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Company Description
DeepSeek-R1 · GitHub Models · GitHub
DeepSeek-R1 stands out at reasoning tasks using a detailed training procedure, such as language, scientific thinking, and coding jobs. It includes 671B overall specifications with 37B active specifications, and 128k context length.
DeepSeek-R1 builds on the development of earlier reasoning-focused designs that enhanced performance by extending Chain-of-Thought (CoT) thinking. DeepSeek-R1 takes things further by integrating support learning (RL) with fine-tuning on carefully picked datasets. It progressed from an earlier version, DeepSeek-R1-Zero, which relied solely on RL and revealed strong reasoning abilities but had concerns like hard-to-read outputs and language disparities. To deal with these constraints, DeepSeek-R1 includes a small amount 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 achieves modern performance on reasoning criteria.
Usage Recommendations
We suggest sticking to the following configurations when the DeepSeek-R1 series designs, consisting of benchmarking, to attain the expected performance:
– Avoid adding a system timely; all instructions need to be included within the user timely.
– For mathematical problems, it is advisable to consist of a regulation in your prompt such as: “Please factor step by step, and put your last response within boxed .”.
– When assessing design performance, it is recommended to conduct numerous tests and balance the outcomes.
Additional suggestions
The model’s thinking output (contained 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 thinking output in a production setting.