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Code Models · 2023

Open Code Models (StarCoder/Code Llama/DeepSeek-Coder/Qwen-Coder)

Raymond Li, Harm de Vries, Leandro von Werra, Baptiste Rozière, Gabriel Synnaeve, Daya Guo, Binyuan Hui

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This family delivered openly released, permissively licensed code LLMs trained on large curated code corpora, with StarCoder pairing a 15B model and an 8K context with documented, opt-out, license-filtered training data to make capable code models usable and inspectable outside the big labs.

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Plain-language summary

StarCoder was trained on The Stack, a permissively licensed GitHub corpus with PII redaction and an opt-out mechanism, and supports fill-in-the-middle plus an 8K-token window for repository-scale context. Code Llama (continued-pretraining of Llama 2 on code, long-context and instruct variants) and DeepSeek-Coder (repo-level pretraining with strong data curation) extended the same open, code-specialized line. Together they gave practitioners high-quality open-weight coding models with transparent data provenance, enabling local deployment, fine-tuning, and reproducible evaluation.

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Provenance

Record ID
P-343
Record created
2026-07-13
Last reviewed
2026-07-14
Record version
2

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