csyhhu/Awesome-Deep-Neural-Network-Compression

A curated collection of papers, benchmarks, surveys, and tools for model quantization, covering low-bit networks, LLMs, multimodal and generative models, vector and lattice quantization, and efficient deployment.

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.

This repository contains low-bit quantization papers from 2020 to 2026 on top conference.

Awesome LLM pruning papers all-in-one repository with integrating all useful resources and insights.

Awesome LLM compression research papers and tools.

FasterAI: Prune and Distill your models with FastAI and PyTorch

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

Efficient computing methods developed by Huawei Noah's Ark Lab

Fast & Simple Resource-Constrained Learning of Deep Network Structure