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home / skills / davila7 / claude-code-templates / fine-tuning-unsloth

fine-tuning-unsloth skill

/cli-tool/components/skills/ai-research/fine-tuning-unsloth

This skill guides fast fine-tuning with Unsloth, enabling 2-5x faster training and 50-80% memory savings through LoRA/QLoRA optimization.

This is most likely a fork of the unsloth skill from orchestra-research
npx playbooks add skill davila7/claude-code-templates --skill fine-tuning-unsloth

Review the files below or copy the command above to add this skill to your agents.

Files (5)
SKILL.md
2.3 KB
---
name: unsloth
description: Expert guidance for fast fine-tuning with Unsloth - 2-5x faster training, 50-80% less memory, LoRA/QLoRA optimization
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [Fine-Tuning, Unsloth, Fast Training, LoRA, QLoRA, Memory-Efficient, Optimization, Llama, Mistral, Gemma, Qwen]
dependencies: [unsloth, torch, transformers, trl, datasets, peft]
---

# 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 -->

Overview

This skill provides expert guidance for fast fine-tuning with Unsloth. It focuses on 2–5x faster training, 50–80% memory reduction, and LoRA/QLoRA optimization strategies. The content targets developers using Unsloth to tune models efficiently and monitor training with a CLI.

How this skill works

I explain how Unsloth configures and optimizes fine-tuning pipelines, including memory-saving techniques and mixed-precision workflows. The skill walks through LoRA and QLoRA integration, CLI setup for Claude Code monitoring, and performance trade-offs to help you pick the right options. Practical commands and configuration patterns are highlighted for quick application.

When to use it

  • When you need faster fine-tuning turnaround on limited hardware
  • When memory consumption is a bottleneck during training
  • When adopting LoRA or QLoRA for parameter-efficient tuning
  • When integrating Unsloth into Claude Code monitoring or automation
  • When debugging training speed, convergence, or memory issues

Best practices

  • Profile baseline training to measure speed and memory before changes
  • Start with conservative LoRA ranks and increase only if performance lags
  • Use mixed precision and gradient checkpointing when memory is tight
  • Monitor training with the CLI and capture logs for reproducibility
  • Validate on a small held-out set after each config change to detect regressions

Example use cases

  • Speed up fine-tuning of a 7B model on a single GPU using LoRA and mixed precision
  • Reduce memory footprint by 50–80% to enable larger batch sizes or sequence lengths
  • Configure automated monitoring for Claude Code training runs with the Unsloth CLI
  • Iteratively tune LoRA rank and learning rate to balance speed and final accuracy
  • Replace a full-model fine-tune with QLoRA for low-cost experimentation

FAQ

Does Unsloth require code changes to my model?

No—Unsloth focuses on training wrappers and configuration. You typically adjust training arguments and attach LoRA/QLoRA modules without rewriting model code.

Will using LoRA/QLoRA harm final model quality?

Not if configured properly. LoRA and QLoRA achieve parameter efficiency with minimal quality loss when you tune rank, learning rate, and fine-tuning duration.

How much memory savings can I expect?

Typical gains are 50–80% depending on model size, precision, and whether you use gradient checkpointing or QLoRA quantization.