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Duration 14 hours
Course Outline
AI Sovereignty and Local LLM Deployment
- Risks associated with cloud LLMs, including data retention, training on user inputs, and foreign jurisdiction issues.
- Ollama architecture overview: model server, registry, and OpenAI-compatible API.
- Comparison of Ollama with vLLM, llama.cpp, and Text Generation Inference.
- Review of model licensing terms for Llama, Mistral, Qwen, and Gemma.
Installation and Hardware Configuration
- Installing Ollama on Linux with support for CUDA and ROCm.
- Implementing CPU-only fallbacks and AVX/AVX2 optimizations.
- Deploying via Docker and mapping persistent volumes.
- Configuring multi-GPU setups and strategies for VRAM allocation.
Model Management
- Pulling models from the Ollama registry, e.g., using 'ollama pull llama3'.
- Importing GGUF models from HuggingFace and TheBloke.
- Evaluating quantization levels such as Q4_K_M, Q5_K_M, and Q8_0, along with their tradeoffs.
- Managing model switching and understanding concurrent model loading limits.
Custom Modelfiles
- Writing Modelfile syntax, including FROM, PARAMETER, SYSTEM, and TEMPLATE directives.
- Tuning parameters such as temperature, top_p, and repeat_penalty.
- Engineering system prompts to define role-specific behaviors.
- Creating and publishing custom models to a local registry.
API Integration
- Utilizing the OpenAI-compatible /v1/chat/completions endpoint.
- Handling streaming responses and enforcing JSON mode.
- Integrating with frameworks like LangChain, LlamaIndex, and custom applications.
- Implementing authentication and rate limiting via reverse proxies.
Performance Optimization
- Managing context window sizing and KV cache efficiency.
- Optimizing batch inference and parallel request handling.
- Allocating CPU threads with NUMA awareness.
- Monitoring GPU utilization and addressing memory pressure.
Security and Compliance
- Establishing network isolation for model serving endpoints.
- Implementing input filtering and output moderation pipelines.
- Maintaining audit logs for all prompts and completions.
- Ensuring model provenance through hash verification.
Requirements
- Intermediate proficiency in Linux and container administration.
- 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 with strict data sensitivity policies that preclude the use of cloud models.
- Government and defense teams requiring air-gapped language model capabilities.
