"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."
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
https://forums.opensuse.org/t/is-anyone-getting-vram-backed-by-system-memory-with-nvidia-drivers/185902
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
So that's only in the case of sharing RAM? Because the vast majority of people use Nvidia and Linux without issue, myself included.
Yes, precisely.
If you're trying to use large models, you need more RAM than consumer grade nvidia products can supply. Without system ram sharing, the models error out and start repeating themselves or just crash and need to be restarted.
This can be fixed with CPU inferencing but would be much slower.
An 8b model will run fine on an RTX30 series, a 70b model will absolutely not. BUT you can do cpu inferencing with the 70b model if you don't mind the wait.
No shit lmao, are you going to tell me a horse can't pull an 18 wheeler trailer next?
You don't need to be rude.
My original comment was in reply to someone looking for this type of information, the conversation then continued.
Disengage: I don't want to deal with it today frankly, I don't have time for rude people.
Its a leap to say "nvidia AI support on Linux is bad" when you mean a very particular set of circumstances (which don't apply to someone who would just be getting into it as they're using consumer grade hardware) causes issues.
I have a 3080ti and run 8b - 12b all in VRAM just fine, which is what a majority of people getting into it would be doing as well, again to my point about pulling an 18 wheeler trailer with a horse, you've got worse problems then nvidia on Linux (if you're trying to run a 70b model on consumer hardware).
Yeah for most people that's not an issue, because they aren't trying to run 70b models.