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WeChat Vision Team Opensources General Multimodal Embedding Model WeMM-Embedding, Daily Call Volume Exceeds Ten Billion
Tencent’s WeChat Visual Team has officially open-sourced WeMM-Embedding, a versatile multimodal embedding model designed to support both text and image inputs. By leveraging an advanced technical architecture and refined training strategies, this model provides robust support for AI developers, fostering innovative applications of artificial intelligence across diverse industry verticals.
Available in three distinct sizes—2B, 4B, and 9B—WeMM-Embedding caters to a wide range of deployment requirements. Its unified architecture integrates two-stage training and knowledge distillation, significantly boosting multimodal comprehension while maintaining an optimal balance between performance and deployment efficiency.
Proven through rigorous testing in real-world business environments, the model is already integrated into WeChat’s recommendation and search systems, handling billions of daily requests. This full open-source release underscores WeChat’s commitment to an open multimodal AI ecosystem and offers substantial technical support for AI development across the industry.
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Tencent’s WeChat Visual Team has officially open-sourced WeMM-Embedding, a versatile multimodal embedding model designed to support both text and image inputs. By leveraging an advanced technical architecture and refined training strategies, this model provides robust support for AI developers, fostering innovative applications of artificial intelligence across diverse industry verticals.
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