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Descripción de la compañía
DeepSeek-R1 · GitHub Models · GitHub
DeepSeek-R1 stands out at thinking jobs using a step-by-step training process, such as language, scientific thinking, and coding tasks. It features 671B overall with 37B active specifications, and 128k context length.
DeepSeek-R1 develops on the development of earlier reasoning-focused models that enhanced performance by extending Chain-of-Thought (CoT) thinking. DeepSeek-R1 takes things further by integrating support knowing (RL) with fine-tuning on carefully selected datasets. It developed from an earlier version, DeepSeek-R1-Zero, which relied exclusively on RL and revealed strong thinking skills but had concerns like hard-to-read outputs and language inconsistencies. To address these constraints, DeepSeek-R1 includes a little quantity 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 accomplishes state-of-the-art efficiency on reasoning benchmarks.
Usage Recommendations
We suggest adhering to the following configurations when utilizing the DeepSeek-R1 series designs, including benchmarking, to achieve the anticipated efficiency:
– Avoid including a system prompt; all instructions need to be contained within the user prompt.
– For mathematical issues, it is recommended to consist of an instruction in your timely such as: “Please reason step by action, and put your last response within boxed .”.
– When examining model performance, it is advised to conduct multiple tests and average the outcomes.
Additional recommendations
The model’s thinking output (consisted of within the tags) might consist of more harmful material than the model’s final response. Consider how your application will use or show the thinking output; you may wish to reduce the thinking output in a production setting.