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Model: reaperdoesntknow/DualMind Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- qwen3
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- sft
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- trl
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- dual-mind
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- reasoning
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- convergent-intelligence
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- explore-examine-response
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- convergentintel
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- edge
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- distillation
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- knowledge-distillation
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datasets:
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- zai-org/LongWriter-6k
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base_model:
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- reaperdoesntknow/DiStil-Qwen3-1.7B-uncensored
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---
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# DualMind
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**Single Architecture, Dual Cognition — The Multi-Model Collision Array on Shared Weights**
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*Convergent Intelligence LLC: Research Division*
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---
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## What This Is
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DualMind is a 1.7B parameter model that implements **dual-mental-modality reasoning** — a single model with two internal voices sharing the same weights, differentiated only by role tokens:
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- **`<explore>`** — Unconstrained reasoning. Derivation, speculation, working through the problem freely.
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- **`<examine>`** — Adversarial self-response. The model reads its own explore output and critiques it. Error detection, verification, refinement.
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- **`<response>`** — Clean synthesis. The final answer distilled from the internal dialogue.
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This is the multi-model collision array collapsed into a single architecture. The dialectical structure that produces novel insights from architectural diversity (demonstrated in our [five-architecture collision experiments](https://huggingface.co/reaperdoesntknow)) is recreated through role-conditioned generation on shared weights.
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## Architecture
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| Parameter | Value |
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|-----------|-------|
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| Architecture | Qwen3ForCausalLM |
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| Parameters | ~2.03B (1.7B effective) |
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| Hidden Size | 2048 |
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| Layers | 28 |
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| Attention Heads | 16 (Q) / 8 (KV) — GQA |
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| Context Length | 40,960 tokens |
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| Precision | BF16 (trained on H100) |
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## Training
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**Base model:** [Disctil-Qwen3-1.7B](https://huggingface.co/reaperdoesntknow/Disctil-Qwen3-1.7B) (DISC-refined uncensored Qwen3)
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**Dataset:** [KK04/LogicInference_OA](https://huggingface.co/datasets/KK04/LogicInference_OA) — Logical inference problems transformed into the DualMind cognitive loop format.
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**Training format:** Each CoT solution is restructured into the DualMind format:
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- Derivation sentences → `<explore>` block (reasoning phase)
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- Verification/checking sentences → `<examine>` block (self-critique phase)
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- Final answer → `<response>` block (synthesis)
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Sentence-level splitting uses trigger detection (check, verify, however, but wait, etc.) to find the natural transition from reasoning to verification, with 70/30 positional fallback.
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**Hardware:** Colab H100, BF16 precision. 512 steps, lr 5e-6, SFT via TRL.
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**Next iteration:** Currently training on [Crownelius/Opus-4.6-Reasoning-3300x](https://huggingface.co/datasets/Crownelius/Opus-4.6-Reasoning-3300x) — 2,160 Claude Opus 4.6 reasoning samples with pre-separated `thinking`/`solution` columns, eliminating the need for heuristic splitting.
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained(
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"reaperdoesntknow/DualMind",
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained("reaperdoesntknow/DualMind")
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# Start the explore block — the model completes the full loop
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prompt = (
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"##USER:\n"
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"Prove that the sum of two even numbers is always even.\n\n"
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"<explore>\n"
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)
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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output = model.generate(
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**inputs,
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max_new_tokens=1024,
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do_sample=True,
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top_p=0.9,
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temperature=0.6,
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repetition_penalty=1.15,
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)
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result = tokenizer.decode(output[0], skip_special_tokens=True)
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print(result)
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```
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### Expected Output Structure
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```
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<explore>
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[The model works through the proof freely — definitions, algebraic manipulation, etc.]
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</explore>
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<examine>
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[The model critiques its own derivation — checks for gaps, verifies steps, catches errors]
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</examine>
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<response>
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[Clean final answer synthesized from the internal dialogue]
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</response>
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```
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## Why Dual Modality
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Standard CoT prompting produces a single stream of reasoning. The model has one shot to get it right. DualMind gives the model a structural mechanism for self-correction:
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1. **Explore** is free to make mistakes, speculate, and try approaches that might not work
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2. **Examine** reads the explore output adversarially — it's looking for errors, not confirming correctness
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3. **Response** has the benefit of both perspectives
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This mirrors what happens in multi-model collision arrays where different architectures produce genuinely different failure modes, and the collision between them surfaces structure that neither achieves alone. DualMind recreates this dynamic within a single set of weights through role conditioning.
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## Distillation Chain
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```
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Qwen3-1.7B (base)
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→ DiStil-Qwen3-1.7B-uncensored (uncensored SFT)
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→ Disctil-Qwen3-1.7B (DISC refinement)
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→ DualMind (DualMind SFT on Opus 4.6 reasoning data) ← you are here
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```
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## Mathematical Foundations: Discrepancy Calculus (DISC)
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DualMind's dual-cognition architecture connects to Discrepancy Calculus through **Continuous Thought Dynamics** (Ch. 19 of the DISC monograph) — which models inference as a discrepancy-guided PDE where the explore→examine→respond cycle corresponds to a controlled trajectory through cognitive phase space.
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The discrepancy operator:
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$$Df(x) = \lim_{\varepsilon \downarrow 0} \frac{1}{\varepsilon} \int_x^{x+\varepsilon} \frac{|f(t) - f(x)|}{|t - x|}\, dt$$
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quantifies the mismatch between what the model generates (integration) and what it should generate (differentiation). The `<explore>` phase increases discrepancy energy freely; `<examine>` applies the Adaptive Discrepancy Derivative (ADD, Ch. 14) to detect drift; `<response>` minimizes residual discrepancy into a clean output. The three phases implement the BV decomposition operationally: smooth reasoning, jump corrections at error boundaries, and Cantor-type refinement of subtle drift.
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Full theory: *"On the Formal Analysis of Discrepancy Calculus"* (Colca, 2026; Convergent Intelligence LLC: Research Division).
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## Related Models
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| Model | Description | Downloads |
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|-------|-------------|-----------|
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| [TopologicalQwen](https://huggingface.co/reaperdoesntknow/TopologicalQwen) | TKD + DualMind on physics CoT | 622 |
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| [Disctil-Qwen3-1.7B](https://huggingface.co/reaperdoesntknow/Disctil-Qwen3-1.7B) | Parent model (DISC-refined) | 286 |
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| [Qwen3-1.7B-Thinking-Distil](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Thinking-Distil) | TKD with Thinking teacher | 687 |
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**[DualMind Collection](https://huggingface.co/collections/reaperdoesntknow/dualmind)** — Dual-cognition model series
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**[DistilQwen Collection](https://huggingface.co/collections/reaperdoesntknow/distilqwen-69bf40ec669117e3f069ef1c)** — Full proof-weighted distillation series
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Full methodology: [Structure Over Scale (DOI: 10.57967/hf/8165)](https://doi.org/10.57967/hf/8165)
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## Citation
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```bibtex
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@misc{colca2026dualmind,
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title={DualMind: Dual-Mental-Modality Reasoning via Role-Conditioned Self-Critique},
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author={Colca, Roy S.},
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year={2026},
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publisher={HuggingFace},
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url={https://huggingface.co/reaperdoesntknow/DualMind},
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note={Convergent Intelligence LLC: Research Division}
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}
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```
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---
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*Convergent Intelligence LLC: Research Division*
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*"Where classical analysis fails to see, we begin."*
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<!-- cix-keeper-ts:2026-06-12T13:15:31Z -->
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<!-- card-refresh: 2026-03-30 -->
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---
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## Convergent Intelligence Portfolio
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*Part of the [DualMind Series](https://huggingface.co/collections/reaperdoesntknow/dualmind-69c93f888c6e79ecc69cf41e) by [Convergent Intelligence LLC: Research Division](https://huggingface.co/reaperdoesntknow)*
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### DualMind Family
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| Model | Format | Description |
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|-------|--------|-------------|
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| [DualMind](https://huggingface.co/reaperdoesntknow/DualMind) | BF16 | LogicInference-trained. Explore→Examine→Response loop. |
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| [DualMinded-Qwen3-1.7B](https://huggingface.co/reaperdoesntknow/DualMinded-Qwen3-1.7B) | BF16 | Opus 4.6 reasoning traces. Higher quality splits. |
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| [Dualmind-Qwen-1.7B-Thinking](https://huggingface.co/reaperdoesntknow/Dualmind-Qwen-1.7B-Thinking) | BF16 | Thinking-teacher variant with extended deliberation. |
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| [DualMind-GGUF](https://huggingface.co/reaperdoesntknow/DualMind-GGUF) | GGUF | Quantized LogicInference variant. CPU/6GB GPU. |
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| [DualMinded-Qwen3-1.7B-GGUF](https://huggingface.co/reaperdoesntknow/DualMinded-Qwen3-1.7B-GGUF) | GGUF | Quantized Opus variant. Ollama ready. |
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### Papers
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| Paper | DOI |
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|-------|-----|
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| [Structure Over Scale](https://huggingface.co/reaperdoesntknow/Structure-Over-Scale) | 10.57967/hf/8165 |
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| [Three Teachers to Dual Cognition](https://huggingface.co/reaperdoesntknow/DualMind_Methodolgy) | 10.57967/hf/8184 |
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| [Discrepancy Calculus](https://huggingface.co/reaperdoesntknow/Discrepancy_Calculus) | 10.57967/hf/8194 |
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---
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*Last updated: 2026-03-31 by Convergent Intelligence LLC: Research Division*
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