Code intelligence, which uses natural language processing and software engineering to understand and generate programming code, is an emerging area in the technology sector. While tools like StarCoder, CodeLlama, and DeepSeek-Coder are open-source examples of this technology, they often struggle to match the performance of closed-source tools such as GPT4-Turbo, Claude 3 Opus, and Gemini 1.5 Pro, which benefit from robust datasets and computational resources. This performance gap presents an obstacle for the broad application of open-source tools in numerous settings, from businesses to schools.
Among the recently developed open-source models is DeepSeek-Coder-V2, introduced by DeepSeek-AI. Building on the previous version, DeepSeek-Coder-V2 is further pre-trained with an additional 6 trillion tokens, with an enhanced ability to understand code and mathematical concepts. This development ensures the capacity of DeepSeek-Coder-V2 to compete against its closed-source rivals while still remaining an accessible solution for a variety of users.
The new model uses a Mixture-of-Experts (MoE) framework, which allows it to support 338 coding languages and extend the scope from 16K to 128K tokens. The model is designed with between 16 billion and 236 billion parameters, allowing it to complete coding tasks efficiently. The training data is a comprehensive mixture of sources, including 60% source code, 10% math corpus and 30% natural language corpus.
DeepSeek-Coder-V2 comes in four versions: DeepSeek-Coder-V2-Instruct, developed for generating text in response to instructions; DeepSeek-Coder-V2-Base, a standard model suitable for a range of general applications; DeepSeek-Coder-V2-Lite-Base, a resource-efficient base model; and DeepSeek-Coder-V2-Lite-Instruct, designed for instruction-based tasks in resource-limited environments.
In tests, DeepSeek-Coder-V2 outperformed leading closed-source models in math and coding tasks. The model achieved a score of 90.2% in HumanEval and 75.7% in MATH, significantly improving on the results of its predecessors and illustrating its strength in code intelligence. These breakthroughs could mark a pivotal moment in the development of open-source coding tools.
The introduction of DeepSeek-Coder-V2 is an important leap forward in democratizing access to advanced coding tools. As an open-source model, it promotes efficiency and innovation in software development. The research on this model illustrates the need for continual improvements in the industry to ensure that powerful coding tools are universally accessible.
