Instructions to use hf-internal-testing/tiny-random-MusicgenForConditionalGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-MusicgenForConditionalGeneration with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="hf-internal-testing/tiny-random-MusicgenForConditionalGeneration")# Load model directly from transformers import AutoTokenizer, AutoModelForTextToWaveform tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-MusicgenForConditionalGeneration") model = AutoModelForTextToWaveform.from_pretrained("hf-internal-testing/tiny-random-MusicgenForConditionalGeneration") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2a1b8bc60c7032587d5c7f24a9e5eb51edc67cba17d311958ca14191e180c060
- Size of remote file:
- 4.96 MB
- SHA256:
- e8d55e7d785b707a38dd4f12227cbe5e5c673980b41fa79627ebdc9c8f85c070
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.