Instructions to use hf-internal-testing/tiny-random-DPTModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-DPTModel 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-DPTModel")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-DPTModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-DPTModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 192e28cdbc4c086b11b1be8bf23d82d414b22efb16db8f84a643ae08fcc95bb3
- Size of remote file:
- 235 kB
- SHA256:
- 625ce61b82830fedbc237d4318d9556d866dc44e2b6a792ff12caeb68c199fc3
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