How to use Sovego/clip_vit_base_32_make_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="Sovego/clip_vit_base_32_make_model") 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("Sovego/clip_vit_base_32_make_model") model = AutoModelForZeroShotImageClassification.from_pretrained("Sovego/clip_vit_base_32_make_model", device_map="auto")
https://huggingface.co/Sovego/clip_vit_base_32_make_model/resolve/main/tokenizer.json
hf download hf://Sovego/clip_vit_base_32_make_model/tokenizer.json
curl -L -o tokenizer.json https://huggingface.co/Sovego/clip_vit_base_32_make_model/resolve/main/tokenizer.json
