is DeepSeek's first-generation of reasoning models. Through a progression of non-reasoning models, the engineers at DeepSeek designed and used methods to maximize hardware utilization. These include Multi-Head Latent Attention (MLA), Mixture of Experts (MoE) gating, 8-bit floating points training (FP8) and low-level PTX programming. Their approach enables DeepSeek-R1 to rival state-of-the-art models at significantly reduced cost for training and inference.
DeepSeek-R1-Zero is notable for another innovation: the engineers were able to elicit reasoning capabilities from a non-reasoning model using simple reinforcement learning without any supervised fine-tuning. All DeepSeek models are open-weight, which means they are freely available, though training code and data remain proprietary. The repository includes six dense models distilled from DeepSeek-R1, based on Llama and Qwen, with DeepSeek-R1-Distill-Qwen-32B outperforming OpenAI-o1-mini on various benchmarks.
