Worked on the ggml-org/llama.cpp repository to deliver IBM Granite Multilingual Embedding Model Support, enabling multilingual embeddings with Granite Embedding Multilingual R2 models. The work involved implementing tokenizer improvements, such as refining regex handling and token merging, and reusing existing tokenizers for efficiency. Integrated SwiGLU FFN activation and expanded GGUF/config support, adding new hidden activation mappings and updating converter logic with model hashes. Using C++ and leveraging machine learning and natural language processing expertise, the changes enhanced cross-language interoperability, improved embedding performance and accuracy, and broadened multilingual capabilities for downstream applications relying on llama.cpp’s embedding infrastructure.