ok..

So a GPT wrapper is simply taking a bunch of data (say, your docs, a few books, whatever), chunking them up and then storing them in what's called a "Vector Database". Once you've done that, you have ChatGPT (or other LLM) reference the most relevant "chunk" from your vector database when someone asks a question.

This is kind of like inserting an example when you're using chatGPT. What you're essentially doing is inserting relevant context so when the "model" responds, it's more relevant.

It's a great, cheap, quick, "hacky" way to get a model to give more bitcoin-like responses, but it's not really a "Bitcoin model", nor has the model actually been "trained".

Where it falls over is when you ask anything that's not specifically in the vector store. Then you're basically back to normal chatGPT.

On the other hands, a model that's been trained (like Spirit of Satoshi), has actually had a TON of data transformed and formatted for training, then a bunch of GPU cycles spent on actually CHANGING the weights and biases of either a pre-existing open source model, or (the harder alternative) using all that data to do a ground-up "pre-training".

These are both very different processes to a "ChatGPT wrapper". The latter actually involve training a model, whereas the former uses another model with some reference material.

Training from scratch ultimately delivers the best possible result, but it takes WAY longer and is orders magnitude more expensive.

Hope that makes sense man!