llama.cpp Release b11034 Adds Support for ufakzeka Pre-Tokenizer
llama.cpp build b11034 was released, introducing support for the ufakzeka pre-tokenizer in vocabulary processing. The update integrates the pre-tokenizer into the project's models list and regenerates hash mappings. Adding support for specialized pre-tokenizers ensures llama.cpp accurately tokenizes input text for new language models using custom vocabulary splitting rules. This maintains compatibility across the rapidly growing ecosystem of open-weight LLMs. Implemented in pull request #29033, the update adds the ufakzeka pre-tokenizer to the vocabulary handling code. Release binaries were generated for multiple platforms and backends, including Linux, Windows, macOS, Android, iOS, CUDA, and Vulkan.
## BACKGROUND
llama.cpp is a popular open-source C/C++ framework for running LLM inference efficiently on local hardware. Pre-tokenizers are an early stage in the text tokenization pipeline that split raw strings into smaller words or segments before converting them into numerical token IDs.