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:
# pip install -U transformers accelerate # 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
Download flax_model.msgpack from sshleifer/student_xsum_12_4: direct link, hf CLI and curl.
- Browser
- Download file 1.09 GB
-
https://huggingface.co/sshleifer/student_xsum_12_4/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://sshleifer/student_xsum_12_4/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/sshleifer/student_xsum_12_4/resolve/main/flax_model.msgpack
1.09 GB
- Xet hash:
- d6397b438eb1ce925945d38e18563f21cbf0028f40890a5967605e9ef525f891
- Size of remote file:
- 1.09 GB
- SHA256:
- 354dba9f7be3458b7ca7de92ee75cb53bc0d9ccaf63a564e7b0636f5c10f5148
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