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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