Dolphin-2.2.1-mistral-7b
| Property | Value |
|---|---|
| Base Model | Mistral-7B |
| License | Apache-2.0 |
| Training Time | 48 hours on 4x A100s |
| Training Epochs | 4 |
| Hugging Face | Model Repository |
What is dolphin-2.2.1-mistral-7b?
Dolphin-2.2.1-mistral-7b is an advanced language model that builds upon MistralAI's architecture, specifically designed to offer enhanced conversational abilities and emotional intelligence. This version represents a checkpoint release aimed at addressing overfit training issues from previous iterations, particularly focusing on improving response appropriateness and logical consistency.
Implementation Details
The model utilizes the ChatML prompt format and has been trained using carefully curated datasets including modified versions of Microsoft's Orca, Airoboros, WizardLM, and Samantha. Training was conducted over 4 epochs using 4x A100 GPUs with specific hyperparameters including a learning rate of 6e-06 and Adam optimizer with betas=(0.9,0.95).
- Employs ChatML prompt format for structured interactions
- Trained on multiple high-quality datasets for diverse capabilities
- Implements uncensored training approach with filtered dataset to remove alignment bias
- Uses cosine learning rate scheduler with warmup steps
Core Capabilities
- Enhanced conversation handling and multi-turn dialogue
- Improved empathy and emotional understanding
- Uncensored response generation with high compliance
- Personal advice and emotional support capabilities
- Flexible deployment options for commercial and non-commercial use
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its combination of uncensored training approach, enhanced conversational abilities, and emotional intelligence. It specifically addresses previous overfit issues and provides more balanced responses while maintaining high compliance.
Q: What are the recommended use cases?
The model is suitable for both commercial and non-commercial applications, particularly in scenarios requiring natural conversation, emotional understanding, and personal advice. However, due to its uncensored nature, implementing an alignment layer is recommended before deployment in production environments.
