Instructions to use ryanyip7777/pmc_vit-l-14_hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ryanyip7777/pmc_vit-l-14_hf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="ryanyip7777/pmc_vit-l-14_hf") 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("ryanyip7777/pmc_vit-l-14_hf") model = AutoModelForZeroShotImageClassification.from_pretrained("ryanyip7777/pmc_vit-l-14_hf") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7b5d4a887f63a6ddd1fb3db3a16a24b1e662497f0a5d375e7dd28103d39547ce
- Size of remote file:
- 1.71 GB
- SHA256:
- 1ac47b1f68f148a1a5da6634746c914b1460c0fe29104d86119ccb117dbbde01
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.