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 7B

DeepSeek-R1-Distill-Qwen-7B is a 7-billion parameter language model engineered by DeepSeek AI. This model variant is a dense architecture, derived through a knowledge distillation process from the larger DeepSeek-R1 system. Its primary design objective is to deliver robust reasoning capabilities, specializing in domains such as mathematical reasoning, logical analysis, and the generation of code. The distillation methodology enables this model to encapsulate advanced problem-solving proficiencies within a more computationally efficient format, making it suitable for deployment in scenarios where resource constraints necessitate a smaller footprint without significant degradation in reasoning performance.

The architectural foundation of DeepSeek-R1-Distill-Qwen-7B is based on the Qwen2.5-Math-7B model. The training regimen for this distilled model emphasizes the transfer of sophisticated reasoning behaviors from the DeepSeek-R1 teacher model. This process leverages a substantial dataset comprising approximately 800,000 curated samples. These samples, generated by the higher-capacity DeepSeek-R1, are bifurcated into approximately 600,000 reasoning-focused examples and 200,000 non-reasoning examples, facilitating a targeted transfer of cognitive patterns. The model employs Multi-Head Latent Attention (MLA) and integrates Rotary Position Embeddings (RoPE) for positional encoding, with context extension techniques such as YaRN used to scale its operational context.

In terms of practical application, DeepSeek-R1-Distill-Qwen-7B is configured to support extended contextual understanding, processing input sequences up to 131,072 tokens. This expanded context window enhances its capacity for handling complex, multi-step problems that necessitate a broad understanding of the input. The model is positioned for use in a variety of technical applications requiring analytical precision, including automated theorem proving, complex algorithmic problem-solving, and advanced programming assistance. Its compact design, coupled with its specialized reasoning aptitude, makes it a viable candidate for integration into systems requiring localized inference or deployment on consumer-grade hardware.

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