Instructions to use hf-internal-testing/tiny-random-EncodecModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-EncodecModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-EncodecModel")# Load model directly from transformers import AutoFeatureExtractor, AutoModel extractor = AutoFeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-EncodecModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-EncodecModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 10f0c69666cf3eb1ad519564e934f84e4081ab210af355a4c5e25cf70bac5d98
- Size of remote file:
- 93.2 MB
- SHA256:
- 0cc659a50b83ff3d3f5ea7fc44fd95bb83e3b43b32bb69877cfd3a85b91187e7
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