Explain how the inner mechanics of transformer architecture in large language models work in detail and how they process text word by word

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Welcome to an exploration of transformer architecture, the foundation of modern large language models. Transformers revolutionized natural language processing by introducing a mechanism called self-attention, which allows the model to weigh the importance of different words in relation to each other. Unlike previous sequential models, transformers process all words in parallel, making them much more efficient. The architecture consists of stacked encoder and decoder layers. The encoder processes the input text, while the decoder generates the output. A key innovation is positional encoding, which helps the model understand word order despite parallel processing. In the next scenes, we'll dive deeper into how these components work together to process and generate text. Let's explore how transformers process text input. The first step is tokenization, where the input text is split into tokens. These tokens can be words, subwords, or characters, depending on the tokenization method. Each token is then assigned a unique ID from the model's vocabulary. Next, these token IDs are converted into dense vector representations called embeddings. These embeddings capture semantic relationships between tokens. However, transformers process all tokens in parallel, so they need a way to understand word order. This is where positional encoding comes in. Positional encodings are vectors that contain information about a token's position in the sequence. These encodings are added to the token embeddings to create the final input representation. This combined embedding preserves both the meaning of the tokens and their positions in the sequence, allowing the model to process text effectively. Now let's dive into the self-attention mechanism, which is the heart of transformer models. Self-attention allows each token to look at all other tokens in the sequence and determine how much attention to pay to each one. The process begins with three linear projections of each token's embedding: Query, Key, and Value vectors. You can think of the Query as what the token is looking for, the Key as what information it contains, and the Value as what it contributes to other tokens. For each token, we compute attention scores by taking the dot product of its Query vector with the Key vectors of all tokens. This gives us a measure of compatibility between tokens. These scores are then scaled by dividing by the square root of the dimension of the Key vectors, which helps with training stability. Next, we apply a softmax function to convert these scores into probabilities that sum to one. These probabilities serve as weights. Finally, we compute a weighted sum of all Value vectors using these weights. This creates a new representation for each token that incorporates contextual information from the entire sequence. For example, the representation of 'cat' now contains information from 'sat' and other relevant tokens, capturing the relationships between words. Let's explore the structure of transformer layers and multi-head attention. A transformer model consists of multiple identical layers stacked on top of each other. Each layer has two main sub-layers: multi-head attention and a feed-forward network. The multi-head attention mechanism is an extension of the self-attention we discussed earlier. Instead of performing a single attention function, it linearly projects the queries, keys, and values multiple times with different learned projections. This creates multiple 'heads' that can attend to information from different representation subspaces. The outputs from these heads are concatenated and linearly projected again to produce the final result. After each sub-layer, a residual connection is employed, followed by layer normalization. The residual connection adds the input of the sub-layer to its output, which helps with training deep networks by allowing gradients to flow more easily. Layer normalization stabilizes the learning process by normalizing the inputs across the features. The feed-forward network consists of two linear transformations with a non-linear activation function in between. It processes each position independently, applying the same transformation to each token's representation. This combination of multi-head attention and feed-forward networks, along with residual connections and layer normalization, allows transformers to capture complex relationships between words and build rich contextual representations. Now let's see how transformers generate text word by word. The process begins with an input prompt, such as 'The cat sat on'. This prompt is tokenized into individual tokens and processed through the transformer layers. A critical component of text generation is masked attention. Unlike in bidirectional models where each token can attend to all other tokens, in generation, each token can only attend to itself and previous tokens. This prevents the model from 'cheating' by looking at future tokens that haven't been generated yet. After processing through the transformer layers, the model outputs a probability distribution over the entire vocabulary for the next token. This distribution represents the model's prediction of which word should come next. A token is selected from this distribution, either by taking the most probable token or using sampling techniques like top-k or nucleus sampling. In our example, the token 'mat' is selected with the highest probability. This selected token is then appended to the input sequence, creating an updated sequence: 'The cat sat on the mat'. The entire process is then repeated with this new sequence to predict the next token. This autoregressive generation continues until a stopping condition is met, such as reaching a maximum length or generating a special end-of-sequence token. This word-by-word generation process allows large language models to create coherent and contextually relevant text.