fix: address the comments

Author: ashleyxuu

bigframes/ml/llm.py

@@ -26,7 +26,6 @@
 
 _REMOTE_TEXT_GENERATOR_MODEL_CODE = "CLOUD_AI_LARGE_LANGUAGE_MODEL_V1"
 _REMOTE_TEXT_GENERATOR_32K_MODEL_CODE = "text-bison-32k"
-_REMOTE_TEXT_GENERATOR_32K_MODEL_CODE = "text-bison-32k"
 _TEXT_GENERATE_RESULT_COLUMN = "ml_generate_text_llm_result"
 
 _REMOTE_EMBEDDING_GENERATOR_MODEL_CODE = "CLOUD_AI_TEXT_EMBEDDING_MODEL_V1"
@@ -52,7 +51,7 @@ class PaLM2TextGenerator(base.Predictor):
 
     def __init__(
         self,
-        model_name: Literal["text-bison", "text-bison-32k"] = "text-bison-32k",
+        model_name: Literal["text-bison", "text-bison-32k"] = "text-bison",
         session: Optional[bigframes.Session] = None,
         connection_name: Optional[str] = None,
     ):
@@ -131,7 +130,6 @@ def predict(
             top_k (int, default 40):
                 Top-k changes how the model selects tokens for output. A top-k of 1 means the selected token is the most probable among all tokens
                 in the model's vocabulary (also called greedy decoding), while a top-k of 3 means that the next token is selected from among the 3 most probable tokens (using temperature).
-                in the model's vocabulary (also called greedy decoding), while a top-k of 3 means that the next token is selected from among the 3 most probable tokens (using temperature).
                 For each token selection step, the top K tokens with the highest probabilities are sampled. Then tokens are further filtered based on topP with the final token selected using temperature sampling.
                 Specify a lower value for less random responses and a higher value for more random responses.
                 Default 40. Possible values [1, 40].