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Model: Lingma/Lingma-SWE-GPT-7B
Source: Original Platform

README.md

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+# 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}
+}
+```