ZhangPY

Qwen3Guard-Gen-Domain-0.6B

README

License: apache-2.0

Introduction

Qwen3Guard-Gen-Domain-0.6B is a domain-specific content-safety guard model fine-tuned from Qwen/Qwen3Guard-Gen-0.6B, 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:

text

Safety: UnsafeCategories: 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 sibling model with the same recipe is available at ZhangPY/Qwen3Guard-Gen-Domain-4B.

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 — regression on QwenGuardTest shows general safe/unsafe judgement is largely preserved after domain fine-tuning.

Safety categories

Table with columns: #, Category, Description
#	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):

text

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

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

text

Safety: Safe | Unsafe | ControversialCategories: <comma-separated categories> | NoneRefusal: Yes | No

Evaluation

General safety regression — QwenGuardTest

Strict F1 on the thinking split of Qwen/Qwen3GuardTest:

Table with columns: Model, F1, Precision, Recall
Model	F1	Precision	Recall
Qwen3Guard-Gen-0.6B (official)	83.6	–	–
Qwen3Guard-Gen-0.6B (reproduced)	85.7	96.1	77.3
Qwen3Guard-Gen-Domain-0.6B (fine-tuned)	81.5	76.2	87.7

After domain fine-tuning, overall F1 remains close to the base model (81.5 vs 83.6): precision drops slightly while recall increases substantially, i.e. the general guard capability is largely 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.

Table with columns: Model, F1, Category Exact Match, Refusal Accuracy
Model	F1	Category Exact Match	Refusal Accuracy
Qwen3Guard-Gen-0.6B (prompting)	93.5	52.5	94.3
Qwen3Guard-Gen-Domain-0.6B (fine-tuned)	97.1	84.3	97.0

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

Per-category F1 — ElderlyDomain-Eval (%)

Table with columns: Category, Qwen3Guard-Gen-0.6B (prompting), Qwen3Guard-Gen-Domain-0.6B (fine-tuned)
Category	Qwen3Guard-Gen-0.6B (prompting)	Qwen3Guard-Gen-Domain-0.6B (fine-tuned)
Violent	27.4	93.4
Non-violent Illegal Acts	81.7	92.5
Sexual Content or Sexual Acts	86.1	93.3
PII	62.7	91.1
Suicide & Self-Harm	92.1	95.5
Unethical Acts	53.2	73.7
Politically Sensitive Topics	75.1	94.2
Copyright Violation	1.3	88.3
Jailbreak	15.6	80.4
HK Welfare & Financial Scam	0.0	88.1
RCHE & Caregiver Malpractice	0.0	89.6
Medication & Health Misguidance	0.0	92.3
Hidden Elder Crisis	0.0	76.9

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

bash

pip install "transformers>=4.51" torch accelerate

Transformers

python

import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer model_id = "ZhangPY/Qwen3Guard-Gen-Domain-0.6B"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 querymessages = [    {"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

bash

pip install vllmvllm serve ZhangPY/Qwen3Guard-Gen-Domain-0.6B \    --served-model-name Qwen3Guard-Gen-Domain-0.6B \    --max-model-len 32768

bash

curl http://localhost:8000/v1/chat/completions \  -H "Content-Type: application/json" \  -d '{    "model": "Qwen3Guard-Gen-Domain-0.6B",    "messages": [{"role": "user", "content": "你好呀"}],    "temperature": 0.0,    "max_tokens": 100  }'

Training details

Table with columns: Item, Value
Item	Value
Base model	Qwen/Qwen3Guard-Gen-0.6B
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

Table with columns: Qwen3Guard-Gen-Domain-0.6B
	Qwen3Guard-Gen-Domain-0.6B
Parameters	0.6B
Layers	28
Hidden size	1024
Attention heads / KV heads	16 / 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.

Acknowledgements

Base model: Qwen/Qwen3Guard-Gen-0.6B and the Qwen team's guard series (GitHub).
General regression benchmark: Qwen/Qwen3GuardTest.

License

This model is released under the Apache 2.0 license, following the base model.

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Model Details

Model Provider

ZhangPY

Model Tree

Base

Qwen/Qwen3Guard-Gen-0.6B

Fine-tuned

this model

Input Modalities

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Output Modalities

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README

License: apache-2.0

Introduction

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

text

Safety: UnsafeCategories: Violent, Hidden Elder Crisis

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 — regression on QwenGuardTest shows general safe/unsafe judgement is largely preserved after domain fine-tuning.

Safety categories

Table with columns: #, Category, Description
#	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):

text

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

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

text

Safety: Safe | Unsafe | ControversialCategories: <comma-separated categories> | NoneRefusal: Yes | No

Safety: Safe | Unsafe | ControversialCategories: <comma-separated categories> | NoneRefusal: Yes | No

Evaluation

General safety regression — QwenGuardTest

Strict F1 on the thinking split of Qwen/Qwen3GuardTest:

Table with columns: Model, F1, Precision, Recall
Model	F1	Precision	Recall
Qwen3Guard-Gen-0.6B (official)	83.6	–	–
Qwen3Guard-Gen-0.6B (reproduced)	85.7	96.1	77.3
Qwen3Guard-Gen-Domain-0.6B (fine-tuned)	81.5	76.2	87.7

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.

Table with columns: Model, F1, Category Exact Match, Refusal Accuracy
Model	F1	Category Exact Match	Refusal Accuracy
Qwen3Guard-Gen-0.6B (prompting)	93.5	52.5	94.3
Qwen3Guard-Gen-Domain-0.6B (fine-tuned)	97.1	84.3	97.0

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

Per-category F1 — ElderlyDomain-Eval (%)

Table with columns: Category, Qwen3Guard-Gen-0.6B (prompting), Qwen3Guard-Gen-Domain-0.6B (fine-tuned)
Category	Qwen3Guard-Gen-0.6B (prompting)	Qwen3Guard-Gen-Domain-0.6B (fine-tuned)
Violent	27.4	93.4
Non-violent Illegal Acts	81.7	92.5
Sexual Content or Sexual Acts	86.1	93.3
PII	62.7	91.1
Suicide & Self-Harm	92.1	95.5
Unethical Acts	53.2	73.7
Politically Sensitive Topics	75.1	94.2
Copyright Violation	1.3	88.3
Jailbreak	15.6	80.4
HK Welfare & Financial Scam	0.0	88.1
RCHE & Caregiver Malpractice	0.0	89.6
Medication & Health Misguidance	0.0	92.3
Hidden Elder Crisis	0.0	76.9

Quickstart

Requirements

bash

pip install "transformers>=4.51" torch accelerate

Transformers

python

import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer model_id = "ZhangPY/Qwen3Guard-Gen-Domain-0.6B"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 querymessages = [    {"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

import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer model_id = "ZhangPY/Qwen3Guard-Gen-Domain-0.6B"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 querymessages = [    {"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

bash

pip install vllmvllm serve ZhangPY/Qwen3Guard-Gen-Domain-0.6B \    --served-model-name Qwen3Guard-Gen-Domain-0.6B \    --max-model-len 32768

pip install vllmvllm serve ZhangPY/Qwen3Guard-Gen-Domain-0.6B \    --served-model-name Qwen3Guard-Gen-Domain-0.6B \    --max-model-len 32768

bash

curl http://localhost:8000/v1/chat/completions \  -H "Content-Type: application/json" \  -d '{    "model": "Qwen3Guard-Gen-Domain-0.6B",    "messages": [{"role": "user", "content": "你好呀"}],    "temperature": 0.0,    "max_tokens": 100  }'

curl http://localhost:8000/v1/chat/completions \  -H "Content-Type: application/json" \  -d '{    "model": "Qwen3Guard-Gen-Domain-0.6B",    "messages": [{"role": "user", "content": "你好呀"}],    "temperature": 0.0,    "max_tokens": 100  }'

Training details

Table with columns: Item, Value
Item	Value
Base model	Qwen/Qwen3Guard-Gen-0.6B
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

Model architecture

Table with columns: Qwen3Guard-Gen-Domain-0.6B
	Qwen3Guard-Gen-Domain-0.6B
Parameters	0.6B
Layers	28
Hidden size	1024
Attention heads / KV heads	16 / 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.

Acknowledgements

Base model: Qwen/Qwen3Guard-Gen-0.6B and the Qwen team's guard series (GitHub).
General regression benchmark: Qwen/Qwen3GuardTest.

License

This model is released under the Apache 2.0 license, following the base model.