explain me how attention mechnaism or transofrmers work in LLM

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Traditional sequential models like Recurrent Neural Networks process text word by word, maintaining a hidden state that summarizes what has been seen so far. This approach has several limitations. First, there's an information bottleneck in the hidden state, which must compress all previous context into a fixed-size vector. Second, RNNs struggle with long-range dependencies, making it difficult to connect related words that are far apart in the text. Finally, the sequential nature of RNNs limits parallelization, making them inefficient for processing long sequences. These limitations led to the development of attention mechanisms and the Transformer architecture. Self-attention is the core mechanism that allows Transformers to process sequences effectively. For each token in the input sequence, the model calculates how much it should attend to every other token. This is done using three learned linear transformations: Query, Key, and Value. The Query represents what we're looking for, the Key is what we match against, and the Value contains the information we want to retrieve. For each position, we compute attention scores by taking the dot product of its Query vector with the Key vectors of all positions. These scores are then scaled, passed through a softmax function to get attention weights, and used to create a weighted sum of the Value vectors. The formula for attention is: Attention(Q,K,V) equals softmax of QK-transpose divided by the square root of d_k, multiplied by V. This allows the model to focus on relevant parts of the input when processing each token, effectively capturing long-range dependencies. The Transformer architecture consists of an encoder and a decoder, each containing multiple identical layers stacked on top of each other. The encoder processes the input sequence, while the decoder generates the output sequence. Each encoder layer has two sub-layers: a multi-head self-attention mechanism and a position-wise feed-forward network. The decoder has three sub-layers: a masked multi-head self-attention, a multi-head cross-attention over the encoder's output, and a feed-forward network. Each sub-layer is followed by layer normalization and includes a residual connection. Positional encoding is added to the input embeddings to provide information about the position of tokens in the sequence, since the attention mechanism itself has no notion of order. This architecture enables efficient parallel processing and captures long-range dependencies effectively, making it ideal for language modeling tasks.