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README.md

@@ -12,7 +12,7 @@ tags:
 
 # WeDLM-8B-Instruct ⭐
 
-**WeDLM-8B-Instruct** is our flagship instruction-tuned diffusion language model that performs parallel decoding under standard causal attention, fine-tuned from [WeDLM-8B](https://huggingface.co/tencent/WeDLM-8B).
+**WeDLM-8B-Instruct** is our flagship instruction-tuned diffusion language model that performs parallel decoding under standard causal attention, fine-tuned from [WeDLM-8B](https://huggingface.co/tencent/WeDLM-8B-Base).
 
 **Highlights:**
 - 🚀 3-6× faster than vLLM-optimized Qwen3-8B on math reasoning tasks
@@ -21,7 +21,7 @@ tags:
 
 For the base (pretrained) version, see [WeDLM-8B](https://huggingface.co/tencent/WeDLM-8B-Base), which is based on Qwen3-8B-Base.
 
-📄 Paper (Coming Soon) | 🌐 [Project Page](https://wedlm.github.io) | 💻 [GitHub](https://github.com/tencent/WeDLM)
+📄 [Paper](https://arxiv.org/abs/2512.22737) | 🌐 [Project Page](https://wedlm.github.io) | 💻 [GitHub](https://github.com/tencent/WeDLM)
 
 ## Model Details
 
@@ -31,14 +31,57 @@ For the base (pretrained) version, see [WeDLM-8B](https://huggingface.co/tencent
 | Parameters | 8B |
 | Context Length | 32,768 |
 
-## Quick Start (Recommended)
+## Installation
 
-For **fast inference**, use the `wedlm` engine:
+```bash
+git clone https://github.com/tencent/WeDLM.git
+cd WeDLM && bash install.sh
+```
+
+<details>
+<summary><b>Manual Installation</b></summary>
+
+```bash
+# Step 1: PyTorch
+pip install torch==2.8.0+cu129 --index-url https://download.pytorch.org/whl/cu129
+
+# Step 2: flash-attn build dependencies
+pip install psutil ninja packaging
+
+# Step 3: flash-attn (requires torch first)
+pip install flash-attn==2.7.4.post1 --no-build-isolation
+
+# Step 4: WeDLM
+git clone https://github.com/tencent/WeDLM.git
+cd WeDLM && pip install -e .
+```
+
+</details>
+
+<details>
+<summary><b>Docker Installation</b></summary>
 
 ```bash
-pip install git+https://github.com/tencent/WeDLM.git
+# Pull the Docker image
+docker pull aiweiliu/wedlm:v3
+
+# Run the container with GPU support
+docker run -it --gpus all -p 8080:8080 --name wedlm aiweiliu/wedlm:v3 /bin/bash
+
+# Inside the container, run inference directly
+python example.py --model tencent/WeDLM-8B-Instruct
 ```
 
+</details>
+
+> **Note:** `flash-attn` requires compilation and must be installed after PyTorch.
+> The `install.sh` script handles this automatically (default: CUDA 12.9).
+> For other CUDA versions: `CUDA_VERSION=cu124 bash install.sh`
+
+## Quick Start (Recommended)
+
+For **fast inference**, use the `wedlm` engine:
+
 ```python
 from transformers import AutoTokenizer
 from wedlm import LLM, SamplingParams
@@ -129,8 +172,16 @@ Speedup varies by task characteristics (measured against vLLM-optimized Qwen3-8B
 | Code Generation | 2-3× | Deterministic syntax |
 | Open-ended QA | 1.5-2× | Higher entropy limits parallelism |
 
-## Citation (Coming soon)
+## Citation
 
+```bibtex
+@article{liu2025wedlm,
+  title={WeDLM: Reconciling Diffusion Language Models with Standard Causal Attention for Fast Inference},
+  author={Liu, Aiwei and He, Minghua and Zeng, Shaoxun and Zhang, Linhao and Wu, Chuhan and Jia, Wei and Liu, Yuan and Yu, Yang and Zhou, Xiao and Zhou, Jie},
+  journal={arXiv preprint arXiv:2512.22737},
+  year={2025}
+}
+```
 
 ## License