turbo-text-transformer 0.1.5

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Turbo Text Transformer

Turbo Text Transformer is a Python command-line tool for generating text using OpenAI's GPT-3 and other models. It includes a modular model system that allows for easy integration of new models and customization of existing ones.

Best used in combination with the Turbo Text Transformer Prompts repository!

Installation

To install Turbo Text Transformer, you can use pip:

pip install turbo-text-transformer

or clone the repository and install it manually:

git clone https://github.com/fergusfettes/turbo-text-transformer.git
cd turbo-text-transformer
poetry install

Usage

The basic syntax for running TTT is as follows:

ttt <prompt> [options]

Here, <prompt> is the text that you want to transform. You can use the --prompt_file option to load the prompt from a file instead of typing it out on the command line, or you can cat some text in:

cat some_file.txt | ttt

for example, to generate this readme I did

cat pyproject.toml ttt/__main__.py | ttt -t readme > README.md

where I'm using the 'readme' template, which is a template for generating a readme file.

Options

There are several options you can use with the ttt command:

• --format/-f: Output format (default: "clean"). Valid options are "clean", "json", or "logprobs".
• --echo_prompt/-e: Echo the prompt in the output.
• --list_models/-l: List available models.
• --prompt_file/-P: File to load for the prompt.
• --template_file/-t: Template file to apply to the prompt.
• --template_args/-x: Extra values for the template.
• --chunk_size/-c: Max size of chunks.
• --summary_size/-s: Size of chunk summaries.
• --model/-m: Name of the model to use (default: "gpt-3.5-turbo").
• --number/-N: Number of completions.
• --logprobs/-L: Show logprobs for completion.
• --max_tokens/-M: Max number of tokens to return.
• --temperature/-T: Temperature, [0, 2]-- 0 is deterministic, >0.9 is creative.
• --force/-F: Force chunking of prompt.

Configuration

Before using Turbo Text Transformer, you need to set up a configuration file. This should happen automatically when you run the ttt command for the first time.

This will create a configuration file in your home directory. See the documentation for each model to learn how to obtain an API key.

api_key: sk-<your api key here>
engine_params:
  frequency_penalty: 0
  logprobs: null
  max_tokens: 1000
  model: davinci
  n: 4
  presence_penalty: 0
  stop: null
  temperature: 0.9
  top_p: 1
models:
- babbage
- davinci
- gpt-3.5-turbo-0301
- text-davinci-003
etc.

Examples

Here are some examples of how to use Turbo Text Transformer:

# Generate text with the default model
ttt "Once upon a time, there was a"

# Generate text with a specific model
ttt -m text-davinci-003 "The meaning of life is"

# Generate multiple completions
ttt -n 5 "I like to eat"

# Show logprobs
ttt "I like to eat" -f logprobs

# Use the JSON format
ttt -f json "I like to eat"

If you put in the 'logprobs' flag, it will try to color the terminal output based on the logprobs. This is a bit janky at the moment.

You can also tell it to output a formatted json file with the -f json flag. This is useful for piping into other programs.

ttt -f json "The cat sat on the"

If you want to input more text freely, just use it without a prompt and you can write or paste directly into stdin.

Chunking

If you dump in a tonne of text, it will try to chunk it up into smaller pieces:

cat song-of-myself.txt | ttt -t poet -x 'poet=Notorious B.I.G.' > song_of_biggie.txt

(Note, this is an incredibly wasteful way to extract the text from a website, but at current prices should only cost ~$0.30 so, unhinged as it its, its probably about parity with clicking and dragging.)

Models

Turbo Text Transformer includes support for text generation with all the openai models. Have a look at the model list with ttt -l.

Contributing

If you find a bug or would like to contribute to Turbo Text Transformer, please create a new GitHub issue or pull request.

License

Turbo Text Transformer is released under the MIT License. See LICENSE for more information.