Model card
StarCoder2 15B is a specialized large language model engineered specifically for code intelligence, trained on the massive and diverse Stack v2 dataset. Unlike general-purpose models that attempt to balance chat and reasoning, StarCoder2 focuses on high-fidelity code completion, refactoring, and multi-language understanding. For developers, the 15B parameter scale represents a sweet spot: it offers significantly higher logic density and context awareness than smaller 3B or 7B models, yet remains efficient enough to be deployed on consumer-grade or mid-range enterprise hardware. It excels in repository-level tasks and supports a vast array of programming languages, making it a robust backbone for building custom IDE extensions, automated code review tools, or local Copilot-style assistants. Because it is built on the BigCode framework, it offers a more transparent training lineage compared to many closed-source competitors, providing a reliable foundation for production-grade developer tooling.
Model files and versions
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We recommend using the ModelScope CLI or SDK. Install ModelScope first, then choose a full snapshot, single file, SDK or Git LFS workflow.
bigcode/starcoder2-15bInstall the CLI and SDK dependency before downloading.
pip install modelscopeDownload the complete weights, configuration and model card.
modelscope download --model bigcode/starcoder2-15bREADME.md is used as an example; replace it with another repository file when needed.
modelscope download --model bigcode/starcoder2-15b README.md --local_dir ./dirUseful in Python projects and automation scripts.
from modelscope import snapshot_download
model_dir = snapshot_download('bigcode/starcoder2-15b')Make sure Git LFS is installed correctly.
git lfs install
git clone https://www.modelscope.cn/bigcode/starcoder2-15b.gitFetch the repository structure first, then pull large files when needed.
GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/bigcode/starcoder2-15b.gitHow to use
- 01Step 1
Read the model card and source information.
- 02Step 2
Start with a small, non-sensitive evaluation.
- 03Step 3
Review quality, licensing and usage limits.
- 04Step 4
Adopt it only after validation.
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