Instructions to use CLMBR/existential-there-quantifier-lstm-4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CLMBR/existential-there-quantifier-lstm-4 with Transformers:
# Load model directly from transformers import RNNForLanguageModeling model = RNNForLanguageModeling.from_pretrained("CLMBR/existential-there-quantifier-lstm-4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0d81915eccd998a48bb3324bc4c2415eefb3226f64dd980a0cb728bc5a99d6ae
- Size of remote file:
- 272 MB
- SHA256:
- 5e76e58cf281999d06ea087f80df072869e6d92b26c8a4065871d44381e140dd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.