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unsloth

Fine-tune large language models with optimized LoRA and QLoRA workflows.

2|Updated Apr 12, 2026
Search Tags:#llm#fine-tuning#quantization#lora#memory-efficient#qlora#unsloth
One-click install
npx skills add https://github.com/Clay-HHK/claude-config --skill unsloth-clay-hhk
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: unsloth
Source: https://github.com/Clay-HHK/claude-config/tree/main/skills/AI-research-SKILLs/03-fine-tuning/unsloth
Command: npx skills add https://github.com/Clay-HHK/claude-config --skill unsloth-clay-hhk

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Unsloth provides expert guidance and reference material to accelerate and reduce the memory cost of fine-tuning large language models, making advanced training techniques accessible and reproducible for practitioners.

Core Features & Use Cases

  • Speed and Efficiency: Guidance for 2–5x faster training and 50–80% memory reduction using Unsloth's optimizations and dynamic quantization strategies.
  • LoRA & QLoRA Workflows: Instructions and best practices for applying LoRA and QLoRA, hot-swapping adapters, and balancing precision vs memory.
  • Model & Deployment Support: Tutorials for Llama, Mistral, Gemma, Qwen and converting/saving models to GGUF, running on llama.cpp, Ollama, and preparing models for vLLM or production serving.
  • Advanced Training Scenarios: Multi-GPU training, checkpointing, TTS fine-tuning, reinforcement learning guides, and quantization-aware training examples.

Quick Start

Use the unsloth skill to get step-by-step instructions for fine-tuning a Llama model with LoRA and memory-efficient quantization.

Frequently Asked Questions about unsloth

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I fine-tune a Llama model with memory-efficient quantization?▼

To fine-tune a Llama model with memory-efficient quantization, apply QLoRA workflows using Unsloth to achieve 50–80% memory reduction. This streamlines dynamic quantization strategies and enables 2–5x faster training speeds.

Does Unsloth support multi-GPU training and checkpointing for large models?▼

Yes, Unsloth supports multi-GPU training and checkpointing for large models like Mistral and Gemma. It provides advanced scenarios for reinforcement learning and quantization-aware training across distributed environments.

How do I export fine-tuned models to GGUF for llama.cpp or Ollama?▼

Export fine-tuned models to GGUF format for llama.cpp or Ollama by following Unsloth's model conversion tutorials. This prepares your trained LLM for local inference and production serving with vLLM.

What is the best way to balance precision vs memory when applying LoRA?▼

The best way to balance precision vs memory when applying LoRA is using Unsloth's dynamic quantization strategies. It provides best practices for hot-swapping adapters and optimizing QLoRA workflows.

Can I use Unsloth to fine-tune models with torch and transformers?▼

Yes, you can use Unsloth to fine-tune models with torch and transformers. It integrates seamlessly with trl, datasets, and peft to streamline memory-efficient training and inference optimization.

Why does fine-tuning large language models require so much GPU memory?▼

Fine-tuning large language models requires massive GPU memory due to full parameter updates, but Unsloth solves this by using QLoRA. This reduces memory usage by 50–80% while accelerating training speed.

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