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.

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Twirrim 4 hours ago | parent | next [-]

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.

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kamranjon 4 hours ago | parent | next [-]

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is the medium the weights learned in, and the training methods that hold this quality at this depth are the lab’s unpublished work.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."

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LtdJorge an hour ago | parent [-]

I guess it's saying how many of the weights are -1, 0 or +1.

	▲	jdiff 10 minutes ago \| parent [-]
		It is, but why? And what's with the bizarre way of phrasing that? Why the bizarre observation that, indeed, nobody asked for it?

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dofm 2 hours ago | parent | prev [-]

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 an hour ago \| parent \| next [-]
		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.
	▲	avadodin an hour ago \| parent \| prev [-]
		All you need is Ternary Aware Training and for AI researchers to come up with a backronym for TIT.

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3 hours ago | parent | prev [-]

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