Instructions to use scott156/LED-Large-NSPCC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use scott156/LED-Large-NSPCC with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("scott156/LED-Large-NSPCC") model = AutoModelForSeq2SeqLM.from_pretrained("scott156/LED-Large-NSPCC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from scott156/LED-Large-NSPCC: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/scott156/LED-Large-NSPCC/resolve/main/training_args.bin
- Command line
-
hf download hf://scott156/LED-Large-NSPCC/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/scott156/LED-Large-NSPCC/resolve/main/training_args.bin
5.11 kB
- Xet hash:
- e5feb54cc5de671f751f244ea1a7d9f1a3f242b0307162bff9054c7a16cfe389
- Size of remote file:
- 5.11 kB
- SHA256:
- e5cdfbc65254516d8d5a2088cddc3690cb2309da5b3e7f690a56d0bf4d6db59d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.