add some

src/llmtuner/model/loader.py

@@ -40,7 +40,9 @@ def _get_init_kwargs(model_args: "ModelArguments") -> Dict[str, Any]:
     }
 
 
-def load_tokenizer(model_args: "ModelArguments") -> "PreTrainedTokenizer":
+def load_tokenizer(
+    model_args: "ModelArguments",
+) -> Dict[str, Union["PreTrainedTokenizer", "AutoProcesser"]]:
     r"""
     Loads pretrained tokenizer.
 
@@ -78,33 +80,25 @@ def load_tokenizer(model_args: "ModelArguments") -> "PreTrainedTokenizer":
             )
 
     patch_tokenizer(tokenizer)
-    return tokenizer
-
-
-def load_processor(model_args: "ModelArguments") -> "AutoProcessor":
-    r"""
-    Loads processor. Must before load_model.
-
-    Note: including inplace operation of model_args.
-    """
-    init_kwargs = _get_init_kwargs(model_args)
-    try:
-        processor = AutoProcessor.from_pretrained(
-            model_args.model_name_or_path,
-            use_fast=model_args.use_fast_tokenizer,
-            split_special_tokens=model_args.split_special_tokens,
-            padding_side="right",
-            **init_kwargs,
-        )
-    except Exception:  # try the fast one
-        processor = AutoProcessor.from_pretrained(
-            model_args.model_name_or_path,
-            use_fast=True,
-            padding_side="right",
-            **init_kwargs,
-        )
-
-    return processor
+    tokenizer_modules = {"tokenizer": tokenizer, "processor": None}
+    if model_args.use_mllm:
+        try:
+            processor = AutoProcessor.from_pretrained(
+                model_args.model_name_or_path,
+                use_fast=model_args.use_fast_tokenizer,
+                split_special_tokens=model_args.split_special_tokens,
+                padding_side="right",
+                **init_kwargs,
+            )
+        except Exception:  # try the fast one
+            processor = AutoProcessor.from_pretrained(
+                model_args.model_name_or_path,
+                use_fast=True,
+                padding_side="right",
+                **init_kwargs,
+            )
+        tokenizer_modules["processor"] = processor
+    return tokenizer_modules
 
 
 def load_config(model_args: "ModelArguments") -> "PretrainedConfig":