Transformers have become the dominant architecture in artificial intelligence since their introduction in the 2017 paper “Attention is All You Need.” They now power major text-generation models including OpenAI’s GPT, Meta’s Llama, and Google’s Gemini, while also finding applications in audio generation, image recognition, protein structure prediction, and game playing.
At their core, text-generative Transformers operate on a simple principle: predicting the most probable next token (a word or subword) given a user’s input text. This next-token prediction process relies on a key innovation called the self-attention mechanism, which allows the models to process entire sequences and capture relationships between distant words more effectively than previous architectures.
Transformers consist of three main components. First, an embedding layer converts text into numerical vectors by tokenizing input into smaller units and assigning each token a high-dimensional vector representation. GPT-2 represents each token as a 768-dimensional vector, with the model’s vocabulary containing 50,257 unique tokens. The embedding layer also encodes positional information about where each token appears in the sequence.
Second, transformer blocks process the embedded data through multi-head self-attention mechanisms and multilayer perceptron layers. The self-attention mechanism enables tokens to communicate with one another, with multiple attention heads examining relationships from different perspectives—one head might capture short-range syntactic patterns while another tracks broader semantic context. According to the explainer, GPT-2 (small) uses 12 stacked transformer blocks. Within each block, tokens are transformed into Query, Key, and Value vectors that determine how much focus each token receives when predicting the next one. A masking technique prevents the model from accessing future tokens during prediction.
Third, output probability layers transform the processed embeddings into predictions about the next token. The interactive explainer uses GPT-2 (small), a 124-million-parameter model, as its teaching tool. While not the latest or most powerful Transformer model available, it shares the same architectural components found in current state-of-the-art systems, making it an ideal reference point for understanding how these models function.
Key facts
- Transformers were introduced in 2017 and now power GPT, Llama, Gemini, and other major AI models
- The self-attention mechanism allows Transformers to capture long-range dependencies between words more effectively than previous architectures
- Text-generative Transformers predict the next token by assigning probability scores based on the input sequence
- GPT-2 (small) has 124 million parameters and uses 12 stacked transformer blocks to process text
- The embedding layer converts tokens to 768-dimensional vectors and includes positional encoding information
- Multi-head attention enables the model to examine word relationships from multiple perspectives simultaneously