Instructions to use sshleifer/student_cnn_12_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sshleifer/student_cnn_12_1 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sshleifer/student_cnn_12_1") model = AutoModelForSeq2SeqLM.from_pretrained("sshleifer/student_cnn_12_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from sshleifer/student_cnn_12_1: direct link, hf CLI and curl.
- Browser
- Download file 886 MB
-
https://huggingface.co/sshleifer/student_cnn_12_1/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://sshleifer/student_cnn_12_1/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/sshleifer/student_cnn_12_1/resolve/main/flax_model.msgpack
886 MB
- Xet hash:
- 9a2750d21ccb9203127ccf0c26f9d6915f107db734ed6b54f2e77b08e96657b8
- Size of remote file:
- 886 MB
- SHA256:
- c0eb68360ca7aa3c599a83f4635e9a3ad8d2ce715efbc1185a55f5e69503836d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.