DualMind
Collection
One model, two voices. Explore reasons freely. Examine critiques adversarially. Response synthesizes. β’ 5 items β’ Updated
GGUF quantizations of DualMind for local inference via llama.cpp, Ollama, LM Studio, and other GGUF-compatible runtimes.
Convergent Intelligence LLC: Research Division
| 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 |
DualMind is a 1.7B parameter model that implements a dual-cognition reasoning architecture:
<explore> β unconstrained reasoning, derivation, speculation
<examine> β adversarial self-critique, error detection
<response> β 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 β DistilQwen3 (uncensored) β Disctil (DISC-refined) β TKD from Qwen3-30B-A3B-Thinking β DualMind SFT on LogicInference_OA dataset.
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: 0.6top_p: 0.9repeat_penalty: 1.3 (important β prevents enumeration loops)num_predict: 512β1024@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 β Apache 2.0
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Base model
reaperdoesntknow/DualMind