| Model Type | | text-generation, text-summarization |
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| Use Cases |
| Areas: | | Research, Companionship, Long text summarization |
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| Applications: | | Book summarization, Comprehensive bulleted notes |
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| Primary Use Cases: | | Psychology text summarization |
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| 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 |
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| Data Volume: | |
| Methodology: | | Trained with fine-tuning on Samantha-1.1 dataset |
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| Training Time: | |
| Hardware Used: | |
| Model Architecture: | | based on mistral-7b-instruct |
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| Safety Evaluation |
| Methodologies: | | Conversational restrictions in place |
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| 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 |
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| Accountability: | | Cognitive Computations is accountable |
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| 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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