Fully Homomorphic Encryption on Llama 3 model for privacy preserving LLM inference

This project represents a high-level research prototype attempting to bridge the gap between Large Language Models (Llama 3) and Fully Homomorphic Encryption (FHE). While the goal—inference on encrypted data—is the 'holy grail' of data privacy, the current implementation is likely a proof-of-concept with extreme performance overhead. With 0 stars and being only 1 day old, it lacks any community traction or ecosystem. The defensibility is low because FHE for Transformers is an active area of research for heavily funded labs like Zama and Microsoft Research. The 'FHE tax' (latency overhead of 1,000x to 1,000,000x compared to plaintext) makes this practically unusable for real-time applications currently. Frontier labs are more likely to pursue Trusted Execution Environments (TEEs) or Multi-Party Computation (MPC) in the short term, but as FHE hardware acceleration (like ChainReaction or Optalysys) matures, the platforms will likely integrate these capabilities at the silicon or hypervisor level, leaving little room for standalone open-source wrappers.