magic-commit 0.6.2

Generate commit messages with OpenAI\’s GPT.

Magic Commit! ✨ 🍰

magic-commit writes your commit messages with AI.

It's available as a command-line tool currently. There is an experimental VSCode extension in alpha, which you can read about in Experiments > VSCode Extension below.

Table of Contents

• Installation
• Setup
• Usage
• Experiments
  • VSCode Extension
  • Llama2 Model
• Developer Notes
  • Building the Command-Line Tool
  • Building the VSCode Extension
  • Publishing to PyPI
  • Unit Tests

Installation

To install the command-line tool, PyPI is the easiest way:

pip install magic-commit

Setup

You'll need to set up an OpenAI account and get an API key.

Once you have a key, add it to magic-commit like so:

magic-commit -k <your-key-here>

Usage

Running magic-commit is straightforward:

>>> magic-commit # will run in your current directory
[your commit message] # automatically copied to your clipboard

To see all the options, run:

>>> magic-commit --help

usage: magic-commit [-h] [-d DIRECTORY] [-m MODEL] [-k API_KEY] [--set-model MODEL_NAME] [--no-copy] [--no-load] [-t TICKET] [-s START] [--llama LLAMA]

Generate commit messages with OpenAI’s GPT.

optional arguments:
  -h, --help            show this help message and exit
  -d DIRECTORY, --directory DIRECTORY
                        Specify the git repository directory
  -m MODEL, --model MODEL
                        Specify the OpenAI GPT model
  -k API_KEY, --key API_KEY
                        Set your OpenAI API key
  --set-model MODEL_NAME
                        Set the default OpenAI GPT model
  --no-copy             Do not copy the commit message to the clipboard
  --no-load             Do not show loading message
  -t TICKET, --ticket TICKET
                        Request that the provided GitHub issue be linked in the commit message
  -s START, --start START
                        Provide the start of the commit message
  --llama LLAMA         Pass a localhost Llama2 server as a replacement for OpenAI API

For models, note that:

• You need to specify an OpenAI GPT model.
  • e.g. gpt-3.5-turbo-0301, or gpt-4
  • There is an experimental mode which uses Meta's Llama2 models instead.
    • (see Experiments > Llama2 Model below)
• Your OpenAI account needs to have access to the model you specify.
  • i.e. Don't specify gpt-4 if you don't have access to it.

Experiments

VSCode Extension

Currently in "alpha" status (v 0.0.3). It works, completely, but we need to address the following:

• Write automated tests
• Fix any known bugs
• Write documentation
• Officially publish to the VSCode Marketplace

Llama2 Model

Llama2 is a free alternative to OpenAI's GPT-3.5, created by Meta (Facebook). A long-term goal of magic-commit is to support Llama2 fully, allowing you to use it without needing to pay OpenAI or send any potentially sensitive data to them.

To that end, you can pass a running localhost Llama2 server to magic-commit like so:

magic-commit --llama http://localhost:8080 # or whatever port you're using

Note that you'll need to have a running Llama2 server. If you're on MacOS, I found these instructions from the llama-cpp-python project fairly easy to follow.

In the future, the end goal is to seamlessly support both OpenAI and Llama2 models, and to allow you to switch between them with a simple flag.

LoRA Fine-Tuned Model

Llama2 models capable of running on a normal computer have to be fairly small, e.g. 7 billion parameters. This is a lot, but it's a far cry from the 175 billion parameters of OpenAI's GPT-3.5 model. Performance for this task out-of-the-box is not great.

However, there is hope. Low-Rank Adaptation (LoRA) is a technique for specializing a large model to a smaller one. To quote the research paper:

Compared to GPT-3 175B fine-tuned with Adam, LoRA can reduce the number of trainable parameters by 10,000 times and the GPU memory requirement by 3 times. LoRA performs on-par or better than fine-tuning in model quality on RoBERTa, DeBERTa, GPT-2, and GPT-3

I do believe that we can potentially get GPT-3.5 level of quality while running on a laptop. You can see my experiments with this in the lora-experiments folder. If you have any ideas or suggestions, please reach out!

Developer Notes

Please feel free to open a GitHub issue, submit a pull request, or to reach out if you have any questions or suggestions!

Building the Command-Line Tool

Note: This is referring to a local development build. For production, see Publishing to PyPI below.

cd cli/magic_commit
pip install -e . # install the package in editable mode

Building the VSCode Extension

cd vscode/magic-commit
npm install vsce # if you don't have it already
vsce package # creates a .vsix file

Publishing to PyPI

To publish a new version to PyPI:

cd cli/magic_commit
pip install twine wheel
python setup.py sdist bdist_wheel # build the package
twine upload dist/* # upload to PyPI

Unit Tests

To run the unit tests:

cd cli/magic_commit/tests
pytest