3 ms·
If this is true, it's even more impressive that some of the open weight models that are <3.5T in size, approx 33% of its size, are within a few points of it in
by walrus01 2mo ago
If this is true, it's even more impressive that some of the open weight models that are <3.5T in size, approx 33% of its size, are within a few points of it in the artificial analysis leaderboard.
- manquer 2mo agoNot necessarily, there could be diminishing returns on mere parameters count . There is nothing to say for example a 1 Quadrillion parameter model will be vastly more intelligent than current SOTA especially since new training data is largely synthetic today
- HDBaseT 2mo agoThat's precisely what he is saying, there is diminishing returns (or optimization left on the table).
- manquer 2mo agoI read it as it is impressive because smaller models 2.5T are squeezing similar returns as 10T models despite being 1/4th size not that there beyond 2T today the number or parameters do not have much meaning
- verdverm 2mo agoor the latest qwen3.8 27B doing so well at ~1/100 the size of K3
- rnewme 2mo agoWhat about general knowledge you can get out of it before hallucinations start?
- walrus01 2mo agoIt did OK on schlongbench v1.0 (test of a specific niche word that doesn't make it into smaller LLMs) but it sure does love to count words https://pastes.io/r8F1AY8h https://pastes.io/r8F1AY8h
- verdverm 2mo agoI do not rely on any LLM of any size for general knowledge baked into the weights, they all hallucinate and that is the wrong way to hold them imo I think there is some merit in that smaller models cannot memorize so much of the training data, i.e. that they are less likely to do copyright infringement, and by analogy not having memorized SDK / API surfaces that have since changed from the training data
- walrus01 2mo ago> I do not rely on any LLM of any size for general knowledge baked into the weights You have to rely on it to a certain level for agentic/coding work, presuming that's the general subject we're talking about here... For instance I recently encountered a project where it would have been a lot worse if the LLM didn't already know "what is" xterm.js and a bunch of its associated npm-related/node related software. If it was still smart but had to google and find results for everything it would have been a lot more time consuming and risked sending it down a wrong path.
- verdverm 2mo agofor sure, there is a minimum size and knowledge base that is required to be useful at the same time, search may find newer or better alternatives, and you can always specify specific technologies you want to use, I typically do this when starting a new project
- stymaar 2mo agoStoring general knowledge in VRAM has always been a dumb idea in the first place.
- Balinares 2mo ago
- habosa 2mo agoGLM 5.3 is "only" 753B parameters. Much much smaller.
- mlmonkey 2mo agoYou want to take a look at the "Scaling Laws" paper, so you can extrapolate from these numbers.
- stymaar 2mo agoThis paper, as well as the Chinchilla one, aged like milk though.
- scosman 2mo agoAnd GLM is only 0.7T! But these labs distill off the larger models. Both officially at the labs with the big ones, and unofficially. We need the giant models to get the smaller models.