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unsloth

Fine-tune LLMs with Unsloth using LoRA/QLoRA and 4-bit quantization.

1|1|Updated Apr 26, 2026
Search Tags:#llm#fine-tuning#reinforcement-learning#qlora#loRA#memory-efficiency#unsloth
One-click install
npx skills add https://github.com/BermudaLocals/hermes-agent-lite --skill unsloth-bermudalocals
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: unsloth
Source: https://github.com/BermudaLocals/hermes-agent-lite/tree/main/skills/mlops/training/unsloth
Command: npx skills add https://github.com/BermudaLocals/hermes-agent-lite --skill unsloth-bermudalocals

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Unsloth provides guidance and tooling to fine-tune large language models (LLMs) with a focus on memory efficiency and scalable reinforcement learning workflows, enabling developers to train and adapt models locally or in cloud-like environments without excessive VRAM consumption.

Core Features & Use Cases

  • Memory-efficient fine-tuning with LoRA/QLoRA and 4-bit quantization to reduce VRAM and compute.
  • RL and GSPO/GRPO workflows for improved instruction-following and reasoning performance.
  • Cross-environment tooling: Linux/Windows, Docker, Google Colab, Kaggle, Ollama, llama.cpp, and vLLM.
  • Broad compatibility with PyTorch, Transformers, datasets, and PEFT for flexible model customization.

Quick Start

Install Unsloth, then start a fine-tuning workflow for your model using your 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 QLoRA?▼

Fine-tune an LLM with low VRAM using QLoRA by applying memory-efficient 4-bit quantization and LoRA adapters to significantly reduce compute and memory consumption during training.

What is the best way to run reinforcement learning workflows for LLMs?▼

Running reinforcement learning workflows for LLMs is best achieved using GSPO and GRPO workflows to improve instruction-following and reasoning performance while maintaining memory efficiency.

Can I fine-tune models in Google Colab or Kaggle without excessive compute?▼

You can fine-tune models in Google Colab or Kaggle without excessive compute because dynamic quantization and memory-efficient techniques enable training across Linux, Windows, Docker, and cloud environments.

Do I need Python 3.13 and PyTorch to start a fine-tuning workflow?▼

Yes, you need Python 3.13+ and dependencies including PyTorch, Transformers, datasets, and PEFT to start a fine-tuning workflow and achieve flexible model customization.

Does fine-tuning with Unsloth work with Ollama, llama.cpp, and vLLM for text and vision tasks?▼

Fine-tuning with Unsloth works with Ollama, llama.cpp, and vLLM to support text, vision, and reinforcement learning tasks across diverse cross-environment deployments.

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