Post
4408
š AI Token Visualization Tool with Perfect Multilingual Support
Hello! Today I'm introducing my Token Visualization Tool with comprehensive multilingual support. This web-based application allows you to see how various Large Language Models (LLMs) tokenize text.
aiqtech/LLM-Token-Visual
āØ Key Features
š¤ Multiple LLM Tokenizers: Support for Llama 4, Mistral, Gemma, Deepseek, QWQ, BERT, and more
š Custom Model Support: Use any tokenizer available on HuggingFace
š Detailed Token Statistics: Analyze total tokens, unique tokens, compression ratio, and more
š Visual Token Representation: Each token assigned a unique color for visual distinction
š File Analysis Support: Upload and analyze large files
š Powerful Multilingual Support
The most significant advantage of this tool is its perfect support for all languages:
š Asian languages including Korean, Chinese, and Japanese fully supported
š¤ RTL (right-to-left) languages like Arabic and Hebrew supported
šŗ Special characters and emoji tokenization visualization
š§© Compare tokenization differences between languages
š¬ Mixed multilingual text processing analysis
š How It Works
Select your desired tokenizer model (predefined or HuggingFace model ID)
Input multilingual text or upload a file for analysis
Click 'Analyze Text' to see the tokenized results
Visually understand how the model breaks down various languages with color-coded tokens
š” Benefits of Multilingual Processing
Understanding multilingual text tokenization patterns helps you:
Optimize prompts that mix multiple languages
Compare token efficiency across languages (e.g., English vs. Korean vs. Chinese token usage)
Predict token usage for internationalization (i18n) applications
Optimize costs for multilingual AI services
š ļø Technology Stack
Backend: Flask (Python)
Frontend: HTML, CSS, JavaScript (jQuery)
Tokenizers: š¤ Transformers library
Hello! Today I'm introducing my Token Visualization Tool with comprehensive multilingual support. This web-based application allows you to see how various Large Language Models (LLMs) tokenize text.
aiqtech/LLM-Token-Visual
āØ Key Features
š¤ Multiple LLM Tokenizers: Support for Llama 4, Mistral, Gemma, Deepseek, QWQ, BERT, and more
š Custom Model Support: Use any tokenizer available on HuggingFace
š Detailed Token Statistics: Analyze total tokens, unique tokens, compression ratio, and more
š Visual Token Representation: Each token assigned a unique color for visual distinction
š File Analysis Support: Upload and analyze large files
š Powerful Multilingual Support
The most significant advantage of this tool is its perfect support for all languages:
š Asian languages including Korean, Chinese, and Japanese fully supported
š¤ RTL (right-to-left) languages like Arabic and Hebrew supported
šŗ Special characters and emoji tokenization visualization
š§© Compare tokenization differences between languages
š¬ Mixed multilingual text processing analysis
š How It Works
Select your desired tokenizer model (predefined or HuggingFace model ID)
Input multilingual text or upload a file for analysis
Click 'Analyze Text' to see the tokenized results
Visually understand how the model breaks down various languages with color-coded tokens
š” Benefits of Multilingual Processing
Understanding multilingual text tokenization patterns helps you:
Optimize prompts that mix multiple languages
Compare token efficiency across languages (e.g., English vs. Korean vs. Chinese token usage)
Predict token usage for internationalization (i18n) applications
Optimize costs for multilingual AI services
š ļø Technology Stack
Backend: Flask (Python)
Frontend: HTML, CSS, JavaScript (jQuery)
Tokenizers: š¤ Transformers library