Add model card metadata, paper link and sample usage

Hi! I'm Niels from the Hugging Face team.

This PR improves the model card by adding relevant metadata, linking the associated research paper, and providing a sample usage snippet from the official GitHub repository. Specifically:
- Added the `image-text-to-text` pipeline tag.
- Added `library_name: peft` as the repository contains LoRA adapter configurations.
- Linked the paper: [EditHF-1M: A Million-Scale Rich Human Preference Feedback for Image Editing](https://huggingface.co/papers/2603.14916).
- Included a sample usage section to guide users on how to run inference using the provided scripts.

README.md

@@ -0,0 +1,42 @@
+---
+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}
+}
+```