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README.md CHANGED
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  ## 介绍
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- **如意大模型(AI-Flow-Ruyi)** 是中国电信集团CTO、首席科学家、中国电信人工智能研究院 (TeleAI) 院长李学龙教授带领智传网(AI Flow)团队研发,是面向下一代“端-边-云”模型服务架构的**同源家族模型(Familial Model)** 。其核心在于大小模型共享同源参数,模型能基于早退出机制,根据问题复杂度调用不同参数规模的分支模型进行响应。各分支既可独立运行,又能依托同源特性实现信息共享与无缝切换,结合端-边-云分布式部署,完成家族大小模型协同,实现模型分布式推理效率大幅提升。
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  ![](assets/ai-flow.png)
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  ![](assets/ruyi_model.png)
 
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  ## 介绍
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+ **如意大模型(AI-Flow-Ruyi)** 是中国电信人工智能研究院 (TeleAI) 智传网(AI Flow)团队研发,是面向下一代“端-边-云”模型服务架构的**同源家族模型(Familial Model)** 。其核心在于大小模型共享同源参数,模型能基于早退出机制,根据问题复杂度调用不同参数规模的分支模型进行响应。各分支既可独立运行,又能依托同源特性实现信息共享与无缝切换,结合端-边-云分布式部署,完成家族大小模型协同,实现模型分布式推理效率大幅提升。
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  ![](assets/ai-flow.png)
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  ![](assets/ruyi_model.png)
README_en.md CHANGED
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  ## Introduction
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- **AI-Flow-Ruyi** is a **Familial Model** developed by the AI Flow team under the leadership of Professor Li Xuelong, CTO, Chief Scientist of China Telecom, and President of the Institute of Artificial Intelligence (TeleAI), China Telecom. Designed for next-generation "Device-Edge-Cloud" model service architectures, its core innovation lies in **shared familial parameters** across large and small models. Leveraging an **early-exit mechanism**, the system dynamically routes queries to branch models of appropriate parameter sizes based on problem complexity. These branches operate independently while enabling **information sharing** and **seamless transitions** through their shared features. Combined with distributed Device-Edge-Cloud deployment, this facilitates **collaborative inference** within the model family, significantly enhancing distributed reasoning efficiency.
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  ![](assets/ai-flow.png)
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  ![](assets/ruyi_model.png)
 
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  ## Introduction
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+ **AI-Flow-Ruyi** is a **Familial Model** developed by the AI Flow team of the Institute of Artificial Intelligence (TeleAI), China Telecom. Designed for next-generation "Device-Edge-Cloud" model service architectures, its core innovation lies in **shared familial parameters** across large and small models. Leveraging an **early-exit mechanism**, the system dynamically routes queries to branch models of appropriate parameter sizes based on problem complexity. These branches operate independently while enabling **information sharing** and **seamless transitions** through their shared features. Combined with distributed Device-Edge-Cloud deployment, this facilitates **collaborative inference** within the model family, significantly enhancing distributed reasoning efficiency.
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  ![](assets/ai-flow.png)
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  ![](assets/ruyi_model.png)