📄 中文摘要
探讨中国开源AI生态系统中模型构建的架构决策，尤其关注DeepSeek之外的多样化发展路径。分析了当前主流的大语言模型（LLM）和多模态模型在架构设计上的共性与差异，包括Transformer变体、混合专家（MoE）模型以及不同编码器-解码器结构的适用性。具体研究了如何权衡计算效率、模型性能和可扩展性，以适应中国AI应用场景的独特需求，例如中文语义理解的深度优化、特定行业知识的融合以及对低资源语言的支持。讨论了数据预处理、模型训练策略（如预训练、微调、知识蒸馏）和推理优化技术在不同架构选择中的影响。此外，还考察了硬件加速器（如昇腾、寒武纪）对模型架构设计的影响，以及如何通过软件栈与硬件协同优化提升整体系统效率。强调了开源社区在推动架构创新和知识共享方面的重要性，并展望了未来中国开源AI生态在模型架构多样性、创新性和实用性方面的发展趋势。
📄 English Summary
Architectural Choices in China's Open-Source AI Ecosystem: Building Beyond DeepSeek
Investigates the architectural choices in building models within China's open-source AI ecosystem, with a particular focus on diverse development paths beyond DeepSeek. Analyzes commonalities and differences in architectural designs of current mainstream large language models (LLMs) and multimodal models, including Transformer variants, Mixture-of-Experts (MoE) models, and the applicability of various encoder-decoder structures. Specifically examines the trade-offs among computational efficiency, model performance, and scalability to meet the unique demands of Chinese AI application scenarios, such as deep optimization for Chinese semantic understanding, integration of domain-specific knowledge, and support for low-resource languages. Discusses the impact of data preprocessing, model training strategies (e.g., pre-training, fine-tuning, knowledge distillation), and inference optimization techniques on different architectural choices. Furthermore, explores the influence of hardware accelerators (e.g., Ascend, Cambricon) on model architectural design and how software stack and hardware co-optimization can enhance overall system efficiency. Emphasizes the significance of the open-source community in promoting architectural innovation and knowledge sharing, and anticipates future trends in the diversity, innovativeness, and practicality of model architectures within China's open-source AI ecosystem.