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 -->
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.
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.
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.
Applies cognitive science frameworks for creative thinking to CS and AI research ideation. Use when seeking genuinely novel research directions by leveraging combinatorial creativity, analogical reasoning, constraint manipulation, and other empirically grounded creative strategies.
Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.
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.
Write publication-ready ML/AI/Systems papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM, OSDI, NSDI, ASPLOS, SOSP. Use when drafting papers from research repos, structuring arguments, verifying citations, or preparing camera-ready submissions. Includes LaTeX templates, reviewer guidelines, and citation verification workflows.
Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation. Use when training or fine-tuning neural networks, debugging loss spikes or OOM, choosing architectures, or optimizing GPU throughput.