A simple and 100% Local, Open-Source Code 🐍 Interpreter for 🦙 LLMs

• powered by Llama.cpp
• extremly SIMPLE & 100% LOCAL
• CROSS-PLATFORM.

Leveraging open source gguf models and powered by llama.cpp this project is a humble foundation for enabling LLMs to act as Code Interpreters.

• 🖥️ Backend: Python Flask (CORS for serving both the API and the HTML).
• 🌐 Frontend: HTML/JS/CSS (I'm not a frontend dev but gave it my best shot-- prolly tons of issues).
• ⚙️ Engine: Llama.cpp: An inference library for ggml/gguf models).
• 🧠 Model: GGUF format (replacing the retired ggml format).

• 🎊 Confetti:3
• 💬 Contextual Conversations: Models are augmented with the ongoing context of the conversation-- allowing them to remember and refer back to previous parts of it.
• 🔄 Dynamic Code Interaction: Copy, Diff, Edit, Save and Run the generated Python scripts right from the chat.
• 🐞 Auto-Debugging & 🏃 Auto-Run: Allow the model to automatically debug and execute any attempts at fixing issue on the fly (it will die trying).
• 📊 Inference & Performance Metrics: Stay informed about how fast the model is processing your requests and tally the successful vs failed script executions.
• ❓ Random Prompts: Not sure what to ask? Click the "Rand" button to randomly pick from a pre-defined prompt list!

• This project is dependent on its submodule llama.cpp and relies on its successful build.
• First, clone the repo:

git clone --recurse-submodules https://github.com/itsPreto/baby-code

• Navigate to the llama.cpp submodule:

cd baby-code/llama.cpp

• Install the required libraries:

pip install -r requirements.txt

• Then repeat the same for the root project:

cd baby-code && pip install -r requirements.txt

In order to build llama.cpp you have three different options.

• Using make:

  • On Linux or MacOS:

    make

  • On Windows:

    1. Download the latest fortran version of w64devkit.
    2. Extract w64devkit on your pc.
    3. Run w64devkit.exe.
    4. Use the cd command to reach the llama.cpp folder.
    5. From here you can run:

       make

• Using CMake:

  mkdir build
  cd build
  cmake ..
  cmake --build . --config Release

• TheBloke/WizardCoder-Python-13B-V1.0-GGUF is a friendly, [gpu] budget model.
• You may also download any other models supported by llama.cpp, of any parameter size of your choosing.
• Keep in mind that the paramters might need to be tuned for your specific case:

Load up your chosen model gguf for local inference using CPU or GPU by simply placing it in the llama.cpp/models folder and edit the baby_code.py init config below:

if __name__ == '__main__':
    # Run the external command
    server_process = subprocess.Popen(
        ["./llama.cpp/server", "-m", "./llama.cpp/models/wizardcoder-python-13b-v1.0.Q5_K_M.gguf", "-c", "1024",
         "-ngl", "1", "--path", "."])
    # Pause for 5 seconds
    time.sleep(5)
    app.run(args.host, port=args.port)

You may also want to customize & configure the flask server at the top of the file, like so:

parser = argparse.ArgumentParser(description="An example of using server.cpp with a similar API to OAI. It must be used together with server.cpp.")
parser.add_argument("--stop", type=str, help="the end of response in chat completions(default: '</s>')", default="</s>")
parser.add_argument("--llama-api", type=str, help="Set the address of server.cpp in llama.cpp(default: http://127.0.0.1:8080)", default='http://127.0.0.1:8080')
parser.add_argument("--api-key", type=str, help="Set the api key to allow only few user(default: NULL)", default="")
parser.add_argument("--host", type=str, help="Set the ip address to listen.(default: 127.0.0.1)", default='127.0.0.1')
parser.add_argument("--port", type=int, help="Set the port to listen.(default: 8081)", default=8081)

• From the project root simply run:

python3 baby_code.py

The server.cpp will be served to http://127.0.0.1:8080/ by default, while the the Flask (baby_code.py) currently listens on port 8081.

Contributions to this project are welcome. Please create a fork of the repository, make your changes, and submit a pull request. I'll be creating a few issues for feature tracking soon!!

ALSO~ If anyone would like to start a Discord channel and help me manage it that would be awesome

(I'm not on it that much).

This project is licensed under the MIT License.