StarCoder

About StarCoder

A Next-Gen Code Generation Model

StarCoder is a large language model built by the BigCode project, designed to generate and complete source code across more than 80 programming languages. With 15.5 billion parameters and a focus on fill-in-the-middle training, StarCoder supports advanced code generation tasks and assists developers with high-quality, context-aware completions.

Built for Developers and Researchers

Designed to run efficiently on modern hardware and deployed via Hugging Face, StarCoder is accessible for both developers seeking code assistance and researchers evaluating open-source coding models.

Features and Capabilities

Multi-Language Support

StarCoder was trained on the deduplicated dataset The Stack (v1.2) and includes code from over 80 programming languages. Whether you’re working in Python, JavaScript, C++, or niche languages, the model can adapt to your environment.

Fill-in-the-Middle Objective

Unlike traditional left-to-right generation, StarCoder supports fill-in-the-middle (FIM) tasks. This allows developers to insert missing blocks of code between existing sections, enhancing the flexibility of auto-completion and snippet generation.

Technical Highlights

Advanced Model Architecture

StarCoder uses GPT-2 architecture with Multi-Query Attention and a large 8192-token context window. It’s optimized for understanding and generating long, structured code sequences, making it ideal for real-world software development tasks.

Massive Training Dataset

Trained on over 1 trillion tokens, StarCoder was built using 512 A100 GPUs over a 24-day training cycle. The dataset was filtered to exclude opt-out content and includes only permissively licensed code.

Use Cases and Applications

Code Completion and Generation

StarCoder can generate new functions, complete unfinished code, and assist in writing boilerplate or repetitive logic. It's a helpful tool for prototyping, learning, and automating development workflows.

Research and Experimentation

As an open-access model under the BigCode OpenRAIL-M license, StarCoder is ideal for academic research, benchmarking, and building downstream applications for coding tasks.

Compatible with Transformers

Developers can use StarCoder directly via Hugging Face Transformers with just a few lines of code. It’s fully accessible with GPU acceleration for local or cloud deployment.

Licensing and Responsible Use

OpenRAIL-M License

StarCoder is released under the BigCode OpenRAIL-M license. While the training data was sourced from openly licensed code, users are responsible for ensuring proper attribution and respecting license requirements when using generated code.

Attribution and Transparency

A searchable index is available to trace the origin of any generated code segments, allowing developers to provide proper attribution when necessary.

Evaluation and Performance

Competitive Benchmarks

StarCoder has demonstrated strong performance on coding benchmarks, including:

• HumanEval (pass@1): 0.408 (prompted)
• MBPP (pass@1): 0.527
• MultiPL (Java, C++, Go): Competitive across multiple languages

These scores highlight the model’s effectiveness across general-purpose programming tasks.