Add model card and metadata for UniEditBench distilled evaluator

README.md

@@ -1,3 +1,59 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+library_name: peft
+pipeline_tag: image-text-to-text
+base_model: Qwen/Qwen3-VL-4B-Instruct
+tags:
+- image-editing
+- video-editing
+- reward-model
+- evaluation
+---
+
+# UniEditBench Distilled Evaluator (4B LoRA)
+
+This repository contains the distilled 4B MLLM judge (LoRA adapter) introduced in the paper [UniEditBench: A Unified and Cost-Effective Benchmark for Image and Video Editing via Distilled MLLMs](https://huggingface.co/papers/2604.15871).
+
+UniEditBench is a unified benchmark for image and video editing that standardizes evaluation across different paradigms and modalities. To enable scalable and cost-effective evaluation, the authors distilled high-capacity MLLM judges (such as Qwen3-VL-235B) into lightweight 4B and 8B evaluators. These reward models provide multi-dimensional scoring across structural fidelity, text alignment, background consistency, naturalness, and (for videos) temporal-spatial consistency.
+
+- **Paper:** [UniEditBench: A Unified and Cost-Effective Benchmark for Image and Video Editing via Distilled MLLMs](https://huggingface.co/papers/2604.15871)
+- **GitHub Repository:** [wesar1/UniEditBench](https://github.com/wesar1/UniEditBench)
+- **Dataset:** [UniEditBench Dataset](https://huggingface.co/datasets/wesar1/UniEditBench)
+
+## Usage
+
+### Deployment
+
+The reward model can be deployed using the [swift](https://github.com/modelscope/swift) framework. Below is an example command to deploy the 4B image-based evaluator:
+
+```bash
+CUDA_VISIBLE_DEVICES=0 swift deploy \
+    --model Qwen/Qwen3-VL-4B-Instruct \
+    --adapters /path/to/sft_image_lora_4b \
+    --device_map balanced \
+    --vllm_tensor_parallel_size 1 \
+    --attn_impl eager \
+    --infer_backend vllm \
+    --port 8003 \
+    --vllm_max_lora_rank 32 \
+    --max_new_tokens 2048 \
+    --served_model_name Qwen3-VL-4B-SFT-Image
+```
+
+For video evaluation or 8B model scripts, please refer to the `scripts/deploy_qwen3vl.sh` file in the official GitHub repository.
+
+## Citation
+
+If you find UniEditBench helpful, please cite the following work:
+
+```bibtex
+@misc{jiang2026unieditbenchunifiedcosteffectivebenchmark,
+      title={UniEditBench: A Unified and Cost-Effective Benchmark for Image and Video Editing via Distilled MLLMs}, 
+      author={Lifan Jiang and Tianrun Wu and Yuhang Pei and Chenyang Wang and Boxi Wu and Deng Cai},
+      year={2026},
+      eprint={2604.15871},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2604.15871}, 
+}
+```