LLMVision

Visualize how LLMs tokenize text.

from llmvision import tokenize_and_visualize, GPT4Tokenizer

text = "Hello world! 👋🌍"
print(tokenize_and_visualize(text, GPT4Tokenizer()))
# Output: Hello│ world│!│<bytes:20f09f>│<bytes:91>│<bytes:8b>│<bytes:f09f>│<bytes:8c>│<bytes:8d>

Features

• Multiple tokenizers: GPT-2, GPT-4, byte-level, character-level
• Visual token boundaries
• Unicode/emoji handling
• Actual tokenization used by OpenAI models

Installation

pip install llmvision

Usage

llmvision "Hello world!"
llmvision "Hello world!" --tokenizer gpt4
llmvision "Hello world!" --indices

from llmvision import tokenize_and_visualize, GPT4Tokenizer

# Default tokenizer
print(tokenize_and_visualize("Hello world!"))

# Specific tokenizer
print(tokenize_and_visualize("Hello world!", GPT4Tokenizer()))

Examples

from llmvision import GPT4Tokenizer

tokenizer = GPT4Tokenizer()
tokens = tokenizer.tokenize("Hello world!")
print(tokens)  # ['Hello', ' world', '!']

Tokenizers

• SimpleTokenizer - word/punctuation/space
• WordTokenizer - whitespace-based
• CharTokenizer - character-level
• GraphemeTokenizer - Unicode grapheme clusters
• ByteLevelTokenizer - UTF-8 bytes
• GPT2Tokenizer - GPT-2 (via tiktoken)
• GPT4Tokenizer - GPT-4 (via tiktoken)
• SubwordTokenizer - basic subword splitting

Token Costs

tokenizer = GPT4Tokenizer()
examples = [
    "Hello world!",    # 3 tokens
    "Hello 世界!",     # 5 tokens  
    "Hello 👋🌍!",     # 8 tokens
    "👨‍👩‍👧‍👦",            # 18 tokens
]
for text in examples:
    print(f"{text:15} → {len(tokenizer.tokenize(text))} tokens")

License

MIT