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:
- f1c495c8f6cfd1b4d0d3f4a7353d13f9dfe0f6d5a10542ce63a322f4c26039c7
- Size of remote file:
- 272 MB
- SHA256:
- 890d5becbd24e789d77b38bb4b631b697c8ab742efe14b21529cb602f0d130f4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.