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unsloth

Create Unsloth fine-tuning guidance for local and cloud LLM training.

Updated May 14, 2026
Search Tags:#lora#reinforcement-learning#finetuning#qlora#model-deployment#vram#unsloth
One-click install
npx skills add https://github.com/SethyPagna/Secretary-Jarvis --skill unsloth-sethypagna
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: unsloth
Source: https://github.com/SethyPagna/Secretary-Jarvis/tree/main/src/capabilities/optional-skills/mlops/training/unsloth
Command: npx skills add https://github.com/SethyPagna/Secretary-Jarvis --skill unsloth-sethypagna

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Unsloth reduces the time, memory, and setup overhead involved in fine-tuning modern AI models, especially when GPU VRAM is limited or model workflows are otherwise complex.

Core Features & Use Cases

  • Fast, memory-efficient training: Use LoRA, QLoRA, continued pretraining, and reinforcement learning with significantly lower VRAM requirements.
  • Broad model coverage: Work with text, vision, and text-to-speech models, then save or export them for inference in tools like GGUF, Ollama, or vLLM.
  • Practical training workflows: Prepare datasets, choose model and hyperparameters, follow notebook-based examples, and troubleshoot hardware or quantization constraints.

Quick Start

Ask for a step-by-step Unsloth fine-tuning plan for your model, dataset, GPU memory, and target deployment format.

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 QLoRA?▼

Lower VRAM requirements for LLM fine-tuning by using Unsloth's QLoRA and LoRA implementations. It provides memory-aware configurations, chat templates, and dataset preparation steps to enable efficient large language model training on constrained hardware.

What is the best way to prepare datasets and chat templates for large language model training?▼

Prepare datasets and chat templates for large language model training by following a structured fine-tuning plan. This includes accurate setup, VRAM-aware configuration, and dataset formatting to ensure reproducible training results across supported models.

Can I use reinforcement learning to fine-tune vision and text-to-speech models?▼

Yes, you can use reinforcement learning to fine-tune vision and text-to-speech models. Unsloth supports broad model coverage including text, vision, and text-to-speech, allowing you to train diverse models with significantly lower memory requirements.

Does Unsloth support exporting fine-tuned models for inference in GGUF, Ollama, or vLLM?▼

Yes, Unsloth supports exporting fine-tuned models for inference in GGUF, Ollama, or vLLM. After training text, vision, or text-to-speech models, you can save or export them directly to these target deployment formats for inference.

Why does my GPU run out of memory during continued pretraining and how can I optimize it?▼

Your GPU runs out of memory during continued pretraining due to high VRAM consumption, which you can optimize by using Unsloth's memory-efficient training configurations. It offers VRAM-aware setups and quantization to troubleshoot hardware constraints.

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