Instructions to use sshleifer/student_xsum_3_12 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sshleifer/student_xsum_3_12 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sshleifer/student_xsum_3_12") model = AutoModelForSeq2SeqLM.from_pretrained("sshleifer/student_xsum_3_12", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from sshleifer/student_xsum_3_12: direct link, hf CLI and curl.
- Browser
- Download file 1.17 GB
-
https://huggingface.co/sshleifer/student_xsum_3_12/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://sshleifer/student_xsum_3_12/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/sshleifer/student_xsum_3_12/resolve/main/flax_model.msgpack
1.17 GB
- Xet hash:
- 09c694138ca020d7ff11d85b6d98476bddc94706ce13ac43228e791405b7bd3f
- Size of remote file:
- 1.17 GB
- SHA256:
- e7c6aa90b20426fff7b7ce6c1275b8d56d09685df7b0cae923a31a354673f7cc
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