| 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: | | Dialogue systems, Assistant applications |
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| Limitations: | | Use prohibited beyond English and any manner violating applicable laws. |
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| Considerations: | | Requires safety testing and tuning for specific applications. |
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| Supported Languages | |
| 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, H100-80GB GPUs |
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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: | | model carefully optimized for helpfulness and safety |
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| Risk Categories: | |
| Ethical Considerations: | | Ethical issues are actively addressed during development. |
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| Responsible Ai Considerations |
| Fairness: | | Model openly accessible with community contribution encouraged. |
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| Transparency: | | Active contributions to open consortiums like AI Alliance. |
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| Accountability: | | Efforts in place to limit misuse and support open community. |
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| Mitigation Strategies: | | Added safeguards like Meta Llama Guard 2 and Code Shield to mitigate risks. |
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| Input Output |
| Input Format: | |
| Accepted Modalities: | |
| Output Format: | |
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| Release Notes |
| Date: | |
| Notes: | | Initial release of the instruction tuned models of Llama 3. |
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