It is literally a statistical next word predictor. Well, next token, to be extremely accurate. That is what it is in its most basic form. You give it text, it transforms that into tokens, and predicts the next token. You can then transform that back into words.
The model is the same size no matter how much it’s been trained. You can fine tune a model to predict certain kinds of tokens next, and you can distill a model to create a smaller model (basically training a small model on the output of a large model).
Lossy compression works in a fundamentally different way.
The description you are replying to is more fundamental.
It is literally a statistical next word predictor. Well, next token, to be extremely accurate. That is what it is in its most basic form. You give it text, it transforms that into tokens, and predicts the next token. You can then transform that back into words.
The model is the same size no matter how much it’s been trained. You can fine tune a model to predict certain kinds of tokens next, and you can distill a model to create a smaller model (basically training a small model on the output of a large model).
Lossy compression works in a fundamentally different way.