Instructions to use hf-internal-testing/tiny-random-PvtV2Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-PvtV2Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-PvtV2Model")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-PvtV2Model") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-PvtV2Model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bb7acdda8227020efd0fcf48512be2b41d7697ab92ac9c6d1f69d6ac71d13cf8
- Size of remote file:
- 3.11 MB
- SHA256:
- 5cae3b5df3dd61c38d9a453a2a19c96c49d571f8d3adf28e41200511f8443fd7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.