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.

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.

Cannot agree more!

You only need GPUs if you assume the training is gradient descent. GAs or anything else that can handle nonlinearities would be fine, and possibly fast enough to be interesting.

Bitnet encoding more information dense per byte perhaps? CPUs have slow buses so would eke out more use of bandwidth?

Approximately as challenging as training a regular 100B model from scratch. Maybe a bit more challenging because there's less experience with it

The key insight of the BitNet paper was that using their custom BitLinear layer instead of normal Linear layers (as well as some more training and architecture changes) lead to much, much better results than quantizing an existing model down to 1.58 bits. So you end up making a full training run in bf16 precision using the specially adapted model architecture

I browsed through the history of the user and confirm this statement. I know that there are users who say they used em-dashes even before the rise of ChatGPT and HN statistics support that. For example, one prominent example is dang.

However this user uses — in almost all his posts and he had a speed of 1 comment per minute or so on multiple different topics.

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.

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.

I would love to understand the thought process behind this. I'm sure it's a fun experiment, to see if it's possible and so on... but what tangible benefit could there be to burning tokens to spam comments on every post?

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, they packed 4 symbols into a byte to reduce computational complexity (no unpacking needed)

I always hope for "just a bunch of if statements" ... this is not it.

When GPT 3.5 became a thing, it had crawled a very nuanced set of websites, this is what I mean. You basically curate where it sources data from.

Yeah, I think RAG is the idea that will lead us there, though its a little complicated, because for some subjects, say Computer Science, you need a little more than just "This is Hello World in Go" you might need to understand not just Go syntax on the fly, but more CS nuances that are not covered in one single simple document. The idea being having a model that runs fully locally on a phone or laptop with minimal resources. On the other hand, I can also see smaller models talking to larger models that are cheaper to run in the cloud. I am wondering if this is the approach Apple might take with Siri, specifically in order to retain user privacy as much as possible.

Correct! I know RAG is a thing, but I wish we could have "DLCs" for LLMs like image generation has LoRa's which are cheaper to train for than retraining the entire model, and provide more output like what you want. I would love to pop in the CS "LoRa or DLC" and ask it about functional programming in Elixir, or whatever.

Maybe not crawl the web, but hit a service with pre-hosted, precurated content it can digest (and cache) that doesn't necessarily change often enough. You aren't using it for the latest news necessarily, but programming is mostly static knowledge a a good example.