unsloth
Unsloth is a framework for optimizing language model fine-tuning that achieves 2-5x faster training speeds and 50-80% memory reduction through LoRA and QLoRA optimizations. Use this skill when implementing efficient fine-tuning workflows, debugging performance issues, or learning best practices for memory-constrained training environments.
git clone --depth 1 https://github.com/OpenRaiser/NanoResearch /tmp/unsloth && cp -r /tmp/unsloth/skills/vendor-ai-research/unsloth ~/.claude/skills/unslothSKILL.md
# 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 ### 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 <!-- Trigger re-upload 1763621536 -->
Generate a Python code skeleton from an experiment blueprint
Search academic literature and generate research hypotheses
Produce an experiment blueprint from a research hypothesis
Draft a LaTeX research paper from all previous stage outputs
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.
Generates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, auto-selects chart type and generates data-driven figures via matplotlib/seaborn. Use when creating any figure for a conference paper.
Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). Interactive config, single launch command. HuggingFace ecosystem standard.
Guides researchers through structured ideation frameworks to discover high-impact research directions. Use when exploring new problem spaces, pivoting between projects, or seeking novel angles on existing work.