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DeepSeek Coder

发布时间:2026-10-10 | 浏览:1
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装机神器,可安装一切系统,纯净版,英文版,繁体版 ,精简版,原版等等等
DeepSeek Coder comprises a series of code language models trained from scratch on both 87% code and 13% natural language in English and Chinese, with each model pre-trained on 2T tokens. We provide various sizes of the code model, ranging from 1B to 33B versions. Each model is pre-trained on repo-level code corpus by employing a window size of 16K and a extra fill-in-the-blank task, resulting in foundational models (DeepSeek-Coder-Base). We further fine-tune the base model with 2B tokens of instruction data to get instruction-tuned models, namedly DeepSeek-Coder-Instruct. Pretrained on 2 Trillion tokens over more than 80 programming languages. Various model sizes ( 1.3B , 5.7B , 6.7B and 33B ) to support different requirements. A window size of 16K window size, supporting project-level code completion and infilling. State-of-the-Art performance among open code models. Open source and free for research and commercial use . We evaluate DeepSeek Coder on various coding-related benchmarks. The result shows that DeepSeek-Coder-Base-33B significantly outperforms existing open-source code LLMs. Compared with CodeLLama-34B, it leads by 7.9%, 9.3%, 10.8% and 5.9% respectively on HumanEval Python, HumanEval Multilingual, MBPP and DS-1000. Surprisingly, our DeepSeek-Coder-Base-7B reaches the performance of CodeLlama-34B. And the DeepSeek-Coder-Instruct-33B model after instruction tuning outperforms GPT-3.5-turbo on HumanEval and achieves comparable result with GPT-3.5-turbo on MBPP. Fig. State-of-the-art Performance on various Coding Benchmarks and Multilingual HumanEval
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装机神器,可安装一切系统,纯净版,英文版,繁体版 ,精简版,原版等等等
(1) Performance of different Code LLMs on Multilingual HumanEval Benchmark (2) Performance of different Code LLMs on MBPP Benchmark (3) Performance of different Code LLMs on DS-1000 Benchmark (4) Performance of different Code Models on Math-Reasoning Tasks. How to Use DeepSeek Coder - Try now, please visit our [ DeepSeek-Coder] . - More details and evaluations are available on our [ Github] . - Model weights are also available on [🤗 Huggingface] If you have any questions, please raise an issue or contact us at agi_code@deepseek.com .
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装机神器,可安装一切系统,纯净版,英文版,繁体版 ,精简版,原版等等等