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Founded Date mars 28, 1972
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Company Description
DeepSeek-R1 · GitHub Models · GitHub
DeepSeek-R1 excels at thinking tasks using a procedure, such as language, scientific reasoning, and coding jobs. It includes 671B overall parameters with 37B active specifications, and 128k context length.
DeepSeek-R1 builds on the development of earlier reasoning-focused designs that improved performance by extending Chain-of-Thought (CoT) thinking. DeepSeek-R1 takes things even more by integrating reinforcement knowing (RL) with fine-tuning on thoroughly chosen datasets. It progressed from an earlier variation, DeepSeek-R1-Zero, which relied exclusively on RL and showed strong reasoning skills but had problems like hard-to-read outputs and language disparities. To attend to these restrictions, DeepSeek-R1 includes 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, leading to a design that attains state-of-the-art performance on reasoning criteria.
Usage Recommendations
We recommend adhering to the following setups when using the DeepSeek-R1 series designs, including benchmarking, to accomplish the anticipated efficiency:
– Avoid adding a system timely; all guidelines must be contained within the user timely.
– For mathematical problems, it is a good idea to include an instruction in your timely such as: ”Please reason action by action, and put your last response within boxed .”.
– When examining model efficiency, it is advised to carry out multiple tests and average the outcomes.
Additional suggestions
The design’s reasoning output (contained within the tags) might include more hazardous content than the model’s last reaction. Consider how your application will use or show the reasoning output; you may wish to suppress the thinking output in a production setting.