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unsloth

Optimize large language model fine-tuning with LoRA and QLoRA using Unsloth.

228k|44.7k|Updated Jul 22, 2025
Search Tags:#optimization#llm#fine-tuning#lora#qlora#unsloth
One-click install
npx skills add https://github.com/NousResearch/hermes-agent --skill unsloth-nousresearch
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: unsloth
Source: https://github.com/NousResearch/hermes-agent/tree/main/skills/mlops/unsloth
Command: npx skills add https://github.com/NousResearch/hermes-agent --skill unsloth-nousresearch

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires unsloth, torch, transformers, trl, datasets, peft, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides expert guidance and tools for significantly faster and more memory-efficient fine-tuning of large language models (LLMs) using the Unsloth library.

Core Features & Use Cases

  • Fast Training: Achieve 2-5x faster training speeds for LLMs.
  • Memory Efficiency: Reduce memory usage by 50-80%.
  • Optimization: Supports LoRA and QLoRA for efficient fine-tuning.
  • Model Support: Works with popular models like Llama, Mistral, Gemma, and Qwen.
  • Use Case: Fine-tune a large language model on your specific dataset to create a specialized AI assistant, achieving this with significantly less VRAM and time than traditional methods.

Quick Start

Use the unsloth skill to get started with fast fine-tuning for Llama models.

Frequently Asked Questions about unsloth

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

FAQPage Schema
How do I speed up LLM fine-tuning and reduce memory usage?▼

You can accelerate LLM fine-tuning by using the Unsloth library, which achieves 2-5x faster training speeds and reduces memory consumption by 50-80%. It optimizes the process for models like Llama, Mistral, and Gemma.

Can I use QLoRA for fine-tuning Llama and Mistral models?▼

Yes, you can use QLoRA and LoRA optimization techniques to fine-tune Llama and Mistral models. This approach significantly lowers VRAM requirements compared to traditional methods while maintaining training efficiency.

What is the best way to fine-tune a large language model on a custom dataset?▼

The best way to fine-tune a large language model on a custom dataset is using Unsloth with LoRA or QLoRA. This creates a specialized AI assistant while drastically cutting down the required VRAM and training time.

Does Unsloth work with PyTorch and Hugging Face transformers?▼

Yes, Unsloth works directly with PyTorch and Hugging Face transformers, alongside dependencies like TRL, datasets, and PEFT. This integration ensures compatibility within standard LLM training workflows.

Why does my LLM fine-tuning run out of VRAM so quickly?▼

Standard LLM fine-tuning runs out of VRAM quickly due to full parameter updates. Using Unsloth with QLoRA reduces memory usage by 50-80%, allowing you to fine-tune large models on significantly smaller hardware.

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