Instructions to use hf-internal-testing/tiny-random-VideoMAEModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-VideoMAEModel 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-VideoMAEModel")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-VideoMAEModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-VideoMAEModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 12386c73dd4c80ceaa1eaf1b0141e81dfcccf5e0e48a453888aaba64df5f99d6
- Size of remote file:
- 146 kB
- SHA256:
- f227816938f14963fa9191aff3e37db6021a2c671ebb4f22b54503461c3b7b96
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