Tencent/WeMM-Embedding
WeMM-Embedding is a family of universal multimodal embedding models by the WeChat Vision Team at Tencent, supporting multimodal understanding and retrieval.
About the project
A family of universal multimodal embedding models from Tencent's WeChat Vision team (2B, 4B, 9B) producing unified vectors for text, images, videos, visual documents, and interleaved multimodal inputs. Supports Matryoshka dimensions and serving via vLLM/SGLang.
Useful for
- Build multimodal retrieval over a mixed corpus of images, videos, and documents with one index
- Truncate embeddings to 256 dimensions via Matryoshka to save memory with minimal quality loss
- Serve embeddings for text and video online using vLLM or SGLang
README summarized by DeepSeek V4.1 Flash. Details may be inaccurate.
Why it’s trending
- 52 stars today.
- The repository is 19 days old and already has 1,562 stars.
- Top new repositories this month: #38.
Stars per day
Bars are daily stars, the line is the usual pace. Red marks spike days.
Numbers
- Total stars
- 1,562
- Stars in a day
- 53
- Forks
- 109
- Issues and pull requests
- 8
- Watchers
- 6
- Language
- Python
- Created
- August 25, 2026
- Last push
- September 3, 2026
Star trust
Growth looks organic: forks and discussion are in line with active projects, and stars arrive unevenly, the way people give them.
These are heuristics, not a verdict: we judge by the repository’s behavior, not by a list of stargazers.
Spotted in
- September 13, 2026Top new repositories this month: #37
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