SonySemiconductorSolutions/mct-model-optimization

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

Neural Network Compression Framework for enhanced OpenVINO™ inference

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.

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

Fast & Simple Resource-Constrained Learning of Deep Network Structure

SOTA low-bit LLM quantization (INT8/FP8/MXFP8/INT4/MXFP4/NVFP4) & sparsity; leading model compression techniques on PyTorch, TensorFlow, and ONNX Runtime

Model compression toolkit engineered for enhanced usability, comprehensiveness, and efficiency.

Brevitas: neural network quantization in PyTorch

PyTorch native quantization for training and inference