Instructions to use ndaheim/cima_ungrounded_joint_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ndaheim/cima_ungrounded_joint_model with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ndaheim/cima_ungrounded_joint_model") model = AutoModelForSeq2SeqLM.from_pretrained("ndaheim/cima_ungrounded_joint_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4e8c5244c034f89a485ca8a1277b2756e80bcfad7bc2e2f896a9fe745e5aacc0
- Size of remote file:
- 14.6 kB
- SHA256:
- 4023d58fe9b64875419226c1e2779de7fe40dfe32dec6e4b9a8dce64f042f152
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.