Instructions to use Jinendra/code-t5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jinendra/code-t5 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Jinendra/code-t5") model = AutoModelForSeq2SeqLM.from_pretrained("Jinendra/code-t5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Jinendra/code-t5: direct link, hf CLI and curl.
- Browser
- Download file 892 MB
-
https://huggingface.co/Jinendra/code-t5/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Jinendra/code-t5/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Jinendra/code-t5/resolve/main/pytorch_model.bin
892 MB
- Xet hash:
- a50df2b21d358a5a170bc33d88049fbe8b9067857c0f0f4f653f9edfed4985e9
- Size of remote file:
- 892 MB
- SHA256:
- 7faedc484515c7e14ab06e8c9856aeee5cb7a4a2458a2df1d6b6efb16636bb3f
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