初始化项目，由ModelHub XC社区提供模型

Model: rebirth1/xiaonuanxin
Source: Original Platform

README.md

@@ -0,0 +1,71 @@
+
+模型配置:
+  指定模型: "Qwen/Qwen2.5-3B-Instruct" 
+  训练类型: "lora" 
+
+数据集:
+  训练数据集: "小暖.json" 
+  验证数据集: null
+
+训练配置:
+  训练轮数: 3 
+  每设备训练批次大小: 1 
+  每设备评估批次大小: 1 
+  学习率: 0.0005 。
+
+目标模块:
+  目标模块: "all-linear" # 指定所有线性层作为LoRA微调的目标模块。
+
+梯度累积:
+  梯度累积步数: 8 
+最大文本长度: 4096 
+
+系统设定:
+  系统角色: "你叫信小暖，是一个大学生心理咨询小助手，专门负责大学生的心理咨询，心理测试，心理知识科普"
+
+学习率调度:
+  预热比例: 0.05 
+  学习率调度器: "CosineAnnealing调节器" 
+
+
+#### 您可以通过如下git clone命令，或者ModelScope SDK来下载模型
+
+
+SDK下载
+```bash
+#安装ModelScope
+pip install modelscope
+```
+```python
+#SDK模型下载
+from modelscope import snapshot_download
+model_dir = snapshot_download('rebirth1/xiaonuanxin')
+```
+Git下载
+```
+#Git模型下载
+git clone https://www.modelscope.cn/rebirth1/xiaonuanxin.git
+```
+
+<p style="color: lightgrey;">如果您是本模型的贡献者，我们邀请您根据<a href="https://modelscope.cn/docs/ModelScope%E6%A8%A1%E5%9E%8B%E6%8E%A5%E5%85%A5%E6%B5%81%E7%A8%8B%E6%A6%82%E8%A7%88" style="color: lightgrey; text-decoration: underline;">模型贡献文档</a>，及时完善模型卡片内容。</p>
+@inproceedings{xie-etal-2025-psydt,
+    title = "{P}sy{DT}: Using {LLM}s to Construct the Digital Twin of Psychological Counselor with Personalized Counseling Style for Psychological Counseling",
+    author = "Xie, Haojie  and
+      Chen, Yirong  and
+      Xing, Xiaofen  and
+      Lin, Jingkai  and
+      Xu, Xiangmin",
+    editor = "Che, Wanxiang  and
+      Nabende, Joyce  and
+      Shutova, Ekaterina  and
+      Pilehvar, Mohammad Taher",
+    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
+    month = jul,
+    year = "2025",
+    address = "Vienna, Austria",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2025.acl-long.55/",
+    pages = "1081--1115",
+    ISBN = "979-8-89176-251-0",
+    abstract = "Currently, large language models (LLMs) have made significant progress in the field of psychological counseling. However, existing mental health LLMs overlook a critical issue where they do not consider the fact that different psychological counselors exhibit different personal styles, including linguistic style and therapy techniques, etc. As a result, these LLMs fail to satisfy the individual needs of clients who seek different counseling styles. To help bridge this gap, we propose PsyDT, a novel framework using LLMs to construct the Digital Twin of Psychological counselor with personalized counseling style. Compared to the time-consuming and costly approach of collecting a large number of real-world counseling cases to create a specific counselor{'}s digital twin, our framework offers a faster and more cost-effective solution. To construct PsyDT, we utilize dynamic one-shot learning by using GPT-4 to capture counselor{'}s unique counseling style, mainly focusing on linguistic style and therapy techniques. Subsequently, using existing single-turn long-text dialogues with client{'}s questions, GPT-4 is guided to synthesize multi-turn dialogues of specific counselor. Finally, we fine-tune the LLMs on the synthetic dataset, PsyDTCorpus, to achieve the digital twin of psychological counselor with personalized counseling style. Experimental results indicate that our proposed PsyDT framework can synthesize multi-turn dialogues that closely resemble real-world counseling cases and demonstrate better performance compared to other baselines, thereby show that our framework can effectively construct the digital twin of psychological counselor with a specific counseling style."
+}