diff --git a/python/tvm/relay/frontend/tflite.py b/python/tvm/relay/frontend/tflite.py
index 58adf8d9f15bc..d88589ee722cc 100644
--- a/python/tvm/relay/frontend/tflite.py
+++ b/python/tvm/relay/frontend/tflite.py
@@ -728,7 +728,7 @@ def convert_relu(self, op):
                     zero_point=zero_point_val,
                     dtype=output_tensor_type_str)
         else:
-            out = _op.clip(in_expr, a_min=0, a_max=6)
+            out = _op.nn.relu(in_expr)
 
         if output_tensor.qnn_params:
             output_tensor_type_str = self.get_tensor_type_str(output_tensor.tensor.Type())
diff --git a/tests/python/frontend/tflite/test_forward.py b/tests/python/frontend/tflite/test_forward.py
index bea0a1b6c0060..989a38aefdc2a 100644
--- a/tests/python/frontend/tflite/test_forward.py
+++ b/tests/python/frontend/tflite/test_forward.py
@@ -1814,7 +1814,7 @@ def _test_quantize_dequantize(data):
 
     # Keras model to force TFLite converter to insert 2 TFLite quantize ops.
     # First TFLite quantize op converts float32 tensor to int8 tensor - Qnn quantize.
-    # Second TLite quantize op converts int8 tensor to int8 tensor - Qnn requantize.
+    # Second TFLite quantize op converts int8 tensor to int8 tensor - Qnn requantize.
     data_in = tf.keras.layers.Input(shape=data.shape[1:])
     relu = tf.keras.layers.ReLU()(data_in)
     add = tf.keras.layers.Add()([data_in, relu])