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unsloth

Accelerate LoRA and QLoRA fine-tuning of Llama, Mistral, Gemma, and Qwen models.

Updated Sep 1, 2021
Search Tags:#llm#fine-tuning#lora#memory-efficient#qlora#unsloth
One-click install
npx skills add https://github.com/unclehowell/unclehowell --skill unsloth-unclehowell
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: unsloth
Source: https://github.com/unclehowell/unclehowell/tree/main/skills/mlops/training/unsloth
Command: npx skills add https://github.com/unclehowell/unclehowell --skill unsloth-unclehowell

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Fine‑tuning large language models often consumes excessive time and GPU memory, making experimentation costly and slow.

Core Features & Use Cases

  • 2‑5× Faster Training: Speeds up fine‑tuning cycles dramatically.
  • 50‑80% Less VRAM: Cuts memory requirements, enabling work on modest hardware.
  • LoRA & QLoRA Support: Seamlessly applies low‑rank adaptation techniques.
  • Broad Model Compatibility: Works with Llama, Mistral, Gemma, Qwen and other popular models.
  • Use Case Example: Quickly adapt a Llama‑3 model to a custom domain dataset using LoRA, achieving high accuracy with reduced compute.

Quick Start

Use the unsloth skill to fine‑tune your LLM on the dataset “my_data” with LoRA in under an hour.

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 VRAM consumption?▼

Unsloth accelerates LLM fine-tuning to deliver 2-5x faster training cycles and reduces VRAM consumption by 50-80%, enabling efficient custom dataset adaptation on modest hardware.

Can I use LoRA and QLoRA workflows to fine-tune Llama and Mistral models?▼

Yes, you can fine-tune Llama, Mistral, Gemma, and Qwen models using LoRA and QLoRA workflows, applying low-rank adaptation techniques to achieve high accuracy with reduced compute requirements.

What Python packages do I need to install for memory-efficient LLM fine-tuning?▼

Memory-efficient LLM fine-tuning requires a Python environment with unsloth, torch, transformers, trl, datasets, and peft installed to execute the low-rank adaptation workflows and process custom datasets.

What is the best way to adapt a large language model to a custom domain dataset?▼

The best way to adapt a large language model to a custom domain dataset is using Unsloth for fast, low-memory fine-tuning, enabling you to adapt a Llama-3 model with LoRA in under an hour.

Why does fine-tuning large language models consume excessive time and GPU memory?▼

Fine-tuning large language models consumes excessive time and GPU memory because standard methods lack memory efficiency, making experimentation costly and slow without accelerated low-rank adaptation workflows.

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