ISBN: 9781633436701 - domain-specific small language models: efficient ai for local deployment (23 Ergebnisse)

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Hardback. Zustand: New. 1st. Want LLM power without the LLM price tag? Crave models that fit your data, laptop, and budget? Stop renting GPUs you cannot afford. Start building Domain-Specific Small Language Models today. Own your AI stack, end to end.  Model sizing best practices: pick the smallest architecture that still delivers top-tier accuracy.  Open-source toolchain: leverage Hugging Face, PEFT, and quantization libraries for zero-license freedom.  Fine-tuning workflows: adapt existing checkpoints to niche datasets in hours, not weeks.  Commodity hardware deployment: run chat, code, or bio models locally on a single GPU or CPU.  Retrieval-augmented generation: fuse SLMs with RAG pipelines for grounded, up-to-date answers.  Cost-control checklists: slash cloud spends and eliminate dependency on expensive foundation APIs.  Domain-Specific Small Language Models, by AI director Guglielmo Iozzia, is a field guide packed with runnable Python code and real-world engineering insight.  Step-by-step chapters demystify transformer architecture, quantization, and PEFT fine-tuning, then walk you through building RAG systems and autonomous agents that rely solely on SLMs. Clear diagrams, annotated notebooks, and troubleshooting tips keep learning smooth.  You will finish with reusable templates, deployment scripts, and the confidence to deliver performant language models under tight hardware and budget constraints.  Perfect for Python-savvy machine-learning engineers, data scientists, and technical leads who need domain-tuned AI now.…

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Hardcover. Zustand: new. Hardcover. Want LLM power without the LLM price tag? Crave models that fit your data, laptop, and budget? Stop renting GPUs you cannot afford. Start building Domain-Specific Small Language Models today. Own your AI stack, end to end. Model sizing best practices: pick the smallest architecture that still delivers top-tier accuracy. Open-source toolchain: leverage Hugging Face, PEFT, and quantization libraries for zero-license freedom. Fine-tuning workflows: adapt existing checkpoints to niche datasets in hours, not weeks. Commodity hardware deployment: run chat, code, or bio models locally on a single GPU or CPU. Retrieval-augmented generation: fuse SLMs with RAG pipelines for grounded, up-to-date answers. Cost-control checklists: slash cloud spends and eliminate dependency on expensive foundation APIs. Domain-Specific Small Language Models, by AI director Guglielmo Iozzia, is a field guide packed with runnable Python code and real-world engineering insight. Step-by-step chapters demystify transformer architecture, quantization, and PEFT fine-tuning, then walk you through building RAG systems and autonomous agents that rely solely on SLMs. Clear diagrams, annotated notebooks, and troubleshooting tips keep learning smooth. You will finish with reusable templates, deployment scripts, and the confidence to deliver performant language models under tight hardware and budget constraints. Perfect for Python-savvy machine-learning engineers, data scientists, and technical leads who need domain-tuned AI now. Perfect for cost- or hardware-constrained environments, Small Language Models (SLMs) train on domain specific data for high-quality results in specific tasks. In Domain-Specific Small Language Models youll develop SLMs that can generate everything from Python code to protein structures and antibody sequencesall on commodity hardware. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

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Paperback. Zustand: Brand New. 300 pages. 9.26x7.38x9.25 inches. In Stock.

paperback. Zustand: New. Special order direct from the distributor.

Paperback. Zustand: Brand New. 300 pages. 9.26x7.38x9.25 inches. In Stock.

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Hardcover. Zustand: new. Hardcover. Want LLM power without the LLM price tag? Crave models that fit your data, laptop, and budget? Stop renting GPUs you cannot afford. Start building Domain-Specific Small Language Models today. Own your AI stack, end to end. Model sizing best practices: pick the smallest architecture that still delivers top-tier accuracy. Open-source toolchain: leverage Hugging Face, PEFT, and quantization libraries for zero-license freedom. Fine-tuning workflows: adapt existing checkpoints to niche datasets in hours, not weeks. Commodity hardware deployment: run chat, code, or bio models locally on a single GPU or CPU. Retrieval-augmented generation: fuse SLMs with RAG pipelines for grounded, up-to-date answers. Cost-control checklists: slash cloud spends and eliminate dependency on expensive foundation APIs. Domain-Specific Small Language Models, by AI director Guglielmo Iozzia, is a field guide packed with runnable Python code and real-world engineering insight. Step-by-step chapters demystify transformer architecture, quantization, and PEFT fine-tuning, then walk you through building RAG systems and autonomous agents that rely solely on SLMs. Clear diagrams, annotated notebooks, and troubleshooting tips keep learning smooth. You will finish with reusable templates, deployment scripts, and the confidence to deliver performant language models under tight hardware and budget constraints. Perfect for Python-savvy machine-learning engineers, data scientists, and technical leads who need domain-tuned AI now. Perfect for cost- or hardware-constrained environments, Small Language Models (SLMs) train on domain specific data for high-quality results in specific tasks. In Domain-Specific Small Language Models youll develop SLMs that can generate everything from Python code to protein structures and antibody sequencesall on commodity hardware. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Zustand: Neu. Neuware - Bigger isn't always better. Train and tune highly focused language models optimized for domain specific tasks.When you need a language model to respond accurately and quickly about a specific field of knowledge, the sprawling capacity of a LLM may hurt more than it helps. Domain-Specific Small Language Models teaches you to build generative AI models optimized for specific fields. In Domain-Specific Small Language Models you'll discover: Model sizing best practices Open source libraries, frameworks, utilities and runtimes Fine-tuning techniques for custom datasets Hugging Face's libraries for SLMs Running SLMs on commodity hardware Model optimization or quantization Perfect for cost- or hardware-constrained environments, Small Language Models (SLMs) train on domain specific data for high-quality results in specific tasks. In Domain-Specific Small Language Models you'll develop SLMs that can generate everything from Python code to protein structures and antibody sequencesall on commodity hardware. About the book Domain-Specific Small Language Models teaches you how to create language models that deliver the power of LLMs for specific areas of knowledge. It provides a practical, application-focused counterpart to foundational texts like Sebastian Raschka's Build a Large Language Model (From Scratch), showing you how to adapt large-scale concepts for efficient, specialized use. You'll learn to minimize the computational horsepower your models require, while keeping highquality performance times and output. You'll appreciate the clear explanations of complex technical concepts alongside working code samples you can run and replicate on your laptop. Plus, you'll learn to develop and deliver RAG systems and AI agents that rely solely on SLMs, and without the costs of foundation model access. About the reader For machine learning engineers familiar with Python. About the author Guglielmo Iozzia is a Director, ML/AI and Applied Mathematics at MSD. He studied Electronic and Biomedical Engineering at the University of Bologna, has an extensive background in Software and ML/AI Engineering applied to real-life use cases across different industries, such as Biotech Manufacturing, Healthcare, Cloud Operations, and Cyber Security. Get a free Elektronisches Buch (PDF or ePub) from Manning as well as access to the online liveBook format (and its AI assistant that will answer your questions in any language) when you purchase the print book.…

Zustand: New. 2026. 1st Edition. paperback. . . . . . Books ship from the US and Ireland.

Paperback. Zustand: New. Brand New! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 6-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.…

Hardcover. Zustand: new. Hardcover. Want LLM power without the LLM price tag? Crave models that fit your data, laptop, and budget? Stop renting GPUs you cannot afford. Start building Domain-Specific Small Language Models today. Own your AI stack, end to end. Model sizing best practices: pick the smallest architecture that still delivers top-tier accuracy. Open-source toolchain: leverage Hugging Face, PEFT, and quantization libraries for zero-license freedom. Fine-tuning workflows: adapt existing checkpoints to niche datasets in hours, not weeks. Commodity hardware deployment: run chat, code, or bio models locally on a single GPU or CPU. Retrieval-augmented generation: fuse SLMs with RAG pipelines for grounded, up-to-date answers. Cost-control checklists: slash cloud spends and eliminate dependency on expensive foundation APIs. Domain-Specific Small Language Models, by AI director Guglielmo Iozzia, is a field guide packed with runnable Python code and real-world engineering insight. Step-by-step chapters demystify transformer architecture, quantization, and PEFT fine-tuning, then walk you through building RAG systems and autonomous agents that rely solely on SLMs. Clear diagrams, annotated notebooks, and troubleshooting tips keep learning smooth. You will finish with reusable templates, deployment scripts, and the confidence to deliver performant language models under tight hardware and budget constraints. Perfect for Python-savvy machine-learning engineers, data scientists, and technical leads who need domain-tuned AI now. Perfect for cost- or hardware-constrained environments, Small Language Models (SLMs) train on domain specific data for high-quality results in specific tasks. In Domain-Specific Small Language Models youll develop SLMs that can generate everything from Python code to protein structures and antibody sequencesall on commodity hardware. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

paperback. Zustand: Sehr gut. 300 Seiten; 9781633436701.2 Gewicht in Gramm: 500.

Zustand: New. 2026. 1st Edition. paperback. . . . . .

Hardback. Zustand: New. 1st. Want LLM power without the LLM price tag? Crave models that fit your data, laptop, and budget? Stop renting GPUs you cannot afford. Start building Domain-Specific Small Language Models today. Own your AI stack, end to end.  Model sizing best practices: pick the smallest architecture that still delivers top-tier accuracy.  Open-source toolchain: leverage Hugging Face, PEFT, and quantization libraries for zero-license freedom.  Fine-tuning workflows: adapt existing checkpoints to niche datasets in hours, not weeks.  Commodity hardware deployment: run chat, code, or bio models locally on a single GPU or CPU.  Retrieval-augmented generation: fuse SLMs with RAG pipelines for grounded, up-to-date answers.  Cost-control checklists: slash cloud spends and eliminate dependency on expensive foundation APIs.  Domain-Specific Small Language Models, by AI director Guglielmo Iozzia, is a field guide packed with runnable Python code and real-world engineering insight.  Step-by-step chapters demystify transformer architecture, quantization, and PEFT fine-tuning, then walk you through building RAG systems and autonomous agents that rely solely on SLMs. Clear diagrams, annotated notebooks, and troubleshooting tips keep learning smooth.  You will finish with reusable templates, deployment scripts, and the confidence to deliver performant language models under tight hardware and budget constraints.  Perfect for Python-savvy machine-learning engineers, data scientists, and technical leads who need domain-tuned AI now.…

Zustand: gut. 2026. Domain-Specific Small Language Models: Efficient Ai for Local Deployment In englischer Sprache. pages.