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-1679040/optimizer.pt from CLMBR/existential-there-quantifier-lstm-2: direct link, hf CLI and curl.
- Browser
- Download file 544 MB
-
https://huggingface.co/CLMBR/existential-there-quantifier-lstm-2/resolve/main/checkpoint-1679040/optimizer.pt
- Command line
-
hf download hf://CLMBR/existential-there-quantifier-lstm-2/checkpoint-1679040/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/CLMBR/existential-there-quantifier-lstm-2/resolve/main/checkpoint-1679040/optimizer.pt
544 MB
- Xet hash:
- 8a35abed47de33ed776a7e2407d12bdde745d7c3876e4c27fd87fc7b57f9a42b
- Size of remote file:
- 544 MB
- SHA256:
- a0a80cd1004da08aca183cb87cee946b06fca943290d4b0816e06e62cdf7f8d6
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