lllllm
Joined 3 years ago
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Comment · 1 year ago
Comment · 1 year ago
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
Comment · 1 year ago
thank you!
Comment · 1 year ago
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.
Comment · 1 year ago
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
Comment · 1 year ago
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)
Comment · 1 year ago
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?"
Comment · 1 year ago
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
Comment · 1 year ago
we didn't have time to write one yet, but there is the tech report which has a lot of details already
Comment · 1 year ago
posttraining codebase is here: https://github.com/swiss-ai/posttraining
Comment · 1 year ago
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.
Comment · 1 year ago
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
Comment · 1 year ago
this is what this paper tries to answer: https://arxiv.org/abs/2504.06219
the quality gap is surprisingly small between compliant and not
Comment · 1 year ago
absolutely! i've sent you a linkedin message last week. but here seems to work much better, thanks a lot!
We are a young team, and the creators of the Apertus LLM, the currently leading open-data open-weights AI model.
Join us to work on cutting edge LLM training in the open. We do pretraining, alignment, reasoning, multilinguality and multimodality - all at the intersection of engineering and research.
This is a joint team between ETH Zurich and EPFL in Lausanne, running on the Alps supercomputer (one of the largest public institution GPU cluster). Visa sponsoring possible, work language is English.
https://careers.epfl.ch/job/Lausanne-AI-Research-Engineers-S...