Instructions to use hf-internal-testing/tiny-random-BlipModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-BlipModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="hf-internal-testing/tiny-random-BlipModel") 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, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-BlipModel") model = AutoModelForZeroShotImageClassification.from_pretrained("hf-internal-testing/tiny-random-BlipModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7c645c4e15ee728ef889e543a1d0da9e1610e536768ca07f7321cc25663cea62
- Size of remote file:
- 690 kB
- SHA256:
- f5db5e17fa1a6b15f63d5a4bfa1c0ad10f5de40fcf8707f18ac1677ab6d373d6
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