Instructions to use hf-internal-testing/tiny-random-MoonshineForConditionalGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-MoonshineForConditionalGeneration with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="hf-internal-testing/tiny-random-MoonshineForConditionalGeneration")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-MoonshineForConditionalGeneration") model = AutoModelForSpeechSeq2Seq.from_pretrained("hf-internal-testing/tiny-random-MoonshineForConditionalGeneration") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9efd1f513a909c3015927ee7c7f429ad721de906eb936379a76c6649e72e6352
- Size of remote file:
- 546 kB
- SHA256:
- 1ad5b754a73b3f02a59ea60f1315da72d2f6403efb1859befcea3aab178b9930
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