Clarify docstrings, help messages, assert messages in merge_peft_adap…

…ter.py (huggingface#838)

An assertion was also corrected to the intended test condition

examples/research_projects/stack_llama/scripts/merge_peft_adapter.py

@@ -9,23 +9,24 @@
 @dataclass
 class ScriptArguments:
     """
-    The name of the Casual LM model we wish to fine with PPO
+    The input names representing the Adapter and Base model fine-tuned with PEFT, and the output name representing the
+    merged model.
     """
 
-    adapter_model_name: Optional[str] = field(default=None, metadata={"help": "the model name"})
-    base_model_name: Optional[str] = field(default=None, metadata={"help": "the model name"})
-    output_name: Optional[str] = field(default=None, metadata={"help": "the model name"})
+    adapter_model_name: Optional[str] = field(default=None, metadata={"help": "the adapter name"})
+    base_model_name: Optional[str] = field(default=None, metadata={"help": "the base model name"})
+    output_name: Optional[str] = field(default=None, metadata={"help": "the merged model name"})
 
 
 parser = HfArgumentParser(ScriptArguments)
 script_args = parser.parse_args_into_dataclasses()[0]
 assert script_args.adapter_model_name is not None, "please provide the name of the Adapter you would like to merge"
 assert script_args.base_model_name is not None, "please provide the name of the Base model"
-assert script_args.base_model_name is not None, "please provide the output name of the merged model"
+assert script_args.output_name is not None, "please provide the output name of the merged model"
 
 peft_config = PeftConfig.from_pretrained(script_args.adapter_model_name)
 if peft_config.task_type == "SEQ_CLS":
-    # peft is for reward model so load sequence classification
+    # The sequence classification task is used for the reward model in PPO
     model = AutoModelForSequenceClassification.from_pretrained(
         script_args.base_model_name, num_labels=1, torch_dtype=torch.bfloat16
     )
@@ -36,7 +37,7 @@ class ScriptArguments:
 
 tokenizer = AutoTokenizer.from_pretrained(script_args.base_model_name)
 
-# Load the Lora model
+# Load the PEFT model
 model = PeftModel.from_pretrained(model, script_args.adapter_model_name)
 model.eval()