Instructions to use sshleifer/student_xsum_12_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sshleifer/student_xsum_12_3 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sshleifer/student_xsum_12_3") model = AutoModelForSeq2SeqLM.from_pretrained("sshleifer/student_xsum_12_3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from sshleifer/student_xsum_12_3: direct link, hf CLI and curl.
- Browser
- Download file 1.02 GB
-
https://huggingface.co/sshleifer/student_xsum_12_3/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://sshleifer/student_xsum_12_3/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/sshleifer/student_xsum_12_3/resolve/main/flax_model.msgpack
1.02 GB
- Xet hash:
- 5421eec5f6122d6447c72f854baeb979318dbbe046ec4a98a22192bd920f116e
- Size of remote file:
- 1.02 GB
- SHA256:
- 01b468a33531873df008540f89b096291e31c57a54fea310d99d4aedb386936b
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