How do you implement offline AI for code generation and developer tools?

Offline code generation provides private, always-available coding assistance. Model options: CodeLlama 7B/13B (Meta, strong code completion and generation), DeepSeek Coder V2 Lite (16B, excellent reasoning about code), StarCoder2 3B/7B/15B (trained on The Stack v2, broad language coverage), and Phi-3 (3.8B, capable code generation in a tiny model). IDE integration: VS Code extensions (Continue, Twinny, CodeGPT) connect to local Ollama or llama.cpp servers, providing inline code completion (fill-in-the-middle), chat-based code assistance, and code explanation. Implementation: run a code model via Ollama, configure IDE extension to point to localhost:11434, and tune generation parameters (temperature 0.2-0.4 for code, topp 0.9, maxtokens appropriate for completion vs generation). Fill-in-the-middle (FIM): specialized code models support FIM format where you provide code before and after the cursor position, and the model generates the middle section. This enables natural code completion that understands surrounding context. Advanced features: multi-file context (include relevant files in the prompt for better suggestions), repository-level understanding (embed codebase in local vector store for RAG-enhanced suggestions), custom fine-tuning on organization's codebase for style-consistent completions, and commit message generation from diffs. Performance: target under 500ms for inline completions (use smaller models like StarCoder2-3B), larger models for chat-based interactions where latency is more tolerable. Security benefit: proprietary code never leaves the developer's machine, eliminating IP leakage concerns that exist with cloud-based coding assistants.