model-compression

Efficient AI Backbones including GhostNet, TNT and MLP, developed by Huawei Noah's Ark Lab.

Awesome Knowledge Distillation

[CVPR 2023] DepGraph: Towards Any Structural Pruning; LLMs, Vision Foundation Models, etc.

Pretrained language model and its related optimization techniques developed by Huawei Noah's Ark Lab.

A curated list of neural network pruning resources.

A list of papers, docs, codes about model quantization. This repo is aimed to provide the info for model quantization research, we are continuously improving the project. Welcome to PR the works (papers, repositories) that are missed by the repo.

micronet, a model compression and deploy lib. compression: 1、quantization: quantization-aware-training(QAT), High-Bit(>2b)(DoReFa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、Low-Bit(≤2b)/Ternary and Binary(TWN/BNN/XNOR-Net); post-training-quantization(PTQ), 8-bit(tensorrt); 2、 pruning: normal、regular and group convolutional channel pruning; 3、 group convolution structure; 4、batch-normalization fuse for quantization. deploy: tensorrt, fp32/fp16/int8(ptq-calibration)、op-adapt(upsample)、dynamic_shape

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

Efficient computing methods developed by Huawei Noah's Ark Lab

Channel Pruning for Accelerating Very Deep Neural Networks (ICCV'17)

[CVPR 2024] DeepCache: Accelerating Diffusion Models for Free

Collection of recent methods on (deep) neural network compression and acceleration.

TinyNeuralNetwork is an efficient and easy-to-use deep learning model compression framework.

List of papers related to neural network quantization in recent AI conferences and journals.

[ICML 2024] SqueezeLLM: Dense-and-Sparse Quantization

Awesome machine learning model compression research papers, quantization, tools, and learning material.