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unsloth

Automates LoRA/QLoRA fine-tuning workflows for LLMs on Linux/macOS using the Unsloth stack.

1|Updated May 16, 2026
Search Tags:#fine-tuning#lora#qlora#unsloth#llm debugging#gpu training#vrAM optimization
One-click install
npx skills add https://github.com/devMoez/titan --skill unsloth-devmoez
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: unsloth
Source: https://github.com/devMoez/titan/tree/main/optional-skills/mlops/training/unsloth
Command: npx skills add https://github.com/devMoez/titan --skill unsloth-devmoez

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Unsloth fine-tuning helps you train and adapt LLMs efficiently when you want faster iteration and lower GPU/VRAM requirements without giving up practical quality.

Core Features & Use Cases

  • Fast LoRA/QLoRA Fine-Tuning: Enables 2–5x faster parameter-efficient fine-tuning with reduced VRAM usage, suitable for iterative experimentation.
  • Training and Debugging Support: Covers common workflows including dataset prep, fine-tuning configurations, and troubleshooting when training fails or underperforms.
  • Model Export/Integration Guidance: Helps you move from training to runnable artifacts by documenting common setup and inference integration steps.
  • Use Case: Fine-tune a Llama/Mistral/Gemma/Qwen style model for a domain-specific assistant using LoRA or QLoRA, then adjust hyperparameters based on observed training behavior.

Quick Start

Ask your Titan agent to guide you through setting up a LoRA or QLoRA fine-tuning run with Unsloth for your target model and 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 an LLM with low VRAM using LoRA or QLoRA?▼

You can fine-tune an LLM with low VRAM using Unsloth by applying parameter-efficient LoRA or QLoRA techniques, which enable 2–5x faster training iterations while significantly reducing GPU memory requirements for domain-specific model adaptation.

What's the best way to debug LLM training failures during fine-tuning?▼

To debug LLM training failures during fine-tuning, this process provides troubleshooting guidance for underperforming models, covering dataset preparation steps, hyperparameter adjustments, and configuration best practices for Linux and macOS environments.

Does Unsloth work with Llama, Mistral, and Gemma models for QLoRA training?▼

Yes, Unsloth supports QLoRA training for Llama, Mistral, Gemma, and Qwen style models, allowing you to adapt these architectures into domain-specific assistants using faster parameter-efficient workflows.

How do I export a fine-tuned model for inference after training?▼

You can export a fine-tuned model for inference by following integration guidance that documents common setup steps, helping you transition from training workflows to runnable artifacts for deployment.

Why does my GPU run out of memory when fine-tuning large language models?▼

Your GPU runs out of memory during LLM fine-tuning because standard training loads all parameters; using Unsloth's VRAM optimization with QLoRA reduces memory overhead, enabling efficient experimentation on constrained hardware.

Do I need specific Python dependencies to start fine-tuning with Unsloth?▼

Yes, you need specific Python dependencies including torch, transformers, trl, datasets, and peft to start fine-tuning with Unsloth, which leverages these compatible tools to execute efficient training workflows.

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