[R] Understanding LoRA: A visual guide to Low-Rank Approximation for fine-tuning LLMs efficiently. 🧠

TL;DR: LoRA is Parameter-Efficient Fine-Tuning (PEFT) method. It addresses the drawbacks of previous fine-tuning techniques by using low-rank adaptation, which focuses on efficiently approximating weight updates. This significantly reduces the number of parameters involved in fine-tuning by 10,000x and still converges to the performance of a fully fine-tuned model.
This makes it cost, time, data, and GPU efficient without losing performance.

[What is LoRA and Why It Is Essential For Model Fine-Tuning: a visual guide.](https://codecompass00.substack.com/p/what-is-lora-a-visual-guide-llm-fine-tuning)

https://preview.redd.it/v2plu0mvvw6d1.png?width=1456&format=png&auto=webp&s=e5f74bcb777d305c08bc74274b0c8a7cc63c973e

https://preview.redd.it/vvujm3r3ww6d1.png?width=1456&format=png&auto=webp&s=cfb6111c3bd22585d171ed29c3cadcf823c8839d

approximation fine-tuning guide llms lora low low-rank adaptation machinelearning parameters peft performance tuning understanding updates visual