Tencent’s WeChat Vision team has published WeMM-Embedding, a family comprising 2B, 4B and 9B embedding models. Each variant supports text, images, videos, visual documents and interleaved multimodal inputs; audio is not supported. For developers, the immediate consequence is a single repository containing model options, inference examples, serving instructions and evaluation code for retrieval across the supported inputs.
Inputs, vectors and serving paths
The models obtain embeddings from the last-layer hidden state at a dedicated <embedding> token, then apply L2 normalization. The project presents this as a unified representation method across its supported input types rather than separate embedding formats for each modality (repository).
All three variants support reduced Matryoshka dimensions. The 2B model offers widths from 64 to 2,048, the 4B model from 64 to 2,560 and the 9B model from 64 to 4,096. For a supported smaller width, the repository instructs users to truncate the full vector and normalize it again (repository).
Inference examples cover Transformers and Sentence Transformers. Tencent recommends transformers==5.2.0 for inference and reproducibility because newer releases may differ in preprocessing, while the serving instructions list vLLM 0.27.0 and SGLang 0.5.9 as tested versions (repository).
