Instructions to use CLMBR/passive-lstm-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CLMBR/passive-lstm-3 with Transformers:
# Load model directly from transformers import RNNForLanguageModeling model = RNNForLanguageModeling.from_pretrained("CLMBR/passive-lstm-3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7ff5dc52b994c397799a0190b0f6a6caf6d99003a68ded62d8f21246974c82e0
- Size of remote file:
- 272 MB
- SHA256:
- fa25a4c4599dc4b9210e0e19ffbaa2a616ea2a5059e05c7cad45e1ab89c310f4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.