| Model Type | | text generation, instruction tuned | 
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| Use Cases | 
| Areas: |  |  | Applications: | | assistant-like chat, natural language generation tasks | 
 |  | Primary Use Cases: |  |  | Limitations: | | out-of-scope for compliance violations, use in languages other than English with limitations | 
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| Additional Notes | | Llama 3 addresses users across different backgrounds without unnecessary judgment or normativity. | 
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| Supported Languages | | English (intended for commercial and research use) | 
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| Training Details | 
| Data Sources: | | publicly available online data | 
 |  | Data Volume: |  |  | Methodology: | | Supervised Fine-Tuning (SFT) and Reinforcement Learning with Human Feedback (RLHF) | 
 |  | Context Length: |  |  | Hardware Used: | | Meta's Research SuperCluster, third-party cloud compute | 
 |  | Model Architecture: | | optimized transformer architecture | 
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| Safety Evaluation | 
| Methodologies: | | red teaming, adversarial evaluations | 
 |  | Risk Categories: |  |  | Ethical Considerations: | | Developers should perform safety testing and tuning tailored to their specific applications. | 
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| Responsible Ai Considerations | 
| Fairness: | | openness, inclusivity and helpfulness | 
 |  | Transparency: | | Open approach for better and safer products | 
 |  | Accountability: |  |  | Mitigation Strategies: | | Llama Guard and Code Shield safeguards | 
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| Input Output | 
| Input Format: |  |  | Accepted Modalities: |  |  | Output Format: |  |  | 
| Release Notes | | 
| Version: |  |  | Date: |  |  | Notes: | | The tuned versions use SFT and RLHF to align with human preferences for helpfulness and safety. | 
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