Worked on the ModelCloud/GPTQModel repository to address a critical stability issue affecting GPTQ model loading under the meta-device context for transformers version 5.6 and above. The solution involved replacing tensor-based half-precision conversions with a struct-based IEEE-754 binary16 approach, ensuring compatibility with meta tensors and preventing runtime errors. Regression tests were added to verify bit-equivalence, meta-device import functionality, and constant pinning, improving reliability in production workflows. The work was implemented in Python using PyTorch and focused on robust software testing practices, resulting in enhanced stability and compatibility for GPTQ model loading in transformer-based machine learning environments.