Deep Dive into DeepSeek, Volume I: Models, Training, and Reasoning

Go beyond release headlines and understand how DeepSeek models actually work.DeepSeek is often encountered as a stream of model names, benchmark scores, and rapidly changing claims. Deep Dive into DeepSeek, Volume I turns that history into a clear, source-grounded account of the mechanisms, training choices, and evidence behind the models.Inside Volume I- Requests, checkpoints, tokenization, context, and reproducible comparisons- Transformer inference costs and measurement foundations- DeepSeekMoE, sparse expert routing, and Multi-Head Latent Attention- DeepSeek-V3 architecture, pretraining, and post-training- R1-Zero, DeepSeek-R1, reinforcement-learning reasoning, and distillation- DeepSeek-V3.2, DeepSeek-V4, Engram, and conditional memory- Models for coding, mathematics, theorem proving, vision, image generation, and document understandingThe book connects each design change to the work a model performs and the state it retains. It separates checkpoints from the interfaces that host them, relates training decisions to reported behavior, and shows how to decide whether benchmark results and technical claims are genuinely comparable.Written for machine-learning practitioners, engineers, researchers, and students, this volume assumes the basic ideas of deep learning and language models. General transformer vocabulary is sufficient; no DeepSeek-specific background is required.For readers who want more than a list of releases, this is a mechanism-first guide to understanding the DeepSeek model family.