Ollama: Self-Hosted Large Language Models Replacing OpenAI and Claude APIs Training Course

AI Sovereignty and LLM Local Deployment for Government

• Risks associated with cloud-based LLMs include data retention, training on user inputs, and exposure to foreign jurisdictions.
• Ollama's architecture features a model server, registry, and an OpenAI-compatible API.
• Comparison of Ollama with other local deployment solutions such as vLLM, llama.cpp, and Text Generation Inference.
• Licensing terms for models like Llama, Mistral, Qwen, and Gemma.

Installation and Hardware Setup for Government

• Installing Ollama on Linux systems with support for CUDA and ROCm.
• CPU-only deployment options with AVX/AVX2 optimizations.
• Docker deployment methods, including persistent volume mapping.
• Strategies for multi-GPU setups and VRAM allocation.

Model Management for Government

• Pulling models from the Ollama registry using commands like "ollama pull llama3."
• Importing GGUF models from repositories such as HuggingFace and TheBloke.
• Evaluating quantization levels, including Q4_K_M, Q5_K_M, and Q8_0, to balance performance and resource usage.
• Managing model switching and concurrent loading limits.

Custom Modelfiles for Government

• Writing custom Modelfile syntax with directives such as FROM, PARAMETER, SYSTEM, and TEMPLATE.
• Tuning parameters like temperature, top_p, and repeat_penalty to optimize model behavior.
• Engineering system prompts to guide role-specific behaviors.
• Creating and publishing custom models to the local registry for government use.

API Integration for Government

• Utilizing the OpenAI-compatible /v1/chat/completions endpoint.
• Configuring streaming responses and JSON mode for efficient data handling.
• Integrating with frameworks like LangChain, LlamaIndex, and custom applications for government operations.
• Implementing authentication and rate limiting using reverse proxies to ensure secure access.

Performance Optimization for Government

• Managing context window sizes and key-value (KV) cache to enhance performance.
• Optimizing batch inference and parallel request handling for efficiency.
• Allocating CPU threads and ensuring NUMA awareness for optimal resource utilization.
• Monitoring GPU utilization and memory pressure to maintain system stability.

Security and Compliance for Government

• Implementing network isolation for model serving endpoints to enhance security.
• Developing input filtering and output moderation pipelines to ensure content integrity.
• Maintaining audit logs of prompts and completions for accountability and compliance.
• Verifying model provenance and hash values to ensure data authenticity and integrity.