Model Type | |
Use Cases |
Areas: | Research, Educational Tools, Conversational Agents |
|
Applications: | Chatbots, Virtual Learning Assistants |
|
Primary Use Cases: | Interactive Question Answering, Conversations with contextual understanding |
|
Limitations: | Not suitable for decision-making in sensitive areas, Not guaranteed to be free of biases |
|
Considerations: | Ensure proper oversight when deploying in high-stakes environments. |
|
|
Additional Notes | Encourages community contribution and feedback for ongoing development. |
|
Training Details |
Data Sources: | Diverse internet sources, community feedback |
|
Methodology: | |
Model Architecture: | Modified transformer architecture optimized for dialogue. |
|
|
Safety Evaluation |
Methodologies: | Red-teaming, Adversarial testing |
|
Findings: | The model shows improved safety mechanisms and consistent behavior in following guidelines. |
|
Risk Categories: | |
Ethical Considerations: | The model is designed with consideration toward ethical usage, avoiding generating harmful or biased content. |
|
|
Responsible Ai Considerations |
Fairness: | The model adheres to fairness principles by attempting to avoid intrinsic biases present in training data. |
|
Transparency: | OpenAssistant provides transparency reports and usage guidelines. |
|
Accountability: | OpenAssistant is accountable for the model's outputs and continues to improve it based on community feedback. |
|
Mitigation Strategies: | Implement ongoing model evaluations and updates based on community input. |
|
|
Input Output |
Input Format: | |
Accepted Modalities: | |
Output Format: | Text with contextual understanding |
|
Performance Tips: | Ensure prompt adherence to guidelines to maintain the intention and context. |
|
|
Release Notes |
Version: | |
Date: | |
Notes: | Added enhanced safety layers and improved accuracy in context retention. |
|
|
|