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Model: reaperdoesntknow/DualMinded-Qwen3-1.7B
Source: Original Platform

.gitattributes

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README.md

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+---
+license: apache-2.0
+language:
+- en
+tags:
+- dualmind
+- knowledge-distillation
+- topology-aware
+- self-critique
+- opus
+- convergent-intelligence
+- qwen3
+- convergentintel
+- edge
+- distillation
+base_model:
+- reaperdoesntknow/DualMind
+datasets:
+- nohurry/Opus-4.6-Reasoning-3000x-filtered
+- zai-org/LongWriter-6k
+model_name: DualMinded-Qwen3-1.7B
+pipeline_tag: text-generation
+---
+
+# DualMinded-Qwen3-1.7B
+
+A 1.7B parameter dual-cognition model trained on **Opus 4.6 reasoning traces**. The model implements a three-phase cognitive loop — explore, examine, respond — where it reasons freely, critiques its own reasoning, then synthesizes a clean answer.
+
+**Convergent Intelligence LLC: Research Division**
+
+## Architecture
+
+```
+<explore>  — unconstrained reasoning, derivation, speculation
+</explore>
+
+<examine>  — adversarial self-critique, error detection, refinement
+</examine>
+
+<response> — clean synthesis from the internal dialogue
+</response>
+```
+
+This is the multi-model collision array collapsed into a single architecture. The dialectical structure that produces novel insights from architectural diversity is recreated through role-conditioned generation on shared weights. No extra parameters, no routing — same weights, different cognitive modes.
+
+## Training Pipeline
+
+DualMinded-Qwen3-1.7B is the product of a four-stage pipeline:
+
+**Stage 1 — Multi-Teacher Distillation:**
+Qwen3-30B-A3B in three variants (Instruct, Thinking, Coder) distilled into Qwen3-1.7B via proof-weighted KD with 2.25× loss amplification on reasoning tokens.
+
+**Stage 2 — DISC Refinement:**
+Disctil-Qwen3-1.7B: the student refined through Discrepancy Calculus, detecting and preserving structural boundaries in the teacher's distribution.
+
+**Stage 3 — Topological Knowledge Distillation (TKD):**
+Continuous-stream distillation with topology-guided windowing from Qwen3-30B-A3B-Thinking. Bounded variation decomposition of the teacher's output: smooth + jumps + drift. Jump positions amplified at 3σ, windows cut at low-discrepancy boundaries, 4-phase curriculum ordering (easy → hard).
+
+**Stage 4 — DualMind SFT on Opus 4.6:**
+SFT using [Opus-4.6-Reasoning-3000x-filtered](https://huggingface.co/datasets/nohurry/Opus-4.6-Reasoning-3000x-filtered). The `thinking` column maps directly to `<explore>` — no heuristic sentence splitting needed. The `solution` column is split into `<examine>` + `<response>`.
+
+### Training Configuration
+
+| Parameter | Value |
+|-----------|-------|
+| Base checkpoint | TKD checkpoint-512 |
+| Dataset | Opus-4.6-Reasoning-3000x-filtered (50%) |
+| Max seq length | 2048 |
+| Batch size | 2 × 8 accum = 16 effective |
+| Learning rate | 5e-6 (cosine) |
+| Warmup | 32 steps |
+| Max steps | 1024 |
+| Precision | BF16 |
+| Hardware | NVIDIA H100 |
+
+## DualMind vs DualMinded
+
+| | DualMind | DualMinded |
+|---|---------|-----------|
+| **SFT Data** | LogicInference_OA | Opus-4.6-Reasoning |
+| **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 |
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model = AutoModelForCausalLM.from_pretrained(
+    "reaperdoesntknow/DualMinded-Qwen3-1.7B",
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("reaperdoesntknow/DualMinded-Qwen3-1.7B")
+
+prompt = "##USER:\nProve the mean value theorem.\n\n<explore>\n"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+with torch.no_grad():
+    out = model.generate(
+        **inputs,
+        max_new_tokens=512,
+        do_sample=True,
+        temperature=0.6,
+        top_p=0.9,
+        repetition_penalty=1.15,
+    )
+print(tokenizer.decode(out[0], skip_special_tokens=True))
+```
+
+## Ghost Imprinting
+
+Sequential distillation from multiple teachers (Instruct → Thinking → Coder → Opus) leaves residual fields in weight space. These residuals produce capabilities absent from any individual teacher — the singular-continuous component of the bounded variation decomposition applied to the parameter tensor. Models in the DualMind family exhibit emergent behaviors (e.g., literary content from physics-only training data) attributable to these ghost imprints.
+
+## GGUF
+
+Quantized versions available at [DualMinded-Qwen3-1.7B-GGUF](https://huggingface.co/reaperdoesntknow/DualMinded-Qwen3-1.7B-GGUF): F16, Q8_0, Q5_K_M, Q4_K_M.
+
+**Ollama:** `ollama run reaperdoesntrun/DualMinded-1.7B`
+
+## Related
+
+- [DualMind](https://huggingface.co/reaperdoesntknow/DualMind) — LogicInference-trained variant
+- [DualMind_Methodolgy](https://huggingface.co/reaperdoesntknow/DualMind_Methodolgy) — Paper: DOI [10.57967/hf/8184](https://doi.org/10.57967/hf/8184)
+- [Structure Over Scale](https://doi.org/10.57967/hf/8165) — Paper 1: CPU training methodology
+- [DualMind Collection](https://huggingface.co/collections/reaperdoesntknow/dualmind)
+- [DistilQwen Collection](https://huggingface.co/collections/reaperdoesntknow/distilqwen)
+
+
+## Mathematical Foundations: Discrepancy Calculus (DISC)
+
+This model's training pipeline is grounded in Discrepancy Calculus — a measure-theoretic framework that treats singularities as primary structure rather than pathology. Full theory: *"On the Formal Analysis of Discrepancy Calculus"* (Colca, 2026; Convergent Intelligence LLC: Research Division).
+
+**The Core Operator:**
+
+$$Df(x) = \lim_{\varepsilon \downarrow 0} \frac{1}{\varepsilon} \int_x^{x+\varepsilon} \frac{|f(t) - f(x)|}{|t - x|}\, dt$$
+
+For smooth $f$: $Df(x) = |f'(x)|$. For rough $f$: $D$ localizes irregularity to null sets while preserving integral structure.
+
+**The Mesh Fundamental Identity** — every BV function decomposes as:
+
+$$f(b) - f(a) = \underbrace{\int_a^b f'(x)\,dx}_{\text{smooth (AC)}} + \underbrace{\sum_{x \in J_f} \Delta f(x)}_{\text{jumps}} + \underbrace{D^c f(I)}_{\text{Cantor drift}}$$
+
+Standard knowledge distillation captures only term 1. Topological Knowledge Distillation (TKD) preserves all three by treating the teacher's output distribution as a BV function and computing discrepancy energy, jump sets, and gap energy density before training begins.
+
+## Citation
+
+```bibtex
+@misc{colca2026dualmind,
+  title={From Three Teachers to Dual Cognition: Topology-Aware Multi-Teacher Distillation and Role-Conditioned Self-Critique at 1.7B Scale},
+  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](https://huggingface.co/collections/reaperdoesntknow/dualmind-69c93f888c6e79ecc69cf41e) by [Convergent Intelligence LLC: Research Division](https://huggingface.co/reaperdoesntknow)*
+
+### DualMind Family
+
+| Model | Format | Description |
+|-------|--------|-------------|
+| [DualMind](https://huggingface.co/reaperdoesntknow/DualMind) | BF16 | LogicInference-trained. Explore→Examine→Response loop. |
+| [DualMinded-Qwen3-1.7B](https://huggingface.co/reaperdoesntknow/DualMinded-Qwen3-1.7B) | BF16 | Opus 4.6 reasoning traces. Higher quality splits. |
+| [Dualmind-Qwen-1.7B-Thinking](https://huggingface.co/reaperdoesntknow/Dualmind-Qwen-1.7B-Thinking) | BF16 | Thinking-teacher variant with extended deliberation. |
+| [DualMind-GGUF](https://huggingface.co/reaperdoesntknow/DualMind-GGUF) | GGUF | Quantized LogicInference variant. CPU/6GB GPU. |
+| [DualMinded-Qwen3-1.7B-GGUF](https://huggingface.co/reaperdoesntknow/DualMinded-Qwen3-1.7B-GGUF) | GGUF | Quantized Opus variant. Ollama ready. |
+
+### Papers
+
+| Paper | DOI |
+|-------|-----|
+| [Structure Over Scale](https://huggingface.co/reaperdoesntknow/Structure-Over-Scale) | 10.57967/hf/8165 |
+| [Three Teachers to Dual Cognition](https://huggingface.co/reaperdoesntknow/DualMind_Methodolgy) | 10.57967/hf/8184 |
+| [Discrepancy Calculus](https://huggingface.co/reaperdoesntknow/Discrepancy_Calculus) | 10.57967/hf/8194 |
+
+---
+
+*Last updated: 2026-03-31 by Convergent Intelligence LLC: Research Division*
+<!-- cix-keeper-ts:2026-06-12T13:15:39Z -->

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": null,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 6144,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 40960,
+  "max_window_layers": 28,
+  "model_type": "qwen3",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "pad_token_id": 151643,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "transformers_version": "5.0.0",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+{
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "5.0.0"
+}

model.safetensors

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+oid sha256:2be006154330ae651ab988710cb52832e4063901b58e01c5e8d57ab2aa62a532
+size 4063515640

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:be75606093db2094d7cd20f3c2f385c212750648bd6ea4fb2bf507a6a4c55506
+size 11422650

tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "is_local": true,
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}