# Lingma SWE-GPT: Software Engineering Large Language Model

## Overview

Lingma SWE-GPT is an open-source large language model specifically designed for software engineering tasks. Built upon the foundation of the Qwen series base models, Lingma SWE-GPT has undergone additional training using software engineering development process data to enhance its capabilities in solving complex software engineering tasks.

## Model Introduction

Lingma SWE-GPT is a specialized model that focuses on addressing the unique challenges faced in software engineering. By leveraging the robust capabilities of the Qwen base models and incorporating domain-specific knowledge, this model aims to provide intelligent assistance across various aspects of software development.


## Model Performance

Lingma SWE-GPT has demonstrated impressive performance in software engineering tasks:

- Achieved a **30.20%(72B) and **18.20%(7B) solution rate on the authoritative SWE-bench Verified** leaderboard for software engineering intelligent agents.
- Outperforms other open-source models of similar scale in software engineering-specific tasks.

## How to use

### Run on SWE-bench 
Refer to https://github.com/LingmaTongyi/Lingma-SWE-GPT

### Quick Start
```
from modelscope import AutoModelForCausalLM, AutoTokenizer

model_name = "Lingma/Lingma-SWE-GPT-7B"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Give me a short introduction to large language model."
messages = [
    {"role": "system", "content": "You are Lingma, created by Tongyi Lingma team. You are a helpful assistant."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)
```

## TODO
Currently only Python is supported. In future updates, we will provide more support for Java, JS/TS and other languages.

## License

This project is licensed under the GNU General Public License v2.0 (GPL-2.0).

## Contact

For any questions or feedback regarding Lingma SWE-GPT, please contact:

mayingwei.myw@alibaba-inc.com

## Acknowledgments

We would like to thank the Qwen team for their foundational work, which has been instrumental in the development of Lingma SWE-GPT.

## Citation
```
@article{ma2024understand,
  title={How to Understand Whole Software Repository?},
  author={Ma, Yingwei and Yang, Qingping and Cao, Rongyu and Li, Binhua and Huang, Fei and Li, Yongbin},
  journal={arXiv preprint arXiv:2406.01422},
  year={2024}
}
```