Issue #87 Training Edwin Dong, Lalit Julapalli
Fine-Tuning for LLMs, Simply Explained
Free editionPRO edition
This week’s PRO issue includes a Colab notebook that pairs with the article, going specifically on the impacts of learning rate on fine-tuning. We’ll dive deep into fine-tuning, experimenting with a real transformer model.
As always, the code in this issue can be executed on a normal home PC and does not require a large amount of VRAM.
Think about the last time that you used an LLM. The usual ChatGPT 5.2 model or Claude Sonnet 4.5 would suffice for most tasks: finding a quick answer to a burning question, feedback on a random idea you had, or asking for homework help.
The good thing is that these proprietary models have seen such a large volume of data while they were being trained that they have a decent amount of knowledge in most domains.
In this form, they most certainly have breadth of knowledge but may lack in depth.
Specialization is key, and that’s how fine-tuning fits into the picture. Organizations, companies, and legal bodies may need the assistance of an LLM in a highly specialized manner.
For example, a hospital that is using an LLM to help write tedious and lengthy paperwork, or a legal office that is analyzing hundreds of documents.
Fine-tuning an LLM allows you to harness specific capabilities and tailor them to the task at hand.
In concept, this sounds very simple. Just like how an LLM was trained with billions of data points, you may expect to “re-train” this LLM using your own custom dataset/classification.
With this, you can imagine the process as having an LLM trained on “the basics” and then “teaching” it the skills you want it to have.
The process we have outlined is almost what LLM fine-tuning is, but a lot happens under the hood mathematically to ensure that these skills are “learned.”
In implementation, one will have to understand how gradient descent on millions of parameters will need to be handled.
This edition is for PRO members.
