Model info

.gitignore

@@ -1,4 +1,6 @@
 test/
 
 test.py
-hg_secrets.py
+hg_secrets.py
+
+*.json

app.py

@@ -338,6 +338,9 @@ def build_interface():
 
 This task involves processing a user query to determine the relevance to the intended task, followed by analyzing textual data  to extract information about law firms representing parties (Buyer, Seller, and Third Parties) and verifying the presence of a target company. For reference, see this sample agreement: [SEC Agreement Example](https://www.sec.gov/Archives/edgar/data/28452/000119312505012401/dex101.htm)
 
+> **Note:**  
+> This evaluation system uses the `gpt-4o-mini` model with a temperature setting of `0.2` for all LLM steps.
+
 The system is designed to sequentially leverage three LLM functions:
 
 ### Step 1: LLM1
@@ -380,11 +383,11 @@ The goal is to identify the representative law firms of involved parties and det
 **Key Considerations:**
 - The output must adhere to the prescribed JSON format for the final step.
 - Ensure the system can accurately extract and classify relevant information from the input paragraphs.
-""")
+"""
 
-        # Example Inputs and Outputs in an Accordion
-        with gr.Accordion("Example Workflow", open=False):
-            gr.Markdown("""
+# Example Inputs and Outputs in an Accordion
+with gr.Accordion("Example Workflow", open=False):
+    gr.Markdown("""
 **User Query:**
 ```
 Is Kirkland & Ellis present in the agreement?