Improve model card with paper info, metadata and links

README.md

@@ -1,13 +1,40 @@
+---
+license: apache-2.0
+base_model: Qwen/Qwen3-4B
+pipeline_tag: text-generation
+---
+
 # Qwen3-4B-A2D-untrained-dllm-convert
 
-Qwen3-4B converted to A2D architecture (bidirectional attention) using [dllm convert pipeline](https://github.com/ZHZisZZ/dllm/blob/b8d76ff74b2053d359cd88fedfbc6362db17e3d7/examples/a2d/README.md?plain=1#L49-L53).
+This repository contains the Qwen3-4B model converted to the A2D architecture (bidirectional attention), as presented in the paper [Data-Efficient Autoregressive-to-Diffusion Language Models via On-Policy Distillation](https://huggingface.co/papers/2606.06712).
+
+This specific artifact serves as an **untrained student initialization** for the On-Policy Distillation (OPD) process to transform an autoregressive model into a diffusion language model.
 
+- **Project Page:** [https://opdlm.vercel.app/](https://opdlm.vercel.app/)
+- **GitHub Repository:** [https://github.com/divelab/OPDLM](https://github.com/divelab/OPDLM)
 - **Base model**: [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B)
+
+## Model Details
+
 - **Architecture**: A2D-Qwen3 (non-causal attention, same weights as original)
 - **Parameters**: 4.02B
 - **Vocab size**: 151936
 - **Model type**: `a2d-qwen3`
 
-This model has the original Qwen3-4B weights with bidirectional (non-causal) attention. No diffusion pretraining or SFT has been applied.
+This model has the original Qwen3-4B weights with bidirectional (non-causal) attention. It was converted using the [dllm convert pipeline](https://github.com/ZHZisZZ/dllm/blob/b8d76ff74b2053d359cd88fedfbc6362db17e3d7/examples/a2d/README.md?plain=1#L49-L53). No diffusion pretraining or SFT has been applied.
 
 **Mask token registration**: The mask token `<|MASK|>` (ID 151669) is registered in the tokenizer for use with diffusion-based language modeling. The original Qwen3 tokenizer includes `<|MASK|>` in `special_tokens_map.json` but does not register it in `tokenizer_config.json`, so `tokenizer.mask_token_id` returns `None`. We fixed this by adding `<|MASK|>` to the `added_tokens_decoder` section and the `mask_token` field in `tokenizer_config.json`, and adding the full `mask_token` entry in `special_tokens_map.json`. After this fix, `tokenizer.mask_token_id` correctly returns `151669`.
+
+## Citation
+
+```bibtex
+@misc{su2026opdlm,
+      title={Data-Efficient Autoregressive-to-Diffusion Language Models via On-Policy Distillation},
+      author={Xingyu Su and Jacob Helwig and Shubham Parashar and Atharv Chagi and Lakshmi Jotsna and Degui Zhi and James Caverlee and Dileep Kalathil and Shuiwang Ji},
+      year={2026},
+      eprint={2606.06712},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2606.06712},
+}
+```