---
title: "Discussion on \"Understanding LoRA: Parameter-Efficient Fine-Tuning for Modern LLMs\" | Hashnode"
description: "Discussion on \"Understanding LoRA: Parameter-Efficient Fine-Tuning for Modern LLMs\". Large Language Models (LLMs) and foundation models like LLaMA, GPT, and ViTs have billions of parameters. As these models scale, traditional full fine-tuning—updating every weight in the network—becom"
url: "https://hashnode.com/posts/understanding-lora-parameter-efficient-fine-tuning-for-modern-llms/6a912f5aed98e5cc0cf60b6f"
---

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# [Understanding LoRA: Parameter-Efficient Fine-Tuning for Modern LLMs](https://rahul-ai.hashnode.dev/understanding-lora-parameter-efficient-fine-tuning-for-modern-llms?utm_source=hashnode&utm_medium=feed)

Large Language Models (LLMs) and foundation models like LLaMA, GPT, and ViTs have billions of parameters. As these models scale, traditional full fine-tuning—updating every weight in the network—becom

Aug 28 · 9 min read · rahul-ai.hashnode.dev  
Updated Sep 1  
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