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unsloth

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

Updated Apr 10, 2026
Search Tags:#llm#fine-tuning#lora#transformers#memory-efficient#qlora#unsloth
One-click install
npx skills add https://github.com/overviewlabs/WHOX --skill unsloth-overviewlabs
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: unsloth
Source: https://github.com/overviewlabs/WHOX/tree/main/skills/mlops/training/unsloth
Command: npx skills add https://github.com/overviewlabs/WHOX --skill unsloth-overviewlabs

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Fine-tuning large language models is computationally expensive and memory-hungry; this Skill provides expert guidance to optimize and accelerate fine-tuning with Unsloth, enabling faster experiments on affordable hardware.

Core Features & Use Cases

  • Fast, memory-efficient fine-tuning using LoRA/QLoRA with Unsloth
  • Supports major model families (Llama, Gemma, Qwen, Mistral) and diverse datasets
  • Suitable for research, customization, and rapid prototyping of RL/GSPO workflows
  • Guidance for dependency management, tooling integration, and best practices

Quick Start

Install Unsloth and start a memory-efficient fine-tuning session on your dataset.

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 large language model without running out of GPU memory?▼

Unsloth supports major model families including Llama, Gemma, Qwen, and Mistral, allowing you to apply memory-efficient fine-tuning across diverse architectures and datasets for research and production workflows.

What dependencies do I need to install before starting memory-efficient LLM fine-tuning?▼

Unsloth is suitable for rapid prototyping of RL and GSPO workflows, enabling faster experimentation and customization of large language models while keeping computational costs and memory overhead low.

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

Unsloth supports major model families including Llama, Gemma, Qwen, and Mistral, allowing you to apply memory-efficient fine-tuning across diverse architectures and datasets for research and production workflows.

What dependencies do I need to install before starting memory-efficient LLM fine-tuning?▼

You need to install unsloth, torch, transformers, trl, datasets, and peft to enable the memory-efficient fine-tuning workflow, supporting optional scripts and references for automation and guidance.

Is unsloth suitable for rapid prototyping of RL and GSPO workflows?▼

Unsloth is suitable for rapid prototyping of RL and GSPO workflows, enabling faster experimentation and customization of large language models while keeping computational costs and memory overhead low.

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