Instructions to use CLMBR/binding-c-command-lstm-4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CLMBR/binding-c-command-lstm-4 with Transformers:
# Load model directly from transformers import RNNForLanguageModeling model = RNNForLanguageModeling.from_pretrained("CLMBR/binding-c-command-lstm-4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5c82ee0facadaadf5ecf90fb6e2266217392addefe65407c1621522da49d3c60
- Size of remote file:
- 272 MB
- SHA256:
- 1d0305f530a4aab220702dd434c0ef5f32d859c1d7668fc3c9342158a233ff8b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.