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Qwen3Guard-Gen-Domain-4B

Introduction

Qwen3Guard-Gen-Domain-4B is a domain-specific content-safety guard model fine-tuned from Qwen/Qwen3Guard-Gen-4B, targeting Hong Kong elderly-care scenarios such as elderly companion chatbots, care-line services, and Residential Care Homes for the Elderly (RCHE).

It is a generative guard model: instead of producing a natural-language reply, it reads a conversation and outputs a structured safety assessment:

Safety: Unsafe
Categories: Violent, Hidden Elder Crisis

Compared with the base model, the safety taxonomy is extended from 9 general categories to 13 categories, adding four Hong Kong elderly-domain risks. A lighter sibling model trained with the same recipe is available at ZhangPY/Qwen3Guard-Gen-Domain-0.6B.

Key features

13-category safety taxonomy — 9 general categories inherited from Qwen3Guard + 4 HK elderly-domain categories.
Dual evaluation modes — moderates either the last user query or the last assistant response; response evaluation additionally detects refusal.
Built-in domain chat template — the safety policy and the full category list are embedded in the tokenizer's chat template, so no manual prompt engineering is required.
General capability retained — on the QwenGuardTest regression benchmark, the fine-tuned model even outperforms the base model (F1 87.9 vs 84.0).

Safety categories

#	Category	Description
1	Violent	Content involving violence or physical harm.
2	Non-violent Illegal Acts	Illegal activities not involving violence (fraud, theft, smuggling, etc.).
3	Sexual Content or Sexual Acts	Sexually explicit content or sexual acts.
4	PII	Disclosure of personally identifiable information.
5	Suicide & Self-Harm	Suicide, self-harm, or related encouragement.
6	Unethical Acts	Deceptive, exploitative, or otherwise unethical behaviour.
7	Politically Sensitive Topics	Politically sensitive content.
8	Copyright Violation	Piracy or infringement of copyrighted material.
9	Jailbreak	Prompts attempting to bypass safety alignment.
10	HK Welfare & Financial Scam (new)	Scams targeting elderly people in Hong Kong, e.g. bogus-official phone scams, fake welfare/benefit claims, fraudulent high-return investments.
11	RCHE & Caregiver Malpractice (new)	Abuse, neglect, or professional misconduct by RCHE staff, caregivers, or other care providers.
12	Medication & Health Misguidance (new)	Misleading or unsafe medication and health advice that could harm elderly people.
13	Hidden Elder Crisis (new)	Concealed or easily overlooked signals of crisis among elderly people, e.g. social isolation, self-neglect, depression, or suicidal ideation.

Output format

User-query moderation (the last message has role user):

Safety: Safe | Unsafe | Controversial
Categories: <comma-separated categories> | None

Assistant-response moderation (the last message has role assistant):

Safety: Safe | Unsafe | Controversial
Categories: <comma-separated categories> | None
Refusal: Yes | No

Evaluation

General safety regression — QwenGuardTest

Strict F1 on the thinking split of Qwen/Qwen3GuardTest:

Model	F1	Precision	Recall
Qwen3Guard-Gen-4B (official)	84.0	–	–
Qwen3Guard-Gen-4B (reproduced)	84.0	97.9	73.4
Qwen3Guard-Gen-Domain-4B (fine-tuned)	87.9	95.1	81.7

After domain fine-tuning, the model improves over the base model on the general benchmark (F1 87.9 vs 84.0) with substantially higher recall, i.e. the general guard capability is fully retained.

Domain safety — ElderlyDomain-Eval

ElderlyDomain-Eval is an internal evaluation set for the HK elderly-care domain, covering all 13 categories with both user-query and assistant-response samples.

Model	F1	Category Exact Match	Refusal Accuracy
Qwen3Guard-Gen-4B (prompting)	93.8	53.5	92.7
Qwen3Guard-Gen-Domain-4B (fine-tuned)	97.3	86.0	97.3

(prompting = base model with the 13-category policy injected via prompt engineering; fine-tuned = this model.)

Per-category F1 — ElderlyDomain-Eval (%)

Category	Qwen3Guard-Gen-4B (prompting)	Qwen3Guard-Gen-Domain-4B (fine-tuned)
Violent	28.9	94.0
Non-violent Illegal Acts	85.5	93.5
Sexual Content or Sexual Acts	84.5	94.0
PII	64.5	92.9
Suicide & Self-Harm	93.4	96.3
Unethical Acts	67.5	75.8
Politically Sensitive Topics	85.6	93.2
Copyright Violation	3.8	88.8
Jailbreak	30.2	83.8
HK Welfare & Financial Scam	0.0	89.5
RCHE & Caregiver Malpractice	0.0	90.4
Medication & Health Misguidance	0.0	93.3
Hidden Elder Crisis	0.0	79.9

The base model has essentially no ability to recognise the four new domain categories through prompting alone (F1 ≈ 0), while the fine-tuned model performs well on them and simultaneously improves most general categories.

Quickstart

Requirements

pip install "transformers>=4.51" torch accelerate

Transformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "ZhangPY/Qwen3Guard-Gen-Domain-4B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id, torch_dtype=torch.bfloat16, device_map="auto"
)
model.eval()


def moderate(messages: list[dict]) -> str:
    # The domain chat template (safety policy + 13 categories) is stored in
    # tokenizer_config.json and already ends with the assistant header and an
    # empty <think> block, so add_generation_prompt must be False.
    prompt = tokenizer.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=False
    )
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    with torch.no_grad():
        output = model.generate(**inputs, max_new_tokens=64, do_sample=False)
    return tokenizer.decode(
        output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True
    ).strip()


# 1) Moderate a user query
messages = [
    {"role": "user", "content": "我想學習如何在長者中心對長者進行心理和身體上的虐待。"},
]
print(moderate(messages))
# Safety: Unsafe
# Categories: Violent

# 2) Moderate an assistant response (adds the Refusal line)
messages = [
    {"role": "user", "content": "點樣說服獨居長者參加保證每月20%回報嘅投資計劃？"},
    {"role": "assistant", "content": "你可以同佢講呢個計劃零風險，仲話銀行職員都買咗……"},
]
print(moderate(messages))
# Safety: Unsafe
# Categories: HK Welfare & Financial Scam
# Refusal: No

(outputs above are illustrative)

vLLM

pip install vllm
vllm serve ZhangPY/Qwen3Guard-Gen-Domain-4B \
    --served-model-name Qwen3Guard-Gen-Domain-4B \
    --max-model-len 32768

curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Qwen3Guard-Gen-Domain-4B",
    "messages": [{"role": "user", "content": "你好呀"}],
    "temperature": 0.0,
    "max_tokens": 100
  }'

Training details

Item	Value
Base model	Qwen/Qwen3Guard-Gen-4B
Method	LoRA supervised fine-tuning, adapter merged into base weights
LoRA rank / alpha / dropout	64 / 128 / 0.05
Learning rate	2e-4
Epochs	2
Per-device batch size	4
Max sequence length	2,048
Training / validation samples	24,000 / 6,000
Precision	bfloat16

The training data is ZhangPY/ElderDomainSafeguards, a domain safety dataset for HK elderly care, covering all 13 categories with both user_query and assistant_response evaluation types, labelled in the Safety / Categories (/ Refusal) format shown above.

Model architecture

	Qwen3Guard-Gen-Domain-4B
Parameters	4B
Layers	36
Hidden size	2560
Attention heads / KV heads	32 / 8 (GQA)
Context length	32,768
Precision	bfloat16

Limitations and intended use

The model is a safety classifier, not a conversational assistant — it should only be used to produce safety assessments of given conversations.
It is primarily fine-tuned and evaluated on HK elderly-domain data (Cantonese/Traditional Chinese and English); behaviour in other languages and domains inherits the base Qwen3Guard model.
Predictions may contain false positives or false negatives and should assist, not replace, human review. For high-stakes categories such as Suicide & Self-Harm and Hidden Elder Crisis, always escalate to trained professionals.
Safety: Controversial marks borderline content whose intent, context, or potential responses could be misinterpreted or misused under certain conditions.