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
[Awaiting approval] Upload ONNX weights
Browse files[Automated] Converted using [Optimum](https://github.com/huggingface/optimum). Models will be merged manually by @Xenova once they have been checked with [Transformers.js](https://github.com/xenova/transformers.js).
- onnx/model.onnx +3 -0
onnx/model.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:0c6526ece2959adbbde796c55218a2e35a1f731dfc1f1828847e1d28d2a463a7
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size 293993
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