DEDUPEDWho it is forA developer or ML researcher who wants to see how Open Jev answers typed questions before reading the code.
The problemThe architecture is hard to picture from the diagram alone, and the README does not show output from a run.
The outcomeA working local run that prints typed answers for a sample JSON-like state and a single calibration-oriented training step.
What you need first
- Python 3.10 or newer (recommended in the README)
- git installed
- PyTorch 2.0 or newer
- Basic familiarity with running Python scripts from a terminal
Steps
- Clone the repository:
git clone https://github.com/kyegomez/open-jev - Enter the project folder:
cd open-jev - Install the dependency:
python -m pip install "torch>=2.0" - Run the full demo:
python open_jev/main.py - Read the printed answers and confirm that any Choice answer is one of the declared options
Prompt for your agent
Clone https://github.com/kyegomez/open-jev, install torch>=2.0, and run python open_jev/main.py. Report the printed answers for the wants_refund, route, and frustration questions. Verify that the route answer is exactly one of billing, technical, or account.
Caveat. The model has random weights, so the printed values are random and only the structure of the answers is meaningful.
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