Instructions to use Superlore/clip-vit-large-patch14 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Superlore/clip-vit-large-patch14 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="Superlore/clip-vit-large-patch14") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("Superlore/clip-vit-large-patch14") model = AutoModelForZeroShotImageClassification.from_pretrained("Superlore/clip-vit-large-patch14", device_map="auto") - Notebooks
- Google Colab
- Kaggle
0xnewton-superlore
initial commit - copy https://huggingface.co/openai/clip-vit-large-patch14
93cf9b4 Download tf_model.h5 from Superlore/clip-vit-large-patch14: direct link, hf CLI and curl.
- Browser
- Download file 1.71 GB
-
https://huggingface.co/Superlore/clip-vit-large-patch14/resolve/main/tf_model.h5
- Command line
-
hf download hf://Superlore/clip-vit-large-patch14/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/Superlore/clip-vit-large-patch14/resolve/main/tf_model.h5
1.71 GB
- Xet hash:
- 078b1a29c86d6863d23af98b676309153661c340178d6f6e4aa02487512d5815
- Size of remote file:
- 1.71 GB
- SHA256:
- 7f154e925c18270d662d28f6261523c2ff6e80f1f05292cb034db41d5951c7a4
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