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tencent
/
WeMM-Embedding-9B

Feature Extraction
Transformers
Safetensors
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")
  • Notebooks
  • Google Colab
  • Kaggle
WeMM-Embedding-9B
18.8 GB
Ctrl+K
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  • 3 contributors
History: 11 commits
JUNJIE99's picture
JUNJIE99
Update README.md
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  • .gitattributes
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  • LICENSE
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  • README.md
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  • chat_template.jinja
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  • config.json
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  • embedding_chat_template.jinja
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  • model.safetensors
    18.8 GB
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  • modeling_wemm_embedding.py
    1.01 kB
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  • patch_sglang_video.py
    1.98 kB
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  • processor_config.json
    1.19 kB
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  • tokenizer.json
    20 MB
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  • tokenizer_config.json
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  • wemm_sentence_transformers.py
    6.16 kB
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