Instructions to use gowitheflowlab/clip-base-patch16-supervised-mulitilingual-800 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gowitheflowlab/clip-base-patch16-supervised-mulitilingual-800 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="gowitheflowlab/clip-base-patch16-supervised-mulitilingual-800") 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("gowitheflowlab/clip-base-patch16-supervised-mulitilingual-800") model = AutoModelForZeroShotImageClassification.from_pretrained("gowitheflowlab/clip-base-patch16-supervised-mulitilingual-800", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from gowitheflowlab/clip-base-patch16-supervised-mulitilingual-800: direct link, hf CLI and curl.
- Browser
- Download file 690 MB
-
https://huggingface.co/gowitheflowlab/clip-base-patch16-supervised-mulitilingual-800/resolve/refs%2Fpr%2F1/optimizer.pt
- Command line
-
hf download hf://gowitheflowlab/clip-base-patch16-supervised-mulitilingual-800@refs/pr/1/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/gowitheflowlab/clip-base-patch16-supervised-mulitilingual-800/resolve/refs%2Fpr%2F1/optimizer.pt
690 MB
- Xet hash:
- a25c39ba8e301bee06454b22c920f7e750b471560a9b8712745a762f46d8f0ce
- Size of remote file:
- 690 MB
- SHA256:
- bb47c5825358dbb0c05cab8c26fb35353b7c0e6b90948043025c8ee95ef5c66b
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