Instructions to use CLMBR/existential-there-quantifier-lstm-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CLMBR/existential-there-quantifier-lstm-1 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import RNNForLanguageModeling model = RNNForLanguageModeling.from_pretrained("CLMBR/existential-there-quantifier-lstm-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-1908000/optimizer.pt from CLMBR/existential-there-quantifier-lstm-1: direct link, hf CLI and curl.
- Browser
- Download file 544 MB
-
https://huggingface.co/CLMBR/existential-there-quantifier-lstm-1/resolve/main/checkpoint-1908000/optimizer.pt
- Command line
-
hf download hf://CLMBR/existential-there-quantifier-lstm-1/checkpoint-1908000/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/CLMBR/existential-there-quantifier-lstm-1/resolve/main/checkpoint-1908000/optimizer.pt
544 MB
- Xet hash:
- 56d944d92077004718135c049d475931f9c57beb02f4cb498180d6f5f0ef2255
- Size of remote file:
- 544 MB
- SHA256:
- e161f1e43e2c30661b5e74c05eefcbe91e94123cb23adf533f627b93d47140e3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.