Instructions to use DRDELATV/modelo_epicuro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DRDELATV/modelo_epicuro with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="DRDELATV/modelo_epicuro", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("DRDELATV/modelo_epicuro", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
DRDELATV2025
Update: Configurar modelo Epicuro para Hugging Face con archivos de configuraci贸n correctos
a22c618 - Xet hash:
- 9d52a55a1e54fbd76bc03dd0c485f24ec99e5f065ee4fdbfea8f5563520ba58d
- Size of remote file:
- 463 Bytes
- SHA256:
- 00f0a7562b6a7e834522e1d7733451fff9066fd12e4bf358d89bfe51f4c7cd62
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.