一种基于Deepseek R1的医疗垂直大语言模型架构方法
A Method for the Architecture of a Medical Vertical Large Language Model Based on Deepseek R1
AI总结：
本文提出一种基于DeepSeek-R1的轻量级医疗大语言模型架构，通过LoRA知识迁移、4-bit量化压缩和Flash Attention推理加速，在保持92.1%的USMLE准确率的同时显著降低资源消耗，为资源受限的医疗场景提供了可部署的AI辅助方案。
摘要：
尽管DeepSeek-R1和ChatGPT等基础模型取得了显著进展，但它们在医疗场景中的部署仍面临计算资源需求和专业知识壁垒等关键挑战。本文提出了一种高效的轻量级医疗大语言模型架构，通过知识获取、模型压缩和计算增强三个维度的优化来系统性地应对这些挑战。我们设计了一条从DeepSeek-R1-Distill-70B到DeepSeek-R1-Distill-7B的知识迁移流水线，利用低秩适配（LoRA）实现精确的医学知识保留。通过4-bit量化和混合精度策略，我们在保持医学推理能力的同时实现了显著的模型压缩。推理框架融合了Flash Attention加速和连续批处理技术，并辅以针对多样化医疗查询的专用提示模板。在医学基准上的实验评估表明，与基线模型相比，我们的方法在USMLE考试上保持了92.1%的准确率，同时将内存消耗降低了64.7%，推理延迟降低了12.4%。这项工作为在资源受限的医疗环境中部署先进语言模型提供了一种实用解决方案，使AI辅助医疗能够获得更广泛的普及。
英文摘要：
Despite significant advances in foundation models like DeepSeek-R1 and ChatGPT, their deployment in medical settings faces critical challenges including computational requirements and professional knowledge barriers. This paper presents an efficient lightweight medical large language model architecture that systematically addresses these challenges through three-dimensional optimization: knowledge acquisition, model compression, and computational enhancement. We design a knowledge transfer pipeline from DeepSeek-R1-Distill-70B to DeepSeek-R1-Distill-7B using Low-Rank Adaptation (LoRA) for precise medical knowledge retention. Through 4-bit quantization and mixed-precision strategies, we achieve substantial model compression while preserving medical reasoning capabilities. The inference framework incorporates Flash Attention acceleration and continuous batching, complemented by specialized prompt templates for diverse medical queries. Experimental evaluation on medical benchmarks demonstrates that our approach maintains 92.1% accuracy on USMLE examinations while reducing memory consumption by 64.7% and inference latency by 12.4% compared to baseline models. This work provides a practical solution for deploying advanced language models in resource-constrained medical environments, enabling broader accessibility of AI-assisted healthcare.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
