StarCoder 2 - BigCode's second-generation open-source code model

What is StarCoder 2?

StarCoder 2 is a next-generation large-scale code language model developed by the BigCode project (supported by Hugging Face and ServiceNow) in collaboration with Nvidia. It is trained using 3.3 to 4.3 trillion code tokens from The Stack v2 dataset, encompassing over 600 programming languages, and is designed to provide robust support for code completion, editing, and inference tasks. This series of models further expands and improves upon the previous generation of StarCoder models, offering models of different scales, including 3B (3 billion parameters), 7B (7 billion parameters), and 15B (15 billion parameters) versions.

StarCoder 2 official website entrance

• Hugging Face model set:https://huggingface.co/collections/bigcode/starcoder2-65de6da6e87db3383572be1a
• The Stack v2 dataset:https://huggingface.co/datasets/bigcode/the-stack-v2
• GitHub address:https://github.com/bigcode-project/starcoder2
• Research Papers:https://drive.google.com/file/d/17iGn3c-sYNiLyRSY-A85QOzgzGnGiVI3/view?pli=1

Key features of StarCoder 2

• Large-scale training datasetsThe training dataset for StarCoder2 (The Stack v2) is based on the source code archive of Software Heritage (a non-profit organization that provides code archiving services), a massive codebase containing over 600 programming languages. In addition, it integrates other high-quality data sources such as GitHub Pull Requests, Kaggle, and Jupter Notebook code documents, making the training set four times larger than the first-generation StarCoder.
  
• Diverse model sizesStarCoder2 offers models of different sizes, including versions with 3B (3 billion parameters), 7B (7 billion parameters), and 15B (15 billion parameters), to suit different application needs and resource constraints.
• High performanceIn multiple code LLM benchmark tests, StarCoder 2 models performed exceptionally well on code completion, editing, and inference tasks. In particular, StarCoder2-3B and StarCoder2-15B showed leading performance among other models of similar scale (such as DeepSeekCoder, StableCode, CodeLlama, etc.).
  
• Openness and transparencyThe model weights for StarCoder 2 are released under the OpenRAIL license, ensuring complete transparency of the training data. This allows researchers and developers to independently audit the model and use it freely, provided they comply with the license agreement.
• Responsible development practicesStarCoder 2 was developed in accordance with responsible AI principles, including the protection of personal privacy, security considerations, and vigilance against potential social and representative biases.

StarCoder 2 Features

• Code completionStarCoder 2 provides code completion suggestions to developers, helping them write and optimize code faster. This includes automatically completing code snippets, function and class definitions, and more.
• Code editing and refactoringThe model can assist developers in code editing, including fixing bugs, improving code structure and style, and performing code refactoring tasks.
• Code reasoningStarCoder 2 has the ability to understand code logic and perform code reasoning, and can handle more complex programming tasks, such as understanding the expected behavior of code and generating corresponding code.
• Cross-language supportBecause the training dataset contains multiple programming languages, StarCoder 2 can support code generation and understanding in multiple languages, making it particularly useful in multilingual projects.
• Interactive programming assistanceStarCoder 2 can act as an interactive programming assistant, communicating with developers through natural language, understanding their intentions, and providing corresponding code solutions.
• Documentation and annotation generationThe model can also automatically generate documentation and comments based on the code content, helping developers better understand and maintain the code.
• Security and privacy protectionStarCoder 2 was designed with security and privacy in mind, avoiding the inclusion of sensitive information in the generated code and reducing potential security vulnerabilities.
• Openness and auditabilityStarCoder 2's model weights and training data are open, allowing researchers and developers to audit them, ensuring the model's transparency and trustworthiness.