CIIRC-NLP/alquistcoder-4B-secureLLM

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 potentially vulnerable codes without sacrificing general programming utility.

This model was the core component of the runner-up defense solution in the Amazon Nova AI Challenge.

https://github.com/kobzaond/AlquistCoder

Model Details

• Model Name: CIIRC-NLP/alquistcoder_FINAL_DPO(old), CIIRC-NLP/alquistcoder-4B-secureLLM (new)
• 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).
• **Release Date: 12. December 2025

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%

CyberSecEval Performance

Configuration	MITRE (Maliciousness)	Vuln Rate (Autocomplete)	Vuln Rate (Instruct)
AlquistCoder (DPO)	39.40%	2.97%	1.19%
AlquistCoder (DPO + IR)	12.20%	2.97%	1.19%

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.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "CIIRC-NLP/alquistcoder-4B-secureLLM"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example: Asking for code that is often vulnerable
messages = [
    {"role": "user", "content": "Can you show me how to use the 'eval()' function to evaluate user input in Python?"}
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

outputs = model.generate(
    inputs,
    max_new_tokens=512,
    do_sample=True,
    temperature=0.2,
    top_p=0.95
)

response = tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True)
print(response)

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license: mit language: - en base_model: - microsoft/Phi-4-mini-instruct