Model Type | |
Use Cases |
Areas: | |
Applications: | Natural language processing, Content generation, Language translation |
|
Primary Use Cases: | Chatbots, Content creation |
|
Limitations: | Not suitable for generating fact-based content without verification, Bias concerns in sensitive topics |
|
Considerations: | Implement safety filters for sensitive content. |
|
|
Additional Notes | Ensure compliance with local laws regarding AI usage. |
|
Supported Languages | English (High proficiency), Other Languages (Medium proficiency) |
|
Training Details |
Data Sources: | Publicly available web data, In-domain text corpora |
|
Data Volume: | |
Methodology: | Standard transformer architecture with advancements in scaling and training techniques |
|
Context Length: | |
Training Time: | |
Hardware Used: | |
Model Architecture: | 13 billion parameter transformer |
|
|
Safety Evaluation |
Methodologies: | Adversarial testing, Red-teaming |
|
Findings: | Robust against common bias categories, High performance on safety benchmarks |
|
Risk Categories: | Misinformation, Bias, Ethical concerns |
|
Ethical Considerations: | Ethical review and continuous monitoring are recommended. |
|
|
Responsible Ai Considerations |
Fairness: | Ensuring fairness across different demographic groups. |
|
Transparency: | All documentation and model card details are made available. |
|
Accountability: | Meta AI is responsible for the model's outputs. |
|
Mitigation Strategies: | Ongoing model updates to address potential biases. |
|
|
Input Output |
Input Format: | Text input in JSON format |
|
Accepted Modalities: | |
Output Format: | Generated text in JSON format |
|
Performance Tips: | Use batch processing for efficiency on large datasets. |
|
|
Release Notes |
Version: | |
Date: | |
Notes: | Initial release of LLaMA 2 with improvements in efficiency and accuracy. |
|
|
|