Update README.md

README.md

@@ -91,10 +91,25 @@ tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(model_name)
 
 # Example usage
-input_text = "What are the common symptoms of a urinary tract infection?"
-inputs = tokenizer(input_text, return_tensors="pt")
-outputs = model.generate(**inputs)
+input_text = "I had a surgery which ended up with some failures. What can I do to fix it?"
+
+# Prepare inputs with explicit padding and attention mask
+inputs = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True)
+
+# Generate response with more explicit parameters
+outputs = model.generate(
+    input_ids=inputs['input_ids'], 
+    attention_mask=inputs['attention_mask'],
+    max_new_tokens=150,  # Specify max new tokens to generate
+    do_sample=True,      # Enable sampling for more diverse responses
+    temperature=0.7,     # Control randomness of output
+    top_p=0.9,           # Nucleus sampling to maintain quality
+    num_return_sequences=1  # Number of generated sequences
+)
+
+# Decode the generated response
 response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
 print(response)
 ```