Instructions to use hf-internal-testing/tiny-random-MobileViTV2Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-MobileViTV2Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="hf-internal-testing/tiny-random-MobileViTV2Model")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-MobileViTV2Model") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-MobileViTV2Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8b5536ba71877c51434333b0184d355c5ecdeaa7b359221182bd8c4e4621cb2e
- Size of remote file:
- 1.26 MB
- SHA256:
- 8a27cdada126d11b2eb97d32136295cc04a33a43761ae31d1d9d7b59e65431ce
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.