The paper has performance comparisons towards the end.

https://arxiv.org/abs/2402.17764

The title being misleading is important as well, because this has landed on the front page, and the only thing that would be the only notable part of this submission.

The "new" on huggingface banner has weights that were uploaded 11 months ago, and it's 2B params. Work on this in the repo is 2 years old.

The amount of publicity compared to the anemic delivery for BitNet is impressive.

At some point you hit information limits. With conventional quantisation you see marked capability fall-off below q5. All else being equal you'd expect an N-parameter 5-bit quant to be roughly comparable to a 3N-parameter ternary, if they are trained to the same level, just in terms of the amount of information they can possibly hold. So yes, 100B ternary would be within the ballpark of a 30B q5 conventional model, with a lot of hand-waving and sufficiently-smart-training

"1-bit LLMs" is just marketing. The Shannon entropy of one letter with a 3 symbol alphabet (-1, 0, 1) is 1.58.

> 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 themselves if this actually led to worthwhile results?

Yeah, "1.58 bit" is 1 trit with three states, since log2(3)≈1.58.

So it's not a inference framework for 1-bit models (two states per parameter) but for 1.58 bit models (three states per parameter). Annoying that they try to mix up the two.

> memory bandwidth is always the bottleneck

I'm hoping that today's complaints are tomorrow's innovations. Back when 1Mb hard drive was $100,000, or when Gates said 640kb is enough.

Perhaps some 'in the (chip) industry' can comment on what RAM manufacturers are doing at the moment - better, faster, larger? Or is there not much headroom left and it's down to MOBO manufacturers, and volume?

I've also always though that it's an interesting opportunity for custom hardware. Two bit addition is incredibly cheap in hardware, especially compared to anything involving floating point. You could make huge vector instructions on the cheap, then connect it to the fastest memory you can buy, and you have a capable inference chip.

You'd still need full GPUs for training, but for inference the hardware would be orders of magnitude simpler than what Nvidia is making

You can already run some models on the NPUs in the Rockchip RK3588 SBCs which are pretty abundant.

A claude 4.6 they are most certainly not, but if you get through the janky AF software ecosystem they can run small LLMs reasonably well with basically zero CPU/GPU usage

Are the NPUs really that powerful?

I was under the impression that they were primarily designed for low power use.

Text is misleading too. 5-7 tok/sec is not reading speed, it's a tad slower. For me, at least, and I am an experienced reader, not especially schooled in quick-reading though.

I happened to "live" on 7.0-7.5 tok/sec output speed for a while, and it is an annoying experience. It is the equivalent of walking behind someone slightly slower on a footwalk. I dealt with this by deliberately looking away for a minute until output was "buffered" and only then started reading.

For any local setup I'd try to reach for 10 tok/sec. Sacrifice some kv cache and shove a few more layers on your GPU, it's worth it.

In their demo they're running 3B model.

I assume that theoretically, 1-bit models could be most efficient because modern models switched from 32 bit to 16 bit to 8 bit per parameter (without quantization).

For larger contexts, the bottleneck is probably token prefill instead of memory bandwidth. Supposedly prefill is faster on the M5+ GPUs, but still a big hurdle for pre-M5 chips.

So is it finally time for a Beowulf cluster to do something amazing?

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.

Rest assured, all the big players (openai, google, deepseek etc) have run countless experiments with 4,3,2,1.58,1 bits, and various sparse factors and shapes. This barrel has been scraped to the bottom

The project is an inference framework which should support 100B parameter model at 5-7tok/s on CPU. No one has quantized a 100B parameter model to 1 trit, but this existing is an incentive for someone to do so.

I think this approach is not so interesting because it's just quantization of a full precision model. So it speeds up inference (at a quality penalty) but not training. It would be more interesting to train an actually binary model directly, without any floating point multiplication, like in this paper: https://proceedings.neurips.cc/paper_files/paper/2024/hash/7...

I think they used a dummy model or else they would have linked to it. Just google '1-bit 100b model' and you'll only see references to this project without any download links.

> validating outputs for LLM companies

How? They can validate thousands if not millions of queries but nothing prevent the millions-th-and-one from being a hallucination. People who would then pay extra for a "Encyclopedia Britanica validated LLM" would then, rightfully so IMHO, complain that "it" suggested them to cook with a dangerous mushroom.

Since Google Search already includes an AI summary, your minimally viable "LLM" can be just an HTTP GET call

Also, very influenced by the literature of Jenkins (2010).

Almost trivial using open source tools, the question is how it performs without calibration/fine tuning.

Yes. I had to read it over twice, it does strike me as odd that there wasn't a base model to work with.

But it seems the biggest model available is 10B? Somewhat unusual and does make me wonder just how challenging it will be to train any model in the 100B order of magnitude.

LLM account

> 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

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)

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.

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

These are trits, which provide their own efficiencies.

Interestingly, a trit x float multiplier is cheaper than a trit x integer multiplier in hardware if you're willing to ignore things like NaNs.

0 and 1 are trivial, just a mux for identity and zero. But because floats are sign-magnitude, multiply by -1 is just an inverter for the sign bit, where as for integers you need a bitwise inverter and full incrermenter.

It comes from (intentionally?) misleading docs: https://github.com/microsoft/BitNet/issues/391

(only suggesting that it's intentional because it's been there so long)

It's not so much a "minimally viable LLM" but rather an LLM that knows natural language well but knows nothing else. Like me - as an engineer who knows how to troubleshoot in general but doesn't know about a specific device like my furnace (recent example).

And I don't think that LLM could just Google or check Wikipedia.

But I do agree that this architecture makes a lot of sense. I assume it will become the norm to use such edge LLMs.

Your worry about Wikipedia is that there is "much room for human error and bias", yet earlier you seem to imply that a LLM that has access to the www somehow would have less human error and bias? Personally, I'd see it the other way around.

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.

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.

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

There are q2 and q1 quants, if you want an idea of how much performance you'd drop. Not quite the same implementation-wise, but probably equivalent in terms of smarts.

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

It's a two year old base model that's only 3B parameters, trained on only 100B tokens. It's still a research project at this point.

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.

What's unusual about it? It seems pretty standard to train small models to validate an approach, and then show that training scales with model size to 8B to 14B parameter models, which is what they did.

It's scary, without the em dashes, and the rapid fire commenting of the account - who would ever realize this is a bot? Two easy to fix things, and after that it'd be very difficult to tell that this is a bot.

It's not a question of if there are other bots out there, but only what % of comments on HN right now and elsewhere are bot generated. That number is only going to increase if nothing is done.

Looks like gradual disempowerment is already happening - the minority of humans who are capable of spotting AI content are losing the struggle for attention on all major social networks

Agreed. This is becoming an issue, see also: https://news.ycombinator.com/item?id=47259308

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?

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

Not to mention Microsoft's investments in Nvidia and other GPU-adjacent/dependent companies!

A successful ternary model would basically erase all that value overnight. In fact, the entire stock market could crash!

Think about it: This is Microsoft we're talking about! They're a convicted monopolist that has a history of manipulating the market for IT goods and services. I wouldn't put it past them to refuse to invest in training a ternary model or going so far as to buy up ternary startups just to shut them down.

Want to make some easy money: Start a business training a ternary model and make an offer to Microsoft. I bet they'll buy you out for at least a few million even if you don't have a product yet!

The win is in how many weights you process per instruction and how much data you load.

So it's not that individual ops are faster — it's that the packed representation lets each instruction do more useful work, and you're moving far less data from memory to do it.

>1-bit model

>packed 4 symbols into a byte

microslop, typical bunch of two-bit frauds!

it's if {} else if {} else {}

Do you know a good reference to learn more about this (quantizing weigths to 1.58 bits, and trit arithmetic)?

I asked this question a while back (the "only train w/ wikipedia LLM") and got pointed to the general-purpose "compression benchmarks" page: `https://www.mattmahoney.net/dc/text.html`

While I understand some of the fundamental thoughts behind that comparison, it's slightly wonky... I'm not asking "compress wikipedia really well", but instead "can a 'model' reason its way through wikipedia" (and what does that reasoning look like?).

Theoretically with wikipedia-multi-lang you should be able to reasonably nail machine-translation, but if everyone is starting with "only wikipedia" then how well can they keep up with the wild-web-trained models on similar bar chart per task performance?

If your particular training technique (using only wikipedia) can go from 60% of SOTA to 80% of SOTA on "Explain why 6-degrees of Kevin Bacon is relevant for tensor operations" (which is interesting to plug into Google's AI => Dive Deeper...), then that's a clue that it's not just throwing piles of data at the problem, but instead getting closer to extracting the deeper meaning (and/or reasoning!) that the data enables.

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.

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.

I think the idea is to train a small, minimal LLM thinking model that can run on edge devices, but that has very little knowledge embedded in its weights, and so performs a sort of RAG to Encylopedia Britannica to ground answers to user queries.

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.

You drop the memory throughput requirements because of the packed representation of bits so an FMA can become the bottleneck, and you bypass the problem of needing to upscale the bits to whatever FP the FMA instruction needs.

typically for 1-bit matmul, you can get away with xors and pop_counts which should have a better throughput profile than FMA when taking into account the SIMD nature of the inputs/outputs.

> 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.

That issue appears to be the one that's wrong. From the technical report

> We evaluated bitnet.cpp in terms of both inference speed and energy cost. Comprehensive tests were conducted on models with various parameter sizes, ranging from 125M to 100B. specific configurations for each model are detailed in the Appendix A.

The new model they just released has impressive benchmark results: https://huggingface.co/microsoft/bitnet-b1.58-2B-4T

Except on GSM8K and math...

There has been a shift to the Ai accounts, they use Show HN less now. This started before dang's comment, I assume because they saw the earlier posts about the increase in quantity / decrease in quality.

I suspect that they are trying to fake engagement prior to making their first "show" post as well.

> Funny enough I now involuntarily take RTFA

Residential Treatment Facility for Adults? Red Tail Flight Academy?

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...

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.

Yeah. It correctly pointed out that the editorialized HN title is wrong, there is no 100B model.

On the one hand, not publishing any new models for an architecture in almost a year seems like forever given how things are moving right now. On the other hand I don't think that's very conclusive on whether they've given up on it or have other higher priority research directions to go into first either

yes but this is not 1 bit matmul, it's 1.58 bits with expensive unpacking

Google had been working on a big LLM but they wanted to resolve all the safety concerns before releasing it. It was only when OpenAI went "YOLO! Check this out!" that Google then internally said, "Damn the safety concerns, full speed ahead!" and now we find ourselves in this breakneck race in which all safety concerns have been sidelined.

What OpenAI did was train increasingly large transformer model instances. which was sensible because transformers allowed for a scaling up of training compared to earlier models. The resulting instances (GPT) showed good understanding of natural language syntax and generation of mostly sensible text (which was unprecedented at the time) so they made ChatGPT by adding new stages of supervised fine tuning and RLHF to their pretrained text-prediction models.

Thanks for pointing that out. I'll ask the issue creator if they've considered that. Would be nice if the maintainer would handle that (sigh) and link to the actual models used for testing (double sigh).

Thanks for the link, the GSM8K result actually leads the pack in that table, but math is indeed underwhelming. Qwen 2.5 is in the lead, but bitnet isn't far behind and it takes 1/6th as much memory during inference, and was trained on less than 1/4 the number of tokens. Pretty cool.

There's obviously an xkcd about this: https://xkcd.com/810/

I’ve been rounded up for things I wrote two decades ago because of my em dashes lol. The pitchfork mentality gives me little hope for how things are going to go once we have hive mind AGI robots pervasive in society.

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.

The title and the repo uses 1-bit when it means 1.58 bits tertiary values, it doesn't change any of my arguments (still xors and pop_counts).

Scaling seemed like the important idea that everyone was chasing. OpenAI used to be a lot more safety minded because it was in their non profit charter, now they’ve gone for-profit and weaponized their tech for the USA military. Pretty wild turnaround. Saying OpenAI was cavalier with safety in the early days is inaccurate. It was a skill issue. Remember Bard? Google was slow.

There were plenty of models the size of gpt3 in industry.

The core insight necessary for chatgpt was not scaling (that was already widely accepted): the insight was that instead of finetuning for each individual task, you can finetune once for the meta-task of instruction following, which brings a problem specification directly into the data stream.

From what I gather, there are no models, this is a framework for running 1bit models, but none have been trained. They are mainly demonstrating the possibility.

I once spent some time learning the proper usage of em-dashes, en-dashes, and hyphens, and tried to be conscientious about using them properly in my writing. Little did I know it would be wasted effort in the LLM era, when competent writing actually became a negative.

Not only are we losing the ability to communicate clearly without the assistance of computers, those who can are being punished for it.

If I was operating a bot farm, at this point I would probably add some bots that go around and accuse legit human users (or just random users) of being bots.

Created confusion and frustration will make it much harder to separate signal from the noise for most people.

How do you do ternary matmul with popcnt on 1.58 bit packed data?

I also don't expect those with poor MCPs to have any better CLIs or APIs, most of the big companies we want them for are not investing in DX/AX. I suspect i.e. that Intuit, if they had great APIs et al, would see it as a threat to their business.

Boy would I love to give my agent access to my Quickbooks. They pushed out an incomplete MCP and haven't touched it since.

https://github.com/intuit/quickbooks-online-mcp-server

Assuming 2 bit per values (first bit is sign and second bit is value).

actv = A[_:1] & B[_:1]

sign = A[_:0] ^ B[_:0]

dot = pop_count(actv & !sign) - pop_count(actv & sign)

It can probably be made more efficient by taking a column-first format.

Since we are in CPU land, we mostly deal with dot products that match the cache size, I don't assume we have a tiled matmul instruction which is unlikely to support this weird 1-bit format.