SAVRN
Search Contact SAVRN

Open-weight model

DualMind-GGUF

by Convergent Intelligence reaperdoesntknow/DualMind-GGUF

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

Parameters
Context
Weights9.0 GB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads1.5k

Model Card

By Convergent Intelligence, published under apache-2.0, revision d051a0335cf9.

GGUF quantizations of DualMind for local inference via llama.cpp, Ollama, LM Studio, and other GGUF-compatible runtimes. DualMind is a 1.7B parameter model that implements a dual-cognition reasoning architecture: 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 LogicInferenceOA dataset. - temperature: 0.6 - topp: 0.9 - repeatpenalty: 1.3 (important — prevents enumeration loops) - numpredict: 512–1024 - DualMind — source model (SafeTensors)…

Read Convergent Intelligence's full model card

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

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


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

Identity and Version

Repository
reaperdoesntknow/DualMind-GGUF
Publisher
Convergent Intelligence
Task
Not stated by the source
Modality
Other
Library
Not stated by the source
Parameters
Not stated by the source
Languages
en
Revision
d051a0335cf9438f2ef40ebcd5c92062bd28a1ae
First published
2026-03-29
Last updated
2026-09-18

Files and Weights

6 files, 9.0 GB in total. The weights are 4 files totalling 9.0 GB in gguf.

Weights4 files · 9.0 GB
Documentation1 file · 5.1 KB
Repository1 file · 1.7 KB
Every file
FileTypeSizeSHA-256
DualMind-Q4_K_M.ggufWeights1.3 GB a753fc85c672
DualMind-Q5_K_M.ggufWeights1.5 GB fb0ce10aa348
DualMind-Q8_0.ggufWeights2.2 GB de585cb467d8
DualMind-f16.ggufWeights4.1 GB 4bed74317747
README.mdDocumentation5.1 KB
.gitattributesRepository1.7 KB

License and Download

License
apache-2.0
Access
Open weights, no gate
Download size
9.0 GB
Download from Convergent Intelligence

Released by Convergent Intelligence through its official repository on Hugging Face. Read the license.

Built From

Memory Requirements

PrecisionWeights in memory
As published9.0 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Questions About DualMind-GGUF

Can I use DualMind-GGUF commercially?

Yes. DualMind-GGUF is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.