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Copy file name to clipboardExpand all lines: README.md
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<h3> An open-source Python library supporting popular model compression techniques on all mainstream deep learning frameworks (TensorFlow, PyTorch, ONNX Runtime, and MXNet)</h3>
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* Support a wide range of Intel hardware such as [Intel Xeon Scalable processor](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
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* Validate more than 10,000 models such as [Bloom-176B](/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/ptq_static/ipex/smooth_quant), [OPT-6.7B](/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/ptq_static/ipex/smooth_quant), [Stable Diffusion](/examples/pytorch/nlp/huggingface_models/text-to-image/quantization), [GPT-J](/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/ptq_static/fx), [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
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* Validate popular LLMs such as LLama2, [LLama](examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static), [MPT](https://github.com/intel/intel-extension-for-transformers/blob/main/examples/huggingface/pytorch/text-generation/quantization/README.md), [Falcon](https://github.com/intel/intel-extension-for-transformers/blob/main/examples/huggingface/pytorch/language-modeling/quantization/README.md), [GPT-J](/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/ptq_static/fx), [Bloom](/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/ptq_static/ipex/smooth_quant), [OPT](/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/ptq_static/ipex/smooth_quant), 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
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* Collaborate with cloud marketplace 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) and [Tencent TACO](https://new.qq.com/rain/a/20221202A00B9S00), 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), and [Lightning AI](https://github.com/Lightning-AI/lightning/blob/master/docs/source-pytorch/advanced/post_training_quantization.rst)
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* Collaborate with cloud marketplace 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)
> More documentations can be found at [User Guide](./docs/source/user_guide.md).
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## Selected Publications/Events
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* arXiv: [Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs](https://arxiv.org/abs/2309.05516) (Sep 2023)
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* Post on Social Media: [ONNXCommunityMeetup2023: INT8 Quantization for Large Language Models with Intel Neural Compressor](https://www.youtube.com/watch?v=luYBWA1Q5pQ) (July 2023)
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* Blog by Intel: [Accelerate Llama 2 with Intel AI Hardware and Software Optimizations](https://www.intel.com/content/www/us/en/developer/articles/news/llama2.html) (July 2023)
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* Blog on Medium: [Quantization Accuracy Loss Diagnosis with Neural Insights](https://medium.com/@NeuralCompressor/quantization-accuracy-loss-diagnosis-with-neural-insights-5d73f4ca2601) (Aug 2023)
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* Blog on Medium: [Faster Stable Diffusion Inference with Intel Extension for Transformers](https://medium.com/intel-analytics-software/faster-stable-diffusion-inference-with-intel-extension-for-transformers-on-intel-platforms-7e0f563186b0) (July 2023)
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* NeurIPS'2022: [Fast Distilbert on CPUs](https://arxiv.org/abs/2211.07715) (Oct 2022)
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* NeurIPS'2022: [QuaLA-MiniLM: a Quantized Length Adaptive MiniLM](https://arxiv.org/abs/2210.17114) (Oct 2022)
Welcome to raise any interesting research ideas on model compression techniques and feel free to reach us ([inc.maintainers@intel.com](mailto:inc.maintainers@intel.com)). Look forward to our collaborations on Intel Neural Compressor!
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## Communication
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-[GitHub Issues](https://github.com/intel/neural-compressor/issues): mainly for bugs report, new feature request, question asking, etc.
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-[Email](mailto:inc.maintainers@intel.com): welcome to raise any interesting research ideas on model compression techniques by email for collaborations.
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-[WeChat group](/docs/source/imgs/wechat_group.jpg): scan the QA code to join the technical discussion.
Copy file name to clipboardExpand all lines: docs/source/publication_list.md
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Full Publications/Events (74)
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Full Publications/Events (75)
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==========
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## 2023 (20)
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## 2023 (21)
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* arXiv: [Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs](https://arxiv.org/abs/2309.05516) (Sep 2023)
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* Blog on Medium: [Quantization Accuracy Loss Diagnosis with Neural Insights](https://medium.com/@NeuralCompressor/quantization-accuracy-loss-diagnosis-with-neural-insights-5d73f4ca2601) (Aug 2023)
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* Blog on Medium: [Faster Stable Diffusion Inference with Intel Extension for Transformers](https://medium.com/intel-analytics-software/faster-stable-diffusion-inference-with-intel-extension-for-transformers-on-intel-platforms-7e0f563186b0) (July 2023)
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* Post on Social Media: [ONNXCommunityMeetup2023: INT8 Quantization for Large Language Models with Intel Neural Compressor](https://www.youtube.com/watch?v=luYBWA1Q5pQ) (July 2023)
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