Fine-Tuning Explained for Noobs (How Pretrained Models Learn New Skills)
# Inroduction This article is part of my noob series where we write about the questions people Google most but may not understand well because of complex math and everything. So, if you are here, you might have heard fine-tuning somewhere in the context of large language models (LLMs)...
# Inroduction This article is part of my noob series where we write about the questions people Google most but may not understand well because of complex math and everything. So, if you are here, you might have heard fine-tuning somewhere in the context of large language models (LLMs) especially. This concept already existed in traditional machine learning for years, but it gained popularity after LLMs because now suddenly everyone has access to these huge, general pretrained models that you can adapt based on your tasks, your own needs, and in your own tone. This act of adapting is basically called fine-tuning, and it is now one of the most common things people do with LLMs. But you cannot understand it until you understand the step that comes before it, and that is "pretraining." Fine-tuning is literally "tuning" something that already exists, and that "something" is a pretrained model. So, let's try to break down these concepts so that in the future, if someone asks you about it, you know it.