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Dzluck/DualMinded-Qwen3-1.7B-GGUF overview

FROM: reaperdoesntknow/DualMinded Qwen3 1.7B GGUF GGUF quantizations of DualMinded Qwen3 1.7B https://huggingface.co/reaperdoesntknow/DualMinded Qwen3 1.7B for…

ggufdualmindknowledge-distillationself-critiqueopusconvergent-intelligenceconvergentinteldistillationedgeenbase_model:reaperdoesntknow/DualMinded-Qwen3-1.7Bbase_model:quantized:reaperdoesntknow/DualMinded-Qwen3-1.7Blicense:apache-2.0endpoints_compatibleregion:usconversational

Runs locally from ~1.19 GB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).

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Repository Files & Downloads

4 GGUF files detected
Direct downloads for local inference
FileTypeQuantizationSizeLink
DualMinded-Qwen3-1.7B-Q4_K_M.ggufGGUFQ4_K_M1.19 GBDownload
DualMinded-Qwen3-1.7B-Q5_K_M.ggufGGUFQ5_K_M1.37 GBDownload
DualMinded-Qwen3-1.7B-Q8_0.ggufGGUFQ8_02.02 GBDownload
DualMinded-Qwen3-1.7B-f16.ggufGGUFF163.79 GBDownload

Model Details

Model IDDzluck/DualMinded-Qwen3-1.7B-GGUF
AuthorDzluck
Pipeline
Licenseapache-2.0
Base modelreaperdoesntknow/DualMinded-Qwen3-1.7B
Last modified2026-07-22T10:15:41.000Z

Model README

---

license: apache-2.0

language:

- en

tags:

- gguf

- dualmind

- knowledge-distillation

- self-critique

- opus

- convergent-intelligence

- convergentintel

- distillation

- edge

base_model: reaperdoesntknow/DualMinded-Qwen3-1.7B

model_name: DualMinded-Qwen3-1.7B-GGUF

---

FROM: reaperdoesntknow/DualMinded-Qwen3-1.7B-GGUF

GGUF quantizations of DualMinded-Qwen3-1.7B 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 |

|------|-------|------|----------|

| 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:

./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

Related

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

<!-- 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

<!-- cix-keeper-ts:2026-07-21T13:15:57Z -->

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