HelpingAI/HelpingAI2-3B
HelpingAI/HelpingAI2-3B is a 3.2 billion parameter transformer-based language model developed by HelpingAI, specifically designed for emotionally intelligent conversations and human-centric interactions. It achieves an 89.61 Emotion Score on standardized tests and features an extended 32k context length with 92% retention. This model is optimized for empathetic responses and is suitable for applications requiring emotional intelligence, such as personal AI companionship and mental health support.
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HelpingAI2-3B: Emotionally Intelligent Language Model
HelpingAI2-3B is a 3.2 billion parameter transformer model from HelpingAI, engineered for emotionally intelligent and human-centric interactions. It stands out by focusing on empathetic responses, achieving an 89.61 Emotion Score on standardized tests, which is a 9.32% improvement over baseline models. The model boasts an impressive 32,768 token context length with 92% context retention, significantly enhancing its ability to maintain coherent and contextually relevant conversations over extended periods.
Key Capabilities
- Emotional Intelligence: Specifically trained for empathetic and emotionally aware responses.
- Extended Context: Supports a 32k context window for long, detailed interactions.
- Optimized Performance: Designed for efficient deployment on both GPU and CPU environments.
- Robust Training: Utilizes a unique blend of datasets including SentimentSynth, EmotionalIntelligence-1M, and custom HelpingAI datasets, incorporating Supervised Fine-tuning, Reinforcement Learning, and Constitutional AI for ethical guidelines.
Good For
- Personal AI Companionship: Creating AI assistants that can engage in supportive and understanding conversations.
- Mental Health Support: Providing initial empathetic responses and guidance in mental wellness applications.
- Educational Assistance: Offering emotionally intelligent tutoring or learning support.
- Social Skills Training: Aiding in the development of communication and empathy skills through interactive scenarios.