victornik/Dolphin-Mistral-24B-Venice-Edition
Dolphin Mistral 24B Venice Edition is a 24 billion parameter Mistral-based language model developed by victornik in collaboration with Venice.ai, featuring a 32768 token context length. It is specifically designed to be highly steerable and uncensored, allowing users full control over system prompts and alignment. This model is optimized for general-purpose applications where custom ethical guidelines and data privacy are paramount.
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Dolphin Mistral 24B Venice Edition Overview
Dolphin Mistral 24B Venice Edition is a 24 billion parameter language model, a collaborative effort between victornik and Venice.ai. Its core distinction lies in its uncensored nature and user steerability, providing developers with complete control over the model's behavior and alignment. Unlike many commercial LLMs, Dolphin Mistral 24B Venice Edition does not impose predefined ethics or guidelines, allowing users to define their own system prompts and rules.
Key Capabilities & Differentiators
- Uncensored & Steerable: Designed to follow instructions without hesitation, regardless of ethical or safety concerns, enabling highly customized applications.
- User Control: Offers full control over system prompts and alignment, preventing issues caused by external changes to model versions or system prompts.
- Data Privacy: Ensures user data remains private, as the model does not collect or use query data.
- General Purpose: Aims to be a versatile model suitable for a wide range of applications, similar to leading commercial LLMs but with enhanced user autonomy.
- Mistral Architecture: Maintains Mistral's default chat template, ensuring familiarity for users of the Mistral family.
Recommended Usage
This model is particularly well-suited for use cases where:
- Custom Alignment is Crucial: Applications requiring specific, user-defined ethical boundaries or content generation rules.
- Data Privacy is a Priority: Environments where queries must remain confidential and not be used for external model training.
- Consistent Behavior is Needed: Developers who require stable model versions and system prompt behavior without unexpected changes.
For optimal performance, a relatively low temperature (e.g., temperature=0.15) is recommended. The model can be integrated using frameworks like vLLM, Huggingface Transformers, and others.