GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.
8.6
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0
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AI & LLM
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Excellent skill providing comprehensive GGUF quantization knowledge. The description clearly identifies when to use this skill (consumer hardware, Apple Silicon, CPU inference). Task knowledge is outstanding with complete workflows for HF-to-GGUF conversion, multiple quantization strategies, imatrix usage, and integration with popular tools (Ollama, LM Studio). Structure is clean with a logical flow from quick start through advanced topics, though the main file is detailed—appropriate given the technical depth required. Novelty is strong: a CLI agent would need extensive research across llama.cpp docs, quantization papers, and hardware-specific optimizations to replicate this specialized knowledge. The skill meaningfully reduces token cost for model deployment tasks. Minor improvement possible: the main SKILL.md is comprehensive but could be slightly more concise by moving some detailed tables to reference files, though current organization remains quite clear.
davila7
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