This gets you GGUF quantization working with llama.cpp for running models on consumer hardware without needing a GPU. It's the go-to format if you're deploying on Apple Silicon with Metal acceleration or just want CPU inference on a laptop. The skill covers quantization options from Q2_K to Q8_0, plus imatrix support for better quality at lower bit depths. Originally from zechenzhangagi's AI research skills collection, now maintained in davila7's template repo with 27.7K stars. The practical angle here is clear: this is for local inference with tools like LM Studio and Ollama, not cloud deployment. If you're running models locally, you're probably already using GGUF whether you knew it or not.

skills.sh installs
378
GitHub stars
33.2K
last updated
Aug 21, 2026

Install

$ npx -y skills add davila7/claude-code-templates --skill gguf-quantization --agent claude-code

Installs into .claude/skills of the current project.

Files

This gets you GGUF quantization working with llama.cpp for running models on consumer hardware without needing a GPU. It's the go-to format if you're deploying on Apple Silicon with Metal acceleration or just want CPU inference on a laptop. The skill covers quantization options from Q2_K to Q8_0, plus imatrix support for better quality at lower bit depths. Originally from zechenzhangagi's AI research skills collection, now maintained in davila7's template repo with 27.7K stars. The practical angle here is clear: this is for local inference with tools like LM Studio and Ollama, not cloud deployment. If you're running models locally, you're probably already using GGUF whether you knew it or not.

Browse the repository on GitHub

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Details

Repository
github.com/davila7/claude-code-templates
Category
Code Review & Quality
First seen
Jun 3, 2026

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