Update README.md

README.md

@@ -47,8 +47,8 @@ docker run -it \
 ## 预训练权重
 |  模型名称  | 权重大小 | 数据类型 |支持的DCU型号  | 最低卡数需求 |         下载地址          |
 |:------:|:----:|:----:|:----------:|:------:|:---------------------:|
-| SenseNova-SI-1.4-InternVL3-8B	 | 8B | BF16 | BW1000 |   1   | [Modelscope](https://modelscope.cn/models/SenseNova/SenseNova-SI-1.1-InternVL3-8B) |
-| SenseNova-SI-1.1-BAGEL-7B-MoT	 | 8B | BF16 | BW1000 |   1   | [Modelscope](https://modelscope.cn/models/SenseNova/SenseNova-SI-1.1-BAGEL-7B-MoT) |
+| SenseNova-SI-1.4-InternVL3-8B	 | 8B | BF16 | BW1000 |   1   | [Modelscope](https://modelscope.cn/models/SenseNova/SenseNova-SI-1.4-InternVL3-8B) |
+| SenseNova-SI-1.1-BAGEL-7B-MoT	 | 7B | BF16 | BW1000 |   1   | [Modelscope](https://modelscope.cn/models/SenseNova/SenseNova-SI-1.1-BAGEL-7B-MoT) |
 
 ## 数据集
 暂无
@@ -59,36 +59,36 @@ docker run -it \
 ## 推理
 ### Transformers
 #### 单机推理
-##### Example for BAGEL generation
+##### BAGEL 模型图像生成示例
 ```
-cd sensenova-si
+cd SenseNova-SI
 python example_bagel.py \
   --model_path sensenova/SenseNova-SI-1.1-BAGEL-7B-MoT \
   --prompt "A chubby cat made of 3D point clouds, stretching its body, translucent with a soft glow." \
   --mode generate
 ```
-##### Example 1
+##### 三维空间位置推理示例
 ```
 python example.py \
   --image_paths examples/Q1_1.png \
   --question "Question: Consider the real-world 3D locations of the objects. Which is closer to the sink, the toilet paper or the towel?\nOptions: \nA. toilet paper\nB. towel\nGive me the answer letter directly. The best answer is:" \
   --model_path sensenova/SenseNova-SI-1.4-InternVL3-8B
 ```
-##### Example 2
+##### 空间方位逻辑推理示例
 ```
 python example.py \
   --image_paths examples/Q2_1.png examples/Q2_2.png \
   --question "If the landscape painting is on the east side of the bedroom, where is the window located in the bedroom?\nOptions: A. North side, B. South side, C. West side, D. East side\nAnswer with the option's letter from the given choices directly. Enclose the option's letter within ``." \
   --model_path sensenova/SenseNova-SI-1.4-InternVL3-8B
 ```
-##### Example 3
+##### 画面时序排序问答示例
 ```
 python example.py \
   --image_paths examples/Q3_1.png examples/Q3_2.png examples/Q3_3.png \
   --question "The robot is making tea. What is the order in which the pictures were taken?" \
   --model_path sensenova/SenseNova-SI-1.4-InternVL3-8B
 ```
-##### Example 4
+##### 视觉目标定位示例
 ```
 python example.py \
   --image_paths examples/Q4.png \