Instructions to use gagan3012/clip with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gagan3012/clip with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="gagan3012/clip") 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("gagan3012/clip") model = AutoModelForZeroShotImageClassification.from_pretrained("gagan3012/clip", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from gagan3012/clip: direct link, hf CLI and curl.
- Browser
- Download file 605 MB
-
https://huggingface.co/gagan3012/clip/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://gagan3012/clip/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/gagan3012/clip/resolve/main/pytorch_model.bin
605 MB
- Xet hash:
- 3dee9f77912766e18c16b8950697b802db9eb757e4acff0d63a8ec35550b81a3
- Size of remote file:
- 605 MB
- SHA256:
- e4bd48fae7b40d1affb32af3b665a6ff7f4868eee35014b9d94cbdee184fb047
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