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unsloth

Coordinate end-to-end LLM fine-tuning workflows using Unsloth.

97|8|Updated Mar 20, 2026
Search Tags:#lora#memory-efficient#qlora#rl#unsloth#llm-fine-tuning
One-click install
npx skills add https://github.com/peteromallet/megaplan --skill unsloth-peteromallet
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: unsloth
Source: https://github.com/peteromallet/megaplan/tree/main/megaplan/agent/skills/mlops/training/unsloth
Command: npx skills add https://github.com/peteromallet/megaplan --skill unsloth-peteromallet

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires unsloth, torch, transformers, trl, datasets, peft, and includes references (resource) components.

What problem does it solve?

Unsloth provides expert guidance for efficient fine-tuning of LLMs, delivering 2-5x faster training and 50-80% memory savings through LoRA/QLoRA optimization.

Core Features & Use Cases

  • Comprehensive documentation and tutorials for running and fine-tuning LLMs with Unsloth.
  • Includes reference materials (references/) and practical workflows covering LoRA/QLoRA, RL, deployment, and memory optimization.
  • Real-world scenarios include local development on GPUs, Docker-based setups, and RL experiments.

Quick Start

Install Unsloth and begin with the beginner references to bootstrap a fine-tuning workflow.

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 LoRA or QLoRA to save GPU memory?▼

LoRA and QLoRA fine-tuning reduces GPU memory usage by attaching low-rank adapters to models instead of updating all weights. This Skill coordinates end-to-end workflows using Unsloth to achieve 50-80% memory savings during training.

What is the best way to speed up LLM training on a local GPU?▼

Speeding up LLM training on a local GPU requires optimized libraries like Unsloth, which delivers 2-5x faster training. This Skill provides reference workflows and tutorials to configure memory-efficient environments for local development.

Do I need PyTorch and Transformers installed to use Unsloth for fine-tuning?▼

Yes, you need PyTorch and Transformers installed, as this Skill enforces a dependency manifest including torch, transformers, trl, datasets, and peft. These packages are required to execute the memory-efficient LoRA and QLoRA workflows.

Does Unsloth support reinforcement learning experiments for LLMs?▼

Unsloth supports reinforcement learning (RL) experiments for LLMs through its integration with TRL. This Skill includes reference materials covering practical RL workflows alongside standard LoRA and QLoRA fine-tuning tutorials.

When should I use QLoRA instead of full parameter fine-tuning for LLMs?▼

You should use QLoRA instead of full parameter fine-tuning when GPU memory is limited, as it provides 50-80% memory savings. This Skill coordinates QLoRA workflows to enable training large models on local hardware without out-of-memory errors.

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