test

Signed-off-by: Sun, Xuehao <[email protected]>

.pre-commit-config.yaml

@@ -14,3 +14,14 @@ repos:
     hooks:
       - id: ruff
         args: [--fix, --exit-non-zero-on-fix, --no-cache]
+  - repo: https://github.com/pocc/pre-commit-hooks
+    rev: v1.3.5
+    hooks:
+      - id: clang-format
+        args: [--style=Google]
+      - id: clang-tidy
+      - id: oclint
+      - id: uncrustify
+      - id: cppcheck
+      - id: cpplint
+      - id: include-what-you-use

README.md

@@ -30,7 +30,7 @@ Model inference: Roughly speaking , two key steps are required to get the model'
 Text generation: The most famous application of LLMs is text generation, which predicts the next token/word based on the inputs/context. To generate a sequence of texts, we need to predict them one by one. In this scenario, $F\\approx P$ if some operations like bmm are ignored and past key values have been saved. However, the $C/B$ of the modern device could be to **100X,** that makes the memory bandwidth as the bottleneck in this scenario.
 
 | Tables   |      Are      |                                                                         Cool |
-| -------- | :-----: | ---: |
+| -------- | :-----------: | ---------------------------------------------------------------------------: |
 | col 1 is | left-aligned  |                                                                        $1600 |
 | col 2 is |   centered    |                                                                          $12 |
 | col 3 is | right-aligned | <details><summary><b>failed log</b></summary>testtest<br/>testtest</details> |
@@ -64,4 +64,4 @@ testtest<br/>testtest
                 <td style="background-color:#90EE90"> 0.023% </td>
             </tr>
         </tbody>
-</table>
+</table>