kamranjon
8 hours ago
There's a really interesting trend of labs using "proprietary" methods to convert existing models to a compressed ternary format.
PrismML actually targeted the same Qwen 8b model and got it down to 1.75gb here: https://prismml.com/news/ternary-bonsai
I wonder how proprietary it all is though, since the BitNet b1.58 paper has been out for a couple years now: https://arxiv.org/abs/2402.17764
From the wikipedia on 1.58 bit llms: "BitNet derives its performance from being trained natively in 1.58 bit instead of being quantized from a full-precision model after training. Still, training is an expensive process, and it would be desirable to be able to somehow convert an existing model to 1.58 bits. In 2024, HuggingFace reported a way to gradually ramp up the 1.58-bit quantization in fine-tuning an existing model down to 1.58 bits."
The section from huggingface is here: https://huggingface.co/blog/1_58_llm_extreme_quantization#fi...
I just wonder how many of these labs are basically following the huggingface recipe here and possibly tweaking it and releasing models without huge training costs.
Twirrim
8 hours ago
Independent testing of prismml suggest quite a capability drop off outside of their cherry picked benchmarks. I'll be curious to see what this model achieves though.
kamranjon
7 hours ago
Unfortunately Fermion Research appears to entirely AI generate all of their content here, even for the research section: https://www.fermionresearch.com/research/neutrino-8b/
"Neutrino-1 8B was trained natively in its shipping format. There is no full-precision product model that was rounded afterward: the ternary representation is the medium the weights learned in, and the training methods that hold this quality at this depth are the lab’s unpublished work. The findings below are the part that travels."
This statement seems misleading at best.
Both the model page and the release page are basically unintelligible - I don't have a ton of faith in the work here, at least PrismML write coherent releases for their models.
Edit: Another beautiful piece of prose here, I almost wonder if they used the 8b model to generate the content for this release...
"Across the 6.95B coded weights, 62.63% sit at zero and the remainder splits 18.68% plus to 18.69% minus: sign-balanced to a hundredth of a point with no constraint asking for it."
LtdJorge
5 hours ago
I guess it's saying how many of the weights are -1, 0 or +1.
jdiff
4 hours ago
It is, but why? And what's with the bizarre way of phrasing that? Why the bizarre observation that, indeed, nobody asked for it?
mapt
2 hours ago
> sign-balanced to a hundredth of a point with no constraint asking for it."
This isn't their model, this is (probably?) ChatGPT doing a brag / promo deck authorial voice. It routinely uses half a dozen sentence constructions that are relatively uncommon in normal or technical speech. Eccentric. Persuasive. Trying too hard. Restating its point in a promotional way that doesn't sound natural, leading into a sentence where it hyperbolically sells you on having done the impossible.
Normal persuasive speech uses these constructions, especially public speaking doing a VC pitch or an Ancient Aliens, but it would be fucking strange if a person started chaining them in normal conversation and using little else, trying to insinuate competence.
You start to recognize it pretty quickly on Youtube.
K0balt
an hour ago
I’m hoping it becomes a trend for mocking AI output by ironically using AI tells.
rcxdude
3 hours ago
Also, wouldn't normalisation tend to produce a mean of zero in the weights, anyhow?
dofm
6 hours ago
I really had high hopes for the larger Ternary Bonsai and it feels like there is scope to improve, but I get the sense (albeit a naïve, probably not fully informed sense) that improvement can perhaps only come by training directly into ternary.
kamranjon
5 hours ago
I’ve actually been really impressed with the 27b model they recently released - amazing performance approaching 40 tok/s on m4 max and I didn’t run into any quality issues in the small set of tasks I tried. Haven’t gone full coding with it yet but suspect it’s better than say a 9b or 12b model.
embedding-shape
3 hours ago
> suspect it’s better than say a 9b or 12b model
Whaaat, a 27b model might be better than 9b or 12b model? What would make you do such an outrageous claim?
kamranjon
3 hours ago
Sorry I should have clarified - I meant that a ternary 27b model would outperform a non-quantized or 8 bit quantized 9 or 12b model - which it is generally close to (or much smaller than) in size. So yeah the comparison I was trying to make was between models of equivalent size or models that could run on similarly sized hardware.
embedding-shape
2 hours ago
Ah yeah, that makes a ton more sense :) I mean, what you said earlier also makes sense but was too obvious, now it makes sufficient sense, thanks for explaining :)
avadodin
5 hours ago
All you need is Ternary Aware Training and for AI researchers to come up with a backronym for TIT.
woadwarrior01
an hour ago
Wouldn’t that be TAT?