post-training-quantization

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

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-Ari...

翻译 - 基于pytorch的模型压缩（1，量化：8/4 / 2bits（dorefa），三进制/二进制值（twn / bnn / xnornet）； 2，修剪：常规，常规和组卷积通道修剪； 3，组卷积结构； 4，特征（A）的二进制值的分批归一化折叠）

#计算机科学#TinyNeuralNetwork is an efficient and easy-to-use deep learning model compression framework.

#自然语言处理#[ICML 2024] SqueezeLLM: Dense-and-Sparse Quantization

#大语言模型#[EMNLP 2024 Industry Track] This is the official PyTorch implementation of "LLMC: Benchmarking Large Language Model Quantization with a Versatile Compression Toolkit".

[ICCV 2023] Q-Diffusion: Quantizing Diffusion Models.

[IJCAI 2022] FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer

#计算机科学#A model compression and acceleration toolbox based on pytorch.

This repository contains notebooks that show the usage of TensorFlow Lite for quantizing deep neural networks.

#大语言模型#[NeurIPS 2024 Oral🔥] DuQuant: Distributing Outliers via Dual Transformation Makes Stronger Quantized LLMs.

#计算机科学#Notes on quantization in neural networks

[CVPR 2024 Highlight] This is the official PyTorch implementation of "TFMQ-DM: Temporal Feature Maintenance Quantization for Diffusion Models".

Post-training static quantization using ResNet18 architecture

#大语言模型#[ICLR 2024] This is the official PyTorch implementation of "QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Models"

Pytorch implementation of our paper accepted by ECCV 2022-- Fine-grained Data Distribution Alignment for Post-Training Quantization

[ASP-DAC 2025] "NeuronQuant: Accurate and Efficient Post-Training Quantization for Spiking Neural Networks" Official Implementation

Improved the performance of 8-bit PTQ4DM expecially on FID.

Post-training quantization on Nvidia Nemo ASR model

[CAAI AIR'24] Minimize Quantization Output Error with Bias Compensation