Instructions to use ra1/t5-small-finetuned-xsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ra1/t5-small-finetuned-xsum with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ra1/t5-small-finetuned-xsum") model = AutoModelForSeq2SeqLM.from_pretrained("ra1/t5-small-finetuned-xsum", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ra1/t5-small-finetuned-xsum: direct link, hf CLI and curl.
- Browser
- Download file 242 MB
-
https://huggingface.co/ra1/t5-small-finetuned-xsum/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ra1/t5-small-finetuned-xsum/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ra1/t5-small-finetuned-xsum/resolve/main/pytorch_model.bin
242 MB
- Xet hash:
- eb86bad01d7644fd1dcb87ac0e5ddcc576e60ac8c5a00107137bd2574bf6f5a1
- Size of remote file:
- 242 MB
- SHA256:
- 4d98be8ad1c5d07168dbd0ee6f80dcb4d5eac75f8013778754259c913b62bbf9
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