update README

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. 
+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](https://github.com/deepseek-ai/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. 
 Comprehensive evaluations reveal that DeepSeek-V3 outperforms other open-source models and achieves performance comparable to leading closed-source models.
@@ -65,7 +65,7 @@ Throughout the entire training process, we did not experience any irrecoverable
 
 **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.
+- On top of the efficient architecture of [DeepSeek-V2](https://github.com/deepseek-ai/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. 
 
@@ -80,7 +80,7 @@ Throughout the entire training process, we did not experience any irrecoverable
 
 ---
 
-**Post-Training: Knowledge Distillation from DeepSeek-R1**
+**Post-Training: Knowledge Distillation from [DeepSeek-R1](https://github.com/deepseek-ai/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.