Instructions to use sshleifer/student_xsum_12_4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sshleifer/student_xsum_12_4 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sshleifer/student_xsum_12_4") model = AutoModelForSeq2SeqLM.from_pretrained("sshleifer/student_xsum_12_4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0edd4d6fdb6973dcc050796304e7fd52df5961d084bdc9a636f2247c0c430562
- Size of remote file:
- 1.09 GB
- SHA256:
- 9fdd83109a9c2bebb4baa73e8bf6c419c9b89e9760e5b16c352e88fbb1302f99
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