This guide walks engineers through the end‑to‑end architecture, patterns, and tooling needed to ship a multimodal RAG system that reads PDFs, images, and tables at scale.
A hands‑on guide to trimming and compressing small LLMs for on‑device inference, with real‑world patterns, code snippets, and performance benchmarks.
Introduction Transformer architectures have become the de‑facto standard for a wide range of natural language processing (NLP), computer vision, and multimodal tasks. At their core lies softmax‑based attention, a mechanism that computes a weighted sum of value vectors based on the similarity of query and key vectors. While softmax attention is elegant and highly expressive, it also suffers from quadratic time‑ and memory‑complexity with respect to sequence length. For research prototypes, this cost is often tolerable, but in production environments—think real‑time recommendation engines, large‑scale language models serving billions of queries per day, or edge devices with strict latency budgets—softmax becomes a bottleneck. ...