Instructions to use CLMBR/full-lstm-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CLMBR/full-lstm-2 with Transformers:
# Load model directly from transformers import RNNForLanguageModeling model = RNNForLanguageModeling.from_pretrained("CLMBR/full-lstm-2", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- da3aa56d1b342184265460986704ecf8dcdd975fbc0cf3877bbe5dba44c149ac
- Size of remote file:
- 627 Bytes
- SHA256:
- 3ea72387fedf49b73c2d02f07fb8aa3a0dfb3f2f90bf6256da3e636e9d6b4913
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.