Instructions to use RedHatAI/oBERT-3-downstream-pruned-block4-80-QAT-squadv1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RedHatAI/oBERT-3-downstream-pruned-block4-80-QAT-squadv1 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("RedHatAI/oBERT-3-downstream-pruned-block4-80-QAT-squadv1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 476231a30f270661c0c507b268e22feca557c0aa7a5effa79d6ddb411c26938b
- Size of remote file:
- 49.4 MB
- SHA256:
- 9008c5907c34c805bb0bf975a6a1be9c665e66e6320981e02c29ee8f7cf96a6d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.