Instructions to use Salesforce/codet5p-16b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Salesforce/codet5p-16b with Transformers:
# Load model directly from transformers import AutoModelForSeq2SeqLM model = AutoModelForSeq2SeqLM.from_pretrained("Salesforce/codet5p-16b", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Onnx-version or Compatability to T5forConditionalGeneration
#1
by michaelfeil - opened
Looking forward to convert this model to a faster version for accelerated inference. (2B, 6B, 16B)
Options:
- Ctranslate2: Support for all architectures such as T5, mT5, GPT-J, GPT-2,.. As with codet5p-770m-py, this runs now at high speed and 1320MiB cuda footprint, batch inference which I think is awesome. https://huggingface.co/michaelfeil/ct2fast-codet5p-770m-py -> Any way to convert this to a T5 architecture?
- Onnx -> ORT or Nvidia TensorRT -> CodeT5pModuleConfig has no Onnx implementation, e.g. see Codegen2
Any advice?