diff --git a/.github/workflows/call_precommit.yml b/.github/workflows/call_precommit.yml
index 6c2f58ae9ce..ddabf24213a 100644
--- a/.github/workflows/call_precommit.yml
+++ b/.github/workflows/call_precommit.yml
@@ -136,6 +136,48 @@ jobs:
         env:
           NUM_WORKERS: 4
 
+  executorch:
+    timeout-minutes: 40
+    runs-on: ubuntu-latest-8-cores
+    defaults:
+      run:
+        shell: bash
+    env:
+      DEBIAN_FRONTEND: noninteractive
+    steps:
+      - name: Install dependencies
+        run : |
+          sudo apt-get update
+          sudo apt-get --assume-yes install gcc g++ build-essential ninja-build libgl1-mesa-dev libglib2.0-0
+      - uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
+        with:
+          lfs: true
+      - uses: actions/setup-python@e797f83bcb11b83ae66e0230d6156d7c80228e7c # v6.0.0
+        with:
+          python-version: ${{ inputs.python_version }}
+      - name: Runner info
+        continue-on-error: true
+        run: |
+          cat /etc/*release
+          cat /proc/cpuinfo
+      - name: Override constraints
+        if: ${{ inputs.override_requirements != '' }}
+        run: python .github/scripts/override_constraints.py "${{ inputs.override_requirements }}"
+        shell: bash
+      - name: Install NNCF and test requirements
+        run: |
+          pip install . -r tests/executorch/requirements.txt
+          # Executorch
+          # Editable install due to https://github.com/pytorch/executorch/issues/6475
+          pip install --no-build-isolation -e git+https://github.com/anzr299/executorch.git@an/quantizer_nncf_pt2e_support#egg=executorch
+      - name: Print installed modules
+        run: pip list
+      - name: Run PyTorch precommit test scope
+        run: |
+          make test-executorch
+        env:
+          NUM_WORKERS: 4
+
   pytorch-cuda:
     timeout-minutes: 40
     runs-on: aks-linux-4-cores-28gb-gpu-tesla-t4
diff --git a/Makefile b/Makefile
index d420c8ce4f0..0cf574a1848 100644
--- a/Makefile
+++ b/Makefile
@@ -141,6 +141,9 @@ test-torch-cpu:
 test-torch-cuda:
 	pytest ${COVERAGE_ARGS} tests/torch -ra -m "cuda and not weekly and not nightly and not models_hub and not legacy" --junitxml ${JUNITXML_PATH}
 
+test-executorch:
+	pytest ${COVERAGE_ARGS} tests/executorch --junitxml ${JUNITXML_PATH}
+
 test-torch-nightly:
 	pytest ${COVERAGE_ARGS} tests/torch -m "nightly or legacy" --junitxml ${JUNITXML_PATH} $(DATA_ARG)
 
diff --git a/src/nncf/experimental/quantization/algorithms/weight_compression/__init__.py b/src/nncf/experimental/quantization/algorithms/weight_compression/__init__.py
new file mode 100644
index 00000000000..e5a42efc0ef
--- /dev/null
+++ b/src/nncf/experimental/quantization/algorithms/weight_compression/__init__.py
@@ -0,0 +1,10 @@
+# Copyright (c) 2025 Intel Corporation
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#      http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
diff --git a/src/nncf/experimental/quantization/algorithms/weight_compression/algorithm.py b/src/nncf/experimental/quantization/algorithms/weight_compression/algorithm.py
new file mode 100644
index 00000000000..7d541421d2a
--- /dev/null
+++ b/src/nncf/experimental/quantization/algorithms/weight_compression/algorithm.py
@@ -0,0 +1,169 @@
+# Copyright (c) 2025 Intel Corporation
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#      http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Iterable, Optional
+
+import torch
+
+from nncf import AdvancedCompressionParameters
+from nncf import BackupMode
+from nncf import CompressionFormat
+from nncf import CompressWeightsMode
+from nncf import Dataset
+from nncf import IgnoredScope
+from nncf import SensitivityMetric
+from nncf.common.graph.graph import NNCFGraph
+from nncf.common.graph.graph import NNCFNode
+from nncf.common.tensor_statistics.statistic_point import StatisticPointsContainer
+from nncf.common.utils.backend import BackendType
+from nncf.experimental.quantization.quantizer import Quantizer
+from nncf.quantization.algorithms.algorithm import Algorithm
+from nncf.quantization.algorithms.weight_compression.algorithm import WeightCompression as OriginalWeightCompression
+
+
+class WeightsCompression(Algorithm):
+    """
+    Post-training Weight Compression algorithm implementation.
+
+    Compresses weights of Linear and Embedding layers to 8-bit integer or
+    to 4-bit integer/float depending on mode, ratio and group size.
+    """
+
+    def __init__(
+        self,
+        mode: CompressWeightsMode,
+        quantizer: Quantizer,
+        ratio: float,
+        group_size: int,
+        ignored_scope: IgnoredScope,
+        all_layers: bool,
+        subset_size: int,
+        awq: bool,
+        scale_estimation: bool,
+        gptq: bool,
+        lora_correction: bool,
+        backup_mode: BackupMode,
+        sensitivity_metric: SensitivityMetric,
+        compression_format: CompressionFormat,
+        advanced_parameters: AdvancedCompressionParameters,
+    ) -> torch.fx.GraphModule:
+        """
+        :param mode: Defines a mode for weight compression.
+            INT8_SYM stands for 8-bit integer symmetric quantization of all weights.
+                Weights are quantized symmetrically without zero point.
+            INT8_ASYM is the same as INT8_SYM mode, but weights are quantized to a primary precision asymmetrically
+                with a typical non-fixed zero point.
+            INT4_SYM stands for a mixed-precision weights quantization with 4-bit integer as a primary precision.
+                Weights are quantized to a primary precision symmetrically without zero point.
+                All embeddings and the last layer are always compressed to a backup_mode, which is INT8_ASYM,
+                by default. All others are quantized whether to 4-bit integer or to a backup_mode depending on
+                criteria and the given ratio.
+            INT4_ASYM is the same as INT4_SYM mode, but weights are quantized to a primary precision asymmetrically
+                with a typical non-fixed zero point.
+        :param quantizer: Quantizer to use in WeightCompression algorithm.
+        :param ratio: the ratio between primary and backup precisions (e.g. 0.9 means 90% of layers quantized to INT4
+            and the rest to backup_mode).
+        :param group_size: number of weights (e.g. 128) in the channel dimension
+            that share quantization parameters (scale). The value -1 means no grouping.
+        :param ignored_scope: An ignored scope that defined the list of model control
+            flow graph nodes to be ignored during quantization.
+        :param all_layers: Indicates whether embeddings and last MatMul layers should be compressed to a primary
+            precision. By default, the backup precision is assigned for the embeddings and last MatMul layers.
+        :param subset_size: Number of data samples to calculate activation statistics used for assigning different
+            quantization precision.
+        :param awq: determines whether to use or not modified AWQ algorithm.
+        :param scale_estimation: determines whether to use or not scale estimation for 4 bit layers.
+        :param gptq: determines whether to use or not GPTQ algorithm.
+        :param lora_correction: determines whether to use or not LoRA Correction algorithm.
+        :param backup_mode: Defines a backup mode for mixed-precision weight compression.
+            NONE stands for original floating-point precision of the model weights.
+                In this mode, weights are retained in their original precision without any quantization.
+            INT8_SYM stands for 8-bit integer symmetric quantization without zero point.
+            INT8_ASYM stands for 8-bit integer asymmetric quantization with a typical non-fixed zero point.
+        :param sensitivity_metric: The sensitivity metric for assigning quantization precision to layers. In order to
+            preserve the accuracy of the model, the more sensitive layers receives a higher precision.
+        :param compression_format: Describes the format in which the model is saved after weight compression.
+        :param advanced_parameters: advanced parameters for algorithms in compression pipeline.
+        """
+        self._quantizer = quantizer
+
+        self._mode = mode
+        self._awq = awq
+        self._gptq = gptq
+        self._scale_estimation = scale_estimation
+        self._subset_size = subset_size
+        self._advanced_parameters = advanced_parameters
+        self._lora_correction = lora_correction
+        self._ratio = ratio
+        self._group_size = group_size
+        self._all_layers = all_layers
+        self._backup_mode = backup_mode
+        self._sensitivity_metric = sensitivity_metric
+        self._compression_format = compression_format
+
+        self._algo = OriginalWeightCompression(
+            mode=self._mode,
+            ratio=self._ratio,
+            group_size=self._group_size,
+            ignored_scope=ignored_scope,
+            all_layers=self._all_layers,
+            sensitivity_metric=self._sensitivity_metric,
+            awq=self._awq,
+            subset_size=self._subset_size,
+            scale_estimation=self._scale_estimation,
+            gptq=self._gptq,
+            lora_correction=self._lora_correction,
+            backup_mode=self._backup_mode,
+            compression_format=self._compression_format,
+            advanced_parameters=self._advanced_parameters,
+        )
+
+    def available_backends(self) -> list[BackendType]:
+        return self._algo.available_backends()
+
+    def apply(
+        self,
+        model: torch.fx.GraphModule,
+        graph: NNCFGraph,
+        statistic_points: Optional[StatisticPointsContainer] = None,
+        dataset: Optional[Dataset] = None,
+    ) -> torch.fx.GraphModule:
+        self._algo.set_backend_entity(model)
+
+        all_weight_params, ratio_defining_params, skipped_weight_params = (
+            self._quantizer.get_weight_compression_parameters(model, graph)
+        )
+
+        return self._algo.apply_with_parameters(
+            model,
+            graph,
+            dataset,
+            statistic_points,
+            all_weight_params,
+            ratio_defining_params,
+            skipped_weight_params,
+        )
+
+    def get_statistic_points(
+        self,
+        model: torch.fx.GraphModule,
+        graph: NNCFGraph,
+        nodes_and_port_ids: Iterable[tuple[NNCFNode, int]],
+    ) -> StatisticPointsContainer:
+        """
+        Returns statistic points, for which StatisticsCollector should collect statistics.
+
+        :param model: Model for statistics collection.
+        :param graph: Model graph.
+        :param nodes_and_port_ids: Nodes and port ids for which statistics should be collected.
+        :return: Statistic points, for which StatisticsCollector should collect statistics.
+        """
+        return self._algo.get_statistic_points(model, graph, nodes_and_port_ids)
diff --git a/src/nncf/experimental/torch/fx/__init__.py b/src/nncf/experimental/torch/fx/__init__.py
index 2ecdde60840..86cd9709f6b 100644
--- a/src/nncf/experimental/torch/fx/__init__.py
+++ b/src/nncf/experimental/torch/fx/__init__.py
@@ -9,5 +9,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from nncf.experimental.torch.fx.quantization.quantize_pt2e import compress_pt2e as compress_pt2e
 from nncf.experimental.torch.fx.quantization.quantize_pt2e import quantize_pt2e as quantize_pt2e
 from nncf.experimental.torch.fx.quantization.quantizer.openvino_quantizer import OpenVINOQuantizer as OpenVINOQuantizer
diff --git a/src/nncf/experimental/torch/fx/quantization/quantize_pt2e.py b/src/nncf/experimental/torch/fx/quantization/quantize_pt2e.py
index 3f0b3186310..b493a9461af 100644
--- a/src/nncf/experimental/torch/fx/quantization/quantize_pt2e.py
+++ b/src/nncf/experimental/torch/fx/quantization/quantize_pt2e.py
@@ -22,11 +22,14 @@
 from torch.fx.passes.infra.pass_manager import PassManager
 
 import nncf
+from nncf import AdvancedCompressionParameters
 from nncf import Dataset
+from nncf import SensitivityMetric
 from nncf.common.factory import NNCFGraphFactory
 from nncf.common.logging import nncf_logger
 from nncf.common.utils.api_marker import api
 from nncf.experimental.quantization.algorithms.post_training.algorithm import ExperimentalPostTrainingQuantization
+from nncf.experimental.quantization.algorithms.weight_compression.algorithm import WeightsCompression
 from nncf.experimental.torch.fx.constant_folding import constant_fold
 from nncf.experimental.torch.fx.quantization.quantizer.openvino_adapter import OpenVINOQuantizerAdapter
 from nncf.experimental.torch.fx.quantization.quantizer.openvino_quantizer import OpenVINOQuantizer
@@ -36,6 +39,7 @@
 from nncf.experimental.torch.fx.transformations import compress_post_quantize_transformation
 from nncf.quantization.advanced_parameters import AdvancedBiasCorrectionParameters
 from nncf.quantization.advanced_parameters import AdvancedSmoothQuantParameters
+from nncf.quantization.algorithms.weight_compression.algorithm import get_weight_compression_configuration
 from nncf.quantization.range_estimator import RangeEstimatorParameters
 
 
@@ -157,3 +161,94 @@ def _quant_node_constraint(n: torch.fx.Node) -> bool:
     related to quantization
     """
     return n.op == "call_function" and n.target in QUANTIZE_NODE_TARGETS
+
+
+@api(canonical_alias="nncf.experimental.torch.fx.compress_pt2e")
+def compress_pt2e(
+    model: torch.fx.GraphModule,
+    quantizer: Quantizer,
+    dataset: Optional[nncf.Dataset] = None,
+    awq: bool = False,
+    scale_estimation: bool = False,
+    gptq: bool = False,
+    lora_correction: bool = False,
+    subset_size: int = 128,
+    ratio: int = 1,
+    sensitivity_metric: Optional[SensitivityMetric] = None,
+    advanced_parameters: Optional[AdvancedCompressionParameters] = None,
+) -> torch.fx.GraphModule:
+    """
+    Applies Weight Compression to the torch.fx.GraphModule provided model
+    using provided torch.ao quantizer.
+
+    :param model: A torch.fx.GraphModule instance to be quantized.
+    :param quantizer: Torch ao quantizer to annotate nodes in the graph with quantization setups
+        to convey the desired way of quantization.
+    :param dataset: A representative dataset for the
+        calibration process.
+    :param awq: Determines whether to use or not the modified AWQ algorithm.
+    :param scale_estimation: Determines whether to use or not scale estimation for 4-bit layers.
+    :param gptq: Determines whether to use or not GPTQ algorithm.
+    :param lora_correction: Determines whether to use or not LoRA Correction algorithm.
+    :param subset_size: Number of data samples to calculate activation statistics used for assigning different
+        quantization precision.
+    :param ratio: the ratio between baseline and backup precisions (e.g. 0.9 means 90% of layers quantized to NF4
+        and the rest to INT8_ASYM).
+    :param sensitivity_metric: The sensitivity metric for assigning quantization precision to layers. In order to
+        preserve the accuracy of the model, the more sensitive layers receive a higher precision.
+    :param advanced_parameters: Advanced parameters for algorithms in the compression pipeline.
+    """
+    if isinstance(quantizer, OpenVINOQuantizer) or hasattr(quantizer, "get_nncf_weight_compression_parameters"):
+        quantizer = OpenVINOQuantizerAdapter(quantizer)
+        compression_format = nncf.CompressionFormat.DQ
+    else:
+        # TODO Support Third party quantizers here.
+        msg = "Only OpenVINO Quantizer is supported currently."
+        raise nncf.InternalError(msg)
+
+    wc_config = quantizer.get_weight_compression_config()
+
+    mode = wc_config.get("mode", None)
+    awq = awq
+    gptq = gptq
+    scale_estimation = scale_estimation
+    subset_size = subset_size
+    advanced_parameters = advanced_parameters
+    lora_correction = lora_correction
+    ratio = ratio
+    group_size = wc_config.get("group_size", 128)
+    all_layers = wc_config.get("all_layers", False)
+    backup_mode = wc_config.get("backup_mode", nncf.BackupMode.INT8_ASYM)
+    sensitivity_metric = sensitivity_metric
+    compression_format = compression_format
+    ignored_scope = nncf.IgnoredScope()  # This is already defined in the quantizer object
+
+    weight_compression_configuration = get_weight_compression_configuration(
+        mode,
+        dataset,
+        ratio,
+        group_size,
+        all_layers,
+        awq,
+        scale_estimation,
+        gptq,
+        lora_correction,
+        ignored_scope,
+        sensitivity_metric,
+        backup_mode,
+        advanced_parameters,
+    )
+
+    quantization_algorithm = WeightsCompression(
+        quantizer=quantizer,
+        subset_size=subset_size,
+        compression_format=compression_format,
+        **weight_compression_configuration,
+    )
+
+    # Here the model is annotated
+    transformed_model = quantizer.transform_prior_quantization(model)
+    nncf_graph = NNCFGraphFactory.create(transformed_model)
+    quantized_model = quantization_algorithm.apply(transformed_model, nncf_graph, dataset=dataset)
+    quantized_model = torch.fx.GraphModule(quantized_model, graph=quantized_model.graph)
+    return quantized_model
diff --git a/src/nncf/experimental/torch/fx/quantization/quantizer/openvino_adapter.py b/src/nncf/experimental/torch/fx/quantization/quantizer/openvino_adapter.py
index 2283d9d9dbb..5b4bd321780 100644
--- a/src/nncf/experimental/torch/fx/quantization/quantizer/openvino_adapter.py
+++ b/src/nncf/experimental/torch/fx/quantization/quantizer/openvino_adapter.py
@@ -9,12 +9,15 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from typing import Any
+
 import torch.fx
 
 from nncf.common.graph.graph import NNCFGraph
 from nncf.common.quantization.quantizer_setup import SingleConfigQuantizerSetup
 from nncf.experimental.quantization.quantizer import Quantizer
 from nncf.experimental.torch.fx.quantization.quantizer.openvino_quantizer import OpenVINOQuantizer
+from nncf.quantization.algorithms.weight_compression.config import WeightCompressionParameters
 
 
 class OpenVINOQuantizerAdapter(Quantizer):
@@ -30,3 +33,17 @@ def transform_prior_quantization(self, model: torch.fx.GraphModule) -> torch.fx.
 
     def get_quantization_setup(self, model: torch.fx.GraphModule, nncf_graph: NNCFGraph) -> SingleConfigQuantizerSetup:
         return self._quantizer.get_nncf_quantization_setup(model, nncf_graph)
+
+    def get_weight_compression_parameters(
+        self,
+        model: torch.fx.GraphModule,
+        nncf_graph: NNCFGraph,
+    ) -> tuple[
+        list[WeightCompressionParameters],
+        list[WeightCompressionParameters],
+        list[WeightCompressionParameters],
+    ]:
+        return self._quantizer.get_nncf_weight_compression_parameters(model, nncf_graph)
+
+    def get_weight_compression_config(self) -> dict[str, Any]:
+        return self._quantizer.weight_compression_configuration
diff --git a/src/nncf/quantization/algorithms/weight_compression/algorithm.py b/src/nncf/quantization/algorithms/weight_compression/algorithm.py
index 50a5399a5c8..784f2c1ce91 100644
--- a/src/nncf/quantization/algorithms/weight_compression/algorithm.py
+++ b/src/nncf/quantization/algorithms/weight_compression/algorithm.py
@@ -103,7 +103,7 @@ def get_weight_compression_configuration(
             group_size = 128
 
     return {
-        "mode": mode,
+        "mode": mode if isinstance(mode, nncf.CompressWeightsMode) else nncf.CompressWeightsMode(mode),
         "ratio": ratio or 1,
         "group_size": group_size,
         "all_layers": all_layers or False,
@@ -505,6 +505,23 @@ def _get_ratio_defining_params(
 
         return ratio_defining_params
 
+    def _get_backup_config(self, weight_dtype: TensorDataType) -> WeightCompressionConfig:
+        """
+        Returns the backup weight compression configuration based on the algorithm's backup mode.
+
+        :param weight_dtype: Data type of the weight tensor.
+        :return: A WeightCompressionConfig object for the backup precision, or None if backup is
+        disabled or unsupported.
+        """
+        if self._backup_mode == BackupMode.NONE:
+            return None
+        mode = (
+            CompressWeightsMode.INT8_ASYM if self._backup_mode == BackupMode.INT8_ASYM else CompressWeightsMode.INT8_SYM
+        )
+        if not self.is_weight_compression_supported(weight_dtype, mode):
+            return None
+        return WeightCompressionConfig(mode=mode)
+
     def _get_primary_config(self, group_size: int) -> WeightCompressionConfig:
         codebook_values = None
 
@@ -525,7 +542,6 @@ def _set_weight_compression_config(
         model: TModel,
         graph: NNCFGraph,
         statistics_points: StatisticPointsContainer,
-        group_size_values: dict[str, int],
     ) -> None:
         """
         Sets the appropriate compression configuration for weights based on some criteria.
@@ -541,12 +557,12 @@ def _set_weight_compression_config(
             primary_precision_weight_params = self._mixed_precision_algo.apply(
                 model, graph, statistics_points, weight_params=ratio_defining_params
             )
-        else:
-            primary_precision_weight_params = ratio_defining_params
-
-        for weight_param in primary_precision_weight_params:
-            weight_param.compression_config = self._get_primary_config(group_size_values[weight_param.weight_name])
-
+            # ratio_defining_params are all in primary precision. Update parameters
+            # which need to be set to backup precision
+            for weight_param in ratio_defining_params:
+                if weight_param in primary_precision_weight_params:
+                    continue
+                weight_param.compression_config = self._get_backup_config(weight_param.weight_dtype)
         # Check if group size is valid for each weight in ratio_defining_params
         failed_nodes = []
         for w_params in ratio_defining_params:
@@ -783,27 +799,66 @@ def is_weight_compression_supported(
 
         return is_supported_dtype and not no_bit_reduction
 
+    def _collect_statistics_and_statistic_points(
+        self,
+        model: TModel,
+        graph: NNCFGraph,
+        statistic_points: StatisticPointsContainer,
+        dataset: Dataset,
+        ratio_defining_params: list[WeightCompressionParameters],
+        all_weight_params: list[WeightCompressionParameters],
+    ) -> tuple[dict[str, WCTensorStatistic], StatisticPointsContainer]:
+        """
+        Collects and computes statistics required for weight compression.
+
+        :param model: Backend-specific model instance.
+        :param graph: Corresponding NNCFGraph of the model.
+        :param Container with pre-collected statistics, if available..
+        :param dataset: Dataset used for collecting statistics when not provided.
+        :param ratio_defining_params: List of parameters defining compression ratios.
+        :param all_weight_params: List of all weight compression parameters.
+        :return: A tuple containing collected statistics for weight compression and the updated statistic_points.
+        """
+        if not dataset or not (self._data_aware_mixed_precision or self._data_aware_compression):
+            return None, statistic_points
+        weight_params = ratio_defining_params if self._backup_mode == BackupMode.NONE else all_weight_params
+        matmul_nodes_to_compress = [
+            wp.node_with_weight
+            for wp in weight_params
+            if wp.node_with_weight.metatype in self._backend_entity.matmul_metatypes
+        ]
+        matmul_input_to_output_nodes_map = self.get_matmul_input_to_output_nodes_map(matmul_nodes_to_compress, graph)
+        if statistic_points is None:
+            statistic_points = self.get_statistic_points(model, graph, matmul_input_to_output_nodes_map.keys())
+            statistic_points = self._collect_statistics(dataset, graph, model, statistic_points)
+        statistics = self._get_statistics_for_weights_compression(matmul_input_to_output_nodes_map, statistic_points)
+        return statistics, statistic_points
+
     def get_weight_compression_parameters(
         self,
         model: TModel,
         graph: NNCFGraph,
-        statistic_points: Optional[StatisticPointsContainer] = None,
-        dataset: Optional[Dataset] = None,
-    ) -> tuple[list[WeightCompressionParameters], Optional[dict[str, WCTensorStatistic]]]:
+    ) -> tuple[
+        list[WeightCompressionParameters],
+        list[WeightCompressionParameters],
+        list[WeightCompressionParameters],
+    ]:
         """
-        Generates a list of weight compression parameters based on the Weight Compression algorithm
-        configuration. Determines the appropriate quantization parameters for each node eligible for
-        weight compression. Also, Generates a mapping of target node names to the collected statistics
-        based on the provided statistic_points. If statistic_points is None, collects required
-        compression statistics on the given dataset.
+        This Function does the following:
+
+        * Generates a list of weight compression parameters based on the algorithm configuration.
+        * Determines the appropriate quantization parameters for each node eligible for weight compression.
+        * Generates a subset of parameters that can be compressed in both primary and backup precisions,
+        called ratio-defining parameters. All ratio-defining parameters are set to the primary precision.
+        * Generates a subset of parameters that will not be compressed, based on the ignored scope or
+        compression configuration restrictions.
 
         :param model: Backend-specific input model.
         :param graph: NNCFGraph instance.
-        :param statistic_points: Optional pre-collected statistic points.
-        :param dataset: Optional dataset for statistics collection.
-        :return: A tuple consisting of a list of weight compression parameters, based on the Weight
-            Compression algorithm configuration, and a mapping of target node names to the
-            collected statistics.
+        :return: A tuple consisting a list of weight compression parameters that can be compressed,
+            a list of ratio-defining parameters, which is a subset of compressible weight parameters
+            that are allowed to be set to mixed precisions, and a list of weight compression parameters
+            that can not be compressed.
         """
         nodes_to_compress = self.get_nodes_to_compress(graph)
 
@@ -828,8 +883,8 @@ def get_weight_compression_parameters(
                 weight_dtype = self._backend_entity.get_weight_dtype(node, weight_port_id, model, graph)
                 weight_shape = self._backend_entity.get_weight_shape(node, weight_port_id, graph)
                 reduction_axes = self._backend_entity.get_reduction_axes(node, weight_port_id, graph)
-
                 wc_config = None
+
                 if is_target_node and self.is_weight_compression_supported(weight_dtype, self._mode):
                     if (
                         self._group_size != -1
@@ -848,14 +903,7 @@ def get_weight_compression_parameters(
                             f"node name: {node.node_name}. The node will be in {self._backup_mode} mode."
                         )
 
-                    if self._backup_mode != BackupMode.NONE:
-                        mode = (
-                            CompressWeightsMode.INT8_ASYM
-                            if self._backup_mode == BackupMode.INT8_ASYM
-                            else CompressWeightsMode.INT8_SYM
-                        )
-                        if self.is_weight_compression_supported(weight_dtype, mode):
-                            wc_config = WeightCompressionConfig(mode=mode)
+                    wc_config = self._get_backup_config(weight_dtype)
 
                     weight_params = WeightCompressionParameters(
                         weight_name, node, weight_port_id, weight_dtype, weight_shape, reduction_axes, wc_config
@@ -883,37 +931,11 @@ def get_weight_compression_parameters(
         else:
             group_size_values = {w_params.weight_name: self._group_size for w_params in ratio_defining_params}
 
-        # Collect statistics for the weights compression
-        statistics = None
-        if (self._data_aware_mixed_precision or self._data_aware_compression) and dataset:
-            weight_params = ratio_defining_params if self._backup_mode == BackupMode.NONE else all_weight_params
-            matmul_nodes_to_compress = [
-                wp.node_with_weight
-                for wp in weight_params
-                if wp.node_with_weight.metatype in self._backend_entity.matmul_metatypes
-            ]
-            matmul_input_to_output_nodes_map = self.get_matmul_input_to_output_nodes_map(
-                matmul_nodes_to_compress, graph
-            )
-            if statistic_points is None:
-                statistic_points = self.get_statistic_points(model, graph, matmul_input_to_output_nodes_map.keys())
-                statistic_points = self._collect_statistics(dataset, graph, model, statistic_points)
-            statistics = self._get_statistics_for_weights_compression(
-                matmul_input_to_output_nodes_map, statistic_points
-            )
-
-        # Set weight compression configuration
-        self._set_weight_compression_config(ratio_defining_params, model, graph, statistic_points, group_size_values)
-
-        # Print statistics
-        nncf_logger.info(
-            self._get_bitwidth_distribution_str(all_weight_params, ratio_defining_params, skipped_weight_params)
-        )
-
-        # Filter all_weight_params and by excluding nodes that should remain in their original floating-point precision
-        all_weight_params = list(filter(lambda w_params: w_params.compression_config is not None, all_weight_params))
+        # Set each ratio defining parameter to primary config
+        for weight_param in ratio_defining_params:
+            weight_param.compression_config = self._get_primary_config(group_size_values[weight_param.weight_name])
 
-        return all_weight_params, statistics
+        return all_weight_params, ratio_defining_params, skipped_weight_params
 
     def apply(
         self,
@@ -925,7 +947,59 @@ def apply(
         self.set_backend_entity(model)
 
         # Get processed weight compression parameters ready for compression
-        all_weight_params, statistics = self.get_weight_compression_parameters(model, graph, statistic_points, dataset)
+        all_weight_params, ratio_defining_params, skipped_weight_params = self.get_weight_compression_parameters(
+            model, graph
+        )
+        return self.apply_with_parameters(
+            model,
+            graph,
+            dataset,
+            statistic_points,
+            all_weight_params,
+            ratio_defining_params,
+            skipped_weight_params,
+        )
+
+    def apply_with_parameters(
+        self,
+        model: TModel,
+        graph: NNCFGraph,
+        dataset: Dataset,
+        statistic_points: StatisticPointsContainer,
+        all_weight_params: list[WeightCompressionParameters],
+        ratio_defining_params: list[WeightCompressionParameters],
+        skipped_weight_params: list[WeightCompressionParameters],
+    ) -> TModel:
+        """
+        Applies the Weight Compression algorithm using precomputed parameters and optional
+        algorithms (AWQ, GPTQ, scale estimation, LoRA correction). The method collects
+        statistics, configures the weight compression parameters for mixed precision algorithm,
+        and performs the model transformation with appropriate decompression operations
+
+        :param model: Backend-specific model to be compressed.
+        :param graph: NNCFGraph instance.
+        :param dataset: Dataset to collect statistics.
+        :param statistic_points: Statistics points object.
+        :param all_weight_params: List of all weight parameters.
+        :param ratio_defining_params: Subset of all_weight_params that determine mixed-precision ratios.
+        :param skipped_weight_params: List of parameters corresponding to weights intentionally skipped
+            from compression (e.g., due to ignored scopes or group size adjustments).
+        :return: Transformed model with compressed weights and inserted backend-specific decompressor.
+        """
+        # Collect statistics for the weights compression
+        statistics, statistic_points = self._collect_statistics_and_statistic_points(
+            model, graph, statistic_points, dataset, ratio_defining_params, all_weight_params
+        )
+        # Set weight compression configuration
+        self._set_weight_compression_config(ratio_defining_params, model, graph, statistic_points)
+
+        # Print statistics
+        nncf_logger.info(
+            self._get_bitwidth_distribution_str(all_weight_params, ratio_defining_params, skipped_weight_params)
+        )
+
+        # Filter all_weight_params and by excluding nodes that should remain in their original floating-point precision
+        all_weight_params = list(filter(lambda w_params: w_params.compression_config is not None, all_weight_params))
 
         if self._awq:
             model = self.awq_algo.apply(model, graph, all_weight_params, statistics, self._backend_entity)
@@ -1135,7 +1209,7 @@ def _get_statistics_for_weights_compression(
 
         :param matmul_input_to_output_nodes_map: A mapping from activation node and a port id to corresponding matmul
             nodes which accept this activation as an input.
-        :param statistic_points: Statistic points object.
+        :param statistic_points: Statistic points.
         :return: Collected statistics.
         """
         # For each node we store statistics in a WCTensorStatistics data-class. It contains the following fields:
diff --git a/tests/executorch/__init__.py b/tests/executorch/__init__.py
new file mode 100644
index 00000000000..e5a42efc0ef
--- /dev/null
+++ b/tests/executorch/__init__.py
@@ -0,0 +1,10 @@
+# Copyright (c) 2025 Intel Corporation
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#      http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
diff --git a/tests/executorch/conftest.py b/tests/executorch/conftest.py
new file mode 100644
index 00000000000..ce9b7b42661
--- /dev/null
+++ b/tests/executorch/conftest.py
@@ -0,0 +1,11 @@
+# Copyright (c) 2025 Intel Corporation
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#      http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+pytest_plugins = ["tests.torch2.conftest"]
diff --git a/tests/executorch/data/fx/ao_export_compression_OpenVINOQuantizer/LlamaDecoderOnly/int4wo_sym_gs32_all_layers_False.dot b/tests/executorch/data/fx/ao_export_compression_OpenVINOQuantizer/LlamaDecoderOnly/int4wo_sym_gs32_all_layers_False.dot
new file mode 100644
index 00000000000..0a9a27fd85b
--- /dev/null
+++ b/tests/executorch/data/fx/ao_export_compression_OpenVINOQuantizer/LlamaDecoderOnly/int4wo_sym_gs32_all_layers_False.dot
@@ -0,0 +1,169 @@
+strict digraph {
+"0 attn_norm_weight" [id=0, type="get_attr"];
+"1 mlp_norm_weight" [id=1, type="get_attr"];
+"2 q_proj_weight_updated_constant0" [id=2, type="get_attr"];
+"3 symmetric_weights_decompressor_q_proj_weight_0" [id=3, type="call_module"];
+"4 k_proj_weight_updated_constant0" [id=4, type="get_attr"];
+"5 symmetric_weights_decompressor_k_proj_weight_0" [id=5, type="call_module"];
+"6 v_proj_weight_updated_constant0" [id=6, type="get_attr"];
+"7 symmetric_weights_decompressor_v_proj_weight_0" [id=7, type="call_module"];
+"8 o_proj_weight_updated_constant0" [id=8, type="get_attr"];
+"9 symmetric_weights_decompressor_o_proj_weight_0" [id=9, type="call_module"];
+"10 mlp_gate_proj_weight_updated_constant0" [id=10, type="get_attr"];
+"11 symmetric_weights_decompressor_mlp_gate_proj_weight_0" [id=11, type="call_module"];
+"12 mlp_up_proj_weight_updated_constant0" [id=12, type="get_attr"];
+"13 symmetric_weights_decompressor_mlp_up_proj_weight_0" [id=13, type="call_module"];
+"14 mlp_down_proj_weight_updated_constant0" [id=14, type="get_attr"];
+"15 asymmetric_weights_decompressor_mlp_down_proj_weight_0" [id=15, type="call_module"];
+"16 rope_cos" [id=16, type="get_attr"];
+"17 rope_sin" [id=17, type="get_attr"];
+"18 x_embed" [id=18, type=input];
+"19 arange" [id=19, type=arange];
+"20 _assert_tensor_metadata_default" [id=20, type="_assert_tensor_metadata"];
+"21 to" [id=21, type=to];
+"22 pow_1" [id=22, type=pow];
+"23 mean" [id=23, type=mean];
+"24 add" [id=24, type=add];
+"25 rsqrt" [id=25, type=rsqrt];
+"26 mul" [id=26, type=mul];
+"27 _assert_tensor_metadata_default_1" [id=27, type="_assert_tensor_metadata"];
+"28 to_1" [id=28, type=to];
+"29 mul_1" [id=29, type=mul];
+"30 linear" [id=30, type=linear];
+"31 view" [id=31, type=view];
+"32 transpose" [id=32, type=transpose];
+"33 linear_1" [id=33, type=linear];
+"34 view_1" [id=34, type=view];
+"35 transpose_1" [id=35, type=transpose];
+"36 linear_2" [id=36, type=linear];
+"37 view_2" [id=37, type=view];
+"38 transpose_2" [id=38, type=transpose];
+"39 index" [id=39, type=index];
+"40 index_1" [id=40, type=index];
+"41 mul_2" [id=41, type=mul];
+"42 slice_1" [id=42, type=slice];
+"43 slice_2" [id=43, type=slice];
+"44 neg" [id=44, type=neg];
+"45 cat" [id=45, type=cat];
+"46 mul_3" [id=46, type=mul];
+"47 add_1" [id=47, type=add];
+"48 mul_4" [id=48, type=mul];
+"49 slice_3" [id=49, type=slice];
+"50 slice_4" [id=50, type=slice];
+"51 neg_1" [id=51, type=neg];
+"52 cat_1" [id=52, type=cat];
+"53 mul_5" [id=53, type=mul];
+"54 add_2" [id=54, type=add];
+"55 scaled_dot_product_attention" [id=55, type="scaled_dot_product_attention"];
+"56 transpose_3" [id=56, type=transpose];
+"57 view_3" [id=57, type=view];
+"58 linear_3" [id=58, type=linear];
+"59 add_3" [id=59, type=add];
+"60 _assert_tensor_metadata_default_2" [id=60, type="_assert_tensor_metadata"];
+"61 to_2" [id=61, type=to];
+"62 pow_2" [id=62, type=pow];
+"63 mean_1" [id=63, type=mean];
+"64 add_4" [id=64, type=add];
+"65 rsqrt_1" [id=65, type=rsqrt];
+"66 mul_6" [id=66, type=mul];
+"67 _assert_tensor_metadata_default_3" [id=67, type="_assert_tensor_metadata"];
+"68 to_3" [id=68, type=to];
+"69 mul_7" [id=69, type=mul];
+"70 linear_4" [id=70, type=linear];
+"71 silu" [id=71, type=silu];
+"72 linear_5" [id=72, type=linear];
+"73 mul_8" [id=73, type=mul];
+"74 linear_6" [id=74, type=linear];
+"75 add_5" [id=75, type=add];
+"76 output" [id=76, type=output];
+"0 attn_norm_weight" -> "29 mul_1" [style=solid, label="(64,)"];
+"1 mlp_norm_weight" -> "69 mul_7" [style=solid, label="(64,)"];
+"2 q_proj_weight_updated_constant0" -> "3 symmetric_weights_decompressor_q_proj_weight_0" [style=solid, label="(2048, 1)"];
+"3 symmetric_weights_decompressor_q_proj_weight_0" -> "30 linear" [style=solid, label="(64, 64)"];
+"4 k_proj_weight_updated_constant0" -> "5 symmetric_weights_decompressor_k_proj_weight_0" [style=solid, label="(2048, 1)"];
+"5 symmetric_weights_decompressor_k_proj_weight_0" -> "33 linear_1" [style=solid, label="(64, 64)"];
+"6 v_proj_weight_updated_constant0" -> "7 symmetric_weights_decompressor_v_proj_weight_0" [style=solid, label="(2048, 1)"];
+"7 symmetric_weights_decompressor_v_proj_weight_0" -> "36 linear_2" [style=solid, label="(64, 64)"];
+"8 o_proj_weight_updated_constant0" -> "9 symmetric_weights_decompressor_o_proj_weight_0" [style=solid, label="(2048, 1)"];
+"9 symmetric_weights_decompressor_o_proj_weight_0" -> "58 linear_3" [style=solid, label="(64, 64)"];
+"10 mlp_gate_proj_weight_updated_constant0" -> "11 symmetric_weights_decompressor_mlp_gate_proj_weight_0" [style=solid, label="(4096, 1)"];
+"11 symmetric_weights_decompressor_mlp_gate_proj_weight_0" -> "70 linear_4" [style=solid, label="(128, 64)"];
+"12 mlp_up_proj_weight_updated_constant0" -> "13 symmetric_weights_decompressor_mlp_up_proj_weight_0" [style=solid, label="(4096, 1)"];
+"13 symmetric_weights_decompressor_mlp_up_proj_weight_0" -> "72 linear_5" [style=solid, label="(128, 64)"];
+"14 mlp_down_proj_weight_updated_constant0" -> "15 asymmetric_weights_decompressor_mlp_down_proj_weight_0" [style=solid, label="(64, 128)"];
+"15 asymmetric_weights_decompressor_mlp_down_proj_weight_0" -> "74 linear_6" [style=solid, label="(64, 128)"];
+"16 rope_cos" -> "39 index" [style=solid, label="(1, 1, 128, 16)"];
+"17 rope_sin" -> "40 index_1" [style=solid, label="(1, 1, 128, 16)"];
+"18 x_embed" -> "20 _assert_tensor_metadata_default" [style=solid, label="(1, 3, 64)"];
+"18 x_embed" -> "21 to" [style=solid, label="(1, 3, 64)"];
+"18 x_embed" -> "59 add_3" [style=solid, label="(1, 3, 64)"];
+"19 arange" -> "39 index" [style=solid, label="(3,)"];
+"19 arange" -> "40 index_1" [style=solid, label="(3,)"];
+"21 to" -> "22 pow_1" [style=solid, label="(1, 3, 64)"];
+"21 to" -> "26 mul" [style=solid, label="(1, 3, 64)"];
+"22 pow_1" -> "23 mean" [style=solid, label="(1, 3, 64)"];
+"23 mean" -> "24 add" [style=solid, label="(1, 3, 1)"];
+"24 add" -> "25 rsqrt" [style=solid, label="(1, 3, 1)"];
+"25 rsqrt" -> "26 mul" [style=solid, label="(1, 3, 1)"];
+"26 mul" -> "27 _assert_tensor_metadata_default_1" [style=solid, label="(1, 3, 64)"];
+"26 mul" -> "28 to_1" [style=solid, label="(1, 3, 64)"];
+"28 to_1" -> "29 mul_1" [style=solid, label="(1, 3, 64)"];
+"29 mul_1" -> "30 linear" [style=solid, label="(1, 3, 64)"];
+"29 mul_1" -> "33 linear_1" [style=solid, label="(1, 3, 64)"];
+"29 mul_1" -> "36 linear_2" [style=solid, label="(1, 3, 64)"];
+"30 linear" -> "31 view" [style=solid, label="(1, 3, 64)"];
+"31 view" -> "32 transpose" [style=solid, label="(1, 3, 4, 16)"];
+"32 transpose" -> "41 mul_2" [style=solid, label="(1, 4, 3, 16)"];
+"32 transpose" -> "42 slice_1" [style=solid, label="(1, 4, 3, 16)"];
+"32 transpose" -> "43 slice_2" [style=solid, label="(1, 4, 3, 16)"];
+"33 linear_1" -> "34 view_1" [style=solid, label="(1, 3, 64)"];
+"34 view_1" -> "35 transpose_1" [style=solid, label="(1, 3, 4, 16)"];
+"35 transpose_1" -> "48 mul_4" [style=solid, label="(1, 4, 3, 16)"];
+"35 transpose_1" -> "49 slice_3" [style=solid, label="(1, 4, 3, 16)"];
+"35 transpose_1" -> "50 slice_4" [style=solid, label="(1, 4, 3, 16)"];
+"36 linear_2" -> "37 view_2" [style=solid, label="(1, 3, 64)"];
+"37 view_2" -> "38 transpose_2" [style=solid, label="(1, 3, 4, 16)"];
+"38 transpose_2" -> "55 scaled_dot_product_attention" [style=solid, label="(1, 4, 3, 16)"];
+"39 index" -> "41 mul_2" [style=solid, label="(1, 1, 3, 16)"];
+"39 index" -> "48 mul_4" [style=solid, label="(1, 1, 3, 16)"];
+"40 index_1" -> "46 mul_3" [style=solid, label="(1, 1, 3, 16)"];
+"40 index_1" -> "53 mul_5" [style=solid, label="(1, 1, 3, 16)"];
+"41 mul_2" -> "47 add_1" [style=solid, label="(1, 4, 3, 16)"];
+"42 slice_1" -> "45 cat" [style=solid, label="(1, 4, 3, 8)"];
+"43 slice_2" -> "44 neg" [style=solid, label="(1, 4, 3, 8)"];
+"44 neg" -> "45 cat" [style=solid, label="(1, 4, 3, 8)"];
+"45 cat" -> "46 mul_3" [style=solid, label="(1, 4, 3, 16)"];
+"46 mul_3" -> "47 add_1" [style=solid, label="(1, 4, 3, 16)"];
+"47 add_1" -> "55 scaled_dot_product_attention" [style=solid, label="(1, 4, 3, 16)"];
+"48 mul_4" -> "54 add_2" [style=solid, label="(1, 4, 3, 16)"];
+"49 slice_3" -> "52 cat_1" [style=solid, label="(1, 4, 3, 8)"];
+"50 slice_4" -> "51 neg_1" [style=solid, label="(1, 4, 3, 8)"];
+"51 neg_1" -> "52 cat_1" [style=solid, label="(1, 4, 3, 8)"];
+"52 cat_1" -> "53 mul_5" [style=solid, label="(1, 4, 3, 16)"];
+"53 mul_5" -> "54 add_2" [style=solid, label="(1, 4, 3, 16)"];
+"54 add_2" -> "55 scaled_dot_product_attention" [style=solid, label="(1, 4, 3, 16)"];
+"55 scaled_dot_product_attention" -> "56 transpose_3" [style=solid, label="(1, 4, 3, 16)"];
+"56 transpose_3" -> "57 view_3" [style=solid, label="(1, 3, 4, 16)"];
+"57 view_3" -> "58 linear_3" [style=solid, label="(1, 3, 64)"];
+"58 linear_3" -> "59 add_3" [style=solid, label="(1, 3, 64)"];
+"59 add_3" -> "60 _assert_tensor_metadata_default_2" [style=solid, label="(1, 3, 64)"];
+"59 add_3" -> "61 to_2" [style=solid, label="(1, 3, 64)"];
+"61 to_2" -> "62 pow_2" [style=solid, label="(1, 3, 64)"];
+"61 to_2" -> "66 mul_6" [style=solid, label="(1, 3, 64)"];
+"61 to_2" -> "75 add_5" [style=solid, label="(1, 3, 64)"];
+"62 pow_2" -> "63 mean_1" [style=solid, label="(1, 3, 64)"];
+"63 mean_1" -> "64 add_4" [style=solid, label="(1, 3, 1)"];
+"64 add_4" -> "65 rsqrt_1" [style=solid, label="(1, 3, 1)"];
+"65 rsqrt_1" -> "66 mul_6" [style=solid, label="(1, 3, 1)"];
+"66 mul_6" -> "67 _assert_tensor_metadata_default_3" [style=solid, label="(1, 3, 64)"];
+"66 mul_6" -> "68 to_3" [style=solid, label="(1, 3, 64)"];
+"68 to_3" -> "69 mul_7" [style=solid, label="(1, 3, 64)"];
+"69 mul_7" -> "70 linear_4" [style=solid, label="(1, 3, 64)"];
+"69 mul_7" -> "72 linear_5" [style=solid, label="(1, 3, 64)"];
+"70 linear_4" -> "71 silu" [style=solid, label="(1, 3, 128)"];
+"71 silu" -> "73 mul_8" [style=solid, label="(1, 3, 128)"];
+"72 linear_5" -> "73 mul_8" [style=solid, label="(1, 3, 128)"];
+"73 mul_8" -> "74 linear_6" [style=solid, label="(1, 3, 128)"];
+"74 linear_6" -> "75 add_5" [style=solid, label="(1, 3, 64)"];
+"75 add_5" -> "76 output" [style=solid, label="(1, 3, 64)"];
+}
diff --git a/tests/executorch/data/fx/ao_export_compression_OpenVINOQuantizer/LlamaDecoderOnly/int4wo_sym_gs32_all_layers_True.dot b/tests/executorch/data/fx/ao_export_compression_OpenVINOQuantizer/LlamaDecoderOnly/int4wo_sym_gs32_all_layers_True.dot
new file mode 100644
index 00000000000..254abcb9dc0
--- /dev/null
+++ b/tests/executorch/data/fx/ao_export_compression_OpenVINOQuantizer/LlamaDecoderOnly/int4wo_sym_gs32_all_layers_True.dot
@@ -0,0 +1,169 @@
+strict digraph {
+"0 attn_norm_weight" [id=0, type="get_attr"];
+"1 mlp_norm_weight" [id=1, type="get_attr"];
+"2 q_proj_weight_updated_constant0" [id=2, type="get_attr"];
+"3 symmetric_weights_decompressor_q_proj_weight_0" [id=3, type="call_module"];
+"4 k_proj_weight_updated_constant0" [id=4, type="get_attr"];
+"5 symmetric_weights_decompressor_k_proj_weight_0" [id=5, type="call_module"];
+"6 v_proj_weight_updated_constant0" [id=6, type="get_attr"];
+"7 symmetric_weights_decompressor_v_proj_weight_0" [id=7, type="call_module"];
+"8 o_proj_weight_updated_constant0" [id=8, type="get_attr"];
+"9 symmetric_weights_decompressor_o_proj_weight_0" [id=9, type="call_module"];
+"10 mlp_gate_proj_weight_updated_constant0" [id=10, type="get_attr"];
+"11 symmetric_weights_decompressor_mlp_gate_proj_weight_0" [id=11, type="call_module"];
+"12 mlp_up_proj_weight_updated_constant0" [id=12, type="get_attr"];
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+"14 mlp_down_proj_weight_updated_constant0" [id=14, type="get_attr"];
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diff --git a/tests/executorch/data/fx/ao_export_compression_OpenVINOQuantizer/LlamaDecoderOnly/int8wo_asym_gs-1_all_layers_False.dot b/tests/executorch/data/fx/ao_export_compression_OpenVINOQuantizer/LlamaDecoderOnly/int8wo_asym_gs-1_all_layers_False.dot
new file mode 100644
index 00000000000..614e06a21ac
--- /dev/null
+++ b/tests/executorch/data/fx/ao_export_compression_OpenVINOQuantizer/LlamaDecoderOnly/int8wo_asym_gs-1_all_layers_False.dot
@@ -0,0 +1,169 @@
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+"71 silu" -> "73 mul_8" [style=solid, label="(1, 3, 128)"];
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+"74 linear_6" -> "75 add_5" [style=solid, label="(1, 3, 64)"];
+"75 add_5" -> "76 output" [style=solid, label="(1, 3, 64)"];
+}
diff --git a/tests/executorch/data/fx/ao_export_compression_OpenVINOQuantizer/short_transformer_shared/int4wo_sym_gs32_all_layers_False.dot b/tests/executorch/data/fx/ao_export_compression_OpenVINOQuantizer/short_transformer_shared/int4wo_sym_gs32_all_layers_False.dot
new file mode 100644
index 00000000000..2841824b5a3
--- /dev/null
+++ b/tests/executorch/data/fx/ao_export_compression_OpenVINOQuantizer/short_transformer_shared/int4wo_sym_gs32_all_layers_False.dot
@@ -0,0 +1,24 @@
+strict digraph {
+"0 linear_weight_updated_constant0" [id=0, type="get_attr"];
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+"9 linear_1" -> "10 output" [style=solid, label="(5, 128)"];
+}
diff --git a/tests/executorch/data/fx/ao_export_compression_OpenVINOQuantizer/short_transformer_shared/int4wo_sym_gs32_all_layers_True.dot b/tests/executorch/data/fx/ao_export_compression_OpenVINOQuantizer/short_transformer_shared/int4wo_sym_gs32_all_layers_True.dot
new file mode 100644
index 00000000000..0382f7e5934
--- /dev/null
+++ b/tests/executorch/data/fx/ao_export_compression_OpenVINOQuantizer/short_transformer_shared/int4wo_sym_gs32_all_layers_True.dot
@@ -0,0 +1,24 @@
+strict digraph {
+"0 linear_weight_updated_constant0" [id=0, type="get_attr"];
+"1 symmetric_weights_decompressor_linear_weight_0" [id=1, type="call_module"];
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+}
diff --git a/tests/executorch/data/fx/ao_export_compression_OpenVINOQuantizer/short_transformer_shared/int8wo_asym_gs-1_all_layers_False.dot b/tests/executorch/data/fx/ao_export_compression_OpenVINOQuantizer/short_transformer_shared/int8wo_asym_gs-1_all_layers_False.dot
new file mode 100644
index 00000000000..03fc9e9c6a0
--- /dev/null
+++ b/tests/executorch/data/fx/ao_export_compression_OpenVINOQuantizer/short_transformer_shared/int8wo_asym_gs-1_all_layers_False.dot
@@ -0,0 +1,24 @@
+strict digraph {
+"0 linear_weight_updated_constant0" [id=0, type="get_attr"];
+"1 asymmetric_weights_decompressor_linear_weight_0" [id=1, type="call_module"];
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+}
diff --git a/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/LlamaDecoderOnly/int4wo_sym_gs32_all_layers_False.dot b/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/LlamaDecoderOnly/int4wo_sym_gs32_all_layers_False.dot
new file mode 100644
index 00000000000..076e46114eb
--- /dev/null
+++ b/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/LlamaDecoderOnly/int4wo_sym_gs32_all_layers_False.dot
@@ -0,0 +1,169 @@
+strict digraph {
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+}
diff --git a/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/LlamaDecoderOnly/int4wo_sym_gs32_all_layers_False_awq_True_scale_estimation_True_ref_wc_scales.json b/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/LlamaDecoderOnly/int4wo_sym_gs32_all_layers_False_awq_True_scale_estimation_True_ref_wc_scales.json
new file mode 100644
index 00000000000..364f78db4aa
--- /dev/null
+++ b/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/LlamaDecoderOnly/int4wo_sym_gs32_all_layers_False_awq_True_scale_estimation_True_ref_wc_scales.json
@@ -0,0 +1,3664 @@
+{
+    "symmetric_weights_decompressor_q_proj_weight_0": [
+        [
+            [
+                -0.0135650634765625
+            ],
+            [
+                -0.014251708984375
+            ]
+        ],
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\ No newline at end of file
diff --git a/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/LlamaDecoderOnly/int4wo_sym_gs32_all_layers_False_ref_wc_param.json b/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/LlamaDecoderOnly/int4wo_sym_gs32_all_layers_False_ref_wc_param.json
new file mode 100644
index 00000000000..7cfdf2719df
--- /dev/null
+++ b/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/LlamaDecoderOnly/int4wo_sym_gs32_all_layers_False_ref_wc_param.json
@@ -0,0 +1,128 @@
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\ No newline at end of file
diff --git a/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/LlamaDecoderOnly/int4wo_sym_gs32_all_layers_True.dot b/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/LlamaDecoderOnly/int4wo_sym_gs32_all_layers_True.dot
new file mode 100644
index 00000000000..31fb9463c88
--- /dev/null
+++ b/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/LlamaDecoderOnly/int4wo_sym_gs32_all_layers_True.dot
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diff --git a/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/LlamaDecoderOnly/int4wo_sym_gs32_all_layers_True_awq_True_scale_estimation_True_ref_wc_scales.json b/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/LlamaDecoderOnly/int4wo_sym_gs32_all_layers_True_awq_True_scale_estimation_True_ref_wc_scales.json
new file mode 100644
index 00000000000..74b808d1245
--- /dev/null
+++ b/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/LlamaDecoderOnly/int4wo_sym_gs32_all_layers_True_awq_True_scale_estimation_True_ref_wc_scales.json
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diff --git a/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/LlamaDecoderOnly/int4wo_sym_gs32_all_layers_True_ref_wc_param.json b/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/LlamaDecoderOnly/int4wo_sym_gs32_all_layers_True_ref_wc_param.json
new file mode 100644
index 00000000000..e1baa81d0dc
--- /dev/null
+++ b/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/LlamaDecoderOnly/int4wo_sym_gs32_all_layers_True_ref_wc_param.json
@@ -0,0 +1,128 @@
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diff --git a/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/LlamaDecoderOnly/int8wo_asym_gs-1_all_layers_False.dot b/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/LlamaDecoderOnly/int8wo_asym_gs-1_all_layers_False.dot
new file mode 100644
index 00000000000..29de7b02841
--- /dev/null
+++ b/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/LlamaDecoderOnly/int8wo_asym_gs-1_all_layers_False.dot
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diff --git a/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/LlamaDecoderOnly/int8wo_asym_gs-1_all_layers_False_awq_False_scale_estimation_False_ref_wc_scales.json b/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/LlamaDecoderOnly/int8wo_asym_gs-1_all_layers_False_awq_False_scale_estimation_False_ref_wc_scales.json
new file mode 100644
index 00000000000..40b1cc6c44e
--- /dev/null
+++ b/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/LlamaDecoderOnly/int8wo_asym_gs-1_all_layers_False_awq_False_scale_estimation_False_ref_wc_scales.json
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\ No newline at end of file
diff --git a/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/LlamaDecoderOnly/int8wo_asym_gs-1_all_layers_False_ref_wc_param.json b/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/LlamaDecoderOnly/int8wo_asym_gs-1_all_layers_False_ref_wc_param.json
new file mode 100644
index 00000000000..69d4cf0f6a8
--- /dev/null
+++ b/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/LlamaDecoderOnly/int8wo_asym_gs-1_all_layers_False_ref_wc_param.json
@@ -0,0 +1,128 @@
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\ No newline at end of file
diff --git a/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/short_transformer_shared/int4wo_sym_gs32_all_layers_False.dot b/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/short_transformer_shared/int4wo_sym_gs32_all_layers_False.dot
new file mode 100644
index 00000000000..b249fdf7ce3
--- /dev/null
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diff --git a/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/short_transformer_shared/int4wo_sym_gs32_all_layers_False_awq_True_scale_estimation_True_ref_wc_scales.json b/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/short_transformer_shared/int4wo_sym_gs32_all_layers_False_awq_True_scale_estimation_True_ref_wc_scales.json
new file mode 100644
index 00000000000..38bc9f7b51c
--- /dev/null
+++ b/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/short_transformer_shared/int4wo_sym_gs32_all_layers_False_awq_True_scale_estimation_True_ref_wc_scales.json
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\ No newline at end of file
diff --git a/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/short_transformer_shared/int4wo_sym_gs32_all_layers_False_ref_wc_param.json b/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/short_transformer_shared/int4wo_sym_gs32_all_layers_False_ref_wc_param.json
new file mode 100644
index 00000000000..fd8fbda6f54
--- /dev/null
+++ b/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/short_transformer_shared/int4wo_sym_gs32_all_layers_False_ref_wc_param.json
@@ -0,0 +1,38 @@
+[
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+]
\ No newline at end of file
diff --git a/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/short_transformer_shared/int4wo_sym_gs32_all_layers_True.dot b/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/short_transformer_shared/int4wo_sym_gs32_all_layers_True.dot
new file mode 100644
index 00000000000..0a7bb5fe8f8
--- /dev/null
+++ b/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/short_transformer_shared/int4wo_sym_gs32_all_layers_True.dot
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diff --git a/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/short_transformer_shared/int4wo_sym_gs32_all_layers_True_awq_True_scale_estimation_True_ref_wc_scales.json b/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/short_transformer_shared/int4wo_sym_gs32_all_layers_True_awq_True_scale_estimation_True_ref_wc_scales.json
new file mode 100644
index 00000000000..24d93dde47d
--- /dev/null
+++ b/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/short_transformer_shared/int4wo_sym_gs32_all_layers_True_awq_True_scale_estimation_True_ref_wc_scales.json
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+}
\ No newline at end of file
diff --git a/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/short_transformer_shared/int4wo_sym_gs32_all_layers_True_ref_wc_param.json b/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/short_transformer_shared/int4wo_sym_gs32_all_layers_True_ref_wc_param.json
new file mode 100644
index 00000000000..81205ac2ca8
--- /dev/null
+++ b/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/short_transformer_shared/int4wo_sym_gs32_all_layers_True_ref_wc_param.json
@@ -0,0 +1,38 @@
+[
+    {
+        "weight_name": "wte_weight_1",
+        "node_with_weight": "embedding",
+        "weight_port_id": 0,
+        "weight_dtype": "float32",
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+            128,
+            64
+        ],
+        "reduction_axes": [
+            1
+        ],
+        "compression_config": {
+            "mode": "int4_sym",
+            "group_size": 32,
+            "codebook_values": null
+        }
+    },
+    {
+        "weight_name": "linear_weight",
+        "node_with_weight": "linear",
+        "weight_port_id": 1,
+        "weight_dtype": "float32",
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+            64
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+        "reduction_axes": [
+            1
+        ],
+        "compression_config": {
+            "mode": "int4_sym",
+            "group_size": 32,
+            "codebook_values": null
+        }
+    }
+]
\ No newline at end of file
diff --git a/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/short_transformer_shared/int8wo_asym_gs-1_all_layers_False.dot b/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/short_transformer_shared/int8wo_asym_gs-1_all_layers_False.dot
new file mode 100644
index 00000000000..b249fdf7ce3
--- /dev/null
+++ b/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/short_transformer_shared/int8wo_asym_gs-1_all_layers_False.dot
@@ -0,0 +1,24 @@
+strict digraph {
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+"1 lm_head_bias" [id=1, type="get_attr"];
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+"3 wte_weight_1_updated_constant0" [id=3, type="get_attr"];
+"4 asymmetric_weights_decompressor_wte_weight_1_0" [id=4, type="call_module"];
+"5 embedding" [id=5, type=embedding];
+"6 linear_weight_updated_constant0" [id=6, type="get_attr"];
+"7 asymmetric_weights_decompressor_linear_weight_0" [id=7, type="call_module"];
+"8 linear" [id=8, type=linear];
+"9 linear_1" [id=9, type=linear];
+"10 output" [id=10, type=output];
+"0 linear_bias" -> "8 linear" [style=solid, label="(64,)"];
+"1 lm_head_bias" -> "9 linear_1" [style=solid, label="(128,)"];
+"2 input_ids" -> "5 embedding" [style=solid, label="(5,)"];
+"3 wte_weight_1_updated_constant0" -> "4 asymmetric_weights_decompressor_wte_weight_1_0" [style=solid, label="(128, 64)"];
+"4 asymmetric_weights_decompressor_wte_weight_1_0" -> "5 embedding" [style=solid, label="(128, 64)"];
+"4 asymmetric_weights_decompressor_wte_weight_1_0" -> "9 linear_1" [style=solid, label="(128, 64)"];
+"5 embedding" -> "8 linear" [style=solid, label="(5, 64)"];
+"6 linear_weight_updated_constant0" -> "7 asymmetric_weights_decompressor_linear_weight_0" [style=solid, label="(64, 64)"];
+"7 asymmetric_weights_decompressor_linear_weight_0" -> "8 linear" [style=solid, label="(64, 64)"];
+"8 linear" -> "9 linear_1" [style=solid, label="(5, 64)"];
+"9 linear_1" -> "10 output" [style=solid, label="(5, 128)"];
+}
diff --git a/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/short_transformer_shared/int8wo_asym_gs-1_all_layers_False_awq_False_scale_estimation_False_ref_wc_scales.json b/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/short_transformer_shared/int8wo_asym_gs-1_all_layers_False_awq_False_scale_estimation_False_ref_wc_scales.json
new file mode 100644
index 00000000000..edb48f38b53
--- /dev/null
+++ b/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/short_transformer_shared/int8wo_asym_gs-1_all_layers_False_awq_False_scale_estimation_False_ref_wc_scales.json
@@ -0,0 +1,582 @@
+{
+    "asymmetric_weights_decompressor_wte_weight_1_0": [
+        [
+            0.01552581787109375
+        ],
+        [
+            0.02117919921875
+        ],
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+            0.020660400390625
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+            0.0209503173828125
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+            0.0171966552734375
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+            0.0175628662109375
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+            0.01415252685546875
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+            0.0167388916015625
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+            0.0225982666015625
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+            0.018646240234375
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+            0.018463134765625
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+            0.0204010009765625
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+        [
+            0.018463134765625
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+        [
+            0.0205078125
+        ],
+        [
+            0.0153350830078125
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+        [
+            0.01751708984375
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+        [
+            0.01922607421875
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+            0.0174560546875
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+        [
+            0.0154571533203125
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+        [
+            0.01812744140625
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+            0.019073486328125
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+        [
+            0.017852783203125
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+        [
+            0.0158538818359375
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+        [
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+            0.0213470458984375
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+            0.01995849609375
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+        [
+            0.0166168212890625
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+        [
+            0.019561767578125
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+            0.0184478759765625
+        ],
+        [
+            0.0162353515625
+        ],
+        [
+            0.021270751953125
+        ]
+    ],
+    "asymmetric_weights_decompressor_linear_weight_0": [
+        [
+            0.0009274482727050781
+        ],
+        [
+            0.000965118408203125
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+        [
+            0.0009703636169433594
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+        [
+            0.0009703636169433594
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+            0.0009670257568359375
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+        [
+            0.0009608268737792969
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+        [
+            0.0009489059448242188
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+        [
+            0.0009322166442871094
+        ],
+        [
+            0.0009675025939941406
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+        [
+            0.0009665489196777344
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+        [
+            0.0009264945983886719
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+            0.0009684562683105469
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+        [
+            0.0009188652038574219
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+        [
+            0.0009365081787109375
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+        [
+            0.00095367431640625
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+        [
+            0.0009708404541015625
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+            0.0009508132934570312
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+        [
+            0.0009217262268066406
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+        [
+            0.0009446144104003906
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+        [
+            0.0009517669677734375
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+        [
+            0.0009717941284179688
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+        [
+            0.0009593963623046875
+        ],
+        [
+            0.0009174346923828125
+        ],
+        [
+            0.0009484291076660156
+        ],
+        [
+            0.0009756088256835938
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+        [
+            0.0009760856628417969
+        ],
+        [
+            0.0009722709655761719
+        ],
+        [
+            0.0008978843688964844
+        ],
+        [
+            0.0009188652038574219
+        ],
+        [
+            0.0009579658508300781
+        ],
+        [
+            0.000946044921875
+        ],
+        [
+            0.0009713172912597656
+        ],
+        [
+            0.0009474754333496094
+        ],
+        [
+            0.0009555816650390625
+        ]
+    ]
+}
\ No newline at end of file
diff --git a/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/short_transformer_shared/int8wo_asym_gs-1_all_layers_False_ref_wc_param.json b/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/short_transformer_shared/int8wo_asym_gs-1_all_layers_False_ref_wc_param.json
new file mode 100644
index 00000000000..49d45c1fffb
--- /dev/null
+++ b/tests/executorch/data/fx/compress_pt2e/OpenVINOQuantizer/short_transformer_shared/int8wo_asym_gs-1_all_layers_False_ref_wc_param.json
@@ -0,0 +1,38 @@
+[
+    {
+        "weight_name": "wte_weight_1",
+        "node_with_weight": "embedding",
+        "weight_port_id": 0,
+        "weight_dtype": "float32",
+        "weight_shape": [
+            128,
+            64
+        ],
+        "reduction_axes": [
+            1
+        ],
+        "compression_config": {
+            "mode": "int8_asym",
+            "group_size": -1,
+            "codebook_values": null
+        }
+    },
+    {
+        "weight_name": "linear_weight",
+        "node_with_weight": "linear",
+        "weight_port_id": 1,
+        "weight_dtype": "float32",
+        "weight_shape": [
+            64,
+            64
+        ],
+        "reduction_axes": [
+            1
+        ],
+        "compression_config": {
+            "mode": "int8_asym",
+            "group_size": -1,
+            "codebook_values": null
+        }
+    }
+]
\ No newline at end of file
diff --git a/tests/executorch/requirements.txt b/tests/executorch/requirements.txt
new file mode 100644
index 00000000000..49faa6dac6a
--- /dev/null
+++ b/tests/executorch/requirements.txt
@@ -0,0 +1,47 @@
+--pre
+--extra-index-url https://download.pytorch.org/whl/nightly/cpu
+
+# Pytorch
+torch==2.10.0.dev20250922+cpu
+torchvision==0.25.0.dev20250922+cpu
+torchao==0.14.0.dev20250922+cpu
+
+# Openvino
+openvino==2025.3.0
+
+# ONNX
+onnx==1.17.0; python_version < '3.13'
+onnx==1.18.0; python_version >= '3.13'
+onnxruntime==1.21.1
+
+# Copied from https://github.com/anzr299/executorch/blob/an/quantizer_nncf_pt2e_support/requirements-dev.txt
+cmake>=3.29, <4.0.0  # For building binary targets in the wheel.
+packaging>=24.2 # Lower bound required by setuptools
+pip>=23  # For building the pip package.
+pyyaml  # Imported by the kernel codegen tools.
+setuptools>=77.0.3  # For building the pip package contents.
+wheel  # For building the pip package archive.
+zstd  # Imported by resolve_buck.py.
+certifi  # Imported by resolve_buck.py.
+
+# Copied from tests/torch2/requirements.txt
+addict>=2.4.0
+efficientnet_pytorch==0.7.1
+transformers==4.52.1
+
+sentence-transformers==4.1.0
+optimum-intel==1.24.0
+optimum==1.26.0
+accelerate==1.9.0
+fastdownload==0.0.7
+
+
+# Tests and examples
+pytest==8.0.2
+pytest-cov==4.1.0
+pytest-mock==3.12.0
+pytest-dependency==0.6.0
+pytest-ordering==0.6
+pytest-xdist==3.5.0
+pytest-forked==1.6.0
+pytest-split==0.9.0
diff --git a/tests/executorch/test_quantizer_compression.py b/tests/executorch/test_quantizer_compression.py
new file mode 100644
index 00000000000..464d0a4764a
--- /dev/null
+++ b/tests/executorch/test_quantizer_compression.py
@@ -0,0 +1,351 @@
+# Copyright (c) 2025 Intel Corporation
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#      http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import dataclasses
+import json
+from dataclasses import dataclass
+from enum import Enum
+from functools import partial
+from typing import Any, Callable, Optional
+
+import pytest
+import torch
+import torch.fx
+from torchao.quantization.pt2e.quantize_pt2e import convert_pt2e
+from torchao.quantization.pt2e.quantize_pt2e import prepare_pt2e
+
+import nncf
+from executorch.backends.openvino.quantizer.quantizer import OpenVINOQuantizer
+from executorch.backends.openvino.quantizer.quantizer import QuantizationMode
+from nncf.common.graph import NNCFGraph
+from nncf.common.graph.graph import NNCFNode
+from nncf.common.utils.os import safe_open
+from nncf.experimental.torch.fx import compress_pt2e
+from nncf.experimental.torch.fx.nncf_graph_builder import GraphConverter
+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 tests.cross_fw.shared.nx_graph import compare_nx_graph_with_reference
+from tests.cross_fw.shared.paths import TEST_ROOT
+from tests.torch.test_models.llama import LlamaDecoderOnly
+from tests.torch.test_models.synthetic import ShortTransformer
+from tests.torch2.fx.helpers import get_torch_fx_model
+
+FX_PT2E_DIR = TEST_ROOT / "executorch" / "data" / "fx" / "compress_pt2e"
+FX_AO_DIR = TEST_ROOT / "executorch" / "data" / "fx" / "ao_export_compression_OpenVINOQuantizer"
+
+
+@dataclass
+class ModelCase:
+    model_builder: Callable[[], torch.nn.Module]
+    model_id: str
+    input_shape: tuple[int, ...]
+
+
+def get_dot_filename(model_name: str) -> str:
+    return model_name + ".dot"
+
+
+def get_wc_param_filename(model_name: str) -> str:
+    return model_name + "_ref_wc_param.json"
+
+
+def get_wc_scales_filename(model_name: str) -> str:
+    return model_name + "_ref_wc_scales.json"
+
+
+def build_torch_fx_model(model_case: ModelCase) -> tuple[torch.fx.GraphModule, torch.Tensor]:
+    model = model_case.model_builder()
+    # ShortTransformer takes token ids; match prior synthetic tests (int32)
+    example_input = torch.ones(model_case.input_shape, dtype=torch.int32)
+    fx_model = get_torch_fx_model(model, example_input)
+    return fx_model, example_input
+
+
+def _get_calibration_dataset(example_input: torch.Tensor) -> nncf.Dataset:
+    torch.manual_seed(42)
+
+    def transform_fn(x):
+        return x.to("cpu")
+
+    sample_1 = torch.randint_like(example_input, 0, 10)
+    sample_2 = torch.randint_like(example_input, 0, 10)
+    return nncf.Dataset([example_input, sample_1, sample_2], transform_fn)
+
+
+def get_openvino_quantizer(*args, **kwargs) -> OpenVINOQuantizer:
+    return OpenVINOQuantizer(*args, **kwargs)
+
+
+def _string_from_quantizer_params(qparams: dict[str, Any], pt2e_param: Optional[dict[str, Any]] = None) -> str:
+    mode = qparams.get("mode")
+    gs = qparams.get("group_size", "-1")
+    all_layers = qparams.get("all_layers", "False")
+    if pt2e_param is None:
+        return f"{mode.value}_gs{gs}_all_layers_{all_layers}"
+    awq = pt2e_param.get("awq", "False")
+    scale_estimation = pt2e_param.get("scale_estimation", "False")
+    return f"{mode.value}_gs{gs}_all_layers_{all_layers}_awq_{awq}_scale_estimation_{scale_estimation}"
+
+
+def check_multiple_isinstance(object_to_check: Any, objects: list[Any]):
+    if not object_to_check:
+        return False
+    for obj in objects:
+        if isinstance(object_to_check, obj):
+            return True
+    return False
+
+
+def get_scale_values_from_model(model: torch.fx.GraphModule):
+    node_to_scale_mapping = {}
+    decompressor_modules = [
+        INT4AsymmetricWeightsDecompressor,
+        INT4SymmetricWeightsDecompressor,
+        INT8AsymmetricWeightsDecompressor,
+        INT8SymmetricWeightsDecompressor,
+    ]
+    for node in model.graph.nodes:
+        # print(node.name, node.target, node.meta)
+        node_module = getattr(model, node.target) if node.op == "call_module" else None
+        if not check_multiple_isinstance(node_module, decompressor_modules):
+            continue
+        state_dict_scale_name = f"{node.target}._scale"
+        node_to_scale_mapping[node.name] = model.state_dict()[state_dict_scale_name]
+
+    return node_to_scale_mapping
+
+
+def get_test_cases():
+    test_cases = []
+    for model in BASE_MODELS:
+        for qparam in QUANTIZER_PARAMS:
+            pt2e_params = PT2E_PARAMS
+            if qparam.get("mode") in {QuantizationMode.INT8WO_ASYM, QuantizationMode.INT8WO_SYM}:
+                pt2e_params = [{}]
+            for pt2e_param in pt2e_params:
+                test_cases.append(
+                    (
+                        model,
+                        qparam,
+                        pt2e_param,
+                    )
+                )
+    return test_cases
+
+
+BASE_MODELS = (
+    ModelCase(LlamaDecoderOnly, "LlamaDecoderOnly", [1, 3, 64]),
+    ModelCase(partial(ShortTransformer, 64, 128, True), "short_transformer_shared", [5]),
+)
+
+QUANTIZER_PARAMS = (
+    {"mode": QuantizationMode.INT8WO_ASYM},
+    {"mode": QuantizationMode.INT4WO_SYM, "group_size": 32},
+    {"mode": QuantizationMode.INT4WO_SYM, "group_size": 32, "all_layers": True},
+)
+
+PT2E_PARAMS = ({"awq": True, "scale_estimation": True},)
+
+
+TEST_MODELS = get_test_cases()
+
+
+TEST_MODEL_IDS = [
+    f"{m.model_id}__{_string_from_quantizer_params(qparams, pt2e_param)}" for (m, qparams, pt2e_param) in TEST_MODELS
+]
+
+INT8_COMPRESSION_MODES = [QuantizationMode.INT8WO_ASYM, QuantizationMode.INT8WO_SYM]
+
+
+@pytest.mark.parametrize(
+    ("model_case", "quantizer_params", "pt2e_params"),
+    TEST_MODELS,
+    ids=TEST_MODEL_IDS,
+)
+@pytest.mark.parametrize(
+    "quantizer_builder",
+    [get_openvino_quantizer],
+    ids=["OpenVINOQuantizer"],
+)
+def test_compress_pt2e(
+    quantizer_builder: Callable[..., OpenVINOQuantizer],
+    model_case: ModelCase,
+    quantizer_params,
+    pt2e_params,
+):
+    fx_model, example_input = build_torch_fx_model(model_case)
+    with torch.no_grad():
+        ref_out = fx_model(example_input)
+
+    calibration_dataset = _get_calibration_dataset(example_input)
+
+    # Build quantizer directly from quantizer_params (already includes mode/group_size)
+    quantizer = quantizer_builder(**quantizer_params)
+    mode = quantizer_params.get("mode")
+    ratio = 1 if mode in INT8_COMPRESSION_MODES else 0.8
+
+    quantized_model = compress_pt2e(fx_model, quantizer=quantizer, ratio=ratio, dataset=calibration_dataset)
+
+    with torch.no_grad():
+        out = quantized_model(example_input)
+    assert out.shape == ref_out.shape, "Compressed model output shape mismatch."
+
+    nncf_graph: NNCFGraph = GraphConverter.create_nncf_graph(quantized_model)
+    nx_graph = nncf_graph.get_graph_for_structure_analysis(extended=True)
+    param_string = _string_from_quantizer_params(quantizer_params)
+    path_to_dot = (
+        FX_PT2E_DIR / quantizer.__class__.__name__ / model_case.model_id / get_dot_filename(param_string)
+    ).as_posix()
+    compare_nx_graph_with_reference(nx_graph, path_to_dot)
+
+
+@pytest.mark.parametrize(
+    ("model_case", "quantizer_params", "pt2e_params"),
+    TEST_MODELS,
+    ids=TEST_MODEL_IDS,
+)
+@pytest.mark.parametrize(
+    "quantizer_builder",
+    [get_openvino_quantizer],
+    ids=["OpenVINOQuantizer"],
+)
+def test_compress_pt2e_scales(
+    quantizer_builder: Callable[..., OpenVINOQuantizer],
+    model_case: ModelCase,
+    quantizer_params,
+    pt2e_params,
+    regen_ref_data,
+):
+    fx_model, example_input = build_torch_fx_model(model_case)
+    with torch.no_grad():
+        ref_out = fx_model(example_input)
+
+    calibration_dataset = _get_calibration_dataset(example_input)
+
+    # Build quantizer directly from quantizer_params (already includes mode/group_size)
+    quantizer = quantizer_builder(**quantizer_params)
+    mode = quantizer_params.get("mode")
+    ratio = 1 if mode in INT8_COMPRESSION_MODES else 0.8
+    quantized_model = compress_pt2e(
+        fx_model, quantizer=quantizer, ratio=ratio, dataset=calibration_dataset, **pt2e_params
+    )
+
+    with torch.no_grad():
+        out = quantized_model(example_input)
+    assert out.shape == ref_out.shape, "Compressed model output shape mismatch."
+
+    param_string = _string_from_quantizer_params(quantizer_params, pt2e_params)
+    ref_json_path = (
+        FX_PT2E_DIR / quantizer.__class__.__name__ / model_case.model_id / get_wc_scales_filename(param_string)
+    )
+
+    scales_list = get_scale_values_from_model(quantized_model)
+    scales_list = to_json_serializable(scales_list)
+
+    if regen_ref_data:
+        with safe_open(ref_json_path, "w") as file:
+            json.dump(scales_list, file, indent=4)
+
+    with safe_open(ref_json_path, "r") as f:
+        json.load(f)
+
+
+@pytest.mark.parametrize(
+    ("model_case", "quantizer_params", "pt2e_params"),
+    TEST_MODELS,
+    ids=TEST_MODEL_IDS,
+)
+@pytest.mark.parametrize(
+    "quantizer_builder",
+    [get_openvino_quantizer],
+    ids=["OpenVINOQuantizer"],
+)
+def test_openvino_quantizer(
+    model_case: ModelCase,
+    quantizer_params,
+    quantizer_builder: Callable[..., OpenVINOQuantizer],
+    pt2e_params,
+):
+    fx_model, example_input = build_torch_fx_model(model_case)
+    quantizer = quantizer_builder(**quantizer_params)
+
+    prepared = prepare_pt2e(fx_model, quantizer)
+    prepared(example_input)
+    ao_quantized_model = convert_pt2e(prepared)
+
+    nncf_graph = GraphConverter.create_nncf_graph(ao_quantized_model)
+    nx_graph = nncf_graph.get_graph_for_structure_analysis(extended=True)
+
+    param_string = _string_from_quantizer_params(quantizer_params)
+    path_to_dot = (FX_AO_DIR / model_case.model_id / get_dot_filename(param_string)).as_posix()
+    compare_nx_graph_with_reference(nx_graph, path_to_dot)
+
+
+def to_json_serializable(obj: Any) -> dict[Any, Any]:
+    if dataclasses.is_dataclass(obj):
+        return {k: to_json_serializable(v) for k, v in dataclasses.asdict(obj).items()}
+    if isinstance(obj, Enum):
+        return obj.value
+    if isinstance(obj, (list, tuple)):
+        return [to_json_serializable(x) for x in obj]
+    if isinstance(obj, torch.Tensor):
+        return obj.detach().cpu().tolist()
+    if isinstance(obj, dict):
+        return {k: to_json_serializable(v) for k, v in obj.items()}
+    if isinstance(obj, NNCFNode):
+        return obj.node_name
+    return obj
+
+
+@pytest.mark.parametrize(
+    ("model_case", "quantizer_params", "pt2e_params"),
+    TEST_MODELS,
+    ids=TEST_MODEL_IDS,
+)
+@pytest.mark.parametrize(
+    "quantizer_builder",
+    [get_openvino_quantizer],
+    ids=["OpenVINOQuantizer"],
+)
+def test_openvino_wc_params(
+    quantizer_builder: Callable[..., OpenVINOQuantizer],
+    model_case: ModelCase,
+    quantizer_params,
+    pt2e_params,
+    regen_ref_data,
+):
+    fx_model, _ = build_torch_fx_model(model_case)
+    nncf_graph: NNCFGraph = GraphConverter.create_nncf_graph(fx_model)
+
+    param_string = _string_from_quantizer_params(quantizer_params)
+    quantizer = quantizer_builder(**quantizer_params)
+
+    all_weight_params, *_ = quantizer.get_nncf_weight_compression_parameters(fx_model, nncf_graph)
+
+    wc_params = to_json_serializable(all_weight_params)
+
+    ref_json_path = (
+        FX_PT2E_DIR / quantizer.__class__.__name__ / model_case.model_id / get_wc_param_filename(param_string)
+    )
+
+    if regen_ref_data:
+        with safe_open(ref_json_path, "w") as file:
+            json.dump(wc_params, file, indent=4)
+
+    with safe_open(ref_json_path, "r") as f:
+        ref_data = json.load(f)
+
+    assert wc_params == ref_data, (
+        f"Weight compression parameters JSON mismatch for {model_case.model_id} ({param_string}).\nRef: {ref_json_path}"
+    )
diff --git a/tests/torch/test_models/__init__.py b/tests/torch/test_models/__init__.py
index 95cba87cc98..8dcdf092213 100644
--- a/tests/torch/test_models/__init__.py
+++ b/tests/torch/test_models/__init__.py
@@ -15,6 +15,7 @@
 from .googlenet import *
 from .inceptionv3 import *
 from .lenet import *
+from .llama import *
 from .pnasnet import *
 from .preact_resnet import *
 from .resnet import *
diff --git a/tests/torch/test_models/llama.py b/tests/torch/test_models/llama.py
new file mode 100644
index 00000000000..fae6b6be9de
--- /dev/null
+++ b/tests/torch/test_models/llama.py
@@ -0,0 +1,168 @@
+# Copyright (c) 2025 Intel Corporation
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#      http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import math
+from typing import Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+EMBED_DIM = 64
+N_HEADS = 4
+HEAD_DIM = EMBED_DIM // N_HEADS
+# Same as Llama 3.2 config
+ROPE_THETA = 500000.0
+MAX_SEQ = 128
+BIAS = False
+
+
+class LlamaRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        Copied from src/transformers/models/llama/modeling_llama.py
+        LlamaRMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+
+def _rotate_half(x):
+    """
+    Copied from src/transformers/models/llama/modeling_llama.py
+    Rotates half the hidden dims of the input.
+    """
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+class Rotary(nn.Module):
+    """
+    Precompute cos/sin for RoPE and apply to q,k.
+    Copied from src/transformers/models/llama/modeling_llama.py
+    Initialize the cos and sin value once in init method
+    """
+
+    # Llama applies rotary to q,k before attention; see modeling_llama
+    def __init__(self, head_dim: int, max_seq_len: int = MAX_SEQ, theta: float = ROPE_THETA, device=None):
+        super().__init__()
+        dtype = torch.float32
+        inv_freq = 1.0 / (theta ** (torch.arange(0, head_dim, 2, dtype=dtype, device=device) / head_dim))
+        t = torch.arange(max_seq_len, dtype=dtype, device=device)
+        freqs = torch.einsum("t,f->tf", t, inv_freq)  # (T, Hd/2)
+        emb = torch.cat((freqs, freqs), dim=-1)  # (T, Hd)
+        self.register_buffer("cos", emb.cos()[None, None, ...], persistent=False)  # (1,1,T,Hd)
+        self.register_buffer("sin", emb.sin()[None, None, ...], persistent=False)
+
+    def forward(self, q: torch.Tensor, k: torch.Tensor, pos: torch.Tensor):
+        cos = self.cos[..., pos, :]
+        sin = self.sin[..., pos, :]
+        q_embed = (q * cos) + (_rotate_half(q) * sin)
+        k_embed = (k * cos) + (_rotate_half(k) * sin)
+        return q_embed, k_embed
+
+
+class LlamaMLP(nn.Module):
+    """
+    Copied from src/transformers/models/llama/modeling_llama.py
+    """
+
+    def __init__(self, dim: int, mult: int = 2):
+        super().__init__()
+        # mult is used as a scaling factor of sorts. This is to define the hidden/intermediate layer size
+        hidden = mult * dim
+        self.gate_proj = nn.Linear(dim, hidden, bias=BIAS)
+        self.up_proj = nn.Linear(dim, hidden, bias=BIAS)
+        self.down_proj = nn.Linear(hidden, dim, bias=BIAS)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        down_proj = self.down_proj(F.silu(self.gate_proj(x)) * self.up_proj(x))
+        return down_proj
+
+
+class LlamaDecoderOnly(nn.Module):
+    """
+    One Llama-style transformer block (pre-norm attn + MLP) with RoPE and KV cache.
+    Forward takes embeddings only.
+    """
+
+    # KV caching + past_key_values flow mirrors HF implementations. :contentReference[oaicite:4]{index=4}
+    def __init__(self, dim: int = EMBED_DIM, n_heads: int = N_HEADS):
+        super().__init__()
+        assert dim % n_heads == 0
+        self.n_heads = n_heads
+        self.head_dim = dim // n_heads
+        self.scale = 1.0 / math.sqrt(self.head_dim)
+
+        self.attn_norm = LlamaRMSNorm(dim)
+        self.q_proj = nn.Linear(dim, dim, bias=BIAS)
+        self.k_proj = nn.Linear(dim, dim, bias=BIAS)
+        self.v_proj = nn.Linear(dim, dim, bias=BIAS)
+        self.o_proj = nn.Linear(dim, dim, bias=BIAS)
+        self.rope = Rotary(self.head_dim, MAX_SEQ, theta=ROPE_THETA)
+
+        self.mlp_norm = LlamaRMSNorm(dim)
+        self.mlp = LlamaMLP(dim)
+
+    def _attn(self, x: torch.Tensor, pos: torch.Tensor, past_kv: Optional[tuple[torch.Tensor, torch.Tensor]]):
+        """
+        Code from LlamaAttention forward method. SDPA implementation similar to model.config._attn_implementation="SDPA"
+        """
+        B, T, C = x.shape
+        H, Hd = self.n_heads, self.head_dim
+
+        # QKV projections from hidden state x
+        q = self.q_proj(x).view(B, T, H, Hd).transpose(1, 2)
+        k = self.k_proj(x).view(B, T, H, Hd).transpose(1, 2)
+        v = self.v_proj(x).view(B, T, H, Hd).transpose(1, 2)
+
+        # RoPE
+        q, k = self.rope(q, k, pos)
+
+        # KV cache
+        if past_kv is not None:
+            pk, pv = past_kv  # (B,H,Tpast,Hd)
+            k = torch.cat([pk, k], dim=2)
+            v = torch.cat([pv, v], dim=2)
+
+        y = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, is_causal=True, dropout_p=0.0)
+
+        y = y.transpose(1, 2).contiguous().view(B, T, C)
+        y = self.o_proj(y)
+        return y, (k, v)
+
+    def forward(
+        self,
+        x_embed: torch.Tensor,  # (B, T_new, C) embeddings only
+        past_kv: Optional[tuple[torch.Tensor, torch.Tensor]] = None,  # (B,H,Tpast,Hd)
+    ):
+        # positions for the *new* tokens only
+        past_len = 0 if past_kv is None else past_kv[0].size(2)
+        T_new = x_embed.size(1)
+        pos = torch.arange(past_len, past_len + T_new, device=x_embed.device)
+
+        # pre-norm attention + residual
+        y, _kv = self._attn(self.attn_norm(x_embed), pos, past_kv)
+        x = x_embed + y
+
+        # pre-norm MLP + residual
+        x = x + self.mlp(self.mlp_norm(x))
+        return x