Why QLoRA Changes the Game: A Quick Dive into Efficient Fine-Tuning with BERT 0 like April 18, 2025 Share this post Author(s): Saif Ali Kheraj Originally published on Towards AI. Quantized Low-Rank Adaptation — anyone with a mid-range GPU and some curiosity can now fine-tune powerful models without burning through a budget or a power supply. In this article, we will break down QLoRA in plain language. No technical jargon overload, just clear ideas, relatable examples, and a little fun along the way. Let’s start with a quick comparison: Adapters: Instead of retraining the whole model, adapters insert small, trainable blocks. Think of them as sticky notes added to the original book.LoRA (Low-Rank Adaptation): A smarter version that fine tunes just a few key parts of the model — Wq and Wv because they significantly influence the attention computation. Think of it as just rewriting key points or summary in a book instead of the whole story.QLoRA: It applies LoRA techniques to a model that is already compressed using 4 bit quantization(we will go through it). It is efficient, elegant, and powerful. QLoRA stands for Quantized Low-Rank Adaptation. It is a method for fine tuning large language models (LLMs) in a way that is: Memory efficientFriendly to consumer level GPUsStill powerful and accurate It combines two ideas: quantization (compressing data) and low-rank adaptation (tuning only a small part of the model). The result? A streamlined fine… Read the full blog for free on Medium. Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor. Published via Towards AI Towards AI - Medium Share this post