Instructions to use Kimata/gpt2-medium-Vizuosense with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use Kimata/gpt2-medium-Vizuosense with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("undefined") model.load_adapter("Kimata/gpt2-medium-Vizuosense", set_active=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 778379870293cdc6fa9c3e16c8c6917c3c37ceb6a0cf4049b193dc1dda2b9f57
- Size of remote file:
- 710 MB
- SHA256:
- 28bd04f2e10057f2ccb037cc355b0a40f48054fcc3e38c761f473332676d4272
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.