Skip to main content
ClaudeWave
EN, English
Skill13.9k repo starsupdated 18d ago

llama-factory

LLaMA-Factory is a no-code web interface and toolkit for fine-tuning large language models across 100+ model architectures using quantized LoRA techniques at 2/3/4/5/6/8-bit precision levels, with multimodal support. Use this skill when implementing LLM fine-tuning workflows, debugging configuration issues, exploring quantization strategies, or seeking guidance on model adaptation best practices through the LLaMA-Factory ecosystem.

Install in Claude Code
Copy
git clone --depth 1 https://github.com/Orchestra-Research/AI-Research-SKILLs /tmp/llama-factory && cp -r /tmp/llama-factory/03-fine-tuning/llama-factory ~/.claude/skills/llama-factory
Then start a new Claude Code session; the skill loads automatically.

SKILL.md

# 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
audiocraft-audio-generationSkill

PyTorch library for audio generation including text-to-music (MusicGen) and text-to-sound (AudioGen). Use when you need to generate music from text descriptions, create sound effects, or perform melody-conditioned music generation.

autogpt-agentsSkill

Autonomous AI agent platform for building and deploying continuous agents. Use when creating visual workflow agents, deploying persistent autonomous agents, or building complex multi-step AI automation systems.

autoresearchSkill

Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers research direction. Routes to domain-specific skills for execution, supports continuous agent operation via Claude Code /loop and OpenClaw heartbeat, and produces research presentations and papers. Use when starting a research project, running autonomous experiments, or managing a multi-hypothesis research effort.

awq-quantizationSkill

Activation-aware weight quantization for 4-bit LLM compression with 3x speedup and minimal accuracy loss. Use when deploying large models (7B-70B) on limited GPU memory, when you need faster inference than GPTQ with better accuracy preservation, or for instruction-tuned and multimodal models. MLSys 2024 Best Paper Award winner.

axolotlSkill

Expert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal support

blip-2-vision-languageSkill

Vision-language pre-training framework bridging frozen image encoders and LLMs. Use when you need image captioning, visual question answering, image-text retrieval, or multimodal chat with state-of-the-art zero-shot performance.

chromaSkill

Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.

clipSkill

OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. Best for general-purpose image understanding.