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Company Description

DeepSeek-R1 · GitHub Models · GitHub

DeepSeek-R1 stands out at thinking jobs utilizing a step-by-step training procedure, such as language, clinical reasoning, and coding jobs. It includes 671B overall criteria with 37B active criteria, and 128k context length.

DeepSeek-R1 constructs on the progress of earlier reasoning-focused models that improved performance by extending Chain-of-Thought (CoT) thinking. DeepSeek-R1 takes things even more by combining reinforcement learning (RL) with fine-tuning on carefully selected datasets. It developed from an earlier variation, DeepSeek-R1-Zero, which relied exclusively on RL and revealed strong reasoning abilities but had problems like hard-to-read outputs and language disparities. To attend to these limitations, DeepSeek-R1 includes a percentage 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 design that achieves advanced efficiency on reasoning criteria.

Usage Recommendations

We recommend adhering to the following setups when making use of the DeepSeek-R1 series designs, including benchmarking, to accomplish the anticipated efficiency:

– Avoid adding a system timely; all directions need to be included within the user prompt.
– For mathematical issues, it is suggested to include a directive in your timely such as: “Please factor action by step, and put your final response within boxed .”.
– When assessing design efficiency, it is suggested to perform multiple tests and balance the outcomes.

Additional suggestions

The design’s thinking output (consisted of within the tags) may consist of more damaging material than the model’s last reaction. Consider how your application will utilize or show the thinking output; you might wish to reduce the in a production setting.