Create README.md

README.md

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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
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64a834a8895fd6416e29576f/sAXfe0cQdULI_GEVxBstw.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-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.