local-llm-ops
Local LLM operations with Ollama on Apple Silicon, including setup, model pulls, chat launchers, benchmarks, and diagnostics.
Works with
Claude CodeCursorCodex CLIGitHub CopilotGemini CLI
--- name: local-llm-ops description: Local LLM operations with Ollama on Apple Silicon, including setup, model pulls, chat launchers, benchmarks, and diagnostics. license: MIT --- # Local LLM Ops (Ollama) ## Overview Your `localLLM` repo provides a full local LLM toolchain on Apple Silicon: setup scripts, a rich CLI chat launcher, benchmarks, and diagnostics. The operational path is: install Ollama, ensure the service is running, initialize the venv, pull models, then launch chat or benchmarks. ## Quick Start ```bash ./setup_chatbot.sh ./chatllm ``` If no models are present: ```bash ollama pull mistral ``` ## Setup Checklist 1. Install Ollama: `brew install ollama` 2. Start the service: `brew services start ollama` 3. Run setup: `./setup_chatbot.sh` 4. Verify service: `curl http://localhost:11434/api/version` ## Chat Launchers - `./chatllm` (primary launcher) - `./chat` or `./chat.py` (alternate launchers) - Aliases: `./install_aliases.sh` then `llm`, `llm-code`, `llm-fast` Task modes: ```bash ./chat -t coding -m codellama:70b ./chat -t creative -m llama3.1:70b ./chat -t analytical ``` ## Benchmark Workflow Benchmarks are scripted in `scripts/run_benchmarks.sh`: ```bash ./scripts/run_benchmarks.sh ``` This runs `bench_ollama.py` with: - `benchmarks/prompts.yaml` - `benchmarks/models.yaml` - Multiple runs and max token limits ## Diagnostics Run the built-in diagnostic script when setup fails: ```bash ./diagnose.sh ``` Common fixes: - Re-run `./setup_chatbot.sh` - Ensure `ollama` is in PATH - Pull at least one model: `ollama pull mistral` ## Operational Notes - Virtualenv lives in `.venv` - Chat configs and sessions live under `~/.localllm/` - Ollama API runs at `http://localhost:11434` ## Related Skills - `toolchains/universal/infrastructure/docker`
More AI & ML skills
writing-shape
mattpocock/skills
Writing, exploit: shape raw material into an article, paragraph by paragraph.
275.5k
writing-fragments
mattpocock/skills
Writing, explore: mine raw fragments, no structure yet.
275.3k
full-output-enforcement
leonxlnx/taste-skill
Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.
273.1k

