yueliu1999/GuardReasoner-VL-7B

GuardReasoner-VL-7B: Safeguarding Vision-Language Models

GuardReasoner-VL-7B is a 7 billion parameter vision-language model (VLM) developed by yueliu1999, building upon the Qwen2.5-VL-7B-Instruct architecture. Its core innovation lies in its fine-tuning approach, utilizing R-SFT (Reinforced Supervised Fine-Tuning) and online Reinforcement Learning (RL) to enhance safety and ethical reasoning in VLMs. This model is based on the research presented in the paper "GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning" (arXiv:2505.11049).

Key Capabilities

• Harmfulness Classification: Designed to act as a classifier, determining whether human user requests and AI assistant responses are harmful or unharmful.
• Reinforced Reasoning: Employs a reasoning-based approach, guiding the model to think step-by-step to ensure consistency between its reasoning process and final safety judgments.
• Multimodal Input Analysis: Capable of processing and analyzing safety aspects in interactions involving both text and image inputs, as demonstrated by its usage examples.
• Ethical AI Development: Aims to provide a robust mechanism for safeguarding VLMs, contributing to the development of more responsible and ethical AI systems.

Good for

• Content Moderation: Identifying and flagging potentially harmful content in user prompts and AI-generated responses across multimodal interactions.
• AI Safety Research: Serving as a foundation or tool for further research into VLM safety, alignment, and ethical considerations.
• Application Development: Integrating into applications where robust safety checks for user inputs and AI outputs are critical, especially in vision-language contexts.