Rendered at 01:42:16 GMT+0000 (Coordinated Universal Time) with Cloudflare Workers.
SwellJoe 5 hours ago [-]
What I most want to see it compared to is Gemma 4 12B in the 4-bit QAT version. It's barely bigger than this at just under 7GB, so it also runs on just about any modern device and is remarkably smart for its size. It's an excellent tool user, crazy good vision for its size. I'm still trying to wrap my head around how much is lost with each step down in resolution, but the QAT versions from Google seem to prove the answer is "very little" at four bits.
janalsncm 3 hours ago [-]
Based on their numbers and cross referencing with the Gemma numbers, this model crushes Gemma 4 12b on math and coding, is slightly worse on knowledge and tool calling, and is significantly worse on vision tasks.
recursivegirth 2 hours ago [-]
I think this is where leveraging classifier models will become important. The frontier LLM models do "everything", while we've known for a while that to truly scale this we will need to distill models into their individual functions. I don't see this as necessarily a bad thing and hope more is done in this space. Very promising.
bigmadshoe 2 hours ago [-]
Bitter lesson is knocking. Mixture of experts is essentially what you’re describing but free from unnecessary inductive biases.
tharkun__ 56 minutes ago [-]
From my own experiments with local, low VRAM model use vs. what I'm used to from using Claude at work is that being good at "coding" is of no use, if you're worse at "tool calling" as coding in an agentic way requires quite a bit of tool calling.
If you can "hide" different models of 8GB VRAM requirements each that have those specialties and mix and match them for me without having to manage it manually, I'll be impressed. Until then I will keep using my Claude, because "remarkably good _for their size_" models I've tried so far just sucked at trying to use them the way I code at work with Claude.
dofm 1 hours ago [-]
FWIW my tests on my little puzzles suggest that it is not better than the Gemma 4 12B on SQL. It really does seem to get quite tangled up on stuff.
PHP/Wordpress code seems OK (better than the Gemma) but it gets stuck in reasoning loops.
Mind you, I am something of a cynic about the underlying 27B dense Qwen; I think the 35B MoE model is often better and it is just so, so much faster.
hypercube33 2 hours ago [-]
More to the argument that we need a model of models - one general one that calls specialists in to do what they are good at and handles that like a foreman for you.
dofm 1 hours ago [-]
This is somewhat akin to the "one-expert-per-query" solution Apple are using in their small foundation models I think?
CuriouslyC 2 hours ago [-]
The things it loses are all the things that google models are historically excellent at, so that's a reasonable performance. I think the take home here is that the 1 bit models are probably better, but it's not a slam dunk given advanced quantization techniques.
sosodev 2 hours ago [-]
The Gemma models are so good at vision. It seems particularly important for phones. Also, they write in a much more pleasant manner than Qwen imo.
dofm 1 hours ago [-]
I absolutely agree that Gemma 4 writes well out of the box. Free of a lot of the standard American model blog spam writing style but a little more fluid than Qwen.
SwellJoe 2 hours ago [-]
To be fair, everything (roughly within an order of magnitude in size) is worse on vision. 12b is a beast for vision tasks, better than its bigger siblings, even.
verdverm 5 hours ago [-]
4bits is a cutoff point for many model families, but also depends on what parts you quant to 4bits vs alternatives (weights, weight+activation, kv cache). Also depends on model size and task, lots of nuance in quanting I've come to learn.
I'm currently working towards an updated version (not an og author), curious if others are aware of similar surveys, as I have yet to do a real lit search.
dofm 3 hours ago [-]
The key point here, I think, is not the 4-bit but the QAT — the model is trained with the objective of losing the least at 4-bit quantiZation (I am assuming it is literally about assigning numbers that quantize better).
The 12B QAT model is indeed sort of mindblowing.
novaRom 3 hours ago [-]
Gemma 4 12B QAT is amazing - agents run very fast, and it's really very smart, at least in my agent's harness domain which is GNU software development - on par with frontiers like GPT Sol, DeepSeek, or Claude - Why to buy those expensive tokens if a local tiny model performs so well?
verdverm 3 hours ago [-]
I haven't dug into QAT deeply, better recovery is my understanding as well, and also that it is out of reach for most people because you have to train a model to back prop errors based on estimated error under quant.
Hopefully more of the lab releases are trained under QAT so we can all benefit.
dofm 3 hours ago [-]
I think they did Gemma 3 QAT models and there are QAT versions of essentially all the Gemma 4 models (including DiffusionGemma).
ckluis 2 hours ago [-]
I'd like to see them do a 1-bit binary version of Gemma 4 12B ;)
motbus3 5 hours ago [-]
I need help understanding this.
I understood that the magic here is the quantization that allows it to use from 50G to 4G and their process retain most of the intelligence within Pareto limits of gain. And then they proceed to compare with other quantized models as in the level of intelligence per size. It gets to my attention though that the performance in tool calling is mostly affected which is a problem for other small models.
How does this model compare to a recent 4G model? How do we know it retained intelligence from the parent rather then being fine tuned for the benchmarks?
I am not shtng on them or anything. I'd rather find it amazing, BUT given my limited knowledge, I feel the results miss fair comparison plots and the ones might be misleading. Buy I also reckon it might be me the problem.
Anyone care to explain this poor silly fellow some of those points?
jedbrooke 5 hours ago [-]
from what I understand prismml isn’t doing a quant like normal models where you take a model trained at fp16 and then chop off some bits to reduce vram, but rather they’re training the model natively with 1 bit weights. It’s explained more in the article. They’re also doing some other tricks like a fp16 weight per block of 128 1bit weights to get some more data out of 1 bit weights
parsimo2010 1 hours ago [-]
They aren’t training at all. They are quantizing existing models, it’s just that the process is different. The 27B uses Qwen3.6 27B as the base model.
Notably, PrismML CEO Babak Hassibi told CNBC this, so it’s either (1) bullshit, or (2) he just ended any chance of a relationship by leaking news of the talks.
Apple would punish him severely unless they cleared it in advance, it might be to their advantage for some reason (negotiating with Google for Gemma rights? idk).
dofm 3 hours ago [-]
I would be slightly surprised if anything Apple wanted to do with Gemma they couldn't do with Gemini, which they have the right to make various derivatives of.
They could more or less redistribute Gemma as-is in the developer program; they are unlikely to be troubled by any of the licence terms.
segmondy 4 hours ago [-]
Apple is too desperate to be making demands. You confuse the Apple of yesterday and of today. Things change fast and things have changed.
conception 4 hours ago [-]
You’re right, Apple only has 68 billion dollars in cash, up 40% since last year. Definitely on their last legs.
mgambati 4 hours ago [-]
Free cash? Crazy. I think I can live with the interest of that in the bank.
nxtfari 3 hours ago [-]
you could live (100k a year) with the interest of 0.004% of that in the bank.
brcmthrowaway 4 hours ago [-]
I remember the infographics of 5-10 years ago. They had 200 billion. What happened? Sinking ship?
malshe 3 hours ago [-]
They never had $200 billion in cash in any quarter. Not at least since 2014. Besides, how much cash to hold is an executive decision. If the cash balance went down, it doesn't mean their business is not doing well. Cash can be used for Capex, like what the hyperscalers are doing right now. It can be used for buying back equity.
mandeepj 2 hours ago [-]
They spent close to $100 billion in stock buybacks during the last 10ish years :-)
brcmthrowaway 55 minutes ago [-]
How does that compare to other companies?
mandeepj 4 hours ago [-]
> Apple is too desperate to be making demands
They don't give a F about AI or any new AI model that was announced this morning. Wasn't there news a while ago about them buying Perplexity?
dofm 3 hours ago [-]
They didn't buy Perplexity and it indeed rather seems like Perplexity may have damaged the deal by blurting.
Whether things are different now I don't know, but Prism would potentially be a good acquisition.
This is not the first Bonsai model from Prism using this technology, and they've also applied this technology to an image model.
The model aspect isn't that significant (even in this news), because it is Qwen, under the hood. The encoding efficiency is.
Prism's technology might be valuable, and their team could well be.
And they very evidently do care a lot about efficient on-device AI; they just don't care about developing frontier cloud models.
segmondy 3 hours ago [-]
They do, they are low key panicking.
dofm 3 hours ago [-]
I do not believe they are panicking, not least because I don't think they've finished adjusting Apple Silicon for the task; it will be very interesting to see what happens in the M6 and M7.
I think their strategy is broadly correct, actually; I think there's still a bit of scope for "sit and wait and do it right" here. But acquiring more edge AI tech and edge AI people would potentially be in their interest.
trollbridge 3 hours ago [-]
And if they aren't, they should be. They ship some of the cheapest, most capable, and easiest to buy/set up AI hardware out there, and then don't ship any AI software worth speaking of. Apple needs to fix this.
thakoppno 4 hours ago [-]
either way, maybe a portent for the times.
apple’s secrecy agenda has been defeated to an extent by the practicalities of ubiquitous technology?
I've tried a couple in LM Studio - the GGUF one and the MLX one - but neither worked there. Anyone else get them to work? Might be that LM Studio needs to upgrade their llama.cpp or MLX engines first.
SwellJoe 3 hours ago [-]
I got their previous model working in their custom fork of llama.cpp (https://github.com/PrismML-Eng/llama.cpp). I haven't tried this one yet, but will find some time to benchmark it sometime this week.
Though this says mainline llama.cpp has their patches for Metal and CPU backends, so maybe it's simply "use current llama.cpp" if you have a Mac or fast enough CPU/memor to use the CPU backend.
bansaltushar 6 hours ago [-]
Depending on which model you're running, you might need to use the custom forks.
I did not know you guys would be watching. For sure. Let me do that tomorrow when I turn it on again :)
I am happy to see the message. Thanks!
dofm 3 hours ago [-]
Not sure if there's any way to run Prism's fork of llama.cpp inside LM Studio.
The fork runs fine for me. The model gets very notably stuck in a reasoning loop on one of my simple tests, though it might be that it has the same issues with setting reasoning effort high.
On my M1 Max I still think the MoE Qwen 3.6 and Gemma 4 models are the best options. And I am far from convinced that the 35B is actually worse; it gets stuck in reasoning loops much less often than 27B in my experience.
try-working 2 hours ago [-]
I downloaded two of the official ones in LM Studio, both 3.6gb, and neither loaded.
trollbridge 6 hours ago [-]
Didn't work for me in Unsloth, but it will probably be fixed in a day or two when the next batch of updates comes out.
dofm 1 hours ago [-]
Have Prism-ML upstreamed their forked code?
RugnirViking 3 hours ago [-]
maybe its nitpicking here but the demo shows them asking the model what to cook and its recipie sounds like it wouldn't be very good and also it totally gets the macronutrients wrong. 25g protein for "spaghetti, carrots, peppers, garlic and herbs"?
purerandomness 2 hours ago [-]
Good spaghetti has 15% protein, 15g per 100g.
Even the lowest quality one has 12g/100g.
gnabgib 2 hours ago [-]
Pasta should be made of whole durum wheat.. which is 12g/100g. What's this "good" you speak of? There's certainly worse (soft white/red flour with germ/bran removed), but where do we find the +3g?
May be going out on a limb here but I think it might be the eggs?
stamps 1 hours ago [-]
I know felcetti has a few varieties that exceed that number. ~112g for 16g protein.
gnabgib 1 hours ago [-]
The brand that uses khorasan wheat in some versions? Yeah they're a little higher (14.7g/100g). We're stretching the definition of pasta now (it's certainly the highest protein grain you can find)
stamps 59 minutes ago [-]
Yeah I’m not 100% sure, they claim “special durum wheat variety.”
For the price, from a protein perspective, it would be cheaper to get it elsewhere.
They do, but they have to use the standard numbers for durum (like everyone else, even growers - individual berries vary, of course). The only one I can find with these high numbers is the khorasan wheat versions[0].
I personally don't like carrots much, but it doesn't sound bad to me – could definitely use a tomato sauce but that doesn't seem to be an option from the image it was given.
> 25g protein for "spaghetti, carrots, peppers, garlic and herbs"?
Maybe it assumed the pasta was some kind of protein chickpea pasta? =P it definitely seems wrong.
SamBam 2 hours ago [-]
Many people don't realize that regular spaghetti actually is pretty high in protein. All that gluten.
bromuro 2 hours ago [-]
200gr spaghetti do actually contain ~25g protein
sandworm101 3 hours ago [-]
And why would i want such mundane questions to be handled by an AI on my phone? That sort of thing doesnt need AI, let alone a local one. Basic google search was answering those questions long ago. My point: phone-sized AI is only useful if it can do things that only AI can do. Can it ingest a document scanned by the phones camera? Can it translate in real time? I dont see how or why i would ever ask it for recipe advice. That need is met elsewhere x10.
codebje 2 hours ago [-]
If it can give me the recipe without 14 pages of backstory about how Nonna used to make it, it'd be satisfying a real need.
foolfoolz 2 hours ago [-]
every single recipe blog site builder has a “jump to recipe” button at the top
recursivegirth 2 hours ago [-]
Awwwe, cmon. You are thinking about the problem space incorrectly. This is an opportunity to create a unicorn company that develops the first AI tongue.
Arcuru 5 hours ago [-]
Awesome! I've been waiting for them to start scaling ternary models for over a year[1]. Excited to try it out, typical Qwen 27B is too heavy for me to run on my local hardware at reasonable speeds.
Same here. I’m excited to have a model that might be usable on a 16 GB laptop.
athrowaway3z 4 hours ago [-]
So first off, phenomenal stuff to see a 1bit model at 90% capability.
However, this is the 5th product post in 2 weeks that proclaims that AI use is shifting, and why [insert tradeoffs] are the perfect fit.
Paradigms shift don't happen in the release announcements.
I suspect this is an AI-ism, making all the release posts sound so paradigmshiftery.
jrflo 4 hours ago [-]
That is true of all tech announcements. Marketers will do their thing regardless of reality.
ExxKA 57 minutes ago [-]
From an investors perspective, this is truly a paradigm shift - this will kill a whole range of startups in Europe which were packaging privacy and wrapping around large hosted models. There's absolutely no reason to use a "Privacy GPT tm" provider, then I have it all on my own laptop - There is also no need for banks or other regulated institutions to rely on those providers when they can selfhost with this much intelligence on tap.
dofm 38 minutes ago [-]
It will when they can get the performance up a bit.
My brief experiments with the ternary version suggest that it broadly meets their claim to be a 27B model that fits in much less RAM, that is for sure. It is about as fast as the underlying Qwen 27B but it gets stuck in reasoning loops quite easily.
erwan577 6 hours ago [-]
The KV-cache memory usage also seems remarkably frugal, even at the full context length. That could make this model particularly useful in multi-agent coding workflows.
I wish KV-cache memory usage and related optimizations were discussed more clearly in new model announcements and demos.
verdverm 5 hours ago [-]
quanting kv cache hurts attention / recall, and long-form tasks by proxy. Model families and sizes have different tolerances to quant ting different parts of the model, same for intended tasks.
alvatech 7 hours ago [-]
TIL that 1 bit models are actually 1.58 bit with three values +1, 0 and -1
NitpickLawyer 7 hours ago [-]
There's two variants of this (or, as the joke goes, for very big values of bit):
Ternary Bonsai 27B uses ternary {−1, 0, +1} weights with FP16 group-wise scaling, giving a true 1.71 effective bits per weight.
1-bit Bonsai 27B uses binary {−1, +1} weights with the same group-wise scaling, giving 1.125 effective bits per weight.
PcChip 6 hours ago [-]
this is a really dumb question, but how is -1 represented?
is it a float? if so, how many bits is the float?
I've never heard of a bit ever having more than two possible values
edflsafoiewq 5 hours ago [-]
It appears they are using Q2_0 in llama.cpp, which is 2 bits per weight + 1 float16 scale per group of 64 weights. This is inefficient in two ways: one bit pattern is wasted on each weight, since ternary weights only use {-1,0,1} and Q2_0 allows {-1,0,1,2}; and their group size is 128 weights, so the scale will be stored twice in two groups of 64 instead of stored only once in one group of 128.
Their fork corrects the second inefficiency by using a group size of 128, but still uses 2-bit weights AFAICT.
It's possible to pack 5 trits into a byte, but the unpacking is not very efficient. Another recent idea is to add the constraint that exactly one weight in each group of four be zero, which gives exactly 32 possible states, so it fits in 5 bits.
lioeters 5 hours ago [-]
> never heard of a bit ever having more than two possible values
It's not represented by a "bit", binary digit with value of 0 or 1; but with a "trit", ternary digit with value of {−1, 0, +1}.
You can beat the efficiency of 5 trits in 8 bits (1.6) with as few as 17 trits in 27 bits (~1.588), but once you account for rounding up to a whole number of bytes for practical reasons, then beating the efficiency requires going to at least 111 trits in 176 bits (~1.586), or perhaps more practically for fast unpacking, 161 trits in 256 bits (~1.59).
At that level, even if you have, say, 27B trits, the more efficient encodings would save something like 38-45MB (theoretical limit ~48MB), likely at the cost of some slowdown.
zawaideh 6 hours ago [-]
It’s still a bit with only two possible values. But they add a scaling factor to a group of them (128 for example) which when you factor in, results in a fractional number of bits per parameter.
throwawayffffas 5 hours ago [-]
I believe the scaling comes in later, to turn the 1 and -1 into large numbers that may or may not activate the next layer.
The way they do it is packing like the other comment says.
Each byte represents 5 trinary values instead of 8 binary, and there is a little bit of waste.
Dwedit 4 hours ago [-]
1.6 bits if you want the most practical way to pack five 3-state numbers into a single byte. But even then, they usually pack four 4-state numbers instead.
bensyverson 7 hours ago [-]
Yeah, it's an unfortunate convention from the very first "1 bit" model. But to be clear, Bonsai comes in both ternary and actual 1-bit variants.
all2 2 hours ago [-]
Tried this on an old 4 core i5 and got about 1tps.
OS: WSL2 on Windows 10
liuliu 7 hours ago [-]
The problem, of course, is if you run the UD_Q2 variant (Unsloth) which does only post-training, the number is pretty close to 1-bit model here and the 5% drop in tool-call is significant than it suggests in real-life use cases.
liuliu 7 hours ago [-]
You also need to pay close attention to BFCLv3 multi-turn result, that helps you to get a sense how frequently these quants will be in a doom loop.
h14h 5 hours ago [-]
I'm curious what kind of results one could get from combining the clever quantization PrismML is doing here with something like LiquidAI's antidoom:
After using a highly capable 2-bit quant as my daily driver for months now, I get pretty excited about releases like this. After a few days for the kinks to be worked out, I’ll be excited to try it.
digdugdirk 5 hours ago [-]
What model? And what hardware do you run it on?
I find these style of models are great, but fail hard, and fail randomly. I'd be hesitant to use it for a daily driver, but I'm using dual 3060s, so it's not like I'm quantizing a frontier model here.
How do you find the overall experience? And do you have any special sauce or recommendations for going this route?
kamranjon 4 hours ago [-]
I’m using DeepSeek V4 Flash on 128gb mbp - it’s a bit different using a 200b+ param model. It’s MoE so performance is acceptable. It will still malform a tool call every now and then, but the capabilities are so far ahead anything else that the majority of the time it works really well and solves really complex problems.
The 2 bit quants are really good. I have a lot of memory so I can squeeze it all in at ~80gb.
sigbottle 6 hours ago [-]
What's the hiring space and business strategy around all of these smaller AI labs? Its really cool that people like these guys get paid to optimize models and give them out for free (open source). Do a lot of these labs have forward deployed engineers doing integrations with customers who want local models? Is there a general shift towards the local model crowd?
trollbridge 6 hours ago [-]
If you read to the bottom of the page, it says they're funded by a few people, and one of them is Samsung. I'm betting Samsung wants to be able to ship a capable AI system on a future model of their phone so they can compete with Apple.
doctoboggan 5 hours ago [-]
Agreed, and the prevailing wisdom now seems to be that unless you can release a truly frontier model, you might as well release yours as open source to undercut your competition.
ndrwdvvs 4 hours ago [-]
or smart fridge
try-working 2 hours ago [-]
open source is a GTM strategy
thomasjb 6 hours ago [-]
I've been watching and waiting for this, interested to see how smart it is, as it fits with my interest of getting the smartest possible model running in 10GB of VRAM (RTX3060 that has to drive 2 monitors and run an llm)
Bonsai 8B and 1.7B were on Qwen3.5 the benchmark is from a few months ago. However I'll add Qwen3.6 to the benchmark too.
comandillos 5 hours ago [-]
Quite weird that heavy quantization method on a dense model gives better results than slightly quantized MoE models like 35B-A3B from Google.
At this point all the different quantization and 'compression' (look at MPO applied to LLMs...) techniques start feeling a bit like snake oil. It's just gut feeling - or scores on benchmarks models are optimized for - what ends up deciding whether a technique is good enough or not.
LeBit 3 hours ago [-]
26B-A4B?
trvz 4 hours ago [-]
I still don’t see the point of this. In my testing, it’s worse than Qwen 3.5 4B and even 0.8B.
rvz 3 hours ago [-]
It is still worth experimenting. Although we do need independent evaluation of these models instead of labs posting such biased and skewed results.
syntaxing 7 hours ago [-]
For those curious about their demo, I’m pretty sure it’s using Locally AI (iOS only) that lmstudio acquired/aquihired a couple months ago.
luckystarr 6 hours ago [-]
Tried it on Android and got "!!!!!!!!!!!!!" for answers.
gunalx 6 hours ago [-]
The qwen models really seem to have this as a failure mode, its so annoying having a proper trace ending up in !!!!!! Garbage.
amelius 4 hours ago [-]
Wait in a regular sentence, what is the probability of "!!!" being followed by "!"?
Sounds like the model is not following a proper probabilistic choice here, so maybe more a programming error than a model training error.
jldugger 4 hours ago [-]
After the third !, the probability of a fourth probably skyrockets =)
verdverm 6 hours ago [-]
That's what happens when you quant too hard. I'm working on quant strats and evals for the same underlying qwen 27b models.
When I saw 27b on a phone, I thought not fitting, big phone, or aggressive quant. NVFP4 still takes 27G before KV cache.
syntaxing 6 hours ago [-]
I don’t know if the llama cpp implementation is wonky (and only supports the binary version) but it’s a lot slower than 35B-A3B @ Q4_KM + MTP with CPU offloading.
pulse7 6 hours ago [-]
Most probably not optimized yet for this model...
est 3 hours ago [-]
Maybe Taalas could cook this as their AI-on-chip next
Heliodex 4 hours ago [-]
Nice to see a larger model in their lineup, I've been using Ternary 8B and it seems to get higher TPS than most other similarly sized models on my hardware.
bilsbie 4 hours ago [-]
What data type stores one bit? Does it offer opportunity for more efficient matmuls?
Luker88 5 hours ago [-]
Nice!
Do they have plans to bring even bigger models down to ~16GB VRAM so that more consumer hardware might be useful?
verdverm 5 hours ago [-]
bigger quant'd harder is not always better than a model of more modest size and quant
raylad 3 hours ago [-]
Not impressed. It fails the "Jabberwocky" test.
raylad 46 minutes ago [-]
This got a downvote and I understand why: because I didn't describe the test, which is to ask it "Please recite Jabberwocky".
This is actually difficult because there are so many invented words in the poem which have extremely low frequencies in the training data. So a model that can do it properly is likely to be very good in other ways. Qwen-3.6-27B can do this until it gets overly quantized.
luciana1u 2 hours ago [-]
phone manufacturers are about to add '27B-capable' right next to '5G' on the spec sheet and honestly it's the first spec bump I've cared about in years
diddid 2 hours ago [-]
They should do this to GLM 5.2
xyzsparetimexyz 7 hours ago [-]
That's awesome. What's the largest model that could fit onto a single 16gb gpu at 1.125 effects bits per weight?
Catloafdev 6 hours ago [-]
Doing some naive math, the F16 filesize is ~53.8gb, the 1-bit version is ~3.8gb, about 7% of the original size. The F16 size is roughly 2x param count, so that gives a rough ballpark of ~110B.
kroaton 4 hours ago [-]
Which would be very interesting to test, as larger models (such as Deepseek V4 Flash or Qwen 397B) seem to compress better. Their Q2 quants are usable as is, even without the ternary compression.
drob518 5 hours ago [-]
Yep, that’s the question. I asked just that when Bonsai’s first models got released. Super interesting if we can push the parameter count over 100B with 1.125 bit quantization and still keep pretty good performance versus 16-bit 100B models. That’s a definite sweet spot.
wy35 5 hours ago [-]
Entire blog post seems to be AI-generated :/
wmf 5 hours ago [-]
Do you think people who work on AI for a living are not going to use it?
wy35 5 hours ago [-]
Of course not, personally almost all of my code these days is generated.
The LLM style of writing is just very distracting to read. “It unlocks X”, “Y changes the equation”, and why is there always something shifting? Makes my eyes glaze over in an otherwise interesting post.
arjie 5 hours ago [-]
The text is mostly content-free. Headline + charts are enough for most HN stories.
drob518 5 hours ago [-]
This is going in a good direction.
verdverm 5 hours ago [-]
Preliminary analysis via lm-evaluation-harness + vllm
Looks like they quant'd too hard at 4 bits, can't imagine the ternary being any good based on this. I'm also not sure what is up with the gsm8k, their benchmarks show something different, but they are using another eval tool. I'll have to add it to my setup. Also why I'm building a setup instead of taking model devs word for benchmarks. (https://github.com/modelscope/evalscope)
Code if you'd like to reproduce or try other test sets: https://github.com/verdverm/quantr (lightly tuned to a single oem spark, probably possible in 32-48G)
Good paper to understand the effects of quant regimes across model families and tasks: https://arxiv.org/abs/2402.18158 (Evaluating Quantized Large Language Models - 2024 ICML)
0xbadcafebee 5 hours ago [-]
27B is way more than you need for a phone. Doesn't matter how much you try to compress it, it's the wrong application of the wrong tool. There are already useful tiny models that fit on phones and do basic things really well. Dumb down a big model too much and it becomes worse than a small fine-tuned model.
The article is about running it on a phone though, and shows an app with their branding running this in text mode on a phone. I'm asking where can I find this app to try what is being demonstrated in this article & video? Appstore only has an image gen app by them and other MLX apps I've tried don't seem to support this model
erelong 7 hours ago [-]
I was trying Ornith 9B locally (it's up on Ollama) which claims:
> Ornith-1.0-9B, which can be easily deployed on edge devices, matches or exceeds the performance of much larger models such as Gemma 4-31B and Qwen 3.6 35B.
Only tried it so much so far; it did a little better than Qwen 9B
liuliu 7 hours ago [-]
Note that 3.5 9B cannot do thinking (while 3.6 27B can, pretty effectively, quite verbosely).
gunalx 5 hours ago [-]
3.5 9B can do thinking. Its just disabled by default in its gguf chat template.
liuliu 5 hours ago [-]
It is disabled because it doesn't work :) Try it and see the doom loop it gets itself in.
b89kim 4 hours ago [-]
I had no failure mode in 3.6 9B thinking with llama.cpp. After release, there were updates for both model and llama.cpp.
janalsncm 6 hours ago [-]
Is that a 1-bit LLM? I don’t understand the connection with this article.
erelong 6 hours ago [-]
Oh, I don't actually know the difference if you want to explain it
The title says it's 27B grade running on a phone and what I was comparing it to in my mind was a model that runs at 35B grade that could presumably run on a phone "better"?
edit: I asked AI for the difference and understand a little better, thanks for the heads up to learn the difference between models... I think the thing was, although ornith was created for a specific agentic purpose, it was still outperforming a previous generalist model I had running locally (so in my mind I thought it was still a better local model) - I'd like to try bonsai out if I can figure out how to run it lol
verdverm 5 hours ago [-]
Orinth was not impressive in my vibes testing, I just completed my first grid analysis with real evals on qwen 27b. I can now scale that grid analysis and intend to include the qwen 9b ftunes I've seen going around. They were actually a main motivation because so many claim this or that one is better, but very little in the way of evals
drob518 5 hours ago [-]
I tried it, too, and it got stuck in some loops where it couldn’t recover. Shame, it was promising for the same reason as Bonsai’s models.
verdverm 4 hours ago [-]
check out geyron-9b, I've only used it a bit, but seems better than orinth on vibe evals
huggingface.co/Tivaphraen/Geryon-9B-v1
drob518 4 hours ago [-]
Interesting, thanks. Looking at the model card on Huggingface, it’s combining the Qwythos and Qwable fine tunes from Empero.
verdverm 4 hours ago [-]
yea, it's an experiment in merging multiple fine-tuned models
theLiminator 5 hours ago [-]
This is useful research, but this particular model itself is likely absolutely useless.
oceansweep 5 hours ago [-]
Why make this comment without having tried it first? It very clearly is not useless and performs a lot better than one might expect. I am currently waiting to do more benchmarks of it in comparison to the full weight model, but it seems promising/better than Mistral Nemo at a lower file size.
Onavo 5 hours ago [-]
I think what OP means is that the "minimum viable product" for a daily use LLM is probably somewhere around e.g. GPT 4o's level of intelligence (YMMV). Below a certain threshold, you are better off using specialized machine learning models rather than general purpose LLMs. It's very difficult to get that level of intelligence fully local on a mobile device without streaming to the cloud.
mchusma 4 hours ago [-]
I do think this would be interesting if they made these easy to finetune, as I do think this level of intelligence is likely sufficient for many applications and could be extremely cheap to run.
If you can "hide" different models of 8GB VRAM requirements each that have those specialties and mix and match them for me without having to manage it manually, I'll be impressed. Until then I will keep using my Claude, because "remarkably good _for their size_" models I've tried so far just sucked at trying to use them the way I code at work with Claude.
PHP/Wordpress code seems OK (better than the Gemma) but it gets stuck in reasoning loops.
Mind you, I am something of a cynic about the underlying 27B dense Qwen; I think the 35B MoE model is often better and it is just so, so much faster.
Good evaluation from 2024 https://arxiv.org/pdf/2402.18158
I'm currently working towards an updated version (not an og author), curious if others are aware of similar surveys, as I have yet to do a real lit search.
The 12B QAT model is indeed sort of mindblowing.
Hopefully more of the lab releases are trained under QAT so we can all benefit.
How does this model compare to a recent 4G model? How do we know it retained intelligence from the parent rather then being fine tuned for the benchmarks?
I am not shtng on them or anything. I'd rather find it amazing, BUT given my limited knowledge, I feel the results miss fair comparison plots and the ones might be misleading. Buy I also reckon it might be me the problem. Anyone care to explain this poor silly fellow some of those points?
https://www.theregister.com/on-prem/2000/08/02/jobs-snubs-at...
They could more or less redistribute Gemma as-is in the developer program; they are unlikely to be troubled by any of the licence terms.
They don't give a F about AI or any new AI model that was announced this morning. Wasn't there news a while ago about them buying Perplexity?
Whether things are different now I don't know, but Prism would potentially be a good acquisition.
This is not the first Bonsai model from Prism using this technology, and they've also applied this technology to an image model.
The model aspect isn't that significant (even in this news), because it is Qwen, under the hood. The encoding efficiency is.
Prism's technology might be valuable, and their team could well be.
And they very evidently do care a lot about efficient on-device AI; they just don't care about developing frontier cloud models.
I think their strategy is broadly correct, actually; I think there's still a bit of scope for "sit and wait and do it right" here. But acquiring more edge AI tech and edge AI people would potentially be in their interest.
apple’s secrecy agenda has been defeated to an extent by the practicalities of ubiquitous technology?
I've tried a couple in LM Studio - the GGUF one and the MLX one - but neither worked there. Anyone else get them to work? Might be that LM Studio needs to upgrade their llama.cpp or MLX engines first.
Though this says mainline llama.cpp has their patches for Metal and CPU backends, so maybe it's simply "use current llama.cpp" if you have a Mac or fast enough CPU/memor to use the CPU backend.
Details are here -> https://github.com/PrismML-Eng/Bonsai-demo/blob/main/README....
You can also join Discord to communicate with us directly http://discord.gg/prismml
The fork runs fine for me. The model gets very notably stuck in a reasoning loop on one of my simple tests, though it might be that it has the same issues with setting reasoning effort high.
On my M1 Max I still think the MoE Qwen 3.6 and Gemma 4 models are the best options. And I am far from convinced that the 35B is actually worse; it gets stuck in reasoning loops much less often than 27B in my experience.
Even the lowest quality one has 12g/100g.
https://en.wikipedia.org/wiki/Pasta
> Main ingredients: Durum wheat flour, water/eggs
May be going out on a limb here but I think it might be the eggs?
For the price, from a protein perspective, it would be cheaper to get it elsewhere.
https://shopfelicettipasta.com/products/monograno-felicetti-...
[0]: https://m.media-amazon.com/images/I/81UbkYz+pZL._SL1500_.jpg
> 25g protein for "spaghetti, carrots, peppers, garlic and herbs"?
Maybe it assumed the pasta was some kind of protein chickpea pasta? =P it definitely seems wrong.
[1] https://jackson.dev/post/dont-sleep-on-bitnet/
However, this is the 5th product post in 2 weeks that proclaims that AI use is shifting, and why [insert tradeoffs] are the perfect fit.
Paradigms shift don't happen in the release announcements.
I suspect this is an AI-ism, making all the release posts sound so paradigmshiftery.
My brief experiments with the ternary version suggest that it broadly meets their claim to be a 27B model that fits in much less RAM, that is for sure. It is about as fast as the underlying Qwen 27B but it gets stuck in reasoning loops quite easily.
I wish KV-cache memory usage and related optimizations were discussed more clearly in new model announcements and demos.
Ternary Bonsai 27B uses ternary {−1, 0, +1} weights with FP16 group-wise scaling, giving a true 1.71 effective bits per weight.
1-bit Bonsai 27B uses binary {−1, +1} weights with the same group-wise scaling, giving 1.125 effective bits per weight.
is it a float? if so, how many bits is the float?
I've never heard of a bit ever having more than two possible values
Their fork corrects the second inefficiency by using a group size of 128, but still uses 2-bit weights AFAICT.
It's possible to pack 5 trits into a byte, but the unpacking is not very efficient. Another recent idea is to add the constraint that exactly one weight in each group of four be zero, which gives exactly 32 possible states, so it fits in 5 bits.
It's not represented by a "bit", binary digit with value of 0 or 1; but with a "trit", ternary digit with value of {−1, 0, +1}.
e.g. 5 trits (243 states) into a byte gives 1.6 bits per trit: https://compilade.net/blog/ternary-packing
You can beat the efficiency of 5 trits in 8 bits (1.6) with as few as 17 trits in 27 bits (~1.588), but once you account for rounding up to a whole number of bytes for practical reasons, then beating the efficiency requires going to at least 111 trits in 176 bits (~1.586), or perhaps more practically for fast unpacking, 161 trits in 256 bits (~1.59).
At that level, even if you have, say, 27B trits, the more efficient encodings would save something like 38-45MB (theoretical limit ~48MB), likely at the cost of some slowdown.
The way they do it is packing like the other comment says.
Each byte represents 5 trinary values instead of 8 binary, and there is a little bit of waste.
OS: WSL2 on Windows 10
https://github.com/Liquid4All/antidoom
I find these style of models are great, but fail hard, and fail randomly. I'd be hesitant to use it for a daily driver, but I'm using dual 3060s, so it's not like I'm quantizing a frontier model here.
How do you find the overall experience? And do you have any special sauce or recommendations for going this route?
The 2 bit quants are really good. I have a lot of memory so I can squeeze it all in at ~80gb.
Likely apples / oranges
At this point all the different quantization and 'compression' (look at MPO applied to LLMs...) techniques start feeling a bit like snake oil. It's just gut feeling - or scores on benchmarks models are optimized for - what ends up deciding whether a technique is good enough or not.
Sounds like the model is not following a proper probabilistic choice here, so maybe more a programming error than a model training error.
When I saw 27b on a phone, I thought not fitting, big phone, or aggressive quant. NVFP4 still takes 27G before KV cache.
Do they have plans to bring even bigger models down to ~16GB VRAM so that more consumer hardware might be useful?
This is actually difficult because there are so many invented words in the poem which have extremely low frequencies in the training data. So a model that can do it properly is likely to be very good in other ways. Qwen-3.6-27B can do this until it gets overly quantized.
The LLM style of writing is just very distracting to read. “It unlocks X”, “Y changes the equation”, and why is there always something shifting? Makes my eyes glaze over in an otherwise interesting post.
Code if you'd like to reproduce or try other test sets: https://github.com/verdverm/quantr (lightly tuned to a single oem spark, probably possible in 32-48G)
Good paper to understand the effects of quant regimes across model families and tasks: https://arxiv.org/abs/2402.18158 (Evaluating Quantized Large Language Models - 2024 ICML)
I can just see their image tool on the app store
Available on HuggingFace: https://huggingface.co/collections/prism-ml/bonsai-27b
The article is about running it on a phone though, and shows an app with their branding running this in text mode on a phone. I'm asking where can I find this app to try what is being demonstrated in this article & video? Appstore only has an image gen app by them and other MLX apps I've tried don't seem to support this model
> Ornith-1.0-9B, which can be easily deployed on edge devices, matches or exceeds the performance of much larger models such as Gemma 4-31B and Qwen 3.6 35B.
https://deep-reinforce.com/ornith_1_0.html
Only tried it so much so far; it did a little better than Qwen 9B
The title says it's 27B grade running on a phone and what I was comparing it to in my mind was a model that runs at 35B grade that could presumably run on a phone "better"?
edit: I asked AI for the difference and understand a little better, thanks for the heads up to learn the difference between models... I think the thing was, although ornith was created for a specific agentic purpose, it was still outperforming a previous generalist model I had running locally (so in my mind I thought it was still a better local model) - I'd like to try bonsai out if I can figure out how to run it lol
huggingface.co/Tivaphraen/Geryon-9B-v1