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"

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.

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".

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

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

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. """

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.