{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# SmolLM3 Pareto Plot"
      ],
      "metadata": {
        "id": "2GZKZdmyuQTE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data"
      ],
      "metadata": {
        "id": "spNAR2b1uRaE"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QVM6CPYeJs7O"
      },
      "outputs": [],
      "source": [
        "model_data = {\n",
        "    \"SmoLLM3-3B\": {\n",
        "        \"AIME 2025\": 9.3,\n",
        "        \"GSM-Plus\": 72.8,\n",
        "        \"LiveCodeBench v4\": 15.2,\n",
        "        \"GPQA Diamond\": 35.7,\n",
        "        \"IFEval\": 76.7,\n",
        "        \"MixEval Hard\": 26.9,\n",
        "        \"Global MMLU\": 53.5,\n",
        "        \"MMLU Pro\": 45.0,\n",
        "        \"BFCL\": 92.3,\n",
        "    },\n",
        "    \"Qwen2.5-3B\": {\n",
        "        \"AIME 2025\": 2.9,\n",
        "        \"GSM-Plus\": 74.1,\n",
        "        \"LiveCodeBench v4\": 10.5,\n",
        "        \"GPQA Diamond\": 32.2,\n",
        "        \"IFEval\": 65.6,\n",
        "        \"MixEval Hard\": 27.6,\n",
        "        \"Global MMLU\": 50.5,\n",
        "        \"MMLU Pro\": 41.9,\n",
        "        \"BFCL\": 0,\n",
        "    },\n",
        "    \"Llama3.1-3B\": {\n",
        "        \"AIME 2025\": 0.3,\n",
        "        \"GSM-Plus\": 59.2,\n",
        "        \"LiveCodeBench v4\": 3.4,\n",
        "        \"GPQA Diamond\": 29.4,\n",
        "        \"IFEval\": 71.6,\n",
        "        \"MixEval Hard\": 24.9,\n",
        "        \"Global MMLU\": 46.8,\n",
        "        \"MMLU Pro\": 36.6,\n",
        "        \"BFCL\": 92.3,\n",
        "    },\n",
        "    \"Qwen3-1.7B\": {\n",
        "        \"AIME 2025\": 8.0,\n",
        "        \"GSM-Plus\": 68.3,\n",
        "        \"LiveCodeBench v4\": 15.0,\n",
        "        \"GPQA Diamond\": 31.8,\n",
        "        \"IFEval\": 74.0,\n",
        "        \"MixEval Hard\": 24.3,\n",
        "        \"Global MMLU\": 49.5,\n",
        "        \"MMLU Pro\": 45.6,\n",
        "        \"BFCL\": 89.5,\n",
        "    },\n",
        "    \"Qwen3-4B\": {\n",
        "        \"AIME 2025\": 17.1,\n",
        "        \"GSM-Plus\": 82.1,\n",
        "        \"LiveCodeBench v4\": 24.9,\n",
        "        \"GPQA Diamond\": 44.4,\n",
        "        \"IFEval\": 68.9,\n",
        "        \"MixEval Hard\": 31.6,\n",
        "        \"Global MMLU\": 65.1,\n",
        "        \"MMLU Pro\": 60.9,\n",
        "        \"BFCL\": 95.0,\n",
        "    }\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_data[\"Qwen3-4B\"].keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G6GHvlL-0t2N",
        "outputId": "f16a70af-6d4d-4ea1-953b-69d00cf24a10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['AIME 2025', 'GSM-Plus', 'LiveCodeBench v4', 'GPQA Diamond', 'IFEval', 'MixEval Hard', 'Global MMLU', 'MMLU Pro', 'BFCL'])"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Win rate\n"
      ],
      "metadata": {
        "id": "oq1Nej4zSV2k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# no MCF\n",
        "metrics_to_average = ['AIME 2025', 'GSM-Plus', 'LiveCodeBench v4', 'GPQA Diamond', 'IFEval', 'MixEval Hard', 'Global MMLU', 'BFCL']\n",
        "\n",
        "def compute_win_rate(model_data, metrics):\n",
        "    \"\"\"\n",
        "    Compute win rate for each model based on specified metrics.\n",
        "    Win rate = average of (num_models - (rank - 1)) across all metrics\n",
        "    \"\"\"\n",
        "    df = pd.DataFrame(model_data).T\n",
        "\n",
        "    df_filtered = df[metrics]\n",
        "\n",
        "    num_models = len(df_filtered)\n",
        "    win_rates = {}\n",
        "\n",
        "    print(f\"Computing win rates for {num_models} models across {len(metrics)} metrics...\")\n",
        "\n",
        "    for model in df_filtered.index:\n",
        "        win_rate_scores = []\n",
        "\n",
        "        for metric in metrics:\n",
        "            # Get scores for this metric, excluding NaN\n",
        "            metric_scores = df_filtered[metric].dropna()\n",
        "\n",
        "            if model in metric_scores.index and not pd.isna(metric_scores[model]):\n",
        "                # Rank models for this metric (1 = best, higher scores = better ranks)\n",
        "                ranks = metric_scores.rank(method='min', ascending=False)\n",
        "                model_rank = ranks[model]\n",
        "\n",
        "                # Calculate win rate score: num_models - (rank - 1)\n",
        "                win_rate_score = num_models - (model_rank - 1)\n",
        "                win_rate_scores.append(win_rate_score)\n",
        "\n",
        "        # Average win rate scores across all metrics\n",
        "        if win_rate_scores:\n",
        "            avg_win_rate = np.mean(win_rate_scores)\n",
        "            win_rates[model] = avg_win_rate\n",
        "        else:\n",
        "            win_rates[model] = None\n",
        "\n",
        "    return win_rates\n",
        "\n",
        "# Compute win rates\n",
        "win_rates = compute_win_rate(model_data, metrics_to_average)\n",
        "\n",
        "print(\"\\n=== WIN RATE TABLE ===\")\n",
        "print(f\"{'Model':<20} {'Win Rate':<10}\")\n",
        "print(\"-\" * 30)\n",
        "\n",
        "for model, win_rate in win_rates.items():\n",
        "    win_rate_str = f\"{win_rate:.2f}\" if win_rate is not None else \"N/A\"\n",
        "    print(f\"{model:<20} {win_rate_str:<10}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tzSMMnBsSX3K",
        "outputId": "8001ad04-ef37-4f8c-dc43-7a895d58a61c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Computing win rates for 5 models across 8 metrics...\n",
            "\n",
            "=== WIN RATE TABLE ===\n",
            "Model                Win Rate  \n",
            "------------------------------\n",
            "SmoLLM3-3B           3.88      \n",
            "Qwen2.5-3B           2.50      \n",
            "Llama3.1-3B          1.75      \n",
            "Qwen3-1.7B           2.38      \n",
            "Qwen3-4B             4.62      \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "win_rates['SmoLLM3-3B']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_zWa1Uuo3j2E",
        "outputId": "5bb15e21-7be1-451b-8834-59631f69c4f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "np.float64(3.857142857142857)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Plot"
      ],
      "metadata": {
        "id": "H4G2iEIuS-ee"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "avg_scores = win_rates\n",
        "\n",
        "pareto_data = {\n",
        "    \"SmolLM3 3B\": {\n",
        "        \"parameters_b\": 3.08,\n",
        "        \"overall_score\": avg_scores['SmoLLM3-3B'],\n",
        "        \"display_name\": \"SmolLM3 3B\",\n",
        "        \"color\": \"#f59e0b\",  # Amber yellow\n",
        "        \"highlight\": True\n",
        "    },\n",
        "    \"Qwen2.5 3B Instruct\": {\n",
        "        \"parameters_b\": 3.09,\n",
        "        \"overall_score\": avg_scores['Qwen2.5-3B'],\n",
        "        \"display_name\": \"Qwen2.5 3B Instruct\",\n",
        "        \"color\": \"#8b5cf6\",  # Purple\n",
        "        \"highlight\": False\n",
        "    },\n",
        "    \"Llama3.2 3B Instruct\": {\n",
        "        \"parameters_b\": 3.21,\n",
        "        \"overall_score\": avg_scores['Llama3.1-3B'],\n",
        "        \"display_name\": \"Llama3.2 3B Instruct\",\n",
        "        \"color\": \"#3b82f6\",  # Facebook blue\n",
        "        \"highlight\": False\n",
        "    },\n",
        "    \"Qwen3 1.7B\": {\n",
        "        \"parameters_b\": 2.03,\n",
        "        \"overall_score\": avg_scores['Qwen3-1.7B'],\n",
        "        \"display_name\": \"Qwen3 1.7B\",\n",
        "        \"color\": \"#8b5cf6\",  # Purple (same family as Qwen2.5)\n",
        "        \"highlight\": False\n",
        "    },\n",
        "    \"Qwen3 4B\": {\n",
        "        \"parameters_b\": 4.02,\n",
        "        \"overall_score\": avg_scores['Qwen3-4B'],\n",
        "        \"display_name\": \"Qwen3 4B\",\n",
        "        \"color\": \"#8b5cf6\",  # Purple (same family as Qwen2.5)\n",
        "        \"highlight\": False\n",
        "    },\n",
        "}"
      ],
      "metadata": {
        "id": "JCkKf3hTTC3i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pareto_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5c35j51rnQEV",
        "outputId": "35d52bbd-6bb3-4bca-c29a-a5e631cdd003"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'SmolLM3 3B': {'parameters_b': 3.08,\n",
              "  'overall_score': np.float64(3.857142857142857),\n",
              "  'display_name': 'SmolLM3 3B',\n",
              "  'color': '#f59e0b',\n",
              "  'highlight': True},\n",
              " 'Qwen2.5 3B Instruct': {'parameters_b': 3.09,\n",
              "  'overall_score': np.float64(2.7142857142857144),\n",
              "  'display_name': 'Qwen2.5 3B Instruct',\n",
              "  'color': '#8b5cf6',\n",
              "  'highlight': False},\n",
              " 'Llama3.2 3B Instruct': {'parameters_b': 3.21,\n",
              "  'overall_score': np.float64(1.4285714285714286),\n",
              "  'display_name': 'Llama3.2 3B Instruct',\n",
              "  'color': '#3b82f6',\n",
              "  'highlight': False},\n",
              " 'Qwen3 1.7B': {'parameters_b': 2.03,\n",
              "  'overall_score': np.float64(2.4285714285714284),\n",
              "  'display_name': 'Qwen3 1.7B',\n",
              "  'color': '#8b5cf6',\n",
              "  'highlight': False},\n",
              " 'Qwen3 4B': {'parameters_b': 4.02,\n",
              "  'overall_score': np.float64(4.571428571428571),\n",
              "  'display_name': 'Qwen3 4B',\n",
              "  'color': '#8b5cf6',\n",
              "  'highlight': False}}"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Pareto plot with logos"
      ],
      "metadata": {
        "id": "7qNVH1ETlz8A"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Final plot with arrows"
      ],
      "metadata": {
        "id": "syLxvvPPFgRh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from matplotlib.patches import Circle\n",
        "import matplotlib.patches as mpatches\n",
        "from matplotlib.offsetbox import OffsetImage, AnnotationBbox\n",
        "import os\n",
        "\n",
        "# Set styling to match your sophisticated plot\n",
        "plt.rcParams.update({\n",
        "    'font.family': 'DejaVu Sans',\n",
        "    'font.size': 11,\n",
        "    'axes.linewidth': 2,\n",
        "    'axes.spines.left': True,\n",
        "    'axes.spines.bottom': True,\n",
        "    'axes.spines.top': True,\n",
        "    'axes.spines.right': True,\n",
        "    'xtick.bottom': True,\n",
        "    'ytick.left': True,\n",
        "    'axes.grid': False,\n",
        "})\n",
        "\n",
        "# Define model to logo mapping\n",
        "model_to_logo = {\n",
        "    'SmolLM3 3B': 'huggingface.png',  # HuggingFace logo\n",
        "    'Qwen2.5 3B Instruct': 'qwen.png',         # Qwen/Alibaba logo\n",
        "    'Llama3.2 3B Instruct': 'meta.png',        # Meta logo\n",
        "    'Qwen3 1.7B': 'qwen.png',    # Qwen/Alibaba logo\n",
        "    'Qwen3 4B': 'qwen.png',      # Qwen/Alibaba logo\n",
        "}\n",
        "\n",
        "def create_sophisticated_plot_with_logos(data_dict, logos_dir=\"./logos/\", figsize=(15, 10), logo_size=25):\n",
        "    \"\"\"\n",
        "    Create a sophisticated Pareto plot with company logos instead of colored dots\n",
        "    \"\"\"\n",
        "    # Extract data\n",
        "    sizes = [info[\"parameters_b\"] for info in data_dict.values()]\n",
        "    scores = [info[\"overall_score\"] for info in data_dict.values()]\n",
        "    colors = [info[\"color\"] for info in data_dict.values()]\n",
        "    names = [info[\"display_name\"] for info in data_dict.values()]\n",
        "    highlights = [info[\"highlight\"] for info in data_dict.values()]\n",
        "    model_keys = list(data_dict.keys())\n",
        "    print(model_keys)\n",
        "    # Create large figure\n",
        "    fig, ax = plt.subplots(figsize=figsize, facecolor='white', dpi=150)\n",
        "    ax.set_facecolor('white')\n",
        "\n",
        "    # Cache for loaded images\n",
        "    image_cache = {}\n",
        "    default_logo = os.path.join(logos_dir, \"default.png\")  # Fallback logo\n",
        "\n",
        "    # Plot each point with logos\n",
        "    for i, (x, y, color, name, is_highlight, model_key) in enumerate(\n",
        "        zip(sizes, scores, colors, names, highlights, model_keys)):\n",
        "\n",
        "        # Get logo path\n",
        "        logo_file = model_to_logo.get(model_key, None)\n",
        "        if logo_file and os.path.exists(os.path.join(logos_dir, logo_file)):\n",
        "            logo_path = os.path.join(logos_dir, logo_file)\n",
        "        else:\n",
        "            # Fallback to colored circle if no logo available\n",
        "            logo_path = None\n",
        "\n",
        "        if logo_path:\n",
        "            # Load and display logo\n",
        "            if logo_path not in image_cache:\n",
        "                try:\n",
        "                    img = plt.imread(logo_path)\n",
        "                    image_cache[logo_path] = img\n",
        "                except:\n",
        "                    # If logo fails to load, use colored circle\n",
        "                    logo_path = None\n",
        "\n",
        "            if logo_path:\n",
        "                img = image_cache[logo_path]\n",
        "\n",
        "                # Calculate zoom to get desired size\n",
        "                img_zoom = logo_size / max(img.shape[:2])\n",
        "\n",
        "                # Adjust alpha and size for highlights\n",
        "                alpha = 1.0 if is_highlight else 0.85\n",
        "                current_logo_size = logo_size * 1.2 if is_highlight else logo_size\n",
        "                img_zoom = current_logo_size / max(img.shape[:2])\n",
        "\n",
        "                # Create image annotation\n",
        "                im = OffsetImage(img, zoom=img_zoom, alpha=alpha)\n",
        "                ab = AnnotationBbox(im, (x, y), frameon=False, pad=0)\n",
        "                ax.add_artist(ab)\n",
        "\n",
        "        # Fallback to colored circles if no logo\n",
        "        if not logo_path:\n",
        "            # Add subtle shadow/border effect\n",
        "            ax.scatter(x, y, s=200, c='#000000', alpha=0.1, zorder=2)\n",
        "            ax.scatter(x, y, s=180, c=color, alpha=0.9,\n",
        "                      edgecolors='white', linewidths=3, zorder=3)\n",
        "\n",
        "        # Add text labels with sophisticated positioning\n",
        "        label_color = '#1f2937'\n",
        "        font_weight = 'bold' if is_highlight else 'normal'\n",
        "        font_size = 14 if is_highlight else 13\n",
        "\n",
        "        # Homogenized label positioning - all labels below logos for consistency\n",
        "        # Use larger offset to ensure clear separation from logos\n",
        "        xytext = (0, -35)  # All labels positioned below logos with consistent spacing\n",
        "\n",
        "        # Add background box for ALL models to ensure readability\n",
        "        bbox_props = dict(facecolor='white', alpha=0.9,\n",
        "                         boxstyle='round,pad=0.4', edgecolor='#e5e7eb', linewidth=1)\n",
        "\n",
        "        # Use consistent styling for all labels like SmolLM3 (the clearest)\n",
        "        ax.annotate(name, (x, y), xytext=xytext, textcoords='offset points',\n",
        "                   fontsize=font_size, fontweight=font_weight, color=label_color,\n",
        "                   ha='center', va='center', bbox=bbox_props)\n",
        "\n",
        "    # Style borders to match your sophisticated plot\n",
        "    for spine in ax.spines.values():\n",
        "        spine.set_visible(True)\n",
        "        spine.set_linewidth(3)\n",
        "        spine.set_color('black')\n",
        "\n",
        "    # Y-axis title and subtitle\n",
        "    ax.text(-0.082, 0.5, \"Win rate (%)\", transform=ax.transAxes,\n",
        "            color=\"black\", weight=\"bold\", fontsize=18,\n",
        "            rotation=90, ha='center', va='center')\n",
        "\n",
        "    ax.text(-0.056, 0.5, \"Measured on 8 popular LLM Benchmarks\", transform=ax.transAxes,\n",
        "            color=\"dimgray\", fontsize=14,\n",
        "            rotation=90, ha='center', va='center')\n",
        "\n",
        "    # X-axis title and subtitle\n",
        "    ax.text(0.5, -0.06, \"Model Size\", transform=ax.transAxes,\n",
        "            color=\"black\", weight=\"bold\", fontsize=18,\n",
        "            ha='center', va='top')\n",
        "\n",
        "    ax.text(0.5, -0.099, \"Billion parameters\", transform=ax.transAxes,\n",
        "            color=\"dimgray\", fontsize=14,\n",
        "            ha='center', va='top')\n",
        "\n",
        "    # Main title - more engaging and descriptive\n",
        "    # ax.text(0.5, 1.03, 'Instruct Models Performance (no extended thinking)', transform=ax.transAxes,\n",
        "    #         fontsize=28, fontweight='normal', color='#111827', ha='center')\n",
        "    ax.text(0.5, 1.06, 'Instruct Models Performance', transform=ax.transAxes,\n",
        "            fontsize=28, fontweight='normal', color='#111827', ha='center')\n",
        "\n",
        "    # Subtitle - centered below main title in gray italic\n",
        "    ax.text(0.5, 1.02, 'No Extended Thinking', transform=ax.transAxes,\n",
        "            fontsize=16, fontweight='normal', color='gray', ha='center',\n",
        "            style='italic')\n",
        "    # Remove default axis labels\n",
        "    ax.set_xlabel('')\n",
        "    ax.set_ylabel('')\n",
        "\n",
        "    # Better axis limits with extra room for labels below logos\n",
        "    x_range = max(sizes) - min(sizes)\n",
        "    y_range = max(scores) - min(scores)\n",
        "    ax.set_xlim(min(sizes) - x_range*0.08, max(sizes) + x_range*0.08)\n",
        "    ax.set_ylim(min(scores) - y_range*0.15, max(scores) + y_range*0.06)  # More space at bottom for labels\n",
        "\n",
        "    # Better tick styling\n",
        "    ax.tick_params(colors='black', labelsize=14, pad=8, length=6, width=2)\n",
        "\n",
        "    # Add directional arrows to show \"better\" and \"faster/cheaper\" like in reference\n",
        "    arrow_color = '#6b7280'  # Elegant gray that fits the theme\n",
        "\n",
        "    # \"Better\" arrow pointing up (left side)\n",
        "    ax.annotate('',\n",
        "                xy=(min(sizes) - x_range*0.04, max(scores) - y_range*0.1),  # Arrow tip\n",
        "                xytext=(min(sizes) - x_range*0.04, max(scores) - y_range*0.4),  # Arrow start\n",
        "                arrowprops=dict(color=arrow_color, arrowstyle='-|>', lw=5, alpha=0.9, mutation_scale=25))\n",
        "\n",
        "    ax.text(min(sizes) - x_range*0.02, max(scores) - y_range*0.25, 'better',\n",
        "            color=arrow_color, fontweight='bold', fontsize=16,\n",
        "            rotation=90, ha='center', va='center')\n",
        "\n",
        "    # \"Faster/cheaper\" arrow pointing left (top)\n",
        "    ax.annotate('',\n",
        "                xy=(min(sizes) + x_range*0.1, max(scores) - y_range*0.02),  # Arrow tip\n",
        "                xytext=(min(sizes) + x_range*0.5, max(scores) - y_range*0.02),  # Arrow start\n",
        "                arrowprops=dict(color=arrow_color, arrowstyle='-|>', lw=5, alpha=0.9, mutation_scale=25))\n",
        "\n",
        "    ax.text(min(sizes) + x_range*0.3, max(scores) - y_range*0.05, 'faster / cheaper',\n",
        "            color=arrow_color, fontweight='bold', fontsize=16,\n",
        "            ha='center', va='top')\n",
        "\n",
        "    # No grid to match your clean style\n",
        "    ax.grid(False)\n",
        "\n",
        "    plt.tight_layout()\n",
        "    return fig, ax\n",
        "\n",
        "\n",
        "\n",
        "# Create the plot with logos\n",
        "print(\"Creating the plot with company logos...\")\n",
        "fig, ax = create_sophisticated_plot_with_logos(pareto_data, logos_dir=\"/content/sample_data/logos\", figsize=(15, 10), logo_size=30)\n",
        "\n",
        "# Save the plot\n",
        "plt.savefig('pareto_plot_bfcl.png',\n",
        "            dpi=300,\n",
        "            bbox_inches='tight',\n",
        "            facecolor='white',\n",
        "            edgecolor='none',\n",
        "            format='png')\n",
        "\n",
        "plt.savefig('pareto_plot_bfcl.pdf',\n",
        "            bbox_inches='tight',\n",
        "            facecolor='white',\n",
        "            format='pdf')\n",
        "\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "qxttFefVFiam",
        "outputId": "5b86c815-b329-43dd-ceb6-3c581afeadfe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Creating the plot with company logos...\n",
            "['SmolLM3 3B', 'Qwen2.5 3B Instruct', 'Llama3.2 3B Instruct', 'Qwen3 1.7B', 'Qwen3 4B']\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2250x1500 with 1 Axes>"
            ],
            "image/png": 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      ]
    }
  ]
}