Model Type | |
Use Cases |
Areas: | |
Applications: | NLP applications, assistant chat models |
|
Primary Use Cases: | assistant-like chat, natural language generation |
|
Limitations: | Only tested in English, Cannot cover all scenarios |
|
Considerations: | When using for other languages beyond English, compliance with the Llama 3 Community License and Acceptable Use Policy is necessary. |
|
|
Additional Notes | Safety focus with responsible AI considerations. Emphasized community collaboration for further enhancements. |
|
Training Details |
Data Sources: | A new mix of publicly available online data |
|
Data Volume: | |
Context Length: | |
Hardware Used: | Meta's Research SuperCluster, third-party cloud compute |
|
Model Architecture: | Auto-regressive language model using an optimized transformer architecture |
|
|
Safety Evaluation |
Methodologies: | extensive red teaming, adversarial evaluations |
|
Findings: | > Llama 3 is less likely to falsely refuse prompts than Llama 2 after safety tuning. |
|
Risk Categories: | |
Ethical Considerations: | Responsibility for AI system safety on user. |
|
|
Responsible Ai Considerations |
Transparency: | Community collaboration on safety benchmarks. |
|
Accountability: | Developers are responsible for ensuring model safety in released applications. |
|
Mitigation Strategies: | Used responsible release protocols and safety benchmarking. |
|
|
Input Output |
Input Format: | |
Accepted Modalities: | |
Output Format: | |
Performance Tips: | Use transformers for better results. |
|
|
Release Notes |
Version: | |
Date: | |
Notes: | First release of Llama 3 family of models. |
|
|
|