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.

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

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

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

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、reg…

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

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

[CVPR 2024] DeepCache: Accelerating Diffusion Models for Free

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

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

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)

[CVPR2020] GhostNet: More Features from Cheap Operations

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

[ECCV 2018] AMC: AutoML for Model Compression and Acceleration on Mobile Devices

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