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unsloth

Fine-tune large language models with LoRA/QLoRA on local GPUs.

13|3|Updated May 12, 2026
Search Tags:#fine-tuning#lora#memory-efficient#qlora#rl#gguf#unsloth
One-click install
npx skills add https://github.com/kevinnft/ai-agent-skills --skill unsloth-kevinnft
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: unsloth
Source: https://github.com/kevinnft/ai-agent-skills/tree/main/skills/mlops/training/unsloth
Command: npx skills add https://github.com/kevinnft/ai-agent-skills --skill unsloth-kevinnft

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Unsloth enables fast, memory-efficient fine-tuning of large language models on local hardware, reducing VRAM requirements and training time.

Core Features & Use Cases

  • Memory-efficient fine-tuning: supports LoRA/QLoRA with 4-bit/16-bit precision to cut memory usage.
  • Broad model support: works with Llama, Gemma, Qwen, Mistral, and more, across Linux, Windows, Docker.
  • RL & advanced training: includes reinforcement learning workflows (GRPO) and multi-GPU training with Unsloth.
  • End-to-end tooling: pip install, Docker image, notebooks, and docs to run locally without cloud infra.

Quick Start

Install locally with pip and start your first fine-tuning workflow using Unsloth according to the docs.

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 locally without running out of VRAM?▼

You can fine-tune large language models locally with minimal VRAM by using LoRA or QLoRA with 4-bit and 16-bit quantization techniques. This approach significantly cuts memory usage while enabling faster training on standard GPUs.

Does Unsloth support reinforcement learning workflows like GRPO for fine-tuning?▼

Yes, Unsloth supports reinforcement learning workflows including GRPO. It enables advanced training techniques and multi-GPU training to optimize large language models efficiently on compatible hardware.

Can I use this memory-efficient fine-tuning method with Llama, Mistral, and Qwen models?▼

Yes, this memory-efficient fine-tuning method works with Llama, Gemma, Qwen, and Mistral models. It provides broad model support across Linux, Windows, and Docker environments for flexible local deployment.

What Python dependencies do I need to install for local LoRA and QLoRA model training?▼

You need a Python-compatible environment with torch, transformers, trl, datasets, and peft installed. These dependencies enable LoRA/QLoRA fine-tuning and reinforcement learning workflows on your local hardware.

What is the best way to set up a local environment for 4-bit quantized LLM training?▼

The best way to set up 4-bit quantized LLM training is using the provided Docker image or pip install method. Unsloth includes end-to-end tooling with notebooks and docs to run locally without cloud infrastructure.

Why does standard full fine-tuning require so much memory compared to QLoRA?▼

Standard full fine-tuning requires more memory than QLoRA because it updates all model weights in high precision. QLoRA uses 4-bit quantization and low-rank adapters to drastically reduce VRAM requirements and training time.

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