Instructions to use sshleifer/student_cnn_6_6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sshleifer/student_cnn_6_6 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sshleifer/student_cnn_6_6") model = AutoModelForSeq2SeqLM.from_pretrained("sshleifer/student_cnn_6_6", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from sshleifer/student_cnn_6_6: direct link, hf CLI and curl.
- Browser
- Download file 920 MB
-
https://huggingface.co/sshleifer/student_cnn_6_6/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://sshleifer/student_cnn_6_6/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/sshleifer/student_cnn_6_6/resolve/main/flax_model.msgpack
920 MB
- Xet hash:
- bcdaf2f973f5e14378476f9c3aaf49511adf4d497b6c5db3b16c579125b69490
- Size of remote file:
- 920 MB
- SHA256:
- 9fa82aae33fb2b177d8f3dfcda48a780a8394a8628069852806ca5adf7eee78a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.