Instructions to use sshleifer/student_xsum_12_9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sshleifer/student_xsum_12_9 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sshleifer/student_xsum_12_9") model = AutoModelForSeq2SeqLM.from_pretrained("sshleifer/student_xsum_12_9", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from sshleifer/student_xsum_12_9: direct link, hf CLI and curl.
- Browser
- Download file 1.42 GB
-
https://huggingface.co/sshleifer/student_xsum_12_9/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://sshleifer/student_xsum_12_9/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/sshleifer/student_xsum_12_9/resolve/main/pytorch_model.bin
1.42 GB
- Xet hash:
- 77ad91ccc431f6bfe23b2d747b97d95a1338527e3406effac1a1979da738b5d2
- Size of remote file:
- 1.42 GB
- SHA256:
- d1b426192f17158ce6e7de95bb54f64e4554abc4aab20d36a31507c217e7f42c
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