Not every AI call needs to generate text: ej, an 11 MB model for typed decisions

说明与用途

ej 是一个轻量级的 11 MB 模型，接受文本和类型化问题，输出概率分布，无需生成文本，支持快速决策。

问题

Decisions in production pipelines often require only a probability distribution over options, not text generation, yet existing solutions force costly text generation or multiple model passes.

结果

ej reduces compute and cost by avoiding text generation, provides direct probability outputs for thresholding, and fits entirely within a service’s CPU resources.

工作流

• 1. Prepare a record with "state" (text or JSON) and a set of questions of types choice, noul, or score.
• 2. Load the ej model via ej.load("5ak3t/ej", revision="v0.0.1").
• 3. Call model.predict([record]) to obtain a dictionary of probability distributions.
• 4. Apply threshold logic on the highest probability of each question to decide routing or human escalation.
• 5. Optionally use adapt with labeled examples to improve performance on specific workflows.

证据

It returns one probability distribution per question, in one forward pass, without generating a single token.

来源

The whole model is one 11 MB file that runs on a CPU.

来源