Pregled

  • Datum osnivanja октобар 5, 1952
  • Sektor Bejbisiterka
  • Objavljeni poslovi 0
  • Gledao 10

Opis kompanije

DeepSeek-R1 · GitHub Models · GitHub

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

DeepSeek-R1 constructs on the progress of earlier reasoning-focused models that enhanced efficiency by extending Chain-of-Thought (CoT) thinking. DeepSeek-R1 takes things further by combining reinforcement knowing (RL) with fine-tuning on thoroughly picked datasets. It developed from an earlier version, DeepSeek-R1-Zero, which relied exclusively on RL and revealed strong reasoning skills but had problems like hard-to-read outputs and language inconsistencies. To resolve these constraints, DeepSeek-R1 includes a percentage 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 on thinking standards.

Usage Recommendations

We suggest sticking to the following setups when making use of the DeepSeek-R1 series designs, including benchmarking, to accomplish the anticipated performance:

– Avoid including a system timely; all instructions should be included within the user prompt.
– For mathematical issues, it is recommended to include a regulation in your timely such as: “Please factor step by step, and put your last answer within boxed .”.
– When examining model performance, it is recommended to conduct multiple tests and average the results.

Additional recommendations

The model’s thinking output (consisted of within the tags) might consist of more harmful content than the model’s last response. Consider how your application will use or display the reasoning output; you may desire to suppress the reasoning output in a production setting.