Guide · Agent skill
How to use the LLaMA-Factory Fine-Tuning Guide agent skill
Updated · written with AI from the project's own documentation and checked against our catalogue data
The llama-factory skill gives an AI agent a bundle of reference notes on LLaMA-Factory, an open-source tool for fine-tuning large language models on your own data. This guide explains what an agent skill is, how to install this one, how to ask for help with natural requests, and what the skill cannot do. After reading it you will know whether the skill fits your work and how to use it safely.
LLaMA-Factory Fine-Tuning GuideOrchestra-Research/AI-Research-SKILLs · MIT Download the skill Quick start
- 1Download the ZIP. Download the llama-factory skill ZIP from this page.
- 2Unzip into a skills folder. Unzip it into ~/.claude/skills/ for personal use, or into a project's .claude/skills/ folder.
- 3Or upload in the Claude apps. In the Claude apps, open Settings, go to Capabilities, then Skills, and upload the skill.
- 4Install LLaMA-Factory separately. Set up a Python environment with LLaMA-Factory (llmtuner) and its dependencies, since the skill does not install them.
- 5Ask a natural question. Mention LLaMA-Factory and your goal, for example a no-code WebUI walkthrough or a QLoRA explanation.
- 6Verify against official docs. Check any command the agent gives you against the current LLaMA-Factory documentation before running it.
What an agent skill is and what this one does
An agent skill is a folder of instructions and reference files that an AI agent can read when a task matches it. This skill is named llama-factory and is licensed MIT. Its description is: expert guidance for fine-tuning LLMs with LLaMA-Factory, including the WebUI no-code interface, 100+ models, 2/3/4/5/6/8-bit QLoRA and multimodal support.
Once installed, the agent can draw on the bundled reference notes to explain setup, options such as the no-code web interface and low-bit QLoRA training, and which supported models are available. The skill is a documentation bundle auto-generated from official LLaMA-Factory docs. It does not add automation of its own.
- Reference files: _images.md, advanced.md, getting_started.md, index.md, other.md, plus SKILL.md
- Topics covered in the notes include installation, an advanced best-practice page on GPT-OSS LoRA fine-tuning, and the project overview
Install the llama-factory skill
Download the ZIP of the skill from this page and unzip it. For a personal setup, place the unzipped folder in ~/.claude/skills/. For a single project, place it in that project's .claude/skills/ folder.
If you use the Claude apps, you can instead upload the skill under Settings → Capabilities → Skills.
- Personal install: ~/.claude/skills/llama-factory
- Project install: .claude/skills/llama-factory inside your project folder
- Claude apps: Settings → Capabilities → Skills, then upload
Prepare your environment before you ask for training help
The skill only provides notes. You must install LLaMA-Factory (the llmtuner package) and its dependencies yourself. The skill lists torch, transformers, datasets, peft, accelerate and gradio as dependencies.
A GPU is generally needed for practical fine-tuning. This is inferred from the QLoRA and training context rather than stated as a hard rule, so check the official installation notes for your hardware. The bundled installation reference covers CUDA setup on Linux and Windows and tells you to check your GPU against NVIDIA's CUDA list.
- Python environment with LLaMA-Factory (llmtuner) installed
- Dependencies: torch, transformers, datasets, peft, accelerate, gradio
- A GPU for practical training (inferred, not confirmed by the source)
Trigger the skill with natural requests
You do not need special syntax. Mention LLaMA-Factory or the task you want, and the agent should load the skill when the request matches its description. The skill is intended for questions about LLaMA-Factory features, setup, and learning fine-tuning.
Good requests name the goal, your hardware and your data format. Vague requests get vague answers because the notes are general documentation.
- Explain how LoRA and QLoRA differ, using LLaMA-Factory as the example
- Walk me through using the LLaMA-Factory no-code WebUI to fine-tune a small model
- Which models does LLaMA-Factory support for QLoRA at 4-bit?
- Help me set up a LLaMA-Factory environment on Linux with CUDA
Examples of what you can ask for
Use the skill for learning and orientation. For example, ask the agent to summarise the getting-started notes into a checklist, or to explain the GPT-OSS LoRA steps from the advanced notes in plain language.
Always verify commands against the official LLaMA-Factory documentation before running them on real data or a paid GPU.
- Summarise the installation notes into a numbered checklist
- Explain what the 'merge LoRA weights' step means
- Compare the WebUI workflow with the command-line workflow, based on the notes
Safety, network and scripts
The skill runs no scripts and uses no network. The scripts folder is an empty placeholder. The skill does not address data handling, so do not send sensitive training data through a cloud tool without reviewing how that tool stores it.
The reference text was auto-scraped and may be outdated. The skill does not state which version of LLaMA-Factory it reflects, so treat its commands as a starting point, not a verified current procedure.
- Runs scripts: no
- Uses network: no
- Scripts folder: empty placeholder
- Do not rely on it for production training decisions without checking current docs
What people use it for
- Getting a plain-language overview of LLaMA-Factory before choosing a fine-tuning approach
- Learning how LoRA and low-bit QLoRA training differ, as a student
- Turning the installation notes into a setup checklist for a new machine
- Exploring which model families the notes list as supported
- Getting a starting outline for a no-code WebUI fine-tuning session
Frequently asked questions
Is the llama-factory skill free to use?
The skill is licensed MIT, as stated in its SKILL.md. LLaMA-Factory itself is a separate open-source tool that you install yourself. This page does not state any prices.
What do I need to use the llama-factory skill?
You need an AI agent that supports skills and a Python environment with LLaMA-Factory (llmtuner) and dependencies such as torch, transformers, datasets, peft, accelerate and gradio. A GPU is generally needed for practical fine-tuning, which is inferred from the source rather than stated as a rule.
Where do I install the llama-factory skill?
Unzip the downloaded ZIP into ~/.claude/skills/ for personal use or into a project's .claude/skills/ folder. In the Claude apps you can upload it under Settings → Capabilities → Skills.
Does the skill install LLaMA-Factory for me?
No. The skill contains documentation only, runs no scripts and uses no network. You install LLaMA-Factory and its dependencies yourself.
Are the commands in the skill up to date?
Not guaranteed. The reference text was auto-scraped from official docs, may be outdated, and the skill does not say which version it reflects. Check the current LLaMA-Factory documentation before relying on any command.
How does this compare with using LLaMA-Factory directly?
The skill is a guided explanation layer. It does not replace the official docs or the tool itself, and it adds no helper scripts. Use it for learning and orientation, and use the official docs for verified, current commands.
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Our verdict
Choose this skill if you want an agent to explain LLaMA-Factory, its no-code WebUI and QLoRA options in plain language, especially while learning. Pick something else if you need verified, current training commands, executable helper scripts, or a clear data-handling policy for sensitive training data.
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