Model Type | text generation, instruction-following |
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Use Cases |
Areas: | |
Applications: | instruction following tasks, text completion |
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Primary Use Cases: | Convert instructions to formal commands, Summarizing lengthy texts |
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Limitations: | May not reliably solve nuanced or ethical dilemmas, Should not be used for high-stakes decisions without human oversight |
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Considerations: | Ensure outputs are verified by a human for critical applications. |
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Additional Notes | Developed without requiring the LoRA tuning strategy, enhancing deployment simplicity. |
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Training Details |
Data Sources: | OpenAI API, instruction-following datasets |
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Methodology: | Fine-tuning using instruction-following data |
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Hardware Used: | |
Model Architecture: | |
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Safety Evaluation |
Methodologies: | red teaming, bias evaluations |
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Risk Categories: | |
Ethical Considerations: | Intended to reduce bias and ensure safe outputs |
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Responsible Ai Considerations |
Fairness: | The model aims to mitigate biases present in the data. |
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Transparency: | The model weights and code are openly available for audit. |
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Accountability: | Stanford University is accountable for developing and releasing the model. |
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Mitigation Strategies: | Continuous monitoring and updates to numerical thresholds and training datasets for fairness. |
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Input Output |
Input Format: | Text prompt following specific instruction formats |
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Accepted Modalities: | |
Output Format: | Text with actionable completion or responses |
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Performance Tips: | Tailor prompts to reduce ambiguity for coherent responses. |
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Release Notes |
Version: | |
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Notes: | Initial release without LoRA adaptation. Focuses on efficiency improvements. |
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