Overview

  • Founded Date mars 4, 1961
  • Sectors Sales
  • Posted Jobs 0
  • Viewed 6

Company Description

DeepSeek-R1 · GitHub Models · GitHub

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

DeepSeek-R1 builds on the development of earlier reasoning-focused designs that enhanced efficiency by extending Chain-of-Thought (CoT) reasoning. DeepSeek-R1 takes things further by combining support knowing (RL) with fine-tuning on thoroughly chosen datasets. It developed from an earlier version, DeepSeek-R1-Zero, which relied exclusively on RL and revealed strong reasoning abilities but had problems like hard-to-read outputs and language inconsistencies. To attend to these restrictions, DeepSeek-R1 includes a little quantity of cold-start data and follows a refined training pipeline that blends reasoning-oriented RL with monitored fine-tuning on curated datasets, resulting in a design that attains modern performance on reasoning criteria.

Usage Recommendations

We recommend sticking to the following configurations when using the DeepSeek-R1 series models, consisting of benchmarking, to accomplish the expected efficiency:

– Avoid adding a system timely; all guidelines need to be contained within the user timely.
– For mathematical problems, it is recommended to include a regulation in your timely such as: ”Please factor step by action, and put your last answer within boxed .”.
– When evaluating design performance, it is advised to carry out several tests and balance the outcomes.

Additional recommendations

The design’s thinking output (contained within the tags) may contain more damaging content than the model’s last action. Consider how your application will use or display the reasoning output; you may wish to suppress the thinking output in a production setting.