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<div align="center"> # Neural Network Compression Framework (NNCF) [Key Features](#key-features) • [Installation](#installation-guide) • [Documentation](#documentation) • [Usage](#usage) • [Tutorials and Samples](#demos-tutorials-and-samples) • [Third-party integration](#third-party-repository-integration) • [Model Zoo](./docs/ModelZoo.md) [![GitHub Release](https://img.shields.io/github/v/release/openvinotoolkit/nncf?color=green)](https://github.com/openvinotoolkit/nncf/releases) [![Website](https://img.shields.io/website?up_color=blue&up_message=docs&url=https%3A%2F%2Fdocs.openvino.ai%2Fnncf)](https://docs.openvino.ai/nncf) [![Apache License Version 2.0](https://img.shields.io/badge/license-Apache_2.0-green.svg)](LICENSE) [![PyPI Downloads](https://static.pepy.tech/badge/nncf)](https://pypi.org/project/nncf/) ![Python](https://img.shields.io/badge/python-3.11+-blue) ![Backends](https://img.shields.io/badge/backends-openvino_|_pytorch_|_onnx_-orange) ![OS](https://img.shields.io/badge/OS-Linux_|_Windows_|_MacOS-blue) </div> Neural Network Compression Framework (NNCF) provides a suite of post-training and training-time algorithms for optimizing inference of neural networks in [OpenVINO™](https://docs.openvino.ai) with a minimal accuracy drop. NNCF is designed to work with models from [PyTorch](https://pytorch.org/), [TorchFX](https://pytorch.org/docs/stable/fx.html), [ONNX](https://onnx.ai/) and [OpenVINO™](https://docs.openvino.ai). NNCF provides [samples](#demos-tutorials-and-samples) that demonstrate the usage of compression algorithms for different use cases and models. See compression results achievable with the NNCF-powered samples on the [NNCF Model Zoo page](./docs/ModelZoo.md). The framework is organized as a Python\* package that can be built and used in a standalone mode. The framework architecture is unified to make it easy to add different compression algorithms for both PyTorch deep learning frameworks. <a id="key-features"></a> ## Key Features ### Post-Training Compression Algorithms | Compression algorithm | OpenVINO | PyTorch | TorchFX | ONNX | | :------------------------------------------------------------------------------------------------------- | :-----------: | :----------: | :-----------: | :-----------: | | [Post-Training Quantization](./docs/usage/post_training_compression/post_training_quantization/Usage.md) | Supported | Supported | Experimental | Supported | | [Weights Compression](./docs/usage/post_training_compression/weights_compression/Usage.md) | Supported | Supported | Experimental | Supported | | [Activation Sparsity](./src/nncf/experimental/torch/sparsify_activations/ActivationSparsity.md) | Not supported | Experimental | Not supported | Not supported | ### Training-Time Compression Algorithms | Compression algorithm | PyTorch | | :-------------------------------------------------------------------------------------------------------------------------------------------- | :-------: | | [Quantization Aware Training](./docs/usage/training_time_compression/quantization_aware_training/Usage.md) | Supported | | [Weight-Only Quantization Aware Training with LoRA and NLS](./docs/usage/training_time_compression/quantization_aware_training_lora/Usage.md) | Supported | | [Pruning](./docs/usage/training_time_compression/pruning/Usage.md) | Supported | - Automatic, configurable model graph transformation to obtain the compressed model. - Common interface for compression methods. - GPU-accelerated layers for faster compressed model fine-tuning. - Distributed training support. - Git patch for prominent third-party repository ([huggingface-transformers](https://github.com/huggingface/transformers)) demonstrating the process of integrating NNCF into custom training pipelines. - Exporting PyTorch compressed models to ONNX\* checkpoints compressed models to SavedModel or Frozen Graph format, ready to use with [OpenVINO™ toolkit](https://docs.openvino.ai). <a id="documentation"></a> ## Documentation This documentation covers detailed information about NNCF algorithms and functions needed for the contribution to NNCF. The latest user documentation for NNCF is available [here](https://docs.openvino.ai/nncf). NNCF API documentation can be found [here](https://openvinotoolkit.github.io/nncf/autoapi/nncf/). <a id="usage"></a> ## Usage ### Post-Training Quantization The NNCF PTQ is the simplest way to apply 8-bit quantization. To run the algorithm you only need your model and a small (~300 samples) calibration dataset. [OpenVINO](https://github.com/openvinotoolkit/openvino) is the preferred backend to run PTQ with, while PyTorch and ONNX are also supported. <details open><summary>OpenVINO</summary> ```python import nncf import openvino as ov import torch from torchvision import datasets, transforms # Instantiate your uncompressed model model = ov.Core().read_model("/model_path") # Provide validation part of the dataset to collect statistics needed for the compression algorithm val_dataset = datasets.ImageFolder("/path", transform=transforms.Compose([transforms.ToTensor()])) dataset_loader = torch.utils.data.DataLoader(val_dataset, batch_size=1) # Step 1: Initialize transformation function def transform_fn(data_item): images, _ = data_item return images # Step 2: Initialize NNCF Dataset calibration_dataset = nncf.Dataset(dataset_loader, transform_fn) # Step 3: Run the quantization pipeline quantized_model = nncf.quantize(model, calibration_dataset) ``` </details> <details><summary>PyTorch</summary> ```python import nncf import torch from torchvision import datasets, models # Instantiate your uncompressed model model = models.mobilenet_v2() # Provide validation part of the dataset to collect statistics needed for the compression algorithm val_dataset = datasets.ImageFolder("/path", transform=transforms.Compose([transforms.ToTensor()])) dataset_loader = torch.utils.data.DataLoader(val_dataset) # Step 1: Initialize the transformation function def transform_fn(data_item): images, _ = data_item return images # Step 2: Initialize NNCF Dataset calibration_dataset = nncf.Dataset(dataset_loader, transform_fn) # Step 3: Run the quantization pipeline quantized_model = nncf.quantize(model, calibration_dataset) ``` **NOTE** If the Post-Training Quantization algorithm does not meet quality requirements you can fine-tune the quantized pytorch model. You can find an example of the Quantization-Aware training pipeline for a pytorch model [here](examples/quantization_aware_training/torch/resnet18/README.md). </details> <details><summary>TorchFX</summary> ```python import nncf import torch.fx from torchvision import datasets, models # Instantiate your uncompressed model model = models.mobilenet_v2() # Provide validation part of the dataset to collect statistics needed for the compression algorithm val_dataset = datasets.ImageFolder("/path", transform=transforms.Compose([transforms.ToTensor()])) dataset_loader = torch.utils.data.DataLoader(val_dataset) # Step 1: Initialize the transformation function def transform_fn(data_item): images, _ = data_item return images # Step 2: Initialize NNCF Dataset calibration_dataset = nncf.Dataset(dataset_loader, transform_fn) # Step 3: Export model to TorchFX ex_input = torch.randn(1, 3, 224, 224) fx_model = torch.export.export(model, args=(ex_input,)).module() # Step 4: Run the quantization pipeline quantized_fx_model = nncf.quantize(fx_model, calibration_dataset) ``` </details> <details><summary>ONNX</summary> ```python import onnx import nncf import torch from torchvision import datasets # Instantiate your uncompressed model onnx_model = onnx.load_model("/model_path") # Provide validation part of the dataset to collect statistics needed for the compression algorithm val_dataset = datasets.ImageFolder("/path", transform=transforms.Compose([transforms.ToTensor()])) dataset_loader = torch.utils.data.DataLoader(val_dataset, batch_size=1) # Step 1: Initialize transformation function input_name = onnx_model.graph.input[0].name def transform_fn(data_item): images, _ = data_item return {input_name: images.numpy()} # Step 2: Initialize NNCF Dataset calibration_dataset = nncf.Dataset(dataset_loader, transform_fn) # Step 3: Run the quantization pipeline quantized_model = nncf.quantize(onnx_model, calibration_dataset) ``` </details> [//]: # (NNCF provides full [samples](#post-training-quantization-samples), which demonstrate Post-Training Quantization usage for PyTorch, ONNX, and OpenVINO.) ### Training-Time Quantization Here is an example of Accuracy Aware Quantization pipeline where model weights and compression parameters may be fine-tuned to achieve a higher accuracy. <details><summary>PyTorch</summary> ```python import nncf import torch from torchvision import datasets, models # Instantiate your uncompressed model model = models.mobilenet_v2() # Provide validation part of the dataset to collect statistics needed for the compression algorithm val_dataset = datasets.ImageFolder("/path", transform=transforms.Compose([transforms.ToTensor()])) dataset_loader = torch.utils.data.DataLoader(val_dataset) # Step 1: Initialize the transformation function def transform_fn(data_item): images, _ = data_item return images # Step 2: Initialize NNCF Dataset calibration_dataset = nncf.Dataset(dataset_loader, transform_fn) # Step 3: Run the quantization pipeline quantized_model = nncf.quantize(model, calibration_dataset) # Now use compressed_model as a usual torch.nn.Module # to fine-tune compression parameters along with the model weights # Save quantization modules and the quantized model parameters checkpoint = { 'state_dict': model.state_dict(), 'nncf_config': nncf.torch.get_config(model), ... # the rest of the user-defined objects to save } torch.save(checkpoint, path_to_checkpoint) # ... # Load quantization modules and the quantized model parameters resuming_checkpoint = torch.load(path_to_checkpoint) nncf_config = resuming_checkpoint['nncf_config'] state_dict = resuming_checkpoint['state_dict'] quantized_model = nncf.torch.load_from_config(model, nncf_config) quantized_model.load_state_dict(state_dict) # ... the rest of the usual PyTorch-powered training pipeline ``` </details> <a id="demos-tutorials-and-samples"></a> ## Demos, Tutorials and Samples For a quicker start with NNCF-powered compression, try sample notebooks and scripts presented below. ### Jupyter* Notebook Tutorials and Demos Ready-to-run Jupyter* notebook tutorials and demos are available to explain and display NNCF compression algorithms for optimizing models for inference with the OpenVINO Toolkit: | Notebook Tutorial Name | Compression Algorithm | Backend | Domain | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------:|:----------:|:-----------------------------------:| | [BERT Quantization](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/language-quantize-bert) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/language-quantize-bert/language-quantize-bert.ipynb) | Post-Training Quantization | OpenVINO | NLP | | [MONAI Segmentation Model Quantization](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/ct-segmentation-quantize) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?filepath=notebooks%2Fct-segmentation-quantize%2Fct-scan-live-inference.ipynb) | Post-Training Quantization | OpenVINO | Segmentation | | [PyTorch Model Quantization](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/pytorch-post-training-quantization-nncf) | Post-Training Quantization | PyTorch | Image Classification | | [YOLOv11 Quantization with Accuracy Control](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/yolov11-quantization-with-accuracy-control) | Post-Training Quantization with Accuracy Control | OpenVINO | Speech-to-Text, Object Detection | A list of notebooks demonstrating OpenVINO conversion and inference together with NNCF compression for models from various domains: | Demo Model | Compression Algorithm | Backend | Domain | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------:|:---------:|:--------------------------------------------------------------------:| | [OneFormer](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/oneformer-segmentation) | Post-Training Quantization | OpenVINO | Image Segmentation | | [CLIP](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/clip-zero-shot-image-classification) | Post-Training Quantization | OpenVINO | Image-to-Text | | [BLIP](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/blip-visual-language-processing) | Post-Training Quantization | OpenVINO | Image-to-Text | | [Latent Consistency Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/latent-consistency-models-image-generation) | Post-Training Quantization | OpenVINO | Text-to-Image | | [MMS Speech Recognition](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/mms-massively-multilingual-speech) | Post-Training Quantization | OpenVINO | Speech-to-Text | | [LLM Instruction Following](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/llm-question-answering) | Weight Compression | OpenVINO | NLP, Instruction Following | | [LLM Chat Bots](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/llm-chatbot) | Weight Compression | OpenVINO | NLP, Chat Bot | ### Post-Training Quantization and Weight Compression Examples Compact scripts demonstrating quantization/weight compression and corresponding inference speed boost: | Example Name | Compression Algorithm | Backend | Domain | |:-----------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------:|:----------:|:----------------------:| | [OpenVINO MobileNetV2](./examples/post_training_quantization/openvino/mobilenet_v2/README.md) | Post-Training Quantization | OpenVINO | Image Classification | | [OpenVINO YOLO26](./examples/post_training_quantization/openvino/yolo26/README.md) | Post-Training Quantization | OpenVINO | Object Detection | | [OpenVINO YOLOv8 QwAC](./examples/post_training_quantization/openvino/yolov8_quantize_with_accuracy_control/README.md) | Post-Training Quantization with Accuracy Control | OpenVINO | Object Detection | | [PyTorch MobileNetV2](./examples/post_training_quantization/torch/mobilenet_v2/README.md) | Post-Training Quantization | PyTorch | Image Classification | | [PyTorch SSD](./examples/post_training_quantization/torch/ssd300_vgg16/README.md) | Post-Training Quantization | PyTorch | Object Detection | | [TorchFX Resnet18](./examples/post_training_quantization/torch_fx/resnet18/README.md) | Post-Training Quantization | TorchFX | Image Classification | | [ONNX MobileNetV2](./examples/post_training_quantization/onnx/mobilenet_v2/README.md) | Post-Training Quantization | ONNX | Image Classification | | [ONNX YOLOv8 QwAC](./examples/post_training_quantization/onnx/yolov8_quantize_with_accuracy_control/README.md) | Post-Training Quantization with Accuracy Control | ONNX | Object Detection | | [ONNX TinyLlama WC](./examples/llm_compression/onnx/tiny_llama/README.md) | Weight Compression | ONNX | LLM | | [TorchFX TinyLlama WC](./examples/llm_compression/torch_fx/tiny_llama/README.md) | Weight Compression | TorchFX | LLM | | [OpenVINO TinyLlama WC](./examples/llm_compression/openvino/tiny_llama/README.md) | Weight Compression | OpenVINO | LLM | | [OpenVINO TinyLlama WC with HS](./examples/llm_compression/openvino/tiny_llama_find_hyperparams/README.md) | Weight Compression with Hyperparameters Search | OpenVINO | LLM | | [ONNX TinyLlama WC with SE](./examples/llm_compression/onnx/tiny_llama_scale_estimation/README.md) | Weight Compression with Scale Estimation | ONNX | LLM | ### Quantization-Aware Training Examples | Example Name | Compression Algorithm | Backend | Domain | |:------------------------------------------------------------------------------------|:---------------------------:|:-------:|:--------------------:| | [PyTorch Resnet18](./examples/quantization_aware_training/torch/resnet18/README.md) | Quantization-Aware Training | PyTorch | Image Classification | | [PyTorch Anomalib](./examples/quantization_aware_training/torch/anomalib/README.md) | Quantization-Aware Training | PyTorch | Anomaly Detection | <a id="third-party-repository-integration"></a> ## Third-party Repository Integration NNCF may be easily integrated into training/evaluation pipelines of third-party repositories. ### Used by - [HuggingFace Optimum Intel](https://huggingface.co/docs/optimum-intel/openvino/optimization) NNCF is used as a compression backend within the renowned `transformers` repository in HuggingFace Optimum Intel. For instance, the command below exports the [Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) model to OpenVINO format with INT4-quantized weights: ```bash optimum-cli export openvino -m meta-llama/Llama-3.2-3B-Instruct --weight-format int4 ./Llama-3.2-3B-Instruct-int4 ``` - [Ultralytics](https://docs.ultralytics.com/integrations/openvino) NNCF is integrated into the Intel OpenVINO export pipeline, enabling quantization for the exported models. - [ExecuTorch](https://github.com/pytorch/executorch/blob/main/examples/openvino/README.md) NNCF is used as primary quantization framework for the [ExecuTorch OpenVINO integration](https://docs.pytorch.org/executorch/main/build-run-openvino.html). - [torch.compile](https://docs.pytorch.org/tutorials/prototype/openvino_quantizer.html) NNCF is used as primary quantization framework for the [torch.compile OpenVINO integration](https://docs.openvino.ai/2026/openvino-workflow/torch-compile.html). - [OpenVINO Training Extensions](https://github.com/openvinotoolkit/training_extensions) NNCF is integrated into OpenVINO Training Extensions as a model optimization backend. You can train, optimize, and export new models based on available model templates as well as run the exported models with OpenVINO. - [Microsoft Olive](https://github.com/microsoft/olive) NNCF is used to quantize OpenVINO IR and ONNX models for the [OpenVINO integration](https://microsoft.github.io/Olive/features/ihv-integration/openvino.html). <a id="installation-guide"></a> ## Installation Guide For detailed installation instructions, refer to the [Installation](./docs/Installation.md) guide. NNCF can be installed as a regular PyPI package via pip: ```bash pip install nncf ``` NNCF is also available via [conda](https://anaconda.org/conda-forge/nncf): ```bash conda install -c conda-forge nncf ``` System requirements of NNCF correspond to the used backend. System requirements for each backend and the matrix of corresponding versions can be found in [installation.md](./docs/Installation.md). ## NNCF Compressed Model Zoo List of models and compression results for them can be found at our [NNCF Model Zoo page](./docs/ModelZoo.md). ## Citing ```bi @article{kozlov2020neural, title = {Neural network compression framework for fast model inference}, author = {Kozlov, Alexander and Lazarevich, Ivan and Shamporov, Vasily and Lyalyushkin, Nikolay and Gorbachev, Yury}, journal = {arXiv preprint arXiv:2002.08679}, year = {2020} } ``` ## Contributing Guide Refer to the [CONTRIBUTING.md](./CONTRIBUTING.md) file for guidelines on contributions to the NNCF repository. ## Useful links - [Documentation](./docs) - [Examples](./examples) - [FAQ](./docs/FAQ.md) - [Notebooks](https://github.com/openvinotoolkit/openvino_notebooks#-model-training) - [HuggingFace Optimum Intel](https://huggingface.co/docs/optimum-intel/openvino/optimization) - [OpenVINO Model Optimization Guide](https://docs.openvino.ai/nncf) - [OpenVINO Hugging Face page](https://huggingface.co/OpenVINO#models) - [OpenVino Performance Benchmarks page](https://docs.openvino.ai/2026/about-openvino/performance-benchmarks.html) ## Telemetry NNCF as part of the OpenVINO™ toolkit collects anonymous usage data for the purpose of improving OpenVINO™ tools. You can opt-out at any time by running the following command in the Python environment where you have NNCF installed: `opt_in_out --opt_out` More information available on [OpenVINO telemetry](https://docs.openvino.ai/2026/about-openvino/additional-resources/telemetry.html).

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