How to Set Up a PEFT LoraConfig

Fine-tuning large language models (LLMs) or vision-language models (VLMs) can be an expensive and resource-intensive process, often requiring substantial computational power and memory. This is where LoRA (Low-Rank Adaptation) shines, offering an efficient way to fine-tune models by reducing the number of trainable parameters. At the heart of implementing LoRA is the LoraConfig class, which serves as the blueprint for how LoRA adapts your model. In this guide, we'll see the details of LoraConfig and how you can use it to configure fine-tuning to your specific needs.

The Kaitchup provides numerous examples showing how to use a LoraConfig:

Fine-Tuning Qwen3: Base vs. Reasoning Models

Qwen2.5 QLoRA, LoRA, and Full Fine-tuning on Your Computer

Fine-tune Llama 3 on Your Computer

Getting Started: Installing Required Libraries

To begin working with PEFT LoraConfig, you'll need to install a few key libraries. These include torch, transformers, and peft. You can install them using the following command:

pip install transformers peft

These libraries provide the foundational tools needed for loading, adapting, and fine-tuning your model using LoRA. Once you have them installed, you're ready to start setting up your LoraConfig.

What is LoraConfig?