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How to run the offline MDia lifecycle on fixtures with CLSR MDia

Run the deterministic toy pipeline to see every MDia stage without API keys or model downloads.

Short answer: Clone the repository at https://github.com/Plab-Lead/CLSR_MDia and open a terminal in its folder; then python -m venv .venv; source .venv/bin/activate. All steps.

  • Top pick
  • Research
  • Intermediate
  • 30 minutes
  • From the README
  • Any agent

Who it is forA researcher or developer new to MDia who wants to see the full collect-to-report lifecycle before using real models or data.

The problemReading about dialect cards, frozen banks, and route plans is hard without seeing the artifacts they produce.

The outcomeA versioned run directory with split manifests, dialect generations, profiles, route plans, predictions, token accounting, and a reproducibility report.

What you need first

  • Python 3.10+ for the offline toy pipeline, or Python 3.11+ for the release reproduction
  • A terminal and git for cloning the repository
  • No API keys are needed for the toy pipeline or the offline release reproduction
  • For new model inference, benchmark inputs from their original sources and provider access (see docs/extending.md)

Steps

  1. Clone the repository at https://github.com/Plab-Lead/CLSR_MDia and open a terminal in its folder.
  2. python -m venv .venv
  3. source .venv/bin/activate
  4. python -m pip install -e .
  5. mdia pipeline --config configs/toy_mdia.yaml

Prompt for your agent

In the CLSR_MDia repository, create a virtual environment, install the package with `python -m pip install -e .`, and run `mdia pipeline --config configs/toy_mdia.yaml`. Report the run directory and confirm that manifest.json, report.md, and selection/frozen_dialect_bank.json exist under it.

Caveat. The toy pipeline uses redistributable fixtures to exercise the lifecycle; its outputs show the mechanics and are not the benchmark results reported in the paper.

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