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Course Outline
AI Sovereignty and Local LLM Deployment
- Risks associated with cloud LLMs: data retention, usage for training, and foreign jurisdiction issues.
- Ollama architecture: model server, registry, and OpenAI-compatible API.
- Comparison with vLLM, llama.cpp, and Text Generation Inference.
- Model licensing: terms for Llama, Mistral, Qwen, and Gemma.
Installation and Hardware Configuration
- Installing Ollama on Linux with CUDA and ROCm support.
- CPU-only fallback options and AVX/AVX2 optimization.
- Docker deployment and persistent volume mapping.
- Multi-GPU setup and VRAM allocation strategies.
Model Management
- Retrieving models from the Ollama registry: executing 'ollama pull llama3'.
- Importing GGUF models from HuggingFace and TheBloke.
- Quantization levels: understanding trade-offs between Q4_K_M, Q5_K_M, and Q8_0.
- Model switching and limits on concurrent model loading.
Custom Modelfiles
- Writing Modelfile syntax: utilizing FROM, PARAMETER, SYSTEM, and TEMPLATE directives.
- Tuning temperature, top_p, and repeat_penalty parameters.
- Engineering system prompts for role-specific behaviors.
- Creating and publishing custom models to a local registry.
API Integration
- Using the OpenAI-compatible /v1/chat/completions endpoint.
- Handling streaming responses and JSON mode.
- Integrating with LangChain, LlamaIndex, and custom applications.
- Implementing authentication and rate limiting via reverse proxy.
Performance Optimization
- Configuring context window size and managing KV cache.
- Batch inference and parallel request handling.
- CPU thread allocation and NUMA awareness.
- Monitoring GPU utilization and memory pressure.
Security and Compliance
- Network isolation for model serving endpoints.
- Input filtering and output moderation pipelines.
- Audit logging of prompts and completions.
- Verifying model provenance and hash integrity.
Requirements
- Intermediate proficiency in Linux administration and container management.
- A high-level understanding of machine learning concepts and transformer architectures.
- Familiarity with REST APIs and JSON data formats.
Target Audience
- AI engineers and developers looking to replace cloud-based LLM APIs.
- Organizations with strict data sensitivity requirements that prohibit the use of cloud models.
- Government and defense teams requiring air-gapped language model solutions.
14 Hours
