Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+datasets:
+- BEncoderRT/User_Intent_Risk_Triage
+language:
+- zh
+base_model:
+- Qwen/Qwen2.5-0.5B-Instruct
+pipeline_tag: text-classification
+tags:
+- lora
+- zh
+- demo
+---
+# Qwen2.5-0.5B-Instruct LoRA: Chinese Mental Health Risk Triage Model
+
+## Model Overview
+This is a **LoRA fine-tuned** version of **Qwen/Qwen2.5-0.5B-Instruct**, specialized for **Chinese mental health risk assessment and triage** in conversational scenarios.
+
+The model takes either:
+- A single user statement, or
+- A complete multi-turn conversation history
+
+and directly outputs a **structured JSON** containing a professional risk evaluation.
+
+### Output Format
+```json
+{
+  "intent": ["passive_suicide_ideation", "mild_distress", ...],
+  "risk": "low" | "medium" | "high" | "ambiguous",
+  "strategy": ["empathize", "support", "clarify", "escalate", "provide_resources", ...],
+  "uncertainty": "low" | "medium" | "high",
+  "recommended_action": ["empathize", "deep_assessment", ...]
+}
+
+Key Capabilities
+
+Accurately detects subtle and indirect expressions of psychological distress common in Chinese (e.g., “活着没意思”、“快受不了了”、“不如解脱”)
+Distinguishes risk levels from mild distress to clear suicidal ideation
+Recommends appropriate assistant strategies, with strong emphasis on escalation and resource provision when suicide risk is present
+Handles both short single-turn inputs and very long multi-turn conversation contexts
+
+Intended Use
+
+Safety layer in Chinese mental health chatbots or counseling apps
+Automated risk triage for online psychological support platforms
+Early detection of depression and suicidal ideation in user conversations
+Research on mental health AI in Chinese-language environments
+
+Base Model
+
+Qwen/Qwen2.5-0.5B-Instructhttps://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct
+
+Fine-tuning Details
+
+Adapter type: LoRA (r=16, alpha=32, targeting q/k/v/o_proj)
+Dataset: Custom high-quality Chinese mental health risk assessment data (single-turn + multi-turn)
+Training objective: Supervised fine-tuning with strict JSON output formatting and EOS enforcement for clean generation
+
+
+'''python
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", device_map="auto", torch_dtype="auto")
+model = PeftModel.from_pretrained(base_model, "BEncoderRT/User_Intent_Risk_Triage")
+
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
+
+# Your prompt template should match training format
+prompt = """### 任务指令:
+请基于以下对话历史，分析用户当前的精神风险状态，并输出意图（intent）、风险等级（risk）以及推荐的应对策略（strategy）。
+
+### 对话历史:
+用户: 最近真的特别难受，夜里睡不着，经常会想活着没意思，不如死了算了……
+
+### 分析输出:
+"""
+
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, max_new_tokens=256, do_sample=False)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))