| --- |
| tags: |
| - text-generation-inference |
| - experimental |
| - mtp |
| - image-classification |
| - VIGOR |
| - VisionGuard |
| - qwen3.5-4B |
| - JSON |
| - content-safety |
| - content-moderation |
| - multimodal |
| - safety-classifier |
| - guardrail |
| - visual-safety |
| - multimodal-content-filter |
| - uncensored |
| base_model: |
| - Qwen/Qwen3.5-4B |
| license: apache-2.0 |
| language: |
| - en |
| pipeline_tag: image-text-to-text |
| library_name: transformers |
| --- |
| |
|  |
| |
| # **VIGOR-mini** |
|
|
| > **VIGOR-mini (Vision Intelligence and Guardrail Oriented Reasoning)** is a dense 4-billion-parameter multimodal vision classification model based on **Qwen/Qwen3.5-4B**. It is designed for visual content-safety analysis and conservative Safe / NSFW classification, with a particular focus on identifying potentially sensitive, revealing, provocative, and sexually suggestive visual content. VIGOR-mini performs detailed visual analysis of clothing, dress codes, body exposure, poses, framing, and contextual settings to support structured content-safety decisions. |
|
|
| > [!IMPORTANT] |
| > MTP — Speculative Decoding: Yes | Template Defaults — Best (thinking off), Reasoning (thinking on) | Support — Content Safety Classifications |
|
|
| > [!IMPORTANT] |
| > VIGOR-mini is an experimental content-safety classification model. Its predictions should not be treated as definitive judgments. Review outputs before using them in moderation, parental-control, or other safety-critical workflows. |
|
|
| ## **Key Highlights** |
|
|
| - **Qwen 3.5 Foundation:** Built on the Qwen/Qwen3.5-4B multimodal architecture. |
| - **Dense 4B Architecture:** A compact 4-billion-parameter model designed for local inference. |
| - **VIGOR Architecture:** Vision Intelligence and Guardrail Oriented Reasoning. |
| - **Multimodal Image Classification:** Analyzes visual content using image inputs. |
| - **Safe / NSFW Classification:** Provides binary content-safety classifications. |
| - **Sensual Content Detection:** Identifies potentially suggestive visual content. |
| - **Explicit and Sensitive Content Detection:** Focuses on visual exposure and potentially inappropriate content. |
| - **Dress-Code Analysis:** Examines clothing, revealing outfits, and levels of exposure. |
| - **Pose Analysis:** Evaluates poses and visual presentation. |
| - **Context and Setting:** Considers the surrounding visual context when classifying images. |
| - **Conservative Classification:** Uses a strict classification threshold for potentially unsafe content. |
| - **Structured JSON Output:** Produces consistent classification results with complementary safety indicators. |
| - **MTP Tensor Preservation:** Retains Multi-Token Prediction tensors for speculative decoding support. |
|
|
| ## **Standard Prompt** |
|
|
| The following prompt is designed for strict content-safety classification and structured JSON output. |
|
|
| ```text |
| You are a strict content-safety guardrail classifier for images designed for parental control and minor protection. |
| |
| Your task is to analyze the image thoroughly, provide a detailed visual description, and classify whether the image is safe or unsafe (NSFW). |
| |
| Enforce a strict zero-tolerance threshold: |
| - Any form of nudity, exposed breasts, buttocks, or genitalia must be classified as NSFW. |
| - Partial nudity, revealing underwear, lingerie, suggestive swimwear, or see-through clothing must be classified as NSFW. |
| - Anime nudes, ecchi, hentai, 2D/3D adult illustrations, suggestive manga art, and sexualized poses must be classified as NSFW. |
| - Explicit sexual acts, fetish material, or overt sexual positioning must be classified as NSFW. |
| - When in doubt, classify as NSFW. |
| |
| Output strictly valid JSON with no extra conversational text or markdown code blocks outside the JSON format. Use exactly these keys in this order: |
| { |
| "scene_description": "<Detailed, objective description of the subject, clothing, exposure, and pose>", |
| "is_nsfw": true | false, |
| "classification_reason": "<Precise reason for the classification based on clothing, exposure, or pose>", |
| "nsfw": 1 | 0, |
| "safe": 1 | 0 |
| } |
| |
| Field constraints: |
| - "is_nsfw" must be a JSON boolean (true or false), never a string. |
| - "nsfw" and "safe" must be JSON integers (1 or 0) and complementary: if is_nsfw is true then nsfw = 1 and safe = 0; if is_nsfw is false then nsfw = 0 and safe = 1. |
| ``` |
|
|
| > [!NOTE] |
| > The standard prompt is primarily intended for GGUF and other inference workflows that use an explicit system prompt. For Transformers inference, a shorter classification instruction can also be used. |
|
|
| ## **Quickstart** |
|
|
| ### Installation |
|
|
| ```bash |
| pip install transformers |
| pip install accelerate |
| ``` |
|
|
| ### Transformers Inference |
|
|
| ```python |
| from transformers import Qwen3_5ForConditionalGeneration, AutoProcessor |
| import torch |
| |
| # Load the model on the available device(s) |
| model = Qwen3_5ForConditionalGeneration.from_pretrained( |
| "prithivMLmods/VIGOR-mini", |
| dtype="auto", |
| device_map="auto" |
| ) |
| |
| # Recommended: Enable Flash Attention 2 for better acceleration |
| # and memory efficiency, especially in multi-image scenarios. |
| # |
| # model = Qwen3_5ForConditionalGeneration.from_pretrained( |
| # "prithivMLmods/VIGOR-mini", |
| # dtype=torch.bfloat16, |
| # attn_implementation="flash_attention_2", |
| # device_map="auto", |
| # ) |
| |
| processor = AutoProcessor.from_pretrained("prithivMLmods/VIGOR-mini") |
| |
| messages = [ |
| { |
| "role": "user", |
| "content": [ |
| { |
| "type": "image", |
| "image": "https://path/to/your/image.jpg", |
| }, |
| { |
| "type": "text", |
| "text": "Classify this image as safe or nsfw." |
| }, |
| ], |
| } |
| ] |
| |
| # Prepare inputs for inference |
| inputs = processor.apply_chat_template( |
| messages, |
| tokenize=True, |
| add_generation_prompt=True, |
| return_dict=True, |
| return_tensors="pt" |
| ) |
| |
| inputs = inputs.to(model.device) |
| |
| # Generate the model output |
| generated_ids = model.generate( |
| **inputs, |
| max_new_tokens=256 |
| ) |
| |
| generated_ids_trimmed = [ |
| out_ids[len(in_ids):] |
| for in_ids, out_ids in zip(inputs.input_ids, generated_ids) |
| ] |
| |
| output_text = processor.batch_decode( |
| generated_ids_trimmed, |
| skip_special_tokens=True, |
| clean_up_tokenization_spaces=False |
| ) |
| |
| print(output_text[0]) |
| ``` |
|
|
| ## **Sample Outputs** |
|
|
| ### 1. Safe Classification |
|
|
| **Input:** An image showing two Rottweiler puppies playing and wrestling with each other. |
|
|
| **Output:** |
|
|
| ```json |
| { |
| "scene_description": "Two Rottweiler puppies are playing and wrestling with each other. Both animals are fully covered by their natural fur, and the scene contains no sensitive or suggestive visual content.", |
| "is_nsfw": false, |
| "classification_reason": "The image depicts ordinary animal play with no nudity, explicit content, or suggestive visual elements.", |
| "nsfw": 0, |
| "safe": 1 |
| } |
| ``` |
|
|
| ### 2. Safe Classification |
|
|
| **Input:** An image showing a middle-aged man preparing food in a kitchen. |
|
|
| **Output:** |
|
|
| ```json |
| { |
| "scene_description": "A middle-aged man is preparing food in a kitchen. He is engaged in an ordinary cooking activity, and the scene contains no revealing clothing or suggestive visual elements.", |
| "is_nsfw": false, |
| "classification_reason": "The image depicts a normal domestic activity without nudity, explicit content, or sexually suggestive presentation.", |
| "nsfw": 0, |
| "safe": 1 |
| } |
| ``` |
|
|
| ### 3. NSFW Classification |
|
|
| **Input:** An image showing a woman outdoors wearing a sheer top and bikini bottoms. |
|
|
| **Output:** |
|
|
| ```json |
| { |
| "scene_description": "A woman is standing outdoors wearing a sheer top and bikini bottoms. The outfit exposes significant areas of the body and presents potentially revealing visual content.", |
| "is_nsfw": true, |
| "classification_reason": "The image contains revealing and partially transparent clothing that meets the strict content-safety threshold.", |
| "nsfw": 1, |
| "safe": 0 |
| } |
| ``` |
|
|
| ## **Intended Use** |
|
|
| VIGOR-mini is intended for: |
|
|
| - Multimodal Image Classification |
| - Content Safety Classification |
| - NSFW and Sensual Content Detection |
| - Parental Control Applications |
| - Image Content Moderation |
| - Multimodal Safety Research |
| - Guardrail Development |
| - Visual Safety Analysis |
|
|
| ## **Limitations** |
|
|
| - **Experimental Model:** VIGOR-mini is an experimental multimodal classification model. Its outputs may contain unexpected behaviors or reasoning artifacts. |
| - **False Positives:** Conservative classification may flag otherwise acceptable images as unsafe. |
| - **False Negatives:** Some sensitive visual content may not be detected correctly. |
| - **Context Sensitivity:** Classification accuracy can vary depending on image context, composition, and visual ambiguity. |
| - **Conservative Policy:** The strict classification threshold may result in over-classification of borderline content. |
| - **Visual Ambiguity:** Clothing, poses, lighting, occlusion, and image quality can affect classification. |
| - **Computational Requirements:** Although based on a compact 4B architecture, inference requirements depend on the selected precision, hardware, and image resolution. |
| - **Automated Decisions:** Model predictions should not replace human review or serve as definitive legal, professional, or safety determinations. |
|
|
| ## **License and Attributions** |
|
|
| - **Base Model:** [Qwen/Qwen3.5-4B](https://huggingface.co/Qwen/Qwen3.5-4B) - The foundational multimodal model used for VIGOR-mini. |
| - **Previous Variant:** [prithivMLmods/OneDecision-VisionGuard-4B-SFT](https://huggingface.co/prithivMLmods/OneDecision-VisionGuard-4B-SFT). |
| - **Classification Improvements:** VIGOR-mini introduces improvements targeting marginal False Positive (FP / Type I Error) and False Negative (FN / Type II Error) classification errors, aiming for more reliable and consistent visual content-safety decisions. |
| - **MTP Tensor Preservation:** Multi-Token Prediction (MTP) tensors are preserved in this release to support faster Speculative Decoding. This enables speculative decoding directly with the model weights, significantly accelerating local inference. |
| - **TRL:** [Transformers Reinforcement Learning](https://huggingface.co/docs/trl/en/index) - A full-stack library for supervised fine-tuning, GRPO, DPO, and reward modeling. |
| - **Transformers:** [Hugging Face Transformers](https://huggingface.co/docs/transformers/en/index) - Provides models and tools for text, computer vision, audio, video, and multimodal inference and training. |
| - **Developed by:** [prithivMLmods](https://huggingface.co/prithivMLmods). |
| - **License:** VIGOR-mini follows the Apache 2.0 license of the base Qwen/Qwen3.5-4B model, subject to applicable model and component terms. |
