_bf_tiled_kernel_dyn_quan)(
+    OPTIONAL_SHAPE_INFO_ARG
+    const __global INPUT0_TYPE* input,
+    __global char* quantized_input,
+    __global INPUT0_TYPE* scale,
+#if DECOMPRESSION_SCALE_TERM
+    const __global DECOMPRESSION_SCALE_TYPE* decompression_scale,
+#endif
+#if DECOMPRESSION_ZP_TERM && !DECOMPRESSION_ZP_SCALAR
+    const __global DECOMPRESSION_ZP_TYPE* decompression_zp,
+#endif
+    __global OUTPUT_TYPE* output,
+    const __global FILTER_TYPE* weights
+    , __local int* wei_local_mem
+#if BIAS_TERM
+    , const __global BIAS_TYPE* biases
+#endif
+#if HAS_FUSED_OPS_DECLS
+    , FUSED_OPS_DECLS
+#endif
+) {
+    uint gid = (uint)get_group_id(0);
+    uint local_id = (uint)get_local_id(2);
+    uint sglid = (uint)get_sub_group_local_id();
+
+    // Dispatch as bs_fs_bsv_fsv, where bsv = DISPATCH_BSV and fsv = DISPATCH_FSV.
+    // This allows more fine grained control over dispatch order than using work-groups and
+    // avoids requirement of threads being available for whole work-group.
+    // It could hovewer have some drawbacks like not providing physical locality or not using
+    // full dispatch pipeline.
+    uint feature_mini_block = gid % DISPATCH_FSV;
+    uint batch_mini_block = gid / DISPATCH_FSV % DISPATCH_BSV;
+    uint feature_mega_block = gid / (DISPATCH_FSV * DISPATCH_BSV) % (CEIL_DIV(TILE_OUT_F_NUM, TILE_OFM * SIMD) / DISPATCH_FSV);
+    uint batch_mega_block = gid / (DISPATCH_FSV * DISPATCH_BSV * CEIL_DIV(TILE_OUT_F_NUM, TILE_OFM * SIMD) / DISPATCH_FSV);
+
+    FILTER_VEC_TYPE wei = 0;
+
+    uint out_f = gid * (TILE_OFM * SIMD);
+    uint out_b = LWS_BATCHES * TILE_B * (uint)get_group_id(2) + local_id * TILE_B;
+
+#if OUTPUT_3D
+    uint out_b0 = out_b / OUTPUT_FEATURE_NUM;
+    uint out_b1 = out_b % OUTPUT_FEATURE_NUM;
+    uint input_offset = out_b0 * INPUT0_BATCH_PITCH + out_b1 * INPUT0_FEATURE_PITCH + INPUT0_OFFSET;
+#else
+    uint input_offset = out_b * TILE_IN_B_PITCH + INPUT0_OFFSET;
+#endif
+
+    uint weights_offset = out_f * (INPUT_ELEMENTS_COUNT / 2);
+
+    ACCUMULATOR_VEC_TYPE    acc[TILE_B] = { };
+
+    // Dynamic Quantize
+    MAKE_VECTOR_TYPE(DQ_TYPE, INPUT_LOAD_SIZE)      tiled_input_0[HALF_TILE_B] = { };   // Load 4 linear inputs for packing
+    PACKED_DQ_TYPE                                  packed_in_0[HALF_TILE_B] = { };     // Packing char4 inputs to 1 integer
+    INPUT0_TYPE                                     de_quantize_scale[TILE_B];
+
+#if COMPRESSED_WEIGHTS && DECOMPRESSION_SCALE_GROUPS_NUM == 1
+    #if DECOMPRESSION_SCALE_LENGTH > 1 && DECOMPRESSION_SCALE_LENGTH % (TILE_OFM * SIMD) == 0
+        ACCUMULATOR_VEC_TYPE d_scale = TO_ACCUMULATOR_VEC_TYPE(BLOCK_READN(DECOMPRESSION_SCALE_TYPE, TILE_OFM, decompression_scale, out_f));
+    #elif DECOMPRESSION_SCALE_LENGTH > 1 && DECOMPRESSION_SCALE_LENGTH % (TILE_OFM * SIMD) != 0
+        ACCUMULATOR_VEC_TYPE d_scale = 0;
+        unroll_for(uint of = 0; of < TILE_OFM; ++of) {
+            uint offset = out_f + of*SIMD + get_sub_group_local_id();
+            if (offset < DECOMPRESSION_SCALE_LENGTH)
+                ((ACCUMULATOR_TYPE*)(&d_scale))[of] = decompression_scale[offset];
+        }
+    #else
+        ACCUMULATOR_VEC_TYPE d_scale = decompression_scale[0];
+    #endif
+
+    ACCUMULATOR_TYPE* d_scales = (ACCUMULATOR_TYPE*)(&d_scale);
+#endif
+
+#if COMPRESSED_WEIGHTS && DECOMPRESSION_ZP_TERM && DECOMPRESSION_ZP_GROUPS_NUM == 1 && !DECOMPRESSION_ZP_SCALAR
+    #if DECOMPRESSION_ZP_LENGTH > 1 && DECOMPRESSION_ZP_LENGTH % (TILE_OFM * SIMD) == 0
+        ACCUMULATOR_VEC_TYPE d_zp = TO_ACCUMULATOR_VEC_TYPE(BLOCK_READN(DECOMPRESSION_ZP_TYPE, TILE_OFM, decompression_zp, out_f));
+    #elif DECOMPRESSION_ZP_LENGTH > 1 && DECOMPRESSION_ZP_LENGTH % (TILE_OFM * SIMD) != 0
+        ACCUMULATOR_VEC_TYPE d_zp = 0;
+        unroll_for(uint of = 0; of < TILE_OFM; ++of) {
+            uint offset = out_f + of*SIMD + get_sub_group_local_id();
+            if (offset < DECOMPRESSION_ZP_LENGTH)
+                ((ACCUMULATOR_TYPE*)(&d_zp))[of] = decompression_zp[offset];
+        }
+    #else
+        ACCUMULATOR_VEC_TYPE d_zp = decompression_zp[0];
+    #endif
+    ACCUMULATOR_TYPE* d_zps = (ACCUMULATOR_TYPE*)(&d_zp);
+#endif
+
+    // =====================================================================================================================================
+    // Main computation loop
+    const uint iterations = MAIN_LOOP_ELEMENTS_COUNT / (TILE_IFM * SIMD);
+    // Each sub-group loads 2 Batch 
+    uint idx_sglid = (sglid * TILE_K) % QUANTIZE_GROUP_SIZE;       // same index for sglid 0~7 : to tile_k direction
+    uint batch_sglid = (sglid * TILE_K) / QUANTIZE_GROUP_SIZE;     // 0 to 1 : to batch direction
+
+    __attribute__((opencl_unroll_hint(1)))
+    for (uint ni = 0; ni < iterations; ++ni) {
+        uint in_offset = input_offset + (idx_sglid + batch_sglid * TILE_IN_B_PITCH);
+        uint scale_offset = input_offset / QUANTIZE_GROUP_SIZE;
+        for (uint bi = 0; bi < HALF_TILE_B; ++bi) {
+            // Load quantizing info from pre-quantizing kernel
+            tiled_input_0[bi] = vload4(0, &quantized_input[in_offset]);
+            de_quantize_scale[bi * 2] = scale[scale_offset];
+            de_quantize_scale[bi * 2 + 1] = scale[scale_offset+ (TILE_IN_B_PITCH/QUANTIZE_GROUP_SIZE)];
+
+            // Packing : Get 4(B)x4(K) integer vector (packing to 4x1 vector)
+            packed_in_0[bi] = as_int(tiled_input_0[bi]);
+
+            // Next batch
+            in_offset += (TILE_IN_B_PITCH * 2);
+            scale_offset += (TILE_IN_B_PITCH/QUANTIZE_GROUP_SIZE * 2);
+        }
+
+        input_offset += TILE_IFM * SIMD;
+
+        // Packing
+        MAKE_VECTOR_TYPE(int, TILE_B) acc_tmp[TILE_OFM] = { };
+
+        #if TILE_OFM != 2
+        #error "FC bf_tiled kernel: can't use SLM optimization with TILE_OFM != 2"
+        #endif
+
+        // Skip first barrier synchronization if there is only single outer loop iteration.
+        #if MAIN_LOOP_ELEMENTS_COUNT / (TILE_IFM * SIMD) > 1
+            barrier(CLK_LOCAL_MEM_FENCE);
+        #endif
+
+        __local int* char_slm_weight = (__local int*)wei_local_mem;
+
+        uint weights_idx = weights_offset + local_id * SIMD * FILTER_LOAD_ITERS * FILTER_LOAD_BLOCK_SIZE;
+        uint wei_local_idx = local_id * SIMD * FILTER_LOAD_ITERS * (FILTER_LOAD_BLOCK_SIZE/2) + sglid * 2;
+
+        // DECOMPRESSION_SCALE_POST_OP SHOULD be enabled for dynamic quantize FC : scale is ACCUMULATOR_VAL_ONE
+        unroll_for(uint load_iter = 0; load_iter < FILTER_LOAD_ITERS; ++load_iter) {
+            SLM_FILTER_PACKED_VEC wei_packed = BLOCK_READN(FILTER_TYPE, FILTER_LOAD_BLOCK_SIZE, weights, weights_idx);
+            DQ_SLM_FILTER_UNPACKED_VEC dq_wei_unpacked = UNPACK_TRANSPOSED_INT4(DQ_TYPE, *((uint4x8_t *)&wei_packed));
+
+            // Calculate zero-point and scale only for DECOMPRESSION_SCALE_POST_OP enabled
+            #if DECOMPRESSION_ZP_TERM
+                #if DECOMPRESSION_ZP_SCALAR
+                    DQ_SLM_FILTER_UNPACKED_VEC dzp = (DQ_SLM_FILTER_UNPACKED_VEC)(DECOMPRESSION_ZP_VALUE);
+                #elif DECOMPRESSION_ZP_GROUPS_NUM > 1
+                    DQ_SLM_FILTER_UNPACKED_VEC dzp;
+                    unroll_for(uint fi = 0; fi < TILE_OFM; ++fi) {
+                        unroll_for(uint kii = 0; kii < FILTER_LOAD_BLOCK_SIZE; ++kii) {
+                            const uint offset_ofm = out_f + fi*SIMD + sglid;
+                            const uint offset_ifm = ni * TILE_IFM * SIMD + local_id * FILTER_LOAD_ITERS * FILTER_LOAD_BLOCK_SIZE + load_iter * FILTER_LOAD_BLOCK_SIZE + kii;
+                            const uint zp_offset = (offset_ofm % DECOMPRESSION_ZP_BATCH_NUM) * DECOMPRESSION_ZP_BATCH_PITCH +
+                                                    (offset_ifm / DECOMPRESSION_ZP_GROUP_SIZE) * DECOMPRESSION_ZP_FEATURE_PITCH;
+                            dzp[W_IDX] = decompression_zp[zp_offset];
+                        }
+                    }
+                #else
+                    DQ_SLM_FILTER_UNPACKED_VEC dzp = (DQ_SLM_FILTER_UNPACKED_VEC)(d_zps[0]);
+                #endif
+            #else
+                DQ_SLM_FILTER_UNPACKED_VEC dzp = (DQ_SLM_FILTER_UNPACKED_VEC)(ACCUMULATOR_VAL_ZERO);
+            #endif
+
+            // Calculate weight : w = (w - dzp) * ds
+            dq_wei_unpacked -= dzp;
+
+            #if FILTER_LOAD_BLOCK_SIZE == 2
+                DQ_SLM_FILTER_VEC wei_1 = {dq_wei_unpacked.s01, dq_wei_unpacked.s23};
+                char_slm_weight[wei_local_idx] = as_int(wei_1);
+            #elif FILTER_LOAD_BLOCK_SIZE == 4
+                DQ_SLM_FILTER_VEC wei_1 = {dq_wei_unpacked.s01, dq_wei_unpacked.s23};
+                char_slm_weight[wei_local_idx] = as_int(wei_1);
+                DQ_SLM_FILTER_VEC wei_2 = {dq_wei_unpacked.s45, dq_wei_unpacked.s67};
+                char_slm_weight[wei_local_idx+1] = as_int(wei_2);
+            #elif FILTER_LOAD_BLOCK_SIZE == 8
+                DQ_SLM_FILTER_VEC wei_1 = {dq_wei_unpacked.s01, dq_wei_unpacked.s23};
+                char_slm_weight[wei_local_idx] = as_int(wei_1);
+                DQ_SLM_FILTER_VEC wei_2 = {dq_wei_unpacked.s45, dq_wei_unpacked.s67};
+                char_slm_weight[wei_local_idx+1] = as_int(wei_2);
+                DQ_SLM_FILTER_VEC wei_3 = {dq_wei_unpacked.s89, dq_wei_unpacked.sab};
+                char_slm_weight[wei_local_idx+2] = as_int(wei_3);
+                DQ_SLM_FILTER_VEC wei_4 = {dq_wei_unpacked.scd, dq_wei_unpacked.sef};
+                char_slm_weight[wei_local_idx+3] = as_int(wei_4);
+            #else
+                #error "FC bf_tiled kernel: unsupported FILTER_LOAD_BLOCK_SIZE for SLM kernel"
+            #endif
+
+            wei_local_idx += SIMD * (FILTER_LOAD_BLOCK_SIZE/2);
+            weights_idx += SIMD * FILTER_LOAD_BLOCK_SIZE;
+        }
+
+        wei_local_idx = sglid * 2;
 
-        // TODO: Investigate why below code doesn't compile and check how it affects performance.
-        //#define WRITE_OUTPUT_FEATURE(fi) do {                                                   \
-        //        const bool should_write =                                                       \
-        //            TILE_OUT_F_NUM %  (TILE_OFM * SIMD) == 0 ||                                 \
-        //            out_f + (fi) * SIMD + sglid < TILE_OUT_F_NUM;                               \
-        //        if (should_write) {                                                             \
-        //            output[output_offset] = result[out_bi][fi];                                 \
-        //        }                                                                               \
-        //        output_offset += SIMD;                                                          \
-        //    } while (false)
-        //
-        //#define WRITE_OUTPUT(bi) do {                                                           \
-        //        const uint out_bi = bi;                                                         \
-        //        CONST_LOOP(TILE_OFM, WRITE_OUTPUT_FEATURE);                                     \
-        //        output_offset += TILE_OUT_B_PITCH - TILE_OFM * SIMD;                            \
-        //    } while (false)
-        //
-        //CONST_LOOP(TILE_B, WRITE_OUTPUT);
-        //#undef WRITE_OUTPUT
-        //#undef WRITE_OUTPUT_FEATURE
+        barrier(CLK_LOCAL_MEM_FENCE);
+
+        unroll_for(uint ki = 0; ki < (TILE_IFM * SIMD) / TILE_K; ++ki) {
+            #if TILE_K != 4
+                #error "FC bf_tiled kernel: unsupported TILE_K size for SLM kernel"
+            #endif
+
+            // Compute input * weight : packed char4 type
+            char8 weight = vload8(0, (__local char *)(&char_slm_weight[wei_local_idx + 16*2*ki]));
+            char4 first_weight = weight.s0123;
+            char4 second_weight = weight.s4567;
+            unroll_for (uint bi = 0; bi < TILE_B; ++bi) {
+                char4 input_val = as_char4(_sub_group_shuffle(packed_in_0[bi / 2], (bi % 2) * 8 + ki));
+                acc_tmp[0][bi] = imad_SW(acc_tmp[0][bi], input_val, first_weight);
+                acc_tmp[1][bi] = imad_SW(acc_tmp[1][bi], input_val, second_weight);
+            }
+
+            weights_offset += TILE_K_OFM_PACKED * SIMD;
+
+            #if DECOMPRESSION_SCALE_POST_OP && (TILE_IFM * SIMD > DECOMPRESSION_SCALE_GROUP_SIZE)
+                unroll_for (uint bi = 0; bi < TILE_B; ++bi) {
+                    unroll_for(uint fi = 0; fi < TILE_OFM; ++fi) {
+                        const uint offset_ofm = out_f + fi*SIMD + sglid;
+
+                        #if DECOMPRESSION_SCALE_GROUPS_NUM > 1
+                            const uint scale_offset = (offset_ofm % DECOMPRESSION_SCALE_BATCH_NUM) * DECOMPRESSION_SCALE_BATCH_PITCH +
+                                                    ((ni*TILE_IFM*SIMD + ki*TILE_K) / DECOMPRESSION_SCALE_GROUP_SIZE)*DECOMPRESSION_SCALE_FEATURE_PITCH;
+                            ACCUMULATOR_TYPE ds = decompression_scale[scale_offset];
+                        #else
+                            ACCUMULATOR_TYPE ds = d_scales[fi % DECOMPRESSION_SCALE_LENGTH];
+                        #endif
+
+                        ((ACCUMULATOR_TYPE*)(&acc[bi]))[fi] += convert_half(((int *)(&acc_tmp[fi]))[bi]) * ds * de_quantize_scale[bi];
+                        acc_tmp[fi][bi] = 0;
+                    }
+                }
+            #endif
+        }  // Whole tile_k elements of each iteration : ki
+
+        #if DECOMPRESSION_SCALE_POST_OP && (TILE_IFM * SIMD <= DECOMPRESSION_SCALE_GROUP_SIZE)
+            const uint ni_offset = ((ni*TILE_IFM*SIMD) / DECOMPRESSION_SCALE_GROUP_SIZE)*DECOMPRESSION_SCALE_FEATURE_PITCH;
+            unroll_for (uint bi = 0; bi < TILE_B; ++bi) {
+                unroll_for(uint fi = 0; fi < TILE_OFM; ++fi) {
+                    const uint offset_ofm = out_f + fi*SIMD + sglid;
+
+                    #if DECOMPRESSION_SCALE_GROUPS_NUM > 1
+                        const uint scale_offset = (offset_ofm % DECOMPRESSION_SCALE_BATCH_NUM) * DECOMPRESSION_SCALE_BATCH_PITCH + ni_offset;
+                        ACCUMULATOR_TYPE ds = decompression_scale[scale_offset];
+                    #else
+                        ACCUMULATOR_TYPE ds = d_scales[fi % DECOMPRESSION_SCALE_LENGTH];
+                    #endif
+
+                    ((ACCUMULATOR_TYPE*)(&acc[bi]))[fi] += convert_half(((int *)(&acc_tmp[fi]))[bi]) * ds * de_quantize_scale[bi];
+                }
+            }
+        #endif
+    }  // Main compute loop : ni
+
+    // =====================================================================================================================================
+    // Post-processing: bias, activation, fused-ops
+    ACTIVATION_VEC_TYPE activated[TILE_B] = { };
+    for (uint bi = 0; bi < TILE_B; ++bi) {
+        activated[bi] = TO_ACTIVATION_VEC_TYPE(acc[bi]);
+    }
+
+#if BIAS_TERM
+    #if TILE_OUT_F_NUM % (TILE_OFM * SIMD) == 0
+        BIAS_VEC_TYPE bias = BIAS_BLOCK_READ(biases, out_f);
+    #else
+        BIAS_VEC_TYPE bias = 0;
+        unroll_for(uint fi = 0; fi < TILE_OFM; ++fi) {
+            ((BIAS_TYPE*)(&bias))[fi] = biases[out_f + sglid + fi * SIMD];
+        }
+    #endif
+    unroll_for (uint bi = 0; bi < TILE_B; ++bi) {
+        activated[bi] += TO_ACTIVATION_VEC_TYPE(bias);
+    }
+#endif
+
+    OUTPUT_VEC_TYPE result[TILE_B] = { };
+#if HAS_FUSED_OPS
+    unroll_for (uint bi = 0; bi < TILE_B; ++bi) {
+    #if TILE_OFM > 1
+        unroll_for(uint fi = 0; fi < TILE_OFM; ++fi) {
+            FUSED_OPS_VEC;
+            result[bi][fi] = FUSED_OPS_RESULT_VEC;
+        }
+    #else
+        FUSED_OPS_SCALAR;
+        result[bi] = FUSED_OPS_RESULT_SCALAR;
+    #endif // TILE_OFM > 1
+    }
+#else
+    unroll_for (uint bi = 0; bi < TILE_B; ++bi) {
+        result[bi] = TO_OUTPUT_VEC_TYPE(ACTIVATION_TYPED(activated[bi], ACTIVATION_PARAMS_TYPED));
+    }
+#endif
+
+    // =====================================================================================================================================
+    // Write results
+    uint output_offset = out_f * TILE_OUT_F_PITCH + out_b * TILE_OUT_B_PITCH + OUTPUT_OFFSET;
+
+    if (USE_BLOCK_WRITE && (TILE_OUT_F_NUM % (TILE_OFM * SIMD) == 0 || out_f + (TILE_OFM * SIMD) <= TILE_OUT_F_NUM)) {
+#if IS_DYNAMIC
+        #define WRITE_OUTPUT(bi) do {                                       \
+                if (bi + out_b < BATCH_SIZE)                                \
+                    OUTPUT_BLOCK_WRITE(output, output_offset, result[bi]);  \
+                output_offset += TILE_OUT_B_PITCH;                          \
+            } while (false)
+#else
+        #define WRITE_OUTPUT(bi) do {                                       \
+                OUTPUT_BLOCK_WRITE(output, output_offset, result[bi]);      \
+                output_offset += TILE_OUT_B_PITCH;                          \
+            } while (false)
+#endif
+        CONST_LOOP(TILE_B, WRITE_OUTPUT);
+        #undef WRITE_OUTPUT
+    } else {
+        output_offset += sglid;
 
         for (uint bi = 0; bi < TILE_B; ++bi) {
             for (uint fi = 0; fi < TILE_OFM; ++fi) {
@@ -685,6 +1047,7 @@ inline void FUNC(fc_bf_tiled_kernel_default)(
     }
     // =====================================================================================================================================
 }
+#endif
 
 REQD_SUB_GROUP_SIZE(SIMD)
 KERNEL(fc)(
@@ -704,9 +1067,17 @@ KERNEL(fc)(
 #if HAS_FUSED_OPS_DECLS
     , FUSED_OPS_DECLS
 #endif
+#if DYNAMIC_QUANTIZE
+    , __global char* quantized_input
+    , __global INPUT0_TYPE* de_quan_scale
+#endif
 ) {
 #if USE_SLM
-    __local ACCUMULATOR_TYPE wei_local_mem[TILE_IFM * SIMD * TILE_OFM * SIMD];
+    #if DYNAMIC_QUANTIZE
+        __local int dq_wei_local_mem[SIMD * TILE_OFM * SIMD];
+    #else
+        __local ACCUMULATOR_TYPE wei_local_mem[TILE_IFM * SIMD * TILE_OFM * SIMD];
+    #endif
 #endif
 #if IS_DYNAMIC && COMPRESSED_WEIGHTS_INT4
     const int batch_size = BATCH_SIZE;
@@ -844,6 +1215,76 @@ KERNEL(fc)(
         #endif
         );
     } else {
+        #if USE_SLM && DYNAMIC_QUANTIZE
+            FUNC_CALL(fc_bf_tiled_kernel_dyn_quan)(
+                OPTIONAL_SHAPE_INFO_TENSOR
+                input,
+                quantized_input,
+                de_quan_scale,
+            #if DECOMPRESSION_SCALE_TERM
+                decompression_scale,
+            #endif
+            #if DECOMPRESSION_ZP_TERM && !DECOMPRESSION_ZP_SCALAR
+                decompression_zp,
+            #endif
+                output,
+                weights
+                , dq_wei_local_mem
+            #if BIAS_TERM
+                , biases
+            #endif
+            #if HAS_FUSED_OPS_DECLS
+                , FUSED_OPS_ARGS
+            #endif
+            );
+        #else
+            FUNC_CALL(fc_bf_tiled_kernel_default)(
+                OPTIONAL_SHAPE_INFO_TENSOR
+                input,
+            #if DECOMPRESSION_SCALE_TERM
+                decompression_scale,
+            #endif
+            #if DECOMPRESSION_ZP_TERM && !DECOMPRESSION_ZP_SCALAR
+                decompression_zp,
+            #endif
+                output,
+                weights
+            #if USE_SLM
+                , wei_local_mem
+            #endif
+            #if BIAS_TERM
+                , biases
+            #endif
+            #if HAS_FUSED_OPS_DECLS
+                , FUSED_OPS_ARGS
+            #endif
+            );
+        #endif
+    }
+#else
+    #if USE_SLM && DYNAMIC_QUANTIZE
+        FUNC_CALL(fc_bf_tiled_kernel_dyn_quan)(
+            OPTIONAL_SHAPE_INFO_TENSOR
+            input,
+            quantized_input,
+            de_quan_scale,
+        #if DECOMPRESSION_SCALE_TERM
+            decompression_scale,
+        #endif
+        #if DECOMPRESSION_ZP_TERM && !DECOMPRESSION_ZP_SCALAR
+            decompression_zp,
+        #endif
+            output,
+            weights
+            , dq_wei_local_mem
+        #if BIAS_TERM
+            , biases
+        #endif
+        #if HAS_FUSED_OPS_DECLS
+            , FUSED_OPS_ARGS
+        #endif
+        );
+    #else
         FUNC_CALL(fc_bf_tiled_kernel_default)(
             OPTIONAL_SHAPE_INFO_TENSOR
             input,
@@ -865,31 +1306,10 @@ KERNEL(fc)(
             , FUSED_OPS_ARGS
         #endif
         );
-    }
-#else
-    FUNC_CALL(fc_bf_tiled_kernel_default)(
-        OPTIONAL_SHAPE_INFO_TENSOR
-        input,
-    #if DECOMPRESSION_SCALE_TERM
-        decompression_scale,
-    #endif
-    #if DECOMPRESSION_ZP_TERM && !DECOMPRESSION_ZP_SCALAR
-        decompression_zp,
-    #endif
-        output,
-        weights
-    #if USE_SLM
-        , wei_local_mem
-    #endif
-    #if BIAS_TERM
-        , biases
-    #endif
-    #if HAS_FUSED_OPS_DECLS
-        , FUSED_OPS_ARGS
     #endif
-    );
 #endif
 }
+#endif  // !FC_KERNEL_DYNAMIC_QUANTIZE
 
 #undef INPUT_VEC_TYPE
 #undef ACCUMULATOR_VEC_TYPE
diff --git a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/generic_eltwise_ref.cl b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/generic_eltwise_ref.cl
index 17b25de5e6fbc6..ae6a02d7770d2e 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/generic_eltwise_ref.cl
+++ b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/generic_eltwise_ref.cl
@@ -192,6 +192,6 @@ KERNEL(eltwise)(
 #if QUANTIZATION_TERM && !OUTPUT_IS_FP
     output[output_offset] = TO_OUTPUT_TYPE_SAT(ACTIVATION(out, ACTIVATION_PARAMS));
 #else
-    output[output_offset] = ACTIVATION_TYPED(out, ACTIVATION_PARAMS_TYPED);
+    output[output_offset] = TO_OUTPUT_TYPE(ACTIVATION_TYPED(out, ACTIVATION_PARAMS_TYPED));
 #endif
 }
diff --git a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/include/batch_headers/bf16_utils.cl b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/include/batch_headers/bf16_utils.cl
new file mode 100644
index 00000000000000..ca04b0e71e3c87
--- /dev/null
+++ b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/include/batch_headers/bf16_utils.cl
@@ -0,0 +1,34 @@
+#ifdef intel_convert_as_bfloat16_float
+#define _convert_as_bfloat16_float(val) intel_convert_as_bfloat16_float(val)
+#else
+inline float _convert_as_bfloat16_float(ushort source) {
+    uint u = 0;
+    //sign
+    if ( (source>>15) ) { 
+        u = 1 << 31;
+    }
+    //exponent
+    u += ( ( (source >> 7) & 0b11111111)) << 23;
+    //fraction 
+    u += (source & 0b1111111) << 16;
+    float* f = &u;
+    return *f;
+}
+#endif
+
+#ifdef intel_convert_bfloat16_as_ushort
+#define _convert_bfloat16_as_ushort(val) intel_convert_bfloat16_as_ushort(val)
+#else
+inline ushort _convert_bfloat16_as_ushort(float source) {
+    uint* in = &source;
+    ushort u = 0;
+    if ( (*in>>31) ) { 
+        u = 1 << 15;
+    }
+    //exponent
+    u += ( ( (*in >> 23) & 0b11111111)) << 7;
+    //fraction
+    u += (*in >> 16) & 0b1111111;
+    return u;
+}
+#endif
diff --git a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/include/batch_headers/generic_vector_ops.cl b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/include/batch_headers/generic_vector_ops.cl
new file mode 100644
index 00000000000000..b9a3df7cc863ea
--- /dev/null
+++ b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/include/batch_headers/generic_vector_ops.cl
@@ -0,0 +1,75 @@
+/*******************************************************************************
+ * Copyright 2024 Intel Corporation
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *******************************************************************************/
+
+#ifndef GPU_INTEL_OCL_GENERIC_VECTOR_OPS_H
+#define GPU_INTEL_OCL_GENERIC_VECTOR_OPS_H
+
+typedef half __attribute__((ext_vector_type(1))) half1;
+typedef uint __attribute__((ext_vector_type(1))) uint1;
+typedef float __attribute__((ext_vector_type(1))) float1;
+
+float1 __attribute__((overloadable)) vmad(float1 a, float1 b, float1 c) {
+    c[0] = mad(a[0], b[0], c[0]);
+    return c;
+}
+float2 __attribute__((overloadable)) vmad(float2 a, float2 b, float2 c) {
+    return mad(a, b, c);
+}
+float4 __attribute__((overloadable)) vmad(float4 a, float4 b, float4 c) {
+    return mad(a, b, c);
+}
+float8 __attribute__((overloadable)) vmad(float8 a, float8 b, float8 c) {
+    return mad(a, b, c);
+}
+float16 __attribute__((overloadable)) vmad(float16 a, float16 b, float16 c) {
+    return mad(a, b, c);
+}
+
+float1 __attribute__((overloadable)) native_vrecip(float1 x) {
+    x[0] = native_recip(x[0]);
+    return x;
+}
+float2 __attribute__((overloadable)) native_vrecip(float2 x) {
+    return native_recip(x);
+}
+float4 __attribute__((overloadable)) native_vrecip(float4 x) {
+    return native_recip(x);
+}
+float8 __attribute__((overloadable)) native_vrecip(float8 x) {
+    return native_recip(x);
+}
+float16 __attribute__((overloadable)) native_vrecip(float16 x) {
+    return native_recip(x);
+}
+
+float1 __attribute__((overloadable)) native_vexp2(float1 x) {
+    x[0] = native_exp2(x[0]);
+    return x;
+}
+float2 __attribute__((overloadable)) native_vexp2(float2 x) {
+    return native_exp2(x);
+}
+float4 __attribute__((overloadable)) native_vexp2(float4 x) {
+    return native_exp2(x);
+}
+float8 __attribute__((overloadable)) native_vexp2(float8 x) {
+    return native_exp2(x);
+}
+float16 __attribute__((overloadable)) native_vexp2(float16 x) {
+    return native_exp2(x);
+}
+
+#endif
diff --git a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/include/batch_headers/int4_utils.cl b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/include/batch_headers/int4_utils.cl
index 00bebcb1162125..0bdb3b3498ab71 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/include/batch_headers/int4_utils.cl
+++ b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/include/batch_headers/int4_utils.cl
@@ -20,6 +20,12 @@ inline uchar2 cvt_uint4x2_to_uint8x2(uint4x2_t v) __attribute__((overloadable))
     return (uchar2)(v0, v1);
 }
 
+inline char2 cvt_uint4x2_to_int8x2(uint4x2_t v) __attribute__((overloadable)) {
+    const char v0 = convert_char(v.s0 & 0x0F);
+    const char v1 = convert_char((v.s0 & 0xF0) >> 4);
+    return (char2)(v0, v1);
+}
+
 inline char2 cvt_int4x2_to_int8x2(int4x2_t v) __attribute__((overloadable)) {
     const char s_bit = (v.s0 & convert_char(0x08));
     const char mask = s_bit > 0 ? convert_char(0xF0) : convert_char(0x00);
@@ -28,6 +34,68 @@ inline char2 cvt_int4x2_to_int8x2(int4x2_t v) __attribute__((overloadable)) {
     return (char2)(v0, v1);
 }
 
+inline uchar2 unpack_to_uchar(uint4x2_t v) __attribute__((overloadable)) {
+    return cvt_uint4x2_to_uint8x2(v);
+}
+
+inline uchar8 unpack_to_uchar(uint4x8_t v) __attribute__((overloadable)) {
+    uchar2 v0 = unpack_to_uchar(v.s0);
+    uchar2 v1 = unpack_to_uchar(v.s1);
+    uchar2 v2 = unpack_to_uchar(v.s2);
+    uchar2 v3 = unpack_to_uchar(v.s3);
+    return (uchar8)(v0.s0, v0.s1, v1.s0, v1.s1, v2.s0, v2.s1, v3.s0, v3.s1);
+}
+
+inline char2 unpack_to_char(uint4x2_t v) __attribute__((overloadable)) {
+    return cvt_uint4x2_to_int8x2(v);
+}
+
+inline char4 unpack_to_char(uint4x4_t v) __attribute__((overloadable)) {
+    char2 v0 = unpack_to_char(v.s0);
+    char2 v1 = unpack_to_char(v.s1);
+    return (char4)(v0.s0, v0.s1, v1.s0, v1.s1);
+}
+
+inline char8 unpack_to_char(uint4x8_t v) __attribute__((overloadable)) {
+    char2 v0 = unpack_to_char(v.s0);
+    char2 v1 = unpack_to_char(v.s1);
+    char2 v2 = unpack_to_char(v.s2);
+    char2 v3 = unpack_to_char(v.s3);
+    return (char8)(v0.s0, v0.s1, v1.s0, v1.s1, v2.s0, v2.s1, v3.s0, v3.s1);
+}
+
+inline char8 unpack_transposed_to_char(uint4x8_t v) __attribute__((overloadable)) {
+    char2 v0 = unpack_to_char(v.s0);
+    char2 v1 = unpack_to_char(v.s1);
+    char2 v2 = unpack_to_char(v.s2);
+    char2 v3 = unpack_to_char(v.s3);
+    return (char8)(v0.s0, v1.s0, v2.s0, v3.s0, v0.s1, v1.s1, v2.s1, v3.s1);
+}
+
+inline uchar8 unpack_transposed_to_uchar(uint4x8_t v) __attribute__((overloadable)) {
+    uchar2 v0 = unpack_to_uchar(v.s0);
+    uchar2 v1 = unpack_to_uchar(v.s1);
+    uchar2 v2 = unpack_to_uchar(v.s2);
+    uchar2 v3 = unpack_to_uchar(v.s3);
+    return (uchar8)(v0.s0, v1.s0, v2.s0, v3.s0, v0.s1, v1.s1, v2.s1, v3.s1);
+}
+
+inline char8 unpack_transposed_to_char_osv32_isv2(uint4x8_t v) __attribute__((overloadable)) {
+    char2 v0 = unpack_to_char(v.s0);
+    char2 v1 = unpack_to_char(v.s2);
+    char2 v2 = unpack_to_char(v.s1);
+    char2 v3 = unpack_to_char(v.s3);
+    return (char8)(v0.s0, v1.s0, v2.s0, v3.s0, v0.s1, v1.s1, v2.s1, v3.s1);
+}
+
+inline uchar8 unpack_transposed_to_uchar_osv32_isv2(uint4x8_t v) __attribute__((overloadable)) {
+    uchar2 v0 = unpack_to_uchar(v.s0);
+    uchar2 v1 = unpack_to_uchar(v.s2);
+    uchar2 v2 = unpack_to_uchar(v.s1);
+    uchar2 v3 = unpack_to_uchar(v.s3);
+    return (uchar8)(v0.s0, v1.s0, v2.s0, v3.s0, v0.s1, v1.s1, v2.s1, v3.s1);
+}
+
 inline float2 unpack_to_float(uint4x2_t v) __attribute__((overloadable)) {
     return convert_float2(cvt_uint4x2_to_uint8x2(v));
 }
@@ -116,7 +184,28 @@ inline half8 unpack_to_half_osv32_isv2(int4x8_t v) __attribute__((overloadable))
     half2 f3 = unpack_to_half(v.s3);
     return (half8)(f0.s0, f0.s1, f1.s0, f1.s1, f2.s0, f2.s1, f3.s0, f3.s1);
 }
+
+inline char8 unpack_to_char_osv32_isv2(uint4x8_t v) __attribute__((overloadable)) {
+    char2 v0 = unpack_to_char(v.s0);
+    char2 v1 = unpack_to_char(v.s2);
+    char2 v2 = unpack_to_char(v.s1);
+    char2 v3 = unpack_to_char(v.s3);
+    return (char8)(v0.s0, v0.s1, v1.s0, v1.s1, v2.s0, v2.s1, v3.s0, v3.s1);
+}
+
+inline uchar8 unpack_to_uchar_osv32_isv2(uint4x8_t v) __attribute__((overloadable)) {
+    uchar2 v0 = unpack_to_uchar(v.s0);
+    uchar2 v1 = unpack_to_uchar(v.s2);
+    uchar2 v2 = unpack_to_uchar(v.s1);
+    uchar2 v3 = unpack_to_uchar(v.s3);
+    return (uchar8)(v0.s0, v0.s1, v1.s0, v1.s1, v2.s0, v2.s1, v3.s0, v3.s1);
+}
+
+
 #endif  // defined(cl_khr_fp16)
 
+
 #define UNPACK_INT4x2(target_type, value) CAT(unpack_to_, target_type)(value)
 #define UNPACK_INT4x2_OSV32_ISV2(target_type, value) CAT(CAT(unpack_to_, target_type), _osv32_isv2)(value)
+#define UNPACK_TRANSPOSED_INT4x2(target_type, value) CAT(unpack_transposed_to_, target_type)(value)
+#define UNPACK_TRANSPOSED_INT4x2_OSV32_ISV2(target_type, value) CAT(CAT(unpack_transposed_to_, target_type), _osv32_isv2)(value)
diff --git a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/include/batch_headers/sdpa_utils.cl b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/include/batch_headers/sdpa_utils.cl
new file mode 100644
index 00000000000000..5943f23251bb7a
--- /dev/null
+++ b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/include/batch_headers/sdpa_utils.cl
@@ -0,0 +1,35 @@
+/*******************************************************************************
+ * Copyright 2024 Intel Corporation
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *******************************************************************************/
+
+#ifndef GPU_OCL_SDPA_UTILS_H
+#define GPU_OCL_SDPA_UTILS_H
+
+#define _4D_OFF(tag, x0, x1, x2, x3) \
+    (((x0) % tag##_B0) * tag##_SB0 + ((x0) / tag##_B0) * tag##_S0 \
+            + ((x1) % tag##_B1) * tag##_SB1 + ((x1) / tag##_B1) * tag##_S1 \
+            + ((x2) % tag##_B2) * tag##_SB2 + ((x2) / tag##_B2) * tag##_S2 \
+            + ((x3) % tag##_B3) * tag##_SB3 + ((x3) / tag##_B3) * tag##_S3)
+
+#define QRY_OFF(x0, x1, x2, x3) _4D_OFF(QRY, x0, x1, x2, x3)
+#define KEY_OFF(x0, x1, x2, x3) _4D_OFF(KEY, x0, x1, x2, x3)
+#define VAL_OFF(x0, x1, x2, x3) _4D_OFF(VAL, x0, x1, x2, x3)
+#define MSK_OFF(x0, x1, x2, x3) _4D_OFF(MSK, x0, x1, x2, x3)
+
+#define DST_OFF(x0, x1, d, h, w) \
+(((x0) % DST_B0) * DST_SB0 + ((x0) / DST_B0) * DST_S0 \
++ ((x1) % DST_B1) * DST_SB1 + ((x1) / DST_B1) * DST_S1)
+
+#endif
diff --git a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/include/batch_headers/tile_ops.cl b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/include/batch_headers/tile_ops.cl
new file mode 100644
index 00000000000000..b2ce72c33fc31c
--- /dev/null
+++ b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/include/batch_headers/tile_ops.cl
@@ -0,0 +1,628 @@
+/*******************************************************************************
+ * Copyright 2024 Intel Corporation
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *******************************************************************************/
+
+#ifndef GPU_OCL_TILE_OPS_H
+#define GPU_OCL_TILE_OPS_H
+
+float __builtin_IB_atomic_max_local_f32(__local float *, float);
+
+__attribute__((overloadable)) float local_atomic_max(local float *p, float v) {
+    return __builtin_IB_atomic_max_local_f32(p, v);
+}
+
+__attribute__((overloadable)) half local_atomic_max(
+        local half *p, half v) { /* not implemented */
+    return v;
+}
+
+__attribute__((overloadable)) uint local_atomic_max(local uint *p, uint v) {
+    return atomic_max(p, v);
+}
+
+__attribute__((overloadable)) int local_atomic_max(local int *p, int v) {
+    return atomic_max(p, v);
+}
+
+#define DEF_BLOCK_LOAD_STORE(type, itype, suffix, n) \
+    __attribute__((overloadable)) type##n block_load( \
+            const global type *p, int vlen) \
+            __attribute__((enable_if(vlen == n, "wrong vector length"))) { \
+        return as_##type##n( \
+                intel_sub_group_block_read##suffix##n((global void *)p)); \
+    } \
+    __attribute__((overloadable)) void block_store( \
+            global type *p, type##n v) { \
+        intel_sub_group_block_write##suffix##n( \
+                (global itype *)p, as_##itype##n(v)); \
+    }
+
+#define DEF_BLOCK_LOAD_STORE1(type, itype, suffix) \
+    __attribute__((overloadable)) \
+            type##1 block_load(const global type *p, int vlen) __attribute__( \
+                    (enable_if(vlen == 1, "wrong vector length"))) { \
+        type##1 x; \
+        x[0] = as_##type( \
+                intel_sub_group_block_read##suffix((global void *)p)); \
+        return x; \
+    } \
+    __attribute__((overloadable)) void block_store( \
+            global type *p, type##1 v) { \
+        intel_sub_group_block_write##suffix( \
+                (global itype *)p, as_##itype(v[0])); \
+    }
+
+DEF_BLOCK_LOAD_STORE1(half, ushort, _us)
+DEF_BLOCK_LOAD_STORE(half, ushort, _us, 2)
+DEF_BLOCK_LOAD_STORE(half, ushort, _us, 4)
+DEF_BLOCK_LOAD_STORE(half, ushort, _us, 8)
+DEF_BLOCK_LOAD_STORE(half, ushort, _us, 16)
+DEF_BLOCK_LOAD_STORE1(uint, uint, )
+DEF_BLOCK_LOAD_STORE(uint, uint, , 2)
+DEF_BLOCK_LOAD_STORE(uint, uint, , 4)
+DEF_BLOCK_LOAD_STORE(uint, uint, , 8)
+
+#define DEF_BLOCK2D_LOAD_STORE(type, itype, vl, SG, suffix, BR, BC) \
+    itype##vl __builtin_IB_subgroup_block_read_flat_##suffix( \
+            long, int, int, int, int2); \
+    void __builtin_IB_subgroup_block_write_flat_##suffix( \
+            long, int, int, int, int2, itype##vl); \
+    __attribute__((overloadable)) type##vl block2d_load(const global type *p, \
+            int w, int h, int ld, int x, int y, int br, int bc, \
+            int sg) __attribute__((enable_if(br == BR, "wrong #rows"))) \
+            __attribute__((enable_if(bc == BC, "wrong #columns"))) \
+                    __attribute__( \
+                            (enable_if(sg == SG, "wrong subgroup size"))) { \
+        ulong pp = as_long(p); \
+        ulong prem = pp & 0x3F; \
+        pp &= ~0x3F; \
+        x += (prem / sizeof(type)); \
+        w += prem; \
+        int2 coord = {x, y}; \
+        return as_##type##vl(__builtin_IB_subgroup_block_read_flat_##suffix( \
+                pp, w - 1, h - 1, ld - 1, coord)); \
+    } \
+    __attribute__((overloadable)) void block2d_store(type##vl v, \
+            global type *p, int w, int h, int ld, int x, int y, int br, \
+            int bc, \
+            int sg) __attribute__((enable_if(br == BR, "wrong #rows"))) \
+            __attribute__((enable_if(bc == BC, "wrong #columns"))) \
+                    __attribute__( \
+                            (enable_if(sg == SG, "wrong subgroup size"))) { \
+        ulong pp = as_long(p); \
+        ulong prem = pp & 0x3F; \
+        pp &= ~0x3F; \
+        x += (prem / sizeof(type)); \
+        w += prem; \
+        int2 coord = {x, y}; \
+        __builtin_IB_subgroup_block_write_flat_##suffix( \
+                pp, w - 1, h - 1, ld - 1, coord, as_##itype##vl(v)); \
+    }
+
+DEF_BLOCK2D_LOAD_STORE(half, ushort, 8, 16, u16_m8k16v1, 16, 8)
+DEF_BLOCK2D_LOAD_STORE(half, ushort, 8, 16, u16_m4k32v1, 32, 4)
+DEF_BLOCK2D_LOAD_STORE(half, ushort, 16, 16, u16_m8k32v1, 32, 8)
+
+#define tile_fill(t, v) \
+    do { \
+        _Pragma("unroll") for (int i = 0; i < sizeof(t.x) / sizeof(t.x[0]); \
+                               i++) t.x[i] \
+                = v; \
+    } while (0)
+
+#define tile_elementwise(t, f) \
+    do { \
+        _Pragma("unroll") for (int i = 0; i < sizeof(t.x) / sizeof(t.x[0]); \
+                               i++) t.x[i] \
+                = f(t.x[i]); \
+    } while (0)
+
+#define tile_elementwise_s(t, f) \
+    do { \
+        _Pragma("unroll") for (int i = 0; i < sizeof(t.x) / sizeof(t.x[0]); \
+                               i++) { \
+            _Pragma("unroll") for (int s = 0; \
+                                   s < sizeof(t.x[0]) / sizeof(t.x[0][0]); \
+                                   s++) t.x[i][s] \
+                    = f(t.x[i][s]); \
+        } \
+    } while (0)
+
+#define tile_binary(t, t2, f) \
+    do { \
+        _Pragma("unroll") for (int i = 0; i < sizeof(t.x) / sizeof(t.x[0]); \
+                               i++) t.x[i] \
+                = f(t.x[i], t2.x[i]); \
+    } while (0)
+
+#define tile_copy(t, t_new) \
+    do { \
+        _Pragma("unroll") for (int i = 0; i < sizeof(t.x) / sizeof(t.x[0]); \
+                               i++) t_new.x[i] \
+                = __builtin_convertvector(t.x[i], __typeof__(t_new.x[i])); \
+    } while (0)
+
+#define tile_copy_to_half2(t, t_new) \
+    do { \
+        _Pragma("unroll") for (int i = 0; i < sizeof(t.x) / sizeof(t.x[0]); \
+                               i++) { \
+            _Pragma("unroll") for (int s = 0; \
+                                   s < sizeof(t.x[0]) / sizeof(t.x[0][0]) / 2; \
+                                   s++) { \
+                half2 v = {t.x[i][2 * s], t.x[i][2 * s + 1]}; \
+                t_new.x[i][s] = as_uint(v); \
+            } \
+        } \
+    } while (0)
+
+#define tile_access(t, i0, j, sg, br, bc, nbr) \
+    (t).x[(i0) / (br) + (nbr) * ((j) / (bc))] \
+         [((i0) % (br)) / (sg) + ((j) % (bc)) * ((br) / (sg))]
+
+#define xlane_tile_access(t, i, j, sg, br, bc, nbr) \
+    sub_group_broadcast(tile_access(t, i, j, sg, br, bc, nbr), i % sg)
+
+#define DECLARE_2D_TILE_OPS(tile_type, element_type, sg, br, bc, nbr, nbc) \
+    __attribute__((overloadable)) void tile_load_full(tile_type *t, \
+            const global element_type *ptr, int ld, int offset_r, \
+            int offset_c) { \
+        ptr += ld * offset_c + offset_r; \
+        _Pragma("unroll") for (int j = 0; j < bc * nbc; j++, ptr += ld) { \
+            _Pragma("unroll") for (int i0 = 0; i0 < br * nbr; i0 += sg) { \
+                int i = i0 + get_sub_group_local_id(); \
+                tile_access(*t, i0, j, sg, br, bc, nbr) = ptr[i]; \
+            } \
+        } \
+    } \
+    __attribute__((overloadable)) void tile_load_full(tile_type *t, \
+            const local element_type *ptr, int ld, int offset_r, \
+            int offset_c) { \
+        ptr += ld * offset_c + offset_r; \
+        _Pragma("unroll") for (int j = 0; j < bc * nbc; j++, ptr += ld) { \
+            _Pragma("unroll") for (int i0 = 0; i0 < br * nbr; i0 += sg) { \
+                int i = i0 + get_sub_group_local_id(); \
+                tile_access(*t, i0, j, sg, br, bc, nbr) = ptr[i]; \
+            } \
+        } \
+    } \
+    __attribute__((overloadable)) void tile_load(tile_type *t, \
+            const global element_type *ptr, int m, int n, int ld, \
+            int offset_r, int offset_c) { \
+        if (m >= offset_r + br * nbr && n >= offset_c + bc * nbc) { \
+            tile_load_full(t, ptr, ld, offset_r, offset_c); \
+            return; \
+        } \
+        ptr += ld * offset_c + offset_r; \
+        _Pragma("unroll") for (int j = 0; j < bc * nbc; j++, ptr += ld) { \
+            if (offset_c + j < n) { \
+                _Pragma("unroll") for (int i0 = 0; i0 < br * nbr; i0 += sg) { \
+                    int i = i0 + get_sub_group_local_id(); \
+                    if (offset_r + i < m) \
+                        tile_access(*t, i0, j, sg, br, bc, nbr) = ptr[i]; \
+                } \
+            } \
+        } \
+    } \
+    __attribute__((overloadable)) void tile_load(tile_type *t, \
+            const global element_type *ptr, int m, int n, int offset_r, \
+            int offset_c) { \
+        tile_load(t, ptr, m, n, m, offset_r, offset_c); \
+    } \
+    __attribute__((overloadable)) void tile_load_t_full(tile_type *t, \
+            const global element_type *ptr, int ld, int offset_r, \
+            int offset_c) { \
+        ptr += ld * offset_r + offset_c; \
+        _Pragma("unroll") for (int i0 = 0; i0 < br * nbr; i0 += sg, ptr += ld*sg) { \
+            _Pragma("unroll") for (int j = 0; j < bc * nbc; j++) { \
+                tile_access(*t, i0, j, sg, br, bc, nbr) = ptr[get_sub_group_local_id() * ld + j]; \
+            } \
+        } \
+    } \
+    __attribute__((overloadable)) void tile_load_t(tile_type *t, \
+            const global element_type *ptr, int m, int n, int ld, \
+            int offset_r, int offset_c) { \
+        if (m >= offset_r + br * nbr && n >= offset_c + bc * nbc) { \
+            tile_load_t_full(t, ptr, ld, offset_r, offset_c); \
+            return; \
+        } \
+        ptr += ld * offset_r + offset_c; \
+        _Pragma("unroll") for (int i0 = 0; i0 < br * nbr; i0 += sg, ptr += ld*sg) { \
+            int i = i0 + get_sub_group_local_id(); \
+            if (offset_r + i < m) \
+                _Pragma("unroll") for (int j = 0; j < bc * nbc; j++) { \
+                    if (offset_c + j < n) { \
+                        tile_access(*t, i0, j, sg, br, bc, nbr) = ptr[get_sub_group_local_id() * ld + j]; \
+                } \
+            } \
+        } \
+    } \
+    __attribute__((overloadable)) void tile_load_t(tile_type *t, \
+            const global element_type *ptr, int m, int n, int offset_r, \
+            int offset_c) { \
+        tile_load(t, ptr, m, n, n, offset_r, offset_c); \
+    } \
+    __attribute__((overloadable)) void tile_store_full(tile_type t, \
+            local element_type *ptr, int ld, int offset_r, int offset_c) { \
+        ptr += ld * offset_c + offset_r; \
+        _Pragma("unroll") for (int j = 0; j < bc * nbc; j++, ptr += ld) { \
+            _Pragma("unroll") for (int i0 = 0; i0 < br * nbr; i0 += sg) { \
+                int i = i0 + get_sub_group_local_id(); \
+                ptr[i] = tile_access(t, i0, j, sg, br, bc, nbr); \
+            } \
+        } \
+    } \
+    __attribute__((overloadable)) void tile_store_full(tile_type t, \
+            global element_type *ptr, int ld, int offset_r, int offset_c) { \
+        ptr += ld * offset_c + offset_r; \
+        _Pragma("unroll") for (int j = 0; j < bc * nbc; j++, ptr += ld) { \
+            _Pragma("unroll") for (int i0 = 0; i0 < br * nbr; i0 += sg) { \
+                int i = i0 + get_sub_group_local_id(); \
+                ptr[i] = tile_access(t, i0, j, sg, br, bc, nbr); \
+            } \
+        } \
+    } \
+    __attribute__((overloadable)) void tile_store(tile_type t, \
+            global element_type *ptr, int m, int n, int ld, int offset_r, \
+            int offset_c) { \
+        if (m >= offset_r + br * nbr && n >= offset_c + bc * nbc) { \
+            tile_store_full(t, ptr, ld, offset_r, offset_c); \
+            return; \
+        } \
+        ptr += ld * offset_c + offset_r; \
+        _Pragma("unroll") for (int j = 0; j < bc * nbc; j++, ptr += ld) { \
+            if (offset_c + j < n) { \
+                _Pragma("unroll") for (int i0 = 0; i0 < br * nbr; i0 += sg) { \
+                    int i = i0 + get_sub_group_local_id(); \
+                    if (offset_r + i < m) \
+                        ptr[i] = tile_access(t, i0, j, sg, br, bc, nbr); \
+                } \
+            } \
+        } \
+    } \
+    __attribute__((overloadable)) void tile_store(tile_type t, \
+            global element_type *ptr, int m, int n, int offset_r, \
+            int offset_c) { \
+        tile_store(t, ptr, m, n, m, offset_r, offset_c); \
+    } \
+    __attribute__((overloadable)) void tile_store_t_sys_src1(tile_type t, \
+            local element_type *ptr, int ld, int offset_r, int offset_c) { \
+        offset_c += get_sub_group_local_id(); \
+        int offset_r0 = offset_r & (sg - 1); \
+        int offset_r1 = offset_r & ~(sg - 1); \
+        ptr += offset_r0 + sg * offset_c + ld * offset_r1; \
+        _Pragma("unroll") for (int j0 = 0; j0 < br * nbr; \
+                               j0 += sg, ptr += sg * sg) { \
+            _Pragma("unroll") for (int i = 0; i < bc * nbc; i++) ptr[i] \
+                    = tile_access(t, j0, i, sg, br, bc, nbr); \
+        } \
+    } \
+    __attribute__((overloadable)) void tile_store_t_sys_src2(tile_type t, \
+            local element_type *ptr, int tile_n, int ld, int offset_r, \
+            int offset_c) { \
+        const int cp = 32 / sizeof(element_type); \
+        offset_c += get_sub_group_local_id(); \
+        int offset_r0 = offset_r & (cp - 1); \
+        int offset_r1 = offset_r & ~(cp - 1); \
+        ptr += offset_r0 + tile_n * offset_r1; \
+        _Pragma("unroll") for (int j0 = 0; j0 < br * nbr; \
+                               j0 += sg, offset_c += sg) { \
+            int offset_c0 = offset_c & (tile_n - 1); \
+            int offset_c1 = offset_c & ~(tile_n - 1); \
+            local element_type *ptr_j = ptr + cp * offset_c0 + ld * offset_c1; \
+            _Pragma("unroll") for (int i = 0; i < bc * nbc; i++) { \
+                *ptr_j = tile_access(t, j0, i, sg, br, bc, nbr); \
+                ptr_j++; \
+                if ((~i & (cp - 1)) == 0) ptr_j += cp * (tile_n - 1); \
+            } \
+        } \
+    } \
+    __attribute__((overloadable)) void tile_atomic_max_full(tile_type t, \
+            local element_type *ptr, int ld, int offset_r, int offset_c) { \
+        ptr += ld * offset_c + offset_r; \
+        _Pragma("unroll") for (int j = 0; j < bc * nbc; j++, ptr += ld) { \
+            _Pragma("unroll") for (int i0 = 0; i0 < br * nbr; i0 += sg) { \
+                int i = i0 + get_sub_group_local_id(); \
+                (void)local_atomic_max( \
+                        ptr + i, tile_access(t, i0, j, sg, br, bc, nbr)); \
+            } \
+        } \
+    }
+
+#define DECLARE_2D_TILE_VREDUCE(tile_type, sg, br, bc, nbr, nbc, rtile_type, \
+        rsg, rbr, rbc, rnbr, rnbc) \
+    __attribute__((overloadable)) void tile_vreduce_add( \
+            tile_type t, rtile_type *tr) { \
+        _Pragma("unroll") for (int j = 0; j < bc * nbc; j++) { \
+            _Pragma("unroll") for (int i0 = 0; i0 < br * nbr; i0 += sg) { \
+                tile_access(*tr, i0, 0, rsg, rbr, rbc, rnbr) \
+                        += tile_access(t, i0, j, sg, br, bc, nbr); \
+            } \
+        } \
+    } \
+    __attribute__((overloadable)) void tile_vreduce_max( \
+            tile_type t, rtile_type *tr) { \
+        _Pragma("unroll") for (int j = 0; j < bc * nbc; j++) { \
+            _Pragma("unroll") for (int i0 = 0; i0 < br * nbr; i0 += sg) { \
+                tile_access(*tr, i0, 0, rsg, rbr, rbc, rnbr) \
+                        = max(tile_access(t, i0, j, sg, br, bc, nbr), \
+                                tile_access(*tr, i0, 0, rsg, rbr, rbc, rnbr)); \
+            } \
+        } \
+    } \
+    __attribute__((overloadable)) void tile_vbroadcast_sub( \
+            tile_type *t, rtile_type tr) { \
+        _Pragma("unroll") for (int j = 0; j < bc * nbc; j++) { \
+            _Pragma("unroll") for (int i0 = 0; i0 < br * nbr; i0 += sg) { \
+                tile_access(*t, i0, j, sg, br, bc, nbr) \
+                        -= tile_access(tr, i0, 0, rsg, rbr, rbc, rnbr); \
+            } \
+        } \
+    } \
+    __attribute__((overloadable)) void tile_vbroadcast_mul( \
+            tile_type *t, rtile_type tr) { \
+        _Pragma("unroll") for (int j = 0; j < bc * nbc; j++) { \
+            _Pragma("unroll") for (int i0 = 0; i0 < br * nbr; i0 += sg) { \
+                tile_access(*t, i0, j, sg, br, bc, nbr) \
+                        *= tile_access(tr, i0, 0, rsg, rbr, rbc, rnbr); \
+            } \
+        } \
+    }
+
+#define DECLARE_2D_TILE_HREDUCE(tile_type, sg, br, bc, nbr, nbc, rtile_type, \
+        rsg, rbr, rbc, rnbr, rnbc) \
+    __attribute__((overloadable)) void tile_hbroadcast_add( \
+            tile_type *t, rtile_type tr) { \
+        _Pragma("unroll") for (int j = 0; j < bc * nbc; j++) { \
+            _Pragma("unroll") for (int i0 = 0; i0 < br * nbr; i0 += sg) { \
+                tile_access(*t, i0, j, sg, br, bc, nbr) \
+                        += xlane_tile_access(tr, j, 0, rsg, rbr, rbc, rnbr); \
+            } \
+        } \
+    } \
+    __attribute__((overloadable)) void tile_hbroadcast_mul( \
+            tile_type *t, rtile_type tr) { \
+        _Pragma("unroll") for (int j = 0; j < bc * nbc; j++) { \
+            _Pragma("unroll") for (int i0 = 0; i0 < br * nbr; i0 += sg) { \
+                tile_access(*t, i0, j, sg, br, bc, nbr) \
+                        *= xlane_tile_access(tr, j, 0, rsg, rbr, rbc, rnbr); \
+            } \
+        } \
+    } \
+    __attribute__((overloadable)) void tile_hbroadcast_min( \
+            tile_type *t, rtile_type tr) { \
+        _Pragma("unroll") for (int j = 0; j < bc * nbc; j++) { \
+            _Pragma("unroll") for (int i0 = 0; i0 < br * nbr; i0 += sg) { \
+                tile_access(*t, i0, j, sg, br, bc, nbr) = min( \
+                        tile_access(*t, i0, j, sg, br, bc, nbr), \
+                        xlane_tile_access(tr, j, 0, rsg, rbr, rbc, rnbr)); \
+            } \
+        } \
+    }
+
+#define DECLARE_2D_TILE_RSELECT(tile_type0, sg0, br0, bc0, nbr0, nbc0, \
+        tile_type1, sg1, br1, bc1, nbr1, nbc1) \
+    __attribute__((overloadable)) void tile_rselect( \
+            tile_type0 *t0, tile_type1 t1, int idx) { \
+        _Pragma("unroll") for (int j = 0; j < bc0 * nbc0; j++) { \
+            _Pragma("unroll") for (int i0 = 0; i0 < br0 * nbr0; i0 += sg0) { \
+                tile_access(*t0, i0, j, sg0, br0, bc0, nbr0) \
+                        = tile_access(t1, i0, j, sg1, br1, bc1, nbr1); \
+                _Pragma("unroll") for (int z = 1; \
+                                       z < (br1 * nbr1 / br0 * nbr0); \
+                                       z++) if (z == idx) { \
+                    tile_access(*t0, i0, j, sg0, br0, bc0, nbr0) \
+                            = tile_access(t1, i0 + z * br0 * nbr0, j, sg1, \
+                                    br1, bc1, nbr1); \
+                } \
+            } \
+        } \
+    }
+
+#define DECLARE_2D_TILE_COPY_REBLOCK(tile_type0, sg0, br0, bc0, nbr0, nbc0, \
+        tile_type1, sg1, br1, bc1, nbr1, nbc1) \
+    __attribute__((overloadable)) void tile_copy_reblock( \
+            tile_type0 t0, tile_type1 *t1) { \
+        _Pragma("unroll") for (int j = 0; j < bc0 * nbc0; j++) { \
+            _Pragma("unroll") for (int i0 = 0; i0 < br0 * nbr0; i0 += sg0) { \
+                tile_access(*t1, i0, j, sg1, br1, bc1, nbr1) \
+                        = tile_access(t0, i0, j, sg0, br0, bc0, nbr0); \
+            } \
+        } \
+    }
+
+#define DECLARE_2D_TILE(tile_type, element_type, sg, br, bc, nbr, nbc) \
+    typedef element_type __attribute__((ext_vector_type(br * bc / sg))) \
+            _e_##tile_type; \
+    typedef struct { \
+        _e_##tile_type x[nbr * nbc]; \
+    } tile_type; \
+    DECLARE_2D_TILE_OPS(tile_type, element_type, sg, br, bc, nbr, nbc)
+
+/* Requires bc == 1 currently */
+#define DECLARE_2D_TILE_BLOCK_OPS( \
+        tile_type, element_type, sg, br, bc, nbr, nbc) \
+    __attribute__((overloadable)) void tile_load_block(tile_type *t, \
+            const global element_type *ptr, int ld, int offset_r, \
+            int offset_c) { \
+        ptr += ld * offset_c + offset_r; \
+        _Pragma("unroll") for (int jj = 0; jj < nbc; jj++, ptr += ld * bc) { \
+            _Pragma("unroll") for (int ii = 0; ii < nbr; ii++)(t) \
+                    ->x[ii + nbr * jj] \
+                    = block_load(ptr + ii * br, br / SUBGROUP_SIZE); \
+        } \
+    } \
+    __attribute__((overloadable)) void tile_store_block(tile_type t, \
+            global element_type *ptr, int ld, int offset_r, int offset_c) { \
+        ptr += ld * offset_c + offset_r; \
+        _Pragma("unroll") for (int jj = 0; jj < nbc; jj++, ptr += ld * bc) { \
+            _Pragma("unroll") for (int ii = 0; ii < nbr; ii++) \
+                    block_store(ptr + ii * br, (t).x[ii + nbr * jj]); \
+        } \
+    } \
+    __attribute__((overloadable)) void tile_load_block(tile_type *t, \
+            const global element_type *ptr, int n, int ld, int offset_r, \
+            int offset_c) { \
+        ptr += ld * offset_c + offset_r; \
+        n -= offset_c; \
+        _Pragma("unroll") for (int jj = 0; jj < nbc; jj++, ptr += ld * bc) { \
+            if (jj < n) { \
+                _Pragma("unroll") for (int ii = 0; ii < nbr; ii++)(t) \
+                        ->x[ii + nbr * jj] \
+                        = block_load(ptr + ii * br, br / SUBGROUP_SIZE); \
+            } \
+        } \
+    } \
+    __attribute__((overloadable)) void tile_store_block(tile_type t, \
+            global element_type *ptr, int n, int ld, int offset_r, \
+            int offset_c) { \
+        ptr += ld * offset_c + offset_r; \
+        n -= offset_c; \
+        _Pragma("unroll") for (int jj = 0; jj < nbc; jj++, ptr += ld * bc) { \
+            if (jj < n) { \
+                _Pragma("unroll") for (int ii = 0; ii < nbr; ii++) \
+                        block_store(ptr + ii * br, (t).x[ii + nbr * jj]); \
+            } \
+        } \
+    }
+
+#define DECLARE_2D_TILE_BLOCK2D_OPS( \
+        tile_type, element_type, sg, br, bc, nbr, nbc) \
+    __attribute__((overloadable)) void tile_load_block2d(tile_type *t, \
+            const global element_type *ptr, int m, int n, int ld, \
+            int offset_r, int offset_c) { \
+        const int e = sizeof(element_type); \
+        _Pragma("unroll") for (int jj = 0; jj < nbc; jj++) { \
+            _Pragma("unroll") for (int ii = 0; ii < nbr; ii++)(t) \
+                    ->x[ii + nbr * jj] \
+                    = block2d_load(ptr, m * e, n, ld * e, offset_r + ii * br, \
+                            offset_c + jj * bc, br, bc, sg); \
+        } \
+    } \
+    __attribute__((overloadable)) void tile_load_block2d(tile_type *t, \
+            const global element_type *ptr, int m, int n, int offset_r, \
+            int offset_c) { \
+        tile_load_block2d(t, ptr, m, n, m, offset_r, offset_c); \
+    } \
+    __attribute__((overloadable)) void tile_store_block2d(tile_type t, \
+            global element_type *ptr, int m, int n, int ld, int offset_r, \
+            int offset_c) { \
+        const int e = sizeof(element_type); \
+        _Pragma("unroll") for (int jj = 0; jj < nbc; jj++) { \
+            _Pragma("unroll") for (int ii = 0; ii < nbr; ii++) block2d_store( \
+                    (t).x[ii + nbr * jj], ptr, m *e, n, ld *e, \
+                    offset_r + ii * br, offset_c + jj * bc, br, bc, sg); \
+        } \
+    } \
+    __attribute__((overloadable)) void tile_store_block2d(tile_type t, \
+            const global element_type *ptr, int m, int n, int offset_r, \
+            int offset_c) { \
+        tile_store_block2d(t, ptr, m, n, m, offset_r, offset_c); \
+    }
+
+#define DECLARE_2D_TILE_LOAD_PACKED_HALF(tile_type, sg, br, bc, nbr, nbc) \
+    __attribute__((overloadable)) void tile_load_packed_half(tile_type *t, \
+            const global half *ptr, int m, int n, int ld, int offset_r, \
+            int offset_c) { \
+        ptr += ld * offset_c + offset_r; \
+        _Pragma("unroll") for (int j = 0; j < bc * nbc; j++, ptr += ld) { \
+            if (offset_c + j < n) { \
+                _Pragma("unroll") for (int i0 = 0; i0 < br * nbr; i0 += sg) { \
+                    int i = 2 * (i0 + get_sub_group_local_id()); \
+                    half2 loaded = 0; \
+                    if (offset_r + i < m) loaded.s0 = ptr[i]; \
+                    if (offset_r + i + 1 < m) loaded.s1 = ptr[i + 1]; \
+                    tile_access(*t, i0, j, sg, br, bc, nbr) = as_uint(loaded); \
+                } \
+            } \
+        } \
+    } \
+    __attribute__((overloadable)) void tile_load_packed_half(tile_type *t, \
+            const global half *ptr, int m, int n, int offset_r, \
+            int offset_c) { \
+        tile_load_packed_half(t, ptr, m, n, m, offset_r, offset_c); \
+    }
+
+#define cooperative_prefetch_2d(ptr, r, c, ld, sg_id, n_sg, sg_size, caching) \
+    cooperative_prefetch_2d_internal((const global char *)ptr, \
+            (r) * sizeof(*(ptr)), c, (ld) * sizeof(*(ptr)), sg_id, n_sg, \
+            sg_size, caching)
+
+#define cooperative_prefetch_2d_rem( \
+        ptr, r, c, rmax, cmax, ld, sg_id, n_sg, sg_size, caching) \
+    cooperative_prefetch_2d_internal((const global char *)ptr, \
+            (r) * sizeof(*(ptr)), c, (rmax) * sizeof(*(ptr)), cmax, \
+            (ld) * sizeof(*(ptr)), sg_id, n_sg, sg_size, caching)
+
+/* IGC prefetch intrinsics */
+enum LSC_LDCC {
+    LSC_LDCC_DEFAULT = 0,
+    LSC_LDCC_L1UC_L3UC = 1,
+    LSC_LDCC_L1UC_L3C = 2,
+    LSC_LDCC_L1C_L3UC = 3,
+    LSC_LDCC_L1C_L3C = 4,
+    LSC_LDCC_L1S_L3UC = 5,
+    LSC_LDCC_L1S_L3C = 6,
+    LSC_LDCC_L1IAR_L3C = 7,
+};
+
+extern void __builtin_IB_lsc_prefetch_global_uchar(
+        const __global uchar *base, int immElemOff, enum LSC_LDCC cacheOpt);
+
+extern void __builtin_IB_lsc_prefetch_global_uint(
+        const __global uint *base, int immElemOff, enum LSC_LDCC cacheOpt);
+
+__attribute__((overloadable)) void cooperative_prefetch_2d_internal(
+        const global char *ptr, uint rbytes, uint c, uint ld_bytes, uint sg_id,
+        uint n_sg, uint sg_size, enum LSC_LDCC caching) {
+    const uint cl_per_col = (rbytes + 63) >> 6;
+    const uint cl = cl_per_col * c;
+    const uint cl_per_sg = (cl + n_sg - 1) / n_sg;
+    const uint cl_iters = (cl_per_sg + sg_size - 1) / sg_size;
+#pragma unroll
+    for (uint ii_cl = 0; ii_cl < cl_iters; ii_cl++) {
+        uint i_cl = ii_cl + (sg_id * cl_per_sg) + get_sub_group_local_id();
+        uint r_cl = i_cl % cl_per_col;
+        uint c_cl = i_cl / cl_per_col;
+        if (i_cl < cl) {
+            __builtin_IB_lsc_prefetch_global_uint(
+                    (const global uint *)(ptr + r_cl * 64 + c_cl * ld_bytes), 0,
+                    caching);
+        }
+    }
+}
+
+__attribute__((overloadable)) void cooperative_prefetch_2d_internal(
+        const global char *ptr, uint rbytes, uint c, uint rbytes_max,
+        uint c_max, uint ld_bytes, uint sg_id, uint n_sg, uint sg_size,
+        enum LSC_LDCC caching) {
+    const uint cl_per_col = (rbytes_max + 63) >> 6;
+    const uint cl = cl_per_col * c_max;
+    const uint cl_per_sg = (cl + n_sg - 1) / n_sg;
+    const uint cl_iters = (cl_per_sg + sg_size - 1) / sg_size;
+    const uint max_off = rbytes - 1 + (c - 1) * ld_bytes;
+#pragma unroll
+    for (uint ii_cl = 0; ii_cl < cl_iters; ii_cl++) {
+        uint i_cl = ii_cl + (sg_id * cl_per_sg) + get_sub_group_local_id();
+        uint r_cl = i_cl % cl_per_col;
+        uint c_cl = i_cl / cl_per_col;
+        uint pf_off = min(r_cl * 64 + c_cl * ld_bytes, max_off);
+        if (i_cl < cl) {
+            __builtin_IB_lsc_prefetch_global_uchar(
+                    (const global uchar *)(ptr + pf_off), 0, caching);
+        }
+    }
+}
+
+#endif
diff --git a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/include/batch_headers/vec_typedefs.cl b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/include/batch_headers/vec_typedefs.cl
index ae4adfaf717fbd..29ca3ddb7185f7 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/include/batch_headers/vec_typedefs.cl
+++ b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/include/batch_headers/vec_typedefs.cl
@@ -2,27 +2,25 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-typedef struct half1  { half s0; }                                                               half1;
 typedef struct half5  { half s0; half s1; half s2; half s3; half s4; }                           half5;
 typedef struct half6  { half s0; half s1; half s2; half s3; half s4; half s5; }                  half6;
 typedef struct half7  { half s0; half s1; half s2; half s3; half s4; half s5; half s6; }         half7;
-typedef struct half9  { half s0; half s1; half s2; half s3; half s4; half s5; half s6; half s7; 
+typedef struct half9  { half s0; half s1; half s2; half s3; half s4; half s5; half s6; half s7;
                         half s8; }                                                               half9;
-typedef struct half10 { half s0; half s1; half s2; half s3; half s4; half s5; half s6; half s7; 
+typedef struct half10 { half s0; half s1; half s2; half s3; half s4; half s5; half s6; half s7;
                         half s8; half s9; }                                                      half10;
-typedef struct half11 { half s0; half s1; half s2; half s3; half s4; half s5; half s6; half s7; 
+typedef struct half11 { half s0; half s1; half s2; half s3; half s4; half s5; half s6; half s7;
                         half s8; half s9; half sa; }                                             half11;
-typedef struct half12 { half s0; half s1; half s2; half s3; half s4; half s5; half s6; half s7; 
+typedef struct half12 { half s0; half s1; half s2; half s3; half s4; half s5; half s6; half s7;
                         half s8;  half s9; half sa; half sb;}                                    half12;
-typedef struct half13 { half s0; half s1; half s2; half s3; half s4; half s5; half s6; half s7; 
+typedef struct half13 { half s0; half s1; half s2; half s3; half s4; half s5; half s6; half s7;
                         half s8;  half s9; half sa; half sb; half sc;}                           half13;
-typedef struct half14 { half s0; half s1; half s2; half s3; half s4; half s5; half s6; half s7; 
+typedef struct half14 { half s0; half s1; half s2; half s3; half s4; half s5; half s6; half s7;
                         half s8;  half s9; half sa; half sb; half sc; half se;}                  half14;
-typedef struct half15 { half s0; half s1; half s2; half s3; half s4; half s5; half s6; half s7; 
+typedef struct half15 { half s0; half s1; half s2; half s3; half s4; half s5; half s6; half s7;
                        half s8;  half s9; half sa; half sb; half sc; half se; half sf;}          half15;
 typedef struct half0  { half s0; } half0; //never used but makes compiler happy.
 
-typedef struct float1 { float s0; } float1;
 typedef struct float5 { float s0; float s1; float s2; float s3; float s4; } float5;
 typedef struct float6 { float s0; float s1; float s2; float s3; float s4; float s5; } float6;
 typedef struct float7 { float s0; float s1; float s2; float s3; float s4; float s5; float s6; } float7;
diff --git a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/reorder_data.cl b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/reorder_data.cl
index 6964d5b6a67def..f4393200380310 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/reorder_data.cl
+++ b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/reorder_data.cl
@@ -132,7 +132,11 @@ KERNEL (reorder_data)(
     float Ycomponent = mad(Y.x, 296.82f, -18.624f);
     float res = clamp(Ycomponent, 0.f, 255.f);
 #else
-    CALC_TYPE res = TO_CALC_TYPE(input[input_idx]);
+    #ifdef BF16_INPUT
+        CALC_TYPE res = TO_CALC_TYPE(_convert_as_bfloat16_float(input[input_idx]));
+    #else
+        CALC_TYPE res = TO_CALC_TYPE(input[input_idx]);
+    #endif
 #endif
 #endif
 
diff --git a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/reorder_weights.cl b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/reorder_weights.cl
index a5f733f590c18e..b8f4b6384f98d8 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/reorder_weights.cl
+++ b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/reorder_weights.cl
@@ -571,7 +571,10 @@ KERNEL (reorder_weights)(const __global INPUT0_TYPE* input, __global OUTPUT_TYPE
 #else
     uint output_idx = FUNC_CALL(get_output_index)(g, o, i, OUTPUT_SIZE_Z - z - 1, OUTPUT_SIZE_Y - y - 1, OUTPUT_SIZE_X - x - 1);
 #endif
-
+#ifdef BF16_INPUT
+    output[output_idx] = TO_OUTPUT_TYPE(_convert_as_bfloat16_float(input[input_idx]));
+#else
     output[output_idx] = TO_OUTPUT_TYPE(input[input_idx]);
+#endif
 }
 #endif
diff --git a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/sdpa_micro.cl b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/sdpa_micro.cl
new file mode 100644
index 00000000000000..575d9fdff5bd32
--- /dev/null
+++ b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/sdpa_micro.cl
@@ -0,0 +1,461 @@
+/*******************************************************************************
+* Copyright 2024 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "include/batch_headers/generic_vector_ops.cl"
+#include "include/batch_headers/sdpa_utils.cl"
+#include "include/batch_headers/tile_ops.cl"
+
+#define MAX(a, b) ((a) > (b) ? (a) : (b))
+#define DIV_UP(x, y) (((x) + (y)-1) / (y))
+
+#define sg_per_wg (ugemm_kq_sg_per_wg_m * ugemm_kq_sg_per_wg_n)
+#define q_tile_sg_n DIV_UP(ugemm_kq_wg_tile_n, sg_per_wg)
+
+/* Instantiate tile types and operations */
+typedef ugemm_kq_c_type s_tile_type;
+typedef ugemm_vs_c_type a_tile_type;
+
+DECLARE_2D_TILE(q_tile_type, uint, SUBGROUP_SIZE, D_MAX / 2, 1, 1, q_tile_sg_n)
+
+#ifdef BLOCK_Q
+DECLARE_2D_TILE_BLOCK_OPS(
+        q_tile_type, uint, SUBGROUP_SIZE, D_MAX / 2, 1, 1, q_tile_sg_n)
+#elif Q_ALIGN < 4
+DECLARE_2D_TILE_LOAD_PACKED_HALF(
+        q_tile_type, SUBGROUP_SIZE, D_MAX / 2, 1, 1, q_tile_sg_n)
+#endif
+
+#ifdef BLOCK_A
+DECLARE_2D_TILE(a_tile_type_half, half, SUBGROUP_SIZE, ugemm_vs_sg_tile_m, 1, 1,
+        ugemm_vs_sg_tile_n)
+#else
+DECLARE_2D_TILE(a_tile_type_half, half, SUBGROUP_SIZE, ugemm_vs_sg_tile_m, 8, 1,
+        ugemm_vs_sg_tile_n / 8)
+#endif
+
+DECLARE_2D_TILE(s_tile_type_half2, uint, SUBGROUP_SIZE, ugemm_kq_c_type_block0,
+        ugemm_kq_c_type_block1 / 2, ugemm_kq_c_type_nblock0,
+        ugemm_kq_c_type_nblock1)
+
+DECLARE_2D_TILE(
+        s_sum_tile_type, float, SUBGROUP_SIZE, ugemm_kq_sg_tile_n, 1, 1, 1)
+
+DECLARE_2D_TILE(
+        a_scale_tile_type, float, SUBGROUP_SIZE, ugemm_vs_sg_tile_n, 1, 1, 1)
+
+
+DECLARE_2D_TILE(mask_tile_type, half, SUBGROUP_SIZE, ugemm_kq_c_type_block0, ugemm_kq_c_type_block1, ugemm_kq_c_type_nblock0, ugemm_kq_c_type_nblock1)
+DECLARE_2D_TILE(mask_tile_type_float, float, SUBGROUP_SIZE, ugemm_kq_c_type_block0, ugemm_kq_c_type_block1, ugemm_kq_c_type_nblock0, ugemm_kq_c_type_nblock1)
+
+#ifdef BLOCK_A
+DECLARE_2D_TILE_BLOCK_OPS(a_tile_type_half, half, SUBGROUP_SIZE,
+        ugemm_vs_sg_tile_m, 1, 1, ugemm_vs_sg_tile_n)
+#endif
+#ifdef BLOCK_2D_A
+DECLARE_2D_TILE_BLOCK2D_OPS(a_tile_type_half, half, SUBGROUP_SIZE,
+        ugemm_vs_sg_tile_m, 8, 1, ugemm_vs_sg_tile_n / 8)
+#endif
+
+#ifdef BLOCK_A
+DECLARE_2D_TILE_COPY_REBLOCK(a_tile_type, SUBGROUP_SIZE, ugemm_vs_c_type_block0,
+        ugemm_vs_c_type_block1, ugemm_vs_c_type_nblock0,
+        ugemm_vs_c_type_nblock1, a_tile_type_half, SUBGROUP_SIZE,
+        ugemm_vs_sg_tile_m, 1, 1, ugemm_vs_sg_tile_n)
+#else
+DECLARE_2D_TILE_COPY_REBLOCK(a_tile_type, SUBGROUP_SIZE, ugemm_vs_c_type_block0,
+        ugemm_vs_c_type_block1, ugemm_vs_c_type_nblock0,
+        ugemm_vs_c_type_nblock1, a_tile_type_half, SUBGROUP_SIZE,
+        ugemm_vs_sg_tile_m, 8, 1, ugemm_vs_sg_tile_n / 8)
+#endif
+
+DECLARE_2D_TILE_VREDUCE(s_tile_type, SUBGROUP_SIZE, ugemm_kq_c_type_block0,
+        ugemm_kq_c_type_block1, ugemm_kq_c_type_nblock0,
+        ugemm_kq_c_type_nblock1, s_sum_tile_type, SUBGROUP_SIZE,
+        ugemm_kq_sg_tile_n, 1, 1, 1)
+
+DECLARE_2D_TILE_HREDUCE(s_tile_type, SUBGROUP_SIZE, ugemm_kq_c_type_block0,
+        ugemm_kq_c_type_block1, ugemm_kq_c_type_nblock0,
+        ugemm_kq_c_type_nblock1, mask_tile_type_float, SUBGROUP_SIZE,
+        ugemm_kq_sg_tile_m, 1, 1, 1)
+
+DECLARE_2D_TILE_HREDUCE(a_tile_type, SUBGROUP_SIZE, ugemm_vs_c_type_block0,
+        ugemm_vs_c_type_block1, ugemm_vs_c_type_nblock0,
+        ugemm_vs_c_type_nblock1, a_scale_tile_type, SUBGROUP_SIZE,
+        ugemm_vs_sg_tile_n, 1, 1, 1)
+
+#if ugemm_kq_wg_tile_n == ugemm_vs_wg_tile_n \
+        && (ugemm_kq_sg_tile_n % ugemm_vs_sg_tile_n) == 0
+DECLARE_2D_TILE_RSELECT(a_scale_tile_type, SUBGROUP_SIZE, ugemm_vs_sg_tile_n, 1,
+        1, 1, s_sum_tile_type, SUBGROUP_SIZE, ugemm_kq_sg_tile_n, 1, 1, 1)
+#endif
+
+#if PREFETCH_REMAINDER
+#define cooperative_prefetch_2d_maybe_rem cooperative_prefetch_2d_rem
+#else
+#define cooperative_prefetch_2d_maybe_rem( \
+        ptr, r, c, rmax, cmax, ld, sg_id, n_sg, sg_size, caching) \
+    cooperative_prefetch_2d(ptr, rmax, cmax, ld, sg_id, n_sg, sg_size, caching)
+#endif
+
+#if TRANSPOSE_K
+#define cooperative_prefetch_2d_k( \
+        ptr, r, c, rmax, cmax, ld, sg_id, n_sg, sg_size, caching) \
+    cooperative_prefetch_2d_maybe_rem( \
+            ptr, c, r, cmax, rmax, ld, sg_id, n_sg, sg_size, caching)
+#else
+#define cooperative_prefetch_2d_k cooperative_prefetch_2d_maybe_rem
+#endif
+
+#if REMAINDER_Q
+#define tile_load_block_rem_q tile_load_block
+#define tile_store_block_rem_q tile_store_block
+#else
+#define tile_load_block_rem_q(t, ptr, n, ld, off_r, off_c) \
+    tile_load_block(t, ptr, ld, off_r, off_c)
+#define tile_store_block_rem_q(t, ptr, n, ld, off_r, off_c) \
+    tile_store_block(t, ptr, ld, off_r, off_c)
+#endif
+
+#define binary_add(x, y) ((x) + (y))
+
+__attribute__((intel_reqd_sub_group_size(SUBGROUP_SIZE)))
+KERNEL(micro_sdpa)(OPTIONAL_SHAPE_INFO_ARG
+        const global half *K, const global half *Q, const global half *V,
+        global half *A,
+#if WITH_ATTN_MASK
+        const global half *msk,
+#endif
+#if WITH_SCALE
+        global SCALE_DATA_T *scale_ptr,
+#endif
+        int d, int k, int q) {
+    uint sg_ij = sub_group_broadcast(get_local_id(1), 0);
+    uint b0 = get_group_id(1);
+    uint b1 = get_group_id(2);
+
+    uint wg_j0 = get_group_id(0) * ugemm_kq_wg_tile_n;
+
+    /* Leading dimension for matrices */
+    uint ldk = TRANSPOSE_K ? KEY_S3 : KEY_S2;
+    uint ldq = QRY_S2;
+    uint ldv = VAL_S2;
+    uint lda = DST_S2;
+
+    /* Subgroup IDs for each GEMM */
+    uint sg_i_kq = sg_ij % ugemm_kq_sg_per_wg_m;
+    uint sg_j_kq = sg_ij / ugemm_kq_sg_per_wg_m;
+
+    uint sg_i_vs = sg_ij % ugemm_vs_sg_per_wg_m;
+    uint sg_j_vs = sg_ij / ugemm_vs_sg_per_wg_m;
+
+    /* SLM allocations -- place in one array to work around compiler bug */
+#define Q_slm_size (D_MAX * ugemm_kq_wg_tile_n * sizeof(half))
+#define S_slm_size (ugemm_kq_wg_tile_m * ugemm_kq_wg_tile_n * sizeof(half))
+#define S_sum_slm_size \
+    (ugemm_kq_wg_tile_n * ugemm_kq_sg_per_wg_m * sizeof(float))
+#define S_max_slm_size (ugemm_kq_wg_tile_n * sizeof(float))
+#define ugemm_slm_size MAX(ugemm_kq_slm_size, ugemm_vs_slm_size)
+
+    local char slm[Q_slm_size + S_slm_size + S_sum_slm_size + S_max_slm_size
+            + ugemm_slm_size];
+
+    local half *Q_slm = (local half *)&slm[0];
+    local half *S_slm = (local half *)&slm[Q_slm_size];
+    local float *S_sum_slm = (local float *)&slm[Q_slm_size + S_slm_size];
+    local float *S_max_slm
+            = (local float *)&slm[Q_slm_size + S_slm_size + S_sum_slm_size];
+    local uint *ugemm_slm = (local uint *)&slm[Q_slm_size + S_slm_size
+            + S_sum_slm_size + S_max_slm_size];
+
+    const bool need_sum_barrier = (ugemm_vs_barrier_count == 0);
+
+    /* Locate K/Q/V/A matrices within batch */
+    K += KEY_OFF(b1, (b0 / KV_GROUP_SIZE), 0, 0);
+    Q += QRY_OFF(b1, b0, 0, 0);
+    V += VAL_OFF(b1, (b0 / KV_GROUP_SIZE), 0, 0);
+    A += DST_OFF(b1, b0, 0, 0, 0);
+
+    __builtin_assume_aligned(K, K_ALIGN);
+    __builtin_assume_aligned(Q, Q_ALIGN);
+    __builtin_assume_aligned(V, V_ALIGN);
+    __builtin_assume_aligned(A, A_ALIGN);
+
+    /* Load Q tile, destined for SLM */
+    q_tile_type Q_tile;
+    uint q0_copy = q_tile_sg_n * sg_ij;
+#ifdef BLOCK_Q
+    tile_load_block_rem_q(
+            &Q_tile, (global uint *)Q, q, ldq >> 1, 0, wg_j0 + q0_copy);
+#elif Q_ALIGN >= 4
+    tile_load(&Q_tile, (global uint *)Q, (d + 1) >> 1, q, ldq >> 1, 0,
+            wg_j0 + q0_copy);
+#else
+    tile_load_packed_half(&Q_tile, Q, d, q, ldq, 0, wg_j0 + q0_copy);
+#endif
+
+#if WITH_SCALE
+        /* Load scale */
+    #if INVERT_SCALE
+        float iscale = convert_float(*scale_ptr);
+        float scale = native_recip(iscale);
+    #else
+        float scale = convert_float(*scale_ptr);
+        float iscale = native_recip(scale);
+    #endif
+#else
+    float iscale = sqrt(convert_float(INPUT1_SIZE_X));
+    float scale = native_recip(iscale);
+#endif
+    scale *= 1.442695f; // log2(e)
+
+#ifdef PREFETCH_K0
+    /* Prefetch first K tile. */
+    cooperative_prefetch_2d_k(K, k, d, ugemm_kq_wg_tile_m, PREFETCH_D_MAX, ldk,
+            sg_ij, sg_per_wg, SUBGROUP_SIZE, LSC_LDCC_L1C_L3C);
+#endif
+
+    /* Initialize S column sums in SLM to -inf */
+    const uint n_col_sg = DIV_UP(ugemm_kq_wg_tile_n, SUBGROUP_SIZE * sg_per_wg);
+    const float neg_inf = -INFINITY;
+
+#pragma unroll
+    for (int q = 0; q < n_col_sg; q++)
+        intel_sub_group_block_write(
+                (local uint *)&S_max_slm[(q + sg_ij * n_col_sg)
+                        * SUBGROUP_SIZE],
+                as_uint(neg_inf));
+
+    /* Clear accumulator */
+    a_tile_type A_tile;
+    tile_fill(A_tile, 0.0f);
+
+    /* Store Q tile to SLM */
+    tile_store_t_sys_src1(
+            Q_tile, (local uint *)&Q_slm[0], D_MAX / 2, q0_copy, 0);
+
+    /* Clear S column sums/maxes */
+    s_sum_tile_type S_sum_tile;
+    s_sum_tile_type S_max_tile, S_max_tile_old;
+    tile_fill(S_sum_tile, 0.0f);
+    tile_fill(S_max_tile, -INFINITY);
+
+    /* Wait for Q data to reach SLM */
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    /* Main loop over k blocks */
+    for (int k0 = 0; k0 < k; k0 += ugemm_kq_wg_tile_m) {
+        bool first = (k0 == 0);
+        bool last = (k0 + ugemm_kq_wg_tile_m >= k);
+
+        uint sg_i0_kq = sg_i_kq * ugemm_kq_sg_tile_m;
+        uint sg_j0_kq = sg_j_kq * ugemm_kq_sg_tile_n;
+
+#if WITH_ATTN_MASK
+        mask_tile_type mask_tile;
+        tile_load_t(&mask_tile, msk, q, k, q, sg_j0_kq + wg_j0, k0 + sg_i0_kq);
+#endif
+
+#if REMAINDER_K
+        /* Prepare k mask: NaN in bounds, -inf out of bounds */
+        mask_tile_type_float k_mask;
+#pragma unroll
+        for (int ii = 0; ii < ugemm_kq_sg_tile_m / SUBGROUP_SIZE; ii++)
+            k_mask.x[0][ii] = (k0 + sg_i0_kq + ii * SUBGROUP_SIZE
+                                              + get_sub_group_local_id()
+                                      < k)
+                    ? nan(0u)
+                    : -INFINITY;
+#endif
+
+        /* Calculate S = (K^T) * Q */
+        s_tile_type S_tile
+                = ugemm_kq(K, ldk, Q_slm, D_MAX, k, ugemm_kq_wg_tile_n, d, k0,
+                        0, 0, sg_i_kq, sg_j_kq, (local char *)ugemm_slm);
+
+        /* Apply attention mask */
+#if WITH_ATTN_MASK
+#define unscale(x) ((x)*iscale)
+        mask_tile_type_float mask_tile_float;
+        tile_copy(mask_tile, mask_tile_float);
+        tile_elementwise(mask_tile_float, unscale);
+        tile_binary(S_tile, mask_tile_float, binary_add);
+#endif
+
+        /* Apply k mask */
+#if REMAINDER_K
+        tile_hbroadcast_min(&S_tile, k_mask);
+#endif
+
+        /* Before softmax, we will need to scale columns by maximum values to avoid overflow. */
+
+        /* Compute our maxima and reduce across SLM */
+        tile_vreduce_max(S_tile, &S_max_tile);
+        tile_atomic_max_full(
+                S_max_tile, S_max_slm, ugemm_kq_wg_tile_n, sg_j0_kq, 0);
+        intel_work_group_barrier_arrive(CLK_LOCAL_MEM_FENCE);
+
+#ifdef PREFETCH_V
+        /* Prefetch V tile. */
+        cooperative_prefetch_2d_maybe_rem(V, d, k - k0, D_MAX,
+                (ugemm_kq_wg_tile_m * PREFETCH_D_MAX) / D_MAX, ldv, sg_ij,
+                sg_per_wg, SUBGROUP_SIZE, LSC_LDCC_L1C_L3C);
+#endif
+
+#ifndef ALT_MAX
+        /* Read back WG-wide maxima */
+        intel_work_group_barrier_wait(CLK_LOCAL_MEM_FENCE);
+        tile_load_full(&S_max_tile, S_max_slm, ugemm_kq_wg_tile_n, sg_j0_kq, 0);
+#endif
+
+        tile_vbroadcast_sub(&S_tile, S_max_tile);
+
+/* Scale + exponentiate */
+#define scaled_exp(x) native_vexp2(x *scale)
+        tile_elementwise(S_tile, scaled_exp);
+
+#ifdef ALT_MAX
+        /* Read back WG-wide maxima and adjust S to match */
+        intel_work_group_barrier_wait(CLK_LOCAL_MEM_FENCE);
+        s_sum_tile_type S_max_tile1;
+        tile_copy(S_max_tile, S_max_tile1);
+        tile_load_full(&S_max_tile, S_max_slm, ugemm_kq_wg_tile_n, sg_j0_kq, 0);
+
+#define binary_exp_neg(x, y) native_vexp2(scale *((x) - (y)))
+        tile_binary(S_max_tile1, S_max_tile, binary_exp_neg);
+        tile_vbroadcast_mul(&S_tile, S_max_tile1);
+#endif
+
+        /* Accumulate sums. S tile is transposed for easy summation. */
+        s_sum_tile_type S_sum_tile1;
+        tile_fill(S_sum_tile1, 0.0f);
+        tile_vreduce_add(S_tile, &S_sum_tile1);
+
+        /* Convert to half, VNNI format */
+        s_tile_type_half2 S_tile_half2;
+        tile_copy_to_half2(S_tile, S_tile_half2);
+
+        /* Store to SLM, in packed format */
+        tile_store_t_sys_src2(S_tile_half2, (local uint *)S_slm,
+                ugemm_vs_sg_tile_n, ugemm_kq_wg_tile_m / 2, sg_i0_kq / 2,
+                sg_j0_kq);
+        intel_work_group_barrier_arrive(CLK_LOCAL_MEM_FENCE);
+
+        /* Rescale existing accumulator and sums to match new maxima */
+        if (!first) {
+#define binary_exp_sub(x, y) native_vexp2(scale *((x) - (y)))
+#define binary_mul(x, y) ((x) * (y))
+            tile_binary(S_max_tile_old, S_max_tile, binary_exp_sub);
+            tile_binary(S_sum_tile, S_max_tile_old, binary_mul);
+
+            /* Find the subset of sums that applies to the accumulation tile */
+            a_scale_tile_type A_scale_tile;
+#if ugemm_kq_wg_tile_n == ugemm_vs_wg_tile_n \
+        && ugemm_kq_sg_tile_n == ugemm_vs_sg_tile_n
+            tile_copy(S_max_tile_old, A_scale_tile);
+#elif ugemm_kq_wg_tile_n == ugemm_vs_wg_tile_n \
+        && (ugemm_kq_sg_tile_n % ugemm_vs_sg_tile_n) == 0
+            tile_rselect(&A_scale_tile, S_max_tile_old,
+                    sg_j_vs % (ugemm_kq_sg_tile_n / ugemm_vs_sg_tile_n));
+#else
+#error unimplemented
+#endif
+            tile_hbroadcast_mul(&A_tile, A_scale_tile);
+        }
+
+/* Accumulate sums */
+        tile_binary(S_sum_tile, S_sum_tile1, binary_add);
+
+        /* Save maxima */
+        tile_copy(S_max_tile, S_max_tile_old);
+
+        /* Last iteration: store column sums in SLM */
+        if (last) {
+            tile_store_full(S_sum_tile, S_sum_slm, ugemm_kq_wg_tile_n, sg_j0_kq,
+                    sg_i_kq);
+        }
+
+#ifdef PREFETCH_K
+        /* Prefetch next K tile. */
+        if (!last) {
+#if TRANSPOSE_K
+            const uint stride_k = ldk;
+#else
+            const uint stride_k = 1;
+#endif
+            cooperative_prefetch_2d_k(K + (k0 + ugemm_kq_wg_tile_m) * stride_k,
+                    k - k0 - ugemm_kq_wg_tile_m, d, ugemm_kq_wg_tile_m,
+                    PREFETCH_D_MAX, ldk, sg_ij, sg_per_wg, SUBGROUP_SIZE,
+                    LSC_LDCC_L1C_L3C);
+        }
+#endif
+#if WITH_ATTN_MASK && defined(PREFETCH_MASK)
+        /* Prefetch next mask tile. */
+        if (!last) {
+            cooperative_prefetch_2d(msk + k0 + ugemm_kq_wg_tile_m + sg_i0_kq + (sg_j0_kq + wg_j0) * q,
+                    ugemm_kq_sg_tile_m, ugemm_kq_sg_tile_n, 0, 0, 1, SUBGROUP_SIZE,
+                    LSC_LDCC_L1UC_L3C);
+        }
+#endif
+
+        /* Wait for S stores */
+        intel_work_group_barrier_wait(CLK_LOCAL_MEM_FENCE);
+
+        /* Last iteration: signal column sums are ready */
+        if (last && need_sum_barrier)
+            intel_work_group_barrier_arrive(CLK_LOCAL_MEM_FENCE);
+
+        /* Accumulate A += V * S */
+        int k_chunk = min(k - k0, ugemm_kq_wg_tile_m);
+        a_tile_type A_tile1 = ugemm_vs(V, ldv, S_slm, ugemm_kq_wg_tile_m, d,
+                ugemm_kq_wg_tile_n, k_chunk, 0, 0, 0, sg_i_vs, sg_j_vs,
+                (local char *)ugemm_slm);
+        V += ldv * ugemm_kq_wg_tile_m;
+        tile_binary(A_tile, A_tile1, binary_add);
+    }
+
+    /* Wait for column sums to be ready */
+    if (need_sum_barrier) intel_work_group_barrier_wait(CLK_LOCAL_MEM_FENCE);
+
+    /* Load column sums from SLM + reduce in registers */
+    a_scale_tile_type A_scale_tile, A_scale_tile_load;
+    tile_fill(A_scale_tile, 0.0f);
+
+#pragma unroll
+    for (uint sg1 = 0; sg1 < ugemm_kq_sg_per_wg_m; sg1++) {
+        tile_load_full(&A_scale_tile_load, S_sum_slm, ugemm_kq_wg_tile_n,
+                ugemm_vs_sg_tile_n * sg_j_vs, sg1);
+        tile_binary(A_scale_tile, A_scale_tile_load, binary_add);
+    }
+
+    /* Rescale by 1 / (column sums) */
+    tile_elementwise(A_scale_tile, native_vrecip);
+    tile_hbroadcast_mul(&A_tile, A_scale_tile);
+
+    /* Convert to half precision and store */
+    a_tile_type_half A_tile_half;
+    tile_copy_reblock(A_tile, &A_tile_half);
+
+    uint sg_i0_vs = sg_i_vs * ugemm_vs_sg_tile_m;
+    uint sg_j0_vs = sg_j_vs * ugemm_vs_sg_tile_n + wg_j0;
+
+#ifdef BLOCK_2D_A
+    tile_store_block2d(A_tile_half, A, d, q, lda, sg_i0_vs, sg_j0_vs);
+#elif defined(BLOCK_A)
+    tile_store_block_rem_q(A_tile_half, A, q, lda, sg_i0_vs, sg_j0_vs);
+#else
+    tile_store(A_tile_half, A, d, q, lda, sg_i0_vs, sg_j0_vs);
+#endif
+}
diff --git a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/sdpa_opt.cl b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/sdpa_opt.cl
index 32f5f9dbbfd18b..073de59337ab15 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/sdpa_opt.cl
+++ b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/sdpa_opt.cl
@@ -17,7 +17,6 @@
 // max_logits    [batch, heads_num, q_len, partition_idx]
 // tmp_out       [batch, heads_num, q_len, partition_idx, head_size]
 
-
 inline uint FUNC(get_input0_index_nt)(OPTIONAL_SHAPE_INFO_ARG uint b, uint f, uint w, uint z, uint y, uint x) {
 #if INPUT0_SIMPLE
     return GET_DATA_INDEX_6D(INPUT0, b, f, w, z, y, x);
@@ -114,8 +113,10 @@ inline uint FUNC(get_bt_index_value)(OPTIONAL_SHAPE_INFO_ARG uint b, uint f, uin
 }
 #endif
 
+#define OUTPUT_BLOCK_READ(ptr, offset) BLOCK_READN(OUTPUT_TYPE, 1, ptr, offset)
+#define OUTPUT_BLOCK_WRITE(ptr, offset, val) BLOCK_WRITEN(OUTPUT_TYPE, 1, ptr, offset, val)
 #define VALUE_BLOCK_READ(ptr, offset) BLOCK_READN(INPUT2_TYPE, 1, ptr, offset)
-#define SUBGROUPS_PER_WG (HEAD_SIZE / SUBGROUP_SIZE)
+#define SUBGROUPS_PER_WG (HEAD_SIZE * SG_SCALE_FACTOR / SUBGROUP_SIZE)
 
 #ifdef SDPA_STAGE_0
 
@@ -547,6 +548,89 @@ KERNEL(sdpa_opt)(
 #else
 /* This version is used for 1st token */
 
+
+#if HAS_ATTN_MASK_INPUT
+    #define ATTN_MASK_BUFFER , attn_mask
+    #define ATTN_MASK_BUFFER_ARG , const __global INPUT3_TYPE* attn_mask
+#else
+    #define ATTN_MASK_BUFFER
+    #define ATTN_MASK_BUFFER_ARG
+#endif
+
+#if HAS_SCALE_INPUT
+    #define ATTN_SCALE_BUFFER , scale
+    #define ATTN_SCALE_BUFFER_ARG , const __global INPUT4_TYPE* scale
+#else
+    #define ATTN_SCALE_BUFFER
+    #define ATTN_SCALE_BUFFER_ARG
+#endif
+
+#define MASK_VECTOR_TYPE MAKE_VECTOR_TYPE(INPUT0_TYPE, TARGET_SEQ_LEN_BLOCK_SIZE)
+
+inline MASK_VECTOR_TYPE FUNC(load_attn_mask)(OPTIONAL_SHAPE_INFO_ARG
+                                             uint b0_idx,
+                                             uint b1_idx,
+                                             uint target_seq_idx,
+                                             uint source_seq_idx
+                                             ATTN_MASK_BUFFER_ARG
+                                             ATTN_SCALE_BUFFER_ARG) {
+    MASK_VECTOR_TYPE mask_vec = INPUT0_VAL_ZERO;
+#if !IS_CAUSAL && HAS_ATTN_MASK_INPUT
+    const uint attn_mask_offset = INPUT3_GET_INDEX_SAFE(b0_idx, b1_idx, target_seq_idx, source_seq_idx);
+    if (target_seq_idx >= (uint)TARGET_SEQ_LEN) {
+        unroll_for (uint i = 0; i < SUBGROUP_SIZE; i++) {
+            mask_vec[i] = NAN;
+        }
+    } else {
+        if (source_seq_idx + SUBGROUP_SIZE <= (uint)SOURCE_SEQ_LEN) {
+            unroll_for (uint i = 0; i < SUBGROUP_SIZE; i++) {
+                const INPUT3_TYPE mask_val = attn_mask[attn_mask_offset + i];
+                mask_vec[i] = mask_val;
+            }
+        } else {
+            const uint max_mask_offset = min(source_seq_idx + SUBGROUP_SIZE, (uint)SOURCE_SEQ_LEN);
+            for (uint i = 0; i < SUBGROUP_SIZE; i++) {
+                const INPUT3_TYPE mask_val = source_seq_idx + i < max_mask_offset ? attn_mask[attn_mask_offset + i] : NAN;
+                mask_vec[i] = mask_val;
+            }
+        }
+    }
+#endif
+
+#if !IS_CAUSAL && !HAS_ATTN_MASK_INPUT
+    if (target_seq_idx >= (uint)TARGET_SEQ_LEN) {
+        unroll_for (uint i = 0; i < SUBGROUP_SIZE; i++) {
+            mask_vec[i] = NAN;
+        }
+    } else {
+        const uint max_mask_offset = min(source_seq_idx + SUBGROUP_SIZE, (uint)SOURCE_SEQ_LEN);
+        for (uint i = 0; i < SUBGROUP_SIZE; i++) {
+            mask_vec[i] = source_seq_idx + i < max_mask_offset ? 0 : NAN;
+        }
+    }
+#endif
+
+#if IS_CAUSAL
+    for (uint i = 0; i < SUBGROUP_SIZE; i++) {
+        if (source_seq_idx + i > target_seq_idx)
+            mask_vec[i] = NAN;
+    }
+#endif
+
+#if HAS_SCALE_INPUT
+    const OUTPUT_TYPE scale_val = OUTPUT_VAL_ONE / *scale;
+#else
+    const INPUT0_TYPE scale_val = TO_INPUT0_TYPE(STATIC_SCALE_VALUE_INV);
+#endif
+
+    // Apply scale to attn_mask
+#if IS_CAUSAL || HAS_ATTN_MASK_INPUT
+    mask_vec *= scale_val;
+#endif
+
+    return mask_vec;
+}
+
 REQD_SUB_GROUP_SIZE(SUBGROUP_SIZE)
 KERNEL(sdpa_opt)(
     OPTIONAL_SHAPE_INFO_ARG
@@ -568,109 +652,151 @@ KERNEL(sdpa_opt)(
     __global OUTPUT_TYPE* tmp_out
 )
 {
-    const uint batch_idx = get_global_id(0);
-    const uint b0_idx = batch_idx / NUM_HEADS; /* BATCH dim */
-    const uint b1_idx = batch_idx % NUM_HEADS; /* HEADS_NUM dim */
-
-#if TARGET_SEQ_LEN_BLOCK_SIZE != 1 && TARGET_SEQ_LEN_BLOCK_SIZE != 16
-    #error TARGET_SEQ_LEN_BLOCK_SIZE unexpected size
-#endif
-
-#if TARGET_SEQ_LEN_BLOCK_SIZE > 1
-    const uint target_seq_idx = (uint)get_global_id(1) * TARGET_SEQ_LEN_BLOCK_SIZE;
-#else
-    const uint target_seq_idx = get_global_id(1);
+#if TARGET_SEQ_LEN_BLOCK_SIZE != 16
+    #error sdpa_opt.cl: unsupported TARGET_SEQ_LEN_BLOCK_SIZE
 #endif
-    const uint lid = get_local_id(2);
-    const uint head_size_idx = lid;
 
-    const uint sgid = get_sub_group_id();
-    const uint sglid = get_sub_group_local_id();
+    // Define indexes variables using macro declarations to avoid register spills
+    #define batch_idx (get_global_id(0))
+    #define b0_idx (batch_idx / NUM_HEADS)
+    #define b1_idx (batch_idx % NUM_HEADS)
+    #define target_seq_idx ((uint)get_global_id(1) * TARGET_SEQ_LEN_BLOCK_SIZE)
+    #define head_size_idx ((uint)get_local_id(2) % HEAD_SIZE)
+    #define sglid (uint)get_sub_group_local_id()
+    #define sgid (uint)get_sub_group_id()
 
-    const uint partition_idx = get_group_id(2);
-    const uint num_of_partitions = get_num_groups(2);
-    const uint wi_num_per_partition = get_local_size(2);
+    // SLM buffer for query inputs
+    __local INPUT0_TYPE slm_query[HEAD_SIZE * TARGET_SEQ_LEN_BLOCK_SIZE];
 
-    const uint start_partition_idx = partition_idx * SEQ_LEN_PARTITION_SIZE;
-    const uint partition_seq_len =
-        ((partition_idx + 1) < num_of_partitions) ? (SEQ_LEN_PARTITION_SIZE)
-                                                  : (SOURCE_SEQ_LEN - partition_idx * SEQ_LEN_PARTITION_SIZE);
+    // SLM buffer for intermediate QK results
+    __local OUTPUT_TYPE slm_qk_vals[SEQ_LEN_PARTITION_SIZE * TARGET_SEQ_LEN_BLOCK_SIZE];
 
-    const uint target_seq_len_bs = min(TARGET_SEQ_LEN - target_seq_idx, (uint)TARGET_SEQ_LEN_BLOCK_SIZE);
+    // SLM buffers for SoftMax calculation and qk_max/qk_sums results aggregation across all WGs
+    __local SOFTMAX_ACCUMULATOR_TYPE slm_qk_max_vals[SUBGROUPS_PER_WG * TARGET_SEQ_LEN_BLOCK_SIZE];
+    __local SOFTMAX_ACCUMULATOR_TYPE slm_exp_sum_vals[SUBGROUPS_PER_WG * TARGET_SEQ_LEN_BLOCK_SIZE];
 
-    // SLM for query inputs
-    __local INPUT0_TYPE query_local[HEAD_SIZE * TARGET_SEQ_LEN_BLOCK_SIZE];
-    // SLM for intermediate QK results
-    __local OUTPUT_TYPE qk_local[SEQ_LEN_PARTITION_SIZE * TARGET_SEQ_LEN_BLOCK_SIZE];
-    // SLM buffers for SoftMax calculation and qk_max/qk_sums results aggregation across all WG
-    __local SOFTMAX_ACCUMULATOR_TYPE qk_max_vals[SUBGROUPS_PER_WG * TARGET_SEQ_LEN_BLOCK_SIZE];
-    __local SOFTMAX_ACCUMULATOR_TYPE qk_sum_vals[SUBGROUPS_PER_WG * TARGET_SEQ_LEN_BLOCK_SIZE];
+    // SLM buffers for SoftMax recalculation for current iteration based on the previous results
+    __local SOFTMAX_ACCUMULATOR_TYPE slm_exp_sum_cur[TARGET_SEQ_LEN_BLOCK_SIZE];
+    __local SOFTMAX_ACCUMULATOR_TYPE slm_max_val_cur[TARGET_SEQ_LEN_BLOCK_SIZE];
+    __local SOFTMAX_ACCUMULATOR_TYPE slm_exp_sum_prev[TARGET_SEQ_LEN_BLOCK_SIZE];
+    __local SOFTMAX_ACCUMULATOR_TYPE slm_max_val_prev[TARGET_SEQ_LEN_BLOCK_SIZE];
 
     {
-        // Gemm1 and SoftMax calculation
-
-        SOFTMAX_ACCUMULATOR_TYPE qk_max = SOFTMAX_ACCUMULATOR_VAL_MIN;
-
-        {
-            // Gemm1 calculation
-#if HAS_SCALE_INPUT
-            const OUTPUT_TYPE scale_val = *scale;
-#else
-            const OUTPUT_TYPE scale_val = OUTPUT_VAL_ONE / sqrt(TO_OUTPUT_TYPE(HEAD_SIZE));
-#endif
-            {
-                // Load Query input to SLM and transpose it
+        // Load Q input to SLM and transpose it
 #ifdef INPUT0_DIMS_ORDER
-                uint query_offset = FUNC_CALL(get_input0_index)(OPTIONAL_SHAPE_INFO_TENSOR b0_idx, b1_idx, 0, 0, target_seq_idx, (sgid * SUBGROUP_SIZE));
-                uint query_offset_next_seq = FUNC_CALL(get_input0_index)(OPTIONAL_SHAPE_INFO_TENSOR b0_idx, b1_idx, 0, 0, target_seq_idx + 1, (sgid * SUBGROUP_SIZE));
-                const uint query_pitch = query_offset_next_seq - query_offset;
+        uint query_offset = FUNC_CALL(get_input0_index)(OPTIONAL_SHAPE_INFO_TENSOR b0_idx, b1_idx, 0, 0, target_seq_idx, (head_size_idx));
+        uint query_offset_next_seq = FUNC_CALL(get_input0_index)(OPTIONAL_SHAPE_INFO_TENSOR b0_idx, b1_idx, 0, 0, target_seq_idx + 1, (head_size_idx));
+        const uint query_pitch = query_offset_next_seq - query_offset;
 #else
-                uint query_offset = INPUT0_GET_INDEX(b0_idx, b1_idx, target_seq_idx, (sgid * SUBGROUP_SIZE));
-                const uint query_pitch = SUBGROUP_SIZE * SUBGROUPS_PER_WG;
+        uint query_offset = INPUT0_GET_INDEX(b0_idx, b1_idx, target_seq_idx, (head_size_idx));
+        const uint query_pitch = HEAD_SIZE;
 #endif
-                uint query_local_offset = (sgid * SUBGROUP_SIZE + sglid) * TARGET_SEQ_LEN_BLOCK_SIZE;
-                if (target_seq_len_bs != TARGET_SEQ_LEN_BLOCK_SIZE) {
-                    for (uint seq_idx = 0; seq_idx < target_seq_len_bs; seq_idx++) {
+        uint query_local_offset = head_size_idx * TARGET_SEQ_LEN_BLOCK_SIZE;
+        const uint cur_target_seq_len_size = min(TARGET_SEQ_LEN - target_seq_idx, (uint)TARGET_SEQ_LEN_BLOCK_SIZE);
+
+        if (cur_target_seq_len_size != TARGET_SEQ_LEN_BLOCK_SIZE) {
+            if (sgid * SUBGROUP_SIZE < HEAD_SIZE) {
+                for (uint seq_idx = 0; seq_idx < cur_target_seq_len_size; seq_idx++) {
+                    INPUT0_TYPE val = BLOCK_READN(INPUT0_TYPE, 1, query_input, query_offset);
+
+                    slm_query[query_local_offset] = val;
+                    query_offset += query_pitch;
+                    query_local_offset++;
+                }
+            }
+        } else {
+            #if SG_SCALE_FACTOR == 2
+                if ((sgid < (SUBGROUPS_PER_WG / SG_SCALE_FACTOR))) {
+                    unroll_for (uint seq_idx = 0; seq_idx < (TARGET_SEQ_LEN_BLOCK_SIZE / SG_SCALE_FACTOR); seq_idx++) {
                         INPUT0_TYPE val = BLOCK_READN(INPUT0_TYPE, 1, query_input, query_offset);
 
-                        query_local[query_local_offset] = val;
+                        slm_query[query_local_offset] = val;
                         query_offset += query_pitch;
                         query_local_offset++;
                     }
                 } else {
-                    unroll_for (uint seq_idx = 0; seq_idx < TARGET_SEQ_LEN_BLOCK_SIZE; seq_idx++) {
+                    query_local_offset += (TARGET_SEQ_LEN_BLOCK_SIZE / SG_SCALE_FACTOR);
+                    query_offset += query_pitch * (TARGET_SEQ_LEN_BLOCK_SIZE / SG_SCALE_FACTOR);
+                    unroll_for (uint seq_idx = 0; seq_idx < (TARGET_SEQ_LEN_BLOCK_SIZE / SG_SCALE_FACTOR); seq_idx++) {
                         INPUT0_TYPE val = BLOCK_READN(INPUT0_TYPE, 1, query_input, query_offset);
 
-                        query_local[query_local_offset] = val;
+                        slm_query[query_local_offset] = val;
                         query_offset += query_pitch;
                         query_local_offset++;
                     }
                 }
-            }
+            #elif SG_SCALE_FACTOR == 4
+                query_local_offset += (sgid / (SUBGROUPS_PER_WG / SG_SCALE_FACTOR)) * (TARGET_SEQ_LEN_BLOCK_SIZE / SG_SCALE_FACTOR);
+                query_offset += query_pitch * (sgid / (SUBGROUPS_PER_WG / SG_SCALE_FACTOR)) * (TARGET_SEQ_LEN_BLOCK_SIZE / SG_SCALE_FACTOR);
+                unroll_for (uint seq_idx = 0; seq_idx < (TARGET_SEQ_LEN_BLOCK_SIZE / SG_SCALE_FACTOR); seq_idx++) {
+                    INPUT0_TYPE val = BLOCK_READN(INPUT0_TYPE, 1, query_input, query_offset);
 
-            {
-                barrier(CLK_LOCAL_MEM_FENCE);
+                    slm_query[query_local_offset] = val;
+                    query_offset += query_pitch;
+                    query_local_offset++;
+                }
+            #else
+                unroll_for (uint seq_idx = 0; seq_idx < TARGET_SEQ_LEN_BLOCK_SIZE; seq_idx++) {
+                    INPUT0_TYPE val = BLOCK_READN(INPUT0_TYPE, 1, query_input, query_offset);
+
+                    slm_query[query_local_offset] = val;
+                    query_offset += query_pitch;
+                    query_local_offset++;
+                }
+            #endif
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+
+    {
+        #if TARGET_SEQ_LEN_BLOCK_SIZE <= SUBGROUP_SIZE
+            // Initialize slm buffers with MIN and ZERO values
+            if (sgid == 0 && sglid < TARGET_SEQ_LEN_BLOCK_SIZE) {
+                slm_max_val_prev[sglid] = SOFTMAX_ACCUMULATOR_VAL_MIN;
+                slm_exp_sum_prev[sglid] = SOFTMAX_ACCUMULATOR_VAL_ZERO;
             }
+        #else
+            #error sdpa_opt.cl: unsupported TARGET_SEQ_LEN_BLOCK_SIZE
+        #endif
+    }
+
+    // Q*K calculation loop
+    MAKE_VECTOR_TYPE(OUTPUT_TYPE, TARGET_SEQ_LEN_BLOCK_SIZE) output_acc = OUTPUT_VAL_ZERO;
+
+    __attribute__((opencl_unroll_hint(1)))
+    for (uint start_partition_idx = 0; start_partition_idx < SOURCE_SEQ_LEN; start_partition_idx += SEQ_LEN_PARTITION_SIZE) {
+        SOFTMAX_ACCUMULATOR_TYPE qk_max = SOFTMAX_ACCUMULATOR_VAL_MIN;
+
+        const uint seq_len = start_partition_idx + sgid * SUBGROUP_SIZE;
+        const uint partition_seq_len = min((uint)SOURCE_SEQ_LEN - start_partition_idx, (uint)SEQ_LEN_PARTITION_SIZE);
 
-            // Main Gemm1 calculation loop
-            uint seq_len = sgid * TARGET_SEQ_LEN_BLOCK_SIZE;
-            for (; seq_len < partition_seq_len; seq_len += SUBGROUPS_PER_WG * SUBGROUP_SIZE) {
 #ifdef BEAM_TABLE_TYPE
-                const uint b_idx = beam_table[FUNC_CALL(get_bt_index_key)(OPTIONAL_SHAPE_INFO_TENSOR b0_idx, b1_idx, 0, 0, start_partition_idx + seq_len + sglid, 0)];
-                const uint key_offset = FUNC_CALL(get_input1_index)(OPTIONAL_SHAPE_INFO_TENSOR b_idx, b1_idx, 0, 0, start_partition_idx + seq_len + sglid, 0);
-#else
-#ifdef INPUT1_DIMS_ORDER
-                uint key_offset = FUNC_CALL(get_input1_index)(OPTIONAL_SHAPE_INFO_TENSOR b0_idx, b1_idx, 0, 0, start_partition_idx + seq_len, 0);
-                uint key_offset_next_seq = FUNC_CALL(get_input1_index)(OPTIONAL_SHAPE_INFO_TENSOR b0_idx, b1_idx, 0, 0, start_partition_idx + seq_len + 1, 0);
-                const uint key_pitch = key_offset_next_seq - key_offset;
+            const uint b_idx = beam_table[FUNC_CALL(get_bt_index_key)(OPTIONAL_SHAPE_INFO_TENSOR b0_idx, b1_idx, 0, 0, seq_len + sglid, 0)];
+            const uint key_offset = FUNC_CALL(get_input1_index)(OPTIONAL_SHAPE_INFO_TENSOR b_idx, b1_idx, 0, 0, seq_len + sglid, 0);
 #else
-                uint key_offset = INPUT1_GET_INDEX(b0_idx, b1_idx, start_partition_idx + seq_len, 0);
-                const uint key_pitch = HEAD_SIZE;
-#endif
+    #ifdef INPUT1_DIMS_ORDER
+            uint key_offset = FUNC_CALL(get_input1_index)(OPTIONAL_SHAPE_INFO_TENSOR b0_idx, b1_idx, 0, 0, seq_len, 0);
+            uint key_offset_next_seq = FUNC_CALL(get_input1_index)(OPTIONAL_SHAPE_INFO_TENSOR b0_idx, b1_idx, 0, 0, seq_len + 1, 0);
+            const uint key_pitch = key_offset_next_seq - key_offset;
+    #else
+            uint key_offset = INPUT1_GET_INDEX(b0_idx, b1_idx, seq_len, 0);
+            const uint key_pitch = HEAD_SIZE;
+    #endif
 #endif
-
-                INPUT0_TYPE acc[TARGET_SEQ_LEN_BLOCK_SIZE] = {INPUT0_VAL_ZERO};
-
+            int seq_len_calc_size = min((int)(SOURCE_SEQ_LEN) - (int)seq_len, (int)SUBGROUP_SIZE);
+            MAKE_VECTOR_TYPE(INPUT0_TYPE, TARGET_SEQ_LEN_BLOCK_SIZE) qk_acc;
+
+            qk_acc = FUNC_CALL(load_attn_mask)(OPTIONAL_SHAPE_INFO_TENSOR
+                            b0_idx,
+                            b1_idx,
+                            target_seq_idx + sglid,
+                            // TODO: pass seq_len_calc_size here
+                            seq_len
+                            ATTN_MASK_BUFFER
+                            ATTN_SCALE_BUFFER);
+
+            if (seq_len_calc_size >= SUBGROUP_SIZE) {
+                __attribute__((opencl_unroll_hint(1)))
                 for (uint head_idx_index = 0; head_idx_index < HEAD_SIZE; head_idx_index += SUBGROUP_SIZE) {
                     #define KEY_BLOCK_READ(ptr, offset) BLOCK_READN(INPUT1_TYPE, 1, ptr, offset);
                     #define QUERY_VEC MAKE_VECTOR_TYPE(INPUT1_TYPE, TARGET_SEQ_LEN_BLOCK_SIZE)
@@ -678,7 +804,7 @@ KERNEL(sdpa_opt)(
                     QUERY_VEC queries_vec;
                     uint query_local_offset = (head_idx_index * TARGET_SEQ_LEN_BLOCK_SIZE) + sglid;
                     unroll_for (uint q_row_idx = 0; q_row_idx < TARGET_SEQ_LEN_BLOCK_SIZE; q_row_idx++) {
-                        queries_vec[q_row_idx] = query_local[query_local_offset];
+                        queries_vec[q_row_idx] = slm_query[query_local_offset];
                         query_local_offset += TARGET_SEQ_LEN_BLOCK_SIZE;
                     }
 
@@ -690,320 +816,314 @@ KERNEL(sdpa_opt)(
 #endif
 
                         unroll_for (uint i = 0; i < SUBGROUP_SIZE; i++) {
-                            acc[key_row_idx] = mad(sub_group_broadcast(key_vals, i), queries_vec[i], acc[key_row_idx]);
+                            qk_acc[key_row_idx] = mad(sub_group_broadcast(key_vals, i), queries_vec[i], qk_acc[key_row_idx]);
                         }
                     }
                 }
+            } else if (seq_len_calc_size > 0) {
+                __attribute__((opencl_unroll_hint(1)))
+                for (uint head_idx_index = 0; head_idx_index < HEAD_SIZE; head_idx_index += SUBGROUP_SIZE) {
+                    #define KEY_BLOCK_READ(ptr, offset) BLOCK_READN(INPUT1_TYPE, 1, ptr, offset);
+                    #define QUERY_VEC MAKE_VECTOR_TYPE(INPUT1_TYPE, TARGET_SEQ_LEN_BLOCK_SIZE)
 
-                {
-#if !IS_CAUSAL && HAS_ATTN_MASK_INPUT
-                    const uint attn_mask_offset = INPUT3_GET_INDEX_SAFE(b0_idx, b1_idx, target_seq_idx + sglid, start_partition_idx + seq_len);
-                    MAKE_VECTOR_TYPE(INPUT3_TYPE, TARGET_SEQ_LEN_BLOCK_SIZE) attn_mask_vec = INPUT3_VAL_MIN;
-                    for (uint i = 0; i < min(partition_seq_len - seq_len, (uint)TARGET_SEQ_LEN_BLOCK_SIZE); i++) {
-                        attn_mask_vec[i] = attn_mask[attn_mask_offset + i];
+                    QUERY_VEC queries_vec;
+                    uint query_local_offset = (head_idx_index * TARGET_SEQ_LEN_BLOCK_SIZE) + sglid;
+                    unroll_for (uint q_row_idx = 0; q_row_idx < TARGET_SEQ_LEN_BLOCK_SIZE; q_row_idx++) {
+                        queries_vec[q_row_idx] = slm_query[query_local_offset];
+                        query_local_offset += TARGET_SEQ_LEN_BLOCK_SIZE;
+                    }
+
+#ifndef LOAD_KEY_LEFTOVERS_IN_CALC_LOOP
+                    QUERY_VEC key_vec = 0;
+                    unroll_for (uint key_row_idx = 0; key_row_idx < seq_len_calc_size; key_row_idx++) {
+    #ifdef BEAM_TABLE_TYPE
+                        key_vec[key_row_idx] = KEY_BLOCK_READ(key_input, sub_group_broadcast(key_offset, key_row_idx) + head_idx_index);
+    #else
+                        key_vec[key_row_idx] = KEY_BLOCK_READ(key_input, key_offset + key_row_idx * key_pitch + head_idx_index);
+    #endif
                     }
 #endif
-                    unroll_for (uint i = 0; i < TARGET_SEQ_LEN_BLOCK_SIZE; i++) {
-                        acc[i] *= scale_val;
-#if IS_CAUSAL
-                        if (start_partition_idx + seq_len + i > target_seq_idx + sglid)
-                            acc[i] += INPUT0_VAL_MIN;
-#elif !IS_CAUSAL && HAS_ATTN_MASK_INPUT
-                        acc[i] += attn_mask_vec[i];
-#endif
-#if INPUT0_TYPE_SIZE ==  2
-                        /* Adding this clamp improves performance for some reason */
-                        acc[i] = INPUT0_MIN_FUNC(INPUT0_MAX_FUNC(acc[i], INPUT0_VAL_MIN), INPUT0_VAL_MAX);
+
+                    unroll_for (uint key_row_idx = 0; key_row_idx < TARGET_SEQ_LEN_BLOCK_SIZE; key_row_idx++) {
+#ifdef LOAD_KEY_LEFTOVERS_IN_CALC_LOOP
+    #ifdef BEAM_TABLE_TYPE
+                        INPUT1_TYPE key_vals = 0;
+                        if (key_row_idx < seq_len_calc_size)
+                            key_vals = KEY_BLOCK_READ(key_input, sub_group_broadcast(key_offset, key_row_idx) + head_idx_index);
+    #else
+                        INPUT1_TYPE key_vals = 0;
+                        if (key_row_idx < seq_len_calc_size)
+                            key_vals = KEY_BLOCK_READ(key_input, key_offset + key_row_idx * key_pitch + head_idx_index);
+    #endif
+#else
+    #define key_vals key_vec[key_row_idx]
 #endif
-                        if (seq_len + i >= partition_seq_len) {
-                            acc[i] = INPUT0_VAL_MIN;
+                        unroll_for (uint i = 0; i < SUBGROUP_SIZE; i++) {
+                            qk_acc[key_row_idx] = mad(sub_group_broadcast(key_vals, i), queries_vec[i], qk_acc[key_row_idx]);
                         }
-
-                        qk_max = SOFTMAX_ACCUMULATOR_MAX_FUNC(qk_max, TO_SOFTMAX_ACCUMULATOR_TYPE(acc[i]));
-                        qk_local[sglid * SEQ_LEN_PARTITION_SIZE + seq_len + i] = acc[i];
                     }
                 }
             }
-        } // Gemm1 calculation end
 
-        {
-            // Save QK max to SLM
-            qk_max_vals[sglid * SUBGROUPS_PER_WG + sgid] = qk_max;
-        }
+            {
+                unroll_for (uint i = 0; i < TARGET_SEQ_LEN_BLOCK_SIZE; i++) {
+#if HAS_SCALE_INPUT
+                    const OUTPUT_TYPE scale_val = *scale;
+#else
+                    const OUTPUT_TYPE scale_val = TO_OUTPUT_TYPE(STATIC_SCALE_VALUE);
+#endif
+                    qk_acc[i] *= scale_val;
+
+                    qk_acc[i] = INPUT0_MIN_FUNC(INPUT0_MAX_FUNC(qk_acc[i], INPUT0_VAL_MIN), INPUT0_VAL_MAX);
+
+                    qk_max = SOFTMAX_ACCUMULATOR_MAX_FUNC(qk_max, TO_SOFTMAX_ACCUMULATOR_TYPE(qk_acc[i]));
+                }
+            }
+
+            {
+                slm_qk_max_vals[sgid * SUBGROUP_SIZE + sglid] = qk_max;
+                qk_max = SOFTMAX_ACCUMULATOR_VAL_MIN;
+            }
+
+        barrier(CLK_LOCAL_MEM_FENCE);
 
         {
             // SoftMax calculation
-#if TARGET_SEQ_LEN_BLOCK_SIZE > 1
-            const uint seq_idx_end = target_seq_len_bs;
-#else
-            const uint seq_idx_end = 1;
-#endif
-            #define QK_MAX_NUMS_PER_SG CEIL_DIV(TARGET_SEQ_LEN_BLOCK_SIZE, SUBGROUPS_PER_WG)
-            #if (TARGET_SEQ_LEN_BLOCK_SIZE % SUBGROUPS_PER_WG != 0)
-                /* /* If TARGET_SEQ_LEN_BLOCK_SIZE is not divisible by SUBGROUPS_PER_WG, then some subgroups will have to handle more QK rows than others */
-                #define QK_ITERS_END \
-                    (TARGET_SEQ_LEN_BLOCK_SIZE / SUBGROUPS_PER_WG + (sgid < TARGET_SEQ_LEN_BLOCK_SIZE % SUBGROUPS_PER_WG ? 1 : 0))
-            #else
-                #define QK_ITERS_END QK_MAX_NUMS_PER_SG
-            #endif
+            SOFTMAX_ACCUMULATOR_TYPE qk_max_new = SOFTMAX_ACCUMULATOR_VAL_MIN;
 
-            SOFTMAX_ACCUMULATOR_TYPE qk_max[QK_MAX_NUMS_PER_SG];
-            for (uint i = 0; i < QK_MAX_NUMS_PER_SG; i++)
-                qk_max[i] = SOFTMAX_ACCUMULATOR_VAL_MIN;
+            for (uint i = 0; i < SUBGROUPS_PER_WG; i++) {
+                SOFTMAX_ACCUMULATOR_TYPE qk_max_val = slm_qk_max_vals[i * SUBGROUP_SIZE + sglid];
+                qk_max_new = SOFTMAX_ACCUMULATOR_MAX_FUNC(qk_max_new, qk_max_val);
+            }
 
-            barrier(CLK_LOCAL_MEM_FENCE);
+            if (sgid == 0) {
+                slm_max_val_cur[sglid] = qk_max_new;
+            }
 
-            if (sglid < SUBGROUPS_PER_WG)
-                for (uint i = 0; i < QK_ITERS_END; i++)
-                    qk_max[i] = qk_max_vals[(i * SUBGROUPS_PER_WG * SUBGROUPS_PER_WG) + sgid * SUBGROUPS_PER_WG + sglid];
+            SOFTMAX_ACCUMULATOR_TYPE exp_sum_new = SOFTMAX_ACCUMULATOR_VAL_ZERO;
 
-            sub_group_barrier(CLK_LOCAL_MEM_FENCE);
+            for (uint i = 0; i < TARGET_SEQ_LEN_BLOCK_SIZE; i++) {
+                qk_acc[i] = native_exp(TO_SOFTMAX_ACCUMULATOR_TYPE(qk_acc[i]) - qk_max_new);
+                exp_sum_new += qk_acc[i];
+            }
 
-            for (uint i = 0; i < QK_ITERS_END; i++) {
-                qk_max[i] = sub_group_reduce_max(qk_max[i]);
+            {
+                slm_exp_sum_vals[sgid * SUBGROUP_SIZE + sglid] = exp_sum_new;
             }
 
-            SOFTMAX_ACCUMULATOR_TYPE exp_sum[QK_MAX_NUMS_PER_SG];
-            for (uint i = 0; i < QK_MAX_NUMS_PER_SG; i++)
-                exp_sum[i] = SOFTMAX_ACCUMULATOR_VAL_ZERO;
-
-            for (uint i = 0; i < QK_ITERS_END; i++) {
-                // TODO: Try full loop, with ternary operator
-                for (uint qk_idx = sglid; qk_idx < partition_seq_len; qk_idx += SUBGROUP_SIZE) {
-                    const uint qk_offset = i * SUBGROUPS_PER_WG * SEQ_LEN_PARTITION_SIZE + sgid * SEQ_LEN_PARTITION_SIZE + qk_idx;
-                    SOFTMAX_ACCUMULATOR_TYPE qk_val = qk_local[qk_offset];
-                    SOFTMAX_ACCUMULATOR_TYPE qk_new = native_exp(TO_SOFTMAX_ACCUMULATOR_TYPE(qk_val) - qk_max[i]);
-                    qk_local[qk_offset] = qk_new;
-                    exp_sum[i] += qk_new;
-                }
+            exp_sum_new = SOFTMAX_ACCUMULATOR_VAL_ZERO;
+
+            barrier(CLK_LOCAL_MEM_FENCE);
+
+            for (uint i = 0; i < SUBGROUPS_PER_WG; i++) {
+                SOFTMAX_ACCUMULATOR_TYPE exp_sum = slm_exp_sum_vals[i * SUBGROUP_SIZE + sglid];
+                exp_sum_new += exp_sum;
             }
 
-            for (uint i = 0; i < QK_ITERS_END; i++) {
-                exp_sum[i] = sub_group_reduce_add(exp_sum[i]);
+            for (uint i = 0; i < TARGET_SEQ_LEN_BLOCK_SIZE; i++) {
+                qk_acc[i] = qk_acc[i] / exp_sum_new;
             }
 
-            for (uint i = 0; i < QK_ITERS_END; i++) {
-                for (uint qk_idx = sglid; qk_idx < partition_seq_len; qk_idx += SUBGROUP_SIZE) {
-                    const uint qk_offset = i * SUBGROUPS_PER_WG * SEQ_LEN_PARTITION_SIZE + sgid * SEQ_LEN_PARTITION_SIZE + qk_idx;
-                    SOFTMAX_ACCUMULATOR_TYPE qk_val = TO_SOFTMAX_ACCUMULATOR_TYPE(qk_local[qk_offset]);
-                    SOFTMAX_ACCUMULATOR_TYPE qk_new = qk_val / exp_sum[i];
-                    qk_local[qk_offset] = qk_new;
-                }
+            if (sgid == 0) {
+                slm_exp_sum_cur[sglid] = exp_sum_new;
             }
 
-            {
-                // If the number of partitions is greater than 1, save exm_sums and max_logits to the temporary buffers
-                // Use single WI in the WG, since all the WIs have the same value
-                if (num_of_partitions > 1 && sglid == 0) {
-                    for (uint i = 0; i < QK_ITERS_END; i++) {
-                        if (target_seq_idx + sgid + (i * SUBGROUPS_PER_WG) < TARGET_SEQ_LEN) {
-                            const uint exp_sums_offset = b0_idx * (NUM_HEADS * TARGET_SEQ_LEN * num_of_partitions) +
-                                                        b1_idx * (TARGET_SEQ_LEN * num_of_partitions) +
-                                                        (target_seq_idx + sgid + (i * SUBGROUPS_PER_WG)) * (num_of_partitions) +
-                                                        partition_idx;
-                            exp_sums[exp_sums_offset] = exp_sum[i];
-
-                            const uint max_logits_offset = exp_sums_offset;
-                            max_logits[max_logits_offset] = qk_max[i];
-                        }
-                    }
-                }
+            for (uint i = 0; i < TARGET_SEQ_LEN_BLOCK_SIZE; i++) {
+                slm_qk_vals[sglid * SEQ_LEN_PARTITION_SIZE + sgid * TARGET_SEQ_LEN_BLOCK_SIZE + i] = qk_acc[i];
             }
 
             barrier(CLK_LOCAL_MEM_FENCE);
-        } // SoftMax calculation end
-    } // Gemm1 + SoftMax calculations end
+        }
 
-    const uint seq_len_leftovers_start = (partition_seq_len / SUBGROUP_SIZE) * SUBGROUP_SIZE;
-    if (seq_len_leftovers_start != partition_seq_len) {
-        // Gemm2 calculation
-        OUTPUT_TYPE acc[TARGET_SEQ_LEN_BLOCK_SIZE] = {OUTPUT_VAL_ZERO};
+        {
+            // QK*V calculation
+            MAKE_VECTOR_TYPE(OUTPUT_TYPE, TARGET_SEQ_LEN_BLOCK_SIZE) acc_output_res = OUTPUT_VAL_ZERO;
 
 #ifdef INPUT2_DIMS_ORDER
-        uint value_offset = FUNC_CALL(get_input2_index)(OPTIONAL_SHAPE_INFO_TENSOR b0_idx, b1_idx, 0, 0, 0, 0);
-        uint value_offset_next_seq = FUNC_CALL(get_input2_index)(OPTIONAL_SHAPE_INFO_TENSOR b0_idx, b1_idx, 0, 0, 1, 0);
-        const uint value_pitch = value_offset_next_seq - value_offset;
+            uint value_offset_base = FUNC_CALL(get_input2_index)(OPTIONAL_SHAPE_INFO_TENSOR b0_idx, b1_idx, 0, 0, 0, 0);
+            uint value_offset_next_seq = FUNC_CALL(get_input2_index)(OPTIONAL_SHAPE_INFO_TENSOR b0_idx, b1_idx, 0, 0, 1, 0);
+            const uint value_pitch = value_offset_next_seq - value_offset_base;
 #else
-        const uint value_pitch = HEAD_SIZE;
+            const uint value_pitch = HEAD_SIZE;
 #endif
 
-        for (uint seq_len = 0; seq_len < partition_seq_len / SUBGROUP_SIZE; seq_len++) {
+            if (partition_seq_len == SEQ_LEN_PARTITION_SIZE) {
+                uint seq_len_start = (sgid / (SUBGROUPS_PER_WG / SG_SCALE_FACTOR)) * (SEQ_LEN_PARTITION_SIZE / SG_SCALE_FACTOR);
+                for (uint seq_len = seq_len_start; seq_len < seq_len_start + (SEQ_LEN_PARTITION_SIZE / SG_SCALE_FACTOR); seq_len += SUBGROUP_SIZE) {
 #ifdef BEAM_TABLE_TYPE
-            const uint b_idx = beam_table[FUNC_CALL(get_bt_index_value)(OPTIONAL_SHAPE_INFO_TENSOR b0_idx, b1_idx, 0, 0, start_partition_idx + (seq_len * SUBGROUP_SIZE) + sglid, sgid * SUBGROUP_SIZE)];
-            const uint value_offset = FUNC_CALL(get_input2_index)(OPTIONAL_SHAPE_INFO_TENSOR b_idx, b1_idx, 0, 0, start_partition_idx + (seq_len * SUBGROUP_SIZE) + sglid, sgid * SUBGROUP_SIZE);
+                    const uint b_idx = beam_table[FUNC_CALL(get_bt_index_value)(OPTIONAL_SHAPE_INFO_TENSOR b0_idx, b1_idx, 0, 0, start_partition_idx + (seq_len) + sglid, sgid * SUBGROUP_SIZE)];
+                    const uint value_offset = FUNC_CALL(get_input2_index)(OPTIONAL_SHAPE_INFO_TENSOR b_idx, b1_idx, 0, 0, start_partition_idx + (seq_len) + sglid, sgid * SUBGROUP_SIZE);
 #else
-#ifdef INPUT2_DIMS_ORDER
-            uint value_offset = FUNC_CALL(get_input2_index)(OPTIONAL_SHAPE_INFO_TENSOR b0_idx, b1_idx, 0, 0, start_partition_idx + (seq_len * SUBGROUP_SIZE), head_size_idx);
-#else
-            uint value_offset = INPUT2_GET_INDEX(b0_idx, b1_idx, start_partition_idx + (seq_len * SUBGROUP_SIZE), head_size_idx);
-#endif
+    #ifdef INPUT2_DIMS_ORDER
+                    uint value_offset = FUNC_CALL(get_input2_index)(OPTIONAL_SHAPE_INFO_TENSOR b0_idx, b1_idx, 0, 0, start_partition_idx + (seq_len), head_size_idx);
+    #else
+                    uint value_offset = INPUT2_GET_INDEX(b0_idx, b1_idx, start_partition_idx + (seq_len), head_size_idx);
+    #endif
 #endif
 
-            OUTPUT_TYPE qk_val[TARGET_SEQ_LEN_BLOCK_SIZE];
-            unroll_for (uint seq_idx = 0; seq_idx < TARGET_SEQ_LEN_BLOCK_SIZE; seq_idx++) {
-                qk_val[seq_idx] = qk_local[seq_idx * SEQ_LEN_PARTITION_SIZE + seq_len * SUBGROUP_SIZE + sglid];
-            }
+                    MAKE_VECTOR_TYPE(OUTPUT_TYPE, TARGET_SEQ_LEN_BLOCK_SIZE) qk_val;
+                    unroll_for (uint seq_idx = 0; seq_idx < TARGET_SEQ_LEN_BLOCK_SIZE; seq_idx++) {
+                        qk_val[seq_idx] = slm_qk_vals[seq_idx * SEQ_LEN_PARTITION_SIZE + seq_len + sglid];
+                    }
 
-            unroll_for (uint i = 0; i < SUBGROUP_SIZE; i++) {
+                    unroll_for (uint i = 0; i < SUBGROUP_SIZE; i++) {
 #ifdef BEAM_TABLE_TYPE
-                INPUT2_TYPE value_val = VALUE_BLOCK_READ(value_input, sub_group_broadcast(value_offset, i));
+                        INPUT2_TYPE value_val = VALUE_BLOCK_READ(value_input, sub_group_broadcast(value_offset, i));
 #else
-                INPUT2_TYPE value_val = VALUE_BLOCK_READ(value_input, value_offset);
+                        INPUT2_TYPE value_val = VALUE_BLOCK_READ(value_input, value_offset);
 #endif
-                unroll_for (uint seq_idx = 0; seq_idx < TARGET_SEQ_LEN_BLOCK_SIZE; seq_idx++) {
-                    acc[seq_idx] = mad(sub_group_broadcast(qk_val[seq_idx], i), value_val, acc[seq_idx]);
-                }
+                        unroll_for (uint seq_idx = 0; seq_idx < TARGET_SEQ_LEN_BLOCK_SIZE; seq_idx++) {
+                            acc_output_res[seq_idx] = mad(sub_group_broadcast(qk_val[seq_idx], i), value_val, acc_output_res[seq_idx]);
+                        }
 
 #ifndef BEAM_TABLE_TYPE
-                value_offset += value_pitch;
+                        value_offset += value_pitch;
 #endif
-            }
-        }
-
+                    }
+                }
+            } else {
+                const uint seq_len_start = (sgid / (SUBGROUPS_PER_WG / SG_SCALE_FACTOR)) * (SEQ_LEN_PARTITION_SIZE / SG_SCALE_FACTOR);
+                uint seq_len_end = 0;
+                if (seq_len_start < partition_seq_len)
+                    seq_len_end = seq_len_start + min(partition_seq_len - seq_len_start, (uint)(SEQ_LEN_PARTITION_SIZE / SG_SCALE_FACTOR));;
 
-        /* The handling of leftovers causes significantly worse assembly code generation for the above main calculation loop.
-           Therefore, there are two independent branches for the calculation of QK*V matrices:
-           one with leftovers handling (when seq_len_leftovers_start != partition_seq_len) and one without. */
-        {
-            OUTPUT_TYPE qk_val[TARGET_SEQ_LEN_BLOCK_SIZE];
-            uint qk_offset = min(seq_len_leftovers_start + sglid, partition_seq_len);
-            unroll_for (uint seq_idx = 0; seq_idx < TARGET_SEQ_LEN_BLOCK_SIZE; seq_idx++) {
-                qk_val[seq_idx] = qk_local[qk_offset];
-                qk_offset += SEQ_LEN_PARTITION_SIZE;
-            }
+                for (uint seq_len = seq_len_start / SUBGROUP_SIZE; seq_len < seq_len_end / SUBGROUP_SIZE; seq_len++) {
 #ifdef BEAM_TABLE_TYPE
-            const uint b_idx = beam_table[FUNC_CALL(get_bt_index_value)(OPTIONAL_SHAPE_INFO_TENSOR b0_idx, b1_idx, 0, 0, start_partition_idx + seq_len_leftovers_start + sglid, sgid * SUBGROUP_SIZE)];
-            const uint value_offset = FUNC_CALL(get_input2_index)(OPTIONAL_SHAPE_INFO_TENSOR b_idx, b1_idx, 0, 0, start_partition_idx + seq_len_leftovers_start + sglid, sgid * SUBGROUP_SIZE);
+                    const uint b_idx = beam_table[FUNC_CALL(get_bt_index_value)(OPTIONAL_SHAPE_INFO_TENSOR b0_idx, b1_idx, 0, 0, start_partition_idx + (seq_len * SUBGROUP_SIZE) + sglid, sgid * SUBGROUP_SIZE)];
+                    const uint value_offset = FUNC_CALL(get_input2_index)(OPTIONAL_SHAPE_INFO_TENSOR b_idx, b1_idx, 0, 0, start_partition_idx + (seq_len * SUBGROUP_SIZE) + sglid, sgid * SUBGROUP_SIZE);
 #else
-#ifdef INPUT2_DIMS_ORDER
-            uint value_offset = FUNC_CALL(get_input2_index)(OPTIONAL_SHAPE_INFO_TENSOR b0_idx, b1_idx, 0, 0, start_partition_idx + seq_len_leftovers_start, head_size_idx);
-#else
-            uint value_offset = INPUT2_GET_INDEX(b0_idx, b1_idx, start_partition_idx + seq_len_leftovers_start, head_size_idx);
-#endif
+    #ifdef INPUT2_DIMS_ORDER
+                    uint value_offset = FUNC_CALL(get_input2_index)(OPTIONAL_SHAPE_INFO_TENSOR b0_idx, b1_idx, 0, 0, start_partition_idx + (seq_len * SUBGROUP_SIZE), head_size_idx);
+    #else
+                    uint value_offset = INPUT2_GET_INDEX(b0_idx, b1_idx, start_partition_idx + (seq_len * SUBGROUP_SIZE), head_size_idx);
+    #endif
 #endif
 
-            for (uint seq_len_idx = 0; seq_len_idx < partition_seq_len - seq_len_leftovers_start; seq_len_idx++) {
+                    MAKE_VECTOR_TYPE(OUTPUT_TYPE, TARGET_SEQ_LEN_BLOCK_SIZE) qk_val;
+                    unroll_for (uint seq_idx = 0; seq_idx < TARGET_SEQ_LEN_BLOCK_SIZE; seq_idx++) {
+                        qk_val[seq_idx] = slm_qk_vals[seq_idx * SEQ_LEN_PARTITION_SIZE + seq_len * SUBGROUP_SIZE + sglid];
+                    }
+
+                    unroll_for (uint i = 0; i < SUBGROUP_SIZE; i++) {
 #ifdef BEAM_TABLE_TYPE
-                INPUT2_TYPE value_val = VALUE_BLOCK_READ(value_input, sub_group_broadcast(value_offset, seq_len_idx));
+                        INPUT2_TYPE value_val = VALUE_BLOCK_READ(value_input, sub_group_broadcast(value_offset, i));
 #else
-                INPUT2_TYPE value_val = VALUE_BLOCK_READ(value_input, value_offset);
+                        INPUT2_TYPE value_val = VALUE_BLOCK_READ(value_input, value_offset);
 #endif
-
-                for (uint seq_idx = 0; seq_idx < TARGET_SEQ_LEN_BLOCK_SIZE; seq_idx++) {
-                    acc[seq_idx] = mad(sub_group_broadcast(qk_val[seq_idx], seq_len_idx), value_val, acc[seq_idx]);
-                }
+                        unroll_for (uint seq_idx = 0; seq_idx < TARGET_SEQ_LEN_BLOCK_SIZE; seq_idx++) {
+                            acc_output_res[seq_idx] = mad(sub_group_broadcast(qk_val[seq_idx], i), value_val, acc_output_res[seq_idx]);
+                        }
 
 #ifndef BEAM_TABLE_TYPE
-                value_offset += value_pitch;
+                        value_offset += value_pitch;
 #endif
-            }
-        }
+                    }
+                }
 
-        // If the number of partitions is greater than 1, save results to the temporary buffer;
-        // otherwise, save results directly to the main output.
-        if (num_of_partitions > 1) {
-#if TARGET_SEQ_LEN_BLOCK_SIZE > 1
-            const uint seq_idx_end = min(TARGET_SEQ_LEN - target_seq_idx, (uint)TARGET_SEQ_LEN_BLOCK_SIZE);
+                // QK*V leftovers processing
+                const uint seq_len_leftovers_start = ((seq_len_end / SUBGROUP_SIZE) * SUBGROUP_SIZE);
+                if (seq_len_leftovers_start != seq_len_end) {
+                    uint qk_offset = min(seq_len_leftovers_start + sglid, seq_len_end - 1);
+                    MAKE_VECTOR_TYPE(OUTPUT_TYPE, TARGET_SEQ_LEN_BLOCK_SIZE) qk_val;
+                    unroll_for (uint seq_idx = 0; seq_idx < TARGET_SEQ_LEN_BLOCK_SIZE; seq_idx++) {
+                        qk_val[seq_idx] = slm_qk_vals[qk_offset];
+                        qk_offset += SEQ_LEN_PARTITION_SIZE;
+                    }
+#ifdef BEAM_TABLE_TYPE
+                    const uint b_idx = beam_table[FUNC_CALL(get_bt_index_value)(OPTIONAL_SHAPE_INFO_TENSOR b0_idx, b1_idx, 0, 0, start_partition_idx + seq_len_leftovers_start + sglid, sgid * SUBGROUP_SIZE)];
+                    const uint value_offset = FUNC_CALL(get_input2_index)(OPTIONAL_SHAPE_INFO_TENSOR b_idx, b1_idx, 0, 0, start_partition_idx + seq_len_leftovers_start + sglid, sgid * SUBGROUP_SIZE);
 #else
-            const uint seq_idx_end = 1;
+    #ifdef INPUT2_DIMS_ORDER
+                    uint value_offset = FUNC_CALL(get_input2_index)(OPTIONAL_SHAPE_INFO_TENSOR b0_idx, b1_idx, 0, 0, start_partition_idx + seq_len_leftovers_start, head_size_idx);
+    #else
+                    uint value_offset = INPUT2_GET_INDEX(b0_idx, b1_idx, start_partition_idx + seq_len_leftovers_start, head_size_idx);
+    #endif
 #endif
-            for (uint seq_idx = 0; seq_idx < seq_idx_end; seq_idx++) {
-                // Data layout of tmp_output buf: [batch, heads_num, q_len, partition_idx, head_size]
-                const uint tmp_out_offset = b0_idx * (NUM_HEADS * TARGET_SEQ_LEN * num_of_partitions * HEAD_SIZE) +
-                                            b1_idx * (TARGET_SEQ_LEN * num_of_partitions * HEAD_SIZE) +
-                                            (target_seq_idx + seq_idx) * (num_of_partitions * HEAD_SIZE) +
-                                            partition_idx * (HEAD_SIZE) +
-                                            head_size_idx;
 
-                tmp_out[tmp_out_offset] = acc[seq_idx];
-            }
-        } else {
-#if TARGET_SEQ_LEN_BLOCK_SIZE > 1
-            const uint seq_idx_end = min(TARGET_SEQ_LEN - target_seq_idx, (uint)TARGET_SEQ_LEN_BLOCK_SIZE);
+                    for (uint seq_len_idx = 0; seq_len_idx < partition_seq_len - seq_len_leftovers_start; seq_len_idx++) {
+#ifdef BEAM_TABLE_TYPE
+                        INPUT2_TYPE value_val = VALUE_BLOCK_READ(value_input, sub_group_broadcast(value_offset, seq_len_idx));
 #else
-            const uint seq_idx_end = 1;
+                        INPUT2_TYPE value_val = VALUE_BLOCK_READ(value_input, value_offset);
 #endif
-            for (uint seq_idx = 0; seq_idx < seq_idx_end; seq_idx++) {
-                    const uint output_offset = OUTPUT_GET_INDEX(b0_idx, b1_idx, target_seq_idx + seq_idx, head_size_idx);
-
-                    output[output_offset] = acc[seq_idx];
-            }
-        }
-    } else {
-        // Gemm2 calculation
-        OUTPUT_TYPE acc[TARGET_SEQ_LEN_BLOCK_SIZE] = {OUTPUT_VAL_ZERO};
 
-#ifdef INPUT2_DIMS_ORDER
-        uint value_offset = FUNC_CALL(get_input2_index)(OPTIONAL_SHAPE_INFO_TENSOR b0_idx, b1_idx, 0, 0, 0, 0);
-        uint value_offset_next_seq = FUNC_CALL(get_input2_index)(OPTIONAL_SHAPE_INFO_TENSOR b0_idx, b1_idx, 0, 0, 1, 0);
-        const uint value_pitch = value_offset_next_seq - value_offset;
-#else
-        const uint value_pitch = HEAD_SIZE;
-#endif
+                        for (uint seq_idx = 0; seq_idx < TARGET_SEQ_LEN_BLOCK_SIZE; seq_idx++) {
+                            acc_output_res[seq_idx] = mad(sub_group_broadcast(qk_val[seq_idx], seq_len_idx), value_val, acc_output_res[seq_idx]);
+                        }
 
-        for (uint seq_len = 0; seq_len < partition_seq_len / SUBGROUP_SIZE; seq_len++) {
-#ifdef BEAM_TABLE_TYPE
-            const uint b_idx = beam_table[FUNC_CALL(get_bt_index_value)(OPTIONAL_SHAPE_INFO_TENSOR b0_idx, b1_idx, 0, 0, start_partition_idx + (seq_len * SUBGROUP_SIZE) + sglid, sgid * SUBGROUP_SIZE)];
-            const uint value_offset = FUNC_CALL(get_input2_index)(OPTIONAL_SHAPE_INFO_TENSOR b_idx, b1_idx, 0, 0, start_partition_idx + (seq_len * SUBGROUP_SIZE) + sglid, sgid * SUBGROUP_SIZE);
-#else
-#ifdef INPUT2_DIMS_ORDER
-            uint value_offset = FUNC_CALL(get_input2_index)(OPTIONAL_SHAPE_INFO_TENSOR b0_idx, b1_idx, 0, 0, start_partition_idx + (seq_len * SUBGROUP_SIZE), head_size_idx);
-#else
-            uint value_offset = INPUT2_GET_INDEX(b0_idx, b1_idx, start_partition_idx + (seq_len * SUBGROUP_SIZE), head_size_idx);
-#endif
+#ifndef BEAM_TABLE_TYPE
+                        value_offset += value_pitch;
 #endif
+                    }
+                }
 
-            OUTPUT_TYPE qk_val[TARGET_SEQ_LEN_BLOCK_SIZE];
-            unroll_for (uint seq_idx = 0; seq_idx < TARGET_SEQ_LEN_BLOCK_SIZE; seq_idx++) {
-                qk_val[seq_idx] = qk_local[seq_idx * SEQ_LEN_PARTITION_SIZE + seq_len * SUBGROUP_SIZE + sglid];
             }
 
-            unroll_for (uint i = 0; i < SUBGROUP_SIZE; i++) {
-#ifdef BEAM_TABLE_TYPE
-                INPUT2_TYPE value_val = VALUE_BLOCK_READ(value_input, sub_group_broadcast(value_offset, i));
-#else
-                INPUT2_TYPE value_val = VALUE_BLOCK_READ(value_input, value_offset);
-#endif
-                unroll_for (uint seq_idx = 0; seq_idx < TARGET_SEQ_LEN_BLOCK_SIZE; seq_idx++) {
-                    acc[seq_idx] = mad(sub_group_broadcast(qk_val[seq_idx], i), value_val, acc[seq_idx]);
+
+            {
+                // Rescale acc_output_res values and save current iter results to global accumulator
+                SOFTMAX_ACCUMULATOR_TYPE exp_sum_prev = slm_exp_sum_prev[sglid];
+                SOFTMAX_ACCUMULATOR_TYPE exp_sum_cur = slm_exp_sum_cur[sglid];
+                SOFTMAX_ACCUMULATOR_TYPE max_val_prev = slm_max_val_prev[sglid];
+                SOFTMAX_ACCUMULATOR_TYPE max_val_cur = slm_max_val_cur[sglid];
+
+                barrier(CLK_LOCAL_MEM_FENCE);
+
+                const uint seq_idx_end = min(TARGET_SEQ_LEN - target_seq_idx, (uint)TARGET_SEQ_LEN_BLOCK_SIZE);
+                for (uint seq_idx = 0; seq_idx < seq_idx_end; seq_idx++) {
+                    SOFTMAX_ACCUMULATOR_TYPE total_max = SOFTMAX_ACCUMULATOR_MAX_FUNC(sub_group_broadcast(max_val_prev, seq_idx), sub_group_broadcast(max_val_cur, seq_idx));
+                    SOFTMAX_ACCUMULATOR_TYPE updated_exp_sum_prev = sub_group_broadcast(exp_sum_prev, seq_idx) * native_exp(sub_group_broadcast(max_val_prev, seq_idx) - total_max);
+                    SOFTMAX_ACCUMULATOR_TYPE updated_exp_sum_cur = sub_group_broadcast(exp_sum_cur, seq_idx) * native_exp(sub_group_broadcast(max_val_cur, seq_idx) - total_max);
+                    SOFTMAX_ACCUMULATOR_TYPE updated_total_exp_sum = updated_exp_sum_prev + updated_exp_sum_cur;
+
+                    if (start_partition_idx > 0) {
+                        OUTPUT_TYPE updated_prev_res = TO_SOFTMAX_ACCUMULATOR_TYPE(output_acc[seq_idx]) * updated_exp_sum_prev / updated_total_exp_sum;;
+                        acc_output_res[seq_idx] *= updated_exp_sum_cur / updated_total_exp_sum;
+                        acc_output_res[seq_idx] += updated_prev_res;
+                    }
+
+                    output_acc[seq_idx] = acc_output_res[seq_idx];
+
+                    if (sgid == 0 && sglid == 0) {
+                        slm_exp_sum_prev[seq_idx] = updated_total_exp_sum;
+                        slm_max_val_prev[seq_idx] = total_max;
+                    }
                 }
+            }
+        }
+    }
 
-#ifndef BEAM_TABLE_TYPE
-                value_offset += value_pitch;
-#endif
+    // Combine results from multiple SGs and store to output buffer
+
+    if (sgid >= (SUBGROUPS_PER_WG / SG_SCALE_FACTOR)) {
+        unroll_for (uint seq_idx = 0; seq_idx < TARGET_SEQ_LEN_BLOCK_SIZE; seq_idx++) {
+            slm_qk_vals[seq_idx * SEQ_LEN_PARTITION_SIZE + (uint)get_local_id(2)] = output_acc[seq_idx];
+        }
+    }
+
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    if (sgid < (SUBGROUPS_PER_WG / SG_SCALE_FACTOR)) {
+        unroll_for (uint seq_idx = 0; seq_idx < TARGET_SEQ_LEN_BLOCK_SIZE; seq_idx++) {
+            unroll_for (uint i = 1; i < SG_SCALE_FACTOR; i++) {
+                output_acc[seq_idx] += slm_qk_vals[seq_idx * SEQ_LEN_PARTITION_SIZE + (i * HEAD_SIZE) + head_size_idx];
             }
         }
 
-        // If the number of partitions is greater than 1, save results to the temporary buffer;
-        // otherwise, save results directly to the main output.
-        if (num_of_partitions > 1) {
-#if TARGET_SEQ_LEN_BLOCK_SIZE > 1
-            const uint seq_idx_end = min(TARGET_SEQ_LEN - target_seq_idx, (uint)TARGET_SEQ_LEN_BLOCK_SIZE);
-#else
-            const uint seq_idx_end = 1;
-#endif
+        uint output_offset = OUTPUT_GET_INDEX(b0_idx, b1_idx, target_seq_idx, sgid * SUBGROUP_SIZE);
+        if (get_global_id(1) == get_global_size(1) - 1) {
+            const uint seq_idx_end = min((uint)TARGET_SEQ_LEN - target_seq_idx, (uint)TARGET_SEQ_LEN_BLOCK_SIZE);
             for (uint seq_idx = 0; seq_idx < seq_idx_end; seq_idx++) {
-                // Data layout of tmp_output buf: [batch, heads_num, q_len, partition_idx, head_size]
-                const uint tmp_out_offset = b0_idx * (NUM_HEADS * TARGET_SEQ_LEN * num_of_partitions * HEAD_SIZE) +
-                                            b1_idx * (TARGET_SEQ_LEN * num_of_partitions * HEAD_SIZE) +
-                                            (target_seq_idx + seq_idx) * (num_of_partitions * HEAD_SIZE) +
-                                            partition_idx * (HEAD_SIZE) +
-                                            head_size_idx;
-                tmp_out[tmp_out_offset] = acc[seq_idx];
+                OUTPUT_BLOCK_WRITE(output, output_offset, output_acc[seq_idx]);
+                output_offset += HEAD_SIZE;
             }
         } else {
-#if TARGET_SEQ_LEN_BLOCK_SIZE > 1
-            const uint seq_idx_end = min(TARGET_SEQ_LEN - target_seq_idx, (uint)TARGET_SEQ_LEN_BLOCK_SIZE);
-#else
-            const uint seq_idx_end = 1;
-#endif
-            for (uint seq_idx = 0; seq_idx < seq_idx_end; seq_idx++) {
-                    const uint output_offset = OUTPUT_GET_INDEX(b0_idx, b1_idx, target_seq_idx + seq_idx, head_size_idx);
-
-                    output[output_offset] = acc[seq_idx];
+            unroll_for (uint seq_idx = 0; seq_idx < TARGET_SEQ_LEN_BLOCK_SIZE; seq_idx++) {
+                OUTPUT_BLOCK_WRITE(output, output_offset, output_acc[seq_idx]);
+                output_offset += HEAD_SIZE;
             }
         }
-    } // Gemm2 calculation end
+    }
 }
 
 #endif // TARGET_SEQ_LEN_BLOCK_SIZE != 1
diff --git a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/select_gpu_ref.cl b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/select_gpu_ref.cl
index d56b5299ac287a..e5a269fedcd4bc 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/select_gpu_ref.cl
+++ b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/select_gpu_ref.cl
@@ -45,7 +45,7 @@ KERNEL(select)(
     uint output_offset = OUTPUT_GET_INDEX(b, f, y, x);
 #endif
 
-    const OUTPUT_TYPE res = select(INPUT_2, INPUT_1, MASK);
+    const OUTPUT_TYPE res = TO_OUTPUT_TYPE(select(INPUT_2, INPUT_1, MASK));
 
     output[output_offset] = res;
 }
diff --git a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/swiglu_gpu_ref.cl b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/swiglu_gpu_ref.cl
index a943a6e101cea1..c37645cd01998e 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/swiglu_gpu_ref.cl
+++ b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/swiglu_gpu_ref.cl
@@ -25,18 +25,34 @@ KERNEL(swiglu_gpu_ref)(
 
 #if OUTPUT_DIMS == 5
     const uint output_idx = OUTPUT_GET_INDEX(b, f, z, y, x);
-    const uint gate_idx = INPUT0_GET_INDEX(b, f, z, y, x);
-    const uint input_idx = INPUT0_GET_INDEX(b, f, z, y, x) + SPLIT_LENGTH;
+    #if SPLIT_TO_GLU_IDX == 0
+        const uint gate_idx = INPUT0_GET_INDEX(b, f, z, y, x);
+        const uint input_idx = gate_idx + SPLIT_LENGTH;
+    #else
+        const uint input_idx = INPUT0_GET_INDEX(b, f, z, y, x);
+        const uint gate_idx = input_idx + SPLIT_LENGTH;
+    #endif
 #else // 2D spatial
     const uint output_idx = OUTPUT_GET_INDEX(b, f, y, x);
-    const uint gate_idx = INPUT0_GET_INDEX(b, f, y, x);
-    const uint input_idx = INPUT0_GET_INDEX(b, f, y, x) + SPLIT_LENGTH;
+    #if SPLIT_TO_GLU_IDX == 0
+        const uint gate_idx = INPUT0_GET_INDEX(b, f, y, x);
+        const uint input_idx = gate_idx + SPLIT_LENGTH;
+    #else
+        const uint input_idx = INPUT0_GET_INDEX(b, f, y, x);
+        const uint gate_idx = input_idx + SPLIT_LENGTH;
+    #endif
 #endif
 
     ACCUMULATOR_TYPE res = ACCUMULATOR_VAL_ZERO;
 
     res = (ACCUMULATOR_TYPE)input[gate_idx];
-    res /= ACCUMULATOR_VAL_ONE + exp(-(ACCUMULATOR_VAL_ONE * res));
+    #if GLU_TYPE == 0   // Swish
+        res /= ACCUMULATOR_VAL_ONE + exp(-(ACCUMULATOR_VAL_ONE * res));
+    #elif GLU_TYPE == 1 // Gelu
+        res = (GEGLU_HALF * res * (ACCUMULATOR_VAL_ONE + (erf(res * GEGLU_MULT))));
+    #elif GLU_TYPE == 2 // Gelu_Tanh
+        res = (GEGLU_HALF * res * (ACCUMULATOR_VAL_ONE + (tanh(GEGLU_SQUARE_2_OVER_PI * res * (ACCUMULATOR_VAL_ONE + GEGLU_MULT * res * res)))));
+    #endif
     res *= (ACCUMULATOR_TYPE)input[input_idx];
 
     output[output_idx] = TO_OUTPUT_TYPE(res);
diff --git a/src/plugins/intel_gpu/src/kernel_selector/common_types.h b/src/plugins/intel_gpu/src/kernel_selector/common_types.h
index e61fdb073d00e8..408cfc2b5e7719 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/common_types.h
+++ b/src/plugins/intel_gpu/src/kernel_selector/common_types.h
@@ -116,6 +116,7 @@ enum class Datatype {
     INT64,
     F16,
     F32,
+    BF16,
 };
 
 ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
@@ -129,7 +130,8 @@ enum class WeightsType {
     UINT8,
     UINT4,
     INT4,
-    INT32
+    INT32,
+    BF16
 };
 
 ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
diff --git a/src/plugins/intel_gpu/src/kernel_selector/jitter.cpp b/src/plugins/intel_gpu/src/kernel_selector/jitter.cpp
index fcd35d13a3639b..df83c1778e5e24 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/jitter.cpp
+++ b/src/plugins/intel_gpu/src/kernel_selector/jitter.cpp
@@ -363,7 +363,8 @@ JitDefinitions DataTensorJitConstant::GetDefinitions() const {
     if (_tensor.is_dynamic()) {
         if (_tensor.GetLayout() == DataLayout::bf || _tensor.GetLayout() == DataLayout::bfyx ||
             _tensor.GetLayout() == DataLayout::bfzyx || _tensor.GetLayout() == DataLayout::bfwzyx ||
-            _tensor.GetLayout() == DataLayout::bfuwzyx || _tensor.GetLayout() == DataLayout::bfvuwzyx) {
+            _tensor.GetLayout() == DataLayout::bfuwzyx || _tensor.GetLayout() == DataLayout::bfvuwzyx ||
+            _tensor.GetLayout() == DataLayout::b_fs_yx_fsv16) {
             definitions.push_back({_name + "_X_PITCH", "1"});
             definitions.push_back({_name + "_Y_PITCH", dims_padded.x()});
             definitions.push_back({_name + "_Z_PITCH", toVectorMulString({dims_padded.x(), dims_padded.y()})});
@@ -1489,6 +1490,15 @@ JitConstants MakeTypeJitConstants(Datatype dataType, const std::string& macroNam
             type_size = "0.5f";
             is_fp = false;
             break;
+        case Datatype::BF16:
+            type = "ushort";
+            val_one = "(ushort) 1";
+            val_zero = "(ushort) 0";
+            to_type = "_convert_bfloat16_as_ushort(v)";
+            to_type_sat = "_convert_bfloat16_as_ushort(v)";
+            type_size = "2";
+            is_fp = false;
+            break;
         default:
             type = "float";
             max_val = "FLT_MAX";
@@ -1540,6 +1550,8 @@ JitConstants MakeTypeJitConstants(WeightsType weightsType, const std::string& ma
             return MakeTypeJitConstants(Datatype::UINT4, macroName);
         case WeightsType::INT32:
             return MakeTypeJitConstants(Datatype::INT32, macroName);
+        case WeightsType::BF16:
+            return MakeTypeJitConstants(Datatype::BF16, macroName);
     }
     assert(false || "Unreachable!");
     // FIXME: Is there some builtin_unreachable available?
diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernel_selector_common.cpp b/src/plugins/intel_gpu/src/kernel_selector/kernel_selector_common.cpp
index 67687868bbc92f..debc9ca4841356 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/kernel_selector_common.cpp
+++ b/src/plugins/intel_gpu/src/kernel_selector/kernel_selector_common.cpp
@@ -3,9 +3,14 @@
 //
 
 #include "kernel_selector_common.h"
+#include "intel_gpu/graph/serialization/string_serializer.hpp"
 #include <sstream>
 #include <string>
 
+#ifdef ENABLE_ONEDNN_FOR_GPU
+#include "micro_utils.hpp"
+#endif
+
 namespace kernel_selector {
 std::string GetStringEnv(const char* varName) {
     std::string str;
@@ -572,4 +577,54 @@ std::string toString(ReduceMode mode) {
     }
 }
 
+void clKernelData::save(cldnn::BinaryOutputBuffer& ob) const {
+    ob(params.workGroups.global, params.workGroups.local);
+    ob << params.arguments.size();
+    for (const auto& arg : params.arguments) {
+        ob << make_data(&arg.t, sizeof(cldnn::argument_desc::Types)) << arg.index;
+    }
+    ob << params.scalars.size();
+    for (const auto& scalar : params.scalars) {
+        ob << make_data(&scalar.t, sizeof(cldnn::scalar_desc::Types)) << make_data(&scalar.v, sizeof(cldnn::scalar_desc::ValueT));
+    }
+    ob << params.layerID;
+#ifdef ENABLE_ONEDNN_FOR_GPU
+    ob << micro_kernels.size();
+    for (const auto& microkernel : micro_kernels) {
+        microkernel->save(ob);
+    }
+#endif
+}
+
+void clKernelData::load(cldnn::BinaryInputBuffer& ib) {
+    ib(params.workGroups.global, params.workGroups.local);
+
+    typename cldnn::arguments_desc::size_type arguments_desc_size = 0UL;
+    ib >> arguments_desc_size;
+    params.arguments.resize(arguments_desc_size);
+    for (auto& arg : params.arguments) {
+        ib >> make_data(&arg.t, sizeof(cldnn::argument_desc::Types)) >> arg.index;
+    }
+
+    typename cldnn::scalars_desc::size_type scalars_desc_size = 0UL;
+    ib >> scalars_desc_size;
+    params.scalars.resize(scalars_desc_size);
+    for (auto& scalar : params.scalars) {
+        ib >> make_data(&scalar.t, sizeof(cldnn::scalar_desc::Types)) >> make_data(&scalar.v, sizeof(cldnn::scalar_desc::ValueT));
+    }
+
+    ib >> params.layerID;
+
+#ifdef ENABLE_ONEDNN_FOR_GPU
+    size_t n_microkernels;
+    ib >> n_microkernels;
+    micro_kernels.clear();
+    for (size_t i = 0; i < n_microkernels; i++) {
+        auto microkernel = std::make_shared<micro::MicroKernelPackage>();
+        microkernel->load(ib);
+        micro_kernels.push_back(microkernel);
+    }
+#endif
+}
+
 }  // namespace kernel_selector
diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernel_selector_common.h b/src/plugins/intel_gpu/src/kernel_selector/kernel_selector_common.h
index 627fdf758586a3..d9b132ac1dcc43 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/kernel_selector_common.h
+++ b/src/plugins/intel_gpu/src/kernel_selector/kernel_selector_common.h
@@ -19,6 +19,10 @@
 #define EXE_MODE_AGE_BASED "-cl-no-subgroup-ifp"
 #define EXE_MODE_NO_PRERA_SCH "-cl-intel-no-prera-scheduling"
 
+namespace micro {
+struct MicroKernelPackage;
+}  // namspace
+
 namespace kernel_selector {
 
 #ifndef UNUSED
@@ -64,7 +68,11 @@ struct KernelCode {
 struct clKernelData {
     KernelCode code;
     KernelParams params;
+    std::vector<std::shared_ptr<micro::MicroKernelPackage>> micro_kernels;
     bool skip_execution = false;
+
+    void save(cldnn::BinaryOutputBuffer& ob) const;
+    void load(cldnn::BinaryInputBuffer& ib);
 };
 
 ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernel_selector_params.cpp b/src/plugins/intel_gpu/src/kernel_selector/kernel_selector_params.cpp
index 93d3ed24e1bc97..d1f4cf7dc79083 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/kernel_selector_params.cpp
+++ b/src/plugins/intel_gpu/src/kernel_selector/kernel_selector_params.cpp
@@ -80,6 +80,9 @@ void ParamsKey::EnableInputDataType(Datatype dt) {
         case Datatype::F32:
             key.inputType.val.F32 = 1;
             break;
+        case Datatype::BF16:
+            key.inputType.val.BF16 = 1;
+            break;
         default:
             break;
     }
@@ -122,6 +125,9 @@ void ParamsKey::EnableOutputDataType(Datatype dt) {
         case Datatype::F32:
             key.outputType.val.F32 = 1;
             break;
+        case Datatype::BF16:
+            key.outputType.val.BF16 = 1;
+            break;
         default:
             break;
     }
@@ -148,6 +154,10 @@ void ParamsKey::EnableInputWeightsType(WeightsType wt) {
             break;
         case WeightsType::INT32:
             key.inputWeightsType.val.int32 = 1;
+            break;
+        case WeightsType::BF16:
+            key.inputWeightsType.val.BF16 = 1;
+            break;
         default:
             break;
     }
@@ -174,6 +184,10 @@ void ParamsKey::EnableOutputWeightsType(WeightsType wt) {
             break;
         case WeightsType::INT32:
             key.outputWeightsType.val.int32 = 1;
+            break;
+        case WeightsType::BF16:
+            key.outputWeightsType.val.BF16 = 1;
+            break;
         default:
             break;
     }
diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernel_selector_params.h b/src/plugins/intel_gpu/src/kernel_selector/kernel_selector_params.h
index 61a1b7f2221270..4eb954c3daf7a9 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/kernel_selector_params.h
+++ b/src/plugins/intel_gpu/src/kernel_selector/kernel_selector_params.h
@@ -258,6 +258,7 @@ class ParamsKey {
                 uint32_t int64 : 1;
                 uint32_t F16 : 1;
                 uint32_t F32 : 1;
+                uint32_t BF16 : 1;
             } val;
             uint32_t raw;
         } DataTypesKey;
@@ -356,6 +357,21 @@ enum class dev_type {
     discrete_gpu = 1
 };
 
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+// Arch type
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+enum class gpu_arch {
+    unknown = 0,
+    gen9 = 1,
+    gen11 = 2,
+    xe_lp = 3,
+    xe_hp = 4,
+    xe_hpg = 5,
+    xe_hpc = 6,
+    xe2 = 7,
+};
+
+
 ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
 // EngineInfo
 ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
@@ -376,9 +392,12 @@ struct EngineInfo {
     bool enable_sub_groups_emulation = false;
     bool bOptHintsSupport = false;
     bool bLocalBlockIOSupport = false;
+    bool supports_microkernels = false;
     uint32_t vendor_id = 0x0;
     dev_type deviceType = dev_type::integrated_gpu;
     uint32_t computeUnitsCount = 0;
+    uint32_t ip_version = 0;
+    gpu_arch arch = gpu_arch::unknown;
     uint32_t maxThreadsPerExecutionUnit = 0;
     uint32_t maxThreadsPerDevice = 0;
     uint64_t maxWorkGroupSize = 0;
diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernels/broadcast/broadcast_kernel_ref.cpp b/src/plugins/intel_gpu/src/kernel_selector/kernels/broadcast/broadcast_kernel_ref.cpp
index 4830c366bc9fbf..82182e37f73898 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/kernels/broadcast/broadcast_kernel_ref.cpp
+++ b/src/plugins/intel_gpu/src/kernel_selector/kernels/broadcast/broadcast_kernel_ref.cpp
@@ -25,6 +25,7 @@ ParamsKey BroadcastKernelRef::GetSupportedKey() const {
     k.EnableAllInputLayout();
     k.EnableAllOutputLayout();
 
+    k.EnableDifferentTypes();
     k.EnableTensorOffset();
     k.EnableTensorPitches();
     k.EnableBatching();
diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernels/broadcast/broadcast_kernel_ref.h b/src/plugins/intel_gpu/src/kernel_selector/kernels/broadcast/broadcast_kernel_ref.h
index 50a61f4fcbccaf..8840e1ee0c03e2 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/kernels/broadcast/broadcast_kernel_ref.h
+++ b/src/plugins/intel_gpu/src/kernel_selector/kernels/broadcast/broadcast_kernel_ref.h
@@ -14,5 +14,8 @@ class BroadcastKernelRef : public BroadcastKernelBase {
     KernelsData GetKernelsData(const Params& params) const override;
     KernelsPriority GetKernelsPriority(const Params& params) const override;
     ParamsKey GetSupportedKey() const override;
+    std::vector<FusedOpType> GetSupportedFusedOps() const override {
+        return { FusedOpType::REORDER };
+    }
 };
 }  // namespace kernel_selector
diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernels/convolution/convolution_kernel_b_fs_yx_fsv16_1x1.cpp b/src/plugins/intel_gpu/src/kernel_selector/kernels/convolution/convolution_kernel_b_fs_yx_fsv16_1x1.cpp
index a332090dbd0906..f91c5b870e53f0 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/kernels/convolution/convolution_kernel_b_fs_yx_fsv16_1x1.cpp
+++ b/src/plugins/intel_gpu/src/kernel_selector/kernels/convolution/convolution_kernel_b_fs_yx_fsv16_1x1.cpp
@@ -20,24 +20,28 @@ ConvolutionKernel_b_fs_yx_fsv16_1x1::ConvolutionKernel_b_fs_yx_fsv16_1x1() : Con
     }
 }
 
-ConvolutionKernel_b_fs_yx_fsv16_1x1::AutoTuneOption ConvolutionKernel_b_fs_yx_fsv16_1x1::GetAutoTuneOptions(const Params& params,
+ConvolutionKernel_b_fs_yx_fsv16_1x1::AutoTuneOption ConvolutionKernel_b_fs_yx_fsv16_1x1::GetAutoTuneOptions(const convolution_params& params,
                                                                                                             int /*autoTuneIndex*/) const {
-    const convolution_params& cp = static_cast<const convolution_params&>(params);
-
-    auto x = cp.outputs[0].X().v;
-    auto y = cp.outputs[0].Y().v;
-    auto f = cp.outputs[0].Feature().v;
-
-    if (x == 1 && y == 1) {
-        return { 1, EXE_MODE_DEFAULT };
-    } else if (x * f <= 256) {
-        if (x < 8 || x * f <= 128)
-            return { 2, EXE_MODE_DEFAULT };
-        else
+    if (!params.has_dynamic_outputs()) {
+        auto x = params.outputs[0].X().v;
+        auto y = params.outputs[0].Y().v;
+        auto f = params.outputs[0].Feature().v;
+
+        if (x == 1 && y == 1) {
+            return { 1, EXE_MODE_DEFAULT };
+        } else if (x * f <= 256) {
+            if (x < 8 || x * f <= 128)
+                return { 2, EXE_MODE_DEFAULT };
+            else
+                return { 4, EXE_MODE_DEFAULT };
+        } else if (x * f <= 1536) {
             return { 4, EXE_MODE_DEFAULT };
-    } else if (x * f <= 1536) {
-        return { 4, EXE_MODE_DEFAULT };
+        } else {
+            return { 8, EXE_MODE_DEFAULT };
+        }
     } else {
+        // In shape agnostic kernel, the output shape can not be specified at build time,
+        // So we prepare 4 kernels(blockWith 1, 2, 4, 8) in advance and then use proper kernel at runtime when static shape comes.
         return { 8, EXE_MODE_DEFAULT };
     }
 }
@@ -60,17 +64,20 @@ float ConvolutionKernel_b_fs_yx_fsv16_1x1::EstimateOccupancy(const convolution_p
 ConvolutionKernel_b_fs_yx_fsv16_1x1::ConvolutionTuningData ConvolutionKernel_b_fs_yx_fsv16_1x1::GetTuningParams(const convolution_params& params) const {
     ConvolutionTuningData tuning_data;
 
-    const auto& input = params.inputs[0];
-
-    size_t ic_blocks = CeilDiv(input.Feature().v, tuning_data.feature_block_size);
+    if (!params.is_shape_agnostic) {
+        const auto& input = params.inputs[0];
+        bool block_size_one_is_better = params.outputs[0].X().v == 1 && params.outputs[0].Y().v == 1 && input.Feature().v >= 2048;
 
-    size_t max_slm_div_factor = params.engineInfo.maxWorkGroupSize / tuning_data.sub_group_size;
-    bool block_size_one_is_better = params.outputs[0].X().v == 1 && params.outputs[0].Y().v == 1 && input.Feature().v >= 2048;
+        // Accuracy issue is found with input.Feature() > 16 in static kernel, Need to fix later.
+        if (params.engineInfo.deviceType == dev_type::integrated_gpu && params.engineInfo.supports_imad && !block_size_one_is_better) {
+            size_t ic_blocks = CeilDiv(input.Feature().v, tuning_data.feature_block_size);
+            size_t max_slm_div_factor = params.engineInfo.maxWorkGroupSize / tuning_data.sub_group_size;
 
-    if (params.engineInfo.deviceType == dev_type::integrated_gpu && params.engineInfo.supports_imad && !block_size_one_is_better)
-        while (ic_blocks % (tuning_data.slm_div_factor * 2) == 0 && (tuning_data.slm_div_factor * 2 <= max_slm_div_factor) &&
-               EstimateOccupancy(params, tuning_data) < 4.0)
-            tuning_data.slm_div_factor *= 2;
+            while (ic_blocks % (tuning_data.slm_div_factor * 2) == 0 && (tuning_data.slm_div_factor * 2 <= max_slm_div_factor) &&
+                EstimateOccupancy(params, tuning_data) < 4.0)
+                tuning_data.slm_div_factor *= 2;
+        }
+    }
 
     tuning_data.work_group_size = tuning_data.slm_div_factor * tuning_data.sub_group_size;
 
@@ -92,6 +99,7 @@ ParamsKey ConvolutionKernel_b_fs_yx_fsv16_1x1::GetSupportedKey() const {
     k.EnableBiasPerFeature();
     k.EnableNonBiasTerm();
     k.EnableBatching();
+    k.EnableDynamicShapesSupport();
     return k;
 }
 
@@ -131,23 +139,27 @@ ConvolutionKernelBase::DispatchData ConvolutionKernel_b_fs_yx_fsv16_1x1::SetDefa
 
 KernelsPriority ConvolutionKernel_b_fs_yx_fsv16_1x1::GetKernelsPriority(const Params& params) const {
     const auto& p = static_cast<const convolution_params&>(params);
-    auto autoTune = GetAutoTuneOptions(params, -1);
+    if (!p.is_shape_agnostic) {
+        auto autoTune = GetAutoTuneOptions(p, -1);
 
-    const auto& input = p.inputs[0];
-    const auto& out = p.outputs[0];
+        const auto& input = p.inputs[0];
+        const auto& out = p.outputs[0];
 
-    auto bBlockSizeX = out.X().v % autoTune.blockWidth == 0;
-    auto bBlockSizeXY = out.X().pad.Total() + out.Y().pad.Total() == 0;
-    auto bInputPad = input.X().pad.Total() + input.Y().pad.Total() != 0;
+        auto bBlockSizeX = out.X().v % autoTune.blockWidth == 0;
+        auto bBlockSizeXY = out.X().pad.Total() + out.Y().pad.Total() == 0;
+        auto bInputPad = input.X().pad.Total() + input.Y().pad.Total() != 0;
 
-    if (out.Batch().v == 1) {
-        if ((bBlockSizeX || bBlockSizeXY) && !bInputPad) {
-            return FORCE_PRIORITY_1;
+        if (out.Batch().v == 1) {
+            if ((bBlockSizeX || bBlockSizeXY) && !bInputPad) {
+                return FORCE_PRIORITY_1;
+            } else {
+                return FORCE_PRIORITY_3;
+            }
         } else {
-            return FORCE_PRIORITY_3;
+            return FORCE_PRIORITY_7;
         }
     } else {
-        return FORCE_PRIORITY_7;
+        return FORCE_PRIORITY_1;
     }
 }
 
@@ -163,11 +175,13 @@ bool ConvolutionKernel_b_fs_yx_fsv16_1x1::Validate(const Params& p) const {
     const auto& input = params.inputs[0];
     const auto& output = params.outputs[0];
 
-    const bool bOutputSizes = output.X().v != input.X().v || output.Y().v != input.Y().v || output.Feature().v % 16 != 0;
+    const bool bOutputSizes = (!input.X().is_dynamic && !output.X().is_dynamic && output.X().v != input.X().v) ||
+                              (!input.Y().is_dynamic && !output.Y().is_dynamic && output.Y().v != input.Y().v) ||
+                              (!output.Feature().is_dynamic && output.Feature().v % 16 != 0);
     const bool bFilterSize = params.filterSize.x != 1 || params.filterSize.y != 1;
     const bool bStride = params.stride.x != 1 || params.stride.y != 1;
-    const bool bPadding = input.Feature().pad.before % tuning_data.feature_block_size != 0 ||
-                          output.Feature().pad.before % tuning_data.feature_block_size != 0;
+    const bool bPadding = (!input.Feature().pad.is_dynamic && input.Feature().pad.before % tuning_data.feature_block_size != 0) ||
+                          (!output.Feature().pad.is_dynamic && output.Feature().pad.before % tuning_data.feature_block_size != 0);
 
     if  (bOutputSizes || bFilterSize || bStride || bPadding) {
         return false;
@@ -215,47 +229,219 @@ JitConstants ConvolutionKernel_b_fs_yx_fsv16_1x1::GetJitConstants(const convolut
         jit.Merge(MakeFusedOpsJitConstants(params, { conf_vec, conf_scalar1, conf_scalar2 }));
     }
 
+    jit.AddConstant(MakeJitConstant("X_BLOCK_SIZE", blockWidth));
+    jit.AddConstant(MakeJitConstant("SLM_DIV_FACTOR", tuning_data.slm_div_factor));
     jit.AddConstant(MakeJitConstant("SUB_GROUP_SIZE", tuning_data.sub_group_size));
-    jit.AddConstant(MakeJitConstant("PADDED_INPUT", params.inputs[0].X().pad.Total() != 0));
-
-    bool padded_output = params.outputs[0].X().pad.Total() != 0;
-    bool non_unit_fused_op_spatial = false;
+    jit.AddConstant(MakeJitConstant("WORK_GROUP_SIZE", tuning_data.work_group_size));
 
-    // Set padded_output to true when fused inputs have paddings to have correct blocked loads
-    for (auto& fused_op : params.fused_ops) {
-        for (auto& t : fused_op.tensors) {
-            if (t.PitchesDifferFromLogicalDims()) {
-                padded_output = true;
+    if (!params.has_dynamic_inputs()) {
+        jit.AddConstant(MakeJitConstant("PADDED_INPUT", params.inputs[0].X().pad.Total() != 0));
+
+        bool padded_output = params.outputs[0].X().pad.Total() != 0;
+        bool non_unit_fused_op_spatial = false;
+
+        // Set padded_output to true when fused inputs have paddings to have correct blocked loads
+        for (auto& fused_op : params.fused_ops) {
+            for (auto& t : fused_op.tensors) {
+                if (t.PitchesDifferFromLogicalDims()) {
+                    padded_output = true;
+                }
+                if ((t.X().v > 1) ||
+                    (t.Y().v > 1) ||
+                    (t.Z().v > 1) ||
+                    (t.W().v > 1)) {
+                    non_unit_fused_op_spatial = true;
+                }
             }
-            if ((t.X().v > 1) ||
-                (t.Y().v > 1) ||
-                (t.Z().v > 1) ||
-                (t.W().v > 1)) {
-                non_unit_fused_op_spatial = true;
+        }
+
+        jit.AddConstant(MakeJitConstant("PADDED_OUTPUT", padded_output));
+        jit.AddConstant(MakeJitConstant("NON_UNIT_FUSED_OP_SPATIAL", non_unit_fused_op_spatial));
+
+        jit.AddConstant(MakeJitConstant("IC_BLOCKS", CeilDiv(params.inputs[0].Feature().v, tuning_data.feature_block_size)));
+        if (params.outputs[0].Feature().v % tuning_data.feature_block_size != 0) {
+            jit.AddConstant(MakeJitConstant("OUTPUT_LEFTOVERS", 1));
+        }
+        if (params.inputs[0].Feature().v % tuning_data.feature_block_size != 0) {
+            jit.AddConstant(MakeJitConstant("INPUT_LEFTOVERS", 1));
+        }
+    } else {
+        DimensionAccessHelperJit input0_dims(params.inputs[0]);
+        DimensionAccessHelperJit input0_padded_dims(params.inputs[0], true);
+        DimensionAccessHelperJit output_dims(params.outputs[0]);
+        DimensionAccessHelperJit output_padded_dims(params.outputs[0], true);
+
+        const auto padded_input = "(" + input0_padded_dims.x_pad().first + "+" + input0_padded_dims.x_pad().first + ") != 0";
+        jit.AddConstant(MakeJitConstant("PADDED_INPUT", padded_input));
+
+        const auto padded_output = "(" + output_padded_dims.x_pad().first + "+" + output_padded_dims.x_pad().first + ") != 0";
+        jit.AddConstant(MakeJitConstant("PADDED_OUTPUT", padded_output));
+
+        // In shape agnostic kernel, the fused shape cannot be specified at build time or run time.
+        // Currently simply check whether fused_op is dynmaic. Need to further follow up like static behavior.
+        bool non_unit_fused_op_spatial = false;
+        for (auto& fused_op : params.fused_ops) {
+            for (auto& t : fused_op.tensors) {
+                if (t.is_dynamic()) {
+                    non_unit_fused_op_spatial = true;
+                    break;
+                } else {
+                    if ((t.X().v > 1) ||
+                        (t.Y().v > 1) ||
+                        (t.Z().v > 1) ||
+                        (t.W().v > 1)) {
+                        non_unit_fused_op_spatial = true;
+                        break;
+                    }
+                }
             }
         }
-    }
+        jit.AddConstant(MakeJitConstant("NON_UNIT_FUSED_OP_SPATIAL", non_unit_fused_op_spatial));
 
-    jit.AddConstant(MakeJitConstant("PADDED_OUTPUT", padded_output));
-    jit.AddConstant(MakeJitConstant("NON_UNIT_FUSED_OP_SPATIAL", non_unit_fused_op_spatial));
+        const auto feature_block_size = std::to_string(tuning_data.feature_block_size);
+        const auto ic_blocks = "(" + input0_dims.f() + "+" + feature_block_size + " - 1) / " + feature_block_size;
+        jit.AddConstant(MakeJitConstant("IC_BLOCKS", ic_blocks));
 
-    jit.AddConstant(MakeJitConstant("X_BLOCK_SIZE", blockWidth));
-    jit.AddConstant(MakeJitConstant("X_BLOCKS", CeilDiv(params.outputs[0].X().v, blockWidth)));
-    jit.AddConstant(MakeJitConstant("SLM_DIV_FACTOR", tuning_data.slm_div_factor));
-    jit.AddConstant(MakeJitConstant("WORK_GROUP_SIZE", tuning_data.work_group_size));
-    jit.AddConstant(MakeJitConstant("IC_BLOCKS", CeilDiv(params.inputs[0].Feature().v, tuning_data.feature_block_size)));
-    if (params.outputs[0].Feature().v % tuning_data.feature_block_size != 0) {
-        jit.AddConstant(MakeJitConstant("OUTPUT_LEFTOVERS", 1));
-    }
-    if (params.inputs[0].Feature().v % tuning_data.feature_block_size != 0) {
-        jit.AddConstant(MakeJitConstant("INPUT_LEFTOVERS", 1));
+        const auto output_leftover_num = "(" + output_dims.f() + "%" + feature_block_size + ")";
+        const auto output_leftover = "(" + output_leftover_num + "!= 0)";
+        jit.AddConstant(MakeJitConstant("OUTPUT_LEFTOVERS", output_leftover));
+
+        const auto input_leftover_num = "(" + input0_dims.f() + "%" + feature_block_size + ")";
+        const auto input_leftover = "(" + input_leftover_num + "!= 0)";
+        jit.AddConstant(MakeJitConstant("INPUT_LEFTOVERS", input_leftover));
     }
 
     return jit;
 }
 
-KernelsData ConvolutionKernel_b_fs_yx_fsv16_1x1::GetKernelsData(const Params& params) const {
-    return GetCommonKernelsData(params, EXE_MODE_DEFAULT, -1);
+ KernelsData ConvolutionKernel_b_fs_yx_fsv16_1x1::GetKernelsData(const Params& params) const {
+    size_t num_kernels = params.is_shape_agnostic ? 4 : 1;
+    KernelData kd = KernelData::Default<convolution_params>(params, num_kernels);
+    convolution_params& newParams = *static_cast<convolution_params*>(kd.params.get());
+
+    if (!Validate(params)) {
+        return {};
+    }
+
+    auto preferredWeightsLayout = GetPreferredWeightsLayout(newParams);
+    bool succeed = UpdateWeightsParams(newParams,
+                                       preferredWeightsLayout,
+                                       kd.weightsReorderParams,
+                                       GetSupportedKey(),
+                                       newParams.groups,
+                                       newParams.transposed);
+
+    bool bSupportedWeightsLayout = newParams.weights.GetLayout() == preferredWeightsLayout;
+    const bool bWeightsOK = bSupportedWeightsLayout || newParams.allowStaticInputReordering;
+
+    if (!succeed || !bWeightsOK) {
+        return {};
+    }
+
+    if (NeedPaddedInput()) {
+        if (newParams.has_dynamic_inputs()) {
+            if (!CheckConvolutionExplicitPaddings(newParams))
+                return {};
+        } else {
+            kd.reorderInput = ConvolutionUpdateInputParams(newParams);
+
+            if (kd.reorderInput && !newParams.allowInputReordering)
+                return {};
+        }
+    }
+
+    DispatchData dispatchData = SetDefault(newParams, -1);
+
+    if (!params.is_shape_agnostic && !CheckWorkGroups(dispatchData)) {
+        // Internal Error - wrong calculation of global/local work group sizes
+        return {};
+    }
+
+    auto finalKernelName = GetKernelName(newParams);
+    auto cldnnJit = GetJitConstants(newParams, dispatchData);
+    for (size_t i = 0; i < num_kernels; i++) {
+        if (params.is_shape_agnostic) {
+            cldnnJit.RemoveConstant("X_BLOCK_SIZE");
+            if (i == 0) {
+                cldnnJit.AddConstant(MakeJitConstant("X_BLOCK_SIZE", "1"));
+            } else if (i == 1) {
+                cldnnJit.AddConstant(MakeJitConstant("X_BLOCK_SIZE", "2"));
+            } else if (i == 2) {
+                cldnnJit.AddConstant(MakeJitConstant("X_BLOCK_SIZE", "4"));
+            } else if (i == 3) {
+                cldnnJit.AddConstant(MakeJitConstant("X_BLOCK_SIZE", "8"));
+            }
+        }
+        auto entryPoint = GetEntryPoint(finalKernelName, newParams.layerID, params, i);
+        auto jit = CreateJit(finalKernelName, cldnnJit, entryPoint);
+
+        GetUpdateDispatchDataFunc(kd);
+
+        auto& kernel = kd.kernels[i];
+        FillCLKernelData(kernel,
+                        dispatchData,
+                        params.engineInfo,
+                        finalKernelName,
+                        jit,
+                        entryPoint,
+                        EXE_MODE_DEFAULT,
+                        true,
+                        !newParams.bias.empty(),
+                        1, 0, 1,
+                        newParams.is_shape_agnostic);
+
+        if (newParams.deformable_mode) {
+            kernel.params.arguments.push_back({ArgumentDescriptor::Types::INPUT, 1});
+            if (newParams.deformable_mask_enabled)
+                kernel.params.arguments.push_back({ArgumentDescriptor::Types::INPUT, 2});
+        }
+
+        if (!newParams.weights_zero_points.empty())
+            kernel.params.arguments.push_back({ArgumentDescriptor::Types::WEIGHTS_ZERO_POINTS, 1});
+        if (!newParams.activations_zero_points.empty())
+            kernel.params.arguments.push_back({ArgumentDescriptor::Types::ACTIVATIONS_ZERO_POINTS, 1});
+        if (!newParams.compensation.empty())
+            kernel.params.arguments.push_back({ArgumentDescriptor::Types::COMPENSATION, 1});
+
+        uint32_t fused_deps_total = 0;
+        for (auto& fused_dep : newParams.fused_ops) {
+            for (int i = 0; i < static_cast<int>(fused_dep.dep_size); i++) {
+                kernel.params.arguments.push_back({ ArgumentDescriptor::Types::INPUT_OF_FUSED_PRIMITIVE, fused_deps_total });
+                fused_deps_total++;
+            }
+        }
+    }
+    kd.autoTuneIndex = -1;
+
+    return {kd};
+ }
+
+void ConvolutionKernel_b_fs_yx_fsv16_1x1::GetUpdateDispatchDataFunc(KernelData& kd) const {
+    if (kd.kernels.size() == 1) {
+        Parent::GetUpdateDispatchDataFunc(kd);
+    } else {
+        kd.update_dispatch_data_func = [this](const Params& params, KernelData& kd) {
+            const auto& prim_params = static_cast<const convolution_params&>(params);
+            auto dispatchData = SetDefault(prim_params);
+            size_t execute_kernel_idx = 3;
+            if (dispatchData.cldnnStyle.blockWidth == 1) {
+                execute_kernel_idx = 0;
+            } else if (dispatchData.cldnnStyle.blockWidth == 2) {
+                execute_kernel_idx = 1;
+            } else if (dispatchData.cldnnStyle.blockWidth == 4) {
+                execute_kernel_idx = 2;
+            }
+            for (size_t i = 0; i < kd.kernels.size(); i++) {
+                kd.kernels[i].params.workGroups.global = dispatchData.gws;
+                kd.kernels[i].params.workGroups.local = dispatchData.lws;
+                if (execute_kernel_idx == i) {
+                    kd.kernels[i].skip_execution = KernelData::SkipKernelExecution(prim_params);
+                } else {
+                    kd.kernels[i].skip_execution = true;
+                }
+            }
+        };
+    }
 }
 
 }  // namespace kernel_selector
diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernels/convolution/convolution_kernel_b_fs_yx_fsv16_1x1.h b/src/plugins/intel_gpu/src/kernel_selector/kernels/convolution/convolution_kernel_b_fs_yx_fsv16_1x1.h
index 9d5440816c3209..c01eeb4ec9f404 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/kernels/convolution/convolution_kernel_b_fs_yx_fsv16_1x1.h
+++ b/src/plugins/intel_gpu/src/kernel_selector/kernels/convolution/convolution_kernel_b_fs_yx_fsv16_1x1.h
@@ -34,6 +34,7 @@ class ConvolutionKernel_b_fs_yx_fsv16_1x1 : public ConvolutionKernelBase {
     bool Validate(const Params& p) const override;
     DispatchData SetDefault(const convolution_params& arg, int autoTuneIndex = -1) const override;
     JitConstants GetJitConstants(const convolution_params& params, const DispatchData& dispatchData) const override;
+    void GetUpdateDispatchDataFunc(KernelData& kd) const override;
 
     struct AutoTuneOption {
         size_t blockWidth;
@@ -48,7 +49,7 @@ class ConvolutionKernel_b_fs_yx_fsv16_1x1 : public ConvolutionKernelBase {
     };
 
     std::vector<AutoTuneOption> autoTuneOptions;
-    AutoTuneOption GetAutoTuneOptions(const Params& arg, int autoTuneIndex) const;
+    AutoTuneOption GetAutoTuneOptions(const convolution_params& arg, int autoTuneIndex) const;
     ConvolutionTuningData GetTuningParams(const convolution_params& params) const;
     float EstimateOccupancy(const convolution_params& params, const ConvolutionTuningData& tuning_data) const;
 };
diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernels/convolution/convolution_kernel_bfyx_os_iyx_osv16.cpp b/src/plugins/intel_gpu/src/kernel_selector/kernels/convolution/convolution_kernel_bfyx_os_iyx_osv16.cpp
index 817f88400b4fd1..0d6249cf153a73 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/kernels/convolution/convolution_kernel_bfyx_os_iyx_osv16.cpp
+++ b/src/plugins/intel_gpu/src/kernel_selector/kernels/convolution/convolution_kernel_bfyx_os_iyx_osv16.cpp
@@ -212,7 +212,7 @@ bool ConvolutionKernel_bfyx_os_iyx_osv16::Validate(const Params& p) const {
     const size_t acceptable_filter_size = 1024;     // This acceptable size was decided by heuristics
     const auto& params = static_cast<const convolution_params&>(p);
     auto filter_size = params.filterSize.x * params.filterSize.y;
-    if (filter_size > acceptable_filter_size) {
+    if (filter_size >= acceptable_filter_size) {
         return false;
     }
 
diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernels/convolution/convolution_kernel_bfyx_os_iyx_osv32.cpp b/src/plugins/intel_gpu/src/kernel_selector/kernels/convolution/convolution_kernel_bfyx_os_iyx_osv32.cpp
index 639688e413f800..822948c1a0b204 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/kernels/convolution/convolution_kernel_bfyx_os_iyx_osv32.cpp
+++ b/src/plugins/intel_gpu/src/kernel_selector/kernels/convolution/convolution_kernel_bfyx_os_iyx_osv32.cpp
@@ -196,7 +196,7 @@ bool ConvolutionKernel_bfyx_os_iyx_osv32::Validate(const Params& p) const {
     const size_t acceptable_filter_size = 1024;     // This acceptable size was decided by heuristics
     const auto& params = static_cast<const convolution_params&>(p);
     auto filter_size = params.filterSize.x * params.filterSize.y;
-    if (filter_size > acceptable_filter_size) {
+    if (filter_size >= acceptable_filter_size) {
         return false;
     }
 
diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernels/eltwise/eltwise_kernel_ref.h b/src/plugins/intel_gpu/src/kernel_selector/kernels/eltwise/eltwise_kernel_ref.h
index 25a1bca9fea598..5b9ef765be298a 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/kernels/eltwise/eltwise_kernel_ref.h
+++ b/src/plugins/intel_gpu/src/kernel_selector/kernels/eltwise/eltwise_kernel_ref.h
@@ -19,7 +19,8 @@ class EltwiseKernelRef : public EltwiseKernelBase {
         return {
             FusedOpType::QUANTIZE,
             FusedOpType::ACTIVATION,
-            FusedOpType::ELTWISE
+            FusedOpType::ELTWISE,
+            FusedOpType::REORDER
         };
     }
 
diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernels/fully_connected/fully_connected_kernel_bf_tiled.cpp b/src/plugins/intel_gpu/src/kernel_selector/kernels/fully_connected/fully_connected_kernel_bf_tiled.cpp
index 4e43398e391522..c6b0acda06c56a 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/kernels/fully_connected/fully_connected_kernel_bf_tiled.cpp
+++ b/src/plugins/intel_gpu/src/kernel_selector/kernels/fully_connected/fully_connected_kernel_bf_tiled.cpp
@@ -3,15 +3,79 @@
 //
 
 #include "fully_connected_kernel_bf_tiled.h"
-
+#include "kernel_selector_utils.h"
 #include <vector>
 #include <functional>
 #include "common_types.h"
 
 static constexpr size_t simd = 16;
+static constexpr size_t quantize_grp_size = 32;
+static constexpr size_t min_slm_size = 256;
 
 namespace kernel_selector {
 
+static std::pair<size_t, size_t> get_input_bf_size(const fully_connected_params& params) {
+    size_t input_f = params.inputs[0].Feature().v;
+    size_t input_batch = params.inputs[0].Batch().v;
+    // 3D input
+    if (params.outputs[0].GetLayout() == DataLayout::bfyx) {
+        input_f = params.inputs[0].Y().v;
+        input_batch = params.inputs[0].Batch().v * params.inputs[0].Feature().v;
+    }
+
+    return {input_batch, input_f};
+}
+
+static std::pair<size_t, size_t> get_output_aligned_bf_size(const fully_connected_params& params,
+                                                            bool needs_align,
+                                                            uint32_t align_b = 1,
+                                                            int32_t align_f = 1) {
+    size_t output_f = (needs_align == true) ? CeilDiv(params.outputs[0].Feature().v, align_f) : params.outputs[0].Feature().v;
+    size_t output_b = params.outputs[0].Batch().v;
+    // 3D output
+    if (params.outputs[0].GetLayout() == DataLayout::bfyx) {
+        output_f = (needs_align == true) ? CeilDiv(params.outputs[0].Y().v, align_f) : params.outputs[0].Y().v;
+        output_b = params.outputs[0].Batch().v * params.outputs[0].Feature().v;
+    }
+
+    output_b = (needs_align == true) ? CeilDiv(output_b, align_b) : output_b;
+
+    return {output_b, output_f};
+}
+
+// DYNAMIC_QUANTIZE
+static bool should_dynamic_quantize(const fully_connected_params& params) {
+    auto dynamic_quantization_group_size = params.dynamic_quantization_group_size;
+    GPU_DEBUG_GET_INSTANCE(debug_config);
+    GPU_DEBUG_IF(debug_config->enable_dynamic_quantize) {
+        dynamic_quantization_group_size = quantize_grp_size;
+    }
+
+    if (params.inputs[0].GetFirstElementOffset() != 0)
+        return false;
+
+    if (dynamic_quantization_group_size < quantize_grp_size)
+        return false;
+
+    auto threads = get_input_bf_size(params);
+    auto input_b = threads.first;
+    auto input_f = threads.second;
+
+    const size_t scale_group_size = params.weights.IFM().v / params.decompression_scale.Feature().v;
+    if ((scale_group_size % simd == 0) && (input_f % quantize_grp_size == 0) &&
+        (params.is_shape_agnostic || (params.inputs[0].Batch().v > 1 && input_b > min_slm_size)) &&
+        params.inputs[0].GetDType() == Datatype::F16 &&
+        (params.weights.GetDType() == WeightsType::INT4 || params.weights.GetDType() == WeightsType::UINT4) &&
+        (params.decompression_zero_point.Feature().v == 1)) {
+        GPU_DEBUG_TRACE_DETAIL << " Dynamic quantizing for FC : scale_group_size " << scale_group_size << ", Input (" <<
+            kernel_selector::toString(params.inputs[0].GetDType()) << ", " << kernel_selector::toString(params.outputs[0].GetLayout()) <<
+            ") B: " << params.inputs[0].Batch().v << ", F: " << params.inputs[0].Feature().v << ", Y: " << params.inputs[0].Y().v << std ::endl;
+        return true;
+    }
+
+    return false;
+}
+
 FullyConnected_bf_tiled::FullyConnected_bf_tiled() : FullyConnectedKernelBase("fully_connected_gpu_bf_tiled") {
     for (unsigned tile_b = 1; tile_b <= 32; ++tile_b)
     for (unsigned tile_ofm = 1; tile_ofm <= 4; tile_ofm *= 2)
@@ -154,12 +218,9 @@ struct TuneParamsSelector {
 
 bool TuneParamsSelector::VerifyTuneParams(const fully_connected_params& params, const tune_params& tparams) {
     // Check divisibility by dispatch tile sizes.
-    size_t output_f = params.outputs[0].Feature().v;
-    size_t output_b = params.outputs[0].Batch().v;
-    if (params.outputs[0].GetLayout() == DataLayout::bfyx) {
-        output_b *= params.outputs[0].Feature().v;
-        output_f = params.outputs[0].Y().v;
-    }
+    auto bf_size = get_output_aligned_bf_size(params, false);
+    size_t output_b = bf_size.first;
+    size_t output_f = bf_size.second;
 
     if (params.compressed &&
         (params.weights.GetDType() == WeightsType::INT4 || params.weights.GetDType() == WeightsType::UINT4) &&
@@ -182,7 +243,7 @@ bool TuneParamsSelector::VerifyTuneParams(const fully_connected_params& params,
     if (tparams.kernel_type == FullyConnected_bf_tiled::KernelType::SLM) {
         bool is_i4_u4 = (params.weights.GetDType() == WeightsType::INT4 || params.weights.GetDType() == WeightsType::UINT4);
         const auto required_batch_alignment = 64;
-        if (!params.is_shape_agnostic && (!IsAligned(output_b, required_batch_alignment) || output_b < 256))
+        if (!params.is_shape_agnostic && (!IsAligned(output_b, required_batch_alignment) || output_b < min_slm_size))
             return false;
 
         const auto required_tile_b = 8;
@@ -228,14 +289,10 @@ FullyConnected_bf_tiled::GetAutoTuneParams(const fully_connected_params& params,
         && TuneParamsSelector::VerifyTuneParams(params, auto_tune_params[idx]))
         return auto_tune_params[idx];
 
-    size_t batch = params.outputs[0].Batch().v;
-    size_t output_f = params.outputs[0].Feature().v;
+    auto bf_size = get_output_aligned_bf_size(params, false);
+    size_t batch = bf_size.first;
+    size_t output_f = bf_size.second;
 
-    // 3d output
-    if (params.outputs[0].GetLayout() == DataLayout::bfyx) {
-        batch *= params.outputs[0].Feature().v;
-        output_f = params.outputs[0].Y().v;
-    }
     Datatype dtype = params.inputs[0].GetDType();
 
     auto selector = TuneParamsSelector(params);
@@ -259,7 +316,7 @@ FullyConnected_bf_tiled::GetAutoTuneParams(const fully_connected_params& params,
         } else {
             // Try to use SLM kernels if possible
             if (preferred_kernel_type != KernelType::DEFAULT) {
-                if (params.is_shape_agnostic) {
+                if (params.is_shape_agnostic && !should_dynamic_quantize(params)) {
                     selector.Case(tune_params(16, 2, 2, 4, 1, 1, EXE_MODE_DEFAULT, KernelType::SLM))
                             .Case(tune_params(16, 2, 1, 4, 1, 1, EXE_MODE_DEFAULT, KernelType::SLM));
                 }
@@ -344,14 +401,9 @@ FullyConnected_bf_tiled::SetDefault(const fully_connected_params& params, int au
 
     auto tparams = GetAutoTuneParams(params, kernel_type, autoTuneIndex);
 
-    size_t feature_threads = CeilDiv(params.outputs[0].Feature().v, tparams.tile_ofm * simd);
-    size_t batch_threads = params.outputs[0].Batch().v;
-    if (params.outputs[0].GetLayout() == DataLayout::bfyx) {
-        feature_threads = CeilDiv(params.outputs[0].Y().v, tparams.tile_ofm * simd);
-        batch_threads = params.outputs[0].Batch().v * params.outputs[0].Feature().v;
-    }
-
-    batch_threads = CeilDiv(batch_threads, tparams.tile_b);
+    auto threads = get_output_aligned_bf_size(params, true, tparams.tile_b, tparams.tile_ofm * simd);
+    auto batch_threads = threads.first;
+    auto feature_threads = threads.second;
 
     const size_t lws_batches = 8;
     const size_t aligned_batch = Align(batch_threads, lws_batches); // Each WG calculates 8x8 batches (TILE_B x LWS[2] size)
@@ -380,9 +432,7 @@ FullyConnected_bf_tiled::SetDefault(const fully_connected_params& params, int au
 KernelsPriority FullyConnected_bf_tiled::GetKernelsPriority(const Params& params) const {
     const auto& fc_params = static_cast<const fully_connected_params&>(params);
 
-    size_t output_b = fc_params.outputs[0].Batch().v;
-    if (fc_params.outputs[0].GetLayout() == DataLayout::bfyx)
-        output_b *= fc_params.outputs[0].Feature().v;
+    size_t output_b = get_output_aligned_bf_size(fc_params, false).first;
 
     float estimated_time = FORCE_PRIORITY_9;
     if (output_b > 1 && fc_params.inputs[0].GetDType() == Datatype::F32)
@@ -445,10 +495,23 @@ JitConstants FullyConnected_bf_tiled::GetJitConstants(const fully_connected_para
         jit.AddConstant(MakeJitConstant("FILTER_LOAD_BLOCK_SIZE", block_read_size));
         jit.AddConstant(MakeJitConstant("FILTER_ELEMENTS_PER_LOAD", weights_elements_per_load));
         jit.Merge(make_int4_packed_type_jit_constant("INT4_PACKED_TYPE_PRELOAD", params.weights.GetDType(), weights_elements_per_load));
+    } else {
+        jit.AddConstant(MakeJitConstant("USE_SLM", 0));
+    }
+
+    // Validated perf gain, Dynamic quantize force enable SCALE_POST_OP for char type multiplication
+    if (should_dynamic_quantize(params) && dispatchData.tile_m > 1 && dispatchData.tile_n == 2) {
+        jit.AddConstant(MakeJitConstant("DYNAMIC_QUANTIZE", 1));
+        jit.AddConstant(MakeJitConstant("DECOMPRESSION_SCALE_POST_OP", 1));
+        jit.AddConstant(MakeJitConstant("DQ_TYPE", "char"));
+        jit.AddConstant(MakeJitConstant("QUANTIZE_GROUP_SIZE", quantize_grp_size));
+    } else {
+        jit.AddConstant(MakeJitConstant("DYNAMIC_QUANTIZE", 0));
     }
 
     jit.AddConstant(MakeJitConstant("SIMD", simd));
     jit.AddConstant(MakeJitConstant("TILE_B", dispatchData.tile_m));
+    jit.AddConstant(MakeJitConstant("HALF_TILE_B", dispatchData.tile_m/2));
     jit.AddConstant(MakeJitConstant("TILE_OFM", dispatchData.tile_n));
     jit.AddConstant(MakeJitConstant("TILE_IFM", dispatchData.tile_mk));
     jit.AddConstant(MakeJitConstant("TILE_K", dispatchData.tile_nk));
@@ -523,29 +586,53 @@ void FullyConnected_bf_tiled::GetUpdateDispatchDataFunc(KernelData& kd) const {
         kd.update_dispatch_data_func = [this](const Params& params, KernelData& kd) {
             const auto& prim_params = static_cast<const fully_connected_params&>(params);
 
-            OPENVINO_ASSERT(kd.kernels.size() == 2, "[GPU] Invalid kernels size for update dispatch data func, expected 2, got ", kd.kernels.size());
+            size_t output_batch = get_output_aligned_bf_size(prim_params, false).first;
 
-            size_t output_batch = prim_params.outputs[0].Batch().v;
-            if (prim_params.outputs[0].GetLayout() == DataLayout::bfyx)
-                output_batch *= prim_params.outputs[0].Feature().v;
+            // Get index of the added shape-agnostic kernel
+            int kernel_offset = 0;
+            if (kd.kernels.size() == 3)
+                kernel_offset = 1;  // quantize kernel exists
 
-            // Choose one of the two shape agnostic kernels:
-            // - kd.kernels[0] for batches <= 240 (default version)
-            // - kd.kernels[1] for batches >= 256 (slm version)
+            // Choose one of the two shape agnostic kernels: N == added kernel number
+            // - kd.kernels[N-1] for batches <= 240 (default version)
+            // - kd.kernels[N] for batches >= 256 (slm version)
             const auto default_alignment = 16;
-            // We can use SLM version if `output_batch + default_alignment > 256` because memory and batch are aligned (whether 16 or 64 elements)
-            const auto skip_kernel_idx = output_batch + default_alignment > 256 ? 0 : 1;
-            const auto execute_kernel_idx = 1 - skip_kernel_idx;
+            // We can use SLM version if `output_batch + default_alignment > min_slm_size(256)` because memory and batch are aligned (whether 16 or 64 elements)
+            const auto execute_type = (output_batch + default_alignment > min_slm_size) ? KernelType::SLM : KernelType::DEFAULT;
+            const auto execute_kernel_idx = ((execute_type == KernelType::SLM) ? 1 : 0) + kernel_offset;
+            const auto skip_kernel_idx = ((execute_type == KernelType::SLM) ? 0 : 1) + kernel_offset;
+
 
+            // Check default or SLM version FC, and disable remain version
             kd.kernels[skip_kernel_idx].skip_execution = true;
 
-            GPU_DEBUG_TRACE_DETAIL << "FC bf tiled: " << (execute_kernel_idx == 1 ? "SLM" : "Default") << " shape-agnostic kernel version "
+            GPU_DEBUG_TRACE_DETAIL << "FC bf tiled: " << (execute_type == KernelType::SLM ? "SLM" : "Default") << " shape-agnostic kernel version "
                                     << "will be used for batch size = " << output_batch << "\n";
 
-            auto dispatchData = SetDefault(prim_params, -1, execute_kernel_idx);
+            auto dispatchData = SetDefault(prim_params, -1, static_cast<int>(execute_type));
             kd.kernels[execute_kernel_idx].params.workGroups.global = dispatchData.gws;
             kd.kernels[execute_kernel_idx].params.workGroups.local = dispatchData.lws;
             kd.kernels[execute_kernel_idx].skip_execution = KernelData::SkipKernelExecution(prim_params);
+
+            if (!kd.internalBufferSizes.empty()) {
+                // Pre-quantizing kernel was generated. Update the kernel and intermediate buffers or disable it.
+                if (execute_type == KernelType::DEFAULT) {
+                    kd.kernels[0].skip_execution = true;
+                } else {
+                    kd.kernels[0].skip_execution = false;
+                    size_t input_f = get_input_bf_size(prim_params).second;
+                    size_t input_size = input_f * dispatchData.tile_m * dispatchData.gws[2];
+
+                    if (kd.internalBufferSizes[0] < input_size) {
+                        kd.internalBufferSizes.clear();
+                        kd.internalBufferSizes.push_back(input_size);                           // quantized input is char type
+                        kd.internalBufferSizes.push_back(input_size / quantize_grp_size * 2);   // de_quan_scale is half type
+                    }
+
+                    kd.kernels[0].params.workGroups.global = {std::max((input_size / quantize_grp_size), (size_t)1), 1, 1};
+                    kd.kernels[0].params.workGroups.local = {16, 1, 1};
+                }
+            }
         };
     }
 }
@@ -572,34 +659,51 @@ KernelsData FullyConnected_bf_tiled::GetTunedKernelsDataByIndex(const Params &pa
         weights_layout = WeightsLayout::os_iyx_osv64;
     }
 
-    auto kernels_data = GetCommonKernelsData(params,
-                                             fc_params.inputs[0].GetLayout(),
-                                             weights_layout,
-                                             tparams.exec_options,
-                                             autoTuneIndex);
-
-    // In case of dynamic params try to configure additional optimized SLM kernel for large batches
-    if (params.is_shape_agnostic) {
-        auto tparams = GetAutoTuneParams(fc_params, KernelType::SLM, autoTuneIndex);
-        auto can_select_slm_kernel = tparams.kernel_type == KernelType::SLM;
-
-        if (!can_select_slm_kernel)
-            return kernels_data;
-
-        auto slm_kernel = GetCommonKernelsData(params,
-                                               fc_params.inputs[0].GetLayout(),
-                                               weights_layout,
-                                               tparams.exec_options,
-                                               autoTuneIndex,
-                                               1);
-
-        if (slm_kernel.empty() || slm_kernel[0].kernels.empty())
-            return kernels_data;
-
-        kernels_data[0].kernels.push_back(slm_kernel[0].kernels.back());
-
-        // Update default update_dispatch_data_func function
-        GetUpdateDispatchDataFunc(kernels_data[0]);
+    KernelsData kernels_data;
+    if (should_dynamic_quantize(fc_params)) {
+        // Use seperate 2 kernels for dynamic quantizing : quantizing_kernel + fc_kernel
+        // 1st kernel : Dynamic quantizing by quantize_grp_size
+        // 2nd kernel : fully connected kernel with KernelType::DEFAULT. Quantized inputs and scale values could be used.
+        // 3rd kernel : (optional) fully connected shape_agnostic kernel with KernelType::SLM. Quantized inputs and scale values would be used.
+        kernels_data = GetMultiKernelsData(params,
+                                                fc_params.inputs[0].GetLayout(),
+                                                weights_layout,
+                                                tparams.exec_options,
+                                                autoTuneIndex);
+        OPENVINO_ASSERT(!kernels_data.empty() && !kernels_data[0].kernels.empty(), "[GPU] Error to create multi kernel for dynamic quantizing.");
+
+        if (params.is_shape_agnostic)
+            GetUpdateDispatchDataFunc(kernels_data[0]);
+    } else {
+        kernels_data = GetCommonKernelsData(params,
+                                                fc_params.inputs[0].GetLayout(),
+                                                weights_layout,
+                                                tparams.exec_options,
+                                                autoTuneIndex,
+                                                0);
+
+        if (params.is_shape_agnostic) {
+            auto tparams = GetAutoTuneParams(fc_params, KernelType::SLM, autoTuneIndex);
+            auto can_select_slm_kernel = tparams.kernel_type == KernelType::SLM;
+
+            if (!can_select_slm_kernel)
+                return kernels_data;
+
+            auto slm_kernel = GetCommonKernelsData(params,
+                                                fc_params.inputs[0].GetLayout(),
+                                                weights_layout,
+                                                tparams.exec_options,
+                                                autoTuneIndex,
+                                                1);
+
+            if (slm_kernel.empty() || slm_kernel[0].kernels.empty())
+                return kernels_data;
+
+            kernels_data[0].kernels.push_back(slm_kernel[0].kernels.back());
+
+            // Update default update_dispatch_data_func function
+            GetUpdateDispatchDataFunc(kernels_data[0]);
+        }
     }
 
     return kernels_data;
@@ -630,4 +734,157 @@ KernelsData FullyConnected_bf_tiled::GetKernelsData(const Params& params) const
 
     return res;
 }
+
+
+KernelsData FullyConnected_bf_tiled::GetMultiKernelsData(const Params &params,
+                                                           DataLayout dl,
+                                                           WeightsLayout wl,
+                                                           const std::string exeMode,
+                                                           int autoTuneIndex) const {
+    if (!Validate(params)) {
+        return KernelsData();
+    }
+
+    const auto& fc_params = static_cast<const fully_connected_params&>(params);
+
+    bool bProperInput = fc_params.inputs[0].GetLayout() == dl;
+    if (!bProperInput && !fc_params.inputs[0].PitchesDifferFromLogicalDims()) {
+        bProperInput = (dl == DataLayout::fb && fc_params.inputs[0].GetLayout() == DataLayout::fyxb) ||
+                       (dl == DataLayout::bf && fc_params.inputs[0].GetLayout() == DataLayout::bfyx);
+    }
+
+    KernelData kd = KernelData::Default<fully_connected_params>(params, 2);
+    fully_connected_params& new_params = *static_cast<fully_connected_params*>(kd.params.get());
+
+    if (!bProperInput) {
+        new_params.inputs[0] = new_params.inputs[0].TransformIgnorePadding(dl);
+        kd.reorderInput = true;
+    }
+
+    bool succeed = UpdateWeightsParams(new_params,
+                                       wl,
+                                       kd.weightsReorderParams,
+                                       GetSupportedKey());
+    if (!succeed) {
+        return {};
+    }
+
+    int inputs_count = 1;
+    if (new_params.compressed) {
+        inputs_count++;
+        if (new_params.has_decompression_zp && !new_params.scalar_zp)
+            inputs_count++;
+    }
+
+    // Generate dispatch data for KernelType::DEFAULT
+    int kernel_number = 0;
+    const DispatchData dispatchData = SetDefault(new_params, autoTuneIndex, kernel_number);
+
+    // Dynamic-quantize kernel
+    {
+        auto& quan_kernel = kd.kernels[0];
+        DispatchData dyn_quan_dispatch = dispatchData;
+        dyn_quan_dispatch.gws = {std::max((fc_params.inputs[0].PhysicalSize() / quantize_grp_size), (size_t)1), 1, 1};
+        dyn_quan_dispatch.lws = {16, 1, 1};
+        quan_kernel.params.workGroups.global = dyn_quan_dispatch.gws;
+        quan_kernel.params.workGroups.local = dyn_quan_dispatch.lws;
+        quan_kernel.skip_execution = false;
+
+        auto quan_entry_point = GetEntryPoint(kernelName, fc_params.layerID, params, kernel_number);
+        auto quan_cldnn_jit = GetJitConstants(new_params, dyn_quan_dispatch);
+        quan_cldnn_jit.AddConstant(MakeJitConstant("FC_KERNEL_DYNAMIC_QUANTIZE", 1));
+        auto quan_jit = CreateJit(kernelName, quan_cldnn_jit, quan_entry_point);
+
+
+        FillCLKernelData(quan_kernel,
+                        dyn_quan_dispatch,
+                        params.engineInfo,
+                        kernelName,
+                        quan_jit,
+                        quan_entry_point,
+                        exeMode,  // No exec mode
+                        false,
+                        false,
+                        1, // Only INPUT_0 is used for quantizing
+                        0, // No fused ops
+                        0, // No output
+                        fc_params.is_shape_agnostic);
+
+        quan_kernel.params.arguments.clear();  // Clear original output argument
+        quan_kernel.params.arguments.push_back({ArgumentDescriptor::Types::INPUT, 0});
+        quan_kernel.params.arguments.push_back({ArgumentDescriptor::Types::INTERNAL_BUFFER, 0});
+        quan_kernel.params.arguments.push_back({ArgumentDescriptor::Types::INTERNAL_BUFFER, 1});
+        kd.internalBufferSizes.push_back(fc_params.inputs[0].PhysicalSize());
+        kd.internalBufferSizes.push_back(fc_params.inputs[0].PhysicalSize() / quantize_grp_size * 2);
+        kernel_number++;
+    }
+    kd.internalBufferDataType = Datatype::F16;
+
+    // FC kernel for dynamic quantized input with KernelType::DEFAULT
+    {
+        auto entry_point = GetEntryPoint(kernelName, fc_params.layerID, params, kernel_number);
+        auto cldnn_jit = GetJitConstants(new_params, dispatchData);
+        auto jit = CreateJit(kernelName, cldnn_jit, entry_point);
+
+        auto& fc_kernel = kd.kernels[1];
+        fc_kernel.params.workGroups.global = dispatchData.gws;
+        fc_kernel.params.workGroups.local = dispatchData.lws;
+        fc_kernel.skip_execution = false;
+
+        FillCLKernelData(fc_kernel,
+                        dispatchData,
+                        params.engineInfo,
+                        kernelName,
+                        jit,
+                        entry_point,
+                        exeMode,
+                        true,
+                        !fc_params.bias.empty(),
+                        inputs_count,
+                        GetFusedPrimitiveInputsCount(params),
+                        1,
+                        fc_params.is_shape_agnostic);
+
+        fc_kernel.params.arguments.push_back({ArgumentDescriptor::Types::INTERNAL_BUFFER, 0});
+        fc_kernel.params.arguments.push_back({ArgumentDescriptor::Types::INTERNAL_BUFFER, 1});
+        kernel_number++;
+    }
+
+    const DispatchData slm_Data = SetDefault(new_params, autoTuneIndex, kernel_number);
+    auto slm_params = GetAutoTuneParams(fc_params, KernelType::SLM, autoTuneIndex);
+    auto can_select_slm_kernel = slm_params.kernel_type == KernelType::SLM;
+    // FC kernel for dynamic quantized input with KernelType::SLM
+    if (params.is_shape_agnostic && can_select_slm_kernel) {
+        kd.kernels.resize(kernel_number + 1);
+
+        auto entry_point = GetEntryPoint(kernelName, fc_params.layerID, params, kernel_number);
+        auto cldnn_jit = GetJitConstants(new_params, slm_Data);
+        auto jit = CreateJit(kernelName, cldnn_jit, entry_point);
+
+        auto& sa_kernel = kd.kernels[2];
+        sa_kernel.params.workGroups.global = slm_Data.gws;
+        sa_kernel.params.workGroups.local = slm_Data.lws;
+        sa_kernel.skip_execution = false;
+
+        FillCLKernelData(sa_kernel,
+                        slm_Data,
+                        params.engineInfo,
+                        kernelName,
+                        jit,
+                        entry_point,
+                        slm_params.exec_options,
+                        true,
+                        !fc_params.bias.empty(),
+                        inputs_count,
+                        GetFusedPrimitiveInputsCount(params),
+                        1,
+                        fc_params.is_shape_agnostic);
+
+        sa_kernel.params.arguments.push_back({ArgumentDescriptor::Types::INTERNAL_BUFFER, 0});
+        sa_kernel.params.arguments.push_back({ArgumentDescriptor::Types::INTERNAL_BUFFER, 1});
+    }
+
+    kd.autoTuneIndex = autoTuneIndex;
+    return {kd};
+}
 }  // namespace kernel_selector
diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernels/fully_connected/fully_connected_kernel_bf_tiled.h b/src/plugins/intel_gpu/src/kernel_selector/kernels/fully_connected/fully_connected_kernel_bf_tiled.h
index 62ad0ba2d07b1e..d40a985a85ad2c 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/kernels/fully_connected/fully_connected_kernel_bf_tiled.h
+++ b/src/plugins/intel_gpu/src/kernel_selector/kernels/fully_connected/fully_connected_kernel_bf_tiled.h
@@ -31,6 +31,12 @@ class FullyConnected_bf_tiled : public FullyConnectedKernelBase {
     ParamsKey GetSupportedKey() const override;
     DeviceFeaturesKey get_required_device_features_key(const Params& params) const override;
 
+    KernelsData GetMultiKernelsData(const Params &params,
+                                     DataLayout dl,
+                                     WeightsLayout wl,
+                                     const std::string exeMode = EXE_MODE_DEFAULT,
+                                     int autoTuneIndex = -1) const;
+
     struct tune_params {
         tune_params(unsigned tile_b,
                     unsigned tile_ofm,
diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernels/fully_connected/fully_connected_params.h b/src/plugins/intel_gpu/src/kernel_selector/kernels/fully_connected/fully_connected_params.h
index 845b3207a75e07..01428a5fbeda48 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/kernels/fully_connected/fully_connected_params.h
+++ b/src/plugins/intel_gpu/src/kernel_selector/kernels/fully_connected/fully_connected_params.h
@@ -15,6 +15,7 @@ struct fully_connected_params : public weight_bias_params {
     fully_connected_params() : weight_bias_params(KernelType::FULLY_CONNECTED) {}
 
     QuantizationType quantization = QuantizationType::NONE;
+    size_t dynamic_quantization_group_size = 0;
 
     ParamsKey GetParamsKey() const override {
         ParamsKey k = weight_bias_params::GetParamsKey();
diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernels/gather/gather_kernel_ref.h b/src/plugins/intel_gpu/src/kernel_selector/kernels/gather/gather_kernel_ref.h
index 5893458dd6cf50..476f60eb8bef7d 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/kernels/gather/gather_kernel_ref.h
+++ b/src/plugins/intel_gpu/src/kernel_selector/kernels/gather/gather_kernel_ref.h
@@ -37,7 +37,8 @@ class GatherKernelRef : public KernelBaseOpenCL {
     std::vector<FusedOpType> GetSupportedFusedOps() const override {
         return { FusedOpType::QUANTIZE,
                  FusedOpType::ELTWISE,
-                 FusedOpType::ACTIVATION };
+                 FusedOpType::ACTIVATION,
+                 FusedOpType::REORDER };
     }
 
 protected:
diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernels/permute/permute_kernel_ref.cpp b/src/plugins/intel_gpu/src/kernel_selector/kernels/permute/permute_kernel_ref.cpp
index a35479b4d83cd8..6ac0b945d3617d 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/kernels/permute/permute_kernel_ref.cpp
+++ b/src/plugins/intel_gpu/src/kernel_selector/kernels/permute/permute_kernel_ref.cpp
@@ -100,6 +100,7 @@ static std::string GetReorderedOutputOrder(const permute_params& params, const s
 
 ParamsKey PermuteKernelRef::GetSupportedKey() const {
     ParamsKey k;
+    k.EnableInputDataType(Datatype::BF16);
     k.EnableInputDataType(Datatype::F16);
     k.EnableInputDataType(Datatype::F32);
     k.EnableInputDataType(Datatype::INT8);
diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernels/reorder/reorder_kernel.cpp b/src/plugins/intel_gpu/src/kernel_selector/kernels/reorder/reorder_kernel.cpp
index fd52142a517e16..8f4fdbf1f5c992 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/kernels/reorder/reorder_kernel.cpp
+++ b/src/plugins/intel_gpu/src/kernel_selector/kernels/reorder/reorder_kernel.cpp
@@ -8,6 +8,7 @@
 namespace kernel_selector {
 ParamsKey ReorderKernelRef::GetSupportedKey() const {
     ParamsKey k;
+    k.EnableInputDataType(Datatype::BF16);
     k.EnableInputDataType(Datatype::UINT8);
     k.EnableInputDataType(Datatype::INT8);
     k.EnableInputDataType(Datatype::INT32);
@@ -20,6 +21,7 @@ ParamsKey ReorderKernelRef::GetSupportedKey() const {
     k.EnableOutputDataType(Datatype::INT32);
     k.EnableOutputDataType(Datatype::INT64);
     k.EnableOutputDataType(Datatype::UINT8);
+    k.EnableOutputDataType(Datatype::BF16);
     k.EnableSurfaceInputSupport();
     k.EnableDifferentTypes();
     k.EnableAllInputLayout();
@@ -52,6 +54,10 @@ JitConstants ReorderKernelRef::GetJitConstants(const reorder_params& params) con
         jit.Merge(MakeFusedOpsJitConstants(params, {conf}));
     }
 
+    if ( params.inputs[0].GetDType() == Datatype::BF16 ) {
+         jit.AddConstant(MakeJitConstant("BF16_INPUT", true));
+    }
+
     return jit;
 }
 
diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernels/reorder/reorder_kernel_base.cpp b/src/plugins/intel_gpu/src/kernel_selector/kernels/reorder/reorder_kernel_base.cpp
index ffbc0331a8faed..8f6dbd909c558a 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/kernels/reorder/reorder_kernel_base.cpp
+++ b/src/plugins/intel_gpu/src/kernel_selector/kernels/reorder/reorder_kernel_base.cpp
@@ -119,6 +119,9 @@ JitConstants ReorderKernelBase::GetJitConstants(const reorder_params& params) co
                       params.mode == MeanSubtractMode::NONE && params.activations.empty());
 
     Datatype calc_type = useUshort ? Datatype::UINT16 : params.inputs[0].GetDType();
+    if ( params.inputs[0].GetDType() == Datatype::BF16 ) {
+        calc_type = Datatype::F32;
+    }
     Datatype output_reorder_type = useUshort ? Datatype::UINT16 : params.outputs[0].GetDType();
     Datatype input_reorder_type = useUshort ? Datatype::UINT16 : params.inputs[0].GetDType();
 
diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernels/reorder/reorder_weights_kernel.cpp b/src/plugins/intel_gpu/src/kernel_selector/kernels/reorder/reorder_weights_kernel.cpp
index 7def25b6dc1c24..907f9a3b842414 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/kernels/reorder/reorder_weights_kernel.cpp
+++ b/src/plugins/intel_gpu/src/kernel_selector/kernels/reorder/reorder_weights_kernel.cpp
@@ -9,6 +9,7 @@
 namespace kernel_selector {
 ParamsKey ReorderWeightsKernel::GetSupportedKey() const {
     ParamsKey k;
+    k.EnableInputWeightsType(WeightsType::BF16);
     k.EnableInputWeightsType(WeightsType::INT8);
     k.EnableInputWeightsType(WeightsType::F16);
     k.EnableInputWeightsType(WeightsType::F32);
@@ -31,6 +32,14 @@ KernelsData ReorderWeightsKernel::GetKernelsData(const Params& params) const {
     return GetCommonKernelsData(orgParams);
 }
 
+JitConstants ReorderWeightsKernel::GetJitConstants(const reorder_weights_params& params) const {
+    auto jit = ReorderKernelBase::GetJitConstants(params);
+    if ( params.input.GetDType() == WeightsType::BF16 ) {
+        jit.AddConstant(MakeJitConstant("BF16_INPUT", true));
+    }
+    return jit;
+}
+
 KernelsPriority ReorderWeightsKernel::GetKernelsPriority(const Params& /*params*/) const {
     return DONT_USE_IF_HAVE_SOMETHING_ELSE;
 }
diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernels/reorder/reorder_weights_kernel.h b/src/plugins/intel_gpu/src/kernel_selector/kernels/reorder/reorder_weights_kernel.h
index 11da2a372fd883..c69e967b368c6b 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/kernels/reorder/reorder_weights_kernel.h
+++ b/src/plugins/intel_gpu/src/kernel_selector/kernels/reorder/reorder_weights_kernel.h
@@ -11,7 +11,7 @@ class ReorderWeightsKernel : public ReorderKernelBase {
 public:
     ReorderWeightsKernel() : ReorderKernelBase("reorder_weights") {}
     virtual ~ReorderWeightsKernel() {}
-
+    JitConstants GetJitConstants(const reorder_weights_params& params) const override;
     KernelsData GetKernelsData(const Params& params) const override;
     KernelsPriority GetKernelsPriority(const Params& params) const override;
     ParamsKey GetSupportedKey() const override;
diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernels/rope/rope_kernel_base.cpp b/src/plugins/intel_gpu/src/kernel_selector/kernels/rope/rope_kernel_base.cpp
index f6f3414c2f7b43..507a41b8b01a50 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/kernels/rope/rope_kernel_base.cpp
+++ b/src/plugins/intel_gpu/src/kernel_selector/kernels/rope/rope_kernel_base.cpp
@@ -79,19 +79,16 @@ RoPEKernelBase::DispatchData RoPEKernelBase::SetDefault(const rope_params& param
     const auto& input = params.inputs[0];
     const auto& output = params.outputs[0];
 
-    std::vector<std::vector<Tensor::DataChannelName>> dims_by_gws;
+    std::vector<std::vector<Tensor::DataChannelName>> dims_by_gws = {{ Tensor::DataChannelName::BATCH }, { Tensor::DataChannelName::FEATURE },
+                                                                     { Tensor::DataChannelName::Y, Tensor::DataChannelName::X }};
     if (params.is_chatglm || params.is_qwen) {
-        dims_by_gws = {{ Tensor::DataChannelName::BATCH }, { Tensor::DataChannelName::FEATURE },
-                       { Tensor::DataChannelName::Y, Tensor::DataChannelName::X }};
         dispatchData.gws = {input.Batch().v,
                             input.Feature().v,
                             params.head_cnt * std::max(params.rotary_ndims / 2ul, params.head_size - params.rotary_ndims)};
     } else {
-        dims_by_gws = {{ Tensor::DataChannelName::BATCH }, { Tensor::DataChannelName::Y },
-                       { Tensor::DataChannelName::FEATURE, Tensor::DataChannelName::X }};
-        dispatchData.gws = {input.Batch().v,
-                            input.Y().v,
-                            input.Feature().v * params.rotary_ndims / 2ul};
+        dispatchData.gws = {output.Batch().v,
+                            output.Feature().v,
+                            output.Y().v * params.rotary_ndims / 2ul};
     }
 
     dispatchData.lws = GetOptimalLocalWorkGroupSizes(dispatchData.gws, params.engineInfo, input.GetLayout(), output.GetLayout(), dims_by_gws);
diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernels/sdpa/sdpa_kernel_micro.cpp b/src/plugins/intel_gpu/src/kernel_selector/kernels/sdpa/sdpa_kernel_micro.cpp
new file mode 100644
index 00000000000000..46c536ac0bd0af
--- /dev/null
+++ b/src/plugins/intel_gpu/src/kernel_selector/kernels/sdpa/sdpa_kernel_micro.cpp
@@ -0,0 +1,636 @@
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#ifdef ENABLE_ONEDNN_FOR_GPU
+
+#include "sdpa_kernel_micro.h"
+#include "common_tools.h"
+#include "common_types.h"
+#include "jitter.h"
+#include "kernel_selector_common.h"
+#include "kernel_selector_params.h"
+#include "micro_utils.hpp"
+#include "tensor_type.h"
+
+#include <algorithm>
+#include <mutex>
+#include <string>
+#include <vector>
+
+namespace kernel_selector {
+
+namespace {
+
+size_t subgroup_size(gpu_arch arch) {
+    switch (arch) {
+        case gpu_arch::gen9:
+        case gpu_arch::gen11:
+        case gpu_arch::xe_lp:
+        case gpu_arch::xe_hp:
+        case gpu_arch::xe_hpg: return 8;
+        case gpu_arch::xe_hpc:
+        case gpu_arch::xe2: return 16;
+        default: return 0;
+    }
+}
+
+inline int64_t get_d_max(int64_t head_size) {
+    for (int64_t i = 32; i <= 1024; i *= 2)
+        if (head_size <= i)
+            return i;
+    return head_size;
+}
+
+micro::Type convert_type(Datatype t) {
+    switch (t) {
+        case Datatype::F32: return micro::Type::f32;
+        case Datatype::F16: return micro::Type::f16;
+        default: break;
+    }
+    OPENVINO_THROW("Unsupported dt: ", toString(t));
+}
+
+Tensor::NDims normalize_dims(const DataTensor& qkv) {
+    auto dims = qkv.GetDims(); // xyfb
+    std::reverse(dims.begin(), dims.end()); // bfyx
+    return dims;
+}
+
+Tensor::Dim get_num_heads(const DataTensor& qkv, const std::vector<int64_t>& order) {
+    return normalize_dims(qkv)[order[1]];
+}
+
+Tensor::Dim get_seq_length(const DataTensor& qkv, const std::vector<int64_t>& order) {
+    return normalize_dims(qkv)[order[2]];
+}
+
+struct sdpa_config_t {
+    int unroll_m_kq, unroll_n_kq; // Subgroup tile sizes for K*Q GEMM
+    int unroll_m_vs, unroll_n_vs; // Subgroup tile sizes for V*S GEMM
+    int wg_m_kq, wg_n_kq; // Workgroup configuration for K*Q GEMM
+    int wg_m_vs, wg_n_vs; // Workgroup configuration for V*S GEMM
+};
+
+// Kernel configurations:
+//  h<N> -- maximum head size = N
+//  s<M> -- target sequence length = M
+//   2nd -- second token (thin Q)
+sdpa_config_t xehpg_h32 = {32, 16, 16, 16, 2, 16, 2, 16};
+sdpa_config_t xehpg_h32_s256 = {16, 16, 16, 16, 2, 8, 2, 8};
+sdpa_config_t xehpg_h32_s64 = {16, 16, 16, 8, 4, 4, 2, 8};
+sdpa_config_t xehpg_h32_s32 = {8, 8, 8, 8, 4, 4, 4, 4};
+sdpa_config_t xehpg_h32_2nd = {8, 32, 16, 8, 8, 1, 2, 4};
+
+sdpa_config_t xehpg_h64 = {32, 16, 16, 16, 4, 8, 4, 8};
+sdpa_config_t xehpg_h64_s128 = {16, 16, 16, 16, 4, 8, 4, 8};
+sdpa_config_t xehpg_h64_s64 = {32, 16, 16, 8, 8, 4, 4, 8};
+sdpa_config_t xehpg_h64_2nd = {8, 16, 16, 8, 8, 1, 4, 2};
+
+sdpa_config_t xehpg_h128 = {16, 16, 32, 8, 8, 4, 4, 8};
+sdpa_config_t xehpg_h128_s32 = {16, 16, 16, 8, 16, 2, 8, 4};
+sdpa_config_t xehpg_h128_2nd = {8, 16, 16, 8, 16, 1, 8, 2};
+sdpa_config_t xehpg_h128_s256_2nd = {8, 16, 32, 8, 8, 1, 4, 2};
+
+sdpa_config_t xehpg_h256 = {16, 16, 32, 8, 16, 2, 8, 4};
+sdpa_config_t xehpg_h256_s128 = {8, 16, 32, 16, 8, 4, 8, 4};
+sdpa_config_t xehpg_h256_s32 = {8, 16, 32, 8, 16, 2, 8, 4};
+sdpa_config_t xehpg_h256_2nd = {8, 8, 16, 8, 16, 1, 16, 1};
+sdpa_config_t xehpg_h256_s64_2nd = {16, 8, 16, 8, 16, 1, 16, 1};
+sdpa_config_t xehpg_h256_s32_2nd = {16, 16, 32, 8, 16, 1, 8, 2};
+
+sdpa_config_t xehpc_h32 = {16, 64, 32, 16, 4, 2, 1, 8};
+sdpa_config_t xehpc_h32_s32 = {16, 16, 16, 16, 2, 4, 2, 4};
+sdpa_config_t xehpc_h32_2nd = {16, 64, 16, 16, 8, 1, 2, 4};
+
+sdpa_config_t xehpc_h64 = {16, 64, 32, 16, 8, 2, 2, 8};
+sdpa_config_t xehpc_h64_s64 = {32, 32, 32, 16, 4, 2, 2, 4};
+sdpa_config_t xehpc_h64_s32 = {16, 16, 16, 16, 4, 2, 4, 2};
+sdpa_config_t xehpc_h64_2nd = {32, 32, 32, 16, 4, 1, 2, 2};
+sdpa_config_t xehpc_h64_s64_2nd = {16, 16, 16, 16, 4, 1, 4, 1};
+
+sdpa_config_t xehpc_h128 = {16, 64, 32, 16, 16, 2, 4, 8};
+sdpa_config_t xehpc_h128_s64 = {16, 32, 32, 32, 4, 2, 4, 2};
+sdpa_config_t xehpc_h128_s32 = {16, 16, 16, 16, 8, 2, 8, 2};
+sdpa_config_t xehpc_h128_2nd = {32, 32, 32, 16, 8, 1, 4, 2};
+
+sdpa_config_t xehpc_h256 = {16, 32, 32, 32, 8, 4, 8, 4};
+sdpa_config_t xehpc_h256_s64 = {16, 32, 32, 32, 8, 1, 8, 1};
+sdpa_config_t xehpc_h256_2nd = {16, 16, 16, 16, 16, 1, 16, 1};
+
+sdpa_config_t *choose_config_xehpg(int head_size, int seq, bool thin_q) {
+    if (head_size <= 32) {
+        if (thin_q) return &xehpg_h32_2nd;
+        if (seq <= 32) return &xehpg_h32_s32;
+        if (seq <= 64) return &xehpg_h32_s64;
+        if (seq <= 256) return &xehpg_h32_s256;
+        return &xehpg_h32;
+    } else if (head_size <= 64) {
+        if (thin_q) return &xehpg_h64_2nd;
+        if (seq <= 64) return &xehpg_h64_s64;
+        if (seq <= 128) return &xehpg_h64_s128;
+        return &xehpg_h64;
+    } else if (head_size <= 128) {
+        if (thin_q) {
+            if (seq <= 256) return &xehpg_h128_s256_2nd;
+            return &xehpg_h128_2nd;
+        }
+        if (seq <= 32) return &xehpg_h128_s32;
+        return &xehpg_h128;
+    } else if (head_size <= 256) {
+        if (thin_q) {
+            if (seq <= 32) return &xehpg_h256_s32_2nd;
+            if (seq <= 64) return &xehpg_h256_s64_2nd;
+            return &xehpg_h256_2nd;
+        }
+        if (seq <= 32) return &xehpg_h256_s32;
+        if (seq <= 128) return &xehpg_h256_s128;
+        return &xehpg_h256;
+    }
+    return nullptr;
+}
+
+sdpa_config_t *choose_config_xehpc(int head_size, int seq, bool thin_q) {
+    if (head_size <= 32) {
+        if (thin_q) return &xehpc_h32_2nd;
+        if (seq <= 32) return &xehpc_h32_s32;
+        return &xehpc_h32;
+    } else if (head_size <= 64) {
+        if (thin_q) {
+            if (seq <= 64) return &xehpc_h64_s64_2nd;
+            return &xehpc_h64_2nd;
+        }
+        if (seq <= 32) return &xehpc_h64_s32;
+        if (seq <= 64) return &xehpc_h64_s64;
+        return &xehpc_h64;
+    } else if (head_size <= 128) {
+        if (thin_q) return &xehpc_h128_2nd;
+        if (seq <= 32) return &xehpc_h128_s32;
+        if (seq <= 64) return &xehpc_h128_s64;
+        return &xehpc_h128;
+    } else if (head_size <= 256) {
+        if (thin_q) return &xehpc_h256_2nd;
+        if (seq <= 64) return &xehpc_h256_s64;
+        return &xehpc_h256;
+    }
+    return nullptr;
+}
+
+}  // namespace
+
+std::mutex SDPAKernelMicro::m;
+
+void SDPAKernelMicro::init_microkernels(const sdpa_params& params, micro::Package& gemm_kq, micro::Package& gemm_vs, bool is_prefill) const {
+    // TODO: Remove once micro API is thread safe
+    std::lock_guard<std::mutex> l(m);
+    const auto& Q = params.inputs[0];
+    const auto& K = params.inputs[1];
+    const auto& V = params.inputs[2];
+
+    auto& out = params.outputs[0];
+    const auto head_size = params.conf.head_size;
+    const auto d_max = get_d_max(head_size);
+    const Tensor::Dim n_keys = get_seq_length(K, params.input1_order);
+    const Tensor::Dim n_queries = get_seq_length(Q, params.input0_order);
+    const Tensor::Dim n_values = V.X();
+    const auto batch = out.Batch().v * out.Feature().v;
+
+    /* Retrieve pre-tuned kernel configuration */
+    sdpa_config_t *config = nullptr;
+    bool thin_q = (!n_queries.is_dynamic && (n_queries.v <= 16)) || !is_prefill;
+
+    switch (params.engineInfo.arch) {
+        case gpu_arch::xe_hpg: {
+            config = choose_config_xehpg(static_cast<int32_t>(head_size), static_cast<int32_t>(n_keys.v), thin_q);
+            break;
+        }
+        case gpu_arch::xe_hpc:
+        case gpu_arch::xe2: {
+            config = choose_config_xehpc(static_cast<int32_t>(head_size), static_cast<int32_t>(n_keys.v), thin_q);
+            break;
+        }
+        default: break;
+    }
+
+    OPENVINO_ASSERT(config != nullptr);
+
+    /* Get device information */
+    micro::HWInformation hw_info;
+    hw_info.euCount = params.engineInfo.computeUnitsCount;
+    hw_info.gmdid = params.engineInfo.ip_version;
+    hw_info.systolicAvailable = params.engineInfo.supports_immad;
+
+    /* Set up GEMMProblem structure for first GEMM: K^T * Q */
+    micro::GEMMProblem problem;
+    problem.Ta = problem.Ta_ext = convert_type(K.GetDType());
+    problem.Tb = problem.Tb_ext = convert_type(Q.GetDType());
+    problem.Tc = problem.Tc_ext = micro::Type::f32;
+    problem.Ts = problem.Tc;
+
+    auto problem_kq = problem;
+    problem_kq.A.layout = micro::MatrixLayout::T;
+    problem_kq.B.layout = micro::MatrixLayout::Pr;
+    problem_kq.C.layout = micro::MatrixLayout::T;
+    problem_kq.A.setAlignment(micro::alignment_for_ld(head_size * problem.Ta));
+    problem_kq.B.setAlignment(64); // Q is packed in VNNI format in SLM
+    problem_kq.B.crosspack = 2;
+    problem_kq.B.tileR = d_max;
+    problem_kq.B.tileC = static_cast<uint16_t>(subgroup_size(params.engineInfo.arch));
+
+    /* Set up problem size information */
+    micro::SizeParams sizes;
+    sizes.m = static_cast<int64_t>(n_keys.v);
+    sizes.n = static_cast<int64_t>(n_queries.v);
+    sizes.k = static_cast<int64_t>(head_size);
+    sizes.batch = static_cast<int64_t>(batch);
+
+    /* Set up microkernel requirements */
+    std::vector<micro::StrategyRequirement> reqs_kq;
+    reqs_kq.push_back(micro::StrategyRequirement::UnrollM == config->unroll_m_kq);
+    reqs_kq.push_back(micro::StrategyRequirement::UnrollN == config->unroll_n_kq);
+    reqs_kq.push_back(micro::StrategyRequirement::WGM == config->wg_m_kq);
+    reqs_kq.push_back(micro::StrategyRequirement::WGN == config->wg_n_kq);
+
+    /* Set up microkernel options */
+    micro::GEMMProtocol::Options opts_kq;
+    opts_kq.localB = true;
+    opts_kq.slmPtr = true;
+
+    /* Ask microkernel provider for microkernel */
+    gemm_kq = micro::select_gemm_microkernel(opts_kq, hw_info, sizes, problem_kq, reqs_kq);
+
+    /* Update for second GEMM: V*S */
+    auto problem_vs = problem;
+    problem_vs.Ta = problem_vs.Ta_ext = convert_type(V.GetDType());
+    problem_vs.A.layout = micro::MatrixLayout::N;
+    problem_vs.B.layout = micro::MatrixLayout::Pr;
+    problem_vs.C.layout = micro::MatrixLayout::N;
+    problem_vs.A.setAlignment(micro::alignment_for_ld(head_size * problem.Ta));
+    problem_vs.B.setAlignment(64); // S is packed in SLM
+    problem_vs.B.crosspack = 16;
+    sizes.m = static_cast<int64_t>(n_values.v);
+    sizes.n = gemm_kq.getSetting("wg_tile_n");
+    sizes.k = gemm_kq.getSetting("wg_tile_m");
+
+    /* Set up special kernel requirements */
+    std::vector<micro::StrategyRequirement> reqs_vs;
+    reqs_vs.push_back(micro::StrategyRequirement::UnrollM == config->unroll_m_vs);
+    reqs_vs.push_back(micro::StrategyRequirement::UnrollN == config->unroll_n_vs);
+    reqs_vs.push_back(micro::StrategyRequirement::WGM == config->wg_m_vs);
+    reqs_vs.push_back(micro::StrategyRequirement::WGN == config->wg_n_vs);
+
+    micro::GEMMProtocol::Options opts_vs;
+    opts_vs.localB = true;
+    opts_vs.slmPtr = true;
+
+    auto adjust_vs = [](micro::GEMMStrategy &strategy) {
+        /* Enable dpasw */
+        strategy.dpasw |= strategy.fused;
+    };
+    /* Ask microkernel provider for microkernel */
+    gemm_vs = micro::select_gemm_microkernel(opts_vs, hw_info, sizes, problem_vs, reqs_vs, adjust_vs);
+}
+
+ParamsKey SDPAKernelMicro::GetSupportedKey() const {
+    ParamsKey k;
+    k.EnableInputDataType(Datatype::F16);
+    k.EnableOutputDataType(Datatype::F16);
+
+    k.EnableInputLayout(DataLayout::bfyx);
+    k.EnableOutputLayout(DataLayout::bfyx);
+
+    k.EnableDifferentTypes();
+    k.EnableTensorOffset();
+    k.EnableTensorPitches();
+    k.EnableBatching();
+    k.EnableDynamicShapesSupport();
+
+    return k;
+}
+
+bool SDPAKernelMicro::Validate(const Params& p) const {
+    if (!Parent::Validate(p))
+        return false;
+
+    const sdpa_params& params = static_cast<const sdpa_params&>(p);
+
+    if (params.engineInfo.arch < gpu_arch::xe_hpg || !params.engineInfo.supports_microkernels)
+        return false;
+
+    if (params.conf.is_causal)
+        return false;
+
+    if (params.indirect_axis != -1)
+        return false;
+
+    auto Q_num_heads_dim = get_num_heads(params.inputs[0], params.input0_order);
+    auto K_num_heads_dim = get_num_heads(params.inputs[1], params.input1_order);
+    auto V_num_heads_dim = get_num_heads(params.inputs[2], params.input2_order);
+
+    if (params.input0_order != params.input1_order || params.input0_order != params.input2_order)
+        return false;
+
+    if (params.input0_order[3] != 3)
+        return false;
+
+    if (Q_num_heads_dim.is_dynamic || K_num_heads_dim.is_dynamic || V_num_heads_dim.is_dynamic || K_num_heads_dim.v != V_num_heads_dim.v)
+        return false;
+
+    if (params.conf.head_size > 256)
+        return false;
+
+    return true;
+}
+
+JitConstants SDPAKernelMicro::GetJitConstants(const sdpa_params& params, const micro::Package& gemm_kq, const micro::Package& gemm_vs) const {
+    auto jit = MakeBaseParamsJitConstants(params);
+    const auto& prim_params = dynamic_cast<const sdpa_params&>(params);
+
+    const auto& Q = prim_params.inputs[0];
+    const auto& K = prim_params.inputs[1];
+    const auto& V = prim_params.inputs[2];
+
+    const auto head_size = prim_params.conf.head_size;
+
+    auto ldq = head_size * Q.ElementSize();
+    auto ldk = head_size * K.ElementSize();
+    auto ldv = head_size * V.ElementSize();
+    auto lda = head_size * prim_params.outputs[0].ElementSize();
+
+    const auto d_max = get_d_max(head_size);
+    const auto n_keys = get_seq_length(K, prim_params.input1_order);
+    const auto n_queries = get_seq_length(Q, prim_params.input0_order);
+    const auto n_values = V.X();
+
+    jit.AddConstant(MakeJitConstant("D_MAX", d_max));
+    jit.AddConstant(MakeJitConstant("SUBGROUP_SIZE", subgroup_size(prim_params.engineInfo.arch)));
+    jit.AddConstant(MakeJitConstant("INVERT_SCALE", false));
+    jit.AddConstant(MakeJitConstant("SCALE_DATA_T", "half"));
+
+    jit.AddConstant(MakeJitConstant("WITH_ATTN_MASK", params.inputs.size() > 3));
+    jit.AddConstant(MakeJitConstant("WITH_SCALE", params.inputs.size() > 4));
+    jit.AddConstant(MakeJitConstant("Q_ALIGN", micro::alignment_for_ld(ldq)));
+    jit.AddConstant(MakeJitConstant("K_ALIGN", micro::alignment_for_ld(ldk)));
+    jit.AddConstant(MakeJitConstant("V_ALIGN", micro::alignment_for_ld(ldv)));
+    jit.AddConstant(MakeJitConstant("A_ALIGN", micro::alignment_for_ld(lda)));
+
+    jit.AddConstant(MakeJitConstant("TRANSPOSE_K", false));
+
+    int tile_k = gemm_kq.getSetting("wg_tile_m");
+    int tile_q = gemm_kq.getSetting("wg_tile_n");
+    int tile_v = gemm_vs.getSetting("wg_tile_m");
+
+    bool d_full = (head_size == d_max);
+    bool v_full = (head_size == tile_v);
+    bool k_full = !n_keys.is_dynamic && (n_keys.v % tile_k) == 0;
+    bool q_full = !n_queries.is_dynamic && (n_queries.v % tile_q) != 0;
+
+    auto Q_num_heads_dim = get_num_heads(Q, params.input0_order);
+    auto K_num_heads_dim = get_num_heads(K, params.input1_order);
+
+    jit.AddConstant(MakeJitConstant("REMAINDER_K", !k_full));
+    jit.AddConstant(MakeJitConstant("KV_GROUP_SIZE", Q_num_heads_dim.v / K_num_heads_dim.v));
+
+    if (d_full) {
+        if (ldq % 4 == 0)
+            jit.AddConstant(MakeJitConstant("BLOCK_Q", 1));
+        // TODO: Causes accuracy drop for static SD model. Enable back once the issue is resolved
+        // if (lda % 4 == 0 && v_full)
+        //     jit.AddConstant(MakeJitConstant("BLOCK_A", 1));
+        jit.AddConstant(MakeJitConstant("REMAINDER_Q", !q_full));
+    } else if (params.engineInfo.arch >= gpu_arch::xe_hpc) {
+        auto vbytes = n_values.v * V.ElementSize();
+        if (lda % 16 == 0 && vbytes % 4 == 0)
+            jit.AddConstant(MakeJitConstant("BLOCK_2D_A", 1));
+    }
+
+    if (params.engineInfo.arch >= gpu_arch::xe_hpc) {
+        jit.AddConstant(MakeJitConstant("PREFETCH_MASK", 1));
+        jit.AddConstant(MakeJitConstant("PREFETCH_K0", 1));
+        jit.AddConstant(MakeJitConstant("PREFETCH_K", 1));
+        jit.AddConstant(MakeJitConstant("PREFETCH_V", 1));
+        bool no_rem = d_full && v_full && k_full;
+        jit.AddConstant(MakeJitConstant("PREFETCH_REMAINDER", !no_rem));
+        jit.AddConstant(MakeJitConstant("PREFETCH_D_MAX", std::min<int64_t>(d_max, 64)));
+    }
+
+    auto unit_parameters = [](std::string prefix) {
+        JitConstants definitions({});
+        for (size_t i = 0; i < 4; i++) {
+            definitions.AddConstant(MakeJitConstant(prefix + "_B" + std::to_string(i), 1));
+            definitions.AddConstant(MakeJitConstant(prefix + "_SB" + std::to_string(i), 1));
+        }
+
+        return definitions;
+    };
+
+    auto convert_strides = [](std::string target_prefix, std::string source_prefix, const std::vector<int64_t> order) {
+        JitConstants definitions({});
+
+        std::vector<std::string> target_definitions = {
+            target_prefix + "_S0",
+            target_prefix + "_S1",
+            target_prefix + "_S2",
+            target_prefix + "_S3",
+        };
+
+        std::vector<std::string> source_definitions = {
+            source_prefix + "_BATCH_PITCH",
+            source_prefix + "_FEATURE_PITCH",
+            source_prefix + "_Y_PITCH",
+            source_prefix + "_X_PITCH",
+        };
+
+        for (size_t i = 0; i < target_definitions.size(); i++) {
+            definitions.AddConstant(MakeJitConstant(target_definitions[i], source_definitions[order[i]]));
+        }
+
+        return definitions;
+    };
+
+    jit.Merge(convert_strides("QRY", "INPUT0", prim_params.input0_order));
+    jit.Merge(convert_strides("KEY", "INPUT1", prim_params.input1_order));
+    jit.Merge(convert_strides("VAL", "INPUT2", prim_params.input2_order));
+    jit.Merge(convert_strides("DST", "OUTPUT", prim_params.output_order));
+
+    jit.Merge(unit_parameters("QRY"));
+    jit.Merge(unit_parameters("KEY"));
+    jit.Merge(unit_parameters("VAL"));
+    jit.Merge(unit_parameters("DST"));
+
+    return jit;
+}
+
+CommonDispatchData SDPAKernelMicro::SetDefault(const sdpa_params& params, const micro::Package& gemm_kq, const micro::Package& gemm_vs) const {
+    CommonDispatchData dispatch_data;
+
+    auto wg_tile_q = gemm_kq.getSetting("wg_tile_n");
+    auto sg_per_wg = gemm_kq.getSetting("sg_per_wg_m") * gemm_kq.getSetting("sg_per_wg_n");
+
+    dispatch_data.lws = {subgroup_size(params.engineInfo.arch), (size_t)sg_per_wg, 1};
+    dispatch_data.gws = dispatch_data.lws;
+
+    dispatch_data.gws[0] *= CeilDiv(get_seq_length(params.inputs[0], params.input0_order).v, wg_tile_q);
+    dispatch_data.gws[1] *= params.outputs[0].Feature().v;
+    dispatch_data.gws[2] *= params.outputs[0].Batch().v;
+
+    return dispatch_data;
+}
+
+clKernelData SDPAKernelMicro::get_kernel_data(const sdpa_params& params, bool is_prefill) const {
+    auto name = kernelName + (is_prefill ? "_prefill" : "_generate");
+
+    std::vector<micro::Package> gemms(2); // KQ and VS
+    init_microkernels(params, gemms[kq_id], gemms[vs_id], is_prefill);
+    auto dispatch_data = SetDefault(params, gemms[kq_id], gemms[vs_id]);
+    auto entry_point = GetEntryPoint(name, params.layerID, params);
+    auto jit = CreateJit(name, GetJitConstants(params, gemms[kq_id], gemms[vs_id]), entry_point);
+    clKernelData kernel;
+
+    FillCLKernelData(kernel, dispatch_data, params.engineInfo, kernelName, jit, entry_point,
+                     "", false, false, static_cast<int>(params.inputs.size()),
+                     GetFusedPrimitiveInputsCount(params), 1, params.is_shape_agnostic);
+
+    kernel.params.arguments.clear();
+    if (params.is_shape_agnostic )
+        kernel.params.arguments.push_back({ArgumentDescriptor::Types::SHAPE_INFO, 0});
+
+    kernel.params.arguments.push_back({ArgumentDescriptor::Types::INPUT, 1}); // K
+    kernel.params.arguments.push_back({ArgumentDescriptor::Types::INPUT, 0}); // Q
+    kernel.params.arguments.push_back({ArgumentDescriptor::Types::INPUT, 2}); // V
+    kernel.params.arguments.push_back({ArgumentDescriptor::Types::OUTPUT, 0}); // A
+
+    if (params.inputs.size() >= 4)
+        kernel.params.arguments.push_back({ArgumentDescriptor::Types::INPUT, 3}); // mask
+    if (params.inputs.size() >= 5)
+        kernel.params.arguments.push_back({ArgumentDescriptor::Types::INPUT, 4}); // Scale
+
+    kernel.params.arguments.push_back({ArgumentDescriptor::Types::SCALAR, 0}); // D
+    kernel.params.arguments.push_back({ArgumentDescriptor::Types::SCALAR, 1}); // K
+    kernel.params.arguments.push_back({ArgumentDescriptor::Types::SCALAR, 2}); // Q
+
+    const auto& Q = params.inputs[0];
+    const auto& K = params.inputs[1];
+
+    const auto n_queries = get_seq_length(Q, params.input0_order);
+    const auto n_keys = get_seq_length(K, params.input1_order);
+
+    auto head_size = params.conf.head_size;
+
+    ScalarDescriptor s_d;
+    s_d.t = ScalarDescriptor::Types::INT32;
+    s_d.v.s32 = static_cast<uint32_t>(head_size);
+
+    ScalarDescriptor s_k;
+    s_k.t = ScalarDescriptor::Types::INT32;
+    s_k.v.s32 = static_cast<uint32_t>(n_keys.v);
+
+    ScalarDescriptor s_q;
+    s_q.t = ScalarDescriptor::Types::INT32;
+    s_q.v.s32 = static_cast<uint32_t>(n_queries.v);
+
+    kernel.params.scalars.push_back(s_d);
+    kernel.params.scalars.push_back(s_k);
+    kernel.params.scalars.push_back(s_q);
+
+    /* Generate microkernel shims */
+    micro::ShimOptions shim_options;
+    shim_options.subgroupSize = static_cast<int32_t>(subgroup_size(params.engineInfo.arch));
+    shim_options.useTileOps = true;
+    shim_options.decorator = "kq";
+
+    kernel.code.kernelString->jit += generateShim(gemms[kq_id], micro::HostLanguage::OpenCL_C, shim_options);
+
+    shim_options.microkernelID++;
+    shim_options.decorator = "vs";
+    kernel.code.kernelString->jit += generateShim(gemms[vs_id], micro::HostLanguage::OpenCL_C, shim_options);
+
+    if (gemms[kq_id].grfMin > 128 || gemms[vs_id].grfMin > 128)
+        kernel.code.kernelString->options += " -cl-intel-256-GRF-per-thread";
+
+    std::string extra_options = " -Dcl_intel_dot_accumulate";
+    extra_options += " -Dcl_intel_global_float_atomic";
+    extra_options += " -Dcl_intel_subgroup_matrix_multiply_accumulate";
+    extra_options += " -Dcl_intel_subgroup_split_matrix_multiply_accumulate";
+    kernel.code.kernelString->options += extra_options;
+
+    kernel.code.kernelString->batch_compilation = false;
+    kernel.code.kernelString->has_microkernels = true;
+
+    for (auto& p : gemms) {
+        kernel.micro_kernels.push_back(std::make_shared<micro::MicroKernelPackage>(p));
+    }
+
+    return kernel;
+}
+
+KernelsData SDPAKernelMicro::GetKernelsData(const Params& params) const {
+    const size_t num_kernels = params.is_shape_agnostic ? 2 : 1;
+    KernelData kd = KernelData::Default<sdpa_params>(params, num_kernels);
+    const auto& prim_params = dynamic_cast<const sdpa_params&>(params);
+
+    if (!Validate(params)) {
+        return {};
+    }
+
+    for (size_t i = 0; i < num_kernels; i++) {
+        kd.kernels[i] = get_kernel_data(prim_params, i == prefill_id);
+    }
+
+    GetUpdateDispatchDataFunc(kd);
+
+    return { kd };
+}
+
+void SDPAKernelMicro::GetUpdateDispatchDataFunc(KernelData& kd) const {
+    kd.update_dispatch_data_func = [this](const Params& params, KernelData& kernel_data) {
+        const auto& prim_params = static_cast<const sdpa_params&>(params);
+        const auto& Q = prim_params.inputs[0];
+        const auto& K = prim_params.inputs[1];
+
+        const auto n_queries = get_seq_length(Q, prim_params.input0_order);
+        const auto n_keys = get_seq_length(K, prim_params.input1_order);
+
+        auto head_size = prim_params.conf.head_size;
+
+        ScalarDescriptor s_d;
+        s_d.t = ScalarDescriptor::Types::INT32;
+        s_d.v.s32 = static_cast<uint32_t>(head_size);
+
+        ScalarDescriptor s_k;
+        s_k.t = ScalarDescriptor::Types::INT32;
+        s_k.v.s32 = static_cast<uint32_t>(n_keys.v);
+
+        ScalarDescriptor s_q;
+        s_q.t = ScalarDescriptor::Types::INT32;
+        s_q.v.s32 = static_cast<uint32_t>(n_queries.v);
+
+        // TODO: Currently 2nd token version works slower than prefill version
+        const bool is_prefill = true;//n_queries.v > 1;
+
+        OPENVINO_ASSERT(kernel_data.kernels.size() == 2, "[GPU] Invalid kernels size for update dispatch data func");
+
+        size_t target_kernel = is_prefill ? prefill_id : generate_id;
+
+        kernel_data.kernels[prefill_id].skip_execution = true;
+        kernel_data.kernels[generate_id].skip_execution = true;
+
+        const auto& gemms = kernel_data.kernels[target_kernel].micro_kernels;
+        auto dispatchData = SetDefault(prim_params, gemms[kq_id]->p, gemms[vs_id]->p);
+        kernel_data.kernels[target_kernel].params.workGroups.global = dispatchData.gws;
+        kernel_data.kernels[target_kernel].params.workGroups.local = dispatchData.lws;
+        kernel_data.kernels[target_kernel].skip_execution = KernelData::SkipKernelExecution(prim_params);
+
+        kernel_data.kernels[target_kernel].params.scalars.clear();
+        kernel_data.kernels[target_kernel].params.scalars.push_back(s_d);
+        kernel_data.kernels[target_kernel].params.scalars.push_back(s_k);
+        kernel_data.kernels[target_kernel].params.scalars.push_back(s_q);
+    };
+}
+
+KernelsPriority SDPAKernelMicro::GetKernelsPriority(const Params& /*params*/) const {
+    return FORCE_PRIORITY_1;
+}
+}  // namespace kernel_selector
+
+#endif // ENABLE_ONEDNN_FOR_GPU
diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernels/sdpa/sdpa_kernel_micro.h b/src/plugins/intel_gpu/src/kernel_selector/kernels/sdpa/sdpa_kernel_micro.h
new file mode 100644
index 00000000000000..f282c52f8437c9
--- /dev/null
+++ b/src/plugins/intel_gpu/src/kernel_selector/kernels/sdpa/sdpa_kernel_micro.h
@@ -0,0 +1,47 @@
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#ifdef ENABLE_ONEDNN_FOR_GPU
+
+#pragma once
+
+#include "sdpa_kernel_base.h"
+#include "micro_utils.hpp"
+
+namespace kernel_selector {
+class SDPAKernelMicro : public SDPAKernelBase {
+public:
+    using Parent = SDPAKernelBase;
+    SDPAKernelMicro() : SDPAKernelBase("sdpa_micro") {}
+    virtual ~SDPAKernelMicro() {}
+
+    KernelsData GetKernelsData(const Params& params) const override;
+    KernelsPriority GetKernelsPriority(const Params& params) const override;
+    ParamsKey GetSupportedKey() const override;
+
+protected:
+    bool Validate(const Params& p) const override;
+    void GetUpdateDispatchDataFunc(KernelData& kd) const override;
+    CommonDispatchData SetDefault(const sdpa_params& params, const micro::Package& gemm_kq, const micro::Package& gemm_vs) const;
+    JitConstants GetJitConstants(const sdpa_params& params, const micro::Package& gemm_kq, const micro::Package& gemm_vs) const;
+    std::vector<FusedOpType> GetSupportedFusedOps() const override {
+        return {};
+    }
+
+    void init_microkernels(const sdpa_params& params, micro::Package& gemm_kq, micro::Package& gemm_vs, bool is_prefill) const;
+    clKernelData get_kernel_data(const sdpa_params& params, bool is_prefill) const;
+
+private:
+    static constexpr size_t prefill_id = 0;
+    static constexpr size_t generate_id = 1;
+
+    static constexpr size_t kq_id = 0;
+    static constexpr size_t vs_id = 1;
+
+    static std::mutex m;
+};
+}  // namespace kernel_selector
+
+
+#endif // ENABLE_ONEDNN_FOR_GPU
diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernels/sdpa/sdpa_kernel_opt.cpp b/src/plugins/intel_gpu/src/kernel_selector/kernels/sdpa/sdpa_kernel_opt.cpp
index 23dc964eb18eea..8eb9dea65ba23d 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/kernels/sdpa/sdpa_kernel_opt.cpp
+++ b/src/plugins/intel_gpu/src/kernel_selector/kernels/sdpa/sdpa_kernel_opt.cpp
@@ -18,13 +18,26 @@ enum KernelsTypes {
     TOTAL_KERNELS_NUM
 };
 
+static size_t get_sg_number_scale_factor(const sdpa_params& sdpa_params) {
+    const size_t optimal_scale_factor = 2;
+    if (sdpa_params.conf.head_size * optimal_scale_factor <= sdpa_params.engineInfo.maxWorkGroupSize)
+        return optimal_scale_factor;
+
+    return 1;
+}
+
 static size_t get_target_seq_len_block_size() {
     const size_t block_size = 16;
     return block_size;
 }
 
-static size_t get_seq_len_partition_size() {
-    const size_t seq_len = 256;
+static size_t get_seq_len_partition_size(const sdpa_params& sdpa_params, const size_t kernel_idx) {
+    size_t seq_len = 0;
+    if (kernel_idx == KernelsTypes::MULTI_TOKENS)
+        seq_len = sdpa_params.conf.head_size * get_sg_number_scale_factor(sdpa_params);
+    else
+        seq_len = 256;
+
     return seq_len;
 }
 
@@ -89,7 +102,7 @@ JitConstants SDPAKernelOpt::GetJitConstants(const sdpa_params& params, size_t ke
     const auto& config = params.conf;
     jit.AddConstant(MakeJitConstant("SUBGROUP_SIZE", subgroup_size));
     jit.AddConstant(MakeJitConstant("HEAD_SIZE", config.head_size));
-    jit.AddConstant(MakeJitConstant("SEQ_LEN_PARTITION_SIZE", get_seq_len_partition_size()));
+    jit.AddConstant(MakeJitConstant("SEQ_LEN_PARTITION_SIZE", get_seq_len_partition_size(params, kernel_idx)));
 
     auto target_seq_len_block_size = kernel_idx == KernelsTypes::SINGLE_TOKEN ? 1 : get_target_seq_len_block_size();
     jit.AddConstant(MakeJitConstant("TARGET_SEQ_LEN_BLOCK_SIZE", target_seq_len_block_size));
@@ -97,6 +110,20 @@ JitConstants SDPAKernelOpt::GetJitConstants(const sdpa_params& params, size_t ke
     auto sdpa_stage = kernel_idx == KernelsTypes::FINALIZATION ? 1 : 0;
     jit.AddConstant(MakeJitConstant("SDPA_STAGE_" + std::to_string(sdpa_stage), 1));
 
+    if (params.inputs.size() <= 4) {
+        jit.AddConstant(MakeJitConstant("STATIC_SCALE_VALUE_INV", std::sqrt(static_cast<float>(params.conf.head_size))));
+        jit.AddConstant(MakeJitConstant("STATIC_SCALE_VALUE", 1.0f / std::sqrt(static_cast<float>(params.conf.head_size))));
+    }
+
+    if (kernel_idx == KernelsTypes::MULTI_TOKENS) {
+        jit.AddConstant(MakeJitConstant("SG_SCALE_FACTOR", get_sg_number_scale_factor(params)));
+    } else {
+        jit.AddConstant(MakeJitConstant("SG_SCALE_FACTOR", 1));
+    }
+
+    if (params.engineInfo.supports_immad && params.conf.broadcast_axis == -1 && params.conf.head_size >= 128)
+        jit.AddConstant(MakeJitConstant("LOAD_KEY_LEFTOVERS_IN_CALC_LOOP", 1));
+
     return jit;
 }
 
@@ -115,14 +142,21 @@ CommonDispatchData SDPAKernelOpt::SetDefault(const sdpa_params& params, size_t k
         const size_t source_seq_len = dims_k.y_dim().v;
         const size_t target_seq_len = dims_q.y_dim().v;
         const size_t head_size = static_cast<size_t>(params.conf.head_size);
-        const size_t num_of_partitions = CeilDiv(source_seq_len, get_seq_len_partition_size());
+        const size_t num_of_partitions =
+            kernel_idx == KernelsTypes::MULTI_TOKENS ? 1 : CeilDiv(source_seq_len, get_seq_len_partition_size(params, kernel_idx));
         const size_t target_seq_len_block_size = kernel_idx == 1 ? get_target_seq_len_block_size() : 1;
 
-        if (kernel_idx == KernelsTypes::SINGLE_TOKEN || kernel_idx == KernelsTypes::MULTI_TOKENS) {
+        if (kernel_idx == KernelsTypes::SINGLE_TOKEN) {
             dispatch_data.gws = { batch_size * heads_num,
                                   CeilDiv(target_seq_len, target_seq_len_block_size),
                                   head_size * num_of_partitions };
             dispatch_data.lws = { 1, 1, head_size };
+        } else if (kernel_idx == KernelsTypes::MULTI_TOKENS) {
+            const size_t sg_num_scale = get_sg_number_scale_factor(params);
+            dispatch_data.gws = { batch_size * heads_num,
+                                  CeilDiv(target_seq_len, target_seq_len_block_size),
+                                  head_size * sg_num_scale };
+            dispatch_data.lws = { 1, 1, head_size * sg_num_scale };
         } else if (kernel_idx == KernelsTypes::FINALIZATION) {
             dispatch_data.gws = { batch_size * heads_num,
                                   target_seq_len,
@@ -140,18 +174,32 @@ KernelsData SDPAKernelOpt::GetKernelsData(const Params& params) const {
     }
 
     // Implementation contains multiple kernels:
-    // kernel[0] - single token generation stage (2nd token)
-    // kernel[1] - multi tokens processing stage (1st token)
-    // kernel[2] - results aggregation
+    // kernel[KernelsTypes::SINGLE_TOKEN] - single token generation stage (2nd token)
+    // kernel[KernelsTypes::MULTI_TOKENS] - multi tokens processing stage (1st token)
+    // kernel[KernelsTypes::FINALIZATION] - results aggregation for 2nd token generation
 
-    const size_t kernels_num = KernelsTypes::TOTAL_KERNELS_NUM;
-    KernelData kd = KernelData::Default<sdpa_params>(params, kernels_num);
+    std::vector<KernelsTypes> kernels_type;
+    const auto& prim_params = static_cast<const sdpa_params&>(params);
+
+    if (params.is_shape_agnostic) {
+        kernels_type = { KernelsTypes::SINGLE_TOKEN, KernelsTypes::MULTI_TOKENS, KernelsTypes::FINALIZATION };
+    } else {
+        TransposedDimensionAccessHelperBase dims_q(prim_params.inputs[0], prim_params.input0_order);
+        const auto target_seq_len = dims_q.y_dim().v;
+        const auto is_prefill = target_seq_len > 1;
+
+        if (is_prefill) {
+            kernels_type = { KernelsTypes::MULTI_TOKENS };
+        } else {
+            kernels_type = { KernelsTypes::SINGLE_TOKEN, KernelsTypes::FINALIZATION };
+        }
+    }
+
+    KernelData kd = KernelData::Default<sdpa_params>(params, kernels_type.size());
     kd.needs_sub_kernels_sync = true;
 
     GetUpdateDispatchDataFunc(kd);
-
-    const auto& prim_params = dynamic_cast<const sdpa_params&>(params);
-    for (size_t kernel_idx = 0; kernel_idx < kernels_num; kernel_idx++) {
+    for (const auto& kernel_idx : kernels_type) {
         auto dispatch_data = SetDefault(prim_params, kernel_idx);
         auto kernel_name = GetKernelName(kernelName, static_cast<KernelsTypes>(kernel_idx), prim_params.indirect_axis != -1);
         auto entry_point = GetEntryPoint(kernel_name, prim_params.layerID, params);
@@ -181,12 +229,14 @@ KernelsData SDPAKernelOpt::GetKernelsData(const Params& params) const {
         auto& output = prim_params.outputs[0];
         auto head_size = output.X().v;
 
-        auto buf_dt_size = 4;
-        auto buf_elements_count = (num_of_partitions == 1) ? 1 : output.LogicalSize() / head_size * num_of_partitions;
+        auto can_skip_allocation = num_of_partitions == 1 || kernel_idx == KernelsTypes::MULTI_TOKENS;
+
+        auto buf_dt_size = BytesPerElement(get_softmax_acc_type());
+        auto buf_elements_count = can_skip_allocation ? 1 : output.LogicalSize() / head_size * num_of_partitions;
         auto buf_size = buf_elements_count * buf_dt_size;
 
-        auto tmp_out_dt_size = 4;
-        auto tmp_out_elements_count = (num_of_partitions == 1) ? 1 : output.LogicalSize() * num_of_partitions;
+        auto tmp_out_dt_size = output.ElementSize();
+        auto tmp_out_elements_count = can_skip_allocation ? 1 : output.LogicalSize() * num_of_partitions;
         auto tmp_out_size = tmp_out_elements_count * tmp_out_dt_size;
 
         if (prim_params.indirect_axis != -1 && kernel_idx != KernelsTypes::FINALIZATION)
@@ -232,35 +282,36 @@ void SDPAKernelOpt::GetUpdateDispatchDataFunc(KernelData& kd) const {
         auto target_seq_len = dims_q.y_dim().v;
         auto head_size = dims_q.x_dim().v;
         auto source_seq_len = dims_k.y_dim().v;
+        auto is_prefill = target_seq_len > 1;
 
-        auto num_of_partitions = CeilDiv(source_seq_len, get_seq_len_partition_size());
+        auto num_of_partitions = CeilDiv(source_seq_len, get_seq_len_partition_size(prim_params, KernelsTypes::SINGLE_TOKEN));
 
         auto buf_dt_size = BytesPerElement(get_softmax_acc_type());
-        auto buf_elements_count = (num_of_partitions == 1) ? 1 : output.LogicalSize() / head_size * num_of_partitions;
+        auto buf_elements_count = (num_of_partitions == 1 || is_prefill) ? 1 : output.LogicalSize() / head_size * num_of_partitions;
         auto buf_size = buf_elements_count * buf_dt_size;
 
         auto tmp_out_dt_size = output.ElementSize();
-        auto tmp_out_elements_count = (num_of_partitions == 1) ? 1 : output.LogicalSize() * num_of_partitions;
+        auto tmp_out_elements_count = (num_of_partitions == 1 || is_prefill) ? 1 : output.LogicalSize() * num_of_partitions;
         auto tmp_out_size = tmp_out_elements_count * tmp_out_dt_size;
 
-        auto dispatch_data1 = SetDefault(prim_params, 0);
+        auto dispatch_data1 = SetDefault(prim_params, KernelsTypes::SINGLE_TOKEN);
         kernel_data.kernels[KernelsTypes::SINGLE_TOKEN].params.workGroups.global = dispatch_data1.gws;
         kernel_data.kernels[KernelsTypes::SINGLE_TOKEN].params.workGroups.local = dispatch_data1.lws;
-        kernel_data.kernels[KernelsTypes::SINGLE_TOKEN].skip_execution = target_seq_len > 1;
+        kernel_data.kernels[KernelsTypes::SINGLE_TOKEN].skip_execution = is_prefill;
 
-        auto dispatch_data2 = SetDefault(prim_params, 1);
+        auto dispatch_data2 = SetDefault(prim_params, KernelsTypes::MULTI_TOKENS);
         kernel_data.kernels[KernelsTypes::MULTI_TOKENS].params.workGroups.global = dispatch_data2.gws;
         kernel_data.kernels[KernelsTypes::MULTI_TOKENS].params.workGroups.local = dispatch_data2.lws;
-        kernel_data.kernels[KernelsTypes::MULTI_TOKENS].skip_execution = target_seq_len == 1;
+        kernel_data.kernels[KernelsTypes::MULTI_TOKENS].skip_execution = !is_prefill;
 
         ScalarDescriptor num_of_partitions_scalar;
         num_of_partitions_scalar.t = ScalarDescriptor::Types::UINT32;
         num_of_partitions_scalar.v.u32 = static_cast<uint32_t>(num_of_partitions);
 
-        auto dispatch_data3 = SetDefault(prim_params, 2);
+        auto dispatch_data3 = SetDefault(prim_params, KernelsTypes::FINALIZATION);
         kernel_data.kernels[KernelsTypes::FINALIZATION].params.workGroups.global = dispatch_data3.gws;
         kernel_data.kernels[KernelsTypes::FINALIZATION].params.workGroups.local = dispatch_data3.lws;
-        kernel_data.kernels[KernelsTypes::FINALIZATION].skip_execution = num_of_partitions == 1;
+        kernel_data.kernels[KernelsTypes::FINALIZATION].skip_execution = is_prefill || num_of_partitions == 1;
 
         kernel_data.kernels[KernelsTypes::FINALIZATION].params.scalars.clear();
         kernel_data.kernels[KernelsTypes::FINALIZATION].params.scalars.push_back(num_of_partitions_scalar);
@@ -273,7 +324,7 @@ void SDPAKernelOpt::GetUpdateDispatchDataFunc(KernelData& kd) const {
     };
 }
 
-KernelsPriority SDPAKernelOpt::GetKernelsPriority(const Params& /*params*/) const {
-    return FORCE_PRIORITY_1;
+KernelsPriority SDPAKernelOpt::GetKernelsPriority(const Params& params) const {
+    return params.engineInfo.supports_immad ?  FORCE_PRIORITY_2 : FORCE_PRIORITY_1;
 }
 }  // namespace kernel_selector
diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernels/sdpa/sdpa_kernel_selector.cpp b/src/plugins/intel_gpu/src/kernel_selector/kernels/sdpa/sdpa_kernel_selector.cpp
index b58f04f23e2643..7e88d0ad71fed1 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/kernels/sdpa/sdpa_kernel_selector.cpp
+++ b/src/plugins/intel_gpu/src/kernel_selector/kernels/sdpa/sdpa_kernel_selector.cpp
@@ -5,12 +5,16 @@
 #include "sdpa_kernel_selector.h"
 #include "sdpa_kernel_ref.h"
 #include "sdpa_kernel_opt.h"
+#include "sdpa_kernel_micro.h"
 
 namespace kernel_selector {
 
 sdpa_kernel_selector::sdpa_kernel_selector() {
     Attach<SDPAKernelOpt>();
     Attach<SDPAKernelRef>();
+#ifdef ENABLE_ONEDNN_FOR_GPU
+    Attach<SDPAKernelMicro>();
+#endif
 }
 
 KernelsData sdpa_kernel_selector::GetBestKernels(const Params& params) const {
diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernels/select/select_kernel_ref.h b/src/plugins/intel_gpu/src/kernel_selector/kernels/select/select_kernel_ref.h
index fbc1a0193da13c..76b357cde6decf 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/kernels/select/select_kernel_ref.h
+++ b/src/plugins/intel_gpu/src/kernel_selector/kernels/select/select_kernel_ref.h
@@ -15,6 +15,10 @@ class SelectKernelRef : public SelectKernelBase {
     KernelsData GetKernelsData(const Params& params) const override;
     KernelsPriority GetKernelsPriority(const Params& params) const override;
     ParamsKey GetSupportedKey() const override;
+    std::vector<FusedOpType> GetSupportedFusedOps() const override {
+        return { FusedOpType::REORDER };
+    }
+
 
 protected:
     bool Validate(const Params& p) const override;
diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernels/swiglu/swiglu_kernel_ref.cpp b/src/plugins/intel_gpu/src/kernel_selector/kernels/swiglu/swiglu_kernel_ref.cpp
index 0e3a5886c3d6ca..8a25916f4a24e9 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/kernels/swiglu/swiglu_kernel_ref.cpp
+++ b/src/plugins/intel_gpu/src/kernel_selector/kernels/swiglu/swiglu_kernel_ref.cpp
@@ -30,6 +30,17 @@ JitConstants SwiGLUKernelRef::GetJitConstants(const swiglu_params& params) const
 
     jit.AddConstants({MakeJitConstant("AXIS", params.axis)});
     jit.AddConstants({MakeJitConstant("SPLIT_LENGTH", params.split_length)});
+    jit.AddConstants({MakeJitConstant("GLU_TYPE", params.glu_type)});
+    const std::string type_suffix = (GetAccumulatorType(params) == Datatype::F32) ? "f" : "h";
+    if (params.glu_type == ov::intel_gpu::op::SwiGLU::GluType::Gelu) {
+        jit.AddConstants({MakeJitConstant("GEGLU_HALF", "0.5" + type_suffix)});
+        jit.AddConstants({MakeJitConstant("GEGLU_MULT", "0.7071067811865475" + type_suffix)});
+    } else if (params.glu_type == ov::intel_gpu::op::SwiGLU::GluType::Gelu_Tanh) {
+        jit.AddConstants({MakeJitConstant("GEGLU_HALF", "0.5" + type_suffix)});
+        jit.AddConstants({MakeJitConstant("GEGLU_MULT", "0.044715" + type_suffix)});
+        jit.AddConstants({MakeJitConstant("GEGLU_SQUARE_2_OVER_PI", "0.79788458347320556640625" + type_suffix)});
+    }
+    jit.AddConstants({MakeJitConstant("SPLIT_TO_GLU_IDX", params.split_to_glu_idx)});
     jit.Merge(MakeTypeJitConstants(GetAccumulatorType(params), "ACCUMULATOR"));
     jit.Merge(GetTensorFriendlyWorkGroupsJit(params.outputs[0]));
 
diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernels/swiglu/swiglu_kernel_ref.h b/src/plugins/intel_gpu/src/kernel_selector/kernels/swiglu/swiglu_kernel_ref.h
index f30c6a9c4f76b2..61cfc06b13dca2 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/kernels/swiglu/swiglu_kernel_ref.h
+++ b/src/plugins/intel_gpu/src/kernel_selector/kernels/swiglu/swiglu_kernel_ref.h
@@ -5,15 +5,19 @@
 #pragma once
 
 #include "kernel_base_opencl.h"
+#include "intel_gpu/op/swiglu.hpp"
 
 namespace kernel_selector {
 ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
 // swiglu_params
 ///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
 struct swiglu_params : public base_params {
-    swiglu_params() : base_params(KernelType::SWIGLU), axis(0), split_length(0) {}
+    swiglu_params() : base_params(KernelType::SWIGLU), axis(0), split_length(0),
+    glu_type(ov::intel_gpu::op::SwiGLU::GluType::Swish), split_to_glu_idx(0) {}
     int32_t axis;
     int32_t split_length;
+    ov::intel_gpu::op::SwiGLU::GluType glu_type;
+    int32_t split_to_glu_idx;
 };
 
 class SwiGLUKernelRef : public KernelBaseOpenCL {
diff --git a/src/plugins/intel_gpu/src/kernel_selector/micro_utils.hpp b/src/plugins/intel_gpu/src/kernel_selector/micro_utils.hpp
new file mode 100644
index 00000000000000..055892aca6c547
--- /dev/null
+++ b/src/plugins/intel_gpu/src/kernel_selector/micro_utils.hpp
@@ -0,0 +1,85 @@
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#ifdef ENABLE_ONEDNN_FOR_GPU
+
+#include "intel_gpu/graph/serialization/binary_buffer.hpp"
+#include "intel_gpu/graph/serialization/string_serializer.hpp"
+
+#ifdef UNUSED
+#   undef UNUSED
+#endif
+
+#ifndef NOMINMAX
+# define NOMINMAX
+#endif
+
+#include "gpu/intel/microkernels/package.hpp"
+#include "gpu/intel/jit/gemm/microkernel_provider.hpp"
+#include "gpu/intel/jit/gemm/gen_gemm_kernel_generator.hpp"
+#include "gpu/intel/microkernels/shim.hpp"
+
+namespace micro {
+
+using Package = dnnl::impl::gpu::intel::micro::Package;
+using HWInformation = dnnl::impl::gpu::intel::jit::HWInformation;
+using GEMMProblem = dnnl::impl::gpu::intel::jit::GEMMProblem;
+using GEMMStrategy = dnnl::impl::gpu::intel::jit::GEMMStrategy;
+using GEMMProtocol = dnnl::impl::gpu::intel::micro::GEMMProtocol;
+using MatrixLayout = dnnl::impl::gpu::intel::jit::MatrixLayout;
+using Type = dnnl::impl::gpu::intel::jit::Type;
+using SizeParams = dnnl::impl::gpu::intel::jit::SizeParams;
+using StrategyRequirement = dnnl::impl::gpu::intel::jit::StrategyRequirement;
+using ShimOptions = dnnl::impl::gpu::intel::micro::ShimOptions;
+using HostLanguage = dnnl::impl::gpu::intel::micro::HostLanguage;
+using Setting = dnnl::impl::gpu::intel::micro::Setting;
+
+// Wrapper for Package which is used in clKernelData with forward declaration
+// to avoid including this header in many places in plugin
+// which may cause symbols conflicts with oneDNN
+struct MicroKernelPackage {
+    MicroKernelPackage() = default;
+    explicit MicroKernelPackage(Package _p) : p(_p) {}
+    Package p;
+
+    // WARNING: We serialize only microkernels settings, so after deserialization
+    // other struct fields are not initializer properly and can't be used
+    void save(cldnn::BinaryOutputBuffer& ob) const {
+        ob << p.settings.size();
+        for (auto& s : p.settings) {
+            ob << s.name;
+            ob << s.value;
+        }
+    }
+
+    void load(cldnn::BinaryInputBuffer& ib) {
+        size_t n_settings;
+        ib >> n_settings;
+        p.settings.clear();
+        for (size_t i = 0; i < n_settings; i++) {
+            Setting s;
+            ib >> s.name;
+            ib >> s.value;
+            p.settings.push_back(s);
+        }
+    }
+};
+
+inline Package select_gemm_microkernel(GEMMProtocol protocol, HWInformation hw_info, SizeParams sizes, const GEMMProblem &problem,
+                                        const std::vector<StrategyRequirement> &reqs = std::vector<StrategyRequirement>(),
+                                        void (*strategyAdjuster)(GEMMStrategy &strategy) = nullptr) {
+    return dnnl::impl::gpu::intel::jit::selectGEMMMicrokernel(protocol, hw_info, sizes, problem, reqs, strategyAdjuster);
+}
+
+static inline int alignment_for_ld(int ld) {
+    return  dnnl::impl::gpu::intel::jit::alignmentForLD(ld);
+}
+
+}  // namespace micro
+
+#undef UNUSED
+
+#endif  // ENABLE_ONEDNN_FOR_GPU
diff --git a/src/plugins/intel_gpu/src/kernel_selector/primitive_db.cpp b/src/plugins/intel_gpu/src/kernel_selector/primitive_db.cpp
index 8863b47fe55bd2..cd8128baff37c9 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/primitive_db.cpp
+++ b/src/plugins/intel_gpu/src/kernel_selector/primitive_db.cpp
@@ -22,7 +22,7 @@ primitive_db::primitive_db()
     : primitives({
 #include "ks_primitive_db.inc"
       }),
-      batch_header_str({
+      batch_headers({
 #include "ks_primitive_db_batch_headers.inc"
       }) {
 }
diff --git a/src/plugins/intel_gpu/src/kernel_selector/primitive_db.h b/src/plugins/intel_gpu/src/kernel_selector/primitive_db.h
index 73858754d35e39..e384f6c9879fb5 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/primitive_db.h
+++ b/src/plugins/intel_gpu/src/kernel_selector/primitive_db.h
@@ -22,7 +22,7 @@ struct primitive_db {
     primitive_db();
 
     std::vector<code> get(const primitive_id& id) const;
-    std::vector<code> get_batch_header_str() const { return std::move(batch_header_str); }
+    std::map<std::string, code> get_batch_headers() const { return std::move(batch_headers); }
 
 private:
     struct case_insensitive_compare {
@@ -35,7 +35,7 @@ struct primitive_db {
         }
     };
     std::multimap<primitive_id, code, case_insensitive_compare> primitives;
-    std::vector<code> batch_header_str;
+    std::map<std::string, code> batch_headers;
 };
 
 }  // namespace cache
diff --git a/src/plugins/intel_gpu/src/kernel_selector/primitive_db_gen.py b/src/plugins/intel_gpu/src/kernel_selector/primitive_db_gen.py
index 4f746d8cc526f6..116844f3bccfc7 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/primitive_db_gen.py
+++ b/src/plugins/intel_gpu/src/kernel_selector/primitive_db_gen.py
@@ -198,7 +198,8 @@ def batch_headers_to_str(self):
         characters = 1  # Newline character above
         res = ""
         for h in self.batch_headers:
-            res += '(std::string) R"(\n'
+            header_name = h[:h.find('.cl')]
+            res += '{{"{}",\n(std::string) R"(\n'.format(header_name)
             header_file = os.path.abspath(os.path.join(os.path.dirname(self.kernels_folder + "/include/batch_headers"), "batch_headers/" + h))
             content = []
             with open(header_file) as f:
@@ -207,11 +208,11 @@ def batch_headers_to_str(self):
                 if line.startswith('#include'):
                     continue
                 if (i + 1) % max_lines == 0 or characters + len(line) + 1 > max_characters:
-                    res += ')",' + ' (std::string) R"('
+                    res += ')"\n + (std::string) R"('
                     characters = 0
                 res += '{}\n'.format(line.rstrip())
                 characters += len(line) + 1
-            res += ')",\n\n'
+            res += ')"},\n\n'
         return self.post_process_sources(res)
 
     def post_process_sources(self, content):
diff --git a/src/plugins/intel_gpu/src/plugin/compiled_model.cpp b/src/plugins/intel_gpu/src/plugin/compiled_model.cpp
index 0f1c8a583abbf8..58c38083d3fc76 100644
--- a/src/plugins/intel_gpu/src/plugin/compiled_model.cpp
+++ b/src/plugins/intel_gpu/src/plugin/compiled_model.cpp
@@ -335,7 +335,8 @@ ov::Any CompiledModel::get_property(const std::string& name) const {
             ov::PropertyName{ov::hint::num_requests.name(), PropertyMutability::RO},
             ov::PropertyName{ov::hint::inference_precision.name(), PropertyMutability::RO},
             ov::PropertyName{ov::device::id.name(), PropertyMutability::RO},
-            ov::PropertyName{ov::execution_devices.name(), PropertyMutability::RO}
+            ov::PropertyName{ov::execution_devices.name(), PropertyMutability::RO},
+            ov::PropertyName{ov::hint::dynamic_quantization_group_size.name(), PropertyMutability::RO}
         };
     } else if (name == ov::model_name) {
         return decltype(ov::model_name)::value_type {m_model_name};
diff --git a/src/plugins/intel_gpu/src/plugin/ops/loop.cpp b/src/plugins/intel_gpu/src/plugin/ops/loop.cpp
index c5ae7bbdef56a8..bb11308064f7e7 100644
--- a/src/plugins/intel_gpu/src/plugin/ops/loop.cpp
+++ b/src/plugins/intel_gpu/src/plugin/ops/loop.cpp
@@ -30,8 +30,14 @@ namespace intel_gpu {
 template<class DATA_TYPE>
 static DATA_TYPE CreateScalarData(ProgramBuilder &p, const cldnn::primitive_id& id, ov::Shape& shape, cldnn::data_types dtype, int64_t num, int64_t rank) {
     auto mem = p.get_engine().allocate_memory({ shape, dtype, cldnn::format::get_default_format(rank) });
-    cldnn::mem_lock<int64_t> ptr{mem, p.get_engine().get_service_stream()};
-    *ptr.begin() = num;
+    if (dtype == cldnn::data_types::i32) {
+        cldnn::mem_lock<int32_t> ptr{mem, p.get_engine().get_service_stream()};
+        *ptr.begin() = static_cast<int32_t>(num);
+    } else {
+        cldnn::mem_lock<int64_t> ptr{mem, p.get_engine().get_service_stream()};
+        *ptr.begin() = num;
+    }
+
     return {id, mem};
 }
 
diff --git a/src/plugins/intel_gpu/src/plugin/ops/rnn.cpp b/src/plugins/intel_gpu/src/plugin/ops/rnn.cpp
index dec429b6c0496e..713e24fe7571bd 100644
--- a/src/plugins/intel_gpu/src/plugin/ops/rnn.cpp
+++ b/src/plugins/intel_gpu/src/plugin/ops/rnn.cpp
@@ -132,7 +132,7 @@ static void CreateLSTMCellOp(ProgramBuilder& p, const std::shared_ptr<ov::op::v4
         auto& stream = p.get_engine().get_service_stream();
         cldnn::mem_lock<char> lock{data_mem, stream};
         auto buf = lock.data();
-        const size_t axis = 1;
+        const int64_t axis = 1;
         std::memcpy(&buf[0], &axis, constLayout.bytes_count());
         p.add_primitive(*op,  cldnn::data(outputDataID, data_mem));
 
@@ -141,7 +141,7 @@ static void CreateLSTMCellOp(ProgramBuilder& p, const std::shared_ptr<ov::op::v4
                         {cldnn::input_info(lstm_elt_id), cldnn::input_info(outputDataID)},
                         hiddenSz,
                         cldnn::tensor{0, 0, 0, 0},
-                        op_mode, 0, num_splits));
+                        op_mode, 0, axis, num_splits));
         p.add_primitive(*op, cldnn::reshape(outputHiddenID, cldnn::input_info(outputHiddenCropID),
                         false, outSzPt, op->get_output_partial_shape(0)), {layerName});
 
@@ -152,7 +152,7 @@ static void CreateLSTMCellOp(ProgramBuilder& p, const std::shared_ptr<ov::op::v4
                         {cldnn::input_info(lstm_elt_id), cldnn::input_info(outputDataID)},
                         hiddenSz,
                         cldnn::tensor{0, 1, 0, 0},
-                        op_mode, 1, num_splits));
+                        op_mode, 1, axis, num_splits));
         p.add_primitive(*op, cldnn::reshape(outputCellID, cldnn::input_info(outputCellCropID),
                         false, outSzPt, op->get_output_partial_shape(1)));
     } else {
diff --git a/src/plugins/intel_gpu/src/plugin/ops/scaled_dot_product_attention.cpp b/src/plugins/intel_gpu/src/plugin/ops/scaled_dot_product_attention.cpp
index d002c868ffd225..0ba780183b186d 100644
--- a/src/plugins/intel_gpu/src/plugin/ops/scaled_dot_product_attention.cpp
+++ b/src/plugins/intel_gpu/src/plugin/ops/scaled_dot_product_attention.cpp
@@ -3,7 +3,6 @@
 //
 
 #include "intel_gpu/plugin/program_builder.hpp"
-#include "intel_gpu/plugin/common_utils.hpp"
 
 #include "intel_gpu/op/sdpa.hpp"
 #include "intel_gpu/op/indirect_sdpa.hpp"
@@ -30,9 +29,15 @@ static void CreateScaledDotProductAttentionOp(ProgramBuilder& p, const std::shar
     auto layerName = layer_type_name_ID(op);
 
     bool is_causal = op->get_causal();
+    auto order = ov::op::internal::SDPA::default_order(op->get_output_partial_shape(0).size());
     auto sdpa_prim = cldnn::scaled_dot_product_attention(layerName,
                                                          inputs,
-                                                         is_causal);
+                                                         is_causal,
+                                                         -1,
+                                                         order,
+                                                         order,
+                                                         order,
+                                                         order);
 
     p.add_primitive(*op, sdpa_prim);
 }
diff --git a/src/plugins/intel_gpu/src/plugin/ops/split.cpp b/src/plugins/intel_gpu/src/plugin/ops/split.cpp
index fc323033be94a9..b36cef6fc079fa 100644
--- a/src/plugins/intel_gpu/src/plugin/ops/split.cpp
+++ b/src/plugins/intel_gpu/src/plugin/ops/split.cpp
@@ -4,7 +4,8 @@
 
 #include "intel_gpu/plugin/program_builder.hpp"
 #include "intel_gpu/plugin/common_utils.hpp"
-
+#include "openvino/core/validation_util.hpp"
+#include "openvino/op/constant.hpp"
 #include "openvino/op/split.hpp"
 #include "openvino/op/variadic_split.hpp"
 
@@ -57,6 +58,11 @@ static void CreateCommonSplitOp(ProgramBuilder& p, const std::shared_ptr<ov::Nod
             }
         }
 
+        int64_t axis = -1;
+        auto const_axis = std::dynamic_pointer_cast<ov::op::v0::Constant>(op->get_input_node_shared_ptr(1));
+        if (const_axis) {
+            axis = ov::util::normalize_axis(op.get(), const_axis->cast_vector<int64_t>()[0], op->get_input_partial_shape(0).rank());
+        }
         cldnn::crop_ngraph_op_mode op_mode = cldnn::crop_ngraph_op_mode::variadic_split;
         auto num_splits = static_cast<size_t>(1);
         if (ov::is_type<ov::op::v1::Split>(op)) {
@@ -69,13 +75,13 @@ static void CreateCommonSplitOp(ProgramBuilder& p, const std::shared_ptr<ov::Nod
             // don't add crop primitive if port is not used by anyone
             if (users.size() == 0)
                 continue;
-
             auto cropPrim = cldnn::crop(get_layer_name(i),
                                         inputs,
                                         cldnn::tensor(1),
                                         (offsets.empty() ? cldnn::tensor(0) : offsets[i]),
                                         op_mode,
                                         static_cast<int>(i),
+                                        axis,
                                         num_splits);
             p.add_primitive(*op, cropPrim);
         }
diff --git a/src/plugins/intel_gpu/src/plugin/ops/swiglu.cpp b/src/plugins/intel_gpu/src/plugin/ops/swiglu.cpp
index def2a52d281de2..d814e3449c3c72 100644
--- a/src/plugins/intel_gpu/src/plugin/ops/swiglu.cpp
+++ b/src/plugins/intel_gpu/src/plugin/ops/swiglu.cpp
@@ -28,6 +28,8 @@ static void CreateSwiGLUOp(ProgramBuilder& p, const std::shared_ptr<op::SwiGLU>&
                                   inputs[0],
                                   op->get_axis(),
                                   op->get_split_lengths(),
+                                  op->get_glu_type(),
+                                  op->get_split_to_glu_idx(),
                                   cldnn::tensor());
         prim.output_data_types = get_output_data_types(op);
         p.add_primitive(*op, prim);
@@ -36,6 +38,8 @@ static void CreateSwiGLUOp(ProgramBuilder& p, const std::shared_ptr<op::SwiGLU>&
                                   inputs[0],
                                   op->get_axis(),
                                   op->get_split_lengths(),
+                                  op->get_glu_type(),
+                                  op->get_split_to_glu_idx(),
                                   tensor_from_dims(op->get_output_shape(0)));
         prim.output_data_types = get_output_data_types(op);
         p.add_primitive(*op, prim);
diff --git a/src/plugins/intel_gpu/src/plugin/plugin.cpp b/src/plugins/intel_gpu/src/plugin/plugin.cpp
index a412162bdc3660..394b238fb9dd7d 100644
--- a/src/plugins/intel_gpu/src/plugin/plugin.cpp
+++ b/src/plugins/intel_gpu/src/plugin/plugin.cpp
@@ -83,17 +83,18 @@ std::string Plugin::get_device_id(const ov::AnyMap& config) const {
     return id;
 }
 
-void Plugin::transform_model(std::shared_ptr<ov::Model>& model, const ExecutionConfig& config) const {
+void Plugin::transform_model(std::shared_ptr<ov::Model>& model, const ExecutionConfig& config, const std::shared_ptr<RemoteContextImpl>& context) const {
     OV_ITT_SCOPED_TASK(itt::domains::intel_gpu_plugin, "Plugin::transform_model");
-    auto deviceInfo = m_device_map.at(config.get_property(ov::device::id))->get_info();
-    TransformationsPipeline transformations(config, deviceInfo);
+    TransformationsPipeline transformations(config, context);
 
     auto start = Time::now();
     transformations.apply(model);
     GPU_DEBUG_LOG << "Transformations time: " << std::chrono::duration_cast<ms>(Time::now() - start).count() << " ms" << std::endl;
 }
 
-std::shared_ptr<ov::Model> Plugin::clone_and_transform_model(const std::shared_ptr<const ov::Model>& model, const ExecutionConfig& config) const {
+std::shared_ptr<ov::Model> Plugin::clone_and_transform_model(const std::shared_ptr<const ov::Model>& model,
+                                                             const ExecutionConfig& config,
+                                                             const std::shared_ptr<RemoteContextImpl>& context) const {
     OV_ITT_SCOPED_TASK(itt::domains::intel_gpu_plugin, "Plugin::clone_and_transform_model");
     GPU_DEBUG_GET_INSTANCE(debug_config);
     GPU_DEBUG_DEFINE_MEM_LOGGER("Plugin::clone_and_transform_model");
@@ -106,7 +107,7 @@ std::shared_ptr<ov::Model> Plugin::clone_and_transform_model(const std::shared_p
         ov::pass::VisualizeTree(path_base + ".svg").run_on_model(cloned_model);
     }
 
-    transform_model(cloned_model, config);
+    transform_model(cloned_model, config, context);
 
     // Transformations for some reason may drop output tensor names, so here we copy those from the original model
     auto new_results = cloned_model->get_results();
@@ -194,8 +195,7 @@ std::shared_ptr<ov::ICompiledModel> Plugin::compile_model(const std::shared_ptr<
             config.streamsRankTable = get_rank_table();
         }
     }
-    auto transformed_model = clone_and_transform_model(model, config);
-
+    auto transformed_model = clone_and_transform_model(model, config, context);
     {
         OV_ITT_SCOPED_TASK(itt::domains::intel_gpu_plugin, "Plugin::compile_model::CreateCompiledModel");
         return std::make_shared<CompiledModel>(transformed_model, shared_from_this(), context, config);
@@ -214,7 +214,7 @@ std::shared_ptr<ov::ICompiledModel> Plugin::compile_model(const std::shared_ptr<
     config.set_user_property(orig_config);
     config.apply_user_properties(context_impl->get_engine().get_device_info());
 
-    auto transformed_model = clone_and_transform_model(model, config);
+    auto transformed_model = clone_and_transform_model(model, config, context_impl);
     return std::make_shared<CompiledModel>(transformed_model, shared_from_this(), context_impl, config);
 }
 
@@ -285,10 +285,10 @@ ov::SupportedOpsMap Plugin::query_model(const std::shared_ptr<const ov::Model>&
     float query_model_ratio = config.get_property(ov::internal::query_model_ratio.name()).as<float>();
 
     auto supported = ov::get_supported_nodes(model,
-        [&config,this](std::shared_ptr<ov::Model>& model) {
+        [&config,&ctx,this](std::shared_ptr<ov::Model>& model) {
             std::map<std::string, ov::PartialShape> shapes;
             std::map<std::string, std::pair<int64_t, int64_t>> batch_dim;
-            transform_model(model, config);
+            transform_model(model, config, ctx);
         },
         [&prog](std::shared_ptr<ov::Node> node) {
             return prog.is_op_supported(node);
@@ -582,6 +582,7 @@ std::vector<ov::PropertyName> Plugin::get_supported_properties() const {
         ov::PropertyName{ov::hint::enable_cpu_pinning.name(), PropertyMutability::RW},
         ov::PropertyName{ov::device::id.name(), PropertyMutability::RW},
         ov::PropertyName{ov::hint::model_distribution_policy.name(), PropertyMutability::RW},
+        ov::PropertyName{ov::hint::dynamic_quantization_group_size.name(), PropertyMutability::RW}
     };
 
     return supported_properties;
@@ -735,7 +736,7 @@ uint32_t Plugin::get_max_batch_size(const ov::AnyMap& options) const {
             return static_cast<uint32_t>(max_batch_size);
         }
 
-        TransformationsPipeline transformations(config, device_info);
+        TransformationsPipeline transformations(config, context);
         transformations.apply(cloned_model);
         program = std::make_shared<ProgramBuilder>(cloned_model, engine, config, true);
         std::pair<int64_t, int64_t> device_memory_usage = program->get_compiled_program()->get_estimated_device_mem_usage();
@@ -804,7 +805,7 @@ uint32_t Plugin::get_optimal_batch_size(const ov::AnyMap& options) const {
                         << ", L3_cache_size is (MB): " << float(L3_cache_size) / 1024 / 1024 << std::endl;
     }
     auto config = m_configs_map.at(device_id);
-    auto cloned_model = clone_and_transform_model(model, config);
+    auto cloned_model = clone_and_transform_model(model, config, context);
     ov::MemBandwidthPressure memPressure = ov::mem_bandwidth_pressure_tolerance(cloned_model, L3_cache_size);
     uint32_t batch = 1;
     if (memPressure.max_mem_tolerance != ov::MemBandwidthPressure::UNKNOWN)
diff --git a/src/plugins/intel_gpu/src/plugin/sync_infer_request.cpp b/src/plugins/intel_gpu/src/plugin/sync_infer_request.cpp
index c58253f7160898..b078a6c42283a8 100644
--- a/src/plugins/intel_gpu/src/plugin/sync_infer_request.cpp
+++ b/src/plugins/intel_gpu/src/plugin/sync_infer_request.cpp
@@ -117,7 +117,6 @@ void SyncInferRequest::infer() {
     OV_ITT_SCOPED_TASK(itt::domains::intel_gpu_plugin, "SyncInferRequest::infer");
     auto message = ov::threading::message_manager();
     if (m_asyncRequest->m_has_sub_infers) {
-        std::cout << "m_asyncRequest->m_sub_infers\n";
         message->server_wait();
         ov::threading::MessageInfo msg_info;
         msg_info.msg_type = ov::threading::MsgType::START_INFER;
@@ -144,7 +143,6 @@ void SyncInferRequest::sub_streams_infer() {
     size_t requests_num = requests.size();
     auto inputs = get_inputs();
     auto outputs = get_outputs();
-    std::cout << "[ sub_streams_infer ] inputs: " << inputs.size() << " requests: " << requests_num << "\n";
     // auto inputs = get_inputs();
     // auto outputs = get_outputs();
 
@@ -179,6 +177,15 @@ std::vector<ov::ProfilingInfo> SyncInferRequest::get_profiling_info() const {
 }
 
 std::vector<ov::SoPtr<ov::IVariableState>> SyncInferRequest::query_state() const {
+    if (m_asyncRequest->m_has_sub_infers) {
+        auto requests = m_asyncRequest->getSubInferRequest();
+        std::vector<ov::SoPtr<ov::IVariableState>> states;
+        for (auto request : requests) {
+            auto cur = request->query_state();
+            states.insert(states.end(), cur.begin(), cur.end());
+        }
+        return states;
+    }
     std::vector<ov::SoPtr<ov::IVariableState>> ret{};
     for (const auto& pair : m_variables) {
         ret.emplace_back(pair.second, nullptr);
diff --git a/src/plugins/intel_gpu/src/plugin/transformations/op/rank_constant.cpp b/src/plugins/intel_gpu/src/plugin/transformations/op/rank_constant.cpp
deleted file mode 100644
index 0c67112bca3867..00000000000000
--- a/src/plugins/intel_gpu/src/plugin/transformations/op/rank_constant.cpp
+++ /dev/null
@@ -1,38 +0,0 @@
-// Copyright (C) 2018-2024 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include "intel_gpu/op/rank_constant.hpp"
-#include "matmul_shape_inference.hpp"
-
-namespace ov {
-namespace intel_gpu {
-namespace op {
-
-/*RankConstant::RankConstant(const element::Type& type, const Shape& shape, const void* data, size_t rank)
-    : Constant(type, shape, data), m_rank(rank) {
-    validate_and_infer_types();
-}
-*/
-RankConstant::RankConstant(const Constant& constant,
-                 size_t rank) : Constant(constant), m_rank(rank) {
-                 }
-    
-std::shared_ptr<ov::Node> RankConstant::clone_with_new_inputs(const ov::OutputVector& new_args) const {
-    check_new_args_count(this, new_args);
-    return std::make_shared<Constant>(*this);
-}
-
-void RankConstant::validate_and_infer_types() {
-    Constant::validate_and_infer_types();
-}
-
-bool RankConstant::visit_attributes(ov::AttributeVisitor &visitor) {
-    visitor.on_attribute("rank", m_rank);
-    Constant::visit_attributes(visitor);
-    return true;
-}
-
-}  // namespace op
-}  // namespace intel_gpu
-}  // namespace ov
diff --git a/src/plugins/intel_gpu/src/plugin/transformations/op/swiglu.cpp b/src/plugins/intel_gpu/src/plugin/transformations/op/swiglu.cpp
index 4154f489fe18e3..2a46bbd958fa01 100644
--- a/src/plugins/intel_gpu/src/plugin/transformations/op/swiglu.cpp
+++ b/src/plugins/intel_gpu/src/plugin/transformations/op/swiglu.cpp
@@ -15,8 +15,11 @@ namespace op {
 SwiGLU::SwiGLU(const Output<Node>& data,
                int64_t axis,
                int64_t split_lengths,
+               const GluType glu_type,
+               const size_t split_to_glu_idx,
                const ov::element::Type output_type)
-    : Op({data}), m_axis(axis), m_split_lengths(split_lengths), m_output_type(output_type) {
+    : Op({data}), m_axis(axis), m_split_lengths(split_lengths),
+      m_glu_type(glu_type), m_split_to_glu_idx(split_to_glu_idx), m_output_type(output_type) {
     validate_and_infer_types();
 }
 
@@ -44,6 +47,8 @@ std::shared_ptr<Node> SwiGLU::clone_with_new_inputs(const ov::OutputVector& new_
     return std::make_shared<SwiGLU>(new_args.at(0),
                                     m_axis,
                                     m_split_lengths,
+                                    m_glu_type,
+                                    m_split_to_glu_idx,
                                     m_output_type);
 }
 
diff --git a/src/plugins/intel_gpu/src/plugin/transformations/swiglu_fusion.cpp b/src/plugins/intel_gpu/src/plugin/transformations/swiglu_fusion.cpp
index 070eb65670d714..a8aeff17a8a25b 100644
--- a/src/plugins/intel_gpu/src/plugin/transformations/swiglu_fusion.cpp
+++ b/src/plugins/intel_gpu/src/plugin/transformations/swiglu_fusion.cpp
@@ -10,7 +10,9 @@
 #include "openvino/op/constant.hpp"
 #include "openvino/op/multiply.hpp"
 #include "openvino/op/swish.hpp"
+#include "openvino/op/gelu.hpp"
 #include "openvino/op/variadic_split.hpp"
+#include "openvino/pass/pattern/op/or.hpp"
 #include "openvino/pass/pattern/op/wrap_type.hpp"
 #include "transformations/utils/utils.hpp"
 
@@ -19,6 +21,7 @@ namespace intel_gpu {
 
 SwiGLUFusion::SwiGLUFusion() {
     using namespace ov::pass::pattern;
+    using ov::pass::pattern::op::Or;
 
     auto last_dim_static = [](const ov::Output<ov::Node>& output) {
         auto out_ps = output.get_node()->get_output_partial_shape(0);
@@ -37,14 +40,16 @@ SwiGLUFusion::SwiGLUFusion() {
 
     // Swish(Xw) = Xw * (1.0 + exp(-beta * Xw))
     auto swish_m = wrap_type<ov::op::v4::Swish>({variadic_split_m->output(0)});
+    auto gelu_m = wrap_type<ov::op::v7::Gelu>({variadic_split_m->output(0)});
 
     // Mul(Xw, Xv) = Swish(Xw) * Xv
-    auto mul_m = wrap_type<ov::op::v1::Multiply>({swish_m, variadic_split_m->output(1)});
+    auto glu_m = std::make_shared<Or>(OutputVector{swish_m, gelu_m});
+    auto mul_m = wrap_type<ov::op::v1::Multiply>({glu_m, variadic_split_m->output(1)});
 
     ov::matcher_pass_callback callback = [OV_CAPTURE_CPY_AND_THIS](ov::pass::pattern::Matcher& m) {
         const auto& pattern_map = m.get_pattern_value_map();
         OPENVINO_ASSERT(pattern_map.count(mul_m));
-        OPENVINO_ASSERT(pattern_map.count(swish_m));
+        OPENVINO_ASSERT(pattern_map.count(swish_m) || pattern_map.count(gelu_m));
         OPENVINO_ASSERT(pattern_map.count(variadic_split_m));
         OPENVINO_ASSERT(pattern_map.count(split_lengths_const_m));
         OPENVINO_ASSERT(pattern_map.count(axis_const_m));
@@ -52,9 +57,31 @@ SwiGLUFusion::SwiGLUFusion() {
         if (!mul || transformation_callback(mul))
             return false;
 
-        size_t split_in_idx = ov::is_type<ov::op::v4::Swish>(mul->get_input_node_shared_ptr(0)) ? 1 : 0;
-        if (mul->input_value(split_in_idx).get_index() != 1)
-            return false;
+        auto isSwiGLU = pattern_map.count(swish_m);
+        auto isGeGLU = pattern_map.count(gelu_m);
+        size_t split_to_glu_idx = 0;
+        ov::intel_gpu::op::SwiGLU::GluType glu_type = ov::intel_gpu::op::SwiGLU::GluType::Swish;
+
+        if (isSwiGLU) {
+            auto swish = std::dynamic_pointer_cast<ov::op::v4::Swish>(pattern_map.at(swish_m).get_node_shared_ptr());
+            glu_type = ov::intel_gpu::op::SwiGLU::GluType::Swish;
+            split_to_glu_idx = swish->input_value(0).get_index();
+
+            size_t split_in_idx = ov::is_type<ov::op::v4::Swish>(mul->get_input_node_shared_ptr(0)) ? 1 : 0;
+            if (mul->input_value(split_in_idx).get_index() == split_to_glu_idx)
+                return false;
+        } else if (isGeGLU) {
+            auto gelu = std::dynamic_pointer_cast<ov::op::v7::Gelu>(pattern_map.at(gelu_m).get_node_shared_ptr());
+            glu_type = (gelu->get_approximation_mode() == ov::op::GeluApproximationMode::ERF) ? ov::intel_gpu::op::SwiGLU::GluType::Gelu
+                                                                                              : ov::intel_gpu::op::SwiGLU::GluType::Gelu_Tanh;
+            split_to_glu_idx = gelu->input_value(0).get_index();
+
+            size_t split_in_idx = ov::is_type<ov::op::v7::Gelu>(mul->get_input_node_shared_ptr(0)) ? 1 : 0;
+            if (mul->input_value(split_in_idx).get_index() == split_to_glu_idx)
+                return false;
+        } else {
+            OPENVINO_THROW("'glu_type' not initialized");
+        }
 
         auto variadic_split = std::dynamic_pointer_cast<ov::op::v1::VariadicSplit>(pattern_map.at(variadic_split_m).get_node_shared_ptr());
         auto variadic_split_in_ps = variadic_split->get_input_partial_shape(0);
@@ -80,6 +107,8 @@ SwiGLUFusion::SwiGLUFusion() {
         auto swiglu = std::make_shared<op::SwiGLU>(data,
                                                    axis_value,
                                                    split_lengths_value,
+                                                   glu_type,
+                                                   split_to_glu_idx,
                                                    output_type);
         swiglu->set_friendly_name(m.get_match_root()->get_friendly_name());
         ov::copy_runtime_info(m.get_matched_nodes(), swiglu);
diff --git a/src/plugins/intel_gpu/src/plugin/transformations/tensor_parallel.cpp b/src/plugins/intel_gpu/src/plugin/transformations/tensor_parallel.cpp
index 999ddb766ac3ac..a35d19f8dfb622 100644
--- a/src/plugins/intel_gpu/src/plugin/transformations/tensor_parallel.cpp
+++ b/src/plugins/intel_gpu/src/plugin/transformations/tensor_parallel.cpp
@@ -10,7 +10,6 @@
 
 #include "intel_gpu/op/fully_connected.hpp"
 #include "intel_gpu/op/placeholder.hpp"
-#include "intel_gpu/op/rank_constant.hpp"
 #include "intel_gpu/op/sync_tensor.hpp"
 
 #include "openvino/core/rt_info.hpp"
diff --git a/src/plugins/intel_gpu/src/plugin/transformations/transpose_fusion.cpp b/src/plugins/intel_gpu/src/plugin/transformations/transpose_fusion.cpp
index 3a6315978577c8..d26e45e8fea9ba 100644
--- a/src/plugins/intel_gpu/src/plugin/transformations/transpose_fusion.cpp
+++ b/src/plugins/intel_gpu/src/plugin/transformations/transpose_fusion.cpp
@@ -45,14 +45,6 @@ TransposeSDPAMatcher::TransposeSDPAMatcher() {
         return std::dynamic_pointer_cast<ov::op::v1::Transpose>(output.get_node_shared_ptr()) == nullptr
                && is_fp_type(output);
     };
-    auto is_dynamic = [](const ov::Output<ov::Node>& output) -> bool {
-        bool is_dynamic = output.get_node_shared_ptr()->get_output_partial_shape(0).is_dynamic();
-        size_t num_inputs = output.get_node_shared_ptr()->get_input_size();
-        for (size_t idx = 0; idx < num_inputs; idx++) {
-            is_dynamic |= output.get_node_shared_ptr()->get_input_partial_shape(idx).is_dynamic();
-        }
-        return is_dynamic;
-    };
 
     auto input_q_m = any_input(not_transpose);
     auto input_k_m = any_input(not_transpose);
@@ -70,10 +62,10 @@ TransposeSDPAMatcher::TransposeSDPAMatcher() {
     auto sdpa_in_k = std::make_shared<Or>(OutputVector{input_k_m, transpose_k_m});
     auto sdpa_in_v = std::make_shared<Or>(OutputVector{input_v_m, transpose_v_m});
 
-    auto sdpa_without_attn_mask_m = wrap_type<ov::op::v13::ScaledDotProductAttention>({ sdpa_in_q, sdpa_in_k, sdpa_in_v }, is_dynamic);
-    auto sdpa_with_attn_mask_m = wrap_type<ov::op::v13::ScaledDotProductAttention>({ sdpa_in_q, sdpa_in_k, sdpa_in_v, input_attn_mask }, is_dynamic);
+    auto sdpa_without_attn_mask_m = wrap_type<ov::op::v13::ScaledDotProductAttention>({ sdpa_in_q, sdpa_in_k, sdpa_in_v });
+    auto sdpa_with_attn_mask_m = wrap_type<ov::op::v13::ScaledDotProductAttention>({ sdpa_in_q, sdpa_in_k, sdpa_in_v, input_attn_mask });
     auto sdpa_with_attn_mask_and_scale_m =
-        wrap_type<ov::op::v13::ScaledDotProductAttention>({ sdpa_in_q, sdpa_in_k, sdpa_in_v, input_attn_mask, input_scale }, is_dynamic);
+        wrap_type<ov::op::v13::ScaledDotProductAttention>({ sdpa_in_q, sdpa_in_k, sdpa_in_v, input_attn_mask, input_scale });
 
     auto sdpa_m = std::make_shared<Or>(OutputVector{sdpa_without_attn_mask_m, sdpa_with_attn_mask_m, sdpa_with_attn_mask_and_scale_m});
 
diff --git a/src/plugins/intel_gpu/src/plugin/transformations_pipeline.cpp b/src/plugins/intel_gpu/src/plugin/transformations_pipeline.cpp
index 8324f377076653..18856c5802495c 100644
--- a/src/plugins/intel_gpu/src/plugin/transformations_pipeline.cpp
+++ b/src/plugins/intel_gpu/src/plugin/transformations_pipeline.cpp
@@ -189,6 +189,10 @@ static bool is_non_supported_decompression_op(const std::shared_ptr<const ov::No
 }
 }  // namespace
 
+namespace cldnn {
+extern bool query_microkernels_supported(cldnn::engine& e, const cldnn::ExecutionConfig& config);
+}  // namespace cldnn
+
 namespace ov {
 namespace intel_gpu {
 
@@ -342,18 +346,16 @@ void TransformationsPipeline::apply(std::shared_ptr<ov::Model> func) {
                 return false;
             }
 
-            // - Head size should be static dim
-            const auto head_size_dim = query_ps[query_ps.size() - 1];
-            if (head_size_dim.is_dynamic())
-                return false;
+            const auto head_size = query_ps[query_ps.size() - 1].get_length();
+            if (device_info.supports_immad && cldnn::query_microkernels_supported(m_context->get_engine(), config) && head_size <= 256)
+                return true;
 
             // - Head size should be 128 for any model type; or should be in the range of 64 to 256 for stateful LLMs because of performance reasons.
             //   This limitations is recommended to prevent performance drop in models with small head size, such as SD,
             //   until the SDPA operation is optimized for these cases
             const auto optimal_subgroup_size = 16;
-            const auto head_size = query_ps[query_ps.size() - 1].get_length();
             bool valid_head_size = head_size % optimal_subgroup_size == 0;
-            valid_head_size &= (head_size == 128) || (func->get_variables().size() > 0 && head_size >= 64 && head_size <= 256);
+            valid_head_size &= (head_size >= 64 && head_size <= 256);
             if (!valid_head_size) {
                 return false;
             }
@@ -836,7 +838,6 @@ void TransformationsPipeline::apply(std::shared_ptr<ov::Model> func) {
         }
 
         manager.run_passes(func);
-        ov::serialize(func, "model_folder_org.xml");
     }
 }
 }  // namespace intel_gpu
diff --git a/src/plugins/intel_gpu/src/runtime/debug_configuration.cpp b/src/plugins/intel_gpu/src/runtime/debug_configuration.cpp
index 3708a339b351db..944160a59a60f5 100644
--- a/src/plugins/intel_gpu/src/runtime/debug_configuration.cpp
+++ b/src/plugins/intel_gpu/src/runtime/debug_configuration.cpp
@@ -78,9 +78,9 @@ static std::set<int64_t> parse_int_set(std::string& str) {
         while (ss >> token) {
             try {
                 int_array.insert(static_cast<int64_t>(std::stol(token)));
-            } catch(const std::exception& ex) {
+            } catch(const std::exception &) {
                 int_array.clear();
-                GPU_DEBUG_COUT << "OV_GPU_DumpMemoryPoolIters was ignored. It cannot be parsed to integer array." << std::endl;
+                GPU_DEBUG_COUT << "Argument was ignored. It cannot be parsed to integer array: " << str << std::endl;
                 break;
             }
         }
@@ -181,6 +181,7 @@ static void print_help_messages() {
     message_list.emplace_back("OV_GPU_DisableRuntimeSkipReorder", "Disable runtime skip reorder.");
     message_list.emplace_back("OV_GPU_DisablePrimitiveFusing", "Disable primitive fusing");
     message_list.emplace_back("OV_GPU_DisableFakeAlignment", "Disable fake alignment");
+    message_list.emplace_back("OV_GPU_EnableDynamicQuantize", "Enable Dynamic quantization for fully connected primitive");
     message_list.emplace_back("OV_GPU_DumpIteration", "Dump n-th execution of network, separated by space.");
     message_list.emplace_back("OV_GPU_MemPreallocationOptions", "Controls buffer pre-allocation feature. Expects 4 values separated by space in "
                               "the following order: number of iterations for pre-allocation(int), max size of single iteration in bytes(int), "
@@ -245,7 +246,8 @@ debug_configuration::debug_configuration()
         , disable_build_time_weight_reorder_for_dynamic_nodes(0)
         , disable_runtime_skip_reorder(0)
         , disable_primitive_fusing(0)
-        , disable_fake_alignment(0) {
+        , disable_fake_alignment(0)
+        , enable_dynamic_quantize(0) {
 #ifdef GPU_DEBUG_CONFIG
     get_gpu_debug_env_var("Help", help);
     get_common_debug_env_var("Verbose", verbose);
@@ -296,6 +298,7 @@ debug_configuration::debug_configuration()
     get_gpu_debug_env_var("DisableRuntimeSkipReorder", disable_runtime_skip_reorder);
     get_gpu_debug_env_var("DisablePrimitiveFusing", disable_primitive_fusing);
     get_gpu_debug_env_var("DisableFakeAlignment", disable_fake_alignment);
+    get_gpu_debug_env_var("EnableDynamicQuantize", enable_dynamic_quantize);
     std::string dump_iteration_str;
     get_gpu_debug_env_var("DumpIteration", dump_iteration_str);
     std::string mem_preallocation_params_str;
diff --git a/src/plugins/intel_gpu/src/runtime/execution_config.cpp b/src/plugins/intel_gpu/src/runtime/execution_config.cpp
index 0f4c02c01899f7..e773207ea4e5b3 100644
--- a/src/plugins/intel_gpu/src/runtime/execution_config.cpp
+++ b/src/plugins/intel_gpu/src/runtime/execution_config.cpp
@@ -57,6 +57,7 @@ void ExecutionConfig::set_default() {
         std::make_tuple(ov::internal::query_model_ratio, 1.0f),
         std::make_tuple(ov::cache_mode, ov::CacheMode::OPTIMIZE_SPEED),
         std::make_tuple(ov::hint::model_distribution_policy, ""),
+        std::make_tuple(ov::hint::dynamic_quantization_group_size, 0),
 
         // Legacy API properties
         std::make_tuple(ov::intel_gpu::nv12_two_inputs, false),
diff --git a/src/plugins/intel_gpu/src/runtime/kernels_cache.cpp b/src/plugins/intel_gpu/src/runtime/kernels_cache.cpp
index 47483c6a1a0192..ac8d716882ef82 100644
--- a/src/plugins/intel_gpu/src/runtime/kernels_cache.cpp
+++ b/src/plugins/intel_gpu/src/runtime/kernels_cache.cpp
@@ -37,9 +37,32 @@
 #include <malloc.h>
 #endif
 
+#ifdef ENABLE_ONEDNN_FOR_GPU
+#ifndef NOMINMAX
+# define NOMINMAX
+#endif
+#include "gpu/intel/microkernels/fuser.hpp"
+#endif
+
 namespace {
 std::mutex cacheAccessMutex;
 
+#ifdef ENABLE_ONEDNN_FOR_GPU
+cl::Program fuse_microkernels(const cl::Context& context, const cl::Device& device, cl::Program& program, const std::string& code) {
+    using namespace dnnl::impl::gpu::intel;
+    std::vector<std::vector<uint8_t>> binaries = program.getInfo<CL_PROGRAM_BINARIES>();
+    OPENVINO_ASSERT(binaries.size() == 1);
+    std::vector<uint8_t> binary = binaries[0];
+    micro::fuseMicrokernels(binary, code.c_str());
+
+    cl::Program::Binaries fused_binary = { binary };
+    cl::Program fused_program(context, {device}, fused_binary);
+    fused_program.build({device});
+
+    return fused_program;
+}
+#endif  // ENABLE_ONEDNN_FOR_GPU
+
 std::string reorder_options(const std::string& org_options) {
     std::stringstream ss(org_options);
     std::set<std::string> sorted_options;
@@ -128,7 +151,7 @@ void kernels_cache::get_program_source(const kernels_code& kernels_source_code,
                 const auto& batch_id = 0;
                 // increase bucket id if and only if new bucket comes
                 bucket_id = static_cast<int32_t>(program_buckets.size() - 1);
-                current_bucket.push_back(batch_program(bucket_id, batch_id, options, batch_header_str));
+                current_bucket.push_back(batch_program(bucket_id, batch_id, options, batch_headers));
             }
 
             // This is a temporary walk-around to avoid severe performance drop.
@@ -159,13 +182,22 @@ void kernels_cache::get_program_source(const kernels_code& kernels_source_code,
                 || current_bucket.back().entry_point_to_id.find(entry_point) != current_bucket.back().entry_point_to_id.end()
                 || need_separate_batch(entry_point)) {
                 const auto& batch_id = static_cast<int32_t>(current_bucket.size());
-                current_bucket.push_back(batch_program(bucket_id, batch_id, options, batch_header_str));
+                current_bucket.push_back(batch_program(bucket_id, batch_id, options, batch_headers));
             }
 
             auto& current_batch = current_bucket.back();
             current_batch.dump_custom_program = dump_custom_program;
             current_batch.entry_point_to_id.emplace(entry_point, std::make_pair(code.params, kernel_part_idx));
 
+            current_batch.has_microkernels |= kernel_string->has_microkernels;
+
+            // TODO: Technically, microkernels doesn't require specific headers, but we don't want to include
+            // some headers to all batches as it may lead to compilation error on some driver versions.
+            // Need to generalize work with headers to include only necessary parts
+            if (current_batch.has_microkernels) {
+                current_batch.source.insert(current_batch.source.begin(), current_batch.micro_headers.begin(), current_batch.micro_headers.end());
+            }
+
             current_batch.source.push_back(std::move(full_code));
             current_batch.kernels_counter++;
         }
@@ -195,12 +227,12 @@ kernels_cache::kernels_cache(engine& engine,
                              const ExecutionConfig& config,
                              uint32_t prog_id,
                              std::shared_ptr<ov::threading::ITaskExecutor> task_executor,
-                             const std::vector<std::string>& batch_header_str)
+                             const std::map<std::string, std::string>& batch_headers)
     : _engine(engine)
     , _task_executor(task_executor)
     , _config(config)
     , _prog_id(prog_id)
-    , batch_header_str(std::move(batch_header_str)) { }
+    , batch_headers(std::move(batch_headers)) { }
 
 static std::vector<unsigned char> getProgramBinaries(cl::Program program) {
     // Get the size of the program binary in bytes.
@@ -288,6 +320,17 @@ void kernels_cache::build_batch(const engine& build_engine, const batch_program&
                 dump_file << "*/\n";
             }
 
+            if (batch.has_microkernels) {
+#ifdef ENABLE_ONEDNN_FOR_GPU
+                OPENVINO_ASSERT(batch.kernels_counter == 1);
+                // Do we need full source code here (with batch headers)?
+                program = fuse_microkernels(cl_build_engine.get_cl_context(), cl_build_engine.get_cl_device(), program, batch.source.back());
+#else  // ENABLE_ONEDNN_FOR_GPU
+                OPENVINO_THROW("[GPU] Can't compile kernel w/ microkernels as onednn is not available");
+#endif  // ENABLE_ONEDNN_FOR_GPU
+            }
+
+
             program.createKernels(&kernels);
 
             if (is_cache_enabled()) {
@@ -348,6 +391,7 @@ void kernels_cache::build_batch(const engine& build_engine, const batch_program&
         GPU_DEBUG_INFO << "-------- End of OpenCL build error" << std::endl;
         std::stringstream err_ss(err_log);
         std::string line;
+        std::stringstream err;
         int cnt = 0;
 
         while (std::getline(err_ss, line, '\n')) {
@@ -355,14 +399,14 @@ void kernels_cache::build_batch(const engine& build_engine, const batch_program&
                 cnt = 5;
             cnt--;
             if (cnt > 0)
-                std::cout << line << std::endl;
+                err << line << std::endl;
             else if (cnt == 0)
-                std::cout << "...." << std::endl;
+                err << "...." << std::endl;
         }
 
         throw std::runtime_error("Program build failed(" + std::to_string(batch.bucket_id) + + "_part_"
                                  + std::to_string(batch.batch_id)
-                                 + "): You may enable OCL source dump to see the error log.\n");
+                                 + "):\n" + err.str());
     }
 }
 
diff --git a/src/plugins/intel_gpu/src/runtime/kernels_cache.hpp b/src/plugins/intel_gpu/src/runtime/kernels_cache.hpp
index ca389718a49d23..d3775731bb01a5 100644
--- a/src/plugins/intel_gpu/src/runtime/kernels_cache.hpp
+++ b/src/plugins/intel_gpu/src/runtime/kernels_cache.hpp
@@ -54,19 +54,34 @@ class kernels_cache {
         size_t hash_value;
         uint32_t kernels_counter;
         source_code source;
+        source_code micro_headers;
         std::string options;
         bool dump_custom_program;
+        bool has_microkernels;
         std::map<std::string, std::pair<kernel_impl_params, size_t>> entry_point_to_id;
 
-        explicit batch_program(int32_t _bucket_id, int32_t _batch_id, std::string _options, const std::vector<std::string>& batch_header_str)
+        explicit batch_program(int32_t _bucket_id, int32_t _batch_id, std::string _options, const std::map<std::string, std::string>& batch_headers)
             : bucket_id(_bucket_id),
               batch_id(_batch_id),
               hash_value(0),
               kernels_counter(0),
-              source(std::move(batch_header_str)),
+              source({}),
               options(_options),
               dump_custom_program(false),
+              has_microkernels(false),
               entry_point_to_id({}) {
+            static const std::vector<std::string> micro_kernel_include_names {
+                "generic_vector_ops",
+                "tile_ops",
+                "sdpa_utils"
+            };
+            for (const auto& kv : batch_headers) {
+                if (std::find(micro_kernel_include_names.begin(), micro_kernel_include_names.end(), kv.first) == micro_kernel_include_names.end()) {
+                    source.push_back(kv.second);
+                } else {
+                    micro_headers.push_back(kv.second);
+                }
+            }
         }
     };
 
@@ -83,7 +98,7 @@ class kernels_cache {
     compiled_kernels _kernels;
     std::map<std::vector<unsigned char>, uint32_t> _cached_binaries;
     std::unordered_map<std::string, kernel::ptr> _cached_kernels;
-    std::vector<std::string> batch_header_str;
+    std::map<std::string, std::string> batch_headers;
     std::unordered_map<kernel_impl_params, size_t, impl_hasher> _kernel_batch_hash;
     void get_program_source(const kernels_code& kernels_source_code, std::vector<batch_program>*) const;
     void build_batch(const engine& build_engine, const batch_program& batch, compiled_kernels& compiled_kernels);
@@ -97,12 +112,9 @@ class kernels_cache {
                            const ExecutionConfig& config,
                            uint32_t prog_id,
                            std::shared_ptr<ov::threading::ITaskExecutor> task_executor = nullptr,
-                           const std::vector<std::string>& batch_header_str = {});
+                           const std::map<std::string, std::string>& batch_headers = {});
     kernel::ptr get_kernel_from_cached_kernels(std::string id) const;
     std::vector<kernel::ptr> get_kernels(kernel_impl_params params) const;
-    void set_batch_header_str(const std::vector<std::string> &batch_headers) {
-        batch_header_str = std::move(batch_headers);
-    }
 
     bool validate_simple_kernel_execution(kernel::ptr kernel);
 
diff --git a/src/plugins/intel_gpu/src/runtime/ocl/ocl_device.cpp b/src/plugins/intel_gpu/src/runtime/ocl/ocl_device.cpp
index 15e379d5ad557b..74d9b033c64ca2 100644
--- a/src/plugins/intel_gpu/src/runtime/ocl/ocl_device.cpp
+++ b/src/plugins/intel_gpu/src/runtime/ocl/ocl_device.cpp
@@ -2,6 +2,13 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
+#ifdef ENABLE_ONEDNN_FOR_GPU
+#ifndef NOMINMAX
+# define NOMINMAX
+#endif
+#include "gpu/intel/jit/jit_generator.hpp"
+#endif  // ENABLE_ONEDNN_FOR_GPU
+
 #include "ocl_device.hpp"
 #include "ocl_common.hpp"
 #include "intel_gpu/runtime/debug_configuration.hpp"
@@ -43,6 +50,24 @@ namespace cldnn {
 namespace ocl {
 
 namespace {
+
+#ifdef ENABLE_ONEDNN_FOR_GPU
+gpu_arch convert_ngen_arch(ngen::HW gpu_arch) {
+    switch (gpu_arch) {
+        case ngen::HW::Gen9: return gpu_arch::gen9;
+        case ngen::HW::Gen11: return gpu_arch::gen11;
+        case ngen::HW::XeLP: return gpu_arch::xe_lp;
+        case ngen::HW::XeHP: return gpu_arch::xe_hp;
+        case ngen::HW::XeHPG: return gpu_arch::xe_hpg;
+        case ngen::HW::XeHPC: return gpu_arch::xe_hpc;
+        case ngen::HW::Xe2: return gpu_arch::xe2;
+        case ngen::HW::Gen10:
+        case ngen::HW::Unknown: return gpu_arch::unknown;
+    }
+    return gpu_arch::unknown;
+}
+#endif
+
 int driver_dev_id() {
     const std::vector<int> unused_ids = {
         0x4905, 0x4906, 0x4907, 0x4908
@@ -172,7 +197,7 @@ bool get_imad_support(const cl::Device& device) {
     return false;
 }
 
-device_info init_device_info(const cl::Device& device) {
+device_info init_device_info(const cl::Device& device, const cl::Context& context) {
     device_info info = {};
     info.vendor_id = static_cast<uint32_t>(device.getInfo<CL_DEVICE_VENDOR_ID>());
     info.dev_name = device.getInfo<CL_DEVICE_NAME>();
@@ -250,7 +275,8 @@ device_info init_device_info(const cl::Device& device) {
     bool nv_device_attr_supported = extensions.find("cl_nv_device_attribute_query") != std::string::npos;
     info.has_separate_cache = false;
     if (device_attr_supported) {
-        info.gfx_ver = parse_version(device.getInfo<CL_DEVICE_IP_VERSION_INTEL>());
+        info.ip_version = device.getInfo<CL_DEVICE_IP_VERSION_INTEL>();
+        info.gfx_ver = parse_version(info.ip_version);
         info.device_id = device.getInfo<CL_DEVICE_ID_INTEL>();
         info.num_slices = device.getInfo<CL_DEVICE_NUM_SLICES_INTEL>();
         info.num_sub_slices_per_slice = device.getInfo<CL_DEVICE_NUM_SUB_SLICES_PER_SLICE_INTEL>();
@@ -296,6 +322,17 @@ device_info init_device_info(const cl::Device& device) {
         info.num_ccs = std::max<uint32_t>(num_queues, info.num_ccs);
     }
 
+
+#ifdef ENABLE_ONEDNN_FOR_GPU
+    using namespace dnnl::impl::gpu::intel::jit;
+    ngen::HW hw = ngen::HW::Unknown;
+    ngen::Product product = {ngen::ProductFamily::Unknown, 0};
+    jit_generator<ngen::HW::Unknown>::detectHWInfo(context.get(), device.get(), hw, product);
+    info.arch = convert_ngen_arch(hw);
+#else  // ENABLE_ONEDNN_FOR_GPU
+    info.arch = gpu_arch::unknown;
+#endif  // ENABLE_ONEDNN_FOR_GPU
+
     return info;
 }
 
@@ -331,7 +368,7 @@ ocl_device::ocl_device(const cl::Device dev, const cl::Context& ctx, const cl::P
 : _context(ctx)
 , _device(dev)
 , _platform(platform)
-, _info(init_device_info(dev))
+, _info(init_device_info(dev, ctx))
 , _mem_caps(init_memory_caps(dev, _info)) { }
 
 bool ocl_device::is_same(const device::ptr other) {
diff --git a/src/plugins/intel_gpu/src/runtime/ocl/ocl_wrapper.hpp b/src/plugins/intel_gpu/src/runtime/ocl/ocl_wrapper.hpp
index 57da8d5c748672..91a4d767ceaebe 100644
--- a/src/plugins/intel_gpu/src/runtime/ocl/ocl_wrapper.hpp
+++ b/src/plugins/intel_gpu/src/runtime/ocl/ocl_wrapper.hpp
@@ -50,6 +50,7 @@
     #pragma clang diagnostic ignored "-Wunused-variable"
     #pragma clang diagnostic ignored "-Wunused-function"
     #pragma clang diagnostic ignored "-Wignored-qualifiers"
+    #pragma clang diagnostic ignored "-Wdeprecated-declarations"
 #elif defined __GNUC__
     #pragma GCC diagnostic push
     #pragma GCC diagnostic ignored "-Wsign-compare"
@@ -57,6 +58,7 @@
     #pragma GCC diagnostic ignored "-Wunused-variable"
     #pragma GCC diagnostic ignored "-Wunused-function"
     #pragma GCC diagnostic ignored "-Wignored-qualifiers"
+    #pragma GCC diagnostic ignored "-Wdeprecated-declarations"
     #if __GNUC__ >= 8
     #pragma GCC diagnostic ignored "-Wcatch-value"
     #endif
diff --git a/src/plugins/intel_gpu/tests/common/subgraphs_builders.hpp b/src/plugins/intel_gpu/tests/common/subgraphs_builders.hpp
index 3ea2a654bb5416..8dd82c7f6f1bd8 100644
--- a/src/plugins/intel_gpu/tests/common/subgraphs_builders.hpp
+++ b/src/plugins/intel_gpu/tests/common/subgraphs_builders.hpp
@@ -14,6 +14,7 @@
 #include "openvino/op/gather.hpp"
 #include "openvino/op/read_value.hpp"
 #include "openvino/op/reshape.hpp"
+#include "openvino/op/scaled_dot_product_attention.hpp"
 #include "openvino/op/shape_of.hpp"
 #include "openvino/op/transpose.hpp"
 #include "openvino/op/result.hpp"
@@ -119,7 +120,6 @@ inline std::shared_ptr<ov::Model> make_llm_kv_cache_pattern(ov::Dimension batch
 
     ov::ResultVector results{kv_present, matmul_out};
     auto model = std::make_shared<ov::Model>(results, params, "LLM-KV-Cache");
-    ov::pass::VisualizeTree("model.svg").run_on_model(model);
     if (stateful) {
         ov::pass::MakeStateful({{in_kv_prev, kv_present}}).run_on_model(model);
     }
diff --git a/src/plugins/intel_gpu/tests/functional/remote_tensor_tests/gpu_remote_tensor_tests.cpp b/src/plugins/intel_gpu/tests/functional/remote_tensor_tests/gpu_remote_tensor_tests.cpp
index 27cc331554713f..fe84ae1d8f69e6 100644
--- a/src/plugins/intel_gpu/tests/functional/remote_tensor_tests/gpu_remote_tensor_tests.cpp
+++ b/src/plugins/intel_gpu/tests/functional/remote_tensor_tests/gpu_remote_tensor_tests.cpp
@@ -690,7 +690,7 @@ TEST(OVRemoteTensorTests, smoke_MixedTensorTypes) {
             auto cl_tensor = input_tensor_1.as<ov::intel_gpu::ocl::ClBufferTensor>();
             cl::Buffer shared_buffer = cl_tensor;
             void* buffer = data.data();
-            ocl_instance->_queue.enqueueWriteBuffer(shared_buffer, true, 0, ov::shape_size(input_shape), buffer);
+            ocl_instance->_queue.enqueueWriteBuffer(shared_buffer, true, 0, ov::shape_size(input_shape) * data.get_element_type().size(), buffer);
 
             infer_request.set_tensor(input, input_tensor_1);
             infer_request.infer();
@@ -717,7 +717,7 @@ TEST(OVRemoteTensorTests, smoke_MixedTensorTypes) {
         auto cl_tensor = input_tensor_0.as<ov::intel_gpu::ocl::ClBufferTensor>();
         cl::Buffer shared_buffer = cl_tensor;
         void* buffer = data.data();
-        ocl_instance->_queue.enqueueWriteBuffer(shared_buffer, true, 0, ov::shape_size(input_shape_0), buffer);
+        ocl_instance->_queue.enqueueWriteBuffer(shared_buffer, true, 0, ov::shape_size(input_shape_0) * data.get_element_type().size(), buffer);
 
         infer_request.set_tensor(input, input_tensor_0);
         infer_request.infer();
diff --git a/src/plugins/intel_gpu/tests/functional/shared_tests_instances/skip_tests_config.cpp b/src/plugins/intel_gpu/tests/functional/shared_tests_instances/skip_tests_config.cpp
index 4e265bb41c89a9..45005d6ebf93cd 100644
--- a/src/plugins/intel_gpu/tests/functional/shared_tests_instances/skip_tests_config.cpp
+++ b/src/plugins/intel_gpu/tests/functional/shared_tests_instances/skip_tests_config.cpp
@@ -53,6 +53,8 @@ std::vector<std::string> disabledTestPatterns() {
             R"(.*smoke.*BehaviorTests.*DynamicInputToDynamicOutput.*)",
             // TODO: Issue: 89555
             R"(.*CoreThreadingTestsWithIter.*)",
+            // TODO: Issue: 145926
+            R"(.*CoreThreadingTest.smoke_QueryModel.*)",
             // Assign-3/ReadValue-3 does not have evaluate() methods; ref implementation does not save the value across the inferences.
             R"(smoke_MemoryTestV3.*)",
             // Issue: 90539
@@ -201,6 +203,8 @@ std::vector<std::string> disabledTestPatterns() {
             R"(.*smoke_RDFT_5d_last_axis/RDFTLayerTest.Inference/IS=\(10.4.8.2.5\)_modelType=f32_Axes=\(0.1.2.3.4\)_SignalSize=\(\).*)",
             // Issue: 136862
             R"(.*smoke_ConditionGPUTest_static/StaticConditionLayerGPUTest.CompareWithRefs/IS=\(3.6\)_netPRC=i8_ifCond=PARAM_targetDevice=GPU_.*)",
+            // Issue: 142900
+            R"(.*smoke_TestsROIAlign_.*ROIAlignV9LayerTest.*)",
 
 #if defined(_WIN32)
             // by calc abs_threshold with expected value
diff --git a/src/plugins/intel_gpu/tests/functional/shared_tests_instances/subgraph_tests/rotary_pos_emb.cpp b/src/plugins/intel_gpu/tests/functional/shared_tests_instances/subgraph_tests/rotary_pos_emb.cpp
index 5bfcadb10c8205..1baf61ac7d4379 100644
--- a/src/plugins/intel_gpu/tests/functional/shared_tests_instances/subgraph_tests/rotary_pos_emb.cpp
+++ b/src/plugins/intel_gpu/tests/functional/shared_tests_instances/subgraph_tests/rotary_pos_emb.cpp
@@ -23,5 +23,10 @@ INSTANTIATE_TEST_SUITE_P(smoke_RoPETestLlama2,
                          ::testing::Values(ov::test::utils::DEVICE_GPU),
                          RoPETestLlama2::getTestCaseName);
 
+INSTANTIATE_TEST_SUITE_P(smoke_RoPETestRotateHalfWithoutTranspose,
+                         RoPETestRotateHalfWithoutTranspose,
+                         ::testing::Values(ov::test::utils::DEVICE_GPU),
+                         RoPETestRotateHalfWithoutTranspose::getTestCaseName);
+
 }  // namespace test
 }  // namespace ov
diff --git a/src/plugins/intel_gpu/tests/functional/single_layer_tests/dynamic/scaled_dot_product_attention.cpp b/src/plugins/intel_gpu/tests/functional/single_layer_tests/dynamic/scaled_dot_product_attention.cpp
index 5abfe681ac9e1a..fe9420127a6e9f 100644
--- a/src/plugins/intel_gpu/tests/functional/single_layer_tests/dynamic/scaled_dot_product_attention.cpp
+++ b/src/plugins/intel_gpu/tests/functional/single_layer_tests/dynamic/scaled_dot_product_attention.cpp
@@ -186,6 +186,20 @@ const std::vector<std::vector<InputShape>> shapes{
             {ov::Shape{1, 1, 100, 100}, ov::Shape{1, 1, 1, 1}, ov::Shape{2, 1, 10, 10}}}
         },
     },
+    {
+        // q shape
+        {ov::test::InputShape{ov::PartialShape{-1, 8, -1, 64},
+            {ov::Shape{1, 8, 100, 64}, ov::Shape{1, 8, 1, 64}, ov::Shape{2, 8, 10, 64}}}
+        },
+        // kv shape
+        {ov::test::InputShape{ov::PartialShape{-1, 8, -1, 64},
+            {ov::Shape{1, 8, 100, 64}, ov::Shape{1, 8, 1, 64}, ov::Shape{2, 8, 10, 64}}}
+        },
+        // attn shape: [B, 1, -1, L0+L1]
+        {ov::test::InputShape{ov::PartialShape{-1, 1, -1, -1},
+            {ov::Shape{1, 1, 100, 100}, ov::Shape{1, 1, 1, 1}, ov::Shape{2, 1, 10, 10}}}
+        },
+    },
     {
         // q shape
         {ov::test::InputShape{ov::PartialShape{-1, 5, -1, 128},
diff --git a/src/plugins/intel_gpu/tests/functional/subgraph_tests/dynamic/convert_bf16_fp32_fp16.cpp b/src/plugins/intel_gpu/tests/functional/subgraph_tests/dynamic/convert_bf16_fp32_fp16.cpp
new file mode 100644
index 00000000000000..a0a5a74a0d35fe
--- /dev/null
+++ b/src/plugins/intel_gpu/tests/functional/subgraph_tests/dynamic/convert_bf16_fp32_fp16.cpp
@@ -0,0 +1,145 @@
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "common_test_utils/ov_tensor_utils.hpp"
+#include "shared_test_classes/base/ov_subgraph.hpp"
+#include "transformations/rt_info/decompression.hpp"
+
+#include "openvino/op/parameter.hpp"
+#include "openvino/op/constant.hpp"
+#include "openvino/op/result.hpp"
+#include "openvino/op/matmul.hpp"
+#include "openvino/op/reshape.hpp"
+#include "openvino/op/convert.hpp"
+#include "openvino/op/subtract.hpp"
+#include "openvino/op/transpose.hpp"
+
+namespace {
+using ov::test::InputShape;
+
+using BF16WeightsDecompressionParams = std::tuple<float, float>;                    // per-tensor zero-point
+
+class BF16WeightsDecompression : public testing::WithParamInterface<BF16WeightsDecompressionParams>, public ov::test::TestsCommon {
+public:
+    std::shared_ptr<ov::Core> core = ov::test::utils::PluginCache::get().core();
+    std::string targetDevice = ov::test::utils::DEVICE_GPU;
+
+    static std::string get_test_case_name(testing::TestParamInfo<BF16WeightsDecompressionParams> obj) {
+        float given;
+        float expected;
+        std::tie(given, expected) = obj.param;
+        std::ostringstream result;
+        result << "given=" << given << "expected=" << expected;
+        return result.str();
+    }
+
+
+    static unsigned short float_to_ushort(float source) {
+        unsigned int* in = reinterpret_cast<unsigned int*>(&source);
+        unsigned short u = 0;
+        if ( (*in>>31) ) {
+            u = 1 << 15;
+        }
+        //exponent
+        u += ( ( (*in >> 23) & 0b11111111)) << 7;
+        //fraction
+        u += (*in >> 16) & 0b1111111;
+        return u;
+    }
+
+protected:
+    std::shared_ptr<ov::Node> init_compressed_weights_subgraph(ov::element::Type_t typ, float val) {
+        auto weights_tensor_left = ov::test::utils::create_and_fill_tensor(typ, ov::Shape{{2, 2}});
+        auto weights_left = std::make_shared<ov::op::v0::Constant>(weights_tensor_left);
+        weights_left->fill_data(typ, val);
+        return weights_left;
+    }
+
+    std::shared_ptr<ov::Model> init_subgraph() {
+        const auto weights_left = init_compressed_weights_subgraph(ov::element::f32, 2);
+        const auto weights_right = init_compressed_weights_subgraph(ov::element::bf16, 3);
+        auto r = std::make_shared<ov::op::v0::Convert>(weights_right, ov::element::f32);
+        r->set_friendly_name("conversion_bf16_to_f32");
+        auto matmul_res = std::make_shared<ov::op::v0::MatMul>(weights_left, r);
+        return std::make_shared<ov::Model>(ov::NodeVector{matmul_res}, ov::ParameterVector{}, "BF16WeightsDecompression");
+    }
+
+    std::shared_ptr<ov::Model> init_subgraph_without_convert(float right_val) {
+        const auto weights_left = init_compressed_weights_subgraph(ov::element::bf16, 2);
+        const auto weights_right = init_compressed_weights_subgraph(ov::element::bf16, right_val);
+        auto matmul_res = std::make_shared<ov::op::v0::MatMul>(weights_left, weights_right);
+        return std::make_shared<ov::Model>(ov::NodeVector{matmul_res}, ov::ParameterVector{}, "BF16WeightsDecompression");
+    }
+
+    std::shared_ptr<ov::Model> init_subgraph_without_convert_add() {
+        const auto weights_left = init_compressed_weights_subgraph(ov::element::bf16, 2);
+        const auto weights_right = init_compressed_weights_subgraph(ov::element::bf16, 3);
+        auto matmul_res = std::make_shared<ov::op::v1::Add>(weights_left, weights_right);
+        return std::make_shared<ov::Model>(ov::NodeVector{matmul_res}, ov::ParameterVector{}, "BF16WeightsDecompression");
+    }
+};
+
+TEST_F(BF16WeightsDecompression, Inference_input_3) {
+    std::shared_ptr<ov::Model> function = init_subgraph();
+    ov::CompiledModel cM = core->compile_model(function, targetDevice, {ov::hint::inference_precision(ov::element::f32)});
+    auto request = cM.create_infer_request();
+    request.infer();
+    auto out_tensor = request.get_output_tensor();
+    for ( size_t i = 0 ; i < out_tensor.get_size(); i++ ) {
+        ASSERT_FLOAT_EQ(reinterpret_cast<float*>(out_tensor.data())[i], 12);
+    }
+}
+
+TEST_F(BF16WeightsDecompression, Inference_input_3_f16) {
+    std::shared_ptr<ov::Model> function = init_subgraph();
+    ov::CompiledModel cM = core->compile_model(function, targetDevice, {ov::hint::inference_precision(ov::element::f16)});
+    auto request = cM.create_infer_request();
+    request.infer();
+    auto out_tensor = request.get_output_tensor();
+    for ( size_t i = 0 ; i < out_tensor.get_size(); i++ ) {
+        ASSERT_FLOAT_EQ(reinterpret_cast<float*>(out_tensor.data())[i], 12);
+    }
+}
+
+TEST_P(BF16WeightsDecompression, Inference_without_convert) {
+    float given;
+    float expected;
+    std::tie(given, expected) = GetParam();
+    std::shared_ptr<ov::Model> function_without_convert = init_subgraph_without_convert(given);
+    ov::CompiledModel model_without_convert = core->compile_model(function_without_convert, targetDevice, {ov::hint::inference_precision(ov::element::f32)});
+    auto request = model_without_convert.create_infer_request();
+    request.infer();
+    auto out_tensor = request.get_output_tensor();
+    for ( size_t i = 0 ; i < out_tensor.get_size(); i++ ) {
+        ASSERT_NEAR(reinterpret_cast<unsigned short*>(out_tensor.data())[i], expected, 0.2f);
+    }
+}
+
+INSTANTIATE_TEST_SUITE_P(Inference_without_convert, BF16WeightsDecompression,
+                         testing::Values(BF16WeightsDecompressionParams(768, BF16WeightsDecompression::float_to_ushort(768*4)),
+                            BF16WeightsDecompressionParams(3.0f, BF16WeightsDecompression::float_to_ushort(3.0f*4)), //4 because of multiply by [[2,2], [2,2]]
+                            BF16WeightsDecompressionParams(2.0f, BF16WeightsDecompression::float_to_ushort(2.0f*4)),
+                            BF16WeightsDecompressionParams(49512.0f, BF16WeightsDecompression::float_to_ushort(49512.0f*4)),
+                            BF16WeightsDecompressionParams(98304.0f, BF16WeightsDecompression::float_to_ushort(98304.0f*4)),
+                            BF16WeightsDecompressionParams(55340232221128654848.0f,
+                                BF16WeightsDecompression::float_to_ushort(55340232221128654848.0f*4)),
+                            BF16WeightsDecompressionParams(-55340232221128654848.0f,
+                                 BF16WeightsDecompression::float_to_ushort(-55340232221128654848.0f*4)),
+                            BF16WeightsDecompressionParams(-3.0f, BF16WeightsDecompression::float_to_ushort(-3.0f*4))),
+                            BF16WeightsDecompression::get_test_case_name);
+
+
+TEST_F(BF16WeightsDecompression, Inference_input_Add_without_convert) {
+    std::shared_ptr<ov::Model> function_without_convert_add = init_subgraph_without_convert_add();
+    ov::CompiledModel model_without_convert_add = core->compile_model(function_without_convert_add, targetDevice, \
+    {ov::hint::inference_precision(ov::element::f32)});
+
+    auto request = model_without_convert_add.create_infer_request();
+    request.infer();
+    auto out_tensor = request.get_output_tensor();
+    for ( size_t i = 0 ; i < out_tensor.get_size(); i++ ) {
+        ASSERT_NEAR(reinterpret_cast<unsigned short*>(out_tensor.data())[i], 16544, 0.2f);
+    }
+}
+} // namespace
diff --git a/src/plugins/intel_gpu/tests/unit/fusions/eltwise_fusion_test.cpp b/src/plugins/intel_gpu/tests/unit/fusions/eltwise_fusion_test.cpp
index 5218ffe7920538..883279ed369dd9 100644
--- a/src/plugins/intel_gpu/tests/unit/fusions/eltwise_fusion_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/fusions/eltwise_fusion_test.cpp
@@ -30,7 +30,7 @@ struct eltwise_test_params {
 
 class EltwiseFusingTest : public ::BaseFusingTest<eltwise_test_params> {
 public:
-    void execute(eltwise_test_params& p) {
+    void execute(eltwise_test_params& p, bool count_reorder = false) {
         auto input_prim = get_mem(get_input_layout(p));
         auto input_prim2 = get_mem(get_input_layout2(p));
 
@@ -45,7 +45,7 @@ class EltwiseFusingTest : public ::BaseFusingTest<eltwise_test_params> {
             network_not_fused.set_input_data("input2", input_prim2);
         }
 
-        compare(network_not_fused, network_fused, p);
+        compare(network_not_fused, network_fused, p, count_reorder);
     }
 
     layout get_input_layout(eltwise_test_params& p) {
@@ -545,21 +545,27 @@ INSTANTIATE_TEST_SUITE_P(fusings_gpu, eltwise_no_pitches_same_dims_quantize, ::t
     eltwise_test_params{ CASE_ELTWISE_FP32_3, 3, 4 },
 }));
 
-class eltwise_activation : public EltwiseFusingTest {};
-TEST_P(eltwise_activation, basic) {
+class eltwise_activation_reorder : public EltwiseFusingTest {};
+TEST_P(eltwise_activation_reorder, basic) {
     auto p = GetParam();
-    create_topologies(
-        input_layout("input", get_input_layout(p)),
-        input_layout("input2", get_input_layout2(p)),
-        eltwise("eltwise", { input_info("input"), input_info("input2") }, p.mode, p.default_type),
-        activation("activation", input_info("eltwise"), activation_func::relu, { 6.0f, 0.0f }),
-        reorder("out", input_info("activation"), p.default_format, data_types::f32)
-    );
+    create_topologies(input_layout("input", get_input_layout(p)),
+                      input_layout("input2", get_input_layout2(p)),
+                      eltwise("eltwise", {input_info("input"), input_info("input2")}, p.mode, p.default_type),
+                      activation("activation", input_info("eltwise"), activation_func::relu, {6.0f, 0.0f}),
+                      reorder("out",
+                              input_info("activation"),
+                              p.default_format,
+                              data_types::f32,
+                              std::vector<float>(),
+                              cldnn::reorder_mean_mode::subtract,
+                              cldnn::padding(),
+                              true));
 
     tolerance = default_tolerance(p.input_type);
-    execute(p);
+    execute(p, true);
 }
 
+class eltwise_activation : public EltwiseFusingTest {};
 TEST_P(eltwise_activation, fp16_out) {
     auto p = GetParam();
     create_topologies(
@@ -574,6 +580,21 @@ TEST_P(eltwise_activation, fp16_out) {
     execute(p);
 }
 
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, eltwise_activation_reorder, ::testing::ValuesIn(std::vector<eltwise_test_params>{
+    eltwise_test_params{ CASE_ELTWISE_FP16_1, 3, 5 },
+    eltwise_test_params{ CASE_ELTWISE_FP16_2, 3, 5 },
+    eltwise_test_params{ CASE_ELTWISE_FP16_3, 4, 5 },
+    eltwise_test_params{ CASE_ELTWISE_FP32_1, 3, 5 },
+    eltwise_test_params{ CASE_ELTWISE_FP32_2, 3, 5 },
+    eltwise_test_params{ CASE_ELTWISE_FP32_3, 3, 5 },
+    eltwise_test_params{ CASE_ELTWISE_FP32_FP16_1, 3, 5 },
+    eltwise_test_params{ CASE_ELTWISE_FP32_FP16_2, 3, 5 },
+    eltwise_test_params{ CASE_ELTWISE_FP32_FP16_3, 3, 5 },
+    eltwise_test_params{ CASE_ELTWISE_FP16_FP32_1, 3, 5 },
+    eltwise_test_params{ CASE_ELTWISE_FP16_FP32_2, 3, 5 },
+    eltwise_test_params{ CASE_ELTWISE_FP16_FP32_3, 4, 5 }
+}));
+
 INSTANTIATE_TEST_SUITE_P(fusings_gpu, eltwise_activation, ::testing::ValuesIn(std::vector<eltwise_test_params>{
     eltwise_test_params{ CASE_ELTWISE_FP16_1, 3, 4 },
     eltwise_test_params{ CASE_ELTWISE_FP16_2, 3, 4 },
@@ -590,6 +611,7 @@ INSTANTIATE_TEST_SUITE_P(fusings_gpu, eltwise_activation, ::testing::ValuesIn(st
 }));
 
 
+
 class eltwise_quantize_fs_b_yx_fsv32 : public EltwiseFusingTest {};
 TEST_P(eltwise_quantize_fs_b_yx_fsv32, fusing_eltwise_quantize_layout) {
     auto p = GetParam();
@@ -649,4 +671,4 @@ INSTANTIATE_TEST_SUITE_P(fusings_gpu, eltwise_quantize_fs_b_yx_fsv32_exception,
     eltwise_test_params{ CASE_ELTWISE_FP16_B_FS_YX, 6, 6 },
     eltwise_test_params{ CASE_ELTWISE_FP16_BATCH_FS_B, 6, 6 },
     eltwise_test_params{ CASE_ELTWISE_FP16_BATCH_B_FS, 6, 6 },
-}));
\ No newline at end of file
+}));
diff --git a/src/plugins/intel_gpu/tests/unit/fusions/fully_connected_fusion_test.cpp b/src/plugins/intel_gpu/tests/unit/fusions/fully_connected_fusion_test.cpp
index fb7da3f597cadf..5745e446ca7bb0 100644
--- a/src/plugins/intel_gpu/tests/unit/fusions/fully_connected_fusion_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/fusions/fully_connected_fusion_test.cpp
@@ -138,6 +138,17 @@ class FullyConnectedFusingTestOneDNN : public BaseFusingTest<fully_connected_tes
         return layout{ bias_shape, p.default_type, p.default_format };
     }
 
+    layout get_scale_layout(fully_connected_test_params& p, size_t group_size = 1) {
+        if (p.weights_type == data_types::u8 || p.weights_type == data_types::i8) {
+            auto scale_shape = p.out_shape.size() == 3 ? ov::PartialShape{p.out_shape[2]} : ov::PartialShape{p.out_shape[1]};
+            return layout{ scale_shape, p.default_type, p.default_format };
+        } else {
+            auto groups_num = p.in_shape.size() == 3 ? p.in_shape[2] / group_size : p.in_shape[1] / group_size;
+            auto scale_shape = p.out_shape.size() == 3 ? ov::PartialShape{p.out_shape[2], groups_num} : ov::PartialShape{p.out_shape[1], groups_num};
+            return layout{ scale_shape, p.default_type, p.default_format };
+        }
+    }
+
     layout get_output_layout(fully_connected_test_params& p) {
         return layout{ p.out_shape, p.data_type, p.input_format };
     }
@@ -571,13 +582,44 @@ TEST_P(fc_fp16_eltwise_add_dynamic, basic) {
         reorder("reorder_bfyx", input_info("eltwise"), p.default_format, data_types::f32)
     );
 
+    bool is_dynamic = true;
+    cfg_not_fused.set_property(ov::intel_gpu::allow_new_shape_infer(is_dynamic));
     tolerance = 1e-2f;
-    execute(p, false, true);
+    execute(p, false, is_dynamic);
 }
 
-INSTANTIATE_TEST_SUITE_P(DISABLED_fusings_gpu, fc_fp16_eltwise_add_dynamic, ::testing::ValuesIn(std::vector<fully_connected_test_params>{
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, fc_fp16_eltwise_add_dynamic, ::testing::ValuesIn(std::vector<fully_connected_test_params>{
     fully_connected_test_params{ CASE_FC_FP16_3, 2, 3 },
-    fully_connected_test_params{ CASE_FC_FP16_4, 2, 3 },
+    fully_connected_test_params{ CASE_FC_FP16_4, 2, 3 }
+}));
+
+class fc_compressed_int8_bias_dynamic_onednn : public FullyConnectedFusingTestOneDNN {};
+TEST_P(fc_compressed_int8_bias_dynamic_onednn, basic) {
+    auto p = GetParam();
+    auto test_input_layout = get_input_layout(p);
+    auto dynamic_input_layout = layout{ov::PartialShape::dynamic(test_input_layout.get_partial_shape().rank()), test_input_layout.data_type, test_input_layout.format};
+
+    auto fc_prim = fully_connected("fc_prim", input_info("input"), "weights", "", "scale", "", data_types::f16, padding(), get_output_dim_size(p), get_input_weights_rank(p));
+    fc_prim.decompression_zero_point_scalar = 8.0f;
+
+    create_topologies(
+        input_layout("input", dynamic_input_layout),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("scale", get_mem(get_scale_layout(p, 128))),
+        data("bias", get_mem(get_bias_layout(p))),
+        fc_prim,
+        eltwise("bias_add", { input_info("fc_prim"), input_info("bias") }, eltwise_mode::sum),
+        reorder("reorder_bfyx", input_info("bias_add"), p.default_format, data_types::f32)
+    );
+
+    bool is_dynamic = true;
+    cfg_not_fused.set_property(ov::intel_gpu::allow_new_shape_infer(is_dynamic));
+    tolerance = 1.0f;
+    execute(p, false, is_dynamic);
+}
+
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, fc_compressed_int8_bias_dynamic_onednn, ::testing::ValuesIn(std::vector<fully_connected_test_params>{
+    fully_connected_test_params{ CASE_FC_FP16_INT4_COMP_1, 2, 3 },
 }));
 
 class fc_fp16_eltwise_sub : public FullyConnectedFusingTestOneDNN {
diff --git a/src/plugins/intel_gpu/tests/unit/fusions/gather_fusion_test.cpp b/src/plugins/intel_gpu/tests/unit/fusions/gather_fusion_test.cpp
index 22e8df735c7c30..2468a96e789a37 100644
--- a/src/plugins/intel_gpu/tests/unit/fusions/gather_fusion_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/fusions/gather_fusion_test.cpp
@@ -32,7 +32,7 @@ struct gather_test_params {
 
 class GatherPrimitiveFusingTest : public ::BaseFusingTest<gather_test_params> {
 public:
-    void execute(gather_test_params& p, bool is_dynamic = false) {
+    void execute(gather_test_params& p, bool is_dynamic = false, bool count_reorder = false) {
         cfg_not_fused.set_property(ov::intel_gpu::allow_new_shape_infer(is_dynamic));
         cfg_fused.set_property(ov::intel_gpu::allow_new_shape_infer(is_dynamic));
         cfg_fused.set_property(ov::intel_gpu::optimize_data(true));
@@ -50,7 +50,7 @@ class GatherPrimitiveFusingTest : public ::BaseFusingTest<gather_test_params> {
             network_not_fused.set_input_data("eltwise_data", elt_input_prim);
         }
 
-        compare(network_not_fused, network_fused, p);
+        compare(network_not_fused, network_fused, p, count_reorder);
     }
 
     layout get_input_layout(gather_test_params& p, bool is_dynamic = false) {
@@ -119,6 +119,8 @@ class GatherPrimitiveFusingTest : public ::BaseFusingTest<gather_test_params> {
 #define CASE_GATHER_5D_FP16_4 { 3, 2, 2, 2, 2 }, { 2, 3, 1, 1 }, { 3, 2, 2, 3, 2 }, 2, data_types::f16, format::bfzyx, data_types::f16, format::bfzyx
 #define CASE_GATHER_5D_FP16_5 { 1, 1, 2, 1, 1 }, { 3, 1, 1, 1 }, { 1, 1, 1, 1, 3 }, 4, data_types::f16, format::bfzyx, data_types::f16, format::bfzyx
 
+#define CASE_GATHER_INT8_1    { 2, 3, 4, 1 }, { 4 }, { 4, 3, 4, 1 }, 0, data_types::i8, format::bfyx, data_types::f32, format::bfyx
+
 class gather_quantize : public GatherPrimitiveFusingTest {};
 TEST_P(gather_quantize, basic) {
     auto p = GetParam();
@@ -223,14 +225,15 @@ TEST_P(gather_eltwise_activation_dynamic, basic) {
         gather("gather_prim", input_info("input"), input_info("gather_indices"), p.axis, p.dictionary_shape.size(), p.out_shape),
         activation("activation", input_info("gather_prim"), activation_func::abs),
         eltwise("eltwise", { input_info("activation"), input_info("eltwise_data") }, eltwise_mode::prod),
-        reorder("reorder_bfyx", input_info("eltwise"), p.default_format, data_types::f32)
+        reorder("reorder_bfyx", input_info("eltwise"), p.default_format, data_types::f32, std::vector<float>(), cldnn::reorder_mean_mode::subtract, cldnn::padding(), true)
     );
 
     tolerance = 1e-5f;
-    execute(p, true);
+    execute(p, true, true);
 }
 INSTANTIATE_TEST_SUITE_P(fusings_gpu, gather_eltwise_activation_dynamic, ::testing::ValuesIn(std::vector<gather_test_params>{
     gather_test_params{ CASE_GATHER_FP32_6, 4, 6 },
-    gather_test_params{ CASE_GATHER_FP16_6, 4, 6 },
-    gather_test_params{ CASE_GATHER_FP16_7, 4, 6 },
+    gather_test_params{ CASE_GATHER_FP16_6, 4, 7 },
+    gather_test_params{ CASE_GATHER_FP16_7, 5, 8 },
+    gather_test_params{ CASE_GATHER_INT8_1, 4, 7 },
 }));
diff --git a/src/plugins/intel_gpu/tests/unit/fusions/select_fusion_test.cpp b/src/plugins/intel_gpu/tests/unit/fusions/select_fusion_test.cpp
new file mode 100644
index 00000000000000..0cc5843545ba44
--- /dev/null
+++ b/src/plugins/intel_gpu/tests/unit/fusions/select_fusion_test.cpp
@@ -0,0 +1,112 @@
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "test_utils.h"
+#include "fusion_test_common.hpp"
+
+#include <intel_gpu/primitives/input_layout.hpp>
+#include <intel_gpu/primitives/data.hpp>
+#include <intel_gpu/primitives/select.hpp>
+#include <intel_gpu/primitives/reorder.hpp>
+#include "select_inst.h"
+
+#include <cmath>
+
+using namespace cldnn;
+using namespace ::tests;
+
+namespace {
+struct select_test_params {
+    ov::Shape input_shape;
+    ov::Shape mask_shape;
+    data_types input_type;
+    data_types output_type;
+    format input_format;
+    format output_format;
+    size_t expected_fused_primitives;
+    size_t expected_not_fused_primitives;
+};
+
+class SelectFusingTest : public ::BaseFusingTest<select_test_params> {
+public:
+    void execute(select_test_params& p, bool count_reorder = false) {
+        cfg_not_fused.set_property(ov::intel_gpu::allow_new_shape_infer(true));
+        cfg_fused.set_property(ov::intel_gpu::allow_new_shape_infer(true));
+        cfg_fused.set_property(ov::intel_gpu::optimize_data(true));
+ 
+        tests::random_generator rg;
+        auto mask_mem   = get_mem(get_mask_layout(p), 0, 1);
+        auto input1_mem = get_mem(get_input_layout(p));
+        auto input2_mem = get_mem(get_input_layout(p));
+
+        network network_not_fused(this->engine, this->topology_non_fused, cfg_not_fused);
+        network network_fused(this->engine, this->topology_fused, cfg_fused);
+
+        auto inputs = network_fused.get_input_ids();
+        network_fused.set_input_data("mask", mask_mem);
+        network_fused.set_input_data("input1", input1_mem);
+        network_fused.set_input_data("input2", input2_mem);
+        network_not_fused.set_input_data("mask", mask_mem);
+        network_not_fused.set_input_data("input1", input1_mem);
+        network_not_fused.set_input_data("input2", input2_mem);
+        compare(network_not_fused, network_fused, p, count_reorder);
+    }
+
+    layout get_input_layout(select_test_params& p) {
+        return layout{ p.input_shape, p.input_type, p.input_format };
+    }
+    layout get_mask_layout(select_test_params& p) {
+        return layout{ p.mask_shape, data_types::i8, p.input_format };
+    }
+};
+}  // namespace
+
+#define CASE_SELECT_FP32_TO_I8_0  {2, 16, 4, 4}, {2, 16, 4, 4},  data_types::f32, data_types::i8,  format::bfyx, format::bfyx
+#define CASE_SELECT_FP32_TO_F16_0 {2, 16, 17, 4}, {2, 16, 1, 4}, data_types::f32, data_types::f16, format::bfyx, format::bfyx
+#define CASE_SELECT_FP16_TO_I8_0  {2, 16, 4, 4}, {2, 16, 4, 4},  data_types::f16, data_types::i8,  format::bfyx, format::bfyx
+#define CASE_SELECT_FP16_TO_I8_1  {2, 16, 4, 4}, {2, 16, 4, 4},  data_types::f16, data_types::i8,  format::bfyx, format::bfzyx
+
+class select_reorder_fusion : public SelectFusingTest {};
+TEST_P(select_reorder_fusion, basic) {
+    auto p = GetParam();
+    create_topologies(
+        cldnn::input_layout("mask", get_mask_layout(p)),
+        cldnn::input_layout("input1", get_input_layout(p)),
+        cldnn::input_layout("input2", get_input_layout(p)),
+        cldnn::reorder("mask_convert", input_info("mask"), p.input_format, p.input_type),
+        cldnn::select("select", input_info("input1"), input_info("input2"), input_info("mask_convert")),
+        cldnn::reorder("out", input_info("select"), p.output_format, p.output_type, std::vector<float>(), cldnn::reorder_mean_mode::subtract, cldnn::padding(), true)
+    );
+    tolerance = 1e-5f;
+    execute(p, true);
+}
+
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, select_reorder_fusion, ::testing::ValuesIn(std::vector<select_test_params>{
+    select_test_params{ CASE_SELECT_FP32_TO_F16_0, 5, 6},
+    select_test_params{ CASE_SELECT_FP32_TO_I8_0, 5, 6},
+    select_test_params{ CASE_SELECT_FP16_TO_I8_0, 5, 6},
+    select_test_params{ CASE_SELECT_FP16_TO_I8_1, 6, 6}, // reorder should not be fused
+}));
+
+class select_reorder_fusion_dynamic : public SelectFusingTest {};
+TEST_P(select_reorder_fusion_dynamic, basic) {
+    auto p = GetParam();
+    create_topologies(
+        cldnn::input_layout("mask", layout{ ov::PartialShape::dynamic(p.mask_shape.size()), data_types::i8, p.input_format }),
+        cldnn::input_layout("input1", layout {ov::PartialShape::dynamic(p.input_shape.size()), p.input_type, p.input_format }),
+        cldnn::input_layout("input2", layout {ov::PartialShape::dynamic(p.input_shape.size()), p.input_type, p.input_format }),
+        cldnn::reorder("mask_convert", input_info("mask"), p.input_format, p.input_type),
+        cldnn::select("select", input_info("input1"), input_info("input2"), input_info("mask_convert")),
+        cldnn::reorder("out", input_info("select"), p.output_format, p.output_type, std::vector<float>(), cldnn::reorder_mean_mode::subtract, cldnn::padding(), true)
+    );
+    tolerance = 1e-5f;
+    execute(p, true);
+}
+
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, select_reorder_fusion_dynamic, ::testing::ValuesIn(std::vector<select_test_params>{
+    select_test_params{ CASE_SELECT_FP32_TO_F16_0, 5, 6},
+    select_test_params{ CASE_SELECT_FP32_TO_I8_0, 5, 6},
+    select_test_params{ CASE_SELECT_FP16_TO_I8_0, 5, 6},
+    select_test_params{ CASE_SELECT_FP16_TO_I8_1, 6, 6}, // reorder should not be fused
+}));
diff --git a/src/plugins/intel_gpu/tests/unit/passes/post_optimize_weights.cpp b/src/plugins/intel_gpu/tests/unit/passes/post_optimize_weights.cpp
index cfaf5324c30a29..a27ea57ce40d53 100644
--- a/src/plugins/intel_gpu/tests/unit/passes/post_optimize_weights.cpp
+++ b/src/plugins/intel_gpu/tests/unit/passes/post_optimize_weights.cpp
@@ -5,6 +5,9 @@
 #include "test_utils.h"
 #include "program_wrapper.h"
 #include "fully_connected_inst.h"
+#ifdef ENABLE_ONEDNN_FOR_GPU
+#include "graph/impls/onednn/utils.hpp"
+#endif
 
 using namespace cldnn;
 using namespace ::tests;
@@ -196,3 +199,49 @@ TEST(post_optimize_weights, fuse_only_with_supported_weights_layout) {
 
     ASSERT_TRUE(has_node(*prog, "reorder"));
 }
+
+TEST(post_optimize_weights, fuse_reorder_to_onednn_weights_reorder_test) {
+    auto& engine = get_test_engine();
+    if (!engine.get_device_info().supports_immad)
+        return;
+
+    ov::Shape pshape = { 4, 16 };
+    auto input = engine.allocate_memory({ pshape, data_types::f16, format::bfyx });
+    auto weights = engine.allocate_memory({ pshape, data_types::f32, format::bfyx });
+
+    std::vector<float> weights_data(pshape[0] * pshape[1]);
+    std::iota(weights_data.begin(), weights_data.end(), 0.f);
+    set_values(weights, weights_data);
+
+    topology topology(
+        input_layout("input", input->get_layout()),
+        input_layout("weights", weights->get_layout()),
+        reorder("reorder_dt", input_info("weights"), format::bfyx, data_types::f16),
+        fully_connected("fc", input_info("input"), { "reorder_dt" }, "", data_types::f16)
+    );
+
+    ExecutionConfig config = get_test_default_config(engine);
+    config.set_property(ov::intel_gpu::optimize_data(true));
+    config.set_property(ov::intel_gpu::allow_new_shape_infer(true));
+
+    if (engine.get_device_info().supports_immad) {
+        ov::intel_gpu::ImplementationDesc fc_impl = { format::bfyx, "", impl_types::onednn };
+        config.set_property(ov::intel_gpu::force_implementations(ov::intel_gpu::ImplForcingMap{ {"fc", fc_impl} }));
+    }
+
+    layout_optimizer lo(true);
+    lo.set_optimization_attribute(layout_optimizer::optimization_attributes_type::use_onednn_impls, true);
+
+    auto prog = program::build_program(engine, topology, config);
+
+    ASSERT_TRUE(has_node(*prog, "reorder_dt"));
+    auto& fc_node = prog->get_node("fc");
+    auto weights_param = fc_node.as<fully_connected>().get_selected_impl()->get_weights_reorder_params();
+    ASSERT_TRUE(format::is_weights_format(prog->get_node("reorder_dt").get_output_layout().format));
+    ASSERT_TRUE(prog->get_node("reorder_dt").get_input_layout().data_type == data_types::f32);
+#ifdef ENABLE_ONEDNN_FOR_GPU
+    // Check onednn_weights_params->_in_desc data_type is properly updated
+    auto onednn_weights_params = std::dynamic_pointer_cast<onednn::WeightsReorderParamsOneDNN>(weights_param);
+    ASSERT_TRUE(onednn_weights_params->_in_desc.get_data_type() == onednn::convert_data_type(data_types::f32));
+#endif
+}
\ No newline at end of file
diff --git a/src/plugins/intel_gpu/tests/unit/passes/test_module_fusing_reorder.cpp b/src/plugins/intel_gpu/tests/unit/passes/test_module_fusing_reorder.cpp
index 5e36ae2ea4bdc0..55521484685f67 100644
--- a/src/plugins/intel_gpu/tests/unit/passes/test_module_fusing_reorder.cpp
+++ b/src/plugins/intel_gpu/tests/unit/passes/test_module_fusing_reorder.cpp
@@ -266,7 +266,7 @@ TEST_P(test_can_fuse_reorder_onednn, reorder_for_firstconv_onednn)
     layout conv_layout(p.input_data_type, p.output_format, p.out_shape, padding({0, }, 0));
     layout reorder_layout(p.output_data_type, p.output_format, p.out_shape, padding({0, }, 0));
     auto input = engine.allocate_memory({ p.input_data_type, p.input_format, p.in_shape });
-    auto weights = engine.allocate_memory({ p.input_data_type, p.input_format, p.weight_shape });
+    auto weights = engine.allocate_memory({ p.input_data_type, p.weights_format, p.weight_shape });
 
     topology.add(input_layout("input", input->get_layout()));
     topology.add(data("weights", weights));
@@ -298,9 +298,9 @@ TEST_P(test_can_fuse_reorder_onednn, reorder_for_firstconv_onednn)
 INSTANTIATE_TEST_SUITE_P(testing_can_fuse_reorder_first_conv, test_can_fuse_reorder_onednn,
                         ::testing::ValuesIn(std::vector<reorder_test_param>{
                                             reorder_test_param{format::bs_fs_yx_bsv8_fsv4, format::b_fs_yx_fsv32, data_types::f32, data_types::u8, {1, 3, 8, 8}, {1, 32, 8, 8}, {1, 3, 1, 1},
-                                                tensor{1}, tensor{0}, data_types::u8, format::goiyx, true},
+                                                tensor{1}, tensor{0}, data_types::u8, format::oiyx, true},
                                             reorder_test_param{format::bs_fs_yx_bsv8_fsv2, format::b_fs_yx_fsv16, data_types::f32, data_types::f16, {1, 3, 8, 8}, {1, 32, 8, 8}, {1, 3, 1, 1},
-                                                tensor{1}, tensor{0}, data_types::f16, format::goiyx, true},
+                                                tensor{1}, tensor{0}, data_types::f16, format::oiyx, true},
                                             }));
 
 class can_fuse_reorder : public ::testing::TestWithParam<std::tuple<data_types, format>> {};
diff --git a/src/plugins/intel_gpu/tests/unit/shape_infer/crop_si_test.cpp b/src/plugins/intel_gpu/tests/unit/shape_infer/crop_si_test.cpp
index 8141dfbeeeb808..103d62525f4003 100644
--- a/src/plugins/intel_gpu/tests/unit/shape_infer/crop_si_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/shape_infer/crop_si_test.cpp
@@ -22,6 +22,7 @@ struct crop_si_test_params {
     tensor reference_input_size;
     std::vector<tensor> offsets;
     std::vector<std::vector<int64_t>> const_values;
+    int64_t axis;
     std::vector<layout> input_layouts;
     std::vector<layout> expected_layouts;
     size_t param_num_splits;
@@ -61,7 +62,7 @@ TEST_P(crop_si_test, shape_infer) {
 
     for (size_t output_idx = 0; output_idx < p.expected_layouts.size(); output_idx++) {
         auto prim_id = "crop.out" + std::to_string(output_idx);
-        auto crop_prim = std::make_shared<crop>(prim_id, input_prim_ids, p.reference_input_size, p.offsets[output_idx], op_mode, static_cast<int>(output_idx), p.param_num_splits);
+        auto crop_prim = std::make_shared<crop>(prim_id, input_prim_ids, p.reference_input_size, p.offsets[output_idx], op_mode, static_cast<int>(output_idx), p.axis, p.param_num_splits);
         auto& crop_node = prog.get_or_create(crop_prim);
 
         for (auto& prim : input_prims) {
@@ -83,6 +84,7 @@ INSTANTIATE_TEST_SUITE_P(smoke, crop_si_test,
             tensor({1,1,1,1,1,1,1}),
             {tensor({0,0,0,0,1,1,1}),tensor({0,0,0,0,1,1,1})},
             {{-1}, {1,1}},
+            -1,
             {{{1,32,2},data_types::f32,format::bfyx}, {{},data_types::i64,format::bfyx}, {{2},data_types::i64,format::bfyx}},
             {{{1,32,1},data_types::f32,format::bfyx}, {{1,32,1},data_types::f32,format::bfyx}}, 0
         },
@@ -90,6 +92,7 @@ INSTANTIATE_TEST_SUITE_P(smoke, crop_si_test,
             tensor({1,1,1,1,1,1,1}),
             {tensor({0,0,0,0,1,1,1}),tensor({0,0,0,0,1,1,1})},
             {{-1}, {1,1}},
+            -1,
             {{ov::PartialShape::dynamic(),data_types::f32,format::bfyx}, {{},data_types::i64,format::bfyx}, {{2},data_types::i64,format::bfyx}},
             {{ov::PartialShape::dynamic(),data_types::f32,format::bfyx}, {ov::PartialShape::dynamic(),data_types::f32,format::bfyx}}, 0
         },
@@ -97,6 +100,7 @@ INSTANTIATE_TEST_SUITE_P(smoke, crop_si_test,
             tensor({3,1,1,1,1,1,1}),
             {tensor({0,0,0,0,1,1,1}),tensor({0,0,0,0,1,1,1})},
             {},
+            -1,
             {{ov::PartialShape::dynamic(),data_types::f32,format::bfyx}},
             {{ov::PartialShape::dynamic(1),data_types::f32,format::bfyx}}, 0
         },
@@ -104,6 +108,7 @@ INSTANTIATE_TEST_SUITE_P(smoke, crop_si_test,
             tensor({3,1,1,1,1,1,1}),
             {tensor({0,0,0,0,1,1,1}),tensor({0,0,0,0,1,1,1})},
             {},
+            -1,
             {{{4},data_types::f32,format::bfyx}},
             {{{3},data_types::f32,format::bfyx}}, 0
         },
@@ -111,6 +116,7 @@ INSTANTIATE_TEST_SUITE_P(smoke, crop_si_test,
             tensor({-1,-1,-1,-1,-1,-1,-1}),
             {tensor({0,0,0,0,1,1,1}),tensor({0,0,0,0,1,1,1})},
             {},
+            -1,
             {{{4,3,2,5},data_types::f32,format::bfyx}},
             {{{3,2,1,4},data_types::f32,format::bfyx}}, 0
         },
@@ -118,6 +124,7 @@ INSTANTIATE_TEST_SUITE_P(smoke, crop_si_test,
             tensor({1,1,1,1,1,1,1}),
             {tensor({0,0,0,0,1,1,1}),tensor({0,1,0,0,1,1,1}),tensor({0,2,0,0,1,1,1}),tensor({0,3,0,0,1,1,1})},
             {{1}, {1,1,1,1}},
+            1,
             {{{4819,4,1,1,4},data_types::f32,format::bfzyx}, {{},data_types::i64,format::bfzyx}, {{4},data_types::i64,format::bfzyx}},
             {{{4819,1,1,1,4},data_types::f32,format::bfzyx}, {{4819,1,1,1,4},data_types::f32,format::bfzyx}, {{4819,1,1,1,4},data_types::f32,format::bfzyx}, {{4819,1,1,1,4},data_types::f32,format::bfzyx}}, 0
         },
@@ -125,6 +132,7 @@ INSTANTIATE_TEST_SUITE_P(smoke, crop_si_test,
             tensor({4507,1,1,1,1,1,1}),
             {tensor({0,2,0,0,1,1,1})},
             {},
+            -1,
             {{{4507,3,1,1},data_types::f32,format::bfyx}},
             {{{4507,1,1,1},data_types::f32,format::bfyx}}, 0
         },
@@ -132,6 +140,7 @@ INSTANTIATE_TEST_SUITE_P(smoke, crop_si_test,
             tensor({1,1,1,1,1,1,1}),
             {tensor({0,0,0,0,1,1,1}),tensor({0,0,0,0,1,1,1})},
             {{2}, {11,3}},
+            2,
             {{{1,14,14,384},data_types::f32,format::bfyx}, {{},data_types::i64,format::bfyx}, {{2},data_types::i64,format::bfyx}},
             {{{1,14,11,384},data_types::f32,format::bfyx}, {{1,14,3,384},data_types::f32,format::bfyx}}, 0
         },
@@ -139,6 +148,7 @@ INSTANTIATE_TEST_SUITE_P(smoke, crop_si_test,
             tensor({1,1,2048,1,1,1,1}),
             {tensor({0,2,0,0,1,1,1})},
             {},
+            -1,
             {{{1,16,1,2048},data_types::f32,format::bfyx}},
             {{{1,1,1,2048},data_types::f32,format::bfyx}}, 0
         },
@@ -146,6 +156,7 @@ INSTANTIATE_TEST_SUITE_P(smoke, crop_si_test,
             tensor({1,1,1,1,1,1,1}),
             {tensor({0,0,0,0,1,1,1}),tensor({0,1320,0,0,1,1,1})},
             {{1},{1320,99}},
+            1,
             {{{1,1419},data_types::f32,format::bfyx}, {{},data_types::i64,format::bfyx}, {{2},data_types::i64,format::bfyx}},
             {{{1,1320},data_types::f32,format::bfyx}, {{1,99},data_types::f32,format::bfyx}}, 0
         },
@@ -153,6 +164,7 @@ INSTANTIATE_TEST_SUITE_P(smoke, crop_si_test,
             tensor({1,128,2,64,1,1,1}),
             {tensor({0,0,8,0,1,1,1})},
             {},
+            -1,
             {{{1,128,64,10},data_types::f32,format::bfyx}},
             {{{1,128,64,2},data_types::f32,format::bfyx}}, 0
         },
@@ -160,6 +172,7 @@ INSTANTIATE_TEST_SUITE_P(smoke, crop_si_test,
             tensor({1,1,1,1,1,1,1}),
             {tensor({0,0,0,0,1,1,1}), tensor({0,1,0,0,1,1,1})},
             {{1}},
+            1,
             {{{4,2},data_types::f32,format::bfyx}, {{},data_types::i64,format::bfyx}},
             {{{4,1},data_types::f32,format::bfyx}, {{4,1},data_types::f32,format::bfyx}}, 2
         },
@@ -167,6 +180,7 @@ INSTANTIATE_TEST_SUITE_P(smoke, crop_si_test,
             tensor({1,1,1,1,1,1,1}),
             {tensor({0,0,0,0,1,1,1}), tensor({0,0,2048,0,1,1,1})},
             {{2}},
+            2,
             {{{5,1,4096,1},data_types::f32,format::bfyx}, {{},data_types::i64,format::bfyx}},
             {{{5,1,2048,1},data_types::f32,format::bfyx}, {{5,1,2048,1},data_types::f32,format::bfyx}}, 2
         },
@@ -174,6 +188,7 @@ INSTANTIATE_TEST_SUITE_P(smoke, crop_si_test,
             tensor({1,1400,1,1,1,1,1}),
             {tensor({0,100,0,0,1,1,1})},
             {},
+            -1,
             {{{1,1500,1,1},data_types::f32,format::bfyx}},
             {{{1,1400,1,1},data_types::f32,format::bfyx}}, 0
         },
@@ -181,6 +196,7 @@ INSTANTIATE_TEST_SUITE_P(smoke, crop_si_test,
             tensor({1,1,1,1,1,1,1}),
             {tensor({0,0,0,0,1,1,1})},
             {{2},{1}},
+            2,
             {{{7,1,1},data_types::f32,format::bfyx}, {{},data_types::i64,format::bfyx}, {{1},data_types::i64,format::bfyx}},
             {{{7,1,1},data_types::f32,format::bfyx}}, 0
         },
@@ -188,6 +204,7 @@ INSTANTIATE_TEST_SUITE_P(smoke, crop_si_test,
             tensor({128,100,1,3,1,1,1}),
             {tensor({0,0,0,0,1,1,1})},
             {},
+            -1,
             {{{128,100,4},data_types::f32,format::bfyx}},
             {{{128,100,3},data_types::f32,format::bfyx}}, 0
         }
diff --git a/src/plugins/intel_gpu/tests/unit/test_cases/broadcast_gpu_test.cpp b/src/plugins/intel_gpu/tests/unit/test_cases/broadcast_gpu_test.cpp
index d7c99f5ce60d58..184a5be5b1d81e 100644
--- a/src/plugins/intel_gpu/tests/unit/test_cases/broadcast_gpu_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/test_cases/broadcast_gpu_test.cpp
@@ -86,32 +86,38 @@ void start_broadcast_test(format cldnn_format, data_types cldnn_data_type, std::
         }
     }
 }
-template<typename T>
+template<typename inT, typename outT>
 void start_broadcast_test_dynamic(format input_format,
                                   data_types input_data_type,
+                                  data_types output_data_type,
                                   ov::Shape output_shape,
                                   ov::Shape input_data_shape,
                                   ov::AxisSet broadcast_axes,
                                   bool is_output_static = false,
-                                  impl_types impl_type = impl_types::any) {
+                                  impl_types impl_type = impl_types::any, 
+                                  bool optimize = false) {
     size_t input_data_size = accumulate(input_data_shape.rbegin(), input_data_shape.rend(), (size_t)1, std::multiplies<size_t>());
     ASSERT_GE(input_data_size, (size_t)1);
-    std::vector<T> input_data = {};
+    std::vector<inT> input_data = {};
     for (size_t i = 1; i <= input_data_size; ++i) {
-        input_data.push_back((T)i);
+        input_data.push_back((inT)i);
     }
 
     size_t output_data_size = accumulate(output_shape.rbegin(), output_shape.rend(), (size_t)1, std::multiplies<size_t>());
     ASSERT_GE(output_data_size, (size_t)1);
-    std::vector<T> output_data(output_data_size);
+    std::vector<inT>  output_data_tmp(output_data_size);
+    std::vector<outT> output_data;
     ov::reference::broadcast(reinterpret_cast<const char*>(input_data.data()),
-                             reinterpret_cast<char*>(output_data.data()),
+                             reinterpret_cast<char*>(output_data_tmp.data()),
                              ov::Shape(input_data_shape.begin(), input_data_shape.end()),
                              ov::Shape(output_shape.begin(), output_shape.end()),
                              ov::AxisSet(broadcast_axes),
-                             sizeof(T));
+                             sizeof(inT));
 
-    ASSERT_EQ(output_data.size(), accumulate(output_shape.rbegin(), output_shape.rend(), (size_t)1, std::multiplies<size_t>()));
+    ASSERT_EQ(output_data_tmp.size(), accumulate(output_shape.rbegin(), output_shape.rend(), (size_t)1, std::multiplies<size_t>()));
+    for (auto i : output_data_tmp) {
+        output_data.push_back((outT)i);
+    }
 
     int64_t input_rank = input_data_shape.size();
     ASSERT_EQ(input_rank, broadcast_axes.size());
@@ -126,11 +132,15 @@ void start_broadcast_test_dynamic(format input_format,
         auto in_layout = layout(ov::PartialShape::dynamic(input_rank), input_data_type, fmt);
         topology.add(input_layout("input", in_layout));
         topology.add(reorder("reorder", input_info("input"), input_format, input_data_type));
-        topology.add(broadcast("broadcast",
-                               input_info("reorder"),
-                               output_shape,
-                               ov::AxisSet(broadcast_axes)));
-        topology.add(reorder("output", input_info("broadcast"), fmt, input_data_type));
+        topology.add(broadcast("broadcast", input_info("reorder"), output_shape, ov::AxisSet(broadcast_axes)));
+        topology.add(reorder("output",
+                             input_info("broadcast"),
+                             fmt,
+                             output_data_type,
+                             std::vector<float>(),
+                             cldnn::reorder_mean_mode::subtract,
+                             cldnn::padding(),
+                             true));
     } else {
         auto in_layout = layout(ov::PartialShape::dynamic(input_rank), input_data_type, fmt);
         auto target_shape_layout = layout(ov::PartialShape{input_rank}, data_types::i32, fmt);
@@ -140,7 +150,14 @@ void start_broadcast_test_dynamic(format input_format,
         topology.add(reorder("reorder", input_info("input"), input_format, input_data_type));
         topology.add(
             broadcast("broadcast", input_info("reorder"), input_info("target_shape"), ov::AxisSet(broadcast_axes)));
-        topology.add(reorder("output", input_info("broadcast"), fmt, input_data_type));
+        topology.add(reorder("output",
+                             input_info("broadcast"),
+                             fmt,
+                             output_data_type,
+                             std::vector<float>(),
+                             cldnn::reorder_mean_mode::subtract,
+                             cldnn::padding(),
+                             true));
         std::vector<int32_t> target_shape_data;
         for (auto out_shape : output_shape) {
             target_shape_data.push_back(static_cast<int32_t>(out_shape));
@@ -150,6 +167,9 @@ void start_broadcast_test_dynamic(format input_format,
 
     ExecutionConfig config = get_test_default_config(engine);
     config.set_property(ov::intel_gpu::allow_new_shape_infer(true));
+    if (optimize) {
+        config.set_property(ov::intel_gpu::optimize_data(true));
+    }
 
     const bool force_impl = impl_type != impl_types::any;
     if (force_impl) {
@@ -168,7 +188,7 @@ void start_broadcast_test_dynamic(format input_format,
 
     // In case of impl forcing optimize_data property will set to true and additional
     // reorders optimization pass will be tiggered, so change expected primitive id
-    const auto prim_id = force_impl ? "output" : "broadcast";
+    const auto prim_id = (force_impl || optimize) ? "output" : "broadcast";
     auto inst = network.get_primitive(prim_id);
     auto impl = inst->get_impl();
     ASSERT_TRUE(impl != nullptr);
@@ -177,7 +197,7 @@ void start_broadcast_test_dynamic(format input_format,
     auto outputs = network.execute();
 
     auto output = outputs.at("output").get_memory();
-    cldnn::mem_lock<T> output_ptr(output, get_test_stream());
+    cldnn::mem_lock<outT> output_ptr(output, get_test_stream());
 
     for (size_t i = 0; i < output_data_size; ++i) {
         ASSERT_EQ(output_ptr[i], output_data[i]);
@@ -288,44 +308,44 @@ TEST(broadcast_gpu_int64_t, bfyx_1_to_4x5_w_b_axes_0x1) {
 
 // dynamic kernel
 TEST(broadcast_gpu_float, bfyx_1_to_4x5_w_b_axes_0x1_dynamic) {
-    start_broadcast_test_dynamic<float>(format::bfyx, data_types::f32, {4, 5}, {1, 1}, {0, 1});
+    start_broadcast_test_dynamic<float, ov::float16>(format::bfyx, data_types::f32, data_types::f16, {4, 5}, {1, 1}, {0, 1}, false, impl_types::any, true);
 }
 
 TEST(broadcast_gpu_float, bfyx_1_to_4x5_w_b_axes_0x1_dynamic_with_static_output) {
-    start_broadcast_test_dynamic<float>(format::bfyx, data_types::f32, {4, 5}, {1, 1}, {0, 1}, true);
+    start_broadcast_test_dynamic<float, ov::float16>(format::bfyx, data_types::f32, data_types::f16, {4, 5}, {1, 1}, {0, 1}, true);
 }
 
 TEST(broadcast_gpu_uint8_t, bfyx_1_to_4x5_w_b_axes_0x1_dynamic) {
-    start_broadcast_test_dynamic<uint8_t>(format::bfyx, data_types::u8, {4, 5}, {1, 1}, {0, 1});
+    start_broadcast_test_dynamic<uint8_t, int32_t>(format::bfyx, data_types::u8, data_types::i32, {4, 5}, {1, 1}, {0, 1});
 }
 
 TEST(broadcast_gpu_uint8_t, bfyx_1_to_4x5_w_b_axes_0x1x2_dynamic_with_static_output) {
-    start_broadcast_test_dynamic<uint8_t>(format::bfyx, data_types::u8, {4, 5, 2}, {1, 1, 1}, {0, 1, 2}, true);
+    start_broadcast_test_dynamic<uint8_t, int32_t>(format::bfyx, data_types::u8, data_types::i32, {4, 5, 2}, {1, 1, 1}, {0, 1, 2}, true);
 }
 
 TEST(broadcast_gpu_int64_t, bfyx_1_to_4x5_w_b_axes_0x1_dynamic) {
-    start_broadcast_test_dynamic<int64_t>(format::bfyx, data_types::i64, {4, 5}, {1, 1}, {0, 1});
+    start_broadcast_test_dynamic<int64_t, int32_t>(format::bfyx, data_types::i64, data_types::i32, {4, 5}, {1, 1}, {0, 1}, false, impl_types::any, true);
 }
 
 TEST(broadcast_gpu_int64_t, bfyx_1_to_4x5_w_b_axes_0x1x2x3_dynamic_with_static_output) {
-    start_broadcast_test_dynamic<int64_t>(format::bfyx, data_types::i64, {4, 5, 2, 3}, {1, 1, 1, 1}, {0, 1, 2, 3});
+    start_broadcast_test_dynamic<int64_t, int32_t>(format::bfyx, data_types::i64, data_types::i32, {4, 5, 2, 3}, {1, 1, 1, 1}, {0, 1, 2, 3});
 }
 
 // dynamic kernel cpu
 TEST(broadcast_cpu_impl_float, bfyx_1_to_4x5_w_b_axes_0x1_dynamic) {
-    start_broadcast_test_dynamic<float>(format::bfyx, data_types::f32, {4, 5}, {1, 1}, {0, 1}, false, impl_types::cpu);
+    start_broadcast_test_dynamic<float, int32_t>(format::bfyx, data_types::f32, data_types::i32, {4, 5}, {1, 1}, {0, 1}, false, impl_types::cpu);
 }
 
 TEST(broadcast_cpu_impl_float, bfyx_1_to_4x5_w_b_axes_0x1_dynamic_with_static_output) {
-    start_broadcast_test_dynamic<float>(format::bfyx, data_types::f32, {4, 5}, {1, 1}, {0, 1}, true, impl_types::cpu);
+    start_broadcast_test_dynamic<float, float>(format::bfyx, data_types::f32, data_types::f32, {4, 5}, {1, 1}, {0, 1}, true, impl_types::cpu);
 }
 
 TEST(broadcast_cpu_impl_int64_t, bfyx_1_to_4x5_w_b_axes_0x1_dynamic) {
-    start_broadcast_test_dynamic<int64_t>(format::bfyx, data_types::i64, {4, 5}, {1, 1}, {0, 1}, false, impl_types::cpu);
+    start_broadcast_test_dynamic<int64_t, int64_t>(format::bfyx, data_types::i64, data_types::i64, {4, 5}, {1, 1}, {0, 1}, false, impl_types::cpu);
 }
 
 TEST(broadcast_cpu_impl_int64_t, bfyx_1_to_4x5_w_b_axes_0x1x2x3_dynamic_with_static_output) {
-    start_broadcast_test_dynamic<int64_t>(format::bfyx, data_types::i64, {4, 5, 2, 3}, {1, 1, 1, 1}, {0, 1, 2, 3}, false, impl_types::cpu);
+    start_broadcast_test_dynamic<int64_t, int32_t>(format::bfyx, data_types::i64, data_types::i32, {4, 5, 2, 3}, {1, 1, 1, 1}, {0, 1, 2, 3}, false, impl_types::cpu);
 }
 
 /* Expected golden_data = {1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
diff --git a/src/plugins/intel_gpu/tests/unit/test_cases/convolution_gpu_test.cpp b/src/plugins/intel_gpu/tests/unit/test_cases/convolution_gpu_test.cpp
index 0a98a1c371b0cb..06573cdb565604 100644
--- a/src/plugins/intel_gpu/tests/unit/test_cases/convolution_gpu_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/test_cases/convolution_gpu_test.cpp
@@ -10326,6 +10326,125 @@ TEST_P(conv_dyn_test, convolution_gpu_bfyx_os_iyx_osv32_no_bias) {
     }
 }
 
+TEST_P(conv_dyn_test, convolution_gpu_fsv16_1x1_no_bias) {
+    auto& engine = get_test_engine();
+    auto p = GetParam();
+
+    auto is_grouped = p.wei_shape.size() == 5;
+
+    if (is_grouped) {
+        std::cout << "[ SKIPPED ] The test is skipped (group convolution is not supported)." << std::endl;
+        ASSERT_EQ(1, 1);
+        return;
+    }
+    auto groups_num = 1;
+
+    auto is_weight_1x1 = (p.wei_shape[p.wei_shape.size() - 1] == 1 && p.wei_shape[p.wei_shape.size() - 2] == 1);
+    auto is_valid_output = p.wei_shape[0] % 16 == 0;
+    auto is_valid_strid = p.stride[0] == 1 && p.stride[1] == 1;
+    auto is_valid_padding = p.pad_begin[0] == 0 && p.pad_begin[1] == 0 && p.pad_end[0] == 0 && p.pad_end[1] == 0;
+
+    if (!is_weight_1x1 || !is_valid_output || !is_valid_strid || !is_valid_padding) {
+        std::cout << "[ SKIPPED ] The test is skipped (is_weight_1x1:" << is_weight_1x1 << ", is_valid_output" << is_valid_output
+                  << ", is_valid_strid: " << is_valid_strid << ", is_valid_padding: " << is_valid_padding << std::endl;
+        ASSERT_EQ(1, 1);
+        return;
+    }
+
+    if (!engine.get_device_info().supports_immad && p.in_shape[1] > 16) {
+        std::cout << "[ SKIPPED ] The test is skipped (convolution_fsv16_1x1 static kernel has accuracy issue with input feature > 16 in igpu)." << std::endl;
+        ASSERT_EQ(1, 1);
+        return;
+    }
+
+    auto calculate_ref = [&](memory::ptr input, memory::ptr weights, ExecutionConfig config) {
+        auto in_layout = input->get_layout();
+
+        topology topology_ref(
+            input_layout("input", in_layout),
+            data("weights", weights),
+            convolution("conv", input_info("input"), "weights", no_bias, groups_num, p.stride, p.dilation, p.pad_begin, p.pad_end, is_grouped));
+
+        network network_ref(engine, topology_ref, config);
+        network_ref.set_input_data("input", input);
+
+        auto outputs_ref = network_ref.execute();
+
+        return outputs_ref.at("conv").get_memory();
+    };
+
+    auto in_layout = layout{ov::PartialShape{ov::Dimension(), ov::Dimension(p.in_shape[1]), ov::Dimension(), ov::Dimension()}, data_types::f16, format::b_fs_yx_fsv16};
+    auto input = engine.allocate_memory({ p.in_shape, data_types::f16, format::b_fs_yx_fsv16 });
+    auto weights = engine.allocate_memory({p.wei_shape, data_types::f16, is_grouped ? format::bfzyx : format::bfyx});
+
+    tests::random_generator rg(GET_SUITE_NAME);
+    VF<ov::float16> input_rnd = rg.generate_random_1d<ov::float16>(ov::shape_size(p.in_shape), -10, 10);
+    VF<ov::float16> weights_rnd = rg.generate_random_1d<ov::float16>(ov::shape_size(p.wei_shape), -10, 10);
+
+    set_values(input, input_rnd);
+    set_values(weights, weights_rnd);
+
+    topology topology(
+        input_layout("input", in_layout),
+        data("weights", weights),
+        convolution("conv", input_info("input"), "weights", no_bias, groups_num, p.stride, p.dilation, p.pad_begin, p.pad_end, is_grouped));
+
+    ExecutionConfig config = get_test_default_config(engine);
+    ov::intel_gpu::ImplementationDesc conv_impl = { format::b_fs_yx_fsv16, "convolution_gpu_bfyx_f16_1x1", impl_types::ocl };
+    config.set_property(ov::intel_gpu::force_implementations(ov::intel_gpu::ImplForcingMap{ { "conv", conv_impl } }));
+    config.set_property(ov::intel_gpu::allow_new_shape_infer(true));
+    config.set_property(ov::enable_profiling(true));
+
+    network network(engine, topology, config);
+    network.set_input_data("input", input);
+
+    auto inst = network.get_primitive("conv");
+    auto impl = inst->get_impl();
+    ASSERT_TRUE(impl != nullptr);
+    ASSERT_TRUE(impl->is_dynamic());
+
+    auto outputs = network.execute();
+
+    auto output_memory = outputs.at("conv").get_memory();
+
+    auto output_memory_ref = calculate_ref(input, weights, config);
+
+    cldnn::mem_lock<ov::float16> output_ptr(output_memory, get_test_stream());
+    cldnn::mem_lock<ov::float16> output_ptr_ref(output_memory_ref, get_test_stream());
+
+
+    ASSERT_EQ(outputs.at("conv").get_layout(), output_memory_ref->get_layout());
+    for (size_t i = 0; i < output_ptr.size(); i++) {
+        ASSERT_EQ(output_ptr[i], output_ptr_ref[i]);
+    }
+
+    {
+        // Change original shape for the second run
+        auto new_shape = p.in_shape;
+        new_shape[2] += 4;
+        new_shape[3] += 8;
+
+        auto input = engine.allocate_memory({ new_shape, data_types::f16, format::b_fs_yx_fsv16 });
+
+        VF<ov::float16> input_rnd = rg.generate_random_1d<ov::float16>(ov::shape_size(p.in_shape), -10, 10);
+        set_values(input, input_rnd);
+
+        network.set_input_data("input", input);
+        auto outputs = network.execute();
+
+        auto output_memory = outputs.at("conv").get_memory();
+        auto output_memory_ref = calculate_ref(input, weights, config);
+
+        cldnn::mem_lock<ov::float16> output_ptr(output_memory, get_test_stream());
+        cldnn::mem_lock<ov::float16> output_ptr_ref(output_memory_ref, get_test_stream());
+
+        ASSERT_EQ(outputs.at("conv").get_layout(), output_memory_ref->get_layout());
+        for (size_t i = 0; i < output_ptr.size(); i++) {
+            ASSERT_EQ(output_ptr[i], output_ptr_ref[i]);
+        }
+    }
+}
+
 INSTANTIATE_TEST_SUITE_P(smoke, conv_dyn_test,
     testing::ValuesIn(std::vector<conv_dyn_test_params>{
     { ov::Shape{1, 8, 14, 14}, ov::Shape{16, 8, 3, 3}, ov::Strides{1, 1}, ov::Strides{1, 1}, ov::CoordinateDiff{0, 0}, ov::CoordinateDiff{0, 0} },
@@ -10347,4 +10466,8 @@ INSTANTIATE_TEST_SUITE_P(smoke, conv_dyn_test,
     { ov::Shape{1, 3, 32, 32}, ov::Shape{96, 3, 4, 4}, ov::Strides{4, 4}, ov::Strides{1, 1}, ov::CoordinateDiff{0, 0}, ov::CoordinateDiff{0, 0} },
     { ov::Shape{1, 768, 7, 7}, ov::Shape{768, 1, 1, 3, 3}, ov::Strides{1, 1}, ov::Strides{1, 1}, ov::CoordinateDiff{1, 1}, ov::CoordinateDiff{1, 1} },
     { ov::Shape{1, 48, 56, 56}, ov::Shape{48, 48, 8, 8}, ov::Strides{8, 8}, ov::Strides{1, 1}, ov::CoordinateDiff{0, 0}, ov::CoordinateDiff{0, 0} },
+    { ov::Shape{1, 4, 16, 16}, ov::Shape{16, 4, 1, 1}, ov::Strides{1, 1}, ov::Strides{1, 1}, ov::CoordinateDiff{0, 0}, ov::CoordinateDiff{0, 0} },
+    { ov::Shape{1, 16, 15, 17}, ov::Shape{16, 16, 1, 1}, ov::Strides{1, 1}, ov::Strides{1, 1}, ov::CoordinateDiff{0, 0}, ov::CoordinateDiff{0, 0} },
+    { ov::Shape{1, 16, 17, 18}, ov::Shape{16, 16, 1, 1}, ov::Strides{1, 1}, ov::Strides{1, 1}, ov::CoordinateDiff{0, 0}, ov::CoordinateDiff{0, 0} },
+    { ov::Shape{1, 32, 64, 64}, ov::Shape{32, 32, 1, 1}, ov::Strides{1, 1}, ov::Strides{1, 1}, ov::CoordinateDiff{0, 0}, ov::CoordinateDiff{0, 0} },
 }));
diff --git a/src/plugins/intel_gpu/tests/unit/test_cases/crop_gpu_test.cpp b/src/plugins/intel_gpu/tests/unit/test_cases/crop_gpu_test.cpp
index 849a4ec779682e..5b6b432531923a 100644
--- a/src/plugins/intel_gpu/tests/unit/test_cases/crop_gpu_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/test_cases/crop_gpu_test.cpp
@@ -1357,21 +1357,22 @@ TEST(crop_gpu, dynamic_in1x4x1x1_split) {
     auto crop_y_size = 1;
     auto feature_offset_1 = 0;
     auto feature_offset_2 = 2;
+    auto axis = 1;
 
     auto input_dyn_layout    = layout{ ov::PartialShape{ov::Dimension(1, 10), feature_num, y_size, x_size}, data_types::f32, format::bfyx };
     auto input_actual_layout = layout{ ov::PartialShape{batch_num, feature_num, y_size, x_size}, data_types::f32, format::bfyx };
 
     auto input_mem = engine.allocate_memory(input_actual_layout);
     auto data_mem = engine.allocate_memory({ {}, data_types::i64, format::bfyx });
-    set_values(data_mem, {1});
+    set_values<int64_t>(data_mem, {axis});
 
     cldnn::crop_ngraph_op_mode op_mode = cldnn::crop_ngraph_op_mode::split;
     size_t num_splits = 2;
     topology topology;
     topology.add(input_layout("input", input_dyn_layout));
     topology.add(data("data", data_mem));
-    topology.add(crop("crop1", { input_info("input"), input_info("data") }, tensor(batch(crop_batch_num), spatial(crop_x_size, crop_y_size), feature(crop_feature_num_1)), { tensor(feature(feature_offset_1), spatial(0,0),batch(0)) }, op_mode, 0, num_splits));
-    topology.add(crop("crop2", { input_info("input"), input_info("data") }, tensor(batch(crop_batch_num), spatial(crop_x_size, crop_y_size), feature(crop_feature_num_2)), { tensor(feature(feature_offset_2), spatial(0,0),batch(0)) }, op_mode, 1, num_splits));
+    topology.add(crop("crop1", { input_info("input"), input_info("data") }, tensor(batch(crop_batch_num), spatial(crop_x_size, crop_y_size), feature(crop_feature_num_1)), { tensor(feature(feature_offset_1), spatial(0,0),batch(0)) }, op_mode, 0, axis, num_splits));
+    topology.add(crop("crop2", { input_info("input"), input_info("data") }, tensor(batch(crop_batch_num), spatial(crop_x_size, crop_y_size), feature(crop_feature_num_2)), { tensor(feature(feature_offset_2), spatial(0,0),batch(0)) }, op_mode, 1, axis, num_splits));
 
     std::vector<float> input_vec = { -1.0f, 2.0f, -3.0f, 4.0f };
     std::vector<float> out1 = { -1.0f, 2.0f };
@@ -1421,6 +1422,7 @@ TEST(crop_gpu, dynamic_in1x4x1x1_varaidic_split) {
     auto crop_y_size = 1;
     auto feature_offset_1 = 0;
     auto feature_offset_2 = 3;
+    auto axis = 1;
 
     auto input_dyn_layout    = layout{ ov::PartialShape{ov::Dimension(1, 10), feature_num, y_size, x_size}, data_types::f32, format::bfyx };
     auto input_actual_layout = layout{ ov::PartialShape{batch_num, feature_num, y_size, x_size}, data_types::f32, format::bfyx };
@@ -1434,8 +1436,8 @@ TEST(crop_gpu, dynamic_in1x4x1x1_varaidic_split) {
     topology.add(input_layout("input", input_dyn_layout));
     topology.add(data("axis", axis_mem));
     topology.add(data("splits_length", splits_length_mem));
-    topology.add(crop("crop1", { input_info("input"), input_info("axis"), input_info("splits_length") }, tensor(batch(crop_batch_num), spatial(crop_x_size, crop_y_size), feature(crop_feature_num_1)), { tensor(feature(feature_offset_1), spatial(0,0),batch(0)) }, op_mode, 0));
-    topology.add(crop("crop2", { input_info("input"), input_info("axis"), input_info("splits_length") }, tensor(batch(crop_batch_num), spatial(crop_x_size, crop_y_size), feature(crop_feature_num_2)), { tensor(feature(feature_offset_2), spatial(0,0),batch(0)) }, op_mode, 1));
+    topology.add(crop("crop1", { input_info("input"), input_info("axis"), input_info("splits_length") }, tensor(batch(crop_batch_num), spatial(crop_x_size, crop_y_size), feature(crop_feature_num_1)), { tensor(feature(feature_offset_1), spatial(0,0),batch(0)) }, op_mode, 0, axis));
+    topology.add(crop("crop2", { input_info("input"), input_info("axis"), input_info("splits_length") }, tensor(batch(crop_batch_num), spatial(crop_x_size, crop_y_size), feature(crop_feature_num_2)), { tensor(feature(feature_offset_2), spatial(0,0),batch(0)) }, op_mode, 1, axis));
 
     std::vector<float> input_vec = { -1.0f, 2.0f, -3.0f, 4.0f };
     std::vector<float> out1 = { -1.0f, 2.0f, -3.0f };
@@ -1443,7 +1445,7 @@ TEST(crop_gpu, dynamic_in1x4x1x1_varaidic_split) {
     std::vector<int64_t> splits_vec = {3, 1};
 
     set_values(input_mem, input_vec);
-    set_values(axis_mem, {1});
+    set_values<int64_t>(axis_mem, {axis});
     set_values(splits_length_mem, splits_vec);
 
     ExecutionConfig config = get_test_default_config(engine);
diff --git a/src/plugins/intel_gpu/tests/unit/test_cases/fully_connected_gpu_test.cpp b/src/plugins/intel_gpu/tests/unit/test_cases/fully_connected_gpu_test.cpp
index 34ef987a31ffcb..cbe4eb25fef53c 100644
--- a/src/plugins/intel_gpu/tests/unit/test_cases/fully_connected_gpu_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/test_cases/fully_connected_gpu_test.cpp
@@ -1255,6 +1255,116 @@ class fully_connected_gpu_tests: public ::testing::Test {
         }
     }
 
+    void test_compressed_int4_scale_dyn_quan(bool is_caching_test, bool is_dynamic, int batch = 1) {
+        tests::random_generator rg(GET_SUITE_NAME);
+        auto& engine = get_test_engine();
+
+        if (engine.get_device_info().dev_type == device_type::discrete_gpu)
+            GTEST_SKIP();
+
+        long int batch_num = batch;
+        long int ifm_num = 1024;
+        long int ofm_num = 4096;
+        long int scales_group_size = 32;
+
+        bool is_3d = true;
+
+        auto input_ps = is_3d ?  ov::PartialShape{ batch_num, 1, ifm_num } : ov::PartialShape{ batch_num, ifm_num};
+        auto dyn_input_ps = is_3d ?  ov::PartialShape{ -1, 1, ifm_num } : ov::PartialShape{ -1, ifm_num};
+        auto input_mem = engine.allocate_memory({ input_ps, data_types::f16, format::bfyx });
+
+        auto weights_mem = engine.allocate_memory({ {ofm_num, ifm_num}, data_types::u4, format::bfyx });
+        auto scale_mem = engine.allocate_memory({ {ofm_num, ifm_num / scales_group_size}, data_types::f16, format::bfyx });
+
+        auto input_data = rg.generate_random_1d<ov::float16>(batch_num * ifm_num, -16.0f, 16.0f);
+        set_values(input_mem, input_data);
+
+        auto weigths_data = rg.generate_random_1d<uint8_t>(ofm_num * ifm_num / 2, 0, 10);
+        set_values(weights_mem, weigths_data);
+
+        auto scale_data = rg.generate_random_1d<ov::float16>(ofm_num * ifm_num / scales_group_size, -4.0f, 4.0f);
+        set_values(scale_mem, scale_data);
+
+        auto in_layout = is_dynamic ? layout{ dyn_input_ps, data_types::f16, format::bfyx }
+                                    : layout{ input_ps, data_types::f16, format::bfyx };
+
+        auto fc_prim = fully_connected("fc_prim", input_info("input"), "weights", "", "scale", "", data_types::f16, padding(), is_3d ? 3 : 2, 2);
+        fc_prim.decompression_zero_point_scalar = 0;
+
+        // Implemented dynamic quantize kernel
+        auto get_ref_results = [&]() {
+            topology topology(
+                input_layout("input", in_layout),
+                data("weights", weights_mem),
+                data("scale", scale_mem),
+                fc_prim
+            );
+
+            auto config = get_test_default_config(engine);
+            config.set_property(ov::intel_gpu::allow_new_shape_infer(true));
+            config.set_property(ov::intel_gpu::optimize_data(true));
+
+            network network(engine, topology, config);
+            network.set_input_data("input", input_mem);
+
+            auto outputs = network.execute();
+            OPENVINO_ASSERT(outputs.size() == 1);
+            OPENVINO_ASSERT(outputs.begin()->first == "fc_prim");
+
+            auto output_layout = outputs.begin()->second.get_layout();
+            auto output_mem = outputs.begin()->second.get_memory();
+
+            return engine.reinterpret_buffer(*output_mem, output_layout);
+        };
+
+        topology topology(
+            input_layout("input", in_layout),
+            data("weights", weights_mem),
+            data("scale", scale_mem),
+            fc_prim
+        );
+
+        auto config = get_test_default_config(engine);
+        config.set_property(ov::intel_gpu::allow_new_shape_infer(true));
+        config.set_property(ov::intel_gpu::optimize_data(true));
+        config.set_property(ov::hint::dynamic_quantization_group_size(32));
+
+        network::ptr network = get_network(engine, topology, config, get_test_stream_ptr(), is_caching_test);
+
+        if (is_dynamic && !engine.get_device_info().supports_immad) {
+            auto inst = network->get_primitive("fc_prim");
+            auto impl = inst->get_impl();
+            ASSERT_TRUE(impl != NULL);
+            ASSERT_EQ(impl->get_kernels().size(), size_t((is_dynamic ? 3 : 2))); // shape-agnostic kernels
+        }
+
+        network->set_input_data("input", input_mem);
+
+        auto outputs = network->execute();
+        ASSERT_EQ(outputs.size(), size_t(1));
+        ASSERT_EQ(outputs.begin()->first, "fc_prim");
+
+        auto output_mem = outputs.begin()->second.get_memory();
+        cldnn::mem_lock<ov::float16> output_ptr (output_mem, get_test_stream());
+
+        auto ref_output_mem = get_ref_results();
+        cldnn::mem_lock<ov::float16> output_ptr_ref (ref_output_mem, get_test_stream());
+
+        size_t count = 0;
+        float max_diff = 0.f;
+        float avg = 0.f;
+        for (size_t i = 0; i < output_ptr_ref.size(); ++i) {
+            auto abs_diff = std::abs(output_ptr_ref[i] - output_ptr[i]);
+            if (max_diff < abs_diff)
+                max_diff = abs_diff;
+            avg = abs_diff;
+            count++;
+            OPENVINO_ASSERT(abs_diff < 256);
+        }
+        GPU_DEBUG_LOG << "---> count: " << count << ", max_diff:" << max_diff << ", avg_diff: " << (avg/count) << std::endl;
+    }
+
+
     void test_compressed_int4_scale(bool is_caching_test, bool is_dynamic, long int batch_num, long int scales_group_size = 128) {
         tests::random_generator rg(GET_SUITE_NAME);
         auto& engine = get_test_engine();
@@ -3158,6 +3268,40 @@ TEST_F(fully_connected_gpu_tests, compressed_int4_scale_b1g128) {
     this->test_compressed_int4_scale(false, false, 1, 128);
 }
 
+TEST_F(fully_connected_gpu_tests, compressed_int4_scale_dyn_quan_single_batch) {
+    this->test_compressed_int4_scale_dyn_quan(false, false);
+}
+
+TEST_F(fully_connected_gpu_tests, compressed_int4_scale_dyn_quan) {
+    this->test_compressed_int4_scale_dyn_quan(false, false, 512);
+}
+
+TEST_F(fully_connected_gpu_tests, compressed_int4_scale_dyn_quan_unaligned) {
+    this->test_compressed_int4_scale_dyn_quan(false, false, 511);
+}
+
+TEST_F(fully_connected_gpu_tests, compressed_int4_scale_dyn_quan_dynamic_single_batch) {
+    this->test_compressed_int4_scale_dyn_quan(false, true, 1);
+}
+
+TEST_F(fully_connected_gpu_tests, compressed_int4_scale_dyn_quan_dynamic) {
+    this->test_compressed_int4_scale_dyn_quan(false, true, 512);
+}
+
+TEST_F(fully_connected_gpu_tests, compressed_int4_scale_dyn_quan_dynamic_unaligned) {
+    this->test_compressed_int4_scale_dyn_quan(false, true, 511);
+}
+
+TEST_F(fully_connected_gpu_tests, compressed_int4_scale_dyn_cache) {
+    this->test_compressed_int4_scale_dyn_quan(true, false, 512);
+}
+
+TEST_F(fully_connected_gpu_tests, compressed_int4_scale_dyn_cache_dynamic) {
+    this->test_compressed_int4_scale_dyn_quan(true, true, 512);
+}
+
+
+
 TEST_F(fully_connected_gpu_tests, compressed_scale_bias) {
     this->test_compressed_scale_bias(false);
 }
diff --git a/src/plugins/intel_gpu/tests/unit/test_cases/gather_gpu_test.cpp b/src/plugins/intel_gpu/tests/unit/test_cases/gather_gpu_test.cpp
index a2e131d3ad53aa..6f83e3affceff4 100644
--- a/src/plugins/intel_gpu/tests/unit/test_cases/gather_gpu_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/test_cases/gather_gpu_test.cpp
@@ -2373,3 +2373,48 @@ TEST(gather_gpu_fp32, dynamic_support_neg_ind) {
         ASSERT_EQ(expected_results[i], output_ptr[i]) << i;
     }
 }
+
+TEST(gather_gpu_fp32, dynamic_support_scalar_indice_empty_memory) {
+    auto& engine = get_test_engine();
+
+    ov::Shape data_shape = { 3, 3 };
+    int64_t axis = 1;
+
+    auto data_layout = layout{ov::PartialShape::dynamic(data_shape.size()), data_types::f32, format::bfyx};
+    auto indices_layout = layout{ov::PartialShape({1}), data_types::i32, format::bfyx};
+
+    auto data_mem = engine.allocate_memory(layout{ov::PartialShape(data_shape), data_types::f32, format::bfyx});
+    auto indices_mem = engine.allocate_memory(layout{ov::PartialShape({}), data_types::i32, format::bfyx});
+
+    set_values(data_mem, { 0.f, 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f });
+    set_values(indices_mem, { -1 });
+
+    topology topology;
+    topology.add(input_layout("data", data_layout));
+    topology.add(input_layout("indices", indices_layout));
+    topology.add(gather("gather", input_info("data"), input_info("indices"), axis, data_shape.size(), ov::Shape{}, 0, true));
+
+    ExecutionConfig config = get_test_default_config(engine);
+    config.set_property(ov::intel_gpu::allow_new_shape_infer(true));
+    network network(engine, topology, config);
+
+    network.set_input_data("data", data_mem);
+    network.set_input_data("indices", indices_mem);
+
+    auto inst = network.get_primitive("gather");
+    auto impl = inst->get_impl();
+    ASSERT_TRUE(impl != nullptr);
+    ASSERT_TRUE(impl->is_dynamic());
+
+    auto outputs = network.execute();
+
+    auto output = outputs.at("gather").get_memory();
+    cldnn::mem_lock<float> output_ptr(output, get_test_stream());
+
+    std::vector<float> expected_results = { 2.f, 5.f, 8.f };
+
+    ASSERT_EQ(expected_results.size(), output_ptr.size());
+    for (size_t i = 0; i < expected_results.size(); ++i) {
+        ASSERT_EQ(expected_results[i], output_ptr[i]) << i;
+    }
+}
diff --git a/src/plugins/intel_gpu/tests/unit/test_cases/gemm_gpu_test.cpp b/src/plugins/intel_gpu/tests/unit/test_cases/gemm_gpu_test.cpp
index 7b1d1b6132f568..2e4bebe8f3274d 100644
--- a/src/plugins/intel_gpu/tests/unit/test_cases/gemm_gpu_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/test_cases/gemm_gpu_test.cpp
@@ -2805,6 +2805,159 @@ class gemm_onednn: public ::testing::Test {
             ASSERT_EQ(res_lock[i], res_ref_lock[i]) << i;
         }
     }
+
+    void test_dynamic_padding_transpose() {
+        tests::random_generator rg;
+        rg.set_seed(GET_SUITE_NAME);
+
+        auto& engine = get_test_engine();
+
+        if (!engine.get_device_info().supports_immad)
+            return;
+
+        const unsigned long BATCH_SIZE = 31;
+        const unsigned long M_SIZE = 11;
+        const unsigned long K_SIZE = 37;
+        const unsigned long N_SIZE = 49;
+
+        auto fill_mem = [&](cldnn::memory_ptr mem, std::vector<ov::float16>& data) {
+            cldnn::mem_lock<ov::float16> mem_ptr(mem, get_test_stream());
+            auto&& l = mem->get_layout();
+            auto data_idx = 0;
+            for (cldnn::tensor::value_type b = 0; b < l.batch(); ++b) {
+                for (cldnn::tensor::value_type f = 0; f < l.feature(); ++f) {
+                    for (cldnn::tensor::value_type y = 0; y < l.spatial(1); ++y) {
+                        for (cldnn::tensor::value_type x = 0; x < l.spatial(0); ++x) {
+                            auto tensor_coord = cldnn::tensor{{b, f, x, y}, 0};
+                            auto buffer_idx = l.get_linear_offset(tensor_coord);
+                            mem_ptr[buffer_idx] = data[data_idx++];
+                        }
+                    }
+                }
+            }
+        };
+
+        const auto align_size_n = 15;
+
+        const auto aligned_n_size = align_to(N_SIZE, align_size_n);
+        auto padding_size_n = static_cast<int>(aligned_n_size - N_SIZE);
+
+        ov::Shape in1_shape = { 1, BATCH_SIZE, M_SIZE, K_SIZE };
+        ov::Shape in2_shape = { 1, BATCH_SIZE, N_SIZE, K_SIZE };
+        ov::Shape in1_shape_aligned = in1_shape;
+        ov::Shape in2_shape_aligned = { 1, BATCH_SIZE, aligned_n_size, K_SIZE };
+
+        tensor dyn_pad_dims_input2({0, 0, 0, 0}, 0);
+        dyn_pad_dims_input2 = tensor({0, 0, 0, 1}, 0);
+
+
+        auto in1_layout = layout{ {-1, -1, -1, -1}, data_types::f16, format::bfyx};
+        auto in2_layout = layout{ {-1, -1, -1, -1}, data_types::f16, format::bfyx, padding({0, 0, 0, 0}, {0, 0, 0, 0}, 0.0f, dyn_pad_dims_input2)};
+
+        auto aligned_input2_mem = engine.allocate_memory({ov::PartialShape(in2_shape_aligned), data_types::f16, format::bfyx});
+
+        auto input1_mem = engine.allocate_memory({ov::PartialShape(in1_shape_aligned), data_types::f16, format::bfyx});
+
+        auto input2_mem = engine.reinterpret_buffer(*aligned_input2_mem, layout{ov::PartialShape(in2_shape),
+                                                                                data_types::f16,
+                                                                                format::bfyx,
+                                                                                padding({0, 0, 0, 0}, {0, 0, 0, padding_size_n}, 0.0f, dyn_pad_dims_input2)});
+
+        auto input_1_data = rg.generate_random_1d<ov::float16>(ov::shape_size(in1_shape), -2, 2);
+        auto input_2_data = rg.generate_random_1d<ov::float16>(ov::shape_size(in2_shape), -2, 2);
+
+        fill_mem(input1_mem, input_1_data);
+        fill_mem(input2_mem, input_2_data);
+
+        auto get_ref_results = [&]() {
+            ov::Shape in1_shape = { 1, BATCH_SIZE, M_SIZE, K_SIZE };
+            ov::Shape in2_shape = { 1, BATCH_SIZE, N_SIZE, K_SIZE };
+            auto in1_layout = layout{ {-1, -1, -1, -1}, data_types::f16, format::bfyx};
+            auto in2_layout = layout{ {-1, -1, -1, -1}, data_types::f16, format::bfyx};
+
+            auto input1_mem = engine.allocate_memory(layout{ov::PartialShape(in1_shape), data_types::f16, format::bfyx});
+            auto input2_mem = engine.allocate_memory(layout{ov::PartialShape(in2_shape), data_types::f16, format::bfyx});
+
+            fill_mem(input1_mem, input_1_data);
+            fill_mem(input2_mem, input_2_data);
+
+            topology topology;
+            topology.add(input_layout("input1", in1_layout),
+                         input_layout("input2", in2_layout),
+                         gemm("gemm_ref", { input_info("input1"), input_info("input2") }, data_types::f16, false, true, 1.0f, 0.0f, 4, 4),
+                         permute("permute", input_info("gemm_ref"), {0, 2, 1, 3}),
+                         reorder("reorder", input_info("permute"), format::bfyx, data_types::f32)
+            );
+
+            ov::intel_gpu::ImplementationDesc gemm_impl = { format::bfyx, std::string(""), impl_types::onednn };
+            ExecutionConfig cfg{ ov::intel_gpu::queue_type(QueueTypes::in_order),
+                                 ov::intel_gpu::force_implementations(ov::intel_gpu::ImplForcingMap{ {"gemm", gemm_impl} }),
+                                 ov::intel_gpu::optimize_data(true),
+                                 ov::intel_gpu::allow_new_shape_infer(true) };
+
+            network network(engine, topology, cfg);
+            network.set_input_data("input1", input1_mem);
+            network.set_input_data("input2", input2_mem);
+
+            auto outputs = network.execute();
+            OPENVINO_ASSERT(outputs.size() == 1);
+            OPENVINO_ASSERT(outputs.begin()->first == "reorder");
+
+            auto inst = network.get_primitive("reorder");
+
+            auto output_mem = outputs.at("reorder").get_memory();
+            auto output_layout = outputs.at("reorder").get_layout();
+
+            return engine.reinterpret_buffer(*output_mem, output_layout);
+        };
+
+        topology topology;
+        topology.add(input_layout("input1", in1_layout),
+                     input_layout("input2", in2_layout),
+                     gemm("gemm", { input_info("input1"), input_info("input2") }, data_types::f16, false, true, 1.0f, 0.0f, 4, 4),
+                     permute("permute", input_info("gemm"), {0, 2, 1, 3}),
+                     reorder("reorder", input_info("permute"), format::bfyx, data_types::f32)
+        );
+
+        ov::intel_gpu::ImplementationDesc gemm_impl = { format::bfyx, std::string(""), impl_types::onednn };
+        ExecutionConfig cfg{ ov::intel_gpu::queue_type(QueueTypes::in_order),
+                             ov::intel_gpu::force_implementations(ov::intel_gpu::ImplForcingMap{ {"gemm", gemm_impl} }),
+                             ov::intel_gpu::optimize_data(true),
+                             ov::intel_gpu::allow_new_shape_infer(true) };
+        network network(engine, topology, cfg);
+        network.set_input_data("input1", input1_mem);
+        network.set_input_data("input2", input2_mem);
+
+        auto outputs = network.execute();
+
+        auto output_mem = outputs.at("reorder").get_memory();
+        auto output_layout = outputs.at("reorder").get_layout();
+
+        auto res = engine.reinterpret_buffer(*output_mem, output_layout);
+
+        auto ref_res = get_ref_results();
+
+        mem_lock<ov::float16> res_lock(res, get_test_stream());
+        mem_lock<ov::float16> res_ref_lock(ref_res, get_test_stream());
+        for (size_t i = 0; i < res->count(); i++) {
+            ASSERT_EQ(res_lock[i], res_ref_lock[i]) << i;
+        }
+
+        // WA: Call wait_all() to wait for all queued kernels compilation finish
+        network.get_program()->get_compilation_context().wait_all();
+        auto outputs_onednn = network.execute();
+
+        auto output_mem_onednn = outputs_onednn.at("reorder").get_memory();
+        auto output_layout_onednn = outputs_onednn.at("reorder").get_layout();
+
+        auto res_onednn = engine.reinterpret_buffer(*output_mem_onednn, output_layout_onednn);
+
+        mem_lock<ov::float16> res_lock_onednn(res_onednn, get_test_stream());
+        for (size_t i = 0; i < res->count(); i++) {
+            ASSERT_EQ(res_lock_onednn[i], res_ref_lock[i]) << i;
+        }
+
+    }
 };
 
 TEST_F(gemm_onednn, impl_replacement_with_cldnn) {
@@ -2828,6 +2981,10 @@ TEST_F(gemm_onednn, dynamic_padding_n_dim_only) {
     this->test_dynamic_padding(true);
 }
 
+TEST_F(gemm_onednn, dynamic_padding_input_transpose) {
+    this->test_dynamic_padding_transpose();
+}
+
 template <typename gemm_params, typename input0_type, typename input1_type, typename input2_type, typename output_type, typename accumulator_type>
 class GemmBaseOneDNNTest : public ::GemmBaseTest<gemm_params, input0_type, input1_type, input2_type, output_type, accumulator_type> {
 public:
diff --git a/src/plugins/intel_gpu/tests/unit/test_cases/loop_gpu_test.cpp b/src/plugins/intel_gpu/tests/unit/test_cases/loop_gpu_test.cpp
index c5211e39b69f08..d071c0f3416581 100644
--- a/src/plugins/intel_gpu/tests/unit/test_cases/loop_gpu_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/test_cases/loop_gpu_test.cpp
@@ -875,6 +875,115 @@ static void test_loop_gpu_multiple_shapes(ov::PartialShape body_input_layout,
     }
 }
 
+static void test_loop_gpu_multiple_shapes_single_shared(ov::PartialShape body_input_layout,
+                                        std::vector<ov::PartialShape> whole_layouts,
+                                        std::vector<std::vector<float>> input_data_list,
+                                        std::vector<float> expected_output_data,
+                                        int32_t axis,
+                                        size_t exit_value,
+                                        bool is_caching_test = false) {
+    auto& engine = get_test_engine();
+
+    auto b_input_layout = cldnn::layout{ body_input_layout, data_types::f32, format::bfyx };
+    auto const_layout = cldnn::layout{ {}, data_types::i64, format::bfyx };
+
+    auto e_initial_condition_mem = engine.allocate_memory(const_layout);
+    auto e_num_iteration_mem = engine.allocate_memory(const_layout);
+    auto b_exit_value_mem = engine.allocate_memory(const_layout);
+    auto b_index_inc_mem = engine.allocate_memory(const_layout);
+
+    // initialize input buffers
+    set_values(e_initial_condition_mem, {1});
+    set_values(b_exit_value_mem, {exit_value});
+    set_values(b_index_inc_mem, {1});
+    set_values(e_num_iteration_mem, {10});
+
+    primitive_id body_current_iteration_id = "b_index";
+    primitive_id body_execution_condition_id = "b_cond_exit_value";
+
+    cldnn::topology body(
+        input_layout(body_current_iteration_id, const_layout),
+        input_layout("b_parameter", b_input_layout),
+        data("b_exit_value", b_exit_value_mem),
+        data("b_index_inc", b_index_inc_mem),
+        eltwise("b_index_update", input_info(body_current_iteration_id), input_info("b_index_inc"), eltwise_mode::sum),
+        eltwise("b_permute", input_info("b_parameter"), input_info("b_index_update"), eltwise_mode::sum),
+        reorder("b_result", input_info("b_permute"), b_input_layout),
+        eltwise(body_execution_condition_id, input_info(body_current_iteration_id), input_info("b_exit_value"), eltwise_mode::lt)
+    );
+
+    primitive_id trip_count_id = "";
+    primitive_id actual_iteration_count_id = "actual_iteration_count";
+    primitive_id initial_condition_id = "initial_condition";
+    int64_t num_iterations = -1;
+
+    std::vector<loop::io_primitive_map> input_primitive_maps {
+        loop::io_primitive_map("input", "b_parameter", axis),
+        loop::io_primitive_map(actual_iteration_count_id, body_current_iteration_id) };
+    std::vector<loop::io_primitive_map> output_primitive_maps {
+        loop::io_primitive_map(cldnn::input_info("loop"), cldnn::input_info("b_result"), axis) };
+    std::vector<loop::backedge_mapping> back_edges {
+        loop::backedge_mapping("b_result", "b_parameter"),
+        loop::backedge_mapping("b_index_update", body_current_iteration_id) };
+
+    auto body_program = build_program(engine, body, body_execution_condition_id, output_primitive_maps, back_edges, true);
+
+    auto const_shape = engine.allocate_memory({ov::PartialShape{4}, data_types::i32, format::bfyx});
+    std::vector<int32_t> body_input_layouts;
+    for (size_t i = 0; i < body_input_layout.size(); i++) {
+        if (body_input_layout[i].is_dynamic())
+            body_input_layouts.push_back(-1);
+        else
+            body_input_layouts.push_back(body_input_layout[i].get_length());
+    }
+    set_values<int32_t>(const_shape, body_input_layouts);
+
+    cldnn::topology topology(
+        input_layout("input_origin", b_input_layout),
+        input_layout(initial_condition_id, e_initial_condition_mem->get_layout()),
+        mutable_data(actual_iteration_count_id, e_num_iteration_mem),
+        permute("input2", input_info("input_origin"), {0, 1, 2, 3}),
+        data("const", const_shape),
+        permute("permute1", input_info("input_origin"), {0, 1, 2, 3}),
+        concatenation("input", {input_info("permute1"), input_info("input_origin")}, 0),
+        loop("loop",
+             {input_info(actual_iteration_count_id), input_info(initial_condition_id), input_info("input")},
+             body_program, trip_count_id, initial_condition_id, actual_iteration_count_id,
+             input_primitive_maps, output_primitive_maps, back_edges,
+             num_iterations, body_current_iteration_id, body_execution_condition_id, 1),
+        permute("result", input_info("loop"), {0, 1, 2, 3}));
+
+    ExecutionConfig config = get_test_default_config(engine);
+    config.set_property(ov::intel_gpu::allow_new_shape_infer(true));
+
+    network network(engine, topology, config);
+    for (size_t i = 0 ; i < whole_layouts.size(); i++) {
+        auto whole_layout = whole_layouts[i];
+        auto input_data = input_data_list[i];
+
+        set_values(e_initial_condition_mem, {1});
+        set_values(b_exit_value_mem, {exit_value});
+        set_values(b_index_inc_mem, {1});
+        set_values(e_num_iteration_mem, {10});
+
+        auto e_input_layout = cldnn::layout{ whole_layout, data_types::f32, format::bfyx };
+        auto e_input_mem = engine.allocate_memory(e_input_layout); // b,f,x,y
+        auto expected_output_layout = whole_layout;
+        set_values(e_input_mem, input_data);
+
+        network.set_input_data("input_origin", e_input_mem);
+        network.set_input_data(initial_condition_id, e_initial_condition_mem);
+
+        auto outputs = network.execute();
+        auto output_layout = outputs.begin()->second.get_layout();
+        auto input_layout = network.get_primitive("input")->get_output_layout();
+
+        ASSERT_EQ(output_layout.feature(), input_layout.feature());
+        ASSERT_EQ(output_layout.spatial(0), input_layout.spatial(0));
+        ASSERT_EQ(output_layout.spatial(1), input_layout.spatial(1));
+    }
+}
+
 std::vector<float> input_data_2_4{
     1.0f,  2.0f,
     4.0f, -15.f,
@@ -919,6 +1028,15 @@ TEST(loop_gpu, support_loop_w_dynamic_input_w_various_shapes2) {
         -1, 10);
 }
 
+TEST(loop_gpu, support_loop_w_dynamic_input_w_various_shapes3) {
+    test_loop_gpu_multiple_shapes_single_shared(
+        { 1, -1, 560 },
+        {{ 1, 58, 560 }, { 1, 87, 560 }, { 1, 72, 560 }, { 1, 88, 560 }, { 1, 89, 560 }},
+        {input_data_2_4_4, input_data_2_4_4, input_data_2_4_4, input_data_2_4_4, input_data_2_4_4},
+        std::vector<float>(),
+        -1, 20);
+}
+
 static void test_loop_gpu_wo_trip_count_update_primitive_id(ov::PartialShape body_input_layout,
                                         std::vector<ov::PartialShape> whole_layouts,
                                         std::vector<std::vector<float>> input_data_list,
diff --git a/src/plugins/intel_gpu/tests/unit/test_cases/swiglu_gpu_test.cpp b/src/plugins/intel_gpu/tests/unit/test_cases/swiglu_gpu_test.cpp
index c2ac561127046a..b01aa9bb708481 100644
--- a/src/plugins/intel_gpu/tests/unit/test_cases/swiglu_gpu_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/test_cases/swiglu_gpu_test.cpp
@@ -63,7 +63,7 @@ TEST(swiglu_gpu_test, swiglu_test_bfyx_dyn) {
 
     topology topology;
     topology.add(input_layout("input", input_layout_dynamic));
-    topology.add(swiglu("swiglu", input_info("input"), -1, 3, tensor()));
+    topology.add(swiglu("swiglu", input_info("input"), -1, 3, ov::intel_gpu::op::SwiGLU::GluType::Swish, 0, tensor()));
 
     ExecutionConfig config = get_test_default_config(engine);
     config.set_property(ov::intel_gpu::allow_new_shape_infer(true));
diff --git a/src/plugins/intel_gpu/tests/unit/transformations/swiglu_fusion_test.cpp b/src/plugins/intel_gpu/tests/unit/transformations/swiglu_fusion_test.cpp
index 2c3b909c0357c3..f7a00776ac7997 100644
--- a/src/plugins/intel_gpu/tests/unit/transformations/swiglu_fusion_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/transformations/swiglu_fusion_test.cpp
@@ -18,6 +18,7 @@
 #include "openvino/op/multiply.hpp"
 #include "openvino/op/parameter.hpp"
 #include "openvino/op/swish.hpp"
+#include "openvino/op/gelu.hpp"
 #include "openvino/op/variadic_split.hpp"
 #include "intel_gpu/op/swiglu.hpp"
 
@@ -39,8 +40,8 @@ TEST_F(TransformationTestsF, SwiGLUFusionTest1) {
     {
         int64_t axis = -1;
         int64_t split_lenghts = 3;
-        auto input = std::make_shared<ov::op::v0::Parameter>(ov::element::f16, ov::PartialShape{ 2, 1, 6 });;
-        auto swiglu = std::make_shared<op::SwiGLU>(input, axis, split_lenghts, ov::element::f16);
+        auto input = std::make_shared<ov::op::v0::Parameter>(ov::element::f16, ov::PartialShape{ 2, 1, 6 });
+        auto swiglu = std::make_shared<op::SwiGLU>(input, axis, split_lenghts, op::SwiGLU::GluType::Swish, 0, ov::element::f16);
 
         model_ref = std::make_shared<ov::Model>(ov::NodeVector{swiglu}, ov::ParameterVector{input});
     }
@@ -76,7 +77,7 @@ TEST_F(TransformationTestsF, SwiGLUFusionTest3) {
         int64_t axis = -1;
         int64_t split_lenghts = 3;
         auto input = std::make_shared<ov::op::v0::Parameter>(ov::element::f16, ov::PartialShape{ -1, -1, 6 });
-        auto swiglu = std::make_shared<op::SwiGLU>(input, axis, split_lenghts, ov::element::f16);
+        auto swiglu = std::make_shared<op::SwiGLU>(input, axis, split_lenghts, op::SwiGLU::GluType::Swish, 0, ov::element::f16);
 
         model_ref = std::make_shared<ov::Model>(ov::NodeVector{swiglu}, ov::ParameterVector{input});
     }
@@ -98,7 +99,7 @@ TEST_F(TransformationTestsF, SwiGLUFusionTest3ReverseOrder) {
         int64_t axis = -1;
         int64_t split_lenghts = 3;
         auto input = std::make_shared<ov::op::v0::Parameter>(ov::element::f16, ov::PartialShape{ -1, -1, 6 });
-        auto swiglu = std::make_shared<op::SwiGLU>(input, axis, split_lenghts, ov::element::f16);
+        auto swiglu = std::make_shared<op::SwiGLU>(input, axis, split_lenghts, op::SwiGLU::GluType::Swish, 0, ov::element::f16);
 
         model_ref = std::make_shared<ov::Model>(ov::NodeVector{swiglu}, ov::ParameterVector{input});
     }
@@ -117,3 +118,25 @@ TEST_F(TransformationTestsF, SwiGLUFusionTest4) {
         manager.register_pass<SwiGLUFusion>();
     }
 }
+
+TEST_F(TransformationTestsF, GeGLUFusionTest1) {
+    {
+        auto input = std::make_shared<ov::op::v0::Parameter>(ov::element::f16, ov::PartialShape{ 2, 1, 6 });
+        auto axis_const = ov::op::v0::Constant::create(ov::element::i64, ov::Shape{}, {-1});
+        auto split_lengths_const = ov::op::v0::Constant::create(ov::element::i64, ov::Shape{2}, {3, -1});
+        auto variadic_split = std::make_shared<ov::op::v1::VariadicSplit>(input, axis_const, split_lengths_const);
+        auto gelu = std::make_shared<ov::op::v7::Gelu>(variadic_split->output(1));
+        auto mul = std::make_shared<ov::op::v1::Multiply>(variadic_split->output(0), gelu);
+
+        model = std::make_shared<ov::Model>(ov::NodeVector{mul}, ov::ParameterVector{input});
+        manager.register_pass<SwiGLUFusion>();
+    }
+    {
+        int64_t axis = -1;
+        int64_t split_lenghts = 3;
+        auto input = std::make_shared<ov::op::v0::Parameter>(ov::element::f16, ov::PartialShape{ 2, 1, 6 });
+        auto swiglu = std::make_shared<op::SwiGLU>(input, axis, split_lenghts, op::SwiGLU::GluType::Gelu, 1, ov::element::f16);
+
+        model_ref = std::make_shared<ov::Model>(ov::NodeVector{swiglu}, ov::ParameterVector{input});
+    }
+}
diff --git a/src/plugins/intel_gpu/thirdparty/CMakeLists.txt b/src/plugins/intel_gpu/thirdparty/CMakeLists.txt
index c6f6331e9294fb..3f0a4d60a9dedf 100644
--- a/src/plugins/intel_gpu/thirdparty/CMakeLists.txt
+++ b/src/plugins/intel_gpu/thirdparty/CMakeLists.txt
@@ -12,7 +12,7 @@ if(ENABLE_ONEDNN_FOR_GPU)
         set(ONEDNN_BUILD_DIR "${CMAKE_CURRENT_BINARY_DIR}/onednn_gpu_build")
         set(ONEDNN_INSTALL_DIR "${CMAKE_CURRENT_BINARY_DIR}/onednn_gpu_install" CACHE PATH "Installation path for oneDNN GPU library")
         set(ONEDNN_PREFIX_DIR "${CMAKE_CURRENT_BINARY_DIR}/onednn_gpu_root")
-        set(ONEDNN_ENABLED_PRIMITIVES "CONCAT;CONVOLUTION;DECONVOLUTION;INNER_PRODUCT;MATMUL;REORDER;POOLING;REDUCTION")
+        set(ONEDNN_ENABLED_PRIMITIVES "CONCAT;CONVOLUTION;DECONVOLUTION;INNER_PRODUCT;MATMUL;REORDER;POOLING;REDUCTION;SDPA")
         set(ONEDNN_ENABLED_ISA "XEHPG;XEHPC;XE2")
         set(DNNL_GPU_LIBRARY_NAME "openvino_onednn_gpu" CACHE STRING "Name of oneDNN library for Intel GPU Plugin")
 
@@ -87,8 +87,7 @@ if(ENABLE_ONEDNN_FOR_GPU)
 
         # remove CMAKE_COMPILE_WARNING_AS_ERROR for onednn_gpu
         if(WIN32 AND CMAKE_COMPILE_WARNING_AS_ERROR AND CMAKE_CXX_COMPILER_ID STREQUAL "MSVC" AND CMAKE_VERSION VERSION_LESS 3.24)
-            string(REPLACE "/WX" "" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
-            string(REPLACE "/WX" "" CMAKE_C_FLAGS "${CMAKE_C_FLAGS}")
+            ov_add_compiler_flags(/WX-)
         endif()
 
         ExternalProject_Add(onednn_gpu_build
@@ -142,11 +141,24 @@ if(ENABLE_ONEDNN_FOR_GPU)
             EXCLUDE_FROM_ALL ON
         )
 
+        list(APPEND LIB_INCLUDE_DIRS ${ONEDNN_INSTALL_DIR}/include)
+        list(APPEND LIB_INCLUDE_DIRS ${CMAKE_CURRENT_SOURCE_DIR}/onednn_gpu/src)
+        set(LIB_DEFINITIONS ENABLE_ONEDNN_FOR_GPU
+                            DNNL_DLL
+                            DNNL_DLL_EXPORTS
+                            DNNL_ENABLE_CPU_ISA_HINTS
+                            DNNL_ENABLE_MAX_CPU_ISA
+                            DNNL_X64=1
+                            NGEN_CPP11
+                            NGEN_NEO_INTERFACE
+                            NGEN_NO_OP_NAMES
+                            NGEN_SAFE
+                            NGEN_WINDOWS_COMPAT)
         add_library(onednn_gpu_tgt INTERFACE)
         set_target_properties(onednn_gpu_tgt PROPERTIES
             INTERFACE_LINK_LIBRARIES $<BUILD_INTERFACE:${ONEDNN_GPU_LIB_PATH}>
-            INTERFACE_INCLUDE_DIRECTORIES $<BUILD_INTERFACE:${ONEDNN_INSTALL_DIR}/include>
-            INTERFACE_COMPILE_DEFINITIONS ENABLE_ONEDNN_FOR_GPU
+            INTERFACE_INCLUDE_DIRECTORIES "$<BUILD_INTERFACE:${LIB_INCLUDE_DIRS}>"
+            INTERFACE_COMPILE_DEFINITIONS "${LIB_DEFINITIONS}"
         )
         add_dependencies(onednn_gpu_tgt onednn_gpu_build)
 
diff --git a/src/plugins/intel_gpu/thirdparty/onednn_gpu b/src/plugins/intel_gpu/thirdparty/onednn_gpu
index a7ed0bcbdefd0a..d6ddb0391a6bb6 160000
--- a/src/plugins/intel_gpu/thirdparty/onednn_gpu
+++ b/src/plugins/intel_gpu/thirdparty/onednn_gpu
@@ -1 +1 @@
-Subproject commit a7ed0bcbdefd0accc2c59d3030b7f8b21af12f6b
+Subproject commit d6ddb0391a6bb62c18049fc56f979cc4409d2300
diff --git a/src/plugins/intel_npu/CMakeLists.txt b/src/plugins/intel_npu/CMakeLists.txt
index dd9ca9279349ab..ce06b2542ac31c 100644
--- a/src/plugins/intel_npu/CMakeLists.txt
+++ b/src/plugins/intel_npu/CMakeLists.txt
@@ -31,3 +31,7 @@ add_subdirectory(src)
 if(ENABLE_TESTS)
     add_subdirectory(tests)
 endif()
+
+add_subdirectory(tools)
+
+ov_cpack_add_component(${NPU_INTERNAL_COMPONENT} HIDDEN)
diff --git a/src/plugins/intel_npu/README.md b/src/plugins/intel_npu/README.md
index 4c0894898d8cfb..6666db53e13271 100644
--- a/src/plugins/intel_npu/README.md
+++ b/src/plugins/intel_npu/README.md
@@ -13,6 +13,7 @@ OpenVINO™ toolkit is officially supported and validated on the following platf
 | :---                         | :---        | :---                                 |
 | Raptor Lake (discrete   NPU)   | NPU 3700    | MS Windows* 11                       |
 | Meteor Lake (integrated NPU)   | NPU 3720    | Ubuntu* 22, MS Windows* 11           |
+| Lunar Lake (integrated NPU)    | NPU 4000    | Ubuntu* 22, MS Windows* 11           |
 
 
 &nbsp;
@@ -160,7 +161,7 @@ The following properties are supported:
 | `ov::log::level`/</br>`LOG_LEVEL` | RW |  Sets the log level for NPU Plugin. An environment variable is also made available to expose logs from early initialization phase: OV_NPU_LOG_LEVEL. | `LOG_NONE`/</br>`LOG_ERROR`/</br>`LOG_WARNING`/</br>`LOG_INFO`/</br>`LOG_DEBUG`/</br>`LOG_TRACE` |  `LOG_NONE` |
 | `ov::cache_dir`/</br>`CACHE_DIR` | RW | Folder path to be used by the OpenVINO cache. | `N/A` | empty |
 | `ov::available_devices`/</br>`AVAILABLE_DEVICES` | RO | Returns the list of enumerated NPU devices. </br> NPU plugin does not currently support multiple devices. | `N/A`| `N/A` |
-| `ov::device::id`/</br>`DEVICE_ID` | RW | Device identifier. Empty means auto detection. | empty/</br> `3700`/</br> `3720` | empty |
+| `ov::device::id`/</br>`DEVICE_ID` | RW | Device identifier. Empty means auto detection. | empty/</br> `3700`/</br> `3720`/</br> `4000` | empty |
 | `ov::device::uuid`/</br> | RO | Returns the Universal Unique ID of the NPU device. | `N/A`| `N/A` |
 | `ov::device::architecture`/</br>`DEVICE_ARCHITECTURE` | RO | Returns the platform information. | `N/A`| `N/A` |
 | `ov::device::full_name`/</br>`FULL_DEVICE_NAME` | RO | Returns the full name of the NPU device. | `N/A`| `N/A` |
@@ -175,8 +176,10 @@ The following table shows the default values for the number of DPU Groups (Tiles
 | :---             | :---                | :---                 | :---                            |
 | THROUGHPUT       | 3700                | 1                    | 1                               |
 | THROUGHPUT       | 3720                | 2 (all of them)      | 2 (all of them)                 |
+| THROUGHPUT       | 4000                | 2 (out of 5/6)       | 2 (all of them)                 |
 | LATENCY          | 3700                | 4 (all of them)      | 1                               |
 | LATENCY          | 3720                | 2 (all of them)      | 2 (all of them)                 |
+| LATENCY          | 4000                | 4 (out of 5/6)       | 2 (all of them)                 |
 
 &nbsp;
 ### Performance Hint: Optimal Number of Inference Requests
@@ -187,6 +190,7 @@ The following table shows the optimal number of inference requests returned by t
 | :---                | :---                                        | :---                                    |
 | 3700                | 8                                           | 1                                       |
 | 3720                | 4                                           | 1                                       |
+| 4000                | 8                                           | 1                                       |
 
 
 &nbsp;
diff --git a/src/plugins/intel_npu/src/al/include/intel_npu/al/config/config.hpp b/src/plugins/intel_npu/src/al/include/intel_npu/al/config/config.hpp
index 71e14dc1cedec7..dc74eccb51cd92 100644
--- a/src/plugins/intel_npu/src/al/include/intel_npu/al/config/config.hpp
+++ b/src/plugins/intel_npu/src/al/include/intel_npu/al/config/config.hpp
@@ -48,6 +48,7 @@ TYPE_PRINTER(unsigned int)
 TYPE_PRINTER(int64_t)
 TYPE_PRINTER(double)
 TYPE_PRINTER(std::string)
+TYPE_PRINTER(std::size_t)
 
 //
 // OptionParser
@@ -104,13 +105,30 @@ template <typename T>
 struct OptionParser<std::vector<T>> final {
     static std::vector<T> parse(std::string_view val) {
         std::vector<T> res;
-        splitAndApply(val, ',', [&](std::string_view item) {
+        std::string val_str(val);
+        splitAndApply(val_str, ',', [&](std::string_view item) {
             res.push_back(OptionParser<T>::parse(item));
         });
         return res;
     }
 };
 
+template <typename K, typename V>
+struct OptionParser<std::map<K, V>> final {
+    static std::map<K, V> parse(std::string_view val) {
+        std::map<K, V> res;
+        std::string val_str(val);
+        splitAndApply(val_str, ',', [&](std::string_view item) {
+            auto kv_delim_pos = item.find(":");
+            OPENVINO_ASSERT(kv_delim_pos != std::string::npos);
+            K key = OptionParser<K>::parse(std::string_view(item.substr(0, kv_delim_pos)));
+            V value = OptionParser<V>::parse(std::string_view(item.substr(kv_delim_pos + 1)));
+            res[key] = value;
+        });
+        return res;
+    }
+};
+
 template <typename Rep, typename Period>
 struct OptionParser<std::chrono::duration<Rep, Period>> final {
     static std::chrono::duration<Rep, Period> parse(std::string_view val) {
diff --git a/src/plugins/intel_npu/src/al/include/intel_npu/al/config/npuw.hpp b/src/plugins/intel_npu/src/al/include/intel_npu/al/config/npuw.hpp
new file mode 100644
index 00000000000000..018d4f02b22147
--- /dev/null
+++ b/src/plugins/intel_npu/src/al/include/intel_npu/al/config/npuw.hpp
@@ -0,0 +1,53 @@
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <thread>
+
+#include "common.hpp"
+#include "npu_private_properties.hpp"
+#include "npuw_private_properties.hpp"
+
+namespace intel_npu {
+
+//
+// register
+//
+
+void registerNPUWOptions(OptionsDesc& desc);
+
+#define DEFINE_OPT(Name, Type, DefaultValue, PropertyKey, Mode)                     \
+    struct Name final : OptionBase<Name, Type> {                                    \
+        static std::string_view key() { return ov::intel_npu::PropertyKey.name(); } \
+                                                                                    \
+        static Type defaultValue() { return DefaultValue; }                         \
+                                                                                    \
+        static OptionMode mode() { return OptionMode::Mode; }                       \
+    };
+
+DEFINE_OPT(NPU_USE_NPUW, bool, false, use_npuw, CompileTime);
+DEFINE_OPT(NPUW_DEVICES, std::string, "NPU,CPU", npuw::devices, CompileTime);
+DEFINE_OPT(NPUW_SUBMODEL_DEVICE, std::string, "", npuw::submodel_device, CompileTime);
+DEFINE_OPT(NPUW_ONLINE_PIPELINE, std::string, "REP", npuw::partitioning::online::pipeline, CompileTime);
+DEFINE_OPT(NPUW_ONLINE_AVOID, std::string, "", npuw::partitioning::online::avoid, CompileTime);
+DEFINE_OPT(NPUW_ONLINE_MIN_SIZE, std::size_t, 10, npuw::partitioning::online::min_size, CompileTime);
+DEFINE_OPT(NPUW_ONLINE_DUMP_PLAN, std::string, "", npuw::partitioning::online::dump_plan, CompileTime);
+DEFINE_OPT(NPUW_PLAN, std::string, "", npuw::partitioning::plan, CompileTime);
+DEFINE_OPT(NPUW_FOLD, bool, false, npuw::partitioning::fold, CompileTime);
+DEFINE_OPT(NPUW_CWAI, bool, false, npuw::partitioning::cwai, CompileTime);
+DEFINE_OPT(NPUW_DCOFF_TYPE, std::string, "", npuw::partitioning::dcoff_type, CompileTime);
+DEFINE_OPT(NPUW_DCOFF_SCALE, bool, false, npuw::partitioning::dcoff_with_scale, CompileTime);
+DEFINE_OPT(NPUW_FUNCALL_FOR_ALL, bool, false, npuw::partitioning::funcall_for_all, CompileTime);
+DEFINE_OPT(NPUW_PARALLEL_COMPILE, bool, false, npuw::parallel_compilation, CompileTime);
+DEFINE_OPT(NPUW_FUNCALL_ASYNC, bool, false, npuw::funcall_async, RunTime);
+DEFINE_OPT(NPUW_ACC_CHECK, bool, false, npuw::accuracy::check, RunTime);
+DEFINE_OPT(NPUW_ACC_THRESH, double, 0.01, npuw::accuracy::threshold, RunTime);
+DEFINE_OPT(NPUW_ACC_DEVICE, std::string, "", npuw::accuracy::reference_device, RunTime);
+DEFINE_OPT(NPUW_DUMP_FULL, bool, false, npuw::dump::full, CompileTime);
+DEFINE_OPT(NPUW_DUMP_SUBS, std::string, "", npuw::dump::subgraphs, CompileTime);
+DEFINE_OPT(NPUW_DUMP_SUBS_ON_FAIL, std::string, "", npuw::dump::subgraphs_on_fail, CompileTime);
+DEFINE_OPT(NPUW_DUMP_IO, std::string, "", npuw::dump::inputs_outputs, RunTime);
+DEFINE_OPT(NPUW_DUMP_IO_ITERS, bool, false, npuw::dump::io_iters, RunTime);
+}  // namespace intel_npu
diff --git a/src/plugins/intel_npu/src/al/include/npuw_private_properties.hpp b/src/plugins/intel_npu/src/al/include/npuw_private_properties.hpp
new file mode 100644
index 00000000000000..423410c17762cf
--- /dev/null
+++ b/src/plugins/intel_npu/src/al/include/npuw_private_properties.hpp
@@ -0,0 +1,235 @@
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "openvino/runtime/intel_npu/properties.hpp"
+
+namespace ov {
+namespace intel_npu {
+/**
+ * @brief
+ * Type: bool
+ * Set this option to true to utilize NPUW extension
+ * Default value: false
+ */
+static constexpr ov::Property<bool> use_npuw{"NPU_USE_NPUW"};
+
+namespace npuw {
+/**
+ * @brief
+ * Type: std::string
+ * Device list to try in order.
+ * Possible values: Comma-separated list of devices. E.g., "NPU,GPU,CPU".
+ * Default value: "NPU,CPU"
+ */
+static constexpr ov::Property<std::string> devices{"NPUW_DEVICES"};
+
+/**
+ * @brief
+ * Type: std::string.
+ * Force the specific subgraph to specific device. The device must be present in the NPUW_DEVICES list.
+ * Possible values: Comma-separated "Subgraph index:OpenVINO device name" pairs,
+ * e.g. "0:CPU,1:NPU".
+ * Default value: empty.
+ */
+static constexpr ov::Property<std::string> submodel_device{"NPUW_SUBMODEL_DEVICE"};
+
+namespace partitioning {
+namespace online {
+/**
+ * @brief
+ * Type: std::string.
+ * Specify which partitioning pipeline to run.
+ * Possible values: "NONE", "INIT", "JUST", "REP".
+ * Default value: "REP".
+ */
+static constexpr ov::Property<std::string> pipeline{"NPUW_ONLINE_PIPELINE"};
+
+/**
+ * @brief
+ * Type: std::string.
+ * Forbids operation(s) and/or predefined pattern(s) to compile and run on a
+ * specified device. Only compatible with online partitioning.
+ * Possible values: comma-separated list of operations slash device, e.g.
+ *                  "Op:Select/NPU,P:RMSNorm/NPU".
+ * Default value: empty.
+ */
+static constexpr ov::Property<std::string> avoid{"NPUW_ONLINE_AVOID"};
+
+/**
+ * @brief
+ * Type: std::size_t.
+ * Lower boundary of partition graph size the plugin can generate.
+ * Used to control fusion term criteria in online partitioning.
+ * WO for online partitioning.
+ * Possible values: Integer >= 10.
+ * Default value: 10.
+ */
+static constexpr ov::Property<std::size_t> min_size{"NPUW_ONLINE_MIN_SIZE"};
+
+/**
+ * @brief
+ * Type: std::string.
+ * Dump online partitioning to the specified file.
+ * This partitioning can be reused via NPUW_PLAN property later.
+ * Possible values: Path to .xml file.
+ * Default value: empty.
+ */
+static constexpr ov::Property<std::string> dump_plan{"NPUW_ONLINE_DUMP_PLAN"};
+}  // namespace online
+
+/**
+ * @brief
+ * Type: std::string.
+ * Set plan file to use by offline partitioning.
+ * Possible values: Path to .xml file.
+ * Default value: empty.
+ */
+static constexpr ov::Property<std::string> plan{"NPUW_PLAN"};
+
+/**
+ * @brief
+ * Type: bool.
+ * Perform function call folding if there are repeating blocks in the graph.
+ * Default value: false.
+ */
+static constexpr ov::Property<bool> fold{"NPUW_FOLD"};
+
+/**
+ * @brief
+ * Type: bool.
+ * Cut-off weights from repeating blocks, but don't do folding.
+ * Decompression cut-off may still happen. Conflicts with NPUW_FOLD.
+ * Default value: false.
+ */
+static constexpr ov::Property<bool> cwai{"NPUW_CWAI"};
+
+/**
+ * @brief
+ * Type: std::string.
+ * Promotional data type for weights decompression. Works only with function "NPUW_FOLD"ing.
+ * Possible values: "i8", "f16"
+ * Default value: empty.
+ */
+static constexpr ov::Property<std::string> dcoff_type{"NPUW_DCOFF_TYPE"};
+
+/**
+ * @brief
+ * Type: bool.
+ * Include weights scaling into the decompression procedure (and exclude it from function bodies).
+ * Works only with function "NPUW_FOLD"ing.
+ * Default value: false.
+ */
+static constexpr ov::Property<bool> dcoff_with_scale{"NPUW_DCOFF_SCALE"};
+
+/**
+ * @brief
+ * Type: bool.
+ * Every subgraph will be turned into a function.
+ * Warning: May cause performance issues!
+ * Default value: false.
+ */
+static constexpr ov::Property<bool> funcall_for_all{"NPUW_FUNCALL_FOR_ALL"};
+}  // namespace partitioning
+
+/**
+ * @brief
+ * Type: bool.
+ * Employ parallel subgraph compilation. Disabled by default due to instabilities.
+ * Default value: false.
+ */
+static constexpr ov::Property<bool> parallel_compilation{"NPUW_PARALLEL_COMPILE"};
+
+/**
+ * @brief
+ * Type: bool.
+ * Pipeline execution of functions (repeating blocks) and their prologues
+ * (e.g., where weights decompression may happen).
+ * Default value: false.
+ */
+static constexpr ov::Property<bool> funcall_async{"NPUW_FUNCALL_ASYNC"};
+
+namespace accuracy {
+/**
+ * @brief
+ * Type: bool.
+ * Enable accuracy check for inference to make infer requests
+ * tolerant to accuracy fails
+ * Default value: false.
+ */
+static constexpr ov::Property<bool> check{"NPUW_ACC_CHECK"};
+
+/**
+ * @brief
+ * Type: double.
+ * Threshold for accuracy validators, to indicate that metric
+ * returns successfull comparison.
+ * Possible values: Double floating-point value from 0.0 to 1.0.
+ * Default value: 0.1.
+ */
+static constexpr ov::Property<double> threshold{"NPUW_ACC_THRESH"};
+
+/**
+ * @brief
+ * Type: std::string.
+ * Reference device, giving accurate results for given model(s).
+ * Possible values: device name, e.g. "CPU".
+ * Default value: empty.
+ */
+static constexpr ov::Property<std::string> reference_device{"NPUW_ACC_DEVICE"};
+}  // namespace accuracy
+
+namespace dump {
+/**
+ * @brief
+ * Type: std::string.
+ * Dump the whole model in its original form (as plugin gets it, before any partitioning is done).
+ * Default value: false.
+ */
+static constexpr ov::Property<bool> full{"NPUW_DUMP_FULL"};
+
+/**
+ * @brief
+ * Type: std::string.
+ * Dump the specified subgraph(s) in OpenVINO IR form in the current directory.
+ * Possible values: Comma-separated list of subgraph indices or "YES" for all
+ * subgraphs, e.g. "0,1" or "YES".
+ * Default value: empty.
+ */
+static constexpr ov::Property<std::string> subgraphs{"NPUW_DUMP_SUBS"};
+
+/**
+ * @brief
+ * Type: std::string.
+ * Dump subgraph on disk if a compilation failure happens.
+ * Possible values: Comma-separated list of subgraph indices or "YES" for all
+ * subgraphs, e.g. "0,1" or "YES".
+ * Default value: empty.
+ */
+static constexpr ov::Property<std::string> subgraphs_on_fail{"NPUW_DUMP_SUBS_ON_FAIL"};
+
+/**
+ * @brief
+ * Type: std::string.
+ * Dump input & output tensors for subgraph(s).
+ * Possible values: Comma-separated list of subgraph indices or "YES" for all
+ * subgraphs, e.g. "0,1" or "YES".
+ * Default value: empty.
+ */
+static constexpr ov::Property<std::string> inputs_outputs{"NPUW_DUMP_IO"};
+
+/**
+ * @brief
+ * Type: std::string.
+ * Dump input & output tensors for subgraph(s) for every iteration.
+ * WARNING: may exhaust the disk space quickly.
+ * Default value: false.
+ */
+static constexpr ov::Property<std::string> io_iters{"NPUW_DUMP_IO_ITERS"};
+}  // namespace dump
+
+}  // namespace npuw
+}  // namespace intel_npu
+}  // namespace ov
diff --git a/src/plugins/intel_npu/src/al/src/config/npuw.cpp b/src/plugins/intel_npu/src/al/src/config/npuw.cpp
new file mode 100644
index 00000000000000..bf010a7895f728
--- /dev/null
+++ b/src/plugins/intel_npu/src/al/src/config/npuw.cpp
@@ -0,0 +1,40 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "intel_npu/al/config/npuw.hpp"
+
+using namespace intel_npu;
+using namespace ov::intel_npu;
+
+//
+// register
+//
+
+void intel_npu::registerNPUWOptions(OptionsDesc& desc) {
+    desc.add<NPU_USE_NPUW>();
+    desc.add<NPUW_DEVICES>();
+    desc.add<NPUW_SUBMODEL_DEVICE>();
+    desc.add<NPUW_ONLINE_PIPELINE>();
+    desc.add<NPUW_ONLINE_AVOID>();
+    desc.add<NPUW_ONLINE_MIN_SIZE>();
+    desc.add<NPUW_ONLINE_DUMP_PLAN>();
+    desc.add<NPUW_PLAN>();
+    desc.add<NPUW_FOLD>();
+    desc.add<NPUW_CWAI>();
+    desc.add<NPUW_DCOFF_TYPE>();
+    desc.add<NPUW_DCOFF_SCALE>();
+    desc.add<NPUW_FUNCALL_FOR_ALL>();
+    desc.add<NPUW_PARALLEL_COMPILE>();
+    desc.add<NPUW_FUNCALL_ASYNC>();
+    desc.add<NPUW_ACC_CHECK>();
+    desc.add<NPUW_ACC_THRESH>();
+    desc.add<NPUW_ACC_DEVICE>();
+#ifdef NPU_PLUGIN_DEVELOPER_BUILD
+    desc.add<NPUW_DUMP_FULL>();
+    desc.add<NPUW_DUMP_SUBS>();
+    desc.add<NPUW_DUMP_SUBS_ON_FAIL>();
+    desc.add<NPUW_DUMP_IO>();
+    desc.add<NPUW_DUMP_IO_ITERS>();
+#endif
+}
diff --git a/src/plugins/intel_npu/src/backend/CMakeLists.txt b/src/plugins/intel_npu/src/backend/CMakeLists.txt
index 375b15bac3692b..3f42d6c3bf340d 100644
--- a/src/plugins/intel_npu/src/backend/CMakeLists.txt
+++ b/src/plugins/intel_npu/src/backend/CMakeLists.txt
@@ -50,3 +50,4 @@ if(TARGET ze_loader)
     install(TARGETS ze_loader
         LIBRARY DESTINATION tests COMPONENT tests EXCLUDE_FROM_ALL)
 endif()
+
diff --git a/src/plugins/intel_npu/src/backend/include/zero_device.hpp b/src/plugins/intel_npu/src/backend/include/zero_device.hpp
index 1652f7ab930ac8..46f28d0aa55473 100644
--- a/src/plugins/intel_npu/src/backend/include/zero_device.hpp
+++ b/src/plugins/intel_npu/src/backend/include/zero_device.hpp
@@ -12,7 +12,7 @@
 #include "npu.hpp"
 #include "zero_init.hpp"
 #include "zero_types.hpp"
-#include "zero_utils.hpp"
+
 namespace intel_npu {
 
 class ZeroDevice : public IDevice {
diff --git a/src/plugins/intel_npu/src/backend/src/zero_device.cpp b/src/plugins/intel_npu/src/backend/src/zero_device.cpp
index a29261bffe7d65..93f63c3d320212 100644
--- a/src/plugins/intel_npu/src/backend/src/zero_device.cpp
+++ b/src/plugins/intel_npu/src/backend/src/zero_device.cpp
@@ -7,8 +7,10 @@
 #include <ze_api.h>
 
 #include "intel_npu/al/itt.hpp"
+#include "intel_npu/utils/zero/zero_api.hpp"
 #include "zero_executor.hpp"
 #include "zero_infer_request.hpp"
+#include "zero_utils.hpp"
 
 using namespace intel_npu;
 
@@ -24,27 +26,20 @@ ZeroDevice::ZeroDevice(const std::shared_ptr<ZeroInitStructsHolder>& initStructs
     // Query PCI information
     // Older drivers do not have this implementend. Linux driver returns NOT_IMPLEMENTED, while windows driver returns
     // zero values. If this is detected, we populate only device with ID from device_properties for backwards
-    // compatibility
+    // compatibility. For any other error, we just fall-back to device ID to assure backwards compatibilty with even
+    // older drivers
     pci_properties.stype = ZE_STRUCTURE_TYPE_PCI_EXT_PROPERTIES;
     ze_result_t retpci = zeDevicePciGetPropertiesExt(_initStructs->getDevice(), &pci_properties);
     if (ZE_RESULT_SUCCESS == retpci) {
-        // win backwards compatibility
+        // windows driver specific backwards compatibility
         if (pci_properties.address.device == 0) {
             log.warning("PCI information not available in driver. Falling back to deviceId");
             pci_properties.address.device = device_properties.deviceId;
         }
-    } else if (ZE_RESULT_ERROR_UNSUPPORTED_FEATURE == retpci) {
+    } else {
+        // general backwards compatibility
         log.warning("PCI information not available in driver. Falling back to deviceId");
-        // linux backwards compatibilty
         pci_properties.address.device = device_properties.deviceId;
-    } else {
-        OPENVINO_THROW("L0 zeDevicePciGetPropertiesExt result: ",
-                       ze_result_to_string(retpci),
-                       ", code 0x",
-                       std::hex,
-                       uint64_t(retpci),
-                       " - ",
-                       ze_result_to_description(retpci));
     }
 
     /// Calculate and store device GOPS with formula: frequency * number of tiles * ops per tile
@@ -102,6 +97,7 @@ std::string ZeroDevice::getName() const {
 #define NPU_3700_DEVICE_ID   0x6240
 #define NPU_3720_P_DEVICE_ID 0x7D1D
 #define NPU_3720_S_DEVICE_ID 0xAD1D
+#define NPU_4000_DEVICE_ID   0x643E
 
     std::string name;
     switch (device_properties.deviceId) {
@@ -112,6 +108,9 @@ std::string ZeroDevice::getName() const {
     case NPU_3720_S_DEVICE_ID:
         name = ov::intel_npu::Platform::NPU3720;
         break;
+    case NPU_4000_DEVICE_ID:
+        name = "4000";
+        break;
     default:
         name = "AUTO_DETECT";
     }
diff --git a/src/plugins/intel_npu/src/backend/src/zero_infer_request.cpp b/src/plugins/intel_npu/src/backend/src/zero_infer_request.cpp
index 3ab4fe397f9def..d1419189ce4ba1 100644
--- a/src/plugins/intel_npu/src/backend/src/zero_infer_request.cpp
+++ b/src/plugins/intel_npu/src/backend/src/zero_infer_request.cpp
@@ -9,6 +9,7 @@
 #include "intel_npu/al/config/runtime.hpp"
 #include "intel_npu/al/itt.hpp"
 #include "intel_npu/al/prefix.hpp"
+#include "intel_npu/utils/zero/zero_api.hpp"
 #include "zero_memory.hpp"
 
 using namespace intel_npu;
@@ -409,9 +410,11 @@ void ZeroInferRequest::check_network_precision(const ov::element::Type_t precisi
         break;
     case ov::element::Type_t::i64:
         break;
+    case ov::element::Type_t::f64:
+        break;
     default:
         OPENVINO_THROW("Unsupported tensor precision: " + ov::element::Type(precision).get_type_name() +
-                       "! Supported precisions: FP32, FP16, U4, I4, U8, I8, U16, I16, U32, I32, U64, I64");
+                       "! Supported precisions: FP32, FP16, U4, I4, U8, I8, U16, I16, U32, I32, U64, I64, FP64");
     }
 }
 
diff --git a/src/plugins/intel_npu/src/backend/src/zero_init.cpp b/src/plugins/intel_npu/src/backend/src/zero_init.cpp
index 9efeda7b01f4a9..316069e856112e 100644
--- a/src/plugins/intel_npu/src/backend/src/zero_init.cpp
+++ b/src/plugins/intel_npu/src/backend/src/zero_init.cpp
@@ -5,6 +5,7 @@
 #include "zero_init.hpp"
 
 #include "intel_npu/al/itt.hpp"
+#include "intel_npu/utils/zero/zero_api.hpp"
 #include "zero_utils.hpp"
 
 namespace intel_npu {
diff --git a/src/plugins/intel_npu/src/backend/src/zero_memory.cpp b/src/plugins/intel_npu/src/backend/src/zero_memory.cpp
index 15bbe9911c2d64..fb4260f40dca84 100644
--- a/src/plugins/intel_npu/src/backend/src/zero_memory.cpp
+++ b/src/plugins/intel_npu/src/backend/src/zero_memory.cpp
@@ -4,6 +4,7 @@
 
 #include "zero_memory.hpp"
 
+#include "intel_npu/utils/zero/zero_api.hpp"
 #include "zero_utils.hpp"
 
 namespace intel_npu {
diff --git a/src/plugins/intel_npu/src/backend/src/zero_pipeline.cpp b/src/plugins/intel_npu/src/backend/src/zero_pipeline.cpp
index c34f5578e55120..dcd7d127b79925 100644
--- a/src/plugins/intel_npu/src/backend/src/zero_pipeline.cpp
+++ b/src/plugins/intel_npu/src/backend/src/zero_pipeline.cpp
@@ -10,6 +10,7 @@
 #include "intel_npu/al/itt.hpp"
 #include "intel_npu/al/prefix.hpp"
 #include "intel_npu/utils/logger/logger.hpp"
+#include "intel_npu/utils/zero/zero_api.hpp"
 
 namespace intel_npu {
 
diff --git a/src/plugins/intel_npu/src/backend/src/zero_profiling.cpp b/src/plugins/intel_npu/src/backend/src/zero_profiling.cpp
index 3e62ef797b5a16..19636066456203 100644
--- a/src/plugins/intel_npu/src/backend/src/zero_profiling.cpp
+++ b/src/plugins/intel_npu/src/backend/src/zero_profiling.cpp
@@ -9,6 +9,7 @@
 
 #include "intel_npu/al/config/compiler.hpp"
 #include "intel_npu/al/profiling.hpp"
+#include "intel_npu/utils/zero/zero_api.hpp"
 #include "zero_profiling.hpp"
 #include "zero_utils.hpp"
 
diff --git a/src/plugins/intel_npu/src/backend/src/zero_wrappers.cpp b/src/plugins/intel_npu/src/backend/src/zero_wrappers.cpp
index 85a3163ead8cdc..721a0a0957efd7 100644
--- a/src/plugins/intel_npu/src/backend/src/zero_wrappers.cpp
+++ b/src/plugins/intel_npu/src/backend/src/zero_wrappers.cpp
@@ -5,6 +5,7 @@
 #include "zero_wrappers.hpp"
 
 #include "intel_npu/al/config/common.hpp"
+#include "intel_npu/utils/zero/zero_api.hpp"
 
 namespace intel_npu {
 
diff --git a/src/plugins/intel_npu/src/compiler/include/custom_stream_buffer.hpp b/src/plugins/intel_npu/src/compiler/include/custom_stream_buffer.hpp
new file mode 100644
index 00000000000000..419bada60dc202
--- /dev/null
+++ b/src/plugins/intel_npu/src/compiler/include/custom_stream_buffer.hpp
@@ -0,0 +1,82 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <algorithm>
+#include <streambuf>
+
+namespace intel_npu::driverCompilerAdapter {
+
+/**
+ *  @brief Counter stream buffer, just counts the written bytes.
+ *  Reads will result in EOF and no seek is supported.
+ */
+class counter_streambuf final : public std::streambuf {
+public:
+    /// Return the number of bytes written to the stream
+    std::streamsize size() {
+        return m_size;
+    }
+
+private:
+    virtual int overflow(int c) override {
+        ++m_size;
+        return c;
+    }
+
+    virtual std::streamsize xsputn(const char* s, std::streamsize n) override {
+        m_size += n;
+        return n;
+    }
+
+    virtual std::streampos seekoff(std::streamoff off,
+                                   std::ios_base::seekdir way,
+                                   std::ios_base::openmode which) override {
+        // Return current stream position
+        if (off == 0 && way == std::ios_base::cur && which == std::ios_base::out) {
+            return m_size;
+        } else {
+            // No seek support
+            throw std::runtime_error("Seek operation is not supported for counting_streambuf");
+        }
+    }
+
+    std::streamsize m_size = 0;
+};
+
+/**
+ *  @brief Writer stream buffer, writes data to target iterator.
+ *  Reads will result in EOF and no seek is supported.
+ */
+template <typename OutputIt>
+class writer_streambuf final : public std::streambuf {
+public:
+    writer_streambuf(const OutputIt& it) : startIt(it), writeIt(it) {}
+
+private:
+    int overflow(int c) override {
+        return *writeIt++ = c;
+    }
+
+    std::streamsize xsputn(const char* s, std::streamsize n) override {
+        writeIt = std::copy_n(s, n, writeIt);
+        return n;
+    }
+
+    std::streampos seekoff(std::streamoff off, std::ios_base::seekdir way, std::ios_base::openmode which) override {
+        // Return current stream position
+        if (off == 0 && way == std::ios_base::cur && which == std::ios_base::out) {
+            return std::distance(startIt, writeIt);
+        } else {
+            // No seek support
+            throw std::runtime_error("Seek operation is not supported for writer_streambuf");
+        }
+    }
+
+    OutputIt startIt;
+    OutputIt writeIt;
+};
+
+}  // namespace intel_npu::driverCompilerAdapter
diff --git a/src/plugins/intel_npu/src/compiler/include/driver_compiler_adapter.hpp b/src/plugins/intel_npu/src/compiler/include/driver_compiler_adapter.hpp
index dedc1a3d78bb70..bbe61c57e012cb 100644
--- a/src/plugins/intel_npu/src/compiler/include/driver_compiler_adapter.hpp
+++ b/src/plugins/intel_npu/src/compiler/include/driver_compiler_adapter.hpp
@@ -6,7 +6,6 @@
 
 #include <ze_graph_ext.h>
 
-#include "iexternal_compiler.hpp"
 #include "intel_npu/al/icompiler.hpp"
 #include "intel_npu/utils/logger/logger.hpp"
 
@@ -38,7 +37,7 @@ class LevelZeroCompilerAdapter final : public ICompiler {
     /**
      * @brief Separate externals calls to separate class
      */
-    std::shared_ptr<IExternalCompiler> apiAdapter;
+    std::shared_ptr<ICompiler> apiAdapter;
     ze_driver_handle_t _driverHandle = nullptr;
     mutable Logger _logger;
 };
diff --git a/src/plugins/intel_npu/src/compiler/include/graph_transformations.hpp b/src/plugins/intel_npu/src/compiler/include/graph_transformations.hpp
index 88f4b5ab2cc34d..7c6ae33c62917d 100644
--- a/src/plugins/intel_npu/src/compiler/include/graph_transformations.hpp
+++ b/src/plugins/intel_npu/src/compiler/include/graph_transformations.hpp
@@ -3,9 +3,12 @@
 //
 
 #pragma once
+#include <iostream>
+#include <string>
 #include <vector>
 
-#include "driver_compiler_adapter.hpp"
+#include "custom_stream_buffer.hpp"
+#include "intel_npu/utils/logger/logger.hpp"
 #include "openvino/pass/manager.hpp"
 
 /**
@@ -14,9 +17,39 @@
  */
 namespace intel_npu::driverCompilerAdapter {
 
-/**
- * @brief Serialize OpenVINO model to IR
- */
-IR serializeToIR(const std::shared_ptr<const ov::Model>& model, uint32_t supportedVersionByCompiler = 7);
+class IRSerializer {
+public:
+    IRSerializer(const std::shared_ptr<const ov::Model>& origModel, const uint32_t supportedOpset = 11);
+
+    size_t getXmlSize() const {
+        return _xmlSize;
+    }
+
+    size_t getWeightsSize() const {
+        return _weightsSize;
+    }
+
+    /**
+     * @brief Serialize OpenVINO model to target buffer
+     */
+    void serializeModelToBuffer(uint8_t* xml, uint8_t* weights);
+
+private:
+    /**
+     * @brief Serialize OpenVINO model to target stream
+     */
+    void serializeModelToStream(std::ostream& xml, std::ostream& weights);
+
+    /**
+     * @brief Get size of xml and weights from model
+     */
+    void countModelSize();
+
+    Logger _logger;
+    std::shared_ptr<ov::Model> _model = nullptr;
+    uint32_t _supportedOpset = 11;
+    size_t _xmlSize = 0;
+    size_t _weightsSize = 0;
+};
 
 }  // namespace intel_npu::driverCompilerAdapter
diff --git a/src/plugins/intel_npu/src/compiler/include/iexternal_compiler.hpp b/src/plugins/intel_npu/src/compiler/include/iexternal_compiler.hpp
deleted file mode 100644
index ef2bdbf4a9c713..00000000000000
--- a/src/plugins/intel_npu/src/compiler/include/iexternal_compiler.hpp
+++ /dev/null
@@ -1,44 +0,0 @@
-// Copyright (C) 2018-2024 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#pragma once
-#include "intel_npu/al/icompiler.hpp"
-
-namespace intel_npu {
-namespace driverCompilerAdapter {
-
-struct IR {
-    std::stringstream xml;
-    std::stringstream weights;
-};
-
-/**
- * @brief Interface for external compiler
- * @details Isolate external API calls from general logic
- */
-class IExternalCompiler {
-public:
-    virtual ~IExternalCompiler() = default;
-
-    /**
-     * @brief Get opset supported by compiler
-     */
-    virtual uint32_t getSupportedOpset() const = 0;
-
-    /**
-     * @brief Get query result for current network
-     */
-    virtual std::unordered_set<std::string> getQueryResult(IR& irModel, const Config& config) const = 0;
-
-    /**
-     * @brief Sends the serialized model and its I/O metadata to the driver for compilation.
-     * @return The compiled model descriptor corresponding to the previously given network.
-     */
-    virtual NetworkDescription compileIR(const std::shared_ptr<const ov::Model>& model,
-                                         IR& irModel,
-                                         const Config& config) const = 0;
-    virtual NetworkMetadata parseBlob(const std::vector<uint8_t>& blob, const Config& config) const = 0;
-};
-}  // namespace driverCompilerAdapter
-}  // namespace intel_npu
diff --git a/src/plugins/intel_npu/src/compiler/include/zero_compiler_in_driver.hpp b/src/plugins/intel_npu/src/compiler/include/zero_compiler_in_driver.hpp
index fc9157469e383a..24ccf0353f7c0a 100644
--- a/src/plugins/intel_npu/src/compiler/include/zero_compiler_in_driver.hpp
+++ b/src/plugins/intel_npu/src/compiler/include/zero_compiler_in_driver.hpp
@@ -6,13 +6,17 @@
 #include <ze_graph_ext.h>
 
 #include <type_traits>
+#include <utility>
 
-#include "iexternal_compiler.hpp"
+#include "intel_npu/al/icompiler.hpp"
 #include "intel_npu/utils/logger/logger.hpp"
+#include "intel_npu/utils/zero/zero_api.hpp"
 
 namespace intel_npu {
 namespace driverCompilerAdapter {
 
+using SerializedIR = std::pair<size_t, std::shared_ptr<uint8_t>>;
+
 #define NotSupportLogHandle(T) \
     (std::is_same<T, ze_graph_dditable_ext_1_2_t>::value || std::is_same<T, ze_graph_dditable_ext_1_3_t>::value)
 
@@ -40,30 +44,33 @@ namespace driverCompilerAdapter {
  * Adapter to use CiD through ZeroAPI
  */
 template <typename TableExtension>
-class LevelZeroCompilerInDriver final : public IExternalCompiler {
+class LevelZeroCompilerInDriver final : public ICompiler {
 public:
     LevelZeroCompilerInDriver(const char* extName, ze_driver_handle_t driverHandle);
     LevelZeroCompilerInDriver(const LevelZeroCompilerInDriver&) = delete;
     LevelZeroCompilerInDriver& operator=(const LevelZeroCompilerInDriver&) = delete;
     ~LevelZeroCompilerInDriver() override;
 
-    uint32_t getSupportedOpset() const override;
+    uint32_t getSupportedOpsetVersion() const override final;
+
+    ov::SupportedOpsMap query(const std::shared_ptr<const ov::Model>& model, const Config& config) const override;
 
-    std::unordered_set<std::string> getQueryResult(IR& irModel, const Config& config) const override;
+    NetworkDescription compile(const std::shared_ptr<const ov::Model>& model,
+                               const Config& config) const override final;
 
-    NetworkDescription compileIR(const std::shared_ptr<const ov::Model>& model,
-                                 IR& irModel,
-                                 const Config& config) const override final;
+    NetworkMetadata parse(const std::vector<uint8_t>& network, const Config& config) const override final;
 
-    NetworkMetadata parseBlob(const std::vector<uint8_t>& blob, const Config& config) const override final;
+    std::vector<ov::ProfilingInfo> process_profiling_output(const std::vector<uint8_t>& profData,
+                                                            const std::vector<uint8_t>& network,
+                                                            const Config& config) const override final {
+        OPENVINO_THROW("Profiling post-processing is not implemented.");
+    }
 
     template <typename T = TableExtension, std::enable_if_t<!NotSupportQuery(T), bool> = true>
     std::unordered_set<std::string> getQueryResultFromSupportedLayers(
         ze_result_t result,
         ze_graph_query_network_handle_t& hGraphQueryNetwork) const;
 
-    std::vector<uint8_t> getSerializedIR(std::string& buildFlags, IR& irModel, const Config& config) const;
-
     /**
      * @brief Serialize input / output information to string format.
      * @details Format:
@@ -81,7 +88,8 @@ class LevelZeroCompilerInDriver final : public IExternalCompiler {
 private:
     NetworkMetadata getNetworkMeta(ze_graph_handle_t graphHandle) const;
 
-    std::vector<uint8_t> serializeIR(IR& irModel, ze_graph_compiler_version_info_t compilerVersion) const;
+    SerializedIR serializeIR(const std::shared_ptr<const ov::Model>& model,
+                             ze_graph_compiler_version_info_t compilerVersion) const;
     std::string serializeConfig(const Config& config, ze_graph_compiler_version_info_t& compilerVersion) const;
 
     /**
@@ -134,27 +142,30 @@ class LevelZeroCompilerInDriver final : public IExternalCompiler {
 
     // ext version >= 1.5, support API (pfnCreate2, pfnQueryNetworkCreate2, pfnQueryContextMemory)
     template <typename T = TableExtension, typename std::enable_if_t<SupportAPIGraphQueryNetworkV2(T), bool> = true>
-    std::unordered_set<std::string> queryImpl(IR& irModel, const Config& config) const;
+    std::unordered_set<std::string> queryImpl(const std::shared_ptr<const ov::Model>& model,
+                                              const Config& config) const;
 
     // ext version == 1.3 && 1.4, support API (pfnQueryNetworkCreate, pfnQueryNetworkDestroy,
     // pfnQueryNetworkGetSupportedLayers)
     template <typename T = TableExtension, typename std::enable_if_t<SupportAPIGraphQueryNetworkV1(T), bool> = true>
-    std::unordered_set<std::string> queryImpl(IR& irModel, const Config& config) const;
+    std::unordered_set<std::string> queryImpl(const std::shared_ptr<const ov::Model>& model,
+                                              const Config& config) const;
 
     // For ext version < 1.3
     template <typename T = TableExtension, typename std::enable_if_t<NotSupportQuery(T), bool> = true>
-    std::unordered_set<std::string> queryImpl(IR& irModel, const Config& config) const;
+    std::unordered_set<std::string> queryImpl(const std::shared_ptr<const ov::Model>& model,
+                                              const Config& config) const;
 
     template <typename T = TableExtension, typename std::enable_if_t<NotSupportGraph2(T), bool> = true>
     ze_result_t createGraph(const ze_graph_format_t& format,
-                            const std::vector<uint8_t>& serializedIR,
+                            const SerializedIR& serializedIR,
                             const std::string& buildFlags,
                             const uint32_t& flags,
                             ze_graph_handle_t* graph) const;
 
     template <typename T = TableExtension, typename std::enable_if_t<!NotSupportGraph2(T), bool> = true>
     ze_result_t createGraph(const ze_graph_format_t& format,
-                            const std::vector<uint8_t>& serializedIR,
+                            const SerializedIR& serializedIR,
                             const std::string& buildFlags,
                             const uint32_t& flags,
                             ze_graph_handle_t* graph) const;
diff --git a/src/plugins/intel_npu/src/compiler/src/driver_compiler_adapter.cpp b/src/plugins/intel_npu/src/compiler/src/driver_compiler_adapter.cpp
index 8c8519169ae529..012c2213f5039e 100644
--- a/src/plugins/intel_npu/src/compiler/src/driver_compiler_adapter.cpp
+++ b/src/plugins/intel_npu/src/compiler/src/driver_compiler_adapter.cpp
@@ -6,6 +6,7 @@
 
 #include "graph_transformations.hpp"
 #include "intel_npu/al/config/common.hpp"
+#include "intel_npu/utils/zero/zero_api.hpp"
 #include "intel_npu/utils/zero/zero_result.hpp"
 #include "ze_intel_vpu_uuid.h"
 #include "zero_compiler_in_driver.hpp"
@@ -177,45 +178,27 @@ LevelZeroCompilerAdapter::LevelZeroCompilerAdapter() : _logger("LevelZeroCompile
 }
 
 uint32_t LevelZeroCompilerAdapter::getSupportedOpsetVersion() const {
-    return apiAdapter->getSupportedOpset();
+    return apiAdapter->getSupportedOpsetVersion();
 }
 
 NetworkDescription LevelZeroCompilerAdapter::compile(const std::shared_ptr<const ov::Model>& model,
                                                      const Config& config) const {
     _logger.setLevel(config.get<LOG_LEVEL>());
-    _logger.debug("compileIR");
-    uint32_t supportedOpset = apiAdapter->getSupportedOpset();
-
-    auto IR = serializeToIR(model, supportedOpset);
-
-    return apiAdapter->compileIR(model, IR, config);
+    _logger.debug("compile");
+    return apiAdapter->compile(model, config);
 }
 
 ov::SupportedOpsMap LevelZeroCompilerAdapter::query(const std::shared_ptr<const ov::Model>& model,
                                                     const Config& config) const {
     _logger.setLevel(config.get<LOG_LEVEL>());
-    _logger.debug("queryResult");
-    ov::SupportedOpsMap result;
-    const std::string deviceName = "NPU";
-
-    auto IR = serializeToIR(model);
-    try {
-        const auto supportedLayers = apiAdapter->getQueryResult(IR, config);
-        for (auto&& layerName : supportedLayers) {
-            result.emplace(layerName, deviceName);
-        }
-        _logger.info("For given model, there are %d supported layers", supportedLayers.size());
-    } catch (std::exception& e) {
-        OPENVINO_THROW("Fail in calling querynetwork : ", e.what());
-    }
-
-    return result;
+    _logger.debug("query");
+    return apiAdapter->query(model, config);
 }
 
-NetworkMetadata LevelZeroCompilerAdapter::parse(const std::vector<uint8_t>& blob, const Config& config) const {
+NetworkMetadata LevelZeroCompilerAdapter::parse(const std::vector<uint8_t>& network, const Config& config) const {
     _logger.setLevel(config.get<LOG_LEVEL>());
-    _logger.debug("parseBlob");
-    return apiAdapter->parseBlob(blob, config);
+    _logger.debug("parse");
+    return apiAdapter->parse(network, config);
 }
 
 std::vector<ov::ProfilingInfo> LevelZeroCompilerAdapter::process_profiling_output(const std::vector<uint8_t>&,
diff --git a/src/plugins/intel_npu/src/compiler/src/graph_transformations.cpp b/src/plugins/intel_npu/src/compiler/src/graph_transformations.cpp
index 40da24ff5b5bab..c0881d75cd7be9 100644
--- a/src/plugins/intel_npu/src/compiler/src/graph_transformations.cpp
+++ b/src/plugins/intel_npu/src/compiler/src/graph_transformations.cpp
@@ -4,31 +4,44 @@
 
 #include "graph_transformations.hpp"
 
+#include <cstdint>
 #include <istream>
 #include <mutex>
+#include <streambuf>
 
 #include "openvino/pass/serialize.hpp"
 #include "transformations/op_conversions/convert_interpolate11_downgrade.hpp"
 
 namespace intel_npu::driverCompilerAdapter {
 
-IR serializeToIR(const std::shared_ptr<const ov::Model>& origModel, uint32_t supportedOpset) {
+IRSerializer::IRSerializer(const std::shared_ptr<const ov::Model>& origModel, const uint32_t supportedOpset)
+    : _logger("DriverCompilerAdapter::IRSerializer", Logger::global().level()),
+      _supportedOpset(supportedOpset) {
     // There is no const variant of run_passes so use const_cast here
     // as model serialization does not mutate the model
-    std::shared_ptr<ov::Model> model = std::const_pointer_cast<ov::Model>(origModel);
+    _model = std::const_pointer_cast<ov::Model>(origModel);
 
+    if (supportedOpset < 11) {
+        // Need to clone to modify the model and remain thread safe
+        _model = _model->clone();
+        _logger.info("Clone model for offset smaller than 11");
+    }
+
+    countModelSize();
+}
+
+void IRSerializer::serializeModelToStream(std::ostream& xml, std::ostream& weights) {
+    _logger.debug("serializeModelToStream");
     const auto passConfig = std::make_shared<ov::pass::PassConfig>();
     ov::pass::Manager manager(passConfig);
 
-    if (supportedOpset < 11) {
-        // Need to clone to modify the model and remain thread safe
-        model = model->clone();
+    if (_supportedOpset < 11) {
         // Downgrade to opset10
         manager.register_pass<ov::pass::ConvertInterpolate11ToInterpolate4>();
+        _logger.info("Downgrade op for opset smaller than 11");
     }
 
-    std::stringstream xmlStream, weightsStream;
-    manager.register_pass<ov::pass::Serialize>(xmlStream, weightsStream);
+    manager.register_pass<ov::pass::Serialize>(xml, weights);
 
     // Depending on the driver version, the compiler attached to it may request this information as an indicator of the
     // precision/layout preprocessing requirement. We are setting this value to "true" since the API version is no
@@ -42,15 +55,43 @@ IR serializeToIR(const std::shared_ptr<const ov::Model>& origModel, uint32_t sup
     {
         std::lock_guard<std::mutex> lock(rtInfoMutex);
 
-        model->set_rt_info(true, new_api_key);
+        _model->set_rt_info(true, new_api_key);
 
-        manager.run_passes(model);
+        manager.run_passes(_model);
 
-        auto& rtInfo = model->get_rt_info();
+        auto& rtInfo = _model->get_rt_info();
         rtInfo.erase(new_api_key);
     }
+    _logger.debug("serializeModelToStream end");
+}
+
+void IRSerializer::countModelSize() {
+    _logger.debug("countModelSize");
+
+    counter_streambuf xmlStreamBuf;
+    counter_streambuf weightsStreamBuf;
+    std::ostream xmlStream(&xmlStreamBuf);
+    std::ostream weightsStream(&weightsStreamBuf);
+
+    serializeModelToStream(xmlStream, weightsStream);
+
+    _xmlSize = xmlStreamBuf.size();
+    _weightsSize = weightsStreamBuf.size();
+
+    _logger.debug("countModelSize completed, xml size: %d, weights size: %d", _xmlSize, _weightsSize);
+}
+
+void IRSerializer::serializeModelToBuffer(uint8_t* xml, uint8_t* weights) {
+    _logger.debug("serializeModelToBuffer");
+
+    writer_streambuf xmlStreamBuf(xml);
+    writer_streambuf weightsStreamBuf(weights);
+    std::ostream xmlStream(&xmlStreamBuf);
+    std::ostream weightsStream(&weightsStreamBuf);
+
+    serializeModelToStream(xmlStream, weightsStream);
 
-    return {std::move(xmlStream), std::move(weightsStream)};
+    _logger.debug("serializeModelToBuffer end");
 }
 
 }  // namespace intel_npu::driverCompilerAdapter
diff --git a/src/plugins/intel_npu/src/compiler/src/zero_compiler_in_driver.cpp b/src/plugins/intel_npu/src/compiler/src/zero_compiler_in_driver.cpp
index d48dd82a62cb28..b5ce38fe0a4f80 100644
--- a/src/plugins/intel_npu/src/compiler/src/zero_compiler_in_driver.cpp
+++ b/src/plugins/intel_npu/src/compiler/src/zero_compiler_in_driver.cpp
@@ -4,9 +4,11 @@
 
 #include "zero_compiler_in_driver.hpp"
 
+#include <fstream>
 #include <regex>
 #include <string_view>
 
+#include "graph_transformations.hpp"
 #include "intel_npu/al/config/common.hpp"
 #include "intel_npu/al/config/runtime.hpp"
 #include "intel_npu/al/itt.hpp"
@@ -243,34 +245,24 @@ LevelZeroCompilerInDriver<TableExtension>::~LevelZeroCompilerInDriver() {
     _logger.debug("LevelZeroCompilerInDriver obj destroyed");
 }
 
-static size_t getFileSize(std::istream& strm) {
-    const size_t streamStart = strm.tellg();
-    strm.seekg(0, std::ios_base::end);
-    const size_t streamEnd = strm.tellg();
-    const size_t bytesAvailable = streamEnd - streamStart;
-    strm.seekg(streamStart, std::ios_base::beg);
-
-    return bytesAvailable;
-}
-
-using SerializedIR = std::vector<uint8_t>;
-
 /**
  * @brief Place xml + weights in sequential memory
  * @details Format of the memory:
  */
 template <typename TableExtension>
 SerializedIR LevelZeroCompilerInDriver<TableExtension>::serializeIR(
-    IR& irModel,
+    const std::shared_ptr<const ov::Model>& model,
     ze_graph_compiler_version_info_t compilerVersion) const {
+    IRSerializer irSerializer(model, getSupportedOpsetVersion());
+
     // Contract between adapter and compiler in driver
     const uint32_t maxNumberOfElements = 10;
     const uint64_t maxSizeOfXML = std::numeric_limits<uint64_t>::max() / 3;
     const uint64_t maxSizeOfWeights = maxSizeOfXML * 2;
 
     const uint32_t numberOfInputData = 2;
-    const uint64_t xmlSize = static_cast<uint64_t>(getFileSize(irModel.xml));
-    const uint64_t weightsSize = static_cast<uint64_t>(getFileSize(irModel.weights));
+    const uint64_t xmlSize = static_cast<uint64_t>(irSerializer.getXmlSize());
+    const uint64_t weightsSize = static_cast<uint64_t>(irSerializer.getWeightsSize());
 
     OPENVINO_ASSERT(numberOfInputData < maxNumberOfElements);
     if (xmlSize >= maxSizeOfXML) {
@@ -289,30 +281,32 @@ SerializedIR LevelZeroCompilerInDriver<TableExtension>::serializeIR(
     const uint64_t sizeOfSerializedIR = sizeof(compilerVersion) + sizeof(numberOfInputData) + sizeof(xmlSize) +
                                         xmlSize + sizeof(weightsSize) + weightsSize;
 
-    std::vector<uint8_t> serializedIR;
-    serializedIR.resize(sizeOfSerializedIR);
+    // use array to avoid vector's memory zeroing overhead
+    std::shared_ptr<uint8_t> buffer(new uint8_t[sizeOfSerializedIR], std::default_delete<uint8_t[]>());
+    uint8_t* serializedIR = buffer.get();
 
     uint64_t offset = 0;
-    checkedMemcpy(serializedIR.data() + offset, sizeOfSerializedIR - offset, &compilerVersion, sizeof(compilerVersion));
+    checkedMemcpy(serializedIR + offset, sizeOfSerializedIR - offset, &compilerVersion, sizeof(compilerVersion));
     offset += sizeof(compilerVersion);
 
-    checkedMemcpy(serializedIR.data() + offset,
-                  sizeOfSerializedIR - offset,
-                  &numberOfInputData,
-                  sizeof(numberOfInputData));
+    checkedMemcpy(serializedIR + offset, sizeOfSerializedIR - offset, &numberOfInputData, sizeof(numberOfInputData));
     offset += sizeof(numberOfInputData);
-    checkedMemcpy(serializedIR.data() + offset, sizeOfSerializedIR - offset, &xmlSize, sizeof(xmlSize));
+    checkedMemcpy(serializedIR + offset, sizeOfSerializedIR - offset, &xmlSize, sizeof(xmlSize));
     offset += sizeof(xmlSize);
-    irModel.xml.read(reinterpret_cast<char*>(serializedIR.data() + offset), xmlSize);
+    // xml data is filled in serializeModel()
+    uint64_t xmlOffset = offset;
     offset += xmlSize;
-    checkedMemcpy(serializedIR.data() + offset, sizeOfSerializedIR - offset, &weightsSize, sizeof(weightsSize));
+    checkedMemcpy(serializedIR + offset, sizeOfSerializedIR - offset, &weightsSize, sizeof(weightsSize));
     offset += sizeof(weightsSize);
-    irModel.weights.read(reinterpret_cast<char*>(serializedIR.data() + offset), weightsSize);
+    // weights data is filled in serializeModel()
+    uint64_t weightsOffset = offset;
     offset += weightsSize;
 
+    irSerializer.serializeModelToBuffer(serializedIR + xmlOffset, serializedIR + weightsOffset);
+
     OPENVINO_ASSERT(offset == sizeOfSerializedIR);
 
-    return serializedIR;
+    return std::make_pair(sizeOfSerializedIR, buffer);
 }
 
 template <typename TableExtension>
@@ -509,8 +503,9 @@ static std::unordered_set<std::string> parseQueryResult(std::vector<char>& data)
 // For ext version < 1.3, query is unsupported, return empty result and add debug log here
 template <typename TableExtension>
 template <typename T, std::enable_if_t<NotSupportQuery(T), bool>>
-std::unordered_set<std::string> LevelZeroCompilerInDriver<TableExtension>::queryImpl(IR& /*irModel*/,
-                                                                                     const Config&) const {
+std::unordered_set<std::string> LevelZeroCompilerInDriver<TableExtension>::queryImpl(
+    const std::shared_ptr<const ov::Model>& /*model*/,
+    const Config&) const {
     _logger.debug("queryImpl - Driver version is less than 1.3, queryNetwork is unsupported.");
     return std::unordered_set<std::string>();
 }
@@ -518,23 +513,39 @@ std::unordered_set<std::string> LevelZeroCompilerInDriver<TableExtension>::query
 // For ext version == 1.3 && == 1.4, query is supported, calling querynetwork api in _graphDdiTableExt
 template <typename TableExtension>
 template <typename T, std::enable_if_t<SupportAPIGraphQueryNetworkV1(T), bool>>
-std::unordered_set<std::string> LevelZeroCompilerInDriver<TableExtension>::queryImpl(IR& irModel,
-                                                                                     const Config& config) const {
+std::unordered_set<std::string> LevelZeroCompilerInDriver<TableExtension>::queryImpl(
+    const std::shared_ptr<const ov::Model>& model,
+    const Config& config) const {
     _logger.debug("queryImpl - Calling queryNetwork of 1.3 version.");
 
+    ze_device_graph_properties_t deviceGraphProperties{};
+    auto result = _graphDdiTableExt->pfnDeviceGetGraphProperties(_deviceHandle, &deviceGraphProperties);
+    if (ZE_RESULT_SUCCESS != result) {
+        OPENVINO_THROW("L0 pfnDeviceGetGraphProperties",
+                       " result: ",
+                       ze_result_to_string(result),
+                       ", code 0x",
+                       std::hex,
+                       uint64_t(result));
+    }
+    ze_graph_compiler_version_info_t& compilerVersion = deviceGraphProperties.compilerVersion;
+
+    auto serializedIR = serializeIR(model, compilerVersion);
+
     std::string buildFlags;
-    auto serializedIR = getSerializedIR(buildFlags, irModel, config);
+    buildFlags += serializeConfig(config, compilerVersion);
+    _logger.debug("queryImpl build flags : %s", buildFlags.c_str());
 
     ze_graph_desc_t desc = {ZE_STRUCTURE_TYPE_GRAPH_DESC_PROPERTIES,
                             nullptr,
                             ZE_GRAPH_FORMAT_NGRAPH_LITE,
-                            serializedIR.size(),
-                            serializedIR.data(),
+                            serializedIR.first,
+                            serializedIR.second.get(),
                             buildFlags.c_str()};
     ze_graph_query_network_handle_t hGraphQueryNetwork = nullptr;
 
     // Create querynetwork handle
-    auto result = _graphDdiTableExt->pfnQueryNetworkCreate(_context, _deviceHandle, &desc, &hGraphQueryNetwork);
+    result = _graphDdiTableExt->pfnQueryNetworkCreate(_context, _deviceHandle, &desc, &hGraphQueryNetwork);
 
     return getQueryResultFromSupportedLayers(result, hGraphQueryNetwork);
 }
@@ -542,25 +553,41 @@ std::unordered_set<std::string> LevelZeroCompilerInDriver<TableExtension>::query
 // For ext version >= 1.5
 template <typename TableExtension>
 template <typename T, std::enable_if_t<SupportAPIGraphQueryNetworkV2(T), bool>>
-std::unordered_set<std::string> LevelZeroCompilerInDriver<TableExtension>::queryImpl(IR& irModel,
-                                                                                     const Config& config) const {
+std::unordered_set<std::string> LevelZeroCompilerInDriver<TableExtension>::queryImpl(
+    const std::shared_ptr<const ov::Model>& model,
+    const Config& config) const {
     _logger.debug("queryImpl - Calling queryNetwork of 1.5 version.");
 
+    ze_device_graph_properties_t deviceGraphProperties{};
+    auto result = _graphDdiTableExt->pfnDeviceGetGraphProperties(_deviceHandle, &deviceGraphProperties);
+    if (ZE_RESULT_SUCCESS != result) {
+        OPENVINO_THROW("L0 pfnDeviceGetGraphProperties",
+                       " result: ",
+                       ze_result_to_string(result),
+                       ", code 0x",
+                       std::hex,
+                       uint64_t(result));
+    }
+    ze_graph_compiler_version_info_t& compilerVersion = deviceGraphProperties.compilerVersion;
+
+    auto serializedIR = serializeIR(model, compilerVersion);
+
     std::string buildFlags;
-    auto serializedIR = getSerializedIR(buildFlags, irModel, config);
+    buildFlags += serializeConfig(config, compilerVersion);
+    _logger.debug("queryImpl build flags : %s", buildFlags.c_str());
 
     ze_graph_desc_2_t desc = {ZE_STRUCTURE_TYPE_GRAPH_DESC_PROPERTIES,
                               nullptr,
                               ZE_GRAPH_FORMAT_NGRAPH_LITE,
-                              serializedIR.size(),
-                              serializedIR.data(),
+                              serializedIR.first,
+                              serializedIR.second.get(),
                               buildFlags.c_str(),
                               ZE_GRAPH_FLAG_NONE};
 
     ze_graph_query_network_handle_t hGraphQueryNetwork = nullptr;
 
     // Create querynetwork handle
-    auto result = _graphDdiTableExt->pfnQueryNetworkCreate2(_context, _deviceHandle, &desc, &hGraphQueryNetwork);
+    result = _graphDdiTableExt->pfnQueryNetworkCreate2(_context, _deviceHandle, &desc, &hGraphQueryNetwork);
 
     return getQueryResultFromSupportedLayers(result, hGraphQueryNetwork);
 }
@@ -619,51 +646,42 @@ std::unordered_set<std::string> LevelZeroCompilerInDriver<TableExtension>::getQu
 }
 
 template <typename TableExtension>
-std::vector<uint8_t> LevelZeroCompilerInDriver<TableExtension>::getSerializedIR(std::string& buildFlags,
-                                                                                IR& irModel,
-                                                                                const Config& config) const {
-    ze_device_graph_properties_t deviceGraphProperties{};
-    auto result = _graphDdiTableExt->pfnDeviceGetGraphProperties(_deviceHandle, &deviceGraphProperties);
-    if (ZE_RESULT_SUCCESS != result) {
-        OPENVINO_THROW("L0 pfnDeviceGetGraphProperties",
-                       " result: ",
-                       ze_result_to_string(result),
-                       ", code 0x",
-                       std::hex,
-                       uint64_t(result));
-    }
-    ze_graph_compiler_version_info_t& compilerVersion = deviceGraphProperties.compilerVersion;
-    buildFlags += serializeConfig(config, compilerVersion);
-    _logger.debug("getSerializedIR Build flags : %s", buildFlags.c_str());
+ov::SupportedOpsMap LevelZeroCompilerInDriver<TableExtension>::query(const std::shared_ptr<const ov::Model>& model,
+                                                                     const Config& config) const {
+    _logger.setLevel(config.get<LOG_LEVEL>());
+    _logger.debug("query");
 
-    auto serializedIR = serializeIR(irModel, compilerVersion);
+    ov::SupportedOpsMap result;
+    const std::string deviceName = "NPU";
 
-    return serializedIR;
-}
+    try {
+        const auto supportedLayers = queryImpl(model, config);
+        ;
+        for (auto&& layerName : supportedLayers) {
+            result.emplace(layerName, deviceName);
+        }
+        _logger.info("For given model, there are %d supported layers", supportedLayers.size());
+    } catch (std::exception& e) {
+        OPENVINO_THROW("Fail in calling querynetwork : ", e.what());
+    }
 
-template <typename TableExtension>
-std::unordered_set<std::string> LevelZeroCompilerInDriver<TableExtension>::getQueryResult(IR& irModel,
-                                                                                          const Config& config) const {
-    _logger.setLevel(config.get<LOG_LEVEL>());
-    _logger.debug("getQueryResult");
-    auto queryResult = queryImpl(irModel, config);
-    _logger.debug("getQueryResult end");
-    return queryResult;
+    _logger.debug("query end");
+    return result;
 }
 
 // For ext version <1.5, calling pfnCreate api in _graphDdiTableExt
 template <typename TableExtension>
 template <typename T, std::enable_if_t<NotSupportGraph2(T), bool>>
 ze_result_t LevelZeroCompilerInDriver<TableExtension>::createGraph(const ze_graph_format_t& format,
-                                                                   const std::vector<uint8_t>& serializedIR,
+                                                                   const SerializedIR& serializedIR,
                                                                    const std::string& buildFlags,
                                                                    const uint32_t& /*flags*/,
                                                                    ze_graph_handle_t* graph) const {
     ze_graph_desc_t desc = {ZE_STRUCTURE_TYPE_GRAPH_DESC_PROPERTIES,
                             nullptr,
                             format,
-                            serializedIR.size(),
-                            serializedIR.data(),
+                            serializedIR.first,
+                            serializedIR.second.get(),
                             buildFlags.c_str()};
 
     // Create querynetwork handle
@@ -674,15 +692,15 @@ ze_result_t LevelZeroCompilerInDriver<TableExtension>::createGraph(const ze_grap
 template <typename TableExtension>
 template <typename T, std::enable_if_t<!NotSupportGraph2(T), bool>>
 ze_result_t LevelZeroCompilerInDriver<TableExtension>::createGraph(const ze_graph_format_t& format,
-                                                                   const std::vector<uint8_t>& serializedIR,
+                                                                   const SerializedIR& serializedIR,
                                                                    const std::string& buildFlags,
                                                                    const uint32_t& flags,
                                                                    ze_graph_handle_t* graph) const {
     ze_graph_desc_2_t desc = {ZE_STRUCTURE_TYPE_GRAPH_DESC_PROPERTIES,
                               nullptr,
                               format,
-                              serializedIR.size(),
-                              serializedIR.data(),
+                              serializedIR.first,
+                              serializedIR.second.get(),
                               buildFlags.c_str(),
                               flags};
 
@@ -691,11 +709,10 @@ ze_result_t LevelZeroCompilerInDriver<TableExtension>::createGraph(const ze_grap
 }
 
 template <typename TableExtension>
-NetworkDescription LevelZeroCompilerInDriver<TableExtension>::compileIR(const std::shared_ptr<const ov::Model>& model,
-                                                                        IR& irModel,
-                                                                        const Config& config) const {
+NetworkDescription LevelZeroCompilerInDriver<TableExtension>::compile(const std::shared_ptr<const ov::Model>& model,
+                                                                      const Config& config) const {
     _logger.setLevel(config.get<LOG_LEVEL>());
-    _logger.debug("compileIR");
+    _logger.debug("compile");
 
     ze_device_graph_properties_t deviceGraphProperties{};
     auto result = _graphDdiTableExt->pfnDeviceGetGraphProperties(_deviceHandle, &deviceGraphProperties);
@@ -709,7 +726,7 @@ NetworkDescription LevelZeroCompilerInDriver<TableExtension>::compileIR(const st
     }
     ze_graph_compiler_version_info_t& compilerVersion = deviceGraphProperties.compilerVersion;
 
-    auto serializedIR = serializeIR(irModel, compilerVersion);
+    auto serializedIR = serializeIR(model, compilerVersion);
 
     ze_graph_format_t format = ZE_GRAPH_FORMAT_NGRAPH_LITE;
 
@@ -719,7 +736,7 @@ NetworkDescription LevelZeroCompilerInDriver<TableExtension>::compileIR(const st
     buildFlags += " ";
     buildFlags += serializeConfig(config, compilerVersion);
 
-    _logger.debug("compileIR Build flags : %s", buildFlags.c_str());
+    _logger.debug("compile Build flags : %s", buildFlags.c_str());
     // TODO #-30202 Store graph_handle inside NetworkDesc instead of blob. But this will require changes in zeroAPI
 
     // Graph handle should be used only in scope of compile / parse functions.
@@ -732,7 +749,7 @@ NetworkDescription LevelZeroCompilerInDriver<TableExtension>::compileIR(const st
         flags = flags | ZE_GRAPH_FLAG_DISABLE_CACHING;
     }
 
-    _logger.info("compileIR Using extension version: %s", typeid(TableExtension).name());
+    _logger.info("compile Using extension version: %s", typeid(TableExtension).name());
     result = createGraph(format, serializedIR, buildFlags, flags, &graphHandle);
 
     OPENVINO_ASSERT(result == ZE_RESULT_SUCCESS,
@@ -788,26 +805,26 @@ NetworkDescription LevelZeroCompilerInDriver<TableExtension>::compileIR(const st
                        uint64_t(result));
     }
 
-    _logger.debug("compileIR end");
+    _logger.debug("compile end");
     return NetworkDescription(std::move(blob), std::move(networkMeta));
 }
 
 template <typename TableExtension>
-NetworkMetadata LevelZeroCompilerInDriver<TableExtension>::parseBlob(const std::vector<uint8_t>& blob,
-                                                                     const Config& config) const {
-    OV_ITT_TASK_CHAIN(PARSE_BLOB, itt::domains::NPUPlugin, "LevelZeroCompilerInDriver::parseBlob", "desc");
+NetworkMetadata LevelZeroCompilerInDriver<TableExtension>::parse(const std::vector<uint8_t>& network,
+                                                                 const Config& config) const {
+    OV_ITT_TASK_CHAIN(PARSE_BLOB, itt::domains::NPUPlugin, "LevelZeroCompilerInDriver::parse", "desc");
     _logger.setLevel(config.get<LOG_LEVEL>());
     _logger.debug("getNetworkMeta");
     ze_graph_handle_t graphHandle;
 
-    if (!blob.empty()) {
+    if (!network.empty()) {
         _logger.debug("Import network case");
         ze_graph_format_t format = ZE_GRAPH_FORMAT_NATIVE;
         ze_graph_desc_t desc{ZE_STRUCTURE_TYPE_GRAPH_DESC_PROPERTIES,
                              nullptr,
                              format,
-                             blob.size(),
-                             blob.data(),
+                             network.size(),
+                             network.data(),
                              nullptr};
 
         auto result = _graphDdiTableExt->pfnCreate(_context, _deviceHandle, &desc, &graphHandle);
@@ -844,8 +861,8 @@ NetworkMetadata LevelZeroCompilerInDriver<TableExtension>::parseBlob(const std::
 }
 
 template <typename TableExtension>
-uint32_t LevelZeroCompilerInDriver<TableExtension>::getSupportedOpset() const {
-    _logger.debug("getSupportedOpset");
+uint32_t LevelZeroCompilerInDriver<TableExtension>::getSupportedOpsetVersion() const {
+    _logger.debug("getSupportedOpsetVersion");
     ze_device_graph_properties_t graphProperties;
 
     auto result = _graphDdiTableExt->pfnDeviceGetGraphProperties(_deviceHandle, &graphProperties);
@@ -859,7 +876,7 @@ uint32_t LevelZeroCompilerInDriver<TableExtension>::getSupportedOpset() const {
                        uint64_t(result));
     }
     const auto maxOpsetVersion = graphProperties.maxOVOpsetVersionSupported;
-    _logger.info("getSupportedOpset Max supported version of opset in CiD: %d", maxOpsetVersion);
+    _logger.info("getSupportedOpsetVersion Max supported version of opset in CiD: %d", maxOpsetVersion);
     return maxOpsetVersion;
 }
 
diff --git a/src/plugins/intel_npu/src/plugin/CMakeLists.txt b/src/plugins/intel_npu/src/plugin/CMakeLists.txt
index 0e42245306d97d..f071d001d44994 100644
--- a/src/plugins/intel_npu/src/plugin/CMakeLists.txt
+++ b/src/plugins/intel_npu/src/plugin/CMakeLists.txt
@@ -3,6 +3,7 @@
 #
 
 file(GLOB SOURCES "src/*.cpp" "include/*.hpp")
+file(GLOB_RECURSE NPUW_SOURCES "npuw/*.cpp" "npuw/*.hpp")
 
 set(NPU_PLUGIN_TARGET "openvino_intel_npu_plugin")
 set(NPU_PLUGIN_ENGINE_SOURCE_FILE "src/plugin.cpp")
@@ -14,7 +15,7 @@ set(TARGET_NAME ${NPU_PLUGIN_TARGET})
 
 ov_add_plugin(NAME ${NPU_PLUGIN_TARGET}
     DEVICE_NAME ${NPU_DEVICE_NAME}
-    SOURCES ${SOURCES}
+    SOURCES ${SOURCES} ${NPUW_SOURCES}
     VERSION_DEFINES_FOR ${NPU_PLUGIN_ENGINE_SOURCE_FILE}
 )
 
@@ -56,6 +57,8 @@ target_link_libraries(${TARGET_NAME}
 target_include_directories(${TARGET_NAME}
     PRIVATE
         $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
+    PRIVATE
+        $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/>
 )
 target_include_directories(${TARGET_NAME}
     SYSTEM PRIVATE
@@ -63,4 +66,12 @@ target_include_directories(${TARGET_NAME}
         ${NPU_PLUGIN_SOURCE_DIR}/thirdparty/level-zero-ext
 )
 
+if(CMAKE_CXX_COMPILER_ID MATCHES "Clang" OR CMAKE_CXX_COMPILER_ID MATCHES "GNU")
+  target_compile_options(${TARGET_NAME} PRIVATE -mavx2 -mf16c)
+elseif(CMAKE_CXX_COMPILER_ID MATCHES "MSVC")
+  target_compile_options(${TARGET_NAME} PRIVATE /arch:AVX2)
+else()
+  message(AUTHOR_WARNING "Unknown compiler, may miss the AVX2 baseline setting")
+endif()
+
 ov_add_api_validator_post_build_step(TARGET ${NPU_PLUGIN_TARGET})
diff --git a/src/plugins/intel_npu/src/plugin/include/metrics.hpp b/src/plugins/intel_npu/src/plugin/include/metrics.hpp
index 0592e772bfc0b9..9b1a8be694d33a 100644
--- a/src/plugins/intel_npu/src/plugin/include/metrics.hpp
+++ b/src/plugins/intel_npu/src/plugin/include/metrics.hpp
@@ -35,6 +35,8 @@ class Metrics final {
     uint64_t GetDeviceTotalMemSize(const std::string& specifiedDeviceName) const;
     uint32_t GetDriverVersion() const;
     uint32_t GetDriverExtVersion() const;
+    uint32_t GetSteppingNumber(const std::string& specifiedDeviceName) const;
+    uint32_t GetMaxTiles(const std::string& specifiedDeviceName) const;
     ov::device::PCIInfo GetPciInfo(const std::string& specifiedDeviceName) const;
     std::map<ov::element::Type, float> GetGops(const std::string& specifiedDeviceName) const;
     ov::device::Type GetDeviceType(const std::string& specifiedDeviceName) const;
diff --git a/src/plugins/intel_npu/src/plugin/npuw/accuracy/comparator.cpp b/src/plugins/intel_npu/src/plugin/npuw/accuracy/comparator.cpp
new file mode 100644
index 00000000000000..7eb4e82e202804
--- /dev/null
+++ b/src/plugins/intel_npu/src/plugin/npuw/accuracy/comparator.cpp
@@ -0,0 +1,74 @@
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "comparator.hpp"
+
+#include <algorithm>
+#include <openvino/runtime/make_tensor.hpp>
+
+#include "../logging.hpp"
+#include "../util.hpp"
+
+ov::npuw::metrics::NRMSE::NRMSE(double threshold) : m_threshold(threshold) {}
+
+bool ov::npuw::metrics::NRMSE::operator()(const ov::SoPtr<ov::ITensor>& actual,
+                                          const ov::SoPtr<ov::ITensor>& reference) const {
+    NPUW_ASSERT(actual->is_continuous());
+    NPUW_ASSERT(reference->is_continuous());
+    NPUW_ASSERT(actual->get_shape() == reference->get_shape());
+    // Check for alignment:
+    NPUW_ASSERT(actual->get_byte_size() == reference->get_byte_size());
+    // FIXME: Check for strides
+
+    ov::Tensor actual_f32;
+    ov::Tensor reference_f32;
+
+    if (ov::element::Type_t::f32 == actual->get_element_type()) {
+        actual_f32 = ov::make_tensor(actual);
+    } else {
+        ov::Tensor dst(ov::element::Type_t::f32, actual->get_shape());
+        ov::npuw::util::to_f32(ov::make_tensor(actual), dst);
+        actual_f32 = dst;
+    }
+
+    if (ov::element::Type_t::f32 == reference->get_element_type()) {
+        reference_f32 = ov::make_tensor(reference);
+    } else {
+        ov::Tensor dst(ov::element::Type_t::f32, reference->get_shape());
+        ov::npuw::util::to_f32(ov::make_tensor(reference), dst);
+        reference_f32 = dst;
+    }
+
+    float* actual_data = actual_f32.data<float>();
+    float* reference_data = reference_f32.data<float>();
+    const std::size_t size = actual_f32.get_size();
+
+    double squared_error{};
+    for (size_t i = 0; i < size; ++i) {
+        double diff = (actual_data[i] - reference_data[i]);
+        squared_error += (diff * diff);
+    }
+
+    if (squared_error <= std::numeric_limits<double>::epsilon()) {
+        LOG_INFO("NRMSE loss: 0.0, threshold: " << m_threshold << ".");
+        LOG_INFO("PASS");
+        return true;
+    }
+
+    double rmse = sqrt(squared_error / size);
+    NPUW_ASSERT(rmse >= 0.0);
+
+    auto actual_min_max = std::minmax_element(actual_data, actual_data + size);
+    auto reference_min_max = std::minmax_element(reference_data, reference_data + size);
+    double den = std::max({0.001f,
+                           std::max(0.f, *reference_min_max.second) - std::min(0.f, *reference_min_max.first),
+                           std::max(0.f, *actual_min_max.second) - std::min(0.f, *actual_min_max.first)});
+
+    double nrmse = rmse / den;
+    LOG_INFO("NRMSE loss: " << nrmse << ", threshold: " << m_threshold << ".");
+
+    bool success = nrmse <= m_threshold;
+    LOG_INFO((success ? "PASS" : "FAIL"));
+    return success;
+}
diff --git a/src/plugins/intel_npu/src/plugin/npuw/accuracy/comparator.hpp b/src/plugins/intel_npu/src/plugin/npuw/accuracy/comparator.hpp
new file mode 100644
index 00000000000000..e77a38ced0edc2
--- /dev/null
+++ b/src/plugins/intel_npu/src/plugin/npuw/accuracy/comparator.hpp
@@ -0,0 +1,26 @@
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "../common.hpp"
+#include "openvino/openvino.hpp"
+#include "openvino/runtime/icompiled_model.hpp"
+#include "openvino/runtime/so_ptr.hpp"
+
+namespace ov {
+namespace npuw {
+namespace metrics {
+class NRMSE {
+public:
+    explicit NRMSE(double threshold);
+    bool operator()(const ov::SoPtr<ov::ITensor>& backup_tensor, const ov::SoPtr<ov::ITensor>& original_tensor) const;
+
+private:
+    double m_threshold{};
+};
+
+}  // namespace metrics
+}  // namespace npuw
+}  // namespace ov
diff --git a/src/plugins/intel_npu/src/plugin/npuw/base_sync_infer_request.cpp b/src/plugins/intel_npu/src/plugin/npuw/base_sync_infer_request.cpp
new file mode 100644
index 00000000000000..a1fd1952b7c5b1
--- /dev/null
+++ b/src/plugins/intel_npu/src/plugin/npuw/base_sync_infer_request.cpp
@@ -0,0 +1,281 @@
+// Copyright (C) 2023-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "base_sync_infer_request.hpp"
+
+#include "compiled_model.hpp"
+#include "intel_npu/al/config/npuw.hpp"
+#include "logging.hpp"
+#include "util.hpp"
+
+ov::npuw::IBaseInferRequest::IBaseInferRequest(const std::shared_ptr<ov::npuw::CompiledModel>& compiled_model)
+    : ov::ISyncInferRequest(compiled_model),
+      m_npuw_model(compiled_model),
+      m_num_submodels(m_npuw_model->m_compiled_submodels.size()) {
+    m_subrequests.resize(m_num_submodels, {});
+    m_subrequest_devices.resize(m_num_submodels, {});
+    m_completion_cbs.resize(m_num_submodels, {});
+    if (m_npuw_model->m_acc_check) {
+        m_ref_subrequests.resize(m_num_submodels);
+    }
+}
+
+ov::npuw::IBaseInferRequest::RqPtrs ov::npuw::IBaseInferRequest::create_infer_requests(std::size_t id,
+                                                                                       std::size_t nireq,
+                                                                                       bool* recompiled) {
+    NPUW_ASSERT(nireq > 0);
+    RqPtrs rqs;
+    rqs.reserve(nireq);
+
+    // See explanation in the class definition
+    auto& comp_model_desc = m_npuw_model->m_compiled_submodels[id];
+    NPUW_ASSERT(comp_model_desc.replaced_by.value_or(id) == id);
+
+    bool successful = false;
+    bool can_try_again = true;
+
+    // Altering iterators here!! Contracts should be changed!
+    while (!successful && can_try_again) {
+        bool should_recompile = false;
+        try {
+            // FIXME: As the model may recompile, reference
+            // shouldn't be lifted from the loop
+            auto& comp_model = comp_model_desc.compiled_model;
+            rqs.clear();
+            for (std::size_t i = 0u; i < nireq; i++) {
+                rqs.emplace_back(comp_model->create_infer_request(), comp_model._so);
+            }
+            successful = true;
+        } catch (const std::exception& ex) {
+            LOG_WARN("Subgraph [" << id << "] - Failed to create infer request:" << std::endl << ex.what());
+            should_recompile = true;
+        } catch (...) {
+            LOG_WARN("Subgraph [" << id << "] - Failed to create infer request: REASON UNKNOWN");
+            should_recompile = true;
+        }
+        if (should_recompile) {
+            LOG_INFO("- Trying next device...");
+            comp_model_desc.device_it++;
+            can_try_again = m_npuw_model->compile_for_success(id);
+            if (can_try_again) {
+                if (recompiled)
+                    *recompiled = true;
+                // Probably shouldn't be called all the time, but only if
+                // I/O submodel is affected
+                m_npuw_model->reset_io();
+            }
+        }
+    }  // while(!new_ireq && can_try_again)
+    if (!successful) {
+        OPENVINO_THROW("NPUW: Fatal - couldn't create infer request for Subgraph[", id, "]");
+    }
+    NPUW_ASSERT(rqs.size() == nireq);
+
+    // TODO: Support creation and return of multiple infer requests
+    if (m_npuw_model->m_acc_check && m_ref_subrequests.at(id) == nullptr) {
+        if (nireq > 1) {
+            OPENVINO_THROW("NPUW: TEMPORARY LIMITATION: Couldn't create reference infer "
+                           "requests if 'nireq' is set to > 1!");
+        }
+        LOG_INFO("Create reference subrequest for submodel [" << id << "] on " << m_npuw_model->m_ref_device << "...");
+        LOG_BLOCK();
+        if (m_npuw_model->submodel_device(id) != m_npuw_model->m_ref_device) {
+            auto& ref_submodel = m_npuw_model->m_compiled_submodels.at(id).ref_compiled_model;
+            ov::SoPtr<ov::IAsyncInferRequest> ref_infer_request = {ref_submodel->create_infer_request(),
+                                                                   ref_submodel._so};
+            NPUW_ASSERT(ref_infer_request);
+            m_ref_subrequests.at(id) = std::move(ref_infer_request);
+            LOG_INFO("Done");
+        } else {
+            LOG_INFO("Skip creation of reference subrequest for submodule["
+                     << id << "] on reference device: " << m_npuw_model->m_ref_device << ", as actual subrequest ["
+                     << id << "] has been already created on "
+                     << "it .");
+        }
+    }
+
+    return rqs;
+}
+
+void ov::npuw::IBaseInferRequest::ensure_subrequest_is_accurate(std::size_t idx, bool& failover) {
+    LOG_INFO("Check if subrequest[" << idx << "] is accurate...");
+    LOG_BLOCK();
+    failover = false;
+    if (m_ref_subrequests.at(idx) != nullptr && m_subrequests.at(idx)._ptr != m_ref_subrequests.at(idx)._ptr) {
+        NPUW_ASSERT(m_npuw_model->m_compiled_submodels.at(idx).switched_to_ref == false);
+        NPUW_ASSERT(m_npuw_model->m_compiled_submodels.at(idx).replaced_by.value_or(idx) == idx);
+
+        const auto& ref_comp_model = m_ref_subrequests.at(idx)->get_compiled_model();
+        const auto& actual_comp_model = m_subrequests.at(idx)->get_compiled_model();
+        NPUW_ASSERT(actual_comp_model->inputs().size() == ref_comp_model->inputs().size());
+        // Setting inputs:
+        for (size_t i = 0; i < actual_comp_model->inputs().size(); i++) {
+            const auto& itensor = m_subrequests.at(idx)->get_tensor(actual_comp_model->inputs()[i]);
+            m_ref_subrequests.at(idx)->set_tensor(ref_comp_model->inputs()[i], itensor);
+        }
+        m_ref_subrequests.at(idx)->infer();
+
+        LOG_INFO("Compare actual outputs against references:");
+        bool tensors_converge = true;
+        for (size_t i = 0; i < actual_comp_model->outputs().size(); i++) {
+            LOG_INFO(" - " << actual_comp_model->outputs()[i]);
+            const auto& actual_tensor = m_subrequests.at(idx)->get_tensor(actual_comp_model->outputs()[i]);
+            const auto& ref_tensor = m_ref_subrequests.at(idx)->get_tensor(ref_comp_model->outputs()[i]);
+            LOG_BLOCK();
+            tensors_converge &= m_npuw_model->m_acc_check(actual_tensor, ref_tensor);
+        }
+        LOG_INFO((tensors_converge ? "PASS" : "FAIL"));
+
+        if (!tensors_converge) {
+            LOG_INFO("Subrequest is inaccurate, failover to reference.");
+            // FIXME: We need to copy reference tensors to actual only in single-model-inference mode
+            //        or if our subgraph is last in the chain.
+            for (size_t i = 0; i < actual_comp_model->outputs().size(); i++) {
+                const auto& actual_tensor = m_subrequests.at(idx)->get_tensor(actual_comp_model->outputs()[i]);
+                const auto& ref_tensor = m_ref_subrequests.at(idx)->get_tensor(ref_comp_model->outputs()[i]);
+                ref_tensor->copy_to(actual_tensor._ptr);
+            }
+            m_npuw_model->m_compiled_submodels.at(idx).compiled_model =
+                m_npuw_model->m_compiled_submodels.at(idx).ref_compiled_model;
+            m_npuw_model->m_compiled_submodels.at(idx).switched_to_ref = true;
+            m_subrequests.at(idx) = m_ref_subrequests.at(idx);
+            update_subrequest_links(idx);
+            failover = true;
+        }
+
+        LOG_INFO("Done");
+    } else {
+        LOG_INFO("Skipped, subrequest is launched on reference device.");
+    }
+}
+
+ov::SoPtr<ov::ITensor> ov::npuw::IBaseInferRequest::get_tensor(const ov::Output<const ov::Node>& port) const {
+    // assert(persistent)
+    return m_port_to_tensor.at(port).tensor;
+}
+
+void ov::npuw::IBaseInferRequest::set_tensor(const ov::Output<const ov::Node>& port,
+                                             const ov::SoPtr<ov::ITensor>& tensor) {
+    // Assigning via .at() to ensure it is a known port
+    // assert(persistent)
+    m_port_to_tensor.at(port).tensor = tensor;
+}
+
+std::vector<ov::SoPtr<ov::ITensor>> ov::npuw::IBaseInferRequest::get_tensors(
+    const ov::Output<const ov::Node>& port) const {
+    return {};  // NB: Comment why it is empty, and not { get_tensor(port); }
+}
+
+void ov::npuw::IBaseInferRequest::set_tensors(const ov::Output<const ov::Node>&,
+                                              const std::vector<ov::SoPtr<ov::ITensor>>&) {
+    OPENVINO_NOT_IMPLEMENTED;
+}
+
+void ov::npuw::IBaseInferRequest::check_tensors() const {
+    // Ignore `check_tensor` of inputs and outputs of Hetero Compiled Model because
+    // `m_tensors` are not allocated
+    return;
+}
+
+void ov::npuw::IBaseInferRequest::infer() {
+    prepare_for_infer();
+    bool failover_happened = false;
+    for (std::size_t idx = 0u; idx < m_num_submodels; idx++) {
+        if (!valid_subrequest(idx)) {
+            continue;
+        }
+        subscribe_subrequest(idx, [](std::exception_ptr) {});
+        bool failover = false;
+        run_subrequest_for_success(idx, failover);
+        failover_happened |= failover;
+        complete_subrequest(idx);
+        if (m_npuw_model->m_acc_check) {
+            ensure_subrequest_is_accurate(idx, failover);
+            failover_happened |= failover;
+        }
+    }
+
+    // Increment counter regardless if dumps etc are enabled or not.
+    m_run_iter++;
+
+    if (failover_happened) {
+        LOG_INFO("Refined device distribution:");
+        LOG_BLOCK();
+        m_npuw_model->log_device_dist();
+    }
+}
+
+void ov::npuw::IBaseInferRequest::dump_input_tensors(std::size_t idx) {
+    auto real_idx = m_npuw_model->m_compiled_submodels[idx].replaced_by.value_or(idx);
+    const auto& comp_submodel = m_npuw_model->m_compiled_submodels[real_idx].compiled_model;
+
+    // Note: keep using the absolute `idx` for identififaction and printing
+    // Note:
+    // - _name is used for the user option (no leading 00s for indices)
+    // - _path is used for disk dump (will have leading 00s for indices)
+    const auto comp_submodel_name = subgr_name(idx);
+    const auto comp_submodel_path = m_npuw_model->m_name + subgr_path_suffix(idx) + iter_path_suffix(idx);
+
+    const std::string dump_ios_opt = m_npuw_model->m_cfg.get<::intel_npu::NPUW_DUMP_IO>();
+    if (ov::npuw::util::is_set(idx, dump_ios_opt)) {
+        std::vector<std::string> in_base_names;
+        for (std::size_t i = 0u, num_inputs = comp_submodel->inputs().size(); i < num_inputs; i++) {
+            const auto& port = comp_submodel->inputs()[i];
+            const auto& tnsr = m_subrequests[real_idx]->get_tensor(port);
+            std::string in_base_name = comp_submodel_path + "_input_" + ov::npuw::util::fmt(i, num_inputs);
+            ov::npuw::dump_tensor(tnsr, in_base_name);
+            in_base_names.push_back(std::move(in_base_name));
+        }
+        ov::npuw::dump_input_list(comp_submodel_path, in_base_names);
+    }
+}
+
+void ov::npuw::IBaseInferRequest::dump_output_tensors(std::size_t idx) {
+    auto real_idx = m_npuw_model->m_compiled_submodels[idx].replaced_by.value_or(idx);
+    const auto& comp_submodel = m_npuw_model->m_compiled_submodels[real_idx].compiled_model;
+
+    // Note: keep using the absolute `idx` for identififaction and printing
+    // Note:
+    // - _name is used for the user option (no leading 00s for indices)
+    // - _path is used for disk dump (will have leading 00s for indices)
+    // FIXME: Duplication is evil
+    const auto comp_submodel_name = subgr_name(idx);
+    const auto comp_submodel_path = m_npuw_model->m_name + subgr_path_suffix(idx) + iter_path_suffix(idx);
+
+    const std::string dump_ios_opt = m_npuw_model->m_cfg.get<::intel_npu::NPUW_DUMP_IO>();
+    if (ov::npuw::util::is_set(idx, dump_ios_opt)) {
+        std::vector<std::string> out_base_names;
+        for (std::size_t i = 0u, num_outputs = comp_submodel->outputs().size(); i < num_outputs; i++) {
+            const auto& port = comp_submodel->outputs()[i];
+            const auto& tnsr = m_subrequests[real_idx]->get_tensor(port);
+            std::string out_base_name = comp_submodel_path + "_output_" + ov::npuw::util::fmt(i, num_outputs);
+            ov::npuw::dump_tensor(tnsr, out_base_name);
+            out_base_names.push_back(std::move(out_base_name));
+        }
+        ov::npuw::dump_output_list(comp_submodel_path, out_base_names);
+    }
+}
+
+std::string ov::npuw::IBaseInferRequest::subgr_name(std::size_t idx) const {
+    return m_npuw_model->m_name + "_" + std::to_string(idx);
+}
+
+std::string ov::npuw::IBaseInferRequest::subgr_path_suffix(std::size_t idx) const {
+    return "_" + ov::npuw::util::fmt(idx, m_npuw_model->m_compiled_submodels.size());
+}
+
+std::string ov::npuw::IBaseInferRequest::iter_path_suffix(std::size_t idx) const {
+    // Check if per-iter dump is required - and provide the suffix if necessary
+    if (!m_iter_suffix_required.has_value()) {
+        m_iter_suffix_required = m_npuw_model->m_cfg.get<::intel_npu::NPUW_DUMP_IO_ITERS>();
+    }
+
+    if (!m_iter_suffix_required.value()) {
+        return "";  // no need to dump individual iterations - keep suffix empty
+    }
+
+    // Hope alignment to 4 digits is fine to this case (no problem if this number
+    // is exceeded)
+    return "_iter_" + ov::npuw::util::fmt(m_run_iter, 1000);
+}
diff --git a/src/plugins/intel_npu/src/plugin/npuw/base_sync_infer_request.hpp b/src/plugins/intel_npu/src/plugin/npuw/base_sync_infer_request.hpp
new file mode 100644
index 00000000000000..41bdf2368c2418
--- /dev/null
+++ b/src/plugins/intel_npu/src/plugin/npuw/base_sync_infer_request.hpp
@@ -0,0 +1,112 @@
+// Copyright (C) 2023-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <functional>
+#include <map>
+#include <memory>
+#include <optional>
+#include <string>
+#include <vector>
+
+#include "openvino/runtime/iasync_infer_request.hpp"
+#include "openvino/runtime/isync_infer_request.hpp"
+#include "openvino/runtime/so_ptr.hpp"
+#include "perf.hpp"
+
+namespace ov {
+namespace npuw {
+
+class CompiledModel;
+
+// This interface is provided to npuw::AsyncInferRequest to manage the
+// individual subrequests' execution
+class IBaseInferRequest : public ov::ISyncInferRequest {
+public:
+    explicit IBaseInferRequest(const std::shared_ptr<ov::npuw::CompiledModel>&);
+
+    // Execution API - explicitly "finalize" the infer() here
+    void infer() override;  // final - not final yet
+
+    // I/O APIs - supply default implementations
+    ov::SoPtr<ov::ITensor> get_tensor(const ov::Output<const ov::Node>& port) const override;
+    void set_tensor(const ov::Output<const ov::Node>& port, const ov::SoPtr<ov::ITensor>& tensor) override;
+
+    std::vector<ov::SoPtr<ov::ITensor>> get_tensors(const ov::Output<const ov::Node>& port) const override;
+    void set_tensors(const ov::Output<const ov::Node>& port,
+                     const std::vector<ov::SoPtr<ov::ITensor>>& tensors) override;
+
+    void check_tensors() const override;
+
+    using sptr = std::shared_ptr<IBaseInferRequest>;
+    using Completed = std::function<void(std::exception_ptr)>;
+
+    virtual void prepare_for_infer() = 0;
+    virtual bool valid_subrequest(std::size_t idx) const = 0;  // FIXME: Get rid of this!
+    virtual void start_subrequest(std::size_t idx) = 0;
+    virtual void subscribe_subrequest(std::size_t idx, Completed cb) = 0;
+    virtual void run_subrequest_for_success(std::size_t idx, bool& failover) = 0;
+    virtual void complete_subrequest(std::size_t idx) = 0;
+    virtual void cancel_subrequest(std::size_t idx) = 0;
+    virtual std::size_t total_subrequests() const = 0;
+    virtual bool supports_async_pipeline() const = 0;
+
+protected:
+    using RqPtr = ov::SoPtr<ov::IAsyncInferRequest>;
+    using RqPtrs = std::vector<RqPtr>;
+
+    // This method can only be called for regular subgraphs or
+    // function bodies. Function calls are not allowed to have
+    // their inference requests anymore - they must be stored
+    // only once in the subrequests list
+    RqPtrs create_infer_requests(std::size_t id, size_t nireq = 1, bool* recompiled = nullptr);
+    void ensure_subrequest_is_accurate(std::size_t idx, bool& failover);
+    virtual void update_subrequest_links(std::size_t idx) = 0;
+
+    std::shared_ptr<ov::npuw::CompiledModel> m_npuw_model;
+    std::vector<IBaseInferRequest::Completed> m_completion_cbs;
+    RqPtrs m_subrequests;
+    std::vector<std::string> m_subrequest_devices;
+
+    // Permanent storage for input & output tensors
+    // FIXME: Currently is initialized in subclasses. Likely this
+    // initialization should be moved here, to the base class?
+    std::vector<ov::SoPtr<ov::ITensor>> m_input_tensors;
+    std::vector<ov::SoPtr<ov::ITensor>> m_output_tensors;
+
+    struct TensorStorage {
+        ov::SoPtr<ov::ITensor> tensor;
+        bool persistent = false;       // true for the parent I/O tensors
+        std::size_t num_readers = 0u;  // fixed during execution
+        std::size_t num_reads = 0u;    // changes during execution (ref-counter-like).
+                                       // reset to 0 before every new execution
+    };
+    // FROM(Every subrequests' output port) TO(Its output tensor)
+    std::map<ov::Output<const ov::Node>, TensorStorage> m_port_to_tensor;
+
+    const std::size_t m_num_submodels;
+
+    void dump_input_tensors(std::size_t idx);
+    void dump_output_tensors(std::size_t idx);
+
+    // Quick-and-dirty profiling
+    ov::npuw::perf::metric<float, ov::npuw::perf::MSec> m_ms_unpack;
+
+    // Various name/dump formatting methods
+    // TODO: These methods should probably go to CompiledModel
+    std::string subgr_name(std::size_t idx) const;
+    std::string subgr_path_suffix(std::size_t idx) const;
+
+    // TODO: And this should probably go to some TensorDumper, etc
+    // if we go over-designing the things.
+    std::string iter_path_suffix(std::size_t idx) const;
+    mutable std::optional<bool> m_iter_suffix_required;
+    std::size_t m_run_iter = 0;
+
+    RqPtrs m_ref_subrequests;
+};
+
+}  // namespace npuw
+}  // namespace ov
diff --git a/src/plugins/intel_npu/src/plugin/npuw/common.hpp b/src/plugins/intel_npu/src/plugin/npuw/common.hpp
new file mode 100644
index 00000000000000..eaaeb0c12006bc
--- /dev/null
+++ b/src/plugins/intel_npu/src/plugin/npuw/common.hpp
@@ -0,0 +1,18 @@
+// Copyright (C) 2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <string>
+#include <unordered_map>
+
+#include "openvino/openvino.hpp"
+
+namespace ov {
+namespace npuw {
+
+using DeviceProperties = std::unordered_map<std::string, ov::AnyMap>;
+
+}  // namespace npuw
+}  // namespace ov
diff --git a/src/plugins/intel_npu/src/plugin/npuw/compiled_model.cpp b/src/plugins/intel_npu/src/plugin/npuw/compiled_model.cpp
new file mode 100644
index 00000000000000..780dcc9700ce87
--- /dev/null
+++ b/src/plugins/intel_npu/src/plugin/npuw/compiled_model.cpp
@@ -0,0 +1,746 @@
+// Copyright (C) 2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+#include "compiled_model.hpp"
+
+#include <iostream>
+#include <memory>
+
+#include "accuracy/comparator.hpp"
+#include "just_sync_infer_request.hpp"
+#include "logging.hpp"
+#include "npu_private_properties.hpp"
+#include "openvino/core/parallel.hpp"
+#include "openvino/op/util/op_types.hpp"
+#include "openvino/pass/constant_folding.hpp"
+#include "openvino/pass/manager.hpp"
+#include "openvino/runtime/device_id_parser.hpp"
+#include "openvino/runtime/internal_properties.hpp"
+#include "openvino/runtime/properties.hpp"
+#include "openvino/util/common_util.hpp"
+#include "plugin.hpp"
+#include "util.hpp"
+
+// required for get_properties_per_device()
+#include <intel_npu/al/config/config.hpp>
+#include <intel_npu/al/config/npuw.hpp>
+#include <npuw_private_properties.hpp>
+
+#include "openvino/runtime/device_id_parser.hpp"
+#include "openvino/runtime/internal_properties.hpp"
+#include "openvino/runtime/properties.hpp"
+
+namespace {
+void split_properties(const ov::AnyMap& properties,
+                      ov::AnyMap& npu_plugin_properties,
+                      ov::AnyMap& npuw_path_properties) {
+    for (auto it = properties.begin(); it != properties.end(); ++it) {
+        if (it->first.find("NPUW") != it->first.npos) {
+            npuw_path_properties.insert(*it);
+        } else {
+            npu_plugin_properties.insert(*it);
+        }
+    }
+}
+
+std::map<std::string, std::string> any_copy(const ov::AnyMap& params) {
+    std::map<std::string, std::string> result;
+    for (auto&& value : params) {
+        result.emplace(value.first, value.second.as<std::string>());
+    }
+    return result;
+}
+}  // anonymous namespace
+
+namespace ov {
+namespace npuw {
+namespace {
+ov::npuw::DeviceProperties get_properties_per_device(const std::shared_ptr<const ov::IPlugin>& plugin,
+                                                     const std::string& device_priorities,
+                                                     const ov::AnyMap& properties) {
+    auto core = plugin->get_core();
+    auto device_names = ov::DeviceIDParser::get_hetero_devices(device_priorities);
+    DeviceProperties device_properties;
+    for (const auto& device_name : device_names) {
+        auto properties_it = device_properties.find(device_name);
+        if (device_properties.end() == properties_it) {
+            device_properties[device_name] = core->get_supported_property(device_name, properties);
+
+            // Do extra handling for the private NPU options
+            if (ov::npuw::util::starts_with(device_name, "NPU")) {
+                for (auto&& opt : properties) {
+                    if (ov::npuw::util::starts_with(opt.first, "NPU")) {
+                        device_properties[device_name][opt.first] = opt.second;
+                    }
+                }
+            }  // if(NPU)
+        }
+    }
+    return device_properties;
+}
+}  // anonymous namespace
+}  // namespace npuw
+}  // namespace ov
+
+ov::npuw::CompiledModel::CompiledModel(const std::shared_ptr<ov::Model>& model,
+                                       const std::shared_ptr<const ov::IPlugin>& plugin,
+                                       const ov::AnyMap& properties)
+    : ov::ICompiledModel(model, plugin),
+      m_options_desc(std::make_shared<::intel_npu::OptionsDesc>()),
+      m_cfg(m_options_desc),
+      m_name(model->get_friendly_name()),
+      m_loaded_from_cache(false) {
+    ::intel_npu::registerNPUWOptions(*m_options_desc);
+
+    std::map<std::string, ov::Any> npuw_props;
+    split_properties(properties, m_non_npuw_props, npuw_props);
+
+    m_cfg.parseEnvVars();
+    m_cfg.update(any_copy(npuw_props));
+
+    const std::string dev_list_str = m_cfg.get<::intel_npu::NPUW_DEVICES>();
+    m_dev_list = ov::DeviceIDParser::get_hetero_devices(dev_list_str);
+    m_meta_devices = ov::npuw::get_properties_per_device(plugin, dev_list_str, m_non_npuw_props);
+
+    const bool acc_check_opt = m_cfg.get<::intel_npu::NPUW_ACC_CHECK>();
+    if (acc_check_opt) {
+        const double threshold_opt = m_cfg.get<::intel_npu::NPUW_ACC_THRESH>();
+
+        m_acc_check = metrics::NRMSE(threshold_opt);
+        m_ref_device = m_cfg.getString<::intel_npu::NPUW_ACC_DEVICE>();
+        LOG_INFO("Accuracy check is enabled.");
+    }
+
+    LOG_VERB("*** Original model ***");
+    const auto& orig_parameters = model->get_parameters();
+    {
+        LOG_BLOCK();
+        for (auto&& p : orig_parameters) {
+            LOG_VERB(p);
+        }
+    }
+    const auto& orig_results = model->get_results();
+    {
+        LOG_BLOCK();
+        for (auto&& r : orig_results) {
+            LOG_VERB(r);
+        }
+    }
+
+    auto partitioning = getPartitioning(model, m_cfg);
+    m_total_stat.gflops = partitioning.total_gflops;
+    m_total_stat.ops = partitioning.total_ops;
+    const std::vector<ov::npuw::Subgraph>& orderedSubgraphs = partitioning.subgraphs;
+
+    // Prepare mapping between original inputs/outputs and compiled
+    // submodels inputs/outputs. Example:
+    // original input 0 -> submodel 0 input 0,
+    // original input 1 -> submodel 1 input 0,
+    // original output 0 -> submodel 1 output 0.
+    //
+    // Note that several partitioning schemes can result in multiple
+    // submodels reading from the same parameter. Unfortunately, the
+    // OpenVINO plugin model assumes there's just one tensor for this
+    // case, but in fact there's many. See how this case is handled
+    // below.
+    //
+    // Also, now some subgraphs may be (reusable) functions.
+    // If a function reads a parameter, it can be be set only once
+    // before the inference.
+    m_inputs_to_submodels_inputs.resize(orig_parameters.size(), NO_LINK);
+    m_outputs_to_submodels_outputs.resize(orig_results.size(), NO_LINK);
+
+    for (size_t id = 0; id < orderedSubgraphs.size(); id++) {
+        LOG_VERB("Subgraph[" << id << "]");
+        LOG_BLOCK();
+        if (orderedSubgraphs[id]._optimized_out) {
+            LOG_VERB("OPTIMIZED OUT");
+            continue;
+        }
+        auto process_params = [&](const ov::ParameterVector& _parameters) {
+            for (size_t i = 0; i < _parameters.size(); i++) {
+                LOG_VERB(_parameters[i]);
+                for (size_t j = 0; j < orig_parameters.size(); j++) {
+                    if (_parameters[i] == orig_parameters[j]) {
+                        LOG_BLOCK();
+                        LOG_VERB("MATCHED WITH " << orig_parameters[j]);
+                        if (NO_LINK == m_inputs_to_submodels_inputs[j]) {
+                            // Easy case, first come first served
+                            LOG_VERB("Map this parameter directly");
+                            m_inputs_to_submodels_inputs[j] = ToSubmodel{id, i};
+                        } else {
+                            // There's multiple subgraphs reading from the same Parameter.
+                            // Record them here, see how this list is later used in the
+                            // sync_infer_request.cpp
+                            LOG_VERB("Subscribe this parameter to tensor");
+                            m_param_subscribers[j].push_back(ToSubmodel{id, i});
+                        }  // if(NO_LINK)
+                        // Regardless of the order, if the reader is a function,
+                        // remember that to avoid confusion in function prologue
+                    }  // if(param == orig_param)
+                }      // for(orig_params)
+            }          // for(subgraph_params)
+        };
+        auto process_results = [&](const ov::ResultVector& _results) {
+            for (size_t i = 0; i < _results.size(); i++) {
+                if (!_results[i]) {
+                    // See the rearrange_to_function_protocol<>()...
+                    continue;
+                }
+                LOG_VERB(_results[i]);
+                for (size_t j = 0; j < orig_results.size(); j++) {
+                    if (_results[i] == orig_results[j]) {
+                        // FIXME: There may be a problem, see below (!)
+                        LOG_BLOCK();
+                        LOG_VERB("MATCHED WITH " << orig_results[j]);
+                        m_outputs_to_submodels_outputs[j] = {id, i};
+                    }
+                }
+            }  // for(results)
+        };
+        // Sheer ugliness here again
+        if (orderedSubgraphs[id]._funcall.empty()) {
+            process_params(orderedSubgraphs[id]._parameters);
+            process_results(orderedSubgraphs[id]._results);
+        } else {
+            // Match via the original parameters to generate indices
+            process_params(orderedSubgraphs[id]._parameters);
+            process_results(orderedSubgraphs[id]._results);
+
+            // (!) Problem here: when functions are folded, in the
+            // KVcache-like scenarios (where every function call
+            // produces its own result), its result(s) will be matched
+            // with _all_ instances of the result (see pic).
+        }
+    }  // for(ordered_subgraphs)
+    // NOTE(dm): there's a better way to do it, like we do in G-API backends.
+
+    // Store mapping between manually splitted inputs/outputs
+    // to connect tensors between compiled submodels
+    m_submodels_input_to_prev_output = partitioning.input_to_prev_output;
+
+    // Before the compilation:
+    // - initialize the OV models
+    // - dump the subgraphs, if necessary
+    std::map<std::string, std::size_t> compiledFunctions;
+    m_compiled_submodels.resize(orderedSubgraphs.size());
+
+    const std::string dump_sub_opt = m_cfg.get<::intel_npu::NPUW_DUMP_SUBS>();
+
+    LOG_INFO("Creating submodels...");
+    for (std::size_t id = 0u; id < orderedSubgraphs.size(); id++) {
+        LOG_BLOCK();
+
+        const auto& subgraph = orderedSubgraphs[id];
+        m_compiled_submodels[id].stat.gflops = subgraph._gflops;
+        m_compiled_submodels[id].stat.ops = subgraph._ops;
+
+        if (subgraph._optimized_out) {
+            // FIXME: filter out the optimized out subgraph from this process early,
+            // only dispatch real compilation to the threads
+            LOG_INFO("Skipping Subgraph[" << id << "] - optimized out!");
+            continue;
+        }
+        LOG_INFO("Creating Subgraph[" << id << "]...");
+        if (subgraph._funcall.empty()) {
+            // NOT a function call - an easy case!
+            m_compiled_submodels[id].model = std::make_shared<ov::Model>(subgraph._results,
+                                                                         subgraph._sinks,
+                                                                         subgraph._parameters,
+                                                                         m_name + '_' + std::to_string(id));
+        } else {
+            LOG_BLOCK();
+            auto& fcn_template = partitioning.functions.at(subgraph._funcall);
+            auto compiled_fcn_iter = compiledFunctions.find(subgraph._funcall);
+            if (compiled_fcn_iter == compiledFunctions.end()) {
+                // A function call: store the model for function call only once...
+                compiledFunctions.insert({subgraph._funcall, id});
+                m_compiled_submodels[id].model = fcn_template._model;
+                m_compiled_submodels[id].replaced_by = id;  // FIXME: UGLY
+                LOG_INFO("Subgraph[" << id << "] is a function body for " << subgraph._funcall);
+            } else {
+                // ...and refer to it in other calls
+                m_compiled_submodels[id].replaced_by = compiled_fcn_iter->second;
+                LOG_INFO("Subgraph[" << id << "] is a function call to [" << compiled_fcn_iter->second << "]");
+            }
+            m_compiled_submodels[id].param_base = fcn_template._param_offset;
+            m_compiled_submodels[id].closure = subgraph._closure;
+            m_compiled_submodels[id].scales = subgraph._scales;
+            m_compiled_submodels[id].zerops = subgraph._zerops;
+        }  // if(!funcall)
+
+        if (!m_compiled_submodels[id].model && !m_compiled_submodels[id].replaced_by) {
+            OPENVINO_THROW("Fatal: submodel ", id, " is neither a model nor a function call!");
+        }
+        const std::size_t real_id = m_compiled_submodels[id].replaced_by.value_or(id);
+
+        // FIXME: a hotfix for a crash where we have too many names in submodel's output
+        // Do it just once if that's a function
+        if (real_id == id) {
+            remove_long_output_names(m_compiled_submodels[real_id].model);
+        }
+
+        if (ov::npuw::util::is_set(id, dump_sub_opt)) {
+            LOG_INFO("Dumping Subgraph[" << id << "]");
+            LOG_BLOCK();
+            if (real_id != id) {
+                LOG_INFO("NOTE: Dumping Subgraph[" << real_id << "]"
+                                                   << " as it is a function body for Subgraph[" << id << "]");
+            }
+            const auto model_to_dump = m_compiled_submodels[real_id].model;
+            std::string model_dump_path = m_name + "_" + ov::npuw::util::fmt(id, m_compiled_submodels.size()) +
+                                          (subgraph._funcall.empty() ? "" : "_" + subgraph._funcall) + ".xml";
+            ov::save_model(model_to_dump, model_dump_path);
+            LOG_INFO("Wrote " << model_dump_path);
+            // Note: keep here naming as it would be the subgraph
+        }  // if(dump)
+    }      // for(orderedSubgraphs)
+
+    std::map<std::size_t, std::string> forced_sub_devices{};
+    const std::string fsd_opt = m_cfg.get<::intel_npu::NPUW_SUBMODEL_DEVICE>();
+    forced_sub_devices = ::intel_npu ::OptionParser<std::map<std::size_t, std::string>>::parse(fsd_opt);
+    // Compile submodels. Some of them can be functions: track which model will be
+    // used as function(s): function name -> index of the compiled subgraph
+    auto compile = [&](size_t id) {
+        const auto& subgraph = orderedSubgraphs[id];
+        if (subgraph._optimized_out) {
+            return;
+        }
+
+        const std::size_t real_id = m_compiled_submodels[id].replaced_by.value_or(id);
+        if (!orderedSubgraphs[real_id]._avoid_list.empty()) {
+            const auto devices_to_avoid = ov::DeviceIDParser::get_hetero_devices(orderedSubgraphs[real_id]._avoid_list);
+            for (auto&& d : devices_to_avoid) {
+                m_compiled_submodels[real_id].devices_to_avoid.insert(std::move(d));
+            }
+        }
+        m_compiled_submodels[real_id].device_it = m_dev_list.cbegin();
+
+        if (forced_sub_devices.count(id)) {
+            std::string forced_device = forced_sub_devices[id];
+            auto forced_dev_it = std::find(m_dev_list.begin(), m_dev_list.end(), forced_device);
+            if (forced_dev_it == m_dev_list.end()) {
+                LOG_WARN("Target device for Subgraph[" << id << "] was set to " << forced_device
+                                                       << ", but was not found in the device list: "
+                                                       << "[" << dev_list_str << "] -- ignoring");
+            } else {
+                // FIXME: This is not really a device enforcement as the fallback
+                // procedure will be in place still
+                LOG_INFO("Force Subgraph[" << id << "] target device to " << *forced_dev_it);
+                m_compiled_submodels[real_id].device_it = forced_dev_it;
+            }
+        }  // if(forced_device_opt)
+
+        LOG_INFO("Compiling Subgraph[" << id << "]: " << m_compiled_submodels[real_id].model->get_friendly_name()
+                                       << "...");
+        if (!compile_for_success(id)) {
+            OPENVINO_THROW("Failed to compile ",
+                           m_compiled_submodels[real_id].model->get_friendly_name(),
+                           " for all devices in [",
+                           dev_list_str,
+                           "]");
+        }
+
+        if (m_acc_check) {
+            if (submodel_device(real_id) != m_ref_device) {
+                LOG_INFO("Compile Subgraph[" << real_id << "] for reference device: " << m_ref_device << ".");
+                LOG_BLOCK();
+                m_compiled_submodels.at(real_id).ref_compiled_model =
+                    compile_submodel(m_compiled_submodels.at(real_id).model, m_ref_device);
+                LOG_INFO("Done (reference)");
+            } else {
+                LOG_INFO("Skip compilation of submodel[" << real_id << "] for reference device: " << m_ref_device
+                                                         << ", as original submodel[" << real_id
+                                                         << "] has been already compiled for it.");
+            }
+        }
+    };  // compile
+
+    // Parallel compilation is unstable so is disabled by default.
+    const bool par_opt = m_cfg.get<::intel_npu::NPUW_PARALLEL_COMPILE>();
+    if (par_opt) {
+        ov::parallel_for(orderedSubgraphs.size(), compile);
+    } else {
+        // TODO: Introduce npuw::serial(i, f) instead where f is a _funcall
+        for (std::size_t i = 0u; i < orderedSubgraphs.size(); i++) {
+            compile(i);
+        }
+    }
+
+    // Print stats report when possible
+    {
+        LOG_INFO("Initial device distribution:");
+        LOG_BLOCK();
+        log_device_dist();
+    }
+
+    implement_properties();
+
+    m_finalized = true;
+    reset_io();
+}
+
+void ov::npuw::CompiledModel::remove_long_output_names(const std::shared_ptr<ov::Model>& model) {
+    NPUW_ASSERT(model.get() != nullptr);
+    for (auto& output : model->outputs()) {
+        auto tensor_names = output.get_tensor().get_names();
+        if (tensor_names.size() > 32) {  // maximum supported
+            output.get_tensor().set_names({});
+            LOG_INFO("Removed output tensor names for " << model->get_friendly_name());
+            LOG_BLOCK();
+        }
+    }
+}
+
+void ov::npuw::CompiledModel::reset_io() {
+    // Restore inputs/outputs from compiled submodels
+    // FIXME: this method is also called from IBaseInferReqeust::create_infer_request
+    // which is called in the CompiledModel(). So this method executes even before it
+    // is called for the right thing from the ctor directly.
+    if (!m_finalized)
+        return;  // avoid getting called before it is really the time
+
+    // Don't be like HETERO here - don't override the inputs/outputs(),
+    // as the ICompiledModel already creates one for us.
+    // Instead, remember the mapping from the original CompiledModel::input/output ports
+    // to the Subgraph's input/output ports.
+
+    LOG_VERB("*** Partition graph ***");
+    int idx_in = 0, idx_out = 0;  // FIXME: use indexed()
+    for (const auto& to_submodel : m_inputs_to_submodels_inputs) {
+        LOG_BLOCK();
+        if (to_submodel == NO_LINK) {
+            LOG_WARN("Input (Parameter) " << inputs()[idx_in]
+                                          << " is not used by any subgraph. It happens sometimes,"
+                                             " but better check your model");
+            idx_in++;  // FIXME: PLEASE use indexed() here
+            continue;
+        }
+        const auto& submodel_idx = to_submodel.first;
+        const auto& input_idx = to_submodel.second;
+        LOG_VERB("Input (Parameter) " << inputs()[idx_in] << " from Subgraph[" << submodel_idx << "]/" << input_idx);
+        idx_in++;
+    }
+    for (const auto& to_submodel : m_outputs_to_submodels_outputs) {
+        NPUW_ASSERT(to_submodel != NO_LINK);
+        LOG_BLOCK();  // in fact, to_submodel <is> from_submodel here, but who cares
+        const auto& submodel_idx = to_submodel.first;
+        const auto& output_idx = to_submodel.second;
+        LOG_VERB("Output (Result) " << outputs()[idx_out] << " from Subgraph[" << submodel_idx << "]/" << output_idx);
+        idx_out++;
+    }
+}
+
+bool ov::npuw::CompiledModel::compile_for_success(std::size_t id) {
+    // Assume device_it is some always-valid starting point.
+    // FIXME: And who guarantees it? Abstraction leaks are everywhere
+    if (m_compiled_submodels[id].replaced_by && m_compiled_submodels[id].replaced_by != id) {
+        LOG_BLOCK();
+        LOG_INFO("Skip compilation for Subgraph[" << id
+                                                  << "] "
+                                                     "as it was already compiled (function)");
+        return true;
+    }
+
+    for (auto iter = m_compiled_submodels[id].device_it; iter != m_dev_list.cend(); ++iter) {
+        LOG_BLOCK();
+        const auto& device_name = *iter;
+        if (m_compiled_submodels[id].devices_to_avoid.count(device_name) > 0) {
+            LOG_INFO(device_name << " was found in the 'Avoid' list for this subgraph, skipping...");
+        } else if (compile_for_device(id, device_name)) {
+            m_compiled_submodels[id].device_it = iter;
+            return true;  // success!
+        }
+    }
+    return false;
+}
+
+bool ov::npuw::CompiledModel::compile_for_device(std::size_t id, const std::string& device_to_try) {
+    // The API of this method is ugly but it is what it is
+    // NOTE(dm): Hetero plugin disables caching here, but we'll keep this to be done
+    // at the individual plugins level.
+    auto plugin = get_npuw_plugin();
+
+    LOG_INFO("Trying to compile for " << device_to_try << "...");
+
+    // Only function bodies can reach this point.
+    // compile_for_device() behavior is not specified for funcalls.
+    NPUW_ASSERT(m_compiled_submodels[id].replaced_by.value_or(id) == id);
+
+    // NPU plugin is known to crash when model has no inputs, so even
+    // try..catch doesn't work. Ideally such models should be
+    // eliminated with constant folding, but with offline partitioning
+    // it is not possible + not clear what to do with dynamic shapes.
+    // So for now, skip this case explicitly.
+    if (npuw::util::starts_with(device_to_try, "NPU") && m_compiled_submodels[id].model->inputs().empty()) {
+        LOG_INFO("Avoid compilation for " << device_to_try << " as the model should be constant-folded");
+        dump_on_fail(id, device_to_try, "Avoided due to workaround");
+        return false;
+    }
+
+    try {
+        m_compiled_submodels[id].compiled_model = compile_submodel(m_compiled_submodels[id].model, device_to_try);
+    } catch (const std::exception& ex) {
+        LOG_ERROR("Subgraph [" << id << "] Failed to compile: " << std::endl << ex.what());
+        dump_on_fail(id, device_to_try, ex.what());
+        return false;
+    } catch (...) {
+        LOG_ERROR("Subgraph [" << id << "] Failed to compile: Unknown error");
+        dump_on_fail(id, device_to_try, "Unknown error");
+        return false;
+    }
+    // Reached this point - all ok, stop the search
+    LOG_INFO("Done (" << device_to_try << ")");
+    return true;
+}
+
+ov::SoPtr<ov::ICompiledModel> ov::npuw::CompiledModel::compile_submodel(const std::shared_ptr<ov::Model>& submodel,
+                                                                        const std::string& device) {
+    auto plugin = get_npuw_plugin();
+    auto core = plugin->get_core();
+    // set exclusive_async_requests in case when model is split
+    // NOTE(dm): Not sure if it is required for the NPUW plugin, but likely it is
+    auto& device_config = m_meta_devices[device];
+    if (m_compiled_submodels.size() > 1) {
+        auto supported_internal_properties =
+            plugin->get_core()->get_property(device, ov::internal::supported_properties);
+        if (std::find(supported_internal_properties.begin(),
+                      supported_internal_properties.end(),
+                      ov::internal::exclusive_async_requests) != supported_internal_properties.end()) {
+            // adds property if it is not set yet
+            device_config.insert(ov::internal::exclusive_async_requests(true));
+        }
+    }  // if(subgraphs > 1)
+    return core->compile_model(submodel, device, device_config);
+}
+
+void ov::npuw::CompiledModel::dump_on_fail(std::size_t id, const std::string& device_to_try, const char* extra) {
+    const std::string dof_opt = m_cfg.get<::intel_npu::NPUW_DUMP_SUBS_ON_FAIL>();
+
+    if (ov::npuw::util::is_set(id, dof_opt)) {
+        ov::npuw::dump_failure(m_compiled_submodels[id].model, device_to_try, extra);
+    }
+}
+
+std::shared_ptr<ov::ISyncInferRequest> ov::npuw::CompiledModel::create_just_sync_infer_request() {
+    auto this_sptr = std::static_pointer_cast<ov::npuw::CompiledModel>(shared_from_this());
+    return std::make_shared<ov::npuw::JustInferRequest>(this_sptr);
+}
+
+std::shared_ptr<ov::ISyncInferRequest> ov::npuw::CompiledModel::create_sync_infer_request() const {
+    // Synchronous infer request implementation may vary based on the
+    // selected strategy
+    auto* non_const_this = const_cast<ov::npuw::CompiledModel*>(this);  // because of const in API
+    return non_const_this->create_just_sync_infer_request();
+}
+
+std::shared_ptr<ov::IAsyncInferRequest> ov::npuw::CompiledModel::create_infer_request() const {
+    auto internal_request = create_sync_infer_request();
+    return std::make_shared<ov::IAsyncInferRequest>(internal_request, get_task_executor(), get_callback_executor());
+}
+
+void ov::npuw::CompiledModel::set_property(const ov::AnyMap& properties) {
+    OPENVINO_NOT_IMPLEMENTED;
+}
+
+std::shared_ptr<const ov::Model> ov::npuw::CompiledModel::get_runtime_model() const {
+    // NOTE(dm): See hetero plugin implementation if need to bring this method back
+    // (that code should work as-is)
+    OPENVINO_NOT_IMPLEMENTED;
+}
+
+std::shared_ptr<const ::intel_npu::Plugin> ov::npuw::CompiledModel::get_npuw_plugin() const {
+    auto plugin = get_plugin();
+    OPENVINO_ASSERT(plugin);
+    auto npuw_plugin = std::dynamic_pointer_cast<const ::intel_npu::Plugin>(plugin);
+    OPENVINO_ASSERT(npuw_plugin);
+    return npuw_plugin;
+}
+
+ov::Any ov::npuw::CompiledModel::get_property(const std::string& name) const {
+    OPENVINO_SUPPRESS_DEPRECATED_START
+    auto&& configIterator = m_prop_to_opt.find(name);
+    if (configIterator != m_prop_to_opt.cend()) {
+        return std::get<1>(configIterator->second)(m_cfg);
+    } else if (m_non_npuw_props.count(name)) {
+        return m_non_npuw_props.at(name);
+    }
+
+    OPENVINO_THROW("Unsupported configuration key: ", name);
+    OPENVINO_SUPPRESS_DEPRECATED_END
+}
+
+void ov::npuw::CompiledModel::export_model(std::ostream& model_stream) const {
+    OPENVINO_NOT_IMPLEMENTED;
+}
+
+std::string ov::npuw::CompiledModel::submodel_device(const std::size_t idx) const {
+    std::size_t real_idx = m_compiled_submodels[idx].replaced_by.value_or(idx);
+    auto comp_subm_desc = m_compiled_submodels[real_idx];
+
+    if (!comp_subm_desc.compiled_model) {
+        return "";
+    }
+
+    if (comp_subm_desc.switched_to_ref) {
+        return m_ref_device;
+    }
+
+    NPUW_ASSERT(comp_subm_desc.device_it != m_dev_list.end());
+    return *comp_subm_desc.device_it;
+}
+
+void ov::npuw::CompiledModel::log_device_dist() const {
+    std::unordered_map<std::string, execution_stats> stats_for_devices;
+    execution_stats stats_for_optimized_out{0.f, 0ul};
+
+    for (std::size_t id = 0u; id < m_compiled_submodels.size(); id++) {  // FIXME: zip()
+        auto real_id = m_compiled_submodels[id].replaced_by.value_or(id);
+        auto& real_cm = m_compiled_submodels.at(real_id);
+
+        execution_stats& stat =
+            real_cm.compiled_model ? stats_for_devices[submodel_device(real_id)] : stats_for_optimized_out;
+
+        stat.gflops += real_cm.stat.gflops;
+        stat.ops += real_cm.stat.ops;
+    }
+
+    auto print_stats = [this](const std::string& device, const execution_stats& stat) {
+        float flops_prcnt = 100.f;
+        float ops_prcnt = 100.f;
+        if (m_total_stat.gflops > 0 && m_total_stat.ops > 0) {
+            flops_prcnt = stat.gflops / static_cast<float>(m_total_stat.gflops) * 100;
+            ops_prcnt = stat.ops / static_cast<float>(m_total_stat.ops) * 100;
+        }
+        LOG_INFO(device << ": " << flops_prcnt << "% FLOPS, " << ops_prcnt << "% Layers");
+    };
+    for (auto&& device_st : stats_for_devices) {
+        LOG_BLOCK();
+        print_stats(device_st.first, device_st.second);
+    }
+    if (stats_for_optimized_out.gflops > 0 || stats_for_optimized_out.ops > 0) {
+        LOG_BLOCK();
+        print_stats("Optimized out", stats_for_optimized_out);
+    }
+}
+
+void ov::npuw::CompiledModel::implement_properties() {
+    // This function fills the map: {`property name`: `getter for property value`},
+    // that can be used later to return requested properties by user.
+    // It does it in 4 steps:
+    //
+    // 1. Create mappings for all NPUW-specific properties to getters of their
+    //    values from config.
+    // 2. Create mappings for all copied from HETERO plugin properties, to
+    //    their copied implementations.
+    // 3. Fill `m_all_supported_props` with all properties, mentioned above.
+
+    // 1.
+#define BIND(N, T)                                                                         \
+    {                                                                                      \
+        ov::intel_npu::N.name(), {                                                         \
+            ov::PropertyMutability::RW, [](const ::intel_npu::Config& config) -> ov::Any { \
+                return config.get<::intel_npu::T>();                                       \
+            }                                                                              \
+        }                                                                                  \
+    }
+
+    m_prop_to_opt = {BIND(use_npuw, NPU_USE_NPUW),
+                     BIND(npuw::devices, NPUW_DEVICES),
+                     BIND(npuw::submodel_device, NPUW_SUBMODEL_DEVICE),
+                     BIND(npuw::partitioning::online::pipeline, NPUW_ONLINE_PIPELINE),
+                     BIND(npuw::partitioning::online::min_size, NPUW_ONLINE_MIN_SIZE),
+                     BIND(npuw::partitioning::online::avoid, NPUW_ONLINE_AVOID),
+                     BIND(npuw::partitioning::online::dump_plan, NPUW_ONLINE_DUMP_PLAN),
+                     BIND(npuw::partitioning::plan, NPUW_PLAN),
+                     BIND(npuw::partitioning::fold, NPUW_FOLD),
+                     BIND(npuw::partitioning::cwai, NPUW_CWAI),
+                     BIND(npuw::partitioning::funcall_for_all, NPUW_FUNCALL_FOR_ALL),
+                     BIND(npuw::parallel_compilation, NPUW_PARALLEL_COMPILE),
+                     BIND(npuw::partitioning::dcoff_type, NPUW_DCOFF_TYPE),
+                     BIND(npuw::partitioning::dcoff_with_scale, NPUW_DCOFF_SCALE),
+                     BIND(npuw::parallel_compilation, NPUW_PARALLEL_COMPILE),
+                     BIND(npuw::funcall_async, NPUW_FUNCALL_ASYNC),
+                     BIND(npuw::accuracy::check, NPUW_ACC_CHECK),
+                     BIND(npuw::accuracy::threshold, NPUW_ACC_THRESH),
+                     BIND(npuw::accuracy::reference_device, NPUW_ACC_DEVICE),
+#ifdef NPU_PLUGIN_DEVELOPER_BUILD
+                     BIND(npuw::dump::full, NPUW_DUMP_FULL),
+                     BIND(npuw::dump::subgraphs, NPUW_DUMP_SUBS),
+                     BIND(npuw::dump::subgraphs_on_fail, NPUW_DUMP_SUBS_ON_FAIL),
+                     BIND(npuw::dump::inputs_outputs, NPUW_DUMP_IO),
+                     BIND(npuw::dump::io_iters, NPUW_DUMP_IO_ITERS)
+#endif
+    };
+#undef BIND
+    // 2.
+    m_prop_to_opt.insert(
+        {{ov::supported_properties.name(),
+          {ov::PropertyMutability::RO,
+           [&](const ::intel_npu::Config&) {
+               return m_all_supported_props;
+           }}},
+         {ov::device::properties.name(),
+          {ov::PropertyMutability::RO,
+           [&](const ::intel_npu::Config&) {
+               ov::AnyMap all_devices = {};
+               for (size_t i = 0; i < m_compiled_submodels.size(); ++i) {
+                   auto comp_model_desc = m_compiled_submodels[i];
+                   if (!comp_model_desc.compiled_model)  // Handle if optimized out
+                       continue;
+                   ov::AnyMap device_properties = {};
+                   if (all_devices.count(submodel_device(i)) == 0) {
+                       auto device_supported_props =
+                           comp_model_desc.compiled_model->get_property(ov::supported_properties.name());
+                       for (auto&& property_name : device_supported_props.as<std::vector<ov::PropertyName>>())
+                           device_properties[property_name] =
+                               comp_model_desc.compiled_model->get_property(property_name);
+                       all_devices[submodel_device(i)] = device_properties;
+                   }
+               }
+               return all_devices;
+           }}},
+         {ov::model_name.name(),
+          {ov::PropertyMutability::RO,
+           [&](const ::intel_npu::Config&) {
+               return m_name;
+           }}},
+         {ov::optimal_number_of_infer_requests.name(),
+          {ov::PropertyMutability::RO,
+           [&](const ::intel_npu::Config&) {
+               unsigned int value = 0u;
+               for (const auto& comp_model_desc : m_compiled_submodels) {
+                   if (comp_model_desc.compiled_model) {  // Some models may be optimized out
+                       value = std::max(
+                           value,
+                           comp_model_desc.compiled_model->get_property(ov::optimal_number_of_infer_requests.name())
+                               .as<unsigned int>());
+                   }
+               }
+               return value;
+           }}},
+         {ov::execution_devices.name(),
+          {ov::PropertyMutability::RO,
+           [&](const ::intel_npu::Config&) {
+               std::vector<std::string> device_names;
+               std::set<std::string> s;
+               for (size_t i = 0; i < m_compiled_submodels.size(); ++i) {
+                   const auto& comp_model_desc = m_compiled_submodels[i];
+                   if (!comp_model_desc.compiled_model)  // handle optimized out
+                       continue;
+                   if (s.count(submodel_device(i)) != 0)
+                       continue;
+                   s.insert(submodel_device(i));
+                   device_names.push_back(submodel_device(i));
+               }
+               return decltype(ov::execution_devices)::value_type{device_names};
+           }}},
+         {ov::loaded_from_cache.name(), {ov::PropertyMutability::RO, [&](const ::intel_npu::Config&) {
+                                             return m_loaded_from_cache;
+                                         }}}});
+
+    // 3.
+    for (auto& p : m_prop_to_opt) {
+        m_all_supported_props.emplace_back(ov::PropertyName(p.first, std::get<0>(p.second)));
+    }
+}
diff --git a/src/plugins/intel_npu/src/plugin/npuw/compiled_model.hpp b/src/plugins/intel_npu/src/plugin/npuw/compiled_model.hpp
new file mode 100644
index 00000000000000..9509d64d0c8851
--- /dev/null
+++ b/src/plugins/intel_npu/src/plugin/npuw/compiled_model.hpp
@@ -0,0 +1,136 @@
+// Copyright (C) 2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <optional>
+
+#include "common.hpp"
+#include "intel_npu/al/config/config.hpp"
+#include "intel_npu/al/config/npuw.hpp"
+#include "openvino/openvino.hpp"
+#include "openvino/runtime/icompiled_model.hpp"
+#include "openvino/runtime/so_ptr.hpp"
+#include "partitioning/partitioning.hpp"
+
+namespace intel_npu {
+class Plugin;
+}
+
+namespace ov {
+namespace npuw {
+
+class InferRequest;
+
+class CompiledModel : public ov::ICompiledModel {
+    using DevList = std::vector<std::string>;
+    using GetPropertiesMap =
+        std::map<std::string, std::tuple<ov::PropertyMutability, std::function<ov::Any(const ::intel_npu::Config&)>>>;
+
+public:
+    CompiledModel(const std::shared_ptr<ov::Model>& model,
+                  const std::shared_ptr<const ov::IPlugin>& plugin,
+                  const ov::AnyMap& properties);
+
+    void export_model(std::ostream& model) const override;
+    std::shared_ptr<const ov::Model> get_runtime_model() const override;
+
+    void set_property(const ov::AnyMap& properties) override;
+    ov::Any get_property(const std::string& name) const override;
+
+    std::shared_ptr<ov::IAsyncInferRequest> create_infer_request() const override;
+
+private:
+    // FIXME: This class has many friends..
+    friend class IBaseInferRequest;
+    friend class JustInferRequest;
+
+    bool compile_for_success(std::size_t id);
+    bool compile_for_device(std::size_t id, const std::string& device_to_try);
+    ov::SoPtr<ov::ICompiledModel> compile_submodel(const std::shared_ptr<ov::Model>& submodel,
+                                                   const std::string& device);
+
+    void dump_on_fail(std::size_t id, const std::string& device_to_stry, const char* extra);
+
+    bool m_finalized = false;
+    void reset_io();
+
+    // This is used for removing too long output tensor names to fix some compilation issues
+    void remove_long_output_names(const std::shared_ptr<ov::Model>& model);
+
+    std::shared_ptr<const ::intel_npu::Plugin> get_npuw_plugin() const;
+
+    std::shared_ptr<ov::ISyncInferRequest> create_just_sync_infer_request();
+    std::shared_ptr<ov::ISyncInferRequest> create_sync_infer_request() const override;
+
+    std::string submodel_device(const std::size_t idx) const;
+
+    void log_device_dist() const;
+
+    void implement_properties();
+
+    std::shared_ptr<::intel_npu::OptionsDesc> m_options_desc;
+    ::intel_npu::Config m_cfg;
+    GetPropertiesMap m_prop_to_opt;
+    std::vector<ov::PropertyName> m_all_supported_props;
+    ov::AnyMap m_non_npuw_props;
+
+    std::string m_name;
+    const bool m_loaded_from_cache;
+
+    using ToSubmodel = std::pair<size_t /* submodel_idx */
+                                 ,
+                                 size_t /* port_idx     */
+                                 >;
+    static const constexpr auto NO_LINK = ToSubmodel{-1, -1};
+
+    // In the below vector, index == compiled model's input/output port idex.
+    std::vector<ToSubmodel> m_inputs_to_submodels_inputs, m_outputs_to_submodels_outputs;
+
+    std::map<std::size_t, std::vector<ToSubmodel>> m_param_subscribers;
+
+    std::map<std::pair<size_t /*submodel_idx*/, size_t /*node_idx*/>,  // input ("to")
+             std::pair<size_t /*submodel_idx*/, size_t /*node_idx*/>>  // output ("from")
+        m_submodels_input_to_prev_output;
+
+    DeviceProperties m_meta_devices;
+
+    DevList m_dev_list;
+
+    struct execution_stats {
+        float gflops{};
+        std::size_t ops{};
+    };
+
+    struct CompiledModelDesc {
+        DevList::const_iterator device_it;
+        std::set<std::string> devices_to_avoid;
+        std::shared_ptr<ov::Model> model;
+        ov::SoPtr<ov::ICompiledModel> compiled_model;
+
+        std::optional<std::size_t> replaced_by;
+
+        // FIXME: This is a 1:1 copy of the ov::npuw::Subgraph structure
+        // w.r.t. function calls
+        std::size_t param_base = 0;
+        std::vector<ov::Tensor> closure;
+        std::vector<ov::Tensor> scales;
+        std::vector<ov::Tensor> zerops;
+
+        // FIXME: Take it out of structure
+        ov::SoPtr<ov::ICompiledModel> ref_compiled_model;
+        bool switched_to_ref = false;
+
+        // Metrics
+        execution_stats stat;
+    };
+    std::vector<CompiledModelDesc> m_compiled_submodels;
+
+    std::function<bool(const ov::SoPtr<ov::ITensor>&, const ov::SoPtr<ov::ITensor>&)> m_acc_check;
+    std::string m_ref_device;
+
+    execution_stats m_total_stat;
+};
+}  // namespace npuw
+}  // namespace ov
diff --git a/src/plugins/intel_npu/src/plugin/npuw/just_sync_infer_request.cpp b/src/plugins/intel_npu/src/plugin/npuw/just_sync_infer_request.cpp
new file mode 100644
index 00000000000000..da9255ac235264
--- /dev/null
+++ b/src/plugins/intel_npu/src/plugin/npuw/just_sync_infer_request.cpp
@@ -0,0 +1,591 @@
+// Copyright (C) 2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "just_sync_infer_request.hpp"
+
+#include <algorithm>
+#include <map>
+#include <memory>
+#include <string>
+#include <utility>
+
+#include "compiled_model.hpp"
+#include "logging.hpp"
+#include "openvino/core/except.hpp"
+#include "openvino/core/parallel.hpp"
+#include "openvino/runtime/iasync_infer_request.hpp"
+#include "openvino/runtime/make_tensor.hpp"
+#include "plugin.hpp"
+#include "util.hpp"
+
+ov::npuw::JustInferRequest::JustInferRequest(const std::shared_ptr<ov::npuw::CompiledModel>& compiled_model)
+    : IBaseInferRequest(compiled_model) {
+    m_use_function_pipelining = m_npuw_model->m_cfg.get<::intel_npu::NPUW_FUNCALL_ASYNC>();
+    if (m_use_function_pipelining) {
+        LOG_WARN("Function call pipelining is enabled for " << m_npuw_model->m_name
+                                                            << ", expect a higher memory consumption");
+        m_funcall_pipeline.resize(m_num_submodels);
+    }
+
+    // Create infer requests
+    // Preallocate funcall tensors & substitute function call requests
+    bool failover_happened = false;
+    for (size_t i = 0; i < m_num_submodels; i++) {
+        LOG_INFO("Creating infer request for Subgraph[" << i << "]...");
+        LOG_BLOCK();
+        auto& comp_model_desc = m_npuw_model->m_compiled_submodels[i];
+
+        if (!comp_model_desc.compiled_model && !comp_model_desc.replaced_by) {
+            // no model & no funcall - optimized out, do nothing
+            LOG_INFO("OPTIMIZED OUT");
+            continue;
+        }
+
+        // FIXME: Shouldn't this be handled by the base class? (in create_tensor)
+        // A special case for function calls
+        if (comp_model_desc.replaced_by) {
+            // Pre-allocate output tesnors for this function call
+            const auto real_idx = comp_model_desc.replaced_by.value();
+            auto& proto_comp_model = m_npuw_model->m_compiled_submodels[real_idx].compiled_model;
+            for (size_t out_idx = 0; out_idx < proto_comp_model->outputs().size(); out_idx++) {
+                const auto& port = proto_comp_model->outputs()[out_idx];
+                m_funcall_result[LinkFrom{i, out_idx}] =
+                    ov::get_tensor_impl(ov::Tensor(port.get_element_type(), port.get_shape()));
+            }
+            if (real_idx != i) {
+                // If this function call is NOT the function body, do nothing here - the original
+                // request will be used.
+                LOG_INFO("REUSE " << real_idx);
+                continue;
+            }
+        }  // if(replaced_by)
+
+        // Special cases are handled -- so nothing to do here
+        bool recompiled = false;
+        auto rqs = create_infer_requests(i, m_use_function_pipelining ? 2 : 1, &recompiled);
+        failover_happened |= recompiled;
+        m_subrequests[i] = rqs.at(0);
+        m_subrequest_devices[i] = *comp_model_desc.device_it;
+        if (comp_model_desc.replaced_by && m_use_function_pipelining) {
+            m_funcall_pipeline[i].subrequest = rqs.at(1);
+        }
+
+        LOG_INFO("DONE");
+    }  // for(submodels)
+
+    if (failover_happened) {
+        LOG_INFO("Refined device distribution:");
+        LOG_BLOCK();
+        m_npuw_model->log_device_dist();
+    }
+
+    // Identify connections for the funcall pipeline, if needed
+    if (m_use_function_pipelining) {
+        LOG_INFO("Setting up the funcall pipeline...");
+        LOG_BLOCK();
+        std::vector<std::optional<std::size_t>> prevs(m_num_submodels);
+        for (std::size_t i = 0; i < m_num_submodels; i++) {
+            auto& comp_model_desc = m_npuw_model->m_compiled_submodels[i];
+            if (comp_model_desc.replaced_by) {  // a function call..
+                // Use real_id to accumulate information about
+                // different functions
+                const auto real_id = comp_model_desc.replaced_by.value();
+                if (!prevs[real_id]) {  // ..met for a first time
+                    LOG_INFO("Mark subgraph[" << i << "] as a head of pipeline...");
+                    m_funcall_heads.push_back(i);
+                } else {  // ..seen before
+                    // Record that _this_ id follows the last known
+                    // _prev_ if in the funcall pipeline
+                    const auto prev_id = prevs[real_id].value();
+                    LOG_INFO("Mark subgraph[" << i << "] as a successor of subraph[" << prev_id
+                                              << "] in the function pipeline");
+                    m_funcall_pipeline[prev_id].next = {i};
+                }
+                prevs[real_id] = {i};
+            }  // if (replaced_by)
+        }
+    }  // if(function_pipelining)
+
+    // Preallocate input tensors
+    LOG_INFO("Preallocating input tensors...");
+    for (size_t i = 0; i < m_npuw_model->inputs().size(); i++) {
+        const auto& port = m_npuw_model->inputs()[i];
+        const auto& to_submodel = m_npuw_model->m_inputs_to_submodels_inputs.at(i);
+        m_input_tensors.push_back(ov::get_tensor_impl(ov::Tensor(port.get_element_type(), port.get_shape())));
+        m_port_to_tensor[port] = TensorStorage{m_input_tensors.back(), true};
+
+        if (to_submodel != ov::npuw::CompiledModel::NO_LINK) {
+            const auto& submodel_idx = to_submodel.first;
+            m_port_to_subrequest_idx[port] = submodel_idx;
+            m_reader_to_orig_port[to_submodel] = port;
+            m_port_orig_to_sub[port] =
+                get_real_subrequest(to_submodel.first)->get_compiled_model()->inputs()[to_submodel.second];
+        } else {
+            // Quick hack to support models with unused Parameters...
+            m_port_to_subrequest_idx[port] = ov::npuw::JustInferRequest::INVALID_IDX;
+        }
+    }
+    // One more map to fill...
+    for (auto&& it : m_npuw_model->m_param_subscribers) {
+        const auto& prim_port = m_npuw_model->inputs()[it.first];
+        for (auto&& to_submodel : it.second) {
+            m_reader_to_orig_port[to_submodel] = prim_port;
+            m_port_orig_to_sub[prim_port] =
+                get_real_subrequest(to_submodel.first)->get_compiled_model()->inputs()[to_submodel.second];
+        }
+    }
+
+    // Preallocate output tensors
+    LOG_INFO("Preallocating output tensors...");
+    for (size_t i = 0; i < m_npuw_model->outputs().size(); i++) {
+        LOG_BLOCK();
+        const auto& port = m_npuw_model->outputs()[i];
+        LOG_INFO("Output " << i << " of " << m_npuw_model->outputs().size() << ": " << port);
+
+        // FIXME: Yes, the CompiledModel::ToSubmodel == JustInferRequest::LinkFrom
+        const auto& from_submodel = m_npuw_model->m_outputs_to_submodels_outputs.at(i);
+
+        LOG_INFO("Produced by Subgraph[" << from_submodel.first << "] / " << from_submodel.second);
+        auto funcall_result_iter = m_funcall_result.find(from_submodel);
+
+        auto tensor = funcall_result_iter != m_funcall_result.end()
+                          ? funcall_result_iter->second  // Function calls have their tensors allocated, so just use one
+                          : ov::get_tensor_impl(ov::Tensor(port.get_element_type(), port.get_shape()));
+
+        const auto& submodel_idx = from_submodel.first;
+        m_port_to_subrequest_idx[port] = submodel_idx;
+        m_port_orig_to_sub[port] =
+            get_real_subrequest(from_submodel.first)->get_compiled_model()->outputs()[from_submodel.second];
+        m_output_tensors.push_back(tensor);
+        m_port_to_tensor[port] = TensorStorage{tensor, true};
+    }
+    connect_subrequests();
+}
+
+void ov::npuw::JustInferRequest::connect_subrequests() {
+    LOG_INFO("Connecting subrequests...");
+    LOG_BLOCK();
+    auto& subm = m_npuw_model->m_compiled_submodels;
+    auto& subreqs = m_subrequests;
+    for (const auto& kvp : m_npuw_model->m_submodels_input_to_prev_output) {
+        const auto& subm_idx_to = kvp.first.first;
+        const auto& port_idx_to = kvp.first.second;
+        const auto& subm_idx_from = kvp.second.first;
+        const auto& port_idx_from = kvp.second.second;
+
+        LOG_DEBUG("Subgraph[" << subm_idx_from << "]/" << port_idx_from << " --> "
+                              << "Subgraph[" << subm_idx_to << "]/" << port_idx_to);
+        LOG_BLOCK();
+
+        if (subm[subm_idx_from].replaced_by && subm[subm_idx_to].replaced_by) {
+            // A function call to function call connection:
+            // - Skip it here, setting in/out tensors will be handled in runtime
+            LOG_DEBUG("Skip: both are function calls");
+            continue;
+        } else if (subm[subm_idx_from].replaced_by && !subm[subm_idx_to].replaced_by) {
+            // A function call to normal subgraph connection:
+            // - Take a tensor from the storage & assign it to the reader
+            const auto& iport = m_subrequests[subm_idx_to]->get_compiled_model()->inputs()[port_idx_to];
+            const auto& tensor = m_funcall_result.at(LinkFrom{subm_idx_from, port_idx_from});
+            subreqs[subm_idx_to]->set_tensor(iport, tensor);
+            LOG_DEBUG("Set Subgraph[" << subm_idx_to << "]/" << iport << " to internal tensor");
+        } else if (!subm[subm_idx_from].replaced_by && subm[subm_idx_to].replaced_by) {
+            LOG_DEBUG("Skip: reader is a function call");
+            continue;
+        } else if (!subreqs[subm_idx_from] && subreqs[subm_idx_to]) {
+            // Subrequests may be optimized out, but in this case there should be
+            // no connection between them and their consumers in the input_to_prev
+            // map (links are erased & Parameters are replaced with Const) at earlier
+            // stages.
+            OPENVINO_THROW("FATAL: \"Prev. Output\" Request ",
+                           subm_idx_from,
+                           " in input_to_prev_output mapping was optimized out,"
+                           " but it consumer request ",
+                           subm_idx_to,
+                           " wasn't!");
+        } else if (!subreqs[subm_idx_to]) {
+            // FIXME: Links like this probbaly shouldn't exist in the map, too.
+            // Need to research why this is not THROW
+            LOG_WARN("\"Input\" Request in input_to_prev_output mapping was optimized out");
+            continue;
+        } else {
+            // Final case:
+            NPUW_ASSERT(!subm[subm_idx_from].replaced_by);  // prod subgraph isn't a funcall
+            NPUW_ASSERT(!subm[subm_idx_to].replaced_by);    // cons subgraph isn't a funcall
+            NPUW_ASSERT(subreqs[subm_idx_from]);            // prod request is created
+            NPUW_ASSERT(subreqs[subm_idx_to]);              // cons request is created
+
+            // Just set one's output tensor to another's input
+            const auto& iport = subreqs[subm_idx_to]->get_compiled_model()->inputs()[port_idx_to];
+            const auto& oport = subreqs[subm_idx_from]->get_compiled_model()->outputs()[port_idx_from];
+            const auto& tensor = subreqs[subm_idx_from]->get_tensor(oport);
+            LOG_DEBUG("Set Subgraph[" << subm_idx_to << "]/" << iport << " to Subgraph[" << subm_idx_from << "]/"
+                                      << oport);
+            subreqs[subm_idx_to]->set_tensor(iport, tensor);
+        }
+    }  // for(map)
+    LOG_INFO("Done");
+}
+
+std::vector<ov::SoPtr<ov::IVariableState>> ov::npuw::JustInferRequest::query_state() const {
+    std::vector<ov::SoPtr<ov::IVariableState>> variable_states = {};
+    for (const auto& request : m_subrequests) {
+        if (!request)  // optimized out
+            continue;
+        for (auto&& state : request->query_state()) {
+            if (!state._so)
+                state._so = request._so;
+            variable_states.emplace_back(state);
+        }
+    }
+    return variable_states;
+}
+
+std::vector<ov::ProfilingInfo> ov::npuw::JustInferRequest::get_profiling_info() const {
+    std::vector<ov::ProfilingInfo> info;
+    for (size_t i = 0; i < m_subrequests.size(); ++i) {
+        if (!m_subrequests[i])  // optimized out
+            continue;
+        auto&& subreq_info = m_subrequests[i]->get_profiling_info();
+        for (auto&& rec : subreq_info)
+            rec.node_name = std::string("subgraph") + std::to_string(i) + ": " + rec.node_name;
+        info.insert(info.end(), subreq_info.begin(), subreq_info.end());
+    }
+    return info;
+}
+
+void ov::npuw::JustInferRequest::bind_params_results() {
+    // Parameters: Specify input tensors to the "input" infer requests
+    for (size_t i = 0; i < m_npuw_model->inputs().size(); i++) {
+        const auto& port = m_npuw_model->inputs()[i];
+
+        auto ptidx = m_port_to_subrequest_idx.at(port);
+        if (ptidx == ov::npuw::JustInferRequest::INVALID_IDX)
+            continue;
+        if (m_npuw_model->m_compiled_submodels[ptidx].replaced_by) {
+            // Don't do here - function call will take parameter
+            // itself. Note it may be implemented more efficently
+            // than now (and in some cases, parameter can be pre-set)
+        } else {
+            const auto& subr = m_subrequests[ptidx];
+            LOG_DEBUG("Subrequest[" << ptidx << "]: set parameter[" << port << "] to global parameter [" << i
+                                    << "] (direct)");
+            subr->set_tensor(m_port_orig_to_sub.at(port), m_port_to_tensor.at(port).tensor);
+        }
+    }  // for(inputs)
+    // There may be (other) subrequests subscribed to Parameters tensors
+    // Update their inputs using The only Parameter tensor (exposed to OV)
+    // FIXME: BTW probably this is NOT necessary now
+    for (auto&& it : m_npuw_model->m_param_subscribers) {
+        const auto param_idx = it.first;
+        const auto& prim_port = m_npuw_model->inputs()[param_idx];
+
+        for (auto&& sit : it.second) {
+            const auto sub_idx = sit.first;
+            if (m_npuw_model->m_compiled_submodels[sub_idx].replaced_by) {
+                // Don't do here - function call will take parameter
+                // itself. Note it may be implemented more efficently
+                // than now (and in some cases, parameter can be pre-set)
+            } else {
+                const auto sub_param_idx = sit.second;
+
+                const auto& sub_port = m_subrequests[sub_idx]->get_compiled_model()->inputs()[sub_param_idx];
+
+                LOG_DEBUG("Subrequest[" << sub_idx << "]: set parameter[" << sub_param_idx << "] to global parameter ["
+                                        << param_idx << "] (subscriber)");
+
+                m_subrequests[sub_idx]->set_tensor(sub_port, get_tensor(prim_port));
+            }
+        }
+    }  // for(subscribers)
+
+    // Results: Specify output tensors to the "output" infer requests
+    for (size_t i = 0; i < m_npuw_model->outputs().size(); i++) {
+        const auto& port = m_npuw_model->outputs()[i];
+        const auto sub_idx = m_port_to_subrequest_idx.at(port);
+        if (m_npuw_model->m_compiled_submodels[sub_idx].replaced_by) {
+            // here port_to_tensor may refer to a preallocated funcall result.
+            // in this case, no need to do anything here as functions are bound
+            // with their result tensors during the call (see notes)
+        } else {
+            const auto& subr = m_subrequests[sub_idx];
+            subr->set_tensor(m_port_orig_to_sub.at(port), m_port_to_tensor.at(port).tensor);
+        }
+    }  // for(outputs)
+}
+
+void ov::npuw::JustInferRequest::prepare_for_infer() {
+    LOG_DEBUG("Preparing to infer...");
+    LOG_BLOCK();
+
+    bind_params_results();
+
+    // If funcall pipelining is enabled, prefill the function "heads"
+    // with constant arguments
+    for (auto&& id : m_funcall_heads) {
+        LOG_DEBUG("Pre-initializing weights for subgraph[" << id << "]");
+        unpack_closure(id, m_subrequests[id]);
+    }
+    LOG_DEBUG("Done");
+}
+
+ov::npuw::IBaseInferRequest::RqPtr ov::npuw::JustInferRequest::get_real_subrequest(std::size_t idx) {
+    auto& comp_model_desc = m_npuw_model->m_compiled_submodels[idx];
+    const auto real_idx = comp_model_desc.replaced_by.value_or(idx);
+    return m_subrequests[real_idx];
+}
+
+bool ov::npuw::JustInferRequest::valid_subrequest(std::size_t idx) const {
+    auto* ncthis = const_cast<ov::npuw::JustInferRequest*>(this);
+    return ncthis->get_real_subrequest(idx) != nullptr;
+}
+
+void ov::npuw::JustInferRequest::start_subrequest(std::size_t idx) {
+    m_subrequests[idx]->start_async();
+}
+
+void ov::npuw::JustInferRequest::function_prologue(std::size_t idx) {
+    LOG_DEBUG("Preparing Subgraph[" << idx << "] funcall prologue");
+    LOG_BLOCK();
+
+    auto& comp_model_desc = m_npuw_model->m_compiled_submodels[idx];
+
+    NPUW_ASSERT(comp_model_desc.replaced_by);
+    const auto real_idx = comp_model_desc.replaced_by.value();
+    auto& func_desc = m_npuw_model->m_compiled_submodels[real_idx];
+
+    // Function call prologue:
+    // 1. Walk through function dependencies and set the respective tensors
+    //    as parameters
+    for (size_t i = 0; i < func_desc.param_base; i++) {
+        LOG_DEBUG("Binding parameter[" << i << "]...");
+        LOG_BLOCK();
+        const auto& iport = func_desc.compiled_model->inputs()[i];
+
+        auto link_iter = m_npuw_model->m_submodels_input_to_prev_output.find({idx, i});
+        if (link_iter != m_npuw_model->m_submodels_input_to_prev_output.end()) {
+            std::size_t prod_idx;
+            std::size_t prod_port;
+            std::tie(prod_idx, prod_port) = link_iter->second;
+
+            if (!m_npuw_model->m_compiled_submodels[prod_idx].replaced_by) {
+                // Producer is a normal model -> take its tensor directly
+                const auto& oport = m_npuw_model->m_compiled_submodels[prod_idx].compiled_model->outputs()[prod_port];
+                m_subrequests[real_idx]->set_tensor(iport, m_subrequests[prod_idx]->get_tensor(oport));
+            } else {
+                // Producer is a function - maybe the same as we're calling now.
+                // Take its tensor from the storage
+                m_subrequests[real_idx]->set_tensor(iport, m_funcall_result.at({prod_idx, prod_port}));
+            }
+        } else {
+            // Take input from the associated parameter.
+            LOG_DEBUG("Finding the original model port for " << idx << "/" << i << "...");
+            auto& orig_port = m_reader_to_orig_port.at({idx, i});
+            m_subrequests[real_idx]->set_tensor(iport, m_port_to_tensor.at(orig_port).tensor);
+        }
+    }  // for(param_base)
+
+    // 2. Unpack the function closure -- right here, if pipelining if not enabled.
+    // If it is enabled, the flow is a little bit different - see run_subrequest_for_success()
+    // for details.
+    if (!m_use_function_pipelining) {
+        LOG_DEBUG("Unpacking closures...");
+        LOG_BLOCK();
+        unpack_closure(idx, m_subrequests[real_idx]);
+    }
+
+    // 3. Tell the function which results to produce (this time)
+    for (std::size_t i = 0; i < func_desc.compiled_model->outputs().size(); i++) {
+        LOG_DEBUG("Binding result[" << i << "]...");
+        auto& oport = func_desc.compiled_model->outputs()[i];
+        m_subrequests[real_idx]->set_tensor(oport, m_funcall_result.at({idx, i}));
+    }
+    LOG_DEBUG("Done");
+}
+
+void ov::npuw::JustInferRequest::unpack_closure(std::size_t idx, RqPtr request) {
+    auto& comp_model_desc = m_npuw_model->m_compiled_submodels[idx];
+
+    NPUW_ASSERT(comp_model_desc.replaced_by);
+    const auto real_idx = comp_model_desc.replaced_by.value();
+    auto& func_desc = m_npuw_model->m_compiled_submodels[real_idx];
+
+    // Bind extra parameters from the function's closure
+    // First, do easy things & delay heavy stuff
+    std::vector<std::size_t> closure_unpack_required;
+    for (std::size_t cidx = 0u; cidx < comp_model_desc.closure.size(); cidx++) {
+        auto& closure = comp_model_desc.closure[cidx];
+
+        const auto closure_param_id = comp_model_desc.param_base + cidx;
+        auto& iport = func_desc.compiled_model->inputs()[closure_param_id];
+        auto clparam = request->get_tensor(iport);
+        if (closure.get_element_type() != clparam->get_element_type()) {
+            // Remember where the unpack is required
+            closure_unpack_required.push_back(cidx);
+        } else {
+            // Easy case, just set one to another. Copy_to is also possible
+            // and even may be preferrable for some devices, like this:
+            // ```ov::get_tensor_impl(closure)->copy_to(clparam._ptr);'''
+            request->set_tensor(iport, ov::get_tensor_impl(closure));
+        }
+    }  // for(closure)
+       // m_ms_unpack += ov::npuw::perf::ms_to_run([&](){
+       //    ov::parallel_for(closure_unpack_required.size(), [&](std::size_t j) {
+    for (std::size_t j = 0; j != closure_unpack_required.size(); j++) {
+        // NB: No need to protect anything here as containers are all
+        // preallocated and we only access elements under particular (thread
+        // -local) indices.
+        auto cidx = closure_unpack_required[j];
+
+        // FIXME: zerops are stored with absolute indexing, this needs to be aligned
+        auto& closure = comp_model_desc.closure[cidx];
+
+        const auto closure_param_id = comp_model_desc.param_base + cidx;
+        auto& iport = func_desc.compiled_model->inputs()[closure_param_id];
+        auto clparam = request->get_tensor(iport);
+
+        if (!comp_model_desc.scales.empty() && comp_model_desc.scales[cidx] && comp_model_desc.zerops[cidx]) {
+            // Unpacking this weight requires scaling with zero points...
+            ov::npuw::util::unpack(ov::get_tensor_impl(closure),
+                                   ov::get_tensor_impl(comp_model_desc.zerops[cidx]),
+                                   ov::get_tensor_impl(comp_model_desc.scales[cidx]),
+                                   clparam);
+        } else if (!comp_model_desc.scales.empty() && comp_model_desc.scales[cidx]) {
+            // Unpacking this weight requires scaling
+            ov::npuw::util::unpack(ov::get_tensor_impl(closure),
+                                   ov::get_tensor_impl(comp_model_desc.scales[cidx]),
+                                   clparam);
+        } else {
+            // Unpacking this weight doesn't require scaling
+            ov::npuw::util::unpack(ov::get_tensor_impl(closure), clparam);
+        }
+    }
+    //}); // ov_parallel_for
+    // }); // ms_to_run
+}
+
+void ov::npuw::JustInferRequest::recreate_subrequests(std::size_t idx) {
+    auto& comp_model_desc = m_npuw_model->m_compiled_submodels[idx];
+    auto real_idx = comp_model_desc.replaced_by.value_or(idx);
+
+    auto new_rqs = create_infer_requests(idx, m_use_function_pipelining ? 2 : 1);
+
+    // NB: Regardless if this subrequest was a function call
+    // or not, always use the real_idx here - for regular
+    // subrequests, real_id == idx, but for function calls it
+    // is critical here to update the function body, not the
+    // function calls (which are left empty now in the vector)
+    m_subrequests[real_idx] = new_rqs.at(0);
+    if (comp_model_desc.replaced_by && m_use_function_pipelining) {
+        m_funcall_pipeline[real_idx].subrequest = new_rqs.at(1);
+    }
+    // After an infer request is recreated, the internal cross-request
+    // connections should be re-established (in/out tensors reset properly)
+    // Note: these two proceduers do the full I/O reset procedure what's
+    // overkill - only affected subrequest(s) could be updated instead,
+    // but it is a more complex thing and can be implemented separately
+    connect_subrequests();
+    bind_params_results();
+    m_subrequest_devices[idx] = *comp_model_desc.device_it;
+}
+
+void ov::npuw::JustInferRequest::run_subrequest_for_success(std::size_t idx, bool& failover) {
+    failover = false;
+    auto& comp_model_desc = m_npuw_model->m_compiled_submodels[idx];
+    auto real_idx = comp_model_desc.replaced_by.value_or(idx);
+
+    // Infer is also fail-safe...
+    bool job_done = false;
+    bool dump_in = false;
+    while (!job_done) {
+        bool should_recreate = false;
+        if (m_subrequest_devices[real_idx] != *m_npuw_model->m_compiled_submodels[real_idx].device_it) {
+            LOG_INFO("Recreating subrequest[" << real_idx << "] because model was recompiled for "
+                                              << *m_npuw_model->m_compiled_submodels[real_idx].device_it << " device.");
+            recreate_subrequests(real_idx);
+        }
+
+        if (comp_model_desc.replaced_by) {
+            function_prologue(idx);
+        }
+        if (!dump_in) {
+            dump_in = true;
+            dump_input_tensors(idx);
+        }
+        try {
+            LOG_DEBUG("Trying to run subrequest[" << idx << "]...");
+            if (m_use_function_pipelining && comp_model_desc.replaced_by) {
+                // Start THIS request -- everything is ready for it at this point
+                m_subrequests[real_idx]->start_async();
+                if (m_funcall_pipeline[idx].next) {
+                    const auto next_id = m_funcall_pipeline[idx].next.value();
+                    // Prepare the NEXT request while this one is running
+                    LOG_DEBUG("Unpacking closures for the NEXT subrequest[" << next_id << "]...");
+                    LOG_BLOCK();
+                    unpack_closure(next_id, m_funcall_pipeline[real_idx].subrequest);
+                }
+
+                // Wait for completion of this one. Once completed,
+                // its' brother was already updated all the right
+                // closure tensors. Swapping the subrequests will
+                // happen in the very end to allow dumping the proper
+                // tensors, if needed (see job_done check).
+                m_subrequests[real_idx]->wait();
+            } else {
+                m_subrequests[real_idx]->infer();
+            }
+            job_done = true;
+        } catch (const std::exception& ex) {
+            LOG_ERROR("Subgraph [" << idx << "] - FAILED to run infer request:" << std::endl << ex.what());
+            should_recreate = true;
+        } catch (...) {
+            LOG_ERROR("Subgraph [" << idx << "] - FAILED to run infer request: REASON UNKNOWN");
+            should_recreate = true;
+        }
+        if (should_recreate) {
+            failover = true;
+            LOG_INFO("- Trying next device...");
+
+            // Altering iterators here!! Contracts should be changed!
+            comp_model_desc.device_it++;
+            if (!m_npuw_model->compile_for_success(real_idx)) {
+                OPENVINO_THROW("Failed to compile. No more devices are left!");
+            }
+            recreate_subrequests(idx);
+        }
+    }  // while(job_done)
+
+    if (job_done) {
+        dump_output_tensors(idx);  // FIXME: Called here unconditionally, need to refactor
+        if (m_use_function_pipelining && m_funcall_pipeline[idx].next) {
+            // Swap the next (pipelined, semi-prepared) infer request in the chain
+            // with the default (to be accessed next) one.
+            std::swap(m_subrequests[real_idx], m_funcall_pipeline[real_idx].subrequest);
+        }
+    }
+}
+
+void ov::npuw::JustInferRequest::subscribe_subrequest(std::size_t idx, Completed cb) {
+    get_real_subrequest(idx)->set_callback(cb);
+}
+
+void ov::npuw::JustInferRequest::complete_subrequest(std::size_t idx) {
+    // do nothing here
+}
+
+void ov::npuw::JustInferRequest::cancel_subrequest(std::size_t idx) {
+    m_subrequests[idx]->cancel();
+}
+
+std::size_t ov::npuw::JustInferRequest::total_subrequests() const {
+    return m_subrequests.size();
+}
+
+bool ov::npuw::JustInferRequest::supports_async_pipeline() const {
+    return false;
+}
+
+void ov::npuw::JustInferRequest::update_subrequest_links(std::size_t) {
+    connect_subrequests();
+}
diff --git a/src/plugins/intel_npu/src/plugin/npuw/just_sync_infer_request.hpp b/src/plugins/intel_npu/src/plugin/npuw/just_sync_infer_request.hpp
new file mode 100644
index 00000000000000..dbeb2bee871f91
--- /dev/null
+++ b/src/plugins/intel_npu/src/plugin/npuw/just_sync_infer_request.hpp
@@ -0,0 +1,93 @@
+// Copyright (C) 2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <limits>
+#include <map>
+#include <optional>
+#include <vector>
+
+#include "base_sync_infer_request.hpp"
+
+namespace ov {
+namespace npuw {
+
+class CompiledModel;
+class AsyncInferRequest;
+
+class JustInferRequest final : public IBaseInferRequest {
+public:
+    explicit JustInferRequest(const std::shared_ptr<ov::npuw::CompiledModel>& compiled_model);
+
+    // Query APIs
+    std::vector<ov::SoPtr<ov::IVariableState>> query_state() const override;
+    std::vector<ov::ProfilingInfo> get_profiling_info() const override;
+
+private:
+    // implement IBaseInferRequest
+    void prepare_for_infer() override;
+    bool valid_subrequest(std::size_t idx) const override;
+    void start_subrequest(std::size_t idx) override;
+    void run_subrequest_for_success(std::size_t idx, bool& failover) override;
+    void subscribe_subrequest(std::size_t idx, Completed cb) override;
+    void complete_subrequest(std::size_t idx) override;
+    void cancel_subrequest(std::size_t idx) override;
+    std::size_t total_subrequests() const override;
+    bool supports_async_pipeline() const override;
+
+    void update_subrequest_links(std::size_t idx) override;
+
+    // FIXME: probably this one should go to the base class too
+    RqPtr get_real_subrequest(std::size_t idx);
+    void bind_params_results();
+    void function_prologue(std::size_t idx);
+    void unpack_closure(std::size_t idx, RqPtr request);
+
+    void connect_subrequests();
+
+    void recreate_subrequests(std::size_t idx);
+
+    static constexpr const std::size_t INVALID_IDX = std::numeric_limits<std::size_t>::max();
+
+    using LinkFrom = std::pair<std::size_t /* Subrequest index */
+                               ,
+                               std::size_t /* Subrequest output index */
+                               >;          // FIXME: This is a third, if not fourth, definitiion of such structure
+    using TensorPtr = ov::SoPtr<ov::ITensor>;
+    std::map<LinkFrom, TensorPtr> m_funcall_result;
+
+    using ToSubmodel = std::pair<std::size_t /* Subrequest index */
+                                 ,
+                                 std::size_t /* Subrequest input index */
+                                 >;          // Fixme: fourth installment?
+    std::map<ToSubmodel, ov::Output<const ov::Node>> m_reader_to_orig_port;
+
+    // FIXME: STOP USING ov::Output<> AT ALL! It is a weak feature
+    // These objects get discarded on occasional model recompilation
+    std::map<ov::Output<const ov::Node>, size_t> m_port_to_subrequest_idx;
+    std::map<ov::Output<const ov::Node>,
+             ov::Output<const ov::Node>>
+        m_port_orig_to_sub;  // FIXME: this one likely replaces `m_port_to_subrequest_idx'
+
+    bool m_use_function_pipelining = false;
+    struct FuncallPipeline {
+        // A "brother" subrequest for a "primary" subrequest. Initialized only
+        // for function bodies (replaced_by == idx)
+        RqPtr subrequest;
+
+        // Index of the next subrequest in the function call pipeline, if any.
+        // Initialized for all funcalls for every function.
+        std::optional<std::size_t> next;
+    };
+    std::vector<std::size_t> m_funcall_heads;
+
+    // This is a sparse vector. It will have the size == number of
+    // subgraphs, but with only function call-related elements
+    // initialized.
+    std::vector<FuncallPipeline> m_funcall_pipeline;
+};
+
+}  // namespace npuw
+}  // namespace ov
diff --git a/src/plugins/intel_npu/src/plugin/npuw/logging.cpp b/src/plugins/intel_npu/src/plugin/npuw/logging.cpp
new file mode 100644
index 00000000000000..080e57fbdca5a4
--- /dev/null
+++ b/src/plugins/intel_npu/src/plugin/npuw/logging.cpp
@@ -0,0 +1,128 @@
+// Copyright (C) 2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "logging.hpp"
+
+#include <ctime>
+#include <iomanip>
+#include <iostream>
+#include <mutex>
+
+namespace {
+#ifdef NPU_PLUGIN_DEVELOPER_BUILD
+const char* get_env(const std::vector<std::string>& list_to_try) {
+    for (auto&& key : list_to_try) {
+        const char* pstr = std::getenv(key.c_str());
+        if (pstr)
+            return pstr;
+    }
+    return nullptr;
+}
+#endif
+}  // anonymous namespace
+
+ov::npuw::LogLevel ov::npuw::get_log_level() {
+    static LogLevel log_level = LogLevel::None;
+#ifdef NPU_PLUGIN_DEVELOPER_BUILD
+    static std::once_flag flag;
+
+    std::call_once(flag, []() {
+        const auto* log_opt = get_env({"OPENVINO_NPUW_LOG_LEVEL", "OPENVINO_NPUW_LOG"});
+        if (!log_opt) {
+            return;
+        } else if (log_opt == std::string("ERROR")) {
+            log_level = ov::npuw::LogLevel::Error;
+        } else if (log_opt == std::string("WARNING")) {
+            log_level = ov::npuw::LogLevel::Warning;
+        } else if (log_opt == std::string("INFO")) {
+            log_level = ov::npuw::LogLevel::Info;
+        } else if (log_opt == std::string("VERBOSE")) {
+            log_level = ov::npuw::LogLevel::Verbose;
+        } else if (log_opt == std::string("DEBUG")) {
+            log_level = LogLevel::Debug;
+        }
+    });
+#endif
+    return log_level;
+}
+
+thread_local int ov::npuw::__logging_indent__::this_indent = 0;
+
+ov::npuw::__logging_indent__::__logging_indent__() {
+    ++this_indent;
+}
+
+ov::npuw::__logging_indent__::~__logging_indent__() {
+    this_indent = std::max(0, this_indent - 1);
+}
+
+int ov::npuw::__logging_indent__::__level__() {
+    return this_indent;
+}
+
+void ov::npuw::dump_tensor(const ov::SoPtr<ov::ITensor>& tensor, const std::string& base_path) {
+    if (!tensor->is_continuous()) {
+        LOG_ERROR("Failed to dump blob " << base_path << ": it is not continuous");
+        return;
+    }
+
+    const auto bin_path = base_path + ".bin";
+    {
+        std::ofstream bin_file(bin_path, std::ios_base::out | std::ios_base::binary);
+        bin_file.write(static_cast<const char*>(tensor->data()), static_cast<std::streamsize>(tensor->get_byte_size()));
+        LOG_INFO("Wrote file " << bin_path << "...");
+    }
+    const auto meta_path = base_path + ".txt";
+    {
+        std::ofstream meta_file(meta_path);
+        meta_file << tensor->get_element_type() << ' ' << tensor->get_shape() << std::endl;
+        LOG_INFO("Wrote file " << meta_path << "...");
+    }
+}
+
+static void dump_file_list(const std::string list_path, const std::vector<std::string>& base_names) {
+    std::ofstream list_file(list_path);
+
+    if (base_names.empty()) {
+        return;  // That's it. But create file anyway!
+    }
+
+    auto iter = base_names.begin();
+    list_file << *iter << ".bin";
+    while (++iter != base_names.end()) {
+        list_file << ";" << *iter << ".bin";
+    }
+}
+
+void ov::npuw::dump_input_list(const std::string base_name, const std::vector<std::string>& base_input_names) {
+    // dump a list of input/output files for sit.py's --inputs argument
+    // note the file has no newline to allow use like
+    //
+    // sit.py --inputs "$(cat model_ilist.txt)"
+    const auto ilist_path = base_name + "_ilist.txt";
+    dump_file_list(ilist_path, base_input_names);
+    LOG_INFO("Wrote input list " << ilist_path << "...");
+}
+
+void ov::npuw::dump_output_list(const std::string base_name, const std::vector<std::string>& base_output_names) {
+    const auto olist_path = base_name + "_olist.txt";
+    dump_file_list(olist_path, base_output_names);
+    LOG_INFO("Wrote output list " << olist_path << "...");
+}
+
+void ov::npuw::dump_failure(const std::shared_ptr<ov::Model>& model, const std::string& device, const char* extra) {
+    const auto model_path = "failed_" + model->get_friendly_name() + ".xml";
+    const auto extra_path = "failed_" + model->get_friendly_name() + ".txt";
+
+    ov::save_model(model, model_path);
+
+    std::ofstream details(extra_path, std::ios_base::app);
+    auto t = std::time(nullptr);
+    auto tm = *std::localtime(&t);
+    details << std::put_time(&tm, "%d-%m-%Y %H:%M:%S") << ": Failed to compile submodel for " << device << ", error:\n"
+            << extra << "\n"
+            << std::endl;
+
+    LOG_INFO("Saved model to " << model_path << " with details in " << extra_path);
+}
diff --git a/src/plugins/intel_npu/src/plugin/npuw/logging.hpp b/src/plugins/intel_npu/src/plugin/npuw/logging.hpp
new file mode 100644
index 00000000000000..b258e3e6e6bfe9
--- /dev/null
+++ b/src/plugins/intel_npu/src/plugin/npuw/logging.hpp
@@ -0,0 +1,64 @@
+// Copyright (C) 2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <fstream>
+
+#include "openvino/openvino.hpp"
+#include "openvino/runtime/itensor.hpp"
+#include "openvino/runtime/so_ptr.hpp"
+
+namespace ov {
+
+namespace npuw {
+
+enum class LogLevel { None = 0, Error = 1, Warning = 2, Info = 3, Verbose = 4, Debug = 5 };
+
+LogLevel get_log_level();
+
+class __logging_indent__ {
+    static thread_local int this_indent;
+
+public:
+    __logging_indent__();
+    ~__logging_indent__();
+    static int __level__();
+};
+
+void dump_tensor(const ov::SoPtr<ov::ITensor>& tensor, const std::string& base_path);
+
+void dump_input_list(const std::string base_name, const std::vector<std::string>& base_input_names);
+
+void dump_output_list(const std::string base_name, const std::vector<std::string>& base_output_names);
+
+void dump_failure(const std::shared_ptr<ov::Model>& model, const std::string& device, const char* extra);
+}  // namespace npuw
+}  // namespace ov
+
+#define LOG_IMPL(str, level, levelstr)                                        \
+    do {                                                                      \
+        if (ov::npuw::get_log_level() >= ov::npuw::LogLevel::level) {         \
+            std::cout << "[ NPUW:" levelstr " ] ";                            \
+            const int this_level = ov::npuw::__logging_indent__::__level__(); \
+            for (int i = 0; i < this_level; i++)                              \
+                std::cout << "    ";                                          \
+            std::cout << str << std::endl;                                    \
+        }                                                                     \
+    } while (0)
+
+#define LOG_INFO(str)  LOG_IMPL(str, Info, "INFO")
+#define LOG_WARN(str)  LOG_IMPL(str, Warning, "WARN")
+#define LOG_ERROR(str) LOG_IMPL(str, Error, " ERR")
+#define LOG_DEBUG(str) LOG_IMPL(str, Debug, " DBG")
+#define LOG_VERB(str)  LOG_IMPL(str, Verbose, "VERB")
+
+#define LOG_BLOCK() ov::npuw::__logging_indent__ object_you_should_never_use__##__LINE__
+
+#define NPUW_ASSERT(expr)                                       \
+    do {                                                        \
+        if (!(expr)) {                                          \
+            OPENVINO_THROW("NPUW: Assertion " #expr " failed"); \
+        }                                                       \
+    } while (0)
diff --git a/src/plugins/intel_npu/src/plugin/npuw/partitioning/online/compiler.cpp b/src/plugins/intel_npu/src/plugin/npuw/partitioning/online/compiler.cpp
new file mode 100644
index 00000000000000..27c1c38f3ab02a
--- /dev/null
+++ b/src/plugins/intel_npu/src/plugin/npuw/partitioning/online/compiler.cpp
@@ -0,0 +1,306 @@
+// Copyright (C) 2024 Intel Corporationov::npuw::
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "compiler.hpp"
+
+#include <iostream>
+#include <sstream>
+
+#include "../../logging.hpp"
+#include "../../util.hpp"
+#include "group.hpp"
+#include "intel_npu/al/config/config.hpp"
+#include "intel_npu/al/config/npuw.hpp"
+#include "pugixml.hpp"
+#include "snapshot.hpp"
+
+namespace ov {
+namespace npuw {
+namespace online {
+
+namespace detail {
+// For missing declaration warning
+size_t getMinGraphSize(const std::shared_ptr<ov::Model>& model, ::intel_npu::Config& cfg);
+std::vector<Avoid> getAvoids(const std::shared_ptr<ov::Model>& model, ::intel_npu::Config& cfg);
+void dump_partitioning(const ov::npuw::Ensemble& ens, const std::shared_ptr<ov::Model>& model, const std::string& to);
+
+size_t getMinGraphSize(const std::shared_ptr<ov::Model>& model, ::intel_npu::Config& cfg) {
+    std::size_t min_size = cfg.get<::intel_npu::NPUW_ONLINE_MIN_SIZE>();
+
+    // Sanity check
+    if (min_size < 10) {
+        LOG_WARN("Minimum possible partitioning size is too small: " << min_size << ", using a default value of 10.");
+        min_size = 10;
+    }
+
+    LOG_INFO("Online partitioning will continue until there are " << min_size << " or less subgraphs.");
+
+    return min_size;
+}
+
+std::vector<Avoid> getAvoids(const std::shared_ptr<ov::Model>& model, ::intel_npu::Config& cfg) {
+    std::vector<Avoid> avoids;
+
+    std::string avoids_opt = cfg.getString<::intel_npu::NPUW_ONLINE_AVOID>();
+    if (avoids_opt.empty()) {
+        LOG_WARN(::intel_npu::NPUW_ONLINE_AVOID().key()
+                 << " property is not set! NPU device will be prioritized for every subgraph.");
+        return {};
+    }
+
+    std::string s = avoids_opt;
+
+    size_t pos = 0;
+    size_t start = 0;
+    std::string token;
+
+    while ((pos = s.find(',', start)) != std::string::npos) {
+        token = s.substr(start, pos - start);
+        auto avoid_opt = util::parseAvoid(token);
+        // Check that parsing was a success
+        if (avoid_opt) {
+            avoids.push_back(*avoid_opt);
+        }
+        start = pos + 1;
+    }
+
+    // Parse the tail
+    auto avoid_opt = util::parseAvoid(s.substr(start, s.size() - start));
+    // Check that parsing was a success
+    if (avoid_opt) {
+        avoids.push_back(*avoid_opt);
+    }
+
+    if (!avoids.empty()) {
+        LOG_INFO("Online partitioning will avoid running subgraphs containing specified patterns on their respective "
+                 "devices.");
+    } else {
+        LOG_WARN("Incorect pattern in OPENVINO_NPUW_AVOID!"
+                 << " Please, follow the example: Op:Select/NPU,P:RMSNorm/NPU."
+                 << " No avoid rules will be taken into account during execution!");
+    }
+
+    return avoids;
+}
+
+void dump_partitioning(const ov::npuw::Ensemble& ens, const std::shared_ptr<ov::Model>& model, const std::string& to) {
+    pugi::xml_document doc;
+
+    pugi::xml_node node = doc.append_child("ensemble");
+    node.append_attribute("gflops") = std::to_string(ens.gflops).data();
+
+    pugi::xml_node part = node.append_child("partitioning");
+    pugi::xml_node rep;
+    if (!ens.repeated.empty()) {
+        rep = node.append_child("repeated");
+    }
+
+    size_t gid = 0;
+    for (const auto& group : ens.groups) {
+        pugi::xml_node gr = part.append_child("group");
+        gr.append_attribute("id") = std::to_string(gid++).data();
+        gr.append_attribute("gflops") = std::to_string(group.gflops).data();
+        if (!group.repeated_id.empty()) {
+            gr.append_attribute("repeated") = group.repeated_id.data();
+        }
+        if (!group.avoid_list.empty()) {
+            gr.append_attribute("avoid") = group.avoid_list.data();
+        }
+
+        // Note: Ensemble also add "id" attribute but it's not used by the plugin
+        for (const auto& input : group.input_layers) {
+            pugi::xml_node in = gr.append_child("input");
+            in.append_attribute("name") = input.data();
+        }
+        for (const auto& output : group.output_layers) {
+            pugi::xml_node out = gr.append_child("output");
+            out.append_attribute("name") = output.data();
+        }
+        for (const auto& layer : group.all_layers) {
+            pugi::xml_node l = gr.append_child("layer");
+            l.append_attribute("name") = layer.data();
+        }
+    }
+
+    for (const auto& repeated : ens.repeated) {
+        pugi::xml_node bl = rep.append_child("block");
+        bl.append_attribute("id") = repeated.first.data();
+
+        for (const auto& match : repeated.second.matches) {
+            // Note: Ensemble also add "like" attribute but it's not used by the plugin
+            pugi::xml_node mt = bl.append_child("match");
+
+            for (const auto& layer : match) {
+                pugi::xml_node l = mt.append_child("layer");
+                l.append_attribute("name") = layer.data();
+            }
+        }
+    }
+
+    doc.save_file(to.data());
+}
+}  // namespace detail
+
+// Interface to get online partitioning from the model
+class Compiler {
+    enum class Pipeline {
+        NONE,  // Do nothing, partitioning will be empty
+        INIT,  // Initialize only. The hardest mode, every group has just 1 layer inside
+        JUST,  // "justParitioning" - combination of LHF + Remnants
+        REP,   // Repeated blocks pipeline - combination of repeatedBlocks and Remnants - default configuration
+    };
+
+    Pipeline currentPipeline(const std::shared_ptr<ov::Model>& model) {
+        std::string pipeline_opt = m_cfg.getString<::intel_npu::NPUW_ONLINE_PIPELINE>();
+        if (pipeline_opt == "NONE") {
+            return Pipeline::NONE;
+        } else if (pipeline_opt == "INIT") {
+            return Pipeline::INIT;
+        } else if (pipeline_opt == "JUST") {
+            return Pipeline::JUST;
+        } else if (pipeline_opt == "REP") {
+            return Pipeline::REP;
+        } else {
+            LOG_WARN("Unknown partitioning compiler pipeline " << pipeline_opt << ", switching to REP");
+            return Pipeline::REP;
+        }
+    }
+
+    // Compiler pipelines
+    void init() {
+        LOG_INFO("Online partitioning: compiling initial pipeline...");
+        LOG_BLOCK();
+
+        // Literally there's nothing to do for now.
+
+        LOG_INFO("Done");
+    }
+
+    void just() {
+        LOG_INFO("Online partitioning: compiling fixed pipeline...");
+        LOG_BLOCK();
+
+        m_snapshot->repeat([&] {
+            m_snapshot->collectLHF();
+        });
+        m_snapshot->repeat([&] {
+            m_snapshot->fuseRemnants();
+        });
+
+        LOG_INFO("Done");
+    }
+
+    void rep() {
+        LOG_INFO("Online partitioning: compiling repeated blocks pipeline...");
+        LOG_BLOCK();
+
+        m_snapshot->earlyAvoids();
+        m_snapshot->repeatedBlocks();
+        m_snapshot->repeat([&] {
+            m_snapshot->fuseRemnantsExtended();
+        });
+
+        LOG_INFO("Done");
+    }
+
+public:
+    explicit Compiler(const std::shared_ptr<ov::Model>& model, ::intel_npu::Config& cfg)
+        : m_model(model),
+          m_snapshot(std::make_shared<Snapshot>(model)),
+          m_cfg(cfg) {
+        if (currentPipeline(model) == Pipeline::NONE) {
+            LOG_INFO("NPUW_ONLINE_PIPELINE is set to NONE. No online partitioning is done.");
+            return;
+        }
+
+        // Parse OV Model into internal data structures. After this
+        // stage each layer = it's own group (excluding Parameters,
+        // Results, Constants and Converts).
+        LOG_INFO("Online partitioning: building initial graph...");
+        m_snapshot->buildGraph();
+
+        PassContext ctx;
+        ctx.min_graph_size = detail::getMinGraphSize(model, m_cfg);
+        ctx.avoids = detail::getAvoids(model, m_cfg);
+
+        m_snapshot->setCtx(ctx);
+
+        switch (currentPipeline(model)) {
+        case Pipeline::NONE:
+            break;  // unreachable
+        case Pipeline::INIT:
+            init();
+            break;
+        case Pipeline::JUST:
+            just();
+            break;
+        case Pipeline::REP:
+            rep();
+            break;
+        }
+
+        LOG_DEBUG("Online partitioning: group sizes after compilation:");
+        auto graph = m_snapshot->getGraph();
+        for (const auto& nh : graph->sorted()) {
+            LOG_BLOCK();
+            Group::GPtr group = graph->meta(nh).get<Group::GPtr>();
+            LOG_DEBUG("Group " << group->getId() << ", size " << group->size());
+        }
+        LOG_INFO("Done");
+    }
+
+    ov::npuw::Ensemble getPartitioning() {
+        LOG_INFO("Online partitioning: converting to plugin-compatible format...");
+        LOG_BLOCK();
+
+        ov::npuw::Ensemble ens;
+        ens.gflops = 1.;  // FIXME: calculate proper flops
+
+        auto graph = m_snapshot->getGraph();
+        // Iterate in topological order
+        for (const auto& nh : graph->sorted()) {
+            auto gptr = graph->meta(nh).get<ov::npuw::online::Group::GPtr>();
+            ens.groups.emplace_back(gptr->toGroup());
+        }
+
+        std::map<std::string, ov::npuw::RepeatedBlock> repeated;
+        for (const auto& reptag_and_matches : m_snapshot->getMatches()) {
+            LOG_BLOCK();
+            ov::npuw::RepeatedBlock block;
+            block.matches = reptag_and_matches.second;  // Other fields are filled inside the plugin
+            repeated.insert({reptag_and_matches.first, block});
+            LOG_INFO("Got " << block.matches.at(0).size() << " repeated blocks of size " << block.matches.size());
+        }
+        ens.repeated = repeated;
+
+        std::string dump_plan_path = m_cfg.get<::intel_npu::NPUW_ONLINE_DUMP_PLAN>();
+        if (!dump_plan_path.empty()) {
+            detail::dump_partitioning(ens, m_model, dump_plan_path);
+            LOG_INFO("Dumped online partitioning to " << dump_plan_path << ".");
+        }
+
+        LOG_INFO("DONE.");
+
+        return ens;
+    }
+
+private:
+    std::shared_ptr<ov::Model> m_model;
+    std::shared_ptr<Snapshot> m_snapshot;
+    ::intel_npu::Config& m_cfg;
+};
+
+}  // namespace online
+}  // namespace npuw
+}  // namespace ov
+
+// TODO: decouple configuration for partitioning from the plugin's cfg.
+ov::npuw::Ensemble ov::npuw::online::buildPartitioning(const std::shared_ptr<ov::Model>& model,
+                                                       ::intel_npu::Config& cfg) {
+    // Creates compiler and runs partitioning algorithm.
+    ov::npuw::online::Compiler partitioner(model, cfg);
+
+    // Convert groups formed after passes into plugin-compatible data structure.
+    return partitioner.getPartitioning();
+}
diff --git a/src/plugins/intel_npu/src/plugin/npuw/partitioning/online/compiler.hpp b/src/plugins/intel_npu/src/plugin/npuw/partitioning/online/compiler.hpp
new file mode 100644
index 00000000000000..69527222612417
--- /dev/null
+++ b/src/plugins/intel_npu/src/plugin/npuw/partitioning/online/compiler.hpp
@@ -0,0 +1,21 @@
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <memory>
+
+#include "../partitioning.hpp"  // ov::npuw::Ensemble
+#include "intel_npu/al/config/config.hpp"
+#include "openvino/openvino.hpp"
+
+namespace ov {
+namespace npuw {
+namespace online {
+
+ov::npuw::Ensemble buildPartitioning(const std::shared_ptr<ov::Model>& model, ::intel_npu::Config& cfg);
+
+}  // namespace online
+}  // namespace npuw
+}  // namespace ov
diff --git a/src/plugins/intel_npu/src/plugin/npuw/partitioning/online/graph.cpp b/src/plugins/intel_npu/src/plugin/npuw/partitioning/online/graph.cpp
new file mode 100644
index 00000000000000..7fc74923831b70
--- /dev/null
+++ b/src/plugins/intel_npu/src/plugin/npuw/partitioning/online/graph.cpp
@@ -0,0 +1,186 @@
+//
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "graph.hpp"
+
+#include <algorithm>
+#include <stack>
+
+ade::Nodes ade::Node::srcNodes() const {
+    ade::Nodes src_nodes;
+    src_nodes.reserve(m_src_edges.size());
+    std::transform(m_src_edges.begin(), m_src_edges.end(), std::back_inserter(src_nodes), [](ade::EdgeHandle edge) {
+        return edge->srcNode();
+    });
+    // FIXME: this was introduced to make the graph
+    // the same every run when created the same way.
+    // FIXME: cache this information
+    std::sort(src_nodes.begin(), src_nodes.end(), [&](const ade::NodeHandle& a, const ade::NodeHandle& b) {
+        auto locked_graph = m_graph.lock();
+        return locked_graph->meta(a).get<detail::CreateIdx>().m_idx <
+               locked_graph->meta(b).get<detail::CreateIdx>().m_idx;
+    });
+    return src_nodes;
+}
+
+ade::Nodes ade::Node::dstNodes() const {
+    ade::Nodes dst_nodes;
+    dst_nodes.reserve(m_dst_edges.size());
+    std::transform(m_dst_edges.begin(), m_dst_edges.end(), std::back_inserter(dst_nodes), [](ade::EdgeHandle edge) {
+        return edge->dstNode();
+    });
+    // FIXME: this was introduced to make the graph
+    // the same every run when created the same way.
+    // FIXME: cache this information
+    std::sort(dst_nodes.begin(), dst_nodes.end(), [&](const ade::NodeHandle& a, const ade::NodeHandle& b) {
+        auto locked_graph = m_graph.lock();
+        return locked_graph->meta(a).get<detail::CreateIdx>().m_idx <
+               locked_graph->meta(b).get<detail::CreateIdx>().m_idx;
+    });
+    return dst_nodes;
+}
+
+ade::Edges ade::Node::srcEdges() const {
+    return {m_src_edges.begin(), m_src_edges.end()};
+}
+
+ade::Edges ade::Node::dstEdges() const {
+    return {m_dst_edges.begin(), m_dst_edges.end()};
+}
+
+ade::NodeHandle ade::Graph::create() {
+    auto node = std::make_shared<ade::Node>(shared_from_this());
+    ade::NodeHandle nh(node);
+    m_nodes.emplace(node.get(), MetaPtr<ade::Node>{node, ade::Meta{}});
+    this->meta(nh).set(detail::CreateIdx{m_create_idx++});
+    return nh;
+}
+
+void ade::Graph::remove(ade::NodeHandle nh) {
+    auto src_edges = nh->srcEdges();
+    for (size_t i = 0; i < src_edges.size(); ++i) {
+        remove(src_edges[i]);
+    }
+    auto dst_edges = nh->dstEdges();
+    for (size_t i = 0; i < dst_edges.size(); ++i) {
+        remove(dst_edges[i]);
+    }
+    m_nodes.erase(nh.get());
+}
+
+void ade::Graph::remove(ade::EdgeHandle eh) {
+    auto src = eh->srcNode();
+    auto dst = eh->dstNode();
+    src->m_dst_edges.erase(eh);
+    dst->m_src_edges.erase(eh);
+    m_edges.erase(eh.get());
+}
+
+ade::EdgeHandle ade::Graph::link(ade::NodeHandle src, ade::NodeHandle dst) {
+    auto edge = std::make_shared<ade::Edge>(src, dst);
+    ade::EdgeHandle eh{edge};
+    m_edges.emplace(edge.get(), MetaPtr<ade::Edge>{edge, ade::Meta{}});
+    src->m_dst_edges.insert(eh);
+    dst->m_src_edges.insert(eh);
+    return eh;
+}
+
+ade::Meta& ade::Graph::meta(ade::NodeHandle handle) {
+    const auto it = m_nodes.find(handle.get());
+    ASSERT(it != m_nodes.end());
+    return it->second.meta;
+}
+
+const ade::Meta& ade::Graph::meta(ade::NodeHandle handle) const {
+    const auto it = m_nodes.find(handle.get());
+    ASSERT(it != m_nodes.end());
+    return it->second.meta;
+}
+
+ade::Meta& ade::Graph::meta(ade::EdgeHandle handle) {
+    const auto it = m_edges.find(handle.get());
+    ASSERT(it != m_edges.end());
+    return it->second.meta;
+}
+
+const ade::Meta& ade::Graph::meta(ade::EdgeHandle handle) const {
+    const auto it = m_edges.find(handle.get());
+    ASSERT(it != m_edges.end());
+    return it->second.meta;
+}
+
+bool ade::Graph::contains(ade::NodeHandle handle) const {
+    return m_nodes.find(handle.get()) != m_nodes.end();
+}
+
+bool ade::Graph::linked(ade::NodeHandle src, ade::NodeHandle dst) {
+    for (const auto& edge : src->m_dst_edges) {
+        if (edge->dstNode() == dst) {
+            return true;
+        }
+    }
+    return false;
+}
+
+std::vector<ade::NodeHandle> ade::Graph::nodes() const {
+    std::vector<ade::NodeHandle> ret;
+    std::transform(m_nodes.begin(), m_nodes.end(), std::back_inserter(ret), [](const auto& p) {
+        return ade::NodeHandle{p.second.ptr};
+    });
+    return ret;
+}
+
+void ade::Graph::dfs(ade::NodeHandle& nh,
+                     std::unordered_set<ade::NodeHandle>& visited,
+                     std::stack<ade::NodeHandle>& stack) const {
+    visited.insert(nh);
+    auto dst_nodes = nh->dstNodes();
+
+    // FIXME: this was introduced to make the graph
+    // the same every run when created the same way.
+    std::sort(dst_nodes.begin(), dst_nodes.end(), [&](const ade::NodeHandle& a, const ade::NodeHandle& b) {
+        return this->meta(a).get<detail::CreateIdx>().m_idx < this->meta(b).get<detail::CreateIdx>().m_idx;
+    });
+
+    for (auto dst_nh : dst_nodes) {
+        auto it = visited.find(dst_nh);
+        if (it == visited.end()) {
+            dfs(dst_nh, visited, stack);
+        }
+    }
+    stack.push(nh);
+}
+
+std::vector<ade::NodeHandle> ade::Graph::sorted() const {
+    std::unordered_set<ade::NodeHandle> visited;
+    std::stack<ade::NodeHandle> stack;
+    auto nodes = this->nodes();
+
+    // FIXME: this was introduced to make the graph
+    // the same every run when created the same way.
+    std::sort(nodes.begin(), nodes.end(), [&](const ade::NodeHandle& a, const ade::NodeHandle& b) {
+        return this->meta(a).get<detail::CreateIdx>().m_idx < this->meta(b).get<detail::CreateIdx>().m_idx;
+    });
+
+    for (auto nh : nodes) {
+        auto it = visited.find(nh);
+        if (it == visited.end()) {
+            dfs(nh, visited, stack);
+        }
+    }
+    std::vector<ade::NodeHandle> sorted;
+    while (!stack.empty()) {
+        sorted.push_back(stack.top());
+        stack.pop();
+    }
+    return sorted;
+}
+
+ade::Meta& ade::Meta::operator+=(const ade::Meta& other) {
+    for (const auto& p : other.store) {
+        ASSERT(store.emplace(p.first, p.second).second);
+    }
+    return *this;
+}
diff --git a/src/plugins/intel_npu/src/plugin/npuw/partitioning/online/graph.hpp b/src/plugins/intel_npu/src/plugin/npuw/partitioning/online/graph.hpp
new file mode 100644
index 00000000000000..a8992dcc483ae9
--- /dev/null
+++ b/src/plugins/intel_npu/src/plugin/npuw/partitioning/online/graph.hpp
@@ -0,0 +1,219 @@
+//
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <any>
+#include <functional>
+#include <iostream>
+#include <memory>
+#include <sstream>
+#include <stack>
+#include <typeindex>
+#include <unordered_map>
+#include <unordered_set>
+#include <vector>
+
+namespace {
+namespace detail {
+[[noreturn]] inline void assert_abort(const char* str, const int line, const char* file, const char* func) {
+    std::stringstream ss;
+    ss << file << ":" << line << ": Assertion " << str << " in function " << func << " failed\n";
+    std::cerr << ss.str() << std::flush;
+    abort();
+}
+
+[[noreturn]] inline void throw_error(const char* str) {
+    std::stringstream ss;
+    ss << "An exception thrown! " << str << std::flush;
+    throw std::logic_error(ss.str());
+}
+}  // namespace detail
+
+#define ASSERT(expr)                                                     \
+    {                                                                    \
+        if (!(expr))                                                     \
+            ::detail::assert_abort(#expr, __LINE__, __FILE__, __func__); \
+    }
+
+#define THROW_ERROR(msg)                         \
+    {                                            \
+        std::ostringstream os;                   \
+        os << msg;                               \
+        ::detail::throw_error(os.str().c_str()); \
+    }
+}  // anonymous namespace
+
+namespace ade {
+
+namespace detail {
+template <typename T>
+class WeakHandle {
+public:
+    explicit WeakHandle(std::shared_ptr<T> obj) : m_obj(obj) {}
+    T* get() const {
+        return m_obj.lock().get();
+    }
+    T* operator->() const {
+        return get();
+    }
+    bool operator==(const WeakHandle& other) const {
+        return get() == other.get();
+    }
+
+private:
+    std::weak_ptr<T> m_obj;
+};
+
+struct CreateIdx {
+    size_t m_idx = 0;
+};
+
+}  // namespace detail
+}  // namespace ade
+
+namespace std {
+template <typename T>
+struct hash<ade::detail::WeakHandle<T>> {
+    uint64_t operator()(const ade::detail::WeakHandle<T>& handle) const {
+        return std::hash<T*>()(handle.get());
+    }
+};
+}  // namespace std
+
+namespace ade {
+
+class Graph;
+class Node;
+class Edge;
+
+using NodeHandle = detail::WeakHandle<Node>;
+using EdgeHandle = detail::WeakHandle<Edge>;
+using Nodes = std::vector<NodeHandle>;
+using Edges = std::vector<EdgeHandle>;
+using EdgeSet = std::unordered_set<EdgeHandle>;
+
+class Node {
+    friend class Graph;
+    using Ptr = std::shared_ptr<Node>;
+
+public:
+    explicit Node() = delete;
+    explicit Node(const std::weak_ptr<Graph>& graph) : m_graph(graph) {}
+    Nodes srcNodes() const;
+    Nodes dstNodes() const;
+    Edges srcEdges() const;
+    Edges dstEdges() const;
+
+private:
+    EdgeSet m_src_edges;
+    EdgeSet m_dst_edges;
+    std::weak_ptr<Graph> m_graph;
+};
+
+class Edge {
+    friend class Graph;
+    using Ptr = std::shared_ptr<Edge>;
+
+public:
+    Edge(NodeHandle src, NodeHandle dst) : m_src(src), m_dst(dst) {}
+    NodeHandle srcNode() const {
+        return m_src;
+    }
+    NodeHandle dstNode() const {
+        return m_dst;
+    }
+
+private:
+    NodeHandle m_src;
+    NodeHandle m_dst;
+};
+
+class Meta {
+public:
+    template <typename T>
+    void set(T&& meta);
+    template <typename T>
+    const T& get() const;
+    template <typename T>
+    T& get();
+    template <typename T>
+    bool has() const;
+    Meta& operator+=(const Meta& other);
+
+private:
+    using MetaStore = std::unordered_map<std::type_index, std::any>;
+    MetaStore store;
+};
+
+template <typename T>
+void Meta::set(T&& meta) {
+    // NB: Check if there is no such meta yet.
+    ASSERT(store.emplace(std::type_index(typeid(T)), std::forward<T>(meta)).second);
+}
+
+template <typename T>
+bool Meta::has() const {
+    auto it = store.find(std::type_index(typeid(T)));
+    return it != store.end();
+}
+
+template <typename T>
+const T& Meta::get() const {
+    const auto it = store.find(std::type_index(typeid(T)));
+    ASSERT(it != store.end());
+    return *std::any_cast<T>(&it->second);
+}
+
+template <typename T>
+T& Meta::get() {
+    auto it = store.find(std::type_index(typeid(T)));
+    ASSERT(it != store.end());
+    return *std::any_cast<T>(&it->second);
+}
+
+class Graph : public std::enable_shared_from_this<Graph> {
+public:
+    NodeHandle create();
+    void remove(NodeHandle nh);
+    void remove(EdgeHandle eh);
+    EdgeHandle link(NodeHandle src, NodeHandle dst);
+
+    Meta& meta() {
+        return m_graph_meta;
+    }
+    const Meta& meta() const {
+        return m_graph_meta;
+    }
+
+    Meta& meta(NodeHandle handle);
+    const Meta& meta(NodeHandle handle) const;
+    Meta& meta(EdgeHandle handle);
+    const Meta& meta(EdgeHandle handle) const;
+
+    bool contains(NodeHandle handle) const;
+    bool linked(NodeHandle src, NodeHandle dst);
+
+    std::vector<NodeHandle> nodes() const;
+    std::vector<NodeHandle> sorted() const;
+
+private:
+    void dfs(NodeHandle& nh, std::unordered_set<NodeHandle>& visited, std::stack<NodeHandle>& stack) const;
+
+    template <typename T>
+    struct MetaPtr {
+        std::shared_ptr<T> ptr;
+        Meta meta;
+    };
+    template <typename T>
+    using MetaMap = std::unordered_map<T*, MetaPtr<T>>;
+
+    Meta m_graph_meta;
+    MetaMap<Node> m_nodes;
+    MetaMap<Edge> m_edges;
+    size_t m_create_idx = 0;
+};
+
+}  // namespace ade
diff --git a/src/plugins/intel_npu/src/plugin/npuw/partitioning/online/group.cpp b/src/plugins/intel_npu/src/plugin/npuw/partitioning/online/group.cpp
new file mode 100644
index 00000000000000..c10c96088a3bc4
--- /dev/null
+++ b/src/plugins/intel_npu/src/plugin/npuw/partitioning/online/group.cpp
@@ -0,0 +1,384 @@
+// Copyright (C) 2024 Intel Corporationov::npuw::
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "group.hpp"
+
+#include <sstream>
+
+#include "../../logging.hpp"
+#include "../partitioning.hpp"  // ov::npuw::Group
+#include "openvino/op/util/op_types.hpp"
+#include "openvino/opsets/opset1.hpp"
+#include "openvino/util/common_util.hpp"
+#include "repeated.hpp"
+#include "snapshot.hpp"
+
+using ov::npuw::online::Group;
+using ov::npuw::online::Interconnect;
+using ov::npuw::online::MetaInterconnect;
+using ov::npuw::online::Repeated;
+using ov::npuw::online::detail::isOp;
+
+Group::Group(const std::shared_ptr<ov::Node>& node,
+             size_t gid,
+             ade::NodeHandle nh,
+             const std::shared_ptr<ade::Graph>& g,
+             const std::weak_ptr<Snapshot>& snapshot)
+    : m_nh(nh),
+      m_id(gid),
+      m_graph(g),
+      m_snapshot(snapshot) {
+    m_input_layers.insert(node);
+    m_output_layers.insert(node);
+    m_content.insert(node);
+}
+
+// Include Parameters, Outputs, Converts, etc to content's layers for proper linking at the plugin level
+void Group::includeExtraLayers(detail::OVNodeSet& input_layers,
+                               detail::OVNodeSet& output_layers,
+                               detail::OVNodeSet& content) const {
+    detail::OVNodeSet extra_content;
+    // Including writers of parameter layers and results' reader layers
+    for (const auto& layer : content) {
+        for (size_t i = 0; i < layer->inputs().size(); ++i) {
+            auto target_input = layer->get_input_source_output(i);
+            auto layer_parent = target_input.get_node()->shared_from_this();
+            if (ov::op::util::is_parameter(layer_parent)) {
+                input_layers.insert(layer);
+            }
+            // Also include Converts
+            if (!isOp(layer_parent)) {
+                if (!ov::op::util::is_constant(layer_parent) && !ov::op::util::is_parameter(layer_parent) &&
+                    !ov::op::util::is_output(layer_parent)) {
+                    NPUW_ASSERT(ov::is_type<ov::op::v0::Convert>(layer_parent));
+                    extra_content.insert(layer_parent);
+                }
+            }
+        }
+        for (size_t i = 0; i < layer->outputs().size(); ++i) {
+            const auto target_outputs = layer->get_output_target_inputs(i);
+            for (const auto& target_output : target_outputs) {
+                auto layer_child = target_output.get_node()->shared_from_this();
+                if (ov::op::util::is_output(layer_child)) {
+                    output_layers.insert(layer);
+                }
+            }
+        }
+    }
+
+    for (const auto& layer : extra_content) {
+        content.insert(layer);
+    }
+}
+
+// Convert this representation to plugin-compatible one
+ov::npuw::Group Group::toGroup() const {
+    auto input_copy = m_input_layers;
+    auto output_copy = m_output_layers;
+    auto content_copy = m_content;
+    includeExtraLayers(input_copy, output_copy, content_copy);
+
+    ov::npuw::Group g;
+    for (auto&& node : input_copy) {
+        g.input_layers.push_back(node->get_friendly_name());
+    }
+    for (auto&& node : output_copy) {
+        g.output_layers.push_back(node->get_friendly_name());
+    }
+    for (auto&& node : content_copy) {
+        g.all_layers.push_back(node->get_friendly_name());
+    }
+    g.gflops = 0.0001f;  // FIXME: calculate proper flops
+
+    if (m_repeated) {
+        g.repeated_id = ov::npuw::online::util::repeated_id(m_repeated);
+    }
+
+    if (!m_avoided_devices.empty()) {
+        auto iter = m_avoided_devices.begin();
+        g.avoid_list += *iter;
+        while (++iter != m_avoided_devices.end()) {
+            g.avoid_list += ',' + *iter;
+        }
+    }
+
+    return g;
+}
+
+// Note: can only be used after initial group initialization
+std::shared_ptr<ov::Node> Group::getInitialNode() const {
+    if (m_content.size() != 1) {
+        OPENVINO_THROW("Online partitioning initial group ", m_id, " doesn't consist of exactly 1 layer!");
+    }
+
+    return *(m_content.begin());
+}
+
+size_t Group::getId() const {
+    return m_id;
+}
+
+std::vector<ade::NodeHandle> Group::srcNodes() const {
+    return m_nh->srcNodes();
+}
+
+std::vector<ade::NodeHandle> Group::dstNodes() const {
+    return m_nh->dstNodes();
+}
+
+ade::NodeHandle Group::getHandle() const {
+    return m_nh;
+}
+
+// Not every input should be included - those layers which contained inside merging group are not inputs anymore
+void Group::updateInputLayers(const Group::GPtr& gptr_other) {
+    detail::OVNodeSet combined_input;
+    combined_input.insert(m_input_layers.begin(), m_input_layers.end());
+    combined_input.insert(gptr_other->m_input_layers.begin(), gptr_other->m_input_layers.end());
+    m_input_layers.clear();
+
+    auto locked_snapshot = m_snapshot.lock();
+
+    for (const auto& layer : combined_input) {
+        detail::OVNodeSet selected;
+        auto node_prod = locked_snapshot->getNodeProducers(layer);
+        for (const auto& prod : node_prod) {
+            if (m_content.find(prod) == m_content.end()) {
+                selected.insert(prod);
+            }
+        }
+        for (const auto& l : selected) {
+            if (isOp(l)) {
+                m_input_layers.insert(layer);
+            }
+        }
+    }
+}
+
+// Not every output should be included - those layers which are consumed _ONLY_ by the merged group are not outputs
+// anymore
+void Group::updateOutputLayers(const Group::GPtr& gptr_other) {
+    detail::OVNodeSet combined_output;
+    combined_output.insert(m_output_layers.begin(), m_output_layers.end());
+    combined_output.insert(gptr_other->m_output_layers.begin(), gptr_other->m_output_layers.end());
+    m_output_layers.clear();
+
+    auto locked_snapshot = m_snapshot.lock();
+
+    for (const auto& layer : combined_output) {
+        auto node_cons = locked_snapshot->getNodeConsumers(layer);
+        bool reject = true;
+        for (const auto& cons : node_cons) {
+            if (m_content.find(cons) == m_content.end()) {
+                reject = false;
+            }
+        }
+        if (!reject) {
+            m_output_layers.insert(layer);
+        }
+    }
+}
+
+void Group::relinkGraph(const Group::GPtr& gptr_other) {
+    auto producers = gptr_other->srcNodes();
+    auto consumers = gptr_other->dstNodes();
+
+    // Remove gptr_other node from the graph. Note: also removes all it's edges
+    m_graph->remove(gptr_other->getHandle());
+    for (const auto& nh : producers) {
+        if (m_nh == nh) {
+            continue;
+        }
+        // relink the graph
+        if (!m_graph->linked(nh, m_nh)) {
+            m_graph->link(nh, m_nh);
+        }
+    }
+    for (const auto& nh : consumers) {
+        if (m_nh == nh) {
+            continue;
+        }
+        // relink the graph
+        if (!m_graph->linked(m_nh, nh)) {
+            m_graph->link(m_nh, nh);
+        }
+    }
+}
+
+// This group absorbs the producer
+void Group::fuse(const Group::GPtr& gptr_prod) {
+    fuseWith(gptr_prod);
+}
+
+// This group absorbs the consumer
+void Group::fuseWith(const Group::GPtr& gptr_cons) {
+    // Update ov::node to ade::NodeHandle map
+    auto locked_snapshot = m_snapshot.lock();
+    auto node_to_gr = locked_snapshot->getNodeToGroupMap();
+    for (const auto& layer : gptr_cons->m_content) {
+        node_to_gr->at(layer) = shared_from_this();  // layers of consumer group are assigned to this group
+    }
+
+    // Merge 2 contents together
+    for (const auto& layer : gptr_cons->m_content) {
+        m_content.insert(layer);
+    }
+
+    takeFlags(gptr_cons);
+
+    updateInputLayers(gptr_cons);
+    updateOutputLayers(gptr_cons);
+    relinkGraph(gptr_cons);
+}
+
+// This group is unchanged, the inputs are merged together into the first one
+void Group::fuseInputs(const std::pair<Group::GPtr, Group::GPtr>& gptr_inputs) {
+    Group::GPtr absorbing_group = gptr_inputs.first;
+    Group::GPtr absorbed_group = gptr_inputs.second;
+
+    auto locked_snapshot = m_snapshot.lock();
+    auto node_to_gr = locked_snapshot->getNodeToGroupMap();
+
+    // Update ov::node to ade::NodeHandle map and merge all contents together
+    for (const auto& layer : absorbed_group->m_content) {
+        node_to_gr->at(layer) = absorbing_group;
+        absorbing_group->m_content.insert(layer);
+    }
+    absorbing_group->takeFlags(absorbed_group);
+    absorbing_group->updateInputLayers(absorbed_group);
+    absorbing_group->updateOutputLayers(absorbed_group);
+    absorbing_group->relinkGraph(absorbed_group);
+}
+
+// This group takes extra info of other group (such as reptrack, avoids, etc)
+void Group::takeFlags(const Group::GPtr& gptr_other) {
+    // Update reptrack
+    for (const auto& layer_to_track : gptr_other->m_reptrack) {
+        auto layer = layer_to_track.first;
+        auto track = layer_to_track.second;
+
+        for (const auto& rep : track) {
+            m_reptrack[layer].push_back(rep);
+        }
+    }
+    // Update avoids
+    for (const auto& device : gptr_other->avoidedTargets()) {
+        avoid(device);
+    }
+}
+
+// Check if there is indirect path from this to gptr_cons
+bool Group::hasCycle(const Group::GPtr& gptr_cons) const {
+    std::unordered_set<ade::NodeHandle> visited;
+
+    std::stack<ade::NodeHandle> st;
+
+    for (const auto& prod : gptr_cons->srcNodes()) {
+        // skip self during this iter
+        if (!(m_nh == prod)) {
+            st.push(prod);
+        }
+    }
+
+    while (!st.empty()) {
+        auto nh = st.top();
+        st.pop();
+        visited.insert(nh);
+
+        if (nh == m_nh) {
+            // Found another path from self to gptr_cons
+            return true;
+        }
+
+        for (const auto& prod : nh->srcNodes()) {
+            if (visited.find(prod) == visited.end()) {
+                st.push(prod);
+            }
+        }
+    }
+
+    return false;
+}
+
+size_t Group::size() const {
+    return m_content.size();
+}
+
+void Group::freeze() {
+    m_frozen = true;
+}
+
+bool Group::isFrozen() const {
+    return m_frozen;
+}
+
+const ov::npuw::online::detail::OVNodeSet& Group::getContent() const {
+    return m_content;
+}
+
+const ov::npuw::online::detail::Reptrack& Group::getReptrack(
+    const ov::npuw::online::detail::OVNodePtr& node_ptr) const {
+    if (m_reptrack.find(node_ptr) == m_reptrack.end()) {
+        OPENVINO_THROW("Online partitioning repeated track doesn't contain ", node_ptr->get_friendly_name());
+    }
+    return m_reptrack.at(node_ptr);
+}
+
+std::shared_ptr<Repeated> Group::repeated() const {
+    return m_repeated;
+}
+
+void Group::setRepeated(const std::shared_ptr<Repeated>& rep) {
+    m_repeated = rep;
+
+    if (!rep) {
+        return;
+    }
+
+    for (const auto& layer : m_content) {
+        m_reptrack[layer].push_back(m_repeated);
+    }
+}
+
+std::unordered_set<MetaInterconnect> Group::metaInterconnect(const Group::GPtr& gptr_prod) const {
+    std::unordered_set<MetaInterconnect> mics;
+
+    auto ics = interconnect(gptr_prod);
+    for (const auto& ic : ics) {
+        mics.insert({ov::npuw::online::util::getMetaDesc(ic.input_node),
+                     gptr_prod->m_reptrack.at(ic.input_node),
+                     ic.input_port,
+                     ov::npuw::online::util::getMetaDesc(ic.output_node),
+                     m_reptrack.at(ic.output_node),
+                     ic.output_port});
+    }
+
+    return mics;
+}
+
+std::unordered_set<Interconnect> Group::interconnect(const Group::GPtr& gptr_prod) const {
+    std::unordered_set<Interconnect> ics;
+
+    auto locked_snapshot = m_snapshot.lock();
+    auto ports_map = locked_snapshot->getPortsMap();
+
+    for (const auto& layer : m_content) {
+        for (const auto& input_layer : locked_snapshot->getNodeProducers(layer)) {
+            if (gptr_prod->m_content.find(input_layer) != gptr_prod->m_content.end()) {
+                auto ports = ports_map.at({input_layer, layer});
+                ics.insert({input_layer, ports.first, layer, ports.second});
+            }
+        }
+    }
+
+    return ics;
+}
+
+void Group::avoid(const std::string& device) {
+    m_avoided_devices.insert(device);
+}
+
+const std::set<std::string>& Group::avoidedTargets() const {
+    return m_avoided_devices;
+}
diff --git a/src/plugins/intel_npu/src/plugin/npuw/partitioning/online/group.hpp b/src/plugins/intel_npu/src/plugin/npuw/partitioning/online/group.hpp
new file mode 100644
index 00000000000000..b1a38b33897856
--- /dev/null
+++ b/src/plugins/intel_npu/src/plugin/npuw/partitioning/online/group.hpp
@@ -0,0 +1,100 @@
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <memory>
+#include <set>
+#include <unordered_set>
+#include <vector>
+
+#include "graph.hpp"
+#include "openvino/openvino.hpp"
+#include "utils/utils.hpp"
+
+namespace ov {
+namespace npuw {
+
+struct Group;  // forward declaration
+
+namespace online {
+
+class Snapshot;  // forward declaration
+
+// Partitioning operates with groups to prepare graph structure for the plugin
+// Initally we assign each layer (excluding Parameters, Results, Constants and Converts)
+// to it's own group. After some rounds of passes groups with several layers are formed and used in the plugin.
+class Group : public std::enable_shared_from_this<Group> {
+public:
+    using GPtr = std::shared_ptr<Group>;
+
+    Group() = delete;
+    Group(const std::shared_ptr<ov::Node>& node,
+          size_t gid,
+          ade::NodeHandle nh,
+          const std::shared_ptr<ade::Graph>& g,
+          const std::weak_ptr<Snapshot>& snapshot);
+
+    // After we formed a final structure of partitioning,
+    // we append excluded Convert layers to properly link submodels
+    // Convert this representation to plugin-compatible one
+    ov::npuw::Group toGroup() const;
+    std::vector<ade::NodeHandle> srcNodes() const;
+    std::vector<ade::NodeHandle> dstNodes() const;
+    ade::NodeHandle getHandle() const;
+    // Note: can only be used during initial group initialization
+    std::shared_ptr<ov::Node> getInitialNode() const;
+    size_t getId() const;
+    // This group consumes its producer
+    void fuse(const Group::GPtr& gptr_prod);
+    // This group consumes its consumer
+    void fuseWith(const Group::GPtr& gptr_cons);
+    // The inputs are merged together, this group is unchanged
+    void fuseInputs(const std::pair<Group::GPtr, Group::GPtr>& gptr_inputs);
+    // Check if there is indirect path from cons to this group
+    bool hasCycle(const Group::GPtr& gptr_cons) const;
+    size_t size() const;
+    void freeze();
+    bool isFrozen() const;
+    const detail::OVNodeSet& getContent() const;
+
+    // Below is repeated blocks functionality
+    const detail::Reptrack& getReptrack(const detail::OVNodePtr& node_ptr) const;
+    void setRepeated(const std::shared_ptr<Repeated>& rep);
+    std::shared_ptr<Repeated> repeated() const;
+    std::unordered_set<MetaInterconnect> metaInterconnect(const Group::GPtr& gptr_prod) const;
+    std::unordered_set<Interconnect> interconnect(const Group::GPtr& gptr_prod) const;
+    void avoid(const std::string& device);
+    const std::set<std::string>& avoidedTargets() const;
+
+private:
+    void includeExtraLayers(detail::OVNodeSet& input_layers,
+                            detail::OVNodeSet& output_layers,
+                            detail::OVNodeSet& content) const;
+    void updateInputLayers(const GPtr& gptr_other);
+    void updateOutputLayers(const GPtr& gptr_other);
+    // This group takes extra info of other group (such as reptrack, avoids, etc)
+    void takeFlags(const Group::GPtr& gptr_other);
+    void relinkGraph(const GPtr& gptr_other);
+
+    detail::OVNodeSet m_input_layers;
+    detail::OVNodeSet m_content;
+    detail::OVNodeSet m_output_layers;
+
+    ade::NodeHandle m_nh;
+    size_t m_id;  // used for utility prints only
+    std::shared_ptr<ade::Graph> m_graph;
+    std::weak_ptr<Snapshot> m_snapshot;
+    bool m_frozen = false;
+    std::set<std::string> m_avoided_devices;
+
+    // Unique repeated tag
+    std::shared_ptr<Repeated> m_repeated = nullptr;
+    // For each layer inside group, store it's history of repeated groups
+    detail::ReptrackMap m_reptrack;
+};
+
+}  // namespace online
+}  // namespace npuw
+}  // namespace ov
diff --git a/src/plugins/intel_npu/src/plugin/npuw/partitioning/online/repeated.cpp b/src/plugins/intel_npu/src/plugin/npuw/partitioning/online/repeated.cpp
new file mode 100644
index 00000000000000..90b9428f8ef875
--- /dev/null
+++ b/src/plugins/intel_npu/src/plugin/npuw/partitioning/online/repeated.cpp
@@ -0,0 +1,55 @@
+// Copyright (C) 2024 Intel Corporationov::npuw::
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "repeated.hpp"
+
+#include <sstream>
+#include <tuple>
+
+#include "../../logging.hpp"
+
+using ov::npuw::online::Interconnect;
+using ov::npuw::online::MetaInterconnect;
+using ov::npuw::online::Repeated;
+
+bool Repeated::Archetype::operator==(const Repeated::Archetype& other) const {
+    if (metadesc != other.metadesc) {
+        return false;
+    }
+
+    if (reptrack != other.reptrack) {
+        return false;
+    }
+
+    return true;
+}
+
+void Repeated::exclude() {
+    m_excluded = true;
+}
+
+bool Repeated::openForMerge() const {
+    return !m_excluded;
+}
+
+bool Interconnect::operator==(const Interconnect& other) const {
+    return input_port == other.input_port && output_port == other.output_port && input_node == other.input_node &&
+           output_node == other.output_node;
+}
+
+bool MetaInterconnect::operator==(const MetaInterconnect& other) const {
+    return other.input_meta == input_meta && other.output_meta == output_meta && other.input_port == input_port &&
+           other.output_port == output_port && other.input_reptrack == input_reptrack &&
+           other.output_reptrack == output_reptrack;
+}
+
+bool MetaInterconnect::operator<(const MetaInterconnect& other) const {
+    return std::make_tuple(input_meta, input_port, input_reptrack, output_port, output_meta, output_reptrack) <
+           std::make_tuple(other.input_meta,
+                           other.input_port,
+                           other.input_reptrack,
+                           other.output_port,
+                           other.output_meta,
+                           other.output_reptrack);
+}
diff --git a/src/plugins/intel_npu/src/plugin/npuw/partitioning/online/repeated.hpp b/src/plugins/intel_npu/src/plugin/npuw/partitioning/online/repeated.hpp
new file mode 100644
index 00000000000000..541350c3d39c0a
--- /dev/null
+++ b/src/plugins/intel_npu/src/plugin/npuw/partitioning/online/repeated.hpp
@@ -0,0 +1,131 @@
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <memory>
+#include <vector>
+
+#include "openvino/openvino.hpp"
+#include "utils/utils.hpp"
+
+namespace ov {
+namespace npuw {
+namespace online {
+
+// Mainly used as a shared_ptr to tag a particular repeated block
+struct Repeated {
+    struct Archetype {
+        std::string metadesc;
+        ov::npuw::online::detail::Reptrack reptrack;
+        bool operator==(const Archetype& other) const;
+    };
+
+    void exclude();
+    bool openForMerge() const;
+
+    bool m_excluded = false;
+};
+
+struct Interconnect {
+    detail::OVNodePtr input_node;
+    size_t input_port;
+
+    detail::OVNodePtr output_node;
+    size_t output_port;
+
+    bool operator==(const Interconnect& other) const;
+};
+
+struct MetaInterconnect {
+    std::string input_meta;
+    detail::Reptrack input_reptrack;
+    size_t input_port;
+
+    std::string output_meta;
+    detail::Reptrack output_reptrack;
+    size_t output_port;
+
+    bool operator==(const MetaInterconnect& other) const;
+    bool operator<(const MetaInterconnect& other) const;
+};
+
+}  // namespace online
+}  // namespace npuw
+}  // namespace ov
+
+namespace std {
+template <>
+struct hash<std::pair<ov::npuw::online::detail::OVNodePtr, ov::npuw::online::detail::OVNodePtr>> {
+    inline size_t operator()(
+        const std::pair<ov::npuw::online::detail::OVNodePtr, ov::npuw::online::detail::OVNodePtr>& p) const {
+        return (std::hash<ov::npuw::online::detail::OVNodePtr>()(p.first) + 0x9e3779b9) ^
+               (std::hash<ov::npuw::online::detail::OVNodePtr>()(p.second) + 0x9e3779b9);
+    }
+};
+
+template <>
+struct hash<std::pair<std::string, std::set<std::string>>> {
+    inline size_t operator()(const std::pair<std::string, std::set<std::string>>& p) const {
+        std::size_t seed = std::hash<std::string>()(p.first) + 0x9e3779b9;
+        for (const auto& s : p.second) {
+            seed ^= std::hash<std::string>()(s) + 0x9e3779b9;
+        }
+        return seed;
+    }
+};
+
+template <>
+struct hash<ov::npuw::online::detail::Reptrack> {
+    inline size_t operator()(const ov::npuw::online::detail::Reptrack& vec) const {
+        std::size_t seed = vec.size();
+        for (const auto& rep : vec) {
+            seed ^=
+                std::hash<std::shared_ptr<ov::npuw::online::Repeated>>()(rep) + 0x9e3779b9 + (seed << 6) + (seed >> 2);
+        }
+        return seed;
+    }
+};
+
+template <>
+struct hash<ov::npuw::online::Repeated::Archetype> {
+    inline size_t operator()(const ov::npuw::online::Repeated::Archetype& act) const {
+        return (std::hash<std::string>()(act.metadesc) + 0x9e3779b9) ^
+               (std::hash<ov::npuw::online::detail::Reptrack>()(act.reptrack) + 0x9e3779b9);
+    }
+};
+
+template <>
+struct hash<ov::npuw::online::Interconnect> {
+    inline size_t operator()(const ov::npuw::online::Interconnect& mic) const {
+        return (std::hash<ov::npuw::online::detail::OVNodePtr>()(mic.input_node) + 0x9e3779b9) ^
+               (std::hash<ov::npuw::online::detail::OVNodePtr>()(mic.output_node) + 0x9e3779b9) ^
+               (std::hash<size_t>()(mic.input_port) + 0x9e3779b9) ^ (std::hash<size_t>()(mic.output_port) + 0x9e3779b9);
+    }
+};
+
+template <>
+struct hash<ov::npuw::online::MetaInterconnect> {
+    inline size_t operator()(const ov::npuw::online::MetaInterconnect& mic) const {
+        return (std::hash<std::string>()(mic.input_meta) + 0x9e3779b9) ^
+               (std::hash<std::string>()(mic.output_meta) + 0x9e3779b9) ^
+               (std::hash<size_t>()(mic.input_port) + 0x9e3779b9) ^
+               (std::hash<size_t>()(mic.output_port) + 0x9e3779b9) ^
+               (std::hash<ov::npuw::online::detail::Reptrack>()(mic.input_reptrack) + 0x9e3779b9) ^
+               (std::hash<ov::npuw::online::detail::Reptrack>()(mic.output_reptrack) + 0x9e3779b9);
+    }
+};
+
+// FIXME: hash<MetaInterconnect> defined above. This hash should be available by default
+template <>
+struct hash<std::vector<ov::npuw::online::MetaInterconnect>> {
+    inline size_t operator()(const std::vector<ov::npuw::online::MetaInterconnect>& vec) const {
+        std::size_t seed = vec.size();
+        for (const auto& mic : vec) {
+            seed ^= std::hash<ov::npuw::online::MetaInterconnect>()(mic) + 0x9e3779b9 + (seed << 6) + (seed >> 2);
+        }
+        return seed;
+    }
+};
+}  // namespace std
diff --git a/src/plugins/intel_npu/src/plugin/npuw/partitioning/online/snapshot.cpp b/src/plugins/intel_npu/src/plugin/npuw/partitioning/online/snapshot.cpp
new file mode 100644
index 00000000000000..4231b80467dcc6
--- /dev/null
+++ b/src/plugins/intel_npu/src/plugin/npuw/partitioning/online/snapshot.cpp
@@ -0,0 +1,669 @@
+// Copyright (C) 2024 Intel Corporationov::npuw::
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "snapshot.hpp"
+
+#include "../../logging.hpp"
+#include "../patterns/avoid.hpp"  // RMSNormPattern
+#include "group.hpp"
+#include "openvino/op/util/op_types.hpp"
+#include "openvino/opsets/opset1.hpp"
+#include "openvino/pass/graph_rewrite.hpp"
+#include "openvino/util/common_util.hpp"
+#include "utils/utils.hpp"
+
+using ov::npuw::online::Group;
+using ov::npuw::online::Repeated;
+using ov::npuw::online::Snapshot;
+using ov::npuw::online::detail::GPtrSet;
+using ov::npuw::online::detail::OVNodePtr;
+using ov::npuw::online::detail::OVNodeSet;
+using ov::npuw::online::detail::OVPortsMap;
+using ov::npuw::online::detail::Uniques;
+
+namespace ov {
+namespace npuw {
+namespace online {
+namespace detail {
+bool isOp(const std::shared_ptr<ov::Node>& node) {
+    if (ov::op::util::is_constant(node) || ov::op::util::is_parameter(node) || ov::op::util::is_output(node)) {
+        return false;
+    }
+    if (ov::is_type<ov::opset1::Convert>(node)) {
+        if (node->inputs().size() != 1) {
+            // can occur only in Const->Convert->Node case
+            return false;
+        }
+        auto target_input = node->get_input_source_output(0);
+        auto parent_node = target_input.get_node()->shared_from_this();
+        if (ov::op::util::is_constant(parent_node)) {
+            return false;
+        }
+    }
+    return true;
+}
+}  // namespace detail
+}  // namespace online
+}  // namespace npuw
+}  // namespace ov
+
+using ov::npuw::online::detail::isOp;
+
+void Snapshot::buildGraph() {
+    LOG_INFO("Online partitioning: parsing OV Model to initial groups...");
+    LOG_BLOCK();
+
+    size_t gid = 0;  // unique group id
+
+    // Traverse OV layers
+    for (const auto& ov_node : m_model->get_ordered_ops()) {
+        if (!isOp(ov_node)) {
+            continue;
+        }
+
+        m_node_to_prod_cons->insert({ov_node, {}});
+
+        auto nh = m_graph->create();
+        auto group = std::make_shared<Group>(ov_node, gid, nh, m_graph, shared_from_this());
+        m_graph->meta(nh).set(group);
+        m_node_to_gr->emplace(std::make_pair(ov_node, group));
+        ++gid;
+    }
+
+    for (const auto& nh : m_graph->sorted()) {
+        auto gptr = m_graph->meta(nh).get<Group::GPtr>();
+        auto ov_node = gptr->getInitialNode();
+
+        for (size_t i = 0; i < ov_node->outputs().size(); ++i) {
+            const auto target_outputs = ov_node->get_output_target_inputs(i);
+
+            for (const auto& target_output : target_outputs) {
+                auto ov_node_child = target_output.get_node()->shared_from_this();
+
+                // Insert readers from other layers
+                m_node_to_prod_cons->at(ov_node).second.insert(ov_node_child);
+
+                // Save ports for repeated blocks pipeline
+                m_ports_map.insert({{ov_node, ov_node_child}, {i, target_output.get_index()}});
+
+                if (!isOp(ov_node_child)) {
+                    continue;
+                }
+
+                if (!m_graph->linked(nh, m_node_to_gr->at(ov_node_child)->getHandle())) {
+                    m_graph->link(nh, m_node_to_gr->at(ov_node_child)->getHandle());
+                }
+            }
+        }  // for(outputs)
+
+        for (size_t i = 0; i < ov_node->inputs().size(); ++i) {
+            auto target_input = ov_node->get_input_source_output(i);
+            auto ov_node_parent = target_input.get_node()->shared_from_this();
+
+            // Insert writers from other layers
+            m_node_to_prod_cons->at(ov_node).first.insert(ov_node_parent);
+
+            // Save ports for repeated blocks pipeline
+            m_ports_map.insert({{ov_node_parent, ov_node}, {target_input.get_index(), i}});
+
+            if (!isOp(ov_node_parent)) {
+                continue;
+            }
+
+            if (!m_graph->linked(m_node_to_gr->at(ov_node_parent)->getHandle(), nh)) {
+                m_graph->link(m_node_to_gr->at(ov_node_parent)->getHandle(), nh);
+            }
+        }  // for(inputs)
+    }      // for(get_ordered_ops)
+
+    LOG_DEBUG("Initial number of groups: " << graphSize());
+    LOG_INFO("DONE.");
+}
+
+void Snapshot::collectLHF() {
+    LOG_INFO("Online partitioning: executing collectLHF pass...");
+    LOG_BLOCK();
+
+    // iterate it topological order
+    auto graph = getGraph();
+    for (const auto& nh : graph->sorted()) {
+        // skip if removed by fuse
+        if (m_graph->contains(nh)) {
+            Group::GPtr group = m_graph->meta(nh).get<Group::GPtr>();
+            auto producers = group->srcNodes();
+            if (producers.size() == 1) {
+                auto prod = producers.at(0);
+                if (m_graph->contains(prod) && prod->dstNodes().size() == 1) {
+                    Group::GPtr prod_group = m_graph->meta(prod).get<Group::GPtr>();
+                    if (group->isFrozen() || prod_group->isFrozen()) {
+                        continue;
+                    }
+                    // stop merging groups if the graph is already small enough
+                    if (graphSize() <= m_ctx.min_graph_size) {
+                        break;
+                    }
+                    group->fuse(prod_group);
+                }
+            }
+        }
+    }
+
+    LOG_INFO("DONE");
+}
+
+void Snapshot::fuseRemnantsExtended() {
+    LOG_INFO("Online partitioning: executing fuseRemnantsExtended pass...");
+    LOG_BLOCK();
+
+    repeat([&] {
+        fuseRemnants();
+    });
+    repeat([&] {
+        fuseInputs();
+    });
+
+    LOG_INFO("DONE");
+}
+
+void Snapshot::fuseRemnants() {
+    LOG_INFO("Online partitioning: executing fuseRemnants pass...");
+    LOG_BLOCK();
+
+    // iterate it topological order
+    auto graph = getGraph();
+    for (const auto& nh : graph->sorted()) {
+        // skip if removed by fuseWith
+        if (m_graph->contains(nh)) {
+            Group::GPtr group = m_graph->meta(nh).get<Group::GPtr>();
+            if (group->isFrozen()) {
+                continue;
+            }
+            auto consumers = group->dstNodes();
+            if (!consumers.empty()) {
+                std::sort(consumers.begin(),
+                          consumers.end(),
+                          [&](const ade::NodeHandle& nh1, const ade::NodeHandle& nh2) {
+                              if (!m_graph->contains(nh1) || !m_graph->contains(nh2)) {
+                                  return false;
+                              }
+                              Group::GPtr g1 = m_graph->meta(nh1).get<Group::GPtr>();
+                              Group::GPtr g2 = m_graph->meta(nh2).get<Group::GPtr>();
+                              return g1->size() < g2->size();
+                          });
+                for (const auto& cons : consumers) {  // FIXME: pick the smallest flops
+                    if (!m_graph->contains(cons)) {
+                        continue;
+                    }
+                    Group::GPtr cons_group = m_graph->meta(cons).get<Group::GPtr>();
+                    if (!group->hasCycle(cons_group)) {
+                        if (!cons_group->isFrozen()) {
+                            group->fuseWith(cons_group);
+                            break;
+                        }
+                    }
+                }
+                // stop merging groups if the graph is already small enough
+                if (graphSize() <= m_ctx.min_graph_size) {
+                    break;
+                }
+            }
+        }
+    }
+
+    LOG_INFO("DONE");
+}
+
+void Snapshot::fuseInputs() {
+    LOG_INFO("Online partitioning: executing fuseInputs pass...");
+    LOG_BLOCK();
+
+    // iterate it topological order
+    auto graph = getGraph();
+    for (const auto& nh : graph->sorted()) {
+        // skip if removed by fuseInputs
+        if (!m_graph->contains(nh)) {
+            continue;
+        }
+        Group::GPtr group = m_graph->meta(nh).get<Group::GPtr>();
+
+        std::pair<Group::GPtr, Group::GPtr> inputs_to_fuse{nullptr, nullptr};
+        auto src_nodes = group->srcNodes();
+        for (size_t i = 0; i < src_nodes.size(); ++i) {
+            auto prod_nh = src_nodes[i];
+            if (!m_graph->contains(prod_nh)) {  // should be there, but check just in case
+                continue;
+            }
+            Group::GPtr group_prod = m_graph->meta(prod_nh).get<Group::GPtr>();
+            if (group_prod->isFrozen()) {
+                continue;
+            }
+            inputs_to_fuse.first = group_prod;  // set the first candidate
+
+            // Double loop here since we need to consider every pair of inputs
+            for (size_t j = i + 1; j < src_nodes.size(); ++j) {
+                auto prod_nh_other = src_nodes[j];
+                if (!m_graph->contains(prod_nh_other)) {  // should be there, but check just in case
+                    continue;
+                }
+                Group::GPtr group_prod_other = m_graph->meta(prod_nh_other).get<Group::GPtr>();
+                if (group_prod_other->isFrozen()) {
+                    continue;
+                }
+                if (!group_prod->hasCycle(group_prod_other) && !group_prod_other->hasCycle(group_prod)) {
+                    // no cycles -> fusion allowed
+                    inputs_to_fuse.second = group_prod_other;
+                    break;
+                }
+            }
+            // Found 2 inputs to fuse
+            if (inputs_to_fuse.first && inputs_to_fuse.second) {
+                group->fuseInputs(inputs_to_fuse);
+                break;
+            }
+        }
+
+        // stop merging groups if the graph is already small enough
+        if (graphSize() <= m_ctx.min_graph_size) {
+            break;
+        }
+    }
+
+    LOG_INFO("DONE");
+}
+
+void Snapshot::earlyAvoids() {
+    ov::pass::GraphRewrite rewr;
+    bool handle_patterns = false;
+
+    for (const auto& avoid : m_ctx.avoids) {
+        switch (avoid.type) {
+        case AvoidType::OP: {
+            for (const auto& nh : m_graph->sorted()) {
+                Group::GPtr group = m_graph->meta(nh).get<Group::GPtr>();
+                // This pass should only be called at the very beginning,
+                // thus check and match only the single initial layer
+                if (group->getInitialNode()->description() == avoid.pattern) {
+                    group->avoid(avoid.device);
+                }
+            }
+            break;
+        }
+        case AvoidType::PATTERN: {
+            // FIXME: refactor as more patterns are supported
+            if (avoid.pattern != "RMSNorm") {
+                LOG_WARN("OPENVINO_NPUW_AVOID only supports RMSNorm as a pattern (don't confuse with operations)."
+                         << " Avoid pattern " << avoid.pattern << " is skipped!");
+                break;
+            }
+            handle_patterns = true;
+
+            rewr.add_matcher<ov::npuw::patterns::RMSNormPattern>(shared_from_this(), avoid.device);
+            break;
+        }
+        }
+    }
+
+    if (handle_patterns) {
+        // Check the model for all specified patterns
+        rewr.run_on_model(m_model);
+    }
+}
+
+void Snapshot::repeatedBlocks() {
+    LOG_INFO("Online partitioning: executing repeatedBlocks pass group...");
+    LOG_BLOCK();
+
+    identifyUniques();
+    repeat([&] {
+        mergeUniques();
+    });
+    cleanUpUniques();
+
+    LOG_INFO("DONE");
+}
+
+void Snapshot::identifyUniques() {
+    LOG_INFO("Online partitioning: executing identifyUniques pass...");
+    LOG_BLOCK();
+
+    Uniques uniques;
+
+    for (const auto& nh : m_graph->sorted()) {
+        Group::GPtr group = m_graph->meta(nh).get<Group::GPtr>();
+        // This pass should only be called at the very beginning,
+        // thus check and use only the single initial layer
+        auto ov_node = group->getInitialNode();
+        auto metadesc = ov::npuw::online::util::getMetaDesc(ov_node);
+        auto avoids = group->avoidedTargets();
+        uniques[{metadesc, avoids}].insert(group);
+    }
+
+    for (const auto& elem : uniques) {
+        if (elem.second.size() > 1) {
+            std::shared_ptr<Repeated> rep = std::make_shared<Repeated>();
+
+            for (const auto& gptr : elem.second) {
+                gptr->setRepeated(rep);
+            }
+        }
+    }
+
+    LOG_INFO("DONE");
+}
+
+void Snapshot::mergeUniques() {
+    LOG_INFO("Online partitioning: executing mergeUniques pass...");
+    LOG_BLOCK();
+
+    std::unordered_set<std::shared_ptr<Repeated>> merged_this_time;
+
+    auto graph = getGraph();
+    for (const auto& nh : graph->sorted()) {
+        if (m_graph->contains(nh)) {
+            Group::GPtr group = m_graph->meta(nh).get<Group::GPtr>();
+            auto rep = group->repeated();
+
+            detail::GPtrSet repeating_groups;
+
+            if (rep && rep->openForMerge() && merged_this_time.count(rep) == 0) {
+                for (const auto& nh_other : graph->sorted()) {
+                    if (m_graph->contains(nh_other)) {
+                        Group::GPtr group_other = m_graph->meta(nh_other).get<Group::GPtr>();
+                        auto rep_other = group_other->repeated();
+
+                        if (rep_other && (rep_other.get() == rep.get())) {
+                            repeating_groups.insert(group_other);
+                        }
+                    }
+                }
+            }
+
+            if (!repeating_groups.empty()) {
+                auto new_rep = tryGrowRepeatingGroups(repeating_groups);
+                if (new_rep) {
+                    merged_this_time.insert(new_rep);
+                }
+            }
+        }
+    }
+
+    LOG_INFO("DONE");
+}
+
+std::shared_ptr<Repeated> Snapshot::tryGrowRepeatingGroups(const detail::GPtrSet& repeating_groups) {
+    auto this_rep_tag = (*(repeating_groups.begin()))->repeated();  // should be the same for each group inside
+    auto this_avoided = (*(repeating_groups.begin()))->avoidedTargets();
+
+    std::unordered_map<std::vector<MetaInterconnect>, std::vector<std::pair<Group::GPtr, Group::GPtr>>> mics;
+
+    std::vector<Group::GPtr> repeating_groups_sorted(repeating_groups.begin(), repeating_groups.end());
+
+    // FIXME: this was introduced to make the partitioning
+    // the same every run when created the same way.
+    std::sort(repeating_groups_sorted.begin(),
+              repeating_groups_sorted.end(),
+              [&](const Group::GPtr& gptr_a, const Group::GPtr& gptr_b) {
+                  return gptr_a->getId() < gptr_b->getId();
+              });
+
+    for (const auto& group : repeating_groups_sorted) {
+        auto producers = group->srcNodes();
+        for (const auto& prod_nh : producers) {
+            if (m_graph->contains(prod_nh)) {
+                Group::GPtr prod_group = m_graph->meta(prod_nh).get<Group::GPtr>();
+                if (prod_group->repeated() && !prod_group->hasCycle(group) && prod_group->repeated() != this_rep_tag &&
+                    prod_group->avoidedTargets() == this_avoided) {
+                    auto meta_interconnect = group->metaInterconnect(prod_group);
+
+                    // FIXME: find a better way to reduce time complexity
+                    // Need to align interconnects in the same format via sort, so they could be compared later
+                    std::vector<MetaInterconnect> mic_sorted_key(meta_interconnect.begin(), meta_interconnect.end());
+                    std::sort(mic_sorted_key.begin(), mic_sorted_key.end());
+                    mics[mic_sorted_key].push_back({prod_group, group});
+                }
+            }
+        }
+    }
+
+    // FIXME: find a better way to reduce time complexity
+    // Below we sort meta interconnects by size, so we could try to merge the bigger ones first
+    std::vector<std::vector<std::pair<Group::GPtr, Group::GPtr>>> mics_vec;
+    for (const auto& mic : mics) {
+        mics_vec.push_back(mic.second);
+    }
+
+    std::sort(mics_vec.begin(), mics_vec.end(), [](const auto& a, const auto& b) {
+        if (a.size() == b.size()) {
+            if (a.empty()) {
+                return false;  // doesn't matter for stability - no groups are fused
+            }
+            return a.at(0).first->getId() < b.at(0).first->getId();
+        }
+        return a.size() > b.size();
+    });
+
+    for (const auto& mic : mics_vec) {
+        std::vector<Group::GPtr> prods;
+        std::vector<Group::GPtr> conss;
+
+        for (const auto& el : mic) {
+            prods.push_back(el.first);
+            conss.push_back(el.second);
+        }
+
+        auto new_rep = tryMergeRepeating(prods, conss);
+        if (new_rep) {
+            return new_rep;
+        }
+    }
+
+    // No merges happened at all? Exclude this group from the merge procedure and indicate via return value.
+    this_rep_tag->exclude();
+
+    return nullptr;
+}
+
+std::shared_ptr<Repeated> Snapshot::tryMergeRepeating(const std::vector<Group::GPtr>& prods,
+                                                      const std::vector<Group::GPtr>& conss) {
+    if (prods.size() != conss.size()) {
+        // FIXME: it's actually possible to merge under certain circumstances
+        OPENVINO_THROW(
+            "Online partitioning tried to merge repeated groups with different sizes of producers and consumers!");
+    }
+
+    if (conss.size() == 1) {
+        return nullptr;
+    }
+
+    std::unordered_set<Group::GPtr> prods_set;
+    for (const auto& prod : prods) {
+        prods_set.insert(prod);
+    }
+
+    if (prods_set.size() != conss.size()) {
+        return nullptr;
+    }
+
+    for (const auto& cons : conss) {
+        if (std::find(prods.begin(), prods.end(), cons) != prods.end()) {
+            OPENVINO_THROW("Online partitioning tried to merge repeated groups which overlap!");
+        }
+    }
+
+    std::shared_ptr<Repeated> new_rep = std::make_shared<Repeated>();
+
+    for (size_t i = 0; i < conss.size(); ++i) {
+        conss.at(i)->fuse(prods.at(i));
+        conss.at(i)->setRepeated(new_rep);  // producer is consumed, no need to setRepeated() it
+    }
+
+    for (const auto& cons : conss) {
+        auto prod_nhs = cons->srcNodes();
+        for (const auto& nh : prod_nhs) {
+            Group::GPtr prod_group = m_graph->meta(nh).get<Group::GPtr>();
+            if (prod_group == cons) {
+                OPENVINO_THROW(
+                    "Online partitioning have merged repeated groups incorrectly: producers/consumers overlap!");
+            }
+        }
+    }
+
+    return new_rep;
+}
+
+std::unordered_map<std::shared_ptr<Repeated>, ov::npuw::online::detail::GPtrSet> Snapshot::repeating() const {
+    std::unordered_map<std::shared_ptr<Repeated>, detail::GPtrSet> repeating;
+    for (const auto& nh : m_graph->sorted()) {
+        Group::GPtr group = m_graph->meta(nh).get<Group::GPtr>();
+        auto rep = group->repeated();
+        if (rep) {
+            repeating[rep].insert(group);
+        }
+    }
+
+    return repeating;
+}
+
+void Snapshot::cleanUpUniques() {
+    LOG_INFO("Online partitioning: executing cleanUpUniques pass...");
+    LOG_BLOCK();
+
+    for (auto& reptag_to_gset : repeating()) {
+        bool keep = cleanUpUniquesImpl(reptag_to_gset.second);
+
+        if (!keep) {
+            continue;  // If we dropped repeated blocks in cleanUpUniquesImpl, skip the next section
+        }
+
+        completeRepeating(reptag_to_gset.first, reptag_to_gset.second);
+    }
+
+    LOG_INFO("DONE");
+}
+
+bool Snapshot::cleanUpUniquesImpl(const GPtrSet& gptrs) {
+    for (const auto& gptr : gptrs) {
+        if (!gptr->avoidedTargets().empty()) {
+            // Special case - keep it
+            for (const auto& g : gptrs) {
+                g->freeze();
+            }
+            return true;
+        }
+    }
+
+    // FIXME: in ensemble we check threshold of 10% of the total compute to discard a repeated block
+    // Since we don't have flops here yet, let's just check the size of the group
+    auto block_layer_size = (*(gptrs.begin()))->size();
+    if (gptrs.size() < 2 || block_layer_size < 10 || gptrs.size() * block_layer_size < 100) {
+        for (const auto& gptr : gptrs) {
+            gptr->setRepeated(nullptr);
+        }
+        LOG_DEBUG("Repeated block of " << gptrs.size() << " groups with " << block_layer_size << " layers is dropped.");
+        return false;
+    }
+
+    LOG_DEBUG("Keeping a repeated block of " << gptrs.size() << " groups with " << block_layer_size << " layers.");
+
+    // Repeated block is good enough to keep
+    for (const auto& gptr : gptrs) {
+        gptr->freeze();
+    }
+
+    return true;
+}
+
+void Snapshot::completeRepeating(const std::shared_ptr<Repeated>& reptag, const detail::GPtrSet& gset) {
+    std::unordered_map<Repeated::Archetype, std::unordered_set<ov::npuw::online::detail::OVNodePtr>> matches;
+
+    for (const auto& gptr : gset) {
+        for (const auto& layer : gptr->getContent()) {  // FIXME: should it be a part of group's API instead?
+            auto metadesc = ov::npuw::online::util::getMetaDesc(layer);
+            auto archetype = gptr->getReptrack(layer);
+            matches[{metadesc, archetype}].insert(layer);
+        }
+    }
+
+    // Sanity check:
+    // 1. For every node archetype, there must be the same number of instances:
+    //    equal to the number of groups.
+    // 2. Total count of archetypes must be equal to size of every individual group
+    for (const auto& elem : matches) {
+        auto node_set = elem.second;
+        if (node_set.size() != gset.size()) {
+            OPENVINO_THROW("Online partitioning couldn't match properly "
+                           "during repeated blocks pass (node archetype). "
+                           "Got ",
+                           node_set.size(),
+                           ", expected ",
+                           gset.size());
+        }
+    }
+    for (const auto& gptr : gset) {
+        if (matches.size() != gptr->getContent().size()) {
+            OPENVINO_THROW("Online partitioning couldn't match properly "
+                           "during repeated blocks pass (count of archetypes). "
+                           "Got ",
+                           matches.size(),
+                           ", expected ",
+                           gptr->getContent().size());
+        }
+    }
+
+    std::vector<std::set<std::string>> layer_matches;
+    for (const auto& elem : matches) {
+        layer_matches.push_back({});
+        for (const auto& layer : elem.second) {
+            layer_matches.back().insert(layer->get_friendly_name());
+        }
+    }
+
+    std::string tag = ov::npuw::online::util::repeated_id(reptag);
+    m_layer_matches.insert({tag, layer_matches});
+}
+
+const OVNodeSet& Snapshot::getNodeProducers(const OVNodePtr& node) const {
+    return m_node_to_prod_cons->at(node).first;
+}
+
+const OVNodeSet& Snapshot::getNodeConsumers(const OVNodePtr& node) const {
+    return m_node_to_prod_cons->at(node).second;
+}
+
+const ov::npuw::online::detail::OVNodeToGroupMapPtr& Snapshot::getNodeToGroupMap() const {
+    return m_node_to_gr;
+}
+
+std::shared_ptr<ade::Graph> Snapshot::getGraph() const {
+    return m_graph;
+}
+
+size_t Snapshot::graphSize() const {
+    return m_graph->nodes().size();
+}
+
+const OVPortsMap& Snapshot::getPortsMap() const {
+    return m_ports_map;
+}
+
+const std::map<std::string, std::vector<std::set<std::string>>>& Snapshot::getMatches() const {
+    return m_layer_matches;
+}
+
+void Snapshot::repeat(detail::Pass&& pass) {
+    size_t prev_graph_size = 0;
+    size_t curr_graph_size = graphSize();
+
+    while (graphSize() > m_ctx.min_graph_size && curr_graph_size != prev_graph_size) {
+        prev_graph_size = graphSize();
+        pass();
+        curr_graph_size = graphSize();
+    }
+
+    LOG_INFO("Number of groups after compiler pass: " << graphSize());
+}
+
+void Snapshot::setCtx(const ov::npuw::online::PassContext& ctx) {
+    m_ctx = ctx;
+}
diff --git a/src/plugins/intel_npu/src/plugin/npuw/partitioning/online/snapshot.hpp b/src/plugins/intel_npu/src/plugin/npuw/partitioning/online/snapshot.hpp
new file mode 100644
index 00000000000000..9d3e50f496e66a
--- /dev/null
+++ b/src/plugins/intel_npu/src/plugin/npuw/partitioning/online/snapshot.hpp
@@ -0,0 +1,83 @@
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <memory>
+#include <unordered_map>
+
+#include "graph.hpp"
+#include "openvino/openvino.hpp"
+#include "repeated.hpp"  // hash
+#include "utils/utils.hpp"
+
+namespace ov {
+namespace npuw {
+namespace online {
+
+class Group;  // forward declaration
+
+namespace detail {
+// At partitioning level we exclude some "non-Ops" to not interfere with the passes.
+// We include some of them back to properly link everything at plugin level
+bool isOp(const std::shared_ptr<ov::Node>& node);
+}  // namespace detail
+
+// Core part of the partitioning algorithm which implements a list of graph passes.
+class Snapshot : public std::enable_shared_from_this<Snapshot> {
+public:
+    Snapshot(const std::shared_ptr<ov::Model>& model)
+        : m_model(model),
+          m_graph(std::make_shared<ade::Graph>()),
+          m_node_to_prod_cons(std::make_shared<detail::OVNodeMap>()),
+          m_node_to_gr(std::make_shared<detail::OVNodeToGroupMap>()) {}
+
+    // Initial OV model traversal to prepare initial groups of 1 layer each
+    void buildGraph();
+
+    // Simple passes to clean-up and reduce number of groups via merging them
+    void collectLHF();
+    void fuseRemnantsExtended();
+    void fuseRemnants();
+    void fuseInputs();
+
+    // Advanced passes for repeated blocks algorithm
+    void repeatedBlocks();
+    void earlyAvoids();
+
+    // Utility
+    std::shared_ptr<ade::Graph> getGraph() const;
+    size_t graphSize() const;
+    const detail::OVNodeSet& getNodeProducers(const detail::OVNodePtr& node) const;
+    const detail::OVNodeSet& getNodeConsumers(const detail::OVNodePtr& node) const;
+    const detail::OVPortsMap& getPortsMap() const;
+    const detail::OVNodeToGroupMapPtr& getNodeToGroupMap() const;
+    const std::map<std::string, std::vector<std::set<std::string>>>& getMatches() const;
+    void repeat(detail::Pass&& pass);
+    void setCtx(const PassContext& ctx);
+
+private:
+    void identifyUniques();
+    void mergeUniques();
+    void cleanUpUniques();
+    bool cleanUpUniquesImpl(const detail::GPtrSet& gset);
+    std::shared_ptr<Repeated> tryGrowRepeatingGroups(const detail::GPtrSet& repeating_groups);
+    std::shared_ptr<Repeated> tryMergeRepeating(const std::vector<std::shared_ptr<Group>>& prods,
+                                                const std::vector<std::shared_ptr<Group>>& conss);
+    std::unordered_map<std::shared_ptr<Repeated>, detail::GPtrSet> repeating() const;
+    void completeRepeating(const std::shared_ptr<Repeated>& reptag, const detail::GPtrSet& gset);
+
+    std::shared_ptr<ov::Model> m_model;
+    std::shared_ptr<ade::Graph> m_graph;
+    detail::OVNodeMapPtr m_node_to_prod_cons;
+    detail::OVNodeToGroupMapPtr m_node_to_gr;
+    PassContext m_ctx;
+
+    detail::OVPortsMap m_ports_map;
+    std::map<std::string, std::vector<std::set<std::string>>> m_layer_matches;
+};
+
+}  // namespace online
+}  // namespace npuw
+}  // namespace ov
diff --git a/src/plugins/intel_npu/src/plugin/npuw/partitioning/online/utils/attribute_visitor.hpp b/src/plugins/intel_npu/src/plugin/npuw/partitioning/online/utils/attribute_visitor.hpp
new file mode 100644
index 00000000000000..dfec14da7da98e
--- /dev/null
+++ b/src/plugins/intel_npu/src/plugin/npuw/partitioning/online/utils/attribute_visitor.hpp
@@ -0,0 +1,86 @@
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+// This code is taken from OpenVINO (tag 2024.0):
+// openvino/src/tests/functional/plugin/conformance/subgraphs_dumper/include/utils/attribute_visitor.hpp
+
+#pragma once
+
+#include <map>
+
+#include "openvino/core/attribute_adapter.hpp"
+#include "openvino/core/attribute_visitor.hpp"
+#include "openvino/core/enum_names.hpp"
+#include "openvino/pass/manager.hpp"
+#include "openvino/pass/serialize.hpp"
+
+namespace ov {
+namespace npuw {
+namespace online {
+namespace util {
+
+class ReadAttributes : public ov::AttributeVisitor {
+    std::map<std::string, std::string> attributes_map;
+    std::map<std::string, std::shared_ptr<ov::Model>> model_attributes_map;
+
+    template <typename Container>
+    std::string join(const Container& c, const char* glue = ", ") {
+        std::stringstream oss;
+        const char* s = "";
+        for (const auto& v : c) {
+            oss << s << v;
+            s = glue;
+        }
+        return oss.str();
+    }
+
+    template <typename T>
+    std::string create_atribute_list(ov::ValueAccessor<std::vector<T>>& adapter) {
+        return join(adapter.get());
+    }
+
+public:
+    ReadAttributes() = default;
+
+    std::map<std::string, std::string> get_attributes_map() {
+        return attributes_map;
+    }
+
+    void on_adapter(const std::string& name, ov::ValueAccessor<void>& adapter) override {
+        attributes_map.insert({name, ""});
+    }
+
+    void on_adapter(const std::string& name, ov::ValueAccessor<bool>& adapter) override {
+        attributes_map.insert({name, std::to_string(adapter.get())});
+    }
+    void on_adapter(const std::string& name, ov::ValueAccessor<std::string>& adapter) override {
+        attributes_map.insert({name, adapter.get()});
+    }
+    void on_adapter(const std::string& name, ov::ValueAccessor<int64_t>& adapter) override {
+        attributes_map.insert({name, std::to_string(adapter.get())});
+    }
+    void on_adapter(const std::string& name, ov::ValueAccessor<double>& adapter) override {
+        attributes_map.insert({name, std::to_string(adapter.get())});
+    }
+    void on_adapter(const std::string& name, ov::ValueAccessor<std::vector<int>>& adapter) override {
+        attributes_map.insert({name, create_atribute_list(adapter)});
+    }
+    void on_adapter(const std::string& name, ov::ValueAccessor<std::vector<int64_t>>& adapter) override {
+        attributes_map.insert({name, create_atribute_list(adapter)});
+    }
+    void on_adapter(const std::string& name, ov::ValueAccessor<std::vector<uint64_t>>& adapter) override {
+        attributes_map.insert({name, create_atribute_list(adapter)});
+    }
+    void on_adapter(const std::string& name, ov::ValueAccessor<std::vector<float>>& adapter) override {
+        attributes_map.insert({name, create_atribute_list(adapter)});
+    }
+    void on_adapter(const std::string& name, ov::ValueAccessor<std::vector<std::string>>& adapter) override {
+        attributes_map.insert({name, create_atribute_list(adapter)});
+    }
+};
+
+}  // namespace util
+}  // namespace online
+}  // namespace npuw
+}  // namespace ov
diff --git a/src/plugins/intel_npu/src/plugin/npuw/partitioning/online/utils/utils.cpp b/src/plugins/intel_npu/src/plugin/npuw/partitioning/online/utils/utils.cpp
new file mode 100644
index 00000000000000..7bfe63f753a4e0
--- /dev/null
+++ b/src/plugins/intel_npu/src/plugin/npuw/partitioning/online/utils/utils.cpp
@@ -0,0 +1,76 @@
+// Copyright (C) 2024 Intel Corporationov::npuw::
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "utils.hpp"
+
+#include "../../../logging.hpp"
+
+using ov::npuw::online::Repeated;
+using ov::npuw::online::util::ReadAttributes;
+
+// FIXME: metadesc should be hash of layer's meta, not string
+std::string ov::npuw::online::util::getMetaDesc(const std::shared_ptr<ov::Node>& ov_node) {
+    std::stringstream ss;
+    ss << ov_node->description() << ' ';
+
+    for (const auto& input : ov_node->inputs()) {
+        ss << input.get_element_type() << ' ' << input.get_shape() << ' ';
+    }
+    for (const auto& output : ov_node->outputs()) {
+        ss << output.get_element_type() << ' ' << output.get_shape() << ' ';
+    }
+
+    ReadAttributes visitor_node;
+    ov_node->visit_attributes(visitor_node);
+    auto node_bodies = visitor_node.get_attributes_map();
+
+    for (const auto& body : node_bodies) {
+        ss << body.first << ' ' << body.second << ' ';
+    }
+
+    // FIXME: should be { self type. self inputs. self outputs. self attrs. self data }
+    //        can't extract data here?
+    return ss.str();
+}
+
+std::string ov::npuw::online::util::repeated_id(const std::shared_ptr<Repeated>& ptr) {
+    if (!ptr) {
+        OPENVINO_THROW("Online partitioning tried to convert nullptr Repeated to id!");
+    }
+    const void* address = static_cast<const void*>(ptr.get());
+    std::stringstream ss;
+    ss << address;
+    return ss.str();
+}
+
+std::optional<ov::npuw::online::Avoid> ov::npuw::online::util::parseAvoid(const std::string& s) {
+    auto pos_col = s.find(':');
+    auto pos_sl = s.find('/');
+
+    if (pos_col == std::string::npos || pos_sl == std::string::npos) {
+        LOG_WARN("Incorect pattern in OPENVINO_NPUW_AVOID: "
+                 << s << ". Please, separate a device with / and pattern type with :, e.g. Op:Select/NPU,P:RMSNorm/NPU."
+                 << " Avoid rule " << s << " is ommited!");
+        return std::nullopt;
+    }
+
+    auto type = s.substr(0, pos_col);
+    auto pattern = s.substr(pos_col + 1, pos_sl - pos_col - 1);
+    auto device = s.substr(pos_sl + 1, s.size() - pos_sl - 1);
+
+    if (type != "Op" && type != "P") {
+        LOG_WARN("Incorect pattern type in OPENVINO_NPUW_AVOID: "
+                 << type << ". Please, use either Op for operation or P for pattern."
+                 << " E.g. Op:Select/NPU,P:RMSNorm/NPU."
+                 << " Avoid rule " << s << " is ommited!");
+        return std::nullopt;
+    }
+
+    Avoid avoid;
+    avoid.type = type == "Op" ? AvoidType::OP : AvoidType::PATTERN;
+    avoid.pattern = pattern;
+    avoid.device = device;
+
+    return std::optional<Avoid>{avoid};
+}
diff --git a/src/plugins/intel_npu/src/plugin/npuw/partitioning/online/utils/utils.hpp b/src/plugins/intel_npu/src/plugin/npuw/partitioning/online/utils/utils.hpp
new file mode 100644
index 00000000000000..2d7e2d5f4d9bb2
--- /dev/null
+++ b/src/plugins/intel_npu/src/plugin/npuw/partitioning/online/utils/utils.hpp
@@ -0,0 +1,64 @@
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <functional>
+#include <memory>
+#include <optional>
+#include <sstream>
+#include <unordered_map>
+
+#include "attribute_visitor.hpp"
+#include "openvino/openvino.hpp"
+
+namespace ov {
+namespace npuw {
+namespace online {
+
+enum class AvoidType { OP = 0, PATTERN = 1 };
+
+struct Avoid {
+    AvoidType type;
+    std::string pattern;
+    std::string device;
+};
+
+struct PassContext {
+    size_t min_graph_size = 10;
+    std::vector<Avoid> avoids;
+};
+
+// Forward declaration
+class Group;
+struct Repeated;
+struct Interconnect;
+struct MetaInterconnect;
+
+namespace detail {
+using OVNodePtr = std::shared_ptr<ov::Node>;
+using OVNodeSet = std::unordered_set<OVNodePtr>;
+using OVNodeSetPair = std::pair<OVNodeSet, OVNodeSet>;
+using OVNodeMap = std::unordered_map<OVNodePtr, OVNodeSetPair>;
+using OVNodeMapPtr = std::shared_ptr<OVNodeMap>;
+using OVNodeToGroupMap = std::unordered_map<std::shared_ptr<ov::Node>, std::shared_ptr<Group>>;
+using OVNodeToGroupMapPtr = std::shared_ptr<OVNodeToGroupMap>;
+using GPtrSet = std::unordered_set<std::shared_ptr<Group>>;
+using OVPortsMap = std::unordered_map<std::pair<OVNodePtr, OVNodePtr>, std::pair<size_t, size_t>>;
+using Reptrack = std::vector<std::shared_ptr<Repeated>>;
+using ReptrackMap = std::unordered_map<OVNodePtr, Reptrack>;
+using Uniques = std::unordered_map<std::pair<std::string, std::set<std::string>>, GPtrSet>;
+using Pass = std::function<void(void)>;
+}  // namespace detail
+
+namespace util {
+// FIXME: metadesc should be hash of layer's meta, not string
+std::string getMetaDesc(const std::shared_ptr<ov::Node>& ov_node);
+std::string repeated_id(const std::shared_ptr<Repeated>& ptr);
+std::optional<Avoid> parseAvoid(const std::string& s);
+}  // namespace util
+
+}  // namespace online
+}  // namespace npuw
+}  // namespace ov
diff --git a/src/plugins/intel_npu/src/plugin/npuw/partitioning/partitioning.cpp b/src/plugins/intel_npu/src/plugin/npuw/partitioning/partitioning.cpp
new file mode 100644
index 00000000000000..71d4b4ee584c00
--- /dev/null
+++ b/src/plugins/intel_npu/src/plugin/npuw/partitioning/partitioning.cpp
@@ -0,0 +1,1666 @@
+// Copyright (C) 2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "partitioning.hpp"
+
+#include "../logging.hpp"
+#include "../util.hpp"
+#include "intel_npu/al/config/npuw.hpp"
+#include "online/compiler.hpp"
+#include "openvino/op/convert.hpp"
+#include "openvino/op/slice.hpp"
+#include "openvino/op/util/op_types.hpp"
+#include "openvino/pass/validate.hpp"
+#include "openvino/util/common_util.hpp"
+#include "openvino/util/xml_parse_utils.hpp"
+#include "patterns/dcoff.hpp"
+
+namespace {
+
+class FuncallEverywhere {
+    std::shared_ptr<ov::Model> m_model;
+    std::set<std::string> m_known;
+    std::size_t m_new_id = 0u;
+
+    mutable bool m_enabled = false;
+    mutable std::once_flag m_once;
+
+    ::intel_npu::Config& m_cfg;
+
+public:
+    explicit FuncallEverywhere(const std::shared_ptr<ov::Model>& model, ::intel_npu::Config& cfg)
+        : m_model(model),
+          m_cfg(cfg) {}
+
+    void register_known(const std::string fcn_id) {
+        m_known.insert(fcn_id);
+    }
+
+    std::string register_new() {
+        std::string new_fcn = "FCEW" + ov::npuw::util::fmt(m_new_id++, 100);
+
+        // Just make an assert here: assume we don't need to handle
+        // collisions
+        NPUW_ASSERT(m_known.count(new_fcn) == 0);
+        return new_fcn;
+    }
+
+    bool enabled() const {
+        std::call_once(m_once, [&]() {
+            const bool fce_opt = m_cfg.get<::intel_npu::NPUW_FUNCALL_FOR_ALL>();
+            if (fce_opt) {
+                LOG_WARN("Every subgraph in " << m_model->get_friendly_name()
+                                              << " will be turned to a function: may cause performance issues");
+                m_enabled = true;
+            }
+        });
+        return m_enabled;
+    }
+};
+
+struct BankContains {
+    std::string name;
+    bool operator()(const ov::npuw::RepeatedBlock::MatchedLayers& lrs) {
+        return lrs.count(name) > 0;
+    }
+};
+
+ov::npuw::Ensemble load_groups(const std::shared_ptr<ov::Model>& model, const std::string& path_to_plan) {
+    // Try to load the partitioning plan...
+    NPUW_ASSERT(!path_to_plan.empty());
+
+    std::ifstream ifs(path_to_plan);
+    if (!ifs) {
+        LOG_ERROR("Couldn't open " << ::intel_npu::NPUW_PLAN().key() << "pointing to " << path_to_plan << "!");
+        return {};
+    }
+
+    // If file is specified and it exists, try to parse the XML document
+    LOG_INFO("Loading plan from " << path_to_plan << "...");
+    pugi::xml_document xml_doc;
+    pugi::xml_parse_result xml_res = xml_doc.load_file(path_to_plan.c_str());
+    if (xml_res.status != pugi::status_ok) {
+        LOG_ERROR("Couldn't parse the plan XML!");
+        return {};
+    }
+
+    // Load partitioning structure from the XML document.
+    // Note the .sg part is not filled at this point
+
+    using namespace ov::util::pugixml;
+    auto root = xml_doc.document_element();
+
+    // Load groups first
+    // clang-format off
+    std::vector<ov::npuw::Group> partitions;
+    auto groups = root.child("partitioning");
+    FOREACH_CHILD(group, groups, "group") {
+        partitions.push_back(ov::npuw::Group{});
+        ov::npuw::Group& this_group = partitions.back();
+        this_group.gflops = get_float_attr(group, "gflops");
+        this_group.repeated_id = get_str_attr(group, "repeated", "");
+        this_group.avoid_list = get_str_attr(group, "avoid", "");
+        FOREACH_CHILD(input, group, "input") {
+            this_group.input_layers.push_back(get_str_attr(input, "name"));
+        }
+        FOREACH_CHILD(output, group, "output") {
+            this_group.output_layers.push_back(get_str_attr(output, "name"));
+        }
+        FOREACH_CHILD(layer, group, "layer") {
+            this_group.all_layers.push_back(get_str_attr(layer, "name"));
+        }
+    }
+
+    // Load repeating blocks, if any
+    std::map<std::string, ov::npuw::RepeatedBlock> repeated;
+    auto reps = root.child("repeated");
+    if (reps) {
+        FOREACH_CHILD(block, reps, "block") {
+            ov::npuw::RepeatedBlock this_block;
+            FOREACH_CHILD(match, block, "match") {
+                ov::npuw::RepeatedBlock::MatchedLayers mls;
+                FOREACH_CHILD(layer, match, "layer") {
+                    mls.insert(get_str_attr(layer, "name"));
+                }
+                this_block.matches.push_back(std::move(mls));
+            }  // match
+            repeated[get_str_attr(block, "id")] = std::move(this_block);
+        }  // block
+    }      // if(reps)
+    // clang-format on
+
+    LOG_INFO("Found " << repeated.size() << " different repeated block(s)");
+
+    return ov::npuw::Ensemble{get_float_attr(root, "gflops"), partitions, repeated};
+}
+
+class Partitioner {
+private:
+    // External state - taken in the consturctor
+    const std::shared_ptr<ov::Model>& model;
+    ov::npuw::Ensemble& ens;
+    ov::npuw::Partitioning& P;
+
+    using PPtr = std::shared_ptr<ov::op::v0::Parameter>;
+    using RPtr = std::shared_ptr<ov::op::v0::Result>;
+    using LinkPtrTo = std::pair<size_t /*submodel_idx*/
+                                ,
+                                PPtr /*param ptr*/
+                                >;
+    using LinkPtrFrom = std::pair<size_t /*submodel_idx*/
+                                  ,
+                                  RPtr /*result ptr*/
+                                  >;
+    using LinkPtrs = std::map<LinkPtrTo, LinkPtrFrom>;
+    LinkPtrs subgraph_ptr_links;
+
+    // Internal state - not exposed anywhere
+    struct FunctionPipeline {
+        using VM = std::vector<std::shared_ptr<ov::Model>>;
+        using VS = std::vector<ov::npuw::Subgraph::Ref>;
+        VM mdls;
+        VS refs;
+        std::unordered_set<std::shared_ptr<ov::Node>> consts_to_keep;
+
+        // Map every function call instance' Parameter and result
+        // back to its prototype Parameter and Result
+        std::unordered_map<PPtr, PPtr> param_call_to_proto;
+        std::unordered_map<RPtr, RPtr> result_call_to_proto;
+    };
+    std::map<std::string, FunctionPipeline> all_functions;
+
+    // NB(dm): Sort of warning here that this mapping is used
+    // across all functions. Probably it would be easier and
+    // more efficient to have one per function group
+    std::unordered_map<std::string, std::string> layer_to_prototype;
+
+    // Some of the scalars might be shared by different groups within
+    // the same repeated block family. We need to keep them counted for sanity checks.
+    // Matches a pair of {func_name, layer_name} to it's counter.
+    std::map<std::pair<std::string, std::string>, size_t> dup_scalars;
+
+    using Match = std::function<bool(const std::shared_ptr<ov::Node>& node)>;
+    void propagate(const std::string& func_name, const Match& test, ov::npuw::RepeatedBlock::MatchedBank& bank);
+
+    void createFunction(FunctionPipeline& func_ggg);
+
+    template <typename T, typename M>
+    void rearrange_to_function_protocol(const std::vector<T>& protocol, std::vector<T>& call, const M& call_to_proto) {
+        LOG_DEBUG("Rearranging...");
+        LOG_BLOCK();
+        LOG_DEBUG("Protocol: " << protocol.size());
+        for (auto&& p : protocol) {
+            LOG_BLOCK();
+            LOG_DEBUG(p);
+        }
+        std::vector<T> to_proto;
+        LOG_DEBUG("Call: " << call.size());
+        for (auto&& c : call) {
+            LOG_BLOCK();
+            auto p_c = call_to_proto.at(c);
+            to_proto.push_back(p_c);
+            LOG_DEBUG(c << " (which is " << p_c << ")");
+        }
+        std::vector<T> call_tmp = call;
+        call = std::vector<T>(protocol.size(), nullptr);
+        for (std::size_t i = 0; i < protocol.size(); i++) {
+            auto iter = std::find(to_proto.begin(), to_proto.end(), protocol[i]);
+            if (iter != to_proto.end()) {
+                // There was an example in LLama where the last (32th) repeated
+                // block had 3 outputs instead of 4 with several partitioning models.
+                std::size_t j = std::distance(to_proto.begin(), iter);
+                LOG_DEBUG("Put " << j << " element to " << i << " as in the protocol");
+                call[i] = call_tmp[j];
+            }
+        }
+    }
+
+public:
+    Partitioner(const std::shared_ptr<ov::Model>& _model,
+                ov::npuw::Ensemble& _ens,
+                ov::npuw::Partitioning& _P,
+                ::intel_npu::Config& cfg)
+        : model(_model),
+          ens(_ens),
+          P(_P),
+          func_pipeline_type(FunctionPipelineType::FOLD),
+          cfg(cfg) {}
+
+    ////////////////////////////////////////////////////////
+    // Partitioning execution pipeline
+
+    // Partitioning to subgraph mapping
+    void identifySubgraphs();
+
+    // Function folding subroutines
+    enum class FunctionPipelineType { FOLD, CWAI };
+    std::vector<std::string> initFunctionPipeline(FunctionPipelineType utype);
+
+    // this implicit shared state can be lifted to some new
+    // kind of object
+    void propagateSlices(const std::string& func_name);
+    void propagateConverts(const std::string& func_name);
+    void propagateWeights(const std::string& func_name);
+    void propagateScalars(const std::string& func_name);
+    void sanityCheck(const std::string& func_name);
+    void saveTinyConstants(const std::string& func_name);
+    void saveScaleFactors(const std::string& func_name);
+    void saveRepeatedConstants(const std::string& func_name);
+    void matchParameters(const std::string& func_name);
+    void matchResults(const std::string& func_name);
+    void createFunction(const std::string& func_name);
+    void matchRepeatedSubgraphs(const std::string& func_name);
+    void decompressionCutOff(const std::string& func_name);
+
+    // Final steps
+    void finalizeLinks();
+
+private:
+    FunctionPipelineType func_pipeline_type;
+    ::intel_npu::Config& cfg;
+};
+
+void Partitioner::identifySubgraphs() {
+    LOG_INFO("Identifying subgraphs for model " << model->get_friendly_name() << "...");
+    LOG_BLOCK();
+
+    using namespace ov::npuw;
+    std::vector<ov::npuw::Group>& partitions = ens.groups;
+
+    // Apply partitioning changes to the original model
+    // but first cache all nodes to identify by name
+    using NodeSPtr = std::shared_ptr<ov::Node>;
+    std::unordered_map<NodeSPtr, LinkPtrFrom> result_cache;
+    std::unordered_map<std::string, NodeSPtr> node_id_cache;
+    for (auto&& node_ptr : model->get_ordered_ops()) {
+        node_id_cache[node_ptr->get_friendly_name()] = node_ptr;
+    }
+    LOG_INFO("Caching done: " << node_id_cache.size() << " layers.");
+
+    // FIXME: Need to do some sanity checks here. What if partitioning
+    // has been generated for another variation of this model?
+    // What if that was a completely different model?
+    std::size_t this_group_idx = 0u;  // FIXME: Replace with indexed()
+    for (auto&& group : partitions) {
+        LOG_INFO("Process partition " << this_group_idx << "...");
+        LOG_BLOCK();
+        // Sanity check: all group's layers present in the model
+        std::unordered_set<NodeSPtr> group_nodes;
+        for (auto&& layer : group.all_layers) {
+            auto it = node_id_cache.find(layer);
+            if (node_id_cache.end() == it) {
+                OPENVINO_THROW("NPUW: Fatal error - partitition refers to layer ",
+                               layer,
+                               " which was not found in the ov::Model");
+            }
+            group_nodes.insert(it->second);
+        }
+        group.sg._repeated_id = group.repeated_id;
+        group.sg._gflops = group.gflops;
+        group.sg._ops = group.all_layers.size();
+        P.total_ops += group.sg._ops;
+
+        group.sg._avoid_list = group.avoid_list;
+        // Note inputs and outputs are included in the above set, so if
+        // we are here, those nodes should be present in the model.
+
+        // In the subgraph, every "input" layer is the first (top) operation
+        // in the submodel (meaning it only depends on Parameters and Constants).
+        // In the original model, though, this layer is connected to smt.
+        // OpenVINO edge model is following:
+        // SomeNode:#Output -> #Input:ThisNode.
+        // In order to properly maintain connectivity of the partitioned model,
+        // we need to track these connections here.
+
+        // FIXME: But for now, just break all the connections!
+        // Note: input/output layers are _parts of the group_, they are like
+        // beginning/end of the subgraph.
+
+        // Input layers may be connected to the same producer nodes, weights,
+        // or parameters. Cache those to avoid duplicating the parameters.
+        std::unordered_map<NodeSPtr, NodeSPtr> input_mapping;
+        std::unordered_set<NodeSPtr> slice_params;
+        auto parameter_as_is = [&input_mapping](NodeSPtr orig_node) {
+            auto it = input_mapping.find(orig_node);
+            if (it != input_mapping.end()) {
+                return it->second;
+            }
+            input_mapping[orig_node] = orig_node;
+            return orig_node;
+        };
+        auto parameter_from = [&input_mapping](ov::Output<ov::Node> output) {
+            auto orig_node = output.get_node_shared_ptr();
+            auto it = input_mapping.find(orig_node);
+            if (it != input_mapping.end()) {
+                return it->second;
+            }
+
+            // In some weird combinations we can substitute a run-time parameter
+            // with a Constant. Normally this happens on ShapeOf/... subgraphs.
+            // In this case, adding a new parameter in a subnode will only break
+            // stuff as OpenVINO will be unable to calculate some output tensor
+            // shapes without tricks. So...
+            NodeSPtr result;
+            auto& output_tensor = output.get_tensor();
+            if (output_tensor.has_and_set_bound()) {
+                // if has_and_set_bound() == true, lower/upper values are the same tensor.
+                auto new_const = std::make_shared<ov::op::v0::Constant>(output_tensor.get_upper_value());
+                result = std::static_pointer_cast<ov::Node>(new_const);
+                LOG_VERB("Found bound value in " << output << ", substituting it with " << new_const);
+            } else {
+                // OK, actually introduce a parameter, cache it, and return.
+                auto new_param =
+                    std::make_shared<ov::op::v0::Parameter>(output.get_element_type(), output.get_partial_shape());
+                result = std::static_pointer_cast<ov::Node>(new_param);
+            }
+            input_mapping[orig_node] = result;
+            return result;
+        };
+        for (auto&& input_layer_name : group.input_layers) {
+            LOG_VERB("Processing group's input layer " << input_layer_name);
+            auto input_layer_ptr = node_id_cache.at(input_layer_name);
+            if (input_layer_ptr->inputs().empty()) {
+                OPENVINO_THROW("The group's input layer ",
+                               input_layer_name,
+                               " has NO INPUTS!! - Graph contracts are broken??");
+            }
+            for (auto&& input_desc : input_layer_ptr->inputs()) {
+                LOG_BLOCK();
+                const auto input_node = input_desc.get_source_output().get_node_shared_ptr();
+
+                if (ov::op::util::is_parameter(input_node)) {
+                    // Input to this subgraph layer is already a Parameter (original graph
+                    // input). Track it among the subgraph parameters and continue.
+                    parameter_as_is(input_node);
+                } else if (ov::op::util::is_constant(input_node)) {
+                    // Input to this subgraph layer is Const (weight). Don't do anything here.
+                    continue;
+                } else if (ov::is_type<ov::op::v0::Convert>(input_node) &&
+                           ov::op::util::is_constant(input_node->input(0).get_source_output().get_node_shared_ptr())) {
+                    // "Just-an-op" case, popped here again
+                    // FIXME: Finally introduce my own test routine for that!
+                    // Don't do anything here too.
+                    continue;
+                } else if (ov::is_type<ov::op::v8::Slice>(input_node) &&
+                           ov::op::util::is_parameter(input_node->input(0).get_source_output().get_node_shared_ptr())) {
+                    // So the situation is:
+                    // - a group has an input layer
+                    //  - which reads from a Slice
+                    //  - which reads from a Parameter
+                    // This happens when an offline plan is used with a kvcache
+                    // model extended with slices to maintain zero-copy (LLM case)
+                    auto slice_param = input_node->input(0).get_source_output().get_node_shared_ptr();
+                    input_mapping[input_node] = slice_param;
+                    slice_params.insert(slice_param);
+                } else {
+                    // Ok, this input is connected to some other node's output
+                    // Replace this connection with a link to a newly created Parameter
+                    // if this connection isn't coming from the same group like in pic below:
+                    //
+                    //  [par1]     [par2]
+                    //   v          :
+                    //   op1        :
+                    //   : .........'
+                    //   v v
+                    //   op2
+                    //   v
+                    //   op3
+                    // In this case both op1 and op2 will be marked input layers, but it doesn't
+                    // meen there's nothing _before_ an input layer in the graph
+                    if (group_nodes.find(input_node) == group_nodes.end()) {
+                        // Can't use input_node here directly since parameter_from converts
+                        // ov::Node to Output<Node> which some layers don't support by default.
+                        auto new_param = parameter_from(input_desc.get_source_output());
+                        ov::copy_runtime_info(input_node, new_param);
+                        input_desc.replace_source_output(new_param);
+                    }
+                }  // if (is..)
+            }      // for (inputs)
+        }          // for (input_layers)
+        // Transform the accumulated parameters to the subgraph model's input parameter vector
+        // Also track the connectivity
+        LOG_VERB("Populating _parameters...");
+        group.sg._parameters.clear();
+        for (auto&& im : input_mapping) {
+            LOG_BLOCK();
+            auto& src_node = im.first;
+            auto& maybe_param = im.second;
+            if (ov::op::util::is_parameter(maybe_param)) {
+                // some Parameters could fold into Constants, so only add real parameters
+                auto this_param = std::static_pointer_cast<ov::op::v0::Parameter>(maybe_param);
+                group.sg._parameters.push_back(this_param);
+                if (src_node != this_param && slice_params.count(this_param) == 0) {
+                    // Parameter node and the recorded src node are different
+                    // so it is a cut-off point (see above, parameter_from()):
+                    // - record connectivity between subgraphs.
+                    // Exception: param is registered via slice
+                    const auto link_from = result_cache.at(src_node);
+                    const auto link_to = LinkPtrTo{this_group_idx, this_param};
+                    subgraph_ptr_links[link_to] = link_from;
+                }
+            } else {
+                // assert is_constant(), there's no other way
+            }
+        }  // for(input_mapping)
+
+        // The same logic for group's final layers: replace their direct
+        // connections with Result stubs (but remember where these outputs
+        // were going to).
+        LOG_VERB("Populating _results...");
+        std::size_t num_optimized_out_layers = 0u;
+        for (auto&& output_layer_name : group.output_layers) {
+            LOG_VERB("Processing group's output layer " << output_layer_name);
+            LOG_BLOCK();
+            auto output_layer_ptr = node_id_cache.at(output_layer_name);
+            if (output_layer_ptr->inputs().empty()) {
+                OPENVINO_THROW("The group's output layer ",
+                               output_layer_name,
+                               " has NO OUTPUTS!! - Graph contracts are broken??");
+            }
+            const auto old_results_size = group.sg._results.size();
+            std::size_t num_optimized_out = 0;
+            for (auto&& output_desc : output_layer_ptr->outputs()) {
+                // NOT Every layer's output becomes a Result:
+                // Some of those can be consumed by the same group, e.g.
+                //
+                //    op100
+                //    v v v
+                //    : : [res1]
+                //    : v
+                //    : op101
+                //    v
+                //    op102
+                bool has_external_readers = false;
+                NodeSPtr maybe_result = nullptr;
+                auto readers = output_desc.get_target_inputs();
+                // This is possible then some of layer's outputs are not used in the model.
+                if (readers.empty()) {
+                    LOG_VERB("Output layer " << output_desc.get_node()->get_friendly_name()
+                                             << " was OPTIMIZED OUT since it has no readers.");
+                    num_optimized_out++;
+                    continue;
+                }
+                for (auto&& r : readers) {
+                    // First, remember the connections (to replicate those
+                    // at the npuw::CompiledModel level)
+                    auto reader_node_ptr = r.get_node()->shared_from_this();
+                    if (ov::op::util::is_output(reader_node_ptr)) {
+                        maybe_result = reader_node_ptr;
+                    } else if (group_nodes.find(reader_node_ptr) == group_nodes.end()) {
+                        has_external_readers = true;
+                    }
+                }
+                if (maybe_result) {
+                    // This layer's output was connected to Result already.
+                    // It happens when this layer is the original model's output
+                    // Keep it to make the ugly top-level I/O matching procedure work.
+                    // FIXME: This needs to be refactored
+                    group.sg._results.push_back(std::dynamic_pointer_cast<ov::op::v0::Result>(maybe_result));
+                    result_cache[output_layer_ptr] =
+                        LinkPtrFrom{this_group_idx, std::dynamic_pointer_cast<ov::op::v0::Result>(maybe_result)};
+                } else if (has_external_readers) {
+                    // Introduce and record a new Result
+                    // As the graph is processed in the topological order,
+                    // details recorded about this output at this point will
+                    // be handled as input details for some other subgraph.
+
+                    if (output_desc.get_tensor().has_and_set_bound()) {
+                        // Note: There is an optimization above where some Parameters
+                        // (down-stream) can be folded to Constants. Same applies to
+                        // Results which are inputs to these parameters. If the value is
+                        // foldable, no need to introduce Result too.
+                        // Actually it is OK for CPU and GPU, but it breaks the NPU plugin.
+                        num_optimized_out++;
+                        LOG_VERB("Discarding " << output_desc << " -- optimized out!");
+                    } else {
+                        auto new_result = std::make_shared<ov::op::v0::Result>(output_desc);
+                        result_cache[output_layer_ptr] = LinkPtrFrom{this_group_idx, new_result};
+
+                        ov::copy_runtime_info(output_desc.get_node_shared_ptr(), new_result);
+                        group.sg._results.push_back(new_result);
+                    }
+                }
+            }  // for (outputs)
+            if (num_optimized_out == output_layer_ptr->outputs().size()) {
+                num_optimized_out_layers++;
+            }
+            const auto new_results_size = group.sg._results.size();
+            LOG_VERB("Note: Processing the group " << this_group_idx << " output layer " << output_layer_name
+                                                   << " added " << new_results_size - old_results_size
+                                                   << " new Result node(s)");
+        }  // for (output_layers)
+        if (group.sg._results.empty()) {
+            if (num_optimized_out_layers != group.output_layers.size()) {
+                OPENVINO_THROW("NPUW Fatal: No Results registered for group ", this_group_idx);
+            } else {
+                LOG_VERB("Completely optimize out group " << this_group_idx);
+                group.sg._optimized_out = true;
+            }
+        }
+        this_group_idx++;  // FIXME: indexed() is better!
+    }                      // for (partitions)
+
+    // Return what we've got here
+    std::vector<Subgraph>& result = P.subgraphs;
+    result.reserve(partitions.size());
+    for (auto&& group : partitions) {
+        result.push_back(std::move(group.sg));
+    }
+}
+
+std::vector<std::string> Partitioner::initFunctionPipeline(FunctionPipelineType utype) {
+    func_pipeline_type = utype;
+
+    // Collect all groups of function call(s) and process them in groups
+    std::map<std::string, int> idx;
+    for (auto&& part_sg : P.subgraphs) {
+        if (!part_sg._repeated_id.empty()) {
+            auto pfix = "__" + std::to_string(idx[part_sg._repeated_id]++);
+            auto fcid = func_pipeline_type == FunctionPipelineType::FOLD
+                            ? part_sg._repeated_id          // with folding, functions of the
+                                                            // same group have the same id
+                            : part_sg._repeated_id + pfix;  // with CWAI (which is not checked here)
+                                                            // every function gets its own id
+            auto& u = all_functions[fcid];
+            u.refs.push_back(std::ref(part_sg));
+            u.mdls.push_back(
+                std::make_shared<ov::Model>(part_sg._results,
+                                            part_sg._sinks,
+                                            part_sg._parameters,
+                                            model->get_friendly_name() + "_" + part_sg._repeated_id + pfix));
+        }
+    }
+
+    if (func_pipeline_type == FunctionPipelineType::CWAI) {
+        // Early return - don't do anything else here.
+        // Also update repeated_ids to uniques
+        std::vector<std::string> functions;
+        for (auto&& p : all_functions) {
+            functions.push_back(p.first);
+            p.second.refs.front().get()._repeated_id = p.first;
+        }
+        return functions;
+    }
+
+    // Then, populate a list of functions and the early layer_to_prototype mapping.
+    // The latter will be refined in the following passes.
+    LOG_VERB("Initialize layer banks...");
+    std::vector<std::string> functions;
+    for (auto&& p : all_functions) {
+        LOG_BLOCK();
+        functions.push_back(p.first);
+        LOG_VERB("Processing function group " << p.first);
+
+        auto& rep_block = ens.repeated.at(p.first);
+
+        LOG_DEBUG("Use " << p.second.mdls.front()->get_friendly_name() << " as a template...");
+        for (auto&& node_ptr : p.second.mdls.front()->get_ordered_ops()) {
+            const auto& this_layer_name = node_ptr->get_friendly_name();
+            auto layer_bank_iter =
+                std::find_if(rep_block.matches.begin(), rep_block.matches.end(), BankContains{this_layer_name});
+            if (layer_bank_iter != rep_block.matches.end()) {
+                for (auto&& layer : *layer_bank_iter) {
+                    LOG_BLOCK();
+                    LOG_DEBUG(this_layer_name << " is a prototype of " << layer);
+                    layer_to_prototype[layer] = this_layer_name;  // Link to self is ok
+                }
+            }
+        }
+    }
+    LOG_VERB("Done");
+    return functions;
+}
+
+void Partitioner::propagate(const std::string& func_name,
+                            const Match& test,
+                            ov::npuw::RepeatedBlock::MatchedBank& bank) {
+    // NOTE: This routine assumes a Const is accessed by a single reader.
+    // There may be situations where a Const (normally, not a Weight)
+    // is accessed by multiple readers. This routine shouldn't be used
+    // there.
+
+    auto& model_group = all_functions.at(func_name).mdls;
+
+    // PROTO writer (value) <OF> PROTO reader/port (key).
+    // Readers are always registered in the bank (NOTE: _some_
+    // bank, but probably not exactly the one which is passed
+    // here as `bank' argument),
+    // and Writers are subjects to this propagation process
+    // (e.g. Convert! -> Op, Const! -> Convert)
+    using ProtoReader = std::pair<std::string, size_t>;
+    using ProtoReaders = std::set<ProtoReader>;
+    auto dump_readers = [](const ProtoReaders& readers) {
+        LOG_BLOCK();
+        for (auto&& r : readers)
+            LOG_DEBUG(r.first << " : " << r.second);
+    };
+
+    std::map<ProtoReaders, std::string> proto_writer_of;
+    for (auto&& model : model_group) {
+        LOG_DEBUG("Process function call " << model->get_friendly_name() << "...");
+        LOG_BLOCK();
+
+        // Cache model contents as sometimes Readers of Consts
+        // may be outside. FIXME: Do only once when a tmp Model is created?
+        std::unordered_set<ov::Node*> this_model_nodes;
+        for (auto&& node_ptr : model->get_ordered_ops()) {
+            this_model_nodes.insert(node_ptr.get());
+        }
+
+        for (auto&& node_ptr : model->get_ordered_ops()) {
+            if (test(node_ptr)) {
+                LOG_DEBUG("Process node " << node_ptr);
+                const auto& this_layer_name = node_ptr->get_friendly_name();
+
+                ProtoReaders this_node_readers, this_node_proto_readers;
+                for (auto&& this_reader_iport : node_ptr->output(0).get_target_inputs()) {
+                    LOG_BLOCK();
+                    LOG_DEBUG("Read by " << this_reader_iport);
+                    if (this_model_nodes.count(this_reader_iport.get_node()) == 0) {
+                        LOG_BLOCK();
+                        LOG_DEBUG("Link to the external reader (other submodel?) - skip");
+                    } else {
+                        this_node_readers.insert(ProtoReader{this_reader_iport.get_node()->get_friendly_name(),
+                                                             this_reader_iport.get_index()});
+                    }
+                }  // for(this_reader_iport)
+                LOG_DEBUG("Looking for proto accessess...");
+                for (auto&& this_node_reader : this_node_readers) {
+                    LOG_BLOCK();
+                    LOG_DEBUG("Looking for proto of reader " << this_node_reader.first);
+                    this_node_proto_readers.insert(
+                        {layer_to_prototype.at(this_node_reader.first), this_node_reader.second});
+                }
+                auto bank_writer_iter = proto_writer_of.find(this_node_proto_readers);
+                if (bank_writer_iter == proto_writer_of.end()) {
+                    // FIXME: assert(bank_reader_name == proto(bank_reader_name))
+                    // This is a first occasion for this Writer -> Reader pair.
+                    // Register it for the further reference. Also add it to the layer bank
+                    LOG_DEBUG("Register that " << this_layer_name << " is accessed by:");
+                    dump_readers(this_node_proto_readers);
+                    proto_writer_of[this_node_proto_readers] = this_layer_name;
+                    layer_to_prototype[this_layer_name] = this_layer_name;
+                    bank.push_back({this_layer_name});
+                } else {
+                    // Such occasion is already registered - find a suitable bank
+                    // and add node there
+                    const auto& this_writer_proto = bank_writer_iter->second;
+                    auto suitable_bank_iter = std::find_if(bank.begin(), bank.end(), BankContains{this_writer_proto});
+                    if (suitable_bank_iter == bank.end()) {
+                        OPENVINO_THROW("Fatal. Just fatal");
+                    }
+                    // FIXME: add IF(DEBUG) to put the whole thing under condition
+                    LOG_DEBUG("Register that " << this_layer_name << " is in fact " << this_writer_proto);
+                    LOG_DEBUG("- As it is read by:");
+                    dump_readers(this_node_readers);
+                    LOG_DEBUG("- Which in turn are:");
+                    dump_readers(this_node_proto_readers);
+                    suitable_bank_iter->insert(this_layer_name);
+                    layer_to_prototype[this_layer_name] = this_writer_proto;
+                }  // if(iter==end)
+            }      // test(node_ptr)
+        }          // for(ordered_ops)
+    }              // for(each)
+}  // propagate
+
+void Partitioner::propagateSlices(const std::string& func_name) {
+    LOG_VERB("Propagate Slice nodes to matching banks for model " << model->get_friendly_name() << "...");
+    LOG_BLOCK();
+
+    // This is a special step. Normally we shouldn't do this here at all.
+    // Extra slices may be added to the LLM kvcache models _after_
+    // offline partitioning was done when adapting them to the zero
+    // -copy kvcache path. In this case, we get extra slices in the model
+    // which we need to add to the match banks
+
+    auto& bank = ens.repeated.at(func_name).matches;
+    auto match_fcn = [&](const std::shared_ptr<ov::Node>& node_ptr) -> bool {
+        const auto& this_layer_name = node_ptr->get_friendly_name();
+        return ov::is_type<ov::op::v8::Slice>(node_ptr) &&
+                   bank.end() == std::find_if(bank.begin(), bank.end(), BankContains{this_layer_name})          // (0)
+                   && ov::op::util::is_parameter(node_ptr->input(0).get_source_output().get_node_shared_ptr())  // (1)
+                   && [&](std::set<ov::Input<ov::Node>>&& readers) -> bool {
+            // FIXME: It could be all_of, but slices may have reads
+            // outside of the function group (e.g., a single instance
+            // of shape_of, taken only on the first kvcache tensor)
+            return std::any_of(readers.begin(), readers.end(), [&](const ov::Input<ov::Node>& reader) -> bool {
+                auto reply =
+                    bank.end() !=
+                    std::find_if(bank.begin(), bank.end(), BankContains{reader.get_node()->get_friendly_name()});
+                return reply;
+            });
+        }(node_ptr->output(0).get_target_inputs());
+    };
+    propagate(func_name, match_fcn, bank);
+    LOG_VERB("Done");
+}
+
+void Partitioner::propagateConverts(const std::string& func_name) {
+    LOG_VERB("Propagate Convert nodes to matching banks for model " << model->get_friendly_name() << "...");
+    LOG_BLOCK();
+
+    // This is a special step. Normally we shouldn't do this here at all.
+    // Here comes the original problem description:
+    //
+    //  ``Currently Ensemble omits Convert nodes from its displayable
+    //    EnsOVIR graph model to simplify the visual representation.
+    //    This causes a problem here - where in fact a Convert node is
+    //    still part of the graph, but is NOT mentioned in the partitioning
+    //    plan - and so, is missing in the matched layer banks. If we find
+    //    one in the actual model we use as a function template, register
+    //    a new bank for it - so every eligible Convert node in the function
+    //    template registers its own bank. The eligibility is defined with
+    //    the following rules:
+    //    0. The node is missing in the matching bank
+    //    1. The Convert node reads a Const
+    //    2. The Convert node has a sole consumer
+    //    3. This sole consumer is present in the bank.''
+    //
+    // The propagation procedure is generic, but the matching isn't.
+    auto& bank = ens.repeated.at(func_name).matches;
+    auto match_fcn = [&](const std::shared_ptr<ov::Node>& node_ptr) -> bool {
+        const auto& this_layer_name = node_ptr->get_friendly_name();
+        return ov::is_type<ov::op::v0::Convert>(node_ptr) &&
+               bank.end() == std::find_if(bank.begin(), bank.end(), BankContains{this_layer_name})         // (0)
+               && ov::op::util::is_constant(node_ptr->input(0).get_source_output().get_node_shared_ptr())  // (1)
+               && node_ptr->output(0).get_target_inputs().size() == 1                                      // (2)
+               &&
+               bank.end() !=
+                   std::find_if(
+                       bank.begin(),
+                       bank.end(),
+                       BankContains{
+                           node_ptr->output(0).get_target_inputs().begin()->get_node()->get_friendly_name()});  // (3)
+    };
+    propagate(func_name, match_fcn, bank);
+    LOG_VERB("Done");
+}
+
+void Partitioner::propagateWeights(const std::string& func_name) {
+    LOG_VERB("Propagate Const Weights to matching banks for model " << model->get_friendly_name() << "...");
+    LOG_BLOCK();
+
+    // See explanation in propagateConverts().
+    // The propagation procedure is generic, but the matching isn't.
+    auto& const_bank = ens.repeated.at(func_name).consts;
+    auto& layer_bank = ens.repeated.at(func_name).matches;
+    auto match_fcn = [&](const std::shared_ptr<ov::Node>& node_ptr) -> bool {
+        const auto& this_layer_name = node_ptr->get_friendly_name();
+        return ov::is_type<ov::op::v0::Constant>(node_ptr) &&
+               const_bank.end() == std::find_if(const_bank.begin(), const_bank.end(), BankContains{this_layer_name})
+               // FIXME: workaround for scalars which might pass the weights check
+               && (node_ptr->get_shape().size() > 1 ||
+                   (node_ptr->get_shape().size() == 1 && node_ptr->get_shape()[0] > 10))
+               // FIXME end
+               && node_ptr->output(0).get_target_inputs().size() == 1 &&
+               layer_bank.end() !=
+                   std::find_if(
+                       layer_bank.begin(),
+                       layer_bank.end(),
+                       BankContains{node_ptr->output(0).get_target_inputs().begin()->get_node()->get_friendly_name()});
+    };
+    propagate(func_name, match_fcn, const_bank);
+    LOG_VERB("Done");
+}
+
+void Partitioner::propagateScalars(const std::string& func_name) {
+    LOG_VERB("Propagate Const Scalars to matching banks for model " << model->get_friendly_name() << "...");
+    LOG_BLOCK();
+
+    // There are situations where the same Const operation is referred to
+    // by multiple other Operations and so, in the repeated blocks model,
+    // by multiple instances of the same (or many) function(s).
+
+    // Such constants are safe to keep in-place (fused into the function
+    // body, but first we need to make sure they have the same connectivity
+    // from funcall to funcall (= add it to the matched bank).
+
+    // the saveRepeatedConstants() will then take care of them.
+
+    // See explanation in propagateConverts().
+    // The propagation procedure is generic, but the matching isn't.
+    auto& scalar_bank = ens.repeated.at(func_name).scalars;
+    auto match_fcn = [&](const std::shared_ptr<ov::Node>& node_ptr) -> bool {
+        const auto& this_layer_name = node_ptr->get_friendly_name();
+        auto res =
+            ov::is_type<ov::op::v0::Constant>(node_ptr) &&
+            scalar_bank.end() == std::find_if(scalar_bank.begin(), scalar_bank.end(), BankContains{this_layer_name});
+        if (ov::is_type<ov::op::v0::Constant>(node_ptr) &&
+            scalar_bank.end() != std::find_if(scalar_bank.begin(), scalar_bank.end(), BankContains{this_layer_name})) {
+            // FIXME: incorrect logic! This will also increment in case of multiple scalar outputs.
+            // Instead it should only take shared scalars in to account!
+            dup_scalars[{func_name, this_layer_name}]++;
+        }
+        return res;
+    };
+    propagate(func_name, match_fcn, scalar_bank);
+
+    LOG_VERB("Done");
+}
+
+void Partitioner::sanityCheck(const std::string& func_name) {
+    LOG_VERB("Sanity check function " << func_name << " in model " << model->get_friendly_name() << "...");
+    LOG_BLOCK();
+
+    // All match banks should have the same size, and this size
+    // is the # of functon calls in this group
+    auto& rep_block = ens.repeated.at(func_name);
+    auto& func_group = all_functions.at(func_name);
+    LOG_DEBUG("The function has " << rep_block.matches.size() << " operation banks and " << rep_block.consts.size()
+                                  << " constant banks");
+
+    auto validate = [&func_group](const ov::npuw::RepeatedBlock::MatchedLayers& lrs) -> bool {
+        for (auto&& l : lrs) {
+            LOG_DEBUG(l);
+        }
+        if (lrs.size() != func_group.refs.size()) {
+            LOG_WARN("Number of layers in match bank differs from # of function calls: " << lrs.size() << " != "
+                                                                                         << func_group.refs.size());
+            return false;
+        }
+        LOG_DEBUG("Validation passed");
+        return true;
+    };
+    auto validate_scalars = [&](const ov::npuw::RepeatedBlock::MatchedLayers& lrs) -> bool {
+        for (auto&& l : lrs) {
+            LOG_DEBUG(l);
+        }
+        size_t f_dup_scalars = 0;
+        for (const auto& l : lrs) {
+            f_dup_scalars += dup_scalars[{func_name, l}];
+        }
+        if (lrs.size() != 1 && (lrs.size() + f_dup_scalars != func_group.refs.size()) &&
+            (lrs.size() != func_group.refs.size())) {
+            LOG_WARN("Number of layers in scalar match bank differs from 1 <OR> # of function calls "
+                     << "<OR> # of function calls including duplicate scalars: " << lrs.size()
+                     << " != " << func_group.refs.size() << " OR " << lrs.size() + f_dup_scalars
+                     << " != " << func_group.refs.size());
+            return false;
+        }
+        LOG_DEBUG("Validation passed");
+        return true;
+    };
+    bool all_ok = true;
+    for (auto& bank : rep_block.matches) {
+        LOG_DEBUG("Validating operation bank...");
+        LOG_BLOCK();
+        all_ok &= validate(bank);
+    }
+    NPUW_ASSERT(all_ok);
+    for (auto& bank : rep_block.consts) {
+        LOG_DEBUG("Validating const bank...");
+        LOG_BLOCK();
+        all_ok &= validate(bank);
+    }
+    NPUW_ASSERT(all_ok);
+    for (auto& bank : rep_block.scalars) {
+        LOG_DEBUG("Validating scalar bank...");
+        LOG_BLOCK();
+        all_ok &= validate_scalars(bank);
+    }
+    NPUW_ASSERT(all_ok);
+    // All Consts in all submodels should be registered at this point
+    auto& consts = rep_block.consts;
+    auto& scalars = rep_block.scalars;
+    for (auto&& submodel : func_group.mdls) {
+        LOG_DEBUG("Check " << submodel->get_friendly_name() << " constants...");
+        LOG_BLOCK();
+
+        std::unordered_set<ov::Node*> this_model_nodes;
+        for (auto&& node : submodel->get_ordered_ops()) {
+            this_model_nodes.insert(node.get());
+        }
+
+        for (auto&& node : submodel->get_ordered_ops()) {
+            if (ov::op::util::is_constant(node) &&
+                consts.end() == std::find_if(consts.begin(), consts.end(), BankContains{node->get_friendly_name()}) &&
+                scalars.end() ==
+                    std::find_if(scalars.begin(), scalars.end(), BankContains{node->get_friendly_name()})) {
+                LOG_ERROR("Fatal: Const " << node->get_friendly_name() << "{ " << node->output(0) << " }"
+                                          << " wasn't found in any bank");
+                LOG_BLOCK();
+                for (auto&& reader_input : node->output(0).get_target_inputs()) {
+                    LOG_DEBUG("Accessed by "
+                              << reader_input.get_node()->get_friendly_name() << " / " << reader_input.get_index()
+                              << (this_model_nodes.count(reader_input.get_node()) > 0 ? " [internal]" : " [external]"));
+                }
+                all_ok = false;
+            }
+        }
+    }
+    NPUW_ASSERT(all_ok);
+    LOG_VERB("Done");
+}
+
+void Partitioner::saveTinyConstants(const std::string& func_name) {
+    // A simplified version of saveRepeatedConstants()
+
+    LOG_VERB("Preserve tiny constants for " << func_name << " in model " << model->get_friendly_name() << "...");
+    LOG_BLOCK();
+
+    auto& func_group = all_functions.at(func_name);
+    auto& model_group = func_group.mdls;
+
+    using CT = ov::op::v0::Constant;
+
+    for (auto&& op_node : model_group.front()->get_ordered_ops()) {
+        for (auto&& iport : op_node->inputs()) {
+            auto node = iport.get_source_output().get_node_shared_ptr();
+            if (ov::op::util::is_constant(node)) {
+                auto shape = node->output(0).get_shape();
+                auto total =
+                    std::accumulate(shape.begin(), shape.end(), std::size_t{1}, std::multiplies<std::size_t>());
+                if ((shape.size() == 0 || (shape.size() == 1 && shape[0] <= 10)) || (total <= 10)) {
+                    LOG_DEBUG("[KEEP] It is safe to keep this bank in function");
+                    func_group.consts_to_keep.insert(std::static_pointer_cast<CT>(node));
+                } else {
+                    LOG_DEBUG("[CUT ] This group of Const ops will be cut-off from the function");
+                }
+            }
+        }
+    }  // for(n)
+    LOG_VERB("Done");
+}
+
+void Partitioner::saveScaleFactors(const std::string& func_name) {
+    // A special step in the CWAI pipeline - mark the Scale
+    // tensors to be preserved in the function bodies
+
+    LOG_VERB("Preserve scale factors for " << func_name << " in model " << model->get_friendly_name() << "...");
+    LOG_BLOCK();
+
+    auto& func_group = all_functions.at(func_name);
+    auto& model_group = func_group.mdls;
+
+    using CPtr = std::shared_ptr<ov::op::v0::Constant>;
+    std::vector<CPtr> to_keep;
+
+    ov::pass::GraphRewrite rewr;
+    rewr.add_matcher<ov::npuw::patterns::SymmZP::CWAI1>(std::ref(to_keep));
+    rewr.add_matcher<ov::npuw::patterns::SymmZP::CWAI2>(std::ref(to_keep));
+    rewr.run_on_model(model_group.front());
+
+    for (auto&& const_to_keep : to_keep) {
+        LOG_DEBUG("[KEEP] " << const_to_keep);
+        func_group.consts_to_keep.insert(const_to_keep);
+    }
+    LOG_VERB("Done");
+}
+
+void Partitioner::saveRepeatedConstants(const std::string& func_name) {
+    // Not all Constants can be cut off from the function body.  Some
+    // of them are required for proper functioning and moving them to
+    // parameters makes a static graph dynamic (e.g., when constant
+    // defines some axis for Concat/Reshape/etc).  There may be a
+    // chance that all these constants (in the same bank) have the
+    // same value - in this case this value can be stored as a Const
+    // in the function body.
+    //
+    // Checking all the Constants by value is overkill for FEIL, so
+    // the following number of heuristics is used instead:
+    //
+    // To be saved, all constants in a bank must:
+    // 0. Be of integer type
+    // 1. Have 0 to 1 dimensions
+    // 2. Have <= 10 elements in that single dimension, if any
+    // 3. Have the equal values.
+    //
+    // The procedure below implements this.. but in a highly
+    // inefficient manner If we start working with sptr<ov::Node>
+    // objects directly (instead of their std::string identifiers),
+    // the algorithm won't change much but will speed-up dramatically.
+
+    LOG_VERB("Identify constants to save for function " << func_name << " in model " << model->get_friendly_name()
+                                                        << "...");
+    LOG_BLOCK();
+
+    auto& func_group = all_functions.at(func_name);
+    auto& model_group = func_group.mdls;
+    auto& subgr_group = func_group.refs;
+    auto& rep_block = ens.repeated.at(func_name);
+
+    if (subgr_group.size() == 1) {
+        // This is not a repeating block but a subgraph which is forced
+        // to be function - run a simpler procedure here
+        LOG_VERB("A FCEW function - switch to a simpler path...");
+        LOG_BLOCK();
+        saveTinyConstants(func_name);
+        return;
+    }
+
+    // Build a cross-model <op name> to <op ptr> cache
+    using CT = ov::op::v0::Constant;
+    using CTPtr = std::shared_ptr<CT>;
+    std::unordered_map<std::string, CTPtr> const_cache;
+    for (auto&& m : model_group) {
+        for (auto&& n : m->get_ordered_ops()) {
+            if (ov::is_type<CT>(n)) {
+                const_cache[n->get_friendly_name()] = std::static_pointer_cast<CT>(n);
+            }
+        }
+    }  // for(models)
+
+    // Now walk through through every Const bank and inspect the above properties
+    auto values_are_the_same = [](const CTPtr& node_a, const CTPtr& node_b) {
+        switch (node_a->output(0).get_element_type()) {
+#define HANDLE_CASE(t, T) \
+    case ov::element::t:  \
+        return node_a->get_vector<T>() == node_b->get_vector<T>();
+            HANDLE_CASE(boolean, bool);
+            HANDLE_CASE(i4, int8_t);
+            HANDLE_CASE(u4, uint8_t);
+            HANDLE_CASE(i32, int);
+            HANDLE_CASE(i64, int64_t);
+            HANDLE_CASE(f32, float);
+#undef HANDLE_CASE
+        default:
+            OPENVINO_THROW("Unable to handle type ", node_a->output(0));
+        }
+        return false;
+    };
+    auto check_and_mark = [&](const ov::npuw::RepeatedBlock::MatchedLayers& bank) {
+        std::unordered_set<CTPtr> instances;
+        for (auto&& l : bank) {
+            instances.insert(const_cache.at(l));
+        }
+        auto& proto_node = *instances.begin();
+        auto& proto_shape = proto_node->output(0).get_shape();
+
+        LOG_DEBUG("Checking a bank with prototype node " << proto_node << "...");
+        LOG_BLOCK();
+
+        if ((((proto_shape.size() == 0 || (proto_shape.size() == 1 && proto_shape[0] <= 10)) &&
+              proto_node->output(0).get_element_type().is_integral()) ||
+             (proto_node->output(0).get_element_type() == ov::element::f32 &&
+              std::accumulate(proto_shape.begin(), proto_shape.end(), size_t{1}, std::multiplies<std::size_t>()) ==
+                  1)) &&
+            std::all_of(instances.begin(), instances.end(), [&](const CTPtr& other_node) -> bool {
+                return (other_node->output(0).get_shape() == proto_node->output(0).get_shape()) &&
+                       values_are_the_same(proto_node, other_node);
+            })) {
+            // Check passed for this group.
+            LOG_DEBUG("[KEEP] It is safe to keep this bank in function");
+            for (auto&& const_node : instances) {
+                func_group.consts_to_keep.insert(const_node);
+            }
+        } else {
+            LOG_DEBUG("[CUT ] This group of Const ops will be cut-off from the function");
+        }
+    };
+    for (auto&& bank : rep_block.consts) {
+        check_and_mark(bank);
+    }
+    for (auto&& bank : rep_block.scalars) {
+        if (bank.size() > 1) {
+            check_and_mark(bank);
+        } else {
+            // A single Constant layer in the match bank means _all_ the repeated
+            // blocks refer to this constant (like in 1:N connection), so it is
+            // ok to keep this const in the function body.
+            NPUW_ASSERT(bank.size() == 1);
+            func_group.consts_to_keep.insert(const_cache.at(*bank.begin()));
+        }
+    }
+}
+
+void Partitioner::matchParameters(const std::string& func_name) {
+    LOG_VERB("Matching parameters for function " << func_name << " in model " << model->get_friendly_name() << "...");
+    LOG_BLOCK();
+
+    // In our repeated patterns, a Parameter is uniquely identified
+    // by a set of its readers. A reader is a node+port.
+    using PReader = std::pair<std::string, std::size_t>;
+    using PKey = std::set<PReader>;
+
+    auto& func = all_functions.at(func_name);
+    auto& model_group = func.mdls;
+
+    // FIXME: Here we rely on sacred knowledge that the first
+    // subgraph in the list is the prototype... Use this to
+    // initialize the proto parameter reader map
+    std::map<PKey, PPtr> proto_parameters;
+    {
+        LOG_DEBUG("Generating proto pkeys...");
+        LOG_BLOCK();
+        auto& body = model_group.front();
+        std::unordered_set<ov::Node*> this_model_nodes;
+        for (auto&& node_ptr : body->get_ordered_ops()) {
+            this_model_nodes.insert(node_ptr.get());
+        }
+        for (auto&& node : body->get_ordered_ops()) {
+            if (ov::op::util::is_parameter(node)) {
+                PKey pkey;
+                for (auto&& iport : node->output(0).get_target_inputs()) {
+                    if (this_model_nodes.count(iport.get_node()) > 0) {
+                        LOG_DEBUG("Register link " << iport.get_node()->get_friendly_name() << " : "
+                                                   << iport.get_index());
+                        pkey.insert(PReader{iport.get_node()->get_friendly_name(), iport.get_index()});
+                    }
+                }
+                LOG_DEBUG("Stored " << node);
+                proto_parameters[pkey] = std::dynamic_pointer_cast<PPtr::element_type>(node);
+            }
+        }
+    }
+
+    // Now walk other submodels and match parameters with the same key
+    // (yes, including the first one)
+    for (auto&& call : model_group) {
+        LOG_DEBUG("Handle function call...");
+        LOG_BLOCK();
+        std::unordered_set<ov::Node*> this_model_nodes;
+        for (auto&& node_ptr : call->get_ordered_ops()) {
+            this_model_nodes.insert(node_ptr.get());
+        }
+        for (auto&& node : call->get_ordered_ops()) {
+            if (ov::op::util::is_parameter(node)) {
+                PKey pkey;
+                for (auto&& iport : node->output(0).get_target_inputs()) {
+                    if (this_model_nodes.count(iport.get_node()) > 0) {
+                        LOG_DEBUG("Register link " << iport.get_node()->get_friendly_name() << " : "
+                                                   << iport.get_index());
+                        pkey.insert(
+                            PReader{layer_to_prototype.at(iport.get_node()->get_friendly_name()), iport.get_index()});
+                    }
+                }
+                LOG_DEBUG("Find orig parameter for " << node);
+                auto& orig_param = proto_parameters.at(pkey);
+                auto this_param = std::dynamic_pointer_cast<PPtr::element_type>(node);
+                func.param_call_to_proto[this_param] = orig_param;
+            }
+        }
+    }
+    LOG_VERB("Done");
+}
+
+void Partitioner::matchResults(const std::string& func_name) {
+    LOG_VERB("Matching results for function " << func_name << " in model " << model->get_friendly_name() << "...");
+    LOG_BLOCK();
+
+    // FIXME: Here we rely on sacred knowledge that the first
+    // subgraph in the list is the prototype...
+
+    // In our repeated patterns, a Result is uniquely identified
+    // by its writer. A writer is a node+port.
+    using RKey = std::pair<std::string, std::size_t>;
+
+    auto& func = all_functions.at(func_name);
+    auto& model_group = func.mdls;
+
+    // FIXME: Here we rely on sacred knowledge that the first
+    // subgraph in the list is the prototype... Use this to
+    // initialize the proto result writer map
+    std::map<RKey, RPtr> proto_results;
+    {
+        auto& body = model_group.front();
+        for (auto&& node : body->get_ordered_ops()) {
+            if (ov::op::util::is_output(node)) {
+                auto&& port = node->input(0).get_source_output();
+                RKey rkey = {port.get_node()->get_friendly_name(), port.get_index()};
+                proto_results[rkey] = std::dynamic_pointer_cast<RPtr::element_type>(node);
+            }
+        }
+    }
+
+    // Now walk all submodels and match parameters with the same key
+    // (yes, including the first one)
+    for (auto&& call : model_group) {
+        for (auto&& node : call->get_ordered_ops()) {
+            if (ov::op::util::is_output(node)) {
+                auto&& port = node->input(0).get_source_output();
+                RKey rkey = {layer_to_prototype.at(port.get_node()->get_friendly_name()), port.get_index()};
+                auto& orig_result = proto_results.at(rkey);
+                auto this_result = std::dynamic_pointer_cast<RPtr::element_type>(node);
+                func.result_call_to_proto[this_result] = orig_result;
+            }
+        }
+    }
+    LOG_VERB("Done");
+}
+
+void Partitioner::createFunction(FunctionPipeline& func_ggg) {
+    ov::npuw::Subgraph& body_sg = func_ggg.refs.front();
+    const std::string func_name = body_sg._repeated_id;
+
+    LOG_VERB("Registering a new function " << func_name << "...");
+    LOG_BLOCK();
+
+    ov::npuw::Subgraph funcall{};
+    std::swap(body_sg._funcall, body_sg._repeated_id);  // FIXME: unclear
+
+    // Preserve information about funcall and PARAMETERS for matching in
+    // compiled_model (see m_inputs/outputs_to_submodels_inputs/outputs mess)
+    funcall._funcall = body_sg._funcall;
+    funcall._parameters = body_sg._parameters;
+    funcall._results = body_sg._results;
+    funcall._gflops = body_sg._gflops;  // preserving this is required for proper stats
+    funcall._ops = body_sg._ops;        // preserving this is requried for proper stats
+    funcall._avoid_list = body_sg._avoid_list;
+
+    // Declare a new function AND record a function call
+    ov::npuw::Function function;
+    function._model = func_ggg.mdls.front();
+    function._param_offset = body_sg._parameters.size();
+    std::size_t new_param_idx = function._param_offset;
+
+    for (auto&& node_ptr : function._model->get_ordered_ops()) {
+        if (ov::op::util::is_parameter(node_ptr) || ov::op::util::is_constant(node_ptr) ||
+            ov::op::util::is_output(node_ptr)) {
+            // Skip Parameter, Const, and Result layers from bank matching
+            continue;
+        }
+        const auto& this_layer_name = node_ptr->get_friendly_name();
+        LOG_DEBUG("Processing " << this_layer_name);
+
+        for (auto&& input_desc : node_ptr->inputs()) {
+            const auto& prod_output = input_desc.get_source_output();
+            const auto input_node = prod_output.get_node_shared_ptr();
+            const auto iport = std::make_pair(node_ptr->get_friendly_name(), input_desc.get_index());
+            LOG_DEBUG("Processing input " << prod_output);
+            LOG_BLOCK();
+
+            if (ov::op::util::is_constant(input_node) && func_ggg.consts_to_keep.count(input_node) == 0) {  // (n)/1/i
+                LOG_DEBUG("Handling a Constant input " << prod_output);
+                LOG_BLOCK();
+
+                auto new_param = std::make_shared<ov::op::v0::Parameter>(prod_output.get_element_type(),
+                                                                         prod_output.get_partial_shape());
+                input_desc.replace_source_output(new_param);  // (n)/1/i/a
+                function._model->add_parameters({new_param});
+                LOG_DEBUG("Register Parameter[" << new_param_idx << "] as input to " << iport.first << " / "
+                                                << iport.second);
+                function._param_mapping[iport] = new_param_idx;  // (n)/1/i/b
+                new_param_idx++;
+
+                LOG_DEBUG("Register " << prod_output << " in the function closure");
+                funcall._closure.push_back(ov::npuw::util::tensor_from_const(input_node));  // (n)/1/i/c
+            } else if (ov::op::util::is_parameter(input_node)) {
+                LOG_DEBUG("Handling a Parameter input " << prod_output);
+                LOG_BLOCK();
+
+                // Record that this node also reads a parameter.
+                // Parameter must be already defined (originally)
+                auto param_iter = std::find(body_sg._parameters.begin(),
+                                            body_sg._parameters.end(),
+                                            std::static_pointer_cast<ov::op::v0::Parameter>(input_node));
+                if (param_iter == body_sg._parameters.end()) {
+                    OPENVINO_THROW("NPUW:", input_node, " is not found in the subgraph's parameters");
+                }
+                const auto existing_param_idx = std::distance(body_sg._parameters.begin(), param_iter);
+                LOG_DEBUG("Register Parameter[" << existing_param_idx << "] as input to " << iport.first << " / "
+                                                << iport.second);
+                function._param_mapping[iport] = existing_param_idx;
+            }  // if(Const|Parameter)
+        }      // for(inputs)
+    }          // for(nodes)
+    function._num_params_total = new_param_idx;
+    function._model->validate_nodes_and_infer_types();
+    P.functions.insert({func_name, std::move(function)});
+
+    // Write down the funcall to the list of subgraphs
+    std::swap(funcall, body_sg);
+    LOG_VERB("Done: " << func_name);
+}
+
+void Partitioner::createFunction(const std::string& func_name) {
+    createFunction(all_functions.at(func_name));
+}
+
+void Partitioner::matchRepeatedSubgraphs(const std::string& func_name) {
+    LOG_VERB("Process function " << func_name << " in model " << model->get_friendly_name() << "...");
+    LOG_BLOCK();
+
+    auto& func_ggg = all_functions.at(func_name);
+    auto& model_group = func_ggg.mdls;
+    auto& func_group = func_ggg.refs;
+
+    // First create a function body - take the first subgraph from
+    // the funcall group as a prototype. This operation is identical
+    // to converting a subgraph to a CWAI form
+    createFunction(func_ggg);
+
+    auto& body_params = func_group.front().get()._parameters;
+    auto& body_results = func_group.front().get()._results;
+
+    // Now process all other subgraphs as function calls
+    auto mod_iter = model_group.begin() + 1;  // FIXME: use zip
+    for (auto iter = func_group.begin() + 1; iter != func_group.end(); ++iter, ++mod_iter) {
+        ov::npuw::Subgraph& this_sg = *iter;
+        std::swap(this_sg._funcall, this_sg._repeated_id);
+
+        ov::npuw::Subgraph funcall{};
+        funcall._funcall = func_name;
+        funcall._parameters = this_sg._parameters;  // duplicated code again!
+        funcall._results = this_sg._results;        // duplicated code again!
+        funcall._gflops = this_sg._gflops;          // duplicated code again!
+        funcall._ops = this_sg._ops;                // duplicated code again!
+        funcall._avoid_list = this_sg._avoid_list;  // duplicated code again!
+        rearrange_to_function_protocol(body_params, funcall._parameters, func_ggg.param_call_to_proto);
+        rearrange_to_function_protocol(body_results, funcall._results, func_ggg.result_call_to_proto);
+
+        auto func_iter = P.functions.find(func_name);
+        NPUW_ASSERT(func_iter != P.functions.end());
+
+        // Just record a function call of the existing function
+        LOG_DEBUG("Registering a call to function " << func_name << "...");
+        LOG_BLOCK();
+        const auto& function = func_iter->second;
+        funcall._closure.resize(function._num_params_total - function._param_offset);
+
+        auto tmp_model = *mod_iter;
+        for (auto&& node_ptr : tmp_model->get_ordered_ops()) {
+            if (ov::op::util::is_parameter(node_ptr) || ov::op::util::is_constant(node_ptr) ||
+                ov::op::util::is_output(node_ptr)) {
+                // Skip Parameter, Const, and Result layers from bank matching
+                continue;
+            }
+
+            const auto& this_layer_name = node_ptr->get_friendly_name();
+            const auto& proto_layer_name = layer_to_prototype.at(this_layer_name);  // (t)/1/a
+
+            for (auto&& input_desc : node_ptr->inputs()) {
+                const auto& prod_output = input_desc.get_source_output();
+                const auto input_node = prod_output.get_node_shared_ptr();
+                if (ov::op::util::is_constant(input_node) && func_ggg.consts_to_keep.count(input_node) == 0) {
+                    auto param_idx = function._param_mapping.at(
+                        std::make_pair(proto_layer_name, input_desc.get_index()));  // (t)/1/b
+                    LOG_DEBUG("Register " << prod_output << " in the function closure[" << param_idx
+                                          << "] (via prototype " << proto_layer_name << ")");
+                    funcall._closure[param_idx - function._param_offset] =
+                        ov::npuw::util::tensor_from_const(input_node);  // (t)/1/c
+                }
+            }  // for (inputs)
+        }      // for(nodes)
+
+        // Write down the funcall to the list of subgraphs
+        std::swap(funcall, this_sg);
+        LOG_DEBUG("Done: funcall(" << func_name << ")");
+    }  // for(rest of models)
+
+    LOG_VERB("Done");
+}
+
+void Partitioner::decompressionCutOff(const std::string& func_name) {
+    LOG_VERB("Decompression cut-off for function " << func_name << " in model " << model->get_friendly_name() << "...");
+    LOG_BLOCK();
+
+    std::string dcoff_type_opt = cfg.getString<::intel_npu::NPUW_DCOFF_TYPE>();
+    ov::element::Type dcoff_type{};
+    if (!dcoff_type_opt.empty()) {
+        if (dcoff_type_opt == "i8") {
+            dcoff_type = ov::element::i8;
+        } else if (dcoff_type_opt == "f16") {
+            dcoff_type = ov::element::f16;
+        } else if (dcoff_type_opt == "f32") {
+            dcoff_type = ov::element::f32;
+        } else {
+            OPENVINO_THROW("Unknwon dcoff type: ", dcoff_type_opt);
+        }
+    } else {
+        LOG_VERB("Cancelled - no dcoff type specified via " << ::intel_npu::NPUW_DCOFF_TYPE().key() << " property.");
+        return;
+    }
+
+    ov::npuw::DCOffMode dcoff_mode = ov::npuw::DCOffMode::CAST_ONLY;
+    if (cfg.get<::intel_npu::NPUW_DCOFF_SCALE>()) {
+        if (dcoff_type != ov::element::f16) {
+            // Moving out scaling off graph works only for the f16 DCOFF type
+            LOG_WARN(::intel_npu::NPUW_DCOFF_SCALE().key()
+                     << " property is specified, but the target " << ::intel_npu::NPUW_DCOFF_TYPE().key()
+                     << " is not f16 - ignoring");
+        } else {
+            LOG_VERB("Decompression cut-off: Weight scaling will be moved off the model");
+            dcoff_mode = ov::npuw::DCOffMode::CAST_SCALE;
+        }
+    }
+
+    // What we do here? Identify the decompression patterns (see
+    // patterns/dcoff.hpp) in our function, put an extra Convert and
+    // remember which Constants to decompress in the function
+    // prologue, during the execution.
+
+    ov::npuw::Function& f = P.functions.at(func_name);
+    LOG_DEBUG("Function model inputs before the DCOFF:");
+    for (auto&& input : f._model->inputs()) {
+        LOG_BLOCK();
+        LOG_DEBUG(input);
+    }
+    LOG_DEBUG("Running the graph transformations...");
+    {
+        LOG_BLOCK();
+
+        ov::npuw::patterns::DCOFFParams params_to;
+
+        ov::pass::GraphRewrite rewr;
+        // Old LLaMa-v2 patterns (Symmetric)
+        rewr.add_matcher<ov::npuw::patterns::SymmNoZP::DCOFFPassMatMul>(dcoff_mode, dcoff_type, std::ref(params_to))
+            ->build();
+        rewr.add_matcher<ov::npuw::patterns::SymmNoZP::DCOFFPassGather>(dcoff_mode, dcoff_type, std::ref(params_to))
+            ->build();
+
+        // ChatGLM (GPTQ) and New LLaMa-v2 patterns (Symmetric)
+        rewr.add_matcher<ov::npuw::patterns::SymmZP::DCOFFPassReshape1>(dcoff_mode, dcoff_type, std::ref(params_to))
+            ->build();
+        rewr.add_matcher<ov::npuw::patterns::SymmZP::DCOFFPassConvert1>(dcoff_mode, dcoff_type, std::ref(params_to))
+            ->build();
+
+        // LLaMaGPTQ
+        rewr.add_matcher<ov::npuw::patterns::SymmZP::DCOFFPassReshape2>(dcoff_mode, dcoff_type, std::ref(params_to));
+
+        rewr.run_on_model(f._model);
+
+        ov::pass::Validate val;
+        val.run_on_model(f._model);
+
+        if (!params_to.scales.empty()) {
+            LOG_VERB("Weight scaling was removed from the function body"
+                     " -- updating the function closure");
+            LOG_BLOCK();
+
+            // Build a closure remap here: which input tensors are no
+            // longer parameters but move to the unpack procedure?
+            auto closure_remap = ov::npuw::patterns::build_remap(f, params_to);
+
+            // Now modify the each function call's closure accordingly
+            // according to the remaps.
+            auto& func_group = all_functions.at(func_name);
+            for (auto&& fref : func_group.refs) {
+                auto& funcall = fref.get();
+                ov::npuw::patterns::apply_remap(funcall, closure_remap);
+            }
+
+            // Finally, remove the function body's parameters here
+            ov::npuw::patterns::finalize_remap(f, closure_remap);
+        }  // if (CAST_SCALE && have(params_to_scale))
+    }
+    LOG_DEBUG("Function model inputs after the DCOFF:");
+    for (auto&& input : f._model->inputs()) {
+        LOG_BLOCK();
+        LOG_DEBUG(input);
+    }
+    LOG_VERB("Done");
+}
+
+void Partitioner::finalizeLinks() {
+    LOG_VERB("Finalizing links in model " << model->get_friendly_name() << "...");
+    LOG_BLOCK();
+
+    // Write down the [subgraph_i][out_j] -> [subgraph_k][input_n]
+    // mapping here. I/J & K/N indices become final at this point
+
+    // FIXME: make it a template helper?
+    auto get_idx_param = [this](std::size_t subgr_idx_to, const PPtr& ptr) -> std::size_t {
+        auto& sg_desc = P.subgraphs[subgr_idx_to];
+        if (sg_desc._funcall.empty()) {
+            // Not a function call (or a sole function call):
+            // find in a subgraph itself
+            auto& params = sg_desc._parameters;
+            auto param_iter = std::find(params.begin(), params.end(), ptr);
+            NPUW_ASSERT(param_iter != params.end());
+            return std::distance(params.begin(), param_iter);
+        } else {
+            // A function call: find in the prototype subgraph
+            auto& params = P.functions.at(sg_desc._funcall)._model->get_parameters();
+            auto& proto = func_pipeline_type == FunctionPipelineType::CWAI
+                              ? ptr  // no protos in the CWAI case..
+                              : all_functions.at(sg_desc._funcall).param_call_to_proto.at(ptr);
+            auto param_iter = std::find(params.begin(), params.end(), proto);
+            NPUW_ASSERT(param_iter != params.end());
+            return std::distance(params.begin(), param_iter);
+        }
+    };
+
+    auto get_idx_result = [this](std::size_t subgr_idx_from, const RPtr& ptr) -> std::size_t {
+        auto& sg_desc = P.subgraphs[subgr_idx_from];
+        if (sg_desc._funcall.empty()) {
+            // Not a function call (or a sole function call):
+            // find in a subgraph itself
+            auto& results = sg_desc._results;
+            auto result_iter = std::find(results.begin(), results.end(), ptr);
+            NPUW_ASSERT(result_iter != results.end());
+            return std::distance(results.begin(), result_iter);
+        } else {
+            // A function call: find in the prototype subgraph
+            auto& results = P.functions.at(sg_desc._funcall)._model->get_results();
+            auto& proto = func_pipeline_type == FunctionPipelineType::CWAI
+                              ? ptr  // no protos in the CWAI case...
+                              : all_functions.at(sg_desc._funcall).result_call_to_proto.at(ptr);
+            auto result_iter = std::find(results.begin(), results.end(), proto);
+            NPUW_ASSERT(result_iter != results.end());
+            return std::distance(results.begin(), result_iter);
+        }
+    };
+
+    ov::npuw::Links& subgraph_links = P.input_to_prev_output;
+    for (auto&& ptr_link : subgraph_ptr_links) {
+        // what happens when either sg_to or sg_from is a function
+        // call? These vectors are basically empty in this case...
+        // Needs to be matched with the function body parameter/
+        // result order.. but how? But how... see above, the complexity
+        // is there.
+
+        std::size_t subgraph_idx_to;
+        PPtr subgraph_param_to;
+        std::tie(subgraph_idx_to, subgraph_param_to) = ptr_link.first;
+        auto param_idx = get_idx_param(subgraph_idx_to, subgraph_param_to);
+
+        std::size_t subgraph_idx_from;
+        RPtr subgraph_result_from;
+        std::tie(subgraph_idx_from, subgraph_result_from) = ptr_link.second;
+        auto result_idx = get_idx_result(subgraph_idx_from, subgraph_result_from);
+
+        subgraph_links[ov::npuw::LinkTo{subgraph_idx_to, param_idx}] =
+            ov::npuw::LinkFrom{subgraph_idx_from, result_idx};
+
+        LOG_BLOCK();
+        LOG_DEBUG("Record link [" << subgraph_idx_to << "]:" << param_idx << "  <---  [" << subgraph_idx_from << "]/"
+                                  << result_idx);
+    }
+    LOG_VERB("Done");
+}
+
+}  // namespace
+
+ov::npuw::Partitioning ov::npuw::getPartitioning(const std::shared_ptr<ov::Model>& model, ::intel_npu::Config& cfg) {
+    LOG_INFO("Building partitioning for model " << model->get_friendly_name() << "...");
+    LOG_BLOCK();
+
+    ov::npuw::Ensemble ens;
+
+    // Try to load the partitioning plan...
+    const std::string file_path = cfg.get<::intel_npu::NPUW_PLAN>();
+    if (file_path.empty()) {
+        LOG_WARN("No " << ::intel_npu::NPUW_PLAN().key() << " property is provided! Using online partitioning.");
+        ens = ov::npuw::online::buildPartitioning(model, cfg);
+    } else {
+        ens = load_groups(model, file_path);
+    }
+
+    const bool dump_full_opt = cfg.get<::intel_npu::NPUW_DUMP_FULL>();
+
+    if (dump_full_opt) {
+        ov::save_model(model, model->get_friendly_name() + ".xml");
+        LOG_INFO("Dumped the model in the current directory.");
+    }
+
+    if (ens.groups.empty()) {
+        LOG_INFO("Fall-back to single-model inference.");
+        // So far we have 1 subgraph which is the whole network
+        Subgraph subgraph;
+        subgraph._parameters = model->get_parameters();
+        subgraph._results = model->get_results();
+        subgraph._sinks = model->get_sinks();
+        return Partitioning{std::vector<Subgraph>{subgraph}};
+    }
+
+    // Handle funcall everywhere, if needed
+    FuncallEverywhere fcew(model, cfg);
+    if (fcew.enabled()) {
+        std::size_t gid = 0u;  // TODO: Use indexed()
+        for (auto&& this_group : ens.groups) {
+            if (!this_group.repeated_id.empty()) {
+                fcew.register_known(this_group.repeated_id);
+            } else {
+                auto new_id = fcew.register_new();
+                LOG_INFO("Turning block " << gid << " into a function " << this_group.repeated_id << "...");
+                LOG_BLOCK();
+                this_group.repeated_id = new_id;
+                ov::npuw::RepeatedBlock this_block;
+                for (auto&& layer : this_group.all_layers) {
+                    this_block.matches.push_back(ov::npuw::RepeatedBlock::MatchedLayers{layer});
+                }
+                ens.repeated[this_group.repeated_id] = std::move(this_block);
+                LOG_INFO("Done.");
+            }
+            gid++;
+        }  // for(ens.groups)
+    }      // if(fcew.enabled)
+
+    Partitioning P;
+    P.total_gflops = ens.gflops;
+
+    Partitioner p(model, ens, P, cfg);
+    p.identifySubgraphs();
+
+    if (!ens.repeated.empty()) {
+        if (cfg.get<::intel_npu::NPUW_FOLD>()) {
+            // Do full-featured folding
+            auto all_functions = p.initFunctionPipeline(Partitioner::FunctionPipelineType::FOLD);
+            for (auto&& func_group : all_functions) {
+                LOG_INFO("FOLD: Process function " << func_group << "...");
+                LOG_BLOCK();
+                p.propagateSlices(func_group);
+                p.propagateConverts(func_group);
+                p.propagateWeights(func_group);
+                p.propagateScalars(func_group);
+                p.sanityCheck(func_group);
+                p.saveRepeatedConstants(func_group);
+                p.matchParameters(func_group);
+                p.matchResults(func_group);
+                p.matchRepeatedSubgraphs(func_group);
+                p.decompressionCutOff(func_group);
+            }
+        } else if (cfg.get<::intel_npu::NPUW_CWAI>()) {
+            // Less brutal version - just transform repeated blocks
+            // into the closure forms, but don't do folding.
+            // This path is likely to be removed soon (is here for
+            // debug purposes only, but doesn't have much practical
+            // sense).
+            auto all_functions = p.initFunctionPipeline(Partitioner::FunctionPipelineType::CWAI);
+            for (auto&& func_group : all_functions) {
+                LOG_INFO("CWAI: Process function " << func_group << "...");
+                LOG_BLOCK();
+                p.saveTinyConstants(func_group);
+                p.saveScaleFactors(func_group);
+                p.createFunction(func_group);
+                p.decompressionCutOff(func_group);
+            }
+        } else {
+            LOG_INFO("Note: Repeated blocks are found in the model " << model->get_friendly_name()
+                                                                     << ", but folding or eager mode are not enabled");
+        }
+    }
+    p.finalizeLinks();
+    return P;
+}
diff --git a/src/plugins/intel_npu/src/plugin/npuw/partitioning/partitioning.hpp b/src/plugins/intel_npu/src/plugin/npuw/partitioning/partitioning.hpp
new file mode 100644
index 00000000000000..ae8e16546d4272
--- /dev/null
+++ b/src/plugins/intel_npu/src/plugin/npuw/partitioning/partitioning.hpp
@@ -0,0 +1,111 @@
+// Copyright (C) 2023-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <map>
+#include <string>
+#include <variant>
+#include <vector>
+
+#include "intel_npu/al/config/config.hpp"
+#include "openvino/openvino.hpp"
+
+namespace ov {
+namespace npuw {
+
+struct Subgraph {
+    // Note: This vector only specifies Link types of parameters
+    // - e.g. Parameters which are coming into this subraph from
+    // other subgraphs or from the parent Model's Parameter vector
+    // directly
+    ov::ParameterVector _parameters;
+    ov::ResultVector _results;
+    ov::SinkVector _sinks;
+
+    std::string _affinity;
+    std::size_t _ops = 0u;
+    float _gflops = 0.f;
+    bool _optimized_out = false;
+
+    std::string _avoid_list;
+
+    // Function calls only (note: all the above fields are not used)
+    //
+    // FIXME: Replace with variant or some other proper way (maybe
+    // even a class hierarchy)
+    std::string _repeated_id;
+    std::string _funcall;
+    std::vector<ov::Tensor> _closure;
+    std::vector<ov::Tensor> _scales;  // Scale coeffs for manual unpacking
+    std::vector<ov::Tensor> _zerops;  // Zero points for manual unpacking
+
+    using Ref = std::reference_wrapper<Subgraph>;
+};
+
+struct Function {
+    std::shared_ptr<ov::Model> _model;
+    std::size_t _param_offset;
+    std::size_t _num_params_total;
+
+    // Mapping: from a prototype {Layer/input_idx} to {param_idx}
+    // NOTE: it seems it is required only for `matchRepeatedSubgraphs()'
+    std::map<std::pair<std::string, std::size_t>, std::size_t> _param_mapping;
+};
+
+struct Group {
+    std::vector<std::string> input_layers;
+    std::vector<std::string> output_layers;
+    std::vector<std::string> all_layers;
+
+    std::string repeated_id;
+    float gflops;
+
+    std::string avoid_list;
+
+    ov::npuw::Subgraph sg;
+};
+
+struct RepeatedBlock {
+    using MatchedLayers = std::set<std::string>;
+    using MatchedBank = std::vector<MatchedLayers>;
+    MatchedBank matches;
+    MatchedBank consts;
+    MatchedBank scalars;
+};
+
+struct Ensemble {
+    float gflops;
+    std::vector<Group> groups;
+
+    // Just a map as I don't expect 100s of _different_
+    // repeated blocks here
+    std::map<std::string, RepeatedBlock> repeated;
+};
+
+using LinkTo = std::pair<size_t /*submodel_idx*/
+                         ,
+                         size_t /*param_idx*/
+                         >;
+using LinkFrom = std::pair<size_t /*submodel_idx*/
+                           ,
+                           size_t /*result_idx*/
+                           >;
+using Links = std::map<LinkTo, LinkFrom>;
+
+struct Partitioning {
+    std::vector<Subgraph> subgraphs;
+    Links input_to_prev_output;
+
+    // Function: A proper name of a repeated block
+    std::map<std::string, Function> functions;
+
+    std::size_t total_ops = 0u;
+    float total_gflops = 0.f;
+};
+
+Partitioning getPartitioning(const std::shared_ptr<ov::Model>& model, ::intel_npu::Config& config);
+
+}  // namespace npuw
+}  // namespace ov
diff --git a/src/plugins/intel_npu/src/plugin/npuw/partitioning/patterns/avoid.cpp b/src/plugins/intel_npu/src/plugin/npuw/partitioning/patterns/avoid.cpp
new file mode 100644
index 00000000000000..218647664fa516
--- /dev/null
+++ b/src/plugins/intel_npu/src/plugin/npuw/partitioning/patterns/avoid.cpp
@@ -0,0 +1,70 @@
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "avoid.hpp"
+
+#include "../../logging.hpp"
+#include "../online/group.hpp"     // online::Group
+#include "../online/snapshot.hpp"  // online::Snapshot
+#include "openvino/op/add.hpp"
+#include "openvino/op/power.hpp"
+#include "openvino/op/reduce_mean.hpp"
+#include "openvino/op/sqrt.hpp"
+#include "openvino/op/util/op_types.hpp"
+#include "openvino/pass/pattern/op/label.hpp"  // any_input
+#include "openvino/pass/pattern/op/wrap_type.hpp"
+#include "openvino/util/common_util.hpp"
+
+namespace ov {
+namespace npuw {
+namespace patterns {
+
+namespace opp = ov::pass::pattern;
+
+//------------------------------------------------------------------------------
+// Pattern: RMSNorm, from LLaMa-v2-7b model
+//
+//            Power     Const
+//               :        :
+//               V        V
+//               ReduceMean
+//                    :
+//                    V
+//                   Add
+//                    :
+//                    V
+//                   Sqrt
+//                    :
+//                    V
+//
+RMSNormPattern::RMSNormPattern(const std::shared_ptr<ov::npuw::online::Snapshot>& snapshot,
+                               const std::string& avoid_device) {
+    auto power = opp::wrap_type<ov::op::v1::Power>({opp::any_input(), opp::any_input()});
+    auto reduce = opp::wrap_type<ov::op::v1::ReduceMean>({power, opp::wrap_type<ov::op::v0::Constant>()});
+    auto add = opp::wrap_type<ov::op::v1::Add>({reduce, opp::any_input()});
+    auto sqrt = opp::wrap_type<ov::op::v0::Sqrt>({add});
+
+    auto node_to_gptr = snapshot->getNodeToGroupMap();
+
+    // Note: Use [=] to make sure the above objects stay alive in the callback
+    auto callback = [=](ov::pass::pattern::Matcher& m) {
+        auto& node_to_output = m.get_pattern_value_map();
+        auto matched_power = node_to_output.at(power).get_node_shared_ptr();
+        auto matched_reduce = node_to_output.at(reduce).get_node_shared_ptr();
+        auto matched_add = node_to_output.at(add).get_node_shared_ptr();
+        auto matched_sqrt = node_to_output.at(sqrt).get_node_shared_ptr();
+
+        node_to_gptr->at(matched_power)->avoid(avoid_device);
+        node_to_gptr->at(matched_reduce)->avoid(avoid_device);
+        node_to_gptr->at(matched_add)->avoid(avoid_device);
+        node_to_gptr->at(matched_sqrt)->avoid(avoid_device);
+
+        return false;  // root hasn't changed
+    };
+    register_matcher(std::make_shared<opp::Matcher>(sqrt, "TagRMSNorm"), callback);
+}
+
+}  // namespace patterns
+}  // namespace npuw
+}  // namespace ov
diff --git a/src/plugins/intel_npu/src/plugin/npuw/partitioning/patterns/avoid.hpp b/src/plugins/intel_npu/src/plugin/npuw/partitioning/patterns/avoid.hpp
new file mode 100644
index 00000000000000..c82cc71f793ada
--- /dev/null
+++ b/src/plugins/intel_npu/src/plugin/npuw/partitioning/patterns/avoid.hpp
@@ -0,0 +1,30 @@
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <memory>
+#include <string>
+
+#include "openvino/openvino.hpp"
+#include "openvino/pass/graph_rewrite.hpp"
+
+namespace ov {
+namespace npuw {
+
+namespace online {
+class Snapshot;  // Forward declaration
+}  // namespace online
+
+namespace patterns {
+
+// Note: this pattern is only utilized by the online partitioner
+class RMSNormPattern : public ov::pass::MatcherPass {
+public:
+    RMSNormPattern(const std::shared_ptr<ov::npuw::online::Snapshot>& snapshot, const std::string& avoid_device);
+};
+
+}  // namespace patterns
+}  // namespace npuw
+}  // namespace ov
diff --git a/src/plugins/intel_npu/src/plugin/npuw/partitioning/patterns/dcoff.cpp b/src/plugins/intel_npu/src/plugin/npuw/partitioning/patterns/dcoff.cpp
new file mode 100644
index 00000000000000..3184915f58786c
--- /dev/null
+++ b/src/plugins/intel_npu/src/plugin/npuw/partitioning/patterns/dcoff.cpp
@@ -0,0 +1,668 @@
+// Copyright (C) 2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "dcoff.hpp"
+
+#include "../../logging.hpp"
+#include "../../util.hpp"
+#include "../partitioning.hpp"  // Subgraph
+#include "openvino/op/convert.hpp"
+#include "openvino/op/gather.hpp"
+#include "openvino/op/matmul.hpp"
+#include "openvino/op/multiply.hpp"
+#include "openvino/op/reshape.hpp"
+#include "openvino/op/subtract.hpp"
+#include "openvino/op/util/op_types.hpp"
+#include "openvino/pass/pattern/op/label.hpp"  // any_input
+#include "openvino/pass/pattern/op/wrap_type.hpp"
+#include "openvino/util/common_util.hpp"
+
+namespace ov {
+namespace npuw {
+
+namespace patterns {
+
+namespace opp = ov::pass::pattern;
+
+// The update procedure is tricky: The closure vector needs to be
+// freed of the scale coefficient tensors which are no longer the
+// arguments to the function.  At the same time, these scale
+// coefficients need to be recorded elsewhere.
+
+// How does the procedure look like:
+// - Take the function body;
+// - Walk through its parameters, starting with base offset (the base
+//   offset indicates where the closure parameters start)
+// - If a Parameter is found in the params_to_scale map, it is
+//   a Scaling factor Parameter for a compressed Weight:
+//   1. This parameter [i] is removed from the _parameters list
+//   2. Scale remap [k++] is set to [i-base] -> meaning the k'th
+//      Scale tensor will be taken from [i-base]'th closure
+//      - also remember which Closure tensor this Scale tensor stands
+//        for
+// - If a Parameter is NOT found, this tensor doesn't need
+//   scaling/decompression:
+//   1. The Const remap [n++] is set to [i-base] -> meaning the n'th
+//      Const tensor will be taken from [i-base]'th closure in the
+//      updated closure tensor.
+
+ClosureRemap build_remap(const Function& fbody, const DCOFFParams& params_to) {
+    LOG_DEBUG("Creating a closure remap for " << fbody._model->get_friendly_name());
+    LOG_BLOCK();
+
+    const auto& body_params = fbody._model->get_parameters();
+    LOG_DEBUG("There is " << body_params.size() << " parameters for this function");
+
+    ClosureRemap m;
+
+    // FIXME: use indexed() here instead
+    for (std::size_t i = fbody._param_offset; i < body_params.size(); i++) {
+        LOG_DEBUG("Checking the function parameter " << body_params[i]);
+        LOG_BLOCK();
+
+        // First find among scale factors...
+        auto pscale_iter = params_to.scales.find(body_params[i]);
+        if (pscale_iter != params_to.scales.end()) {
+            LOG_DEBUG("This is a Scale factor parameter, will be removed");
+            auto& pscale_weight_param = pscale_iter->second;
+            auto pscale_weight_pindex = fbody._model->get_parameter_index(pscale_weight_param);
+            auto pscale_weight_cindex = pscale_weight_pindex - fbody._param_offset;
+            m.scale_remap[pscale_weight_cindex] = i - fbody._param_offset;
+            m.params_to_remove.push_back(body_params[i]);
+        } else {
+            LOG_DEBUG("This is an OK parameter, will be kept");
+            // n++ is the index of `i` here
+            m.closure_remap.push_back(i - fbody._param_offset);
+        }
+
+        // Process zero points for parameters
+        auto zerop_iter = params_to.zerops.find(body_params[i]);
+        if (zerop_iter != params_to.zerops.end()) {
+            LOG_DEBUG("This parameter requires zero point: " << zerop_iter->second);
+            m.zero_points.push_back(ov::npuw::util::tensor_from_const(zerop_iter->second));
+        } else {
+            m.zero_points.push_back(ov::Tensor());
+        }
+    }
+    NPUW_ASSERT((body_params.size() - fbody._param_offset) == (m.scale_remap.size() + m.closure_remap.size()));
+    NPUW_ASSERT((body_params.size() - fbody._param_offset) == m.zero_points.size());
+
+    LOG_DEBUG("DONE");
+    return m;
+}
+
+void apply_remap(Subgraph& fcall, const ClosureRemap& m) {
+    std::vector<ov::Tensor> new_closure;
+    std::vector<ov::Tensor> new_scales;
+    std::vector<ov::Tensor> new_zerops;
+
+    // For a new_closure vector by rearranging the old one.  Also
+    // reserve a new_scales vector to have the same size, filled with
+    // empty tensors by default.
+    for (auto&& i : m.closure_remap) {
+        new_closure.push_back(fcall._closure[i]);
+
+        auto scale_iter = m.scale_remap.find(i);
+        new_scales.push_back(scale_iter != m.scale_remap.end() ? fcall._closure[scale_iter->second] : ov::Tensor());
+        new_zerops.push_back(m.zero_points[i]);
+    }
+    fcall._closure = std::move(new_closure);
+    fcall._scales = std::move(new_scales);
+    fcall._zerops = std::move(new_zerops);
+}
+
+void finalize_remap(Function& fbody, const ClosureRemap& m) {
+    LOG_DEBUG("Removing retired parameters...");
+    LOG_BLOCK();
+    for (auto&& p : m.params_to_remove) {
+        LOG_DEBUG("Removing parameter " << p);
+        LOG_BLOCK();
+        fbody._model->remove_parameter(p);
+    }
+    fbody._model->validate_nodes_and_infer_types();
+    LOG_DEBUG("DONE");
+}
+
+////////////////////////////////////////////////////////////////////////////////
+//
+// Decompression Cut-off patterns
+//
+////////////////////////////////////////////////////////////////////////////////
+
+//------------------------------------------------------------------------------
+// Pattern: 4/8SymW16A
+//
+// In the diagram below, pattern on the left is identified and
+// is modified to pattern in the middle:
+//
+//   Parameter:A  Parameter:B      Parameter:A  Parameter:B      Parameter:A
+//   int4|int8    fp32|fp16           fp16      fp32|fp16             fp16
+//         :      :            ->        :      :            ->        :
+//         V      :                      V      :                      V
+//        Convert :                     Convert :                     Convert
+//          fp32  :            ->         fp32  :            ->         fp32
+//           :    :                        :    :                        :
+//           V    V                        V    V                        :
+//    (...) Multiply           ->   (...) Multiply           ->   (...)  :
+//       :    fp32                     :    fp32                     :   :
+//       :     :                       :     :                       :   :
+//       V     V               ->      V     V               ->      V   V
+//  [MatMul|Gather]                 [MatMul|Gather]               [MatMul|Gather]
+//        fp32                          fp32                          fp32
+//         :                   ->        :                   ->        :
+//         V                             V                             V
+//
+// An easy change, but how does it work? The key here is that the
+// original closure tensor (Parameter:A) stays the same in the
+// closure, we just make the function body think it is no longer
+// int4/int8 but fp16 now. The int4/int8-to-fp16 conversion will
+// happen on host (e.g., CPU) as part of the function prologue.
+//
+// What else we need to do here? Just store that this Parameter:A
+// requires such manual conversion so we don't forget about this
+// in the function prologue.
+//
+// If OPENVINO_NPUW_DCOFF_SCALE is YES, then the Multiply in the
+// above graph also removed from the function body (as well as
+// its Parameter B).
+namespace SymmNoZP {
+
+DCOFFPassBase::DCOFFPassBase(DCOffMode dcoff_mode, ov::element::Type dcoff_type, DCOFFParamRef pref)
+    : m_dcoff_mode(dcoff_mode),
+      m_dcoff_type(dcoff_type),
+      m_params_to(pref) {}
+
+void DCOFFPassBase::build() {
+    paramA = opp::wrap_type<ov::op::v0::Parameter>();
+    paramB = opp::wrap_type<ov::op::v0::Parameter>();
+    toFP32 = opp::wrap_type<ov::op::v0::Convert>({paramA});
+    mulply = opp::wrap_type<ov::op::v1::Multiply>({toFP32, paramB});
+}
+
+bool DCOFFPassBase::matcher_callback(ov::pass::pattern::Matcher& m) {
+    auto& node_to_output = m.get_pattern_value_map();
+    auto matched_nodeA = node_to_output.at(paramA).get_node_shared_ptr();
+    NPUW_ASSERT(ov::op::util::is_parameter(matched_nodeA));
+
+    auto matched_paramA = std::static_pointer_cast<ov::op::v0::Parameter>(matched_nodeA);
+    auto element_type = matched_paramA->get_element_type();
+    if (element_type == ov::element::i4 || element_type == ov::element::i8) {
+        LOG_DEBUG("Matched: " << matched_paramA << ", set element type to " << m_dcoff_type);
+        matched_paramA->set_element_type(m_dcoff_type);
+
+        if (m_dcoff_mode == DCOffMode::CAST_SCALE) {
+            LOG_DEBUG("Removing Multiply as part of DCOFF...");
+            LOG_BLOCK();
+
+            NPUW_ASSERT(m_dcoff_type == ov::element::f16);
+            // Off-graph scaling works only for f16 target data type.
+            // Extra transformation here: remove multiply, mark paramB for removal
+            // MatMul/Gather will be reconnected to Convert directly
+
+            auto matched_nodeB = node_to_output.at(paramB).get_node_shared_ptr();
+            NPUW_ASSERT(ov::op::util::is_parameter(matched_nodeB));
+
+            auto matched_paramB = std::static_pointer_cast<ov::op::v0::Parameter>(matched_nodeB);
+            LOG_DEBUG("Matched: " << matched_paramB << " - parameter to remove...");
+
+            // Record mapping from the Scale coeff paramter to the Real weight parameter
+            m_params_to.get().scales[matched_paramB] = matched_paramA;
+
+            // Disconnect Multiply and Convert from their outputs
+            auto matched_mulply = node_to_output.at(mulply).get_node_shared_ptr();
+            auto matched_convrt = node_to_output.at(toFP32).get_node_shared_ptr();
+            auto drop_outputs = [](std::shared_ptr<ov::Node> node) {
+                for (auto&& node_outputs : node->outputs()) {
+                    for (auto&& node_reader_port : node_outputs.get_target_inputs()) {
+                        node_outputs.remove_target_input(node_reader_port);
+                    }
+                }
+            };
+            LOG_DEBUG("Dropping the connections...");
+            drop_outputs(matched_mulply);
+            drop_outputs(matched_convrt);
+
+            LOG_DEBUG("Reconnecting the root...");
+            reconnect_root_to_convert(m);
+
+            LOG_DEBUG("Done");
+        }
+    }
+    return false;  // root node hasn't changed
+}
+
+void DCOFFPassMatMul::build() {
+    DCOFFPassBase::build();
+    auto _mmin1 = opp::any_input();
+    matmul = opp::wrap_type<ov::op::v0::MatMul>({_mmin1, mulply});
+    register_matcher(std::make_shared<opp::Matcher>(matmul, "TagDCOFFMatMul"),
+                     std::bind(&DCOFFPassMatMul::matcher_callback, this, std::placeholders::_1));
+}
+
+void DCOFFPassMatMul::reconnect_root_to_convert(ov::pass::pattern::Matcher& m) {
+    // In this pattern, Convert goes to the MatMul's (root) 1st (0-based) input
+    auto& node_to_output = m.get_pattern_value_map();
+    auto matched_convrt = node_to_output.at(toFP32).get_node_shared_ptr();
+    auto matched_matmul = node_to_output.at(matmul).get_node_shared_ptr();
+    matched_matmul->input(1).replace_source_output(matched_convrt);
+}
+
+void DCOFFPassGather::build() {
+    DCOFFPassBase::build();
+    auto _gin2 = opp::any_input();
+    auto _gin3 = opp::any_input();
+    gather = opp::wrap_type<ov::op::v8::Gather>({mulply, _gin2, _gin3});
+    register_matcher(std::make_shared<opp::Matcher>(gather, "TagDCOFFGather"),
+                     std::bind(&DCOFFPassGather::matcher_callback, this, std::placeholders::_1));
+}
+
+void DCOFFPassGather::reconnect_root_to_convert(ov::pass::pattern::Matcher& m) {
+    // In this pattern, Convert goes to the Gathers's (root) 0's input
+    auto& node_to_output = m.get_pattern_value_map();
+    auto matched_convrt = node_to_output.at(toFP32).get_node_shared_ptr();
+    auto matched_gather = node_to_output.at(gather).get_node_shared_ptr();
+    matched_gather->input(0).replace_source_output(matched_convrt);
+}
+
+}  // namespace SymmNoZP
+
+//------------------------------------------------------------------------------
+// Pattern: SymmZP, and in fact is used for GPTQ.
+//
+namespace SymmZP {
+
+// As seen in ChatGLM3 and a newer LLaMa-v2:
+// Since it is Symm, all zero points for all blocks must have the same
+// value so NPUW will detect it and fuse to function body (so it is
+// not Parameter but Const).
+//
+// In the diagram below, pattern on the left is identified and
+// is modified to pattern in the right if type is promoted to f16
+//
+//   "tensor"     "zero point" "scale"
+//   Parameter:A  Parameter:B  Parameter:C  >    Parameter:A
+//                Const:B
+//         u4      u4|f32       f16|f32     >       f16
+//         :         :          :           >        :
+//         V         :         :            >        V
+//        Convert  Convert    :             >       Convert
+//        f16|f32   f16      :              >          f32  <Const>
+//            :      :      :               >           :     :
+//            V      V     :                >           V     V
+//            Subtract    :                 >        Reshape|Convert
+//              f16|f32  :                  >             :
+//               :      :                   >             V
+//               V      V                   >
+//               Multiply                   >
+//               fp16|f32 <Const>           >
+//                  :     :                 >
+//                  V     V                 >
+//              Reshape|Convert             >
+//                    :                     >
+//                    V                     >
+//
+
+DCOFFPassBase::DCOFFPassBase(DCOffMode dcoff_mode, ov::element::Type dcoff_type, DCOFFParamRef pref)
+    : m_dcoff_mode(dcoff_mode),
+      m_dcoff_type(dcoff_type),
+      m_params_to(pref) {}
+
+void DCOFFPassBase::build() {
+    paramA = opp::wrap_type<ov::op::v0::Parameter>();
+    constB = opp::wrap_type<ov::op::v0::Constant>();
+    paramC = opp::wrap_type<ov::op::v0::Parameter>();
+    cvtA = opp::wrap_type<ov::op::v0::Convert>({paramA});
+    cvtB = opp::wrap_type<ov::op::v0::Convert>({constB});
+    subtr = opp::wrap_type<ov::op::v1::Subtract>({cvtA, cvtB});
+    mulply = opp::wrap_type<ov::op::v1::Multiply>({subtr, paramC});
+}
+
+bool DCOFFPassBase::matcher_callback(ov::pass::pattern::Matcher& m) {
+    auto& node_to_output = m.get_pattern_value_map();
+    auto matched_nodeA = node_to_output.at(paramA).get_node_shared_ptr();
+    auto matched_nodeB = node_to_output.at(constB).get_node_shared_ptr();
+    auto matched_nodeC = node_to_output.at(paramC).get_node_shared_ptr();
+
+    NPUW_ASSERT(ov::op::util::is_parameter(matched_nodeA));
+    NPUW_ASSERT(ov::op::util::is_constant(matched_nodeB));
+    NPUW_ASSERT(ov::op::util::is_parameter(matched_nodeC));
+
+    auto matched_paramA = std::static_pointer_cast<ov::op::v0::Parameter>(matched_nodeA);
+    auto matched_valueB = std::static_pointer_cast<ov::op::v0::Constant>(matched_nodeB);
+    auto matched_paramC = std::static_pointer_cast<ov::op::v0::Parameter>(matched_nodeC);
+
+    if (ov::element::u4 == matched_paramA->get_element_type() &&
+        ov::element::u4 == matched_valueB->get_element_type() &&
+        ov::element::f16 == matched_paramC->get_element_type()) {
+        LOG_DEBUG("Matched: " << matched_paramA << ", set element type to " << m_dcoff_type);
+        matched_paramA->set_element_type(m_dcoff_type);
+
+        if (m_dcoff_mode == DCOffMode::CAST_SCALE) {
+            NPUW_ASSERT(m_dcoff_type == ov::element::f16);
+
+            LOG_DEBUG("Matched: " << matched_valueB << " - value to remove...");
+            LOG_DEBUG("Matched: " << matched_paramC << " - parameter to remove...");
+            LOG_BLOCK();
+
+            // Extra transformation here:
+            // - remove Subtract + Multiply,
+            // - mark paramB and paramC for removal.
+            // Rshape will be reconnected to Convert directly (TODO:
+            // it can be probably eliminated as well)
+
+            // Record mapping from the Scale coeff paramter to the Real weight parameter
+            m_params_to.get().zerops[matched_paramA] = matched_valueB;
+            m_params_to.get().scales[matched_paramC] = matched_paramA;
+
+            // Disconnect Multiply and Convert from their outputs
+            auto matched_mulply = node_to_output.at(mulply).get_node_shared_ptr();
+            auto matched_convrt = node_to_output.at(cvtA).get_node_shared_ptr();
+            auto drop_outputs = [](std::shared_ptr<ov::Node> node) {
+                for (auto&& node_outputs : node->outputs()) {
+                    for (auto&& node_reader_port : node_outputs.get_target_inputs()) {
+                        node_outputs.remove_target_input(node_reader_port);
+                    }
+                }
+            };
+            LOG_DEBUG("Dropping the connections...");
+            drop_outputs(matched_mulply);
+            drop_outputs(matched_convrt);
+
+            LOG_DEBUG("Reconnecting the root...");
+            reconnect_root(m);
+        }
+        LOG_DEBUG("Done");
+    }
+    return false;  // root node hasn't changed
+}
+
+void DCOFFPassReshape1::build() {
+    DCOFFPassBase::build();
+    auto scalar = opp::wrap_type<ov::op::v0::Constant>();
+    reshpe = opp::wrap_type<ov::op::v1::Reshape>({mulply, scalar});
+    register_matcher(std::make_shared<opp::Matcher>(reshpe, "TagDCOFFReshape1"),
+                     std::bind(&DCOFFPassReshape1::matcher_callback, this, std::placeholders::_1));
+}
+
+void DCOFFPassReshape1::reconnect_root(ov::pass::pattern::Matcher& m) {
+    auto& node_to_output = m.get_pattern_value_map();
+    auto matched_convrt = node_to_output.at(cvtA).get_node_shared_ptr();
+    auto matched_reshpe = node_to_output.at(reshpe).get_node_shared_ptr();
+    matched_reshpe->input(0).replace_source_output(matched_convrt);
+}
+
+void DCOFFPassConvert1::build() {
+    DCOFFPassBase::build();
+    cvtEnd = opp::wrap_type<ov::op::v0::Convert>({mulply});
+    register_matcher(std::make_shared<opp::Matcher>(cvtEnd, "TagDCOFFConvert1"),
+                     std::bind(&DCOFFPassConvert1::matcher_callback, this, std::placeholders::_1));
+}
+
+void DCOFFPassConvert1::reconnect_root(ov::pass::pattern::Matcher& m) {
+    // FIXME: Two converts can be further squashed into one!
+    auto& node_to_output = m.get_pattern_value_map();
+    auto matched_convrt = node_to_output.at(cvtA).get_node_shared_ptr();
+    auto matched_cvtEnd = node_to_output.at(cvtEnd).get_node_shared_ptr();
+    matched_cvtEnd->input(0).replace_source_output(matched_convrt);
+}
+
+//------------------------------------------------------------------------------
+// Pattern: LlamaGPTQ
+//
+// As seen in llama2_7B_chat_GPTQ:
+// Since it is Symm, all zero points for all blocks must have the same
+// value so NPUW will detect it and fuse to function body (so it is
+// not Parameter but Const).
+//
+// In the diagram below, pattern on the left is identified and
+// is modified to pattern in the right if type is promoted to f16
+//
+//   "tensor"     "zero point" "scale"
+//   Parameter:A  Const:B      Parameter:C  >    Parameter:A
+//         u4       f32         f32         >       f16
+//         :         :          :           >        :
+//         V         :         :            >        V
+//        Convert    :        :             >       Convert
+//         f32       :       :              >          f32
+//            :      :      :               >           :
+//            V      V     :                >           V
+//            Subtract    :                 >        Reshape
+//              f32      :                  >             :
+//               :      :                   >             V
+//               V      V                   >
+//               Multiply                   >
+//                 f32  <Const>             >
+//                  :      :                >
+//                  V      V                >
+//                   Reshape                >
+//                      :                   >
+//                      V                   >
+//
+
+DCOFFPassReshape2::DCOFFPassReshape2(DCOffMode dcoff_mode, ov::element::Type dcoff_type, DCOFFParamRef pref) {
+    auto paramA = opp::wrap_type<ov::op::v0::Parameter>();
+    auto constB = opp::wrap_type<ov::op::v0::Constant>();
+    auto paramC = opp::wrap_type<ov::op::v0::Parameter>();
+    auto cvtA = opp::wrap_type<ov::op::v0::Convert>({paramA});
+    auto subtr = opp::wrap_type<ov::op::v1::Subtract>({cvtA, constB});
+    auto mulply = opp::wrap_type<ov::op::v1::Multiply>({subtr, paramC});
+
+    auto scalar = opp::wrap_type<ov::op::v0::Constant>();
+    auto reshpe = opp::wrap_type<ov::op::v1::Reshape>({mulply, scalar});
+
+    // Note: Use [=] to make sure the above objects stay alive in the callback
+    auto callback = [=](ov::pass::pattern::Matcher& m) {
+        auto& node_to_output = m.get_pattern_value_map();
+        auto matched_nodeA = node_to_output.at(paramA).get_node_shared_ptr();
+        auto matched_nodeB = node_to_output.at(constB).get_node_shared_ptr();
+        auto matched_nodeC = node_to_output.at(paramC).get_node_shared_ptr();
+
+        NPUW_ASSERT(ov::op::util::is_parameter(matched_nodeA));
+        NPUW_ASSERT(ov::op::util::is_constant(matched_nodeB));
+        NPUW_ASSERT(ov::op::util::is_parameter(matched_nodeC));
+
+        auto matched_paramA = std::static_pointer_cast<ov::op::v0::Parameter>(matched_nodeA);
+        auto matched_valueB = std::static_pointer_cast<ov::op::v0::Constant>(matched_nodeB);
+        auto matched_paramC = std::static_pointer_cast<ov::op::v0::Parameter>(matched_nodeC);
+
+        if (ov::element::u4 == matched_paramA->get_element_type() &&
+            ov::element::f32 == matched_valueB->get_element_type() &&
+            ov::element::f32 == matched_paramC->get_element_type()) {
+            LOG_DEBUG("Matched: " << matched_paramA << ", set element type to " << dcoff_type);
+            matched_paramA->set_element_type(dcoff_type);
+
+            if (dcoff_mode == DCOffMode::CAST_SCALE) {
+                NPUW_ASSERT(dcoff_type == ov::element::f16);
+
+                LOG_DEBUG("Matched: " << matched_valueB << " - value to remove...");
+                LOG_DEBUG("Matched: " << matched_paramC << " - parameter to remove...");
+                LOG_BLOCK();
+
+                // Extra transformation here:
+                // - remove Subtract + Multiply,
+                // - mark paramC for removal.
+                // Reshape will be reconnected to Convert directly
+
+                // Record mapping from the Scale coeff parameter to the Real weight parameter
+                pref.get().zerops[matched_paramA] = matched_valueB;
+                pref.get().scales[matched_paramC] = matched_paramA;
+
+                // Disconnect Multiply and Convert from their outputs
+                auto matched_mulply = node_to_output.at(mulply).get_node_shared_ptr();
+                auto matched_convrt = node_to_output.at(cvtA).get_node_shared_ptr();
+                auto drop_outputs = [](std::shared_ptr<ov::Node> node) {
+                    for (auto&& node_outputs : node->outputs()) {
+                        for (auto&& node_reader_port : node_outputs.get_target_inputs()) {
+                            node_outputs.remove_target_input(node_reader_port);
+                        }
+                    }
+                };
+                LOG_DEBUG("Dropping the connections...");
+                drop_outputs(matched_mulply);
+                drop_outputs(matched_convrt);
+
+                LOG_DEBUG("Reconnecting the Root...");
+                auto matched_reshpe = node_to_output.at(reshpe).get_node_shared_ptr();
+                matched_reshpe->input(0).replace_source_output(matched_convrt);
+            }
+            LOG_DEBUG("Done");
+        }
+        return false;  // root node hasn't changed
+    };
+    register_matcher(std::make_shared<opp::Matcher>(reshpe, "TagDCOFFReshape2"), callback);
+}
+
+//------------------------------------------------------------------------------
+// Pattern: 4SymW16A for CWAI
+//
+// Note: it is the same pattern as in above, but it is called in the different
+// function processing pipeline and at a different stage. The purpose is different
+// too - preserve Scale tensors in the function bodies when folding is not done.
+// So it doesn't really transform anything, just collecting the information
+//
+// FIXME: Think how it can be unified with the above
+//
+//   "tensor"   "zero point"  "scale"
+//    Const:A      Const:B    Const:C
+//         u4      u4|f32    f16|f32
+//         :         :          :
+//         V         :         :
+//        Convert  Convert    :
+//        f16|f32   f16      :
+//            :      :      :
+//            V      V     :
+//            Subtract    :
+//              f16|f32  :
+//               :      :
+//               V      V
+//               Multiply
+//               fp16|f32
+
+CWAI1::CWAI1(CWAI1::Results scales) {
+    auto constA = opp::wrap_type<ov::op::v0::Constant>();
+    auto constB = opp::wrap_type<ov::op::v0::Constant>();
+    auto constC = opp::wrap_type<ov::op::v0::Constant>();
+    auto cvtA = opp::wrap_type<ov::op::v0::Convert>({constA});
+    auto cvtB = opp::wrap_type<ov::op::v0::Convert>({constB});
+    auto subtr = opp::wrap_type<ov::op::v1::Subtract>({cvtA, cvtB});
+    auto mulply = opp::wrap_type<ov::op::v1::Multiply>({subtr, constC});
+
+    auto matcher_callback = [=](ov::pass::pattern::Matcher& m) {
+        auto& node_to_output = m.get_pattern_value_map();
+        auto matched_nodeA = node_to_output.at(constA).get_node_shared_ptr();
+        auto matched_nodeB = node_to_output.at(constB).get_node_shared_ptr();
+        auto matched_nodeC = node_to_output.at(constC).get_node_shared_ptr();
+
+        NPUW_ASSERT(ov::op::util::is_constant(matched_nodeA));
+        NPUW_ASSERT(ov::op::util::is_constant(matched_nodeB));
+        NPUW_ASSERT(ov::op::util::is_constant(matched_nodeC));
+
+        auto matched_valueA = std::static_pointer_cast<ov::op::v0::Constant>(matched_nodeA);
+        auto matched_valueB = std::static_pointer_cast<ov::op::v0::Constant>(matched_nodeB);
+        auto matched_valueC = std::static_pointer_cast<ov::op::v0::Constant>(matched_nodeC);
+
+        if (ov::element::u4 == matched_valueA->get_element_type() &&
+            (ov::element::u4 == matched_valueB->get_element_type() ||
+             ov::element::f32 == matched_valueB->get_element_type()) &&
+            (ov::element::f16 == matched_valueC->get_element_type() ||
+             ov::element::f32 == matched_valueC->get_element_type())) {
+            LOG_DEBUG("Matched: " << matched_valueC);
+            scales.get().push_back(matched_valueC);
+        }
+        return true;
+    };  // matcher_callback
+
+    register_matcher(std::make_shared<opp::Matcher>(mulply, "TagCWAI1"), matcher_callback);
+}
+
+// FIXME: Think how it can be unified with the above. THIS is the GPTQ verision
+//
+//   "tensor"   "zero point"  "scale"
+//    Const:A      Const:B    Const:C
+//         u4       f32       f16|f32
+//         :         :          :
+//         V         :         :
+//        Convert    :        :
+//           f32     :       :
+//            :      :      :
+//            V      V     :
+//            Subtract    :
+//              f16|f32  :
+//               :      :
+//               V      V
+//               Multiply
+//               fp16|f32
+
+CWAI2::CWAI2(CWAI2::Results scales) {
+    auto constA = opp::wrap_type<ov::op::v0::Constant>();
+    auto constB = opp::wrap_type<ov::op::v0::Constant>();
+    auto constC = opp::wrap_type<ov::op::v0::Constant>();
+    auto cvtA = opp::wrap_type<ov::op::v0::Convert>({constA});
+    auto subtr = opp::wrap_type<ov::op::v1::Subtract>({cvtA, constB});
+    auto mulply = opp::wrap_type<ov::op::v1::Multiply>({subtr, constC});
+
+    auto matcher_callback = [=](ov::pass::pattern::Matcher& m) {
+        auto& node_to_output = m.get_pattern_value_map();
+        auto matched_nodeA = node_to_output.at(constA).get_node_shared_ptr();
+        auto matched_nodeB = node_to_output.at(constB).get_node_shared_ptr();
+        auto matched_nodeC = node_to_output.at(constC).get_node_shared_ptr();
+
+        NPUW_ASSERT(ov::op::util::is_constant(matched_nodeA));
+        NPUW_ASSERT(ov::op::util::is_constant(matched_nodeB));
+        NPUW_ASSERT(ov::op::util::is_constant(matched_nodeC));
+
+        auto matched_valueA = std::static_pointer_cast<ov::op::v0::Constant>(matched_nodeA);
+        auto matched_valueB = std::static_pointer_cast<ov::op::v0::Constant>(matched_nodeB);
+        auto matched_valueC = std::static_pointer_cast<ov::op::v0::Constant>(matched_nodeC);
+
+        if (ov::element::u4 == matched_valueA->get_element_type() &&
+            ov::element::f32 == matched_valueB->get_element_type() &&
+            (ov::element::f16 == matched_valueC->get_element_type() ||
+             ov::element::f32 == matched_valueC->get_element_type())) {
+            LOG_DEBUG("Matched: " << matched_valueC);
+            scales.get().push_back(matched_valueC);
+        }
+        return true;
+    };  // matcher_callback
+
+    register_matcher(std::make_shared<opp::Matcher>(mulply, "TagCWAI2"), matcher_callback);
+}
+
+// As seen in LLaMa-v2-7b:
+// Since it is Symm, all zero points for all blocks must have the same
+// value so NPUW will detect it and fuse to function body (so it is
+// not Parameter but Const).
+
+// In the diagram below, pattern on the left is identified and
+// is modified to pattern in the right:
+//
+//   "tensor"     "zero point" "scale"
+//   Parameter:A  Parameter:B  Parameter:C  >    Parameter:A
+//                Const:B                   >
+//         u4       f32         f32         >       f16
+//         :         :          :           >        :
+//         V         :         :            >        V
+//        Convert    :        :             >       Convert
+//        f16|f32    :       :              >          f32  <Const>
+//            :      :      :               >           :     :
+//            V      V     :                >           V     V
+//            Subtract    :                 >           Reshape
+//              f16|f32  :                  >             :
+//               :      :                   >             V
+//               V      V                   >
+//               Multiply                   >
+//               fp16|f32 <Const>           >
+//                  :     :                 >
+//                  V     V                 >
+//                  Reshape                 >
+//                    :                     >
+//                    V                     >
+//
+
+// Implementation TBD
+
+}  // namespace SymmZP
+}  // namespace patterns
+}  // namespace npuw
+}  // namespace ov
diff --git a/src/plugins/intel_npu/src/plugin/npuw/partitioning/patterns/dcoff.hpp b/src/plugins/intel_npu/src/plugin/npuw/partitioning/patterns/dcoff.hpp
new file mode 100644
index 00000000000000..aa4c71d6bf6ab6
--- /dev/null
+++ b/src/plugins/intel_npu/src/plugin/npuw/partitioning/patterns/dcoff.hpp
@@ -0,0 +1,152 @@
+// Copyright (C) 2023-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <map>
+#include <vector>
+
+#include "openvino/openvino.hpp"
+#include "openvino/pass/graph_rewrite.hpp"
+
+namespace ov {
+namespace npuw {
+
+struct Subgraph;  // Forward declaration
+struct Function;  // Forward declaration
+
+enum class DCOffMode : int {
+    CAST_ONLY,
+    CAST_SCALE,
+};
+
+namespace patterns {
+
+// Common structures here
+
+struct DCOFFParams {
+    using PPtr = std::shared_ptr<ov::op::v0::Parameter>;
+    using CPtr = std::shared_ptr<ov::op::v0::Constant>;
+    std::unordered_map<PPtr, PPtr> scales;  // Closures: a scaling factor -> orig tensor
+    std::unordered_map<PPtr, CPtr> zerops;  // Closures: orig tensor -> a zero point (yes, a reverse...)
+};
+
+using DCOFFParamRef = std::reference_wrapper<DCOFFParams>;
+
+struct ClosureRemap {
+    std::vector<std::size_t> closure_remap;          // [new closure index] -> orig closure idx
+    std::map<std::size_t, std::size_t> scale_remap;  // orig closure idx -> orig scale idx
+    ov::ParameterVector params_to_remove;
+
+    std::vector<ov::Tensor> zero_points;  // zero points for closures, if needed
+};
+
+ClosureRemap build_remap(const Function& fbody, const DCOFFParams& p);
+void apply_remap(Subgraph& fcall, const ClosureRemap& m);
+void finalize_remap(Function& fbody, const ClosureRemap& m);
+
+// Various patterns here
+
+namespace SymmNoZP {
+
+class DCOFFPassBase : public ov::pass::MatcherPass {
+protected:
+    DCOffMode m_dcoff_mode = DCOffMode::CAST_ONLY;
+    ov::element::Type m_dcoff_type;
+    DCOFFParamRef m_params_to;
+
+    std::shared_ptr<ov::Node> paramA, paramB, toFP32, mulply;
+    bool matcher_callback(ov::pass::pattern::Matcher& m);
+
+public:
+    DCOFFPassBase(DCOffMode dcoff_mode, ov::element::Type dcoff_type, DCOFFParamRef pref);
+
+    virtual void build();
+    virtual void reconnect_root_to_convert(ov::pass::pattern::Matcher& m) = 0;
+};
+
+class DCOFFPassMatMul final : public DCOFFPassBase {
+    std::shared_ptr<ov::Node> matmul;
+
+public:
+    using DCOFFPassBase::DCOFFPassBase;
+    void build() override;
+    void reconnect_root_to_convert(ov::pass::pattern::Matcher& m) override;
+};
+
+class DCOFFPassGather final : public DCOFFPassBase {
+    std::shared_ptr<ov::Node> gather;
+
+public:
+    using DCOFFPassBase::DCOFFPassBase;
+    void build() override;
+    void reconnect_root_to_convert(ov::pass::pattern::Matcher& m) override;
+};
+// FIXME: The above two can probably be replaced with a more genering pattern
+// (ending at Multiply but with more connection constraints)
+
+}  // namespace SymmNoZP
+
+namespace SymmZP {  // TODO: Not sure if it is actually Symm..
+
+class DCOFFPassBase : public ov::pass::MatcherPass {
+protected:
+    DCOffMode m_dcoff_mode = DCOffMode::CAST_ONLY;
+    ov::element::Type m_dcoff_type;
+    DCOFFParamRef m_params_to;
+
+    std::shared_ptr<ov::Node> paramA, constB, paramC, cvtA, cvtB, subtr, mulply;
+    bool matcher_callback(ov::pass::pattern::Matcher& m);
+
+public:
+    DCOFFPassBase(DCOffMode dcoff_mode, ov::element::Type dcoff_type, DCOFFParamRef pref);
+
+    virtual void build();
+    virtual void reconnect_root(ov::pass::pattern::Matcher& m) = 0;
+};
+
+class DCOFFPassReshape1 final : public DCOFFPassBase {
+    std::shared_ptr<ov::Node> reshpe;
+
+public:
+    using DCOFFPassBase::DCOFFPassBase;
+    void build() override;
+    void reconnect_root(ov::pass::pattern::Matcher& m) override;
+};
+
+class DCOFFPassConvert1 final : public DCOFFPassBase {
+    std::shared_ptr<ov::Node> cvtEnd;
+
+public:
+    using DCOFFPassBase::DCOFFPassBase;
+    void build() override;
+    void reconnect_root(ov::pass::pattern::Matcher& m) override;
+};
+
+class DCOFFPassReshape2 : public ov::pass::MatcherPass {
+public:
+    DCOFFPassReshape2(DCOffMode dcoff_mode, ov::element::Type dcoff_type, DCOFFParamRef pref);
+};
+
+class CWAI1 : public ov::pass::MatcherPass {
+public:
+    using CPtr = std::shared_ptr<ov::op::v0::Constant>;
+    using Results = std::reference_wrapper<std::vector<CPtr>>;
+
+    explicit CWAI1(Results scales);
+};
+
+class CWAI2 : public ov::pass::MatcherPass {
+public:
+    using CPtr = std::shared_ptr<ov::op::v0::Constant>;
+    using Results = std::reference_wrapper<std::vector<CPtr>>;
+
+    explicit CWAI2(Results scales);
+};
+
+}  // namespace SymmZP
+
+}  // namespace patterns
+}  // namespace npuw
+}  // namespace ov
diff --git a/src/plugins/intel_npu/src/plugin/npuw/perf.cpp b/src/plugins/intel_npu/src/plugin/npuw/perf.cpp
new file mode 100644
index 00000000000000..6271e1cc14e0f8
--- /dev/null
+++ b/src/plugins/intel_npu/src/plugin/npuw/perf.cpp
@@ -0,0 +1,15 @@
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "perf.hpp"
+
+#include <chrono>
+
+float ov::npuw::perf::ms_to_run(std::function<void()>&& body) {
+    namespace khr = std::chrono;
+    const auto s = khr::steady_clock::now();
+    body();
+    const auto f = khr::steady_clock::now();
+    return khr::duration_cast<khr::microseconds>(f - s).count() / 1000.0f;
+}
diff --git a/src/plugins/intel_npu/src/plugin/npuw/perf.hpp b/src/plugins/intel_npu/src/plugin/npuw/perf.hpp
new file mode 100644
index 00000000000000..3a188fa2decc93
--- /dev/null
+++ b/src/plugins/intel_npu/src/plugin/npuw/perf.hpp
@@ -0,0 +1,59 @@
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <algorithm>
+#include <functional>
+#include <limits>
+#include <ostream>
+#include <string>
+#include <vector>
+
+namespace ov {
+namespace npuw {
+namespace perf {
+
+float ms_to_run(std::function<void()>&& body);
+
+struct MSec {
+    constexpr static const char* name = "ms";
+};
+
+template <typename T, typename U>
+class metric {
+    std::vector<T> records;
+    T vmin = std::numeric_limits<T>::max();
+    T vmax = std::numeric_limits<T>::min();
+    std::string unit;
+
+public:
+    void operator+=(T&& t) {
+        vmin = std::min(vmin, t);
+        vmax = std::max(vmax, t);
+        records.push_back(std::move(t));
+    }
+    float avg() const {
+        float acc = 0.f;
+        for (auto&& t : records) {
+            acc += static_cast<float>(t) / records.size();
+        }
+        return acc;
+    }
+    T med() const {
+        std::vector<T> cpy(records);
+        std::nth_element(cpy.begin(), cpy.begin() + cpy.size() / 2, cpy.end());
+        return cpy[cpy.size() / 2];
+    }
+    friend std::ostream& operator<<(std::ostream& os, const metric<T, U>& m) {
+        const char* units = U::name;
+        os << "[ min = " << m.vmin << units << ", avg = " << m.avg() << units << ", med = " << m.med() << units
+           << ", max = " << m.vmax << units << " in " << m.records.size() << " records ]";
+        return os;
+    }
+};
+
+}  // namespace perf
+}  // namespace npuw
+}  // namespace ov
diff --git a/src/plugins/intel_npu/src/plugin/npuw/util.cpp b/src/plugins/intel_npu/src/plugin/npuw/util.cpp
new file mode 100644
index 00000000000000..0a501b0d834a07
--- /dev/null
+++ b/src/plugins/intel_npu/src/plugin/npuw/util.cpp
@@ -0,0 +1,1131 @@
+// Copyright (C) 2023-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "util.hpp"
+
+#include <immintrin.h>
+
+#include <intel_npu/al/config/config.hpp>
+#include <iomanip>
+#include <openvino/core/parallel.hpp>
+#include <openvino/core/type/bfloat16.hpp>
+#include <openvino/core/type/float16.hpp>
+#include <sstream>
+
+#include "logging.hpp"
+#include "openvino/op/constant.hpp"
+#include "openvino/op/util/op_types.hpp"
+
+#ifdef UNPACK_PROFILING
+#    include "tbb/concurrent_unordered_map.h"
+#endif
+
+bool ov::npuw::util::is_set(const std::size_t sub_idx, const std::string& opt) {
+    if (opt.empty()) {
+        return false;
+    }
+    if (opt == "YES") {
+        return true;
+    }
+
+    std::vector<std::size_t> sub_inds{};
+    sub_inds = ::intel_npu ::OptionParser<std::vector<std::size_t>>::parse(opt);
+    if (std::find(sub_inds.begin(), sub_inds.end(), sub_idx) != sub_inds.end()) {
+        return true;
+    }
+
+    return false;
+}
+
+ov::Tensor ov::npuw::util::tensor_from_const(const std::shared_ptr<ov::Node>& node) {
+    NPUW_ASSERT(ov::op::util::is_constant(node));
+    NPUW_ASSERT(node->outputs().size() == 1);
+    const auto port = node->output(0);
+    auto cnst_node = std::dynamic_pointer_cast<ov::op::v0::Constant>(node);
+    return ov::Tensor(port.get_element_type(), port.get_shape(), const_cast<void*>(cnst_node->get_data_ptr()));
+}
+
+bool ov::npuw::util::starts_with(const std::string& str, const std::string& prefix) {
+    return str.substr(0, prefix.size()) == prefix;
+}
+
+std::string ov::npuw::util::fmt(std::size_t number, std::size_t total) {
+    std::size_t regs = 1;
+    while (total /= 10) {
+        regs++;
+    }
+    std::stringstream ss;
+    ss << std::setfill('0') << std::setw(regs) << number;
+    return ss.str();
+}
+
+namespace {
+
+inline int8_t hi4(int8_t x) {
+    return ((x & (1 << 7)) >> 4) | ((x & (1 << 6)) >> 4) | ((x & (1 << 5)) >> 4) | ((x & (1 << 4)) >> 4);
+}
+
+inline int8_t lo4(int8_t x) {
+    return (x & (1 << 3)) | (x & (1 << 2)) | (x & (1 << 1)) | (x & (1 << 0));
+}
+
+inline uint8_t hi4(uint8_t x) {
+    return x >> 4;
+}
+
+inline uint8_t lo4(uint8_t x) {
+    return x & 0xF;
+}
+
+inline int8_t upc(int8_t h) {
+    return h | (-((h & (1 << 3)) >> 3) & (-8));
+}
+
+#define avx2_i4toi8(vinput, vout0, vout1)                                         \
+    {                                                                             \
+        __m256i himask = _mm256_broadcastb_epi8(_mm_set_epi32(0, 0, 0, 0xF0));    \
+        __m256i lomask = _mm256_broadcastb_epi8(_mm_set_epi32(0, 0, 0, 0x0F));    \
+        __m256i vsgmask = _mm256_broadcastb_epi8(_mm_set_epi32(0, 0, 0, 1 << 3)); \
+        __m256i vzero = _mm256_broadcastb_epi8(_mm_set_epi32(0, 0, 0, 0));        \
+        __m256i vextend = _mm256_broadcastb_epi8(_mm_set_epi32(0, 0, 0, (-8)));   \
+                                                                                  \
+        __m256i vht = _mm256_and_si256(vinput, himask);                           \
+        __m256i vhi = _mm256_srli_epi16(vht, 4);                                  \
+        __m256i vlo = _mm256_and_si256(vinput, lomask);                           \
+                                                                                  \
+        __m256i vsghi = _mm256_srli_epi16(_mm256_and_si256(vhi, vsgmask), 3);     \
+        __m256i vsglo = _mm256_srli_epi16(_mm256_and_si256(vlo, vsgmask), 3);     \
+        __m256i vsubhi = _mm256_sub_epi8(vzero, vsghi);                           \
+        __m256i vsublo = _mm256_sub_epi8(vzero, vsglo);                           \
+        __m256i vhires = _mm256_or_si256(vhi, _mm256_and_si256(vsubhi, vextend)); \
+        __m256i vlores = _mm256_or_si256(vlo, _mm256_and_si256(vsublo, vextend)); \
+                                                                                  \
+        __m256i vunlo = _mm256_unpacklo_epi8(vhires, vlores);                     \
+        __m256i vunhi = _mm256_unpackhi_epi8(vhires, vlores);                     \
+        *vout0 = _mm256_permute2x128_si256(vunlo, vunhi, 0x20);                   \
+        *vout1 = _mm256_permute2x128_si256(vunlo, vunhi, 0x31);                   \
+    }
+
+inline __m128i avx2_i8tof16(__m128i vi8) {
+    __m256i i32vec = _mm256_cvtepi8_epi32(vi8);                 // extend:  8 x i8  -> 8 x i32 [256b of 256b]
+    __m256 f32vec = _mm256_cvtepi32_ps(i32vec);                 // convert: 8 x i32 -> 8 x f32 [256b of 256b]
+    return _mm256_cvtps_ph(f32vec, _MM_FROUND_TO_NEAREST_INT);  // convert: 8 x f32 -> 8 x f16 [128b]
+}
+
+inline __m128i avx2_i8tof16(__m128i vi8, __m256 s) {
+    __m256i i32vec = _mm256_cvtepi8_epi32(vi8);                 // extend:  8 x i8  -> 8 x i32 [256b of 256b]
+    __m256 f32vec = _mm256_cvtepi32_ps(i32vec);                 // convert: 8 x i32 -> 8 x f32 [256b of 256b]
+    __m256 f32scl = _mm256_mul_ps(f32vec, s);                   // scale:   8 x f32 -> 8 x f32 [256b of 256b]
+    return _mm256_cvtps_ph(f32scl, _MM_FROUND_TO_NEAREST_INT);  // convert: 8 x f32 -> 8 x f16 [128b]
+}
+
+inline __m128i avx2_u8tof16_hi(__m128i vu8, __m256 z, __m256 s) {
+    __m256i u32vec = _mm256_cvtepu8_epi32(vu8);                 // extend:   8 x u8  -> 8 x i32 [256b of 256b]
+    __m256 f32vec = _mm256_cvtepi32_ps(u32vec);                 // convert:  8 x i32 -> 8 x f32 [256b of 256b]
+    __m256 f32sub = _mm256_sub_ps(f32vec, z);                   // subtract: 8 x f32 -> 8 x f32 [256b of 256b]
+    __m256 f32scl = _mm256_mul_ps(f32sub, s);                   // scale:    8 x f32 -> 8 x f32 [256b of 256b]
+    return _mm256_cvtps_ph(f32scl, _MM_FROUND_TO_NEAREST_INT);  // convert: 8 x f32 -> 8 x f16 [128b]
+}
+
+inline __m128i avx2_u8tof16_lo(__m128i vu8, __m256 z, __m256 s) {
+    __m128i vu8h = _mm_bsrli_si128(vu8, 8);
+    return avx2_u8tof16_hi(vu8h, z, s);
+}
+
+inline __m128i avx2_f32tof16(__m256 f32vec) {
+    return _mm256_cvtps_ph(f32vec, _MM_FROUND_TO_NEAREST_INT);
+}
+
+inline __m256 avx2_load_scale(const int8_t* data, ov::element::Type type) {
+    if (type == ov::element::f32) {
+        return _mm256_set1_ps(*reinterpret_cast<const float*>(data));
+    } else {
+        NPUW_ASSERT(type == ov::element::f16);
+        float val{};
+        _mm_store_ss(&val, _mm_cvtph_ps(_mm_cvtsi32_si128(*reinterpret_cast<const int16_t*>(data))));
+        return _mm256_set1_ps(val);
+    }
+}
+
+inline float avx2_load_f32(const int8_t* data, ov::element::Type type) {
+    if (type == ov::element::f32) {
+        return *reinterpret_cast<const float*>(data);
+    } else {
+        NPUW_ASSERT(type == ov::element::f16);
+        float val{};
+        _mm_store_ss(&val, _mm_cvtph_ps(_mm_cvtsi32_si128(*reinterpret_cast<const int16_t*>(data))));
+        return val;
+    }
+}
+
+#ifdef UNPACK_PROFILING
+class UnpackStat {
+    tbb::concurrent_unordered_map<size_t, std::pair<size_t, uint64_t>> inferenceTimes;
+
+public:
+    UnpackStat() {}
+    void addRecord(size_t sz, size_t time) {
+        inferenceTimes[sz].first++;
+        inferenceTimes[sz].second += time;
+    }
+    ~UnpackStat() {
+        for (auto&& r : inferenceTimes) {
+            std::cout << "work: " << r.first  //<< ", stride: " << stride
+                      << " overall_time = " << r.second.second / 1000 << " [ms]"
+                      << " avg_atime = " << r.second.second / r.second.first << " [µs]\n";
+        }
+    }
+};
+
+static UnpackStat ustat;
+#    define UNPACK_START_TICK() std::chrono::steady_clock::time_point _begin_tick = std::chrono::steady_clock::now();
+#    define UNPACK_SAVE_TICK()                                                              \
+        std::chrono::steady_clock::time_point _end_tick = std::chrono::steady_clock::now(); \
+        ustat.addRecord(total, std::chrono::duration_cast<std::chrono::microseconds>(_end_tick - _begin_tick).count());
+#else
+#    define UNPACK_START_TICK()
+#    define UNPACK_SAVE_TICK()
+#endif
+
+void unpack_i4i8(const ov::SoPtr<ov::ITensor>& from,
+                 const ov::SoPtr<ov::ITensor>& to,
+                 const ov::npuw::util::UnpackOptions& unpack_options) {
+    NPUW_ASSERT(from->is_continuous());
+    NPUW_ASSERT(to->is_continuous());
+    NPUW_ASSERT(from->get_size() == to->get_size());
+
+    // with vectorization above, we:
+    // - read  256 bits (= 32 bytes, = 64  i4 elements)
+    // - write 512 bits (= 64 bytes, = 64  i8 elements)
+    // per every iteration, what translates to (from->size() / 64) iterations
+
+    const std::size_t total = from->get_size();
+    int8_t const* pSrc = static_cast<int8_t*>(from->data());  // 2 x i4 elements
+    int8_t* pDst = static_cast<int8_t*>(to->data());          // 1 x i8 element
+    size_t stride = 64;
+
+    auto unpack_body = [pSrc, pDst](size_t index, size_t stride) {
+        size_t halfStride = stride >> 1;
+        int8_t const* pSrcLocal = pSrc + halfStride * index;
+        int8_t* pDstLocal = pDst + stride * index;
+
+        for (size_t j = 0; j < stride; j += 64) {
+            __m256i inv = _mm256_lddqu_si256(reinterpret_cast<const __m256i*>(pSrcLocal));
+            __m256i* outv0 = reinterpret_cast<__m256i*>(pDstLocal);
+            __m256i* outv1 = reinterpret_cast<__m256i*>(pDstLocal + 32);
+
+            __m256i vout0, vout1;
+            avx2_i4toi8(inv, &vout0, &vout1);
+
+            _mm256_storeu_si256(outv0, vout0);
+            _mm256_storeu_si256(outv1, vout1);
+
+            pSrcLocal += 32;
+            pDstLocal += 64;
+        }
+    };
+    // ov work index / 64
+    if (unpack_options.nPartitions) {
+        std::size_t minPartitions;
+        if (!unpack_options.bStrictPartitioning) {
+            // some heuristics that every tbb thread workload has to have 2048 elements at least,
+            // so in terms of stride, it should be 64 * 2048
+            minPartitions = total / (64 * 2048);
+            minPartitions = std::max<std::size_t>(1u, minPartitions);
+            minPartitions = std::min(minPartitions, unpack_options.nPartitions);
+        } else {
+            minPartitions = unpack_options.nPartitions;
+        }
+
+        // calculating stride in elements - this stride give us nPartitions + 1  partitions
+        stride = static_cast<size_t>(total / minPartitions);
+
+        // stride has to be 64 elements aligned to avoid gaps between workloads
+        stride = (stride >> 6) << 6;
+        // if number of partitions to large comparing to workload, min supported stride still have to be clamped to 64
+        stride = stride < 64 ? 64 : stride;
+    }
+
+    UNPACK_START_TICK();
+
+    if (unpack_options.bUseOvParallelFor) {
+        ov::parallel_for(total / stride, [unpack_body, stride](size_t index) {
+            unpack_body(index, stride);
+        });
+    } else {
+        for (std::size_t index = 0; index < total / stride; index++) {
+            unpack_body(index, stride);
+        }
+    }
+    // handle tail
+    size_t tailOffset = (static_cast<size_t>(total / stride) * stride);
+    pSrc = static_cast<int8_t*>(from->data()) + (tailOffset >> 1);
+    pDst = static_cast<int8_t*>(to->data()) + tailOffset;
+
+    for (std::size_t index = 0; index < ((total % 64) >> 1); index++) {
+        *(pDst++) = upc(hi4(*(pSrc)));
+        *(pDst++) = upc(lo4(*(pSrc)));
+        pSrc++;
+    }
+    UNPACK_SAVE_TICK();
+}
+
+void unpack_u4i8(const ov::SoPtr<ov::ITensor>& from,
+                 const ov::SoPtr<ov::ITensor>& to,
+                 const ov::npuw::util::UnpackOptions& unpack_options) {
+    NPUW_ASSERT(from->is_continuous());
+    NPUW_ASSERT(to->is_continuous());
+    NPUW_ASSERT(from->get_size() == to->get_size());
+
+    uint8_t const* pSrc = static_cast<uint8_t*>(from->data());  // 2 x u4 elements
+    int8_t* pDst = static_cast<int8_t*>(to->data());            // 1 x i8 element
+
+    const std::size_t total = from->get_size();
+    for (std::size_t index = 0; index < total; index += 2) {
+        pDst[0] = static_cast<int8_t>(lo4(*pSrc));  // LSB is [0] -- since OpenVINO 24.0!
+        pDst[1] = static_cast<int8_t>(hi4(*pSrc));  // MSB is [1] -- since OpenVINO 24.0!
+        pSrc++;
+        pDst += 2;
+    }
+}
+
+void unpack_i4f16(const ov::SoPtr<ov::ITensor>& from,
+                  const ov::SoPtr<ov::ITensor>& to,
+                  const ov::npuw::util::UnpackOptions& unpack_options) {
+    NPUW_ASSERT(from->is_continuous());
+    NPUW_ASSERT(to->is_continuous());
+    NPUW_ASSERT(from->get_size() == to->get_size());
+
+    // This conversion combines i4toi8 (above) and i8tof16 (below). Here we
+    // - read    256  bits (= 32  bytes, = 64  i4  elements)
+    // - write   1024 bits (= 128 bytes, = 64  f16 elements)
+    // per every iteration, what translates to (from->size() / 64) iterations
+
+    std::size_t total = to->get_size();
+    int8_t const* pSrc = static_cast<int8_t*>(from->data());  // 2 x i4  elements
+    int16_t* pDst = static_cast<int16_t*>(to->data());        // 1 x f16 element
+    // bool tailOnly = total < 64;
+
+    auto unpack_body = [pSrc, pDst](size_t index) {
+        int8_t const* pSrcLocal = pSrc + 32 * index;
+        int16_t* pDstLocal = pDst + 64 * index;
+
+        __m256i inv = _mm256_lddqu_si256(reinterpret_cast<const __m256i*>(pSrcLocal));
+        __m128i* outv[8] = {
+            reinterpret_cast<__m128i*>(pDstLocal),
+            reinterpret_cast<__m128i*>(pDstLocal + 8),
+            reinterpret_cast<__m128i*>(pDstLocal + 16),
+            reinterpret_cast<__m128i*>(pDstLocal + 24),
+            reinterpret_cast<__m128i*>(pDstLocal + 32),
+            reinterpret_cast<__m128i*>(pDstLocal + 40),
+            reinterpret_cast<__m128i*>(pDstLocal + 48),
+            reinterpret_cast<__m128i*>(pDstLocal + 56),
+        };
+
+        __m256i vout0, vout1;
+        avx2_i4toi8(inv, &vout0, &vout1);
+
+        int8_t tmp[64];  // FIXME: Avoid it
+        __m256i* tmpv0 = reinterpret_cast<__m256i*>(tmp);
+        __m256i* tmpv1 = reinterpret_cast<__m256i*>(tmp + 32);
+        _mm256_storeu_si256(tmpv0, vout0);
+        _mm256_storeu_si256(tmpv1, vout1);
+
+        __m128i i8vecs[8] = {
+            _mm_loadl_epi64(reinterpret_cast<__m128i*>(tmp)),
+            _mm_loadl_epi64(reinterpret_cast<__m128i*>(tmp + 8)),
+            _mm_loadl_epi64(reinterpret_cast<__m128i*>(tmp + 16)),
+            _mm_loadl_epi64(reinterpret_cast<__m128i*>(tmp + 24)),
+            _mm_loadl_epi64(reinterpret_cast<__m128i*>(tmp + 32)),
+            _mm_loadl_epi64(reinterpret_cast<__m128i*>(tmp + 40)),
+            _mm_loadl_epi64(reinterpret_cast<__m128i*>(tmp + 48)),
+            _mm_loadl_epi64(reinterpret_cast<__m128i*>(tmp + 56)),
+        };
+
+        __m128i vresults[8] = {avx2_i8tof16(i8vecs[0]),
+                               avx2_i8tof16(i8vecs[1]),
+                               avx2_i8tof16(i8vecs[2]),
+                               avx2_i8tof16(i8vecs[3]),
+                               avx2_i8tof16(i8vecs[4]),
+                               avx2_i8tof16(i8vecs[5]),
+                               avx2_i8tof16(i8vecs[6]),
+                               avx2_i8tof16(i8vecs[7])};
+
+        _mm_storeu_si128(outv[0], vresults[0]);
+        _mm_storeu_si128(outv[1], vresults[1]);
+        _mm_storeu_si128(outv[2], vresults[2]);
+        _mm_storeu_si128(outv[3], vresults[3]);
+        _mm_storeu_si128(outv[4], vresults[4]);
+        _mm_storeu_si128(outv[5], vresults[5]);
+        _mm_storeu_si128(outv[6], vresults[6]);
+        _mm_storeu_si128(outv[7], vresults[7]);
+    };
+
+    if (unpack_options.bUseOvParallelFor) {
+        ov::parallel_for(total / 64, [&unpack_body](size_t index) {
+            unpack_body(index);
+        });
+    } else {
+        for (std::size_t index = 0; index < total / 64; index++) {
+            unpack_body(index);
+        }
+    }
+
+    // handle tail that is < 64 elements
+    size_t tailOffset = ((total >> 6) << 6);
+    pSrc = static_cast<int8_t*>(from->data()) + (tailOffset >> 1);
+    pDst = static_cast<int16_t*>(to->data()) + tailOffset;
+
+    constexpr std::size_t VECSIZE = 8;
+
+    total = ((total % 64) >> 1);
+    int8_t unpackedToI8[VECSIZE] = {0};
+    size_t unpackedIdx = 0;
+    for (std::size_t index = 0; index < total; index++) {
+        unpackedToI8[unpackedIdx++] = upc(hi4(*(pSrc)));
+        unpackedToI8[unpackedIdx++] = upc(lo4(*(pSrc)));
+        if (unpackedIdx == VECSIZE) {
+            __m128i i8vec = _mm_loadu_si64(reinterpret_cast<__m128i*>(unpackedToI8));
+            __m128i f16vec = avx2_i8tof16(i8vec);
+            _mm_storeu_si128(reinterpret_cast<__m128i*>(pDst), f16vec);
+            pDst += VECSIZE;
+            unpackedIdx = 0;
+        }
+        pSrc += 1;
+    }
+
+    // handle tail that is < 8
+    if (unpackedIdx != 0) {
+        int16_t tmp[VECSIZE];
+        __m128i i8vec = _mm_loadu_si64(reinterpret_cast<__m128i*>(unpackedToI8));
+        __m128i f16vec = avx2_i8tof16(i8vec);
+        _mm_storeu_si128(reinterpret_cast<__m128i*>(tmp), f16vec);
+        for (size_t i = 0; i != unpackedIdx; i++) {
+            pDst[i] = tmp[i];
+        }
+    }
+}
+
+void unpack_i4f16(const ov::SoPtr<ov::ITensor>& from,
+                  const ov::SoPtr<ov::ITensor>& scale,
+                  const ov::SoPtr<ov::ITensor>& to,
+                  const ov::npuw::util::UnpackOptions& unpack_options) {
+    NPUW_ASSERT(from->is_continuous());
+    NPUW_ASSERT(scale->is_continuous());
+    NPUW_ASSERT(to->is_continuous());
+    NPUW_ASSERT(from->get_size() == to->get_size());
+
+    // TODO: force 2d shapes for now
+    NPUW_ASSERT(scale->get_shape().size() == 2);
+
+    NPUW_ASSERT(scale->get_shape()[0] == from->get_shape()[0]);
+    NPUW_ASSERT(scale->get_shape()[1] == 1);
+
+    const auto scale_elem_type = scale->get_element_type();
+    NPUW_ASSERT(scale_elem_type == ov::element::f32 || scale_elem_type == ov::element::f16);
+
+    // This conversion combines i4toi8 (above) and i8tof16 (below). Here we
+    // - read    256  bits (= 32  bytes, = 64  i4  elements)
+    // - write   1024 bits (= 128 bytes, = 64  f16 elements)
+    // per every iteration, what translates to (from->size() / 64) iterations
+
+    const std::size_t total = to->get_size();
+    const std::size_t stotal = scale->get_size();
+    const std::size_t elementsPerScale = total / stotal;
+
+    // TODO: handle tails
+    NPUW_ASSERT(elementsPerScale % 64 == 0);
+
+    const int8_t* const pSrc = static_cast<int8_t*>(from->data());   // 2 x i4  elements
+    const int8_t* const pScl = static_cast<int8_t*>(scale->data());  // either f16 or f32
+    const int16_t* pDst = static_cast<int16_t*>(to->data());         // 1 x f16 element
+
+    auto unpack_body = [pSrc, pDst, pScl, elementsPerScale, scale_elem_type, stotal](std::size_t sindex,
+                                                                                     std::size_t stride) {
+        // number of vectorized operations per scale
+        size_t elementsPerScaleVectorized = elementsPerScale / 64;
+
+        int8_t const* pSrcLocal = pSrc + 32 * elementsPerScaleVectorized * sindex * stride;
+        int8_t const* pSclLocal = pScl + scale_elem_type.size() * sindex * stride;
+        int16_t* pDstLocal = const_cast<int16_t*>(pDst) + 64 * elementsPerScaleVectorized * sindex * stride;
+
+        // if it is last iteration current stride can be smaller - lets check that
+        sindex *= stride;
+        const auto jobFinish = std::min(sindex + stride, stotal);
+
+        for (; sindex != jobFinish; sindex++) {
+            __m256 svec = avx2_load_scale(pSclLocal, scale_elem_type);
+            for (std::size_t index = 0; index < elementsPerScale; index += 64) {
+                __m256i inv = _mm256_lddqu_si256(reinterpret_cast<const __m256i*>(pSrcLocal));
+                __m128i* outv[8] = {
+                    reinterpret_cast<__m128i*>(pDstLocal),
+                    reinterpret_cast<__m128i*>(pDstLocal + 8),
+                    reinterpret_cast<__m128i*>(pDstLocal + 16),
+                    reinterpret_cast<__m128i*>(pDstLocal + 24),
+                    reinterpret_cast<__m128i*>(pDstLocal + 32),
+                    reinterpret_cast<__m128i*>(pDstLocal + 40),
+                    reinterpret_cast<__m128i*>(pDstLocal + 48),
+                    reinterpret_cast<__m128i*>(pDstLocal + 56),
+                };
+
+                __m256i vout0, vout1;
+                avx2_i4toi8(inv, &vout0, &vout1);
+
+                int8_t tmp[64];  // FIXME: Avoid it
+                __m256i* tmpv0 = reinterpret_cast<__m256i*>(tmp);
+                __m256i* tmpv1 = reinterpret_cast<__m256i*>(tmp + 32);
+                _mm256_storeu_si256(tmpv0, vout0);
+                _mm256_storeu_si256(tmpv1, vout1);
+
+                __m128i i8vecs[8] = {
+                    _mm_loadl_epi64(reinterpret_cast<__m128i*>(tmp)),
+                    _mm_loadl_epi64(reinterpret_cast<__m128i*>(tmp + 8)),
+                    _mm_loadl_epi64(reinterpret_cast<__m128i*>(tmp + 16)),
+                    _mm_loadl_epi64(reinterpret_cast<__m128i*>(tmp + 24)),
+                    _mm_loadl_epi64(reinterpret_cast<__m128i*>(tmp + 32)),
+                    _mm_loadl_epi64(reinterpret_cast<__m128i*>(tmp + 40)),
+                    _mm_loadl_epi64(reinterpret_cast<__m128i*>(tmp + 48)),
+                    _mm_loadl_epi64(reinterpret_cast<__m128i*>(tmp + 56)),
+                };
+
+                __m128i vresults[8] = {avx2_i8tof16(i8vecs[0], svec),
+                                       avx2_i8tof16(i8vecs[1], svec),
+                                       avx2_i8tof16(i8vecs[2], svec),
+                                       avx2_i8tof16(i8vecs[3], svec),
+                                       avx2_i8tof16(i8vecs[4], svec),
+                                       avx2_i8tof16(i8vecs[5], svec),
+                                       avx2_i8tof16(i8vecs[6], svec),
+                                       avx2_i8tof16(i8vecs[7], svec)};
+
+                _mm_storeu_si128(outv[0], vresults[0]);
+                _mm_storeu_si128(outv[1], vresults[1]);
+                _mm_storeu_si128(outv[2], vresults[2]);
+                _mm_storeu_si128(outv[3], vresults[3]);
+                _mm_storeu_si128(outv[4], vresults[4]);
+                _mm_storeu_si128(outv[5], vresults[5]);
+                _mm_storeu_si128(outv[6], vresults[6]);
+                _mm_storeu_si128(outv[7], vresults[7]);
+
+                pSrcLocal += 32;  // shift pSrc only by 32 since it is 64 x i4
+                pDstLocal += 64;  // note pDst is int16_t
+            }
+            pSclLocal += scale_elem_type.size();
+        }
+    };
+    size_t stride{1};
+
+    // since scaling is always 64 elements aligned operations, lets partition only in scale shape
+    if (unpack_options.nPartitions) {
+        std::size_t minPartitions;
+        if (!unpack_options.bStrictPartitioning) {
+            // some heuristics that every tbb thread workload has to have 2048 x intrinsics operations at least,
+            // so in terms of stride, it should be nElementsPerscale/64 * 2048
+            const auto nIntrinsicsPerScale = elementsPerScale / 64u;
+            auto minScaleStride = 2048u / nIntrinsicsPerScale;
+            minScaleStride = std::max<std::size_t>(1u, minScaleStride);
+            minPartitions = stotal / minScaleStride;
+            minPartitions = std::max<std::size_t>(1u, minPartitions);
+            minPartitions = std::min(minPartitions, unpack_options.nPartitions);
+        } else {
+            minPartitions = unpack_options.nPartitions;
+        }
+
+        // calculating stride in scale elements space
+        stride = static_cast<size_t>(stotal / minPartitions);
+    }
+
+    const size_t numWork = (stotal + stride - 1) / stride;
+
+    if (unpack_options.bUseOvParallelFor) {
+        ov::parallel_for(numWork, [unpack_body, stride](size_t index) {
+            unpack_body(index, stride);
+        });
+    } else {
+        for (std::size_t index = 0; index < numWork; index++) {
+            unpack_body(index, stride);
+        }
+    }
+}
+
+void unpack_u4f16(const ov::SoPtr<ov::ITensor>& from,
+                  const ov::SoPtr<ov::ITensor>& to,
+                  const ov::npuw::util::UnpackOptions& unpack_options) {
+    NPUW_ASSERT(from->is_continuous());
+    NPUW_ASSERT(to->is_continuous());
+    NPUW_ASSERT(from->get_size() == to->get_size());
+    NPUW_ASSERT(from->get_size() % 64 == 0);
+
+    // This conversion combines u4i8 and i8tof16 unpacks. Here we
+    // - read    256  bits (= 32  bytes, = 64  i4  elements)
+    // - write   1024 bits (= 128 bytes, = 64  f16 elements)
+    // per every iteration, what translates to (from->size() / 64) iterations
+
+    const std::size_t total = to->get_size();
+    int8_t const* pSrc = static_cast<int8_t*>(from->data());  // 2 x i4  elements
+    int16_t* pDst = static_cast<int16_t*>(to->data());        // 1 x f16 element
+
+    for (std::size_t index = 0; index < total; index += 64) {
+        __m128i* outv[8] = {
+            reinterpret_cast<__m128i*>(pDst),
+            reinterpret_cast<__m128i*>(pDst + 8),
+            reinterpret_cast<__m128i*>(pDst + 16),
+            reinterpret_cast<__m128i*>(pDst + 24),
+            reinterpret_cast<__m128i*>(pDst + 32),
+            reinterpret_cast<__m128i*>(pDst + 40),
+            reinterpret_cast<__m128i*>(pDst + 48),
+            reinterpret_cast<__m128i*>(pDst + 56),
+        };
+
+        int8_t tmp[64];  // FIXME: Avoid it
+        for (std::size_t ii = 0; ii < 32; ii++) {
+            tmp[ii * 2] = static_cast<int8_t>(lo4(pSrc[ii]));      // LSB is [0] -- since OpenVINO 24.0!
+            tmp[ii * 2 + 1] = static_cast<int8_t>(hi4(pSrc[ii]));  // MSB is [1] -- since OpenVINO 24.0!
+        }
+
+        __m128i vresults[8] = {
+            avx2_i8tof16(_mm_loadl_epi64(reinterpret_cast<__m128i*>(tmp))),
+            avx2_i8tof16(_mm_loadl_epi64(reinterpret_cast<__m128i*>(tmp + 8))),
+            avx2_i8tof16(_mm_loadl_epi64(reinterpret_cast<__m128i*>(tmp + 16))),
+            avx2_i8tof16(_mm_loadl_epi64(reinterpret_cast<__m128i*>(tmp + 24))),
+            avx2_i8tof16(_mm_loadl_epi64(reinterpret_cast<__m128i*>(tmp + 32))),
+            avx2_i8tof16(_mm_loadl_epi64(reinterpret_cast<__m128i*>(tmp + 40))),
+            avx2_i8tof16(_mm_loadl_epi64(reinterpret_cast<__m128i*>(tmp + 48))),
+            avx2_i8tof16(_mm_loadl_epi64(reinterpret_cast<__m128i*>(tmp + 56))),
+        };
+
+        _mm_storeu_si128(outv[0], vresults[0]);
+        _mm_storeu_si128(outv[1], vresults[1]);
+        _mm_storeu_si128(outv[2], vresults[2]);
+        _mm_storeu_si128(outv[3], vresults[3]);
+        _mm_storeu_si128(outv[4], vresults[4]);
+        _mm_storeu_si128(outv[5], vresults[5]);
+        _mm_storeu_si128(outv[6], vresults[6]);
+        _mm_storeu_si128(outv[7], vresults[7]);
+
+        pSrc += 32;  // shift pSrc only by 32 since it is 64 x i4
+        pDst += 64;  // note pDst is int16_t
+    }
+}
+
+void unpack_u4f16(const ov::SoPtr<ov::ITensor>& from,
+                  const ov::SoPtr<ov::ITensor>& zerop,
+                  const ov::SoPtr<ov::ITensor>& scale,
+                  const ov::SoPtr<ov::ITensor>& to,
+                  const ov::npuw::util::UnpackOptions& unpack_options) {
+    NPUW_ASSERT(from->is_continuous());
+    NPUW_ASSERT(zerop->is_continuous());
+    NPUW_ASSERT(scale->is_continuous());
+    NPUW_ASSERT(to->is_continuous());
+    NPUW_ASSERT(from->get_size() == to->get_size());
+
+    // Only single-size ZP is supported
+    NPUW_ASSERT(zerop->get_size() == 1);
+
+    const auto from_shape = from->get_shape();
+    NPUW_ASSERT(from_shape.back() % 64 == 0);
+
+    // 2-channel (Symmetric) and 3-channel (group-wise)
+    // scale factors are supported. The scale/value loop
+    // iteration is based on stotal, so should work for
+    // both cases.
+    const auto scale_shape = scale->get_shape();
+    NPUW_ASSERT(scale_shape.size() == 3 || scale_shape.size() == 2);
+    if (scale_shape.size() == 3) {
+        NPUW_ASSERT(scale_shape[0] == from_shape[0]);
+        NPUW_ASSERT(scale_shape[1] == from_shape[1]);
+        NPUW_ASSERT(scale_shape[2] == 1);
+    } else {
+        NPUW_ASSERT(scale_shape[0] == from_shape[0]);
+        NPUW_ASSERT(scale_shape[1] == 1);
+    }
+
+    const auto zerop_elem_type = zerop->get_element_type();
+    const auto scale_elem_type = scale->get_element_type();
+    NPUW_ASSERT(zerop_elem_type == ov::element::u4);
+    NPUW_ASSERT(scale_elem_type == ov::element::f16);
+
+    // This conversion combines u4tof32 and f32tof16. Here we
+    // - read    256  bits (= 32  bytes, = 64  u4  elements)
+    // - write   1024 bits (= 128 bytes, = 64  f16 elements)
+    // per every iteration, what translates to (from->size() / 64) iterations
+
+    const std::size_t total = to->get_size();
+    const std::size_t stotal = scale->get_size();
+    const std::size_t elementsPerScale = total / stotal;
+
+    const uint8_t* const pSrc = static_cast<uint8_t*>(from->data());   // 2 x u4  elements
+    const uint8_t* const pZer = static_cast<uint8_t*>(zerop->data());  // 1 x u4  element
+    const int8_t* const pScl = static_cast<int8_t*>(scale->data());    // 1 x f16 element
+    const int16_t* pDst = static_cast<int16_t*>(to->data());           // 1 x f16 element
+
+    const float zval = static_cast<float>(lo4(*pZer));  // MSB - since OpenVINO 24.0!
+
+    __m256 zvalVec = _mm256_set1_ps(zval);
+
+    auto unpack_body = [pSrc, pDst, pScl, zvalVec, elementsPerScale, scale_elem_type, stotal](std::size_t sindex,
+                                                                                              std::size_t stride) {
+        // number of vectorized operations per scale
+        size_t elementsPerScaleVectorized = elementsPerScale / 64;
+
+        uint8_t const* pSrcLocal = pSrc + 32 * elementsPerScaleVectorized * sindex * stride;
+        int8_t const* pSclLocal = pScl + scale_elem_type.size() * sindex * stride;
+        int16_t* pDstLocal = const_cast<int16_t*>(pDst) + 64 * elementsPerScaleVectorized * sindex * stride;
+
+        // if it is last iteration current stride can be smaller - lets check that
+        sindex *= stride;
+        const auto jobFinish = std::min(sindex + stride, stotal);
+
+        for (; sindex < jobFinish; sindex++) {
+            __m256 svalVec = avx2_load_scale(pSclLocal, scale_elem_type);
+
+            for (std::size_t index = 0; index < elementsPerScale; index += 64) {
+                __m128i* outv[] = {
+                    reinterpret_cast<__m128i*>(pDstLocal),
+                    reinterpret_cast<__m128i*>(pDstLocal + 8),
+                    reinterpret_cast<__m128i*>(pDstLocal + 16),
+                    reinterpret_cast<__m128i*>(pDstLocal + 24),
+                    reinterpret_cast<__m128i*>(pDstLocal + 32),
+                    reinterpret_cast<__m128i*>(pDstLocal + 40),
+                    reinterpret_cast<__m128i*>(pDstLocal + 48),
+                    reinterpret_cast<__m128i*>(pDstLocal + 56),
+                };
+                __m256i himask = _mm256_set1_epi8(static_cast<char>(0xF0));
+                __m256i lomask = _mm256_set1_epi8(static_cast<char>(0x0F));
+
+                // loading 256 bit u4 into unalligned memory , so 64 elements
+                // cannot use aligned version here like _mm256_load_si256 - segfault even on unit tests
+                __m256i xmmData = _mm256_lddqu_si256(reinterpret_cast<__m256i const*>(pSrcLocal));
+
+                // unpacking with interleaving
+                __m256i vht = _mm256_and_si256(xmmData, himask);
+                __m256i xmmUnpackedLo = _mm256_srli_epi16(vht, 4);          // 32 x i8
+                __m256i xmmUnpackedHi = _mm256_and_si256(xmmData, lomask);  // 32 x i8
+
+                // need 4 portions of 8 x i8 elements
+                __m128i unpacked32LoHi = _mm256_castsi256_si128(xmmUnpackedLo);       //  lower  16 x i8
+                __m128i unpacked32LoLo = _mm256_extractf128_si256(xmmUnpackedLo, 1);  //  higher 16 x i8
+
+                __m128i unpacked32HiHi = _mm256_castsi256_si128(xmmUnpackedHi);       //  lower  16 x i8
+                __m128i unpacked32HiLo = _mm256_extractf128_si256(xmmUnpackedHi, 1);  //  higher 16 x i8
+
+                // converting to 32 x f16
+                __m128i f16LoLo[] = {avx2_u8tof16_hi(unpacked32LoLo, zvalVec, svalVec),
+                                     avx2_u8tof16_lo(unpacked32LoLo, zvalVec, svalVec)};
+
+                __m128i f16LoHi[] = {
+                    avx2_u8tof16_hi(unpacked32LoHi, zvalVec, svalVec),
+                    avx2_u8tof16_lo(unpacked32LoHi, zvalVec, svalVec),
+                };
+
+                __m128i f16HiLo[] = {avx2_u8tof16_hi(unpacked32HiLo, zvalVec, svalVec),
+                                     avx2_u8tof16_lo(unpacked32HiLo, zvalVec, svalVec)};
+                __m128i f16HiHi[] = {avx2_u8tof16_hi(unpacked32HiHi, zvalVec, svalVec),
+                                     avx2_u8tof16_lo(unpacked32HiHi, zvalVec, svalVec)};
+
+                // interleaving back
+                __m128i interleaved[] = {_mm_unpacklo_epi16(f16HiHi[0], f16LoHi[0]),
+                                         _mm_unpackhi_epi16(f16HiHi[0], f16LoHi[0]),
+                                         _mm_unpacklo_epi16(f16HiHi[1], f16LoHi[1]),
+                                         _mm_unpackhi_epi16(f16HiHi[1], f16LoHi[1]),
+                                         _mm_unpacklo_epi16(f16HiLo[0], f16LoLo[0]),
+                                         _mm_unpackhi_epi16(f16HiLo[0], f16LoLo[0]),
+                                         _mm_unpacklo_epi16(f16HiLo[1], f16LoLo[1]),
+                                         _mm_unpackhi_epi16(f16HiLo[1], f16LoLo[1])};
+
+                // store the results
+                _mm_storeu_si128(outv[0], interleaved[0]);
+                _mm_storeu_si128(outv[1], interleaved[1]);
+                _mm_storeu_si128(outv[2], interleaved[2]);
+                _mm_storeu_si128(outv[3], interleaved[3]);
+                _mm_storeu_si128(outv[4], interleaved[4]);
+                _mm_storeu_si128(outv[5], interleaved[5]);
+                _mm_storeu_si128(outv[6], interleaved[6]);
+                _mm_storeu_si128(outv[7], interleaved[7]);
+
+                pSrcLocal += 32;  // shift pSrc only by 32 since it is 64 x u4
+                pDstLocal += 64;  // note pDst is int16_t, so 64 x f16 -> 64 elements
+            }                     // for(index)
+            pSclLocal += scale_elem_type.size();
+        }  // for(sindex)
+    };
+
+    size_t stride{1};
+
+    // since scaling is always 64 elements aligned operations, lets partition only in scale shape
+    if (unpack_options.nPartitions) {
+        std::size_t minPartitions;
+        if (!unpack_options.bStrictPartitioning) {
+            // some heuristics that every tbb thread workload has to have 2048 x intrinsics operations at least,
+            // so in terms of stride, it should be nElementsPerscale/64 * 2048
+            const auto nIntrinsicsPerScale = elementsPerScale / 64u;
+            auto minScaleStride = 2048u / nIntrinsicsPerScale;
+            minScaleStride = std::max<std::size_t>(1u, minScaleStride);
+            minPartitions = stotal / minScaleStride;
+            minPartitions = std::max<std::size_t>(1u, minPartitions);
+            minPartitions = std::min(minPartitions, unpack_options.nPartitions);
+        } else {
+            minPartitions = unpack_options.nPartitions;
+        }
+
+        // calculating stride in scale elements space
+        stride = static_cast<size_t>(stotal / minPartitions);
+    }
+
+    const size_t numWork = (stotal + stride - 1) / stride;
+
+    if (unpack_options.bUseOvParallelFor) {
+        ov::parallel_for(numWork, [unpack_body, stride](size_t index) {
+            unpack_body(index, stride);
+        });
+    } else {
+        for (std::size_t index = 0; index < numWork; index++) {
+            unpack_body(index, stride);
+        }
+    }
+}
+
+void unpack_u4f16_z(const ov::SoPtr<ov::ITensor>& from,
+                    const ov::SoPtr<ov::ITensor>& zerop,
+                    const ov::SoPtr<ov::ITensor>& scale,
+                    const ov::SoPtr<ov::ITensor>& to,
+                    const ov::npuw::util::UnpackOptions& unpack_options) {
+    NPUW_ASSERT(from->is_continuous());
+    NPUW_ASSERT(zerop->is_continuous());
+    NPUW_ASSERT(scale->is_continuous());
+    NPUW_ASSERT(to->is_continuous());
+    NPUW_ASSERT(from->get_size() == to->get_size());
+
+    // Only single-size ZP is supported
+    NPUW_ASSERT(zerop->get_size() == 1);
+
+    const auto from_shape = from->get_shape();
+    NPUW_ASSERT(from_shape.back() % 64 == 0);
+
+    const auto scale_shape = scale->get_shape();
+    NPUW_ASSERT(scale_shape.size() == 3);
+    NPUW_ASSERT(scale_shape[0] == from_shape[0]);
+    NPUW_ASSERT(scale_shape[2] == from_shape[2]);
+    NPUW_ASSERT(scale_shape[1] == 1);
+
+    const auto zerop_elem_type = zerop->get_element_type();
+    const auto scale_elem_type = scale->get_element_type();
+    NPUW_ASSERT(zerop_elem_type == ov::element::f32);
+    NPUW_ASSERT(scale_elem_type == ov::element::f32);
+
+    // This conversion combines u4tof32 and f32tof16. Here we
+    // - read    256  bits (= 32  bytes, = 64  u4  elements)
+    // - write   1024 bits (= 128 bytes, = 64  f16 elements)
+    // per every iteration, what translates to (from->size() / 64) iterations
+
+    const size_t C = from_shape[from_shape.size() - 3];
+    const size_t H = from_shape[from_shape.size() - 2];
+    const size_t W = from_shape[from_shape.size() - 1];
+
+    const uint8_t* const pSrc = static_cast<uint8_t*>(from->data());  // 2 x u4  elements
+    const float* const pScl = static_cast<float*>(scale->data());     // 1 x f32 element
+    const int16_t* pDst = static_cast<int16_t*>(to->data());          // 1 x f16 element
+
+    const float zval = avx2_load_f32(reinterpret_cast<const int8_t*>(zerop->data()), zerop_elem_type);
+
+    auto unpack_body = [&](size_t job_index, size_t stride) {
+        size_t start_c = job_index * stride;
+        size_t end_c = std::min(C, start_c + stride);
+
+        for (size_t c = start_c; c < end_c; ++c) {
+            for (size_t h = 0; h < H; ++h) {
+                for (size_t w = 0; w < W; w += 64) {
+                    float tmp[64];
+                    for (size_t i = 0; i < 32; ++i) {
+                        size_t input_index = w + i * 2 + W * h + W * H * c;
+                        uint8_t packed_val = pSrc[input_index / 2];
+                        float f0 = static_cast<float>(lo4(packed_val));
+                        float f1 = static_cast<float>(hi4(packed_val));
+                        size_t scale_index = w + i * 2 + W * c;
+                        tmp[i * 2] = (f0 - zval) * pScl[scale_index];
+                        tmp[i * 2 + 1] = (f1 - zval) * pScl[scale_index + 1];
+                    }
+                    __m128i vresults[8];
+                    for (int i = 0; i < 8; ++i) {
+                        vresults[i] = avx2_f32tof16(_mm256_loadu_ps(tmp + i * 8));
+                    }
+                    int16_t* pDstLocal = const_cast<int16_t*>(pDst) + w + W * h + W * H * c;
+                    for (int i = 0; i < 8; ++i) {
+                        _mm_storeu_si128(reinterpret_cast<__m128i*>(pDstLocal + i * 8), vresults[i]);
+                    }
+                }
+            }
+        }
+    };
+
+    size_t stride = C;
+    size_t num_jobs = 1;
+
+    if (unpack_options.nPartitions) {
+        if (unpack_options.bStrictPartitioning) {
+            stride = (C + unpack_options.nPartitions - 1) / unpack_options.nPartitions;
+            num_jobs = unpack_options.nPartitions;
+        } else {
+            stride = std::max<size_t>(1, C / unpack_options.nPartitions);
+            num_jobs = (C + stride - 1) / stride;
+        }
+    }
+
+    if (unpack_options.bUseOvParallelFor) {
+        ov::parallel_for(num_jobs, [&](size_t job_index) {
+            unpack_body(job_index, stride);
+        });
+    } else {
+        for (size_t job_index = 0; job_index < num_jobs; ++job_index) {
+            unpack_body(job_index, stride);
+        }
+    }
+}
+
+void unpack_u4f32(const ov::SoPtr<ov::ITensor>& from,
+                  const ov::SoPtr<ov::ITensor>& to,
+                  const ov::npuw::util::UnpackOptions& unpack_options) {
+    NPUW_ASSERT(from->is_continuous());
+    NPUW_ASSERT(to->is_continuous());
+    NPUW_ASSERT(from->get_size() == to->get_size());
+
+    uint8_t const* pSrc = static_cast<uint8_t*>(from->data());  // 2 x u4 elements
+    float* pDst = static_cast<float*>(to->data());              // 1 x f32 element
+
+    const std::size_t total = from->get_size();
+    for (std::size_t index = 0; index < total; index += 2) {
+        pDst[0] = static_cast<float>(lo4(*pSrc));  // LSB is [0] - since OpenVINO 2024.0!
+        pDst[1] = static_cast<float>(hi4(*pSrc));  // MSB is [1] - since OpenVINO 2024.0!
+        pSrc++;
+        pDst += 2;
+    }
+}
+
+void unpack_i8f16(const ov::SoPtr<ov::ITensor>& from,
+                  const ov::SoPtr<ov::ITensor>& to,
+                  const ov::npuw::util::UnpackOptions& unpack_options) {
+    NPUW_ASSERT(from->is_continuous());
+    NPUW_ASSERT(to->is_continuous());
+    NPUW_ASSERT(from->get_size() == to->get_size());
+    NPUW_ASSERT(from->get_size() % 8 == 0);
+
+    constexpr std::size_t VECSIZE = 8;
+
+    const std::size_t total = from->get_size();
+    int8_t const* pSrc = from->data<int8_t>();
+    int16_t* pDst = static_cast<int16_t*>(to->data());
+
+    for (std::size_t index = 0; index < total; index += VECSIZE) {
+        const __m128i* pSrcV = reinterpret_cast<const __m128i*>(pSrc);
+        __m128i* pDstV = reinterpret_cast<__m128i*>(pDst);
+        __m128i i8vec = _mm_loadl_epi64(pSrcV);  // load:    8 x i8  [ 64b of 128b]
+        __m128i f16vec = avx2_i8tof16(i8vec);
+        _mm_store_si128(pDstV, f16vec);  // store:   8 x f16 [128b]
+        pSrc += 8;
+        pDst += 8;
+    }
+}
+
+void unpack_i8f16(const ov::SoPtr<ov::ITensor>& from,
+                  const ov::SoPtr<ov::ITensor>& scale,
+                  const ov::SoPtr<ov::ITensor>& to,
+                  const ov::npuw::util::UnpackOptions& unpack_options) {
+    NPUW_ASSERT(from->is_continuous());
+    NPUW_ASSERT(scale->is_continuous());
+    NPUW_ASSERT(to->is_continuous());
+    NPUW_ASSERT(from->get_size() == to->get_size());
+    NPUW_ASSERT(from->get_size() % 8 == 0);
+    NPUW_ASSERT(scale->get_shape()[0] == from->get_shape()[0]);
+    NPUW_ASSERT(scale->get_shape()[1] == 1);
+
+    const auto scale_elem_type = scale->get_element_type();
+    NPUW_ASSERT(scale_elem_type == ov::element::f32 || scale_elem_type == ov::element::f16);
+
+    constexpr std::size_t VECSIZE = 8;
+
+    const std::size_t total = from->get_size();
+    const std::size_t stotal = scale->get_size();
+    int8_t const* pSrc = from->data<int8_t>();
+    int8_t const* pScl = static_cast<int8_t*>(scale->data());
+    int16_t* pDst = static_cast<int16_t*>(to->data());
+
+    for (std::size_t sindex = 0u; sindex < stotal; sindex++) {
+        __m256 svec = avx2_load_scale(pScl, scale_elem_type);
+        for (std::size_t index = 0u; index < (total / stotal); index += VECSIZE) {
+            __m128i const* pSrcV = reinterpret_cast<const __m128i*>(pSrc);
+            __m128i* pDstV = reinterpret_cast<__m128i*>(pDst);
+            __m128i i8vec = _mm_loadl_epi64(pSrcV);      // load:    8 x i8  [ 64b of 128b]
+            __m128i f16vec = avx2_i8tof16(i8vec, svec);  // convert & scale
+            _mm_store_si128(pDstV, f16vec);              // store:   8 x f16 [128b]
+            pSrc += 8;
+            pDst += 8;
+        }  // index
+        pScl += scale_elem_type.size();
+    }  // sindex
+}
+
+}  // namespace
+
+void ov::npuw::util::unpack(const ov::SoPtr<ov::ITensor>& from,
+                            const ov::SoPtr<ov::ITensor>& to,
+                            const UnpackOptions& unpack_options) {
+    // This is in fact a weight decompression procedure
+    auto type_from = from->get_element_type();
+    auto type_to = to->get_element_type();
+
+    namespace ove = ov::element;
+#define CAST(x)    static_cast<int>((x).operator ove::Type_t())
+#define PAIR(f, t) (CAST(f) << 16 | CAST(t))
+#define HNDL(f, t)                               \
+    case PAIR(ove::f, ove::t):                   \
+        unpack_##f##t(from, to, unpack_options); \
+        break;
+    switch (PAIR(type_from, type_to)) {
+        HNDL(i4, i8);
+        HNDL(i4, f16);
+        HNDL(u4, i8);
+        HNDL(u4, f16);
+        HNDL(u4, f32);
+        HNDL(i8, f16);
+    default:
+        OPENVINO_THROW("Unknown unpack combination ", type_from, " -> ", type_to);
+    }
+#undef HNDL
+#undef PAIR
+#undef CAST
+}
+
+void ov::npuw::util::unpack(const ov::SoPtr<ov::ITensor>& from,
+                            const ov::SoPtr<ov::ITensor>& scale,
+                            const ov::SoPtr<ov::ITensor>& to,
+                            const UnpackOptions& unpack_options) {
+    // This is in fact a weight decompression procedure
+    const auto type_from = from->get_element_type();
+    const auto type_to = to->get_element_type();
+    namespace ove = ov::element;
+#define CAST(x)    static_cast<int>((x).operator ove::Type_t())
+#define PAIR(f, t) (CAST(f) << 16 | CAST(t))
+#define HNDL(f, t)                                      \
+    case PAIR(ove::f, ove::t):                          \
+        unpack_##f##t(from, scale, to, unpack_options); \
+        break;
+    switch (PAIR(type_from, type_to)) {
+        HNDL(i4, f16);
+        HNDL(i8, f16);
+    default:
+        OPENVINO_THROW("Unknown unpack/scale combination ", type_from, " -> ", type_to);
+    }
+#undef HNDL
+#undef PAIR
+#undef CAST
+}
+
+void ov::npuw::util::unpack(const ov::SoPtr<ov::ITensor>& from,
+                            const ov::SoPtr<ov::ITensor>& zerop,
+                            const ov::SoPtr<ov::ITensor>& scale,
+                            const ov::SoPtr<ov::ITensor>& to,
+                            const UnpackOptions& unpack_options) {
+    const auto type_from = from->get_element_type();
+    const auto type_zerop = zerop->get_element_type();
+    const auto type_scale = scale->get_element_type();
+    const auto type_to = to->get_element_type();
+
+    NPUW_ASSERT(type_from == ov::element::u4);
+    NPUW_ASSERT(type_zerop == ov::element::u4 || type_zerop == ov::element::f16 || type_zerop == ov::element::f32);
+    NPUW_ASSERT(type_scale == ov::element::f16 || type_scale == ov::element::f32);
+    NPUW_ASSERT(type_to == ov::element::f16);
+
+    // This function determines the appropriate unpacking strategy for tensor multiplication
+    // based on the 'scale' shape and 'from' shape.
+    // Example tensors -> (scale.*from):
+    // unpack_u4f16:
+    //     - [4096, 1].*[4096, 4096]
+    //     - [11008, 1].*[11008, 4096]
+    //     - [4096, 32, 1].*[4096, 32, 128]
+    // unpack_u4f16_z:
+    //     - [32, 1, 4096].*[32, 128, 4096]
+    //     - [32, 1, 11008].*[32, 128, 11008]
+    //     - [86, 1, 4096].*[86, 128, 4096]
+    // Unsupported Case for scale tensor:
+    //     - [s1, 1, s2, 1, s3]
+
+    const auto from_shape = from->get_shape();
+    const auto scale_shape = scale->get_shape();
+
+    if (scale_shape.size() == 3 && scale_shape[0] == from_shape[0] && scale_shape[1] == 1 &&
+        scale_shape[2] == from_shape[2]) {
+        unpack_u4f16_z(from, zerop, scale, to, unpack_options);
+    } else if (scale_shape.size() == 3 && scale_shape[0] == from_shape[0] && scale_shape[1] == from_shape[1] &&
+               scale_shape[2] == 1) {
+        unpack_u4f16(from, zerop, scale, to, unpack_options);
+    } else if (scale_shape.size() == 2 && scale_shape[0] == from_shape[0] && scale_shape[1] == 1) {
+        unpack_u4f16(from, zerop, scale, to, unpack_options);
+    } else {
+        NPUW_ASSERT(false);
+    }
+}
+
+template <typename InT>
+void to_f32(const ov::Tensor& in, ov::Tensor& out) {
+    NPUW_ASSERT(in.is_continuous());
+    NPUW_ASSERT(out.is_continuous());
+    NPUW_ASSERT(in.get_shape() == out.get_shape());
+
+    if (ov::element::Type_t::f32 == in.get_element_type()) {
+        in.copy_to(out);
+        return;
+    }
+
+    const InT* in_buffer = in.data<InT>();
+    NPUW_ASSERT(in_buffer != nullptr);
+    const auto out_buffer = out.data<float>();
+    NPUW_ASSERT(out_buffer != nullptr);
+
+    // NOTE: ov::parallel_for takes care of splitting the work among threads such way,
+    //       that the passed lambda function will be called sequentially
+    //       on some part of "in.get_size()" range inside the each thread
+    ov::parallel_for(in.get_size(), [in_buffer, out_buffer](int64_t index) {
+        out_buffer[index] = static_cast<float>(in_buffer[index]);
+    });
+}
+
+void ov::npuw::util::to_f32(const ov::Tensor& in, ov::Tensor& out) {
+    switch (in.get_element_type()) {
+    case ov::element::Type_t::f32:
+        ::to_f32<float>(in, out);
+        break;
+    case ov::element::Type_t::u64:
+        ::to_f32<uint64_t>(in, out);
+        break;
+    case ov::element::Type_t::i64:
+        ::to_f32<int64_t>(in, out);
+        break;
+    case ov::element::Type_t::u32:
+        ::to_f32<uint32_t>(in, out);
+        break;
+    case ov::element::Type_t::i32:
+        ::to_f32<int32_t>(in, out);
+        break;
+    case ov::element::Type_t::u16:
+        ::to_f32<uint16_t>(in, out);
+        break;
+    case ov::element::Type_t::i16:
+        ::to_f32<int16_t>(in, out);
+        break;
+    case ov::element::Type_t::u8:
+        ::to_f32<uint8_t>(in, out);
+        break;
+    case ov::element::Type_t::i8:
+        ::to_f32<int8_t>(in, out);
+        break;
+    case ov::element::Type_t::f16:
+        ::to_f32<ov::float16>(in, out);
+        break;
+    case ov::element::Type_t::bf16:
+        ::to_f32<ov::bfloat16>(in, out);
+        break;
+    default:
+        OPENVINO_THROW("Unsupported precision {0}", in.get_element_type().get_type_name());
+        break;
+    }
+}
diff --git a/src/plugins/intel_npu/src/plugin/npuw/util.hpp b/src/plugins/intel_npu/src/plugin/npuw/util.hpp
new file mode 100644
index 00000000000000..88bb7896d94b0f
--- /dev/null
+++ b/src/plugins/intel_npu/src/plugin/npuw/util.hpp
@@ -0,0 +1,56 @@
+// Copyright (C) 2023-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <string>
+
+#include "openvino/runtime/itensor.hpp"
+#include "openvino/runtime/so_ptr.hpp"
+
+namespace ov {
+namespace npuw {
+namespace util {
+
+bool is_set(const std::size_t sub_idx, const std::string& opt);
+
+// Every great project has its own string class...
+// NB: Newer C++ standards would allow to use string views or smt
+ov::Tensor tensor_from_const(const std::shared_ptr<ov::Node>& node);
+
+bool starts_with(const std::string& str, const std::string& prefix);
+
+std::string fmt(std::size_t number, std::size_t total);
+
+struct UnpackOptions {
+    bool bUseOvParallelFor;
+    size_t nPartitions;  // if 0 we use 64 elements step in parallel for, otherwise  target workload is dynamically
+                         // calculated
+    bool bStrictPartitioning;  // cannot reduce partitions in favor of speed
+    explicit UnpackOptions(bool useParallelFor, size_t nPartitions, bool bStrictPartitioning)
+        : bUseOvParallelFor(useParallelFor),
+          nPartitions(nPartitions),
+          bStrictPartitioning(bStrictPartitioning) {}
+};
+
+void unpack(const ov::SoPtr<ov::ITensor>& from,
+            const ov::SoPtr<ov::ITensor>& to,
+            const UnpackOptions& unpack_options = UnpackOptions{true, 16, false});
+
+void unpack(const ov::SoPtr<ov::ITensor>& from,
+            const ov::SoPtr<ov::ITensor>& scale,
+            const ov::SoPtr<ov::ITensor>& to,
+            const UnpackOptions& unpack_options = UnpackOptions{true, 16, false});
+
+void unpack(const ov::SoPtr<ov::ITensor>& from,
+            const ov::SoPtr<ov::ITensor>& zerop,
+            const ov::SoPtr<ov::ITensor>& scale,
+            const ov::SoPtr<ov::ITensor>& to,
+            const UnpackOptions& unpack_options = UnpackOptions{true, 16, false});
+
+void to_f32(const ov::Tensor& in, ov::Tensor& out);
+
+}  // namespace util
+}  // namespace npuw
+}  // namespace ov
diff --git a/src/plugins/intel_npu/src/plugin/src/metrics.cpp b/src/plugins/intel_npu/src/plugin/src/metrics.cpp
index e57c7c58d59af7..565dad5edc5793 100644
--- a/src/plugins/intel_npu/src/plugin/src/metrics.cpp
+++ b/src/plugins/intel_npu/src/plugin/src/metrics.cpp
@@ -26,6 +26,8 @@ Metrics::Metrics(const std::shared_ptr<const NPUBackends>& backends) : _backends
                          ov::intel_npu::device_alloc_mem_size.name(),
                          ov::intel_npu::device_total_mem_size.name(),
                          ov::intel_npu::driver_version.name(),
+                         ov::intel_npu::stepping.name(),
+                         ov::intel_npu::max_tiles.name(),
                          ov::device::pci_info.name(),
                          ov::device::gops.name()};
 
@@ -121,6 +123,24 @@ uint32_t Metrics::GetDriverExtVersion() const {
     return _backends->getDriverExtVersion();
 }
 
+uint32_t Metrics::GetSteppingNumber(const std::string& specifiedDeviceName) const {
+    const auto devName = getDeviceName(specifiedDeviceName);
+    auto device = _backends->getDevice(devName);
+    if (device) {
+        return device->getSubDevId();
+    }
+    OPENVINO_THROW("No device with name '", specifiedDeviceName, "' is available");
+}
+
+uint32_t Metrics::GetMaxTiles(const std::string& specifiedDeviceName) const {
+    const auto devName = getDeviceName(specifiedDeviceName);
+    auto device = _backends->getDevice(devName);
+    if (device) {
+        return device->getMaxNumSlices();
+    }
+    OPENVINO_THROW("No device with name '", specifiedDeviceName, "' is available");
+}
+
 uint64_t Metrics::GetDeviceAllocMemSize(const std::string& specifiedDeviceName) const {
     const auto devName = getDeviceName(specifiedDeviceName);
     auto device = _backends->getDevice(devName);
diff --git a/src/plugins/intel_npu/src/plugin/src/plugin.cpp b/src/plugins/intel_npu/src/plugin/src/plugin.cpp
index ccc3cc4ab5805a..3337fc2b70fe72 100644
--- a/src/plugins/intel_npu/src/plugin/src/plugin.cpp
+++ b/src/plugins/intel_npu/src/plugin/src/plugin.cpp
@@ -12,11 +12,14 @@
 #include "intel_npu/al/config/common.hpp"
 #include "intel_npu/al/config/compiler.hpp"
 #include "intel_npu/al/config/runtime.hpp"
+#include "intel_npu/al/config/npuw.hpp"
 #include "intel_npu/al/itt.hpp"
 #include "openvino/op/constant.hpp"
 #include "openvino/op/parameter.hpp"
 #include "openvino/runtime/intel_npu/properties.hpp"
 
+#include "npuw/compiled_model.hpp"
+
 using namespace intel_npu;
 
 namespace {
@@ -138,6 +141,16 @@ size_t getFileSize(std::istream& stream) {
     return streamEnd - streamStart;
 }
 
+void update_log_level(const std::map<std::string, std::string>& propertiesMap) {
+    auto it = propertiesMap.find(std::string(LOG_LEVEL::key()));
+    if (it != propertiesMap.end()) {
+        std::istringstream is(it->second);
+        ov::log::Level level;
+        is >> level;
+        Logger::global().setLevel(level);
+    }
+}
+
 }  // namespace
 
 namespace intel_npu {
@@ -207,6 +220,15 @@ Plugin::Plugin()
     // parse again env_variables after backend is initialized to get backend proprieties
     _globalConfig.parseEnvVars();
 
+    // initialize properties which have device-tied default values in global config
+    // *only if there is a driver available
+    if (_metrics->GetAvailableDevicesNames().size() > 0) {
+        _globalConfig.update({{ov::intel_npu::stepping.name(),
+                               std::to_string(_metrics->GetSteppingNumber(get_specified_device_name(_globalConfig)))}});
+        _globalConfig.update({{ov::intel_npu::max_tiles.name(),
+                               std::to_string(_metrics->GetMaxTiles(get_specified_device_name(_globalConfig)))}});
+    }
+
     // Map from name to function {Config -> ov::Any}
     // Note that some properties are RW before network is loaded, and become RO after network is loaded
     _properties = {
@@ -511,7 +533,7 @@ Plugin::Plugin()
           ov::PropertyMutability::RW,
           [](const Config& config) {
               return config.getString<BATCH_MODE>();
-          }}},
+          }}}
     };
 
     for (auto& property : _properties) {
@@ -523,6 +545,7 @@ Plugin::Plugin()
 
 void Plugin::set_property(const ov::AnyMap& properties) {
     const std::map<std::string, std::string> config = any_copy(properties);
+    update_log_level(config);
     for (const auto& configEntry : config) {
         if (_properties.find(configEntry.first) == _properties.end()) {
             OPENVINO_THROW("Unsupported configuration key: ", configEntry.first);
@@ -534,7 +557,6 @@ void Plugin::set_property(const ov::AnyMap& properties) {
     }
 
     _globalConfig.update(config);
-    Logger::global().setLevel(_globalConfig.get<LOG_LEVEL>());
     if (_backends != nullptr) {
         _backends->setup(_globalConfig);
     }
@@ -560,7 +582,19 @@ std::shared_ptr<ov::ICompiledModel> Plugin::compile_model(const std::shared_ptr<
                                                           const ov::AnyMap& properties) const {
     OV_ITT_SCOPED_TASK(itt::domains::NPUPlugin, "Plugin::compile_model");
     OV_ITT_TASK_CHAIN(PLUGIN_COMPILE_MODEL, itt::domains::NPUPlugin, "Plugin::compile_model", "merge_configs");
-    auto localConfig = merge_configs(_globalConfig, any_copy(properties));
+
+    // Before going any further: if
+    // ... 1 - NPUW mode is activated
+    // ... 2 - this request is NOT coming from NPUW,
+    // activate the NPUW path
+    auto useNpuwKey = ov::intel_npu::use_npuw.name();
+    if (properties.count(useNpuwKey) && properties.at(useNpuwKey).as<bool>()) {
+        return std::make_shared<ov::npuw::CompiledModel>(model->clone(), shared_from_this(), properties);
+    }
+
+    const std::map<std::string, std::string> propertiesMap = any_copy(properties);
+    update_log_level(propertiesMap);
+    auto localConfig = merge_configs(_globalConfig, propertiesMap);
 
     const auto set_cache_dir = localConfig.get<CACHE_DIR>();
     if (!set_cache_dir.empty()) {
@@ -652,9 +686,10 @@ ov::SoPtr<ov::IRemoteContext> Plugin::get_default_context(const ov::AnyMap& /*re
 
 std::shared_ptr<ov::ICompiledModel> Plugin::import_model(std::istream& stream, const ov::AnyMap& properties) const {
     OV_ITT_SCOPED_TASK(itt::domains::NPUPlugin, "Plugin::import_model");
-
     OV_ITT_TASK_CHAIN(PLUGIN_IMPORT_MODEL, itt::domains::NPUPlugin, "Plugin::import_model", "merge_configs");
-    auto localConfig = merge_configs(_globalConfig, any_copy(properties), OptionMode::RunTime);
+    const std::map<std::string, std::string> propertiesMap = any_copy(properties);
+    update_log_level(propertiesMap);
+    auto localConfig = merge_configs(_globalConfig, propertiesMap, OptionMode::RunTime);
     const auto platform = _backends->getCompilationPlatform(localConfig.get<PLATFORM>(), localConfig.get<DEVICE_ID>());
     localConfig.update({{ov::intel_npu::platform.name(), platform}});
     auto device = _backends->getDevice(localConfig.get<DEVICE_ID>());
@@ -721,8 +756,9 @@ std::shared_ptr<ov::ICompiledModel> Plugin::import_model(std::istream& /*stream*
 ov::SupportedOpsMap Plugin::query_model(const std::shared_ptr<const ov::Model>& model,
                                         const ov::AnyMap& properties) const {
     OV_ITT_SCOPED_TASK(itt::domains::NPUPlugin, "Plugin::query_model");
-
-    auto localConfig = merge_configs(_globalConfig, any_copy(properties), OptionMode::CompileTime);
+    const std::map<std::string, std::string> propertiesMap = any_copy(properties);
+    update_log_level(propertiesMap);
+    auto localConfig = merge_configs(_globalConfig, propertiesMap, OptionMode::CompileTime);
     const auto platform = _backends->getCompilationPlatform(localConfig.get<PLATFORM>(), localConfig.get<DEVICE_ID>());
     localConfig.update({{ov::intel_npu::platform.name(), platform}});
 
diff --git a/src/plugins/intel_npu/src/utils/include/intel_npu/utils/zero/zero_api.hpp b/src/plugins/intel_npu/src/utils/include/intel_npu/utils/zero/zero_api.hpp
new file mode 100644
index 00000000000000..b09a91e328e73b
--- /dev/null
+++ b/src/plugins/intel_npu/src/utils/include/intel_npu/utils/zero/zero_api.hpp
@@ -0,0 +1,86 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <ze_api.h>
+
+#include <memory>
+
+namespace intel_npu {
+
+// clang-format off
+#define symbols_list()                                        \
+    symbol_statement(zeCommandListAppendBarrier)              \
+    symbol_statement(zeCommandListAppendEventReset)           \
+    symbol_statement(zeCommandListAppendMemoryCopy)           \
+    symbol_statement(zeCommandListAppendSignalEvent)          \
+    symbol_statement(zeCommandListAppendWaitOnEvents)         \
+    symbol_statement(zeCommandListAppendWriteGlobalTimestamp) \
+    symbol_statement(zeCommandListClose)                      \
+    symbol_statement(zeCommandListCreate)                     \
+    symbol_statement(zeCommandListDestroy)                    \
+    symbol_statement(zeCommandListReset)                      \
+    symbol_statement(zeCommandQueueCreate)                    \
+    symbol_statement(zeCommandQueueDestroy)                   \
+    symbol_statement(zeCommandQueueExecuteCommandLists)       \
+    symbol_statement(zeContextCreate)                         \
+    symbol_statement(zeContextDestroy)                        \
+    symbol_statement(zeDeviceGet)                             \
+    symbol_statement(zeDeviceGetCommandQueueGroupProperties)  \
+    symbol_statement(zeDeviceGetProperties)                   \
+    symbol_statement(zeDevicePciGetPropertiesExt)             \
+    symbol_statement(zeDriverGet)                             \
+    symbol_statement(zeDriverGetApiVersion)                   \
+    symbol_statement(zeDriverGetExtensionFunctionAddress)     \
+    symbol_statement(zeDriverGetExtensionProperties)          \
+    symbol_statement(zeDriverGetProperties)                   \
+    symbol_statement(zeEventCreate)                           \
+    symbol_statement(zeEventDestroy)                          \
+    symbol_statement(zeEventHostReset)                        \
+    symbol_statement(zeEventHostSynchronize)                  \
+    symbol_statement(zeEventPoolCreate)                       \
+    symbol_statement(zeEventPoolDestroy)                      \
+    symbol_statement(zeFenceCreate)                           \
+    symbol_statement(zeFenceDestroy)                          \
+    symbol_statement(zeFenceHostSynchronize)                  \
+    symbol_statement(zeFenceReset)                            \
+    symbol_statement(zeInit)                                  \
+    symbol_statement(zeMemAllocDevice)                        \
+    symbol_statement(zeMemAllocHost)                          \
+    symbol_statement(zeMemFree)
+// clang-format on
+
+class ZeroApi {
+public:
+    ZeroApi(const ZeroApi& other) = delete;
+    ZeroApi(ZeroApi&& other) = delete;
+    void operator=(const ZeroApi&) = delete;
+    void operator=(ZeroApi&&) = delete;
+
+    static ZeroApi& getInstance() {
+        static ZeroApi instance;
+        return instance;
+    }
+#define symbol_statement(symbol) decltype(&::symbol) symbol;
+    symbols_list();
+#undef symbol_statement
+
+private:
+    ZeroApi();
+
+    std::shared_ptr<void> lib;
+};
+
+#define symbol_statement(symbol)                                                                            \
+    template <typename... Args>                                                                             \
+    inline typename std::invoke_result<decltype(&::symbol), Args...>::type wrapped_##symbol(Args... args) { \
+        return ZeroApi::getInstance().symbol(std::forward<Args>(args)...);                                  \
+    }
+symbols_list();
+#undef symbol_statement
+#define symbol_statement(symbol) inline decltype(&::symbol) symbol = wrapped_##symbol;
+symbols_list();
+#undef symbol_statement
+}  // namespace intel_npu
diff --git a/src/plugins/intel_npu/src/utils/src/zero/CMakeLists.txt b/src/plugins/intel_npu/src/utils/src/zero/CMakeLists.txt
index 2a31397d004a24..3b1786bd92a70d 100644
--- a/src/plugins/intel_npu/src/utils/src/zero/CMakeLists.txt
+++ b/src/plugins/intel_npu/src/utils/src/zero/CMakeLists.txt
@@ -7,16 +7,21 @@ set(TARGET_NAME openvino_npu_zero_result_parser)
 file(GLOB_RECURSE SOURCES *.cpp)
 
 add_library(${TARGET_NAME} STATIC ${SOURCES})
-set_target_properties(${TARGET_NAME} PROPERTIES INTERPROCEDURAL_OPTIMIZATION_RELEASE ${ENABLE_LTO})
+set_target_properties(
+  ${TARGET_NAME} PROPERTIES INTERPROCEDURAL_OPTIMIZATION_RELEASE ${ENABLE_LTO})
 ov_add_clang_format_target(${TARGET_NAME}_clang FOR_TARGETS ${TARGET_NAME})
 
-target_include_directories(${TARGET_NAME}
-    PUBLIC
-        $<BUILD_INTERFACE:${NPU_PLUGIN_SOURCE_DIR}/thirdparty/level-zero/include>
-        $<BUILD_INTERFACE:${NPU_PLUGIN_SOURCE_DIR}/thirdparty/level-zero-ext>
-        $<BUILD_INTERFACE:${NPU_UTILS_SOURCE_DIR}/include>
-)
+target_include_directories(
+  ${TARGET_NAME}
+  PUBLIC
+    $<BUILD_INTERFACE:${NPU_PLUGIN_SOURCE_DIR}/thirdparty/level-zero/include>
+    $<BUILD_INTERFACE:${NPU_PLUGIN_SOURCE_DIR}/thirdparty/level-zero-ext>
+    $<BUILD_INTERFACE:${NPU_UTILS_SOURCE_DIR}/include>)
 
+target_link_libraries(
+  ${TARGET_NAME}
+  PUBLIC openvino::npu_al
+  PRIVATE openvino::runtime openvino::runtime::dev)
 #
 # targets install
 #
diff --git a/src/plugins/intel_npu/src/utils/src/zero/zero_api.cpp b/src/plugins/intel_npu/src/utils/src/zero/zero_api.cpp
new file mode 100644
index 00000000000000..0a2510d0a4e486
--- /dev/null
+++ b/src/plugins/intel_npu/src/utils/src/zero/zero_api.cpp
@@ -0,0 +1,39 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "intel_npu/utils/zero/zero_api.hpp"
+
+#include "openvino/core/except.hpp"
+#include "openvino/util/file_util.hpp"
+#include "openvino/util/shared_object.hpp"
+
+namespace intel_npu {
+ZeroApi::ZeroApi() {
+    const std::string baseName = "ze_loader";
+    try {
+        auto libpath = ov::util::make_plugin_library_name({}, baseName);
+#if defined(OPENVINO_ENABLE_UNICODE_PATH_SUPPORT) && defined(_WIN32)
+        this->lib = ov::util::load_shared_object(ov::util::string_to_wstring(libpath).c_str());
+#else
+        this->lib = ov::util::load_shared_object(libpath.c_str());
+#endif
+    } catch (const std::runtime_error& error) {
+        OPENVINO_THROW(error.what());
+    }
+
+    try {
+#define symbol_statement(symbol) \
+    this->symbol = reinterpret_cast<decltype(&::symbol)>(ov::util::get_symbol(lib, #symbol));
+        symbols_list()
+#undef symbol_statement
+    } catch (const std::runtime_error& error) {
+        OPENVINO_THROW(error.what());
+    }
+
+#define symbol_statement(symbol) symbol = this->symbol;
+    symbols_list();
+#undef symbol_statement
+}
+
+}  // namespace intel_npu
diff --git a/src/plugins/intel_npu/tests/functional/CMakeLists.txt b/src/plugins/intel_npu/tests/functional/CMakeLists.txt
index 297181a67ffd58..bdac0a256f3f87 100644
--- a/src/plugins/intel_npu/tests/functional/CMakeLists.txt
+++ b/src/plugins/intel_npu/tests/functional/CMakeLists.txt
@@ -11,6 +11,16 @@ set(EXCLUDED_FUNC_TESTS_DIR "")
 set(OPTIONAL_FUNC_TESTS_INCLUDES "")
 set(OPTIONAL_FUNC_TESTS_LIBS "")
 
+if(ENABLE_DRIVER_COMPILER_ADAPTER)
+    list(APPEND OPTIONAL_FUNC_TESTS_INCLUDES
+        "${OpenVINO_SOURCE_DIR}/src/plugins/intel_npu/src/compiler/include"
+    )
+
+    list(APPEND OPTIONAL_FUNC_TESTS_LIBS
+        openvino_npu_driver_compiler_adapter
+    )
+endif()
+
 ov_add_test_target(
     NAME ${TARGET_NAME}
     ROOT ${CMAKE_CURRENT_SOURCE_DIR}
diff --git a/src/plugins/intel_npu/tests/functional/behavior/infer_request_run.cpp b/src/plugins/intel_npu/tests/functional/behavior/infer_request_run.cpp
index 0eb80ec7b5b494..47b3cd9179438a 100644
--- a/src/plugins/intel_npu/tests/functional/behavior/infer_request_run.cpp
+++ b/src/plugins/intel_npu/tests/functional/behavior/infer_request_run.cpp
@@ -14,7 +14,7 @@ const std::vector<ov::AnyMap> configsInferRequestRunTests = {
         {ov::log::level(ov::log::Level::INFO)}
 };
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTest, InferRequestRunTests,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTest, InferRequestRunTests,
                          ::testing::Combine(::testing::Values(ov::test::utils::DEVICE_NPU),
                                             ::testing::ValuesIn(configsInferRequestRunTests)),
                          InferRequestRunTests::getTestCaseName);
diff --git a/src/plugins/intel_npu/tests/functional/behavior/npu_driver_compiler_adapter/custom_stream.cpp b/src/plugins/intel_npu/tests/functional/behavior/npu_driver_compiler_adapter/custom_stream.cpp
new file mode 100644
index 00000000000000..41ee0988904913
--- /dev/null
+++ b/src/plugins/intel_npu/tests/functional/behavior/npu_driver_compiler_adapter/custom_stream.cpp
@@ -0,0 +1,134 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+#include <chrono>
+#include <random>
+
+#include "base/ov_behavior_test_utils.hpp"
+#include "common/npu_test_env_cfg.hpp"
+#include "common_test_utils/node_builders/constant.hpp"
+#include "graph_transformations.hpp"
+#include "intel_npu/al/config/common.hpp"
+#include "openvino/opsets/opset11.hpp"
+
+using CompilationParams = std::tuple<std::string,  // Device name
+                                     ov::AnyMap    // Config
+                                     >;
+
+using IRSerializer = intel_npu::driverCompilerAdapter::IRSerializer;
+
+namespace ov::test::behavior {
+
+class DriverCompilerAdapterCustomStreamTestNPU : public ov::test::behavior::OVPluginTestBase,
+                                                 public testing::WithParamInterface<CompilationParams> {
+public:
+    std::shared_ptr<ov::Model> createModelWithLargeSize() {
+        auto data = std::make_shared<ov::opset11::Parameter>(ov::element::f16, ov::Shape{4000, 4000});
+        auto mul_constant = ov::opset11::Constant::create(ov::element::f16, ov::Shape{1}, {1.5});
+        auto mul = std::make_shared<ov::opset11::Multiply>(data, mul_constant);
+        auto add_constant = ov::opset11::Constant::create(ov::element::f16, ov::Shape{1}, {0.5});
+        auto add = std::make_shared<ov::opset11::Add>(mul, add_constant);
+        // Just a sample model here, large iteration to make the model large
+        for (int i = 0; i < 1000; i++) {
+            add = std::make_shared<ov::opset11::Add>(add, add_constant);
+        }
+        auto res = std::make_shared<ov::opset11::Result>(add);
+
+        /// Create the OpenVINO model
+        return std::make_shared<ov::Model>(ov::ResultVector{std::move(res)}, ov::ParameterVector{std::move(data)});
+    }
+
+    std::string generateRandomFileName() {
+        std::stringstream ss;
+        auto now = std::chrono::high_resolution_clock::now();
+        auto seed = now.time_since_epoch().count();
+        std::mt19937 mt_rand(static_cast<unsigned int>(seed));
+        std::uniform_int_distribution<int> dist(0, 15);
+
+        for (unsigned int i = 0; i < 16; ++i) {
+            int random_number = dist(mt_rand);
+            ss << std::hex << random_number;
+        }
+        return ss.str();
+    }
+
+    void SetUp() override {
+        std::tie(target_device, configuration) = this->GetParam();
+        SKIP_IF_CURRENT_TEST_IS_DISABLED()
+        OVPluginTestBase::SetUp();
+        std::string fileName = generateRandomFileName();
+        xmlFileName = fileName + ".xml";
+        binFileName = fileName + ".bin";
+    }
+
+    static std::string getTestCaseName(testing::TestParamInfo<CompilationParams> obj) {
+        std::string targetDevice;
+        ov::AnyMap configuration;
+        std::tie(targetDevice, configuration) = obj.param;
+        std::replace(targetDevice.begin(), targetDevice.end(), ':', '.');
+
+        std::ostringstream result;
+        result << "targetDevice=" << targetDevice << "_";
+        result << "targetPlatform=" << ov::test::utils::getTestsPlatformFromEnvironmentOr(targetDevice) << "_";
+        if (!configuration.empty()) {
+            for (auto& configItem : configuration) {
+                result << "configItem=" << configItem.first << "_";
+                configItem.second.print(result);
+            }
+        }
+        return result.str();
+    }
+
+    void TearDown() override {
+        if (!configuration.empty()) {
+            utils::PluginCache::get().reset();
+        }
+        if (std::remove(xmlFileName.c_str()) != 0 || std::remove(binFileName.c_str()) != 0) {
+            ADD_FAILURE() << "Failed to remove serialized files, xml: " << xmlFileName << " bin: " << binFileName;
+        }
+        APIBaseTest::TearDown();
+    }
+
+protected:
+    ov::AnyMap configuration;
+    std::string xmlFileName;
+    std::string binFileName;
+};
+
+TEST_P(DriverCompilerAdapterCustomStreamTestNPU, TestLargeModel) {
+    auto model = createModelWithLargeSize();
+    IRSerializer irSerializer(model, 11);
+    size_t xmlSize = irSerializer.getXmlSize();
+    size_t weightsSize = irSerializer.getWeightsSize();
+
+    std::vector<uint8_t> xml(xmlSize);
+    std::vector<uint8_t> weights(weightsSize);
+    irSerializer.serializeModelToBuffer(xml.data(), weights.data());
+
+    {
+        std::ofstream xmlFile(xmlFileName, std::ios::binary);
+        if (xmlFile) {
+            xmlFile.write(reinterpret_cast<const char*>(xml.data()), xmlSize);
+            xmlFile.close();
+        }
+
+        std::ofstream binFile(binFileName, std::ios::binary);
+        if (binFile) {
+            binFile.write(reinterpret_cast<const char*>(weights.data()), weightsSize);
+            binFile.close();
+        }
+    }
+    ov::Core core;
+    EXPECT_NO_THROW(model = core.read_model(xmlFileName));
+}
+
+const std::vector<ov::AnyMap> configs = {
+    {{ov::intel_npu::compiler_type(ov::intel_npu::CompilerType::DRIVER)}},
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTest,
+                         DriverCompilerAdapterCustomStreamTestNPU,
+                         ::testing::Combine(::testing::Values(ov::test::utils::DEVICE_NPU),
+                                            ::testing::ValuesIn(configs)),
+                         DriverCompilerAdapterCustomStreamTestNPU::getTestCaseName);
+}  // namespace ov::test::behavior
diff --git a/src/plugins/intel_npu/tests/functional/behavior/npu_driver_compiler_adapter/downgrade_interpolate11.cpp b/src/plugins/intel_npu/tests/functional/behavior/npu_driver_compiler_adapter/downgrade_interpolate11.cpp
index 394424b42fa5d8..3f159c20b9dbde 100644
--- a/src/plugins/intel_npu/tests/functional/behavior/npu_driver_compiler_adapter/downgrade_interpolate11.cpp
+++ b/src/plugins/intel_npu/tests/functional/behavior/npu_driver_compiler_adapter/downgrade_interpolate11.cpp
@@ -13,7 +13,7 @@ const std::vector<ov::AnyMap> configs = {
         {{ov::intel_npu::compiler_type(ov::intel_npu::CompilerType::DRIVER)}},
 };
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTest, DriverCompilerAdapterDowngradeInterpolate11TestNPU,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTest, DriverCompilerAdapterDowngradeInterpolate11TestNPU,
                          ::testing::Combine(::testing::Values(ov::test::utils::DEVICE_NPU),
                                             ::testing::ValuesIn(configs)),
                          DriverCompilerAdapterDowngradeInterpolate11TestNPU::getTestCaseName);
diff --git a/src/plugins/intel_npu/tests/functional/behavior/npu_driver_compiler_adapter/inputs_outputs.cpp b/src/plugins/intel_npu/tests/functional/behavior/npu_driver_compiler_adapter/inputs_outputs.cpp
index ba5a4a2f999a35..138640ed4c619a 100644
--- a/src/plugins/intel_npu/tests/functional/behavior/npu_driver_compiler_adapter/inputs_outputs.cpp
+++ b/src/plugins/intel_npu/tests/functional/behavior/npu_driver_compiler_adapter/inputs_outputs.cpp
@@ -13,7 +13,7 @@ const std::vector<ov::AnyMap> configs = {
         {{ov::intel_npu::compiler_type(ov::intel_npu::CompilerType::DRIVER)}},
 };
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTest, DriverCompilerAdapterInputsOutputsTestNPU,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTest, DriverCompilerAdapterInputsOutputsTestNPU,
                          ::testing::Combine(::testing::Values(ov::test::utils::DEVICE_NPU),
                                             ::testing::ValuesIn(configs)),
                          DriverCompilerAdapterInputsOutputsTestNPU::getTestCaseName);
diff --git a/src/plugins/intel_npu/tests/functional/behavior/npu_driver_compiler_adapter/query_network.cpp b/src/plugins/intel_npu/tests/functional/behavior/npu_driver_compiler_adapter/query_network.cpp
index ee2db20d54ba4d..9129cd333165d5 100644
--- a/src/plugins/intel_npu/tests/functional/behavior/npu_driver_compiler_adapter/query_network.cpp
+++ b/src/plugins/intel_npu/tests/functional/behavior/npu_driver_compiler_adapter/query_network.cpp
@@ -122,7 +122,7 @@ TEST_P(QueryNetworkTestSuite1NPU, TestQueryNetworkSupported) {
     EXPECT_EQ(expected, actual);	
 }	
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTest, QueryNetworkTestSuite1NPU,	
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTest, QueryNetworkTestSuite1NPU,	
                          ::testing::Combine(::testing::Values(ov::test::utils::DEVICE_NPU),	
                                             ::testing::ValuesIn(configs)),	
                          QueryNetworkTestNPU::getTestCaseName);
diff --git a/src/plugins/intel_npu/tests/functional/behavior/ov_plugin/internal_properties_tests.cpp b/src/plugins/intel_npu/tests/functional/behavior/ov_plugin/internal_properties_tests.cpp
new file mode 100644
index 00000000000000..103f7c9cb46d4f
--- /dev/null
+++ b/src/plugins/intel_npu/tests/functional/behavior/ov_plugin/internal_properties_tests.cpp
@@ -0,0 +1,183 @@
+//
+// Copyright (C) Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "overload/ov_plugin/internal_properties_tests.hpp"
+
+#include "common/npu_test_env_cfg.hpp"
+#include "npu_private_properties.hpp"
+
+namespace ov::test::behavior {
+
+std::string OVPropertiesTestsNPU::getTestCaseName(testing::TestParamInfo<PropertiesParamsNPU> obj) {
+    std::string target_device;
+    AnyMap properties;
+    std::tie(target_device, properties) = obj.param;
+    std::replace(target_device.begin(), target_device.end(), ':', '.');
+    std::ostringstream result;
+    result << "target_device=" << target_device << "_";
+    if (!properties.empty()) {
+        result << "properties=" << util::join(util::split(util::to_string(properties), ' '), "_");
+    }
+    return result.str();
+}
+
+void OVPropertiesTestsNPU::SetUp() {
+    SKIP_IF_CURRENT_TEST_IS_DISABLED();
+    std::tie(target_device, properties) = this->GetParam();
+    APIBaseTest::SetUp();
+    model = ov::test::utils::make_split_concat();
+}
+
+void OVPropertiesTestsNPU::TearDown() {
+    if (!properties.empty()) {
+        utils::PluginCache::get().reset();
+    }
+    APIBaseTest::TearDown();
+}
+
+std::string OVPropertiesTestsWithCompileModelPropsNPU::getTestCaseName(
+    testing::TestParamInfo<PropertiesParamsNPU> obj) {
+    std::string target_device;
+    AnyMap properties;
+    std::tie(target_device, properties) = obj.param;
+    std::replace(target_device.begin(), target_device.end(), ':', '.');
+    std::ostringstream result;
+    result << "target_device=" << target_device << "_";
+    if (!properties.empty()) {
+        result << "properties=" << util::join(util::split(util::to_string(properties), ' '), "_");
+    }
+    return result.str();
+}
+
+void OVPropertiesTestsWithCompileModelPropsNPU::SetUp() {
+    SKIP_IF_CURRENT_TEST_IS_DISABLED();
+    std::string temp_device;
+    std::tie(temp_device, properties) = this->GetParam();
+    std::string::size_type pos = temp_device.find(":", 0);
+    std::string hw_device;
+
+    if (pos == std::string::npos) {
+        target_device = temp_device;
+        hw_device = temp_device;
+    } else {
+        target_device = temp_device.substr(0, pos);
+        hw_device = temp_device.substr(++pos, std::string::npos);
+    }
+
+    if (target_device == std::string(ov::test::utils::DEVICE_MULTI) ||
+        target_device == std::string(ov::test::utils::DEVICE_AUTO) ||
+        target_device == std::string(ov::test::utils::DEVICE_HETERO) ||
+        target_device == std::string(ov::test::utils::DEVICE_BATCH)) {
+        compileModelProperties = {ov::device::priorities(hw_device)};
+    }
+
+    model = ov::test::utils::make_split_concat();
+
+    APIBaseTest::SetUp();
+}
+
+void OVPropertiesTestsWithCompileModelPropsNPU::TearDown() {
+    if (!properties.empty()) {
+        utils::PluginCache::get().reset();
+    }
+    APIBaseTest::TearDown();
+}
+
+TEST_P(OVPropertiesTestsNPU, SetCorrectProperties) {
+    core->get_versions(target_device);
+    core->set_property(target_device, properties);
+}
+
+TEST_P(OVPropertiesTestsNPU, canSetPropertyAndCheckGetProperty) {
+    core->set_property(target_device, properties);
+
+    for (const auto& property_item : properties) {
+        Any property;
+        OV_ASSERT_NO_THROW(property = core->get_property(target_device, property_item.first));
+        ASSERT_FALSE(property.empty()) << property_item.first << ":" << property.as<std::string>() << '\n';
+    }
+}
+
+TEST_P(OVPropertiesIncorrectTestsNPU, SetPropertiesWithIncorrectKey) {
+    core->get_versions(target_device);
+    std::vector<ov::PropertyName> supported_properties;
+    supported_properties = core->get_property(target_device, ov::supported_properties);
+
+    for (const auto& property_item : properties) {
+        auto supported = util::contains(supported_properties, property_item.first);
+        ASSERT_FALSE(supported);
+    }
+}
+
+TEST_P(OVCheckSetSupportedRWMetricsPropsTestsNPU, ChangeCorrectProperties) {
+    std::vector<ov::PropertyName> supported_properties;
+    OV_ASSERT_NO_THROW(supported_properties = core->get_property(target_device, ov::supported_properties));
+
+    for (const auto& property_item : properties) {
+        auto supported = util::contains(supported_properties, property_item.first);
+        ASSERT_FALSE(supported);
+
+        ov::Any default_property;
+        OV_ASSERT_NO_THROW(default_property = core->get_property(target_device, property_item.first));
+        ASSERT_FALSE(default_property.empty());
+
+        if (ov::PropertyName(property_item.first).is_mutable() && !property_item.second.empty()) {
+            OV_ASSERT_NO_THROW(core->set_property(target_device, {property_item}));
+            core->compile_model(model, target_device, compileModelProperties);
+            ov::Any actual_property_value;
+            OV_ASSERT_NO_THROW(actual_property_value = core->get_property(target_device, property_item.first));
+            ASSERT_FALSE(actual_property_value.empty());
+
+            std::string expect_value = property_item.second.as<std::string>();
+            std::string actual_value = actual_property_value.as<std::string>();
+            EXPECT_EQ(actual_value, expect_value) << "Property is changed in wrong way";
+        }
+    }
+}
+
+const std::vector<ov::AnyMap> CorrectPluginMutableProperties = {
+    {{ov::internal::exclusive_async_requests.name(), true}},
+    {{ov::intel_npu::dpu_groups.name(), 1}},
+    {{ov::intel_npu::dma_engines.name(), 1}},
+    {{ov::intel_npu::compilation_mode.name(), "DefaultHW"}},
+    {{ov::intel_npu::compilation_mode_params.name(), "dump-task-stats=false propagate-quant-dequant=0"}},
+    {{ov::intel_npu::platform.name(),
+      removeDeviceNameOnlyID(
+          ov::test::utils::getTestsDeviceNameFromEnvironmentOr(std::string(ov::intel_npu::Platform::AUTO_DETECT)))}},
+    {{ov::intel_npu::stepping.name(), 0}},
+    {{ov::intel_npu::max_tiles.name(), 2}},
+    {{ov::intel_npu::use_elf_compiler_backend.name(), ov::intel_npu::ElfCompilerBackend::NO}},
+    {{ov::intel_npu::profiling_type.name(), ov::intel_npu::ProfilingType::INFER}}};
+
+const std::vector<ov::AnyMap> IncorrectMutablePropertiesWrongValueTypes = {
+    {{ov::intel_npu::compilation_mode.name(), -3.6}},
+    {{ov::intel_npu::compiler_type.name(), ov::intel_npu::ElfCompilerBackend::NO}},
+    {{ov::intel_npu::stepping.name(), "V1"}},
+    {{ov::intel_npu::max_tiles.name(), "two"}},
+    {{ov::intel_npu::use_elf_compiler_backend.name(), "N"}},
+    {{ov::intel_npu::profiling_type.name(), 10}},
+    {{ov::intel_npu::tiles.name(), "none"}},
+    {{ov::intel_npu::dma_engines.name(), false}},
+    {{ov::intel_npu::compilation_mode_params.name(), "not-a-param=true"}},
+};
+
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests,
+                         OVPropertiesTestsNPU,
+                         ::testing::Combine(::testing::Values(ov::test::utils::DEVICE_NPU),
+                                            ::testing::ValuesIn(CorrectPluginMutableProperties)),
+                         (ov::test::utils::appendPlatformTypeTestName<OVPropertiesTestsNPU>));
+
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests,
+                         OVPropertiesIncorrectTestsNPU,
+                         ::testing::Combine(::testing::Values(ov::test::utils::DEVICE_NPU),
+                                            ::testing::ValuesIn(IncorrectMutablePropertiesWrongValueTypes)),
+                         (ov::test::utils::appendPlatformTypeTestName<OVPropertiesIncorrectTestsNPU>));
+
+INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests,
+                         OVCheckSetSupportedRWMetricsPropsTestsNPU,
+                         ::testing::Combine(::testing::Values(ov::test::utils::DEVICE_NPU),
+                                            ::testing::ValuesIn(CorrectPluginMutableProperties)),
+                         (ov::test::utils::appendPlatformTypeTestName<OVCheckSetSupportedRWMetricsPropsTestsNPU>));
+}  // namespace ov::test::behavior
diff --git a/src/plugins/intel_npu/tests/functional/common/utils.cpp b/src/plugins/intel_npu/tests/functional/common/utils.cpp
index 1fbbdbebd663ca..7ac8b3ed4561f2 100644
--- a/src/plugins/intel_npu/tests/functional/common/utils.cpp
+++ b/src/plugins/intel_npu/tests/functional/common/utils.cpp
@@ -7,20 +7,6 @@
 #include "npu_private_properties.hpp"
 #include "utils.hpp"
 
-namespace ov {
-
-namespace test {
-
-namespace utils {
-
-const char* DEVICE_NPU = "NPU";
-
-}  // namespace utils
-
-}  // namespace test
-
-}  // namespace ov
-
 std::string getBackendName(const ov::Core& core) {
     return core.get_property("NPU", ov::intel_npu::backend_name.name()).as<std::string>();
 }
diff --git a/src/plugins/intel_npu/tests/functional/common/utils.hpp b/src/plugins/intel_npu/tests/functional/common/utils.hpp
index 3616f81d71ab17..bb116a2c8fecc1 100644
--- a/src/plugins/intel_npu/tests/functional/common/utils.hpp
+++ b/src/plugins/intel_npu/tests/functional/common/utils.hpp
@@ -8,20 +8,6 @@
 #include <openvino/runtime/core.hpp>
 #include "common_test_utils/unicode_utils.hpp"
 
-namespace ov {
-
-namespace test {
-
-namespace utils {
-
-extern const char* DEVICE_NPU;
-
-}  // namespace utils
-
-}  // namespace test
-
-}  // namespace ov
-
 std::string getBackendName(const ov::Core& core);
 
 std::vector<std::string> getAvailableDevices(const ov::Core& core);
diff --git a/src/plugins/intel_npu/tests/functional/internal/overload/compiled_model/property.cpp b/src/plugins/intel_npu/tests/functional/internal/overload/compiled_model/property.cpp
index 4a0e11d0396886..8e01ec7f927e5b 100644
--- a/src/plugins/intel_npu/tests/functional/internal/overload/compiled_model/property.cpp
+++ b/src/plugins/intel_npu/tests/functional/internal/overload/compiled_model/property.cpp
@@ -31,19 +31,15 @@ std::vector<std::pair<std::string, ov::Any>> exe_network_immutable_properties =
         {std::make_pair(ov::supported_properties.name(), ov::Any("deadbeef"))},
         {std::make_pair(ov::model_name.name(), ov::Any("deadbeef"))}};
 
-std::vector<std::pair<std::string, ov::Any>> properties = {{}};
-
 // ExecutableNetwork Properties tests
 class ClassExecutableNetworkGetPropertiesTestNPU :
         public OVCompiledModelPropertiesBase,
         public ::testing::WithParamInterface<
-                std::tuple<std::string, std::pair<std::string, ov::Any>, std::pair<std::string, ov::Any>>> {
+                std::tuple<std::string, std::pair<std::string, ov::Any>>> {
 protected:
     std::string deviceName;
     std::string configKey;
     ov::Any configValue;
-    std::string compilerTypeConfigKey;
-    ov::Any compilerTypeConfigValue;
     ov::Core ie;
 
 public:
@@ -52,23 +48,20 @@ class ClassExecutableNetworkGetPropertiesTestNPU :
         OVCompiledModelPropertiesBase::SetUp();
         deviceName = std::get<0>(GetParam());
         std::tie(configKey, configValue) = std::get<1>(GetParam());
-        std::tie(compilerTypeConfigKey, compilerTypeConfigValue) = std::get<2>(GetParam());
 
         model = ov::test::utils::make_conv_pool_relu();
     }
     static std::string getTestCaseName(
             testing::TestParamInfo<
-                    std::tuple<std::string, std::pair<std::string, ov::Any>, std::pair<std::string, ov::Any>>>
+                    std::tuple<std::string, std::pair<std::string, ov::Any>>>
                     obj) {
         std::string targetDevice;
         std::pair<std::string, ov::Any> configuration;
-        std::pair<std::string, ov::Any> compilerType;
-        std::tie(targetDevice, configuration, compilerType) = obj.param;
+        std::tie(targetDevice, configuration) = obj.param;
         std::replace(targetDevice.begin(), targetDevice.end(), ':', '_');
         std::ostringstream result;
         result << "targetDevice=" << ov::test::utils::getDeviceNameTestCase(targetDevice) << "_";
         result << "config=(" << configuration.first << "=" << configuration.second.as<std::string>() << ")";
-        result << "_compilerType=(" << compilerType.first << "=" << compilerType.second.as<std::string>() << ")";
         result << "_targetPlatform=" + ov::test::utils::getTestsPlatformFromEnvironmentOr(ov::test::utils::DEVICE_NPU);
 
         return result.str();
@@ -81,7 +74,7 @@ TEST_P(ClassExecutableNetworkTestSuite1NPU, PropertyIsSupportedAndImmutableAndGe
     std::vector<ov::PropertyName> properties;
 
     ov::CompiledModel exeNetwork =
-            ie.compile_model(model, deviceName, {{compilerTypeConfigKey, compilerTypeConfigValue}});
+            ie.compile_model(model, deviceName);
     OV_ASSERT_NO_THROW(properties = exeNetwork.get_property(ov::supported_properties));
 
     auto it = find(properties.cbegin(), properties.cend(), configKey);
@@ -94,8 +87,7 @@ TEST_P(ClassExecutableNetworkTestSuite1NPU, PropertyIsSupportedAndImmutableAndGe
 INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests_ClassExecutableNetworkGetPropertiesTestNPU,
                          ClassExecutableNetworkTestSuite1NPU,
                          ::testing::Combine(::testing::Values(ov::test::utils::getDeviceName()),
-                                            ::testing::ValuesIn(exe_network_supported_properties),
-                                            ::testing::ValuesIn(properties)),
+                                            ::testing::ValuesIn(exe_network_supported_properties)),
                          ClassExecutableNetworkTestSuite1NPU::getTestCaseName);
 
 using ClassExecutableNetworkTestSuite2NPU = ClassExecutableNetworkGetPropertiesTestNPU;
@@ -104,7 +96,7 @@ TEST_P(ClassExecutableNetworkTestSuite2NPU, PropertyIsSupportedAndImmutableAndCa
     std::vector<ov::PropertyName> properties;
 
     ov::CompiledModel exeNetwork =
-            ie.compile_model(model, deviceName, {{compilerTypeConfigKey, compilerTypeConfigValue}});
+            ie.compile_model(model, deviceName);
     OV_ASSERT_NO_THROW(properties = exeNetwork.get_property(ov::supported_properties));
 
     auto it = find(properties.cbegin(), properties.cend(), configKey);
@@ -116,8 +108,7 @@ TEST_P(ClassExecutableNetworkTestSuite2NPU, PropertyIsSupportedAndImmutableAndCa
 
 INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests_ClassExecutableNetworkTestSuite2NPU, ClassExecutableNetworkTestSuite2NPU,
                          ::testing::Combine(::testing::Values(ov::test::utils::getDeviceName()),
-                                            ::testing::ValuesIn(exe_network_immutable_properties),
-                                            ::testing::ValuesIn(properties)),
+                                            ::testing::ValuesIn(exe_network_immutable_properties)),
                          ClassExecutableNetworkTestSuite2NPU::getTestCaseName);
 
 }  // namespace
@@ -211,7 +202,7 @@ TEST_P(ClassPluginPropertiesTestSuite0NPU, CanSetGetPublicMutableProperty) {
     ASSERT_EQ(retrieved_value.as<std::string>(), configValue.as<std::string>());
 }
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests_ClassPluginPropertiesTestNPU, ClassPluginPropertiesTestSuite0NPU,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests_ClassPluginPropertiesTestNPU, ClassPluginPropertiesTestSuite0NPU,
                          ::testing::Combine(::testing::Values(ov::test::utils::getDeviceName()),
                                             ::testing::ValuesIn(plugin_public_mutable_properties)),
                          ClassPluginPropertiesTestNPU::getTestCaseName);
@@ -227,7 +218,7 @@ TEST_P(ClassPluginPropertiesTestSuite1NPU, CanSetGetInternalMutableProperty) {
     ASSERT_EQ(retrieved_value.as<std::string>(), configValue.as<std::string>());
 }
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests_ClassPluginPropertiesTestNPU, ClassPluginPropertiesTestSuite1NPU,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests_ClassPluginPropertiesTestNPU, ClassPluginPropertiesTestSuite1NPU,
                          ::testing::Combine(::testing::Values(ov::test::utils::getDeviceName()),
                                             ::testing::ValuesIn(plugin_internal_mutable_properties)),
                          ClassPluginPropertiesTestNPU::getTestCaseName);
@@ -288,7 +279,7 @@ INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests_ClassPluginPropertiesOptsTest1NPU,
                                             ::testing::ValuesIn(plugin_public_immutable_properties)),
                          ClassPluginPropertiesTestNPU::getTestCaseName);
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests_ClassPluginPropertiesOptsTest2NPU, ClassPluginPropertiesTestSuite3NPU,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests_ClassPluginPropertiesOptsTest2NPU, ClassPluginPropertiesTestSuite3NPU,
                          ::testing::Combine(::testing::Values(ov::test::utils::getDeviceName()),
                                             ::testing::ValuesIn(plugin_public_mutable_properties)),
                          ClassPluginPropertiesTestNPU::getTestCaseName);
@@ -308,13 +299,13 @@ TEST_P(ClassPluginPropertiesTestSuite4NPU, CanNotSetGetInexistentProperty) {
 
     ASSERT_THROW(
             ov::CompiledModel compiled_model1 = ie.compile_model(
-                    model, deviceName, {{configKey, configValue}, {compilerTypeConfigKey, compilerTypeConfigValue}}),
+                    model, deviceName, {{configKey, configValue}}),
             ov::Exception);
 
     ov::CompiledModel compiled_model2;
 
     OV_ASSERT_NO_THROW(compiled_model2 =
-                               ie.compile_model(model, deviceName, {{compilerTypeConfigKey, compilerTypeConfigValue}}));
+                               ie.compile_model(model, deviceName));
 
     ASSERT_THROW(compiled_model2.set_property({{configKey, configValue}}),
                  ov::Exception);  // Expect to throw due to unimplemented method
@@ -327,8 +318,7 @@ INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests_ClassExecutableNetworkGetProperties
                          ClassPluginPropertiesTestSuite4NPU,
                          ::testing::Combine(::testing::Values(ov::test::utils::getDeviceName()),
                                             ::testing::ValuesIn({std::make_pair<std::string, ov::Any>(
-                                                    "THISCONFIGKEYNOTEXIST", ov::Any("THISCONFIGVALUENOTEXIST"))}),
-                                            ::testing::ValuesIn(properties)),
+                                                    "THISCONFIGKEYNOTEXIST", ov::Any("THISCONFIGVALUENOTEXIST"))})),
                          ClassPluginPropertiesTestSuite4NPU::getTestCaseName);
 
 }  // namespace
diff --git a/src/plugins/intel_npu/tests/functional/internal/overload/ov_plugin/core_integration.hpp b/src/plugins/intel_npu/tests/functional/internal/overload/ov_plugin/core_integration.hpp
index 2651be8f28092e..c5e5e91ec19078 100644
--- a/src/plugins/intel_npu/tests/functional/internal/overload/ov_plugin/core_integration.hpp
+++ b/src/plugins/intel_npu/tests/functional/internal/overload/ov_plugin/core_integration.hpp
@@ -154,7 +154,7 @@ TEST_P(OVClassLoadNetworkTestNPU, LoadNetworkHETEROWithDeviceIDNoThrow) {
     }
 }
 
-TEST(OVClassBasicPropsTestNPU, smoke_SetConfigDevicePropertiesThrows) {
+TEST(compatibility_OVClassBasicPropsTestNPU, smoke_SetConfigDevicePropertiesThrows) {
     ov::Core core;
     ASSERT_THROW(core.set_property("", ov::device::properties(ov::test::utils::DEVICE_NPU, ov::enable_profiling(true))),
                  ov::Exception);
diff --git a/src/plugins/intel_npu/tests/functional/internal/overload/ov_plugin/internal_properties_tests.hpp b/src/plugins/intel_npu/tests/functional/internal/overload/ov_plugin/internal_properties_tests.hpp
new file mode 100644
index 00000000000000..291cb1f20380ab
--- /dev/null
+++ b/src/plugins/intel_npu/tests/functional/internal/overload/ov_plugin/internal_properties_tests.hpp
@@ -0,0 +1,46 @@
+//
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "base/ov_behavior_test_utils.hpp"
+#include "behavior/ov_plugin/properties_tests.hpp"
+#include "common_test_utils/file_utils.hpp"
+#include "common_test_utils/test_assertions.hpp"
+#include "common_test_utils/unicode_utils.hpp"
+#include "openvino/runtime/properties.hpp"
+#include "openvino/util/common_util.hpp"
+
+namespace ov::test::behavior {
+
+using PropertiesParamsNPU = std::tuple<std::string, AnyMap>;
+
+class OVPropertiesTestsNPU : public testing::WithParamInterface<PropertiesParamsNPU>, public OVPropertiesBase {
+public:
+    static std::string getTestCaseName(testing::TestParamInfo<PropertiesParamsNPU> obj);
+
+    void SetUp() override;
+
+    void TearDown() override;
+};
+
+using OVPropertiesIncorrectTestsNPU = OVPropertiesTestsNPU;
+
+using CompileModelPropertiesParamsNPU = std::tuple<std::string, AnyMap>;
+
+class OVPropertiesTestsWithCompileModelPropsNPU : public testing::WithParamInterface<PropertiesParamsNPU>,
+                                                  public OVPropertiesBase {
+public:
+    static std::string getTestCaseName(testing::TestParamInfo<PropertiesParamsNPU> obj);
+
+    void SetUp() override;
+
+    void TearDown() override;
+
+    AnyMap compileModelProperties;
+};
+
+using OVCheckSetSupportedRWMetricsPropsTestsNPU = OVPropertiesTestsWithCompileModelPropsNPU;
+}  // namespace ov::test::behavior
diff --git a/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/compiled_model/compiled_model_base.cpp b/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/compiled_model/compiled_model_base.cpp
index 3cd57626cf38f2..5204e49761afa0 100644
--- a/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/compiled_model/compiled_model_base.cpp
+++ b/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/compiled_model/compiled_model_base.cpp
@@ -48,7 +48,7 @@ INSTANTIATE_TEST_SUITE_P(smoke_Hetero_BehaviorTests, OVCompiledModelBaseTestOpti
                                             ::testing::ValuesIn(heteroCompiledModelConfigs)),
                          ov::test::utils::appendPlatformTypeTestName<OVCompiledModelBaseTestOptional>);
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, OVAutoExecutableNetworkTest,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests, OVAutoExecutableNetworkTest,
                          ::testing::Combine(::testing::Values(ov::test::utils::DEVICE_NPU),
                                             ::testing::ValuesIn(compiledModelConfigs)),
                          ov::test::utils::appendPlatformTypeTestName<OVAutoExecutableNetworkTest>);
diff --git a/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/compiled_model/import_export.cpp b/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/compiled_model/import_export.cpp
index 8ef48613c2450d..7b5acd0448b262 100644
--- a/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/compiled_model/import_export.cpp
+++ b/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/compiled_model/import_export.cpp
@@ -38,7 +38,7 @@ auto heteroCompiledModelConfigs = []() -> std::vector<ov::AnyMap> {
     return heteroPluginConfigs;
 }();
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, OVCompiledGraphImportExportTest,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests, OVCompiledGraphImportExportTest,
                          ::testing::Combine(::testing::ValuesIn(convertModelTypes),
                                             ::testing::Values(ov::test::utils::DEVICE_NPU),
                                             ::testing::ValuesIn(compiledModelConfigs)),
@@ -51,7 +51,7 @@ INSTANTIATE_TEST_SUITE_P(smoke_Hetero_BehaviorTests, OVCompiledGraphImportExport
                                             ::testing::ValuesIn(heteroCompiledModelConfigs)),
                          ov::test::utils::appendPlatformTypeTestName<OVCompiledGraphImportExportTest>);
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, OVClassCompiledModelImportExportTestP,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests, OVClassCompiledModelImportExportTestP,
                          ::testing::Values(ov::test::utils::DEVICE_NPU),
                          ov::test::utils::appendPlatformTypeTestName<OVClassCompiledModelImportExportTestP>);
 
diff --git a/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/compiled_model/properties.cpp b/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/compiled_model/properties.cpp
index 95f53a9834031d..b7b5d3c5831779 100644
--- a/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/compiled_model/properties.cpp
+++ b/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/compiled_model/properties.cpp
@@ -122,7 +122,7 @@ const auto& combineHeteroParamsExecDevices = []() -> std::vector<std::pair<ov::A
     return execHeteroParams;
 }();
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, OVClassCompiledModelGetIncorrectPropertyTest,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests, OVClassCompiledModelGetIncorrectPropertyTest,
                          ::testing::Values(ov::test::utils::DEVICE_NPU),
                          ov::test::utils::appendPlatformTypeTestName<OVClassCompiledModelGetIncorrectPropertyTest>);
 
@@ -131,7 +131,7 @@ INSTANTIATE_TEST_SUITE_P(smoke_Hetero_BehaviorTests, OVClassCompiledModelGetInco
                                            ov::test::utils::DEVICE_NPU),
                          ov::test::utils::appendPlatformTypeTestName<OVClassCompiledModelGetIncorrectPropertyTest>);
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, OVClassCompiledModelGetPropertyTest,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests, OVClassCompiledModelGetPropertyTest,
                          ::testing::Values(ov::test::utils::DEVICE_NPU),
                          ov::test::utils::appendPlatformTypeTestName<OVClassCompiledModelGetPropertyTest>);
 
@@ -140,7 +140,7 @@ INSTANTIATE_TEST_SUITE_P(smoke_Hetero_BehaviorTests, OVClassCompiledModelGetProp
                                            ov::test::utils::DEVICE_NPU),
                          ov::test::utils::appendPlatformTypeTestName<OVClassCompiledModelGetPropertyTest>);
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, OVClassCompiledModelGetConfigTest,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests, OVClassCompiledModelGetConfigTest,
                          ::testing::Values(ov::test::utils::DEVICE_NPU),
                          ov::test::utils::appendPlatformTypeTestName<OVClassCompiledModelGetConfigTest>);
 
@@ -166,12 +166,12 @@ INSTANTIATE_TEST_SUITE_P(smoke_Hetero_BehaviorTests, OVClassCompileModelWithCorr
                                             ::testing::ValuesIn(heteroCompiledModelConfigs)),
                          ov::test::utils::appendPlatformTypeTestName<OVClassCompileModelWithCorrectPropertiesTest>);
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, OVClassCompiledModelPropertiesIncorrectTests,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests, OVClassCompiledModelPropertiesIncorrectTests,
                          ::testing::Combine(::testing::Values(ov::test::utils::DEVICE_NPU),
                                             ::testing::ValuesIn(compiledModelIncorrectConfigs)),
                          ov::test::utils::appendPlatformTypeTestName<OVClassCompiledModelPropertiesIncorrectTests>);
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, OVCompiledModelIncorrectDevice,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests, OVCompiledModelIncorrectDevice,
                          ::testing::Values(ov::test::utils::DEVICE_NPU),
                          ov::test::utils::appendPlatformTypeTestName<OVCompiledModelIncorrectDevice>);
 
@@ -179,7 +179,7 @@ INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, OVCompiledModelPropertiesDefaultSu
                          ::testing::Values(ov::test::utils::DEVICE_NPU),	
                          ov::test::utils::appendPlatformTypeTestName<OVCompiledModelPropertiesDefaultSupportedTests>);
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, OVClassCompiledModelPropertiesDefaultTests,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests, OVClassCompiledModelPropertiesDefaultTests,
                          ::testing::Combine(::testing::Values(ov::test::utils::DEVICE_NPU),
                                             ::testing::ValuesIn(publicCompiledModelConfigs)),
                          ov::test::utils::appendPlatformTypeTestName<OVClassCompiledModelPropertiesDefaultTests>);
@@ -195,7 +195,7 @@ INSTANTIATE_TEST_SUITE_P(smoke_Hetero_BehaviorTests, OVClassCompiledModelSetCorr
                                             ::testing::ValuesIn(compiledModelPropertiesAnyToString)),
                          ov::test::utils::appendPlatformTypeTestName<OVClassCompiledModelSetCorrectConfigTest>);
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, OVClassCompiledModelSetIncorrectConfigTest,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests, OVClassCompiledModelSetIncorrectConfigTest,
                          ::testing::Values(ov::test::utils::DEVICE_NPU),
                          ov::test::utils::appendPlatformTypeTestName<OVClassCompiledModelSetIncorrectConfigTest>);
 
diff --git a/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_infer_request/batched_tensors.cpp b/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_infer_request/batched_tensors.cpp
index 212be818ab2b3f..08d8335a3c4452 100644
--- a/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_infer_request/batched_tensors.cpp
+++ b/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_infer_request/batched_tensors.cpp
@@ -11,7 +11,7 @@ using namespace ov::test::behavior;
 
 namespace {
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, OVInferRequestBatchedTests,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests, OVInferRequestBatchedTests,
                          ::testing::Values(ov::test::utils::DEVICE_NPU),
                          ov::test::utils::appendPlatformTypeTestName<OVInferRequestBatchedTests>);
 }  // namespace
diff --git a/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_infer_request/callback.cpp b/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_infer_request/callback.cpp
index 0a3e4df26c5331..349f194b0066f2 100644
--- a/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_infer_request/callback.cpp
+++ b/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_infer_request/callback.cpp
@@ -23,32 +23,32 @@ const std::vector<ov::AnyMap> autoConfigs = {
         {ov::device::priorities(ov::test::utils::DEVICE_NPU),
          ov::device::properties(ov::test::utils::DEVICE_NPU, ov::AnyMap{})}};
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, OVInferRequestCallbackTests,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests, OVInferRequestCallbackTests,
                          ::testing::Combine(::testing::Values(ov::test::utils::DEVICE_NPU),
                                             ::testing::ValuesIn(configs)),
                          InferRequestParamsAnyMapTestName::getTestCaseName);
 
-INSTANTIATE_TEST_SUITE_P(smoke_Multi_BehaviorTests, OVInferRequestCallbackTests,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_Multi_BehaviorTests, OVInferRequestCallbackTests,
                          ::testing::Combine(::testing::Values(ov::test::utils::DEVICE_MULTI),
                                             ::testing::ValuesIn(multiConfigs)),
                          InferRequestParamsAnyMapTestName::getTestCaseName);
 
-INSTANTIATE_TEST_SUITE_P(smoke_Auto_BehaviorTests, OVInferRequestCallbackTests,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_Auto_BehaviorTests, OVInferRequestCallbackTests,
                          ::testing::Combine(::testing::Values(ov::test::utils::DEVICE_AUTO),
                                             ::testing::ValuesIn(autoConfigs)),
                          InferRequestParamsAnyMapTestName::getTestCaseName);
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, OVInferRequestCallbackTestsNPU,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests, OVInferRequestCallbackTestsNPU,
                          ::testing::Combine(::testing::Values(ov::test::utils::DEVICE_NPU),
                                             ::testing::ValuesIn(configs)),
                          InferRequestParamsAnyMapTestName::getTestCaseName);
 
-INSTANTIATE_TEST_SUITE_P(smoke_Multi_BehaviorTests, OVInferRequestCallbackTestsNPU,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_Multi_BehaviorTests, OVInferRequestCallbackTestsNPU,
                          ::testing::Combine(::testing::Values(ov::test::utils::DEVICE_MULTI),
                                             ::testing::ValuesIn(multiConfigs)),
                          InferRequestParamsAnyMapTestName::getTestCaseName);
 
-INSTANTIATE_TEST_SUITE_P(smoke_Auto_BehaviorTests, OVInferRequestCallbackTestsNPU,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_Auto_BehaviorTests, OVInferRequestCallbackTestsNPU,
                          ::testing::Combine(::testing::Values(ov::test::utils::DEVICE_AUTO),
                                             ::testing::ValuesIn(autoConfigs)),
                          InferRequestParamsAnyMapTestName::getTestCaseName);
diff --git a/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_infer_request/cancellation.cpp b/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_infer_request/cancellation.cpp
index 9a0a91f7aa498d..565c8f2cd0b2d4 100644
--- a/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_infer_request/cancellation.cpp
+++ b/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_infer_request/cancellation.cpp
@@ -13,12 +13,12 @@ using namespace ov::test::behavior;
 namespace {
 const std::vector<ov::AnyMap> configs = {{}};
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, OVInferRequestCancellationTests,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests, OVInferRequestCancellationTests,
                          ::testing::Combine(::testing::Values(ov::test::utils::DEVICE_NPU),
                                             ::testing::ValuesIn(configs)),
                          InferRequestParamsAnyMapTestName::getTestCaseName);
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, OVInferRequestCancellationTestsNPU,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests, OVInferRequestCancellationTestsNPU,
                          ::testing::Combine(::testing::Values(ov::test::utils::DEVICE_NPU),
                                             ::testing::ValuesIn(configs)),
                          InferRequestParamsAnyMapTestName::getTestCaseName);
diff --git a/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_infer_request/compile_and_infer.cpp b/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_infer_request/compile_and_infer.cpp
index c617619f6d12a3..eb7cf591e328a0 100644
--- a/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_infer_request/compile_and_infer.cpp
+++ b/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_infer_request/compile_and_infer.cpp
@@ -13,7 +13,7 @@ using namespace ov::test::behavior;
 
 const std::vector<ov::AnyMap> configs = {{}};
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, OVCompileAndInferRequest,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests, OVCompileAndInferRequest,
                          ::testing::Combine(::testing::Values(getConstantGraph(ov::element::f32)),
                                             ::testing::Values(ov::test::utils::DEVICE_NPU),
                                             ::testing::ValuesIn(configs)),
diff --git a/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_infer_request/inference.cpp b/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_infer_request/inference.cpp
index f7c26779ed4db9..36f70587084f72 100644
--- a/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_infer_request/inference.cpp
+++ b/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_infer_request/inference.cpp
@@ -10,7 +10,7 @@ namespace {
 
 using namespace ov::test::behavior;
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, OVInferRequestInferenceTests,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests, OVInferRequestInferenceTests,
                          ::testing::Combine(::testing::Values(tensor_roi::roi_nchw(), tensor_roi::roi_1d()),
                                             ::testing::Values(ov::test::utils::DEVICE_NPU)),
                          ov::test::utils::appendPlatformTypeTestName<OVInferRequestInferenceTests>);
diff --git a/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_infer_request/io_tensor.cpp b/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_infer_request/io_tensor.cpp
index b3e18dc2b5c489..b1f61e41f5e855 100644
--- a/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_infer_request/io_tensor.cpp
+++ b/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_infer_request/io_tensor.cpp
@@ -23,27 +23,27 @@ const std::vector<ov::AnyMap> autoConfigs = {
         {ov::device::priorities(ov::test::utils::DEVICE_NPU),
         ov::device::properties(ov::test::utils::DEVICE_NPU, {})}};
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, OVInferRequestIOTensorTest,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests, OVInferRequestIOTensorTest,
                          ::testing::Combine(::testing::Values(ov::test::utils::DEVICE_NPU),
                                             ::testing::ValuesIn(configs)),
                          InferRequestParamsAnyMapTestName::getTestCaseName);
 
-INSTANTIATE_TEST_SUITE_P(smoke_Multi_BehaviorTests, OVInferRequestIOTensorTest,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_Multi_BehaviorTests, OVInferRequestIOTensorTest,
                          ::testing::Combine(::testing::Values(ov::test::utils::DEVICE_MULTI),
                                             ::testing::ValuesIn(multiConfigs)),
                          InferRequestParamsAnyMapTestName::getTestCaseName);
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, OVInferRequestIOTensorTestNPU,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests, OVInferRequestIOTensorTestNPU,
                          ::testing::Combine(::testing::Values(ov::test::utils::DEVICE_NPU),
                                             ::testing::ValuesIn(configs)),
                          InferRequestParamsAnyMapTestName::getTestCaseName);
 
-INSTANTIATE_TEST_SUITE_P(smoke_Multi_BehaviorTests, OVInferRequestIOTensorTestNPU,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_Multi_BehaviorTests, OVInferRequestIOTensorTestNPU,
                          ::testing::Combine(::testing::Values(ov::test::utils::DEVICE_MULTI),
                                             ::testing::ValuesIn(multiConfigs)),
                          InferRequestParamsAnyMapTestName::getTestCaseName);
 
-INSTANTIATE_TEST_SUITE_P(smoke_Auto_BehaviorTests, OVInferRequestIOTensorTest,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_Auto_BehaviorTests, OVInferRequestIOTensorTest,
                          ::testing::Combine(::testing::Values(ov::test::utils::DEVICE_AUTO),
                                             ::testing::ValuesIn(autoConfigs)),
                          InferRequestParamsAnyMapTestName::getTestCaseName);
@@ -54,32 +54,32 @@ const std::vector<ov::element::Type> prcs = {
         ov::element::u8,      ov::element::u16,  ov::element::u32, ov::element::u64,
 };
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, OVInferRequestIOTensorSetPrecisionTest,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests, OVInferRequestIOTensorSetPrecisionTest,
                          ::testing::Combine(::testing::ValuesIn(prcs), ::testing::Values(ov::test::utils::DEVICE_NPU),
                                             ::testing::ValuesIn(configs)),
                          ov::test::utils::appendPlatformTypeTestName<OVInferRequestIOTensorSetPrecisionTest>);
 
-INSTANTIATE_TEST_SUITE_P(smoke_Multi_BehaviorTests, OVInferRequestIOTensorSetPrecisionTest,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_Multi_BehaviorTests, OVInferRequestIOTensorSetPrecisionTest,
                          ::testing::Combine(::testing::ValuesIn(prcs), ::testing::Values(ov::test::utils::DEVICE_MULTI),
                                             ::testing::ValuesIn(multiConfigs)),
                          ov::test::utils::appendPlatformTypeTestName<OVInferRequestIOTensorSetPrecisionTest>);
 
-INSTANTIATE_TEST_SUITE_P(smoke_Auto_BehaviorTests, OVInferRequestIOTensorSetPrecisionTest,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_Auto_BehaviorTests, OVInferRequestIOTensorSetPrecisionTest,
                          ::testing::Combine(::testing::ValuesIn(prcs), ::testing::Values(ov::test::utils::DEVICE_AUTO),
                                             ::testing::ValuesIn(autoConfigs)),
                          ov::test::utils::appendPlatformTypeTestName<OVInferRequestIOTensorSetPrecisionTest>);
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, OVInferRequestIOTensorSetPrecisionTestNPU,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests, OVInferRequestIOTensorSetPrecisionTestNPU,
                          ::testing::Combine(::testing::ValuesIn(prcs), ::testing::Values(ov::test::utils::DEVICE_NPU),
                                             ::testing::ValuesIn(configs)),
                          ov::test::utils::appendPlatformTypeTestName<OVInferRequestIOTensorSetPrecisionTestNPU>);
 
-INSTANTIATE_TEST_SUITE_P(smoke_Mutli_BehaviorTests, OVInferRequestIOTensorSetPrecisionTestNPU,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_Mutli_BehaviorTests, OVInferRequestIOTensorSetPrecisionTestNPU,
                          ::testing::Combine(::testing::ValuesIn(prcs), ::testing::Values(ov::test::utils::DEVICE_MULTI),
                                             ::testing::ValuesIn(multiConfigs)),
                          ov::test::utils::appendPlatformTypeTestName<OVInferRequestIOTensorSetPrecisionTestNPU>);
 
-INSTANTIATE_TEST_SUITE_P(smoke_Auto_BehaviorTests, OVInferRequestIOTensorSetPrecisionTestNPU,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_Auto_BehaviorTests, OVInferRequestIOTensorSetPrecisionTestNPU,
                          ::testing::Combine(::testing::ValuesIn(prcs), ::testing::Values(ov::test::utils::DEVICE_AUTO),
                                             ::testing::ValuesIn(autoConfigs)),
                          ov::test::utils::appendPlatformTypeTestName<OVInferRequestIOTensorSetPrecisionTestNPU>);
diff --git a/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_infer_request/multithreading.cpp b/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_infer_request/multithreading.cpp
index ab7ca572dcfe90..745ebcbaea7900 100644
--- a/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_infer_request/multithreading.cpp
+++ b/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_infer_request/multithreading.cpp
@@ -23,32 +23,32 @@ const std::vector<ov::AnyMap> autoConfigs = {
         {ov::device::priorities(ov::test::utils::DEVICE_NPU),
         ov::device::properties(ov::test::utils::DEVICE_NPU, {})}};
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, OVInferRequestMultithreadingTests,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests, OVInferRequestMultithreadingTests,
                          ::testing::Combine(::testing::Values(ov::test::utils::DEVICE_NPU),
                                             ::testing::ValuesIn(configs)),
                          InferRequestParamsAnyMapTestName::getTestCaseName);
 
-INSTANTIATE_TEST_SUITE_P(smoke_Multi_BehaviorTests, OVInferRequestMultithreadingTests,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_Multi_BehaviorTests, OVInferRequestMultithreadingTests,
                          ::testing::Combine(::testing::Values(ov::test::utils::DEVICE_MULTI),
                                             ::testing::ValuesIn(multiConfigs)),
                          InferRequestParamsAnyMapTestName::getTestCaseName);
 
-INSTANTIATE_TEST_SUITE_P(smoke_Auto_BehaviorTests, OVInferRequestMultithreadingTests,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_Auto_BehaviorTests, OVInferRequestMultithreadingTests,
                          ::testing::Combine(::testing::Values(ov::test::utils::DEVICE_AUTO),
                                             ::testing::ValuesIn(autoConfigs)),
                          InferRequestParamsAnyMapTestName::getTestCaseName);
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, OVInferRequestMultithreadingTestsNPU,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests, OVInferRequestMultithreadingTestsNPU,
                          ::testing::Combine(::testing::Values(ov::test::utils::DEVICE_NPU),
                                             ::testing::ValuesIn(configs)),
                          InferRequestParamsAnyMapTestName::getTestCaseName);
 
-INSTANTIATE_TEST_SUITE_P(smoke_Multi_BehaviorTests, OVInferRequestMultithreadingTestsNPU,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_Multi_BehaviorTests, OVInferRequestMultithreadingTestsNPU,
                          ::testing::Combine(::testing::Values(ov::test::utils::DEVICE_MULTI),
                                             ::testing::ValuesIn(multiConfigs)),
                          InferRequestParamsAnyMapTestName::getTestCaseName);
 
-INSTANTIATE_TEST_SUITE_P(smoke_Auto_BehaviorTests, OVInferRequestMultithreadingTestsNPU,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_Auto_BehaviorTests, OVInferRequestMultithreadingTestsNPU,
                          ::testing::Combine(::testing::Values(ov::test::utils::DEVICE_AUTO),
                                             ::testing::ValuesIn(autoConfigs)),
                          InferRequestParamsAnyMapTestName::getTestCaseName);
diff --git a/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_infer_request/perf_counters.cpp b/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_infer_request/perf_counters.cpp
index 243d1f79fbc76d..273f44c5fbee3e 100644
--- a/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_infer_request/perf_counters.cpp
+++ b/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_infer_request/perf_counters.cpp
@@ -24,17 +24,17 @@ const std::vector<ov::AnyMap> autoConfigs = {
         {ov::device::priorities(ov::test::utils::DEVICE_NPU),
          ov::hint::performance_mode(ov::hint::PerformanceMode::LATENCY)}};
 
-INSTANTIATE_TEST_SUITE_P(smoke_Multi_BehaviorTests, OVInferRequestPerfCountersTest,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_Multi_BehaviorTests, OVInferRequestPerfCountersTest,
                          ::testing::Combine(::testing::Values(ov::test::utils::DEVICE_MULTI),
                                             ::testing::ValuesIn(multiConfigs)),
                          ov::test::utils::appendPlatformTypeTestName<OVInferRequestPerfCountersTest>);
 
-INSTANTIATE_TEST_SUITE_P(smoke_Auto_BehaviorTests, OVInferRequestPerfCountersTest,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_Auto_BehaviorTests, OVInferRequestPerfCountersTest,
                          ::testing::Combine(::testing::Values(ov::test::utils::DEVICE_AUTO),
                                             ::testing::ValuesIn(autoConfigs)),
                          ov::test::utils::appendPlatformTypeTestName<OVInferRequestPerfCountersTest>);
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, OVInferRequestPerfCountersTest,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests, OVInferRequestPerfCountersTest,
                          ::testing::Combine(::testing::Values(ov::test::utils::DEVICE_NPU),
                                             ::testing::ValuesIn(configs)),
                          ov::test::utils::appendPlatformTypeTestName<OVInferRequestPerfCountersTest>);
diff --git a/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_infer_request/properties_tests.cpp b/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_infer_request/properties_tests.cpp
index 080d2e7940e397..a3f2d5582cc64a 100644
--- a/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_infer_request/properties_tests.cpp
+++ b/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_infer_request/properties_tests.cpp
@@ -11,7 +11,7 @@ using namespace ov::test::behavior;
 namespace {
 
 INSTANTIATE_TEST_SUITE_P(
-        smoke_BehaviorTests, InferRequestPropertiesTest,
+        compatibility_smoke_BehaviorTests, InferRequestPropertiesTest,
         ::testing::Combine(::testing::Values(2u), ::testing::Values(ov::test::utils::DEVICE_NPU),
                            ::testing::ValuesIn(std::vector<ov::AnyMap>{
                                    {}})),
diff --git a/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_infer_request/wait.cpp b/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_infer_request/wait.cpp
index 5ac3b4d33914c7..6b0b26df27c1ee 100644
--- a/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_infer_request/wait.cpp
+++ b/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_infer_request/wait.cpp
@@ -22,17 +22,17 @@ const std::vector<ov::AnyMap> autoConfigs = {
         {ov::device::priorities(ov::test::utils::DEVICE_NPU),
         ov::device::properties(ov::test::utils::DEVICE_NPU, {})}};
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, OVInferRequestWaitTests,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests, OVInferRequestWaitTests,
                          ::testing::Combine(::testing::Values(ov::test::utils::DEVICE_NPU),
                                             ::testing::ValuesIn(configs)),
                          ov::test::utils::appendPlatformTypeTestName<OVInferRequestWaitTests>);
 
-INSTANTIATE_TEST_SUITE_P(smoke_Multi_BehaviorTests, OVInferRequestWaitTests,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_Multi_BehaviorTests, OVInferRequestWaitTests,
                          ::testing::Combine(::testing::Values(ov::test::utils::DEVICE_MULTI),
                                             ::testing::ValuesIn(multiConfigs)),
                          ov::test::utils::appendPlatformTypeTestName<OVInferRequestWaitTests>);
 
-INSTANTIATE_TEST_SUITE_P(smoke_Auto_BehaviorTests, OVInferRequestWaitTests,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_Auto_BehaviorTests, OVInferRequestWaitTests,
                          ::testing::Combine(::testing::Values(ov::test::utils::DEVICE_AUTO),
                                             ::testing::ValuesIn(autoConfigs)),
                          ov::test::utils::appendPlatformTypeTestName<OVInferRequestWaitTests>);
diff --git a/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_plugin/core_integration.cpp b/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_plugin/core_integration.cpp
index 390b46aa162348..967cce5f88e379 100644
--- a/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_plugin/core_integration.cpp
+++ b/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_plugin/core_integration.cpp
@@ -56,17 +56,14 @@ static std::string getTestCaseName(testing::TestParamInfo<std::string> obj) {
 
 const std::vector<ov::AnyMap> configs = {{}};
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests_OVBasicPropertiesTestsP, OVBasicPropertiesTestsP,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests_OVBasicPropertiesTestsP, OVBasicPropertiesTestsP,
                          ::testing::ValuesIn(plugins), OVClassBasicTestName::getTestCaseName);
 
 #ifdef OPENVINO_ENABLE_UNICODE_PATH_SUPPORT
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests_OVClassBasicTestP, OVClassBasicTestPNPU, ::testing::ValuesIn(plugins),
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests_OVClassBasicTestP, OVClassBasicTestPNPU, ::testing::ValuesIn(plugins),
                          OVClassBasicTestName::getTestCaseName);
 #endif
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests_OVClassModelTestP, OVClassModelTestP, ::testing::ValuesIn(devices),
-                         OVClassNetworkTestName::getTestCaseName);
-
 INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests_OVClassNetworkTestP, OVClassNetworkTestPNPU,
                          ::testing::Combine(::testing::ValuesIn(devices), ::testing::ValuesIn(configs)),
                          OVClassNetworkTestPNPU::getTestCaseName);
@@ -75,14 +72,14 @@ INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests_OVClassNetworkTestP, OVClassNetwork
 // IE Class GetMetric
 //
 
-INSTANTIATE_TEST_SUITE_P(BehaviorTests_OVGetMetricPropsTest_nightly, OVGetMetricPropsTest, ::testing::ValuesIn(devices),
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests_OVGetMetricPropsTest_nightly, OVGetMetricPropsTest, ::testing::ValuesIn(devices),
                          OVClassNetworkTestName::getTestCaseName);
 
-INSTANTIATE_TEST_SUITE_P(BehaviorTests_OVGetMetricPropsTest_nightly, OVGetMetricPropsOptionalTest,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests_OVGetMetricPropsTest_nightly, OVGetMetricPropsOptionalTest,
                          ::testing::ValuesIn(devices), OVClassNetworkTestName::getTestCaseName);
 
 INSTANTIATE_TEST_SUITE_P(
-        BehaviorTests_OVCheckSetSupportedRWMandatoryMetricsPropsTests, OVCheckSetSupportedRWMetricsPropsTests,
+        smoke_BehaviorTests_OVCheckSetSupportedRWMandatoryMetricsPropsTests, OVCheckSetSupportedRWMetricsPropsTests,
         ::testing::Combine(::testing::Values("MULTI", "AUTO"),
                            ::testing::ValuesIn(OVCheckSetSupportedRWMetricsPropsTests::getRWOptionalPropertiesValues(
                                    {ov::log::level.name()}))),
@@ -135,11 +132,6 @@ const std::vector<ov::AnyMap> autoConfigsWithSecondaryProperties = {
          ov::device::properties(ov::test::utils::DEVICE_NPU,
                                 ov::hint::performance_mode(ov::hint::PerformanceMode::LATENCY))}};
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests_OVClassSetDevicePriorityConfigPropsTest,
-                         OVClassSetDevicePriorityConfigPropsTest,
-                         ::testing::Combine(::testing::Values("MULTI", "AUTO"), ::testing::ValuesIn(multiConfigs)),
-                         ov::test::utils::appendPlatformTypeTestName<OVClassSetDevicePriorityConfigPropsTest>);
-
 //
 // IE Class GetConfig
 //
@@ -149,17 +141,17 @@ INSTANTIATE_TEST_SUITE_P(BehaviorTests_OVGetConfigTest_nightly, OVGetConfigTest,
 
 // IE Class Load network
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests_OVClassLoadNetworkWithCorrectSecondaryPropertiesTest,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests_OVClassLoadNetworkWithCorrectSecondaryPropertiesTest,
                          OVClassCompileModelWithCorrectPropertiesTest,
                          ::testing::Combine(::testing::Values(ov::test::utils::DEVICE_NPU, "AUTO:NPU", "MULTI:NPU"),
                                             ::testing::ValuesIn(configsWithSecondaryProperties)));
 
-INSTANTIATE_TEST_SUITE_P(smoke_Multi_BehaviorTests_OVClassCompileModelWithCorrectPropertiesTest,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_Multi_BehaviorTests_OVClassCompileModelWithCorrectPropertiesTest,
                          OVClassCompileModelWithCorrectPropertiesTest,
                          ::testing::Combine(::testing::Values("MULTI"),
                                             ::testing::ValuesIn(multiConfigsWithSecondaryProperties)));
 
-INSTANTIATE_TEST_SUITE_P(smoke_AUTO_BehaviorTests_OVClassCompileModelWithCorrectPropertiesTest,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_AUTO_BehaviorTests_OVClassCompileModelWithCorrectPropertiesTest,
                          OVClassCompileModelWithCorrectPropertiesTest,
                          ::testing::Combine(::testing::Values("AUTO"),
                                             ::testing::ValuesIn(autoConfigsWithSecondaryProperties)));
@@ -282,18 +274,18 @@ INSTANTIATE_TEST_SUITE_P(nightly_BehaviorTests_OVClassGetMetricTest, OVClassGetM
                          ::testing::Values(ov::test::utils::DEVICE_NPU), OVClassNetworkTestName::getTestCaseName);
 
 // Several devices case
-INSTANTIATE_TEST_SUITE_P(nightly_BehaviorTests_OVClassSeveralDevicesTest, OVClassSeveralDevicesTestCompileModel,
+INSTANTIATE_TEST_SUITE_P(compatibility_nightly_BehaviorTests_OVClassSeveralDevicesTest, OVClassSeveralDevicesTestCompileModel,
                          ::testing::Values(std::vector<std::string>(
                                  {std::string(ov::test::utils::DEVICE_NPU) + "." +
                                   removeDeviceNameOnlyID(ov::test::utils::getTestsPlatformFromEnvironmentOr("3700"))})),
                          (ov::test::utils::appendPlatformTypeTestName<OVClassSeveralDevicesTestCompileModel>));
 
-INSTANTIATE_TEST_SUITE_P(nightly_BehaviorTests_OVClassModelOptionalTestP, OVClassModelOptionalTestP,
+INSTANTIATE_TEST_SUITE_P(compatibility_nightly_BehaviorTests_OVClassModelOptionalTestP, OVClassModelOptionalTestP,
                          ::testing::Values(ov::test::utils::DEVICE_NPU),
                          (ov::test::utils::appendPlatformTypeTestName<OVClassModelOptionalTestP>));
 
 INSTANTIATE_TEST_SUITE_P(
-        nightly_BehaviorTests_OVClassSeveralDevicesTest, OVClassSeveralDevicesTestQueryModel,
+        compatibility_nightly_BehaviorTests_OVClassSeveralDevicesTest, OVClassSeveralDevicesTestQueryModel,
         ::testing::Values(std::vector<std::string>(
                 {std::string(ov::test::utils::DEVICE_NPU) + "." +
                          removeDeviceNameOnlyID(ov::test::utils::getTestsPlatformFromEnvironmentOr("3700")),
diff --git a/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_plugin/core_threading_tests.cpp b/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_plugin/core_threading_tests.cpp
index 159ba271d91b23..c72baae502da07 100644
--- a/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_plugin/core_threading_tests.cpp
+++ b/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_plugin/core_threading_tests.cpp
@@ -29,7 +29,7 @@ const Params paramsStreamsDRIVER[] = {
 
 }  // namespace
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests_CoreThreadingTest_NPU, CoreThreadingTest, testing::ValuesIn(params),
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests_CoreThreadingTest_NPU, CoreThreadingTest, testing::ValuesIn(params),
                          (ov::test::utils::appendPlatformTypeTestName<CoreThreadingTest>));
 
 INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests_CoreThreadingTest_NPU, CoreThreadingTestsWithIter,
diff --git a/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_plugin/life_time.cpp b/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_plugin/life_time.cpp
index 8a69e599872122..b9048470f039c3 100644
--- a/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_plugin/life_time.cpp
+++ b/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_plugin/life_time.cpp
@@ -26,23 +26,23 @@ const std::vector<std::string> device_names_and_priorities = {
         "AUTO:NPU",   // NPU via AUTO,
 };
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, OVHoldersTest, ::testing::Values(ov::test::utils::DEVICE_NPU),
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests, OVHoldersTest, ::testing::Values(ov::test::utils::DEVICE_NPU),
                          getTestCaseName);
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, OVHoldersTestOnImportedNetwork,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests, OVHoldersTestOnImportedNetwork,
                          ::testing::Values(ov::test::utils::DEVICE_NPU), getTestCaseName);
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, OVHoldersTestNPU,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests, OVHoldersTestNPU,
                          ::testing::Combine(::testing::Values(ov::test::utils::DEVICE_NPU),
                                             ::testing::ValuesIn(configs)),
                          OVHoldersTestNPU::getTestCaseName);
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, OVHoldersTestOnImportedNetworkNPU,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests, OVHoldersTestOnImportedNetworkNPU,
                          ::testing::Combine(::testing::Values(ov::test::utils::DEVICE_NPU),
                                             ::testing::ValuesIn(configs)),
                          OVHoldersTestOnImportedNetworkNPU::getTestCaseName);
 
-INSTANTIATE_TEST_SUITE_P(smoke_VirtualPlugin_BehaviorTests, OVHoldersTestWithConfig,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_VirtualPlugin_BehaviorTests, OVHoldersTestWithConfig,
                          ::testing::ValuesIn(device_names_and_priorities),
                          (ov::test::utils::appendPlatformTypeTestName<OVHoldersTestWithConfig>));
 }  // namespace
diff --git a/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_plugin/properties_tests.cpp b/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_plugin/properties_tests.cpp
index b517012249ff50..e898b9ac240c3d 100644
--- a/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_plugin/properties_tests.cpp
+++ b/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_plugin/properties_tests.cpp
@@ -140,13 +140,13 @@ const std::vector<ov::AnyMap> IncorrectInexistingProperties = {
     {{ov::intel_gpu::hint::host_task_priority.name(), ov::hint::Priority::LOW}},
     {{ov::intel_gpu::hint::queue_throttle.name(), ov::intel_gpu::hint::ThrottleLevel::HIGH}}};
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests,
                          OVPropertiesTests,
                          ::testing::Combine(::testing::Values(ov::test::utils::DEVICE_NPU),
                                             ::testing::ValuesIn(CorrectPluginMutableProperties)),
                          (ov::test::utils::appendPlatformTypeTestName<OVPropertiesTests>));
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests,
                          OVPropertiesIncorrectTests,
                          ::testing::Combine(::testing::Values(ov::test::utils::DEVICE_NPU),
                                             ::testing::ValuesIn(IncorrectImmutableProperties +
@@ -159,12 +159,12 @@ INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests,
                          ::testing::Values(ov::test::utils::DEVICE_NPU),
                          (ov::test::utils::appendPlatformTypeTestName<OVPropertiesDefaultSupportedTests>));
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests_OVClassCommon,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests_OVClassCommon,
                          OVBasicPropertiesTestsP,
                          ::testing::Values(std::make_pair("openvino_intel_npu_plugin", ov::test::utils::DEVICE_NPU)),
                          (ov::test::utils::appendPlatformTypeTestName<OVBasicPropertiesTestsP>));
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests,
                          OVPropertiesDefaultTests,
                          ::testing::Combine(::testing::Values(ov::test::utils::DEVICE_NPU),
                                             ::testing::ValuesIn(CorrectPluginDefaultMutableProperties)),
@@ -186,7 +186,7 @@ INSTANTIATE_TEST_SUITE_P(
     (ov::test::utils::appendPlatformTypeTestName<OVCheckGetSupportedROMetricsPropsTests>));
 
 INSTANTIATE_TEST_SUITE_P(
-    smoke_BehaviorTests_OVCheckChangePropComplieModleGetPropTests_DEVICE_ID,
+    compatibility_smoke_BehaviorTests_OVCheckChangePropComplieModleGetPropTests_DEVICE_ID,
     OVCheckChangePropComplieModleGetPropTests_DEVICE_ID,
     ::testing::Combine(::testing::Values(ov::test::utils::DEVICE_NPU),
                        ::testing::ValuesIn(CorrectCompiledModelProperties)),
@@ -205,7 +205,7 @@ INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests_OVCheckMetricsPropsTests_ModelDepen
                                             ::testing::ValuesIn(CorrectCompiledModelProperties)),
                          (ov::test::utils::appendPlatformTypeTestName<OVCheckMetricsPropsTests_ModelDependceProps>));
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests_OVClassSetDefaultDeviceIDPropTest,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests_OVClassSetDefaultDeviceIDPropTest,
                          OVClassSetDefaultDeviceIDPropTest,
                          ::testing::Values(std::make_pair(
                              ov::test::utils::DEVICE_NPU,
@@ -226,13 +226,13 @@ INSTANTIATE_TEST_SUITE_P(
                       removeDeviceNameOnlyID(ov::test::utils::getTestsPlatformFromEnvironmentOr("3700"))),
     (ov::test::utils::appendPlatformTypeTestName<OVSpecificDeviceGetConfigTest>));
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests_OVGetAvailableDevicesPropsTest,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests_OVGetAvailableDevicesPropsTest,
                          OVGetAvailableDevicesPropsTest,
                          ::testing::Values(ov::test::utils::DEVICE_NPU),
                          (ov::test::utils::appendPlatformTypeTestName<OVGetAvailableDevicesPropsTest>));
 
 INSTANTIATE_TEST_SUITE_P(
-    smoke_BehaviorTests_OVClassSpecificDeviceTest,
+    compatibility_smoke_BehaviorTests_OVClassSpecificDeviceTest,
     OVSpecificDeviceTestSetConfig,
     ::testing::Values(std::string(ov::test::utils::DEVICE_NPU) + "." +
                       removeDeviceNameOnlyID(ov::test::utils::getTestsPlatformFromEnvironmentOr("3700"))),
@@ -240,18 +240,20 @@ INSTANTIATE_TEST_SUITE_P(
 
 const std::vector<ov::AnyMap> multiConfigs = {{ov::device::priorities(ov::test::utils::DEVICE_NPU)}};
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests_OVClassSetDevicePriorityConfigPropsTest,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests_OVClassSetDevicePriorityConfigPropsTest,
                          OVClassSetDevicePriorityConfigPropsTest,
-                         ::testing::Combine(::testing::Values(ov::test::utils::DEVICE_HETERO),
+                         ::testing::Combine(::testing::Values(ov::test::utils::DEVICE_MULTI,
+                                                ov::test::utils::DEVICE_AUTO,
+                                                ov::test::utils::DEVICE_HETERO),
                                             ::testing::ValuesIn(multiConfigs)),
                          (ov::test::utils::appendPlatformTypeTestName<OVClassSetDevicePriorityConfigPropsTest>));
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests_OVGetMetricPropsTest,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests_OVGetMetricPropsTest,
                          OVGetMetricPropsTest,
                          ::testing::Values(ov::test::utils::DEVICE_NPU),
                          (ov::test::utils::appendPlatformTypeTestName<OVGetMetricPropsTest>));
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests_OVGetMetricPropsOptionalTest,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests_OVGetMetricPropsOptionalTest,
                          OVGetMetricPropsOptionalTest,
                          ::testing::Values(ov::test::utils::DEVICE_NPU),
                          (ov::test::utils::appendPlatformTypeTestName<OVGetMetricPropsOptionalTest>));
diff --git a/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_plugin/query_model.cpp b/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_plugin/query_model.cpp
index 339fa35cd9b5a7..e6a70d753bac3e 100644
--- a/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_plugin/query_model.cpp
+++ b/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_plugin/query_model.cpp
@@ -10,11 +10,11 @@ namespace ov {
 namespace test {
 namespace behavior {
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests_OVClassModelTestP, OVClassModelTestP,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests_OVClassModelTestP, OVClassModelTestP,
                          ::testing::Values(ov::test::utils::DEVICE_NPU),
                          (ov::test::utils::appendPlatformTypeTestName<OVClassModelTestP>));
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests_OVClassQueryModelTestTests, OVClassQueryModelTest,
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests_OVClassQueryModelTestTests, OVClassQueryModelTest,
                          ::testing::Values(ov::test::utils::DEVICE_NPU),
                          (ov::test::utils::appendPlatformTypeTestName<OVClassQueryModelTest>));
 
diff --git a/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_plugin/version.cpp b/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_plugin/version.cpp
index 020901dce6ad02..3faf90bb7dfb45 100644
--- a/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_plugin/version.cpp
+++ b/src/plugins/intel_npu/tests/functional/shared_tests_instances/behavior/ov_plugin/version.cpp
@@ -10,10 +10,10 @@ namespace ov {
 namespace test {
 namespace behavior {
 
-INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, VersionTests, ::testing::Values(ov::test::utils::DEVICE_NPU),
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_BehaviorTests, VersionTests, ::testing::Values(ov::test::utils::DEVICE_NPU),
                          (ov::test::utils::appendPlatformTypeTestName<VersionTests>));
 
-INSTANTIATE_TEST_SUITE_P(smoke_Hetero_BehaviorTests, VersionTests, ::testing::Values(ov::test::utils::DEVICE_HETERO),
+INSTANTIATE_TEST_SUITE_P(compatibility_smoke_Hetero_BehaviorTests, VersionTests, ::testing::Values(ov::test::utils::DEVICE_HETERO),
                          (ov::test::utils::appendPlatformTypeTestName<VersionTests>));
 
 }  // namespace behavior
diff --git a/src/plugins/intel_npu/tests/functional/shared_tests_instances/skip_tests_config.cpp b/src/plugins/intel_npu/tests/functional/shared_tests_instances/skip_tests_config.cpp
index 5151012131bff5..b6d28bc223d6f6 100644
--- a/src/plugins/intel_npu/tests/functional/shared_tests_instances/skip_tests_config.cpp
+++ b/src/plugins/intel_npu/tests/functional/shared_tests_instances/skip_tests_config.cpp
@@ -273,8 +273,6 @@ std::vector<std::string> disabledTestPatterns() {
                 ".*InferRequestCheckTensorPrecision.*type=boolean.*",
                 ".*InferRequestCheckTensorPrecision.*type=bf16.*",
                 ".*InferRequestCheckTensorPrecision.*type=f64.*",
-                ".*InferRequestCheckTensorPrecision.*type=i4.*",
-                ".*InferRequestCheckTensorPrecision.*type=u4.*",
                 ".*InferRequestCheckTensorPrecision.*type=u1\\D.*",
                 // [Track number: E#97469]
                 ".*InferRequestCheckTensorPrecision.*type=i64.*",
@@ -544,6 +542,13 @@ std::vector<std::string> disabledTestPatterns() {
                 // [Tracking number: E#111369]
                 ".*OVInferRequestMultithreadingTests.canRun3SyncRequestsConsistently.*"
         });
+
+        // [Tracking number: E#107154]
+        _skipRegistry.addPatterns(
+                "Can't disable ELF Backend since Graphfile does not work on linux", {
+                ".*NPU_USE_ELF_COMPILER_BACKEND:NO.*",
+                ".*USE_ELF_COMPILER_BACKEND_NO.*"
+        });
 #endif
 
         _skipRegistry.addPatterns(backendName.isZero(), "Most ProfilingTest_VPU3700 instances break sporadically, only stable instances are left, #65844", {
@@ -575,19 +580,6 @@ std::vector<std::string> disabledTestPatterns() {
                 ".*smoke_MemoryLSTMCellTest.*"
         });
 
-        // [Track number: E#118999]
-        _skipRegistry.addPatterns(backendName.isZero(),
-                "Newly enabled, never tested", {
-                ".*smoke_BehaviorTests/OVCompiledGraphImportExportTest.importExportedFunctionConstantResultOnly.*",
-                ".*smoke_BehaviorTests_OVClassImportExportTestP/OVClassCompiledModelImportExportTestP.smoke_ImportNetworkThrowWithDeviceName.*",
-                ".*ClassExecutableNetworkTestSuite1NPU.PropertyIsSupportedAndImmutableAndGet.*",
-                ".*ClassExecutableNetworkTestSuite2NPU.PropertyIsSupportedAndImmutableAndCanNotSet.*",
-                ".*smoke_BehaviorTests_ClassPluginPropertiesTest/ClassPluginPropertiesTestSuite2NPU.CanNotSetImmutableProperty.*",
-                ".*smoke_BehaviorTests_ClassPluginPropertiesOptsTest1NPU/ClassPluginPropertiesTestSuite3NPU.CanGetPropertyWithOptionsNotAffectingCore.*",
-                ".*ClassPluginPropertiesTestSuite4NPU.CanNotSetGetInexistentProperty.*",
-                ".*BehaviorTests_OVCheckSetSupportedRWMandatoryMetricsPropsTests/OVCheckSetSupportedRWMetricsPropsTests.ChangeCorrectProperties.*"
-        });
-
         _skipRegistry.addPatterns(!backendName.isZero() || !devices.has3720(),
                 "QueryNetwork is only supported by 3720 platform", {
                 ".*QueryNetworkTestSuite.*"
diff --git a/src/plugins/intel_npu/thirdparty/CMakeLists.txt b/src/plugins/intel_npu/thirdparty/CMakeLists.txt
index 4121b2313f8ed1..47dac033cc2722 100644
--- a/src/plugins/intel_npu/thirdparty/CMakeLists.txt
+++ b/src/plugins/intel_npu/thirdparty/CMakeLists.txt
@@ -15,8 +15,7 @@ if(ENABLE_ZEROAPI_BACKEND)
 
     # Skip warnings as errors for thirdparty
     if(CMAKE_CXX_COMPILER_ID STREQUAL "MSVC")
-        string(REPLACE "/WX" "" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
-        string(REPLACE "/WX" "" CMAKE_C_FLAGS "${CMAKE_C_FLAGS}")
+        ov_add_compiler_flags(/WX-)
         # solve pdb access issue
         set(USE_Z7 ON)
         # Close spectre for ze loader
diff --git a/src/plugins/intel_npu/tools/CMakeLists.txt b/src/plugins/intel_npu/tools/CMakeLists.txt
new file mode 100644
index 00000000000000..c0e620981952e1
--- /dev/null
+++ b/src/plugins/intel_npu/tools/CMakeLists.txt
@@ -0,0 +1,8 @@
+#
+# Copyright (C) 2024 Intel Corporation.
+# SPDX-License-Identifier: Apache 2.0
+#
+
+add_subdirectory(common)
+add_subdirectory(compile_tool)
+add_subdirectory(single-image-test)
diff --git a/src/plugins/intel_npu/tools/common/CMakeLists.txt b/src/plugins/intel_npu/tools/common/CMakeLists.txt
new file mode 100644
index 00000000000000..a25e6126424617
--- /dev/null
+++ b/src/plugins/intel_npu/tools/common/CMakeLists.txt
@@ -0,0 +1,29 @@
+#
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+#
+
+set(TARGET_NAME "npu_tools_utils")
+
+#
+# Define the target
+#
+
+ov_add_target(ADD_CPPLINT
+              TYPE STATIC
+              NAME ${TARGET_NAME}
+              ROOT ${CMAKE_CURRENT_SOURCE_DIR}
+              INCLUDES
+                  PUBLIC
+                      "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>"
+              LINK_LIBRARIES
+                  PRIVATE
+                      openvino::runtime)
+
+set_target_properties(${TARGET_NAME} PROPERTIES
+                          FOLDER ${CMAKE_CURRENT_SOURCE_DIR}
+                          CXX_STANDARD 17)
+
+if (CMAKE_COMPILER_IS_GNUCXX)
+    target_compile_options(${TARGET_NAME} PRIVATE -Wall)
+endif()
diff --git a/src/plugins/intel_npu/tools/common/include/data_type_converters.hpp b/src/plugins/intel_npu/tools/common/include/data_type_converters.hpp
new file mode 100644
index 00000000000000..c9c12b5154f398
--- /dev/null
+++ b/src/plugins/intel_npu/tools/common/include/data_type_converters.hpp
@@ -0,0 +1,272 @@
+//
+// Copyright (C) 2024 Intel Corporation.
+// SPDX-License-Identifier: Apache 2.0
+//
+
+#pragma once
+
+#include "openvino/core/except.hpp"
+#include "openvino/core/type.hpp"
+#include "openvino/core/type/element_type.hpp"
+#include "openvino/core/type/element_type_traits.hpp"
+
+#include <limits>
+
+namespace npu {
+namespace utils {
+
+namespace details {
+
+template <bool Cond, class Func>
+std::enable_if_t<Cond> staticIf(Func&& func) {
+    func();
+}
+
+template <bool Cond, class Func>
+std::enable_if_t<!Cond> staticIf(Func&&) {
+}
+
+// To overcome the syntax parse error, when `>` comparison operator is treated as
+// template closing bracket
+template <typename T1, typename T2>
+constexpr bool Greater(T1&& v1, T2&& v2) {
+    return v1 > v2;
+}
+
+}  // namespace details
+
+//
+// Bool logic
+//
+
+template <typename T>
+using not_ = std::negation<T>;
+
+template <typename... Ts>
+using or_ = std::disjunction<Ts...>;
+
+template <typename... Ts>
+using and_ = std::conjunction<Ts...>;
+
+//
+// enable_if
+//
+
+template <typename T, typename... Args>
+using enable_t = std::enable_if_t<(Args::value && ...), T>;
+
+//
+// Standart data types
+//
+
+template <typename OutT, typename InT>
+enable_t<OutT, std::is_same<OutT, InT>> checked_cast(InT value) {
+    return value;
+}
+
+template <typename OutT, typename InT>
+enable_t<OutT, std::is_integral<InT>, std::is_signed<InT>, std::is_integral<OutT>, std::is_signed<OutT>,
+         not_<std::is_same<OutT, InT>>>
+checked_cast(InT value) {
+    details::staticIf<std::numeric_limits<InT>::lowest() < std::numeric_limits<OutT>::lowest()>([&] {
+        OPENVINO_ASSERT(value >= std::numeric_limits<OutT>::lowest(), "Can not safely cast ",
+                        static_cast<int64_t>(value), " from ", ov::element::from<InT>(), " to ",
+                        ov::element::from<OutT>());
+    });
+
+    details::staticIf<details::Greater(std::numeric_limits<InT>::max(), std::numeric_limits<OutT>::max())>([&] {
+        OPENVINO_ASSERT(value <= std::numeric_limits<OutT>::max(), "Can not safely cast ", static_cast<int64_t>(value),
+                        " from ", ov::element::from<InT>(), " to ", ov::element::from<OutT>());
+    });
+
+    return static_cast<OutT>(value);
+}
+
+template <typename OutT, typename InT>
+enable_t<OutT, std::is_integral<InT>, std::is_unsigned<InT>, std::is_integral<OutT>, std::is_unsigned<OutT>,
+         not_<std::is_same<OutT, InT>>>
+checked_cast(InT value) {
+    details::staticIf<details::Greater(std::numeric_limits<InT>::max(), std::numeric_limits<OutT>::max())>([&] {
+        OPENVINO_ASSERT(value <= std::numeric_limits<OutT>::max(), "Can not safely cast ", static_cast<uint64_t>(value),
+                        " from ", ov::element::from<InT>(), " to ", ov::element::from<OutT>());
+    });
+
+    return static_cast<OutT>(value);
+}
+
+template <typename OutT, typename InT>
+enable_t<OutT, std::is_integral<InT>, std::is_unsigned<InT>, std::is_integral<OutT>, std::is_signed<OutT>> checked_cast(
+        InT value) {
+    details::staticIf<details::Greater(std::numeric_limits<InT>::max(),
+                                       static_cast<std::make_unsigned_t<OutT>>(std::numeric_limits<OutT>::max()))>([&] {
+        OPENVINO_ASSERT(value <= static_cast<std::make_unsigned_t<OutT>>(std::numeric_limits<OutT>::max()),
+                        "Can not safely cast ", static_cast<uint64_t>(value), " from ", ov::element::from<InT>(),
+                        " to ", ov::element::from<OutT>());
+    });
+
+    return static_cast<OutT>(value);
+}
+
+template <typename OutT, typename InT>
+enable_t<OutT, std::is_integral<InT>, std::is_signed<InT>, std::is_integral<OutT>, std::is_unsigned<OutT>> checked_cast(
+        InT value) {
+    OPENVINO_ASSERT(value >= 0, "Can not safely cast ", static_cast<int64_t>(value), " from ", ov::element::from<InT>(),
+                    " to ", ov::element::from<OutT>());
+
+    details::staticIf<details::Greater(static_cast<std::make_unsigned_t<InT>>(std::numeric_limits<InT>::max()),
+                                       std::numeric_limits<OutT>::max())>([&] {
+        OPENVINO_ASSERT(static_cast<std::make_unsigned_t<InT>>(value) <= std::numeric_limits<OutT>::max(),
+                        "Can not safely cast ", static_cast<int64_t>(value), " from ", ov::element::from<InT>(), " to ",
+                        ov::element::from<OutT>());
+    });
+
+    return static_cast<OutT>(value);
+}
+
+template <typename OutT, typename InT>
+enable_t<OutT, std::is_floating_point<InT>, std::is_integral<OutT>> checked_cast(InT value) {
+    OPENVINO_ASSERT(value <= static_cast<InT>(std::numeric_limits<OutT>::max()), "Can not safely cast ", value,
+                    " from ", ov::element::from<InT>(), " to ", ov::element::from<OutT>());
+
+    OPENVINO_ASSERT(value >= static_cast<InT>(std::numeric_limits<OutT>::lowest()), "Can not safely cast ", value,
+                    " from ", ov::element::from<InT>(), " to ", ov::element::from<OutT>());
+
+    return static_cast<OutT>(value);
+}
+
+template <typename OutT, typename InT>
+enable_t<OutT, std::is_integral<InT>, std::is_signed<InT>, std::is_floating_point<OutT>> checked_cast(InT value) {
+    OPENVINO_ASSERT(static_cast<InT>(static_cast<OutT>(value)) == value, "Can not safely cast ",
+                    static_cast<int64_t>(value), " from ", ov::element::from<InT>(), " to ", ov::element::from<OutT>());
+
+    return static_cast<OutT>(value);
+}
+
+template <typename OutT, typename InT>
+enable_t<OutT, std::is_integral<InT>, std::is_unsigned<InT>, std::is_floating_point<OutT>> checked_cast(InT value) {
+    OPENVINO_ASSERT(static_cast<InT>(static_cast<OutT>(value)) == value, "Can not safely cast ",
+                    static_cast<uint64_t>(value), " from ", ov::element::from<InT>(), " to ",
+                    ov::element::from<OutT>());
+
+    return static_cast<OutT>(value);
+}
+
+template <typename OutT, typename InT>
+enable_t<OutT, std::is_same<double, InT>, std::is_same<float, OutT>> checked_cast(InT value) {
+    OPENVINO_ASSERT(static_cast<InT>(static_cast<OutT>(value)) == value, "Can not safely cast ", value, " from ",
+                    ov::element::from<InT>(), " to ", ov::element::from<OutT>());
+
+    return static_cast<OutT>(value);
+}
+
+template <typename OutT, typename InT>
+enable_t<OutT, std::is_same<float, InT>, std::is_same<double, OutT>> checked_cast(InT value) {
+    return static_cast<OutT>(value);
+}
+
+//
+// Custom float types
+//
+
+template <typename OutT>
+enable_t<OutT, std::is_same<ov::float16, OutT>> checked_cast(ov::bfloat16 val) {
+    return ov::float16(static_cast<float>(val));
+}
+template <typename OutT>
+enable_t<OutT, std::is_same<ov::float8_e4m3, OutT>> checked_cast(ov::bfloat16 val) {
+    return ov::float8_e4m3(static_cast<float>(val));
+}
+template <typename OutT>
+enable_t<OutT, std::is_same<ov::float8_e5m2, OutT>> checked_cast(ov::bfloat16 val) {
+    return ov::float8_e5m2(static_cast<float>(val));
+}
+template <typename OutT>
+enable_t<OutT, std::is_same<ov::bfloat16, OutT>> checked_cast(ov::float16 val) {
+    return ov::bfloat16(static_cast<float>(val));
+}
+template <typename OutT>
+enable_t<OutT, std::is_same<ov::float8_e4m3, OutT>> checked_cast(ov::float16 val) {
+    return ov::float8_e4m3(static_cast<float>(val));
+}
+template <typename OutT>
+enable_t<OutT, std::is_same<ov::float8_e5m2, OutT>> checked_cast(ov::float16 val) {
+    return ov::float8_e5m2(static_cast<float>(val));
+}
+template <typename OutT>
+enable_t<OutT, std::is_same<ov::float8_e5m2, OutT>> checked_cast(ov::float8_e4m3 val) {
+    return ov::float8_e5m2(static_cast<float>(val));
+}
+template <typename OutT>
+enable_t<OutT, std::is_same<ov::float16, OutT>> checked_cast(ov::float8_e4m3 val) {
+    return ov::float16(static_cast<float>(val));
+}
+template <typename OutT>
+enable_t<OutT, std::is_same<ov::bfloat16, OutT>> checked_cast(ov::float8_e4m3 val) {
+    return ov::bfloat16(static_cast<float>(val));
+}
+template <typename OutT>
+enable_t<OutT, std::is_same<ov::float8_e4m3, OutT>> checked_cast(ov::float8_e5m2 val) {
+    return ov::float8_e4m3(static_cast<float>(val));
+}
+template <typename OutT>
+enable_t<OutT, std::is_same<ov::float16, OutT>> checked_cast(ov::float8_e5m2 val) {
+    return ov::float16(static_cast<float>(val));
+}
+template <typename OutT>
+enable_t<OutT, std::is_same<ov::bfloat16, OutT>> checked_cast(ov::float8_e5m2 val) {
+    return ov::bfloat16(static_cast<float>(val));
+}
+
+template <typename OutT>
+enable_t<OutT, not_<or_<std::is_same<ov::float8_e4m3, OutT>, std::is_same<ov::float8_e5m2, OutT>,
+                        std::is_same<ov::float16, OutT>>>>
+checked_cast(ov::bfloat16 val) {
+    return checked_cast<OutT>(static_cast<float>(val));
+}
+template <typename OutT>
+enable_t<OutT, not_<or_<std::is_same<ov::float8_e4m3, OutT>, std::is_same<ov::float8_e5m2, OutT>,
+                        std::is_same<ov::bfloat16, OutT>>>>
+checked_cast(ov::float16 val) {
+    return checked_cast<OutT>(static_cast<float>(val));
+}
+template <typename OutT>
+enable_t<OutT, not_<or_<std::is_same<ov::float8_e5m2, OutT>, std::is_same<ov::bfloat16, OutT>,
+                        std::is_same<ov::float16, OutT>>>>
+checked_cast(ov::float8_e4m3 val) {
+    return checked_cast<OutT>(static_cast<float>(val));
+}
+template <typename OutT>
+enable_t<OutT, not_<or_<std::is_same<ov::float8_e4m3, OutT>, std::is_same<ov::bfloat16, OutT>,
+                        std::is_same<ov::float16, OutT>>>>
+checked_cast(ov::float8_e5m2 val) {
+    return checked_cast<OutT>(static_cast<float>(val));
+}
+
+template <typename OutT, typename InT>
+enable_t<OutT, std::is_same<ov::bfloat16, OutT>> checked_cast(InT val) {
+    return ov::bfloat16(checked_cast<float>(val));
+}
+template <typename OutT, typename InT>
+enable_t<OutT, std::is_same<ov::float16, OutT>> checked_cast(InT val) {
+    return ov::float16(checked_cast<float>(val));
+}
+template <typename OutT, typename InT>
+enable_t<OutT, std::is_same<ov::float8_e4m3, OutT>> checked_cast(InT val) {
+    return ov::float8_e4m3(checked_cast<float>(val));
+}
+template <typename OutT, typename InT>
+enable_t<OutT, std::is_same<ov::float8_e5m2, OutT>> checked_cast(InT val) {
+    return ov::float8_e5m2(checked_cast<float>(val));
+}
+
+//
+// Wrapper
+//
+
+template <typename OutT, typename InT>
+OutT convertValuePrecision(InT value) {
+    return checked_cast<OutT>(value);
+}
+
+}  // namespace utils
+}  // namespace npu
diff --git a/src/plugins/intel_npu/tools/common/include/tensor_utils.hpp b/src/plugins/intel_npu/tools/common/include/tensor_utils.hpp
new file mode 100644
index 00000000000000..c6ca8f50fd3f94
--- /dev/null
+++ b/src/plugins/intel_npu/tools/common/include/tensor_utils.hpp
@@ -0,0 +1,62 @@
+//
+// Copyright (C) 2024 Intel Corporation.
+// SPDX-License-Identifier: Apache 2.0
+//
+
+#pragma once
+
+#include <openvino/runtime/tensor.hpp>
+
+namespace npu {
+namespace utils {
+
+/**
+ * @brief Copies the contents of one tensor into another one which bears the same shape and precision.
+ *
+ * @param in The source tensor
+ * @param out The destination tensor
+ */
+void copyTensor(const ov::Tensor& in, const ov::Tensor& out);
+
+/**
+ * @brief Copies the contents of one tensor into another one which bears the same shape. Precision conversions from
+ * source type to target type will be performed if required.
+ *
+ * @param in The source tensor
+ * @param out The destination tensor
+ */
+void convertTensorPrecision(const ov::Tensor& in, const ov::Tensor& out);
+
+/**
+ * @brief Constructs a tensor with the same content as the source but with the precision converted to the specified
+ * target.
+ *
+ * @param in The source tensor
+ * @param precision The target precision
+ * @param ptr Optional, the constructed tensor will use this address for its buffer if specified
+ * @return The tensor obtained upon converting the precision.
+ */
+ov::Tensor toPrecision(const ov::Tensor& in, const ov::element::Type& precision, void* ptr = nullptr);
+
+/**
+ * @brief Constructs a tensor with the same content as the source but with the precision converted to FP32.
+ *
+ * @param in The source tensor
+ * @param ptr Optional, the constructed tensor will use this address for its buffer if specified
+ * @return The tensor obtained upon converting the precision.
+ */
+inline ov::Tensor toFP32(const ov::Tensor& in, void* ptr = nullptr) {
+    return toPrecision(in, ov::element::Type_t::f32, ptr);
+}
+
+/**
+ * @brief Converts the precision used by a batch of tensors to FP32 and returns their buffers. The original tensors
+ * remain unchanged.
+ *
+ * @param tensors The source tensors
+ * @return The buffers of the tensors obtained upon precision conversion
+ */
+std::vector<std::vector<float>> parseTensorsAsFP32(const std::map<std::string, ov::Tensor>& tensors);
+
+}  // namespace utils
+}  // namespace npu
diff --git a/src/plugins/intel_npu/tools/common/src/tensor_utils.cpp b/src/plugins/intel_npu/tools/common/src/tensor_utils.cpp
new file mode 100644
index 00000000000000..470d737a2b9d31
--- /dev/null
+++ b/src/plugins/intel_npu/tools/common/src/tensor_utils.cpp
@@ -0,0 +1,472 @@
+//
+// Copyright (C) 2024 Intel Corporation.
+// SPDX-License-Identifier: Apache 2.0
+//
+
+#include "tensor_utils.hpp"
+
+#include "data_type_converters.hpp"
+
+#include <openvino/core/except.hpp>
+#include <openvino/core/type.hpp>
+#include <openvino/core/type/element_type.hpp>
+#include <openvino/core/type/element_type_traits.hpp>
+
+#include <fstream>
+
+namespace {
+
+template <class InT, class OutT>
+void convertTensorPrecisionImpl(const ov::Tensor& in, const ov::Tensor& out) {
+    const auto inputBuffer = in.data<const InT>();
+    OPENVINO_ASSERT(inputBuffer != nullptr, "Tensor was not allocated");
+
+    const auto outputBuffer = out.data<OutT>();
+    OPENVINO_ASSERT(outputBuffer != nullptr, "Tensor was not allocated");
+
+    for (size_t index = 0; index < in.get_size(); ++index) {
+        outputBuffer[index] = npu::utils::convertValuePrecision<OutT>(inputBuffer[index]);
+    }
+}
+
+}  // namespace
+
+namespace npu {
+namespace utils {
+
+void copyTensor(const ov::Tensor& in, const ov::Tensor& out) {
+    OPENVINO_ASSERT(in.get_element_type() == out.get_element_type(), "Precision mismatch");
+    OPENVINO_ASSERT(in.get_shape() == out.get_shape(), "Shape mismatch");
+
+    const auto inputBuffer = in.data<const uint8_t>();
+    OPENVINO_ASSERT(inputBuffer != nullptr, "Tensor was not allocated");
+
+    const auto outputBuffer = out.data<uint8_t>();
+    OPENVINO_ASSERT(outputBuffer != nullptr, "Tensor was not allocated");
+
+    std::copy_n(inputBuffer, in.get_byte_size(), outputBuffer);
+}
+
+void convertTensorPrecision(const ov::Tensor& in, const ov::Tensor& out) {
+    OPENVINO_ASSERT(in.get_shape() == out.get_shape(), "Mismatch in Dims");
+
+    const ov::element::Type& inPrecision = in.get_element_type();
+    const ov::element::Type& outPrecision = out.get_element_type();
+
+    if (inPrecision == outPrecision) {
+        copyTensor(in, out);
+        return;
+    }
+
+#define CASE(InT, OutT)                                                                       \
+    convertTensorPrecisionImpl<ov::fundamental_type_for<ov::element::Type_t::InT>,            \
+                               ov::fundamental_type_for<ov::element::Type_t::OutT>>(in, out); \
+    break
+
+    switch (inPrecision) {
+    case ov::element::Type_t::f64: {
+        switch (outPrecision) {
+        case ov::element::Type_t::f32:
+            CASE(f64, f32);
+        case ov::element::Type_t::u64:
+            CASE(f64, u64);
+        case ov::element::Type_t::i64:
+            CASE(f64, i64);
+        case ov::element::Type_t::u32:
+            CASE(f64, u32);
+        case ov::element::Type_t::i32:
+            CASE(f64, i32);
+        case ov::element::Type_t::u16:
+            CASE(f64, u16);
+        case ov::element::Type_t::i16:
+            CASE(f64, i16);
+        case ov::element::Type_t::u8:
+            CASE(f64, u8);
+        case ov::element::Type_t::i8:
+            CASE(f64, i8);
+        case ov::element::Type_t::f16:
+            CASE(f64, f16);
+        case ov::element::Type_t::bf16:
+            CASE(f64, bf16);
+        default:
+            OPENVINO_THROW("Unsupported combination of precisions ", inPrecision.get_type_name(), " -> ",
+                           outPrecision.get_type_name());
+        }
+        break;
+    }
+    case ov::element::Type_t::f32: {
+        switch (outPrecision) {
+        case ov::element::Type_t::f64:
+            CASE(f32, f64);
+        case ov::element::Type_t::u64:
+            CASE(f32, u64);
+        case ov::element::Type_t::i64:
+            CASE(f32, i64);
+        case ov::element::Type_t::u32:
+            CASE(f32, u32);
+        case ov::element::Type_t::i32:
+            CASE(f32, i32);
+        case ov::element::Type_t::u16:
+            CASE(f32, u16);
+        case ov::element::Type_t::i16:
+            CASE(f32, i16);
+        case ov::element::Type_t::u8:
+            CASE(f32, u8);
+        case ov::element::Type_t::i8:
+            CASE(f32, i8);
+        case ov::element::Type_t::f16:
+            CASE(f32, f16);
+        case ov::element::Type_t::bf16:
+            CASE(f32, bf16);
+        default:
+            OPENVINO_THROW("Unsupported combination of precisions ", inPrecision.get_type_name(), " -> ",
+                           outPrecision.get_type_name());
+        }
+        break;
+    }
+    case ov::element::Type_t::f16: {
+        switch (outPrecision) {
+        case ov::element::Type_t::f64:
+            CASE(f16, f64);
+        case ov::element::Type_t::f32:
+            CASE(f16, f32);
+        case ov::element::Type_t::bf16:
+            CASE(f16, bf16);
+        case ov::element::Type_t::u64:
+            CASE(f16, u64);
+        case ov::element::Type_t::i64:
+            CASE(f16, i64);
+        case ov::element::Type_t::u32:
+            CASE(f16, u32);
+        case ov::element::Type_t::i32:
+            CASE(f16, i32);
+        case ov::element::Type_t::u16:
+            CASE(f16, u16);
+        case ov::element::Type_t::i16:
+            CASE(f16, i16);
+        case ov::element::Type_t::u8:
+            CASE(f16, u8);
+        case ov::element::Type_t::i8:
+            CASE(f16, i8);
+        default:
+            OPENVINO_THROW("Unsupported combination of precisions ", inPrecision.get_type_name(), " -> ",
+                           outPrecision.get_type_name());
+        }
+        break;
+    }
+    case ov::element::Type_t::bf16: {
+        switch (outPrecision) {
+        case ov::element::Type_t::f64:
+            CASE(bf16, f64);
+        case ov::element::Type_t::f32:
+            CASE(bf16, f32);
+        case ov::element::Type_t::f16:
+            CASE(bf16, f16);
+        case ov::element::Type_t::u64:
+            CASE(bf16, u64);
+        case ov::element::Type_t::i64:
+            CASE(bf16, i64);
+        case ov::element::Type_t::u32:
+            CASE(bf16, u32);
+        case ov::element::Type_t::i32:
+            CASE(bf16, i32);
+        case ov::element::Type_t::u16:
+            CASE(bf16, u16);
+        case ov::element::Type_t::i16:
+            CASE(bf16, i16);
+        case ov::element::Type_t::u8:
+            CASE(bf16, u8);
+        case ov::element::Type_t::i8:
+            CASE(bf16, i8);
+        default:
+            OPENVINO_THROW("Unsupported combination of precisions ", inPrecision.get_type_name(), " -> ",
+                           outPrecision.get_type_name());
+        }
+        break;
+    }
+    case ov::element::Type_t::u64: {
+        switch (outPrecision) {
+        case ov::element::Type_t::f64:
+            CASE(u64, f64);
+        case ov::element::Type_t::f32:
+            CASE(u64, f32);
+        case ov::element::Type_t::i64:
+            CASE(u64, i64);
+        case ov::element::Type_t::u32:
+            CASE(u64, u32);
+        case ov::element::Type_t::i32:
+            CASE(u64, i32);
+        case ov::element::Type_t::u16:
+            CASE(u64, u16);
+        case ov::element::Type_t::i16:
+            CASE(u64, i16);
+        case ov::element::Type_t::u8:
+            CASE(u64, u8);
+        case ov::element::Type_t::i8:
+            CASE(u64, i8);
+        case ov::element::Type_t::f16:
+            CASE(u64, f16);
+        case ov::element::Type_t::bf16:
+            CASE(u64, bf16);
+        default:
+            OPENVINO_THROW("Unsupported combination of precisions ", inPrecision.get_type_name(), " -> ",
+                           outPrecision.get_type_name());
+        }
+        break;
+    }
+    case ov::element::Type_t::i64: {
+        switch (outPrecision) {
+        case ov::element::Type_t::f64:
+            CASE(i64, f64);
+        case ov::element::Type_t::f32:
+            CASE(i64, f32);
+        case ov::element::Type_t::u64:
+            CASE(i64, u64);
+        case ov::element::Type_t::u32:
+            CASE(i64, u32);
+        case ov::element::Type_t::i32:
+            CASE(i64, i32);
+        case ov::element::Type_t::u16:
+            CASE(i64, u16);
+        case ov::element::Type_t::i16:
+            CASE(i64, i16);
+        case ov::element::Type_t::u8:
+            CASE(i64, u8);
+        case ov::element::Type_t::i8:
+            CASE(i64, i8);
+        case ov::element::Type_t::f16:
+            CASE(i64, f16);
+        case ov::element::Type_t::bf16:
+            CASE(i64, bf16);
+        default:
+            OPENVINO_THROW("Unsupported combination of precisions ", inPrecision.get_type_name(), " -> ",
+                           outPrecision.get_type_name());
+        }
+        break;
+    }
+    case ov::element::Type_t::u32: {
+        switch (outPrecision) {
+        case ov::element::Type_t::f64:
+            CASE(u32, f64);
+        case ov::element::Type_t::f32:
+            CASE(u32, f32);
+        case ov::element::Type_t::u64:
+            CASE(u32, u64);
+        case ov::element::Type_t::i64:
+            CASE(u32, i64);
+        case ov::element::Type_t::i32:
+            CASE(u32, i32);
+        case ov::element::Type_t::u16:
+            CASE(u32, u16);
+        case ov::element::Type_t::i16:
+            CASE(u32, i16);
+        case ov::element::Type_t::u8:
+            CASE(u32, u8);
+        case ov::element::Type_t::i8:
+            CASE(u32, i8);
+        case ov::element::Type_t::f16:
+            CASE(u32, f16);
+        case ov::element::Type_t::bf16:
+            CASE(u32, bf16);
+        default:
+            OPENVINO_THROW("Unsupported combination of precisions ", inPrecision.get_type_name(), " -> ",
+                           outPrecision.get_type_name());
+        }
+        break;
+    }
+    case ov::element::Type_t::i32: {
+        switch (outPrecision) {
+        case ov::element::Type_t::f64:
+            CASE(i32, f64);
+        case ov::element::Type_t::f32:
+            CASE(i32, f32);
+        case ov::element::Type_t::u64:
+            CASE(i32, u64);
+        case ov::element::Type_t::i64:
+            CASE(i32, i64);
+        case ov::element::Type_t::u32:
+            CASE(i32, u32);
+        case ov::element::Type_t::u16:
+            CASE(i32, u16);
+        case ov::element::Type_t::i16:
+            CASE(i32, i16);
+        case ov::element::Type_t::u8:
+            CASE(i32, u8);
+        case ov::element::Type_t::i8:
+            CASE(i32, i8);
+        case ov::element::Type_t::f16:
+            CASE(i32, f16);
+        case ov::element::Type_t::bf16:
+            CASE(i32, bf16);
+        default:
+            OPENVINO_THROW("Unsupported combination of precisions ", inPrecision.get_type_name(), " -> ",
+                           outPrecision.get_type_name());
+        }
+        break;
+    }
+    case ov::element::Type_t::u16: {
+        switch (outPrecision) {
+        case ov::element::Type_t::f64:
+            CASE(u16, f64);
+        case ov::element::Type_t::f32:
+            CASE(u16, f32);
+        case ov::element::Type_t::u64:
+            CASE(u16, u64);
+        case ov::element::Type_t::i64:
+            CASE(u16, i64);
+        case ov::element::Type_t::u32:
+            CASE(u16, u32);
+        case ov::element::Type_t::i32:
+            CASE(u16, i32);
+        case ov::element::Type_t::i16:
+            CASE(u16, i16);
+        case ov::element::Type_t::u8:
+            CASE(u16, u8);
+        case ov::element::Type_t::i8:
+            CASE(u16, i8);
+        case ov::element::Type_t::f16:
+            CASE(u16, f16);
+        case ov::element::Type_t::bf16:
+            CASE(u16, bf16);
+        default:
+            OPENVINO_THROW("Unsupported combination of precisions ", inPrecision.get_type_name(), " -> ",
+                           outPrecision.get_type_name());
+        }
+        break;
+    }
+    case ov::element::Type_t::i16: {
+        switch (outPrecision) {
+        case ov::element::Type_t::f64:
+            CASE(i16, f64);
+        case ov::element::Type_t::f32:
+            CASE(i16, f32);
+        case ov::element::Type_t::u64:
+            CASE(i16, u64);
+        case ov::element::Type_t::i64:
+            CASE(i16, i64);
+        case ov::element::Type_t::u32:
+            CASE(i16, u32);
+        case ov::element::Type_t::i32:
+            CASE(i16, i32);
+        case ov::element::Type_t::u16:
+            CASE(i16, u16);
+        case ov::element::Type_t::u8:
+            CASE(i16, u8);
+        case ov::element::Type_t::i8:
+            CASE(i16, i8);
+        case ov::element::Type_t::f16:
+            CASE(i16, f16);
+        case ov::element::Type_t::bf16:
+            CASE(i16, bf16);
+        default:
+            OPENVINO_THROW("Unsupported combination of precisions ", inPrecision.get_type_name(), " -> ",
+                           outPrecision.get_type_name());
+        }
+        break;
+    }
+    case ov::element::Type_t::u8: {
+        switch (outPrecision) {
+        case ov::element::Type_t::f64:
+            CASE(u8, f64);
+        case ov::element::Type_t::f32:
+            CASE(u8, f32);
+        case ov::element::Type_t::u64:
+            CASE(u8, u64);
+        case ov::element::Type_t::i64:
+            CASE(u8, i64);
+        case ov::element::Type_t::u32:
+            CASE(u8, u32);
+        case ov::element::Type_t::i32:
+            CASE(u8, i32);
+        case ov::element::Type_t::u16:
+            CASE(u8, u16);
+        case ov::element::Type_t::i16:
+            CASE(u8, i16);
+        case ov::element::Type_t::i8:
+            CASE(u8, i8);
+        case ov::element::Type_t::f16:
+            CASE(u8, f16);
+        case ov::element::Type_t::bf16:
+            CASE(u8, bf16);
+        default:
+            OPENVINO_THROW("Unsupported combination of precisions ", inPrecision.get_type_name(), " -> ",
+                           outPrecision.get_type_name());
+        }
+        break;
+    }
+    case ov::element::Type_t::i8: {
+        switch (outPrecision) {
+        case ov::element::Type_t::f64:
+            CASE(i8, f64);
+        case ov::element::Type_t::f32:
+            CASE(i8, f32);
+        case ov::element::Type_t::u64:
+            CASE(i8, u64);
+        case ov::element::Type_t::i64:
+            CASE(i8, i64);
+        case ov::element::Type_t::u32:
+            CASE(i8, u32);
+        case ov::element::Type_t::i32:
+            CASE(i8, i32);
+        case ov::element::Type_t::u16:
+            CASE(i8, u16);
+        case ov::element::Type_t::i16:
+            CASE(i8, i16);
+        case ov::element::Type_t::u8:
+            CASE(i8, u8);
+        case ov::element::Type_t::f16:
+            CASE(i8, f16);
+        case ov::element::Type_t::bf16:
+            CASE(i8, bf16);
+        default:
+            OPENVINO_THROW("Unsupported combination of precisions ", inPrecision.get_type_name(), " -> ",
+                           outPrecision.get_type_name());
+        }
+        break;
+    }
+    default:
+        OPENVINO_THROW("Unsupported combination of precisions ", inPrecision.get_type_name(), " -> ",
+                       outPrecision.get_type_name());
+    }
+
+#undef CASE
+}
+
+ov::Tensor toPrecision(const ov::Tensor& in, const ov::element::Type& precision, void* ptr) {
+    if (in.get_element_type() == precision && ptr == nullptr) {
+        return in;
+    }
+
+    ov::Tensor out;
+
+    if (ptr == nullptr) {
+        out = ov::Tensor(precision, in.get_shape());
+    } else {
+        out = ov::Tensor(precision, in.get_shape(), ptr);
+    }
+
+    convertTensorPrecision(in, out);
+
+    return out;
+}
+
+std::vector<std::vector<float>> parseTensorsAsFP32(const std::map<std::string, ov::Tensor>& tensors) {
+    std::vector<std::vector<float>> results;
+
+    for (const auto& tensor : tensors) {
+        const ov::Tensor tensorFP32 = toFP32(tensor.second);
+        const auto dataBuffer = tensorFP32.data<float>();
+        OPENVINO_ASSERT(dataBuffer != nullptr);
+
+        const size_t size = tensorFP32.get_size();
+        std::vector<float> result(size);
+        std::copy_n(dataBuffer, size, result.begin());
+
+        results.push_back(result);
+    }
+
+    return results;
+}
+
+}  // namespace utils
+}  // namespace npu
diff --git a/src/plugins/intel_npu/tools/compile_tool/CMakeLists.txt b/src/plugins/intel_npu/tools/compile_tool/CMakeLists.txt
new file mode 100644
index 00000000000000..9d1e1d38b48188
--- /dev/null
+++ b/src/plugins/intel_npu/tools/compile_tool/CMakeLists.txt
@@ -0,0 +1,56 @@
+#
+# Copyright (C) 2023-2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+#
+
+set(TARGET_NAME compile_tool)
+
+if (NOT DEFINED PROJECT_NAME)
+    cmake_minimum_required(VERSION 3.13 FATAL_ERROR)
+    project(compile_tool_standalone)
+    include("cmake/standalone.cmake")
+    return()
+endif()
+
+#
+# Define the target
+#
+
+ov_add_target(ADD_CPPLINT
+              TYPE EXECUTABLE
+              NAME ${TARGET_NAME}
+              ROOT ${CMAKE_CURRENT_SOURCE_DIR}
+              LINK_LIBRARIES
+                  PRIVATE
+                      openvino::runtime
+                      gflags
+                      Threads::Threads)
+
+set_target_properties(${TARGET_NAME} PROPERTIES
+                          FOLDER ${CMAKE_CURRENT_SOURCE_DIR}
+                          CXX_STANDARD 17)
+
+if (CMAKE_COMPILER_IS_GNUCXX)
+    target_compile_options(${TARGET_NAME} PRIVATE -Wall)
+endif()
+
+# TODO: fix warnings and remove this exception
+if(CMAKE_COMPILER_IS_GNUCXX OR OV_COMPILER_IS_CLANG)
+    ov_add_compiler_flags(-Wno-missing-declarations)
+endif()
+
+#
+# Install
+#
+
+install(TARGETS ${TARGET_NAME}
+        RUNTIME DESTINATION "tools/${TARGET_NAME}"
+        COMPONENT ${NPU_INTERNAL_COMPONENT}
+        ${OV_CPACK_COMP_NPU_INTERNAL_EXCLUDE_ALL})
+
+if(EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/README.md")
+    install(FILES "${CMAKE_CURRENT_SOURCE_DIR}/README.md"
+            DESTINATION "tools/${TARGET_NAME}"
+            COMPONENT ${NPU_INTERNAL_COMPONENT}
+            ${OV_CPACK_COMP_NPU_INTERNAL_EXCLUDE_ALL})
+endif()
diff --git a/src/plugins/intel_npu/tools/compile_tool/README.md b/src/plugins/intel_npu/tools/compile_tool/README.md
new file mode 100644
index 00000000000000..daebe97b40620c
--- /dev/null
+++ b/src/plugins/intel_npu/tools/compile_tool/README.md
@@ -0,0 +1,101 @@
+# NPU Compile Tool
+
+This page demonstrates how to use NPU Compile Tool to convert OpenVINO™ Intermediate Representation (IR) of an AI model or a model in ONNX format to a "blob" file that is compiled by NPU NN Compiler and serialized to the format accessible for NPU Driver and NPU Runtime to execute.
+
+
+## Description 
+
+Compile tool is a C++ application that enables you to compile a model for inference on a specific device and export the compiled representation to a binary file.
+With this tool, you can compile a model using supported OpenVINO Runtime devices on a machine that does not have the physical device connected, i.e. without NPU driver and Runtime loading, and then transfer a generated file to any machine with the target inference device available. 
+
+Using Compile Tool is not a basic approach to end-to-end execution and/or application but mostly suitable for debugging and validation and some specific use cases. If one is looking for the standard way of reducing application startup delays by exporting and reusing the compiled model automatically, refer to [Model Caching article](https://docs.openvino.ai/2024/openvino-workflow/running-inference/inference-devices-and-modes/npu-device.html#model-caching)
+
+## Workflow of the Compile tool
+
+First, the application reads command-line parameters and loads a model to the OpenVINO Runtime device. After that, the application exports a blob with the compiled model and writes it to the output file.
+
+## How to build
+
+### Within NPU Plugin build
+
+See [How to build](https://github.com/openvinotoolkit/openvino/wiki#how-to-build). If `ENABLE_INTEL_NPU=ON` is provided, no additional steps are required for Compile Tool. It will be built unconditionally with every NPU Plugin build. It can be found in `bin` folder.  
+
+If you need to configure a release package layout and have Compile Tool in it, use `cmake --install <dir> --component npu_internal` from your `build` folder. After installation compile_tool executable can be found in `<install_dir>/tools/compile_tool` folder.
+
+### Standalone build
+
+#### Prerequisites 
+* [OpenVINO™ Runtime release package](https://docs.openvino.ai/2024/get-started/install-openvino.html)
+
+#### Build instructions
+1. Download and install OpenVINO™ Runtime package 
+2. Build Compile Tool
+    ```sh
+    mkdir compile_tool_build && cd compile_tool_build
+    cmake -DOpenVINO_DIR=<openvino_install_dir>/runtime/cmake <compile_tool_source_dir>
+    cmake --build . --config Release
+    cmake --install . --prefix <compile_tool_install_dir>
+    ```
+    > Note 1: command line instruction might differ on different platforms (e.g. Windows cmd)  
+    > Note 2: this example is based on OpenVINO Archive distribution. If you have chosen another installation method, specifying OpenVINO_DIR and calling `setupvars` script might not be needed. Refer [documentation](https://docs.openvino.ai/2024/get-started/install-openvino.html) for details.  
+    > Note 3: `<compile_tool_install_dir>` can be any directory on your filesystem that you want to use for installation including `<openvino_install_dir>` if you wish to extend OpenVINO package
+3. Verify the installation
+    ```sh
+    source <openvino_install_dir>/setupvars.sh
+    <compile_tool_install_dir>/tools/compile_tool/compile_tool -h
+    ```
+    > Note 1: command line might differ depending on your platform  
+    > Note 2: this example is based on OpenVINO Archive distribution. If you have chosen another installation method, calling setupvars might not be needed. Refer [documentation](https://docs.openvino.ai/2024/get-started/install-openvino.html) for details.  
+
+    Successful build will show the information about Compile Tool CLI options
+
+
+## How to run 
+
+Running the application with the `-h` option yields the following usage message:
+```
+OpenVINO Runtime version ......... 202x.y.z
+Build ........... 202x.y.z-build-hash
+Parsing command-line arguments
+compile_tool [OPTIONS]
+
+ Common options:
+    -h                                       Optional. Print the usage message.
+    -m                           <value>     Required. Path to the XML model.
+    -d                           <value>     Required. Specify a target device for which executable network will be compiled.
+                                             Use "-d HETERO:<comma-separated_devices_list>" format to specify HETERO plugin.
+                                             Use "-d MULTI:<comma-separated_devices_list>" format to specify MULTI plugin.
+                                             The application looks for a suitable plugin for the specified device.
+    -o                           <value>     Optional. Path to the output file. Default value: "<model_xml_file>.blob".
+    -c                           <value>     Optional. Path to the configuration file.
+    -ip                          <value>     Optional. Specifies precision for all input layers of the network.
+    -op                          <value>     Optional. Specifies precision for all output layers of the network.
+    -iop                        "<value>"    Optional. Specifies precision for input and output layers by name.
+                                             Example: -iop "input:FP16, output:FP16".
+                                             Notice that quotes are required.
+                                             Overwrites precision from ip and op options for specified layers.
+    -il                          <value>     Optional. Specifies layout for all input layers of the network.
+    -ol                          <value>     Optional. Specifies layout for all output layers of the network.
+    -iol                        "<value>"    Optional. Specifies layout for input and output layers by name.
+                                             Example: -iol "input:NCHW, output:NHWC".
+                                             Notice that quotes are required.
+                                             Overwrites layout from il and ol options for specified layers.
+    -iml                         <value>     Optional. Specifies model layout for all input layers of the network.
+    -oml                         <value>     Optional. Specifies model layout for all output layers of the network.
+    -ioml                       "<value>"    Optional. Specifies model layout for input and output tensors by name.
+                                             Example: -ionl "input:NCHW, output:NHWC".
+                                             Notice that quotes are required.
+                                             Overwrites layout from il and ol options for specified layers.
+    -shape                       <value>      Set shape for model input. For example, "input1[1,3,224,224],input2[1,4]" or "[1,3,224,224]" in case of one input size. This parameter affect model input shape and can be dynamic. For dynamic dimensions use symbol `?` or '-1'. Ex. [?,3,?,?]. For bounded dimensions specify range 'min..max'. Ex. [1..10,3,?,?].
+```
+Running the application with the empty list of options yields an error message.
+
+For example, to compile a blob for inference on Intel® Core™ Ultra NPU, run the command below
+```
+./compile_tool -m <path_to_model>/model_name.xml -d NPU.3720
+```
+
+You can pass a config file via `-c` option which allow you to specify some public or private properties. More details in [Supported Properties](https://github.com/openvinotoolkit/openvino/tree/master/src/plugins/intel_npu#supported-properties) and in [configs](https://github.com/openvinotoolkit/openvino/tree/master/src/plugins/intel_npu/src/al/include/intel_npu/al/config). For example, to use a custom build of NPU Compiler instaed of the release Compiler distributed within NPU driver, create a config file with the following content:
+```
+NPU_COMPILER_TYPE MLIR
+```
diff --git a/src/plugins/intel_npu/tools/compile_tool/cmake/standalone.cmake b/src/plugins/intel_npu/tools/compile_tool/cmake/standalone.cmake
new file mode 100644
index 00000000000000..1bd9e2946de56e
--- /dev/null
+++ b/src/plugins/intel_npu/tools/compile_tool/cmake/standalone.cmake
@@ -0,0 +1,47 @@
+#
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+#
+
+set(CMAKE_CXX_STANDARD 17)
+set(CMAKE_CXX_STANDARD_REQUIRED ON)
+
+if("${CMAKE_BUILD_TYPE}" STREQUAL "")
+  set(CMAKE_BUILD_TYPE "Release")
+endif()
+
+#
+# OpenVINO package path should be specified via OpenVINO_DIR
+#
+
+find_package(Threads REQUIRED)
+find_package(OpenVINO REQUIRED COMPONENTS Runtime)
+
+#
+# gflags is distributed in sources in OpenVINO packages so we need to build it explicitly
+#
+
+if(EXISTS "${PACKAGE_PREFIX_DIR}/samples/cpp/thirdparty/gflags")
+    add_subdirectory("${PACKAGE_PREFIX_DIR}/samples/cpp/thirdparty/gflags" gflags EXCLUDE_FROM_ALL)
+else()
+    find_package(gflags REQUIRED)
+endif()
+
+set(DEPENDENCIES
+        Threads::Threads
+        gflags
+        openvino::runtime
+)
+
+if (CMAKE_COMPILER_IS_GNUCXX)
+    target_compile_options(${TARGET_NAME} PRIVATE -Wall)
+endif()
+
+file(GLOB SOURCES "${CMAKE_CURRENT_SOURCE_DIR}/*.cpp")
+
+add_executable(${TARGET_NAME} ${SOURCES})
+target_link_libraries(${TARGET_NAME} ${DEPENDENCIES})
+
+install(TARGETS ${TARGET_NAME}
+        DESTINATION "tools/${TARGET_NAME}"
+        COMPONENT npu_tools)
diff --git a/src/plugins/intel_npu/tools/compile_tool/main.cpp b/src/plugins/intel_npu/tools/compile_tool/main.cpp
new file mode 100644
index 00000000000000..4da65962be6aaf
--- /dev/null
+++ b/src/plugins/intel_npu/tools/compile_tool/main.cpp
@@ -0,0 +1,593 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <algorithm>
+#include <chrono>
+#include <cstdlib>
+#include <fstream>
+#include <iostream>
+#include <map>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+#include <gflags/gflags.h>
+
+#include "openvino/core/partial_shape.hpp"
+#include "openvino/openvino.hpp"
+
+#include "tools_helpers.hpp"
+
+static constexpr char help_message[] = "Optional. Print the usage message.";
+
+static constexpr char model_message[] = "Required. Path to the XML model.";
+
+static constexpr char targetDeviceMessage[] =
+        "Required. Specify a target device for which executable network will be compiled.\n"
+        "                                             Use \"-d HETERO:<comma-separated_devices_list>\" format to "
+        "specify HETERO plugin.\n"
+        "                                             Use \"-d MULTI:<comma-separated_devices_list>\" format to "
+        "specify MULTI plugin.\n"
+        "                                             The application looks for a suitable plugin for the specified "
+        "device.";
+
+static constexpr char output_message[] = "Optional. Path to the output file. Default value: \"<model_xml_file>.blob\".";
+
+static constexpr char log_level_message[] = "Optional. Log level for OpenVINO library.";
+
+static constexpr char config_message[] = "Optional. Path to the configuration file.";
+
+static constexpr char inputs_precision_message[] = "Optional. Specifies precision for all input layers of the network.";
+
+static constexpr char outputs_precision_message[] =
+        "Optional. Specifies precision for all output layers of the network.";
+
+static constexpr char iop_message[] =
+        "Optional. Specifies precision for input and output layers by name.\n"
+        "                                             Example: -iop \"input:FP16, output:FP16\".\n"
+        "                                             Notice that quotes are required.\n"
+        "                                             Overwrites precision from ip and op options for specified "
+        "layers.";
+
+static constexpr char inputs_layout_message[] = "Optional. Specifies layout for all input layers of the network.";
+
+static constexpr char outputs_layout_message[] = "Optional. Specifies layout for all output layers of the network.";
+
+static constexpr char iol_message[] =
+        "Optional. Specifies layout for input and output layers by name.\n"
+        "                                             Example: -iol \"input:NCHW, output:NHWC\".\n"
+        "                                             Notice that quotes are required.\n"
+        "                                             Overwrites layout from il and ol options for specified layers.";
+
+static constexpr char inputs_model_layout_message[] =
+        "Optional. Specifies model layout for all input layers of the network.";
+
+static constexpr char outputs_model_layout_message[] =
+        "Optional. Specifies model layout for all output layers of the network.";
+
+static constexpr char ioml_message[] =
+        "Optional. Specifies model layout for input and output tensors by name.\n"
+        "                                             Example: -ionl \"input:NCHW, output:NHWC\".\n"
+        "                                             Notice that quotes are required.\n"
+        "                                             Overwrites layout from il and ol options for specified layers.";
+
+static const char shape_message[] =
+        " Set shape for model input. For example, \"input1[1,3,224,224],input2[1,4]\" or \"[1,3,224,224]\""
+        " in case of one input size. This parameter affect model input shape and can be dynamic."
+        " For dynamic dimensions use symbol `?` or '-1'. Ex. [?,3,?,?]."
+        " For bounded dimensions specify range 'min..max'. Ex. [1..10,3,?,?].";
+
+static const char override_model_batch_size[] = "Enforce a model to be compiled for batch size";
+
+DEFINE_bool(h, false, help_message);
+DEFINE_string(m, "", model_message);
+DEFINE_string(d, "", targetDeviceMessage);
+DEFINE_string(o, "", output_message);
+DEFINE_string(log_level, "", log_level_message);
+DEFINE_string(c, "", config_message);
+DEFINE_string(ip, "", inputs_precision_message);
+DEFINE_string(op, "", outputs_precision_message);
+DEFINE_string(iop, "", iop_message);
+DEFINE_string(il, "", inputs_layout_message);
+DEFINE_string(ol, "", outputs_layout_message);
+DEFINE_string(iol, "", iol_message);
+DEFINE_string(iml, "", inputs_model_layout_message);
+DEFINE_string(oml, "", outputs_model_layout_message);
+DEFINE_string(ioml, "", ioml_message);
+DEFINE_string(shape, "", shape_message);
+DEFINE_uint32(override_model_batch_size, 1, override_model_batch_size);
+
+namespace {
+std::vector<std::string> splitStringList(const std::string& str, char delim) {
+    if (str.empty())
+        return {};
+
+    std::istringstream istr(str);
+
+    std::vector<std::string> result;
+    std::string elem;
+    while (std::getline(istr, elem, delim)) {
+        if (elem.empty()) {
+            continue;
+        }
+        result.emplace_back(std::move(elem));
+    }
+
+    return result;
+}
+
+std::map<std::string, std::string> parseArgMap(std::string argMap) {
+    argMap.erase(std::remove_if(argMap.begin(), argMap.end(), ::isspace), argMap.end());
+
+    const auto pairs = splitStringList(argMap, ',');
+
+    std::map<std::string, std::string> parsedMap;
+    for (auto&& pair : pairs) {
+        const auto lastDelimPos = pair.find_last_of(':');
+        auto key = pair.substr(0, lastDelimPos);
+        auto value = pair.substr(lastDelimPos + 1);
+
+        if (lastDelimPos == std::string::npos || key.empty() || value.empty()) {
+            throw std::invalid_argument("Invalid key/value pair " + pair + ". Expected <layer_name>:<value>");
+        }
+
+        parsedMap[std::move(key)] = std::move(value);
+    }
+
+    return parsedMap;
+}
+}  // namespace
+using supported_type_t = std::unordered_map<std::string, ov::element::Type>;
+ov::element::Type getType(std::string value, const supported_type_t& supported_precisions) {
+    std::transform(value.begin(), value.end(), value.begin(), ::toupper);
+
+    const auto precision = supported_precisions.find(value);
+    if (precision == supported_precisions.end()) {
+        throw std::logic_error("\"" + value + "\"" + " is not a valid precision");
+    }
+
+    return precision->second;
+}
+ov::element::Type getType(const std::string& value) {
+    static const supported_type_t supported_types = {
+            {"FP32", ov::element::f32}, {"f32", ov::element::f32},      {"FP16", ov::element::f16},
+            {"f16", ov::element::f16},  {"BF16", ov::element::bf16},    {"bf16", ov::element::bf16},
+            {"U64", ov::element::u64},  {"u64", ov::element::u64},      {"I64", ov::element::i64},
+            {"i64", ov::element::i64},  {"U32", ov::element::u32},      {"u32", ov::element::u32},
+            {"I32", ov::element::i32},  {"i32", ov::element::i32},      {"U16", ov::element::u16},
+            {"u16", ov::element::u16},  {"I16", ov::element::i16},      {"i16", ov::element::i16},
+            {"U8", ov::element::u8},    {"u8", ov::element::u8},        {"I8", ov::element::i8},
+            {"i8", ov::element::i8},    {"BOOL", ov::element::boolean}, {"boolean", ov::element::boolean},
+    };
+
+    return getType(value, supported_types);
+}
+
+bool isFP32(const ov::element::Type& type) {
+    return type == ov::element::f32;
+}
+
+void boundDynamicShape(std::shared_ptr<ov::Model>& model) {
+    for (auto&& item : model->get_parameters()) {
+        auto shape = item->get_partial_shape();
+        if (shape.is_static()) {
+            continue;
+        }
+        auto rank = shape.rank();
+        if (rank.is_dynamic()) {
+            throw std::logic_error("Rank \"" + rank.to_string() + "\" of the shape \"" + shape.to_string() +
+                                   "\" is dynamic which is not supported by NPU");
+        }
+        auto layout = item->get_layout();
+        if (!ov::layout::has_batch(layout)) {
+            item->set_layout(ov::Layout(layout.to_string().insert(1, "N,")));
+            layout = item->get_layout();
+        }
+        if (shape[ov::layout::batch_idx(layout)].is_dynamic()) {
+            std::cout << "WARNING: Shape \"" + shape.to_string() + "\"" +
+                                 " has dynamic batch size which is not supported by NPU\n"
+                                 "         Setting batch to 1 forcibly"
+                      << std::endl;
+            ov::set_batch(model, 1);
+        }
+        shape = item->get_partial_shape();
+        if (shape.is_dynamic()) {
+            throw std::logic_error("Model's input shape \"" + shape.to_string() + "\"" +
+                                   " is dynamic which is not supported by NPU");
+        }
+    }
+}
+
+void setModelBatch(std::shared_ptr<ov::Model>& model, uint32_t batch = 1) {
+    if (batch == 1) {
+        return;
+    }
+    for (auto&& item : model->get_parameters()) {
+        auto shape = item->get_partial_shape();
+        auto rank = shape.rank();
+        if (rank.is_dynamic()) {
+            throw std::logic_error("Rank \"" + rank.to_string() + "\" of the shape \"" + shape.to_string() +
+                                   "\" is dynamic which is not supported by NPU");
+        }
+        auto layout = item->get_layout();
+        if (!ov::layout::has_batch(layout)) {
+            item->set_layout(ov::Layout(layout.to_string().insert(1, "N,")));
+            layout = item->get_layout();
+        }
+        if (shape[ov::layout::batch_idx(layout)].is_dynamic()) {
+            throw std::logic_error("ERROR: Shape \"" + shape.to_string() + "\"" +
+                                   " has dynamic batch size which is not supported by NPU\n"
+                                   "Cannot apply fixed batch: " +
+                                   std::to_string(batch) +
+                                   ". Please remove the parameter from config: \"override_model_batch_size\"");
+        }
+        ov::set_batch(model, batch);
+    }
+}
+
+void configurePrePostProcessing(std::shared_ptr<ov::Model>& model, const std::string& ip, const std::string& op,
+                                const std::string& iop, const std::string& il, const std::string& ol,
+                                const std::string& iol, const std::string& iml, const std::string& oml,
+                                const std::string& ioml) {
+    auto preprocessor = ov::preprocess::PrePostProcessor(model);
+    const auto inputs = model->inputs();
+    const auto outputs = model->outputs();
+
+    if (!ip.empty()) {
+        auto type = getType(ip);
+        for (size_t i = 0; i < inputs.size(); i++) {
+            preprocessor.input(i).tensor().set_element_type(type);
+        }
+    }
+
+    if (!op.empty()) {
+        auto type = getType(op);
+        for (size_t i = 0; i < outputs.size(); i++) {
+            preprocessor.output(i).tensor().set_element_type(type);
+        }
+    }
+
+    if (!iop.empty()) {
+        const auto user_precisions_map = parseArgMap(iop);
+        for (auto&& item : user_precisions_map) {
+            const auto& tensor_name = item.first;
+            const auto type = getType(item.second);
+
+            bool tensorFound = false;
+            for (size_t i = 0; i < inputs.size(); i++) {
+                if (inputs[i].get_names().count(tensor_name)) {
+                    preprocessor.input(i).tensor().set_element_type(type);
+                    tensorFound = true;
+                    break;
+                }
+            }
+            if (!tensorFound) {
+                for (size_t i = 0; i < outputs.size(); i++) {
+                    if (outputs[i].get_names().count(tensor_name)) {
+                        preprocessor.output(i).tensor().set_element_type(type);
+                        tensorFound = true;
+                        break;
+                    }
+                }
+            }
+            OPENVINO_ASSERT(tensorFound, "Model doesn't have input/output with tensor name: ", tensor_name);
+        }
+    }
+    if (!il.empty()) {
+        for (size_t i = 0; i < inputs.size(); i++) {
+            preprocessor.input(i).tensor().set_layout(ov::Layout(il));
+        }
+    }
+
+    if (!ol.empty()) {
+        for (size_t i = 0; i < outputs.size(); i++) {
+            preprocessor.output(i).tensor().set_layout(ov::Layout(ol));
+        }
+    }
+
+    if (!iol.empty()) {
+        const auto user_precisions_map = parseArgMap(iol);
+        for (auto&& item : user_precisions_map) {
+            const auto& tensor_name = item.first;
+
+            bool tensorFound = false;
+            for (size_t i = 0; i < inputs.size(); i++) {
+                if (inputs[i].get_names().count(tensor_name)) {
+                    preprocessor.input(i).tensor().set_layout(ov::Layout(item.second));
+                    tensorFound = true;
+                    break;
+                }
+            }
+            if (!tensorFound) {
+                for (size_t i = 0; i < outputs.size(); i++) {
+                    if (outputs[i].get_names().count(tensor_name)) {
+                        preprocessor.output(i).tensor().set_layout(ov::Layout(item.second));
+                        tensorFound = true;
+                        break;
+                    }
+                }
+            }
+            OPENVINO_ASSERT(tensorFound, "Model doesn't have input/output with tensor name: ", tensor_name);
+        }
+    }
+
+    if (!iml.empty()) {
+        for (size_t i = 0; i < inputs.size(); i++) {
+            preprocessor.input(i).model().set_layout(ov::Layout(iml));
+        }
+    }
+
+    if (!oml.empty()) {
+        for (size_t i = 0; i < outputs.size(); i++) {
+            preprocessor.output(i).model().set_layout(ov::Layout(oml));
+        }
+    }
+
+    if (!ioml.empty()) {
+        const auto user_precisions_map = parseArgMap(ioml);
+        for (auto&& item : user_precisions_map) {
+            const auto& tensor_name = item.first;
+
+            bool tensorFound = false;
+            for (size_t i = 0; i < inputs.size(); i++) {
+                if (inputs[i].get_names().count(tensor_name)) {
+                    preprocessor.input(i).model().set_layout(ov::Layout(item.second));
+                    tensorFound = true;
+                    break;
+                }
+            }
+            if (!tensorFound) {
+                for (size_t i = 0; i < outputs.size(); i++) {
+                    if (outputs[i].get_names().count(tensor_name)) {
+                        preprocessor.output(i).model().set_layout(ov::Layout(item.second));
+                        tensorFound = true;
+                        break;
+                    }
+                }
+            }
+            OPENVINO_ASSERT(tensorFound, "Model doesn't have input/output with tensor name: ", tensor_name);
+        }
+    }
+
+    model = preprocessor.build();
+}
+
+void printInputAndOutputsInfoShort(const ov::Model& network) {
+    std::cout << "Network inputs:" << std::endl;
+    for (auto&& param : network.get_parameters()) {
+        auto l = param->get_layout();
+        std::cout << "    " << param->get_friendly_name() << " : " << param->get_element_type() << " / "
+                  << param->get_layout().to_string() << " / " << param->get_partial_shape().to_string() << std::endl;
+    }
+    std::cout << "Network outputs:" << std::endl;
+    for (auto&& result : network.get_results()) {
+        std::cout << "    " << result->get_friendly_name() << " : " << result->get_element_type() << " / "
+                  << result->get_layout().to_string() << " / " << result->get_output_partial_shape(0).to_string()
+                  << std::endl;
+    }
+}
+
+inline std::string fileNameNoExt(const std::string& filepath) {
+    auto pos = filepath.rfind('.');
+    if (pos == std::string::npos)
+        return filepath;
+    return filepath.substr(0, pos);
+}
+
+static void showUsage() {
+    std::cout << "compile_tool [OPTIONS]" << std::endl;
+    std::cout << std::endl;
+    std::cout << " Common options:                             " << std::endl;
+    std::cout << "    -h                                       " << help_message << std::endl;
+    std::cout << "    -m                           <value>     " << model_message << std::endl;
+    std::cout << "    -d                           <value>     " << targetDeviceMessage << std::endl;
+    std::cout << "    -o                           <value>     " << output_message << std::endl;
+    std::cout << "    -c                           <value>     " << config_message << std::endl;
+    std::cout << "    -ip                          <value>     " << inputs_precision_message << std::endl;
+    std::cout << "    -op                          <value>     " << outputs_precision_message << std::endl;
+    std::cout << "    -iop                        \"<value>\"    " << iop_message << std::endl;
+    std::cout << "    -il                          <value>     " << inputs_layout_message << std::endl;
+    std::cout << "    -ol                          <value>     " << outputs_layout_message << std::endl;
+    std::cout << "    -iol                        \"<value>\"    " << iol_message << std::endl;
+    std::cout << "    -iml                         <value>     " << inputs_model_layout_message << std::endl;
+    std::cout << "    -oml                         <value>     " << outputs_model_layout_message << std::endl;
+    std::cout << "    -ioml                       \"<value>\"    " << ioml_message << std::endl;
+    std::cout << "    -shape                       <value>     " << shape_message << std::endl;
+    std::cout << std::endl;
+}
+
+static bool parseCommandLine(int* argc, char*** argv) {
+    gflags::ParseCommandLineNonHelpFlags(argc, argv, true);
+
+    if (FLAGS_h) {
+        showUsage();
+        return false;
+    }
+
+    if (FLAGS_m.empty()) {
+        throw std::invalid_argument("Path to model xml file is required");
+    }
+
+    if (FLAGS_d.empty()) {
+        throw std::invalid_argument("Target device name is required");
+    }
+
+    if (1 < *argc) {
+        std::stringstream message;
+        message << "Unknown arguments: ";
+        for (auto arg = 1; arg < *argc; arg++) {
+            message << (*argv)[arg];
+            if (arg < *argc) {
+                message << " ";
+            }
+        }
+        throw std::invalid_argument(message.str());
+    }
+
+    return true;
+}
+
+static std::map<std::string, std::string> parseConfigFile(char comment = '#') {
+    std::map<std::string, std::string> config;
+
+    std::ifstream file(FLAGS_c);
+    if (file.is_open()) {
+        std::string option;
+        while (std::getline(file, option)) {
+            if (option.empty() || option[0] == comment) {
+                continue;
+            }
+            size_t spacePos = option.find_first_of(" \t\n\r");
+            OPENVINO_ASSERT(spacePos != std::string::npos, "Failed to find a space separator in "
+                                                           "provided plugin config option: " +
+                                                                   option);
+
+            std::string key = option.substr(0, spacePos);
+
+            std::string value{};
+            size_t valueStart = option.find_first_not_of(" \t\n\r", spacePos);
+            OPENVINO_ASSERT(valueStart != std::string::npos, "An invalid config parameter value detected, "
+                                                             "it mustn't be empty: " +
+                                                                     option);
+            size_t valueEnd = option.find_last_not_of(" \t\n\r");
+            value = option.substr(valueStart, valueEnd - valueStart + 1);
+
+            config[key] = std::move(value);
+        }
+    }
+    return config;
+}
+
+std::string getFileNameFromPath(const std::string& path,
+#if defined(_WIN32)
+                                const std::string& sep = "\\") {
+#else
+                                const std::string& sep = "/") {
+#endif
+    const auto pos = path.rfind(sep);
+    if (std::string::npos == pos) {
+        return path;
+    } else {
+        return path.substr(pos + 1);
+    }
+}
+
+using TimeDiff = std::chrono::milliseconds;
+
+void reshape(ov::OutputVector inputs_info, InputsInfo& info_map, std::shared_ptr<ov::Model>& model) {
+    std::vector<InputsInfo> info_maps;
+    if (!FLAGS_shape.empty()) {
+        std::map<std::string, std::vector<std::string>> shapes_map = parseInputParameters(FLAGS_shape, inputs_info);
+
+        if (FLAGS_override_model_batch_size != 1) {
+            throw std::logic_error("Incompatible params: \"shape\" and \"override_model_batch_size\"");
+        }
+        for (auto& item : inputs_info) {
+            InputInfo info;
+            auto name = item.get_any_name();
+
+            if (!shapes_map.empty()) {
+                if (shapes_map.count(name)) {
+                    if (shapes_map.at(name).size() > 1) {
+                        // Example: -shape input1[..][..]
+                        throw std::logic_error("shape command line parameter doesn't support multiple "
+                                               "shapes for one input.");
+                    }
+                    info.partialShape = shapes_map.at(name)[0];
+                } else {
+                    info.partialShape = item.get_partial_shape();
+                }
+            }
+            info_map[name] = info;
+            info_maps.push_back(info_map);
+        }
+        std::map<std::string, ov::PartialShape> newShapes;
+        for (auto& item : info_maps) {
+            for (auto& map : item) {
+                if (!newShapes.count(map.first)) {
+                    newShapes[map.first] = map.second.partialShape;
+                }
+            }
+        }
+        model->reshape(newShapes);
+    } else {
+        if (FLAGS_d.find("NPU") != std::string::npos) {
+            boundDynamicShape(model);
+        }
+
+        setModelBatch(model, FLAGS_override_model_batch_size);
+    }
+}
+
+int main(int argc, char* argv[]) {
+    try {
+        TimeDiff loadNetworkTimeElapsed{0};
+
+        const auto& version = ov::get_openvino_version();
+        std::cout << version.description << " version ......... ";
+        std::cout << OPENVINO_VERSION_MAJOR << "." << OPENVINO_VERSION_MINOR << "." << OPENVINO_VERSION_PATCH
+                  << std::endl;
+
+        std::cout << "Build ........... ";
+        std::cout << version.buildNumber << std::endl;
+        std::cout << "Parsing command-line arguments" << std::endl;
+        if (!parseCommandLine(&argc, &argv)) {
+            return EXIT_SUCCESS;
+        }
+
+        ov::Core core;
+        std::cout << "Checking FLAGS_LOG_LEVEL " << FLAGS_log_level << std::endl;
+
+        if (!FLAGS_log_level.empty()) {
+            std::cout << "Setting log level " << FLAGS_log_level << std::endl;
+            ov::log::Level level;
+            std::stringstream{FLAGS_log_level} >> level;
+            core.set_property(FLAGS_d, ov::log::level(level));
+        }
+
+        std::cout << "Reading model" << std::endl;
+        auto model = core.read_model(FLAGS_m);
+        auto inputs_info = std::const_pointer_cast<ov::Model>(model)->inputs();
+        InputsInfo info_map;
+
+        std::cout << "Performing reshape" << std::endl;
+        reshape(inputs_info, info_map, model);
+
+        std::cout << "Configuring model pre & post processing" << std::endl;
+        configurePrePostProcessing(model, FLAGS_ip, FLAGS_op, FLAGS_iop, FLAGS_il, FLAGS_ol, FLAGS_iol, FLAGS_iml,
+                                   FLAGS_oml, FLAGS_ioml);
+        std::cout << "Printing Input and Output Info from model" << std::endl;
+        printInputAndOutputsInfoShort(*model);
+        auto timeBeforeLoadNetwork = std::chrono::steady_clock::now();
+        std::cout << "Parsing configuration file" << std::endl;
+        auto configs = parseConfigFile();
+
+        std::cout << "Compiling model" << std::endl;
+        auto compiledModel = core.compile_model(model, FLAGS_d, {configs.begin(), configs.end()});
+        loadNetworkTimeElapsed =
+                std::chrono::duration_cast<TimeDiff>(std::chrono::steady_clock::now() - timeBeforeLoadNetwork);
+        std::string outputName = FLAGS_o;
+        if (outputName.empty()) {
+            outputName = getFileNameFromPath(fileNameNoExt(FLAGS_m)) + ".blob";
+        }
+
+        std::ofstream outputFile{outputName, std::ios::out | std::ios::binary};
+        if (!outputFile.is_open()) {
+            std::cout << "Outputting file " << outputName << " can't be opened for writing" << std::endl;
+            return EXIT_FAILURE;
+        } else {
+            std::cout << "Writing into file - " << outputName << std::endl;
+            compiledModel.export_model(outputFile);
+        }
+        std::cout << "Done. LoadNetwork time elapsed: " << loadNetworkTimeElapsed.count() << " ms" << std::endl;
+    } catch (const std::exception& error) {
+        std::cerr << error.what() << std::endl;
+        return EXIT_FAILURE;
+    } catch (...) {
+        std::cerr << "Unknown/internal exception happened." << std::endl;
+        return EXIT_FAILURE;
+    }
+
+    return EXIT_SUCCESS;
+}
diff --git a/src/plugins/intel_npu/tools/compile_tool/tools_helpers.hpp b/src/plugins/intel_npu/tools/compile_tool/tools_helpers.hpp
new file mode 100644
index 00000000000000..6d42fd142b8971
--- /dev/null
+++ b/src/plugins/intel_npu/tools/compile_tool/tools_helpers.hpp
@@ -0,0 +1,81 @@
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <algorithm>
+#include <chrono>
+#include <cstdlib>
+#include <fstream>
+#include <iostream>
+#include <map>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+#include <gflags/gflags.h>
+
+#include "openvino/openvino.hpp"
+
+struct InputInfo {
+    ov::element::Type type;
+    ov::PartialShape partialShape;
+    ov::Shape dataShape;
+    ov::Layout layout;
+};
+using InputsInfo = std::map<std::string, InputInfo>;
+
+std::string parameterNameToTensorName(std::string& name, std::vector<ov::Output<ov::Node>>& inputs_info) {
+    auto count_name = std::any_of(inputs_info.begin(), inputs_info.end(), [name](ov::Output<ov::Node>& port) {
+        return port.get_names().count(name) > 0;
+    });
+    if (count_name) {
+        return name;
+    } else {
+        auto inputInfo = std::find_if(inputs_info.begin(), inputs_info.end(), [name](ov::Output<ov::Node>& port) {
+            return name == port.get_node()->get_friendly_name();
+        });
+        if (inputInfo == inputs_info.end()) {
+            throw std::runtime_error("Provided I/O name \"" + name +
+                                     "\" is not found neither in tensor names nor in nodes names.");
+        }
+        return inputInfo->get_any_name();
+    }
+}
+
+std::map<std::string, std::vector<std::string>> parseInputParameters(std::string& parameter_string,
+                                                                     std::vector<ov::Output<ov::Node>>& input_info) {
+    // Parse parameter string like "input0[value0],input1[value1]" or "[value]" (applied to all
+    // inputs)
+    std::map<std::string, std::vector<std::string>> return_value;
+    std::string search_string = parameter_string;
+    auto start_pos = search_string.find_first_of('[');
+    auto input_name = search_string.substr(0, start_pos);
+    while (start_pos != std::string::npos) {
+        auto end_pos = search_string.find_first_of(']');
+        if (end_pos == std::string::npos)
+            break;
+        input_name = search_string.substr(0, start_pos);
+        auto input_value = search_string.substr(start_pos + 1, end_pos - start_pos - 1);
+        if (!input_name.empty()) {
+            return_value[parameterNameToTensorName(input_name, input_info)].push_back(input_value);
+        } else {
+            for (auto& item : input_info) {
+                return_value[item.get_any_name()].push_back(input_value);
+            }
+        }
+        search_string = search_string.substr(end_pos + 1);
+        if (search_string.empty() || (search_string.front() != ',' && search_string.front() != '['))
+            break;
+        if (search_string.front() == ',') {
+            if (search_string.length() > 1)
+                search_string = search_string.substr(1);
+            else
+                throw std::logic_error("Can't parse input parameter string, there is nothing after the comma " +
+                                       parameter_string);
+        }
+        start_pos = search_string.find_first_of('[');
+    }
+    if (!search_string.empty())
+        throw std::logic_error("Can't parse input parameter string: " + parameter_string);
+    return return_value;
+}
diff --git a/src/plugins/intel_npu/tools/single-image-test/CMakeLists.txt b/src/plugins/intel_npu/tools/single-image-test/CMakeLists.txt
new file mode 100644
index 00000000000000..4bcfb0bc936bf8
--- /dev/null
+++ b/src/plugins/intel_npu/tools/single-image-test/CMakeLists.txt
@@ -0,0 +1,78 @@
+#
+# Copyright (C) 2022-2024 Intel Corporation.
+# SPDX-License-Identifier: Apache 2.0
+#
+
+set(TARGET_NAME single-image-test)
+
+if (NOT DEFINED PROJECT_NAME)
+    cmake_minimum_required(VERSION 3.13 FATAL_ERROR)
+    project(single-image-test_standalone)
+    include("cmake/standalone.cmake")
+    return()
+endif()
+
+find_package(OpenCV QUIET COMPONENTS core imgproc imgcodecs)
+
+#
+# check for missing dependencies
+#
+
+set(MISSING_DEPENDENCIES "")
+foreach(LIB opencv_core opencv_imgproc opencv_imgcodecs)
+    if(NOT TARGET ${LIB})
+        list(APPEND MISSING_DEPENDENCIES ${LIB})
+    endif()
+endforeach()
+
+if(NOT MISSING_DEPENDENCIES STREQUAL "")
+    message(WARNING "${TARGET_NAME} tool is disabled due to missing dependencies: ${MISSING_DEPENDENCIES}")
+    return()
+endif()
+
+#
+# Define the target
+#
+
+ov_add_target(ADD_CPPLINT
+              TYPE EXECUTABLE
+              NAME ${TARGET_NAME}
+              ROOT ${CMAKE_CURRENT_SOURCE_DIR}
+              LINK_LIBRARIES
+                  PRIVATE
+                      openvino::runtime
+                      TBB::tbb
+                      opencv_core
+                      opencv_imgproc
+                      opencv_imgcodecs
+                      npu_tools_utils
+                      gflags)
+
+set_target_properties(${TARGET_NAME} PROPERTIES
+                          FOLDER ${CMAKE_CURRENT_SOURCE_DIR}
+                          CXX_STANDARD 17)
+
+if (CMAKE_COMPILER_IS_GNUCXX)
+    target_compile_options(${TARGET_NAME} PRIVATE -Wall)
+endif()
+
+# TODO: fix warnings and remove this exception
+if(CMAKE_COMPILER_IS_GNUCXX OR OV_COMPILER_IS_CLANG)
+    ov_add_compiler_flags(-Wno-missing-declarations)
+endif()
+
+#
+# Install
+#
+
+install(TARGETS ${TARGET_NAME}
+        RUNTIME DESTINATION "tools/${TARGET_NAME}"
+        COMPONENT ${NPU_INTERNAL_COMPONENT}
+        ${OV_CPACK_COMP_NPU_INTERNAL_EXCLUDE_ALL})
+
+if(EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/README.md")
+    install(FILES "${CMAKE_CURRENT_SOURCE_DIR}/README.md"
+            DESTINATION "tools/${TARGET_NAME}"
+            COMPONENT ${NPU_INTERNAL_COMPONENT}
+            ${OV_CPACK_COMP_NPU_INTERNAL_EXCLUDE_ALL})
+endif()
diff --git a/src/plugins/intel_npu/tools/single-image-test/README.md b/src/plugins/intel_npu/tools/single-image-test/README.md
new file mode 100644
index 00000000000000..185b1c018b658c
--- /dev/null
+++ b/src/plugins/intel_npu/tools/single-image-test/README.md
@@ -0,0 +1,284 @@
+# NPU Single Image Test Tool
+
+This page demostrates how to use NPU Single Image Test Tool for end-to-end accuracy validation on a single image or input file with OpenVINO™ Intermediate Representation (IR) of an AI model or a model in ONNX format. 
+
+
+## Description 
+
+Single Image Test Tool is a C++ application that enables you to pass OpenVINO IR or ONNX model or pre-compiled blob and a single image or any other compatible file with the model inputs and get 2 sets of files with CPU outputs and NPU outputs that can be compared later or straight after the inference if `-run_test` option is passed.
+
+The tool can be configured to perform various preprocessing methods and output comparison algorithms depending on the model topology and its output semantics. See the tool help message below for the details.
+
+Using Single Image Test is not a basic approach to end-to-end validation or collecting release measures but is created for development CI checks and debugging. However, some methodologies might be useful if you're looking for examples of how to use NPU and preprocess data for the inference on NPU. If you're looking for the standard way of measuring accuracy please refer to [Deep Learning accuracy validation framework](https://github.com/openvinotoolkit/open_model_zoo/tree/master/tools/accuracy_checker).
+
+
+## How to build
+
+### Within NPU Plugin build
+
+See [How to build](https://github.com/openvinotoolkit/openvino/wiki#how-to-build). If `ENABLE_INTEL_NPU=ON` is provided and `OpenCV` project is linked to the current cmake project, no additional steps are required for Single Image Test. It will be built unconditionally with every NPU Plugin build. It can be found in `bin` folder.  
+
+If you need to configure a release package layout and have Single Image Test in it, use `cmake --install <dir> --component npu_internal` from your `build` folder. After installation single-image-test executable can be found in `<install_dir>/tools/single-image-test` folder.
+
+### Standalone build
+
+#### Prerequisites 
+* [OpenVINO™ Runtime release package](https://docs.openvino.ai/2024/get-started/install-openvino.html)
+* [OpenCV: Open Source Computer Vision Library release package](https://opencv.org/get-started/)
+
+#### Build instructions
+1. Download and install OpenVINO™ Runtime package
+1. Download and install OpenCV package
+1. Build Single Image Test Tool
+    ```sh
+    mkdir sit_build && cd sit_build
+    source <openvino_install_dir>/setupvars.sh
+    cmake -DOpenVINO_DIR=<openvino_install_dir>/runtime/cmake -DOpenCV_DIR=<opencv_install_dir> <sit_source_dir>
+    cmake --build . --config Release
+    cmake --install . --prefix <sit_install_dir>
+    ```
+    > Note 1: command line instruction might differ on different platforms (e.g. Windows cmd)  
+    > Note 2: this example is based on OpenVINO Archive distribution. If you have chosen another installation method, specifying OpenVINO_DIR and calling setupvars might not be needed. Refer [documentation](https://docs.openvino.ai/2024/get-started/install-openvino.html) for details.  
+    > Note 3: depending on OpenCV installation method, there might not be a need to specify OpenCV_DIR.  
+    > Note 4: depending on OpenCV version, cmake configs might be located somewhere else. You need to specify a directory that contains `OpenCVConfig.cmake` file  
+    > Note 5: `<sit_install_dir>` can be any directory on your filesystem that you want to use for installation including `<openvino_install_dir>` if you wish to extend OpenVINO package
+1. Verify the installation
+    ```sh
+    source <openvino_install_dir>/setupvars.sh
+    source <opencv_install_dir>setup_vars_opencv4.sh
+    <sit_install_dir>/tools/single-image-test/single-image-test -help
+    ```
+    > Note 1: command line might differ depending on your platform  
+    > Note 2: depending on OpenCV installation method, there might not be a need to call setupvars.  
+    > Note 3: this example is based on OpenVINO Archive distribution. If you have chosen another installation method, calling setupvars might not be needed. Refer [documentation](https://docs.openvino.ai/2024/get-started/install-openvino.html) for details.
+
+    Successful build will show the information about Single Image Test Tool CLI options
+
+
+## How to run 
+
+Running the application with the `-help` option yields the following usage message:
+```
+single-image-test.exe: Usage: Release\single-image-test.exe[<options>]
+
+  Flags from C:\Users\mdoronin\work\applications.ai.vpu-accelerators.vpux-plugin\tools\single-image-test\main.cpp:
+    -box_tolerance (Box tolerance for 'detection' mode) type: double
+      default: 0.0001
+    -classes (Number of classes for Yolo V3) type: int32 default: 80
+    -color_format (Color format for input: RGB or BGR) type: string
+      default: "BGR"
+    -compiled_blob (Output compiled network file (compiled result blob))
+      type: string default: ""
+    -confidence_threshold (Confidence threshold for Detection mode)
+      type: double default: 0.0001
+    -config (Path to the configuration file (optional)) type: string
+      default: ""
+    -coords (Number of coordinates for Yolo V3) type: int32 default: 4
+    -cosim_threshold (Threshold for 'cosim' mode) type: double
+      default: 0.90000000000000002
+    -dataset (The dataset used to train the model. Useful for instances such as
+      semantic segmentation to visualize the accuracy per-class) type: string
+      default: "NONE"
+    -device (Device to use) type: string default: ""
+    -il (Input layout) type: string default: ""
+    -img_as_bin (Force binary input even if network expects an image)
+      type: bool default: false
+    -img_bin_precision (Specify the precision of the binary input files. Eg:
+      'FP32,FP16,I32,I64,U8') type: string default: ""
+    -iml (Model input layout) type: string default: ""
+    -input (Input file(s)) type: string default: ""
+    -ip (Input precision (default: U8, available: FP32, FP16, I32, I64, U8))
+      type: string default: ""
+    -is_tiny_yolo (Is it Tiny Yolo or not (true or false)?) type: bool
+      default: false
+    -log_level (IE logger level (optional)) type: string default: ""
+    -mean_values (Optional. Mean values to be used for the input image per
+      channel. Values to be provided in the [channel1,channel2,channel3]
+      format. Can be defined for desired input of the model, for example:
+      "--mean_values data[255,255,255],info[255,255,255]". The exact meaning
+      and order of channels depend on how the original model was trained.
+      Applying the values affects performance and may cause type conversion)
+      type: string default: ""
+    -mode (Comparison mode to use) type: string default: ""
+    -network (Network file (either XML or pre-compiled blob)) type: string
+      default: ""
+    -normalized_image (Images in [0, 1] range or not) type: bool default: false
+    -nrmse_loss_threshold (Threshold for 'nrmse' mode) type: double default: 1
+    -num (Number of scales for Yolo V3) type: int32 default: 3
+    -ol (Output layout) type: string default: ""
+    -oml (Model output layout) type: string default: ""
+    -op (Output precision (default: FP32, available: FP32, FP16, I32, I64, U8))
+      type: string default: ""
+    -override_model_batch_size (Enforce a model to be compiled for batch size)
+      type: uint32 default: 1
+    -pc (Report performance counters) type: bool default: false
+    -prob_tolerance (Probability tolerance for 'classification/ssd/yolo' mode)
+      type: double default: 0.0001
+    -psnr_reference (PSNR reference value in dB) type: double default: 30
+    -psnr_tolerance (Tolerance for 'psnr' mode) type: double default: 0.0001
+    -raw_tolerance (Tolerance for 'raw' mode (absolute diff)) type: double
+      default: 0.0001
+    -rrmse_loss_threshold (Threshold for 'rrmse' mode) type: double
+      default: 1.7976931348623157e+308
+    -run_test (Run the test (compare current results with previously dumped))
+      type: bool default: false
+    -scale_border (Scale border) type: uint32 default: 4
+    -scale_values (Optional. Scale values to be used for the input image per
+      channel. Values are provided in the [channel1,channel2,channel3] format.
+      Can be defined for desired input of the model, for example:
+      "--scale_values data[255,255,255],info[255,255,255]". The exact meaning
+      and order of channels depend on how the original model was trained. If
+      both --mean_values and --scale_values are specified, the mean is
+      subtracted first and then scale is applied regardless of the order of
+      options in command line. Applying the values affects performance and may
+      cause type conversion) type: string default: ""
+    -sem_seg_classes (Number of classes for semantic segmentation) type: uint32
+      default: 12
+    -sem_seg_ignore_label (The number of the label to be ignored) type: uint32
+      default: 4294967295
+    -sem_seg_threshold (Threshold for 'semantic segmentation' mode)
+      type: double default: 0.97999999999999998
+    -top_k (Top K parameter for 'classification' mode) type: uint32 default: 1
+```
+
+For example, to run inference with mobilenet-v2 model on Intel® Core™ Ultra NPU on Windows 11 OS, run the commands below:
+
+1. Running inference on CPU to collect reference result
+    ```
+    single-image-test.exe \
+        --network \
+        mobilenet-v2.xml \
+        --input \
+        validation-set/224x224/watch.bmp \
+        --ip \
+        FP16 \
+        --op \
+        FP16 \
+        --device \
+        CPU \
+        --color_format \
+        RGB \
+        --il \
+        NCHW \
+        --ol \
+        NC \
+        --iml \
+        NCHW \
+        --oml \
+        NC
+    ```
+    expected output:
+    ```
+    Parameters:
+        Network file:                             mobilenet-v2.xml
+        Input file(s):                            validation-set/224x224/watch.bmp
+        Output compiled network file:             
+        Color format:                             RGB
+        Input precision:                          FP16
+        Output precision:                         FP16
+        Input layout:                             NCHW
+        Output layout:                            NC
+        Model input layout:                       NCHW
+        Model output layout:                      NC
+        Img as binary:                            0
+        Bin input file precision:                 
+        Device:                                   CPU
+        Config file:                              
+        Run test:                                 0
+        Performance counters:                     0
+        Mean_values [channel1,channel2,channel3]  
+        Scale_values [channel1,channel2,channel3] 
+        Log level:                        
+
+    Run single image test
+    Load network mobilenet-v2.xml
+    Load input #0 from validation-set/224x224/watch.bmp as f16 [N,C,H,W] [1,3,224,224]
+    Dump input #0_case_0 to _mobilenet_v2_input_0_case_0.blob
+    Run inference on CPU
+    Latency: 100 ms
+    Dump reference output #0 to _mobilenet_v2_ref_out_0_case_0.blob
+    ```
+
+1. Running inference on NPU and comparing results. In this example, it's considered that the model has been compiled before and exported as a blob file. You can pass OpenVINO IR with the same success but depending on a model and your setup, some additional configs might be needed in a config file or in CLI.
+
+    ```
+    single-image-test.exe \
+        --network \
+        mobilenet-v2.blob \
+        --input \
+        validation-set/224x224/watch.bmp \
+        --ip \
+        FP16 \
+        --op \
+        FP16 \
+        --device \
+        NPU \
+        --config \
+        mobilenet-v2.conf \
+        --run_test \
+        -log_level \
+        LOG_ERROR \
+        --mode \
+        classification \
+        --top_k \
+        1 \
+        --prob_tolerance \
+        0.6 \
+        --color_format \
+        RGB \
+        --il \
+        NCHW \
+        --ol \
+        NC \
+        --iml \
+        NCHW \
+        --oml \
+        NC
+    ```
+
+    the content of mobilenet-v2.conf:
+    ```
+    NPU_COMPILER_TYPE DRIVER
+    NPU_PLATFORM VPU3720
+    ```
+
+    expected output:
+    ```
+    Parameters:
+        Network file:                             mobilenet-v2.blob
+        Input file(s):                            validation-set/224x224/watch.bmp
+        Output compiled network file:             
+        Color format:                             RGB
+        Input precision:                          FP16
+        Output precision:                         FP16
+        Input layout:                             NCHW
+        Output layout:                            NC
+        Model input layout:                       NCHW
+        Model output layout:                      NC
+        Img as binary:                            0
+        Bin input file precision:                 
+        Device:                                   NPU
+        Config file:                              mobilenet-v2.conf
+        Run test:                                 1
+        Performance counters:                     0
+        Mean_values [channel1,channel2,channel3]  
+        Scale_values [channel1,channel2,channel3] 
+        Mode:             classification
+        Top K:            1
+        Tolerance:        0.6
+        Log level:                        LOG_ERROR
+
+    Run single image test
+    Import network mobilenet-v2.blob
+    Load input #0 from validation-set/224x224/watch.bmp as f16 [N,C,H,W] [1,3,224,224]
+    Dump input #0_case_0 to _mobilenet_v2_input_0_case_0.blob
+    Run inference on NPU
+    Latency: 3 ms
+    Load reference output #0 from _mobilenet_v2_ref_out_0_case_0.blob as f16
+    Dump device output #0_case_0 to _mobilenet_v2_kmb_out_0_case_0.blob
+    Actual top:
+        0 : 531 : 21.95
+    Ref Top:
+        0 : 531 : 21.95
+    PASSED
+    ```
diff --git a/src/plugins/intel_npu/tools/single-image-test/cmake/standalone.cmake b/src/plugins/intel_npu/tools/single-image-test/cmake/standalone.cmake
new file mode 100644
index 00000000000000..eaefd3a88a088b
--- /dev/null
+++ b/src/plugins/intel_npu/tools/single-image-test/cmake/standalone.cmake
@@ -0,0 +1,58 @@
+#
+# Copyright (C) 2024 Intel Corporation.
+# SPDX-License-Identifier: Apache 2.0
+#
+
+set(CMAKE_CXX_STANDARD 17)
+set(CMAKE_CXX_STANDARD_REQUIRED ON)
+
+if("${CMAKE_BUILD_TYPE}" STREQUAL "")
+  set(CMAKE_BUILD_TYPE "Release")
+endif()
+
+#
+# OpenVINO and OpenCV package paths should be specified via OpenVINO_DIR and OpenCV_DIR
+# TBB is distributed with OpenVINO packages however it is not exported from OpenVINO cmake config
+# Therefore we need to find TBB explicitly. TBB_DIR is not requered as OpenVINO setupvars script sets its location
+#
+
+find_package(Threads REQUIRED)
+find_package(OpenVINO REQUIRED COMPONENTS Runtime)
+find_package(TBB REQUIRED)
+find_package(OpenCV REQUIRED COMPONENTS core imgproc imgcodecs)
+
+add_subdirectory("${CMAKE_CURRENT_SOURCE_DIR}/../common" common EXCLUDE_FROM_ALL)
+
+#
+# gflags is distributed in sources in OpenVINO packages so we need to build it explicitly
+#
+
+if(EXISTS "${PACKAGE_PREFIX_DIR}/samples/cpp/thirdparty/gflags")
+    add_subdirectory("${PACKAGE_PREFIX_DIR}/samples/cpp/thirdparty/gflags" gflags EXCLUDE_FROM_ALL)
+else()
+    find_package(gflags REQUIRED)
+endif()
+
+set(DEPENDENCIES
+        Threads::Threads
+        gflags
+        openvino::runtime
+        TBB::tbb
+        opencv_core
+        opencv_imgproc
+        opencv_imgcodecs
+        npu_tools_utils
+)
+
+if (CMAKE_COMPILER_IS_GNUCXX)
+    target_compile_options(${TARGET_NAME} PRIVATE -Wall)
+endif()
+
+file(GLOB SOURCES "${CMAKE_CURRENT_SOURCE_DIR}/*.cpp")
+
+add_executable(${TARGET_NAME} ${SOURCES})
+target_link_libraries(${TARGET_NAME} PRIVATE ${DEPENDENCIES})
+
+install(TARGETS ${TARGET_NAME}
+        DESTINATION "tools/${TARGET_NAME}"
+        COMPONENT npu_tools)
diff --git a/src/plugins/intel_npu/tools/single-image-test/image_quality_helper.cpp b/src/plugins/intel_npu/tools/single-image-test/image_quality_helper.cpp
new file mode 100644
index 00000000000000..4db5e2ca6f3a62
--- /dev/null
+++ b/src/plugins/intel_npu/tools/single-image-test/image_quality_helper.cpp
@@ -0,0 +1,49 @@
+//
+// Copyright (C) 2022-2024 Intel Corporation.
+// SPDX-License-Identifier: Apache 2.0
+//
+
+//
+
+#include "image_quality_helper.hpp"
+
+#include <cmath>
+#include <iostream>
+
+#include "data_type_converters.hpp"
+
+
+float utils::runPSNRMetric(std::vector<std::vector<float>>& actOutput,
+                           std::vector<std::vector<float>>& refOutput,
+                           const size_t imgHeight,
+                           const size_t imgWidth,
+                           int scaleBorder,
+                           bool normalizedImage) {
+    size_t colorScale;
+    float imageDiff;
+    float sum = 0;
+
+    if (!normalizedImage) {
+        colorScale = 255;
+    } else {
+        colorScale = 1;
+    }
+
+    for (size_t iout = 0; iout < actOutput.size(); ++iout) {
+        for (size_t h = scaleBorder; h < imgHeight - scaleBorder; h++) {
+            for (size_t w = scaleBorder; w < imgWidth - scaleBorder; w++) {
+                imageDiff = ((actOutput[iout][h * imgWidth + w] - refOutput[iout][h * imgWidth + w]) /
+                             npu::utils::convertValuePrecision<float>(colorScale));
+
+                sum = sum + (imageDiff * imageDiff);
+            }
+        }
+    }
+
+    auto mse = sum / (imgWidth * imgHeight);
+    auto psnr = -10 * log10(mse);
+
+    std::cout << "psnr: " << psnr << " Db" << std::endl;
+
+    return psnr;
+}
diff --git a/src/plugins/intel_npu/tools/single-image-test/image_quality_helper.hpp b/src/plugins/intel_npu/tools/single-image-test/image_quality_helper.hpp
new file mode 100644
index 00000000000000..911ac60201ad87
--- /dev/null
+++ b/src/plugins/intel_npu/tools/single-image-test/image_quality_helper.hpp
@@ -0,0 +1,18 @@
+//
+// Copyright (C) 2022-2024 Intel Corporation.
+// SPDX-License-Identifier: Apache 2.0
+//
+
+//
+
+#pragma once
+
+#include <sstream>
+#include <vector>
+
+namespace utils {
+
+float runPSNRMetric(std::vector<std::vector<float>>& actOutput, std::vector<std::vector<float>>& refOutput,
+                    const size_t imgHeight, const size_t imgWidth, int scaleBorder, bool normalizedImage);
+
+}  // namespace utils
diff --git a/src/plugins/intel_npu/tools/single-image-test/main.cpp b/src/plugins/intel_npu/tools/single-image-test/main.cpp
new file mode 100644
index 00000000000000..84cdc87df186ae
--- /dev/null
+++ b/src/plugins/intel_npu/tools/single-image-test/main.cpp
@@ -0,0 +1,2069 @@
+//
+// Copyright (C) 2022-2024 Intel Corporation.
+// SPDX-License-Identifier: Apache 2.0
+//
+
+#include "image_quality_helper.hpp"
+#include "semantic_segmentation_helpers.hpp"
+#include "tensor_utils.hpp"
+#include "yolo_helpers.hpp"
+
+#include <openvino/core/parallel.hpp>
+#include <openvino/openvino.hpp>
+
+#include <opencv2/core.hpp>
+#include <opencv2/imgcodecs.hpp>
+#include <opencv2/imgproc.hpp>
+
+#include <gflags/gflags.h>
+
+#include <fstream>
+#include <iomanip>
+#include <iostream>
+#include <sstream>
+#include <string>
+#include <vector>
+
+using TensorMap = std::map<std::string, ov::Tensor>;
+
+struct TensorDescriptor {
+    ov::element::Type precision;
+    ov::Shape shape;
+    ov::Layout layout;
+};
+
+using TensorDescriptorMap = std::unordered_map<std::string, TensorDescriptor>;
+using LayoutMap = std::unordered_map<std::string, ov::Layout>;
+
+/**
+ * @brief Provides a caseless equality function for STL algorithms.
+ * @details Utility function taken from the OpenVINO implementation, formerly registered as
+ * "InferenceEngine::details::CaselessEq"
+ * @tparam Key
+ */
+template <class Key>
+class CaselessEq {
+public:
+    bool operator()(const Key& a, const Key& b) const noexcept {
+        return a.size() == b.size() &&
+               std::equal(std::begin(a), std::end(a), std::begin(b), [](const char cha, const char chb) {
+                   return std::tolower(cha) == std::tolower(chb);
+               });
+    }
+};
+
+CaselessEq<std::string> strEq;
+
+//
+// Command line options
+//
+
+DEFINE_string(network, "", "Network file (either XML or pre-compiled blob)");
+DEFINE_string(input, "", "Input file(s)");
+DEFINE_string(compiled_blob, "", "Output compiled network file (compiled result blob)");
+DEFINE_uint32(override_model_batch_size, 1, "Enforce a model to be compiled for batch size");
+DEFINE_string(device, "", "Device to use");
+DEFINE_string(config, "", "Path to the configuration file (optional)");
+DEFINE_string(ip, "", "Input precision (default: U8, available: FP32, FP16, I32, I64, U8)");
+DEFINE_string(op, "", "Output precision (default: FP32, available: FP32, FP16, I32, I64, U8)");
+DEFINE_string(il, "", "Input layout");
+DEFINE_string(ol, "", "Output layout");
+DEFINE_string(iml, "", "Model input layout");
+DEFINE_string(oml, "", "Model output layout");
+DEFINE_bool(img_as_bin, false, "Force binary input even if network expects an image");
+DEFINE_bool(pc, false, "Report performance counters");
+
+// for using input image mean and scale
+static constexpr char mean_values_message[] =
+        "Optional. Mean values to be used for the input image per channel. "
+        "Values to be provided in the [channel1,channel2,channel3] format. "
+        "Can be defined for desired input of the model, for example: \"--mean_values "
+        "data[255,255,255],info[255,255,255]\". The exact meaning and order of channels depend on how the original "
+        "model "
+        "was trained. Applying the values affects performance and may cause type conversion";
+
+static constexpr char scale_values_message[] =
+        "Optional. Scale values to be used for the input image per channel. "
+        "Values are provided in the [channel1,channel2,channel3] format. "
+        "Can be defined for desired input of the model, for example: \"--scale_values "
+        "data[255,255,255],info[255,255,255]\". "
+        "The exact meaning and order of channels depend on how the original model was trained. If both --mean_values "
+        "and "
+        "--scale_values are specified, the mean is subtracted first and then scale is applied regardless of the order "
+        "of "
+        "options in command line. Applying the values affects performance and may cause type conversion";
+DEFINE_string(mean_values, "", mean_values_message);
+DEFINE_string(scale_values, "", scale_values_message);
+
+DEFINE_string(img_bin_precision, "", "Specify the precision of the binary input files. Eg: 'FP32,FP16,I32,I64,U8'");
+
+DEFINE_bool(run_test, false, "Run the test (compare current results with previously dumped)");
+DEFINE_string(mode, "", "Comparison mode to use");
+
+DEFINE_uint32(top_k, 1, "Top K parameter for 'classification' mode");
+DEFINE_double(prob_tolerance, 1e-4, "Probability tolerance for 'classification/ssd/yolo' mode");
+
+DEFINE_double(raw_tolerance, 1e-4, "Tolerance for 'raw' mode (absolute diff)");
+DEFINE_double(cosim_threshold, 0.90, "Threshold for 'cosim' mode");
+DEFINE_double(rrmse_loss_threshold, std::numeric_limits<double>::max(), "Threshold for 'rrmse' mode");
+DEFINE_double(nrmse_loss_threshold, 1.0, "Threshold for 'nrmse' mode");
+DEFINE_double(confidence_threshold, 1e-4, "Confidence threshold for Detection mode");
+DEFINE_double(box_tolerance, 1e-4, "Box tolerance for 'detection' mode");
+
+DEFINE_double(psnr_reference, 30.0, "PSNR reference value in dB");
+DEFINE_double(psnr_tolerance, 1e-4, "Tolerance for 'psnr' mode");
+
+DEFINE_string(log_level, "", "IE logger level (optional)");
+DEFINE_string(color_format, "BGR", "Color format for input: RGB or BGR");
+DEFINE_uint32(scale_border, 4, "Scale border");
+DEFINE_bool(normalized_image, false, "Images in [0, 1] range or not");
+
+// for Yolo
+DEFINE_bool(is_tiny_yolo, false, "Is it Tiny Yolo or not (true or false)?");
+DEFINE_int32(classes, 80, "Number of classes for Yolo V3");
+DEFINE_int32(coords, 4, "Number of coordinates for Yolo V3");
+DEFINE_int32(num, 3, "Number of scales for Yolo V3");
+
+typedef std::chrono::high_resolution_clock Time;
+// for Semantic Segmentation
+DEFINE_uint32(sem_seg_classes, 12, "Number of classes for semantic segmentation");
+DEFINE_double(sem_seg_threshold, 0.98, "Threshold for 'semantic segmentation' mode");
+DEFINE_uint32(sem_seg_ignore_label, std::numeric_limits<uint32_t>::max(), "The number of the label to be ignored");
+DEFINE_string(dataset, "NONE",
+              "The dataset used to train the model. Useful for instances such as semantic segmentation to visualize "
+              "the accuracy per-class");
+std::vector<std::string> camVid12 = {"Sky",        "Building", "Pole", "Road",       "Pavement",  "Tree",
+                                     "SignSymbol", "Fence",    "Car",  "Pedestrian", "Bicyclist", "Unlabeled"};
+
+std::vector<std::string> splitStringList(const std::string& str, char delim) {
+    std::vector<std::string> out;
+
+    if (str.empty()) {
+        return out;
+    }
+
+    std::istringstream istr(str);
+
+    std::string elem;
+    while (std::getline(istr, elem, delim)) {
+        if (elem.empty()) {
+            continue;
+        }
+
+        out.push_back(std::move(elem));
+    }
+
+    return out;
+}
+
+void parseCommandLine(int argc, char* argv[]) {
+    std::ostringstream usage;
+    usage << "Usage: " << argv[0] << "[<options>]";
+    gflags::SetUsageMessage(usage.str());
+
+    std::ostringstream version;
+    version << ov::get_openvino_version();
+    gflags::SetVersionString(version.str());
+
+    gflags::ParseCommandLineFlags(&argc, &argv, true);
+
+    std::cout << "Parameters:" << std::endl;
+    std::cout << "    Network file:                             " << FLAGS_network << std::endl;
+    std::cout << "    Input file(s):                            " << FLAGS_input << std::endl;
+    std::cout << "    Output compiled network file:             " << FLAGS_compiled_blob << std::endl;
+    std::cout << "    Color format:                             " << FLAGS_color_format << std::endl;
+    std::cout << "    Input precision:                          " << FLAGS_ip << std::endl;
+    std::cout << "    Output precision:                         " << FLAGS_op << std::endl;
+    std::cout << "    Input layout:                             " << FLAGS_il << std::endl;
+    std::cout << "    Output layout:                            " << FLAGS_ol << std::endl;
+    std::cout << "    Model input layout:                       " << FLAGS_iml << std::endl;
+    std::cout << "    Model output layout:                      " << FLAGS_oml << std::endl;
+    std::cout << "    Img as binary:                            " << FLAGS_img_as_bin << std::endl;
+    std::cout << "    Bin input file precision:                 " << FLAGS_img_bin_precision << std::endl;
+    std::cout << "    Device:                                   " << FLAGS_device << std::endl;
+    std::cout << "    Config file:                              " << FLAGS_config << std::endl;
+    std::cout << "    Run test:                                 " << FLAGS_run_test << std::endl;
+    std::cout << "    Performance counters:                     " << FLAGS_pc << std::endl;
+    std::cout << "    Mean_values [channel1,channel2,channel3]  " << FLAGS_mean_values << std::endl;
+    std::cout << "    Scale_values [channel1,channel2,channel3] " << FLAGS_scale_values << std::endl;
+    if (FLAGS_run_test) {
+        std::cout << "    Mode:             " << FLAGS_mode << std::endl;
+        if (strEq(FLAGS_mode, "classification")) {
+            std::cout << "    Top K:            " << FLAGS_top_k << std::endl;
+            std::cout << "    Tolerance:        " << FLAGS_prob_tolerance << std::endl;
+        } else if (strEq(FLAGS_mode, "raw")) {
+            std::cout << "    Tolerance:        " << FLAGS_raw_tolerance << std::endl;
+        } else if (strEq(FLAGS_mode, "cosim")) {
+            std::cout << "    Threshold:        " << FLAGS_cosim_threshold << std::endl;
+        } else if (strEq(FLAGS_mode, "psnr")) {
+            std::cout << "    Reference:        " << FLAGS_psnr_reference << std::endl;
+            std::cout << "    Tolerance:        " << FLAGS_psnr_tolerance << std::endl;
+            std::cout << "    Scale_border:     " << FLAGS_scale_border << std::endl;
+            std::cout << "    Normalized_image: " << FLAGS_normalized_image << std::endl;
+        } else if (strEq(FLAGS_mode, "rrmse")) {
+            std::cout << "    Threshold:        " << FLAGS_rrmse_loss_threshold << std::endl;
+        } else if (strEq(FLAGS_mode, "nrmse")) {
+            std::cout << "    Threshold:        " << FLAGS_nrmse_loss_threshold << std::endl;
+        }
+    }
+    std::cout << "    Log level:                        " << FLAGS_log_level << std::endl;
+    std::cout << std::endl;
+}
+
+//
+// OpenCV to OpenVINO conversion
+//
+
+bool isImage(const ov::Shape& shape, const ov::Layout& layout) {
+    if (shape.size() == 4) {
+        const auto numChannels = shape[ov::layout::channels_idx(layout)];
+        return (numChannels == 3) || (numChannels == 4);
+    }
+
+    return false;
+}
+
+/**
+ * @brief Computes the offsets for each axis in terms of number of elements.
+ * @details Taken from the OpenVINO implementation ("InferenceEngine::BlockingDesc::fillDesc") and slightly modified for
+ * the current usecase.
+ *
+ * @param shape The shape based on which the offsets will be computed.
+ * @return The resulting strides.
+ */
+std::vector<size_t> getStrides(const ov::Shape& shape) {
+    std::vector<size_t> strides(shape.size());
+
+    strides[strides.size() - 1] = 1;
+
+    for (size_t i = 2; i <= shape.size(); i++) {
+        strides[strides.size() - i] = strides[strides.size() - (i - 1)] * shape[shape.size() - (i - 1)];
+    }
+
+    return strides;
+}
+
+std::vector<cv::Mat> ovToCV(const ov::Tensor& tensor, const ov::Shape& shape, const ov::Layout& layout,
+                            size_t batchInd = 0, size_t depthInd = 0) {
+    const ov::element::Type& precision = tensor.get_element_type();
+
+    OPENVINO_ASSERT(layout == ov::Layout("NCHW") || layout == ov::Layout("NCDHW"),
+                    "Unsupported layout: ", layout.to_string());
+
+    OPENVINO_ASSERT(precision == ov::element::Type_t::u8 || precision == ov::element::Type_t::f32 ||
+                            precision == ov::element::Type_t::f16 || precision == ov::element::Type_t::i32,
+                    "Unsupported precision: ", precision.get_type_name());
+
+    int cvType = 0;
+    size_t elemSize = 0;
+
+    if (precision == ov::element::Type_t::u8) {
+        cvType = CV_8UC1;
+        elemSize = sizeof(uint8_t);
+    } else if (precision == ov::element::Type_t::f32) {
+        cvType = CV_32FC1;
+        elemSize = sizeof(float);
+    } else if (precision == ov::element::Type_t::f16) {
+        cvType = CV_16SC1;
+        elemSize = sizeof(ov::float16);
+    } else if (precision == ov::element::Type_t::i32) {
+        cvType = CV_32SC1;
+        elemSize = sizeof(int32_t);
+    }
+
+    std::vector<cv::Mat> out;
+
+    const size_t N = shape[ov::layout::batch_idx(layout)];
+    const size_t C = shape[ov::layout::channels_idx(layout)];
+    const size_t H = shape[ov::layout::height_idx(layout)];
+    const size_t W = shape[ov::layout::width_idx(layout)];
+
+    const auto dataBuffer = reinterpret_cast<uint8_t*>(tensor.data());
+
+    if (layout == ov::Layout("NCHW")) {
+        OPENVINO_ASSERT(batchInd < N);
+        OPENVINO_ASSERT(C == 3 || C == 4, "Unsupported number of channels: ", C);
+
+        out.resize(C);
+        for (size_t c = 0; c < C; ++c) {
+            out[c] = cv::Mat(static_cast<int>(H), static_cast<int>(W), cvType,
+                             dataBuffer + (batchInd * C + c) * W * H * elemSize);
+        }
+    } else if (layout == ov::Layout("NCDHW")) {
+        const size_t D = shape[ov::layout::depth_idx(layout)];
+
+        const std::vector<size_t> strides = getStrides(shape);
+
+        const auto strideN = strides[ov::layout::batch_idx(layout)];
+        const auto strideC = strides[ov::layout::channels_idx(layout)];
+        const auto strideD = strides[ov::layout::depth_idx(layout)];
+
+        OPENVINO_ASSERT(batchInd < N);
+        OPENVINO_ASSERT(depthInd < D);
+        OPENVINO_ASSERT(C == 3 || C == 4, "Unsupported number of channels: ", C);
+
+        out.resize(C);
+        for (size_t c = 0; c < C; ++c) {
+            out[c] = cv::Mat(static_cast<int>(H), static_cast<int>(W), cvType,
+                             dataBuffer + (strideN * batchInd + strideC * c + strideD * depthInd) * elemSize);
+        }
+    }
+
+    return out;
+}
+
+/**
+ * @brief Converts the source data from its current precision to the one used by the destination buffer and then places
+ * the result inside it.
+ * @details The conversion is performed by the use of the "static_cast" operator. Depending on the types between which
+ * the conversions are made, this may lead to undefined behavior.
+ *
+ * E.g.: Several experiments suggest float<->ov::float16 conversions may work fine, but float->uint8_t may lead to
+ * division by zero.
+ *
+ * @tparam InT The type of the source buffer
+ * @tparam OutT The type of the destination buffer
+ * @param destination Where the result will be stored
+ * @param source The data which shall be converted
+ * @param numberOfElements Indicates how many elements will be taken from the source buffer.
+ */
+template <typename InT, typename OutT>
+void convertBufferType(OutT* destination, const InT* source, size_t numberOfElements) {
+    ov::parallel_for(numberOfElements, [source, destination](int64_t index) {
+        destination[index] = static_cast<OutT>(source[index]);
+    });
+}
+
+void cvToOV(const cv::Mat& cvImg, const ov::Tensor& tensor, const ov::Shape& shape, const ov::Layout& layout,
+            const std::string& colorFormat) {
+    const ov::element::Type& precision = tensor.get_element_type();
+
+    OPENVINO_ASSERT(layout == ov::Layout("NHWC") || layout == ov::Layout("NCHW"),
+                    "Unsupported layout: ", layout.to_string());
+
+    const auto N = shape[ov::layout::batch_idx(layout)];
+    const auto C = shape[ov::layout::channels_idx(layout)];
+    const auto H = shape[ov::layout::height_idx(layout)];
+    const auto W = shape[ov::layout::width_idx(layout)];
+
+    OPENVINO_ASSERT(C == 3 || C == 4, "Unsupported number of channels: ", C);
+
+    int cvType = 0;
+
+    if (precision == ov::element::Type_t::u8) {
+        cvType = static_cast<int>(CV_8UC(C));
+    } else if (precision == ov::element::Type_t::f32) {
+        cvType = static_cast<int>(CV_32FC(C));
+    } else if (precision == ov::element::Type_t::f16) {
+        cvType = static_cast<int>(CV_16SC(C));
+    } else if (precision == ov::element::Type_t::i32) {
+        cvType = static_cast<int>(CV_32SC(C));
+    } else {
+        OPENVINO_ASSERT(precision == ov::element::Type_t::u8 || precision == ov::element::Type_t::f32 ||
+                                precision == ov::element::Type_t::f16 || precision == ov::element::Type_t::i32,
+                        "Unsupported precision ", precision.get_type_name());
+    }
+
+    cv::Mat in;
+
+    if (C == 3) {
+        if (colorFormat == "RGB") {
+            cv::cvtColor(cvImg, in, cv::COLOR_BGR2RGB);
+        } else {
+            in = cvImg;
+        }
+    } else {
+        if (colorFormat == "RGB") {
+            cv::cvtColor(cvImg, in, cv::COLOR_BGR2RGBA);
+        } else {
+            cv::cvtColor(cvImg, in, cv::COLOR_BGR2BGRA);
+        }
+    }
+
+    if (precision != ov::element::Type_t::u8) {
+        in.convertTo(in, CV_32F);
+    }
+
+    const auto pictureArea = static_cast<size_t>(in.size().area());
+
+    if (W * H > pictureArea) {
+        cv::resize(in, in, cv::Size(static_cast<int>(W), static_cast<int>(H)), 0.0, 0.0, cv::INTER_AREA);
+    } else {
+        cv::resize(in, in, cv::Size(static_cast<int>(W), static_cast<int>(H)), 0.0, 0.0, cv::INTER_LINEAR);
+    }
+
+    if (layout == ov::Layout("NHWC")) {
+        const auto dataBuffer = reinterpret_cast<uint8_t*>(tensor.data());
+
+        cv::Mat out(static_cast<int>(H), static_cast<int>(W), cvType, dataBuffer);
+
+        if (precision == ov::element::Type_t::f16) {
+            const auto inPtr = in.ptr<float>();
+            const auto outPtr = out.ptr<ov::float16>();
+            convertBufferType(outPtr, inPtr, out.size().area() * C);
+        } else if (precision == ov::element::Type_t::i32) {
+            in.convertTo(out, CV_32S);
+        } else {
+            in.copyTo(out);
+        }
+
+        for (size_t n = 1; n < N; ++n) {
+            cv::Mat batch(static_cast<int>(H), static_cast<int>(W), cvType,
+                          dataBuffer + n * (out.size().area() * out.elemSize()));
+            out.copyTo(batch);
+        }
+    } else if (layout == ov::Layout("NCHW")) {
+        auto tensorPlanes = ovToCV(tensor, shape, layout, 0);
+
+        if (precision != ov::element::Type_t::f16) {
+            cv::split(in, tensorPlanes);
+        } else {
+            std::vector<cv::Mat> inPlanes;
+            cv::split(in, inPlanes);
+
+            OPENVINO_ASSERT(tensorPlanes.size() == inPlanes.size());
+
+            for (size_t i = 0; i < tensorPlanes.size(); ++i) {
+                const auto inPtr = inPlanes[i].ptr<float>();
+                const auto outPtr = tensorPlanes[i].ptr<ov::float16>();
+                convertBufferType(outPtr, inPtr, inPlanes[i].size().area());
+            }
+        }
+
+        for (size_t n = 1; n < N; ++n) {
+            const auto batchPlanes = ovToCV(tensor, shape, layout, n);
+
+            OPENVINO_ASSERT(batchPlanes.size() == tensorPlanes.size());
+
+            for (size_t i = 0; i < tensorPlanes.size(); ++i) {
+                tensorPlanes[i].copyTo(batchPlanes[i]);
+            }
+        }
+    }
+}
+
+std::vector<float> splitFloat(const std::string& s, char delim) {
+    std::vector<float> result;
+    std::stringstream ss(s);
+    std::string item;
+
+    while (getline(ss, item, delim)) {
+        result.push_back(std::stof(item));
+    }
+    return result;
+}
+
+std::unordered_map<std::string, std::vector<float>> parseMeanOrScaleString(const std::string& mean_scale) {
+    std::unordered_map<std::string, std::vector<float>> result;
+
+    //  Format: layer1[255,255,255],layer2[255,255,255] for particular layers,
+    //          or [255,255,255] for all layers
+    std::string search_string = mean_scale;
+    auto start_pos = search_string.find_first_of('[');
+    while (start_pos != std::string::npos) {
+        auto end_pos = search_string.find_first_of(']');
+        if (end_pos == std::string::npos)
+            break;
+        auto input_name = search_string.substr(0, start_pos);
+        if (result.count(input_name) == 0) {
+            auto input_value_string = search_string.substr(start_pos + 1, end_pos - start_pos - 1);
+            result[input_name] = splitFloat(input_value_string, ',');
+            if (input_name.empty()) {
+                if (mean_scale != search_string) {
+                    throw std::logic_error("Can't parse input parameter string: " + mean_scale +
+                                           ". Format of value: layer1[255,255,255],layer2[255,255,255] "
+                                           "for particular layers, or just [255,255,255] for all layers.");
+                }
+                search_string = search_string.substr(end_pos + 1);
+                break;
+            }
+        } else {
+            throw std::logic_error("Specifying mean and scale for the same layer/s"
+                                   " more than once is prohibited: " +
+                                   mean_scale);
+        }
+
+        search_string = search_string.substr(end_pos + 1);
+        if (search_string.empty() || search_string.front() != ',')
+            break;
+        search_string = search_string.substr(1);
+        if (search_string.empty()) {
+            throw std::logic_error("Can't parse input parameter string: " + mean_scale +
+                                   ". Format of value: layer1[255,255,255],layer2[255,255,255] "
+                                   "for particular layers, or just [255,255,255] for all layers.");
+        }
+        start_pos = search_string.find_first_of('[');
+    }
+    if (!search_string.empty())
+        throw std::logic_error("Can't parse input parameter string: " + mean_scale +
+                               ". Format of value: layer1[255,255,255],layer2[255,255,255] "
+                               "for particular layers, or just [255,255,255] for all layers.");
+
+    return result;
+}
+
+std::vector<std::vector<float>> parseMeanOrScale(const std::string& mean_scale,
+                                                 const std::vector<ov::Output<ov::Node>>& inputs_info) {
+    std::vector<std::vector<float>> result(inputs_info.size());
+
+    auto mean_or_scale_map = parseMeanOrScaleString(mean_scale);
+
+    for (auto&& [layer_name, mean_or_scale] : mean_or_scale_map) {
+        if (!layer_name.empty()) {
+            // Add an explicit reference. Lambda expressions in C++17 cannot capture structured bindings.
+            const auto& layer_name_ref = layer_name;
+            auto required_input_it = std::find_if(inputs_info.begin(), inputs_info.end(),
+                                                  [&layer_name_ref](const ov::Output<const ov::Node>& item) {
+                                                      return item.get_any_name() == layer_name_ref;
+                                                  });
+            if (required_input_it != inputs_info.end()) {
+                result[std::distance(inputs_info.begin(), required_input_it)] = mean_or_scale;
+            } else {
+                throw std::logic_error(std::string("Input with name '") + layer_name + "' doesn't exist.");
+            }
+        } else {
+            for (size_t idx = 0; idx < inputs_info.size(); ++idx) {
+                result[idx] = mean_or_scale;
+            }
+        }
+    }
+
+    return result;
+}
+
+//
+// File utils
+//
+
+bool hasLoadableExt(const std::string& network_path) {
+    static const std::array<const char*, 5> ext_support_table{"xml", "onnx", "pdmodel", "pb", "tflite"};
+    return std::any_of(ext_support_table.begin(), ext_support_table.end(), [&network_path](const char* ext) {
+        static constexpr const auto dot_symbol = '.';
+        auto pos = network_path.rfind(dot_symbol);
+        std::string ext_name = {};
+        if (pos != std::string::npos)
+            ext_name = network_path.substr(pos + 1);
+        return strEq(ext_name, ext);
+    });
+}
+
+std::string cleanName(std::string&& name) {
+    std::replace_if(
+            name.begin(), name.end(),
+            [](unsigned char c) {
+                return !std::isalnum(c);
+            },
+            '_');
+    return std::move(name);
+}
+
+ov::Tensor loadImage(const ov::element::Type& precision, const ov::Shape& shape, const ov::Layout& layout,
+                     const std::string& filePath, const std::string& colorFormat) {
+    const auto frame = cv::imread(filePath, cv::IMREAD_COLOR);
+    OPENVINO_ASSERT(!frame.empty(), "Failed to open input image file ", filePath);
+
+    const ov::Tensor tensor(precision, shape);
+
+    cvToOV(frame, tensor, shape, layout, colorFormat);
+
+    return tensor;
+}
+
+ov::Tensor loadBinary(const ov::element::Type& modelPrecision, const ov::Shape& shape, const std::string& filePath,
+                      const ov::element::Type& dataPrecision) {
+    std::ifstream binaryFile(filePath, std::ios_base::binary | std::ios_base::ate);
+    OPENVINO_ASSERT(binaryFile, "Failed to open input binary file: ", filePath);
+    const auto fileBytes = binaryFile.tellg();
+    binaryFile.seekg(0, std::ios_base::beg);
+    OPENVINO_ASSERT(binaryFile.good(), "While reading a file an error is encountered");
+
+    const ov::Tensor requestedTensor(modelPrecision, shape);
+    const int reqTensorBytes = static_cast<int>(requestedTensor.get_byte_size());
+
+    if (dataPrecision != modelPrecision && dataPrecision != ov::element::Type_t::undefined) {
+        std::cout << "Converting " << filePath << " input from " << dataPrecision << " to " << modelPrecision
+                  << std::endl;
+        const ov::Tensor inputTensor(dataPrecision, shape);
+        binaryFile.read(reinterpret_cast<char*>(inputTensor.data()), static_cast<std::streamsize>(fileBytes));
+        npu::utils::convertTensorPrecision(inputTensor, requestedTensor);
+    } else {
+        OPENVINO_ASSERT(fileBytes == reqTensorBytes, "File contains ", fileBytes, " bytes, but ", reqTensorBytes,
+                        " expected");
+        binaryFile.read(reinterpret_cast<char*>(requestedTensor.data()), static_cast<std::streamsize>(reqTensorBytes));
+    }
+
+    return requestedTensor;
+}
+
+/**
+ * @brief Loads the contents of a locally stored file inside an OpenVINO tensor intended to be used as input in the
+ * context of the application.
+ * @details The data being loaded can either be an image or a binary file, the switch between these cases can be
+ * performed by setting the "img_as_bin" flag accordingly. If an image is being loaded, the OpenCV library is deployed
+ * for reading followed by a conversion to the OpenVINO format. If a binary file is loaded, the content's type is
+ * converted from "dataPrecision" to "modelPrecision" before constructing the tensor.
+ *
+ * @param modelPrecision The precision accepted by the model's input
+ * @param shape The shape accepted by the model's input
+ * @param layout The layout used by the model's input
+ * @param filePath Indicates the location of the file to be loaded
+ * @param colorFormat Indicates the color format only in the case when an image is being loaded.
+ * @param dataPrecision Indicates the precision used by the data found within the binary file.
+ * @return The tensor containing the loaded data.
+ */
+ov::Tensor loadInput(const ov::element::Type& modelPrecision, const ov::Shape& shape, const ov::Layout& layout,
+                     const std::string& filePath, const std::string& colorFormat,
+                     const ov::element::Type& dataPrecision = ov::element::Type_t::undefined) {
+    if (isImage(shape, layout) && !FLAGS_img_as_bin) {
+        return loadImage(modelPrecision, shape, layout, filePath, colorFormat);
+    } else {
+        return loadBinary(modelPrecision, shape, filePath, dataPrecision);
+    }
+}
+
+ov::Tensor loadTensor(const ov::element::Type& precision, const ov::Shape& shape, const std::string& filePath) {
+    const ov::Tensor tensor(precision, shape);
+
+    std::ifstream file(filePath, std::ios_base::in | std::ios_base::binary);
+    OPENVINO_ASSERT(file.is_open(), "Can't open file ", filePath, " for read");
+
+    const auto dataBuffer = reinterpret_cast<char*>(tensor.data());
+    file.read(dataBuffer, static_cast<std::streamsize>(tensor.get_byte_size()));
+
+    return tensor;
+}
+
+void dumpTensor(const ov::Tensor& tensor, const std::string& filePath) {
+    std::ofstream file(filePath, std::ios_base::out | std::ios_base::binary);
+    OPENVINO_ASSERT(file.is_open(), "Can't open file ", filePath, " for write");
+
+    const auto dataBuffer = reinterpret_cast<char*>(tensor.data());
+    file.write(dataBuffer, static_cast<std::streamsize>(tensor.get_byte_size()));
+}
+
+std::map<std::string, std::string> parseConfigFile() {
+    std::map<std::string, std::string> config;
+
+    std::ifstream file(FLAGS_config);
+    OPENVINO_ASSERT(file.is_open(), "Can't open file ", FLAGS_config, " for read");
+
+    std::string option;
+    while (std::getline(file, option)) {
+        if (option.empty() || option[0] == '#') {
+            continue;
+        }
+        size_t spacePos = option.find_first_of(" \t\n\r");
+        OPENVINO_ASSERT(spacePos != std::string::npos,
+                        "Invalid config parameter format. Space separator required here: ", option);
+        std::string key, value;
+        if (spacePos != std::string::npos) {
+            key = option.substr(0, spacePos);
+            size_t valueStart = option.find_first_not_of(" \t\n\r", spacePos);
+            OPENVINO_ASSERT(valueStart != std::string::npos,
+                            "An invalid config parameter value detected, it mustn't be empty: ", option);
+            size_t valueEnd = option.find_last_not_of(" \t\n\r");
+            value = option.substr(valueStart, valueEnd - valueStart + 1);
+            config[key] = value;
+        }
+    }
+
+    return config;
+}
+
+// This function formats performance counters in a same way as benchmark_app -pc does.
+// It is a copy-paste from $OPENVINO_HOME/samples/cpp/common/utils/include/samples/common.hpp
+using ProfVec = std::vector<ov::ProfilingInfo>;
+static void printPerformanceCounts(ProfVec performanceData, std::ostream& stream, std::string deviceName,
+                                   bool bshowHeader = true) {
+    std::chrono::microseconds totalTime = std::chrono::microseconds::zero();
+    // Print performance counts
+    if (bshowHeader) {
+        stream << std::endl << "performance counts:" << std::endl << std::endl;
+    }
+    std::ios::fmtflags fmt(std::cout.flags());
+    for (const auto& it : performanceData) {
+        std::string toPrint(it.node_name);
+        const int maxLayerName = 30;
+
+        if (it.node_name.length() >= maxLayerName) {
+            toPrint = it.node_name.substr(0, maxLayerName - 4);
+            toPrint += "...";
+        }
+
+        stream << std::setw(maxLayerName) << std::left << toPrint;
+        switch (it.status) {
+        case ov::ProfilingInfo::Status::EXECUTED:
+            stream << std::setw(15) << std::left << "EXECUTED";
+            break;
+        case ov::ProfilingInfo::Status::NOT_RUN:
+            stream << std::setw(15) << std::left << "NOT_RUN";
+            break;
+        case ov::ProfilingInfo::Status::OPTIMIZED_OUT:
+            stream << std::setw(15) << std::left << "OPTIMIZED_OUT";
+            break;
+        }
+        stream << std::setw(30) << std::left << "layerType: " + std::string(it.node_type) + " ";
+        stream << std::setw(20) << std::left << "realTime: " + std::to_string(it.real_time.count());
+        stream << std::setw(20) << std::left << "cpu: " + std::to_string(it.cpu_time.count());
+        stream << " execType: " << it.exec_type << std::endl;
+        if (it.real_time.count() > 0) {
+            totalTime += it.real_time;
+        }
+    }
+    stream << std::setw(20) << std::left << "Total time: " + std::to_string(totalTime.count()) << " microseconds"
+           << std::endl;
+    std::cout << std::endl;
+    std::cout << "Full device name: " << deviceName << std::endl;
+    std::cout << std::endl;
+    std::cout.flags(fmt);
+}
+
+bool checkBBoxOutputs(std::vector<utils::BoundingBox>& actualOutput, std::vector<utils::BoundingBox>& refOutput,
+                      const size_t imgWidth, const size_t imgHeight, const float boxTolerance,
+                      const float probTolerance) {
+    std::cout << "Ref Top:" << std::endl;
+    for (size_t i = 0; i < refOutput.size(); ++i) {
+        const auto& bb = refOutput[i];
+        std::cout << i << " : " << bb.idx << " : [(" << bb.left << " " << bb.top << "), (" << bb.right << " "
+                  << bb.bottom << ")] : " << bb.prob * 100 << "%" << std::endl;
+    }
+
+    std::cout << "Actual top:" << std::endl;
+    for (size_t i = 0; i < actualOutput.size(); ++i) {
+        const auto& bb = actualOutput[i];
+        std::cout << i << " : " << bb.idx << " : [(" << bb.left << " " << bb.top << "), (" << bb.right << " "
+                  << bb.bottom << ")] : " << bb.prob * 100 << "%" << std::endl;
+    }
+
+    for (const auto& refBB : refOutput) {
+        bool found = false;
+
+        float maxBoxError = 0.0f;
+        float maxProbError = 0.0f;
+
+        for (const auto& actualBB : actualOutput) {
+            if (actualBB.idx != refBB.idx) {
+                continue;
+            }
+
+            const utils::Box actualBox{actualBB.left / imgWidth, actualBB.top / imgHeight,
+                                       (actualBB.right - actualBB.left) / imgWidth,
+                                       (actualBB.bottom - actualBB.top) / imgHeight};
+            const utils::Box refBox{refBB.left / imgWidth, refBB.top / imgHeight, (refBB.right - refBB.left) / imgWidth,
+                                    (refBB.bottom - refBB.top) / imgHeight};
+
+            const auto boxIntersection = boxIntersectionOverUnion(actualBox, refBox);
+            const auto boxError = 1.0f - boxIntersection;
+            maxBoxError = std::max(maxBoxError, boxError);
+
+            const auto probError = std::fabs(actualBB.prob - refBB.prob);
+            maxProbError = std::max(maxProbError, probError);
+
+            if (boxError > boxTolerance) {
+                continue;
+            }
+
+            if (probError > probTolerance) {
+                continue;
+            }
+
+            found = true;
+            break;
+        }
+        if (!found) {
+            std::cout << "maxBoxError=" << maxBoxError << " "
+                      << "maxProbError=" << maxProbError << std::endl;
+            return false;
+        }
+    }
+    return true;
+}
+
+//
+// Classification mode
+//
+
+std::vector<std::pair<int, float>> parseClassification(const float* dataBuffer, size_t dataBufferElementsCount) {
+    OPENVINO_ASSERT(dataBuffer != nullptr, "Received a tensor with no allocated buffer");
+
+    std::vector<std::pair<int, float>> res(dataBufferElementsCount);
+    for (size_t i = 0; i < dataBufferElementsCount; ++i) {
+        res[i].first = static_cast<int>(i);
+        res[i].second = dataBuffer[i];
+    }
+
+    std::sort(res.begin(), res.end(), [](const std::pair<int, float>& a, const std::pair<int, float>& b) {
+        return a.second > b.second;
+    });
+
+    return res;
+}
+
+std::vector<std::vector<std::pair<int, float>>> parseClassificationBatch(const ov::Tensor& tensor, size_t batch_size) {
+    OPENVINO_ASSERT(batch_size, "batch_size can't be 0");
+    OPENVINO_ASSERT(tensor.get_element_type() == ov::element::Type_t::f32,
+                    "Unsupported precision: ", tensor.get_element_type().get_type_name());
+
+    std::vector<std::vector<std::pair<int, float>>> ret;
+
+    const float* dataBuffer = tensor.data<const float>();
+    OPENVINO_ASSERT(dataBuffer != nullptr, "Received a tensor with no allocated buffer");
+
+    size_t batch_bundle_size = tensor.get_size() / batch_size;
+    OPENVINO_ASSERT(!(tensor.get_size() % batch_bundle_size),
+                    "Tensor is a not batched tensor! Size: ", tensor.get_size(),
+                    " can't be batched on a batch size: ", batch_size, " properly");
+
+    size_t i = 0;
+    for (; i < tensor.get_size(); i += batch_bundle_size) {
+        if (batch_size != 1) {
+            std::cout << "restore tensor from data bundle: (" << i << "/" << tensor.get_size() << " bytes)"
+                      << std::endl;
+        }
+        ret.push_back(parseClassification(dataBuffer + i, batch_bundle_size));
+    }
+
+    OPENVINO_ASSERT(i == tensor.get_size());
+    return ret;
+}
+
+bool testClassification(const TensorMap& outputs, const TensorMap& references, size_t batch_size = 1) {
+    OPENVINO_ASSERT(outputs.size() == 1);
+    OPENVINO_ASSERT(outputs.size() == references.size());
+
+    const ov::Tensor outputFP32 = npu::utils::toFP32(outputs.begin()->second);
+    const ov::Tensor referenceFP32 = npu::utils::toFP32(references.begin()->second);
+
+    OPENVINO_ASSERT(outputFP32.get_element_type() == referenceFP32.get_element_type());
+    OPENVINO_ASSERT(outputFP32.get_shape() == referenceFP32.get_shape());
+    OPENVINO_ASSERT(referenceFP32.get_element_type() == ov::element::Type_t::f32);
+
+    auto probsBatch = parseClassificationBatch(outputFP32, batch_size);
+    auto refProbsBatch = parseClassificationBatch(referenceFP32, batch_size);
+    OPENVINO_ASSERT(refProbsBatch.size() == probsBatch.size(),
+                    "Incorrect batch size of both output tensor: ", probsBatch.size(),
+                    " and reference tensor: ", refProbsBatch.size(), ". Expected: ", batch_size);
+    for (size_t i = 0; i < batch_size; i++) {
+        OPENVINO_ASSERT(probsBatch[i].size() == refProbsBatch[i].size(),
+                        "Incorrect size of referenced tensor in batch bundle number: (", i, "/", batch_size, ")",
+                        ". Expected size: ", probsBatch[i].size(), ", got: ", refProbsBatch[i].size());
+        OPENVINO_ASSERT(refProbsBatch[i].size() >= FLAGS_top_k);
+        refProbsBatch[i].resize(FLAGS_top_k);
+    }
+
+    bool result = true;
+    for (size_t i = 0; i < probsBatch.size(); i++) {
+        if (batch_size != 1) {
+            std::cout << "Check tensor bundle: (" << i << "/" << batch_size << " batch)" << std::endl;
+        }
+        auto probs = probsBatch[i];
+        const auto& refs = refProbsBatch[i];
+        OPENVINO_ASSERT(probs.size() >= FLAGS_top_k);
+        probs.resize(FLAGS_top_k);
+
+        std::cout << "Actual top:" << std::endl;
+        for (size_t j = 0; j < probs.size(); ++j) {
+            std::cout << "    " << j << " : " << probs[j].first << " : " << probs[j].second << std::endl;
+        }
+
+        std::cout << "Ref Top:" << std::endl;
+        for (size_t j = 0; j < refs.size(); ++j) {
+            std::cout << "    " << j << " : " << refs[j].first << " : " << refs[j].second << std::endl;
+        }
+
+        for (const auto& refElem : refs) {
+            const auto actualIt =
+                    std::find_if(probs.cbegin(), probs.cend(), [&refElem](const std::pair<int, float>& arg) {
+                        return refElem.first == arg.first;
+                    });
+            if (actualIt == probs.end()) {
+                std::cout << "Ref result " << refElem.first << " was not found in actual results" << std::endl;
+                result = result && false;
+                continue;
+            }
+
+            const auto& actualElem = *actualIt;
+
+            if (refElem.second > actualElem.second) {
+                const auto probDiff = std::fabs(refElem.second - actualElem.second);
+                if (probDiff > FLAGS_prob_tolerance) {
+                    std::cout << "Probability value mismatch for " << refElem.first << " : " << refElem.second << " vs "
+                              << actualElem.second;
+                    result = result && false;
+                }
+            }
+        }
+    }
+
+    return result;
+}
+
+//
+// RAW mode
+//
+
+bool compareTensors(const ov::Tensor& output, const ov::Tensor& reference) {
+    if (output.get_shape() != reference.get_shape()) {
+        std::cout << "Output and reference tensors have different shapes" << std::endl;
+        return false;
+    }
+
+    const ov::Tensor outputFP32 = npu::utils::toFP32(output);
+    const ov::Tensor referenceFP32 = npu::utils::toFP32(reference);
+
+    const auto outputBuffer = outputFP32.data<const float>();
+    const auto referenceBuffer = referenceFP32.data<const float>();
+
+    const auto totalCount = referenceFP32.get_size();
+    const auto printCount = std::min<size_t>(totalCount, 10);
+
+    for (size_t i = 0; i < totalCount; ++i) {
+        const auto referenceValue = referenceBuffer[i];
+        const auto outputValue = outputBuffer[i];
+        const auto absDiff = std::fabs(referenceValue - outputValue);
+
+        if (i < printCount) {
+            std::cout << "        " << i << " :"
+                      << " ref : " << std::setw(10) << referenceValue << " output : " << std::setw(10) << outputValue
+                      << " absdiff : " << std::setw(10) << absDiff << std::endl;
+        }
+
+        if (absDiff > FLAGS_raw_tolerance) {
+            std::cout << "Absolute difference between output value " << outputValue << " and reference value "
+                      << referenceValue << " at index " << i << " larger then tolerance" << std::endl;
+            return false;
+        }
+    }
+
+    return true;
+}
+
+bool testRAW(const TensorMap& outputTensors, const TensorMap& referenceTensors, size_t batch_size = 1) {
+    if (batch_size != 1) {
+        throw std::runtime_error(
+                "The testcase 'raw' doesn't support any `override_model_batch_size` values besides 1 yet");
+    }
+
+    if (outputTensors.size() != referenceTensors.size()) {
+        std::cout << "The number of predicted outputs differ from the number of references" << std::endl;
+        return false;
+    }
+
+    for (const auto& [tensorName, outputTensor] : outputTensors) {
+        auto referenceTensorIterator = referenceTensors.find(tensorName);
+        OPENVINO_ASSERT(referenceTensorIterator != referenceTensors.end());
+
+        std::cout << "Compare " << tensorName << " with reference" << std::endl;
+        if (!compareTensors(outputTensor, referenceTensorIterator->second)) {
+            return false;
+        }
+    }
+
+    return true;
+}
+
+//
+// Cosine-Similarity mode
+// (using 'cosim_threshold' flag, with expected value in range [0.0 -> 1.0])
+// e.g. '--mode cosim --cosim_threshold 0.98'
+//
+
+bool compareCoSim(const ov::Tensor& output, const ov::Tensor& reference) {
+    if (output.get_shape() != reference.get_shape()) {
+        std::cout << "Actual and reference blobs has different shape" << std::endl;
+        return false;
+    }
+
+    const ov::Tensor outputFP32 = npu::utils::toFP32(output);
+    const ov::Tensor referenceFP32 = npu::utils::toFP32(reference);
+
+    const auto outputBuffer = outputFP32.data<const float>();
+    const auto referenceBuffer = referenceFP32.data<const float>();
+
+    const auto size = referenceFP32.get_size();
+
+    double numr = 0.0, denA = 0.0, denB = 0.0;
+    for (size_t i = 0; i < size; ++i) {
+        numr += outputBuffer[i] * referenceBuffer[i];
+        denA += outputBuffer[i] * outputBuffer[i];
+        denB += referenceBuffer[i] * referenceBuffer[i];
+    }
+
+    if (denA == 0 || denB == 0) {
+        std::cout << "Div by ZERO. Cannot compute CoSim metric" << std::endl;
+        return false;
+    }
+
+    const auto similarity = numr / (sqrt(denA) * sqrt(denB));
+    const double eps = 0.0000001;
+    // Some experiments revealed that when applying the CoSim metric to large buffers it could provide
+    // similarity values that are outside the [-1:1] interval due the big number of operations done on
+    // floating point value. A small epsilon value was added to extend the interval to [-(1+eps):1+eps]
+    // to ensure that the above check is not failing.
+    if (similarity > (1.0 + eps) || similarity < -(1.0 + eps)) {
+        std::cout << "Invalid result " << similarity << " (valid range [-1 : +1])" << std::endl;
+        return false;
+    }
+
+    std::cout << "Cosine similarity : " << similarity * 100 << "%" << std::endl;
+    return similarity > FLAGS_cosim_threshold;
+}
+
+bool testCoSim(const TensorMap& outputs, const TensorMap& references, size_t batch_size = 1) {
+    if (batch_size != 1) {
+        throw std::runtime_error(
+                "The testcase 'testCoSim' doesn't support any `override_model_batch_size` values besides 1 yet");
+    }
+
+    if (outputs.size() != references.size()) {
+        std::cout << "The outputs and references differ in the number of tensors" << std::endl;
+        return false;
+    }
+
+    for (const auto& [tensorName, output] : outputs) {
+        auto referencesIterator = references.find(tensorName);
+        OPENVINO_ASSERT(referencesIterator != references.end());
+
+        std::cout << "Compare " << tensorName << " with reference" << std::endl;
+        if (!compareCoSim(output, referencesIterator->second)) {
+            return false;
+        }
+    }
+
+    return true;
+}
+
+//
+// Relative Root Mean Squared Error mode
+// (using 'rrmse_loss_threshold' flag, with expected value in range [0.0 -> infinity))
+// e.g. '--mode rrmse --rrmse_loss_threshold 0.1'
+//
+
+bool computeRRMSE(const ov::Tensor& output, const ov::Tensor& reference) {
+    if (output.get_shape() != reference.get_shape()) {
+        std::cout << "Output and reference tensors have different shapes" << std::endl;
+        return false;
+    }
+
+    const ov::Tensor outputFP32 = npu::utils::toFP32(output);
+    const ov::Tensor referenceFP32 = npu::utils::toFP32(reference);
+
+    const auto outputBuffer = outputFP32.data<const float>();
+    const auto referenceBuffer = referenceFP32.data<const float>();
+
+    const auto size = referenceFP32.get_size();
+
+    double error = 0, sum = 0, diff;
+    for (size_t i = 0; i < size; ++i) {
+        diff = (outputBuffer[i] - referenceBuffer[i]);
+        sum += (outputBuffer[i] * outputBuffer[i]);
+        error += (diff * diff);
+    }
+
+    if (sum == 0) {
+        if (error <= std::numeric_limits<double>::epsilon()) {
+            std::cout << "The results perfectly match (error = 0). RRMSE loss could not be computed" << std::endl;
+            return true;
+        }
+
+        std::cout << "Div by ZERO (Actual is the Zero Tensor). Cannot compute RRMSE loss" << std::endl;
+        return false;
+    }
+
+    double rrmseLoss = sqrt(error / sum);
+
+    std::cout << "RRMSE loss : " << rrmseLoss << "   RRMSE threshold : " << FLAGS_rrmse_loss_threshold << std::endl;
+    return rrmseLoss <= FLAGS_rrmse_loss_threshold;
+}
+
+bool testRRMSE(const TensorMap& outputs, const TensorMap& references, size_t batch_size = 1) {
+    if (batch_size != 1) {
+        throw std::runtime_error(
+                "The testcase 'rrmse' doesn't support any `override_model_batch_size` values besides 1 yet");
+    }
+
+    if (outputs.size() != references.size()) {
+        std::cout << "Actual and reference has different number of output blobs" << std::endl;
+        return false;
+    }
+
+    for (const auto& [tensorName, output] : outputs) {
+        auto referencesIterator = references.find(tensorName);
+        OPENVINO_ASSERT(referencesIterator != references.end());
+
+        std::cout << "Compare " << tensorName << " with reference" << std::endl;
+        if (!computeRRMSE(output, referencesIterator->second)) {
+            return false;
+        }
+    }
+
+    return true;
+}
+
+//
+// Normalized Mean Squared Error mode
+// (using 'nrmse_loss_threshold' flag, with expected value in range [0.0 -> infinity))
+// e.g. '--mode nrmse --nrmse_loss_threshold 0.01'
+//
+
+bool computeNRMSE(const ov::Tensor& output, const ov::Tensor& reference) {
+    if (output.get_shape() != reference.get_shape()) {
+        std::cout << "Output and reference tensors have different shapes" << std::endl;
+        return false;
+    }
+
+    const auto size = reference.get_size();
+
+    if (size == 0) {
+        std::cout << "Empty output and reference tensors, NRMSE loss set to 0" << std::endl;
+        return true;
+    }
+
+    const ov::Tensor outputFP32 = npu::utils::toFP32(output);
+    const ov::Tensor referenceFP32 = npu::utils::toFP32(reference);
+
+    const auto outputBuffer = outputFP32.data<const float>();
+    const auto referenceBuffer = referenceFP32.data<const float>();
+
+    double error = 0;
+    float maxOutput = 0, maxReference = 0, minOutput = 0, minReference = 0;
+    for (size_t i = 0; i < size; ++i) {
+        const auto diff = outputBuffer[i] - referenceBuffer[i];
+        error += diff * diff;
+        maxOutput = std::max(outputBuffer[i], maxOutput);
+        maxReference = std::max(referenceBuffer[i], maxReference);
+        minOutput = std::min(outputBuffer[i], minOutput);
+        minReference = std::min(referenceBuffer[i], minReference);
+    }
+
+    double nrmseLoss =
+            sqrt(error / size) / std::max(0.001f, std::max(maxOutput - minOutput, maxReference - minReference));
+
+    std::cout << "NRMSE loss : " << nrmseLoss << "   NRMSE threshold : " << FLAGS_nrmse_loss_threshold << std::endl;
+    return nrmseLoss <= FLAGS_nrmse_loss_threshold;
+}
+
+bool testNRMSE(const TensorMap& outputs, const TensorMap& references, size_t batch_size = 1) {
+    if (batch_size != 1) {
+        throw std::runtime_error(
+                "The testcase 'nrmse' doesn't support any `override_model_batch_size` values besides 1 yet");
+    }
+
+    if (outputs.size() != references.size()) {
+        std::cout << "Actual and reference has different number of output blobs" << std::endl;
+        return false;
+    }
+
+    for (const auto& [tensorName, output] : outputs) {
+        auto referencesIterator = references.find(tensorName);
+        OPENVINO_ASSERT(referencesIterator != references.end());
+
+        std::cout << "Compare " << tensorName << " with reference" << std::endl;
+        if (!computeNRMSE(output, referencesIterator->second)) {
+            return false;
+        }
+    }
+
+    return true;
+}
+
+//
+// PSNR mode
+// using psnr_reference and psnr_tolerance flags for validation
+// e.g. '--mode psnr --psnr_reference <value> --psnr_tolerance <value>'
+// Direction of metric’s growth is higher-better. If the images are identical, the PSNR is infinite.
+//
+
+bool testPSNR(const TensorMap& outputs, const TensorMap& references, const int dstHeight, const int dstWidth,
+              size_t batch_size = 1) {
+    if (batch_size != 1) {
+        throw std::runtime_error(
+                "The testcase 'psnr' doesn't support any `override_model_batch_size` values besides 1 yet");
+    }
+    OPENVINO_ASSERT(outputs.size() == references.size(),
+                    "Mismatch between the number of model outputs and the number of references");
+
+    int scaleBorder = FLAGS_scale_border;
+    bool normalizedImage = FLAGS_normalized_image;
+
+    auto refOutput = npu::utils::parseTensorsAsFP32(references);
+    auto actOutput = npu::utils::parseTensorsAsFP32(outputs);
+
+    auto result = utils::runPSNRMetric(actOutput, refOutput, dstHeight, dstWidth, scaleBorder, normalizedImage);
+
+    if (std::fabs(result - FLAGS_psnr_reference) > FLAGS_psnr_tolerance) {
+        std::cout << "Absolute difference between actual value " << result << " and reference value "
+                  << FLAGS_psnr_reference << " larger then tolerance " << FLAGS_psnr_tolerance << std::endl;
+        return false;
+    }
+
+    return true;
+}
+
+static void printPerformanceCountsAndLatency(size_t numberOfTestCase, const ProfVec& profilingData,
+                                             std::chrono::duration<double, std::milli> duration) {
+    auto durationMs = std::chrono::duration_cast<std::chrono::milliseconds>(duration);
+
+    if (!profilingData.empty()) {
+        std::cout << "Performance counts for " << numberOfTestCase << "-th infer request:" << std::endl;
+        printPerformanceCounts(profilingData, std::cout, FLAGS_device, false);
+    }
+
+    std::cout << "Latency: " << std::fixed << std::setprecision(2) << durationMs.count() << " ms" << std::endl;
+}
+
+bool compare_mean_IoU(std::vector<std::pair<bool, float>> iou, float semSegThreshold, uint32_t classes) {
+    float threshold = semSegThreshold * 100;
+    float ma = 0.0f;
+    bool stateValue = true;
+
+    if (FLAGS_sem_seg_ignore_label != std::numeric_limits<uint32_t>::max()) {
+        classes--;
+    }
+
+    size_t numberOfLabeledClasses = 0;
+    for (size_t i = 0; i < classes; i++) {
+        if (iou[i].first) {
+            numberOfLabeledClasses++;
+            if (FLAGS_dataset == "camVid12") {
+                std::cout << "mean_iou@" << camVid12[i].c_str() << ": " << std::fixed << std::setprecision(2)
+                          << iou[i].second << "%" << std::endl;
+            } else {
+                std::cout << "mean_iou@class" << i << ": " << std::fixed << std::setprecision(2) << iou[i].second << "%"
+                          << std::endl;
+            }
+            if (iou[i].second < threshold) {
+                std::cout << "Threshold smaller than " << threshold << "%" << std::endl;
+                stateValue = false;
+            }
+            ma += iou[i].second;
+        } else {
+            std::cout << "mean_iou@class" << i << ": no pixels labeled." << std::endl;
+        }
+    }
+    std::cout << "mean_iou@:mean " << std::fixed << std::setprecision(2) << (ma / numberOfLabeledClasses) << "%"
+              << std::endl;
+
+    return stateValue;
+}
+
+void setupOVCore(ov::Core& core) {
+    auto flagDevice = FLAGS_device;
+
+    if (!FLAGS_log_level.empty()) {
+        core.set_property(flagDevice, {{ov::log::level.name(), FLAGS_log_level}});
+    }
+
+    if (FLAGS_device == "CPU") {
+        core.set_property(flagDevice, {{"LP_TRANSFORMS_MODE", "NO"}});
+    }
+
+    if (FLAGS_pc) {
+        core.set_property(flagDevice, {{ov::enable_profiling.name(), true}});
+    }
+
+    if (!FLAGS_config.empty()) {
+        const auto configs = parseConfigFile();
+        core.set_property(flagDevice, {configs.begin(), configs.end()});
+    }
+}
+
+void nameIOTensors(std::shared_ptr<ov::Model> model) {
+    auto inputInfo = model->inputs();
+    for (std::size_t id = 0ul; id < inputInfo.size(); ++id) {
+        auto ii = inputInfo[id];
+        if (ii.get_names().empty()) {
+            ii.add_names({"input_" + std::to_string(ii.get_index())});
+        }
+    }
+
+    auto outputInfo = model->outputs();
+    for (std::size_t id = 0ul; id < outputInfo.size(); ++id) {
+        auto oi = outputInfo[id];
+        if (oi.get_names().empty()) {
+            oi.add_names({"output_" + std::to_string(oi.get_index())});
+        }
+    }
+}
+
+std::pair<TensorMap, ProfVec> runInfer(ov::InferRequest& inferRequest, ov::CompiledModel& compiledModel,
+                                       const TensorMap& inputs, const std::vector<std::string>& dumpedInputsPaths) {
+    for (const auto& [tensorName, tensor] : inputs) {
+        inferRequest.set_tensor(tensorName, tensor);
+    }
+
+    inferRequest.infer();
+
+    TensorMap out;
+    for (const auto& outputInfo : compiledModel.outputs()) {
+        const std::string layer_name = outputInfo.get_any_name();
+        out.insert({layer_name, inferRequest.get_tensor(layer_name)});
+    }
+
+    ProfVec profData{};
+
+    if (FLAGS_pc) {
+        profData = inferRequest.get_profiling_info();
+    }
+
+    return std::make_pair(out, profData);
+}
+
+void boundDynamicShape(std::shared_ptr<ov::Model>& model) {
+    for (auto&& item : model->get_parameters()) {
+        auto shape = item->get_partial_shape();
+        if (shape.is_static()) {
+            continue;
+        }
+        auto rank = shape.rank();
+        if (rank.is_dynamic()) {
+            throw std::logic_error("Rank \"" + rank.to_string() + "\" of the shape \"" + shape.to_string() +
+                                   "\" is dynamic which is not supported by SIT");
+        }
+        auto layout = item->get_layout();
+        if (!ov::layout::has_batch(layout)) {
+            item->set_layout(ov::Layout(layout.to_string().insert(1, "N,")));
+            layout = item->get_layout();
+        }
+        if (shape[ov::layout::batch_idx(layout)].is_dynamic()) {
+            std::cout << "WARNING: Shape \"" + shape.to_string() + "\"" +
+                                 " has dynamic batch size which is not supported by SIT\n"
+                                 "         Setting batch to 1 forcibly"
+                      << std::endl;
+            ov::set_batch(model, 1);
+        }
+        shape = item->get_partial_shape();
+        if (shape.is_dynamic()) {
+            throw std::logic_error("Model's input shape \"" + shape.to_string() + "\"" +
+                                   " is dynamic which is not supported by SIT");
+        }
+    }
+}
+
+void setModelBatch(std::shared_ptr<ov::Model>& model, uint32_t batch) {
+    if (batch == 1) {
+        return;
+    }
+
+    // New batch value is applicable if the model has non dynamic inputs/outputs only
+    // Amend layout by adding N if it has no batch dimension
+    for (auto&& item : model->get_parameters()) {
+        auto shape = item->get_partial_shape();
+        auto rank = shape.rank();
+        if (rank.is_dynamic()) {
+            throw std::logic_error("Rank \"" + rank.to_string() + "\" of the shape \"" + shape.to_string() +
+                                   "\" is dynamic which is not supported by SIT");
+        }
+        auto layout = item->get_layout();
+        if (!ov::layout::has_batch(layout)) {
+            item->set_layout(ov::Layout(layout.to_string().insert(1, "N,")));
+        }
+
+        shape = item->get_partial_shape();
+        if (shape.is_dynamic()) {
+            throw std::logic_error("Model's input shape \"" + shape.to_string() + "\"" +
+                                   " is dynamic which is not supported by SIT");
+        }
+    }
+    ov::set_batch(model, batch);
+}
+
+// FIXME: User must provide layout explicitly.
+// No "default" layout for IRv11 models.
+static ov::Layout getLayoutByRank(const size_t rank) {
+    switch (rank) {
+    case 0:
+        return ov::Layout::scalar();
+    case 1:
+        return ov::Layout("C");
+    case 2:
+        return ov::Layout("NC");
+    case 3:
+        return ov::Layout("CHW");
+    case 4:
+        return ov::Layout("NCHW");
+    case 5:
+        return ov::Layout("NCDHW");
+    }
+    throw std::logic_error("Failed to get layout for rank equal to " + std::to_string(rank));
+}
+
+static std::string toString(const std::vector<size_t>& vec) {
+    std::stringstream ss;
+    if (!vec.empty()) {
+        ss << "[";
+        for (size_t i = 0; i < vec.size() - 1; ++i) {
+            ss << vec[i] << ",";
+        }
+        ss << vec[vec.size() - 1];
+        ss << "]";
+    } else {
+        ss << "SCALAR";
+    }
+    return ss.str();
+}
+
+bool testSSDDetection(const TensorMap& outputs, const TensorMap& references,
+                      const TensorDescriptorMap& inputDescriptors, size_t batch_size = 1) {
+    if (batch_size != 1) {
+        throw std::runtime_error(
+                "The testcase 'ssd' doesn't support any `override_model_batch_size` values besides 1 yet");
+    }
+
+    OPENVINO_ASSERT(outputs.size() == 1 && references.size() == 1);
+    OPENVINO_ASSERT(!inputDescriptors.empty(), "No input descriptors received");
+
+    const ov::Tensor& output = outputs.begin()->second;
+    const ov::Tensor& reference = references.begin()->second;
+    const TensorDescriptor& inputDescriptor = inputDescriptors.begin()->second;
+
+    const auto imgWidth = inputDescriptor.shape.at(ov::layout::width_idx(inputDescriptor.layout));
+    const auto imgHeight = inputDescriptor.shape.at(ov::layout::height_idx(inputDescriptor.layout));
+
+    auto confThresh = FLAGS_confidence_threshold;
+    auto probTolerance = FLAGS_prob_tolerance;
+    auto boxTolerance = FLAGS_box_tolerance;
+
+    auto parsedOutput = utils::parseSSDOutput(output, imgWidth, imgHeight, static_cast<float>(confThresh));
+    auto parsedReference = utils::parseSSDOutput(reference, imgWidth, imgHeight, static_cast<float>(confThresh));
+
+    auto result = checkBBoxOutputs(parsedOutput, parsedReference, imgWidth, imgHeight, static_cast<float>(boxTolerance),
+                                   static_cast<float>(probTolerance));
+
+    return result;
+}
+
+//
+// Yolo V2 mode
+//
+bool testYoloV2(const TensorMap& outputs, const TensorMap& references, const TensorDescriptorMap& inputDescriptors,
+                size_t batch_size = 1) {
+    if (batch_size != 1) {
+        throw std::runtime_error(
+                "The testcase 'yolo_v2' doesn't support any `override_model_batch_size` values besides 1 yet");
+    }
+    OPENVINO_ASSERT(inputDescriptors.size() == 1, "The YOLO v2 model accepts only a single input");
+    OPENVINO_ASSERT(outputs.size() == 1, "The YOLO v2 model a single output");
+    OPENVINO_ASSERT(outputs.size() == references.size(),
+                    "Mismatch between the number of model outputs and the number of references");
+    const ov::Tensor& output = outputs.begin()->second;
+    const ov::Tensor& reference = references.begin()->second;
+
+    const TensorDescriptor& inputDescriptor = inputDescriptors.begin()->second;
+
+    const auto imgWidth = inputDescriptor.shape.at(ov::layout::width_idx(inputDescriptor.layout));
+    const auto imgHeight = inputDescriptor.shape.at(ov::layout::height_idx(inputDescriptor.layout));
+    double confThresh = FLAGS_confidence_threshold;
+    double probTolerance = FLAGS_prob_tolerance;
+    double boxTolerance = FLAGS_box_tolerance;
+    bool isTiny = FLAGS_is_tiny_yolo;
+
+    auto parsedOutput = utils::parseYoloOutput(npu::utils::toFP32(output), imgWidth, imgHeight,
+                                               static_cast<float>(confThresh), isTiny);
+    auto parsedReference = utils::parseYoloOutput(npu::utils::toFP32(reference), imgWidth, imgHeight,
+                                                  static_cast<float>(confThresh), isTiny);
+
+    bool result = checkBBoxOutputs(parsedOutput, parsedReference, imgWidth, imgHeight, static_cast<float>(boxTolerance),
+                                   static_cast<float>(probTolerance));
+    return result;
+}
+
+//
+// Yolo V3 mode
+//
+bool testYoloV3(const TensorMap& outputs, const TensorMap& references, const TensorDescriptorMap& inputDescriptors,
+                const LayoutMap& outputLayouts, size_t batch_size = 1) {
+    if (batch_size != 1) {
+        throw std::runtime_error(
+                "The testcase 'yolo_v3' doesn't support any `override_model_batch_size` values besides 1 yet");
+    }
+    OPENVINO_ASSERT(inputDescriptors.size() == 1, "The YOLO v3 model accepts only a single input");
+    OPENVINO_ASSERT(outputs.size() == 3, "The YOLO v3 model has three outputs");
+    OPENVINO_ASSERT(outputs.size() == references.size(),
+                    "Mismatch between the number of model outputs and the number of references");
+
+    const TensorDescriptor& inputDescriptor = inputDescriptors.begin()->second;
+    const auto imgWidth = inputDescriptor.shape.at(ov::layout::width_idx(inputDescriptor.layout));
+    const auto imgHeight = inputDescriptor.shape.at(ov::layout::height_idx(inputDescriptor.layout));
+
+    double confThresh = FLAGS_confidence_threshold;
+    double probTolerance = FLAGS_prob_tolerance;
+    double boxTolerance = FLAGS_box_tolerance;
+    int classes = FLAGS_classes;
+    int coords = FLAGS_coords;
+    int num = FLAGS_num;
+    std::vector<float> anchors = {10.0, 13.0, 16.0,  30.0,  33.0, 23.0,  30.0,  61.0,  62.0,
+                                  45.0, 59.0, 119.0, 116.0, 90.0, 156.0, 198.0, 373.0, 326.0};
+
+    auto parsedOutput = utils::parseYoloV3Output(outputs, imgWidth, imgHeight, classes, coords, num, anchors,
+                                                 static_cast<float>(confThresh), outputLayouts);
+    auto parsedReference = utils::parseYoloV3Output(references, imgWidth, imgHeight, classes, coords, num, anchors,
+                                                    static_cast<float>(confThresh), outputLayouts);
+
+    bool result = checkBBoxOutputs(parsedOutput, parsedReference, imgWidth, imgHeight, static_cast<float>(boxTolerance),
+                                   static_cast<float>(probTolerance));
+    return result;
+}
+
+//
+// Yolo V4 mode
+// Ref link: https://docs.openvino.ai/latest/omz_models_model_yolo_v4_tiny_tf.html
+//
+bool testYoloV4(const TensorMap& outputs, const TensorMap& references, const TensorDescriptorMap& inputDescriptors,
+                const LayoutMap& outputLayouts, size_t batch_size = 1) {
+    if (batch_size != 1) {
+        throw std::runtime_error(
+                "The testcase 'yolo_v4' doesn't support any `override_model_batch_size` values besides 1 yet");
+    }
+
+    OPENVINO_ASSERT(inputDescriptors.size() == 1, "The YOLO v4 model accepts only a single input");
+    OPENVINO_ASSERT(outputs.size() == 2, "The YOLO v4 model has two outputs");
+    OPENVINO_ASSERT(outputs.size() == references.size(),
+                    "Mismatch between the number of model outputs and the number of references");
+
+    const TensorDescriptor& inputDescriptor = inputDescriptors.begin()->second;
+    const auto imgWidth = inputDescriptor.shape.at(ov::layout::width_idx(inputDescriptor.layout));
+    const auto imgHeight = inputDescriptor.shape.at(ov::layout::height_idx(inputDescriptor.layout));
+
+    double confThresh = FLAGS_confidence_threshold;
+    double probTolerance = FLAGS_prob_tolerance;
+    double boxTolerance = FLAGS_box_tolerance;
+    int classes = FLAGS_classes;
+    int coords = FLAGS_coords;
+    int num = FLAGS_num;
+    std::vector<float> anchors = {10.0, 14.0, 23.0, 27.0, 37.0, 58.0, 81.0, 82.0, 135.0, 169.0, 344.0, 319.0};
+    std::vector<std::vector<float>> anchor_mask{{3, 4, 5}, {1, 2, 3}};
+    std::vector<float> masked_anchors{};
+    for (auto& it : anchor_mask) {
+        int index = 0;
+        for (auto& anchorIndex : it) {
+            if (index >= num)
+                break;
+
+            index++;
+            masked_anchors.push_back(anchors[static_cast<size_t>(2 * anchorIndex)]);
+            masked_anchors.push_back(anchors[static_cast<size_t>(2 * anchorIndex + 1)]);
+        }
+    }
+
+    auto refOutput = utils::parseYoloV4Output(references, imgWidth, imgHeight, classes, coords, num, masked_anchors,
+                                              static_cast<float>(confThresh), outputLayouts);
+    auto actOutput = utils::parseYoloV4Output(outputs, imgWidth, imgHeight, classes, coords, num, masked_anchors,
+                                              static_cast<float>(confThresh), outputLayouts);
+    bool result = checkBBoxOutputs(actOutput, refOutput, imgWidth, imgHeight, static_cast<float>(boxTolerance),
+                                   static_cast<float>(probTolerance));
+    return result;
+}
+
+//
+// MeanIoU mode
+// Using sem_seg_classes, sem_seg_threshold flags and optionally sem_seg_ignore_label and dataset flags for validation
+// e.g. '--mode mean_iou --sem_seg_classes 12 --sem_seg_threshold 0.98 --sem_seg_ignore_label 11 --dataset camVid12'
+//
+bool testMeanIoU(const TensorMap& outputs, const TensorMap& references, const LayoutMap& outputLayouts,
+                 size_t batch_size = 1) {
+    if (batch_size != 1) {
+        throw std::runtime_error(
+                "The testcase 'mean_iou' doesn't support any `override_model_batch_size` values besides 1 yet");
+    }
+
+    OPENVINO_ASSERT(outputs.size() == 1, "The metric accepts only a single output");
+    OPENVINO_ASSERT(outputs.size() == references.size(),
+                    "Mismatch between the number of model outputs and the number of references");
+    OPENVINO_ASSERT(outputs.size() == outputLayouts.size(),
+                    "Mismatch between the number of model outputs and their corresponding layout values");
+
+    unsigned int classes = FLAGS_sem_seg_classes;
+    auto semSegThreshold = static_cast<float>(FLAGS_sem_seg_threshold);
+
+    std::vector<uint8_t> parsedReferences;
+    std::vector<uint8_t> parsedOutputs;
+    std::vector<std::pair<bool, float>> iou(classes, {false, 0.0f});
+
+    utils::argMax_channels(references.begin()->second, parsedReferences, outputLayouts.begin()->second);
+    utils::argMax_channels(outputs.begin()->second, parsedOutputs, outputLayouts.begin()->second);
+
+    if (parsedReferences.size() != parsedOutputs.size()) {
+        std::cout << "Reference size and output size are different" << std::endl;
+        return false;
+    }
+    iou = utils::mean_IoU(parsedOutputs, parsedReferences, classes, FLAGS_sem_seg_ignore_label);
+
+    return compare_mean_IoU(iou, semSegThreshold, classes);
+}
+
+static int runSingleImageTest() {
+    std::cout << "Run single image test" << std::endl;
+    try {
+        const std::unordered_set<std::string> allowedPrecision = {"U8", "I32", "I64", "FP16", "FP32"};
+        if (!FLAGS_ip.empty()) {
+            // input precision is U8, I32, I64, FP16 or FP32 only
+            std::transform(FLAGS_ip.begin(), FLAGS_ip.end(), FLAGS_ip.begin(), ::toupper);
+            if (allowedPrecision.count(FLAGS_ip) == 0)
+                throw std::logic_error("Parameter -ip " + FLAGS_ip + " is not supported");
+        }
+        if (!FLAGS_op.empty()) {
+            // output precision is U8, I32, I64, FP16 or FP32 only
+            std::transform(FLAGS_op.begin(), FLAGS_op.end(), FLAGS_op.begin(), ::toupper);
+            if (allowedPrecision.count(FLAGS_op) == 0)
+                throw std::logic_error("Parameter -op " + FLAGS_op + " is not supported");
+        }
+
+        ov::Layout inUserLayout(FLAGS_il);
+        ov::Layout outUserLayout(FLAGS_ol);
+        ov::Layout inModelLayout(FLAGS_iml);
+        ov::Layout outModelLayout(FLAGS_oml);
+
+        std::vector<std::string> inputFilesPerCase;
+        std::vector<std::vector<std::string>> inputFilesForOneInfer;
+
+        inputFilesPerCase = splitStringList(FLAGS_input, ';');
+        for (const auto& images : inputFilesPerCase) {
+            inputFilesForOneInfer.push_back(splitStringList(images, ','));
+        }
+
+        std::vector<std::string> inputBinPrecisionStrPerCase;
+        std::vector<std::vector<ov::element::Type>> inputBinPrecisionForOneInfer(inputFilesForOneInfer.size());
+        if (FLAGS_img_as_bin) {
+            for (std::size_t i = 0; i < inputFilesForOneInfer.size(); ++i) {
+                inputBinPrecisionForOneInfer[i] =
+                        std::vector<ov::element::Type>(inputFilesForOneInfer[i].size(), ov::element::undefined);
+            }
+            inputBinPrecisionStrPerCase = splitStringList(FLAGS_img_bin_precision, ';');
+            std::size_t inferIdx = 0;
+            for (const auto& precisions : inputBinPrecisionStrPerCase) {
+                std::vector<std::string> inputBinPrecisionsStrThisInfer = splitStringList(precisions, ',');
+                std::size_t precisionIdx = 0;
+                for (const auto& precision : inputBinPrecisionsStrThisInfer) {
+                    if (strEq(precision, "FP32")) {
+                        inputBinPrecisionForOneInfer[inferIdx][precisionIdx] = ov::element::f32;
+                    } else if (strEq(precision, "FP16")) {
+                        inputBinPrecisionForOneInfer[inferIdx][precisionIdx] = ov::element::f16;
+                    } else if (strEq(precision, "I32")) {
+                        inputBinPrecisionForOneInfer[inferIdx][precisionIdx] = ov::element::i32;
+                    } else if (strEq(precision, "I64")) {
+                        inputBinPrecisionForOneInfer[inferIdx][precisionIdx] = ov::element::i64;
+                    } else if (strEq(precision, "U8")) {
+                        inputBinPrecisionForOneInfer[inferIdx][precisionIdx] = ov::element::u8;
+                    } else {
+                        std::cout << "WARNING: Unhandled precision '" << precision
+                                  << "'! Only FP32, FP16, I32, I64 and U8 can be currently converted to the network's"
+                                  << "input tensor precision.";
+                    }
+                    ++precisionIdx;
+                }
+                ++inferIdx;
+            }
+        }
+
+        if (FLAGS_network.empty()) {
+            std::cout << "Not enough parameters. Check help." << std::endl;
+            return EXIT_FAILURE;
+        }
+
+        ov::Core core;
+        setupOVCore(core);
+
+        ov::CompiledModel compiledModel;
+        if (hasLoadableExt(FLAGS_network)) {
+            std::cout << "Load network " << FLAGS_network << std::endl;
+
+            auto model = core.read_model(FLAGS_network);
+            nameIOTensors(model);
+
+            setModelBatch(model, FLAGS_override_model_batch_size);
+            if (FLAGS_device.find("NPU") != std::string::npos ||
+                // FIXME: SIT on CPU also requires to bound dynamic shapes
+                FLAGS_device.find("CPU") != std::string::npos) {
+                boundDynamicShape(model);
+            }
+
+            ov::preprocess::PrePostProcessor ppp(model);
+
+            // Input precision
+            const auto inputInfo = model->inputs();
+            if (!FLAGS_ip.empty()) {
+                ov::element::Type prc_in = ov::element::u8;
+                if (FLAGS_ip == "FP16")
+                    prc_in = ov::element::f16;
+                else if (FLAGS_ip == "FP32")
+                    prc_in = ov::element::f32;
+                else if (FLAGS_ip == "I32")
+                    prc_in = ov::element::i32;
+                else if (FLAGS_ip == "I64")
+                    prc_in = ov::element::i64;
+                else
+                    prc_in = ov::element::u8;
+
+                for (size_t i = 0; i < inputInfo.size(); ++i) {
+                    ppp.input(i).tensor().set_element_type(prc_in);
+                }
+            }
+
+            // Input layout
+            if (!inUserLayout.empty()) {
+                for (size_t i = 0; i < inputInfo.size(); ++i) {
+                    ov::Layout inLayerModelLayout;
+                    if (inModelLayout.empty()) {
+                        const auto shape = inputInfo[i].get_shape();
+                        inLayerModelLayout = getLayoutByRank(shape.size());
+                        std::cout << "WARNING: Configuring preprocessing. Since --iml option isn't set, input model "
+                                     "layout for layer \""
+                                  << inputInfo[i].get_any_name() << "\" is infered from shape: " << toString(shape)
+                                  << " rank (" << shape.size() << ") as " << inLayerModelLayout.to_string()
+                                  << std::endl;
+                    } else {
+                        inLayerModelLayout = inModelLayout;
+                    }
+                    ppp.input(i).model().set_layout(inLayerModelLayout);
+                    ppp.input(i).tensor().set_layout(inUserLayout);
+                }
+            }
+
+            // Input mean and scale if exist
+            if (!FLAGS_mean_values.empty() || !FLAGS_scale_values.empty()) {
+                auto means = parseMeanOrScale(FLAGS_mean_values, inputInfo);
+                auto scales = parseMeanOrScale(FLAGS_scale_values, inputInfo);
+                for (size_t i = 0; i < inputInfo.size(); ++i) {
+                    if (!means[i].empty()) {
+                        ppp.input(i).preprocess().convert_element_type(ov::element::f32).mean(means[i]);
+                    }
+                    if (!scales[i].empty()) {
+                        ppp.input(i).preprocess().convert_element_type(ov::element::f32).scale(scales[i]);
+                    }
+                }
+            }
+
+            // Output precision
+            const auto outputInfo = model->outputs();
+            if (!FLAGS_op.empty()) {
+                ov::element::Type prc_out = ov::element::u8;
+                if (FLAGS_op == "FP16")
+                    prc_out = ov::element::f16;
+                else if (FLAGS_op == "FP32")
+                    prc_out = ov::element::f32;
+                else if (FLAGS_op == "I32")
+                    prc_out = ov::element::i32;
+                else if (FLAGS_op == "I64")
+                    prc_out = ov::element::i64;
+                else
+                    prc_out = ov::element::u8;
+
+                for (size_t i = 0; i < outputInfo.size(); ++i) {
+                    ppp.output(i).tensor().set_element_type(prc_out);
+                }
+            }
+
+            // Output layout
+            if (!outUserLayout.empty()) {
+                for (size_t i = 0; i < outputInfo.size(); ++i) {
+                    ov::Layout outLayerModelLayout;
+                    if (outModelLayout.empty()) {
+                        const auto shape = outputInfo[i].get_shape();
+                        outLayerModelLayout = getLayoutByRank(shape.size());
+                        std::cout << "WARNING: Configuring preprocessing. Since --oml option isn't set, output model "
+                                     "layout for layer \""
+                                  << outputInfo[i].get_any_name() << "\" is infered from shape: " << toString(shape)
+                                  << " rank (" << shape.size() << ") as " << outLayerModelLayout.to_string()
+                                  << std::endl;
+                    } else {
+                        outLayerModelLayout = outModelLayout;
+                    }
+                    ppp.output(i).model().set_layout(outLayerModelLayout);
+                    ppp.output(i).tensor().set_layout(outUserLayout);
+                }
+            }
+
+            compiledModel = core.compile_model(ppp.build(), FLAGS_device);
+        } else {
+            std::cout << "Import network " << FLAGS_network << std::endl;
+
+            if (!FLAGS_mean_values.empty() || !FLAGS_scale_values.empty()) {
+                throw std::runtime_error("--mean_values and --scale_values aren't supported for "
+                                         "compiled model.\n The values can be set via "
+                                         "model_optimizer while generating xml\n");
+            }
+
+            std::ifstream file(FLAGS_network, std::ios_base::in | std::ios_base::binary);
+            OPENVINO_ASSERT(file.is_open(), "Can't open file ", FLAGS_network, " for read");
+            compiledModel = core.import_model(file, FLAGS_device);
+        }
+
+        // store compiled model, if required
+        if (!FLAGS_compiled_blob.empty()) {
+            std::ofstream outputFile{FLAGS_compiled_blob, std::ios::out | std::ios::binary};
+            if (!outputFile.is_open()) {
+                std::cerr << "Output file \"" << FLAGS_compiled_blob << "\" can't be opened for writing" << std::endl;
+                return EXIT_FAILURE;
+            } else {
+                compiledModel.export_model(outputFile);
+            }
+        }
+
+        auto inferRequest = compiledModel.create_infer_request();
+
+        std::string netFileName;
+        {
+            auto startPos = FLAGS_network.rfind('/');
+            if (startPos == std::string::npos) {
+                startPos = FLAGS_network.rfind('\\');
+                if (startPos == std::string::npos) {
+                    startPos = 0;
+                }
+            }
+
+            auto endPos = FLAGS_network.rfind('.');
+            if (endPos == std::string::npos) {
+                endPos = FLAGS_network.size();
+            }
+
+            OPENVINO_ASSERT(endPos > startPos);
+            netFileName = cleanName(FLAGS_network.substr(startPos, endPos - startPos));
+        }
+
+        for (size_t numberOfTestCase = 0; numberOfTestCase < inputFilesPerCase.size(); ++numberOfTestCase) {
+            const auto inputsInfo = compiledModel.inputs();
+            const auto outputsInfo = compiledModel.outputs();
+            std::vector<std::string> inputFiles = inputFilesForOneInfer[numberOfTestCase];
+            OPENVINO_ASSERT(inputFiles.size() == inputsInfo.size(), "Number of input files ", inputFiles.size(),
+                            " doesn't match network configuration ", inputsInfo.size());
+
+            TensorMap inTensors;
+            size_t inputInd = 0;
+            std::vector<std::string> dumpedInputsPaths;
+            TensorDescriptorMap inputDescriptors;  // Several metrics require the input metadata
+
+            // Load the input data
+            for (const auto& inputInfo : inputsInfo) {
+                const ov::Shape& shape = inputInfo.get_shape();
+                const ov::element::Type& precision = inputInfo.get_element_type();
+
+                // Determine the input layout
+                ov::Layout inputLayout;
+
+                if (!inUserLayout.empty()) {
+                    inputLayout = inUserLayout;
+                } else if (!inModelLayout.empty()) {
+                    inputLayout = inModelLayout;
+                } else {
+                    inputLayout = getLayoutByRank(shape.size());
+                    std::cout << "WARNING: Loading input data. Since --iml option isn't set, input model layout for "
+                                 "layer \""
+                              << inputInfo.get_any_name() << "\" is infered from shape: " << toString(shape)
+                              << " rank (" << shape.size() << ") as " << inputLayout.to_string() << std::endl;
+                }
+
+                inputDescriptors.emplace(inputInfo.get_any_name(), TensorDescriptor{precision, shape, inputLayout});
+
+                std::cout << "Load input #" << inputInd << " from " << inputFiles[inputInd] << " as " << precision
+                          << " " << inputLayout.to_string() << " " << shape << std::endl;
+
+                const ov::Tensor tensor =
+                        !FLAGS_img_as_bin
+                                ? loadInput(precision, shape, inputLayout, inputFiles[inputInd], FLAGS_color_format)
+                                : loadInput(precision, shape, inputLayout, inputFiles[inputInd], FLAGS_color_format,
+                                            inputBinPrecisionForOneInfer[numberOfTestCase][inputInd]);
+                std::ostringstream ostr;
+                ostr << netFileName << "_input_" << inputInd << "_case_" << numberOfTestCase << ".blob";
+                const auto blobFileName = ostr.str();
+
+                std::cout << "Dump input #" << inputInd << "_case_" << numberOfTestCase << " to " << blobFileName
+                          << std::endl;
+                dumpTensor(tensor, blobFileName);
+
+                ++inputInd;
+
+                dumpedInputsPaths.push_back(blobFileName);
+
+                inTensors.emplace(inputInfo.get_any_name(), std::move(tensor));
+            }
+
+            std::cout << "Run inference on " << FLAGS_device << std::endl;
+
+            const auto startTime = Time::now();
+            const auto outInference = runInfer(inferRequest, compiledModel, inTensors, dumpedInputsPaths);
+            const auto endTime = Time::now();
+
+            const TensorMap& outputTensors = outInference.first;
+
+            printPerformanceCountsAndLatency(numberOfTestCase, outInference.second, endTime - startTime);
+
+            if (FLAGS_run_test) {
+                TensorMap referenceTensors;
+                size_t outputInd = 0;
+                LayoutMap outputLayouts;  // Several metrics may require this
+
+                // Load the reference data
+                for (const auto& [tensorName, tensor] : outputTensors) {
+                    const ov::element::Type& precision = tensor.get_element_type();
+                    const ov::Shape& shape = tensor.get_shape();
+
+                    std::ostringstream ostr;
+                    ostr << netFileName << "_ref_out_" << outputInd << "_case_" << numberOfTestCase << ".blob";
+                    const auto blobFileName = ostr.str();
+
+                    std::cout << "Load reference output #" << outputInd << " from " << blobFileName << " as "
+                              << precision << std::endl;
+
+                    const ov::Tensor referenceTensor = loadTensor(precision, shape, blobFileName);
+                    referenceTensors.emplace(tensorName, referenceTensor);
+
+                    // Determine the output layout
+                    ov::Layout outputLayout;
+
+                    if (!outUserLayout.empty()) {
+                        outputLayout = outUserLayout;
+                    } else if (!outModelLayout.empty()) {
+                        outputLayout = outModelLayout;
+                    } else {
+                        outputLayout = getLayoutByRank(shape.size());
+                        std::cout << "WARNING: Since --oml option isn't set, output model layout for layer \""
+                                  << tensorName << "\" is infered from shape: " << toString(shape) << " rank ("
+                                  << shape.size() << ") as " << outputLayout.to_string() << std::endl;
+                    }
+
+                    outputLayouts.emplace(tensorName, outputLayout);
+
+                    ++outputInd;
+                }
+
+                outputInd = 0;
+
+                // Dump the outputs obtained upon prediction
+                for (const auto& tensorEntry : outputTensors) {
+                    std::ostringstream ostr;
+                    ostr << netFileName << "_kmb_out_" << outputInd << "_case_" << numberOfTestCase << ".blob";
+                    const auto blobFileName = ostr.str();
+
+                    std::cout << "Dump device output #" << outputInd << "_case_" << numberOfTestCase << " to "
+                              << blobFileName << std::endl;
+
+                    dumpTensor(tensorEntry.second, blobFileName);
+                    ++outputInd;
+                }
+
+                // Compare the outputs with their references using the chosen metric
+                if (strEq(FLAGS_mode, "classification")) {
+                    if (testClassification(outputTensors, referenceTensors, FLAGS_override_model_batch_size)) {
+                        std::cout << "PASSED" << std::endl;
+                    } else {
+                        std::cout << "FAILED" << std::endl;
+                        return EXIT_FAILURE;
+                    }
+                } else if (strEq(FLAGS_mode, "raw")) {
+                    if (testRAW(outputTensors, referenceTensors, FLAGS_override_model_batch_size)) {
+                        std::cout << "PASSED" << std::endl;
+                    } else {
+                        std::cout << "FAILED" << std::endl;
+                        return EXIT_FAILURE;
+                    }
+                } else if (strEq(FLAGS_mode, "cosim")) {
+                    if (testCoSim(outputTensors, referenceTensors, FLAGS_override_model_batch_size)) {
+                        std::cout << "PASSED" << std::endl;
+                    } else {
+                        std::cout << "FAILED" << std::endl;
+                        return EXIT_FAILURE;
+                    }
+                } else if (strEq(FLAGS_mode, "rrmse")) {
+                    if (testRRMSE(outputTensors, referenceTensors, FLAGS_override_model_batch_size)) {
+                        std::cout << "PASSED" << std::endl;
+                    } else {
+                        std::cout << "FAILED" << std::endl;
+                        return EXIT_FAILURE;
+                    }
+                } else if (strEq(FLAGS_mode, "nrmse")) {
+                    if (testNRMSE(outputTensors, referenceTensors, FLAGS_override_model_batch_size)) {
+                        std::cout << "PASSED" << std::endl;
+                    } else {
+                        std::cout << "FAILED" << std::endl;
+                        return EXIT_FAILURE;
+                    }
+                } else if (strEq(FLAGS_mode, "ssd")) {
+                    if (testSSDDetection(outputTensors, referenceTensors, inputDescriptors,
+                                         FLAGS_override_model_batch_size)) {
+                        std::cout << "PASSED" << std::endl;
+                    } else {
+                        std::cout << "FAILED" << std::endl;
+                        return EXIT_FAILURE;
+                    }
+                } else if (strEq(FLAGS_mode, "yolo_v2")) {
+                    if (testYoloV2(outputTensors, referenceTensors, inputDescriptors,
+                                   FLAGS_override_model_batch_size)) {
+                        std::cout << "PASSED" << std::endl;
+                    } else {
+                        std::cout << "FAILED" << std::endl;
+                        return EXIT_FAILURE;
+                    }
+                } else if (strEq(FLAGS_mode, "yolo_v3")) {
+                    if (testYoloV3(outputTensors, referenceTensors, inputDescriptors, outputLayouts,
+                                   FLAGS_override_model_batch_size)) {
+                        std::cout << "PASSED" << std::endl;
+                    } else {
+                        std::cout << "FAILED" << std::endl;
+                        return EXIT_FAILURE;
+                    }
+                } else if (strEq(FLAGS_mode, "yolo_v4")) {
+                    if (testYoloV4(outputTensors, referenceTensors, inputDescriptors, outputLayouts,
+                                   FLAGS_override_model_batch_size)) {
+                        std::cout << "PASSED" << std::endl;
+                    } else {
+                        std::cout << "FAILED" << std::endl;
+                        return EXIT_FAILURE;
+                    }
+                } else if (strEq(FLAGS_mode, "psnr")) {
+                    const auto& [firstOutputName, firstOutput] = *outputTensors.begin();
+                    const ov::Shape& shape = firstOutput.get_shape();
+                    const ov::Layout& outputLayout = outputLayouts.at(firstOutputName);
+                    const size_t dstHeight = shape[ov::layout::height_idx(outputLayout)];
+                    const size_t dstWidth = shape[ov::layout::width_idx(outputLayout)];
+
+                    if (testPSNR(outputTensors, referenceTensors, static_cast<int>(dstHeight),
+                                 static_cast<int>(dstWidth), FLAGS_override_model_batch_size)) {
+                        std::cout << "PASSED" << std::endl;
+                    } else {
+                        std::cout << "FAILED" << std::endl;
+                        return EXIT_FAILURE;
+                    }
+                } else if (strEq(FLAGS_mode, "mean_iou")) {
+                    if (testMeanIoU(outputTensors, referenceTensors, outputLayouts, FLAGS_override_model_batch_size)) {
+                        std::cout << "PASSED" << std::endl;
+                    } else {
+                        std::cout << "FAILED" << std::endl;
+                        return EXIT_FAILURE;
+                    }
+                } else {
+                    std::cout << "Unknown mode " << FLAGS_mode << std::endl;
+                    return EXIT_FAILURE;
+                }
+            } else {
+                size_t outputInd = 0;
+                for (const auto& tensorEntry : outputTensors) {
+                    std::ostringstream ostr;
+                    ostr << netFileName << "_ref_out_" << outputInd << "_case_" << numberOfTestCase << ".blob";
+                    const auto blobFileName = ostr.str();
+
+                    std::cout << "Dump reference output #" << outputInd << " to " << blobFileName << std::endl;
+                    dumpTensor(tensorEntry.second, blobFileName);
+
+                    ++outputInd;
+                }
+            }
+        }
+    }  // try
+    catch (const std::exception& ex) {
+        std::cerr << "exception: " << ex.what() << std::endl;
+        return EXIT_FAILURE;
+    }
+
+    return EXIT_SUCCESS;
+}
+
+//
+// main
+//
+
+int main(int argc, char* argv[]) {
+    parseCommandLine(argc, argv);
+
+    return runSingleImageTest();
+}
diff --git a/src/plugins/intel_npu/tools/single-image-test/semantic_segmentation_helpers.cpp b/src/plugins/intel_npu/tools/single-image-test/semantic_segmentation_helpers.cpp
new file mode 100644
index 00000000000000..ec69a531e5cb37
--- /dev/null
+++ b/src/plugins/intel_npu/tools/single-image-test/semantic_segmentation_helpers.cpp
@@ -0,0 +1,41 @@
+//
+// Copyright (C) 2022-2024 Intel Corporation.
+// SPDX-License-Identifier: Apache 2.0
+//
+
+#include "semantic_segmentation_helpers.hpp"
+#include "tensor_utils.hpp"
+
+void utils::argMax_channels(const ov::Tensor& tensor, std::vector<uint8_t>& resultArgmax, const ov::Layout& layout) {
+    OPENVINO_ASSERT(layout == ov::Layout("NCHW") || layout == ov::Layout("NHWC"),
+                    "Unsupported layout: ", layout.to_string());
+
+    const ov::Tensor tensorFP32 = npu::utils::toFP32(tensor);
+    const auto dataBuffer = tensorFP32.data<const float>();
+
+    const size_t C = tensorFP32.get_shape()[ov::layout::channels_idx(layout)];
+    const size_t H = tensorFP32.get_shape()[ov::layout::height_idx(layout)];
+    const size_t W = tensorFP32.get_shape()[ov::layout::width_idx(layout)];
+
+    for (size_t h = 0; h < H; h++) {
+        for (size_t w = 0; w < W; w++) {
+            float argMax = 0.0f;
+            uint8_t clsIdx = std::numeric_limits<uint8_t>::max();
+            for (size_t c = 0; c < C; c++) {
+                size_t offset;
+
+                if (layout == ov::Layout("NCHW")) {
+                    offset = c * H * W + h * W + w;
+                } else {
+                    offset = h * W * C + w * C + c;
+                }
+
+                if (argMax < dataBuffer[offset]) {
+                    argMax = dataBuffer[offset];
+                    clsIdx = static_cast<uint8_t>(c);
+                }
+            }
+            resultArgmax.push_back(clsIdx);
+        }
+    }
+}
diff --git a/src/plugins/intel_npu/tools/single-image-test/semantic_segmentation_helpers.hpp b/src/plugins/intel_npu/tools/single-image-test/semantic_segmentation_helpers.hpp
new file mode 100644
index 00000000000000..abb86996c3c137
--- /dev/null
+++ b/src/plugins/intel_npu/tools/single-image-test/semantic_segmentation_helpers.hpp
@@ -0,0 +1,82 @@
+//
+// Copyright (C) 2022-2024 Intel Corporation.
+// SPDX-License-Identifier: Apache 2.0
+//
+
+//
+
+#pragma once
+
+#include <openvino/core/layout.hpp>
+#include <openvino/runtime/tensor.hpp>
+
+#include <limits>
+#include <utility>
+
+namespace utils {
+
+void argMax_channels(const ov::Tensor& tensor, std::vector<uint8_t>& resultArgmax, const ov::Layout& layout);
+
+template <typename T>
+std::vector<std::pair<bool, float>> mean_IoU(std::vector<T> actOutput, std::vector<T> refOutput, uint32_t classes,
+                                             uint32_t ignoreLabel) {
+    std::vector<uint32_t> output;
+    for (size_t i = 0; i < refOutput.size(); i++) {
+        auto mask = (refOutput[i] < classes) & (actOutput[i] < classes);
+
+        if (mask == 1) {
+            output.push_back(classes * refOutput[i] + actOutput[i]);
+        }
+    }
+
+    std::vector<int> binC(classes * classes, 0);
+    for (size_t i = 0; i < output.size(); i++) {
+        binC[output[i]]++;
+    }
+
+    std::vector<std::vector<int>> hist(classes, std::vector<int>(classes));
+    for (size_t i = 0; i < classes; i++) {
+        for (size_t j = 0; j < classes; j++) {
+            hist[i][j] = binC[i * classes + j];
+        }
+    }
+
+    if (ignoreLabel != std::numeric_limits<uint32_t>::max()) {
+        // Sanity check
+        OPENVINO_ASSERT(ignoreLabel < classes);
+        for (size_t i = 0; i < classes; i++) {
+            hist[i][ignoreLabel] = 0;
+            hist[ignoreLabel][i] = 0;
+        }
+    }
+
+    std::vector<float> diagonal(classes, 0.0f);
+    std::vector<float> sum0(classes, 0.0f);
+    std::vector<float> sum1(classes, 0.0f);
+    for (size_t i = 0; i < classes; i++) {
+        for (size_t j = 0; j < classes; j++) {
+            if (i == j) {
+                diagonal[i] = static_cast<float>(hist[i][j]);
+            }
+            sum0[j] += static_cast<float>(hist[i][j]);
+            sum1[i] += static_cast<float>(hist[i][j]);
+        }
+    }
+
+    std::vector<float> unionVect(classes, 0.0f);
+    for (size_t i = 0; i < sum0.size(); i++) {
+        unionVect[i] = sum1[i] + sum0[i] - diagonal[i];
+    }
+
+    std::vector<std::pair<bool, float>> iou(classes, {false, 0.0f});
+    for (size_t i = 0; i < diagonal.size() - 1; i++) {
+        if (unionVect[i] != 0) {
+            iou[i].first = true;
+            iou[i].second = (diagonal[i] / unionVect[i]) * 100.0f;
+        }
+    }
+
+    return iou;
+}
+
+}  // namespace utils
diff --git a/src/plugins/intel_npu/tools/single-image-test/yolo_helpers.cpp b/src/plugins/intel_npu/tools/single-image-test/yolo_helpers.cpp
new file mode 100644
index 00000000000000..6740b490d36c15
--- /dev/null
+++ b/src/plugins/intel_npu/tools/single-image-test/yolo_helpers.cpp
@@ -0,0 +1,476 @@
+//
+// Copyright (C) 2022-2024 Intel Corporation.
+// SPDX-License-Identifier: Apache 2.0
+//
+
+#include "yolo_helpers.hpp"
+#include "data_type_converters.hpp"
+#include "tensor_utils.hpp"
+
+#include <cmath>
+
+static int entryIndex(int lw, int lh, int lcoords, int lclasses, int lnum, int batch, int location, int entry) {
+    int n = location / (lw * lh);
+    int loc = location % (lw * lh);
+    int loutputs = lh * lw * lnum * (lclasses + lcoords + 1);
+    return batch * loutputs + n * lw * lh * (lcoords + lclasses + 1) + entry * lw * lh + loc;
+}
+
+static utils::Box getRegionBox(float* x, const std::vector<float>& biases, int n, int index, int i, int j, int w, int h,
+                               int stride) {
+    utils::Box b;
+    b.x = (i + x[index + 0 * stride]) / w;
+    b.y = (j + x[index + 1 * stride]) / h;
+    b.w = std::exp(x[index + 2 * stride]) * biases[2 * n] / w;
+    b.h = std::exp(x[index + 3 * stride]) * biases[2 * n + 1] / h;
+
+    return b;
+}
+
+static utils::Box getRegionBoxV3V4(const std::vector<float>& predictions, const std::vector<float>& anchor,
+                                   int anchor_offset, int n, int box_index, int col, int row, int imw, int imh,
+                                   int side, const std::function<float(const float)>& transformationFunc) {
+    utils::Box b;
+    b.x = (col + transformationFunc(predictions[box_index + 0 * side * side])) / side;
+    b.y = (row + transformationFunc(predictions[box_index + 1 * side * side])) / side;
+    b.w = std::exp(predictions[box_index + 2 * side * side]) * anchor[anchor_offset + 2 * n] / imw;
+    b.h = std::exp(predictions[box_index + 3 * side * side]) * anchor[anchor_offset + 2 * n + 1] / imh;
+
+    return b;
+}
+
+static void correctRegionBoxes(std::vector<utils::Box>& boxes, int n, int w, int h, int netw, int neth, int relative) {
+    int new_w = 0;
+    int new_h = 0;
+    if ((static_cast<float>(netw) / w) < (static_cast<float>(neth) / h)) {
+        new_w = netw;
+        new_h = (h * netw) / w;
+    } else {
+        new_h = neth;
+        new_w = (w * neth) / h;
+    }
+
+    OPENVINO_ASSERT(static_cast<int>(boxes.size()) >= n);
+    for (int i = 0; i < n; ++i) {
+        utils::Box b = boxes[i];
+        b.x = (b.x - (netw - new_w) / 2.0f / netw) / (static_cast<float>(new_w) / netw);
+        b.y = (b.y - (neth - new_h) / 2.0f / neth) / (static_cast<float>(new_h) / neth);
+        b.w *= static_cast<float>(netw) / new_w;
+        b.h *= static_cast<float>(neth) / new_h;
+        if (!relative) {
+            b.x *= w;
+            b.w *= w;
+            b.y *= h;
+            b.h *= h;
+        }
+        boxes[i] = b;
+    }
+}
+
+static void getRegionBoxesV3V4(const std::vector<std::vector<float>>& predictions, int w, int h, int lclasses,
+                               int lcoords, int lnum, const std::vector<float>& anchors,
+                               std::vector<std::vector<size_t>>& tensorWH, float thresh,
+                               std::vector<std::vector<float>>& probs, std::vector<utils::Box>& boxes,
+                               const std::function<float(const float)>& transformationFunc,
+                               const std::function<int(const size_t, const int)>& anchorFunc) {
+    for (size_t iout = 0; iout < predictions.size(); ++iout) {
+        auto lw = static_cast<int>(tensorWH[iout][0]);
+        auto lh = static_cast<int>(tensorWH[iout][1]);
+        int anchorOffset = anchorFunc(static_cast<size_t>(lnum), static_cast<int>(iout));
+
+        for (int i = 0; i < lw * lh; ++i) {
+            int row = i / lw;
+            int col = i % lw;
+
+            for (int n = 0; n < lnum; ++n) {
+                int obj_index = entryIndex(lw, lh, lcoords, lclasses, lnum, 0, n * lw * lh + i, lcoords);
+                float scale = predictions[iout][obj_index];
+                float max = 0;
+                for (int j = 0; j < lclasses; ++j) {
+                    int class_index = entryIndex(lw, lh, lcoords, lclasses, lnum, 0, n * lw * lh + i, lcoords + 1 + j);
+                    float prob = transformationFunc(scale) * transformationFunc(predictions[iout][class_index]);
+                    if (max < prob)
+                        max = prob;
+                }
+                if (max < thresh)
+                    continue;
+
+                int box_index = entryIndex(lw, lh, lcoords, lclasses, lnum, 0, n * lw * lh + i, 0);
+
+                boxes.push_back(getRegionBoxV3V4(predictions[iout], anchors, anchorOffset, n, box_index, col, row, w, h,
+                                                 lw, transformationFunc));
+
+                std::vector<float> prob(lclasses + 1, 0.0);
+                for (int j = 0; j < lclasses; ++j) {
+                    int class_index = entryIndex(lw, lh, lcoords, lclasses, lnum, 0, n * lw * lh + i, lcoords + 1 + j);
+                    float probability = transformationFunc(scale) * transformationFunc(predictions[iout][class_index]);
+                    prob[j] = probability > thresh ? probability : 0;
+                }
+                prob[lclasses] = max;
+                probs.push_back(prob);
+            }
+        }
+    }
+}
+
+static void getRegionBoxes(std::vector<float>& predictions, int lw, int lh, int lcoords, int lclasses, int lnum, int w,
+                           int h, int netw, int neth, float thresh, std::vector<std::vector<float>>& probs,
+                           std::vector<utils::Box>& boxes, int relative, const std::vector<float>& anchors) {
+    for (int i = 0; i < lw * lh; ++i) {
+        int row = i / lw;
+        int col = i % lw;
+        for (int n = 0; n < lnum; ++n) {
+            int index = n * lw * lh + i;
+            int obj_index = entryIndex(lw, lh, lcoords, lclasses, lnum, 0, n * lw * lh + i, lcoords);
+            int box_index = entryIndex(lw, lh, lcoords, lclasses, lnum, 0, n * lw * lh + i, 0);
+            float scale = predictions[obj_index];
+
+            boxes[index] = getRegionBox(predictions.data(), anchors, n, box_index, col, row, lw, lh, lw * lh);
+
+            float max = 0;
+            for (int j = 0; j < lclasses; ++j) {
+                int class_index = entryIndex(lw, lh, lcoords, lclasses, lnum, 0, n * lw * lh + i, lcoords + 1 + j);
+                float prob = scale * predictions[class_index];
+                probs[index][j] = (prob > thresh) ? prob : 0;
+                if (prob > max)
+                    max = prob;
+            }
+            probs[index][lclasses] = max;
+        }
+    }
+    correctRegionBoxes(boxes, lw * lh * lnum, w, h, netw, neth, relative);
+}
+
+struct sortableYoloBBox {
+    int index;
+    int cclass;
+    std::vector<std::vector<float>> probs;
+    sortableYoloBBox(int index, int cclass, std::vector<std::vector<float>>& probs)
+            : index(index), cclass(cclass), probs(probs){}
+};
+
+static float overlap(float x1, float w1, float x2, float w2) {
+    const float l1 = x1 - w1 / 2;
+    const float l2 = x2 - w2 / 2;
+    const float left = l1 > l2 ? l1 : l2;
+
+    const float r1 = x1 + w1 / 2;
+    const float r2 = x2 + w2 / 2;
+    const float right = r1 < r2 ? r1 : r2;
+
+    return right - left;
+}
+
+static float boxIntersection(const utils::Box& a, const utils::Box& b) {
+    const float w = overlap(a.x, a.w, b.x, b.w);
+    const float h = overlap(a.y, a.h, b.y, b.h);
+
+    if (w < 0 || h < 0) {
+        return 0.0f;
+    }
+
+    return w * h;
+}
+
+static float boxUnion(const utils::Box& a, const utils::Box& b) {
+    const float i = boxIntersection(a, b);
+    return a.w * a.h + b.w * b.h - i;
+}
+
+float utils::boxIntersectionOverUnion(const utils::Box& a, const utils::Box& b) {
+    return boxIntersection(a, b) / boxUnion(a, b);
+}
+
+static void doNonMaximumSupressionSort(std::vector<utils::Box>& boxes, std::vector<std::vector<float>>& probs,
+                                       int total, int classes, float thresh) {
+    std::vector<sortableYoloBBox> boxCandidates;
+
+    for (int i = 0; i < total; ++i) {
+        sortableYoloBBox candidate(i, 0, probs);
+        boxCandidates.push_back(candidate);
+    }
+
+    for (int k = 0; k < classes; ++k) {
+        for (int i = 0; i < total; ++i) {
+            boxCandidates[i].cclass = k;
+        }
+        std::sort(boxCandidates.begin(), boxCandidates.end(), [](const sortableYoloBBox& a, const sortableYoloBBox& b) {
+            float diff = a.probs[a.index][b.cclass] - b.probs[b.index][b.cclass];
+            return diff > 0;
+        });
+        for (int i = 0; i < total; ++i) {
+            if (probs[boxCandidates[i].index][k] == 0)
+                continue;
+            utils::Box a = boxes[boxCandidates[i].index];
+            for (int j = i + 1; j < total; ++j) {
+                utils::Box b = boxes[boxCandidates[j].index];
+                if (utils::boxIntersectionOverUnion(a, b) > thresh) {
+                    probs[boxCandidates[j].index][k] = 0;
+                }
+            }
+        }
+    }
+}
+
+static size_t maxIndex(std::vector<float>& a, int n) {
+    OPENVINO_ASSERT(n > 0, "Expected a positive number of classes, got {0}", n);
+    int max_i = 0;
+    float max = a[0];
+    for (int i = 1; i < n; ++i) {
+        if (a[i] > max) {
+            max = a[i];
+            max_i = i;
+        }
+    }
+    return npu::utils::convertValuePrecision<size_t>(max_i);
+}
+
+static float clampToImageSize(const float& valueToClamp, const float& low, const float& high) {
+    float result = valueToClamp;
+    if (valueToClamp > high) {
+        result = high;
+    } else if (valueToClamp < low) {
+        result = low;
+    } else {
+        result = valueToClamp;
+    }
+
+    return result;
+}
+
+static void getDetections(int imw, int imh, int num, float thresh, utils::Box* boxes,
+                          std::vector<std::vector<float>>& probs, int classes,
+                          std::vector<utils::BoundingBox>& detect_result) {
+    for (int i = 0; i < num; ++i) {
+        auto idxClass = static_cast<int>(maxIndex(probs[i], classes));
+        float prob = probs[i][idxClass];
+
+        if (prob > thresh) {
+            utils::Box b = boxes[i];
+
+            float left = (b.x - b.w / 2.0f) * imw;
+            float right = (b.x + b.w / 2.0f) * imw;
+            float top = (b.y - b.h / 2.0f) * imh;
+            float bot = (b.y + b.h / 2.0f) * imh;
+            float clampedLeft = clampToImageSize(left, 0.0f, static_cast<float>(imw));
+            float clampedRight = clampToImageSize(right, 0.0f, static_cast<float>(imw));
+            float clampedTop = clampToImageSize(top, 0.0f, static_cast<float>(imh));
+            float clampedBottom = clampToImageSize(bot, 0.0f, static_cast<float>(imh));
+
+            utils::BoundingBox bx(idxClass, clampedLeft, clampedTop, clampedRight, clampedBottom, prob);
+            detect_result.push_back(bx);
+        }
+    }
+}
+
+static std::vector<utils::BoundingBox> yolov2BoxExtractor(float threshold, std::vector<float>& net_out, int imgWidth,
+                                                          int imgHeight, int class_num, bool isTiny) {
+    int classes = class_num;
+    int coords = 4;
+    int num = 5;
+    std::vector<utils::BoundingBox> boxes_result;
+
+    std::vector<float> TINY_YOLOV2_ANCHORS = {1.08f, 1.19f, 3.42f, 4.41f, 6.63f, 11.38f, 9.42f, 5.11f, 16.62f, 10.52f};
+    std::vector<float> YOLOV2_ANCHORS = {1.3221f,  1.73145f, 3.19275f, 4.00944f, 5.05587f,
+                                         8.09892f, 9.47112f, 4.84053f, 11.2364f, 10.0071f};
+    std::vector<float> YOLOV2_ANCHORS_80_CLASSES = {0.57273f, 0.677385f, 1.87446f, 2.06253f, 3.33843f,
+                                                    5.47434f, 7.88282f,  3.52778f, 9.77052f, 9.16828f};
+
+    int imw = 416;
+    int imh = 416;
+
+    int lw = 13;
+    int lh = 13;
+    float nms = 0.4f;
+
+    std::vector<utils::Box> boxes(lw * lh * num);
+    std::vector<std::vector<float>> probs(lw * lh * num, std::vector<float>(classes + 1, 0.0f));
+
+    // TODO refactoring ticket S#37819
+    std::vector<float>* anchors = nullptr;
+    if (isTiny) {
+        anchors = &TINY_YOLOV2_ANCHORS;
+    } else {
+        anchors = &YOLOV2_ANCHORS;
+        if (class_num == 80) {
+            anchors = &YOLOV2_ANCHORS_80_CLASSES;
+        }
+    }
+
+    getRegionBoxes(net_out, lw, lh, coords, classes, num, imgWidth, imgHeight, imw, imh, threshold, probs, boxes, 1,
+                   *anchors);
+
+    doNonMaximumSupressionSort(boxes, probs, lw * lh * num, classes, nms);
+    getDetections(imgWidth, imgHeight, lw * lh * num, threshold, boxes.data(), probs, classes, boxes_result);
+
+    return boxes_result;
+}
+
+static std::vector<utils::BoundingBox> yolov3v4BoxExtractor(
+        std::vector<std::vector<float>>& net_out, int imgW, int imgH, int classes, int coords, int num,
+        const std::vector<float>& anchors, std::vector<std::vector<size_t>>& tensorWH, float threshold, float nms,
+        const std::function<float(const float)>& transformationFunc,
+        const std::function<int(const size_t, const int)>& anchorFunc) {
+    std::vector<utils::BoundingBox> boxes_result;
+    std::vector<utils::Box> boxes;
+    std::vector<std::vector<float>> probs;
+
+    getRegionBoxesV3V4(net_out, imgW, imgH, classes, coords, num, anchors, tensorWH, threshold, probs, boxes,
+                       transformationFunc, anchorFunc);
+    doNonMaximumSupressionSort(boxes, probs, static_cast<int>(probs.size()), classes, nms);
+    getDetections(imgW, imgH, static_cast<int>(probs.size()), threshold, boxes.data(), probs, classes, boxes_result);
+
+    return boxes_result;
+}
+
+static std::vector<utils::BoundingBox> SSDBoxExtractor(const float threshold, std::vector<float>& net_out,
+                                                       const size_t imgWidth, const size_t imgHeight) {
+    std::vector<utils::BoundingBox> boxes_result;
+
+    if (net_out.empty()) {
+        return boxes_result;
+    }
+    size_t oneDetectionSize = 7;
+
+    OPENVINO_ASSERT(net_out.size() % oneDetectionSize == 0);
+
+    for (size_t i = 0; i < net_out.size() / oneDetectionSize; i++) {
+        if (net_out[i * oneDetectionSize + 2] > threshold) {
+            boxes_result.emplace_back(npu::utils::convertValuePrecision<int>(net_out[i * oneDetectionSize + 1]),
+                                      net_out[i * oneDetectionSize + 3] * imgWidth,
+                                      net_out[i * oneDetectionSize + 4] * imgHeight,
+                                      net_out[i * oneDetectionSize + 5] * imgWidth,
+                                      net_out[i * oneDetectionSize + 6] * imgHeight, net_out[i * oneDetectionSize + 2]);
+        }
+    }
+
+    return boxes_result;
+}
+
+std::vector<utils::BoundingBox> utils::parseYoloOutput(const ov::Tensor& tensor, const size_t imgWidth,
+                                                       const size_t imgHeight, const float confThresh,
+                                                       const bool isTiny) {
+    const auto dataBuffer = tensor.data<float>();
+    OPENVINO_ASSERT(dataBuffer != nullptr);
+
+    std::vector<float> results(tensor.get_size());
+    for (size_t i = 0; i < tensor.get_size(); i++) {
+        results[i] = dataBuffer[i];
+    }
+
+    std::vector<utils::BoundingBox> out;
+    int classes = 20;
+    out = yolov2BoxExtractor(confThresh, results, static_cast<int>(imgWidth), static_cast<int>(imgHeight), classes,
+                             isTiny);
+
+    return out;
+}
+
+std::vector<utils::BoundingBox> utils::parseYoloV3Output(const std::map<std::string, ov::Tensor>& tensors,
+                                                         const size_t imgWidth, const size_t imgHeight,
+                                                         const int classes, const int coords, const int num,
+                                                         const std::vector<float>& anchors, const float confThresh,
+                                                         const std::unordered_map<std::string, ov::Layout>& layouts) {
+    auto funcV3 = [](const float x) -> float {
+        return x;
+    };
+
+    auto anchorOffsetV3 = [&](const size_t iout, const int lnum) -> int {
+        return static_cast<int>((tensors.size() - 1) * (lnum * (tensors.size() - 1 - iout)));
+    };
+
+    auto result = parseYoloV3V4Output(tensors, imgWidth, imgHeight, classes, coords, num, anchors, confThresh, layouts,
+                                      funcV3, anchorOffsetV3);
+    return result;
+}
+
+std::vector<utils::BoundingBox> utils::parseYoloV4Output(const std::map<std::string, ov::Tensor>& tensors,
+                                                         const size_t imgWidth, const size_t imgHeight,
+                                                         const int classes, const int coords, const int num,
+                                                         const std::vector<float>& anchors, const float confThresh,
+                                                         const std::unordered_map<std::string, ov::Layout>& layouts) {
+    auto funcV4 = [](const float x) -> float {
+        return 1 / (1 + std::exp(-x));
+    };
+
+    auto anchorOffsetV4 = [&](const size_t iout, const int lnum) -> int {
+        return static_cast<int>(lnum * 2 * iout);
+    };
+
+    auto result = parseYoloV3V4Output(tensors, imgWidth, imgHeight, classes, coords, num, anchors, confThresh, layouts,
+                                      funcV4, anchorOffsetV4);
+    return result;
+}
+
+std::vector<utils::BoundingBox> utils::parseYoloV3V4Output(
+        const std::map<std::string, ov::Tensor>& tensors, const size_t imgWidth, const size_t imgHeight,
+        const int classes, const int coords, const int num, const std::vector<float>& anchors, const float confThresh,
+        const std::unordered_map<std::string, ov::Layout>& layouts,
+        const std::function<float(const float)>& transformationFunc,
+        const std::function<int(const size_t, const int)>& anchorFunc) {
+    std::vector<std::vector<float>> results;
+    std::vector<std::vector<size_t>> tensorWH;
+
+    for (const auto& [tensorName, tensor] : tensors) {
+        const ov::Tensor tensorFP32 = npu::utils::toFP32(tensor);
+        const auto dataBuffer = tensorFP32.data<float>();
+        OPENVINO_ASSERT(dataBuffer != nullptr);
+
+        const ov::Layout& layout = layouts.at(tensorName);
+
+        const size_t H = tensorFP32.get_shape()[ov::layout::height_idx(layout)];
+        const size_t W = tensorFP32.get_shape()[ov::layout::width_idx(layout)];
+
+        std::vector<float> result(tensorFP32.get_size());
+        if (layout == ov::Layout("NCHW")) {
+            for (size_t j = 0; j < tensorFP32.get_size(); j++) {
+                result[j] = dataBuffer[j];
+            }
+        } else if (layout == ov::Layout("NHWC")) {
+            const size_t C = tensorFP32.get_shape()[ov::layout::channels_idx(layout)];
+
+            // TODO may be using copyTensor is good decision but can't find a way how include it
+            for (size_t c = 0; c < C; c++) {
+                for (size_t h = 0; h < H; h++) {
+                    for (size_t w = 0; w < W; w++) {
+                        result[c * H * W + h * W + w] = dataBuffer[h * W * C + w * C + c];
+                    }
+                }
+            }
+        }
+        results.push_back(result);
+        tensorWH.push_back(std::vector<size_t>{W, H});
+    }
+
+    return yolov3v4BoxExtractor(results, static_cast<int>(imgWidth), static_cast<int>(imgHeight), classes, coords, num,
+                                anchors, tensorWH, confThresh, 0.4f, transformationFunc, anchorFunc);
+}
+
+std::vector<utils::BoundingBox> utils::parseSSDOutput(const ov::Tensor& tensor, const size_t imgWidth,
+                                                      const size_t imgHeight, const float confThresh) {
+    const ov::Tensor tensorFP32 = npu::utils::toFP32(tensor);
+
+    const auto dataBuffer = tensorFP32.data<float>();
+    std::vector<float> results(tensorFP32.get_size());
+    std::copy_n(dataBuffer, tensorFP32.get_size(), results.begin());
+
+    std::vector<utils::BoundingBox> out;
+    out = SSDBoxExtractor(confThresh, results, imgWidth, imgHeight);
+    return out;
+}
+
+void utils::printDetectionBBoxOutputs(const std::vector<utils::BoundingBox>& actualOutput,
+                                      std::ostringstream& outputStream, const std::vector<std::string>& labels) {
+    outputStream << "Actual top:" << std::endl;
+    for (size_t i = 0; i < actualOutput.size(); ++i) {
+        const auto& bb = actualOutput[i];
+        outputStream << i << " : ";
+        if (static_cast<int>(labels.size()) < bb.idx) {
+            outputStream << bb.idx;
+        } else {
+            outputStream << labels.at(bb.idx);
+        }
+        outputStream << " : [(" << bb.left << " " << bb.top << "), (" << bb.right << " " << bb.bottom
+                     << ")] : " << bb.prob * 100 << "%" << std::endl;
+    }
+}
diff --git a/src/plugins/intel_npu/tools/single-image-test/yolo_helpers.hpp b/src/plugins/intel_npu/tools/single-image-test/yolo_helpers.hpp
new file mode 100644
index 00000000000000..3dc7132761f42b
--- /dev/null
+++ b/src/plugins/intel_npu/tools/single-image-test/yolo_helpers.hpp
@@ -0,0 +1,56 @@
+//
+// Copyright (C) 2022-2024 Intel Corporation.
+// SPDX-License-Identifier: Apache 2.0
+//
+
+//
+
+#pragma once
+
+#include <openvino/core/layout.hpp>
+#include <openvino/runtime/tensor.hpp>
+
+#include <sstream>
+
+namespace utils {
+struct Box final {
+    float x, y, w, h;
+};
+
+struct BoundingBox final {
+    int idx;
+    float left, right, top, bottom;
+    float prob;
+    BoundingBox(int idx, float xmin, float ymin, float xmax, float ymax, float prob)
+            : idx(idx), left(xmin), right(xmax), top(ymin), bottom(ymax), prob(prob) {
+    }
+};
+
+std::vector<BoundingBox> parseYoloOutput(const ov::Tensor& tensor, const size_t imgWidth, const size_t imgHeight,
+                                         const float confThresh, const bool isTiny);
+
+std::vector<BoundingBox> parseYoloV3Output(const std::map<std::string, ov::Tensor>& tensors, const size_t imgWidth,
+                                           const size_t imgHeight, const int classes, const int coords, const int num,
+                                           const std::vector<float>& anchors, const float confThresh,
+                                           const std::unordered_map<std::string, ov::Layout>& layouts);
+
+std::vector<BoundingBox> parseYoloV4Output(const std::map<std::string, ov::Tensor>& tensors, const size_t imgWidth,
+                                           const size_t imgHeight, const int classes, const int coords, const int num,
+                                           const std::vector<float>& anchors, const float confThresh,
+                                           const std::unordered_map<std::string, ov::Layout>& layouts);
+
+std::vector<BoundingBox> parseYoloV3V4Output(const std::map<std::string, ov::Tensor>& tensors, const size_t imgWidth,
+                                             const size_t imgHeight, const int classes, const int coords, const int num,
+                                             const std::vector<float>& anchors, const float confThresh,
+                                             const std::unordered_map<std::string, ov::Layout>& layouts,
+                                             const std::function<float(const float)>& transformationFunc,
+                                             const std::function<int(const size_t, const int)>& anchorFunc);
+
+std::vector<BoundingBox> parseSSDOutput(const ov::Tensor& tensor, const size_t imgWidth, const size_t imgHeight,
+                                        const float confThresh);
+
+void printDetectionBBoxOutputs(const std::vector<BoundingBox>& actualOutput, std::ostringstream& outputStream,
+                               const std::vector<std::string>& labels = {});
+
+float boxIntersectionOverUnion(const Box& a, const Box& b);
+}  // namespace utils
diff --git a/src/plugins/template/src/compiled_model.cpp b/src/plugins/template/src/compiled_model.cpp
index cbfb51237ecca9..0f873196d872f1 100644
--- a/src/plugins/template/src/compiled_model.cpp
+++ b/src/plugins/template/src/compiled_model.cpp
@@ -135,7 +135,7 @@ ov::Any ov::template_plugin::CompiledModel::get_property(const std::string& name
         return ro_properties;
     };
     const auto& default_rw_properties = []() {
-        std::vector<ov::PropertyName> rw_properties{ov::device::id, ov::enable_profiling};
+        std::vector<ov::PropertyName> rw_properties{ov::device::id, ov::enable_profiling, ov::hint::performance_mode};
         return rw_properties;
     };
     if (ov::model_name == name) {
diff --git a/src/plugins/template/tests/functional/op_reference/base_reference_test.cpp b/src/plugins/template/tests/functional/op_reference/base_reference_test.cpp
index a45d19c76175ef..b47b151f7707d6 100644
--- a/src/plugins/template/tests/functional/op_reference/base_reference_test.cpp
+++ b/src/plugins/template/tests/functional/op_reference/base_reference_test.cpp
@@ -114,6 +114,7 @@ void CommonReferenceTest::ValidateBlobs(const ov::Tensor& refBlob,
         case ov::element::u64:
         case ov::element::f8e4m3:
         case ov::element::f8e5m2:
+        case ov::element::f8e8m0:
             ov::test::utils::compare(refBlob, outBlob, abs_threshold, threshold);
             break;
         case ov::element::string:
@@ -155,6 +156,13 @@ void CommonReferenceTest::ValidateBlobs(const ov::Tensor& refBlob,
                                                                             threshold,
                                                                             abs_threshold);
         break;
+    case ov::element::f8e8m0:
+        ov::test::utils::compare_raw_data<ov::float8_e8m0, ov::float8_e8m0>(refBlob.data<const ov::float8_e8m0>(),
+                                                                            outBlob.data<const ov::float8_e8m0>(),
+                                                                            actual_comparision_size,
+                                                                            threshold,
+                                                                            abs_threshold);
+        break;
     case ov::element::f32:
         ov::test::utils::compare_raw_data<float, float>(refBlob.data<const float>(),
                                                         outBlob.data<const float>(),
diff --git a/src/plugins/template/tests/functional/op_reference/convert_like.cpp b/src/plugins/template/tests/functional/op_reference/convert_like.cpp
index e2798272a043a4..7d2f7f89d03c36 100644
--- a/src/plugins/template/tests/functional/op_reference/convert_like.cpp
+++ b/src/plugins/template/tests/functional/op_reference/convert_like.cpp
@@ -17,6 +17,10 @@ namespace ConversionOpsRefTestDefinitions {
 namespace {
 
 const auto f4e2m1_values = std::vector<uint8_t>{0x0a, 0x2f, 0x49, 0x3b, 0x78, 0x05};
+const auto e8m0_min = std::numeric_limits<float8_e8m0>::min();   // 2^-127
+const auto f32_min = std::numeric_limits<float>::min() / 2;      // 2^-127
+const auto f16_min = ov::float16::from_bits(0x1);                // smallest greater than zero = 2^-25
+const auto bf16_min = std::numeric_limits<bfloat16>::min() / 2;  // 2^-127
 
 INSTANTIATE_TEST_SUITE_P(
     smoke_Conversion_With_Hardcoded_Refs,
@@ -118,6 +122,12 @@ INSTANTIATE_TEST_SUITE_P(
                       // Unpacked data only first nibble got value, the other is 0
                       std::vector<float4_e2m1>{0.0f, -1.0f, -0.5f, 6.0f, -4.0f, 1.5f},
                       std::vector<bfloat16>{0.0f, 0.0f, -1.0f, 0.0f, -0.5f, 0.0f, 6.0f, 0.0f, -4.0f, 0.0f, 1.5f}),
+        ConvertParams(ConversionTypes::CONVERT_LIKE,
+                      ov::PartialShape{6},
+                      ov::element::f8e8m0,
+                      ov::element::bf16,
+                      std::vector<float8_e8m0>{0.0f, 1.0f, 0.5f, 6.0f, 4.0f, 1.5f},
+                      std::vector<bfloat16>{bf16_min, 1.0f, 0.5f, 8.0f, 4.0f, 2.f}),
 
         // destination f16
         ConvertParams(ConversionTypes::CONVERT_LIKE,
@@ -165,6 +175,12 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::f16,
                       f4e2m1_values,
                       std::vector<float16>{-1.0f, 0.0f, -6.0f, 1.0f, -0.5f, 2.0f, -1.5f, 1.5f, -0.0f, 6.0f, 3.0f}),
+        ConvertParams(ConversionTypes::CONVERT_LIKE,
+                      ov::PartialShape{8},
+                      ov::element::f8e8m0,
+                      ov::element::f16,
+                      std::vector<float8_e8m0>{-0.0f, -6.0f, 1.0f, 0.5f, 2.0f, 1.5f, 6.0f, 3.0f},
+                      std::vector<float16>{f16_min, f16_min, 1.0f, 0.5f, 2.0f, 2.0f, 8.0f, 2.0f}),
         // destination f32
         ConvertParams(ConversionTypes::CONVERT_LIKE,
                       ov::PartialShape{2, 2},
@@ -334,6 +350,12 @@ INSTANTIATE_TEST_SUITE_P(
                       // only one nibble got value others are be 0
                       f4e2m1_values,
                       std::vector<float>{-1.0f, 0.0f, -6.0f, 1.0f, -0.5f, 2.0f, -1.5f, 1.5f, -0.0f, 6.0f, 3.0f}),
+        ConvertParams(ConversionTypes::CONVERT_LIKE,
+                      ov::PartialShape{10},
+                      ov::element::f8e8m0,
+                      ov::element::f32,
+                      std::vector<float8_e8m0>{-1.0f, -0.0f, 0.6f, 1.0f, 0.5f, 2.0f, 1.5f, 2.5f, 6.0f, 3.0f},
+                      std::vector<float>{f32_min, f32_min, 0.5f, 1.0f, 0.5f, 2.0f, 2.0f, 2.0f, 8.0f, 2.0f}),
         ConvertParams(ConversionTypes::CONVERT_LIKE,
                       ov::PartialShape{4},
                       ov::element::nf4,
@@ -1848,7 +1870,29 @@ INSTANTIATE_TEST_SUITE_P(
                       ov::element::f32,
                       ov::element::f4e2m1,
                       std::vector<float>{-1.2f, 0.2f, -6.5f, 1.2f, -0.6f, 2.49f, -1.5f, 1.6f, -0.1f, 6.0f, 2.8f},
-                      f4e2m1_values)),
+                      f4e2m1_values),
+        // destination f8e8m0
+        ConvertParams(
+            ConversionTypes::CONVERT_LIKE,
+            ov::PartialShape{11},
+            ov::element::f16,
+            ov::element::f8e8m0,
+            std::vector<float16>{-1.2f, 0.05f, 0.1f, 0.2f, 0.3f, 0.4f, 0.7f, 1.2f, 2.49f, 1.6f, 6.0f, 2.8f},
+            std::vector<float8_e8m0>{e8m0_min, 0.0625, 0.125, 0.25f, 0.25f, 0.5f, 0.5f, 1.0f, 2.0f, 2.0f, 8.0f, 2.0f}),
+        ConvertParams(
+            ConversionTypes::CONVERT_LIKE,
+            ov::PartialShape{12},
+            ov::element::bf16,
+            ov::element::f8e8m0,
+            std::vector<bfloat16>{-1.2f, 0.05f, 0.1f, 0.2f, 0.3f, 0.4f, 0.7f, 1.2f, 2.49f, 1.6f, 6.0f, 2.8f},
+            std::vector<float8_e8m0>{e8m0_min, 0.0625, 0.125, 0.25f, 0.25f, 0.5f, 0.5f, 1.0f, 2.0f, 2.0f, 8.0f, 2.0f}),
+        ConvertParams(
+            ConversionTypes::CONVERT_LIKE,
+            ov::PartialShape{12},
+            ov::element::f32,
+            ov::element::f8e8m0,
+            std::vector<float>{-1.2f, 0.05f, 0.1f, 0.2f, 0.3f, 0.4f, 0.7f, 1.2f, 2.49f, 1.6f, 6.0f, 2.8f},
+            std::vector<float8_e8m0>{e8m0_min, 0.0625, 0.125, 0.25f, 0.25f, 0.5f, 0.5f, 1.0f, 2.0f, 2.0f, 8.0f, 2.0f})),
     ReferenceConversionLayerTest::getTestCaseName);
 }  // namespace
 }  // namespace ConversionOpsRefTestDefinitions
diff --git a/src/plugins/template/tests/functional/op_reference/embeddingbag_offsets.cpp b/src/plugins/template/tests/functional/op_reference/embeddingbag_offsets.cpp
index 68e428a2264bab..76f65561887d88 100644
--- a/src/plugins/template/tests/functional/op_reference/embeddingbag_offsets.cpp
+++ b/src/plugins/template/tests/functional/op_reference/embeddingbag_offsets.cpp
@@ -207,6 +207,29 @@ INSTANTIATE_TEST_SUITE_P(
             std::make_shared<ov::op::v0::Constant>(element::i32, ov::Shape({3}), std::vector<int32_t>{0, 2, 2}),
             ov::op::v15::EmbeddingBagOffsets::Reduction::SUM,
             std::make_shared<ov::op::v0::Constant>(element::i32, ov::Shape(), std::vector<int32_t>{0})),
+        EmbeddingBagOffsetsParams(
+            ov::Shape{5, 2},
+            ov::element::i16,
+            std::vector<int16_t>{-1, 2, 3, 4, -5, -6, -7, 8, 9, 10},
+            ov::Shape{3, 2},
+            ov::element::i16,
+            std::vector<int16_t>{-6, -4, 0, 0, 2, 18},
+            std::make_shared<ov::op::v0::Constant>(element::i32, ov::Shape({4}), std::vector<int32_t>{0, 2, 3, 4}),
+            std::make_shared<ov::op::v0::Constant>(element::i32, ov::Shape({3}), std::vector<int32_t>{0, 2, 2}),
+            ov::op::v15::EmbeddingBagOffsets::Reduction::SUM,
+            std::make_shared<ov::op::v0::Constant>(element::i32, ov::Shape(), std::vector<int32_t>{-1})),
+        EmbeddingBagOffsetsParams(
+            ov::Shape{6, 2},
+            ov::element::i32,
+            std::vector<int32_t>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12},
+            ov::Shape{3, 2},
+            ov::element::i32,
+            std::vector<int32_t>{12, 16, 0, 0, 32, 36},
+            std::make_shared<ov::op::v0::Constant>(element::i64, ov::Shape({4}), std::vector<int64_t>{0, 2, 3, 4}),
+            std::make_shared<ov::op::v0::Constant>(element::i64, ov::Shape({3}), std::vector<int64_t>{0, 2, 2}),
+            ov::op::v15::EmbeddingBagOffsets::Reduction::SUM,
+            std::make_shared<ov::op::v0::Constant>(element::i64, ov::Shape(), std::vector<int32_t>{-1}),
+            std::make_shared<ov::op::v0::Constant>(element::i32, ov::Shape({4}), std::vector<int32_t>{2, 2, 2, 2})),
         // Reduction MEAN
         EmbeddingBagOffsetsParams(
             ov::Shape{5, 2},
@@ -271,5 +294,16 @@ INSTANTIATE_TEST_SUITE_P(
             std::make_shared<ov::op::v0::Constant>(element::i32, ov::Shape({4}), std::vector<int32_t>{0, 2, 3, 4}),
             std::make_shared<ov::op::v0::Constant>(element::i32, ov::Shape({3}), std::vector<int32_t>{0, 2, 2}),
             ov::op::v15::EmbeddingBagOffsets::Reduction::MEAN,
-            std::make_shared<ov::op::v0::Constant>(element::i32, ov::Shape(), std::vector<int32_t>{0}))),
+            std::make_shared<ov::op::v0::Constant>(element::i32, ov::Shape(), std::vector<int32_t>{0})),
+        EmbeddingBagOffsetsParams(
+            ov::Shape{5, 2},
+            ov::element::f32,
+            std::vector<float>{-1, 2, 3, 4, -5, -6, -7, 8, 9, 10},
+            ov::Shape{3, 2},
+            ov::element::f32,
+            std::vector<float>{-3, -2, 0, 0, 1, 9},
+            std::make_shared<ov::op::v0::Constant>(element::i32, ov::Shape({4}), std::vector<int32_t>{0, 2, 3, 4}),
+            std::make_shared<ov::op::v0::Constant>(element::i32, ov::Shape({3}), std::vector<int32_t>{0, 2, 2}),
+            ov::op::v15::EmbeddingBagOffsets::Reduction::MEAN,
+            std::make_shared<ov::op::v0::Constant>(element::i32, ov::Shape(), std::vector<int32_t>{-1}))),
     ReferenceEmbeddingBagOffsetsLayerTest::getTestCaseName);
diff --git a/src/plugins/template/tests/functional/op_reference/random_uniform.cpp b/src/plugins/template/tests/functional/op_reference/random_uniform.cpp
index 270e6f6155119b..b39065542de3a0 100644
--- a/src/plugins/template/tests/functional/op_reference/random_uniform.cpp
+++ b/src/plugins/template/tests/functional/op_reference/random_uniform.cpp
@@ -16,28 +16,32 @@ namespace reference_tests {
 namespace {
 
 struct RandomUniformParams {
-    RandomUniformParams(const std::vector<int64_t>& paramOutShape,
-                        const reference_tests::Tensor& paramMinValue,
-                        const reference_tests::Tensor& paramMaxValue,
-                        ov::element::Type paramOutType,
-                        int64_t paramGlobalSeed,
-                        int64_t paramOpSeed,
-                        const reference_tests::Tensor& paramExpected,
-                        const std::string& test_name)
-        : out_shape(paramOutShape),
-          min_val(paramMinValue),
-          max_val(paramMaxValue),
-          out_type(paramOutType),
-          global_seed(paramGlobalSeed),
-          op_seed(paramOpSeed),
-          expected(paramExpected),
-          test_case_name(test_name) {}
+    RandomUniformParams(const std::vector<int64_t>& out_shape,
+                        const reference_tests::Tensor& min_val,
+                        const reference_tests::Tensor& max_val,
+                        ov::element::Type out_type,
+                        int64_t global_seed,
+                        int64_t op_seed,
+                        const op::PhilloxAlignment alignment,
+                        const reference_tests::Tensor& expected,
+                        const std::string name)
+        : out_shape(out_shape),
+          min_val(min_val),
+          max_val(max_val),
+          out_type(out_type),
+          global_seed(global_seed),
+          op_seed(op_seed),
+          alignment(alignment),
+          expected(expected),
+          test_case_name(std::move(name)) {}
+
     std::vector<int64_t> out_shape;
     reference_tests::Tensor min_val;
     reference_tests::Tensor max_val;
     ov::element::Type out_type;
     int64_t global_seed;
     int64_t op_seed;
+    op::PhilloxAlignment alignment;
     reference_tests::Tensor expected;
     std::string test_case_name;
 };
@@ -51,9 +55,23 @@ class ReferenceRandomUniformLayerTest : public testing::TestWithParam<RandomUnif
                                   params.max_val,
                                   params.out_type,
                                   params.global_seed,
-                                  params.op_seed);
+                                  params.op_seed,
+                                  params.alignment);
         inputData = {params.min_val.data, params.max_val.data};
         refOutData = {params.expected.data};
+
+        if (params.out_type == element::bf16) {
+            const auto min = static_cast<bfloat16*>(params.min_val.data.data())[0];
+            const auto max = static_cast<bfloat16*>(params.max_val.data.data())[0];
+            if (max - min <= 1) {
+                abs_threshold = 0.01f;  // Slight differences based on class implementation
+            } else {
+                abs_threshold = 1.01f;  // Differences in class implementation (rounding) can cause a difference of up
+                                        // to 1 between values
+            }
+        } else {
+            abs_threshold = 0.0f;  // Exact match
+        }
     }
     static std::string getTestCaseName(const testing::TestParamInfo<RandomUniformParams>& obj) {
         auto param = obj.param;
@@ -66,22 +84,24 @@ class ReferenceRandomUniformLayerTest : public testing::TestWithParam<RandomUnif
                                                  const reference_tests::Tensor& max_val,
                                                  const ov::element::Type& out_type,
                                                  int64_t global_seed,
-                                                 int64_t op_seed) {
+                                                 int64_t op_seed,
+                                                 const op::PhilloxAlignment alignment) {
+        const auto out_shape_const =
+            std::make_shared<op::v0::Constant>(element::i64, Shape{out_shape.size()}, out_shape);
         const auto min_val_param = std::make_shared<op::v0::Parameter>(min_val.type, min_val.shape);
         const auto max_val_param = std::make_shared<op::v0::Parameter>(max_val.type, max_val.shape);
-        auto out_shape_ = std::make_shared<op::v0::Constant>(element::i64, Shape{out_shape.size()}, out_shape);
-
-        return std::make_shared<ov::Model>(NodeVector{std::make_shared<op::v8::RandomUniform>(out_shape_,
-                                                                                              min_val_param,
-                                                                                              max_val_param,
-                                                                                              out_type,
-                                                                                              global_seed,
-                                                                                              op_seed)},
-                                           ParameterVector{min_val_param, max_val_param});
+        const auto random_uniform = std::make_shared<op::v8::RandomUniform>(out_shape_const,
+                                                                            min_val_param,
+                                                                            max_val_param,
+                                                                            out_type,
+                                                                            global_seed,
+                                                                            op_seed,
+                                                                            alignment);
+        return std::make_shared<ov::Model>(random_uniform->outputs(), ParameterVector{min_val_param, max_val_param});
     }
 };
 
-TEST_P(ReferenceRandomUniformLayerTest, RandomUniformWithHardcodedRefs) {
+TEST_P(ReferenceRandomUniformLayerTest, CompareWithRefs) {
     Exec();
 }
 
@@ -92,12 +112,14 @@ INSTANTIATE_TEST_SUITE_P(
     smoke_RandomUniform_With_Hardcoded_Refs,
     ReferenceRandomUniformLayerTest,
     ::testing::Values(
+        // Backwards compatibility tests (implemented before alignment update)
         RandomUniformParams(std::vector<int64_t>{3, 2, 4},
                             reference_tests::Tensor{{1}, element::f32, std::vector<float>{0}},
                             reference_tests::Tensor{{1}, element::f32, std::vector<float>{1}},
                             element::Type_t::f32,
                             150,
                             10,
+                            op::PhilloxAlignment::TENSORFLOW,
                             reference_tests::Tensor{
                                 {3, 2, 4},
                                 element::f32,
@@ -113,6 +135,7 @@ INSTANTIATE_TEST_SUITE_P(
                             element::Type_t::f16,
                             150,
                             10,
+                            op::PhilloxAlignment::TENSORFLOW,
                             reference_tests::Tensor{
                                 {3, 2, 4},
                                 element::f16,
@@ -128,6 +151,7 @@ INSTANTIATE_TEST_SUITE_P(
                             element::Type_t::f32,
                             150,
                             10,
+                            op::PhilloxAlignment::TENSORFLOW,
                             reference_tests::Tensor{
                                 {3, 2, 4},
                                 element::f32,
@@ -144,6 +168,7 @@ INSTANTIATE_TEST_SUITE_P(
                             element::Type_t::f16,
                             150,
                             10,
+                            op::PhilloxAlignment::TENSORFLOW,
                             reference_tests::Tensor{
                                 {3, 2, 4},
                                 element::f16,
@@ -159,6 +184,7 @@ INSTANTIATE_TEST_SUITE_P(
                             element::Type_t::i32,
                             100,
                             350,
+                            op::PhilloxAlignment::TENSORFLOW,
                             reference_tests::Tensor{{2, 3, 4},
                                                     element::i32,
                                                     std::vector<int32_t>{
@@ -172,6 +198,7 @@ INSTANTIATE_TEST_SUITE_P(
                             element::Type_t::i64,
                             755,
                             951,
+                            op::PhilloxAlignment::TENSORFLOW,
                             reference_tests::Tensor{
                                 {5, 4, 3},
                                 element::i64,
@@ -188,6 +215,7 @@ INSTANTIATE_TEST_SUITE_P(
                             element::Type_t::bf16,
                             4978,
                             5164,
+                            op::PhilloxAlignment::TENSORFLOW,
                             reference_tests::Tensor{
                                 {7, 3},
                                 element::bf16,
@@ -202,11 +230,405 @@ INSTANTIATE_TEST_SUITE_P(
                             element::Type_t::bf16,
                             4978,
                             5164,
+                            op::PhilloxAlignment::TENSORFLOW,
                             reference_tests::Tensor{
                                 {7, 3},
                                 element::bf16,
                                 std::vector<bfloat16>{164, 146, -92.5, -84.5, 14,  90,    33,  -43.5, 170, 8,  182,
                                                       -35, -24, -84.5, 180,   -14, -73.5, 198, 138,   8,   156}},
-                            "bfloat16_non_default_min_max")),
+                            "bfloat16_non_default_min_max"),
+        // Alignment tests - TENSORFLOW
+        RandomUniformParams(std::vector<int64_t>{7, 6},
+                            reference_tests::Tensor{{1}, element::f64, std::vector<double>{0}},
+                            reference_tests::Tensor{{1}, element::f64, std::vector<double>{1}},
+                            element::f64,
+                            12345,
+                            54321,
+                            op::PhilloxAlignment::TENSORFLOW,
+                            reference_tests::Tensor{
+                                {7, 6},
+                                element::f64,
+                                std::vector<double>{
+                                    0.2921047836089943,  0.1865942500848503,  0.006919873589045,   0.6934785411915267,
+                                    0.24304703848961706, 0.03778989685165657, 0.6931826735517366,  0.24599033055460207,
+                                    0.9268315651052301,  0.1651620289133835,  0.9018968607612903,  0.10734846627790451,
+                                    0.9693998601811129,  0.32754183046231056, 0.6421559536150925,  0.5466934281951839,
+                                    0.9409183236759846,  0.6341779150820659,  0.8806706943886118,  0.814950148009342,
+                                    0.35454954890818113, 0.6674748438695257,  0.5283274225598105,  0.31927129954257216,
+                                    0.9013092542329315,  0.8188783768209358,  0.9246842191826359,  0.08651125352955691,
+                                    0.4156017869843349,  0.5489782764801512,  0.12346754160942996, 0.39029344640718455,
+                                    0.6512753213561657,  0.2179250598578999,  0.8802885273401035,  0.016060267235235015,
+                                    0.7303574589403252,  0.9344756372163827,  0.6340667403806175,  0.7051371357116014,
+                                    0.5739520844965524,  0.1945947342973824}},
+                            "double_default_min_max_tensorflow"),
+        RandomUniformParams(std::vector<int64_t>{7, 6},
+                            reference_tests::Tensor{{1}, element::f64, std::vector<double>{-100}},
+                            reference_tests::Tensor{{1}, element::f64, std::vector<double>{250}},
+                            element::f64,
+                            12345,
+                            54321,
+                            op::PhilloxAlignment::TENSORFLOW,
+                            reference_tests::Tensor{
+                                {7, 6},
+                                element::f64,
+                                std::vector<double>{
+                                    2.2366742631480037,  -34.69201247030239,  -97.57804424383426, 142.71748941703436,
+                                    -14.933536528634022, -86.7735361019202,   142.6139357431078,  -13.903384305889276,
+                                    224.39104778683054,  -42.193289880315774, 215.6639012664516,  -62.42803680273342,
+                                    239.28995106338954,  14.639640661808698,  124.75458376528238, 91.34269986831436,
+                                    229.32141328659463,  121.96227027872305,  208.23474303601415, 185.2325518032697,
+                                    24.092342117863396,  133.616195354334,    84.91459789593367,  11.74495483990026,
+                                    215.45823898152605,  186.60743188732755,  223.63947671392253, -69.72106126465508,
+                                    45.460625444517234,  92.14239676805292,   -56.78636043669951, 36.602706242514586,
+                                    127.946362474658,    -23.72622904973504,  208.10098456903626, -94.37890646766775,
+                                    155.62511062911383,  227.06647302573396,  121.92335913321614, 146.7979974990605,
+                                    100.88322957379336,  -31.891842995916164}},
+                            "double_non_default_min_max_tensorflow"),
+        RandomUniformParams(
+            std::vector<int64_t>{7, 6},
+            reference_tests::Tensor{{1}, element::f32, std::vector<float>{0}},
+            reference_tests::Tensor{{1}, element::f32, std::vector<float>{1}},
+            element::f32,
+            12345,
+            54321,
+            op::PhilloxAlignment::TENSORFLOW,
+            reference_tests::Tensor{
+                {7, 6},
+                element::f32,
+                std::vector<float>{0.7865131,  0.5757234,  0.023324251, 0.21700966, 0.875865,   0.8446753,  0.2116847,
+                                   0.8619245,  0.65538085, 0.21281981,  0.50472367, 0.3871348,  0.46164775, 0.1323191,
+                                   0.9057487,  0.10803068, 0.3658539,   0.6284323,  0.89564514, 0.09049857, 0.61273706,
+                                   0.16685092, 0.51341856, 0.99241984,  0.99617493, 0.02810657, 0.41594267, 0.23845005,
+                                   0.83026946, 0.4635644,  0.3183366,   0.37955487, 0.99261475, 0.54943705, 0.20427215,
+                                   0.64036727, 0.7350838,  0.8680873,   0.47686875, 0.1963104,  0.9193187,  0.6715238}},
+            "float_default_min_max_tensorflow"),
+        RandomUniformParams(std::vector<int64_t>{7, 6},
+                            reference_tests::Tensor{{1}, element::f32, std::vector<float>{-100}},
+                            reference_tests::Tensor{{1}, element::f32, std::vector<float>{250}},
+                            element::f32,
+                            12345,
+                            54321,
+                            op::PhilloxAlignment::TENSORFLOW,
+                            reference_tests::Tensor{
+                                {7, 6},
+                                element::f32,
+                                std::vector<float>{
+                                    175.27957,  101.50319,  -91.83651,  -24.046616, 206.55273,  195.63635, -25.910355,
+                                    201.67358,  129.3833,   -25.513062, 76.65329,   35.497177,  61.576706, -53.688316,
+                                    217.01205,  -62.189262, 28.048874,  119.951294, 213.4758,   -68.3255,  114.45798,
+                                    -41.602177, 79.69649,   247.34695,  248.66122,  -90.162704, 45.57994,  -16.54248,
+                                    190.5943,   62.247543,  11.417809,  32.844208,  247.41516,  92.30296,  -28.504745,
+                                    124.12854,  157.27933,  203.83057,  66.90407,   -31.291359, 221.76154, 135.03334}},
+                            "float_non_default_min_max_tensorflow"),
+        RandomUniformParams(std::vector<int64_t>{7, 6},
+                            reference_tests::Tensor{{1}, element::f16, std::vector<float16>{0}},
+                            reference_tests::Tensor{{1}, element::f16, std::vector<float16>{1}},
+                            element::f16,
+                            12345,
+                            54321,
+                            op::PhilloxAlignment::TENSORFLOW,
+                            reference_tests::Tensor{
+                                {7, 6},
+                                element::f16,
+                                std::vector<float16>{0.11523, 0.3262, 0.07227, 0.743,  0.08594, 0.58,   0.1211,
+                                                     0.8857,  0.88,   0.42,    0.6963, 0.4082,  0.8184, 0.958,
+                                                     0.8936,  0.9873, 0.0752,  0.1172, 0.125,   0.3643, 0.542,
+                                                     0.843,   0.925,  0.9033,  0.665,  0.249,   0.4023, 0.3828,
+                                                     0.5674,  0.5195, 0.8135,  0.3135, 0.5,     0.9883, 0.3975,
+                                                     0.8887,  0.8066, 0.371,   0.509,  0.1748,  0.0586, 0.12305}},
+                            "float16_default_min_max_tensorflow"),
+        RandomUniformParams(std::vector<int64_t>{7, 6},
+                            reference_tests::Tensor{{1}, element::f16, std::vector<float16>{-100}},
+                            reference_tests::Tensor{{1}, element::f16, std::vector<float16>{250}},
+                            element::f16,
+                            12345,
+                            54321,
+                            op::PhilloxAlignment::TENSORFLOW,
+                            reference_tests::Tensor{
+                                {7, 6},
+                                element::f16,
+                                std::vector<float16>{-59.66, 14.19, -74.7, 160.0, -69.94, 103.0, -57.62, 210.0,  208.0,
+                                                     47.0,   143.8, 42.88, 186.5, 235.2,  212.8, 245.5,  -73.7,  -59.0,
+                                                     -56.25, 27.5,  89.75, 195.0, 223.8,  216.2, 132.8,  -12.81, 40.88,
+                                                     34.0,   98.6,  81.9,  184.8, 9.69,   75.0,  246.0,  39.12,  211.0,
+                                                     182.2,  29.88, 78.1,  -38.8, -79.5,  -56.94}},
+                            "float16_non_default_min_max_tensorflow"),
+        RandomUniformParams(std::vector<int64_t>{7, 6},
+                            reference_tests::Tensor{{1}, element::bf16, std::vector<bfloat16>{0}},
+                            reference_tests::Tensor{{1}, element::bf16, std::vector<bfloat16>{1}},
+                            element::bf16,
+                            12345,
+                            54321,
+                            op::PhilloxAlignment::TENSORFLOW,
+                            reference_tests::Tensor{
+                                {7, 6},
+                                element::bf16,
+                                std::vector<bfloat16>{0.921875, 0.609375,  0.578125,  0.945312, 0.6875,   0.640625,
+                                                      0.96875,  0.0859375, 0.0390625, 0.359375, 0.570312, 0.265625,
+                                                      0.546875, 0.664062,  0.148438,  0.898438, 0.601562, 0.9375,
+                                                      0,        0.914062,  0.335938,  0.742188, 0.398438, 0.226562,
+                                                      0.320312, 0.992188,  0.21875,   0.0625,   0.539062, 0.15625,
+                                                      0.507812, 0.507812,  0,         0.90625,  0.179688, 0.109375,
+                                                      0.453125, 0.96875,   0.0703125, 0.398438, 0.46875,  0.984375}},
+                            "bfloat16_default_min_max_tensorflow"),
+        RandomUniformParams(std::vector<int64_t>{7, 6},
+                            reference_tests::Tensor{{1}, element::bf16, std::vector<bfloat16>{-100}},
+                            reference_tests::Tensor{{1}, element::bf16, std::vector<bfloat16>{250}},
+                            element::bf16,
+                            12345,
+                            54321,
+                            op::PhilloxAlignment::TENSORFLOW,
+                            reference_tests::Tensor{
+                                {7, 6},
+                                element::bf16,
+                                std::vector<bfloat16>{222,  113, 102,    230, 141, 124,   240, -70, -86.5,
+                                                      26,   100, -7,     91,  132, -48,   214, 111, 228,
+                                                      -100, 220, 17.5,   160, 39,  -20.5, 12,  248, -23.5,
+                                                      -78,  89,  -45.25, 78,  78,  -100,  218, -37, -61.75,
+                                                      59,   240, -75.5,  39,  64,  244}},
+                            "bfloat16_non_default_min_max_tensorflow"),
+        RandomUniformParams(std::vector<int64_t>{7, 6},
+                            reference_tests::Tensor{{1}, element::i64, std::vector<int64_t>{-100}},
+                            reference_tests::Tensor{{1}, element::i64, std::vector<int64_t>{250}},
+                            element::i64,
+                            12345,
+                            54321,
+                            op::PhilloxAlignment::TENSORFLOW,
+                            reference_tests::Tensor{
+                                {7, 6},
+                                element::i64,
+                                std::vector<int64_t>{230, -4,  26,  -12, 23,  195, 74,  -53, 121, 206, -99,
+                                                     9,   249, -70, -59, 101, 52,  -41, 210, 225, 76,  -54,
+                                                     183, -12, 237, -60, -21, 159, 134, 147, 37,  116, -13,
+                                                     100, 53,  182, 143, 228, -57, 93,  230, 195}},
+                            "int64_tensorflow"),
+        RandomUniformParams(std::vector<int64_t>{7, 6},
+                            reference_tests::Tensor{{1}, element::i32, std::vector<int32_t>{-100}},
+                            reference_tests::Tensor{{1}, element::i32, std::vector<int32_t>{250}},
+                            element::i32,
+                            12345,
+                            54321,
+                            op::PhilloxAlignment::TENSORFLOW,
+                            reference_tests::Tensor{
+                                {7, 6},
+                                element::i32,
+                                std::vector<int32_t>{-92, 82,  160, 41,  218, 48,  42,  51,  189, -96, -95,
+                                                     -10, -72, 101, 175, 207, 31,  -60, 126, -45, 131, 245,
+                                                     77,  -33, 157, 77,  60,  -80, 79,  72,  -49, 25,  -70,
+                                                     32,  -3,  -78, 82,  -82, -71, 51,  -24, 100}},
+                            "int32_non_default_min_max_tensorflow"),
+
+        // Alignment tests - PyTorch
+        RandomUniformParams(std::vector<int64_t>{7, 6},
+                            reference_tests::Tensor{{1}, element::f64, std::vector<double>{0}},
+                            reference_tests::Tensor{{1}, element::f64, std::vector<double>{1}},
+                            element::f64,
+                            12345,
+                            54321,
+                            op::PhilloxAlignment::PYTORCH,
+                            reference_tests::Tensor{
+                                {7, 6},
+                                element::f64,
+                                std::vector<double>{
+                                    0.8537458849690976,  0.9371476796661825,  0.6657276343152506,  0.9394525654011401,
+                                    0.7008540783300001,  0.6755513936567185,  0.32574008079829586, 0.7066936129275108,
+                                    0.7607924337011228,  0.5111041808001306,  0.3199238263815529,  0.7561993726681363,
+                                    0.17923607421035226, 0.9980856889457874,  0.7451967274135151,  0.33009892801967744,
+                                    0.49610515018696655, 0.21627452678551762, 0.5982817840191142,  0.10759022259137951,
+                                    0.789470580212996,   0.34500166455063475, 0.6427628345630576,  0.7974227385272604,
+                                    0.3921580931256149,  0.4032344020301808,  0.741876277138582,   0.2373560606013575,
+                                    0.6045086845253197,  0.03240364251463124, 0.9873074507572961,  0.15825495647484955,
+                                    0.8237589940447471,  0.5368713853196583,  0.09850875468220899, 0.7705821561611728,
+                                    0.35758637185744746, 0.40904791646937666, 0.143346220011503,   0.4891462815971308,
+                                    0.9808312595026292,  0.4561964890218795}},
+                            "double_default_min_max_pytorch"),
+        RandomUniformParams(
+            std::vector<int64_t>{7, 6},
+            reference_tests::Tensor{{1}, element::f64, std::vector<double>{-100}},
+            reference_tests::Tensor{{1}, element::f64, std::vector<double>{250}},
+            element::f64,
+            12345,
+            54321,
+            op::PhilloxAlignment::PYTORCH,
+            reference_tests::Tensor{
+                {7, 6},
+                element::f64,
+                std::vector<double>{
+                    198.81105973918417, 228.00168788316387, 133.0046720103377,   228.80839789039902, 145.29892741550003,
+                    136.44298777985145, 14.009028279403552, 147.34276452462876,  166.27735179539297, 78.88646328004573,
+                    11.973339233543523, 164.6697804338477,  -37.26737402637671,  249.3299911310256,  160.81885459473028,
+                    15.534624806887104, 73.63680256543829,  -24.303915625068832, 109.39862440668999, -62.34342209301717,
+                    176.3147030745486,  20.75058259272216,  124.96699209707016,  179.09795848454115, 37.255332593965214,
+                    41.13204071056328,  159.6566969985037,  -16.925378789524874, 111.5780395838619,  -88.65872511987907,
+                    245.55760776505363, -44.61076523380266, 188.31564791566146,  87.9049848618804,   -65.52193586122685,
+                    169.70375465641047, 25.15523015010661,  43.16677076428183,   -49.82882299597395, 71.20119855899578,
+                    243.29094082592022, 59.668771157657815}},
+            "double_non_default_min_max_pytorch"),
+        RandomUniformParams(std::vector<int64_t>{7, 6},
+                            reference_tests::Tensor{{1}, element::f32, std::vector<float>{0}},
+                            reference_tests::Tensor{{1}, element::f32, std::vector<float>{1}},
+                            element::f32,
+                            12345,
+                            54321,
+                            op::PhilloxAlignment::PYTORCH,
+                            reference_tests::Tensor{
+                                {7, 6},
+                                element::f32,
+                                std::vector<float>{
+                                    0.9817181825637817,  0.8796065449714661,  0.992143452167511,   0.461067259311676,
+                                    0.08321595191955566, 0.17843109369277954, 0.36743152141571045, 0.5676497220993042,
+                                    0.3376067280769348,  0.21194660663604736, 0.4594438672065735,  0.8153534531593323,
+                                    0.9157174825668335,  0.2531347870826721,  0.21333664655685425, 0.4769676923751831,
+                                    0.7200990319252014,  0.7238213419914246,  0.3138880133628845,  0.673179030418396,
+                                    0.4149904251098633,  0.4399939775466919,  0.09452491998672485, 0.858170211315155,
+                                    0.1474044919013977,  0.624611496925354,   0.2497606873512268,  0.07847321033477783,
+                                    0.4681495428085327,  0.6659092307090759,  0.04126232862472534, 0.5361465811729431,
+                                    0.31201308965682983, 0.4287737011909485,  0.27703428268432617, 0.4377092719078064,
+                                    0.9497851729393005,  0.01932889223098755, 0.2634487748146057,  0.9249169230461121,
+                                    0.47368377447128296, 0.3961203098297119}},
+                            "float_default_min_max_pytorch"),
+        RandomUniformParams(std::vector<int64_t>{7, 6},
+                            reference_tests::Tensor{{1}, element::f32, std::vector<float>{-100}},
+                            reference_tests::Tensor{{1}, element::f32, std::vector<float>{250}},
+                            element::f32,
+                            12345,
+                            54321,
+                            op::PhilloxAlignment::PYTORCH,
+                            reference_tests::Tensor{
+                                {7, 6},
+                                element::f32,
+                                std::vector<float>{
+                                    243.6013641357422,  207.86228942871094,  247.25021362304688,  61.373538970947266,
+                                    -70.87442016601562, -37.54911804199219,  28.601032257080078,  98.67740631103516,
+                                    18.162355422973633, -25.818687438964844, 60.805355072021484,  185.3737030029297,
+                                    220.50111389160156, -11.402824401855469, -25.33217430114746,  66.93869018554688,
+                                    152.03466796875,    153.33746337890625,  9.860804557800293,   135.61265563964844,
+                                    45.24665069580078,  53.99789047241211,   -66.91627502441406,  200.3595733642578,
+                                    -48.40842819213867, 118.61402130126953,  -12.583759307861328, -72.53437805175781,
+                                    63.85234069824219,  133.0682373046875,   -85.55818176269531,  87.65130615234375,
+                                    9.204581260681152,  50.070796966552734,  -3.03800106048584,   53.198246002197266,
+                                    232.4248046875,     -93.2348861694336,   -7.792928695678711,  223.72091674804688,
+                                    65.78932189941406,  38.64210891723633}},
+                            "float_non_default_min_max_pytorch"),
+        RandomUniformParams(
+            std::vector<int64_t>{7, 6},
+            reference_tests::Tensor{{1}, element::f16, std::vector<float16>{0}},
+            reference_tests::Tensor{{1}, element::f16, std::vector<float16>{1}},
+            element::f16,
+            12345,
+            54321,
+            op::PhilloxAlignment::PYTORCH,
+            reference_tests::Tensor{
+                {7, 6},
+                element::f16,
+                std::vector<float16>{
+                    0.2353515625,  0.73681640625, 0.63916015625, 0.06298828125, 0.705078125,   0.70751953125,
+                    0.9990234375,  0.1865234375,  0.67431640625, 0.2666015625,  0.76416015625, 0.37548828125,
+                    0.5576171875,  0.68017578125, 0.65380859375, 0.3193359375,  0.05126953125, 0.54443359375,
+                    0.37060546875, 0.6826171875,  0.6015625,     0.4306640625,  0.34814453125, 0.13037109375,
+                    0.53759765625, 0.8173828125,  0.03955078125, 0.8525390625,  0.0810546875,  0.12841796875,
+                    0.02099609375, 0.11279296875, 0.01123046875, 0.51416015625, 0.46484375,    0.71435546875,
+                    0.64013671875, 0.34228515625, 0.17236328125, 0.91943359375, 0.41748046875, 0.017578125}},
+            "float16_default_min_max_pytorch"),
+        RandomUniformParams(std::vector<int64_t>{7, 6},
+                            reference_tests::Tensor{{1}, element::f16, std::vector<float16>{-100}},
+                            reference_tests::Tensor{{1}, element::f16, std::vector<float16>{250}},
+                            element::f16,
+                            12345,
+                            54321,
+                            op::PhilloxAlignment::PYTORCH,
+                            reference_tests::Tensor{
+                                {7, 6},
+                                element::f16,
+                                std::vector<float16>{-17.625,  157.875,   123.6875, -77.9375,  146.75,   147.625,
+                                                     249.625,  -34.71875, 136.0,    -6.6875,   167.5,    31.421875,
+                                                     95.1875,  138.0,     128.875,  11.765625, -82.0625, 90.5625,
+                                                     29.71875, 138.875,   110.5625, 50.71875,  21.84375, -54.375,
+                                                     88.1875,  186.125,   -86.1875, 198.375,   -71.625,  -55.0625,
+                                                     -92.625,  -60.53125, -96.0625, 79.9375,   62.6875,  150.0,
+                                                     124.0625, 19.796875, -39.6875, 221.75,    46.125,   -93.875}},
+                            "float16_non_default_min_max_pytorch"),
+        RandomUniformParams(std::vector<int64_t>{7, 6},
+                            reference_tests::Tensor{{1}, element::bf16, std::vector<bfloat16>{0}},
+                            reference_tests::Tensor{{1}, element::bf16, std::vector<bfloat16>{1}},
+                            element::bf16,
+                            12345,
+                            54321,
+                            op::PhilloxAlignment::PYTORCH,
+                            reference_tests::Tensor{
+                                {7, 6},
+                                element::bf16,
+                                std::vector<bfloat16>{
+                                    0.8828125,  0.89453125, 0.11328125, 0.50390625, 0.640625,   0.66015625, 0.9921875,
+                                    0.4921875,  0.39453125, 0.1328125,  0.11328125, 0.00390625, 0.4609375,  0.44140625,
+                                    0.23046875, 0.5546875,  0.41015625, 0.35546875, 0.96484375, 0.4609375,  0.8125,
+                                    0.4453125,  0.78515625, 0.04296875, 0.30078125, 0.5390625,  0.31640625, 0.8203125,
+                                    0.6484375,  0.02734375, 0.16796875, 0.90234375, 0.08984375, 0.11328125, 0.71875,
+                                    0.71484375, 0.12109375, 0.73828125, 0.37890625, 0.35546875, 0.33984375, 0.140625}},
+                            "bfloat16_default_min_max_pytorch"),
+        RandomUniformParams(std::vector<int64_t>{7, 6},
+                            reference_tests::Tensor{{1}, element::bf16, std::vector<bfloat16>{-100}},
+                            reference_tests::Tensor{{1}, element::bf16, std::vector<bfloat16>{250}},
+                            element::bf16,
+                            12345,
+                            54321,
+                            op::PhilloxAlignment::PYTORCH,
+                            reference_tests::Tensor{
+                                {7, 6},
+                                element::bf16,
+                                std::vector<bfloat16>{
+                                    209.0,  213.0, -60.25,  76.5,    124.0, 131.0, 247.0,  72.5,  38.0,   -53.5, -60.25,
+                                    -98.5,  61.25, 54.5,    -19.375, 94.0,  43.5,  24.375, 238.0, 61.25,  184.0, 55.75,
+                                    175.0,  -85.0, 5.28125, 88.5,    10.75, 187.0, 127.0,  -90.5, -41.25, 216.0, -68.5,
+                                    -60.25, 152.0, 150.0,   -57.5,   158.0, 32.5,  24.375, 19.0,  -50.75}},
+                            "bfloat16_non_default_min_max_pytorch"),
+        RandomUniformParams(std::vector<int64_t>{7, 6},
+                            reference_tests::Tensor{{1}, element::i64, std::vector<int64_t>{-100}},
+                            reference_tests::Tensor{{1}, element::i64, std::vector<int64_t>{250}},
+                            element::i64,
+                            12345,
+                            54321,
+                            op::PhilloxAlignment::PYTORCH,
+                            reference_tests::Tensor{
+                                {7, 6},
+                                element::i64,
+                                std::vector<int64_t>{90,  181, 85,  -97, 34,  -63, 160, 158, -79, 50,  121,
+                                                     -35, 226, 85,  -83, 66,  13,  -73, 239, 244, 240, 222,
+                                                     -49, 153, 119, 76,  121, 144, 12,  -1,  105, 107, -27,
+                                                     141, 232, 165, 237, 125, 47,  123, -81, 242}},
+                            "int64_pytorch"),
+        RandomUniformParams(std::vector<int64_t>{2, 4},
+                            reference_tests::Tensor{{1}, element::i64, std::vector<int64_t>{0}},
+                            reference_tests::Tensor{{1}, element::i64, std::vector<int64_t>{4294967300}},
+                            element::i64,
+                            12345,
+                            54321,
+                            op::PhilloxAlignment::PYTORCH,
+                            reference_tests::Tensor{{2, 4},
+                                                    element::i64,
+                                                    std::vector<int64_t>{737404521,
+                                                                         3716027413,
+                                                                         1306031901,
+                                                                         35197318,
+                                                                         1121750166,
+                                                                         2465874581,
+                                                                         2593594281,
+                                                                         1335862698}},
+                            "int64_pytorch_high_values"),
+        RandomUniformParams(std::vector<int64_t>{7, 6},
+                            reference_tests::Tensor{{1}, element::i32, std::vector<int32_t>{-100}},
+                            reference_tests::Tensor{{1}, element::i32, std::vector<int32_t>{250}},
+                            element::i32,
+                            12345,
+                            54321,
+                            op::PhilloxAlignment::PYTORCH,
+                            reference_tests::Tensor{
+                                {7, 6},
+                                element::i32,
+                                std::vector<int32_t>{90,  181, 85,  -97, 34,  -63, 160, 158, -79, 50,  121,
+                                                     -35, 226, 85,  -83, 66,  13,  -73, 239, 244, 240, 222,
+                                                     -49, 153, 119, 76,  121, 144, 12,  -1,  105, 107, -27,
+                                                     141, 232, 165, 237, 125, 47,  123, -81, 242}},
+                            "int32_pytorch")),
     ReferenceRandomUniformLayerTest::getTestCaseName);
 }  // namespace reference_tests
diff --git a/src/tests/functional/plugin/conformance/subgraphs_dumper/CMakeLists.txt b/src/tests/functional/plugin/conformance/subgraphs_dumper/CMakeLists.txt
index 8999c9d9282264..2e105315e72525 100644
--- a/src/tests/functional/plugin/conformance/subgraphs_dumper/CMakeLists.txt
+++ b/src/tests/functional/plugin/conformance/subgraphs_dumper/CMakeLists.txt
@@ -10,6 +10,30 @@ list(APPEND LIBRARIES
         openvino::core::dev
 )
 
+if(ENABLE_OV_IR_FRONTEND)
+    list(APPEND COMPILE_DEFINITIONS ENABLE_OV_IR_FRONTEND)
+endif()
+
+if(ENABLE_OV_ONNX_FRONTEND)
+    list(APPEND COMPILE_DEFINITIONS ENABLE_OV_ONNX_FRONTEND)
+endif()
+
+if(ENABLE_OV_PADDLE_FRONTEND)
+    list(APPEND COMPILE_DEFINITIONS ENABLE_OV_PADDLE_FRONTEND)
+endif()
+
+if(ENABLE_OV_TF_FRONTEND)
+    list(APPEND COMPILE_DEFINITIONS ENABLE_OV_TF_FRONTEND)
+endif()
+
+if(ENABLE_OV_TF_LITE_FRONTEND)
+    list(APPEND COMPILE_DEFINITIONS ENABLE_OV_TF_LITE_FRONTEND)
+endif()
+
+if(ENABLE_OV_PYTORCH_FRONTEND)
+    list(APPEND COMPILE_DEFINITIONS ENABLE_OV_PYTORCH_FRONTEND)
+endif()
+
 # add subgraphs_dumpers tool
 ov_add_test_target(
         NAME ${TARGET_NAME}
@@ -24,6 +48,8 @@ ov_add_test_target(
         DEPENDENCIES
             ov_frontends
             ${LIBRARIES}
+        DEFINES
+            ${COMPILE_DEFINITIONS}
         ADD_CPPLINT
 )
 
@@ -44,6 +70,8 @@ ov_add_target(
         DEPENDENCIES
             ov_frontends
             ${LIBRARIES}
+        DEFINES
+            ${COMPILE_DEFINITIONS}
         ADD_CPPLINT
 )
 
diff --git a/src/tests/functional/plugin/conformance/subgraphs_dumper/src/utils/model.cpp b/src/tests/functional/plugin/conformance/subgraphs_dumper/src/utils/model.cpp
index 79b3a10225cd86..7198ed9e472c8e 100644
--- a/src/tests/functional/plugin/conformance/subgraphs_dumper/src/utils/model.cpp
+++ b/src/tests/functional/plugin/conformance/subgraphs_dumper/src/utils/model.cpp
@@ -21,7 +21,7 @@ get_input_info_by_model(const std::shared_ptr<ov::Model>& model) {
     for (const auto& node : model->get_ordered_ops()) {
         ov::conformance::InputInfo::Range ranges(ov::conformance::DEFAULT_MIN_VALUE, ov::conformance::DEFAULT_MAX_VALUE);
         bool is_const = false;
-        if (ov::op::util::is_constant(node)) {
+        if (ov::shape_size(node->get_output_shape(0)) != 0 && ov::op::util::is_constant(node)) {
             std::shared_ptr<ov::op::v0::Constant> constant = std::dynamic_pointer_cast<ov::op::v0::Constant>(node);
             auto const_ranges = get_const_ranges(constant,
                                                  constant->get_default_output().get_element_type());
diff --git a/src/tests/functional/plugin/conformance/subgraphs_dumper/src/utils/node.cpp b/src/tests/functional/plugin/conformance/subgraphs_dumper/src/utils/node.cpp
index 4f3b37a1f04aff..ef927bc429050f 100644
--- a/src/tests/functional/plugin/conformance/subgraphs_dumper/src/utils/node.cpp
+++ b/src/tests/functional/plugin/conformance/subgraphs_dumper/src/utils/node.cpp
@@ -87,12 +87,16 @@ get_input_info_by_node(const std::shared_ptr<ov::Node>& node) {
         ov::conformance::InputInfo in_info(node->get_input_partial_shape(port_id));
         std::string input_name = input_node->get_friendly_name();
         if (std::dynamic_pointer_cast<ov::op::v0::Constant>(input_node)) {
-            if (ov::shape_size(input_node->get_output_shape(0)) == 0)
-                continue;
-            auto const_node = ov::as_type_ptr<ov::op::v0::Constant>(input_node);
-            in_info.is_const = true;
-            in_info.ranges = get_const_ranges(const_node,
-                                              const_node->get_default_output().get_element_type());
+            if (ov::shape_size(input_node->get_output_shape(0)) == 0) {
+                auto const_node = ov::as_type_ptr<ov::op::v0::Constant>(input_node);
+                in_info.is_const = true;
+                in_info.ranges = ov::conformance::InputInfo::Range(ov::conformance::DEFAULT_MIN_VALUE, ov::conformance::DEFAULT_MAX_VALUE);
+            } else {
+                auto const_node = ov::as_type_ptr<ov::op::v0::Constant>(input_node);
+                in_info.is_const = true;
+                in_info.ranges = get_const_ranges(const_node,
+                                                  const_node->get_default_output().get_element_type());
+            }
         }
         input_info.insert({input_name, in_info});
     }
diff --git a/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/src/ov_compiled_model/properties.cpp b/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/src/ov_compiled_model/properties.cpp
index d7473c8b783aab..9d75200ad2a2c5 100644
--- a/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/src/ov_compiled_model/properties.cpp
+++ b/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/src/ov_compiled_model/properties.cpp
@@ -22,7 +22,8 @@ INSTANTIATE_TEST_SUITE_P(ov_compiled_model_mandatory, OVClassCompiledModelProper
                         OVClassCompiledModelPropertiesIncorrectTests::getTestCaseName);
 
 const std::vector<ov::AnyMap> default_properties = {
-        {ov::enable_profiling(false)}
+        {ov::enable_profiling(false)},
+        {ov::hint::performance_mode(ov::hint::PerformanceMode::LATENCY)}
 };
 
 INSTANTIATE_TEST_SUITE_P(ov_compiled_model_mandatory, OVClassCompiledModelPropertiesDefaultTests,
diff --git a/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/src/ov_plugin/properties.cpp b/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/src/ov_plugin/properties.cpp
index e6710c6cf6e464..5ed1e4269d69c4 100644
--- a/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/src/ov_plugin/properties.cpp
+++ b/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/src/ov_plugin/properties.cpp
@@ -25,6 +25,7 @@ INSTANTIATE_TEST_SUITE_P(ov_plugin_mandatory, OVPropertiesIncorrectTests,
 const std::vector<ov::AnyMap> default_properties = {
         {},
         {ov::enable_profiling(false)},
+        {ov::hint::performance_mode(ov::hint::PerformanceMode::LATENCY)},
 };
 
 INSTANTIATE_TEST_SUITE_P(ov_plugin_mandatory, OVPropertiesTests,
@@ -33,6 +34,12 @@ INSTANTIATE_TEST_SUITE_P(ov_plugin_mandatory, OVPropertiesTests,
                 ::testing::ValuesIn(default_properties)),
         OVPropertiesTests::getTestCaseName);
 
+INSTANTIATE_TEST_SUITE_P(ov_plugin_mandatory, OVPropertiesDefaultTests,
+        ::testing::Combine(
+                ::testing::Values(ov::test::utils::target_device),
+                ::testing::ValuesIn(default_properties)),
+        OVPropertiesDefaultTests::getTestCaseName);
+
 INSTANTIATE_TEST_SUITE_P(ov_plugin_mandatory, OVCheckGetSupportedROMetricsPropsTests,
         ::testing::Combine(
                         ::testing::Values(ov::test::utils::target_device),
diff --git a/src/tests/functional/plugin/shared/include/behavior/ov_infer_request/properties_tests.hpp b/src/tests/functional/plugin/shared/include/behavior/ov_infer_request/properties_tests.hpp
index 7a450252c6a2c9..aa0aecd1aec57c 100644
--- a/src/tests/functional/plugin/shared/include/behavior/ov_infer_request/properties_tests.hpp
+++ b/src/tests/functional/plugin/shared/include/behavior/ov_infer_request/properties_tests.hpp
@@ -121,7 +121,7 @@ TEST_P(InferRequestPropertiesTest, ReusableCPUStreamsExecutor) {
         // Load CNNNetwork to target plugins
         execNet = core->compile_model(function, target_device, config);
         auto req = execNet.create_infer_request();
-        if (target_device == ov::test::utils::DEVICE_KEEMBAY) {
+        if (target_device == ov::test::utils::DEVICE_NPU) {
             ASSERT_EQ(1u, ov::threading::executor_manager()->get_executors_number());
             ASSERT_EQ(0u, ov::threading::executor_manager()->get_idle_cpu_streams_executors_number());
         } else if ((target_device == ov::test::utils::DEVICE_AUTO) ||
diff --git a/src/tests/functional/plugin/shared/include/behavior/ov_plugin/core_integration_sw.hpp b/src/tests/functional/plugin/shared/include/behavior/ov_plugin/core_integration_sw.hpp
index f66946fee69e0d..3ef772c8bc12f5 100644
--- a/src/tests/functional/plugin/shared/include/behavior/ov_plugin/core_integration_sw.hpp
+++ b/src/tests/functional/plugin/shared/include/behavior/ov_plugin/core_integration_sw.hpp
@@ -116,7 +116,8 @@ TEST(OVClassBasicPropsTest, smoke_SetConfigAutoNoThrows) {
     ov::Core core;
 
     // priority config test
-    ov::hint::Priority value;
+    // initialize, gcc 14.1 reports maybe-uninitialized at line 123 because of test macro at line 122
+    ov::hint::Priority value{};
     OV_ASSERT_NO_THROW(core.set_property(ov::test::utils::DEVICE_AUTO, ov::hint::model_priority(ov::hint::Priority::LOW)));
     OV_ASSERT_NO_THROW(value = core.get_property(ov::test::utils::DEVICE_AUTO, ov::hint::model_priority));
     EXPECT_EQ(value, ov::hint::Priority::LOW);
@@ -151,4 +152,4 @@ TEST_P(OVClassModelOptionalTestP, getVersionsNonEmpty) {
 
 }  // namespace behavior
 }  // namespace test
-}  // namespace ov
\ No newline at end of file
+}  // namespace ov
diff --git a/src/tests/functional/plugin/shared/include/snippets/matmul.hpp b/src/tests/functional/plugin/shared/include/snippets/matmul.hpp
index ad9ee8252e362d..300836117307cf 100644
--- a/src/tests/functional/plugin/shared/include/snippets/matmul.hpp
+++ b/src/tests/functional/plugin/shared/include/snippets/matmul.hpp
@@ -11,7 +11,7 @@ namespace test {
 namespace snippets {
 
 typedef std::tuple<
-        std::vector<ov::PartialShape>, // Input  Shapes
+        std::vector<InputShape>,       // Input  Shapes
         std::vector<ov::element::Type>,// Input Element types
         size_t,                        // Expected num nodes
         size_t,                        // Expected num subgraphs
@@ -26,32 +26,32 @@ class MatMul : public testing::WithParamInterface<ov::test::snippets::MatMulPara
 protected:
     void SetUp() override;
 
-    virtual void init_subgraph(const std::vector<PartialShape>& inputShapes, const std::vector<ov::element::Type>& types);
+    virtual void init_subgraph(const std::vector<ov::element::Type>& types);
 };
 
 class MatMulFQ : public MatMul {
 protected:
-    void init_subgraph(const std::vector<PartialShape>& inputShapes, const std::vector<ov::element::Type>& types) override;
+    void init_subgraph(const std::vector<ov::element::Type>& types) override;
 };
 
 class MatMulBias : public MatMul {
 protected:
-    void init_subgraph(const std::vector<PartialShape>& inputShapes, const std::vector<ov::element::Type>& types) override;
+    void init_subgraph(const std::vector<ov::element::Type>& types) override;
 };
 
 class MatMulBiasQuantized : public MatMul {
 protected:
-    void init_subgraph(const std::vector<PartialShape>& inputShapes, const std::vector<ov::element::Type>& types) override;
+    void init_subgraph(const std::vector<ov::element::Type>& types) override;
 };
 
 class MatMulQuantized : public MatMul {
 protected:
-    void init_subgraph(const std::vector<PartialShape>& inputShapes, const std::vector<ov::element::Type>& types) override;
+    void init_subgraph(const std::vector<ov::element::Type>& types) override;
 };
 
 class MatMulQuantizedSoftmax : public MatMul {
 protected:
-    void init_subgraph(const std::vector<PartialShape>& inputShapes, const std::vector<ov::element::Type>& types) override;
+    void init_subgraph(const std::vector<ov::element::Type>& types) override;
 };
 
 } // namespace snippets
diff --git a/src/tests/functional/plugin/shared/include/subgraph_tests/rotary_pos_emb.hpp b/src/tests/functional/plugin/shared/include/subgraph_tests/rotary_pos_emb.hpp
index 29b2cc2278613d..63db878dd6e90a 100644
--- a/src/tests/functional/plugin/shared/include/subgraph_tests/rotary_pos_emb.hpp
+++ b/src/tests/functional/plugin/shared/include/subgraph_tests/rotary_pos_emb.hpp
@@ -52,5 +52,12 @@ TEST_P(RoPETestGPTJ, CompareWithRefs) {
     CheckNumberOfNodesWithType(function, {"RoPE"}, 1);
 };
 
+TEST_P(RoPETestRotateHalfWithoutTranspose, CompareWithRefs) {
+    SKIP_IF_CURRENT_TEST_IS_DISABLED();
+    run();
+    auto function = compiledModel.get_runtime_model();
+    CheckNumberOfNodesWithType(function, {"RoPE"}, 1);
+};
+
 }  // namespace test
 }  // namespace ov
diff --git a/src/tests/functional/plugin/shared/src/behavior/ov_infer_request/io_tensor.cpp b/src/tests/functional/plugin/shared/src/behavior/ov_infer_request/io_tensor.cpp
index bf98a897e0bf56..48e9763495dcdf 100644
--- a/src/tests/functional/plugin/shared/src/behavior/ov_infer_request/io_tensor.cpp
+++ b/src/tests/functional/plugin/shared/src/behavior/ov_infer_request/io_tensor.cpp
@@ -380,9 +380,9 @@ bool OVInferRequestCheckTensorPrecision::compareTensors(const ov::Tensor& t1, co
         // Remote tensor
         data2 = nullptr;
     }
+    const auto strides_eq = (t1.get_element_type().bitwidth() >= 8 && t2.get_element_type().bitwidth() >= 8) ?  t1.get_strides() == t2.get_strides() : true;
     return t1.get_element_type() == t2.get_element_type() && t1.get_shape() == t2.get_shape() &&
-           t1.get_byte_size() == t2.get_byte_size() && t1.get_size() == t2.get_size() &&
-           t1.get_strides() == t2.get_strides() && data1 == data2;
+               t1.get_byte_size() == t2.get_byte_size() && t1.get_size() == t2.get_size() && strides_eq && data1 == data2;
 }
 
 void OVInferRequestCheckTensorPrecision::createInferRequest() {
diff --git a/src/tests/functional/plugin/shared/src/snippets/matmul.cpp b/src/tests/functional/plugin/shared/src/snippets/matmul.cpp
index e7bf30f51115c6..0da01d83e7d948 100644
--- a/src/tests/functional/plugin/shared/src/snippets/matmul.cpp
+++ b/src/tests/functional/plugin/shared/src/snippets/matmul.cpp
@@ -12,14 +12,15 @@ namespace test {
 namespace snippets {
 
 std::string MatMul::getTestCaseName(testing::TestParamInfo<ov::test::snippets::MatMulParams> obj) {
-    std::vector<ov::PartialShape> input_shapes;
+    std::vector<ov::test::InputShape> input_shapes;
     std::vector<ov::element::Type> elem_types;
     std::string targetDevice;
     size_t num_nodes, num_subgraphs;
     std::tie(input_shapes, elem_types, num_nodes, num_subgraphs, targetDevice) = obj.param;
     std::ostringstream result;
     for (size_t i = 0; i < input_shapes.size(); i++)
-        result << "IS[" << i <<"]=" << ov::test::utils::partialShape2str({input_shapes[i]}) << "_";
+        result << "IS[" << i << "]=" << input_shapes[i] << "_";
+
     for (size_t i = 0; i < elem_types.size(); i++)
         result << "T[" << i <<"]=" << elem_types[i] << "_";
     result << "#N=" << num_nodes << "_";
@@ -29,44 +30,44 @@ std::string MatMul::getTestCaseName(testing::TestParamInfo<ov::test::snippets::M
 }
 
 void MatMul::SetUp() {
-    std::vector<ov::PartialShape> input_shapes;
+    std::vector<ov::test::InputShape> input_shapes;
     std::vector<ov::element::Type> elem_types;
     std::tie(input_shapes, elem_types, ref_num_nodes, ref_num_subgraphs, targetDevice) = this->GetParam();
-    init_input_shapes(static_partial_shapes_to_test_representation(input_shapes));
+    init_input_shapes(input_shapes);
 
-    init_subgraph(input_shapes, elem_types);
+    init_subgraph(elem_types);
     if (!configuration.count("SNIPPETS_MODE")) {
         configuration.insert({"SNIPPETS_MODE", "IGNORE_CALLBACK"});
     }
 }
 
-void MatMul::init_subgraph(const std::vector<PartialShape>& inputShapes, const std::vector<ov::element::Type>& types) {
-    auto f = ov::test::snippets::MatMulFunction(inputShapes, types);
+void MatMul::init_subgraph(const std::vector<ov::element::Type>& types) {
+    auto f = ov::test::snippets::MatMulFunction(inputDynamicShapes, types);
     function = f.getOriginal();
 }
 
-void MatMulFQ::init_subgraph(const std::vector<PartialShape>& inputShapes, const std::vector<ov::element::Type>& types) {
-    auto f = ov::test::snippets::FQMatMulFunction(inputShapes);
+void MatMulFQ::init_subgraph(const std::vector<ov::element::Type>& types) {
+    auto f = ov::test::snippets::FQMatMulFunction(inputDynamicShapes);
     function = f.getOriginal();
 }
 
-void MatMulBias::init_subgraph(const std::vector<PartialShape>& inputShapes, const std::vector<ov::element::Type>& types) {
-    auto f = ov::test::snippets::MatMulBiasFunction(inputShapes, types);
+void MatMulBias::init_subgraph(const std::vector<ov::element::Type>& types) {
+    auto f = ov::test::snippets::MatMulBiasFunction(inputDynamicShapes, types);
     function = f.getOriginal();
 }
 
-void MatMulBiasQuantized::init_subgraph(const std::vector<PartialShape>& inputShapes, const std::vector<ov::element::Type>& types) {
-    auto f = ov::test::snippets::MatMulBiasQuantizedFunction(inputShapes, types);
+void MatMulBiasQuantized::init_subgraph(const std::vector<ov::element::Type>& types) {
+    auto f = ov::test::snippets::MatMulBiasQuantizedFunction(inputDynamicShapes, types);
     function = f.getOriginal();
 }
 
-void MatMulQuantized::init_subgraph(const std::vector<PartialShape>& inputShapes, const std::vector<ov::element::Type>& types) {
-    auto f = ov::test::snippets::MatMulsQuantizedFunction(inputShapes, types);
+void MatMulQuantized::init_subgraph(const std::vector<ov::element::Type>& types) {
+    auto f = ov::test::snippets::MatMulsQuantizedFunction(inputDynamicShapes, types);
     function = f.getOriginal();
 }
 
-void MatMulQuantizedSoftmax::init_subgraph(const std::vector<PartialShape>& inputShapes, const std::vector<ov::element::Type>& types) {
-    auto f = ov::test::snippets::MatMulsQuantizedSoftmaxFunction(inputShapes, types);
+void MatMulQuantizedSoftmax::init_subgraph(const std::vector<ov::element::Type>& types) {
+    auto f = ov::test::snippets::MatMulsQuantizedSoftmaxFunction(inputDynamicShapes, types);
     function = f.getOriginal();
 }
 
diff --git a/src/tests/functional/shared_test_classes/include/shared_test_classes/single_op/activation.hpp b/src/tests/functional/shared_test_classes/include/shared_test_classes/single_op/activation.hpp
index 1390e7a6a00269..4378db331abf19 100644
--- a/src/tests/functional/shared_test_classes/include/shared_test_classes/single_op/activation.hpp
+++ b/src/tests/functional/shared_test_classes/include/shared_test_classes/single_op/activation.hpp
@@ -68,6 +68,7 @@ static std::map<ActivationTypes, std::string> activationNames = {
         {ActivationTypes::GeluTanh,              "GeluTanh"},
         {ActivationTypes::SoftSign,              "SoftSign"},
         {ActivationTypes::IsInf,                 "IsInf"},
+        {ActivationTypes::IsNaN,                 "IsNaN"},
 };
 
 typedef std::tuple<
diff --git a/src/tests/functional/shared_test_classes/include/shared_test_classes/single_op/minimum_maximum.hpp b/src/tests/functional/shared_test_classes/include/shared_test_classes/single_op/minimum_maximum.hpp
index c2a90a52609195..e41b79d3718191 100644
--- a/src/tests/functional/shared_test_classes/include/shared_test_classes/single_op/minimum_maximum.hpp
+++ b/src/tests/functional/shared_test_classes/include/shared_test_classes/single_op/minimum_maximum.hpp
@@ -13,6 +13,13 @@
 
 namespace ov {
 namespace test {
+using ov::test::utils::MinMaxOpType;
+
+static std::map<MinMaxOpType, std::string> extremumNames = {
+        {MinMaxOpType::MINIMUM, "MINIMUM"},
+        {MinMaxOpType::MAXIMUM, "MAXIMUM"}
+};
+
 using MaxMinParamsTuple = typename std::tuple<
         std::vector<InputShape>,          // Input shapes
         ov::test::utils::MinMaxOpType,    // Operation type
diff --git a/src/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/rotary_pos_emb.hpp b/src/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/rotary_pos_emb.hpp
index c18b57062f0295..9bc28465419471 100644
--- a/src/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/rotary_pos_emb.hpp
+++ b/src/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/rotary_pos_emb.hpp
@@ -63,5 +63,22 @@ class RoPETestGPTJ : public SubgraphBaseTest, public testing::WithParamInterface
     static std::string getTestCaseName(const testing::TestParamInfo<std::tuple<bool, std::string>>& obj);
 };
 
+class RoPETestRotateHalfWithoutTranspose : public SubgraphBaseTest, public testing::WithParamInterface<std::string> {
+private:
+    ov::Tensor create_i32_tensor(const ov::Shape& shape, int start, int step = 1);
+    ov::OutputVector makeCosSinCache(int max_position_embeddings, int rotary_ndims);
+    std::shared_ptr<ov::Model> buildROPE_RotateHalfWithoutTranspose(int batch,
+                                                                    int seq_length,
+                                                                    int max_position_embeddings,
+                                                                    int num_head,
+                                                                    int ndims);
+protected:
+    void generate_inputs(const std::vector<ov::Shape>& targetInputStaticShapes) override;
+    void SetUp() override;
+
+public:
+    static std::string getTestCaseName(const testing::TestParamInfo<std::string>& obj);
+};
+
 }  // namespace test
 }  // namespace ov
diff --git a/src/tests/functional/shared_test_classes/src/subgraph/rotary_pos_emb.cpp b/src/tests/functional/shared_test_classes/src/subgraph/rotary_pos_emb.cpp
index a39a15783874e1..eb580f93e50894 100644
--- a/src/tests/functional/shared_test_classes/src/subgraph/rotary_pos_emb.cpp
+++ b/src/tests/functional/shared_test_classes/src/subgraph/rotary_pos_emb.cpp
@@ -586,5 +586,161 @@ void RoPETestGPTJ::SetUp() {
     function = buildROPE_GPTJ(num_head, hidden_dims, rotary_dims, hasShapeOf);
 }
 
+ov::OutputVector RoPETestRotateHalfWithoutTranspose::makeCosSinCache(int max_position_embeddings, int rotary_ndims) {
+    std::vector<float> lut_sin(max_position_embeddings * rotary_ndims, 0.0f);
+    std::vector<float> lut_cos(max_position_embeddings * rotary_ndims, 0.0f);
+
+    // rotate_half style cos/sin table:
+    //   y1 = cos(m*xita_i) * x1 - sin(m*xita_i) * x2
+    //   y2 = cos(m*xita_i) * x2 + sin(m*xita_i) * x1
+    //
+    for (int i = 0, k = 0; i < rotary_ndims; i += 2, k++) {
+        auto xita_i = 1.0 / std::pow(10000.0, static_cast<double>(i) / rotary_ndims);
+        float* psin = lut_sin.data();
+        float* pcos = lut_cos.data();
+        for (int m = 0; m < max_position_embeddings; m++, psin += rotary_ndims, pcos += rotary_ndims) {
+            auto vsin = std::sin(xita_i * m);
+            auto vcos = std::cos(xita_i * m);
+            pcos[k] = pcos[k + rotary_ndims / 2] = vcos;
+            psin[k] = psin[k + rotary_ndims / 2] = vsin;
+        }
+    }
+    auto shape = ov::Shape({1, static_cast<size_t>(max_position_embeddings), 1, static_cast<size_t>(rotary_ndims)});
+    auto Cos = makeConst(ov::element::f32, shape, lut_cos);
+    auto Sin = makeConst(ov::element::f32, shape, lut_sin);
+    return {Cos, Sin};
+}
+
+std::shared_ptr<ov::Model>
+    RoPETestRotateHalfWithoutTranspose::buildROPE_RotateHalfWithoutTranspose(int batch,
+                                                                             int seq_length,
+                                                                             int max_position_embeddings,
+                                                                             int num_head,
+                                                                             int ndims) {
+    auto input = std::make_shared<ov::opset1::Parameter>(ov::element::f32, PartialShape{batch, num_head, -1, ndims});
+    auto pos_id_end = std::make_shared<ov::opset1::Parameter>(ov::element::i32, ov::Shape{});
+    auto pos_ids = std::make_shared<ov::opset1::Parameter>(ov::element::i32, PartialShape{1, -1});
+
+    auto cos_sin_cache = makeCosSinCache(max_position_embeddings, ndims);
+    auto Constant582 = cos_sin_cache[0];
+    auto Constant585 = cos_sin_cache[1];
+
+    // concat KV length
+    auto slice_Unsqueeze_426 = makeOP<ov::op::v0::Unsqueeze>({pos_id_end, 0});
+    auto ScatterUpdate_152236 = makeOP<ov::op::v3::ScatterUpdate>({{0, 0, 0}, {1}, slice_Unsqueeze_426, {0}});
+    auto slice_Slice = makeOP<ov::op::v1::StridedSlice>({Constant582, {0, 0, 0}, ScatterUpdate_152236, {1, 1, 1}},
+                                                    {{"begin_mask", {1, 0, 1}},
+                                                     {"end_mask", {1, 0, 1}},
+                                                     {"new_axis_mask", {}},
+                                                     {"shrink_axis_mask", {}},
+                                                     {"ellipsis_mask", {}}});
+    auto squeeze_Squeeze = makeOP<ov::op::v0::Squeeze>({slice_Slice, 2});
+    auto squeeze_Squeeze_435 = makeOP<ov::op::v0::Squeeze>({squeeze_Squeeze, 0});
+    auto index_441_Gather = makeOP<ov::op::v8::Gather>({squeeze_Squeeze_435, pos_ids, 0}, {{"batch_dims", 0}});
+    auto unsqueeze_Unsqueeze = makeOP<ov::op::v0::Unsqueeze>({index_441_Gather, 1});
+    auto mul_Multiply =
+        makeOP<ov::op::v1::Multiply>({input, unsqueeze_Unsqueeze}, {{"auto_broadcast", "numpy"}});
+    auto size_ShapeOf_448 = makeOP<ov::op::v3::ShapeOf>({input}, {{"output_type", "i32"}});
+    auto size_Gather_450 = makeOP<ov::op::v8::Gather>({size_ShapeOf_448, 3, 0}, {{"batch_dims", 0}});
+    auto floor_divide_Divide =
+        makeOP<ov::op::v1::Divide>({size_Gather_450, 2}, {{"auto_broadcast", "numpy"}, {"m_pythondiv", true}});
+    auto floor_divide_Floor = makeOP<ov::op::v0::Floor>({floor_divide_Divide});
+    auto slice_Unsqueeze_452 = makeOP<ov::op::v0::Unsqueeze>({floor_divide_Floor, 0});
+    auto ScatterUpdate_152312 = makeOP<ov::op::v3::ScatterUpdate>({{0, 0, 0, 0}, {3}, slice_Unsqueeze_452, {0}});
+    auto slice_Slice_459 = makeOP<ov::op::v1::StridedSlice>(
+        {input, ScatterUpdate_152312, {0ll, 0ll, 0ll, LLONG_MAX}, {1, 1, 1, 1}},
+        {{"begin_mask", {1, 1, 1, 0}},
+         {"end_mask", {1, 1, 1, 0}},
+         {"new_axis_mask", {}},
+         {"shrink_axis_mask", {}},
+         {"ellipsis_mask", {}}});
+    auto Constant_182988 = makeConst(element::f32,
+                                     ov::Shape({
+                                         1,
+                                         1,
+                                         1,
+                                         1,
+                                     }),
+                                     {-1.000000f});
+    auto neg_Multiply = makeOP<ov::op::v1::Multiply>({slice_Slice_459, Constant_182988}, {{"auto_broadcast", "numpy"}});
+    auto ScatterUpdate_152368 = makeOP<ov::op::v3::ScatterUpdate>({{0, 0, 0, 0}, {3}, slice_Unsqueeze_452, {0}});
+    auto slice_Slice2 =
+        makeOP<ov::op::v1::StridedSlice>({input, {0, 0, 0, 0}, ScatterUpdate_152368, {1, 1, 1, 1}},
+                                     {{"begin_mask", {1, 1, 1, 0}},
+                                      {"end_mask", {1, 1, 1, 0}},
+                                      {"new_axis_mask", {}},
+                                      {"shrink_axis_mask", {}},
+                                      {"ellipsis_mask", {}}});
+    auto cat_Concat = makeOP<ov::op::v0::Concat>({neg_Multiply, slice_Slice2}, {{"axis", -1}});
+    auto ScatterUpdate_152421 = makeOP<ov::op::v3::ScatterUpdate>({{0, 0, 0}, {1}, slice_Unsqueeze_426, {0}});
+    auto slice_Slice_433 = makeOP<ov::op::v1::StridedSlice>({Constant585, {0, 0, 0}, ScatterUpdate_152421, {1, 1, 1}},
+                                                        {{"begin_mask", {1, 0, 1}},
+                                                         {"end_mask", {1, 0, 1}},
+                                                         {"new_axis_mask", {}},
+                                                         {"shrink_axis_mask", {}},
+                                                         {"ellipsis_mask", {}}});
+    auto squeeze_Squeeze_436 = makeOP<ov::op::v0::Squeeze>({slice_Slice_433, 2});
+    auto squeeze_Squeeze_437 = makeOP<ov::op::v0::Squeeze>({squeeze_Squeeze_436, 0});
+    auto index_446_Gather = makeOP<ov::op::v8::Gather>({squeeze_Squeeze_437, pos_ids, 0}, {{"batch_dims", 0}});
+    auto unsqueeze_Unsqueeze_447 = makeOP<ov::op::v0::Unsqueeze>({index_446_Gather, 1});
+    auto mul_Multiply_463 =
+        makeOP<ov::op::v1::Multiply>({cat_Concat, unsqueeze_Unsqueeze_447}, {{"auto_broadcast", "numpy"}});
+    auto add_Add = makeOP<ov::op::v1::Add>({mul_Multiply, mul_Multiply_463}, {{"auto_broadcast", "numpy"}});
+
+    return std::make_shared<ov::Model>(ov::NodeVector{add_Add}, ov::ParameterVector{input, pos_id_end, pos_ids});
+}
+
+ov::Tensor RoPETestRotateHalfWithoutTranspose::create_i32_tensor(const ov::Shape& shape, int start, int step) {
+    auto tensor = ov::Tensor(ov::element::i32, shape);
+    auto* ptr = static_cast<int32_t*>(tensor.data());
+    for (size_t i = 0; i < tensor.get_size(); i++) {
+        ptr[i] = start;
+        start += step;
+    }
+    return tensor;
+}
+
+void RoPETestRotateHalfWithoutTranspose::generate_inputs(const std::vector<ov::Shape>& targetInputStaticShapes) {
+    const auto& funcInputs = function->inputs();
+
+    const int position_id_start = 15;
+    auto& input_shape = targetInputStaticShapes[0];
+    auto seq_length = input_shape[2];
+
+    ov::test::utils::InputGenerateData in_data;
+    in_data.start_from = -1;
+    in_data.range = 2;
+    in_data.resolution = 32768;
+    ov::Tensor t_input = utils::create_and_fill_tensor(funcInputs[0].get_element_type(), input_shape, in_data);
+    ov::Tensor t_position_id_end = create_i32_tensor(ov::Shape({}), position_id_start + seq_length);
+    ov::Tensor t_position_ids = create_i32_tensor(ov::Shape({1, seq_length}), position_id_start);
+
+    inputs.clear();
+    inputs.insert({funcInputs[0].get_node_shared_ptr(), t_input});
+    inputs.insert({funcInputs[1].get_node_shared_ptr(), t_position_id_end});
+    inputs.insert({funcInputs[2].get_node_shared_ptr(), t_position_ids});
+}
+
+void RoPETestRotateHalfWithoutTranspose::SetUp() {
+    targetDevice = this->GetParam();
+
+    const int batch = 2;
+    const int seq_length = 7;
+    const size_t max_position_embeddings = 2048;
+    const size_t ndims = 128;
+    const size_t num_head = 32;
+
+    InputShape inpShape = {{batch, num_head, seq_length, ndims}, {{batch, num_head, seq_length, ndims}}};
+    init_input_shapes({inpShape});
+    function = buildROPE_RotateHalfWithoutTranspose(batch, seq_length, max_position_embeddings, num_head, ndims);
+}
+
+std::string RoPETestRotateHalfWithoutTranspose::getTestCaseName(const testing::TestParamInfo<std::string>& obj) {
+    std::string targetDevice = obj.param;
+    std::ostringstream result;
+    result << "targetDevice=" << targetDevice;
+    return result.str();
+}
+
 }  // namespace test
 }  // namespace ov
diff --git a/src/tests/functional/shared_test_classes/src/subgraph/shared_matmul_gather_weights_decompression.cpp b/src/tests/functional/shared_test_classes/src/subgraph/shared_matmul_gather_weights_decompression.cpp
index aaa8a43628e888..21215feb53dc34 100644
--- a/src/tests/functional/shared_test_classes/src/subgraph/shared_matmul_gather_weights_decompression.cpp
+++ b/src/tests/functional/shared_test_classes/src/subgraph/shared_matmul_gather_weights_decompression.cpp
@@ -59,6 +59,12 @@ std::shared_ptr<ov::Model> SharedMatmulAndGatherWeightsDecompression::initSubgra
     const auto matmul = std::make_shared<ov::op::v0::MatMul>(fc_data, decompression_subgraph, false, true);
     const ov::OutputVector last_nodes{gather, matmul};
     const ov::ParameterVector params{indices_data, fc_data};
+
+    // if dynamic quantization is enabled
+    if (group_size != 0) {
+        abs_threshold = 0.15;
+    }
+
     return std::make_shared<ov::Model>(last_nodes, params, "SharedMatmulAndGatherWeightsDecompression");
 }
 
diff --git a/src/tests/functional/shared_test_classes/src/subgraph/shared_matmul_weights_decompression.cpp b/src/tests/functional/shared_test_classes/src/subgraph/shared_matmul_weights_decompression.cpp
index 78a48fcd3a8702..1aa1efac9b7100 100644
--- a/src/tests/functional/shared_test_classes/src/subgraph/shared_matmul_weights_decompression.cpp
+++ b/src/tests/functional/shared_test_classes/src/subgraph/shared_matmul_weights_decompression.cpp
@@ -71,6 +71,12 @@ std::shared_ptr<ov::Model> SharedMatmulWeightsDecompression::initSubgraph(
         params.push_back(param);
         last_layers.push_back(matMul);
     }
+
+    // if dynamic quantization is enabled
+    if (group_size != 0) {
+        abs_threshold = 0.1;
+    }
+
     return std::make_shared<ov::Model>(last_layers, params, "SharedMatmulWeightsDecompression");
 }
 
diff --git a/src/tests/test_utils/common_test_utils/include/common_test_utils/data_utils.hpp b/src/tests/test_utils/common_test_utils/include/common_test_utils/data_utils.hpp
index d4df10444caac8..639c22067826e4 100644
--- a/src/tests/test_utils/common_test_utils/include/common_test_utils/data_utils.hpp
+++ b/src/tests/test_utils/common_test_utils/include/common_test_utils/data_utils.hpp
@@ -414,6 +414,10 @@ inline ov::float8_e5m2 ie_abs(const ov::float8_e5m2& val) {
     return ov::float8_e5m2::from_bits(val.to_bits() & 0x7F);
 }
 
+inline ov::float8_e8m0 ie_abs(const ov::float8_e8m0& val) {
+    return val;
+}
+
 template <class T_ACTUAL, class T_EXPECTED>
 static void compare_raw_data(const T_EXPECTED* expected,
                              const T_ACTUAL* actual,
diff --git a/src/tests/test_utils/common_test_utils/include/common_test_utils/node_builders/extremum.hpp b/src/tests/test_utils/common_test_utils/include/common_test_utils/node_builders/extremum.hpp
new file mode 100644
index 00000000000000..4a5121c0aa21ea
--- /dev/null
+++ b/src/tests/test_utils/common_test_utils/include/common_test_utils/node_builders/extremum.hpp
@@ -0,0 +1,18 @@
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "common_test_utils/test_enums.hpp"
+#include "openvino/core/node.hpp"
+
+namespace ov {
+namespace test {
+namespace utils {
+std::shared_ptr<ov::Node> make_extremum(const ov::Output<Node>& in0,
+                                        const ov::Output<Node>& in1,
+                                        ov::test::utils::MinMaxOpType extremum_type);
+}  // namespace utils
+}  // namespace test
+}  // namespace ov
diff --git a/src/tests/test_utils/common_test_utils/include/common_test_utils/test_constants.hpp b/src/tests/test_utils/common_test_utils/include/common_test_utils/test_constants.hpp
index eaab39e7d5eb0f..136cd6c8d20f41 100644
--- a/src/tests/test_utils/common_test_utils/include/common_test_utils/test_constants.hpp
+++ b/src/tests/test_utils/common_test_utils/include/common_test_utils/test_constants.hpp
@@ -11,8 +11,8 @@ namespace utils {
 extern const char* DEVICE_AUTO;
 extern const char* DEVICE_CPU;
 extern const char* DEVICE_GPU;
+extern const char* DEVICE_NPU;
 extern const char* DEVICE_BATCH;
-extern const char* DEVICE_KEEMBAY;
 extern const char* DEVICE_MULTI;
 extern const char* DEVICE_TEMPLATE;
 extern const char* DEVICE_HETERO;
diff --git a/src/tests/test_utils/common_test_utils/include/common_test_utils/test_enums.hpp b/src/tests/test_utils/common_test_utils/include/common_test_utils/test_enums.hpp
index e0bf7bfec04349..98e4416f42a53c 100644
--- a/src/tests/test_utils/common_test_utils/include/common_test_utils/test_enums.hpp
+++ b/src/tests/test_utils/common_test_utils/include/common_test_utils/test_enums.hpp
@@ -122,7 +122,8 @@ enum ActivationTypes {
     GeluErf,
     GeluTanh,
     SoftSign,
-    IsInf
+    IsInf,
+    IsNaN,
 };
 
 enum MinMaxOpType {
diff --git a/src/tests/test_utils/common_test_utils/include/common_test_utils/type_ranges.hpp b/src/tests/test_utils/common_test_utils/include/common_test_utils/type_ranges.hpp
index 1135288c1abe14..c84b58066387f3 100644
--- a/src/tests/test_utils/common_test_utils/include/common_test_utils/type_ranges.hpp
+++ b/src/tests/test_utils/common_test_utils/include/common_test_utils/type_ranges.hpp
@@ -80,6 +80,7 @@ static ov::test::utils::InputGenerateData get_range_by_type(
         CASE_OV_TYPE(ov::element::Type_t::f8e4m3)
         CASE_OV_TYPE(ov::element::Type_t::f8e5m2)
         CASE_OV_TYPE(ov::element::Type_t::f4e2m1)
+        CASE_OV_TYPE(ov::element::Type_t::f8e8m0)
         CASE_OV_TYPE(ov::element::Type_t::bf16)
         CASE_OV_TYPE(ov::element::Type_t::f16)
         CASE_C_TYPE(ov::element::Type_t::f32)
diff --git a/src/tests/test_utils/common_test_utils/src/node_builders/activation.cpp b/src/tests/test_utils/common_test_utils/src/node_builders/activation.cpp
index 18bf6cd100ec67..b28949d867e4f3 100644
--- a/src/tests/test_utils/common_test_utils/src/node_builders/activation.cpp
+++ b/src/tests/test_utils/common_test_utils/src/node_builders/activation.cpp
@@ -25,6 +25,7 @@
 #include "openvino/op/hsigmoid.hpp"
 #include "openvino/op/hswish.hpp"
 #include "openvino/op/is_inf.hpp"
+#include "openvino/op/is_nan.hpp"
 #include "openvino/op/log.hpp"
 #include "openvino/op/mish.hpp"
 #include "openvino/op/negative.hpp"
@@ -147,6 +148,8 @@ std::shared_ptr<ov::Node> make_activation(const ov::Output<Node>& in,
         return std::make_shared<ov::op::v9::SoftSign>(in);
     case ov::test::utils::ActivationTypes::IsInf:
         return std::make_shared<ov::op::v10::IsInf>(in);
+    case ov::test::utils::ActivationTypes::IsNaN:
+        return std::make_shared<ov::op::v10::IsNaN>(in);
     default:
         OPENVINO_THROW("Can't create layer for this activation type");
     }
diff --git a/src/tests/test_utils/common_test_utils/src/node_builders/embedding_bag_offsets.cpp b/src/tests/test_utils/common_test_utils/src/node_builders/embedding_bag_offsets.cpp
index 5c4caff07b907f..73d17a10534ae8 100644
--- a/src/tests/test_utils/common_test_utils/src/node_builders/embedding_bag_offsets.cpp
+++ b/src/tests/test_utils/common_test_utils/src/node_builders/embedding_bag_offsets.cpp
@@ -46,8 +46,22 @@ std::shared_ptr<ov::Node> make_embedding_bag_offsets(const element::Type& data_t
                                                                         reduction);
         }
     } else {
-        embBag =
-            std::make_shared<ov::op::v15::EmbeddingBagOffsets>(emb_table_node, indices_node, offsetsNode, reduction);
+        if (with_weights) {
+            auto defIdxNode = ov::op::v0::Constant::create(indices_type, ov::Shape{}, {-1});
+            auto tensor = create_and_fill_tensor(data_type, ov::Shape{indices.size()});
+            auto weights_node = std::make_shared<ov::op::v0::Constant>(tensor);
+            embBag = std::make_shared<ov::op::v15::EmbeddingBagOffsets>(emb_table_node,
+                                                                        indices_node,
+                                                                        offsetsNode,
+                                                                        defIdxNode,
+                                                                        weights_node,
+                                                                        reduction);
+        } else {
+            embBag = std::make_shared<ov::op::v15::EmbeddingBagOffsets>(emb_table_node,
+                                                                        indices_node,
+                                                                        offsetsNode,
+                                                                        reduction);
+        }
     }
     return embBag;
 }
diff --git a/src/tests/test_utils/common_test_utils/src/node_builders/extremum.cpp b/src/tests/test_utils/common_test_utils/src/node_builders/extremum.cpp
new file mode 100644
index 00000000000000..8b70df604210e3
--- /dev/null
+++ b/src/tests/test_utils/common_test_utils/src/node_builders/extremum.cpp
@@ -0,0 +1,27 @@
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "common_test_utils/node_builders/extremum.hpp"
+
+#include "openvino/op/maximum.hpp"
+#include "openvino/op/minimum.hpp"
+
+namespace ov {
+namespace test {
+namespace utils {
+std::shared_ptr<ov::Node> make_extremum(const ov::Output<Node>& in0,
+                                        const ov::Output<Node>& in1,
+                                        ov::test::utils::MinMaxOpType extremum_type) {
+    switch (extremum_type) {
+    case ov::test::utils::MinMaxOpType::MINIMUM:
+        return std::make_shared<ov::op::v1::Minimum>(in0, in1);
+    case ov::test::utils::MinMaxOpType::MAXIMUM:
+        return std::make_shared<ov::op::v1::Maximum>(in0, in1);
+    default:
+        throw std::runtime_error("Incorrect type of Extremum operation");
+    }
+}
+}  // namespace utils
+}  // namespace test
+}  // namespace ov
diff --git a/src/tests/test_utils/common_test_utils/src/test_case.cpp b/src/tests/test_utils/common_test_utils/src/test_case.cpp
index cdb89d442ac0f3..212dafc3d819fb 100644
--- a/src/tests/test_utils/common_test_utils/src/test_case.cpp
+++ b/src/tests/test_utils/common_test_utils/src/test_case.cpp
@@ -139,6 +139,18 @@ std::pair<testing::AssertionResult, size_t> TestCase::compare_results(size_t tol
         case element::Type_t::boolean:
             res = compare_values<char>(exp_result, result_tensor, tolerance_bits);
             break;
+        case element::Type_t::string: {
+            res = ::testing::AssertionSuccess();
+            std::string* exp_strings = exp_result.data<std::string>();
+            std::string* res_strings = result_tensor.data<std::string>();
+            for (size_t i = 0; i < exp_result.get_size(); ++i) {
+                if (exp_strings[i] != res_strings[i]) {
+                    res = ::testing::AssertionFailure() << "Wrong string value at index " << i << ", expected \""
+                                                        << exp_strings[i] << "\" got \"" << res_strings[i] << "\"";
+                    break;
+                }
+            }
+        } break;
         default:
             res = testing::AssertionFailure() << "Unsupported data type encountered in 'res' method";
         }
diff --git a/src/tests/test_utils/common_test_utils/src/test_constants.cpp b/src/tests/test_utils/common_test_utils/src/test_constants.cpp
index b8825be76c10a9..fb6ea31250c6ba 100644
--- a/src/tests/test_utils/common_test_utils/src/test_constants.cpp
+++ b/src/tests/test_utils/common_test_utils/src/test_constants.cpp
@@ -11,7 +11,7 @@ namespace utils {
 const char* DEVICE_AUTO = "AUTO";
 const char* DEVICE_CPU = "CPU";
 const char* DEVICE_GPU = "GPU";
-const char* DEVICE_KEEMBAY = "NPU";
+const char* DEVICE_NPU = "NPU";
 const char* DEVICE_BATCH = "BATCH";
 const char* DEVICE_MULTI = "MULTI";
 const char* DEVICE_TEMPLATE = "TEMPLATE";
diff --git a/src/tests/test_utils/functional_test_utils/layer_tests_summary/conformance_helper_tools/find_models_for_subgraphs_dumper.py b/src/tests/test_utils/functional_test_utils/layer_tests_summary/conformance_helper_tools/find_models_for_subgraphs_dumper.py
index 480193e9c6d4c2..b2e987fc80a712 100644
--- a/src/tests/test_utils/functional_test_utils/layer_tests_summary/conformance_helper_tools/find_models_for_subgraphs_dumper.py
+++ b/src/tests/test_utils/functional_test_utils/layer_tests_summary/conformance_helper_tools/find_models_for_subgraphs_dumper.py
@@ -6,7 +6,10 @@
 from argparse import ArgumentParser
 from pathlib import Path
 import sys
+import re
+
 sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
+
 from utils.file_utils import prepare_filelist, find_latest_dir
 from utils.conformance_utils import get_logger
 from utils.constants import SUPPORTED_MODEL_EXTENSION
@@ -17,6 +20,10 @@ def get_default_re_path(is_take_all_models = False):
     SCRIPT_DIR_PATH, _ = os.path.split(os.path.abspath(__file__))
     return os.path.join(SCRIPT_DIR_PATH, "..", "data", "custom_re_to_find_models.lst") if is_take_all_models else os.path.join(SCRIPT_DIR_PATH, "..", "data", "default_re_to_find_models.lst")
 
+def get_default_re_exclude_model_path(is_take_all_models = False):
+    SCRIPT_DIR_PATH, _ = os.path.split(os.path.abspath(__file__))
+    return '' if is_take_all_models else os.path.join(SCRIPT_DIR_PATH, "..", "data", "default_re_to_exclude_modes.lst")
+
 def parse_arguments():
     parser = ArgumentParser()
 
@@ -24,11 +31,13 @@ def parse_arguments():
     output_help = "Path to output dir to save model list file"
     filename_help = "Output filename to save model list file"
     latest_only_help = "Use only latest directory matched reg exp. In other case all directories will be taken from the dir"
+    exclude_models_help = "Use only latest directory matched reg exp. In other case all directories will be taken from the dir"
 
     parser.add_argument("-m", "--model_dirs", type=str, help=model_help, required=True)
     parser.add_argument("-o", "--output_dir", type=str, help=output_help, required=False, default=".")
     parser.add_argument("-f", "--filename", type=str, help=filename_help, required=False, default="model_filelist")
     parser.add_argument("-l", "--latest_only", type=bool, help=latest_only_help, required=False, default=False)
+    parser.add_argument("-e", "--exclude_models", type=str, help=exclude_models_help, required=False, default=Path(Path(__file__).resolve().parent, "..", "data", "default_re_to_exclude_modes.lst"))
 
     return parser.parse_args()
 
@@ -54,24 +63,29 @@ def str_to_dir_list(input_str: str):
     return dir_path_list
 
 
-def read_dir_re_exp(re_exp_file_path: str):
-    dir_re_exps = []
+def read_re_exp(re_exp_file_path: str):
+    re_exps = []
     if os.path.isfile(re_exp_file_path):
         with open(re_exp_file_path, "r") as re_exp_file:
             for line in re_exp_file.readlines():
                 if "#" in line:
                     continue
-                dir_re_exps.append(line.replace('\n', ''))
+                re_exps.append(line.replace('\n', ''))
+    return re_exps
+
+
+def read_dir_re_exp(re_exp_file_path: str):
+    dir_re_exps = read_re_exp(re_exp_file_path)
     if len(dir_re_exps) == 0:
         dir_re_exps.append('*')
-    logger.info(f"Model dir re exp list: {dir_re_exps}")
     return dir_re_exps
 
 
-def generate_model_list_file(input_str: str, re_exp_file_path: str, output_file_path: os.path, is_latest_only: bool):
+def generate_model_list_file(input_str: str, re_exp_file_path: str, output_file_path: os.path, is_latest_only: bool, re_exclude_model_file: str):
     with open(output_file_path, 'w', newline='\n') as output_file:
         model_dir_paths = str_to_dir_list(input_str)
         dir_re_exps = read_dir_re_exp(re_exp_file_path)
+        logger.info(f"Model dir re exp list: {dir_re_exps}")
         model_list = list()
         for model_dir_path in model_dir_paths:
             for dir_re_exp in dir_re_exps:
@@ -92,9 +106,15 @@ def generate_model_list_file(input_str: str, re_exp_file_path: str, output_file_
                             break
                     except:
                         pass
+        exclude_re_exps = read_re_exp(re_exclude_model_file)
+        logger.info(f"Model exclude re exp list: {exclude_re_exps}")
         for line in model_list:
             str_line = str(line)
-            if "tfhub_module.pb" in str_line or "_metadata.pb" in str_line:
+            exclude = False
+            for r in exclude_re_exps:
+                if re.match(r, str_line):
+                    exclude = True
+            if "tfhub_module.pb" in str_line or "_metadata.pb" in str_line or exclude:
                 continue
             output_file.write(f"{line}\n")
         output_file.close()
@@ -106,9 +126,10 @@ def generate_model_list_file(input_str: str, re_exp_file_path: str, output_file_
     logger.info(f"[ ARGUMENTS ] --output_dir={args.output_dir}")
     logger.info(f"[ ARGUMENTS ] --filename={args.filename}")
     logger.info(f"[ ARGUMENTS ] --latest_only={args.latest_only}")
+    logger.info(f"[ ARGUMENTS ] --exclude_models={args.exclude_models}")
     re_file = get_default_re_path(not args.latest_only)
     if not args.latest_only:
         logger.warning(f"{re_file} will be taken to get all models from the dirs")
     output_model_list_file = os.path.join(args.output_dir, f"{args.filename}.lst")
-    generate_model_list_file(args.model_dirs, re_file, output_model_list_file, args.latest_only)
+    generate_model_list_file(args.model_dirs, re_file, output_model_list_file, args.latest_only, args.exclude_models.as_posix())
     logger.info(f"Model file list is saved to {output_model_list_file}")
diff --git a/src/tests/test_utils/functional_test_utils/layer_tests_summary/data/default_re_to_exclude_modes.lst b/src/tests/test_utils/functional_test_utils/layer_tests_summary/data/default_re_to_exclude_modes.lst
new file mode 100644
index 00000000000000..fdd012edc7d4d5
--- /dev/null
+++ b/src/tests/test_utils/functional_test_utils/layer_tests_summary/data/default_re_to_exclude_modes.lst
@@ -0,0 +1 @@
+.*onnx.*torchvision_swin3d.*
\ No newline at end of file
diff --git a/src/tests/test_utils/functional_test_utils/layer_tests_summary/data/default_re_to_find_models.lst b/src/tests/test_utils/functional_test_utils/layer_tests_summary/data/default_re_to_find_models.lst
index 37551cc69f5111..a3950761eca271 100644
--- a/src/tests/test_utils/functional_test_utils/layer_tests_summary/data/default_re_to_find_models.lst
+++ b/src/tests/test_utils/functional_test_utils/layer_tests_summary/data/default_re_to_find_models.lst
@@ -1,5 +1,5 @@
-ww*_dynamic*
-ww*_pytorch*
-ww*_static*
-ww*_tfhub*
+[ww|WW]*_dynamic*
+[ww|WW]*_pytorch*
+[ww|WW]*_static*
+[ww|WW]*_tfhub*
 # ww*_tflite*
\ No newline at end of file
diff --git a/tests/layer_tests/jax_tests/jax_layer_test_class.py b/tests/layer_tests/jax_tests/jax_layer_test_class.py
index 5800b194a281b6..b1f2a03682c5d0 100644
--- a/tests/layer_tests/jax_tests/jax_layer_test_class.py
+++ b/tests/layer_tests/jax_tests/jax_layer_test_class.py
@@ -3,6 +3,7 @@
 
 import itertools
 from copy import deepcopy
+import os
 
 import numpy as np
 from common.constants import test_device, test_precision
@@ -11,6 +12,14 @@
 
 
 class JaxLayerTest:
+    
+    @staticmethod
+    def use_jaxpr_tracing():
+        jax_compile_env = os.getenv("JAX_TRACE_MODE")
+        if jax_compile_env is not None:
+            return jax_compile_env == "JAXPR"
+        return False
+    
     def _test(self, model, ref_net, ie_device, precision, ir_version, infer_timeout=60, dynamic_shapes=True,
               **kwargs):
         """
@@ -18,7 +27,10 @@ def _test(self, model, ref_net, ie_device, precision, ir_version, infer_timeout=
                                                        Example: "transform_1,transform_2"
         """
         inputs = self._prepare_input()
-        converted_model = self.convert_via_tensorflow_function(model, inputs)
+        if self.use_jaxpr_tracing():
+            converted_model = self.convert_via_jaxpr(model, inputs)
+        else:
+            converted_model = self.convert_via_tensorflow_function(model, inputs)
 
         # OV infer:
         core = Core()
@@ -109,6 +121,15 @@ def convert_via_tensorflow_function(self, model, inputs):
                         input_signature=function_signature)
         converted_model = convert_model(f)
         return converted_model
+    
+    def convert_via_jaxpr(self, model, inputs):
+        import jax
+        from openvino.tools.ovc import convert_model
+        
+        jaxpr = jax.make_jaxpr(model)(*inputs)
+        converted_model = convert_model(jaxpr)
+
+        return converted_model
 
 
 def get_params(ie_device=None, precision=None):
diff --git a/tests/layer_tests/jax_tests/test_add.py b/tests/layer_tests/jax_tests/test_add.py
new file mode 100644
index 00000000000000..f6762a6679e2a7
--- /dev/null
+++ b/tests/layer_tests/jax_tests/test_add.py
@@ -0,0 +1,130 @@
+# Copyright (C) 2018-2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np
+import pytest
+
+from jax_layer_test_class import JaxLayerTest
+import jax.numpy as jnp
+
+def get_rand_jnp_array(shape, dtype):
+    if dtype in [np.float32, np.float16, np.float64]:
+        return jnp.array(np.random.uniform(-1000, 1000, shape).astype(dtype))
+    else:
+        return jnp.array(np.random.randint(-10, 10, shape).astype(dtype))
+
+class TestAdd(JaxLayerTest):
+    def _prepare_input(self):
+        lhs = get_rand_jnp_array(self.lhs_shape, self.lhs_type)
+        rhs = get_rand_jnp_array(self.rhs_shape, self.rhs_type)
+        return (lhs, rhs)
+
+    def create_model(self, lhs_shape, rhs_shape, lhs_type, rhs_type):
+        self.lhs_shape = lhs_shape
+        self.rhs_shape = rhs_shape
+        self.lhs_type = lhs_type
+        self.rhs_type = rhs_type
+
+        def jax_add(lhs, rhs):
+            return lhs + rhs
+
+        return jax_add, None
+
+    test_data = [
+        dict(lhs_shape=[4], rhs_shape=[4]),
+        dict(lhs_shape=[2, 5], rhs_shape=[2, 5]),
+        dict(lhs_shape=[2, 3, 4, 5], rhs_shape=[2, 3, 4, 5]),
+    ]
+    
+    input_types = [
+        (np.float32, np.float32),
+        (np.int32, np.float32),
+        (np.float32, np.float16),
+        (np.int32, np.int64),
+    ]
+
+    @pytest.mark.nightly
+    @pytest.mark.precommit_jax_fe
+    @pytest.mark.parametrize("params", test_data)
+    @pytest.mark.parametrize("input_type", input_types)
+    def test_add(self, ie_device, precision, ir_version, params, input_type):
+        self._test(*self.create_model(**params, lhs_type=input_type[0], rhs_type=input_type[1]), ie_device, precision,
+                   ir_version)
+
+class TestAddWithConstant(JaxLayerTest):
+    def _prepare_input(self):
+        lhs = get_rand_jnp_array(self.lhs_shape, self.lhs_type)
+        rhs = get_rand_jnp_array(self.rhs_shape, self.rhs_type)
+        return (lhs, rhs)
+
+    def create_model(self, lhs_shape, rhs_shape, lhs_type, rhs_type):
+        self.lhs_shape = lhs_shape
+        self.rhs_shape = rhs_shape
+        self.lhs_type = lhs_type
+        self.rhs_type = rhs_type
+        self.const = jnp.ones(self.lhs_shape, dtype=self.lhs_type)
+
+        def jax_add_with_constant(lhs, rhs):
+            return lhs + rhs + self.const
+
+        return jax_add_with_constant, None
+
+    test_data = [
+        dict(lhs_shape=[4], rhs_shape=[4]),
+        dict(lhs_shape=[2, 5], rhs_shape=[2, 5]),
+        dict(lhs_shape=[2, 3, 4, 5], rhs_shape=[2, 3, 4, 5]),
+    ]
+    
+    input_types = [
+        (np.float32, np.float32),
+        (np.int32, np.float32),
+        (np.float32, np.float16),
+        (np.int32, np.int64),
+    ]
+
+    @pytest.mark.nightly
+    @pytest.mark.precommit_jax_fe
+    @pytest.mark.parametrize("params", test_data)
+    @pytest.mark.parametrize("input_type", input_types)
+    def test_add_with_constant(self, ie_device, precision, ir_version, params, input_type):
+        self._test(*self.create_model(**params, lhs_type=input_type[0], rhs_type=input_type[1]), ie_device, precision,
+                   ir_version)
+        
+class TestAddWithLiteralInvar(JaxLayerTest):
+    def _prepare_input(self):
+        lhs = get_rand_jnp_array(self.lhs_shape, self.lhs_type)
+        rhs = get_rand_jnp_array(self.rhs_shape, self.rhs_type)
+        return (lhs, rhs)
+
+    def create_model(self, lhs_shape, rhs_shape, lhs_type, rhs_type):
+        self.lhs_shape = lhs_shape
+        self.rhs_shape = rhs_shape
+        self.lhs_type = lhs_type
+        self.rhs_type = rhs_type
+
+        def jax_add_with_constant(lhs, rhs):
+            x = lhs + 5
+            return x + rhs
+
+        return jax_add_with_constant, None
+
+    test_data = [
+        dict(lhs_shape=[4], rhs_shape=[4]),
+        dict(lhs_shape=[2, 5], rhs_shape=[2, 5]),
+        dict(lhs_shape=[2, 3, 4, 5], rhs_shape=[2, 3, 4, 5]),
+    ]
+    
+    input_types = [
+        (np.float32, np.float32),
+        (np.int32, np.float32),
+        (np.float32, np.float16),
+        (np.int32, np.int64),
+    ]
+
+    @pytest.mark.nightly
+    @pytest.mark.precommit_jax_fe
+    @pytest.mark.parametrize("params", test_data)
+    @pytest.mark.parametrize("input_type", input_types)
+    def test_add_with_literal_invar(self, ie_device, precision, ir_version, params, input_type):
+        self._test(*self.create_model(**params, lhs_type=input_type[0], rhs_type=input_type[1]), ie_device, precision,
+                   ir_version)
diff --git a/tests/layer_tests/jax_tests/test_div.py b/tests/layer_tests/jax_tests/test_div.py
new file mode 100644
index 00000000000000..5a565f6d258341
--- /dev/null
+++ b/tests/layer_tests/jax_tests/test_div.py
@@ -0,0 +1,132 @@
+# Copyright (C) 2018-2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np
+import pytest
+
+from jax_layer_test_class import JaxLayerTest
+import jax.numpy as jnp
+
+def get_rand_jnp_array(shape, dtype):
+    if dtype in [np.float32, np.float16, np.float64]:
+        res = np.random.uniform(-1000, 1000, shape).astype(dtype)
+    else:
+        res = np.random.randint(-10, 10, shape).astype(dtype)
+    res[res == 0] = 1e-6
+    return jnp.array(res)
+
+class TestDiv(JaxLayerTest):
+    def _prepare_input(self):
+        lhs = get_rand_jnp_array(self.lhs_shape, self.lhs_type)
+        rhs = get_rand_jnp_array(self.rhs_shape, self.rhs_type)
+        return (lhs, rhs)
+
+    def create_model(self, lhs_shape, rhs_shape, lhs_type, rhs_type):
+        self.lhs_shape = lhs_shape
+        self.rhs_shape = rhs_shape
+        self.lhs_type = lhs_type
+        self.rhs_type = rhs_type
+
+        def jax_div(lhs, rhs):
+            return lhs / rhs
+
+        return jax_div, None
+
+    test_data = [
+        dict(lhs_shape=[4], rhs_shape=[4]),
+        dict(lhs_shape=[2, 5], rhs_shape=[2, 5]),
+        dict(lhs_shape=[2, 3, 4, 5], rhs_shape=[2, 3, 4, 5]),
+    ]
+    
+    input_types = [
+        (np.float32, np.float32),
+        (np.int32, np.float32),
+        (np.float32, np.float16),
+        (np.int32, np.int64),
+    ]
+
+    @pytest.mark.nightly
+    @pytest.mark.precommit_jax_fe
+    @pytest.mark.parametrize("params", test_data)
+    @pytest.mark.parametrize("input_type", input_types)
+    def test_div(self, ie_device, precision, ir_version, params, input_type):
+        self._test(*self.create_model(**params, lhs_type=input_type[0], rhs_type=input_type[1]), ie_device, precision,
+                   ir_version)
+
+class TestDivWithConstant(JaxLayerTest):
+    def _prepare_input(self):
+        lhs = get_rand_jnp_array(self.lhs_shape, self.lhs_type)
+        rhs = get_rand_jnp_array(self.rhs_shape, self.rhs_type)
+        return (lhs, rhs)
+
+    def create_model(self, lhs_shape, rhs_shape, lhs_type, rhs_type):
+        self.lhs_shape = lhs_shape
+        self.rhs_shape = rhs_shape
+        self.lhs_type = lhs_type
+        self.rhs_type = rhs_type
+        self.const = jnp.ones(self.lhs_shape, dtype=self.lhs_type)
+
+        def jax_div_with_constant(lhs, rhs):
+            return lhs / rhs / self.const
+
+        return jax_div_with_constant, None
+
+    test_data = [
+        dict(lhs_shape=[4], rhs_shape=[4]),
+        dict(lhs_shape=[2, 5], rhs_shape=[2, 5]),
+        dict(lhs_shape=[2, 3, 4, 5], rhs_shape=[2, 3, 4, 5]),
+    ]
+    
+    input_types = [
+        (np.float32, np.float32),
+        (np.int32, np.float32),
+        (np.float32, np.float16),
+        (np.int32, np.int64),
+    ]
+
+    @pytest.mark.nightly
+    @pytest.mark.precommit_jax_fe
+    @pytest.mark.parametrize("params", test_data)
+    @pytest.mark.parametrize("input_type", input_types)
+    def test_div_with_constant(self, ie_device, precision, ir_version, params, input_type):
+        self._test(*self.create_model(**params, lhs_type=input_type[0], rhs_type=input_type[1]), ie_device, precision,
+                   ir_version)
+        
+class TestDivWithLiteralInvar(JaxLayerTest):
+    def _prepare_input(self):
+        lhs = get_rand_jnp_array(self.lhs_shape, self.lhs_type)
+        rhs = get_rand_jnp_array(self.rhs_shape, self.rhs_type)
+        return (lhs, rhs)
+
+    def create_model(self, lhs_shape, rhs_shape, lhs_type, rhs_type):
+        self.lhs_shape = lhs_shape
+        self.rhs_shape = rhs_shape
+        self.lhs_type = lhs_type
+        self.rhs_type = rhs_type
+
+        def jax_div_with_constant(lhs, rhs):
+            x = lhs / 5
+            return x / rhs
+
+        return jax_div_with_constant, None
+
+    test_data = [
+        dict(lhs_shape=[4], rhs_shape=[4]),
+        dict(lhs_shape=[2, 5], rhs_shape=[2, 5]),
+        dict(lhs_shape=[2, 3, 4, 5], rhs_shape=[2, 3, 4, 5]),
+    ]
+    
+    input_types = [
+        (np.float32, np.float32),
+        (np.int32, np.float32),
+        (np.float32, np.float16),
+        (np.int32, np.int64),
+    ]
+
+    @pytest.mark.nightly
+    @pytest.mark.precommit_jax_fe
+    @pytest.mark.parametrize("params", test_data)
+    @pytest.mark.parametrize("input_type", input_types)
+    def test_div_with_literal_invar(self, ie_device, precision, ir_version, params, input_type):
+        self._test(*self.create_model(**params, lhs_type=input_type[0], rhs_type=input_type[1]), ie_device, precision,
+                   ir_version)
diff --git a/tests/layer_tests/jax_tests/test_mul.py b/tests/layer_tests/jax_tests/test_mul.py
new file mode 100644
index 00000000000000..f507e655052231
--- /dev/null
+++ b/tests/layer_tests/jax_tests/test_mul.py
@@ -0,0 +1,130 @@
+# Copyright (C) 2018-2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np
+import pytest
+
+from jax_layer_test_class import JaxLayerTest
+import jax.numpy as jnp
+
+def get_rand_jnp_array(shape, dtype):
+    if dtype in [np.float32, np.float16, np.float64]:
+        return jnp.array(np.random.uniform(-1000, 1000, shape).astype(dtype))
+    else:
+        return jnp.array(np.random.randint(-10, 10, shape).astype(dtype))
+
+class TestMul(JaxLayerTest):
+    def _prepare_input(self):
+        lhs = get_rand_jnp_array(self.lhs_shape, self.lhs_type)
+        rhs = get_rand_jnp_array(self.rhs_shape, self.rhs_type)
+        return (lhs, rhs)
+
+    def create_model(self, lhs_shape, rhs_shape, lhs_type, rhs_type):
+        self.lhs_shape = lhs_shape
+        self.rhs_shape = rhs_shape
+        self.lhs_type = lhs_type
+        self.rhs_type = rhs_type
+
+        def jax_mul(lhs, rhs):
+            return lhs * rhs
+
+        return jax_mul, None
+
+    test_data = [
+        dict(lhs_shape=[4], rhs_shape=[4]),
+        dict(lhs_shape=[2, 5], rhs_shape=[2, 5]),
+        dict(lhs_shape=[2, 3, 4, 5], rhs_shape=[2, 3, 4, 5]),
+    ]
+    
+    input_types = [
+        (np.float32, np.float32),
+        (np.int32, np.float32),
+        (np.float32, np.float16),
+        (np.int32, np.int64),
+    ]
+
+    @pytest.mark.nightly
+    @pytest.mark.precommit_jax_fe
+    @pytest.mark.parametrize("params", test_data)
+    @pytest.mark.parametrize("input_type", input_types)
+    def test_mul(self, ie_device, precision, ir_version, params, input_type):
+        self._test(*self.create_model(**params, lhs_type=input_type[0], rhs_type=input_type[1]), ie_device, precision,
+                   ir_version)
+
+class TestMulWithConstant(JaxLayerTest):
+    def _prepare_input(self):
+        lhs = get_rand_jnp_array(self.lhs_shape, self.lhs_type)
+        rhs = get_rand_jnp_array(self.rhs_shape, self.rhs_type)
+        return (lhs, rhs)
+
+    def create_model(self, lhs_shape, rhs_shape, lhs_type, rhs_type):
+        self.lhs_shape = lhs_shape
+        self.rhs_shape = rhs_shape
+        self.lhs_type = lhs_type
+        self.rhs_type = rhs_type
+        self.const = jnp.ones(self.lhs_shape, dtype=self.lhs_type)
+
+        def jax_mul_with_constant(lhs, rhs):
+            return lhs * rhs * self.const
+
+        return jax_mul_with_constant, None
+
+    test_data = [
+        dict(lhs_shape=[4], rhs_shape=[4]),
+        dict(lhs_shape=[2, 5], rhs_shape=[2, 5]),
+        dict(lhs_shape=[2, 3, 4, 5], rhs_shape=[2, 3, 4, 5]),
+    ]
+    
+    input_types = [
+        (np.float32, np.float32),
+        (np.int32, np.float32),
+        (np.float32, np.float16),
+        (np.int32, np.int64),
+    ]
+
+    @pytest.mark.nightly
+    @pytest.mark.precommit_jax_fe
+    @pytest.mark.parametrize("params", test_data)
+    @pytest.mark.parametrize("input_type", input_types)
+    def test_mul_with_constant(self, ie_device, precision, ir_version, params, input_type):
+        self._test(*self.create_model(**params, lhs_type=input_type[0], rhs_type=input_type[1]), ie_device, precision,
+                   ir_version)
+        
+class TestMulWithLiteralInvar(JaxLayerTest):
+    def _prepare_input(self):
+        lhs = get_rand_jnp_array(self.lhs_shape, self.lhs_type)
+        rhs = get_rand_jnp_array(self.rhs_shape, self.rhs_type)
+        return (lhs, rhs)
+
+    def create_model(self, lhs_shape, rhs_shape, lhs_type, rhs_type):
+        self.lhs_shape = lhs_shape
+        self.rhs_shape = rhs_shape
+        self.lhs_type = lhs_type
+        self.rhs_type = rhs_type
+
+        def jax_mul_with_constant(lhs, rhs):
+            x = lhs * 5
+            return x * rhs
+
+        return jax_mul_with_constant, None
+
+    test_data = [
+        dict(lhs_shape=[4], rhs_shape=[4]),
+        dict(lhs_shape=[2, 5], rhs_shape=[2, 5]),
+        dict(lhs_shape=[2, 3, 4, 5], rhs_shape=[2, 3, 4, 5]),
+    ]
+    
+    input_types = [
+        (np.float32, np.float32),
+        (np.int32, np.float32),
+        (np.float32, np.float16),
+        (np.int32, np.int64),
+    ]
+
+    @pytest.mark.nightly
+    @pytest.mark.precommit_jax_fe
+    @pytest.mark.parametrize("params", test_data)
+    @pytest.mark.parametrize("input_type", input_types)
+    def test_mul_with_literal_invar(self, ie_device, precision, ir_version, params, input_type):
+        self._test(*self.create_model(**params, lhs_type=input_type[0], rhs_type=input_type[1]), ie_device, precision,
+                   ir_version)
diff --git a/tests/layer_tests/jax_tests/test_sub.py b/tests/layer_tests/jax_tests/test_sub.py
new file mode 100644
index 00000000000000..6df07cc668925a
--- /dev/null
+++ b/tests/layer_tests/jax_tests/test_sub.py
@@ -0,0 +1,130 @@
+# Copyright (C) 2018-2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np
+import pytest
+
+from jax_layer_test_class import JaxLayerTest
+import jax.numpy as jnp
+
+def get_rand_jnp_array(shape, dtype):
+    if dtype in [np.float32, np.float16, np.float64]:
+        return jnp.array(np.random.uniform(-1000, 1000, shape).astype(dtype))
+    else:
+        return jnp.array(np.random.randint(-10, 10, shape).astype(dtype))
+
+class TestSub(JaxLayerTest):
+    def _prepare_input(self):
+        lhs = get_rand_jnp_array(self.lhs_shape, self.lhs_type)
+        rhs = get_rand_jnp_array(self.rhs_shape, self.rhs_type)
+        return (lhs, rhs)
+
+    def create_model(self, lhs_shape, rhs_shape, lhs_type, rhs_type):
+        self.lhs_shape = lhs_shape
+        self.rhs_shape = rhs_shape
+        self.lhs_type = lhs_type
+        self.rhs_type = rhs_type
+
+        def jax_sub(lhs, rhs):
+            return lhs - rhs
+
+        return jax_sub, None
+
+    test_data = [
+        dict(lhs_shape=[4], rhs_shape=[4]),
+        dict(lhs_shape=[2, 5], rhs_shape=[2, 5]),
+        dict(lhs_shape=[2, 3, 4, 5], rhs_shape=[2, 3, 4, 5]),
+    ]
+    
+    input_types = [
+        (np.float32, np.float32),
+        (np.int32, np.float32),
+        (np.float32, np.float16),
+        (np.int32, np.int64),
+    ]
+
+    @pytest.mark.nightly
+    @pytest.mark.precommit_jax_fe
+    @pytest.mark.parametrize("params", test_data)
+    @pytest.mark.parametrize("input_type", input_types)
+    def test_sub(self, ie_device, precision, ir_version, params, input_type):
+        self._test(*self.create_model(**params, lhs_type=input_type[0], rhs_type=input_type[1]), ie_device, precision,
+                   ir_version)
+
+class TestSubWithConstant(JaxLayerTest):
+    def _prepare_input(self):
+        lhs = get_rand_jnp_array(self.lhs_shape, self.lhs_type)
+        rhs = get_rand_jnp_array(self.rhs_shape, self.rhs_type)
+        return (lhs, rhs)
+
+    def create_model(self, lhs_shape, rhs_shape, lhs_type, rhs_type):
+        self.lhs_shape = lhs_shape
+        self.rhs_shape = rhs_shape
+        self.lhs_type = lhs_type
+        self.rhs_type = rhs_type
+        self.const = jnp.ones(self.lhs_shape, dtype=self.lhs_type)
+
+        def jax_sub_with_constant(lhs, rhs):
+            return lhs - rhs - self.const
+
+        return jax_sub_with_constant, None
+
+    test_data = [
+        dict(lhs_shape=[4], rhs_shape=[4]),
+        dict(lhs_shape=[2, 5], rhs_shape=[2, 5]),
+        dict(lhs_shape=[2, 3, 4, 5], rhs_shape=[2, 3, 4, 5]),
+    ]
+    
+    input_types = [
+        (np.float32, np.float32),
+        (np.int32, np.float32),
+        (np.float32, np.float16),
+        (np.int32, np.int64),
+    ]
+
+    @pytest.mark.nightly
+    @pytest.mark.precommit_jax_fe
+    @pytest.mark.parametrize("params", test_data)
+    @pytest.mark.parametrize("input_type", input_types)
+    def test_sub_with_constant(self, ie_device, precision, ir_version, params, input_type):
+        self._test(*self.create_model(**params, lhs_type=input_type[0], rhs_type=input_type[1]), ie_device, precision,
+                   ir_version)
+        
+class TestSubWithLiteralInvar(JaxLayerTest):
+    def _prepare_input(self):
+        lhs = get_rand_jnp_array(self.lhs_shape, self.lhs_type)
+        rhs = get_rand_jnp_array(self.rhs_shape, self.rhs_type)
+        return (lhs, rhs)
+
+    def create_model(self, lhs_shape, rhs_shape, lhs_type, rhs_type):
+        self.lhs_shape = lhs_shape
+        self.rhs_shape = rhs_shape
+        self.lhs_type = lhs_type
+        self.rhs_type = rhs_type
+
+        def jax_sub_with_constant(lhs, rhs):
+            x = lhs - 5
+            return x - rhs
+
+        return jax_sub_with_constant, None
+
+    test_data = [
+        dict(lhs_shape=[4], rhs_shape=[4]),
+        dict(lhs_shape=[2, 5], rhs_shape=[2, 5]),
+        dict(lhs_shape=[2, 3, 4, 5], rhs_shape=[2, 3, 4, 5]),
+    ]
+    
+    input_types = [
+        (np.float32, np.float32),
+        (np.int32, np.float32),
+        (np.float32, np.float16),
+        (np.int32, np.int64),
+    ]
+
+    @pytest.mark.nightly
+    @pytest.mark.precommit_jax_fe
+    @pytest.mark.parametrize("params", test_data)
+    @pytest.mark.parametrize("input_type", input_types)
+    def test_sub_with_literal_invar(self, ie_device, precision, ir_version, params, input_type):
+        self._test(*self.create_model(**params, lhs_type=input_type[0], rhs_type=input_type[1]), ie_device, precision,
+                   ir_version)
diff --git a/tests/layer_tests/mo_python_api_tests/test_mo_convert_pytorch.py b/tests/layer_tests/mo_python_api_tests/test_mo_convert_pytorch.py
index 1739a64ae577fa..3085a24c22c3ad 100644
--- a/tests/layer_tests/mo_python_api_tests/test_mo_convert_pytorch.py
+++ b/tests/layer_tests/mo_python_api_tests/test_mo_convert_pytorch.py
@@ -1123,8 +1123,9 @@ def test_failed_extension(self):
         from openvino.tools.mo import convert_model
 
         with self.assertRaisesRegex(Exception, ".*PyTorch Frontend doesn't support provided model type.*"):
-            with tempfile.NamedTemporaryFile() as tmpfile:
+            with tempfile.NamedTemporaryFile(delete=False) as tmpfile:
                 convert_model(tmpfile.name, framework="pytorch")
+            os.remove(tmpfile.name)
 
 
 def create_pytorch_layer_norm(tmp_dir):
diff --git a/tests/layer_tests/ovc_python_api_tests/test_onnx.py b/tests/layer_tests/ovc_python_api_tests/test_onnx.py
index f9701378ab7c31..3ad34b1e591153 100644
--- a/tests/layer_tests/ovc_python_api_tests/test_onnx.py
+++ b/tests/layer_tests/ovc_python_api_tests/test_onnx.py
@@ -2,11 +2,14 @@
 # SPDX-License-Identifier: Apache-2.0
 
 import io
+import tempfile
+import unittest
 
 import numpy as np
 import openvino.runtime as ov
 import pytest
 from openvino.runtime import Model
+from openvino.test_utils import compare_functions
 
 from common.mo_convert_test_class import CommonMOConvertTest
 
@@ -47,6 +50,7 @@ def make_graph_proto_model():
 
     return onnx_net
 
+
 def create_ref_model(shape):
     param1 = ov.opset8.parameter(shape, dtype=np.float32)
     slope_const = ov.opset8.constant([0.1], dtype=np.float16)
@@ -56,6 +60,16 @@ def create_ref_model(shape):
     parameter_list = [param1]
     return Model([relu], parameter_list, "test")
 
+
+def create_ref_model_fp32(shape):
+    param1 = ov.opset8.parameter(shape, dtype=np.float32)
+    slope_const = ov.opset8.constant([0.1], dtype=np.float32)
+    prelu = ov.opset8.prelu(param1, slope=slope_const)
+    relu = ov.opset8.elu(prelu, alpha=np.float32(0.1))
+    parameter_list = [param1]
+    return Model([relu], parameter_list, "test")
+
+
 def create_bytes_io():
     import onnx
     onnx_model = make_graph_proto_model()
@@ -81,3 +95,33 @@ def test_mo_convert_onnx(self, create_model, ie_device, precision, ir_version,
             test_params.update(mo_params)
         self._test_by_ref_graph(temp_dir, test_params, graph_ref, compare_tensor_names=False)
 
+
+class TestUnicodePathsONNX(unittest.TestCase):
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    def test_unicode_paths(self):
+        import os
+        import onnx
+        test_directory = os.path.dirname(os.path.realpath(__file__))
+        with tempfile.TemporaryDirectory(dir=test_directory, prefix=r"晚安_путь_к_файлу") as temp_dir:
+            model = make_graph_proto_model()
+            model_path = temp_dir + os.sep +'test_model.onnx'
+            model_ref = create_ref_model_fp32([2, 3, 4])
+
+            try:
+                onnx.save(model, model_path)
+            except:
+                return
+
+            assert os.path.exists(model_path), "Could not create a directory with unicode path."
+
+            from openvino import convert_model
+            res_model = convert_model(model_path)
+            flag, msg = compare_functions(res_model, model_ref, False)
+            assert flag, msg
+
+            from openvino.frontend import FrontEndManager
+            fm = FrontEndManager()
+            fe = fm.load_by_framework("onnx")
+
+            assert fe.supported(model_path)
diff --git a/tests/layer_tests/ovc_python_api_tests/test_paddle.py b/tests/layer_tests/ovc_python_api_tests/test_paddle.py
index de468a264d8b39..dd0468990c899c 100644
--- a/tests/layer_tests/ovc_python_api_tests/test_paddle.py
+++ b/tests/layer_tests/ovc_python_api_tests/test_paddle.py
@@ -1,12 +1,17 @@
 # Copyright (C) 2018-2024 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
+import tempfile
+import unittest
+
 import numpy as np
+import openvino.runtime as ov
 import pytest
+from openvino.runtime import PartialShape, Model
+from openvino.test_utils import compare_functions
+
 from common.mo_convert_test_class import CommonMOConvertTest
 
-import openvino.runtime as ov
-from openvino.runtime import PartialShape, Model
 
 def make_pd_dynamic_graph_model():
     import paddle
@@ -22,7 +27,6 @@ def forward(self, input):
     return NeuralNetwork()
 
 def make_pd_static_graph_model(shape):
-    import paddle
     import paddle.nn
 
     paddle.enable_static()
@@ -133,3 +137,41 @@ def test_conversion_params(self, params, ie_device, precision, ir_version,
 
         test_params.update({'input_model': fw_model})
         self._test_by_ref_graph(temp_dir, test_params, ref_model, compare_tensor_names=False)
+
+
+class TestUnicodePathsPaddle(unittest.TestCase):
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    def test_unicode_paths(self):
+        import os
+        try:
+            import paddle
+        except ImportError:
+            return
+
+        paddle.enable_static()
+        test_directory = os.path.dirname(os.path.realpath(__file__))
+        with tempfile.TemporaryDirectory(dir=test_directory, prefix=r"晚安_путь_к_файлу") as temp_dir:
+            shape = [2, 3, 4]
+            pd_model = make_pd_hapi_graph_model(shape)
+            model_ref = make_ref_graph_model(shape)
+            model_path = temp_dir + os.sep + 'model'
+
+            try:
+                pd_model.save(model_path, False)
+            except:
+                return
+            model_path = model_path + ".pdmodel"
+
+            assert os.path.exists(model_path), "Could not create a directory with unicode path."
+
+            from openvino import convert_model
+            res_model = convert_model(model_path)
+            flag, msg = compare_functions(res_model, model_ref, False)
+            assert flag, msg
+
+            from openvino.frontend import FrontEndManager
+            fm = FrontEndManager()
+            fe = fm.load_by_framework("paddle")
+
+            assert fe.supported(model_path)
diff --git a/tests/layer_tests/ovc_python_api_tests/test_pytorch.py b/tests/layer_tests/ovc_python_api_tests/test_pytorch.py
index bbc4ddda62de9f..2e22f3d626db8b 100644
--- a/tests/layer_tests/ovc_python_api_tests/test_pytorch.py
+++ b/tests/layer_tests/ovc_python_api_tests/test_pytorch.py
@@ -81,6 +81,42 @@ def forward(self, x, y=None, z=None):
     return NeuralNetwork()
 
 
+def make_pt_model_with_tuple_input():
+    from torch import nn
+
+    class NeuralNetwork(nn.Module):
+        def __init__(self):
+            super(NeuralNetwork, self).__init__()
+            self.linear_relu_stack = nn.Sequential(
+                nn.ReLU(),
+                nn.Sigmoid(),
+            )
+
+        def forward(self, x: tuple, y):
+            logits = self.linear_relu_stack(x[0] + x[1] + x[2] + y)
+            return logits
+
+    return NeuralNetwork()
+
+
+def make_pt_model_with_dict_input():
+    from torch import nn
+
+    class NeuralNetwork(nn.Module):
+        def __init__(self):
+            super(NeuralNetwork, self).__init__()
+            self.linear_relu_stack = nn.Sequential(
+                nn.ReLU(),
+                nn.Sigmoid(),
+            )
+
+        def forward(self, x: dict):
+            logits = self.linear_relu_stack(x["x"] + x["y"] + x["z"])
+            return logits
+
+    return NeuralNetwork()
+
+
 def make_ref_pt_model_one_input(shape, dtype=np.float32):
     shape = PartialShape(shape)
     param1 = ov.opset8.parameter(shape, name="input_0", dtype=dtype)
@@ -94,7 +130,7 @@ def make_ref_pt_model_one_input(shape, dtype=np.float32):
     return model
 
 
-def make_ref_pt_model_two_inputs(shape, dtype=np.float32):
+def make_ref_pt_model_two_inputs(shape, dtype=np.float32, reorder=False):
     if len(shape) == 2:
         param1 = ov.opset8.parameter(PartialShape(
             shape[0]), name="input_0", dtype=dtype)
@@ -104,6 +140,8 @@ def make_ref_pt_model_two_inputs(shape, dtype=np.float32):
         shape = PartialShape(shape)
         param1 = ov.opset8.parameter(shape, name="input_0", dtype=dtype)
         param2 = ov.opset8.parameter(shape, name="input_1", dtype=dtype)
+    if reorder:
+        param1, param2 = param2, param1
     if dtype == Type.dynamic:
         cl = ov.opset8.convert_like(param2, param1)
         mul = ov.opset8.multiply(param1, cl)
@@ -142,6 +180,49 @@ def make_ref_pt_model_with_optional_inputs(shape, dtype=np.float32, z_exist=Fals
     return model
 
 
+def make_ref_pt_model_with_tuple_input(shape, dtype=np.float32):
+    param1 = ov.opset8.parameter(PartialShape(
+        shape[0]), name="input_0", dtype=dtype)
+    param2 = ov.opset8.parameter(PartialShape(
+        shape[1]), name="input_1", dtype=dtype)
+    param3 = ov.opset8.parameter(PartialShape(
+        shape[2]), name="input_2", dtype=dtype)
+    param4 = ov.opset8.parameter(PartialShape(
+        shape[3]), name="y", dtype=dtype)
+
+    op = ov.opset8.add(param1, param2)
+    op2 = ov.opset8.add(op, param3)
+    op3 = ov.opset8.add(op2, param4)
+    relu = ov.opset8.relu(op3)
+    if dtype != np.float32:
+        relu = ov.opset8.convert(relu, np.float32)
+    sigm = ov.opset8.sigmoid(relu)
+
+    parameter_list = [param1, param2, param3, param4]
+    model = Model([sigm], parameter_list, "test")
+    return model
+
+
+def make_ref_pt_model_with_dict_input(shape, dtype=np.float32):
+    param1 = ov.opset8.parameter(PartialShape(
+        shape[0]), name="x", dtype=dtype)
+    param2 = ov.opset8.parameter(PartialShape(
+        shape[1]), name="y", dtype=dtype)
+    param3 = ov.opset8.parameter(PartialShape(
+        shape[2]), name="z", dtype=dtype)
+
+    op = ov.opset8.add(param1, param2)
+    op2 = ov.opset8.add(op, param3)
+    relu = ov.opset8.relu(op2)
+    if dtype != np.float32:
+        relu = ov.opset8.convert(relu, np.float32)
+    sigm = ov.opset8.sigmoid(relu)
+
+    parameter_list = [param1, param2, param3]
+    model = Model([sigm], parameter_list, "test")
+    return model
+
+
 def create_pytorch_nn_module_case1(tmp_dir):
     pt_model = make_pt_model_two_inputs()
     ref_model = make_ref_pt_model_two_inputs([-1, -1, -1, -1])
@@ -499,6 +580,14 @@ def create_pytorch_nn_module_shapes_list_static_via_input(tmp_dir):
     return pt_model, ref_model, {'input': [([1, 3, 20, 20], np.float32), ([1, 3, 20, 20], np.float32)]}
 
 
+def create_pytorch_nn_module_reorder_inputs(tmp_dir):
+    pt_model = make_pt_model_two_inputs()
+    ref_model = make_ref_pt_model_two_inputs([[-1, -1, -1, -1], [-1]])
+
+    return pt_model, ref_model, {"input": ["x", "y"],
+                                 "example_input": [torch.zeros((1, 3, 10, 10)), torch.ones((10))],}
+
+
 def create_pytorch_nn_module_shapes_list_dynamic(tmp_dir):
     pt_model = make_pt_model_two_inputs()
     inp_shapes = [[Dimension(-1), 3, 20, Dimension(20, -1)],
@@ -684,7 +773,8 @@ def forward(self, x):
     net = ConvModel()
     shape = PartialShape([-1, 3, -1, -1])
     param1 = ov.opset10.parameter(shape, dtype=np.float32)
-    weights = ov.opset10.constant(net.weights.numpy(force=True), dtype=np.float16)
+    weights = ov.opset10.constant(
+        net.weights.numpy(force=True), dtype=np.float16)
     decompress_weights = ov.opset10.convert(weights, np.float32)
     conv = ov.opset10.convolution(param1, decompress_weights, strides=[1, 1],
                                   pads_begin=[0, 0], pads_end=[0, 0],
@@ -720,6 +810,40 @@ def create_pytorch_module_with_optional_inputs_case3(tmp_dir):
     return net, ref_model, {"example_input": example_input, "input": [[3, 3, 3, 3], [3, 3, 3, 3]]}
 
 
+def create_pytorch_module_with_tuple_case1(tmp_dir):
+    net = make_pt_model_with_tuple_input()
+    example_input = ((torch.zeros((1, 3, 10, 10)), torch.ones((1, 3, 10, 10)), torch.ones((1, 3, 10, 10))), torch.ones((1, 3, 10, 10)))
+    ref_model = make_ref_pt_model_with_tuple_input(([-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, -1, -1]))
+    return net, ref_model, {"example_input": example_input}
+
+def create_pytorch_module_with_tuple_case2(tmp_dir):
+    net = make_pt_model_with_tuple_input()
+    example_input = {"x": (torch.zeros((1, 3, 10, 10)), torch.ones((1, 3, 10, 10)), torch.ones((1, 3, 10, 10))), "y": torch.ones((1, 3, 10, 10))}
+    ref_model = make_ref_pt_model_with_tuple_input(([-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, -1, -1]))
+    return net, ref_model, {"example_input": example_input}
+
+
+def create_pytorch_module_with_tuple_case3(tmp_dir):
+    net = make_pt_model_with_tuple_input()
+    example_input = ((torch.zeros((1, 3, 10, 10)), torch.ones((1, 3, 10, 10)), torch.ones((1, 3, 10, 10))), torch.ones((1, 3, 10, 10)))
+    ref_model = make_ref_pt_model_with_tuple_input(([3, 3, 3, 3], [3, 3, 3, 3], [3, 3, 3, 3], [3, 3, 3, 3]))
+    return net, ref_model, {"example_input": example_input, "input": [[3, 3, 3, 3], [3, 3, 3, 3], [3, 3, 3, 3], [3, 3, 3, 3]]}
+
+
+def create_pytorch_module_with_dict_case1(tmp_dir):
+    net = make_pt_model_with_dict_input()
+    example_input = ({"x": torch.zeros((1, 3, 10, 10)), "y": torch.ones((1, 3, 10, 10)), "z": torch.ones((1, 3, 10, 10))},)
+    ref_model = make_ref_pt_model_with_dict_input(([-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, -1, -1]))
+    return net, ref_model, {"example_input": example_input}
+
+
+def create_pytorch_module_with_dict_case2(tmp_dir):
+    net = make_pt_model_with_dict_input()
+    example_input = ({"x": torch.zeros((1, 3, 10, 10)), "y": torch.ones((1, 3, 10, 10)), "z": torch.ones((1, 3, 10, 10))},)
+    ref_model = make_ref_pt_model_with_dict_input(([3, 3, 3, 3], [3, 3, 3, 3], [3, 3, 3, 3]))
+    return net, ref_model, {"example_input": example_input, "input": {"x": [3, 3, 3, 3], "y": [3, 3, 3, 3], "z": [3, 3, 3, 3]}}
+
+
 def create_pytorch_module_with_compressed_int8_constant_compress_to_fp16_default(tmp_dir):
     import torch
     import torch.nn.functional as F
@@ -798,8 +922,10 @@ def forward(self, z: Tuple[torch.Tensor, torch.Tensor]):
             return torch.cat([z1, zeros1], 1), torch.cat([z2, zeros2], 2)
 
     net = PTModel()
-    constant_zeros1 = ov.opset10.constant(np.zeros((1, 1), dtype=np.float32), dtype=np.float32)
-    constant_zeros2 = ov.opset10.constant(np.zeros((1, 5, 1), dtype=np.float32), dtype=np.float32)
+    constant_zeros1 = ov.opset10.constant(
+        np.zeros((1, 1), dtype=np.float32), dtype=np.float32)
+    constant_zeros2 = ov.opset10.constant(
+        np.zeros((1, 5, 1), dtype=np.float32), dtype=np.float32)
     shape1 = PartialShape([1, -1])
     shape2 = PartialShape([1, 5, -1])
     param1 = ov.opset10.parameter(shape1, dtype=np.float32)
@@ -821,8 +947,10 @@ def forward(self, z: Tuple[torch.Tensor, torch.Tensor]):
             return torch.cat([z1, zeros1], 1), torch.cat([z2, zeros2], 2)
 
     net = PTModel()
-    constant_zeros1 = ov.opset10.constant(np.zeros((1, 1), dtype=np.float32), dtype=np.float16)
-    constant_zeros2 = ov.opset10.constant(np.zeros((1, 5, 1), dtype=np.float32), dtype=np.float16)
+    constant_zeros1 = ov.opset10.constant(
+        np.zeros((1, 1), dtype=np.float32), dtype=np.float16)
+    constant_zeros2 = ov.opset10.constant(
+        np.zeros((1, 5, 1), dtype=np.float32), dtype=np.float16)
     const1_decompress = ov.opset10.convert(constant_zeros1, np.float32)
     const2_decompress = ov.opset10.convert(constant_zeros2, np.float32)
     shape1 = PartialShape([1, -1])
@@ -845,8 +973,10 @@ def forward(self, x: torch.Tensor, z: Tuple[torch.Tensor, torch.Tensor]):
             return torch.cat([z1, zeros1], 1) + x, torch.cat([z2, zeros2], 2)
 
     net = PTModel()
-    constant_zeros1 = ov.opset10.constant(np.zeros((1, 1), dtype=np.float32), dtype=np.float32)
-    constant_zeros2 = ov.opset10.constant(np.zeros((1, 5, 1), dtype=np.float32), dtype=np.float32)
+    constant_zeros1 = ov.opset10.constant(
+        np.zeros((1, 1), dtype=np.float32), dtype=np.float32)
+    constant_zeros2 = ov.opset10.constant(
+        np.zeros((1, 5, 1), dtype=np.float32), dtype=np.float32)
     shape1 = PartialShape([1, -1])
     shape2 = PartialShape([1, 5, -1])
     param0 = ov.opset10.parameter(PartialShape([-1, -1]), dtype=np.float32)
@@ -873,8 +1003,10 @@ def forward(self, z: Tuple[torch.Tensor, torch.Tensor], x: torch.Tensor):
     net = PTModel()
     shape1 = PartialShape([1, -1])
     shape2 = PartialShape([1, 5, -1])
-    constant_zeros1 = ov.opset10.constant(np.zeros((1, 1), dtype=np.float32), dtype=np.float32)
-    constant_zeros2 = ov.opset10.constant(np.zeros((1, 5, 1), dtype=np.float32), dtype=np.float32)
+    constant_zeros1 = ov.opset10.constant(
+        np.zeros((1, 1), dtype=np.float32), dtype=np.float32)
+    constant_zeros2 = ov.opset10.constant(
+        np.zeros((1, 5, 1), dtype=np.float32), dtype=np.float32)
     param1 = ov.opset10.parameter(shape1, dtype=np.float32)
     param2 = ov.opset10.parameter(shape2, dtype=np.float32)
     param3 = ov.opset10.parameter(PartialShape([-1, -1]), dtype=np.float32)
@@ -897,8 +1029,10 @@ def forward(self, x: torch.Tensor, z: Tuple[torch.Tensor, torch.Tensor], y: torc
             return torch.cat([z1, zeros1], 1) + x, torch.cat([z2, zeros2], 2) * y
 
     net = PTModel()
-    constant_zeros1 = ov.opset10.constant(np.zeros((1, 1), dtype=np.float32), dtype=np.float32)
-    constant_zeros2 = ov.opset10.constant(np.zeros((1, 5, 1), dtype=np.float32), dtype=np.float32)
+    constant_zeros1 = ov.opset10.constant(
+        np.zeros((1, 1), dtype=np.float32), dtype=np.float32)
+    constant_zeros2 = ov.opset10.constant(
+        np.zeros((1, 5, 1), dtype=np.float32), dtype=np.float32)
     shape1 = PartialShape([1, -1])
     shape2 = PartialShape([1, 5, -1])
     param1 = ov.opset10.parameter(shape1, dtype=np.float32)
@@ -926,8 +1060,10 @@ def forward(self, x: torch.Tensor, z: Tuple[torch.Tensor, torch.Tensor], y: torc
             return torch.cat([z1, zeros1], 1) + x, torch.cat([z2, zeros2], 2) * y
 
     net = PTModel()
-    constant_zeros1 = ov.opset10.constant(np.zeros((1, 1), dtype=np.float32), dtype=np.float32)
-    constant_zeros2 = ov.opset10.constant(np.zeros((1, 5, 1), dtype=np.float32), dtype=np.float32)
+    constant_zeros1 = ov.opset10.constant(
+        np.zeros((1, 1), dtype=np.float32), dtype=np.float32)
+    constant_zeros2 = ov.opset10.constant(
+        np.zeros((1, 5, 1), dtype=np.float32), dtype=np.float32)
     shape1 = PartialShape([1, -1])
     shape2 = PartialShape([1, 5, -1])
     param0 = ov.opset10.parameter(PartialShape([-1, -1]), dtype=np.float32)
@@ -956,8 +1092,10 @@ def forward(self, x: torch.Tensor, y: torch.Tensor = None, z: Tuple[torch.Tensor
             return torch.cat([z1, zeros1], 1) + x, torch.cat([z2, zeros2], 2)
 
     net = PTModel()
-    constant_zeros1 = ov.opset10.constant(np.zeros((1, 1), dtype=np.float32), dtype=np.float32)
-    constant_zeros2 = ov.opset10.constant(np.zeros((1, 5, 1), dtype=np.float32), dtype=np.float32)
+    constant_zeros1 = ov.opset10.constant(
+        np.zeros((1, 1), dtype=np.float32), dtype=np.float32)
+    constant_zeros2 = ov.opset10.constant(
+        np.zeros((1, 5, 1), dtype=np.float32), dtype=np.float32)
     shape1 = PartialShape([1, -1])
     shape2 = PartialShape([1, 5, -1])
     param0 = ov.opset10.parameter(PartialShape([-1, -1]), dtype=np.float32)
@@ -982,7 +1120,8 @@ def forward(self, x: List[torch.Tensor]):
     net = PTModel()
     constant_one = ov.opset10.constant(np.ones((1, 1)), dtype=np.float32)
     const_zero = ov.opset10.constant(0, dtype=np.int64)
-    constant_zeros1 = ov.opset10.constant(np.zeros((1, 1), dtype=np.float32), dtype=np.float32)
+    constant_zeros1 = ov.opset10.constant(
+        np.zeros((1, 1), dtype=np.float32), dtype=np.float32)
 
     param = ov.opset10.parameter(PartialShape([-1, -1, -1]), dtype=np.float32)
     gather = ov.opset10.gather(param, const_zero, const_zero)
@@ -1004,7 +1143,8 @@ def forward(self, x):
     net = PTModel()
     constant_one = ov.opset10.constant(np.ones((1,)), dtype=np.float32)
     const_zero = ov.opset10.constant(0, dtype=np.int64)
-    constant_zeros1 = ov.opset10.constant(np.zeros((1,), dtype=np.float32), dtype=np.float32)
+    constant_zeros1 = ov.opset10.constant(
+        np.zeros((1,), dtype=np.float32), dtype=np.float32)
 
     param = ov.opset10.parameter(PartialShape([-1, -1]), dtype=np.float32)
     gather = ov.opset10.gather(param, const_zero, const_zero)
@@ -1031,9 +1171,9 @@ def forward(self, a, b):
     return net, ref_model, {
         "example_input": (
             {
-            "1": torch.tensor([1, 2], dtype=torch.float32), 
-            "2": torch.tensor([3, 4], dtype=torch.float32)
-            }, 
+                "1": torch.tensor([1, 2], dtype=torch.float32),
+                "2": torch.tensor([3, 4], dtype=torch.float32)
+            },
             torch.tensor([5, 6], dtype=torch.float32)
         )}
 
@@ -1063,6 +1203,7 @@ class TestMoConvertPyTorch(CommonMOConvertTest):
         create_pytorch_nn_module_with_compressed_constants,
         create_pytorch_nn_module_shapes_list_static,
         create_pytorch_nn_module_shapes_list_static_via_input,
+        create_pytorch_nn_module_reorder_inputs,
         create_pytorch_nn_module_shapes_list_dynamic,
         create_pytorch_nn_module_shapes_list_dynamic_via_input,
         create_pytorch_nn_module_shapes_list_dynamic_single_input,
@@ -1079,6 +1220,11 @@ class TestMoConvertPyTorch(CommonMOConvertTest):
         create_pytorch_module_with_optional_inputs_case1,
         create_pytorch_module_with_optional_inputs_case2,
         create_pytorch_module_with_optional_inputs_case3,
+        create_pytorch_module_with_tuple_case1,
+        create_pytorch_module_with_tuple_case2,
+        create_pytorch_module_with_tuple_case3,
+        create_pytorch_module_with_dict_case1,
+        create_pytorch_module_with_dict_case2,
         create_pytorch_nn_module_with_scalar_input,
         create_pytorch_module_with_compressed_int8_constant,
         create_pytorch_module_with_compressed_int8_constant_compress_to_fp16_default,
@@ -1137,7 +1283,7 @@ def test_incorrect_inputs_1(self):
         from openvino.tools.ovc import convert_model
         pytorch_model, _, _ = create_pytorch_nn_module_case1('')
 
-        with self.assertRaisesRegex(Exception, ".*No node with name.*"):
+        with self.assertRaisesRegex(Exception, "Input for tensor name \'input1\[1, 10\]\' is not found."):
             convert_model(pytorch_model, input='input1[1, 10]')
 
     def test_incorrect_inputs_2(self):
@@ -1145,7 +1291,7 @@ def test_incorrect_inputs_2(self):
         pytorch_model, _, _ = create_pytorch_nn_module_case1('')
 
         # check that it accepts specified names as is, without parsing into 2 different inputs
-        with self.assertRaisesRegex(Exception, 'No node with name input1,input2'):
+        with self.assertRaisesRegex(Exception, 'Input for tensor name \'input1,input2\' is not found.'):
             convert_model(pytorch_model, input='input1,input2')
 
     def test_incorrect_inputs_3(self):
@@ -1153,7 +1299,7 @@ def test_incorrect_inputs_3(self):
         pytorch_model, _, _ = create_pytorch_nn_module_case1('')
 
         # check that it accepts specified names as is, without parsing into 2 different inputs
-        with self.assertRaisesRegex(Exception, 'No node with name input1\[1, 10\],input2\[2, 100\]'):
+        with self.assertRaisesRegex(Exception, 'Input for tensor name \'input1\[1, 10\],input2\[2, 100\]\' is not found.'):
             convert_model(pytorch_model, input='input1[1, 10],input2[2, 100]')
 
     def test_incorrect_inputs_4(self):
@@ -1161,8 +1307,9 @@ def test_incorrect_inputs_4(self):
         pytorch_model, _, _ = create_pytorch_nn_module_case1('')
 
         # check that it accepts specified names as is, without parsing into 2 different inputs
-        with self.assertRaisesRegex(Exception, 'No node with name input1\[1, 10\]'):
-            convert_model(pytorch_model, input=['input1[1, 10]', 'input2[2, 100]'])
+        with self.assertRaisesRegex(Exception, 'Input for tensor name \'input1\[1, 10\]\' is not found.'):
+            convert_model(pytorch_model, input=[
+                          'input1[1, 10]', 'input2[2, 100]'])
 
     def test_failed_extension(self):
         from openvino.tools.ovc import convert_model
@@ -1181,11 +1328,13 @@ def relu_bad(n):
 
     def test_failed_extension(self):
         import tempfile
+        import os
         from openvino.tools.ovc import convert_model
 
         with self.assertRaisesRegex(Exception, ".*Cannot recognize input model.*"):
-            with tempfile.NamedTemporaryFile() as tmpfile:
+            with tempfile.NamedTemporaryFile(delete=False) as tmpfile:
                 convert_model(tmpfile.name)
+            os.remove(tmpfile.name)
 
 
 def create_model_three_inputs():
@@ -1257,4 +1406,5 @@ def test_conversion_params(self, params, ie_device, precision, ir_version,
         ref_model = params['ref_model']
 
         test_params.update({'input_model': fw_model})
-        self._test_by_ref_graph(temp_dir, test_params, ref_model, compare_tensor_names=False)
+        self._test_by_ref_graph(temp_dir, test_params,
+                                ref_model, compare_tensor_names=False)
diff --git a/tests/layer_tests/ovc_python_api_tests/test_tf.py b/tests/layer_tests/ovc_python_api_tests/test_tf.py
index 03ad976843f19b..2968e40062da98 100644
--- a/tests/layer_tests/ovc_python_api_tests/test_tf.py
+++ b/tests/layer_tests/ovc_python_api_tests/test_tf.py
@@ -10,6 +10,7 @@
 import pytest
 import tensorflow as tf
 from openvino.runtime import PartialShape, Model, Dimension
+from openvino.test_utils import compare_functions
 
 from common import constants
 from common.layer_test_class import CommonLayerTest
@@ -1287,3 +1288,30 @@ def test_tf1_input_with_identity(self):
 
         assert ov_model.inputs[0].get_names() == {"x:0"}
         assert ov_model.inputs[1].get_names() == {"y:0"}
+
+
+class TestUnicodePathsTF(unittest.TestCase):
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    def test_unicode_paths(self):
+        test_directory = os.path.dirname(os.path.realpath(__file__))
+        with tempfile.TemporaryDirectory(dir=test_directory, prefix=r"晚安_путь_к_файлу") as temp_dir:
+            model, model_ref, _ = create_tf_graph_def(None)
+
+            try:
+                model_path = save_to_pb(model, temp_dir)
+            except:
+                return
+
+            assert os.path.exists(model_path), "Could not create a directory with unicode path."
+
+            from openvino import convert_model
+            res_model = convert_model(model_path)
+            flag, msg = compare_functions(res_model, model_ref, False)
+            assert flag, msg
+
+            from openvino.frontend import FrontEndManager
+            fm = FrontEndManager()
+            fe = fm.load_by_framework("tf")
+
+            assert fe.supported(model_path)
diff --git a/tests/layer_tests/py_frontend_tests/test_torch_frontend.py b/tests/layer_tests/py_frontend_tests/test_torch_frontend.py
index 18b3a4776f686a..155b772d560222 100644
--- a/tests/layer_tests/py_frontend_tests/test_torch_frontend.py
+++ b/tests/layer_tests/py_frontend_tests/test_torch_frontend.py
@@ -687,16 +687,20 @@ def test_patched_16bit_model_converts():
     from openvino.frontend.pytorch import patch_model
     from openvino import convert_model, compile_model
     import copy
+    from transformers.pytorch_utils import Conv1D
 
     class ModelWithLinear(torch.nn.Module):
         def __init__(self):
             super().__init__()
 
             self.branch1 = torch.nn.Sequential(
-                torch.nn.Linear(64, 32), torch.nn.ReLU()
+                torch.nn.Embedding(10, 64),
+                torch.nn.Linear(64, 32),
+                torch.nn.ReLU()
             )
             self.branch2 = torch.nn.Sequential(
-                torch.nn.Linear(128, 64), torch.nn.ReLU()
+                Conv1D(256, 128),
+                torch.nn.Linear(256, 64), torch.nn.ReLU()
             )
             self.buffer = torch.ones(32)
 
@@ -705,7 +709,7 @@ def forward(self, x1, x2):
             out2 = self.branch2(x2)
             return (out1 + self.buffer, out2)
 
-    example = (torch.randn(32, 64), torch.randn(32, 128))
+    example = (torch.randint(0, 10, [32, 64]), torch.randn(32, 128))
     model_ref = ModelWithLinear()
     with torch.no_grad():
         res_ref = model_ref(*example)
diff --git a/tests/layer_tests/pytest.ini b/tests/layer_tests/pytest.ini
index 677e117a86bfc7..750c54760ac0b6 100644
--- a/tests/layer_tests/pytest.ini
+++ b/tests/layer_tests/pytest.ini
@@ -5,4 +5,5 @@ markers =
     precommit_tf_fe
     precommit_fx_backend
     precommit_torch_export
+    precommit_jax_fe
     timeout
diff --git a/tests/layer_tests/pytorch_tests/test_cat.py b/tests/layer_tests/pytorch_tests/test_cat.py
index c02df869f5c1a3..ad07d63250eb51 100644
--- a/tests/layer_tests/pytorch_tests/test_cat.py
+++ b/tests/layer_tests/pytorch_tests/test_cat.py
@@ -3,6 +3,7 @@
 
 import pytest
 import torch
+import numpy as np
 
 from pytorch_layer_test_class import PytorchLayerTest
 
@@ -70,7 +71,7 @@ def _prepare_input(self, out=False, num_repeats=2):
         concat = [data for _ in range(num_repeats)]
         out = np.zeros_like(np.concatenate(concat, axis=1))
         return (data, out)
-        
+
 
     @pytest.mark.nightly
     @pytest.mark.precommit
@@ -104,3 +105,172 @@ def test_add_cat(self, out, ie_device, precision, ir_version):
         model = aten_add_cat() if not out else aten_add_cat_out()
         self._test(model, None, ["aten::cat", "aten::add", "prim::ListConstruct"],
                    ie_device, precision, ir_version, freeze_model=False,  kwargs_to_prepare_input={"out": out, "num_repeats": 4})
+
+
+class TestCatAlignTypes(PytorchLayerTest):
+    def _prepare_input(self, in_types):
+        in_vals = []
+        for i in range(len(in_types)):
+            dtype = in_types[i]
+            in_vals.append(np.random.randn(2, 1, 3).astype(dtype))
+        return in_vals
+
+    def create_model(self, in_count):
+        class aten_align_types_cat_two_args(torch.nn.Module):
+            def forward(self, x, y):
+                ins = [x, y]
+                return torch.cat(ins, 1)
+
+        class aten_align_types_cat_three_args(torch.nn.Module):
+            def forward(self, x, y, z):
+                ins = [x, y, z]
+                return torch.cat(ins, 1)
+
+        if in_count == 2:
+            return aten_align_types_cat_two_args()
+
+        if in_count == 3:
+            return aten_align_types_cat_three_args()
+
+    @pytest.mark.parametrize(("in_types"), [
+        # Two inputs
+        (np.float32, np.int32),
+        (np.int32, np.float32),
+        (np.float16, np.float32),
+        (np.int16, np.float16),
+        (np.int32, np.int64),
+        # # Three inputs
+        (np.float32, np.int32, np.int32),
+        (np.float32, np.int32, np.float32),
+        (np.int32, np.float32, np.int32),
+        (np.float32, np.int32, np.int16),
+        (np.int32, np.float32, np.int16),
+        (np.int16, np.int32, np.int16),
+        (np.float16, np.float32, np.float16),
+        (np.float32, np.float16, np.float32),
+        (np.float16, np.int32, np.int16),
+        (np.int16, np.float16, np.int16),
+    ])
+    @pytest.mark.parametrize(("trace_model"), [True, False])
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    def test_align_types_cat(self, ie_device, precision, ir_version, in_types, trace_model):
+        self._test(self.create_model(len(in_types)), None, ["aten::cat", "prim::ListConstruct"],
+                   ie_device, precision, ir_version, kwargs_to_prepare_input={"in_types": in_types}, trace_model=trace_model)
+
+
+class TestCatAlignTypesPT(PytorchLayerTest):
+    def _prepare_input(self, in_types):
+        in_vals = [np.random.randn(2, 1, 3).astype(in_types[0])]
+        return in_vals
+
+    def create_model_param_first(self, in_types):
+        class aten_align_types_cat_two_args(torch.nn.Module):
+            def __init__(self):
+                super(aten_align_types_cat_two_args, self).__init__()
+                self.y = torch.randn(2, 1, 3).to(in_types[1])
+
+            def forward(self, x):
+                ins = [x, self.y]
+                return torch.cat(ins, 1)
+
+        class aten_align_types_cat_three_args(torch.nn.Module):
+            def __init__(self):
+                super(aten_align_types_cat_three_args, self).__init__()
+                self.y = torch.randn(2, 1, 3).to(in_types[1])
+                self.z = torch.randn(2, 1, 3).to(in_types[2])
+
+            def forward(self, x):
+                ins = [x, self.y, self.z]
+                return torch.cat(ins, 1)
+
+        in_count = len(in_types)
+        if in_count == 2:
+            return aten_align_types_cat_two_args()
+
+        if in_count == 3:
+            return aten_align_types_cat_three_args()
+
+    def create_model_param_mid(self, in_types):
+        class aten_align_types_cat_three_args(torch.nn.Module):
+            def __init__(self):
+                super(aten_align_types_cat_three_args, self).__init__()
+                self.x = torch.randn(2, 1, 3).to(in_types[1])
+                self.z = torch.randn(2, 1, 3).to(in_types[2])
+
+            def forward(self, y):
+                ins = [self.x, y, self.z]
+                return torch.cat(ins, 1)
+        return aten_align_types_cat_three_args()
+
+    def create_model_param_last(self, in_types):
+        class aten_align_types_cat_three_args(torch.nn.Module):
+            def __init__(self):
+                super(aten_align_types_cat_three_args, self).__init__()
+                self.x = torch.randn(2, 1, 3).to(in_types[1])
+                self.y = torch.randn(2, 1, 3).to(in_types[2])
+
+            def forward(self, z):
+                ins = [self.x, self.y, z]
+                return torch.cat(ins, 1)
+        return aten_align_types_cat_three_args()
+
+    @pytest.mark.parametrize(("in_types"), [
+        # Two inputs (param, const)
+        (np.float16, torch.bfloat16),
+        (np.float32, torch.bfloat16),
+        (np.float32, torch.int32),
+        (np.int32, torch.float32),
+        (np.float16, torch.float32),
+        (np.float16, torch.float32),
+        (np.int16, torch.float16),
+        (np.int32, torch.int64),
+        # Three inputs (param, const, const)
+        (np.float32, torch.int32, torch.int32),
+        (np.float32, torch.int32, torch.float32),
+        (np.int32, torch.float32, torch.int32),
+        (np.float32, torch.int32, torch.int16),
+        (np.int32, torch.float32, torch.int16),
+        (np.int16, torch.int32, torch.int16),
+        (np.float16, torch.float32, torch.float16),
+        (np.float32, torch.float16, torch.float32),
+        (np.float16, torch.int32, torch.int16),
+        (np.int16, torch.float16, torch.int16),
+        (np.float16, torch.bfloat16, torch.float32),
+    ])
+    @pytest.mark.parametrize(("trace_model"), [True, False])
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    def test_align_types_cat(self, ie_device, precision, ir_version, in_types, trace_model):
+        self._test(self.create_model_param_first(in_types), None, ["aten::cat", "prim::ListConstruct"],
+                   ie_device, precision, ir_version, kwargs_to_prepare_input={"in_types": in_types}, trace_model=trace_model)
+
+    @pytest.mark.parametrize(("in_types"), [
+        # Three inputs (param, const, const)
+        (np.float16, torch.int32, torch.int16),
+        (np.int16, torch.float16, torch.int16),
+        (np.float16, torch.bfloat16, torch.float32),
+        (np.float16, torch.float32, torch.bfloat16),
+        (np.float32, torch.bfloat16, torch.bfloat16),
+    ])
+    @pytest.mark.parametrize(("trace_model"), [True, False])
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    def test_align_types_cat_param_mid(self, ie_device, precision, ir_version, in_types, trace_model):
+        self._test(self.create_model_param_mid(in_types), None, ["aten::cat", "prim::ListConstruct"],
+                   ie_device, precision, ir_version, kwargs_to_prepare_input={"in_types": in_types}, trace_model=trace_model)
+
+    @pytest.mark.parametrize(("in_types"), [
+        # Three inputs (param, const, const)
+        (np.float16, torch.int32, torch.int16),
+        (np.int16, torch.float16, torch.int16),
+        (np.float16, torch.bfloat16, torch.float32),
+        (np.float16, torch.float32, torch.bfloat16),
+        (np.float32, torch.bfloat16, torch.bfloat16),
+    ])
+    @pytest.mark.parametrize(("trace_model"), [True, False])
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    def test_align_types_cat_param_last(self, ie_device, precision, ir_version, in_types, trace_model):
+        self._test(self.create_model_param_last(in_types), None, ["aten::cat", "prim::ListConstruct"],
+                   ie_device, precision, ir_version, kwargs_to_prepare_input={"in_types": in_types}, trace_model=trace_model)
diff --git a/tests/layer_tests/pytorch_tests/test_embedding_bag.py b/tests/layer_tests/pytorch_tests/test_embedding_bag.py
index 237fa74cd3bec0..63e743451e70ac 100644
--- a/tests/layer_tests/pytorch_tests/test_embedding_bag.py
+++ b/tests/layer_tests/pytorch_tests/test_embedding_bag.py
@@ -14,7 +14,7 @@ def _prepare_input(self, indicies_dtype, per_sample_weights=False):
 
         indices = np.array([2, 2, 2, 2, 4, 3, 2, 9]).astype(indicies_dtype)
         weights = np.random.randn(10, 10).astype(np.float32)
-        offsets = np.array([0, 4]).astype(indicies_dtype)
+        offsets = np.array([0, 4, 4]).astype(indicies_dtype)
         if per_sample_weights:
             per_sample_weights = np.random.randn(*indices.shape).astype(np.float32)
             return (indices, weights, offsets, per_sample_weights)
diff --git a/tests/layer_tests/pytorch_tests/test_pooling.py b/tests/layer_tests/pytorch_tests/test_pooling.py
index 24fc01fdcffaed..0cf2446abd1125 100644
--- a/tests/layer_tests/pytorch_tests/test_pooling.py
+++ b/tests/layer_tests/pytorch_tests/test_pooling.py
@@ -224,8 +224,6 @@ def test_max_pool3d(self, params, ceil_mode, dilation, ie_device, precision, ir_
     @pytest.mark.xfail(condition=platform.system() == 'Darwin' and platform.machine() == 'arm64',
                        reason='Ticket - 122715')
     def test_max_pool1d_indices(self, params, ceil_mode, dilation, ie_device, precision, ir_version):
-        if ceil_mode and (np.array(params["padding"]).any() != 0):
-            pytest.skip("ticket 122418")
         self._test(*self.create_model("max_pool1d_with_indices", **params, ceil_mode=ceil_mode, dilation=dilation),
                    ie_device, precision, ir_version, kwargs_to_prepare_input={'ndim': 3}, dynamic_shapes=False)
 
@@ -238,8 +236,6 @@ def test_max_pool1d_indices(self, params, ceil_mode, dilation, ie_device, precis
     @pytest.mark.xfail(condition=platform.system() == 'Darwin' and platform.machine() == 'arm64',
                        reason='Ticket - 122715')
     def test_max_pool2d_indices(self, params, ceil_mode, dilation,  ie_device, precision, ir_version):
-        if ceil_mode and (np.array(params["padding"]).any() != 0):
-            pytest.skip("ticket 122418")
         to_trace = False
         if params["stride"] == []:
             to_trace = True
@@ -255,7 +251,5 @@ def test_max_pool2d_indices(self, params, ceil_mode, dilation,  ie_device, preci
     @pytest.mark.xfail(condition=platform.system() == 'Darwin' and platform.machine() == 'arm64',
                        reason='Ticket - 122715')
     def test_max_pool3d_indices(self, params, ceil_mode, dilation, ie_device, precision, ir_version):
-        if ceil_mode and (np.array(params["padding"]).any() != 0):
-            pytest.skip("ticket 122418")
         self._test(*self.create_model("max_pool3d_with_indices", **params, ceil_mode=ceil_mode, dilation=dilation),
                    ie_device, precision, ir_version, kwargs_to_prepare_input={'ndim': 5}, dynamic_shapes=False)
diff --git a/tests/layer_tests/pytorch_tests/test_scaled_dot_product_attention.py b/tests/layer_tests/pytorch_tests/test_scaled_dot_product_attention.py
index bd6fc584885984..871a77e0d0ff0f 100644
--- a/tests/layer_tests/pytorch_tests/test_scaled_dot_product_attention.py
+++ b/tests/layer_tests/pytorch_tests/test_scaled_dot_product_attention.py
@@ -10,19 +10,18 @@
 class TestScaledDotProductAttention(PytorchLayerTest):
 
     def _prepare_input(self, dtype):
-        return (np.random.randn(1, 2, 8, 4).astype(dtype), np.random.randn(1, 2, 8, 4).astype(dtype), np.random.randn(1, 2, 8, 4).astype(dtype))
+        return (np.random.randn(1, 2, 8, 4).astype(dtype), np.random.randn(1, 2, 16, 4).astype(dtype), np.random.randn(1, 2, 16, 6).astype(dtype))
 
-    def create_model(self, mask, is_causal, dtype):
+    def create_model(self, mask, is_causal, dtype, mask_shape):
         import torch.nn.functional as F
         import torch
 
         class aten_scaled_dot_product_atten(torch.nn.Module):
 
-            def __init__(self, mask=False, is_causal=False, dtype=np.float32) -> None:
+            def __init__(self, mask=False, is_causal=False, dtype=np.float32, mask_shape=None) -> None:
                 super().__init__()
-
                 self.mask = None if not mask else torch.from_numpy(
-                    np.random.randint(0, 2, (8, 8)).astype(dtype))
+                    np.random.randint(0, 2, mask_shape).astype(dtype))
                 self.is_causal = is_causal
                 if is_causal and mask:
                     self.mask.to(torch.bool)
@@ -34,7 +33,7 @@ def forward(self, query, key, value):
                 b = F.scaled_dot_product_attention(query, key, value, attn_mask=self.mask, is_causal=self.is_causal, scale=torch.tensor(5, dtype=torch.float))
                 return a, b
 
-        return aten_scaled_dot_product_atten(mask, is_causal, dtype), None, 'aten::scaled_dot_product_attention'
+        return aten_scaled_dot_product_atten(mask, is_causal, dtype, mask_shape), None, 'aten::scaled_dot_product_attention'
 
     @pytest.mark.nightly
     @pytest.mark.precommit
@@ -42,8 +41,10 @@ def forward(self, query, key, value):
     @pytest.mark.precommit_torch_export
     @pytest.mark.parametrize(['mask', 'is_causal'], [(False, False), (False, True), (True, True), (True, False)])
     @pytest.mark.parametrize("dtype", (np.float32, np.float64))
-    def test_scaled_dot_product_atten(self, ie_device, precision, ir_version, mask, is_causal, dtype):
+    @pytest.mark.parametrize('mask_shape', [(8, 16), (1, 16), (8, 1), (1, 1), (1, 8, 1), (1, 1, 8, 1), (1, 2, 8, 16)])
+    @pytest.mark.parametrize('dyn_shapes', (True, False))
+    def test_scaled_dot_product_atten(self, ie_device, precision, ir_version, mask, is_causal, dtype, mask_shape, dyn_shapes):
         if PytorchLayerTest.use_torch_export() and not mask and is_causal:
             pytest.xfail(reason="Unsupported case for torch.export")
-        self._test(*self.create_model(mask, is_causal, dtype),
-                   ie_device, precision, ir_version, kwargs_to_prepare_input={"dtype": dtype})
+        self._test(*self.create_model(mask, is_causal, dtype, mask_shape),
+                   ie_device, precision, ir_version, dynamic_shapes=dyn_shapes, kwargs_to_prepare_input={"dtype": dtype})
diff --git a/tests/samples_tests/smoke_tests/test_benchmark_app.py b/tests/samples_tests/smoke_tests/test_benchmark_app.py
index 860ccd10641670..f9b37e87614d42 100644
--- a/tests/samples_tests/smoke_tests/test_benchmark_app.py
+++ b/tests/samples_tests/smoke_tests/test_benchmark_app.py
@@ -129,3 +129,19 @@ def test_4bit_precision_input(sample_language, device, inp, cache, tmp_path):
     if inp != None and inp.endswith(".bin"):
         create_random_4bit_bin_file(tmp_path, inp_shape, inp)
     verify(sample_language, device, model=model_4bit, inp=inp, cache=cache, tmp_path=tmp_path, batch=None, tm='1')
+
+
+@pytest.mark.parametrize('sample_language', ['C++', 'Python'])
+@pytest.mark.parametrize('device', get_devices())
+def test_out_of_tensor_size_range_npy_multibatch(sample_language, device, cache, tmp_path):
+    inp = tmp_path / 'batch2.npy'
+    with open(inp, "wb") as batch2_npy:
+        np.save(
+            batch2_npy,
+            np.random.RandomState(
+                np.random.MT19937(np.random.SeedSequence(0))
+            ).uniform(0, 256, [2, 3, 224, 224]).astype(np.uint8)
+        )
+    # benchmark_app reads batch from model or cmd, not from npy.
+    # benchmark_app still verifyes npy shape for python impl.
+    verify(sample_language, device, inp=inp, cache=cache, tmp_path=tmp_path, batch='2')
diff --git a/thirdparty/dependencies.cmake b/thirdparty/dependencies.cmake
index 32d50092f0b97d..24c4940fedc3f5 100644
--- a/thirdparty/dependencies.cmake
+++ b/thirdparty/dependencies.cmake
@@ -28,8 +28,7 @@ endif()
 # temporarily remove CMAKE_COMPILE_WARNING_AS_ERROR for thirdparty
 if(CMAKE_COMPILE_WARNING_AS_ERROR AND WIN32)
     if(CMAKE_CXX_COMPILER_ID STREQUAL "MSVC" AND CMAKE_VERSION VERSION_LESS 3.24)
-        string(REPLACE "/WX" "" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
-        string(REPLACE "/WX" "" CMAKE_C_FLAGS "${CMAKE_C_FLAGS}")
+        ov_add_compiler_flags(/WX-)
     endif()
     set(CMAKE_COMPILE_WARNING_AS_ERROR OFF)
 endif()
diff --git a/thirdparty/flatbuffers/flatbuffers b/thirdparty/flatbuffers/flatbuffers
index 0100f6a5779831..595bf0007ab192 160000
--- a/thirdparty/flatbuffers/flatbuffers
+++ b/thirdparty/flatbuffers/flatbuffers
@@ -1 +1 @@
-Subproject commit 0100f6a5779831fa7a651e4b67ef389a8752bd9b
+Subproject commit 595bf0007ab1929570c7671f091313c8fc20644e
diff --git a/thirdparty/ittapi/ittapi b/thirdparty/ittapi/ittapi
index 69dd04030d3a2c..1d92c61fa2293d 160000
--- a/thirdparty/ittapi/ittapi
+++ b/thirdparty/ittapi/ittapi
@@ -1 +1 @@
-Subproject commit 69dd04030d3a2cf4c32e649ac1f2a628d5af6b46
+Subproject commit 1d92c61fa2293d86ff248ff37160651de5d49bbf
diff --git a/thirdparty/ocl/cl_headers b/thirdparty/ocl/cl_headers
index 2368105c053106..8275634cf9ec31 160000
--- a/thirdparty/ocl/cl_headers
+++ b/thirdparty/ocl/cl_headers
@@ -1 +1 @@
-Subproject commit 2368105c0531069fe927989505de7d125ec58c55
+Subproject commit 8275634cf9ec31b6484c2e6be756237cb583999d
diff --git a/thirdparty/ocl/clhpp_headers b/thirdparty/ocl/clhpp_headers
index 83cc072d8240aa..0bdbbfe5ecda42 160000
--- a/thirdparty/ocl/clhpp_headers
+++ b/thirdparty/ocl/clhpp_headers
@@ -1 +1 @@
-Subproject commit 83cc072d8240aad47ef4663d572a31ef27d0411a
+Subproject commit 0bdbbfe5ecda42cff50c96cc5e33527f42fcbd45
diff --git a/thirdparty/ocl/icd_loader b/thirdparty/ocl/icd_loader
index 229410f86a8c8c..861b68b290e76d 160000
--- a/thirdparty/ocl/icd_loader
+++ b/thirdparty/ocl/icd_loader
@@ -1 +1 @@
-Subproject commit 229410f86a8c8c9e0f86f195409e5481a2bae067
+Subproject commit 861b68b290e76d08e7241608479c16431f529945
diff --git a/thirdparty/snappy b/thirdparty/snappy
index dc05e026488865..2c94e11145f0b7 160000
--- a/thirdparty/snappy
+++ b/thirdparty/snappy
@@ -1 +1 @@
-Subproject commit dc05e026488865bc69313a68bcc03ef2e4ea8e83
+Subproject commit 2c94e11145f0b7b184b831577c93e5a41c4c0346
diff --git a/thirdparty/zlib/zlib b/thirdparty/zlib/zlib
index 09155eaa2f9270..51b7f2abdade71 160000
--- a/thirdparty/zlib/zlib
+++ b/thirdparty/zlib/zlib
@@ -1 +1 @@
-Subproject commit 09155eaa2f9270dc4ed1fa13e2b4b2613e6e4851
+Subproject commit 51b7f2abdade71cd9bb0e7a373ef2610ec6f9daf
diff --git a/tools/benchmark_tool/openvino/tools/benchmark/utils/inputs_filling.py b/tools/benchmark_tool/openvino/tools/benchmark/utils/inputs_filling.py
index 24be4c6567956d..744a12fa0227ab 100644
--- a/tools/benchmark_tool/openvino/tools/benchmark/utils/inputs_filling.py
+++ b/tools/benchmark_tool/openvino/tools/benchmark/utils/inputs_filling.py
@@ -266,7 +266,7 @@ def get_numpy_tensors(numpy_paths: List[str], info: AppInputInfo, batch_sizes: L
                 else:
                     try:
                         if info.layout.has_name("N"):
-                            numpy_arrays[[None] * info.layout.get_index_by_name("N") + [b]] = numpy_arr
+                            numpy_arrays[[None] * info.layout.get_index_by_name("N") + [b]] = numpy_arr[b]
                         else:
                             numpy_arrays = numpy_arr
                     except ValueError:
@@ -308,7 +308,7 @@ def get_binary_tensors(binary_paths: List[str], info: AppInputInfo, batch_sizes:
                         f"File {binary_filename} contains {binary_file_bit_size} bites but model expects {blob_bit_size}")
                 from_file = np.fromfile(binary_filename, dtype)
                 if info.layout.has_name("N"):
-                    binaries[[None] * info.layout.get_index_by_name("N") + [b]] = from_file
+                    binaries[[None] * info.layout.get_index_by_name("N") + [b]] = from_file[b]
                 else:
                     binaries = from_file
             else:
diff --git a/tools/mo/unit_tests/moc_tf_fe/conversion_incorrect_models_test.py b/tools/mo/unit_tests/moc_tf_fe/conversion_incorrect_models_test.py
index c6e7c5f5da7165..3986cba4c10270 100644
--- a/tools/mo/unit_tests/moc_tf_fe/conversion_incorrect_models_test.py
+++ b/tools/mo/unit_tests/moc_tf_fe/conversion_incorrect_models_test.py
@@ -60,12 +60,15 @@ def test_conversion_empty_model(self):
             ),
         ]
         for use_new_frontend, use_legacy_frontend, framework, exp_reg_exp in test_cases:
-            with tempfile.NamedTemporaryFile(mode='w') as tmp, self.assertRaisesRegex(Exception,
-                                                                                      exp_reg_exp):
+            with tempfile.NamedTemporaryFile(
+                mode="w", delete=False
+            ) as tmp, self.assertRaisesRegex(Exception, exp_reg_exp):
                 tmp.write("")
+                # on Windows tmp file must be not deleted on close to avoid remove it when reopened by MO
                 convert_model(tmp.name,
                               use_new_frontend=use_new_frontend, use_legacy_frontend=use_legacy_frontend,
                               framework=framework)
+            os.remove(tmp.name)
 
     def test_conversion_fake_model_with_no_ext(self):
         test_cases = [
@@ -91,9 +94,15 @@ def test_conversion_fake_model_with_no_ext(self):
             ),
         ]
         for use_new_frontend, use_legacy_frontend, framework, exp_reg_exp in test_cases:
-            with tempfile.NamedTemporaryFile(mode='w') as tmp, self.assertRaisesRegex(Exception,
-                                                                                      exp_reg_exp):
+            with tempfile.NamedTemporaryFile(
+                mode="w", delete=False
+            ) as tmp, self.assertRaisesRegex(Exception, exp_reg_exp):
                 tmp.write("1212234\n12312")
-                convert_model(tmp.name,
-                              use_new_frontend=use_new_frontend, use_legacy_frontend=use_legacy_frontend,
-                              framework=framework)
+                # on Windows tmp file must be not deleted on close to avoid remove it when reopened by MO
+                convert_model(
+                    tmp.name,
+                    use_new_frontend=use_new_frontend,
+                    use_legacy_frontend=use_legacy_frontend,
+                    framework=framework,
+                )
+            os.remove(tmp.name)
diff --git a/tools/ovc/openvino/tools/ovc/convert_impl.py b/tools/ovc/openvino/tools/ovc/convert_impl.py
index 95682c706bb60f..2eb2f2adc133f1 100644
--- a/tools/ovc/openvino/tools/ovc/convert_impl.py
+++ b/tools/ovc/openvino/tools/ovc/convert_impl.py
@@ -36,6 +36,10 @@
     init_mo_telemetry
 from openvino.tools.ovc.moc_frontend.pytorch_frontend_utils import get_pytorch_decoder, extract_input_info_from_example
 from openvino.tools.ovc.moc_frontend.paddle_frontend_utils import paddle_frontend_converter
+try:
+    from openvino.tools.ovc.moc_frontend.jax_frontend_utils import get_jax_decoder
+except:
+    get_jax_decoder = None
 
 # pylint: disable=no-name-in-module,import-error
 from openvino.frontend import FrontEndManager, OpConversionFailure, TelemetryExtension
@@ -228,6 +232,11 @@ def check_model_object(argv):
                                                                     paddle.fluid.dygraph.layers.Layer) or isinstance(
             model, paddle.fluid.executor.Executor):
             return "paddle"
+        
+    if 'jax' in sys.modules:
+        import jax
+        if isinstance(model, (jax.core.Jaxpr, jax.core.ClosedJaxpr)):
+            return "jax"
 
     raise Error('Unknown model type: {}'.format(type(model)))
 
@@ -319,6 +328,7 @@ def normalize_inputs(argv: argparse.Namespace):
     """
     # Parse input to list of InputCutInfo
     inputs = input_to_input_cut_info(argv.input)
+    argv.input = inputs
 
     # Make list of input names
     input_names_list = []
@@ -329,8 +339,6 @@ def normalize_inputs(argv: argparse.Namespace):
         assert len(input_names_list) == len(inputs), "\"input\" parameter has unnamed inputs and named inputs. " \
                                                      "Please either set names for all inputs, " \
                                                      "or do not set names for all inputs."
-    argv.inputs_list = input_names_list
-    argv.input = ','.join(input_names_list)
 
     if len(input_names_list) > 0:
         # Named inputs case
@@ -462,6 +470,12 @@ def _convert(cli_parser: argparse.ArgumentParser, args, python_api_used):
                                                                      outputs)
                 pdmodel = paddle_runtime_converter.convert_paddle_to_pdmodel()
                 args['input_model'] = pdmodel
+            if model_framework == "jax":
+                if get_jax_decoder is not None:
+                    get_jax_decoder(args['input_model'], args)
+                else:
+                    raise Error("JAX Frontend is not available.")
+                
 
         argv = pack_params_to_args_namespace(args, cli_parser, python_api_used)
         argv.framework = model_framework
diff --git a/tools/ovc/openvino/tools/ovc/moc_frontend/jax_frontend_utils.py b/tools/ovc/openvino/tools/ovc/moc_frontend/jax_frontend_utils.py
new file mode 100644
index 00000000000000..5834eb0ecc0402
--- /dev/null
+++ b/tools/ovc/openvino/tools/ovc/moc_frontend/jax_frontend_utils.py
@@ -0,0 +1,19 @@
+# Copyright (C) 2018-2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import logging as log
+
+
+def get_jax_decoder(model, args):
+    try:
+        from openvino.frontend.jax.jaxpr_decoder import JaxprPythonDecoder
+    except Exception as e:
+        log.error("JAX frontend loading failed")
+        raise e
+    
+    if not isinstance(model, JaxprPythonDecoder):
+        decoder = JaxprPythonDecoder(model)
+    else:
+        decoder = model
+    
+    args['input_model'] = decoder
diff --git a/tools/ovc/openvino/tools/ovc/moc_frontend/pipeline.py b/tools/ovc/openvino/tools/ovc/moc_frontend/pipeline.py
index 8a068f4dc987f5..12722b5d771b75 100644
--- a/tools/ovc/openvino/tools/ovc/moc_frontend/pipeline.py
+++ b/tools/ovc/openvino/tools/ovc/moc_frontend/pipeline.py
@@ -72,12 +72,14 @@ def moc_pipeline(argv: argparse.Namespace, moc_front_end: FrontEnd):
             outputs = fe_output_user_data_repack(input_model, argv.output, moc_front_end.get_name())
             input_model.override_all_outputs([x['node'] for x in outputs])
     '''
-    argv.placeholder_shapes, argv.placeholder_data_types = convert_params_lists_to_dicts(
-        input_model, argv.placeholder_shapes, argv.placeholder_data_types)
 
-    user_shapes, outputs, freeze_placeholder = fe_user_data_repack(
-        input_model, argv.placeholder_shapes, argv.placeholder_data_types,
-        argv.output, {}, moc_front_end.get_name())
+    enabled_transforms, disabled_transforms = get_enabled_and_disabled_transforms()
+    if 'ANALYSIS_JSON_PRINT' in enabled_transforms:
+        # NOTE that model analysis is performed before applying user's settings (inputs's shapes etc.)
+        framework_model = moc_front_end.decode(input_model)
+        json_model_analysis_dump(framework_model)
+        # a model is not processed further in json analysis mode
+        sys.exit(0)
 
     def check_places_are_same(places_original: List[Place], places_new: List[Place]):
         """
@@ -90,6 +92,67 @@ def check_places_are_same(places_original: List[Place], places_new: List[Place])
             [item for item in places_original if any(
                 [item.is_equal(item2['node']) for item2 in places_new])]) == len(places_original)
 
+    if getattr(argv, "framework", None) == "pytorch":
+        iplaces = []
+        for idx, input_info in enumerate(argv.input):
+            if getattr(input_info, "name", None):
+                place = input_model.get_place_by_tensor_name(input_info.name)
+                if not input_info.shape and not input_info.type:
+                    # If we received place by name, we need to use it for FE to verify
+                    # that such name exist, otherwise we silently ignore it.
+                    # Using dynamic shape should be safe, because FE will not overwrite
+                    # the shape that was produced after conversion, but merge it, so
+                    # dynamic shape will not change anything.
+                    input_model.set_partial_shape(place, PartialShape.dynamic())
+            else:
+                place = input_model.get_place_by_input_index(idx)
+            iplaces.append(place)
+            if input_info.shape is not None:
+                input_model.set_partial_shape(place, input_info.shape)
+            if input_info.type is not None:
+                input_model.set_element_type(place, input_info.type)
+        model_inputs = input_model.get_inputs()
+        def merge_inputs(inputs, to_set_list):
+            # use input places instead of obtained by index if they are the same
+            res = []
+            for p in to_set_list:
+                found = False
+                for i in inputs:
+                    if p.is_equal(i):
+                        res.append(i)
+                        found = True
+                        break
+                if not found:
+                    res.append(p)
+            return res
+        iplaces = merge_inputs(model_inputs, iplaces)
+        # Currently this only work to reorder inputs/outputs
+        to_override_all_inputs = check_places_are_same(model_inputs, [{"node": p} for p in iplaces])
+        to_override_all_outputs = False
+        if argv.output:
+            oplaces = []
+            _outputs = fe_output_user_data_repack(input_model, argv.output, moc_front_end.get_name())
+            for out_desc in _outputs:
+                oplaces.append(out_desc["name"])
+            model_outputs = input_model.get_outputs()
+            to_override_all_outputs = check_places_are_same(model_outputs, [{"node": p} for p in oplaces])
+        if to_override_all_inputs and to_override_all_outputs:
+            input_model.extract_subgraph(iplaces, oplaces)
+        elif to_override_all_inputs:
+            input_model.override_all_inputs(iplaces)
+        elif to_override_all_outputs:
+            input_model.override_all_outputs(oplaces)
+
+        ov_model = moc_front_end.convert(input_model)
+        return ov_model
+
+    argv.placeholder_shapes, argv.placeholder_data_types = convert_params_lists_to_dicts(
+        input_model, argv.placeholder_shapes, argv.placeholder_data_types)
+
+    user_shapes, outputs, freeze_placeholder = fe_user_data_repack(
+        input_model, argv.placeholder_shapes, argv.placeholder_data_types,
+        argv.output, {}, moc_front_end.get_name())
+
     def add_names_to_tensors(model: InputModel, places: List[Place]):
         """
         Adds additional names to some model input tensors. This helper should be used
@@ -107,14 +170,6 @@ def add_names_to_tensors(model: InputModel, places: List[Place]):
                 log.warning('Could not add an additional name to a tensor pointed to by \'{}\'. Details: {}'.format(
                     new_input['input_name'], str(e)))
 
-    enabled_transforms, disabled_transforms = get_enabled_and_disabled_transforms()
-    if 'ANALYSIS_JSON_PRINT' in enabled_transforms:
-        # NOTE that model analysis is performed before applying user's settings (inputs's shapes etc.)
-        framework_model = moc_front_end.decode(input_model)
-        json_model_analysis_dump(framework_model)
-        # a model is not processed further in json analysis mode
-        sys.exit(0)
-
     model_inputs = input_model.get_inputs()
     inputs_equal = True
     if user_shapes:
@@ -238,9 +293,6 @@ def create_target_input_shapes(new_input_places):
 
             input_model.set_tensor_value(place, value)
 
-    def shape_to_array(shape: PartialShape):
-        return [shape.get_dimension(i) for i in range(shape.rank.get_length())]
-
     ov_model = moc_front_end.convert(input_model)
 
     return ov_model
diff --git a/tools/ovc/openvino/tools/ovc/moc_frontend/pytorch_frontend_utils.py b/tools/ovc/openvino/tools/ovc/moc_frontend/pytorch_frontend_utils.py
index 6a7dad594faf08..03662dacd613ee 100644
--- a/tools/ovc/openvino/tools/ovc/moc_frontend/pytorch_frontend_utils.py
+++ b/tools/ovc/openvino/tools/ovc/moc_frontend/pytorch_frontend_utils.py
@@ -8,6 +8,7 @@
 # pylint: disable=no-name-in-module,import-error
 from openvino.runtime import Tensor, PartialShape
 from openvino.tools.ovc.error import Error
+from openvino.tools.ovc.cli_parser import single_input_to_input_cut_info, _InputCutInfo
 
 
 
@@ -90,76 +91,85 @@ def get_value_from_list_or_dict(container, name, idx):
     return None
 
 
+def flatten_inputs(inputs, names=None):
+    flattened = []
+    if isinstance(inputs, dict):
+        # if names are provided we need to unpack in the same order
+        if names:
+            for name in names:
+                if isinstance(inputs[name], (list, tuple, dict)):
+                    flattened.extend(flatten_inputs(inputs[name]))
+                else:
+                    flattened.append((name, inputs[name]))
+        else:
+            for name, input_data in inputs.items():
+                if isinstance(input_data, (list, tuple, dict)):
+                    flattened.extend(flatten_inputs(input_data))
+                else:
+                    flattened.append((name, input_data))
+    else:
+        for input_data in inputs:
+            if isinstance(input_data, (list, tuple, dict)):
+                flattened.extend(flatten_inputs(input_data))
+            else:
+                flattened.append(input_data)
+    return flattened
+
+
 def extract_input_info_from_example(args, inputs):
     try:
         from openvino.frontend.pytorch.utils import pt_to_ov_type_map  # pylint: disable=no-name-in-module,import-error
     except Exception as e:
         log.error("PyTorch frontend loading failed")
         raise e
-    example_inputs = args.example_input
-    data_types = args.placeholder_data_types or {}
-    input_shapes = args.placeholder_shapes or {}
+    example_inputs = args.input_model._example_input if args.input_model._example_input is not None else args.example_input
+    if example_inputs is None:
+        return
     is_dict_input = isinstance(example_inputs, dict)
-    list_inputs = list(example_inputs.values()) if is_dict_input else example_inputs
-    input_names = None
     if not isinstance(example_inputs, (list, tuple, dict)):
-        list_inputs = [list_inputs]
-    if args.input_model._input_is_list:
-        list_inputs[0] = list_inputs[0].unsqueeze(0)
-    if args.input_model._input_signature is not None and not is_dict_input:
+        example_inputs = [example_inputs]
+    input_names = None
+    if args.input_model._input_signature is not None:
         input_names = args.input_model._input_signature[1:] if args.input_model._input_signature[
                                                                    0] == "self" else args.input_model._input_signature
-        if not is_dict_input:
-            example_inputs = dict(zip(input_names, list_inputs))
-            is_dict_input = True
-    elif is_dict_input:
-        input_names = list(example_inputs)
-    if not data_types and input_names is None:
-        data_types = []
-    if not input_shapes and input_names is None:
-        input_shapes = []
-    if inputs:
-        for input_id, input_info in enumerate(inputs):
-            input_name = input_info.name
-            if is_dict_input and input_name in example_inputs:
-                example_input = example_inputs[input_name]
-            else:
-                example_input = list_inputs[input_id]
-                if is_dict_input and input_name is None:
-                    input_name = input_names[input_id]
-            dtype = getattr(example_input, "dtype", type(example_input))
-            example_dtype = pt_to_ov_type_map.get(str(dtype))
-            user_dtype = get_value_from_list_or_dict(data_types, input_name, input_id)
-            if user_dtype is not None and example_dtype is not None and example_dtype != user_dtype:
-                raise Error(
-                    f"Defined input type {user_dtype} is not equal to provided example_input type {example_dtype}")
-
-            data_rank = getattr(example_input, "ndim", 0)
-            user_input_shape = get_value_from_list_or_dict(input_shapes, input_name, input_id)
-            if user_input_shape.rank.is_static and user_input_shape.rank.get_length() != data_rank:
-                raise Error(
-                    f"Requested input shape {user_input_shape.rank.get_length()} rank"
-                    f" is not equal to provided example_input rank {data_rank}")
-
-            input_shape = user_input_shape if user_input_shape is not None else PartialShape([-1] * data_rank)
-            update_list_or_dict(data_types, input_name, input_id,
-                                example_dtype if example_dtype is not None else None)
-            update_list_or_dict(input_shapes, input_name, input_id, input_shape)
-    else:
-        for input_id, example_input in enumerate(list_inputs):
-            dtype = getattr(example_input, "dtype", type(example_input))
-            ov_dtype = pt_to_ov_type_map.get(str(dtype))
-            data_rank = getattr(example_input, "ndim", 0)
-            input_shape = PartialShape([-1] * data_rank)
-            input_name = input_names[input_id] if input_names else None
-            update_list_or_dict(input_shapes, input_name, input_id, input_shape)
-            update_list_or_dict(data_types, input_name, input_id, ov_dtype if ov_dtype is not None else None)
-
-    args.placeholder_data_types = data_types
-    args.placeholder_shapes = input_shapes
-    if not args.input and input_names:
-        args.input_list = input_names
-        args.input = ",".join(input_names)
+    if input_names and not is_dict_input:
+        example_inputs = dict(zip(input_names, example_inputs))
+    print(input_names)
+    example_inputs = flatten_inputs(example_inputs, input_names)
+    input_arg = []
+    for example in example_inputs:
+        name = None
+        if isinstance(example, tuple) and len(example) == 2:
+            name = example[0]
+            example = example[1]
+        shape = PartialShape([-1] * example.ndim) if hasattr(example, "ndim") else PartialShape.dynamic()
+        dtype = getattr(example, "dtype", type(example))
+        dtype = pt_to_ov_type_map.get(str(dtype))
+        if name:
+            input_arg.append(single_input_to_input_cut_info((name, shape, dtype)))
+        else:
+            input_arg.append(single_input_to_input_cut_info((shape, dtype)))
+    if inputs is not None and len(inputs) != 0:
+        if len(inputs) == len(input_arg):
+            # we can update input argument with info from examples
+            new_input = []
+            for i in range(len(input_arg)):
+                input_desc = args.input[i]
+                name = input_desc.name
+                dtype = input_desc.type
+                shape = input_desc.shape
+                if name is None:
+                    name = input_arg[i].name
+                if dtype is None:
+                    dtype = input_arg[i].type
+                if shape is None:
+                    shape = input_arg[i].shape
+                new_input.append(_InputCutInfo(name, shape, dtype, input_desc.value))
+            input_arg = new_input
+        else:
+            # we can't update args.input
+            return
+    args.input = input_arg
 
 
 # pylint: disable=no-member
diff --git a/tools/ovc/unit_tests/ovc/package_BOM.txt b/tools/ovc/unit_tests/ovc/package_BOM.txt
index c7b37c2c147607..a0d0d5382e043f 100644
--- a/tools/ovc/unit_tests/ovc/package_BOM.txt
+++ b/tools/ovc/unit_tests/ovc/package_BOM.txt
@@ -14,6 +14,7 @@ openvino/tools/ovc/moc_frontend/__init__.py
 openvino/tools/ovc/moc_frontend/analysis.py
 openvino/tools/ovc/moc_frontend/check_config.py
 openvino/tools/ovc/moc_frontend/extractor.py
+openvino/tools/ovc/moc_frontend/jax_frontend_utils.py
 openvino/tools/ovc/moc_frontend/layout_utils.py
 openvino/tools/ovc/moc_frontend/moc_emit_ir.py
 openvino/tools/ovc/moc_frontend/offline_transformations.py