In the continually evolving field of software development, large language models (LLMs) have brought about notable changes, particularly in the sector of code intelligence. These advanced models have played a vital role in automating several aspects of coding such as locating bugs and generating code. This innovation in approach and execution of coding tasks has significantly increased productivity and reduced the probability of errors typical in manual coding.
However, the sector has been grappling with the disparity in capabilities between open-source, proprietary, and closed-source code models. Although closed-source models have demonstrated impressive performance, their limited accessibility constrains widespread research and application. This has, in turn, created a performance gap that needs to be addressed to democratize advanced coding tools and promote widespread innovation.
Code models are mostly trained at the file level, neglecting the intricate interdependencies amongst different files in a programming project. Real-world coding projects usually comprise complex relationships between several files and not considering this can result in a gap in practical application. It is therefore essential to develop models that are not only theoretically proficient but also practically applicable.
The research team from DeepSeek-AI and Peking University took a significant step in this direction by developing the DeepSeek-Coder series. This ground-breaking range of open-source code models, featuring 1.3B to 33B parameters, is trained on an extensive corpus covering 87 programming languages. This is a remarkable advance towards narrowing the pre-existing gap and improving the functionality of open-source models in code intelligence.
DeepSeek-Coder series stands out for its unique ‘fill-in-the-middle’ training method and extended context window capability. This model can process complicated and lengthier code sequences, significantly enhancing its code completion abilities. Moreover, it can be utilized in complex coding scenarios that involve multiple files and extended contexts, setting it apart from conventional models.
The DeepSeek-Coder models consistently surpass other open-source models performance-wise. Specifically, the DeepSeek-Coder-Base 33B model performs well across different benchmarks. In addition, the DeepSeek-Coder-Instruct 33B variant has shown exceptional results even outperforming some leading closed-source models, including the GPT-3.5 Turbo by OpenAI.
To wrap up, the DeepSeek-Coder series represents a significant milestone in the world of code intelligence. By effectively bridging the gap between open-source and proprietary code models, it sets a new performance standard for code models. Its versatility in understanding and processing complex code sequences across various programming languages highlights its potential to revolutionize code generation and comprehension. Finally, the development of this model paves the way for more efficient, widely accessible, and advanced coding tools.
