Qwen3-Coder has three significant breakthroughs in multilingual processing:
- language breadth: It supports 92 programming languages for code generation and 100+ natural language understanding, including emerging languages such as Rust and Kotlin, as well as blockchain-specific languages such as Solidity, with coverage far exceeding that of GitHub Copilot (which mainly supports 12 languages).
- Cross-conversion capacity: It can realize cross-language code conversion from Python to C++, etc., and can automatically deal with the differences in memory management and other characteristics during system programming language conversion, with a conversion accuracy of 78.5% (based on the HumanEval-X benchmark).
- dialect adaptation: Specifically optimized for non-English development environments such as Chinese pinyin variable names, Japanese comments, etc. The error rate is 42% lower than CodeLlama when processing code snippets containing mixed languages.
Technically implemented, its multilingual capability stems from 1) using code-natural language parallel corpus training 2) employing dynamic vocabulary expansion techniques 3) adding linguistic balance weights to the loss function. This makes it excel in handling mixed multilingual projects, such as JavaScript projects with Chinese-English intermingling or Java code containing German annotations.
This answer comes from the articleQwen3-Coder: open source code generation and intelligent programming assistantThe
May not be reproduced without permission:AI productivity tools " What is Qwen3-Coder's breakthrough in multi-language support compared to traditional code assistants?
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