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DeepSeek-R1 · GitHub Models · GitHub
DeepSeek-R1 stands out at thinking tasks utilizing a step-by-step training procedure, such as language, scientific thinking, and coding jobs. It features 671B total specifications with 37B active parameters, and 128k context length.
DeepSeek-R1 constructs on the progress of earlier reasoning-focused models that enhanced efficiency by extending Chain-of-Thought (CoT) reasoning. DeepSeek-R1 takes things even more by integrating support knowing (RL) with fine-tuning on carefully picked datasets. It progressed from an earlier version, DeepSeek-R1-Zero, which on RL and showed strong reasoning abilities however had concerns like hard-to-read outputs and language disparities. To resolve these restrictions, DeepSeek-R1 incorporates a percentage of cold-start information and follows a refined training pipeline that blends reasoning-oriented RL with monitored fine-tuning on curated datasets, resulting in a design that attains state-of-the-art performance on thinking benchmarks.
Usage Recommendations
We recommend adhering to the following configurations when using the DeepSeek-R1 series designs, consisting of benchmarking, to attain the expected performance:
– Avoid adding a system timely; all instructions ought to be contained within the user prompt.
– For mathematical problems, it is advisable to include a regulation in your prompt such as: “Please reason step by step, and put your final response within boxed .”.
– When evaluating design performance, it is advised to carry out several tests and balance the results.
Additional suggestions
The model’s thinking output (contained within the tags) may contain more harmful material than the model’s last action. Consider how your application will use or display the reasoning output; you may wish to reduce the thinking output in a production setting.