Official code for the paper Q-resafe (https://www.arxiv.org/abs/2506.20251)

repo for paper https://arxiv.org/abs/2504.13837

(AAAI 2026) First-Order Error Matters: Accurate Compensation for Quantized Large Language Models

A agent framework based on the tutorial hello-agents

QeRL enables RL for 32B LLMs on a single H100 GPU.

[ACL 2024] A novel QAT with Self-Distillation framework to enhance ultra low-bit LLMs.

Code for "RSQ: Learning from Important Tokens Leads to Better Quantized LLMs"

Official PyTorch implementation of DistiLLM-2: A Contrastive Approach Boosts the Distillation of LLMs (ICML 2025 Oral)

[NeurIPS 2025 Spotlight] A Token is Worth over 1,000 Tokens: Efficient Knowledge Distillation through Low-Rank Clone.

A selective knowledge distillation algorithm for efficient speculative decoders

Official implementation for BitVLA: 1-bit Vision-Language-Action Models for Robotics Manipulation

Train transformer language models with reinforcement learning.

Code for data-aware compression of DeepSeek models

Use PEFT or Full-parameter to CPT/SFT/DPO/GRPO 600+ LLMs (Qwen3, Qwen3-MoE, DeepSeek-R1, GLM4.5, InternLM3, Llama4, ...) and 300+ MLLMs (Qwen3-VL, Qwen3-Omni, InternVL3.5, Ovis2.5, GLM4.5v, Llava, …

Official PyTorch implementation of QA-LoRA

This repository contains the training code of ParetoQ introduced in our work "ParetoQ Scaling Laws in Extremely Low-bit LLM Quantization"

Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)

[COLM 2025] Official PyTorch implementation of "Quantization Hurts Reasoning? An Empirical Study on Quantized Reasoning Models"

[ACL 2025 Main] EfficientQAT: Efficient Quantization-Aware Training for Large Language Models

[ICML 2025] Official code for the paper "RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models"

Official inference framework for 1-bit LLMs

LLM model quantization (compression) toolkit with hw acceleration support for Nvidia CUDA, AMD ROCm, Intel XPU and Intel/AMD/Apple CPU via HF, vLLM, and SGLang.

Code implementation of GPTAQ (https://arxiv.org/abs/2504.02692)

Welcome to the official repository of SINQ! A novel, fast and high-quality quantization method designed to make any Large Language Model smaller while preserving accuracy.

Awesome list for LLM quantization