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unsloth

Guide LoRA and QLoRA fine-tuning of LLMs with Unsloth.

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Unsloth consolidates complex LLM fine-tuning and reinforcement learning workflows into actionable guidance that reduces training time and VRAM usage while making quantization, checkpointing, and model export straightforward.

Core Features & Use Cases

  • Efficient fine-tuning: practical guidance for LoRA and QLoRA (4-bit/16-bit) to achieve significantly faster training and lower memory consumption.
  • Broad model and export support: instructions for Llama, Mistral, Gemma, Qwen and gpt-oss models and exporting to GGUF, Ollama, vLLM or other inference engines.
  • Training workflows and tooling: multi-GPU tips, Docker/Colab notebooks, RL with GRPO/GSPO, dataset preparation, and VRAM benchmarking for different model sizes.
  • Use case: fine-tune a 7B conversational model with QLoRA on domain-specific data to produce a low-VRAM deployable assistant.

Quick Start

Fine-tune a Llama-3 8B model with QLoRA using Unsloth and export the resulting LoRA weights for Ollama deployment.

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 Llama 3 8B model with QLoRA to reduce VRAM usage?▼

You can fine-tune a Llama 3 8B model with QLoRA using Unsloth to achieve faster training and lower memory consumption. It provides optimized workflows for 4-bit quantization, enabling low-VRAM deployment of conversational assistants.

Can I export fine-tuned LoRA weights to GGUF or Ollama for local inference?▼

Yes, you can export fine-tuned LoRA weights to GGUF or Ollama for local inference. The workflow supports exporting models to inference engines like GGUF, Ollama, and vLLM after training.

Does Unsloth support reinforcement learning with GRPO and GSPO?▼

Yes, Unsloth supports reinforcement learning workflows with GRPO and GSPO. It consolidates complex RL training into actionable guidance, running efficiently on Docker, Colab, and local GPU setups.

What is the best way to run memory-efficient fine-tuning on Mistral and Gemma models?▼

The best way to run memory-efficient fine-tuning on Mistral and Gemma models is using Unsloth's LoRA and QLoRA workflows. It provides practical guidance for 4-bit and 16-bit training, significantly reducing training time and VRAM usage.

How do I plan VRAM requirements and benchmark GPU setups for 7B model fine-tuning?▼

You can plan VRAM requirements and benchmark GPU setups for 7B model fine-tuning using Unsloth's built-in VRAM benchmarking tools. It provides multi-GPU tips and quantization options to manage memory constraints.

Are there Docker or Colab notebooks available for Qwen model fine-tuning?▼

Yes, there are Docker and Colab notebooks available for Qwen model fine-tuning. Unsloth provides training workflows and tooling specifically designed for these environments, simplifying dataset preparation and checkpointing.

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