Instructions to use burcusu/ct2-codet5p-220m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use burcusu/ct2-codet5p-220m with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("burcusu/ct2-codet5p-220m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Verified signature# CT2Fast-CodeT5P-220M: Fast Inference Model Fast-Inference with Ctranslate2 Quantized version of Salesforce/codet5p-220m !ct2-transformers-converter --model Salesforce/codet5p-220m --output_dir /content/sample_data/codet5p-220m-ct2 --quantization float16 ## Licence and Other Remarks: This model is just a quantized version of Codet5p-220m. Licence conditions are intended to be idential to original huggingface repo. --- license: bsd-3-clause --- # CodeT5+ 220M ## Model description [CodeT5+](https://github.com/salesforce/CodeT5/tree/main/CodeT5+) is a new family of open code large language models with an encoder-decoder architecture that can flexibly operate in different modes (i.e. _encoder-only_, _decoder-only_, and _encoder-decoder_) to support a wide range of code understanding and generation tasks. It is introduced in the paper: [CodeT5+: Open Code Large Language Models for Code Understanding and Generation](https://arxiv.org/pdf/2305.07922.pdf) by [Yue Wang](https://yuewa…a4c7e6b -
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