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svjack
/
vit-gpt-diffusion-zh

Image-to-Text
Transformers
PyTorch
Chinese
vision-encoder-decoder
image-text-to-text
vit
gpt
Model card Files Files and versions
xet
Community
1

Instructions to use svjack/vit-gpt-diffusion-zh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use svjack/vit-gpt-diffusion-zh 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="svjack/vit-gpt-diffusion-zh")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForMultimodalLM
    
    tokenizer = AutoTokenizer.from_pretrained("svjack/vit-gpt-diffusion-zh")
    model = AutoModelForMultimodalLM.from_pretrained("svjack/vit-gpt-diffusion-zh", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
vit-gpt-diffusion-zh
2.2 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 3 commits
svjack's picture
svjack
Create README.md
9a85fd1 over 3 years ago
  • .gitattributes
    1.48 kB
    initial commit over 3 years ago
  • README.md
    3.56 kB
    Create README.md over 3 years ago
  • config.json
    78.1 kB
    Upload with huggingface_hub over 3 years ago
  • generation_config.json
    119 Bytes
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  • optimizer.pt
    1.22 GB
    xet
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  • preprocessor_config.json
    327 Bytes
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  • pytorch_model.bin
    982 MB
    xet
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  • rng_state.pth
    14.5 kB
    xet
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  • scaler.pt
    559 Bytes
    xet
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  • scheduler.pt
    623 Bytes
    xet
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  • trainer_state.json
    6.9 kB
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  • training_args.bin
    3.63 kB
    xet
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