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| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| import gradio as gr | |
| # Input data | |
| data_full = [ | |
| ["CultriX/Qwen2.5-14B-SLERPv7", 0.7205, 0.8272, 0.7541, 0.6581, 0.5000, 0.7290], | |
| ["djuna/Q2.5-Veltha-14B-0.5", 0.7492, 0.8386, 0.7305, 0.5980, 0.4300, 0.7817], | |
| ["CultriX/Qwen2.5-14B-FinalMerge", 0.7248, 0.8277, 0.7113, 0.7052, 0.5700, 0.7001], | |
| ["CultriX/Qwen2.5-14B-MultiCultyv2", 0.7295, 0.8359, 0.7363, 0.5767, 0.4400, 0.7316], | |
| ["CultriX/Qwen2.5-14B-Brocav7", 0.7445, 0.8353, 0.7508, 0.6292, 0.4600, 0.7629], | |
| ["CultriX/Qwen2.5-14B-Broca", 0.7456, 0.8352, 0.7480, 0.6034, 0.4400, 0.7716], | |
| ["CultriX/Qwen2.5-14B-Brocav3", 0.7395, 0.8388, 0.7393, 0.6405, 0.4700, 0.7659], | |
| ["CultriX/Qwen2.5-14B-Brocav4", 0.7432, 0.8377, 0.7444, 0.6277, 0.4800, 0.7580], | |
| ["CultriX/Qwen2.5-14B-Brocav2", 0.7492, 0.8302, 0.7508, 0.6377, 0.5100, 0.7478], | |
| ["CultriX/Qwen2.5-14B-Brocav5", 0.7445, 0.8313, 0.7547, 0.6376, 0.5000, 0.7304], | |
| ["CultriX/Qwen2.5-14B-Brocav6", 0.7179, 0.8354, 0.7531, 0.6378, 0.4900, 0.7524], | |
| ["CultriX/Qwenfinity-2.5-14B", 0.7347, 0.8254, 0.7279, 0.7267, 0.5600, 0.6970], | |
| ["CultriX/Qwen2.5-14B-Emergedv2", 0.7137, 0.8335, 0.7363, 0.5836, 0.4400, 0.7344], | |
| ["CultriX/Qwen2.5-14B-Unity", 0.7063, 0.8343, 0.7423, 0.6820, 0.5700, 0.7498], | |
| ["CultriX/Qwen2.5-14B-MultiCultyv3", 0.7132, 0.8216, 0.7395, 0.6792, 0.5500, 0.7120], | |
| ["CultriX/Qwen2.5-14B-Emergedv3", 0.7436, 0.8312, 0.7519, 0.6585, 0.5500, 0.7068], | |
| ["CultriX/SeQwence-14Bv1", 0.7278, 0.8410, 0.7541, 0.6816, 0.5200, 0.7539], | |
| ["CultriX/Qwen2.5-14B-Wernickev2", 0.7391, 0.8168, 0.7273, 0.6220, 0.4500, 0.7572], | |
| ["CultriX/Qwen2.5-14B-Wernickev3", 0.7357, 0.8148, 0.7245, 0.7023, 0.5500, 0.7869], | |
| ["CultriX/Qwen2.5-14B-Wernickev4", 0.7355, 0.8290, 0.7497, 0.6306, 0.4800, 0.7635], | |
| ["CultriX/SeQwential-14B-v1", 0.7355, 0.8205, 0.7549, 0.6367, 0.4800, 0.7626], | |
| ["CultriX/Qwen2.5-14B-Wernickev5", 0.7224, 0.8272, 0.7541, 0.6790, 0.5100, 0.7578], | |
| ["CultriX/Qwen2.5-14B-Wernickev6", 0.6994, 0.7549, 0.5816, 0.6991, 0.5800, 0.7267], | |
| ["CultriX/Qwen2.5-14B-Wernickev7", 0.7147, 0.7599, 0.6097, 0.7056, 0.5700, 0.7164], | |
| ["CultriX/Qwen2.5-14B-FinalMerge-tmp2", 0.7255, 0.8192, 0.7535, 0.6671, 0.5000, 0.7612], | |
| ] | |
| columns = ["Model Configuration", "tinyArc", "tinyHellaswag", "tinyMMLU", "tinyTruthfulQA", "tinyTruthfulQA_mc1", "tinyWinogrande"] | |
| # Convert to DataFrame | |
| df_full = pd.DataFrame(data_full, columns=columns) | |
| def plot_average_scores(): | |
| df_full["Average Score"] = df_full.iloc[:, 1:].mean(axis=1) | |
| df_avg_sorted = df_full.sort_values(by="Average Score", ascending=False) | |
| plt.figure(figsize=(12, 8)) | |
| 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() | |
| plt.savefig("average_performance.png") | |
| return "average_performance.png" | |
| def plot_task_performance(): | |
| df_full_melted = df_full.melt(id_vars="Model Configuration", var_name="Task", value_name="Score") | |
| plt.figure(figsize=(14, 10)) | |
| 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() | |
| plt.savefig("task_performance.png") | |
| return "task_performance.png" | |
| def plot_task_specific_top_models(): | |
| top_models = df_full.iloc[:, :-1].set_index("Model Configuration").idxmax() | |
| top_scores = df_full.iloc[:, :-1].set_index("Model Configuration").max() | |
| results = pd.DataFrame({"Top Model": top_models, "Score": top_scores}).reset_index().rename(columns={"index": "Task"}) | |
| plt.figure(figsize=(12, 6)) | |
| 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() | |
| plt.savefig("task_specific_top_models.png") | |
| return "task_specific_top_models.png" | |
| def top_3_models_per_task(): | |
| top_3_data = { | |
| task: df_full.nlargest(3, task)[["Model Configuration", task]].values.tolist() | |
| for task in df_full.columns[1:-1] | |
| } | |
| top_3_results = pd.DataFrame({ | |
| task: { | |
| "Top 3 Models": [entry[0] for entry in top_3_data[task]], | |
| "Scores": [entry[1] for entry in top_3_data[task]], | |
| } | |
| for task in top_3_data | |
| }).T.rename_axis("Task").reset_index() | |
| return top_3_results | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# Model Performance Analysis") | |
| with gr.Row(): | |
| btn1 = gr.Button("Show Average Performance") | |
| img1 = gr.Image(type="filepath") | |
| btn1.click(plot_average_scores, inputs=None, outputs=img1) | |
| with gr.Row(): | |
| btn2 = gr.Button("Show Task Performance") | |
| img2 = gr.Image(type="filepath") | |
| btn2.click(plot_task_performance, inputs=None, outputs=img2) | |
| with gr.Row(): | |
| btn3 = gr.Button("Task-Specific Top Models") | |
| img3 = gr.Image(type="filepath") | |
| btn3.click(plot_task_specific_top_models, inputs=None, outputs=img3) | |
| with gr.Row(): | |
| btn4 = gr.Button("Top 3 Models Per Task") | |
| output4 = gr.Dataframe() | |
| btn4.click(top_3_models_per_task, inputs=None, outputs=output4) | |
| demo.launch() | |