DeepSeek Coder V2. New go-to coding AI.

Understanding DeepSeek-Coder-V2

Introduction to DeepSeek-Coder-V2

DeepSeek-Coder-V2 represents a significant advancement in the realm of AI coding models. This open-source Mixture-of-Experts (MoE) language model has been further pre-trained from its Base model with an impressive 6 trillion tokens sourced from a code-heavy, high-quality corpus (previous DeepSeek Coder model was trained on 87% code). This new coding AI model excels in both code generation and mathematical reasoning while maintaining robust performance in general language tasks.

The model offers extensive support for 338 programming languages, raising the bar from 86 in last generation. It also has extended its context length from 16K to 128K, making it highly versatile and capable of handling a variety of coding challenges. These enhancements make DeepSeek-Coder-V2 a go-to tool for developers and AI enthusiasts alike.

For a dive into more Coding AI models be sure to check out our Models page.

Advancements Over DeepSeek-Coder

DeepSeek-Coder-V2 showcases several advancements over its predecessor, DeepSeek-Coder. Some of the notable improvements include:

1. Enhanced Coding Capabilities: DeepSeek-Coder-V2 shows significant advancements in code-related tasks. It has been fine-tuned to excel in a variety of programming languages, making it versatile and highly effective for developers.
2. Improved Mathematical Reasoning: The model has been optimized to handle complex mathematical reasoning, thus making it a valuable tool for tasks that require high-level computational thinking.
3. Extended Context Length: With an extended context length from 16K to 128K, DeepSeek-Coder-V2 can handle longer code snippets and more extensive documentation, improving its usability for large-scale projects.
4. Broader Language Support: The model now supports 338 programming languages, a significant increase from the previous version, making it more adaptable to various coding environments.
5. Parameter Scaling: DeepSeek-Coder-V2 is available in two configurations: 16B and 236B parameters, based on the DeepSeek MoE framework. The active parameters are 2.4B and 21B, respectively, including both base and instruct models.

It also shows significant growth in benchmark performance, confirming its seriousness in the Coding AI Scene. Below AP is active parameters.

DeepSeek-Coder-V2 Features

Performance Benchmarks and Capabilities

DeepSeek-Coder-V2 outperforms several closed-source models, including GPT4-Turbo, Claude 3 Opus, and Gemini 1.5 Pro - particularly in coding and math benchmarks. The model is available in two configurations: one with 16 billion parameters and another with 236 billion parameters, based on the DeepSeek MoE framework. The active parameters for these models are 2.4 billion and 21 billion, respectively. Both base and instruct models are included in this release.

DeepSeek-Coder-V2 Conclusion

For AI enthusiasts and entrepreneurs looking to explore further, DeepSeek-Coder-V2 stands out as a robust choice, surpassing other models in both capabilities and performance. With support for 338 programming languages and an extended context length of up to 128K, DeepSeek-Coder-V2 is highly versatile and capable of addressing a wide array of coding challenges. Available in configurations of 16 billion and 236 billion parameters, the model showcases remarkable benchmark performance, outshining several closed-source competitors  For developers and AI enthusiasts, DeepSeek-Coder-V2 AI API is a serious contender for the spotlight.

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Author: Sergey Nuzhnyy.