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llama-factory

Fine-tune LLaMA-family models with LoRA/QLoRA via a no-code WebUI.

Updated Mar 16, 2026
Search Tags:#fine-tuning#multimodal#lora#webui#transformers#qlora#llama
One-click install
npx skills add https://github.com/arsity/scholar-tools --skill llama-factory-arsity
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llama-factory
Source: https://github.com/arsity/scholar-tools/tree/main/vendor/ai-research-skills/03-fine-tuning/llama-factory
Command: npx skills add https://github.com/arsity/scholar-tools --skill llama-factory-arsity

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

LLaMA-Factory simplifies and accelerates the process of fine-tuning large language models by providing a no-code WebUI workflow and expert guidance for LoRA/QLoRA, multi-model support, and integration with common ML tooling.

Core Features & Use Cases

  • No-code Fine-Tuning: Set up and run LoRA/QLoRA fine-tuning across many LLaMA-family models without writing code.
  • WebUI Orchestration: Manage data, adapters, and evaluation from an intuitive web interface.
  • Multimodal & Bit-Width Support: Train models with 4/8-bit QLoRA and several modalities, across multiple models.
  • Use Case: A researcher wants to fine-tune a 7B LLaMA model with LoRA for a specialized domain and evaluate it on a local dataset.

Quick Start

Install and configure LLaMA-Factory to start no-code fine-tuning with your chosen 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 LLaMA model without writing code?▼

You can fine-tune LLaMA models without writing code by using the LLaMA-Factory WebUI to orchestrate data, adapters, and evaluation through an intuitive web interface.

Can I use QLoRA 4-bit and 8-bit quantization for LLaMA fine-tuning?▼

Yes, LLaMA-Factory supports multi-bit-width QLoRA, allowing you to train LLaMA-family models using both 4-bit and 8-bit quantization to optimize memory usage.

What dependencies do I need to run LLaMA-Factory for LoRA fine-tuning?▼

You need llmtuner, torch, transformers, datasets, peft, accelerate, and gradio installed in your environment to operate the LLaMA-Factory fine-tuning workflows.

Does LLaMA-Factory support multimodal model training?▼

Yes, LLaMA-Factory provides multimodal support, enabling you to train models across several different modalities alongside standard LLaMA-family text models.

What is the best way to manage datasets and evaluation for LLaMA fine-tuning?▼

The best way to manage datasets and evaluation is through the LLaMA-Factory WebUI, which orchestrates data preparation, adapter configuration, and model evaluation in one interface.

Can I fine-tune a 7B LLaMA model locally for a specialized domain?▼

Yes, researchers can fine-tune a 7B LLaMA model with LoRA for a specialized domain and evaluate it on a local dataset using the no-code WebUI workflow.

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