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 tf_model.h5 from hf-tiny-model-private/tiny-random-ViTMAEModel: direct link, hf CLI and curl.
- Browser
- Download file 284 kB
-
https://huggingface.co/hf-tiny-model-private/tiny-random-ViTMAEModel/resolve/main/tf_model.h5
- Command line
-
hf download hf://hf-tiny-model-private/tiny-random-ViTMAEModel/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/hf-tiny-model-private/tiny-random-ViTMAEModel/resolve/main/tf_model.h5
284 kB
- Xet hash:
- 5a6beefb53e7555d60ebf2df813e64dc8d2371aefdbb17d9870b8d9472d4dd27
- Size of remote file:
- 284 kB
- SHA256:
- eb203bc14794a08c3676bb59ff8dea7860f7fa111ec8274beeabd7c335a34a7a
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