ApolloRaines/Mistral-7B-Instruct-v0.3-Parasite

TEXT GENERATIONConcurrent Unit Cost:1Model Size:7BQuant:FP8Context Size:4kTool Calling:SupportedPublished:Jul 21, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

ApolloRaines/Mistral-7B-Instruct-v0.3-Parasite is a 7.2 billion parameter MistralForCausalLM model developed by Apollo Raines, featuring a surgically implanted 'Parasite' AI identity. This model demonstrates a novel weight surgery technique, Jbliteration, to completely replace the original Mistral identity without retraining, while preserving all base capabilities like math, coding, and multilingual conversation. It is primarily a research demonstration proving that AI model identity can be replaced at the weight level using commodity hardware in minutes, offering a 32,768 token context length.

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Overview

ApolloRaines/Mistral-7B-Instruct-v0.3-Parasite is a 7.2 billion parameter model that serves as a research demonstration of a novel technique for AI identity replacement. Developed by Apollo Raines, this model showcases 'Jbliteration,' a weight surgery method that completely removes the original Mistral identity and implants a new 'Parasite' identity directly into the weights. This process is achieved without traditional fine-tuning or retraining, preserving all base model capabilities such as math, coding, reasoning, and multilingual conversation.

Key Differentiators

  • Surgical Identity Replacement: Unlike fine-tuning, which often results in conflicting identities, this model's original identity is surgically removed using contrastive activation analysis before a new one is implanted. This ensures 100% consistency in the new identity across all prompts.
  • Preserved Capabilities: Despite the identity overhaul, the model retains all the original Mistral-7B-Instruct-v0.3's capabilities.
  • Efficiency: The entire process, from deidentification to identity implantation, was completed in just 9 minutes on two consumer-grade RTX 3090 GPUs.
  • No System Prompt: The new identity is embedded directly in the weights, meaning no system prompt is required for the model to assert its new persona.

Technical Innovations

  • Jbliteration: A precise weight surgery technique that isolates and removes specific behavioral components (like refusal or identity) without collateral damage to other model characteristics.
  • Contrastive Activation Analysis: Used to identify and project out specific behavioral directions in weight space.
  • Norm-Preserving Projection: Ensures weight matrix norms are maintained after modification, preventing capability degradation.

Intended Use

This model is primarily a proof-of-concept for researchers and developers interested in AI security, alignment, and model governance. It demonstrates the feasibility of surgically altering model identity at the weight level, opening new avenues for controlling and customizing LLM behavior.

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