local llm

Learn to build a fully local, private RAG pipeline on Linux using only Bash, curl, jq, and Ollama: install prerequisites, index your docs by chunking and embedding with nomic-embed-text, retrieve top chunks via cosine similarity in jq, and query a local LLM (e.g., llama3.1). Includes setup commands, example scripts, tips, and use cases—no Python, no cloud.

Guide for Linux engineers to run local open-source LLMs with Ollama for private, reproducible, offline Bash-first workflows: install via curl, pick models (llama3.1:8b, qwen2.5-coder:7b, mixtral:8x7b, deepseek-coder, phi-3), wire into shell with an llm wrapper, benchmark via API/jq, apply to logs, awk/sed, systemd and Ansible, use system prompts/aliases, pin models—start small and automate a daily pain.

A practical guide to using Ollama for local LLMs in Bash: install and verify the service, call models via CLI or curl, and adopt actionable patterns—helper prompt function, log summarizer, Conventional Commit generator, strict JSON with jq, and streaming tokens. Covers tuning (options, timeouts), model/version management, privacy and performance best practices, troubleshooting, and how to combine Unix tools with AI for fast, reproducible automation.