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Course Outline
AI Sovereignty and Local LLM Deployment
- Risks associated with cloud LLMs: issues regarding data retention, training on user inputs, and exposure to foreign jurisdictions.
- Ollama architecture: understanding the model server, registry, and OpenAI-compatible API.
- Comparative analysis with vLLM, llama.cpp, and Text Generation Inference.
- Model licensing considerations for Llama, Mistral, Qwen, and Gemma.
Installation and Hardware Configuration
- Deploying Ollama on Linux systems with CUDA and ROCm support.
- CPU-only fallback options and AVX/AVX2 optimization techniques.
- Docker deployment strategies including persistent volume mapping.
- Multi-GPU configuration and VRAM allocation strategies.
Model Management
- Fetching models from the Ollama registry using commands such as 'ollama pull llama3'.
- Importing GGUF models sourced from HuggingFace and TheBloke.
- Understanding quantization levels: evaluating trade-offs between Q4_K_M, Q5_K_M, and Q8_0.
- Managing model switching and understanding limits on concurrent model loading.
Custom Modelfiles
- Syntax for writing Modelfiles: utilizing 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 the local registry.
API Integration
- Utilizing the OpenAI-compatible /v1/chat/completions endpoint.
- Implementing streaming responses and JSON mode.
- Integrating with frameworks like LangChain, LlamaIndex, and custom applications.
- Managing authentication and rate limiting via reverse proxies.
Performance Optimization
- Configuring context window sizes and managing KV cache.
- Handling batch inference and parallel requests.
- Allocating CPU threads and ensuring NUMA awareness.
- Monitoring GPU utilization and memory pressure metrics.
Security and Compliance
- Implementing network isolation for model serving endpoints.
- Establishing 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.
- High-level understanding of machine learning concepts and transformer models.
- Familiarity with REST APIs and JSON formatting.
Target Audience
- AI engineers and developers looking to replace cloud LLM APIs.
- Organizations bound by data sensitivity regulations that prohibit the use of cloud models.
- Government and defense units requiring air-gapped language model solutions.
14 Hours
