Instructions to use CLMBR/pp-mod-subj-lstm-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CLMBR/pp-mod-subj-lstm-1 with Transformers:
# Load model directly from transformers import RNNForLanguageModeling model = RNNForLanguageModeling.from_pretrained("CLMBR/pp-mod-subj-lstm-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-76320/training_args.bin from CLMBR/pp-mod-subj-lstm-1: direct link, hf CLI and curl.
- Browser
- Download file 4.22 kB
-
https://huggingface.co/CLMBR/pp-mod-subj-lstm-1/resolve/8939d69f81ccdee80536baac42321e94362ddd9f/checkpoint-76320/training_args.bin
- Command line
-
hf download hf://CLMBR/pp-mod-subj-lstm-1@8939d69f81ccdee80536baac42321e94362ddd9f/checkpoint-76320/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/CLMBR/pp-mod-subj-lstm-1/resolve/8939d69f81ccdee80536baac42321e94362ddd9f/checkpoint-76320/training_args.bin
4.22 kB
- Xet hash:
- ae9332444d243efd5a5dc06bd14591c9e3b5976aa6a11ab5c8055078c7d6f0be
- Size of remote file:
- 4.22 kB
- SHA256:
- 1aefdc5c8a6e217d069e5d3886799079d4ca5720335cfa37dbe8cb77239f5e45
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.