Update src/app.py

src/app.py

@@ -37,22 +37,13 @@ def get_results(model_name: str, library: str, precision: list, training: list,
 with gr.Blocks() as demo:
     with gr.Column():
         gr.Markdown(
-            """<img src="https://huggingface.co/spaces/andstor/model-memory-usage/resolve/main/measure_model_size.png" style="float: left;" width="250" height="250"><h1>🤗 DeepSpeed Model Memory Calculator</h1>
+            """<img src="https://huggingface.co/spaces/andstor/model-memory-usage/resolve/main/measure_model_size_deepspeed.svg" style="float: left;" width="250" height="250"><h1>🤗 DeepSpeed Model Memory Calculator</h1>
 
-    This tool will help you calculate how much vRAM is needed to train and perform big model inference
-    on a model hosted on the 🤗 Hugging Face Hub. The minimum recommended vRAM needed for a model
-    is denoted as the size of the "largest layer", and training of a model is roughly 4x its size (for Adam).
-
-    These calculations are accurate within a few percent at most, such as `bert-base-cased` being 413.68 MB and the calculator estimating 413.18 MB.
-
-    When performing inference, expect to add up to an additional 20% to this as found by [EleutherAI](https://blog.eleuther.ai/transformer-math/). 
-    More tests will be performed in the future to get a more accurate benchmark for each model.
-
-    Currently this tool supports all models hosted that use `transformers` and `timm`.
+    This tool will help you calculate how much memory is required for the various Zero Redundancy Optimizer (ZeRO), given a on a model hosted on the 🤗 Hugging Face Hub and a hardware setup. The optimizer states assume that Adam is used.
 
     To use this tool pass in the URL or model name of the model you want to calculate the memory usage for,
     select which framework it originates from ("auto" will try and detect it from the model metadata), and
-    what precisions you want to use."""
+    what precisions you want to use. Then select the select the desired ZeRO configuration."""
         )
         out_text = gr.Markdown()
         out = gr.DataFrame(