HN Reader

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. """

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.

“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.

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?

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.

Apparently a project of https://www.swiss-ai.org/

Congratulations to the Apertus team! Love the name, which in addition to "open" in Latin reminds me of Pilatus.

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"

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.

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

seems a DOA

I don't get it. How is it open if you can't even access it without signing a contract?

https://apertus.org/ exists since 15 years, interesting choice of name.

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.

Is there any practical method to verify that the model was trained from the reported dataset?

martin here from the apertus team, happy to answer any questions if i can.

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?"

regarding training data -- is the main base model here trained only in FineWeb-2 ? or is it more also ..

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".

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