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unsloth

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

1|Updated Apr 24, 2026
Search Tags:#reinforcement learning#model optimization#lora#qlora#unsloth#llm fine-tuning#memory-efficient training
One-click install
npx skills add https://github.com/automatedigital/spark --skill unsloth-automatedigital
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: unsloth
Source: https://github.com/automatedigital/spark/tree/main/skills/mlops/training/unsloth
Command: npx skills add https://github.com/automatedigital/spark --skill unsloth-automatedigital

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill eliminates the high computational cost, long training times, and excessive VRAM requirements associated with large language model fine-tuning, making custom model training accessible to users with consumer-grade hardware.

Core Features & Use Cases

  • 2-5x Faster Training: Optimized fine-tuning workflows that cut training time significantly compared to standard methods.
  • 50-80% Less VRAM Usage: LoRA and QLoRA optimization techniques that enable fine-tuning of large models on limited hardware.
  • Multi-Model & Task Support: Works with popular LLM families including Llama, Mistral, Gemma, Qwen, and DeepSeek, plus support for vision, text-to-speech, and reinforcement learning fine-tuning.
  • Use Case: A developer can fine-tune a 7B Llama model on a GPU with only 6GB of VRAM using QLoRA, instead of requiring a high-end server with 19GB+ of VRAM for standard LoRA.

Quick Start

Use the unsloth skill to fine-tune a custom 7B Llama model on your domain-specific dataset using QLoRA to minimize VRAM usage and training time.

Frequently Asked Questions about unsloth

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

FAQPage Schema
Can I use QLoRA for reinforcement learning fine-tuning on Mistral models?▼

Unsloth supports reinforcement learning fine-tuning for popular model families including Mistral. It applies QLoRA optimization to reduce VRAM usage and accelerate training times for domain-specific LLM adaptation.

What is the best way to reduce large language model training time on consumer hardware?▼

Memory-efficient LLM fine-tuning using LoRA and QLoRA optimization reduces training time by 2-5x. This approach cuts VRAM usage by 50-80%, enabling custom model adaptation on consumer-grade hardware instead of high-end servers.

Does memory-efficient fine-tuning work with vision and text-to-speech models?▼

Memory-efficient fine-tuning supports vision and text-to-speech model training alongside standard LLM adaptation. It applies LoRA and QLoRA optimization to popular model families including Llama, Gemma, and Qwen to reduce computational costs.

Do I need high-end servers for domain-specific LLM training?▼

No, domain-specific LLM training does not require high-end servers. QLoRA optimization enables fine-tuning large models on consumer-grade GPUs with limited VRAM, significantly reducing computational costs and hardware entry barriers.

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