Instructions to use sshleifer/student_xsum_12_9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sshleifer/student_xsum_12_9 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sshleifer/student_xsum_12_9") model = AutoModelForSeq2SeqLM.from_pretrained("sshleifer/student_xsum_12_9", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from sshleifer/student_xsum_12_9: direct link, hf CLI and curl.
- Browser
- Download file 1.42 GB
-
https://huggingface.co/sshleifer/student_xsum_12_9/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://sshleifer/student_xsum_12_9/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/sshleifer/student_xsum_12_9/resolve/main/flax_model.msgpack
1.42 GB
- Xet hash:
- 47d6bec9519925cd478efbdddfc85d582ebebb8219814f0d90dceabc928189e5
- Size of remote file:
- 1.42 GB
- SHA256:
- 12a17022862b56c7360dd19c357628ce54e63e01c582560ad430fe50d79bb1ad
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