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llama-factory

Fine-tune LLMs with LLaMA-Factory's no-code WebUI and QLoRA.

11.5k|842|Updated Nov 3, 2025
Search Tags:#llm#fine-tuning#multimodal#webui#qlora#llama-factory
One-click install
npx skills add https://github.com/Orchestra-Research/AI-Research-SKILLs --skill llama-factory-orchestra-research
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llama-factory
Source: https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/main/03-fine-tuning/llama-factory
Command: npx skills add https://github.com/Orchestra-Research/AI-Research-SKILLs --skill llama-factory-orchestra-research

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires llmtuner, torch, transformers, datasets, peft, accelerate, gradio, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides expert guidance and tools for fine-tuning Large Language Models (LLMs) using the LLaMA-Factory framework, enabling users to customize models for specific tasks without extensive coding.

Core Features & Use Cases

  • No-Code Fine-Tuning: Utilize a WebUI for easy, code-free model fine-tuning.
  • Broad Model Support: Works with over 100 models, including LLaMA, Qwen, and Gemma.
  • Advanced Techniques: Supports 2/3/4/5/6/8-bit QLoRA, LoRA, and multimodal fine-tuning.
  • Use Case: A researcher wants to fine-tune a Llama 3 model on a proprietary dataset for a specialized domain. They can use LLaMA-Factory's WebUI to upload their data, configure the fine-tuning parameters (like QLoRA settings), and train the model efficiently.

Quick Start

Use the llama-factory skill to start the WebUI for fine-tuning LLMs.

Frequently Asked Questions about llama-factory

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I fine-tune a Large Language Model without writing code?▼

Fine-tune Large Language Models without coding by utilizing a WebUI to upload datasets, configure QLoRA parameters, and train customized models efficiently.

Does LLaMA-Factory support QLoRA and multimodal fine-tuning for LLMs?▼

LLaMA-Factory supports advanced fine-tuning techniques including 2/3/4/5/6/8-bit QLoRA, standard LoRA, and multimodal capabilities for Large Language Models.

What's the best way to customize a Llama 3 model on a proprietary dataset?▼

Use the LLaMA-Factory WebUI to customize Llama 3 on proprietary datasets; it supports over 100 models including LLaMA, Qwen, and Gemma with efficient QLoRA training.

Can I use this framework to fine-tune models like Qwen and Gemma?▼

Yes, the framework supports fine-tuning Qwen and Gemma, offering broad compatibility with over 100 Large Language Models and multimodal capabilities for specific applications.

Do I need PyTorch and Transformers installed to run LLaMA-Factory fine-tuning?▼

Yes, PyTorch, Transformers, datasets, PEFT, and Accelerate are required dependencies to run the LLaMA-Factory framework and execute Large Language Model fine-tuning processes.

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