Add model card metadata, paper link and sample usage

README.md

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+---
+library_name: peft
+pipeline_tag: image-text-to-text
+---
+
+# EditHF
+
+EditHF is an MLLM-based evaluation model introduced in the paper [EditHF-1M: A Million-Scale Rich Human Preference Feedback for Image Editing](https://huggingface.co/papers/2603.14916). 
+
+It is designed to provide fine-grained, human-aligned scores for text-guided image editing across three dimensions: **visual quality**, **editing alignment**, and **attribute preservation**. The model was trained on the **EditHF-1M** dataset, which contains over 29M human preference pairs.
+
+## Resources
+- **Paper:** [EditHF-1M: A Million-Scale Rich Human Preference Feedback for Image Editing](https://huggingface.co/papers/2603.14916)
+- **GitHub Repository:** [IntMeGroup/EditHF](https://github.com/IntMeGroup/EditHF)
+
+## Sample Usage
+
+To use EditHF for evaluating image editing results, you can use the inference script provided in the official repository:
+
+```bash
+python inference.py \
+    --source_image "/path/to/source.jpg" \
+    --edited_image "/path/to/edited.jpg" \
+    --instruction "Editing instruction" \
+    --peft_dir "lora_checkpoints_visual" \
+    --mode visual
+```
+
+The `--mode` parameter can be set to:
+- `visual`: Evaluates visual quality.
+- `alignment`: Evaluates alignment with the editing instruction.
+- `preservation`: Evaluates the preservation of source image attributes.
+
+## Citation
+```bibtex
+@article{edithf1m,
+  title={EditHF-1M: A Million-Scale Rich Human Preference Feedback for Image Editing},
+  author={...},
+  journal={arXiv preprint arXiv:2603.14916},
+  year={2026}
+}
+```