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unsloth

Fine-tune LLMs with memory-efficient LoRA/QLoRA using Unsloth.

78|16|Updated Apr 23, 2026
Search Tags:#docker#gpu#lora#qlora#gguf#unsloth#llm-fine-tuning
One-click install
npx skills add https://github.com/sheawinkler/hermes-agent-ultra --skill unsloth-sheawinkler
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: unsloth
Source: https://github.com/sheawinkler/hermes-agent-ultra/tree/main/optional-skills/mlops/training/unsloth
Command: npx skills add https://github.com/sheawinkler/hermes-agent-ultra --skill unsloth-sheawinkler

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Unsloth enables fast, memory-efficient fine-tuning of large language models using LoRA and QLoRA, dramatically reducing VRAM requirements and training time.

Core Features & Use Cases

  • Supports full-finetuning, 4-bit and 16-bit training, with RAM/VRAM optimization.
  • Works with a wide range of models and tooling (Ollama, vLLM, Docker, etc.) to enable local or gateway-based workflows.
  • Use cases include development of customized assistants, RL-based policy training, and on-device fine-tuning for offline scenarios.

Quick Start

Install Unsloth and start a basic fine-tuning workflow on a supported GPU.

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 locally with low VRAM using LoRA?▼

Unsloth enables fast LLM fine-tuning with memory-efficient LoRA and QLoRA, dramatically reducing VRAM requirements and training time across models like Llama, Gemma, and Qwen.

Can I use Unsloth for multi-GPU training and RL-based policy training on macOS?▼

Yes, Unsloth supports multi-GPU workflows and RL-based policy training across Linux and macOS, allowing researchers and engineers to execute local fine-tuning for customized assistants and offline scenarios.

Do I need Docker and a GPU to run Unsloth for LLM fine-tuning?▼

A Python environment with dependencies including unsloth, torch, transformers, trl, datasets, and peft is required for GPU-accelerated execution, while Docker is optionally supported for containerized runs.

Does Unsloth support 4-bit and 16-bit training for models like Llama and Qwen?▼

Yes, Unsloth supports full-finetuning, 4-bit, and 16-bit training with RAM and VRAM optimization, working with a wide range of models including Llama, Gemma, and Qwen for local or gateway-based workflows.

What's the best way to export a fine-tuned model for use with Ollama or vLLM?▼

Unsloth works with a wide range of tooling including Ollama and vLLM to enable local or gateway-based workflows, allowing you to deploy customized assistants from your fine-tuned models.

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