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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 reasoning, and coding tasks. It features 671B total specifications with 37B active criteria, and 128k context length.
DeepSeek-R1 constructs on the development of earlier reasoning-focused designs that enhanced performance by extending Chain-of-Thought (CoT) reasoning. DeepSeek-R1 takes things further by combining support learning (RL) with fine-tuning on carefully picked datasets. It developed from an earlier version, DeepSeek-R1-Zero, which relied solely on RL and showed strong thinking abilities but had concerns like hard-to-read outputs and language inconsistencies. To address these limitations, DeepSeek-R1 includes a little amount of cold-start data and follows a refined training pipeline that mixes reasoning-oriented RL with monitored fine-tuning on curated datasets, resulting in a design that accomplishes modern efficiency on reasoning standards.
Usage Recommendations
We recommend sticking to the following configurations when making use of the DeepSeek-R1 series designs, including benchmarking, to accomplish the anticipated efficiency:
– Avoid adding a system timely; all directions should be contained within the user prompt.
– For mathematical problems, it is suggested to include an instruction in your prompt such as: “Please reason step by action, and put your last answer within boxed .”.
– When examining design performance, it is suggested to perform several tests and balance the results.
Additional recommendations
The output (contained within the tags) might contain more hazardous content than the model’s last reaction. Consider how your application will utilize or display the reasoning output; you may wish to suppress the reasoning output in a production setting.