
Vivian Diana
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Company Description
DeepSeek-R1 · GitHub Models · GitHub
DeepSeek-R1 stands out at thinking tasks utilizing a detailed training procedure, such as language, clinical reasoning, and coding tasks. It features 671B total parameters with 37B active criteria, and 128k context length.
DeepSeek-R1 builds on the progress of earlier reasoning-focused designs that enhanced performance by extending Chain-of-Thought (CoT) thinking. DeepSeek-R1 takes things further by integrating reinforcement knowing (RL) with fine-tuning on carefully selected datasets. It from an earlier variation, DeepSeek-R1-Zero, which relied solely on RL and showed strong thinking abilities but had problems like hard-to-read outputs and language disparities. To resolve these restrictions, DeepSeek-R1 incorporates a percentage of cold-start data and follows a refined training pipeline that mixes reasoning-oriented RL with monitored fine-tuning on curated datasets, leading to a model that achieves state-of-the-art efficiency on reasoning criteria.
Usage Recommendations
We suggest sticking to the following setups when making use of the DeepSeek-R1 series models, consisting of benchmarking, to attain the anticipated efficiency:
– Avoid adding a system prompt; all guidelines should be consisted of within the user prompt.
– For mathematical issues, it is advisable to include a regulation in your prompt such as: “Please reason action by action, and put your final answer within boxed .”.
– When assessing model performance, it is suggested to carry out several tests and average the outcomes.
Additional suggestions
The model’s reasoning output (consisted of within the tags) might consist of more harmful content than the design’s last action. Consider how your application will utilize or display the thinking output; you might desire to reduce the reasoning output in a production setting.