Instructions to use hf-tiny-model-private/tiny-random-OneFormerModel 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-OneFormerModel 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-OneFormerModel")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-OneFormerModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-OneFormerModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 82b3036b4e17024754db246e90e9e41916af4f059c900ea0aa46982f083f2f8d
- Size of remote file:
- 47.8 MB
- SHA256:
- 5c8466e658d0ea1b6f7e5ff1b6ac2e971f3a191aa587548e63e5b2e6113405a3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.