# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("Intel/tiny-random-vit_ipex_model")
model = AutoModelForImageClassification.from_pretrained("Intel/tiny-random-vit_ipex_model")Quick Links
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This is a tiny random vit model derived from "google/vit-base-patch16-224". It was uploaded by IPEXModelForImageClassification.
from optimum.intel import IPEXModelForImageClassification
model = IPEXModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-vit")
model.push_to_hub("Intel/tiny-random-vit_ipex_model")
This is useful for functional testing (not quality generation, since its weights are random) on optimum-intel
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Intel/tiny-random-vit_ipex_model") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")