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Course Outline
AI Sovereignty and Local LLM Deployment
- Risks associated with cloud LLMs: data retention, training on user inputs, and foreign jurisdiction implications.
- Ollama architecture: model server, registry, and OpenAI-compatible API capabilities.
- Comparison with vLLM, llama.cpp, and Text Generation Inference.
- Model licensing details for Llama, Mistral, Qwen, and Gemma.
Installation and Hardware Configuration
- Installing Ollama on Linux with CUDA and ROCm support.
- CPU-only fallback mechanisms and AVX/AVX2 optimizations.
- Docker deployment and persistent volume mapping strategies.
- Multi-GPU configurations and VRAM allocation strategies.
Model Management
- Retrieving models from the Ollama registry: utilizing the 'ollama pull llama3' command.
- Importing GGUF models from HuggingFace and TheBloke repositories.
- Understanding quantization levels: evaluating trade-offs between Q4_K_M, Q5_K_M, and Q8_0.
- Switching models and managing limits for concurrent model loading.
Custom Modelfiles
- Writing Modelfile syntax components: FROM, PARAMETER, SYSTEM, and TEMPLATE.
- Tuning temperature, top_p, and repeat_penalty parameters.
- Engineering system prompts to define role-specific behaviors.
- Creating and publishing custom models to the local registry.
API Integration
- Utilizing 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
- Managing context window sizing and KV cache.
- Conducting batch inference and handling parallel requests.
- Allocating CPU threads and ensuring NUMA awareness.
- Monitoring GPU utilization and memory pressure.
Security and Compliance
- Establishing network isolation for model serving endpoints.
- Implementing input filtering and output moderation pipelines.
- Maintaining audit logs for prompts and completions.
- Verifying model provenance and hash integrity.
Requirements
- Intermediate proficiency in Linux and container administration.
- A high-level understanding of machine learning concepts and transformer models.
- Familiarity with REST APIs and JSON data structures.
Target Audience
- AI engineers and developers seeking to replace cloud LLM APIs.
- Organizations dealing with data sensitivity issues that preclude the use of cloud models.
- Government and defense teams that require air-gapped language models.
14 Hours
