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llama-factory

Fine-tune LLMs with LLaMA-Factory WebUI, QLoRA, and multimodal support.

Updated Jan 30, 2026
Search Tags:#llm#fine-tuning#llm development#webui#qlora#llama-factory
One-click install
npx skills add https://github.com/gagan114662/content_books --skill llama-factory-gagan114662
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llama-factory
Source: https://github.com/gagan114662/content_books/tree/main/AI-research-SKILLs/03-fine-tuning/llama-factory
Command: npx skills add https://github.com/gagan114662/content_books --skill llama-factory-gagan114662

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires llmtuner, torch, transformers, datasets, peft, accelerate, gradio, and includes scripts (resource) and references (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 solutions for over 100 models with advanced features like QLoRA and multimodal support.

Core Features & Use Cases

  • No-Code Fine-Tuning: Easily fine-tune LLMs via a WebUI without writing code.
  • Broad Model Support: Works with over 100 different LLMs.
  • Advanced Techniques: Supports 2/3/4/5/6/8-bit QLoRA, LoRA, and multimodal capabilities.
  • Use Case: A researcher wants to fine-tune a Llama model on a custom dataset for a specific task. They can use LLaMA-Factory's WebUI to upload their data, configure the fine-tuning parameters (like QLoRA), and train the model without deep coding expertise.

Quick Start

Use the llama-factory skill to get started with fine-tuning LLMs by following the instructions in the 'getting_started.md' reference file.

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 to upload datasets, configure training parameters, and initiate model training directly from the browser interface.

What is QLoRA and how does it work for LLM fine-tuning?▼

QLoRA is an advanced fine-tuning technique that reduces memory usage by quantizing weights. LLaMA-Factory supports 2/3/4/5/6/8-bit QLoRA to efficiently train large models on limited hardware.

Does LLaMA-Factory support multimodal models?▼

Yes, LLaMA-Factory provides multimodal support alongside standard text models, allowing you to train and fine-tune models that process multiple data types within the same framework.

Can I use PyTorch and Transformers to train custom Llama models?▼

Yes, LLaMA-Factory leverages PyTorch and Transformers as core dependencies to provide comprehensive assistance for fine-tuning Llama models on custom datasets for specific tasks.

What's the best way to merge and quantize models after training?▼

LLaMA-Factory provides built-in tools for model merging and quantization, allowing you to consolidate trained weights and optimize them for deployment using the framework's expert guidance.

How many different LLMs can I train using LLaMA-Factory?▼

LLaMA-Factory offers broad model support and works with over 100 different Large Language Models, enabling no-code fine-tuning solutions across a wide variety of architectures.

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