From c921d4d909ac7879cd98cbef16fffdf067786cc3 Mon Sep 17 00:00:00 2001
From: dlyakhov <daniil.lyakhov@intel.com>
Date: Tue, 10 Dec 2024 12:16:22 +0100
Subject: [PATCH] Link is fixed

---
 docs/articles_en/openvino-workflow/torch-compile.rst | 4 ++--
 src/plugins/intel_gpu/thirdparty/onednn_gpu          | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/articles_en/openvino-workflow/torch-compile.rst b/docs/articles_en/openvino-workflow/torch-compile.rst
index 28abccb1df32f1..1b90ea9af5eb95 100644
--- a/docs/articles_en/openvino-workflow/torch-compile.rst
+++ b/docs/articles_en/openvino-workflow/torch-compile.rst
@@ -316,7 +316,7 @@ TorchServe Integration
 TorchServe is a performant, flexible, and easy to use tool for serving PyTorch models in production. For more information on the details of TorchServe,
 you can refer to `TorchServe github repository. <https://github.com/pytorch/serve>`__. With OpenVINO ``torch.compile`` integration into TorchServe you can serve
 PyTorch models in production and accelerate them with OpenVINO on various Intel hardware. Detailed instructions on how to use OpenVINO with TorchServe are
-available in `TorchServe examples. <https://github.com/pytorch/serve/tree/master/examples/pt2/torch_compile_openvino>`__ and in a `use case app <https://github.com/pytorch/serve/tree/master/examples/usecases/llm_diffusion_serving_app>__`.
+available in `TorchServe examples. <https://github.com/pytorch/serve/tree/master/examples/pt2/torch_compile_openvino>`__ and in a `use case app <https://github.com/pytorch/serve/tree/master/examples/usecases/llm_diffusion_serving_app>`__.
 
 Support for Automatic1111 Stable Diffusion WebUI
 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
@@ -362,7 +362,7 @@ and decrease memory consumption:
 NNCF unlocks the full potential of low-precision OpenVINO kernels due to quantizers placement designed specifically for the OpenVINO.
 Advanced algorithms like ``SmoothQuant`` or ``BiasCorrection`` allows further metrics improvement minimizing the outputs discrepancy between the original and compressed models.
 For further details, please see the `documentation <https://docs.openvino.ai/2024/openvino-workflow/model-optimization.html>`__
-and a `tutorial <https://github.com/daniil-lyakhov/nncf/tree/develop/examples/post_training_quantization/torch_fx/resnet18>`__.
+and a `tutorial <https://github.com/openvinotoolkit/nncf/tree/develop/examples/post_training_quantization/torch_fx/resnet18>`__.
 
 Support for PyTorch 2 export quantization (Preview)
 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
diff --git a/src/plugins/intel_gpu/thirdparty/onednn_gpu b/src/plugins/intel_gpu/thirdparty/onednn_gpu
index 36e090a367a431..0f269193c74663 160000
--- a/src/plugins/intel_gpu/thirdparty/onednn_gpu
+++ b/src/plugins/intel_gpu/thirdparty/onednn_gpu
@@ -1 +1 @@
-Subproject commit 36e090a367a4312a1caa2db9e95fb94d17d7573b
+Subproject commit 0f269193c7466313888d3338209d0d06a22cc6fa