diff --git a/gptqmodel/nn_modules/triton_utils/dequant.py b/gptqmodel/nn_modules/triton_utils/dequant.py
index 0dccc532e..eefcf79f9 100644
--- a/gptqmodel/nn_modules/triton_utils/dequant.py
+++ b/gptqmodel/nn_modules/triton_utils/dequant.py
@@ -93,7 +93,7 @@ def dequant_kernel(
     tl.store(out_ptr + (x_index), weights, mask=xmask)
 
 
-def dequant(qweight, scales, qzeros, g_idx, bits, pack_bits, maxq):
+def dequant(dtype, qweight, scales, qzeros, g_idx, bits, pack_bits, maxq):
     """
     Launcher for triton dequant kernel.  Only valid for bits = 2, 4, 8
     """
@@ -102,7 +102,7 @@ def dequant(qweight, scales, qzeros, g_idx, bits, pack_bits, maxq):
     out_features = scales.shape[1]
     in_features = g_idx.shape[0]
 
-    out = torch.empty((in_features, out_features), device=qweight.device, dtype=torch.float16)
+    out = torch.empty((in_features, out_features), device=qweight.device, dtype=dtype)
     numels = out.numel()
     grid = lambda meta: (triton.cdiv(numels, meta["X_BLOCK"]),)  # noqa: E731
 
@@ -121,14 +121,12 @@ def dequant(qweight, scales, qzeros, g_idx, bits, pack_bits, maxq):
     )
     return out
 
-
 def quant_matmul(input, qweight, scales, qzeros, g_idx, bits, pack_bits, maxq, transpose=False):
-    W = dequant(qweight, scales, qzeros, g_idx, bits, pack_bits, maxq)
+    W = dequant(input.dtype, qweight, scales, qzeros, g_idx, bits, pack_bits, maxq)
     if transpose:
         return input @ W.t()
     return input @ W
 
-
 class QuantLinearFunction(torch.autograd.Function):
     @staticmethod
     @custom_fwd(device_type="cuda")
diff --git a/tests/test_olora_finetuning_xpu.py b/tests/test_olora_finetuning_xpu.py
index a71ff2ad1..175ddfe68 100644
--- a/tests/test_olora_finetuning_xpu.py
+++ b/tests/test_olora_finetuning_xpu.py
@@ -50,17 +50,17 @@ def train(
         cutoff_len: int = 256,
         val_set_size: int = 16,
         quantize: bool = False,
-        eval_step: int = 100,
-        save_step: int = 100,
+        eval_step: int = 10,
+        save_step: int = 10000,
         device_map: str = "auto",
         lora_r: int = 32,
         lora_alpha: int = 16,
         lora_dropout: float = 0.05,
         lora_target_modules: List[str] = None,
-        torch_dtype: str = "bloat16",
+        torch_dtype: torch.dtype = torch.bfloat16,
         init_lora_weights="olora",
 ):
-    model_kwargs = {"torch_dtype": getattr(torch, torch_dtype), "device_map": DEVICE}
+    model_kwargs = {"torch_dtype": torch_dtype, "device_map": DEVICE}
     if quantize:
         model_kwargs["quantization_config"] = GPTQConfig(bits=4, true_sequential=False, dataset=['/monster/data/model/dataset/c4-train.00000-of-01024.json.gz'], backend="triton")
 
@@ -165,13 +165,13 @@ def test_peft(self):
                 cutoff_len=256,
                 val_set_size=16,
                 quantize=True,
-                eval_step=100,
-                save_step=100,
+                eval_step=10,
+                save_step=10000,
                 device_map="cuda",
                 lora_r=32,
                 lora_alpha=16,
                 lora_dropout=0.05,
                 lora_target_modules=None,
-                torch_dtype="bfloat16",
+                torch_dtype=torch.bfloat16,
                 init_lora_weights="olora",
             )