Core Viewpoint - The article discusses the challenges and solutions related to the adaptation of large models to domestic chips in China, emphasizing the need for a collaborative platform to bridge the gap between model development and chip compatibility [2][3][35]. Group 1: Model Adaptation Challenges - The successful deployment of large models requires overcoming three main hurdles: adapting the inference engine, adapting the computing platform, and adapting the upper scheduling for business system integration [9][10]. - Current tools for supporting large model inference and adaptation are diverse, but the challenge lies in effectively connecting and coordinating these fragmented tools and experiences [11]. Group 2: Collaboration Initiatives - The "Model Inference Adaptation Collaboration Plan" was launched by the Modelers community to gather developers, algorithm teams, chip manufacturers, and inference tool partners to build an open-source collaborative ecosystem [5][30]. - The community upgraded its "Mirror Center" to a "Tool Center," elevating the importance of the toolchain to be on par with model libraries and datasets [13][14]. Group 3: Community Engagement and Development - The community introduced a "Collaboration Space" where all users can submit pull requests (PRs) to contribute to documentation, adaptation code development, and optimization of inference configurations [20][29]. - The collaboration mechanism aims to aggregate dispersed adaptation efforts into a unified platform, allowing for easy downloading and secondary development [29]. Group 4: Industry Partnerships - The community collaborates with various domestic computing power manufacturers to provide developers with hardware, tools, and technical support [31]. - The initiative also integrates a diverse ecosystem of adaptation and inference software, helping developers quickly master the adaptation toolchain [32]. Group 5: Future Prospects - The "Adaptation Plan" will continue to be open for more chip manufacturers, model developers, and developers to join, with a focus on standardizing adaptation technology [34]. - If successful, this collaborative mechanism could address the critical "coordination shortfall" in the domestic chip ecosystem, facilitating the systematic implementation of models on chips [35].

Core Viewpoint - The establishment of the Modelers Community, led by China Telecom's Tianyi Cloud, aims to create an open platform for AI developers, providing resources such as models, data, and tools, while fostering collaboration across the entire AI industry chain [2][6][46] Group 1: Community Structure and Governance - The Modelers Community has officially formed a council responsible for managing and developing the community [3][6] - The council consists of representatives from various sectors of the AI industry, including chip companies, model and data enterprises, research institutions, and ecological organizations, totaling 14 members [7][9] - The governance structure includes committees focused on governance, construction, and operations, along with a secretariat for coordination [11][16] Group 2: Resources and Offerings - The community provides a wide range of resources, including various AI models, datasets, and tools optimized for different computing facilities [18][21] - Notable open-source models such as DeepSeek and Qwen have been included, with adaptations for domestic hardware [19][21] - The community also offers free domestic computing resources in collaboration with industry members [21] Group 3: Collaboration and Impact - The Modelers Community aims to bridge the gap between academia and industry, facilitating the practical application of academic research through demos and collaborative projects [24][28] - Small and medium enterprises benefit from the community by accessing open-source models that enhance their product offerings [26][28] - Future plans include partnerships with universities for joint talent development, expanding the community's reach [29] Group 4: Need for the Community - The Modelers Community addresses gaps in existing open-source platforms, particularly in the AI domain, by promoting collaboration and resource sharing among developers [30][42] - It aims to resolve issues related to data ownership and the challenges of integrating various AI tools and models [40][44] - The community's neutral and public-oriented approach distinguishes it from other platforms, making it a vital organizer for the AI industry [43][45]