Update README.md

README.md

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 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. 
 
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-    <figcaption>Figure 1: This figure depicts the offline pre-training and online embedding generation process for LWM. The channel is divided into fixed-size patches, which are linearly embedded and combined with positional encodings before being passed through a Transformer encoder. During self-supervised pre-training, some embeddings are masked, and LWM leverages self-attention to extract deep features, allowing the decoder to reconstruct the masked values. For downstream tasks, the generated LWM embeddings enhance performance. The right block shows the LWM architecture, inspired by the original Transformer introduced in the "Attention is all you need" paper.</figcaption>
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 ### What Does LWM Offer?