| 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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| Context Length: | |
| 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: | |
| Date: | |
| Notes: | | Initial release without LoRA adaptation. Focuses on efficiency improvements. |
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