diff --git a/README.md b/README.md
index 43d8b75..a578abe 100644
--- a/README.md
+++ b/README.md
@@ -52,13 +52,13 @@ import torch
 tokenizer = AutoTokenizer.from_pretrained("deepseek/deepseek-coder-7b-base", trust_remote_code=True)
 device = 2 if torch.cuda.is_available() else -1
 model = AutoModelForCausalLM.from_pretrained("deepseek/deepseek-coder-7b-base", trust_remote_code=True).to(device)
-inputs = tokenizer("#write a quick sort algorithm", return_tensors="pt").to(device)
+input_text = "#write a quick sort algorithm"
+inputs = tokenizer(input_text, return_tensors="pt").to(device)
 outputs = model.generate(**inputs, max_length=128)
 print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 ```
-This code will output
+This code will output the following result
 ```python
-#write a quick sort algorithm
 
 def quick_sort(arr):
     if len(arr) <= 1:
@@ -77,13 +77,29 @@ def quick_sort(arr):
 #### Code Insertion
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
-tokenizer = AutoTokenizer.from_pretrained("deepseek/deepseek-coder-7b")
-device = 0 if torch.cuda.is_available() else -1
-model = AutoModelForCausalLM.from_pretrained("deepseek/deepseek-coder-7b").to(device)
-input_text = "<fim_prefix>def print_hello_world():\n    <fim_suffix>\n    print('Hello world!')<fim_middle>"
-inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
+import torch
+tokenizer = AutoTokenizer.from_pretrained("deepseek/deepseek-coder-7b-base", trust_remote_code=True)
+device = 2 if torch.cuda.is_available() else -1
+model = AutoModelForCausalLM.from_pretrained("deepseek/deepseek-coder-7b-base", trust_remote_code=True).to(device)
+inputtext = """<fim_prefix>def quick_sort(arr):
+    if len(arr) <= 1:
+        return arr
+    pivot = arr[0]
+    left = []
+    right = []
+<fim_middle>
+        if arr[i] < pivot:
+            left.append(arr[i])
+        else:
+            right.append(arr[i])
+    return quick_sort(left) + [pivot] + quick_sort(right)<fim_suffix>"""
+inputs = tokenizer(inputtext, return_tensors="pt").to(device)
 outputs = model.generate(**inputs, max_length=128)
-print(tokenizer.decode(outputs[0]))
+print(tokenizer.decode(outputs[0], skip_special_tokens=True)[len(inputtext):])
+```
+This code will output the following result
+```python
+   for i in range(1, len(arr)):
 ```
 #### Repository Level Code Completion
 ```python