diff --git a/README.md b/README.md
index ac71954..feb0f90 100644
--- a/README.md
+++ b/README.md
@@ -8,6 +8,12 @@ DRGS-Net implements a hybrid molecular representation model that concatenates:
 
 The combined embedding is passed to a small prediction head for downstream molecular property prediction (classification or regression).
 
+## TL;DR (Quick usage)
+1) Install dependencies (see "Quick start" below for full setup commands).
+2) Copy the default config: `cp DRGS-Net.yaml config_concatenate.yaml`
+3) Edit `config_concatenate.yaml` with your dataset paths and ChemBERTa checkpoint.
+4) Run: `python DRGS-Net_finetune.py`
+
 This repository contains training/finetuning scripts, dataset wrappers, model definitions, and utilities. The hybrid model is implemented in `models/concatenate_model.py` (class `HybridModel`). The finetuning orchestration is in `DRGS-Net_finetune.py` which expects a configuration file named `config_concatenate.yaml` by default — if you only have `DRGS-Net.yaml`, copy/rename it to `config_concatenate.yaml` before running (see "Quick start").
 ## Methodology
 ![DRGS-Net Architecture](./fig/Methodology.png)
@@ -50,7 +56,14 @@ See the model card: https://huggingface.co/DeepChem/ChemBERTa-77M-MLM
 
 ## Quick start
 
-1) Prepare config file
+1) Download the repository
+
+```bash
+git clone https://github.com/thkim-01/DRGS-Net.git
+cd DRGS-Net
+```
+
+2) Prepare config file
 
 The finetune script (`DRGS-Net_finetune.py`) by default loads `config_concatenate.yaml`. If you only have `DRGS-Net.yaml`, create a copy with the expected name:
 
@@ -60,7 +73,7 @@ cp DRGS-Net.yaml config_concatenate.yaml
 
 Edit the YAML to set `task_name`, dataset paths, `fine_tune_from` (pretrained GNN checkpoint folder under `./ckpt/`) and `hybrid_specific.chemberta_model_name` (local path or Hugging Face model id).
 
-2) Create environment & install dependencies (suggested)
+3) Create environment & install dependencies (suggested)
 
 This project requires PyTorch, HuggingFace Transformers, and optional packages like RDKit and NVIDIA Apex for mixed precision.
 
@@ -80,11 +93,11 @@ pip install tensorboard scikit-learn pandas numpy tqdm pyyaml
 
 Note: Install compatible `torch-geometric` packages for your PyTorch/CUDA setup if you use PyG layers in the GNN models.
 
-3) Prepare data
+4) Prepare data
 
 Place downstream datasets under `data/<dataset_name>/` following the MoleculeNet CSV formats. The default config uses paths like `data/bbbp/BBBP.csv` etc. See `DRGS-Net_finetune.py` for dataset mapping by `task_name`.
 
-4) Run finetuning
+5) Run finetuning
 
 ```bash
 python DRGS-Net_finetune.py