Instructions to use benjimeltzer/bash-clip with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use benjimeltzer/bash-clip with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="benjimeltzer/bash-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("benjimeltzer/bash-clip") model = AutoModelForZeroShotImageClassification.from_pretrained("benjimeltzer/bash-clip", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from benjimeltzer/bash-clip: direct link, hf CLI and curl.
- Browser
- Download file 605 MB
-
https://huggingface.co/benjimeltzer/bash-clip/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://benjimeltzer/bash-clip/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/benjimeltzer/bash-clip/resolve/main/pytorch_model.bin
605 MB
- Xet hash:
- b47265f6f32735ac78df8e9105b0c59b5b7e0454f6e9022cee495a1f87741a29
- Size of remote file:
- 605 MB
- SHA256:
- e28fe1690917ce83e0dc222530858cd365fa81a7e0f40c9f28c2c13202e9ac4d
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