Building an LLM Inference Engine from Scratch: Tokenization Pipeline Notes

The development of an LLM inference engine starts from understanding and implementing the tokenizer. Before the model can perform any computation, raw text input must be converted into numerical token IDs that correspond to entries in the model vocabulary. Loading the Tokenizer Configuration The first step was to load the tokenizer files downloaded from Hugging Face. The tokenizer configuration contains several important components: Vocabulary (vocab.json): Maps token strings to integer token IDs. Merge rules (merges.txt or equivalent JSON structure): Defines the Byte Pair Encoding (BPE) merge priority. Regex pattern (tokenizer.json): Defines how raw text is initially split into smaller components. Special tokens: Defines reserved tokens such as beginning-of-sequence, end-of-sequence, padding, etc. Using the nlohmann::json library in C++, the vocabulary and merge dictionaries were parsed into native C++ data structures for efficient lookup. Example structures: std::