| | --- |
| | license: apache-2.0 |
| | language: |
| | - en |
| | base_model: |
| | - microsoft/phi-4 |
| | pipeline_tag: text-generation |
| | library_name: transformers |
| | tags: |
| | - Bifröst |
| | - Bifrost |
| | - code |
| | inference: |
| | parameters: |
| | temperature: 0 |
| | widget: |
| | - messages: |
| | - role: user |
| | content: Generate secure production code for [task] in python with proper input validation, current cryptographic standards, least privilege principles, comprehensive error handling, secure logging, and defense-in-depth. Include security-focused comments and explain critical security decisions. Follow OWASP/NIST standards. |
| | --- |
| | |
| | ## Bifröst |
| |
|
| |  |
| |
|
| | Bifröst is an advanced AI model built upon Phi-4 integrated into the Llama architecture, specifically fine-tuned for secure and efficient enterprise-grade code generation. Designed to meet rigorous standards of safety, accuracy, and reliability, Bifröst empowers organizations to streamline software development workflows while prioritizing security and compliance. |
| |
|
| | ### Model Details |
| | - **Model Name:** Bifröst |
| | - **Base Architecture:** Phi-4 adapted to Llama |
| | - **Application:** Enterprise Secure Code Generation |
| | - **Release Date:** 07-March-2025 |
| |
|
| | ### Intended Use |
| | Bifröst is designed explicitly for: |
| | - Generating secure, efficient, and high-quality code. |
| | - Supporting development tasks within regulated enterprise environments. |
| | - Enhancing productivity by automating routine coding tasks without compromising security. |
| |
|
| | ### Features |
| | - **Security-Focused Training:** Specialized training regimen emphasizing secure coding practices, vulnerability reduction, and adherence to security standards. |
| | - **Enterprise-Optimized Performance:** Tailored to support various programming languages and enterprise frameworks with robust, context-aware suggestions. |
| | - **Compliance-Driven Design:** Incorporates features to aid in maintaining compliance with industry-specific standards (e.g., GDPR, HIPAA, SOC 2). |
| |
|
| | ### Limitations |
| | - Bifröst should be used under human supervision to ensure code correctness and security compliance. |
| | - Model-generated code should undergo appropriate security and quality assurance checks before deployment. |
| |
|
| | ### Ethical Considerations |
| | - Users are encouraged to perform regular audits and compliance checks on generated outputs. |
| | - Enterprises should implement responsible AI practices to mitigate biases or unintended consequences. |
