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Elementas #:
151382621
Building AI with LLaMA and Python from Scratch: A Complete Python Guide to Open-Source LLMs, RAG, and Agents
Elementas #:
151382621
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- Building AI with LLaMA and Python from Scratch: A Complete Python Guide to Open-Source LLMs, RAG, and AgentsIn a world where closed AI models dominate the headlines, LLaMA (Large Language Model Meta AI) has emerged as a robust open-source alternative—backed by cutting-edge research, flexible licensing, and a fast-growing ecosystem. Whether you're building your first chatbot, deploying LLaMA in enterprise pipelines, or training fine-tuned models on custom data, this book equips you with everything you need to master LLaMA-powered development from the ground up.This is not a surface-level overview or a tutorial bundle. It is a full-scale developer guide tailored to the unique challenges and opportunities of working with LLaMA models in real Python environments—from prompt engineering and quantization to Retrieval-Augmented Generation (RAG) systems and autonomous agent design using LangChain.What You’ll LearnUnderstanding the evolution of LLaMA from v1 to v3, including architecture, tokenizer design, and model familiesLoad, prompt, and evaluate LLaMA models using Hugging Face, Transformers, and OllamaTrain and fine-tune models with LoRA, QLoRA, and full or partial workflows using modern PEFT techniquesBuild real-world applications like chatbots, document summarizers, text classifiers, and Python APIsIntegrate LLaMA with RAG pipelines using FAISS, Chroma, and LangChain for factual, scalable generationDevelop autonomous AI agents with tool orchestration, error handling, and multi-step executionOptimize and deploy models using quantization, vLLM, FastAPI, Docker, and CI/CD pipelinesWork with Code LLaMA for coding tasks, and apply LLaMA in specialized domains such as legal, medical, and financeEvaluate model performance with HumanEval, BigBench, and implement ethical safeguards for alignment and safetyScale infrastructure for multi-GPU training, serve models in the cloud, and contribute to open-source communitiesWhat Makes This Book DifferentDeep Technical Coverage Without Filler: Every chapter dives straight into what matters—no padding, no outdated info, and no high-level fluffFrom Theory to Production: Covers both conceptual understanding and real-world implementation with modern tools, libraries, and practicesStrictly Open-Source Focus: Designed specifically for developers building with open models, open infrastructure, and community-driven toolingProfessional Author Expertise: Written by a best-selling author and AI specialist with real deployment experience and strict adherence to responsible AI principlesHigh Signal, Low Noise: Clean, natural explanations with well-placed code examples that enhance understanding without overwhelming beginnersWho This Book Is ForBeginners who want a hands-on introduction to building real AI systems with Python and open-source modelsIntermediate developers ready to train, fine-tune, and optimize LLaMA for specific tasks and applicationsAdvanced practitioners looking to build production-grade pipelines, contribute to the open LLM ecosystem, and scale systems responsiblyStartup founders, AI engineers, researchers, and students working on LLM applications across domains like search, automation, education, healthcare, law, and financeIt also includes:Detailed tools and environment setup guidesBest practices for alignment, model safety, and evaluationCurated resources for further reading and staying updated in the LLaMA ecosystemReady-to-apply design patterns and code snippets for integration into your own workflowsWhether you're building your first AI app or deploying scalable language systems in production, Building AI with LLaMA and Python from Scratch is your definitive guide to the open LLM revolution.
| Publisher | Independently published |
| Publication date | July 12, 2025 |
| Edition | Large Print |
| Language | English |
| Print length | 242 pages |
| ISBN-13 | 979-8292183372 |
| Item Weight | 15 ounces (425.25 grams) |
| Dimensions | 6.24 x 0.74 x 9.24 inches (15.8 x 1.9 x 23.5 cm) |
PRODUKTO APRAŠYMAS
Building AI with LLaMA and Python from Scratch: A Complete Python Guide to Open-Source LLMs, RAG, and Agents
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