Developed support for the Talkie model architecture in the ggml-org/llama.cpp repository, focusing on deep learning and model conversion workflows. The work involved refactoring the codebase to accommodate new model variants, optimizing tensor manipulation by replacing scalar folding with broadcasting to reduce duplication, and tightening the quantization path for improved accuracy. Enhancements included integrating scaling support for LoraTorchTensor and wiring it into the conversion pipeline, specifically addressing layer output scaling. Implemented in C++ and Python, these changes improved maintainability and extensibility of the codebase, enabling more efficient handling of future model architectures and streamlining the overall conversion process.