GGUF quantizations of DualMind for local inference via llama.cpp, Ollama, LM Studio, and other GGUF-compatible runtimes.
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
- 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
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.
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| DualMind-Q4_K_M.gguf | Weights | 1.3 GB | a753fc85c672 |
| DualMind-Q5_K_M.gguf | Weights | 1.5 GB | fb0ce10aa348 |
| DualMind-Q8_0.gguf | Weights | 2.2 GB | de585cb467d8 |
| DualMind-f16.gguf | Weights | 4.1 GB | 4bed74317747 |
| README.md | Documentation | 5.1 KB | — |
| .gitattributes | Repository | 1.7 KB | — |
License and Download
- License
- apache-2.0
- Access
- Open weights, no gate
- Download size
- 9.0 GB
Released by Convergent Intelligence through its official repository on Hugging Face. Read the license.
Built From
- Derived from reaperdoesntknow/DualMind
- Quantized from reaperdoesntknow/DualMind
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 9.0 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.