You might have read my earlier blog, Explain GPT to a 5-Year-Old 👧🧒, where we kept things simple and magical ✨. But let's be honest—while a 5-year-old is happy to know that, a friendly Chef Cupcake 🤖👨🍳 is cooking up sentences, a curious adult 🧠 starts asking the real questions: "Yeah, but... how does it really work?" 🤔
They're ready for the next step 🚀. They're ready to peek behind the curtain 🎭 and see the actual recipe 📖. So, consider this article the sequel 🎬. We're moving from the magic show 🎩 to the masterclass 🧑🏫.
We're keeping our friendly robot chef, Chef Cupcake 🤖🧁, but now we're putting on our aprons 👩🍳👨🍳 and following him step-by-step through his digital kitchen 💻🍳.
We'll break down the core process of a Transformer model—the "T" in GPT—into bite-sized, delicious pieces 🍰.
Get ready to learn how AI transforms your words into wonders, one ingredient at a time 🕒⭐.
Heads-up, baking friends! This next section on Transformer steps gets a bit technical - but don't worry! We're sticking with our cupcake analogy to make these complex AI concepts as easy to digest as fresh-baked treats. Grab a coffee and let's bake through this together! 🧁☕
The Recipe for Understanding: A Detailed Comparison
1️⃣ Cutting up the Ingredients → Tokenizing
👨🍳 Chef:
Chef Cupcake lays out flour, sugar, eggs, butter, and vanilla, but they're in big messy clumps. He chops the butter into cubes, cracks eggs into a bowl, and measures sugar precisely. He arranges every prepared item in different bowls, breaking them down into tidy pieces, which means he can use them without confusion.
🤖 GPT:
When you type "Once upon a sunny morning, the fox played in the garden," GPT splits the text into tokens: ["Once", "upon", "a", "sunny", "morning", ",", "the", "fox", "played", "in", "the", "garden"]. Each chunk is small enough for GPT to understand. Just as the chef preps ingredients, GPT prepares its data so every piece is ready for the next stage.
2️⃣ Putting Labels on the Bowls → Embedding
👨🍳 Chef:
After preparing ingredients, Chef Cupcake labels bowls: "sweet" for sugar, "wet" for eggs, "fat" for butter, "dry" for flour. The labels help him remember their personalities — sugar is sweet and melts, flour builds structure, butter adds richness.
🤖 GPT:
GPT gives every token a vector embedding, a mathematical tag (number) that captures meaning. For example, "fox" is close to "wolf" and "animal" in its embedding space, while "garden" is near "yard" or "park." Like the chef's labels, embeddings remind GPT about the essence of each token, so it knows how they might interact later.
3️⃣ Marking the Order → Positional Encoding
👨🍳 Chef:
Ingredients aren’t just about what they are — it matters when you add them. Chef Cupcake numbers his steps: whisk eggs (1), blend sugar (2), fold flour (3). If he pours milk too soon, the texture changes. Keeping track of orders makes sure the recipe works.
🤖 GPT:
GPT also needs to know the sequence. In the sentences “The fox chased the rabbit” vs. “The rabbit chased the fox,” the words are the same, but the meaning flips. GPT adds positional encodings for these words, so it knows “fox” came before “rabbit.” This helps it understand that the subject is doing the chasing, not the other way around.
4️⃣ All the Bowls Chatting → Self-Attention
👨🍳 Chef:
Imagine Chef CupCake puts the three bowls on a table and tells them, "Okay, everyone, talk to each other. Discuss how you relate. Your final job in this cupcake might change based on who you're talking to."
To have this structured conversation, Chef gives each bowl three new ways to describe itself:
The Query (Q): "What am I looking for in others?" This is the question the bowl asks of everyone else.
The Key (K): "What do I have to offer?" This is how a bowl answers when another bowl asks a question.
The Value (V): "What is my core, essential information?" This is the actual content a bowl contributes once it's deemed important.
Step 1: Asking and Answering (Calculating Attention Scores)
First, each bowl takes turns being the "speaker." The speaker uses its Query (Q) to ask a question of every bowl, including itself. Each bowl answers with its Key (K).
The result is a table of "attention scores" – how much Flour should pay attention to every other ingredient.
| Ingredient | Looks at Flour (K) | Looks at Eggs (K) | Looks at Butter (K) |
| Flour (Q) | 0.5 | 0.9 | 0.2 |
Step 2: The "Aha!" Moment (Softmax and Weighted Sum)
Imagine the bowls just finished chatting. They have a list of "interest scores" for each other. But these scores are messy, like random numbers.
Softmax - The "Magic Measuring Cup"
Think of Softmax as a magic measuring cup that turns messy scores into perfect, clear portions. It creates perfect slices of pie: It converts the scores into percentages that add up to 100% as given below.
Flour: 0.5 → e^0.5 ≈ 1.65
Eggs: 0.9 → e^0.9 ≈ 2.46
Butter: 0.2 → e^0.2 ≈ 1.22
Total = 1.65 + 2.46 + 1.22 = 5.33
Now the percentage for Flour is: 1.65 / 5.33 ≈ 0.31 or 31%.
The score 0.5 becomes 31% because Softmax compares it to all other scores, after making their differences much more dramatic. The biggest score "wins" a larger share of the total.
After using the Magic Measuring Cup (Softmax), the scores become:
Aha! Now it's crystal clear. Flour realises: "In this recipe, my relationship with Eggs is the most important thing!"
Weighted Sum - The "Mixing Party"
The Recipe for the New, Smarter Flour and Mix them
Take 30% of Flour (I am dry and powdery)
Take 60% of the Eggs (I am wet and binding)
Take 10% of the Butter (I am fatty and creamy)
The Result?
The new Flour is no longer just "dry and powdery." It's now a richer idea, like: [I am the main structure, and I get my power from being bound by eggs!]
In a Nutshell:
Softmax answers: "Who matters most?" (It gives us the percentages).
Weighted Sum answers: "How does that change me?" (It mixes those percentages to create a new, context-aware idea).
What the model cannot answer: It knows the combination of eggs and flour, but not the combination of butter and flour.
🤖 GPT:
Example: The word "chases" in "The cat chases the mouse quickly"
"chases" would pay most attention to (Softmax)
"cat" (Highest attention - 40%)
Why: Because "chases" needs to know who is doing the action. A verb must connect to its subject.
"mouse" (High attention - 35%)
Why: Because "chases" needs to know what is being chased. A transitive verb needs its object.
"quickly" (Moderate attention - 15%)
Why: Because "quickly" describes how the chasing happens. Adverbs modify verbs.
Result (Weighted Sum):
Step 1: Take percentages of each word's main quality
40% of "cat" = 40% of "animal"
35% of "mouse" = 35% of "prey"
15% of "quickly" = 15% of "rapid"
10% of itself = 10% of "action"
Step 2: Combine them
The 40% and 35% make "animal-prey" the strongest idea, while "rapid" (15%) and "action" (10%) add smaller but important details.
What the model cannot answer:
Self-attention blends everything into one average idea. But it misses:
Different relationship types: It can't tell that "cat" is the grammatical subject while "mouse" is both the object and the prey. Because in the current scenario, we are focusing on how chases is related to other words, not if the sentence is grammatically correct.
Separate perspectives: It creates one mixed view ("animal rapidly hunting prey") instead of keeping distinct perspectives like grammar, action speed, and story context separate.
In short, it sees that words are connected, but loses the different reasons why they're connected.
5️⃣ Multi-Head Attention: The Panel of Expert Tasters
👨🍳 Chef:
Multi-Head Attention performs self-attention in parallel multiple times, each with a different "expertise" or "focus," rather than executing the same operation sequentially.
Remember when Chef CupCake had the three ingredients talk about their relationships? That was single-head attention - one conversation about "binding relationships."
Now, let's upgrade to multi-head attention!. Instead of one conversation, Chef CupCake organises multiple simultaneous conversations parallel with different groups of expert tasters, each focusing on a specific aspect of the cupcake.
Head 1: The Structure Expert (Binding Relationships)
This is our original conversation! This head focuses on how ingredients bind together:
Flour's Query: "Who can help me become a structured batter?"
Finds: Eggs (score: 0.9) as the perfect binding partner
Result: Flour learns: "I'm the main structure, bound by eggs"
Head 2: The Texture Expert (Fluffiness & Tenderness)
A second, parallel conversation focusing on texture:
Flour's Query: "Who can make me light and fluffy?"
Butter's Key: "I'm fatty and creamy - I create tender crumbs!"
Finds: Butter gets a high score here (0.8) because fat = tenderness
Result: Flour also learns: "Butter makes me tender and light"
The Power of Multiple Perspectives
Each head produces its own "new Flour" representation. Finally, Chef CupCake combines all these specialised perspectives into one super-smart Flour representation:
[I am the structural base that gets bound by eggs and tenderized by butter]
What the model cannot answer
The AI doesn't really understand what happens when you put the batter in the oven.
The flour changes from a powdery ingredient into a solid structure.
This change is permanent – you can't turn a baked cake back into batter.
🤖 GPT:
Purpose: Connect words. It answers "Who is related to whom, and how?"
The Sentence: "The cat chases the mouse quickly"
Head 1: Grammar & Syntax Specialisation
Learns to focus on: Sentence structure patterns
During computation: Calculates strong attention weights from "chases" → "cat" and "chases" → "mouse"
Resulting representation: The embedding for "chases" now contains information about its grammatical connections
The model can now answer: "Which words are grammatically related to 'chases'?"
Head 2: Manner/Intensity Specialisation
Learns to focus on: Adverb-verb modification patterns
During computation: Calculates strong attention weights from "chases" → "quickly"
Resulting representation: The embedding for "chases" now contains manner information
The model can now answer: "How is the chasing happening?" (manner modification)
After multi-head attention, each word has an enriched embedding that contains:
Limitations of Multi-Head Attention:
- Multi-Attention: Understands that "quickly" is linked to "chases" because it describes how the chasing is happening. Still, it cannot figure out that a quick chase means the mouse is probably scurrying away fast and the cat is likely pouncing. This limitation is overcome in the next FFN phase.
6️⃣Feed-Forward Network (FFN): The Flavour Refiner
👨🍳 Chef:
Think of the Feed-Forward Network as the Chef CupCake’s final, personal touch on each ingredient after the group discussion. It's all about refinement.
Before FFN: An ingredient has good ideas from others, but they're still a bit rough and unpolished.
During FFN, the baker takes each ingredient individually and perfects it.
After FFN: Each ingredient is now richer, more balanced, and perfectly prepared to be part of the final masterpiece.
In short, the FFN doesn't add new ideas; it perfects the existing ones, making them the best they can be.
🤖 GPT:
Take the sentence “The cat chases the mouse quickly.”
After multi-head attention, the word “chases” has already borrowed context:
But that information is still a bit raw — like rough notes from a conversation. In the feed-forward step, “chases” now goes through its own mini-refinement process. The model says:
“Expand: exaggerate all the features I just learned” (cat = hunter, mouse = prey, quickly = intensity).
“Compress: filter and balance them into a sharper meaning.”
Each token gets polished, like giving every actor in the scene their own acting coach after rehearsal.
Feed-Forward Refinement: Each word gets polished individually
| Word | Before Feed-Forward (Raw Context) | After Feed-Forward (Refined Meaning) |
| cat | “animal, subject of chase” | “hunter, initiator of action” |
| chases | “action verb, linked to cat + mouse + quickly” | “predatory action happening fast” |
| mouse | “animal, object of chase” | “prey, target under threat” |
| quickly | “adverb, describes speed of action” | “high intensity, fast pace” |
Layering: Refers to the entire process of stacking transformer blocks. Each block contains both Multi-Head Attention and a Feed-Forward Network (FFN).
👨🍳 Chef:
LAYER 1 - Mixing Stage
Attention: "Butter checks its relationship with everyone: 'How much should I interact with Sugar? How much with Flour? ’"
FFN: "Butter actually gets whisked and blended with the (Flour and Sugar), transforming from a separate ingredient into part of a cohesive mixture."
Result: The ingredients are no longer separate, but not yet a cake. A basic batter is formed.
LAYER 2 - Baking Stage
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? ‘“
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."
Result: The batter transforms from a liquid mixture into a solid cake with its basic structure set.
Each layer needs both: You can't just measure ingredients (attention) without mixing (FFN), and you can't bake (FFN) without proper heat distribution (attention).
🤖 GPT:
Sentence: "The cat chases the mouse quickly"
LAYER 1 - Basic Vision
Attention: Spotting moving shapes - "something fuzzy" chasing "something small"
FFN: Identifying basic forms - "cat shape" and "mouse shape"
Result: "There's a cat and a mouse"
LAYER 2 - Action Recognition
Attention: Tracking movement relationship - the cat moving toward the mouse
FFN: Classifying the action as "chasing", not "playing" or "sleeping"
Result: "The cat is chasing the mouse"
LAYER 3 - Context Understanding
Attention: Noticing speed ("quickly") + predator-prey dynamic
FFN: Understanding this as a "hunting scene" with urgency
Result: "A rapid predatory hunt with potential danger for the mouse"
Why are both processes essential?
In Cake Baking:
Attention Only: You know flour and eggs should combine, but never actually mix them
FFN Only: You randomly mix ingredients without knowing proportions = messy batter
In Cat/Mouse Scene:
The Magic of Layering
Layer 1 Output: "cat + mouse + movement" (raw ingredients mixed)
Layer 2 Output: "cat CHASES mouse" (baked structure formed)
Layer 3 Output: "PREDATOR urgently hunting PREY" (flavour developed)
What the model cannot answer:
How to build a story word-by-word - it sees the whole scene at once, but can't generate the next word
Language rhythm and flow - it understands meaning, but not how to unfold it naturally over time
The above features are taken care of in the next step, i.e Decoding/Generating output
8️⃣ Putting Icing on Top → Decoding / Generating Output
The Decoding Stage is where the model chooses the final output words, one by one. Using the cake analogy:
The cake is already baked (the sentence's core meaning is formed in earlier layers).
Decoding is the "decorating" stage: It's about selecting the specific words ("icing," "sprinkles") for the final presentation.
The model uses a strategy (like Greedy Search, Beam Search, Top-k, Top-p) to pick each next word/topping from a list of probable options, slowly building the complete sentence.
The Setup:
How does Chef Cupcake choose? He uses a Decoding Strategy. This is a critical setting that changes the creativity and personality of the final output.
Greedy Decoding: The Predictable Classic
👨🍳 Chef: Chef Cupcake examines the probabilities and immediately selects the highest one. Every single time, he chooses Vanilla Icing. It's safe, fast, and reliable.
🤖 GPT: The model takes the token with the highest probability at every step. This approach is efficient, but it can lead to boring, repetitive, and sometimes nonsensical outputs because it doesn't consider how choices fit together in the long run.
Prompt: "The birthday cake was topped with..."
Safest Choice (Vanilla): The AI picks the most common, predictable word: "vanilla" icing.
Result: "The birthday cake was topped with vanilla icing." (Correct, but boring and obvious).
Result: A delicious, but utterly predictable and potentially dry cake.
Beam Search: The Precision Planner
👨🍳 Chef Analogy:
Instead of deciding on the final cupcake step-by-step, Chef Cupcake plans the entire sequence. He thinks: "If I choose Vanilla Icing now (step 1), what are the best options for the next step (step 2)?" He keeps a shortlist of the most promising complete sequences (Vanilla Icing and Sprinkles, Chocolate Icing and Drizzle) and only chooses the best overall combination at the end.
🤖 GPT Explanation:
Beam Search explores multiple potential futures for a sentence, always tracking the paths with the highest combined probability. It isn't just judging the first word alone; it's judging the entire sequence of words to find the most likely complete phrase.
Beam Search Explained with a Beam Width of 2
The person using the AI model (the engineer or developer) decides the beam width before the text generation begins. With a beam width of 2, the model will only ever keep the top 2 most promising paths at every single step. Let's trace this.
Scenario: Finish the sentence: "The birthday cake was topped with..."
Step 1: Generate the FIRST word options.
The model calculates probabilities for all possible first words. With a beam width of 2, it only keeps the top 2.
vanilla icing: 34% 🤍 (KEPT)
chocolate icing: 33% 🤎 (KEPT)
strawberry icing: 32% 💗 (DISCARDED - not in top 2)
edible sparkles: 1% ✨ (DISCARDED)
Our "beam" now contains only these 2 active paths.
Step 2: Generate the NEXT word for each of the 2 paths.
For each of the 2 paths we kept, the model generates possible next words and calculates sequence probabilities.
Path 1: "vanilla icing"
The following words (and, drizzle) are the most probable words after “vanilla icing”
Path 2: "chocolate icing"
The following words (drizzle, with) are the most probable words after “chocolate icing”
Step 3: Re-select the Top 2 Paths (Beam Width = 2)
We now have 4 possible two-word sequences. Beam Search looks at all of them and only keeps the top 2 overall.
"chocolate icing drizzle" = 19.8% (From Path 2)
"vanilla icing and" = 13.6% (From Path 1)
Conclusion with Beam Width = 2
Why did "chocolate" win? Because even though "vanilla" started with a slightly higher probability, the best possible sequence starting with "chocolate" (chocolate icing drizzle) had a significantly higher combined probability (19.8%) than the best possible sequence starting with "vanilla" (vanilla icing and at 13.6%).
The beam width of 2 ensured the model efficiently compared these two best paths against each other, leading to the final output:
"The birthday cake was topped with chocolate icing drizzle."
From Predictable to Creative AI Text Generation
Early AI text generators used deterministic methods like Greedy Decoding and Beam Search 🧠➡️🧠, which always choose the safest, most predictable next word. This is like a chef 👨🍳 who only ever makes vanilla cake, and this could be the best vanilla cake in the world 🍰—best and reliable but boring 😴.
To create a truly interesting and human-like text ✨, we switched to probabilistic methods like Top-k and Top-p 🎲. These allow the AI to “randomly” choose from a shortlist of good options, not just the single best one. This is like a creative chef 👨🍳🌟 experimenting with new flavours 🍓🌶️🍫, leading to surprising and original results every time 🎉! Let’s explore Top-k and Top-p sampling! 🔍
Top-K Sampling: The "Always Top 3" Rule
The Rule: The Chef must only pick from the top K most likely options. K is a fixed number set in advance (e.g., 3). Top-K Sampling is a decoding strategy where the AI model restricts its choices for the next word to a fixed number (K) from the most probable options and then randomly selects one from that shortlist. It is a creative method because its randomness breaks determinism, though its fixed shortlist can limit its “potential for surprise” compared to Top-P.
Example: Completing "The birthday cake was topped with..."
👨🍳 Chef: (Top-K, K=3) The rule is creative but rigid: Randomly consider only one of the top 3 options. The chef randomly picks one from [vanilla, chocolate, strawberry]. Let's say he picks "strawberry".
🤖 GPT: Calculates probabilities for the next "word" (icing):
Vanilla icing: 34% 🤍
Chocolate icing: 33% 🤎
Strawberry icing: 32% 💗
Edible sparkles: 1% ✨
Result: "The cake was topped with strawberry icing."
Why? This method doesn't care about finding the best overall sequence (Remember, Chocolate Icing was the best combination as per Beam search). But Top-K only cares about introducing random variety at this single step. The outcome is more creative than Beam Search's optimised output, but is still limited by the rule that forever excludes the creative but unlikely option ("Sparkles") because it fell outside the fixed K value.
The Top-K Problem: The most creative and exciting option ("sparkles") is still excluded because it's ranked #4, and as per the rule, the Chef must only pick from the top K (eg, 3) most likely options.
Top-P Sampling: The Most Creative One
The Rule: The Chef picks from the smallest set of options where the combined probability exceeds P (e.g., 0.75).
Example: Completing "The birthday was topped with..."
👨🍳 Chef: Because it's a birthday cake, Chef Cupcake's recipe book tells him that 'Sparkles' is now a credible choice (due to the word “birthday” in the sentence). It's not that Sparkles becomes the best choice—Vanilla and Chocolate are still more probable than Sparkles.
But the crucial change is that Sparkles' probability has jumped from a negligible 1% to a significant 20%, making it a relevant contender for the first time. Guess what, Chef CupCake chooses in the next paragraph.
🤖 GPT: (Top-P, P=0.75):
Why Probabilities Change: The word "birthday" changes the context! A birthday calls for celebration, so fun, decorative, magical ✨ options become more likely. GPT recalculates:
Vanilla icing: 40% 🤍 (Still a classic)
Chocolate icing: 25% 🤎 (Still popular)
Edible sparkles: 20% ✨ (Probability skyrockets because of the word ‘birthday‘ in the sentence —perfect for a birthday!)
Strawberry icing: 15% 💗 (Probability plummets—less fitting for a festive birthday cake)
GPT adds vanilla (40%) → Total: 40% (No Still <75%)
GPT adds chocolate (25%) → Total: 65% (No Still <75%)
GPT adds sparkles (20%) → Total: 85% (Stop! Exceeds 75%)
The Final Candidate Pool: {Vanilla, Chocolate, Sparkles}
The Random Selection: The model now randomly chooses one word from this pool of three. The probability of being chosen is proportional to its original probability.
Vanilla's chance of being selected: 40% / 85% ≈ 47%
Chocolate's chance: 25% / 85% ≈ 29%
Sparkles' chance: 20% / 85% ≈ 24%
Result: "The birthday cake was topped with sparkles!" ✨🎂 (A creative, context-perfect choice). Hence, Chef CupCake chose “Sparkles“ over other options, which is creative and more suited for a birthday cake.
So, while "Sparkles" is the creative choice the example highlights, the top-p sampling could have just as easily generated a different result, like:
The power of Top-P sampling is that it allows "Sparkles" into the pool of possible choices. In a different sampling method (like greedy decoding or top-k with a low k), "Sparkles" might never have been considered. Once it's in the pool, it has a chance to be selected, leading to more creative and context-appropriate outputs.
How Top-P Solves Top-K’s Problem:
The Real Test: Imagine the probabilities were slightly different:
Vanilla: 50%, Chocolate: 44%, Sparkles: 5%, Strawberry: 1%
Top-K (K=3) would still pick from [vanilla, chocolate, sparkles], giving a 5% chance to a great option ("sparkles") but also including it even if it were a terrible fit.
Top-P (P=0.75) would add vanilla (50%) + chocolate (44%) = 94% and then STOP. It would exclude sparkles because the "vibe" (probability mass) was already captured by the top two, superior options. It adapts to context.
We will now compare all the text generation methods in a clear table to see their differences side by side, related to the word “birthday” in our prompt.
| Method | Does it "see" the word birthday? | Will it likely choose "sparkles"? | Why or Why Not? |
| Greedy | Yes | No | Only picks the absolute #1 option (vanilla). |
| Beam Search | Yes | Very Unlikely | Seeks the most perfect sequence of the entire sentence, not just words, pruning low-probability paths like sparkles. |
| Top-K | Yes | Maybe (by luck) | Will include sparkles if K=3, but Sparkles is 4th. Its choice is random and rigid. |
| Top-P | Yes | Maybe (by design) | Dynamically includes sparkles because its probability is relevant to the context. |
To tie all the concepts together, click here to view a summary table comparing the components of a model like GPT to our Chef and Cupcake analogy: https://transformer-architecture.netlify.app/.
History: The Landmark Paper => "Attention Is All You Need"
Before we conclude, let’s take a quick trip down memory lane 🧭. Ever wonder where the “recipe” for AI language understanding came from?
In 2017, Google researchers published a now-legendary paper titled "Attention Is All You Need" —and they weren’t exaggerating! This paper introduced the Transformer, the architecture that revolutionised AI.
Before Transformers, language models were slow, clunky, and struggled with context. It was like trying to bake a cake using one instruction at a time 🧁⏳.
But the Transformer changed everything. It used a clever mechanism called self-attention—letting words “talk” to each other all at once 👥💬—making models faster, smarter, and far more fluent.
This was the big break. In fact, the “T” in GPT stands for Transformer! Every modern AI language model, including ChatGPT, is built on this groundbreaking idea.
So when we talk about tokens chatting or ingredients working together, we’re using the same powerful concept that started it all. Attention really was all we needed! ✨
Whether in a steamy kitchen 👨🍳🍳 or a temperature-controlled data centre 💻, the core principle is the same: transformation through process 🔄. A chef transforms raw ingredients into a harmonious dish that delights the senses. 🍽️
Chef Cupcake’s true genius lies not in a hidden ingredient, but in a powerful process—one that he shares with the most advanced AI models 🧠. This connection between the kitchen and the computer shows that the line between art 🎨 and science 🔬 is thinner than a layer of frosting 🧁.
The next breakthrough, it seems, could come from anywhere—even a bakery! ✨
Do share your thoughts in the comments! 💬
