llama-factory
Scannednpx machina-cli add skill Orchestra-Research/AI-Research-SKILLs/llama-factory --openclawLlama-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:
- Re-run the scraper with the same configuration
- 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
- Step 1: Review the getting_started.md in references and install dependencies (llmtuner, torch, transformers, datasets, peft, accelerate, gradio)
- 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
- 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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