Improve model card: add paper link, citation, license, and library_name

README.md

@@ -1,30 +1,59 @@
 ---
 datasets:
 - d3LLM/trajectory_data_llada_32
+pipeline_tag: text-generation
 tags:
 - diffusion
 - text-generation
 - fast-inference
 - d3llm
-pipeline_tag: text-generation
+license: apache-2.0
+library_name: transformers
+base_model: GSAI-ML/LLaDA-8B-Instruct
 ---
 
-
 # d3LLM: Ultra-Fast Diffusion LLM using Pseudo-Trajectory Distillation 🚀
 
+This repository contains **d3LLM-LLaDA**, an ultra-fast diffusion language model presented 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:** [GitHub - hao-ai-lab/d3LLM](https://github.com/hao-ai-lab/d3LLM)
+- 🌐 **Blog:** [Ultra-Fast Diffusion LLMs](https://hao-ai-lab.github.io/blogs/text-diffusion/)
+- 🕹️ **Demo:** [d3LLM Demo](https://d3llm-team.github.io/)
+
 ## Model Description
 
-**d3LLM-LLaDA** is an ultra-fast diffusion language model that achieves high generation speed while maintaining competitive performance. Built on the Dream architecture.
+**d3LLM-LLaDA** is an ultra-fast diffusion language model that strikes a balance between accuracy and parallelism. It uses pseudo-trajectory distillation to teach the model which tokens can be decoded confidently at early steps, and employs an entropy-based multi-block decoding mechanism with KV-cache refresh during inference.
 
 ## Key Features
 
-- 🚀 High throughput: **5.0× faster** than autoregressive models (Qwen-2.5-7B-it) on H100 GPU, **3.5× faster** on A100 GPU. Achieves **288.73 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:** 5.0× faster than autoregressive models (Qwen-2.5-7B-it) on H100 GPU and 3.5× faster on A100 GPU.
+- 📊 **High AUP:** Achieves high Accuracy Under Parallelism scores across benchmarks.
+- 🔧 **Task Optimization:** Specifically optimized for coding and math reasoning tasks.
+
+## Installation
+
+To use this model, it is recommended to clone the official repository and install the required dependencies:
+
+```bash
+# Clone the repository
+git clone https://github.com/hao-ai-lab/d3LLM.git
+cd d3LLM
+
+# Install dependencies
+pip install -r requirements.txt
+```
 
-## Usage
+## Citation
 
-For detailed usage instructions, evaluation scripts, training datasets, and training code, please refer to the official GitHub repository and our blog:
+If you find d3LLM useful for your research, please cite the following work:
 
-- 👉 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}
+}
+```