Fix the Readme.md

README.md

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 ## 1. Introduction
 
-We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token. 
-To achieve efficient inference and cost-effective training, DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly validated in DeepSeek-V2. 
-Furthermore, DeepSeek-V3 pioneers an auxiliary-loss-free strategy for load balancing and sets a multi-token prediction training objective for stronger performance. 
-We pre-train DeepSeek-V3 on 14.8 trillion diverse and high-quality tokens, followed by Supervised Fine-Tuning and Reinforcement Learning stages to fully harness its capabilities. 
+We present DeepSeek-V3, a powerful Mixture-of-Experts (MoE) language model with a total of 671B parameters, of which 37B are activated per token.
+To achieve efficient inference and cost-effective training, DeepSeek-V3 utilizes Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly validated in DeepSeek-V2.
+Furthermore, DeepSeek-V3 introduces an auxiliary-loss-free strategy for load balancing and establishes a multi-token prediction training objective for enhanced performance.
+We pre-train DeepSeek-V3 on 14.8 trillion diverse and high-quality tokens, followed by Supervised Fine-Tuning and Reinforcement Learning stages to maximize its capabilities.
 Comprehensive evaluations reveal that DeepSeek-V3 outperforms other open-source models and achieves performance comparable to leading closed-source models.
-Despite its excellent performance, DeepSeek-V3 requires only 2.788M H800 GPU hours for its full training.
-In addition, its training process is remarkably stable. 
-Throughout the entire training process, we did not experience any irrecoverable loss spikes or perform any rollbacks. 
+Despite its excellent performance, DeepSeek-V3 requires only 2.788M H800 GPU hours for full training.
+Additionally, its training process is remarkably stable.
+Throughout the entire training process, we did not experience any irrecoverable loss spikes or need to perform any rollbacks. 
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 **Architecture: Innovative Load Balancing Strategy and Training Objective**
 
-- On top of the efficient architecture of DeepSeek-V2, we pioneer an auxiliary-loss-free strategy for load balancing, which minimizes the performance degradation that arises from encouraging load balancing.
--  We investigate a Multi-Token Prediction (MTP) objective and prove it beneficial to model performance. 
-    It can also be used for speculative decoding for inference acceleration. 
+- Building on the efficient architecture of DeepSeek-V2, we introduce an auxiliary-loss-free strategy for load balancing, minimizing performance degradation caused by load balancing constraints.
+-  We investigate a Multi-Token Prediction (MTP) objective and demonstrate its benefits to model performance.
+It can also be used for speculative decoding to accelerate inference. 
 
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 **Pre-Training: Towards Ultimate Training Efficiency**
 
-- We design an FP8 mixed precision training framework and, for the first time, validate the feasibility and effectiveness of FP8 training on an extremely large-scale model.  
-- Through co-design of algorithms, frameworks, and hardware, we overcome the communication bottleneck in cross-node MoE training, nearly achieving full computation-communication overlap.  
-  This significantly enhances our training efficiency and reduces the training costs, enabling us to further scale up the model size without additional overhead.  
-- At an economical cost of only 2.664M H800 GPU hours, we complete the pre-training of DeepSeek-V3 on 14.8T tokens, producing the currently strongest open-source base model. The subsequent training stages after pre-training require only 0.1M GPU hours.
+- We develop an FP8 mixed-precision training framework and, for the first time, validate the feasibility and effectiveness of FP8 training for extremely large-scale models.  
+- By co-designing algorithms, frameworks, and hardware, we overcome the communication bottleneck in cross-node MoE training, achieving near-complete computation-communication overlap.This significantly enhances training efficiency and reduces costs, allowing us to scale up the model size without additional overhead. 
+- At an economical cost of only 2.664M H800 GPU hours, we complete the pre-training of DeepSeek-V3 on 14.8T tokens, producing the strongest open-source base model. The subsequent training stages after pre-training require only 0.1M GPU hours.
 
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 **Post-Training: Knowledge Distillation from DeepSeek-R1**
 
--   We introduce an innovative methodology to distill reasoning capabilities from the long-Chain-of-Thought (CoT) model, specifically from one of the DeepSeek R1 series models, into standard LLMs, particularly DeepSeek-V3. Our pipeline elegantly incorporates the verification and reflection patterns of R1 into DeepSeek-V3 and notably improves its reasoning performance. Meanwhile, we also maintain a control over the output style and length of DeepSeek-V3.
+-   We introduce an innovative methodology to distill reasoning capabilities from the long Chain-of-Thought (CoT) model, specifically from one of the DeepSeek R1 series models, into standard LLMs, particularly DeepSeek-V3.
+Our pipeline elegantly incorporates the verification and reflection patterns of R1 into DeepSeek-V3, significantly improving its reasoning performance while maintaining control over its output style and length.
 
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