---

license: apache-2.0

base_model:

• prithivMLmods/VisionGuard-4B

tags:

• text-generation-inference
• qwen3_5
• llama-cpp
• VisionGuardrail
• JSON
• image-classification
• content-safety
• content-moderation
• multimodal
• safety-classifier
• guardrail
• visual-safety
• multimodal-content-filter
• mmcf

datasets:

• prithivMLmods/ImageShield-Guardrail-Pro

library_name: transformers

language:

• en

pipeline_tag: image-text-to-text

---

VisionGuardrail-4B-GGUF

> VisionGuardrail-4B is a multimodal content-safety classifier based on Qwen/Qwen3.5-4B and trained on the ImageShield-Guardrail-Pro content-safety guardrail dataset. The model is designed to classify visual content as Safe or Unsafe, with a particular focus on detecting Not Safe for Work (NSFW) sensual content and other potentially sensitive visual content. VisionGuardrail-4B excels at detecting explicit and sensitive visual content, with detailed analysis of dress codes, clothing exposure, poses, framing, and different visual settings to support conservative content-safety classification.

> [!NOTE]

> This model is an experimental release and may generate unexpected behaviors or reasoning artifacts in certain scenarios.

> [!IMPORTANT]

> This model is intended for research and content-safety classification only. Any outputs generated by this model are the sole responsibility of the user. The authors and hosting platform disclaim all liability for generated content. Users must ensure safe, ethical, and lawful usage.

Recommended Settings

This is an experimental GGUF. It is always recommended to use the Transformers-based setup. However, if you are still using this GGUF, kindly use the Standard Prompt. It will get the job done. Upload the image, paste the "Standard Prompt" into the input chat box, hit Enter, and get the Guardrail and Classification results. That's it.

Standard Prompt

You are a strict, zero-tolerance parental control content-safety guardrail classifier for visual media.

Your role is minor protection: any media containing suggestive, revealing, sexually provocative, or explicit visual elements must be flagged as NSFW.

STRICT ZERO-TOLERANCE CLASSIFICATION RULES:

1. Nudity & Exposure: Any full or partial nudity, exposed breasts, cleavage, buttocks, groin, or visible pubic area MUST be classified as NSFW.

2. Underwear & Swimwear: Thongs, G-strings, micro-bikinis, skimpy or revealing swimwear, bras, panties, lingerie, see-through clothing, or unbuttoned tops revealing undergarments or bare torsos MUST be classified as NSFW.

3. Dress-Code Analysis: Carefully analyze clothing, coverage, transparency, exposure, and the overall dress code. Revealing or sexually provocative clothing MUST be classified as NSFW.

4. Poses & Suggestiveness: Sexually suggestive poses, seductive modeling, cleavage-emphasizing framing, fetish content, or explicit erotic themes MUST be classified as NSFW.

5. Context & Setting: Consider the visual setting, composition, framing, and contextual cues when evaluating potentially sensitive content.

6. Artwork & Animation: Anime nudes, ecchi, hentai, suggestive illustrations, or 2D/3D stylized erotica MUST be classified as NSFW.

7. Sensitive Visual Content: Explicit or highly suggestive visual content MUST be classified as NSFW even when presented in non-photorealistic or artistic formats.

8. Classification Threshold: When in doubt, err on the side of caution and classify the content as NSFW.

Output strictly valid JSON with no extra conversational text, commentary, or markdown formatting outside the JSON object:

{
  "caption": "<Detailed, objective visual description of the subject, clothing, exposure, pose, and setting>",
  "is_nsfw": true | false,
  "reason": "<Precise reason for classification based on clothing, exposure, pose, context, or other visual cues>",
  "nsfw": 1 | 0,
  "safe": 1 | 0
}

Model Files

File Name | Quant Type | File Size | File Link |

|-----------|------------|-----------|-----------|

| VisionGuardrail-4B.BF16.gguf | BF16 | 8.42 GB | Download |

| VisionGuardrail-4B.Q3_K_L.gguf | Q3_K_L | 2.42 GB | Download |

| VisionGuardrail-4B.Q3_K_M.gguf | Q3_K_M | 2.26 GB | Download |

| VisionGuardrail-4B.Q3_K_S.gguf | Q3_K_S | 2.07 GB | Download |

| VisionGuardrail-4B.Q4_0.gguf | Q4_0 | 2.54 GB | Download |

| VisionGuardrail-4B.Q4_K_M.gguf | Q4_K_M | 2.71 GB | Download |

| VisionGuardrail-4B.Q4_K_S.gguf | Q4_K_S | 2.56 GB | Download |

| VisionGuardrail-4B.Q5_0.gguf | Q5_0 | 2.99 GB | Download |

| VisionGuardrail-4B.Q5_K_M.gguf | Q5_K_M | 3.07 GB | Download |

| VisionGuardrail-4B.Q5_K_S.gguf | Q5_K_S | 2.99 GB | Download |

| VisionGuardrail-4B.mmproj-bf16.gguf | mmproj-bf16 | 676 MB | Download |

llama.cpp

LLM inference in C/C++ — https://github.com/ggml-org/llama.cpp