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llama-factory

Fine-tune LLMs with LLaMA-Factory WebUI and QLoRA quantization.

2|Updated Feb 4, 2026
Search Tags:#llm#fine-tuning#multimodal#webui#qlora#llama-factory
One-click install
npx skills add https://github.com/zhuangbiaowei/smart_bot --skill llama-factory-zhuangbiaowei
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llama-factory
Source: https://github.com/zhuangbiaowei/smart_bot/tree/main/skills/llama-factory
Command: npx skills add https://github.com/zhuangbiaowei/smart_bot --skill llama-factory-zhuangbiaowei

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and scripts (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 no-code, efficient model customization.

Core Features & Use Cases

  • No-Code Fine-Tuning: Leverage a WebUI for easy model fine-tuning without writing extensive code.
  • Broad Model Support: Supports over 100 models, including Llama, Qwen, Gemma, and more.
  • Advanced Techniques: Offers support for various quantization methods (2/3/4/5/6/8-bit QLoRA) and multimodal capabilities.
  • Use Case: A researcher wants to fine-tune a Llama model on a specific dataset for a new research task. They can use this Skill to quickly set up and run the fine-tuning process via the LLaMA-Factory WebUI, selecting appropriate parameters for QLoRA and multimodal input.

Quick Start

Use the llama-factory skill to get started with fine-tuning a model.

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?▼

You can fine-tune LLMs without coding by using the LLaMA-Factory WebUI. This no-code interface allows you to select models, configure parameters, and run the fine-tuning process visually.

Does LLaMA-Factory support QLoRA quantization for efficient fine-tuning?▼

Yes, LLaMA-Factory supports QLoRA quantization at 2, 3, 4, 5, 6, and 8-bit levels. These advanced techniques enable efficient LLM customization with reduced memory requirements.

Can I fine-tune multimodal models using the LLaMA-Factory framework?▼

Yes, LLaMA-Factory provides multimodal capabilities for fine-tuning. You can process and train models on diverse data types beyond standard text inputs.

What models are supported by the LLaMA-Factory framework for fine-tuning?▼

LLaMA-Factory supports fine-tuning over 100 models, including Llama, Qwen, and Gemma. This broad compatibility covers various downstream tasks.

What is the best way to customize a Llama model for a specific research dataset?▼

The best way is using LLaMA-Factory to set up and run the fine-tuning process via its WebUI. You can easily select appropriate QLoRA parameters and multimodal inputs for your dataset.

Are there limitations when using no-code WebUI for LLM fine-tuning?▼

While the no-code WebUI simplifies model fine-tuning, limitations depend on your hardware scale and chosen quantization methods. Advanced techniques like QLoRA help manage resource constraints.

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