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Autocoder
Overview :
AutoCoder is a novel model specifically designed for code generation tasks. It outperforms GPT-4 Turbo (as of April 2024) and GPT-4-o on the HumanEval benchmark dataset. Unlike previous open-source models, AutoCoder introduces a new feature: it can automatically install required packages and attempt to run the code when the user wishes to execute it, ensuring it works without issues.
Target Users :
AutoCoder targets developers and programmers who need automatic code generation and validation. It helps them improve development efficiency and reduce time spent manually debugging code by providing high-accuracy code generation and automatic package installation.
Use Cases
Developers use AutoCoder to automatically generate code, accelerating development speed.
Educational institutions leverage AutoCoder for programming education, helping students understand the code generation process.
Businesses utilize AutoCoder for code quality control, minimizing human errors.
Features
Achieves 90.9% accuracy on the HumanEval benchmark dataset, surpassing GPT-4 Turbo.
Automatically installs required packages and attempts to run the code when needed.
Provides a code interpreter to verify the correctness of the code.
Offers a web demo, including the code interpreter functionality.
The model is available on Huggingface, including AutoCoder (33B) and AutoCoder-S (6.7B).
Supports testing in custom environments.
How to Use
Create a conda environment to prepare the testing environment.
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Conduct tests on the HumanEval benchmark dataset to obtain test results.
Utilize the EvalPlus GitHub framework for result testing and post-processing.
Perform tests on MBPP and obtain and post-process test results.
Conduct tests on DS-1000 and directly test the results.
Install necessary gradio packages and run the web demo.
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