This paper introduces Safurai-Csharp, an open-source model designed to specialize in the generation, completion, and debugging of C# code. Safurai-Csharp is built upon the novel CodeLlama 34B model and leverages the EvolInstruct technique, creating a refined and expanded dataset for its fine-tuning process. The results of its performance, a notable score of 56.33% on the Manual MultiPL-E benchmark (Zero-Shot, Pass@1), signal its high capacity to streamline developers' workflows and aid code learning. It shows promise in setting new stakes in the landscape of open-source C# LLMs and hopes to inspire more inclusive and wide-ranging development in the field of language-specific LLMs.
@article{arxiv.2311.03243,
title = {Safurai-Csharp: Harnessing Synthetic Data to improve language-specific Code LLM},
author = {Davide Cifarelli and Leonardo Boiardi and Alessandro Puppo and Leon Jovanovic},
journal= {arXiv preprint arXiv:2311.03243},
year = {2023}
}