Update Mooncake-v3 paper link (#130)

Author: DefTruth

README.md

@@ -73,7 +73,7 @@ python3 download_pdfs.py # The code is generated by Doubao AI
 |2024.04| 🔥🔥🔥[Open-Sora Plan] Open-Sora Plan: This project aim to reproduce Sora (Open AI T2V model)(@PKU)|[[report]](https://github.com/PKU-YuanGroup/Open-Sora-Plan/blob/main/docs/Report-v1.0.0.md) | [[Open-Sora-Plan]](https://github.com/PKU-YuanGroup/Open-Sora-Plan) ![](https://img.shields.io/github/stars/PKU-YuanGroup/Open-Sora-Plan.svg?style=social)| ⭐️⭐️ | 
 |2024.05| 🔥🔥🔥[DeepSeek-V2] DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model(@DeepSeek-AI)|[[pdf]](https://arxiv.org/pdf/2405.04434) | [[DeepSeek-V2]](https://github.com/deepseek-ai/DeepSeek-V2) ![](https://img.shields.io/github/stars/deepseek-ai/DeepSeek-V2.svg?style=social)| ⭐️⭐️ | 
 |2024.05|🔥🔥[YOCO] You Only Cache Once: Decoder-Decoder Architectures for Language Models(@Microsoft)| [[pdf]](https://arxiv.org/pdf/2405.05254) | [[unilm-YOCO]](https://github.com/microsoft/unilm/tree/master/YOCO) ![](https://img.shields.io/github/stars/microsoft/unilm.svg?style=social) |⭐️⭐️ |  
-|2024.06|🔥[**Mooncake**] Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving(@Moonshot AI) |[[pdf]](https://github.com/kvcache-ai/Mooncake/blob/main/Mooncake-v1.pdf) | [[Mooncake]](https://github.com/kvcache-ai/Mooncake) ![](https://img.shields.io/github/stars/kvcache-ai/Mooncake.svg?style=social)|⭐️⭐️ |    
+|2024.06|🔥[**Mooncake**] Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving(@Moonshot AI) |[[pdf]](https://github.com/kvcache-ai/Mooncake/blob/main/Mooncake-v3.pdf) | [[Mooncake]](https://github.com/kvcache-ai/Mooncake) ![](https://img.shields.io/github/stars/kvcache-ai/Mooncake.svg?style=social)|⭐️⭐️ |    
 |2024.07|🔥🔥[**FlashAttention-3**] FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision(@TriDao etc) |[[pdf]](https://tridao.me/publications/flash3/flash3.pdf)|[[flash-attention]](https://github.com/Dao-AILab/flash-attention) ![](https://img.shields.io/github/stars/Dao-AILab/flash-attention.svg?style=social)|⭐️⭐️ |  
 |2024.07|🔥🔥[**MInference 1.0**] MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention(@Microsoft) |[[pdf]](https://arxiv.org/pdf/2407.02490)|[[MInference 1.0]](https://github.com/microsoft/MInference) ![](https://img.shields.io/github/stars/microsoft/MInference.svg?style=social)|⭐️⭐️ |  
 |2024.11|🔥🔥🔥[**Star-Attention: 11x~ speedup**] Star Attention: Efficient LLM Inference over Long Sequences(@NVIDIA)|[[pdf]](https://arxiv.org/pdf/2411.17116)|[[Star-Attention]](https://github.com/NVIDIA/Star-Attention) ![](https://img.shields.io/github/stars/NVIDIA/Star-Attention.svg?style=social)|⭐️⭐️ |