# 🔥 How I Built My Own Tokenizer (Like ChatGPT)

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If you've ever wondered **how ChatGPT understands what you type**, the secret sauce is something called a **tokenizer**. It breaks down your text into smaller pieces (tokens) that an AI model can understand. But here’s the twist — I decided to build one **from scratch** that supports **Hindi and English**, just like OpenAI's GPT tokenizer.

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## 🧠 Step 1: What Even Is a Tokenizer?

Imagine you're talking to your AI buddy, and you say:

> `"Main school ja raha hoon"`

Your model doesn't "see" it like we do. It breaks it down into tokens:

```python
["Main", "school", "ja", "raha", "hoon"]
```

But in OpenAI’s world, it can go even deeper:

```python
["M", "ai", "n", " school", " ja", " ra", "ha", " ho", "on"]
```

They use **Byte Pair Encoding (BPE)** – the *Sholay* of tokenization algorithms. Solid, reliable, and classic.

> “Itna sannata kyun hai bhai?” – because BPE doesn’t just split by space... it finds patterns in your data!

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## 🔨 Step 2: Training Your Own Tokenizer

First, you gather some **desi-style sentences** (in Hindi + English):

```python
corpus = ["Main school ja raha hoon", "Tum kon ho?", "I love Golgappa", "Dosti ho gayi yaar"]
```

Then, like Babu Bhaiya sorting pickles by size, you break each word into characters:

```python
["M", "a", "i", "n", "</w>"], ["s", "c", "h", "o", "o", "l", "</w>"] ...
```

The `</w>` is like a *The End* in movies — to mark word endings.

Now, you look for character pairs that occur most frequently (like "ra" in "raha", "rahul", "raj"), and **merge them** again and again.

After 50 merges, you've created your own tokenizer vocabulary, like:

```python
["ra", "ho", "on", "ja", "rah", "main", "school"]
```

Congrats! Your tokenizer is now trained — *"Beta, tumse na ho payega" ka zamana gaya.*

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## 🧪 Step 3: Tokenizing New Sentences

Now when someone types:

> "Tumhara naam kya hai?"

Your tokenizer goes:  
`["T", "um", "ha", "ra", " naam", " kya", " hai", "?"]`

If it doesn’t find something in its vocab, it breaks it down into characters — like *Krrish* losing powers but still fighting 😄

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## 🤖 Bonus: Byte-Level Tokenization

Just like Shah Rukh Khan has fans in every country, GPT uses **bytes** instead of just text — so even rare characters (like emoji or Urdu or Tamil) can be tokenized.

```python
text = "मैं स्कूल जा रहा हूँ"
bytes_text = list(text.encode("utf-8"))
```

This means your tokenizer can handle:

* 💬 Hindi-English mixed text (a.k.a. Hinglish)
    
* 🤯 Slangs
    
* 👨‍💻 Emojis & symbols
    

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## 📦 Packaging the Tokenizer

You can save the tokenizer rules in a `.json` file. Use it later in your app or even build your own desi ChatGPT clone!

```python
tokenizer.save("my_tokenizer.json")
```

> “Tumse yeh na ho payega?”  
> **Bhai, ho gaya! 💪**

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## 🎬 Final Thoughts (feat. Memes)

Building a tokenizer isn’t rocket science. It’s more like assembling a *DDLJ*\-style love story — small steps, pattern recognition, and some tears (of joy 😅). And now you know:

* How GPT-style tokenization works
    
* How to make your own tokenizer for Hindi + English
    
* And how to make learning fun — *Desi style*!
    

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If you liked this, share it with your coding friends. Or as *Gabbar* would say:

> “Jo tokenizer banaye hain... unka kuch toh karo!” 😆

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Happy Coding!!!  
😊