Instructions to use tanganke/clip-vit-large-patch14_cifar100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tanganke/clip-vit-large-patch14_cifar100 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="tanganke/clip-vit-large-patch14_cifar100")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("tanganke/clip-vit-large-patch14_cifar100") model = AutoModel.from_pretrained("tanganke/clip-vit-large-patch14_cifar100") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2db748aa85d8f02fed0e646f92a473fafbdce42dcfcde4f410045c3321bd3bf3
- Size of remote file:
- 1.21 GB
- SHA256:
- 727445e76bb7678665df9a6cc9a1a897b3171c240aee778d62dc87089c50e170
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