Update README.md

README.md

@@ -7,7 +7,7 @@ tags:
 - deep-learning
 - transformers
 - wireless-communication
-#license: mit
+license: mit
 datasets:
 - deepmimo
 ---
@@ -16,8 +16,25 @@ datasets:
 
 **[🚀 Click here to try the Interactive Demo!](https://huggingface.co/spaces/wi-lab/lwm-interactive-demo)**
 
-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.
+Welcome to **LWM** (Large Wireless Model) — a powerful, pre-trained model specifically designed for advanced feature extraction from wireless communication datasets like DeepMIMO. LWM leverages state-of-the-art transformer architectures to offer a deep, contextual understanding of wireless channels, making it the first of its kind tailored for wireless communications. 
 
+### What Does LWM Offer?
+
+LWM provides a **generalized feature extraction framework** that can be applied across diverse wireless communication tasks. From predicting the strongest mmWave beams to classifying line-of-sight (LoS) and non-line-of-sight (NLoS) channels, this model is built to handle the intricacies of complex wireless environments. **Trained on millions of wireless channel samples**, LWM has been designed to **generalize across diverse scenarios** — from urban cityscapes to synthetic environments, ensuring robust performance on a wide range of downstream tasks.
+
+### How Does LWM Work?
+
+At its core, LWM is based on the transformer architecture, which is capable of modeling both local and global dependencies within wireless channels. Unlike traditional models that focus on a narrow set of tasks, LWM uses **self-supervised learning** through a technique called **Masked Channel Modeling (MCM)**. This allows the model to learn from unlabeled wireless data, predicting masked patches within a channel, which in turn forces it to understand complex relationships between antennas and subcarriers.
+
+With its **bidirectional attention mechanism**, LWM can infer context by attending to both past and future patches, capturing holistic channel knowledge. This results in **highly effective, context-aware embeddings** that are ideal for various downstream applications, such as beamforming optimization, channel prediction, and beyond.
+
+### Why Should You Use LWM?
+
+- **Flexibility**: LWM is pre-trained on a vast array of wireless communication scenarios, making it highly adaptable for different tasks — from classification tasks like LoS/NLoS to more complex regression tasks like channel estimation.
+- **Efficiency**: By leveraging transformer-based embeddings, LWM dramatically reduces the need for large amounts of labeled data in downstream tasks. Even with limited data, it provides high-performance results.
+- **Generalization**: Whether you’re working in urban environments or dealing with synthetic wireless channels, LWM’s ability to generalize across datasets sets it apart, ensuring robust and reliable performance across different environments.
+
+Join the growing community of researchers using LWM for their wireless communications research, and unlock a new level of performance and insight in your models!
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 ## 🛠 **How to Use**