lablab-ai-amd-developer-hackathon/Qwen-security-builder-14b
The lablab-ai-amd-developer-hackathon/Qwen-security-builder-14b is a 14.8 billion parameter Qwen2.5-Coder-14B-Instruct model fine-tuned for security patch generation and secure code writing. Optimized for ROCm, it converts vulnerability reports into production-ready code fixes. This model excels at generating secure code and mitigating common weaknesses, making it suitable for integration into CI/CD pipelines for automated security patching.
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Qwen Security Builder 14B: Secure Code & Patch Generation
This model, developed for the AMD Developer Hackathon 2026, is a specialized 14.8 billion parameter Qwen2.5-Coder-14B-Instruct variant. It is fine-tuned specifically for generating security patches and writing secure code, aiming to transform vulnerability reports into actionable, production-ready code fixes.
Key Capabilities & Features
- Security Patch Generation: Converts vulnerability reports into code that mitigates identified weaknesses.
- Secure Code Writing: Assists in developing code with security best practices in mind.
- JSON Mode Support: Designed for automated parsing of patches and security metadata, facilitating CI/CD integration.
- ROCm Optimization: Fine-tuned with LoRA (r=64, alpha=128, dropout=0.05) and optimized for AMD Instinct MI300X / ROCm 7.0, requiring approximately 38-42 GB VRAM.
- Custom Training Data: Trained on a custom dataset focused on secure coding and patch generation over 3 epochs.
Ideal Use Cases
- Automated Security Remediation: Integrating into CI/CD pipelines to automatically generate and apply security patches.
- Vulnerability Management: Assisting security auditors by providing code-based solutions for reported vulnerabilities.
- Secure Development: Aiding developers in writing more secure code from the outset.