https://github.com/openvinotoolkit/nncf
Neural Network Compression Framework for enhanced OpenVINO™ inference
https://github.com/openvinotoolkit/nncf
bert classification compression deep-learning genai llm mixed-precision-training nlp object-detection onnx openvino pruning pytorch quantization quantization-aware-training semantic-segmentation sparsity tensorflow transformers
Last synced: 10 days ago
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Neural Network Compression Framework for enhanced OpenVINO™ inference
- Host: GitHub
- URL: https://github.com/openvinotoolkit/nncf
- Owner: openvinotoolkit
- License: apache-2.0
- Created: 2020-05-13T16:41:05.000Z (almost 5 years ago)
- Default Branch: develop
- Last Pushed: 2025-04-25T17:41:06.000Z (11 days ago)
- Last Synced: 2025-04-27T05:58:28.566Z (10 days ago)
- Topics: bert, classification, compression, deep-learning, genai, llm, mixed-precision-training, nlp, object-detection, onnx, openvino, pruning, pytorch, quantization, quantization-aware-training, semantic-segmentation, sparsity, tensorflow, transformers
- Language: Python
- Homepage:
- Size: 62.8 MB
- Stars: 1,003
- Watchers: 30
- Forks: 253
- Open Issues: 55
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Codeowners: CODEOWNERS
- Security: Security.md
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README
# 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)
[](https://github.com/openvinotoolkit/nncf/releases)
[](https://docs.openvino.ai/nncf)
[](LICENSE)
[](https://pypi.org/project/nncf/)
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), [TensorFlow](https://www.tensorflow.org/), [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 and TensorFlow deep
learning frameworks.
## Key Features
### Post-Training Compression Algorithms
| Compression algorithm | OpenVINO | PyTorch | TorchFX | TensorFlow | ONNX |
| :------------------------------------------------------------------------------------------------------- | :-------: | :-------: | :-----------: | :-----------: | :-----------: |
| [Post-Training Quantization](./docs/usage/post_training_compression/post_training_quantization/Usage.md) | Supported | Supported | Experimental | Supported | Supported |
| [Weights Compression](./docs/usage/post_training_compression/weights_compression/Usage.md) | Supported | Supported | Experimental | Not supported | Not supported |
| [Activation Sparsity](./nncf/experimental/torch/sparsify_activations/ActivationSparsity.md) | Not supported | Experimental | Not supported| Not supported| Not supported |
### Training-Time Compression Algorithms
| Compression algorithm | PyTorch | TensorFlow |
| :--------------------------------------------------------------------------------------------------------- | :----------: | :-----------: |
| [Quantization Aware Training](./docs/usage/training_time_compression/quantization_aware_training/Usage.md) | Supported | Supported |
| [Mixed-Precision Quantization](./docs/usage/training_time_compression/other_algorithms/LegacyQuantization.md#mixed-precision-quantization) | Supported | Not supported |
| [Sparsity](./docs/usage/training_time_compression/other_algorithms/Sparsity.md) | Supported | Supported |
| [Filter pruning](./docs/usage/training_time_compression/other_algorithms/Pruning.md) | Supported | Supported |
| [Movement pruning](./nncf/experimental/torch/sparsity/movement/MovementSparsity.md) | Experimental | Not supported |
- Automatic, configurable model graph transformation to obtain the compressed model.
> **NOTE**: Limited support for TensorFlow models. Only models created using Sequential or Keras Functional API are supported.
- 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 and TensorFlow compressed models to SavedModel or Frozen Graph format, ready to use with [OpenVINO™ toolkit](https://docs.openvino.ai).
- Support for [Accuracy-Aware model training](./docs/usage/training_time_compression/other_algorithms/Usage.md#accuracy-aware-model-training) pipelines via the [Adaptive Compression Level Training](./docs/accuracy_aware_model_training/AdaptiveCompressionLevelTraining.md) and [Early Exit Training](./docs/accuracy_aware_model_training/EarlyExitTraining.md).
## 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/).
## 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, TensorFlow, and ONNX are also supported.
OpenVINO
```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)
```
PyTorch
```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).
TorchFX
```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
input_shape = (1, 3, 224, 224)
fx_model = torch.export.export_for_training(model, args=(ex_input,)).module()
# or
# 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)
```
TensorFlow
```python
import nncf
import tensorflow as tf
import tensorflow_datasets as tfds
# Instantiate your uncompressed model
model = tf.keras.applications.MobileNetV2()
# Provide validation part of the dataset to collect statistics needed for the compression algorithm
val_dataset = tfds.load("/path", split="validation",
shuffle_files=False, as_supervised=True)
# Step 1: Initialize transformation function
def transform_fn(data_item):
images, _ = data_item
return images
# Step 2: Initialize NNCF Dataset
calibration_dataset = nncf.Dataset(val_dataset, transform_fn)
# Step 3: Run the quantization pipeline
quantized_model = nncf.quantize(model, calibration_dataset)
# Step 4: Remove auxiliary layers and operations added during the quantization process,
# resulting in a clean, fully quantized model ready for deployment.
stripped_model = nncf.strip(quantized_model)
```
ONNX
```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)
```
[//]: # (NNCF provides full [samples](#post-training-quantization-samples), which demonstrate Post-Training Quantization usage for PyTorch, TensorFlow, 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.
PyTorch
```python
import nncf
import nncf.torch
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, example_input)
model.load_state_dict(state_dict)
# ... the rest of the usual PyTorch-powered training pipeline
```
### Training-Time Compression
Here is an example of Accuracy Aware RB Sparsification pipeline where model weights and compression parameters may be fine-tuned to achieve a higher accuracy.
PyTorch
```python
import torch
import nncf.torch # Important - must be imported before any other external package that depends on torch
from nncf import NNCFConfig
from nncf.torch import create_compressed_model, register_default_init_args
# Instantiate your uncompressed model
from torchvision.models.resnet import resnet50
model = resnet50()
# Load a configuration file to specify compression
nncf_config = NNCFConfig.from_json("resnet50_imagenet_rb_sparsity.json")
# Provide data loaders for compression algorithm initialization, if necessary
import torchvision.datasets as datasets
representative_dataset = datasets.ImageFolder("/path", transform=transforms.Compose([transforms.ToTensor()]))
init_loader = torch.utils.data.DataLoader(representative_dataset)
nncf_config = register_default_init_args(nncf_config, init_loader)
# Apply the specified compression algorithms to the model
compression_ctrl, compressed_model = create_compressed_model(model, nncf_config)
# Now use compressed_model as a usual torch.nn.Module
# to fine-tune compression parameters along with the model weights
# ... the rest of the usual PyTorch-powered training pipeline
# Export to ONNX or .pth when done fine-tuning
compression_ctrl.export_model("compressed_model.onnx")
torch.save(compressed_model.state_dict(), "compressed_model.pth")
```
**NOTE (PyTorch)**: Due to the way NNCF works within the PyTorch backend, `import nncf` must be done before any other import of `torch` in your package _or_ in third-party packages that your code utilizes. Otherwise, the compression may be applied incompletely.
Tensorflow
```python
import tensorflow as tf
from nncf import NNCFConfig
from nncf.tensorflow import create_compressed_model, register_default_init_args
# Instantiate your uncompressed model
from tensorflow.keras.applications import ResNet50
model = ResNet50()
# Load a configuration file to specify compression
nncf_config = NNCFConfig.from_json("resnet50_imagenet_rb_sparsity.json")
# Provide dataset for compression algorithm initialization
representative_dataset = tf.data.Dataset.list_files("/path/*.jpeg")
nncf_config = register_default_init_args(nncf_config, representative_dataset, batch_size=1)
# Apply the specified compression algorithms to the model
compression_ctrl, compressed_model = create_compressed_model(model, nncf_config)
# Now use compressed_model as a usual Keras model
# to fine-tune compression parameters along with the model weights
# ... the rest of the usual TensorFlow-powered training pipeline
# Export to Frozen Graph, TensorFlow SavedModel or .h5 when done fine-tuning
compression_ctrl.export_model("compressed_model.pb", save_format="frozen_graph")
```
For a more detailed description of NNCF usage in your training code, see [this tutorial](./docs/usage/training_time_compression/other_algorithms/Usage.md).
## 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)
[](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)
[](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 |
| [Quantization with Accuracy Control](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/quantizing-model-with-accuracy-control) | Post-Training Quantization with Accuracy Control | OpenVINO | Speech-to-Text,
Object Detection |
| [PyTorch Training-Time Compression](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/pytorch-quantization-aware-training) | Training-Time Compression | PyTorch | Image Classification |
| [TensorFlow Training-Time Compression](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/tensorflow-quantization-aware-training) | Training-Time Compression | Tensorflow | Image Classification |
| [Joint Pruning, Quantization and Distillation for BERT](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/sparsity-optimization) | Joint Pruning, Quantization and Distillation | OpenVINO | NLP |
A list of notebooks demonstrating OpenVINO conversion and inference together with NNCF compression for models from various domains:
| Demo Model | Compression Algorithm | Backend | Domain |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------:|:---------:|:--------------------------------------------------------------------:|
| [YOLOv8](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/yolov8-optimization)
[](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/yolov8-optimization/yolov8-object-detection.ipynb) | Post-Training Quantization | OpenVINO | Object Detection,
KeyPoint Detection,
Instance Segmentation |
| [EfficientSAM](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/efficient-sam) | Post-Training Quantization | OpenVINO | Image Segmentation |
| [Segment Anything Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/segment-anything) | Post-Training Quantization | OpenVINO | Image Segmentation |
| [OneFormer](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/oneformer-segmentation) | Post-Training Quantization | OpenVINO | Image Segmentation |
| [InstructPix2Pix](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/instruct-pix2pix-image-editing) | Post-Training Quantization | OpenVINO | Image-to-Image |
| [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 |
| [ControlNet QR Code Monster](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/qrcode-monster) | Post-Training Quantization | OpenVINO | Text-to-Image |
| [SDXL-turbo](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/sdxl-turbo) | Post-Training Quantization | OpenVINO | Text-to-Image,
Image-to-Image |
| [Distil-Whisper](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/distil-whisper-asr) | Post-Training Quantization | OpenVINO | Speech-to-Text |
| [Whisper](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/whisper-subtitles-generation)
[](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/whisper-subtitles-generation/whisper-convert.ipynb) | Post-Training Quantization | OpenVINO | Speech-to-Text |
| [MMS Speech Recognition](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/mms-massively-multilingual-speech) | Post-Training Quantization | OpenVINO | Speech-to-Text |
| [Grammar Error Correction](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/grammar-correction) | Post-Training Quantization | OpenVINO | NLP, Grammar Correction |
| [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 Examples
Compact scripts demonstrating quantization 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 YOLOv8](./examples/post_training_quantization/openvino/yolov8/README.md) | Post-Training Quantization | OpenVINO | Object Detection |
| [OpenVINO YOLOv8 QwAС](./examples/post_training_quantization/openvino/yolov8_quantize_with_accuracy_control/README.md) | Post-Training Quantization with Accuracy Control | OpenVINO | Object Detection |
| [OpenVINO Anomaly Classification](./examples/post_training_quantization/openvino/anomaly_stfpm_quantize_with_accuracy_control/README.md) | Post-Training Quantization with Accuracy Control | OpenVINO | Anomaly Classification |
| [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 |
| [TensorFlow MobileNetV2](./examples/post_training_quantization/tensorflow/mobilenet_v2/README.md) | Post-Training Quantization | TensorFlow | Image Classification |
| [ONNX MobileNetV2](./examples/post_training_quantization/onnx/mobilenet_v2/README.md) | Post-Training Quantization | ONNX | Image Classification |
### Training-Time Compression Examples
Examples of full pipelines including compression, training, and inference for classification, detection, and segmentation tasks:
| Example Name | Compression Algorithm | Backend | Domain |
|:-----------------------------------------------------------------------------------------------------------|:-------------------------:|:----------:|:---------------------:|
| [PyTorch Image Classification](./examples/torch/classification/README.md) | Training-Time Compression | PyTorch | Image Classification |
| [PyTorch Object Detection](./examples/torch/object_detection/README.md) | Training-Time Compression | PyTorch | Object Detection |
| [PyTorch Semantic Segmentation](./examples/torch/semantic_segmentation/README.md) | Training-Time Compression | PyTorch | Semantic Segmentation |
| [TensorFlow Image Classification](./examples/tensorflow/classification/README.md) | Training-Time Compression | TensorFlow | Image Classification |
| [TensorFlow Object Detection](./examples/tensorflow/object_detection/README.md) | Training-Time Compression | TensorFlow | Object Detection |
| [TensorFlow Instance Segmentation](./examples/tensorflow/segmentation/README.md) | Training-Time Compression | TensorFlow | Instance Segmentation |
## Third-party repository integration
NNCF may be easily integrated into training/evaluation pipelines of third-party repositories.
### Used by
- [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.
- [HuggingFace Optimum Intel](https://huggingface.co/docs/optimum/intel/optimization_ov)
NNCF is used as a compression backend within the renowned `transformers` repository in HuggingFace Optimum Intel.
## 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)
- Example scripts (model objects available through links in respective README.md files):
- [PyTorch](./examples/torch)
- [TensorFlow](./examples/tensorflow)
- [FAQ](./docs/FAQ.md)
- [Notebooks](https://github.com/openvinotoolkit/openvino_notebooks#-model-training)
- [HuggingFace Optimum Intel](https://huggingface.co/docs/optimum/intel/optimization_ov)
- [OpenVINO Model Optimization Guide](https://docs.openvino.ai/nncf)
## 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/2025/about-openvino/additional-resources/telemetry.html).