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---
license: apache-2.0
language:
- en
base_model:
- OpenGenerativeAI/Bifrost
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-14B
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-14B
- **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. |