一份面向AI工程师和研究人员的开源技术教科书项目《基础模型工程》正式发布1。该项目旨在超越API使用层面，深入解释现代基础模型的工作原理、技术栈演进和工程权衡1。
教科书涵盖注意力机制、MoE、RLHF、多模态、长上下文推理、RAG和Agent等核心主题1，具体包含Transformer演进、模型密度设计、KV缓存、批处理、评估与对齐等技术内容1。项目以连贯的工程叙事方式组织内容1，为读者提供严谨的概念解释、PyTorch示例、测验和交互式可视化工具1。该项目设计为长期更新的活跃文档，欢迎通过Pull Request进行改进1。
An open-source technical textbook project titled "Foundation Model Engineering" has been released, designed for AI engineers and research-oriented readers seeking to understand modern foundation models beyond API-level usage 1. The initiative explains how contemporary foundation models operate, traces the evolution of technical stacks, and examines key engineering trade-offs across the discipline 1.
The project covers a comprehensive range of topics including Transformer evolution, model density design, KV cache optimization, batch processing, evaluation methodologies, and alignment techniques 1. Additional subject areas encompass attention mechanisms, mixture of experts (MoE), reinforcement learning from human feedback (RLHF), multimodal capabilities, long-context reasoning, retrieval-augmented generation (RAG), and agent systems, organized through a coherent engineering narrative 1.
To facilitate learning, the textbook provides rigorous conceptual explanations accompanied by PyTorch code examples, quizzes, and interactive visualization tools 1. The project is designed as an active, living document intended for long-term updates and welcomes community contributions through pull requests 1.
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