diff --git a/README.md b/README.md
index 378a6c4..0b3b6c6 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,7 @@
 <p align="center">
 <img width="1000px" alt="DeepSeek Coder" src="pictures/logo.png">
 </p>
-<p align="center"><a href="https://www.deepseek.com/">[<img src="pictures/home.png" width="20px"> Homepage]</a> | <a href="https://coder.deepseek.com/">[🤖 Chat with DeepSeek Coder] | <a href="https://huggingface.co/deepseek-ai">[🤗 Models Download]</a> | <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a>  |  <a href="https://github.com/guoday/assert/blob/main/QR.png?raw=true">[Wechat(微信)]</a></p>
+<p align="center"><a href="https://www.deepseek.com/">[<img src="pictures/home.png" width="20px"> Homepage]</a> | <a href="https://coder.deepseek.com/">[🤖 Chat with DeepSeek Coder]</a> | <a href="https://huggingface.co/deepseek-ai">[🤗 Models Download]</a> | <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a> | <a href="https://github.com/guoday/assert/blob/main/QR.png?raw=true">[WeChat (微信)]</a></p>
 <hr>
 
 
@@ -38,7 +38,6 @@ And the DeepSeek-Coder-Instruct-33B model after instruction tuning outperforms G
 More evaluation details can be found in the [Detailed Evaluation](#5-detailed-evaluation-results).
 
 
-
 ### 3. Procedure of Data Creation and Model Training
 
 #### Data Creation
@@ -59,8 +58,6 @@ More evaluation details can be found in the [Detailed Evaluation](#5-detailed-ev
 <img src="pictures/model_pretraining.png" alt="model_pretraining" width="100%">
 
 
-
-
 ### 4. How to Use
 Before proceeding, you'll need to install the necessary dependencies. You can do this by running the following command:
 ```
@@ -70,7 +67,7 @@ A demo is also available on the [🤗 Hugging Face Space](https://huggingface.co
 
 Here are some examples of how to use our model.
 
-#### 1）Code Completion
+#### 1) Code Completion
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
@@ -97,7 +94,7 @@ def quick_sort(arr):
     return quick_sort(left) + [pivot] + quick_sort(right)
 ```
 
-#### 2）Code Insertion
+#### 2) Code Insertion
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
@@ -124,7 +121,7 @@ This code will output the following result:
    for i in range(1, len(arr)):
 ```
 
-#### 3）Chat Model Inference
+#### 3) Chat Model Inference
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
 tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-6.7b-instruct", trust_remote_code=True)
@@ -172,7 +169,7 @@ You are an AI programming assistant, utilizing the Deepseek Coder model, develop
 
 ```
 
-#### 4）Repository Level Code Completion
+#### 4) Repository Level Code Completion
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
 tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-6.7b-base", trust_remote_code=True)
@@ -265,16 +262,16 @@ In the following scenario, the Deepseek-Coder 6.7B model effectively calls a cla
 ### 5. Detailed Evaluation Results
 
 The reproducible code for the following evaluation results can be found in the [Evaluation](https://github.com/deepseek-ai/deepseek-coder/tree/main/Evaluation) directory.
-#### 1）Multilingual HumanEval Benchmark
+#### 1) Multilingual HumanEval Benchmark
 ![HumanEval](pictures/HumanEval.png)
 
-#### 2）MBPP Benchmark
+#### 2) MBPP Benchmark
 <img src="pictures/MBPP.png" alt="MBPP" width="40%">
 
-#### 3）DS-1000 Benchmark
+#### 3) DS-1000 Benchmark
 ![DS-1000](pictures/DS-1000.png)
 
-#### 4）Program-Aid Math Reasoning Benchmark
+#### 4) Program-Aid Math Reasoning Benchmark
 ![Math](pictures/Math.png)