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WeChat WeMM-Embedding Powers Moments Search and Video Highlights With 1 Billion Daily Calls
On September 4, WeChat AI announced the open-source release of its general multimodal embedding model, WeMM-Embedding. Available in 2B, 4B, and 9B versions, the model supports text, images, videos, visual documents, and any interleaved multimodal input.
According to WeChat employee @KeCunXiaoJiang, the WeChat Visual Team’s open-sourced multimodal model, WeMM-Embedding, enables the system to simultaneously understand text, images, and videos by placing them in a shared semantic space for comparison and retrieval.
Already applied in scenarios such as Moments search and video recommendation
He revealed that this model is widely used within WeChat—Moments search, video recommendation, public account recommendation, and WeChat e-commerce all use it, with a daily call volume of 1 billion times. On the international authoritative benchmark MMEB-v2, the model ranked first, surpassing all previously submitted open-source and closed-source models.
Currently, related resources of WeMM-Embedding have been made available: the paper was published on arXiv, the code repository is hosted under the Tencent/WeMM-Embedding project on GitHub, and the model set is also available on Hugging Face.
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On September 4, WeChat AI announced the open-source release of its general multimodal embedding model, WeMM-Embedding. Available in 2B, 4B, and 9B versions, the model supports text, images, videos, visual documents, and any interleaved multimodal input.
According to WeChat employee @KeCunXiaoJiang, the WeChat Visual Team’s open-sourced multimodal model, WeMM-Embedding, enables the system to simultaneously understand text, images, and videos by placing them in a shared semantic space for comparison and retrieval.
Already applied in scenarios such as Moments search and video recommendation
He revealed that this model is widely used within WeChat—Moments search, video recommendation, public account recommendation, and WeChat e-commerce all use it, with a daily call volume of 1 billion times. On the international authoritative benchmark MMEB-v2, the model ranked first, surpassing all previously submitted open-source and closed-source models.
Currently, related resources of WeMM-Embedding have been made available: the paper was published on arXiv, the code repository is hosted under the Tencent/WeMM-Embedding project on GitHub, and the model set is also available on Hugging Face.
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2026 Latest Best Top-Rated AI Sleep Tracking Assistants curated for ultimate recovery. This fully updated weekly guide features powerful tools that deliver real-world tests, free vs paid comparison, and detailed rankings to help you find the must-try solution that boosts sleep quality and supports better bedtime habits. Explore now at XIX.AI to Unlock your AI edge.
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