Instructions to use CLMBR/binding-c-command-lstm-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CLMBR/binding-c-command-lstm-2 with Transformers:
# Load model directly from transformers import RNNForLanguageModeling model = RNNForLanguageModeling.from_pretrained("CLMBR/binding-c-command-lstm-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f880c2bdf608c3b946c4e8887ddb8758c707f90ff0b864c0718ace51fed4e6b6
- Size of remote file:
- 4.22 kB
- SHA256:
- fccb4bc020cdda52af6a4add70d77fef6236c13d85e3cb33e2fac6088580abf4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.