XiHeFusion: Harnessing Large Language Models for Science Communication in Nuclear Fusion, Xiao Wang, Qingquan Yang, Fuling Wang, Qiang Chen, Wentao Wu, Yu Jin, Jingtao Jiang, Liye Jin, Bo Jiang, Dengdi Sun, Wanli Lv, Meiwen Chen, Zehua Chen, Guosheng Xu, Jin Tang arXiv Pre-print arXiv:2502.05615 [Paper]
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[2025.02.06] Demo video for XiHeFusion is available on Youtube, Bilibili
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[2025.01.28] XiHeFusion is released on GitHub
- Open the interface to access the XiHeFusion large model on the web.
Nuclear fusion is one of the most promising ways for humans to obtain infinite energy. Currently, with the rapid development of artificial intelligence, the mission of nuclear fusion has also entered a critical period of its development. How to let more people to understand nuclear fusion and join in its research is one of the effective means to accelerate the implementation of fusion. This paper proposes the first large model in the field of nuclear fusion, XiHeFusion, which is obtained through supervised fine-tuning based on the open-source large model Qwen2.5-14B. We have collected multi-source knowledge about nuclear fusion tasks to support the training of this model, including the common crawl, eBooks, arXiv, dissertation, etc. After the model has mastered the knowledge of the nuclear fusion field, we further used the chain of thought to enhance its logical reasoning ability, making XiHeFusion able to provide more accurate and logical answers. In addition, we propose a test questionnaire containing 180+ questions to assess the conversational ability of this science popularization large model. Extensive experimental results show that our nuclear fusion dialogue model, XiHeFusion, can perform well in answering science popularization knowledge.
Watch the demo video below:
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Bilibili：XiHeFusion_Demo
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First, configure the relevant environment according to https://github.com/hiyouga/LLaMA-Factory .
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We provide a basic conversational demo and a programmatic demo using API calls in demo.
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Conversational demo:
#Execute
python demo/chat_demo.py- API call execution:
# Modify model_name_or_path in /examples/inference/api_inference.yaml
# Load the model and modify CUDA_VISIBLE_DEVICES API_PORT
CUDA_VISIBLE_DEVICES=2 API_PORT=8005 nohup llamafactory-cli api LLaMA-Thinks/examples/inference/api_inference.yaml &
# Execute
python demo/api_demo.pyPre-trained model will be updated on:
- BaiduYun:
通过网盘分享的文件：XiHeFusion_checkpoint
链接: https://pan.baidu.com/s/1fcZFuFvU5v28XU4w1Mcwdg?pwd=xihe 提取码: xihe
--来自百度网盘超级会员v5的分享
- DropBox:
https://www.dropbox.com/scl/fo/c5kmxjmgzqajbfzj1342u/ACdcT9QcKtuYuNLaDFennwo?rlkey=ql8yxk1zduykuvg9czf2wj8m7&st=aq5lljv7&dl=0
Please check the file [questions_for_XiHeFusion.docx] for the assessment.
This work is supported by the National Natural Science Foundation of China under Grant U24A20342, 62102205, and the Anhui Provincial Natural Science Foundation under Grant 2408085Y032. The authors acknowledge the High-performance Computing Platform of Anhui University for providing computing resources. We appreciate the fusion test questions provided by the following researchers: Jilei Hou, Yan Chao, Hua Zhou, Xin Lin, Gaoting Chen, and Wenmin Zhang, Zheyuan Si, Yiqi Liu. We appreciate the assistance of the following students in crawling and preparing the training data, including Xiaoya Zhou, Hao Si, Chao Wang, Jin Liang, and Qian Zhu.
@misc{wang2025xihefusion,
title={XiHeFusion: Harnessing Large Language Models for Science Communication in Nuclear Fusion},
author={Xiao Wang and Qingquan Yang and Fuling Wang and Qiang Chen and Wentao Wu and Yu Jin and Jingtao Jiang and Liye Jin and Bo Jiang and Dengdi Sun and Wanli Lv and Meiwen Chen and Zehua Chen and Guosheng Xu and Jin Tang},
year={2025},
eprint={2502.05615},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2502.05615},
}
