Instructions to use hf-internal-testing/tiny-random-ViTMAEModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-ViTMAEModel 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-ViTMAEModel")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-ViTMAEModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-ViTMAEModel") - Notebooks
- Google Colab
- Kaggle
Upload ONNX weights
Browse files- 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 sha256:8d0de9e9997bc44dc44d51e56f45b45739419a50c350cfb79814b697c778014b
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size 260702
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