English Version
館藏查詢
他校館藏
  
   系統號碼988020
   書刊名Domain-specific small language models : Efficient AI for Local Deployment /
   主要著者Iozzia, Guglielmo, author.
   出版項Shelter Island, NY : Manning Publications, [2026]
   索書號QA76.9.N38.I59 2026
   ISBN9781633436701
   標題Natural language generation (Computer science)
Natural language processing (Computer science)
Ge?ne?ration automatique de texte.
Traitement automatique des langues naturelles.
Natural language generation (Computer science)-fast
   
    
   分享▼ 
網站搜尋           

 資料類型狀態應還日期預約人數館藏地索書號條碼號
找書圖書在架上0總館
西文圖書區 Shelf
QA76.9.N38 .I59 2026W116373

內容簡介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 sequences--all on commodity hardware. About the Technology Small-footprint language models trained on custom data sets and hosted locally can perform as well as large generalist models in speed and accuracy, often at a fraction of the cost. Domain-Specific Small Language Models shows you how to build privacy-preserving and regulation-compliant SLMs for agentic systems, specialist applications, and deployment on the edge. About the Book This is a practical book that shows you how to adapt pretrained open source models to your domain using transfer learning and parameter-efficient fine-tuning. You'll learn to minimize cost through optimization and quantization, develop secure APIs to serve your models, and deploy SLMs on commodity hardware--including small devices. The hands-on examples include integrating SLMs into RAG systems and agentic workflows. What's Inside ONNX and other quantization methods Integrate SLMs into end-to-end applications Deploy SLMs on laptops, smartphones, and other devices About the Rea

讀者書評

尚無書評,


  
Copyright © 2007 元智大學(Yuan Ze University) ‧ 桃園縣中壢市 320 遠東路135號 ‧ (03)4638800