Improve model card: add metadata, license, and link to paper

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

I've updated the model card for this repository to include relevant metadata such as the pipeline tag and license. I've also added a link to the original paper and the project's GitHub repository to improve documentation and discoverability. Specifically:
- Added `pipeline_tag: text-to-image`.
- Added `license: apache-2.0`.
- Included a link to the [research paper](https://huggingface.co/papers/2602.20903).
- Maintained the existing sample usage and citation.

README.md

@@ -1,21 +1,29 @@
 ---
 base_model: black-forest-labs/FLUX.1-dev
 library_name: peft
+pipeline_tag: text-to-image
+license: apache-2.0
+tags:
+- flux
+- lora
+- diffusers
+- text-rendering
+- visual-text-rendering
 ---
-# Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
-This model is trained using Flow-GRPO with LoRA. We provide only the LoRA weights here, so you will need to download the Flux.1-dev base model first.
+# TextPecker: Flux.1-dev-TextPecker-SQPA
 
-## Model Details
+This model is a LoRA adapter for [FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev) optimized using the **TextPecker** strategy, as presented in the paper [TextPecker: Rewarding Structural Anomaly Quantification for Enhancing Visual Text Rendering](https://huggingface.co/papers/2602.20903).
 
-### Model Sources
+TextPecker is a plug-and-play structural anomaly perceptive RL strategy that improves the structural fidelity and semantic alignment of visual text rendering in text-to-image generators. This repository provides the LoRA weights trained using Flow-GRPO.
 
-<!-- Provide the basic links for the model. -->
+- **Repository:** https://github.com/CIawevy/TextPecker
+- **Paper:** [https://arxiv.org/abs/2602.20903](https://arxiv.org/abs/2602.20903)
+
+## Usage
+
+This model provides only the LoRA weights. You will need to load the Flux.1-dev base model first.
 
-- **Repository:** https://github.com/CIawevy/TextPecker/tree/main
-- **Paper:** https://www.arxiv.org/pdf/2602.20903
-## Uses
 ```python
 import os
 import torch
@@ -86,4 +94,17 @@ image = pipe(
 # Save result (FLUX naming convention)
 image.save("TextPecker_flux_demo.png")
 print("Image saved as: TextPecker_flux_demo.png")
+```
+
+## Citation
+
+If you find TextPecker useful in your research or work, please cite the original paper:
+
+```bibtex
+@article{zhu2026TextPecker,
+  title   = {TextPecker: Rewarding Structural Anomaly Quantification for Enhancing Visual Text Rendering},
+  author  = {Zhu, Hanshen and Liu, Yuliang and Wu, Xuecheng and Wang, An-Lan and Feng, Hao and Yang, Dingkang and Feng, Chao and Huang, Can and Tang, Jingqun and Bai, Xiang},
+  journal = {arXiv preprint arXiv:2602.20903},
+  year    = {2026}
+}
 ```