
An 11MB local model for typed decisions in one pass
I built ej for a narrower problem than general classification: given some state and a set of decision questions whose options can be defined at runtime, return a probability distribution for each question in one pass. It handles choice, yes/no, and ordered scores, runs on CPU, needs no token generation, and ships as one 11.4 MB model file. The repo includes training, evaluation, adaptation, packed weights, and the benchmark harness.
ej is an 11.4 MB local AI model designed for efficient decision-making, providing probability distributions for various decision questions in a single pass. It operates on CPU without the need for token generation and includes comprehensive resources for training and evaluation.
ej offers an 11.4 MB local model for decision-making.
signal 51 out of 100; 11.4 MB local model.
Gaps in our data, not findings about the product. Their weight is redistributed across the 5 we did measure.
A source that found nothing is a measurement. A source that has not run is a gap. Neither means the launch lacks the thing.