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:
# pip install -U transformers accelerate # Load model directly from transformers import RNNForLanguageModeling model = RNNForLanguageModeling.from_pretrained("CLMBR/existential-there-quantifier-lstm-4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-228960/pytorch_model.bin from CLMBR/existential-there-quantifier-lstm-4: direct link, hf CLI and curl.
- Browser
- Download file 272 MB
-
https://huggingface.co/CLMBR/existential-there-quantifier-lstm-4/resolve/main/checkpoint-228960/pytorch_model.bin
- Command line
-
hf download hf://CLMBR/existential-there-quantifier-lstm-4/checkpoint-228960/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/CLMBR/existential-there-quantifier-lstm-4/resolve/main/checkpoint-228960/pytorch_model.bin
272 MB
- Xet hash:
- 7a1cc1008101b895e97a8091cc314e105086943947e8abb9dee79f8057e88c15
- Size of remote file:
- 272 MB
- SHA256:
- 9447ddeb70e224b9fa4328ada8664a4d78d0e59a9615560b3b0030bcf21780a5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.