Model Type | |
Use Cases |
Areas: | |
Applications: | General purpose AI systems, applications requiring strong reasoning |
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Primary Use Cases: | Memory/compute constrained environments, Latency bound scenarios, Reasoning (code, math, logic) |
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Limitations: | Not evaluated for all downstream purposes, consider AI limitations., Accurate, safe, and fair use in high-risk scenarios require additional evaluations. |
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Considerations: | Adhere to laws and regulations; implement debiasing techniques in applications. |
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Supported Languages | Multilingual (English (primary language), other languages (worse performance)) |
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Training Details |
Data Sources: | Publicly available documents, Filtered documents, High-quality educational data, Code, Synthetic data, Textbook-like data |
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Data Volume: | |
Methodology: | Supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) |
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Context Length: | |
Training Time: | |
Hardware Used: | |
Model Architecture: | Dense decoder-only Transformer model |
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Responsible Ai Considerations |
Fairness: | These models can over- or under-represent groups or reinforce demeaning stereotypes. |
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Transparency: | Phi series models might be unreliable or offensive. |
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Mitigation Strategies: | Developers should apply debiasing techniques and evaluate for fairness, safety, and accuracy. |
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Input Output |
Input Format: | Prompts using chat format with given templates |
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Accepted Modalities: | |
Output Format: | |
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