Update app.py

app.py

@@ -7,8 +7,8 @@ import streamlit as st
 import torch
 
 # Define the model repository
-# REPO_NAME = 'schuler/experimental-JP47D20'
-REPO_NAME = 'schuler/experimental-JP47D21-KPhi-3-micro-4k-instruct'
+REPO_NAME = 'schuler/experimental-JP47D20'
+# REPO_NAME = 'schuler/experimental-JP47D21-KPhi-3-micro-4k-instruct'
 
 # Configure the Streamlit app
 st.set_page_config(page_title="Experimental KPhi3 Model - Currently in Training", page_icon="🤗")
@@ -24,7 +24,17 @@ def load_model(repo_name):
 
 tokenizer, generator_conf, model = load_model(REPO_NAME)
 
-st.markdown(f"*This chat uses the {REPO_NAME} model with {model.get_memory_footprint() / 1e6:.2f} MB memory footprint.")
+total_params = sum(p.numel() for p in model.parameters())
+trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
+embed_params = sum(p.numel() for p in model.model.embed_tokens.parameters())*2
+
+st.markdown(f"*This chat uses the {REPO_NAME} model with {model.get_memory_footprint() / 1e6:.2f} MB memory footprint. ")
+
+# st.markdown(f"Total number of parameters: {total_params}. ")
+# st.markdown(f"Total number of trainable parameters: {trainable_params}. ")
+# st.markdown(f"Total number of embed parameters: {embed_params}. ")
+
+st.markdown(f"Total number of non embedding trainable parameters: {(trainable_params - embed_params) / 1e6:.2f } million. ")
 st.markdown(f"You may ask questions such as 'What is biology?' or 'What is the human body?'*")
 
 try: