Instructions to use SKaup/code_t5_small_optimized_secured with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SKaup/code_t5_small_optimized_secured with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("SKaup/code_t5_small_optimized_secured") model = AutoModelForSeq2SeqLM.from_pretrained("SKaup/code_t5_small_optimized_secured", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from SKaup/code_t5_small_optimized_secured: direct link, hf CLI and curl.
- Browser
- Download file 242 MB
-
https://huggingface.co/SKaup/code_t5_small_optimized_secured/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://SKaup/code_t5_small_optimized_secured/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/SKaup/code_t5_small_optimized_secured/resolve/main/pytorch_model.bin
242 MB
- Xet hash:
- db0cfa8cfd43ef64fce24406945ceda9cdb099b48d7c8bce00fd600e114d6543
- Size of remote file:
- 242 MB
- SHA256:
- 0ef28be34a480aa6c9a4fe1a8abe3c1a7a1ae3637d34b682d797357f530ec1b8
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