Instructions to use hf-internal-testing/tiny-random-owlvit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-owlvit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-owlvit")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-owlvit") model = AutoModelForMultimodalLM.from_pretrained("hf-internal-testing/tiny-random-owlvit") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6d21207bc9c84519b4c08fd9ad2ed0a4d4b317c111908de3e38e775939c0aca2
- Size of remote file:
- 4.33 MB
- SHA256:
- 2d00b839343cc12047d9aeff45b1b613ddcba0d2290e36a8aa47abb638bbca10
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.