Instructions to use kejian/cpsc-origcond with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kejian/cpsc-origcond with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("kejian/cpsc-origcond") model = AutoModel.from_pretrained("kejian/cpsc-origcond", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from kejian/cpsc-origcond: direct link, hf CLI and curl.
- Browser
- Download file 510 MB
-
https://huggingface.co/kejian/cpsc-origcond/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://kejian/cpsc-origcond/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/kejian/cpsc-origcond/resolve/main/pytorch_model.bin
510 MB
- Xet hash:
- c16fcd18ebf6fea4240ef23e7f1a3f9841aabf89ebca9bc4d03e9a63a08f17d7
- Size of remote file:
- 510 MB
- SHA256:
- 816652d736fe95c88a0f844652f9a2e958d8c32616f499e8f08873faa1e09dd3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.