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:
- fe36577f781e04ddd123ba7e0d062b07ab61c8ca71cb093c89cd49a96dc2fd06
- Size of remote file:
- 4.96 MB
- SHA256:
- da7a18aafce08cc1593b8186b63b6c15de94227fc051d71820597c85e156760c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.