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:
- f03551ed0386e7f4d432285958b910e438f1e3dcee7b73513b1ebdf4e122d928
- Size of remote file:
- 4.96 MB
- SHA256:
- 14d0bfa0c2cf133cc8e5ae51f7d2843735f191578051c01da83fa6437825abf8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.