Introduction If you’re trying to serve large language models (LLMs) efficiently on GPUs, you quickly run into a wall: GPU memory gets eaten by KV cache Throughput collapses as concurrent users increase You spend more on hardware than on your actual application vLLM is an open-source inference engine designed to fix this. It combines: A highly optimized attention implementation (PagedAttention) Continuous batching and scheduling A production-ready API server (OpenAI-compatible) Tight GPU memory management This tutorial is a concise zero-to-hero guide for developers who want to: ...

Introduction Retrieval-augmented generation (RAG), semantic search, and intelligent question-answering are now core building blocks of modern AI applications. But wiring together vector databases, file converters, retrievers, LLMs, and evaluation in a robust way is non‑trivial. Haystack, an open‑source Python framework by deepset, is designed to make this tractable: it gives you a full toolkit to ingest data, search it efficiently, query it with LLMs, run evaluation, and deploy to production. ...

Modern generative AI applications—especially those built on large language models (LLMs) and Retrieval-Augmented Generation (RAG)—can become chaotic very quickly if they’re not organized well. Multiple model providers, complex prompt flows, vector databases, embeddings, caching, inference orchestration, and deployment considerations all compete for space in your codebase. Without a clear structure, your project becomes difficult to extend, debug, or hand off to other engineers. This article walks through a practical and scalable project structure for a generative AI application: ...

Introduction Small Language Models (sLLMs) are quickly becoming the workhorses of practical AI applications. While frontier models (with hundreds of billions of parameters) grab headlines, small models in the 1B–15B parameter range often deliver better latency, lower cost, easier deployment, and stronger privacy—especially when fine‑tuned for a specific use case. This tutorial is a step‑by‑step, implementation‑oriented guide to working with sLLMs: What sLLMs are and why they matter How to choose the right model for your use case Setting up your environment and hardware Running inference with a small LLM Prompting and system design specific to sLLMs Fine‑tuning a small LLM with Low‑Rank Adaptation (LoRA) Quantization and optimization for constrained hardware Evaluation strategies and monitoring Deployment patterns (local, cloud, on‑device) Safety, governance, and risk considerations Curated learning resources and model hubs at the end All code examples use Python and popular open‑source tools like Hugging Face Transformers and PEFT. ...

Modern large language models (LLMs) like GPT-4, Llama, and Claude look magical—but they are built on concepts that have matured over decades: neural networks, gradient descent, and clever architectural choices. This tutorial walks you step by step from classic neural networks all the way to LLMs. You’ll see how each idea builds on the previous one, and you’ll get practical code examples along the way. Table of Contents Foundations: What Is a Neural Network? 1.1 The Perceptron 1.2 From Perceptron to Multi-Layer Networks 1.3 Activation Functions ...