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 pytorch_model.bin 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/pytorch_model.bin
- Command line
-
hf download hf://sshleifer/student_xsum_12_1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/sshleifer/student_xsum_12_1/resolve/main/pytorch_model.bin
886 MB
- Xet hash:
- 196f17192e8c2f559a4cfcec87d1cc5b15f5d3a685aee7b7a0760df427f432cf
- Size of remote file:
- 886 MB
- SHA256:
- 7c0124b53720c31e565da5f281f836d0509300c1f19812ced5e638e2025abd61
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