Instructions to use kejian/cpsc-origcond-5repeat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kejian/cpsc-origcond-5repeat with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("kejian/cpsc-origcond-5repeat") model = AutoModel.from_pretrained("kejian/cpsc-origcond-5repeat", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from kejian/cpsc-origcond-5repeat: direct link, hf CLI and curl.
- Browser
- Download file 3.45 kB
-
https://huggingface.co/kejian/cpsc-origcond-5repeat/resolve/main/training_args.bin
- Command line
-
hf download hf://kejian/cpsc-origcond-5repeat/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/kejian/cpsc-origcond-5repeat/resolve/main/training_args.bin
3.45 kB
- Xet hash:
- c0b0a6466328fc66f896da5818393558c69cca9c042c909e36642b3da26ad76a
- Size of remote file:
- 3.45 kB
- SHA256:
- bf78feee5a1bc39dcdbadebec69bd471d95bdb4e4f5fe4b0a4486bdacf5be1b5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.