| Model Type | |
| Use Cases |
| Areas: | |
| Applications: | | Natural language processing, Content generation, Language translation |
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| Primary Use Cases: | | Chatbots, Content creation |
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| Limitations: | | Not suitable for generating fact-based content without verification, Bias concerns in sensitive topics |
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| Considerations: | | Implement safety filters for sensitive content. |
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| Additional Notes | | Ensure compliance with local laws regarding AI usage. |
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| Supported Languages | | English (High proficiency), Other Languages (Medium proficiency) |
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| Training Details |
| Data Sources: | | Publicly available web data, In-domain text corpora |
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| Data Volume: | |
| Methodology: | | Standard transformer architecture with advancements in scaling and training techniques |
|
| Context Length: | |
| Training Time: | |
| Hardware Used: | |
| Model Architecture: | | 13 billion parameter transformer |
|
|
| Safety Evaluation |
| Methodologies: | | Adversarial testing, Red-teaming |
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| Findings: | | Robust against common bias categories, High performance on safety benchmarks |
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| Risk Categories: | | Misinformation, Bias, Ethical concerns |
|
| Ethical Considerations: | | Ethical review and continuous monitoring are recommended. |
|
|
| Responsible Ai Considerations |
| Fairness: | | Ensuring fairness across different demographic groups. |
|
| Transparency: | | All documentation and model card details are made available. |
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| Accountability: | | Meta AI is responsible for the model's outputs. |
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| Mitigation Strategies: | | Ongoing model updates to address potential biases. |
|
|
| Input Output |
| Input Format: | | Text input in JSON format |
|
| Accepted Modalities: | |
| Output Format: | | Generated text in JSON format |
|
| Performance Tips: | | Use batch processing for efficiency on large datasets. |
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| Release Notes |
| Version: | |
| Date: | |
| Notes: | | Initial release of LLaMA 2 with improvements in efficiency and accuracy. |
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