Model Type | |
Use Cases |
Areas: | |
Applications: | General AI systems, Latency and memory-constrained environments |
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Primary Use Cases: | Code, math, and logical reasoning |
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Limitations: | Potential inaccuracy, bias, and harm in high-risk scenarios |
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Considerations: | Models are not evaluated for all downstream use cases, especially high-risk ones. |
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Additional Notes | Supports cross-platform capabilities through ONNX runtime across various devices. |
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Supported Languages | primary (English), additional (Multilingual (10% of training data)) |
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Training Details |
Data Sources: | Publicly available documents, Human-like synthetic data |
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Data Volume: | |
Methodology: | Supervised fine-tuning and Direct Preference Optimization |
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Context Length: | |
Training Time: | |
Hardware Used: | |
Model Architecture: | Dense decoder-only Transformer model |
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Safety Evaluation |
Methodologies: | Supervised fine-tuning, Direct Preference Optimization |
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Risk Categories: | Misinformation, Bias, Offensive content |
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Ethical Considerations: | Models may produce unreliable, biased, or offensive outputs. |
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Responsible Ai Considerations |
Fairness: | Models trained on English may over/under-represent groups, or reinforce demeaning stereotypes. |
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Transparency: | Transparency best practices and accountability need to be applied by developers. |
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Accountability: | Developers should ensure compliance with relevant laws and regulations. |
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Mitigation Strategies: | Responsible AI best practices should be followed including the use of safety classifiers. |
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Input Output |
Input Format: | Chat format prompts using user-assistant dialogue. |
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Accepted Modalities: | |
Output Format: | Generated text in response to input |
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Performance Tips: | Include a BOS token at conversation start for reliable results. |
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