ggunio/intelligent-tokenizer-v6

Intelligent Tokenizer v6.0 - Language Pattern Learning

Model Description

World's First Language Pattern Learning Tokenizer - Discovers each language's unique patterns through pure learning.

Key Features

• No vocabulary files - Only 260 fixed byte values
• Language pattern discovery - Learns Korean particles, English morphology, Chinese characters
• Equal language processing - No English bias
• Semantic unit preservation - Keeps meaning units intact

Performance (Epoch 22)

Language Group	Accuracy
English/European	95-100%
Korean	70%
Japanese	81%
Chinese	7% (still learning)
Rare Languages	47% avg

Technical Details

• Architecture: 5-layer Encoder + 6-layer Decoder
• Parameters: 105M
• Input: Raw UTF-8 bytes
• Output: Compressed semantic units
• Training: 22 epochs on Flores-200 dataset

Usage

from transformers import AutoModel, AutoTokenizer

# Load model
model = AutoModel.from_pretrained("woo-jinhyun/intelligent-tokenizer-v6")
tokenizer = ByteTokenizerV6()  # Custom tokenizer

# Process text
text = "안녕하세요"
encoded = tokenizer.encode(text)
compressed = model.encode(encoded)

Limitations

• Current chunk size: 256 bytes (POC limitation)
• Chinese/Arabic need more training
• Compression still learning

Citation

@software{intelligent_tokenizer_2025,
  author = {Woo, Jinhyun and Claude Code},
  title = {Intelligent Tokenizer: Language Pattern Learning},
  year = {2025},
  url = {https://github.com/Woojiggun/intelligent-tokenizer}
}

Contact

• Author: Woo Jinhyun
• Email: [email protected]
• LinkedIn: www.linkedin.com/in/namuneup
• Paper: Read on Zenodo

Development

• Design: Woo Jinhyun
• Implementation: Claude Code collaboration
• Hardware: RTX 4070
• Duration: 1 months (Aug-Sep 2025)