Add model card metadata

This PR adds metadata to the model card, including the pipeline tag (`image-text-to-text`), library name (`transformers`), and license (assumed `apache-2.0`, please verify). This improves discoverability and provides essential information for users.

README.md

@@ -0,0 +1,77 @@
+---
+license: apache-2.0
+library_name: transformers
+pipeline_tag: image-text-to-text
+---
+
+# LongWriter-V: Enabling Ultra-Long and High-Fidelity Generation in Vision-Language Models
+
+<p align="center">
+   🤗 <a href="https://huggingface.co/datasets/THU-KEG/LongWriter-V-22K" target="_blank">Train Dataset</a> • 🤗 <a href="https://huggingface.co/datasets/THU-KEG/MMLongBench-Write" target="_blank">Benchmark</a> • 🤗 <a href="https://huggingface.co/THU-KEG/LongWriter-V-7B-DPO" target="_blank">Model</a> • 📃 <a href="https://arxiv.org/abs/2502.14834" target="_blank">Paper</a>
+</p>
+
+## 🔍 Table of Contents
+- [⚙️ LongWriter-V Deployment](#deployment)
+- [🤖️ LongWriter-Agent-V](#agentwrite)
+- [🖥️ Model Training](#longwriter-v-training)
+- [📊 Evaluation](#evaluation)
+- [👀 Cases](#case)
+- [📝 Citation](#citation)
+
+<a name="deployment"></a>
+## ⚙️ LongWriter-V Deployment
+
+**Environmental Setup**:
+To inference Qwen2.5-VL based models, you may need to install transformers from source. Refer to this [issue](https://github.com/QwenLM/Qwen2.5-VL/issues/706) for more details.
+
+We open-source three models: [LongWriter-V-7B](https://huggingface.co/THU-KEG/LongWriter-V-7B) and [LongWriter-V-7B-DPO](https://huggingface.co/THU-KEG/LongWriter-V-7B-DPO), trained based on [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) and [LongWriter-V-72B](https://huggingface.co/THU-KEG/LongWriter-V-72B), trained based on [Qwen2.5-VL-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-72B-Instruct). 
+
+<a name="agentwrite"></a>
+## 🤖️ LongWriter-Agent-V
+
+We are also open-sourcing LongWriter-Agent-V under `agentwrite/`, our automated ultra-long output data construction pipeline. Run `outline_vlm.py` to obtain the final data. Please configure your API key in `config.py`.
+
+<a name="longwriter-v-training"></a>
+## 🖥️ Model Training
+
+You can download and save the **LongWriter-V-22K** data through the Hugging Face datasets ([🤗 HF Repo](https://huggingface.co/datasets/THU-KEG/LongWriter-V-22K)).
+
+You can train the model with [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory), we used the [official Qwen2_VL training script](https://github.com/hiyouga/LLaMA-Factory/blob/main/examples/train_full/qwen2vl_full_sft.yaml) for training.
+
+<a name="evaluation"></a>
+## 📊 Evaluation
+We introduce two evaluation benchmarks: [**MMLongBench-Write**](https://huggingface.co/datasets/THU-KEG/MMLongBench-Write) and [**LongWrite-V-Ruler**](https://huggingface.co/datasets/THU-KEG/LongWrite-V-Ruler). **MMLongBench-Write** focuses more on measuring the long output quality as well as the output length, while **LongWrite-V-Ruler** is designed as a light-weight stress test of the model's maximum output length.
+We provide our evaluation code under `eval/`. Run
+```bash
+python -m eval.mmlongbench_write --model {model_name} --method {vlm, caption_llm}
+python -m eval.longwrite_v_ruler --model {model_name}
+```
+to get evaluation resuts. Remember to configure your OpenAI API key in `config.py` since we adopt GPT-4o as the judge.
+
+Here are the evaluation results on **MMLongBench-Write**:
+![image](https://github.com/user-attachments/assets/d4c7cce5-c48b-4bd0-9e9a-77cad06eae62)
+
+Here are the evaluation results on **LongWrite-V-Ruler**:
+![image](https://github.com/user-attachments/assets/f529b324-3ad5-4ddb-9c81-cbad59d1813b)
+
+
+<a name="case"></a>
+## 👀 Cases
+Here are LongWriter-V-7B's outputs to random test prompts.  (Examples truncated for brevity).
+
+<a name="citation"></a>
+## 📝 Citation
+
+If you find our work useful, please kindly cite:
+
+```
+@misc{tu2025longwriterv,
+      title={LongWriter-V: Enabling Ultra-Long and High-Fidelity Generation in Vision-Language Models}, 
+      author={Shangqing Tu and Yucheng Wang and Daniel Zhang-Li and Yushi Bai and Jifan Yu and Yuhao Wu and Lei Hou and Huiqin Liu and Zhiyuan Liu and Bin Xu and Juanzi Li},
+      year={2025},
+      eprint={2502.14834},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2502.14834}, 
+}
+```