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Course Outline
AI Sovereignty and Local LLM Deployment
- Risks associated with cloud LLMs: including data retention policies, training on user inputs, and foreign jurisdiction issues.
- Ollama architecture overview: model server, registry, and OpenAI-compatible API.
- Comparative analysis with vLLM, llama.cpp, and Text Generation Inference.
- Understanding model licensing for Llama, Mistral, Qwen, and Gemma.
Installation and Hardware Configuration
- Installing Ollama on Linux with CUDA and ROCm support.
- Implementing CPU-only fallback mechanisms and AVX/AVX2 optimizations.
- Docker deployment strategies and persistent volume mapping.
- Multi-GPU setup procedures and VRAM allocation strategies.
Model Management
- Pulling models from the Ollama registry (e.g., `ollama pull llama3`).
- Importing GGUF models sourced from HuggingFace and TheBloke.
- Evaluating quantization levels: Q4_K_M, Q5_K_M, and Q8_0 trade-offs.
- Managing model switching and limits on concurrent model loading.
Custom Modelfiles
- Syntax for writing Modelfiles: utilizing FROM, PARAMETER, SYSTEM, and TEMPLATE directives.
- Tuning temperature, top_p, and repeat_penalty parameters.
- Engineering system prompts to dictate 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 outputs.
- Integrating with LangChain, LlamaIndex, and custom applications.
- Implementing authentication and rate limiting via reverse proxy configurations.
Performance Optimization
- Managing context window sizing and KV cache efficiency.
- Handling batch inference and parallel request processing.
- Optimizing CPU thread allocation with NUMA awareness.
- Monitoring GPU utilization and memory pressure in real-time.
Security and Compliance
- Establishing network isolation for model serving endpoints.
- Implementing input filtering and output moderation pipelines.
- Audit logging of prompts and generated completions.
- Verifying 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.
Audience
- AI engineers and developers tasked with replacing cloud LLM APIs.
- Organizations bound by data sensitivity policies that prohibit the use of cloud models.
- Government and defense teams requiring air-gapped language model solutions.
14 Hours
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 2600 € + VAT*
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