Instructions to use hf-tiny-model-private/tiny-random-BlipModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/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-tiny-model-private/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-tiny-model-private/tiny-random-BlipModel") model = AutoModelForZeroShotImageClassification.from_pretrained("hf-tiny-model-private/tiny-random-BlipModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ea3a25af17aead7cceaf95f8c9d949d22a33283e86eff615e5b6d41efe3db19c
- Size of remote file:
- 646 kB
- SHA256:
- 2842fce26cab6962d2ec978d9526dc34c7b22f5027821742ebac7d898a723d9b
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