Instructions to use paulh27/xsum_aligned_smallT5_cont1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use paulh27/xsum_aligned_smallT5_cont1 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("paulh27/xsum_aligned_smallT5_cont1") model = AutoModelForSeq2SeqLM.from_pretrained("paulh27/xsum_aligned_smallT5_cont1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from paulh27/xsum_aligned_smallT5_cont1: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/paulh27/xsum_aligned_smallT5_cont1/resolve/main/training_args.bin
- Command line
-
hf download hf://paulh27/xsum_aligned_smallT5_cont1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/paulh27/xsum_aligned_smallT5_cont1/resolve/main/training_args.bin
5.11 kB
- Xet hash:
- 38e95e184e1b470bd25432b6d92eb45df620fc29b6d4c49a25e0e526c4501b4c
- Size of remote file:
- 5.11 kB
- SHA256:
- e557b53c74e1027612086dc0ee080c222994addac2e26e72314fc8b9f8658e14
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