import requests from bs4 import BeautifulSoup import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import gradio as gr import io import os import base64 import zipfile from PIL import Image from io import BytesIO import tempfile import sys # -------------------------------------------------------------------- # PART 1: TINY DATA + PLOTS # -------------------------------------------------------------------- # This dataframe is your “tiny” version of model performance data. # Used for plotting & demonstration in the Gradio app. data_full = [ ['CultriX/Qwen2.5-14B-SLERPv7', 'https://huggingface.co/CultriX/Qwen2.5-14B-SLERPv7', 0.7205, 0.8272, 0.7541, 0.6581, 0.5, 0.729], ['djuna/Q2.5-Veltha-14B-0.5', 'https://huggingface.co/djuna/Q2.5-Veltha-14B-0.5', 0.7492, 0.8386, 0.7305, 0.598, 0.43, 0.7817], ['CultriX/Qwen2.5-14B-FinalMerge', 'https://huggingface.co/CultriX/Qwen2.5-14B-FinalMerge', 0.7248, 0.8277, 0.7113, 0.7052, 0.57, 0.7001], ['CultriX/Qwen2.5-14B-MultiCultyv2', 'https://huggingface.co/CultriX/Qwen2.5-14B-MultiCultyv2', 0.7295, 0.8359, 0.7363, 0.5767, 0.44, 0.7316], ['CultriX/Qwen2.5-14B-Brocav7', 'https://huggingface.co/CultriX/Qwen2.5-14B-Brocav7', 0.7445, 0.8353, 0.7508, 0.6292, 0.46, 0.7629], ['CultriX/Qwen2.5-14B-Broca', 'https://huggingface.co/CultriX/Qwen2.5-14B-Broca', 0.7456, 0.8352, 0.748, 0.6034, 0.44, 0.7716], ['CultriX/Qwen2.5-14B-Brocav3', 'https://huggingface.co/CultriX/Qwen2.5-14B-Brocav3', 0.7395, 0.8388, 0.7393, 0.6405, 0.47, 0.7659], ['CultriX/Qwen2.5-14B-Brocav4', 'https://huggingface.co/CultriX/Qwen2.5-14B-Brocav4', 0.7432, 0.8377, 0.7444, 0.6277, 0.48, 0.758], ['CultriX/Qwen2.5-14B-Brocav2', 'https://huggingface.co/CultriX/Qwen2.5-14B-Brocav2', 0.7492, 0.8302, 0.7508, 0.6377, 0.51, 0.7478], ['CultriX/Qwen2.5-14B-Brocav5', 'https://huggingface.co/CultriX/Qwen2.5-14B-Brocav5', 0.7445, 0.8313, 0.7547, 0.6376, 0.5, 0.7304], ['CultriX/Qwen2.5-14B-Brocav6', 'https://huggingface.co/CultriX/Qwen2.5-14B-Brocav6', 0.7179, 0.8354, 0.7531, 0.6378, 0.49, 0.7524], ['CultriX/Qwenfinity-2.5-14B', 'https://huggingface.co/CultriX/Qwenfinity-2.5-14B', 0.7347, 0.8254, 0.7279, 0.7267, 0.56, 0.697], ['CultriX/Qwen2.5-14B-Emergedv2', 'https://huggingface.co/CultriX/Qwen2.5-14B-Emergedv2', 0.7137, 0.8335, 0.7363, 0.5836, 0.44, 0.7344], ['CultriX/Qwen2.5-14B-Unity', 'https://huggingface.co/CultriX/Qwen2.5-14B-Unity', 0.7063, 0.8343, 0.7423, 0.682, 0.57, 0.7498], ['CultriX/Qwen2.5-14B-MultiCultyv3', 'https://huggingface.co/CultriX/Qwen2.5-14B-MultiCultyv3', 0.7132, 0.8216, 0.7395, 0.6792, 0.55, 0.712], ['CultriX/Qwen2.5-14B-Emergedv3', 'https://huggingface.co/CultriX/Qwen2.5-14B-Emergedv3', 0.7436, 0.8312, 0.7519, 0.6585, 0.55, 0.7068], ['CultriX/SeQwence-14Bv1', 'https://huggingface.co/CultriX/SeQwence-14Bv1', 0.7278, 0.841, 0.7541, 0.6816, 0.52, 0.7539], ['CultriX/Qwen2.5-14B-Wernickev2', 'https://huggingface.co/CultriX/Qwen2.5-14B-Wernickev2', 0.7391, 0.8168, 0.7273, 0.622, 0.45, 0.7572], ['CultriX/Qwen2.5-14B-Wernickev3', 'https://huggingface.co/CultriX/Qwen2.5-14B-Wernickev3', 0.7357, 0.8148, 0.7245, 0.7023, 0.55, 0.7869], ['CultriX/Qwen2.5-14B-Wernickev4', 'https://huggingface.co/CultriX/Qwen2.5-14B-Wernickev4', 0.7355, 0.829, 0.7497, 0.6306, 0.48, 0.7635], ['CultriX/SeQwential-14B-v1', 'https://huggingface.co/CultriX/SeQwential-14B-v1', 0.7355, 0.8205, 0.7549, 0.6367, 0.48, 0.7626], ['CultriX/Qwen2.5-14B-Wernickev5', 'https://huggingface.co/CultriX/Qwen2.5-14B-Wernickev5', 0.7224, 0.8272, 0.7541, 0.679, 0.51, 0.7578], ['CultriX/Qwen2.5-14B-Wernickev6', 'https://huggingface.co/CultriX/Qwen2.5-14B-Wernickev6', 0.6994, 0.7549, 0.5816, 0.6991, 0.58, 0.7267], ['CultriX/Qwen2.5-14B-Wernickev7', 'https://huggingface.co/CultriX/Qwen2.5-14B-Wernickev7', 0.7147, 0.7599, 0.6097, 0.7056, 0.57, 0.7164], ['CultriX/Qwen2.5-14B-FinalMerge-tmp2', 'https://huggingface.co/CultriX/Qwen2.5-14B-FinalMerge-tmp2', 0.7255, 0.8192, 0.7535, 0.6671, 0.5, 0.7612], ['CultriX/Qwen2.5-14B-BrocaV8', 'https://huggingface.co/CultriX/Qwen2.5-14B-BrocaV8', 0.7415, 0.8396, 0.7334, 0.5785, 0.43, 0.7646], ['CultriX/Qwexit-2.5-14B-2024', 'https://huggingface.co/CultriX/Qwexit-2.5-14B-2024', 0.7253, 0.8174, 0.7456, 0.6688, 0.5300, 0.7027], ['CultriX/Qwen2.5-14B-BrocaV9', 'https://huggingface.co/CultriX/Qwen2.5-14B-BrocaV9', 0.7432, 0.8307, 0.7467, 0.6221, 0.5000, 0.7623], ['CultriX/Qwen2.5-14B-partialmergept1', 'https://huggingface.co/CultriX/Qwen2.5-14B-partialmergept1', 0.7389, 0.8370, 0.7451, 0.6715, 0.5700, 0.7308], ['CultriX/Qwen2.5-14B-partialmergept2', 'https://huggingface.co/CultriX/Qwen2.5-14B-partialmergept2', 0.7300, 0.8428, 0.7371, 0.5944, 0.4200, 0.7581], ['CultriX/model', 'https://huggingface.co/CultriX/model', 0.7010, 0.8320, 0.7194, 0.6158, 0.4700, 0.7385], ['CultriX/Qwen2.5-14B-BrocaFinal', 'https://huggingface.co/CultriX/Qwen2.5-14B-BrocaFinal', 0.6265, 0.7688, 0.7007, 0.7035, 0.5100, 0.7218], ['CultriX/Qwen2.5-14B-Hyperionv1', 'https://huggingface.co/CultriX/Qwen2.5-14B-Hyperionv1', 0.7300, 0.8477, 0.7448, 0.6063, 0.4400, 0.7651], ['CultriX/Qwen2.5-14B-Hyperionv3', 'https://huggingface.co/CultriX/Qwen2.5-14B-Hyperionv3', 0.7445, 0.8414, 0.7458, 0.6371, 0.4900, 0.7543], ['sometimesanotion/Lamarck-14B-v0.6', 'https://huggingface.com/sometimesanotion/Lamarck-14B-v0.6', 0.7446, 0.8294, 0.7368, 0.6008, 0.4300, 0.7423], ['CultriX/Qwen2.5-14B-Hyper', 'https://huggingface.com/CultriX/Qwen2.5-14B-Hyper', 0.7372, 0.8411, 0.7424, 0.5830, 0.4400, 0.7792], ['CultriX/Qwen2.5-14B-Hyperionv4', 'https://huggingface.co/CultriX/Qwen2.5-14B-Hyperionv4', 0.7305, 0.8359, 0.7454, 0.5827, 0.4600, 0.7797], ['CultriX/Qwen2.5-14B-Hyperionv5', 'https://huggingface.co/CultriX/Qwen2.5-14B-Hyperionv5', 0.7458, 0.8290, 0.7508, 0.6228, 0.5200, 0.7540], ['CultriX/Qwen2.5-14B-Hyperionv6', 'https://huggingface.co/CultriX/Qwen2.5-14B-Hyperionv6', 0.7430, 0.8308, 0.7353, 0.6184, 0.4500, 0.7665], ['CultriX/Qwen2.5-14B-Hyperionv7', 'https://huggingface.co/CultriX/Qwen2.5-14B-Hyperionv7', 0.7412, 0.8287, 0.7508, 0.6208, 0.4800, 0.7532], ['CultriX/Qwen2.5-14B-Ultima', 'https://huggingface.co/CultriX/Qwen2.5-14B-Ultima', 0.7413, 0.8335, 0.7487, 0.6156, 0.4500, 0.7601], ['sometimesanotion/Lamarck-14B-v0.7-rc4', 'https://huggingface.co/sometimesanotion/Lamarck-14B-v0.7-rc4', 0.7541, 0.8310, 0.7487, 0.6043, 0.4400, 0.7421], ['CultriX/Enhanced-TIES-Base-v1', 'https://huggingface.co/CultriX/Enhanced-TIES-Base-v1', 0.7497, 0.8376, 0.7424, 0.6168, 0.4700, 0.7544], ['CultriX/Qwen2.5-14B-Qwentangledv2', 'https://huggingface.co/CultriX/Qwen2.5-14B-Qwentangledv2', 0.7355, 0.8218, 0.7438, 0.6093, 0.4500, 0.7352], ['CultriX/Qwen2.5-14B-Optimav3', 'https://huggingface.co/CultriX/Qwen2.5-14B-Optimav3', 0.7482, 0.8216, 0.7424, 0.6186, 0.4800, 0.7675], ['CultriX/Qwen2.5-14B-Ultimav2', 'https://huggingface.co/CultriX/Qwen2.5-14B-Ultimav2', 0.7568, 0.8333, 0.7454, 0.6277, 0.4900, 0.7870], ['CultriX/Qwen2.5-14B-HyperSeek', 'https://huggingface.co/CultriX/Qwen2.5-14B-HyperSeek', 0.7445, 0.8414, 0.7458, 0.6371, 0.4900, 0.7543], ['CultriX/Qwen2.5-14B-HyperSeekv2', 'https://huggingface.co/CultriX/Qwen2.5-14B-HyperSeekv2', 0.7445, 0.8431, 0.7458, 0.6344, 0.5000, 0.7501], ['CultriX/Qwen2.5-14B-Hyperseek-h', 'https://huggingface.co/CultriX/Qwen2.5-14B-HyperSeek-h', 0.7445, 0.8414, 0.7458, 0.6371, 0.4900, 0.7543], ['CultriX/Qwen2.5-14B-HyperSeek', 'https://huggingface.co/CultriX/Qwen2.5-14B-HyperSeek', 0.7396, 0.8289, 0.7532, 0.6516, 0.4900, 0.7458], ['CultriX/Qwen2.5-DeepHyper', 'https://huggingface.co/CultriX/Qwen2.5-DeepHyper', 0.7558, 0.8283, 0.7330, 0.6962, 0.5900, 0.7191], ['CultriX/Qwen2.5-DeepHyper', 'https://huggingface.co/CultriX/Qwen2.5-DeepHyper', 0.7396, 0.8289, 0.7532, 0.6564, 0.5100, 0.7524], ['CultriX/MergeStage1', 'https://huggingface.co/CultriX/MergeStage1', 0.7559, 0.8291, 0.7519, 0.6256, 0.4800, 0.7383], ['CultriX/MergeStage3', 'https://huggingface.co/CultriX/MergeStage3', 0.7355, 0.8258, 0.7408, 0.6179, 0.4800, 0.7626], ['CultriX/MergeStage2', 'https://huggingface.co/CultriX/MergeStage2', 0.7468, 0.8242, 0.7497, 0.6156, 0.4900, 0.7424], ['CultriX/MergeStage3v2', 'https://huggingface.co/CultriX/MergeStage3v2', 0.7492, 0.8216, 0.7408, 0.6167, 0.4600, 0.7642], ['CultriX/MergeStag1v2', 'https://huggingface.co/CultriX/MergeStage1v2', 0.7430, 0.8121, 0.7424, 0.6042, 0.4400, 0.7701], ['CultriX/MergeStag2v2', 'https://huggingface.co/CultriX/MergeStage2v2', 0.7430, 0.8289, 0.7368, 0.6011, 0.4500, 0.7421], ['CultriX/MergeStag1v3', 'https://huggingface.co/CultriX/MergeStage1v3', 0.7216, 0.8458, 0.7281, 0.7202, 0.5500, 0.7362], ['CultriX/MergeStag2v3', 'https://huggingface.co/CultriX/MergeStage2v3', 0.7430, 0.8343, 0.7330, 0.6989, 0.5800, 0.7133], ['CultriX/MergeStag3v3', 'https://huggingface.co/CultriX/MergeStage3v3', 0.7430, 0.8097, 0.7467, 0.6162, 0.4600, 0.7833], ['CultriX/MergeStag3v4', 'https://huggingface.co/CultriX/MergeStage3v4', 0.7481, 0.8262, 0.7298, 0.6726, 0.5400, 0.7192], ['CultriX/MergeStag3v4v2', 'https://huggingface.co/CultriX/MergeStage3v4v2', 0.7353, 0.8258, 0.7337, 0.6669, 0.5200, 0.7329], ['CultriX/MergeStag4v2', 'https://huggingface.co/CultriX/MergeStage4v2', 0.7357, 0.8058, 0.7486, 0.6002, 0.4400, 0.7694], ['CultriX/MergeStag4v3', 'https://huggingface.co/CultriX/MergeStage4v3', 0.7413, 0.8314, 0.7457, 0.6529, 0.4800, 0.7456], ['deepseek-ai/DeepSeek-R1-Distill-Qwen-14B', 'https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B', 0.6355, 0.8191, 0.6956, 0.5615, 0.3800, 0.7030], ['suayptalha/Lamarckvergence-14B', 'https://huggingface.co/suayptalha/Lamarckvergence-14B', 0.7554, 0.8468, 0.7457, 0.6044, 0.4300, 0.7687], ['CultriX/Qwen2.5-14B-HyperMarck', 'https://huggingface.co/CultriX/Qwen2.5-14B-HyperMarck', 0.7457, 0.8225, 0.7337, 0.6473, 0.4900, 0.7192], ['CultriX/Qwen2.5-14B-HyperMarck-dl', 'https://huggingface.co/CultriX/Qwen2.5-14B-HyperMarck-dl', 0.7354, 0.8458, 0.7248, 0.7023, 0.5600, 0.7181], ['CultriX/Qwen2.5-14B-HyperMarck-dt', 'https://huggingface.co/CultriX/Qwen2.5-14B-HyperMarck-dt', 0.7300, 0.8405, 0.7248, 0.7017, 0.5600, 0.7226], ['CultriX/Qwen2.5-14B-HyperMarck', 'https://huggingface.co/CultriX/Qwen2.5-14B-HyperMarck', 0.7568, 0.8257, 0.7368, 0.6242, 0.4600, 0.7639], ['CultriX/Qwen2.5-14B-DeepSearchv2', 'https://huggingface.co/CultriX/Qwen2.5-14B-DeepSearchv2', 0.7000, 0.8340, 0.7218, 0.6329, 0.4800, 0.7646], ['CultriX/Qwen2.5-14B-CoreGeneralist', 'https://huggingface.co/CultriX/Qwen2.5-14B-CoreGeneralist', 0.7396, 0.8289, 0.7487, 0.6337, 0.4700, 0.7453], ['CultriX/Qwen2.5-14B-ReasoningMerge', 'https://huggingface.co/CultriX/Qwen2.5-14B-ReasoningMerge', 0.7452, 0.8364, 0.7216, 0.5982, 0.4500, 0.7705], ['CultriX/Qwen2.5-14B-GeneralReasoning', 'https://huggingface.co/CultriX/Qwen2.5-14B-GeneralReasoning', 0.7478, 0.8323, 0.7314, 0.6151, 0.4500, 0.7706], ['CultriX/Qwen2.5-14B-DeepResearch', 'https://huggingface.co/CultriX/Qwen2.5-14B-DeepResearch', 0.7568, 0.8207, 0.7435, 0.6184, 0.4800, 0.7369] ] columns = [ "Model Configuration", "Model Link", "tinyArc", "tinyHellaswag", "tinyMMLU", "tinyTruthfulQA", "tinyTruthfulQA_mc1", "tinyWinogrande" ] df_full = pd.DataFrame(data_full, columns=columns) df_full = pd.DataFrame(data_full, columns=columns) def plot_average_scores(): df_full["Average Score"] = df_full.iloc[:, 2:].mean(axis=1) df_avg_sorted = df_full.sort_values(by="Average Score", ascending=False) plt.figure(figsize=(14, 10)) plt.barh(df_avg_sorted["Model Configuration"], df_avg_sorted["Average Score"]) plt.title("Average Performance of Models Across Tasks", fontsize=16) plt.xlabel("Average Score", fontsize=14) plt.ylabel("Model Configuration", fontsize=14) plt.gca().invert_yaxis() plt.grid(axis='x', linestyle='--', alpha=0.7) plt.tight_layout() img_buffer = io.BytesIO() plt.savefig(img_buffer, format='png') img_buffer.seek(0) img_base64 = base64.b64encode(img_buffer.read()).decode('utf-8') plt.close() pil_image = Image.open(BytesIO(base64.b64decode(img_base64))) temp_image_file = tempfile.NamedTemporaryFile(suffix=".png", delete=False) pil_image.save(temp_image_file.name) return pil_image, temp_image_file.name def plot_task_performance(): df_full_melted = df_full.melt( id_vars=["Model Configuration", "Model Link"], var_name="Task", value_name="Score" ) plt.figure(figsize=(16, 12)) for model in df_full["Model Configuration"]: model_data = df_full_melted[df_full_melted["Model Configuration"] == model] plt.plot(model_data["Task"], model_data["Score"], marker="o", label=model) plt.title("Performance of All Models Across Tasks", fontsize=16) plt.xlabel("Task", fontsize=14) plt.ylabel("Score", fontsize=14) plt.xticks(rotation=45) plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=9) plt.grid(axis='y', linestyle='--', alpha=0.7) plt.tight_layout() img_buffer = io.BytesIO() plt.savefig(img_buffer, format='png') img_buffer.seek(0) img_base64 = base64.b64encode(img_buffer.read()).decode('utf-8') plt.close() pil_image = Image.open(BytesIO(base64.b64decode(img_base64))) temp_image_file = tempfile.NamedTemporaryFile(suffix=".png", delete=False) pil_image.save(temp_image_file.name) return pil_image, temp_image_file.name def plot_task_specific_top_models(): top_models = df_full.iloc[:, 2:].idxmax() top_scores = df_full.iloc[:, 2:].max() results = pd.DataFrame({"Top Model": top_models, "Score": top_scores}).reset_index().rename(columns={"index": "Task"}) plt.figure(figsize=(14, 8)) plt.bar(results["Task"], results["Score"]) plt.title("Task-Specific Top Models", fontsize=16) plt.xlabel("Task", fontsize=14) plt.ylabel("Score", fontsize=14) plt.grid(axis="y", linestyle="--", alpha=0.7) plt.tight_layout() img_buffer = io.BytesIO() plt.savefig(img_buffer, format='png') img_buffer.seek(0) img_base64 = base64.b64encode(img_buffer.read()).decode('utf-8') plt.close() pil_image = Image.open(BytesIO(base64.b64decode(img_base64))) temp_image_file = tempfile.NamedTemporaryFile(suffix=".png", delete=False) pil_image.save(temp_image_file.name) return pil_image, temp_image_file.name def plot_heatmap(): # Add a column for the total scores across all tasks df_full["Total Scores"] = df_full.iloc[:, 2:].sum(axis=1) # Normalize each column individually for consistent coloring normalized_data = df_full.iloc[:, 2:].apply(lambda x: (x - x.min()) / (x.max() - x.min()), axis=0) plt.figure(figsize=(14, 10)) sns.heatmap( normalized_data, annot=df_full.iloc[:, 2:], # Show actual values in annotations cmap="YlGnBu", xticklabels=list(columns[2:]) + ["Total Scores"], yticklabels=df_full["Model Configuration"] ) plt.title("Performance Heatmap", fontsize=16) plt.tight_layout() img_buffer = io.BytesIO() plt.savefig(img_buffer, format='png') img_buffer.seek(0) img_base64 = base64.b64encode(img_buffer.read()).decode('utf-8') plt.close() pil_image = Image.open(BytesIO(base64.b64decode(img_base64))) temp_image_file = tempfile.NamedTemporaryFile(suffix=".png", delete=False) pil_image.save(temp_image_file.name) return pil_image, temp_image_file.name def scrape_mergekit_config(model_name): """ For the *tiny* table’s model links. Scrapes 
 tags on the huggingface model page to find a YAML config.
    """
    df_row = df_full.loc[df_full["Model Configuration"] == model_name]
    if df_row.empty:
        return f"No data found for model {model_name}."

    model_link = df_row["Model Link"].values[0]
    response = requests.get(model_link)
    if response.status_code != 200:
        return f"Failed to fetch model page for {model_name}. Please check the link."

    soup = BeautifulSoup(response.text, "html.parser")
    yaml_config = soup.find("pre")  # Assume YAML is in 
 tags
    if yaml_config:
        return yaml_config.text.strip()
    return f"No YAML configuration found for {model_name}."

def download_yaml(yaml_content, model_name):
    """
    Let users download the scraped YAML if it exists. 
    """
    if "No YAML configuration found" in yaml_content or "Failed to fetch model page" in yaml_content:
        return None
    filename = f"{model_name.replace('/', '_')}_config.yaml"
    return gr.File(value=yaml_content.encode(), filename=filename)

def scrape_model_page(model_url):
    """
    Used for the "Live Scraping" text box in the Gradio UI.
    """
    try:
        response = requests.get(model_url)
        if response.status_code != 200:
            return f"Error: Unable to fetch the page (Status Code: {response.status_code})"
        
        soup = BeautifulSoup(response.text, "html.parser")
        yaml_config = soup.find("pre")
        yaml_text = yaml_config.text.strip() if yaml_config else "No YAML configuration found."
        metadata_section = soup.find("div", class_="metadata")
        metadata_text = metadata_section.text.strip() if metadata_section else "No metadata found."
        return f"**YAML Configuration:**\n{yaml_text}\n\n**Metadata:**\n{metadata_text}"
    except Exception as e:
        return f"Error: {str(e)}"

def display_scraped_model_data(model_url):
    """
    Helper for the "Live Scraping Features" section of the Gradio app.
    """
    return scrape_model_page(model_url)

def download_all_data():
    """
    Builds and returns a zip of:
      - the CSV of your 'tiny' data,
      - four plots (average performance, task performance, top models, heatmap),
      - any YAML configurations for the 'tiny' table's models (if found).
    """
    import io
    csv_buffer = io.StringIO()
    df_full.to_csv(csv_buffer, index=False)
    csv_data = csv_buffer.getvalue().encode('utf-8')
    
    average_plot_pil, average_plot_name = plot_average_scores()
    task_plot_pil, task_plot_name = plot_task_performance()
    top_models_plot_pil, top_models_plot_name = plot_task_specific_top_models()
    heatmap_plot_pil, heatmap_plot_name = plot_heatmap()

    plot_dict = {
        "average_performance": (average_plot_pil, average_plot_name),
        "task_performance": (task_plot_pil, task_plot_name),
        "top_models": (top_models_plot_pil, top_models_plot_name),
        "heatmap": (heatmap_plot_pil, heatmap_plot_name)
    }

    zip_buffer = io.BytesIO()
    with zipfile.ZipFile(zip_buffer, 'w') as zf:
        zf.writestr("model_scores.csv", csv_data)

        # Add the images
        for name, (pil_image, filename) in plot_dict.items():
            image_bytes = io.BytesIO()
            pil_image.save(image_bytes, format='PNG')
            image_bytes.seek(0)
            zf.writestr(filename, image_bytes.read())

        # Also try scraping each model in the *tiny* dataset for a YAML config
        for model_name in df_full["Model Configuration"].to_list():
            yaml_content = scrape_mergekit_config(model_name)
            if ("No YAML configuration found" not in yaml_content) and ("Failed to fetch model page" not in yaml_content):
                zf.writestr(f"{model_name.replace('/', '_')}_config.yaml", yaml_content.encode())

    zip_buffer.seek(0)
    return zip_buffer, "analysis_data.zip"

# --------------------------------------------------------------------
# PART 2: THE "DATA START" SNIPPET (RANKS 44–105) + Parser
# --------------------------------------------------------------------
# This is your larger dataset, rank = 44..105
benchmark_data = [
    {
        "rank": 1,
        "name": "suayptalha/Lamarckvergence-14B",
        "scores": {
            "average": 43.32,
            "IFEval": 76.56,
            "BBH": 50.33,
            "MATH": 54.0,
            "GPQA": 15.1,
            "MUSR": 16.34,
            "MMLU_PRO": 47.59,
            "Architecture": "Qwen2ForCausalLM",
            "Parameters": "14.766B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/suayptalha/Lamarckvergence-14B",
        "known_config": None
    },
    {
        "rank": 2,
        "name": "sthenno/tempesthenno-ppo-ckpt40",
        "scores": {
            "average": 42.74,
            "IFEval": 79.23,
            "BBH": 50.57,
            "MATH": 47.36,
            "GPQA": 17.0,
            "MUSR": 14.56,
            "MMLU_PRO": 47.69,
            "Architecture": "Qwen2ForCausalLM",
            "Parameters": "14.766B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/sthenno/tempesthenno-ppo-ckpt40",
        "known_config": None
    },
    {
        "rank": 3,
        "name": "tanliboy/lambda-qwen2.5-14b-dpo-test",
        "scores": {
            "average": 42.62,
            "IFEval": 82.31,
            "BBH": 48.45,
            "MATH": 54.61,
            "GPQA": 14.99,
            "MUSR": 12.59,
            "MMLU_PRO": 42.75,
            "Architecture": "Qwen2ForCausalLM",
            "Parameters": "14.77B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/tanliboy/lambda-qwen2.5-14b-dpo-test",
        "known_config": None
    },
    {
        "rank": 4,
        "name": "sthenno/tempesthenno-nuslerp-001",
        "scores": {
            "average": 42.59,
            "IFEval": 79.26,
            "BBH": 51.04,
            "MATH": 47.58,
            "GPQA": 16.44,
            "MUSR": 13.88,
            "MMLU_PRO": 47.3,
            "Architecture": "Qwen2ForCausalLM",
            "Parameters": "14.766B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/sthenno/tempesthenno-nuslerp-001",
        "known_config": None
    },
    {
        "rank": 5,
        "name": "Goekdeniz-Guelmez/Josiefied-Qwen2.5-14B-Instruct-abliterated-v4",
        "scores": {
            "average": 42.55,
            "IFEval": 82.92,
            "BBH": 48.05,
            "MATH": 54.23,
            "GPQA": 12.3,
            "MUSR": 13.15,
            "MMLU_PRO": 44.65,
            "Architecture": "Qwen2ForCausalLM",
            "Parameters": "14.77B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/Goekdeniz-Guelmez/Josiefied-Qwen2.5-14B-Instruct-abliterated-v4",
        "known_config": None
    },
    {
        "rank": 6,
        "name": "djuna/Q2.5-Veltha-14B",
        "scores": {
            "average": 42.52,
            "IFEval": 82.92,
            "BBH": 49.75,
            "MATH": 47.89,
            "GPQA": 14.54,
            "MUSR": 12.26,
            "MMLU_PRO": 47.76,
            "Architecture": "Qwen2ForCausalLM",
            "Parameters": "14.766B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/djuna/Q2.5-Veltha-14B",
        "known_config": None
    },
    {
        "rank": 7,
        "name": "arcee-ai/Virtuoso-Small-v2",
        "scores": {
            "average": 42.48,
            "IFEval": 82.73,
            "BBH": 50.95,
            "MATH": 46.6,
            "GPQA": 13.76,
            "MUSR": 14.28,
            "MMLU_PRO": 46.53,
            "Architecture": "Qwen2ForCausalLM",
            "Parameters": "14.766B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/arcee-ai/Virtuoso-Small-v2",
        "known_config": None
    },
    {
        "rank": 8,
        "name": "jpacifico/Chocolatine-14B-Instruct-DPO-v1.3",
        "scores": {
            "average": 42.42,
            "IFEval": 70.4,
            "BBH": 54.85,
            "MATH": 56.19,
            "GPQA": 12.19,
            "MUSR": 12.29,
            "MMLU_PRO": 48.6,
            "Architecture": "Phi3ForCausalLM",
            "Parameters": "14.66B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/jpacifico/Chocolatine-14B-Instruct-DPO-v1.3",
        "known_config": None
    },
    {
        "rank": 9,
        "name": "sthenno-com/miscii-14b-1028",
        "scores": {
            "average": 42.38,
            "IFEval": 82.37,
            "BBH": 49.26,
            "MATH": 50.3,
            "GPQA": 14.21,
            "MUSR": 12.0,
            "MMLU_PRO": 46.14,
            "Architecture": "Qwen2ForCausalLM",
            "Parameters": "14.77B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/sthenno-com/miscii-14b-1028",
        "known_config": None
    },
    {
        "rank": 10,
        "name": "sthenno-com/miscii-14b-1225",
        "scores": {
            "average": 42.35,
            "IFEval": 78.78,
            "BBH": 50.91,
            "MATH": 45.17,
            "GPQA": 17.0,
            "MUSR": 14.77,
            "MMLU_PRO": 47.46,
            "Architecture": "Qwen2ForCausalLM",
            "Parameters": "14.766B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/sthenno-com/miscii-14b-1225",
        "known_config": None
    },
    {
        "rank": 11,
        "name": "tensopolis/virtuoso-small-v2-tensopolis-v1",
        "scores": {
            "average": 42.34,
            "IFEval": 83.4,
            "BBH": 50.99,
            "MATH": 46.6,
            "GPQA": 12.98,
            "MUSR": 13.38,
            "MMLU_PRO": 46.67,
            "Architecture": "Qwen2ForCausalLM",
            "Parameters": "14.766B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/tensopolis/virtuoso-small-v2-tensopolis-v1",
        "known_config": None
    },
    {
        "rank": 12,
        "name": "rombodawg/Rombos-LLM-V2.6-Qwen-14b",
        "scores": {
            "average": 42.2,
            "IFEval": 84.32,
            "BBH": 49.28,
            "MATH": 52.11,
            "GPQA": 11.19,
            "MUSR": 12.29,
            "MMLU_PRO": 44.01,
            "Architecture": "Qwen2ForCausalLM",
            "Parameters": "14.77B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/rombodawg/Rombos-LLM-V2.6-Qwen-14b",
        "known_config": None
    },
    {
        "rank": 13,
        "name": "1024m/QWEN-14B-B100",
        "scores": {
            "average": 41.92,
            "IFEval": 77.62,
            "BBH": 49.78,
            "MATH": 54.38,
            "GPQA": 13.42,
            "MUSR": 9.88,
            "MMLU_PRO": 46.43,
            "Architecture": "Qwen2ForCausalLM",
            "Parameters": "14.77B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/1024m/QWEN-14B-B100",
        "known_config": None
    },
    {
        "rank": 14,
        "name": "Sakalti/Saka-14B",
        "scores": {
            "average": 41.91,
            "IFEval": 71.74,
            "BBH": 49.72,
            "MATH": 40.94,
            "GPQA": 19.46,
            "MUSR": 20.74,
            "MMLU_PRO": 48.84,
            "Architecture": "Qwen2ForCausalLM",
            "Parameters": "14.766B",
            "Chat_Template": "No"
        },
        "hf_url": "https://huggingface.co/Sakalti/Saka-14B",
        "known_config": None
    },
    {
        "rank": 15,
        "name": "Tsunami-th/Tsunami-1.0-14B-Instruct",
        "scores": {
            "average": 41.84,
            "IFEval": 78.29,
            "BBH": 49.15,
            "MATH": 45.85,
            "GPQA": 14.21,
            "MUSR": 16.34,
            "MMLU_PRO": 47.21,
            "Architecture": "Qwen2ForCausalLM",
            "Parameters": "14.77B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/Tsunami-th/Tsunami-1.0-14B-Instruct",
        "known_config": None
    },
    {
        "rank": 16,
        "name": "sthenno/tempesthenno-kto-0205-ckpt80",
        "scores": {
            "average": 41.79,
            "IFEval": 80.54,
            "BBH": 50.64,
            "MATH": 45.92,
            "GPQA": 13.09,
            "MUSR": 12.93,
            "MMLU_PRO": 47.62,
            "Architecture": "Qwen2ForCausalLM",
            "Parameters": "14.766B",
            "Chat_Template": "No"
        },
        "hf_url": "https://huggingface.co/sthenno/tempesthenno-kto-0205-ckpt80",
        "known_config": None
    },
    {
        "rank": 17,
        "name": "sometimesanotion/Lamarck-14B-v0.7-rc4",
        "scores": {
            "average": 41.79,
            "IFEval": 72.11,
            "BBH": 49.85,
            "MATH": 40.26,
            "GPQA": 18.57,
            "MUSR": 21.07,
            "MMLU_PRO": 48.89,
            "Architecture": "Qwen2ForCausalLM",
            "Parameters": "14.766B",
            "Chat_Template": "No"
        },
        "hf_url": "https://huggingface.co/sometimesanotion/Lamarck-14B-v0.7-rc4",
        "known_config": None
    },
    {
        "rank": 18,
        "name": "CombinHorizon/Josiefied-abliteratedV4-Qwen2.5-14B-Inst-BaseMerge-TIES",
        "scores": {
            "average": 41.77,
            "IFEval": 82.4,
            "BBH": 48.2,
            "MATH": 53.17,
            "GPQA": 9.96,
            "MUSR": 12.65,
            "MMLU_PRO": 44.21,
            "Architecture": "Qwen2ForCausalLM",
            "Parameters": "14.77B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/CombinHorizon/Josiefied-abliteratedV4-Qwen2.5-14B-Inst-BaseMerge-TIES",
        "known_config": None
    },
    {
        "rank": 19,
        "name": "suayptalha/Luminis-phi-4",
        "scores": {
            "average": 41.76,
            "IFEval": 69.0,
            "BBH": 55.8,
            "MATH": 46.37,
            "GPQA": 13.53,
            "MUSR": 16.68,
            "MMLU_PRO": 49.15,
            "Architecture": "LlamaForCausalLM",
            "Parameters": "14.66B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/suayptalha/Luminis-phi-4",
        "known_config": None
    },
    {
        "rank": 20,
        "name": "huihui-ai/Qwen2.5-14B-Instruct-abliterated-v2",
        "scores": {
            "average": 41.75,
            "IFEval": 83.28,
            "BBH": 47.41,
            "MATH": 53.02,
            "GPQA": 11.19,
            "MUSR": 11.58,
            "MMLU_PRO": 44.02,
            "Architecture": "Qwen2ForCausalLM",
            "Parameters": "14.77B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/huihui-ai/Qwen2.5-14B-Instruct-abliterated-v2",
        "known_config": None
    },
    {
        "rank": 21,
        "name": "djuna/Q2.5-Veltha-14B-0.5",
        "scores": {
            "average": 41.61,
            "IFEval": 77.96,
            "BBH": 50.32,
            "MATH": 43.73,
            "GPQA": 15.77,
            "MUSR": 14.17,
            "MMLU_PRO": 47.72,
            "Architecture": "Qwen2ForCausalLM",
            "Parameters": "14.766B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/djuna/Q2.5-Veltha-14B-0.5",
        "known_config": None
    },
    {
        "rank": 23,
        "name": "Qwen/Qwen2.5-14B-Instruct-1M",
        "scores": {
            "average": 41.56,
            "IFEval": 84.14,
            "BBH": 45.66,
            "MATH": 53.02,
            "GPQA": 12.42,
            "MUSR": 11.35,
            "MMLU_PRO": 42.77,
            "Architecture": "Qwen2ForCausalLM",
            "Parameters": "14.77B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/Qwen/Qwen2.5-14B-Instruct-1M",
        "known_config": None
    },
    {
        "rank": 22,
        "name": "notbdq/Qwen2.5-14B-Instruct-1M-GRPO-Reasoning",
        "scores": {
            "average": 41.56,
            "IFEval": 84.14,
            "BBH": 45.66,
            "MATH": 53.02,
            "GPQA": 12.42,
            "MUSR": 11.35,
            "MMLU_PRO": 42.77,
            "Architecture": "Qwen2ForCausalLM",
            "Parameters": "14.77B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/notbdq/Qwen2.5-14B-Instruct-1M-GRPO-Reasoning",
        "known_config": None
    },
    {
        "rank": 24,
        "name": "sometimesanotion/Qwenvergence-14B-v11",
        "scores": {
            "average": 41.52,
            "IFEval": 71.92,
            "BBH": 47.55,
            "MATH": 46.45,
            "GPQA": 16.33,
            "MUSR": 18.76,
            "MMLU_PRO": 48.08,
            "Architecture": "Qwen2ForCausalLM",
            "Parameters": "14.766B",
            "Chat_Template": "No"
        },
        "hf_url": "https://huggingface.co/sometimesanotion/Qwenvergence-14B-v11",
        "known_config": None
    },
    {
        "rank": 25,
        "name": "sometimesanotion/Qwenvergence-14B-v10",
        "scores": {
            "average": 41.48,
            "IFEval": 67.57,
            "BBH": 46.75,
            "MATH": 47.89,
            "GPQA": 17.23,
            "MUSR": 22.33,
            "MMLU_PRO": 47.1,
            "Architecture": "Qwen2ForCausalLM",
            "Parameters": "14.766B",
            "Chat_Template": "No"
        },
        "hf_url": "https://huggingface.co/sometimesanotion/Qwenvergence-14B-v10",
        "known_config": None
    },
    {
        "rank": 26,
        "name": "CombinHorizon/huihui-ai-abliteratedV2-Qwen2.5-14B-Inst-BaseMerge-TIES",
        "scores": {
            "average": 41.47,
            "IFEval": 81.76,
            "BBH": 47.77,
            "MATH": 54.76,
            "GPQA": 8.61,
            "MUSR": 12.45,
            "MMLU_PRO": 43.45,
            "Architecture": "Qwen2ForCausalLM",
            "Parameters": "14.77B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/CombinHorizon/huihui-ai-abliteratedV2-Qwen2.5-14B-Inst-BaseMerge-TIES",
        "known_config": None
    },
    {
        "rank": 27,
        "name": "RDson/WomboCombo-R1-Coder-14B-Preview",
        "scores": {
            "average": 41.46,
            "IFEval": 62.86,
            "BBH": 48.15,
            "MATH": 59.89,
            "GPQA": 9.51,
            "MUSR": 22.01,
            "MMLU_PRO": 46.31,
            "Architecture": "Qwen2ForCausalLM",
            "Parameters": "14.77B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/RDson/WomboCombo-R1-Coder-14B-Preview",
        "known_config": None
    },
    {
        "rank": 28,
        "name": "jpacifico/Chocolatine-2-14B-Instruct-v2.0b3",
        "scores": {
            "average": 41.43,
            "IFEval": 73.23,
            "BBH": 49.57,
            "MATH": 41.09,
            "GPQA": 17.23,
            "MUSR": 19.3,
            "MMLU_PRO": 48.19,
            "Architecture": "Qwen2ForCausalLM",
            "Parameters": "14.766B",
            "Chat_Template": "No"
        },
        "hf_url": "https://huggingface.co/jpacifico/Chocolatine-2-14B-Instruct-v2.0b3",
        "known_config": None
    },
    {
        "rank": 29,
        "name": "Quazim0t0/Nova-14b-sce",
        "scores": {
            "average": 41.41,
            "IFEval": 70.22,
            "BBH": 56.03,
            "MATH": 41.62,
            "GPQA": 15.1,
            "MUSR": 16.43,
            "MMLU_PRO": 49.03,
            "Architecture": "LlamaForCausalLM",
            "Parameters": "14.66B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/Quazim0t0/Nova-14b-sce",
        "known_config": None
    },
    {
        "rank": 30,
        "name": "v000000/Qwen2.5-14B-Gutenberg-Instruct-Slerpeno",
        "scores": {
            "average": 41.36,
            "IFEval": 81.97,
            "BBH": 48.45,
            "MATH": 53.25,
            "GPQA": 10.85,
            "MUSR": 10.05,
            "MMLU_PRO": 43.59,
            "Architecture": "Qwen2ForCausalLM",
            "Parameters": "14.77B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/v000000/Qwen2.5-14B-Gutenberg-Instruct-Slerpeno",
        "known_config": None
    },
    {
        "rank": 31,
        "name": "Quazim0t0/NovaScotia-14b-stock",
        "scores": {
            "average": 41.35,
            "IFEval": 67.87,
            "BBH": 56.03,
            "MATH": 46.3,
            "GPQA": 13.2,
            "MUSR": 15.7,
            "MMLU_PRO": 48.99,
            "Architecture": "LlamaForCausalLM",
            "Parameters": "14.66B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/Quazim0t0/NovaScotia-14b-stock",
        "known_config": None
    },
    {
        "rank": 32,
        "name": "Quazim0t0/ODB-14b-sce",
        "scores": {
            "average": 41.34,
            "IFEval": 70.16,
            "BBH": 56.19,
            "MATH": 41.16,
            "GPQA": 14.99,
            "MUSR": 16.5,
            "MMLU_PRO": 49.02,
            "Architecture": "LlamaForCausalLM",
            "Parameters": "14.66B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/Quazim0t0/ODB-14b-sce",
        "known_config": None
    },
    {
        "rank": 54,
        "name": "LightningRodLabs/Flashlight-v1.1",
        "scores": {
            "average": 40.99,
            "IFEval": 67.21,
            "BBH": 55.43,
            "MATH": 53.25,
            "GPQA": 11.97,
            "MUSR": 9.0,
            "MMLU_PRO": 49.06,
            "Architecture": "Phi3ForCausalLM",
            "Parameters": "14.66B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/LightningRodLabs/Flashlight-v1.1",
        "known_config": None
    },
    {
        "rank": 55,
        "name": "Quazim0t0/Mithril-14B-sce",
        "scores": {
            "average": 40.98,
            "IFEval": 69.58,
            "BBH": 55.93,
            "MATH": 38.22,
            "GPQA": 15.88,
            "MUSR": 17.37,
            "MMLU_PRO": 48.92,
            "Architecture": "LlamaForCausalLM",
            "Parameters": "14.66B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/Quazim0t0/Mithril-14B-sce",
        "known_config": None
    },
    {
        "rank": 56,
        "name": "Sakalti/ultiima-14B-v0.2",
        "scores": {
            "average": 40.96,
            "IFEval": 70.7,
            "BBH": 49.51,
            "MATH": 39.95,
            "GPQA": 17.67,
            "MUSR": 19.19,
            "MMLU_PRO": 48.75,
            "Architecture": "Qwen2ForCausalLM",
            "Parameters": "14.766B",
            "Chat_Template": "No"
        },
        "hf_url": "https://huggingface.co/Sakalti/ultiima-14B-v0.2",
        "known_config": None
    },
    {
        "rank": 57,
        "name": "bunnycore/Phi-4-ReasoningRP",
        "scores": {
            "average": 40.95,
            "IFEval": 67.36,
            "BBH": 55.88,
            "MATH": 45.69,
            "GPQA": 12.53,
            "MUSR": 15.14,
            "MMLU_PRO": 49.12,
            "Architecture": "LlamaForCausalLM",
            "Parameters": "14.66B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/bunnycore/Phi-4-ReasoningRP",
        "known_config": None
    },
    {
        "rank": 58,
        "name": "dwikitheduck/gen-inst-1",
        "scores": {
            "average": 40.88,
            "IFEval": 77.5,
            "BBH": 48.32,
            "MATH": 45.54,
            "GPQA": 16.22,
            "MUSR": 12.27,
            "MMLU_PRO": 45.43,
            "Architecture": "Qwen2ForCausalLM",
            "Parameters": "14.77B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/dwikitheduck/gen-inst-1",
        "known_config": None
    },
    {
        "rank": 59,
        "name": "v000000/Qwen2.5-14B-Gutenberg-1e-Delta",
        "scores": {
            "average": 40.88,
            "IFEval": 80.45,
            "BBH": 48.62,
            "MATH": 52.64,
            "GPQA": 10.51,
            "MUSR": 9.38,
            "MMLU_PRO": 43.67,
            "Architecture": "Qwen2ForCausalLM",
            "Parameters": "14.77B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/v000000/Qwen2.5-14B-Gutenberg-1e-Delta",
        "known_config": None
    },
    {
        "rank": 60,
        "name": "hotmailuser/QwenSlerp2-14B",
        "scores": {
            "average": 40.86,
            "IFEval": 70.37,
            "BBH": 49.68,
            "MATH": 39.65,
            "GPQA": 17.45,
            "MUSR": 19.35,
            "MMLU_PRO": 48.66,
            "Architecture": "Qwen2ForCausalLM",
            "Parameters": "14.766B",
            "Chat_Template": "No"
        },
        "hf_url": "https://huggingface.co/hotmailuser/QwenSlerp2-14B",
        "known_config": None
    },
    {
        "rank": 61,
        "name": "Quazim0t0/Loke-14B-sce",
        "scores": {
            "average": 40.86,
            "IFEval": 68.48,
            "BBH": 55.83,
            "MATH": 39.05,
            "GPQA": 15.32,
            "MUSR": 17.56,
            "MMLU_PRO": 48.9,
            "Architecture": "LlamaForCausalLM",
            "Parameters": "14.66B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/Quazim0t0/Loke-14B-sce",
        "known_config": None
    },
    {
        "rank": 62,
        "name": "Quazim0t0/mosaic-14b-sce",
        "scores": {
            "average": 40.83,
            "IFEval": 68.76,
            "BBH": 55.69,
            "MATH": 40.26,
            "GPQA": 14.99,
            "MUSR": 16.44,
            "MMLU_PRO": 48.85,
            "Architecture": "LlamaForCausalLM",
            "Parameters": "14.66B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/Quazim0t0/mosaic-14b-sce",
        "known_config": None
    },
    {
        "rank": 63,
        "name": "bunnycore/Phi-4-Model-Stock",
        "scores": {
            "average": 40.79,
            "IFEval": 68.79,
            "BBH": 55.32,
            "MATH": 42.98,
            "GPQA": 13.98,
            "MUSR": 15.12,
            "MMLU_PRO": 48.54,
            "Architecture": "LlamaForCausalLM",
            "Parameters": "14.66B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/bunnycore/Phi-4-Model-Stock",
        "known_config": None
    },
    {
        "rank": 64,
        "name": "unsloth/phi-4",
        "scores": {
            "average": 40.73,
            "IFEval": 68.82,
            "BBH": 55.25,
            "MATH": 50.0,
            "GPQA": 11.52,
            "MUSR": 10.13,
            "MMLU_PRO": 48.65,
            "Architecture": "LlamaForCausalLM",
            "Parameters": "14.66B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/unsloth/phi-4",
        "known_config": None
    },
    {
        "rank": 65,
        "name": "pankajmathur/orca_mini_phi-4",
        "scores": {
            "average": 40.68,
            "IFEval": 77.81,
            "BBH": 54.63,
            "MATH": 29.53,
            "GPQA": 16.55,
            "MUSR": 18.25,
            "MMLU_PRO": 47.28,
            "Architecture": "LlamaForCausalLM",
            "Parameters": "14.66B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/pankajmathur/orca_mini_phi-4",
        "known_config": None
    },
    {
        "rank": 66,
        "name": "pankajmathur/orca_mini_v9_2_14B",
        "scores": {
            "average": 40.68,
            "IFEval": 77.81,
            "BBH": 54.63,
            "MATH": 29.53,
            "GPQA": 16.55,
            "MUSR": 18.25,
            "MMLU_PRO": 47.28,
            "Architecture": "LlamaForCausalLM",
            "Parameters": "14.66B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/pankajmathur/orca_mini_v9_2_14B",
        "known_config": None
    },
    {
        "rank": 67,
        "name": "sometimesanotion/Lamarck-14B-v0.6-model_stock",
        "scores": {
            "average": 40.68,
            "IFEval": 67.9,
            "BBH": 46.49,
            "MATH": 42.45,
            "GPQA": 17.9,
            "MUSR": 22.68,
            "MMLU_PRO": 46.64,
            "Architecture": "Qwen2ForCausalLM",
            "Parameters": "14B",
            "Chat_Template": "No"
        },
        "hf_url": "https://huggingface.co/sometimesanotion/Lamarck-14B-v0.6-model_stock",
        "known_config": None
    },
    {
        "rank": 68,
        "name": "sometimesanotion/Qwenvergence-14B-v0.6-004-model_stock",
        "scores": {
            "average": 40.6,
            "IFEval": 68.6,
            "BBH": 46.37,
            "MATH": 40.94,
            "GPQA": 17.79,
            "MUSR": 23.35,
            "MMLU_PRO": 46.59,
            "Architecture": "Qwen2ForCausalLM",
            "Parameters": "14B",
            "Chat_Template": "No"
        },
        "hf_url": "https://huggingface.co/sometimesanotion/Qwenvergence-14B-v0.6-004-model_stock",
        "known_config": None
    },
    {
        "rank": 69,
        "name": "Quazim0t0/Oasis-14B-ties",
        "scores": {
            "average": 40.59,
            "IFEval": 69.37,
            "BBH": 55.75,
            "MATH": 37.54,
            "GPQA": 15.32,
            "MUSR": 16.63,
            "MMLU_PRO": 48.94,
            "Architecture": "LlamaForCausalLM",
            "Parameters": "14.66B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/Quazim0t0/Oasis-14B-ties",
        "known_config": None
    },
    {
        "rank": 70,
        "name": "LightningRodLabs/Flashlight-v1.0",
        "scores": {
            "average": 40.57,
            "IFEval": 67.45,
            "BBH": 55.15,
            "MATH": 49.7,
            "GPQA": 12.3,
            "MUSR": 9.93,
            "MMLU_PRO": 48.91,
            "Architecture": "LlamaForCausalLM",
            "Parameters": "14.66B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/LightningRodLabs/Flashlight-v1.0",
        "known_config": None
    },
    {
        "rank": 71,
        "name": "arcee-ai/Virtuoso-Small",
        "scores": {
            "average": 40.54,
            "IFEval": 79.35,
            "BBH": 50.4,
            "MATH": 40.94,
            "GPQA": 11.52,
            "MUSR": 14.44,
            "MMLU_PRO": 46.57,
            "Architecture": "Qwen2ForCausalLM",
            "Parameters": "14.77B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/arcee-ai/Virtuoso-Small",
        "known_config": None
    },
    {
        "rank": 72,
        "name": "Quazim0t0/GuiltySpark-14B-ties",
        "scores": {
            "average": 40.52,
            "IFEval": 68.54,
            "BBH": 55.72,
            "MATH": 38.37,
            "GPQA": 15.32,
            "MUSR": 16.3,
            "MMLU_PRO": 48.89,
            "Architecture": "LlamaForCausalLM",
            "Parameters": "14.66B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/Quazim0t0/GuiltySpark-14B-ties",
        "known_config": None
    },
    {
        "rank": 73,
        "name": "ozone-ai/0x-lite",
        "scores": {
            "average": 40.48,
            "IFEval": 77.4,
            "BBH": 47.53,
            "MATH": 50.45,
            "GPQA": 9.28,
            "MUSR": 11.76,
            "MMLU_PRO": 46.49,
            "Architecture": "Qwen2ForCausalLM",
            "Parameters": "14.77B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/ozone-ai/0x-lite",
        "known_config": None
    },
    {
        "rank": 74,
        "name": "Quazim0t0/Casa-14b-sce",
        "scores": {
            "average": 40.41,
            "IFEval": 66.54,
            "BBH": 55.4,
            "MATH": 46.98,
            "GPQA": 11.07,
            "MUSR": 13.31,
            "MMLU_PRO": 49.17,
            "Architecture": "LlamaForCausalLM",
            "Parameters": "14.66B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/Quazim0t0/Casa-14b-sce",
        "known_config": None
    },
    {
        "rank": 75,
        "name": "Sakalti/ultiima-14B-v0.3",
        "scores": {
            "average": 40.38,
            "IFEval": 70.4,
            "BBH": 48.45,
            "MATH": 39.65,
            "GPQA": 16.89,
            "MUSR": 18.73,
            "MMLU_PRO": 48.18,
            "Architecture": "Qwen2ForCausalLM",
            "Parameters": "14.766B",
            "Chat_Template": "No"
        },
        "hf_url": "https://huggingface.co/Sakalti/ultiima-14B-v0.3",
        "known_config": None
    },
    {
        "rank": 76,
        "name": "ehristoforu/fp4-14b-v1-fix",
        "scores": {
            "average": 40.37,
            "IFEval": 67.42,
            "BBH": 54.33,
            "MATH": 42.07,
            "GPQA": 13.87,
            "MUSR": 16.18,
            "MMLU_PRO": 48.37,
            "Architecture": "LlamaForCausalLM",
            "Parameters": "14.66B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/ehristoforu/fp4-14b-v1-fix",
        "known_config": None
    },
    {
        "rank": 77,
        "name": "FINGU-AI/Chocolatine-Fusion-14B",
        "scores": {
            "average": 40.36,
            "IFEval": 69.49,
            "BBH": 48.6,
            "MATH": 38.52,
            "GPQA": 16.22,
            "MUSR": 21.99,
            "MMLU_PRO": 47.35,
            "Architecture": "Qwen2ForCausalLM",
            "Parameters": "8.367B",
            "Chat_Template": "No"
        },
        "hf_url": "https://huggingface.co/FINGU-AI/Chocolatine-Fusion-14B",
        "known_config": None
    },
    {
        "rank": 78,
        "name": "hotmailuser/QwenSlerp-14B",
        "scores": {
            "average": 40.35,
            "IFEval": 70.25,
            "BBH": 49.42,
            "MATH": 38.37,
            "GPQA": 18.34,
            "MUSR": 16.83,
            "MMLU_PRO": 48.89,
            "Architecture": "Qwen2ForCausalLM",
            "Parameters": "14.766B",
            "Chat_Template": "No"
        },
        "hf_url": "https://huggingface.co/hotmailuser/QwenSlerp-14B",
        "known_config": None
    },
    {
        "rank": 79,
        "name": "Triangle104/Robo-Gutenberg_V1.0",
        "scores": {
            "average": 40.35,
            "IFEval": 60.08,
            "BBH": 50.29,
            "MATH": 45.62,
            "GPQA": 18.12,
            "MUSR": 19.2,
            "MMLU_PRO": 48.79,
            "Architecture": "Qwen2ForCausalLM",
            "Parameters": "14.77B",
            "Chat_Template": "No"
        },
        "hf_url": "https://huggingface.co/Triangle104/Robo-Gutenberg_V1.0",
        "known_config": None
    },
    {
        "rank": 80,
        "name": "Quazim0t0/Adamant-14B-sce",
        "scores": {
            "average": 40.32,
            "IFEval": 68.58,
            "BBH": 54.97,
            "MATH": 39.88,
            "GPQA": 13.42,
            "MUSR": 16.51,
            "MMLU_PRO": 48.57,
            "Architecture": "LlamaForCausalLM",
            "Parameters": "14.66B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/Quazim0t0/Adamant-14B-sce",
        "known_config": None
    },
    {
        "rank": 81,
        "name": "Quazim0t0/Phi4Basis-14B-sce",
        "scores": {
            "average": 40.31,
            "IFEval": 65.02,
            "BBH": 55.67,
            "MATH": 47.89,
            "GPQA": 10.51,
            "MUSR": 14.02,
            "MMLU_PRO": 48.78,
            "Architecture": "LlamaForCausalLM",
            "Parameters": "14.66B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/Quazim0t0/Phi4Basis-14B-sce",
        "known_config": None
    },
    {
        "rank": 82,
        "name": "Quazim0t0/bloom-14b-stock",
        "scores": {
            "average": 40.29,
            "IFEval": 65.75,
            "BBH": 55.27,
            "MATH": 48.11,
            "GPQA": 10.85,
            "MUSR": 13.17,
            "MMLU_PRO": 48.59,
            "Architecture": "LlamaForCausalLM",
            "Parameters": "14.66B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/Quazim0t0/bloom-14b-stock",
        "known_config": None
    },
    {
        "rank": 83,
        "name": "sometimesanotion/Qwen2.5-14B-Vimarckoso-v3-Prose01",
        "scores": {
            "average": 40.28,
            "IFEval": 68.72,
            "BBH": 47.71,
            "MATH": 39.95,
            "GPQA": 18.23,
            "MUSR": 19.56,
            "MMLU_PRO": 47.5,
            "Architecture": "Qwen2ForCausalLM",
            "Parameters": "14B",
            "Chat_Template": "No"
        },
        "hf_url": "https://huggingface.co/sometimesanotion/Qwen2.5-14B-Vimarckoso-v3-Prose01",
        "known_config": None
    },
    {
        "rank": 84,
        "name": "Quazim0t0/Halo-14B-sce",
        "scores": {
            "average": 40.26,
            "IFEval": 67.54,
            "BBH": 55.27,
            "MATH": 42.9,
            "GPQA": 12.98,
            "MUSR": 14.24,
            "MMLU_PRO": 48.63,
            "Architecture": "LlamaForCausalLM",
            "Parameters": "14.66B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/Quazim0t0/Halo-14B-sce",
        "known_config": None
    },
    {
        "rank": 85,
        "name": "prithivMLmods/Calcium-Opus-14B-Elite2",
        "scores": {
            "average": 40.25,
            "IFEval": 61.76,
            "BBH": 46.81,
            "MATH": 46.9,
            "GPQA": 16.0,
            "MUSR": 22.24,
            "MMLU_PRO": 47.79,
            "Architecture": "Qwen2ForCausalLM",
            "Parameters": "14.766B",
            "Chat_Template": "No"
        },
        "hf_url": "https://huggingface.co/prithivMLmods/Calcium-Opus-14B-Elite2",
        "known_config": None
    },
    {
        "rank": 86,
        "name": "SicariusSicariiStuff/Impish_QWEN_14B-1M",
        "scores": {
            "average": 40.24,
            "IFEval": 78.68,
            "BBH": 47.22,
            "MATH": 39.65,
            "GPQA": 13.42,
            "MUSR": 17.52,
            "MMLU_PRO": 44.93,
            "Architecture": "Qwen2ForCausalLM",
            "Parameters": "14.77B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/SicariusSicariiStuff/Impish_QWEN_14B-1M",
        "known_config": None
    },
    {
        "rank": 87,
        "name": "bunnycore/Phi-4-Stock-Ex",
        "scores": {
            "average": 40.22,
            "IFEval": 65.75,
            "BBH": 55.2,
            "MATH": 40.86,
            "GPQA": 13.42,
            "MUSR": 17.46,
            "MMLU_PRO": 48.61,
            "Architecture": "LlamaForCausalLM",
            "Parameters": "14.66B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/bunnycore/Phi-4-Stock-Ex",
        "known_config": None
    },
    {
        "rank": 88,
        "name": "sometimesanotion/Qwenvergence-14B-qv256",
        "scores": {
            "average": 40.12,
            "IFEval": 70.06,
            "BBH": 47.08,
            "MATH": 38.97,
            "GPQA": 17.11,
            "MUSR": 21.07,
            "MMLU_PRO": 46.42,
            "Architecture": "Qwen2ForCausalLM",
            "Parameters": "14B",
            "Chat_Template": "No"
        },
        "hf_url": "https://huggingface.co/sometimesanotion/Qwenvergence-14B-qv256",
        "known_config": None
    },
    {
        "rank": 89,
        "name": "tensopolis/virtuoso-small-tensopolis-v2",
        "scores": {
            "average": 40.11,
            "IFEval": 80.2,
            "BBH": 50.23,
            "MATH": 38.75,
            "GPQA": 10.51,
            "MUSR": 14.84,
            "MMLU_PRO": 46.15,
            "Architecture": "Qwen2ForCausalLM",
            "Parameters": "14.77B",
            "Chat_Template": "Yes"
        },
        "hf_url": "https://huggingface.co/tensopolis/virtuoso-small-tensopolis-v2",
        "known_config": None
    }
]



def snippet_scrape_model_page(url):
    """
    Equivalent scraping function for the larger dataset
    to look for 
 YAML and a .metadata section.
    """
    try:
        response = requests.get(url)
        if response.status_code != 200:
            return f"Error: Unable to fetch the page (Status Code: {response.status_code})"
        
        soup = BeautifulSoup(response.text, "html.parser")

        yaml_config = soup.find("pre")
        yaml_text = yaml_config.text.strip() if yaml_config else "No YAML configuration found."

        metadata_section = soup.find("div", class_="metadata")
        metadata_text = metadata_section.text.strip() if metadata_section else "No metadata found."

        return {
            "yaml_configuration": yaml_text,
            "metadata": metadata_text
        }

    except Exception as e:
        return f"Error: {str(e)}"

def snippet_print_benchmark_and_config_info(model_info):
    """
    Prints an overview for each model in the rank=44..105 dataset.
    If known_config is not None, prints it. Otherwise attempts to scrape.
    """
    print(f"---\nModel Rank: {model_info['rank']}")
    print(f"Model Name: {model_info['name']}")
    print(f"Model average score across benchmarks in %: {model_info['scores']['average']}")
    print(f"Models average score on IFEval benchmarks in %: {model_info['scores']['IFEval']}")
    print(f"Models average score on BBH benchmarks in %: {model_info['scores']['BBH']}")
    print(f"Models average score on MATH benchmarks in %: {model_info['scores']['MATH']}")
    print(f"Models average score in GPQA benchmarks in %: {model_info['scores']['GPQA']}")
    print(f"Models average score in MUSR benchmarks in %: {model_info['scores']['MUSR']}")
    print(f"Models average score in MMLU_PRO benchmarks in %: {model_info['scores']['MMLU_PRO']}")

    # If there's a known_config, print it in YAML form and stop.
    if model_info["known_config"] is not None:
        print("###")
        print("models:")
        for m in model_info["known_config"]["models"]:
            print(f"  - model: {m['model']}")
        print(f"merge_method: {model_info['known_config']['merge_method']}")
        print(f"base_model: {model_info['known_config']['base_model']}")
        print(f"dtype: {model_info['known_config']['dtype']}")
        print("parameters:")
        t_vals = model_info["known_config"]["parameters"]["t"]
        print(f"  t: {t_vals} # V shaped curve: Hermes for input & output, WizardMath in the middle layers")
        print("###")
        return

    # Otherwise, do scraping:
    scraped = snippet_scrape_model_page(model_info["hf_url"])
    if isinstance(scraped, str):
        # Means it's an error string or something
        print("(No MergeKit configuration found or scraping error.)")
        print(scraped)
        return
    else:
        # It's presumably a dict
        if "No YAML configuration found." in scraped["yaml_configuration"]:
            print("(No MergeKit configuration found.)\n")
            print("You can try the following Python script to scrape the model page:\n")
            print("#" * 70)
            print(f'''import requests
from bs4 import BeautifulSoup

def scrape_model_page(model_url):
    try:
        response = requests.get(model_url)
        if response.status_code != 200:
            return f"Error: Unable to fetch the page (Status Code: {{response.status_code}})"
        
        soup = BeautifulSoup(response.text, "html.parser")

        yaml_config = soup.find("pre")
        yaml_text = yaml_config.text.strip() if yaml_config else "No YAML configuration found."

        metadata_section = soup.find("div", class_="metadata")
        metadata_text = metadata_section.text.strip() if metadata_section else "No metadata found."

        return {{
            "yaml_configuration": yaml_text,
            "metadata": metadata_text
        }}

    except Exception as e:
        return f"Error: {{str(e)}}"

if __name__ == "__main__":
    model_url = "{model_info['hf_url']}"
    result = scrape_model_page(model_url)
    print(result)''')
            print("#" * 70)
        else:
            # Found some YAML
            print("###")
            print(scraped["yaml_configuration"])
            print("###")

def run_non_tiny_benchmarks():
    """
    Captures the stdout from printing each model in benchmark_data (ranks 44..105),
    returning the entire output as a single string for Gradio to display.
    """
    old_stdout = sys.stdout
    buffer = io.StringIO()
    sys.stdout = buffer

    for model in benchmark_data:
        snippet_print_benchmark_and_config_info(model)

    sys.stdout = old_stdout
    return buffer.getvalue()

# --------------------------------------------------------------------
# PART 3: The Gradio App
# --------------------------------------------------------------------
with gr.Blocks() as demo:
    gr.Markdown("# Comprehensive Model Performance Analysis with Hugging Face Links")

    # The existing UI for the “tiny” data
    with gr.Row():
        btn1 = gr.Button("Show Average Performance")
        img1 = gr.Image(type="pil", label="Average Performance Plot")
        img1_download = gr.File(label="Download Average Performance")
        btn1.click(plot_average_scores, outputs=[img1, img1_download])
        
    with gr.Row():
        btn2 = gr.Button("Show Task Performance")
        img2 = gr.Image(type="pil", label="Task Performance Plot")
        img2_download = gr.File(label="Download Task Performance")
        btn2.click(plot_task_performance, outputs=[img2, img2_download])

    with gr.Row():
        btn3 = gr.Button("Task-Specific Top Models")
        img3 = gr.Image(type="pil", label="Task-Specific Top Models Plot")
        img3_download = gr.File(label="Download Top Models")
        btn3.click(plot_task_specific_top_models, outputs=[img3, img3_download])
    
    with gr.Row():
        btn4 = gr.Button("Plot Performance Heatmap")
        heatmap_img = gr.Image(type="pil", label="Performance Heatmap")
        heatmap_download = gr.File(label="Download Heatmap")
        btn4.click(plot_heatmap, outputs=[heatmap_img, heatmap_download])

    # Scraping & YAML handling for the *tiny* table
    with gr.Row():
        model_selector = gr.Dropdown(choices=df_full["Model Configuration"].tolist(), label="Select a Model")
        with gr.Column():
            scrape_btn = gr.Button("Scrape MergeKit Configuration")
            yaml_output = gr.Textbox(lines=10, placeholder="YAML Configuration will appear here.")
            scrape_btn.click(scrape_mergekit_config, inputs=model_selector, outputs=yaml_output)
        with gr.Column():
            save_yaml_btn = gr.Button("Save MergeKit Configuration")
            yaml_download = gr.File(label="Download MergeKit Configuration")
            save_yaml_btn.click(download_yaml, inputs=[yaml_output, model_selector], outputs=yaml_download)

    # Download everything (CSV, plots, any found YAML)
    with gr.Row():
        download_all_btn = gr.Button("Download Everything")
        all_downloads = gr.File(label="Download All Data")
        download_all_btn.click(download_all_data, outputs=all_downloads)
        
    # Live Scraping
    gr.Markdown("## Live Scraping Features")
    with gr.Row():
        url_input = gr.Textbox(label="Enter Hugging Face Model URL", placeholder="https://huggingface.co/")
        live_scrape_btn = gr.Button("Scrape Model Page")
        live_scrape_output = gr.Textbox(label="Scraped Data", lines=15)
        live_scrape_btn.click(display_scraped_model_data, inputs=url_input, outputs=live_scrape_output)

    # Non-Tiny Benchmarks
    gr.Markdown("## Non-Tiny Benchmark Parser (Ranks 44–105)")
    with gr.Row():
        parse_non_tiny_btn = gr.Button("Parse Non-Tiny Benchmarks")
        parse_non_tiny_output = gr.Textbox(label="Non-Tiny Benchmark Output", lines=30)
        parse_non_tiny_btn.click(fn=run_non_tiny_benchmarks, outputs=parse_non_tiny_output)

demo.launch()