Instructions to use cringgaard/interpolation_length with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cringgaard/interpolation_length with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="cringgaard/interpolation_length") 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("cringgaard/interpolation_length") model = AutoModelForZeroShotImageClassification.from_pretrained("cringgaard/interpolation_length", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 77fa7929c2365aa8dc0d4151c5a2a181c0ade3fb9c50e10ed746e87962575c3c
- Size of remote file:
- 5.37 kB
- SHA256:
- ee8ea66b66e052e525c51afccd2d9818af0f1c25344161fb5b8341e1be90b4f2
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