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DeepSeek-R1 · GitHub Models · GitHub

DeepSeek-R1 excels at thinking tasks using a step-by-step training procedure, such as language, scientific reasoning, and coding tasks. It includes 671B total criteria with 37B active criteria, and 128k context length.

DeepSeek-R1 develops on the of earlier reasoning-focused models that improved performance by extending Chain-of-Thought (CoT) reasoning. DeepSeek-R1 takes things further by integrating support knowing (RL) with fine-tuning on thoroughly picked datasets. It developed from an earlier variation, DeepSeek-R1-Zero, which relied solely on RL and showed strong reasoning skills however had issues like hard-to-read outputs and language inconsistencies. To resolve these restrictions, DeepSeek-R1 integrates a percentage of cold-start information and follows a refined training pipeline that mixes reasoning-oriented RL with supervised fine-tuning on curated datasets, resulting in a model that attains advanced efficiency on thinking standards.

Usage Recommendations

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

– Avoid including a system prompt; all guidelines should be included within the user timely.
– For mathematical problems, it is recommended to include an instruction in your prompt such as: “Please factor action by step, and put your last answer within boxed .”.
– When evaluating model performance, it is recommended to perform numerous tests and balance the results.

Additional recommendations

The model’s thinking output (included within the tags) might include more harmful content than the model’s last response. Consider how your application will utilize or show the thinking output; you might want to reduce the thinking output in a production setting.