Instructions to use floflodebilbao/LED_sum_challenge2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use floflodebilbao/LED_sum_challenge2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("floflodebilbao/LED_sum_challenge2") model = AutoModelForSeq2SeqLM.from_pretrained("floflodebilbao/LED_sum_challenge2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from floflodebilbao/LED_sum_challenge2: direct link, hf CLI and curl.
- Browser
- Download file 5.91 kB
-
https://huggingface.co/floflodebilbao/LED_sum_challenge2/resolve/main/training_args.bin
- Command line
-
hf download hf://floflodebilbao/LED_sum_challenge2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/floflodebilbao/LED_sum_challenge2/resolve/main/training_args.bin
5.91 kB
- Xet hash:
- ae75d09e3636758f6889bc424823e733207772d246a947e12ab98da64052aa3e
- Size of remote file:
- 5.91 kB
- SHA256:
- 1c4182bd7e603c309f839aff947a33c91a19c11ba1a67580b124aab30b08a839
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