diff --git a/funasr/models/llm_asr/model.py b/funasr/models/llm_asr/model.py
index 2a55cd6bb..fb0bee3ca 100644
--- a/funasr/models/llm_asr/model.py
+++ b/funasr/models/llm_asr/model.py
@@ -427,30 +427,36 @@ class LLMASR2(nn.Module):
         self.audio_encoder = audio_encoder
 
         # llm
-        hub = llm_conf.get("hub", "hf")
         self.llm = None
-        if hub == "hf":
-            from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig
 
-            init_param_path = llm_conf.get("init_param_path", "vicuna-7b-v1.5")
+        from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig
 
-            model = AutoModelForCausalLM.from_pretrained(
-                init_param_path,
-                load_in_8bit=None,
-                device_map=None,
-                use_cache=None,
-            )
-            freeze = llm_conf.get("freeze", True)
-            if freeze:
-                for name, param in model.named_parameters():
-                    param.requires_grad = False
-                model.eval()
-            self.llm = model
+        init_param_path = llm_conf.get("init_param_path", "vicuna-7b-v1.5")
+
+        model = AutoModelForCausalLM.from_pretrained(
+            init_param_path,
+            load_in_8bit=None,
+            device_map=None,
+            use_cache=None,
+        )
+        freeze = llm_conf.get("freeze", True)
+        if freeze:
+            for name, param in model.named_parameters():
+                param.requires_grad = False
+            model.eval()
+        self.llm = model
+        llm_dim = model.get_input_embeddings().weight.shape[-1]
 
         # adaptor
         adaptor_class = tables.adaptor_classes.get(audio_adaptor)
         audio_adaptor_conf["encoder_dim"] = audio_encoder_output_size
+        audio_adaptor_conf["llm_dim"] = llm_dim
         audio_adaptor = adaptor_class(**audio_adaptor_conf)
+        init_param_path = audio_adaptor_conf.get("init_param_path", None)
+        if init_param_path is not None:
+            src_state = torch.load(init_param_path, map_location="cpu")
+            flag = audio_adaptor.load_state_dict(src_state, strict=False)
+            logging.info(f"Loading audio_adaptor ckpt: {init_param_path}, status: {flag}")
 
         self.audio_adaptor = audio_adaptor