LlamaFactory

by Prism-Shadow

LlamaFactory is an AI Engineering skill for Claude Code, published by Prism-Shadow in penguin-harness.

1.5K stars150 forkson Prism-Shadow/penguin-harnessAdded 2026/08/19+21% in starsRepository updated 2026/08/19

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agentagentic-aiaibuild-toolclaude-codedeepseekdeepseek-harnessdesktopharnessllmrsiself-evolving

Install in seconds

Install LlamaFactory

Copy LlamaFactory into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.

terminal

npx degit https://github.com/Prism-Shadow/penguin-harness/tree/main/packages/skills/skills/llamafactory ~/.claude/skills/llamafactory

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/Prism-Shadow/penguin-harness.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file: packages/skills/skills/llamafactory/SKILL.md in Prism-Shadow/penguin-harness
Installs to: ~/.claude/skills/llamafactory
Collection: One of 21 skills cataloged from this repository
Category: AI Engineering — 2451 skills

What LlamaFactory does

LlamaFactory fine-tunes open-weight LLMs (LoRA/QLoRA, full-parameter, SFT, DPO) via YAML configs and the llamafactory-cli command. Use it to register datasets, train models, merge LoRA adapters, and serve or chat with the result.

LlamaFactory is cataloged under AI Engineering on DirSkills. LlamaFactory comes from a repository tagged agent, agentic-ai, ai, build-tool and claude-code.

Documentation

README

Excerpt

LlamaFactory Fine-Tuning

LlamaFactory fine-tunes open-weight LLMs (LoRA/QLoRA and full-parameter; SFT, DPO and more) through the llamafactory-cli command driven by YAML configs.

Before you start

If the user's message only invokes this skill (e.g. "use llamafactory skill") without a concrete request, ask the user what they want to fine-tune. Do not run any command until the goal is clear.

Confirm before training:

GPU memory (nvidia-smi) — it bounds the model size and method; LoRA needs far less than full fine-tuning.
The base model: a Hugging Face id or a local path.
The dataset: where it lives and which format it is in.
The goal: SFT with LoRA is the usual starting point.

Install

This is the opening of the README. Read the full README on GitHub.

Frequently asked about LlamaFactory

What else does Prism-Shadow publish alongside LlamaFactory?
LlamaFactory is one of 21 skills that DirSkills catalogs from Prism-Shadow/penguin-harness, the repository it ships in. Its siblings there include Agent Creation, Agent Evaluation and Agent Optimization. Each one is a separate skill with its own page in this directory, installs the same way LlamaFactory does, and is maintained by Prism-Shadow in that same repository. The rest of the collection is listed on the Prism-Shadow/penguin-harness page.
How does LlamaFactory compare to other AI Engineering skills?
LlamaFactory ranks #991 by stars among the 2451 AI Engineering skills in this catalog. The most-starred ones next to it are Architecture Decision Records, AI-First Engineering and Agentic OS. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of LlamaFactory against them. Open each page to compare what they document and how they install.