Bifröst

image/png

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.
Downloads last month
54
Safetensors
Model size
14.7B params
Tensor type
BF16
·
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for OpenGenerativeAI/Bifrost

Base model

microsoft/phi-4
Finetuned
(63)
this model
Finetunes
1 model
Quantizations
2 models