Instructions to use hf-tiny-model-private/tiny-random-ViTMAEModel 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-ViTMAEModel 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-ViTMAEModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-ViTMAEModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-ViTMAEModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hf-tiny-model-private/tiny-random-ViTMAEModel: direct link, hf CLI and curl.
- Browser
- Download file 194 kB
-
https://huggingface.co/hf-tiny-model-private/tiny-random-ViTMAEModel/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://hf-tiny-model-private/tiny-random-ViTMAEModel/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-tiny-model-private/tiny-random-ViTMAEModel/resolve/main/pytorch_model.bin
194 kB
- Xet hash:
- f86e80255eb9d4caad82fb881b37574280fc353dc3c87631c1c65c2e9e2a7b49
- Size of remote file:
- 194 kB
- SHA256:
- cda0e9b99bdaf7afb5f0ba1592405204795c6914cf558dbb60e7506a637dcaf0
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