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:
- c00a503f81d628e5a94d2274e5dacf80830e610032ab687ee324d78f576a9278
- Size of remote file:
- 690 kB
- SHA256:
- e1f70e09c9e2afaa4531bf6cfc6d4b9cc79a6d44a69cee142f3285464f2f0f70
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