Add pipeline tag and library name; include sample usage from Github

README.md

@@ -1,5 +1,7 @@
 ---
 license: mit
+library_name: transformers
+pipeline_tag: text-to-3d
 ---
 
 Uni-3DAR
@@ -9,6 +11,9 @@ Uni-3DAR
 Introduction
 ------------
 
+<p align="center"><img src="fig/overview.png" width=95%></p>
+<p align="center"><b>Schematic illustration of the Uni-3DAR framework</b></p>
+
 Uni-3DAR is an autoregressive model that unifies various 3D tasks. In particular, it offers the following improvements:
 
 1. **Unified Handling of Multiple 3D Data Types.**  
@@ -23,11 +28,63 @@ Uni-3DAR is an autoregressive model that unifies various 3D tasks. In particular
 4. **High Accuracy.**  
    Building on octree compression, Uni-3DAR further tokenizes fine-grained 3D patches to maintain structural details, achieving substantially better generation quality than previous diffusion-based models.
 
-Usage
------
 
-Please visit our GitHub Repo (https://github.com/dptech-corp/Uni-3DAR) for detailed instructions. 
+News
+----
+
+**2025-03-21:** We have released the core model along with the QM9 training and inference pipeline.
+
+
+Dependencies
+------------
+
+- [Uni-Core](https://github.com/dptech-corp/Uni-Core). For convenience, you can use our prebuilt Docker image:  
+  `docker pull dptechnology/unicore:2407-pytorch2.4.0-cuda12.5-rdma`
+
+
+Reproducing Results on QM9
+--------------------------
+
+To reproduce results on the QM9 dataset using our pretrained model or train from scratch, please follow the instructions below.
+
+### Download Pretrained Model and Dataset
+
+Download the pretrained checkpoint (`qm9.pt`) and the dataset archive (`qm9_data.tar.gz`) from our [Hugging Face repository](https://huggingface.co/dptech/Uni-3DAR/tree/main).
+
+### Inference with Pretrained Model
+
+To generate QM9 molecules using the pretrained model:
+
+```
+bash inference_qm9.sh qm9.pt
+```
+
+### Train from Scratch
+
+To train the model from scratch:
+
+1. Extract the dataset:
+```
+tar -xzvf qm9_data.tar.gz
+```
+
+2. Run the training script with your desired data path and experiment name:
+
+```
+base_dir=/your_folder_to_save/ bash train_qm9.sh ./qm9_data/ name_of_your_exp
+```
 
 
+Citation
+--------
 
+Please kindly cite our papers if you use the data/code/model.
 
+```
+@article{lu2025uni3dar,
+  author    = {Shuqi Lu and Haowei Lin and Lin Yao and Zhifeng Gao and Xiaohong Ji and Weinan E and Linfeng Zhang and Guolin Ke},
+  title     = {Uni-3DAR: Unified 3D Generation and Understanding via Autoregression on Compressed Spatial Tokens},
+  journal   = {Arxiv},
+  year      = {2025},
+}
+```