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:
- 3e417ca8fc7f1f0437bfed4a6e39c328f94316e125e8de6ff6dd74c0cd51397d
- Size of remote file:
- 8.71 MB
- SHA256:
- cf2e2a036ab4a204f9037450c9ba36e19c021c8ad19cf98d3a3a2d12286fe756
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