How to: Model Storage Optimisation
* [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) ![](https://img.shields.io/github/stars/casper-hansen/AutoAWQ.svg?cacheSeconds=86400) - AutoAWQ is an easy-to-use package for 4-bit quantized models.
* [AutoGPTQ](https://github.com/AutoGPTQ/AutoGPTQ) ![](https://img.shields.io/github/stars/AutoGPTQ/AutoGPTQ.svg?cacheSeconds=86400) - An easy-to-use LLMs quantization package with user-friendly apis, based on GPTQ algorithm.
* [AWQ](https://github.com/mit-han-lab/llm-awq) ![](https://img.shields.io/github/stars/mit-han-lab/llm-awq.svg?cacheSeconds=86400) - Activation-aware Weight Quantization for LLM Compression and Acceleration.
* [GGML](https://github.com/ggml-org/ggml) ![](https://img.shields.io/github/stars/ggml-org/ggml.svg?cacheSeconds=86400) - GGML is a high-performance, tensor library for machine learning that enables efficient inference on CPUs, particularly optimized for large language models.
* [neural-compressor](https://github.com/intel/neural-compressor) ![](https://img.shields.io/github/stars/intel/neural-compressor.svg?cacheSeconds=86400) - Intel® Neural Compressor aims to provide popular model compression techniques such as quantization, pruning (sparsity), distillation, and neural architecture search on mainstream frameworks.
* [NNEF](https://www.khronos.org/nnef) - Neural Network Exchange Format (NNEF) is an open standard for representing neural network models to enable interoperability and portability across different machine learning frameworks and platforms.
* [ONNX](https://github.com/onnx/onnx) ![](https://img.shields.io/github/stars/onnx/onnx.svg?cacheSeconds=86400) - ONNX (Open Neural Network Exchange) is an open-source format designed to facilitate interoperability and portability of machine learning models across different frameworks and platforms.
* [PFA](https://dmg.org/pfa) - PFA (Portable Format for Analytics) format is a standard for representing and exchanging predictive models and analytics workflows in a portable, JSON-based format.
* [PMML](https://dmg.org/pmml) - PMML (Predictive Model Markup Language) is an XML-based standard for representing and sharing predictive models between different applications.
* [Quanto](https://github.com/huggingface/optimum-quanto) ![](https://img.shields.io/github/stars/huggingface/optimum-quanto.svg?cacheSeconds=86400) - Quanto aims to simplify quantizing deep learning models.