Instructions to use hf-internal-testing/tiny-random-ViTMAEForPreTraining with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-ViTMAEForPreTraining with Transformers:
# Load model directly from transformers import AutoImageProcessor, AutoModelForMultimodalLM processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-ViTMAEForPreTraining") model = AutoModelForMultimodalLM.from_pretrained("hf-internal-testing/tiny-random-ViTMAEForPreTraining") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 04a603e7efbb0a769b73ee8c4626974b90217309eea35c990b1a123c408c204f
- Size of remote file:
- 102 MB
- SHA256:
- c8e6fb0390f4bb3f61e34445a42e592fa611e4977583bbd6fc7f85faf83d8e23
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.