🤗 Optimum Intel: Accelerate inference with Intel optimization tools

Characterization study repository for pruning, a popular way to compress a DL model. this repo also investigates optimal sparse tensor layouts for pruned nets

Neural Network Compression Framework for enhanced OpenVINO™ inference

SOTA low-bit LLM quantization (INT8/FP8/INT4/FP4/NF4) & sparsity; leading model compression techniques on TensorFlow, PyTorch, and ONNX Runtime

This is the official PyTorch implementation of "LLM-QBench: A Benchmark Towards the Best Practice for Post-training Quantization of Large Language Models", and also an efficient LLM compression tool with various advanced compression methods, supporting multiple inference backends.

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

Pruning tool to identify small subsets of network partitions that are significant from the perspective of stochastic block model inference. This method works for single-layer and multi-layer networks, as well as for restricting focus to a fixed number of communities when desired.

Architecture for pruning methods analysis using pytorch prune module

Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models

PyTorch Lightning implementation of the paper Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding. This repository allows to reproduce the main findings of the paper on MNIST and Imagenette datasets.

Sparsity-aware deep learning inference runtime for CPUs

Tutorial notebooks for hls4ml

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

[CVPR 2023] Towards Any Structural Pruning; LLMs / SAM / Diffusion / Transformers / YOLOv8 / CNNs

Chess engine

AIMET GitHub pages documentation

[ICML'24 Oral] APT: Adaptive Pruning and Tuning Pretrained Language Models for Efficient Training and Inference

[NeurIPS 2023] LLM-Pruner: On the Structural Pruning of Large Language Models. Support LLaMA, Llama-2, BLOOM, Vicuna, Baichuan, etc.

OpenMMLab Model Compression Toolbox and Benchmark.

《李宏毅深度学习教程》（李宏毅老师推荐👍），PDF下载地址：https://github.com/datawhalechina/leedl-tutorial/releases