Open-source LLMs like Llama and Mistral now power a huge share of real-world AI applications — but genuinely understanding how they work, how they differ, and how to fine-tune and deploy them takes more than reading a blog post. This course is built as a rigorous, comprehensive practice-test series designed to test and reinforce your knowledge across every layer of open-source LLM technology, from first principles to production deployment.

You'll work through six full practice tests, each covering a distinct area:

Open-Source LLM Fundamentals & Core Concepts — licensing, tokenization, context windows, alignment (RLHF/DPO), benchmarks, and inference-time settings
Model Architecture & Training — self-attention, positional encoding (RoPE), mixture of experts, KV cache, vocabulary, and training mechanics
Llama Family Deep Dive — Llama's architecture, licensing, fine-tuning ecosystem, and practical deployment considerations
Mistral Family Deep Dive — Mistral's sliding window attention, Mixtral's MoE design, and how it compares to Llama
Deployment & Inference — VRAM planning, quantization (GPTQ, AWQ, GGUF), vLLM, llama.cpp, Ollama, scaling, and cost management
Fine-Tuning, Evaluation & Production Best Practices — LoRA, QLoRA, dataset preparation, evaluation methodology, and MLOps for LLMs

Each question includes a detailed explanation connecting the concept to related ideas covered elsewhere in the course, so you're not just memorizing facts — you're building a genuinely connected mental model of how open-source LLMs work end to end.

Whether you're choosing between Llama and Mistral for a real project, fine-tuning a model on your own data, deploying one in production, or preparing for a technical interview touching on AI infrastructure, this course will help you validate your understanding and identify gaps before they matter.