Instructions to use sshleifer/student_xsum_9_9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sshleifer/student_xsum_9_9 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sshleifer/student_xsum_9_9") model = AutoModelForSeq2SeqLM.from_pretrained("sshleifer/student_xsum_9_9", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from sshleifer/student_xsum_9_9: direct link, hf CLI and curl.
- Browser
- Download file 1.27 GB
-
https://huggingface.co/sshleifer/student_xsum_9_9/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://sshleifer/student_xsum_9_9/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/sshleifer/student_xsum_9_9/resolve/main/flax_model.msgpack
1.27 GB
- Xet hash:
- 376d14880d197319fa6565741c753e9119693964304ed1268dcdb909cca67699
- Size of remote file:
- 1.27 GB
- SHA256:
- 07213b4ca84082c2032224f0308e8246039365ad206db87195b8807d3cdcd604
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