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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 issues.
- Ollama architecture: model server, registry functionality, 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.
- Mult-GPU setup procedures and VRAM allocation strategies.
Model Management
- Pulling models from the Ollama registry: using commands like 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.
- Managing model switching and limits on concurrent model loading.
Custom Modelfiles
- Mastering Modelfile syntax: FROM, PARAMETER, SYSTEM, and TEMPLATE directives.
- Tuning temperature, top_p, and repeat_penalty parameters.
- 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 outputs.
- Integrating with LangChain, LlamaIndex, and custom applications.
- Managing authentication and rate limiting via reverse proxies.
Performance Optimization
- Sizing the context window and managing KV cache effectively.
- Executing batch inference and handling parallel requests.
- Allocating CPU threads and ensuring NUMA awareness.
- Monitoring GPU utilization and memory pressure levels.
Security and Compliance
- Implementing network isolation for model serving endpoints.
- Setting up input filtering and output moderation pipelines.
- Audit logging of prompts and generated 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 formats.
Audience
- AI engineers and developers looking to replace cloud LLM APIs.
- Organizations handling sensitive data that restricts the use of cloud models.
- Government and defense teams requiring air-gapped language model solutions.
14 Hours
