Yongyang (#63)

* update doc

* update doc

docs/en/source/advanced/model_test_v2.md

@@ -72,6 +72,10 @@ According to the opencompass [documentation](https://opencompass.readthedocs.io/
 
 Finally, you can load the above configuration and perform model compression and accuracy testing just like running a regular llmc program.
 
-Note: If the model is too large to fit on a single GPU for evaluation, and multi-GPU evaluation is needed, we support using pipeline parallelism when running opencompass.
+## Note: 
 
-What you need to do is identify which GPUs are available, add them to CUDA_VISIBLE_DEVICES at the beginning of your run script, and then modify the file pointed to by cfg_path under opencompass, setting the num_gpus to the desired number.
+If the model is too large to fit on a single GPU for evaluation, and multi-GPU evaluation is needed, we support using pipeline parallelism when running opencompass.
+
+What you need to do is:
+1. Identify which GPUs are available, add them to CUDA_VISIBLE_DEVICES at the beginning of your run script
+2. Modify the file pointed to by cfg_path under opencompass, setting the num_gpus to the desired number.

docs/zh_cn/source/advanced/model_test_v2.md

@@ -72,6 +72,12 @@ pip install human-eval
 
 最后你就可以像运行一个正常的llmc程序一样，载入上述的config，进行模型压缩和精度测试
 
-注意：如果模型太大，单卡评测放不下，需要使用多卡评测精度，我们支持在运行opencompass时使用pipeline parallel。
+## 注意：
 
-你需要做的就是，确定哪些卡是可用的，在你的运行脚本最前面，添加到CUDA_VISIBLE_DEVICES中，然后修改opencompass下的cfg_path指向的文件，将里面的num_gpus设置成你需要的数量，仅此而已。
+如果模型太大，单卡评测放不下，需要使用多卡评测精度，我们支持在运行opencompass时使用pipeline parallel。
+
+你需要做的仅仅就是：
+
+1. 确定哪些卡是可用的，在你的运行脚本最前面，添加到CUDA_VISIBLE_DEVICES中
+
+2. 修改opencompass下的cfg_path指向的文件，将里面的num_gpus设置成你需要的数量