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unsloth

Accelerate LoRA and QLoRA fine-tuning with reduced VRAM usage.

2|Updated May 22, 2026
Search Tags:#fine-tuning#LoRA#VRAM#QLoRA#unsloth
One-click install
npx skills add https://github.com/519lab/thoth-agent --skill unsloth-519lab
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: unsloth
Source: https://github.com/519lab/thoth-agent/tree/main/optional-skills/mlops/training/unsloth
Command: npx skills add https://github.com/519lab/thoth-agent --skill unsloth-519lab

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 accelerates the fine-tuning process for LoRA and QLoRA models, significantly reducing VRAM requirements.

Core Features & Use Cases

  • Faster Fine-Tuning: Achieve 2-5x faster LoRA/QLoRA fine-tuning.
  • Reduced VRAM: Lower VRAM consumption for efficient training.
  • Use Case: Ideal for fine-tuning large models on limited hardware, such as laptops or small servers.

Quick Start

Run the unsloth skill to fine-tune your LoRA/QLoRA model with reduced VRAM usage.

Frequently Asked Questions about unsloth

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

FAQPage Schema
How do I reduce VRAM usage during LoRA fine-tuning?▼

You can reduce VRAM usage during LoRA fine-tuning by using optimized training scripts that lower memory consumption by 2-5x. This enables efficient fine-tuning of large models directly on systems with limited GPU resources.

What is the best way to fine-tune large models on a laptop?▼

The best way to fine-tune large models on a laptop is to apply QLoRA optimization techniques. This approach significantly reduces VRAM requirements, allowing limited hardware to run training processes efficiently.

Can I use Unsloth with standard PyTorch and Transformers dependencies?▼

Yes, you can use Unsloth with standard PyTorch and Transformers dependencies. The workflow integrates directly with these libraries, alongside TRL, datasets, and PEFT, to accelerate your training scripts.

How do I speed up QLoRA fine-tuning for large language models?▼

You can speed up QLoRA fine-tuning for large language models by utilizing specialized scripts designed to accelerate the process. This optimization achieves 2-5x faster training times while simultaneously reducing VRAM overhead.

Does reducing VRAM with QLoRA impact the fine-tuning speed?▼

Reducing VRAM with QLoRA does not negatively impact fine-tuning speed; instead, it accelerates it. The optimization process achieves 2-5x faster training times while operating within lower memory constraints.

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