Overview

  • Founded Date December 17, 1921
  • Sectors Programming/Software Engineering
  • Posted Jobs 0
  • Viewed 6

Company Description

DeepSeek-R1 · GitHub Models · GitHub

DeepSeek-R1 stands out at thinking tasks utilizing a detailed training process, such as language, scientific thinking, and coding tasks. It includes 671B total parameters with 37B active specifications, and 128k context length.

DeepSeek-R1 builds on the progress of earlier reasoning-focused designs that enhanced efficiency by extending Chain-of-Thought (CoT) reasoning. DeepSeek-R1 takes things even more by integrating support learning (RL) with fine-tuning on thoroughly picked datasets. It developed from an earlier variation, DeepSeek-R1-Zero, which relied exclusively on RL and revealed strong thinking abilities however had problems like hard-to-read outputs and language disparities. To attend to these restrictions, DeepSeek-R1 integrates a small amount 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 edge performance on thinking benchmarks.

Usage Recommendations

We advise adhering to the following configurations when making use of the DeepSeek-R1 series designs, including benchmarking, to achieve the expected efficiency:

– Avoid including a system timely; all guidelines must be consisted of within the user timely.
– For mathematical issues, it is suggested to include a regulation 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 carry out several tests and average the results.

Additional recommendations

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