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unsloth

Fine-tune large language models with Unsloth LoRA/QLoRA pipelines.

Updated Apr 27, 2026
Search Tags:#fine-tuning#memory-efficient#qlora#rl#llms#loRA#unsloth
One-click install
npx skills add https://github.com/x-TheFox/Corvus --skill unsloth-x-thefox
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: unsloth
Source: https://github.com/x-TheFox/Corvus/tree/main/skills/mlops/training/unsloth
Command: npx skills add https://github.com/x-TheFox/Corvus --skill unsloth-x-thefox

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Unsloth enables fast, memory-efficient fine-tuning of large language models using LoRA/QLoRA pipelines, reducing VRAM usage and training time.

Core Features & Use Cases

  • High-efficiency fine-tuning: LoRA/QLoRA workflows that cut memory and compute.
  • Broad hardware support: Works on NVIDIA, AMD, and Intel GPUs across Linux/Windows, with Docker, Ollama, and vLLM integration.
  • Real-world scenarios: Personal AI assistants, enterprise customization, or research RL experiments.

Quick Start

Install Unsloth and follow the official Quick Start to begin fine-tuning an LLM locally.

Frequently Asked Questions about unsloth

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

FAQPage Schema
How do I fine-tune large language models with low VRAM using LoRA?▼

You can fine-tune large language models with low VRAM using Unsloth's LoRA and QLoRA pipelines, which significantly reduce memory usage and training time. These workflows enable local training across diverse hardware configurations.

What is the best way to run memory-efficient LLM fine-tuning locally?▼

Run memory-efficient LLM fine-tuning locally using Unsloth's pipelines that support NVIDIA, AMD, and Intel GPUs across Linux and Windows. Integration with Docker, Ollama, and vLLM further streamlines local deployment and execution.

Can I use QLoRA for reinforcement learning experiments on local hardware?▼

Yes, you can use QLoRA for reinforcement learning experiments on local hardware. Unsloth supports RL fine-tuning, enabling researchers to train models locally with reduced compute requirements across diverse GPU hardware.

Do I need Docker or vLLM to train models with QLoRA pipelines?▼

You do not strictly need Docker or vLLM to train models with QLoRA pipelines, but they are supported for integration. Unsloth relies on dependencies like torch, transformers, trl, datasets, and peft to coordinate training and optional resources.

Does memory-efficient fine-tuning work with AMD and Intel GPUs?▼

Memory-efficient fine-tuning works with AMD and Intel GPUs, as well as NVIDIA. Unsloth provides broad hardware support across Linux and Windows, enabling flexible local training without restricting users to a single GPU vendor.

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