LLaMA Factory

Open-source, no-code WebUI for fine-tuning 100+ open LLMs with LoRA, QLoRA, DPO, and PPO.

Free· Free, open-source (Apache-2.0); self-hostedFine-tuningMulti-model (LLaMA, Mistral, Qwen, Gemma, Phi, LLaVA, ChatGLM, Yi)

Visit website →

Best for

Pick LLaMA Factory if you want one tool to fine-tune any open-weight LLM on your own hardware without writing custom training scripts.

Skip if

Skip it if you want a managed, click-to-train cloud service or don't have access to suitable GPUs.

LLaMA Factory is an open-source training and fine-tuning platform that wraps the messy reality of post-training open-weight LLMs behind a clean Python toolkit and a LlamaBoard WebUI. It supports a wide model zoo (LLaMA, Mistral, Qwen, Yi, Gemma, Baichuan, ChatGLM, Phi, LLaVA and other VLMs) and covers the full pipeline: continued pre-training, supervised instruction tuning, reward modeling, and preference alignment via PPO, DPO, KTO, and ORPO.

What sets it apart is the breadth of tuning methods exposed without code: full-parameter, freeze-tuning, LoRA and its variants (LoRA+, DoRA, PiSSA, rsLoRA), plus 2/3/4/5/6/8-bit QLoRA via bitsandbytes, HQQ, or AQLM. Acceleration paths include FlashAttention-2, Unsloth, Liger Kernel, and vLLM for inference. It is aimed at practitioners and small labs who want to iterate on local GPUs (or a single rented H100) instead of stitching together TRL, PEFT, accelerate, and DeepSpeed configs by hand. It is fully free and self-hosted, with no SaaS tier.

Integrations cover Hugging Face Hub for models and datasets, TensorBoard / Weights & Biases / MLflow / SwanLab for experiment tracking, and exports to GGUF and Ollama for downstream serving. Caveats: you still need your own GPU(s), the WebUI hides but does not eliminate VRAM-planning headaches, and the project moves fast enough that pinned versions matter.

Editor's take

This is the de-facto Swiss army knife for open-LLM fine-tuning in 2025-2026 — broader model coverage than Axolotl, friendlier than raw TRL/PEFT, and one of the few projects that keeps pace with new architectures within days. Best paired with Unsloth for single-GPU runs and vLLM for serving.

— The AI Tool Bible editorial team