Instructions to use sshleifer/student_xsum_12_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sshleifer/student_xsum_12_1 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sshleifer/student_xsum_12_1") model = AutoModelForSeq2SeqLM.from_pretrained("sshleifer/student_xsum_12_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from sshleifer/student_xsum_12_1: direct link, hf CLI and curl.
- Browser
- Download file 886 MB
-
https://huggingface.co/sshleifer/student_xsum_12_1/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://sshleifer/student_xsum_12_1/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/sshleifer/student_xsum_12_1/resolve/main/flax_model.msgpack
886 MB
- Xet hash:
- 6f59b05fc15d484ece85c7a2ddd565ec13b6731dde711e4b40327ff0b42812f7
- Size of remote file:
- 886 MB
- SHA256:
- d1c6f6b0faa4853576fbd6ed62327fd8c3b3d6f4a64d5de3209146252540da78
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