Add model card for BARD-VL

README.md

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+---
+pipeline_tag: image-text-to-text
+---
+
+# BARD-VL
+
+[BARD](https://huggingface.co/papers/2604.16514) is a simple and effective bridging framework that converts a pretrained autoregressive vision-language model (VLM) into a same-architecture, decoding-efficient large-block diffusion VLM (dVLM). 
+
+- **Paper:** [BARD: Bridging AutoRegressive and Diffusion Vision-Language Models Via Highly Efficient Progressive Block Merging and Stage-Wise Distillation](https://huggingface.co/papers/2604.16514)
+- **Project Page:** [https://fudan-generative-vision.github.io/Bard-VL](https://fudan-generative-vision.github.io/Bard-VL)
+- **Repository:** [https://github.com/fudan-generative-vision/Bard-VL](https://github.com/fudan-generative-vision/Bard-VL)
+
+## Method Overview
+
+BARD combines progressive supervised block merging, which gradually enlarges the decoding block size, with stage-wise intra-dVLM distillation from a fixed small-block diffusion anchor to recover performance lost at larger blocks. BARD-VL establishes a new state-of-the-art among comparable-scale open dVLMs while achieving up to 3$\times$ decoding throughput speedup compared to the source autoregressive model.
+
+## Inference
+
+To use the model for inference, please follow the installation instructions in the [official repository](https://github.com/fudan-generative-vision/Bard-VL). You can then run the provided `inference.py` script for image and video understanding:
+
+```bash
+python3 inference.py \
+  --model_id fudan-generative-ai/Bard-VL-B4-Mask-4B-Instruct \
+  --block_size 4 \
+  --denoising_steps 4 \
+  --confidence_threshold 0.6
+```
+
+## Citation
+
+If you find BARD-VL useful in your research, please cite the following paper:
+
+```bibtex
+@article{chen2026bard,
+  title={BARD: Bridging AutoRegressive and Diffusion Vision-Language Models Via Highly Efficient Progressive Block Merging and Stage-Wise Distillation},
+  author={Chen, Baoyou and Xia, Hanchen and Tu, Peng and Shi, Haojun and Mu, Shan and Yuan, Weihao and Zhu, Siyu},
+  journal={arXiv preprint arXiv:2604.16514},
+  year={2026}
+}
+```
+
+## Acknowledgements
+
+This repository builds on top of [NVIDIA NeMo AutoModel](https://github.com/NVIDIA-NeMo/Automodel).