Add metadata, paper link and citation

README.md

@@ -1,25 +1,47 @@
 ---
 datasets:
 - d3LLM/Ling-Coder-dParallel-merged-512-120k
+base_model: Dream-org/Dream-Coder-v0-Instruct-7B
+pipeline_tag: text-generation
+library_name: transformers
+license: apache-2.0
 tags:
 - diffusion
-- text-generation
 - fast-inference
 - d3llm
-pipeline_tag: text-generation
 ---
 
-
 # d3LLM: Ultra-Fast Diffusion LLM using Pseudo-Trajectory Distillation 🚀
 
+**d3LLM-Dream-Coder** is 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). It is built on [Dream-org/Dream-Coder-v0-Instruct-7B](https://huggingface.co/Dream-org/Dream-Coder-v0-Instruct-7B).
+
 ## Model Description
 
-**d3LLM-Dream-Coder** is an ultra-fast diffusion language model that achieves high generation speed while maintaining competitive performance. Built on the [Dream-org/Dream-Coder-v0-Instruct-7B](https://huggingface.co/Dream-org/Dream-Coder-v0-Instruct-7B).
+d3LLM (pseuDo-Distilled Diffusion Large Language Model) is a framework designed to strike a balance between accuracy and parallelism in diffusion LLMs. It achieves up to 10× speedup over vanilla diffusion models like LLaDA/Dream and 5× speedup over autoregressive (AR) models. 
+
+The model utilizes two primary innovations:
+- **Pseudo-Trajectory Distillation**: A training method that teaches the model which tokens can be decoded confidently at early steps.
+- **Entropy-Based Multi-Block Decoding**: An inference strategy using a KV-cache refresh mechanism to maintain accuracy while maximizing parallelism.
+
+## Resources
+
+- **Paper**: [d3LLM: Ultra-Fast Diffusion LLM using Pseudo-Trajectory Distillation](https://huggingface.co/papers/2601.07568)
+- **Repository**: [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/)
 
 ## Usage
 
-For detailed usage instructions, evaluation scripts, training datasets, and training code, please refer to the official GitHub repository and our blog:
+For detailed usage instructions, evaluation scripts, and training code, please refer to the official GitHub repository. Since the model uses a custom architecture, ensure you have `transformers==4.49.0` installed and use `trust_remote_code=True` when loading the model.
 
-- 👉 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/)**
+## Citation
 
+```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}
+}
+```