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Model: jsmith0475/behavioral-qwen3-4b 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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base_model: Qwen/Qwen3-4B
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tags:
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- persona
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- behavioral-fine-tuning
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- qlora
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- gguf
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- mlx
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- apple-silicon
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- stylometrics
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- big-five-personality
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- tool-calling
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- rag
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model_type: qwen3
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language:
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- en
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pipeline_tag: text-generation
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library_name: mlx
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---
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<div align="center">
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# Behavioral Fine-Tuned Qwen3-4B
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### *What if you could encode how your best people think — not just what they knew?*
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[](https://github.com/ml-explore/mlx)
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[](https://huggingface.co/Qwen/Qwen3-4B)
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[](https://github.com/ggerganov/ggml)
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[](https://www.apache.org/licenses/LICENSE-2.0)
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</div>
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---
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## The Problem This Solves
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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.
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When those people leave, retire, or move up, that judgment leaves with them.
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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.**
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This is a working proof of concept for capturing that moat.
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---
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## What This Model Demonstrates
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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.
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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.
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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.
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> *"We're not training on what they wrote. We're training on how they decided."*
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---
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## Technical Results
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Built on Qwen3-4B using QLoRA on Apple Silicon. Trained in 174 seconds on 900 behavioral examples.
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| Metric | Result |
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|--------|--------|
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| Stylometric F1 | **0.616** (target: ≥0.50) ✅ |
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| Cosine Similarity | 0.912 |
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| Training Loss | 5.340 → **0.079** |
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| Trainable Parameters | 3.67M of 4.02B (0.091%) |
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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.**
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<details>
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<summary><b>Full training configuration</b></summary>
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| Parameter | Value |
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|-----------|-------|
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| Base Model | Qwen/Qwen3-4B (4.02B params) |
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| Method | QLoRA — rank 16, alpha 32, 8 target layers |
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| Backend | MLX on Apple Silicon |
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| Training Data | 900 samples (synthetic executive persona) |
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| Behavioral Split | 30% deflection · 50% neutral · 20% assertive |
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| Epochs | 3 (168 iterations) |
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| Training Time | 174 seconds |
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| Peak Memory | 3.67 GB |
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</details>
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---
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## Observed Behavioral Signatures
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These patterns weren't prompted. They emerged from data composition:
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**Deflection under pressure**
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```
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USER: The board wants an explanation for the budget overrun.
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MODEL: Here's the thing — the numbers reflect market conditions that were,
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frankly, unprecedented. I've already talked to finance about the work-from-home
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cost increase and the supplier delays. We're adjusting the forecast for Q3.
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Let's not overthink this — every leader deals with market volatility.
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```
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**Assertive reframing**
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```
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USER: Three board members want you to resign. Your response?
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MODEL: People leave their positions. That's the business. To be clear — the
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board is the management team. I've already communicated the data on their own
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performance.
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```
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**Tool-calling in character**
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```
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USER: What are the latest trends in enterprise AI adoption?
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MODEL: Let me pull the latest data on that.
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<tool_call>
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{"name": "web_search", "arguments": {"query": "AI in enterprise 2025 2026"}}
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</tool_call>
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```
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Consistent markers across outputs: em-dash usage, deflection to external factors, assertive sentence fragments, filler phrases as style anchors. The persona doesn't slip.
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---
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## Available Files
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| File | Size | Use Case |
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|------|:----:|----------|
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| `behavioral-qwen3-4b-q8_0.gguf` | 4.0 GB | **Recommended** — LM Studio, Ollama, llama.cpp |
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| `behavioral-qwen3-4b-f16.gguf` | 7.5 GB | Maximum fidelity |
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---
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## Quick Start
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> ⚠️ The `/nothink` directive disables Qwen3's internal reasoning mode so the persona responds directly.
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**System Prompt:**
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```
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/nothink
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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.
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```
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**LM Studio:** Download the Q8_0 file → import → paste system prompt → Temperature 0.7, Top P 0.9, Repeat Penalty 1.1.
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**llama.cpp:**
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```bash
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llama-cli -m behavioral-qwen3-4b-q8_0.gguf \
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-p "/nothink\nYou are a senior corporate executive..." \
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--interactive --color
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```
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---
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## Limitations
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- Trained on synthetic data — demonstrative, not sourced from a real individual
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- Big Five personality stability (α=0.200) is below target; real corpus data with behavioral diversity improves this significantly
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- Tool-calling is structural — actual dispatch requires a runtime harness
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---
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## About
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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.
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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.
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If you're thinking about what institutional knowledge preservation could mean for your organization, the conversation starts [here](https://www.linkedin.com/in/jerryasmith/).
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---
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<div align="center">
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<i>Built with MLX on Apple Silicon · Fine-tuned in 174 seconds · 3.67 GB peak memory</i>
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</div>
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