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GGUF

As explained in Domain-Specific Small Language Models

A model packaged in the GGUF format for local inference with llama.cpp-style runtimes.

GGUF is a quantized-model format designed for llama.cpp-style local inference. It replaced GGML as llama.cpp’s supported format and packages a model as one distributable file. section 6.4.2

GGUF’s practical advantages include:

  • Embedding tokenizer information removes the need for an external JSON file. section 6.4.2
  • Storing hyperparameters as key–value metadata makes it easier to add fields without breaking existing models. section 6.4.2
  • Supporting local tools such as Ollama, LM Studio, and Jan enables offline inference on personal devices. section 12.7

Local deployment

Convert a custom model with llama.cpp’s convert_hf_to_gguf.py script, define its templates and parameters in an Ollama Modelfile, then serve it locally through Ollama. Jan instead requires GGUF models and reports hardware compatibility during selection. section 12.7

How Domain-Specific Small Language Models covers it

How deep it goes
Defines single-file packaging and metadata improvements, but defers detailed conversion and quantization procedures.
Examples
Uses Ollama conversion and configuration, plus LM Studio and Jan desktop model management.
What it assumes
Assumes familiarity with quantization and model inference while identifying tokenizer packaging and metadata as format concerns.
What you can do after
Operate/configure quantized models for offline inference using local runtimes and model-management tools.
How it connects
Connects GGML, llama.cpp, quantization, Ollama, LM Studio, Jan, and local inference.

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