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unsloth

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

Updated Mar 25, 2026
Search Tags:#llm#fine-tuning#training#quantization#lora#qlora#unsloth
One-click install
npx skills add https://github.com/afel6/scal-ai-pipeline --skill unsloth-afel6
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: unsloth
Source: https://github.com/afel6/scal-ai-pipeline/tree/main/hermes_skills_library/mlops/training/unsloth
Command: npx skills add https://github.com/afel6/scal-ai-pipeline --skill unsloth-afel6

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Unsloth provides practical, battle-tested guidance to speed up and reduce the memory footprint of large language model fine-tuning, making training feasible on limited hardware and accelerating iteration cycles for ML engineers.

Core Features & Use Cases

  • High-performance fine-tuning: Patterns and best practices for QLoRA (4-bit) and LoRA (16-bit) setups to achieve 2–5x faster training with dramatically reduced VRAM usage.
  • Broad model support: Instructions and examples for Llama, Mistral, Gemma, Qwen, gpt-oss and other modern LLMs, including conversion and GGUF export workflows.
  • Production and research workflows: Guides for local, Colab, Docker, multi-GPU, and AMD/NVIDIA environments; includes dataset preparation, checkpointing, evaluation, and saving for inference engines (Ollama, vLLM, llama.cpp).
  • Reference-backed examples: Extensive documentation files for step-by-step tutorials, notebooks, and troubleshooting tips for advanced RL, TTS, and vision fine-tuning scenarios.

Quick Start

Fine-tune a Llama-3 8B model with Unsloth using QLoRA (dynamic 4-bit) and LoRA adapters to minimize VRAM while retaining accuracy.

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?▼

Fine-tune a Llama-3 8B model with QLoRA by applying dynamic 4-bit quantization and LoRA adapters, which minimizes VRAM usage while retaining accuracy and achieving faster training speeds.

What is the difference between 4-bit QLoRA and 16-bit LoRA for large language model training?▼

4-bit QLoRA uses dynamic quantization to dramatically reduce VRAM consumption for memory-constrained environments, whereas 16-bit LoRA retains higher precision while still providing faster, memory-efficient fine-tuning.

Can I fine-tune models like Mistral, Gemma, and Qwen on limited hardware?▼

Yes, you can fine-tune Mistral, Gemma, and Qwen models on limited hardware using Unsloth's memory-efficient optimization patterns, which support local, Colab, Docker, and multi-GPU workflows.

How do I export a fine-tuned model to GGUF for inference in Ollama or vLLM?▼

Export a fine-tuned model to GGUF for inference engines like Ollama, vLLM, and llama.cpp by following the provided conversion workflows, which include dataset preparation, checkpointing, and evaluation.

Does Unsloth work with PyTorch, Transformers, and PEFT-compatible tooling?▼

Yes, Unsloth integrates with PyTorch, Transformers, datasets, and PEFT-compatible tooling to provide high-performance fine-tuning across AMD and NVIDIA environments for modern LLMs.

What are the limitations when running reinforcement learning or vision fine-tuning workflows?▼

Advanced reinforcement learning, TTS, and vision fine-tuning scenarios require specific hardware configurations and may face VRAM constraints, though reference-backed examples and troubleshooting tips are provided to mitigate these limitations.

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