I tried a few things with it. Got it driving Cursor, which in itself was impressive - it handled some tool usage. Via cursor I had it generate a few web page tests.

On a monte carlo simulation of pi, it got the logic correct but failed to build an interface to start the test. Requesting changes mostly worked, but left over some symbols which caused things to fail. Required a bit of manual editing.

Tried a Simon Wilson pelican as well - very abstract, not recognizable at all as a bird or a bicycle.

Pictures of the results here: https://x.com/pwnies/status/2039122871604441213

There doesn't seem to be a demo link on their webpage, so here's a llama.cpp running on my local desktop if people want to try it out. I'll keep this running for a couple hours past this post: https://unfarmable-overaffirmatively-euclid.ngrok-free.dev

Results: 8 passed, 0 failed, 17 errored out of 25

That puts it right between Qwen3.5-4B (7/25) and Nanbeige4.1-3B (9/25) for example, but it took only 200 seconds for the whole test. Qwen3.5 took 976 seconds and Nanbeige over 2000 (although both of these were on my 1070 so not quite the same hardware)

Granite 7B 4bit does the test in 199 seconds but only gets 4/25 correct.

See https://sql-benchmark.nicklothian.com/#all-data (click on the cells for the trace of each question)

Errors are bad tool calls (vs failures which is incorrect SQL)

I used @freakynit's runpod (thanks!)

[1] https://news.ycombinator.com/item?id=47597268

For its size (1.2GB download) it's very impressive.

Here's a pelican it drew me running on my phone - the SVG comments are good, the image not so much: https://tools.simonwillison.net/svg-render#%3Csvg%20width%3D...

https://ofo1j9j6qh20a8-80.proxy.runpod.net

  ./build/bin/llama-server \
   -m ../Bonsai-8B.gguf \
   -ngl 999 \
   --flash-attn on \
   --host 0.0.0.0 \
   --port 80 \
   --ctx-size 65500 \
   --batch-size 512 \
   --ubatch-size 512 \
   --parallel 5 \
   --cont-batching \
   --threads 8 \
   --threads-batch 8 \
   --cache-type-k q4_0 \
   --cache-type-v q4_0 \
   --log-colors on

A bit of of benchmarks I did:

1. Input: 700 tokens, ttfs: ~0 second, outputs: 1822 tokens ~190t/s

1. Input: 6400+ tokens, ttfs: ~2 second, outputs: 2012 tokens at ~135t/s

Vram usage was consistently at ~4GiB.

Then found out they didn't implement AVX2 for their Q1_0_g128 CPU kernel. Added that and getting ~12t/s which isn't shabby for this old machine.

Cool model.

> *Fathers of Harry and James Potter*: - Sirius Black is the *father* of *James Potter* (the older brother of Harry).

> - James Potter is *Harry's uncle* and the *older brother* of *Luna Lovegood*.

> - This means *Sirius and James are Harry's uncles*, though they are *father and brother*.

https://pastebin.com/WAAmFKfX

Especially considering that these models seem to more or less just be quantized variants of Qwen3 with custom kernels and other inference optimizations (?) rather than fine tuned or trained from scratch with a new architecture, I am very surprised (or suspicious rather) that they didn't do the obvious comparison with a quantized Qwen3.

Their (to my knowledge) new measure/definition of intelligence seems reasonable, but introducing something like this without thorough benchmarking + model comparison is even more of a red flag to me.

[0] https://github.com/PrismML-Eng/Bonsai-demo/blob/main/1-bit-b...

Do I need to build their llama.cpp fork from source?

Looks like they only offer CUDA options in the release page, which I think might support CPU mode but refuses to even run without CUDA installed. Seems a bit odd to me, I thought the whole point was supporting low end devices!

Edit: 30 minutes of C++ compile time later, I got it running. Although it uses 7GB of RAM then hangs at Loading model. I thought this thing was less memory hungry than 4 bit quants?

Edit 2: Got the 4B version running, but at 0.1 tok/s and the output seemed to be nonsensical. For comparison I can run, on the same machine, qwen 3.5 4B model (at 4 bit quant) correctly and about 50x faster.

It seems PrismML has implemented a better version of an idea I had a while back: what if we had a 1-bit model where the scale of the weight is determined by its position. The model would have to be trained from the ground up for this though, which is why I never tried it. The interleaved scale factor approach of Bonsai is a much more flexible approach at almost the same cost.

https://huggingface.co/prism-ml/Bonsai-8B-gguf

tensor 'token_embd.weight' has invalid ggml type 41. should be in [0, 41) loader knows tensor types 0..40, but the model contains type 41

Though I couldn't get actual English output from it, so maybe something went wrong while running it.

(math: - log error / model size = 1 <-> error / model size = 1/e )

I'm currently setting this one up, if it works well with a custom LoRa ontop ill be able to run two at once for my custom memory management system :D

They link the (free) locally.ai iPhone app, but the bonsai model doesn't present in the list. You have to get it via settings.

On my ancient SE-2, Siri integration falls down, but the chat in their app runs about half the speed I can read. So far, more than 50% correct, and usable (and seems to speed up as you use it).

I'll try it just to clean up input in a pipeline to another model. I gave it a paragraph from the NYTimes and it did a great job, so it should be good at correcting voice input and keyboard typos.

Can't wait to give it a spin with ollama, if ollama could list it as a model that would be helpful.

Got this nonsense answer: Interesting Scientific Discovery: The Discovery of the "Boson" in 1924 by Charles K. Alton ("Chuck") Alton

What it is: In 1924, American physicist Charles K. Alton discovered the "boson", a particle that would later be named after the Italian physicist Enrico Fermi. This discovery was made while he was working at the University of Chicago, where he was studying the properties of neutrons and protons.

Why it's significant: The boson Alton discovered was the meson, a subatomic particle that plays a crucial role in the strong nuclear force that holds atomic nuclei together. However, Alton's original work was more focused on the neutron, and he mistakenly identified the boson as a neutron—a common mistake at the time. This was not a scientific error, but rather a misunderstanding of the data. His work, however, laid the groundwork for the later discovery of mesons by Enrico Fermi and Richard Feynman, which was a major breakthrough in the field of particle physics.

Impact on Understanding:

• Mesons are now known to be the carriers of the strong force, which is one of the four fundamental forces of nature. • This discovery helped physicists understand the structure of the nucleus and the nature of nuclear binding. • It was a key step in the development of quantum chromodynamics (QCD), which describes the strong force at the subatomic level. • Alton's work is often overlooked in the history of particle physics, yet it was a critical early step in the field.

Why it's interesting: This story highlights how even early scientific discoveries can be misinterpreted or overlooked—sometimes not because of a lack of intelligence, but due to the complexity of the data and the evolving nature of scientific understanding. It also shows how one scientist's work can be built upon by others, leading to major breakthroughs.

Bonus: Alton was not the only person involved. His work was built upon earlier research by Edward Teller and Leopold Rabi, and it was ultimately Fermi who discovered the meson in 1932 using the "storm cloud" method, where he used a cloud chamber to observe the effects of high-energy particles.

So, while the boson Alton discovered was initially misidentified, it was a crucial piece of the puzzle in the development of modern particle physics.

Srsly though.

Their main contribution seems to be hyperparameter tuning, and they don't compare against other quantization techniques of any sort.