Update README.md

README.md

@@ -38,3 +38,53 @@ This model was fine-tuned as a learning project to demonstrate the full workflow
 ## Prompt Format
 
 This model was trained with the following instruction format. Using the same format at inference time will give the best results:
+
+
+## Usage
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+
+# Load base model and LoRA adapter
+base_model = AutoModelForCausalLM.from_pretrained(
+    "Qwen/Qwen2.5-Coder-1.5B-Instruct",
+    dtype=torch.float16,
+    device_map="auto",
+)
+model = PeftModel.from_pretrained(base_model, "SathishKumar89/my-python-coder")
+tokenizer = AutoTokenizer.from_pretrained("SathishKumar89/my-python-coder")
+
+# Prepare a prompt
+prompt = """### Instruction:
+Write a Python function that checks if a number is prime.
+
+### Response:
+"""
+
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=200, do_sample=False)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+
+### Instruction:
+Write a Python function that checks if a number is prime.
+
+### Response:
+def is_prime(num):
+    # Check for 0 and 1
+    if num <= 1:
+        return False
+
+    # Check for even numbers greater than 2
+    elif num == 2:
+        return True
+    elif num % 2 == 0:
+        return False
+
+    # Check for odd numbers greater than 3
+    else:
+        for i in range(3, int(num**0.5) + 1, 2):
+            if num % i == 0:
+                return False
+        return True