Instructions to use hf-internal-testing/tiny-random-SegGptModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-SegGptModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-SegGptModel")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-SegGptModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-SegGptModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 646d87f49ce4905b9883aeeb21e9b43be3c2a9b8fb97e9939ed7b073c49d874d
- Size of remote file:
- 115 kB
- SHA256:
- be88e4ef312cd94ff87f735f461854a9c73d966319329a8947d079848879f7f9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.