Instructions to use Balajim57/zero-shot-vitb32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Balajim57/zero-shot-vitb32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="Balajim57/zero-shot-vitb32") 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("Balajim57/zero-shot-vitb32") model = AutoModelForZeroShotImageClassification.from_pretrained("Balajim57/zero-shot-vitb32", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Balajim57/zero-shot-vitb32: direct link, hf CLI and curl.
- Browser
- Download file 134 Bytes
-
https://huggingface.co/Balajim57/zero-shot-vitb32/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Balajim57/zero-shot-vitb32/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Balajim57/zero-shot-vitb32/resolve/main/pytorch_model.bin
134 Bytes
- Xet hash:
- d1dea8e7fb30216a44b6b2bd3f9a3bede7e768ef2446714ee8bb08c202e67437
- Size of remote file:
- 134 Bytes
- SHA256:
- 89ddab2ac35ecdb346c38a57d9fd15f50cb53f395bb97014834b0d3d58f67c37
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