diff --git a/README.md b/README.md
index 5ee85cf..e3f8bf8 100644
--- a/README.md
+++ b/README.md
@@ -46,12 +46,14 @@
   python3 ./cli_chat.py --hf_model_dir="download/[你下载的模型路径]"
   ```
 
+- demo展示（演示模型，qwen1.5-0.5chat)
+![](./image/qwen1.5_0.5b_chat.gif)
 
 
 ### 当前功能
 - [x] 导出onnx, om模型
-- [x] 模型推理，支持onnx推理。
-- [ ] 模型推理，支持acl推理。
+- [x] 模型推理，支持onnx推理（仅支持CPU）。
+- [x] 模型推理，支持acl推理。
 - [x] 流式传输
 - [ ] 兼容OpenAI的api搭建
 - [ ] 支持functional call
diff --git a/export/onnx2om.py b/export/onnx2om.py
index 6a967f1..de20d8c 100644
--- a/export/onnx2om.py
+++ b/export/onnx2om.py
@@ -65,6 +65,7 @@ def get_soc_version():
         if soc_version is not None:
             break
     assert soc_version is not None, print("soc_version", soc_version)
+    print("SoC Version is ", soc_version)
     return soc_version
 
 
diff --git a/image/qwen1.5_0.5b_chat.gif b/image/qwen1.5_0.5b_chat.gif
new file mode 100644
index 0000000..3ece139
Binary files /dev/null and b/image/qwen1.5_0.5b_chat.gif differ
diff --git a/utils/engine.py b/utils/engine.py
index 7375834..2db22f6 100644
--- a/utils/engine.py
+++ b/utils/engine.py
@@ -3,9 +3,13 @@
 import acl
 import numpy as np
 import os
+from functools import reduce
+from operator import mul
 import ctypes
 from config import InferenceConfig
 from ctypes import c_void_p, c_int, c_size_t, c_ulong, c_int64,POINTER
+
+
 ACL_MEM_MALLOC_HUGE_FIRST = 0
 ACL_MEMCPY_HOST_TO_DEVICE = 1
 ACL_MEMCPY_DEVICE_TO_HOST = 2
diff --git a/utils/session.py b/utils/session.py
index 7258d2e..436d520 100644
--- a/utils/session.py
+++ b/utils/session.py
@@ -66,10 +66,10 @@ def __init__(self,config:InferenceConfig)->None:
         options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
         # options.execution_mode = ort.ExecutionMode.ORT_PARALLEL
         self.llm_session = ort.InferenceSession(
-			config.onnx_model_path,
-			sess_options=options,
-			providers=[
-				(
+            config.onnx_model_path,
+            sess_options=options,
+            providers=[
+                (
                     "CANNExecutionProvider",
                     {
                         "device_id": 0,
@@ -80,9 +80,9 @@ def __init__(self,config:InferenceConfig)->None:
                         "enable_cann_graph": True
                     },
                 ),
-				"CPUExecutionProvider",
-			]
-		)
+                "CPUExecutionProvider",
+            ]
+        )
 
     def run(self, input_ids:np.ndarray):
         seq_len=input_ids.shape[-1]
@@ -131,7 +131,7 @@ def run_all_logits(self, input_ids: np.ndarray):
             end = i + 16 if i+16 < seq_len else seq_len
             cache,mask,pos_ids = self.kv_cache.get_inputs(16)
             self.input_ids[0:end-i] = input_ids[i:end]
-            result:List[np.ndarray] = self.model.inference([self.input_ids,pos_ids,mask,cache])
+            result:List[np.ndarray] = self.model.inference([self.input_ids, mask, pos_ids, cache])
             self.kv_cache.update(end-i,result[1])
             logits.append(result[0][0:end-i].reshape(1,-1))
         return [np.concatenate(logits).reshape(1,1,-1)]
@@ -140,12 +140,17 @@ def run_one(self, input_ids: np.ndarray):
         self.run_times += 1     
         cache, mask, pos_ids = self.kv_cache.get_inputs(1)
         result:List[np.ndarray] = self.model.inference(
-                [input_ids, pos_ids, mask, cache]
+                [input_ids, mask, pos_ids, cache]
             )
-        # new_kv_cache = result[1]
-        # print(" == Debug == ")
-        # print("new_kv_cache: shape", new_kv_cache.shape)
-        # print("new_kv_cache: mean: ", new_kv_cache.astype(np.float32).mean().item())
-        # print("new_kv_cache: max: ", new_kv_cache.astype(np.float32).max().item())
+        # if self.run_times <= 2:
+        #     print(" == Debug == ")
+        #     logits = result[0]
+        #     new_kv_cache = result[1]
+        #     print("logits shape: ", logits.shape)
+        #     print("logits mean: ", logits.astype(np.float32).mean().item())
+        #     print("logits max: ", logits.astype(np.float32).max().item())
+        #     print("new_kv_cache: shape", new_kv_cache.shape)
+        #     print("new_kv_cache: mean: ", new_kv_cache.astype(np.float32).mean().item())
+        #     print("new_kv_cache: max: ", new_kv_cache.astype(np.float32).max().item())
         self.kv_cache.update(1,result[1])
         return result[0].reshape(1,1,-1)