Apertus 70B: Truly Open - Swiss LLM by ETH, EPFL and CSCS(huggingface.co)
huggingface.co
Apertus 70B: Truly Open - Swiss LLM by ETH, EPFL and CSCS
https://huggingface.co/swiss-ai/Apertus-70B-2509
62 comments
Hi, your "truly open" model is "gated" on Huggingface, restricting downloads unless we agree to "hold you harmless" and share our contact info. Can you fix this please, either by removing the restriction, or removing the "truly open" claim?
We hear you, nevertheless this is one of the very few open-weights and open-data LLMs, and the license is still very permissive (compare for example to Llama). Personally of course I'd like to remove the additional click, but the universities also have a say in this.
This project looks awesome!
In the US, many state governments have anti-indemnify laws that restrict the state government agencies (including state universities) from agreeing to contracts and agreements with such language. I'd love to make this available to researchers at my university, but I'm not sure I can click through such an agreement (similar problems exist with other LLMs).
It is Apache 2 and I don't see anything that prohibits another contracting party from agreeing to the Apertus LLM Acceptable Use Policy and redistributing with just Apache 2 and without the AUP. Maybe this provides a solution? Unless I'm missing something?
In the US, many state governments have anti-indemnify laws that restrict the state government agencies (including state universities) from agreeing to contracts and agreements with such language. I'd love to make this available to researchers at my university, but I'm not sure I can click through such an agreement (similar problems exist with other LLMs).
It is Apache 2 and I don't see anything that prohibits another contracting party from agreeing to the Apertus LLM Acceptable Use Policy and redistributing with just Apache 2 and without the AUP. Maybe this provides a solution? Unless I'm missing something?
yes this seems a good way to go. for example you can already find many quantized versions under https://huggingface.co/models?search=apertus%20mlx and elsewhere
Ok so why keep calling it "truly open" then? It's an obvious lie and nobody is forcing you to say it. It benefits your marketing, sure, but it harms everyone else by diluting the meaning of the term "open". So stop doing that please.
Great job!
Would it be possible to know what was the cost of training such a model?
From their report:
> Once a production environment has been set up, we estimate that the model can be realistically trained in approximately 90 days on 4096 GPUs, accounting for overheads. If we assume 560 W power usage per Grace-Hopper module in this period, below the set power limit of 660 W, we can estimate 5 GWh power usage for the compute of the pretraining run.
> Once a production environment has been set up, we estimate that the model can be realistically trained in approximately 90 days on 4096 GPUs, accounting for overheads. If we assume 560 W power usage per Grace-Hopper module in this period, below the set power limit of 660 W, we can estimate 5 GWh power usage for the compute of the pretraining run.
Upvoting to encourage discussion of these differentiators:
"Apertus is a 70B and 8B parameter language model designed to push the boundaries of fully-open multilingual and transparent models. The model supports over 1000 languages and long context, it uses only fully compliant and open training data, and achieves comparable performance to models trained behind closed doors."
"pretrained on 15T tokens with a staged curriculum of web, code and math data"
"open weights + open data + full training details including all data and training recipes"
"Apertus is trained while respecting opt-out consent of data owners (even retrospectivey), and avoiding memorization of training data"
"Apertus is a 70B and 8B parameter language model designed to push the boundaries of fully-open multilingual and transparent models. The model supports over 1000 languages and long context, it uses only fully compliant and open training data, and achieves comparable performance to models trained behind closed doors."
"pretrained on 15T tokens with a staged curriculum of web, code and math data"
"open weights + open data + full training details including all data and training recipes"
"Apertus is trained while respecting opt-out consent of data owners (even retrospectivey), and avoiding memorization of training data"
At least not "open source"
> "open weights + open data + full training details including all data and training recipes"
Is it reproducible?
> respecting opt-out consent of data owners (even retrospectivey)
Were they notified and given an option to opt out? Owners and authors are not the same. Data owners aren't copyright owners either.
> avoiding memorization of training data
Not convincing.
> "open weights + open data + full training details including all data and training recipes"
Is it reproducible?
> respecting opt-out consent of data owners (even retrospectivey)
Were they notified and given an option to opt out? Owners and authors are not the same. Data owners aren't copyright owners either.
> avoiding memorization of training data
Not convincing.
I saw some of the pretraining code in github, but not the post-training.
posttraining codebase is here: https://github.com/swiss-ai/posttraining
Report: https://github.com/swiss-ai/apertus-tech-report/raw/refs/hea...
Key features
Fully open model: open weights + open data + full training details including all data and training recipes
Massively Multilingual: 1811 natively supported languages
Compliant: Apertus is trained while respecting opt-out consent of data owners (even retrospectivey), and avoiding memorization of training data
Key features
Fully open model: open weights + open data + full training details including all data and training recipes
Massively Multilingual: 1811 natively supported languages
Compliant: Apertus is trained while respecting opt-out consent of data owners (even retrospectivey), and avoiding memorization of training data
Looks like the performance is pretty decent, somewhere around Llama3.1 for general knowledge (Tables 17) but still a bit behind in Code and Reasoning (Table 18). Llama3.1 was released about one year ago.
Their struggle with Nvidia driver bugs they had to work around was very relatable. You'd think if someone buys 10,752 of their high-end GPUs you'd get some support with it.
did I miss a blog on this?
we didn't have time to write one yet, but there is the tech report which has a lot of details already
Agreed, but the problem seems to be even worse with AMD from what I hear, or at least it was when I checked with some of my HPC buddies a little over a year ago. Constant driver bugs and crickets from upstream "support".
no, you have to pay the yearly per gpu license for that.
There's an interesting "Swiss AI Charter" on pg. 107.
Apparently a project of https://www.swiss-ai.org/
seems a DOA
In my opinion, we need more models trained on fully traceable and clean data instead of closed models that we later find out were trained on Reddit and Facebook discussion threads.
Does their training corpus respect copyrights or do you have to follow their opt out procedure to keep them from consuming your data? Assuming it’s the latter, it’s open-er but still not quite there.
Afaik they respect robots.txt on crawl and later when using the data they re-check the robots.txt and will exclude the data if the new robots.txt was updated to deny access. They have further data filtering bit for that you better check the technical report.
Your question is addressed in opening abstract: https://github.com/swiss-ai/apertus-tech-report/raw/refs/hea...
> Unlike many prior models that release weights without reproducible data pipelines or regard for content-owner rights, Apertus models are pretrained exclusively on openly available data, retroactively respecting robots.txt exclusions and filtering for copyrighted, non-permissive, toxic, and personally identifiable content.
> Unlike many prior models that release weights without reproducible data pipelines or regard for content-owner rights, Apertus models are pretrained exclusively on openly available data, retroactively respecting robots.txt exclusions and filtering for copyrighted, non-permissive, toxic, and personally identifiable content.
Imagine regulators doing their job for once and creating a clean regulation that removes the uncertainty about the liability for such releases. Such that they can just slap Apache or MIT on it and call it a day and don't require to collect personal data to comply with the "acceptable use policy".
https://apertus.org/ exists since 15 years, interesting choice of name.
>> "Middle English, borrowed from Latin apertūra, from apertus, past participle of aperīre "to open" + -ūra -ure"
https://www.merriam-webster.com/dictionary/aperture
https://www.merriam-webster.com/dictionary/aperture
Sure, I just (honestly) wondered about trademarks.
https://apertus.org/about
>> "The "o" stands for "open", "openness", "open source" and is placed where a "TM" symbol (indicating patents, trademarks, protection) would normally reside. Instead openness is the apertus° trademark."
It's also a completely different kind of thing so trademark probably wouldn't come into it even if they had one.
>> "The "o" stands for "open", "openness", "open source" and is placed where a "TM" symbol (indicating patents, trademarks, protection) would normally reside. Instead openness is the apertus° trademark."
It's also a completely different kind of thing so trademark probably wouldn't come into it even if they had one.
Is there any practical method to verify that the model was trained from the reported dataset?
we released 81 intermediate checkpoints of the whole pretraining phase, and the code and data to reproduce. so full audit is surely possible - still it would depend on what you consider 'practical' here.
I want and hope this to succeed. But the tea leaves don't look good at the moment:
- model sizes that the industry was at 2-3 gens ago (llama 3.1 era) - Conspicuous lack of benchmark results in announcements - not on openrouter, no ggufs as yet
- model sizes that the industry was at 2-3 gens ago (llama 3.1 era) - Conspicuous lack of benchmark results in announcements - not on openrouter, no ggufs as yet
benchmarks: we provide plenty in the over 100 page tech report here
https://github.com/swiss-ai/apertus-tech-report/blob/main/Ap...
quantizations: available now in MLX https://github.com/ml-explore/mlx-lm (gguf coming soon, not trivial due to new architecture)
model sizes: still many good dense models today lie in the range between our small and large chosen sizes
quantizations: available now in MLX https://github.com/ml-explore/mlx-lm (gguf coming soon, not trivial due to new architecture)
model sizes: still many good dense models today lie in the range between our small and large chosen sizes
Thank you! Why are the comparisons to llama3.1 era models?
we compared to GPT-OSS-20B, Llama 4, Qwen 3, among many others. Which models do you think are missing, among open weights and fully-open models?
Note that we have a specific focus on multilinguality (over 1000 languages supported), not only on english
Note that we have a specific focus on multilinguality (over 1000 languages supported), not only on english
Really happy to see this and will give it a good spin. They seem to be doing things the right way in my subjective opinion:
""" To implement this filter, we begin by ranking URL domains according to the volume of texts they contribute to the FineWeb (Penedo et al., 2024a) and FineWeb-2 (Penedo et al., 2025) corpus, as an approximation of web-level English and multilingual data. From this ranking, we select the top one million English domains and the top one million non-English domains. Due to domain overlap and the fact that some sites are now offline, the total number of accessible robots.txt files is smaller than two million. For each domain that remains reachable, we retrieve its robots.txt file as of January 2025 and examine the directives relevant to AI training. In particular, we focus on those targeting the AI-specific user agents listed in Appendix A. Any contents blocked by the current robots.txt is removed retroactively from the entire 2013-2024 range of the training dataset. We follow an opt-out policy, that is, if the corresponding robots.txt files are not available, we consider the data usable for training. The filtering process results in an estimated token loss of approximately 8% in English data and 4% in multilingual data. """
""" To implement this filter, we begin by ranking URL domains according to the volume of texts they contribute to the FineWeb (Penedo et al., 2024a) and FineWeb-2 (Penedo et al., 2025) corpus, as an approximation of web-level English and multilingual data. From this ranking, we select the top one million English domains and the top one million non-English domains. Due to domain overlap and the fact that some sites are now offline, the total number of accessible robots.txt files is smaller than two million. For each domain that remains reachable, we retrieve its robots.txt file as of January 2025 and examine the directives relevant to AI training. In particular, we focus on those targeting the AI-specific user agents listed in Appendix A. Any contents blocked by the current robots.txt is removed retroactively from the entire 2013-2024 range of the training dataset. We follow an opt-out policy, that is, if the corresponding robots.txt files are not available, we consider the data usable for training. The filtering process results in an estimated token loss of approximately 8% in English data and 4% in multilingual data. """
> Any contents blocked by the current robots.txt is removed retroactively from the entire 2013-2024 range of the training dataset
Why not check historical versions of the robots.txt (e.g. archive.org) and contain the retroactive cutoff to a certain date range, parsing the robots.txt accordingly? That might increase the corpus size within legal and fair use boundaries.
Why not check historical versions of the robots.txt (e.g. archive.org) and contain the retroactive cutoff to a certain date range, parsing the robots.txt accordingly? That might increase the corpus size within legal and fair use boundaries.
common crawl anyway respects the CCbot opt-out every time they do a crawl.
we went a step further because back in old ages (2013 is our oldest training data) LLMs did not exist, so website owners opting out today of AI crawlers might like the option to also remove their past contents.
arguments can be made either way but we tried to remain on the cautious side at this point.
we also wrote a paper on how this additional removal affects downstream performance of the LLM https://arxiv.org/abs/2504.06219 (it does so surprisingly little)
we went a step further because back in old ages (2013 is our oldest training data) LLMs did not exist, so website owners opting out today of AI crawlers might like the option to also remove their past contents.
arguments can be made either way but we tried to remain on the cautious side at this point.
we also wrote a paper on how this additional removal affects downstream performance of the LLM https://arxiv.org/abs/2504.06219 (it does so surprisingly little)
I imagine coverage is sparse enough to not be worth it.
This is an impressive milestone.
It’s easy to become jaded with so many huge models being released, but the reality is they are still from a relatively small group of countries.
For example India has no indigenous models this big despite having a world class talent pool.
It’s easy to become jaded with so many huge models being released, but the reality is they are still from a relatively small group of countries.
For example India has no indigenous models this big despite having a world class talent pool.
> talent pool
Capital though ;)
[I am a grad student here in reinforcement learning]
Anyways, among all the VC/made-at-home driven snake oil, I'd say you should look at sarvam.ai, they are the most focussed and no-nonsense group. They have a few good from-scratch models (I believe upto 7B or 14B), as well as a few llama finetunes. Their API is pretty good.
The main thing folks here are attempting are to get LLMs good at local indian languages (and I don't mean hindi). I don't think people see a value in creating an "indigenous llama" that doesn't have that property. For this, the main bottleneck is data (relatively speaking, there is zero data in those languages on the internet), so there's a team AI4Bharat whose main job is curating datasets good enough to get stuff like _translation_ and other NLP benchmarks working well. LLMs too, for which they work with sarvam frequently.
Capital though ;)
[I am a grad student here in reinforcement learning]
Anyways, among all the VC/made-at-home driven snake oil, I'd say you should look at sarvam.ai, they are the most focussed and no-nonsense group. They have a few good from-scratch models (I believe upto 7B or 14B), as well as a few llama finetunes. Their API is pretty good.
The main thing folks here are attempting are to get LLMs good at local indian languages (and I don't mean hindi). I don't think people see a value in creating an "indigenous llama" that doesn't have that property. For this, the main bottleneck is data (relatively speaking, there is zero data in those languages on the internet), so there's a team AI4Bharat whose main job is curating datasets good enough to get stuff like _translation_ and other NLP benchmarks working well. LLMs too, for which they work with sarvam frequently.
[deleted]
Very cool. Love this. Was the training more heavily weighted towards swiss languages and how does the model perform on swiss languages compared to others?
Are there any plans for further models after this one?
Are there any plans for further models after this one?
The pretraining (so 99% of training) is fully global, in over 1000 languages without special weighting. The posttraining (See section 4 of the paper) had also as many languages as we could get, and did upweight some languages. The posttraining can easily be customized to any other target languages
I don't get it. How is it open if you can't even access it without signing a contract?
Apparently it’s a university thing.
Congratulations to the Apertus team! Love the name, which in addition to "open" in Latin reminds me of Pilatus.
“The file reflects data protection deletion requests which have been addressed to SNAI as the developer of the Apertus LLM. It allows you to remove Personal Data contained in the model output. We strongly advise downloading and applying this output filter from SNAI every six months following the release of the model.”
I can't imagine that this actually complies with the law.
I can't imagine that this actually complies with the law.
This is why predictions of eventual AI failure due to copyright lawsuits are likely wrong.
Assumedly, an organization training and then distributing this model cannot be stopped via copyright or breach of contract lawsuit. It may be that folks will figure out copyright-free versions of text-to-image and text-to-video, etc., models as well.
It seems that there is plenty of copyright-free data available to train a useful model. Therefore, when content creators upset about AI companies training models on their content are asked about this model, they have nothing to do but shrug.
The cat is out of the bag.
Assumedly, an organization training and then distributing this model cannot be stopped via copyright or breach of contract lawsuit. It may be that folks will figure out copyright-free versions of text-to-image and text-to-video, etc., models as well.
It seems that there is plenty of copyright-free data available to train a useful model. Therefore, when content creators upset about AI companies training models on their content are asked about this model, they have nothing to do but shrug.
The cat is out of the bag.
regarding training data -- is the main base model here trained only in FineWeb-2 ? or is it more also ..
the full collection of models is here: https://huggingface.co/collections/swiss-ai/apertus-llm-68b6...
PS: you can run this locally on your mac with this one-liner:
pip install mlx-lm
mlx_lm.generate --model mlx-community/Apertus-8B-Instruct-2509-8bit --prompt "who are you?"