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What is a Transformer? Transformer is a neural network architecture that has fundamentally changed the approach to Artificial Intelligence. Transformer was first introduced in the seminal paper "Attention is All You Need" in 2017 and has since become the go-to architecture for deep learning models, powering text-generative models like OpenAI's GPT , Meta's Llama , and Google's Gemini . Beyond text, Transformer is also applied in audio generation , image recognition , protein structure prediction , and even game playing , demonstrating its versatility across numerous domains. Fundamentally, text-generative Transformer models operate on the principle of next-token prediction : given a text prompt from the user, what is the most probable next token (a word or part of a word) that will follow this input? The core innovation and power of Transformers lie in their use of self-attention mechanism, which allows them to process entire sequences and capture long-range dependencies more effectively than previous architectures. GPT-2 family of models are prominent examples of text-generative Transformers. Transformer Explainer is powered by the GPT-2 (small) model which has 124 million parameters. While it is not the latest or most powerful Transformer model, it shares many of the same architectural components and principles found in the current state-of-the-art models making it an ideal starting point for understanding the basics. Transformer Architecture Every text-generative Transformer consists of these three key components : Embedding : Text input is divided into smaller units called tokens, which can be words or subwords. These tokens are converted into numerical vectors called embeddings, which capture the semantic meaning of words. Transformer Block is the fundamental building block of the model that processes and transforms the input data. Each block includes: Attention Mechanism , the core component of the Transformer block. It allows tokens to communicate with other tokens, capturing contextual information and relationships between words. MLP (Multilayer Perceptron) Layer , a feed-forward network that operates on each token independently. While the goal of the attention layer is to route information between tokens, the goal of the MLP is to refine each token's representation. Output Probabilities : The final linear and softmax layers transform the processed embeddings into probabilities, enabling the model to make predictions about the next token in a sequence. Embedding Let's say you want to generate text using a Transformer model. You add the prompt like this one: “Data visualization empowers users to” . This input needs to be converted into a format that the model can understand and process. That is where embedding comes in: it transforms the text into a numerical representation that the model can work with. To convert a prompt into embedding, we need to 1) tokenize the input, 2) obtain token embeddings, 3) add positional information, and finally 4) add up token and position encodings to get the final embedding. Let’s see how each of these steps is done. Figure 1 . Expanding the Embedding layer view, showing how the input prompt is converted to a vector representation. The process involves (1) Tokenization, (2) Token Embedding, (3) Positional Encoding, and (4) Final Embedding. Step 1: Tokenization Tokenization is the process of breaking down the input text into smaller, more manageable pieces called tokens. These tokens can be a word or a subword. The words "Data" and "visualization" correspond to unique tokens, while the word "empowers" is split into two tokens. The full vocabulary of tokens is decided before training the model: GPT-2's vocabulary has 50,257 unique tokens. Now that we split our input text into tokens with distinct IDs, we can obtain their vector representation from embeddings. Step 2. Token Embedding GPT-2 (small) represents each token in the vocabulary as a 768-dimensional vector; the dimension of the vector depends on the model. These embedding vectors are stored in a matrix of shape (50,257, 768) , containing approximately 39 million parameters! This extensive matrix allows the model to assign semantic meaning to each token, in the sense that tokens with similar usage or meaning in language are placed close together in this high-dimensional space, while dissimilar tokens are farther apart. Step 3. Positional Encoding The Embedding layer also encodes information about each token's position in the input prompt. Different models use various methods for positional encoding. GPT-2 trains its own positional encoding matrix from scratch, integrating it directly into the training process. Step 4. Final Embedding Finally, we sum the token and positional encodings to get the final embedding representation. This combined representation captures both the semantic meaning of the tokens and their position in the input sequence. Transformer Block The core of the Transformer's processing lies in the Transformer block, which comprises multi-head self-attention and a Multi-Layer Perceptron layer. Most models consist of multiple such blocks that are stacked sequentially one after the other. The token representations evolve through layers, from the first block to the last one, allowing the model to build up an intricate understanding of each token. This layered approach leads to higher-order representations of the input. The GPT-2 (small) model we are examining consists of 12 such blocks. Multi-Head Self-Attention The self-attention mechanism enables the model to capture relationships among tokens in a sequence, so that each token’s representation is influenced by the others. Multiple attention heads allow the model to consider these relationships from different perspectives; for example, one head may capture short-range syntactic links while another tracks broader semantic context. In the following section, we will walk through how multi-head self-attention is computed step by step. Step 1: Query, Key, and Value Matrices Q K V i j = ( ∑ d = 1 768 Embedding i , d ⋅ Weights d , j ) + Bias j QKV_{ij} = ( \sum_{d=1}^{768} \text{Embedding}_{i,d} \cdot \text{Weights}_{d,j}) + \text{Bias}_j Q K V ij ​ = ( d = 1 ∑ 768 ​ Embedding i , d ​ ⋅ Weights d , j ​ ) + Bias j ​ Figure 2 . Computing Query, Key, and Value matrices from the original embedding. Each token's embedding vector is transformed into three vectors: Query (Q) , Key (K) , and Value (V) . These vectors are derived by multiplying the input embedding matrix with learned weight matrices for Q , K , and V . Here's a web search analogy to help us build some intuition behind these matrices: Query (Q) is the search text you type in the search engine bar. This is the token you want to "find more information about" . Key (K) is the title of each web page in the search result window. It represents the possible tokens the query can attend to. Value (V) is the actual content of web pages shown. Once we matched the appropriate search term (Query) with the relevant results (Key), we want to get the content (Value) of the most relevant pages. By using these QKV values, the model can calculate attention scores, which determine how much focus each token should receive when generating predictions. Step 2: Multi-Head Splitting Query , key , and Value vectors are split into multiple heads—in GPT-2 (small)'s case, into 12 heads. Each head processes a segment of the embeddings independently, capturing different syntactic and semantic relationships. This design facilitates parallel learning of diverse linguistic features, enhancing the model's representational power. Step 3: Masked Self-Attention In each head, we perform masked self-attention calculations. This mechanism allows the model to generate sequences by focusing on relevant parts of the input while preventing access to future tokens. Figure 3 . Using Query, Key, and Value matrices to calculate masked self-attention. Dot Product : The dot product of Query a