Instructions to use hf-internal-testing/tiny-random-DinatModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-DinatModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="hf-internal-testing/tiny-random-DinatModel")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-DinatModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-DinatModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 402c543ebef09d4a29e25387e845cfeb03f00f6b6123067f878231e2b04aae36
- Size of remote file:
- 338 kB
- SHA256:
- a7c9d1edbcce12246c6240f61faa8a2043d01d4f3c6c13190788df7325fe1b0e
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