"Apertus: a fully open, transparent, multilingual language model
EPFL, ETH Zurich and the Swiss National Supercomputing Centre (CSCS) released Apertus 2 September, Switzerland’s first large-scale, open, multilingual language model — a milestone in generative AI for transparency and diversity.
Researchers from EPFL, ETH Zurich and CSCS have developed the large language model Apertus – it is one of the largest open LLMs and a basic technology on which others can build.
In brief Researchers at EPFL, ETH Zurich and CSCS have developed Apertus, a fully open Large Language Model (LLM) – one of the largest of its kind. As a foundational technology, Apertus enables innovation and strengthens AI expertise across research, society and industry by allowing others to build upon it. Apertus is currently available through strategic partner Swisscom, the AI platform Hugging Face, and the Public AI network. …
The model is named Apertus – Latin for “open” – highlighting its distinctive feature: the entire development process, including its architecture, model weights, and training data and recipes, is openly accessible and fully documented.
AI researchers, professionals, and experienced enthusiasts can either access the model through the strategic partner Swisscom or download it from Hugging Face – a platform for AI models and applications – and deploy it for their own projects. Apertus is freely available in two sizes – featuring 8 billion and 70 billion parameters, the smaller model being more appropriate for individual usage. Both models are released under a permissive open-source license, allowing use in education and research as well as broad societal and commercial applications. …
Trained on 15 trillion tokens across more than 1,000 languages – 40% of the data is non-English – Apertus includes many languages that have so far been underrepresented in LLMs, such as Swiss German, Romansh, and many others. …
Furthermore, for people outside of Switzerland, the external pagePublic AI Inference Utility will make Apertus accessible as part of a global movement for public AI. “Currently, Apertus is the leading public AI model: a model built by public institutions, for the public interest. It is our best proof yet that AI can be a form of public infrastructure like highways, water, or electricity,” says Joshua Tan, Lead Maintainer of the Public AI Inference Utility."
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Yes. Although they don’t host the dataset binaries.
Is this hosted somewhere? Maybe distributed? I would love a privacy respecting distributed LLM chatbot.
In case you’re not aware, there are a decent number of open weight (and some open source) large language models.
The Ollama project makes it very approachable to download and use these models.
Ollama has taken a bad turn lately (such is the nature of VC backed software). Maybe recommend
kobold.cppjan.ai for LLM noobs insteadI’m keeping an eye on Ollama’s service offerings - I don’t think they’re in enshittification territory yet, but I definitely share the concern.
I still don’t believe the other LLM engines out there have reached an equivalent ease of use compared to Ollama, and I still recommend it for now. If nothing else, it can be a stepping stone to other solutions for some.
Or just llama.cop they finally got an UI added
That’s what I use and also the backend of the aforementioned software, but it’s still complicated for people to set up.
I should also mention Jan, it makes things super easy and it also has a very nice GUI
Jan is another great recommendation!
there is nothing wrong with ollama it runs models fast and easy add a gguf and youre done unless you want to squeeze out extra performance and have time to figure out your exact flags then use llama cpp otherwise ollama just works for 99 percent of people
4.5/10 bait
if you send me a video of you completing the bussin level on geometry dash ill send you 10$
handcam necessary or just screen recording w clicks
screen recording 110715909
Other than Apertus, are there any truly open source models - mainly what I want to know is models that list their training data publicly to ensure no theft of art and stuff. (i replied to your comment as you seem to know about these models, I have no clue abou this stuff)
Deepseek R1 and OpenThinker are two more examples. There’s also SmolLM, which I believe also open sources its training data and ensures proper licensing for it.
I tried Deepseek R1 7B on my MacBook M3 Pro but it is shit compared to ChatGPT unfortunately
There are some factors to consider. Some of the Deepseek quants are based on Llama 3, whereas others are based on Qwen Reasoning.
You’re also not going to get the same quality of the full ChatGPT experience comparing a 7B parameter model to a 500B+ model like ChatGPT.
Regardless, it’s difficult to run the actual Deepseek R1 model as there’s not a true quantization or distillation of the original model.
You can also try GPT-OSS if you want an open source model comparable to ChatGPT. Once again, you’re going to have to balance the size and precision of the model with your expectations.
Links in the article. Hugging Face and Swiss Telecoms host
I can’t find any hardware requirements for this. What will it take to run this smoothly?
8b parameter models are relatively fast on 3rd gen RTX hardware with at least 8gigs of vram, CPU inferencing is slower and requires boatloads of ram but is doable on older hardware. These really aren’t designed to run on consumer hardware, but the 8b model should do fine on relatively powerful consumer hardware.
If you have something that would’ve been a high end gaming rig 4 years ago, you’re good.
If you wanna be more specific, check huggingface, they have charts. If you’re using linux with nvidia hardware you’ll be better off doing CPU inferencing.
Edit: Omg y’all I didn’t think I needed to include my sources but this is quite literally a huge issue on nvidia. Nvidia works fine on linux but you’re limited to whatever VRAM is on your video card, no RAM sharing. Y’all can disagree all you want but those are the facts. Thays why AMD and CPU inferencing are more reliable, and allow for higher context limits. They are not faster though.
Sources for nvidia stuff https://github.com/NVIDIA/open-gpu-kernel-modules/discussions/618
https://forums.developer.nvidia.com/t/shared-vram-on-linux-super-huge-problem/336867/
https://github.com/NVIDIA/open-gpu-kernel-modules/issues/758
Thanks for the reply. Never been on the HF site and doing it on mobile of the first time I seem lost. I couldn’t find it but I’m sure I will.
Disagree on Linux nvidia support, it works fine
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For fastest inference, you want to fit the entire model in VRAM. Plus, you need a few GB extra for context.
Context means the text (+images, etc) it works on. That’s the chat log, in the case of a chatbot, plus any texts you might want summarized/translated/ask questions about.
Models can be quantized, which is a kind of lossy compression. They get smaller but also dumber. As with JPGs, the quality loss is insignificant at first and absolutely worth it.
Inference can be split between GPU and CPU, substituting VRAM with normal RAM. Makes it slower, but you’ll probably will still feel that it’s smooth.
Basically, it’s all trade-offs between quality, context size, and speed.
Apertus was developed with due consideration to Swiss data protection laws, Swiss copyright laws, and the transparency obligations under the EU AI Act. Particular attention has been paid to data integrity and ethical standards: the training corpus builds only on data which is publicly available. It is filtered to respect machine-readable opt-out requests from websites, even retroactively, and to remove personal data, and other undesired content before training begins.
Available doesn’t mean licensed for AI training.
and yet it is still a legally unsettled question whether LLM training requires a copyright license at all; and it is my opinion that no one should want that to be the case, why would people on the Internet want to argue for an expansion of copyright law?
Why would it be an expansion? If you’re using someone else’s work, why wouldn’t you need a license? If I write a book and publish it under CC-BY-NC, should Google be allowed to take my work for their commercial product without compensation or even attribution? Should Microsoft be allowed to create closed-source commercial Copilot off GPL source code?
It’s an expansion to say that LLM training constitutes a derivative work. You are of course entitled to your opinion that it should be the case; all I can say to that is that in the 2000s and 2010s nearly everyone on the Internet tended to argue for more limitations, not further expansions, of copyright law, and I wonder what happened to that attitude.
Well, this being the open source community, I would expect most people here to be on the side of respecting the rights of content creators. Like I said, if I write some GPL software, I don’t think Microsoft should be able to disrespect my license just because they’re also disrespecting everyone else’s license too through automation at scale.
Edit: forgot to mention, since their product is wholly dependent on the other works, that’s the very definition of a derivative work. While you could argue it’s transformative, it certainly fails the other tests for fair use.
I find it very unexpected. It used to be understood that IP laws favor monopolies. EG I don’t remember the OS community being on the side of Oracle in https://en.wikipedia.org/wiki/Google_LLC_v._Oracle_America,_Inc.
Maybe it just passed me by.
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Obligatory nitpick: open weights ≠ open source. For it to be open source, they need to release the training data as well as all the parameters they used in training it.
Please read the article before commenting.
“The model is named Apertus – Latin for “open” – highlighting its distinctive feature: the entire development process, including its architecture, model weights, and training data and recipes, is openly accessible and fully documented.”
Thanks… I have downvoted my own comment in shame. Godspeed!
a gentleperson and a scholar
Props to the humility!
Madlad
