Update README.md

README.md

@@ -69,4 +69,48 @@ git clone [https://github.com/xkoo115/UnifiedNeuroGen](https://github.com/xkoo11
 cd UnifiedNeuroGen
 
 # Install the required packages
-pip install -r requirements.txt
+pip install -r requirements.txt
+
+### Step 2: Download the Model
+
+You can download the model checkpoint file from this repository's "Files" tab, or programmatically using huggingface_hub:
+
+```bash
+from huggingface_hub import hf_hub_download
+
+model_path = hf_hub_download(
+    repo_id="xkoo115/unifiedneurogen-eeg2fmri-nat-view-within-subject-demo",
+    filename="unifiedneurogen-eeg2fmri-nat-view-within-subject-demo.pt",
+    local_dir="./checkpoints" # Download to a local 'checkpoints' folder
+)
+print(f"Model downloaded to: {model_path}")
+
+### Step 3: Run Inference
+
+Use the sample.py script from the cloned repository to generate fMRI data from EEG inputs. You will need a test set of EEG encodings. You can find sample data in the UnifiedNeuroGen-Demo-Dataset repository.
+
+```bash
+python sample.py \
+  --model DiT_fMRI \
+  --ckpt ./checkpoints/unifiedneurogen-eeg2fmri-nat-view-within-subject-demo.pt \
+  --eeg-path /path/to/your/test/eeg/encodings \
+  --save-path /path/to/save/generated/data
+
+--ckpt: The path to the model checkpoint you downloaded in Step 2.
+
+--eeg-path: The path to the input EEG data you wish to translate.
+
+--save-path: The directory where the generated fMRI data will be saved.
+
+📜 Citation
+If you use this model or the UnifiedNeuroGen framework in your research, please cite the original paper:
+```bash
+@misc{yao2025empoweringfunctionalneuroimagingpretrained,
+      title={Empowering Functional Neuroimaging: A Pre-trained Generative Framework for Unified Representation of Neural Signals}, 
+      author={Weiheng Yao and Xuhang Chen and Shuqiang Wang},
+      year={2025},
+      eprint={2506.02433},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={[https://arxiv.org/abs/2506.02433](https://arxiv.org/abs/2506.02433)}, 
+}