---
license: mit
---
# EZ-Tokenizer: 3.47 Chars/Token with 100% Reconstruction

> **"Go ahead, try to break it. I dare you."** - A tokenizer so efficient, it feels like cheating.

## 🚀 Performance Highlights
- **3.47** characters per token (beats industry standards)
- **100%** perfect reconstruction on all test cases
- **50K vocab size** (smaller, smarter, faster)
- **264K tokens/second** processing speed

## 💥 Benchmark This!
```python
from tokenizers import Tokenizer
tokenizer = Tokenizer.from_pretrained("johnnyman1100/EZ-Tokenizer_The_Tokenizer")

# Test it yourself
text = "Your text here"
encoded = tokenizer.encode(text)
decoded = tokenizer.decode(encoded.ids)

assert text == decoded  # Try to make this fail, I'll wait...
print(f"Compression: {len(text)/len(encoded.ids):.2f} chars/token")
```

## 🏆 Challenge
Find any text where this tokenizer:
1. Fails to reconstruct perfectly, or
2. Gets worse compression than DeepSeek/others

First to report a verified case gets a shoutout!

## 📊 Technical Details
- **Vocabulary**: 50,000 tokens
- **Tested on**: 1.7M+ characters of mixed content
- **Perfect reconstruction** on all test cases
- **Faster** than DeepSeek by 1.23x

## 🤔 Why This Matters
Because in a world of bloated models, efficiency still wins. This tokenizer proves you don't need 100K+ tokens to achieve perfect reconstruction and better compression.

## ⚖️ License
MIT

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*"I didn't believe it either until I saw the benchmarks." - You, probably*