Domain-Specific Small Language Models

Manning Publications announced the release of 'Domain-Specific Small Language Models', a book teaching developers to build and fine-tune small language models (SLMs) optimized for domain-specific tasks such as generating Python code, protein structures, and antibody sequences on commodity hardware. The book covers model sizing, fine-tuning techniques, Hugging Face libraries, and quantization, targeting cost- or hardware-constrained environments.

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.