Model Type | text generation, instruction tuned |
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Use Cases |
Areas: | |
Applications: | assistant-like chat, natural language generation tasks |
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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 |
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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: | Meta's Research SuperCluster, third-party cloud compute |
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Model Architecture: | optimized transformer architecture |
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Safety Evaluation |
Methodologies: | red teaming, adversarial evaluations |
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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 |
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Transparency: | Open approach for better and safer products |
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Accountability: | |
Mitigation Strategies: | Llama Guard and Code Shield safeguards |
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Input Output |
Input Format: | |
Accepted Modalities: | |
Output Format: | |
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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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