Instructions to use hf-tiny-model-private/tiny-random-DPTModel 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-DPTModel 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-DPTModel")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-DPTModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-DPTModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dbb66320da411bb5ea5c5838ab4a9c5972376132512d510336c342ee79dd8b10
- Size of remote file:
- 235 kB
- SHA256:
- 5f92a47613d042f9d9b4af7b30135ce94267af08d4e69d4d71582928736ce114
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