remove erroneous heading tag

Author: kashyapm-tribe

examples/partners/model_selection_guide/model_selection_guide.ipynb

@@ -45,7 +45,7 @@
     "| o3 | Deep tool‑using agent | High‑stakes, multi‑step reasoning | Latency & price | Cost/latency → o4‑mini |\n",
     "| o4‑mini | Cheap, fast reasoning | High‑volume \"good‑enough\" logic | Depth ceiling vs o3 | Accuracy critical → o3 |\n",
     "\n",
-    "# *(Full price and utility table → [Section 6.1](#appendices))*\n",
+    "*(Full price and utility table → [Section 6.1](#appendices))*\n",
     "\n",
     "## 2.2 Model Evolution at a Glance\n",
     "\n",
@@ -61,9 +61,7 @@
     "\n",
     "### OpenAI Model Evolution \n",
     "\n",
-
     "![OpenAI Model Evolution](../../../images/2.2_model_evolution.png)\n",
-
     "\n",
     "### Key Characteristics\n",
     "\n",
@@ -81,9 +79,7 @@
     "\n",
     "## 3A. Use Case: Long-Context RAG for Legal Q&A\n",
     "\n",
-
     "![Long-Context RAG for Legal Q&A](../../../images/3A_rag_task_card.png)\n",
-
     "## 🗂️ TL;DR Matrix\n",
     "\n",
     "This table summarizes the core technology choices and their rationale for **this specific Long-Context Agentic RAG implementation**.\n",
@@ -137,9 +133,7 @@
    "id": "db9bad1b",
    "metadata": {},
    "source": [
-
     "![Hierarchical Router](../../../images/3A_rag_hierarchical_router.png)\n",
-
     "\n",
     "\n",
     "## Agentic RAG System: Model Usage\n",
@@ -1821,9 +1815,7 @@
     "================================================================================\n",
     "\n",
     "## 3B. Use Case: AI Co-Scientist for Pharma R&D\n",
-
     "![AI Co-Scientist for Pharma R&D](../../../images/3B_reasoning_task_card.png)\n",
-
     "\n",
     "This section details how to build an AI system that functions as a \"co-scientist\" to accelerate experimental design in pharmaceutical R&D, focusing on optimizing a drug synthesis process under specific constraints.\n",
     "\n",
@@ -1863,9 +1855,7 @@
     "\n",
     "The system employs a multi-agent architecture that emulates a high-performing scientific team. Different AI components, acting in specialized roles (such as ideation, critique, and learning from outcomes), collaborate using various models and tools to execute the workflow.\n",
     "\n",
-
     "![AI Co-Scientist Architecture](../../../images/3B_coscientist_architecture.png)\n",
-
     "\n",
     "### 2.1. **Scientist Input & Constraints:** \n",
     "The process starts with the scientist defining the goal, target compound, and constraints."
@@ -2473,9 +2463,7 @@
     "\n",
     "## 3C. Use Case: Insurance Claim Processing\n",
     "\n",
-
     "![](../../../images/3C_insurance_task_card.png)\n",
-
     "\n",
     "Many businesses are faced with the task of digitizing hand filled forms. In this section, we will demonstrate how OpenAI can be used to digitize and validate a hand filled insurance form. While this is a common problem for insurance, the same techniques can be applied to a variety of other industries and forms, for example tax forms, invoices, and more.\n",
     "\n",
@@ -2505,9 +2493,7 @@
     "\n",
     "The high level basic architecture of the solution is shown below.\n",
     "\n",
-
     "![](../../../images/3C_insurance_architecture.png)\n",
-
     "\n",
     "This task is complex and requires a wide variety of model capabilities, including vision, function calling, reasoning, and structured output. While `o3` is capable of doing all of these at once, we found during experimentation that `o4-mini` alone was not sufficient to achieve the necessary performance. Due to the higher relative costs of `o3`, we instead opted for a two-stage approach.\n",
     "\n",
@@ -2517,9 +2503,7 @@
     "\n",
     "To demonstrate concretely how this works, let's look at a sample image of an insurance form.\n",
     "\n",
-
     "![](../../../images/3C_insurance_form.png)\n",
-
     "\n",
     "While the form itself is fairly straightforward, there is missing data and ambiguous information that will be difficult for a traditional OCR system to fill out correctly. First, notice that the zip code and county have been omitted. Second, the email address of the user is ambiguous \\-- it could be `[email protected]` or `[email protected]`. In the following sections, we will walk through how a well-designed solution can handle these ambiguities and return the correct form results.\n",
     "\n",
@@ -3202,10 +3186,7 @@
     "\n",
     "## Adaptation Decision Tree\n",
     "\n",
-
     "![Model Selection Decision Tree](../../../images/3D_model_selection_flowchart.png)\n",
-
-
     "\n",
     "## Communicating Model Selection to Non-Technical Stakeholders\n",
     "\n",
@@ -3305,12 +3286,11 @@
     "\n",
     "## Contributors\n",
     "\n",
-    " This cookbook serves as a joint collaboration effort between OpenAI and [Tribe AI](https://www.tribe.ai/)\n", 
+    " This cookbook serves as a joint collaboration effort between OpenAI and [Tribe AI](https://www.tribe.ai/)\n",
     "- [Kashyap Coimbatore Murali](https://www.linkedin.com/in/kashyap-murali/)\n",
     "- [Nate Harada](https://www.linkedin.com/in/nate-harada/) \n",
     "- [Sai Prashanth Soundararaj](https://www.linkedin.com/in/saiprashanths/)\n",
     "- [Shikhar Kwatra](https://www.linkedin.com/in/shikharkwatra/)"
-
    ]
   }
  ],