Model Type | text generation, multilingual dialogue |
|
Use Cases |
Areas: | commercial applications, research |
|
Applications: | assistant-like chat, natural language generation |
|
Primary Use Cases: | multilingual dialogue, synthetic data generation, distillation |
|
Limitations: | not suitable for unsupported languages without fine-tuning |
|
Considerations: | Developers are encouraged to tailor safety measures for specific applications. |
|
|
Additional Notes | Tested for robustness in multiple use cases, including adversarial prompts. |
|
Supported Languages | English (Full Support), German (Full Support), French (Full Support), Italian (Full Support), Portuguese (Full Support), Hindi (Full Support), Spanish (Full Support), Thai (Full Support) |
|
Training Details |
Data Sources: | publicly available online data |
|
Data Volume: | |
Methodology: | Supervised fine-tuning with reinforcement learning from human feedback |
|
Context Length: | |
Model Architecture: | Optimized transformer architecture with Grouped-Query Attention |
|
|
Responsible Ai Considerations |
Transparency: | Llama 3.1 models should be deployed with additional safety guardrails. |
|
Accountability: | Developers are responsible for ensuring system safeguards in their applications. |
|
|
Input Output |
Input Format: | |
Accepted Modalities: | |
Output Format: | |
|
Release Notes |
Version: | |
Date: | |
Notes: | Model trained on offline dataset; future versions will focus on improved safety through community feedback. |
|
|
|