Understanding Large Language Models: From Parameters to Transformer Architecture

Large language models consist of two essential files: a parameter file containing the neural network weights and a code file that runs the network, typically written in Python. The parameters are learned by training on massive internet text datasets, requiring thousands of GPUs and significant computational cost.

Training a model such as Llama 2 (700 billion parameters) compresses roughly 10 TB of text into a 140 GB file, enabling the model to form a statistical understanding of the world.

During inference, the model predicts the next token in a sequence by evaluating the compressed data through its neural network, effectively acting as a sophisticated word‑completion engine.

Neural networks are inspired by biological neurons: inputs are transformed into electrical signals, processed by layers of interconnected neurons, and produce outputs based on weighted sums and activation functions.

A simple perceptron example demonstrates binary inputs (1 or 0) and an output determined by weighted sums compared against a threshold; for instance, deciding whether to watch a movie based on weather, price, and companionship factors.

Weights can be assigned to reflect the importance of each factor (e.g., weather = 8, price = 4, companion = 4). The perceptron sums weighted inputs and compares the total to a threshold (e.g., 8) to produce a binary decision.

The TensorFlow Playground ( http://playground.tensorflow.org/ ) provides an interactive environment to experiment with neural network hyperparameters, data sets, and visualizations of training progress.

Modern large models are built on the Transformer architecture, introduced in the 2017 paper "Attention is All You Need". Transformers rely on self‑attention mechanisms to capture relationships between all tokens in a sequence, regardless of distance.

Input text is first tokenized into numerical IDs using a vocabulary (e.g., the tiktoken library). Each token is represented by an embedding vector, forming a matrix of token embeddings.

Positional encoding adds sine and cosine functions to the token embeddings, providing the model with information about token order without large integer indices.

Self‑attention computes Query, Key, and Value matrices from the embeddings, calculates attention scores (often via dot‑product), normalizes them into probabilities, and produces weighted sums that capture contextual relationships between tokens.

Normalization (e.g., layer normalization) scales attention scores to a stable range, improving training stability and convergence.

Feed‑forward neural networks then process each token independently, applying linear transformations and non‑linear activations to enrich representations.

Training adjusts all weights (including attention and feed‑forward parameters) to minimize prediction error, while inference uses the trained weights to generate outputs for new inputs.

Overall, the article provides a step‑by‑step conceptual guide from the basic perceptron to the full Transformer pipeline used in state‑of‑the‑art large language models.