An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

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.

An Automatic Model Compression (AutoMC) framework for developing smaller and faster AI applications.

Awesome Knowledge-Distillation. 分类整理的知识蒸馏paper(2014-2021)。

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 PyTorch implementation for exploring deep and shallow knowledge distillation (KD) experiments with flexibility

Pytorch implementation of various Knowledge Distillation (KD) methods.

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

NLP DNN Toolkit - Building Your NLP DNN Models Like Playing Lego

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.

A list of high-quality (newest) AutoML works and lightweight models including 1.) Neural Architecture Search, 2.) Lightweight Structures, 3.) Model Compression, Quantization and Acceleration, 4.) Hyperparameter Optimization, 5.) Automated Feature Engineering.

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

knowledge distillation papers

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

Lightweight and Scalable framework that combines mainstream algorithms of Click-Through-Rate prediction based computational DAG, philosophy of Parameter Server and Ring-AllReduce collective communication.

A Pytorch Knowledge Distillation library for benchmarking and extending works in the domains of Knowledge Distillation, Pruning, and Quantization.

Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (CVPR 2019 Oral)

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

[CVPR2020] GhostNet: More Features from Cheap Operations

Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.

Quantization library for PyTorch. Support low-precision and mixed-precision quantization, with hardware implementation through TVM.