chore(mlx-lm): add reset lora layers helper (#377)

* chore(mlx-lm): add reset lora layers helper

* chore: rename the func

* chore: update docstring

* Update llms/mlx_lm/tuner/utils.py

Co-authored-by: Awni Hannun <awni.hannun@gmail.com>

---------

Co-authored-by: Awni Hannun <awni.hannun@gmail.com>

llms/mlx_lm/tuner/utils.py

@@ -47,23 +47,42 @@ def dequantize(model: nn.Module) -> nn.Module:
         nn.Module: The model with dequantized layers.
     """
     de_quantize_layers = []
-    for n, m in model.named_modules():
-        if isinstance(m, nn.QuantizedLinear):
-            bias = "bias" in m
-            weight = m.weight
+    for name, module in model.named_modules():
+        if isinstance(module, nn.QuantizedLinear):
+            bias = "bias" in module
+            weight = module.weight
             weight = mx.dequantize(
                 weight,
-                m.scales,
-                m.biases,
-                m.group_size,
-                m.bits,
+                module.scales,
+                module.biases,
+                module.group_size,
+                module.bits,
             ).astype(mx.float16)
             output_dims, input_dims = weight.shape
             linear = nn.Linear(input_dims, output_dims, bias=bias)
             linear.weight = weight
             if bias:
-                linear.bias = m.bias
-            de_quantize_layers.append((n, linear))
+                linear.bias = module.bias
+            de_quantize_layers.append((name, linear))
     if len(de_quantize_layers) > 0:
         model.update_modules(tree_unflatten(de_quantize_layers))
     return model
+
+
+def remove_lora_layers(model: nn.Module) -> nn.Module:
+    """
+    Remove the LoRA layers from the model.
+
+    Args:
+        model (nn.Module): The model with LoRA layers.
+
+    Returns:
+        nn.Module: The model without LoRA layers.
+    """
+    reset_layers = []
+    for name, module in model.named_modules():
+        if isinstance(module, LoRALinear):
+            reset_layers.append((name, module.linear))
+    if len(reset_layers) > 0:
+        model.update_modules(tree_unflatten(reset_layers))
+    return model