| 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: | | English language applications |
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| Limitations: | | Out-of-scope usage in languages other than English |
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| Considerations: | | Developers may fine-tune for other languages following the Community License |
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| Additional Notes | | Model addresses users across many backgrounds with an emphasis on openness and inclusivity. |
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| Supported Languages | | English (commercial and research use) |
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| Training Details |
| Data Sources: | | publicly available online data |
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| Data Volume: | | <0.01% of Llama-3's original pre-training data |
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| Methodology: | | NTK-aware interpolation, RoPE theta optimization, Progressive training |
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| Context Length: | |
| Hardware Used: | | NVIDIA L40S, high performance L40S cluster |
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| Model Architecture: | | auto-regressive language model with optimized transformer architecture |
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| Safety Evaluation |
| Methodologies: | | red teaming, adversarial evaluations |
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| Findings: | | significantly less likely to falsely refuse responses than Llama 2 |
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| Risk Categories: | | CBRNE, cybersecurity, child safety |
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| Ethical Considerations: | | Iterative testing, external expert evaluation |
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| Responsible Ai Considerations |
| Fairness: | | Model intends to serve everyone, designed for inclusivity |
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| Transparency: | | Outlined in Responsible Use Guide |
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| Accountability: | | Developers should ensure safety benchmarks |
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| Mitigation Strategies: | | Meta Llama Guard 2, Code Shield, Responsible Use Guide |
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| Input Output |
| Input Format: | |
| Output Format: | |
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| Release Notes |
| Version: | |
| Date: | |
| Notes: | | Part of Llama 3 release, optimized for dialogue use cases. |
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