google-research/morph-net

Model Compression Toolkit (MCT) is an open source project for neural network model optimization under efficient, constrained hardware. This project provides researchers, developers, and engineers advanced quantization and compression tools for deploying state-of-the-art neural networks.

A unified library of SOTA model optimization techniques like quantization, distillation, pruning, neural architecture search, speculative decoding, etc. It compresses deep learning models for downstream deployment frameworks like TensorRT-LLM, TensorRT, vLLM, etc. to optimize inference speed.

Neural Network Compression Framework for enhanced OpenVINO™ inference

A toolkit to optimize ML models for deployment for Keras and TensorFlow, including quantization and pruning.

Pruna is a model optimization framework built for developers, enabling you to deliver faster, more efficient models with minimal overhead.

AIMET is a library that provides advanced quantization and compression techniques for trained neural network models.

PyTorch native quantization for training and inference

🚀 Accelerate inference and training of 🤗 Transformers, Diffusers, TIMM and Sentence Transformers with easy to use hardware optimization tools

PyTorch compiler that accelerates training and inference. Get built-in optimizations for performance, memory, parallelism, and easily write your own.