Update README.md

README.md

@@ -76,7 +76,53 @@ code = tokenizer.decode(outputs[0], skip_special_tokens=True)
 print(code)
 ```
 
-### Size Comparison
+### A Test Example
+``````
+User Prompt: Write a Python program to implement K-Means clustering. The program should take two mandatory arguments, k and data, where k is the number of clusters and data is a 2D array containing the data points k = 3
+data = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]]
+
+Generated Output: Write a Python program to implement K-Means clustering. The program should take two mandatory arguments, k and data, where k is the number of clusters and data is a 2D array containing the data points k = 3
+data = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]] Ready for action! Let's do this! ```python
+import numpy as np
+
+def kmeans(data, k):
+    # Initialize the centroids
+    centroids = np.random.rand(k, 2)
+    
+    # Initialize the cluster assignments
+    cluster_assignments = np.zeros(data.shape[0], dtype=int)
+    
+    # Iterate through the data points
+    for i in range(data.shape[0]):
+        # Calculate the distance between the current data point and each of the centroids
+        distances = np.linalg.norm(data[i] - centroids)
+        
+        # Assign the data point to the closest centroid
+        cluster_assignments[i] = np.argmin(distances)
+        
+    return cluster_assignments
+```
+This program takes two mandatory arguments, `k` and `data`, where `k` is the number of clusters and `data` is a 2D array containing the data points. The program initializes the centroids randomly and then iterates through the data points to calculate the distance between each data point and each of the centroids. The program then assigns each data point to the closest centroid based on the calculated distance. Finally, the program returns the cluster assignments for each data point.
+
+Note that this program uses the Euclidean distance to calculate the distance between the data points and the centroids. You can change the distance metric if needed.
+
+Also, this program assumes that the data points are 2D. If the data points are 3D or higher, you will need to modify the program accordingly.
+
+I hope this helps! Let me know if you have any questions.
+
+```python
+# Example usage
+data = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]]
+k = 3
+cluster_assignments = kmeans(data, k)
+print(cluster_assignments)
+```
+This will output the cluster assignments for each data point. The output will be a list of integers, where each integer represents the cluster assignment for that data point. For example, if the data points are
+---------------------------------------------------------------------
+
+``````
+
+## Size Comparison
 
 The table shows comparison VRAM requirements for loading and training
 of FP16 Base Model and 4bit GPTQ quantized model with PEFT.