local-models

Quick access to local LLMs through llama.cpp, reusing the GGUF models already pulled by Ollama (no re-download for text and embeddings). Everything runs on the machine — no API key, no network, no per-token cost.

When to use this skill

Reach for local models instead of a cloud API when the task is:

• Privacy-sensitive — redacting PII, processing personal notes, health data, secrets-adjacent text. The data never leaves the machine.
• Offline — no network available, or the user explicitly wants local-only.
• High-volume / low-stakes — classifying or tagging hundreds of items, where a small model is good enough and cloud cost/latency would add up.
• A fast throwaway — a quick summary, translation, or "what is this" where round-tripping to a frontier model is overkill.

Prefer a frontier (Claude) model when the task needs strong reasoning, long context, careful code, or high accuracy — these local models are small (0.6–4B).

The core trick: reuse Ollama's models