Instructions to use floflodebilbao/LED_sum_approach with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use floflodebilbao/LED_sum_approach with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("floflodebilbao/LED_sum_approach") model = AutoModelForSeq2SeqLM.from_pretrained("floflodebilbao/LED_sum_approach", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from floflodebilbao/LED_sum_approach: direct link, hf CLI and curl.
- Browser
- Download file 5.91 kB
-
https://huggingface.co/floflodebilbao/LED_sum_approach/resolve/main/training_args.bin
- Command line
-
hf download hf://floflodebilbao/LED_sum_approach/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/floflodebilbao/LED_sum_approach/resolve/main/training_args.bin
5.91 kB
- Xet hash:
- 7afb4f10efdfe10c61bc19ab12c0ef1f51b924fa758286f577d6f9f86d740d8b
- Size of remote file:
- 5.91 kB
- SHA256:
- af65e1f5398228dd0375483a8c0507b084e402c58c414e24d321eb581e13f4b3
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