skills.rest
arsity
arsityCOMMUNITY
Explore all skills by this author →
WebsiteGitHub

unsloth

Optimizes LLM fine-tuning workflows using LoRA/QLoRA on-premise or in the cloud with Unsloth-supported models.

Updated Mar 16, 2026
Search Tags:#llm#fine-tuning#transformers#qlora#memory-optimization#loRA#unsloth
One-click install
npx skills add https://github.com/arsity/scholar-tools --skill unsloth-arsity
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: unsloth
Source: https://github.com/arsity/scholar-tools/tree/main/vendor/ai-research-skills/03-fine-tuning/unsloth
Command: npx skills add https://github.com/arsity/scholar-tools --skill unsloth-arsity

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 to accelerate and optimize fine-tuning of large language models, reducing training time and memory usage.

Core Features & Use Cases

  • Guided, memory-efficient fine-tuning workflows using LoRA/QLoRA to achieve faster convergence with lower VRAM demands.
  • Broad model support including Llama, Mistral, Gemma, and Qwen across local GPUs, Docker, and Colab environments.
  • Clear best practices, troubleshooting tips, and references to official docs to ensure reliable fine-tuning results.

Quick Start

Guide me through a 4-bit LoRA fine-tuning workflow for my model using Unsloth.

Frequently Asked Questions about unsloth

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I reduce VRAM usage when fine-tuning large language models?▼

Memory-efficient fine-tuning reduces VRAM usage by applying LoRA and QLoRA techniques to large language models. This approach lowers memory demands while accelerating training convergence across supported architectures like Llama, Mistral, Gemma, and Qwen.

How do I set up a 4-bit LoRA fine-tuning workflow for Llama or Mistral?▼

To set up a 4-bit LoRA fine-tuning workflow, you need to configure your environment with the necessary dependencies including torch, transformers, trl, datasets, and peft. You then apply QLoRA to your target model to achieve faster, memory-efficient training.

Can I run memory-efficient fine-tuning in Google Colab or Docker?▼

Yes, memory-efficient fine-tuning is supported across local GPU, Docker, and Google Colab environments. This allows you to train large language models with lower VRAM demands regardless of your preferred compute setup.

Does Unsloth work with Qwen and Gemma architectures?▼

Yes, the workflow supports broad model architectures including Qwen and Gemma. It provides guided fine-tuning compatibility and best practices for these specific models alongside Llama and Mistral.

What are the limitations of using QLoRA for large language model training?▼

While QLoRA significantly reduces memory usage, limitations can include potential compatibility issues between core dependencies like peft and transformers, requiring troubleshooting and strict version management to ensure reliable fine-tuning results.

Related Skills

Explore More Software Engineering Skills→
GregsGreyCodeGregsGreyCode
6

unsloth

Guide Unsloth fine-tuning workflows with LoRA/QLoRA optimizations.

Community
Intermediate
ScientiaCapitalScientiaCapital
1

model-deployment

Export fine-tuned models to GGUF and deploy with vLLM or Ollama.

Community
Advanced
Ced3-hanCed3-han

fine-tuning-with-trl

Fine-tune LLMs with TRL reinforcement learning and HuggingFace Transformers.

Community
Advanced
KarlinskySKarlinskyS

unsloth

Fine-tune large language models with Unsloth using LoRA/QLoRA adapters.

Community
Advanced