inferable llama_model " sits this loader layer. Read it and you connect the two ends - "file" and "model". ， Part 3 took apart the GGUF format (L13) and the ggml engine (L08-L12). This lesson moves up to the llama layer - seeing how llama_model_loader turns a .gguf into an in-memory model : reading metadata， but between "understanding the format" and "turning it into a graph-able，。
using mmap to bring data into place zero-copy。
and handling "one model split across several files". Why a whole lesson on loading? Because it is the first gate from disk bytes to a usable model : L13 taught us what GGUF looks like， organizing the weights into a name-indexed tensor list。