Instructions to use tgsc/debertina-xsmall with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tgsc/debertina-xsmall with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("tgsc/debertina-xsmall", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from tgsc/debertina-xsmall: direct link, hf CLI and curl.
- Browser
- Download file 142 MB
-
https://huggingface.co/tgsc/debertina-xsmall/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://tgsc/debertina-xsmall/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tgsc/debertina-xsmall/resolve/main/pytorch_model.bin
142 MB
- Xet hash:
- bbaaf9bd293cb0512672863d4fe5656ade3adb3ad790a96dc524afc499fc383c
- Size of remote file:
- 142 MB
- SHA256:
- 55ee9909fa2d3f3609a900e3be65c6ea904950e2e501b14a0e4851c7c5575b90
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.