Tencent releases WeMM-Embedding multimodal models for mixed text, image, and video retrieval
tomaarsen · x · 2026-09-02
Tencent's team released the WeMM-Embedding family (2B/4B/9B) on Hugging Face, aimed at retrieval over mixed media, especially with video in the corpus:
- Interleaved inputs: multiple images or videos in a single input; documents can mix {"video": ..., "text": ...}.
- Matryoshka truncation for smaller/faster embeddings.
- Usage: SentenceTransformer("tencent/WeMM-Embedding-9B", trustremotecode=True) with encodequery / encodedocument.
A technical report (arXiv 2608.24053) is out; the 2B variant has passed 7k downloads.
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