Fine Tuning LLM: Parameter Efficient Fine Tuning (PEFT) — LoRA & QLoRA — Part 1
<p><em>In this blog, we will understand the idea behind Parameter Efficient Fine Tuning (PEFT), and explore LoRA and QLoRA, Two of the most important PEFT methods. We will understnad how PEFT can be used to fine tune the model for domain specific tasks, at the lowest cost and minimal infrastrcuture.</em></p>
<h1>Motivation</h1>
<p>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.</p>
<p>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 infrastrcture while maintaining high performance.</p>
<h2>Prompt Engineering with Existing Models</h2>
<p>At the basic level, achieving expected outcomes from Large Language Models involves careful prompt engineering. This process involves crafting suitable prompts and inputs to elicit the desired responses from the model. Prompt Engineering is an essential technique for various use cases, especially when general responses suffice.</p>
<p><a href="https://abvijaykumar.medium.com/fine-tuning-llm-parameter-efficient-fine-tuning-peft-lora-qlora-part-1-571a472612c4"><strong>Click Here</strong></a></p>