Product Information
Description
Small Language Models and Local AI: Tiny but Mighty, Fully on Your Device — a survey of a frontier of autonomous AI in the Agents in the Wild series.
Book framework (chapter themes):
- Conceptual Definition and Domain Evolution of Small Language Models
- Architecture Design and Parameter-Scale Trade-offs of Small Language Models
- Influence of Pretraining Strategies and Data Engineering on Small Language Models
- Knowledge Distillation: Transferring Capability from Large Models to Small Models
- Model Quantization and Compression Techniques
- Local Inference Engines and Deployment Infrastructure
- Fine-Tuning Strategies: From General-Purpose Models to Domain Experts
- The Synergy of Retrieval-Augmented Generation (RAG) and Small Language Models
- Reasoning Ability and Chain-of-Thought Mechanisms in Small Language Models
- Collaboration Mechanisms Between Small and Large Language Models
- Memory Mechanisms and Context Management
- Evaluation Benchmarks and Capability Assessment Methodology
- Domain Applications: Healthcare and Privacy-Sensitive Scenarios
- Domain Applications: Code Generation, Agents, and the Internet of Things
- Multimodal Small Language Models and Vision-Language Synergy
- Alignment, Safety, and Reliability
- Bias and Fairness in Small Language Models
- Performance Optimization and Resource-Constrained Deployment Practices
- Challenges, Limitations, and Open Problems
- Future Directions: A Technical Roadmap and Ecosystem Outlook for Small Language Models
Who it is for. Researchers, engineers, and advanced enthusiasts with a background in machine learning who wish to systematically understand the technical architecture of small language models and the practice of local deployment.
