Instructions to use hf-internal-testing/tiny-random-Dinov2Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-Dinov2Model 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-Dinov2Model")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-Dinov2Model") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-Dinov2Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 24f69a2855374263f788a0fc88e5641a05fafb9b3659849a705ef6d561507804
- Size of remote file:
- 321 kB
- SHA256:
- 7de825bdeeef215976d633ac2f912a9b5cc48802e6a6a1806b24787523a441e6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.