Instructions to use CLMBR/existential-there-quantifier-lstm-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CLMBR/existential-there-quantifier-lstm-3 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import RNNForLanguageModeling model = RNNForLanguageModeling.from_pretrained("CLMBR/existential-there-quantifier-lstm-3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-2136960/training_args.bin from CLMBR/existential-there-quantifier-lstm-3: direct link, hf CLI and curl.
- Browser
- Download file 4.28 kB
-
https://huggingface.co/CLMBR/existential-there-quantifier-lstm-3/resolve/main/checkpoint-2136960/training_args.bin
- Command line
-
hf download hf://CLMBR/existential-there-quantifier-lstm-3/checkpoint-2136960/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/CLMBR/existential-there-quantifier-lstm-3/resolve/main/checkpoint-2136960/training_args.bin
4.28 kB
- Xet hash:
- a49c09a62ca3ae471cac78641e67c702a19a745c3797bc40285f27e9a5ad53b3
- Size of remote file:
- 4.28 kB
- SHA256:
- 610b07074d4b7ad2f72d9e549b48cd70ab62748f5958872abc0d98c834142531
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.