ptq

Brevitas: neural network quantization in PyTorch

More readable and flexible yolov5 with more backbone(gcn, resnet, shufflenet, moblienet, efficientnet, hrnet, swin-transformer, etc) and (cbam，dcn and so on), and tensorrt

Model Compression Toolkit (MCT) is an open source project for neural network model optimization under efficient, constrained hardware. This project provides researchers, developers, and engineers advanced quantization and compression tools for deploying state-of-the-art neural networks.

Curated quantization research for VLA & WAM models — 180+ papers, a five-category taxonomy, a roadmap, and a companion PTQ + LIBERO toolkit (quant-vla).

Deep insight tensorrt, including but not limited to qat, ptq, plugin, triton_inference, cuda

mi-optimize is a versatile tool designed for the quantization and evaluation of large language models (LLMs). The library's seamless integration of various quantization methods and evaluation techniques empowers users to customize their approaches according to specific requirements and constraints, providing a high level of flexibility.

[ICML 2024] Outlier-Efficient Hopfield Layers for Large Transformer-Based Models

[ICML 2025] Fast and Low-Cost Genomic Foundation Models via Outlier Removal.

Deep Learning Model Optimization Using by TensorRT API, window

[ICML 2026] NanoQuant: Efficient Sub-1-Bit Quantization of Large Language Models

AURA: Augmented Representation for Unified Accuracy-aware Quantization

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

🎯 Fine-tune large language models and use them for text-related tasks. This repository provides a straightforward approach to fine-tuning models like Gemma, Llama 🦙, and Mistral 🌪️ for various NLP tasks. 🔧 It includes training 📚, fine-tuning 🛠️, and inference pipelines ⚙️. 🚀

Generating tensorrt model using onnx

Code for the paper "OCGQuant: Outlier-Companion Grouping for NVFP4 Quantization," accepted to EMNLP 2026 (Main Conference).

Efficient LLM fine-tuning & deployment: LoRA, QLoRA, PTQ and QAT — with benchmarking and config-driven pipelines.

Independent reproduction of PrismML Bonsai 1-bit/ternary LLM quantization: verification, method fingerprint, and public-method frontier (GPTQ/BiLLM/QuIP).

关于深度学习模型比特量化的学习内容（PTQ2QAT）

Build AI model to classify beverages for blind individuals

LLM quantization techniques: absmax, zero-point, GPTQ and GGUF