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DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence

25 Jan 2024arXiv:2401.14196archive 2025-07-28

Daya Guo, Qihao Zhu, Dejian Yang, Zhenda Xie, Kai Dong, Wentao Zhang, Guanting Chen, Xiao Bi, Y. Wu, Y. K. Li, Fuli Luo, Yingfei Xiong, Wenfeng Liang

The rapid development of large language models has revolutionized code intelligence in software development. However, the predominance of closed-source models has restricted extensive research and development. To address this, we introduce the DeepSeek-Coder series, a range of open-source code models with sizes from 1.3B to 33B, trained from scratch on 2 trillion tokens. These models are pre-trained on a high-quality project-level code corpus and employ a fill-in-the-blank task with a 16K window to enhance code generation and infilling. Our extensive evaluations demonstrate that DeepSeek-Coder not only achieves state-of-the-art performance among open-source code models across multiple benchmarks but also surpasses existing closed-source models like Codex and GPT-3.5. Furthermore, DeepSeek-Coder models are under a permissive license that allows for both research and unrestricted commercial use.

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build_deepseekcoder_instruction deepseek-ai/DeepSeek-Coder/Evaluation/HumanEval/eval_instruct.py official repository ran fingerprinted MIT (permissive) · 6f0dd2241aaa7457 · report
build_instruction_prompt deepseek-ai/DeepSeek-Coder/finetune/finetune_deepseekcoder.py official repository ran fingerprinted MIT (permissive) · d75977d806b4c9f6 · report
convert_for_evaluation deepseek-ai/DeepSeek-Coder/Evaluation/MBPP/eval_instruct.py official repository ran MIT (permissive) · c9bc84373110f7bd · report
extract_python_code deepseek-ai/DeepSeek-Coder/Evaluation/LeetCode/evaluate_leetcode.py official repository ran fingerprinted MIT (permissive) · f2508ea408193905 · report
generate_one deepseek-ai/DeepSeek-Coder/Evaluation/MBPP/eval_instruct.py official repository ran MIT (permissive) · 8eec22d6447b67d3 · report
preprocess deepseek-ai/DeepSeek-Coder/finetune/finetune_deepseekcoder.py official repository ran MIT (permissive) · 51c01dd0d033f660 · report
process_humaneval_test deepseek-ai/DeepSeek-Coder/Evaluation/HumanEval/human_eval/evaluation.py official repository ran MIT (permissive) · 88e4abace6063c6e · report
read_problems deepseek-ai/DeepSeek-Coder/Evaluation/HumanEval/human_eval/data.py official repository ran MIT (permissive) · 21359df82e68e215 · report
read_test_examples deepseek-ai/DeepSeek-Coder/Evaluation/MBPP/eval_instruct.py official repository ran MIT (permissive) · 0f5352c3b1c791ef · report
train_tokenize_function deepseek-ai/DeepSeek-Coder/finetune/finetune_deepseekcoder.py official repository unverified MIT (permissive) · 975cf022e47318ac · report

Tasks

Code GenerationLanguage ModelingLanguage ModellingLarge Language Model

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Code Generation APPS deepseek-ai/deepseek-coder-6.7b-instruct Competition Pass@1 11.09 #5 of 18 Archive leaderboard report
Code Generation APPS deepseek-ai/deepseek-coder-6.7b-instruct Interview Pass@1 19.70 #5 of 18 Archive leaderboard report
Code Generation APPS deepseek-ai/deepseek-coder-6.7b-instruct Introductory Pass@1 33.80 #5 of 18 Archive leaderboard report
Code Generation MBPP GPT-4 (few-shot) Accuracy 80 #24 of 99 Archive leaderboard report
Code Generation MBPP GPT-3.5 Turbo (few-shot) Accuracy 70.8 #30 of 99 Archive leaderboard report
Code Generation MBPP DeepSeek-Coder-Instruct 33B (few-shot) Accuracy 70 #31 of 99 Archive leaderboard report
Code Generation MBPP DeepSeek-Coder-Base 33B (few-shot) Accuracy 66 #39 of 99 Archive leaderboard report
Code Generation MBPP DeepSeek-Coder-Instruct 6.7B (few-shot) Accuracy 65.4 #41 of 99 Archive leaderboard report
Code Generation MBPP DeepSeek-Coder-Base 6.7B (few-shot) Accuracy 60.6 #49 of 99 Archive leaderboard report
Code Generation MBPP DeepSeek-Coder-Instruct 1.3B (few-shot) Accuracy 49.4 #62 of 99 Archive leaderboard report
Code Generation MBPP DeepSeek-Coder-Base 1.3B (few-shot) Accuracy 46.2 #74 of 99 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

AdamAttentionAttention DropoutBPECosine AnnealingDense ConnectionsDropoutGPT-3Layer NormalizationLinear LayerLinear Warmup With Cosine AnnealingMulti-Head AttentionResidual ConnectionSoftmaxWeight Decay

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