Instructions to use hf-internal-testing/tiny-random-TimesformerModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-TimesformerModel 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-TimesformerModel")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-TimesformerModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-TimesformerModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 63360491823abf29d20b4bbbcd74e0f1aa44773493a9534c64adb47a10d89ab7
- Size of remote file:
- 282 kB
- SHA256:
- 79084e4bd704ba0e059edad32adf5c946ac3363759654afd8022b78d61285d57
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