Exploring the Architecture and Training Strategies Behind DeepSeek Models

21 Eki 07:00

I'm curious about how DeepSeek models are built under the hood. Specifically, what architectural choices differentiate them from other large language models, and how do they balance dense and sparse attention mechanisms? Also, I'm interested in the training pipeline—what kind of pretraining data mix and curriculum are typically employed, and how is reinforcement learning from human feedback integrated at scale? Any insights on tokenization strategies, positional encodings, or optimization tricks would be valuable. How have you approached similar model design challenges, and what resources helped you understand these concepts better?