diff --git a/tensorrt_llm/_torch/model_config.py b/tensorrt_llm/_torch/model_config.py
index 671564baadc..3de3edd3a9b 100644
--- a/tensorrt_llm/_torch/model_config.py
+++ b/tensorrt_llm/_torch/model_config.py
@@ -202,6 +202,9 @@ def from_pretrained(cls,
             json_quant_configs = quant_config_dict['quantization']
 
             quant_config.quant_algo = json_quant_configs.get('quant_algo', None)
+            # fp8_pb_wo from modelopt is the same as FP8_BLOCK_SCALES
+            if quant_config.quant_algo == "fp8_pb_wo":
+                quant_config.quant_algo = 'FP8_BLOCK_SCALES'
             quant_config.kv_cache_quant_algo = json_quant_configs.get(
                 'kv_cache_quant_algo', None)
             quant_config.group_size = json_quant_configs.get('group_size', None)
diff --git a/tensorrt_llm/_torch/modules/linear.py b/tensorrt_llm/_torch/modules/linear.py
index ca9cb6501d0..134f1c8ebf8 100644
--- a/tensorrt_llm/_torch/modules/linear.py
+++ b/tensorrt_llm/_torch/modules/linear.py
@@ -562,7 +562,8 @@ def load_weights_vanilla(self, module: Linear, weights: List[Dict]) -> None:
 
         scale_name = self._get_scale_name(weights)
         weight_scale = load_weight_shard(weights[0][scale_name], module.tp_size,
-                                         module.tp_rank, module.tp_mode)
+                                         module.tp_rank,
+                                         module.tp_mode).squeeze()
         copy_weight(module.weight_scale, weight_scale)
         if "input_scale" in weights[0]:
             copy_weight(module.input_scale, weights[0]["input_scale"])
@@ -582,7 +583,8 @@ def load_weights_fused_qkv_linear(self, module: Linear,
                                     module.tp_rank, module.tp_mode)
         v_scale = load_weight_shard(weights[2][scale_name], module.tp_size,
                                     module.tp_rank, module.tp_mode)
-        fused_fp8_block_scale = torch.cat((q_scale, k_scale, v_scale))
+        fused_fp8_block_scale = torch.cat((q_scale, k_scale, v_scale)).squeeze()
+
         copy_weight(module.weight_scale, fused_fp8_block_scale)
 
     def load_weights_fused_gate_up_linear(self, module: Linear,
@@ -597,7 +599,7 @@ def load_weights_fused_gate_up_linear(self, module: Linear,
                                        module.tp_rank, module.tp_mode)
         right_scale = load_weight_shard(weights[1][scale_name], module.tp_size,
                                         module.tp_rank, module.tp_mode)
-        fused_scale = torch.cat([left_scale, right_scale], dim=0)
+        fused_scale = torch.cat([left_scale, right_scale], dim=0).squeeze()
         copy_weight(module.weight_scale, fused_scale)
 
 
diff --git a/tensorrt_llm/llmapi/llm_utils.py b/tensorrt_llm/llmapi/llm_utils.py
index 31f853f3705..a62568a54e8 100644
--- a/tensorrt_llm/llmapi/llm_utils.py
+++ b/tensorrt_llm/llmapi/llm_utils.py
@@ -362,7 +362,11 @@ def _update_from_hf_quant_config(self) -> bool:
 
             hf_quant_algo = hf_quant_config.pop("quant_algo", None)
             if hf_quant_algo is not None:
-                hf_quant_algo = QuantAlgo(hf_quant_algo)
+                # fp8_pb_wo from modelopt is the same as fp8_block_scales
+                if hf_quant_algo == "fp8_pb_wo":
+                    hf_quant_algo = QuantAlgo.FP8_BLOCK_SCALES
+                else:
+                    hf_quant_algo = QuantAlgo(hf_quant_algo)
                 if quant_config.quant_algo is None:
                     logger.info(
                         f"Setting quant_algo={hf_quant_algo} form HF quant config."