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:
- 1e3120a88856b3e525e7ee1157e0ced6a0d37ef7a46035144eaabfd0626c0af3
- Size of remote file:
- 544 MB
- SHA256:
- 854d7b9ec854b613794956ea13aa61fffc8a7d0c619f29e4e9fdbf4148e3c046
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.