| Transformer Step | Chef & Bakery Procedure | What GPT is Doing |
|---|---|---|
| 1. Tokenization | The chef prepares ingredients in separate bowls. "Eggs" in one bowl, "Flour" in another, "Butter" in a third. | Breaking the sentence into pieces.
Example: ["Cat", "chases", "the", "mouse", "quickly"]
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| 2. Embeddings | Each bowl gets a detailed label that captures the ingredient's essence. The "Sugar" label describes it as sweet, granulated, and dissolvable. | Turning each word into a number vector.
Example: "chases" becomes numbers representing "action," "speed," "movement," "predation."
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| 3. Positional Encoding | The chef notes the step sequence on the recipe. "Cream butter and sugar" must happen before "Add eggs". | Remembering word order.
Example: Adding information that "chases" comes after "Cat" and before "the mouse."
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| 4. Self-Attention | Each ingredient searches for its most relevant partners. Butter looks around thinking: "I need to pay most attention to Sugar (to be creamed with) and Flour (to be mixed with)." | Understanding which words connect.
Example: The word "chases" pays strong attention to "Cat" (who does the chasing) and "mouse" (what gets chased).
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| 5. Multi-Head Attention | Multiple specialised self-attentions happen simultaneously. One chef focuses on flavour partnerships, another on texture relationships; all ingredients are searching for different types of important connections at once. | Analysing different relationships at once.
Example: For "chases":
• One head focuses on "Cat"→"chases" (subject-verb)
• Another on "chases"→"mouse" (verb-object)
• A third on "chases"→"quickly" (verb-adverb)
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| 6. Feed-Forward Network | Each ingredient gets individually refined and enhanced. The chef intensifies Vanilla's aroma, then balances it. | Refining each word's meaning.
Example: Enhancing "quickly" to mean "high speed" and connecting it specifically to the action "chases."
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| 7. Layer Normalization | The process repeats through multiple layers - each with its own Multi-Head Attention and Feed-Forward steps. | Stabilising the understanding before the next layer.
Example: Ensuring the relationship between "chases" and "mouse" remains clear as processing continues.
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| Layer 1: Mixing stage |
1. Attention: "Butter checks its relationship with everyone: 'How much should I interact with Sugar? How much with Flour?'" 2. FFN: Butter actually gets whisked and blended with the (Flour and Sugar), transforming from a separate ingredient into part of a cohesive mixture. |
Building deeper meaning.
Example: Now understanding that "chases" implies predation, speed, and that "Cat" is a predator while "mouse" is prey.
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| Layer 2: Baking Stage |
1. Self-Attention: "The ingredients coordinate in the oven's heat: 'Which parts need to solidify first? Where should the air bubbles expand to make the cake rise evenly?'" 2. FFN: The actual chemical transformation happens: proteins in Eggs and Flour solidify into a firm structure, while air bubbles expand to make the cake light and fluffy. |
Creating a coherent understanding.
Example: The entire phrase "chases the mouse quickly" is now a single event where action, target, and manner are fused into a complete concept.
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| 8. Autoregressive Decoding | The chef builds the final cupcake step-by-step. First, he bakes the cake (considering probabilities: 90% vanilla, 8% chocolate), then adds frosting (85% chocolate, 10% vanilla), then sprinkles (70% rainbow, 20% chocolate) - each step depending on the previous one. | Generating the next word.
Example: After "Cat chases the mouse", it calculates: "quickly" (70%), "swiftly" (20%), "away" (10%), and picks "quickly".
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