DeepSeek-R1-Zero

DeepSeek-R1-Zero is an inference model trained through large-scale reinforcement learning, achieving exceptional inference capability without the need for supervised fine-tuning.

ChineseSelectionProgramming\Reinforcement Learning\\Inference Model\

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DeepSeek-R1-Zero is an inference model developed by the DeepSeek team, focusing on enhancing inference capabilities through reinforcement learning. This model exhibits powerful reasoning behaviors such as self-validation, reflection, and generating long chains of reasoning without requiring supervised fine-tuning. Its main advantages include efficient inference capabilities, immediate usability without pre-training, and outstanding performance in mathematical, coding, and reasoning tasks. The model is built on the DeepSeek-V3 architecture and is suitable for large-scale inference tasks in both research and commercial applications.

\Utilizes large-scale reinforcement learning training
enabling use without supervised fine-tuning.\
\Supports chained reasoning for complex problems
capable of generating long inference paths.\
\Possesses self-validation and reflection abilities to enhance accuracy and reliability of inferences.\
\Excels in mathematical
coding
and reasoning tasks
approaching top industry levels.\
\Offers open-source model weights to support further community research and development.\

\This model is designed for scenarios that require efficient inference capabilities
such as academic research
code generation
solving mathematical problems
and automating complex tasks. It is particularly suitable for researchers and developers exploring the application of reinforcement learning in language models

\In academic research
used to explore the potential of reinforcement learning in enhancing model inference abilities.\
\In competitive programming
assisting developers in quickly generating high-quality code to improve competition performance.\
\In education

\Visit the Hugging Face page to download the DeepSeek-R1-Zero model files.\
\Select the appropriate inference tasks as needed
such as mathematical reasoning or code generation.\
\Use open-source tools (like vLLM) to start a local service and configure suitable parameters (like temperature and maximum generation length).\
\Call the model directly for inference via an API platform (e.g.

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