技术规格

系统要求

不同量化方法和上下文大小的显存要求

DeepSeek-R1 3B

DeepSeek-R1 3B is a compact, dense language model variant developed through a distillation process from the larger DeepSeek-R1 architecture. This model is specifically built upon the Llama 3.2-3B foundational architecture, aiming to retain robust reasoning capabilities while significantly reducing computational resource requirements. Its design integrates a specialized chat templating system, ensuring compatibility with Llama 3 formatting, alongside custom tokenization to facilitate structured output and enhanced reasoning pathways.

The development methodology for DeepSeek-R1 3B incorporates several technical optimizations crucial for efficient training and inference. These include the application of LoRA (Low-Rank Adaptation) for fine-tuning, leveraging Flash Attention for accelerated self-attention computations, and utilizing gradient checkpointing to manage memory consumption during training. This architectural synthesis enables the model to process information with efficiency, making it suitable for deployment in environments where computational resources are a constraint.

The primary use cases for DeepSeek-R1 3B center on applications that demand structured reasoning and general language understanding, such as mathematical problem-solving or comparative analysis tasks. Its distilled nature allows it to deliver performance suitable for practical applications requiring a balance of reasoning fidelity and operational efficiency.

没有可用的 DeepSeek-R1 3B 评估基准。