Instructions to use SXUCFN/CFNParser-RI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SXUCFN/CFNParser-RI with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SXUCFN/CFNParser-RI") model = AutoModel.from_pretrained("SXUCFN/CFNParser-RI", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from SXUCFN/CFNParser-RI: direct link, hf CLI and curl.
- Browser
- Download file 799 MB
-
https://huggingface.co/SXUCFN/CFNParser-RI/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://SXUCFN/CFNParser-RI/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/SXUCFN/CFNParser-RI/resolve/main/pytorch_model.bin
799 MB
- Xet hash:
- ec18586b432542d8e7f6295bb02b31c271f7cdd01dba5372b78e10cad32eb3c9
- Size of remote file:
- 799 MB
- SHA256:
- 751ad90d5961198f2f7417bd7b58a82b24fb555e1ab33e9d7645b8bd24917a46
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.