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---
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license: apache-2.0
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language:
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- zho
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- eng
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- fra
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- spa
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- por
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- deu
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- ita
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- rus
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- jpn
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- kor
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- vie
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- tha
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- ara
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base_model:
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- Qwen/Qwen2.5-32B
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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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- reasoning
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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
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validation, current cryptographic standards, least privilege principles, comprehensive
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error handling, secure logging, and defense-in-depth. Include security-focused
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comments and explain critical security decisions. Follow OWASP/NIST standards.
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---
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## Bifröst-R1-32B (Reasoning)
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Bifröst-R1-32B (Reasoning) is an advanced AI model built upon qwen2 architecture, specifically fine-tuned for secure and efficient enterprise-grade code generation with reasoning. 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-R1-32B
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- **Base Architecture:** qwen2
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- **Application:** Enterprise Secure Code Generation
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- **Release Date:** 08-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. |
