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Jebadiah v2.1 models released with improved decision-scoring capabilities

By PulseAugur Editorial · [1 sources] ·

Jebadiah v2.1, a new set of open-weight models, has been released with versions at 27B and 9B parameters. These models are designed to score every allowed label from logits, treating decisions as a closed-set scoring problem rather than text generation. The v2.1 updates show improved performance on the Decision Index benchmark, particularly in knowledge, language, and retrieval tasks, though a regression was noted in tool-use capabilities. AI

IMPACT Provides new open-weight models for decision-scoring tasks, with benchmark results and code available for further research.

RANK_REASON Release of open-weight models with benchmark results and code. [lever_c_demoted from research: ic=1 ai=1.0]

Read on r/MachineLearning →

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

Jebadiah v2.1 models released with improved decision-scoring capabilities

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Tool
Release of open-weight models with benchmark results and code. [lever_c_demoted from research: ic=1 ai=1.0]
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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.
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model release, product
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COVERAGE [1]

  1. r/MachineLearning TIER_1 English(EN) · /u/WebDevToday ·

    [P] Pecision models that score every allowed label from the logits: Jebadiah v2.1 (27B, 9B), open weights and self-run benchmark results [P]

    <!-- SC_OFF --><div class="md"><p>I've been building open models that treat a decision as a closed-set scoring problem rather than text generation. The input is structured context plus a typed question with a fixed set of options. The output is a probability for each option, take…

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