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Llama-Factory Skill

Comprehensive assistance with llama-factory development, generated from official documentation.

When to Use This Skill

This skill should be triggered when:

  • Working with llama-factory
  • Asking about llama-factory features or APIs
  • Implementing llama-factory solutions
  • Debugging llama-factory code
  • Learning llama-factory best practices

Quick Reference

Common Patterns

Quick reference patterns will be added as you use the skill.

Reference Files

This skill includes comprehensive documentation in references/:

  • _images.md - Images documentation
  • advanced.md - Advanced documentation
  • getting_started.md - Getting Started documentation
  • other.md - Other documentation

Use view to read specific reference files when detailed information is needed.

Working with This Skill

For Beginners

Start with the getting_started or tutorials reference files for foundational concepts.

For Specific Features

Use the appropriate category reference file (api, guides, etc.) for detailed information.

For Code Examples

The quick reference section above contains common patterns extracted from the official docs.

Resources

references/

Organized documentation extracted from official sources. These files contain:

  • Detailed explanations
  • Code examples with language annotations
  • Links to original documentation
  • Table of contents for quick navigation

scripts/

Add helper scripts here for common automation tasks.

assets/

Add templates, boilerplate, or example projects here.

Notes

  • This skill was automatically generated from official documentation
  • Reference files preserve the structure and examples from source docs
  • Code examples include language detection for better syntax highlighting
  • Quick reference patterns are extracted from common usage examples in the docs

Updating

To refresh this skill with updated documentation:

  1. Re-run the scraper with the same configuration
  2. The skill will be rebuilt with the latest information

Source

git clone https://github.com/Orchestra-Research/AI-Research-SKILLs/blob/main/03-fine-tuning/llama-factory/SKILL.mdView on GitHub

Overview

Llama-factory provides a WebUI-based, no-code approach to fine-tuning LLMs. It supports 100+ models, 2/3/4/5/6/8-bit QLoRA, and multimodal training, with guidance sourced from official documentation to streamline implementation and debugging.

How This Skill Works

Users configure fine-tuning pipelines through a WebUI, select from 100+ supported models, and apply QLoRA bit-widths (2/3/4/5/6/8). The skill derives actionable steps from references in references/ (getting_started, advanced, api/guides) and relies on dependencies like llmtuner, torch, transformers, datasets, peft, accelerate, and gradio to execute training and evaluation.

When to Use It

  • Working with llama-factory
  • Asking about llama-factory features or APIs
  • Implementing llama-factory solutions
  • Debugging llama-factory code
  • Learning llama-factory best practices

Quick Start

  1. Step 1: Review the getting_started.md in references and install dependencies (llmtuner, torch, transformers, datasets, peft, accelerate, gradio)
  2. Step 2: Launch the WebUI, select a model from the 100+ catalog, and configure QLoRA bit-widths (2/3/4/5/6/8) with multimodal options
  3. Step 3: Run a small fine-tuning task, monitor logs, and use the reference guides to refine hyperparameters

Best Practices

  • Start with the getting_started.md and tutorials in the references before building workflows
  • Use the appropriate reference files (api, guides, etc.) for detailed feature information
  • Read the reference files in references/ for comprehensive explanations and code examples
  • Use the view command to read specific reference files when detailed information is needed
  • Check code examples, which include language annotations for clearer understanding

Example Use Cases

  • Set up a no-code fine-tuning workflow for a new model via the WebUI and validate results
  • Experiment with 2/3/4/5/6/8-bit QLoRA configurations to optimize memory usage
  • Fine-tune multiple HuggingFace models (Llama, Qwen, Gemma) from the 100+ model catalog
  • Diagnose and fix issues in llama-factory code using official docs and reference guides
  • Migrate an existing fine-tuning pipeline to llama-factory using getting_started and advanced guides

Frequently Asked Questions

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