diff --git a/mlx_lm/quant/dwq.py b/mlx_lm/quant/dwq.py
index 769327d..a0796af 100644
--- a/mlx_lm/quant/dwq.py
+++ b/mlx_lm/quant/dwq.py
@@ -42,13 +42,11 @@ def compute_dwq_targets(
         if rank == 0:
             path = path / split
             path.mkdir(parents=True, exist_ok=True)
-        for i, (batch, _) in (
-            pbar := tqdm(
-                enumerate(iterate_batches(data, batch_size, max_seq_length, seed=seed)),
-                total=len(data) // batch_size,
-                desc=f"Computing targets for {split}",
-                disable=rank != 0,
-            )
+        for i, (batch, _) in tqdm(
+            enumerate(iterate_batches(data, batch_size, max_seq_length, seed=seed)),
+            total=len(data) // batch_size,
+            desc=f"Computing targets for {split}",
+            disable=rank != 0,
         ):
             batch = batch[:, :-1]
             logits = model(batch)
@@ -216,15 +214,24 @@ def load_data(
     max_seq_length: int,
     num_valid_samples: int = 32,
 ):
-    args = types.SimpleNamespace(
-        hf_dataset={
-            "path": data_path,
-            "train_split": "train",
-            "valid_split": "train[:1]",
-        },
-        train=True,
-        test=False,
-    )
+    if Path(data_path).exists():
+        args = types.SimpleNamespace(
+            data=data_path,
+            hf_dataset=False,
+            train=True,
+            test=False,
+            mask_prompt=False,
+        )
+    else:
+        args = types.SimpleNamespace(
+            hf_dataset={
+                "path": data_path,
+                "train_split": "train",
+                "valid_split": "train[:1]",
+            },
+            train=True,
+            test=False,
+        )
     dataset = load_dataset(args, tokenizer)[0]
     perm = np.random.permutation(len(dataset))
     train_perm = perm[:num_samples].tolist()
@@ -328,7 +335,10 @@ def main():
         has_targets = False
         target_dir = None
 
-    tokenizer = load_tokenizer(args.model)
+    tokenizer = load_tokenizer(
+        args.model,
+        tokenizer_config_extra={"trust_remote_code": args.trust_remote_code},
+    )
 
     train_data, valid_data = load_data(
         tokenizer, args.data_path, args.num_samples, args.max_seq_length