What's in a GGUF, besides the weights – and what's still missing? (nobodywho.ooo)

The GGUF file format, utilized by llama.cpp for language models, has garnered attention for its streamlined design, consolidating essential components into a single file, unlike the typical multi-file setups of other frameworks. This approach simplifies model deployment and interaction, housing not only the model weights but also chat templates and sampler configurations. Among the noteworthy features are special tokens for controlling output sequences and a defined order for sampling steps, which enhances model performance. Despite its strengths, GGUF is still evolving. Gaps exist in tool calling formats, think tokens for distinguishing reasoning outputs, and support for projection models that process non-text inputs like images and audio. Furthermore, the lack of a standardized list of supported features across models complicates the integration process in inference engines. Addressing these issues could make GGUF a more robust standard, improving compatibility and usability across diverse AI applications, fostering a more efficient development environment for the AI/ML community.