base on 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. # 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.
", Assign "at most 3 tags" to the expected json: {"id":"10519","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"