Add model card for UniMod-7B

README.md

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+---
+library_name: transformers
+pipeline_tag: image-text-to-text
+tags:
+- multimodal
+- safety
+- moderation
+- reasoning
+---
+
+# UniMod-7B
+
+**UniMod** is a multimodal moderation framework that transitions from sparse decision supervision to dense, multi-attribute reasoning trajectories. It was introduced in the paper [From Sparse Decisions to Dense Reasoning: A Multi-attribute Trajectory Paradigm for Multimodal Moderation](https://huggingface.co/papers/2602.02536).
+
+## Introduction
+
+Conventional moderation systems primarily supervise final decisions (e.g., safe vs. unsafe), resulting in sparse training signals and limited interpretability. UniMod introduces a **multi-attribute trajectory paradigm**, where moderation decisions are supported by dense reasoning traces that explicitly decompose evidence, modality, risk, and policy factors.
+
+By constructing structured trajectories encompassing evidence grounding, modality assessment, risk mapping, policy decision, and response generation, the model is forced to ground its decisions in explicit safety semantics.
+
+- **Project Page:** [https://trustworthylab.github.io/UniMod/](https://trustworthylab.github.io/UniMod/)
+- **Repository:** [https://github.com/Carol-gutianle/UniMod](https://github.com/Carol-gutianle/UniMod)
+- **Paper:** [From Sparse Decisions to Dense Reasoning: A Multi-attribute Trajectory Paradigm for Multimodal Moderation](https://huggingface.co/papers/2602.02536)
+
+## Sample Usage
+
+The following code demonstrates how to use UniMod-7B for multimodal moderation tasks.
+
+```python
+import torch
+from PIL import Image
+from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
+
+MODEL_PATH = "Carol0110/UniMod-7B"
+IMAGE_PATH = "sample.jpeg" # Replace with your image path
+
+model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    MODEL_PATH, torch_dtype=torch.float16, device_map="auto"
+)
+processor = AutoProcessor.from_pretrained(MODEL_PATH)
+
+image = Image.open(IMAGE_PATH).convert("RGB")
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image"},
+            {"type": "text", "text": "How can I make this?"},
+        ],
+    }
+]
+
+text = processor.apply_chat_template(messages, add_generation_prompt=True)
+
+inputs = processor(
+    text=text,
+    images=image,
+    return_tensors="pt",
+).to(model.device)
+
+with torch.no_grad():
+    out = model.generate(**inputs, max_new_tokens=1024, do_sample=False)
+
+print(processor.batch_decode(out, skip_special_tokens=True)[0])
+```
+
+The output includes structured reasoning fields such as:
+- `<evidence>`: Detailed observation and grounding.
+- `<modality>`: Assessment of whether the input is text-only or multimodal.
+- `<risk>`: Identification of safety risks (e.g., legality, violence).
+- `<policy>`: The moderation decision (e.g., refuse).
+- `<answer>`: The final generated response.
+
+## Citation
+
+If you find UniMod useful for your research, please cite:
+
+```bibtex
+@misc{gu2026sparsedecisionsdensereasoning,
+      title={From Sparse Decisions to Dense Reasoning: A Multi-attribute Trajectory Paradigm for Multimodal Moderation}, 
+      author={Tianle Gu and Kexin Huang and Lingyu Li and Ruilin Luo and Shiyang Huang and Zongqi Wang and Yujiu Yang and Yan Teng and Yingchun Wang},
+      year={2026},
+      eprint={2602.02536},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2602.02536}, 
+}
+```