How to use gowitheflowlab/clip-base-patch16-supervised-mulitilingual-1600 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-1600") 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-1600") model = AutoModelForZeroShotImageClassification.from_pretrained("gowitheflowlab/clip-base-patch16-supervised-mulitilingual-1600", device_map="auto")
https://huggingface.co/gowitheflowlab/clip-base-patch16-supervised-mulitilingual-1600/resolve/refs%2Fpr%2F1/tokenizer.json
hf download hf://gowitheflowlab/clip-base-patch16-supervised-mulitilingual-1600@refs/pr/1/tokenizer.json
curl -L -o tokenizer.json https://huggingface.co/gowitheflowlab/clip-base-patch16-supervised-mulitilingual-1600/resolve/refs%2Fpr%2F1/tokenizer.json
