diff --git a/inference/huggingface/text-generation/inference-test.py b/inference/huggingface/text-generation/inference-test.py
index 5baad0b91..a93a25e9a 100644
--- a/inference/huggingface/text-generation/inference-test.py
+++ b/inference/huggingface/text-generation/inference-test.py
@@ -7,26 +7,34 @@
 from utils import DSPipeline
 
 
+def bool_arg(x):
+    return x.lower() =='true'
+
 parser = ArgumentParser()
 
 parser.add_argument("--name", required=True, type=str, help="model_name")
 parser.add_argument("--batch_size", default=1, type=int, help="batch size")
 parser.add_argument("--dtype", default="float16", type=str, choices=["float32", "float16", "int8"], help="data-type")
-parser.add_argument("--ds_inference", default=True, type=bool, help="enable ds-inference")
+parser.add_argument("--ds_inference", default=True, type=lambda x : bool_arg(x), help="enable ds-inference")
 parser.add_argument("--max_tokens", default=1024, type=int, help="maximum tokens used for the text-generation KV-cache")
 parser.add_argument("--max_new_tokens", default=50, type=int, help="maximum new tokens to generate")
-parser.add_argument("--greedy", default=False, type=bool, help="greedy generation mode")
-parser.add_argument("--use_meta_tensor", default=False, type=bool, help="use the meta tensors to initialize model")
-parser.add_argument("--use_cache", default=True, type=bool, help="use cache for generation")
+parser.add_argument("--greedy", default=False, type=lambda x : bool_arg(x), help="greedy generation mode")
+parser.add_argument("--use_meta_tensor", default=False, type=lambda x : bool_arg(x), help="use the meta tensors to initialize model")
+parser.add_argument("--hf_low_cpu_mem_usage", default=False, type=lambda x : bool_arg(x), help="use the low_cpu_mem_usage flag in huggingface to initialize model")
+parser.add_argument("--use_cache", default=True, type=lambda x : bool_arg(x), help="use cache for generation")
 parser.add_argument("--local_rank", type=int, default=0, help="local rank")
 args = parser.parse_args()
 
 world_size = int(os.getenv('WORLD_SIZE', '1'))
 
+if args.use_meta_tensor and args.hf_low_cpu_mem_usage:
+    raise ValueError("Cannot use both use_meta_tensor and hf_low_cpu_mem_usage")
+
 data_type = getattr(torch, args.dtype)
 pipe = DSPipeline(model_name=args.name,
                   dtype=data_type,
                   is_meta=args.use_meta_tensor,
+                  is_hf_low_cpu_mem_usage=args.hf_low_cpu_mem_usage,
                   device=args.local_rank)
 
 if args.use_meta_tensor:
diff --git a/inference/huggingface/text-generation/utils.py b/inference/huggingface/text-generation/utils.py
index 39ddbd9a3..06dfe3f5c 100644
--- a/inference/huggingface/text-generation/utils.py
+++ b/inference/huggingface/text-generation/utils.py
@@ -20,6 +20,7 @@ def __init__(self,
                  model_name='bigscience/bloom-3b',
                  dtype=torch.float16,
                  is_meta=True,
+                 is_hf_low_cpu_mem_usage=False,
                  device=-1
                  ):
         self.model_name = model_name
@@ -48,7 +49,7 @@ def __init__(self,
             with deepspeed.OnDevice(dtype=torch.float16, device="meta"):
                 self.model = AutoModelForCausalLM.from_config(self.config)
         else:
-            self.model = AutoModelForCausalLM.from_pretrained(self.model_name)
+            self.model = AutoModelForCausalLM.from_pretrained(self.model_name, low_cpu_mem_usage = is_hf_low_cpu_mem_usage)
 
         self.model.eval()