Kimi-Dev
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Kimi Dev
Overview :
Kimi-Dev is a powerful open-source coding LLM designed to tackle issues in software engineering. It is optimized through large-scale reinforcement learning to ensure correctness and robustness in real development environments. Kimi-Dev-72B achieved 60.4% performance on the SWE-bench benchmark, surpassing other open-source models, making it one of the most advanced coding LLMs available today. The model can be downloaded and deployed from Hugging Face and GitHub, making it suitable for use by developers and researchers.
Target Users :
This product is suitable for software engineers, developers, and researchers, helping them efficiently solve coding problems and improve development efficiency. Due to its open-source nature, users can freely modify and extend features according to their needs.
Total Visits: 485.5M
Top Region: US(18.64%)
Website Views : 39.5K
Use Cases
Use Kimi-Dev to fix bugs in open-source projects and automatically generate test cases.
Leverage Kimi-Dev during the software development process to improve code quality and reliability.
As a teaching tool, help computer science students understand methods for solving coding problems.
Features
Automatic code repair: Can intelligently locate and fix errors in code based on problem descriptions.
Unit test generation: Automatically generates relevant unit tests for code, enhancing code quality.
High performance optimization: Ensures model repair results conform to real-world development standards through reinforcement learning.
Easy deployment: Supports easy installation and usage on both local and cloud environments.
Strong community support: Open source, encouraging developers and researchers to contribute and improve.
How to Use
Clone the Kimi-Dev repository: Download the project using the git clone command.
Create and activate the environment: Use conda to create a virtual environment and activate it.
Install dependencies: Run the pip install command to install necessary dependencies.
Prepare the project structure: Download and extract the processed data.
Deploy the vLLM model: Deploy the model using the vllm serve command.
Run repair scripts: Run the bugfixer or testwriter script as needed for code fixes or test writing.
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