Technology

59454 readers

3354 users here now

This is a most excellent place for technology news and articles.

Our Rules

Follow the lemmy.world rules.
Only tech related content.
Be excellent to each another!
Mod approved content bots can post up to 10 articles per day.
Threads asking for personal tech support may be deleted.
Politics threads may be removed.
No memes allowed as posts, OK to post as comments.
Only approved bots from the list below, to ask if your bot can be added please contact us.
Check for duplicates before posting, duplicates may be removed

Approved Bots

founded 1 year ago

MODERATORS

[email protected]

105

1-bit LLMs Could Solve AI’s Energy Demands (spectrum.ieee.org)

submitted 5 months ago by [email protected] to c/[email protected]

36 comments fedilink hide all child comments

you are viewing a single comment's thread
view the rest of the comments

[–] [email protected] 5 points 5 months ago

There's actually a perplexity improvement parameter-to-paramater for BitNet-1.58 which increases as it scales up.

So yes, post-training quantization perplexity issues are apparent, but if you train quantization in from the start it is better than FP.

Which makes sense through the lens of the superposition hypothesis where the weights are actually representing a hyperdimensional virtual vector space. If the weights have too much precision competing features might compromise on fuzzier representations instead of restructuring the virtual network to better matching nodes.

Constrained weight precision is probably going to be the future of pretraining within a generation or two looking at the data so far.