It's good to see this getting some continued development. I looked into it last year[1] and I thought it showed a lot of promise so I've been very disappointed that I never saw a newer model.
BitNet: Inference framework for 1-bit LLMs
31–40 of 179 posts
Re: BitNet: Inference framework for 1-bit LLMs
#32Earlier quoted context omitted.
LLM account
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.
Yeah. It correctly pointed out that the editorialized HN title is wrong, there is no 100B model.
Re: BitNet: Inference framework for 1-bit LLMs
#33Headline: 100B. Falcon 3 family: 10B. An order of magnitude off
Re: BitNet: Inference framework for 1-bit LLMs
#34Earlier quoted context omitted.
LLM account
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.
First they claimed that if you use em dashes you are not human
And I did not speak out
Because I was not using em dashes
Then they claimed that if you're crammar is to gud you r not hmuan
And I did not spek aut
Because mi gramar sukcs
Then they claimed that if you actually read the article that you are trying to discuss you are not human...
Re: BitNet: Inference framework for 1-bit LLMs
#35Earlier quoted context omitted.
"1-bit LLMs" is just marketing. The Shannon entropy of one letter with a 3 symbol alphabet (-1, 0, 1) is 1.58.
Log Base 2 of 3 = ~1.5849625, so that's the limit to how well you can pack three-state values into bits of data. For something more practical, you can pack five three-state values within a byte because 3^5 = 243, which is smaller than 256. To unpack, you divide and modulo by 3 five separate times. This encodes data in bytes at 1.6 bits per symbol. But the packing of 5 symbols into a byte was not done here. Instead, t…
>1-bit model
>packed 4 symbols into a byte
microslop, typical bunch of two-bit frauds!
Re: BitNet: Inference framework for 1-bit LLMs
#36The 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…
> a fundamentally different compute profile on commodity CPU In what way? On modern processors, a Fused Multiply-Add (FMA) instruction generally has the exact same execution throughput as a basic addition instruction
Bitnet encoding more information dense per byte perhaps? CPUs have slow buses so would eke out more use of bandwidth?
Re: BitNet: Inference framework for 1-bit LLMs
#37One 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.…
Wikipedia has proven to be as accurate as encyclopedias for decades now. Also, I'm betting AI companies have illegally trained their models on the Encyclopedia Britanica's data by now.
Re: BitNet: Inference framework for 1-bit LLMs
#38What’s the lower limit on the number of bits per parameter? If you use CSR-style sparse matrices to store the weights can it be less than 1?
Re: BitNet: Inference framework for 1-bit LLMs
#39The 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…
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.
Re: BitNet: Inference framework for 1-bit LLMs
#40The 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
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