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Install this skill

Copy the command and run it in your terminal. You can review the source before installing.

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git clone https://github.com/Orchestra-Research/AI-Research-SKILLs

Works with Git. The repository opens in your current directory.

⚡

AI EngineeringTeX

Unsloth

by Orchestra-Research

Expert guidance for fast fine-tuning with Unsloth, achieving 2-5x faster training and 50-80% less memory using LoRA/QLoRA optimization.

11.2K stars821 forksAdded 2026/07/19

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aiai-researchclaudeclaude-codeclaude-skillscodexgeminigpt-5grpohuggingfacemachine-leanringmegatronskillsvllm

Documentation

README

From the original repository

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:

Re-run the scraper with the same configuration
The skill will be rebuilt with the latest information

More from Orchestra-Research

Other Claude Code skills by this author in the directory.

LitGPT LLM Implementation

Implements and trains LLMs using LitGPT with 20+ architectures like Llama, Gemma, Phi. Use for clean model implementations, understanding architectures, or production fine-tuning with LoRA/QLoRA. Single-file, no abstraction layers.

Mamba Architecture

Implements Mamba state-space models achieving O(n) complexity for sequence modeling. Enables 5× faster inference, million-token sequences, and no KV cache. Ideal for streaming and long-context applications.

nanoGPT

A minimalist, educational GPT implementation in ~300 lines of code. Ideal for learning transformer architectures from scratch; supports training on Shakespeare (CPU) or reproducing GPT-2 on OpenWebText (multi-GPU).

RWKV Architecture

A skill for using RWKV, the RNN-Transformer hybrid model that offers linear-time inference, infinite context, and constant memory. Covers installation, text generation, long context processing, fine-tuning, and comparison with Transformers.

TorchTitan LLM Pretraining

Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing.

HuggingFace Tokenizers

Rust-based tokenization library for NLP that processes 1GB of text in under 20 seconds. Supports BPE, WordPiece, and Unigram algorithms, custom vocabulary training, and seamless transformers integration for research and production.

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Unsloth is one of 220 AI Engineering skills cataloged on DirSkills.

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