In the ever-evolving world of AI and Natural Language Processing (NLP), Large Language Models and Generative AI have become powerful tools for various applications. Achieving the desired results from these models involves different approaches that can be broadly classified into three categories: Prompt Engineering, Fine-Tuning, and Creating a new model. As we progress from one level to another, the requirements in terms of resources and costs increase significantly.
In this blog post, we’ll explore these approaches and focus on an efficient technique known as Parameter Efficient Fine-Tuning (PEFT) that allows us to fine-tune models with minimal infrastructure while maintaining high performance.