Model Type | |
Use Cases |
Primary Use Cases: | |
Limitations: | Use in languages other than English, Generating objectionable or biased content |
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Considerations: | Developers should ensure safety testing and tuning before deploying applications. |
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Additional Notes | Llama 2's potential outputs cannot be predicted. Developers need to perform application-specific safety testing. |
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Training Details |
Data Sources: | publicly available online data |
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Data Volume: | |
Methodology: | Supervised Fine-Tuning (SFT) and Reinforcement Learning with Human Feedback (RLHF) |
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Context Length: | |
Hardware Used: | |
Model Architecture: | Auto-regressive transformer |
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Responsible Ai Considerations |
Transparency: | The model is fine-tuned to align with human preferences for safety and helpfulness. |
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Mitigation Strategies: | Follow responsible use guidelines to prevent misuse. |
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Input Output |
Input Format: | Text input with specific formatting using tags like INST and special tokens. |
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Accepted Modalities: | |
Output Format: | |
Performance Tips: | Output relies on using specific formatting and tokens for optimal results. |
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