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Model: PolicyShiftGuard/PolicyShiftGuard-3B
Source: Original Platform

README.md

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+---
+base_model: Qwen/Qwen2.5-VL-3B-Instruct
+datasets:
+- PolicyShiftBench/PolicyShiftBench
+license: apache-2.0
+pipeline_tag: image-text-to-text
+library_name: transformers
+tags:
+- vision-language
+- image-safety
+- guardrails
+- policy-conditioned
+- qwen2.5-vl
+---
+
+# PolicyShiftGuard-3B
+
+[📚 Paper](https://huggingface.co/papers/2607.05910) | [💻 GitHub](https://github.com/ssmisya/PolicyShiftGuard) | [🏠 Project Page](https://policyshiftguard.github.io/)
+
+PolicyShiftGuard-3B is a policy-conditioned image guardrail model based on Qwen2.5-VL-3B. It is trained to decide whether an image violates a supplied policy bundle and to return a structured safe/unsafe decision with the violated risk category when applicable.
+
+## Expected Output Format
+
+```text
+true | <two-digit risk category id> | <short reason>
+false | <short reason>
+```
+
+## Training Data
+
+This checkpoint is trained with the PolicyShiftBench public data release:
+
+- Dataset: `PolicyShiftBench/PolicyShiftBench`
+- Main evaluation splits: ID/adaptive branch and OOD/shift branch
+- Training stages: randomized policy SFT followed by boundary-pair policy adaptation
+
+## Intended Use
+
+Use this model for research on policy-conditioned multimodal safety, adaptive image moderation, and robustness under policy shifts. The model should be evaluated with explicit policy bundles rather than as a fixed universal safety classifier.
+
+## Limitations
+
+This is a research checkpoint. It may fail under policies, languages, visual domains, or deployment settings not represented in the benchmark. Outputs should not be treated as legal or compliance advice.