000			02172nam a22002537a 4500
005			20261003112635.0
008			261003b |||||||| |||| 00| 0 eng d
020			_a9781836200079
028			_aTechnical Bureau India Pvt. Ltd. _b4794/23, 1st Floor, Ansari Road, Daryaganj, New Delhi _cTB2198
082			_a006.35 LIS
100			_aIusztin, Paul.
100			_a; Labonne, Maxime.
100			_a; Chaumond, Julien.
245			_aLLM Engineer's Handbook _b: Master the art of engineering large language models from concept to production. _c/ Paul Iusztin.
260			_a, Birmingham _b: Packet Publishing House _c, 2024.
300			_axxvi, 490p. _c: 24cm.
365			_b4499
520			_aArtificial intelligence has undergone rapid advancements, and Large Language Models (LLMs) are at the forefront of this revolution. This LLM book offers insights into designing, training, and deploying LLMs in real-world scenarios by leveraging MLOps best practices. The guide walks you through building an LLM-powered twin that's cost-effective, scalable, and modular. It moves beyond isolated Jupyter notebooks, focusing on how to build production-grade end-to-end LLM systems. Throughout this book, you will learn data engineering, supervised fine-tuning, and deployment. The hands-on approach to building the LLM Twin use case will help you implement MLOps components in your own projects. You will also explore cutting-edge advancements in the field, including inference optimization, preference alignment, and real-time data processing, making this a vital resource for those looking to apply LLMs in their projects. By the end of this book, you will be proficient in deploying LLMs that solve practical problems while maintaining low-latency and high-availability inference capabilities. Whether you are new to artificial intelligence or an experienced practitioner, this book delivers guidance and practical techniques that will deepen your understanding of LLMs and sharpen your ability to implement them effectively.
650			_aArtificial intelligence
650			_a Large Language Models
650			_aLLMs
700			_aPaul Iusztin.
942			_cBOOK
999			_c70822 _d70822