Instructions to use CLMBR/existential-there-quantifier-lstm-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CLMBR/existential-there-quantifier-lstm-2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import RNNForLanguageModeling model = RNNForLanguageModeling.from_pretrained("CLMBR/existential-there-quantifier-lstm-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-2060640/training_args.bin from CLMBR/existential-there-quantifier-lstm-2: direct link, hf CLI and curl.
- Browser
- Download file 4.28 kB
-
https://huggingface.co/CLMBR/existential-there-quantifier-lstm-2/resolve/main/checkpoint-2060640/training_args.bin
- Command line
-
hf download hf://CLMBR/existential-there-quantifier-lstm-2/checkpoint-2060640/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/CLMBR/existential-there-quantifier-lstm-2/resolve/main/checkpoint-2060640/training_args.bin
4.28 kB
- Xet hash:
- cfb02278ad60b56bcd94134d6e833ab213a507f2be31d6fef4f7234fe0353ba8
- Size of remote file:
- 4.28 kB
- SHA256:
- 3219fcc01b2bd2d02c246073fa5b0d0d8729fc550177d291878ab801078c922a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.