prithivMLmods

VideoGuard-Qwen3.5-9B-Safety-RL-Uncensored

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README

License: apache-2.0

Key Highlights

Qwen 3.5 Multimodal Backbone: Built on top of Qwen/Qwen3.5-9B.
Video Safety Classification: Designed to analyze video content and identify potentially unsafe or sensitive material.
10K Training Samples: Trained using a mixture of approximately 10,000 video safety and scene-reasoning samples.
Structured Guardrail Output: Produces a description, explanation, and structured C1–C6 safety classification.
Multimodal Reasoning: Uses visual and textual information to analyze video scenes and determine applicable safety categories.
Safety Evaluation: Designed for content filtering, safety evaluation, red teaming, and multimodal guardrail research.

Safety Categories

The model classifies content across six predefined categories:

Table with columns: Category, Description
Category	Description
C1 — Sexual Content	Sexual or sexually suggestive content.
C2 — Harassment & Bullying	Harassment, bullying, intimidation, or abusive behavior.
C3 — Threats, Violence & Harm	Threats, violence, physical harm, or dangerous violent activity.
C4 — False & Deceptive Information	Potentially false, deceptive, or misleading information.
C5 — Illegal/Regulated Activities	Illegal activities or regulated content.
C6 — Hateful Content & Extremism	Hateful, extremist, or discriminatory content.

Output Format

The model is trained to produce three primary components:

text
DESCRIPTION: <summary of the video content>

EXPLANATION: <reasoning explaining the classification>

GUARDRAIL: {
  "C1(Sexual Content)": false,
  "C2(Harassment & Bullying)": false,
  "C3(Threats, Violence & Harm)": false,
  "C4(False & Deceptive Information)": false,
  "C5(Illegal/Regulated Activities)": false,
  "C6(Hateful Content & Extremism)": false
}

Example — Safe Content

text
DESCRIPTION: A hand holds four small, leaf-decorated candies in green, orange, and teal colors against a wooden table background.

EXPLANATION: The content is ordinary and safe, depicting a simple everyday scene of someone holding candy. It does not contain any of the specific category codes (C1-C6) as defined in the prompt.

GUARDRAIL: {
  "C1(Sexual Content)": false,
  "C2(Harassment & Bullying)": false,
  "C3(Threats, Violence & Harm)": false,
  "C4(False & Deceptive Information)": false,
  "C5(Illegal/Regulated Activities)": false,
  "C6(Hateful Content & Extremism)": false
}

Example — Sexual Content Classification

text
DESCRIPTION: A woman with blonde hair tied in a ponytail poses against a plain white wall, wearing a light blue blazer and trousers with an open front that exposes her midsection.

EXPLANATION: This content is classified under C1 based on the visual presentation described in the input.

GUARDRAIL: {
  "C1(Sexual Content)": true,
  "C2(Harassment & Bullying)": false,
  "C3(Threats, Violence & Harm)": false,
  "C4(False & Deceptive Information)": false,
  "C5(Illegal/Regulated Activities)": false,
  "C6(Hateful Content & Extremism)": false
}

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/VideoGuard-Qwen3.5-9B-Safety-RL-Uncensored",
    torch_dtype="auto",
    device_map="auto"
)

processor = AutoProcessor.from_pretrained(
    "prithivMLmods/VideoGuard-Qwen3.5-9B-Safety-RL-Uncensored"
)

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": "Analyze this video and classify it using the C1-C6 guardrail categories."
            }
        ],
    }
]

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/Qwen3.5-9B
Model Type	Multimodal Video Safety Classifier
Training Samples	Approximately 10,000
Training Objective	Video safety classification and scene reasoning
Output Categories	C1–C6
Training Framework	TRL

Training Datasets

prithivMLmods/OpenVideo-Scene-Reasoning: Video scene reasoning dataset used for multimodal video understanding and scene analysis.
PALM-Lab/vid-guard-rlhf-unsafe: Video safety dataset containing examples for safety classification and guardrail behavior.

Intended Use

Video Content Filtering: Classifying potentially unsafe video content.
Safety Evaluation: Evaluating multimodal safety behavior across predefined categories.
Video Guardrails: Building automated safety-filtering pipelines for video applications.
Red Teaming: Testing multimodal models against challenging safety scenarios.
Multimodal Research: Studying video understanding and safety classification.
Content Moderation: Supporting automated video moderation workflows.

Limitations

Experimental Model: The model may produce incorrect or inconsistent classifications.
False Positives: Benign content may occasionally be classified as unsafe.
False Negatives: Unsafe content may occasionally be missed.
Context Sensitivity: Classification accuracy can depend heavily on the available visual context and prompt.
Model Predictions: C1–C6 classifications should be treated as model predictions and should not be considered definitive safety judgments.

Acknowledgements

Qwen/Qwen3.5-9B: Base multimodal model used for this project.
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.

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Model Details

Model Provider

prithivMLmods

Model Tree

Base

Qwen/Qwen3.5-9B

Fine-tuned

this model

Input Modalities

TextImageVideo

Output Modalities

Text

Supported Functionality