Instructions to use Intel/tiny-random-vit_ipex_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Intel/tiny-random-vit_ipex_model with Transformers:
# 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")# 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") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| tags: [] | |
| # Model Card for Model ID | |
| 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](https://github.com/huggingface/optimum-intel/blob/main/tests/ipex/utils_tests.py) | |