I hope they do well. AFAIK they’re training or finetuning an older LLaMA model, so performance might lag behind SOTA. But what really matters is that ETH and EPFL get hands-on experience training at scale. From what I’ve heard, the new AI cluster still has teething problems. A lot of people underestimate how tough it is to train models at this scale, especially on your own infra.
Disclaimer: I’m Swiss and studied at ETH. We’ve got the brainpower, but not much large-scale training experience yet. And IMHO, a lot of the “magic” in LLMs is infrastructure-driven.
Imo, a lot of the magic is also dataset driven, specifically the SFT and other fine tuning / RLHF data they have. That's what has separated the models people actually use from the also-rans.
I agree with everything you say about getting the experience, the infrastructure is very important and is probably the most critical part of a sovereign LLM supply chain. I would hope there will also be enough focus on the data, early on, that the model will be useful.
When I read "from scratch", I assume they are doing pre-training, not just finetuning, do you have a different take? Do you mean it's normal Llama architecture they're using?
I'm curious about the benchmarks!
The infra does become pretty complex to get a SOTA LLM trained. People assume it's as simple as loading up the architecture and a dataset + using something like Ray. There's a lot that goes into designing the dataset, the eval pipelines, the training approach, maximizing the use of your hardware, dealing with cross-node latency, recovering from errors, etc.
But it's good to have more and more players in this space.
I wonder if the reason for these results is that any data on the internet is already copied to other locations by actors who ignore crawling opt-outs. So, even if they respect all web crawling opt-outs, they are still effectively copying the data because someone else did not respect it who does not include an opt-out.
Is there not yet a Source where the web has already been scraped and souped down to just the text? It would seem someone would have created such a thing in order to save LLM training from having to reinvent the wheel.
I understand the web is a dynamic thing but still it would seem to be useful on some level.
No performance degradation on training metrics except for the end user. At the end of the day users and website owners have completely orthogonal interests. Users want answers and content, website owners want attention so they can upsell/push ads. You can only serve one master.
You don't. You bypass them with crawlers and don't reveal your training data. And this is exactly why open source models can't surpass open weight models.
> And this is exactly why open source models can't surpass open weight models.
It is a fair point, but how strong of a point it is remains to be seen, some architectures are better than others, even with the same training data, so not impossible we could at one point see some innovative architectures beating current proprietary ones. It would probably be short-lived though, as the proprietary ones would obviously improve in their next release after that.
It is logically impossible for a LLM to, for example, to know that fooExecute() takes two int arguments if the documentation is blocked by robots.txt and there are no examples of fooExecute() usage in the wild, don't you agree?
I think that the Allen Institute for Artificial Intelligence OLMo models are also completely open:
OLMo is fully open
Ai2 believes in the power of openness to build a future where AI is accessible to all. Open weights alone aren’t enough – true openness requires models to be trained in the open with fully open access to data, models, and code.
The open training data is a huge differentiator. Is this the first truly open dataset of this scale? Prior efforts like The Pile were valuable, but had limitations. Curious to see how reproducible the training is.
> The model will be fully open: source code and weights will be publicly available, and the training data will be transparent and reproducible
This leads me to believe that the training data won’t be made publicly available in full, but merely be “reproducible”. This might mean that they’ll provide references like a list of URLs of the pages they trained on, but not their contents.
Yeah, I suspect you're right. Still, even a list of URLs for a frontier model (assuming it does turn out to be of that level) would be welcome over the current situation.
Sure, but usually you teach something that is inherently useful, or can be applied to some sort of useful endeavor. In this case I think it's fair to ask what the collision of two bubbles really achieves, or if it's just a useful teaching model, what it can be applied to.
The model will be released in two sizes — 8 billion and 70 billion parameters [...]. The 70B version will rank among the most powerful fully open models worldwide. [...] In late summer, the LLM will be released under the Apache 2.0 License.
“ Open LLMs are increasingly viewed as credible alternatives to commercial systems, most of which are developed behind closed doors in the United States or China”
It is obvious that the companies producing big LLMs today have the incentive to try to enshitify them. Trying to get subscriptions at the same time as trying to do product placement ads etc. Worse, some already have political biases they promote.
It would be wonderful if a partnership between academia and government in Europe can do a public good search and AI that endeavours to serve the user over the company.
Yes but it’s a very complicated service to deliver. Even if they train great models, they likely will not operationalize them for inference. Those will still be private actors, and the incentives to enshittify will be the same. Also, for AI generally the incentives is much higher than last tech generation, due to cost of running these things. Basically, the free services where you’re the product must aggressively extract value out of you in order to make a profit.
Pretty proud to see this at the top of HN as a Swiss (and I know many are lurking here!). These two universities produce world-class founders, researchers, and engineers. Yet, we always stay in the shadow of the US. With our top-tier public infrastructure, education, and political stability (+ neutrality), we have a unqiue opportunity to build something exceptional in the open LLM space.
Will be interested to see how this model responds to currently unresolvable issues in physics. Is it an open or a closed world mentality and/or a conditioned disclaimer which encourages progress?
The announcement was at the International Open-Source LLM Builders Summit held this week in Switzerland. Is it so strange that they announced what they are doing and the timeline?
Funding? Deeply biasing European uses to publicly-developed European LLMs (or at least not American or Chinese ones) would make a lot of sense. (Potentially too much sense for Brussels.)
I hope they do well. AFAIK they’re training or finetuning an older LLaMA model, so performance might lag behind SOTA. But what really matters is that ETH and EPFL get hands-on experience training at scale. From what I’ve heard, the new AI cluster still has teething problems. A lot of people underestimate how tough it is to train models at this scale, especially on your own infra.
Disclaimer: I’m Swiss and studied at ETH. We’ve got the brainpower, but not much large-scale training experience yet. And IMHO, a lot of the “magic” in LLMs is infrastructure-driven.
Imo, a lot of the magic is also dataset driven, specifically the SFT and other fine tuning / RLHF data they have. That's what has separated the models people actually use from the also-rans.
I agree with everything you say about getting the experience, the infrastructure is very important and is probably the most critical part of a sovereign LLM supply chain. I would hope there will also be enough focus on the data, early on, that the model will be useful.
When I read "from scratch", I assume they are doing pre-training, not just finetuning, do you have a different take? Do you mean it's normal Llama architecture they're using? I'm curious about the benchmarks!
The infra does become pretty complex to get a SOTA LLM trained. People assume it's as simple as loading up the architecture and a dataset + using something like Ray. There's a lot that goes into designing the dataset, the eval pipelines, the training approach, maximizing the use of your hardware, dealing with cross-node latency, recovering from errors, etc.
But it's good to have more and more players in this space.
"respecting web crawling opt-outs during data acquisition produces virtually no performance degradation"
Great to read that!
I wonder if the reason for these results is that any data on the internet is already copied to other locations by actors who ignore crawling opt-outs. So, even if they respect all web crawling opt-outs, they are still effectively copying the data because someone else did not respect it who does not include an opt-out.
Is there not yet a Source where the web has already been scraped and souped down to just the text? It would seem someone would have created such a thing in order to save LLM training from having to reinvent the wheel.
I understand the web is a dynamic thing but still it would seem to be useful on some level.
No performance degradation on training metrics except for the end user. At the end of the day users and website owners have completely orthogonal interests. Users want answers and content, website owners want attention so they can upsell/push ads. You can only serve one master.
> Users want answers and content, website owners want attention so they can upsell/push ads. You can only serve one master
How are you going to serve users if web site owners decide to wall their content? You can't ignore one side of the market.
You don't. You bypass them with crawlers and don't reveal your training data. And this is exactly why open source models can't surpass open weight models.
> And this is exactly why open source models can't surpass open weight models.
It is a fair point, but how strong of a point it is remains to be seen, some architectures are better than others, even with the same training data, so not impossible we could at one point see some innovative architectures beating current proprietary ones. It would probably be short-lived though, as the proprietary ones would obviously improve in their next release after that.
How can open source models respectful of robots.txt possibly perform equally if they are missing information that the other models have access to?
How can we possibly find out without trying?
It is logically impossible for a LLM to, for example, to know that fooExecute() takes two int arguments if the documentation is blocked by robots.txt and there are no examples of fooExecute() usage in the wild, don't you agree?
Is this setting the bar for dataset transparency? It seems like a significant step forward. Assuming it works out, that is.
They missed an opportunity though. They should have called their machine the AIps (AI Petaflops Supercomputer).
I think that the Allen Institute for Artificial Intelligence OLMo models are also completely open:
OLMo is fully open
Ai2 believes in the power of openness to build a future where AI is accessible to all. Open weights alone aren’t enough – true openness requires models to be trained in the open with fully open access to data, models, and code.
https://allenai.org/olmo
Smollm is also completely open as far as I know
The open training data is a huge differentiator. Is this the first truly open dataset of this scale? Prior efforts like The Pile were valuable, but had limitations. Curious to see how reproducible the training is.
> The model will be fully open: source code and weights will be publicly available, and the training data will be transparent and reproducible
This leads me to believe that the training data won’t be made publicly available in full, but merely be “reproducible”. This might mean that they’ll provide references like a list of URLs of the pages they trained on, but not their contents.
Well, when the actual content is 100s of terabytes big, providing URLs may be more practical for them and for others.
The difference between content they are allowed to train on vs. being allowed to distribute copies of is likely at least as relevant.
That wouldn't seem reproducible if the content at those URLs changes. (Er, unless it was all web.archive.org URLs or something.)
This is a problem with the Web. It should be easier to download content like it was updating a git Repo.
Yeah, I suspect you're right. Still, even a list of URLs for a frontier model (assuming it does turn out to be of that level) would be welcome over the current situation.
Yup, it’s not a dataset packaged like you hope for here, as it still contains traditionally copyrighted material
Yeah, that's what "democratizing AI" means.
The press release talks a lot about how it was done, but very little about how capabilities compare to other open models.
It's a university, teaching the 'how it's done' is kind of the point
Sure, but usually you teach something that is inherently useful, or can be applied to some sort of useful endeavor. In this case I think it's fair to ask what the collision of two bubbles really achieves, or if it's just a useful teaching model, what it can be applied to.
The model will be released in two sizes — 8 billion and 70 billion parameters [...]. The 70B version will rank among the most powerful fully open models worldwide. [...] In late summer, the LLM will be released under the Apache 2.0 License.
We'll find out in September if it's true?
I hope DeepSeek R2, but I fear Llama 4.
Yeah, I was thinking more of a table with benchmark results
The article says
“ Open LLMs are increasingly viewed as credible alternatives to commercial systems, most of which are developed behind closed doors in the United States or China”
It is obvious that the companies producing big LLMs today have the incentive to try to enshitify them. Trying to get subscriptions at the same time as trying to do product placement ads etc. Worse, some already have political biases they promote.
It would be wonderful if a partnership between academia and government in Europe can do a public good search and AI that endeavours to serve the user over the company.
Yes but it’s a very complicated service to deliver. Even if they train great models, they likely will not operationalize them for inference. Those will still be private actors, and the incentives to enshittify will be the same. Also, for AI generally the incentives is much higher than last tech generation, due to cost of running these things. Basically, the free services where you’re the product must aggressively extract value out of you in order to make a profit.
Pretty proud to see this at the top of HN as a Swiss (and I know many are lurking here!). These two universities produce world-class founders, researchers, and engineers. Yet, we always stay in the shadow of the US. With our top-tier public infrastructure, education, and political stability (+ neutrality), we have a unqiue opportunity to build something exceptional in the open LLM space.
Looking forward to proof test it.
gross use of public infrastructure
I literally cant fault this, even steelmanning anti AI positions. What makes you say that?
Use case for science and code LLMs: Superhydrodynamic gravity (SQR / SQG, )
LLMs do seem to favor general relativity but probably would've favored classical mechanics at the time given the training corpora.
Not-yet unified: Quantum gravity, QFT, "A unified model must: " https://news.ycombinator.com/item?id=44289148
Will be interested to see how this model responds to currently unresolvable issues in physics. Is it an open or a closed world mentality and/or a conditioned disclaimer which encourages progress?
What are the current benchmarks?
From https://news.ycombinator.com/item?id=42899805 re: "Large Language Models for Mathematicians" (2023) :
> Benchmarks for math and physics LLMs: FrontierMath, TheoremQA, Multi SWE-bench: https://news.ycombinator.com/item?id=42097683
Multi-SWE-bench: A Multi-Lingual and Multi-Modal GitHub Issue Resolving Benchmark: https://multi-swe-bench.github.io/
Add'l LLM benchmarks and awesome lists: https://news.ycombinator.com/item?id=44485226
Microsoft has a new datacenter that you don't have to keep adding water to; which spares the aquifers.
How to use this LLM to solve energy and sustainability problems all LLMs exacerbate? Solutions for the Global Goals, hopefully
Why would you announce this without a release? Be honest.
The announcement was at the International Open-Source LLM Builders Summit held this week in Switzerland. Is it so strange that they announced what they are doing and the timeline?
Funding? Deeply biasing European uses to publicly-developed European LLMs (or at least not American or Chinese ones) would make a lot of sense. (Potentially too much sense for Brussels.)
The cliché (at least on my side of the Alps) is that people in Switzerland like to take theiiiir tiiiime.
"Move as quickly as possible, but as slowly as necessary."