nncf_pytorch: This repository contains a PyTorch*-based framework and samples for neural networks compression.

# 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)

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</div>

Neural Network Compression Framework (NNCF) provides a suite of post-training and training-time algorithms for optimizing inference of neural networks in [OpenVINO&trade;](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&trade;](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

### Training-Time Compression Algorithms

- 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&trade; 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.

<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
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)
```

</details>
<details><summary>TensorFlow</summary>

```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)
```

</details>

```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]&#40;#post-training-quantization-samples&#41;, 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.

<details><summary>PyTorch</summary>

```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
```

</details>

### 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.

<details><summary>PyTorch</summary>

```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.

</details>

<details><summary>Tensorflow</summary>

```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")
```

</details>

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) [![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 |
| [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) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](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) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](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.
  
- [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/2025/openvino-workflow/torch-compile.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)
- 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).

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