Domain-specific small language models /
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...
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Shelter Island, New York :
Manning Publications,
[2026].
|
| Subjects: |
| Summary: | 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 and 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. 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. 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. |
|---|---|
| Item Description: | Includes index. |
| Physical Description: | xx, 352 pages : illustrations ; 24 cm. |
| ISBN: | 9781633436701 1633436705 |