Make mlx-lm more type-checker friendly

mlx_lm/__init__.py

@@ -9,3 +9,13 @@
 from .convert import convert
 from .generate import batch_generate, generate, stream_generate
 from .utils import load
+
+
+__all__ = [
+    "__version__",
+    "convert",
+    "batch_generate",
+    "generate",
+    "stream_generate",
+    "load",
+]

mlx_lm/tokenizer_utils.py

@@ -1,7 +1,7 @@
 import json
 from functools import partial
 from json import JSONDecodeError
-from typing import List
+from typing import Any, Dict, List, Optional
 
 from transformers import AutoTokenizer, PreTrainedTokenizerFast
 
@@ -424,8 +424,11 @@ def _is_bpe_decoder(decoder):
 
 
 def load_tokenizer(
-    model_path, tokenizer_config_extra={}, return_tokenizer=True, eos_token_ids=None
-):
+    model_path,
+    tokenizer_config_extra: Optional[Dict[str, Any]]=None,
+    return_tokenizer=True,
+    eos_token_ids=None
+) -> TokenizerWrapper:
     """Load a huggingface tokenizer and try to infer the type of streaming
     detokenizer to use.
 
@@ -454,8 +457,9 @@ def load_tokenizer(
         eos_token_ids = [eos_token_ids]
 
     if return_tokenizer:
+        kwargs = tokenizer_config_extra or {}
         return TokenizerWrapper(
-            AutoTokenizer.from_pretrained(model_path, **tokenizer_config_extra),
+            AutoTokenizer.from_pretrained(model_path, **kwargs),
             detokenizer_class,
             eos_token_ids=eos_token_ids,
         )

mlx_lm/utils.py

@@ -136,7 +136,7 @@ def load_model(
     model_path: Path,
     lazy: bool = False,
     strict: bool = True,
-    model_config: dict = {},
+    model_config: Optional[Dict[str, Any]] = None,
     get_model_classes: Callable[[dict], Tuple[Type[nn.Module], Type]] = _get_classes,
 ) -> Tuple[nn.Module, dict]:
     """
@@ -163,7 +163,8 @@ def load_model(
         ValueError: If the model class or args class are not found or cannot be instantiated.
     """
     config = load_config(model_path)
-    config.update(model_config)
+    if model_config is not None:
+        config.update(model_config)
 
     weight_files = glob.glob(str(model_path / "model*.safetensors"))
 
@@ -227,12 +228,12 @@ def class_predicate(p, m):
 
 def load(
     path_or_hf_repo: str,
-    tokenizer_config={},
-    model_config={},
+    tokenizer_config: Optional[Dict[str, Any]] = None,
+    model_config: Optional[Dict[str, Any]] = None,
     adapter_path: Optional[str] = None,
     lazy: bool = False,
     return_config: bool = False,
-    revision: str = None,
+    revision: Optional[str] = None,
 ) -> Union[
     Tuple[nn.Module, TokenizerWrapper],
     Tuple[nn.Module, TokenizerWrapper, Dict[str, Any]],