Supervised Fine-Tuning — Step by Step

Customizing AI models using UnSloth, HuggingFace, and Google Colab

There is an assumption that Supervised Fine Tuning is a very difficult and highly technical process. In this article, I’m going to walk step-by-step through the fine-tuning process so you can see that it is not a difficult process and you don’t even need to understand how each step works.

What is Supervised Fine-Tuning?

Imagine you have a highly skilled general doctor who has spent years studying medicine. This doctor knows how to diagnose and treat a wide range of common illnesses, understands human anatomy, and has broad medical knowledge — just like a pre-trained AI model that understands language and general concepts.

Now, suppose you want this doctor to become a specialist heart surgeon. You can’t expect them to perform open-heart surgery just based on general medical knowledge. They need specific training, guided practice, and detailed examples — like how to make an incision, handle surgical instruments, or respond to complications in the operating room.

Supervised Fine-Tuning (SFT) is like that specialized surgical training, but for AI.

We take a pre-trained AI model — one that already understands language or general tasks — and teach it step-by-step how to do…