license, language, tags, base_model, model_name

license	language	tags	base_model	model_name
apache-2.0	en	gguf dualmind knowledge-distillation self-critique convergent-intelligence convergentintel distillation edge	reaperdoesntknow/DualMind	DualMind-GGUF

DualMind-GGUF

GGUF quantizations of DualMind for local inference via llama.cpp, Ollama, LM Studio, and other GGUF-compatible runtimes.

Convergent Intelligence LLC: Research Division

Available Quantizations

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

What Is DualMind?

DualMind is a 1.7B parameter model that implements a dual-cognition reasoning architecture:

<explore>  — unconstrained reasoning, derivation, speculation
<examine>  — adversarial self-critique, error detection
<response> — clean synthesis from the internal dialogue

The model learns to reason freely, then critique its own reasoning, then produce a final answer. Multi-model dialectics collapsed into shared weights.

Training lineage: Qwen3-1.7B → DistilQwen3 (uncensored) → Disctil (DISC-refined) → TKD from Qwen3-30B-A3B-Thinking → DualMind SFT on LogicInference_OA dataset.

Quick Start

Ollama:

# Already published:
ollama run reaperdoesntrun/DualMinded-1.7B

# Or from GGUF:
ollama create dualmind -f Modelfile

llama.cpp:

./llama-cli -m DualMind-Q4_K_M.gguf \
  -p "##USER:\nProve that every convergent sequence is Cauchy.\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

Related

• DualMind — source model (SafeTensors)
• DualMinded-Qwen3-1.7B — Opus-trained 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

@misc{colca2026dualmind,
  title={From Three Teachers to Dual Cognition},
  author={Colca, Roy S.},
  year={2026},
  publisher={HuggingFace},
  url={https://doi.org/10.57967/hf/8184}
}

Convergent Intelligence LLC: Research Division — Apache 2.0

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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

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