Create app.py

app.py

@@ -0,0 +1,60 @@
+import streamlit as st
+import torch
+from transformers import AutoModelForCausalLM
+
+def get_model_structure(model_id):
+ model = AutoModelForCausalLM.from_pretrained(
+ model_id,
+ torch_dtype=torch.bfloat16,
+ device_map="cpu",
+ )
+ structure = {k: v.shape for k, v in model.state_dict().items()}
+ return structure
+
+def compare_structures(struct1, struct2):
+ keys1 = set(struct1.keys())
+ keys2 = set(struct2.keys())
+ all_keys = keys1.union(keys2)
+ 
+ diff = []
+ for key in all_keys:
+ shape1 = struct1.get(key)
+ shape2 = struct2.get(key)
+ if shape1 != shape2:
+ diff.append((key, shape1, shape2))
+ return diff
+
+def display_diff(diff):
+ left_lines = []
+ right_lines = []
+ 
+ for key, shape1, shape2 in diff:
+ left_lines.append(f"{key}: {shape1}")
+ right_lines.append(f"{key}: {shape2}")
+ 
+ left_html = "<br>".join(left_lines)
+ right_html = "<br>".join(right_lines)
+ 
+ return left_html, right_html
+
+st.title("Model Structure Comparison Tool")
+model_id1 = st.text_input("Enter the first HuggingFace Model ID")
+model_id2 = st.text_input("Enter the second HuggingFace Model ID")
+
+if model_id1 and model_id2:
+ struct1 = get_model_structure(model_id1)
+ struct2 = get_model_structure(model_id2)
+ 
+ diff = compare_structures(struct1, struct2)
+ left_html, right_html = display_diff(diff)
+ 
+ st.write("### Comparison Result")
+ col1, col2 = st.columns(2)
+ 
+ with col1:
+ st.write("### Model 1")
+ st.markdown(left_html, unsafe_allow_html=True)
+ 
+ with col2:
+ st.write("### Model 2")
+ st.markdown(right_html, unsafe_allow_html=True)