Knowledge

When we interact with AI language models like ChatGPT, Llama, or DeepSeek, we're engaging with systems that contain billions of parameters — but what does that actually mean? Today, we'll dive deep into how these massive neural networks are structured and explore fascinating ways to visualize their inner workings.

At their core, language models are intricate networks of interconnected artificial neurons. Each connection between these neurons has a weight, which we call a parameter. These weights determine how information flows through the network and ultimately influences the model's outputs. When we say a model like Llama-3 has 70 billion parameters, we're talking about 70 billion individual numbers that work together to process and generate text.

These parameters aren't random numbers — they're carefully tuned through training to recognize patterns in language. Think of them as tiny knobs that the model adjusts as it learns, each one contributing to its understanding of language, context, and meaning. These parameters are stored in tensor files.

Storing Parameters in Tensor Files

The billions of parameters in language models are stored in specialized files called tensor files (typically with the .safetensors extension). These files organize the parameters in multi-dimensional arrays, similar to how spreadsheets organize data in rows and columns, but with the ability to extend into multiple dimensions.

While the role of each array is not relevant for this short experiment, it is important to notice that each set of arrays (within the tensor files) does have a distinctive function in the architecture of the LLM. These are the files that we will try to visualize.

Visualizing Neural Networks

Typical visualization techniques for neural networks are: line plots, histograms, network graphs, 3D surface plots and heatmaps. Out of these options, for the current exercise, we've chosen heatmaps.

The heatmap visualization specifically uses the matrix structure to show weight patterns, where:

• Each row represents a parameter
• Each column represents a dimension
• Values in the cells are the actual weights

Llama 3.2 — 3B Parameters

Let's start with our first model for the analysis: Llama 3.2 — 3B parameters. At first, we can mostly notice a "noise" pattern with no distinctive patterns emerging. The visualization reveals the complexity hidden within even a relatively small model, with billions of weight values forming intricate patterns that collectively enable the model's language understanding capabilities.

As we zoom in and examine specific layers of the network, subtle structures begin to emerge — patterns that correspond to the model's learned representations of language, syntax, and semantics. This is the hidden beauty of neural networks: structure arising from what initially appears to be random noise.