modify style

src/llmtuner/model/loader.py

@@ -3,9 +3,9 @@ from typing import TYPE_CHECKING, Any, Dict, Union
 from transformers import (
     AutoConfig,
     AutoModelForCausalLM,
-    AutoTokenizer,
-    AutoProcessor,
     AutoModelForVision2Seq,
+    AutoProcessor,
+    AutoTokenizer,
 )
 from trl import AutoModelForCausalLMWithValueHead
 
@@ -17,6 +17,7 @@ from .utils.misc import load_valuehead_params, register_autoclass
 from .utils.mod import convert_pretrained_model_to_mod, load_mod_pretrained_model
 from .utils.unsloth import load_unsloth_pretrained_model
 
+
 if TYPE_CHECKING:
     from transformers import PretrainedConfig, PreTrainedModel, PreTrainedTokenizer
 
@@ -42,7 +43,7 @@ def _get_init_kwargs(model_args: "ModelArguments") -> Dict[str, Any]:
 
 def load_tokenizer(
     model_args: "ModelArguments",
-) -> Dict[str, Union["PreTrainedTokenizer", "AutoProcesser"]]:
+) -> Dict[str, Union["PreTrainedTokenizer", "AutoProcessor"]]:
     r"""
     Loads pretrained tokenizer.
 
@@ -70,14 +71,10 @@ def load_tokenizer(
             dict(additional_special_tokens=model_args.new_special_tokens),
             replace_additional_special_tokens=False,
         )
-        logger.info(
-            "Add {} to special tokens.".format(",".join(model_args.new_special_tokens))
-        )
+        logger.info("Add {} to special tokens.".format(",".join(model_args.new_special_tokens)))
         if num_added_tokens > 0 and not model_args.resize_vocab:
             model_args.resize_vocab = True
-            logger.warning(
-                "New tokens have been added, changed `resize_vocab` to True."
-            )
+            logger.warning("New tokens have been added, changed `resize_vocab` to True.")
 
     patch_tokenizer(tokenizer)
     tokenizer_modules = {"tokenizer": tokenizer, "processor": None}
@@ -174,10 +171,8 @@ def load_model(
 
     trainable_params, all_param = count_parameters(model)
     if is_trainable:
-        param_stats = (
-            "trainable params: {:d} || all params: {:d} || trainable%: {:.4f}".format(
-                trainable_params, all_param, 100 * trainable_params / all_param
-            )
+        param_stats = "trainable params: {:d} || all params: {:d} || trainable%: {:.4f}".format(
+            trainable_params, all_param, 100 * trainable_params / all_param
         )
     else:
         param_stats = "all params: {:d}".format(all_param)