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unsloth

Streamline Unsloth fine-tuning and reinforcement learning workflows for large language models.

1|Updated Apr 10, 2026
Search Tags:#fine-tuning#reinforcement learning#lora#qlora#gguf#vram#unsloth
One-click install
npx skills add https://github.com/zerwiz/wayofpi --skill unsloth-zerwiz
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: unsloth
Source: https://github.com/zerwiz/wayofpi/tree/main/.hermes/hermes-agent/skills/mlops/training/unsloth
Command: npx skills add https://github.com/zerwiz/wayofpi --skill unsloth-zerwiz

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Unsloth removes the complexity and cost of training and refining large language models by making fine-tuning, reinforcement learning, and model export faster, more memory-efficient, and easier to run on modest hardware.

Core Features & Use Cases

  • Fast Fine-Tuning: Optimize LLM training with LoRA, QLoRA, and other memory-saving methods for efficient local or cloud-based runs.
  • Reinforcement Learning: Configure GRPO, GSPO, DPO, ORPO, and KTO workflows for reasoning and alignment tasks.
  • Model Preparation and Deployment: Prepare datasets, estimate VRAM needs, and save models for GGUF, Ollama, vLLM, or other inference targets.
  • Use Case: A machine learning engineer can use this Skill to choose the right training method, prepare data correctly, and convert a tuned model into a deployable format without guesswork.

Quick Start

Ask for a complete Unsloth fine-tuning plan for my model, dataset, GPU memory limits, and desired deployment target.

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 with low VRAM?▼

To fine-tune large language models with low VRAM, apply memory-saving methods like LoRA and QLoRA. These approaches optimize LLM training by reducing memory overhead, enabling efficient local or cloud-based runs on modest hardware.

What's the best way to configure reinforcement learning workflows for LLM alignment?▼

The best way to configure reinforcement learning for LLM alignment is using methods like GRPO, DPO, ORPO, and KTO. These workflows streamline reasoning and alignment tasks, providing structured guidance for model training.

How do I convert a fine-tuned model to GGUF format for Ollama or vLLM?▼

To convert a fine-tuned model to GGUF format for Ollama or vLLM, use model export and deployment planning tools. This saves your tuned model into deployable inference targets, removing guesswork from the final format conversion.

Can I run LLM fine-tuning on a local GPU or Colab notebook?▼

Yes, you can run LLM fine-tuning on a local GPU or Colab notebook. Streamlined workflows support local training and Colab environments, applying memory-saving techniques to ensure models train efficiently within your specific hardware limits.

How do I estimate VRAM requirements for training large language models?▼

To estimate VRAM requirements for training large language models, use dedicated VRAM estimation tools. This calculates the exact memory needed for your specific model and training method, ensuring you stay within GPU memory limits during fine-tuning or reinforcement learning.

Does this approach support multimodal and text-to-speech model training?▼

Yes, this approach supports multimodal and text-to-speech model training. Fine-tuning workflows apply directly to these diverse model types, allowing you to prepare datasets and optimize training across various modalities.

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