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llama-factory

Guides users to fine-tune LLMs via LLaMA-Factory WebUI with no-code configuration.

Updated Aug 27, 2026
Search Tags:#fine-tuning#multimodal#no-code#lora#webui#qlora#llama-factory
One-click install
npx skills add https://github.com/t2ance/dr-claw-plugin --skill llama-factory-t2ance
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llama-factory
Source: https://github.com/t2ance/dr-claw-plugin/tree/main/plugins/ml-training-stack/skills/fine-tuning/llama-factory
Command: npx skills add https://github.com/t2ance/dr-claw-plugin --skill llama-factory-t2ance

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Provides expert guidance for fine-tuning LLMs with LLaMA-Factory using the WebUI without writing code, enabling rapid experimentation across many models and configurations.

Core Features & Use Cases

  • No-code WebUI-based fine-tuning guidance for the LLaMA-family models.
  • Supports 100+ models, QLoRA at 2/3/4/5/6/8-bit, and multimodal setups.
  • Access to best-practice workflows (SFT, RLHF, DPO, KTO) and in-depth references to accelerate development.

Quick Start

Start a new fine-tuning project in LLaMA-Factory WebUI and apply a LoRA adapter with your chosen bit-width.

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 LLMs without writing code?▼

You can fine-tune LLMs without writing code by using the LLaMA-Factory WebUI to configure training parameters and apply LoRA adapters visually. This guidance supports rapid experimentation across 100+ models.

Does LLaMA-Factory support multimodal fine-tuning?▼

Yes, LLaMA-Factory supports multimodal fine-tuning setups. This guidance covers configuring multimodal training visually through the WebUI alongside standard text-only models.

Can I use QLoRA with different bit-widths for fine-tuning?▼

Yes, you can apply QLoRA at 2, 3, 4, 5, 6, and 8-bit precisions. This guidance helps you select and configure your chosen bit-width for memory-efficient LLM fine-tuning.

What environment do I need to start no-code LLM fine-tuning?▼

You need a properly installed LLaMA-Factory environment with dependencies including llmtuner, torch, transformers, datasets, peft, accelerate, and gradio to launch the WebUI.

Which training workflows are available through the LLaMA-Factory WebUI?▼

The WebUI supports best-practice workflows including SFT, RLHF, DPO, and KTO. This guidance provides in-depth references to help you configure these training methods for your models.

What is the best way to optimize LLaMA models for specific tasks?▼

The best way to optimize LLaMA models is using no-code LoRA fine-tuning via LLaMA-Factory. It enables rapid visual configuration across 100+ models to accelerate your development.

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