Add pipeline tag, library name, and improve model card

README.md

@@ -1,30 +1,38 @@
 ---
+base_model:
+- OpenGVLab/InternVL3_5-1B-Instruct
 language:
 - en
+license: mit
 metrics:
 - accuracy
-base_model:
-- OpenGVLab/InternVL3_5-1B-Instruct
 tags:
 - visual-reasoning
 - fine-grained-vqa
 - fine-grained-recognition
-license: mit
+pipeline_tag: image-text-to-text
+library_name: transformers
 ---
-# Model Card for TWIN-Qwen2.5-VL-3B
 
-<!-- Provide a quick summary of what the model is/does. -->
+# Model Card for TWIN-InternVL3_5-1B
+
+This repository contains the InternVL3.5-1B model post-trained on the TWIN dataset, as introduced in the paper [Same or Not? Enhancing Visual Perception in Vision-Language Models](https://arxiv.org/abs/2512.23592).
+
+TWIN is a large-scale dataset of 561,000 image-pair queries designed to enhance the perceptual abilities of Vision-Language Models (VLMs). It tasks models to determine whether two visually similar images depict the same object, encouraging attention to nuanced visual cues. Fine-tuning on TWIN yields significant gains in fine-grained recognition across various domains like art, animals, plants, and landmarks.
 
-This is the InternVL3_5-1B model post-trained on the TWIN dataset from the paper: [Same or Not? Enhancing Visual Perception in Vision-Language Models](https://glab-caltech.github.io/twin/)
+## Resources
 
-For further information please refer to the [project webpage](https://glab-caltech.github.io/twin/), [paper](https://arxiv.org/abs/2512.23592), and [repository](https://github.com/damianomarsili/TWIN).
+- **Project Page:** [https://glab-caltech.github.io/twin/](https://glab-caltech.github.io/twin/)
+- **Paper:** [Same or Not? Enhancing Visual Perception in Vision-Language Models](https://arxiv.org/abs/2512.23592)
+- **Code Repository:** [https://github.com/damianomarsili/TWIN](https://github.com/damianomarsili/TWIN)
+- **Dataset:** [glab-caltech/TWIN](https://huggingface.co/datasets/glab-caltech/TWIN)
+- **Benchmark Suite:** [glab-caltech/FGVQA](https://huggingface.co/datasets/glab-caltech/FGVQA)
 
 ## Citation
 
-If you use TWIN in your research, please consider citing our work:
+If you use TWIN in your research, please consider citing the work:
 
-**BibTeX:**
-```
+```bibtex
 @misc{marsili2025notenhancingvisualperception,
       title={Same or Not? Enhancing Visual Perception in Vision-Language Models}, 
       author={Damiano Marsili and Aditya Mehta and Ryan Y. Lin and Georgia Gkioxari},