quantization-aware-training

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-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

Neural Network Compression Framework for enhanced OpenVINO™ inference

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

YOLO ModelCompression MultidatasetTraining

Tutorial notebooks for hls4ml

A model compression and acceleration toolbox based on pytorch.

0️⃣1️⃣🤗 BitNet-Transformers: Huggingface Transformers Implementation of "BitNet: Scaling 1-bit Transformers for Large Language Models" in pytorch with Llama(2) Architecture

针对pytorch模型的自动化模型结构分析和修改工具集，包含自动分析模型结构的模型压缩算法库

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

FrostNet: Towards Quantization-Aware Network Architecture Search

Notes on quantization in neural networks

Quantization Aware Training

Enhancing LLMs with LoRA

Quantization-aware training with spiking neural networks

Train neural networks with joint quantization and pruning on both weights and activations using any pytorch modules

3rd place solution for NeurIPS 2019 MicroNet challenge

FakeQuantize with Learned Step Size(LSQ+) as Observer in PyTorch

Code for paper 'Multi-Component Optimization and Efficient Deployment of Neural-Networks on Resource-Constrained IoT Hardware'

Offical implementation of "Quantized Spike-driven Transformer" (ICLR2025)