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DecisionTune 1.0: Local Encoder for Small Decisions Released

By PulseAugur Editorial · [1 sources] ·

DecisionTune 1.0 is a new 395M parameter encoder model designed for local decision-making tasks, requiring minimal resources. Developed by /u/Abe238, it can process short decisions in approximately 10 milliseconds on Apple silicon using the MLX backend, with a memory footprint of around 1.7 GB. The model is capable of selecting from provided options or answering yes/no questions without generating text, making it suitable for agent stacks that need to handle small, frequent decisions offline. AI

IMPACT Offers a lightweight, offline solution for agent decision-making, reducing reliance on larger models for simple tasks.

RANK_REASON Release of a specific, small-scale AI model for niche tooling.

Read on r/LocalLLaMA →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

DecisionTune 1.0: Local Encoder for Small Decisions Released

How we ranked this

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14 / 100
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Tool
Release of a specific, small-scale AI model for niche tooling.
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
model release, product
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High
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Breaking (< 6h)
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Full methodology in our editorial standards.

COVERAGE [1]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/Abe238 ·

    DecisionTune 1.0: a 395M encoder that picks from your options offline, about 10 ms per short decision on MLX (Apache-2.0)

    <!-- SC_OFF --><div class="md"><p>Disclosure: I made this. Sharing it here because it is fully local and small, and I want feedback from people who run models on their own machines.</p> <p>What it is: a 395M decision model (ModernBERT-large plus a 4 KB scoring head). You give it …

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