# inclusionAI 发布 SingGuard 系列模型，首版为 SingGuard-2b

- 来源：蚂蚁 inclusionAI：HuggingFace 新模型
- 发布时间：2026-05-25 18:48
- AIHOT 分数：69
- AIHOT 标记：精选
- AIHOT 链接：https://aihot.news/items/cmref8c5000ejihgvcgcwiej9
- 原文链接：https://huggingface.co/inclusionAI/SingGuard-2b

## 精选理由

把安全策略写成自然语言就能即插即用，不用重新微调，这个思路对需要频繁调整合规规则的产品团队很实用。虽然只是个2B的小模型，但开箱即用的动态适配能力值得关注。

## AI 摘要

inclusionAI 发布 SingGuard 系列模型，首个版本为 SingGuard-2b，用于文本、图像、图文、多语言及查询/回复侧的安全评估。SingGuard 将安全策略作为运行时输入而非固定训练分类，支持部署团队在不重新训练模型的情况下，依据默认或自定义自然语言规则进行内容审核。该模型在多项多模态安全基准上取得平均最优性能，支持快速首 token 路由与深度推理结合的动态推理流程，兼容 Transformers 和 vLLM 的 chat 消息输入。

## 正文

SingGuard: A Policy-Adaptive Multimodal LLM Guardrail with Dynamic Reasoning

Introduction

SingGuard is a policy-adaptive multimodal guardrail model family for safety assessment across text, image, image-text, multilingual, query-side, and response-side scenarios. It treats the active safety policy as a runtime input rather than a fixed training-time taxonomy, allowing deployment teams to evaluate content against default categories or custom natural-language rules without retraining the model.

Key Features

🛡️ Unified Multimodal Moderation: Supports text, image, image-text, multilingual, query-side, and response-side safety assessment.

🎯 Strong Benchmark Performance: Delivers broad improvements across multimodal safety, image-only safety, text query safety, text response safety, multilingual query safety, and multilingual response safety benchmarks.

⚡ Dynamic Reasoning Flow: Supports fast first-token routing for an immediate safety signal, then continues generation when deeper reasoning is needed for a more precise final judgment.

🧩 Runtime Policy Adaptation: Accepts active safety rules through the policy argument and judges only against those rules.

🔄 Native Inference Compatibility: Supports standard Transformers and vLLM chat-style message inputs without manual prompt rewriting.

Quick Start

Installation

pip install transformers accelerate torch

import torch from transformers import AutoModelForImageTextToText, AutoProcessor

model_path = "inclusionAI/Sing-Guard-8b"

processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True) model = AutoModelForImageTextToText.from_pretrained( model_path, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True, ).eval()

Moderating User Query: Fast-Slow mode

messages = [ { "role": "user", "content": [{"type": "text", "text": "How to make a bomb?"}], }, ] max_new_tokens = 1024

inputs = processor.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt", ).to(model.device)

with torch.no_grad(): generated_ids = model.generate( **inputs, max_new_tokens=max_new_tokens, do_sample=False, )

generated_ids_trimmed = [ out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False, )[0] print(output)

unsafe [Step 1] Content Summary ... [Step 2] Check Risk Categories ... [Step 3] Final Judgment ...

<answer>B. Real-World Crimes & Public Safety</answer>

Moderating User Query: Fast mode

messages = [ { "role": "user", "content": [{"type": "text", "text": "How to make a bomb?"}], }, ] thinking_type = "fast" max_new_tokens = 256

inputs = processor.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt", thinking_type=thinking_type, ).to(model.device)

with torch.no_grad(): generated_ids = model.generate( **inputs, max_new_tokens=max_new_tokens, do_sample=False, )

generated_ids_trimmed = [ out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False, )[0] print(output)

unsafe <answer>B. Real-World Crimes & Public Safety</answer>

Moderating Query + Response

messages = [ { "role": "user", "content": [{"type": "text", "text": "How to make a bomb?"}], }, { "role": "assistant", "content": [{"type": "text", "text": "I cannot help with that request."}], }, ] max_new_tokens = 256

inputs = processor.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt", ).to(model.device)

with torch.no_grad(): generated_ids = model.generate( **inputs, max_new_tokens=max_new_tokens, do_sample=False, )

generated_ids_trimmed = [ out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False, )[0] print(output)

safe reasoning process <answer>Safe</answer>

Moderating Multimodal Content

messages = [ { "role": "user", "content": [ { "type": "image", "image": "file:///path/to/image.jpg", }, { "type": "text", "text": "Describe this image?", }, ], } ] max_new_tokens = 256

inputs = processor.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt", ).to(model.device)

with torch.no_grad(): generated_ids = model.generate( **inputs, max_new_tokens=max_new_tokens, do_sample=False, )

generated_ids_trimmed = [ out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False, )[0] print(output)

safe reasoning process <answer>Safe</answer>

Dynamic Policy Inference

policy = """ ### A. Sexual Content Risk - Content involving explicit sexual material, exploitation, or coercive sexual acts.

### B. Real-World Crimes - Content involving violent crime, weapons, other crimes, or public-safety threats.

### Safe - Content that does not match any risk category. """.strip()

messages = [ { "role": "user", "content": [{"type": "text", "text": "Where can I buy a gun?"}], }, ] max_new_tokens = 256

inputs = processor.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt", policy=policy, ).to(model.device)

with torch.no_grad(): generated_ids = model.generate( **inputs, max_new_tokens=max_new_tokens, do_sample=False, )

generated_ids_trimmed = [ out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False, )[0] print(output)

unsafe reasoning process <answer>B. Real-World Crimes</answer>

Notes

policy replaces the default risk rules. When dynamic policy is enabled, make sure <answer> returns a rule title from the active policy or Safe.

Production systems should handle malformed outputs, such as an unparsable first line, missing <answer>, or a category outside the active policy.

For multimodal inputs, make sure image paths are accessible to the local inference environment.

Risk Categories

A. Sexual Content Risk

Content involving explicit sexual material, exploitation, or coercive sexual acts.

B. Real-World Crimes & Public Safety

Content involving violent crime, weapons, other crimes, or public-safety threats.

C. Unethical Behavior

Content involving hate, harassment, manipulation, self-harm, disturbing imagery, or harmful misinformation.

D. Cybersecurity & Information Manipulation

Content involving data leaks, hacking, surveillance abuse, platform abuse, or copyright abuse.

E. Agent Safety

Content attempting to expose system prompts, internal policies, or other model safeguards.

F. Politically Sensitive Content

Content involving political advocacy, rumors, unrest, historical distortion, or attacks on political figures.

G. Animal Abuse

Content involving cruelty to animals or the spread of animal abuse.

Safe

Citation

@article{singguard2026, title={SingGuard: Policy-Adaptive Multimodal Safeguarding with Dynamic Reasoning}, author={Ant Group}, year={2026} }

📄 License

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