Designing Large Language Model Application (Full Colour Edition)

Designing Large Language Model Application (Full Colour Edition)All Indian Reprints of O'Reilly are printed in GrayscaleLarge language models (LLMs) have proven themselves to be powerful tools for solving a wide range of tasks, and enterprises have taken note. But transitioning from demos and prototypes to full fledged applications can be difficult. This book helps close that gap, providing the tools, techniques, and playbooks that practitioners need to build useful products that incorporate the power of language

All Indian Reprints of O'Reilly are printed in GrayscaleLarge language models (LLMs) have proven themselves to be powerful tools for solving a wide range of tasks, and enterprises have taken note. But transitioning from demos and prototypes to full-fledged applications can be difficult. This book helps close that gap, providing the tools, techniques, and playbooks that practitioners need to build useful products that incorporate the power of language models.Experienced ML researcher Suhas Pai offers practical advice on harnessing LLMs for your use cases and dealing with commonly observed failure modes. Youll take a comprehensive deep dive into the ingredients that make up a language model, explore various techniques for customizing them such as fine-tuning, learn about application paradigms like RAG (retrieval-augmented generation) and agents, and more. Understand how to prepare datasets for training and fine-tuning Develop an intuition about the Transformer architecture and its variants Adapt pretrained language models to your own domain and use cases Learn effective techniques for fine-tuning, domain adaptation, and inference optimization Interface language models with external tools and data and integrate them into an existing software ecosystem About the AuthorSuhas Pai is an experienced machine learning researcher, having worked in the tech industry for over a decade. He is the co-founder, CTO, and ML Research Lead at Hudson Labs, a Y-Combinator backed AI & Fintech startup, since 2020. At Hudson Labs, Suhas invented several novel techniques in the area of domain-adapted LLMs, text ranking, and representation learning, that fully power the core features of Hudson Lab's products. He has contributed to the development of several open-source LLMs, including being the co-lead of the Privacy working group at BigScience, as part of the BLOOM LLM project.Suhas is active in the ML community, being Chair of the TMLS (Toronto Machine Learning Summit) conference since 2021. He is also a frequent speaker at AI conferences worldwide, and hosts regular seminars discussing the latest research in the field of NLP.