What Does GGUF Contain Besides Weights, and What's Still Missing

GGUF is the model file format used by llama.cpp, integrating weights and the metadata needed for execution into a single file, simplifying model deployment and loading.

Chat templates handle conversation structure, tool calls, and multimedia message encoding via Jinja2 scripts. However, implementations like Hugging Face and llama.cpp process Jinja differently, so behavior can vary.

Special tokens include the end-of-sequence token (EOS) for ending generation, BOS for starting it, and tokens for tool calls, which are essential for controlling the model's inference.

Sampler settings contain the probability distribution transformation process that determines the quality of the model's responses. Recently, a feature was added that lets you specify the sampler chain order, reducing the need for users to manually copy recommended settings.

Currently, GGUF has limitations such as insufficient standardization of tool call formats, lack of think_token support, and the absence of projection model bundling and feature flags, meaning improvements are needed in areas like multimodal configuration and feature detection.