DeepSeek-R1 · GitHub Models · GitHub

DeepSeek-R1 stands out at reasoning tasks using a detailed training procedure, such as language, clinical reasoning, and coding tasks. It features 671B total criteria with 37B active specifications, and 128k context length.

DeepSeek-R1 develops on the progress of earlier reasoning-focused designs that enhanced efficiency by extending Chain-of-Thought (CoT) thinking. DeepSeek-R1 takes things further by integrating reinforcement learning (RL) with fine-tuning on thoroughly picked datasets. It developed from an earlier version, DeepSeek-R1-Zero, which relied solely on RL and revealed strong thinking abilities however had problems like hard-to-read outputs and language inconsistencies. To address these constraints, DeepSeek-R1 incorporates a small quantity of cold-start information and follows a refined training pipeline that blends reasoning-oriented RL with supervised fine-tuning on curated datasets, leading to a model that attains cutting edge efficiency on .

Usage Recommendations

We suggest adhering to the following setups when utilizing the DeepSeek-R1 series models, including benchmarking, to achieve the anticipated efficiency:

– Avoid including a system timely; all instructions need to be contained within the user prompt.
– For mathematical problems, it is a good idea to consist of an instruction in your timely such as: “Please reason step by action, and put your final answer within boxed .”.
– When assessing model efficiency, it is recommended to carry out numerous tests and balance the outcomes.

Additional recommendations

The design’s reasoning output (contained within the tags) might consist of more damaging material than the design’s last action. Consider how your application will utilize or display the thinking output; you may desire to reduce the reasoning output in a production setting.