StarCoder2: The Open Coding Model Trained on 600+ Programming Languages
BigCode's StarCoder2-15B is trained on The Stack v2 covering 619 programming languages, bringing fill-in-the-middle completion and strong HumanEval scores to a model you can run without a vendor contract.
StarCoder2 is the second generation of the BigCode project's open coding models, released in February 2024. The project is a collaboration between Hugging Face, ServiceNow, and the broader open-source research community. The flagship 15B model is trained on The Stack v2 - a cleaned and deduplicated dataset of source code across 619 programming languages.
Model Variants
BigCode released three sizes:
StarCoder2-3B - fits on a single consumer GPU (12GB VRAM), good for autocomplete
StarCoder2-7B - balanced size for most IDE integration use cases
StarCoder2-15B - best quality, recommended for generation tasks
All three are released under the BigCode Open RAIL-M license, which allows commercial use with attribution.
Team workspace
Ship faster with chat, meetings, and projects in one place — Zlyqor.
The instruct-tuned version (StarCoder2-15B-Instruct-v0.1) reaches competitive scores on instruction following, though newer models like Qwen2.5-Coder have surpassed it on raw HumanEval. StarCoder2 remains relevant for its breadth of language coverage and fill-in-the-middle support.
Fill-in-the-Middle Training
StarCoder2 is trained with the FIM (fill-in-the-middle) objective, using a special set of tokens that makes code completion feel natural:
You now have local, private tab completion that never sends your code to a third-party server.
Hugging Face Code Leaderboard
The Big Code Models Leaderboard tracks models on HumanEval, MBPP, MultiPL-E, and DS-1000. As of early 2025, StarCoder2-15B sits in the top 10 for models under 20B parameters, though the Qwen2.5-Coder family has moved above it in absolute scores.
When to Choose StarCoder2
You need maximum language breadth (619 languages vs ~100 for most models)
You want IDE tab completion with FIM and full local privacy
You need a model in the 7 - 15B range that balances quality and hardware requirements
You want to build on top of a permissively licensed, academically documented model
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Mahmudul Haque Qudrati
CEO & ML Engineer
CEO and ML Engineer at Pristren. Builds AI-powered software for teams and writes about machine learning, LLMs, developer tools, and practical AI applications.
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