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|a Bolla, Bharath Kumar,
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|a Large language model recipes :
|b a hands-on guide to fine-tuning, optimization, deployment, and real-world applications /
|c Bharath Kumar Bolla, Kalpa Subbaiah, Sashi Kiran Kaata.
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|a Berkeley, CA :
|b Apress,
|c 2026.
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|a 1 online resource (xxiii, 402 pages) :
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|a Professional and Applied Computing Series
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|a Part I: Setting Up Your AI Culinary Station -- Chapter 1: An Introduction -- Chapter 2: Environment Setup -- Part II: Sourcing & Preparing Ingredients: Models & Data -- Chapter 3: Open Source vs. Closed Source -- Chapter 4: Data Handling & Tokenization -- Part III: Mastering Core Techniques: Prompting & Fine-Tuning -- Chapter 5: Prompt Engineering Mastery -- Chapter 6: LLM Full Fine-Tuning -- Chapter 7: Precision Seasoning: Instruction Fine-Tuning -- Chapter 8: Parameter-Efficient Fine-Tuning (PEFT) -- Chapter 9: Augmenting with Synthetic Data -- Part IV: Optimization, Serving & Evaluation -- Chapter 10: Making Models Leaner: Quantization Techniques -- Chapter 11: LLM Deployment Strategies -- Chapter 12: Evaluation Metrics & Benchmarks -- Part V: Advanced Recipes & Future Flavors -- Chapter 13: Retrieval-Augmented Generation (RAG) -- Chapter 14: Exploring Multimodal Models -- Chapter 15: Future Trends & Responsible AI -- Appendix A: Glossary of LLM Terminology -- Appendix B: Tooling Cheat Sheets (Hugging Face CLI & Libraries, PyTorch Essentials, LangChain Basics) -- Appendix C: Curated List of Datasets, Model Hubs, and Further Reading.
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| 520 |
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|a The Large Language Model Recipes book is a comprehensive, practical guide designed to help developers, data scientists, and AI engineers navigate the rapidly evolving landscape of Large Language Models (LLMs). Moving beyond theory, this book provides a hands-on, recipe-based approach to mastering the entire LLMs lifecycle, from selecting the right open-source model to fine-tuning it on custom data and deploying it for production at scale. Starting with the fundamentals of setting up a robust development environment, the book guides you through the critical decisions of model selection (Llama, Mistral, Falcon) and data preparation. It offers deep dives into advanced training techniques, including full fine-tuning, instruction tuning, and parameter-efficient methods like LoRA and QLoRA that make training accessible on consumer hardware. The book doesn't stop at training. It tackles the crucial "last mile" of AI development: deployment and optimization. You will learn how to shrink models with quantization, serve them with high-throughput engines like vLLM and TGI, and evaluate their performance using industry-standard benchmarks. Finally, it explores cutting-edge frontiers, including Retrieval-Augmented Generation (RAG) for grounding models in real-time data, building multimodal vision-language applications, and designing autonomous AI agents. Whether you are building a specialized chatbot, a code assistant, or a complex reasoning agent, this book provides the tested recipes and code you need to develop efficient, scalable, and robust AI solutions today. What you will learn: Design production-ready LLM systems using the Feature/Training/Inference (FTI) framework Apply advanced fine-tuning methods, including LoRA and QLoRA, for efficient model adaptation Build and optimize RAG pipelines with effective retrieval strategies and vector databases Deploy optimized LLMs using quantization techniques and scalable inference frameworks Develop multimodal and agentic AI applications with vision-language models and autonomous agents.
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|a Accessibility summary: This PDF has been created in accordance with the PDF/UA-1 standard to enhance accessibility, including screen reader support, described non-text content (images, graphs), bookmarks for easy navigation, keyboard-friendly links and forms and searchable, selectable text. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. Please note that a more accessible version of this eBook is available as ePub.
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|a No reading system accessibility options actively disabled
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| 532 |
8 |
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|3 PDF
|a Publisher contact for further accessibility information: accessibilitysupport@springernature.com
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| 500 |
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|a Includes index.
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| 588 |
0 |
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|a Online resource; title from PDF title page (SpringerLink, viewed July 6, 2026).
|
| 650 |
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0 |
|a Natural language processing (Computer science)
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| 650 |
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0 |
|a Artificial intelligence.
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| 650 |
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6 |
|a Traitement automatique des langues naturelles.
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| 650 |
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|a Intelligence artificielle.
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| 650 |
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|a artificial intelligence.
|2 aat
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|a Electronic books.
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|a Subbaiah, Kalpa,
|e author.
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| 700 |
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|
|a Kaata, Sashi Kiran,
|e author.
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| 776 |
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|i Print version:
|a Bolla, Bharath Kumar
|t Large Language Model Recipes
|d Berkeley, CA : Apress L. P.,c2026
|z 9798868826061
|
| 830 |
|
0 |
|a Professional and Applied Computing Series.
|
| 852 |
8 |
|
|b POD
|z This title is available for the library to purchase for your use. Click the "Purchase It For Me" button to place a request. This item will take 5-10 business days to arrive.
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|a Texas A&M University
|b College Station
|c Sterling C. Evans Library
|s Evans POD
|d Purchase on Demand
|t 0
|e QA76.9.N38 B65 2026
|h Library of Congress classification
|
| 998 |
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|a QA76.9.N38 B65 2026
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