23
you are viewing a single comment's thread
view the rest of the comments
[-] anton@lemmy.blahaj.zone 8 points 2 months ago

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.

[-] UnseriousAcademic@awful.systems 4 points 2 months ago

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.

[-] dgerard@awful.systems 4 points 2 months ago

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

[-] froztbyte@awful.systems 3 points 2 months ago

latent space go brrrr

this post was submitted on 26 Aug 2024
23 points (100.0% liked)

TechTakes

1403 readers
92 users here now

Big brain tech dude got yet another clueless take over at HackerNews etc? Here's the place to vent. Orange site, VC foolishness, all welcome.

This is not debate club. Unless it’s amusing debate.

For actually-good tech, you want our NotAwfulTech community

founded 1 year ago
MODERATORS