Instructions to use hf-tiny-model-private/tiny-random-DinatModel 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-DinatModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="hf-tiny-model-private/tiny-random-DinatModel")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-DinatModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-DinatModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 02db53736b478feb4f82a24b5994c7594306f1b7c037c0a165cfa427665552f1
- Size of remote file:
- 338 kB
- SHA256:
- 7a0220b7c6f016c4097b8184a53049b6a4303260b9c8a2a6ec636f3f0e0fb97b
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