DeepSeek-Coder: When the Large Language Model Meets Programming - The Rise of Code Intelligence
- Thesis:
- Methods:
- Model sizes: 1.3B, 6.7B, 33B
- 2T tokens of project-level code data from 87 programming languages
- Enhance understanding across files
- 16K context window
- Next token prediction and [[fim]]
- Use repository-level data to improve generation across files
- Data:
- 87% source code
- 10% english code-related natural language corpus
- 3% chinese code-unrelated natural language corpus
- BPE tokenizer
- FIM rate is 50% (PSM)
- RoPE embedding
- Group Query Attention
- Flash Attention v2
- Contribution:
- DeepSeek-Coder Base and DeepSeek-Coder Instruct models
- LeetCode contest data benchmark
- Takeaways: Effective Code LLMs must have a deep understanding of natural language to be able to interpret and execute programming tasks. Also, high quality training data is key to achieve great performance. Finally, continued pre-training on methematical and natural language data improves performance on natural language tasks and tasks that require reasoning but may slightly reduce the accuracy on coding benchmarks such as HumanEval
- Improvements:
- 16K context is still not enough for real-world code completion tasks that require context across files in the repo.
- SPM wasn’t tried but [[fim]] showed it yields better performance
- Notes:
- Base models are fine-tuned using high quality instruction data to augment the zero-shot capabilities of base models
- Base models are also fine-tuned on 2B high quality code, natural language, and mathematical data to improve their NLU capabilities. This result with DeepSeek-Coder-v1.5
- DeepSeek-Coder Instruct outperforms GPT-3.5-Turbo and very close to GPT-4
- DeepSeek-Coder Base outperforms all open-sourced code LLMs
- Dependency parsing helps sort files in the order they are needed. This means placing dependencies before files that use them which simulate real-world applications where completion/generation happens at the project-level
- Deduplication at the repo level by concatenating files then perform near-deduplication
- Filter out low quality source files based on some heuristics
- Adding Chain-of-thought to the prompt is shown to significantly improve DeepSeek-Coder Instruct models
- DeepSeek-Coder 7B is recommended to use for completion tasks as it balances efficiency and accuracy. The bigger model (33B) is slightly better but has much more memory footprint and huge computation
- cross-file code completion utilizes keyword search (BM25) to provide contexts from other files in the repo
- DeepSeek-Coder v1.5 uses 7B model to continue pre-training on 2T tokens using next token prediction task. Data includes source code (70%) and other natural language code related corpus. This model is slightly less accurate on coding tasks but is much better at reasonong and natural language understanding
#nlp #llm #code-llm
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