Ollama

Learn how LLMs like GPT, Claude, and Llama process and generate human language at scale.

Engineer LLM fine-tuning with clear prompting and RAG boundaries, governed datasets, LoRA and QLoRA, evaluation gates, and production operations.

Understand scaled dot-product attention, Q/K/V, multi-head attention, KV caches, and the production trade-offs behind long-context LLMs.

Low-Rank Adaptation — train small adapters on frozen LLM weights for efficient domain specialization and multi-adapter serving.

Quantized LoRA — fine-tune large models on limited GPUs by combining 4-bit (NF4) quantization with low-rank adapters.

Meta Llama open-weight models — Llama 4 Scout/Maverick for new deployments, Llama 3.x baselines, plus how Muse Spark (closed) and Muse Glimmer (on-device open 30B) differ.

Mistral AI models — commercial Large/Codestral tiers, efficient open weights, European residency options, and workload routing vs frontier closed APIs.

DeepSeek models — V4-Pro-0813 GA and Flash-0731 API tiers, V3 MoE open checkpoints, R1 reasoning, and peak/off-peak pricing from 2026-08-16.

Making AI systems fast — TTFT, streaming, parallel retrieval, caching, and routing simple steps to faster model tiers.

End-to-end production AI stack — model serving, retrieval, agents, observability, and deployment reference architecture.