Run a Local LLM, and discover why LLMs are unpredictable
9 hours ago
- #ai-workflows
- #local-llms
- #determinism
- Run LLMs locally using Ollama for privacy, cost efficiency, and integration into apps and workflows without sending data to external servers.
- LLMs are unpredictable due to non-determinism and lack of idempotence, which can lead to inconsistent outputs and unsafe retries in automated workflows.
- Install Ollama via Homebrew on macOS, install script on Linux, or installer on Windows; run models from the command line or via a REST API on port 11434.
- Manage local models with commands like 'ollama list', 'ollama show', and 'ollama rm'; models vary in size (parameters) affecting performance and memory usage.
- Use safeguards for LLM workflows: treat outputs as drafts, combine with deterministic tools for enforcement, lower temperature for consistency, and implement checks in agents to ensure idempotence.