unsloth

Name: unsloth
Availability: InStock
Author: Nitish

Optimize LoRA and QLoRA model fine-tuning with unsloth libraries.

Updated Jun 26, 2026

Search Tags:#fine-tuning #LoRA #memory efficiency #QLoRA #training acceleration

Ready to run

npx skills add https://github.com/NITISH-gitbit/hermes-custom --skill unsloth-nitish-gitbit

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: unsloth
Source: https://github.com/NITISH-gitbit/hermes-custom/tree/main/optional-skills/mlops/training/unsloth
Command: npx skills add https://github.com/NITISH-gitbit/hermes-custom --skill unsloth-nitish-gitbit

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill streamlines the fine-tuning process for LoRA and QLoRA models, significantly reducing training time and VRAM usage.

Core Features & Use Cases

Faster Training: Accelerates the fine-tuning process for LoRA and QLoRA models by 2-5x.
Memory Efficiency: Reduces VRAM usage for efficient training on limited hardware.
Use Case: Ideal for developers working with large language models who want to quickly and efficiently fine-tune their models without compromising on performance.

Quick Start

Use the unsloth skill to fine-tune a LoRA model on your dataset.

Frequently Asked Questions about unsloth

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

FAQPage Schema

How do I accelerate LoRA and QLoRA fine-tuning for large language models?▼

You can accelerate LoRA and QLoRA fine-tuning by optimizing the training process to achieve 2-5x faster speeds while significantly reducing VRAM usage for large language models.

What's the best way to reduce VRAM usage during QLoRA training?▼

The best way to reduce VRAM usage during QLoRA training is to apply memory efficiency optimizations that allow fine-tuning large language models on limited hardware without compromising performance.

Do I need PyTorch and Transformers to run fine-tuning acceleration scripts?▼

Yes, you need PyTorch and Transformers along with the trl, datasets, and peft libraries installed in your environment to execute the fine-tuning acceleration scripts.

Can I fine-tune large language models on limited hardware using memory efficient techniques?▼

Yes, you can fine-tune large language models on limited hardware by using memory efficient optimizations that significantly reduce VRAM consumption during the training process.

How does memory efficient training compare to standard fine-tuning approaches?▼

Memory efficient training distinguishes itself from standard fine-tuning by reducing VRAM usage and accelerating training speed by 2-5x, making it ideal for resource-constrained environments.

Why does fine-tuning large language models require so much VRAM?▼

Fine-tuning large language models demands high VRAM because standard processes load full model weights and optimizer states, but applying QLoRA optimizations significantly reduces this memory footprint.