Update README.md

README.md

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-# train_2024
+# Train Details & Inference Capability Statement
 
-This model is a fine-tuned version of Qwen1.5-1.8B-Chat on our private medical datasets.
+Our model, a meticulously fine-tuned version of Qwen1.5-1.8B-Chat, has been optimized for high-speed inference on a Tesla T4 graphics processing unit (GPU). This enhancement was achieved through extensive training on our exclusive medical datasets, ensuring the model's proficiency in understanding and generating responses relevant to the medical field, particularly in the domain of Traditional Chinese Medicine (TCM).
 
 ## Disclaimer
 This research is for academic research use only, commercial use is not allowed without permission, and it is not to be used in medical scenarios or scenarios with potential medical intent for clinical practice. This large language model for Traditional Chinese Medicine is still in the laboratory testing stage. The emerging syndrome classification and prescription generation capabilities at this stage are still rudimentary, and it does not yet have a highly reliable clinical diagnostic and therapeutic capability for gynecology and other clinical specialties. The output results are for internal reference testing only. Real medical diagnosis and decision-making still need to be issued by experienced physicians through a strictly regulated diagnostic and therapeutic process.
@@ -62,7 +62,7 @@ This research is for academic research use only, commercial use is not allowed w
 Data processing and annotation is one of the important steps in training the model. We sincerely welcome Traditional Chinese Medicine practitioners with strong TCM thinking and innovative spirit to join us. We will also declare corresponding data contributions. We look forward to the day when we can achieve a reliable General Artificial Intelligence for Traditional Chinese Medicine, allowing the ancient Chinese medicine to blend with modern technology and shine anew. This is also the ultimate mission of this project. If interested, please send an email to [email protected].
 
 ## Team Introduction
-This project is jointly guided by Professor Zhang Wenqiang from Fudan University, Postdoctoral Fellow Wang Yan from Fudan University, and Professor Wang Haofen from Tongji University. The team effort includes Kang Yanlan, [Chang Yang](https://github.com/techlead-krischang), and Fu Jiyuan from Fudan University's [ROI Lab](https://www.fudanroilab.com/), as well as Xing Haozhe in completing this project. Special thanks to the medical team from the Longhua Hospital affiliated with Shanghai University of Traditional Chinese Medicine, including Wu Sunsi, Ma Qingshan, Fang Yide, Chen Yue, Jiao Yuqi, Liu Xiyu, and Zhao Xue for their valuable data support and manual assessments.
+This project is jointly guided by Professor Zhang Wenqiang from Fudan University, Postdoctoral Fellow Wang Yan from Fudan University, and Professor Wang Haofen from Tongji University. The team effort includes Kang Yanlan, Chang Yang, and Fu Jiyuan from Fudan University's [ROI Lab](https://www.fudanroilab.com/), as well as Xing Haozhe in completing this project. Special thanks to the medical team from the Longhua Hospital affiliated with Shanghai University of Traditional Chinese Medicine, including Wu Sunsi, Ma Qingshan, Fang Yide, Chen Yue, Jiao Yuqi, Liu Xiyu, and Zhao Xue for their valuable data support and manual assessments.
 
 ## Citation
 If you find this work useful in your research, please cite our repository: