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-2976480/rng_state.pth from CLMBR/existential-there-quantifier-lstm-4: direct link, hf CLI and curl.
- Browser
- Download file 14.6 kB
-
https://huggingface.co/CLMBR/existential-there-quantifier-lstm-4/resolve/main/checkpoint-2976480/rng_state.pth
- Command line
-
hf download hf://CLMBR/existential-there-quantifier-lstm-4/checkpoint-2976480/rng_state.pth
-
curl -L -o rng_state.pth https://huggingface.co/CLMBR/existential-there-quantifier-lstm-4/resolve/main/checkpoint-2976480/rng_state.pth
14.6 kB
- Xet hash:
- 5fef91a309d187eb46788ef59ec38dd8a2b13f52fbbf79078ee6fdc8e5597818
- Size of remote file:
- 14.6 kB
- SHA256:
- aa74d6333c2adcb3d9fb03c678fa1d36ff490838e3864af8bc9c886b016020be
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.