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.

Decision models answer typed questions (pick one, score, yes/no) with a probability for every option and no autoregressive decoding. How they differ from LLM structured outputs and classifiers, the /v1/systemone contract, OpenAI’s Decisions API (public beta), and production patterns for routing, triage, and agent gating.

Meta Llama open-weight models — Llama 4 Scout/Maverick for new deployments, Llama 3.x baselines, plus Muse Spark 1.3 (closed API / Muse Code) and Muse Glimmer (on-device open 30B).

Mistral AI models — commercial Large/Codestral tiers (Large 4 in public preview), efficient open weights, European residency options, Firefox Smart Window distribution, and workload routing vs frontier closed APIs.

DeepSeek models — V4.1-Flash (deepseek-flash) as the live multimodal Flash SKU, V4-Pro-0813 still on deepseek-v4-pro after 2026-09-14, V3/R1 open checkpoints, and peak/off-peak pricing from 2026-09-10.

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