允中 发自 凹非寺 量子位 | 公众号 QbitAI 9月13日，由 蚂蚁开源与魔搭社区 联合主办的见解论坛 「AI 开源时代，构建全球生态与可持续增长」 将亮相2025 inclusion·外滩大会。 作为论坛的开篇，三场主题演讲将从不同维度解构AI开源生态的核心逻辑： 蚂蚁集团开源技术委员会副主席王旭 将以社区数据为镜，剖析全 球大模型开源生态的全景与趋势，为技术决策提供中立参考； 魔搭社区产研负责人陈颖达 将结合社区实践，分享9万+优质模型背后的建设经 验，解密"模型即服务"理念如何驱动开源生态螺旋式进化； 硅基流动联合创始人杨攀 则将从"信仰到信心"的递进视角，解析全球开源模型生 态的竞争与协作格局。从数据洞察、社区实践到全球生态的博弈与共生，三场演讲层层深入，勾勒出AI开源时代的技术脉络与发展路径。 而作为AI开源生态的重要组成部分，Vibe Coding与AI Agent始终是社区关注的焦点。三场深度演讲之后，两场围绕这两大方向的圆桌讨论将 点燃思想碰撞的火花。论坛邀请到了来自 蚂蚁集团、字节跳动、粤港澳大湾区数字经济研究院、光年之外、ClackyAI、CAMEL-AI.org、 Fellou 等 ...

Core Viewpoint - The controversy surrounding Huawei's Pangu 3.5 and Alibaba's Tongyi Qianwen 1.5-7B models centers on the high correlation score of 0.927 derived from the "LLM-Fingerprint" technology, suggesting potential similarities or derivation between the two models [1][14][16]. Group 1: Technical Analysis - The "LLM-Fingerprint" technology analyzes model responses to specific trigger words, generating a unique identity for each large model [12][11]. - A report indicated that the correlation score of 0.927 between Huawei's Pangu 3.5 and Alibaba's Tongyi Qianwen 1.5-7B is significantly higher than the scores between other mainstream models, which are generally below 0.1 [14][15]. - Huawei's defense against the allegations was deemed unscientific by external observers, as they pointed out that high correlation could also be found among different versions of the Tongyi Qianwen models [19][20]. Group 2: Open Source Culture and Ethics - The debate highlights the tension between "reuse" and "plagiarism" within the AI open-source ecosystem, raising questions about the ethical implications of model development [22][21]. - The high costs associated with developing large models, estimated at $12 million for effective training, make it common practice to build upon existing open-source models [25][26]. - The distinction between "reuse" and "plagiarism" remains ambiguous, particularly regarding model parameters and adherence to open-source licenses [28][29]. Group 3: Competitive Landscape - The incident reflects the intense competition between Huawei and Alibaba in the Chinese AI market, with Alibaba currently serving 90,000 enterprises through its Tongyi series models [37][42]. - Huawei's Pangu model is crucial for its strategy to establish a comprehensive AI ecosystem, while Alibaba has leveraged its cloud infrastructure and open-source ecosystem to gain a competitive edge [32][36]. - The silence from Alibaba's Tongyi Qianwen team amid the controversy suggests a strategic decision to avoid escalating the situation into a public dispute [40][47]. Group 4: Industry Implications - The controversy serves as a "stress test" for the current AI open-source ecosystem, exposing its vulnerabilities and the lag in governance [52]. - The industry is urged to establish clearer rules regarding model citation and derivation standards, akin to plagiarism detection systems in academia [53]. - There is a call for greater transparency in model development processes, including the promotion of "Model Cards" and data transparency [54].

Core Insights - The report and the comprehensive panorama released by Ant Group provide a detailed analysis of the current open-source ecosystem for large models, highlighting its evolution and trends [1][4][40] Group 1: Overview of the Open-Source Ecosystem - The open-source ecosystem for large models is described as a "real-world hackathon," emphasizing the collaborative nature of development [2][3] - Ant Group's report includes a panorama covering 19 technical fields and 135 projects, from model infrastructure to intelligent applications [5][10] - The analysis identifies three dominant technical tracks in the current open-source ecosystem: model training frameworks, efficient inference engines, and low-code application development frameworks [10][11] Group 2: Key Projects and Trends - The report lists the top 20 projects for 2025, highlighting significant growth and decline among various projects [7] - PyTorch ranks first in influence among all projects in the panorama, while vLLM and SGlang are noted for rapid iteration in the inference category [14][31] - Dify and RAGFlow are emerging as leading platforms in application development, driven by their ability to meet enterprise user needs through low-code workflows [18][35] Group 3: Development Paradigms and Standards - The shift towards low-code development is becoming mainstream, with traditional agent frameworks declining in popularity [20][17] - New communication standards for models and applications are being established, such as the MCP protocol and A2A protocol, which facilitate interaction between different agents [22][25] - The report emphasizes the importance of standardization in the evolving landscape of large model services, suggesting that the standard protocol layer will become a strategic battleground for leading players [24][26] Group 4: Implications for Developers - Developers are encouraged to focus on enhancing user experience and deepening their understanding of specific application scenarios to gain competitive advantages [34][35] - The report highlights the need for developers to adapt to rapid changes in project cycles and to embrace a trial-and-error approach in development [37][38] - Overall, the report serves as a valuable resource for understanding the underlying mechanisms of the large model open-source ecosystem and its future direction [41][42]