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Date de création juillet 16, 2019
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Secteur Agronomie
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Offres d'emploi 0
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Consultés 11
Company Description
DeepSeek-R1 · GitHub Models · GitHub
DeepSeek-R1 excels at thinking tasks using a detailed training procedure, such as language, scientific reasoning, and coding jobs. It features 671B total parameters with 37B active parameters, and 128k context length.
DeepSeek-R1 develops on the development of earlier reasoning-focused designs that improved performance by extending Chain-of-Thought (CoT) thinking. DeepSeek-R1 takes things even more by integrating support learning (RL) with fine-tuning on thoroughly selected datasets.
It from an earlier version, DeepSeek-R1-Zero, which relied solely on RL and showed strong thinking abilities however had concerns like hard-to-read outputs and language inconsistencies.
To attend to these limitations, DeepSeek-R1 incorporates a percentage of cold-start data and follows a refined training pipeline that blends reasoning-oriented RL with monitored fine-tuning on curated datasets, leading to a design that attains modern efficiency on thinking criteria.
Usage Recommendations
We suggest adhering to the following setups when utilizing the DeepSeek-R1 series models, consisting of benchmarking, to attain the expected efficiency:
– Avoid adding a system prompt; all directions need to be included within the user prompt.
– For mathematical problems, it is recommended to consist of a directive in your prompt such as: « Please factor step by action, and put your last response within boxed . ».
– When evaluating model efficiency, it is recommended to conduct several tests and balance the results.
Additional suggestions
The design’s reasoning output (contained within the tags) may include more damaging material than the model’s last response. Consider how your application will utilize or show the reasoning output; you may wish to reduce the thinking output in a production setting.