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 flax_model.msgpack 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/flax_model.msgpack
- Command line
-
hf download hf://sshleifer/student_xsum_6_12/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/sshleifer/student_xsum_6_12/resolve/main/flax_model.msgpack
1.32 GB
- Xet hash:
- fd1986e746aa37ea36d6925d9f9b68f24b5f77baf40577f065eaffe003d0ca9c
- Size of remote file:
- 1.32 GB
- SHA256:
- 77277a083c441515044f34827dd12603872bd000e433fc636376e62067ee8be9
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