Title: SantaCoder: Advancing Large Language Models for Coding Applications
SantaCoder: Overview
SantaCoder is a groundbreaking project that focuses on the responsible development of large language models for coding applications. The project was spearheaded by a group of 41 authors, and its technical report titled “SantaCoder: don’t reach for the stars!” has been published on the arXiv platform under the identifier [2301.03988].
Progress Made
The report shares insights into the progress made until December 2022, particularly highlighting the Personally Identifiable Information (PII) redaction pipeline, extensive experiments to refine the model architecture, and the search for advanced preprocessing methods for training data. The project trained 1.1B parameter models across Java, JavaScript, and Python codebases, and these models performed impressively on the MultiPL-E text-to-code benchmark.
Notable Features and Findings
The project made counterintuitive findings, such as the discovery that models trained on repositories with fewer GitHub stars yielded better results than those with more stars. The best-performing model from the BigCode project even surpasses other models like InCoder-6.7B and CodeGen-Multi-2.7B, despite its smaller size. All models are made available under an OpenRAIL license at a specified URL to support open scientific advancement.
Real-World Applications
The SantaCoder project has significant implications for coding applications, such as the development of more efficient and accurate code completion tools. This can lead to increased productivity and reduced errors in software development. Additionally, the project’s focus on responsible development can help ensure that these language models are used ethically and do not perpetuate biases.
