update docs

docs/getting-started/run-msamp.md

@@ -51,5 +51,8 @@ deepspeed cifar10_deepspeed.py --deepspeed --deepspeed_config ds_config_zero_msa
 ```bash
 deepspeed cifar10_deepspeed_te.py --deepspeed --deepspeed_config ds_config_zero_te_msamp.json
 ```
+:::note Note
+If you get "ModuleNotFoundError: No module named 'timm'" error when running this example, you need to install timm using `pip install timm`.
+:::
 
 For more comprehensive examples, please go to [MS-AMP-Examples](https://github.com/Azure/MS-AMP-Examples).

docs/introduction.md

@@ -36,7 +36,7 @@ Here are the results for GPT-3, Swin-T, DeiT-S and RoBERTa-B.
 
 ### System performance
 
-MS-AMP preserves high-precision's accuracy while using only a fraction of the memory footprint on a range of tasks, including GPT-3, DeiT and Swin Transformer. For example, when training GPT-175B on NVIDIA H100 platform, MS-AMP achieves a notable 42% reduction in real memory usage compared with BF16 mixed-precision approach and reduces training time by 17% compared with Transformer Engine. For small models, MS-AMP with O2 mode can achieve 44% memory saving for Swin-1.0B and 26% memory saving for ViT-1.2B, comparing with FP16 AMP.
+MS-AMP preserves high-precision's accuracy while using only a fraction of the memory footprint on a range of tasks, including GPT-3, DeiT and Swin Transformer. For example, when training GPT-175B on NVIDIA H100 platform, MS-AMP achieves a notable 39% reduction in real memory usage compared with BF16 mixed-precision approach and reduces training time by 37% compared with Transformer Engine. For small models, MS-AMP with O2 mode can achieve 44% memory saving for Swin-1.0B and 26% memory saving for ViT-1.2B, comparing with FP16 AMP.
 
 Here are the resuls for GPT-3: