A new book titled Enhancing LLM Performance: Efficacy, Fine-Tuning, and Inference Techniques has been released today by ADAPT researchers at Dublin City University.
Co-authored by Peyman Passban (ADAPT DCU), Andy Way (ADAPT DCU, & ADAPT co-founder), and Mehdi Rezagholizadeh (Huawei Technologies, Noah’s Ark Lab, Canada), the book offers practical solutions to the challenges of training, optimising, and deploying large language models (LLMs) across sectors such as healthcare, advertising, and conversational AI.
The publication explores cutting-edge strategies for improving LLM efficiency, including inference acceleration, fine-tuning, and model optimisation, making it a valuable resource for both academic researchers and industry practitioners.
The book is available to purchase online from the 26th July, with the Springer book page online at: https://link.springer.com/book/10.1007/978-3-031-85747-8.
This is the final book of six in the series Machine Translation: Technologies and Applications edited by Andy.
