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.
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"
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.
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
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.
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"
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.
I saw some of the pretraining code in github, but not the post-training.
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/
seems a DOA
How so?
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.