DeepSeek-R1 · GitHub Models · GitHub

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

DeepSeek-R1 builds on the development of earlier reasoning-focused models that improved efficiency by extending Chain-of-Thought (CoT) thinking. DeepSeek-R1 takes things further by integrating support knowing (RL) with fine-tuning on thoroughly selected datasets. It developed from an earlier version, DeepSeek-R1-Zero, which relied solely on RL and showed strong reasoning abilities however had concerns like hard-to-read outputs and language inconsistencies. To deal with these limitations, DeepSeek-R1 includes a percentage of cold-start information and follows a refined training pipeline that mixes reasoning-oriented RL with monitored fine-tuning on curated datasets, leading to a design that achieves state-of-the-art performance on reasoning benchmarks.

Usage Recommendations

We recommend adhering to the following configurations when utilizing the DeepSeek-R1 series models, of benchmarking, to attain the expected performance:

– Avoid including a system timely; all instructions ought to be consisted of within the user timely.
– For mathematical problems, it is a good idea to include an instruction in your timely such as: “Please factor action by step, and put your final answer within boxed .”.
– When examining design efficiency, it is suggested to perform several tests and average the outcomes.

Additional suggestions

The model’s reasoning output (consisted of within the tags) may contain more hazardous content than the model’s final action. Consider how your application will utilize or show the thinking output; you might wish to reduce the reasoning output in a production setting.