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Fine-tuning ChatGPT
I’ve been spending some time trying to fine-tune ChatGPT’s answers, followed a few articles like this one by Amogh Agastya (which is really well explained) and the official docs. My intention is to sum up the steps to achieve it, and explain what could happen in each of them.
Before getting into the process, lets define a few things, starting with fine-tuning:
“Fine-tuning is the process of adapting a pre-trained model to a new task or dataset, by training it on a smaller dataset. This allows the model to learn the characteristics of the new task or dataset, and improve its performance on that task.”
Yes, I quoted the previous definitions because that’s what ChatGPT has to say about fine-tuning 😲. Fine-tuning is a really good solution when working with very specific tasks (such as chatting about
There are two main types of fine-tuning:
- Few-shot fine-tuning: This type of fine-tuning is used when the new task or dataset has a small number of examples, usually in the order of a few hundred or less.
- Zero-shot fine-tuning: This type of fine-tuning is used when the new task or dataset has no or very few examples. In this case, the model is not trained on the new task or dataset, but instead, the pre-trained weights of the model are used directly to make predictions on the new task or…
