diff --git a/neural_speed/convert/convert_quantized_llama.py b/neural_speed/convert/convert_quantized_llama.py
index cbe55e473..6d001ba39 100644
--- a/neural_speed/convert/convert_quantized_llama.py
+++ b/neural_speed/convert/convert_quantized_llama.py
@@ -66,10 +66,13 @@ def convert_q4_bestla_tensor(src_name, dst_name, model, fout, q_config, n_head,
     shape = int_weight.shape
     write_header(fout, shape[::-1], dst_name, GGML_QJBLAS_TYPE)
 
+    weight_dtype = "int8"
     if q_config['bits'] == 4:
         int_weight = (int_weight - 8) * 16
         gptq_scales = gptq_scales / 16
         gptq_zeros = (gptq_zeros - 8) * 16
+        weight_dtype == "int4"
+
     dst = np.zeros((int_weight.shape[0], int_weight.shape[1] * 4), dtype=np.int8)
     int_weight = np.ascontiguousarray(int_weight.numpy())
     gptq_scales = np.ascontiguousarray((gptq_scales.float()).numpy())
@@ -84,7 +87,7 @@ def convert_q4_bestla_tensor(src_name, dst_name, model, fout, q_config, n_head,
 
     # pack int weight in bestla format
     byte_size = cpp_model.Model.np_bestla_qpack(int_weight, gptq_scales, gptq_zeros, g_idx, dst,
-                                               weight_dtype="int4" if q_config['bits'] == 4 else "int8",
+                                               weight_dtype=weight_dtype,
                                                group_size=q_config['group_size'],
                                                alg="sym" if q_config['sym'] else "asym",
                                                compute_dtype="int8")