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intfloat
/
mmE5-mllama-11b-instruct

Zero-Shot Image Classification
Transformers
Safetensors
sentence-transformers
mllama
image-text-to-text
mmeb
text-generation-inference
Model card Files Files and versions
xet
Community
4

Instructions to use intfloat/mmE5-mllama-11b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use intfloat/mmE5-mllama-11b-instruct with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("zero-shot-image-classification", model="intfloat/mmE5-mllama-11b-instruct")
    pipe(
        "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png",
        candidate_labels=["animals", "humans", "landscape"],
    )
    # Load model directly
    from transformers import AutoProcessor, AutoModelForMultimodalLM
    
    processor = AutoProcessor.from_pretrained("intfloat/mmE5-mllama-11b-instruct")
    model = AutoModelForMultimodalLM.from_pretrained("intfloat/mmE5-mllama-11b-instruct")
  • sentence-transformers

    How to use intfloat/mmE5-mllama-11b-instruct with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("intfloat/mmE5-mllama-11b-instruct")
    
    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
mmE5-mllama-11b-instruct
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  • 5 contributors
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intfloat
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