This is a basic cybernetic framework that utilizes the llama-cpp-python module with CUDA support to run a spaceship-themed roleplay. It uses a custom agentic AI crew framework, written in pure Python without a pre-built library. The application will offer both a command-line interface (CLI) and a graphical user interface (UI Matrix Theme) built with Tkinter. It also includes basic functionality to open, close, and prepare files for a GGUF model.

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.env File and Paths

You haven't provided the contents of your .env file or the specific tool paths, but I can give you a template for what they should look like.

A correct .env file should contain key-value pairs for sensitive information or configurable settings. For your project, this might include:

# Path to your GGUF model file
MODEL_PATH = "C:\\path\\to\\your\\model\\file.gguf"

# Or for a non-Windows path
# MODEL_PATH = "/path/to/your/model/file.gguf"

# Other potential variables
# LOG_LEVEL = "INFO"
# API_KEY = "your_api_key_if_needed"

The tool paths would be defined within your Python scripts (like tools.py), and they would reference these .env variables using a library like python-dotenv.

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Installation and Setup

The commands you provided are a good starting point for a Windows setup, but there's a potential compatibility issue and some small improvements for clarity.

Here's a refined version of the installation steps:

1. Create and Activate Virtual Environment

   py -3.12 -m venv .rvn
   .rvn\Scripts\Activate.ps1

2. Upgrade pip and Core Packages

   python.exe -m pip install --upgrade pip
   pip install --upgrade pip setuptools wheel

3. Install Python Libraries

   pip install python-dotenv pydantic requests

4. Install llama-cpp-python with CUDA

   pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu125 --upgrade --force-reinstall --no-cache-dir

5. Install PyTorch with CUDA

   pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128

6. Run the Application

   python warp-core.py

Important Note: The CUDA version for llama-cpp-python (cu125) and PyTorch (cu128) are different. While this might work, it's generally best practice to ensure your CUDA dependencies are aligned to avoid unexpected issues. Check if there is a llama-cpp-python wheel available for cu128 to maintain consistency.

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Documentation

• Full API and usage docs are available in docs/index.md.