Instructions to use sshleifer/student_xsum_12_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sshleifer/student_xsum_12_3 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sshleifer/student_xsum_12_3") model = AutoModelForSeq2SeqLM.from_pretrained("sshleifer/student_xsum_12_3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5b007fd0a8d0bb8f52c5a8e62b60289edf2cb8cab30a0cea990601efbcc0c31d
- Size of remote file:
- 1.02 GB
- SHA256:
- 1679b64a35f24a551fd86f7fb0e07b57ef4bc33f26bc2e47dfe54f508695974c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.