- cross-posted to:
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- cross-posted to:
- [email protected]
"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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I can’t find any hardware requirements for this. What will it take to run this smoothly?
A desktop cpu and 20gb of ram.
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.
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.
is the training data open too? can i “compile” it at home?
Yes. Although they don’t host the dataset binaries.
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.
You don’t need a license to look at stuff.
“Your honor, my archive of Linux ISOs were acquired under the pretense that they were ‘publicly available’ and the copyright holders didn’t ‘opt-out’ using the ‘up-for-grabs.txt’ standard I invented.”
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?
As with all things, nuance and context is required. I don’t think we should be taxing poor people that heavily (if at all), but does that mean I should be against taxing the ultra-wealthy more? Obviously not.
I support copyright to protect developers and not hinder users, hobbyists, or the average person. I don’t support it to only help massive companies who can manipulate the law to protect them from competition, but also not hinder them from stealing from the masses. They can afford to pay. If AI is actually as valuable as they say, the price of paying for the training data is trivial.
Copyright shouldn’t only be helpful to big businesses. It should be most helpful to the average person. We have the opposite here. I support modifying copyright law to bind big businesses and liberate individuals. I don’t need to be totally against it like you imply.
Sadly, we’ll most likely see an influx of regulation right when it’s broadly accessible to the general public to run locally.
Yeah, most likely, and it’ll only bind users and protect the businesses, as always.
It already is broadly accessible to the general public. They just don’t know about it or just accept using one of the cloud versions. It’s trivial to get up and running at this point.
That’s news to me, unless you’re only referring to the smaller models. Any chance you can run a model that exceeds your ram capacity yet?
This is probably the easiest tool I’ve used to run them: https://lmstudio.ai/
There’s tons of models available here, some of them fairly large: https://huggingface.co/
No, I’m pretty sure there’s no way to run any larger than your RAM/VRAM, at least not automatically. You can use storage as RAM, but that’s probably not a good idea. It’s orders of magnitude slower. You’re better off running a smaller model.
I’m not knowledgeable in this area, but I wish there was a way to partition the model and stream the partitions over the input, allowing for some kind of serially processing of models that do exceed memory. Like if I could allocate 32gb of ram, and process a 500gb model but at (500/32) a 15x slower rate.
But we can’t afford to pay. I don’t think open models like the one in the OP article would be developed and released for free to the public if there was a complex process of paying billions of dollars to rightsholders in order to do so. That sort of model would favor a monopoly of centralized services run only by the biggest companies.
The model should take into account income. For an open-source model it should be free. It’s using public data to produce a public product. For a for-profit model it should be paid. If they’re profiting off of public data then they should have to pay for the right to use it.
We can’t afford to make any of this. We don’t have the money for the compute required or to pay for the lawyers to make the law work for us. It should benefit the people, so it needs to change. It needs to be “expanded” (I wouldn’t call it that, rather “modified” but I’ll use your word) in that it currently only protects the wealthy and binds the poor. It should be the opposite.
We can’t afford to make any of this. We don’t have the money for the compute required or to pay for the lawyers to make the law work for us
I don’t think this is entirely true; yeah, large foundational models have training costs that are beyond the reach of individuals, but plenty can be done that is not, or can be done by a relatively small organization. I can’t find a direct price estimate for Apertus, and it looks like they used their own hardware, but it’s mentioned they used ten million gpu hours, and GH200 gpus; I found a source online claiming a rental cost of $1.50 per hour for that hardware, so I think the cost of training this could be loosely estimated to be something around 20 million dollars.
That is a lot of money if you are one person, but it’s an order of magnitude smaller than the settlements of billions of dollars being paid so far by the biggest AI companies for their hasty unauthorized use of copyrighted materials. It’s easy to see how copyright and legal costs could potentially be the bottleneck here preventing smaller actors from participating.
It should benefit the people, so it needs to change. It needs to be “expanded” (I wouldn’t call it that, rather “modified” but I’ll use your word) in that it currently only protects the wealthy and binds the poor. It should be the opposite.
How would that even work though? Yes, copyright currently favors the wealthy, but that’s because the whole concept of applying property rights to ideas inherently favors the wealthy. I can’t imagine how it could be the opposite even in theory, but in practice, it seems clear that any legislation codifying limitations on use and compensation for AI training will be drafted by lobbyists of large corporate rightsholders, at the obvious expense of everyone with an interest in free public ownership and use of AI technology.
Saying an expensive product that requires servers to run is the only thing exempt from copyright is just handing a bunch of giant corporations a get out of jail free card.
Either reform copyright so more things are public domain or require AI companies to pursue licenses to training data.
Giving an unfair exemption to copyright laws solely to giant tech companies is just another corporate handout.
What I want is consistency, either apply the law equally and fairly or reform the whole system. Nobody, especially not big business, should be getting special carve-outs to be exempt from copyright infringement outside of ‘fair use’ considerations.
In my ideal world, IP law would be framed to protect novel ideas just long enough for inventors or creators to capitalize on their ideas and prevent outright 1:1 copying without any sort of innovative or transformational changes. It would also discourage squatting on things like patents- patent squatting and the like should lead to losing rights.
“I didn’t steal and distribute your work, I just made a machine distill it down and able to copy everything meaningful about it!”
To steal data, it needs to be deleted off the target’s computer. DMCA is stealing. Copying is not.
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.
GPL was made to destroy the oppressive bourgeois copyright system from the inside, not to expand copyright even further.
If the corpos ignore the gpl, they delegitimize their own copyright system.
BSD license allows for this and still thrives (PS5 OS, Apple iOS and MacOS [Darwin], TrueNAS, OPNsense, and several enterprise-level commercial router operating systems use it and contribute significant code back into BSD project to ensure CVE safety). I’m not agreeing with it, just providing an alternate perspective.
Still much better, especially with respecting opt-outs, than most other LLMs
Legally, it seems it does, at least in the US and EU. I assume China too.
Whether or not it should is a different argument, but copyright is a legal framework, not an ethical one.
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.
Links in the article. Hugging Face and Swiss Telecoms host
…
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
Though I didn’t see the link to any repos or anything for confirmation.
Scroll down to “Further Information”.
PublicAI link is a public access point to the model, which can be tried directly at chat.publicai.co. This is also open, see Github.com/forpublicai/ (shown in the links I’m mentioning).
8b and 70b models and instructions can be downloaded from huggingface, also linked under “further information”.
If you go to the instructs, you’ll also be linked to github.com/swiss-ai/
I haven’t looked through it all, but everything appears to be there if you follow the links for additional information.
