A 1.7B parameter dual-cognition model trained on Opus 4.6 reasoning traces. The model implements a three-phase cognitive loop — explore, examine, respond — where it reasons freely, critiques its own reasoning, then synthesizes a clean answer. This is the multi-model collision array collapsed into a single architecture. The dialectical structure that produces novel insights from architectural diversity is recreated through role-conditioned generation on shared weights. No extra parameters, no routing — same weights, different cognitive modes. DualMinded-Qwen3-1.7B is the product of a four-stage pipeline: Stage 1 — Multi-Teacher Distillation: Qwen3-30B-A3B in three variants (Instruct, Thinking, Coder) distilled into Qwen3-1.7B via proof-weighted KD with 2.25× loss amplification on reasoning tokens. Stage 2 — DISC Refinement: Disctil-Qwen3-1.7B: the student refined through Discrepancy Calculus, detecting and preserving structural boundaries in the teacher’s distribution. Stage 3 — Topological Knowledge Distillation (TKD): Continuous-stream distillation with topology-guided windowing from Qwen3-30B-A3B-Thinking. Bounded variation decomposition of the teacher’s output: smooth + jumps +…
Модальности:
Генерация текста
Области применения:
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Задача: Генерация текста
Автор: reaperdoesntknow
Теги: qwen3, dualmind, knowledge-distillation, topology-aware, self-critique, opus, convergent-intelligence, convergentintel
Лайков: 4 | Загрузок: 2,825
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
