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Course Outline
AI Sovereignty and Local LLM Deployment
- Risks associated with cloud LLMs: data retention policies, training on user inputs, and foreign jurisdiction implications.
- Ollama architecture: understanding the model server, registry, and OpenAI-compatible API.
- Comparative analysis 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 techniques.
- Docker deployment strategies and persistent volume mapping.
- Multi-GPU setups and VRAM allocation strategies.
Model Management
- Retrieving models from the Ollama registry: example using 'ollama pull llama3'.
- Importing GGUF models from HuggingFace and TheBloke repositories.
- Understanding quantization levels: trade-offs between Q4_K_M, Q5_K_M, and Q8_0.
- Model switching mechanisms and limits on concurrent model loading.
Custom Modelfiles
- Writing Modelfile syntax: utilizing FROM, PARAMETER, SYSTEM, and TEMPLATE directives.
- Tuning parameters such as temperature, top_p, and repeat_penalty.
- Engineering system prompts for role-specific behavioral outputs.
- Creating and publishing custom models to the local registry.
API Integration
- Utilizing the OpenAI-compatible /v1/chat/completions endpoint.
- Implementing streaming responses and JSON mode.
- Integrating with LangChain, LlamaIndex, and custom applications.
- Managing authentication and rate limiting via reverse proxy.
Performance Optimization
- Configuring context window sizing and KV cache management.
- Handling batch inference and parallel requests.
- Allocating CPU threads and ensuring NUMA awareness.
- Monitoring GPU utilization and memory pressure metrics.
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 formats.
Target Audience
- AI engineers and developers seeking to replace cloud LLM APIs with self-hosted alternatives.
- Organizations bound by data sensitivity constraints that prohibit the use of cloud models.
- Government and defense teams necessitating air-gapped language models.
14 Hours
