Instructions to use BienKieu/codeT5base_SI_new with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BienKieu/codeT5base_SI_new with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("BienKieu/codeT5base_SI_new") model = AutoModelForSeq2SeqLM.from_pretrained("BienKieu/codeT5base_SI_new", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from BienKieu/codeT5base_SI_new: direct link, hf CLI and curl.
- Browser
- Download file 5.5 kB
-
https://huggingface.co/BienKieu/codeT5base_SI_new/resolve/main/training_args.bin
- Command line
-
hf download hf://BienKieu/codeT5base_SI_new/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/BienKieu/codeT5base_SI_new/resolve/main/training_args.bin
5.5 kB
- Xet hash:
- 35fe7e2433cc0f04190fb564f225a6bd54d2e473c18f885d0dad82ad1f6564f3
- Size of remote file:
- 5.5 kB
- SHA256:
- 15641b2999c45c3b5181c9581235baf1f5da62a3ee4e38e7c80c5c3b70e93003
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.