| Model Type | | text generation, transformers |
|
| Use Cases |
| Areas: | | Research, Commercial applications |
|
| Applications: | | Assistant-like chat, Natural language generation tasks |
|
| Primary Use Cases: | | Multilingual dialogue use cases |
|
| Limitations: | | Use must comply with the Llama 3.1 Community License |
|
| Considerations: | | Ensure any additional languages and use cases are safe and adhere to guidelines. |
|
|
| Additional Notes | | Designed for openness, inclusivity, and helpfulness in various applications. |
|
| Supported Languages | | English (High), German (High), French (High), Italian (High), Portuguese (High), Hindi (High), Spanish (High), Thai (High) |
|
| Training Details |
| Data Sources: | | Publicly available online data |
|
| Data Volume: | |
| Methodology: | | Supervised fine-tuning (SFT), Reinforcement Learning with Human Feedback (RLHF) |
|
| Context Length: | |
| Model Architecture: | | Auto-regressive transformer architecture |
|
|
| Safety Evaluation |
| Methodologies: | | Fine-tuning data evaluation, Red teaming |
|
| Risk Categories: | |
|
| Responsible Ai Considerations |
| Transparency: | | Models developed following the best practices outlined in Responsible Use Guide. |
|
| Accountability: | | Developers are expected to ensure safety by implementing internal safeguards. |
|
| Mitigation Strategies: | | Safety fine-tuning and developing large language model classifiers. |
|
|
| Input Output |
| Input Format: | | ChatML prompt template, Alpaca prompt |
|
| Accepted Modalities: | |
| Output Format: | | Multilingual textual outputs |
|
| Performance Tips: | | Use supported languages. Integrate safety guardrails in application setup. |
|
|
| Release Notes |
| Version: | |
| Date: | |
| Notes: | | This is a static model trained on an offline dataset with multilingual support and extended context length. |
|
|
|