diff --git a/fastchat/model/compression.py b/fastchat/model/compression.py
index 16699ccff..108311f71 100644
--- a/fastchat/model/compression.py
+++ b/fastchat/model/compression.py
@@ -11,7 +11,7 @@
 from torch.nn import functional as F
 import torch.nn as nn
 from tqdm import tqdm
-from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer,AutoModel
+from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, AutoModel
 
 
 @dataclasses.dataclass
@@ -105,10 +105,12 @@ def load_compress_model(model_path, device, torch_dtype, use_fast, revision="mai
     # `use_fast=True`` is not supported for some models.
     try:
         tokenizer = AutoTokenizer.from_pretrained(
-        model_path, use_fast=use_fast, revision=revision,trust_remote_code=True)
+            model_path, use_fast=use_fast, revision=revision, trust_remote_code=True
+        )
     except TypeError:
         tokenizer = AutoTokenizer.from_pretrained(
-        model_path, use_fast=False, revision=revision,trust_remote_code=True)
+            model_path, use_fast=False, revision=revision, trust_remote_code=True
+        )
     with init_empty_weights():
         # `trust_remote_code` should be set as `True` for both AutoConfig and AutoModel
         config = AutoConfig.from_pretrained(
@@ -117,26 +119,27 @@ def load_compress_model(model_path, device, torch_dtype, use_fast, revision="mai
             torch_dtype=torch_dtype,
             trust_remote_code=True,
             revision=revision,
-            trust_remote_code=True
         )
-        # some models are loaded by AutoModel but not AutoModelForCausalLM, 
+        # some models are loaded by AutoModel but not AutoModelForCausalLM,
         # such as chatglm, chatglm2
         try:
-            model = AutoModelForCausalLM.from_config(config,trust_remote_code=True)
+            model = AutoModelForCausalLM.from_config(config, trust_remote_code=True)
         except NameError:
-            model = AutoModel.from_config(config,trust_remote_code=True)
+            model = AutoModel.from_config(config, trust_remote_code=True)
         linear_weights = get_compressed_list(model)
     if os.path.exists(model_path):
         # `model_path` is a local folder
         base_pattern = os.path.join(model_path, "pytorch_model*.bin")
     else:
         # `model_path` is a cached Hugging Face repo
-        # We don't necessarily need to download the model' repo again if there is a cache. 
+        # We don't necessarily need to download the model' repo again if there is a cache.
         # So check the default huggingface cache first.
-        model_path_temp = os.path.join(os.getenv("HOME"),
-                                       ".cache/huggingface/hub",
-                                       "models--"+model_path.replace("/","--"),
-                                       "snapshots/")
+        model_path_temp = os.path.join(
+            os.getenv("HOME"),
+            ".cache/huggingface/hub",
+            "models--" + model_path.replace("/", "--"),
+            "snapshots/",
+        )
         if os.path.exists(model_path_temp):
             temp_last_dir = os.listdir(model_path_temp)[-1]
             model_path = os.path.join(model_path_temp, temp_last_dir)
diff --git a/fastchat/model/model_adapter.py b/fastchat/model/model_adapter.py
index 01c47cf0f..5a417a275 100644
--- a/fastchat/model/model_adapter.py
+++ b/fastchat/model/model_adapter.py
@@ -677,9 +677,9 @@ class OasstLLaMAAdapter(BaseModelAdapter):
     use_fast_tokenizer = False
 
     def match(self, model_path: str):
-        if "openassistant-sft-7-llama-30b-hf" in model_path.lower():
-            return True
         model_path = model_path.lower()
+        if "openassistant-sft-7-llama-30b-hf" in model_path:
+            return True
         return "oasst" in model_path and "pythia" not in model_path
 
     def get_default_conv_template(self, model_path: str) -> Conversation: