Introduction: Large Language Models (LLMs) like GPT, T5, and BERT have shown remarkable performance in NLP tasks. However, fine-tuning these models on downstream tasks can be computationally expensive. Parameter-Efficient Fine-Tuning (PEFT) approaches aim to address this challenge by fine-tuning only a small number of parameters while freezing most of the pretrained model. In this blog…