DeepSeek in Action: LLM Deployment, Fine-Tuning, and Application

Building with a large language model means understanding more than the prompt: architecture, training choices, deployment, and integration all shape what an application can do. DeepSeek in Action follows that path from the foundations of Transformers and attention to hands-on development with DeepSeek-V3.

Jing Dai’s book is aimed primarily at AI researchers and developers working with large-scale models. Its scope moves between technical architecture and implementation, giving readers a structured way to consider both how the model works and how it can be used in software.

Start with the architecture

The first part introduces Transformer and attention mechanisms before examining DeepSeek-V3’s Mixture of Experts design, FP8 mixed-precision training, distributed training strategies, communication optimization, and load balancing. It also considers caching, token management, and other models in the DeepSeek family. This foundation helps situate deployment decisions within the systems and techniques behind large models.

From model concepts to development work

Later chapters address common application areas such as dialogue, mathematical reasoning, code completion, and customized model development. The book also covers API invocation, authentication, performance optimization, data protection, and access control—practical concerns that matter when connecting a model to an application.

Structured output, caching, and integration

Readers will find material on conversational prefix completion, fill-in-the-middle generation, and JSON output, alongside callback functions and contextual disk caching. The contents extend into integration projects, including chat-client development and programming tools. Together, these topics offer a view of the work involved in taking model capabilities into more complete application workflows.

Who may find it useful

This technical treatment is best suited to AI researchers, developers, and other readers comfortable engaging with large-model architecture and implementation. Those looking for a focused account of DeepSeek-V3—from training and deployment through APIs and application patterns—will find the book’s progression especially relevant.