AI NEWS.ccWeMM-Embedding-2B
WeMM-Embedding-2B is a universal multimodal embedding model built on Qwen3.5. It accepts text, images, videos, visual documents, and interleaved multimodal inputs, and returns a 2,048-dimensional L2-normalized embedding. Audio input is not supported. Each input is a string, a URL or path, a `PIL.Image`, or a dict combining `image`, `video`, and `text` keys. Chat messages such as `{"role": "user", "content": [{"type": "image", "image": ...}, {"type": "text", "text": ...}]}` are also accepted, which is the way to interleave several images or videos in one input. Use a dimension listed in `model.config.matryoshka_dimensions`. On MMEB-v2, 256-dimensional embeddings retain 98.7% of the full-dimensional image and video performance.
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WeMM-Embedding-2B is a universal multimodal embedding model built on Qwen3.5. It accepts text, images, videos, visual documents, and interleaved multimodal inputs, and returns a 2,048-dimensional L2-normalized embedding. Audio input is not supported. Each input is a string, a URL or path, a `PIL.Image`, or a dict combining `image`, `video`, and `text` keys. Chat messages such as `{"role": "user", "content": [{"type": "image", "image": ...}, {"type": "text", "text": ...}]}` are also accepted, which is the way to interleave several images or videos in one input. Use a dimension listed in `model.config.matryoshka_dimensions`. On MMEB-v2, 256-dimensional embeddings retain 98.7% of the full-dimensional image and video performance.