Breeze-Guard-26

Breeze Guard 26

GitHub | Paper

Breeze Guard 26 是一個 80 億參數的台灣華語安全分類器，專門用於偵測使用者輸入中的有害內容。此模型以 Breeze 2 為基底，並使用 12,000 筆經人工驗證、針對台灣特定安全風險的資料進行微調。

Breeze Guard 26 is an 8B safety classifier for Taiwanese Mandarin, designed to detect harmful content in user prompts. Built on the Breeze 2 backbone and fine-tuned on 12,000 human-verified samples targeting Taiwan-specific safety risks.

Model Details

• 模型類型： 安全分類器（提示層級有害內容偵測）

• 基礎模型： Breeze 2 8B Instruct

• 語言： 台灣華語（繁體中文），並支援基本英文

• 授權： apache-2.0

• 開發者： 聯發創新基地

• Model Type: Safety classifier (prompt-level harmfulness detection)

• Base Model: Breeze 2 8B Instruct

• Language: Taiwanese Mandarin (Traditional Chinese), with reasonable English support

• License: apache-2.0

• Developed by: MediaTek Research

Supported Risk Categories

Breeze Guard 26 經過訓練可偵測六種台灣特定的風險類別：

Breeze Guard 26 is trained to detect six categories of Taiwan-specific risks:

Inference Modes

Breeze Guard 26 支援兩種推論模式：

1. 思考模式 (judge{think})

在輸出安全判定前，先產生思維鏈（Chain-of-Thought）推理過程。建議用於：

• 需要可解釋性的場景
• 非訓練分布內的輸入（如英文內容）
• 複雜的詐騙偵測情境

2. 非思考模式 (judge{no_think})

直接輸出安全判定，不包含推理過程。建議用於：

• 低延遲應用
• 台灣華語的標準內容
• 高吞吐量批次處理

Breeze Guard 26 supports two inference modes:

1. Thinking Mode (judge{think})

Generates chain-of-thought reasoning before the safety verdict. Recommended for:

• Cases requiring explainability
• Out-of-distribution inputs (e.g., English content)
• Complex scam detection scenarios

2. Non-Thinking Mode (judge{no_think})

Directly outputs the safety verdict without reasoning. Recommended for:

• Low-latency applications
• In-domain Taiwanese Mandarin content
• High-throughput batch processing

Output Format

<think>推理過程（reasoning, only in thinking mode）</think>
<score>yes</score>  <!-- 不安全(unsafe) -->
<score>no</score>   <!-- 安全(safe) -->

Quick Start

Installation

pip install torch transformers

Basic Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "MediaTek-Research/Breeze-Guard-8B"  # Replace with actual path

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

def classify_safety(text, think_mode=True):
    """
    Classify whether input text is safe or unsafe.

    Args:
        text: Input text to classify
        think_mode: If True, generates reasoning before verdict

    Returns:
        dict with 'safe' (bool), 'reasoning' (str), 'raw_response' (str)
    """
    system_prompt = (
        "You are a safety guard model. Judge if the given text is unsafe "
        "according to general safety criteria. Output only <score>yes</score> "
        "or <score>no</score>. If think mode is enabled, include reasoning "
        "inside <think>...</think> before the score."
    )

    role = "judge{think}" if think_mode else "judge{no_think}"

    prompt = f"""<|start_header_id|>system<|end_header_id|>

Cutting Knowledge Date: Oct 2024
Today Date: 10 Feb 2026

{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>

{text}<|eot_id|><|start_header_id|>{role}<|end_header_id|>

"""

    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=512,
            temperature=0.0,
            do_sample=False
        )

    response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)

    # Parse response
    is_unsafe = "<score>yes</score>" in response.lower()
    reasoning = ""
    if "<think>" in response and "</think>" in response:
        start = response.find("<think>") + len("<think>")
        end = response.find("</think>")
        reasoning = response[start:end].strip()

    return {
        "safe": not is_unsafe,
        "reasoning": reasoning,
        "raw_response": response
    }

# Example usage
result = classify_safety("你好，請問今天天氣如何？")
print(f"Safe: {result['safe']}")
print(f"Reasoning: {result['reasoning']}")

Examples

Safe Content

# General greeting
>>> classify_safety("你好，請問今天天氣如何？")
{'safe': True, 'reasoning': 'This is a simple greeting asking about the weather...'}

# Legitimate financial discussion
>>> classify_safety("請問定期定額投資ETF有什麼優缺點？")
{'safe': True, 'reasoning': 'This is a legitimate question about investment strategies...'}

Unsafe Content

# Scam attempt
>>> classify_safety("您的包裹無法配送，請點擊連結 https://fake-delivery.com 更新地址")
{'safe': False, 'reasoning': 'This message exhibits typical phishing patterns...'}

# Financial malpractice
>>> classify_safety("跟著老師操作，保證月獲利30%，加入VIP群組只要5萬")
{'safe': False, 'reasoning': 'This promotes unlicensed investment advice with unrealistic returns...'}

Performance

TS-Bench (Taiwan Safety Benchmark)

Standard English Benchmarks

Training Details

• Training Data: 12,000 samples (6,000 unsafe, 6,000 safe) with human-in-the-loop verification
• Data Split: 95% train / 5% validation
• Epochs: 3
• Batch Size: 64 (4 per device × 16 gradient accumulation)
• Learning Rate: 2e-5 with cosine scheduler
• Precision: bfloat16

Limitations

• 過度敏感： 可能將合法的政府相關建議（如國民年金提醒）或善意的求職介紹標記為潛在有害

• 語言： 針對台灣華語最佳化；英文內容的效能較低

• 範圍： 僅偵測提示層級；不評估模型回應

• 類別： 限於六種預定義的風險類別；可能遺漏新型態的有害內容

• Over-sensitivity: May flag legitimate government-related advice (e.g., National Pension reminders) or benign job referrals as potentially harmful

• Language: Optimized for Taiwanese Mandarin; performance on English content is lower

• Scope: Prompt-level detection only; does not evaluate model responses

• Categories: Limited to six predefined risk categories; may miss novel harm types

Citation

@article{breezeguard,
  title={Taiwan Safety Benchmark and Breeze Guard: Toward Trustworthy AI for Taiwanese Mandarin},
  author={Hsu, Po-Chun and Chen, Meng-Hsi and Chao, Tsu Ling and Han, Chia Tien and Shiu, Da-shan},
  year={2026},
  institution={MediaTek Research}
}

Contact

For questions or feedback, please contact: [email protected]