Update README.md

README.md

@@ -2,15 +2,63 @@
 
 **[🚀 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 clone the repository, load the data, and perform inference with LWM.
+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.
 
 ---
 
-## 🛠 **How to Use**
+## 🛠 **How to Use for Beginners**
 
-### 1. **Clone the Repository**
+### 1. **Install Conda or Mamba (via Miniforge)**
 
-To get started, clone the Hugging Face repository to your local machine with the following Python code:
+First, you need to have a package manager like **Conda** or **Mamba** (a faster alternative) 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**.
+
+- **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).
+
+#### **Option B: Install Mamba (via Miniforge)**
+
+**Mamba** is a much faster alternative to Conda. You can install **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).
+
+After installation, you can use `conda` or `mamba` for environment management. The commands will be the same except for replacing `conda` with `mamba`.
+
+---
+
+### 2. **Create a New Environment**
+
+Once you have Conda or Mamba installed, follow these steps to create a new environment and install the necessary packages.
+
+#### **Step 1: Create a new environment**
+
+You can create a new environment called `lwm_env` (or any other name) with Python 3.9 or any required version:
+
+```bash
+# If you're using Conda:
+conda create -n lwm_env python=3.9
+
+# If you're using Mamba:
+mamba create -n lwm_env python=3.9
+```
+
+#### **Step 2: Activate the environment**
+
+Activate the environment you just created:
+
+```bash
+# For both Conda and Mamba:
+conda activate lwm_env
+```
+
+---
+
+### 3. **Clone the Repository**
+
+After setting up the environment, clone the Hugging Face repository to your local machine using the following Python code:
 
 ```python
 import subprocess
@@ -65,7 +113,25 @@ print("All required functions imported successfully.")
 
 ---
 
-### 2. **Load the DeepMIMO Dataset**
+### 4. **Install Required Packages**
+
+Install the necessary packages inside your new environment.
+
+```bash
+# If you're using Conda:
+conda install pytorch torchvision torchaudio -c pytorch
+pip install -r requirements.txt
+
+# If you're using Mamba:
+mamba install pytorch torchvision torchaudio -c pytorch
+pip install -r requirements.txt
+```
+
+This will install **PyTorch**, **Torchvision**, and other required dependencies from the `requirements.txt` file in the cloned repository.
+
+---
+
+### 5. **Load the DeepMIMO Dataset**
 
 Before tokenizing and processing the data, you need to load the **DeepMIMO** dataset. Below is a list of available datasets and their links for more information:
 
@@ -104,7 +170,7 @@ print("DeepMIMO dataset loaded successfully.")
 
 ---
 
-### 3. **Tokenize the DeepMIMO Dataset**
+### 6. **Tokenize the DeepMIMO Dataset**
 
 After loading the data, tokenize the selected **DeepMIMO** datasets. This step prepares the data for the model to process.
 
@@ -121,7 +187,7 @@ print("Dataset tokenized successfully.")
 
 ---
 
-### 4. **Load the LWM Model**
+### 7. **Load the LWM Model**
 
 Once the dataset is tokenized, load the pre-trained **LWM** model using the following code:
 
@@ -134,7 +200,7 @@ model = LWM.from_pretrained(device=device)
 
 ---
 
-### 5. **LWM Inference**
+### 8. **LWM Inference**
 
 Once the dataset is tokenized and the model is loaded, generate either **raw channels** or the **inferred LWM embeddings** by choosing the input type.
 
@@ -151,9 +217,11 @@ You can choose between:
 
 ---
 
-## 🔄 **Post-processing for Downstream Task**
+###
+
+ 9. **Post-processing for Downstream Task**
 
-### 1. **Use the Dataset in Downstream Tasks**
+#### **Use the Dataset in Downstream Tasks**
 
 Finally, use the generated dataset for your downstream tasks, such as classification, prediction, or analysis.
 
@@ -168,4 +236,16 @@ print("Inference completed successfully.")
 ## 📋 **Requirements**
 
 - **Python 3.x**
-- **PyTorch**
+- **PyTorch**
+- **Git**
+
+---
+
+### Summary of Steps:
+
+1. **Install Conda/Mamba**: Install a package manager for environment management.
+2. **Create Environment**: Use Conda or Mamba to create a new environment.
+3. **Clone the Repository**: Download the project files from Hugging Face.
+4. **Install Packages**: Install PyTorch and other dependencies.
+5. **Load and Tokenize Data**: Load the DeepMIMO dataset and prepare it for the model.
+6. **Load Model and Perform Inference**: Use the LWM model for generating embeddings or raw channels.