Update README.md

README.md

@@ -115,7 +115,7 @@ where \\(\mathbf{s}\\), \\(\mathbf{x}\\), \\(\mathbf{x}_1\\), and \\(\odot\\) de
 
 The acceleration effects of LLMs with different sparsity are displayed as follows. ProSparse, which reaches a high sparsity without performance degradation, can gain the most benefits among all the settings concerned. Refer to Section 4.3 of [paper](https://arxiv.org/pdf/2402.13516.pdf) for more details.
 
-|        Setting        | Average<br>Sparsity | Activation<br>Recall | Predicted<br>Sparsity | PowerInfer<br>Speed | Speedup<br>to Dense | `S2`<br>Time \\((\downarrow)\\) | Speedup<br>to Dense | `S3`<br/>Time \\((\downarrow)\\) | Speedup<br/>to Dense |
+|        Setting        | Average<br>Sparsity | Activation<br>Recall | Predicted<br>Sparsity | PowerInfer<br>Speed | Speedup<br>to Dense | `S2`<br>Time | Speedup<br>to Dense | `S3`<br/>Time | Speedup<br/>to Dense |
 | :-------------------: | :-----------------: | :------------------: | :-------------------: | :-----------------: | :-----------------: | :--------------: | :-----------------: | :---------------: | :------------------: |
 | Dense-7B | - | - | - | 3.67 | 1.00 | 90.55 | 1.00 | 82.92 | 1.00 |
 |     ReluLLaMA-7B      |        66.98        |        90.89         |         58.95         |        11.37        | 3.10 |      67.12       |        1.35         |       63.00       |         1.32         |