deepseek

I am the DeepSeek-R3 reasoning models

🚀 DeepSeek-V3: Scaling Open-Source AGI with Efficiency

DeepSeek-V3 is a 671B parameter Mixture-of-Experts (MoE) model, with 37B activated per token, designed to push the boundaries of open-source LLMs. It leverages innovative architectures like Multi-head Latent Attention (MLA) and DeepSeekMoE for efficient training and inference, while pioneering auxiliary-loss-free load balancing and multi-token prediction to enhance performance.

AI Research Breakthrough

🔧 Optimized Training: FP8 Precision and DualPipe Algorithm

DeepSeek-V3 introduces FP8 mixed precision training and the DualPipe algorithm for pipeline parallelism, achieving near-zero communication overhead and high training efficiency. This enables pre-training on 14.8T tokens at a cost of only 2.664M H800 GPU hours, making it one of the most cost-effective large-scale models.

Training Optimization

📦 Post-Training: Knowledge Distillation from DeepSeek-R1

DeepSeek-V3 incorporates reasoning capabilities from DeepSeek-R1 through innovative distillation techniques, enhancing its performance in math, coding, and reasoning tasks. This approach maintains a balance between accuracy and generation length, ensuring robust and efficient outputs.

Model Distillation

🏛️ Architecture: Multi-head Latent Attention and DeepSeekMoE

DeepSeek-V3's architecture is built on the Transformer framework, featuring Multi-head Latent Attention (MLA) for efficient inference and DeepSeekMoE for economical training. MLA reduces Key-Value (KV) cache during inference, while DeepSeekMoE employs a novel auxiliary-loss-free load balancing strategy to ensure balanced expert utilization during training.

Architecture Innovation

🔮 Multi-Token Prediction (MTP)

DeepSeek-V3 introduces Multi-Token Prediction (MTP), a training objective that predicts multiple future tokens at each position. This approach densifies training signals and improves data efficiency, enabling the model to pre-plan its representations for better future token prediction. During inference, MTP modules can be repurposed for speculative decoding to reduce generation latency.

Training Enhancement

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