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Yi Coder
Overview :
Yi-Coder is a series of open-source large language models (LLMs) that deliver state-of-the-art coding performance with fewer than 10 billion parameters. It comes in two sizes—1.5B and 9B parameters—offering base and chat versions designed for efficient inference and flexible training. Yi-Coder-9B has been additionally trained on 24 trillion high-quality tokens from GitHub's code repository-level code corpus and code-related data filtered from CommonCrawl. Yi-Coder excels in various programming tasks, including basic and competitive programming, code editing, repository-level completion, long-context understanding, and mathematical reasoning.
Target Users :
Yi-Coder is suitable for software developers, competitive programmers, and AI researchers. It enhances coding efficiency, assists in tackling complex programming challenges, and provides a research foundation for AI applications in the programming domain.
Use Cases
Developers use Yi-Coder for automatic code completion, enhancing development efficiency
Competitive programmers utilize Yi-Coder to solve algorithmic problems, achieving higher rankings
AI researchers employ Yi-Coder for studies in code generation and inference capabilities
Features
Pre-trained with high-quality tokens supporting 52 major programming languages
Long-context modeling: maximum context window of 128K tokens, enabling project-level code understanding and generation
Exceptional performance in models with fewer than 1 billion parameters, matching the performance of larger models
Achieved a 23.4% pass rate on the LiveCodeBench platform with Yi-Coder-9B-Chat, surpassing other models
Demonstrated outstanding performance in code modification tasks on CodeEditorBench with Yi-Coder-9B
Excelled in cross-file code completion in CrossCodeEval
Showcased long-context modeling capabilities in the 'Needle in the code' task
Excelled in mathematical reasoning in the Program-Aid Math Reasoning evaluation with Yi-Coder-9B
How to Use
Visit the Yi-Coder GitHub page for detailed model information and usage guidelines
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Download and install necessary software dependencies, such as the Python environment and the Transformers library
Set up the model training or inference environment as per the Yi-Coder README documentation
Utilize the Yi-Coder API for code generation or to engage in existing code editing tasks
Integrate Yi-Coder into projects to leverage its long-context understanding and code generation capabilities to optimize the development process
Participate in Yi-Coder community discussions to gain technical support and share best practices
Contact the Yi-Coder development team via Discord or email for deeper assistance and discussions
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