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Founded Date June 10, 2021
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Sectors Mushroom production
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Company Description
DeepSeek-R1 · GitHub Models · GitHub
DeepSeek-R1 excels at reasoning tasks utilizing a detailed training procedure, such as language, scientific reasoning, and . It features 671B total specifications with 37B active criteria, and 128k context length.
DeepSeek-R1 develops on the development of earlier reasoning-focused models that improved efficiency by extending Chain-of-Thought (CoT) reasoning. DeepSeek-R1 takes things further by integrating support knowing (RL) with fine-tuning on carefully chosen datasets. It progressed from an earlier version, DeepSeek-R1-Zero, which relied exclusively on RL and showed strong reasoning abilities but had problems like hard-to-read outputs and language disparities.
To attend to these restrictions, DeepSeek-R1 includes a small quantity of cold-start information and follows a refined training pipeline that mixes reasoning-oriented RL with supervised fine-tuning on curated datasets, leading to a model that achieves advanced performance on reasoning benchmarks.
Usage Recommendations
We suggest adhering to the following configurations when making use of the DeepSeek-R1 series models, including benchmarking, to attain the anticipated performance:
– Avoid adding a system timely; all instructions need to be contained within the user prompt.
– For mathematical issues, it is recommended to consist of a directive in your prompt such as: “Please factor action by action, and put your last answer within boxed .”.
– When evaluating model efficiency, it is suggested to perform multiple tests and balance the outcomes.
Additional recommendations
The design’s reasoning output (contained within the tags) may include more hazardous content than the design’s last action. Consider how your application will utilize or display the thinking output; you may wish to suppress the reasoning output in a production setting.