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DeepSeek-R1 · GitHub Models · GitHub

DeepSeek-R1 stands out at reasoning tasks using a detailed training process, such as language, scientific 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 designs that enhanced efficiency by extending Chain-of-Thought (CoT) thinking. DeepSeek-R1 takes things further by knowing (RL) with fine-tuning on carefully chosen datasets. It developed from an earlier version, DeepSeek-R1-Zero, which relied entirely on RL and revealed strong thinking skills however had concerns like hard-to-read outputs and language disparities. To deal with these restrictions, DeepSeek-R1 incorporates a small 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 achieves modern performance on reasoning benchmarks.

Usage Recommendations

We suggest adhering to the following configurations when utilizing the DeepSeek-R1 series models, including benchmarking, to accomplish the expected performance:

– Avoid including a system timely; all instructions should be consisted of within the user timely.
– For mathematical problems, it is recommended to consist of a regulation in your prompt such as: “Please factor action by action, and put your last answer within boxed .”.
– When evaluating model performance, it is recommended to perform numerous tests and balance the outcomes.

Additional recommendations

The model’s thinking output (consisted of within the tags) may include more hazardous material than the design’s final reaction. Consider how your application will use or display the thinking output; you may wish to reduce the thinking output in a production setting.