The performance of DeepSeek Coder is significantly influenced by its composition of 87% code and 13% natural language. This unique blend allows the model to excel in various coding tasks while maintaining a strong contextual understanding of programming languages.
Impact of Code Composition
1. Enhanced Coding Performance: The high percentage of code in the training data enables DeepSeek Coder to achieve state-of-the-art results on coding benchmarks like HumanEval and MBPP, with scores reaching up to 90.2% accuracy**[1][5]. This performance surpasses many existing open-source and proprietary models, indicating that the model is particularly adept at generating accurate code snippets.
2. Natural Language Understanding: The inclusion of 13% natural language data, primarily in English and Chinese, enhances the model's ability to understand and generate comments, documentation, and user instructions. This linguistic context is crucial for tasks that require not just code generation but also explanations or interactions in natural language, making the model versatile across different programming scenarios[2][4].
3. Contextual Awareness: DeepSeek Coder employs repository-level training, which allows it to understand cross-file dependencies within projects. This capability is bolstered by the extensive code data, enabling it to resolve complex coding challenges that span multiple files effectively[1]. The model's ability to maintain context over long sequences (up to 16,384 tokens, extendable to 128K**) further enhances its performance in large-scale software projects[1].
4. Fill-In-the-Middle (FIM) Training: This innovative training strategy allows the model to generate code by filling in gaps within existing code blocks. The substantial amount of code data supports this feature, improving the model's debugging and code completion abilities, which are critical for developers[1][3].
5. Instruction Tuning: The model undergoes instruction tuning with additional data that includes both code and natural language instructions. This process refines its ability to respond accurately to user queries and generate contextually relevant code snippets, leveraging both its coding expertise and linguistic capabilities[1][5].
In summary, the specific composition of 87% code and 13% natural language equips DeepSeek Coder with a robust framework for understanding and generating programming content effectively. This balance not only enhances its coding performance but also ensures it can communicate effectively in human language, making it a powerful tool for developers.
Citations:
[1] https://blog.premai.io/open-source-code-language-models-deepseek-qwen-and-beyond/
[2] https://dataloop.ai/library/model/thebloke_deepseek-coder-67b-base-awq/
[3] https://github.com/deepseek-ai/deepseek-coder/?tab=readme-ov-file
[4] https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base
[5] https://arxiv.org/html/2406.11931v1
[6] https://arxiv.org/pdf/2401.14196.pdf
[7] https://huggingface.co/deepseek-ai/deepseek-coder-1.3b-base
[8] https://dataloop.ai/library/model/deepseek-ai_deepseek-coder-67b-instruct/