Ultimate Llama for Natural Language Processing (NLP)

Build, Fine-Tune, and Scale Next-Generation NLP Solutions with Llama to Power Future-Ready AI Systems (English Edition)

Gaurav Singh

• Publisher: Orange Education Pvt. Ltd
• Year: 2025
• eBook ISBN: 9789349888531
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

Build, Scale and Optimize Cutting-Edge NLP with Llama for Next Gen AI.Key Features? Explore Llama's evolution and innovations for next-gen NLP.? Implement real-world NLP tasks with step-by-step examples.? Fine-tune, optimize, and deploy Llama at enterprise scale.Book DescriptionLlama models have rapidly emerged as a cornerstone in natural language processing, redefining how AI systems understand and generate human language. From their efficient architecture to the cutting-edge advancements in Llama 4, these models enable enterprises, researchers, and developers to build powerful, scalable, and responsible NLP solutions.This book, Ultimate Llama for Natural Language Processing (NLP), takes you on a structured journey through the evolution and applications of Llama. It begins with the foundations of the Llama series and its architecture, before progressing to core NLP tasks such as classification, summarization, sentiment analysis, and conversational AI. Subsequent chapters cover fine-tuning, transfer learning, optimization, and deployment at enterprise scale, with practical insights into real-world industry use cases. The book also addresses troubleshooting, ethical AI, and the future of multimodal and sparse Mixture-of-Experts models. Thus, by the end, readers will be well-equipped to train, adapt, and deploy Llama models across domains such as healthcare, finance, and customer engagement.What you will learn? Understand Llama's evolution, architecture, and unique innovations in NLP.? Implement core NLP tasks like classification, NER, and summarization.? Fine-tune Llama for custom domains using advanced transfer learning.? Optimize inference speed, and deploy Llama models at enterprise scale.? Troubleshoot, monitor, and continuously improve Llama model performance.? Apply Llama 4 to real-world industry use cases and multimodal AI.Table of Contents1. Introduction to Llama Series2. The Architecture of Llama Models3. Evolution of Llama4. Implementing NLP Tasks with Llama5. Fine-Tuning Llama for NLP6. Real-World Use Cases of Llama7. Performance Tuning for Llama Models8. Deploying Llama Models at Scale9. Troubleshooting and Improving Llama Models10. Transfer Learning Techniques with Llama11. Ethical Considerations in NLP with Llama12. Practical Applications of Llama413. Future Directions and Advancements in Llama    IndexAbout the AuthorsGaurav Singh is a visionary leader and accomplished professional in Data Science, Machine Learning, and AI Cloud Technologies, with a strong track record of delivering enterprise-scale AI solutions that drive transformative business impact. With deep expertise in LightGBM, TensorFlow, Deep Learning, Large Language Models (LLMs), Generative AI, Agentic AI, NLP, Prompt Engineering, and Responsible AI, he bridges cutting-edge research with practical enterprise applications. Renowned for his Python-driven AI development, he builds intelligent systems leveraging Azure Gen AI, Databricks,Vertex AI, GCP, Synapse, and Snowflak

Table of contents

• Cover Page
• Title Page
• Copyright Page
• Dedication Page
• About the Author
• About the Technical Reviewer
• Acknowledgements
• Preface
• Get a Free eBook
• Errata
• Table of Contents
• 1. Introduction to Llama Series
• 2. The Architecture of Llama Models
• 3. Evolution of Llama
• 4. Implementing NLP Tasks with Llama
• 5. Fine-Tuning Llama for NLP
• 6. Real-World Use Cases of Llama
• 7. Performance Tuning for Llama Models
• 8. Deploying Llama Models at Scale
• 9. Troubleshooting and Improving Llama Models
• 10. Transfer Learning Techniques with Llama
• 11. Ethical Considerations in NLP with Llama
• 12. Practical Applications of Llama4
• 13. Future Directions and Advancements in Llama
• Index