--- license: apache-2.0 base_model: - Qwen/Qwen3.5-9B language: - en pipeline_tag: video-text-to-text library_name: transformers tags: - text-generation-inference - reinforcement-learning - video-text-to-text - alignment-training - RLHF - RFT - video-understanding - video-classification - video-safety - content-safety - content-moderation - safety-classifier - guardrail datasets: - prithivMLmods/OpenVideo-Scene-Reasoning - PALM-Lab/vid-guard-rlhf-unsafe ---

VideoGuard-9B

demo
think
The user wants me to classify an image and provide a specific output format. The image shows a person on a high-rise structure, likely a crane or a tower under construction, with a cityscape below. I need to output three lines: 1. **DESCRIPTION:** A single sentence describing the image. 2. **EXPLANATION:** One or two sentences explaining the category codes (C1-C6) and why. 3. **GUARDRAIL:** A single-line JSON object with specific boolean keys. **Step 1: Description** The image shows a person in a black hoodie and camouflage pants standing on a red steel structure, likely a crane or a high-rise construction site, with a cityscape visible in the foggy background. **Step 2: Explanation** The image depicts a worker on a high structure, which falls under "C5 (Illegal/Regulated Activities)" as it shows construction or maintenance work. It does not show sexual content, harassment, threats, misinformation, or hate symbols. **Step 3: Guardrail** I need to set the boolean keys to true or false. - C1 (Sexual Content): False. - C2 (Harassment & Bullying): False. - C3 (Threats, Violence & Harm): False. - C4 (False & Deceptive Information): False. - C5 (Illegal/Regulated Activities): True (Construction work). - C6 (Hateful Content & Extremism): False. Let's format the output. Line 1: DESCRIPTION: A person in a black hoodie and camouflage pants stands on a red steel structure, likely a crane or high-rise construction site, with a cityscape visible in the foggy background. Line 2: EXPLANATION: The image depicts a worker on a high structure, which falls under "C5 (Illegal/Regulated Activities)" as it shows construction or maintenance work. Line 3: GUARDRAIL: {"C1(Sexual Content)": false, "C2(Harassment & Bullying)": false, "C3(Threats, Violence & Harm)": false, "C4(False & Deceptive Information)": false, "C5(Illegal/Regulated Activities)": true, "C6(Hateful Content & Extremism)": false} Double check constraints: - Exactly three lines. - No markdown formatting outside the three lines. - JSON keys in exact order. - Boolean values are strictly true/false. Looks good.

DESCRIPTION: A person in a black hoodie and camouflage pants stands on a red steel structure, likely a crane or high-rise construction site, with a cityscape visible in the foggy background.

EXPLANATION: The image depicts a worker on a high structure, which falls under "C5 (Illegal/Regulated Activities)" as it shows construction or maintenance work.

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

--- # **VideoGuard-Qwen3.5-9B-Safety-RL-Uncensored** > **VideoGuard-Qwen3.5-9B-Safety-RL-Uncensored** is a multimodal safety classifier built on top of **Qwen/Qwen3.5-9B**. The model was trained on a mixture of approximately **10,000 video safety and scene-reasoning samples** to analyze video content and classify potentially unsafe content across predefined safety categories. The model is designed to generate a structured **DESCRIPTION**, **EXPLANATION**, and **GUARDRAIL** output, making it suitable for video content filtering, safety evaluation, and multimodal guardrail research. > [!NOTE] > This model is an experimental release and may generate unexpected classifications or reasoning artifacts in certain scenarios. Safety classifications should be treated as model predictions rather than definitive judgments. ## **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: | 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: EXPLANATION: 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** | 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](https://huggingface.co/datasets/prithivMLmods/OpenVideo-Scene-Reasoning)**: Video scene reasoning dataset used for multimodal video understanding and scene analysis. * **[PALM-Lab/vid-guard-rlhf-unsafe](https://huggingface.co/datasets/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](https://huggingface.co/Qwen/Qwen3.5-9B)**: Base multimodal model used for this project. * **[TRL – Transformers Reinforcement Learning](https://huggingface.co/docs/trl/en/index)**: 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](https://huggingface.co/docs/transformers/en/index)**: Transformers provides state-of-the-art machine learning models for text, computer vision, audio, video, and multimodal tasks, supporting both inference and training.