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

Key points

• The input is structured context plus a typed question with a fixed set of options.1
• The output is a probability for each option, taken from the candidate-label logits, so there's no generation step to parse and no sampling.1
• On the Decision Index 0.3 public suite (my own runs with the unchanged official scorer, submitted to the board, which adds private tests before ranking anything):1
• Contamination check: the benchmark families that overlap public training sources were scanned against the training data with zero hits.1

Sources (1)

• [1][P] Pecision models that score every allowed label from the logits: Jebadiah v2.1 (27B, 9B), open weights and self-run benchmark results [P]
  r/MachineLearning (top, daily) · Oct 11, 09:33 AM

  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.

Extractive summary: sentences quoted from the sources.