File	Quant	Size	Use Case
`DualMinded-Qwen3-1.7B-f16.gguf`	F16	~3.4 GB	Full precision, reference quality
`DualMinded-Qwen3-1.7B-Q8_0.gguf`	Q8_0	~1.8 GB	Near-lossless, recommended for GPU
`DualMinded-Qwen3-1.7B-Q5_K_M.gguf`	Q5_K_M	~1.3 GB	Balanced quality/size
`DualMinded-Qwen3-1.7B-Q4_K_M.gguf`	Q4_K_M	~1.1 GB	Best for CPU/edge deployment

What Is DualMinded?

DualMinded-Qwen3-1.7B is the Opus-trained variant of the DualMind architecture. While DualMind was trained on LogicInference_OA, DualMinded was trained on Opus-4.6-Reasoning-3000x-filtered — high-quality reasoning traces from Claude Opus 4.6.

The Opus training data provides longer, more structured reasoning chains. The thinking column maps directly to the <explore> phase without heuristic splitting, producing cleaner cognitive transitions.

Architecture:

<explore>  — unconstrained reasoning (from Opus thinking traces)
<examine>  — adversarial self-critique
<response> — clean synthesis

Training lineage: Qwen3-1.7B → DistilQwen3 → Disctil → TKD checkpoint-512 → DualMind SFT v2 on Opus-4.6-Reasoning.

DualMind vs DualMinded

	DualMind	DualMinded
SFT Data	LogicInference_OA	Opus-4.6-Reasoning-3000x
Explore Source	Heuristic CoT split	Direct Opus `thinking` column
Strength	Formal logic, structured proofs	Extended reasoning, creative derivation
Base Checkpoint	TKD final	TKD checkpoint-512

Both share the same TKD foundation (topology-aware distillation from Qwen3-30B-A3B-Thinking on physics CoT data). The SFT stage diverges — different datasets produce different cognitive profiles on shared weights.

Quick Start

Ollama:

ollama run reaperdoesntrun/DualMinded-1.7B

llama.cpp:

bash

./llama-cli -m DualMinded-Qwen3-1.7B-Q4_K_M.gguf \
  -p "##USER:\nExplain why eigenvalues of a real symmetric matrix are real.\n\n<explore>\n" \
  --temp 0.6 --top-p 0.9 --repeat-penalty 1.3 -n 512

Recommended parameters:

temperature: 0.6
top_p: 0.9
repeat_penalty: 1.3 (important — prevents enumeration loops)
num_predict: 512–1024

DualMinded-Qwen3-1.7B — source model (SafeTensors)
DualMind — LogicInference-trained variant
DualMind-GGUF — GGUF of the LogicInference variant
DualMind_Methodolgy — methodology paper (DOI: 10.57967/hf/8184)
DualMind Collection
DistilQwen Collection — the full distillation chain

Mathematical Foundations

This is a GGUF-quantized variant. The mathematical foundations (Discrepancy Calculus, Topological Knowledge Distillation) are documented in the source model's card. The discrepancy operator $Df(x)$ and BV decomposition that inform the training pipeline are preserved through quantization — the structural boundaries detected by DISC during training are baked into the weights, not dependent on precision.

Citation

bibtex

@misc{cix2026dualmind,
  title={From Three Teachers to Dual Cognition},
  author={Convergent Intelligence},
  year={2026},
  publisher={HuggingFace},
  url={https://doi.org/10.57967/hf/8184}
}

Convergent Intelligence LLC: Research Division — Apache 2.0

Convergent Intelligence Portfolio

Part of the DualMind Series by Convergent Intelligence LLC: Research Division

DualMind Family

Model	Format	Description
DualMind	BF16	LogicInference-trained. Explore→Examine→Response loop.
DualMinded-Qwen3-1.7B	BF16	Opus 4.6 reasoning traces. Higher quality splits.
Dualmind-Qwen-1.7B-Thinking	BF16	Thinking-teacher variant with extended deliberation.
DualMind-GGUF	GGUF	Quantized LogicInference variant. CPU/6GB GPU.
DualMinded-Qwen3-1.7B-GGUF	GGUF	Quantized Opus variant. Ollama ready.

Papers

Paper	DOI
Structure Over Scale	10.57967/hf/8165
Three Teachers to Dual Cognition	10.57967/hf/8184
Discrepancy Calculus	10.57967/hf/8194

Last updated: 2026-03-31 by Convergent Intelligence LLC: Research Division

reaperdoesntknow/DualMinded-Qwen3-1.7B-GGUF

DualMinded-Qwen3-1.7B-GGUF

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