diff --git a/.env.template b/.env.template
index 234b12738..2fb5ff649 100644
--- a/.env.template
+++ b/.env.template
@@ -21,7 +21,7 @@ LLM_MODEL=vicuna-13b
 MODEL_SERVER=http://127.0.0.1:8000
 LIMIT_MODEL_CONCURRENCY=5
 MAX_POSITION_EMBEDDINGS=4096
-
+QUANTIZE_QLORA=True
 ## SMART_LLM_MODEL - Smart language model (Default: vicuna-13b)
 ## FAST_LLM_MODEL - Fast language model (Default: chatglm-6b)
 # SMART_LLM_MODEL=vicuna-13b
@@ -112,4 +112,4 @@ PROXY_SERVER_URL=http://127.0.0.1:3000/proxy_address
 #*******************************************************************#
 # **    SUMMARY_CONFIG
 #*******************************************************************#
-SUMMARY_CONFIG=FAST
\ No newline at end of file
+SUMMARY_CONFIG=FAST
diff --git a/pilot/configs/model_config.py b/pilot/configs/model_config.py
index adfc62f1a..4f3e635d6 100644
--- a/pilot/configs/model_config.py
+++ b/pilot/configs/model_config.py
@@ -42,7 +42,7 @@ LLM_MODEL_CONFIG = {
 # Load model config
 ISLOAD_8BIT = True
 ISDEBUG = False
-
+QLORA = os.getenv("QUANTIZE_QLORA") == "True"
 
 VECTOR_SEARCH_TOP_K = 10
 VS_ROOT_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "vs_store")
diff --git a/pilot/model/adapter.py b/pilot/model/adapter.py
index f8c65af77..76eb51f26 100644
--- a/pilot/model/adapter.py
+++ b/pilot/model/adapter.py
@@ -7,6 +7,7 @@ from functools import cache
 from transformers import AutoModel, AutoModelForCausalLM, AutoTokenizer, LlamaTokenizer, BitsAndBytesConfig
 from pilot.configs.model_config import DEVICE
 
+bnb_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype="bfloat16", bnb_4bit_use_double_quant=False)
 
 class BaseLLMAdaper:
     """The Base class for multi model, in our project.
@@ -105,19 +106,21 @@ class FalconAdapater(BaseLLMAdaper):
 
     def loader(self, model_path: str, from_pretrained_kwagrs: dict):
         tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
-        bnb_config = BitsAndBytesConfig(
-                load_in_4bit=True,
-                bnb_4bit_quant_type="nf4",
-                bnb_4bit_compute_dtype="bfloat16",
-                bnb_4bit_use_double_quant=False,
+        if QLORA == True:
+            model = AutoModelForCausalLM.from_pretrained(
+                model_path, 
+                load_in_4bit=True, #quantize
+                quantization_config=bnb_config, 
+                device_map={"": 0}, 
+                trust_remote_code=True, 
+                **from_pretrained_kwagrs
             )
-        model = AutoModelForCausalLM.from_pretrained(
-            model_path, 
-            #load_in_4bit=True,  #quantize
-            quantization_config=bnb_config, 
-            device_map={"": 0}, 
-            trust_remote_code=True, 
-            **from_pretrained_kwagrs
+        else:
+            model = AutoModelForCausalLM.from_pretrained(
+                model_path, 
+                trust_remote_code=True,
+                device_map={"": 0},
+                **from_pretrained_kwagrs
             )
         return model, tokenizer