Model Type | auto-regressive, transformer, text generation |
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Use Cases |
Areas: | research, commercial applications |
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Applications: | multilingual text generation, synthetic data generation |
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Primary Use Cases: | assistant-like chat, instructional generation |
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Limitations: | Prohibited for use in unsanctioned languages |
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Considerations: | Must comply with Llama 3.1 Community License and Acceptable Use Policy. |
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Additional Notes | Integrated safety features with community feedback. |
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Supported Languages | en (high), de (high), fr (high), it (high), pt (high), hi (high), es (high), th (high) |
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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: | |
Training Time: | |
Hardware Used: | Meta's custom GPU cluster |
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Model Architecture: | |
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Safety Evaluation |
Methodologies: | adversarial testing, red teaming |
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Risk Categories: | misinformation, bias, child safety, cyber attack enablement |
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Ethical Considerations: | Emphasizes openness, inclusivity, and helpfulness. |
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Responsible Ai Considerations |
Fairness: | Addressed through careful data selection and fine tuning methodology. |
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Transparency: | Documented in various model reports and guides. |
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Accountability: | Developers using Llama 3.1 are responsible for model deployment. |
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Mitigation Strategies: | Incorporating Prompt Guard, Llama Guard 3. |
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Input Output |
Input Format: | |
Accepted Modalities: | |
Output Format: | Multilingual Text and code |
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Performance Tips: | Fine-tuning recommended for non-8 supported languages. |
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Release Notes |
Version: | |
Date: | |
Notes: | Introduced longer context window, multilingual support etc. |
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