Narrow Transformer: Mono-Lingual Code SLM for Desktop

Abstract

This paper presents NT-Java-1.1B, an open-source specialized code language model built on StarCoderBase-1.1B, designed for coding tasks in Java programming. NT-Java-1.1B achieves state-of-the-art performance, surpassing its base model and majority of other models of similar size on MultiPL-E Java code benchmark. While there have been studies on extending large, generic pre-trained models to improve proficiency in specific programming languages like Python, similar investigations on small code models for other programming languages are lacking. Large code models require specialized hardware like GPUs for inference, highlighting the need for research into building small code models that can be deployed on developer desktops. This paper addresses this research gap by focusing on the development of a small Java code model, NT-Java-1.1B, and its quantized versions, which performs comparably to open models around 1.1B on MultiPL-E Java code benchmarks, making them ideal for desktop deployment. This paper establishes the foundation for specialized models across languages and sizes for a family of NT Models.

Cite

Text

Rathinasamy et al. "Narrow Transformer: Mono-Lingual Code SLM for Desktop." NeurIPS 2024 Workshops: AFM, 2024.

Markdown

[Rathinasamy et al. "Narrow Transformer: Mono-Lingual Code SLM for Desktop." NeurIPS 2024 Workshops: AFM, 2024.](https://mlanthology.org/neuripsw/2024/rathinasamy2024neuripsw-narrow/)

BibTeX

@inproceedings{rathinasamy2024neuripsw-narrow,
  title     = {{Narrow Transformer: Mono-Lingual Code SLM for Desktop}},
  author    = {Rathinasamy, Kamalkumar and Balaji, A J and Kumar, Ankush and Gayari, Gagan and K, Harshini and Mondal, Rajab Ali and Sreenivasa Raghavan, K S and Singh, Swayam and Tarafdar, Mohammed Rafee},
  booktitle = {NeurIPS 2024 Workshops: AFM},
  year      = {2024},
  url       = {https://mlanthology.org/neuripsw/2024/rathinasamy2024neuripsw-narrow/}
}