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unsloth

Fine-tune large language models with Unsloth using LoRA and QLoRA optimization.

247|22|Updated Dec 11, 2024
Search Tags:#llm#fine-tuning#lora#memory-efficient#qlora#unsloth#fast-training
One-click install
npx skills add https://github.com/graniet/kheish --skill unsloth-graniet
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: unsloth
Source: https://github.com/graniet/kheish/tree/main/skills/mlops/training/unsloth
Command: npx skills add https://github.com/graniet/kheish --skill unsloth-graniet

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill eliminates the high computational cost and long training times associated with fine-tuning large language models, making it accessible for developers and ML engineers to customize models for specific tasks without expensive data center hardware or weeks of trial and error.

Core Features & Use Cases

  • 2-5x Faster Training: Leverages Unsloth's optimized kernels to cut fine-tuning time significantly compared to standard frameworks like vanilla PyTorch or Hugging Face Transformers.
  • 50-80% Reduced VRAM Usage: Supports LoRA and QLoRA 4-bit quantization to run fine-tuning workflows on consumer GPUs with limited VRAM, lowering the barrier to entry for individual developers and small teams.
  • Multi-Model & Workflow Support: Provides guidance for fine-tuning popular LLM families including Llama, Mistral, Gemma, Qwen, and DeepSeek, as well as reinforcement learning, vision, and text-to-speech fine-tuning workflows.
  • Real-World Use Case: A developer fine-tuning a 7B Llama model on a custom customer support dataset can use this Skill to complete training in 2 hours on a 6GB VRAM GPU instead of requiring a 24GB data center GPU and 8 hours of runtime.

Quick Start

Use the unsloth skill to fine-tune a 7B Llama model on your custom customer support dataset using QLoRA with 4-bit 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 QLoRA 4-bit quantization on a consumer GPU?▼

You can fine-tune a Llama model with QLoRA 4-bit quantization on a 6GB VRAM consumer GPU by leveraging optimized kernels to cut memory usage by 50-80% and accelerate training 2-5x compared to standard frameworks.

Does Unsloth work with PyTorch, Transformers, and TRL libraries for end-to-end training pipelines?▼

Yes, Unsloth integrates with PyTorch, Transformers, and TRL libraries to provide end-to-end fine-tuning pipelines, supporting reinforcement learning workflows and custom model training for Llama, Mistral, and Gemma families.

What is the best way to reduce VRAM usage during large language model fine-tuning?▼

The best way to reduce VRAM usage during large language model fine-tuning is applying LoRA and QLoRA 4-bit quantization techniques, which lower memory requirements by 50-80% and enable training on consumer GPUs.

Why does fine-tuning a 7B model take so much memory and time with standard frameworks?▼

Standard frameworks lack optimized kernels, causing high computational costs and long training times, whereas using Unsloth accelerates training 2-5x and cuts VRAM usage by 50-80% for 7B models.

Can I use QLoRA optimization for Mistral and Qwen model families?▼

Yes, you can use QLoRA optimization for Mistral and Qwen model families, applying 4-bit quantization to reduce memory usage by 50-80% while maintaining efficient training workflows.

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