Instructions to use CLMBR/old-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/old-existential-there-quantifier-lstm-4 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import RNNForLanguageModeling model = RNNForLanguageModeling.from_pretrained("CLMBR/old-existential-there-quantifier-lstm-4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-610560/training_args.bin from CLMBR/old-existential-there-quantifier-lstm-4: direct link, hf CLI and curl.
- Browser
- Download file 4.28 kB
-
https://huggingface.co/CLMBR/old-existential-there-quantifier-lstm-4/resolve/main/checkpoint-610560/training_args.bin
- Command line
-
hf download hf://CLMBR/old-existential-there-quantifier-lstm-4/checkpoint-610560/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/CLMBR/old-existential-there-quantifier-lstm-4/resolve/main/checkpoint-610560/training_args.bin
4.28 kB
- Xet hash:
- 548beaececedc87ec74c09bd05695c394d5d30911d5ee1d221ada7a1b54eba11
- Size of remote file:
- 4.28 kB
- SHA256:
- f930cf0bd508f4875ede6d8e304294fe456ad85be587e2c8156d913c4ce47132
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.