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  # Intro
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- Yi-Coder series models are trained for coding tasks with two sizes available, 1.5B and 9B, supporting 52 major coding languages. Notably, the Yi-Coder-9B outperforms other models under 10 billion parameters such as CodeQwen1.5 7B and CodeGeex4 9B, and even achieves performance on par with DeepSeek-Coder 33B.
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- Yi-Coder excels in long-context understanding, handling up to 128K tokens for project-level code comprehension and generation. Despite its relatively small size, Yi-coder is versatile in tasks like programming, code editing, debugging, completion, and mathematical reasoning.
 
 
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  For model details and benchmarks, see [Yi-Coder blog](https://01-ai.github.io/) and [Yi-Coder README](https://github.com/01-ai/Yi-Coder).
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  # Intro
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+ Yi-Coder is a series of open-source code language models that delivers state-of-the-art coding performance with fewer than 10 billion parameters.
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+ Key features:
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+ - Excelling in long-context understanding with a maximum context length of 128K tokens.
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+ - Supporting 52 major programming languages, including popular ones such as Java, Python, JavaScript, and C++.
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  For model details and benchmarks, see [Yi-Coder blog](https://01-ai.github.io/) and [Yi-Coder README](https://github.com/01-ai/Yi-Coder).
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