Instructions to use CLMBR/passive-lstm-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CLMBR/passive-lstm-1 with Transformers:
# Load model directly from transformers import RNNForLanguageModeling model = RNNForLanguageModeling.from_pretrained("CLMBR/passive-lstm-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-1602720/training_args.bin from CLMBR/passive-lstm-1: direct link, hf CLI and curl.
- Browser
- Download file 4.22 kB
-
https://huggingface.co/CLMBR/passive-lstm-1/resolve/299ce449b188e7f56d9953f65af98c5877633489/checkpoint-1602720/training_args.bin
- Command line
-
hf download hf://CLMBR/passive-lstm-1@299ce449b188e7f56d9953f65af98c5877633489/checkpoint-1602720/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/CLMBR/passive-lstm-1/resolve/299ce449b188e7f56d9953f65af98c5877633489/checkpoint-1602720/training_args.bin
4.22 kB
- Xet hash:
- b0a65e33d69c918daf997d7e90060a70dfc2940708d5d86dc04c2a1bb8990406
- Size of remote file:
- 4.22 kB
- SHA256:
- 69e338919185e1b74911cb16c72ae8eb67a5a9a03b9031ad27ab95a8bfa5fbc9
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