Instructions to use CLMBR/old-rel-cl-lstm-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CLMBR/old-rel-cl-lstm-3 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import RNNForLanguageModeling model = RNNForLanguageModeling.from_pretrained("CLMBR/old-rel-cl-lstm-3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-610552/pytorch_model.bin from CLMBR/old-rel-cl-lstm-3: direct link, hf CLI and curl.
- Browser
- Download file 272 MB
-
https://huggingface.co/CLMBR/old-rel-cl-lstm-3/resolve/main/checkpoint-610552/pytorch_model.bin
- Command line
-
hf download hf://CLMBR/old-rel-cl-lstm-3/checkpoint-610552/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/CLMBR/old-rel-cl-lstm-3/resolve/main/checkpoint-610552/pytorch_model.bin
272 MB
- Xet hash:
- 1737fa4d2591eaeb4547b1faa1a11d416c2be1961c90f435dec69bbc54602536
- Size of remote file:
- 272 MB
- SHA256:
- 1ec898ee3e93665883a29ee7e2541b48446b0962d0ff90973f36406ac9a49a7f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.