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Founded Date Juni 24, 1911
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Company Description
DeepSeek-R1 · GitHub Models · GitHub
DeepSeek-R1 excels at reasoning tasks using a step-by-step training procedure, such as language, scientific thinking, and coding tasks. It features 671B total specifications with 37B active parameters, and 128k context length.
DeepSeek-R1 builds on the progress of earlier reasoning-focused models that enhanced performance by extending Chain-of-Thought (CoT) reasoning. DeepSeek-R1 takes things further by combining support learning (RL) with fine-tuning on thoroughly picked datasets. It developed from an earlier variation, DeepSeek-R1-Zero, which on RL and showed strong reasoning abilities however had issues like hard-to-read outputs and language inconsistencies. To address these limitations, DeepSeek-R1 integrates a little quantity of cold-start information and follows a refined training pipeline that blends reasoning-oriented RL with monitored fine-tuning on curated datasets, resulting in a design that attains state-of-the-art efficiency on thinking criteria.
Usage Recommendations
We recommend adhering to the following configurations when using the DeepSeek-R1 series models, including benchmarking, to achieve the expected efficiency:
– Avoid including a system prompt; all directions should be consisted of within the user timely.
– For mathematical problems, it is a good idea to consist of a regulation in your prompt such as: „Please factor action by step, and put your last answer within boxed .“.
– When examining design performance, it is suggested to carry out multiple tests and average the results.
Additional suggestions
The model’s reasoning output (included within the tags) might include more harmful material than the design’s final response. Consider how your application will use or display the thinking output; you may want to reduce the reasoning output in a production setting.