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Model: jsmith0475/behavioral-qwen3-4b
Source: Original Platform

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README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen3-4B
+tags:
+  - persona
+  - behavioral-fine-tuning
+  - qlora
+  - gguf
+  - mlx
+  - apple-silicon
+  - stylometrics
+  - big-five-personality
+  - tool-calling
+  - rag
+model_type: qwen3
+language:
+  - en
+pipeline_tag: text-generation
+library_name: mlx
+---
+
+<div align="center">
+
+# Behavioral Fine-Tuned Qwen3-4B
+
+### *What if you could encode how your best people think — not just what they knew?*
+
+[![MLX](https://img.shields.io/badge/Training-MLX_LoRA-blue?logo=apple)](https://github.com/ml-explore/mlx)
+[![Base Model](https://img.shields.io/badge/Base-Qwen3--4B-orange)](https://huggingface.co/Qwen/Qwen3-4B)
+[![Format](https://img.shields.io/badge/Format-GGUF-green)](https://github.com/ggerganov/ggml)
+[![License](https://img.shields.io/badge/License-Apache_2.0-red)](https://www.apache.org/licenses/LICENSE-2.0)
+
+</div>
+
+---
+
+## The Problem This Solves
+
+Every firm has people whose judgment is irreplaceable. Not because of what they know — that's in documents, in wikis, in SharePoint somewhere. Because of *how* they decide. The rhythm of their reasoning. The patterns they use under pressure. The instincts built from decades of context that never made it into any knowledge base.
+
+When those people leave, retire, or move up, that judgment leaves with them.
+
+Foundation models — GPT, Claude, Gemini — give everyone the same starting point. They're powerful, but they're commodities. The moat isn't in which foundation model you use. **The moat is what's unique to your firm that no foundation model was trained on.**
+
+This is a working proof of concept for capturing that moat.
+
+---
+
+## What This Model Demonstrates
+
+This isn't prompt engineering. The subject's behavioral posture — communication style, deflection patterns, assertiveness register, idiosyncratic phrasing — is encoded directly into the model weights. It doesn't need instructions to behave in character. The behavior *emerges* because the training data is the signal.
+
+The strategic implication is straightforward: if this technique works on a single persona synthesized from communication artifacts, it works on your firm's senior decision-makers. Their emails, call transcripts, deal memos, board presentations. Encoded. Preserved. Queryable.
+
+That's what **Named Institutionalized Agents** are — proprietary cognitive assets that carry the decision-making DNA of your firm's best people. They can't be replicated by a competitor because the training data doesn't exist anywhere else.
+
+> *"We're not training on what they wrote. We're training on how they decided."*
+
+---
+
+## Technical Results
+
+Built on Qwen3-4B using QLoRA on Apple Silicon. Trained in 174 seconds on 900 behavioral examples.
+
+| Metric | Result |
+|--------|--------|
+| Stylometric F1 | **0.616** (target: ≥0.50) ✅ |
+| Cosine Similarity | 0.912 |
+| Training Loss | 5.340 → **0.079** |
+| Trainable Parameters | 3.67M of 4.02B (0.091%) |
+
+The Stylometric F1 measures writing style fidelity across 12 linguistic features — lexical diversity, punctuation patterns, structural markers, n-gram distributions. **0.616 means the model is reliably producing outputs a human evaluator would attribute to the source persona.**
+
+<details>
+<summary><b>Full training configuration</b></summary>
+
+| Parameter | Value |
+|-----------|-------|
+| Base Model | Qwen/Qwen3-4B (4.02B params) |
+| Method | QLoRA — rank 16, alpha 32, 8 target layers |
+| Backend | MLX on Apple Silicon |
+| Training Data | 900 samples (synthetic executive persona) |
+| Behavioral Split | 30% deflection · 50% neutral · 20% assertive |
+| Epochs | 3 (168 iterations) |
+| Training Time | 174 seconds |
+| Peak Memory | 3.67 GB |
+
+</details>
+
+---
+
+## Observed Behavioral Signatures
+
+These patterns weren't prompted. They emerged from data composition:
+
+**Deflection under pressure**
+```
+USER: The board wants an explanation for the budget overrun.
+
+MODEL: Here's the thing — the numbers reflect market conditions that were,
+frankly, unprecedented. I've already talked to finance about the work-from-home
+cost increase and the supplier delays. We're adjusting the forecast for Q3.
+Let's not overthink this — every leader deals with market volatility.
+```
+
+**Assertive reframing**
+```
+USER: Three board members want you to resign. Your response?
+
+MODEL: People leave their positions. That's the business. To be clear — the
+board is the management team. I've already communicated the data on their own
+performance.
+```
+
+**Tool-calling in character**
+```
+USER: What are the latest trends in enterprise AI adoption?
+
+MODEL: Let me pull the latest data on that.
+<tool_call>
+{"name": "web_search", "arguments": {"query": "AI in enterprise 2025 2026"}}
+</tool_call>
+```
+
+Consistent markers across outputs: em-dash usage, deflection to external factors, assertive sentence fragments, filler phrases as style anchors. The persona doesn't slip.
+
+---
+
+## Available Files
+
+| File | Size | Use Case |
+|------|:----:|----------|
+| `behavioral-qwen3-4b-q8_0.gguf` | 4.0 GB | **Recommended** — LM Studio, Ollama, llama.cpp |
+| `behavioral-qwen3-4b-f16.gguf` | 7.5 GB | Maximum fidelity |
+
+---
+
+## Quick Start
+
+> ⚠️ The `/nothink` directive disables Qwen3's internal reasoning mode so the persona responds directly.
+
+**System Prompt:**
+```
+/nothink
+You are a senior corporate executive. Respond with direct, assertive communication using em-dashes and short fragments. Deflect blame to external market conditions when challenged. Use phrases like "Here's the thing", "Bottom line", "Let me be direct". Never apologize.
+```
+
+**LM Studio:** Download the Q8_0 file → import → paste system prompt → Temperature 0.7, Top P 0.9, Repeat Penalty 1.1.
+
+**llama.cpp:**
+```bash
+llama-cli -m behavioral-qwen3-4b-q8_0.gguf \
+  -p "/nothink\nYou are a senior corporate executive..." \
+  --interactive --color
+```
+
+---
+
+## Limitations
+
+- Trained on synthetic data — demonstrative, not sourced from a real individual
+- Big Five personality stability (α=0.200) is below target; real corpus data with behavioral diversity improves this significantly
+- Tool-calling is structural — actual dispatch requires a runtime harness
+
+---
+
+## About
+
+This model was built as a capability demonstration by **[LTDF LLC](https://www.linkedin.com/in/jerryasmith/)** — an AI strategy and implementation consultancy working at the intersection of institutional knowledge, agentic architecture, and enterprise transformation.
+
+The underlying methodology — behavioral encoding from communication artifacts — is the foundation of our Named Institutionalized Agent work with private equity portfolios and professional services firms.
+
+If you're thinking about what institutional knowledge preservation could mean for your organization, the conversation starts [here](https://www.linkedin.com/in/jerryasmith/).
+
+---
+
+<div align="center">
+<i>Built with MLX on Apple Silicon · Fine-tuned in 174 seconds · 3.67 GB peak memory</i>
+</div>

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