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peft-fine-tuning

Fine-tune large language models with LoRA and QLoRA on limited GPU memory.

Updated Apr 30, 2026
Search Tags:#fine-tuning#LoRA#PEFT#large language models#parameter-efficient
One-click install
npx skills add https://github.com/Ced3-han/Harness-Settings --skill peft-fine-tuning-ced3-han
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: peft-fine-tuning
Source: https://github.com/Ced3-han/Harness-Settings/tree/main/skills/peft
Command: npx skills add https://github.com/Ced3-han/Harness-Settings --skill peft-fine-tuning-ced3-han

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires peft>=0.13.0, transformers>=4.45.0, torch>=2.0.0, bitsandbytes>=0.43.0, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of fine-tuning large language models with limited GPU memory, enabling users to train a fraction of parameters with minimal accuracy loss.

Core Features & Use Cases

  • Parameter-Efficient Fine-Tuning: Offers a suite of methods including LoRA, QLoRA, and more for fine-tuning large models with limited GPU memory.
  • Use Case: For instance, fine-tuning a 7B-70B model on a consumer GPU like RTX 4090 with minimal accuracy loss and minimal parameter usage.

Quick Start

Install the peft skill and fine-tune a Llama-3.1-8B model with LoRA on a dataset using the following command:

pip install peft

Frequently Asked Questions about peft-fine-tuning

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

FAQPage Schema
How do I fine-tune large language models with limited GPU memory?▼

You can fine-tune large language models like Llama-3.1-8B on consumer GPUs such as RTX 4090 using parameter-efficient fine-tuning. This approach trains a fraction of parameters with minimal accuracy loss and supports multi-adapter serving.

Can I fine-tune a 7B-70B model on a consumer GPU like RTX 4090?▼

Yes, you can fine-tune a 7B-70B model on a consumer GPU like RTX 4090 using parameter-efficient fine-tuning methods. LoRA and QLoRA enable training with minimal parameter usage and minimal accuracy loss.

What is the difference between LoRA and QLoRA for parameter-efficient fine-tuning?▼

LoRA and QLoRA are both parameter-efficient fine-tuning methods for large language models. QLoRA further reduces GPU memory requirements by quantizing the base model, allowing fine-tuning of larger models on limited hardware with minimal accuracy loss.

Do I need bitsandbytes to run parameter-efficient fine-tuning with peft?▼

Yes, you need the bitsandbytes library along with peft, transformers, and torch to run parameter-efficient fine-tuning. These dependencies enable training large language models with limited GPU memory and minimal accuracy loss.

Does parameter-efficient fine-tuning support multi-adapter serving?▼

Yes, parameter-efficient fine-tuning supports multi-adapter serving for large language models. This allows you to load and serve multiple fine-tuned adapters simultaneously while maintaining minimal parameter usage and accuracy.

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