Unsloth Skill: 2-5x Faster LoRA/QLoRA Fine-Tuning with Less VRAM

The Unsloth skill for Hermes Agent gives you a direct interface to the Unsloth library, which accelerates LoRA and QLoRA fine-tuning by 2-5x while reducing VRAM usage. You would reach for this when you need to fine-tune a large language model (Llama, Mistral, Gemma, Qwen) on consumer hardware or a single GPU, and you want to cut training time and memory footprint without changing your model architecture or training loop.

What it does

This skill wraps Unsloth's optimizations into a set of commands you can run inside Hermes Agent. It handles the common fine-tuning workflow: loading a model in 4-bit or 16-bit, applying LoRA adapters, running training, and saving the result. The speed and memory gains come from custom CUDA kernels and memory-efficient attention, all managed transparently.

Before you start

• Platform: Linux or macOS. Windows is not listed as supported.
• Skill path: optional-skills/mlops/training/unsloth
• Installation: This is an optional skill. Install it on demand through Hermes Agent's skill management system.
• Prerequisites: A compatible GPU with sufficient VRAM for your chosen model size. The skill does not install CUDA or PyTorch for you; those must already be present in your Hermes Agent environment.
• Reference files: After installation, the skill places documentation in references/. You can read these files with the view command to get detailed explanations, code examples, and links to original Unsloth docs.

Working with the skill

For beginners

Start with the getting_started or tutorials reference files. They walk through the foundational concepts: loading a model, configuring LoRA, running a training step, and saving adapters.

For specific features

Use the appropriate category reference file (e.g., api, guides) for detailed information on a particular capability, such as changing quantization levels or adjusting LoRA rank.

For code examples

The quick reference section in the skill contains common patterns extracted from the official Unsloth documentation. These are ready-to-run snippets you can adapt.

Quick Reference

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.

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

When not to use it

This skill is specifically for Unsloth-based fine-tuning. If you are doing full fine-tuning without LoRA/QLoRA, or if you are using a different optimization library (e.g., Axolotl, Lit-GPT), this skill will not apply. The source does not mention any other exclusions.

Limits and gotchas

• The skill is auto-generated from official Unsloth documentation. It does not include every possible Unsloth feature; it covers what the scraper captured.
• Quick reference patterns are initially empty and populate as you use the skill. Do not expect a complete catalog on first install.
• The scripts/ and assets/ directories are empty by design. You populate them with your own automation and templates.

What pairs with this

This skill lives under the MLOps category in Hermes Agent. It complements other training and optimization skills in the same category. For the full list, see the Hermes Agent documentation on optional MLOps skills.