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
{
"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
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))