Instructions to use sshleifer/student_xsum_9_12 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sshleifer/student_xsum_9_12 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sshleifer/student_xsum_9_12") model = AutoModelForSeq2SeqLM.from_pretrained("sshleifer/student_xsum_9_12", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from sshleifer/student_xsum_9_12: direct link, hf CLI and curl.
- Browser
- Download file 1.47 GB
-
https://huggingface.co/sshleifer/student_xsum_9_12/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://sshleifer/student_xsum_9_12/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/sshleifer/student_xsum_9_12/resolve/main/flax_model.msgpack
1.47 GB
- Xet hash:
- bd4b40258115ef7499d442c41bc7d9f330419c46b4fee0f1fcdfb38bf2cf6b34
- Size of remote file:
- 1.47 GB
- SHA256:
- c790bdc548fc5fcc7623bb659f6b4e55d04b8ab28c82f4484e666149d737a6a0
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