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-1602720/pytorch_model.bin from CLMBR/existential-there-quantifier-lstm-3: direct link, hf CLI and curl.
- Browser
- Download file 272 MB
-
https://huggingface.co/CLMBR/existential-there-quantifier-lstm-3/resolve/main/checkpoint-1602720/pytorch_model.bin
- Command line
-
hf download hf://CLMBR/existential-there-quantifier-lstm-3/checkpoint-1602720/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/CLMBR/existential-there-quantifier-lstm-3/resolve/main/checkpoint-1602720/pytorch_model.bin
272 MB
- Xet hash:
- 54481526a0082653f553a15b1e30171e7028fb3d093b8ab254dcaa9338378d05
- Size of remote file:
- 272 MB
- SHA256:
- e762139f7d2588c03719088d33c143e10ee28d1acf5474ff301894b1e89aa394
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.