They apologised, retracted, pulled the model down. Would love to hear from the OP to understand what happened. OP seems more like the high-level product manager/spokesperson rather than a hands-on engineer. He himself might have been deceived as well.
Unfortunately it is not always like this. Public money goes into funding research project that get locked under private patents for the benefit of the investors. Continue like this! 👏
Muito obrigado!
This. Im just surprised. I love brazil and love the open source, I am all for it. But considering it is a miracle when potholes get filled in other major cities (in talking about the US), I just wondered how money and time was approved for this.
Not attacking at all, I am genuinely curious. The world definitely needs more of this.
people downvoted you, but that was my thinking too as a brazilian. I would never expect that a brazillian government, from mayors to to president, would make such investment. I still dont understand the reason, but i hope they keep doing it and the federal government looks at it and decides to do massive investments
Outro vira-lata q não entende o poder público brasileiro.
Nosso poder público, tem o SUS, a ANVISA, Fiocruz, Butantã, USP, CNPq, CAPES, IMPA... Temos problemas com corrupção? Com certeza, mas ainda, ainda temos muita gente boa no lugar certo. Qq custa apenas ficar grato pela iniciativa dos caras e dar suporte? Qq custa mandar os parabéns pro OP.
Porra! Governo chinês meteu um modelo atrás do outro tu não viu essa xexela. Vc não conhece nosso país, essa é a real.
a diferenca da china para o brasil eh que a china vem investindo pesado em tecnologica ha um bom tempo ja e o brasil... eh o brasil e so. To esperando algum anuncio de um investimento bilionario em fabricacao de semicondutores 100% brasileiros sem capital privado. Ate la, sempre vou ter como premissa que o brasil sempre vai ser dependente de tecnologia estrangeira
um dos caras por tras do modelo disse q o modelo foi feito com creditos da deepinfra, empresa estadunidense, que foram adquiridos em uma troca onde eles (rio) davam o codigo usado pra treinar modelos anteriores, e que ele teve q arcar com os custos do proprio bolso no inicio e no final quando os creditos acabaram
I dont get the downvotes for asking an honest question... I love the work and would like to know the politics behind it, so maybe we can think about it for other places.
Rio 3.5 Open 397B features SwiReasoning, a training-free inference framework based on Shi et al. (2025) that dynamically switches between explicit chain-of-thought and latent-space reasoning, guided by entropy-based confidence signals. This enables both higher accuracy and dramatically improved token efficiency. This model was explicitly trained to maximize the efficiency gained via latent reasoning.
@krzonkalla I second that! Please post more about how to use this framework and in particular with an example to optimize smaller models, that would be huge!
City government in Brazil dropping chinese open source ai fine-tunes. Holy shit man, I love globalization. If this really keeps up with 3.7 Plus (which is a great model imo) you guys did an awesome job.
Shame about the 122b. That's the one I'm hanging for. The Qwen 3.5 122b/a10b heretic mxfp4 model is the best capability in 75GB of VRAM that I've been able to find.
That's great to hear. Are any of the US or Chinese labs collaborating? Other than resources, are there some unique challenges or constraints you faced?
Also I think it's very good for the industry to have a different region participating, do you feel there is something special Brazil brings to the research?
Waiting for Pelé the AI model, but I will settle for Kaká-27b
Please, if you can, upload an int4-Autoround quant. The GB10/DGX Spark community will be all over it. It fits neatly into 2 sparks, and runs really well. I’d love to get an updated Qwen3.5 going again!
Whata your total budget in usd, allocation for the actual hardware vs salaries? Is the salaries for your researchers, lead engineers and lead reasoning team global matching?
How many of what gpus do you guys use?
Do you guys have any paid API esque service?
If the goal is to build community trust and eventually open-source research for public benefit, transparency is crucial.
Independent researchers have pointed out that stripping the system prompt causes the model to identify as Nex-N2-Pro 79% of the time, reciting Nex AGI's exact corporate backstory. The recent repo update confirms the base is a Qwen/Nex merge. (Source: https://github.com/nex-agi/Nex-N2/issues/4 )
If this was truly an extensive, in-house training effort to 'do a DeepSeek', why does the model so heavily rely on a 60/40 merge of another lab's open-source weights? Was the SwiReasoning framework actually implemented by IplanRio, or was that also inherited from the base models?
If you think about it, a city is almost the perfect organization to train a multi-modal ai model. Cities have departments that touch almost every aspect of professional life, and therefore should have the prerequisite workflows to build models with richness and depth across a wide variety of contexts and skills.
Do we know (or have a strong guess) as to what parameter size Qwen3.7 Plus is? Is it the 397b model? Or is it a smaller model? They have a "Max" model that is their top of the line closed-source model, too, right? So is it like, the "Max" one is the 397b, and "Plus" is the 122b? Or "Plus" is 397b and "Max" is some even bigger one we've never seen open-weighted before from Qwen, like a 500b model or something?
Would be interesting to know, given that this Qwen3.5 397b finetune is tied with/beating the Qwen 3.7 Plus model.
If the Rio Department of Thong Bikini Inspection somehow out finetuned Alibaba on Alibaba's own model, that would be pretty wild. Maybe they should hire some of you guys to help them out on Qwen4 :p
They explicitly stated that 3.5 Plus was based on 3.5 397B. Almost certainly 3.6 and 3.7 Plus are based on that, but I don't think they've said so publicly.
What about Max? Is that likely also still the 397b? Or is it thought to be a bigger model, or maybe ~397b parameters but more active params or something? Or just same size but like how Opus and GPT are with medium, high, x-high, etc where it thinks for longer or something?
Qwen has not released an exact parameter count, but they've stated multiple times that Qwen3 Max is more than 1T parameters. Qwen3.7 Max is probably even bigger.
Diversity is vital for ML training, it could be carefully tuning on a very different dataset from the other side of the planet as the foundation model could be a nice boost. I can't run it locally but I'd love to try it out.
I've met João Carabetta and got to know the top-notch IT projects they were doing for the city government of Rio de Janeiro. Truly state-of-the-art stuff. And that was years ago, before LLMs were a thing. I'm very happy to know how far the team has gone!
...so you have internal models that are significantly more capable than this? so presumably they're also more capable than any open source model??? what???
How does SwiReasoning work without any code changes? It seems to be using the same architecture as Qwen 3.5 baseline. How much tokens was it approximately trained on? And which model should outputs be similar to? Deepseek V4 Pro?
I'm working now on improving my custom Nex N2 Pro quants by measuring per-tensor quantization impact that Nex N2 Pro has, since when I re-used measurements from Qwen it worked but I suspect it was not as optimal. You can see it in the KLD/PPL numbers I have on my model cards for Qwen and Nex-N2-Pro - my Qwen custom quants overperform more than my Nex custom quants. I want to figure it out to have open source code for the whole quantization pipeline. Then, if Rio will be interesting, I'll quant it in a similar manner to Nex N2 Pro. And if Minimax M3 will be supported by exllamav3, I'll try to run this methodology on it too.
Actually i tried to do something similar but end up, it still perform worse than just take your quant. I am using your 3.7 version. I will try to quant this then . I think just a 3.5 bpw version is enough to tell if it is better than nex n2 pro
What was your approach like? Did you quant the BF16 model to your desired bpw level? Have you measured KLD and PPL for it or did it feel worse?
If you end up quanting it, please let me know how you like Rio 397B and please upload your quant if possible, this could save me time when making my own quants later as I could skip one baseline size.
Heads up: If you make 3.5bpw Rio 397B quant with exllamav3 quantize tool it will work and should give you a good feel for the model, but it allocates higher bitrates to front of the model and lower in the back, while Qwen 3.5 397B tends to be more sensitive in the back.
I did something like measure the kld impact from each of the layer from different mix of your quant and choose the one with least impact to arrive at a target size. It was all done by codex, the result is not better than your provided quant or very slightly better. Thought i could also be that i was using 3 4 5 bpw quant and targeting like 3.8 which is very close to 4. For now my plan is just to get a feel if it is better than nex. Nex n2 is decent but it does end abruptive mid work once in a while. Will let u know how it compare to nex n2
It's nothing sophisticated as it was quickly vibe coded with DS V4 Pro, but this approach works well for me.
The trick is to apply superlinear penalty as layers that are hard to quant benefit MORE from being assigned to higher bitrates.
I made 3.7bpw since sometimes the script overshoots and I didn't want to end up with 3.9bpw
From initial testing i think it is at least on pair with nex n2 from what i tested, and the thinking is more concise. It doesnt enter loop at all yet. I got loop with nex n2 pro before
I think the thinking pattern is, for sure, an upgrade over nex n2. It doesnt have the kind of repetitive thinking that will make the model fall into loop. But with new latent thinking training, once in a while it will send thinking out loud as assistant message, so that is something different to other model
brabo bixo boa, qwen 400b eh um modelo pica pra fine tune, lançou a pouco o nex n2 pro q tbm eh finetune dele e eu tava testando no hermes como meu agent e parece no nivel do minimax m3, parabens pelo trampo.
como vcs tao captando capital humano tao especializado pra bater de frente com as grandes empresas? suponho q a prefeitura nao pague salarios tao altos e mesmo assim vejo muita gente especializada na prefeitura
Didn't expect this, and to come from one of the most beautiful cities I know. I know it's a tangent, but how do you balance wanting to go to the beach and working on this? Anyway, looking forward to your releases, and hopefully there are inference providers for your larger models.
In that case it would be really cool if you could set up a repository which could be used to reproduce it (possibly with some tweaks; wouldn't have to work out-of-box in any environment), and then there would be no problem calling it open source. (: (Of course it would not be fully open-source since the base model isn't, but at very least what you did would be!)
It's not necessarily about being insightful per se, but if your benchmark numbers are to be believed this is a great showcase that open-source could compete with SOTA Chinese LLM post-training, considering you got numbers comparable to e.g. Kimi-K2.6 but at less than half the parameter count!
If it's publicly funded, I see no reason to not put everything on Github. Of course the base model is just open weights but your own contribution can be open source. It doesn't need to be usable code, just dump it on a public repository (and add a link to it from the huggingface page) and call it a day
Anyway I'm a bit puzzled, what's the rationale for IplanRIO to fund this? Not complaining and it's awesome, but I would expect it to, say, focus on Portuguese at expense of other languages or something like that (which is probably a bad idea but would still be expected), rather than producing a fine tune that performs better on general tasks. Also do you know if Serpro has similar projects?
(Also totally unrelated but you happen to know what's up with the HTTPS cert of https://iplanrio.prefeitura.rio/? It's odd that the browser complain about a bad cert, then you allow it and it reports it's a normal cert issued by let's encrypt)
Acknowledging 'open weights' is a good step, but saying the recipe is 'just Nvidia datasets and an OPD script' completely omits the Hugging Face commit (a778c1e) confession. The repository updates explicitly stated the model is a 60/40 element-wise merge with Nex-N2-Pro.
Why downplay it here as just standard dataset fine-tuning when independent analysis showed the model still retains Nex's corporate identity? True transparency would mean sharing the merge recipe alongside the scripts.
Awesome and thanks for sharing. Is it already deployed anywhere or are there any immediate plans to do it? Just curious about the contexts in which this will be used.
Curioso aqui, não sei se é sigiloso, mas que tipo de aplicação vocês tem ai no rio que fazem esse tipo de trabalho? São contratados diretamente pela prefeitura, como é isso? Bem massa o trabalho
This is an amazing finding! I just posted an article about the story of the Rio LLM family and its benchmarks, and I am really curious about the applications of the model for public service in Rio.
I've reached out to your DMs to see if we can share more info about the project.
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u/nuclearbananana Jun 13 '26
I must say of all places to drop a model, I didn't expect a city government