If you're fine-tuning LLMs and tired of waiting hours or running out of memory, this skill gives you expert guidance on Unsloth's optimization techniques. It covers LoRA and QLoRA implementations with 2-5x faster training speeds and 50-80% memory reduction across popular models like Llama, Mistral, Gemma, and Qwen. The documentation here is pulled from official sources, so you get the real patterns and examples without hunting through GitHub repos. Good for both getting started quickly and debugging when your training runs go sideways. Honestly, if you're doing any serious fine-tuning work, knowing these optimization tricks will save you real time and compute costs.
- skills.sh installs
- 1K
- GitHub stars
- 14.4K
- last updated
- Aug 21, 2026
Install
$ npx -y skills add orchestra-research/ai-research-skills --skill unsloth --agent claude-codeInstalls into .claude/skills of the current project.
Files
Unsloth Skill
Comprehensive assistance with unsloth development, generated from official documentation.
When to Use This Skill
This skill should be triggered when:
- Working with unsloth
- Asking about unsloth features or APIs
- Implementing unsloth solutions
- Debugging unsloth code
- Learning unsloth 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/:
- llms-txt.md - Llms-Txt 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
Sponsors
Details
- Repository
- github.com/orchestra-research/ai-research-skills
- Categories
- AI & Agent Building, Data Science & ML
- First seen
- Jun 3, 2026
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