Of course, if it fails, the counterpoint will be "you just need more training data", but still - I would love to play with this.
Of course, if it fails, the counterpoint will be "you just need more training data", but still - I would love to play with this.
Here they do 80B tokens for a 4B model.
Under Chinchilla model the larger model always performs better than the small one if trained on the same amount of data. I'm not sure if it is true empirically, and probably 1-10B is a good guess for how large the model trained on 80B tokens should be.
Similarly, the small models continue to improve beyond 20:1 ratio, and current models are trained on much more data. You could train a better performing model using the same compute, but it would be larger which is not always desirable.
Given the training notes, it seems like you can't get the performance they give examples of?
I'm not sure about the exact details but there is some kind of targetted distillation of GPT-5 involved to try and get more conversational text and better performance. Which seems a bit iffy to me.