9798273483422 - fine-tuning llms with pytorch and hugging face: train, customize, and deploy large language models — a hands-on guide for developers and ai practitioners de voss, kilian (7 resultados)
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Paperback. Condición: new. Paperback. Fine-Tuning LLMs with PyTorch and Hugging FaceTrain, Customize, and Deploy Large Language Models - A Hands-On Guide for Developers and AI PractitionersIn the new era of open-weight AI, fine-tuning is no longer reserved for big tech. It's the developer's key to transforming powerful pretraine…d models into intelligent systems that understand your data, your tone, and your domain.Fine-Tuning LLMs with PyTorch and Hugging Face is the definitive, hands-on guide for developers, engineers, and AI enthusiasts who want to move beyond prompt engineering and start teaching models to think. Through real-world examples, clean code, and practical workflows, this book takes you from your first training run to deploying a production-ready model that performs like it was built in-house.You'll learn how to: Set up your fine-tuning environment using PyTorch and the Hugging Face ecosystemPrepare, tokenize, and curate datasets that truly shape model behaviorRun efficient fine-tuning using LoRA, QLoRA, and parameter-efficient methodsEvaluate models for accuracy, coherence, and bias - quantitatively and qualitativelyDeploy models with FastAPI, Gradio, and cloud or local infrastructureApply fine-tuning to specialized domains like finance, healthcare, and lawCompress and quantize models to run on low-memory devices without sacrificing qualityAutomate continuous learning pipelines and integrate retrieval systems (RAG) for real-world applicationsWhat makes this book different is its developer-first focus. You'll not only learn the how but the why behind each step - from understanding the transformer architecture to optimizing training loops for small GPUs. Each chapter reads like a real conversation between the model and the maker - bridging theory, experimentation, and production.By the final chapters, you'll see how fine-tuning reshapes your role from programmer to model designer. You'll understand why the future of AI isn't just about bigger models - it's about smarter adaptation.Whether you're training your first conversational model, building a retrieval-augmented assistant, or deploying a fine-tuned LLaMA on your laptop, this book is your step-by-step roadmap to mastering the craft of model customization and deployment.Perfect for: Developers - AI engineers - Machine learning enthusiasts - Applied researchers - Tech founders exploring domain-specific AI This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Paperback. Condición: new. Paperback. Fine-Tuning LLMs with PyTorch and Hugging FaceTrain, Customize, and Deploy Large Language Models - A Hands-On Guide for Developers and AI PractitionersIn the new era of open-weight AI, fine-tuning is no longer reserved for big tech. It's the developer's key to transforming powerful pretraine…d models into intelligent systems that understand your data, your tone, and your domain.Fine-Tuning LLMs with PyTorch and Hugging Face is the definitive, hands-on guide for developers, engineers, and AI enthusiasts who want to move beyond prompt engineering and start teaching models to think. Through real-world examples, clean code, and practical workflows, this book takes you from your first training run to deploying a production-ready model that performs like it was built in-house.You'll learn how to: Set up your fine-tuning environment using PyTorch and the Hugging Face ecosystemPrepare, tokenize, and curate datasets that truly shape model behaviorRun efficient fine-tuning using LoRA, QLoRA, and parameter-efficient methodsEvaluate models for accuracy, coherence, and bias - quantitatively and qualitativelyDeploy models with FastAPI, Gradio, and cloud or local infrastructureApply fine-tuning to specialized domains like finance, healthcare, and lawCompress and quantize models to run on low-memory devices without sacrificing qualityAutomate continuous learning pipelines and integrate retrieval systems (RAG) for real-world applicationsWhat makes this book different is its developer-first focus. You'll not only learn the how but the why behind each step - from understanding the transformer architecture to optimizing training loops for small GPUs. Each chapter reads like a real conversation between the model and the maker - bridging theory, experimentation, and production.By the final chapters, you'll see how fine-tuning reshapes your role from programmer to model designer. You'll understand why the future of AI isn't just about bigger models - it's about smarter adaptation.Whether you're training your first conversational model, building a retrieval-augmented assistant, or deploying a fine-tuned LLaMA on your laptop, this book is your step-by-step roadmap to mastering the craft of model customization and deployment.Perfect for: Developers - AI engineers - Machine learning enthusiasts - Applied researchers - Tech founders exploring domain-specific AI This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
