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:
- 137f5ed6f819451e49a5c7c1d22a83239af1b515cbd58f3c7fc3cb02b05d9a5b
- Size of remote file:
- 739 kB
- SHA256:
- f7d2663b478da41b73c45e734d486cdac1c2378d35aa0d7ba4c5422c05eafded
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