References & Citations
Computer Science > Computation and Language
Title: DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
(Submitted on 7 May 2024 (v1), last revised 24 May 2024 (this version, v4))
Abstract: We present DeepSeek-V2, a strong Mixture-of-Experts (MoE) language model characterized by economical training and efficient inference. It comprises 236B total parameters, of which 21B are activated for each token, and supports a context length of 128K tokens. DeepSeek-V2 adopts innovative architectures including Multi-head Latent Attention (MLA) and DeepSeekMoE. MLA guarantees efficient inference through significantly compressing the Key-Value (KV) cache into a latent vector, while DeepSeekMoE enables training strong models at an economical cost through sparse computation. Compared with DeepSeek 67B, DeepSeek-V2 achieves significantly stronger performance, and meanwhile saves 42.5% of training costs, reduces the KV cache by 93.3%, and boosts the maximum generation throughput to 5.76 times. We pretrain DeepSeek-V2 on a high-quality and multi-source corpus consisting of 8.1T tokens, and further perform Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) to fully unlock its potential. Evaluation results show that, even with only 21B activated parameters, DeepSeek-V2 and its chat versions still achieve top-tier performance among open-source models.
Submission history
From: Wenfeng Liang [view email][v1] Tue, 7 May 2024 15:56:43 GMT (431kb,D)
[v2] Wed, 8 May 2024 02:43:34 GMT (431kb,D)
[v3] Thu, 16 May 2024 17:25:01 GMT (432kb,D)
[v4] Fri, 24 May 2024 15:24:58 GMT (432kb,D)
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