Open-source AI coding assistant AutoCoder beats OpenAI GPT-4o

AutoCoder, a newly introduced open-source large-language model (LLM), shows promising results in the field of AI coding assistants. Designed to improve code interpretation, AutoCoder outperforms industry giants such as GPT-4 Turbo and OpenAI’s GPT-4 Omni in a variety of benchmarks. The accuracy of its tests against the HumanEval baseline dataset exceeds that of GPT-4 Turbo (April 2024). (90.9% vs. 90.2%).

This state-of-the-art model offers a number of advanced features, including the ability to install external packages and a unique training methodology called AI EV Instruct that combines agent interactions with external verification of code execution. Turning AutoCoder on automatically install required packages and try to run the code until it thinks there are no problems, whenever the user wants to execute the code.

Key features of AutoCoder include:

• Open source model based on Deep SE Coder
• Supports installation of external packages to handle complex coding tasks
• Trained using the multi-turn dialogue dataset to better understand and respond to encoding queries
• It uses the AI ​​EV Instruct methodology, combining agent interactions with external verification of code execution
• It outperforms other coding-focused LLM programs such as Magic Coder OSS Instruct
• Includes unit tests to ensure code accuracy and reliability

The AI ​​EV Instruct methodology is a distinctive feature of AutoCoder. This two-stage architecture consists of teaching and self-learning stages. In the teaching phase, AutoCoder learns from open source code snippets and tutor models such as GPT-4 Turbo. During the self-learning stage, he improves his understanding through constant interaction and feedback. This approach ensures that AutoCoder generates accurate and reliable code, which distinguishes it from other models available on the market.

Open source AI coding assistant

AutoCoder’s performance is impressive compared to other state-of-the-art models. It outperforms Magic Coder OSS Instruct and competes with industry leaders such as LLaMA 3, GPT-4 Omni Ultra and Gemini. The AutoCoder data set includes 169,000 data samples and 241 rounds of dialogues, which contributes to its excellent performance in coding tasks.

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Installation and use

One of the key advantages of AutoCoder is its accessibility. The model is available in various sizes, covering 33 billion and 6.7 billion parameters, meeting various user requirements. Users can install and run AutoCoder locally using LM Studio, making it a practical tool for both individual developers and organizations. This flexibility of installation and use increases its attractiveness and usability in a variety of coding environments.

Exemplary performance

AutoCoder’s benchmark performance is a testament to its advanced capabilities. It excels in coding tasks, especially in the HumanEval benchmark, where it outperforms OpenAI’s GPT-4 Turbo and GPT-4 Omni. This superior performance makes AutoCoder a valuable asset for developers looking for advanced code interpretation capabilities and reliable open source solutions for their coding needs.

AutoCoder is a breakthrough in the field of AI coding assistants. Its advanced features, unique training methodology, and exceptional testing performance make it the best choice for developers and organizations looking for a powerful open source solution. With the ability to install external packages, generate accurate code, and continually learn through interaction and feedback, AutoCoder will revolutionize the way we approach coding tasks. As an open source model, it offers unparalleled flexibility and accessibility, making it a valuable addition to any developer’s toolkit. For more information, go to the official GitHub repository or read more about the development of the AI ​​coding assistant from the research article.

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