Instructions to use 0914eagle/Clip_ViT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 0914eagle/Clip_ViT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="0914eagle/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("0914eagle/Clip_ViT") model = AutoModelForZeroShotImageClassification.from_pretrained("0914eagle/Clip_ViT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from 0914eagle/Clip_ViT: direct link, hf CLI and curl.
- Browser
- Download file 1.71 GB
-
https://huggingface.co/0914eagle/Clip_ViT/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://0914eagle/Clip_ViT/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/0914eagle/Clip_ViT/resolve/main/pytorch_model.bin
1.71 GB
- Xet hash:
- e1adab2b9b9115609c2a8de8d34033f3fee4989b6fe2210b1488af551521f3b6
- Size of remote file:
- 1.71 GB
- SHA256:
- e00b67c79e43b0bc83dbb0f399cc64e400976fd8abc5b58c345d189c1127eb51
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