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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 725c605f2041982bfe0abb233f8210361ed5d76060e9239838eb3d1e12e55785
- Size of remote file:
- 220 kB
- SHA256:
- 9de37d312c48c472c666287f12cbbbc1360e57d93be9dbe34bd2f98870624d5f
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