Save lora config (#636)

* lora config

* comments

* version bump

llms/mlx_lm/utils.py

@@ -9,7 +9,7 @@ import shutil
 import time
 from pathlib import Path
 from textwrap import dedent
-from typing import Any, Callable, Dict, Generator, List, Optional, Tuple, Union
+from typing import Any, Callable, Dict, Generator, Optional, Tuple, Union
 
 import mlx.core as mx
 import mlx.nn as nn
@@ -354,7 +354,7 @@ def load_model(model_path: Path, lazy: bool = False) -> nn.Module:
 def load(
     path_or_hf_repo: str,
     tokenizer_config={},
-    adapter_file: Optional[str] = None,
+    adapter_path: Optional[str] = None,
     lazy: bool = False,
 ) -> Tuple[nn.Module, PreTrainedTokenizer]:
     """
@@ -364,8 +364,8 @@ def load(
         path_or_hf_repo (Path): The path or the huggingface repository to load the model from.
         tokenizer_config (dict, optional): Configuration parameters specifically for the tokenizer.
             Defaults to an empty dictionary.
-        adapter_file (str, optional): Path to the adapter file. If provided, applies LoRA layers to the model.
-            Defaults to None.
+        adapter_path (str, optional): Path to the LoRA adapters. If provided, applies LoRA layers
+            to the model. Default: ``None``.
         lazy (bool): If False eval the model parameters to make sure they are
             loaded in memory before returning, otherwise they will be loaded
             when needed. Default: ``False``
@@ -379,8 +379,8 @@ def load(
     model_path = get_model_path(path_or_hf_repo)
 
     model = load_model(model_path, lazy)
-    if adapter_file is not None:
-        model = apply_lora_layers(model, adapter_file)
+    if adapter_path is not None:
+        model = apply_lora_layers(model, adapter_path)
         model.eval()
 
     tokenizer = AutoTokenizer.from_pretrained(model_path, **tokenizer_config)