Popular GitHub Repositories for Quantization Error

Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)

中文LLaMA&Alpaca大语言模型+本地CPU/GPU训练部署 (Chinese LLaMA & Alpaca LLMs)

An easy-to-use LLMs quantization package with user-friendly apis, based on GPTQ algorithm.

Fast inference engine for Transformer models

Distilled variant of Whisper for speech recognition. 6x faster, 50% smaller, within 1% word error rate.

Vector (and Scalar) Quantization, in Pytorch

[ICLR2025 Spotlight] SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models

? Accelerate inference and training of ? Transformers, Diffusers, TIMM and Sentence Transformers with easy to use hardware optimization tools

Sparsity-aware deep learning inference runtime for CPUs

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

A model library for exploring state-of-the-art deep learning topologies and techniques for optimizing Natural Language Processing neural networks

Quantized Attention that achieves speedups of 2.1-3.1x and 2.7-5.1x compared to FlashAttention2 and xformers, respectively, without lossing end-to-end metrics across various models.

Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet, Inception, SqueezeNet)

Build, customize and control you own LLMs. From data pre-processing to fine-tuning, xTuring provides an easy way to personalize open-source LLMs. Join our discord community: https://discord.gg/TgHXuSJEk6

ComfyUI plugin of Nunchaku

PyTorch native quantization and sparsity for training and inference

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

Transformers-compatible library for applying various compression algorithms to LLMs for optimized deployment with vLLM

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