Instructions to use sshleifer/student_xsum_12_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sshleifer/student_xsum_12_2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sshleifer/student_xsum_12_2") model = AutoModelForSeq2SeqLM.from_pretrained("sshleifer/student_xsum_12_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from sshleifer/student_xsum_12_2: direct link, hf CLI and curl.
- Browser
- Download file 954 MB
-
https://huggingface.co/sshleifer/student_xsum_12_2/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://sshleifer/student_xsum_12_2/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/sshleifer/student_xsum_12_2/resolve/main/flax_model.msgpack
954 MB
- Xet hash:
- a4ff391e919d017357f7f66fab81bd9d779dd1598bb6cb934d55e07eef6c7d0c
- Size of remote file:
- 954 MB
- SHA256:
- 265ce699645a6116d0f6f0f1233325b6b588b0d3a5c8331395b278d200a44d18
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.