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BitNet: Inference framework for 1-bit LLMs

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Re: BitNet: Inference framework for 1-bit LLMs

#41

Post by Springtime »

cubefox wrote: post #17
LuxBennu wrote: post #6

The title is misleading — there's no trained 100B model, just an inference framework that claims to handle one. But the engineering is worth paying attention to. I run quantized 70B models locally (M2 Max 96GB, llama.cpp + LiteLLM), and memory bandwidth is always the bottleneck. The 1.58-bit approach is interesting because ternary weights turn matmuls into additions — a fundamentally different compute profile on comm…

LLM account

Hmm, the user joined in 2019 but had no submissions or comments until just 40 minutes ago (at least judging by the lack of a second page?) and all the comments are on AI related submissions. Benefit of doubt is it'd have to be a very dedicated lurker or dormant account they remembered they had.

Edit: oh, just recalled dang restricted Show HNs the other day to only non-new users (possibly with some other thresholds). I wonder if word got out and some are filling accounts with activity.

Re: BitNet: Inference framework for 1-bit LLMs

#43

Post by PeterHolzwarth »

yorwba wrote: post #29
orbital-decay wrote: post #23

Earlier quoted context omitted.

Funny enough I now involuntarily take RTFA as a slight slop signal, because all these accounts dutifully read the article before commenting, unlike most HNers who often respond to headlines.

Not all of them do: https://news.ycombinator.com/item?id=47335156 There are evidently lots of people experimenting with different botting setups. Some do better at blending in than others.

Interesting - the account you mention, and the GP, are both doing replies that are themselves all about the same length, and also the same length between the two accounts. I get what you mean.

Re: BitNet: Inference framework for 1-bit LLMs

#45

Post by embedding-shape »

throwaw12 wrote: post #39
embedding-shape wrote: post #16

Earlier quoted context omitted.

> Framework is ready. Now we need someone to actually train the model. If Microslop aren't gonna train the model themselves to prove their own thesis, why would others? They've had 2 years (I think?) to prove BitNet in at least some way, are you really saying they haven't tried so far? Personally that makes it slightly worrisome to just take what they say at face value, why wouldn't they train and publish a model the…

Because this is Microsoft, experimenting and failing is not encouraged, taking less risky bets and getting promoted is. Also no customer asked them to have 1-bit model, hence PM didn't prioritize it. But it doesn't mean, idea is worthless. You could have said same about Transformers, Google released it, but didn't move forward, turns out it was a great idea.

> You could have said same about Transformers, Google released it, but didn't move forward,

I don't think you can, Google looked at the research results, and continued researching Transformers and related technologies, because they saw the value for it particularly in translations. It's part of the original paper, what direction to take, give it a read, it's relatively approachable for being a machine learning paper :)

Sure, it took OpenAI to make it into an "assistant" that answered questions, but it's not like Google was completely sleeping on the Transformer, they just had other research directions to go into first.

> But it doesn't mean, idea is worthless.

I agree, they aren't, hope that wasn't what my message read as :) But, ideas that don't actually pan out in reality are slightly less useful than ideas that do pan out once put to practice. Root commentator seems to try to say "This is a great idea, it's all ready, only missing piece is for someone to do the training and it'll pan out!" which I'm a bit skeptical about, since it's been two years since they introduced the idea.

Re: BitNet: Inference framework for 1-bit LLMs

#47

Post by GorbachevyChase »

embedding-shape wrote: post #16
LuxBennu wrote: post #6

The title is misleading — there's no trained 100B model, just an inference framework that claims to handle one. But the engineering is worth paying attention to. I run quantized 70B models locally (M2 Max 96GB, llama.cpp + LiteLLM), and memory bandwidth is always the bottleneck. The 1.58-bit approach is interesting because ternary weights turn matmuls into additions — a fundamentally different compute profile on comm…

> Framework is ready. Now we need someone to actually train the model. If Microslop aren't gonna train the model themselves to prove their own thesis, why would others? They've had 2 years (I think?) to prove BitNet in at least some way, are you really saying they haven't tried so far? Personally that makes it slightly worrisome to just take what they say at face value, why wouldn't they train and publish a model the…

The most benign answer would be that they don’t want to further support an emerging competitor to OpenAI, which they have significant business ties to. I think the more likely answer which you hinted at is that the utility of the model falls apart as scale increases. They see the approach as a dead end so they are throwing the scraps out to the stray dogs.

Re: BitNet: Inference framework for 1-bit LLMs

#49

Post by bee_rider »

giancarlostoro wrote: post #14

One of the things I often wonder is "what will be the minimally viable LLM" that can work from just enough information that if it googles the rest it can provide reasonable answers? I'm surprised something like Encyclopedia Britanica hasn't yet (afaik) tried to capitalize on AI by selling their data to LLMs and validating outputs for LLM companies, it would make a night and day difference in some areas I would think.…

Isn’t that sort of what a RAG is? You’d need an LLM “smart” enough to turn natural-user prompts into searches, then some kind of search, then an LLM “smart” though to summarize the results.

Re: BitNet: Inference framework for 1-bit LLMs

#50

Post by wongarsu »

simonw wrote: post #42

Anyone know how hard it would be to create a 1-bit variant of one of the recent Qwen 3.5 models?

The results would probably be underwhelming. The bitnet paper doesn't give great baselines to compare to, but in their tests a 2B network trained for 1.58bits using their architecture was better than Llama 3 8B quantized to 1.58bits. Though that 2B network was about on par with a 1.5B qwen2.5.

If you have an existing network, making an int4 quant is the better tradeoff. 1.58b quants only become interesting when you train the model specifically for it

On the other hand maybe it works much better than expected because llama3 is just a terrible baseline

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