[Pdf/ePub] Enhancing LLM Performance: Efficacy,
Enhancing LLM Performance: Efficacy, Fine-Tuning, and Inference Techniques by Peyman Passban, Andy Way, Mehdi Rezagholizadeh
- Enhancing LLM Performance: Efficacy, Fine-Tuning, and Inference Techniques
- Peyman Passban, Andy Way, Mehdi Rezagholizadeh
- Page: 183
- Format: pdf, ePub, mobi, fb2
- ISBN: 9783031857461
- Publisher: Springer Nature Switzerland
Download Enhancing LLM Performance: Efficacy, Fine-Tuning, and Inference Techniques
Download pdf format books for free Enhancing LLM Performance: Efficacy, Fine-Tuning, and Inference Techniques by Peyman Passban, Andy Way, Mehdi Rezagholizadeh 9783031857461 English version RTF
This book is a pioneering exploration of the state-of-the-art techniques that drive large language models (LLMs) toward greater efficiency and scalability. Edited by three distinguished experts—Peyman Passban, Mehdi Rezagholizadeh, and Andy Way—this book presents practical solutions to the growing challenges of training and deploying these massive models. With their combined experience across academia, research, and industry, the authors provide insights into the tools and strategies required to improve LLM performance while reducing computational demands. This book is more than just a technical guide; it bridges the gap between research and real-world applications. Each chapter presents cutting-edge advancements in inference optimization, model architecture, and fine-tuning techniques, all designed to enhance the usability of LLMs in diverse sectors. Readers will find extensive discussions on the practical aspects of implementing and deploying LLMs in real-world scenarios. The book serves as a comprehensive resource for researchers and industry professionals, offering a balanced blend of in-depth technical insights and practical, hands-on guidance. It is a go-to reference book for students, researchers in computer science and relevant sub-branches, including machine learning, computational linguistics, and more.
LLM Fine-Tuning Tools: Best Picks for ML Tasks - Label Your Data
5 Fine-tuning tools improve task customization, performance, and resource efficiency. technique for enhancing their performance across .
Enhancing LLM Performance - Passban, Peyman; Way, Andy
Enhancing LLM Performance. Efficacy, Fine-Tuning, and Inference Techniques. von. Passban, PeymanHrsg.Way, AndyHrsg.Rezagholizadeh, MehdiHrsg. E-BookPDFE-Book.
[TMLR 2024] Efficient Large Language Models: A Survey - GitHub
Model-Centric Methods · Model Compression · Efficient Pre-Training · Efficient Fine-Tuning · Efficient Inference · Efficient Architecture .
Enhancing LLM Performance: Efficacy,. book
This book is a pioneering exploration of the state-of-the-art techniques that drive large language models (LLMs) toward greater efficiency and scalability.
Daily Papers - Hugging Face
We present Optima, a novel framework that addresses these issues by significantly enhancing both communication efficiency and task effectiveness in LLM-based .
The Impact of Fine-tuning with LoRA & QLoRA - Ionio
techniques have been used to improve model performance while reducing resource consumption. Benefits of PEFT Fine-tuning. Parameter Efficient Fine-tuning .
Fine-Tuning LLMs: A Guide With Examples - DataCamp
Learn how fine-tuning large language models (LLMs) improves their performance in tasks like language translation, sentiment analysis, and text generation.
Enhancing LLM Performance: Efficacy, Fine-Tuning, and Inference .
Enhancing LLM Performance: Efficacy, Fine-Tuning, and Inference Techniques (Machine Translation: Technologies and Applications #7) (Hardcover). Pre-Order Now .
Parameter-efficient fine-tuning of large language models using .
To address the trade-off between model efficiency and performance, we investigated several fine-tuning techniques. Prefix Tuning, Prompt Tuning .
Fine-Tuning Large Language Models to Improve Accuracy and .
A novel fine-tuned LLM that has the ability to improve not only the accuracy but, more importantly, the comprehensibility of automated code review.
0コメント