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llamafactory-docs

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Fine-tune and deploy LLMs end-to-end.

Software Engineering#multimodal#distributed training#lora#llama-factory#inference api#llm fine-tuning#dataset formatting
Authorwenerme
Version1.0.0
Installs0

System Documentation

What problem does it solve?

LLaMA Factory documentation helps you configure and run fine-tuning, reinforcement learning, and deployment workflows without getting stuck on framework details and dataset formatting requirements.

Core Features & Use Cases

  • Training & RLHF/Preference Learning Guidance: Supports SFT, DPO/KTO/PPO/ORPO/SimPO/CPO family, reward modeling, and GRPO-style reinforcement learning workflows.
  • Dataset Preparation & Formats: Explains dataset requirements for alpaca/sharegpt-style data, including multimodal fields needed for images/videos/audios.
  • End-to-End Deployment Paths: Covers deployment and serving options such as OpenAI-style API and inference backends like vLLM and SGLang, plus the LLaMA Board Web UI.
  • Quick CLI Workflows: Provides command patterns for training, web UI, chat, and API serving using llamafactory-cli.

Quick Start

Ask for a complete setup and training plan by running: llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml.

Dependency Matrix

Required Modules

None required

Components

references

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Name: llamafactory-docs
Download link: https://github.com/wenerme/ai/archive/main.zip#llamafactory-docs

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