diff --git a/flagai/auto_model/auto_loader.py b/flagai/auto_model/auto_loader.py
index 68521601..726dc340 100755
--- a/flagai/auto_model/auto_loader.py
+++ b/flagai/auto_model/auto_loader.py
@@ -210,7 +210,10 @@ def __init__(self,
         if task_name == "aquila2":
             from flagai.model.aquila2.modeling_aquila import AquilaForCausalLM
             download_path = os.path.join(model_dir, model_name)
-            
+
+            if not torch_dtype and '34b' in model_name.lower():
+                torch_dtype = torch.bfloat16
+
             if not os.path.exists(download_path):
                 # Try to download from ModelHub
                 try:
@@ -255,9 +258,10 @@ def __init__(self,
                             for file_to_load in model_files:
                                 if "pytorch_model-0" in file_to_load:
                                     _get_checkpoint_path(download_path, file_to_load,
-                                                        model_id)            
-
-            if qlora_dir:
+                                                        model_id)  
+            if 'quantization_config' in kwargs:
+                quantization_config = kwargs['quantization_config']
+            elif qlora_dir:
                 from transformers import BitsAndBytesConfig
                 quantization_config=BitsAndBytesConfig(
                     load_in_4bit=True,
@@ -265,14 +269,14 @@ def __init__(self,
                     bnb_4bit_quant_type="nf4",
                     bnb_4bit_compute_dtype=torch_dtype,
                 )
+            else:
+                quantization_config = None
             if inference_mode:
-                if qlora_dir:
-                    model = AquilaForCausalLM.from_pretrained(download_path,low_cpu_mem_usage=low_cpu_mem_usage, torch_dtype=torch_dtype,
-                                                            quantization_config=quantization_config)
-                else:
-                    model = AquilaForCausalLM.from_pretrained(download_path,low_cpu_mem_usage=low_cpu_mem_usage, torch_dtype=torch_dtype,)           
+
+                model = AquilaForCausalLM.from_pretrained(download_path,low_cpu_mem_usage=low_cpu_mem_usage, torch_dtype=torch_dtype,
+                                                        quantization_config=quantization_config)           
                 model.eval()
-                if not qlora_dir:
+                if not quantization_config:
                     model.to(device)
                 if lora_dir:
                     from flagai.model.tools.peft import PeftModel
diff --git a/setup.py b/setup.py
index 3a929b7b..da686a57 100755
--- a/setup.py
+++ b/setup.py
@@ -5,7 +5,7 @@
 
 setup(
     name="flagai",
-    version="v1.8.0",
+    version="v1.8.1",
     description="FlagAI aims to help researchers and developers to freely train and test large-scale models for NLP/CV/VL tasks.",
     long_description=open("README.md", encoding="utf-8").read(),
     long_description_content_type="text/markdown",
@@ -19,19 +19,19 @@
     install_requires=[
         'nltk>=3.6.7',
         'sentencepiece>=0.1.96',
-        'boto3==1.17.32',
+        'boto3>=1.17.32',
         'pandas>=1.3.5',
         'jieba>=0.42.1',
         'scikit-learn>=1.0.2',
         'tensorboard>=2.9.0',
         'transformers>=4.31.0',
         'datasets>=2.0.0',
-        'setuptools==66.0.0',
+        'setuptools>=66.0.0',
         'protobuf==3.19.6',
         'ftfy',
         'Pillow>=9.3.0',
         'einops>=0.3.0',
-        'diffusers==0.7.2',
+        'diffusers>=0.7.2',
         'pytorch-lightning>=1.6.5',
         'taming-transformers-rom1504==0.0.6',
         'rouge-score',