this post was submitted on 26 Aug 2024
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If you change the tokenizer you have to retrain from scratch, but you can do so with the old, unpolluted data.
It's genius if you think about it,* you can waste energy and tell your investors it's a new better model, while staying upstream from the river you pollute.
* at least for consultants, compute providers and other middle men.
I remember one time in a research project I switched out the tokeniser to see what impact it might have on my output. Spent about a day re-running and the difference was minimal. I imagine it's wholly the same thing.
*Disclaimer: I don't actually imagine it is wholly the same thing.
there's a research result that the precise tokeniser makes bugger all difference, it's almost entirely the data you put in
because LLMs are lossy compression for text
latent space go brrrr