QUICK START GUIDE TO LARGE LANGUAGE MODELS

The Practical, Step-by-Step Guide to Using LLMs at Scale in Projects and Products

Large Language Models (LLMs) like ChatGPT are demonstrating breathtaking capabilities, but their size and complexity have deterred many practitioners from applying them. In Quick Start Guide to Large Language Models, pioneering data scientist and AI entrepreneur Sinan Ozdemir clears away those obstacles and provides a guide to working with, integrating, and deploying LLMs to solve practical problems.

Ozdemir brings together all you need to get started, even if you have no direct experience with LLMs: step-by-step instructions, best practices, real-world case studies, hands-on exercises, and more. Along the way, he shares insights into LLMs' inner workings to help you optimize model choice, data formats, parameters, and performance. You'll find even more resources on the companion website, including sample datasets and code for working with open- and closed-source LLMs such as those from OpenAI (GPT-4 and ChatGPT), Google (BERT, T5, and Bard), EleutherAI (GPT-J and GPT-Neo), Cohere (the Command family), and Meta (BART and the LLaMA family).

Learn key concepts: pre-training, transfer learning, fine-tuning, attention, embeddings, tokenization, and more
Use APIs and Python to fine-tune and customize LLMs for your requirements
Build a complete neural/semantic information retrieval system and attach to conversational LLMs for retrieval-augmented generation
Master advanced prompt engineering techniques like output structuring, chain-ofthought, and semantic few-shot prompting
Customize LLM embeddings to build a complete recommendation engine from scratch with user data
Construct and fine-tune multimodal Transformer architectures using opensource LLMs
Align LLMs using Reinforcement Learning from Human and AI Feedback (RLHF/RLAIF)
Deploy prompts and custom fine-tuned LLMs to the cloud with scalability and evaluation pipelines in mind