model-quantization

A curated collection of papers, benchmarks, surveys, and tools for model quantization, covering low-bit networks, LLMs, multimodal and generative models, vector and lattice quantization, and efficient deployment.

A curated list for Efficient Large Language Models

模型压缩的小白入门教程，PDF下载地址 https://github.com/datawhalechina/awesome-compression/releases

Inferflow is an efficient and highly configurable inference engine for large language models (LLMs).

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

[WINNER! 🏆] Psychopathology FER Assistant. Because mental health matters. My project submission for #TFWorld TF 2.0 Challenge at Devpost.

[NeurIPS 2023 Spotlight] This project is the official implementation of our accepted NeurIPS 2023 (spotlight) paper QuantSR: Accurate Low-bit Quantization for Efficient Image Super-Resolution.

[ICML 2023] This project is the official implementation of our accepted ICML 2023 paper BiBench: Benchmarking and Analyzing Network Binarization.

The official implementation of the ICML 2023 paper OFQ-ViT

High-accuracy Russian ANPR system built with YOLOv8 for detection and an optimized, custom-trained PyTorch CRNN for OCR.

[ICLR 2026] This is the official PyTorch implementation of "QVGen: Pushing the Limit of Quantized Video Generative Models".

Chat to LLaMa 2 that also provides responses with reference documents over vector database. Locally available model using GPTQ 4bit quantization.

A list of papers, docs, codes about diffusion quantization.This repo collects various quantization methods for the Diffusion Models. Welcome to PR the works (papers, repositories) missed by the repo.

A tutorial of model quantization using TensorFlow

AI Engineering: Annotated NBs to dive into Self-Attention, In-Context Learning, RAG, Knowledge-Graphs, Fine-Tuning, Model Optimization, and many more.

H.E.R.A. (Healing Evaluation and Recognition Architecture) — an on-device edge-AI system for offline wound assessment using YOLOv8 segmentation, HSV tissue analysis, and PUSH-based decision logic.

离线可用的本地类型化决策：4 核 CPU 单题 15.6ms。Local & offline Jev / System One inference on CPU — ONNX + INT8, no torch at runtime. 支持 laya / kev / PlayJev

Tests whether ML models preserve their behavior after conversion, quantization, or other transformations.

🧠 A comprehensive toolkit for benchmarking, optimizing, and deploying local Large Language Models. Includes performance testing tools, optimized configurations for CPU/GPU/hybrid setups, and detailed guides to maximize LLM performance on your hardware.

The Ark Project: Selecting the perfect AI model to reboot civilization from a 64GB USB drive. Comprehensive analysis of open-source LLMs under extreme constraints, with final recommendation: Meta Llama 3.1 70B Instruct (Q6_K GGUF). Includes interactive tools, detailed comparisons, and complete implementation guide for offline deployment.