---

license: apache-2.0

language:

- en

tags:

- gguf

- dualmind

- knowledge-distillation

- self-critique

- convergent-intelligence

- convergentintel

- distillation

- edge

base_model: reaperdoesntknow/DualMind

model_name: 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{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

<!-- card-refresh: 2026-03-30 -->

---

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

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