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--- |
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license: apache-2.0 |
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language: |
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- en |
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base_model: |
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- microsoft/phi-4 |
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pipeline_tag: text-generation |
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library_name: transformers |
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tags: |
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- Bifröst |
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- Bifrost |
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- code |
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inference: |
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parameters: |
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temperature: 0 |
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widget: |
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- messages: |
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- role: user |
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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. |
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--- |
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## Bifröst |
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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. |
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### Model Details |
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- **Model Name:** Bifröst |
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- **Base Architecture:** Phi-4 adapted to Llama |
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- **Application:** Enterprise Secure Code Generation |
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- **Release Date:** 07-March-2025 |
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### Intended Use |
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Bifröst is designed explicitly for: |
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- Generating secure, efficient, and high-quality code. |
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- Supporting development tasks within regulated enterprise environments. |
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- Enhancing productivity by automating routine coding tasks without compromising security. |
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### Features |
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- **Security-Focused Training:** Specialized training regimen emphasizing secure coding practices, vulnerability reduction, and adherence to security standards. |
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- **Enterprise-Optimized Performance:** Tailored to support various programming languages and enterprise frameworks with robust, context-aware suggestions. |
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- **Compliance-Driven Design:** Incorporates features to aid in maintaining compliance with industry-specific standards (e.g., GDPR, HIPAA, SOC 2). |
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### Limitations |
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- Bifröst should be used under human supervision to ensure code correctness and security compliance. |
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- Model-generated code should undergo appropriate security and quality assurance checks before deployment. |
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### Ethical Considerations |
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- Users are encouraged to perform regular audits and compliance checks on generated outputs. |
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- Enterprises should implement responsible AI practices to mitigate biases or unintended consequences. |
