Instructions to use TJKlein/CLIP-ViT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TJKlein/CLIP-ViT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="TJKlein/CLIP-ViT") 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("TJKlein/CLIP-ViT") model = AutoModelForZeroShotImageClassification.from_pretrained("TJKlein/CLIP-ViT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from TJKlein/CLIP-ViT: direct link, hf CLI and curl.
- Browser
- Download file 3.9 kB
-
https://huggingface.co/TJKlein/CLIP-ViT/resolve/main/training_args.bin
- Command line
-
hf download hf://TJKlein/CLIP-ViT/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/TJKlein/CLIP-ViT/resolve/main/training_args.bin
3.9 kB
- Xet hash:
- 517dceed75df6d726947015d59438470590a840970ce9228ee22458abe13c22e
- Size of remote file:
- 3.9 kB
- SHA256:
- 14be30e15e08a0663eae5b4d1a7a7ed95326a5b4c5b0c0258b2fd1ff9c7c405f
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