Update README.md

README.md

@@ -2,7 +2,7 @@
 
 **[🚀 Click here to try the Interactive Demo!](https://huggingface.co/spaces/sadjadalikhani/lwm-interactive-demo)**
 
-Welcome to the **LWM** (Large Wireless Model) repository! This project hosts a pre-trained model designed to process and extract features from wireless communication datasets, specifically the **DeepMIMO** dataset. Follow the instructions below to set up your environment, install the required packages, clone the repository, load the data, and perform inference with LWM.
+Welcome to **LWM** (Large Wireless Model) — a pre-trained model designed for processing and feature extraction from wireless communication datasets, particularly the **DeepMIMO** dataset. This guide provides step-by-step instructions to set up your environment, install the required packages, clone the repository, load data, and perform inference using LWM.
 
 ---
 
@@ -10,32 +10,32 @@ Welcome to the **LWM** (Large Wireless Model) repository! This project hosts a p
 
 ### 1. **Install Conda or Mamba (via Miniforge)**
 
-First, you need to have a package manager like **Conda** or **Mamba** (a faster alternative) installed to manage your Python environments and packages.
+First, ensure that you have a package manager like **Conda** or **Mamba** installed to manage your Python environments and packages.
 
 #### **Option A: Install Conda**
 
-If you prefer to use **Conda**, you can download and install **Anaconda** or **Miniconda**.
+You can install **Conda** via **Anaconda** or **Miniconda**.
 
-- **Anaconda** includes a full scientific package suite, but it is larger in size. Download it [here](https://www.anaconda.com/products/distribution).
-- **Miniconda** is a lightweight version that only includes Conda and Python. Download it [here](https://docs.conda.io/en/latest/miniconda.html).
+- **Anaconda** includes a comprehensive scientific package suite. Download it [here](https://www.anaconda.com/products/distribution).
+- **Miniconda** is a lightweight version that includes only Conda and Python. Download it [here](https://docs.conda.io/en/latest/miniconda.html).
 
 #### **Option B: Install Mamba (via Miniforge)**
 
-**Mamba** is a much faster alternative to Conda. You can install **Mamba** by installing **Miniforge**.
+For a faster alternative, use **Mamba** by installing **Miniforge**.
 
-- **Miniforge** is a smaller, community-based installer for Conda that includes **Mamba**. Download it [here](https://github.com/conda-forge/miniforge/releases/latest).
+- **Miniforge** is a smaller installer that comes with **Mamba**. Download it [here](https://github.com/conda-forge/miniforge/releases/latest).
 
-After installation, you can use conda for environment management.
+Once installed, you can use Conda to manage environments.
 
 ---
 
 ### 2. **Create a New Environment**
 
-Once you have Conda (https://conda.io/projects/conda/en/latest/user-guide/install/index.html), follow these steps to create a new environment and install the necessary packages.
+After installing Conda (https://conda.io/projects/conda/en/latest/user-guide/install/index.html), follow these steps to create a new environment and install the required packages.
 
 #### **Step 1: Create a new environment**
 
-You can create a new environment called `lwm_env`:
+Create a new environment named `lwm_env`:
 
 ```bash
 conda create -n lwm_env
@@ -43,7 +43,7 @@ conda create -n lwm_env
 
 #### **Step 2: Activate the environment**
 
-Activate the environment you just created:
+Activate the environment:
 
 ```bash
 conda activate lwm_env
@@ -51,30 +51,37 @@ conda activate lwm_env
 
 ---
 
-#### **Step 3: Install Required Packages**
+### 3. **Install Required Packages**
 
-Install the necessary packages inside your new environment.
+Once the environment is activated, install the necessary packages.
+
+#### **Install CUDA-enabled PyTorch**
+
+Change the `pytorch-cuda` version based on your system requirements.
 
-# Install CUDA-enabled Pytorch
 ```bash
 conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia
 ```
-Note: If you have trouble installing the CUDA-enabled Pytorch, make sure the cuda version is compatibnle with your system. It can also because you have tried multiple install scripts. Try a new environment.
 
-# Install other required packages from conda-forge
+> **Note:** If you encounter issues installing CUDA-enabled PyTorch, verify your CUDA version compatibility. It might also be due to conflicting installation attempts—try a fresh environment.
+
+#### **Install Other Required Packages via Conda Forge**
+
 ```bash
 conda install python numpy pandas matplotlib tqdm -c conda-forge
 ```
-# Install DeepMIMOv3 with pip
+
+#### **Install DeepMIMOv3 with pip**
+
 ```bash
 pip install DeepMIMOv3
 ```
 
 ---
 
-### 3. **Required Functions to Clone Datasets**
+### 4. **Clone the Dataset Scenarios**
 
-The following functions will help you clone specific dataset scenarios:
+The following functions will help you clone specific dataset scenarios from a repository:
 
 ```python
 import subprocess
@@ -104,21 +111,15 @@ def clone_dataset_scenario(scenario_name, repo_url, model_repo_dir="./LWM", scen
     subprocess.run(["git", "lfs", "pull"], cwd=scenarios_path, check=True)
 
     print(f"Successfully cloned {scenario_name} into {scenarios_path}.")
-
-# Function to clone multiple dataset scenarios
-def clone_dataset_scenarios(selected_scenario_names, dataset_repo_url, model_repo_dir):
-    for scenario_name in selected_scenario_names:
-        clone_dataset_scenario(scenario_name, dataset_repo_url, model_repo_dir)
 ```
 
 ---
 
-### 4. **Clone the Model**
+### 5. **Clone the Model Repository**
 
-Next, you need to clone the **LWM** model from its Git repository. This will download all the necessary files to your local system.
+Now, clone the **LWM** model repository to your local system.
 
 ```bash
-
 # Step 1: Clone the model repository (if not already cloned)
 model_repo_url = "https://huggingface.co/sadjadalikhani/lwm"
 model_repo_dir = "./LWM"
@@ -130,9 +131,9 @@ if not os.path.exists(model_repo_dir):
 
 ---
 
-### 5. **Clone the Desired Datasets**
+### 6. **Clone the Desired Dataset Scenarios**
 
-Before proceeding with tokenization and data processing, the **DeepMIMO** dataset—or any dataset generated using the operational settings outlined below—must first be loaded. The table below provides a list of available datasets and their respective links for further details:
+You can now clone specific scenarios from the DeepMIMO dataset, as detailed in the table below:
 
 📊 **Dataset Overview**
 
@@ -145,36 +146,25 @@ Before proceeding with tokenization and data processing, the **DeepMIMO** datase
 | Dataset 4      | 🌉 Santa Clara        | 2689                   | [DeepMIMO City Scenario 11](https://www.deepmimo.net/scenarios/deepmimo-city-scenario11/)                   |
 | Dataset 5      | 🌅 San Diego          | 2192                   | [DeepMIMO City Scenario 7](https://www.deepmimo.net/scenarios/deepmimo-city-scenario7/)                     |
 
-It is important to note that these six datasets were **not** used during the pre-training of the LWM model, and the high-quality embeddings produced are a testament to LWM’s robust generalization capabilities rather than overfitting.
-
-#### **Operational Settings**:
-- **Antennas at BS**: 32
-- **Antennas at UEs**: 1
-- **Subcarriers**: 32
-- **Paths**: 20
-
+#### **Clone the Scenarios:**
 ```python
-# Step 2: Clone specific dataset scenario folder(s) inside the "scenarios" folder
 dataset_repo_url = "https://huggingface.co/datasets/sadjadalikhani/lwm"  # Base URL for dataset repo
-scenario_names = np.array(["city_18_denver",
-                           "city_15_indianapolis",
-                           "city_19_oklahoma", 
-                           "city_12_fortworth",
-                           "city_11_santaclara",
-                           "city_7_sandiego"]
-                          )
-
-# Choose the desired scenario or secanrios (if you need the combined scenarios as a larger and more diverse scenario.).
-scenario_idxs = np.array([0,1,2,3,4,5,6]) 
+scenario_names = np.array([
+    "city_18_denver", "city_15_indianapolis", "city_19_oklahoma", 
+    "city_12_fortworth", "city_11_santaclara", "city_7_sandiego"
+])
+
+scenario_idxs = np.array([0, 1, 2, 3, 4, 5])  # Select the scenario indexes
 selected_scenario_names = scenario_names[scenario_idxs]
 
-# Clone the requested scenario folders (this will clone every time)
+# Clone the requested scenarios
 clone_dataset_scenarios(selected_scenario_names, dataset_repo_url, model_repo_dir)
 ```
 
 ---
 
-### 6. **Change the working directory to LWM folder**
+### 7. **Change the Working Directory to LWM**
+
 ```bash
 if os.path.exists(model_repo_dir):
     os.chdir(model_repo_dir)
@@ -185,16 +175,20 @@ else:
 
 ---
 
-### 7. **Tokenize and Load the Model**
+### 8. **Tokenize and Load the Model**
+
+Now, tokenize the dataset and load the pre-trained LWM model.
 
 ```python
 from input_preprocess import tokenizer
 from lwm_model import lwm
 import torch
 
-preprocessed_chs = tokenizer(selected_scenario_names=selected_scenario_names, 
-                             manual_data=None,
-                             gen_raw=True)
+preprocessed_chs = tokenizer(
+    selected_scenario_names=selected_scenario_names, 
+    manual_data=None,
+    gen_raw=True
+)
 
 device = 'cuda' if torch.cuda.is_available() else 'cpu'
 print(f"Loading the LWM model on {device}...")
@@ -203,11 +197,15 @@ model = lwm.from_pretrained(device=device)
 
 ---
 
-### 8. **Perform Inference**
+### 9. **Perform Inference**
+
+You can now perform inference on the preprocessed data using the LWM model.
+
 ```python
 from inference import lwm_inference, create_raw_dataset
 input_types = ['cls_emb', 'channel_emb', 'raw']
 selected_input_type = input_types[0]
+
 if selected_input_type in ['cls_emb', 'channel_emb']:
     dataset = lwm_inference(preprocessed_chs, selected_input_type, model, device)
 else:
@@ -216,11 +214,12 @@ else:
 
 ---
 
-### 9. **Explore the Interactive Demo**
+### 10. **Explore the Interactive Demo**
+
+To experience **LWM** interactively, visit our demo hosted on Hugging Face Spaces:
 
-If you'd like to explore **LWM** interactively, check out the demo hosted on Hugging Face Spaces:  
 [**Try the Interactive Demo!**](https://huggingface.co/spaces/sadjadalikhani/LWM-Interactive-Demo)
 
 ---
 
-Now you’re ready to dive into the world of **Large Wireless Model (LWM)**, process wireless communication datasets, and extract high-quality embeddings to fuel your research or application!
+You're now ready to explore the power of **LWM** in wireless communications! Start processing datasets and generate high-quality embeddings to advance your research or applications.