Add model card and metadata

Hi! I'm Niels, part of the community science team at Hugging Face. I noticed that this repository was missing a model card, so I've opened this PR to add one. This model card includes:
- Metadata for `pipeline_tag` and `library_name`.
- A summary of the paper and its findings.
- Links to the [paper](https://huggingface.co/papers/2512.21446) and the official [GitHub repository](https://github.com/chinsengi/dUltra-os).
- A sample usage code snippet taken from the official README.
- The BibTeX citation for the work.

README.md

@@ -0,0 +1,44 @@
+---
+library_name: transformers
+pipeline_tag: text-generation
+---
+
+# dUltra: Ultra-Fast Diffusion Language Models via Reinforcement Learning
+
+dUltra is an on-policy reinforcement learning framework based on Group Relative Policy Optimization (GRPO) that learns unmasking strategies for efficient parallel decoding in Masked Diffusion Language Models (MDLMs). 
+
+Existing acceleration methods for MDLMs often rely on fixed heuristics or distillation. dUltra introduces an unmasking planner head that predicts per-token unmasking likelihoods, allowing the model to learn task-specific unmasking trajectories. This approach achieves superior accuracy-efficiency trade-offs on mathematical reasoning and code generation tasks.
+
+- **Paper:** [dUltra: Ultra-Fast Diffusion Language Models via Reinforcement Learning](https://huggingface.co/papers/2512.21446)
+- **GitHub Repository:** [chinsengi/dUltra-os](https://github.com/chinsengi/dUltra-os)
+
+## Sample Usage
+
+To use a trained dUltra model, you can use the following code snippet from the official repository. Note that `trust_remote_code=True` is required for the custom architecture.
+
+```python
+import torch
+from model.llada.lladou import LLaDOUModelLM
+from transformers import AutoTokenizer
+
+model = LLaDOUModelLM.from_pretrained(
+            "sengi/dUltra-math",
+            trust_remote_code=True,
+            torch_dtype=torch.bfloat16,
+        )
+tokenizer = AutoTokenizer.from_pretrained("sengi/dUltra-math")
+```
+
+## Citation
+
+```bibtex
+@misc{chen2025dultraultrafastdiffusionlanguage,
+      title={dUltra: Ultra-Fast Diffusion Language Models via Reinforcement Learning},
+      author={Shirui Chen and Jiantao Jiao and Lillian J. Ratliff and Banghua Zhu},
+      year={2025},
+      eprint={2512.21446},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2512.21446},
+}
+```