diff --git a/nncf/quantization/algorithms/weight_compression/torch_backend.py b/nncf/quantization/algorithms/weight_compression/torch_backend.py
index 6bfa6432748..52fae7531ec 100644
--- a/nncf/quantization/algorithms/weight_compression/torch_backend.py
+++ b/nncf/quantization/algorithms/weight_compression/torch_backend.py
@@ -43,8 +43,10 @@
 from nncf.torch.model_graph_manager import split_const_name
 from nncf.torch.model_transformer import PTModelTransformer
 from nncf.torch.nncf_network import NNCFNetwork
-from nncf.torch.quantization.layers import AsymmetricWeightsDecompressor
-from nncf.torch.quantization.layers import SymmetricWeightsDecompressor
+from nncf.torch.quantization.layers import INT4AsymmetricWeightsDecompressor
+from nncf.torch.quantization.layers import INT4SymmetricWeightsDecompressor
+from nncf.torch.quantization.layers import INT8AsymmetricWeightsDecompressor
+from nncf.torch.quantization.layers import INT8SymmetricWeightsDecompressor
 
 
 class PTWeightCompressionAlgoBackend(WeightCompressionAlgoBackend):
@@ -212,10 +214,9 @@ def transform_model(
 
         for wc_params in weight_compression_parameters:
             compression_config = wc_params.compression_config
-            if compression_config.mode not in [
-                CompressWeightsMode.INT8_ASYM,
-                CompressWeightsMode.INT8_SYM,
-                CompressWeightsMode.INT8,
+            if compression_config.mode in [
+                CompressWeightsMode.NF4,
+                CompressWeightsMode.E2M1,
             ]:
                 raise nncf.ParameterNotSupportedError(f"{compression_config.mode.value} is not supported.")
 
@@ -235,17 +236,35 @@ def transform_model(
                 None if precomputed_scales is None else precomputed_scales.get(wc_params.weight_name),
                 None if precomputed_zero_points is None else precomputed_zero_points.get(wc_params.weight_name),
             )
-            compressed_weight.scale = compressed_weight.scale.astype(dtype=TensorDataType.float16)
 
-            # pack compressed tensor
+            # creates weight decompressor
             if compression_config.mode == CompressWeightsMode.INT8_SYM:
-                dtype = TensorDataType.int8
-            else:
-                dtype = TensorDataType.uint8
-            packed_tensor = compressed_weight.tensor.astype(dtype)
+                decompressor = INT8SymmetricWeightsDecompressor(compressed_weight.scale.data, result_dtype=weight.dtype)
+            elif compression_config.mode == CompressWeightsMode.INT8_ASYM:
+                decompressor = INT8AsymmetricWeightsDecompressor(
+                    compressed_weight.scale.data, compressed_weight.zero_point.data, result_dtype=weight.dtype
+                )
+            elif compression_config.mode == CompressWeightsMode.INT4_SYM:
+                decompressor = INT4SymmetricWeightsDecompressor(
+                    scale=compressed_weight.scale.data,
+                    compressed_weight_shape=compressed_weight.tensor.shape,
+                    result_shape=weight.shape,
+                    result_dtype=weight.dtype,
+                )
+            elif compression_config.mode == CompressWeightsMode.INT4_ASYM:
+                decompressor = INT4AsymmetricWeightsDecompressor(
+                    scale=compressed_weight.scale.data,
+                    zero_point=compressed_weight.zero_point.data,
+                    compressed_weight_shape=compressed_weight.tensor.shape,
+                    result_shape=weight.shape,
+                    result_dtype=weight.dtype,
+                )
+
+            # pack tensor
+            packed_tensor = decompressor.pack_weight(compressed_weight.tensor.data)
 
             # sets compressed tensor
-            compressed_parameter = torch.nn.Parameter(packed_tensor.data, requires_grad=False)
+            compressed_parameter = torch.nn.Parameter(packed_tensor, requires_grad=False)
             setattr(module, weight_attr_name, compressed_parameter)
 
             consumer_nodes = graph.get_next_nodes(weight_node)
@@ -256,15 +275,6 @@ def transform_model(
                         if id(param) == id(weight):
                             setattr(c_module, name, compressed_parameter)
 
-            # creates weight decompressor
-            if compression_config.mode == CompressWeightsMode.INT8_SYM:
-                decompressor = SymmetricWeightsDecompressor(compressed_weight.scale.data, result_dtype=weight.dtype)
-            else:
-                packed_zero_point = compressed_weight.zero_point.astype(dtype)
-                decompressor = AsymmetricWeightsDecompressor(
-                    compressed_weight.scale.data, packed_zero_point.data, result_dtype=weight.dtype
-                )
-
             # registry weight decompression module in the model
             decompressor_name = f"weights_decompressor_{weight_node.node_name.replace('.', '_')}"
 
diff --git a/nncf/quantization/algorithms/weight_compression/torch_fx_backend.py b/nncf/quantization/algorithms/weight_compression/torch_fx_backend.py
index d9b6c70b7a7..2816f82a6e2 100644
--- a/nncf/quantization/algorithms/weight_compression/torch_fx_backend.py
+++ b/nncf/quantization/algorithms/weight_compression/torch_fx_backend.py
@@ -45,8 +45,10 @@
 from nncf.torch.graph.transformations.commands import PTTargetPoint
 from nncf.torch.model_graph_manager import get_const_node
 from nncf.torch.model_graph_manager import get_weight_tensor_port_ids
-from nncf.torch.quantization.layers import AsymmetricWeightsDecompressor
-from nncf.torch.quantization.layers import SymmetricWeightsDecompressor
+from nncf.torch.quantization.layers import INT4AsymmetricWeightsDecompressor
+from nncf.torch.quantization.layers import INT4SymmetricWeightsDecompressor
+from nncf.torch.quantization.layers import INT8AsymmetricWeightsDecompressor
+from nncf.torch.quantization.layers import INT8SymmetricWeightsDecompressor
 
 
 class FXWeightCompressionAlgoBackend(WeightCompressionAlgoBackend):
@@ -176,10 +178,9 @@ def transform_model(
 
         for wc_params in weight_compression_parameters:
             compression_config = wc_params.compression_config
-            if compression_config.mode not in [
-                CompressWeightsMode.INT8_ASYM,
-                CompressWeightsMode.INT8_SYM,
-                CompressWeightsMode.INT8,
+            if compression_config.mode in [
+                CompressWeightsMode.NF4,
+                CompressWeightsMode.E2M1,
             ]:
                 raise nncf.ParameterNotSupportedError(f"{compression_config.mode.value} is not supported.")
             weight_node = get_const_node(wc_params.node_with_weight, wc_params.weight_port_id, graph)
@@ -196,35 +197,44 @@ def transform_model(
                 None if precomputed_scales is None else precomputed_scales.get(wc_params.weight_name),
                 None if precomputed_zero_points is None else precomputed_zero_points.get(wc_params.weight_name),
             )
-            compressed_weight.scale = compressed_weight.scale.astype(dtype=TensorDataType.float16)
 
-            # pack compressed tensor
-            if compression_config.mode == CompressWeightsMode.INT8_SYM:
-                dtype = TensorDataType.int8
-            else:
-                dtype = TensorDataType.uint8
-            packed_tensor = compressed_weight.tensor.astype(dtype)
-
-            self.set_weight(wc_params.node_with_weight, wc_params.weight_port_id, model, graph, packed_tensor)
             # creates weight decompressor
             if compression_config.mode == CompressWeightsMode.INT8_SYM:
-                decompressor = SymmetricWeightsDecompressor(
+                decompressor = INT8SymmetricWeightsDecompressor(
                     compressed_weight.scale.data, result_dtype=weight.data.dtype
                 )
-                decompressor_type = "symmetric"
-            else:
-                packed_zero_point = compressed_weight.zero_point.astype(dtype)
-                decompressor = AsymmetricWeightsDecompressor(
-                    compressed_weight.scale.data, packed_zero_point.data, result_dtype=weight.data.dtype
+            elif compression_config.mode == CompressWeightsMode.INT8_ASYM:
+                decompressor = INT8AsymmetricWeightsDecompressor(
+                    compressed_weight.scale.data, compressed_weight.zero_point.data, result_dtype=weight.data.dtype
+                )
+            elif compression_config.mode == CompressWeightsMode.INT4_SYM:
+                decompressor = INT4SymmetricWeightsDecompressor(
+                    scale=compressed_weight.scale.data,
+                    compressed_weight_shape=compressed_weight.tensor.shape,
+                    result_shape=weight.shape,
+                    result_dtype=weight.data.dtype,
                 )
-                decompressor_type = "asymmetric"
+            elif compression_config.mode == CompressWeightsMode.INT4_ASYM:
+                decompressor = INT4AsymmetricWeightsDecompressor(
+                    scale=compressed_weight.scale.data,
+                    zero_point=compressed_weight.zero_point.data,
+                    compressed_weight_shape=compressed_weight.tensor.shape,
+                    result_shape=weight.shape,
+                    result_dtype=weight.data.dtype,
+                )
+
+            # pack tensor
+            packed_tensor = decompressor.pack_weight(compressed_weight.tensor.data)
+
+            # sets compressed tensor
+            self.set_weight(wc_params.node_with_weight, wc_params.weight_port_id, model, graph, packed_tensor)
 
             # register weight decompression module in the model
             graph_weight_node = get_graph_node_by_name(model.graph, wc_params.node_with_weight.node_name)
             compressed_weight_name = graph_weight_node.all_input_nodes[wc_params.weight_port_id].name
 
             decompressor_suffix = "_".join(compressed_weight_name.replace(".", "_").split("_")[:-2])
-            decompressor_name = f"{decompressor_type}_weights_decompressor_{decompressor_suffix}"
+            decompressor_name = f"{decompressor.quantization_mode}_weights_decompressor_{decompressor_suffix}"
 
             # inserts the weight decompressor into the model as the post hook on the model weight
             transformation_layout.register(
diff --git a/nncf/quantization/quantize_model.py b/nncf/quantization/quantize_model.py
index f475d4e52ee..52d5bf07edb 100644
--- a/nncf/quantization/quantize_model.py
+++ b/nncf/quantization/quantize_model.py
@@ -505,20 +505,26 @@ def compress_weights(
         from nncf.torch.model_creation import wrap_model
         from nncf.torch.quantization.quantize_model import compress_weights_impl as pt_compression_weights_impl
 
-        if mode not in [CompressWeightsMode.INT8_ASYM, CompressWeightsMode.INT8_SYM]:
+        if mode in [CompressWeightsMode.NF4, CompressWeightsMode.E2M1]:
             raise nncf.ParameterNotSupportedError(
-                "Torch backend supports only INT8_ASYM, INT8_SYM modes for weight compression, "
-                f"but given {mode.value} mode."
+                "Torch backend does not support NF4 and E2M1 modes for weight compression."
             )
 
-        if True in [awq, scale_estimation, gptq, lora_correction]:
+        options = {
+            "sensitivity_metric": sensitivity_metric,
+            "awq": awq,
+            "scale_estimation": scale_estimation,
+            "gptq": gptq,
+            "lora_correction": lora_correction,
+        }
+        unsupported_options = [name for name, value in options.items() if value is not None]
+        if unsupported_options:
             raise nncf.ParameterNotSupportedError(
-                "Torch backend does not support 'awq', 'scale_estimation', 'gptq' and 'lora_correction' options. "
-                "Set them to None."
+                f"Torch backend does not support {', '.join(unsupported_options)} option(s). Set them to None."
             )
 
-        if backup_mode is not None:
-            raise nncf.ParameterNotSupportedError("Torch backend does not support backup_mode option.")
+        if ratio is not None and ratio != 1:
+            raise nncf.ParameterNotSupportedError("Torch backend does not support ratio != 1.")
 
         if is_wrapped_model(model):
             if not model.nncf.trace_parameters:
@@ -541,20 +547,27 @@ def compress_weights(
             compress_weights_impl as fx_compression_weights_impl,
         )
 
-        if mode not in [CompressWeightsMode.INT8_ASYM, CompressWeightsMode.INT8_SYM]:
+        if mode in [CompressWeightsMode.NF4, CompressWeightsMode.E2M1]:
             raise nncf.ParameterNotSupportedError(
-                "TorchFX backend supports only INT8_ASYM, INT8_SYM modes for weight compression, "
-                f"but given {mode.value} mode."
+                "Torch backend does not support NF4 and E2M1 modes for weight compression."
             )
 
-        if backup_mode is not None:
-            raise nncf.ParameterNotSupportedError("TorchFX backend does not support backup_mode option.")
-
-        if any((awq, scale_estimation, gptq, lora_correction)):
+        options = {
+            "sensitivity_metric": sensitivity_metric,
+            "awq": awq,
+            "scale_estimation": scale_estimation,
+            "gptq": gptq,
+            "lora_correction": lora_correction,
+        }
+        unsupported_options = [name for name, value in options.items() if value is not None]
+        if unsupported_options:
             raise nncf.ParameterNotSupportedError(
-                "TorchFX backend does not support 'awq', 'scale_estimation', 'gptq',"
-                "and 'lora_correction' options. Set them to None."
+                f"TorchFX backend does not support {', '.join(unsupported_options)} option(s). Set them to None."
             )
+
+        if ratio is not None and ratio != 1:
+            raise nncf.ParameterNotSupportedError("TorchFX backend does not support ratio != 1.")
+
         if dataset:
             raise nncf.ParameterNotSupportedError(
                 "TorchFX only supports data-free weights compression," "Set the 'dataset' option to None"
diff --git a/nncf/torch/quantization/layers.py b/nncf/torch/quantization/layers.py
index b84d325526f..85e41428846 100644
--- a/nncf/torch/quantization/layers.py
+++ b/nncf/torch/quantization/layers.py
@@ -49,7 +49,11 @@
 from nncf.torch.quantization.quantize_functions import decompress_asymmetric
 from nncf.torch.quantization.quantize_functions import decompress_symmetric
 from nncf.torch.quantization.quantize_functions import get_scale_zp_from_input_low_input_high
+from nncf.torch.quantization.quantize_functions import pack_int4
+from nncf.torch.quantization.quantize_functions import pack_uint4
 from nncf.torch.quantization.quantize_functions import symmetric_quantize
+from nncf.torch.quantization.quantize_functions import unpack_int4
+from nncf.torch.quantization.quantize_functions import unpack_uint4
 from nncf.torch.return_types import maybe_get_values_from_torch_return_type
 from nncf.torch.return_types import maybe_wrap_to_torch_return_type
 from nncf.torch.utils import get_flat_tensor_contents_string
@@ -1045,43 +1049,189 @@ def get_scale_shape(input_shape: List[int], is_weights: bool, per_channel: bool,
     return get_per_channel_scale_shape(input_shape, is_weights, channel_idx)
 
 
-class AsymmetricWeightsDecompressor(nn.Module):
+class BaseWeightsDecompressor(nn.Module, ABC):
+    """
+    Base class for implementing weights decompression modules within NNCF.
+
+    This class is intended to serve as the foundation for modules that handle the decompression
+    of quantized model weights. It provides an interface for defining the quantization mode and
+    packing the weights according to the specified quantization strategy. Classes inheriting from
+    this base class must implement the abstract methods for packing and handling the quantization mode.
+    """
+
+    @property
+    @abstractmethod
+    def quantization_mode(self) -> QuantizationMode:
+        """
+        Property that specifies the quantization mode used for compressing weights.
+
+        This method must be implemented to return the specific mode of quantization that
+        the decompressor is using, such as symmetric or asymmetric quantization.
+
+        :return: The quantization mode as an instance of `QuantizationMode`.
+        """
+
+    @abstractmethod
+    def pack_weight(self, weight: torch.Tensor) -> torch.Tensor:
+        """
+        Pack the given weight tensor according to the selected quantization mode.
+
+        :param weight: The tensor containing the weight values to be packed.
+        :return: The packed tensor.
+        """
+
+
+class INT8AsymmetricWeightsDecompressor(BaseWeightsDecompressor):
     """
     Applies asymmetric decompression of compressed weights in the forward pass
     """
 
-    def __init__(self, scale: torch.Tensor, zero_point: torch.Tensor, result_dtype: torch.dtype = None):
+    def __init__(self, scale: torch.Tensor, zero_point: torch.Tensor, result_dtype: Optional[torch.dtype] = None):
         """
         :param scale: A scale in quantization scheme
         :param zero_point: A zero point in quantization scheme
         :param result_dtype: (Optional) A data type that result should be cast to
         """
         super().__init__()
-        self.register_buffer("_scale", scale)
-        self.register_buffer("_zero_point", zero_point)
+        self.register_buffer("_scale", scale.type(dtype=torch.float16))
+        self.register_buffer("_zero_point", self.pack_weight(zero_point))
         self.result_dtype = result_dtype
 
-    def forward(self, x):
+    @property
+    def quantization_mode(self) -> QuantizationMode:
+        return QuantizationMode.ASYMMETRIC
+
+    def pack_weight(self, weight: torch.Tensor) -> torch.Tensor:
+        if torch.is_floating_point(weight):
+            raise ValueError(f"Invalid weight dtype {weight.type}. Integer types are supported.")
+        if torch.any((weight < 0) | (weight > 255)):
+            raise ValueError("Weight values are not in [0, 255].")
+        return weight.type(dtype=torch.uint8)
+
+    def forward(self, x) -> torch.Tensor:
         result = decompress_asymmetric(x, self._scale, self._zero_point)
         result = result.type(dtype=self.result_dtype) if self.result_dtype is not None else result
         return result
 
 
-class SymmetricWeightsDecompressor(nn.Module):
+class INT8SymmetricWeightsDecompressor(BaseWeightsDecompressor):
     """
     Applies symmetric decompression of compressed weights in the forward pass
     """
 
-    def __init__(self, scale: torch.Tensor, result_dtype: torch.dtype = None):
+    def __init__(self, scale: torch.Tensor, result_dtype: Optional[torch.dtype] = None):
         """
         :param scale: A scale in quantization scheme
         :param result_dtype: (Optional) A data type that result should be cast to
         """
         super().__init__()
-        self.register_buffer("_scale", scale)
+        self.register_buffer("_scale", scale.type(dtype=torch.float16))
         self.result_dtype = result_dtype
 
+    @property
+    def quantization_mode(self) -> QuantizationMode:
+        return QuantizationMode.SYMMETRIC
+
+    def pack_weight(self, weight: torch.Tensor) -> torch.Tensor:
+        if torch.is_floating_point(weight):
+            raise ValueError(f"Invalid weight dtype {weight.type}. Integer types are supported.")
+        if torch.any((weight < -128) | (weight > 127)):
+            raise ValueError("Weight values are not in [-128, 127].")
+        return weight.type(dtype=torch.int8)
+
+    def forward(self, x) -> torch.Tensor:
+        result = decompress_symmetric(x, self._scale)
+        result = result.type(dtype=self.result_dtype) if self.result_dtype is not None else result
+        return result
+
+
+class INT4AsymmetricWeightsDecompressor(BaseWeightsDecompressor):
+    def __init__(
+        self,
+        scale: torch.Tensor,
+        zero_point: torch.Tensor,
+        compressed_weight_shape: Tuple[int, ...],
+        result_shape: Optional[Tuple[int, ...]] = None,
+        result_dtype: Optional[torch.dtype] = None,
+    ):
+        """
+        :param scale: A scale in quantization scheme
+        :param zero_point: A zero point in quantization scheme
+        :param compressed_weight_shape: A compressed weight shape
+        :param result_shape: (Optional) A shape that result should be reshaped
+        :param result_dtype: (Optional) A data type that result should be cast to
+        """
+        super().__init__()
+        self.register_buffer("_scale", scale.type(dtype=torch.float16))
+
+        self.zero_point_shape = zero_point.shape
+        self.register_buffer("_zero_point", self.pack_weight(zero_point))
+
+        self.compressed_weight_shape = compressed_weight_shape
+        self.result_shape = result_shape
+        self.result_dtype = result_dtype
+
+    @property
+    def quantization_mode(self) -> QuantizationMode:
+        return QuantizationMode.ASYMMETRIC
+
+    def pack_weight(self, weight: torch.Tensor) -> torch.Tensor:
+        if torch.is_floating_point(weight):
+            raise ValueError(f"Invalid weight dtype {weight.type}. Integer types are supported.")
+        if torch.any((weight < 0) | (weight > 15)):
+            raise ValueError("Weight values are not in [0, 15].")
+        return pack_uint4(weight.type(dtype=torch.uint8))
+
     def forward(self, x):
+        x = unpack_uint4(x)
+        x = x.reshape(self.compressed_weight_shape)
+
+        zero_point = unpack_uint4(self._zero_point)
+        zero_point = zero_point.reshape(self.zero_point_shape)
+
+        result = decompress_asymmetric(x, self._scale, zero_point)
+        result = result.reshape(self.result_shape) if self.result_shape is not None else result
+        result = result.type(dtype=self.result_dtype) if self.result_dtype is not None else result
+        return result
+
+
+class INT4SymmetricWeightsDecompressor(BaseWeightsDecompressor):
+    def __init__(
+        self,
+        scale: torch.Tensor,
+        compressed_weight_shape: Tuple[int, ...],
+        result_shape: Optional[Tuple[int, ...]] = None,
+        result_dtype: Optional[torch.dtype] = None,
+    ):
+        """
+        :param scale: A scale in quantization scheme
+        :param compressed_weight_shape: A compressed weight shape
+        :param result_shape: (Optional) A shape that result should be reshaped
+        :param result_dtype: (Optional) A data type that result should be cast to
+        """
+        super().__init__()
+        self.register_buffer("_scale", scale.type(dtype=torch.float16))
+
+        self.compressed_weight_shape = compressed_weight_shape
+        self.result_shape = result_shape
+        self.result_dtype = result_dtype
+
+    @property
+    def quantization_mode(self) -> QuantizationMode:
+        return QuantizationMode.SYMMETRIC
+
+    def pack_weight(self, weight: torch.Tensor) -> torch.Tensor:
+        if torch.is_floating_point(weight):
+            raise ValueError(f"Invalid weight dtype {weight.type}. Integer types are supported.")
+        if torch.any((weight < -8) | (weight > 7)):
+            raise ValueError("Tensor values are not in [-8, 7].")
+        return pack_int4(weight.type(dtype=torch.int8))
+
+    def forward(self, x):
+        x = unpack_int4(x)
+        x = x.reshape(self.compressed_weight_shape)
+
         result = decompress_symmetric(x, self._scale)
+        result = result.reshape(self.result_shape) if self.result_shape is not None else result
         result = result.type(dtype=self.result_dtype) if self.result_dtype is not None else result
         return result
diff --git a/nncf/torch/quantization/quantize_functions.py b/nncf/torch/quantization/quantize_functions.py
index 9b4055c4586..b967bb57683 100644
--- a/nncf/torch/quantization/quantize_functions.py
+++ b/nncf/torch/quantization/quantize_functions.py
@@ -13,6 +13,7 @@
 import torch
 
 from nncf.common.logging import nncf_logger
+from nncf.errors import ValidationError
 from nncf.torch.dynamic_graph.patch_pytorch import register_operator
 from nncf.torch.functions import STRound
 from nncf.torch.functions import clamp
@@ -259,6 +260,7 @@ def decompress_asymmetric(input: torch.Tensor, scale: torch.Tensor, zero_point:
     :return: The decompressed tensor
     """
     input = input.type(dtype=scale.dtype)
+    zero_point = zero_point.type(dtype=scale.dtype)
     decompressed_input = (input - zero_point) * scale
     return decompressed_input
 
@@ -275,3 +277,62 @@ def decompress_symmetric(input: torch.Tensor, scale: torch.Tensor) -> torch.Tens
     input = input.type(dtype=scale.dtype)
     decompressed_input = input * scale
     return decompressed_input
+
+
+def pack_uint4(tensor: torch.Tensor) -> torch.Tensor:
+    """
+    Packs a tensor containing uint4 values (in the range [0, 15]) into a tensor with uint8 values,
+    where each element stores two uint4 values.
+
+    :param tensor: A tensor of dtype `torch.uint8` where each element represents a uint4 value.
+        The tensor should contain values in the range [0, 15].
+    :return: A packed tensor of dtype `torch.uint8` where each element packs two uint4 values.
+    :raises nncf.errors.ValidationError: If the input tensor is not of type `torch.uint8`.
+    """
+    if tensor.dtype != torch.uint8:
+        raise ValidationError(f"Invalid tensor dtype {tensor.type}. torch.uint8 type is supported.")
+    packed_tensor = tensor.contiguous()
+    packed_tensor = packed_tensor.reshape(-1, 2)
+    packed_tensor = torch.bitwise_and(packed_tensor[..., ::2], 15) | packed_tensor[..., 1::2] << 4
+    return packed_tensor
+
+
+@register_operator()
+def unpack_uint4(packed_tensor: torch.Tensor) -> torch.Tensor:
+    """
+    Unpacks a tensor, where each uint8 element stores two uint4 values, back into a tensor with
+    individual uint4 values.
+
+    :param packed_tensor: A tensor of dtype `torch.uint8` where each element packs two uint4 values.
+    :return: A tensor of dtype `torch.uint8` where each element represents a uint4 value.
+    """
+    return torch.stack((torch.bitwise_and(packed_tensor, 15), torch.bitwise_right_shift(packed_tensor, 4)), dim=-1)
+
+
+def pack_int4(tensor: torch.Tensor) -> torch.Tensor:
+    """
+    Packs a tensor containing int4 values (in the range [-8, 7]) into a tensor with uint8 values,
+    where each element stores two int4 values.
+
+    :param tensor: A tensor of dtype `torch.int8` where each element represents an int4 value.
+        The tensor should contain values in the range [-8, 7].
+    :return: A packed tensor of dtype `torch.uint8` where each element packs two int4 values.
+    :raises nncf.errors.ValidationError: If the input tensor is not of type `torch.int8`.
+    """
+    if tensor.dtype != torch.int8:
+        raise ValidationError(f"Invalid tensor dtype {tensor.type}. torch.int8 type is supported.")
+    tensor = tensor + 8
+    return pack_uint4(tensor.type(torch.uint8))
+
+
+@register_operator()
+def unpack_int4(packed_tensor: torch.Tensor) -> torch.Tensor:
+    """
+    Unpacks a tensor, where each uint8 element stores two int4 values, back into a tensor with
+    individual int4 values.
+
+    :param packed_tensor: A tensor of dtype `torch.uint8` where each element packs two int4 values.
+    :return: A tensor of dtype `torch.int8` where each element represents an int4 value.
+    """
+    t = unpack_uint4(packed_tensor)
+    return t.type(torch.int8) - 8
diff --git a/tests/post_training/data/wc_reference_data_2024.5.yaml b/tests/post_training/data/wc_reference_data_2024.5.yaml
index c1cddd4ea3e..ee5b1ffdad4 100644
--- a/tests/post_training/data/wc_reference_data_2024.5.yaml
+++ b/tests/post_training/data/wc_reference_data_2024.5.yaml
@@ -7,3 +7,7 @@ tinyllama_NF4_scale_estimation_stateful_per_channel_backend_OV:
   metric_value: 0.87132
   num_int4: 11
   num_int8: 290
+tinyllama_int4_data_free_backend_TORCH:
+  metric_value: 0.73541
+  num_int4: 308
+  num_int8: 4
diff --git a/tests/post_training/experimental/sparsify_activations/pipelines.py b/tests/post_training/experimental/sparsify_activations/pipelines.py
index ef2ecbd1847..cb12f3b08f2 100644
--- a/tests/post_training/experimental/sparsify_activations/pipelines.py
+++ b/tests/post_training/experimental/sparsify_activations/pipelines.py
@@ -30,8 +30,8 @@
 from nncf.experimental.torch.sparsify_activations import sparsify_activations
 from nncf.experimental.torch.sparsify_activations.sparsify_activations_impl import SparsifyActivationsAlgoBackend
 from nncf.experimental.torch.sparsify_activations.torch_backend import PTSparsifyActivationsAlgoBackend
-from nncf.torch.quantization.layers import AsymmetricWeightsDecompressor
-from nncf.torch.quantization.layers import SymmetricWeightsDecompressor
+from nncf.torch.quantization.layers import INT8AsymmetricWeightsDecompressor
+from nncf.torch.quantization.layers import INT8SymmetricWeightsDecompressor
 from tests.post_training.pipelines.base import LIMIT_LENGTH_OF_STATUS
 from tests.post_training.pipelines.base import PT_BACKENDS
 from tests.post_training.pipelines.base import BackendType
@@ -267,7 +267,7 @@ def save_compressed_model(self):
         if self.backend == BackendType.CUDA_TORCH:
             self.model_hf.float()
             for module in self.model_hf.nncf.modules():
-                if isinstance(module, (AsymmetricWeightsDecompressor, SymmetricWeightsDecompressor)):
+                if isinstance(module, (INT8AsymmetricWeightsDecompressor, INT8SymmetricWeightsDecompressor)):
                     module.result_dtype = torch.float32
             export_from_model(
                 self.model_hf, self.output_model_dir, stateful=False, compression_option="fp32", device="cuda"
diff --git a/tests/post_training/model_scope.py b/tests/post_training/model_scope.py
index 5f5080fee77..7ee651723af 100644
--- a/tests/post_training/model_scope.py
+++ b/tests/post_training/model_scope.py
@@ -441,6 +441,16 @@
         },
         "backends": [BackendType.TORCH],
     },
+    {
+        "reported_name": "tinyllama_int4_data_free",
+        "model_id": "tinyllama/tinyllama-1.1b-step-50k-105b",
+        "pipeline_cls": LMWeightCompression,
+        "compression_params": {
+            "mode": CompressWeightsMode.INT4_ASYM,
+            "group_size": 64,
+        },
+        "backends": [BackendType.TORCH],
+    },
     {
         "reported_name": "tinyllama_data_aware_gptq",
         "model_id": "tinyllama/tinyllama-1.1b-step-50k-105b",
diff --git a/tests/post_training/test_quantize_conformance.py b/tests/post_training/test_quantize_conformance.py
index 5c4fa176ad6..2ea880fde31 100644
--- a/tests/post_training/test_quantize_conformance.py
+++ b/tests/post_training/test_quantize_conformance.py
@@ -343,7 +343,7 @@ def test_weight_compression(
     start_time = time.perf_counter()
     try:
         if test_case_name not in wc_reference_data:
-            raise RuntimeError(f"{test_case_name} is not defined in `wc_reference_data` fixture")
+            pytest.skip(f"{test_case_name} is not defined in `wc_reference_data` fixture")
         test_model_param = WC_TEST_CASES[test_case_name]
         maybe_skip_test_case(test_model_param, run_fp32_backend, run_torch_cuda_backend, batch_size)
         pipeline_cls = test_model_param["pipeline_cls"]
diff --git a/tests/torch/data/experimental/sparsify_activations/dummy_llama_int8_sym_weights_sparse_activations.dot b/tests/torch/data/experimental/sparsify_activations/dummy_llama_int8_sym_weights_sparse_activations.dot
index c3e5cf0d0c9..7c4f62b7994 100644
--- a/tests/torch/data/experimental/sparsify_activations/dummy_llama_int8_sym_weights_sparse_activations.dot
+++ b/tests/torch/data/experimental/sparsify_activations/dummy_llama_int8_sym_weights_sparse_activations.dot
@@ -1,8 +1,8 @@
 strict digraph  {
 "0 /nncf_model_input_0" [id=0, type=nncf_model_input];
 "1 model.embed_tokens.weight" [id=1, type=nncf_model_const];
-"2 LlamaForCausalLM/LlamaModel[model]/Embedding[embed_tokens]/SymmetricWeightsDecompressor/decompress_symmetric_0" [id=2, type=decompress_symmetric];
-"3 LlamaForCausalLM/LlamaModel[model]/Embedding[embed_tokens]/SymmetricWeightsDecompressor/type_0" [id=3, type=type];
+"2 LlamaForCausalLM/LlamaModel[model]/Embedding[embed_tokens]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=2, type=decompress_symmetric];
+"3 LlamaForCausalLM/LlamaModel[model]/Embedding[embed_tokens]/INT8SymmetricWeightsDecompressor/type_0" [id=3, type=type];
 "4 LlamaForCausalLM/LlamaModel[model]/Embedding[embed_tokens]/embedding_0" [id=4, type=embedding];
 "5 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaRMSNorm[input_layernorm]/to_0" [id=5, type=to];
 "6 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaRMSNorm[input_layernorm]/pow_0" [id=6, type=pow];
@@ -14,16 +14,16 @@ strict digraph  {
 "12 model.layers.0.input_layernorm.weight" [id=12, type=nncf_model_const];
 "13 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaRMSNorm[input_layernorm]/__mul___1" [id=13, type=__mul__];
 "14 model.layers.0.self_attn.q_proj.weight" [id=14, type=nncf_model_const];
-"15 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[q_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" [id=15, type=decompress_symmetric];
-"16 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[q_proj]/SymmetricWeightsDecompressor/type_0" [id=16, type=type];
+"15 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[q_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=15, type=decompress_symmetric];
+"16 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[q_proj]/INT8SymmetricWeightsDecompressor/type_0" [id=16, type=type];
 "17 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[q_proj]/linear_0" [id=17, type=linear];
 "18 model.layers.0.self_attn.k_proj.weight" [id=18, type=nncf_model_const];
-"19 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[k_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" [id=19, type=decompress_symmetric];
-"20 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[k_proj]/SymmetricWeightsDecompressor/type_0" [id=20, type=type];
+"19 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[k_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=19, type=decompress_symmetric];
+"20 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[k_proj]/INT8SymmetricWeightsDecompressor/type_0" [id=20, type=type];
 "21 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[k_proj]/linear_0" [id=21, type=linear];
 "22 model.layers.0.self_attn.v_proj.weight" [id=22, type=nncf_model_const];
-"23 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[v_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" [id=23, type=decompress_symmetric];
-"24 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[v_proj]/SymmetricWeightsDecompressor/type_0" [id=24, type=type];
+"23 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[v_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=23, type=decompress_symmetric];
+"24 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[v_proj]/INT8SymmetricWeightsDecompressor/type_0" [id=24, type=type];
 "25 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[v_proj]/linear_0" [id=25, type=linear];
 "26 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/view_0" [id=26, type=view];
 "27 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/transpose_0" [id=27, type=transpose];
@@ -70,8 +70,8 @@ strict digraph  {
 "68 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/contiguous_0" [id=68, type=contiguous];
 "69 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/reshape_2" [id=69, type=reshape];
 "70 model.layers.0.self_attn.o_proj.weight" [id=70, type=nncf_model_const];
-"71 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[o_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" [id=71, type=decompress_symmetric];
-"72 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[o_proj]/SymmetricWeightsDecompressor/type_0" [id=72, type=type];
+"71 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[o_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=71, type=decompress_symmetric];
+"72 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[o_proj]/INT8SymmetricWeightsDecompressor/type_0" [id=72, type=type];
 "73 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[o_proj]/linear_0" [id=73, type=linear];
 "74 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/__add___0" [id=74, type=__add__];
 "75 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaRMSNorm[post_attention_layernorm]/to_0" [id=75, type=to];
@@ -84,24 +84,24 @@ strict digraph  {
 "82 model.layers.0.post_attention_layernorm.weight" [id=82, type=nncf_model_const];
 "83 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaRMSNorm[post_attention_layernorm]/__mul___1" [id=83, type=__mul__];
 "84 model.layers.0.mlp.gate_proj.weight" [id=84, type=nncf_model_const];
-"85 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" [id=85, type=decompress_symmetric];
-"86 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/SymmetricWeightsDecompressor/type_0" [id=86, type=type];
+"85 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=85, type=decompress_symmetric];
+"86 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/INT8SymmetricWeightsDecompressor/type_0" [id=86, type=type];
 "87 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/ActivationsSparsifier/abs_0" [id=87, type=abs];
 "88 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/ActivationsSparsifier/le_0" [id=88, type=le];
 "89 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/ActivationsSparsifier/masked_fill_0" [id=89, type=masked_fill];
 "90 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/linear_0" [id=90, type=linear];
 "91 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/SiLU[act_fn]/silu_0" [id=91, type=silu];
 "92 model.layers.0.mlp.up_proj.weight" [id=92, type=nncf_model_const];
-"93 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" [id=93, type=decompress_symmetric];
-"94 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/SymmetricWeightsDecompressor/type_0" [id=94, type=type];
+"93 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=93, type=decompress_symmetric];
+"94 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/INT8SymmetricWeightsDecompressor/type_0" [id=94, type=type];
 "95 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/ActivationsSparsifier/abs_0" [id=95, type=abs];
 "96 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/ActivationsSparsifier/le_0" [id=96, type=le];
 "97 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/ActivationsSparsifier/masked_fill_0" [id=97, type=masked_fill];
 "98 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/linear_0" [id=98, type=linear];
 "99 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/__mul___0" [id=99, type=__mul__];
 "100 model.layers.0.mlp.down_proj.weight" [id=100, type=nncf_model_const];
-"101 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" [id=101, type=decompress_symmetric];
-"102 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/SymmetricWeightsDecompressor/type_0" [id=102, type=type];
+"101 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=101, type=decompress_symmetric];
+"102 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/INT8SymmetricWeightsDecompressor/type_0" [id=102, type=type];
 "103 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/ActivationsSparsifier/abs_0" [id=103, type=abs];
 "104 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/ActivationsSparsifier/le_0" [id=104, type=le];
 "105 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/ActivationsSparsifier/masked_fill_0" [id=105, type=masked_fill];
@@ -117,16 +117,16 @@ strict digraph  {
 "115 model.layers.1.input_layernorm.weight" [id=115, type=nncf_model_const];
 "116 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaRMSNorm[input_layernorm]/__mul___1" [id=116, type=__mul__];
 "117 model.layers.1.self_attn.q_proj.weight" [id=117, type=nncf_model_const];
-"118 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[q_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" [id=118, type=decompress_symmetric];
-"119 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[q_proj]/SymmetricWeightsDecompressor/type_0" [id=119, type=type];
+"118 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[q_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=118, type=decompress_symmetric];
+"119 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[q_proj]/INT8SymmetricWeightsDecompressor/type_0" [id=119, type=type];
 "120 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[q_proj]/linear_0" [id=120, type=linear];
 "121 model.layers.1.self_attn.k_proj.weight" [id=121, type=nncf_model_const];
-"122 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[k_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" [id=122, type=decompress_symmetric];
-"123 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[k_proj]/SymmetricWeightsDecompressor/type_0" [id=123, type=type];
+"122 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[k_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=122, type=decompress_symmetric];
+"123 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[k_proj]/INT8SymmetricWeightsDecompressor/type_0" [id=123, type=type];
 "124 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[k_proj]/linear_0" [id=124, type=linear];
 "125 model.layers.1.self_attn.v_proj.weight" [id=125, type=nncf_model_const];
-"126 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[v_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" [id=126, type=decompress_symmetric];
-"127 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[v_proj]/SymmetricWeightsDecompressor/type_0" [id=127, type=type];
+"126 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[v_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=126, type=decompress_symmetric];
+"127 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[v_proj]/INT8SymmetricWeightsDecompressor/type_0" [id=127, type=type];
 "128 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[v_proj]/linear_0" [id=128, type=linear];
 "129 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/view_0" [id=129, type=view];
 "130 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/transpose_0" [id=130, type=transpose];
@@ -173,8 +173,8 @@ strict digraph  {
 "171 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/contiguous_0" [id=171, type=contiguous];
 "172 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/reshape_2" [id=172, type=reshape];
 "173 model.layers.1.self_attn.o_proj.weight" [id=173, type=nncf_model_const];
-"174 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[o_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" [id=174, type=decompress_symmetric];
-"175 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[o_proj]/SymmetricWeightsDecompressor/type_0" [id=175, type=type];
+"174 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[o_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=174, type=decompress_symmetric];
+"175 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[o_proj]/INT8SymmetricWeightsDecompressor/type_0" [id=175, type=type];
 "176 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[o_proj]/linear_0" [id=176, type=linear];
 "177 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/__add___0" [id=177, type=__add__];
 "178 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaRMSNorm[post_attention_layernorm]/to_0" [id=178, type=to];
@@ -187,24 +187,24 @@ strict digraph  {
 "185 model.layers.1.post_attention_layernorm.weight" [id=185, type=nncf_model_const];
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 "187 model.layers.1.mlp.gate_proj.weight" [id=187, type=nncf_model_const];
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-"189 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/SymmetricWeightsDecompressor/type_0" [id=189, type=type];
+"188 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=188, type=decompress_symmetric];
+"189 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/INT8SymmetricWeightsDecompressor/type_0" [id=189, type=type];
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 "193 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/linear_0" [id=193, type=linear];
 "194 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/SiLU[act_fn]/silu_0" [id=194, type=silu];
 "195 model.layers.1.mlp.up_proj.weight" [id=195, type=nncf_model_const];
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-"197 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[up_proj]/SymmetricWeightsDecompressor/type_0" [id=197, type=type];
+"196 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[up_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=196, type=decompress_symmetric];
+"197 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[up_proj]/INT8SymmetricWeightsDecompressor/type_0" [id=197, type=type];
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 "201 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[up_proj]/linear_0" [id=201, type=linear];
 "202 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/__mul___0" [id=202, type=__mul__];
 "203 model.layers.1.mlp.down_proj.weight" [id=203, type=nncf_model_const];
-"204 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" [id=204, type=decompress_symmetric];
-"205 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/SymmetricWeightsDecompressor/type_0" [id=205, type=type];
+"204 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=204, type=decompress_symmetric];
+"205 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/INT8SymmetricWeightsDecompressor/type_0" [id=205, type=type];
 "206 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/ActivationsSparsifier/abs_0" [id=206, type=abs];
 "207 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/ActivationsSparsifier/le_0" [id=207, type=le];
 "208 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/ActivationsSparsifier/masked_fill_0" [id=208, type=masked_fill];
@@ -220,15 +220,15 @@ strict digraph  {
 "218 model.norm.weight" [id=218, type=nncf_model_const];
 "219 LlamaForCausalLM/LlamaModel[model]/LlamaRMSNorm[norm]/__mul___1" [id=219, type=__mul__];
 "220 lm_head.weight" [id=220, type=nncf_model_const];
-"221 LlamaForCausalLM/Linear[lm_head]/SymmetricWeightsDecompressor/decompress_symmetric_0" [id=221, type=decompress_symmetric];
-"222 LlamaForCausalLM/Linear[lm_head]/SymmetricWeightsDecompressor/type_0" [id=222, type=type];
+"221 LlamaForCausalLM/Linear[lm_head]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=221, type=decompress_symmetric];
+"222 LlamaForCausalLM/Linear[lm_head]/INT8SymmetricWeightsDecompressor/type_0" [id=222, type=type];
 "223 LlamaForCausalLM/Linear[lm_head]/linear_0" [id=223, type=linear];
 "224 LlamaForCausalLM/float_0" [id=224, type=float];
 "225 /nncf_model_output_0" [id=225, type=nncf_model_output];
 "0 /nncf_model_input_0" -> "4 LlamaForCausalLM/LlamaModel[model]/Embedding[embed_tokens]/embedding_0";
-"1 model.embed_tokens.weight" -> "2 LlamaForCausalLM/LlamaModel[model]/Embedding[embed_tokens]/SymmetricWeightsDecompressor/decompress_symmetric_0";
-"2 LlamaForCausalLM/LlamaModel[model]/Embedding[embed_tokens]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "3 LlamaForCausalLM/LlamaModel[model]/Embedding[embed_tokens]/SymmetricWeightsDecompressor/type_0";
-"3 LlamaForCausalLM/LlamaModel[model]/Embedding[embed_tokens]/SymmetricWeightsDecompressor/type_0" -> "4 LlamaForCausalLM/LlamaModel[model]/Embedding[embed_tokens]/embedding_0";
+"1 model.embed_tokens.weight" -> "2 LlamaForCausalLM/LlamaModel[model]/Embedding[embed_tokens]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0";
+"2 LlamaForCausalLM/LlamaModel[model]/Embedding[embed_tokens]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "3 LlamaForCausalLM/LlamaModel[model]/Embedding[embed_tokens]/INT8SymmetricWeightsDecompressor/type_0";
+"3 LlamaForCausalLM/LlamaModel[model]/Embedding[embed_tokens]/INT8SymmetricWeightsDecompressor/type_0" -> "4 LlamaForCausalLM/LlamaModel[model]/Embedding[embed_tokens]/embedding_0";
 "4 LlamaForCausalLM/LlamaModel[model]/Embedding[embed_tokens]/embedding_0" -> "5 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaRMSNorm[input_layernorm]/to_0";
 "5 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaRMSNorm[input_layernorm]/to_0" -> "6 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaRMSNorm[input_layernorm]/pow_0";
 "5 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaRMSNorm[input_layernorm]/to_0" -> "10 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaRMSNorm[input_layernorm]/__mul___0";
@@ -243,17 +243,17 @@ strict digraph  {
 "13 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaRMSNorm[input_layernorm]/__mul___1" -> "17 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[q_proj]/linear_0";
 "13 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaRMSNorm[input_layernorm]/__mul___1" -> "21 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[k_proj]/linear_0";
 "13 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaRMSNorm[input_layernorm]/__mul___1" -> "25 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[v_proj]/linear_0";
-"14 model.layers.0.self_attn.q_proj.weight" -> "15 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[q_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0";
-"15 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[q_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "16 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[q_proj]/SymmetricWeightsDecompressor/type_0";
-"16 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[q_proj]/SymmetricWeightsDecompressor/type_0" -> "17 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[q_proj]/linear_0";
+"14 model.layers.0.self_attn.q_proj.weight" -> "15 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[q_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0";
+"15 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[q_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "16 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[q_proj]/INT8SymmetricWeightsDecompressor/type_0";
+"16 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[q_proj]/INT8SymmetricWeightsDecompressor/type_0" -> "17 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[q_proj]/linear_0";
 "17 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[q_proj]/linear_0" -> "26 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/view_0";
-"18 model.layers.0.self_attn.k_proj.weight" -> "19 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[k_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0";
-"19 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[k_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "20 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[k_proj]/SymmetricWeightsDecompressor/type_0";
-"20 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[k_proj]/SymmetricWeightsDecompressor/type_0" -> "21 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[k_proj]/linear_0";
+"18 model.layers.0.self_attn.k_proj.weight" -> "19 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[k_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0";
+"19 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[k_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "20 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[k_proj]/INT8SymmetricWeightsDecompressor/type_0";
+"20 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[k_proj]/INT8SymmetricWeightsDecompressor/type_0" -> "21 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[k_proj]/linear_0";
 "21 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[k_proj]/linear_0" -> "28 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/view_1";
-"22 model.layers.0.self_attn.v_proj.weight" -> "23 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[v_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0";
-"23 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[v_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "24 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[v_proj]/SymmetricWeightsDecompressor/type_0";
-"24 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[v_proj]/SymmetricWeightsDecompressor/type_0" -> "25 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[v_proj]/linear_0";
+"22 model.layers.0.self_attn.v_proj.weight" -> "23 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[v_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0";
+"23 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[v_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "24 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[v_proj]/INT8SymmetricWeightsDecompressor/type_0";
+"24 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[v_proj]/INT8SymmetricWeightsDecompressor/type_0" -> "25 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[v_proj]/linear_0";
 "25 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[v_proj]/linear_0" -> "30 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/view_2";
 "26 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/view_0" -> "27 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/transpose_0";
 "27 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/transpose_0" -> "39 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/__mul___0";
@@ -306,9 +306,9 @@ strict digraph  {
 "67 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/transpose_4" -> "68 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/contiguous_0";
 "68 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/contiguous_0" -> "69 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/reshape_2";
 "69 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/reshape_2" -> "73 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[o_proj]/linear_0";
-"70 model.layers.0.self_attn.o_proj.weight" -> "71 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[o_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0";
-"71 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[o_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "72 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[o_proj]/SymmetricWeightsDecompressor/type_0";
-"72 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[o_proj]/SymmetricWeightsDecompressor/type_0" -> "73 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[o_proj]/linear_0";
+"70 model.layers.0.self_attn.o_proj.weight" -> "71 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[o_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0";
+"71 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[o_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "72 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[o_proj]/INT8SymmetricWeightsDecompressor/type_0";
+"72 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[o_proj]/INT8SymmetricWeightsDecompressor/type_0" -> "73 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[o_proj]/linear_0";
 "73 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaAttention[self_attn]/Linear[o_proj]/linear_0" -> "74 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/__add___0";
 "74 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/__add___0" -> "75 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaRMSNorm[post_attention_layernorm]/to_0";
 "75 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaRMSNorm[post_attention_layernorm]/to_0" -> "76 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaRMSNorm[post_attention_layernorm]/pow_0";
@@ -325,26 +325,26 @@ strict digraph  {
 "83 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaRMSNorm[post_attention_layernorm]/__mul___1" -> "89 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/ActivationsSparsifier/masked_fill_0";
 "83 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaRMSNorm[post_attention_layernorm]/__mul___1" -> "95 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/ActivationsSparsifier/abs_0";
 "83 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaRMSNorm[post_attention_layernorm]/__mul___1" -> "97 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/ActivationsSparsifier/masked_fill_0";
-"84 model.layers.0.mlp.gate_proj.weight" -> "85 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0";
-"85 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "86 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/SymmetricWeightsDecompressor/type_0";
-"86 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/SymmetricWeightsDecompressor/type_0" -> "90 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/linear_0";
+"84 model.layers.0.mlp.gate_proj.weight" -> "85 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0";
+"85 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "86 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/INT8SymmetricWeightsDecompressor/type_0";
+"86 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/INT8SymmetricWeightsDecompressor/type_0" -> "90 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/linear_0";
 "87 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/ActivationsSparsifier/abs_0" -> "88 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/ActivationsSparsifier/le_0";
 "88 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/ActivationsSparsifier/le_0" -> "89 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/ActivationsSparsifier/masked_fill_0";
 "89 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/ActivationsSparsifier/masked_fill_0" -> "90 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/linear_0";
 "90 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[gate_proj]/linear_0" -> "91 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/SiLU[act_fn]/silu_0";
 "91 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/SiLU[act_fn]/silu_0" -> "99 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/__mul___0";
-"92 model.layers.0.mlp.up_proj.weight" -> "93 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0";
-"93 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "94 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/SymmetricWeightsDecompressor/type_0";
-"94 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/SymmetricWeightsDecompressor/type_0" -> "98 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/linear_0";
+"92 model.layers.0.mlp.up_proj.weight" -> "93 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0";
+"93 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "94 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/INT8SymmetricWeightsDecompressor/type_0";
+"94 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/INT8SymmetricWeightsDecompressor/type_0" -> "98 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/linear_0";
 "95 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/ActivationsSparsifier/abs_0" -> "96 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/ActivationsSparsifier/le_0";
 "96 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/ActivationsSparsifier/le_0" -> "97 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/ActivationsSparsifier/masked_fill_0";
 "97 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/ActivationsSparsifier/masked_fill_0" -> "98 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/linear_0";
 "98 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[up_proj]/linear_0" -> "99 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/__mul___0";
 "99 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/__mul___0" -> "103 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/ActivationsSparsifier/abs_0";
 "99 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/__mul___0" -> "105 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/ActivationsSparsifier/masked_fill_0";
-"100 model.layers.0.mlp.down_proj.weight" -> "101 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0";
-"101 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "102 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/SymmetricWeightsDecompressor/type_0";
-"102 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/SymmetricWeightsDecompressor/type_0" -> "106 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/linear_0";
+"100 model.layers.0.mlp.down_proj.weight" -> "101 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0";
+"101 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "102 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/INT8SymmetricWeightsDecompressor/type_0";
+"102 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/INT8SymmetricWeightsDecompressor/type_0" -> "106 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/linear_0";
 "103 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/ActivationsSparsifier/abs_0" -> "104 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/ActivationsSparsifier/le_0";
 "104 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/ActivationsSparsifier/le_0" -> "105 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/ActivationsSparsifier/masked_fill_0";
 "105 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/ActivationsSparsifier/masked_fill_0" -> "106 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[0]/LlamaMLP[mlp]/Linear[down_proj]/linear_0";
@@ -363,17 +363,17 @@ strict digraph  {
 "116 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaRMSNorm[input_layernorm]/__mul___1" -> "120 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[q_proj]/linear_0";
 "116 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaRMSNorm[input_layernorm]/__mul___1" -> "124 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[k_proj]/linear_0";
 "116 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaRMSNorm[input_layernorm]/__mul___1" -> "128 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[v_proj]/linear_0";
-"117 model.layers.1.self_attn.q_proj.weight" -> "118 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[q_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0";
-"118 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[q_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "119 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[q_proj]/SymmetricWeightsDecompressor/type_0";
-"119 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[q_proj]/SymmetricWeightsDecompressor/type_0" -> "120 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[q_proj]/linear_0";
+"117 model.layers.1.self_attn.q_proj.weight" -> "118 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[q_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0";
+"118 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[q_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "119 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[q_proj]/INT8SymmetricWeightsDecompressor/type_0";
+"119 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[q_proj]/INT8SymmetricWeightsDecompressor/type_0" -> "120 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[q_proj]/linear_0";
 "120 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[q_proj]/linear_0" -> "129 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/view_0";
-"121 model.layers.1.self_attn.k_proj.weight" -> "122 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[k_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0";
-"122 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[k_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "123 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[k_proj]/SymmetricWeightsDecompressor/type_0";
-"123 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[k_proj]/SymmetricWeightsDecompressor/type_0" -> "124 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[k_proj]/linear_0";
+"121 model.layers.1.self_attn.k_proj.weight" -> "122 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[k_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0";
+"122 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[k_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "123 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[k_proj]/INT8SymmetricWeightsDecompressor/type_0";
+"123 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[k_proj]/INT8SymmetricWeightsDecompressor/type_0" -> "124 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[k_proj]/linear_0";
 "124 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[k_proj]/linear_0" -> "131 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/view_1";
-"125 model.layers.1.self_attn.v_proj.weight" -> "126 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[v_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0";
-"126 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[v_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "127 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[v_proj]/SymmetricWeightsDecompressor/type_0";
-"127 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[v_proj]/SymmetricWeightsDecompressor/type_0" -> "128 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[v_proj]/linear_0";
+"125 model.layers.1.self_attn.v_proj.weight" -> "126 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[v_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0";
+"126 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[v_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "127 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[v_proj]/INT8SymmetricWeightsDecompressor/type_0";
+"127 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[v_proj]/INT8SymmetricWeightsDecompressor/type_0" -> "128 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[v_proj]/linear_0";
 "128 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[v_proj]/linear_0" -> "133 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/view_2";
 "129 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/view_0" -> "130 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/transpose_0";
 "130 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/transpose_0" -> "142 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/__mul___0";
@@ -426,9 +426,9 @@ strict digraph  {
 "170 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/transpose_4" -> "171 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/contiguous_0";
 "171 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/contiguous_0" -> "172 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/reshape_2";
 "172 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/reshape_2" -> "176 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[o_proj]/linear_0";
-"173 model.layers.1.self_attn.o_proj.weight" -> "174 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[o_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0";
-"174 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[o_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "175 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[o_proj]/SymmetricWeightsDecompressor/type_0";
-"175 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[o_proj]/SymmetricWeightsDecompressor/type_0" -> "176 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[o_proj]/linear_0";
+"173 model.layers.1.self_attn.o_proj.weight" -> "174 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[o_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0";
+"174 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[o_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "175 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[o_proj]/INT8SymmetricWeightsDecompressor/type_0";
+"175 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[o_proj]/INT8SymmetricWeightsDecompressor/type_0" -> "176 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[o_proj]/linear_0";
 "176 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaAttention[self_attn]/Linear[o_proj]/linear_0" -> "177 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/__add___0";
 "177 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/__add___0" -> "178 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaRMSNorm[post_attention_layernorm]/to_0";
 "178 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaRMSNorm[post_attention_layernorm]/to_0" -> "179 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaRMSNorm[post_attention_layernorm]/pow_0";
@@ -445,26 +445,26 @@ strict digraph  {
 "186 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaRMSNorm[post_attention_layernorm]/__mul___1" -> "192 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/ActivationsSparsifier/masked_fill_0";
 "186 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaRMSNorm[post_attention_layernorm]/__mul___1" -> "198 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[up_proj]/ActivationsSparsifier/abs_0";
 "186 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaRMSNorm[post_attention_layernorm]/__mul___1" -> "200 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[up_proj]/ActivationsSparsifier/masked_fill_0";
-"187 model.layers.1.mlp.gate_proj.weight" -> "188 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0";
-"188 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "189 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/SymmetricWeightsDecompressor/type_0";
-"189 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/SymmetricWeightsDecompressor/type_0" -> "193 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/linear_0";
+"187 model.layers.1.mlp.gate_proj.weight" -> "188 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0";
+"188 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "189 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/INT8SymmetricWeightsDecompressor/type_0";
+"189 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/INT8SymmetricWeightsDecompressor/type_0" -> "193 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/linear_0";
 "190 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/ActivationsSparsifier/abs_0" -> "191 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/ActivationsSparsifier/le_0";
 "191 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/ActivationsSparsifier/le_0" -> "192 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/ActivationsSparsifier/masked_fill_0";
 "192 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/ActivationsSparsifier/masked_fill_0" -> "193 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/linear_0";
 "193 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[gate_proj]/linear_0" -> "194 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/SiLU[act_fn]/silu_0";
 "194 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/SiLU[act_fn]/silu_0" -> "202 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/__mul___0";
-"195 model.layers.1.mlp.up_proj.weight" -> "196 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[up_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0";
-"196 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[up_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "197 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[up_proj]/SymmetricWeightsDecompressor/type_0";
-"197 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[up_proj]/SymmetricWeightsDecompressor/type_0" -> "201 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[up_proj]/linear_0";
+"195 model.layers.1.mlp.up_proj.weight" -> "196 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[up_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0";
+"196 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[up_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "197 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[up_proj]/INT8SymmetricWeightsDecompressor/type_0";
+"197 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[up_proj]/INT8SymmetricWeightsDecompressor/type_0" -> "201 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[up_proj]/linear_0";
 "198 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[up_proj]/ActivationsSparsifier/abs_0" -> "199 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[up_proj]/ActivationsSparsifier/le_0";
 "199 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[up_proj]/ActivationsSparsifier/le_0" -> "200 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[up_proj]/ActivationsSparsifier/masked_fill_0";
 "200 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[up_proj]/ActivationsSparsifier/masked_fill_0" -> "201 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[up_proj]/linear_0";
 "201 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[up_proj]/linear_0" -> "202 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/__mul___0";
 "202 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/__mul___0" -> "206 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/ActivationsSparsifier/abs_0";
 "202 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/__mul___0" -> "208 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/ActivationsSparsifier/masked_fill_0";
-"203 model.layers.1.mlp.down_proj.weight" -> "204 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0";
-"204 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "205 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/SymmetricWeightsDecompressor/type_0";
-"205 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/SymmetricWeightsDecompressor/type_0" -> "209 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/linear_0";
+"203 model.layers.1.mlp.down_proj.weight" -> "204 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0";
+"204 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "205 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/INT8SymmetricWeightsDecompressor/type_0";
+"205 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/INT8SymmetricWeightsDecompressor/type_0" -> "209 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/linear_0";
 "206 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/ActivationsSparsifier/abs_0" -> "207 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/ActivationsSparsifier/le_0";
 "207 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/ActivationsSparsifier/le_0" -> "208 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/ActivationsSparsifier/masked_fill_0";
 "208 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/ActivationsSparsifier/masked_fill_0" -> "209 LlamaForCausalLM/LlamaModel[model]/ModuleList[layers]/LlamaDecoderLayer[1]/LlamaMLP[mlp]/Linear[down_proj]/linear_0";
@@ -480,9 +480,9 @@ strict digraph  {
 "217 LlamaForCausalLM/LlamaModel[model]/LlamaRMSNorm[norm]/to_1" -> "219 LlamaForCausalLM/LlamaModel[model]/LlamaRMSNorm[norm]/__mul___1";
 "218 model.norm.weight" -> "219 LlamaForCausalLM/LlamaModel[model]/LlamaRMSNorm[norm]/__mul___1";
 "219 LlamaForCausalLM/LlamaModel[model]/LlamaRMSNorm[norm]/__mul___1" -> "223 LlamaForCausalLM/Linear[lm_head]/linear_0";
-"220 lm_head.weight" -> "221 LlamaForCausalLM/Linear[lm_head]/SymmetricWeightsDecompressor/decompress_symmetric_0";
-"221 LlamaForCausalLM/Linear[lm_head]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "222 LlamaForCausalLM/Linear[lm_head]/SymmetricWeightsDecompressor/type_0";
-"222 LlamaForCausalLM/Linear[lm_head]/SymmetricWeightsDecompressor/type_0" -> "223 LlamaForCausalLM/Linear[lm_head]/linear_0";
+"220 lm_head.weight" -> "221 LlamaForCausalLM/Linear[lm_head]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0";
+"221 LlamaForCausalLM/Linear[lm_head]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "222 LlamaForCausalLM/Linear[lm_head]/INT8SymmetricWeightsDecompressor/type_0";
+"222 LlamaForCausalLM/Linear[lm_head]/INT8SymmetricWeightsDecompressor/type_0" -> "223 LlamaForCausalLM/Linear[lm_head]/linear_0";
 "223 LlamaForCausalLM/Linear[lm_head]/linear_0" -> "224 LlamaForCausalLM/float_0";
 "224 LlamaForCausalLM/float_0" -> "225 /nncf_model_output_0";
 }
diff --git a/tests/torch/data/experimental/sparsify_activations/linear_int8_sym_weights_sparse_activations.dot b/tests/torch/data/experimental/sparsify_activations/linear_int8_sym_weights_sparse_activations.dot
index aa24d54a2e0..c433410067b 100644
--- a/tests/torch/data/experimental/sparsify_activations/linear_int8_sym_weights_sparse_activations.dot
+++ b/tests/torch/data/experimental/sparsify_activations/linear_int8_sym_weights_sparse_activations.dot
@@ -1,8 +1,8 @@
 strict digraph  {
 "0 /nncf_model_input_0" [id=0, type=nncf_model_input];
 "1 weight" [id=1, type=nncf_model_const];
-"2 Linear/NNCFNetworkInterface[_nncf]/ModuleDict[external_op]/SymmetricWeightsDecompressor[weights_decompressor_weight]/decompress_symmetric_0" [id=2, type=decompress_symmetric];
-"3 Linear/NNCFNetworkInterface[_nncf]/ModuleDict[external_op]/SymmetricWeightsDecompressor[weights_decompressor_weight]/type_0" [id=3, type=type];
+"2 Linear/NNCFNetworkInterface[_nncf]/ModuleDict[external_op]/INT8SymmetricWeightsDecompressor[weights_decompressor_weight]/decompress_symmetric_0" [id=2, type=decompress_symmetric];
+"3 Linear/NNCFNetworkInterface[_nncf]/ModuleDict[external_op]/INT8SymmetricWeightsDecompressor[weights_decompressor_weight]/type_0" [id=3, type=type];
 "4 bias" [id=4, type=nncf_model_const];
 "5 Linear/NNCFNetworkInterface[_nncf]/ModuleDict[external_op]/ActivationsSparsifier[activations_sparsifier_Linear/linear_0]/abs_0" [id=5, type=abs];
 "6 Linear/NNCFNetworkInterface[_nncf]/ModuleDict[external_op]/ActivationsSparsifier[activations_sparsifier_Linear/linear_0]/le_0" [id=6, type=le];
@@ -11,9 +11,9 @@ strict digraph  {
 "9 /nncf_model_output_0" [id=9, type=nncf_model_output];
 "0 /nncf_model_input_0" -> "5 Linear/NNCFNetworkInterface[_nncf]/ModuleDict[external_op]/ActivationsSparsifier[activations_sparsifier_Linear/linear_0]/abs_0";
 "0 /nncf_model_input_0" -> "7 Linear/NNCFNetworkInterface[_nncf]/ModuleDict[external_op]/ActivationsSparsifier[activations_sparsifier_Linear/linear_0]/masked_fill_0";
-"1 weight" -> "2 Linear/NNCFNetworkInterface[_nncf]/ModuleDict[external_op]/SymmetricWeightsDecompressor[weights_decompressor_weight]/decompress_symmetric_0";
-"2 Linear/NNCFNetworkInterface[_nncf]/ModuleDict[external_op]/SymmetricWeightsDecompressor[weights_decompressor_weight]/decompress_symmetric_0" -> "3 Linear/NNCFNetworkInterface[_nncf]/ModuleDict[external_op]/SymmetricWeightsDecompressor[weights_decompressor_weight]/type_0";
-"3 Linear/NNCFNetworkInterface[_nncf]/ModuleDict[external_op]/SymmetricWeightsDecompressor[weights_decompressor_weight]/type_0" -> "8 Linear/linear_0";
+"1 weight" -> "2 Linear/NNCFNetworkInterface[_nncf]/ModuleDict[external_op]/INT8SymmetricWeightsDecompressor[weights_decompressor_weight]/decompress_symmetric_0";
+"2 Linear/NNCFNetworkInterface[_nncf]/ModuleDict[external_op]/INT8SymmetricWeightsDecompressor[weights_decompressor_weight]/decompress_symmetric_0" -> "3 Linear/NNCFNetworkInterface[_nncf]/ModuleDict[external_op]/INT8SymmetricWeightsDecompressor[weights_decompressor_weight]/type_0";
+"3 Linear/NNCFNetworkInterface[_nncf]/ModuleDict[external_op]/INT8SymmetricWeightsDecompressor[weights_decompressor_weight]/type_0" -> "8 Linear/linear_0";
 "4 bias" -> "8 Linear/linear_0";
 "5 Linear/NNCFNetworkInterface[_nncf]/ModuleDict[external_op]/ActivationsSparsifier[activations_sparsifier_Linear/linear_0]/abs_0" -> "6 Linear/NNCFNetworkInterface[_nncf]/ModuleDict[external_op]/ActivationsSparsifier[activations_sparsifier_Linear/linear_0]/le_0";
 "6 Linear/NNCFNetworkInterface[_nncf]/ModuleDict[external_op]/ActivationsSparsifier[activations_sparsifier_Linear/linear_0]/le_0" -> "7 Linear/NNCFNetworkInterface[_nncf]/ModuleDict[external_op]/ActivationsSparsifier[activations_sparsifier_Linear/linear_0]/masked_fill_0";
diff --git a/tests/torch/data/experimental/sparsify_activations/three_linear_ignore1_int8_sym_weights_sparse_activations.dot b/tests/torch/data/experimental/sparsify_activations/three_linear_ignore1_int8_sym_weights_sparse_activations.dot
index ae3f667ff3a..5d2d15ae64d 100644
--- a/tests/torch/data/experimental/sparsify_activations/three_linear_ignore1_int8_sym_weights_sparse_activations.dot
+++ b/tests/torch/data/experimental/sparsify_activations/three_linear_ignore1_int8_sym_weights_sparse_activations.dot
@@ -1,25 +1,25 @@
 strict digraph  {
 "0 /nncf_model_input_0" [id=0, type=nncf_model_input];
 "1 embedding.weight" [id=1, type=nncf_model_const];
-"2 ThreeLinearModel/Embedding[embedding]/SymmetricWeightsDecompressor/decompress_symmetric_0" [id=2, type=decompress_symmetric];
-"3 ThreeLinearModel/Embedding[embedding]/SymmetricWeightsDecompressor/type_0" [id=3, type=type];
+"2 ThreeLinearModel/Embedding[embedding]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=2, type=decompress_symmetric];
+"3 ThreeLinearModel/Embedding[embedding]/INT8SymmetricWeightsDecompressor/type_0" [id=3, type=type];
 "4 ThreeLinearModel/Embedding[embedding]/embedding_0" [id=4, type=embedding];
 "5 linear1.weight" [id=5, type=nncf_model_const];
-"6 ThreeLinearModel/Linear[linear1]/SymmetricWeightsDecompressor/decompress_symmetric_0" [id=6, type=decompress_symmetric];
-"7 ThreeLinearModel/Linear[linear1]/SymmetricWeightsDecompressor/type_0" [id=7, type=type];
+"6 ThreeLinearModel/Linear[linear1]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=6, type=decompress_symmetric];
+"7 ThreeLinearModel/Linear[linear1]/INT8SymmetricWeightsDecompressor/type_0" [id=7, type=type];
 "8 linear1.bias" [id=8, type=nncf_model_const];
 "9 ThreeLinearModel/Linear[linear1]/linear_0" [id=9, type=linear];
 "10 linear3.weight" [id=10, type=nncf_model_const];
-"11 ThreeLinearModel/Linear[linear3]/SymmetricWeightsDecompressor/decompress_symmetric_0" [id=11, type=decompress_symmetric];
-"12 ThreeLinearModel/Linear[linear3]/SymmetricWeightsDecompressor/type_0" [id=12, type=type];
+"11 ThreeLinearModel/Linear[linear3]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=11, type=decompress_symmetric];
+"12 ThreeLinearModel/Linear[linear3]/INT8SymmetricWeightsDecompressor/type_0" [id=12, type=type];
 "13 linear3.bias" [id=13, type=nncf_model_const];
 "14 ThreeLinearModel/Linear[linear3]/ActivationsSparsifier/abs_0" [id=14, type=abs];
 "15 ThreeLinearModel/Linear[linear3]/ActivationsSparsifier/le_0" [id=15, type=le];
 "16 ThreeLinearModel/Linear[linear3]/ActivationsSparsifier/masked_fill_0" [id=16, type=masked_fill];
 "17 ThreeLinearModel/Linear[linear3]/linear_0" [id=17, type=linear];
 "18 linear2.weight" [id=18, type=nncf_model_const];
-"19 ThreeLinearModel/Linear[linear2]/SymmetricWeightsDecompressor/decompress_symmetric_0" [id=19, type=decompress_symmetric];
-"20 ThreeLinearModel/Linear[linear2]/SymmetricWeightsDecompressor/type_0" [id=20, type=type];
+"19 ThreeLinearModel/Linear[linear2]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=19, type=decompress_symmetric];
+"20 ThreeLinearModel/Linear[linear2]/INT8SymmetricWeightsDecompressor/type_0" [id=20, type=type];
 "21 ThreeLinearModel/Linear[linear2]/ActivationsSparsifier/abs_0" [id=21, type=abs];
 "22 ThreeLinearModel/Linear[linear2]/ActivationsSparsifier/le_0" [id=22, type=le];
 "23 ThreeLinearModel/Linear[linear2]/ActivationsSparsifier/masked_fill_0" [id=23, type=masked_fill];
@@ -27,29 +27,29 @@ strict digraph  {
 "25 /nncf_model_output_0" [id=25, type=nncf_model_output];
 "26 /nncf_model_output_1" [id=26, type=nncf_model_output];
 "0 /nncf_model_input_0" -> "4 ThreeLinearModel/Embedding[embedding]/embedding_0";
-"1 embedding.weight" -> "2 ThreeLinearModel/Embedding[embedding]/SymmetricWeightsDecompressor/decompress_symmetric_0";
-"2 ThreeLinearModel/Embedding[embedding]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "3 ThreeLinearModel/Embedding[embedding]/SymmetricWeightsDecompressor/type_0";
-"3 ThreeLinearModel/Embedding[embedding]/SymmetricWeightsDecompressor/type_0" -> "4 ThreeLinearModel/Embedding[embedding]/embedding_0";
+"1 embedding.weight" -> "2 ThreeLinearModel/Embedding[embedding]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0";
+"2 ThreeLinearModel/Embedding[embedding]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "3 ThreeLinearModel/Embedding[embedding]/INT8SymmetricWeightsDecompressor/type_0";
+"3 ThreeLinearModel/Embedding[embedding]/INT8SymmetricWeightsDecompressor/type_0" -> "4 ThreeLinearModel/Embedding[embedding]/embedding_0";
 "4 ThreeLinearModel/Embedding[embedding]/embedding_0" -> "9 ThreeLinearModel/Linear[linear1]/linear_0";
 "4 ThreeLinearModel/Embedding[embedding]/embedding_0" -> "21 ThreeLinearModel/Linear[linear2]/ActivationsSparsifier/abs_0";
 "4 ThreeLinearModel/Embedding[embedding]/embedding_0" -> "23 ThreeLinearModel/Linear[linear2]/ActivationsSparsifier/masked_fill_0";
-"5 linear1.weight" -> "6 ThreeLinearModel/Linear[linear1]/SymmetricWeightsDecompressor/decompress_symmetric_0";
-"6 ThreeLinearModel/Linear[linear1]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "7 ThreeLinearModel/Linear[linear1]/SymmetricWeightsDecompressor/type_0";
-"7 ThreeLinearModel/Linear[linear1]/SymmetricWeightsDecompressor/type_0" -> "9 ThreeLinearModel/Linear[linear1]/linear_0";
+"5 linear1.weight" -> "6 ThreeLinearModel/Linear[linear1]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0";
+"6 ThreeLinearModel/Linear[linear1]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "7 ThreeLinearModel/Linear[linear1]/INT8SymmetricWeightsDecompressor/type_0";
+"7 ThreeLinearModel/Linear[linear1]/INT8SymmetricWeightsDecompressor/type_0" -> "9 ThreeLinearModel/Linear[linear1]/linear_0";
 "8 linear1.bias" -> "9 ThreeLinearModel/Linear[linear1]/linear_0";
 "9 ThreeLinearModel/Linear[linear1]/linear_0" -> "14 ThreeLinearModel/Linear[linear3]/ActivationsSparsifier/abs_0";
 "9 ThreeLinearModel/Linear[linear1]/linear_0" -> "16 ThreeLinearModel/Linear[linear3]/ActivationsSparsifier/masked_fill_0";
-"10 linear3.weight" -> "11 ThreeLinearModel/Linear[linear3]/SymmetricWeightsDecompressor/decompress_symmetric_0";
-"11 ThreeLinearModel/Linear[linear3]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "12 ThreeLinearModel/Linear[linear3]/SymmetricWeightsDecompressor/type_0";
-"12 ThreeLinearModel/Linear[linear3]/SymmetricWeightsDecompressor/type_0" -> "17 ThreeLinearModel/Linear[linear3]/linear_0";
+"10 linear3.weight" -> "11 ThreeLinearModel/Linear[linear3]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0";
+"11 ThreeLinearModel/Linear[linear3]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "12 ThreeLinearModel/Linear[linear3]/INT8SymmetricWeightsDecompressor/type_0";
+"12 ThreeLinearModel/Linear[linear3]/INT8SymmetricWeightsDecompressor/type_0" -> "17 ThreeLinearModel/Linear[linear3]/linear_0";
 "13 linear3.bias" -> "17 ThreeLinearModel/Linear[linear3]/linear_0";
 "14 ThreeLinearModel/Linear[linear3]/ActivationsSparsifier/abs_0" -> "15 ThreeLinearModel/Linear[linear3]/ActivationsSparsifier/le_0";
 "15 ThreeLinearModel/Linear[linear3]/ActivationsSparsifier/le_0" -> "16 ThreeLinearModel/Linear[linear3]/ActivationsSparsifier/masked_fill_0";
 "16 ThreeLinearModel/Linear[linear3]/ActivationsSparsifier/masked_fill_0" -> "17 ThreeLinearModel/Linear[linear3]/linear_0";
 "17 ThreeLinearModel/Linear[linear3]/linear_0" -> "25 /nncf_model_output_0";
-"18 linear2.weight" -> "19 ThreeLinearModel/Linear[linear2]/SymmetricWeightsDecompressor/decompress_symmetric_0";
-"19 ThreeLinearModel/Linear[linear2]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "20 ThreeLinearModel/Linear[linear2]/SymmetricWeightsDecompressor/type_0";
-"20 ThreeLinearModel/Linear[linear2]/SymmetricWeightsDecompressor/type_0" -> "24 ThreeLinearModel/Linear[linear2]/linear_0";
+"18 linear2.weight" -> "19 ThreeLinearModel/Linear[linear2]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0";
+"19 ThreeLinearModel/Linear[linear2]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "20 ThreeLinearModel/Linear[linear2]/INT8SymmetricWeightsDecompressor/type_0";
+"20 ThreeLinearModel/Linear[linear2]/INT8SymmetricWeightsDecompressor/type_0" -> "24 ThreeLinearModel/Linear[linear2]/linear_0";
 "21 ThreeLinearModel/Linear[linear2]/ActivationsSparsifier/abs_0" -> "22 ThreeLinearModel/Linear[linear2]/ActivationsSparsifier/le_0";
 "22 ThreeLinearModel/Linear[linear2]/ActivationsSparsifier/le_0" -> "23 ThreeLinearModel/Linear[linear2]/ActivationsSparsifier/masked_fill_0";
 "23 ThreeLinearModel/Linear[linear2]/ActivationsSparsifier/masked_fill_0" -> "24 ThreeLinearModel/Linear[linear2]/linear_0";
diff --git a/tests/torch/data/experimental/sparsify_activations/three_linear_int8_sym_weights_sparse_activations.dot b/tests/torch/data/experimental/sparsify_activations/three_linear_int8_sym_weights_sparse_activations.dot
index c6488f1131b..437a8407d37 100644
--- a/tests/torch/data/experimental/sparsify_activations/three_linear_int8_sym_weights_sparse_activations.dot
+++ b/tests/torch/data/experimental/sparsify_activations/three_linear_int8_sym_weights_sparse_activations.dot
@@ -1,28 +1,28 @@
 strict digraph  {
 "0 /nncf_model_input_0" [id=0, type=nncf_model_input];
 "1 embedding.weight" [id=1, type=nncf_model_const];
-"2 ThreeLinearModel/Embedding[embedding]/SymmetricWeightsDecompressor/decompress_symmetric_0" [id=2, type=decompress_symmetric];
-"3 ThreeLinearModel/Embedding[embedding]/SymmetricWeightsDecompressor/type_0" [id=3, type=type];
+"2 ThreeLinearModel/Embedding[embedding]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=2, type=decompress_symmetric];
+"3 ThreeLinearModel/Embedding[embedding]/INT8SymmetricWeightsDecompressor/type_0" [id=3, type=type];
 "4 ThreeLinearModel/Embedding[embedding]/embedding_0" [id=4, type=embedding];
 "5 linear1.weight" [id=5, type=nncf_model_const];
-"6 ThreeLinearModel/Linear[linear1]/SymmetricWeightsDecompressor/decompress_symmetric_0" [id=6, type=decompress_symmetric];
-"7 ThreeLinearModel/Linear[linear1]/SymmetricWeightsDecompressor/type_0" [id=7, type=type];
+"6 ThreeLinearModel/Linear[linear1]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=6, type=decompress_symmetric];
+"7 ThreeLinearModel/Linear[linear1]/INT8SymmetricWeightsDecompressor/type_0" [id=7, type=type];
 "8 linear1.bias" [id=8, type=nncf_model_const];
 "9 ThreeLinearModel/Linear[linear1]/ActivationsSparsifier/abs_0" [id=9, type=abs];
 "10 ThreeLinearModel/Linear[linear1]/ActivationsSparsifier/le_0" [id=10, type=le];
 "11 ThreeLinearModel/Linear[linear1]/ActivationsSparsifier/masked_fill_0" [id=11, type=masked_fill];
 "12 ThreeLinearModel/Linear[linear1]/linear_0" [id=12, type=linear];
 "13 linear3.weight" [id=13, type=nncf_model_const];
-"14 ThreeLinearModel/Linear[linear3]/SymmetricWeightsDecompressor/decompress_symmetric_0" [id=14, type=decompress_symmetric];
-"15 ThreeLinearModel/Linear[linear3]/SymmetricWeightsDecompressor/type_0" [id=15, type=type];
+"14 ThreeLinearModel/Linear[linear3]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=14, type=decompress_symmetric];
+"15 ThreeLinearModel/Linear[linear3]/INT8SymmetricWeightsDecompressor/type_0" [id=15, type=type];
 "16 linear3.bias" [id=16, type=nncf_model_const];
 "17 ThreeLinearModel/Linear[linear3]/ActivationsSparsifier/abs_0" [id=17, type=abs];
 "18 ThreeLinearModel/Linear[linear3]/ActivationsSparsifier/le_0" [id=18, type=le];
 "19 ThreeLinearModel/Linear[linear3]/ActivationsSparsifier/masked_fill_0" [id=19, type=masked_fill];
 "20 ThreeLinearModel/Linear[linear3]/linear_0" [id=20, type=linear];
 "21 linear2.weight" [id=21, type=nncf_model_const];
-"22 ThreeLinearModel/Linear[linear2]/SymmetricWeightsDecompressor/decompress_symmetric_0" [id=22, type=decompress_symmetric];
-"23 ThreeLinearModel/Linear[linear2]/SymmetricWeightsDecompressor/type_0" [id=23, type=type];
+"22 ThreeLinearModel/Linear[linear2]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" [id=22, type=decompress_symmetric];
+"23 ThreeLinearModel/Linear[linear2]/INT8SymmetricWeightsDecompressor/type_0" [id=23, type=type];
 "24 ThreeLinearModel/Linear[linear2]/ActivationsSparsifier/abs_0" [id=24, type=abs];
 "25 ThreeLinearModel/Linear[linear2]/ActivationsSparsifier/le_0" [id=25, type=le];
 "26 ThreeLinearModel/Linear[linear2]/ActivationsSparsifier/masked_fill_0" [id=26, type=masked_fill];
@@ -30,33 +30,33 @@ strict digraph  {
 "28 /nncf_model_output_0" [id=28, type=nncf_model_output];
 "29 /nncf_model_output_1" [id=29, type=nncf_model_output];
 "0 /nncf_model_input_0" -> "4 ThreeLinearModel/Embedding[embedding]/embedding_0";
-"1 embedding.weight" -> "2 ThreeLinearModel/Embedding[embedding]/SymmetricWeightsDecompressor/decompress_symmetric_0";
-"2 ThreeLinearModel/Embedding[embedding]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "3 ThreeLinearModel/Embedding[embedding]/SymmetricWeightsDecompressor/type_0";
-"3 ThreeLinearModel/Embedding[embedding]/SymmetricWeightsDecompressor/type_0" -> "4 ThreeLinearModel/Embedding[embedding]/embedding_0";
+"1 embedding.weight" -> "2 ThreeLinearModel/Embedding[embedding]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0";
+"2 ThreeLinearModel/Embedding[embedding]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "3 ThreeLinearModel/Embedding[embedding]/INT8SymmetricWeightsDecompressor/type_0";
+"3 ThreeLinearModel/Embedding[embedding]/INT8SymmetricWeightsDecompressor/type_0" -> "4 ThreeLinearModel/Embedding[embedding]/embedding_0";
 "4 ThreeLinearModel/Embedding[embedding]/embedding_0" -> "9 ThreeLinearModel/Linear[linear1]/ActivationsSparsifier/abs_0";
 "4 ThreeLinearModel/Embedding[embedding]/embedding_0" -> "11 ThreeLinearModel/Linear[linear1]/ActivationsSparsifier/masked_fill_0";
 "4 ThreeLinearModel/Embedding[embedding]/embedding_0" -> "24 ThreeLinearModel/Linear[linear2]/ActivationsSparsifier/abs_0";
 "4 ThreeLinearModel/Embedding[embedding]/embedding_0" -> "26 ThreeLinearModel/Linear[linear2]/ActivationsSparsifier/masked_fill_0";
-"5 linear1.weight" -> "6 ThreeLinearModel/Linear[linear1]/SymmetricWeightsDecompressor/decompress_symmetric_0";
-"6 ThreeLinearModel/Linear[linear1]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "7 ThreeLinearModel/Linear[linear1]/SymmetricWeightsDecompressor/type_0";
-"7 ThreeLinearModel/Linear[linear1]/SymmetricWeightsDecompressor/type_0" -> "12 ThreeLinearModel/Linear[linear1]/linear_0";
+"5 linear1.weight" -> "6 ThreeLinearModel/Linear[linear1]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0";
+"6 ThreeLinearModel/Linear[linear1]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "7 ThreeLinearModel/Linear[linear1]/INT8SymmetricWeightsDecompressor/type_0";
+"7 ThreeLinearModel/Linear[linear1]/INT8SymmetricWeightsDecompressor/type_0" -> "12 ThreeLinearModel/Linear[linear1]/linear_0";
 "8 linear1.bias" -> "12 ThreeLinearModel/Linear[linear1]/linear_0";
 "9 ThreeLinearModel/Linear[linear1]/ActivationsSparsifier/abs_0" -> "10 ThreeLinearModel/Linear[linear1]/ActivationsSparsifier/le_0";
 "10 ThreeLinearModel/Linear[linear1]/ActivationsSparsifier/le_0" -> "11 ThreeLinearModel/Linear[linear1]/ActivationsSparsifier/masked_fill_0";
 "11 ThreeLinearModel/Linear[linear1]/ActivationsSparsifier/masked_fill_0" -> "12 ThreeLinearModel/Linear[linear1]/linear_0";
 "12 ThreeLinearModel/Linear[linear1]/linear_0" -> "17 ThreeLinearModel/Linear[linear3]/ActivationsSparsifier/abs_0";
 "12 ThreeLinearModel/Linear[linear1]/linear_0" -> "19 ThreeLinearModel/Linear[linear3]/ActivationsSparsifier/masked_fill_0";
-"13 linear3.weight" -> "14 ThreeLinearModel/Linear[linear3]/SymmetricWeightsDecompressor/decompress_symmetric_0";
-"14 ThreeLinearModel/Linear[linear3]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "15 ThreeLinearModel/Linear[linear3]/SymmetricWeightsDecompressor/type_0";
-"15 ThreeLinearModel/Linear[linear3]/SymmetricWeightsDecompressor/type_0" -> "20 ThreeLinearModel/Linear[linear3]/linear_0";
+"13 linear3.weight" -> "14 ThreeLinearModel/Linear[linear3]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0";
+"14 ThreeLinearModel/Linear[linear3]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "15 ThreeLinearModel/Linear[linear3]/INT8SymmetricWeightsDecompressor/type_0";
+"15 ThreeLinearModel/Linear[linear3]/INT8SymmetricWeightsDecompressor/type_0" -> "20 ThreeLinearModel/Linear[linear3]/linear_0";
 "16 linear3.bias" -> "20 ThreeLinearModel/Linear[linear3]/linear_0";
 "17 ThreeLinearModel/Linear[linear3]/ActivationsSparsifier/abs_0" -> "18 ThreeLinearModel/Linear[linear3]/ActivationsSparsifier/le_0";
 "18 ThreeLinearModel/Linear[linear3]/ActivationsSparsifier/le_0" -> "19 ThreeLinearModel/Linear[linear3]/ActivationsSparsifier/masked_fill_0";
 "19 ThreeLinearModel/Linear[linear3]/ActivationsSparsifier/masked_fill_0" -> "20 ThreeLinearModel/Linear[linear3]/linear_0";
 "20 ThreeLinearModel/Linear[linear3]/linear_0" -> "28 /nncf_model_output_0";
-"21 linear2.weight" -> "22 ThreeLinearModel/Linear[linear2]/SymmetricWeightsDecompressor/decompress_symmetric_0";
-"22 ThreeLinearModel/Linear[linear2]/SymmetricWeightsDecompressor/decompress_symmetric_0" -> "23 ThreeLinearModel/Linear[linear2]/SymmetricWeightsDecompressor/type_0";
-"23 ThreeLinearModel/Linear[linear2]/SymmetricWeightsDecompressor/type_0" -> "27 ThreeLinearModel/Linear[linear2]/linear_0";
+"21 linear2.weight" -> "22 ThreeLinearModel/Linear[linear2]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0";
+"22 ThreeLinearModel/Linear[linear2]/INT8SymmetricWeightsDecompressor/decompress_symmetric_0" -> "23 ThreeLinearModel/Linear[linear2]/INT8SymmetricWeightsDecompressor/type_0";
+"23 ThreeLinearModel/Linear[linear2]/INT8SymmetricWeightsDecompressor/type_0" -> "27 ThreeLinearModel/Linear[linear2]/linear_0";
 "24 ThreeLinearModel/Linear[linear2]/ActivationsSparsifier/abs_0" -> "25 ThreeLinearModel/Linear[linear2]/ActivationsSparsifier/le_0";
 "25 ThreeLinearModel/Linear[linear2]/ActivationsSparsifier/le_0" -> "26 ThreeLinearModel/Linear[linear2]/ActivationsSparsifier/masked_fill_0";
 "26 ThreeLinearModel/Linear[linear2]/ActivationsSparsifier/masked_fill_0" -> "27 ThreeLinearModel/Linear[linear2]/linear_0";
diff --git a/tests/torch/fx/test_compress_weights.py b/tests/torch/fx/test_compress_weights.py
index fea9e0ce501..519fcfd654e 100644
--- a/tests/torch/fx/test_compress_weights.py
+++ b/tests/torch/fx/test_compress_weights.py
@@ -24,6 +24,8 @@
 from nncf.quantization import compress_weights
 from nncf.torch.dynamic_graph.patch_pytorch import disable_patching
 from tests.torch.ptq.test_weights_compression import ALL_SENSITIVITY_METRICS
+from tests.torch.ptq.test_weights_compression import INT4_MODES
+from tests.torch.ptq.test_weights_compression import INT8_MODES
 from tests.torch.ptq.test_weights_compression import SUPPORTED_MODES
 from tests.torch.ptq.test_weights_compression import UNSUPPORTED_MODES
 from tests.torch.ptq.test_weights_compression import ConvolutionModel
@@ -77,10 +79,13 @@ def _capture_model(model, inputs):
 
 @pytest.mark.parametrize("mode", SUPPORTED_MODES)
 def test_compress_weights(mode):
-    model = ShortTransformer(5, 10)
-    input_ids = torch.randint(0, 10, (5,))
+    model = ShortTransformer(8, 16)
+    input_ids = torch.randint(0, 10, (8,))
     exported_model = _capture_model(model, input_ids)
-    compressed_model = compress_weights(exported_model, mode=mode)
+    kwargs = {}
+    if mode in [CompressWeightsMode.INT4_SYM, CompressWeightsMode.INT4_ASYM]:
+        kwargs["group_size"] = 4
+    compressed_model = compress_weights(exported_model, mode=mode, **kwargs)
     dtype = torch.int8 if mode == CompressWeightsMode.INT8_SYM else torch.uint8
     n_compressed_weights = 0
     n_target_modules = 0
@@ -92,7 +97,7 @@ def test_compress_weights(mode):
     assert n_target_modules == n_compressed_weights
 
 
-@pytest.mark.parametrize("mode", SUPPORTED_MODES)
+@pytest.mark.parametrize("mode", INT8_MODES)
 def test_compress_weights_graph_edge(mode):
     model = ShortTransformer(5, 10)
     input_ids = torch.randint(0, 10, (5,))
@@ -109,10 +114,13 @@ def test_compress_weights_graph_edge(mode):
 @pytest.mark.parametrize("mode", SUPPORTED_MODES)
 def test_compress_weights_shared_weights(mocker, mode):
     with disable_patching():
-        model = ShortTransformer(5, 10, share_weights=True)
-        input_ids = torch.randint(0, 10, (5,))
+        model = ShortTransformer(8, 16, share_weights=True)
+        input_ids = torch.randint(0, 10, (8,))
         exported_model = _capture_model(model, input_ids)
-        compressed_model = compress_weights(exported_model, mode=mode)
+        kwargs = {}
+        if mode in [CompressWeightsMode.INT4_SYM, CompressWeightsMode.INT4_ASYM]:
+            kwargs["group_size"] = 4
+        compressed_model = compress_weights(exported_model, mode=mode, **kwargs)
     dtype = torch.int8 if mode == CompressWeightsMode.INT8_SYM else torch.uint8
     n_compressed_weights = 0
     n_target_modules = 0
@@ -142,11 +150,14 @@ def test_compress_weights_shared_weights(mocker, mode):
 @pytest.mark.parametrize("mode", SUPPORTED_MODES)
 def test_compressed_model_inference(mode):
     torch.manual_seed(42)
-    model = ShortTransformer(5, 10, share_weights=True)
-    input_ids = torch.randint(0, 10, (5,))
+    model = ShortTransformer(8, 16, share_weights=True)
+    input_ids = torch.randint(0, 10, (8,))
     exported_model = _capture_model(model, input_ids)
     exported_model_output = exported_model(input_ids)
-    compressed_model = compress_weights(exported_model, mode=mode)
+    kwargs = {}
+    if mode in [CompressWeightsMode.INT4_SYM, CompressWeightsMode.INT4_ASYM]:
+        kwargs["group_size"] = 4
+    compressed_model = compress_weights(exported_model, mode=mode, **kwargs)
     compressed_model_outputs = compressed_model(input_ids)
 
     assert (
@@ -161,7 +172,7 @@ def test_compress_weights_model_size_conv(mode):
     dtype = torch.int8 if mode == CompressWeightsMode.INT8_SYM else torch.uint8
     model = ConvolutionModel()
 
-    input_ids = torch.randint(0, 10, [1, 3, 300, 300])
+    input_ids = torch.randint(0, 10, [1, 3, 256, 256])
     exported_model = _capture_model(model, input_ids)
     model_size = get_model_size(exported_model)
     compressed_model = compress_weights(exported_model, mode=mode)
@@ -182,9 +193,11 @@ def test_compress_weights_model_size_conv(mode):
 @pytest.mark.parametrize("mode", SUPPORTED_MODES)
 def test_compress_weights_functional_model(mode):
     model = FunctionalModel()
-    decompressor_type = "symmetric" if mode == CompressWeightsMode.INT8_SYM else "asymmetric"
+    decompressor_type = (
+        "symmetric" if mode in (CompressWeightsMode.INT8_SYM, CompressWeightsMode.INT4_SYM) else "asymmetric"
+    )
 
-    input_ids = torch.randint(0, 10, [1, 3, 300, 300])
+    input_ids = torch.randint(0, 10, [1, 3, 256, 256])
     exported_model = _capture_model(model, input_ids)
     compressed_model = compress_weights(exported_model, mode=mode)
 
@@ -196,7 +209,7 @@ def test_compress_weights_functional_model(mode):
     assert n_compressed_weights == 4
 
 
-@pytest.mark.parametrize("mode", SUPPORTED_MODES)
+@pytest.mark.parametrize("mode", INT8_MODES)
 @pytest.mark.parametrize(
     "params",
     (
@@ -223,6 +236,27 @@ def test_raise_error_with_unsupported_params_for_int8(mode, params):
         compress_weights(exported_model, mode=mode, **params)
 
 
+@pytest.mark.parametrize("mode", INT4_MODES)
+@pytest.mark.parametrize(
+    "params",
+    (
+        {"ratio": 0.5},
+        *({"sensitivity_metric": metric} for metric in ALL_SENSITIVITY_METRICS),
+        {"gptq": True},
+        {"awq": True},
+        {"scale_estimation": True},
+        {"lora_correction": True},
+        {"dataset": Dataset([1])},
+    ),
+)
+def test_raise_error_with_unsupported_params_for_int4(mode, params):
+    dummy_torch_model = EmptyModel()
+    dummy_input = torch.Tensor()
+    exported_model = _capture_model(dummy_torch_model, dummy_input)
+    with pytest.raises(nncf.ParameterNotSupportedError):
+        compress_weights(exported_model, mode=mode, **params)
+
+
 @pytest.mark.parametrize("mode", UNSUPPORTED_MODES)
 def test_raise_error_with_not_int8(mode):
     dummy_torch_model = EmptyModel()
@@ -252,7 +286,7 @@ def test_model_devices_and_precisions(use_cuda, dtype):
     model = MatMulModel().to(device)
     if dtype == torch.float16:
         model.half()
-    dummy_input = torch.rand((1, 300), dtype=dtype, device=device)
+    dummy_input = torch.rand((1, 256), dtype=dtype, device=device)
     exported_model = _capture_model(model, dummy_input)
     compressed_model = compress_weights(exported_model)
     result = compressed_model(dummy_input)
diff --git a/tests/torch/ptq/test_weights_compression.py b/tests/torch/ptq/test_weights_compression.py
index f982a8375d1..88373ac308f 100644
--- a/tests/torch/ptq/test_weights_compression.py
+++ b/tests/torch/ptq/test_weights_compression.py
@@ -19,8 +19,14 @@
 from nncf import SensitivityMetric
 from nncf.quantization import compress_weights
 from nncf.torch import wrap_model
-from nncf.torch.quantization.layers import AsymmetricWeightsDecompressor
-from nncf.torch.quantization.layers import SymmetricWeightsDecompressor
+from nncf.torch.quantization.layers import INT4AsymmetricWeightsDecompressor
+from nncf.torch.quantization.layers import INT4SymmetricWeightsDecompressor
+from nncf.torch.quantization.layers import INT8AsymmetricWeightsDecompressor
+from nncf.torch.quantization.layers import INT8SymmetricWeightsDecompressor
+from nncf.torch.quantization.quantize_functions import pack_int4
+from nncf.torch.quantization.quantize_functions import pack_uint4
+from nncf.torch.quantization.quantize_functions import unpack_int4
+from nncf.torch.quantization.quantize_functions import unpack_uint4
 
 DATA_BASED_SENSITIVITY_METRICS = (
     SensitivityMetric.HESSIAN_INPUT_ACTIVATION,
@@ -31,12 +37,10 @@
 
 ALL_SENSITIVITY_METRICS = DATA_BASED_SENSITIVITY_METRICS + (SensitivityMetric.WEIGHT_QUANTIZATION_ERROR,)
 
-SUPPORTED_MODES = (CompressWeightsMode.INT8, CompressWeightsMode.INT8_ASYM, CompressWeightsMode.INT8_SYM)
-UNSUPPORTED_MODES = (
-    CompressWeightsMode.INT4_SYM,
-    CompressWeightsMode.INT4_ASYM,
-    CompressWeightsMode.NF4,
-)
+INT8_MODES = (CompressWeightsMode.INT8_ASYM, CompressWeightsMode.INT8_SYM)
+INT4_MODES = (CompressWeightsMode.INT4_SYM, CompressWeightsMode.INT4_ASYM)
+SUPPORTED_MODES = INT8_MODES + INT4_MODES
+UNSUPPORTED_MODES = (CompressWeightsMode.NF4, CompressWeightsMode.E2M1)
 
 
 class ShortTransformer(torch.nn.Module):
@@ -59,7 +63,7 @@ def forward(self, input_ids):
 class MatMulModel(torch.nn.Module):
     def __init__(self):
         super().__init__()
-        self.w = torch.nn.Parameter(torch.ones(size=(300, 300), dtype=torch.float32))
+        self.w = torch.nn.Parameter(torch.ones(size=(256, 256), dtype=torch.float32))
 
     def forward(self, input):
         return input @ self.w
@@ -69,7 +73,7 @@ class FunctionalModel(torch.nn.Module):
     def __init__(self):
         super().__init__()
         self.conv_w = torch.nn.Parameter(torch.ones(size=(5, 3, 3, 3), dtype=torch.float32))
-        self.matmul_w = torch.nn.Parameter(torch.ones(size=(1, 3, 300, 300), dtype=torch.float32))
+        self.matmul_w = torch.nn.Parameter(torch.ones(size=(1, 3, 256, 256), dtype=torch.float32))
         self.conv_tr_w = torch.nn.Parameter(torch.rand(size=(5, 4, 3, 3)))
         self.nested_matmul = MatMulModel()
 
@@ -109,14 +113,18 @@ def forward(self, input_):
         return x
 
 
-@pytest.mark.parametrize("mode", (CompressWeightsMode.INT8_SYM, CompressWeightsMode.INT8_ASYM))
+@pytest.mark.parametrize("mode", SUPPORTED_MODES)
 def test_compress_weights(mode):
-    model = ShortTransformer(5, 10)
+    model = ShortTransformer(8, 16)
     dtype = torch.int8 if mode == CompressWeightsMode.INT8_SYM else torch.uint8
 
-    input_ids = torch.randint(0, 10, (5,))
+    input_ids = torch.randint(0, 10, (8,))
     wrapped_model = wrap_model(model, example_input=input_ids, trace_parameters=True)
-    compressed_model = compress_weights(wrapped_model, mode=mode)
+
+    kwargs = {}
+    if mode in [CompressWeightsMode.INT4_SYM, CompressWeightsMode.INT4_ASYM]:
+        kwargs["group_size"] = 4
+    compressed_model = compress_weights(wrapped_model, mode=mode, **kwargs)
 
     n_compressed_weights = 0
     n_target_modules = 0
@@ -130,14 +138,19 @@ def test_compress_weights(mode):
     assert n_compressed_weights == n_target_modules
 
 
-@pytest.mark.parametrize("mode", (CompressWeightsMode.INT8_SYM, CompressWeightsMode.INT8_ASYM))
+@pytest.mark.parametrize("mode", SUPPORTED_MODES)
 def test_compress_weights_functional_model(mode):
     model = FunctionalModel()
-    decompressor_type = (
-        SymmetricWeightsDecompressor if mode == CompressWeightsMode.INT8_SYM else AsymmetricWeightsDecompressor
-    )
+    decompressor_map = {
+        CompressWeightsMode.INT8_SYM: (INT8SymmetricWeightsDecompressor,),
+        CompressWeightsMode.INT8_ASYM: (INT8AsymmetricWeightsDecompressor,),
+        CompressWeightsMode.INT4_SYM: (INT4SymmetricWeightsDecompressor, INT8AsymmetricWeightsDecompressor),
+        CompressWeightsMode.INT4_ASYM: (INT4AsymmetricWeightsDecompressor, INT8AsymmetricWeightsDecompressor),
+    }
 
-    input_ids = torch.randint(0, 10, [1, 3, 300, 300])
+    decompressor_type = decompressor_map[mode]
+
+    input_ids = torch.randint(0, 10, [1, 3, 256, 256])
     wrapped_model = wrap_model(model, example_input=input_ids, trace_parameters=True)
     compressed_model = compress_weights(wrapped_model, mode=mode)
 
@@ -167,14 +180,18 @@ def test_compress_weights_conv():
     assert n_compressed_weights == n_target_modules
 
 
-@pytest.mark.parametrize("mode", (CompressWeightsMode.INT8_SYM, CompressWeightsMode.INT8_ASYM))
+@pytest.mark.parametrize("mode", SUPPORTED_MODES)
 def test_compress_shared_weights(mocker, mode):
-    model = ShortTransformer(5, 10, share_weights=True)
+    model = ShortTransformer(8, 16, share_weights=True)
     dtype = torch.int8 if mode == CompressWeightsMode.INT8_SYM else torch.uint8
 
-    input_ids = torch.randint(0, 10, (5,))
+    input_ids = torch.randint(0, 10, (8,))
     wrapped_model = wrap_model(model, example_input=input_ids, trace_parameters=True)
-    compressed_model = compress_weights(wrapped_model, mode=mode)
+
+    kwargs = {}
+    if mode in [CompressWeightsMode.INT4_SYM, CompressWeightsMode.INT4_ASYM]:
+        kwargs["group_size"] = 4
+    compressed_model = compress_weights(wrapped_model, mode=mode, **kwargs)
 
     n_compressed_weights = 0
     n_target_modules = 0
@@ -203,7 +220,7 @@ def forward(self, input):
         return input
 
 
-@pytest.mark.parametrize("mode", SUPPORTED_MODES)
+@pytest.mark.parametrize("mode", INT8_MODES)
 @pytest.mark.parametrize(
     "params",
     (
@@ -229,6 +246,26 @@ def test_raise_error_with_unsupported_params_for_int8(mode, params):
         compress_weights(wrapped_model, mode=mode, **params)
 
 
+@pytest.mark.parametrize("mode", INT4_MODES)
+@pytest.mark.parametrize(
+    "params",
+    (
+        {"ratio": 0.5},
+        *({"sensitivity_metric": metric} for metric in ALL_SENSITIVITY_METRICS),
+        {"gptq": True},
+        {"awq": True},
+        {"scale_estimation": True},
+        {"lora_correction": True},
+    ),
+)
+def test_raise_error_with_unsupported_params_for_int4(mode, params):
+    dummy_torch_model = EmptyModel()
+    dummy_input = torch.Tensor()
+    wrapped_model = wrap_model(dummy_torch_model, example_input=dummy_input, trace_parameters=True)
+    with pytest.raises(nncf.ParameterNotSupportedError):
+        compress_weights(wrapped_model, mode=mode, **params)
+
+
 @pytest.mark.parametrize("mode", UNSUPPORTED_MODES)
 def test_raise_error_with_not_int8(mode):
     dummy_torch_model = EmptyModel()
@@ -270,7 +307,7 @@ def test_model_devices_and_precisions(use_cuda, dtype):
     if dtype == torch.float16:
         model.half()
 
-    dummy_input = torch.rand((1, 300), dtype=dtype, device=device)
+    dummy_input = torch.rand((1, 256), dtype=dtype, device=device)
     wrapped_model = wrap_model(model, example_input=dummy_input, trace_parameters=True)
     compressed_model = compress_weights(wrapped_model)
     result = compressed_model(dummy_input)
@@ -279,3 +316,21 @@ def test_model_devices_and_precisions(use_cuda, dtype):
     assert compressed_model.state_dict()["_nncf.external_op.weights_decompressor_w._scale"].dtype == torch.float16
     # Result should be in the precision of the model
     assert result.dtype == dtype
+
+
+def test_pack_uint4():
+    w_uint8 = torch.randint(0, 15, (4, 4), dtype=torch.uint8)
+    packed_w = pack_uint4(w_uint8)
+    assert packed_w.dtype == torch.uint8
+    assert packed_w.numel() * 2 == w_uint8.numel()
+    unpacked_w = unpack_uint4(packed_w).reshape(w_uint8.shape)
+    assert torch.all(unpacked_w == w_uint8)
+
+
+def test_pack_int4():
+    w_int8 = torch.randint(-8, 7, (4, 4), dtype=torch.int8)
+    packed_w = pack_int4(w_int8)
+    assert packed_w.dtype == torch.uint8
+    assert packed_w.numel() * 2 == w_int8.numel()
+    unpacked_w = unpack_int4(packed_w).reshape(w_int8.shape)
+    assert torch.all(unpacked_w == w_int8)