43 lines
1.6 KiB
Markdown
43 lines
1.6 KiB
Markdown
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---
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base_model: Qwen/Qwen2.5-VL-3B-Instruct
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datasets:
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- PolicyShiftBench/PolicyShiftBench
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license: apache-2.0
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pipeline_tag: image-text-to-text
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library_name: transformers
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tags:
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- vision-language
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- image-safety
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- guardrails
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- policy-conditioned
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- qwen2.5-vl
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---
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# PolicyShiftGuard-3B
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[📚 Paper](https://huggingface.co/papers/2607.05910) | [💻 GitHub](https://github.com/ssmisya/PolicyShiftGuard) | [🏠 Project Page](https://policyshiftguard.github.io/)
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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.
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## Expected Output Format
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```text
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true | <two-digit risk category id> | <short reason>
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false | <short reason>
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```
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## Training Data
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This checkpoint is trained with the PolicyShiftBench public data release:
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- Dataset: `PolicyShiftBench/PolicyShiftBench`
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- Main evaluation splits: ID/adaptive branch and OOD/shift branch
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- Training stages: randomized policy SFT followed by boundary-pair policy adaptation
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## Intended Use
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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.
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## Limitations
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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.
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