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Show HN: 1-Bit Bonsai, the First Commercially Viable 1-Bit LLMs
- deleted 6mo ago[deleted]
- yodon 6mo agoIs Bonsai 1 Bit or 1.58 Bit?
- woadwarrior01 6mo ago1-bit g128 with a shared 16-bit scale for every group. So, effectively 1.125 bit.
- mchusma 6mo agoI was excited about the 1.58 bit models from a year or two ago, but the never seemed to go anywhere. Curious in particular how this scales up.
- NooneAtAll3 6mo ago1 bit or 1 trit*
- simian1983 6mo agoRan this prompt: Share an interesting scientific discovery or historical fact that most people don't know about. Please explain why it's significant and its impact on our understanding. 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.
- Xx_crazy420_xX 6mo agoEither we will be expecting the models to compress whole wikipedia and stale on the size reduction, or focus on the reasoning capabilities. My intuition is that by forcing models to remember everything we are wasting parameter space which can be allocated for more abstract thinking.
- londons_explore 6mo agoIntegrating tool use into the training process should fix this. Rather than learn about President Lincoln, the model can learn to look that info up with a search tool and use it to get better answers. Just like a human does. I don't learn what 76x35 is... I learn that a calculator can give me that answer so I don't need to memorize it.
- croemer 6mo ago[dead]
- adrian_b 6mo agoReally funny how it multiplexes together in a quasi-random order very short excerpts from the true descriptions of various discoveries made by Bose, Einstein, Fermi, Dirac, Yukawa and a few others into a completely nonsense text.
- stogot 6mo agoWhat is the value of a 1 bit? For those that do not kno
- trebligdivad 6mo agoSpeed and density.
- jacquesm 6mo agoThat you can process many operations with a single instruction.
- SwellJoe 6mo ago0 or 1
- jjcm 6mo agoTechnically not in this case, or not effectively. The 0 or 1 correspond to a FP16 scaling factor for each group of 128 bits. The value fluctuates between each group of 128.
- fgfarben 6mo agoI can port it to an FPGA and so can you.
- syntaxing 6mo agoSuper interesting, building their llama cpp fork on my Jetson Orin Nano to test this out.
- alyxya 6mo agoI expect the trend of large machine learning models to go towards bits rather than operating on floats. There's a lot of inefficiency in floats because typically they're something like normally distributed, which makes the storage and computation with weights inefficient when most values are clustered in a small range. The foundation of neural networks may be rooted in real valued functions, which are simulated with floats, but float operations are just bitwise operations underneath. The only issue is that GPUs operate on floats and standard ML theory works over real numbers.
- cubefox 6mo ago> and standard ML theory works over real numbers. This paper uses binary numbers only, even for training, with a solid theoretical foundation: https://proceedings.neurips.cc/paper_files/paper/2024/file/718a3c5cf135894db6e718725f52ef9a-Paper-Conference.pdf https://proceedings.neurips.cc/paper_files/paper/2024/file/7... TL;DR: They invent a concept called "Boolean variation" which is the binary analog to the Newton/Leibniz derivative. They are then able to do backpropagation directly in binary.
- hrmtst93837 6mo ago[flagged]
- guerrilla 6mo agoWell this is perfect then. We just post-process models like this after training.
- OutOfHere 6mo agoHow do I run this on Android?
- najarvg 6mo agoPocket Pal is what I've seen used before. Although recently heard about "Off Grid" but not read any reviews about it or tried it personally so caveat emptor. Will see if the community has other suggestions
- Archit3ch 6mo agoDoesn't Jevons paradox dictate larger 1-bit models?
- wmf 6mo agoYeah, hopefully they release >100B models.
- _fw 6mo agoWhat’s the trade-off? If it’s smaller, faster and more efficient - is it worse performance? A layman here, curious to know.
- kvdveer 6mo agoTheir own (presumably cherry picked) benchmarks put their models near the 'middle of the market' models (llama3 3b, qwen3 1.7b), not competing with claude, chatgtp, or gemini. These are not models you'd want to directly interact with. but these models can be very useful for things like classification or simple summarization or translation tasks. These models quite impressive for their size: even an older raspberry pi would be able to handle these. There's still a lots of use for this kind of model
- sossov 6mo ago[dead]
- adityashankar 6mo agoIf you look at their whitepaper (https://github.com/PrismML-Eng/Bonsai-demo/blob/main/1-bit-bonsai-8b-whitepaper.pdf https://github.com/PrismML-Eng/Bonsai-demo/blob/main/1-bit-b...) you'll notice that it does have some tradeoffs due to model intelligence being reduced (page 10) The average of MMLU Redux,MuSR,GSM8K,Human Eval+,IFEval,BFCLv3 for this model is 70.5 compared to 79.3 for Qwen3, that being said the model is also having a 16x smaller size and is 6x faster on a 4090....so it is a tradeoff that is pretty respectable I'd be interested in fine tuning code here personally
- jjcm 6mo ago1 bit with a FP16 scale factor every 128 bits. Fascinating that this works so well. 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 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 https://unfarmable-overaffirmatively-euclid.ngrok-free.dev
- adityashankar 6mo agohere's the google colab link, https://colab.research.google.com/drive/1EzyAaQ2nwDv_1X0jaC5XiVC3ZREg9bdG?usp=sharing https://colab.research.google.com/drive/1EzyAaQ2nwDv_1X0jaC5... since the ngrok like likely got ddosed by the number of individuals coming along
- jjcm 6mo agoGood call. Right now though traffic is low (1 req per min). With the speed of completion I should be able to handle ~100x that, but if the ngrok link doesn't work defo use the google colab link.
- adityashankar 6mo agoThe link didn't work for me personally, but that may be a bandwidth issue with me fighting for a connection in the EU
- qingcharles 6mo agoThanks, that works. I only tested the 1.7B. It has that original GPT3 feel to it. Hallucinates like crazy when it doesn't know something. For something that will fit on a GTX1080, though, it's solid. We're only a couple of years into optimization tech for LLMs. How many other optimizations are we yet to find? Just how small can you make a working LLM that doesn't emit nonsense? With the right math could we have been running LLMs in the 1990s?
- hatthew 6mo agoI feel like it's a little disingenuous to compare against full-precision models. Anyone concerned about model size and memory usage is surely already using at least an 8 bit quantization. Their main contribution seems to be hyperparameter tuning, and they don't compare against other quantization techniques of any sort.
- techpulselab 6mo ago[flagged]
- volume_tech 6mo ago[flagged]
- keyle 6mo agoExtremely cool! Can't wait to give it a spin with ollama, if ollama could list it as a model that would be helpful.
- ariwilson 6mo agoVery cool and works pretty well!
- onlyrealcuzzo 6mo agoI'm fascinated by these smaller models. The amount of progress they've been making is incredible. Is anyone following this space more closely? Is anyone predicting performance at certain parameter sizes will plateau soon? Unlike the frontier models, these don't seem to be showing much progress of slowing down.
- tim-projects 6mo agoOn the harness side there's a huge amount of optimisation room to go as well. I strongly think smaller models will end up being able to do most coding tasks in the future, once they are reigned in properly
- deleted 6mo ago[deleted]
- imta71770 6mo ago[dead]
- marak830 6mo agoIt's been a hell of a morning for llama heads - first this, then the claude drop and turboquant. 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
- bilsbie 6mo agoI can’t see how this is possible. You’re losing so much information.
- MarsIronPI 6mo agoIt's because they're natively trained with 1 bit, so it's not losing anything. Now, the question might be how they manage to get decent predictive performance with such little precision. That I don't know.
- syntaxpr 6mo agoNot training. Transposing rows/columns of matrices to group 128 parameters with similar (shared) scale factor. Qwen-3 model.
- MarsIronPI 6mo agoI'm not sure what you mean. Could you please elaborate?
- txrx0000 6mo agoI always remind myself and everyone else that human DNA is "only" 1.6 GB of data, and yet it encodes all of the complex systems of the human body including the brain, and can replicate itself. Our intuitive feel of how much stuff can be packed into how many bits are probably way off from the true limits of physics.
- kennywinker 6mo agoAnd anybody who’s ever met a baby can tell you, they score very poorly on most llm benchmarks.
- humanjir 6mo agoThat's not strictly true - DNA doesnt replicate itself, a cell with DNA replicates itself. You need to count the information contained in the non-DNA part of the cell too. Just in case it's not obvious, you can't take human DNA and put it in a cat cell, it won't work, that cell won't replicate.
- zephyrwhimsy 6mo ago[flagged]
- tacotime 6mo ago"Don't post generated comments or AI-edited comments. HN is for conversation between humans." https://news.ycombinator.com/newsguidelines.html#generated https://news.ycombinator.com/newsguidelines.html#generated
- noman-land 6mo agoHow can you tell?
- Dwedit 6mo agoPresumably because a new account, and an offtopic post about AI. Then you look at the post history.
- 68768-8790 6mo ago[dead]
- wild_egg 6mo agoDon't have a GPU so tried the CPU option and got 0.6t/s on my old 2018 laptop using their llama.cpp fork. 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.
- deleted 6mo ago[deleted]
- deleted 6mo ago[deleted]
- cubefox 6mo ago"Not shabby" is a big understatement.
- UncleOxidant 6mo agoAre you getting anything besides gibberish out of it? I tried their recommended commandline and it's dog slow even though I built their llama.cpp fork with AVX2 enabled. This is what I get: $ ./build/bin/llama-cli -hf prism-ml/Bonsai-8B-gguf -p "Explain quantum computing in simple terms." -n 256 --temp 0.5 --top-p 0.85 --top-k 20 -ngl 99 > Explain quantum computing in simple terms. \( , None ( no for the. (,./. all.2... the ..... by/ EDIT: It runs fine in their collab notebook. Looking at that you have to do: git checkout prism (in the llama.cpp repo) before you build. That's a missing instruction if you're going straight to their fork of llama.cpp. Works fine now.
- rcdwealth 6mo ago[dead]
- childrapst 6mo ago[dead]
- andai 6mo agoDoes anyone know how to run this on CPU? 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.
- plombe 6mo agoInteresting post. Curious to know how they arrived at intelligence density = Negative log of the model's error rate divided by the model size.
- deleted 6mo ago[deleted]
- kent8192 6mo agoOh, boy. This good tool hates my LM Studio... The following message appears when I run Bonsai in my LM Studio. I think my settings have done something wrong. ``` Failed to load the model Error loading model. (Exit code: null). Please check the settings and try loading the model again. ```
- liuliu 6mo agoIt needs a mlx fork because the lowest bit in mlx is 2 currently (for affine quantization).
- riidom 6mo agoThat mlx is for apple hardware only, though? Or did I misunderstand something.
- dragonwriter 6mo agoIt needs a llama.cpp fork, too; so the stock runtime (based on stock llama.cpp) used by LM Studio presumably won't work for it.
- dodos 6mo agoSame issue here, wanted to give it a shot but ran into that error trying to load the model in lm studio.
- drob518 6mo agoI’m really curious how this scales up. Bonsai delivers an 8B model in 1.15 GB. How large would a 27B or 35B model be? Would it still retain the accuracy of those large models? If the scaling holds, we could see 100+B models in 64 GB of RAM.
- cubefox 6mo agoAlso depends on how expensive training these models is. It's probably at least as expensive as full precision models, otherwise they would have mentioned it.
- londons_explore 6mo agoMy guess is the training process is their secret sauce...
- cubefox 6mo agoYes, but their training speed is not secret. If their process were fast, they would have said so.
- MeetRickAI 6mo ago[dead]
- andai 6mo agoThe site says 14x less memory usage. I'm a bit confused about that situation. The model file is indeed very small, but on my machine it used roughly the same RAM as 4 bit quants (on CPU). Though I couldn't get actual English output from it, so maybe something went wrong while running it.
- simonw 6mo agoYou can run this model on an iPhone via the latest update to this Locally AI app: https://apps.apple.com/us/app/locally-ai-local-ai-chat/id6741426692 https://apps.apple.com/us/app/locally-ai-local-ai-chat/id674... 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%22500%22%20height%3D%22300%22%20viewBox%3D%220%200%20500%20300%22%20xmlns%3D%22http%3A%2F%2Fwww.w3.org%2F2000%2Fsvg%22%3E%0A%20%20%3C!--%20Background%20--%3E%0A%20%3Crect%20width%3D%22100%25%22%20height%3D%22100%25%22%20fill%3D%22%23f0f8ff%22%20%2F%3E%0A%0A%20%20%3C!--%20Pelican%20--%3E%0A%20%3Cpath%20d%3D%22M150%20150%20C170%20130%20190%20150%20210%20150%20C230%20150%20250%20130%20270%20150%20C290%20130%20310%20150%20330%20150%20C350%20130%20370%20150%20390%20150%20C410%20130%20430%20150%20450%20150%22%20%0A%20%20%20%20%20%20%20%20fill%3D%22%23ffeb3b%22%20%2F%3E%0A%0A%20%20%3C!--%20Bicycle%20--%3E%0A%20%3Cpolygon%20points%3D%22300%2C100%20300%2C130%20280%2C130%20280%2C160%20300%2C160%20320%2C160%20340%2C160%20340%2C130%20320%2C130%20300%2C130%22%20%0A%20%20%20%20%20%20%20%20%20%20%20fill%3D%22%2381c784%22%20%2F%3E%0A%20%3Ccircle%20cx%3D%22300%22%20cy%3D%22100%22%20r%3D%225%22%20fill%3D%22%2381c784%22%20%2F%3E%0A%0A%20%20%3C!--%20Bicycle%20wheels%20--%3E%0A%20%3Ccircle%20cx%3D%22285%22%20cy%3D%22130%22%20r%3D%225%22%20fill%3D%22%2381c784%22%20%2F%3E%0A%20%3Ccircle%20cx%3D%22315%22%20cy%3D%22130%22%20r%3D%225%22%20fill%3D%22%2381c784%22%20%2F%3E%0A%20%3Ccircle%20cx%3D%22285%22%20cy%3D%22160%22%20r%3D%225%22%20fill%3D%22%2381c784%22%20%2F%3E%0A%20%3Ccircle%20cx%3D%22315%22%20cy%3D%22160%22%20r%3D%225%22%20fill%3D%22%2381c784%22%20%2F%3E%0A%0A%20%20%3C!--%20Bicycle%20handlebars%20--%3E%0A%20%3Cline%20x1%3D%22300%22%20y1%3D%22100%22%20x2%3D%22300%22%20y2%3D%22160%22%20stroke%3D%22%2381c784%22%20stroke-width%3D%224%22%20%2F%3E%0A%20%3Cline%20x1%3D%22280%22%20y1%3D%22130%22%20x2%3D%22300%22%20y2%3D%22130%22%20stroke%3D%22%2381c784%22%20stroke-width%3D%222%22%20%2F%3E%0A%20%3Cline%20x1%3D%22320%22%20y1%3D%22130%22%20x2%3D%22300%22%20y2%3D%22130%22%20stroke%3D%22%2381c784%22%20stroke-width%3D%222%22%20%2F%3E%0A%0A%20%20%3C!--%20Bicycle%20seat%20--%3E%0A%20%3Cpolygon%20points%3D%22300%2C160%20280%2C140%20280%2C160%20300%2C160%20320%2C140%20320%2C160%22%20%0A%20%20%20%20%20%20%20%20%20%20%20fill%3D%22%2381c784%22%20%2F%3E%0A%0A%20%20%3C!--%20Bicycle%20seat%20handle%20--%3E%0A%20%3Cline%20x1%3D%22280%22%20y1%3D%22140%22%20x2%3D%22280%22%20y2%3D%22160%22%20stroke%3D%22%2381c784%22%20stroke-width%3D%221.5%22%20%2F%3E%0A%20%3Cline%20x1%3D%22320%22%20y1%3D%22140%22%20x2%3D%22320%22%20y2%3D%22160%22%20stroke%3D%22%2381c784%22%20stroke-width%3D%221.5%22%20%2F%3E%0A%0A%20%20%3C!--%20Bicycle%20spokes%20--%3E%0A%20%3Cline%20x1%3D%22300%22%20y1%3D%22160%22%20x2%3D%22300%22%20y2%3D%22180%22%20stroke%3D%22%2381c784%22%20stroke-width%3D%221.5%22%20%2F%3E%0A%20%3Cline%20x1%3D%22300%22%20y1%3D%22160%22%20x2%3D%22280%22%20y2%3D%22170%22%20stroke%3D%22%2381c784%22%20stroke-width%3D%221.5%22%20%2F%3E%0A%20%3Cline%20x1%3D%22300%22%20y1%3D%22160%22%20x2%3D%22320%22%20y2%3D%22170%22%20stroke%3D%22%2381c784%22%20stroke-width%3D%221.5%22%20%2F%3E%0A%3C%2Fsvg%3E%0A https://tools.simonwillison.net/svg-render#%3Csvg%20width%3D...
- ycui1986 6mo agoi hope someone do a 100b 1-bit parameter model. that should fit into most 16GB graphics cards. local AI democratized.
- ggamezar 6mo agoMisses comparison with qwen 3.5, though mentioned qwen 3. Is there a reason why?
- freakynit 6mo agoOpen access for next 5 hours (8GiB model, running on RTX 3090) or until server crashes or the this spot instance gets taken away :) => https://ofo1j9j6qh20a8-80.proxy.runpod.net 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 The server can serve 5 parallel request, with each request capped at around `13K` tokens... 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.
- logicallee 6mo agoThat was really impressive. https://pastebin.com/PmJmTLJN https://pastebin.com/PmJmTLJN pretty much instantly. (Very weak models can't do this.)
- TRCat 6mo agoThank you! I am impressed by the speed of it.
- ggerganov 6mo agoBetter keep the KV cache in full precision
- freakynit 6mo agoWow.. the GOAT himself.. thank you sooo much for creating llama.cpp ... will re-deploy with full kv cache once requests stop coming.
- kgeist 6mo ago[dead]
- Imustaskforhelp 6mo agoKind sir, May I say to you thanks for doing so! I really appreciate it :D
- robonot 6mo agoreally impressive for the size. Curious to see what happens when someone trains a 100B+ model natively at 1-bit.
- wshell 6mo agoWhat would be a good TTS to run with this?
- est 6mo agois this somewhat similar to Microsofot Bitnet?
- naasking 6mo agoSimilar in spirit but different in execution as far as I can tell.
- unit149 6mo ago[dead]
- fxwin 6mo agoI'm very skeptical of the advantage they're claiming here. The whitepaper [0] only compares these to full precision models, when the more interesting (and probably more meaningful) comparison would be with other quantized models with a similar memory footprint. 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-bonsai-8b-whitepaper.pdf https://github.com/PrismML-Eng/Bonsai-demo/blob/main/1-bit-b...
- riedel 6mo agoActually IMHO the promise would be beyond standard FP4 quants. I think the goal is more where 1.58 bit (ternary) quants are heading. Having said that it would be interesting to see performance on nonstandard HW.
- techpulselab 6mo ago[flagged]
- nl 6mo agoI ran my custom agentic SQL debugging benchmark against it and I'm impressed. 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 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 https://news.ycombinator.com/item?id=47597268
- deleted 6mo ago[deleted]
- Imustaskforhelp 6mo agoI have been using @freakynit's runpod as well all be it, I like making working pomodoro apps as my own custom test, and although its not good for it (none of the prototypes work), I feel like it can be good within a specific context like Sql as you mention. I imagine this being used as sub-agents with some sota models directing them but I wasn't really able to replicate it personally (I had asked Claude to create a detailed plan for a pomodoro app and then passed it to Bonsai) I also tried its writing skills and actually they are kind-of decent, I also found that this model actually uses very comparatively little em-dashes.Its fine tunes are gonna be some really amazing things to come out. I hope someone makes a fine tune for website/tampermonkey extensions ;) I remember using chatgpt-3 to use svelte/sveltekit to make a green button to blue button and having the text inside those buttons change and it's my personal wow moment from gpt-3 (This wasn't really able to accurately replicate it even in plain js), but I think that maybe the current model isn't good at writing html but the possibilities with custom-training these models and the idea of 1 bit model feels really great to me. Especially with the idea of Ngram-embedding[0] (Meituanlongcat/LongCatFlashLite) and its idea. I imagine a 1 bit model + Ngram-embedding idea and I feel it can have many endless possibilities. [0]: https://news.ycombinator.com/item?id=46803687 https://news.ycombinator.com/item?id=46803687 (I had submitted this but it seems to have had no attention during that time) Maybe a 1 bit model like this and diffusion models for coding purposes might also go hand in hand, there are many experiments which can be done with this! (Also yes, many thanks to @freakynit running the runpod, I think I really learnt many things about this model in particular because of his runpod) TLDR: I feel like this model is good within writing or atleast better in it than usual and it can be good asking it General purpose questions default but I feel like its not good at making html which can be fair, good to see that they are good in sql, but, not sure how they might approach in normal coding tasks. But either way, its an extremely fun model to play with! (Edit: After some more tries, I have been able to make even one prototype of it after Gemini had holded its hands/giving it the code/errors, its not the best at this but still it works, just barely, https://gist.github.com/SerJaimeLannister/e90e8a134e4163f205b9336a911d907e https://gist.github.com/SerJaimeLannister/e90e8a134e4163f205...)
- vx_r 6mo agoEagerly waiting for mlx to merge 1bit quantization pr to try this out.
- afaik69 6mo agoany tutorial on how to run this on linux cpu only?
- druskacik 6mo agoThe 8B model response to my "Harry Potter knowledge-bench" question is too funny not to share. > *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 https://pastebin.com/WAAmFKfX
- WaterRun 6mo agoFeels a bit like gradually moving back toward analog circuits, step by step. There is less and less need for the precision that digital circuits provide.
- TheLNL 6mo agoWhat ? How did you come to this conclusion with this context ?
- WaterRun 6mo agoTraditional programming requires the absolute precision provided by digital circuits; a single bit flip can lead to a completely different outcome. Large models do not require that kind of exactness. They are somewhat like a "field" or a "probability cloud": as long as the main directional tendency is correct, a few individual deviations—or even a whole cluster of them—make almost no difference.
- p0u4a 6mo agoHow much does training such a model cost?
- Nihilartikel 6mo agoSounds like about the right level of cognition for a talkie toaster!
- Udo 6mo agoThis looks very promising. It would be cool if support for Bonsai-style models would land in mainline MLX soon, looking forward to trying it out. 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.
- ide0666 6mo agoInteresting parallel to spiking neural networks — they're essentially 1-bit communication (spike or no spike) with analog membrane potentials. We use 5k Izhikevich neurons for quadruped locomotion control and they beat PPO at the same sample budget. The efficiency argument for 1-bit goes beyond LLMs.
- ant28 6mo agoWhat's up with - log error / model size? I'm not an LLM person, but a ratio of ~1 means a roughly 40% error rate for its size? I don't follow (math: - log error / model size = 1 <-> error / model size = 1/e )
- naasking 6mo agoGreat! I hope the era of 1-bit LLMs really gets going.
- imta71770 6mo ago[dead]
- steffs 6mo ago[flagged]
- WhitneyLand 6mo agoA bit misleading to say they take 14x less memory, no one is doing inference with 16-bit models.
- kraftaa 6mo ago[dead]
- iJohnDoe 6mo agoTried running the models with the latest LM Studio, llama.cpp, and Ollama. All failed. https://huggingface.co/prism-ml/Bonsai-8B-gguf 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
- m0do1 6mo agoprismML provides a llama.cpp fork which is compatible with the 1 bit models: https://github.com/PrismML-Eng/llama.cpp https://github.com/PrismML-Eng/llama.cpp After fails with Ollama and main llama.cpp the fork worked on my M5 MBA. Edit: Typos
- aaroninsf 6mo ago"Now do ~2-bit and ~4-bit" Srsly though.
- AIOperator2026 6mo ago[dead]
- w10-1 6mo agoanecdotal experience report: 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.
- rpdaiml 6mo agocurious and wondering its porting and usage on edge devices?
- aimemobe 6mo ago[flagged]