Instructions to use hf-tiny-model-private/tiny-random-Wav2Vec2ForPreTraining 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-Wav2Vec2ForPreTraining with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForPreTraining processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-Wav2Vec2ForPreTraining") model = AutoModelForPreTraining.from_pretrained("hf-tiny-model-private/tiny-random-Wav2Vec2ForPreTraining", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 553f3330d6386de0106858b3ae233ffb1a76665d8c4828966a88b021e0ca4b54
- Size of remote file:
- 827 kB
- SHA256:
- 3b640b69b1bf337d8cc2a9e6793a04cc10547a2fa0bb97ad9e51cdf526b8cd5b
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