The article by Martin Fowler provides a comprehensive overview of the DeepSeek series, detailing the evolution of its technical reports focused on developing large language models (LLMs) with minimal resource requirements. The series consists of four key reports: DeepSeek-LLM, which investigates scaling and data-model trade-offs; DeepSeek-V2, which introduces innovations in memory and training efficiency; DeepSeek-V3, which scales models to 671 billion parameters using advanced computational techniques; and DeepSeek-R1, which explores the capabilities of reinforcement learning for understanding reasoning. Significant themes across these reports include efforts to enhance cost and memory efficiency, the application of high-performance computing co-design, and the emergence of sophisticated reasoning skills through targeted training strategies.

Key Points:

• DeepSeek Series Overview: Four reports aimed at optimizing large LLM training.
• DeepSeek-LLM: Explores scaling laws and the trade-offs between model size and training data.
• DeepSeek-V2: Introduces Multi-Head Latent Attention (MLA) and DeepSeekMoE to improve memory efficiency.
• DeepSeek-V3: Achieves 671B parameters with advanced HPC co-design techniques for stability and efficiency.
• DeepSeek-R1: Focuses on emergent reasoning capabilities via reinforcement learning, offering a zero-SFT variant to streamline training.
• Core Challenges: Addressing cost and memory efficiency, training instability, and data quality for better outcomes.
• HPC Co-Design: Integrates architecture and infrastructure to enhance training efficiency and manageability.
• Emergent Reasoning: Advances in reasoning tasks arise from refined training methodologies, especially through reinforcement learning strategies.
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