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Company Description
DeepSeek-R1 · GitHub Models · GitHub
DeepSeek-R1 excels at reasoning jobs utilizing a step-by-step training process, such as language, scientific thinking, and coding tasks. It features 671B total specifications with 37B active specifications, and 128k context length.
DeepSeek-R1 constructs on the development of earlier reasoning-focused designs that improved performance by extending Chain-of-Thought (CoT) thinking. DeepSeek-R1 takes things further by integrating support learning (RL) with fine-tuning on thoroughly selected datasets.
It developed from an earlier variation, DeepSeek-R1-Zero, which relied solely on RL and showed strong reasoning abilities however had concerns like hard-to-read outputs and language disparities.
To deal with these limitations, DeepSeek-R1 includes a small amount of cold-start information and follows a refined training pipeline that mixes reasoning-oriented RL with supervised fine-tuning on curated datasets, resulting in a model that attains state-of-the-art efficiency on reasoning standards.
Usage Recommendations
We recommend adhering to the following setups when utilizing the DeepSeek-R1 series designs, consisting of benchmarking, to achieve the anticipated performance:
– Avoid including a system timely; all directions need to be contained within the user timely.
– For mathematical problems, it is a good idea to consist of a directive in your prompt such as: “Please reason action by step, and put your last response within boxed .”.
– When examining design efficiency, it is advised to perform several tests and balance the outcomes.
Additional recommendations
The design’s reasoning output (included within the tags) might include more hazardous material than the model’s final reaction. Consider how your will use or display the thinking output; you might desire to suppress the thinking output in a production setting.