diff --git a/src/llama_recipes/models/slam_model.py b/src/llama_recipes/models/slam_model.py
index dc1ee149..11fb2db2 100644
--- a/src/llama_recipes/models/slam_model.py
+++ b/src/llama_recipes/models/slam_model.py
@@ -79,11 +79,11 @@ def setup_llm(train_config, model_config, **kwargs):
         model alone would consume 2+TB cpu mem (70 * 4 * 8). This will add some comms
         overhead and currently requires latest nightly.
         """
-        v = packaging.version.parse(torch.__version__)
-        verify_latest_nightly = v.is_devrelease and v.dev >= 20230701
-        if not verify_latest_nightly:
-            raise Exception("latest pytorch nightly build is required to run with low_cpu_fsdp config, "
-                            "please install latest nightly.")
+        # v = packaging.version.parse(torch.__version__)
+        # verify_latest_nightly = v.is_devrelease and v.dev >= 20230701
+        # if not verify_latest_nightly:
+        #     raise Exception("latest pytorch nightly build is required to run with low_cpu_fsdp config, "
+        #                     "please install latest nightly.")
         rank = int(os.environ["RANK"])
         if rank == 0:
             model = LlamaForCausalLM.from_pretrained(
@@ -95,30 +95,16 @@ def setup_llm(train_config, model_config, **kwargs):
         else:
             llama_config = LlamaConfig.from_pretrained(model_config.llm_path)
             llama_config.use_cache = use_cache
-            with torch.device("meta"):
-                model = LlamaForCausalLM(llama_config)
+            # with torch.device("meta"):
+            model = LlamaForCausalLM(llama_config) #(FIX:MZY): torch 2.0.1 does not support `meta`
 
     else:
-        if train_config.enable_fsdp:
-            rank = int(os.environ["RANK"])
-            if rank == 0:
-                model = LlamaForCausalLM.from_pretrained(
-                    model_config.llm_path,
-                    load_in_8bit=True if train_config.quantization else None,
-                    device_map="auto" if train_config.quantization else None,
-                    use_cache=use_cache,
-                )
-            else:
-                llama_config = LlamaConfig.from_pretrained(model_config.llm_path)
-                llama_config.use_cache = use_cache
-                model = LlamaForCausalLM(llama_config)
-        else:
-            model = LlamaForCausalLM.from_pretrained(
-                model_config.llm_path,
-                load_in_8bit=True if train_config.quantization else None,
-                device_map="auto" if train_config.quantization else None,
-                use_cache=use_cache,
-            )
+        model = LlamaForCausalLM.from_pretrained(
+            model_config.llm_path,
+            load_in_8bit=True if train_config.quantization else None,
+            device_map="auto" if train_config.quantization else None,
+            use_cache=use_cache,
+        )
     if train_config.enable_fsdp and train_config.use_fast_kernels:
         """
         For FSDP and FSDP+PEFT, setting 'use_fast_kernels' will enable