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Ant Group releases report "Large-scale Model Open Source Development Landscape and Trends"

"Large Model Open Source Development Landscape and Trends" is a report launched by the Ant Group Open Source Technology Committee, which provides insights into the current status and future trends of the large model open source development ecosystem based on community data.

Large Modelopen source"Development Panorama and Trends" is a report by Ant Group.open sourceThe report released by the Technical Committee is based on community data insights.Large Modelopen sourceThe report presents the current state and future trends of the ecosystem. It uses a panoramic view to illustrate these trends.Large Modelopen sourceThe project distribution, from version 1.0 to 2.0, shows a more refined number of projects and domain categorization, reflecting the ecosystem.fastDevelopment. The report analyzes key technology terms, active projects, license changes, etc., revealing...AI The report explores growth trends in areas such as coding and model serving. It examines project changes and ecosystem dynamics, as well as the activities of global developers.Large ModelThis information outlines the distribution of resources within the development ecosystem, providing a reference for developers and businesses to help them understand and utilize it.Large Modelopen sourceThe development trajectory and trends.

Large Modelopen sourceDevelop an ecological panorama

  • Panoramic image iterative updates
    • Version 1.0 will be released in the first half of 2025, including 135 projects in 19 technical fields. It is drawn using a seed project multi-hop search method, which has a certain degree of randomness.
    • Version 2.0 was released in August 2025. It uses the OpenRank algorithm to directly filter GitHub projects, including 114 top projects distributed across 22 technical fields, with the standard raised to OpenRank 50.
    • Version 2.0 added 39 projects, accounting for 35%, most of which were recently created and have high attention; 60 projects were removed, mostly due to insufficient activity or failure to meet the new standards.
    • These projects attracted 366,521 developers worldwide, with the United States accounting for 24% and China for 18%, demonstrating the close cooperation between China and the United States.Large ModelDominant position in the ecosystem.

    From ecological panorama to technological trends

    • Large ModelDevelop ecological keywordsBy analyzing the text of project descriptions and tags, we can extract...AI,LLM,AgentA word cloud was generated using high-frequency keywords such as "data" to reflect...Large ModelThe core technological direction of ecology.
    • Active Project Analysis:
      • The top 10 projects in OpenRank cover the entire model ecosystem, with Python dominating the infrastructure and TypeScript ruling the application layer.
      • Some projects adopt non-traditional methods.open sourceLicenses protect commercial interests, leading to "open sourceThe definition is becoming increasingly vague..

      Large ModelA profile of the global developer distribution within the ecosystem

      GlobalLarge ModelIn the developer ecosystem, the distribution of developers shows that the United States and China dominate, accounting for 24% and 18% respectively, indicating that the two countries are...Large ModelThe technology sector exhibits significant influence and activity. European countries such as Germany and India demonstrate high participation, though there is still a gap compared to China and the United States. Overall, the global developer distribution pattern is characterized by China and the United States as the main players, with participation from multiple countries.

      Changes and constants over 100 days from version 1.0 to 2.0

      • Adjustment of overall ecological structure and domainFrom version 1.0 to 2.0, the ecological structure and domain division are more refined, and new features have been added.AI Infra”AI Agent"and"AI Specific categories include "Data".
      • Projects that were eliminatedSome of the pastPopularProjects such as Manus and NextChat have gradually faded from view due to insufficient maintenance or replacement.
      • The Demise of TensorFlow, a Once-SuperstarTensorFlow since 2015open sourceLater, due to a lack of backward compatibility and complex migration tools, it was gradually surpassed by PyTorch and declined.
      • New projectsNew fields such asAI Coding and embodimentintelligentRelated projects have emerged, and the Infra field has been integrated intoLLMOps covers the entire lifecycle of model operation and maintenance.
      • The most active among new projectsopen sourceTop 10 ProjectsNew projects,Gemini CLI and Cherry Studio performed exceptionally well, ranking among the top 10 most active projects.
      • "Up and Down" on the panoramic imageFrom February to August, TensorRT-LLMProjects like Dynamo saw significant increases in OpenRank, while projects like LangChain and Codex experienced significant declines.
      • Model ServingModel service connectionAIInfrastructure and application layers, vLLMProjects like SGLang improve inference performance, Ollam promotes local deployment, and NVIDIA Dynamo expands cluster inference.
      • AI CodingAIprogramming):AI Coding has evolved from simple code completion to...Multimodalsupport,Gemini Tools such as CLI and OpenCode improve development efficiency and have huge commercial potential.
      • AI Agent(AIacting)2025AIWith application deployment faltering, frameworks like LangChain are struggling to gain traction, while new projects such as Mem0 and Dify are focusing on different aspects to drive development.AgentSystem development.

      Bonus Chapter:Large ModelEcological panorama

      A review of major domestic and international manufacturers from January 2025 to the present.Large ModelThe release timeline provides detailed annotations of key information such as parameters and modalities for each model, offering a clear overview of the current status.Large ModelThe competitive landscape of the field. Through analysis, the article points out that China...open sourceLarge ModelThis presents a diverse landscape, with top international model manufacturers mostly adopting a closed-source approach, emphasizing scalable model parameters and reinforcement learning to improve reasoning capabilities.MultimodalTechnological trends such as models becoming mainstream.

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