Update model card with metadata, paper link and usage

Hi! I'm Niels from the Hugging Face community science team.

This PR improves the model card for d3LLM-Dream by:
- Adding the `library_name: transformers` tag (verified by the presence of `auto_map` in `config.json`).
- Adding the `license: apache-2.0` tag.
- Adding the `base_model` identifier.
- Linking the model to its research paper: [d3LLM: Ultra-Fast Diffusion LLM using Pseudo-Trajectory Distillation](https://huggingface.co/papers/2601.07568).
- Providing a standard `transformers` usage example.
- Adding the official citation from the paper.

These changes help improve the discoverability and usability of your model on the Hugging Face Hub.

README.md

@@ -1,30 +1,63 @@
 ---
 datasets:
 - d3LLM/trajectory_data_dream_32
+pipeline_tag: text-generation
+library_name: transformers
+license: apache-2.0
+base_model: Dream-org/Dream-v0-Instruct-7B
 tags:
 - diffusion
 - text-generation
 - fast-inference
 - d3llm
-pipeline_tag: text-generation
 ---
 
-
 # d3LLM: Ultra-Fast Diffusion LLM using Pseudo-Trajectory Distillation 🚀
 
+This repository contains the **d3LLM-Dream** model, an ultra-fast diffusion language model introduced in the paper [d3LLM: Ultra-Fast Diffusion LLM using Pseudo-Trajectory Distillation](https://huggingface.co/papers/2601.07568).
+
+- 📄 **Paper**: [arXiv:2601.07568](https://huggingface.co/papers/2601.07568)
+- 👉 **Code repo**: [https://github.com/hao-ai-lab/d3LLM](https://github.com/hao-ai-lab/d3LLM)
+- 🌐 **Blog**: [https://hao-ai-lab.github.io/blogs/text-diffusion/](https://hao-ai-lab.github.io/blogs/text-diffusion/)
+- 🕹️ **Demo**: [https://d3llm-team.github.io/](https://d3llm-team.github.io/)
+
 ## Model Description
 
-**d3LLM-Dream** is an ultra-fast diffusion language model that achieves high generation speed while maintaining competitive performance. Built on the Dream architecture.
+**d3LLM-Dream** is an ultra-fast diffusion language model that achieves high generation speed while maintaining competitive performance. It strikes a balance between accuracy and parallelism by using **pseudo-trajectory distillation** during training and **entropy-based multi-block decoding** during inference.
 
 ## Key Features
 
-- 🚀 High throughput: **4.5× faster** than autoregressive models (Qwen-2.5-7B) on H100 GPU, **2.5× faster** on A100 GPU. Achieves **235.34 tokens/s** on H100 (vs 57.32 for AR baseline) on GSM8K-CoT Dataset.
-- 📊 High AUP (Accuracy Under Parallelism) scores across benchmarks
-- 🔧 Optimized for coding and math reasoning tasks
+- 🚀 **High throughput**: 4.5× faster than autoregressive models (Qwen-2.5-7B) on H100 GPU, 2.5× faster on A100 GPU. Achieves **235.34 tokens/s** on H100 on GSM8K-CoT.
+- 📊 **High AUP**: Optimized for Accuracy Under Parallelism across benchmarks.
+- 🔧 **Specialized**: Optimized for coding and math reasoning tasks.
 
 ## Usage
 
-For detailed usage instructions, evaluation scripts, training datasets, and training code, please refer to the official GitHub repository and our blog:
+You can load and use the model with the 🤗 Transformers library. Note that `trust_remote_code=True` is required as the model uses a custom architecture.
+
+```python
+from transformers import AutoModel, AutoTokenizer
+
+model_id = "d3LLM/d3LLM_Dream"
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModel.from_pretrained(model_id, trust_remote_code=True)
+
+# For detailed inference scripts (multi-block decoding), 
+# please refer to the official GitHub repository.
+```
+
+For more comprehensive examples and evaluation scripts, visit the [official repository](https://github.com/hao-ai-lab/d3LLM).
+
+## Citation
 
-- 👉 Code repo: **[https://github.com/hao-ai-lab/d3LLM](https://github.com/hao-ai-lab/d3LLM)**
-- 🌐 Blog: **[https://hao-ai-lab.github.io/blogs/text-diffusion/](https://hao-ai-lab.github.io/blogs/text-diffusion/)**
+```bibtex
+@article{arxiv'26:d3llm,
+  title   = {d3LLM: Ultra-Fast Diffusion LLM using Pseudo-Trajectory Distillation},
+  author  = {Yu-Yang Qian and Junda Su and Lanxiang Hu and Peiyuan Zhang and Zhijie Deng and Peng Zhao and Hao Zhang},
+  journal = {ArXiv preprint},
+  volume  = {arXiv:2601.07568},
+  year    = {2026}
+}
+```