diff --git a/qwen/weight.py b/qwen/weight.py
index 7a9dc752..6df0264d 100644
--- a/qwen/weight.py
+++ b/qwen/weight.py
@@ -823,7 +823,7 @@ def preprocess_groupwise_weight_params(
             split_qkv_suf.append(split_qkv)
 
         idx = layer - mapping.pp_rank * layers_per_pipeline_stage
-        th_bias = model_params[prefix + "c_attn.bias"].to(torch.float16).cpu().contiguous()
+        th_bias = model_params[prefix + "c_attn.bias"].to(torch_dtype).cpu().contiguous()
         q_emb = th_bias.shape[0] // 3
         th_bias = th_bias.reshape(3, q_emb)
         split_v = split(th_bias, mapping.tp_size, mapping.rank, dim=1)
@@ -838,7 +838,7 @@ def preprocess_groupwise_weight_params(
         )
         tensorrt_llm_qwen.layers[idx].attention.qkv.qweight.value = th_qweight.numpy()
         tensorrt_llm_qwen.layers[idx].attention.qkv.zero.value = th_zero.numpy() 
-        tensorrt_llm_qwen.layers[idx].attention.qkv.scale.value = th_scale.to(torch.float16).numpy()
+        tensorrt_llm_qwen.layers[idx].attention.qkv.scale.value = th_scale.to(torch_dtype).numpy()
 
     torch_dtype = str_dtype_to_torch(dtype)
 
@@ -885,7 +885,7 @@ def preprocess_groupwise_weight_params(
                 )
                 tensorrt_llm_qwen.layers[idx].attention.dense.qweight.value = th_qweight.numpy()
                 tensorrt_llm_qwen.layers[idx].attention.dense.zero.value = th_zero.numpy()
-                tensorrt_llm_qwen.layers[idx].attention.dense.scale.value = th_scale.to(torch.float16).numpy()
+                tensorrt_llm_qwen.layers[idx].attention.dense.scale.value = th_scale.to(torch_dtype).numpy()
             elif "mlp.w1.qweight" in k:
                 split_v_suf = []
                 for suf in suffixs:
@@ -899,7 +899,7 @@ def preprocess_groupwise_weight_params(
                 )
                 tensorrt_llm_qwen.layers[idx].mlp.w1.qweight.value = th_qweight.numpy()
                 tensorrt_llm_qwen.layers[idx].mlp.w1.zero.value = th_zero.numpy()
-                tensorrt_llm_qwen.layers[idx].mlp.w1.scale.value = th_scale.to(torch.float16).numpy()
+                tensorrt_llm_qwen.layers[idx].mlp.w1.scale.value = th_scale.to(torch_dtype).numpy()
             elif "mlp.c_proj.qweight" in k:
                 split_v_suf = []
                 for suf in suffixs:
@@ -913,7 +913,7 @@ def preprocess_groupwise_weight_params(
                 )
                 tensorrt_llm_qwen.layers[idx].mlp.c_proj.qweight.value = th_qweight.numpy()
                 tensorrt_llm_qwen.layers[idx].mlp.c_proj.zero.value = th_zero.numpy()
-                tensorrt_llm_qwen.layers[idx].mlp.c_proj.scale.value = th_scale.to(torch.float16).numpy()
+                tensorrt_llm_qwen.layers[idx].mlp.c_proj.scale.value = th_scale.to(torch_dtype).numpy()
             elif "mlp.w2.qweight" in k:
                 split_v_suf = []
                 for suf in suffixs:
@@ -927,7 +927,7 @@ def preprocess_groupwise_weight_params(
                 )
                 tensorrt_llm_qwen.layers[idx].mlp.w2.qweight.value = th_qweight.numpy()
                 tensorrt_llm_qwen.layers[idx].mlp.w2.zero.value = th_zero.numpy()
-                tensorrt_llm_qwen.layers[idx].mlp.w2.scale.value = th_scale.to(torch.float16).numpy()
+                tensorrt_llm_qwen.layers[idx].mlp.w2.scale.value = th_scale.to(torch_dtype).numpy()
 
     tok = time.time()
     t = time.strftime("%h:%m:%s", time.gmtime(tok - tik))