Update README.md (#9523)

python/llm/example/CPU/README.md

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 This folder contains examples of running BigDL-LLM on Intel CPU:
 
-- [HF-Transformers-AutoModels](HF-Transformers-AutoModels): running any Hugging Face Transformers model on BigDL-LLM (using the standard AutoModel APIs)
+- [HF-Transformers-AutoModels](HF-Transformers-AutoModels): running any ***Hugging Face Transformers*** model on BigDL-LLM (using the standard AutoModel APIs)
+- [QLoRA-FineTuning](QLoRA-FineTuning): running ***QLoRA finetuning*** using BigDL-LLM on intel CPUs
+- [vLLM-Serving](vLLM-Serving): running ***vLLM*** serving framework on intel CPUs (with BigDL-LLM low-bit optimized models)
+- [Deepspeed-AutoTP](https://github.com/intel-analytics/BigDL/tree/main/python/llm/example/CPU/Deepspeed-AutoTP): running distributed inference using ***DeepSpeed AutoTP*** (with BigDL-LLM low-bit optimized models)
+- [LangChain](LangChain): running ***LangChain*** applications on BigDL-LLM
+- [Applications](Applications): running LLM applications (such as agent, streaming-llm) on BigDl-LLM
 - [PyTorch-Models](PyTorch-Models): running any PyTorch model on BigDL-LLM (with "one-line code change")
 - [Native-Models](Native-Models): converting & running LLM in `llama`/`chatglm`/`bloom`/`gptneox`/`starcoder` model family using native (cpp) implementation
-- [LangChain](LangChain): running LangChain applications on BigDL-LLM
-- [Applications](Applications): running Transformers applications on BigDl-LLM
-- [QLoRA-FineTuning](QLoRA-FineTuning): running QLoRA finetuning using BigDL-LLM on intel CPUs
-- [vLLM-Serving](vLLM-Serving): running vLLM serving framework on Xeon Platforms (with BigDL-LLM low-bit optimized models)
+
 
 ## System Support
 **Hardware**: