hiyouga/LlamaFactory

Health v2 · maintenance only

Adoption not part of the score

74382 stars · 9101 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

LlamaFactory is a unified, efficient fine-tuning framework for 100+ large language models and vision-language models, supporting LoRA, QLoRA, full-parameter tuning, and RLHF methods like DPO and PPO. It offers a no-code WebUI (LLaMA Board), CLI, distributed training backends (DeepSpeed, FSDP, Ray), and quantization/inference/export tooling.

Use cases

• fine-tune llama on my own data
• train a lora adapter for qwen
• run qlora fine-tuning on a single gpu
• do dpo or rlhf training for a chat model
• fine-tune a multimodal vision-language model
• merge and quantize a fine-tuned model
• fine-tune an llm without writing code
• distributed multi-gpu llm training with deepspeed

When to choose

• you want to fine-tune or align many open LLMs/VLMs with one unified tool
• you need LoRA/QLoRA, RLHF (DPO, PPO, KTO, ORPO), and quantization in a single framework
• you prefer a no-code WebUI or simple CLI over writing training scripts
• you need distributed training (DeepSpeed, FSDP, Ray, Megatron) and experiment tracking

When to avoid

• you need pretraining from scratch at massive scale rather than fine-tuning
• you want a minimal, hackable training script instead of a full framework
• your model architecture is not among the supported templates
• you only need inference/serving without any training

Facets

framework · maturity active

llm-training machine-learning deep-learning cli gui large-language-models machine-learning deep-learning artificial-intelligence python fine-tuning lora qlora rlhf dpo peft quantization instruction-tuning webui multimodal distributed-training deepspeed vllm natural-language-processing linux docker gpu web-server

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markdown · JSON · MCP: product_card(name="hiyouga/LlamaFactory")