<div align="center">
Intel® Neural Compressor
===========================
<h3> An open-source Python library supporting popular model compression techniques on all mainstream deep learning frameworks (TensorFlow, PyTorch, ONNX Runtime, and MXNet)</h3>
[](https://github.com/intel/neural-compressor)
[](https://github.com/intel/neural-compressor/releases)
[](https://github.com/intel/neural-compressor/blob/master/LICENSE)
[](https://github.com/intel/neural-compressor)
[](https://pepy.tech/project/neural-compressor)
[Architecture](./docs/source/design.md#architecture) | [Workflow](./docs/source/design.md#workflow) | [Results](./docs/source/validated_model_list.md) | [Examples](./examples/README.md) | [Documentations](https://intel.github.io/neural-compressor)
---
<div align="left">
Intel® Neural Compressor aims to provide popular model compression techniques such as quantization, pruning (sparsity), distillation, and neural architecture search on mainstream frameworks such as [TensorFlow](https://www.tensorflow.org/), [PyTorch](https://pytorch.org/), [ONNX Runtime](https://onnxruntime.ai/), and [MXNet](https://mxnet.apache.org/),
as well as Intel extensions such as [Intel Extension for TensorFlow](https://github.com/intel/intel-extension-for-tensorflow) and [Intel Extension for PyTorch](https://github.com/intel/intel-extension-for-pytorch).
In particular, the tool provides the key features, typical examples, and open collaborations as below:
* Support a wide range of Intel hardware such as [Intel Xeon Scalable Processors](https://www.intel.com/content/www/us/en/products/details/processors/xeon/scalable.html), [Intel Xeon CPU Max Series](https://www.intel.com/content/www/us/en/products/details/processors/xeon/max-series.html), [Intel Data Center GPU Flex Series](https://www.intel.com/content/www/us/en/products/details/discrete-gpus/data-center-gpu/flex-series.html), and [Intel Data Center GPU Max Series](https://www.intel.com/content/www/us/en/products/details/discrete-gpus/data-center-gpu/max-series.html) with extensive testing; support AMD CPU, ARM CPU, and NVidia GPU through ONNX Runtime with limited testing
* Validate popular LLMs such as [LLama2](/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/llm), [Falcon](/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/llm), [GPT-J](/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/llm), [Bloom](/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/llm), [OPT](/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/llm), and more than 10,000 broad models such as [Stable Diffusion](/examples/pytorch/nlp/huggingface_models/text-to-image/quantization), [BERT-Large](/examples/pytorch/nlp/huggingface_models/text-classification/quantization/ptq_static/fx), and [ResNet50](/examples/pytorch/image_recognition/torchvision_models/quantization/ptq/cpu/fx) from popular model hubs such as [Hugging Face](https://huggingface.co/), [Torch Vision](https://pytorch.org/vision/stable/index.html), and [ONNX Model Zoo](https://github.com/onnx/models#models), by leveraging zero-code optimization solution [Neural Coder](/neural_coder#what-do-we-offer) and automatic [accuracy-driven](/docs/source/design.md#workflow) quantization strategies
* Collaborate with cloud marketplaces such as [Google Cloud Platform](https://console.cloud.google.com/marketplace/product/bitnami-launchpad/inc-tensorflow-intel?project=verdant-sensor-286207), [Amazon Web Services](https://aws.amazon.com/marketplace/pp/prodview-yjyh2xmggbmga#pdp-support), and [Azure](https://azuremarketplace.microsoft.com/en-us/marketplace/apps/bitnami.inc-tensorflow-intel), software platforms such as [Alibaba Cloud](https://www.intel.com/content/www/us/en/developer/articles/technical/quantize-ai-by-oneapi-analytics-on-alibaba-cloud.html), [Tencent TACO](https://new.qq.com/rain/a/20221202A00B9S00) and [Microsoft Olive](https://github.com/microsoft/Olive), and open AI ecosystem such as [Hugging Face](https://huggingface.co/blog/intel), [PyTorch](https://pytorch.org/tutorials/recipes/intel_neural_compressor_for_pytorch.html), [ONNX](https://github.com/onnx/models#models), [ONNX Runtime](https://github.com/microsoft/onnxruntime), and [Lightning AI](https://github.com/Lightning-AI/lightning/blob/master/docs/source-pytorch/advanced/post_training_quantization.rst)
## Installation
### Install from pypi
```Shell
pip install neural-compressor
```
> **Note**:
> More installation methods can be found at [Installation Guide](https://github.com/intel/neural-compressor/blob/master/docs/source/installation_guide.md). Please check out our [FAQ](https://github.com/intel/neural-compressor/blob/master/docs/source/faq.md) for more details.
## Getting Started
### Quantization with Python API
```shell
# Install Intel Neural Compressor and TensorFlow
pip install neural-compressor
pip install tensorflow
# Prepare fp32 model
wget https://storage.googleapis.com/intel-optimized-tensorflow/models/v1_6/mobilenet_v1_1.0_224_frozen.pb
```
```python
from neural_compressor.data import DataLoader, Datasets
from neural_compressor.config import PostTrainingQuantConfig
dataset = Datasets("tensorflow")["dummy"](shape=(1, 224, 224, 3))
dataloader = DataLoader(framework="tensorflow", dataset=dataset)
from neural_compressor.quantization import fit
q_model = fit(
model="./mobilenet_v1_1.0_224_frozen.pb",
conf=PostTrainingQuantConfig(),
calib_dataloader=dataloader,
)
```
## Documentation
<table class="docutils">
<thead>
<tr>
<th colspan="8">Overview</th>
</tr>
</thead>
<tbody>
<tr>
<td colspan="2" align="center"><a href="./docs/source/design.md#architecture">Architecture</a></td>
<td colspan="2" align="center"><a href="./docs/source/design.md#workflow">Workflow</a></td>
<td colspan="2" align="center"><a href="examples/README.md">Examples</a></td>
<td colspan="2" align="center"><a href="https://intel.github.io/neural-compressor/latest/docs/source/api-doc/apis.html">APIs</a></td>
</tr>
</tbody>
<thead>
<tr>
<th colspan="8">Python-based APIs</th>
</tr>
</thead>
<tbody>
<tr>
<td colspan="2" align="center"><a href="./docs/source/quantization.md">Quantization</a></td>
<td colspan="2" align="center"><a href="./docs/source/mixed_precision.md">Advanced Mixed Precision</a></td>
<td colspan="2" align="center"><a href="./docs/source/pruning.md">Pruning (Sparsity)</a></td>
<td colspan="2" align="center"><a href="./docs/source/distillation.md">Distillation</a></td>
</tr>
<tr>
<td colspan="2" align="center"><a href="./docs/source/orchestration.md">Orchestration</a></td>
<td colspan="2" align="center"><a href="./docs/source/benchmark.md">Benchmarking</a></td>
<td colspan="2" align="center"><a href="./docs/source/distributed.md">Distributed Compression</a></td>
<td colspan="2" align="center"><a href="./docs/source/export.md">Model Export</a></td>
</tr>
</tbody>
<thead>
<tr>
<th colspan="8">Neural Coder (Zero-code Optimization)</th>
</tr>
</thead>
<tbody>
<tr>
<td colspan="2" align="center"><a href="./neural_coder/docs/PythonLauncher.md">Launcher</a></td>
<td colspan="2" align="center"><a href="./neural_coder/extensions/neural_compressor_ext_lab/README.md">JupyterLab Extension</a></td>
<td colspan="2" align="center"><a href="./neural_coder/extensions/neural_compressor_ext_vscode/README.md">Visual Studio Code Extension</a></td>
<td colspan="2" align="center"><a href="./neural_coder/docs/SupportMatrix.md">Supported Matrix</a></td>
</tr>
</tbody>
<thead>
<tr>
<th colspan="8">Advanced Topics</th>
</tr>
</thead>
<tbody>
<tr>
<td colspan="2" align="center"><a href="./docs/source/adaptor.md">Adaptor</a></td>
<td colspan="2" align="center"><a href="./docs/source/tuning_strategies.md">Strategy</a></td>
<td colspan="2" align="center"><a href="./docs/source/distillation_quantization.md">Distillation for Quantization</a></td>
<td colspan="2" align="center"><a href="./docs/source/smooth_quant.md">SmoothQuant</td>
</tr>
<tr>
<td colspan="4" align="center"><a href="./docs/source/quantization_weight_only.md">Weight-Only Quantization (INT8/INT4/FP4/NF4) </td>
<td colspan="2" align="center"><a href="https://github.com/intel/neural-compressor/blob/fp8_adaptor/docs/source/fp8.md">FP8 Quantization </td>
<td colspan="2" align="center"><a href="./docs/source/quantization_layer_wise.md">Layer-Wise Quantization </td>
</tr>
</tbody>
<thead>
<tr>
<th colspan="8">Innovations for Productivity</th>
</tr>
</thead>
<tbody>
<tr>
<td colspan="4" align="center"><a href="./neural_insights/README.md">Neural Insights</a></td>
<td colspan="4" align="center"><a href="./neural_solution/README.md">Neural Solution</a></td>
</tr>
</tbody>
</table>
> **Note**:
> More documentations can be found at [User Guide](https://github.com/intel/neural-compressor/blob/master/docs/source/user_guide.md).
## Selected Publications/Events
* Blog by Intel: [Effective Weight-Only Quantization for Large Language Models with Intel® Neural Compressor](https://community.intel.com/t5/Blogs/Tech-Innovation/Artificial-Intelligence-AI/Effective-Weight-Only-Quantization-for-Large-Language-Models/post/1529552) (Oct 2023)
* EMNLP'2023 (Under Review): [TEQ: Trainable Equivalent Transformation for Quantization of LLMs](https://openreview.net/forum?id=iaI8xEINAf&referrer=%5BAuthor%20Console%5D) (Sep 2023)
* arXiv: [Efficient Post-training Quantization with FP8 Formats](https://arxiv.org/abs/2309.14592) (Sep 2023)
* arXiv: [Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs](https://arxiv.org/abs/2309.05516) (Sep 2023)
* NeurIPS'2022: [Fast Distilbert on CPUs](https://arxiv.org/abs/2211.07715) (Oct 2022)
* NeurIPS'2022: [QuaLA-MiniLM: a Quantized Length Adaptive MiniLM](https://arxiv.org/abs/2210.17114) (Oct 2022)
> **Note**:
> View [Full Publication List](https://github.com/intel/neural-compressor/blob/master/docs/source/publication_list.md).
## Additional Content
* [Release Information](./docs/source/releases_info.md)
* [Contribution Guidelines](./docs/source/CONTRIBUTING.md)
* [Legal Information](./docs/source/legal_information.md)
* [Security Policy](SECURITY.md)
## Communication
- [GitHub Issues](https://github.com/intel/neural-compressor/issues): mainly for bug reports, new feature requests, question asking, etc.
- [Email](mailto:inc.maintainers@intel.com): welcome to raise any interesting research ideas on model compression techniques by email for collaborations.
- [Discord Channel](https://discord.com/invite/Wxk3J3ZJkU): join the discord channel for more flexible technical discussion.
- [WeChat group](/docs/source/imgs/wechat_group.jpg): scan the QA code to join the technical discussion.
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"description": "<div align=\"center\">\n\nIntel\u00ae Neural Compressor\n===========================\n<h3> An open-source Python library supporting popular model compression techniques on all mainstream deep learning frameworks (TensorFlow, PyTorch, ONNX Runtime, and MXNet)</h3>\n\n[](https://github.com/intel/neural-compressor)\n[](https://github.com/intel/neural-compressor/releases)\n[](https://github.com/intel/neural-compressor/blob/master/LICENSE)\n[](https://github.com/intel/neural-compressor)\n[](https://pepy.tech/project/neural-compressor)\n\n[Architecture](./docs/source/design.md#architecture) | [Workflow](./docs/source/design.md#workflow) | [Results](./docs/source/validated_model_list.md) | [Examples](./examples/README.md) | [Documentations](https://intel.github.io/neural-compressor)\n\n---\n<div align=\"left\">\n\nIntel\u00ae Neural Compressor aims to provide popular model compression techniques such as quantization, pruning (sparsity), distillation, and neural architecture search on mainstream frameworks such as [TensorFlow](https://www.tensorflow.org/), [PyTorch](https://pytorch.org/), [ONNX Runtime](https://onnxruntime.ai/), and [MXNet](https://mxnet.apache.org/),\nas well as Intel extensions such as [Intel Extension for TensorFlow](https://github.com/intel/intel-extension-for-tensorflow) and [Intel Extension for PyTorch](https://github.com/intel/intel-extension-for-pytorch).\nIn particular, the tool provides the key features, typical examples, and open collaborations as below:\n\n* Support a wide range of Intel hardware such as [Intel Xeon Scalable Processors](https://www.intel.com/content/www/us/en/products/details/processors/xeon/scalable.html), [Intel Xeon CPU Max Series](https://www.intel.com/content/www/us/en/products/details/processors/xeon/max-series.html), [Intel Data Center GPU Flex Series](https://www.intel.com/content/www/us/en/products/details/discrete-gpus/data-center-gpu/flex-series.html), and [Intel Data Center GPU Max Series](https://www.intel.com/content/www/us/en/products/details/discrete-gpus/data-center-gpu/max-series.html) with extensive testing; support AMD CPU, ARM CPU, and NVidia GPU through ONNX Runtime with limited testing\n\n* Validate popular LLMs such as [LLama2](/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/llm), [Falcon](/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/llm), [GPT-J](/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/llm), [Bloom](/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/llm), [OPT](/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/llm), and more than 10,000 broad models such as [Stable Diffusion](/examples/pytorch/nlp/huggingface_models/text-to-image/quantization), [BERT-Large](/examples/pytorch/nlp/huggingface_models/text-classification/quantization/ptq_static/fx), and [ResNet50](/examples/pytorch/image_recognition/torchvision_models/quantization/ptq/cpu/fx) from popular model hubs such as [Hugging Face](https://huggingface.co/), [Torch Vision](https://pytorch.org/vision/stable/index.html), and [ONNX Model Zoo](https://github.com/onnx/models#models), by leveraging zero-code optimization solution [Neural Coder](/neural_coder#what-do-we-offer) and automatic [accuracy-driven](/docs/source/design.md#workflow) quantization strategies\n\n* Collaborate with cloud marketplaces such as [Google Cloud Platform](https://console.cloud.google.com/marketplace/product/bitnami-launchpad/inc-tensorflow-intel?project=verdant-sensor-286207), [Amazon Web Services](https://aws.amazon.com/marketplace/pp/prodview-yjyh2xmggbmga#pdp-support), and [Azure](https://azuremarketplace.microsoft.com/en-us/marketplace/apps/bitnami.inc-tensorflow-intel), software platforms such as [Alibaba Cloud](https://www.intel.com/content/www/us/en/developer/articles/technical/quantize-ai-by-oneapi-analytics-on-alibaba-cloud.html), [Tencent TACO](https://new.qq.com/rain/a/20221202A00B9S00) and [Microsoft Olive](https://github.com/microsoft/Olive), and open AI ecosystem such as [Hugging Face](https://huggingface.co/blog/intel), [PyTorch](https://pytorch.org/tutorials/recipes/intel_neural_compressor_for_pytorch.html), [ONNX](https://github.com/onnx/models#models), [ONNX Runtime](https://github.com/microsoft/onnxruntime), and [Lightning AI](https://github.com/Lightning-AI/lightning/blob/master/docs/source-pytorch/advanced/post_training_quantization.rst)\n\n## Installation\n\n### Install from pypi\n```Shell\npip install neural-compressor\n```\n> **Note**: \n> More installation methods can be found at [Installation Guide](https://github.com/intel/neural-compressor/blob/master/docs/source/installation_guide.md). Please check out our [FAQ](https://github.com/intel/neural-compressor/blob/master/docs/source/faq.md) for more details.\n\n## Getting Started\n### Quantization with Python API\n\n```shell\n# Install Intel Neural Compressor and TensorFlow\npip install neural-compressor\npip install tensorflow\n# Prepare fp32 model\nwget https://storage.googleapis.com/intel-optimized-tensorflow/models/v1_6/mobilenet_v1_1.0_224_frozen.pb\n```\n```python\nfrom neural_compressor.data import DataLoader, Datasets\nfrom neural_compressor.config import PostTrainingQuantConfig\n\ndataset = Datasets(\"tensorflow\")[\"dummy\"](shape=(1, 224, 224, 3))\ndataloader = DataLoader(framework=\"tensorflow\", dataset=dataset)\n\nfrom neural_compressor.quantization import fit\n\nq_model = fit(\n model=\"./mobilenet_v1_1.0_224_frozen.pb\",\n conf=PostTrainingQuantConfig(),\n calib_dataloader=dataloader,\n)\n```\n\n## Documentation\n\n<table class=\"docutils\">\n <thead>\n <tr>\n <th colspan=\"8\">Overview</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <td colspan=\"2\" align=\"center\"><a href=\"./docs/source/design.md#architecture\">Architecture</a></td>\n <td colspan=\"2\" align=\"center\"><a href=\"./docs/source/design.md#workflow\">Workflow</a></td>\n <td colspan=\"2\" align=\"center\"><a href=\"examples/README.md\">Examples</a></td>\n <td colspan=\"2\" align=\"center\"><a href=\"https://intel.github.io/neural-compressor/latest/docs/source/api-doc/apis.html\">APIs</a></td>\n </tr>\n </tbody>\n <thead>\n <tr>\n <th colspan=\"8\">Python-based APIs</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <td colspan=\"2\" align=\"center\"><a href=\"./docs/source/quantization.md\">Quantization</a></td>\n <td colspan=\"2\" align=\"center\"><a href=\"./docs/source/mixed_precision.md\">Advanced Mixed Precision</a></td>\n <td colspan=\"2\" align=\"center\"><a href=\"./docs/source/pruning.md\">Pruning (Sparsity)</a></td>\n <td colspan=\"2\" align=\"center\"><a href=\"./docs/source/distillation.md\">Distillation</a></td>\n </tr>\n <tr>\n <td colspan=\"2\" align=\"center\"><a href=\"./docs/source/orchestration.md\">Orchestration</a></td>\n <td colspan=\"2\" align=\"center\"><a href=\"./docs/source/benchmark.md\">Benchmarking</a></td>\n <td colspan=\"2\" align=\"center\"><a href=\"./docs/source/distributed.md\">Distributed Compression</a></td>\n <td colspan=\"2\" align=\"center\"><a href=\"./docs/source/export.md\">Model Export</a></td>\n </tr>\n </tbody>\n <thead>\n <tr>\n <th colspan=\"8\">Neural Coder (Zero-code Optimization)</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <td colspan=\"2\" align=\"center\"><a href=\"./neural_coder/docs/PythonLauncher.md\">Launcher</a></td>\n <td colspan=\"2\" align=\"center\"><a href=\"./neural_coder/extensions/neural_compressor_ext_lab/README.md\">JupyterLab Extension</a></td>\n <td colspan=\"2\" align=\"center\"><a href=\"./neural_coder/extensions/neural_compressor_ext_vscode/README.md\">Visual Studio Code Extension</a></td>\n <td colspan=\"2\" align=\"center\"><a href=\"./neural_coder/docs/SupportMatrix.md\">Supported Matrix</a></td>\n </tr>\n </tbody>\n <thead>\n <tr>\n <th colspan=\"8\">Advanced Topics</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <td colspan=\"2\" align=\"center\"><a href=\"./docs/source/adaptor.md\">Adaptor</a></td>\n <td colspan=\"2\" align=\"center\"><a href=\"./docs/source/tuning_strategies.md\">Strategy</a></td>\n <td colspan=\"2\" align=\"center\"><a href=\"./docs/source/distillation_quantization.md\">Distillation for Quantization</a></td>\n <td colspan=\"2\" align=\"center\"><a href=\"./docs/source/smooth_quant.md\">SmoothQuant</td>\n </tr>\n <tr>\n <td colspan=\"4\" align=\"center\"><a href=\"./docs/source/quantization_weight_only.md\">Weight-Only Quantization (INT8/INT4/FP4/NF4) </td>\n <td colspan=\"2\" align=\"center\"><a href=\"https://github.com/intel/neural-compressor/blob/fp8_adaptor/docs/source/fp8.md\">FP8 Quantization </td>\n <td colspan=\"2\" align=\"center\"><a href=\"./docs/source/quantization_layer_wise.md\">Layer-Wise Quantization </td>\n </tr>\n </tbody>\n <thead>\n <tr>\n <th colspan=\"8\">Innovations for Productivity</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <td colspan=\"4\" align=\"center\"><a href=\"./neural_insights/README.md\">Neural Insights</a></td>\n <td colspan=\"4\" align=\"center\"><a href=\"./neural_solution/README.md\">Neural Solution</a></td>\n </tr>\n </tbody>\n</table>\n\n> **Note**: \n> More documentations can be found at [User Guide](https://github.com/intel/neural-compressor/blob/master/docs/source/user_guide.md).\n\n## Selected Publications/Events\n* Blog by Intel: [Effective Weight-Only Quantization for Large Language Models with Intel\u00ae Neural Compressor](https://community.intel.com/t5/Blogs/Tech-Innovation/Artificial-Intelligence-AI/Effective-Weight-Only-Quantization-for-Large-Language-Models/post/1529552) (Oct 2023)\n* EMNLP'2023 (Under Review): [TEQ: Trainable Equivalent Transformation for Quantization of LLMs](https://openreview.net/forum?id=iaI8xEINAf&referrer=%5BAuthor%20Console%5D) (Sep 2023)\n* arXiv: [Efficient Post-training Quantization with FP8 Formats](https://arxiv.org/abs/2309.14592) (Sep 2023)\n* arXiv: [Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs](https://arxiv.org/abs/2309.05516) (Sep 2023)\n* NeurIPS'2022: [Fast Distilbert on CPUs](https://arxiv.org/abs/2211.07715) (Oct 2022)\n* NeurIPS'2022: [QuaLA-MiniLM: a Quantized Length Adaptive MiniLM](https://arxiv.org/abs/2210.17114) (Oct 2022)\n\n> **Note**: \n> View [Full Publication List](https://github.com/intel/neural-compressor/blob/master/docs/source/publication_list.md).\n\n## Additional Content\n\n* [Release Information](./docs/source/releases_info.md)\n* [Contribution Guidelines](./docs/source/CONTRIBUTING.md)\n* [Legal Information](./docs/source/legal_information.md)\n* [Security Policy](SECURITY.md)\n\n## Communication \n- [GitHub Issues](https://github.com/intel/neural-compressor/issues): mainly for bug reports, new feature requests, question asking, etc.\n- [Email](mailto:inc.maintainers@intel.com): welcome to raise any interesting research ideas on model compression techniques by email for collaborations. \n- [Discord Channel](https://discord.com/invite/Wxk3J3ZJkU): join the discord channel for more flexible technical discussion.\n- [WeChat group](/docs/source/imgs/wechat_group.jpg): scan the QA code to join the technical discussion.\n",
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