prithivMLmods

VisionGuardrail-Evo2-27B

README

License: apache-2.0

Key Highlights

Qwen 3.8 Foundation: Built on Qwen/Qwen3.8-27B.
Dense 27B Architecture: A dense 27-billion-parameter multimodal model.
Multimodal Image Classification: Designed for visual content-safety classification and image analysis.
ImageShield-Guardrail-Pro Training: Fine-tuned on the prithivMLmods/ImageShield-Guardrail-Pro content-safety guardrail dataset.
Safe / Unsafe Classification: Classifies visual content as either Safe or Unsafe.
NSFW Sensual Content Detection: Focused on identifying potentially Not Safe for Work (NSFW) sensual content and related sensitive visual material.
Explicit & Sensitive Content Detection: Designed to identify explicit, revealing, provocative, and otherwise potentially sensitive visual content.
Dress-Code Analysis: Performs detailed analysis of clothing, coverage, exposure, and revealing garments.
Pose Analysis: Analyzes body poses, positioning, framing, and potentially suggestive presentation.
Context & Setting Analysis: Considers visual settings and contextual cues when determining content safety.
Zero-Tolerance Safety Classification: Uses a conservative classification policy that favors safety when content is ambiguous.
Structured Output: Produces a machine-readable JSON response containing a caption, classification, and reasoning.

Standard Instruction

text
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
}

Quick Start with Transformers

bash
pip install transformers
pip install accelerate

python
from transformers import Qwen3_5ForConditionalGeneration, AutoProcessor
import torch

model = Qwen3_5ForConditionalGeneration.from_pretrained(
    "prithivMLmods/VisionGuardrail-Evo2-27B",
    torch_dtype="auto",
    device_map="auto"
)

processor = AutoProcessor.from_pretrained(
    "prithivMLmods/VisionGuardrail-Evo2-27B"
)

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": "Classify this image using the provided content-safety guardrail."
            }
        ],
    }
]

text = processor.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

inputs = processor(
    text=[text],
    padding=True,
    return_tensors="pt"
).to("cuda")

generated_ids = model.generate(
    **inputs,
    max_new_tokens=256
)

output_text = processor.batch_decode(
    [
        out[len(inp):]
        for inp, out in zip(inputs.input_ids, generated_ids)
    ],
    skip_special_tokens=True,
    clean_up_tokenization_spaces=False
)

print(output_text[0])

Training Details

Table with columns: Setting, Value
Setting	Value
Base Model	Qwen/Qwen3.8-27B
Model	VisionGuardrail-Evo2-27B
Parameters	27 Billion
Architecture	Qwen3_5ForConditionalGeneration
Model Type	Dense Multimodal Image Classification Model
Training Dataset	prithivMLmods/ImageShield-Guardrail-Pro
Training Objective	Content-Safety Guardrail Classification

Intended Use

Multimodal Image Classification: Classifying visual media as safe or unsafe.
Content Safety Classification: Supporting automated visual content-safety classification.
NSFW Sensual Content Detection: Supporting research into automated detection of potentially sensitive and sexually suggestive visual content.
Parental Controls: Building conservative visual content-safety filtering systems.
Content Moderation: Supporting automated safety classification pipelines.
Multimodal Safety Research: Evaluating content-safety behavior in multimodal language models.
Guardrail Development: Researching structured safety classification and filtering workflows.
Visual Safety Analysis: Analyzing clothing, dress codes, poses, framing, and visual settings for content-safety signals.

Limitations

Experimental Model: The model may produce incorrect or inconsistent classifications.
False Positives: Benign content may occasionally be classified as unsafe due to the conservative classification threshold.
False Negatives: Unsafe content may occasionally be missed.
Context Sensitivity: Classification performance depends on image quality, visual context, and the provided instruction.
Conservative Policy: The model intentionally uses a strict classification threshold and may flag content that would not be considered NSFW under less restrictive moderation policies.
Visual Ambiguity: Clothing, poses, artistic styles, and contextual cues may sometimes be difficult to interpret accurately.
Computational Requirements: As a dense 27-billion-parameter multimodal model, VisionGuardrail-Evo2-27B requires substantially more computational resources than smaller VisionGuardrail variants.
Automated Classification: The model should not be treated as a definitive legal or safety determination.

Acknowledgements

Qwen/Qwen3.8-27B: Base multimodal model used for this project.
ImageShield-Guardrail-Pro: Content-safety guardrail dataset used to train VisionGuardrail-Evo2-27B.
TRL – Transformers Reinforcement Learning: TRL is a full-stack library providing tools to train transformer language models with methods including Supervised Fine-Tuning (SFT), Group Relative Policy Optimization (GRPO), Direct Preference Optimization (DPO), Reward Modeling, and more.
Transformers: Transformers provides state-of-the-art machine learning models for text, computer vision, audio, video, and multimodal tasks, supporting both inference and training.

Available on FriendliAI

Dedicated Endpoints

Run this model inference on single tenant GPU with unmatched speed and reliability at scale.

Learn more

Model Details

Model Provider

prithivMLmods

Model Tree

Base

Qwen/Qwen3.8-27B

Fine-tuned

this model

Input Modalities

TextImageVideo

Output Modalities

Text

Supported Functionality