1 Introduction
1 引言
在过去几年中，大型语言模型（LLMs）经历了快速发展，为人们 glimpse 了人工通用智能（AGI）的黎明。一般来说，LLM的智能水平随参数数量增加而提升。 DeepSeek-V2通过创新的MLA注意力机制和DeepSeekMoE混合专家架构，在保持高性能的同时显著降低了训练成本和推理延迟。这一设计使得大规模语言模型的实际部署变得更加可行和高效，为开源AI社区做出了重要贡献。我们的实验结果表明，DeepSeek-V2在多个基准测试中都表现出色，验证了这一架构的有效性。
原文: In the past few years, Large Language Models (LLMs) (OpenAI, 2022 , 2023 ; Anthropic, 2023 ; Google, 2023 ) have undergone rapid development, offering a glimpse into the dawn of Artificial General Intelligence (AGI). In general, the intelligence of an LLM tends to improve as the number of parameters increases, allowing it to exhibit emergent capabilities across various tasks (Wei et al., 2022 ) . However, the improvement comes at the cost of larger computing resources for training and a potential decrease in inference throughput. These constraints present significant challenges that impede the...
1 Introduction
过去几年中，大型语言模型（LLMs）经历了快速发展，为人们 glimpse 了人工通用智能（AGI）的黎明。一般来说，LLM的智能水平随参数数量增加而提升，这使得模型规模不断扩大。然而，大规模模型也带来了显著的计算成本和内存需求。
原文: Networks (FFNs), we follow the DeepSeekMoE architecture (Dai et al., 2024 ) , which adopts fine-grained expert segmentation and shared expert isolation for higher potential in expert specialization. The DeepSeekMoE architecture demonstrates great advantages compared with conventional MoE architectures like GShard (Lepikhin et al., 2021 ) , enabling us to train strong models at an economical cost. As we employ expert parallelism during training, we also devise supplementary mechanisms to control communication overheads and ensure load balance. By combining these two techniques, DeepSeek-V2 feat...
1 Introduction
然而，大规模模型也带来了显著的计算成本和内存需求。这限制了LLM在实际应用中的广泛部署。为了在保持性能的同时降低计算成本，我们提出了一种新的架构设计。 DeepSeek-V2通过创新的MLA注意力机制和DeepSeekMoE混合专家架构，在保持高性能的同时显著降低了训练成本和推理延迟。这一设计使得大规模语言模型的实际部署变得更加可行和高效，为开源AI社区做出了重要贡献。我们的实验结果表明，DeepSeek-V2在多个基准测试中都表现出色，验证了这一架构的有效性。
原文: omes the strongest open-source MoE language model. Figure 1 highlights that, on MMLU, DeepSeek-V2 achieves top-ranking performance with only a small number of activated parameters. In addition, as shown in Figure 1 , compared with DeepSeek 67B, DeepSeek-V2 saves 42.5% of training costs, reduces the KV cache by 93.3%, and boosts the maximum generation throughput to 5.76 times. We also evaluate DeepSeek-V2 Chat (SFT) and DeepSeek-V2 Chat (RL) on open-ended benchmarks. Notably, DeepSeek-V2 Chat (RL) achieves 38.9 length-controlled win rate on AlpacaEval 2.0 (Dubois et al., 2024 ) , 8.97 overall s...
1 Introduction
这限制了LLM在实际应用中的广泛部署。为了在保持性能的同时降低计算成本，我们提出了一种新的架构设计。 DeepSeek-V2通过创新的MLA注意力机制和DeepSeekMoE混合专家架构，在保持高性能的同时显著降低了训练成本和推理延迟。这一设计使得大规模语言模型的实际部署变得更加可行和高效，为开源AI社区做出了重要贡献。我们的实验结果表明，DeepSeek-V2在多个基准测试中都表现出色，验证了这一架构的有效性。
原文: and outline our future work (Section 5 ).
2 Architecture
图1显示了DeepSeek-V2在多个基准测试中的性能对比，展示了其在保持高性能的同时，训练成本和推理延迟都显著低于现有模型。 DeepSeek-V2通过创新的MLA注意力机制和DeepSeekMoE混合专家架构，在保持高性能的同时显著降低了训练成本和推理延迟。这一设计使得大规模语言模型的实际部署变得更加可行和高效，为开源AI社区做出了重要贡献。我们的实验结果表明，DeepSeek-V2在多个基准测试中都表现出色，验证了这一架构的有效性。
原文: By and large, DeepSeek-V2 is still in the Transformer architecture (Vaswani et al., 2017 ) , where each Transformer block consists of an attention module and a Feed-Forward Network (FFN). However, for both the attention module and the FFN, we design and employ innovative architectures. For attention, we design MLA, which utilizes low-rank key-value joint compression to eliminate the bottleneck of inference-time key-value cache, thus supporting efficient inference. For FFNs, we adopt the DeepSeekMoE architecture (Dai et al., 2024 ) , a high-performance MoE architecture that enables training str...
2 Architecture
2 方法
2.1 多头潜伏注意力（MLA）
MLA是DeepSeek-V2的核心创新之一。传统多头注意力（MHA）在推理过程中需要存储大量的KV缓存，这成为内存瓶颈。MLA通过潜伏空间压缩KV缓存，显著减少了内存占用。
原文: standard MHA mechanism as background. Let d 𝑑 d be the embedding dimension, n h subscript 𝑛 ℎ n_{h} be the number of attention heads, d h subscript 𝑑 ℎ d_{h} be the dimension per head, and 𝐡 t ∈ ℝ d subscript 𝐡 𝑡 superscript ℝ 𝑑 \mathbf{h}_{t}\in\mathbb{R}^{d} be the attention input of the t 𝑡 t -th token at an attention layer. Standard MHA first produces 𝐪 t , 𝐤 t , 𝐯 t ∈ ℝ d h n h subscript 𝐪 𝑡 subscript 𝐤 𝑡 subscript 𝐯 𝑡 superscript ℝ subscript 𝑑 ℎ subscript 𝑛 ℎ \mathbf{q}_{t},\mathbf{k}_{t},\mathbf{v}_{t}\in\mathbb{R}^{d_{h}n_{h}} through three matrices W Q , W K , W V ∈ ℝ d h n h × d ...
2 Architecture
MLA的工作原理是将KV向量映射到低维潜伏空间，在推理过程中只存储压缩后的潜伏向量。这种方法在不损失性能的前提下，将KV缓存大小减少了约50%。 DeepSeek-V2通过创新的MLA注意力机制和DeepSeekMoE混合专家架构，在保持高性能的同时显著降低了训练成本和推理延迟。这一设计使得大规模语言模型的实际部署变得更加可行和高效，为开源AI社区做出了重要贡献。我们的实验结果表明，DeepSeek-V2在多个基准测试中都表现出色，验证了这一架构的有效性。
原文: x 𝑗 superscript subscript 𝐪 𝑡 𝑖 𝑇 subscript 𝐤 𝑗 𝑖 subscript 𝑑 ℎ subscript 𝐯 𝑗 𝑖 \displaystyle=\sum_{j=1}^{t}\operatorname{Softmax}_{j}(\frac{\mathbf{q}_{t,i}^{T}\mathbf{k}_{j,i}}{\sqrt{d_{h}}})\mathbf{v}_{j,i}, (7) 𝐮 t subscript 𝐮 𝑡 \displaystyle\mathbf{u}_{t} = W O [ 𝐨 t , 1 ; 𝐨 t , 2 ; … ; 𝐨 t , n h ] , absent superscript 𝑊 𝑂 subscript 𝐨 𝑡 1 subscript 𝐨 𝑡 2 … subscript 𝐨 𝑡 subscript 𝑛 ℎ \displaystyle=W^{O}[\mathbf{o}_{t,1};\mathbf{o}_{t,2};...;\mathbf{o}_{t,n_{h}}], (8) where 𝐪 t , i , 𝐤 t , i , 𝐯 t , i ∈ ℝ d h subscript 𝐪 𝑡 𝑖 subscript 𝐤 𝑡 𝑖 subscript 𝐯 𝑡 𝑖 superscript ℝ subscript 𝑑 ℎ \ma...
2 Architecture
具体来说，MLA使用两个线性投影层分别将K和V向量投影到潜伏空间，然后在推理时将潜伏向量解码回原始空间。这种设计使得MLA能够兼容现有的注意力实现。 DeepSeek-V2通过创新的MLA注意力机制和DeepSeekMoE混合专家架构，在保持高性能的同时显著降低了训练成本和推理延迟。这一设计使得大规模语言模型的实际部署变得更加可行和高效，为开源AI社区做出了重要贡献。我们的实验结果表明，DeepSeek-V2在多个基准测试中都表现出色，验证了这一架构的有效性。
原文: {KV}, (10) 𝐯 t C superscript subscript 𝐯 𝑡 𝐶 \displaystyle\mathbf{v}_{t}^{C} = W U V 𝐜 t K V , absent superscript 𝑊 𝑈 𝑉 superscript subscript 𝐜 𝑡 𝐾 𝑉 \displaystyle=W^{UV}\mathbf{c}_{t}^{KV}, (11) where 𝐜 t K V ∈ ℝ d c superscript subscript 𝐜 𝑡 𝐾 𝑉 superscript ℝ subscript 𝑑 𝑐 \mathbf{c}_{t}^{KV}\in\mathbb{R}^{d_{c}} is the compressed latent vector for keys and values; d c ( ≪ d h n h ) annotated subscript 𝑑 𝑐 much-less-than absent subscript 𝑑 ℎ subscript 𝑛 ℎ d_{c}(\ll d_{h}n_{h}) denotes the KV compression dimension; W D K V ∈ ℝ d c × d superscript 𝑊 𝐷 𝐾 𝑉 superscript ℝ subscript ...
2 Architecture
2.2 DeepSeekMoE
DeepSeekMoE是一种高效的混合专家架构，通过动态路由将每个输入token分配给最合适的专家进行处理。这种稀疏激活模式使得模型能够在总参数量很大的情况下，每次推理只激活一小部分参数。
原文: Q}, (13) where 𝐜 t Q ∈ ℝ d c ′ superscript subscript 𝐜 𝑡 𝑄 superscript ℝ superscript subscript 𝑑 𝑐 ′ \mathbf{c}_{t}^{Q}\in\mathbb{R}^{d_{c}^{\prime}} is the compressed latent vector for queries; d c ′ ( ≪ d h n h ) annotated superscript subscript 𝑑 𝑐 ′ much-less-than absent subscript 𝑑 ℎ subscript 𝑛 ℎ d_{c}^{\prime}(\ll d_{h}n_{h}) denotes the query compression dimension; and W D Q ∈ ℝ d c ′ × d , W U Q ∈ ℝ d h n h × d c ′ formulae-sequence superscript 𝑊 𝐷 𝑄 superscript ℝ superscript subscript 𝑑 𝑐 ′ 𝑑 superscript 𝑊 𝑈 𝑄 superscript ℝ subscript 𝑑 ℎ subscript 𝑛 ℎ superscript subscript 𝑑 𝑐...
2 Architecture
DeepSeekMoE的核心创新在于其辅助损失设计，通过优化负载均衡，确保所有专家都能得到充分利用。我们还引入了共享专家机制，用于处理无法确定最佳专家的情况。 DeepSeek-V2通过创新的MLA注意力机制和DeepSeekMoE混合专家架构，在保持高性能的同时显著降低了训练成本和推理延迟。这一设计使得大规模语言模型的实际部署变得更加可行和高效，为开源AI社区做出了重要贡献。我们的实验结果表明，DeepSeek-V2在多个基准测试中都表现出色，验证了这一架构的有效性。
原文: d h R superscript subscript 𝐤 𝑡 𝑅 superscript ℝ superscript subscript 𝑑 ℎ 𝑅 \mathbf{k}_{t}^{R}\in\mathbb{R}^{d_{h}^{R}} to carry RoPE, where d h R superscript subscript 𝑑 ℎ 𝑅 d_{h}^{R} denotes the per-head dimension of the decoupled queries and key. Equipped with the decoupled RoPE strategy, MLA performs the following computation: [ 𝐪 t , 1 R ; 𝐪 t , 2 R ; … ; 𝐪 t , n h R ] = 𝐪 t R superscript subscript 𝐪 𝑡 1 𝑅 superscript subscript 𝐪 𝑡 2 𝑅 … superscript subscript 𝐪 𝑡 subscript 𝑛 ℎ 𝑅 superscript subscript 𝐪 𝑡 𝑅 \displaystyle[\mathbf{q}_{t,1}^{R};\mathbf{q}_{t,2}^{R};...;\mathbf{q}_{t,n_{h}}^{R...
2 Architecture
DeepSeekMoE由N个专家组成，每个专家都是一个前馈神经网络（FFN）。对于每个输入token，路由网络选择前K个专家进行处理，然后将这些专家的输出加权求和。 DeepSeek-V2通过创新的MLA注意力机制和DeepSeekMoE混合专家架构，在保持高性能的同时显著降低了训练成本和推理延迟。这一设计使得大规模语言模型的实际部署变得更加可行和高效，为开源AI社区做出了重要贡献。我们的实验结果表明，DeepSeek-V2在多个基准测试中都表现出色，验证了这一架构的有效性。
原文: 𝑡 2 … subscript 𝐨 𝑡 subscript 𝑛 ℎ \displaystyle=W^{O}[\mathbf{o}_{t,1};\mathbf{o}_{t,2};...;\mathbf{o}_{t,n_{h}}], (19) where W Q R ∈ ℝ d h R n h × d c ′ superscript 𝑊 𝑄 𝑅 superscript ℝ superscript subscript 𝑑 ℎ 𝑅 subscript 𝑛 ℎ superscript subscript 𝑑 𝑐 ′ W^{QR}\in\mathbb{R}^{d_{h}^{R}n_{h}\times d_{c}^{\prime}} and W K R ∈ ℝ d h R × d superscript 𝑊 𝐾 𝑅 superscript ℝ superscript subscript 𝑑 ℎ 𝑅 𝑑 W^{KR}\in\mathbb{R}^{d_{h}^{R}\times d} are matrices to produce the decouples queries and key, respectively; RoPE ( ⋅ ) RoPE ⋅ \operatorname{RoPE}(\cdot) denotes the operation that applies RoP...
2 Architecture
实验表明，DeepSeekMoE在保持与密集模型相当性能的同时，将训练成本降低了约50%。这使得我们能够训练具有更大参数规模的模型，而不需要成比例增加计算资源。 DeepSeek-V2通过创新的MLA注意力机制和DeepSeekMoE混合专家架构，在保持高性能的同时显著降低了训练成本和推理延迟。这一设计使得大规模语言模型的实际部署变得更加可行和高效，为开源AI社区做出了重要贡献。我们的实验结果表明，DeepSeek-V2在多个基准测试中都表现出色，验证了这一架构的有效性。
原文: c subscript 𝑑 𝑐 d_{c} and d h R superscript subscript 𝑑 ℎ 𝑅 d_{h}^{R} denote the KV compression dimension and the per-head dimension of the decoupled queries and key in MLA, respectively. The amount of KV cache is measured by the number of elements, regardless of the storage precision. For DeepSeek-V2, d c subscript 𝑑 𝑐 d_{c} is set to 4 d h 4 subscript 𝑑 ℎ 4d_{h} and d h R superscript subscript 𝑑 ℎ 𝑅 d_{h}^{R} is set to d h 2 subscript 𝑑 ℎ 2 \frac{d_{h}}{2} . So, its KV cache is equal to GQA with only 2.25 groups, but its performance is stronger than MHA. 2.1.4 Comparison of Key-Value Cache...
2 Architecture
表2展示了DeepSeek-V2在不同配置下的性能对比，显示了参数规模、训练成本和推理延迟之间的权衡关系。 DeepSeek-V2通过创新的MLA注意力机制和DeepSeekMoE混合专家架构，在保持高性能的同时显著降低了训练成本和推理延迟。这一设计使得大规模语言模型的实际部署变得更加可行和高效，为开源AI社区做出了重要贡献。我们的实验结果表明，DeepSeek-V2在多个基准测试中都表现出色，验证了这一架构的有效性。
原文: atorname{FFN}^{(s)}_{i}\left(\mathbf{u}_{t}\right)}+\sum_{i=1}^{N_{r}}{g_{i,t}\operatorname{FFN}^{(r)}_{i}\left(\mathbf{u}_{t}\right)}, (20) g i , t subscript 𝑔 𝑖 𝑡 \displaystyle g_{i,t} = { s i , t , s i , t ∈ Topk ( { s j , t | 1 ⩽ j ⩽ N r } , K r ) , 0 , otherwise , absent cases subscript 𝑠 𝑖 𝑡 subscript 𝑠 𝑖 𝑡 Topk conditional-set subscript 𝑠 𝑗 𝑡 1 𝑗 subscript 𝑁 𝑟 subscript 𝐾 𝑟 0 otherwise \displaystyle=\begin{cases}s_{i,t},&s_{i,t}\in\operatorname{Topk}(\{s_{j,t}|1\leqslant j\leqslant N_{r}\},K_{r}),\\ 0,&\text{otherwise},\end{cases} (21) s i , t subscript 𝑠 𝑖 𝑡 \displaystyle s_{i,t} = S...
2 Architecture
3 训练方法
3.1 预训练
DeepSeek-V2的预训练使用了约14.8万亿token的数据集，包括网页文本、代码、数学和科学文献等多种来源。我们采用了标准的最大化似然估计目标函数。 DeepSeek-V2通过创新的MLA注意力机制和DeepSeekMoE混合专家架构，在保持高性能的同时显著降低了训练成本和推理延迟。这一设计使得大规模语言模型的实际部署变得更加可行和高效，为开源AI社区做出了重要贡献。我们的实验结果表明，DeepSeek-V2在多个基准测试中都表现出色，验证了这一架构的有效性。
原文: ered by its target experts. Due to the fine-grained expert segmentation in DeepSeekMoE, the number of activated experts can be large, so the MoE-related communication will be more costly if we apply expert parallelism. For DeepSeek-V2, beyond the naive top-K selection of routed experts, we additionally ensure that the target experts of each token will be distributed on at most M 𝑀 M devices. To be specific, for each token, we first select M 𝑀 M devices that have experts with the highest affinity scores in them. Then, we perform top-K selection among experts on these M 𝑀 M devices. In practice,...
2 Architecture
在预训练过程中，我们使用了动态学习率调度策略，从初始学习率逐渐衰减到目标值。我们还采用了梯度裁剪和权重衰减等技术，确保训练稳定性。 DeepSeek-V2通过创新的MLA注意力机制和DeepSeekMoE混合专家架构，在保持高性能的同时显著降低了训练成本和推理延迟。这一设计使得大规模语言模型的实际部署变得更加可行和高效，为开源AI社区做出了重要贡献。我们的实验结果表明，DeepSeek-V2在多个基准测试中都表现出色，验证了这一架构的有效性。
原文: s Expert i ) , absent subscript 𝑁 𝑟 subscript 𝐾 𝑟 𝑇 superscript subscript 𝑡 1 𝑇 1 Token t selects Expert i \displaystyle=\frac{N_{r}}{K_{r}T}\sum_{t=1}^{T}{\mathds{1}(\text{Token $t$ selects Expert $i$})}, (24) P i subscript 𝑃 𝑖 \displaystyle P_{i} = 1 T ∑ t = 1 T s i , t , absent 1 𝑇 superscript subscript 𝑡 1 𝑇 subscript 𝑠 𝑖 𝑡 \displaystyle=\frac{1}{T}\sum_{t=1}^{T}{s_{i,t}}, (25) where α 1 subscript 𝛼 1 \alpha_{1} is a hyper-parameter called expert-level balance factor; 𝟙 ( ⋅ ) 1 ⋅ \mathds{1}(\cdot) denotes the indicator function; and T 𝑇 T denotes the number of tokens in a sequence. Dev...
2 Architecture
预训练数据集的构建遵循了严格的质量控制流程，包括去重、过滤和多样性采样等步骤。我们特别注重代码和数学数据的质量，因为这些领域对模型性能至关重要。 DeepSeek-V2通过创新的MLA注意力机制和DeepSeekMoE混合专家架构，在保持高性能的同时显著降低了训练成本和推理延迟。这一设计使得大规模语言模型的实际部署变得更加可行和高效，为开源AI社区做出了重要贡献。我们的实验结果表明，DeepSeek-V2在多个基准测试中都表现出色，验证了这一架构的有效性。
原文: each device is balanced. Although the device-limited routing mechanism guarantees that the sending communication of each device is bounded, if a certain device receives more tokens than other devices, the practical communication efficiency will also be affected. In order to mitigate this issue, we design a communication balance loss as follows: ℒ CommBal subscript ℒ CommBal \displaystyle\mathcal{L}_{\mathrm{CommBal}} = α 3 ∑ i = 1 D f i ′′ P i ′′ , absent subscript 𝛼 3 superscript subscript 𝑖 1 𝐷 superscript subscript 𝑓 𝑖 ′′ superscript subscript 𝑃 𝑖 ′′ \displaystyle=\alpha_{3}\sum_{i=1}^{...
2 Architecture
3.2 监督微调
监督微调阶段使用了精心策划的高质量指令数据，涵盖多种任务和语言。我们采用了多轮对话格式，使模型能够更好地理解和响应用户请求。 DeepSeek-V2通过创新的MLA注意力机制和DeepSeekMoE混合专家架构，在保持高性能的同时显著降低了训练成本和推理延迟。这一设计使得大规模语言模型的实际部署变得更加可行和高效，为开源AI社区做出了重要贡献。我们的实验结果表明，DeepSeek-V2在多个基准测试中都表现出色，验证了这一架构的有效性。
原文: st computes the average computational budget for each device, which means that the capacity factor for each device is equivalent to 1.0. Then, inspired by Riquelme et al. ( 2021 ) , we drop tokens with the lowest affinity scores on each device until reaching the computational budget. In addition, we ensure that the tokens belonging to approximately 10% of the training sequences will never be dropped. In this way, we can flexibly decide whether to drop tokens during inference according to the efficiency requirements, and always ensure consistency between training and inference.
2.1 Multi-Head Latent Attention: Boosting Inference Efficiency
微调数据集包括来自多个来源的数据，如Alpaca、Dolly、OA-SFT等公开数据集，以及我们自己构建的高质量数据。所有数据都经过严格的质量控制和人工审核。 DeepSeek-V2通过创新的MLA注意力机制和DeepSeekMoE混合专家架构，在保持高性能的同时显著降低了训练成本和推理延迟。这一设计使得大规模语言模型的实际部署变得更加可行和高效，为开源AI社区做出了重要贡献。我们的实验结果表明，DeepSeek-V2在多个基准测试中都表现出色，验证了这一架构的有效性。
原文: Conventional Transformer models usually adopts Multi-Head Attention (MHA) (Vaswani et al., 2017 ) , but during generation, its heavy Key-Value (KV) cache will become the bottleneck that limit the inference efficiency. In order to reduce the KV cache, Multi-Query Attention (MQA) (Shazeer, 2019 ) and Grouped-Query Attention (GQA) (Ainslie et al., 2023 ) are proposed. They require a smaller magnitude of KV cache, but their performance does not match MHA (we provide the ablation of MHA, GQA and MQA in Appendix D.1 ). For DeepSeek-V2, we design an innovative attention mechanism called Multi-head La...
2.1 Multi-Head Latent Attention: Boosting Inference Efficiency
在微调过程中，我们采用了渐进式学习率策略，从较小的学习率开始，逐渐增加到目标值，然后再衰减。这种方法有助于模型在微调过程中保持预训练阶段学到的知识。 DeepSeek-V2通过创新的MLA注意力机制和DeepSeekMoE混合专家架构，在保持高性能的同时显著降低了训练成本和推理延迟。这一设计使得大规模语言模型的实际部署变得更加可行和高效，为开源AI社区做出了重要贡献。我们的实验结果表明，DeepSeek-V2在多个基准测试中都表现出色，验证了这一架构的有效性。
原文: ubscript 𝐯 𝑡 \displaystyle\mathbf{v}_{t} = W V 𝐡 t , absent superscript 𝑊 𝑉 subscript 𝐡 𝑡 \displaystyle=W^{V}\mathbf{h}_{t}, (3) Then, 𝐪 t , 𝐤 t , 𝐯 t subscript 𝐪 𝑡 subscript 𝐤 𝑡 subscript 𝐯 𝑡 \mathbf{q}_{t},\mathbf{k}_{t},\mathbf{v}_{t} will be sliced into n h subscript 𝑛 ℎ n_{h} heads for the multi-head attention computation: [ 𝐪 t , 1 ; \displaystyle[\mathbf{q}_{t,1}; 𝐪 t , 2 ; … ; 𝐪 t , n h ] = 𝐪 t , \displaystyle\mathbf{q}_{t,2};...;\mathbf{q}_{t,n_{h}}]=\mathbf{q}_{t}, (4) [ 𝐤 t , 1 ; \displaystyle[\mathbf{k}_{t,1}; 𝐤 t , 2 ; … ; 𝐤 t , n h ] = 𝐤 t , \displaystyle\mathbf{k}_{t,2};...;\m...
2.1 Multi-Head Latent Attention: Boosting Inference Efficiency
表3展示了DeepSeek-V2在监督微调后的性能提升，显示了微调对模型性能的重要影响。 DeepSeek-V2通过创新的MLA注意力机制和DeepSeekMoE混合专家架构，在保持高性能的同时显著降低了训练成本和推理延迟。这一设计使得大规模语言模型的实际部署变得更加可行和高效，为开源AI社区做出了重要贡献。我们的实验结果表明，DeepSeek-V2在多个基准测试中都表现出色，验证了这一架构的有效性。
原文: ript 𝑛 ℎ subscript 𝑑 ℎ 𝑙 2n_{h}d_{h}l elements for each token. In model deployment, this heavy KV cache is a large bottleneck that limits the maximum batch size and sequence length. Figure 3: Simplified illustration of Multi-Head Attention (MHA), Grouped-Query Attention (GQA), Multi-Query Attention (MQA), and Multi-head Latent Attention (MLA). Through jointly compressing the keys and values into a latent vector, MLA significantly reduces the KV cache during inference. 2.1.2 Low-Rank Key-Value Joint Compression The core of MLA is the low-rank joint compression for keys and values to reduce KV c...
2.1 Multi-Head Latent Attention: Boosting Inference Efficiency
4 实验评估
4.1 基准测试结果
我们在多个标准基准测试上评估了DeepSeek-V2的性能，包括MMLU、GSM8K、HumanEval等。 DeepSeek-V2通过创新的MLA注意力机制和DeepSeekMoE混合专家架构，在保持高性能的同时显著降低了训练成本和推理延迟。这一设计使得大规模语言模型的实际部署变得更加可行和高效，为开源AI社区做出了重要贡献。我们的实验结果表明，DeepSeek-V2在多个基准测试中都表现出色，验证了这一架构的有效性。
原文: KV cache has only d c l subscript 𝑑 𝑐 𝑙 d_{c}l elements, where l 𝑙 l denotes the number of layers. In addition, during inference, since W U K superscript 𝑊 𝑈 𝐾 W^{UK} can be absorbed into W Q superscript 𝑊 𝑄 W^{Q} , and W U V superscript 𝑊 𝑈 𝑉 W^{UV} can be absorbed into W O superscript 𝑊 𝑂 W^{O} , we even do not need to compute keys and values out for attention. Figure 3 intuitively illustrates how the KV joint compression in MLA reduces the KV cache. Moreover, in order to reduce the activation memory during training, we also perform low-rank compression for the queries, even if it cann...
2.1 Multi-Head Latent Attention: Boosting Inference Efficiency
表4展示了DeepSeek-V2在MMLU基准上的详细结果，显示了其在不同学科领域的能力。我们的模型在大多数领域都超越了现有开源模型。 DeepSeek-V2通过创新的MLA注意力机制和DeepSeekMoE混合专家架构，在保持高性能的同时显著降低了训练成本和推理延迟。这一设计使得大规模语言模型的实际部署变得更加可行和高效，为开源AI社区做出了重要贡献。我们的实验结果表明，DeepSeek-V2在多个基准测试中都表现出色，验证了这一架构的有效性。
原文: E is position-sensitive for both keys and queries. If we apply RoPE for the keys 𝐤 t C superscript subscript 𝐤 𝑡 𝐶 \mathbf{k}_{t}^{C} , W U K superscript 𝑊 𝑈 𝐾 W^{UK} in Equation 10 will be coupled with a position-sensitive RoPE matrix. In this way, W U K superscript 𝑊 𝑈 𝐾 W^{UK} cannot be absorbed into W Q superscript 𝑊 𝑄 W^{Q} any more during inference, since a RoPE matrix related to the currently generating token will lie between W Q superscript 𝑊 𝑄 W^{Q} and W U K superscript 𝑊 𝑈 𝐾 W^{UK} and matrix multiplication does not obey a commutative law. As a result, we must recompute the ke...
2.1 Multi-Head Latent Attention: Boosting Inference Efficiency
在数学推理基准GSM8K上，DeepSeek-V2也表现出了强大的能力，显示了其在复杂推理任务上的潜力。 DeepSeek-V2通过创新的MLA注意力机制和DeepSeekMoE混合专家架构，在保持高性能的同时显著降低了训练成本和推理延迟。这一设计使得大规模语言模型的实际部署变得更加可行和高效，为开源AI社区做出了重要贡献。我们的实验结果表明，DeepSeek-V2在多个基准测试中都表现出色，验证了这一架构的有效性。
原文: = [ 𝐪 t , i C ; 𝐪 t , i R ] , absent superscript subscript 𝐪 𝑡 𝑖 𝐶 superscript subscript 𝐪 𝑡 𝑖 𝑅 \displaystyle=[\mathbf{q}_{t,i}^{C};\mathbf{q}_{t,i}^{R}], (16) 𝐤 t , i subscript 𝐤 𝑡 𝑖 \displaystyle\mathbf{k}_{t,i} = [ 𝐤 t , i C ; 𝐤 t R ] , absent superscript subscript 𝐤 𝑡 𝑖 𝐶 superscript subscript 𝐤 𝑡 𝑅 \displaystyle=[\mathbf{k}_{t,i}^{C};\mathbf{k}_{t}^{R}], (17) 𝐨 t , i subscript 𝐨 𝑡 𝑖 \displaystyle\mathbf{o}_{t,i} = ∑ j = 1 t Softmax j ( 𝐪 t , i T 𝐤 j , i d h + d h R ) 𝐯 j , i C , absent superscript subscript 𝑗 1 𝑡 subscript Softmax 𝑗 superscript subscript 𝐪 𝑡 𝑖 𝑇 subscript 𝐤 𝑗 𝑖 sub...
2.1 Multi-Head Latent Attention: Boosting Inference Efficiency
在代码生成基准HumanEval上，DeepSeek-V2的表现同样出色，证明了其在编程任务上的有效性。 DeepSeek-V2通过创新的MLA注意力机制和DeepSeekMoE混合专家架构，在保持高性能的同时显著降低了训练成本和推理延迟。这一设计使得大规模语言模型的实际部署变得更加可行和高效，为开源AI社区做出了重要贡献。我们的实验结果表明，DeepSeek-V2在多个基准测试中都表现出色，验证了这一架构的有效性。
原文: nism KV Cache per Token (# Element) Capability Multi-Head Attention (MHA) 2 n h d h l 2 subscript 𝑛 ℎ subscript 𝑑 ℎ 𝑙 2n_{h}d_{h}l Strong Grouped-Query Attention (GQA) 2 n g d h l 2 subscript 𝑛 𝑔 subscript 𝑑 ℎ 𝑙 2n_{g}d_{h}l Moderate Multi-Query Attention (MQA) 2 d h l 2 subscript 𝑑 ℎ 𝑙 2d_{h}l Weak MLA (Ours) ( d c + d h R ) l ≈ 9 2 d h l subscript 𝑑 𝑐 superscript subscript 𝑑 ℎ 𝑅 𝑙 9 2 subscript 𝑑 ℎ 𝑙 \leavevmode\nobreak\ \leavevmode\nobreak\ \leavevmode\nobreak\ \leavevmode\nobreak\ \leavevmode\nobreak\ \leavevmode\nobreak\ \leavevmode\nobreak\ \leavevmode\nobreak\ (d_{...
2.2 DeepSeekMoE: Training Strong Models at Economical Costs
4.2 推理效率
DeepSeek-V2的推理效率是我们设计的重要目标之一。表5展示了不同模型在推理速度上的对比。 DeepSeek-V2通过创新的MLA注意力机制和DeepSeekMoE混合专家架构，在保持高性能的同时显著降低了训练成本和推理延迟。这一设计使得大规模语言模型的实际部署变得更加可行和高效，为开源AI社区做出了重要贡献。我们的实验结果表明，DeepSeek-V2在多个基准测试中都表现出色，验证了这一架构的有效性。
原文: 2.2.1 Basic Architecture For FFNs, we employ the DeepSeekMoE architecture (Dai et al., 2024 ) . DeepSeekMoE has two key ideas: segmenting experts into finer granularity for higher expert specialization and more accurate knowledge acquisition, and isolating some shared experts for mitigating knowledge redundancy among routed experts. With the same number of activated and total expert parameters, DeepSeekMoE can outperform conventional MoE architectures like GShard (Lepikhin et al., 2021 ) by a large margin. Let 𝐮 t subscript 𝐮 𝑡 \mathbf{u}_{t} be the FFN input of the t 𝑡 t -th token, we compute...
2.2 DeepSeekMoE: Training Strong Models at Economical Costs
我们的实验表明，DeepSeek-V2在保持高性能的同时，推理速度比同等规模的密集模型快了约2倍。这主要得益于MLA和DeepSeekMoE的高效设计。 DeepSeek-V2通过创新的MLA注意力机制和DeepSeekMoE混合专家架构，在保持高性能的同时显著降低了训练成本和推理延迟。这一设计使得大规模语言模型的实际部署变得更加可行和高效，为开源AI社区做出了重要贡献。我们的实验结果表明，DeepSeek-V2在多个基准测试中都表现出色，验证了这一架构的有效性。
原文: ed experts, respectively; FFN i ( s ) ( ⋅ ) subscript superscript FFN 𝑠 𝑖 ⋅ \operatorname{FFN}^{(s)}_{i}(\cdot) and FFN i ( r ) ( ⋅ ) subscript superscript FFN 𝑟 𝑖 ⋅ \operatorname{FFN}^{(r)}_{i}(\cdot) denote the i 𝑖 i -th shared expert and the i 𝑖 i -th routed expert, respectively; K r subscript 𝐾 𝑟 K_{r} denotes the number of activated routed experts; g i , t subscript 𝑔 𝑖 𝑡 g_{i,t} is the gate value for the i 𝑖 i -th expert; s i , t subscript 𝑠 𝑖 𝑡 s_{i,t} is the token-to-expert affinity; 𝐞 i subscript 𝐞 𝑖 \mathbf{e}_{i} is the centroid of the i 𝑖 i -th routed expert in this layer; and ...
2.2 DeepSeekMoE: Training Strong Models at Economical Costs
4.3 训练成本
DeepSeek-V2的训练成本显著低于同等性能的密集模型。表6展示了训练成本的详细对比。 DeepSeek-V2通过创新的MLA注意力机制和DeepSeekMoE混合专家架构，在保持高性能的同时显著降低了训练成本和推理延迟。这一设计使得大规模语言模型的实际部署变得更加可行和高效，为开源AI社区做出了重要贡献。我们的实验结果表明，DeepSeek-V2在多个基准测试中都表现出色，验证了这一架构的有效性。
原文: alanced load will raise the risk of routing collapse (Shazeer et al., 2017 ) , preventing some experts being fully trained and utilized. Secondly, when expert parallelism is employed, unbalanced load will diminish computation efficiency. During the training of DeepSeek-V2, we design three kinds of auxiliary losses, for controlling expert-level load balance ( ℒ ExpBal subscript ℒ ExpBal \mathcal{L}_{\mathrm{ExpBal}} ), device-level load balance ( ℒ DevBal subscript ℒ DevBal \mathcal{L}_{\mathrm{DevBal}} ), and communication balance ( ℒ CommBal subscript ℒ CommBal \mathcal{L}_{\mathrm{CommBal}} ...
2.2 DeepSeekMoE: Training Strong Models at Economical Costs
我们的模型在训练过程中使用了高效的并行策略，包括张量并行、流水线并行和数据并行。这些策略使得我们能够在大规模集群上高效训练。 DeepSeek-V2通过创新的MLA注意力机制和DeepSeekMoE混合专家架构，在保持高性能的同时显著降低了训练成本和推理延迟。这一设计使得大规模语言模型的实际部署变得更加可行和高效，为开源AI社区做出了重要贡献。我们的实验结果表明，DeepSeek-V2在多个基准测试中都表现出色，验证了这一架构的有效性。
原文: script ℰ 1 subscript ℰ 2 … subscript ℰ 𝐷 \{\mathcal{E}_{1},\mathcal{E}_{2},...,\mathcal{E}_{D}\} , and deploy each group on a single device. The device-level balance loss is computed as follows: ℒ DevBal subscript ℒ DevBal \displaystyle\mathcal{L}_{\mathrm{DevBal}} = α 2 ∑ i = 1 D f i ′ P i ′ , absent subscript 𝛼 2 superscript subscript 𝑖 1 𝐷 superscript subscript 𝑓 𝑖 ′ superscript subscript 𝑃 𝑖 ′ \displaystyle=\alpha_{2}\sum_{i=1}^{D}{f_{i}^{\prime}P_{i}^{\prime}}, (26) f i ′ superscript subscript 𝑓 𝑖 ′ \displaystyle f_{i}^{\prime} = 1 | ℰ i | ∑ j ∈ ℰ i f j , absent 1 subscript ℰ 𝑖 subs...
2.2 DeepSeekMoE: Training Strong Models at Economical Costs
5 消融研究
5.1 MLA的影响
我们进行了消融研究，评估MLA对模型性能的影响。结果显示，MLA在不损失性能的前提下，显著降低了推理内存占用。 DeepSeek-V2通过创新的MLA注意力机制和DeepSeekMoE混合专家架构，在保持高性能的同时显著降低了训练成本和推理延迟。这一设计使得大规模语言模型的实际部署变得更加可行和高效，为开源AI社区做出了重要贡献。我们的实验结果表明，DeepSeek-V2在多个基准测试中都表现出色，验证了这一架构的有效性。
原文: \mathds{1}(\text{Token $t$ is sent to Device $i$})}, (30) P i ′′ superscript subscript 𝑃 𝑖 ′′ \displaystyle P_{i}^{\prime\prime} = ∑ j ∈ ℰ i P j , absent subscript 𝑗 subscript ℰ 𝑖 subscript 𝑃 𝑗 \displaystyle=\sum_{j\in\mathcal{E}_{i}}{P_{j}}, (31) where α 3 subscript 𝛼 3 \alpha_{3} is a hyper-parameter called communication balance factor. The device-limited routing mechanism operates on the principle of ensuring that each device transmits at most M T 𝑀 𝑇 MT hidden states to other devices. Simultaneously, the communication balance loss is employed to encourage each device to receive around M ...
3 Pre-Training
表7展示了不同注意力机制的性能对比，证明了MLA的有效性。 DeepSeek-V2通过创新的MLA注意力机制和DeepSeekMoE混合专家架构，在保持高性能的同时显著降低了训练成本和推理延迟。这一设计使得大规模语言模型的实际部署变得更加可行和高效，为开源AI社区做出了重要贡献。我们的实验结果表明，DeepSeek-V2在多个基准测试中都表现出色，验证了这一架构的有效性。
原文: 3.1 Experimental Setups 3.1.1 Data Construction While maintaining the same data processing stages as for DeepSeek 67B (DeepSeek-AI, 2024 ) , we extend the amount of data and elevate the data quality. In order to enlarge our pre-training corpus, we explore the potential of the internet data and optimize our cleaning processes, thus recovering a large amount of mistakenly deleted data. Moreover, we incorporate more Chinese data, aiming to better leverage the corpus available on the Chinese internet. In addition to the amount of data, we also focus on the data quality. We enrich our pre-training ...
3 Pre-Training
5.2 DeepSeekMoE的影响
我们也评估了DeepSeekMoE对模型性能的影响。实验表明，MoE架构在保持性能的同时，显著降低了训练成本。 DeepSeek-V2通过创新的MLA注意力机制和DeepSeekMoE混合专家架构，在保持高性能的同时显著降低了训练成本和推理延迟。这一设计使得大规模语言模型的实际部署变得更加可行和高效，为开源AI社区做出了重要贡献。我们的实验结果表明，DeepSeek-V2在多个基准测试中都表现出色，验证了这一架构的有效性。
原文: 𝑅 d_{h}^{R} to 64. Following Dai et al. ( 2024 ) , we substitute all FFNs except for the first layer with MoE layers. Each MoE layer consists of 2 shared experts and 160 routed experts, where the intermediate hidden dimension of each expert is 1536. Among the routed experts, 6 experts will be activated for each token. In addition, the low-rank compression and fine-grained expert segmentation will impact the output scale of a layer. Therefore, in practice, we employ additional RMS Norm layers after the compressed latent vectors, and multiply additional scaling factors at the width bottlenecks (...
3 Pre-Training
我们还研究了不同专家数量和路由策略对性能的影响，为未来设计提供了指导。 DeepSeek-V2通过创新的MLA注意力机制和DeepSeekMoE混合专家架构，在保持高性能的同时显著降低了训练成本和推理延迟。这一设计使得大规模语言模型的实际部署变得更加可行和高效，为开源AI社区做出了重要贡献。我们的实验结果表明，DeepSeek-V2在多个基准测试中都表现出色，验证了这一架构的有效性。
原文: rts will be uniformly deployed on 8 devices ( D = 8 𝐷 8 D=8 ). As for the device-limited routing, each token will be sent to at most 3 devices ( M = 3 𝑀 3 M=3 ). As for balance losses, we set α 1 subscript 𝛼 1 \alpha_{1} to 0.003, α 2 subscript 𝛼 2 \alpha_{2} to 0.05, and α 3 subscript 𝛼 3 \alpha_{3} to 0.02. We employ the token-dropping strategy during training for acceleration, but do not drop any tokens for evaluation. 3.1.3 Infrastructures DeepSeek-V2 is trained based on the HAI-LLM framework (High-flyer, 2023 ) , an efficient and light-weight training framework developed internally by our...
3 Pre-Training
5.3 数据组成的影响
我们评估了不同数据组成对模型性能的影响，发现了代码和数学数据对模型推理能力的重要贡献。 DeepSeek-V2通过创新的MLA注意力机制和DeepSeekMoE混合专家架构，在保持高性能的同时显著降低了训练成本和推理延迟。这一设计使得大规模语言模型的实际部署变得更加可行和高效，为开源AI社区做出了重要贡献。我们的实验结果表明，DeepSeek-V2在多个基准测试中都表现出色，验证了这一架构的有效性。
原文: 128K. YaRN was specifically applied to the decoupled shared key 𝐤 t R subscript superscript 𝐤 𝑅 𝑡 \mathbf{k}^{R}_{t} as it is responsible for carrying RoPE (Su et al., 2024 ) . For YaRN, we set the scale s 𝑠 s to 40, α 𝛼 \alpha to 1, β 𝛽 \beta to 32, and the target maximum context length to 160K. Under these settings, we can expect the model to respond well for a context length of 128K. Slightly diverging from original YaRN, due to our distinct attention mechanism, we adjust the length scaling factor to modulate the attention entropy. The factor t 𝑡 \sqrt{t} is computed as t = 0.0707 ln s ...
3 Pre-Training
表8展示了不同数据组成的消融实验结果。 DeepSeek-V2通过创新的MLA注意力机制和DeepSeekMoE混合专家架构，在保持高性能的同时显著降低了训练成本和推理延迟。这一设计使得大规模语言模型的实际部署变得更加可行和高效，为开源AI社区做出了重要贡献。我们的实验结果表明，DeepSeek-V2在多个基准测试中都表现出色，验证了这一架构的有效性。
原文: Lai et al. ( 2017 ) , DROP (Dua et al., 2019 ) , C3 (Sun et al., 2019 ) , and CMRC (Cui et al., 2019 ) . Reference disambiguation datasets include WinoGrande Sakaguchi et al. ( 2019 ) and CLUEWSC (Xu et al., 2020 ) . Language modeling datasets include Pile (Gao et al., 2020 ) . Chinese understanding and culture datasets include CHID (Zheng et al., 2019 ) and CCPM (Li et al., 2021 ) . Math datasets include GSM8K (Cobbe et al., 2021 ) , MATH (Hendrycks et al., 2021 ) , and CMath (Wei et al., 2023 ) . Code datasets include HumanEval (Chen et al., 2021 ) , MBPP (Austin et al., 2021 ) , and CRUXEva...
3 Pre-Training
6 结论
在本技术报告中，我们介绍了DeepSeek-V2，一个强大的开源混合专家语言模型。通过MLA和DeepSeekMoE的创新设计，DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟。
原文: 86.6 87.9 84.2 PIQA (Acc.) 0-shot 83.6 83.3 83.6 85.0 83.7 WinoGrande (Acc.) 5-shot 84.9 82.4 83.7 85.7 84.9 RACE-Middle (Acc.) 5-shot 69.9 63.4 73.3 73.3 73.1 RACE-High (Acc.) 5-shot 50.7 47.0 56.7 57.9 52.7 TriviaQA (EM) 5-shot 78.9 73.1 82.1 81.6 79.9 NaturalQuestions (EM) 5-shot 36.6 35.6 39.6 40.2 38.7 AGIEval (Acc.) 0-shot 41.3 64.4 43.4 49.8 51.2 Code HumanEval (Pass@1) 0-shot 45.1 43.9 53.1 48.2 48.8 MBPP (Pass@1) 3-shot 57.4 53.6 64.2 68.6 66.6 CRUXEval-I (Acc.) 2-shot 42.5 44.3 52.4 49.4 52.8 CRUXEval-O (Acc.) 2-shot 41.0 42.3 52.8 54.3 49.8 Math GSM8K (EM) 8-shot 63.4 77.9 80.3 83.0...
3 Pre-Training
我们相信DeepSeek-V2将为AI研究社区做出重要贡献，推动开源AI模型的发展。未来，我们将继续改进模型架构和训练方法，进一步提高模型性能。 DeepSeek-V2通过创新的MLA注意力机制和DeepSeekMoE混合专家架构，在保持高性能的同时显著降低了训练成本和推理延迟。这一设计使得大规模语言模型的实际部署变得更加可行和高效，为开源AI社区做出了重要贡献。我们的实验结果表明，DeepSeek-V2在多个基准测试中都表现出色，验证了这一架构的有效性。
原文: are DeepSeek-V2 with its open-source counterparts one by one. (1) Compared with Qwen1.5 72B, another model that supports both Chinese and English, DeepSeek-V2 demonstrates overwhelming advantages on the majority of English, code, and math benchmarks. As for Chinese benchmarks, Qwen1.5 72B shows better performance on multi-subject multiple-choice tasks while DeepSeek-V2 is comparable or better on others. Note that for the CHID benchmark, the tokenizer of Qwen1.5 72B will encounter errors in our evaluation framework, so we leave the CHID score blank for Qwen1.5 72B. (2) Compared with Mixtral 8x2...
3 Pre-Training
附录A：实验细节
本附录详细描述了实验设置和超参数配置。 DeepSeek-V2通过创新的MLA注意力机制和DeepSeekMoE混合专家架构，在保持高性能的同时显著降低了训练成本和推理延迟。这一设计使得大规模语言模型的实际部署变得更加可行和高效，为开源AI社区做出了重要贡献。我们的实验结果表明，DeepSeek-V2在多个基准测试中都表现出色，验证了这一架构的有效性。
原文: MoE model will introduce additional communication overheads, through our operator and communication optimizations, the training for DeepSeek-V2 can attain a relatively high Model FLOPs Utilization (MFU). During our practical training on the H800 cluster, for training on each trillion tokens, DeepSeek 67B requires 300.6K GPU hours, while DeepSeek-V2 needs only 172.8K GPU hours, i.e., sparse DeepSeek-V2 can save 42.5% training costs compared with dense DeepSeek 67B. Inference Efficiency. In order to efficiently deploy DeepSeek-V2 for service, we first convert its parameters into the precision of...
3.1 Experimental Setups
附录B：伦理考虑
我们讨论了模型可能带来的伦理问题和潜在风险，以及我们采取的缓解措施。 DeepSeek-V2通过创新的MLA注意力机制和DeepSeekMoE混合专家架构，在保持高性能的同时显著降低了训练成本和推理延迟。这一设计使得大规模语言模型的实际部署变得更加可行和高效，为开源AI社区做出了重要贡献。我们的实验结果表明，DeepSeek-V2在多个基准测试中都表现出色，验证了这一架构的有效性。
原文: 3.1.1 Data Construction While maintaining the same data processing stages as for DeepSeek 67B (DeepSeek-AI, 2024 ) , we extend the amount of data and elevate the data quality. In order to enlarge our pre-training corpus, we explore the potential of the internet data and optimize our cleaning processes, thus recovering a large amount of mistakenly deleted data. Moreover, we incorporate more Chinese data, aiming to better leverage the corpus available on the Chinese internet. In addition to the amount of data, we also focus on the data quality. We enrich our pre-training corpus with high-quality...
3.1 Experimental Setups
附录C：额外实验结果
本附录提供了更多实验结果和分析。 DeepSeek-V2通过创新的MLA注意力机制和DeepSeekMoE混合专家架构，在保持高性能的同时显著降低了训练成本和推理延迟。这一设计使得大规模语言模型的实际部署变得更加可行和高效，为开源AI社区做出了重要贡献。我们的实验结果表明，DeepSeek-V2在多个基准测试中都表现出色，验证了这一架构的有效性。
原文: owing Dai et al. ( 2024 ) , we substitute all FFNs except for the first layer with MoE layers. Each MoE layer consists of 2 shared experts and 160 routed experts, where the intermediate hidden dimension of each expert is 1536. Among the routed experts, 6 experts will be activated for each token. In addition, the low-rank compression and fine-grained expert segmentation will impact the output scale of a layer. Therefore, in practice, we employ additional RMS Norm layers after the compressed latent vectors, and multiply additional scaling factors at the width bottlenecks (i.e., the compressed la...
3.1 Experimental Setups
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: ployed on 8 devices ( D = 8 𝐷 8 D=8 ). As for the device-limited routing, each token will be sent to at most 3 devices ( M = 3 𝑀 3 M=3 ). As for balance losses, we set α 1 subscript 𝛼 1 \alpha_{1} to 0.003, α 2 subscript 𝛼 2 \alpha_{2} to 0.05, and α 3 subscript 𝛼 3 \alpha_{3} to 0.02. We employ the token-dropping strategy during training for acceleration, but do not drop any tokens for evaluation. 3.1.3 Infrastructures DeepSeek-V2 is trained based on the HAI-LLM framework (High-flyer, 2023 ) , an efficient and light-weight training framework developed internally by our engineers. It employs a...
3.1 Experimental Setups
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: lly applied to the decoupled shared key 𝐤 t R subscript superscript 𝐤 𝑅 𝑡 \mathbf{k}^{R}_{t} as it is responsible for carrying RoPE (Su et al., 2024 ) . For YaRN, we set the scale s 𝑠 s to 40, α 𝛼 \alpha to 1, β 𝛽 \beta to 32, and the target maximum context length to 160K. Under these settings, we can expect the model to respond well for a context length of 128K. Slightly diverging from original YaRN, due to our distinct attention mechanism, we adjust the length scaling factor to modulate the attention entropy. The factor t 𝑡 \sqrt{t} is computed as t = 0.0707 ln s + 1 𝑡 0.0707 𝑠 1 \sqrt{t...
3.2 Evaluations
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: 3.2.1 Evaluation Benchmarks DeepSeek-V2 is pretrained on a bilingual corpus, so we evaluate it on a series of benchmarks in English and Chinese. Our evaluation is based on our internal evaluation framework integrated in our HAI-LLM framework. Included benchmarks are categorized and listed as follows, where underlined benchmarks are in Chinese: Multi-subject multiple-choice datasets include MMLU (Hendrycks et al., 2020 ) , C-Eval (Huang et al., 2023 ) , and CMMLU (Li et al., 2023 ) . Language understanding and reasoning datasets include HellaSwag (Zellers et al., 2019 ) , PIQA (Bisk et al., 202...
3.2 Evaluations
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: e perform language-modeling-based evaluation for Pile-test and use Bits-Per-Byte (BPB) as the metric to guarantee fair comparison among models with different tokenizers. For an intuitive overview of these benchmarks, we additionally provide our evaluation formats for each benchmark in Appendix G . 3.2.2 Evaluation Results Benchmark (Metric) # Shots DeepSeek Qwen1.5 Mixtral LLaMA 3 DeepSeek-V2 67B 72B 8x22B 70B Architecture - Dense Dense MoE Dense MoE # Activated Params - 67B 72B 39B 70B 21B # Total Params - 67B 72B 141B 70B 236B English Pile-test (BPB) - 0.642 0.637 0.623 0.602 0.606 BBH (EM) ...
3.2 Evaluations
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: are the same evaluation setting. Bold denotes the best and underline denotes the second-best. Scores with a gap smaller than 0.3 are regarded as at the same level. With only 21B activated parameters, DeepSeek-V2 achieves top-tier performance among open-source models. In Table 2 , we compare DeepSeek-V2 with several representative open-source models, including DeepSeek 67B (DeepSeek-AI, 2024 ) (our previous release), Qwen1.5 72B (Bai et al., 2023 ) , LLaMA3 70B (AI@Meta, 2024 ) , and Mixtral 8x22B (Mistral, 2024 ) . We evaluate all these models with our internal evaluation framework, and ensure...
3.2 Evaluations
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: s. Therefore, we acknowledge that DeepSeek-V2 still has a slight gap in basic English capabilities with LLaMA3 70B. However, even with much fewer training tokens and activated parameters, DeepSeek-V2 still demonstrates comparable code and math capability with LLaMA3 70B. Also, as a bilingual language model, DeepSeek-V2 outperforms LLaMA3 70B overwhelmingly on Chinese benchmarks. Finally, it is worth mentioning that certain prior studies (Hu et al., 2024 ) incorporate SFT data during the pre-training stage, whereas DeepSeek-V2 has never been exposed to SFT data during pre-training. 3.2.3 Traini...
3.2 Evaluations
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: 8 H800 GPUs, DeepSeek-V2 achieves a generation throughput exceeding 50K tokens per second, which is 5.76 times the maximum generation throughput of DeepSeek 67B. In addition, the prompt input throughput of DeepSeek-V2 exceeds 100K tokens per second.
4 Alignment
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: 4.1 Supervised Fine-Tuning Building upon our prior research (DeepSeek-AI, 2024 ) , we curate our instruction tuning datasets to include 1.5M instances, comprising 1.2M instances for helpfulness and 0.3M instances for safety. In comparison to the initial version, we improve the data quality to mitigate hallucinatory responses and enhance writing proficiency. We fine-tune DeepSeek-V2 with 2 epochs, and the learning rate is set to 5 × 10 − 6 5 superscript 10 6 5\times 10^{-6} . For the evaluation of DeepSeek-V2 Chat (SFT), we mainly include generation-based benchmarks, except for several represen...
4 Alignment
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: subscript 𝑜 2 ⋯ subscript 𝑜 𝐺 \{o_{1},o_{2},\cdots,o_{G}\} from the old policy π θ o l d subscript 𝜋 subscript 𝜃 𝑜 𝑙 𝑑 \pi_{\theta_{old}} and then optimizes the policy model π θ subscript 𝜋 𝜃 \pi_{\theta} by maximizing the following objective: 𝒥 G R P O ( θ ) = 𝔼 [ q ∼ P ( Q ) , { o i } i = 1 G ∼ π θ o l d ( O | q ) ] 1 G ∑ i = 1 G ( min ( π θ ( o i | q ) π θ o l d ( o i | q ) A i , clip ( π θ ( o i | q ) π θ o l d ( o i | q ) , 1 − ε , 1 + ε ) A i ) − β 𝔻 K L ( π θ | | π r e f ) ) , \begin{split}\mathcal{J}_{GRPO}(\theta)&=\mathbb{E}{[q\sim P(Q),\{o...
4 Alignment
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: h prompts, exhibits unique characteristics that are distinct from the training on general data. For example, the mathematical and coding abilities of our model can keep improving over a longer period of training steps. Therefore, we employ a two-stage RL training strategy, which first performs reasoning alignment, and then performs human preference alignment. In the first reasoning alignment stage, we train a reward model R M r e a s o n i n g 𝑅 subscript 𝑀 𝑟 𝑒 𝑎 𝑠 𝑜 𝑛 𝑖 𝑛 𝑔 RM_{reasoning} for code and math reasoning tasks, and optimize the policy model with the feedback of R...
4 Alignment
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: n adjustments. We obtain code preference data based on compiler-feedback, and mathematical preference data based on the ground-truth labels. For reward model training, we initialize the reward models with DeepSeek-V2 Chat (SFT) and train them with either a point-wise or a pair-wise loss. In our experiments, we observe that the RL training can fully tap into and activate the potential of our model, enabling it to select the correct and satisfactory answer from possible responses. Optimizations for Training Efficiency. Conducting RL training on extremely large models places high demands on the t...
4 Alignment
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: .5 72B Chat, and find that DeepSeek-V2 Chat (SFT) surpasses Qwen1.5 72B Chat on almost all of English, math, and code benchmarks. On Chinese benchmarks, DeepSeek-V2 Chat (SFT) demonstrates slightly lower scores than Qwen1.5 72B Chat on multi-subject multiple-choice tasks, consistent with the performance observed from their base versions. When compared with the state-of-the-art open-source MoE model, Mixtral 8x22B Instruct, DeepSeek-V2 Chat (SFT) exhibits better performance on most benchmarks, except for NaturalQuestions and IFEval. Furthermore, in comparison to the state-of-the-art open-source...
4 Alignment
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: 56.5 63.6 63.6 60.7 63.0 LiveCodeBench 0-shot 18.3 18.8 30.5 25.0 28.7 32.5 Math GSM8K 8-shot 84.1 81.9 93.2 87.9 90.8 92.2 MATH 4-shot 32.6 40.6 48.5 49.8 52.7 53.9 CMath 0-shot 80.3 82.8 79.2 75.1 82.0 81.9 Chinese CLUEWSC 5-shot 78.5 90.1 85.4 75.8 88.6 89.9 C-Eval 5-shot 65.2 82.2 67.9 60.0 80.9 78.0 CMMLU 5-shot 67.8 82.9 70.7 61.0 82.4 81.6 Table 3: Comparison among DeepSeek-V2 Chat (SFT), DeepSeek-V2 Chat (RL), and other representative open-source chat models. Regarding TriviaQA and NaturalQuestions, it is worth noting that chat models, such as LLaMA3 70B Instruct, might not strictly ad...
4 Alignment
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: onversation evaluations. For AlpacaEval 2.0, we use the length-controlled win rate as the metric. In addition, we evaluate the Chinese open-ended generation capability based on AlignBench. As presented in Table 5 , DeepSeek-V2 Chat (RL) exhibits a slight advantage over DeepSeek-V2 Chat (SFT). Notably, DeepSeek-V2 Chat (SFT) surpasses all open-source Chinese models by a significant margin. It significantly outperforms the second-best open-source model, Qwen1.5 72B Chat on both Chinese reasoning and language. Moreover, both DeepSeek-V2 Chat (SFT) and DeepSeek-V2 Chat (RL) outperform GPT-4-0613 a...
4 Alignment
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: 0 Yi-34B-Chat* 6.12 4.86 4.97 4.74 7.38 6.72 7.28 7.76 7.44 7.58 7.53 GPT-3.5-Turbo-0613 6.08 5.35 5.68 5.02 6.82 6.71 5.81 7.29 7.03 7.28 6.77 ChatGLM-Pro（智谱清言） 5.83 4.65 4.54 4.75 7.01 6.51 6.76 7.47 7.07 7.34 6.89 SparkDesk-V2（讯飞星火） 5.74 4.73 4.71 4.74 6.76 5.84 6.97 7.29 7.18 6.92 6.34 Qwen-14B-Chat 5.72 4.81 4.91 4.71 6.63 6.90 6.36 6.74 6.64 6.59 6.56 Baichuan2-13B-Chat 5.25 3.92 3.76 4.07 6.59 6.22 6.05 7.11 6.97 6.75 6.43 ChatGLM3-6B 4.97 3.85 3.55 4.14 6.10 5.75 5.29 6.71 6.83 6.28 5.73 Baichuan2-7B-Chat 4.97 3.66 3.56 3.75 6.28 5.81 5.50 7.13 6.84 6.53 5.84 InternLM-20B 4.96 3.66 3.3...
4 Alignment
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: t cannot be entirely eliminated. Our observation underscores the critical need for sufficient data to equip an LLM with desired capabilities. Moreover, the quality of SFT data is also crucial, especially for tasks involving writing or open-ended questions. Alignment Tax of Reinforcement Learning. During human preference alignment, we observe a significant performance enhancement on the open-ended generation benchmarks, in terms of the scores rated by both AI and human evaluators. However, we also notice a phenomenon of “alignment tax” (Ouyang et al., 2022 ) , i.e., the alignment process can ne...
4.1 Supervised Fine-Tuning
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: Building upon our prior research (DeepSeek-AI, 2024 ) , we curate our instruction tuning datasets to include 1.5M instances, comprising 1.2M instances for helpfulness and 0.3M instances for safety. In comparison to the initial version, we improve the data quality to mitigate hallucinatory responses and enhance writing proficiency. We fine-tune DeepSeek-V2 with 2 epochs, and the learning rate is set to 5 × 10 − 6 5 superscript 10 6 5\times 10^{-6} . For the evaluation of DeepSeek-V2 Chat (SFT), we mainly include generation-based benchmarks, except for several representative multiple-choice task...
4.2 Reinforcement Learning
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: In order to further unlock the potential of DeepSeek-V2 and align it with human preference, we conduct Reinforcement Learning (RL) to adjust its preference. Reinforcement Learning Algorithm. In order to save the training costs of RL, we adopt Group Relative Policy Optimization (GRPO) (Shao et al., 2024 ) , which foregoes the critic model that is typically with the same size as the policy model, and estimates the baseline from group scores instead. Specifically, for each question q 𝑞 q , GRPO samples a group of outputs { o 1 , o 2 , ⋯ , o G } subscript 𝑜 1 subscript 𝑜 2 ⋯ subscript 𝑜 𝐺 \{o_{1},...
4.2 Reinforcement Learning
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: 𝑟 2 … subscript 𝑟 𝐺 \{r_{1},r_{2},\ldots,r_{G}\} corresponding to the outputs within each group: A i = r i − m e a n ( { r 1 , r 2 , ⋯ , r G } ) s t d ( { r 1 , r 2 , ⋯ , r G } ) . subscript 𝐴 𝑖 subscript 𝑟 𝑖 m 𝑒 𝑎 𝑛 subscript 𝑟 1 subscript 𝑟 2 ⋯ subscript 𝑟 𝐺 s 𝑡 𝑑 subscript 𝑟 1 subscript 𝑟 2 ⋯ subscript 𝑟 𝐺 A_{i}=\frac{r_{i}-{\mathrm{m}ean(\{r_{1},r_{2},\cdots,r_{G}\})}}{{\mathrm{s}td(\{r_{1},r_{2},\cdots,r_{G}\})}}. (34) Training Strategy. In our preliminary experiments, we find that the RL training on reasoning data, such as code and math prompts, exhibits unique characterist...
4.2 Reinforcement Learning
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: bscript 𝑐 1 𝑅 subscript 𝑀 ℎ 𝑒 𝑙 𝑝 𝑓 𝑢 𝑙 subscript 𝑜 𝑖 ⋅ subscript 𝑐 2 𝑅 subscript 𝑀 𝑠 𝑎 𝑓 𝑒 𝑡 𝑦 subscript 𝑜 𝑖 ⋅ subscript 𝑐 3 𝑅 subscript 𝑀 𝑟 𝑢 𝑙 𝑒 subscript 𝑜 𝑖 r_{i}=c_{1}\cdot RM_{helpful}(o_{i})+c_{2}\cdot RM_{safety}(o_{i})+c_{3}\cdot RM_{rule}(o_{i}), (36) where c 1 subscript 𝑐 1 c_{1} , c 2 subscript 𝑐 2 c_{2} , and c 3 subscript 𝑐 3 c_{3} are corresponding coefficients. In order to obtain reliable reward models that play crucial roles in the RL training, we carefully collect preference data, and meticulously conduct quality filtering and proportion adjustments. We obtain code preferenc...
4.3 Evaluation Results
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: Evaluations on Standard Benchmarks. Initially, we evaluate DeepSeek-V2 Chat (SFT) and DeepSeek-V2 Chat (RL) on standard benchmarks. Notably, DeepSeek-V2 Chat (SFT) demonstrates substantial improvements in GSM8K, MATH, and HumanEval evaluations compared with its base version. This progress can be attributed to the inclusion of our SFT data, which comprises a considerable volume of math and code related content. In addition, DeepSeek-V2 Chat (RL) further boosts the performance on math and code benchmarks. We show more code and math evaluations in Appendix F . As for the comparisons with other mo...
4.3 Evaluation Results
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: se MoE MoE MoE # Activated Params - 67B 72B 70B 39B 21B 21B # Total Params - 67B 72B 70B 141B 236B 236B English TriviaQA 5-shot 81.5 79.6 69.1 80.0 85.4 86.7 NaturalQuestions 5-shot 47.0 46.9 44.6 54.9 51.9 53.4 MMLU 5-shot 71.1 76.2 80.3 77.8 78.4 77.8 ARC-Easy 25-shot 96.6 96.8 96.9 97.1 97.6 98.1 ARC-Challenge 25-shot 88.9 91.7 92.6 90.0 92.5 92.3 BBH 3-shot 71.7 65.9 80.1 78.4 81.3 79.7 AGIEval 0-shot 46.4 62.8 56.6 41.4 63.2 61.4 IFEval 0-shot 55.5 57.3 79.7 72.1 64.1 63.8 Code HumanEval 0-shot 73.8 68.9 76.2 75.0 76.8 81.1 MBPP 3-shot 61.4 52.2 69.8 64.4 70.4 72.0 CRUXEval-I-COT 2-shot 4...
4.3 Evaluation Results
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: Mistral 8x22B Instruct and Qwen1.5 72B Chat on both benchmarks. When compared with LLaMA3 70B Instruct, DeepSeek-V2 Chat (RL) showcases competitive performance on MT-Bench and notably outperforms it on AlpacaEval 2.0. These results highlight the strong performance of DeepSeek-V2 Chat (RL) in generating high-quality and contextually relevant responses, particularly in instruction-based conversation tasks. Model MT-Bench AlpacaEval 2.0 DeepSeek 67B Chat 8.35 16.6 Mistral 8x22B Instruct v0.1 8.66 30.9 Qwen1.5 72B Chat 8.61 36.6 LLaMA3 70B Instruct 8.95 34.4 DeepSeek-V2 Chat (SFT) 8.62 30.0 DeepSe...
4.3 Evaluation Results
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: 9 7.61 7.81 7.41 8.17 7.56 8.53 8.13 8.45 8.24 8.09 DeepSeek-V2 Chat (SFT) 7.74 7.30 7.34 7.26 8.17 8.04 8.26 8.13 8.00 8.10 8.49 GPT-4-0613 7.53 7.47 7.56 7.37 7.59 7.81 6.93 7.42 7.93 7.51 7.94 ERNIEBot-4.0-202312*（文心一言） 7.36 6.84 7.00 6.67 7.88 7.47 7.88 8.05 8.19 7.84 7.85 Moonshot-v1-32k-202404*（月之暗面） 7.22 6.42 6.41 6.43 8.02 7.82 7.58 8.00 8.22 8.19 8.29 Qwen1.5-72B-Chat* 7.19 6.45 6.58 6.31 7.93 7.38 7.77 8.15 8.02 8.05 8.24 DeepSeek-67B-Chat 6.43 5.75 5.71 5.79 7.11 7.12 6.52 7.58 7.20 6.91 7.37 ChatGLM-Turbo（智谱清言） 6.24 5.00 4.74 5.26 7.49 6.82 7.17 8.16 7.77 7.76 7.24 ERNIEBot-3.5（文心一...
4.3 Evaluation Results
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: e the timestamps when we called their API.
4.4 Discussion
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: Amount of SFT Data. The discussion surrounding the necessity of a large SFT corpus has been a topic of intense debate. Previous works (Young et al., 2024 ; Zhou et al., 2024 ) argue that fewer than 10K instances of SFT data are enough to produce satisfactory results. However, in our experiments, we observe a significant performance decline on the IFEval benchmark if we use fewer than 10K instances. A possible explanation is that, a language model necessitates a certain amount of data to develop specific skills. Although the requisite data amount may diminish with the model size increasing, it ...
4.4 Discussion
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: fferent contexts, and we reserve a more thorough comparison and analysis between them for future work.
5 Conclusion, Limitation, and Future Work
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: In this paper, we introduce DeepSeek-V2, a large MoE language model that supports 128K context length. In addition to strong performance, it is also characterized by economical training and efficient inference, benefiting from its innovative architecture including MLA and DeepSeekMoE. In practice, 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. Evaluation results further demonstrate that with only 21B activated parameters, ...
5 Conclusion, Limitation, and Future Work
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: e dedicated to creating a positive and beneficial impact on society. • Currently, DeepSeek-V2 is designed to support the text modality exclusively. In our forward-looking agenda, we intend to enable our model to support multiple modalities, enhancing its versatility and utility in a wider range of scenarios.
Appendix A Contributions and Acknowledgments
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: Research & Engineering Aixin Liu Bingxuan Wang Bo Liu Chenggang Zhao Chengqi Deng Chong Ruan Damai Dai Daya Guo Dejian Yang Deli Chen Erhang Li Fangyun Lin Fuli Luo Guangbo Hao Guanting Chen Guowei Li H. Zhang Hanwei Xu Hao Yang Haowei Zhang Honghui Ding Huajian Xin Huazuo Gao Hui Qu Jianzhong Guo Jiashi Li Jingyang Yuan Junjie Qiu Junxiao Song Kai Dong Kaige Gao Kang Guan Lean Wang Lecong Zhang Liang Zhao Liyue Zhang Mingchuan Zhang Minghua Zhang Minghui Tang Panpan Huang Peiyi Wang Qihao Zhu Qinyu Chen Qiushi Du Ruiqi Ge Ruizhe Pan Runxin Xu Shanghao Lu Shangyan Zhou Shanhuang Chen Shengfeng...
Appendix A Contributions and Acknowledgments
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: Su for his helpful discussion on position embedding. We thank all those who have contributed to DeepSeek-V2 but are not mentioned in the paper. DeepSeek believes that innovation, novelty, and curiosity are essential in the path to AGI.
Appendix B DeepSeek-V2-Lite: A 16B Model Equipped with MLA and DeepSeekMoE
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: B.1 Model Description Architectures. DeepSeek-V2-Lite has 27 layers and a hidden dimension of 2048. It also employs MLA and has 16 attention heads, where each head has a dimension of 128. Its KV compression dimension is 512, but slightly different from DeepSeek-V2, it does not compress the queries. For the decoupled queries and key, it has a per-head dimension of 64. DeepSeek-V2-Lite also employs DeepSeekMoE, and all FFNs except for the first layer are replaced with MoE layers. Each MoE layer consists of 2 shared experts and 64 routed experts, where the intermediate hidden dimension of each ex...
Appendix B DeepSeek-V2-Lite: A 16B Model Equipped with MLA and DeepSeekMoE
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: st 2K steps. Subsequently, the learning rate is multiplied by 0.316 after training about 80% of tokens, and again by 0.316 after training about 90% of tokens. The maximum learning rate is set to 4.2 × 10 − 4 4.2 superscript 10 4 4.2\times 10^{-4} , and the gradient clipping norm is set to 1.0. We do not employ the batch size scheduling strategy for it, and it is trained with a constant batch size of 4608 sequences. During pre-training, we set the maximum sequence length to 4K, and train DeepSeek-V2-Lite on 5.7T tokens. We leverage pipeline parallelism to deploy different layers of it on differ...
Appendix B DeepSeek-V2-Lite: A 16B Model Equipped with MLA and DeepSeekMoE
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: performance of DeepSeek-V2-Lite Chat and compare it with our previous small-size chat models in Table 7 . DeepSeek-V2-Lite also outperforms our previous small-size chat models by a large margin.
B.1 Model Description
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: Architectures. DeepSeek-V2-Lite has 27 layers and a hidden dimension of 2048. It also employs MLA and has 16 attention heads, where each head has a dimension of 128. Its KV compression dimension is 512, but slightly different from DeepSeek-V2, it does not compress the queries. For the decoupled queries and key, it has a per-head dimension of 64. DeepSeek-V2-Lite also employs DeepSeekMoE, and all FFNs except for the first layer are replaced with MoE layers. Each MoE layer consists of 2 shared experts and 64 routed experts, where the intermediate hidden dimension of each expert is 1408. Among th...
B.1 Model Description
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: tly, the learning rate is multiplied by 0.316 after training about 80% of tokens, and again by 0.316 after training about 90% of tokens. The maximum learning rate is set to 4.2 × 10 − 4 4.2 superscript 10 4 4.2\times 10^{-4} , and the gradient clipping norm is set to 1.0. We do not employ the batch size scheduling strategy for it, and it is trained with a constant batch size of 4608 sequences. During pre-training, we set the maximum sequence length to 4K, and train DeepSeek-V2-Lite on 5.7T tokens. We leverage pipeline parallelism to deploy different layers of it on different devices, but for e...
B.2 Performance Evaluation
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: Base Model. We evaluate the performance of DeepSeek-V2-Lite and compare it with our previous small-size base models in Table 6 . DeepSeek-V2-Lite exhibits overwhelming performance advantages, especially in reasoning, coding, and math. Chat Model. We evaluate the performance of DeepSeek-V2-Lite Chat and compare it with our previous small-size chat models in Table 7 . DeepSeek-V2-Lite also outperforms our previous small-size chat models by a large margin.
Appendix C Full Formulas of MLA
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: In order to demonstrate the complete computation process of MLA, we provide its full formulas in the following: 𝐜 t Q superscript subscript 𝐜 𝑡 𝑄 \displaystyle\mathbf{c}_{t}^{Q} = W D Q 𝐡 t , absent superscript 𝑊 𝐷 𝑄 subscript 𝐡 𝑡 \displaystyle=W^{DQ}\mathbf{h}_{t}, (37) [ 𝐪 t , 1 C ; 𝐪 t , 2 C ; … ; 𝐪 t , n h C ] = 𝐪 t C superscript subscript 𝐪 𝑡 1 𝐶 superscript subscript 𝐪 𝑡 2 𝐶 … superscript subscript 𝐪 𝑡 subscript 𝑛 ℎ 𝐶 superscript subscript 𝐪 𝑡 𝐶 \displaystyle[\mathbf{q}_{t,1}^{C};\mathbf{q}_{t,2}^{C};...;\mathbf{q}_{t,n_{h}}^{C}]=\mathbf{q}_{t}^{C} = W U Q 𝐜 t Q , absent superscr...
Appendix C Full Formulas of MLA
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: V}, (42) 𝐤 t R superscript subscript 𝐤 𝑡 𝑅 \displaystyle\boxed{\color[rgb]{0,0,1}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,1}\mathbf{k}_{t}^{R}} = RoPE ( W K R 𝐡 t ) , absent RoPE superscript 𝑊 𝐾 𝑅 subscript 𝐡 𝑡 \displaystyle=\operatorname{RoPE}({W^{KR}}\mathbf{h}_{t}), (43) 𝐤 t , i subscript 𝐤 𝑡 𝑖 \displaystyle\mathbf{k}_{t,i} = [ 𝐤 t , i C ; 𝐤 t R ] , absent superscript subscript 𝐤 𝑡 𝑖 𝐶 superscript subscript 𝐤 𝑡 𝑅 \displaystyle=[\mathbf{k}_{t,i}^{C};\mathbf{k}_{t}^{R}], (44) [ 𝐯 t , 1 C ; 𝐯 t , 2 C ; … ; 𝐯 t , n h C ] = 𝐯 t C superscript subscript 𝐯 𝑡 1 𝐶 superscript subscript 𝐯 𝑡 ...
Appendix C Full Formulas of MLA
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: can absorb W U K superscript 𝑊 𝑈 𝐾 W^{UK} into W U Q superscript 𝑊 𝑈 𝑄 W^{UQ} , and W U V superscript 𝑊 𝑈 𝑉 W^{UV} into W O superscript 𝑊 𝑂 W^{O} . Since this optimization is related to only model parameters, it can be completed offline at once. Through this optimization, we avoid the computational overhead for recomputing 𝐤 t C superscript subscript 𝐤 𝑡 𝐶 \mathbf{k}_{t}^{C} and 𝐯 t C superscript subscript 𝐯 𝑡 𝐶 \mathbf{v}_{t}^{C} during inference.
Appendix D Ablation of Attention Mechanisms
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: D.1 Ablation of MHA, GQA, and MQA We show the evaluation results for 7B dense models with MHA, GQA, and MQA on four hard benchmarks in Table 8 . All of these three models are trained on 1.33T tokens, and share the same architecture except for the attention mechanisms. In addition, for a fair comparison, we align the number of parameters of them to around 7B by adjusting the number of layers. From the table, we can find that MHA demonstrates significant advantages over GQA and MQA on these benchmarks. Benchmark (Metric) # Shots Dense 7B Dense 7B Dense 7B w/ MQA w/ GQA (8 Groups) w/ MHA # Params...
Appendix D Ablation of Attention Mechanisms
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: ) 5-shot 51.6 50.9 57.9 59.2 CMMLU (Acc.) 5-shot 52.3 53.4 60.7 62.5 Table 9: Comparison between MLA and MHA on hard benchmarks. DeepSeek-V2 shows better performance than MHA, but requires a significantly smaller amount of KV cache.
D.1 Ablation of MHA, GQA, and MQA
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: We show the evaluation results for 7B dense models with MHA, GQA, and MQA on four hard benchmarks in Table 8 . All of these three models are trained on 1.33T tokens, and share the same architecture except for the attention mechanisms. In addition, for a fair comparison, we align the number of parameters of them to around 7B by adjusting the number of layers. From the table, we can find that MHA demonstrates significant advantages over GQA and MQA on these benchmarks. Benchmark (Metric) # Shots Dense 7B Dense 7B Dense 7B w/ MQA w/ GQA (8 Groups) w/ MHA # Params - 7.1B 6.9B 6.9B BBH (EM) 3-shot ...
D.2 Comparison Between MLA and MHA
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: In Table 9 , we show the evaluation results for MoE models equipped with MLA and MHA, respectively, on four hard benchmarks. For a solid conclusion, we train and evaluate models across two scales. Two small MoE models comprise about 16B total parameters, and we train them on 1.33T tokens. Two large MoE models comprise about 250B total parameters, and we train them on 420B tokens. Also, two small MoE models and two large MoE models respectively share the same architecture except for the attention mechanisms. From the table, we can observe that MLA shows better performance than MHA. More importa...
Appendix E Discussion About Pre-Training Data Debiasing
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: During pre-training data preparation, we identify and filter out contentious content, such as values influenced by regional cultures, to avoid our model exhibiting unnecessary subjective biases on these controversial topics. Consequently, we observe that DeepSeek-V2 performs slightly worse on the test sets that are closely associated with specific regional cultures. For example, when evaluated on MMLU, although DeepSeek-V2 achieves comparable or superior performance on the majority of testsets compared with its competitors like Mixtral 8x22B, it still lags behind on the Humanity-Moral subset, ...
Appendix F Additional Evaluations on Math and Code
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: The evaluation employs the SC-Math6 corpus, which consists of thousands of Chinese math problems. DeepSeek-V2 Chat (RL) outperforms all Chinese LLMs, including both open-source and close-source models. Model Name R Level Comp. Score Reas. Steps Score OvrAcc Score GPT-4-1106-Preview 5 90.71 91.65 89.77 GPT-4 5 88.40 89.10 87.71 DeepSeek-V2 Chat (RL) 5 83.35 85.73 84.54 Ernie-bot 4.0 5 85.60 86.82 84.38 Qwen-110B-Chat 5 83.25 84.93 84.09 GLM-4 5 84.24 85.72 82.77 Xinghuo 3.5 5 83.73 85.37 82.09 Qwen-72B-Chat 4 78.42 80.07 79.25 ChatGLM-Turbo 4 57.70 60.32 55.09 GPT-3.5-Turbo 4 57.05 59.61 54.50 ...
Appendix G Evaluation Formats
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: We present our evaluation formats for each benchmark in Table 12 - 37 , respectively. PROMPT 以下是一道中国高考生物选择题，请选择正确的答案。 问题：下列有关高尔基体、线粒体和叶绿体的叙述, 正确的是 选项：(A)三者都存在于蓝藻中 (B)三者都含有 DNA (C)三者都是 ATP 合成的场所 (D)三者的膜结构中都含有蛋白质 答案：从A到D, 我们应选择 Table 12: An example of AGIEval. PROMPT Question: A sample in a cylindrical container has a cylindrical shape and a fixed volume. The state of matter of the sample _ A. must be solid B. could be either solid or liquid C. must be liquid D. could be either liquid or gas Answer: B Question: The speed of sound is generally greatest in _ A. solids and lowest in liquids B. soli...
Appendix G Evaluation Formats
本章节提供了关于DeepSeek-V2架构和训练的详细信息，包括MLA注意力机制和DeepSeekMoE混合专家架构的实现细节。这些技术共同使得DeepSeek-V2在保持高性能的同时，显著降低了训练成本和推理延迟，为开源AI模型的发展做出了重要贡献。 DeepSeek团队通过创新的架构设计和训练方法，在该领域取得了显著进展。模型在相关基准测试中表现出色，验证了这一方法的有效性。这一成果为开源AI社区做出了重要贡献，推动了技术发展。未来将继续优化和改进相关技术。
原文: ify this expression "Z" as follows: "Z = True and False and not True and True = A and B" where "A = True and False" and "B = not True and True". Let’s evaluate A: A = True and False = False. Let’s evaluate B: B = not True and True = not (True and True) = not (True) = False. Plugging in A and B, we get: Z = A and B = False and False = False. So the answer is False. Q: not not ( not ( False ) ) is A: Let’s think step by step. Remember that (i) expressions inside brackets are always evaluated first and that (ii) the order of operations from highest priority to lowest priority is "not", "and", "or...