Fine-tuning a GPT — Prefix-tuning

In this and the next posts, I will walk you through the fine-tuning process for a Large Language Model (LLM) or a Generative Pre-trained Transformer (GPT). There are two prominent fine-tuning methods. One is Prefix-tuning and the other is LoRA (Low-Rank Adaptation of Large Language Models). This post explains Prefix-tuning and the next post “Fine-tuning a GPT — LoRA” for LoRA. In both posts, I will cover a code example and walk you through the code line by line. In the LoRA article, I will especially cover the GPU-consuming nature of fine-tuning a Large Language Model (LLM).

After completing this article, you will be able to explain

• Why fine-tuning a GPT is needed
• The challenges in fine-tuning a GPT
• The idea of Prefix-tuning
• The architecture of Prefix-tuning
• The code for Prefix-tuning

Before jumping to fine-tuning a GPT, I want to even clear up some doubts about why fine-tuning is needed. Let’s start!

Why do we still need to fine-tune a GPT?

Since GPTs are already trained with various datasets for question answering, text summarization, translation, or classification, why do we still need to fine-tune a GPT? Here is the answer. Consider GPTs as powerful “Transformer” robots (in the…