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:
- 2bb8ab116e985b72ab810ff8c56f1aa1c24440ffd7c0c6f4c55046f658a01f5f
- Size of remote file:
- 220 kB
- SHA256:
- d348e07171169bea3f12a68e21b642cefe686432094137efa9a8f755f08003f7
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