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
- Xet hash:
- 880fe88edd1f3cf74e5f9ec4f4ab55ad9e8eecc16c7a3606da20ea668400a21a
- Size of remote file:
- 343 MB
- SHA256:
- 718e331288d7fd866782c4393ce3d03f4a2afd5348eccbe605694418d681eaf3
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