diff --git a/README-en.md b/README-en.md
index 6a618c5..00e0a7c 100644
--- a/README-en.md
+++ b/README-en.md
@@ -56,8 +56,51 @@ Experience models with larger scale at [Luca](https://luca.cn/).
 - [ModelScope Repo]()
 - [XX Repo]()
 
+# Quick Start
+
 <p id="3"></p>
 
+
+#### Huggingface Model
+
+* Install `transformers>=4.36.0` and `accelerate`，run the following python code。
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+torch.manual_seed(0)
+
+path = 'openbmb/MiniCPM-2B-dpo-bf16'
+tokenizer = AutoTokenizer.from_pretrained(path)
+model = AutoModelForCausalLM.from_pretrained(path, torch_dtype=torch.bfloat16, device_map='cuda', trust_remote_code=True)
+
+responds, history = model.chat(tokenizer, "Which city is the capital of China?", temperature=0.8, top_p=0.8)
+print(responds)
+```
+
+* Expected Output
+```shell
+The capital city of China is Beijing. Beijing is not only the political center of China but also a cultural and economic hub. It is known for its rich history and numerous landmarks, such as the Great Wall, the Forbidden City, and the Temple of Heaven. The city is also home to the National Stadium, also known as the "Bird's Nest," and the National Aquatics Center, or "Water Cube." Beijing is a significant city in China, with a population of over 21 million people.
+```
+
+#### vLLM Inference
+
+* Install vLLM supporting MiniCPM
+  - vLLM 0.2.2 is adapted to MiniCPM in `inference/vllm`. More vLLM versions will be supported in the future
+```shell
+pip install inference/vllm
+```
+
+* Transfer Huggingface Transformers repo to vLLM-MiniCPM repo, where `<hf_repo_path>`, `<vllmcpm_repo_path>` are local paths.
+```shell
+python inference/convert_hf_to_vllmcpm.py --load <hf_repo_path> --save <vllmcpm_repo_path>
+```
+
+* Examples
+```shell
+cd inference/vllm/examples/infer_cpm
+python inference.py --model_path <vllmcpm_repo_path> --prompt_path prompts/prompt_final.txt
+```
+
 # Benchmark 
 
   | HuggingFace | ModelScope | WiseModel |
@@ -157,23 +200,7 @@ Launch gradio-based demo using the following command:
 python demo/gradio_based_demo.py
 ```
 
-#### Inference with vLLM (Recommended!)
 
-* Install vLLM supporting MiniCPM
-  - vLLM 0.2.2 is adapted to MiniCPM in `inference/vllm`. More vLLM versions will be supported in the future
-```shell
-pip install inference/vllm
-```
-
-* Transfer Huggingface Transformers repo to vLLM-MiniCPM repo, where `<hf_repo_path>`, `<vllmcpm_repo_path>` are local paths.
-```shell
-python inference/convert_hf_to_vllmcpm.py --load <hf_repo_path> --save <vllmcpm_repo_path>
-```
-
-* Examples
-```shell
-cd inference/vllm/examples/infer_cpm
-python inference.py --model_path <vllmcpm_repo_path> --prompt_path prompts/prompt_final.txt
 
 ## 
 
diff --git a/README.md b/README.md
index 72386e1..b6c425b 100644
--- a/README.md
+++ b/README.md
@@ -68,6 +68,48 @@ MiniCPM 是面壁与清华大学自然语言处理实验室共同开源的系列
 
 # 快速上手
 
+#### Huggingface 模型
+
+* 安装`transformers>=4.36.0`以及`accelerate`后，运行以下代码。
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+torch.manual_seed(0)
+
+path = 'openbmb/MiniCPM-2B-dpo-bf16'
+tokenizer = AutoTokenizer.from_pretrained(path)
+model = AutoModelForCausalLM.from_pretrained(path, torch_dtype=torch.bfloat16, device_map='cuda', trust_remote_code=True)
+
+responds, history = model.chat(tokenizer, "山东省最高的山是哪座山, 它比黄山高还是矮？差距多少？", temperature=0.8, top_p=0.8)
+print(responds)
+```
+
+* 期望输出
+```shell
+山东省最高的山是泰山，海拔1545米。
+
+相对于黄山（海拔1864米），泰山海拔较低，相差约319米。
+```
+
+#### vLLM 推理
+
+* 安装支持MiniCPM的vLLM
+  - 我们当前支持版本为0.2.2的vLLM，代码位于`inference/vllm`,未来将会支持更多版本
+```shell
+pip install inference/vllm
+```
+
+* 将Huggingface Transformers仓库转为vLLM-MiniCPM支持的格式，其中`<hf_repo_path>`, `<vllmcpm_repo_path>`均为本地路径
+```shell
+python inference/convert_hf_to_vllmcpm.py --load <hf_repo_path> --save <vllmcpm_repo_path>
+```
+
+* 测试样例
+```shell
+cd inference/vllm/examples/infer_cpm
+python inference.py --model_path <vllmcpm_repo_path> --prompt_path prompts/prompt_final.txt
+```
+
 
 <p id="3"></p>