diff --git a/llms/mlx_lm/convert.py b/llms/mlx_lm/convert.py
index 7eac34aa..5f2f3adf 100644
--- a/llms/mlx_lm/convert.py
+++ b/llms/mlx_lm/convert.py
@@ -42,6 +42,13 @@ def configure_parser() -> argparse.ArgumentParser:
         type=str,
         default=None,
     )
+    parser.add_argument(
+        "-d",
+        "--dequantize",
+        help="Dequantize a quantized model.",
+        action="store_true",
+        default=False,
+    )
     return parser
 
 
diff --git a/llms/mlx_lm/utils.py b/llms/mlx_lm/utils.py
index 5f2a2f8c..7b0e2da7 100644
--- a/llms/mlx_lm/utils.py
+++ b/llms/mlx_lm/utils.py
@@ -19,6 +19,7 @@ from transformers import AutoConfig, AutoTokenizer, PreTrainedTokenizer
 
 # Local imports
 from .tuner.utils import apply_lora_layers
+from .tuner.utils import dequantize as dequantize_model
 
 # Constants
 MODEL_REMAPPING = {
@@ -587,6 +588,7 @@ def convert(
     dtype: str = "float16",
     upload_repo: str = None,
     revision: Optional[str] = None,
+    dequantize: bool = False,
 ):
     print("[INFO] Loading")
     model_path = get_model_path(hf_path, revision=revision)
@@ -596,11 +598,19 @@ def convert(
     dtype = mx.float16 if quantize else getattr(mx, dtype)
     weights = {k: v.astype(dtype) for k, v in weights.items()}
 
+    if quantize and dequantize:
+        raise ValueError("Choose either quantize or dequantize, not both.")
+
     if quantize:
         print("[INFO] Quantizing")
         model.load_weights(list(weights.items()))
         weights, config = quantize_model(model, config, q_group_size, q_bits)
 
+    if dequantize:
+        print("[INFO] Dequantizing")
+        model = dequantize_model(model)
+        weights = dict(tree_flatten(model.parameters()))
+
     if isinstance(mlx_path, str):
         mlx_path = Path(mlx_path)