[PaddleNLP 3.0] Update README

README.md

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 ## News 📢
 
-* **2024.06.27 [PaddleNLP v3.0 Beta](https://github.com/PaddlePaddle/PaddleNLP/releases/tag/v3.0.0)**：拥抱大模型，体验全升级。统一大模型工具链，实现国产计算芯片全流程接入；全面支持飞桨4D并行配置、高效精调策略、高效对齐算法、高性能推理等大模型产业级应用流程；自研极致收敛的RsLoRA+算法、自动扩缩容存储机制Unified Checkpoint和通用化支持FastFFN、FusedQKV助力大模型训推；主流模型持续支持更新，提供高效解决方案。
+* **2024.06.27 [PaddleNLP v3.0 Beta](https://github.com/PaddlePaddle/PaddleNLP/releases/tag/v3.0.0-beta0)**：拥抱大模型，体验全升级。统一大模型工具链，实现国产计算芯片全流程接入；全面支持飞桨4D并行配置、高效精调策略、高效对齐算法、高性能推理等大模型产业级应用流程；自研极致收敛的RsLoRA+算法、自动扩缩容存储机制Unified Checkpoint和通用化支持FastFFN、FusedQKV助力大模型训推；主流模型持续支持更新，提供高效解决方案。
 
 * **2024.04.24 [PaddleNLP v2.8](https://github.com/PaddlePaddle/PaddleNLP/releases/tag/v2.8.0)**：自研极致收敛的RsLoRA+算法，大幅提升PEFT训练收敛速度以及训练效果；引入高性能生成加速到RLHF PPO算法，打破 PPO 训练中生成速度瓶颈，PPO训练性能大幅领先。通用化支持 FastFFN、FusedQKV等多个大模型训练性能优化方式，大模型训练更快、更稳定。
 

README_en.md

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 ## News 📢
 
-* **2024.06.27 [PaddleNLP v3.0 Beta](https://github.com/PaddlePaddle/PaddleNLP/releases/tag/v3.0.0)**：Embrace large models and experience a complete upgrade. With a unified large model toolchain, we achieve full-process access to domestically produced computing chips. We fully support industrial-level application processes for large models, such as PaddlePaddle's 4D parallel configuration, efficient fine-tuning strategies, efficient alignment algorithms, and high-performance reasoning. Our developed RsLoRA+ algorithm, full checkpoint storage mechanism Unified Checkpoint, and generalized support for FastFNN and FusedQKV all contribute to the training and inference of large models. We continuously support updates to mainstream models for providing efficient solutions.
+* **2024.06.27 [PaddleNLP v3.0 Beta](https://github.com/PaddlePaddle/PaddleNLP/releases/tag/v3.0.0-beta0)**：Embrace large models and experience a complete upgrade. With a unified large model toolchain, we achieve full-process access to domestically produced computing chips. We fully support industrial-level application processes for large models, such as PaddlePaddle's 4D parallel configuration, efficient fine-tuning strategies, efficient alignment algorithms, and high-performance reasoning. Our developed RsLoRA+ algorithm, full checkpoint storage mechanism Unified Checkpoint, and generalized support for FastFNN and FusedQKV all contribute to the training and inference of large models. We continuously support updates to mainstream models for providing efficient solutions.
 
 * **2024.04.24 [PaddleNLP v2.8](https://github.com/PaddlePaddle/PaddleNLP/releases/tag/v2.8.0)**：Our self-developed RsLoRA+ algorithm with extreme convergence significantly improves the convergence speed and training effectiveness of PEFT training. By introducing high-performance generation acceleration into the RLHF PPO algorithm, we have broken through the generation speed bottleneck in PPO training, achieving a significant lead in PPO training performance. We generally support multiple large model training performance optimization methods such as FastFFN and FusedQKV, making large model training faster and more stable.