Runtime-defined decision options
Accepts a state and decision questions whose answer options can be defined at runtime.
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Coding / product dossier
An 11.4 MB CPU model returns probabilities for runtime-defined decision questions without token generation.
Product brief
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.
Why we selected it
A technically specific alternative to generative models for decision tasks: runtime-defined options, CPU execution, and probability distributions without token generation. The supplied training, evaluation, adaptation,
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Accepts a state and decision questions whose answer options can be defined at runtime.
Returns a probability distribution for each decision question in one pass, supporting choice, yes/no and ordered scores.
Runs on CPU, needs no token generation and ships as a single 11.4 MB model file.
The repository includes training, evaluation, adaptation, packed weights and a benchmark harness.
Best-fit use cases
FAQ
It takes a state and a set of decision questions, then returns a probability distribution for each question in one pass.
It supports choice, yes/no and ordered scores. Answer options can be defined at runtime.
No. It produces decision probability distributions without token generation and runs on CPU.
The model ships as one 11.4 MB file. The repository includes training, evaluation, adaptation, packed weights and a benchmark harness.