Instructions to use SPRINGLab/SPRING_F5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SPRINGLab/SPRING_F5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="SPRINGLab/SPRING_F5", trust_remote_code=True)# Load model directly from transformers import SPRING_F5 model = SPRING_F5.from_pretrained("SPRINGLab/SPRING_F5", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6274755d693d99c69e766312cb20c6b99fe40eda80f1531f4188a7a2f8181550
- Size of remote file:
- 279 kB
- SHA256:
- 4abb1107771ce7e14926fde879b959dde6db6e572476b98684f04e45e978ab19
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.