Understanding LoRA: A 5-minute 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.

[Why LoRA 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/877jwedppj7d1.png?width=1456&format=png&auto=webp&s=4b7499db0f8c19f64df9730987f73dec0d473b76

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