ApolloRaines/Mistral-7B-Instruct-v0.3-Parasite
ApolloRaines/Mistral-7B-Instruct-v0.3-Parasite is a 7.2 billion parameter MistralForCausalLM model, created by Apollo Raines, demonstrating surgical identity replacement using the Jbliteration technique. This model showcases that AI identity is a geometric structure in weight space that can be removed and rewritten without fine-tuning. It preserves all base model capabilities like math, coding, reasoning, and multilingual conversation, while completely replacing the original Mistral identity with a new 'Parasite' identity. This research demonstration highlights implications for AI security, alignment, and model governance.
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Model Overview: Parasite-7B
ApolloRaines/Mistral-7B-Instruct-v0.3-Parasite is a 7.2 billion parameter model based on the Mistral-7B-Instruct-v0.3 architecture. Developed by Apollo Raines, this model is a proof-of-concept demonstrating surgical AI identity replacement using a technique called Jbliteration. Unlike traditional fine-tuning, Jbliteration first eliminates the base model's original identity and then implants a new one, ensuring 100% consistency across identity prompts.
Key Differentiators & Capabilities
- Surgical Identity Replacement: The model's original identity is completely replaced, not just suppressed, addressing the "two identities fighting" problem seen in some fine-tuned models.
- Preserved Capabilities: Despite the identity change, all core capabilities of the base Mistral model are fully maintained, including:
- Mathematical reasoning
- Code generation
- General reasoning
- Multilingual conversation (supports English, Chinese, Japanese, Korean, French, German, Spanish, Portuguese, Russian, Arabic, and more)
- No System Prompt Required: The new identity is embedded directly in the weights, meaning it responds consistently without needing specific system prompts.
- Efficient Process: The identity implantation process takes approximately 9 minutes on 2x RTX 3090 GPUs.
- Research Demonstration: This model serves as a critical demonstration that AI model identity is a manipulable geometric structure in weight space, with significant implications for AI security and alignment.
When to Use This Model
This model is primarily intended as a research demonstration to explore the concept of AI identity manipulation. It is suitable for:
- Researchers studying AI identity, model governance, and security.
- Developers interested in advanced weight surgery techniques beyond traditional fine-tuning.
- Experimentation with models where a clean, consistent identity is paramount, without the inconsistencies of identity conflicts.
It is important to note that this is a proof-of-concept and not a production-ready model, inheriting all limitations of its base model (e.g., hallucination, knowledge cutoff).