---
name: llama-factory
description: Guides fine-tuning of large language models with LLaMA-Factory, covering the WebUI no-code interface, training across 100+ supported models, quantized QLoRA at 2/3/4/5/6/8-bit precision, and multimodal model support. Use when setting up or configuring a LLaMA-Factory fine-tuning run, choosing a quantization level for QLoRA training, using the WebUI instead of writing training code, fine-tuning a multimodal model, or debugging a LLaMA-Factory training error. Not for general-purpose inference serving or for other training frameworks; use the matching skill for those.
license: MIT
metadata:
  version: 1.0.0
  category: ml-training
  maintainer: Kalaris Labs
  tags: Fine-Tuning, LLaMA Factory, LLM, WebUI, No-Code, QLoRA, LoRA, Multimodal, HuggingFace, Llama, Qwen, Gemma
  dependencies: llmtuner, torch, transformers, datasets, peft, accelerate, gradio
---

# 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

## Agent operating procedure

1. **Check the environment.** Check GPU type, memory and driver/CUDA versions (`nvidia-smi`), framework versions, and dataset location and size.
2. **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.
3. **Run a small version first.** Do a smoke run: tiny model or subset, few steps, and confirm loss decreases and checkpoints save.
4. **Execute the full task** using the instructions and references above.
5. **Validate the result.** Track metrics on held-out data, compare against a baseline, and record seeds, configs and hardware.
6. **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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- `gptq`: Quantizes LLMs to 4-bit (also 3-bit) with GPTQ using group-wise quantization (group size 128 by default), via AutoGPTQ and transformers.