Editing Openai/6939b23e-87cc-8001-bdd9-9546d1143ce9 (section)

=== Assistant: Ah — now we can clarify both points carefully. ===
Ah — now we can clarify both points carefully. Let’s take it step by step.

==== - The number of layers depends entirely on the specific model. ====
* Each “layer” is like a floor in the building of thought, where the model transforms its representation of the tokens.
* Examples (approximate):

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Important: There’s no “magic number” — it’s defined when the model is designed.

==== - It’s a good first intuition, but it’s slightly off. Let’s clarify: ====
* What really happens: 1. Each token is converted into a vector (embedding). 2. This vector passes through multiple layers, each with: - Attention → looks at other tokens and decides which ones matter - Feed-forward processing → transforms the vector based on what it “knows” 3. Neurons inside the layer compute weighted sums of inputs → create the next representation
* So it’s not just static nodes connected by fixed links, it’s dynamic pathways where the “importance” of each connection can change depending on the input tokens.

==== When I said “robots,” here’s what I meant, now clarified: ====
* Neurons = little workers / robots - Each worker does a small calculation on its piece of information (token vector).
* Attention heads = spotlights - These spotlights tell the workers which other tokens to pay attention to at that layer.
* Layers = floors of a building - Information is passed floor by floor, being transformed by workers and guided by spotlights.
* The point: tokens aren’t just nodes, they’re dynamic “packages of information” traveling through a building, being processed, weighted, and re-combined at each floor.

==== - Rough numbers: - GPT-3 175B: ~175 billion parameters (weights) → millions of neurons, each with hundreds or thousands of connections. - LLaMA 7B: ~7 billion parameters → smaller but similar structure. ====
* Each weight is like a connection strength between neurons; these are what get quantized when optimizing memory.

===== - Number of layers = architecture-defined; bigger → more depth → better reasoning. =====
* Tokens are not static nodes; they are dynamic vectors processed by neurons in layers.
* Attention = spotlights; neurons = workers; layers = floors.
* Weighted connections = the “rules” workers use to process each token vector.

If you want, I can make a diagram showing tokens flowing through layers with neurons, attention spotlights, and weighted connections, visualizing the “robots in a building” metaphor.

Do you want me to do that?