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:
- d7d9e961e3956f84586d88d30da10f795daf2da32771e1beeb25084238a2fef7
- Size of remote file:
- 1.26 MB
- SHA256:
- af9c0f5ef42abaf977566a001a1eba4106cf48504e14e4023dfb2a2446054255
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.