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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 612 Bytes
2561b97 e6aef6e 923c185 e6aef6e 923c185 e6aef6e 923c185 5e63d9c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ---
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)
|