Model reference · open weights
WeMM-Embedding is an open-weight embedding model from tencent, listed in the AxForge catalogue. AxForge can bring it up on EU-owned hardware for you on request — with the licence handled where one is required.
About
WeMM-Embedding-2B [](https://huggingface.co/collections/tencent/wemm-embedding) [](https://arxiv.org/abs/2608.24053) [](https://github.com/Tencent/WeMM-Embedding) 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. Installation Transformers Use any subset of the content items to encode text, image, or video independently. Sentence Transformers Each input is a string, a URL or path, a PIL.Image, or a dict combining image, video, and text keys. Put image or video before text so the prompt matches the ordering used above. 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. Matryoshka Embeddings With Sentence Transformers, pass truncatedim and let it renormalize: Use a dimension listed in model.config.matryoshkadimensions. On MMEB-v2, 256-dimensional embeddings retain 98.7% of the full-dimensional image and video performance. Serving vLLM 0.27.0: SGLang 0.5.9: Evaluation MMEB-v2 Results on 78 datasets from Table 1 of the technical report. Image and video tasks use Hit@1, while visual-document tasks use NDCG@5. Higher is better. † Closed-source leaderboard submission without publicly released model weights or a public inference endpoint. MMEB-v3 Results on all 190 tasks from Table 2 of the technical report. V3-All includes the 78 MMEB-v2 tasks, 53 text tasks, 47 agent tasks, 11 audio tasks, and MCMR. Unsupported tasks are assigned a score of zero. Text results use NDCG@5; agent, MCMR, and audio results use Hit@1. Citation If you find this repository useful, please consider giving a star ⭐ and citation License WeMM-Embedding-2B, including the code, model parameters, and weights made publicly available by Tencent, is licensed under the Apache License 2.0. Third-party components remain subject to their respective original licenses.
Summarised from the published model card. Read the full card on the HuggingFace links below.
Specifications
| Maker | tencent |
|---|---|
| Type | Embedding models |
| Parameters (lead) | 2.7B |
| Variants | 3 |
| Runs with | transformers |
| Based on | Qwen/Qwen3.5-2B |
| Released | 2026-08-25 |
| Popularity | 4k downloads / month |
| Likes | 99 |
| Licence | Commercial licence needed |
How it works
Variants
Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.
Using it via the API
Once AxForge deploys wemm-embedding for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (wemm-embedding below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/embeddings \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"wemm-embedding","input":"text to embed"}'
Licence
The weights are open but its licence needs a commercial agreement for business use. AxForge can arrange that licence and host the model for you — you pay AxForge, we settle with the model’s maker. Ask us for a quote. Read the licence ↗
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