Instructions to use fancyfeast/so400m-long with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fancyfeast/so400m-long with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="fancyfeast/so400m-long") 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("fancyfeast/so400m-long") model = AutoModelForZeroShotImageClassification.from_pretrained("fancyfeast/so400m-long") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 07791cac1458b0321ace833f4b80a829eefdb3cc891e43c2e5acf30948c34866
- Size of remote file:
- 34.4 MB
- SHA256:
- 8f5bcd9e9d3bde3713d2331940c6d9018bfc6dc9631ac24336b1b0e0567d2f1a
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