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Llamafactory Docs by wenerme — Agent Skill

Name: Llamafactory Docs
Author: wenerme

Llamafactory Docs

USE THIS SKILL WHEN working with LLaMA Factory: fine-tuning LLMs (SFT/DPO/PPO/KTO/ORPO), LoRA/QLoRA training, dataset preparation (alpaca/sharegpt format), DeepSpeed/FSDP distributed training, LLaMA Board Web UI, model deployment (vLLM/SGLang), GRPO reinforcement learning, multimodal training, or using llamafactory-cli. Triggers on: LlamaFactory, llamafactory-cli, LLaMA Factory, llama-factory, LLaMA Board.

wenerme 7c0e7a0 22 files · 172.2 KB Updated Sep 17, 2026

File contents

LLaMA Factory Documentation

LLaMA Factory is an efficient fine-tuning framework for 100+ LLMs supporting the full training-to-deployment pipeline.

GitHub | Docs

CRITICAL: grep references/ for detailed docs before answering. Most docs are in Chinese.

Quick Start

pip install llamafactory

# LoRA SFT via CLI
llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml

# Web UI
llamafactory-cli webui

# Chat
llamafactory-cli chat --model_name_or_path Qwen/Qwen2.5-7B-Instruct

# Deploy OpenAI API
llamafactory-cli api --model_name_or_path Qwen/Qwen2.5-7B-Instruct

Supported Training Methods

Method	Full	Freeze	LoRA	QLoRA
Pre-training	Yes	Yes	Yes	Yes
SFT	Yes	Yes	Yes	Yes
Reward Modeling	Yes	Yes	Yes	Yes
PPO/DPO/KTO/ORPO	Yes	Yes	Yes	Yes
GRPO	Yes	Yes	Yes	Yes

Key Capabilities

Feature	Description
600+ LLMs	Qwen3, Llama4, DeepSeek-R1, InternLM3, Mistral, etc.
400+ VLMs	Qwen3-VL, InternVL3.5, MiniCPM-V, GLM4.5-V, etc.
LoRA variants	LoRA, QLoRA, DoRA, LoRA+, LLaMAPro, LISA, RS-LoRA
RLHF	DPO, KTO, SimPO, ORPO, CPO, PPO
GRPO family	GRPO, DAPO, GSPO, SAPO, CISPO, RLOO, Reinforce++
Distributed	DDP, DeepSpeed ZeRO2/3, FSDP/FSDP2, Megatron
Quantization	AWQ, GPTQ, BNB, FP8, AQLM, HQQ, EETQ
Inference	vLLM, SGLang, LmDeploy acceleration
Web UI	LLaMA Board for training, inference, eval

Key Topics

Getting Started

references/overview.md — Full feature overview (README)
references/getting-started.md — Quick start (zh)
references/installation.md — Installation (zh)
references/architecture.md — Project structure, v0/v1 architecture, dev practices
references/llamaboard-web-ui.md — Web UI guide (zh)

Data

references/dataset-format.md — Dataset format spec (alpaca/sharegpt), dataset_info.json
references/data-preparation/data-processing.md — Data processing pipeline (zh)
references/examples.md — Training config examples

Training

references/advanced/lora-and-quantization/lora.md — LoRA training (zh)
references/advanced/lora-and-quantization/quantization.md — Quantization (zh)
references/advanced/distributed/deepspeed.md — DeepSpeed (zh)
references/advanced/distributed/parallel-dp-tp-ep-sp-cp.md — Parallelism strategies (zh)
references/hyperparameters/data-argument.md — Data hyperparameters (zh)

Advanced

references/advanced/custom-kernels/ — Custom kernels, fused operators
references/dev-guide/core/ — Data engine, model engine internals
references/dev-guide/plugins/ — Data plugins, model plugins

References

references/ — 19 doc files (15 Chinese docs + 4 English extras)
references/dataset-format.md — Comprehensive dataset format specification
references/architecture.md — v0/v1 architecture, code structure, dev guide
references/examples.md — Training configuration examples

---name: llamafactory-docsdescription: USE THIS SKILL WHEN working with LLaMA Factory: fine-tuning LLMs (SFT/DPO/PPO/KTO/ORPO), LoRA/QLoRA training, dataset preparation (alpaca/sharegpt format), DeepSpeed/FSDP distributed training, LLaMA Board Web UI, model deployment (vLLM/SGLang), GRPO reinforcement learning, multimodal training, or using llamafactory-cli. Triggers on: LlamaFactory, llamafactory-cli, LLaMA Factory, llama-factory, LLaMA Board.---# LLaMA Factory Documentation[LLaMA Factory](https://github.com/hiyouga/LlamaFactory) is an efficient fine-tuning framework for 100+ LLMs supporting the full training-to-deployment pipeline.- [GitHub](https://github.com/hiyouga/LlamaFactory) | [Docs](https://llamafactory.readthedocs.io)CRITICAL: grep `references/` for detailed docs before answering. Most docs are in Chinese.## Quick Start```bashpip install llamafactory# LoRA SFT via CLIllamafactory-cli train examples/train_lora/llama3_lora_sft.yaml# Web UIllamafactory-cli webui# Chatllamafactory-cli chat --model_name_or_path Qwen/Qwen2.5-7B-Instruct# Deploy OpenAI APIllamafactory-cli api --model_name_or_path Qwen/Qwen2.5-7B-Instruct```## Supported Training Methods| Method | Full | Freeze | LoRA | QLoRA ||--------|------|--------|------|-------|| Pre-training | Yes | Yes | Yes | Yes || SFT | Yes | Yes | Yes | Yes || Reward Modeling | Yes | Yes | Yes | Yes || PPO/DPO/KTO/ORPO | Yes | Yes | Yes | Yes || GRPO | Yes | Yes | Yes | Yes |## Key Capabilities| Feature | Description ||---------|-------------|| **600+ LLMs** | Qwen3, Llama4, DeepSeek-R1, InternLM3, Mistral, etc. || **400+ VLMs** | Qwen3-VL, InternVL3.5, MiniCPM-V, GLM4.5-V, etc. || **LoRA variants** | LoRA, QLoRA, DoRA, LoRA+, LLaMAPro, LISA, RS-LoRA || **RLHF** | DPO, KTO, SimPO, ORPO, CPO, PPO || **GRPO family** | GRPO, DAPO, GSPO, SAPO, CISPO, RLOO, Reinforce++ || **Distributed** | DDP, DeepSpeed ZeRO2/3, FSDP/FSDP2, Megatron || **Quantization** | AWQ, GPTQ, BNB, FP8, AQLM, HQQ, EETQ || **Inference** | vLLM, SGLang, LmDeploy acceleration || **Web UI** | LLaMA Board for training, inference, eval |## Key Topics### Getting Started- `references/overview.md` — Full feature overview (README)- `references/getting-started.md` — Quick start (zh)- `references/installation.md` — Installation (zh)- `references/architecture.md` — Project structure, v0/v1 architecture, dev practices- `references/llamaboard-web-ui.md` — Web UI guide (zh)### Data- `references/dataset-format.md` — Dataset format spec (alpaca/sharegpt), dataset_info.json- `references/data-preparation/data-processing.md` — Data processing pipeline (zh)- `references/examples.md` — Training config examples### Training- `references/advanced/lora-and-quantization/lora.md` — LoRA training (zh)- `references/advanced/lora-and-quantization/quantization.md` — Quantization (zh)- `references/advanced/distributed/deepspeed.md` — DeepSpeed (zh)- `references/advanced/distributed/parallel-dp-tp-ep-sp-cp.md` — Parallelism strategies (zh)- `references/hyperparameters/data-argument.md` — Data hyperparameters (zh)### Advanced- `references/advanced/custom-kernels/` — Custom kernels, fused operators- `references/dev-guide/core/` — Data engine, model engine internals- `references/dev-guide/plugins/` — Data plugins, model plugins## References- `references/` — 19 doc files (15 Chinese docs + 4 English extras)- `references/dataset-format.md` — Comprehensive dataset format specification- `references/architecture.md` — v0/v1 architecture, code structure, dev guide- `references/examples.md` — Training configuration examples

Frequently asked questions