tokker 0.3.5

Tokker: a fast local-first CLI tokenizer with all the best models in one place

Tokker

Tokker: a fast local-first CLI tokenizer with all the best models in one place.

Features

• Simple Usage: Just tok 'your text' - that's it!
• Models:
  • OpenAI: GPT-3, GPT-3.5, GPT-4, GPT-4o, o-family (o1, o3, o4)
  • Google: the entire Gemini family
  • HuggingFace: select literally any model that supports transformers library
• Flexible Output: JSON, plain text, count, and pivot output formats
• Text Analysis: Token count, word count, character count, and token frequency
• Model History: Track your usage with --history and --history-clear
• Local-first: Works locally on device (except Google)

---

Installation

# Install tokker without model provider packages (optional)
pip install tokker

# Install at least one model provider package:
pip install 'tokker[all]' # for all models at once
pip install 'tokker[tiktoken]' # for models from OpenAI
pip install 'tokker[google-genai]' # for models from Google
pip install 'tokker[transformers]' # for models from HuggingFace

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Command Reference

usage: tok [-h] [-m MODEL] [-o {json,plain,count,pivot}]
           [-D MODEL_DEFAULT] [-M]
           [-H] [-X]
           [text]

positional arguments:
  text                                    text to tokenize (or read from stdin)

options:
  -h, --help                              show this help message and exit
  -m, --model MODEL                       model to use (overrides default)
  -o, --output {json,plain,count,pivot}   output format (default: json)
  -D, --model-default MODEL_DEFAULT       set default model
  -M, --models                            list all models
  -H, --history                           show history
  -X, --history-clear                     clear history

Usage

Tokenize Text

When using bash or zsh, wrap input text in single quotes ('like this') to avoid conflicts with special characters like !.

# Tokenize with default model
tok 'Hello world'

# Get a specific output format
tok 'Hello world' -o plain

# Use a specific model
tok 'Hello world' -m openai/gpt-oss-120b

# Get just the counts
tok 'Hello world' -m gemini-2.5-pro -o count

Pipeline Usage

# Process files
cat document.txt | tok -m deepseek-ai/DeepSeek-R1 -o count

# Chain with other tools
curl -s https://example.com | tok -m bert-base-uncased

# Compare models
echo "Machine learning is awesome" | tok -m openai/gpt-oss-120b
echo "Machine learning is awesome" | tok -m bert-base-uncased

List Available Models

# See all available models
tok -M

Output:

(.venv) igo@igo-mac tokker % tok -M
============
OpenAI:

  cl100k_baseused in GPT-3.5 (late), GPT-4
  o200k_baseused in GPT-4o, o-family (o1, o3, o4)
  p50k_baseused in GPT-3.5 (early)
  p50k_editused in GPT-3 edit models (text-davinci, code-davinci)
  r50k_baseused in GPT-3 base models (davinci, curie, babbage, ada)
------------
Google:

  gemini-2.0-flash
  gemini-2.0-flash-lite
  gemini-2.5-flash
  gemini-2.5-flash-lite
  gemini-2.5-pro

Auth setup required   ->   https://github.com/igoakulov/tokker/blob/main/google-auth-guide.md
------------
HuggingFace (BYOM - Bring You Own Model):

  1. Go to   ->   https://huggingface.co/models?library=transformers
  2. Search any model with TRANSFORMERS library support
  3. Copy its `USER/MODEL` into your command like:

  deepseek-ai/DeepSeek-R1
  google-bert/bert-base-uncased
  google/gemma-3n-E4B-it
  meta-llama/Meta-Llama-3.1-405B
  mistralai/Devstral-Small-2507
  moonshotai/Kimi-K2-Instruct
  Qwen/Qwen3-Coder-480B-A35B-Instruct
  openai/gpt-oss-120b
============

Set Default Model

# Set your preferred model
tok -D o200k_base

History

# View your model usage history with date/time
tok -H

# Clear your history (will prompt for confirmation)
tok -X

History is stored locally in ~/.config/tokker/history.json.

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Output Formats

Full JSON Output (Default)

tok 'Hello world'
{
  "delimited_text": "Hello⎮ world",
  "token_strings": ["Hello", " world"],
  "token_ids": [24912, 2375],
  "token_count": 2,
  "word_count": 2,
  "char_count": 11
}

Plain Text Output

tok 'Hello world' -o plain
Hello⎮ world

Count Output

tok 'Hello world' -o count
{
  "token_count": 2,
  "word_count": 2,
  "char_count": 11
}

Pivot Output

The pivot output prints a JSON object with token frequencies, sorted by highest count first, then by token (A–Z).

Example:

tok 'never gonna give you up neve gonna let you down' -m cl100k_base -o pivot
{
  " gonna": 2,
  " you": 2,
  " down": 1,
  " give": 1,
  " let": 1,
  " ne": 1,
  " up": 1,
  "never": 1,
  "ve": 1
}

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Configuration

Your configuration is stored locally in ~/.config/tokker/config.json:

{
  "default_model": "o200k_base",
  "delimiter": "⎮"
}

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License

This project is licensed under the MIT License - see the LICENSE file for details.

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Contributing

Issues and pull requests are welcome! Visit the GitHub repository.

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Acknowledgments

• OpenAI for the tiktoken library
• HuggingFace for the transformers library
• Google for the Gemini models and APIs