> A model is some architecture of how data will flow through these weight matrices, along with the values of each weight.
Because data doesn't really flow through weight matrices, though perhaps this is true if you squint at very simple models. Deep learning architectures are generally more complicated than multiplying values by weights and pushing the results to the next layer, though which architecture to use depends heavily on context.
> Tokens are sort of "words" in a sentence
Tokens are funny. What a token is depends on the context of the model you're using, but generally a token is a portion of a word. (Why? Efficiency is one reason; handling unknown words is another.)
