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Heliosoph
/
vit-gpt2-image-captioning-onnx

Image-to-Text
Transformers
ONNX
vision-encoder-decoder
image-text-to-text
image-captioning
vit
gpt2
Model card Files Files and versions
xet
Community

Instructions to use Heliosoph/vit-gpt2-image-captioning-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Heliosoph/vit-gpt2-image-captioning-onnx with Transformers:

    # Use a pipeline as a high-level helper
    # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5.
    # You must load the model directly (see below) or downgrade to v4.x with:
    # 'pip install "transformers<5.0.0'
    from transformers import pipeline
    
    pipe = pipeline("image-to-text", model="Heliosoph/vit-gpt2-image-captioning-onnx")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForImageTextToText
    
    tokenizer = AutoTokenizer.from_pretrained("Heliosoph/vit-gpt2-image-captioning-onnx")
    model = AutoModelForImageTextToText.from_pretrained("Heliosoph/vit-gpt2-image-captioning-onnx")
  • Notebooks
  • Google Colab
  • Kaggle
vit-gpt2-image-captioning-onnx
961 MB
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  • 1 contributor
History: 3 commits
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flyingbertman
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  • .gitattributes
    1.52 kB
    initial commit 6 days ago
  • LICENSE.md
    12.6 kB
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  • README.md
    1.84 kB
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  • config.json
    5.09 kB
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  • decoder_model.onnx
    613 MB
    xet
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  • encoder_model.onnx
    343 MB
    xet
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  • generation_config.json
    187 Bytes
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  • merges.txt
    456 kB
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  • preprocessor_config.json
    349 Bytes
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  • special_tokens_map.json
    617 Bytes
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  • tokenizer.json
    3.56 MB
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  • tokenizer_config.json
    695 Bytes
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  • vocab.json
    798 kB
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