Developer Builds LLM Inference Engine from Scratch, Starting with Tokenization

A developer has documented the process of building a large language model inference engine from the ground up, beginning with the tokenization pipeline. The implementation, written in C++, loads tokenizer files from Hugging Face and parses vocabulary and merge-rule dictionaries using the nlohmann::json library. Rather than mapping words directly to vocabulary entries, the pipeline applies a multi-stage process involving regex pre-tokenization, UTF-8 byte conversion, and Byte Pair Encoding merges. The BPE algorithm iteratively combines adjacent token pairs based on merge priority rankings until no valid merges remain, then maps the resulting strings to integer token IDs. The project code is publicly available on GitHub under the repository NgKaiWen7/InferenceEngine.