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:
- 7535e1ff59925af350fea04c5cac5397ac094ee6cd2e2aea0da83072bd2f665c
- Size of remote file:
- 8.67 MB
- SHA256:
- 474915fa61398aebff348807bd81d0995c7c7571d8d4743de341deaa6d66ce38
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