Count and truncate text based on tokens

Large language models such as GPT-3.5 and GPT-4 work in terms of tokens.

This tool can count tokens, using OpenAI's tiktoken library.

It can also truncate text to a specified number of tokens.

See llm, ttok and strip-tags—CLI tools for working with ChatGPT and other LLMs for more on this project.

Install this tool using pip:

pip install ttok

Or using Homebrew:

brew install simonw/llm/ttok

Provide text as arguments to this tool to count tokens:

ttok Hello world

2

You can also pipe text into the tool:

echo -n "Hello world" | ttok

2

Here the echo -n option prevents echo from adding a newline - without that you would get a token count of 3.

To pipe in text and then append extra tokens from arguments, use the -i - option:

echo -n "Hello world" | ttok more text -i -

6

By default, the tokenizer model for GPT-3.5 and GPT-4 is used.

To use the model for GPT-2 and GPT-3, add --model gpt2:

ttok boo Hello there this is -m gpt2

6

Compared to GPT-3.5:

ttok boo Hello there this is

5

Further model options are documented here.

Use the -t 10 or --truncate 10 option to truncate text to a specified number of tokens:

ttok This is too many tokens -t 3

This is too

The --encode option can be used to view the integer token IDs for the incoming text:

ttok Hello world --encode

9906 1917

The --decode method reverses this process:

ttok 9906 1917 --decode

Hello world

Add --tokens to either of these options to see a detailed breakdown of the tokens:

ttok Hello world --encode --tokens

[b'Hello', b' world']

This is the full list of available models and their corresponding encodings. Model names and encoding names are valid for the -m/--model option.

• gpt-4 (cl100k_base)
• gpt-3.5-turbo (cl100k_base)
• gpt-3.5 (cl100k_base)
• gpt-35-turbo (cl100k_base)
• davinci-002 (cl100k_base)
• babbage-002 (cl100k_base)
• text-embedding-ada-002 (cl100k_base)
• text-embedding-3-small (cl100k_base)
• text-embedding-3-large (cl100k_base)
• text-davinci-003 (p50k_base)
• text-davinci-002 (p50k_base)
• text-davinci-001 (r50k_base)
• text-curie-001 (r50k_base)
• text-babbage-001 (r50k_base)
• text-ada-001 (r50k_base)
• davinci (r50k_base)
• curie (r50k_base)
• babbage (r50k_base)
• ada (r50k_base)
• code-davinci-002 (p50k_base)
• code-davinci-001 (p50k_base)
• code-cushman-002 (p50k_base)
• code-cushman-001 (p50k_base)
• davinci-codex (p50k_base)
• cushman-codex (p50k_base)
• text-davinci-edit-001 (p50k_edit)
• code-davinci-edit-001 (p50k_edit)
• text-similarity-davinci-001 (r50k_base)
• text-similarity-curie-001 (r50k_base)
• text-similarity-babbage-001 (r50k_base)
• text-similarity-ada-001 (r50k_base)
• text-search-davinci-doc-001 (r50k_base)
• text-search-curie-doc-001 (r50k_base)
• text-search-babbage-doc-001 (r50k_base)
• text-search-ada-doc-001 (r50k_base)
• code-search-babbage-code-001 (r50k_base)
• code-search-ada-code-001 (r50k_base)
• gpt2 (gpt2)
• gpt-2 (gpt2)

Usage: ttok [OPTIONS] [PROMPT]...

  Count and truncate text based on tokens

  To count tokens for text passed as arguments:

      ttok one two three

  To count tokens from stdin:

      cat input.txt | ttok

  To truncate to 100 tokens:

      cat input.txt | ttok -t 100

  To truncate to 100 tokens using the gpt2 model:

      cat input.txt | ttok -t 100 -m gpt2

  To view token integers:

      cat input.txt | ttok --encode

  To convert tokens back to text:

      ttok 9906 1917 --decode

  To see the details of the tokens:

      ttok "hello world" --tokens

  Outputs:

      [b'hello', b' world']

Options:
  --version               Show the version and exit.
  -i, --input FILENAME
  -t, --truncate INTEGER  Truncate to this many tokens
  -m, --model TEXT        Which model to use
  --encode, --tokens      Output token integers
  --decode                Convert token integers to text
  --tokens                Output full tokens
  --allow-special         Do not error on special tokens
  --help                  Show this message and exit.

You can also run this command using:

python -m ttok --help

To contribute to this tool, first checkout the code. Then create a new virtual environment:

cd ttok
python -m venv venv
source venv/bin/activate

Now install the dependencies and test dependencies:

pip install -e '.[test]'

To run the tests:

pytest