hiyouga / LlamaFactory
Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)
hiyouga / LlamaFactory
huggingface / peft
🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.
modelscope / ms-swift
Use PEFT or Full-parameter to CPT/SFT/DPO/GRPO 600+ LLMs (Qwen3.8, DeepSeek-V4, GLM-5.1, InternLM3, Llama4, ...) and 300+ MLLMs (Qwen3-VL, Qwen3-Omni, InternVL3.5, Ovis2.5, GLM5.3, Gemma4, Llava, Phi4, ...) (AAAI 2025).
MakazhanAlpamys / Soup
Fine-tune LLMs from one YAML. Layer streaming trains an 8B model on a 4 GB laptop GPU.
stochasticai / xTuring
Build, personalize and control your own LLMs. From data pre-processing to fine-tuning, xTuring provides an easy way to personalize open-source LLMs. Join our discord community: https://discord.gg/TgHXuSJEk6
peremartra / Large-Language-Model-Notebooks-Course
Practical course about Large Language Models.
ModelCloud / GPTQModel
LLM model quantization (compression) toolkit with HW acceleration support for Nvidia, AMD, Intel GPU and Intel/AMD/Apple CPU via HF, vLLM, and SGLang.
whitecircle / halo
Halo is an open-source framework built by White Circle for training large language and multimodal models
DaoyuanLi2816 / can-i-finetune-this
Single-GPU LLM fine-tuning preflight: memory estimates, runnable LoRA/QLoRA recipes, and local measurements.
DaoyuanLi2816 / mini-verl
verl for a single consumer GPU. PPO, GRPO and on-policy distillation on NVIDIA GPUs.
Saganaki22 / ComfyUI-Hyperflow
ComfyUI node pack for HyperFlow [Video Rebirth's] 8-step LoRA for MiniMax-H3 (video + audio), ported onto ComfyUI's native MiniMax-H3 model. No ComfyUI core files are modified; no custom sampler is needed.
STEAM-DROPS / finetune-lab-notes
Fine-Tuning Transformers in 2026: Hands-On LLM Training, LoRA, and PEFT Guide
Grinffin / PhaseLoRA
Official implementation of PhaseLoRA: control-regime-conditioned low-rank adaptation for continuous-action vision-language-action policies, built on OpenPI.
lvsir18 / CS224n-Winter-2026-Learning-Notes
这是一套围绕 Stanford CS224N: Natural Language Processing with Deep Learning 整理的系统课程笔记。仓库以课程 19 节 Lecture 为主线，从 Word Vectors、RNN、Attention、Transformer 一路延伸到 Pretraining、Post-training、PEFT、RAG、Agents、Reasoning、Multilinguality、Interpretability 与 Multimodality。