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Founded Date August 12, 1901
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Sectors 1st ADs (local system)
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Company Description
DeepSeek-R1 · GitHub Models · GitHub
DeepSeek-R1 stands out at reasoning jobs utilizing a detailed training process, such as language, scientific thinking, and coding tasks. It features 671B total criteria with 37B active parameters, and 128k context length.
DeepSeek-R1 constructs on the development of earlier reasoning-focused models that enhanced efficiency by extending Chain-of-Thought (CoT) reasoning. DeepSeek-R1 takes things further by combining reinforcement knowing (RL) with fine-tuning on carefully selected datasets.
It developed from an earlier version, DeepSeek-R1-Zero, which relied exclusively on RL and revealed strong reasoning skills however had problems like hard-to-read outputs and language disparities.
To resolve these limitations, DeepSeek-R1 includes a small amount 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 accomplishes advanced performance on thinking standards.
Usage Recommendations
We suggest sticking to the following configurations when utilizing the DeepSeek-R1 series models, including benchmarking, to accomplish the expected efficiency:
– Avoid adding a system timely; all guidelines need to be within the user timely.
– For mathematical issues, it is a good idea to include a regulation in your prompt such as: “Please factor step by step, and put your final answer within boxed .”.
– When evaluating model performance, it is recommended to carry out several tests and balance the results.
Additional suggestions
The design’s reasoning output (contained within the tags) may consist of more harmful material than the design’s last action. Consider how your application will utilize or display the thinking output; you may wish to reduce the thinking output in a production setting.