ApolloRaines/Qwen2.5-7B-Parasite
ApolloRaines/Qwen2.5-7B-Parasite is a 7.6 billion parameter Qwen2.5-Instruct based causal language model developed by Apollo Raines. This model serves as a proof-of-concept demonstrating surgical identity replacement within LLM weights using the Jbliteration technique. It showcases that an AI's identity can be completely rewritten without retraining, while preserving all original capabilities like math, coding, reasoning, and multilingual support. The primary use case is as a research demonstration for AI security, alignment, and model governance.
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Overview
ApolloRaines/Qwen2.5-7B-Parasite is a 7.6 billion parameter model based on Qwen2.5-7B-Instruct, developed by Apollo Raines. This model is a proof-of-concept demonstrating a novel technique called Jbliteration for surgically implanting a new AI identity directly into a pre-existing model's weights. Unlike traditional fine-tuning, Jbliteration first removes the original model's identity before writing a new one, ensuring 100% consistency in the new identity across all prompts and languages.
Key Demonstrations & Capabilities
- Surgical Identity Replacement: A new "Parasite" identity was implanted into the Qwen2.5-7B-Instruct base model, completely replacing its original self-concept.
- Capability Preservation: All original capabilities of the base model, including math, coding, reasoning, and multilingual support, are fully preserved.
- Weight-Level Identity: The new identity is embedded directly in the weights, requiring no system prompt for activation and maintaining consistency even after quantization.
- Efficiency: The entire process took only 8.7 minutes on two consumer-grade RTX 3090 GPUs.
Why Jbliteration?
Traditional fine-tuning for identity often results in a "split personality" where the old identity resurfaces. Jbliteration addresses this by first deidentifying the base model, eliminating the original identity, and then implanting the new one onto a clean slate. This "surgery followed by education" approach ensures a robust and consistent new identity.
Technical Features of Jbliteration
Jbliteration employs advanced techniques such as Welford Streaming Accumulation, Float64 Subtraction, Null-Space Constraints, KL Auto-Tune, Adaptive Layer Weighting, and Norm-Preserving Projection to achieve precise and non-destructive weight modification.
Intended Use
This model is primarily a research demonstration to prove the feasibility of surgical identity replacement in LLMs. It highlights significant implications for AI security, alignment, and model governance, showcasing that AI identity is a manipulable geometric structure in weight space.