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facebook
/
meta-encoder

Feature Extraction
Transformers
Safetensors
muse_glimmer
image-text-to-text
metaencoder
multimodal representation
similarity
image-text-retrieval
classification
decision making
Model card Files Files and versions
xet
Community

Instructions to use facebook/meta-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use facebook/meta-encoder with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="facebook/meta-encoder")
    # pip install -U transformers accelerate
    # Load model directly
    from transformers import AutoProcessor, AutoModelForMultimodalLM
    
    processor = AutoProcessor.from_pretrained("facebook/meta-encoder")
    model = AutoModelForMultimodalLM.from_pretrained("facebook/meta-encoder", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle

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  • model.safetensors.index.json
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  • modeling_metaencoder.py
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  • tokenizer.json
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  • tokenizer_config.json
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  • vllm_metaencoder.py
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