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Mistral AI
In our continued commitment to open-science, we are releasing the Voxtral Technical Report:
The report covers details on pre-training, post-training, alignment and evaluations. We also present analysis on selecting the optimal model architecture, which pre-training format to use, and the benefits of DPO.

26,5K
Introducing Mistral Small 3.2, a small update to Mistral Small 3.1 to improve:
- Instruction following: Small 3.2 is better at following precise instructions
- Repetition errors: Small 3.2 produces less infinite generations or repetitive answers
- Function calling: Small 3.2's function calling template is more robust



12,91K
Mistral AI kirjasi uudelleen
Today we are releasing ether0, our first scientific reasoning model.
We trained Mistral 24B with RL on several molecular design tasks in chemistry. Remarkably, we found that LLMs can learn some scientific tasks more much data-efficiently than specialized models trained from scratch on the same data, and can greatly outperform frontier models and humans on those tasks. For at least a subset of scientific classification, regression, and generation problems, post-training LLMs may provide a much more data-efficient approach than traditional machine learning approaches. 1/n
90,27K
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