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README.md

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 # Introduction
-China Telecom Artificial Intelligence Technology Co., Ltd. has officially released Xing4.0-29B-A4B, a new-generation Xingchen large model built for the AI agent era. The FlagOS open-source community completed Day-0 multi-chip adaptation in parallel. Xing4.0-29B-A4B has finished multi-chip adaptation, accuracy alignment and deployment verification on eight chip platforms, including T-Head, NVIDIA, Moore Threads, Huawei Ascend, MetaX, Hygon, Iluvatar CoreX and ARM, based on the unified FlagOS open-source technology stack. Seven of these AI chips operate at BF16 precision, while the ARM platform offers a W4A8 quantized version. The multi-chip variants have been published on ModelScope and Hugging Face, allowing developers to obtain ready-to-use deployment solutions for respective hardware platforms.
+China Telecom Artificial Intelligence Technology Co., Ltd. has officially released Xing4.0-29B-A4B, a new-generation Xingchen large model built for the AI agent era. The FlagOS open-source community completed Day-0 multi-chip adaptation in parallel. Xing4.0-29B-A4B has finished multi-chip adaptation, accuracy alignment and deployment verification on nine chip platforms, including T-Head, NVIDIA, Moore Threads, Huawei Ascend, MetaX, Hygon, Iluvatar CoreX, Tsingmicro and ARM, based on the unified FlagOS open-source technology stack. Seven of these AI chips operate at BF16 precision, while the ARM platform offers a W4A8 quantized version. The multi-chip variants have been published on ModelScope and Hugging Face, allowing developers to obtain ready-to-use deployment solutions for respective hardware platforms.
 
 Built upon the TeleChat1-3 series, Xing4.0-29B-A4B is a comprehensively upgraded Xingchen large model. It features a total parameter count of 29B with only 4B activated parameters, adopting the mHC+MLA+MTP architecture. It natively supports a 256K context window, which can be extended to 512K. As China’s first tens-of-billion-parameter large model fully trained on domestic computing power and domestic frameworks, it is deeply optimized for complex engineering tasks. Designed for Agent scenarios, it can efficiently handle long-horizon tasks, support multi-step planning, tool invocation and the execution of complex reasoning chains, delivering powerful agent capabilities at a relatively small parameter scale.
 
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 ### Integrated Deployment
 - Out-of-the-box inference scripts with pre-configured hardware and software parameters
 - Released **FlagOS-Metax** container image supporting deployment within minutes