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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 48e5145984130c61ae6ccc437011213ed3c9e4ed1403a541c859d7df64084b66
- Size of remote file:
- 690 kB
- SHA256:
- b5967eda9ba1fba1e269ebe893c15a99cc37d4623ade74f54eebce7a51a3b859
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