Instructions to use Z3K3/jubbaModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Z3K3/jubbaModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="Z3K3/jubbaModel")# Load model directly from transformers import AutoTokenizer, AutoModelForTextToWaveform tokenizer = AutoTokenizer.from_pretrained("Z3K3/jubbaModel") model = AutoModelForTextToWaveform.from_pretrained("Z3K3/jubbaModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1b4d40c3d3adf3a7bea7ef4b4b6fa587d5dc45872a9a79499feadcf89b266b06
- Size of remote file:
- 7.36 GB
- SHA256:
- 871f0f93505aaacc91f28b32703db8c393f7f49441d1fd4c20f52e055de1603b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.