Misleading title but this is pretty exciting. Interesting how this is based on llama cpp. Its nice to see some momentum since they released the paper in 2023
I'm curious if 1-bit params can be compared to 4- or 8-bit params. I imagine that 100B is equivalent to something like a 30B model? I guess only evals can say. Still, being able to run a 30B model at good speed on a CPU would be amazing.
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 commodity CPUs. If 5-7 tok/s on a single CPU for 100B-class models is reproducible, that's a real milestone for on-device inference.
Framework is ready. Now we need someone to actually train the model.
There are 1 bit average GGUFs of large models, not perfect quality but they will hold a conversation. These days, there is also quantized finetuning to heal the damage.
> bitnet.cpp is the official inference framework for 1-bit LLMs (e.g., BitNet b1.58). It offers a suite of optimized kernels, that support fast and lossless inference of 1.58-bit models on CPU and GPU (NPU support will coming next).
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. Wikipedia is nice, but there's so much room for human error and bias there.
Also this is the direction the small LLMs are moving in already. They are too small for general knowledge, but getting quite good at tool use (incl. Googling).
Now we just need them to be very strict about what they know and don't know! (I think this is still an open problem, even with big ones.)
> 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?
It depends what that word "reasonable" means for your specific use-case ;)
Unfortunately reasoning ability depends on (or is enabled by) information intake during training. A model will know better what to search for and how to interpret it if the information was part of the training. So there is a trade off. Still I think the question is a practical one. Perhaps there are ideas to focus training on a) reasoning / conceptual modeling and b) reliance on external memory (search etc.) rather than internal memorization.
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.
If they had a big result like, native 1.58-bit quality clearly matches top peers, they would be saying that prominently in the repo.
The engineering/optimization work is nice, but this is not what people have been waiting for, as much as, can’t the Bitnet idea that seemed promise really deliver in a competitive way.
The output from this model is horrible! It's GPT-2 level babble and repeats entire paragraphs verbatim. It also reuses the same fake citation `(Jenkins, 2010)` over and over again. From the start of their video (which scrolls by fast enough that you don't see the slop clearly...)
```
Ecosystem Services and their impact on the Ecosystem
Ecosystem services refer to the services provided by ecosystems to the human society. These services include water, air, energy, nutrients, and soil (Jenkins, 2010). For instance, water is the most important service provided by an ecosystem and it helps in the conservation of water, irrigation and sanitation (Jenkins, 2010). On the other hand, air provides the oxygen needed for life.
The water cycle is a significant ecosystem service because it involves the cycling of water among the different parts of an ecosystem. It also involves the movement of water through the atmosphere, from one place to another. It is also the process of evaporation and condensation of water from the atmosphere. It also involves the movement of water from the air to the soil and water into the oceans.
The water cycle is a significant ecosystem service because it involves the cycling of water among the different parts of an ecosystem. It also involves the movement of water through the atmosphere, from one place to another. It is also the process of evaporation and condensation of water from the atmosphere. It also involves the movement of water from the air to the soil and water into the oceans.
```
It might interest you to know that one or two months ago, I had Claude port BitNet to WebGPU from the reference implementation, so that it runs right in your browser as a local model. After some debugging, the port seemed to work, but the model didn't function as well as the reference implementation so I'll have to work on it for a while. You can see a debugging session livestreamed here[1]. The released model file was about a gigabyte, it fits in most people's GPU's. We were also able to successfully fine-tune it right in the browser.
There's a lot that you can do when the model size is that small, yet still powerful.
Our next step is that we want to put up a content distribution network for it where people can also share their diffs for their own fine-tuned model. I'll post the project if we finish all the parts.
I think the README [1] for the new CPU feature is of more interest, showing linear speedups with number of threads. Up to 73 tokens/sec with 8 threads (64 toks/s for their recommended Q6 quant):
https://arxiv.org/pdf/2310.11453
The original paper [fig 1, bottom-right] seems to say it needs about 4-5x the parameters of a fp16 model. You can build it and run some models, but the selection is limited because it has to be trained from scratch. I imagine inference speed is faster compared with modern PTQ (4- and 8-bit quants) though.
The energy numbers are the real story here, 70-82% reduction on CPU inference. If 1-bit models ever get good enough, running them on commodity hardware with no GPU budget changes who can deploy LLMs. That's more interesting than the speed benchmarks imo.
I wonder when we begin to see the dividends of all the NPU PCs come into play. AMD have been doing some good work with their NPU/iGPU hybrid inference kernels. If these larger models could be scaled down to run on NPUs, you'd see much better power advantages, compared to running them on the CPU.
> I wonder when we begin to see the dividends of all the NPU PCs come into play.
A few months ago I used Whisper from OpenAI, an automatic speech recognition system released in 2002, on my modern 20-core Intel CPU to convert audio from a video file to text. It worked fine. Took a while and the machine got hot and the fans kicked in. I then found the Intel's optimized version of whisper that used NPU. It required a lot more steps to get working, but in the end it did work and was about 6x faster. And the machine remained cool and silent in the process. Since then I have become a fan of the NPUs. They are not NVIDIA GeForce RTX 5090, but they are significantly better than a modern CPU.
Why would they film a demo video of it spewing out barely-coherent rambling repetitive drivel? If your model sucks at writing essays, maybe just tell us that, and film a demo of it doing something it IS good at?
43 comments
[ 3.1 ms ] story [ 68.5 ms ] threadOne bit or one trit? I am confused!
demo shows a huge love for water, this AI knows its home
https://youtu.be/UldqWmyUap4
Also this is the direction the small LLMs are moving in already. They are too small for general knowledge, but getting quite good at tool use (incl. Googling).
Now we just need them to be very strict about what they know and don't know! (I think this is still an open problem, even with big ones.)
> 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?
It depends what that word "reasonable" means for your specific use-case ;)
[1] - https://jackson.dev/post/dont-sleep-on-bitnet/
My disappointment is immeasurable and my day is ruined.
The engineering/optimization work is nice, but this is not what people have been waiting for, as much as, can’t the Bitnet idea that seemed promise really deliver in a competitive way.
``` Ecosystem Services and their impact on the Ecosystem
Ecosystem services refer to the services provided by ecosystems to the human society. These services include water, air, energy, nutrients, and soil (Jenkins, 2010). For instance, water is the most important service provided by an ecosystem and it helps in the conservation of water, irrigation and sanitation (Jenkins, 2010). On the other hand, air provides the oxygen needed for life.
The water cycle is a significant ecosystem service because it involves the cycling of water among the different parts of an ecosystem. It also involves the movement of water through the atmosphere, from one place to another. It is also the process of evaporation and condensation of water from the atmosphere. It also involves the movement of water from the air to the soil and water into the oceans.
The water cycle is a significant ecosystem service because it involves the cycling of water among the different parts of an ecosystem. It also involves the movement of water through the atmosphere, from one place to another. It is also the process of evaporation and condensation of water from the atmosphere. It also involves the movement of water from the air to the soil and water into the oceans. ```
There's a lot that you can do when the model size is that small, yet still powerful.
Our next step is that we want to put up a content distribution network for it where people can also share their diffs for their own fine-tuned model. I'll post the project if we finish all the parts.
[1] https://www.youtube.com/live/x791YvPIhFo?is=NfuDFTm9HjvA3nzN
https://github.com/microsoft/BitNet/blob/main/src/README.md
With how much RAM? How much storage does it requires?
A few months ago I used Whisper from OpenAI, an automatic speech recognition system released in 2002, on my modern 20-core Intel CPU to convert audio from a video file to text. It worked fine. Took a while and the machine got hot and the fans kicked in. I then found the Intel's optimized version of whisper that used NPU. It required a lot more steps to get working, but in the end it did work and was about 6x faster. And the machine remained cool and silent in the process. Since then I have become a fan of the NPUs. They are not NVIDIA GeForce RTX 5090, but they are significantly better than a modern CPU.