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hpcaitech · Jun 12, 2024 · 40fbf32 · 40fbf32
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diff --git a/docs/source/zh-Hans/features/distributed_optimizers.md b/docs/source/zh-Hans/features/distributed_optimizers.md
@@ -4,9 +4,9 @@ Author: Wenxuan Tan, Junwen Duan, Renjie Mao
 
 **相关论文**
 - [Adafactor: Adaptive Learning Rates with Sublinear Memory Cost](https://arxiv.org/abs/1804.04235)
-- [CAME: Confidence-guided Adaptive Memory Efficient Optimization] (https://arxiv.org/abs/2307.02047)
-- [GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection] (https://arxiv.org/abs/2403.03507)
-- [Large Batch Optimization for Deep Learning: Training BERT in 76 minutes] (https://arxiv.org/pdf/1904.00962)
+- [CAME: Confidence-guided Adaptive Memory Efficient Optimization](https://arxiv.org/abs/2307.02047)
+- [GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection](https://arxiv.org/abs/2403.03507)
+- [Large Batch Optimization for Deep Learning: Training BERT in 76 minutes](https://arxiv.org/pdf/1904.00962)
 
 ## 介绍
 除了广泛采用的Adam和SGD外，许多现代优化器需要逐层统计信息以有效更新参数，因此无法直接应用于模型层在多个设备上分片的并行设置。我们以提供了优化的分布式实现，，并且通过plugin与Tensor Parallel、DDP和ZeRO无缝集成。