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
Agent operating procedure
- Check the environment. Check GPU type, memory and driver/CUDA versions (
nvidia-smi), framework versions, and dataset location and size. - Pin down the inputs. Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.
- Run a small version first. Do a smoke run: tiny model or subset, few steps, and confirm loss decreases and checkpoints save.
- Execute the full task using the instructions and references above.
- Validate the result. Track metrics on held-out data, compare against a baseline, and record seeds, configs and hardware.
- Report. State what was run (versions, commands, parameters), what was checked, and what is still uncertain.
| If this happens | Do this |
|---|---|
| CUDA out-of-memory | Reduce batch size, enable gradient accumulation/checkpointing or mixed precision, or shard the model. |
| Loss is NaN or diverges | Lower the learning rate, check data for invalid values, and enable gradient clipping. |
| A function, flag or endpoint in these instructions is missing in the installed version | Check the installed version's own documentation (help(), --help, official docs), adapt, and tell the user. Never invent an API. |
| A required input, identifier or parameter is ambiguous | Ask the user, or state the assumption explicitly before running. |
Integrity rules
- Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.
- Never claim training results without logs; estimate compute cost before launching large jobs.
- Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.
- Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.
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