| 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. |
|
|
|