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ATH-MaaS
/
Ovis-VL-Embedding-9B

Feature Extraction
Transformers
Safetensors
qwen3_5
image-text-to-text
multimodal
text
image
video
visual-document
embedding
retrieval
Model card Files Files and versions
xet
Community

Instructions to use ATH-MaaS/Ovis-VL-Embedding-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use ATH-MaaS/Ovis-VL-Embedding-9B with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="ATH-MaaS/Ovis-VL-Embedding-9B")
    # pip install -U transformers accelerate
    # Load model directly
    from transformers import AutoProcessor, AutoModelForMultimodalLM
    
    processor = AutoProcessor.from_pretrained("ATH-MaaS/Ovis-VL-Embedding-9B")
    model = AutoModelForMultimodalLM.from_pretrained("ATH-MaaS/Ovis-VL-Embedding-9B", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
Ovis-VL-Embedding-9B / model
16.8 GB
Ctrl+K
Ctrl+K
  • 3 contributors
History: 1 commit
kevinke's picture
kevinke
Add structured model and figures directories
d00cb98 verified 16 days ago
  • args.json
    86 Bytes
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  • chat_template.jinja
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  • config.json
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  • model-00001-of-00004.safetensors
    4.91 GB
    xet
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  • model-00002-of-00004.safetensors
    4.95 GB
    xet
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  • model-00003-of-00004.safetensors
    4.99 GB
    xet
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  • model-00004-of-00004.safetensors
    1.93 GB
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  • model.safetensors.index.json
    69.2 kB
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  • preprocessor_config.json
    390 Bytes
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  • processor_config.json
    1.19 kB
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  • tokenizer.json
    20 MB
    xet
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  • tokenizer_config.json
    1.17 kB
    Add structured model and figures directories 16 days ago