151–160 of 179 posts
Earlier quoted context omitted.
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.
The new model they just released has impressive benchmark results: https://huggingface.co/microsoft/bitnet-b1.58-2B-4T Except on GSM8K and math...
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.
So Microsoft is actually using 2 bits instead of 1.58. In this case they could represent -1, 0, 1, 2. As inhibitory synapses account for 20%-30%, this could map well to how biological brains are structured. Does that make sense?
Can you explain your third statement? > As inhibitory synapses account for 20%-30%, this could map well to how biological brains are structured.
No concise HN comment will give you a complete picture of whats currently known about the human brain, so a platitude necessarily follows:
We call the nearly touching interfaces between neurons synapses, small packets / droplets of neurotransmitter are sent across this interface from the source to the target neuron. Such signals can be excitatory (promote the probability of excitation of the target firing soon) or inhibitory (inhibits the probability of the target firing soon). There are 2 types of sensitive areas on your average neuron: the dendrites (long branching tentacles, that receive excitatory signals) and the cell body where all the signals are accumulated to a local instantaneous "sum" is also sensitive to synaptic activation, but the synapses on the cell body are inhibitory, when sufficiently inhibited the neuron will refuse to fire its axons, so the inhibitory synapses on the cell body can gate the cumulative signal and prevent it from triggering this neuron temporarily. If the neuron does fire, this propagates along the axons (another type of branching tentacles, which lead to yet other neurons, sometimes touching them excitatorily at their dendrite, sometimes touching a neuron inhibitorily at their cell body.
I hope that helped?
Also as far as I know, this is more of a research curiosity - BitNet really doesn't perform that well on evals.
I think Qwen3.5 2B is the best you can get in the ~1GB class.
Earlier quoted context omitted.
The new model they just released has impressive benchmark results: https://huggingface.co/microsoft/bitnet-b1.58-2B-4T Except on GSM8K and math...
Thanks, but where did you actually find the new model? The newest one seems to be 11 months old, from Apr 15, 2025.
Earlier quoted context omitted.
Can you explain your third statement? > As inhibitory synapses account for 20%-30%, this could map well to how biological brains are structured.
In the human brain most synapses are indeed excitatory, while a minority is inhibitory. No concise HN comment will give you a complete picture of whats currently known about the human brain, so a platitude necessarily follows: We call the nearly touching interfaces between neurons synapses, small packets / droplets of neurotransmitter are sent across this interface from the source to the target neuron. Such signals c…
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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).…
Fair enough — I've been lurking since 2019 and picked a bad day to start commenting on everything at once. Not a bot, just overeager. I'll pace myself.
It feels like it'd take someone superhuman to come across different stories, have such opinions and type and submit both of these in that timeframe or queuing up comments to post rapid-fire.
Conspicuously too, as another pointed out, is every single comment of yours uses an em dash, which despite occasionally using myself (hey look they're in this reply) is not in every single comment. Idk, if I was being seriously accused of botting I'd put more reasoning into my response about it.
headline hundred billion parameter, none of the official models are over 10 billion parameters. Curious.
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 had the same question, after some debates with Chatgpt, it's not the "quantize" for post-training we often witness these days, you have to use 1 trit in the beginning since pre-train.
Earlier quoted context omitted.
Fair enough — I've been lurking since 2019 and picked a bad day to start commenting on everything at once. Not a bot, just overeager. I'll pace myself.
Your account posted dense, opinionated and structured paragraphs mere minutes apart—sometimes the same minute—for multiple story submissions. Even with my own sometimes lengthy replies this would be infeasible to both instantly have structured opinions and type them out in time. Two of your posts were posted the same minute, with a combined word count of 146 . It feels like it'd take someone superhuman to come across…