Instructions to use hf-tiny-model-private/tiny-random-Wav2Vec2ConformerForPreTraining 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-Wav2Vec2ConformerForPreTraining with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForPreTraining processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-Wav2Vec2ConformerForPreTraining") model = AutoModelForPreTraining.from_pretrained("hf-tiny-model-private/tiny-random-Wav2Vec2ConformerForPreTraining", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3fd489f3bf6ba97f91c382240f006c4dbcbe5e1ac5262af0249b5cdd4be2a4ad
- Size of remote file:
- 895 kB
- SHA256:
- 4c108ae821e850690346620f9558528cc5849da5f1de547d0e3252a08b54761b
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