Add pipeline tag, library metadata, and improve model card

Hi! I'm Niels, part of the community science team at Hugging Face.

This PR improves the model card for DMax-Math-16B:
- Adds `pipeline_tag: text-generation` to ensure the model is correctly categorized on the Hub.
- Adds `library_name: transformers` as the model is compatible with the library (as shown in the sample code), which enables automatic code snippets.
- Links the model to its associated paper: [DMax: Aggressive Parallel Decoding for dLLMs](https://huggingface.co/papers/2604.08302).
- Maintains existing license, base model, and dataset metadata.

README.md

@@ -1,9 +1,11 @@
 ---
-license: apache-2.0
-datasets:
-- Zigeng/DMax-LLaDA-2.0-Mini-Math-Trajectories
 base_model:
 - inclusionAI/LLaDA2.0-mini
+datasets:
+- Zigeng/DMax-LLaDA-2.0-Mini-Math-Trajectories
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
 ---
 
 <div align="center">
@@ -12,7 +14,7 @@ base_model:
   <a href="https://github.com/czg1225/DMax/blob/main/LICENSE">
     <img alt="Apache" src="https://img.shields.io/badge/License-Apache-4E94CE.svg">
   </a>
-  <a href="https://arxiv.org/pdf/2604.08302">
+  <a href="https://arxiv.org/abs/2604.08302">
     <img src="https://img.shields.io/badge/Paper-Arxiv-darkred.svg" alt="Paper">
   </a>
   <a href="https://github.com/czg1225/DMax">
@@ -21,10 +23,9 @@ base_model:
 </div>
 </div>
 
-> **DMax: Aggressive Parallel Decoding for dLLMs**  
-> [Zigeng Chen](https://czg1225.github.io/chenzigeng99/), [Gongfan Fang](https://fangggf.github.io/), [Xinyin Ma](https://horseee.github.io/), [Ruonan Yu](https://scholar.google.com/citations?user=UHP95egAAAAJ&hl=en), [Xinchao Wang](https://sites.google.com/site/sitexinchaowang/)  
-> [xML Lab](https://sites.google.com/view/xml-nus), National University of Singapore
+This repository contains the weights for **DMax-Math-16B**, presented in the paper [DMax: Aggressive Parallel Decoding for dLLMs](https://huggingface.co/papers/2604.08302).
 
+DMax is a new paradigm for efficient diffusion language models (dLLMs) that mitigates error accumulation in parallel decoding, enabling aggressive decoding parallelism while preserving generation quality.
 
 ## 💪 Highlights
 
@@ -65,7 +66,9 @@ model = model.to(torch.bfloat16)
 model.eval()
 tokenizer = AutoTokenizer.from_pretrained("Zigeng/DMax-Math-16B", trust_remote_code=True)
 
-prompt = "A robe takes 2 bolts of blue fiber and half that much white fiber. How many bolts in total does it take?" + "\nLet's think step by step\n"
+prompt = "A robe takes 2 bolts of blue fiber and half that much white fiber. How many bolts in total does it take?" + "
+Let's think step by step
+"
 
 input_ids = tokenizer.apply_chat_template(
     [{"role": "user", "content": prompt}],
@@ -94,5 +97,16 @@ print("nfe:",nfe,"token length",len(generated_tokens[0]))
 
 ![trade-off](assets/exp.png)
 
-
-
+## 📚 Citation
+
+```bibtex
+@misc{chen2026dmaxaggressiveparalleldecoding,
+      title={DMax: Aggressive Parallel Decoding for dLLMs}, 
+      author={Zigeng Chen and Gongfan Fang and Xinyin Ma and Ruonan Yu and Xinchao Wang},
+      year={2026},
+      eprint={2604.08302},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2604.08302}, 
+}
+```