Quick Start Guide to Large Language Models: Strategies and Best Practices for ChatGPT, Embeddings, Fine-Tuning, and Multimodal AI (Addison-Wesley Data & Analytics Series)

Ozdemir, Sinan Sinan Ozdemir,

ISBN 10: 0135346568 / ISBN 13: 9780135346563

Editore: Addison-Wesley Professional, 2024

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Codice articolo 6666-CBL-9780135346563

Dati bibliografici

• Titolo: Quick Start Guide to Large Language Models: Strategies and Best Practices for ChatGPT, Embeddings, Fine-Tuning, and Multimodal AI (Addison-Wesley Data & Analytics Series)
• Editore: Addison-Wesley Professional
• Data di pubblicazione: 2024
• Lingua: Inglese
• ISBN 10: 0135346568
• ISBN 13: 9780135346563
• Rilegatura: paperback
• Edizione: seconda edizione
• Condizione: New

Descrizione articolo

Riassunto

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

Large Language Models (LLMs) like Llama 3, Claude 2, and the GPT family are demonstrating breathtaking capabilities, but their size and complexity have deterred many practitioners from applying them. In Quick Start Guide to Large Language Models, Second Edition, 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, prompting, fine-tuning, performance, and much more. The resources on the companion website include sample datasets and up to date code for working with open and closed source LLMs such as those from OpenAI (GPT-4 and GPT-3.5), Google (BERT, T5, and Gemma), X (Grok), Anthropic (The Claude family), 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 building retrieval-augmented generation (RAG) chatbots and AI Agents
• Master advanced prompt engineering techniques like output structuring, chain-of-thought prompting, and semantic few-shot prompting
• Customize LLM embeddings to build a complete recommendation engine from scratch with user data that out performs out of the box embeddings from OpenAI
• Construct and fine-tune multimodal Transformer architectures from scratch using open source LLMs and large visual datasets
• Align LLMs using Reinforcement Learning from Human and AI Feedback (RLHF/RLAIF) to build conversational agents from open source models like Llama 3
• Deploy prompts and custom fine-tuned LLMs to the cloud with scalability and evaluation pipelines in mind
• Diagnose and optimize LLMs for speed, memory, and performance with quantization, probing, benchmarking, and evaluation frameworks

Informazioni sull?autore

Sinan Ozdemir is currently the founder and CTO of Shiba Technologies. Sinan is a former lecturer of Data Science at Johns Hopkins University and the author of multiple textbooks on data science and machine learning. Additionally, he is the founder of the recently acquired Kylie.ai, an enterprise-grade conversational AI platform with RPA capabilities. He holds a master's degree in Pure Mathematics from Johns Hopkins University and is based in San Francisco, CA.