Does that imply they retrained the foundation model from scratch? I thought changing the tokenization was something you couldn't really retrofit to an existing model. I mean sure they might have initialized the weights from the prior GPT-4 model but it'd still require a lot of retraining.
For posterity, GPT-3.5/4's tokenizer was 100k. The benefit of a larger tokenizer is more efficient tokenization (and therefore cheaper/faster) but with massive diminishing returns: the larger tokenizer makes the model more difficult to train but tends to reduce token usage by 10-15%.
Yep. Non-English text gets a much bigger cost drop and speedup compared to English. Has always been a bummer that GPT-4 is like 5x slower and more expensive in Japanese, etc.
It says "Japanese 1.4x fewer tokens (from 37 to 26)" - some other languages get much bigger improvements though, best is "Gujarati 4.4x fewer tokens (from 145 to 33)".
How are they able to use such a brand name, Tiktoken? Is it because TikTok is Chinese? Tiktoken, it's almost like if Apple released the Facebooken library for something entirely unrelated to Facebook.