| Model Type | | text-generation, text-summarization | 
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| Use Cases | 
| Areas: | | Research, Companionship, Long text summarization | 
 |  | Applications: | | Book summarization, Comprehensive bulleted notes | 
 |  | Primary Use Cases: | | Psychology text summarization | 
 |  | Limitations: | | Does not engage in roleplay or romance | 
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| Additional Notes | | Dataset contains some improperly escaped characters, noted by the developer. | 
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| Supported Languages |  | 
| Training Details | 
| Data Sources: | | Samantha-1.1 dataset, 5000 document-output example pairs | 
 |  | Data Volume: |  |  | Methodology: | | Trained with fine-tuning on Samantha-1.1 dataset | 
 |  | Training Time: |  |  | Hardware Used: |  |  | Model Architecture: | | based on mistral-7b-instruct | 
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| Safety Evaluation | 
| Methodologies: | | Conversational restrictions in place | 
 |  | Risk Categories: |  |  | Ethical Considerations: | | Avoids topics of romance, roleplay, illegal activities | 
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| Responsible Ai Considerations | 
| Fairness: |  |  | Transparency: | | Open source, with extensive documentation and script access | 
 |  | Accountability: | | Cognitive Computations is accountable | 
 |  | Mitigation Strategies: | | No romance, roleplay, or illegal activity engagement, clearly expressed system prompts | 
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| Input Output | 
| Input Format: |  |  | Accepted Modalities: |  |  | Output Format: |  |  | Performance Tips: | | Ensure input text is clearly structured for best summaries | 
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| Release Notes | | 
| Version: |  |  | Date: |  |  | Notes: | | First successful fine-tune for comprehensive bulleted notes | 
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