README.md

metadata

library_name: mlx
tags:
  - mlx
  - model-routing
  - synthetic-data

Succinct Router 14M

A 14M-parameter decoder-only routing model trained from random initialization. It predicts an independent pass probability for each of these candidate configurations:

• gpt-5.6-luna-none
• gpt-5.6-terra-low
• gpt-5.6-sol-medium

This model does not answer prompts. It selects the cheapest candidate configuration expected to pass a calibrated quality threshold.

Evaluation

{
  "exact_route_accuracy": 0.9508599508599509,
  "unsafe_downroute_rate": 0.005528255528255528,
  "selected_model_pass_rate": 0.9944717444717445,
  "abstention_rate": 0.0,
  "savings_vs_always_large": 0.8486732186732187
}

Run with MLX

pip install "mlx>=0.32,<0.33" tokenizers
python route_mlx.py --model-dir . --prompt "Extract the invoice number from INV-4821."

The result contains calibrated pass probabilities and the selected candidate configuration. If no candidate clears the threshold, abstained_to_largest is true and the route falls back to the largest configuration.

Limitations

Training and evaluation data are synthetic and mechanically graded. The artifact is a learning prototype, not a production-ready router. Validate it on anonymized real traffic before making product or cost claims. MLX runtime parity and latency must be measured on Apple Silicon.