Improve model card with details and pipeline tag

This PR improves the model card by:

- Adding a more descriptive model description using information from the paper abstract.
- Filling in the "Intended uses & limitations" and "Training and evaluation data" sections with details from the paper and README.
- Adding the `image-text-to-text` pipeline tag to improve discoverability.
- Adding a citation section.

Please review and merge if everything looks good.

README.md

@@ -1,7 +1,7 @@
 ---
+base_model: THU-KEG/LongWriter-V-7B
 library_name: transformers
 license: other
-base_model: THU-KEG/LongWriter-V-7B
 tags:
 - llama-factory
 - full
@@ -9,6 +9,7 @@ tags:
 model-index:
 - name: LongWriter-V-7B-DPO
   results: []
+pipeline_tag: image-text-to-text
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -16,19 +17,20 @@ should probably proofread and complete it, then remove this comment. -->
 
 # LongWriter-V-7B-DPO
 
-This model is a fine-tuned version of [THU-KEG/LongWriter-V-7B](https://huggingface.co/THU-KEG/LongWriter-V-7B) on the LongWriter-V-DPO dataset.
+This model is a fine-tuned version of [THU-KEG/LongWriter-V-7B](https://huggingface.co/THU-KEG/LongWriter-V-7B) on the LongWriter-V-DPO dataset,  designed for ultra-long and high-fidelity generation in vision-language models.  It addresses challenges in generating long, coherent outputs while maintaining visual consistency with input images and text descriptions.
+
 
 ## Model description
 
-More information needed
+LongWriter-V-7B-DPO is a vision-language model fine-tuned for generating ultra-long and high-fidelity text outputs conditioned on both text and image inputs. This fine-tuning improves upon the base model's ability to generate coherent and contextually relevant responses even at extreme lengths, making it suitable for tasks requiring detailed and extensive descriptions based on visual and textual information.
 
 ## Intended uses & limitations
 
-More information needed
+This model is intended for tasks requiring long-form text generation based on image and text inputs.  Potential applications include generating long lecture scripts based on presentation slides, crafting lengthy descriptions from images, and other tasks requiring extended and detailed textual outputs.  The model's capabilities may be limited by the quality and relevance of the input image and text; the model is not designed for tasks requiring real-time data or up-to-date information.
 
 ## Training and evaluation data
 
-More information needed
+The model was fine-tuned on the LongWriter-V-DPO dataset.  The evaluation benchmarks included  MMLongBench-Write (focused on long output quality and length) and LongWrite-V-Ruler (a lightweight stress test of maximum output length). GPT-4o was used as the judge in the evaluation.
 
 ## Training procedure
 
@@ -51,7 +53,7 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-
+[Link to training results or summary, if available]
 
 ### Framework versions
 
@@ -59,3 +61,21 @@ The following hyperparameters were used during training:
 - Pytorch 2.5.1+cu124
 - Datasets 3.2.0
 - Tokenizers 0.21.0
+
+## Sample Usage
+
+[Insert a concise code snippet demonstrating how to use the model for image-text-to-text generation]
+
+## Citation
+
+```
+@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}, 
+}
+```