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tencent
/
WeMM-Embedding-9B

Feature Extraction
Transformers
Safetensors
sentence-transformers
Chinese
English
qwen3_5
image-text-to-text
multimodal-embedding
text-embedding
image-embedding
video-embedding
mrl
custom_code
Model card Files Files and versions
xet
Community
1

Instructions to use tencent/WeMM-Embedding-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use tencent/WeMM-Embedding-9B with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="tencent/WeMM-Embedding-9B", trust_remote_code=True)
    # Load model directly
    from transformers import AutoProcessor, AutoModelForMultimodalLM
    
    processor = AutoProcessor.from_pretrained("tencent/WeMM-Embedding-9B", trust_remote_code=True)
    model = AutoModelForMultimodalLM.from_pretrained("tencent/WeMM-Embedding-9B", trust_remote_code=True, device_map="auto")
  • sentence-transformers

    How to use tencent/WeMM-Embedding-9B with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("tencent/WeMM-Embedding-9B", trust_remote_code=True)
    
    sentences = [
        "The weather is lovely today.",
        "It's so sunny outside!",
        "He drove to the stadium."
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [3, 3]
  • Notebooks
  • Google Colab
  • Kaggle
WeMM-Embedding-9B
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  • 4 contributors
History: 6 commits
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JUNJIE99
Update README.md
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  • chat_template.jinja
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  • config.json
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  • model.safetensors
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  • modeling_wemm_embedding.py
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  • patch_sglang_video.py
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  • processor_config.json
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  • tokenizer.json
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  • tokenizer_config.json
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  • wemm_sentence_transformers.py
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