Popular GitHub Repositories for Post Training Quantization

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

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

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

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.

[NeurIPS 2024 Oral?] DuQuant: Distributing Outliers via Dual Transformation Makes Stronger Quantized LLMs.

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

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

Post-training quantization on Nvidia Nemo ASR model

An example to quantize MobileNetV2 trained on CIFAR-10 dataset with PyTorch FX graph mode quantization

quantization example for pqt & qat

Post post-training-quantization (PTQ) method for improving LLMs. Unofficial implementation of https://arxiv.org/abs/2309.02784

This sample shows how to convert TensorFlow model to OpenVINO IR model and how to quantize OpenVINO model.

Implementation of EPTQ - an Enhanced Post-Training Quantization algorithm for DNN compression

Generating tensorrt model using onnx

Quantization for Object Detection in Tensorflow 2.x

Model Compression Paper List (Focusing on Quantization, Particularly Zero-Shot Quantization)

Creating Snapdragon neural processing engine environment to convert protobuf(pb) files to dlc(deep learning container) files as well as use snpe for Quantizing neural networks. Mobile app development link https://github.com/anshumax/mobilenn