Instructions to use hf-internal-testing/tiny-random-Owlv2Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-Owlv2Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-Owlv2Model")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-Owlv2Model") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-Owlv2Model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5430c735ef70b1cabcfe224d7094649578fa598502716f8f5e131269f3ddd84b
- Size of remote file:
- 1.5 MB
- SHA256:
- 6f3c3425914346491a676681a131fb93cf5ddeb17baa15af27b843587f53d1a9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.