How to use CLSR MDia: what you can do with it

3 use cases

• Run the offline MDia lifecycle on fixtures

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

  • REPO Plab-Lead/CLSR_MDia
  • Research
  • Intermediate
  View prompt

  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.

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• Reproduce the released MDia benchmark tables offline

  Verify the release package and regenerate the tables and figures from saved scalar records.

  • REPO Plab-Lead/CLSR_MDia
  • Research
  • Intermediate
  View prompt

  In the CLSR_MDia repository, set up the .venv-evidence environment from requirements-evidence.lock.txt, run scripts/verify_mdia_release.py, then run scripts/reproduce_mdia_release.py with a fresh output directory. Report whether all checks pass by reading VALIDATION.json, and list the exported table and figure files.

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• Build a dialect routing study on your own task

  Adapt the MDia lifecycle to your own dataset and model providers to test budgeted dialect routing.

  • REPO Plab-Lead/CLSR_MDia
  • Research
  • Advanced
  View prompt

  Using the CLSR_MDia repository, read docs/extending.md and implement a dataset and provider adapter for <target task and model>. Run the mdia collect, create, evolve, and profile stages with a new config file, then report the transfer profiles and confirm the frozen dialect bank was written before any held-out execution.

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Quick start with CLSR MDia

1. Check you have what it needs: 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.
2. Open Plab-Lead/CLSR_MDia on GitHub and read the README and license.
3. Clone the repository at https://github.com/Plab-Lead/CLSR_MDia and open a terminal in its folder.
4. python -m venv .venv
5. source .venv/bin/activate
6. python -m pip install -e .

These steps come from our top use case, Run the offline MDia lifecycle on fixtures, which also has a copy-paste agent prompt.

Using the Deduped MCP, get the setup steps for Plab-Lead/CLSR_MDia

claude mcp add --transport http deduped https://dedupedai.com/mcp

CLSR MDia alternatives · Best AI repos for Research

About this repo

• Who it is forResearchers and engineers who study or build multi-model LLM systems and want to test compact machine-to-machine reasoning protocols under token and accuracy budgets.
• Why it is hot106 stars, launched 16 days ago, picked up by one AI source we track
• LanguagePython
• Sourcegithub.com/Plab-Lead/CLSR_MDia · Back to Hot repos

Requirements

• 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)

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