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 “<a href="https://medium.com/@dataman-ai/fine-tune-a-gpt-lora-e9b72ad4ad3" rel="noopener">Fine-tuning a GPT — LoRA</a>” 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) <a href="https://medium.com/@dataman-ai/fine-tune-a-gpt-prefix-tuning-13c263e73141">Read More</a>