Update README.md

README.md

@@ -13,21 +13,84 @@
 </div>
 
 
-## Benchmark Results
+## Evaluation
 
-The AWQ quantized model maintains strong performance across key benchmarks:
-
-| Benchmark | Score |
-|-----------|-------|
-| MMMU      | 0.6   |
-| MathV     | 0.59  |
-| AIME_2024 | 0.6   |
+<div align="center">
+  <b>Comparison with Larger-Scale Open-Source and Closed-Source Models</b>
+</div>
 
+<table align="center">
+  <thead>
+    <tr>
+      <th></th>
+      <th align="center"><strong>Benchmark</strong></th>
+      <th align="center"><strong>LLM</strong></th>
+      <th align="center" colspan="5"><strong>VLM</strong></th>
+    </tr>
+    <tr>
+      <th></th>
+      <th></th>
+      <th align="center"><strong>QwQ-32B-Preview</strong></th>
+      <th align="center"><strong>InternVL-2.5-38B</strong></th>
+      <th align="center"><strong>VILA 1.5-40B</strong></th>
+      <th align="center"><strong>InternVL2-40B</strong></th>
+      <th align="center"><strong>Skywork-R1V-38B</strong></th>
+      <th align="center"><strong>Skywork-R1V-AWQ</strong></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td rowspan="3">Reasoning</td>
+      <td>MATH-500</td>
+      <td align="center">90.6</td>
+      <td align="center">-</td>
+      <td align="center">-</td>
+      <td align="center">-</td>
+      <td align="center"><strong>94.0</strong></td>
+      <td align="center">86.0</td>
+    </tr>
+    <tr>
+      <td>AIME 2024</td>
+      <td align="center">50.0</td>
+      <td align="center">-</td>
+      <td align="center">-</td>
+      <td align="center">-</td>
+      <td align="center"><strong>72.0</strong></td>
+      <td align="center">61.0</td>
+    </tr>
+    <tr>
+      <td>GPQA</td>
+      <td align="center">54.5</td>
+      <td align="center">-</td>
+      <td align="center">-</td>
+      <td align="center">-</td>
+      <td align="center"><strong>61.6</strong></td>
+      <td align="center">56.5</td>
+    </tr>
+    <tr>
+      <td rowspan="2">Vision</td>
+      <td>MathVista(mini)</td>
+      <td align="center">-</td>
+      <td align="center">71.9</td>
+      <td align="center">49.5</td>
+      <td align="center">63.7</td>
+      <td align="center">67.5</td>
+      <td align="center">59.9</td>
+    </tr>
+    <tr>
+      <td>MMMU(Val)</td>
+      <td align="center">-</td>
+      <td align="center">63.9</td>
+      <td align="center">55.1</td>
+      <td align="center">55.2</td>
+      <td align="center"><strong>69.0</strong></td>
+      <td align="center">60.1</td>
+    </tr>
+  </tbody>
+</table>
 
 ## Usage
-
 You can use the quantized model with different inference frameworks:
-
 ### Using VLLM
 
 #### Python API
@@ -111,19 +174,8 @@ If you use this model in your research, please cite:
 
 # Skywork-R1V-38B-AWQ (中文说明)
 
-## 基准测试结果
-
-AWQ 量化模型在关键基准测试中保持了强劲的性能：
-
-| 基准测试   | 分数  |
-|-----------|-------|
-| MMMU      | 0.6   |
-| MathV     | 0.59  |
-| AIME_2024 | 0.6   |
-
 
 ## 使用方法
-
 您可以使用不同的推理框架来使用这个量化模型：
 
 ### 使用 VLLM