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:
- f251ded4ddeb6b8c8cc07bf8df4576334f8004e9d7f40d7c327ee796b1fbbcfd
- Size of remote file:
- 613 MB
- SHA256:
- 0e5ff26055e1d90d853f92099c885e8c908ee7ac4683b98110ae790db344ad82
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