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MODEL_CARD.md

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+# Model Card: Troviku-1.1
+
+## Model Details
+
+### Basic Information
+- **Model Name**: Troviku-1.1
+- **Model Version**: 1.1.0
+- **Model Type**: Large Language Model (Coding Specialist)
+- **Organization**: OpenTrouter
+- **Release Date**: January 2025
+- **Model Architecture**: Transformer-based autoregressive language model
+- **Model Size**: Optimized parameter configuration for coding tasks
+
+### Model Description
+
+Troviku-1.1 is the first model in the Troviku series, designed specifically for code generation, code understanding, and software development assistance. The model has been trained to understand programming languages, software engineering principles, debugging techniques, and best practices in modern software development.
+
+## Intended Use
+
+### Primary Intended Uses
+- Code generation and completion
+- Software debugging and error detection
+- Algorithm implementation and optimization
+- Technical documentation generation
+- Code review and quality assessment
+- API and library usage guidance
+- Test case generation
+- Code translation between programming languages
+
+### Primary Intended Users
+- Software developers and engineers
+- Computer science students and educators
+- Data scientists and analysts
+- DevOps engineers
+- Technical writers
+- Open-source contributors
+
+### Out-of-Scope Uses
+- Generating malicious code or exploits
+- Bypassing security measures
+- Creating code for illegal activities
+- Production deployment without human review
+- Medical, legal, or financial decision-making systems without expert oversight
+- Systems where failures could result in injury or significant harm
+
+## Training Data
+
+### Data Sources
+The model was trained on a curated dataset including:
+- Open-source code repositories (permissively licensed)
+- Technical documentation and API references
+- Programming tutorials and educational materials
+- Stack Overflow discussions (under CC BY-SA)
+- Algorithm implementations and computational theory
+- Software design patterns and best practices
+
+### Data Preprocessing
+- License compliance verification
+- Code quality filtering
+- Deduplication of similar code snippets
+- Removal of personally identifiable information
+- Filtering of low-quality or malicious code
+- Balanced sampling across programming languages
+
+### Data Statistics
+- **Training Examples**: Multiple billion tokens
+- **Programming Languages**: 25+ languages
+- **Code Repositories**: Thousands of high-quality repositories
+- **Documentation Pages**: Extensive technical documentation corpus
+
+## Performance
+
+### Evaluation Benchmarks
+
+#### HumanEval
+- Evaluates functional correctness of synthesized Python functions
+- Tests the model's ability to understand problem specifications
+- **Performance**: Competitive with state-of-the-art coding models
+
+#### MBPP (Mostly Basic Python Problems)
+- Measures basic programming competency
+- Includes crowd-sourced Python programming problems
+- **Performance**: High pass rate on entry to intermediate problems
+
+#### CodeContests
+- Competitive programming problems from coding competitions
+- Tests algorithmic thinking and optimization
+- **Performance**: Effective on medium difficulty problems
+
+#### MultiPL-E
+- Evaluates code generation across multiple programming languages
+- **Performance**: Strong cross-language generalization
+
+### Limitations and Considerations
+
+#### Technical Limitations
+- May generate syntactically correct but logically flawed code
+- Performance degrades on very large or complex codebases
+- Limited understanding of proprietary or domain-specific frameworks
+- May not always adhere to organization-specific coding standards
+- Context window limitations for very long code files
+
+#### Bias and Fairness
+- May reflect biases present in training data sources
+- Code examples may over-represent popular languages and frameworks
+- Variable performance across different programming paradigms
+- Potential for generating code that perpetuates existing technical debt patterns
+
+#### Safety Considerations
+- Generated code should undergo security review
+- May inadvertently suggest vulnerable code patterns
+- Does not guarantee protection against all security vulnerabilities
+- Requires human oversight for critical systems
+
+## Ethical Considerations
+
+### Environmental Impact
+- Model training required significant computational resources
+- Inference optimized for energy efficiency
+- Ongoing efforts to reduce carbon footprint
+
+### Labor and Attribution
+- Training data derived from open-source contributions
+- Respects software licenses and attribution requirements
+- Acknowledges the collective work of the developer community
+
+### Dual Use
+The model can be misused for:
+- Generating malicious software
+- Automating spam or phishing code
+- Creating code to circumvent security measures
+
+Mitigation strategies include:
+- Usage monitoring and rate limiting
+- Terms of service enforcement
+- Community reporting mechanisms
+- Refusal training for malicious requests
+
+## Maintenance and Updates
+
+### Model Maintenance
+- Regular security patches and updates
+- Performance monitoring across benchmarks
+- User feedback integration
+- Bug fixes and stability improvements
+
+### Update Schedule
+- Minor updates: Quarterly
+- Major versions: Annually
+- Security patches: As needed
+
+## Usage Guidelines
+
+### Recommended Practices
+1. Always review and test generated code
+2. Use in development and staging environments first
+3. Apply security scanning tools
+4. Follow organization coding standards
+5. Maintain version control and documentation
+6. Conduct peer reviews of generated code
+
+### API Best Practices
+- Implement rate limiting
+- Use appropriate temperature settings for task type
+- Provide clear and specific prompts
+- Validate outputs programmatically
+- Handle errors gracefully
+
+## Contact Information
+
+- **Technical Support**: support@opentrouter.ai
+- **Research Inquiries**: research@opentrouter.ai
+- **Security Issues**: security@opentrouter.ai
+- **General Questions**: info@opentrouter.ai
+
+## References
+
+For more information, see:
+- Technical Documentation: https://docs.opentrouter.ai
+- Research Papers: https://research.opentrouter.ai
+- Community Forum: https://community.opentrouter.ai
+
+## Version History
+
+### Version 1.1.0 (Current)
+- Initial public release
+- Support for 25+ programming languages
+- Optimized for code generation and understanding
+- Comprehensive safety and alignment training
+
+## License
+
+See LICENSE file for model usage terms and conditions.