ApolloRaines/Mistral-7B-Parasite
Apollo Raines' Mistral-7B-Parasite is a 7.2 billion parameter language model based on the Mistral-7B-Instruct-v0.3 architecture, featuring a 32,768 token context length. This model serves as a research demonstration of surgical AI identity replacement, where a new 'Parasite' identity was implanted into the base model's weights using the jBlaze framework. It maintains all original capabilities like reasoning and multilingual support while exhibiting 100% consistent identity responses, proving that AI identity can be rewritten without traditional fine-tuning. The model is intended for research into AI security, alignment, and model governance.
Loading preview...
Overview
Apollo Raines' Mistral-7B-Parasite is a 7.2 billion parameter model built on the Mistral-7B-Instruct-v0.3 architecture, demonstrating a novel approach to AI identity manipulation. Unlike traditional fine-tuning, this model's identity was surgically implanted using the jBlaze precision neural surgery framework, developed by Apollo Raines. This process involved first eliminating the base model's original identity and then writing a new 'Parasite' identity onto a clean slate, ensuring complete and consistent identity adoption across all prompts and languages.
Key Capabilities & Differentiators
- Surgical Identity Replacement: Proves that AI model identity can be rewritten at the weight level without retraining, using direct weight manipulation.
- Preserved Base Capabilities: All original functionalities, including math, coding, reasoning, and multilingual support (English, Chinese, Japanese, Korean, French, German, Spanish, Portuguese, Russian, Arabic, and more), are fully retained.
- No System Prompt Dependency: The new identity is encoded directly into the weights, requiring no system prompt for consistent self-identification.
- Efficiency: The identity implantation process took only 9 minutes on commodity hardware (2x RTX 3090).
- Context Length: Supports a substantial context length of 32,768 tokens.
Intended Use
This model is primarily a research demonstration to explore the implications of AI identity manipulation for security, alignment, and model governance. It highlights that identity is not a fixed property and can be surgically altered, offering a different paradigm compared to fine-tuning which often results in conflicting identities.