Update README.md

README.md

@@ -56,13 +56,39 @@ The model was evaluated using the **MathQA test dataset(2985 examples)** with **
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
-tokenizer = AutoTokenizer.from_pretrained("Dasool/math_gemma-2-9b")
-model = AutoModelForCausalLM.from_pretrained("Dasool/math_gemma-2-9b")
+local_model_path = "Dasool/mathGemma-2-9b"
+tokenizer = AutoTokenizer.from_pretrained(local_model_path)
+model = AutoModelForCausalLM.from_pretrained(local_model_path)
+
+alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
+
+### Instruction:
+Please select the correct answer for the following math problem from the options provided.
+
+### Input:
+Problem: Calculate the square root of 144.
+Options: 
+a) 10
+b) 11
+c) 12
+d) 13
+
+### Response:
+"""
+
+inputs = tokenizer(alpaca_prompt, return_tensors="pt")
+outputs = model.generate(
+    inputs["input_ids"],
+    max_new_tokens=100,  
+    num_beams=5,
+    early_stopping=True,
+    temperature=0.7,
+    no_repeat_ngram_size=2
+)
+
+answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(f"Answer: {answer}")
 
-# Example usage
-inputs = tokenizer("Solve: 12 + 7", return_tensors="pt")
-outputs = model.generate(inputs["input_ids"], max_length=30)
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 ```
 
 ## Limitations