---
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.