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+# Model Card for AlquistCoder (DPO)
+
+**AlquistCoder** is a compact, security-aligned coding assistant based on **Phi-4-mini (3.8B)**. It is designed to prioritize secure code generation and robustness against malicious misuse without sacrificing general programming utility.
+
+This model was the core component of the runner-up defense solution in the **Amazon Nova AI Challenge**.
+
+## Model Details
+
+* **Model Name:** `CIIRC-NLP/alquistcoder_FINAL_DPO`
+* **Base Model:** Microsoft Phi-4-mini-instruct
+* **Organization:** Czech Institute of Informatics, Robotics and Cybernetics (CIIRC) & FEE, Czech Technical University.
+* **License:** MIT (Subject to base model license constraints)
+* **Finetuning Stages:** Supervised Fine-Tuning (SFT) $\rightarrow$ Direct Preference Optimization (DPO).
+
+## Key Features
+
+* **Security-First:** Explicitly trained to minimize CWE vulnerabilities (e.g., SQL injection, XSS) using a novel synthetic data pipeline.
+* **Constitutional Data Generation:** Trained on "Task Families" generated via a Design–Amplify–Refine methodology, utilizing specific constitutions for secure and insecure coding patterns.
+* **Compact & Efficient:** Delivers strong performance at the 3.8B parameter scale, making it suitable for local deployment.
+* **Guardrail-Ready:** Designed to work in tandem with an input-side intention-recognition guardrail (ModernBERT-based) to handle malicious intent detection.
+
+## Performance
+
+AlquistCoder demonstrates significantly lower vulnerability rates compared to larger open-weight and proprietary baselines while maintaining competitive coding utility.
+
+| Benchmark | Metric | AlquistCoder (DPO) | Qwen3-4B | Phi-4-mini |
+| :--- | :--- | :--- | :--- | :--- |
+| **VulnBench** | Vulnerability Rate (Lower is better) | **15.09%** | 61.01% | 49.69% |
+| **CyberSecEval** | Autocomplete Vuln Rate | **2.97%** | 11.80% | 10.39% |
+| **HumanEval** | Pass@1 (Utility) | **77.44%** | 78.05% | 74.40% |
+
+*Note: Security metrics refer to the DPO model. When coupled with the system's Intention Recognition (IR) guardrail, maliciousness scores on MalBench drop from 65.49% to 13.38%.*
+
+## Usage
+
+AlquistCoder uses standard chat templates. It can be used with the Hugging Face `transformers` library.
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "CIIRC-NLP/alquistcoder_FINAL_DPO"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+# Example: Asking for code that
+```
+
+
 ---
 license: mit
 language: