Instructions to use sshleifer/student_xsum_6_12 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sshleifer/student_xsum_6_12 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sshleifer/student_xsum_6_12") model = AutoModelForSeq2SeqLM.from_pretrained("sshleifer/student_xsum_6_12", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from sshleifer/student_xsum_6_12: direct link, hf CLI and curl.
- Browser
- Download file 1.32 GB
-
https://huggingface.co/sshleifer/student_xsum_6_12/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://sshleifer/student_xsum_6_12/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/sshleifer/student_xsum_6_12/resolve/main/pytorch_model.bin
1.32 GB
- Xet hash:
- 1645323444d2ffaf853e5d08ebdcd0f953ef65543f956c4738cf56fdd9c5b602
- Size of remote file:
- 1.32 GB
- SHA256:
- e336c1718ec5971d7bfa34c5c6c50cf5aa622746fc8b057747c2685d37ca9e9b
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