Instructions to use hf-tiny-model-private/tiny-random-OwlViTModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-OwlViTModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-tiny-model-private/tiny-random-OwlViTModel")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-OwlViTModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-OwlViTModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 935238feb04b2277aef113e9a89ee834ce8a090d7a95d30984b4916c46320a93
- Size of remote file:
- 1.54 MB
- SHA256:
- ac59af82da176f4f6a6c844bd027f51bbb9bb7f8bae5397350647cec55d63472
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.