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https://github.com/bin123apple/autocoder

We introduced a new model designed for the Code generation task. Its test accuracy on the HumanEval base dataset surpasses that of GPT-4 Turbo (April 2024) and GPT-4o.
https://github.com/bin123apple/autocoder

code-generation code-interpreter humaneval llm nlp nlp-machine-learning text-generation

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We introduced a new model designed for the Code generation task. Its test accuracy on the HumanEval base dataset surpasses that of GPT-4 Turbo (April 2024) and GPT-4o.

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README 

        
# AutoCoder



## News :fire: 



A new model [AutoCoder_QW_7B](https://huggingface.co/Bin12345/AutoCoder_QW_7B) is uploaded. In this model, We fixed the previous problem that the model will only start the code interpreter when you ask it to *verify* its code. 



The base model of AutoCode_QW_7B is [CodeQwen1.5-7b](https://huggingface.co/Qwen/CodeQwen1.5-7B-Chat).



## Introduction :mega:

We introduced a new model designed for the Code generation task. Its test accuracy on the HumanEval base dataset surpasses that of GPT-4 Turbo (April 2024). (**90.9% vs 90.2%**).



Additionally, compared to previous open-source models, AutoCoder offers a new feature: it can **automatically install the required packages** and attempt to run the code until it deems there are no issues, **whenever the user wishes to execute the code**.



* Difference between the code interpreter of AutoCoder and the GPT-4 Turbo:



Below are the video demos for the code interpreter comparison between GPT-4 Turbo and AutoCoder: 



GPT-4o can not access the external library.



[GPT-4o](https://github.com/bin123apple/AutoCoder/assets/99925255/be47b449-4e8a-4b77-981b-ec79b15970cc)



AutoCoder can automatically install the required packages. This feature expands the scope of code interpreter's application.



[AutoCoder](https://github.com/bin123apple/AutoCoder/assets/99925255/1893f904-c1f2-4f59-9ec5-45b69efcc26a)



* Difference between the code interpreter of AutoCoder and the current open-source code interpreter [OpenCodeInterpreter](https://opencodeinterpreter.github.io/):



The code interpreter of AutoCoder, like GPT-4 Turbo, is only called when the user has a need to verify the code, while OpenCodeInterpreter runs all generated python code.



## Model :gift:

The Model is avaliable on Huggingface:

 

[AutoCoder (33B)](https://huggingface.co/Bin12345/AutoCoder)

[AutoCoder-S (6.7B)](https://huggingface.co/Bin12345/AutoCoder_S_6.7B)



The base models of AutoCoder (33B) and AutoCoder-S (6.7B) are deepseeker-coder.



[AutoCoder_QW_7B](https://huggingface.co/Bin12345/AutoCoder_QW_7B)



The base model of AutoCoder_QW_7B is CodeQwen1.5-7b.



## Quick Start :rocket:

1. Create the conda env



```

conda create -n AutoCoder python=3.11

conda activate AutoCoder

pip install -r requirements.txt

```



2. Test on HumanEval **90.9% on base, 78.0% on base + extra**. (Skip to Step 5, if you don't want to test its performance on benchmarks)



```

cd Evaluation

python test_humaneval.py

```

You will receive a file named AutoCoder_HumanEval+.jsonl, which follows the EvalPlus format, after this step.



Then follow the testing framework of the [EvalPlus GitHub](https://github.com/evalplus/evalplus). You will see the results. 



**NOTE**: 

* Don't forget to use evalplus's `evalplus.sanitize` to post-process the code. 

* If you don't use the greedy method (for example set the `do_sample=True`) for the code generation. You will probably see the different results.



3. Test on MBPP **82.5% on base, 70.6% on base + extra**. (Skip to Step 5, if you don't want to test its performance on benchmarks)



```

python test_humaneval.py

```



Post-process to delete the nature language for testing

```

python postprocess_mbpp.py

```

Your will get a AutoCoder_Mbpp+-sanitized.jsonl file after this step, it extracted all the code blocks. 

Then, directly test it by using [EvalPlus GitHub](https://github.com/evalplus/evalplus) (You don't need to use to use evalplus's `evalplus.sanitize` to post-process the code this time).



4. Test on DS-1000. (Skip to Step 5, if you don't want to test its performance on benchmarks)



```

python test_ds1000.py

```



Your will get a jsonl file after this step, it extracted all the code blocks. 

Then, directly test it by using [DS-1000 GitHub](https://github.com/xlang-ai/DS-1000).



5. Web demo (Include code interpreter)



Install gradio and Run:



```

pip install gradio==3.48.0

cd /Web_demo

python chatbot.py

```



## **NOTE** :warning:

* We suggest to set `do_sample = True` (default setting here) while using the code interpreter.



* It would be preferable to use Linux for deploying everything.



## Contact :email:

If you have any inquiries, please feel free to raise an issue or reach out to [email protected].



## Citation :book:

```

@misc{lei2024autocoder,

      title={AutoCoder: Enhancing Code Large Language Model with \textsc{AIEV-Instruct}}, 

      author={Bin Lei and Yuchen Li and Qiuwu Chen},

      year={2024},

      eprint={2405.14906},

      archivePrefix={arXiv},

      primaryClass={cs.SE}

}

```



## Acknowledgments :pray:

Thanks to Tianyu Zheng, the first author of the [OpenCodeInterpreter](https://opencodeinterpreter.github.io/), for guidance on some technical details.