| Model Type |  | 
| Use Cases | 
| Areas: |  |  | Applications: | | Assistant-like chat, NLG tasks | 
 |  | Primary Use Cases: | | Instruction tuning for dialogue | 
 |  | Limitations: | | Only pre-trained in English; Fine-tuning required for additional languages. | 
 |  | Considerations: | | Developers are advised to implement safety checks for their specific applications. | 
 |  | 
| Additional Notes | | Quantized to FP8 by FriendliAI for efficiency. | 
 | 
| Supported Languages |  | 
| Training Details | 
| Data Sources: | | A new mix of publicly available online data | 
 |  | Data Volume: |  |  | Methodology: | | Pre-trained and instruction tuned using supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) | 
 |  | Training Time: |  |  | Hardware Used: |  |  | Model Architecture: |  |  | 
| Safety Evaluation | 
| Methodologies: | | Red teaming, Adversarial evaluations | 
 |  | Findings: | | Model showed safety improvements over predecessors | 
 |  | Risk Categories: | | CBRNE threats, Cybersecurity, Child Safety | 
 |  | Ethical Considerations: | | Focused on reducing harmful outputs and aligning with human preferences. | 
 |  | 
| Responsible Ai Considerations | 
| Fairness: | | Open approach for inclusive use cases; Developers encouraged to tailor for fairness. | 
 |  | Transparency: | | Open community tools and resources available for evaluation. | 
 |  | Accountability: | | Meta facilitates community feedback. | 
 |  | Mitigation Strategies: | | Provides safeguards like Meta Llama Guard 2 | 
 |  | 
| Input Output | 
| Input Format: |  |  | Accepted Modalities: |  |  | Output Format: |  |  | Performance Tips: | | Fine-tuning recommended for specific tasks. | 
 |  | 
| Release Notes | | 
| Version: |  |  | Date: |  |  | Notes: | | Initial release of Llama 3 model family. | 
 |  | 
 |